west virginia department of environmental protection

West Virginia 

Regional Haze

State Implementation Plan

to

 Preserve, Protect and Improve

Visibility in Class I Federal Areas

Final

June 2008

Promoting a healthy environment.
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West Virginia Regional Haze SIP Page i

Table of Contents

EXECUTIVE SUMMARY................................................................................................ Page xiii

ACKNOWLEDGMENTS: VISTAS Contractors.............................................................. Page xiv

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

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

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

.............................................................................................................................. Page 1

1.3. General overview of regional haze SIP requirements.......................................... Page 3

1.4. Class I areas within West Virginia....................................................................... Page 4

1.5. State and Federal Land Manager Coordination. .................................................. Page 7

2.0. ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND ESTIMATE OF

NATURAL BACKGROUND CONDITIONS IN CLASS I AREAS. .................... Page 10

2.1. Estimating Natural Conditions for Dolly Sods and Otter Creek Wilderness Areas

............................................................................................................................ Page 13

2.2. Estimating Baseline Conditions for Dolly Sods and Otter Creek Wilderness Areas

............................................................................................................................ Page 14

2.3. Summary of Natural Background and Baseline Conditions for Dolly Sods and Otter

Creek Wilderness Areas..................................................................................... Page 15

2.4. Pollutant Contributions to Visibility Impairment (2000-2004 Baseline Data)

............................................................................................................................ Page 16

3.0. UNIFORM RATE OF PROGRESS TO NATURAL CONDITIONS IN 2064....... Page 21

3.1. Glidepath for the Dolly Sods and Otter Creek Wilderness Areas...................... Page 21

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

DOLLY SODS AND OTTER CREEK WILDERNESS AREAS........................... Page 23

4.1. Baseline Emissions Inventory............................................................................ Page 23

4.1.a. Stationary Point Sources. ............................................................................. Page 24

4.1.a.1.Electric Generating Units (EGUs). ........................................................ Page 24

4.1.a.2.Other Industrial Point Sources (Non-EGUs). ........................................ Page 25

4.1.b. Stationary Area Sources............................................................................... Page 25

4.1.c. Off-Road Mobile Sources............................................................................ Page 26

4.1.d. Highway Mobile Sources............................................................................. Page 27

4.1.e. Biogenic Emissions Sources. ....................................................................... Page 27

4.1.f. Summary 2002 Base G2 (February 2007 version) Baseline Emission Inventory

for West Virginia. ........................................................................................ Page 27

4.1.g. Model Performance Improvements through Emissions Inventory Improvements

............................................................................................................................ Page 28

4.2. Assessment of Relative Contributions from Specific Pollutants and Source Categories

(2002-04 Baseline Data). ................................................................................... Page 29
West Virginia Regional Haze SIP Page ii

5.0. REGIONAL HAZE MODELING METHODS AND INPUTS............................... Page 31

5.1. Analysis Method. ............................................................................................... Page 31

5.2. Model Selection. ................................................................................................ Page 32

5.2.a. Selection of Photochemical Grid Model...................................................... Page 32

5.2.a.1. Criteria. ............................................................................................ Page 32

5.2.a.2. Overview of CMAQ. ....................................................................... Page 32

5.2.b. Selection of Meteorological Model.............................................................. Page 33

5.2.b.1. Criteria. ............................................................................................ Page 33

5.2.b.2. Overview of MM5. .......................................................................... Page 33

5.2.c. Selection of Emissions Processing System................................................. Page 34

5.2.c.1.Criteria. .................................................................................................. Page 34

5.2.c.2. Overview of SMOKE. ..................................................................... Page 35

5.3. Selection of the Modeling Year. ........................................................................ Page 36

5.4. Modeling Domains............................................................................................. Page 37

5.4.a. Horizontal Modeling Domain...................................................................... Page 37

5.4.b. Vertical Modeling Domain. ......................................................................... Page 39

6.0. MODEL PERFORMANCE EVALUATION. ......................................................... Page 40

6.1. Modeling Performance Goals, Criteria and Results........................................... Page 41

6.2. VISTAS Domain-wide Performance. ................................................................ Page 42

6.3. West Virginia Class I Areas Performance. ........................................................ Page 45

7.0. LONG-TERM STRATEGY FOR WEST VIRGINIA’S CLASS I AREAS. .......... Page 48

7.1. Overview of the Long-term Strategy Development Process.............................. Page 48

7.2. Expected Visibility Results in 2018 for West Virginia Class I Areas under existing

and planned emissions controls (Base G2 Inventory)........................................ Page 49

7.2.a. Federal and State Control Requirements. .................................................... Page 49

7.2.b. Additional State programs to reduce emissions........................................... Page 52

7.2.c. Projected 2009 and 2018 Base G2 Emissions Inventories .......................... Page 52

7.2.c.1. Summary of Final Base G2 Emission Inventories for 2009 and 2018

.......................................................................................................... Page 54

7.2.d. Model Results for the 2018 Base G2 Inventory Compared to the Uniform Rate of

Progress Glidepaths for Dolly Sods and Otter Creek Wilderness Areas. .... Page 55

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

at VISTAS Class I areas..................................................................................... Page 60

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

Geographic Areas............................................................................................... Page 64

7.5. Relative Contributions to Visibility Impairment: Geographic Locations of the Largest

Emissions Sources Contributing to Visibility Impairment at Dolly Sods and Otter

Creek Wilderness Areas..................................................................................... Page 66

7.5.a. Back Trajectory Analyses. ........................................................................... Page 66

7.5.b. Residence Time Plots................................................................................... Page 67

7.5.c SO2 Areas of Influence................................................................................. Page 68

7.5.d. Emission Source 2s within SO Areas of Influence........................................ Page 70

7.5.e. Specific Source Types in the Area of Influence for Dolly Sods and Otter Creek

Wilderness Areas. ........................................................................................ Page 84
West Virginia Regional Haze SIP Page iii

7.6. Evaluating 2 Control Options for Specific SO Emissions Sources in Each Area of

Influence............................................................................................................. Page 88

7.6.a. Cost of Compliance...................................................................................... Page 89

7.6.b. Time Necessary for Compliance.................................................................. Page 90

7.6.c. Energy and Non-Air Quality Environmental Impacts.................................. Page 90

7.6.d. Remaining Useful Life................................................................................. Page 90

7.7. Which Control Measures Represent Reasonable Progress for Individual Sources?

............................................................................................................................ Page 90

7.8. What Control Determinations Represent Best Available Retrofit Technology (BART)

for Individual Sources?. ..................................................................................... Page 99

7.8.a. BART-Eligible Sources in West Virginia.................................................... Page 99

7.8.b. Determination of Sources Subject to BART in West Virginia. ................. Page 101

7.8.c. Determination of BART Requirements for Subject-to-BART Sources..... Page 107

7.9. What Additional Emissions Controls Were Considered as part of the Long-Term

Strategy for Visibility Improvement by 2018?................................................. Page 111

8.0. REASONABLE PROGRESS GOALS.................................................................. Page 114

9.0. MONITORING STRATEGY. ............................................................................... Page 115

10.0. INTERSTATE CONSULTATION REGARDING WEST VIRGINIA CONTRIBUTION

TO VISIBILITY IMPAIRMENT IN CLASS I AREAS IN NEIGHBORING STATES

................................................................................................................................ Page 118

11.0. COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONS. .... Page 123

12.0. DETERMINATION OF THE ADEQUACY OF THE EXISTING PLAN........... Page 125
West Virginia Regional Haze SIP Page iv

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List of Appendices

Appendix A: VISTAS Memorandum of Understanding and Bylaws.............................. Page A-1

Appendix B: Conceptual Description.............................................................................. Page B-1

Appendix C: Modeling Analysis Protocol and Quality Assurance Project Plan............. Page C-1

Appendix D: Emissions Preparation and Results. ........................................................... Page D-1

Appendix E: Air Quality and Meteorological Preparations and Results......................... Page E-1

Appendix F: Model Performance Evaluation. ................................................................ Page F-1

Appendix G: Modeling Results and Supplemental Analysis........................................... Page G-1

Appendix H: Reasonable Progress Evaluation/Long Term Strategy............................... Page H-1

Appendix I: Data Access................................................................................................. Page I-1

Appendix J: Consultation Process................................................................................... Page J-1

Appendix K: Recommended Improvements.................................................................... Page K-1

Appendix L: BART Related Documentation. ................................................................. Page L-1

Appendix M: VISTAS Modeling Reports....................................................................... Page M-1

Appendix N: Public Participation Documentation. ......................................................... Page N-1
West Virginia Regional Haze SIP Page vi

List of Tables and Figures

Figure 1.4-1. West Virginia Cass I areas. .......................................................................... Page 5

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

Table 1.5-1. Mandatory Federal Class I Areas in the VISTAS Region

Where Visibility is an Important Value. .......................................................................... Page 9

Figure 2.0-1. Important factors involved in seeing a scenic vista are outlined.. ............... Page 11

Figure 2.0-2. IMPROVE Monitoring sites in the VISTAS Region. ................................ Page 12

Table 2.3-1. Natural Background and Baseline Conditions 

for Dolly Sods and Otter Creek Wilderness Areas. ....................................................... Page 15

Figure 2.4-1. Average light extinction for the 20% Haziest Days in 2000-2004 at VISTAS and

neighboring Class I areas using New IMPROVE algorithm........................ Page 16

Figure 2.4-2. Average light extinction for the 20% Clearest Days in 2000-2004 at VISTAS and

neighboring Class I areas using New IMPROVE algorithm........................ Page 17

Figure 3.1-1. Uniform Rate of Progress Glidepath for the 20% Worst Days at Dolly Sods, WV

........................................................................................................................................ Page 22

Table 4.1. Summary of 2002 Baseline Emissions Inventory for West Virginia. .............. Page 28

Figu 2re 4.2-1. SO Emissions in 2002 in the VISTAS States. .............................................. Page 30

Figu 2re 4.2-2. SO Emissions in 2002 in West Virginia. ...................................................... Page 30

Figure 5.4.a-1.The MM5 horizontal domain is the outer most, blue grid, with the CMAQ 36-km

domain nested in the MM5 domain. ............................................................ Page 38

Figure 5.4.a-2.A more detailed view of the 12-km grid over the VISTAS region.............. Page 39

Figure 6.0-1. Monitoring Networks used for VISTAS 2002 model performance evaluation, and

their location within the VISTAS 12-km domain........................................ Page 40

Table 6.1-1. Model Performance goals and criteria for components of fine particle mass

........................................................................................................................................ Page 41

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.

...................................................................................................................... Page 43

Figure 6.2-2. A zoomed view of the soccer plot depicting both the mean fractional error and

fractional bias for component concentration for all VISTAS sites based on Base

G2 results.. ................................................................................................... Page 43

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

monitors.. ..................................................................................................... Page 44

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.. ................. Page 44

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............................................ Page 45

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

days (bottom).. ............................................................................................. Page 46

F 2igure 7.2.c-1.Base G2 Annual SO emissions for 2002, 2009, and 2018 in the VISTAS states.

...................................................................................................................... Page 53

F xigure 7.2.c-2.Base G2 Annual NO emissions in 2002, 2009 and 2018 in the VISTAS states.

...................................................................................................................... Page 54

Table 7.2.c-1. 2009 Emission Inventory Summary for West Virginia............................. Page 54

Table 7.2.c-2. 2018 Emission Inventory Summary for West Virginia............................. Page 55
West Virginia Regional Haze SIP Page vii

Figure 7.2.d-1.CMAQ 2018 Base G2 results compared to the Uniform Rate of Progress at Dolly

Sods, WV..................................................................................................... Page 56

Figure 7.2.d-2.Projected visibility improvement on 20% worst visibility days at VISTAS and

neighboring Class I areas for the 2018 Base G2a CMAQ run (12 km grid) 

...................................................................................................................... Page 57

Figure 7.2.d-3.CMAQ 2018 Base G2 results at Dolly Sods................................................ Page 58

Figure 7.2.d-4.Projected visibility improvement on 20% best visibility days at VISTAS and

neighboring Class I areas for the 2018 Base G2a CMAQ run (12 km grid)

...................................................................................................................... Page 58

Figure 7.2.d-5.Visibility improvement on 20% haziest days at Dolly Sods between 2000-2004

baseline conditions (left) and 2018 Base G2a projected visibility (right). Image

generated using WINHAZE......................................................................... Page 59

Figure 7.2.d-6.Projected visibility on 20% haziest days for natural background visibility

conditions at Dolly Sods. Image generated using WINHAZE..................... Page 60

Figure 7.3-1. Estimated international emissions contribution to sulfate at VISTAS and

neighboring Class I areas. ............................................................................ Page 62

Figure 7.3-2. Measured PM component concentration on 20% worst visibility days in 2002 (left

bars) and CMAQ-simulated international contributions (right bars) at Dolly Sods,

WV............................................................................................................... Page 63

Figure 7.4-1. CMAQ projections of visibility responses on 20% worst days at Dolly Sods, WV

to 30% reductions from 2009 Base D inventory for visibility-reducing pollutants

in different source categories and geographic areas..................................... Page 65

Figure 7.5.a-1.Example back trajectories for 20% worst visibility days in 2002 for Dolly Sods

...................................................................................................................... Page 67

Figure 7.5.b-1.Residence time plot for 20% worst visibility days in 2000-2004 for Dolly Sods.

Based on trajectories with 100m start height............................................... Page 68

Figu 2re 7.5.c-1.SO Area of Influence plot for sulfate extinction weighted residence time for 20%

worst visibility days in 2000-2004 for Dolly Sods. Based on trajectories with

100m start height.......................................................................................... Page 69

F 2 4 igure 7.5.d-1.Dolly Sods 2002 (left) and 2018 (right) SO distance weighted emissions * SO

extinction-weighted residence time plots..................................................... Page 71

F 2igure 7.5.d-2.2018 SO distance weighted emissions * SO4 extinction weighted residence time

plot for Dolly Sods, WV.. ............................................................................ Page 72

Tab 2le 7.5.d-1. 2018 SO Point Source Contribution to Dolly Sods, WV by State. ......... Page 73

Table 7.5.d-2. Point Source Units Contributing at least 0.5% to Sulfate at Dolly Sods.... Page 74

Table 7.5.d-3. West Virginia Coal-Fired Electric Generating Units and Existing and Scheduled

Controls.......................................................................................................................... Page 76

F 2igure 7.5.d.3.Brigantine, NJ 2002 (left) and 2018 (right) SO distance weighted emissions *

SO4 extinction weighted residence time plots.............................................. Page 78

Tab 2le 7.5-4. 2018 SO Point Source Contribution to Brigantine, NJ by State. ............... Page 78

Figu 2re 7.5.d.4. Great Smokey Mountains, NC and TN 2002 (left) and 2018 (right) SO distance

we 4ighted emissions * SO extinction weighted residence time plots

...................................................................................................................... Page 79

Tab 2le 7.5-5. 2018 SO Point Source Contribution to Great Smokey Mountains, NC and TN by

State................................................................................................................................ Page 79
West Virginia Regional Haze SIP Page viii

Figu 2re 7.5.d.5.James River Face, VA 2002 (left) and 2018 (right) SO distance weighted

emis 4sions * SO extinction weighted residence time plots

...................................................................................................................... Page 80

Tab 2le 7.5-6. 2018 SO Point Source Contribution to James River Face, VA by State.... Page 80

Figu 2re 7.5.d.6. Linville Gorge, NC 2002 (left) and 2018 (right) SO distance weighted emissions

* SO4 extinction weighted residence time plots........................................... Page 81

Tab 2le 7.5-7. 2018 SO Point Source Contribution to Linville Gorge, NC by State......... Page 81

Figu 2re 7.5.d.7. Mammoth Cave, KY 2002 (left) and 2018 (right) SO distance weighted

emissions * SO4 extinction weighted residence time plots.......................... Page 82

Tab 2le 7.5-8. 2018 SO Point Source Contribution to Mammoth Cave, KY by State. ..... Page 82

F 2igure 7.5.d.8. Shenandoah, VA 2002 (left) and 2018 (right) SO distance weighted emissions *

SO4 extinction weighted residence time plots

...................................................................................................................... Page 83

Tab 2le 7.5-9. 2018 SO Point Source Contribution to Shenandoah, VA by State. ........... Page 83

Table 7.5.e.-1. 2018 Emissions Contributions from Major Source Categories in the Area of

Influence for Dolly Sods, WV. .............................................................. Page 85

Tab 2le 7.5.e-2. 2018 SO Emissions contributions from Major Source Categories in the Area

of Influence for Dolly Sods, WV. .......................................................... Page 86

Tab 2le 7.5.e-3. Numbers and Percentages of 2018 SO Emission Units that Contribute to

Sulfate Visibility Impairment at Dolly Sods.................................................................. Page 87

Table 7.7-1. EGU Sources with Contribution $1% to any Class I Area......................... Page 91

Table 7.7-2. Non-EGU Sources to be Evaluated for Reasonable Further Progress......... Page 92

TABLE IV-3. – Ave 2rage Costs per Ton of Annual SO Controls. ...................................... Page 94

Tab 2le IV-4.– Marginal Costs per Ton of Annual SO Controls........................................... Page 94

Tab 2le IV-5. – Estimated Costs per Tons of SO Controlled Under CAIR, Cap Levels Beginning

in 2010 and 2015............................................................................................................ Page 95

Tab 2le 7.7-3. SO Control Costs for Large Preheater Kilns (2006 Dollars)...................... Page 98

Table 7.8.a-1. BART-Eligible Sources in West Virginia. .............................................. Page 99

Table 7.8.b-1. BART Exemption Modeling Results

 12 km Modeling - Maximum Predicted Visibility Impact (delta-deciview) per Class I Areas

...................................................................................................................................... Page 101

Table 7.8.b-2. BART Exemption Modeling Results

 4km Modeling - 98 Percentile Visibility Impact (delta-deciview) per Class I Area

th

...................................................................................................................................... Page 104

Table 7.8.b-3. Exemption Modeling Results for Facilities with > 0.5 deciview of Impact

 4km Modeling - 98 Percentile Visibility Impact (delta-deciview) per Class I Area

th

...................................................................................................................................... Page 105

Table 7.8.b-4. Summary of BART Exemption Requirements for PPG (54-051-00002)

...................................................................................................................................... Page 106

Table 7.8.c-1. Summary of BART Requirements for Mt. Storm (54-023-00003). ......... Page 109

Table 7.8.c-2. Summary of BART Requirements for Capitol Cement (54-003-00006)

...................................................................................................................................... Page 110

Table 7.9-1. VISTAS Ammonia Emissions................................................................... Page 112

Tab 3le 7.9-2. West Virginia NH Emissions. .................................................................. Page 113

Table 8.0-1. West Virginia Reasonable Progress Goals. ............................................... Page 114

Figure 9.0-1. PM2.5 Monitoring Network in West Virginia.. ....................................... Page 117
West Virginia Regional Haze SIP Page ix

Table 10.0-1. 23 WV Electric Generating Units Identified in MANE-VU “EGU ASK”

...................................................................................................................................... Page 120

Table 10.0-2. Contribution of West Virginia 2018 Point Source Sulfate Emissions to Brigantine

...................................................................................................................................... Page 122
West Virginia Regional Haze SIP Page x

List of Acronyms

AoI - Area of Influence

BART - Best Available Retrofit Technology

BRET1 - Breton Wilderness Area, LA

BRIG - Brigantine Wilderness Area, NJ

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 Compact

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

GRSM1 - Great Smokey Mountains National Park, NC and TN

HAP - Hazardous Air Pollutant

HEGL1 - Hercules Glade Wilderness Area, MO

IMPROVE - Interagency Monitoring of Protected Visual Environments

IPM - Integrated Planning Model

JAR1 - 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 particles)

PM10 - Particluate Matter with a diameter of 10 micrometers or less (coarse matter)

POM - Particulate Organic Matter
West Virginia Regional Haze SIP Page xi

PSAT - PM Source Apportionment Technology

PSU/NCAR - Pennsylvania State University/National Center for Atmospheric Research

RAMS - Regional Air Modeling System

RHR - Regional Haze Rule

ROMA1 - Cape Romain Wilderness Area, SC

RPG - Reasonable Progress Goal

RPO - Regional Planning Organization

SAMI - Southern Appalachian Mountain Initiative

SAMA1 - Saint Marks National Wildlife Refuge, FL

SCC - Source Classification Code

SHEN1 - Shenandoah National Park, VA

SHRO1 - Shining Rock Wilderness Area, NC

SIP - State Implementation Plan

SIPS1- Sipsey Wilderness Area, AL

SMOKE - 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 Model

UAM - Urban Airshed Model

UPBU - Upper Buffalo Wilderness Area, AR

EPA - United States Environmental Protection Agency

VISTAS - Visibility Improvement - State and Tribal Association of the Southeast

WVDEP - West Virginia Department of Environmental Protection

WVDAQ - West Virginia Division of Air Quality

VOC - Volatile Organic Compound

VMT - Vehicle Miles Traveled
West Virginia Regional Haze SIP Page xii

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West Virginia Regional Haze SIP Page xiii

EXECUTIVE SUMMARY

Regional haze is defined as visibility impairment that is produced by a multitude of sources and

activities which emit fine particles and their precursors, and which are located across a broad

geographic area. These emissions are transported over large regions, including national parks,

forests and wilderness areas (“Class I” federal areas). The Clean Air Act mandates protection of

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. The states are required to develop and implement air quality

protection plans (State Implementation Plans) to reduce pollution that causes visibility

impairment. These plans should establish goals and emissions reduction strategies based on

trends from various sources including point, area, and mobile (both on-road and non-road)

source emissions, and commercial marine, biogenic, and wildfire and agricultural 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) are currently 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 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, West Virginia has developed a SIP to address visibility impairment in the

State’s Class I Federal Areas – Dolly Sods Wilderness Area and Otter Creek Wilderness Area –

located in the eastern mountains of West Virginia.

In developing the SIP, West Virginia has sought to develop reasonable progress goals and longterm strategies, as well as to examine the reasonableness of BART (Best Available Retrofit

Technology) reductions. As will be discussed further, for the 20 percent worst days, West

Virginia has adopted a reasonable progress goal of 7.3 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 10.4 dv by 2064. Likewise, West Virginia has adopted a

reasonable progress goal for the 20 percent best days which would result in a 1.2 dv reduction in

visibility impairment. A change of one deciview is normally perceptible to the human eye. The

aforementioned predicted reductions in visibility impairment are expected to result from

implementation of existing and planned emission controls which will be discussed in further

detail throughout this SIP document.

In addition to consultation among the VISTAS states, West Virginia has engaged in consultation

with states outside the VISTAS region as well as with federal stakeholders including Federal

Land Managers responsible for the Class I areas and the EPA.
West Virginia Regional Haze SIP Page xiv

ACKNOWLEDGMENTS: VISTAS Contractors

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

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
West Virginia Regional Haze SIP Page 1

West Virginia Regional Haze State Implementation Plan

to Preserve, Protect and Improve Visibility

in Class I Federal Areas

1.0. INTRODUCTION

1.1. What is regional haze?

Regional haze is defined as visibility impairment that is produced by a multitude of sources and

activities which emit fine particles and their precursors, and which are located across a broad

geographic area. These emissions are transported over large regions, including national parks,

forests and wilderness areas (“Class I” Federal areas).

Fine particles may either be emitted directly or formed from emissions of precursors, the most

2 x important of which are sulfur dioxide (SO ) and nitrogen oxides (NO ). Particles affect visibility

through the scattering and absorption of light, and fine particles - particles similar in size to the

wavelength of light - are most efficient, per unit of mass, at reducing visibility. Therefore,

reducing fine particles (particles with a diameter less than 2.5 μm), in the atmosphere is

generally considered to be an effective method of reducing regional haze, and thus improving

vis 2.5 ibility. The most important sources of PM and its precursors are coal-fired power plants,

2.5 industrial boilers and other combustion sources. Other significant contributors to PM and

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

Fine particles also adversely impact human health, especially respiratory and cardiovascular

systems. The United States Environmental Protection Agency (EPA) has set national ambient air

quality standards (NAAQS) for daily and annual levels of fine particles with a diameter smaller

2.5 that 2.5 μm (PM ). 

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

In Section 169A of the 1977 Amendments to the Clean Air Act (CAA), Congress set forth a

program for protecting visibility in Class I 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 or 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. Among them are some of the

nation’s most scenic areas, such as the Grand Canyon. On December 2, 1980, EPA promulgated

regulations to address visibility impairment (45 FR 80084). The 1980 regulations were

developed to address visibility impairment that is “reasonably attributable” to a single source or

small group of sources. These regulations represented the first phase in addressing visibility

impairment and deferred action on regional haze that emanates from a variety of sources until

monitoring, modeling and scientific knowledge about the relationships between pollutants and

visibility impairment improved. 
West Virginia Regional Haze SIP Page 2

In the 1990 Amendments to the CAA, Congress added Section 169B and called on EPA to issue

regional haze rules. The Regional Haze rule (RHR) that EPA promulgated on July 1, 1999 (64

FR 35713), revised the existing visibility regulations in order to 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 EPA that set

out each states’ plan for complying with the regional haze rule, including consultation and

coordination with other states and with federal land managers. The timing of the SIP submittal is

2.5 tied to EPA’s promulgation of designations for the PM National Ambient Air Quality Standard

(NAAQS) at 40 CFR Part 81. States are required to submit a regional haze implementation plan

to EPA within three years after the date of designation. Because EPA promulgated designation

dates on December 17, 2004, regional haze SIPs are due by December 17, 2007.

The regional haze rule addressed the combined visibility effects of various pollution sources

over a wide geographic region. This wide-reaching pollution net meant that all states – even

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

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

cooperation needed to address the visibility issue. West Virginia is among those states that make

up the southeastern portion of the contiguous United States known as VISTAS (Visibility

Improvement – State and Tribal Association of the Southeast), and includes the eastern band of

the Cherokee Indians in addition to the following states: Alabama, Florida, Georgia, Kentucky,

Mississippi, North Carolina, South Carolina, Tennessee, and Virginia.

Figure 1.2-1. Geographical Areas of Regional Planning Organizations
West Virginia Regional Haze SIP Page 3

1.3. General overview of regional haze SIP requirements

The regional haze rule (RHR) at 40 CFR 51.308(d) requires states to demonstrate reasonable

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

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

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

within the State and/or for each mandatory Federal Class I area located outside the State which

may be affected by emissions from sources within the State. States are to establish baseline

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

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

also known as the “glidepath”. 

The regional haze rule then requires States to establish reasonable progress goals (RPGs),

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

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

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

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

visibility for the least impaired days over the same period [see 40 CFR §51.308(d)(1)].

In order to ensure that visibility goals are properly met and set, state implementation plans must

include determinations, for each Class I area, of the baseline visibility conditions (expressed in

deciviews) for the most impaired and least impaired days. SIPs must also contain supporting

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

under natural visibility conditions for the most impaired and least impaired days [see 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 Federal Class I areas within the state [see 40 CFR §51.308(d)(4)].

This first set of reasonable progress goals must be met through measures contained in the state’s

long-term strategy covering the period from the present until 2018. The long term strategy

includes enforceable emission limitations, compliance schedules, and other measures as

necessary to achieve the reasonable progress goals, including all controls required or expected

under all federal and state regulations by 2009 and by 2018. During development of the longterm strategy, states are also required to consider specific factors such as the above-mentioned

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

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

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

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

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

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

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

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

I area. The Clean Air Act section 169A(b) defines BART-eligible sources as sources in 26

specific source categories, in operation within a 15-year period prior to the enactment of the

1977 Clean Air Act Amendments. States must determine BART according to five factors set out
West Virginia Regional Haze SIP Page 4

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

schedules for compliance with BART for each source subject to BART, must be included in the

long-term strategy.

State Implementation Plans for the first review period are due December 17, 2007. These plans

will cover long-term strategies for visibility improvement between baseline conditions in 2000-

2004 and 2018. States are required to evaluate progress toward reasonable progress goals every

five (5) years to assure that installed emissions controls are on track with emissions reduction

forecasts in each SIP. The first interim review is due to EPA five years after submittal of the

initial SIP. If emissions controls fail to meet SIP forecasts, then states should implement

additional emissions controls by 2018 to be consistent with the SIP or revise the SIP to be

consistent with the updated emissions forecast.

The EPA provided several guidance documents listed below to assist the states in

implementation of the regional haze rule requirements. West Virginia followed these guidance

documents in developing the technical analyses reported in this plan.

• Guidance for Tracking Progress Under the Regional Haze Rule (EPA-454/B-03-004,

September 2003).

• Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule

(EPA-454/B-03-005, September 2003).

• Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air

Quality Goals for Ozone, PM2.5, and Regional Haze (EPA, 2007).

• Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program

(EPA, June 2007).

1.4. Class I areas within West Virginia

West Virginia has two Class I areas within its borders: the Dolly Sods Wilderness Area and the 

Otter Creek Wilderness Area. 

The United States Congress designated the Dolly Sods Wilderness in 1975 and it now has a total

of 10,215 acres. All of the wilderness is in West Virginia and it is managed by the Forest

Service.

In the mid-1800s, the Dalhe family used open grassy fields called “sods” for grazing sheep in

this area, which now bears the name Dolly Sods wilderness. The region, located high on the

Allegheny Plateau, is known for its extensive rocky plains, upland bogs, and sweeping vistas. In

the lower elevations, you’ll find a forest of northern hardwoods and laurel thickets. Higher up,

groves of wind stunted red spruce stand near heath barrens where azaleas, mountain laurels,

rhododendron, and blueberries grow. The bogs are unique depressions of sphagnum moss,

cranberries, and the insect-eating sundew plant – an ecosystem you’d expect to see in northern

Canada. Beaver ponds dot the Wilderness and the headwaters of Red Creek spill out of the area.
West Virginia Regional Haze SIP Page 5

The United States Congress designated the Otter Creek Wilderness in 1975 and it now has a total

of 20,000 acres. All of the wilderness is in West Virginia and is managed by the Forest Service.

In a natural bowl between Shavers Mountain (on the east side) and McGowan Mountain (on the

west side) lies Otter Creek Wilderness. Most of the numerous streams in the area flow into Otter

Creek, which runs north across the Wilderness into the Dry Fork River. These streams frequently

flash flood during periods of heavy rain. From the mouth of Otter Creek, the terrain rises to

about 3,900 feet on McGowan Mountain. The area, logged extensively between 1897 and 1914,

now sports a second-growth forest, dense thickets of rhododendron and mountain laurel along

the streams, and a variety of mosses in damper regions. Spruce dominate the higher country and

give wa to hardwoods such as black cherry and yellow birch lower down. Black bears have

returned and are reunited with white-tailed deer, wild turkeys, hares, rabbits, grouse, and several

species of squirrels. Beavers are active in several spots. Timber rattlesnakes may be seen

Although Dolly Sods and Otter Creek are separate and distinct wilderness areas, and both are

mandatory Federal Class I areas, they are being treated as one area for regional haze purposes

due to their close proximity to each other, and the fact that there is only one IMPROVE –

Interagency Monitoring of Protected Visual Environments – monitor which is located at Dolly

Sods. The West Virginia Division of Air Quality (WVDAQ) within the WV Department of

Environmental Protection (WVDEP) is responsible for developing the Regional Haze SIP. This

SIP establishes reasonable progress goals for visibility improvement at these Class I areas, and

prescribes a long term strategy that will achieve those reasonable progress goals within the first

regional haze planning period.

Figure 1.4-1. West Virginia Cass I areas
West Virginia Regional Haze SIP Page 6

In developing this SIP, WVDAQ has also considered that emission sources outside of West

Virginia may affect visibility at Dolly Sods and Otter Creek, and that emissions sources within

West Virginia may affect visibility at Class I areas in neighboring states. Through VISTAS, the

southeastern states have worked together to assess state-by-state contributions to visibility

impairment in specific Class I areas, including those in West Virginia, as well as those affected

by emissions from West Virginia. This technical work is discussed further in Chapters 5, 6 and 7

below. A full discussion of the process and outcome of consultations between West Virginia and

other states is contained in Chapter 10.

Prior to 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 federal land managers, the Environmental Protection

Agency, 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 Mountain 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 sulfur dioxide emissions. SAMI also concluded that

reducing ammonia emissions would be helpful to reduce ammonium nitrate contributions to

visibility impairment. Emissions 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 emissions reductions would occur in the state where emissions

reductions were made. Emissions in surrounding SAMI states and states outside the SAMI

region also contribute to air quality in the SAMI Class I 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 region. SAMI states committed to consider air quality benefits in the Southern

Appalachians as they developed State Implementation Plans for the health standards.

In 2002, the North Carolina legislature passed the North Carolina Clean Smokestack Act (CSA)

the requires North Carolina utilities to reduce emissions of sulfur dioxide and nitrogen oxides.

The SAMI analyses and recommendations supported development of the North Carolina CSA.

While not directly affecting coal-fired power plants in West Virginia, the NC CSA, by reducing

emissions that may be transferred across state borders, will reduce visibility impairment in West

Virginia and is therefore mentioned throughout the SIP as an effective inter-state option to

control emissions.

Congress subsequently considered several legislative bills to reduce sulfur dioxide and nitrogen

oxides from electric generating utilities but ultimately did not adopt any of them. In 2004, the

EPA promulgated the Clean Air Interstate Rule (CAIR) to require emissions reductions for

sulfur dioxide and nitrogen oxides from electric generating utilities in 28 eastern states and the

District of Columbia. West Virginia is a CAIR participating 2 state. The CAIR rule caps SO and

NOx and allows for interstate trading of emissions to yield cost effective reductions. These

reductions will significantly improve visibility in Class I areas in West Virginia.
West Virginia Regional Haze SIP Page 7

1.5. State and Federal Land Manager Coordination

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

consultation between the States and Federal Land Managers on the implementation of the

visibility protection program, including development and review of implementation plan

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

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

the State.

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

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

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

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

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

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

Memorandum of Agreement and Bylaws is enclosed as Appendix A. 

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

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

control levels that will achieve interim visibility goals; and provide adequate documentation to

member agencies so that they can develop their regional haze State/Tribal Implementation Plans

(SIP/TIP). Figure 1.5-1 shows the 18 mandatory Federal Class I areas in the VISTAS Region,

where visibility is an important value. Table 1.5-1 lists these Class I areas.

Figure 1.5-1. Class I Areas in the VISTAS Region
West Virginia Regional Haze SIP Page 8

A technical support document for the regional haze state implementation plans was prepared

cooperatively by VISTAS staff and states to characterize regional haze in the southeastern

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

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

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

existing control programs. Source contributions to VISTAS mandatory Federal Class I areas are

also assessed.
West Virginia Regional Haze SIP Page 9

Table 1.5-1. Mandatory Federal Class I Areas in the VISTAS Region

Where Visibility is an Important Value 1

State Area Name Acreage F ederal

Land

Manager

40 CFR §81.401

Alabama

Sipsey Wilderness Area 24,922 USDA-FS

40 CFR §81.407 

Florida

Chassahowitzka Wilderness Area 23,579 USDI-FWS

Everglades National Park 1,397,429 USDI-NPS

St. Marks Wilderness Area 17,350 USDI-FWS

40 CFR §81.408

Georgia

Cohotta Wilderness Area 36,977 USDA-FS

Okefenokee Wilderness Area 353,981 USDI-FWS

Wolf Island Wilderness Area 5,126 USDI-FWS

40 CFR §81.411

Kentucky

Mammoth Cave National Park 51,303 USDI-NPS

40 CFR §81.422 

North Carolina

Great Smoky Mountains National Park 273,551 USDI-NPS 2

Joyce Kilmer-Slickrock Wilderness Area 13,562 USDA-FS 3

Linville Gorge Wilderness Area 11,786 USDA-FS

Shining Rock Wilderness Area 18,483 USDA-FS

Swanquarter Wilderness Area 8,785 USDI-FWS

40 CFR §81.426 

South Carolina

Cape Romain Wilderness Area 29,000 USDI-FWS

40 CFR §81.428

Tennessee

Great Smoky Mountains National Park 241,207 USDI-NPS 2

Joyce Kilmer-Slickrock Wilderness Area 3,832 USDA-FS 3

40 CFR §81.433

Virginia

James River Face Wilderness Area 8,886 USDA-FS

Shenandoah National Park 190,535 USDI-NPS

40 CFR §81.435 

West Virginia

Dolly Sods Wilderness Area 10,215 USDA-FS

Otter Creek Wilderness Area 20,000 USDA-FS

40 CFR Subpart D, §§81.401 through 81.437 lists those mandatory Federal Class I areas, established under the 1

Clean Air Act Amendments of 1977, where the Administrator, in consultation with the Secretary of the Interior, has

determined visibility to be an important value. The listing of areas where visibility is an important value represents

an evaluation of all international parks (IP), national wilderness areas (Wild) exceeding 5,000 acres, national

memorial parks (NMP) exceeding 5,000 acres, and national parks (NP) exceeding 6,000 acres, in existence on

August 7, 1977. Consultation by EPA with the Federal Land Managers involved: The Department of Interior

(USDI), National Park Service (NPS), and Fish and Wildlife Service (FWS); and the Department of Agriculture

(USDA), Forest Service (FS).

 Great Smoky Mountains National Park, 514,758 acres overall, of which 273,551 acres are in North Carolina, and 2

241,207 acres are in Tennessee.

 Joyce Kilmer-Slickrock Wilderness, 14,033 acres overall, of which 10,201 acres are in North Carolina, and 3,832 3

acres are in Tennessee.
West Virginia Regional Haze SIP Page 10

2.0. ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND

ESTIMATE OF NATURAL BACKGROUND CONDITIONS IN

CLASS I AREAS

The goal of the regional haze rule is to restore natural visibility conditions to the 156 Class I

areas identified in the 1977 Clean Air Act Amendments. 40 CFR -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.

The regional haze rule defines regional haze as “visibility impairment that is caused by the

emission of air pollutants from numerous sources located over a wide geographic area”. The

regional haze rule goes on to define visibility impairment as “any humanly perceptible change in

visibility (light extinction, visual range, contrast, coloration) from that which would have existed

under natural conditions”.

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 ). Light extinction affects the clarity and color of objects being -1

viewed.

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

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

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

incremental change in visibility perceived by the human eye. Most people can easily detect a one

dec ext iview change in visibility. Another useful measure is light extinction, b , which is inversely

related to the deciview.

Figure 2.0-1. Shows the important factors involved in seeing a scenic vista.
West Virginia Regional Haze SIP Page 11

Figure 2.0-1. Important factors involved in seeing a scenic vista are outlined. Image-forming

information from an object is reduced (scattered and absorbed) as it passes

through the atmosphere to the human observer. Air light is also added to the sight

path by scattering processes. Sunlight, light from clouds, and ground-reflected

light impinge on and scatter from particulates located in the sight path. Some of

this scattered light remains in the sight path, and at times it can become so bright

that the image essentially disappears. A final important factor in seeing and

appreciating a scenic vista are the characteristics of the human observer. 

[Source: Introduction to Visibility by William C. Malm, Air Resource Division,

National Park Service]

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

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

of Protected Visual Environments (IMPROVE) program was established in 1985. IMPROVE is

a cooperative measurement effort governed by representatives from Federal, State and regional

organizations. IMPROVE’s objectives are, among other things, to establish current visibility

conditions in Class I areas; to track changes in visibility; to identify emission sources responsible

for man-made visibility impairment; and to provide regional haze monitoring data for all Class I

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 areas and quickly grew

to 30 sites by 1999. Finally, with the enactment of the regional haze rule, the network has been
West Virginia Regional Haze SIP Page 12

expanded to 80 additional sites (for a total of 110 sites) with additional sites for special projects.

Figure 2.0-2 depicts the location of IMPROVE monitoring sites in the VISTAS region.

18 Class I Areas in VISTAS States and Monitor Locations

Figure 2.0-2. IMPROVE Monitoring sites in the VISTAS Region

Some Class I areas employ special optical equipment that measures visibility directly. More

common, however, are instruments which sample the air for the various fine particulate matter

which causes visibility impairment. A mathematical relationship (extinction algorithm) has been

established between these different kinds of particulates and visibility. When the amounts of
West Virginia Regional Haze SIP Page 13

sulfates, nitrates, carbonaceous matter and other sample elements are measured, visibility can be

estimated (reconstructed). 

It is the average of the IMPROVE monitoring data for 2000 through 2004 that can be thought of

as “current” visibility conditions for this initial period. The comparison of initial baseline

conditions to natural visibility conditions indicates the amount of improvement necessary to

attain natural visibility by 2064. Each state must estimate natural visibility levels for Class I

areas within its borders in consultation with Federal Land Managers and other states [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 reductions committed to in

the SIP.

For each Class I area, there are three (3) metrics of visibility that are part of the determination of

reasonable progress:

1) natural conditions,

2) baseline conditions, and

3) current conditions.

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

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

humidity factors. Total light extinction, when converted to deciviews, 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 conditions. It is the average of the

IMPROVE monitoring data for 2000 through 2004 and is equivalent to “current” visibility

conditions for this initial review period. The comparison of initial baseline conditions to natural

conditions indicates the amount of improvement necessary to attain natural visibility by 2064.

Each state must estimate natural visibility levels for Class I areas within its borders in

consultation with the Federal Land Managers (FLMs) 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 if compared to the reductions committed to in the SIP.

2.1. Estimating Natural Conditions for Dolly Sods and Otter Creek Wilderness Areas

Natural background visibility, as defined in Guidance for Estimating Natural Visibility

Conditions Under the Regional Haze Program, EPA-454/B-03-005, September 2003, is based

on annual average concentrations of fine particulate components. The same annual average

natural background visibility is assumed for all Class I areas in the eastern United States

(separate values are estimated for the western United States). Natural background visibility for

the 20 percent worst days is estimated by assuming that the fine particle concentration for natural

background are normally distributed and the 90 percentile of the annual distribution represents th

natural background visibility on the 20% worst days.
West Virginia Regional Haze SIP Page 14

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

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

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

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

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

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

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

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

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

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

between the original and new IMPROVE equation.

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

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

recommended by the IMPROVE Steering Committee. For a more detailed discussion of the two

IMPROVE equations, see Appendix B.

The “old” IMPROVE equation is used to estimate light scattering by particles:

bext . 3 x f (RH) x [Sulfate] + 3 x f (RH) x [Nitrate] + 4 x [Organic Mass] 

+ 10 x [Elemental Carbon] + 1 x [Fine Soil] + 0.6 x [Coarse Mass] + 10.

The “new” IMPROVE equation is used to estimate light scattering by particles:

bext . S L 2.2 x f (RH) x [Small Sulfate] + 4.8 x f (RH) x [Large Sulfate]

+ S L 2.4 x f (RH) x [Small Nitrate] + 5.1 x f (RH) x [Large Nitrate]

+ 2.8 x [Small Organic Mass] + 6.1 x [Large Organic Mass]

+ 10 x [Elemental Carbon] + 1 x [Fine Soil]

+ 1.7 SS f (RH) x [Sea Salt] + 0.6 x [Coarse Mass]

+ Rayleigh Scattering (Site Specific)

+ 2 0.33 x [NO (ppb)].

2.2. Estimating Baseline Conditions for Dolly Sods and Otter Creek Wilderness Areas

Baseline visibility conditions at Dolly Sods and Otter Creek Wilderness Areas are determined

from sampling data collected at the IMPROVE monitoring site located in the Dolly Sods
West Virginia Regional Haze SIP Page 15

Wilderness Area. A five year average (2000 to 2004) was calculated for the 20% worst and the

20% best visibility days in accordance with 40 CFR 51.308(d)(2) and Guidance for Tracking

Progress Under the Regional Haze Rule, EPA-454/B-03-004, September 2003. IMPROVE data

records for Dolly Sods for the period 2000 to 2004 exceed EPA requirements for data

completeness. IMPROVE does not operate a monitor at the Otter Creek Wilderness Area and

considers the IMPROVE monitor data from Dolly Sods to be representative of visibility at Otter

Creek. The light extinction and deciview visibility values for the 20 percent best and 20 percent

worst visibility days at the Class I areas are based on data and calculations included in Appendix

B of this SIP.

2.3. Summary of Natural Background and Baseline Conditions for Dolly Sods and Otter

Creek Wilderness Areas

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

Sods and Otter Creek Wilderness Areas. Note that West Virginia is not considering international

emissions to be a component of natural background. Baseline visibility on the 20 percent worst

days at Dolly Sods is 29.0 dv. Natural background visibility on the 20 percent worst days is

predicted to be 10.4 dv. Values are shown to a precision of one decimal place for technical

accuracy. However, changes of less than ±0.5 dV are likely to be imperceptible. 

Table 2.3-1. Natural Background and Baseline Conditions 

for Dolly Sods and Otter Creek Wilderness Areas

Natural Background Conditions

Class I Area Average for

20 percent

Worst Days

(deciviews)

Average for

20 percent

Best Days

(deciviews)

Average for

20 percent

Worst Days

Bext (Mm )

-1

Average for

20 percent

Best Days

Bext (Mm )

-1

Dolly Sods Wilderness 10.4 3.6 18.5 4.4

Otter Creek Wilderness 10.4 3.6 18.5 4.4

Baseline Visibility Conditions (2000-2004)

Class I Area Average for

20 percent

Worst Days

(deciviews)

Average for

20 percent

Best Days

(deciviews)

Average for

20 percent

Worst Days

Bext (Mm )

-1

Average for

20 percent

Best Days

Bext (Mm )

-1

Dolly Sods Wilderness 29.0 12.3 194.6 35.4

Otter Creek Wilderness 29.0 12.3 194.6 35.4
West Virginia Regional Haze SIP Page 16

2.4. Pollutant Contributions to Visibility Impairment (2000-2004 Baseline Data)

The 20 percent worst visibility days at Dolly Sods and Otter Creek generally occur during the

period May to September. The peak hazy days occur in the summer under stagnant weather

conditions with high relative humidity, high temperatures, and low wind speeds. The 20 percent

best visibility days can occur at any time of the year.

Figure 2.4-1. Average light extinction for the 20% Haziest Days in 2000-2004 at VISTAS and

neighboring Class I areas using New IMPROVE algorithm
West Virginia Regional Haze SIP Page 17

Figure 2.4-2. Average light extinction for the 20% Clearest Days in 2000-2004 at VISTAS

and neighboring Class I areas using New IMPROVE algorithm

Ammonium sulfate 4 2 4, (NH ) SO , is the most important contributor to visibility impairment and

fine particle mass on the 20 percent worst and 20 percent best visibility days at Dolly Sods and

Otter Creek. Sulfate levels on the 20 percent worst days account for 87% of the visibility

impairment. Across the VISTAS region, sulfate levels are higher at the VISTAS inland sites than

at the VISTAS coastal sites (Figure 2.4.1). On the 20 percent clearest days, sulfate levels are

more uniform across the region (Figure 2.4.2). [Note that in these two figures, the levels at Dolly

Sods are also considered to be representative of the levels at Otter Creek.]

The best average visibility and lowest sulfate values on the clearest days occurred at Shining

Rock, NC, at 1621 meter elevation, and is likely influenced on the clearest days by regional

transport of air masses above the boundary layer. Sulfate particles are formed in the atmosphere

2 4 from sulfur dioxide emissions. Sulfate particles occur as hydrogen sulfate, H (SO ), ammonium

bisulfate, HNH4 4 SO 4 2 4 , and ammonium sulfate, (NH ) SO , depending on the availability of

ammonia NH3, in the atmosphere. 
West Virginia Regional Haze SIP Page 18

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

and light extinction on the 20 percent haziest and the 20 percent clearest days at Dolly Sods.

Elevated levels of POM and elemental carbon (EC), indicate impact from wildfires or prescribed

fire. Significant fire impacts are infrequent at Dolly Sods. VISTAS collected additional samples

of carbon at five sites, including Mammoth Cave, KY; Great Smokey Mountains, NC; Millbrook

(Raleigh, NC); Cape Romain, SC; and Shenandoah, VA to better understand sources

contributing to carbon in rural and urban areas. At Shenandoah and other mountain Class I areas

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

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

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

summer. Biogenic carbon emissions at Cape Romain and other coastal sites were lower than

emissions at the forested mountain sites. Carbon from gasoline and diesel engines is a relatively

small contributor at the rural sites. At Millbrook, an urban site, carbon from fossil fuel

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

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

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

2.5 sources of carbon will likely be more effective at reducing levels of PM in urban areas.

Ammonium n 4 3 itrate, NH NO , is formed in the atmosphere by reaction of ammonia and nitrogen

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

temperature. Ammonia preferentially reacts with sulfur dioxide and sulfate before reacting with

nitrogen oxides. Particle nitrate is formed at lower temperatures; at elevated temperatures

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

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

higher on winter days and are more important for the coastal sites where the 20 percent worst

days can occur on winter days. Modeling data (see section 7) indicate that in the VISTAS region

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

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

than controls of nitrogen oxides.

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

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

levels are higher at urban monitors than at the Class I areas. This suggests that control of fossil

2.5 fuel combustion sources would be more effective at reducing PM in urban areas than to

improve visibility in Class I areas.

Soil fine particles are minor contributors to visibility at most sites on most days. Occasional

episodes of elevated fine soil can be attributed to Saharan dust episodes, particularly at the

Everglades, FL, but are rarely seen at Dolly Sods. No control strategies are indicated for fine

soil.

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

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

levels do not contribute significantly to visibility impairment on the 20 percent worst visibility

days. The new IMPROVE equation uses Chloride ion, Cl , from routine IMPROVE -

measurements to calculate sea salt levels. Cl may react with nitrate and volatilize off the filter, -
West Virginia Regional Haze SIP Page 19

so that Cl may underestimate sea salt levels. VISTAS funded sodium ion, Na , analyses of the - +

IMPROVE filters from October 2003 to December 2004 for nine sites. Sodium ion

measurements may overestimate sea salt levels because sodium nitrate is an additional source of

sodium ion that cannot be distinguished from sodium chloride. Our best understanding to date

suggests that using Cl may underestimate sea salt levels by as much as a factor of two. This -

uncertainty has great implication for calculating sea salt levels under natural visibility

conditions.

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

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

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

An unidentified component i 2.5 s reported by IMPROVE as the difference between the total PM

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 the

molecular weights of the other components.

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

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

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

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

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

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

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

that in the VISTAS region and in West Virginia, the most effective means to improve visibility

is to reduce sulfate concentrations.

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

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

and differences in composition of fine particle mass. However, the two networks use different

mea 2.5 surement and analytical techniques, making data comparison difficult. Several PM

nonattainment areas are in close proximity to the Class I areas in the southeastern US, including

Atlanta, GA; Birmingham, AL; Charleston, WV; Chattanooga, TN; Louisville, KY; and

Knoxville, TN. Ammonium sulfate concentrations are comparable between urban and nearby

Class I areas, while organic carbon, elemental carbon and nitrate concentrations are generally

higher in urban areas than the Class I areas. These results suggest that sulfate is widely

distributed regionally while urban areas see additional incremental pollutant loadings from local

emissions sources.

Role of meteorology in determining visibility conditions: specialized mathematical techniques,

including Classification and Regression Tree Analyses (CART) were used to characterize the

relationship between meteorological conditions and visibility conditions at the Class I areas.

Days were assigned to one of five visibility classes ranging from poor to good visibility. Days

were then assigned to bins based on meteorological conditions. For Dolly Sods poor visibility

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

2.5 speeds and elevated PM mass at upwind urban areas. Precipitation was not a good predictor of
West Virginia Regional Haze SIP Page 20

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.
West Virginia Regional Haze SIP Page 21

3.0. UNIFORM RATE OF PROGRESS TO NATURAL CONDITIONS IN

2064

As stated in Section 1.3 above, the regional haze rule directs states to graphically show what

constitutes a “uniform rate of progress” toward natural conditions for each mandatory Federal

Class I area within the State, as well as for each mandatory Federal Class I area located outside

the State that may be affected by emissions from sources within the State. The uniform rate of

progress is also known as the “glidepath.” The glidepath is simply a straight graphical line drawn

from the baseline level of visibility impairment for 2002-2004 to the level representing no

anthropogenic (man-made) impairment in 2064.

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

achieving natural visibility conditions by 2064. Section 40 CFR §51.308(d)(1) of the regional

haze rule requires that reasonable progress goals must both:

1) provide for improvement in visibility for the most impaired days over the period of

the implementation plan; and

2) ensure no degradation in visibility for the least impaired days over the same period.

Uniform rate of progress graphs (glidepaths), were developed for each Class I area in the

VISTAS region. The glidepaths were developed in accordance with EPA’s guidance for tracking

progress and used data collected from the IMPROVE monitoring sites as described in Section 2

of this document. The glidepath is one of the indicators used in setting reasonable progress goals.

3.1. Glidepath for the Dolly Sods and Otter Creek Wilderness Areas

The following is the glidepath for the Dolly Sods and Otter Creek Wilderness Areas. As noted in

Section 1.4, although Dolly Sods and Otter Creek are separate and distinct wilderness areas, and

both are mandatory Class I areas, they are being treated as one area for regional haze purposes

due to their close proximity to each other, and the fact that there is only one IMPROVE monitor,

which is located at Dolly Sods. Natural background visibility is predicted to be 10.4.
West Virginia Regional Haze SIP Page 22

Figure 3.1-1. Uniform Rate of Progress Glidepath for the 20% Worst Days at Dolly Sods, WV
West Virginia Regional Haze SIP Page 23

4.0. NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY

IMPAIRMENT IN DOLLY SODS AND OTTER CREEK

WILDERNESS AREAS

The regional haze rule at 40 CFR 51.308(d)(4) states: 

. . . The implementation plan must also provide for the following:

(i) . . . .

(v) A statewide inventory of emissions of pollutants that are reasonably anticipated

to cause or contribute to visibility impairment in any mandatory Class I Federal area.

The inventory must include emissions for a baseline year, emissions for the most

recent year for which data are available, and estimates of future projected emissions.

The State must also include a commitment to update the inventory periodically.

This Section addresses the emissions inventory requirements of 40 CFR 51.308(d)(4)(v). In

addition, West Virginia commits to periodically updating the inventory as required by the

Consolidated Emissions Reporting Rule 40 CFR 51.30 [60FR39602, 10JUN2002]. 

4.1. Baseline Emissions Inventory

The regional haze rule at 40 CFR 51.308(d)(4)(v) requires a statewide emissions inventory of

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

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

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

ox 2.5 10 ides, fine particulate (PM ), coarse particulate (PM ), ammonia and sulfur dioxide. A more

detailed discussion of the baseline emissions inventory for 2002 is contained in Appendix D.

There are five different emission inventory source classifications: stationary point; stationary

area; on-road (highway); off-road; and biogenic sources. Stationary point sources include those

sources that emit greater than a specified tonnage per year, for which the data is provided at the

process level. Electric generating utilities and industrial sources comprise the major categories

for stationary point sources. 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 (i.e. dry cleaners, service stations, agricultural sources, fires emissions, etc.). These

types of emissions are estimated on a county level. On-road (highway) mobile sources include

cars, 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. Non-road (or offroad) mobile sources are equipment that can move but do not use roadways, i.e., lawn mowers,

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

like stationary area sources, are estimated on a county level. Biogenic sources are the natural

sources like trees, crops, grasses and the natural decay of plants. The emissions from these

sources are estimated on a county level.

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

The first is the actual base year inventory. This inventory reflects the base year emissions that
West Virginia Regional Haze SIP Page 24

correspond to the meteorological data used in the modeling effort, in this case, 2002. These

emissions are used for evaluating 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 emissions changes from year to

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

developed for the electric generating units (EGUs) and the wildfire emissions. The air quality

modeling results, using the typical base year inventory, are used to calculate relative reduction

factors for future years. These relative reduction factors for future years are then used to

demonstrate reasonable progress toward visibility goals.

A synopsis of the inventories used for each source classification is provided below. A more

detailed discussion of the emissions inventory development can be found in Appendix D.

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

a single hazardous air pollutant (HAP), or 25 tpy total HAP are inventoried annually. Minor

(smaller) sources have been inventoried less frequently. The point source emissions data can be

grouped as EGU sources and other industrial point sources, also called non-EGU sources.

4.1.a.1.Electric Generating Units (EGUs)

The actual base year inventory for the EGU sources used 2002 continuous emissions monitoring

(CEM) data reported to the EPA’s Acid Rain program or 2002 hourly emissions data provided

by 2 x the stakeholders. These data provide hourly emissions profiles for SO and NO that can be

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

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

on measured emissions of SO2 x and NO .

EGU source emissions are a significant portion of the emissions inventory. They vary daily and

seasonally as a function of energy demand and utilization as well as outage schedules. Therefore,

a typical base year inventory was developed to avoid potential anomalies in future year

emissions that might be created by relying on a single year (2002) to represent operations. This

approach is consistent with the EPA’s modeling guidance. To develop a typical year 2002

emissions inventory for EGU sources, each unit’s average CEM heat input for 2000 through

2004 was divided by the 2002 actual heat input to generate a unit specific normalizing factor.

This normalizing factor was then multiplied by the 2002 actual emissions. The heat inputs for

the period 2002 through 2004 were used since the modeling current design values use

monitoring data from this same 5-year period. If a unit was shutdown for an entire year during

2000 through 2004 period, the average of the years the unit was operational was used. If a unit

was shutdown in 2002, but not permanently shutdown, the emissions and heat inputs 2001 (or

2000) were used in the normalizing calculations.
West Virginia Regional Haze SIP Page 25

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

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

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

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

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

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

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

specified constraints including air pollution regulations, transmission bottlenecks, and plant

specific operational constraints. The versatility of IPM allows users to specify which constraints

to exercise, and to populate IPM with their own datasets. The EPA also relies on IPM for

regulatory purposes.

The IPM modeling runs took into consideration both CAIR implementation and North Carolina’s

CSA requirements for Duke Power and Progress Energy.

4.1.a.2.Other Industrial Point Sources (Non-EGUs)

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

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

4.1.b. 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 (i.e.,

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

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

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

county level.

West Virginia used the 2002 CERR data as the basis for the stationary area source inventory. In

addition, the VISTAS contractor calculated emissions for animal husbandry, wild fires and

particulate matter from paved and unpaved roads. For other states within the modeling domain,

the state supplied data or the CERR data for 2002 was used. West Virginia updated estimates for

the Residential Fuel/Wood Combustion categories for coal, distillate oil, natural gas, and wood

from the 2002 preliminary NEI file. These data were posted to EPA’s CDX site and made

available to VISTAS’ contractors. Another component of the area source inventory that VISTAS

focused on was related to the development of ammonia emissions from livestock and fertilizers,

as well as PM emissions from paved roads. Version 3.6 of the Carnegie Mellon University
West Virginia Regional Haze SIP Page 26

(CMU) NH3 model was used to improve the NH3 emissions from livestock and fertilizer

application. EPA developed more current paved road PM emission estimates using an improved

methodology. VISTAS obtained the new data and incorporated it into the inventory. The

improved data was resubmitted to the NEI during the final comment period.

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

categories, except for wild fires. For wildfires, a typical year inventory was generated 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 would remain the same for air quality modeling

in both the base and future years. The VISTAS Fire Special Interest Work Group used state

records to ratio the number of acres burned over a longer time period (three or more years, as

available from state records) to 2002 for each fire type. Based on these ratios, the 2002 acreage

was scaled up or down to develop a typical year inventory.

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

the regional haze modeling. Growth factors, supplied by the states or taken from the EPA’s

CAIR emissions projections, were applied to project the controlled emissions to the appropriate

year. If no growth factor was available from either the states or the CAIR growth factor files the

EPA’s Economic Growth and Analysis System Version 5 growth factors were used.

4.1.c. Off-Road Mobile Sources

Off-road, or “non-road”, mobile sources include equipment that can move but do not use the

roadway system, 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 the EPA’s NONROAD2005c model. For the three source categories not included in the

NONROAD model, i.e., aircraft engines, railroad locomotives and commercial marine, more

traditional methods of estimating emissions were used. The same inventory is used for both the

actual and typical base year emissions inventories.

For the source categories estimated using the EPA’s NONROAD model, the model growth

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

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

the commercial marine vessels, railroad locomotive and airport emissions, the VISTAS

contractor calculated the future year emissions using detailed inventory data (both before and

after controls) for 1996 and 2010 obtained from the CAIR Technical Support Document. When

available, state specific growth factors were used. Preliminary data in the NEI listed all of the

CMV river traffic emissions from several West Virginia, Ohio and Kentucky counties at the Port

of Huntington. Emissions were allocated to the respective counties in the West Virginia

correction to the NEI. The data also incorrectly reported residual oil usage for the CMV

category. Spatial emissions allocations improvements were made by the DAQ for several

business and nonroad engine categories as well. Review of the Preliminary NEI showed 0.0 tons

of emissions from the coal mining category in several counties where coal mining is prevalent.

Further analysis revealed that County Business Patterns data is often reported as numeric ranges

rather than as distinct values. When the NONROAD model encounters ranges, it interprets them
West Virginia Regional Haze SIP Page 27

as non-numeric and processes them as 0.0 values. These errors were corrected in the 2002 NEI

and provided to the VISTAS’ contractors.

4.1.d. Highway Mobile Sources

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

inputs for MOBILE include information on the age of vehicles on the roads, the average speeds

on the roads, the mix of vehicles on the roads, any programs in place in an area to reduce

emissions from motor vehicles (e.g., emissions inspection programs), and temperature.

The MOBILE model takes into consideration regulations that affect emissions from this source

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

onroad vehicles using input data reflective of 2002. The MOBILE model then was run for 2009

and 2018 using input data reflective of those years. For West Virginia, the speed, vehicle age and

vehicle mix data are all MOBILE 6.2 defaults. The 2002 vehicle miles traveled (VMT) was

obtained from the West Virginia Department of Transportation (WVDOT) and provided to

VISTAS along with West Virginia-specific temperature data. 

4.1.e. Biogenic Emissions Sources

Biogenic emissions were prepared with the SMOKE-BEIS3 (Biogenic Emissions Inventory

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

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

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

are the same as the emission factors in UAM-BEIS3. 

The basis for the gridded land use data used by BEIS3 is the county land use data in the Biogenic

Emission Landcover Database version 3 (BELD3) provided by the EPA. A separate land

classification scheme, based upon satellite (Advanced Very High Resolution Radiometer

[AVHRR], 1 km spatial resolution) and census information, aided in defining the forest,

agriculture and urban portions of each county.

4.1.f. Summary 2002 Base G2 (February 2007 version) Baseline Emission Inventory for

West Virginia

Table 4.1 is a summary of the 2002 baseline emissions inventory for West Virginia. 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.
West Virginia Regional Haze SIP Page 28

Table 4.1. Summary of 2002 Baseline Emissions Inventory for West Virginia

VOC

(tpy)

NH3

(tpy)

PM10

(tpy)

PM2.5

(tpy)

NOx

(tpy)

SO2

(tpy)

Point 15,775 453 22,076 15,523 277,589 570,153

Area 60,443 9,963 115,346 21,049 12,687 11,667

Non-Road Mobile 18,566 9 1,850 1,728 33,239 2,112

On-Road Mobile 45,284 2,036 1,481 1,068 63,525 2,635

Biogenics 357,850 N/A N/A N/A 2,776 N/A

TOTAL 499,976 12,461 143,771 42,385 390,703 586,568

4.1.g. Model Performance Improvements through Emissions Inventory Improvements

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

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

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

below:

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

estimates for future year utility production and emissions. Continuous Emissions

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

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

provided by utility companies in 2006 through winter 2007.

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

model input and vehicle-miles-traveled data.

• For ammonia emissions from agricultural sources, the Carnegie Mellon University

ammonia model was used to improve annual and monthly estimates.

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

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

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

In 2006, the United States Forest Service and Fish and Wildlife Service provided

projections of increased prescribed burning in 2009 and 2018; these data were

incorporated in the Base G inventory for all states except Florida.

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

Base G.

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

Ocean, ENVIRON created gridded emissions for the VISTAS modeling domain using
West Virginia Regional Haze SIP Page 29

inventory data newly developed for EPA by Corbett at the University of Delaware. These

emissions were incorporated in the Base G modeling.

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

as available.

4.2. Assessment of Relative Contributions from Specific Pollutants and Source

Categories (2002-04 Baseline Data)

Ammonium sulfate is the largest contributor to visibility impairment at Dolly Sods and Otter

Creek and reduction of sulfur dioxide emissions would be the most effective means of reducing

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

states are attributable to electric generating facilities and industrial point sources. As shown in

Figure 4.2-2, 98% of the SO2 emissions in West Virginia are attributable to electric utilities and

industrial point sources.

[This space intentionally blank.]
West Virginia Regional Haze SIP Page 30

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

F 2igure 4.2-2. SO Emissions in 2002 in West Virginia
West Virginia Regional Haze SIP Page 31

5.0. REGIONAL HAZE MODELING METHODS AND INPUTS

Modeling for regional haze was performed by VISTAS for the ten Southeastern states, including

West Virginia. The sections below outline the methods and inputs used by VISTAS for the

regional modeling. Additional details are provided in Appendices C, D and M.

5.1. Analysis Method

The modeling analysis is a complex technical evaluation that begins by selection of the modeling

system. VISTAS chose the following modeling system:

• Meteorological Model: The Pennsylvania State University/National Center for

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

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

studies.

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

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

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

photochemical grid models.

• Air Quality Model: EPA’s Models-3/ Community Multiscale Air Quality (CMAQ)

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

addressing ozone, particulate matter (PM), visibility and acid deposition at regional scale

for periods up to one year.

The EPA’s 2007 Modeling Guidance recommends modeling an entire year or, at a minimum,

several days in each quarter of a year, to adequately represent the range of meteorological

conditions that contribute to elevated levels of fine particulate matter. VISTAS selected the year

2002 as the modeling year for this demonstration. Meteorological inputs were developed for

2002 using the meteorological model. Emissions inventories were also developed for 2002 and

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

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

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

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

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

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

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

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

progress.

The complete modeling protocol used for this analysis can be found in Appendix C.
West Virginia Regional Haze SIP Page 32

5.2. Model Selection

To ensure that a modeling study is defensible, care must be taken in the selection of the models

to be used. The models selected must be scientifically appropriate for the intended application

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

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

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

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

verification at no or low cost. 

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

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

system used for this modeling attainment demonstration.

5.2.a. Selection of Photochemical Grid Model

5.2.a.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 it meets the same several general criteria as a model for an attainment

demonstration for a national ambient air quality standard (NAAQS): 

• The model has received a scientific peer review,

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

• Databases needed to perform the analysis are available and adequate,

• Available past appropriate performance evaluations have shown the model is not biased

toward underestimates or overestimates,

• A protocol on methods and procedures to be followed has been established, 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.

5.2.a.2. Overview of CMAQ

The photochemical model selected for this study was CMAQ version 4.5. For more than a

decade, the EPA has been developing the Models-3 CMAQ modeling system with the

overarching aim of producing a ‘One-Atmosphere’ air quality modeling system capable of

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

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

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

‘holistic’ environmental modeling utilizing state-of-science representations of atmospheric
West Virginia Regional Haze SIP Page 33

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

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

operating science model under the Models-3 framework. 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 one-atmospheric

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

attainment demonstration SIPs, as well as the regional haze SIP. A number of features in

CMAQ’s theoretical formulation and technical implementation make the model well suited for

annual PM modeling.

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

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

5.2.b. Selection of Meteorological Model

5.2.b.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 models ability to accurately replicate important meteorological phenomena in the region

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

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

for the meteorological model to be used in this study:

• Non-Hydrostatic Formulation

• Reasonably current, peer reviewed formulation

• Simulates Cloud Physics

• Publicly available on no or low cost

• Output available in I/O API format 

• Supports Four Dimensional Data Assimilation (FDDA)

• Enhanced treatment of Planetary Boundary Layer heights for AQ modeling

5.2.b.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
West Virginia Regional Haze SIP Page 34

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

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

mesoscale studies. 

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

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

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

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

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

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

different coordinate systems by performing a vertical interpolation, followed by a massconservation 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 threedimensional analyses performed at 12-hour intervals on the outermost grid mesh selected by the

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

analyze standard surface and radiosonde observations, using the National Meteorological

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

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

The 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. MM5 enjoys a

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

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

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

somewhat better photochemical model inputs than alternative models. 

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

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

5.2.c. Selection of Emissions Processing System

5.2.c.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 following
West Virginia Regional Haze SIP Page 35

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

5.2.c.2. Overview of SMOKE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

processing.

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

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

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

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

countries and any pollutant can be processed by the system.

For additional information about the SMOKE model please refer to Modeling Protocol

(Appendix C).
West Virginia Regional Haze SIP Page 36

5.3. Selection of the Modeling Year

A crucial step to SIP modeling is the selection of the period of time to model to represent current

air quality conditions and to project changes in air quality in response to changes in emissions.

The year 2002 was selected as the base year for several reasons. 

The 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 the following principal criteria:

• Simulate a variety of meteorological conditions;

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

baseline design value or visibility impairment;

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

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

monitor violating the NAAQS is based on multiple days.

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

is to model a full, representative year. Moreover, the required relative reduction factor (RRF)

values should be based on model results averaged over the 20 percent worst and the 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 which have already been modeled, (b) choose

periods which are drawn from the years upon which the current design values are based, (c)

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

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

as possible. Finally, the EPA explicitly recommended in its 2007 guidance the use of 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:

• Representativeness of Meteorological Conditions: The VISTAS meteorological

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

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

episodes.

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

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

used Classification and Regression Tree (CART) analyses to evaluate visibility
West Virginia Regional Haze SIP Page 37

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 200 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 five-year baseline period for the VISTAS Class I area. This analysis

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

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

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

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

representativeness of alternative baseline modeling periods from a regulatory standpoint.

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

inventory data for the Comprehensive Emissions Reporting Rule (CERR), it made sense

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

• Years to be used by other RPOs: VISTAS also considered what years other RPOs 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 (January 1 through December 31) 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).

5.4. Modeling Domains

5.4.a. Horizontal Modeling Domain

The EPA’s 2007 modeling guidance recommends a 12-km modeling grid resolution for PM2.5

modeling while a 36-km grid resolution is considered acceptable for regional haze. For the

VISTAS modeling, a coarse 36-km grid resolution was used for modeling the entire United

States and a fine 12-km grid was used to model the eastern United States.

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

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

appropriate for the generally stagnant conditions experienced during West Virginia ozone

episodes. Two-way nesting was not considered due to numerical and computational uncertainty

associated with the technique.

The horizontal coarse grid modeling domain boundaries were determined through a national

effort to develop a common grid projection and boundary. A smaller 12-km grid, modeling

domain was selected in an attempt to balance location of areas of interest, such as ozone and fine
West Virginia Regional Haze SIP Page 38

particulate matter nonattainment areas, as well as Class 1 and wilderness areas for regional haze. 

Processing time was also a factor in choosing a smaller 12-km grid, modeling domain.

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

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

MM5 domain. Figure 5.4.a-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.a-1. The MM5 horizontal domain is the outer most, blue grid, with the CMAQ 36-

km domain nested in the MM5 domain

To achieve finer spatial resolution in the VISTAS states, a one-way nested high resolution

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

the VISTAS region. This is the modeling domain for which the reasonable progress goals are

assessed. 
West Virginia Regional Haze SIP Page 39

Figure 5.4.a-2.A more detailed view of the 12-km grid over the VISTAS region

5.4.b. 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 level. A layer-averaging

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

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

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

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

ambient monitoring data.
West Virginia Regional Haze SIP Page 40

6.0. MODEL PERFORMANCE EVALUATION

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

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

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

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

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

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

the final meteorological and air quality modeling configuration in the modeling protocol

contained in Appendix E and Appendix C, respectively.

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

early version of the VISTAS emissions inventory. In keeping with the one-atmosphere objective

of the CMAQ modeling platform, model performance was evaluated based on measured ozone,

fine particles, and acid deposition in the Air Quality System (AQS), IMPROVE, Speciated

Trends Network (STN), Southern 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.

Figure 6.0-1. Monitoring Networks used for VISTAS 2002 model performance evaluation,

and their location within the VISTAS 12-km domain.
West Virginia Regional Haze SIP Page 41

6.1. Modeling Performance Goals, Criteria and Results

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

particle mass (Table 6.1-1) based on previous model performance for ozone and fine particles.

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

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

other metrics were also calculated and are included in the full model performance evaluation

(Appendix F.2).

Table 6.1-1. Model Performance goals and criteria for components of fine particle mass

Fractional

Bias

Fractional

Error

Comment

#15% #35% Goal for PM model performance based on ozone model

performance, considered excellent performance

#30% #50% Goal for PM model performance, considered good performance

#60% #75% Criteria for PM model performance, considered average

performance. Exceeding this level of performance indicated

fundamental concerns with the modeling system and triggered

diagnostic evaluation.

Several graphic displays of model performance were prepared including:

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

monitoring network and month;

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

monitoring site and month;

3. Spatially average time series plots;

4. Time series plots of monthly fractional bias and error for a species, region and network;

5. Performance goal plots (“soccer plots”) that summarize model performance by species,

region and 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 2007

modeling guidance for Ozone, PM2.5 and Regional Haze. Both “soccer plots” and “bugle plots”

are convenient ways 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
West Virginia Regional Haze SIP Page 42

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 mas (e.g. elemental carbon,

nitrate and soil). 

6.2. VISTAS Domain-wide Performance

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

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

Base G2-Actual annual air quality modeling. Focus is limited to the IMPROVE monitoring

network as these sites are the locations used in projecting visibility improvement goals in the

Class I areas.

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

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

component species will dictate good model performance of total or reconstituted fine particulate

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

extinction will also be provided as a means to discuss the overall model performance for this

Implementation Plan.

In 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.2. The soccer and bugle plots for all of the VISTAS IMPROVE

monitors are included here for summary purposes. The goal and criteria levels used for regional

haze model performance were 30% and 60%, respectively, for mean fractional bias and 50% and

75%, respectively, for mean fractional error. 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 the 20

percent worst days. Plots for individual IMPROVE monitors associated with West Virginia’s

Class I areas are included in Appendix F.2.

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

generally fall outside of the performance criteria thresholds (red box). Sulfates and organics

generally fall within goal thresholds (green box), with a couple of months falling just outside the

goals thresholds but well within the criteria thresholds. In these figures, each point represents a

month. Figure 6.2-3 contains separate soccer plots for each season. The seasonal plots emphasize

poorer nitrate performance in the summer, 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 mean fractional bias (MFB) and mean fractional error

(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 Base G2 modeling results to be representative and acceptable for use in

modeling projection for ozone, particulate matter and regional haze.
West Virginia Regional Haze SIP Page 43

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

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

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

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

Figure 6.2-2. A zoomed view of the soccer plot depicting both the mean fractional error and

fractional bias for component concentration for all VISTAS sites based on Base

G2 results. Each point represents a monthly value as compared to the model

performance criteria (red box) and modeling performance goals (green box).
West Virginia Regional Haze SIP Page 44

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

monitors.

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

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

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

performance criteria (red lines) and modeling performance goals (green lines).
West Virginia Regional Haze SIP Page 45

Figure 6.2-5. Bugle plot of mean fraction error for particulate matter and its component species

for all VISTAS sites based on Base G2 results. Each point represents a monthly

mean fraction bias value as compared to the model performance criteria (red

lines) and modeling performance goals (green lines).

6.3. West Virginia Class I Areas Performance

The following section provides stacked bar charts comparing observed and modeled fine

pa 2.5 rticulate matter (PM )composition. The data have been split into two charts, with the first

displaying the 20 percent best days followed by the chart for the 20 percent worst days. Stacked

bar charts showing mass composition have been developed for the Dolly Sods IMPROVE

monitoring site (see Figure 6.3-1). The Dolly Sods Wilderness Area IMPROVE monitor is used

to represent the Otter Creek Wilderness Area.

The stacked bar chart allows a side by side comparison of each of the days observed and

modeled composition 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 carbon (bextEC)

• Brown = light extinction due to soil (bextSoil)

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

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

modeled bar (right hand bar), so it is easy to identify days when the predicted light extinction for
West Virginia Regional Haze SIP Page 46

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

reconstructed particulate matter light extinction value.

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

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

modeled composition represented by the right hand bar.
West Virginia Regional Haze SIP Page 47

A review of the stacked bar charts reiterates that sulfates are a large contributor to light

extinction in the West Virginia Class I areas on both the 20 percent best days and the 20 percent

worst days. The bar charts also suggest that organic carbon and nitrates are important on the 20

percent best days at Dolly Sods. The bar charts for the 20 percent best days reiterate the general

under-prediction seen in previous sections. The under-prediction of sulfate on most of 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 days chart to the 20 percent worst days chart, one notices that the

various components of particulate pollution play a more prominent role in the 20 percent best

days than with the 20 percent worst days. Also, the species makeup on the 20 percent best days

varies more widely compared to the 20 percent worst days. This suggests that accurately

modeling each species is especially important on the 20 percent best days.

With the bar chart for the 20 percent worst days, it can be seen that model performance does

improve. 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 days series, though some days are still under

predicted.

Overall, the WVDAQ found model performance to fall within acceptable limits. The WVDAQ

further asserts that the one atmosphere modeling performed by the VISTAS contractors is

representative of conditions in the southeastern states and is applicable for use in setting

reasonable progress goals for the Class I areas.
West Virginia Regional Haze SIP Page 48

7.0. LONG-TERM STRATEGY FOR WEST VIRGINIA’S CLASS I

AREAS

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

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

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

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

period from the baseline until 2018. This section discusses development of West Virginia’s longterm strategy.

7.1. Overview of the Long-term Strategy Development Process

The monitored data and modeling analyses cited in sections 2 and 5 above establish that, for the

VISTAS region, the key contributors to regional haze in the 2002-04 baseline period were large

stationary sources of sulfur dioxide emissions. Keeping that key conclusion in mind, this section

addresses the following questions:

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

West Virginia and the VISTAS region, how much visibility improvement, compared to

the glidepath, would be expected at Class I areas in West Virginia between now and

2018?

b. What additional emission controls represent BART in West Virginia?

c. If additional emission reductions were needed, from what pollutants and source

categories would the greatest visibility benefits be realized between the baseline and

2018?

d. Narrowing down further, in what geographic locations are the emissions found which

have the greatest impact on visibility in specific Class I areas?

e. What types of emissions sources do are found in those geographic locations?

f. Which specific individual sources in those geographic locations have the greatest impact

on visibility?

g. What additional emission controls represent reasonable progress for those specific

sources?

h. Given the additional emission reductions expected from reasonable progress and BART,

how much additional visibility improvement, compared to the glidepath, is expected at

Dolly Sods and Otter Creek between the baseline and 2018?
West Virginia Regional Haze SIP Page 49

7.2. Expected Visibility Results in 2018 for West Virginia Class I Areas under existing

and planned emissions controls (Base G2 Inventory)

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

These programs are described in more detail below. 

7.2.a. Federal and State Control Requirements

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

cap emissions 2 x of sulfur dioxide (SO ) and nitrogen oxides (NO ) in the eastern United States by

2015. When fully 2 implemented, CAIR will reduce SO emissions in these states by more than

70 x percent and NO emissions by more than 60 percent from 2003 levels. The CAMR will

reduce utility emissions of mercury from 48 tons a year to 15 tons, a reduction of nearly 70

percent.

NOx x SIP Call. Phase I of the NO SIP call applies to certain Electric Generating Units (EGUs)

and large non-EGUs, including large industrial boilers and turbines, and cement kilns. Those

States affected by x the NO SIP Call in the VISTAS region have developed rules for the control

of NOx x emissions that have been approved by the EPA. This NO SIP Call has resulted in a 68

pe xrcent reduction in NO emissions from large stationary combustion sources. For this analysis,

x

the emissions for NO SIP call affected sources were capped at 2007 levels and carried forward

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

North Carolina Clean Smokestacks Act (CSA). Under the act, enacted in 2002, coal-fired power

plants in North Carolina must achieve a 77-percent cut in nitrogen oxide (NOx) emissions by

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

annua 2 x l caps on both SO and NO emissions for the two primary utility companies in North

Carolina, Duke Energy and Progress Energy. These reductions must be made in North Carolina,

and allowances are not saleable.

Georgia Multi-Pollutant Bill. Georgia enacted regulations in summer 2007 to require coal fired

power 2 x plants in Georgia to reduce SO by approximately 90%, NO by approximately 50%, and

mercury by an estimated 75-85% by 2015. Reductions will affect 21 units at seven facilities. 

The rule requires specific controls on specific units according to a specific schedule. This is not

a cap and trade rule, thus there are no allowances to be banked, sold or traded.

Consent Agreements (Santee Cooper, TECO, VEPCO, Gulf Power, EKPC, Dupont, and Stone

Container).

• Santee Cooper [US District Court of South Carolina, Charleston Division]: A 2004

consent agreement calls for Santee Cooper to install and commence operation of

con 2 x tinuous emission control equipment for PM/SO /NO emissions; comply with systemwide annua 2 x 2 x l PM/SO /NO emissions limits; agree not to buy, sell or trade SO /NO

allowances allocated to Santee Cooper System as a result of said agreement; and to

comply with emission unit limits of said agreement.
West Virginia Regional Haze SIP Page 50

• TECO [US District Court, Middle District of Florida]: Under a settlement agreement, by

2008, Tampa Electric will install permanent emissions-control equipment to meet

stringent pollution limits; implement a series of interim pollution-reduction measures to

reduce emissions while the permanent controls are designed and installed; and retire

pollution emission allowances that Tampa Electric or others could use, or sell to others,

x 2 to emit additional NO , SO and PM. 

• VEPCO [US District Court, Eastern District of Virginia]: Virginia Electric and Power

Co. ag 2reed to spend $1.2 billion between by 2013 to eliminate 237,000 tons of SO and

NOx emissions each year from eight coal-fired electricity generating plants in Virginia

and West Virginia. 

• Gulf Power 7 [State of Florida “Agreement for the Purpose of Ensuring Compliance with

the Ozone Ambient Air Quality Standards”, dated August 28, 2002]: A 2002 agreement

calls for x Gulf Power to upgrade its operation to cut NO emission rates by 61 percent at

its Crist 7 generating plant by 2007 with major reductions beginning in early 2005. The

Crist plant is a significant source of nitrogen oxide emissions in the Pensacola area.

• EKPC [US District Court, Eastern District of Kentucky, Central Division, Lexington]: A

July 2, 2007 consent agreement between the EPA and East Kentucky Power Cooperative

2

requires the utility to reduce its emissions of SO by 54,000 tons per year and its

emis xsions of NO by 8,000 tons per year, by installing and operating selective catalytic

reduction (SCR) technology; low-NOx burners, and PM and mercury Continuous

EMissions Monitors at the utility’s Spurlock, Dale and Cooper Plants. According to the

EPA, total emissions from the plants will decrease 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 CAIR, to meet the reduction requirements of the

consent decree. EKPC is expected to spend $654 million to install pollution controls.

• Dupont [US District Court for the Southern District of Ohio]: A 2007 agreement calls for

E. I. Dupont Nemours & Company’s James River plant to install dual absorption

pollution control equipment by September 1, 2009, resulting in emission reductions of

app 2roximately 1,000 tons SO annually.

• Stone Container [US District Court, Eastern District of Virginia]: A 2004 agreement

calls for the West Point Paper Mill owned by Smurfit/Stone Container to control with a

we 2t scrubber the SO emissions of the #8 Power Boiler. This control device should result

2

in reductions of over 3,500 tons of SO in 2018.

• Alabama Power [US District Court, Northern District of Alabama, Southern Division]:

Under a 2006 Partial Consent Decree APC agreed to spend approximately $200 million

by 2012 to install pollution controls on Plant Miller Units 3 and 4. Upon full

implementation, the agreement will cause APC to reduce approximately 28,000 tons per

year 2 x of emissions of sulfur dioxide (SO ) and nitrogen oxides (NO ) from Plant Miller.
West Virginia Regional Haze SIP Page 51

• AEP [US District Court for the Southern District of Ohio, Eastern Division]: American

Electric x Power agreed to spend $4.6 billion dollars to eliminate 72,000 tons of NO

emissions each year 2 by 2016 and 174,000 tons of SO emissions each year by 2018 from

sixteen plants located in Indiana, Kentucky, Ohio, Virginia and West Virginia.

Heavy Duty Diesel (2007) Engine Standard. EPA set a PM emissions standard for new heavyduty engines of 0.01 grams per brake-horsepower-hour (g/bhp-hr), to take full effect for diesel

eng xines in the 2007 model year. This rule also includes standards for NO and non-methane

hydroca xrbons (NMHC) of 0.20 g/bhp-hr and 0.14 g/bhp-hr, respectively. These NO and NMHC

standards will be phased in together between 2007 and 2010 for diesel engines. Sulfur in diesel

fuel must be lowered to enable modern pollution-control technology to be effective on these

trucks and buses. 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).

Tier 2 Tailpipe. 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 x of vehicles a manufacturer sells each year must have average NO emissions

below a specified value. Tier 2 standards became effective in the 2005 model year.

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

emissions of NOx, hydrocarbons (HC), and carbon monoxide (CO) from several groups of

previously unregulated nonroad engines. Included in these are large industrial spark-ignition

engines and recreational vehicles. Nonroad spark-ignition engines are those powered by

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

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

forklifts, electric generators, airport baggage transport vehicles, and a variety of farm and

construction applications. Nonroad recreational vehicles include snowmobiles, off-highway

motorcycles, and all terrain vehicles. These rules were initially effective in 2004 and will be

fully phased-in by 2012.

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

percent from nonroad diesel equipment, and reduce sulfur levels by 99 percent from current

levels in nonroad diesel fuel starting in 2007. This step will apply to most nonroad diesel fuel in

2010 and to fuel used in locomotives and marine vessels in 2012. (http://www/epa/gov/nonroaddiesel/)

Industrial Boiler/Process Heater/RICE MACT. The EPA issued final rules to substantially

reduce emissions of toxic air pollutants from industrial, commercial and institutional boilers,

process heaters and from stationary reciprocating internal combustion engines (RICE). These

rules reduce emissions of a number of toxic air pollutants, including hydrogen chloride,

mangane 2se, lead, aresenic and mercury by 2009. This rule also reduces emissions of SO and PM

in conjunction with the toxic air pollutant reductions. The applied Maximum Achievable Control

Technology 2 10 (MACT) control efficiencies were 4 percent for SO and 40 percent for PM and

PM2.5. (EPA’s industrial boiler MACT rules were vacated on June 8, 2007. The VISTAS “best
West Virginia Regional Haze SIP Page 52

and final” modeling run will make adjustments to the control assumptions due to this MACT

rule based on input from each state. West Virginia has determined that the controls assumptions

should remain, since case-by-case MACT under CAA Section 112j is triggered and the state

does not expect the controls to be any less stringent than required by the vacated rule.)

Combu xstion Turbine MACT. The projection inventories do not include the NO co-benefit

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

Combustion Engines, which EPA estimates to be small compared to the overall inventory.

VOC 2-, 4-, 7-, and 10-year MACT Standards. Various point source MACTs and associated

emission reductions were implemented. Reductions occurring before 2002 were not counted.

7.2.b. Additional State programs to reduce emissions

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

as required under the Regional Haze Rule 308(d)(3)(v)(A), states are also required to consider

the air quality benefits of measures to mitigate the impacts of construction activities [section

308(d)(3)(v)(B)] and agricultural and forestry smoke management [section 308(d)(3)(v)(E)].

These state measures are discussed in more detail in Section 7.9 and in Appendix H.

7.2.c. 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 West Virginia, and growing the 2002 inventory using EGAS6, MOBILE6.2, 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. These updates for Base G2 are documented in the

MACTEC emissions inventory report “Documentation of the 2002 Base Year and 2009 and

2018 Projection Year Emissions Inventories for VISTAS” dated January 2007.

For non-EGUs, recently updated growth and control data consistent with the data used in EPA’s

CAIR analysis was used and supplemented by State and local agency data and updated forecasts

from the US Department of Energy (US DOE).

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

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

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. West Virginia does not require mobile source controls, other than Stage I

vapor recovery in five (5) counties: Kanawha, Putnam, Cabell, Wayne and Wood. Nonroad
West Virginia Regional Haze SIP Page 53

control data and projections for 1996, 2010, 2015 and 2020 were obtained from the EPA’s Clean

Air Interstate (CAIR) Technical Support Document, and straight line projections were used to

estimate 2009 and 2018 levels.

The following 2 x bar charts show expected decreases in emissions of SO and NO across the

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

a 2re contained in Appendix H). Note that for SO emissions in particular, which are the largest

contributor to haze, emissions from electric generating facilities are expected to decrease

drama 2tically (70%) between 2002 and 2018, with SO reductions from EGUs in West Virginia

of 83%. However, even after implementation of CAIR, EGU emissions are projected to remain

2

the largest contributor to haze, comprising more than half of the remaining SO emissions in

every state.

F 2igure 7.2.c-1. Base G2 Annual SO emissions for 2002, 2009, and 2018 in the VISTAS

states.
West Virginia Regional Haze SIP Page 54

F xigure 7.2.c-2. Base G2 Annual NO emissions in 2002, 2009 and 2018 in the VISTAS

states.

7.2.c.1. Summary of Final Base G2 Emission Inventories for 2009 and 2018

Table 7.2.c-1 is a summary of the 2009 Base G2 emission inventory. See Table 4.1 for a

comparison to the 2002 baseline emissions inventory. The complete inventory and discussion of

the methodology is contained in Appendix D.

Table 7.2.c-1. 2009 Emission Inventory Summary for West Virginia

VOC

(tpy)

NH3

(tpy)

PM10

(tpy)

PM2.5

(tpy)

NOx

(tpy)

SO2

(tpy)

Point 15,171 671 22,747 15,709 124,358 333,463

Area (includes fires) 57,346 10,625 118,106 23,680 14,326 12,284

Non-Road Mobile 18,069 11 1,640 1,528 30,133 359

On-Road Mobile 26,584 2,292 1,145 734 33,392 299

Biogenics 357,850 N/A N/A N/A 2,776 N/A

TOTAL 475,021 13,598 143,637 41,651 209,986 346,404
West Virginia Regional Haze SIP Page 55

Table 7.2.c-2 is a summary of the 2018 emission inventory. The complete inventory and

discussion of the methodology is contained in Appendix D.

Table 7.2.c-2. 2018 Emission Inventory Summary for West Virginia

VOC

(tpy)

NH3

(tpy)

PM10

(tpy)

PM2.5

(tpy)

NOx

(tpy)

SO2

(tpy)

Point 17,952 593 28,084 20,165 94,600 177,517

Area (includes fires) 62,806 11,504 124,566 24,507 15,716 12,849

Non-Road Mobile 14,086 13 1,292 1,198 25,710 56

On-Road Mobile 14,652 2,268 747 369 15,530 231

Biogenics 357,850 N/A N/A N/A 2,776 N/A

TOTAL 467,347 14,377 154,688 46,239 154,332 190,653

7.2.d. Model Results for the 2018 Base G2 Inventory Compared to the Uniform Rate of

Progress Glidepaths for Dolly Sods and Otter Creek Wilderness Areas

Using 2000-2004 IMPROVE monitoring data, the deciview value for the 20 percent best days in

each year are averaged together, producing a single average deciview value for the best days.

Similarly, the deciview values for the 20 percent worst days in each year are averaged together,

producing a single average deciview value for the worst days.

Figure 7.2.d-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 for Dolly Sods (DOSO). The red lines show the target natural

condition in 2064 for the 20 percent worst days, and the incremental deciview 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 emissions controls during the period of the

first long term strategy.
West Virginia Regional Haze SIP Page 56

Figure 7.2.d-1. CMAQ 2018 Base G2 results compared to the Uniform Rate of Progress at

Dolly Sods, WV
West Virginia Regional Haze SIP Page 57

Figure 7.2.d-2. Projected visibility improvement on 20% worst visibility days at VISTAS and

neighboring Class I areas for the 2018 Base G2a CMAQ run (12 km grid) 

Note that for Dolly Sods, visibility improvements on the 20 percent worst days are expected to

be better than the uniform rate of progress glidepath by 2018, based solely on reductions from

existing and planned emissions controls. At Dolly Sods, a 4.3 dv improvement in visibility

would meet the uniform rate of progress in 2018; expected emissions reductions by 2018 are

projected to achieve a 7.3 dv improvement. In fact, as illustrated in Figure 7.2.d-2, visibility

improvements at all the VISTAS mountain Class I areas and most of the coastal Class I areas are

projected to be better than the uniform rate of progress. In Figure 7.2.d-2, the percentage of the

target reduction achieved for Dolly Sods using the new IMPROVE equation is 165%. This

means that the rate of improvement is 65% greater than the uniform rate of progress by 2018.

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 areas. As illustrated in Figure 7.2.d3, visibility on the 20 percent best days is projected to improve at all VISTAS Class I areas as a

result of the 2018 Base G2 emissions reductions. In Figure 7.2.d-3, the percentage of the target

achieved for Dolly Sods is about -12%. Zero percent change would mean no change in visibility;

-12% means that visibility is better than no change, or a 12% improvement (values lower than

current conditions).
West Virginia Regional Haze SIP Page 58

Figure 7.2.d-3. CMAQ 2018 Base G2 results at Dolly Sods.

Figure 7.2.d-4. Projected visibility improvement on 20% best visibility days at VISTAS and

neighboring Class I areas for the 2018 Base G2a CMAQ run (12 km grid)
West Virginia Regional Haze SIP Page 59

The expected change in visibility at Dolly Sods between 2000-2004 baseline conditions on the

20 percent worst days and 2018 projections for Base G2a is illustrated in Figure 7.2.d-4. In

contrast, natural background conditions for the 20 percent worst days are illustrated in Figure

7.2.d-5. These images were generated using WINHAZE, a photographic imaging tool that

accounts for the effect of concentrations of tine 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 need to reach natural background conditions.

Figure 7.2.d-5. Visibility improvement on 20% haziest days at Dolly Sods between 2000-

2004 baseline conditions (left) and 2018 Base G2a projected visibility (right).

Image generated using WINHAZE.
West Virginia Regional Haze SIP Page 60

Figure 7.2.d-6. Projected visibility on 20% haziest days for natural background visibility

conditions at Dolly Sods. Image generated using WINHAZE.

7.3. Relative Contribution from International Emissions to Visibility Impairment in

2018 at VISTAS Class I areas

Em 2.5 issions from Mexico, Canada, Central America, Asia and Africa contribute to PM loadings

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

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

United States, VISTAS used a combination of modeling results from the GEOS-Chem 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 3-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

conditions for the national CMAQ modeling domain, which included portions of Canada and

Mexico. The emissions from these countries were included within the national CMAQ modeling

domain or as part of the boundary conditions outside the national modeling domain, as

appropriate.

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

Class I areas. All international fires are treated as natural emissions in these runs, so both cases

underestimate the contributions from international fires that are due to anthropogenic burning.
West Virginia Regional Haze SIP Page 61

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

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

run both United States and international anthropogenic emissions were removed. The

difference represents international anthropogenic emissions, in the absence of United

States anthropogenic emissions (e.g. compared to 2064 levels). Harvard University

provided GEOS-Chem results to VISTAS for 2002 international contribution on a 4 x 5

degree grid. Concurrently, Harvard modeling for the Electric Power Research Institute

(EPRI) provided GEOS-Chem results for 2001 international contribution on a more

refined 1 x 1 degree grid scale.

2. International emissions are represented by the difference between two CMAQ 36-km

simulations, both using 2018 Base F emissions and boundary conditions from GEOSChem – In the first CMAQ run, all global natural and anthropogenic emissions in 2018

are active. In the second CMAQ run, only global (United States and international) natural

emissions are active. Here the impacts of international emissions are compared against

2018 conditions rather than natural background.

Separate from VISTAS, the CENRAP RPO used PM Source Apportionment Technology

(PSAT) in the CAMx regional air quality model to calculate the impact of Canadian and

Mexican emissions and boundary conditions on visibility at Class I areas in 2002. VISTAS has

compared its results to results from other RPOs on the impact of international emissions and

boundary conditions on visibility at Class I areas in 2002; the results were similar.

As illustrated in Figure 7.3-1, for annual average contributions to sulfate at VISTAS and

neighboring Class I areas, the estimated international contributions are higher at Class I areas

near the Canadian and Mexican borders and along the eastern coast. The estimated international

contribution is higher using CMAQ and PSAT than in the GEOS-Chem runs because the grid

scale is finer (more accurate dispersion of emissions) and because the background atmosphere

includes loadings from current United States anthropogenic emissions (greater photochemical

activity). Similar charts for nitrate and organic carbon mass, for impacts on 20 percent worst

visibility days, and for impacts of international emissions on calculated light extinction are

included in Appendix H.
West Virginia Regional Haze SIP Page 62

Figure 7.3-1. Estimated international emissions contribution to sulfate at VISTAS and

neighboring Class I areas

In Figure 7.3-2 CMAQ projections of contributions from international emissions to PM mass on

20 percent worst visibility days in 2002 at Dolly Sods, West Virginia are compared to United

States domestic contributions to PM components at the site on those days.
West Virginia Regional Haze SIP Page 63

Figure 7.3-2. Measured PM component concentration on 20% worst visibility days in 2002

(left bars) and CMAQ-simulated international contributions (right bars) at

Dolly Sods, WV.

Although VISTAS assessed impacts from international emissions at the Class I areas, these

modeling runs were to provide information to the states to assist them in understanding the

potential impacts at their Class I areas. The modeling showed that at Dolly Sods the impacts

from international emissions were approximately 1 dv of the current conditions. Since good

projected emissions for areas outside the continental United States are not readily available,

there is no real way to assess the impacts from international emissions in 2018, other than to

assume that the impact would be approximately the same as for the baseline conditions.

Therefore, WVDAQ will not be accounting for international emissions in setting the 2018

reasonable progress goals for Dolly Sods and Otter Creek.

Nevertheless, as the atmosphere becomes closer to natural background conditions in the future,

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.
West Virginia Regional Haze SIP Page 64

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

Geographic Areas

An important step toward identifying future reasonable progress measures is to identify the key

pollutants contributing to visibility impairment at each Class I area. To understand the relative

benefit of further reducing emissions from different pollutants, source sectors and geographic

areas, VISTAS engaged the Georgia Institute of Technology to perform emission sensitivity

model runs using CMAQ. Emissions sensitivities were initially performed for three episodes

representing winter and summer conditions: Jan 2002, Jul 2001 and July 2002. These runs used

the initial 2018 projection inventory and considered 30% reductions from specific pollutants,

source categories and geographic areas. Emissions sensitivities were repeated using the 2009

Base D projection inventory and two month-long episodes from 2002; Jun 1 - Jul 10 and Nov 19

- Dec 19. Emissions in 2009 were reduced by 30% for each pollutant sensitivity run. The

pollutant contributions that were evaluated were:

2

• SO from EGU sources in each VISTAS state, other RPOs in the VISTAS 12-km grid,

and Boundary Conditions from outside the 12-km domain

2

• SO from non-EGU point sources in each VISTAS state, other RPOs and Boundary

Conditions

x

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

other RPOs

x

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

3

• NH from all sources in VISTAS and other RPOs

• Volatile Organic Compounds from anthropogenic 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 source sin each VISTAS State and other RPOs

• Primary Carbon from all fires in each VISTAS state and other RPOs

Results are shown in Figure 7.4-1 below for the average of the 20 percent worst visibility days

for Dolly Sods. Responses for the 20 percent worst days were calculated by averaging the

responses for the 20 percent worst days that were modeled in the two episodes. For Dolly Sods,

responses on 9 of the 20 percent worst visibility days were included in the graphic.
West Virginia Regional Haze SIP Page 65

Figure 7.4-1. CMAQ projections of visibility responses on 20% worst days at Dolly Sods, WV

to 30% reductions from 2009 Base D inventory for visibility-reducing pollutants

in different source categories and geographic areas.

As Figure 7.4-1 illustrates, the greatest visibility benefits on the 20 percent worst days for Dolly

Sod 2s are projected to result from further reducing SO emissions from EGUs. At Dolly Sods,

benef 2its are projected from SO reductions from EGUs in several VISTAS states, including

Florida, Georgia, Kentucky, North Carolina, Tennessee, Virginia and West Virginia.

Con 2tributions from other RPOs and SO coming from outside the boundary are also significant.

The greatest benefit would likely be from further EGU reductions in West Virginia, the MRPO,

and 2 from outside the boundary. Additional, smaller benefits would come from additional SO

emission reductions from non-utility, industrial point sources. Within the VISTAS states, the

2

relative importance of SO reductions from non-EGUs is similar to that for EGUs.

Ammonium nit 2.5 rate is a small contributor to PM mass and visibility impairment on the 20

percent worst day x 3 s at Dolly Sods. Therefore the benefits of reducing NO and NH are small. 

Volatile Organic Compounds (VOC) in West Virginia originate primarily from biogenic sources,

as vegetative emissions, and also contribute to visibility impairment. Controlling anthropogenic

sources of VOC has little, if any, visibility benefit at Dolly Sods. Reducing primary carbon from
West Virginia Regional Haze SIP Page 66

point sources, ground level sources or fires is projected to have minimal visibility benefit. This is

consistent with the monitoring data which shows that most measured organic carbon is

secondary in origin and primary carbon is only a small fraction of the total measured carbon

(Appendix B). Reducing carbon from fires was not found to be effective because there was little

fire activity at these sites on the days modeled in the sensitivity analyses.

Note that these results from the emission sensitivity runs are consistent with the conclusions

drawn from the 2000-2004 baseline monitoring data (see Section 2.4 above). The results indicate 

that sulfate is the dominate contributor to visibility impairment on the 20 percent worst days at

all VISTAS sites and that ammonium nitrate can be important for sites where 20 percent worst

day 2s occur in the winter. We conclude that reducing SO emissions from EGU and non-EGU

point sources in West Virginia and the Midwest RPO would have the greatest visibility benefit

for Dolly Sods. Contributions from other VISTAS states and MANE-VU are also significant for

this area.

7.5. Relative Contributions to Visibility Impairment: Geographic Locations of the

Largest Emissions Sources Contributing to Visibility Impairment at Dolly Sods and

Otter Creek Wilderness Areas

Once 2 it was determined that SO emission reductions from EGU and non-EGU point sources in

the VISTAS states would be the most effective sources to control to improve visibility at the

Class I areas, the next step was to identify the specific geographic areas that most likely

2

influence visibility in each Class I area, and then to identify the major SO point sources located

2

in those geographic areas. An SO Area of Influence (AoI) was defined for each Class I area to

represent the geographic area containing sources that would likely have the greatest impact on

visibil 2ity at that Class I area. All SO point sources within these Areas of Influence were

identified and ranked by their 2018 Base G emissions. The following sections contain a broad

overview of the steps in the Area of Influence analyses. See Appendix H for a more detailed

discussion of these analyses and plots for additional Class I areas.

The Area of Influence analysis is not a source apportionment modeling exercise, but rather a

relative metric based on the magnitude of emissions from a source, its distance to the Class I

area(s) of concern, and the sulfate extinction weighted residence time plots, developed using

back trajectories. In other words, it is not an exact quantification of source-by-source

contribution to visibility impairment on the 20% worst days at a specific Class I area, but a

relative metric used to infer this information.

7.5.a. Back Trajectory Analyses

The first step was to generate meteorological back trajectories for IMPROVE monitoring sites in

West Virginia and neighboring Class I areas for the 2000-2004 baseline period. Back trajectory

analyses use interpolated, measured or modeled meteorological fields to estimate the most likely

central path of air masses that arrive at a receptor at a given time. The method essentially follows

a parcel of air backward in hourly steps for a specified length of time. Figure 7.5.a-1 is an

example of a back trajectory analysis for Dolly Sods for the 20 percent worst days in 2002.
West Virginia Regional Haze SIP Page 67

Back Trajectories for 2002 20% Worst Days

Dolly Sods Wilderness Area

Figure 7.5.a-1. Example back trajectories for 20% worst visibility days in 2002 for Dolly

Sods

Trajectories were started at 100 meters and 500 meters above the surface and run backward from

the site for 72 hours. These individual back trajectories for the 20 percent worst days in 2002

were also useful in evaluating model performance for individual days at the Class I areas.

7.5.b. Residence Time Plots

The next step was to plot residence time for each Class I area using five years of back

trajectories for the 20 percent worst visibility days in 2000-2004. Residence time is the

frequency that winds pass over a specific geographic area on the way to a Class I area. Separate

residence time plots were generated using trajectories with 100m and 500m start heights. As

illustrated in Figure 7.5.b-1, winds influencing Dolly Sods on the 20 percent worst days come

from all directions, with a significant southwest-northeast gradient influencing the 20 percent

worst visibility days. 
West Virginia Regional Haze SIP Page 68

Figure 7.5.b-1. Residence time plot for 20% worst visibility days in 2000-2004 for Dolly

Sods. Based on trajectories with 100m start height.

7.5.c SO2 Areas of Influence

The next step was to develop sulfate extinction-weighted residence time plots to define the

geographic area with the highest probability of influencing the receptor on the 20 percent worst

days in 2000-2004 that were dominated by sulfate. Each back trajectory was weighted by sulfate

extinction for that day. This allowed us to focus on the 20 percent worst days that are influenced

by sulfate and place less importance on days influenced by organic carbon from fires. Sulfate

weighted back trajectories for 20 percent worst days were combined for 5 years of data. The

resulting sulfate extinction-weighted residence time plots were used to define the geographic

Area of Influence for 2 sources of SO emissions. In Figure 7.5.c-1 the area representing 10

percent or greater residence time is outlined in red and the area representing 5 percent or greater

residence time is outlined in gray. The VISTAS states focused their analyses on the Area of

Influence defined by 5 percent or greater sulfate extinction-weighted residence time.
West Virginia Regional Haze SIP Page 69

F 2igure 7.5.c-1. SO Area of Influence plot for sulfate extinction weighted residence time for

20% worst visibility days in 2000-2004 for Dolly Sods. Based on trajectories

with 100m start height.
West Virginia Regional Haze SIP Page 70

7.5.d. Emission Source 2s within SO Areas of Influence

Residence time plots were then combined with geographically-gridded emission data based on

the 2002 baseline and 2018 Base G emissions inventories. Plots were generated for the Areas of

Influence defined by trajectories with 100m and 500m start heights. As a way of incorporating

the effects of transport, deposition and chemical transformation of point source emissions along

the path of the trajectories, these data were weighted by 1/d, where d was calculated at the

dis 2tance between grid cell centers, in kilometers. The distance-weighted point source SO

emissions were then combined with the gridded, extinction-weighted back-trajectory residence

times at a spatial resolution of 36-km.

The final step was to combine the residence times and gridded emissions data in plots and data

2

sets. The distance weighted (1/d), gridded point source SO emissions were multiplied by the

total extinction-weighted back-trajectory residence times on a grid cell by grid cell basis. These

results were then normalized by the domain-wide total and displayed as a percentage. The

analysis was done using both the 2002 and 2018 base year inventories.

Dolly Sods

Figure 7.5.d-1 illustrates 2002 and 2018 distance weighted, gridded emissions multiplied by

sulfate-weighted residence time plots for Dolly Sods. These maps help visualize where

emissions reductions will be occurring 2 between 2002 and 2018. The change in SO emissions

between 2002 and 2018 can be seen by comparing emission source strengths in the two plots.

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

Sources that reduce 2 SO emissions by 2018 will show a lower contribution to emissions in the

domain. On the 2018 map, the grid cells with these sources will show a lighter color gradient

2

than on the 2002 map. For example, SO reductions from EGUs in western West Virginia

resulting from CAIR can be seen by comparing the 2002 and 2018 maps. 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.
West Virginia Regional Haze SIP Page 71

F 2igure 7.5.d-1. Dolly Sods 2002 (left) and 2018 (right) SO distance weighted emissions *

SO4 extinction-weighted residence time plots.

F 2igure 7.5.d-2 illustrates the SO distance weighted emissions * sulfate weighted residence time

plots for 2018 emissions for Dolly Sods. This plot illustrates the relative importance of West

Virginia sources compared to sources in neighboring states. Additional analyses, including 2002

and 2018 distance weighted emissions * residence time plots for Dolly Sods and the Class I

areas in neighboring states are contained in Appendix H.

These analyses are serving as the basis for consultation among the VISTAS states.
West Virginia Regional Haze SIP Page 72

F 2igure 7.5.d-2. 2018 SO distance weighted emissions * SO4 extinction weighted residence

time plot for Dolly Sods, WV.

Table 7.5.d-1 shows, in tabular form, the rela 2tive contributions of point source SO emissions

from nearby states to Dolly Sods. Again it should be noted, as stated in the introduction to

section 7.5, that the Area of Influence analysis is not a source apportionment modeling exercise,

but rather a relative metric based on the magnitude of emissions from a source, its distance to the

Class I area(s) of concern, and the sulfate extinction weighted residence time plots, developed

using back trajectories. In other words, it is not an exact quantification of source-by-source

contribution to visibility impairment on the 20% worst days at a specific Class I area, but a

relative metric used to infer this information. 
West Virginia Regional Haze SIP Page 73

Table 7.5.d-1. 2018 SO2 Point Source Contribution to Dolly Sods, WV by State

State Relative Contribution State Relative Contribution

Kentucky 1.34% South Carolina 0.09%

Maryland 11.81% Tennessee 1.25%

North Carolina 0.80% Virginia 5.73%

Ohio 7.37% West Virginia 66.35%

Pennsylvania 5.26%

There are 323 units identified within the Area of Influence which contribute to sulfate at Dolly

Sods, including seventy-seven (77) units in West Virginia. Twenty-nine (29) units have a

relative contribution greater than 0.5% and contribute 72.11% to sulfate, including seventeen

(17) units in West Virginia, 15 of which are EGUs. Table 7.5.d-2 identifies the 29 units within

the AoI for Dolly Sods that contribute at least 0.5% to the total sulfate. Seventeen (17) units have

a relative contribution greater than 1.0% and contribute 64.28% to sulfate, including fourteen

(14) units in West Virginia, all of which are EGUs.

Maryland contributes 11.81% to sulfate at Dolly Sods, with 9.86% attributable to two (2) units,

one of which is subject to BART, at the New Page facility located in Luke, Maryland. WVDAQ

has consulted with the Maryland Department of the Environment and Maryland indicated that

they expect the New Page facility to install a scrubber with at least a 90% control efficiency on

at least one of the two units impacting Dolly Sods. Maryland has not yet finalized their BART

process, and New Page was required to submit their five (5) factor analysis to the Maryland

Department of the Environment by October 31.

Table 7.5.d-3 identifies all existing coal-fired electric generating units in West Virginia and their

current and future controls. As shown in the table all coal-fired EGUs in West Virginia are

currently 2 x 2 controlled for PM, SO and NO or are projected to either shut down or install SO and

NOx 2 controls by 2018. As shown in the table SO controls are currently under construction at

2 x five (5) units (4,007 MW). IPM has predicted the installation of SO and NO controls on an

additional twelve (12) units (2,398 MW) by 2018 and the shutdown of five (5) units (337 MW)

by 2018. 

F 2 4 inally, Figures 7.5.d-3 through 7.5d.8 show the SO distance weighted emissions * SO

extinction-weighted residence time plots (AoI maps) for Brigantine (NJ), Great Smokey

Mountains (NC/TN), James River Face (VA), Linville Gorge (NC), Mammoth Cave (KY), and

Shenandoah (VA). Tables 7.5.d-4 through 7.5.d-9 show, in tabular form, the relative

contributions of point source SO2 emissions within the AoI from nearby states to Brigantine

(NJ), Great Smokey Mountains (NC/TN), James River Face (VA), Linville Gorge (NC),

Mammoth Cave (KY), and Shenandoah (VA). 
West Virginia Regional Haze SIP Page 74

Table 7.5.d-2. Point Source Units Contributing at least 0.5% to Sulfate at Dolly Sods

Source Identification

2002

Base

Year

2018 Base Case AoI and Associated

Metrics

Q/d*RTMax State/Source

Contribution

State FIPS

CNTY

Plant ID

Plant Name

Point

ID

SIC SO2

Emissi

ons

(tpy)

Q

SO2

Emissi

ons

(tpy)

CE

(% )

Distan

ce

(km)

Q/d RT

Max

Q/d *

RTMax

Unit %

Contribu

tion to

Total

Q/d*

RTMax

State %

Contrib

ution to

Total

Q/d*

RTMax

MD 001 001- 0011 New Page - Luke Mill 1 2621 10,160 9,610 0 51.59 186.26 42.35 7,888.1 5.25% 11.81%

MD 001 001- 0011 New Page - Luke Mill 2 2621 8,923 8,441 0 51.59 163.60 42.35 6,928.5 4.61% 11.81%

OH 167 0684000000 Muskingum River Power Plant B006 4911 0 5,034 90 201.89 24.94 49.41 1,232.3 0.82% 7.37%

OH 053 0627010056 Gavin Power Plant B003 4911 19,222 6,479 95 233.68 27.73 28.16 780.9 0.52% 7.37%

OH 053 0627010056 Gavin Power Plant B004 4911 12,601 6,465 90 233.68 27.66 28.16 778.9 0.52% 7.37%

PA 059 4205900006 Allegheny Energy Supply Co/Hatfields Ferry 032 4911 53,016 2,220 90 93.77 23.68 36.51 864.6 0.58% 5.26%

PA 059 4205900006 Allegheny Energy Supply Co/Hatfields Ferry 031 4911 55,695 2,215 90 93.77 23.62 36.51 862.4 0.57% 5.26%

PA 059 4205900006 Allegheny Energy Supply Co/Hatfields Ferry 033 4911 50,001 2,213 90 93.77 23.60 36.51 861.6 0.57% 5.26%

TN 163 0003 Eastman Chemical Company 021520 2869 16,855 16,729 4 397.20 42.12 21.07 887.5 0.59% 1.25%

VA 580 00003 MeadWestVaco Packaging Resource Group 25 2611 8,552 9,997 0 153.90 64.96 24.77 1,609.1 1.07% 5.73%

WV 023 0003 Mount Storm Power Plant 001 4911 8,817 3,191 95 17.36 183.81 74.90 13,767.4 9.16% 66.35%

WV 023 0003 Mount Storm Power Plant 002 4911 9,572 3,191 95 17.36 183.81 74.90 13,767.4 9.16% 66.35%

WV 023 0003 Mount Storm Power Plant 003 4911 2,779 2,923 94.98 17.36 168.39 74.90 12,612.4 8.39% 66.35%

WV 033 0015 Monongahela Power Co - Harrison 003 4911 2,582 5,998 97.98 82.67 72.56 86.63 6,285.9 4.18% 66.35%

WV 033 0015 Monongahela Power Co - Harrison 002 4911 2,731 5,954 97.98 82.67 72.02 86.63 6,239.1 4.15% 66.35%

WV 033 0015 Monongahela Power Co - Harrison 001 4911 3,355 5,908 97.98 82.67 71.47 86.63 6,191.4 4.12% 66.35%

WV 023 0014 North Branch Power Station 002 4911 323 1,018 94.98 18.73 54.33 74.90 4,069.3 2.71% 66.35%

WV 023 0014 North Branch Power Station 001 4911 324 1,004 95 18.73 53.63 74.90 4,016.9 2.67% 66.35%
Table 7.5.d-2. Point Source Units Contributing at least 0.5% to Sulfate at Dolly Sods

Source Identification

2002

Base

Year

2018 Base Case AoI and Associated

Metrics

Q/d*RTMax State/Source

Contribution

State FIPS

CNTY

Plant ID

Plant Name

Point

ID

SIC SO2

Emissi

ons

(tpy)

Q

SO2

Emissi

ons

(tpy)

CE

(% )

Distan

ce

(km)

Q/d RT

Max

Q/d *

RTMax

Unit %

Contribu

tion to

Total

Q/d*

RTMax

State %

Contrib

ution to

Total

Q/d*

RTMax

West Virginia Regional Haze SIP Page 75

WV 073 0005 Monongahela Power Co - Pleasants Power 001 4911 21,507 6,334 90 149.65 42.33 61.20 2,590.6 1.72% 66.35%

WV 073 0005 Monongahela Power Co - Pleasants Power 002 4911 20,090 6,165 90 149.65 41.20 61.20 2,521.4 1.68% 66.35%

WV 061 0001 Monongahela Power Co - Fort Martin Power 001 4911 46,852 4,922 90 78.30 62.86 36.51 2,295.0 1.53% 66.35%

WV 061 0001 Monongahela Power Co - Fort Martin Power 002 4911 42,467 4,890 90 78.30 62.45 36.51 2,280.0 1.52% 66.35%

WV 079 0006 Appalachian Power - John E. Amos 003 4911 39,570 10,821 90 219.63 49.27 39.08 1,925.5 1.28% 66.35%

WV 053 0009 Appalachian Power - Mountatineer 001 4911 39,064 11,433 90 217.38 52.60 30.89 1,624.8 1.08% 66.35%

WV 077 0001 Monongahela Power Co - Albright 003 4911 11,273 660 90 46.21 14.29 87.09 1,244.5 0.83% 66.35%

WV 079 0006 Appalachian Power - John E. Amos 002 4911 27,651 6,694 90 219.63 30.48 39.08 1,191.2 0.79% 66.35%

WV 079 0006 Appalachian Power - John E. Amos 001 4911 30,015 6,613 90 219.63 30.11 39.08 1,176.7 0.78% 66.35%

WV 073 0006 Cabot Corporation - Ohio River Plant 002 2895 2,025 2,803 0 168.40 16.64 61.20 1,018.4 0.68% 66.35%

WV 073 0006 Cabot Corporation - Ohio River Plant 003 2895 1,734 2,400 0 168.40 14.25 61.20 872.1 0.58% 66.35%

TOTAL All Sources 150297.6 100.00% 323

units

Contribution from 0.5%

sources

108,383.7 72.11% 29 units

Contribution from 1.0%

Sources

96,612.8 64.28% 17 units

Note: Units identified in black text are EGUs, units identified in blue text are non-EGUs.

Units identified with bold text have greater than 1.0% contribution to sulfate.
West Virginia Regional Haze SIP 

Table 7.5.d-3. West Virginia Coal-Fired Electric Generating Units and Existing and Schedule

Plant ID Unit MW PM

Controls

SO2

Controls

Yr SO2

ControlsInstalled

NOx

Controls

Yr NOx

ControlsInstalled

Albright 077-0001 1 73 ESP LNB 2018 

2 73 ESP LNB 2018 3 137 ESP Scrubber 2018 LNB/SCR 2018 IPM 

Ft. Martin 061-00001 1 552 ESP Scrubber 2010 SNCR Trim 2004 Cont

const

2 555 ESP Scrubber 2010 SNCR Trim 2004

Grant Town 049-00026 84 Fabric

Filter

Limestone

Injection

Upon

Construction

SNCR 2003

Harrison 033-00015 1 640 ESP Scrubber 1994 SCR 2002

2 640 ESP Scrubber 1994 SCR 20033 640 ESP Scrubber 1994 SCR 2003

John Amos 079-00006 1 800 ESP Scrubber 2009 SCR 2005 Cont

const

2 800 ESP Scrubber 2009 SCR 20043 1300 ESP Scrubber 2008 SCR 2003

Kammer 051-00006 1 210 ESP Scrubber 2018 OFA/SCR 2018 IPM 

2 210 ESP Scrubber 2018 OFA/SCR 2018 IPM 3 210 ESP Scrubber 2018 OFA/SCR 2018 IPM 

KanawhaRiver

039-00006 1 200 ESP Scrubber 2018 OFA/SCR 2018 IPM 

2 200 ESP Scrubber 2018 OFA/SCR 2018 IPM 

Mitchell 039-00006 1 800 ESP Scrubber 2007 SCR 2007

2 800 ESP Scrubber 2007 SCR 2007
Table 7.5.d-3. West Virginia Coal-Fired Electric Generating Units and Existing and Schedule

Plant ID Unit MW PM

Controls

SO2

Controls

Yr SO2

ControlsInstalled

NOx

Controls

Yr NOx

ControlsInstalled

West Virginia Regional Haze SIP 

MEA 061-00027 60 Fabric

Filter

Limestone

Injection

Upon

Construction

LNB Upon

Construction

Mountaineer 053-00009 1300 ESP Scrubber 2007 SCR 2003Mt. Storm 023-00003 1 533 ESP Scrubber 2002 SCR 2003

2 533 ESP Scrubber 2001 SCR 20033 521 ESP Scrubber 1994 SCR 2004

North Branch 023-00014 84 Fabric

Filter

Limestone

Injection

Upon

Construction

LNB Upon

Construction

Philip Sporn 053-00001 1 150 ESP Scrubber 2018 OFA/SCR 2018 IPM 

2 150 ESP Scrubber 2018 OFA/SCR 2018 IPM 3 150 ESP Scrubber 2018 OFA/SCR 2018 IPM 4 150 ESP Scrubber 2018 OFA/SCR 2018 IPM 5 450 ESP Scrubber 2018 LNB/SCR 2018 IPM 

Pleasants 073-00005 1 650 ESP Scrubber 1979 SCR 2003

2 650 ESP Scrubber 1980 SCR 2003

Rivesville 049-00009 7 46 ESP 2018 

8 91 ESP 2018 

Willow Island 073-00004 1 54 ESP LNB 2018 

2 181 ESP Scrubber 2018 LNB/SCR 2018 IPM 
West Virginia Regional Haze SIP Page 78

Brigantine, NJ

F 2igure 7.5.d.3.Brigantine, NJ 2002 (left) and 2018 (right) SO distance weighted emissions *

SO4 extinction weighted residence time plots

Table 7.5-4. 2018 SO2 Point Source Contribution to Brigantine, NJ by State

State Relative Contribution State Relative Contribution

Connecticut 0.05% North Carolina 0.75%

Washington, DC 0.05% Ohio 0.52%

Delaware 27.83% Pennsylvania 13.63%

Maryland 7.67% Virginia 7.90%

New Jersey 40.11% West Virginia 0.94%

New York 0.56%

There are 278 units identified within the Area of Influence which contribute to the sulfate at

Brigantine, including 15 units in West Virginia. Twenty-five (25) units have a relative

contribution greater than 0.5% and contribute 70.75% to sulfate, none of which are located in

West Virginia. Ten (10) units have relative contribution greater than 1.0% and contribute

59.92% to sulfate, none of which are located in West Virginia. Capitol Cement, Kiln 7 (Emission
West Virginia Regional Haze SIP Page 79

Point 010), with a relative contribution of 0.10%, had the largest relative contribution to sulfate

at Brigantine of any West Virginia source. This source is addressed in Section 7.7.

Great Smokey Mountains, NC and TN

Figure 7.5.d.4. Great Smokey 2 Mountains, NC and TN 2002 (left) and 2018 (right) SO distance

we 4ighted emissions * SO extinction weighted residence time plots

Table 7.5-5. 2018 SO2 Point Source Contribution to Great Smokey Mountains, NC 

and TN by State

State Relative Contribution State Relative Contribution

Alabama 3.12% South Carolina 2.44%

Georgia 7.74% Tennessee 74.50%

Kentucky 1.02% Virginia 2.34%

North Carolina 7.43% West Virginia 0.54%

Ohio 0.87%

There are 193 units identified within the Area of Influence which contribute to the sulfate at

Great Smokey Mountains, including five (5) units in West Virginia. Thirty-eight (38) units have
West Virginia Regional Haze SIP Page 80

a relative contribution greater than 0.5% and contribute 79.85% to sulfate, including zero (0)

units in West Virginia. Ten (10) units have relative contribution greater than 1.0% and contribute

61.00% to sulfate, including zero (0) units in West Virginia. 

James River Face, VA

Figure 7.5.d.5. 2James River Face, VA 2002 (left) and 2018 (right) SO distance weighted

emis 4sions * SO extinction weighted residence time plots

Table 7.5-6. 2018 SO2 Point Source Contribution to James River Face, VA by State

State Relative Contribution State Relative Contribution

Washington, DC 0.02% Pennsylvania 1.59%

Delaware 0.59% South Carolina 0.89%

Kentucky 2.02% Tennessee 1.69%

Maryland 3.12% Virginia 62.08%

North Carolina 9.31% West Virginia 15.10%

Ohio 3.60%
West Virginia Regional Haze SIP Page 81

There are 336 units identified within the Area of Influence which contribute to the sulfate at

James River Face, including 66 units in West Virginia. Thirty-nine (39) units have a relative

contribution greater than 0.5% and contribute 49.92% to sulfate, including ten (10) units in West

Virginia, nine (9) of which are EGUs. Twelve (12) units have relative contribution greater than

1.0% and contribute 67.91% to sulfate, including three (3) units in West Virginia, all of which

are EGUs. 

Linville Gorge, NC

Figure 7.5.d.6. L 2inville Gorge, NC 2002 (left) and 2018 (right) SO distance weighted emissions

* SO4 extinction weighted residence time plots

Table 7.5-7. 2018 SO2 Point Source Contribution to Linville Gorge, NC by State

State Relative Contribution State Relative Contribution

Alabama 1.35% South Carolina 3.93%

Georgia 1.98% Tennessee 36.29%

Kentucky 0.90% Virginia 16.53%

North Carolina 34.76% West Virginia 2.49%

Ohio 1.77%

There are 207 units identified within the Area of Influence which contribute to the sulfate at

Linville Gorge, including seventeen (17) units in West Virginia. Thirty-five (35) units have a

relative contribution greater than 0.5% and contribute 74.69% to sulfate, including one (1) unit
West Virginia Regional Haze SIP Page 82

in West Virginia, which is an EGU. Eighteen (18) units have relative contribution greater than

1.0% and contribute 62.57% to sulfate, including zero (0) units in West Virginia. 

Mammoth Cave, KY

Figure 7.5.d.7. Mammoth Cave, KY 2002 ( 2left) and 2018 (right) SO distance weighted

emis 4sions * SO extinction weighted residence time plots

Table 7.5-8. 2018 SO2 Point Source Contribution to Mammoth Cave, KY by State

State Contribution State Contribution

Alabama 4.33% Mississippi 0.04%

Georgia 1.79% Missouri 0.53%

Illinois 0.53% Ohio 3.95%

Indiana 21.22% Tennessee 13.46%

Kentucky 53.60% West Virginia 0.54%

There are 261 units identified within the Area of Influence which contribute to the sulfate at

Mammoth Cave, including five (5) units in West Virginia. Forty-one (41) units have a relative

contribution greater than 0.5% and contribute 66.60% to sulfate, including zero (0) units in West

Virginia. Nineteen (19) units have relative contribution greater than 1.0% and contribute 52.63%

to sulfate, including zero (0) units in West Virginia. 
West Virginia Regional Haze SIP Page 83

Shenandoah, VA

F 2igure 7.5.d.8. Shenandoah, VA 2002 (left) and 2018 (right) SO distance weighted emissions *

SO4 extinction weighted residence time plots

Table 7.5-9. 2018 SO2 Point Source Contribution to Shenandoah, VA by State

State Relative Contribution State Relative Contribution

Washington, DC 0.06% Ohio 4.63%

Delaware 1.46% Pennsylvania 6.11%

Kentucky 1.10% Tennessee 0.36%

Maryland 23.99% Virginia 38.33%

North Carolina 3.97% West Virginia 19.85%

New York 0.16%

There are 300 units identified within the Area of Influence which contribute to the sulfate at

Shenandoah, including 69 units in West Virginia. Thirty-nine (39) units have a relative

contribution greater than 0.5% and contribute 57.54% to sulfate, including thirteen (13) units in

West Virginia, twelve (12) of which are EGUs. Nineteen (19) units have relative contribution

greater than 1.0% and contribute 44.29% to sulfate, including seven (7) units in West Virginia,

six (6) of which are EGUs. 
West Virginia Regional Haze SIP Page 84

7.5.e. Specific Source Types in the Area of Influence for Dolly Sods and Otter Creek

Wilderness Areas

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 at Dolly

Sods and Otter Creek Wilderne 2ss Areas. Lists of SO point sources found within the Area of

Influence for each Class I area were developed using the most current (Base G) VISTAS 2002

base year and 2018 future year emissions. For this purpose the Area of Influence was defined as

the counties with maximum sulfate extinction weighted residence time greater than five (5). For

SO2 sources within each Area of Influence, the following attributes were defined for each

individual unit:

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

2

• SO emissions for 2002 and 2018

• 2018 control efficiency

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

• Emissions divided by distance (Q/d), a metric that accounts for dispersion of emissions

over distance

• Maximum sulfate extinction weighted residence time (Rtmax)

The review was conducted in a top down fashion starting with an analysis of the major source

ca 2tegories in each SO Area of Influence 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 those sources types were reviewed. The contributions from

ma 2jor source categories to the 2018 Base G2 inventory for the SO Area of Influence for Dolly

Sods are listed in Table 7.5.e-1.
West Virginia Regional Haze SIP Page 85

Table 7.5.e.-1. 2018 Emissions Contributions from Major Source Categories in the Area of

Influence for Dolly Sods, WV

This table indicates that for Dolly Sods, Electric Utilities and Industrial Boilers are the two

major source ca 2tegories contributing to 2018 SO emissions in the Area of Influence, even after

implementation of CAIR. Together these two source categories contribute 80 percent of the 2018

SO2 emissions to the Area of Influence for Dolly Sods. Other Fuel Combustion and Other

2

Industrial Processes comprise another 11% of the 2018 SO emissions.

This table can also be used to evaluate the major source categories contributing to emissions of

NOx 3 , NH 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

3

to NH and primary PM. The emissions sensitivities discussed in section 7.4 indicated very small

benef x 3 its to controlling NO , NH and primary PM emissions at Dolly Sods, but if these emissions

were of concern, different source categories would need to be addressed.

The 2 contributions to SO emissions in 2018 from the three highest source categories, Electric

Utilities, Industrial Boilers and Other Fuel Combustion have been further broken out into

subcategories. Table 7.5.e-2 indicates subcategories for the Area of Influence for Dolly Sods.

With 2in Electric Utilities, all the SO emissions are attributable to coal-fired power plants. Within

Industrial Boilers, most emissions are attributable to coal-fired boilers with lesser contributions

from oil and gas boilers. Commercial and institutional coal- and oil-fired boilers have smaller

contributions.
West Virginia Regional Haze SIP Page 86

Table 7.5.e 2-2. 2018 SO Emissions contributions from Major Source Categories in the Area

of Influence for Dolly Sods, WV

These analyses indicate that West Virginia should consider what additional control measures for

electric utilities and industrial boilers are reasonable. The lists of individual sources have also

been used to determine if individual sources in other sources categories are major contributors to

SO2 emissions in the Area of Influence.

As discussed in Appendix H - Reasonable Progress Evaluation and Long Term Strategy, Section

5, the WVDAQ elected to focus on those units that contributed at least one percent to sulfate

visibility impairment at a given Class I area. First, the units with the larger contribution toward

visibility impairment would likely show an environmental benefit under a control evaluation,

and WVDAQ would be able to use that environmental benefit to require controls on a given unit. 

Second, there are several regulatory programs that use a higher threshold than one percent for

evaluation thresholds.

1. The BART rule specifies that a maximum impact of 0.5 dv is an acceptable threshold for

establishing significance. This threshold equates to roughly a 5 percent change in visible

perception. This same significance level is used in the Prevention of Significant

Deterioration/ New Source Review program for the visibility air quality related value. 
West Virginia Regional Haze SIP Page 87

2. The NOx SIP Call laid out a significance level for Section 126 petitions of 4 parts per

million (ppm), that being the level to which a State's contribution to another state's ozone

problem was considered significant. Four ppm represents approximately 3.75 percent of

the 1-hour ozone standard, which was in place at the time EPA promulgated the NOx SIP

Call.

3. For the CAIR rule, a PM contribution of 0.2 ug/m3 was used to demonstrate a significant

impact, which is 1.3% of the annual PM2.5 standard of 15 ug/m3.

4. Lastly, when human health standards are proposed (NAAQS) significant impact levels

(SILS) are assigned which allow sources to determine their significance on air quality in

the area around their facilities. Sources that demonstrate that their "contribution" from

the new or modified sources is less than these significance levels do not have to complete

any further modeling. The SILS represent a percentage of the NAAQS.

After reviewing all averaging periods for the criteria pollutants, WVDAQ determined that the

one percent threshold we were proposing for reasonable further progress was as protective or

more protective than the significant impact levels. The most restrictive threshold identified was

for NOx, which has a NAAQS of 100 ug/m3 and a significance level of 1 ug/m3, which

represents one percent of the total. 

Finally, WVDAQ considered available resources to evaluate the sources in each Class I area’s

sulfate AoI. WVDAQ recognized that there was neither sufficient time or resources available to

evaluate all units within a given AoI. Therefore, a threshold was needed to determine which

units would be evaluated. Table 7.5.e-3 shows that a one percent contribution threshold captures

g 2reater than 64% of the total point source SO contribution to Dolly Sods, while requiring an

evaluation of only 17 units. The next 27% of cumulative contribution may be attributed to units

with individual contribution between 0.1% - 1% and would require analysis of 97 additional

units. The WVDAQ believes that the one percent threshold is appropriate, given the contribution

to the total visibility impairment at each Class I area, and the limited resources available to

conduct the unit-by-unit evaluation for reasonable progress.

Table 7.5.e-3. Numbers and Percentages of 2018 SO2 Emission Units that Contribute

to Sulfate Visibility Impairment at Dolly Sods

Dolly Sods

# Units Contributing > 1% 17

Percentage of total Contribution 64.28%

# Units Contributing > 0.1%, but < 1% 97

Percentage of total Contribution 27.33%
Table 7.5.e-3. Numbers and Percentages of 2018 SO2

 Emission Units that Contribute

to Sulfate Visibility Impairment at Dolly Sods

Dolly Sods

West Virginia Regional Haze SIP Page 88

# Units Contributing < 0.1% 209

Percentage of total Contribution 8.38%

7.6. Evaluating Control Option 2s for Specific SO Emissions Sources in Each Area of

Influence

The next step was to identify emission reductions that have already occurred within each source

category and at specific units. Unit level tables of emission comparisons from 2002 to 2018 were

developed, allowing VISTAS states to review existing emission reductions. These tables

assigned future year control technology from IPM forecasting and state modifications for EGU

and from control efficiency tables for non-EGU point sources.

Once emission control profiles for specific units were defined, the next step was to determine

what, if any, additional control measures would feasibly be available, and to assign costs to those

control measures.

For EGUs, the 2018 IPM files used by VISTAS for EGU sources were obtained and matched to

the 2018 base case inventory of EGU sources. This step was conducted to ensure that

incremental controls assigned to 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 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.

Us 2ing the inventories identified above, VISTAS ran every available SO control strategy in

AirControlNet against the EGU and non-EGU point source inventories to develop a master list

of incremental control strategies for each unit in the VISTAS 36-km domain.

For sources within the Area of Influence for Dolly Sods, the master list of incremental control

measures was sorted to determine the costs of incremental control measures. These data were

combined in a master spreadsheet with the distance from the emission release point to the Class I

area IMPROVE monitor (in km), the 2018 residual emissions and the 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.
West Virginia Regional Haze SIP Page 89

West Virginia used these to evaluate control measures and costs as part its statutory factor

analysis for reasonable further progress.

The regional haze rule requires that states consider the following factors and demonstrate how

these factors were taken into consideration in selecting the reasonable progress goal:

• the costs of compliance

• the time necessary for compliance

• the energy and non-air quality environmental impacts of compliance, and

• the remaining useful life of any potentially affected sources.

7.6.a. Cost of Compliance

As defined in Section 7.5.e, coal-fired electric utilities and coal-fired industrial boilers were the

2

largest source categories contributing to SO in 2018 in the Area of Influence defined for Dolly

Sods. Industrial boilers using oil and commercial and industrial boilers using coal had small

contributions to SO2 in 2018.

Sulfur dioxide emissions from utility, industrial, commercial or institutional boilers can be

controlled either by switching the fuel sources to a lower sulfur fuel content or by installing postcombustion controls. Costs vary by fuel source and boiler type and require source specific

analyses for accuracy.

Bituminous coal is commonly burned in boilers in the eastern United States. Switching to

ano 2ther bituminous coal with lower sulfur content or blending bituminous coals can reduce SO

emissions with the least impact to boiler performance. While sub-bituminous coal has a lower

sulfur content than bituminous coal, it also has a lower heat content and so more coal has to be

burned to generate the same energy output. For boilers that are designed for bituminous coal

only a small fraction of the fuel can be switched to low sulfur sub-bituminous coal without

affecting boiler performance. Costs of boiler modifications to accommodate low sulfur subbituminous coal may make switching to sub-bituminous coal impractical. Contract initiation and

termination costs, differential fuel prices and heat rates, transportation costs, and modification to

boiler operations, fuel handling and waste handling systems will have to be considered on a

case-by-case basis for each source.

Flue gas desulfurization is the common post-combustion control for coal-fired boilers. Flue gas

is passed through an absorbent for sulfur dioxide (generally limestone or lime) in either a wet

scrubber, dry scrubber or spray dryer. A calcium sulfate by-product is produced that may be

further processed to produce gypsum as a commercial by-product. Costs of flue gas

desulfurization include initial construction costs and ongoing operational and maintenance costs

for the absorber tower, sorbent handling, and waste product handling facilities. Costs per ton

vary with boiler size, type and facility siting considerations.

For oil-fired boilers, lower sulfur oil may be an option. Costs need to consider differential fuel

prices and heat rate, boiler modifications, fuel handling costs and maintenance costs.

Conceptually, post-combustion controls could be used for oil boilers, but there is little precedent

for such installations.
West Virginia Regional Haze SIP Page 90

7.6.b. Time Necessary for Compliance

For fuel switching, the time necessary to terminate existing fuel contracts and initiate new

contracts needs to be considered. Generally two to three years may be required. Installation of

post-combustion controls will require 3 or more years depending on market availability of labor

and materials and utility system-wide priorities. Time necessary for compliance will need to be

refined for specific sources.

7.6.c. Energy and Non-Air Quality Environmental Impacts

Switching to lower sulfur fuel or installing post-combustion controls may reduce boiler heat rate

and energy output. Scrubbers and spray dryers will require additional safeguards for fuel

handling and waste handling systems to avoid additional non-air quality environmental impacts

such as increased effluents in waste water discharges and storm water runoff. These factors will

need to be considered specifically for individual sources. Carbon dioxide is emitted as a byproduct of flue gas desulfurization, therefore, impacts of increased carbon dioxide emissions will

need to be considered, particularly if carbon emissions are limited in the future under climate

change mitigation strategies.

7.6.d. Remaining Useful Life

Remaining useful life is specific to the unit for which controls are considered and needs to be

addressed on a case-by-case basis.

7.7. Which Control Measures Represent Reasonable Progress for Individual Sources?

The following summarizes the process for determining reasonable progress for West Virginia

sources. For a detailed discussion of the reasonable progress assessments for all unit’s

contribution greater than one percent (1%) to any Class I area in West Virginia or in neighboring

states, please see Appendix H.

Step 1: Determine pollutants of concern. 

VISTAS evaluated the species contribution on the 20 percent worst days and concluded that

sulfate accounted for greater than 70% of the visibility impairing pollution. The VISTAS States

conc 2luded that controlling SO emissions was the appropriate step in addressing the reasonable

progress assessment for 2018. The VISTAS findings were consistent with the findings of SAMI.

As you may recall, SAMI confirmed that sulfate particles account for the greatest portion of the

haze affecting Class I areas in the Southern Appalachian region and that these sulfates were

produced in large par 2t from SO emissions from coal combustion.

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

Because 2 SO point source emissions in 2018 are projected to represent greater than 95% of the

2

total SO emissions inventory, the VISTAS States concluded that analyses should focus on EGU

and 2 non-EGU point sources of SO emissions.
West Virginia Regional Haze SIP Page 91

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

The WVDAQ calculated the fractional contribution from a 2ll emission units within the SO AoI

for a given Class I area and identified the EGUs with a contribution of one percent (1%) or more

to the sulfate visibility impairment at that Class I area. Again, it should be noted that the AoI

analyses were not source apportionment modeling analyses, but were based on the magnitude of

emissions from a source, its distance to the Class I area(s) of concern, and the sulfate extinction

weighted residence time plots, developed using back trajectories. [Note: No units were identified

with a contribution of $ 1% to sulfate visibility impairment within the AoI for Brigantine, NJ.]

The West Virg 2inia Division of Air Quality (WVDAQ) evaluated the amount of SO reduction

from the EGUs projected due to CAIR. The EGUs in West Virginia are projected to reduce their

2002 2 2 SO emissions by 47 percent by 2009 and a total of 78 percent by 2018. The projected SO

emission reductions are the result of CAIR which relies on unchecked trading and the use of

banked Title IV allowances. Therefore the reductions are not directly enforceable. However,

WVDAQ has issued permits for the installation of controls, and confirmed the commencement

of the installation of controls at the three plants (6 units) projected to reduce emissions by 2009,

and one of the plants (2 units) projected to control by 2018. In fact, the controls on two of the

plants (3 units) were operational by the summer of 2007. The additional controls projected by

IPM on three plants are not as certain due to the rule’s reliance on the banking and trading of

allowances. The WVDAQ 2 intends to re-evaluate the IPM predictions of SO reductions for

CAIR at the time of our first periodic report in 2012 to ensure that the predicted reductions are

indeed taking place where they are expected and needed. Based on the confirmed CAIR controls

and the CAIR controls predicted by IPM, West Virginia has concluded that, at this time, CAIR

constitutes reasonable measures for West Virginia’s EGUs during this first assessment period

(between baseline and 2018). The WVDAQ notes that due to the close proximity of Mt. Storm

and North Branch Power Stations to Dolly Sods and Otter Creek, these plants are of particular

concern.

Table 7.7-1. EGU Sources with Contribution $1% to any Class I Area

Plant ID Plant Point

ID

Class I areas where

Contribution $1% 

023-0003 Mount Storm Power Station 001 Dolly Sods, Shenandoah

1

023-0003 Mount Storm Power Station 002 Dolly Sods, Shenandoah

1

023-0003 Mount Storm Power Station 003 Dolly Sods, Shenandoah

1

033-0015 Harrison Power Station 001 Dolly Sods, Shenandoah

1

033-0015 Harrison Power Station 002 Dolly Sods, Shenandoah

1

033-0015 Harrison Power Station 003 Dolly Sods, Shenandoah

1

023-0014 North Branch Power Station 001 Dolly Sods 2

023-0014 North Branch Power Station 002 Dolly Sods 2
Table 7.7-1. EGU Sources with Contribution $1% to any Class I Area

Plant ID Plant Point

ID

Class I areas where

Contribution $1% 

West Virginia Regional Haze SIP Page 92

073-0005 Pleasants Power Station 001 Dolly Sods, Shenandoah

1

073-0005 Pleasants Power Station 002 Dolly Sods, Shenandoah

1

061-0001 Ft. Martin Power Station 001 Dolly Sods 3

061-0001 Ft. Martin Power Station 002 Dolly Sods 3

079-0006 John Amos Power Station 001 Dolly Sods, James River Face 3

079-0006 John Amos Power Station 002 James River Face 3

079-0006 John Amos Power Station 003 James River Face, Shenandoah 3

053-0009 Mountaineer Power Station 001 Dolly Sods, Shenandoah

1

Cur 2rently equipped wet scrubbers with SO control efficiency >90% 1

Ci 2rculating Fluidized Bed (CFB) boiler with SO control efficiency >90% 2

Cons 2truction has commenced on wet scrubbers with SO control efficiency >90%, to be completed 2008-10 3

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

The WVDAQ calculated the fractional contribution from a 2ll emission units within the SO AoI

for a given Class I area and identified the non-EGUs with a contribution of one percent (1%) or

more to the sulfate visibility impairment at that Class I area. [Note: No units were identified with

a contribution of $ 1% to sulfate visibility impairment within the AoI for Brigantine, NJ.]

Table 7.7-2. Non-EGU Sources to be Evaluated for Reasonable Further Progress

Plant ID Plant Point

ID

Class I areas where

Contribution >1% 

003-006 Capitol Cement 010 Shenandoah

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. 

While all four (4) factors should be considered, any one of them may provide a compelling

reason to exclude a control measure. This is particularly true of control cost. If no cost effective

control exists, then it is not reasonable to require controls. For the purpose of evaluating the cost
West Virginia Regional Haze SIP Page 93

for the reasonable progress assessment in this first regional haze SIP, WVDAQ believes it is not

equitable to require non-EGUs to bear a greater economic burden than EGUs for a given control

strategy. That is, the agency does not believe it is reasonable to potentially subject non-EGUs to

control measures that require a higher cost per ton than that which has been found reasonable for

EGUs. In order 2 to determine the cost effectiveness of SO controls for EGUs, the WVDAQ

reviewed EPA’s CAIR analysis. [See EPA CAIR analysis at 70 FR 25201-25208, 12MAY2005].

The 2 CAIR analysis is a recent and comprehensive review of the cost-effectiveness of EGU SO

controls, which is appropriate to consider as a benchmark in determining a reasonable cost

e 2ffectiveness for SO control for purposes of the reasonable progress assessment.

In the CAIR preamble EPA notes that:

“The 2 SO annual control costs that were presented in the CAIR NPR [Notice of

Proposed Rulemaking] were average costs of $700 per ton and $800 per ton for years

2010 and 2015, respectively, and marginal costs of $700 per ton and $1,000 per ton

for years 2010 and 2015. In addition, the NPR included the results of sensitivity

analy 2ses that examined costs of the proposed SO controls based on the Energy

Information Administration’s projections for electricity growth and natural gas

price 2s. These sensitivity analyses showed marginal SO control costs of $900 per ton

and $1,100 per ton for years 21010 and 2015, respectively.”

EPA goes on to addre 2ss the analysis of SO emission reduction requirements for the CAIR final

rule.

“ 2(I) Reference Lists of Cost-Effective SO Controls

“For today’s action, EPA updated the reference list of controls included in the NPR

of 2 the average and marginal cots per ton of recent SO control actions. The EPA

systematically developed a list of cost information from both recent actions and

proposed actions. The EPA compiled cost information for actions taken by the

Agency, and examined public comments submitted after the NPR was published, to

identify all available control cost information to provide the updated reference list

for today’s preamble. The updated reference list includes both average and marginal

costs of control, to which EPA compares the CAIR control costs, and the list

represents what regulatory decision makers and/or the public believes are the control

costs.

“Table 2 IV-3 provides average costs of SO controls. This table includes average costs

2

for recent BACT permitting decisions for SO . Under EPA’s New Source Review

(NSR) program, if a company is planning to build a new plant or modify an existing

plant such that a significant net increase in emissions will occur, the company must

obtain a NSR permit that addresses controls for air emissions. BACT is the type of

control required by the NSR program for existing sources in attainment areas. The

BACT decisions are determined on a case-by-case basis, usually by State and local

permitting agencies, and reflect consideration of average and incremental cost

effectiveness. These decisions are relevant for EPA’s reference list of average costs
West Virginia Regional Haze SIP Page 94

of SO2 controls, because they represent cost-effective controls that have been

demonstrated.

TABLE IV-3. 2 – Average Costs per Ton of Annual SO Controls

SO2 control action Average cost

per ton

Best Available Control Technology (BACT) Determinations $400-$2,100 1

Nonroad Diesel Engines and Fuel $800 2

Proposed Best Available Retrofit Technology (BART) for

Electric Power Sector

$2,600-$3,400 3

These numbers reflect a range of cost-effectiveness data entered into EPA’s 1

RACT 2/BACT/LAER Clearinghouse (RBLC) for add-on SO controls (www.epe.gov/ttn/catc/).

We identified actions in the data base for large, utility scale, coal-fired boiler units for which

cost effectiveness data were reported. The range of costs shown here is for boilers ranging from

30MW to an estimated 790 MW (we used a conversion factor of 10 mmBtu/hr = 1 MW for

units for which size was reported in mmBtu/hr). Emission limits for these actions ranged from

0.1 mmBtu to 0.27 lb/mmBtu. Add-on controls reported for these units are dry or wet scrubbers

(in one case with added alkali and in one case with a baghouse).Where the dollar-year was not

reported we assumed 1999 dollars. The cost range presented in the NPR was $500-$2,100-

today’s range includes additional BACT costs that were entered into the clearinghouse after the

NPR was published.

Control of Emissions of Air Pollution from Nonroad Diesel Engines and Fuel; Final Rule (69 2

FR 39131; June 29, 2004). The value in this table represents the long-term cost per ton of

emissions reduced from the total fuel and engines program (cost per ton of emissions reduced in

the year 2030). 1999$ per ton.

The EPA IPM modeling 2004, available in the docket. The EPA modeled the Regional Haze 3

Requirements as source specific limits (90 percent SO2 reduction of 0.1 lb/mmBtu rate; except

the five state WRAP region for which we did not model SO2 controls beyond what is done for

the WRAP cap in the base case modeling). Estimated average costs based on this modeling are

$2,600 per ton in 2015 and $3,400 per ton in 2020. 1999$ per ton.

“Table IV-4 provides the marginal cost per ton of recent State and regional decisions

2

for annual SO controls.

Table 2 IV-4.– Marginal Costs per Ton of Annual SO Controls

SO2 control action Marginal cost

per ton

New Hampshire Rule $600 1

WRAP Reg 2ional SO Trading Rule $1,100-$2,200 2
Table 2

 IV-4.– Marginal Costs per Ton of Annual SO Controls

SO2 control action Marginal cost

per ton

West Virginia Regional Haze SIP Page 95

The EPA IPM base case modeling August 2004, available in the docket. (1999$ per ton). We

1

mod 2eled New Hampshire’s State Bill ENV-A2900, which caps SO emissions of all existing

fossil steam units.

2

"An Assessment of Critical Mass for the Regional SO Trading Program,” prepared for the 2

Western Regional Air Partnership Market Trading Forum by ICF Consulting Group, September

27, 2002, available in the docket. This analysis looked at the implications of one or more States

2

choosing to opt-out of the WRAP regional SO trading program. (1999$ per ton).

“(I 2I) Cost Effectiveness of the CAIR Annual SO Reductions

“. . .Table IV-5 provide 2s the average and marginal costs of annual SO reductions

under the CAIR for 2010 and 2015.(When presenting estimated CAIR control costs

in section IV of this preamble, EPA uses “Main Case” to indicate the primary CAIR

IPM analysis, as differentiated from other IPM analyses such as sensitivity runs used

to examine the impacts of varying assumptions about natural gas price and electric

growth.)

Table IV-5. – Estimated Costs per Tons of SO2 Controlled Under CAIR, Cap

Levels Beginning in 2010 and 20151

Type of cost effectiveness 2010 2015

Average Cost – Main Case $500 $700

Marginal Cost – Main Case 700 1,000

Sensitivity Analysis: Marginal Cost Using EIA Growth and

Natural Gas Prices

800 1,200

The EPA IPM modeling 2004, available in the docket. 1999$ per ton. 1

“The 2 2 se estimated SO control costs under the CAIR reflect annual EGU SO caps of

3.6 million tons in 2010 and 2.5 million tons in 2015 within the CAIR region. . . .

“ 2(IV) Cost Effectiveness: Marginal Cost Curves for SO Control 

“. . . Figure IV-1 shows that the “knee” in the 2010 marginal cost-effectiveness curve

– the 2 point where the cost of controlling a ton of SO from EGUs is increasing at a

noticeably higher rate – appears to occur at about $2,000 per ton of SO2. Figure IV2 shows that the “knee” in the 2015 marginal cost-effectiveness curve also appears

2

to occur at about $2,000 per ton of SO . . . . ” 
West Virginia Regional Haze SIP Page 96
West Virginia Regional Haze SIP Page 97

After 2 review of EPA’s CAIR cost analyses the WVDAQ determined that the CAIR SO control

cos 2ts vary by year of analysis (2010 vs. 2015) and may range from $400 to $3,400 per ton SO

removed. Ultimately, EPA found a consistent marginal cost for both years at $2000 per ton,

which WVDAQ believes establishes an appropriate benchmark against which cost effectiveness

may be evaluated for reasonable progress. During the current reasonable progress assessment, no

non-EGU units in West Virginia were identified for additional control because no measures were

found to be cost effective.

The only non-EGU identified as contributing greater than 1% to sulfate visibility impairment at a

Class I area was Capitol Cement, with a 1.27% contribution to sulfate visibility impairment at

Shenandoah. 

Capitol Cement

Capitol Cement is a portland cement manufacturing facility located in Martinsburg, WV. In

2002, ESSROC acquired Riverton Investment Corporation which operated Capitol Cement.

Capitol Cement then applied for a permit to modernize operations at the facility by replacing

certain existing operations and increasing overall plant production capacity. The modernization

project included the replacement of two existing long wet process cement kilns (Kilns 8 and 9)

and associated clinker coolers with a modern large precalciner kiln system and related

equipment. Kiln 7 (point ID 010, identified above), a long wet process kiln, remained at the

facility and is used to make a limited amount of clinker, including certain specialty (colored)

clinkers. 

The modernization project at Capitol Cement was subject to both minor source and major source

permitting requirements, and was required to undergo PSD permitting. Permit R14-026 was

2

issued for the project on June 2, 2005. Under the conditions of permit R14-026 the SO limit for

Kiln 7 was reduced to 900 tpy 2; actual SO emissions in 2002 from Kiln 7 were 1,247 tons and

projec 2ted SO emissions for 2018 were 1,717 tons. [See Appendix H for a copy of the permit and

fact sheet.]

Although only Kiln 7 was identified as requiring a Reasonable Progress analysis, the WVDAQ

determined that the new preheater kiln should also be reviewed with respect to reasonable

p 2rogress, since the allowable SO emissions from the new large preheater kiln are 4,425.8 tpy.

The facility underwent PSD permitting for CO and VOC, and Best Available Control

Technology 2 (BACT) was required for CO and VOC. However, BACT for SO was not required

under PSD netting allowances.

Cost of Compliance - Wet Kiln

VISTAS contracted with Alpine Geophysics to evaluate control options and costs for sources

within the AoI for the Class I areas of concern. Alpine used EPA’s AirControlNet software to

evaluate control options and costs for controls on Kiln 7 at Capitol Cement. The control option

identified was flue gas desulfurization (FGD) with a cost effectiveness of $25,266 per ton. [See

Appendix H.]

Since the cost of compliance for an FGD on Kiln 7 is more than 12 times greater than the cost

effectiveness benchmark, and the facility has taken permit limits to reduce emissions and limit
West Virginia Regional Haze SIP Page 98

ope 2rations of Kiln 7, the WVDAQ determined that there are no cost effective SO controls

available.

Cost of Compliance - Precalciner System

The Mid-Atlantic/Northeast Visibility Union (MANE-VU) RPO contracted with MACTEC, Inc.

to evaluate control options and the four statutory factors for assessing Reasonable Progress

Goals (RPGs) for regional haze in the MANE-VU region. The project report [Assessment of

Reasonable Progress for Regional Haze In MANE-VU Class I Areas, dated July 9, 2007]

2

summarizes the control measures available for SO emissions from electric utilities, industrial

boilers, kilns, residential heating oil and wood combustion. For kilns, the report identifies four

con 2trol options for SO for cement kilns:

• Limiting sulfur content in raw materials and fuel,

• Dry Flue Gas Desulfurization - Spray Dryer Absorption (Dry FGD),

• Wet Flue Gas Desulfurization (Wet FGD), and

• Advanced Flue Gas Desulfurization (Advanced FGD).

Emissions of SO2 from cement kilns are primarily determined by the content of sulfur in the raw

materials and not from the sulfur content of the fuel, as Capitol Cement noted in their permit

application. Capitol Cement’s facility is situated on about 800 acres near Martinsburg, WV,

along with an open pit quarry from which the limestone (a raw material) is obtained. Since the

2

limestone is mined on site, an alternative was not considered to be a viable option for SO

control.

MACTEC, in their report, provided cost estimates for the other three control options identified

above.

Table 2 7.7-3. SO Control Costs for Large Preheater Kilns (2006 Dollars)

Capitol Cost

(10 $) 6

Annual Operating Costs

(10 $) 6

SO2 Cost Effectiveness

2

($/ton SO reduction)

Dry FGD $3.6 - $105.2 $0.9 - $21.0 $10,000 - $72,800

Wet FGD $6.2 - $92.5 $0.9 - $18.9 $9,700 - $64,600

Advanced FGD $17.8 - $58.0 $1.2 - $11.8 $13,600 - $38,000

Since the cost of compliance for all three control options range from 5 to 36 times greater than

2

the cost effectiveness benchmark, the WVDAQ determined that there are no cost effective SO

controls available for the new precalciner system at Capitol Cement.

Time Necessary for Compliance, Energy and Non-Air Impacts, and Remaining Useful Life

The three remaining statutory factors: 1) time necessary for compliance, 2) the energy and nonair quality environmental impacts of compliance, and 3) the remaining useful life of the

emissions unit, while required to be considered, were deemed not applicable, since there were no

cost effective controls to evaluate.
West Virginia Regional Haze SIP Page 99

7.8. What Control Determinations Represent Best Available Retrofit Technology

(BART) for Individual Sources?

Section 169A of the CAA directs States to assess certain large, older emission sources for

additional controls in order to address visibility impacts. States are directed to conduct BART

determinations for such sources in specific source categories, which contribute to visibility

impairment in Class I areas. The 1999 regional haze rule includes the BART requirement, and

directs States to include BART in their regional haze SIPs. On July 6, 2005, U.S. EPA published

a revised final rule, including Appendix Y to 40 CFR part 51, the Guidelines for BART

Determinations Under the Regional Haze Rule (hereinafter referred to as the “BART

Guidelines”) that provides direction to states on determining which of these sources should be

subject to BART, and how to determine BART for each source.

A BART-eligible source is one which has the potential to emit 250 tons or more of a visibilityimpairing air pollutant, was put in place between August 7, 1962 and August 7, 1977, and whose

operations fall within one or more of 26 specifically listed source categories. Under the CAA,

BART is required for any BART-eligible source that a State determines ‘‘emits any air pollutant

which may reasonably be anticipated to cause or contribute to any impairment of visibility in any

such area.’’ 

For those sources subject to BART, Section 169A(g)(7) of the CAA requires that States must

consider the following factors in making BART determinations: 

(1) the costs of compliance, 

(2) the energy and non-air quality environmental impacts of compliance, 

(3) any existing pollution control technology in use at the source, 

(4) the remaining useful life of the source, and 

(5) the degree of improvement in visibility which may reasonably be anticipated to result

from the use of such technology.

7.8.a. BART-Eligible Sources in West Virginia

The following is a list of facilities with BART-eligible sources in West Virginia. See Appendix

H for a detailed description of each BART-eligible emission unit:

Table 7.8.a-1. BART-Eligible Sources in West Virginia

Facility Name Facility ID Location

Allegheny Energy - Ft. Martin 061-00001 Maidsville

Allegheny Energy - Harrison 033-00015 Haywood

Allegheny Energy - Pleasants 073-00005 Willow Island

AEP - Appalachian Power - John Amos 079-00006 St. Albans

AEP - Ohio Power - Mitchell 051-00005 Moundsville
Table 7.8.a-1. BART-Eligible Sources in West Virginia

Facility Name Facility ID Location

West Virginia Regional Haze SIP Page 100

AEP - Appalachian Power - Mountaineer 053-00009 New Haven

Bayer Material Science 051-00009 New Martinsville

Cabot Corp 073-00006 Waverly

Capitol Cement Corporation 003-00006 Martinsburg

Century Aluminum 035-00002 Ravenswood

Clearon Corporation 039-00011 South Charleston

Columbian Chemicals 051-00019 Moundsville

Dominion - Mt. Storm 023-00003 Mt. Storm

Dupont 039-00001 Belle

Kepler Processing 109-00013 Pineville

ERGON Corp - West Virginia, Inc. 029-00008 Newell

GE Advanced Materials 107-00010 Washington

Mittal Steel USA - Weirton, Inc. (formerly Weirton Steel) 029-00001 Weirton

Pinnacle Mining Co. - No. 50 Coal Prep Plant 109-00006 Pineville

Pocahontas Coal Co. - East Gulf Prep Plant 081-00012 Rhodell

PPG Industries 051-00002 Natrium

Mountain State Carbon (formerly Wheeling Pittsburgh Steel) 009-00002 Wheeling

The BART-eligible sources were identified using the methodology in the BART Guidelines. For

an emission unit source to be identified as BART-eligible, we used the criteria from the BART

Guidelines:

• One or more emissions units at the facility fit within one of the 26 categories listed in the

BART Guidelines;

• The emission unit(s) were in existence on August 7, 1977 and began operation at some

point on or after August 7, 1962; and 

• The limited potential emissions from all emission units identified in the previous two

bullets emission units were greater than 250 tons or more per year of any of these

vis 2 x 10ibility-impairing pollutants: SO , NO , and PM .

The BART Guidelines recommend addressing these visibility-impairing pollutants: sulfur

dioxide 2 x (SO ), nitrogen oxides (NO ), and particulate matter, and suggests that States used their
West Virginia Regional Haze SIP Page 101

best judgment in determining whether to address VOC or ammonia emissions. WVDAQ

addre 2 x 10ssed SO and NO , and used particulate matter less than 10 microns in diameter (PM ) as

an indicator for particulate matter to identify BART-eligible units, as the BART Guidelines

recommends. As discussed in detail in Appendix H, VISTAS modeling demonstrated that VOCs

and ammonia from point sources are not significant visibility impairing pollutants. For this

reason, WVDAQ did not evaluate emissions of Volatile Organic Compounds (VOCs) and

ammonia in BART determinations.

7.8.b. Determination of Sources Subject to BART in West Virginia

Under the BART Guidelines, WVDAQ may consider exempting some sources from BART if it

is found that they do not cause or contribute to visibility impairment in a Class I area. In

accordance with the BART guidelines, WVDAQ chose to perform source-specific analyses 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 Guidelines, a contribution threshold

of less than 0.5 deciviews was employed for determining which sources were exempt from

BART.

Twenty-one (21) of West Virginia’s twenty-two (22) BART-eligible sources submitted

exemption modeling demonstrations. Nineteen (19) of the twenty-one (21) sources were able to

demonstrate exemption. Results of these demonstrations are summarized in Tables 7.8.b-1 and

7.8 b-2 below. Additional details are available in Appendix H. Capitol Cement (003-00006) was

the only BART-eligible source which chose not to submit exemption modeling. 

Table 7.8.b-1. BART Exemption Modeling Results

 12 km Modeling - Maximum Predicted Visibility Impact (delta-deciview) per Class I Areas

Company Year Dolly Sods,

WV

Otter Creek,

WV

Shenandoah,

VA

James River

Face, VA

Linville

Gorge, NC

Great Smoky

Mountains, TN

Mountain State Carbon (009-00002)

2001 0.057 0.059 0.091 n/a n/a n/a

2002 0.055 0.051 0.061 n/a n/a n/a

2003 0.048 0.051 0.065 n/a n/a n/a

Mittal Steel USA - Weirton, Inc.

2001 0.131 0.148 0.170 n/a n/a n/a

2002 0.182 0.155 0.081 n/a n/a n/a

2003 0.185 0.299 0.102 n/a n/a n/a
Table 7.8.b-1. BART Exemption Modeling Results

 12 km Modeling - Maximum Predicted Visibility Impact (delta-deciview) per Class I Areas

Company Year Dolly Sods,

WV

Otter Creek,

WV

Shenandoah,

VA

James River

Face, VA

Linville

Gorge, NC

Great Smoky

Mountains, TN

West Virginia Regional Haze SIP Page 102

ERGON Corp. - West Virginia, Inc.

2001 0.031 0.022 0.055 n/a n/a n/a

2002 0.032 0.036 0.026 n/a n/a n/a

2003 0.035 0.054 0.020 n/a n/a n/a

Century Aluminum (035-00002)

2001 0.077 0.081 0.092 0.052 n/a n/a

2002 0.067 0.075 0.071 0.054 n/a n/a

2003 0.037 0.049 0.040 0.030 n/a n/a

DuPont - Belle (039-00001)

2001 0.091 0.087 0.129 0.10 0.107 0.076

2002 0.070 0.078 0.069 0.125 0.096 0.056

2003 0.124 0.124 0.157 0.126 0.206 0.081

Clearon (039-00011)

2001 0.017 0.026 0.019 0.017 0.010 n/a

2002 0.010 0.015 0.012 0.022 0.009 n/a

2003 0.011 0.015 0.020 0.017 0.009 n/a

Pocahontas Coal Co - Eastern Gulf Prep Plant ((081-00012)

2001 0.033 0.038 0.073 0.098 0.062 0.054

2002 0.033 0.037 0.065 0.117 0.052 0.034

2003 0.068 0.065 0.064 0.136 0.044 0.050

GE Woodmark (107-00010)

2001 0.060 0.072 0.061 0.040 n/a n/a

2002 0.036 0.040 0.061 0.033 n/a n/a

2003 0.044 0.055 0.034 0.035 n/a n/a
Table 7.8.b-1. BART Exemption Modeling Results

 12 km Modeling - Maximum Predicted Visibility Impact (delta-deciview) per Class I Areas

Company Year Dolly Sods,

WV

Otter Creek,

WV

Shenandoah,

VA

James River

Face, VA

Linville

Gorge, NC

Great Smoky

Mountains, TN

West Virginia Regional Haze SIP Page 103

Pinnacle Mining - No. 50 Coal Prep Plant (109-00006)

2001 0.022 0.022 0.066 0.075 0.034 0.049

2002 0.019 0.019 0.036 0.069 0.050 0.027

2003 0.039 0.047 0.055 0.120 0.032 0.046

Kepler Processing (109-00013)

2001 0.018 0.013 0.039 0.029 0.023 0.029

2002 0.012 0.010 0.021 0.029 0.026 0.013

2003 0.024 0.031 0.032 0.056 0.022 0.032
West Virginia Regional Haze SIP Page 104

Table 7.8.b-2. BART Exemption Modeling Results

 4km Modeling - 98 Percentile Visibility Impact (delta-deciview) per Class I Area

th

Company Year Dolly Sods,

WV

Otter Creek,

WV

Shenandoah,

VA

James River

Face, VA

Linville

Gorge, NC

Great Smoky

Mountains, TN

AEP - Appalachian Power Co. - John Amos (079-00006)

2001 0.054 0.073 0.062 0.075 0.029 n/a

2002 0.054 0.081 0.055 0.052 0.014 n/a

2003 0.059 0.082 0.074 0.062 0.018 n/a

AEP - Ohio Power Co. - Mitchell (051-00005)

2001 0.094 0.097 0.076 0.031 n/a n/a

2002 0.045 0.058 0.053 0.022 n/a n/a

2003 0.076 0.077 0.066 0.029 n/a n/a

AEP - Appalachian Power Co. - Mountaineer ((053-00009)

2001 0.259 0.344 0.253 0.164 n/a n/a

2002 0.172 0.266 0.281 0.142 n/a n/a

2003 0.201 0.250 0.217 0.142 n/a n/a

Allegheny Energy - Ft. Martin (061-00001)

2001 0.103 0.128 0.10 0.028 n/a n/a

2002 0.098 0.142 0.068 0.027 n/a n/a

2003 0.121 0.113 0.078 0.021 n/a n/a

Allegheny Energy - Harrison (033-00015)

2001 0.216 0.278 0.185 0.067 n/a n/a

2002 0.173 0.253 0.147 0.049 n/a n/a

2003 0.196 0.233 0.181 0.059 n/a n/a

Allegheny Energy - Pleasants (073-00005)

2001 0.059 0.062 0.046 0.019 n/a n/a

2002 0.037 0.053 0.037 0.018 n/a n/a

2003 0.039 0.049 0.036 0.016 n/a n/a
Table 7.8.b-2. BART Exemption Modeling Results

 4km Modeling - 98 Percentile Visibility Impact (delta-deciview) per Class I Area

th

Company Year Dolly Sods,

WV

Otter Creek,

WV

Shenandoah,

VA

James River

Face, VA

Linville

Gorge, NC

Great Smoky

Mountains, TN

West Virginia Regional Haze SIP Page 105

Bayer (051-0009)

2001 0.125 0.0139 0.093 0.056 n/a n/a

2002 0.075 0.121 0.080 0.058 n/a n/a

2003 0.080 0.100 0.073 0.042 n/a n/a

Columbian Chemicals (051-00019)

2001 0.261 0.328 0.378 0.247 n/a n/a

2002 0.295 0.298 0.407 0.228 n/a n/a

2003 0.257 0.296 0.272 0.176 n/a n/a

Cabot Corporation (073-00006)

2001 0.380 0.391 0.425 0.242 n/a n/a

2002 0.405 0.441 0.372 0.370 n/a n/a

2003 0.333 0.396 0.401 0.220 n/a n/a

The exemption modeling results for the BART-eligible facilities which had an impact of greater

than 0.5 deciviews of visibility impact at any Class I area are shown in Table 7.8.b-3. Appendix

H contains more detailed results of the modeling analyses for each BART-eligible source. 

Table 7.8.b-3. Exemption Modeling Results for Facilities with > 0.5 deciview of Impact

 4km Modeling - 98 Percentile Visibility Impact (delta-deciview) per Class I Area

th

Company Year Dolly Sods, WV Otter Creek, WV Shenandoah, VA James River Face, VA

Dominion - Mt. Storm (023-00003)

2001 0.770 0.205 0.538 0.124

2002 0.636 0.286 0.381 0.097

2003 0.416 0.297 0.401 0.098
Table 7.8.b-3. Exemption Modeling Results for Facilities with > 0.5 deciview of Impact

 4km Modeling - 98 Percentile Visibility Impact (delta-deciview) per Class I Area

th

Company Year Dolly Sods, WV Otter Creek, WV Shenandoah, VA James River Face, VA

West Virginia Regional Haze SIP Page 106

PPG Industries ((051-00002)

2001 0.557 0.669 0.786 0.513

2002 0.635 0.615 0.816 0.507

2003 0.578 0.596 0.587 0.364

Capitol Cement (003-00006)

2001 Not available. Capitol Cement elected not to do exemption

modeling. The BART unit is scheduled to be replaced.

2002

2003

Although PPG initially modeled a visibility impact greater than 0.5 deciviews on multiple Class

I areas, PPG elected to accept a permit limit on its BART eligible unit which reduces its

visibility impact to below the exemption threshold of 0.5 deciviews of impact at any Class I area.

Permit, R14-027B, requires 4690.56 tpy 2 of SO emission reductions from Boiler #5 by May 1,

2008. Therefore, PPG is considered BART exempt.

Table 7.8.b-4. Summary of BART Exemption Requirements for PPG (54-051-00002)

Unit: #5 Boiler Unit ID:

003

Pollutant:

SO2

Control

Method:

Multiple

Overall Control

Efficiency: 58%

BART Limit:

1478.8 lbs/hr

MRR

Requirements

Compliance with the Boiler 5 emission limits shall be determined by the use of a CEMS.

The CEMS shall be installed, operated, maintained and certified as accurate under the

applicable requirements of 40CFR60. The date, time and duration of all noncompliance shall be recorded and submitted to the Director in compliance with

45CSR10. 

Other Permit

Requirements

Total 2 SO emissions from Boilers 3, 4 and 5 (Unit ID 002, 001 and 003, respectively)

shall not exceed 3766.8 lb/hr.

Compliance Date May 1, 2008

Seven of West Virginia’s BART-eligible sources are EGUs, with a total of 16 units and a

generating capacity of 11,714 MW, or approximately 80% of West Virginia’s coal-fired

generating capacity. Six of the seven facilities (14 units) have already installed Selective
West Virginia submitted a CAIR SIP to the EPA in June 2006. The submittal was believed to be approvable at that 1

time. EPA subsequently revised various aspects of the the CAIR program through other rule-makings, including

promulgation of a Federal Implementation Plan (FIP). West Virginia then submitted an “abbreviated SIP” to be

consistent with the FIP and related EPA guidance. West Virginia is subject to the CAIR FIP (as modified by the

abbreviated SIP) until a full CAIR SIP is submitted and approved.

West Virginia Regional Haze SIP Page 107

Cataly xtic Reduction (SCRs) to reduce NO , and the remaining facility (2 units) has installed

SNCR, to meet either x the requirements of the NO SIP Call or CAIR. Five of the seven facilities

2

(11 units) currently have scrubbers installed to reduce SO emissions (scrubbers were installed

on 3 of the units in 2007 to meet CAIR). Construction has commenced at the two remaining

facilities (5 units) with startup scheduled in 2008 for one unit, 2009 for two units and 2010 for

the remaining two units. 

The EPA has determined that, as a whole, the CAIR cap-and-trade program improves visibility

more than implementing BART for individual sources in states affected by CAIR. A state that

opts to participate in the CAIR program under 40 CFR Part 96 AAA-EEE need not require

affected BART-elig 2 x ible EGUs to install, operate and maintain BART controls for SO or NO

emissions. Given that all BART-eligible units have already installed or are installing scrubbers

and x NO controls, and since West Virginia is participating in CAIR , and accepts EPA’s overall 1

x 2 finding that CAIR “substitutes” for BART for NO and SO , West Virginia’s EGUs were

allowed to submit BART exemption modeling demonstrations for PM emissions only. Only one

EGU facility (3 units) demonstrated that they contributed significantly to visibility impairment in

any Class I area. 

7.8.c. Determination of BART Requirements for Subject-to-BART Sources

Only Dominion’s Mt. Storm Power Station in Mt. Storm was unable to demonstrate a

contribution of less than 0.5 deciview at all Class I area within 300 km from their BART eligible

sources. Therefore, Mt. Storm was the only source considered to be “subject to BART” and

required to submit a permit application, containing their evaluation of potential BART options

and a proposed BART determination.

Mt. Storm submitted a BART determination on November 3, 2006. A copy of the BART

determination was sent to EPA and the Federal Land Managers for review on January 22, 2007.

On September 5, 2007 the company submitted a permit application to incorporate the results of

the BART determination. The WVDAQ published a legal ad, which commenced the thirty day

public comment period, on September 18, 2007. A copy of the application and draft permit were

sent to the Federal Land Managers for review, via email, on September 17, 2007. Permit R13-

2735 was issued December 13, 2007. [See Appendix L for copies of application, draft permit and

final permit.] The current PM controls at Mt. Storm were determined to satisfy BART, however,

10 the allowable PM emission rate was lowered from 0.05 lb/mmBtu to 0.03 lb/mmBtu, resulting

in a reduction of up to 508 tpy per unit, or a maximum reduction of 1524 tpy. A summary of the

permit requirements is contained in Table 7.8.c-1 below.
West Virginia Regional Haze SIP Page 108

Capitol Cement in Martinsburg chose not to submit exemption modeling, but simply to

acknowledge that Kiln 9 was BART-subject. Capitol Cement had previously applied for and

been granted a PSD (Prevention of Significant Deterioration) permit (R14-026, issued June 2,

2005) for the replacement of two existing long wet process cement kilns and associated clinker

coolers with a modern precalciner system and associated equipment. The only BART-eligible

unit at the facility (Kiln 9) was one of the two kilns being replaced, and the permit included a

requirement for the permanent shutdown of the existing kilns when full-production was achieved

with the replacement kilns, or no later than 180 days after startup. Therefore, the company

requested that the permit be modified to require the permanent shutdown of the Kiln 9 system by

the BART compliance deadline or when full-production was achieved with the replacement

kilns, or no later than 180 days after startup, whichever came first. The WVDAQ issued an

Administrative Update to permit R14-026B on October 11, 2006, requiring that “Operation of

the existing kiln 9 system shall permanently cease after the pre-heater-precalciner kiln system

achieves full production or within 1280 days after the pre-heater-precalciner kiln system first

becomes operational or before the BART compliance deadline (approximately 2013) whichever

comes first”. [See Appendix L for copies of letter and permit.] The modifications at Capitol

Cement a 2 x re expected to result in 1,741.51 tpy of SO reductions, 1,374.81 tpy of NO reductions,

and 10 66.01 tpy of PM reductions. A summary of the BART requirements is contained in Table

7.8.c-3 below.

In accordance with 40 CFR 51.308(e)(1)(iv), West Virginia has required each source “subject to

BART” to install and operate BART, as expeditiously as practicable, but no later than five years

after approval of this SIP. Additionally, in accordance with 40 CFR 51.308(e)(1)(v), West

Virginia has required each BART source to maintain the required control equipment and

establish procedures to ensure such equipment is properly operated and maintained.
West Virginia Regional Haze SIP Page 109

Table 7.8.c-1. Summary of BART Requirements for Mt. Storm (54-023-00003)

Unit: Unit 1 Unit ID:

001

Pollutant:

PM10

(filterable)

Control

Method:

ESP & FGD

Overall

Control

Efficiency:

99.50%

BART Limit:

0.03 lb/mmBtu

(filterable)

MRR

Requirements

The permittee shall calculate the potential particulate matter emissions from Unit 1, Unit

2, and Unit 3 on a daily basis using the monitoring procedures and calculation

methodology outlined in the 45CSR2 monitoring plan. 

The permittee shall record any instance of calculated emissions in excess of the limits

given under Section 4.1 of this permit and any corrective actions therefore taken. 

The permittee shall maintain and operate, at all reasonable times, appropriate equipment

on the ESP and FGD to continuously monitor the performance of each control device. 

PM10 testing on 45CSR2 schedule. 

Other Permit

Requirements

Agg 10 regate 99.50% PM removal efficiency on ESP & FGD

Compliance

Date

December 13, 2007

Unit: Unit 2 Unit ID:

0012

Pollutant:

PM10

(filterable)

Control

Method:

ESP & FGD

Overall

Control

Efficiency:

99.50%

BART Limit:

0.03 lb/mmBtu

(filterable)

MRR

Requirements

Same as Unit 1

Other Permit

Requirements

Agg 10 regate 99.50% PM removal efficiency on ESP & FGD

Compliance

Date

December 13, 2007

Unit: Unit 2 Unit ID:

0013

Pollutant:

PM10

(filterable)

Control

Method:

ESP & FGD

Overall

Control

Efficiency:

99.50%

BART Limit:

0.03 lb/mmBtu

(filterable)

MRR

Requirements

Same as Unit 1

Other Permit

Requirements

Agg 10 regate 99.50% PM removal efficiency on ESP & FGD

Compliance

Date

December 13, 2007
West Virginia Regional Haze SIP Page 110

Table 7.8.c-2. Summary of BART Requirements for Capitol Cement (54-003-00006)

Unit: #9 Kiln Unit ID:

012

Pollutant:

SO2 x , NO ,

PM10

Control

Method:

Shut Down

Overall

Control

Efficiency: N/A

BART Limit:

N/A

MRR

Requirements

Confirm shutdown of kiln

Other Permit

Requirements

N/A

Compliance

Date

Shutdown #9 Kiln within 180 days of startup of the new ph-pc kiln, or before BART

Compliance deadline, whichever comes first
West Virginia Regional Haze SIP Page 111

7.9. What Additional Emissions Controls Were Considered as part of the Long-Term

Strategy for Visibility Improvement by 2018?

40 CFR 51.308(d)(3)(v) of the regional haze rule lists several factors that must be addressed in

each SIP. These factors include the role of fire at the Class I areas and status of state planning for

smoke management, the role of dust and fine soil at the Class I areas and status of plans to

mitigate emissions from construction activities, and the role of ammonia and potential benefits if

emissions from agricultural sources were mitigated.

As discussed in Section 2.4 and shown in Figures 2.4-1 and 2.4-2, carbon sources (such as

agricultural fires, prescribed fires and wildfires) are a relatively minor contributor to visibility

impairment at Dolly Sods. Since carbon is a minor contributor to visibility impairment,

WVDAQ is not proposing to adopt additional requirements for controls on these sources during

the first review period. Nevertheless, West Virginia has a rule in place to regulate emissions

from these sources, 45CSR6 [effective date July 1, 2001] – To Prevent and Control Air Pollution

from Combustion of Refuse – which includes a prohibition of open burning, unless specific

conditions are met. West Virginia does not have a requirement for a Smoke Management Plan to

regulate emissions from prescribed fires, however, West Virginia has proposed revisions to

45CSR6, which include a provision for prescribed burning – the controlled application of fire to

vegetation under specified environmental conditions and precautionary measures which allows

the fire to be confined to a predetermined area for the purpose of accomplishing specifically

planned wildlife and forest management objectives – in accordance with a written prescribed fire

plan approved by the West Virginia Division of Forestry, or in the case of federal lands,

approved by the appropriate Agency Administrator and endorsed by the West Virginia Division

of Forestry. The proposed revisions to 45CSR6 are scheduled to go before the 2008 Legislature

for approval and are expected to become effective in the June 2008 timeframe. WVDAQ has

worked with the WV Division of Forestry and the Federal Land Managers to develop the

language allowing prescribed burns. Strategic prescribed fires, when properly conducted actually

reduce the incidence of wildfires and overall should yield benefits with respect to visibility.

Copies of the current rule and the proposed rule are included in Appendix H.

Also as discussed in Section 2.4 and demonstrated in Figures 2.4-1 and 2.4-2, fine soils are a

relatively minor contributor to visibility impairment at Dolly Sods. Nevertheless, in regard to

construction activities, the WVDAQ has rule 45CSR17 [effective date August 31, 2000] – To

Prevent and Control Particulate Matter Air Pollution from Materials Handling, Preparation,

Storage and Other Sources of Fugitive Particulate Matter – which prohibits the discharge

“beyond the boundary lines of the property lines on which the discharge originates or at any

public or residential location, which causes or contributes to statutory air pollution”. Copies of

this rule are included in Appendix H.

In addition, the WVDAQ has a number of rules in place that require the control of fugitive dust

within plant boundaries, these include 45CSR2 – To Prevent and Control Particulate Air

Pollution form Combustion of Fuel in Indirect Heat Exchangers; 45CSR3 – To Prevent and

Control Air Pollution from the Operation of Hot Mix Asphalt Plants; 45CSR5 – To Prevent and

Control Air Pollution from the Operation of Coal Preparation Plants, Coal Handling Operations

and Coal Refuse Disposal Areas; and 45CSR7 – To Prevent and Control Particulate Matter Air
West Virginia Regional Haze SIP Page 112

Pollution from Manufacturing Processes and Associated Operations. Copies of these rules are

included in Appendix H.

West Virginia also has rules in place to regulate emissions from new sources, including

45CSR13 – Permits for Construction, Modification, Relocation And Operation of Stationary

Sources of Air Pollutants, Notification Requirements, Administrative Updates, Temporary

Permits, General Permits, and Procedures for Evaluation (minor source NSR); 45CSR14 –

Permits for Construction and Major Modification of Major Stationary Sources of Air Pollution

for the Prevention of Significant Deterioration (PSD); and 45CSR19 – Permits for Construction

and Major Modification of Major Stationary Sources of Air Pollution which Cause or Contribute

to Nonattainment (nonattainment NSR). The rules identified above have all been approved by

EPA as part of the State Implementation Plan. 

The West Virginia PSD and nonattainment NSR regulations will address emissions from new

major sources that may locate near a Class I area, or increased emissions from major

modifications to existing sources located near a Class I area. Both the PSD and nonattainment

NSR programs require consultation with the FLMs and require that emissions will not adversely

impact any identified air quality related value, such as visibility.

In regard to agricultural ammonia, West Virginia does not have a large agricultural industry with

correspondingly large ammonia emissions. West Virginia accounts for 1.9 percent of the

ammonia emissions in the VISTAS region, which as shown in Table 7.9-1 is the smallest

contribution in the region. North Carolina, with its large agricultural industry, has ammonia

emissions which totaled 173,083 tons in 2002 (26.1%), or 13.9 times West Virginia’s total. 

Table 7.9-1. VISTAS Ammonia Emissions

State 2002 NH3 Emissions (tons) State % Contribution to Total

Alabama 66,519 10.0

Florida 58,602 8.3

Georgia 95,936 14.5

Kentucky 57,563 8.7

Mississippi 63,929 9.6

North Carolina 173,084 26.1

South Carolina 36,509 5.5

Tennessee 43,258 6.5

Virginia 55,570 8.4

West Virginia 12,461 1.9

Total 663,431 100
West Virginia Regional Haze SIP Page 113

Nevertheless, as shown in Table 7.9-2, the majority (~80%) of the ammonia emissions are

attributable to the area sources sector, with ~90% of those emissions attributable to Agricultural

Livestock Production. Due to the small size of the agricultural industry in West Virginia and the

relatively small amount of ammonia emitted in the state, the WVDAQ has determined that

controls are not warranted on agricultural sources. For a more detailed area source emissions

inventory see Appendix H.

Table 3 7.9-2. West Virginia NH Emissions

Sector 2002

(tons)

2002

(%)

2009

(tons)

2009

(%)

2018

(tons)

2018

(%)

Point 453 3.64 671 4.93 593 4.12

Area 9,963 79.95 10,625 78.14 11,504 80.02

NonRoad 9 0.07 11 0.08 13 0.09

OnROad 2,036 16.34 2,292 16.86 2,268 15.76

Biogenics N/A N/A N/A N/A N/A N/A

Total 12,461 100 13,598 100 14,377 100
West Virginia Regional Haze SIP Page 114

8.0. REASONABLE PROGRESS GOALS

The regional haze rule at 40 CFR 51.308(d)(1) requires States to establish reasonable progress

goals (RPG) for each Class I area within the state (expressed in deciviews) that provide for

reasonable progress towards achieving natural visibility. In addition, EPA released guidance on

June 7, 2007 to use in setting reasonable progress goals. The goals must provide improvement

in visibility for the most impaired days, and ensure no degradation in visibility for the least

impaired days over the State Implementation Plan (SIP) period. The state must also provide an

assessment of the number of years it would take to attain natural visibility conditions if

improvement continues at the rate represented by the reasonable progress goal.

In accordance with the requirements of 40 CFR §51.308(d)(1), this Regional Haze

Implementation Plan establishes reasonable progress goals for Dolly Sods and Otter Creek

Wilderness areas. To calculate the rate of progress represented by each reasonable progress goal,

WVDAQ compared baseline visibility conditions to natural visibility conditions in each class I

area and determined the uniform rate of visibility improvement (in deciviews) that would need to

be maintained during each implementation period in order to attain natural visibility conditions

by 2064. The Reasonable Progress Goals are based on the available Base G2 modeling results,

and represent the best data available at this time.

Table 8.0-1. West Virginia Reasonable Progress Goals

Class I Area Baseline

Visibility

(Deciviews)

Reasonable

Progress Goal

(Deciview

Improvement

Expected by

2018, 20%

Worst Days)

Reasonable

Progress Goal

(Deciview

Improvement

Expected by

2018, 20%

Best Days)

Deciview

Improvement

Needed 2018

- 2064 (20%

Worst Days)

Dolly Sods Wilderness Area 29.0 7.3 1.2 11.3

Otter Creek Wilderness Area 29.0 7.3 1.2 11.3
West Virginia Regional Haze SIP Page 115

9.0. MONITORING STRATEGY

The State Implementation Plan is to be accompanied by a strategy for monitoring regional haze

visibility impairment. Specifically, the Regional Haze Rule at 40 CFR 51.308(d)(4) states:

(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) - (iv) [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 West Virginia, is the

IMPROVE network. Given that IMPROVE monitoring data from 2002-2004 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,

therefore West Virginia 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 West Virginia located at Dolly Sods, which also serves

as the monitoring site for Otter Creek. The IMPROVE measurements are central to West

Virginia’s regional haze monitoring strategy, and it is difficult to imagine how the objectives

listed above could be met without the monitoring provided by IMPROVE. Any reduction in the

scope of the IMPROVE network in West Virginia would jeopardize the State’s ability to

demonstrate reasonable progress toward visibility improvement at Dolly Sods and Otter Creek.

In particular, West Virginia’s regional haze strategy relies on emission reductions that will result

from CAIR which occur at different times and will most likely not be spatially uniform. Of

course, monitoring at every Class I area may important, but it is especially important to
West Virginia Regional Haze SIP Page 116

document the different air quality responses to the emissions reductions at sites like Dolly Sods

that are located in complex terrain. 

The IMPROVE network is periodically assessed to optimize the data acquisition versus the

required resources. Because the current IMPROVE monitor in West Virginia has an extensive

data record that represents a unique airshed, reducing the IMPROVE network by shutting down

the monitoring site would significantly impede tracking progress at Dolly Sods and Otter Creek.

It is critical to the monitoring strategy that IMPROVE will continue to operate this site, the only

one in West Virginia. Alternatively, EPA should ensure that federal funding is available to

operate an equivalent monitor.

Data produced by the IMPROVE monitoring network will be used nearly continuously for

preparing the 5-year progress reports and the 10-year SIP revisions, each of which relies on

analysis of the preceding five years of data. Consequently, the monitoring data from the

IMPROVE sites needs to be readily accessible and to be kept up to date. Presumably, IMPROVE

will continue to process information from its own measurements at about the same pace and with

the same attention to quality as it has shown in the recent past. The VIEWS website has been

maintained by VISTAS and the other Regional Planning Organizations to provide ready access

to the IMPROVE data and data analysis tools. West Virginia is encouraging VISTAS and other

RPOs to maintain VIEWS or a similar data management system to facilitate analysis of the

IMPROVE data.

In addition to the IMPROVE measurements, some ongoing long-term limited monitoring

supported by Federal Land Managers provides additional insight into progress toward regional

haze goals. West Virginia benefits from the data from these measurements, but is not responsible

for the funding decisions to maintain these measurements into the future. Such long-term

monitoring includes a Web camera operated by the USDA Forest Service at Dolly Sods.

Moreover, the WVDAQ operates a PM2.5 network of the filter-based Federal reference method

monitors and filer-based speciation monitors. A map of the various locations around the State is

included in Figure 9.0-1. These PM2.5 measurements help the WVDAQ 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.
West Virginia Regional Haze SIP Page 117

Figure 9.0-1. PM2.5 Monitoring Network in West Virginia.
West Virginia Regional Haze SIP Page 118

10.0. INTERSTATE CONSULTATION REGARDING WEST VIRGINIA

CONTRIBUTION TO VISIBILITY IMPAIRMENT IN CLASS I

AREAS IN NEIGHBORING STATES

The VISTAS states have jointly developed the technical analyses to define the expected

visibility improvement by 2018 under existing federal and state regulations compared to the

un 2iform rate of progress, SO Areas of Influence for each Class I area, and methods to prioritize

contributions from individual sources within the Areas of Influence. The states collectively

accept the conclusions of these analyses.

In December 2006, the VISTAS State Air Directors held their first formal consultation meeting

2

to review Base G modeling results and the SO Areas of Influence analyses. The Air Directors

ag 2reed to look at reasonable control measures for sources on the lists for the SO Areas of

Influence. Each state would consider sources within their state and would identify sources in

neighboring states that they would like to have that state consider. States acknowledged the

review process would differ among states since some Class I areas are projected to achieve

visibility improvements near the uniform rate of progress while most Class I areas are projected

to have greater improvements than uniform rate of progress.

In May 2007, the VISTAS State Air Directors met for their second formal interstate

consultation. States shared their lists of sources in their state and neighboring states for each

Class I area. They also shared their criteria for listing sources and their plans for further

interstate consultation.

West Virginia has evaluated the impact of West Virginia sources on Class I areas in neighboring

states and determined that there are no additional reasonable control measures that should be

implemented to mitigate impacts in Class I areas in neighboring states. West Virginia has

consulted with the responsible states regarding WV’s decision not to require additional controls

for those Class I areas. Analyses of impacts from WV sources are discussed in greater detail in

Appendix H. Neighboring states have shared the controls that are intended for sources that are in

the Areas of Influence for Dolly Sods. For the first control period (five years), West Virginia has

not asked neighboring states to do more than already planned by that state.

The MANE-VU states of New Jersey, New Hampshire, and Vermont sent letters to West

Virginia in the spring of 2007 stating that based on 2002 emissions, WV contributed to visibility

impairment at Class I areas located in those states. MANE-VU states asked West Virginia to

participate in further consultation with these states, and the WVDAQ participated in a

consultation meeting between VISTAS and MANE-VU in Atlanta, Georgia, on August 20.

The MANE-VU states identified 167 EGU stacks, thirteen (13) of which were in West Virginia,

as contributing significantly to visibility impairment at MANE-VU Class I areas in 2002. The 13

stacks identified by MANE-VU vent the emissions from twenty-three (23) EGUs (identified in

Table 10.0-1) in West Virg 2inia. MANE-VU has asked states to control the SO emissions from

these units with a 90% efficiency. MANE-VU also requested a control strategy to provide a 28%

2

reduction in SO emissions from sources, other than EGUs, that would be equivalent to their
West Virginia Regional Haze SIP Page 119

proposed low sulfur residential fuel oil strategy. West Virginia has controlled or is expecting to

control all of the EGUs identified by MANE-VU, with at least a 90% control efficiency by 2018.

As shown in Table 10.0-1, all of the units identified by MANE-VU in their “EGU Ask” are

expected to be controlled by 2018, and thirteen (13) of the units will be controlled with a 95%

eff 2iciency, resulting in an additional 73,015 tons of SO reductions beyond those requested in the

“EGU Ask”. In addition, IPM has predicted the shutdown of five (5) EGUs (337 MW of

capac 2ity) not identified by MANE-VU, these units accounted for a total of 18,849.5 tons of SO

in 2002. As shown in Table 7.5.d-3, by 2018 all the EGUs in West Virginia are expected to

either be 2 controlled for SO or shutdown. 

Wes 2t Virginia’s non-EGUs are predicted to emit 61,704 tons of SO in 2018. MANE-VU in their

“Non-EGU A 2sk” has requested a 28% reduction in these emissions, or 17,277 tons of SO

2

reductions. The additional 91,864 tons of SO reductions achieved by the installation and

operation of more efficient controls on the EGUs and the shutdown of additional EGUs, will

achieve greater reductions than the 28% reduction in the MANE-VU “Non-EGU Ask”. The

WVDAQ believes that these reductions satisfy MANE-VU’s request.

The letters from these states, and responses from WVDAQ, are included as attachments in

Appendix J. West Virginia substantively disagrees with the scope and significance of visibility

impairment that MANE-VU has attributed to emissions from within our state. Past MANE-VU

analyses have focused on 2002 base year emissions and often fail to account for the dramatic

SO2 reductions expected from EGUs in West Virginia under the CAIR.

West Virginia believes that the measures we have adopted to protect visibility in our own Class I

areas are sufficient to address any out of state impacts on other Class I areas. Based on the 2018

VISTAS analyses, it can be shown that the entire state of West Virginia contributes less than 1%

to sulfate in the nearest MANE-VU Class I area, Brigantine.

Table 10.0-2. below illustrates this fact. The assessment is based on sulfate emissions divided

by the distance to the area, multiplied by the maximum residence time. This metric is the same

as that used by West Virginia to evaluate impacts from ourselves and other VISTAS states, and

also to help assess reasonable progress. Moreover, the table demonstrates that no individual unit

in West Virginia contributes more than 0.10% to Q/d * RTMax for Brigantine.
West Virginia Regional Haze SIP Page 120

Table 10.0-1. 23 WV Electric Generating Units Identified in MANE-VU “EGU ASK”

Plant 2ID Unit MW 2002 SO

Emissions

(tons)

SO2

Controls

Y 2r SO

Controls

Installed

SO2

Control

Efficiency

2018

Proj 2ected SO

Emissions

(tons)

Comments

Albright 077-00001 3 137 10,136.5 Scrubber 2018 90% 660.1 IPM Predicted Controls

Ft. Martin 061-00001 1 552 45,228.5 Scrubber 2010 95% 4,922.1 Controls under construction

9,812.1 addi 2tional tons of SO

removed due to greater than

90% Control Efficiency

2 555 45,890.7 Scrubber 2010 95% 4,890.0

John Amos 079-00006 1 800 31,938.0 Scrubber 2009 95% 6,612.7 Controls under construction

24,127.6 addi 2tional tons of SO

removed due to greater than

90% Control Efficiency

2 800 31,465.0 Scrubber 2009 95% 6,693.9

3 1300 44,030.0 Scrubber 2008 95% 10,821.0

Kammer 051-00006 1 210 14,896.0 Scrubber 2018 90% 951.1 IPM Predicted Controls 1

2 210 12,711.0 Scrubber 2018 90% 949.2 IPM Predicted Controls

3 210 14,452.0 Scrubber 2018 90% 948.7 IPM Predicted Controls

Kanawha

River

039-00006 1 200 7,944.0 Scrubber 2018 90% 902.7 IPM Predicted Controls

2 200 7,741.0 Scrubber 2018 90% 900.3 IPM Predicted Controls

M 2itchell 039-00006 1 800 29,568.0 Scrubber 2007 95% 7,646.2 15,228.1 additional tons of SO

removed due to greater than

2 800 29,532.0 Scrubber 2007 95% 7,581.9 90% Control Efficiency

Moun 2taineer 053-00009 1 1300 42,992.0 Scrubber 2007 95% 11,433.5 11,433.5 additional tons of SO

removed due to greater than

90% Control Efficiency
Table 10.0-1. 23 WV Electric Generating Units Identified in MANE-VU “EGU ASK”

Plant 2ID Unit MW 2002 SO

Emissions

(tons)

SO2

Controls

Y 2r SO

Controls

Installed

SO2

Control

Efficiency

2018

Proj 2ected SO

Emissions

(tons)

Comments

West Virginia Regional Haze SIP Page 121

M 2t. Storm 023-00003 1 533 9,958.5 Scrubber 2002 95% 3,191.0 9305.3 additional tons of SO

removed due to greater than

90% Control Efficiency

2 533 10,467.4 Scrubber 2001 95% 3,191.0

3 521 2,944.4 Scrubber 1994 95% 2,923.3

Philip Sporn 053-00001 1 150 6,638.0 Scrubber 2018 90% 668.1 IPM Predicted Controls 1, 2

2 150 6,822.0 Scrubber 2018 90% 647.6 IPM Predicted Controls

3 150 6,939.0 Scrubber 2018 90% 643.0 IPM Predicted Controls

4 150 6,876.0 Scrubber 2018 90% 633.2 IPM Predicted Controls

5 450 13,863.0 Scrubber 2018 90% 1,942.6 IPM Predicted Controls

Pl 2easants 073-00005 1 650 21,667.2 Scrubber 1979 95% 1,575.6 3,109.1 additional tons of SO

removed due to greater than

90% Control Efficiency

2 650 20,241.7 Scrubber 1980 95% 1,533.5

Total 476,943.90 84,880.30 Expected Reductions of

392 2,063.6 tons of SO , 73,015.7

tons greater than the “Ask” of

90% Control on these units

AEP Consent Decree [US District Court for the Southern District of Ohio, Eastern Division], Paragraph 87 requires that a total of at least 600 MW from the 1

following list of Units: Sporn Units 1-4, Clinch River Units 1-3, Tanners Creek 1-3, and/or Kammer Units 1-3 be Retired, Retrofit, or Re-powered by December

31, 2018.

AEP Consent Decree [US District Court for the Southern District of Ohio, Eastern Division], Paragraph 87 requires that Sporn Unit 5 be Retired, Retrofit, or Re- 2

powered by December 31, 2018.
West Virginia Regional Haze SIP Page 122

Table 10.0-2. Contribution of West Virginia 2018 Point Source Sulfate Emissions to Brigantine

Source Identification

2002

Base

Year

2018 Base Case AoI and Associated

Metrics

Q/d*RTMax State/Source

Contribution

State FIPS

CNTY

Plant

ID Plant Name

Point

ID

SIC SO2

Emissi

ons

(tpy)

Q

SO2

Emissi

ons

(tpy)

CE

(% )

Distan

ce

(km)

Q/d RT

Max

Q/d *

RTMax

Unit %

Contribu

tion to

Total

Q/d*

RTMax

State %

Contrib

ution to

Total

Q/d*

RTMax

WV 003 0006 Capitol Cement Corporation 010 3241 1,247 1,717 0 303.36 5.66 15.22 86.1 0.10% 0.94%

WV 051 0005 Ohio Power - Mitchell Plant 001 4911 29,158 7,646 90 546.93 13.98 5.50 76.9 0.09% 0.94%

WV 051 0005 Ohio Power - Mitchell Plant 002 4911 29,575 7,582 90 546.93 13.86 5.50 76.2 0.09% 0.94%

WV 051 0002 PPG Industries, Inc. 003 2812 7,071 7,016 4 549.04 12.78 5.50 70.3 0.08% 0.94%

WV 061 0001 Monongahela Power Co. - Fort Martin Power 001 4911 46,852 4,922 90 469.75 10.48 5.81 60.9 0.07% 0.94%

WV 061 0001 Monongahela Power Co. - Fort Martin Power 002 4911 42,467 4,890 90 469.75 10.41 5.81 60.5 0.07% 0.94%

WV 023 0003 Mount Storm Power Plant 001 4911 8,817 3,191 95 415.55 7.68 7.68 59.0 0.07% 0.94%

WV 023 0003 Mount Storm Power Plant 002 4911 9,572 3,191 95 415.55 7.68 7.68 59.0 0.07% 0.94%

WV 051 0011 Venco Moundsville Calcining Plant K02 2999 4,350 5,638 0 547.45 10.3 5.50 56.7 0.07% 0.94%

WV 023 0003 Mount Storm Power Plant 003 4911 2,779 2,923 94.98 415.55 7.03 7.68 54.0 0.06% 0.94%

WV 051 0011 Venco Moundsville Calcining Plant K01 2999 3,200 4,147 0 547.45 7.58 5.50 41.7 0.05% 0.94%

WV 051 0002 PPG Industries, Inc. 001 2812 3,538 3,510 4 549.04 6.39 5.50 35.1 0.04% 0.94%

WV 051 0002 PPG Industries, Inc. 002 2812 2,069 2,053 4 549.04 3.74 5.50 20.6 0.02% 0.94%

WV 003 0006 Capitol Cement Corporation 012 3241 211 289 0 303.36 0.95 15.22 14.5 0.02% 0.94%

WV 037 0007 Halltown Paperboard Company 001 2631 231 260 4 288.58 0.90 15.22 13.7 0.02% 0.94%

Note: Units identified in black text are EGUs, units identified in blue text are non-EGUs.
West Virginia Regional Haze SIP Page 123

11.0. COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN

REVISIONS

40 CFR §51.308(f) requires West Virginia to revise its regional haze implementation plan and

submit a plan revision to EPA by July 31, 2018 and every ten years thereafter. In accordance

with the requirements listed in 40 CFR §51.308(f), West Virginia has an obligation to revise and

submit this regional haze implementation plan by July 31, 2018 and every ten years thereafter.

In addition, 40 CFR §51.308(g) requires periodic reports evaluating progress towards the

reasonable progress goals established for each mandatory Class I area. In accordance with the

requirements listed in 40 CFR §51.308(g) of the federal rule for regional haze, West Virginia

has an obligation to submit 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 reasonable progress goal for each

mandatory Class I area located within West Virginia and in each mandatory Class I area located

outside West Virginia which may be affected by emissions from within West Virginia. 

The requirements listed in 40 CFR §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 CAIR for EGUs compared to the controls assumed in the modeling.

[The WVDAQ recognizes that the 2018 projections of EGU controls from the IPM runs

represent one solution as to how the CAIR requirements may be met. By the time of the

first periodic report, the WVDAQ 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 the 20 percent best and worst days;

4. Analysis of emissions 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 modification to our visibility monitoring plan.

All requirements listed in 40 CFR §51.308(g) shall be addressed in the SIP revision for

reasonable progress. 

The WVDAQ recognizes that the 2018 projections of EGU controls from the IPM runs represent

a solution as to how the CAIR requirements may be met, not necessarily the solution as to how

the CAIR requirements will be met. By the time the of the first periodic report, the WVDAQ

anticipates that the actual compliance strategy for the various electric utility companies will be

much more defined. An assessment of the actual compliance plans will be completed for the first

periodic report.
West Virginia Regional Haze SIP Page 124

The WVDAQ acknowledges that continuing emissions reductions will be required to achieve

natural background conditions by 2064. Although the first review period focused on sulfate

reductions, future review periods may find that additional sulfate reductions are required and

achievable as control technologies improve. Furthermore, as emissions are reduced from specific

source sectors over time, emissions from other source sectors may become increasingly

significant.

The WVDAQ also commits to ongoing consultation with the FLMs throughout the

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

recent IMPROVE monitoring data and VIEWS data. Consultation between WVDAQ and the

FLMs will include early involvement of the FLMs in the periodic review process and FLMs will

receive copies of revised regional haze SIPs for comment prior to finalization.

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 are recommended, as funding is available, to support the next long term strategy.

The following is an overall summary; Appendix K contains a fuller discussion of possible

technical improvements.

First and foremost, continued improvements are needed in the integrated one-atmosphere air

quality models that are used to project air quality responses to emissions reductions. As our

understanding of partitioning between gaseous and aerosol phases improves, this understanding

needs to be reflected in the models. Improvements can also be made in how the models handle

individual pollutants. Sulfate performance for the CMAQ regional air quality model is good

overall. However, sulfate deposition is frequently overestimated in the models, particularly in the

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

should also be reviewed for sulfate formation over water. Nitrate is overestimated by the model

in the winter and underestimated in the summer, although summer monitored values of nitrate

are very low. Additional improvements in seasonal allocation of ammonia emissions would

improve model estimates of ammonium nitrate formation. Organic carbon is generally

underestimated in the summer months. Improvements are needed in the characterization of both

primary carbon emissions and formation of secondary organic carbon.

Other improvements needed include better tools for organic carbon source apportionment, and

more consistent measurement techniques between rural and urban monitoring networks. To

improve our understanding of the contribution of fire from natural forest fires, prescribed

burning, land clearing and agricultural burning, states need improved record keeping. Additional

improvements to international emissions inventory are also needed, to improve our

understanding of boundary conditions for our modeling domain and of the contributions from

international emissions to pollutant concentrations at the VISTAS Class I areas.
West Virginia Regional Haze SIP Page 125

12.0. DETERMINATION OF THE ADEQUACY OF THE EXISTING PLAN

Depending on the findings of the five-year progress report, West Virginia will take one of the

actions listed in 40 CFR section 51.308(h). The findings of the five-year progress report will

determine which action is appropriate and necessary.

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

1. West Virginia determines that the existing SIP requires no further substantive revision in

order to achieve established goals. West Virginia provides to the Administrator a

negative declaration that further revision of the SIP is not needed at the time.

2. West Virginia determines that the existing SIP may be inadequate to ensure reasonable

progress due to emissions from other states which participated in the regional planning

process. West Virginia provides notification to the Administrator and those states. West

Virginia collaborates with states through the regional planning process to address the

SIP’s deficiencies.

3. West Virginia determines that the current SIP may be inadequate to ensure reasonable

progress due to emissions from another country. West Virginia provides notification,

along with available information, to the Administrator.

4. West Virginia determines that the existing SIP is inadequate to ensure reasonable

progress due to emissions within the state. West Virginia will revise its SIP to address

the plan’s deficiencies within one year.