west virginia department of environmental protection



# West Virginia Fine Particle (PM 2.5 

# ) State Implementation Plan for the Parkersburg Nonattainment Area, Comprising Wood County and the Grant Tax District of Pleasants County

FINAL September 2008

Promoting a healthy environment.

### [This page intentionally left blank.]Page i 

### Table of Contents

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page v List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page vii List of Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page ix Acronyms and Abbreviations

............................................. Page xvii
1.0. INTRODUCTION

.................................................. Page 1 1.1. Request

........................................................ Page 1 1.2. Definition of Fine Particle Matter

................................... Page 1 1.3. Health and Environmental Effects

................................... Page 3 1.4. The PM 2.5 NAAQS

............................................... Page 4 1.5. Association for Southeastern Integrated Planning (ASIP)

................ Page 8 1.6. Clean Air Act Requirements

....................................... Page 9
2.0. SIGNIFICANCE OF PM 2.5 PRECURSOR POLLUTANTS . . . . . . . . . . . . . . . . . Page 10 2.1. Pollutant Contributions in the Parkersburg Nonattainment Area

.......... Page 10 2.2. PM 2.5 Component Species Analysis

................................. Page 13 2.3. Attribution of Emissions Sources

.................................. Page 19 2.4. Identification of Attainment Plan Pollutants and Precursors

.............. Page 23 2.4.a. Direct PM 2.5 emissions

........................................ Page 23 2.4.b. SO 2 emissions

.............................................. Page 23 2.4.c. NO x emissions

.............................................. Page 23 2.4.c. VOC emissions

............................................. Page 24 2.4.d. Ammonia emissions

.......................................... Page 26 2.5. Transportation Conformity

....................................... Page 28
3.0. ATTAINMENT DEMONSTRATION METHODS AND INPUTS

........... Page 37 3.1. Analysis Method

............................................... Page 37 3.2. Model Selection

................................................ Page 38 3.2.a. Selection of Photochemical Grid Model

.......................... Page 38 3.2.a.1. Criteria

.............................................. Page 38 3.2.a.2. Overview of CMAQ

.................................... Page 39 3.2.b. Selection of Meteorological Model

.............................. Page 39 3.2.b.1. Criteria

.............................................. Page 39 3.2.b.2. Overview of MM5

..................................... Page 40 3.2.c. Selection of Emissions Processing System

........................ Page 41 3.2.c.1. Criteria

.............................................. Page 41 3.2.c.2. Overview of SMOKE

................................... Page 41 3.3. Selection of the Modeling Year

.................................... Page 42 3.4. Modeling Domains

.............................................. Page 43 3.4.a. Horizontal Modeling Domain

.................................. Page 43Page ii 3.4.b. Vertical Modeling Domain

.................................... Page 45
4.0. EMISSION INVENTORY

.......................................... Page 46 4.1. Baseline (2002) Emissions Inventory

............................... Page 46 4.1.a. Stationary Sources

........................................... Page 47 4.1.a.1. Stationary Point Sources

................................ Page 47 4.1.a.1.a. Electric Generating Units (EGUs)

...................... Page 47 4.1.a.1.b. Other Industrial Point Sources (NonEGUs)

.............. Page 49 4.1.a.2. Stationary Area Sources

................................ Page 50 4.1.b. Mobile Sources

............................................. Page 51 4.1.b.1. NonRoad Mobile Sources

............................... Page 51 4.1.b.2. Onroad Mobile Sources

................................. Page 53 4.1.c. Biogenic Emissions Sources

................................... Page 53 4.1.d. Summary 2002 Baseline Emissions Inventory for West Virginia

....... Page 54 4.2. Projected 2009 Emissions Inventories

............................... Page 55 4.2.a. Stationary Sources

........................................... Page 56 4.2.a.1. Stationary Point Sources

................................ Page 56 4.2.a.1.a. Electric Generating Units (EGUs)

...................... Page 56 4.2.a.1.b. Other Industrial Point Sources (NonEGUs)

.............. Page 57 4.2.a.2. Stationary Area Sources

................................ Page 58 4.2.b. Mobile Sources

............................................. Page 59 4.2.b.1. Nonroad Mobile Sources

................................ Page 60 4.2.b.2. Onroad Mobile Sources

................................. Page 61 4.2.c. Biogenic Sources

............................................ Page 63 4.2.d. Summary of Final Inventories for 2009

........................... Page 63 4.3. Summary 2002 and 2009 State-wide Emissions Inventories for West Virginia

............................................................. Page 64 4.4. Summary 2009 Region-wide Emissions I nventories for the VISTAS States . Page 65
5.0. MODEL PERFORMANCE EVALUATION

............................ Page 67 5.1. Model Performance Goals, Criteria and Results

....................... Page 69 5.2. VISTAS/ASIP Domain-wide Performance

........................... Page 71 5.3. West Virginia PM 2.5 Model Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 73 5.4. Conclusions of Model Performance Evaluation

....................... Page 76
6.0. CONTROL MEASURES

........................................... Page 78 6.1. Point Source Measures

........................................... Page 78 6.1.a. Control Strategies

........................................... Page 78 6.1.a.1. RACT and Regional Transport Requirements (federal and state regulations)

.......................................... Page 78 6.1.a.1.A. RACT

............................................ Page 78 6.1.a.1.B. NO x SIP Call

...................................... Page 78 6.1.a.1.C. Clean Air Interstate Rule (CAIR)

....................... Page 78 6.1.a.1.D. Standards Regarding Fugitive Dust

.................... Page 79Page iii 6.1.b. Projected Reductions

......................................... Page 80 6.2. Area Source Measures

........................................... Page 81 6.2.a. Control Strategy

............................................. Page 81 6.2.a.1. Open Burning Restrictions (state rule)

..................... Page 81 6.2.b. Projected Reductions

......................................... Page 81 6.3. Non-Road Source Measures

...................................... Page 81 6.3.a. Control Stategies

............................................ Page 81 6.3.a.1 Phase I and Phase II Emissions Standards for Gasoline-Powered Nonroad Utility Engines (federal rule)

............................. Page 81 6.3.a.2. Emissions Standards for Diesel-Powered Nonroad Utility Engines of 50 or More Horsepower (federal rule)

........................ Page 81 6.3.a.3. Emission Standards for Spark-Ignition (SI) Marine Engines (federal rule)

.................................................... Page 82 6.3.a.4. Emission Standards for Large Spark-Ignition (SI) Engines (federal rule)

.................................................... Page 82 6.3.a.5. Standards for Locomotives (federal rule)

................... Page 83 6.3.b. Projected Reductions

......................................... Page 83 6.4. Onroad Source Measures

......................................... Page 85 6.4.a. Control Strategies

........................................... Page 85 6.4.a.1. Federal Tier I new Vehicle Emission and New Federal Evaporative Emissions Standards (federal regulation)

................... Page 85 6.4.a.2. National Low Emission Vehicle Program (federal regulation)

... Page 85 6.4.a.3. Tier 2 Motor Vehicle Emission Regulations (federal regulation)

. Page 86 6.4.a.4. Heavy Duty Diesel Engine Rule (federal regulation)

.......... Page 86 6.4.b. Projected Reductions

......................................... Page 87 6.5. Supplemental Control Measures

................................... Page 88 6.6. Schedule of Adopted Control Measures

............................. Page 90
7.0. ATTAINMENT DEMONSTRATION

................................. Page 92 7.1. Attainment Test Introduction

...................................... Page 92 7.2. Attainment Test Results

.......................................... Page 93 7.3. Supplemental Analyses

.......................................... Page 94 7.3.a. Other Modeling Results

....................................... Page 95 7.3.a.1. EPA’s CAIR Modeling

.................................. Page 95 7.3.a.2. Midwest RPO Modeling

................................. Page 96 7.3.b. Trends in Ambient Air Quality and Emissions

.................. Page 98 7.4. Unmonitored Area Analysis

..................................... Page 100 8.0. CLEAN AIR ACT SECTION 172(C) REQUIREMENTS

................. Page 102 8.1. Reasonably Available Control Measures

............................ Page 102 8.2. Reasonable Further Progress

..................................... Page 103 8.3. Actual Emissions Inventory

...................................... Page 103 8.4. Periodic Emissions Inventory

.................................... Page 104Page iv 8.5. Permitting Requirements for New and Modified Sources

............... Page 104 8.6. Other Measures

............................................... Page 105 8.7. Compliance with Section 110(a)(2)

................................ Page 105 8.8. Equivalent Techniques

.......................................... Page 105 8.9. Contingency Measures

.......................................... Page 106 8.9.a. Enforceability Issues

........................................ Page 106 8.9.b. Evaluations/Required Reductions

.............................. Page 106 8.9.c. Equivalency Ratios

......................................... Page 107 8.9.d. Identified Contingency Measures

.............................. Page 108 9.0. REASONABLY AVAILABLE CONTROL MEASURES (RACM), INCLUDING REASONABLY AVAILABLE CONTROL TECHNOLOGY (RACT) FOR STATIONARY SOURCES

........................................ Page 110 9.1. Statutory and Regulatory Requirements for RACM Analysis

............ Page 110 9.2. Discussion of Reasonable Control Measures

......................... Page 110 9.3. Discussion of RACT

........................................... Page 111 9.3.a. EGU RACT Analysis

........................................ Page 111 9.3.a.1. Background

......................................... Page 111 9.3.a.2. Final Ranking of RACT Candidates

...................... Page 113 9.3.a.3. Why No Additional Controls Will Be Required

.............. Page 113 9.3.a.4. Why Controls Implementation Could Be Delayed

............ Page 116 9.3.a.5. Why RACT Selection Is Site Specific and Costs May Be Underestimated

................................................... Page 117 9.3.b. ICI RACT Analysis

......................................... Page 118 9.3.b.1. Background

......................................... Page 119 9.3.b.2. Final Ranking of RACT Candidates

...................... Page 120 9.3.b.3. Why Controls Implementation Could Be Delayed

............ Page 123 9.3.b.4. Why RACT Selection Is Site Specific and Costs May Be Underestimated

................................................... Page 124 9.4. Evaluation of RACM for Purposes of Achieving Attainment

............ Page 125 9.5. Summary

.................................................... Page 129 9.5.a. RACM Determination

....................................... Page 129 9.5.b. RACT Applicability

......................................... Page 129
10.0. MID-COURSE REVIEW

.......................................... Page 130
11.0. PUBLIC PARTICIPATION

........................................ Page 130
12.0 CONCLUSION

.................................................. Page 130Page v List of Tables
Table 2.1-1. PM 2.5 Concentrations and Design Values for the FRM monitor in the Parkersburg PM 2.5 Nonattainment Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 10 Table 2.4 -1. West Virginia Annual VOC Emissions

............................ Page 24 Table 2.4-2. Annual VOC Emissions for Wood County and the Grant Tax District of Pleasants County

............................................................. Page 24 Table 2.4-3. ASIP Ammonia Emissions

...................................... Page 26 Table 2.4-4. West Virginia Ammonia Emissions

............................... Page 27 Table 2.4-5. Ammonia Emissions for Wood County and Grant Tax District of Pleasants County

................................................................... Page 27 Table 2.5-1. 2002 Base Year Emission Inventory (Tons/Year)

................... Page 30 Table 2.5-2. VMT for Model Years 2002, 2005, 2009, 2015 and 2025

.............. Page 34 Table 2.5-3. Conformity Results

............................................ Page 35 Table 4.1.a.1-1 Comparison of SO 2 and NO xEmissions (tons/year) for EGUs from Actual 2002 Inventory and Typical 2002 Inventory

.................................... Page 49 Table 4.1.c-1. West Virginia Biogenic Emissions

.............................. Page 54 Table 4.1.d-1 2002 Base Year Inventory - WestVirginia Portion of Nonattainment Area

................................................................... Page 54 Table 4.1.d-2. 2002 Base Year Inventory - Ohio Portion of Nonattainment Area

..... Page 55 Table 4.1.d-3. Summary of 2002 Baseline Emissions Inventory for Parkersburg Nonattainment Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 55 Table 4.2.d-1. 2009 Attainment Year Inventory - We st Virginia Portion of Nonattainment Area

................................................................... Page 63 Table 4.2.d-2. 2009 Attainment Year Inventory - Ohio Portion of Nonattainment Area

................................................................... Page 63 Table 4.3-a. 2002 State-wide Emissions Inventory Summary for West Virginia

...... Page 64 Table 4.3.b. 2009 State-wide Emissions I nventory Summary for West Virginia

....... Page 64 Table 5.1-1. VISTAS/ASIP Model Performance Goals and Criteria for Components of Fine Particle Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 70 Table 6.1-1. 2009 Point Source Emission Reductions

........................... Page 80 Table 6.3-1. 2009 Nonroad Emission Reductions

............................. Page 84 Table 6.4-1. Tier I Standards

.............................................. Page 85 Table 6.4-2. Onroad Emission Reductions

................................... Page 87 Table 6.6-1. West Virginia Schedule of Adopted Control Measures Parkersburg Nonattainment Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 90 Table 7.2-1. Quarterly Mean and Annual Mean PM 2.5 Mass Estimates for 2009

.... Page 93 Table 7.3-1. EPA CAIR Modeling Results

.................................... Page 95 Table 7.3-2. LADCO - Round 5 - 2009 Results

................................ Page 96 Table 7.3.b-1. Air Monitoring Data for Parkersburg Area

........................ Page 98 Table 7.4-1. VISTAS Regional Scale Modeling Results

........................ Page 101 Table 8.9-1. Contingency Requirement for PM and PM Precursors

............... Page 106 Table 8.9-2. Quarterly Speciated Relative Reduction Factors (RRFs) for Wood County

.................................................................. Page 107Page vi Table 8.9-2. Contingency Measure Reductions from 2002 Emissions (tons/year)

.... Page 108 Table 8.9-3. Determination of Excess Reductions from Contingency Measures

...... Page 108 Table 9.3.a-1. West Virginia Coal-Fired Power Plants in PM 2.5 Nonattainment Areas . Page 112 Table 9.3.a-2. PM 10 -Primary, PM 2.5 -Primary, SO 2 and NO xfrom West Virginia Coal-Fired Power Plants in PM 2.5 Nonattainment Areas, (tpy) . . . . . . . . . . . . . . . . . Page 112 Table 9.3.a-3. Final Ranking of RACT Candidates for Coal-Fired EGUs for PM 2.5 , NO xand SO 2 in PM 2.5 Nonattainment Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 114 Table 9.3.b-1. ICI Boilers in the West Virginia Portion of the Parkersburg Nonattainment Area

.................................................................. Page 118 Table 9.3.b-2. Final Ranking of RACT Candidates for PM 2.5 from Coal-fired Industrial Boilers in PM 2.5 Nonattainment Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 121 Table 9.3.b-3. Final Ranking of RACT Candidates for PM 2.5 from Oil-fired Industrial Boilers in PM 2.5 Nonattainment Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 121 Table 9.3.b-4. Final Ranking of RACT Candidates for SO 2 from Coal-fired Industrial Boilers in PM 2.5 Nonattainment Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 122 Table 9.3.b-5. Final Ranking of RACT Candidates for SO 2 from Oil-fired Industrial Boilers in PM 2.5 Nonattainment Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 122 Table 9.3.b-6. Final Ranking of RACT Candidates for NO xfrom Industrial Boilers in PM 2.5 Nonattainment Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 123 Table 9.4-1. Analysis of Stationary Source Control Measures to Determine Reasonably Available Control Measures (RACM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 126Page vii List of Figures Figure 1.2-1. Chemical links between the ozone and PM formation processes.

........ Page 3 Figure 1.4-1. West Virginia PM 2.5 Monitoring Network . . . . . . . . . . . . . . . . . . . . . . . . . . Page 6 Figure 1.4-2. Map of West Virginia PM 2.5 Nonattainment Areas . . . . . . . . . . . . . . . . . . . Page 7 Figure 2.1-1. 2002 sample data for the Parkersburg area PM 2.5 monitor. . . . . . . . . . . . . Page 11 Figure 2.1-2. Number of samples per quarter in 2002 at the Parkersburg area monitor (54-107- 1002) with PM 2.5 concentrations greater than 15μ/m 3

. ............... Page 12 Figure 2.2-1. PM 2.5 species contribution in the Parkersburg area, based on EPA SMAT data

.......................................................... Page 14 Figure 2.2-2. Parkersburg 2002 Quarterly Speciati on data based on SMAT tool. Upper left - first quarter (Jan - Mar), Upper right second quarter (Apr - Jun), bottom left - third quarter (Jul - Sep) and bottom right - fourth quarter (Oct - Dec). . . Page 15 Figure 2.2-3. CMAQ modeled changes in PM 2.5 mass for Parkersburg FRM monitor from reducing 2009 VISTAS/ASIP Base D emi ssions by 30% for several different emissions source categories, for the summer period Jun 1 - Jul 10, 2002.

.......................................................... Page 17 Figure 2.2-4. CMAQ modeled changes in PM 2.5 mass for Parkersburg FRM monitor from reducing 2009 VISTAS/ASIP Base D emi ssions by 30% for several different emissions source categories, for the winter period Nov 19 - Dec 19, 2002.

.......................................................... Page 17 Figure 2.2-5. 2002 Monitored Design Values (lef t) and 2009 base G4 12 km projected DVF (middle) and 2009 base G4 36 km projected DVF (right) for all FRM monitors in West Virginia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 18 Figure 2.3-1. Parkersburg nonattainment area SO 2 emissions by source sector, including West Virginia and Ohio emissions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 20 Figure 2.3-2. Statewide West Virginia SO 2 emissions by source sector. . . . . . . . . . . . . Page 21 Figure 2.3-3. Comparison of total West Virginia SO 2 emissions to West Virginia SO 2 emissions in the Parkersburg area. The Parkersburg SO 2 point source emissions account for approximately 12% of the state SO 2 emissions. . . . . . . . . . . . . . . . . . . . . Page 22 Figure 2.4-1. Annual Sensitivities for West Virginia PM 2.5 Non-Attainment Areas (VISTAS Base D emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 25 Figure 2.5-1. Parkersburg PM 2.5 Design Value, 2001-2007

...................... Page 31 Figure 3.4-1. The MM5 horizontal domain is the outer most, blue grid, with the CMAQ 36-km domain nested in the MM5 domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 44 Figure 3.4-2. A more detailed view of the 12-km grid over the VISTAS region

...... Page 45 Figure 4.4-a. Annual SO 2 emissions for 2002, 2009, and 2018 in the VISTAS states. . Page 65 Figure 4.4-b. Annual NO xemissions in 2002, 2009 and 2018 in the VISTAS states. . . Page 66 Figure 5-1. IMPROVE, STN, CASTNet, NADP and SEARCH monitoring sites located in the VISTAS/ASIP region and the VISTAS/ASIP 12 km grid. . . . . . . . . . . . . Page 69 Figure 5.2-1. VISTAS STN Soccer Plots

.................................... Page 71 Figure 5.2-2. VISTAS STN Bugle Plots

..................................... Page 72 Figure 5.2-3. VISTAS FRM Soccer Plots

.................................... Page 72 Figure 5.2-4. VISTAS FRM Bugle Plots

..................................... Page 73Page viii Figure 5.3-1. Scatter plots of predicted and observed fine particulate mass (PM 2.5 ) concentrations for January, April, July and October 2002, FRM sites in West Virginia and the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.

.......................................................... Page 74 Figure 5.3-2. Soccer plot of monthly fractional bias versus fractional gross error for PM 2.5 mass at FRM monitors in West Virginia for the CMAQ 12 km Base G2 Actual base case simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 75 Figure 7.3.b-1. Chart showing trend in PM 2.5 monitoring data in the Parkersburg area.

....................................................... Page 98 Figure 7.3.b-2. Point source emissions for 1999, 2002 and 2005 in the Parkersburg Area.

....................................................... Page 99Page ix List of Appendices Appendix A: Air Quality Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix A-1 Appendix B: Modeling Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix B-1 Appendix B.1: PM 2.5 Conceptual Description: West Virginia PM 2.5 Nonattainment Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix B - 5 Appendix B.2: Modeling Protocol for Associa tion for Southeastern Integrated Planning Emissions and Air Quality Modeling to Address 8-hr Ozone and PM 2.5 Nonattainment Areas in the Southeastern United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix B - 39 Appendix C: Emissions Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C-1 Appendix C.1: Inventory Summaries by Nonattainment Area . . . . . . . . Page Appendix C -7 Appendix C.1.a: Charleston Nonattainment Area . . . . . . . . . . . . . Page Appendix C - 9 Appendix C.1.b: Huntington Nonattainment Area . . . . . . . . . . . . Page Appendix C - 13 Appendix C.1.c: Martinsburg Nonattainment Area . . . . . . . . . . . . Page Appendix C -17 Appendix C.1.d: Parkersburg Nonattainment Area . . . . . . . . . . . Page Appendix C - 21 Appendix C.1.e: Weirton Nonattainment Area . . . . . . . . . . . . . . . Page Appendix C - 25 Appendix C.1.f: Wheeling Nonattainment Area . . . . . . . . . . . . . . Page Appendix C - 29 Appendix C.2: West Virginia Point Source Inventories . . . . . . . . . . . Page Appendix C - 33 Appendix C.2.a: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 35 Appendix C.2.b: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 41 Appendix C.3: West Virginia Area Source Inventories . . . . . . . . . . . Page Appendix C - 47 Appendix C.3.a: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 49 Appendix C.3.b: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 95 Appendix C.4: West Virginia Nonroad Inventories . . . . . . . . . . . . . Page Appendix C - 141 Appendix C.4.a: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 143 Appendix C.4.b: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 317 Appendix C.5: West Virginia Onroad Inventories . . . . . . . . . . . . . . Page Appendix C - 493 Appendix C.5.a: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 495 Appendix C.5.b: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 505 Appendix C.6: Ohio Inventories . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 515 Appendix C.6.a: Point Source Inventories . . . . . . . . . . . . . . . . . Page Appendix C - 517 Appendix C.6.a.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 519 Appendix C.6.a.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 523Page x Appendix C.6.b: Area Source Inventories . . . . . . . . . . . . . . . . . . Page Appendix C - 527 Appendix C.6.b.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 529 Appendix C.6.b.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 543 Appendix C.6.c: Nonroad Inventories . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 557 Appendix C.6.c.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 559 Appendix C.6.c.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 595 Appendix C.6.d: Onroad Inventories . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 631 Appendix C.6.d.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 633 Appendix C.6.d.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 653 Appendix C.7: Kentucky Inventories . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 673 Appendix C.7.a: Point Source Inventories . . . . . . . . . . . . . . . . . Page Appendix C - 675 Appendix C.7.a.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 677 Appendix C.7.a.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 681 Appendix C.7.b: Area Source Inventories . . . . . . . . . . . . . . . . . Page Appendix C - 685 Appendix C.7.b.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 687 Appendix C.7.b.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 695 Appendix C.7.c: Nonroad Inventories . . . . . . . . . . . . . . . . . . . . Page Appendix C - 703 Appendix C.7.c.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 705 Appendix C.7.c.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 717 Appendix C.7.d: Onroad Inventories . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 729 Appendix C.7.d.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 731 Appendix C.7.d.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 737 Appendix C.8: Maryland Inventories . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 743 Appendix C.8.a: Point Source Inventories . . . . . . . . . . . . . . . . . Page Appendix C - 745 Appendix C.8.a.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 747 Appendix C.8.a.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 751 Appendix C.8.b: Area Source Inventories . . . . . . . . . . . . . . . . . Page Appendix C - 755 Appendix C.8.b.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 757 Appendix C.8.b.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 763 Appendix C.8.c: Nonroad Inventories . . . . . . . . . . . . . . . . . . . . Page Appendix C - 769 Appendix C.8.c.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 771 Appendix C.8.c.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 779 Appendix C.8.d: Onroad Inventories . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 787 Appendix C.8.d.1: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 789 Appendix C.8.d.2: 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix C - 795 Appendix D: Emission Inventory Documentation . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 1 Appendix D.1: West Virginia Inventory Documentation . . . . . . . . . . . . Page Appendix D- 5 Appendix D.1.a: Biogenics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 7 Appendix D.1.b: Correspondence . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 21Page xi Appendix D.2: Ohio Inventory Documentation . . . . . . . . . . . . . . . . . Page Appendix D - 135 Appendix D.2.a: LADCO – Base M Strategy Modeling Emissions (Revised) [October 10, 2007] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D- 137 Appendix D.2.b: LADCO IPM Model Parsed File Post Processing Methodology and File Preparation (January 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 153 Appendix D.2.c: LADCO Nonroad Emissi on Inventory Project for Locomotive, Commercial Marine, and Recreational Marine Emission Sources (December 2004)

..... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 165 Appendix D.2.d: Ohio Partial Nonattainment Counties . . . . . . . . Page Appendix D - 255 Appendix D.2.e: Correspondence . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 259 Appendix D.3: Kentucky Inventory Documentation . . . . . . . . . . . . . Page Appendix D - 287 Appendix D.4: Maryland Documentation . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 291 Appendix D.5: Background Documentatio n . . . . . . . . . . . . . . . . . . . . Page Appendix D - 311 Appendix D.5.a: Emission Data Collection for 2002 Regional Haze Inventory Area and Point Sources: Draft Quality Assurance Project Plan (January 2003)

................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 313 Appendix D.5.b: 2002 Southeast Emission Inventory Development for Onroad Mobile and Nonroad Mobile Emission Sources (February 3, 2003) . . . . . Page Appendix D - 340 Appendix D.5.c: Documentation for the Final 1999 National Emission Inventory (Version 3) for Criteria Air Pollutants and Ammonia: Point Sources (January 31, 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 359 Appendix D.5.d: Development of the VISTAS Draft 2002 Mobile Source Emission Inventory (February 9, 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 446

Appendix D.5.e: Development of Growth Fact ors for Future Year Modeling Inventories (April 30, 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 507 Appendix D.5.f: Quality Assurance Project Pl an (Draft) for Visibility Improvement for State and Tribal Association for the South eastern States (VISTAS): Emissions and Air Quality Modeling (November 17, 2004) . . . . . . . . . . . . . . . . . . . . Page Appendix D - 597Page xii Appendix D.5.g: Future Year ElectricityGenerating Sector Emission Inventory Development Using the Integrated the Planning Model (IPM®) in Support of Fine Particle Mass and Visibility in the VISTAS and Midwest RPO Regions (April 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 666 Appendix D.5.h: Inter-RPO IPM Global Parameter Decisions (May 3, 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 710 Appendix D.5.i: Inter-RPO IPM Global Parameter Decisions (May 11, 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 714 Appendix D.5.j: Memorandum from Greg Stella, VISTAS Technical Advisor, Regarding: EGU Emission Factors and Em ission Factor Assignments (June 13, 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 718 Appendix D.5.k: Technical Support Document for VISTAS Emissions and Air Quality Modeling to Support Regional Haze State Implementation Plans (November 14, 2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D -732 Appendix D.5.l: Technical Memorandum: Control Packet Development and Data Sources (July 14, 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix D - 1811

Appendix E: Emissions Inventory QAPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix E-1 Appendix F: Emissions Modeling Documentation . . . . . . . . . . . . . . . . . . . . . Page Appendix F-1 Appendix F.1: Emissions Modeling Documentation . . . . . . . . . . . . . . . . Page Appendix F - 5 Appendix F.2: Development of Temporal Pr ofiles for Electric Generating Units (EGUs) in the VISTAS States: Final Technical Memorandum (27 February 2006)

................. Page Appendix F - 15 Appendix F.3: Processing Mobile Emissions in SMOKE: Is it worth simulating everyday onroad mobile emissions to support 8-hr ozone modeling? . . . . . . . . Page Appendix F - 203 Appendix F.4: 2002 Fire Emission Inventory for the WRAP Region - Phase II (July 22, 2005) Page Appendix F - 227 Appendix G: Meteorological Development Documentation . . . . . . . . . . . . . Page Appendix G-1 Appendix G.1: Characterization of Meteorology and Its Relationship to Fine Particle Mass and Visibility in the VISTAS Region . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix G - 5Page xiii Appendix G.2: Protocol for Annual MM5 Modeling in Support of VISTAS (Visibility Improvement – State and Tribal Association) . . . . . . . . . . . . . . . . . . . Page Appendix G - 327 Appendix H: Model Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix H-1 Appendix I: Modeling Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix I-1 Appendix J: Attainment Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix J-1 Attainment Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix J - 3 Appendix J.1: Graphical Repres entations of 2000-2004 Quarterly PM 2.5 Component Specific DVB and 2009 Quarterly Component Specific DVF for Monitors in West Virginia

........ Page Appendix J - 83 Appendix J.2: Procedures for Estimating Future PM 2.5 Values for the CAIR Final Rule by Application of the (Revised) Speciated Modele d Attainment Test (SMAT) Updated- 11/8/04 Page Appendix J - 117 Appendix K: Adopted State Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K-1 Appendix K.1: Permitting Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 5 45CSR13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 7 45CSR 14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 29 45CSR19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 79 Appendix K.2: Rules that Require Control of Fugitive Dust . . . . . . . Page Appendix K - 127 45CSR2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 139 45CSR3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 163 45CSR5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 169 45CSR7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 183 45CSR17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 201 Appendix K.3: Open Burning Rule . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 207 45CSR6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 209 Appendix K.4: NO xSIP Call Rules . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 219 45CSR1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 221 45CSR26 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 277 Appendix K.5: CAIR Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 321 45CSR39 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 323 45CSR40 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 363 45CSR41 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix K - 413Page xiv Appendix L: Contingency Measures Documentation . . . . . . . . . . . . . . . . . . . Page Appendix L-1 Contingency Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix L - 5 1. Enforceability Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix L - 5 2. Evaluations/Required Reductions . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix L - 5 3. Equivalency Ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix L - 7 4. Identified Contingency Measures . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix L - 8 5. Application of Equivalency Ratios to the Parkersburg Area . . . . Page Appendix L - 9 Appendix L.1: Equivalency Ratio Based on Relative Reduction Factors as Generated by the Community Multiscale Air Quality (CMAQ) Modeling Results . . . . . Page Appendix L - 11 Appendix L.2: Synopsis of the ASIP PM2.5 Sensitivity Study and SO 2 /NO xEquivalency Rations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix L - 17 Appendix L.3: Cytec Industries, Inc. ( 073-00003) Documentation . . . Page Appendix L - 29 Appendix L.4: Cabot Corpor ation. (073-00006) Documentation . . . . Page Appendix L - 57 Appendix M: RACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix M-1 Appendix M.1: EGU RACT Analysis . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix M - 5 Appendix M.2: ICI RACT Analysis . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix M - 119 Appendix M.3: Federal Consent Decrees . . . . . . . . . . . . . . . . . . . . . Page Appendix M - 215 American Electric Power Service Corp. . . . . . . . . . . . . . . . . . . . Page Appendix M - 217 Virginia Electric and Power Company . . . . . . . . . . . . . . . . . . . . Page Appendix M - 323 Appendix M.4: State Consent Orders . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix M - 443 Allegheny Energy - Harrison Power Station . . . . . . . . . . . . . . . . Page Appendix M - 445 Allegheny Energy - Pleasants Power Station . . . . . . . . . . . . . . . Page Appendix M - 451 Appendix N: ASIP and VISTAS Supporting Documentation . . . . . . . . . . . . . Page Appendix N-1 Appendix N.1: Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS . . . . . . . . . . . . . . . . . Page Appendix N - 5 Appendix N.2: Technical Support Document fo r Association for Southeastern Integrated Planning (ASIP) Emissions and Air Quality to Support PM 2.5 State Implementation Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix N - 343Page xv Appendix O: Supporting Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix O - 1 Appendix O.1: LADCO/MRPO Modeling . . . . . . . . . . . . . . . . . . . . . . . Page Appendix O - 5 LADCO Technical Support Document Website . . . . . . . . . . . . . . . . Page Appendix O - 7 LADCO – Round 5 – 2009 Results . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix O - 13 Regional Air Quality Analyses for Ozone, PM 2.5 and Regional Haze: Final Technical Support Document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix O - 15 Appendix O.2: EPA CAIR Modeling . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix O - 185 CAIR Technical Information Website . . . . . . . . . . . . . . . . . . . . . . Page Appendix O - 187 Technical Support Document for the Final Clean Air Interstate Rule - Air Quality Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix O - 193 Appendix P: Transportation Conformity . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page Appendix P - 1 Appendix Q: Public Participation Documentation . . . . . . . . . . . . . . . . . . . . . Page Appendix Q-1Page xvi [This page intentionally left blank]Page xvii Acronyms and Abbreviations
AL Alabama AQI Air Quality Index, daily forecast to the public about air quality on a 24-hour basis ASIP Association for Southeastern Integrated Planning BACT best available control technology BART best available retrofit technology CAAA Clean Air Act Amendments of 1990 CAIR Clean Air Interstate Rule CAMR Clean Air Mercury Rule CFR Code of Federal Regulations CO 2 carbon dioxide CSR Code of State Rules DAQ WVDEP Division of Air Quality DEP West Virginia Department of Environmental Protection EC Elemental carbon EGU electric generating unit EPA U.S. Environmental Protection Agency FL Florida FR Federal Register FRM Federal reference method GA Georgia KY Kentucky LADCO Lake Michigan Air Directors Consortium LAER lowest achievable emission rate LRTP Long Range Transportation Plan lb pound lb/MMBTU pound per million British thermal units MANE-VU Mid-Atlantic/Northeast Visibility Union MARAMA Mid-Atlantic Regional Air Management Association MD Maryland MMBTU million British thermal units MMBTU/hr million BTUs per hour MRPO Midwest Regional Planning Organization MS Missisippi MW megawatt, which equals 1,000,000 watts MWs megawatts N/A not available N 2 O nitrous oxide NAA Nonattainment area NAAQS National Ambient Air Quality Standard NACAA National Association of Clean Air AgenciesPage xviii NARSTO North American Strategy for Tropospheric Ozone. A consortium for atmospheric research in North America, including the U.S., Mexico and Canada. NC North Carolina NESCAUM Northeast States for Coordinated Air Use Management NH 3 ammonia NO nitric oxide NO 2 nitrogen dioxide NO X oxides of nitrogen; typically, NO and NO 2 NSPS New Source Performance Standard NSR New Source Review OC Organic carbon OH Ohio PBW Particle bound water PM particulate matter PM 10 particulate matter having an aerodynamic diameter or 10 microns (0.00039 inches) or less PM 10 -Primary total mass of PM 10 comprising both filterable and condensable fractions PM 2.5 particulate matter having an aerodynamic diameter or 2.5 microns (0.0000975 inches) or less PM 2.5 -Primary total mass of PM 2.5 comprising both filterable and condensable fractions PM 2.5 -Secondary PM 2.5 created in the atmosphere by the reaction of sulfur dioxide and nitrogen oxides with other pollutants PM 2.5 Implementation Rule March 29, 2007, Final Rule to Implement the Fine Particle National Ambient Air Quality Standards (from EPA) PSD Prevention of Significant Deterioration RACM reasonably available control measure RACT reasonably available control technology RBLC EPA’s RACT/BACT/LAER Clearinghouse RFP Reasonable further progress RPO regional planning organization SANDWICH Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbon Hybrid material balance approach for estimating PM2.5 mass composition produced by the PM 2.5 FRM monitors SC South Carolina SESARM Southeastern States Air Resources Managers, Inc. SIP State Implementation Plan SMAT Speciated Modeling Attainment Test SO 2 sulfur dioxide SO 4 sulfate SO X sulfur oxides sq.ft. square feetPage xix STN Speciation Trends Network SUV Sport Utility Vehicle TIP Transportation Improvement Program TN Tennessee TPY tons per year TSP total suspended particulate VA Virginia VISTAS Visibility Improvement State and Territorial Association of the Southeast VMT Vehicle miles traveled VOC Volatile organic compound WV West Virginia WVDAQ West Virginia Division of Air Quality WWW Wood-Washington-Wirt Interstate Planning CommissionPage xx [This page intentionally left blank.]WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 1 

### West Virginia Fine Particle (PM 2.5 

### ) State Implementation Plan for the Parkersburg Nonattainment Area, Comprising Wood County and the Grant Tax District of Pleasants County

1.0. INTRODUCTION The Parkersburg-Marietta, WV-OH PM 2.5 nonattainment area is a multi-state nonattainment area, comprising Wood County and the Grant tax district in Pleasants County, West Virginia, and Washington County, Ohio. The area was designated as nonattainment with the 1997 PM 2.5 National Ambient Air Quality Standard (NAAQS) in the January 5, 2005 Federal Register [70 FR 944], effective April 5, 2005. 1.1. Request The State of West Virginia is requesting that the United States Environmental Protection Agency (EPA) approve this attainment demonstration with a projected attainment date of April 2010 as a revision to the state implementation plan (SIP). The proposed attainment date is within five (5) years of the date of designation, therefore, pursuant to 40 CFR 51.1009(b) a reasonable further progress plan is not required.
1.2. Definition of Fine Particle Matter Fine particle matter, also known as fine particles and PM 2.5 , refers to airborne particles less than or equal to 2.5 micrometers (μm) in diameter, about 1/30th the average width of a human hair. Fine particles are treated as though they are a single pollutant, however they come from many different sources and are composed of many different compounds. PM 2.5 exposure adversely affects human health, especially respiratory and cardiovascular systems. Children, people with heart and lung disease and older adults are particularly sensitive to PM 2.5 exposure.
PM 2.5 can be liquid, solid or can have a solid core surrounded by liquid. PM 2.5 can include material produced by combustion, photochemical reactions, and can contain salt from sea spray and soil-like particles. Particles are distinguished based on the method of formation. Primary particles are emitted directly into the atmosphere and retain the same chemical composition as when they are released. Secondary particles are those formed through chemical reactions involving atmospheric oxygen, water vapor, hydroxyl radicals, nitrates, sulfur dioxide (SO 2 ), oxides of nitrogen (NO x ), and organic gases from natural and anthropogenic sources. PM 2.5 can therefore be composed of varying amounts of different species, including:

! Sulfates ! Nitrates (usually found in the form of ammonium nitrate) ! Ammonium ! Particle bound water ! Elemental carbonWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 2 ! Organic compounds " Primary organic species (from cooking and combustion) " Secondary organic compounds ! Crustal material (includes calcium, aluminum, silicon, magnesium and iron) ! Sea salt (generally only found at coastal monitoring sites) ! Transitional metal ! Potassium (generally from wood burning or cooking) The most significant sources of PM 2.5 and its precursors are coal-fired power plants, industrial boilers and other combustion sources. These emissions are often transported over large distances. Other sources of PM 2.5 emissions include mobile sources, area sources, biogenic, fires, wind blown dust and oceans. Gas-phase precursors SO 2 , NO x , VOCs and ammonia undergo chemical reactions in the atmosphere to form secondary PM (see Figure 1.2-1). Formation of secondary PM depends on numerous factors including the concentration of pr ecursors; the concentrations of other gaseous reactive species; atmospheric conditions including solar radiation, temperature and relative humidity (RH); and the interactions of precursors with pre-existing particles and with cloud or fog droplets. Several atmospheric aerosol species, such as ammonium nitrate and certain organic compounds, are semi-volatile and are found in both gas and particle phases. A variety of meteorological and geographic factors also influen ce the concentration levels of fine particles, including both the regional and local distributi on of urbanized areas, primary and precursor emissions sources, and natural features such as oceans and forests. PM 2.5 concentrations can exceed the national ambient air quality standards (NAAQS) at any time of the year. Therefore, the EPA mandates the year round monitoring of PM 2.5 concentrations throughout the country (40CFR 58, App. D. 4.7). There are 14 Federal Reference Monitors (FRMs) that sample PM 2.5 in West Virginia, one of these monitors is located in the Parkersburg area) (See Figure 1.4-1). The FRMs are filter-based monitors that measure the PM 2.5 mass by passing a measured volume of air through a preweighed filter. The purpose of the FRM system is to determine compliance with the PM 2.5 NAAQS.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 3 Figure 1.2-1. Chemical links between the ozone and PM formation processes. The major precursors are shown in green squares. The organic compounds can be gaseous (always in the gas phase), non-volatile (always in the condensed phase), and semi-volatile (partitioned between the gas and condensed phase). [ Source : Particulate Matter Science for Policy Makers, A NARSTO Assessment , February 2003.]

1.3. Health and Environmental Effects The size of particles is directly linked to their potential for causing health problems. Fine particles less than 2.5 microns in diameter pose the greatest problems because they can lodge deep in the lungs and some may get into the bloodstream. Therefore, exposure to such particlescan affect both the lungs and heart. Particle pollution may occur all year as opposed to groundlevel ozone, which generally occurs during the summer months. Particle pollution exposure is linked to a variety of health problems, including increased respiratory symptoms, such as irritation of the airways, coughing, or difficultybreathing; decreased lung function; aggravated asthma; development of chronic bronchitis; irregular heartbeat; nonfatal heart attacks; and premature death in people with heart or lung disease.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 4 Studies have demonstrated a relationship between increased levels of PM 2.5 and higher rates of death and complications from cardiovascular diseas e. Evidence shows that inhalation of particles leads to direct vascular injury and atherosclerosis, or hardening of the arteries. Environmental effects of particle pollution include reduced visibility, environmental damage and aesthetic damage. PM 2.5 is the major cause of reduced visibility (haze) in parts of the United States, including many of our treasured national parks and wilderness areas. Particles can be carried over long distances by wind and then settle on ground and water. The effects of this settling may include making lakes and streams more acidic, changing the nutrient balance in coastal waters and large river basins, depleting nutrients in soil, damaging sensitive forests and farm crops, and affecting the diversity of ecosystems. Particle pollution can stain and damage stone and other materials, including culturally important objects such as statues and monuments.
1.4. The PM 2.5 NAAQS The Clean Air Act (CAA) requires EPA to set National Ambient Area Quality Standards (NAAQS) for six criteria pollutants, particle pollution (also known as particulate matter (PM)) is one of these. The CAA established two types of national air quality standards for particulate matter. Primary standards set limits to protect public health, including the health of “sensitive” populations, such as asthmatics, children and the elderly. Secondary standards set limits to protect public welfare, including protection against visibility impairment, damage to animals, crops, vegetation and buildings. The nation’s air quality standards for particulate matter were first established in 1971 and were not significantly revised until 1987, when EPA change d the indicator of the standards to regulate inhalable particles less than or equal to 10 microns in diameter (about 1/7 ththe average width of a human hair). Ten years later, in 1997, after a lengthy review, EPA revised the PM standards, setting separate standards for fine particles (PM 2.5 ), equal to or less than 2.5 microns in diameter (about 1/30 ththe average width of a human hair), based on their link to serious health problems ranging from increased symptoms, hospital admissions and emergency room visits for people with heart and lung disease, to premature death in people with heart or lung disease. On July 18, 1997, EPA revised the NAAQS for particulate matter and established primary (health-based) annual standard of and 24-hour standards for PM 2.5 [62 FR 38652]. The annual standard is 15.0 micrograms per cubic meter (μg/m 3 ), based on a 3-year average of annual mean PM 2.5 concentrations. The 1997 24-hour standard is 65 μg/m 3 , based on a 3-year average of the 98 thpercentile of 24-hour concentrations. [Percentile is a value on a scale that indicates the percent of a distribution that is equal to or below it. For example, a concentration value at the 98 thpercentile is equal to or greater than 98 percent of the concentration values.] EPA also revised the secondary standards, making them equal to the primary standards.
The PM 2.5 NAAQS were challenged by numerous litigants and in May 1999, the U.S. Court of Appeals for the D.C. Circuit issued a decision remanding but not vacating, the standards. American Trucking Assoc. v. EPA , 175 F.3d 1027, 1047-48, on rehearing 195 F.3d 4 (D.C. Cir. 1999). The EPA sought review of two aspects of that decision in the U.S. Supreme Court. The Supreme Court upheld the PM 2.5 standards. EPA v. American Trucking Assoc. , 531 U.S. 457WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 5 (2001). In March 2002, the D.C. Circuit rejected all remaining challenges to the PM 2.5 standards, American Trucking Assoc. v EPA, 283 F.3d 355 (D.C. Cir., 2002). The CAA establishes a process for air quality management through the establishment and implementation of the NAAQS. After the promulgation of a new or revised NAAQS, EPA is required to designate areas, pursuant to section 107(d)(1) of the CAA, as attainment, nonattainment or unclassifiable. On January 5, 2005, EPA issued final rule 40 CFR Part 81 – Air Quality Designations and Classifications for the Fine Particles (PM 2.5 ) National Ambient Air Quality Standards [ 70 FR 944 ], that designated areas across the country for the PM 2.5 NAAQS based upon FRM air quality monitoring data for calendar years 2001-2003. The following areas in West Virginia were initially designated as nonattainment: • Charleston, WV [Kanawha and Putnam Counties]; • Huntington-Ashland, WV-KY-OH [Cabell and Wayne Counties, and the Graham Tax District in Mason County]; • Marion County, WV (aka Fairmont CBSA) [Marion County, the Clay Tax District in Harrison County and the Cass Tax District in Monongalia County]; • Martinsburg, WV-Hagerstown, MD [Berkeley County]; • Parkersburg-Marietta, WV-OH [Wood County a nd the Grant Tax District in Pleasants County]; • Steubenville-Weirton, OH-WV [Brooke and Hancock Counties]; and • Wheeling, WV-OH [Marshall and Ohio Counties]. It should be noted that all the areas designated as nonattainment were not in compliance with the annual standard of 15.0 micrograms per cubic meter (μg/m 3 ), based on a 3-year average of annual mean PM 2.5 concentrations. However, all areas of West Virginia were found to be attainment with the co-existing 24-hour standard of 65 μg/m 3 , based on a 3-year average of the 98 thpercentile of 24-hour concentrations. The design values for the Parkersburg nonattainment area for both the annual and 24-hour standards are presented in Table 1.4-1.
Table 1.4-1. Parkersburg Nonattainment Area Design Values for the 1997 Annual and 24-hr PM 2.5 NAAQS 1997 NAAQS 24 hr 3 yr 98% = 65 1997 Annual NAAQS = 15
FRM Site 99- 01 00- 02 01- 03 02- 04 03- 05 04- 06 05- 07 99- 01 00- 02 01- 03 02- 04 03- 05 04- 06 05- 07 107-1002 42 39 37 35 34 35 37 17.6 17.0 16.0 15.2 15.4 15.3 15.4 A violation of the annual NAAQS occurs when the annual mean PM 2.5 concentration averaged over three years is greater than 15.0 μg/m 3 . This three-year average is termed the design value for the monitor. The design value for a PM 2.5 nonattainment area is the highest monitor’s design value in the area.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 6 Reductions in PM 2.5 levels which will result in the area attaining the annual PM 2.5 standard, will have the added benefit of lowering the daily monitored values, which will result in a decrease in the 24-hour design value. The EPA and State air agencies initiated the monitoring process for the PM 2.5 NAAQS in 1999 and deployed the Federal Reference Method (FRM) monitors by January 2001 (see Figure 1-1).

Figure 1.4-1. West Virginia PM 2.5 Monitoring Network

EPA based the designations in the January 5, 2005 final rule on air quality monitoring data for the 3-year period of 2001-2003. Since the designations occurred at the end of 2004, EPA indicated that they would consider 2004 data where feasible in order to evaluate attainment status based on data for the 3-year period 2002-2004. EPA explained that they would consider any complete, quality-assured and certified 2004 PM 2.5 data submitted by any State to EPA by February 22, 2005, if the data indicated that a change in the designation for the entire area would be appropriate.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 7 In the January 5, 2005 final rule, EPA stated that if EPA agreed that a change in designation was appropriate based on the inclusion of 2004 data, then EPA would withdraw the initial designation for the area and issue a designation that reflected the consideration of the new data. On April 14, 2005, EPA issued final rule 40 CFR Part 81 – Air Quality Designations for the Fine Particles (PM 2.5 ) National Ambient Air Quality Standards – Supplemental Amendments [70 FR 19844]. In the April 14, 2005 final action EPA withdrew the nonattainment designation for the Marion County, WV (aka Fairmont CBSA) [Marion County, the Clay Tax District in Harrison County and the Cass Tax District inMonongalia County] and designated the area as Unclassifiable/ Attainment. [See Figure 1-2. Map of WV PM 2.5 Nonattainment Areas.] The PM 2.5 standard designations became effective on April 5, 2005, with state implementation plans due three (3) years later on April 5, 2008.

Figure 1.4-2. Map of West Virginia PM 2.5 Nonattainment Areas States with nonattainment areas are required to submit to EPA by April 5, 2008, an attainment demonstration with associated air quality modeling, any adopted state regulations to reduce emissions of PM 2.5 and precursors, and other supporting information demonstrating that the area will attain the standard as expeditiously as practicable, but no later than five (5) years after designation. EPA will determine the region’s attainment on the basis of air quality data for 2007 - 2009. The West Virginia nonattainment areas, including Parkersburg, are required to attain the standard no later than April, 2010.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 8 This document, the West Virginia Fine Particle (PM 2.5 ) State Implementation Plan for the Parkersburg Nonattainment Area, Comprisi ng Wood County and the Grant Tax District of Pleasants County , is a plan to demonstrate continued improvement and compliance with 1997 PM 2.5 NAAQS in the Parkersburg area in 2009. The Plan consists of the base year inventories for 2002, projection inventories for 2009, an attain ment plan, a demonstration of reasonably available control measures (RACM), including reasonably available control technology (RACT) for point sources, an attainment demonstration, and contingency plans. This Plan has been prepared by the WVDEP in consultation with the Wood-Washington-Wirt Interstate Planning Commission (WWW) to comply with th e CAAA of 1990 and with EPA requirements for the Parkersburg area as stated in EPA’s 2005 designation of the Parkersburg area and EPA’s Clean Air Fine Particle Implementation Rule [72 FR 20586, 25APR2007]. 1.5. Association for Southeastern Integrated Planning (ASIP) When EPA promulgated regional haze regulations on July 1, 1999, it offered an optional approach that groups of states might collaborate in regional analyses of the haze problem. This option showed promise in allowing more cost-effective analyses and the southeastern states opted to follow this option. SESARM - Southeastern States Air Resources Managers, Inc. - accepted responsibility for regional planning organization work tasks on behalf of its member agencies under this option. The SESARM states – Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina and Tennessee – determined that it was appropriate to expand the collaborative effort for the regional haze program beyond their member boundaries and invited Virginia and West Virginia to join the group. This effort was named the Visibility Improvement State and Tribal Association of the Southeast (VISTAS). In 2005 EPA designated areas in eight (8) of the VISTAS states as nonattainment for PM 2.5 and/or 8-hour ozone. The nonattainment designations triggered the requirement for State Implementation Plans (SIP) to be submitted to EPA by April 2008. The guidance from EPA indicated that a significant requirement of the PM 2.5 SIPs will be attainment demonstrations that include modeling analyses to define control strategies and to confirm attainment. The VISTAS states concluded that a collaborative process was the most efficient means for the states to develop information upon which to base their PM 2.5 and 8-hour ozone attainment demonstrations. The local air regulatory agen cies for Jefferson County, AL; Jefferson County, KY; Mecklenburg County, NC; Forsythe County, NC; Knox County, TN; and Shelby County, TN also joined the collaborative. The name for this collaborative effort is the Association for Southeastern Integrated Planning (ASIP). ASIP, through its contractors, performed much of the technical work upon which the Parkersburg attainment demonstration is based.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 9 1.6. Clean Air Act Requirements Section 172(c) of the Clean Air Act contains the general requirements for nonattainment areas. These requirements are listed below and are discussed in more detail in Section 8. Section 172(c) Nonattainment Plan Provisions (1) Reasonably available control measures (RACM) (2) Reasonable further progress (RFP) (3) Actual emissions inventory and periodic emissions inventory (4) New source review (5) Permit requirements for new and modified sources (6) Other measures as may be necessary to provide attainment by specified attainment date (7) Compliance with Section 110(a)(2) (8) Equivalent techniques (9) Contingency measures.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 10 2.0. SIGNIFICANCE OF PM 2.5 PRECURSOR POLLUTANTS [40 CFR §51.1002] 2.1. Pollutant Contributions in the Parkersburg Nonattainment Area Data from the FRM site in the Parkersburg area is displayed below (Figure 2.1-1). Table 2.1-1 contains the quarterly and annual PM 2.5 concentrations for the FRM monitor in the Parkersburg PM 2.5 Nonattainment Area for the three year period used in the nonattainment designation determination. Table 2.1-1 also presents the 2001-2003 PM 2.5 design value for the FRM monitor based on these quarterly and annual averages. The historic quarterly, yearly and design value air quality data for the FRM monitor can be found in Appendix A.

Table 2.1-1. PM 2.5 Concentrations and Design Values for the FRM monitor in the Parkersburg PM 2.5 Nonattainment Area County FRM Site Year 1 st Quarter 2 nd Quarter 3 rd Quarter 4 th Quarter Annual Average Design Value

Wood 107-1002 2001 15.93 18.58 20.73 14.36 17.40 16.03 2002 12.88 17.32 20.98 11.86 15.76 2003 12.88 14.47 19.78 12.61 14.93 It was determined that higher PM 2.5 concentrations are more likely to occur during the second and third quarters, or between April and September, with most occurring between June and September.WV PM2.5SIP for the Parkersburg Nonattainment AreaPage 11Figure 2.1-1. 2002 sample data for the Parkersburg area PM2.5monitor.Parkersburg PM2.5 - 2002(54-107-1002)0.015.030.045.060.075.0Run Date

ug/m3WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 12 FRM data was analyzed to determine if there was a seasonal correlation to higher PM 2.5 values (Figure 2.1-2).

Figure 2.1-2. Number of samples per quarter in 2002 at the Parkersburg area monitor (54-107- 1002) with PM 2.5 concentrations greater than 15μ/m 3

. 

### Parkersburg Area 2002 Samples by Quarter with PM2.5 Concentration > 15 ug/m3

6 14 20

8

0 5 10 15 20 25

Q1 Q2 Q3 Q4 2002Number of Samples
Samples > 15 ug/m3 out of a total of 116 SamplesWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 13 2.2. PM 2.5 Component Species Analysis To fully understand the nature of the PM 2.5 nonattainment issues in the West Virginia nonattainment areas, it is important to analyze the percentage contribution of the individual PM 2.5 component species, both from an annual perspective and seasonally throughout the year. Unfortunately, the FRM monitoring sites only provide a total mass PM 2.5 concentration and do not provide any information concerning the speci ated breakdown of the various components. A separate PM 2.5 monitoring network, the Speciated Trends Network (STN), does allow for the speciation of these components, but the STN PM 2.5 concentration data is not directly comparable to the FRM PM 2.5 concentration data due to a slight difference in the monitoring methodology. This creates an issue in using raw STN PM 2.5 data in an attainment demonstration, because it is not absolutely equivalent to the FRM PM 2.5 data on which the nonattainment designation is based and on which attainment will ultimately be evaluated. To address this issue, Neil Frank with the EPA developed an approach to use the raw STN PM 2.5 data to appropriately estimate the components of PM 2.5 as measured by the FRM monitors. The approach is termed the “ S ulfate, A djusted N itrate, D erived W ater, I nferred C arbonaceous material balance approac H ” method, or SANDWICH. The SANDWICH approach is discussed in greater detail in Appendix J.
Sources of PM 2.5 include all types of combustion activities including motor vehicle emissions, coal-fired power plants, wood and vegetative burning, and certain industrial processes involving nitrates and sulfates. EPA uses the SANDWICH method to chemically characterize ambient PM 2.5 speciation data. Figure 2.2-1 shows that a large portion, about 35%, of annual averaged PM 2.5 composition in the Parkersburg area consists of ammonium sulfate and ammonium nitrate, which are products of reactions of ammonia, sulfates and nitrates in the atmosphere in summer and winter, respectively. Ammonia from sources such as fertilizer and animal feed operations contribute to the formation of ammonium sulfates and ammonium nitrates suspended in the atmosphere. The rest originates from sulfates, carbon and organic compounds from vegetative burning, coal-fired power plants, geological dust, oil combustion, motor vehicle emissions and diesel emissions. Nitrates usually originate from vehicle emissions and power generation.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 14 Figure 2.2-1. PM 2.5 species contribution in the Parkersburg area, based on EPA SMAT data

Organic carbon (OC) levels vary across the y ear (Figure 2.2-2). A combination of sources contribute to organic carbon mass, including bi ogenic, wood burning, vegetative emissions, and fossil fuel combustion by industrial point sources, on-road vehicles and non-road engines, and meat cooking. Elevated levels in the winter are likely due to wood burning (from residential, commercial or industrial wood use, forest fires, prescribed fires, land clearing, or agricultural burning). Elevated levels in summer are likely due to vegetative carbon emissions. Contributions from fossil fuel combustion (gasoline and diesel) are greater in urban areas than in rural areas, but biogenic still dominates. Ammonium sulfate (SO 4 ) is lowest in the winter and highest in the summer (Figure 2.2-2). Point source SO 2 emissions are the predominant precursors of ammonium sulfate. Ammonium nitrate (NO 3 ) is an insignificant contributor to PM 2.5 mass in warmer months and a somewhat larger contributor in the cooler winter months, but always less important than organic carbon and ammonium sulfate (Figure 2.2-2). Ground level (on-road vehicles, non-road engines, area sources) and point (industrial and electric utility) sources of nitrogen oxides and agricultural emissions of ammonia are the primary precursors for ammonium nitrate. Elemental carbon (EC) and fine soil (Crustal) are minor contributors to PM 2.5 mass (Figure 2.2- 2). Fossil fuel combustion and wood burning are th e most important contributors to elemental carbon. Fine soil might be from construction, agricultural activities or windblown dust. 2000-04 Parkersburg (Vienna Neal School) PM2.5 Species Percent Contribution 54-107-1002

4% 3%

39%

31% 4%

10% 9% Crustal EC OC SO4 NO3 NH4 PBW
2000-04 DV = 16.07 ug/m3WV PM2.5SIP for the Parkersburg Nonattainment AreaPage 15Figure 2.2-2. Parkersburg 2002 Quarterly Speciation data based on SMATtool. Upper left - first quarter (Jan - Mar), Upper rightsecond quarter (Apr - Jun), bottom left - third quarter (Jul- Sep) and bottom right - fourth quarter (Oct - Dec).3%3%44%29%0%9%9%3%Parkersburg 2000-2004 DVB Q2PM2.5 Speciation PercentagesCrustalECOCSO4NO3NH4PBWBlank Mass2000-2004 Q2 DVB =16.19 ug/m35%2%31%38%0%11%10%3%Parkersburg 2000-2004 DVB Q3PM2.5 Speciation PercentagesCrustalECOCSO4NO3NH4PBWBlank Mass2000-2004 Q3 DVB =20.01 ug/m33%4%46%23%6%8%6%4%Parkersburg 2000-2004 DVB Q1PM2.5 Speciation PercentagesCrustalECOCSO4NO3NH4PBWBlank Mass2000-2004 Q1 DVB =13.42 ug/m33%4%33%27%10%11%8%4%Parkersburg 2000-2004 DVB Q4PM2.5 Speciation PercentagesCrustalECOCSO4NO3NH4PBWBlank Mass2000-2004 Q4 DVB =12.68 ug/m3WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 16 Georgia Tech ran emissions sensitivities in the CMAQ regional air quality model using the VISTAS 2009 Base D inventory and episodes selected in summer (Jun 1 - Jul 10) and late fall (Nov 19 - Dec 19) 2002 (Figures 2.2-3 and -4). These model runs indicate that the greatest reductions in PM 2.5 mass would be accomplished by reducing SO 2 emissions from electric utilities and industrial point sources. SO 2 reductions in several southeastern states, not just the Parkersburg area, would be beneficial in reducing PM 2.5 in Parkersburg. In both summer and winter the model projects some benefits from reducing local sources of primary carbon from ground sources and from fire emissions. Because biogenic emissions dominate Volatile Organic Carbon (VOC) emissions, the model indicates little benefit in winter and no benefit in summer of reducing anthropogenic VOC emissions. The model projects very little benefit of reducing emissions of nitrogen oxides from either ground level or point sources in West Virginia. In the southeast, ammonium nitrate levels are limited by the availability of ammonia, so reducing ammonia emissions would be a more effective strategy to reduce ammonium nitrate than reducing nitrogen oxide emissions. Finally, the model shows that SO 2 emissions from outside the ASIP 12 km modeling domain (labeled as “BC” or boundary conditions in Figures 2.2-3 and -4) have a comparable contribution to PM 2.5 as local sources of primary carbon (PC). These results demonstrate the regional contribution from SO 2 point sources located several states away from Parkersburg, mostly from the midwest. In summary, SO 2 reductions from electric utilities and industrial point sources are likely to be most effective in reducing PM 2.5 in the Parkersburg area. From the mobile sector, reductions of primary carbon from local sources would be more effective than reductions of either VOC or NO x . ASIP inventories and air quality modeling indicate that installation of SO 2 controls (scrubbers) for electric utilities under EPA’s Clean Air Interstate Rule (CAIR) will significantly reduce PM 2.5 levels in the Parkersburg area by 2009 (figure 2.2-5, see Wood - 1002, middle and right bars).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 17 Figure 2.2-3. CMAQ modeled changes in PM 2.5 mass for Parkersburg FRM monitor from reducing 2009 VISTAS/ASIP Base D emi ssions by 30% for several different emissions source categories, for the summer period Jun 1 - Jul 10, 2002.

Figure 2.2-4. CMAQ modeled changes in PM 2.5 mass for Parkersburg FRM monitor from reducing 2009 VISTAS/ASIP Base D emi ssions by 30% for several different emissions source categories, for the winter period Nov 19 - Dec 19, 2002. 

### Wood, WV (541071002) Summer

-1.20 -1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20

SO2_EGU

S O2 _ nonEGU

N Ox_Ground NOx_Point NH3 VO C

s PC_Ground PC_P o int PC_Fi r es°PM2.5(°g/m3) Bio. Antro. BCs MRPO M-VU CEN VISTAS WV VA TN SC NC MS KY GA FL AL



### Wood, WV (541071002) Winter

-1.40 -1.20 -1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20

SO

2 _

E GU

S O2 _ nonE GU

N Ox _ G

ro

u nd

NOx_Point NH3 V OC

s

P C_

Gr

ound PC_Point P C_Fir e s°PM2.5(°g/m3) Bio. Antro. BCs MRPO M-VU CEN VISTAS WV VA TN SC NC MS KY GA FL ALWV PM2.5SIP for the Parkersburg Nonattainment AreaPage 18Figure 2.2-5. 2002 Monitored Design Values (left) and 2009 baseG4 12 km projected DVF (middle) and 2009 base G4 36 kmprojected DVF (right) for all FRM monitors in West Virginia.West Virginia 2002 Design Value (Left) and2009 base G4 12km projected DVF (Middle) and2009 base G4 36km projected DVF (Right)02468101214161820 Berkeley-0003 Brooke-0005 Cabell-0006 Hancock-0011 Hancock-1004 Harrison-0003 Kanawha-0010 Kanawha-1005 Marion-0006 Marshall-1002 Mercer-0002 Monongalia-0003 Ohio-0010 Raleigh-0002 Summers-0001 Wood-1002West Virginia

ug/m3PBWNH4CrustalECOCNO3SO4blankWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 19 2.3. Attribution of Emissions Sources Precursor pollutants to PM 2.5 can be emitted directly, such as smoke from a fire, or they can form as a result of chemical reactions among gases such as SO 2 , nitrogen dioxide and some organics. Sources of these precursor pollutants include power plants, gasoline and diesel engines, wood combustion and high-temperature industrial processes such as smelters and steel mills. Other sources of PM 2.5 precursor pollutants include mobile sources, area sources, biogenic, fires, wind blown dust and oceans. The speciated analysis of the PM 2.5 concentrations in the Parkersburg PM 2.5 nonattainment area presented above demonstrates that the OC and SO 4 components are the most important portions of the total PM 2.5 mass throughout the year. OC is predominately attributed to biogenic VOC emissions. SO 4 is associated with SO 2 emissions. NH 4 can have a variety of sources including both industrial and natural processes. What little NO 3 is present in the PM 2.5 nonattainment area throughout the year is attributed to NO xfrom combustion sources. Of all these components and associated emission sources, SO 4 is the only significant PM 2.5 component species found throughout the year that is attributed to a set of emissions sources that are controllable through regulatory actions by the West Virginia Division of Air Quality. When evaluated throughout the Parkersburg nonattainment area and across West Virginia, SO 2 is primarily from the point source sector. Figure 2.3-1 presents the SO 2 emissions from the various source sectors in the Parkersburg nonattainment area. The magnitude of the point source sector emissions completely overshadows the emissions from all other source categories. When SO 2 emissions by source category are evaluated acro ss West Virginia (Figure 2.3-2), the vertical scale grows 2.4 times larger than the axis scale used for the nonattainment area SO 2 emissions plot (including West Virginia and Ohio emissi ons). Again, the magnitude of the point source sector completely masks the SO 2 emissions from all other source sectors. Also note that the Parkersburg SO 2 point source emissions account for approximately 12% of the state SO 2 emissions (Figure 2.3-3).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 20 Figure 2.3-1. Parkersburg nonattainment area SO 2 emissions by source sector, including West Virginia and Ohio emissions. Parkersburg PM2.5 Nonattainment Area SO2 Emissions in 2002

33% 67%

0 50,000 100,000 150,000 200,000 250,000

Point Stationary Area Stationary Nonroad Mobile Onroad Mobile BiogenicsAnnual SO2 (Tons/Year)

Ohio SO2 West Virginia SO2WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 21 Figure 2.3-2. Statewide West Virginia SO 2 emissions by source sector. West Virginia Total SO2 Emissions in 2002

0 100,000 200,000 300,000 400,000 500,000 600,000

Point Stationary Area Stationary Nonroad Mobile Onroad Mobile BiogenicsAnnual SO2 (Tons/Year)WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 22 Figure 2.3-3. Comparison of total West Virginia SO 2 emissions to West Virginia SO 2 emissions in the Parkersburg area. The Parkersburg SO 2 point source emissions account for approximately 12% of the state SO 2 emissions. West Virginia Statewide and West Virginina Portion of Parkersburg PM2.5 Nonattainment Area SO2 Emissions in 2002

0 100,000 200,000 300,000 400,000 500,000 600,000

Point Stationary Area Stationary Nonroad Mobile Onroad Mobile BiogenicsAnnual SO2 (Tons/Year)
West Virginia Total SO2 WV Portion of Parkersburg Nonattainment Area SO2WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 23 2.4. Identification of Attainment Plan Pollutants and Precursors The EPA’s Clean Air Fine Particulate Implementation Rule [72 FR 20586, 25APR2007], commonly referred to as the PM 2.5 Implementation Rule, guides states as they develop state implementation plans in response to annual and/or daily PM 2.5 nonattainment. It establishes a hierarchy of precursor pollutants. The significance of each precursor for PM 2.5 has been analyzed and determined by EPA. Based on EPA’s analyses, direct PM 2.5 , SO 2 and NO xwere deemed significant for all nonattainment areas, while NH 3 and other precursors were deemed insignificant at this time. According to the PM 2.5 Implementation Rule, sources of direct PM 2.5 and SO 2 must be evaluated for control measures in all nonattainment areas. Direct PM 2.5 emissions include organic carbon, elemental carbon and crustal material. If emissions of a precursor contribute significantly to PM 2.5 concentrations in the area, then the sources of that precursor should be evaluated for reasonable control measures. EPA found sulfates and carbon to be the most significant fractions of PM 2.5 mass in all nonattainment areas and, therefore, concluded that reductions in SO 2 will lead to a significant net reduction in PM 2.5 concentrations despite a potential slight increase in nitrates.
2.4.a. Direct PM 2.5 emissions 40CFR §51.1002(c) requires States to identif y and evaluate sources of direct PM 2.5 emissions with respect to Reasonable further Progress (RFP) and Reasonably Available Control Measures (RACM), including Reasonably Available Contro l Technology (RACT) for stationary sources. West Virginia addresses RFP and RACM/RACT for direct PM 2.5 emissions in Sections 8 and 9.
2.4.b. SO 2 emissions Sulfate particles are formed in the atmosphere from sulfur dioxide emissions. Sulfate particles occur as hydrogen sulfate, H 2 (SO 4 ), ammonium bisulfate, HNH 4 SO 4 , and ammonium sulfate, (NH 4 ) 2 SO 4 , depending on the availability of ammonia, NH 3 , in the atmosphere. 40CFR §51.1002(c)(1) requires States to address sulfur dioxide (SO 2 ) as a PM 2.5 attainment plan precursor and evaluate sources of SO 2 emissions in the State for control measures. West Virginia addresses RFP and RACM/RACT for SO 2 emissions in Sections 8 and 9.
2.4.c. NO xemissions Ammonium nitrate, NH 4 NO 3 , is formed in the atmosphere by reaction of ammonia and nitrogen oxides. In the ASIP region, the southeastern United States, nitrate formation is limited by the availability of ammonia and by temperature. A mmonia preferentially reacts with sulfur dioxide and sulfate before reacting with nitrogen oxides. Particle nitrate is formed at lower temperatures; at elevated temperatures nitrates remain in the gaseous form. For this reason, particle nitrate levels are very low in the summer and a minor contributor to PM 2.5 concentrations. Particle nitrate concentrations are higher on winter days. Modeling data (see Section 2.2) indicate that in the ASIP region ammonium nitrate formation is limited by ammonia concentrations and suggest that for winter days control of ammonia sources would be more effective in reducing ammonium nitrate levels than control of nitrogen oxides.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 24 40CFR §51.1002(c)(2) requires States to address NO xas a PM 2.5 attainment plan precursor and evaluate sources of NO xemissions in the State for control measures, unless the State provides an appropriate technical demonstration for a specific area showing that NO xemissions from sources in the State do not significantly contribute to PM 2.5 concentrations in the nonattainment area, and EPA concurs. West Virginia addresses RFP and RACM/RACT for NO xemissions in Sections 8 and 9.
2.4.c. VOC emissions In West Virginia anthropogenic emissions of volatile organic compounds (VOCs) are overwhelmed by the biogenic emissions of VOCs from vegetation. Anthropogenic VOC emissions accounted for 28% of the total VOC emissions in the state in 2002, and are projected to account for 25% in 2009 (see Table 2.4-1). In Wood county, anthropogenic emissions accounted for 44% of the VOC emissions in 2002, and are projected to account for 39% of the VOC emissions in 2009 (see Table 2.4-2).
Table 2.4 -1. West Virginia Annual VOC Emissions 2002 (tons) 2002 (%) 2009 (tons) 2009 (%) Area 60,443 12% 57,346 12% On-road 45,284 9% 26,584 6% Non-Road 18,566 4% 18,069 4% Point 15,775 3% 15,171 3% Anthropogenic 142,070 28% 117,170 25% Biogenic 357,850 72% 357,850 75% Total 499,920 100% 475,020 100%

Table 2.4-2. Annual VOC Emissions for Wood County and the Grant Tax District of Pleasants County 2002 (tons) 2002 (%) 2009 (tons) 2009 (%) Area 3,718 20% 3,397 20% On-road 2,100 12% 1,300 8% Non-Road 1,138 6% 1,028 6% Point 1,017 6% 898 5% Anthropogenic 7,973 44% 6,623 39%Table 2.4-2. Annual VOC Emissions for Wood County and the Grant Tax District of Pleasants County 2002 (tons) 2002 (%) 2009 (tons) 2009 (%)

WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 25

Biogenic 10,252 56% 10,252 61% Total 18,225 100% 16,875 100% Sensitivity analyses conducted by Georgia Tech for VISTAS, show that reductions of anthropogenic emissions of VOC would not result in a significant reduction in PM 2.5 concentrations in Wood County (see Figure 2.4-1 below).

Figure 2.4-1. Annual Sensitivities for West Virginia PM 2.5 Non-Attainment Areas (VISTAS Base D emissions, from spreadsheet titled episode_average_so4+no3+nh4+ec+oc_annual_d1_voc_MODIFIED.xls)

In addition, pursuant to 40CFR §51.1002(c)(3) the State is not required to address VOC as a PM 2.5 attainment plan precursor and evaluate sources of VOC emissions in the State for control measures. Therefore, West Virginia is not classi fying VOC as an attainment plan precursor, and is not expending further resources on identifying and controlling VOC sources. 

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### BVOC AVOCWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 26 2.4.d. Ammonia emissions 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 ASIP region, which, as shown in Table 2.4-3, 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 2.4-3. ASIP Ammonia Emissions State 2002 NH 3 Emissions (tons) State Contribution to Total (%) Alabama 66,519 10% Florida 58,602 9% Georgia 95,936 14% Kentucky 57,563 9% Mississippi 63,929 10% North Carolina 173,084 26% South Carolina 36,509 6% Tennessee 43,258 7% Virginia 55,570 8% West Virginia 12,461 2% Total 663,431 100% Note: All numbers rounded to the nearest ton or whole percentage point. Nevertheless, as shown in Table 2.4-4, the majority (~80%) of the ammonia emissions in the state 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 C.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 27 Table 2.4-4. West Virginia Ammonia Emissions Sector 2002 (tons) 2002 (%) 2009 (tons) 2009 (%) Point 453 4% 671 5% Area 9,963 80% 10,625 78% NonRoad 9 0% 11 0% OnRoad 2,036 16% 2,292 17% Biogenics N/A N/A N/A N/A Total 12,461 100% 13,598 100% Note: All numbers rounded to the nearest ton or whole percentage point. Further, as shown in Table 2.4-5, Wood county accounts for only 262 tons of ammonia emissions or approximately 2% of the ammonia emissions in the state, with 45% of the ammonia emissions attributable to area sources.

Table 2.4-5. Ammonia Emissions for Wood Count y and Grant Tax District of Pleasants County Sector 2002 (tons) 2002 (%) 2009 (tons) 2009 (%) Point 52 20% 60 21% Area 117 45% 115 40% NonRoad 1 0% 1 0% OnRoad 92 35% 111 39% Biogenics N/A N/A N/A N/A Total 262 100% 287 100% Note: All numbers rounded to the nearest ton or whole percentage point. In addition, pursuant to 40CFR §51.1002(c)(4) the State is not required to address ammonia as a PM 2.5 attainment plan precursor and evaluate sources of ammonia emissions in the State for control measures. Therefore, West Virginia is not classifying ammonia as an attainment plan precursor, and is not expending further resources on identifying and controlling ammonia sources.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 28 2.5. Transportation Conformity Transportation conformity is a way to ensure that Federal funding and approval are given to those transportation activities which are consistent with air quality goals. It ensures that these transportation activities do not worsen air quality or interfere with the "purpose" of the SIP, which is to meet the National Ambient Air Quality Standards (NAAQS). According to the Clean Air Act, transportation plans, programs, and projects cannot: 1. Create new NAAQS violations; 2. Increase the frequency or severity of existing NAAQS violations; or 3. Delay attainment of the NAAQS. EPA finalized its first transportation conformityrule in November 1993 (subsequently codified in 40 CFR 93, subpart A). States were required to submit related SIP revisions to address the rule provisions by November 1994. The federal rule underwent a series of revisions over the next decade and beyond, some of which were prompted by court decisions. States are no longer required to adopt all of the provisions of the entire federal conformity rule. The three required SIP elements are: 1. Consultation procedures [40 CFR §93.105]; and 2. Procedures for determining regional transportation-related emissions [40 CFR §93.122(a)(4)(ii)]; and 3. Enforceability of design concept and scope and project-level mitigation and control measures [93.125(c)]. The federal requirements apply to areas designated as nonattainment for one or more NAAQS, or which have been redesignated to attainment with federally approved air quality maintenance plans. EPA published a direct final approval of West Virginia’s most recent transportation conformity SIP in the May 2, 2008 Federal Register (73 FR 24175). The responsibletransportation planning entity for the Parkersburg nonattainment area is the Wood-WashingtonWirt Interstate Planning Commission (WWW). For simplicity, the highway planning process may be separated into two parts - The Long Range Transportation Plan (LRTP) - an overall long range transportation network design and implementation process that typically addresses a 20-30 year time frame; and The Transportation Improvement Program (TIP) - a collection of near-term projects that typically address the first 3-5 years of the LRTP and build toward realization of the LRTP. WWW has developed a travel demand model (TDM) to simulate future traffic activity, taking into account projects which are expected to be completed by various analysis years. Outputs from the TDM are then used to develop appropriate inputs for EPA’s MOBILE6.2 emissions factor model. Finally, the activity and emissions factors are processed to generate overall emissions estimates by analysis year.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 29 The emission estimates are compared to: • a base year; or • no-build scenario (if the project(s) were not built); or • emission budgets. The last only applies if EPA has approved or has found adequate emissions budgets which have been submitted as a SIP revision. The appropriate test is determined through the consultation process contained in the MOU. For PM 2.5 , it possible that the pollutant itself and all its various precursors would require analysis. However, as noted elsewhere in this document, ammonia and volatile organic compounds - while recognized as such precursors - are not considered as significant overall to the nonattainment problem. Therefore, they are not considered as attainment plan precursors for SIP development purposes under EPA’s PM 2.5 implementation rule. Further, other pollutants/precursors may be considered significant overall but the highway contribution to those emissions may be insignificant. In that case, the federal Transportation Conformity rule allows such pollutants/precursors to be exempt from conformity analysis under certain circumstances. 40CFR93.109(k) Areas with insignificant motor vehicle emissions. Notwithstanding the other paragraphs in this section, an area is not required to satisfy a regional emissions analysis for §93.118 and/or §93.119 for a given pollutant/precursor and NAAQS, if EPA finds through the adequacyor approval process that a SIP demonstrates that regional motor vehicle emissions are an insignificant contributor to the air quality problem for that pollu tant/precursor and NAAQS. The SIP would have to demonstrate that it would be unreasonable to expect that such an area would experience enough motor vehicle emissions grow th in that pollutant/precursor for a NAAQS violation to occur. Such a finding would be based on a number of factors, including the percentage of motor vehicle em issions in the context of the total SIP inventory, the current state of air quality as determined by monitoring data for that NAAQS, the absence of SIP motor vehicle control measures, and historical trends and future projections of the growth of motor vehicle emissions. . . [emphasis added] For the reasons outlined below, the DEP is herein making a finding that regional highway emissions of PM 2.5 , NO x and SO 2 are insignificant contributors to the nonattainment problem for the Parkersburg area. The finding will become final if EPA concurs and approves this SIP. First, the regional highway pollutant/precursor emissions constitute a relatively small fraction of the overall emissions as shown in the table below.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 30 Table 2.5-1. 2002 Base Year Emi ssion Inventory (Tons/Year) Category County NO x PM 2.5 SO 2 Area Pleasants, WV 171 152 86 Wood, WV 578 987 1,225 WV subtotal 749 1,139 1,311 Washington, OH 188 317 40 Area Subtotal 937 1,456 1,351 NonRoad Pleasants, WV 837 36 41 Wood, WV 1,399 60 112 WV subtotal 2,236 96 153 Washington, OH 1,669 84 92 NonRoad Subtotal 3,905 180 245 OnRoad Pleasants, WV 194 3 9 Wood, WV 2,473 41 102 WV subtotal 2,667 44 111 Washington, OH 2,424 42 112 OnRoad Subtotal 5,091 86 223 Point Pleasants, WV 20,896 853 65,791 Wood, WV 939 215 4,502 WV subtotal 21,835 1,068 70,293 Washington, OH 33,588 681 0 Point Subtotal 55,423 1,749 70,293 % WV OnRoad/ WV Subtotal 9.7% 1.9% 0.2%
TOTALS WV Subtotal 27,487 2,347 71,868 Nonattainment Area Total 65,356 3,471 72,112

% OnRoad/ Total 7.8% 2.5% 0.3%WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 31 From the above, it can be readily determined that the maximum highway contribution to NO x emissions is less than 10%. This true for West Virginia emissions alone and for the combined West Virginia and Ohio emissions within the nonattainment area. The highway contribution to direct PM and SO 2 is below 3%. Therefore, the DEP has determined that the regional highway emissions contribution as a percent of the total SIP inventory is insignificant. Second, the current air quality (CY 2007) is represented by a design value of 15.4 ug/m 3 . This value is slightly higher than the 15.3 ug/m 3 design value upon which the nonattainment designation is based. The graph below [Figure 2.5-1] shows the general improvement of PM 2.5 concentrations from the year 2001 to date. The values dropped from 17.6 μ g/m 3 in 2001 to 17.0 and 16.0 in 2002 and 2003, respectively. This may be attributed to an overall decrease in sulfates from SO 2 reductions mandated by the Acid Rain Program and preliminary implementation of

controls

Figure 2.5-1. Parkersburg PM 2.5 Design Value, 2001-2007

from the Clean Air Interstate Rule. The design value then fluctuated from 15.2 to 15.4 during the 2004-2007 period. This fluctuation is due to the year to year variability in PM 2.5 . It is common to many monitoring sites and, in fact, is much smaller than the year to year variability exhibited by several other West Virginia sites. Overall, the PM concentrations have improved since the beginning of the decade. The area is designated as attainment for all other criteria pollutants. Given the relatively small contribution of the highway emissions to the total SIP inventory, highway emissions are not likely to contribute significantly to the local PM 2.5 mass concentrations. Among other factors, this is largely due to the dominance of sulfates in the 

### Parkersburg PM 2.5 

### Design Value 2001-2007

14.0 14.5 15.0 15.5 16.0 16.5 17.0 17.5 18.0

99-01 00-02 01-03 02-04 03-05 04-06 05-07
Parkersburg PM 2.5 Design ValueWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 32 overall PM mass, coupled with the very low contribution of highway sources to those sulfate totals. Third, historically there have been no West Virginia SIP requirements for motor vehicle control measures. The Wood County portion of the area is subject to Transportation Conformity for the 8-hour ozone standard, with SIP-approved, seasonal budgets for NO xand Volatile Organic Compounds (VOC). Further, the entire nonattainment area is currently subject to Transportation Conformity for the PM 2.5 standard. Emissions analysis has been mandatory for annual highway emissions of direct PM and NO x . However, upon a positive adequacy review or approval of this SIP submittal, no highway emissions analysis will be required under the annual PM 2.5 standard. Highway analysis of seasonal ozone precursors would continue to be mandatory. And, PM 2.5 hotspot analyses would continue to appl y for required projects under 40CFR93.116 and 93.123(b) of the Transportation Conformity Rule. Fourth, WWW has extensively researched the area local historical trends and growth patterns to develop the LRTP and support transportation conformity evaluations. There is no reason to expect highway motor vehicle emissions growth that would lead to a PM 2.5 NAAQS violation. WWW has estimated emissi ons out, at least, to the calendar year 2025 confirming this. The transportation air quality conformity analysis for the WWW area took into account all the regional capacity projects, which are scheduled for implementation through the transportation plan horizon year and the four year TIP. The PM conformity tests were performed for calendar years 2002, 2009, 2015 and 2025, for both the winter and summer representative months [January for winter and July for summer]. For each of those years, vehicle miles traveled (VMT) and Speed were developed by the Federal Functional Class codes within Wood County and Washington County [for both January and July], which are derived from the regional traffic model assignments that are made for each of those years. Pursuant to the Transportation Conformity interim tests, NO xand direct PM were evaluated.
The WWW study area covers the Parkersburg-Marietta Metropo litan area and includes two entire counties: Wood County in West Virginiaand Washington County in Ohio. Parkersburg [WV] along with Marietta and Belpre [OH] are the major urban areas. The metropolitan area has a population of 151,000, out of which 87,000 are in the urbanized area according to 2000 census. The study area includes the urban areas of Parkersburg, Marietta and Belpre in addition to all of Wood County, West Virginia and Washington County, Ohio. The townships of Belpre, Dunham, Fearing, Marietta, Muskingum, Newport and Warren are included in the study, as part of Washington County. The transportation plan, on the other hand, is a comprehensive examination of the future transportation needs for Wood County [WV] and portions of Washington County [OH]. The study area is 571.6 square miles, with Wood County representing 376.9 square miles, and the seven Ohio townships representing 194.7 square miles.
WWW models future highway network activity using the QRS-2 Version 6.0 software package developed by AJH Associates. Advanced Gene ral Network Editor [GNE] version 7.0 was used to edit the base year model network. The WWW QRS-2 traffic model is a 24-hour traffic analysis model with three internal trip purposes: home-based work, home-based non-work, and non-home based; internal to external trips; through trips; and truck trips developed based on roadway functional classes. The model base year is 2000, and the model future year for the Long Range Multimodal Transportation Plan [LRTP] is 2025. In this model, both QRS-2 defaultWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 33 parameters [national averages] and the location-specific parameters derived from the 2002 WWW house hold survey were used. The Wood-Washington-Wirt [WWW] Travel Demand Model Technical Report outlined the tasks and activities in the base year model calibration and validation process. The four major model development tasks are listed as follows: 1. Developing Highway Network System and Related Attributes. 2. Developing Traffic Analysis Zone [T AZ] System and Related Attributes. 3. Deriving Internal-Internal, External-Internal and External-External Trips. 4. Model Calibration and Validation. The regional travel demand model for the base year was executed to obtain the total VMT for the region. The base year estimates of VMT output by the model must be adjusted to match the HPMS VMT estimates on roadway class-specific bases, resulting in HPMS-adjusted VMT estimates for each roadway class. Then the VMT f actors with facility class [which are developed as an average of HPMS and those derived from the local vehicle distribution factors] were used to calculate the VMT for each facility class. The output by the regional travel demand model was input to the MOBILE6.2 emissions model for de veloping the roadway class-specific emission factors for the base year. These emissions factors were combined with the HPMS-adjusted VMT estimates to compute onroad emissions for each roadway class. The resulting emissions estimates are then summed to compute total regional emissions from onroad motor vehicles. For each future year of interest (i.e., analysis year), the regional travel model must first be used to develop roadway-class specific VMT estimates. These estimates are then compared to the base year VMT estimates output by the travel model, to develop roadway-class specific growth rates. The base year HPMS-adjusted VMT estimates above are then multiplied by these growth rates to compute future year HPMS-adjusted VMT estimates. Similar to the base year computation methodology, congested speeds output by the model were input to MOBILE to develop roadway class-sp ecific emissions factors. The HPMS-adjusted VMT estimates for each roadway class were then multiplied by the emissions factors output by MOBILE, and the resulting emissions estimates summed across all roadway classes to compute total regional on-road emissions. The process was followed for each selected analysis year. The above example provides one approach to computing HPMS-adjusted VMT estimates for each analysis year and scenario. There are, however , a number of other variations that could be pursued depending on the robustness of the available data and desired level of analysis. Table 2.5-2 shows the VMT for the model area for the years 2002, 2005, 2009, 2015 and 2025. Appendix A [of the report] lists the VMT by facility type for all the above-listed years.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 34 Table 2.5-2. VMT for Model Years 2002, 2005, 2009, 2015 and 2025 County Name Year Winter VMT Summer VMT Wood 2002 2,295,840 2,604,982 Washington 2002 2,061,782 2,353,125 2002 Total 4,357,622 4,958,107
Wood 2009 2,658,148 3,015,859 Washington 2009 2,224,337 2,538,645 2009 Total 4,882,485 5,554,504
Wood 2015 2,854,781 3,240,181 Washington 2015 2,569,827 2,932,187 2015 Total 5,424,608 6,172,368

Wood 2025 3,111,562 3,531,210 Washington 2025 2,835,467 3,236,821 2025 Total 5,947,029 6,768,031

In June 2005, EPA clarified how transportation conformity was determined in noncontiguous PM 2.5 nonattainment areas. EPA’s clarification was as follows: Noncontiguous PM 2.5 nonattainment areas (NNAs) are areas that c ontain “satellite portions” that are not contiguous with the main portion of the nonattainment area. These satellite areas are designated as part of the overall nonattainment area because they have a point source which contributes, via transport, to the air quality problems of the main geographic portion of the PM 2.5 nonattainment area. We believe that the satellite portions of NNAs best fit the definition of a donut area for transportation conformity purposes. Section 93.101 of the c onformity rule defines “donut areas” as “geographic areas outside a metropolitan planning area boundary, but inside the boundary of a nonattainment or maintenance area that contains any part of a metropolitan area(s). These areas are not isolated rural nonattainment and maintenance areas.” [ http://fhwa.dot.gov/environment/conformity/faqs.htm#pm25 ; question #7] The conformity guidance for “donut” areas was entitled Companion Guidance for the July 1, 2004, Final Transportation Conformity Rule: Conf ormity Implementation in Multi-jurisdictionalWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 35 Nonattainment and Maintenance Areas for Existing and New Air Quality Standards . The partial county of Pleasants is thus considered a “doughnut area” for planning purposes. As part of the PM 2.5 Air Quality analysis, a small portion of Pleasants County [WV] was added to the Wood County area as a “donut”. In this “donut” area there are roads with different classifications. The WVDOT provided WWW with the VM T on these roads [in the “donut area”] for the year 2004. Based upon the advice of the WVDAQ, the modeling area’s growth factor was used to calculate the donut area VMT [with respect to functional class] for the years 2002, 2009, 2015 and 2025. This donut area VMT was added to the Wood County VMT and the Air Quality results were determined based upon that information. Input files were developed for modeling mobile emissions with the Mobile6.2 software by using the VMT split by functional class. The area type parameters were established by the West Virginia Division of Air Quality (WVDAQ) for Wood County. The assumptions related to the derivation of model inputs were developed as part of an interagency consultation process involving the following agencies: • West Virginia Division of Air Quality. • West Virginia Department of Transportation. • Wood-Washington-Wirt Interstate Planning Commission. • Federal Highway Administration. • Ohio Department of Transportation. • U.S. EPA. • Ohio EPA. The results of the Mobile6.2 analysis yields mobile emission rates that can be used along with the VMT by functional class of roadway to derive estimates of total mobile source emissions for NO x and PM 2.5 . The conformity results are represented in Table 2.5-3. The conformity rule required the region’s LRTP to conform to the SIP before FHWA approves the funding of future projects. The conformity evaluation is determined by the processes outlined in the conformity rule. The WWW PM 2.5 nonattainment area has demonstrated conformity with the PM 2.5 transportation conformity rule using the base year interim emissions test.
Table 2.5-3. Conformity Results Year NO x , (tpd) NO x , (Annual) % Change from base PM 2.5 (tpd) PM 2.5 (Annual) % Change from base 2002 6.54 2,388.50 na 0.13 46.26 Na 2009 4.23 1,543.31 -35.4% 0.08 30.64 -33.8% 2015 2.44 890.96 -62.7% 0.06 21.48 -53.6% 2025 1.37 499.97 -79.1% 0.05 18.01 -61.1% The forecasts clearly show that, despite any anticipated growth in the area, significant decreases in highway emissions are expected for all analysis years, for both NO x , and direct PM. The interim conformity tests do not require evaluation of SO 2 . However, federal heavy dutyWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 36 diesel engine standards and low sulfur fuel requi rements are also expected to yield substantial emission decreases commensurate with those shown above. Therefore, the DEP reiterates its conclusion that highway emissions of PM 2.5 , NO x , and SO 2 are insignificant contributors to the nonattainment problem for the Parkersburg area and are therefore exempt from transportation conformity evaluation under 40CFR93.109(k).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 37 3.0. ATTAINMENT DEMONSTRATION METHODS AND INPUTS [40 CFR §51.1007] The attainment modeling for the Parkersburg PM 2.5 nonattainment area was performed in conjunction with the regional haze modeling being done by the Regional Planning Organization (RPO) for the southeastern states, Visibility Improvement State and Tribal Association of the Southeast (VISTAS) and the PM 2.5 and ozone (O 3 ) modeling being done by the Associated of Southeastern Integrated Planning (ASIP). VIST AS and ASIP are run by the ten Southeastern states (Alabama, Florida, Georgia, Kentuc ky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia). Since the regional haze and PM 2.5 modeling both use annual simulations and the regional haze modeling includes an intermediate year that is the attainment year required for the Parkersburg PM 2.5 nonattainment area, the WVDAQ decided to use the modeling for its attainment demonstration. The sections below outline the methods and inputs used by VISTAS/ASIP for the regional modeling. 3.1. Analysis Method The modeling analysis is a complex technical evaluation that begins by selection of the modeling system. VISTAS/ASIP chose the following modeling system: • Meteorological Model: the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Model (MM5) is anonhydrostatic, 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. Additionally, an historical year is selected to model that represents typical meteorological conditions in the Southeast when high ozone, high PM 2.5 and poor visibility are observed throughout the region. Once the historical year is selected, meteorological inputs are developed using the meteorological model. Emissions inventor ies are also developed for the historical year and processed through the emissions model. These inputs are used in the air quality model to predict ozone, PM 2.5 and visibility, with the results compared to historical data. The model performance is evaluated by comparing the model predicted data to the historic air quality data. 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. ASIP selected the yearWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 38 2002 as the modeling year for this demonstra tion. 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/ASIP region. Once model performance was deemed adequate, the current and future year emissions were processed through the emissions model. For this demonstration, the baseline year was 2002, which corresponds to the historic meteorology used in the modeling. The future attainment year the WVDAQ is using for this demonstration is 2009, since the mandatory attainment date for the Parkersburg PM 2.5 nonattainment area is April 5, 2010. The attainment date is set prior to the end of the 2010 calendar year, therefore, attainment of the NAAQS will be required by the end of 2009. The emissions are processed through the air quality model with the meteorological inputs. The air quality modeling results are used to determine a relative reduction in future PM 2.5 , which is used in the attainment. The complete modeling protocol used for this analysis can be found in Appendix B.
3.2. 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 form ulations 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.
3.2.a. Selection of Photochemical Grid Model
3.2.a.1. Criteria For a photochemical grid model to qualify as a candidate for use in an attainment demonstration, a State needs to show that it meets several general criteria: • 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, andWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 39 • 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.
3.2.a.2. Overview of CMAQ The air quality model selected for this study was CMAQ version 4.5, which was the most recent release at the point that the attainment modeling exercise started. 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 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. 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 PM 2.5 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 B). 3.2.b. Selection of Meteorological Model
3.2.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 model’s ability to accurately replicate important meteorological phenomena in the region of study, and the model's ability to interf ace with the rest of the modeling systems – particularly the air quality model. With these issues in mind, the following criteria were established for the meteorological model to be used in this study: • Non-Hydrostatic Formulation • Reasonably current, peer reviewed formulation • Simulates Cloud Physics • Publicly available at no or low cost • Output available in I/O API format • Supports Four Dimensional Data Assimilation (FDDA) • Enhanced treatment of Planetary Boundary Layer heights for air quality modelingWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 40 3.2.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 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 system. 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 interv als on the outermost grid mesh selected by the user. Surface fields are analyzed at three-hour in tervals. 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. Results of detailed performance evaluations of 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 B).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 41 3.2.c. Selection of Emissions Processing System
3.2.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 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 (BEIS-3), • Ability to process emissions for the proposed domain in a reasonable amount of time • Ability to process control strategies • No or low cost for acquisition and maintenance • Expandable to support other species and mechanisms
3.2.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 tem poral 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 schemes forWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 42 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 B). 3.3. 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, PM 2.5, and Regional Haze (Attainment Modeling Guidance) identifies specific goals to consider when selecting one or more episodes for use in demonstrating attainment with the NAAQS. 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. VISTAS adopted a logical, stepwise approach in implementing the Attainment Modeling Guidance in order to identify the most preferable, representative year for 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 conditions for 2000, 2001 and 2002, the candidate modeling years. The year 2002 was found to be representative of conditions in the other two years. Subsequently, these analyses were repeated with the meteorological and air quality monitoring data for 2000 to 2004 to evaluate how well the 2002 modeling year represented the full 2000-2004 baseline period. This analysis confirmed that visibility and PM 2.5 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 G. • Data Availability : In parallel with the CART analysis, episode characterization analyses, collaborative investigations by VISTAS states (e.g., NCDAQ, Georgia DNR, FL DEP)WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 43 intensively studied the availability of PM 2.5 , meteorological, and emissions data and representativeness of alternative baseline modeling periods from a regulatory standpoint. Additionally, 2002 was a year that EPA require d 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 modeling. All of the EPA criteria for model year 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). 3.4. Modeling Domains 3.4.a. Horizontal Modeling Domain The EPA’s 2007 modeling guidance recommends a 12-km modeling grid resolution for PM 2.5 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 nest ed domain. Two-way nesting was not considered due to numerical and computational uncertainty associated with the technique. The horizontal coarse grid modeling domain boundaries were determined through a national effort to develop a common grid projection and boundary. A smaller 12-km grid, modeling domain was selected in an attempt to balance location of areas of interest, such as ozone and fine particulate matter nonattainment areas, as well as Class 1 and wilderness areas for regional haze. Processing time was also a factor in choosing a smaller 12-km grid, modeling domain. The coarse 36-km horizontal grid domain covers the continental United States. This domain was used as the outer grid domain for MM5 modeling with the CMAQ domain nested within the MM5 domain. Figure 3.4-1 shows the MM5 horizontal domain as the outer most, blue grid with the CMAQ 36-km domain nested in the MM5 domain.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 44 Figure 3.4-1. 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 3.4- 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.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 45 Figure 3.4-2. A more detailed view of the 12-km grid over the VISTAS region

3.4.b. Vertical Modeling Domain The vertical grid used in the MM5 modeling primarily defines the CMAQ vertical structure. 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 we re 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.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 46 4.0. EMISSION INVENTORY [40 CFR §51.1008] 4.1. Baseline (2002) Emissions Inventory Clean Air Act Section 172(c)(3) requires that SIPs include a comprehensive, accurate, current inventory of actual emissions. The PM2.5 Impl ementation Rule at 40 CFR §51.1008(b) requires that the base year for this inventory be the most recent calendar year for which a complete inventory was required to be submitted to EPA pursuant to Subpart A of 40 CFR Part 51. The pollutants of concern include volatile organic compounds (VOC), nitrogen oxides (NO x ), fine particulate (PM 2.5 ), coarse particulate (PM 10 ), ammonia (NH 3 ) and sulfur dioxide (SO 2 ). The Implementation Rule further states that the baseline emissions inventory for calendar year 2002 or other suitable year shall be used for attainment planning and RFP plans for areas initially designated nonattainment for the PM 2.5 NAAQS between 2004-2005. Therefore, an inventory was developed for the baseline year 2002 by the WV Department of Environmental Protection and provided to VISTAS’ contractor, MACTEC, for further development. MACTEC performed numerous quality assurance procedures on the data. National Emissions Inventory (NEI) data were compared with previous year’s data to identify missing sources. When emissions data were reported as daily values or in terms of pounds, conversions were made to annual estimates in tons so that annual summaries could be compiled. Pollutant codes were reviewed and standardized to make them compliant with the NEI Input Format (NIF). Ammonia estimates submitted by states were compared with the 2002 Toxics Release Inventory. The results of these and other QA procedures were provided to state and local agencies to review for reasonableness and accuracy. Finally MACTEC took steps to ensure that point source and area source emissions were properly allocated and not double-counted or omitted. More detailed discussion of QA procedures can be found inthe MACTEC emissions inventory report

“ Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008, which is contained in Appendix N. There are five emissions inventory sectors: st ationary point; stationary area; onroad mobile (highway); nonroad mobile (off-road); and bioge nic sources. Stationary point sources include those sources that emit greater than a specified tonnage per year, for which the data are compiled at the process level. Electric generating utilities and industrial sources comprise the major categories for stationary point sources. Stationary area sources (also known as stationary nonpoint sources) are those sources whose individual emissions are relatively small, but due to their large number, the collective emissions can be significant (e.g. dry cleaners, service stations, agricultural sources, fires, etc.). These types of emissions are estimated on a county level. Onroad mobile sources are sources that use the roadway system. Examples include cars, trucks, and motorcycles. The emissions from these sources are estimated by vehicle type and road type and are summed at the county level. Nonroad mobile sources are equipment that can move but do not use roadways, e.g., lawn mowers, construction equipment, railroad locomotives, aircraft, etc. The emissions from these sources are also 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 as well.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 47 In addition to the various source sectors, there are various types of emissions inventories. The first is the actual base year inventory. This inventory reflects the base year emissions that 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. Inthis 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, were used to calculate relative reduction factors for future years. These relative reduction factors for future years were then used to demonstrate attainment with the NAAQS. 4.1.a. Stationary Sources The stationary source sector consists of two main sub-sectors: point and nonpoint sources. In addition, the point source category is further divided into two subcategories: EGUs and nonEGUs. These two inventory types are sometimes reported as separate sectors. Nonpoint sources have historically been known as area sources.
4.1.a.1. Stationary Point Sources Point sources are individual facilities 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 HAPs are inve ntoried annually. State agencies are required to submit annual point source inventories to the EPA by the Consolidated Emissions Reporting Rule (CERR). MACTEC obtained the West Vi rginia base 2002 point source inventory from EPA’s Central Data Exchange (CDX) site located at http://www.epa.gov/cdx/. Various rounds of quality assurance initiated by VISTAS have resulted in changes from the original data as submitted to EPA.
4.1.a.1.a. Electric Generating Units (EGUs) EGU source emissions comprise the most significant portion of the SO 2 and NO xemissions inventories in WV nonattainment areas as well as state-wide. 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 modeling that might be created by relying on a single year (2002) to represent future operations. This approach is consistent with EPA’s modeling guidance. The 2002 typical year inventories provide the baseline modeled air quality condition at each unit against which future year (2009 and 2018) modeling runs were compared to develop relative response factors (RRFs). The 2002 actual EGU emissions data, on the other hand, were used to evaluate model performance so that the future year modeling could be performed with an acceptable level of confidence.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 48 To develop a typical year 2002 emissions i nventory for EGU sources, each unit’s average continuous emission monitor (CEM) heat input for 2000 through 2004 was divided by the 2002 actual heat input to generate a unit-specific normalizing factor. This normalizing factor was then multiplied by the 2002 actual emissions. The heat inputs for the period 2000 through 2004 were used since the modeling current design values use monitoring data from this same 5-year period. If a unit was shut down for an entire year during 2000 through 2004, the average of the years the unit was operational was used. If a unit was shut down in 2002, but not permanently shut down, the emissions and heat inputs for 2001 (or 2000) were used in the normalizing calculations. For purposes of air quality model validation, actual 2002 calendar year emissions for EGU and fire activity were used within the VISTAS domain. During strategy and future year emission runs, “typical year” emissions for these categories were processed. Outside of the VISTAS region, 2002 emissions obtained from th e non-VISTAS regional planning organizations (RPOs) were used in an “as-is” format with no modification made for actual or typical operation. Through a request from VISTAS, Midwest Regi onal Planning Organization (MRPO) provided EGU data for their states, including Washi ngton County, Ohio. MRPO data for Washington County did not contain SO 2 emissions values. Therefore, SO 2 from EGUs in Washington County was not modeled in any processing for 2002. Emi ssions data were provided for other pollutants, however. MRPO does not have a public site available. Their website advises to call Mark Janssen directly for access. The data were downloaded from http://www.ladco.org/oldsite/tech/emis/index.html

. MACTEC used CEM data from EPA’S Clean Ai r Markets Division (CAMD) to prepare the actual 2002 inventory for SO 2 and NO xfor VISTAS states. CEM data can be found at the CAMD Maps and Data web page at http://camddataandmaps.epa.gov/gdm/ . Hourly CAMD emissions were summed and compared with state and local agency NEI submittals. This review showed that the CAMD and NEI annual estimates usually agreed. Minor differences were found to be because facility totals in NEI data contained additional small or emergency units not included in the CAMD database. Typical year and the actual year 2002 emissions data were compared, for the entire VISTAS region. Actual 2002 SO 2 emissions were about 0.5 percent higher than the typical year emissions. The differences on a state-by-state basis ranged from actual emissions being 6.6 percent lower in Florida to 10.9 percent higher in Mississippi. For the entire VISTAS region, actual 2002 NO x emissions were about 0.1 percent lower than the typical year emissions. The differences on a state-by-state basis ranged fr om actual emissions being 9.6 percent lower in Florida to 6.3 percent higher in Mississippi. For We st Virginia typical year emissions were about 3.0% and 3.7% lower for SO 2 and NO xrespectively than actual emissions.
Differences in EGU emissions values between actual year and typical year are shown in Table 4.1.a.1-1 below.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 49 Table 4.1.a.1-1 Comparison of SO 2 and NO xEmissions (tons/year) for EGUs from Actual 2002 Inventory and Typical 2002 Inventory SO 2 Emissions (tons/year) NO xEmissions (tons/year) State Actual 2002 Typical 2002 Percentage Difference Actual 2002 Typical 2002 Percentage Difference AL 447,828 423,736 5.4 161,038 154,704 3.9 FL 453,631 483,590 -6.6 257,677 282,507 -9.6 GA 514,952 517,633 -0.5 147,517 148,126 -0.4 KY 484,057 495,153 -2.3 198,817 201,928 -1.6 MS 67,429 60,086 10.9 43,135 40,433 6.3 NC 477,990 478,489 -0.1 151,854 148,812 2.0 SC 206,399 210,272 -1.9 88,241 88,528 -0.3 TN 334,151 320,146 4.2 157,307 152,137 3.3 VA 241,204 233,691 3.1 86,886 85,081 2.1 WV 516,084 500,381 3.0 230,977 222,437 3.7 Total 3,743,725 3,683,968 1.6 1,523,449 1,497,864 1.7

4.1.a.1.b. Other Industrial Point Sources (NonEGUs) For nonEGU sources, the same inventory is used for both the actual and typical base year emissions inventories. The nonEGU category uses annual emissions as reported for the NEI for the year 2002. West Virginia collected only f ilterable PM emissions during the 2002 inventory cycle. Therefore, MACTEC augmented the filterable PM data submitted by West Virginia as well as for four other VISTAS member states to develop primary PM estimates. Detailed discussion of how PM data were augmented canbe found in the MACTEC emissions inventory report “ Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008 (Appendix N). MACTEC’s procedures for PM augmentation were based on EPA documentation found in Appendix C of Documentation for the Final 1999 National Emissi ons Inventory {Version 3} for Criteria Air Pollutants and Ammonia – Point Sources , January 31, 2004, contained in Appendix D. Through a request from VISTAS, Midwest Re gional Planning Organization (MRPO), provided nonEGU point source data for their states, incl uding Washington County, Ohio. MRPO data for Washington County did not contain SO 2 emissions values. Therefore, SO 2 from nonEGUs was not modeled in any processing for 2002. Emi ssions data were provided other pollutants, however. MRPO does not have a public site available. Their website indicates to call MarkWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 50 Janssen directly for access. The data were downloaded from http://www.ladco.org/oldsite/tech/emis/index.html

. 4.1.a.2. Stationary Area Sources Stationary area sources include sources whose individual emissions are relatively small but due to their large number, their collective emissions can be significant (e.g., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by multiplying an emission factor by some known indicator of collective activity, such as fuel usage, number of households or population. Stationary area source emissions are estimated at the county level. West Virginia used 2002 NEI data developed by th e EPA as the starting point for the area source inventory. 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 MACTEC. MACTEC’s general assumption was that unless determined otherwise, the CERR submittals represented full and complete inventories. Ammonia, several fugitive dust categories, and wildfires were special cases. Carnegie Mellon University (CMU) updated its NH 3 Model after EPA published the 2002 NEI. MACTEC used Version 3.6 of the CMU NH 3 model to calculate NH 3 emissions for all the VISTAS states. Prior to inclusion of the CMU model estimates, MACTEC removed any ammonia records for agricultural livestock or fertilizer emissions from the VISTAS 2002 initial base year inventory. The CMU model version 3.6 was used in large part because it had recently been updated to include the latest (2002) Census of Agriculture animal population statistics. The CMU model can be found at http://www.cmu.edu/ammonia/

. PM 2.5 emissions from several fugitive dust source categories were also updated. During the same time frame that the final VISTAS inventory was being prepared, the Western Regional Air Partnership (WRAP) and EPA had completed their investigation of the overestimation of the PM 2.5 / PM 10 ratio in several fugitive dust categories and EPA was in the process of making revisions to AP-42. Based on data received fr om EPA, VISTAS decided to revise the PM 2.5 emissions from construction, paved roads and unpaved road sources. PM 2.5 emissions were multiplied by 0.67, 0.6, and 0.67 for construction, paved roads and unpaved roads respectively to produce the values found in in the final VISTAS inventory. No changes were made to PM 10 , only to PM 2.5 . For wildfires, a typical year inventory was generated to avoid anomalies in wildfire activity in 2002 compared to longer term averages. Developmen t 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, which included representatives for the Federal Land Managers (FLMs) and EPA, instructed MACTEC to update fire 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.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 51 Typical year fire emissions were developed starting from the actual fire acreage data and emissions calculated for each VISTAS state. State and federal land managers provided West Virginia wildfire and prescribed burning data to VISTAS contractors. MACTEC performed several other global changes on the area source inventory. They removed stage II emissions from the area source sector and included them in the mobile sector of the inventory. MACTEC also adjusted fugitive dust PM 2.5 emissions and added portable fuel container emissions. More detailed discussion of how the VISTAS contractor processed the area source sector inventory can be found in the MACTEC emissions inventory report

“ Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008 (Appendix N). The actual base year inventory serves as the typical base year inventory for all area source categories, except for wildfires. MRPO area source data for Washington County, Ohio were obtained through communication request from VISTAS. MRPO does not have a public site available. Their website indicates to call Mark Janssen directly for access. The data were downloaded from http://www.ladco.org/oldsite/tech/emis/index.html

. 4.1.b. Mobile Sources The mobile source sector consists of two sub-sectors, usually reported as two distinct sectors: nonroad and onroad. In general, mobile sources c onsist of fuel-burning sources that do not have a fixed location. The sub-sectors are discussed in more detail below.
4.1.b.1. NonRoad Mobile Sources Nonroad (also known as “off-road”) mobile sources include equipment that can move but do not use the roadway system. Examples include construction equipment, aircraft, railroad locomotives, lawn and garden equipment. EPA’s NONROAD model was used to develop emission estimates for the majority of the nonroad mobile source categories. The NONROAD model takes into consideration regulations affecting emissions from these source categories and allows the user to change default assumptions by entering data that reflect local conditions. For the three source categories not included in the NONROAD model, aircraft engines, railroad locomotives and commercial marine vessels, traditional methods of estimating emissions were used. Historically, nonroad sector emissions were calculated using methods similar to those of the area source sector and the two sectors share the same file format for reporting to the NEI. The same nonroad inventory was used for both the actual and typical base year emissions inventories. WV DEP staff review of the Preliminary NEI show ed 0.0 tons of emissions from the coal mining equipment category in several counties where coal mining is prevalent. Further investigation revealed that the reference source for nonroad activity data, County Business Patterns, often reports numeric ranges rather than as distinct values. When the NONROAD model encounters ranges, it interprets them as non-numeric and processes them as 0.0 values. In addition to the tonnage of underground coal production by county, West Virginia also produced alternative equipment allocation files indicating the number of employees in air transportation by county, the number of golf courses and country clubs by county, the number of wholesale establishmentsWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 52 by county, the number of employees in logging operations by county, the number of employees in landscaping services by county, the number of employees in manufacturing operations by county, the number of employees in oil and gas drilling and extraction operations by county, and the number of recreational vehicle parks and campgrounds by county. These updated allocation files were provided to EPA. The release of the final NONROAD2005c model contained the corrected allocation files and addressed WV DEP’s concerns. As a result, the entire VISTAS region was modeled using the default allocation files provided with NONROAD2005c. EPA’s update of the nonroad model occurred while the VISTAS nonroad inventory was being developed. Updating to the NONROAD2005c version necessitated extensive revisions to the NONROAD input files. These revisions are docum ented in the MACTEC emissions inventory report “Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008 (Appendix N). NONROAD2005 runs were also performed by MACTEC for Washington County, Ohio using the same procedure as used for the VISTAS region. An initial 2002 base year emissions inventory for aircraft, locomotives, and commercial marine vessels (CMV) was prepared for VISTAS in early 2004 based on data contained in the 2002 NEI. 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 NEI data also incorrectly reported residual oil usage for the CMV category, which was also corrected. Revisions to the initial 2002 emissions inventory were implemented to ensure that the latest state and local data were incorporated as well as to correct an overestimation of PM emissions from aircraft. Revisions were actually implemented in two stages. An initial set of revisions was implemented in the fall of 2004. These were followed by a second set of revisions in 2006, which constitute the final VISTAS inventory. Briefly, MACTEC determined that PM emissions from airports were significantly overestimated. The contractor undertook extensive procedures to identify and correct the error in the data. These procedures are discussed in the MACTEC emissions inventory report “Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008 (Appendix N). Nonroad sector emissions of the categorie s calculated outside of the NONROAD Model for Washington County, Ohio were developed for the MRPO by one of their contractors, Environ. The document, “LADCO NONROAD EMISSION INVENTORY PROJECT FOR LOCOMOTIVE, COMMERCIAL MARINE, AND RECREATIONAL MARINE EMISSION SOURCES, ” can be found at http://www.ladco.org/ladco.php?page=141 . MRPO aircraft emissions data for Washington County, Ohio were obtained thr ough communication request from VISTAS. MRPO does not have a public site available. Their website indicates to call Mark Janssen directly for access. The data were downloaded from http://www.ladco.org/oldsite/tech/emis/index.html

.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 53 4.1.b.2. Onroad Mobile Sources For onroad vehicles, the VISTAS emissions invent ory contractor did not run MOBILE6 directly. Instead data files consistent with Mobile Emissions Estimation Model Version 6 (MOBILE6) were developed and provided to the VISTAS m odeling contractor. These input data files were then run during the VISTAS modeling to generate onroad mobile source emissions using episodic and meteorological specific conditions configured in the sparse matrix operator kernel emissions modeling system (SMOKE) emissions processor. The MOBILE6 module of the SMOKE model was used to develop the onroad mobile source emissions estimates for CO, NO x , NH 3 , SO 2 , PM, and VOC emissions. 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. Onroad mobile source emissions were produced using selected weeks (seven days) of each month and using these days as representative of the entire month. This selection criterion allows for the representation of day-of-the-week variability in the onroad motor vehicles, and models a representation of the meteorological variability in each month. The modeled weeks were selected from mid-month, avoiding inclusion of major holidays.
MRPO onroad mobile emissions data for Wa shington County, Ohio were obtained through communication request from VISTAS. MRPO does not have a public site available. Their website indicates to call Mark Janssen directly for access. The data were downloaded from http://www.ladco.org/oldsite/tech/emis/index.html

. 4.1.c. Biogenic Emissions Sources Natural source emissions can make a significant contribution to total VOC and NO xemissions. Estimating emissions of VOC and NO xfrom natural sources is an essential part of preparing an inventory of ozone precursors. In the past, the impacts of biogenic VOCs were not considered when ozone control strategies to limit emissions of either NO xor VOCs were developed. However, the importance of biogenic VOC emissions in an ozone inventory is appa rent when the biogenic VOC emission estimates are compared to the anthropogenic VOC emission estimates. Calendar year 2002 VOC emission estimates for West Virginia statewide were more than 357,000 tons from biogenic sources compared with less than 16,000 tons from point sources and about 132,000 tons from all anthropogenic sources combined. The biogenic sector accounted for 73% of all VOC emissions in West Virginia. Isoprene, one of the major constituents of biogenic emissions, is very photoreactive, making biogenic emissions an even more important source of VOCs. Because of the interaction between NO xand VOCs in terms of atmospheric ozone levels, biogenic emissions should be included in any invent ory which may be used to predict or monitor atmospheric ozone levels. Inclusion of biogenic emissions is essential for photochemical air quality modeling.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 54 Biogenic emissions are included here to provide context with anthropogenic emissions. Biogenic emissions are assumed to stay constant so projections were not made. Pleasants and Wood County emissions are summarized below.

Table 4.1.c-1. West Virginia Biogenic Emissions State Name County Name FIPS NO (tons/year) VOC (tons/year) West Virginia Pleasants 073 32 4,165 West Virginia Wood 107 67 6,087 West Virginia biogenic data were dow nloaded from EPA’s CHIEF website at http://www.epa.gov/ttn/chief/net/2002inventory.html#biogenic . The biogenic emissions data were copied from the “BEIS3.12 By county” worksheet.

4.1.d. Summary 2002 Baseline Emissions Inventory for West Virginia Table 4.1.d-1 is a summary of the 2002 baseline emissions inventory for the West Virginia portion of the Parkersburg nonattainment area. Table 4.1.d-2 summarizes the 2002 baseline emissions inventory for the Washington C ounty, Ohio portion. Table 4.1.d-3 summarizes the combined inventories for sources from entire Parkersburg nonattainment area. The complete inventory is contained in Appendix C. The em issions summaries for the other VISTAS/ASIP states and Ohio can also be found in Appendix C. See Tables 4.2.d-1 through 4.2.d-3 for the 2009 baseline emissions inventory. The complete inventory is contained in Appendix C.

Table 4.1.d-1 2002 Base Year Inventory - West Virginia Portion of Nonattainment Area Sector NH 3 (tons) NO x (tons) PM 10 (tons) PM 2.5 (tons) SO 2 (tons) VOC (tons) Point 52 21,835 1,769 1,068 70,293 1,162 Area 117 749 5,005 1,139 1,311 3,859 Nonroad 1 2,506 130 120 134 1,138 Onroad 86 2,667 62 44 111 1,956 Biogenic N/A 99 N/A N/A N/A 10,252

Total 255 27,856 6,966 2,372 71,849 18,368WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 55 Table 4.1.d-2. 2002 Base Year Inventory - Ohio Portion of Nonattainment Area Sector NH 3 (tons) NO x (tons) PM 10 (tons) PM 2.5 (tons) SO 2 (tons) VOC (tons) Point 608 33,588 1,612 681 0 1 767 Area 656 188 1,261 317 40 846 Nonroad 1 1,399 64 60 112 502 Onroad 73 2,424 57 42 112 1,577 Biogenic N/A 90 N/A N/A N/A 5,926

Total 1,338 37,690 2,994 1,100 264 1 9,618

1 Note: MRPO did not provide to VISTAS SO 2 point source emissions for Washington County, Ohio. Table 4.1.d-3. Summary of 2002 Baseline Emissions Inventory for Parkersburg Nonattainment Area Sector NH 3 (tons) NO x (tons) PM 10 (tons) PM 2.5 (tons) SO 2 (tons) VOC (tons) Point 660 55,424 3,381 1,749 70,293 1,929 Area 773 937 6,266 1,455 1,351 4,705 Nonroad 2 3,906 194 181 246 1,641 Onroad 159 5,091 119 87 222 3,533 Biogenic N/A 189 N/A N/A N/A 16,178 TOTAL 1,593 65,546 9,960 3,472 72,113 27,985

4.2. Projected 2009 Emissions Inventories The inventory for 2009 accounts for post-2002 em ission 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 using current control information in West Virginia, and growing the 2002 inventory using th e Integrated Planning Model (IPM) for EGUs, EGAS6 for nonEGUs, and NONROAD2005 and MOBILE6.2 for nonroad and onroad estimates.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 56 4.2.a. Stationary Sources As noted previously, the stationary source sector consists of two main sub-sectors: point and nonpoint sources. In addition, the point source category is further divided into two subcategories: EGUs and all other point source types, or nonE GUs. Nonpoint sources have historically been known as area sources.

4.2.a.1. Stationary Point Sources For EGUs, VISTAS relied primarily on the Integrated Planning Model (IPM) to project future generation as well as to calculate the impact of future year control programs. For nonEGUs, VISTAS used growth and control data consistent with the data used in EPA’s Clean Air Interstate Rule (CAIR) analyses, and supplemented these data with available S/L agency knowledge of planned emission controls or ot her changes at specific nonEGUs and updated fuel use forecast data for the U.S. Department of Energy. For both sectors, VISTAS generated 2009 and 2018 inventories for a combined on-the-books (OTB) and on-the-way (OTW) control scenario. The OTB/OTW control scenario accounts for post-2002 emission reductions from promulgated and proposed nonEGU federal control pr ograms as of July 1, 2004; the final CAIR ; and state, local, and site-specific control programs as of October 1, 2007. Section 4.2.a.1.a discusses the EGU projection inventory deve lopment, while Section 4.2.a.1.b discusses the nonEGU projection inventory development.

4.2.a.1.a. Electric Generating Units (EGUs) VISTAS and the Midwest Regional Planning Organization (MRPO) jointly agreed to work together to develop future year emissions based on IPM modeling. In August, 2004 VISTAS and MRPO contracted with ICF Resources, L.L.C., to run IPM to provide utility forecasts for 2009 and 2018 for the electric generating sector of the contiguous United States. IPM is EPA’s preferred model for estimating future year emissions from EGUs. 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 least-cost means of meeting electric generation and capacity requirements while complying with specified constraints including air pollution regulations, transmission bottlenecks, and plant specific operational constraints. The versatility of IPM allows users to specify which constraints to exercise, and to populate IPM with their own data sets. The EPA also relies on IPM for regulatory purposes. The underlying database in this modeling is EPA’s National Electric Energy Data System (NEEDS) released with the CAIR Notice of Data Availability (NODA). The NEEDS database contains the existing and planned/committed unit data in EPA modeling applications of IPM. NEEDS includes basic geographic, operating, air emissions, and other data on these generating units. VISTAS states and stakeholders provided changes for:WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 57 • NO x post-combustion control on existing units • SO 2 scrubbers on existing units • SO 2 emission limitations • PM controls on existing units • Summer net dependable capacity • Heat rate for existing units • SO 2 and NO x control plans based on state rules or enforcement settlements Although “typical year” projections for 2009 emi ssions from EGUs were considered, VISTAS relied primarily on IPM to project future generation as well as to calculate the impact of future emission control programs. The years 2009 and 2018 were explicitly modeled. The IPM modeling runs took into consideration both CAIR implementation as well as other stateand local initiatives and incorporated comments provided by MRPO, CENRAP, and MANEVU which included any enforceable actions, such as the Illinois Power NSR settlement. WV DEP provided the latest information regarding when and where new SO 2 and NO xcontrols are planned to come online. WV DEP also reviewed the IPM results to verify that existing controls and emission rates were properly reflected in the IPM runs. As directed by WV DEP, adjustments to the IPM results were made to specific units with new information that became available as part of the permitting process or other contact with the industry that indicated which units will install controls as a result of CAIR and when these new controls will come online. Review of the initial IPM data indicated that control efficiencies for the Pleasants Units 1 and 2 were set at 79.9% while the true control efficiency is 95%. MACTEC were requested to correct the control efficiencies as well as the controlled emission estimates. The requested changes were made and updates are documented in the MACTEC emissions inventory report “ Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008 (Appendix N). To support PM 2.5 CAMx modeling of the future year 2009, VISTAS’ other primary contractor, Alpine Geophysics, obtained and processed anemission inventory for the 5 MRPO states (Illinois, Indiana, Michigan, Wisconsin, and Ohio). The technical steps that were made as part of the conversion of the MRPO BaseM EGU files into IDA format for PM 2.5 CAMx modeling of the future year 2009 are documented in Appendix G of the MACTEC emissions inventory report referenced above. As noted in section 4.1.a.1.a, MRPO did not provide SO 2 emissions for Washington County, Ohio with their 2002 data. SO 2 emissions were also missing in their 2009 data set. However, IPM-generated data, which contained SO 2 emissions, were used for the 2009 modeling. MRPO provided 2009 emissions data for PM 10 , PM 2.5 , NO x , and VOC.
4.2.a.1.b. Other Industrial Point Sources (NonEGUs) For nonEGUs, MACTEC started with the final 2002 base year inventory and applied updated growth and control data consistent with methods used in EPA’s CAIR analysis. The contractors obtained, reviewed, and applied the most current growth factors developed by EPA, based on forecasts from an updated Regional Economic Models, Inc. (REMI) model (version 5.5) and the latest Annual Energy Outlook published by the Department of Energy (DOE). For programsWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 58 “on-the-books” and “on-the-way,” control assump tions were obtained, reviewed, and applied to the data. VISTAS provided initial future year emissions projections as Excel spreadsheets to WV DEP as well as to the other member state and local agencies for review and comment. Information from the agencies regarding sources that have shut down after 2002 was incorporated and the emissions were set to zero in the projection inventories. There were no shut-downs or new start-ups identified for the 2009 projected inventory in the Parkersburg area. Likewise, information regarding constructi on of new sources was requested and where applicable, included in the 2009 nonEGU inventory. No new sources were identified in the Parkersburg nonattainment area. Washington C ounty, Ohio data were provided to VISTAS contractors for the inventory. To support PM 2.5 CAMx modeling of the future year 2009, VISTAS’ other primary contractor, Alpine Geophysics, obtained and processed into SMOKE format an emission inventory for the 5 MRPO states (Illinois, Indiana, Michigan, Wi sconsin, and Ohio). Appendix G of the MACTEC emissions inventory report “ Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008 (Appendix N) details the technical steps that were made as part of the conversion of the MRPO BaseM nonEGU files into IDA format for PM 2.5 CAMx modeling of the future year 2009. As noted in section 4.1.a.1.b, MRPO did not provide SO 2 emissions to VISTAS for Washington County, Ohio with their 2002 data. SO 2 emissions were also missing in their 2009 data set. However, IPM-generated data, which contained SO 2 emissions, were used for the 2009 modeling. Although nonEGU SO 2 data were not modeled, Muskingum River and Gorsuch power plants accounted for about 96% of SO 2 emissions in Washington County. MRPO provided 2009 emissions data for PM 10 , PM 2.5 , NO x , and VOC.
4.2.a.2. Stationary Area Sources For area sources, MACTEC used two approaches to develop the 2009 and 2018 projection inventories. For nonagricultural sources the fi nal version of the VISTAS 2002 inventory was used. The contractors then back calculated uncontrolled emissions from the 2002 base year inventory based on existing controls reported in the 2002 base year inventory. Controls (including control efficiency, rule effectiveness and rule penetration) provided by the states or originally developed for use in estimating projected emissions for EPA’s Heavy Duty Diesel (HDD) rule-making and used in the CAIR projections were then used to calculate controlled emissions. Growth factors supplied from the states or EPA’ s CAIR emission projections were then applied to project the controlled emissions to 2009. In some cases EGAS Version 5 growth factors were used if no growth factor was available from either the states or the CAIR growth factor files. In a few cases, additional growth factors had to be added for source categories that had not initially been included in the original inventory. Thes e growth factors were obtained from EGAS 5.0. Finally updates to growth factors from EGAS 5.0 were made for fuel fired emission sources. The updated growth factors reflected the most recent data from the Department of Energy’s Annual Energy Outlook (AEO). These data were used to reflect changes in energy efficiency resultingWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 59 from new or updated fuel firing technologies. Ar ea source controls were estimated using known state-level Stage I controls on gasoline dispensing facilities and open burning estimates. The general approach used to calculate projected emissions for agricultural area sources (predominantly NH 3 emission sources) was to use the 2002 base year inventory, which was in turn based on the CMU (Carnegie Mellon University) Ammonia Model version 3.6. Since the base year emissions were uncontrolled, and no future controls for these sources were identified, MACTEC projected the agricultural emissions using state-specific growth if available, otherwise EPA’s Interstate Air Quality Transport Rule (IAQTR)/Ammonia inventory was used to develop the growth factors used to project the revi sed 2002 base year inventory to 2009 or 2018. Since the IAQTR inventory was only used to construct growth factors rather than using the emissions directly, no updated growth factors were pr epared from the CAIR inventory values. Fire emissions were recognized to be inadequate ly represented in previous inventory efforts. Early in the fire inventory development process, representatives from the Fish and Wildlife Service (FWS) and the national Forest Service (FS) were invited to participate. As a result of their collaboration “typical” values were developed to reflect future year fire emissions. More detailed discussion of procedures used by VISTAS to prepare the 2009 area source inventory can be found in the MACTEC emissions inventory report “Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008 (Appendix N). MRPO area source data for Washington County, Ohio were obtained through communication request from VISTAS. MRPO does not have a public site available. Their website indicates to call Mark Janssen directly for access. The data were downloaded from http://www.ladco.org/oldsite/tech/emis/index.html

. 4.2.b. Mobile Sources The general approach for assembling mobile source data was to use as much existing data from preliminary projections as possible and to supplement them with stakeholder input. Separate “on-the-books” and “on-the-way” emission forecasts were developed initially for the mobile source sector. For the final inventories, “on-th e-books” and “on-the-way” were defined with the same strategies and therefore, only a single pr ojection scenario was developed for each forecast year. To ensure consistency across evaluation years, the 2009 and 2018 base case inventories were developed, to the extent practical, using methodologies the same as those employed in developing the onroad portion of the revise d 2002 VISTAS base year inventory. Any modifications to the 2002 inventory methods we re developed in consultation with VISTAS state/local stakeholders. Generally, modifications were only made to properly account for actual changes expected in the intervening period (i.e., between 2002 and 2009 and between 2002 and 2018), but the underlying inventory developmen t methodology was identical, except to the extent requested by VISTAS. Although early versions of the inventory captured Stage II refueling in the area source inventory, Stage II refueling was moved to the on- and nonroad sectors in the final version. West Virginia does not require mobile source controls other thanWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 60 Stage I vapor recovery in five (5) counties: Kanawha, Putnam, Cabell, Wayne and Wood. The future year emissions for the nonroad mobile sources included in the EPA NONROAD model were estimated by running the model for each future year. The future year onroad mobile source inventories were developed by running the MOBILE6 model.
4.2.b.1. Nonroad Mobile Sources The paragraphs that follow briefly describe the projection process used to develop 2009 and 2018 nonroad projection estimates for sources found in the NONROAD model and those sources estimated outside of the model (locomotives, airplanes and commercial marine vessels). MACTEC used NONROAD2005 to develop 2009 and 2018 emission estimates for the source categories covered by the model. All base year revisions were inherently incorporated into the associated projection year revisions. Other specific updates for the projection year s for NONROAD model sources included revising the emission inventory year in the model (as well as various output file naming commands) to be reflective of the projection year. The fuel sulfur content for gasoline and diesel powered equipment was revised. MACTEC also implemented a limited number of local control program changes (national control program changes are handled internally within the NONROAD model, so explicit input file changes are not required). The NONROAD2005 model is capable of handling separate diesel fuel sulfur inputs fo r land-based and marine-based nonroad equipment in a single model execution. Therefore, the differential diesel fuel sulfur values were assembled into a single NONROAD input file. Other than diesel fuel sulfur content and the year of the projections, there are no differences in the methodology used to estimate emissions from NONROAD model sources. All equipment population gr owth and fleet turnover impacts are also handled internally within the NONROAD model, so that explicit input file changes are not required. For the non-NONROAD model source categories, th e 2002 base year emissions inventory for aircraft, locomotives, and commercial marine vessels (CMV) was used to develop emission projections for 2009 and 2018. The base year inventory used to prepare the emission forecasts was the latest available at the time of forecast development. All base year revisions described earlier in this document were inherently incorporated into the associated projection year revisions. The methodology used to develop the 2009 emissions forecasts for aircraft, locomotives, and CMV incorporated “On the Books” and “On the Way” controls. Briefly, the methodology relied on growth and control factors developed from i nventories used in support of EPA rule-makings, and broadly consists of the following steps: (a) Begin with the 2002 base year emission estimates for aircraft, locomotive, and CMV as described above (at the state-county-SCC-pollutant level of detail). (b) Detailed inventory data (both before and after controls) for these same emission sources for 1996, 2010, 2015, and 2020 were obtained from the EPA’S Clean Air Interstate Rule (CAIR) Technical Support Document. Using these data, combined growth and control factors for the period 2002-2009 and 2002-2018 were estimated using straight line interpolationWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 61between 1996 and 2010 (for 2009) and 2015 and 2020 (for 2018). This was done at the statecounty-SCC-pollutant level of detail. (c) The EPA growth and control data were matched against the 2002 VISTAS base year data using state-county-SCC-pollutant as the matc h key. One-to-one matches were not always available so MACTEC developed additional pro cessing steps to develop growth and control factors. The final default growth and control factors were then applied to the 2002 inventory. Projection inventories for 2009 and 2018 for sources inthe states of Illinois, Indiana, and Ohio calculated using the NONROAD model were produced using a methodology identical to that employed to develop the 2002 base year inventory for those states. This method consists of the extraction of a complete set of county-level input data applicable to each of the three states from EPA’s NMIM model. This includes appropriate consideration of all non-default NMIM input files generated by the Midwest Regional Planning Organization as documented earlier in the discussion of the 2002 base year inventory. These input data were then assembled into appropriate input files for the Final NONROAD2005 model and emission estimates were produced using the same procedure employed for the VISTAS region. Non-Nonroad sector emissions for Washington County, Ohio were developed for the MRPO by one of their contractors, Environ. The document, “LADCO NONROAD EMISSION INVENTORY PROJECT FOR LOCOMOTIVE, COMMERCIAL MARINE, AND RECREATIONAL MARINE EMISSION SOURCES, ” can be found at http://www.ladco.org/ladco.php?page=141

. 4.2.b.2. Onroad Mobile Sources MACTEC crafted the 2009 initial inventory for onroad mobile sources using methods identical to those employed for the 2018 preliminary invent ories coupled with any revisions provided by the states during the review of the revised 2002 base year and the 2018 preliminary inventories. Therefore, as was the case for 2018, MACTEC obtaine d from the states any input data revisions, methodological revisions, and local control progr am specifications (to the extent that they differed from 2002/2018). The methodology used to develop the initial 2009 onroad input files was based on forecasting the previously developed 2002 base year input files and is identical to that used for the 2018 methodology with a few exceptions: • The evaluation year was updated to 2009; • the diesel fuel sulfur was revised from 500ppm in the 2002 inputs to 29 ppm for 2009 and 11 ppm for 2018; • diesel sales fractions were updated identically to 2018 except that the diesel sales fractions for 2003-2009 were set at the same value as those for 2002 (rather than 2003- 2018); • VMT mix fractions were updated to 2009 using an identical method to that described for 2018; • All other input data were retained at 2002 values, except as otherwise instructed by individual states (see below). This includes all control program descriptions (I/M, ATP, Stage II, etc.), all other fuel qualities (RVP, oxy content, etc.), all other vehicle descriptive data (registration age distributions, etc.), and all scenario descriptive data.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 62 MACTEC also considered expected VMT growth, modeling temperature, vehicle registration mix, vehicle speed by roadway type, VMT mixes (V MT by vehicle type), diesel sales fractions, state and local fuel standards, vehicle standards, and other local control options. The contractors obtained from the states any input data revi sions, methodological revisions, and local control program specifications. For the most part, 2002 assumptions were carried forward for the 2009 MOBILE6 runs. The basic methodology used to generate the 2009 and 2018 VMT for use in estimating onroad mobile source emissions was that all estimates start from the final VMT estimates used for the 2002 revised base year inventory. Initial 2009 and 2018 VMT estimates were based on linear growth rates for each state, county, and vehicle type as derived from the VMT data assembled by EPA for their most recent HDD rule-making. For states that provided no independent forecast data, which included West Virginia, the estimates were also the final estimates. Diesel sales fractions were updated identically to 2018 except that the diesel sales fractions for 2003-2009 were set at the same value as those for 2002 (rather than 2003-2018). VMT mix fractions were updated to 2009 using an identical method to that used for 2018. All other input data were retained at 2002 values, except as otherwise instructed by individual states. West Virginia provided revised VMT input data. For the development of the final VISTAS 2009 and 2018 inventories and input files, VISTAS states reviewed earlier version inputs, and provided the following corrections, updates and supplemental data: Adding Stage II refueling emissions calculations to the SMOKE processing; revised the HDD compliance; and revised Diesel sulfur values in 2009 to 43 ppm. Diesel sulfur values for 2018 remained at 11 ppm. More detailed discussion of procedures and assumptions used for running the MOBILE6 and NONROAD model are documented in the MACTEC emissions inventory report

“ Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS ” dated March 14, 2008 (Appendix N). MRPO onroad source data for Washington County, Ohio were obtained through communication request from VISTAS. MRPO does not have a public site available. Their website indicates to call Mark Janssen directly for access. The data were downloaded from http://www.ladco.org/oldsite/tech/emis/index.html

.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 63 4.2.c. Biogenic Sources It was assumed that 2002 biogenic emissions w ould be unchanged in 2009. Therefore, no growth factors were applied. 4.2.d. Summary of Final Inventories for 2009 Table 4.2.d-1 is a summary of the 2009 emissions inventory for the West Virginia portion of the Parkersburg nonattainment area. Table 4.2.d-2 is a summary of the 2009 emissions inventory for the Ohio portion of the Parkersburg nonattainment area. Table 4.2.d-3 is a summary of the 2009 emissions inventory for the entire Parkersburg nonattainment area. See Tables 4.1.d-1 through 4.1.d-3 for the 2002 baseline emissions inventory. The complete inventory and discussion of the methodology is contained in Appendices C, D and N. Table 4.2.d-1. 2009 Attainment Year Inventory - West Virginia Portion of Nonattainment Area Sector NH 3 (tons) NO x (tons) PM 10 (tons) PM 2.5 (tons) SO 2 (tons) VOC (tons) Point 60 11,172 1,913 1,139 39,204 1,053 Area 115 784 5,057 1,133 1,404 3,538 Nonroad 1 2,318 118 109 22 1,028 Onroad 104 1,664 50 32 13 1,216 Biogenic NA 99 NA NA NA 10,252

Total 280 16,035 7,138 2,413 40,643 17,086
Table 4.2.d-2. 2009 Attainment Year Inventory - Ohio Portion of Nonattainment Area

Sector NH 3 (tons) NO x (tons) PM 10 (tons) PM 2.5 (tons) SO 2 (tons) VOC (tons) Point 752 6,295 3,786 2,594 13,909 1 836 Area 749 209 1,264 319 64 976 Nonroad 1 1,094 52 49 20 429 Onroad 79 1,405 43 28 11 876 Biogenic NA 90 NA NA NA 5,926

Total 1,580 9,093 5,146 2,990 14,004 9,042
1 Note: MRPO did not provide to VISTAS SO 2 point source 2009 emissions for Washington County, Ohio. However, 2009 SO 2 emissions data were developed from IPM so th at subsequent modeling accounted for EGUS. Although SO 2 emissions from nonEGUs were not modeled, EGU SO 2 accounted for 96% of the Washington County, Ohio point source inventory.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 64 Table 4.2.d-3. 2009 Attainment Year Inventory - Parkersburg Nonattainment Area Sector NH 3 (tons) NO x (tons) PM 10 (tons) PM 2.5 (tons) SO 2 (tons) VOC (tons) Point 812 17,466 5,699 3,733 53,113 1,889 Area 864 993 6,322 1,453 1,468 4,514 Nonroad 2 3,412 170 158 42 1,457 Onroad 183 3,068 93 60 24 2,091 Biogenic NA 189 NA NA NA 16,178

Total 1,861 25,128 12,284 5,403 54,647 26,129 4.3. Summary 2002 and 2009 State-wide Emissions Inventories for West Virginia State-wide emissions inventory summaries for 2002 and 2009 are shown below to provide context. Table 4.3-a contains a summary of the 2002 West Virginia state-wide emissions inventory. Table 4.3-b contains a summary of the 2009 West Virginia state-wide emissions inventory.

Table 4.3-a. 2002 State-wide Emissions Inventory Summary for West Virginia Sector NH 3 (tons) NO x (tons) PM 10 (tons) PM 2.5 (tons) SO 2 (tons) VOC (tons)
Point 453 277,589 22,076 15,523 570,153 15,775 Area 9,963 12,687 115,346 21,049 11,667 60,443 Nonroad 9 33,239 1,850 1,728 2,112 18,566 Onroad 2,036 63,525 1,481 1,068 2,635 45,284 Biogenic NA 2,776 NA NA NA 357,850 TOTAL 12,461 389,816 140,755 39,369 586,568 497,918
Table 4.3.b. 2009 State-wide Emissions Inventory Summary for West Virginia Sector NH 3 (tons) NO x (tons) PM 10 (tons) PM 2.5 (tons) SO 2 (tons) VOC (tons)
Point 644 124,350 17,539 10,578 324,550 14,404 Area 10,625 14,326 118,106 23,680 12,284 57,346 Nonroad 11 30,133 1,640 1,528 359 18,069 Onroad 2,292 38,392 1,145 734 299 26,584Table 4.3.b. 2009 State-wide Emissions Inventory Summary for West Virginia

WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 65

Biogenic N/A 2,776 N/A N/A N/A 357,850 TOTAL 13,572 209,978 138,429 36,520 337,491 474,254 4.4. Summary 2009 Region-wide Emissions Inventories for the VISTAS States The following bar charts show expected decreases in emissions of SO 2 and NO xacross the VISTAS states from 2002 through 2018. Note that for SO 2 emissions in particular, which are the largest contributor to haze and to PM 2.5 , emissions from electric generating facilities are expected to decrease dramatically (70%) between 2002 and 2018, with SO 2 reductions of 83% from EGUs in West Virginia. However, even after implementation of CAIR, EGU emissions are projected to remain the largest contributor to haze, comprising more than half of the remaining SO 2 emissions in every state.

Figure 4.4-a. Annual SO 2 emissions for 2002, 2009, and 2018 in the VISTAS states.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 66 Figure 4.4-b. Annual NO xemissions in 2002, 2009 and 2018 in the VISTAS states.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 67 5.0. MODEL PERFORMANCE EVALUATION The focus of the model performance evaluation is on PM 2.5 and its component species in the urban and PM 2.5 nonattainment areas (NAAs). However, the evaluation of the CMAQ 2002 Base G2 base case simulation was also performed for PM species and visibility primarily at Class I areas as part of the VISTAS regional haze assessment. The VISTAS model evaluation focused on the performance of PM component species and visibility at more rural (e.g., IMPROVE) monitors. ASIP also performed an urban ozone evaluation performance as part of the development of the southeastern states 8-hour ozone SIPs that were due in June 2007. The VISTAS regional PM components and visibility and ASIP urban ozone model performance evaluations are directly relevant to the ASIP PM 2.5 evaluation as they share many of the same precursor and product species and atmospheric processes that produce elevated PM 2.5 levels in the urban and NAAs. The Model Performance Evaluation is contained in Appendix N.2 – Technical Support Document for Association for Southeastern Integrated Planning (ASIP) Emissions and Air Quality Modeling to Support PM 2.5State Implementation Plans (ASIP TSD) and provides details on the ASIP urban and NAAs PM 2.5 mass and PM 2.5 component species model performance evaluation for each ASIP state, the results for West Virginia are summarized in Section 5.3 below. The Model Performance Evaluation also provides a qualitative evaluation of the CMAQ base case for PM 2.5 components and total mass

. [Note: the Model Performance Evaluation is contained in Chapter 3 of the ASIP TSD, an ENVIRON Report by Ralph Morris, which is contained in Appendix N.2 In the VISTAS modeling performance evaluation, the CMAQ results were compared with observational data from the I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE), S peciated T rends N etwork (STN), C lean A ir S tatus T rends Net work (CASTNet), F ederal R eference M ethod (FRM) PM 2.5 mass, N ational A cid D eposition P rogram (NADP) and S outh E ast A erosol R esearch and CH aracterization (SEARCH) study monitoring networks. The VISTAS evaluation focuses primarily on the operational model evaluation of the air quality model’s performance with respect to individual components of fine particulate matter (PM 2.5 ), as good model performance of the PM component species will dictate good model performance of total or reconstituted fine particulate matter from which visibility is based. The FRM network only collects total PM 2.5 mass and so is not as relevant for judging how well the model is predicting the components of light extinction so was not stressed in the VISTAS evaluation, however it is an important component of the ASIP PM 2.5 evaluation. VISTAS also performed a diagnostic evaluation analyzing the ability of the model to reproduce gaseous PM precursor (e.g., SO 2 and NO x ) and product (e.g., HNO 3 ) species. The ASIP model performance evaluation focused on ozone and PM 2.5 performance in urban and nonattainment areas (NAAs). The ASIP ozone model performance evaluation can be found at: http://pah.cert.ucr.edu/vistas/vistas2/evaluation_results/2002ga2a.mpe/
The ASIP PM 2.5 evaluation is summarized in Sections 3.5 and 3.6 of the ASIP TSD with more details provided in Appendices B and C of the ASIP TSD. In addition, the full display of ASIP and VISTAS model evaluation products is available on the project website at: http://pah.cert.ucr.edu/vistas/vistas2/mpe2.shtmlWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 68 A ground-level model evaluation database for 2002 was compiled by the modeling team using several routine and research-grade databases. The focus of the VISTAS evaluation of the CMAQ model was on the PM components that can cause visibility impairment, whereas the ASIP model performance evaluation focus were on PM 2.5 mass and its components. The primary monitoring networks available to evaluate this component of the CMAQ are: • Interagency Monitoring of Protected Visual Environments (IMPROVE); • Clean Air Status and Trends Network (CASTNET); • Southeastern Aerosol Research and Characterization (SEARCH); • EPA Federal Reference Method (FRM) PM 2.5 and PM 10 Mass Networks (EPA-FRM); • EPA Speciation Trends Network (STN) of PM 2.5 species; and • National Acid Deposition Network (NADP). These PM monitoring networks may also provide ozone and other gas phase precursors and product species, and visibility measurements at some sites. There are also gas-phase criteria pollutant measures from the EPA’s Air Quality System (AQS) that were used for the urban ozone evaluation that is discussed elsewhere ( http://pah.cert.ucr.edu/vistas/vistas2/evaluation_results/2002ga2a.mpe/ ). Figure 5-1 shows the locations of the monitoring sites for the IMPROVE, STN, CASTNet, NADP and SEARCH monitoring networks use in the VISTAS and ASIP model evaluation.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 69 Figure 5-1. IMPROVE, STN, CASTNet, NADP and SEARCH monitoring sites located in the VISTAS/ASIP region and the VISTAS/ASIP 12 km grid.

5.1. Model Performance Goals, Criteria and Results To quantify model performance, several statistical measures were calculated and evaluated for all the IMPROVE, STN, CASTNet, SEARCH, FRM and NADP monitors within the VISTAS/ASIP region or VISTAS/ASIP 12 km domain, individually for each monitor and individually for each VISTAS/ASIP state. The st atistical measures selected were based on the recommendations outlined in section 18.4 of the USEPA’s Guidance On The Use Of Models And Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5, and Regional Haze

. In 2004, VISTAS established model performance goals and criteria for components of fine particle mass based on previous model performance for ozone and fine particles (see Table 5.1- 1). EPA modeling guidance for fine particulate matter at the time noted that PM models might not be able to achieve the same level of performance as ozone models. The VISTAS and ASIP model performance evaluation considered several statistical performance measures and displays.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 70 Fractional bias and fractional gross 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 (see Appendices H and N).
Table 5.1-1. VISTAS/ASIP 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 to PM model performance, considered good performance

# ±60% # ±75% Criteria for PM model performance, considered average performance. Exceeding this level of performance indicates fundamental concerns with the modeling system and triggers diagnostic evaluation. Several graphic displays of model performance were prepared including: 1. Scatter plots of predicted and observed concentrations and deposition by species, monitoring network and month; 2. Time series plots of predicted and observed concentrations and deposition by species, monitoring site and month; 3. Spatially averaged time series plots; 4. Time series plots of monthly fractional bi as 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 relativ ely 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, PM 2.5 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 and visibility impairment (e.g. sulfate and organi c 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). Details on the regional model performance evaluation for PM across the VISTAS/ASIP region km grid are provided in Chapter 3 and Appendix B of the VISTAS Technical Support Document that can be found at http://pah.cert.ucr.edu/vistas/vistas2/docs.shtml [also see Appendix N].WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 71 5.2. VISTAS/ASIP Domain-wide Performance As a summary of model performance, soccer and bugle plots for the all of the VISTAS STN and FRM monitors are included here. Plots have been developed for the average monthly concentrations and the performance statistics for all of the PM 2.5 component species (SO 4 , NO 3 , NH 4 , OC, and EC) and reconstructed PM 2.5 total mass from the STN monitoring sites (Figures 5.2-1 and 5.2-2), as well as the total FRM PM 2.5 mass (Figures 5.2-3 and 5.2-4). The soccer plots for monthly average component performance for all the VISTAS/ASIP STN sites shows generally good model performance for most species of PM 2.5 and total PM 2.5 . The exception is the prediction of NO 3 values, which most values fall outside the criteria goal (Figure 5.2-1). There are a few months that fall on the criteria level goal, which is better seen in the zoomed view presented in the image on the right in Figure 5.2-2. However, when the very low concentration of NO 3 is taken into consideration, as presented in the bugle plots (Figures 5.2-2 and 5.2-4), NO 3 performance largely falls within the criteria and goal model performance lines. One can still note a general tendency for under prediction in NO 3 , and other species in right hand image in Figure 5.2-3, which leads to a slight under prediction in total reconstructed PM 2.5 . Monthly total PM 2.5 concentration performance at all the VISTAS/ASIP FRM monitors largely falls within goal level thresholds, with only two months falling just outside goal level performance (Figure 5.2-3). Figure 5.2-4 suggests a slight negative bias in PM 2.5 prediction for most of the year, with mean fractional error valu es remaining within goal levels across the year.

Figure 5.2-1. VISTAS STN Soccer Plots The image on the left is a soccer plot depi cting both the mean fractional error and fractional bias for component concentration for all VISTAS STN monitoring sites. The image on the right is a zoomed view of th e soccer plot on the left to better depict the area inside the criteria/goal lines. Each point represents a monthly value as compared to the model performance criteria (red box) and modeling performance goals (green box).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 72 Figure 5.2-2. VISTAS STN Bugle Plots The image on the left is a bugle plot of the mean fraction bias for particulate matter and its component concentrations for all VI STAS STN monitoring sites. The image on the right is a bugle plot of mean frac tion error for particulate matter and its component species for all VISTAS STN monito ring sites. Each point represents a monthly mean fraction bias value as comp ared to the model performance criteria (red lines) and modeling performance goals (green lines).

Figure 5.2-3. VISTAS FRM Soccer Plots Soccer plots depicting both the mean frac tional error and fractional bias for component concentration for all the VISTAS FRM Monitoring sites. The image on the right is a zoomed view of the soccer plot on the left to better depict the area inside the criteria/goal lines. Each point represents a monthly value as compared to the model performance criteria (red box) and modeling performance goals (green box).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 73 Figure 5.2-4. VISTAS FRM Bugle Plots The image on the left is a bugle plot of the mean fraction bias for particulate matter and its component species concentrations for all the VISTAS FRM Monitoring sites. The image on the right is a bugle plot of mean fraction error for particulate matter and its component species for all the VISTAS FR M Monitoring sites. Each point represents a monthly mean fraction bias value as co mpared to the model performance criteria (red lines) and modeling performance goals (green lines). Overall, the general tendency for the model to have some difficulty in predicting NO 3 , as the monthly average values tend to fall outside the criteria goals for performance in the soccer plots. Part of this under prediction lies in the fact that NO 3 are generally found in low concentration across the southeast, and the model generally has difficulties represented any compound with low atmospheric concentrations. The bugle plots are more encouraging with NO 3 performance, as these plots take into consideration the concentration of the component when evaluating performance. The bugle plots show all components and total PM 2.5 falling within criteria levels, if not goals levels, of model performa nce. The weaker performance of NO 3 accounts for the slight negative bias in the both the total reconstructed PM 2.5 mass from STN sites as well as FRM total PM 2.5 data.

5.3. West Virginia PM 2.5 Model Performance There were no speciated PM 2.5 STN monitoring sites within West Virginia during the 2002 modeling year, so the PM 2.5 model evaluation is limited to the 16 FRM PM 2.5 mass monitoring sites. In January, April and July, CMAQ underestimates PM 2.5 mass across the FRM sites in West Virginia by approximately -30%, -25% and -40% and achieves the PM performance goal in January and April, but not July (Figure 5.3-1). In October the model has near zero bias and 32% error so achieves the ozone model performance goal. The Soccer Plot in Figure 5.3-2 below summarizes the FRM PM 2.5 mass performance in West Virginia in each month. Nine of the months achieve the PM performance goal, with the winter months even achieving or nearly achieving the ozone model performance goal. As seen for the other states, the underprediction bias during the May-July summer months results in the bias falling outside of the PM performance goal, but within the PM performance criteria.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 74 Figure 5.3-1. Scatter plots of predicted and observed fine particulate mass (PM 2.5 ) concentrations for January, April, July and October 2002, FRM sites in West Virginia and the CMAQ 2002 36 and 12 km Base G2 Actual base case simulation.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 75 Figure 5.3-2. Soccer plot of monthly fractional bias versus fractional gross error for PM 2.5 mass at FRM monitors in West Virginia for the CMAQ 12 km Base G2 Actual base case simulation.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 76 5.4. Conclusions of Model Performance Evaluation The quarterly average CMAQ PM 2.5 component species results near the FRM monitors are used in the Speciated Model Attainment Test (SMAT) to project the current year PM 2.5 Design Values to the 2009 future year for comparison with the PM 2.5 NAAQS. SMAT uses the relative changes CMAQ 2002 and 2009 modeling results to scale each of the PM 2.5 components of the current year PM 2.5 Design Values. These model derived scaling factors are referred to as Relative Response Factors (RRFs). The results of the SMAT PM 2.5 Design Value projections for FRM monitoring sites in and near the ASIP states are presented in Chapter 4 of the ASIP TSD (see Appendix N.2) . The results indicate that most of the reductions in the PM 2.5 Design Values between 2002 and 2009 are due to reductions in sulfate concentrations. Thus, performance of the model for sulfate is of most importance in the model performance evaluation. The implications of the major findings in the ASIP model performance evaluation are discussed below in the context of the modeling results used to project future year PM 2.5 attainment through SMAT. • Nitrate Underprediction Tendency : NO 3 is routinely underpredicted during the summer and adjacent months throughout the ASIP region. This underprediction is due to modeled NO x concentrations near zero, when observed values are low, but above zero (typically < 1 μ g/m 3 ). However, NO 3 is almost always a very minor to insignificant contributor to total PM 2.5 mass at FRM monitors in the ASIP region (see stacked bar charts in Chapter 4). In fact, the maximum NO 3 contribution to a 2009 projected PM 2.5 Design Value is 1.0 μ g/m 3 with a median value of 0.2 μ g/m 3 . Thus, the NO 3 performance issues are not considered significant in the PM 2.5 projections. • OCM Underpredictions : The OCM underprediction bias is a cause for concern since it is a major component of the PM 2.5 mass at ASIP FRM monitoring sites with maximum contributions to the 2009 PM 2.5 Design Values of ~8 μ g/m 3 , minimum values of ~3 μ g/m 3 and a median value of ~4 μ g/m 3 . The reasons for the underestimation of OCM are unclear, but the fact that the underpredictions are higher in the urban than rural areas suggest that there may be missing anthropogenic emission sources, or possibly the urban OCM emissions are over diluted across the 12 km grid resolution used in the ASIP modeling. The changes in projected OCM concentrations between the current and projected PM 2.5 Design Values are mostly less than 5% (i.e., 0.95 < RRF OCM < 1.05). Thus, the changes in OCM between the current and future year are having a minor influence on the projected PM 2.5 Design Values.
• EC Performance Issues : The EC underprediction bias at the urban sites is partly due to over dilution of the urban EC emissions due to the coarse (12 km) grid used. For the most part, the CMAQ model performed well for EC and the underprediction would not affect the relative changes in the model response to anthropogenic EC emissions changes. Therefore, any EC performance issues are not a cause for concern, although the model performance for EC was generally good. • Sulfate Underprediction Bias : Although SO 4 is performing well, it does have an underprediction bias that is largest in the summer months. But this underprediction is notWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 77 severe and the model appears to be capturing the temporal and spatial variations in the observed sulfate well and is responding to the SO 2 emission reductions between 2002 and 2009 in a manner as expected. Thus the model performance indicates that the modeled relative changes in SO 4 concentrations are likely a valid response.
• Soil Performance Issues : The CMAQ performance for the Soil species is quite poor. This Soil component of the 2009 projected PM 2.5 Design Value ranges from 0.4 to 1.8 μ g/m 3 . The RRFs for Soil indicate that it is mostly increasing, with summer (Q3) Soil RRFs typically ranging from 1.0 to 1.3. We address the Soil performance issues in Chapter 5 weight of evidence analysis where we assumed that the modeled Soil remains unchanged between 2002 and 2009 which did not significantly affect the projected 2009 PM 2.5 Design Values.
SO 4 reductions dominate the changes in PM 2.5 Design Values between 2002 and 2009. SO 4 performance is quite good in the CMAQ 2002 Base G2 Actual base case simulation, almost always achieving the PM performance goal and frequently also achieving the more stringent ozone performance goal. These factors provide confidence in the Design Value projections using the CMAQ Base G modeling results.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 78 6.0. CONTROL MEASURESThis section is divided into five sections: Point Source Measures, Area Source Measures, NonRoad Source Measures, On-Road Source Measures and Supplemental Control Measures.
6.1. Point Source Measures 6.1.a. Control Strategies
This section documents credit for emission reductions attributable to federal and state requirements on point sources. These credits include: • RACT; • “NO xSIP Call” to reduce ozone transport in the eastern United States; and • EPA’s Clean Air Interstate Rule (CAIR).
6.1.a.1. RACT and Regional Transport Requirements (federal and state regulations)
6.1.a.1.A. RACT
For each PM 2.5 nonattainment area, 40 CFR §51.1010 notes that a SIP revision demonstrating that all reasonable available control measures, including RACT for stationary sources, necessary to demonstrate attainment as expeditiously as practicable, have been adopted. The section of the implementation rules goes on to state that potential measures that are reasonably available, considering technical and economic feasibility, must be adopted as Reasonably Available Control Measures (RACM) if, considered collec tively, they would advance the attainment date by one year or more. As discussed in detail in Section 9, West Virginia determined that there are no additional control measures that could be adopted by January 1, 2008. Further, existing measures, and those planned for implementation by 2009, are expected to enable the area to demonstrate compliance with the 1997 PM 2.5 NAAQS by the 2010 attainment date. As such, no further action on RACT is warranted.
6.1.a.1.B. NOx SIP Call In late 1998, the EPA adopted a rule commonly referred to as the “NO x , SIP Call” to reduce ozone transport in the eastern United States. This regional NO xreduction program required 22 states, including West Virginia, to further reduce large point source NO xemissions to EPAidentified state emission budget levels by 2007. State regulation adoption timelines notwithstanding, the majority of the 22 SIP call states had these regulations in place by 2003/2004.West Virginia has adopted state rules codifying the requirements of the NO xSIP Call, which has resulted in NO xreductions of 77 percent.
6.1.a.1.C. Clean Air Interstate Rule (CAIR) On May 12, 2005, the EPA promulgated the Clean Ar Interstate Rule, which requires reductions in NO xand SO 2 from large fossil fuel fired electric generating units. The rule is set up in two phases with the first phase of NO x reductions required by 2009. The first phase of SO 2 reductions are required by 2010. The rule sets up both annual SO 2 and NO xemissions budgets and an ozoneWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 79 season NO xemission budget. The rule requires that units with a nameplate capacity greater than 25 megawatts (MW) emit no more NO xor SO 2 than their allocations determined by the state either through emission controls or banking and tr ading. West Virginia has adopted state rules codifying the requirements of CAIR, which is expected to result in NO x , reductions of 78 percent by 2015, and SO 2 reductions of 78 percent by 2015.
6.1.a.1.D. Standards Regarding Fugitive Dust
This section documents credits for emission reductions attributable to federal and state requirements on point sources. These credits include visibility standards for existing and modified stationary sources. The WVDAQ has several rules in place that require the control of fugitive dust within plant boundaries, these include 45CSR2 – To Prevent and Control Particulate Air Pollution from 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 Prepara tion Plants, Coal Handling Operations and Coal Refuse Disposal Areas; and 45CSR7 – To Prevent and Control Particulate Matter Air Pollution from Manufacturing Processes and Associated Operations. In addition, 45CSR17 - To Prevent and Control Particulate Matter Air Pollution from Materials handling, Preparation, Storage and Other Sources of Fugitive Particulate Matter prohibits the discharge of fugitive particulate matter beyond the boundary lines of property on which the discharge originates or at any public or residential location, which causes or contributes to statutory air pollution. Copies of these rules are included in Appendix K. West Virginia also has rules in place to regulate emissions from new sources, including 45CSR13 – Permits for Construction, Modificati on, Relocation And Operation of Stationary Sources of Air Pollutants, Notification Requi rements, 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 Sour ces 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 nonattainment NSR and PSD re gulations address emissions from new major sources that may locate in, or near, a nonattainment area, or increase emissions from major modifications to existing sources located in, or near, a nonattainment area. The benefits from the control of fugitive dust are reflected in the 2002 baseline inventory and the 2009 projections thereof. Additional reductions from this measure are not expected.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 80 6.1.b. Projected Reductions The point source NO x , SO 2 and PM 2.5 Direct controls are a phased approach to controlling emissions from power plants and other large fuel combustion sources. The following programs result in emission reductions from point sources that demonstrate progress toward attaining the PM 2.5 standard:
• NO x SIP Call Rule • EPA’s CAIR In West Virginia, the expected emission reductions for 2009 from electric generating utilities were calculated using knowledge of historical em ission rates, adjusted by the expected control efficiencies achieved from various control devices that have been installed. See Appendix D for further point source documentation. Emission reductions resulting from the point source controls, in both the West Virginia and Ohio portions of the Parkersburg area, are presented in the table below.

Table 6.1-1. 2009 Point Source Emission Reductions West Virginia portion of Parkersburg Area Emission Reductions (tons/year) NO xReductions 10,664 SO 2 Reductions 31,090 PM 2.5 Direct Reductions -71 Ohio portion of Parkersburg Area Emission Reductions (tons/year) NO xReductions 27,294 SO 2 Reductions 132,623 PM 2.5 Direct Reductions -1,913WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 81 6.2. Area Source Measures 6.2.a. Control Strategy
6.2.a.1. Open Burning Restrictions (state rule) 45CSR5 - To Prevent and Control Air Pollution from Combustion of Refuse - prohibits the open burning of refuse with limited exceptions. 6.2.b. Projected Reductions The benefits from this program are reflected in the 2002 baseline inventory and the 2009 projections thereof. Additional reductions from this measure are not expected.

6.3. Non-Road Source Measures 6.3.a. Control Stategies
6.3.a.1 Phase I and Phase II Emissions Standards for Gasoline-Powered Nonroad Utility Engines (federal rule)
This measure takes credit for emissions reductions attributable to emissions standards promulgated by the USEPA for small non-road, spark-ignition (SI) (i.e., gasoline-powered) utility engines, as authorized under 42 U.S.C. §7547. The measure affects gasoline-powered (or other SI) lawn and garden equipment, construction equipment, chain saws, and other such utility equipment as chippers and stump grinders, wood splitters, etc., rated at or below 19 kilowatts (kW) [an equivalent of 25 or fewer horsepower (hp) ]. Phase II of the rule applied further controls on handheld and nonhandheld outdoor equipment. Federal emissions standards promulgated under §7547(a) apply to SI nonroad utility engines. The EPA’s Phase I Spark Ignition Final Rule on such emissions standards was published in 60 Federal Register 34581 (July 3, 1995) and was effective beginning August 2, 1995. Compliance was required by the 1997 model year. The Phase II final rule for handheld nonroad equipment was published in 65 Federal Register 24267 (April 25, 2000). The Phase II final rule for nonhandheld equipment was published in 64 Federal Register 15207 (March 30, 1999). This program is implemented by the USEPA, under 42 U.S.C. §7547(a).
6.3.a.2. Emissions Standards for Diesel-Powered Nonroad Utility Engines of 50 or More Horsepower (federal rule)
This measure takes credit for emissions reductions attributable to emissions standards promulgated by the USEPA for nonroad, compression-ignition (i.e., diesel-powered) utility engines, as authorized under 42 U.S.C. §7547. Th e measure affects diesel-powered (or other compression-ignition) construction equipment, indus trial equipment, etc., rated at or above 37 kW (37 kW is approximately equal to 50 horsepower).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 82 Federal emissions standards applicable to compression-ignition nonroad utility engines are promulgated under §7547(a). USEPA’s first rule on such emissions standards was published in 59 Federal Register 31306 (June 17, 1994) and was effective on July 18, 1994. Tier 2 and Tier 3 Emission Standards were promulgated in 1998. This program includes the first set of standards for nonroad diesel engines less than 37 kW (phase in between 1999 and 2000), including marine engines in this size range. It also phases in more stringent Tier 2 emission standards from 2001 to 2006 for all engine sizes and adds yet more stringent Tier 3 standards for engines between 37 and 560 kW (50 and 750 hp) from 2006 to 2008. USEPA adopted a comprehensive national program to greatly reduce emissions from nonroad diesel engines by integrating engine and fuel controls as a system to gain the greatest air quality benefits. This rule was published June 29, 2004. The requirement to reduce sulfur levels in nonroad diesel fuel by more than 99 percent will allow, for the first time, advanced emission control systems to be used on engines used in construction, agricultural, industrial and airport service equipment. This program is implemented by the USEPA, under 42 U.S.C. §7547(a).
6.3.a.3. Emission Standards for Spark-Ignition (SI) Marine Engines (federal rule)
This EPA measure controls exhaust NO xemissions from new spark-ignition (SI) gasoline marine engines, including outboard engines, personal watercraft engines and jet boat engines. EPA is imposing emission standards for two-stroke technology, outboard and personal watercraft engines. This involves increasingly stringent cont rol over the course of a 9-year phase-in period which began in model year 1998. By the end of the phase-in, each manufacturer is required to meet a NO xemission standard. This program is implemented by the USEPA under 42 U.S.C. §7547(a).
6.3.a.4. Emission Standards for Large Spark-Ignition (SI) Engines (federal rule)
This USEPA measure controls emissions from se veral groups of previously unregulated nonroad engines, including large industrial SI engines. The USEPA requirements vary depending upon the type of engine or vehicle, taking into account environmental impacts, usage rates, the need for high performance models, costs, and other factors. The emission standards apply to all new engines sold in the United States and any imported engines manufactured after these standards began. Controls on the category of large industrial SI engines were first required in 2004. Controls on the other engine categories began in years after 2005. Large industrial SI engines are those rated over 19 kW used in a variety of commercial applications; most use liquified petroleum gas, with others operating on gasoline or natural gas.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 83 EPA adopted two tiers of emission standards for large SI engines. The first tier of standards, which started in 2004, are based in a simple laboratory measurement using steady-state procedures. The Tier I standards are the same as those adopted earlier by the California Air Resources Board for engines used in California. Tier 2 standards became effective in 2007. The program is implemented by the USEPA under 42 U.S.C. §7547(a).
6.3.a.5. Standards for Locomotives (federal rule)
This measure sets NO xStandards for locomotive engines manufactured and remanufactured after 2001. This program includes all locomotives originally manufactured from 2002 through 2004. It also applies to the remanufacture of all engines built since 1973. Regulation of the remanufacturing process is critical because locomotives are generally remanufactured five to ten times during their total service lives, which are typically 40 years or more. Three separate sets of emission standards have been adopted, with the applicability of the standards dependent on the date a locomotive is first manufactured. The first set of standards (Tier 0) applies to locomotives and locomotive engine originally manufactured from 1973 through 2001, any time they are manufactured or remanufactured. The second set of standards (Tier 1) applies to locomotives and locomotive engines originally manufactured from 2002 to 2004. These locomotives are required to meet the Tier 1 standards at the time of manufacture and at each subsequent remanufacture. The final set of standards (Tier 2) applies to locomotives and locomotive engines originally manufactured in 2005 and later. Electric locomotives, historic steam-powered locomotives and locomotives manufactured before 1973 do not significantly contribute to the emissions problem and, therefore, are not included in the regulation. This program is implemented by the USEPA under the Final Emission Standards for Locomotives (EPA420-F-97-048) published in December 1997. 6.3.b. Projected Reductions The following nonroad emission reduction measures, discussed above, are calculated using the NONROAD2005 model: • EPA Nonroad Gasoline Engines Rule • EPA Nonroad Diesel Engines Rule • Emissions Standards for Spark-Ignition Marine Engines • Emission Standards for Large Spark-ignition Engines Emission Standards for Locomotives are calculated using the Area Source spreadsheet, but emission benefits are included in the nonroad sector totals. Emission benefits are based on EPA guidance on emission factors for locomotives. In 2009, the reductions are 32.35 percent for NO x and 15 percent for PM 2.5 . NONROAD2005, the current nonroad emissions model approved for use by the EPA, is not designed to calculate the benefits of each of the above control measures individually. As a result,WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 84 this and future SIP revisions will not enumer ate the benefits of individual nonroad control measures. The Table below summarizes the combined benefits from the above control measures on both the West Virginia and Ohio portions of the Parkersburg area.

Table 6.3-1. 2009 Nonroad Emission Reductions West Virginia portion of Parkersburg Area Emission Reductions (tons/year) NO xReductions 189 SO 2 Reductions 111 PM 2.5 Direct Reductions 11 Ohio portion of Parkersburg Area Emission Reductions (tons/year) NO xReductions 305 SO 2 Reductions 92 PM 2.5 Direct Reductions 11WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 85 6.4. Onroad Source Measures 6.4.a. Control Strategies
6.4.a.1. Federal Tier I new Vehicle Emission and New Federal Evaporative Emissions Standards (federal regulation) Under 42 U.S.C. §7521, USEPA issued a new and cleaner set of federal motor vehicle emission standards (Tier I standards), which were phased in begging with model year 1994. The benefits of this program are reflected in the 2002 baseline inventory and the 2009 projections thereof. These federally implemented programs affect li ght-duty vehicles and light-dut trucks (LDT). The Federal Motor Vehicle Control Program requires more stringent exhaust emission standards, as well as a uniform level of evaporative emission controls, demonstrated through the new federal evaporative test procedures. Under 42 U.S.S. §7521(g), all post-1995 model year cars must achieve the Tier I (or Phase I) exhaust standards, which are as follows (emissions are in grams/mile and are related to durability timeframes of 5 yrs/50,ooo miles and 10 yrs/100,000 miles):

Table 6.4-1. Tier I Standards Vehicle Type 5 yrs/50,000 miles 10 yrs/100,000 miles VOCs CO NO x VOCs CO NO x Light-duty vehicles; light-duty trucks (load weight 3,750 lbs) 0.25 3.4 0.4 a 0.31 4.2 0.6 a
Light-duty trucks (load weight 3,751 to 5,750) 0.32 4.4 0.7 b 0.40 5.5 0.97 b

a For diesel-fueled light-duty vehicles and for LDTs at 3,750, before model year 2004, the applicable NO x standards shall be 1.0 at 5 yrs/50,000 miles and 1.25 at 10 yrs/100,000 miles. b This NO x standard does not apply to diesel-fueled trucks of 3,751 to 5,750 lbs.
6.4.a.2. National Low Emission Vehicle Program (federal regulation) Under the National Low-Emission Vehicle (LEV) program, auto manufacturers have agreed to comply with tailpipe standards that are more stringent than USEPA can mandate prior to model year (MY) 2004. Once manufacturers committed to the program, the standards became enforceable in the same manner in which other federal motor vehicle emissions control requirements are enforceable. The program was in place nationwide for MY 2001. The benefits of this program are reflected in the 2002 baseline inventory and the 2009 projections thereof.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 86 These federally implemented programs affect light-duty vehicles and trucks. The National Low Emission Vehicle Program requires more stringent exhaust emission standards than the Federal Motor Vehicle Control Program Tier I (or Phase I) exhaust standards.
6.4.a.3. Tier 2 Motor Vehicle Emission Regulations (federal regulation) The US EPA promulgated a rule on February 10, 2000, requiring more stringent tailpipe emissions standards for all passenger vehicles, including sport utility vehicles (SUVs), minivans, vans and pick-up trucks. These regulations also require lower levels of sulfur in gasoline, which will ensure the effectiveness of low emission-control technologies in vehicles and reduce harmful air pollution. These federally implemented programs affect all passenger vehicles, including sport utility vehicles (SUVs), minivans, vans and pick-up trucks. The new tailpipe and sulfur standards require passenger vehicles to be 77 to 95 percent cleaner than those built before the rule was promulgated and will reduce the sulfur content of gasoline by up to 90 percent. The new tailpipe standards are set at an average standard of 0.07 grams/mile for NO x for all classes of passenger vehicles beginning in 2004. This includes all light-dutytrucks, as well as the largest SUVs. Vehicles weighing less than 6000 pounds are being phasedin to this standard between 2004 and 2007. Beginning in 2004, the refiners and importers of gasoline have the flexibility to manufacture gasoline with a range of sulfur levels as long as all of their production is capped at 300 parts per million (ppm) and their annual corporate average sulfur levels are 120 ppm. In 2005, the refinery average was set at 30 ppm, with a corporate average of 90 ppm and a cap of 300 ppm. Finally, in 2006, refiners met a 30 ppm average sulfur level with a maximum cap of 80 ppm. As newer, cleaner cars enter the national fleet, the new tailpipe standards will significantly reduce emissions of nitrogen oxides from vehicles by about 74 percent by 2030.
6.4.a.4. Heavy Duty Diesel Engine Rule (federal regulation) Under the Heavy-Duty Diesel Engine Rule, truck manufacturers must comply with more stringent tailpipe standards by 2004 and 2007. These standards are enforceable in the same manner that other federal motor vehicle emissions control requirements are enforceable. These federally implemented programs affect heavy-duty diesel engines used in trucks. The Heavy Diesel Engine Rule requires more stri ngent exhaust emission standards. The rule also mandates use of ultra-low sulfur diesel fuel. Sulfur in diesel must be lowered to enable modern pollution-control technology to be effective on these trucks and buses. US EPA requires a 97 percent reduction in the sulfur content of highway diesel fuel from its former level of 500 ppm (low sulfur diesel, or LSD) to 15 ppm (ultra- low sulfur diesel, or ULSD). Refiners began producing the cleaner-burning diesel fuel, ULSD, for use in highway vehicles beginnin June 1, 2006.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 87 6.4.b. Projected Reductions The following onroad emission reduction measures, discussed in greater detail above, are calculated using the MOBILE6 model: • Federal Tier 1 Vehicle Standards, see Section 6.4.a. • National Low Emission Vehicle Standards, see Section 6.4.b. • Federal Tier 2 Vehicle Standards, see Section 6.4.c. • Heavy Duty Diesel Engine Rule, see Section 6.4.d. MOBILE5b, the mobile emissions model used in previous SIPs, was designed to calculate the benefits of each of the above control measures individually. In the update to MOBILE6.2, changes were made to the model, creating synergistic effects between the mobile control measures listed above. These effects make it difficult to calculate incremental benefits from implementation of individual control measures. As a result, this and future SIP revisions will not enumerate the benefits of individual mobile control measures. The table below summarizes the combined benefits from the above control measures in both the West Virginia and Ohio portions of the Parkersburg area. See Appendix D for documentation of the MOBILE 6.2 modeling process.
Table 6.4-2. Onroad Emission Reductions

West Virginia portion of the Parkersburg Area Emission Reductions (tons/year) NO x Reductions 1,003 SO 2 Reductions 98 PM 2.5 Direct Reductions 13 Ohio portion of the Parkersburg Area Emission Reductions (tons/year) NO x Reductions 1,019 SO 2 Reductions 101 PM 2.5 Direct Reductions 14WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 88 6.5. Supplemental Control Measures EPA’s voluntary measures policy, “Guidance on Incorporating Voluntary Mobile Source Emission Reductions Programs in State Implementation Plans,” establishes criteria under which emission reductions from voluntary programs are creditable in a SIP. This policy permits states to develop and implement innovative programs that partner with local jurisdictions, businesses and private citizens to implement emission-reducing behaviors at the local level. Inclusion of the following programs in the control measures portion of this attainment plan is not intended to create an enforceable commitment by the state to implement the programs or to achieve any specific emission reductions projected as a result of implementation of the programs, and the state does not make any such commitment. In addition, the state does not rely on any emission reductions projected as a result of implementation of these programs to demonstrate attainment. While the emission reductions from these programs could be substantial and could lead to significant air quality benefits, actual air quality benefits remain unquantified. Consequently, projected emission reductions from these programs are not included in the emission inventory, the attainment modeling, the r easonable further progress calculations, or any other area of the SIP where specific projected emission reductions are identified. In 2007 the West Virginia Legislature enacted the West Virginia Energy Policy and Development Act of 2007 which created the West Virginia Division of Energy to “. . . provide leadership for developing energy policies emphasizing the increased efficiency of energy use, (and) the increased development and production of new and existing domestic energy sources.” The West Virginia Division of Energy (WVDOE) is responsible for the formulation and implementation of fossil, renewable and energy efficiency initiatives designed to advance energy resource development opportunities and provide energy services to businesses, communities and homeowners in West Virginia. A primary goal of the WVDOE is the development of a state energy plan ( http://www.wvdo.org/community/EOD.pdf ). The WVDOE sponsors a number of programs, including: • Ride Share Program ( http://www.wvdo.org/community/rideshare.html ). Due to the rising gasoline costs and the number of state employees who commute each day, the Ride Share Program, offered through the Energy Efficiency Program, serves as a means of financial savings to participating riders, conservation of fuel, and the reduction of air pollution. • The ENERGY STAR® Change a Light, Change the World Campaign ( http://www.energystar.gov/index.cfm?c=lighting.pr_lighting ). A national challenge to encourage every American to help change the world – one light, one step at a time. The campaign culminates in the fall around ENERGY STAR® Change a Light Day (the first Wednesday in October) with promotions running locally and nationally from October 1 through November 30. If every West Virginia household replaced just one 75-watt incandescent light with a 20-watt compact fluorescent lamp (CFL), the US EPA estimates we could save more than $5 million annually in energy costs. • Clean Air Forum ( http://www.wvdo.org/community/cleanairforum-2006.pdf ). The Clean Air Forum newsletter offers energy information from around the state.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 89 • Alternative Fuels Program ( http://www.wvdo.org/community/fuels.html ). The West Virginia Clean State Program, working clos ely with the US DOE Clean Cities Program, promotes alternate fuel vehicles. • West Virginia Friends of Biodiesel Conference ( http://www.wvdo.org/community/WVFOBPresentations.html ). Sponsored by the WVDOE in October 2007, the conference brought together current and potential biodiesel users to spread the knowledge about this renewable fuel. • Building Professional Energy Training Program ( http://www.wvdo.org/community/code.html ). The West Virginia Energy Efficiency Program Sponsors building code seminars to teach the latest codes and building technologies to local government officials and private-sector building professionals. • Industrial assessment Program ( http://www.wvdo.org/community/ia.html ). Provides industrial assessments to industries that participate in the Industries of the Future-West Virginia (IOF-WV). • Industries of the Future-West Virginia ( http://www.wvdo.org/community/iofwv.html ). IOF-WV is a state-level technical assistance initiative, interacts with the US Department of Energy Industries of the Future Program to provide assistance to energy-intensive industries. Teams from university engineer ing programs provide technical assistance through the Glass Industry Assistance and the Wood Industry Initiative Program. • Lighting Grants Program ( http://www.wvdo.org/community/lgp.html ). This program has provided more than $400,000 for lighting improvements in schools, local governments and nonprofit hospitals. The 464 audits performed have identified $2.5 million in potential annual savings. • Recycling Program ( http://www.wvdo.org/community/recycling.html ). The West Virginia Recycling Directory is updated annually and lists over 500 West Virginia recyclers. • Renewable Energy Program ( http://www.wvdo.org/community/renewable.html ). Solar energy ( http://www.wvdo.org/community/solar.cfm ) represents conceivably the single largest source of energy. Solar energy opportunity has been estimated at 1.5 million barrels of oil energy per year for every square kilometer. The biofuels program ( http://www.wvdo.org/community/biofuels.html ) is looking at energy uses for wood and other biomass resources. The wind program ( http://www.wvdo.org/community/wind.html ) has supported the development of a detailed map of West Virginia wind resources and the promotion of this resource. • Saving Energy in Public Schools Program ( http://www.wvdo.org/community/schoolenergy.html ). Developing a systematic approach to reducing energy use in West Virginia public schools. • West Virginia ENERGY STAR® Program ( http://www.wvdo.org/community/energystar.html ). ENERGY STAR® is a government backed program helping businesses and individuals protect the environment through superior energy efficiency.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 90 6.6. Schedule of Adopted Control Measures
Table 6.6-1. West Virginia Schedule of Adopted Control Measures Parkersburg Nonattainment Area Ref. No. Control Measure Mandate Regulation Number Effective Date

Point Source Measures 6.1.a.1.

6.1.a.1.B.

6.1.a.1.C. RACT and Regional Transport Requirements
NO x SIP Call - nonEGUs
NOx SIP Call - EGUs
CAIR - Annual NO x CAIR - Ozone Season NO x CAIR - Annual SO 2 Federal

45CSR1
45CSR26
45CSR39 45CSR40 45CSR41 10/25/2000 1 Amended: 5/1/2006 5/1/2002 2 Amended: 6/1/2003 5/1/2006 3 5/1/2006 3 5/1/2006 3
6.1.a.1.D. Standards Regarding Fugitive Dust Indirect Heat Exchangers Asphalt Plants Coal Handling & Preparation Manufacturing Process Materials Handling Federal
Federal
Federal
Federal
State 45CSR2
45CSR3
45CSR5
45CSR7
45CSR17 3/15/1972 4 Amended: 8/31/2000 10/01/1966 5 Amended: 8/31/2000 9/01/1968 6 Amended: 8/31/2000 7/1/1969 7 Amended: 8/31/2000 4/8/1980 Amended: 8/31/2000 Area Source Measures 6.2.a.1. Open Burning Restrictions Federal 45CSR6 9/1/1969 8 Amended: 7/1/2001 Nonroad Measures 6.3.a.1. EPA Nonroad Gasoline Engine Rule Federal 40 CFR Parts 90, 91 12/3/1996

6.3.a.2. EPA Nonroad Diesel Engines Rule Federal 40 CFR Part 9 et al. Model Year 2000- 2008 depending on engine sizeTable 6.6-1. West Virginia Schedule of Adopted Control Measures Parkersburg Nonattainment Area Ref. No. Control Measure Mandate Regulation Number Effective Date

WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 91

6.3.a.3. EPA Nonroad SparkIgnition Marine Engine Rule Federal 40 CFR Parts 89, 90,91 Model Year 1998

6.3.a.4. EPA Large Spark-Ignition Engine Rule Federal 40 CFR Parts 89, 90, 91, 94, 1048,1051, 1065, 1068 11/8/2002

6.3.a.5. Standards for Locomotives Federal 63 FR 18998 6/15/1998 Onroad Measures 6.4.a.1. Federal Tier I Vehicle Standards and New Car Evaporative Standards Federal 40 CFR Part 86 Model Year 1994- 1996; Evap. Stds. 1996 6.4.a.2. Nation Low Emissions Vehicle Program Federal 40 CFR Part 86 Model Year 2001

6.4.a.3. Tier 2 Motor Vehicle Emission Standards Federal 65 FR 6698 2/10/2000
6.4.a.4. Heavy-Duty Diesel Engine Rule Federal 63 FR 54694 12/22/1997

Notes: 1 Effective date provided is state effective date ; federal effective date is 11/27/2006 [71 FR 56881]. 2Effective date provided is state effective date ; federal effective date is 7/9/2002 [67 FR 31733]. 3 Effective date provided is state effective date. CA IR rules were submitted to EPA as a SIP revision on 6/1/2006. 4 Effective date provided is state effective date; orig inal federal effective date was 5/31/1972 [37 FR 10902]; federal effective date for most recent amendment is 10/10/2003 [68FR 47473]. 5 Effective date provided is state effective date; orig inal federal effective date was 5/31/1972 [37 FR 10902]; federal effective date for most recent amendment is 12/10/2002 [67 FR 63270]. 6 Effective date provided is state effective date; orig inal federal effective date was 5/31/1972 [37 FR 10902]; federal effective date for most recent amendment is 12/6/2002 [67 FR 62379]. 7 Effective date provided is state effective date; orig inal federal effective date was 5/31/1972 [37 FR 10902]; federal effective date for most recent amendment is 8/4/2003 [68 FR 33010]. 8 Effective date provided is state effective date; original federal effective date was 5/31/1972 [37 FR 10902];federal effective date is 4/11/2003 [68 FR 6627].WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 92 7.0. ATTAINMENT DEMONSTRATION An attainment demonstration consists of (a) analyses that estimate whether selected emissions reductions will result in ambient concentrations that meet the NAAQS, and (b) an identified set of control measures which will result in the required emissions reductions. The necessary emission reductions for both of these attainment demonstration components may be determined by relying on results obtained with air quality models. Section 3.0 of the Attainment Modeling Guidance recommends applying both a modeled attainment test and a subsequent screening test (or unmonitored area analysis) to the air quality modeling results to determine if the annual PM 2.5 NAAQS will be met. Additional technical or corroboratory analyses may also be used as part of a “supplemental analysis” or a more stringent “weight of evidence” determination to supplement the modeled attainment test and to further support a demonstration of attainment of the NAAQS.
7.1. Attainment Test Introduction The purpose of a modeling assessment is to determine if control strategies currently being implemented (“on the books”) and proposed control strategies will lead to attainment of the NAAQS for PM 2.5 by the attainment year of 2009. The modeling is applied in a relative sense, similar to the 8-hour ozone attainment test. However, the PM 2.5 attainment test is more complicated and reflects the fact that PM 2.5 is a mixture. In the test, ambient PM 2.5 is divided into major components, with a separate relative response factor (RRF) and future design value (DVF) calculated for each of the PM 2.5 components. Since the attainment test is calculated on a per species basis, the attainment test for PM 2.5 is referred to as the Speciated Modeled Attainment Test (SMAT). In it entirety, SMAT consists of four basic steps. First, the observed quarterly mean PM 2.5 and quarterly mean composition for each monitor is calculated. This is achieved by multiplying the monitored quarterly mean concentration of PM 2.5 from FRM monitors by the monitored fractional composition of PM 2.5 species for each quarter (e.g., (20% sulfate) x (15.0 μg/m 3 PM 2.5 mass) = 3.0 μg/m 3 sulfate mass). The monitored quarterly mean concentration of PM 2.5 from FRM monitors are 5 year baseline design values (DVB) that are the result of averaging 3 current design values (DVC) that straddle the modeling base year. The fractional composition of PM 2.5 species is derived from STN monitoring site data that has been processed by the “sulfate, adjusted nitrate, derived water, inferred carbonaceous material balance appro ach,” or SANDWICH method, so STN and FRM masses are equivalent. The mean composition derived from the SANDWICH method includes the percent of PM 2.5 that can be attributed to SO 4 , NO 3 , OC, EC, other primary inorganic particulates (or crustal materials), NH 4 , and particle bound water (PBW). The second step is to use model results to derive component specific RRF for each monitor for each quarter. The RRF is basically the ratio of the model’s future projections to the baseline current projections. The RRF for each component is the future year modeled quarterly mean concentration predicted near the monitoring site divided by the base year modeled quaterly mean concentration predicted near the monitoring site.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 93 For the third step, the component specific RRFs are applied to the observe air quality concentrations to projected quarterly species estimates. For each quarter, the current quarterly mean component concentrations (step 1) are multiplied by the component-specific RRF obtained in step 2. This leads to an estimated future quarterly mean concentration for each component. The fourth step sums the quarterly components to get a quarterly mean PM 2.5 value. These quarterly mean values are then averaged to produce a future year annual average PM 2.5 estimate, or future design value (DVF), for each FRM monitoring site. This final value is then compared to the NAAQS (15.0 μg/m 3 ) to determine if attainment is reached. For a more detailed discussion of SMAT and the data sets for each monitor in the nonattainment areas, see Appendix J.
7.2. Attainment Test Results The goal of the SMAT process is to sum the quarterly mean PM 2.5 components to get annual mean PM 2.5 values. Table 7.2-1 displays the quarterly mean and annual mean PM 2.5 estimates for 2009 for the FRM sites in the WV PM 2.5 nonattainment areas.
Table 7.2-1. Quarterly Mean and Annual Mean PM 2.5 Mass Estimates for 2009 (Base G4) AIRS ID State County 1 stQ DVF 2 nd Q DVF 3 rd Q DVF 4 th Q DVF 2009 Annual

DVF 54-003-0003 WV Berkeley 13.3 12.8 15.0 13.2 13.6 54-009-0005 WV Brooke 13.4 14.1 15.1 12.5 13.8 54-011-0006 WV Cabell 13.2 14.2 16.8 12.4 14.1 54-029-0011 WV Hancock 12.6 13.4 14.6 12.0 13.1 54-029-1004 WV Hancock 13.4 14.4 16.3 12.7 14.2 54-033-0003 WV Harrison 10.3 12.0 14.3 9.9 11.6 54-039-0010 WV Kanawha 12.1 13.7 15.7 11.1 13.2 54-039-1005 WV Kanawha 13.6 15.2 17.2 13.0 14.8 54-049-0006 WV Marion 12.3 13.0 15.3 11.3 13.0 54-051-1002 WV Marshall 13.7 13.4 15.0 11.5 13.4 54-055-0002 WV Mercer 8.5 11.4 13.0 9.2 10.5 54-061-0003 WV Monongalia 11.3 12.9 15.4 10.2 12.5 54-069-0010 WV Ohio 12.3 12.9 14.7 11.0 12.7 54-081-0002 WV Raleigh 9.0 11.8 13.7 9.2 10.9 54-089-0001 WV Summers 6.0 9.7 11.3 6.1 8.2Table 7.2-1. Quarterly Mean and Annual Mean PM 2.5 Mass Estimates for 2009 (Base G4) AIRS ID State County 1 stQ DVF 2 nd Q DVF 3 rd Q DVF 4 th Q DVF 2009 Annual

DVF

WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 94

54-107-1002 WV Wood 13.3 14.2 16.2 12.3 14.0 These 2009 DVFs are the final product of the SMAT process and are then compared to the NAAQS (15.0 μg/m 3 ) to determine if the attainment goals will be reached. Since the values at the FRM sites in the nonattainment areas are <15.0 μg/m 3 , all areas have passed the attainment test portion of the attainment demonstration. 7.3. Supplemental Analyses The Attainment Modeling Guidance asserts that all attainment demonstrations should be accompanied by supplemental analyses that further support the modeling conclusions. This supplemental analysis can include additional analyses of air quality, emissions and meteorological data, and consider modeling outputs other than the results of the attainment test. If the attainment test results fall short of the standard, the results of corroboratory analyses may be used in a weight of evidence determination (WOE) to show that attainment is likely despite modeled results, which may be inconclusive. The Attainment Modeling Guidance defines the guidelines for supplemental analysis/WOE for the annual PM 2.5 standard as follows: • Sites with a DVF less than 14.5 μg/m 3 should submit basic supplemental analysis to confirm the outcome of the model attainment test. • Sites with a DVF between 14.5 and 15.5 μg/m 3 should submit a weight of evidence demonstration to aggregate supplemental analyses to support the model attainment demonstration. • Sites with a DVF greater than or equal to 15.5 μg/m 3 should consider additional control measures to ensure attainment, as more qualitative analysis is unlikely to demonstrate attainment. Five of the six West Virginia PM 2.5 nonattainment areas, including Parkersburg, have DVFs lower than the 14.5 μg/m 3 , making the following section an examination of supplemental analyses to corroborate modeling results, rather than a WOE analysis to show attainment. In Section 7.1 of the Attainment Modeling Guidance EPA recommends three basic types of analyses to supplement the primary analyses. They are: 1. Additional modeling 2. Analysis of trends in ambient air quality and emissions 3. Observational models and diagnostic analyses.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 95 7.3.a. Other Modeling Results In Section 7.1 of the Attainment Modeling Guidance EPA suggests several additional modeling exercises that can be performed as part of a supplemental/WOE analysis, including the use of available regional or national scale modeling applications that are suitable for the local area. Other regional or national scale modeling studies that contain results for West Virginia include modeling done for other RPOs and EPA. Other m odeling studies may use different physical and chemical modeling options for their meteorological and air quality runs, which would provide a comparison or sensitivity based on these different options.
7.3.a.1. EPA’s CAIR Modeling One air quality modeling exercise that contains results for West Virginia’s nonattainment areas is the EPA’s modeling for the CAIR. The Technical Support Document for the CAIR, March 2005, provides modeling results with and without the implementation of the CAIR. Differences between the EPA’s modeling and the attainment demonstration are: 1. The meteorology was for 2001, 2. The DVB was the weighted design values for the 1999-2003 period, and 3. The modeling results were for 2010. The DVF was calculated using the CAIR SMAT tool, so methodologies between the CAIR DVF and the values presented in Section 7.2 are the same. The CAIR modeling results are listed in Table 7.3-1.
Table 7.3-1. EPA CAIR Modeling Results State County Average 99-03 2010 Base 2010 CAIR 2015 Base 2015 CAIR WV Berkeley 16.18 15.69 13.43 15.32 12.73 WV Brooke 16.96 16.63 14.42 16.51 14.05 WV Cabell 17.22 17.03 15.08 16.86 14.64 WV Hancock 17.40 17.06 14.89 16.97 14.54 WV Harrison 14.40 14.15 11.90 13.82 11.31 WV Kanawha 17.75 17.56 15.27 17.17 14.66 WV Marion 15.58 15.32 12.90 14.98 12.23 WV Marshall 16.07 15.81 13.46 15.52 12.87 WV Mercer 12.97 12.52 10.82 12.14 10.16 WV Monongalia 14.96 14.77 12.31 14.37 11.40Table 7.3-1. EPA CAIR Modeling Results State County Average 99-03 2010 Base 2010 CAIR 2015 Base 2015 CAIR

WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 96 WV Ohio 15.37 15.14 12.81 14.84 12.22 WV Raleigh 13.54 13.19 11.33 12.80 10.66 WV Summers 10.46 10.21 8.63 9.89 8.07 WV Wood 16.88 16.66 14.14 16.69 13.88 The EPA’s results were for the highest monitor in a county where more than one monitor is located. The EPA’s modeling results predict that the Parkersburg (Wood County) nonattainment area should be below the annual PM 2.5 standard by 2010. Although this is one year later than the attainment year for the area, the EPA’s 2010 CAIR DVF is 0.14 μg/m 3 higher than the WVDEP is showing in the attainment demonstration, but still supports that the area will attain the annual PM 2.5 standard by the attainment year of 2009.
7.3.a.2. Midwest RPO Modeling Another air quality modeling exercise that contains results for West Virginia’s nonattainment areas is the Midwest RPO’s modeling for PM 2.5 . Differences between the Midwest RPO’s modeling and the attainment demonstration are: 1. The meteorology was for 2005, 2. The base year emission inventory was 2005, and 3. The DVB was the weighted design values for the 2001-2005 period. 4. The CAMx modeling platform was used. The Midwest RPO modeling results are listed in Table 7.3-2 and are available at http://ladco.org/Technical_Support_Document.html

.
Table 7.3-2. LADCO - Round 5 - 2009 Results Site ID County 2003- 2005 2004- 2006 2005- 2007 Base Year Design Value Future Year Design Value 5400900051 Brooke 16.8 16.4 16.4 16.5 13.5 5401100061 Cabell 16.3 16.1 16.6 16.3 13.6 5402900111 Hancock 16.4 15.7 15.9 16.0 13.0 5402910041 Hancock 16.6 15.4 15.2 15.7 12.8Table 7.3-2. LADCO - Round 5 - 2009 Results Site ID County 2003- 2005 2004- 2006 2005- 2007 Base Year Design Value Future Year Design Value

WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 97

5403300031 Harrison 13.9 13.9 14.2 14.0 11.6 5403900101 Kanawha 15.1 15.0 15.4 15.2 13.0 5403900111 Kanawha 13.6 13.2 12.2 13.0 11.2 5403910051 Kanawha 16.6 16.4 16.6 16.5 14.4 5404900061 Marion 15.0 14.9 15.3 15.1 12.7 5405110021 Marshall 15.3 15.0 15.2 15.2 12.4 5405500021 Mercer 12.8 13.3 14.5 13.5 10.7 5406100031 Monongalia 14.5 14.1 14.4 14.3 11.9 5406900081 Ohio 14.6 14.1 0.0 14.4 11.7 5406900101 Ohio 15.4 14.3 14.6 14.8 12.0 5408100021 Raleigh 12.9 12.8 13.0 12.9 10.9 5408900011 Summers 10.6 11.2 12.5 11.4 8.7 5410710021 Wood 15.4 15.3 15.4 15.4 12.9WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 98 7.3.b. Trends in Ambient Air Quality and Emissions
Table 7.3.b-1. Air Monitoring Data for Parkersburg Area AIRS ID County 1999 2000 2001 2002 2003 2004 2005 2006 54-107-1002 Wood 17.7 17.8 17.4 15.8 14.9 14.9 16.4 14.7

Figure 7.3.b-1. Chart showing trend in PM 2.5 monitoring data in the Parkersburg area.

As shown in Table 7.3.b-1 and Figure 7.3.b-1 the air quality in the Parkersburg area has been trending downward. In addition, as shown in 7.3.b.2. the emissions of PM, NO xand SO 2 have also been decreasing. See Appendix J, Section 3 for discussion of additional supplemental analyses, including MATS modeling, CAMx modeling, and PSAT modeling. The results of all these models support the conclusion that the Parkersburg area will attain the standard by the April 2010 deadline. 

### Parkersburg

0 2 4 6 8 10 12 14 16 18 20

1999 2000 2001 2002 2003 2004 2005 2006PM2.5 (ug/m3) Annual PM2.5 Standard
Wood (54-107-1002)
Linear (Wood (54-107- 1002))WV PM2.5SIP for the Parkersburg Nonattainment AreaPage 99Figure 7.3.b-2. Point source emissions for 1999, 2002 and 2005 in the Parkersburg Area.Parkersburg Area - PM10-FIL02004006008001000120014001600180020001999 2002 2005

tons/yearPleasants, WVWood, WVParkersburg Area PM25-FIL010020030040050060070080090010001999 2002 2005

tons/yearPleasants, WVWood, WVParkersburg Area - NOx0500010000150002000025000300001999 2002 2005

tons/yearPleasants, WVWood, WVParkersburg Area - SO2010000200003000040000500006000070000800001999 2002 2005

tons/yearPleasants, WVWood, WVWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 100 7.4. Unmonitored Area Analysis
Pursuant to the Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5, and Regional Haze , West Virginia is including an additional review, termed Unmonitored Area Analysis, as part of the State Implementation Plan. This review is intended to ensure that a control strategy leads to reductions in PM 2.5 at other locations which could have baseline (and future) design values exceeding the NAAQS were a monitor located there. The purpose of the analysis is to use a combination of model output and ambient data to identify areas that might exceed the NAAQS if monitors were located there. Considering WV’s particular circumstances, however, we believe a rigorous approach to the unmonitored area analysis is unnecessary as described in terms of actual monitored values, RRFs, Parkersburg Area Terrain Features, and sulfates. As such, WV believes that future year design values will not be greater than the NAAQS in areas that are not monitored. The unmonitored area analysis is intended to be the primary means for identifying high PM 2.5 concentrations outside of traditionally monitored locations. The spatial resolution of the modeling that is the underlying basis of the unmonitored area analysis will determine how well it addresses primary PM 2.5 hotspot issues. The finer the resolution of the grid model, the more likely primary PM 2.5 hotspots will be identified. Based on the monitoring guidance, it is believed that an unmonitored area analysis conducted at 12km or finer resolution is sufficient to address unmonitored PM 2.5 for the annual NAAQS. VISTAS regional scale modeling resulted in a 2009 DVF of 14.0 μ g/m 3 ,for Wood County (AIRS ID 54-107-1002), which is below the NAAQS. VISTAS modeling of future and base year emissions resulted in a spatial field of relative response factors (RRFs). Table 1. contains the VISTAS modeling results, including the Parkersburg area (Wood County). For the Parkersburg area, all RRFs in the spatial field are less than one, indicating a reduction in design values for all grid cells. Interpolation between these grid cells would result in RRFs of less than one for all unmonitored areas, thus indicating a reduction in design values for all grid cells, including those cells that are not monitored. Combining this result with spatial field of monitored values would lead to expectation that all concentrations in unmonitored areas would not be higher than the concentration values at actual monitoring sites. The Parkersburg area is located in the north-west portion of West Virginia and is characterized by rolling terrain with moderate differences in elevation. Elevations in Wood County range from tens of feet to several hundreds of feet. As su ch, it is not expected that actual concentration values in unmonitored areas will exceed either the spatial field of monitored values or the spatial field of RRFs as a result of terrain influences. As a result, terrain influences are not expected to contribute to future year design values that are greater than the NAAQS in unmonitored areas. Finally, since sulfates dominate the individual contribution to PM 2.5 at the Wood County site and is a widespread, dominating component at other sites in the region we do not expect significant PM 2.5 gradients in the Wood County non-attainment area, including the unmonitored areas.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 101 Table 7.4-1. VISTAS Regional Scale Modeling Results AIRS ID County 2002 Annual

DVC 2009 Annual (Base G4a) DVF Difference(2009 DVF2002 DVC) Notes

54-003-0003 Berkeley 16.2 13.6 -2.6 54-009-0005 Brooke 16.7 13.8 -2.9 54-011-0006 Cabell 16.5 14.1 -2.4 54-029-0011 Hancock 15.8 13.1 -2.7 54-029-1004 Hancock 17.3 14.2 -3.1 54-033-0003 Harrison 14.0 11.6 -2.4 54-039-0010 Kanawha 15.4 13.2 -2.2 54-039-0011 Kanawha 12.6 Missing SMAT Data 54-039-1005 Kanawha 17.1 14.8 -2.3 54-049-0006 Marion 15.3 13.0 -2.3 54-051-1002 Marshall 15.6 13.4 -2.2 54-055-0002 Mercer 12.7 10.5 -2.2 54-061-0003 Monongalia 14.8 12.5 -2.3 54-069-0010 Ohio 15.1 12.7 -2.4 54-081-0002 Raleigh 13.1 10.9 -2.2 54-089-0001 Summers 10.1 8.2 -1.9 54-107-1002 Wood 16.1 14.0 -2.1WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 102 8.0. CLEAN AIR ACT SECTION 172(C) REQUIREMENTS Section 172(c) of the Clean Air Act, as amended, contains the general requirements for the nonattainment areas. These requirements are listed below and are discussed in more detail in this Section. Section 172(c) Nonattainment Plan Provisions (1) Reasonably available control measures (RACM) (2) Reasonable further progress (RFP) (3) Actual emissions inventory and periodic emissions inventory (4) New source review (5) Permit requirements for new and modified sources (6) Other measures as may be necessary to provide attainment by specified attainment date (7) Compliance with Section 110(a)(2) (8) Equivalent techniques (9) Contingency measures 8.1. Reasonably Available Control Measures Section 172(c)(1) of the CAA requires SIPs to provide for the implementation of RACM to demonstrate attainment as expeditiously as practicable. A subset of RACM is reasonably available control technology (RACT), which relate s specifically to stationary point sources. In April 2007 the EPA promulgated the PM 2.5 Implementation Rule which established the requirements for RACM and RACT for PM 2.5 nonattainment areas. The EPA stated that if a State projects that an area will attain the standard within 5 years of designation as a result of existing measures (i.e., projected to have a design value of 14.5 μg/m 3 or lower), then the State may conduct a limited RACT and RACM analysis that does not involve additional modeling. Additionally, if a State could not achieve signi ficant emissions reductions during 2008 due to the time required to implement the potential measures or other relevant factors, then the State could conclude that there are no further RACM for the nonattainment area that would advance the attainment date by one year or more. The predicted design value for the Parkersburg area is 14.0 μg/m 3 , well below the annual PM 2.5 standard; therefore, the WVDAQ may conduct a limited RACT and RACM analysis. A few RACM type measures have already been implemented by the WVDAQ, including 45CSR6, and 45CSR17, which were discussed in Section 6. Additional RACM measures could not be implemented during 2008. It would take at least two years to complete rule making and another eighteen months to two years for implementation of any new control measures. There are no other RACM measures available to advance the attainment date. See Section 9 for a detailed discussion of RACM and RACT. Furthermore, the PM 2.5 Implementation Rule includes a presumption that in States that fulfill their CAIR SO 2 emission reduction requirements entirely through EGU emission reductions, compliance by EGU sources with an EPA-approved CAIR SIP or a CAIR FIP would satisfy theirWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 103 SO 2 RACM/RACT requirements. The PM 2.5 Implementation Rule also includes a presumption that in States that are subject to the CAIR annual NO xemission reduction requirements and fulfill these requirements entirely through EGU emission reductions, compliance by EGU sources with an EPA-approved CAIR SIP or a CAIR FIP would satisfy their NO xRACM/RACT requirement for the PM2.5 NAAQS, provided that sources with existing selective catalytic reduction (SCR) emission control technology installed on their boilers operate that technology on a year round basis beginning in 2009. West Virginia is subject to the CAIR and has adopted, and submitted for SIP approval, 45CSR39 – Control of Annual Nitrogen Oxide Emissions to Mitigate Interstate Transport of Fine Particulate Matter and Nitrogen Oxides; and 45CSR41 – Control of Annual Sulfur Dioxide Emissions to Mitigate Interstate Transport of Fine Particulate Matter and Sulfur Dioxide. Both of these rules rely entirely on EGU emission reductions to satisfy the annual NO xand SO 2 reduction requirements. West Virginia is subject to the CAIR FIP until EPA approves the CAIR SIP. West Virginia has a three utility companies that have units equipped with existing SCRs: • Virginia Electric and Power Company - Mt. Storm Power Station • American Electric Power - John Amos, Mountaineer and Mitchell Power Stations • Allegheny Energy Supply - Harrison and Pleasants Power Stations. Virginia Electric and Power Company and Ameri can Electric Power are both subject to federal Consent Decrees which require the year round operation of the existing SCRs beginning January 1, 2008. [See Appendix M for copies of the Consent Decrees.] Allegheny Energy Supply has entered into Consent Orders with the State and agreed to operate the SCRs on the units at Harrison and Pleasants whenever the units are in operation, except for periods of required SCR maintenance, beginning January 1, 2009. [See Appendix M for copies of the Consent Orders.] Therefore, compliance with the CAIR requirements satisfy the SO 2 and NO xRACM/RACT requirements for electric generating units. 8.2. Reasonable Further Progress
In the PM 2.5 Implementation Rule at 40 CFR 51.1009(b), the EPA maintained that an area that demonstrates attainment by 2010 would be considered to have satisfied the RFP requirement and need not submit any additional material to satisfy this requirement. The EPA will view the attainment demonstration as demonstrating that the area is making reasonable progress toward attainment. Herein, the DEP demonstrates that the Parkersburg area will attain by 2010. Therefore, RFP is satisfied and further RFP analysis is unwarranted. 8.3. Actual Emissions Inventory Section 172(c)(3) requires the development of a comprehensive, accurate current inventory of actual emissions from all sources in the nonattainment area. Such inventory is due two years after the effective date of the PM 2.5 non attainment designations, i.e., April 5, 2007. TheWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 104 WVDAQ met this requirement through the submittal of the 2002 emission inventories under the CERR for the West Virginia counties in this nonattainment area. The WVDAQ submitted statewide emissions for point, area, nonroad mobile and highway mobile sources. The final 2002 emission inventories used in the attainment demonstration will go through the public hearing process with the full attainment demonstration, which will include any updates or revisions that are necessary since the CERR submittal. 8.4. Periodic Emissions Inventory Section 172(c) of the CAA requires the submitta l of periodic emission inventories. The WVDAQ plans to meet this requirement through the CERR submittals. As such, the WVDAQ submitted the 2005 emissions inventory June 1, 2007. Similar to the 2002 CERR submittal, the 2005 CERR submittal included statewide emissions for point, area, nonroad mobile and highway mobile sources. 8.5. Permitting Requirements for New and Modified Sources CAA Section 172(c)(5) requires a permit program consistent with the requirements of Section 173. On June 13, 1984, West Virginia requested that EPA approve regulation 45CSR14 "Permits for the Construction and Major Modification of Major Stationary Sources of Air Pollution for the Prevention of Significant Deterioration" (PSD) as a revision to the state implementation plan. Following State response to EPA identified inconsistencies with federal requirements, EPA approved the PSD SIP revision in the Apr il 11, 1986 Federal Register [51 FR 12517]. EPA approved subsequent revisions to the PSD SIP in 1993 [58 FR 34526] and in 1996 [61 FR 54734]. In 2005 West Virginia revised 45CSR14 to incorporate changes required by EPA in the December 31, 2002 Federal Register [67 FR 80186], with the changes becoming effective June 1, 2005. Subsequent to the changes becoming effective the District of Columbia (DC) Circuit Court vacated and remanded key provisions of the December 31, 2002 federal rule. On December 1, 2005 the State requested that EPA approve the revised 45CSR14 as part of the SIP, and on December 22, 2005 requested that EPA not act on the portions of the rule that correspond to the provisions of the federal rule that had been vacated. On November 2, 2006 EPA approved the revisions to 45CSR14 as part of the SIP [see 71 FR 64470]. West Virginia has also implemented as part of its SIP since 1972, 45CSR13 "Permits for Construction, Modification, or Relocation of St ationary Sources of Air Pollutants, and Procedures for Registration and Evaluation" re quiring construction/modification permits for all regulated emission sources. EPA approved the most recent revisions to 45CSR13 as a SIP revision effective April 29, 2003 [68 FR 9559]. In 1983 West Virginia enacted rule 45CSR19 “Requirements for Pre-Construction Review, Determination of Emission Offsets for Proposed New or Modified Stationary Sources of Air Pollutants and Bubble Concept for Intrasource Pollutants” for permitting of major sources and modifications in designated nonattainment areas pursuant to Clean Air Act requirements. This rule was made part of the SIP effective August 1, 1985 [see 50 FR 27247].WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 105 The 1990 CAAA part "D" new source requirements for West Virginia's ozone nonattainment areas were not due for submission to EPA until November 15, 1992. The West Virginia Air Pollution Control Commission, however, adopted (November 10, 1992) by Emergency Rule revisions to existing regulation 45CSR19 "R equirements for Pre-Construction Review, Determination of Emission Offsets for Proposed New or Modified Stationary Sources of Air Pollutants and Emission Trading for Intrasource Pollu tants" reflecting to the extent possible the new CAAA part D requirements (i.e. offsets). The 1992 version of 45CSR19 was adopted as a Legislative Rule on July 7, 1993 and submitted to EPA on August 10, 1993 as a SIP revision. Subsequently EPA notified the State of deficienci es in the SIP revision. West Virginia revised 45CSR19 to correct the identified deficiencies and to incorporate changes required by EPA in the December 31, 2002 Federal Register [67 FR 80186], with the changes becoming effective June 1, 2005 [see Appendix K]. Subsequent to the changes becoming effective the DC Circuit Court vacated and remanded key provisions of the December 31, 2002 federal rule. On December 1, 2005 the State requested that EPA approve the revised 45CSR19 as part of the SIP, and on December 22, 2006 requested that EPA not act on the portions of the rule that correspond to the provisions of the federal rule that had been vacated. On November 2, 2006 EPA approved the revisions to 45CSR19 as part of the SIP [see 71 FR 64468]. Therefore, West Virginia has a nonattainment NSR program which has been approved by EPA.
8.6. Other Measures Section 172(c)(6) requires the nonattainment SIPs to include enforceable limitations and other control measures, along with schedules for compliance as needed to demonstrate attainment. Section 6.0 of this document discusses in detail the Federal and State measures that were modeled to show attainment. Appendix K contains the rules and compliance schedules. 8.7. Compliance with Section 110(a)(2) Section 172(c)(7) of the CAA requires nonattainment SIPs to meet the applicable provisions of Section 110(a)(2). On October 2, 2007, the EPA provided guidance on SIP elements required under Section 110(a)(1) and (2) for the PM 2.5 NAAQS. By letters dated April 3, 2008 and July 9,2008, WVDAQ certified to EPA Region III that specific elements under Section 110(a)(2) have previously been satisfied by certain portions of the West Virginia SIP. On May 22, 2008, WVDAQ submitted a general infrastructure PM2.5 SIP, which covered the remaining elements not previously certified to be in the SIP. 8.8. Equivalent Techniques The WVDAQ believes that the procedures for modeling, emissions inventory and planning follow EPA guidance and is not requesting approva l for equivalent techniques, as envisioned under Section 172(c)(8).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 106 8.9. Contingency Measures Section 172(c)(9) of the Clean Air Act, as amended, requires that nonattainment area SIPs provide for the implementation of specific measures, termed contingency measures, if an area fails to attain the NAAQS in a timely manner or to achieve RFP. Section III(C)(3) of the General Preamble further explains that contingency measures should consist of other available control measures, beyond those necessary to meet the core control requirement to implement reasonably available control measures (RACM) [see Secti on 172(c)(1) of the Act] and, therefore, beyond those reasonably required to expeditiously attain the standards (see 57 FR 13543). Section 172(c)(9) of the Act specifies that contingency measures shall “take effect . . . without further action by the State, or the [EPA] Administrator.” EPA has interpreted this latter requirement [in the General Preamble (at 57 FR 13512)] to mean that no further rulemaking activities by the State or EPA would be needed to effect full implementation of the contingency measures, to occur within 60 days after EPA notif ies the State of its failure to attain the NAAQS in a timely manner or to achieve RFP. EPA recognizes that certain actions, such as no tification of sources, modifications of permits, etc., may be needed before some measures could be implemented. However, States must show that their contingency measures can be implemented with minimal further administrative action on their part and with no additional rulemaking actions such as public hearings or legislative review. 8.9.a. Enforceability Issues All measures and other elements in the SIP must be enforceable by the state/local governments and/or EPA [see Section 172(c)(6) and 110(a)(2)(A) of the Act and 57 FR 13556]. Nonattainment area plan provisions must also c ontain a program to provide for enforcement of control measures and other elements of the SIP [see Section 110(a)(2)(C) of the Act]. The State of West Virginia has the authority to implement and enforce all emission limitations and control measures adopted by this SIP. 8.9.b. Evaluations/Required Reductions The contingency measures for the attainment demonstration must total one year of reductions needed to attain. The inventory is calculated as described in Section 4. Table 8.9-1 shows the calculation of the necessary reductions.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 107 Table 8.9-1. Contingency Requirement for PM and PM Precursors

PM Precursor PM and PM Precursor Emissions 2002-2009 (tons/year)

2002 2009 2002-2009 Contingency Requirement

Calculation (2002-2009) 7 NO x 27,758 15,503 12,255 1,750.7 PM 2.5 Direct 2,372 2,387 0 0.0 SO 2 71,849 40,648 31,201 4,457.3

8.9.c. Equivalency Ratios Contingency reductions must occur on a timetable that is directly related to the attainment SIP schedule. States have no more than one year after notification by EPA of an attainment failure to achieve the contingency plan reductions. For a potential attainment failure, notification would be received in 2010; therefore, the contingency reductions must be achieved no later than 2011. According to EPA guidance, emission reductions fr om different precursors can be used to meet the required contingency target. EPA recommended a method to assess equivalent reductions for different precursors. The recommended approach is to review existing data and sensitivities performed as part of photochemical modeling to estimate the relative impact of different precursors on PM 2.5 concentrations. Basing an equivalency ratio on relative reduction factors as generated by the Community Multiscale Air Quality (CMAQ) modeling results in a quarterly ratio of 1.1 to 1.3 tons of NO xfor each ton of SO 2 (see Appendix L.1). Using sensitivity analyses created by VISTAS and Georgia Tech (see Appendi x L.2), gives an equivalency ratio of 4.5 tons of NO x for each ton of SO 2 in the Parkersburg area.
Table 8.9-2. Quarterly Speciated Relative Reduction Factors (RRFs) for Wood County Quarter Crustal EC OC SO 4 NO 3 NO 3 /SO 4 Q1 1.09996 0.86626 0.98344 0.90487 0.96170 1.1 Q2 1.11903 0.82319 0.96604 0.71783 0.77616 1.1 Q3 1.14873 0.85710 0.96181 0.65552 0.83964 1.3 Q4 1.12030 0.82492 0.98609 0.86338 0.97177 1.1WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 108 8.9.d. Identified Contingency Measures Table 8.9-2 lists the contingency measures identified by West Virginia for the Attainment Demonstration. These measures deliver a total benefit of 246 tons/year of PM 2.5 , 1,093 tons/year of NO x and 9,358 tons/year of SO 2 . The NO xreductions are 596 tons less than the required reduction. However the SO 2 reductions are 6,120 tons greater than required. The excess SO 2 reductions are more than 7 times the required additional NO xreduction, and this ratio is significantly higher than the equivalency assessments described in Section 8.9.c. The contingency measures for the Parkersburg attainment plan are well in excess of the equivalency ratios described in Section 8.9.c and therefore, should be an appropriate backstop for improving air quality if the monitoring network does not demonstrate compliance with the 1997 PM 2.5 NAAQS in 2009.

Table 8.9-2. Contingency Measure Reductions from 2002 Emissions (tons/year ) Contingency Measure NO x (tpy) PM 2.5 (tpy) SO 2 (tpy) Cabot - Plant Shutdown 782 81 6,961 Cytec - Replacement of Coal-fired boilers with gas fired boilers 311 165 2,397 TOTAL REDUCTIONS from Contingency Measures 1,093 246 9,358

Table 8.9-3. Determination of Excess Reductions from Contingency Measures NO x (tpy) PM 2.5 (tpy) SO 2 (tpy) Reductions from Contingency Measures 1,093 246 9,358 Required Reductions from Contingency Measures 1,751 0 4,457 Difference (Excess reductions +, additional reductions needed -) -658 246 4,901 NO x equivalence for SO 2 (Needed NO x reductions/1.1 equivalency ratio) (658 tons NO x= 598 tons SO 2 ) 658 – 598
Excess reductions from Contingency Measures 0 246 4,303

In accordance with EPA’s guidance encouraging early implementation of contingency measures to guard against failure to either meet a milestone or attain, West Virginia has implemented the contingency measures identified in Table 8.9-2 according to the timetable indicated in Section 6. EPA’s guidance on early implementation of control measures is as follows:WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 109 The EPA encourages the early implementation of required control measures and of contingency measures as a means of guarding against failures to meet a milestone or to attain. Any implemented measures (that are not needed for the rate-of-progress requirements or for the attainment requirements) would need to be backfilled only to the extent that they are used to meet a milestone. The reductions from the designated contingency measures are surplus, that is, beyond those contained in the Attainment Demonstration enclosed in this SIP. They will not be used to meet that milestone requirement. As a result, the state will not be required to backfill any contingency measures that they choose to implement in advance of the requirement.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 110 9.0. REASONABLY AVAILABLE CONTRO L MEASURES (RACM), INCLUDING REASONABLY AVAILABLE CONTROL TECHNOLOGY (RACT) FOR STATIONARY SOURCES [40 CFR §51.1010] Section 172(c)(1) of the Clean Air Act requires state implementation plans (SIPs) to include an analysis of reasonably available control measures (RACM). This analysis is designed to ensure that in the Parkersburg region, the State of West Virginia implements all RACM. to demonstrate attainment on the earliest date possible with the annual PM 2.5 standard. 9.1. Statutory and Regulatory Requirements for RACM Analysis The statutory RACM requirement can be found in Section 172(c)(1) of the Clean Air Act, which directs states to “provide for implementation of all reasonably available control measures as expeditiously as practicable.” The regulatory RACM for a PM 2.5 SIP revision can be found at 40 CFR §51.1010. This section requires: (a) For each PM 2.5 nonattainment area, the State shall submit with the attainment demonstration a SIP revision demonstra ting that it has adopted all reasonably available control measures (including RACT for stationary sources) necessary to demonstrate attainment as expeditiously as practicable and to meet any RFP requirements. The SIP revision shall contain the list of potential measures considered by the State, and informati on and analysis sufficient to support the State’s judgement that it has adopted all RACM, including RACT. (b) In determining whether a particular emission reduction measure or set of measures must be adopted as RACM under section 172(c)(1) of the Act, the State must consider the cumulative impact of implementing the available measures. Potential measures that are reasonably available considering technical and economic feasibility must be adopted as RACM if, considered collectively, they would advance the attainment date by one year or more.
9.2. Discussion of Reasonable Control Measures During the period 2002-2005, the state implemented the control measures described in Section 6. Significant additional control measures have already been adopted and are planned for implementation in 2009, including the Clean Air Interstate Rule (CAIR). Modeling data provided in Section 4 demonstrates that continued implementation of these control measures described in Section 6, along with CAIR, will allow the Parkersburg area to come into compliance with the 1997 PM 2.5 NAAQS on or before April 2010, as required by 40 CFR §51.1004.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 111 9.3. Discussion of RACT 9.3.a. EGU RACT Analysis Eight coal-fired power plants having a combined total of 20 utility boilers, which also may becalled electricity generating units (EGUs), operate within four of West Virginia’s six nonattainment areas for particulate matter having an aerodynamic diameter of 2.5 microns or less (PM 2.5 ). Besides PM 2.5 , these boilers emit two other pollutants of concern: sulfur dioxide (SO 2 ) and nitrogen oxides (NO x ). With guidance published by the U.S. Environmental Protection Agency (EPA) and through technical and economic analyses, the Division of Air Quality (DAQ) of the West Virginia Department of Environmental Protection determined potential reasonably available control technology (RACT) candidates for these pollutants from utility boilers. This analysis presents DAQ’s final ranking of those RACT candidates. Based on review of EPA guidance, though, DAQ believes no additional controls for the pollutants of concern will be needed at these power plants’ boilers. However, if controls were warranted, DAQ fore sees at least two obstacles that prevent implementation that would advance the attainment date. 1. One is the timing involved in the design/procure/fabricate/install/accept process for equipment. Based on its analysis, DAQ believes insufficient time exists between now and 2009 for an owner/operator to initiate and then successfully complete the process for traditional or innovative controls, as well as for major upgrades to existing controls. 2. The other is the uncertainty of cost-effectiveness data. DAQ believes selection of any ranked RACT candidate demands site-specific knowledge, including accurate actual cost data, instead of data estimated through regulatory models. Based on its research, DAQ believes available data are outdated or do not reflect current supply-demand pressures that have significantly increased costs in recent years.
9.3.a.1. Background Depending on the non-attainment areas in which these power plants are located, they may or may not generate most of stationary-source emissi ons that contribute directly or indirectly to PM 2.5 non-attainment. Of those emissions, though, direct PM 2.5 emissions have more significance in their ability to directly contribute to non-attainment than SO 2 , which does so indirectly through atmospheric or secondary formation of sulfates; or NO x , which does so through atmospheric formation of nitrates. Two tables generally describe the plants. • Table 9.3.a-1 lists the plants, their respective boilers and sizes, and the non-attainment area in which each plant is located.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 112 • Table 9.3.a-2 lists the respective, current estimated emissions, in tons per year (tpy), from each plant for the three principal pollutants of concern: PM 2.5 -Primary – the sum of filterable and condensable fractions contained in the stack gas measured by an EPA test method – as well as SO 2 and NO x . The table also contains data on emissions of PM 10 -Primary. PM 10 is particulate matter with an aerodynamic diameter of 10 microns or less. PM 10 -Primary comprises the filterable and condensable fractions contained in the stack gas measured by an EPA test method. Note that footnotes for both tables are explained at the bottom of Table 9.3.a-2.
Table 9.3.a-1. West Virginia Coal-Fired Power Plants in PM 2.5 Nonattainment Areas Facility Name Total Boilers Combined Generating Capacity (MW) Nonattainment Area
John Amos 3 2,900 Charleston Kanawha River 2 400 Charleston
Mitchell 2 1,600 Wheeling Kammer 3 630 Wheeling
Mountaineer 1 1,300 Huntington Philip Sporn 5 1,050 Huntington
Pleasants 2 1,300 Parkersburg Willow Island 2 235 Parkersburg

Table 9.3.a-2. PM 10 -Primary, PM 2.5 -Primary, SO 2 and NO xfrom West Virginia Coal-Fired Power Plants in PM 2.5 Nonattainment Areas, (tpy) A Facility Name PM 10 -Primary B Emissions (tpy) PM 2.5 -Primary B Emissions (tpy) SO 2 Emissions (tpy) NO xEmissions (tpy) John Amos 552 271 107,552 44,479 Kanawha River 240 118 15,691 6,138
Mitchell 252 123 59,165 31,163 Kammer 78 56 42,064 14,325Table 9.3.a-2. PM 10 -Primary, PM 2.5 -Primary, SO 2 and NO xfrom West Virginia Coal-Fired Power Plants in PM 2.5 Nonattainment Areas, (tpy) A Facility Name PM 10 -Primary B Emissions (tpy) PM 2.5 -Primary B Emissions (tpy) SO 2 Emissions (tpy) NO xEmissions (tpy)

WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 113

Mountaineer 279 136 43,008 12,883 Philip Sporn 198 96 41,207 13,611
Pleasants 779 338 41,917 13,718 Willow Island 333 210 14,460 5,948 Notes: A Data taken from DEP analysis of 2002 emissions inventory certified data submitted by sources’ owners/operators to EPA’s Clean Air Markets Division as part of the Acid Rain Program. B Primary means that the data include both filterable and condensable fractions of PM.

9.3.a.2. Final Ranking of RACT Candidates To determine RACT candidates that might apply to each of the three pollutants of concern emitted from the EGU category, DAQ followed EPA guidance published in the federal government’s March 29, 2007, Final Rule to Implement the Fine Particle National Ambient Air Quality Standards (PM 2.5 Implementation Rule). Among several things, that guidance required evaluations of technical and economic feasibilities of each candidate technology. For technical feasibility, DAQ compared existing controls at the West Virginia facilities to those expected or potentially used with any coal-fired utility boiler. Cost effectiveness of controls, presented as dollars per ton of respective pollutant removed ($/ton), described the principal test for determining if any control, for any pollutant, was economically feasible. Based on the technical and economic analyses recorded in Chapters 4-6 of the EGU RACT Analysis, Table 9.3.a-3, which DAQ considers to be the summary of its key findings, lists the DAQ’s final ranking of RACT candidates for electric generating units. The chapters just mentioned, which are in Appendix M, contain the respective evaluations of potential RACT for PM 2.5 , SO 2 and NO x . 9.3.a.3. Why No Additional Controls Will Be Required Based on EPA statements about controls equiva lency or superiority among federal air-qualitymanagement programs, however, DAQ believes that no additional controls are needed on the utility boilers in the state’s PM 2.5 non-attainment areas.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 114 Table 9.3.a-3. Final Ranking of RACT Ca ndidates for Coal-Fired EGUs for PM 2.5 , NO x and SO 2 in PM 2.5 Nonattainment Areas Pollutant A Control Technology B,C,D Retrofit, Modification or Other Enhancements

PM 2.5 ESP/dry Improvements/expansion to existing particulate controls ESP/wet Gas conditioning ESP + FF Addition downstream of full-sized FF Addition downstream of smaller technologies such as COHPAC Addition of particle agglomerator upstream of ESP or FF

SO 2 FGD/wet Improvements to existing units E SDA + FF Coal washing FSI/furnace

DSI/duct

NO x LNB + OFA Boiler tuning E Reburn Coal-pulverizer-plus-boiler tuning E

SCR Improved/intelligent soot blowing SNCR Notes: A Pollutants = particulate matter with an aerodynamic diameter of 2.5 microns or less (PM 2.5 ), sulfur dioxide (SO 2 ) and nitrogen oxides (NO x ) B Control equipment for PM = electrostatic precip itator (ESP), fabric filter (FF) or baghouse (BH) C Control equipment for SO 2 = wet scrubber or wet flue-gas desu lferization (wet FGD), furnace sorbent injection (FSI), spray dry absorber (SDA) and duct or dry sorbent injection (DSI) D Control equiment for NO x = selective catalytic reduction (SCR), selective non-catalytic reduction (SNCR), low-NO x burner (LNB) and overfire air (OFA) E By themselves, these enhancements might not be c onsidered RACT. However, they or any others that might give incremental pollutant reduction could be us ed with each other, as appropriate, as well as NO x control technologies such as LNB + OFA, reburn, SCR and SNCR; SO 2 scrubbers; or boilers controlled with jucst ESPs, if compatible with existing control c onfigurations. They could also be used in an SCR-wetFGD-ESP or SNCR-wet-FGD-ESP configuration, if the enhancements were compatible.
For example, for its Clean Air Interstate Rule or CAIR program, EPA states in its PM 2.5 Implementation Rule that CAIR equals RACT for coal-fired utility boilers: Specifically, the final rule includes a presum ption that in States that fulfill their CAIR SO 2 emission reduction requirements entirely through EGU emission reductions (i.e. without reductions from non-EGU sources or allowing non-EGUWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 115 sources to opt in to the CAIR SO 2 trading program), compliance by EGU sources with an EPA-approved CAIR SIP [State Implementation Plan] or a CAIR FIP [Federal Implementation Plan] would satisfy their SO 2 RACT/RACM [Reasonably Available Control Measures] requirements for attaining the fine particle NAAQS [National Ambient Air Quality Standard]. . . For EGUs in the CAIR region, based upon the presumption explained here, States may define RACT/RACM as the CAIR le vel of control on the collective group of sources in the region rather than impose a specific level of control on an individual source. And in EPA’s Oct. 2006 Regional Haze Rule—also known as the BART, or best available retrofit technology, rule—for certain EGUs, that agency states that CAIR-related technologies were better than BART technologies: In the BART rule, EPA presents the results of an analysis showing that controls for EGUs subject to CAIR will result in more visibility improvement in natural areas than BART would have provided. Therefore, States which adopt the CAIR cap and trade program for SO 2 and NO xare allowed to apply CAIR controls as a substitute for controls required under BART because our analysis concluded that CAIR controls are “better than BART” for EGUs in the states subject to CAIR. Therefore, since West Virginia remains subject to CAIR and since the state adopted an approach consistent with EPA’s requirements, DAQ presum es that the state’s CAIR controls for SO 2 and NO x , as well as PM 2.5 , equal RACT. DAQ also presumes that these controls are better than BART controls. If, however, additional PM 2.5 reductions were required from utility boilers beyond the CAIRequals-RACT-for-EGUs statement in EPA’s PM 2.5 Implementation Rule, then more controls or modifications/upgrades to existing controls could be considered from the Table 9.3a-3 menu of potential RACT options. DAQ acknowledges, though, that it may be technically feasible for owners/operators to add more PM controls such as fabric filters or other devices to further polish or cleanse the flue gas by removing more PM 2.5 .But DAQ believes that owners/operators of facilities that have or will have the SCR/wetFGD/ESP combination likely will not add more SO 2 control devices at those facilities. Enhancements to existing SO 2 controls might be an option. However, given the newness of the scrubbers at those facilities that have or will have them, such enhancements are also unlikely. DAQ also does not expect owners/operators of utility boilers that already have NO xcontrols — i.e., LNB, OFA, LNB + OFA, SNCR or SCR—required through the NO xSIP Call to install additional controls. DAQ notes that currently existing SCR and SNCR units at larger utility boilers will operate year-round when CAIR’s NO xPhase I commences. Note that DAQ defines larger utility boilers as those with generating capacities greater than 450 megawatts (MWs).WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 116 9.3.a.4. Why Controls Implementation Could Be Delayed If additional controls or major upgrades were required, DAQ believes those could not be installed and operational by January 2009. Thus, the attainment date could be not advanced by one year. At least five major obstacles prevent realistic timely implementation by 2009 of any RACT candidate involving retrofit of any major air-pollution-control device or major upgrades to any existing technologies—even if an owner/operator initiated the design/procure/fabricate/install/ accept process immediately. 1. While, in the strictest sense, add-on technologies as well as enhancements or major upgrades may be technically feasible and/or cost effective, whether any occur depends on site-specific factors. DAQ believes regulatory estimates cannot accurately or completely describe these factors for a particular facility, nor the costs owners/operators could incur.Nor can regulatory estimates reasonably predict the amount of time the entire add-ontechnology/major-upgrade process may take from decision to final acceptance. 2. Because owners/operators compete simultaneously among themselves for those vendors who have the capacity to supply equipment and services, it is unwise to presume that equipment vendors can and will accept new capital projects. 3. Materials and labor such as boilermakers and steelworkers, for example, to install equipment may not be available. For example, as respondents to a Lake Michigan Air Directors Consortium/Midwest RPO (L ADCO/MRPO) draft RACT document for industrial boilers said: “Because the ICI boiler owners and operators are competing for resources (labor and materials) with the EGU category, there are likely to be impacts and delays in engineering, materials procurement, etc., going forward.” 4. At least one federal program, CAIR, injects negative impacts and delays. Besides the competition noted in point 2, above, DAQ believes that because of CAIR and the NO x SIP Call, costs of some controls have increased significantly in the past few years. Likely, those costs will not plateau until the CAIR and PM 2.5 programs are fully completed. 5. If vendors can be found who will commit to supplying the equipment, based on the technical and economic analyses performed in or referred to by this document, it appears that it could take three to four years, if notfive or six—from decision, to procurement, to design, to fabrication, then installation and through acceptance testing—to implement major add-on technologies at an existing site. Major upgrades may take less time, though DAQ has no estimates of how much less.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 117 9.3.a.5. Why RACT Selection Is Site Specific and Costs May Be Underestimated If any new technology or major upgrade is required, based on technical feasibility and cost effectiveness, DAQ notes two significant c oncerns about the ranked RACT candidates, particularly those requiring significant capital expense, and their selection: 1. Regardless of what candidate measure is selected or combination of measures are selected, final choice at any facility, for any boiler, depends on site-specific factors.Primarily, those include available physical space and compatibility with existing airpollution-control technologies. But among other things, site-specific factors also include combustion processes and conditions, as well as fuels used. 2. Regardless of the estimated costs presented in the EGU RACT analysis, they may underestimate the real-world expenditures that could or would occur. Underestimation could come from out-of-date data that need adjustment for inflation or for increases due to environmental-regulations-induced supply-demand pressures. Underestimation could also come from use of regulatory models or other means, rather than actual data, used to derive these costs. Underestimation may also come from using inadequate retrofit multipliers when approximating costs.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 118 9.3.b. ICI RACT Analysis Industrial, commercial and institutional boilers — IC Is — operate within four of the state’s six non-attainment areas for PM 2.5 . Besides PM 2.5 , these boilers emit two other pollutants of concern: SO 2 and NO x . Table 9.3.b-1 identifies the ICI boilers in the West Virginia portion of the Parkersburg nonattainment area that, in 2002, had actual emissions of 50 tons of either direct PM 2.5 , NO x or SO 2 .

Table 9.3.b-1. ICI Boilers in the West Virginia Portion of the Parkersburg Nonattainment Area Facility ID 2002 NO x (tons) 2002 PM 2.5 (tons) 2002 SO 2 (tons) DuPont Washington Works 107-00001 771 167 4,485 General Electric 107-00010 121 14 17 Cabot Corporation 073-00006 782 81 6,961 Cytec Industries 073-00003 423 217 2,438 With guidance published by EPA and through technical and economic analyses, DAQ determined RACT candidates for these pollutants from ICI boilers. This analysis presents DAQ’s final ranking of those RACT candidates. Even if site-specific cost-effective controls are applicable, however, DAQ foresees at least two obstacles prevent implementation that would advance the attainment date. • One is the timing involved in the design/procure/fabricate/install/accept process for equipment. Based on its analysis, DAQ believes insufficient time exists between now and 2009 for an owner/operator to initiate and then successfully complete the process for major traditional or innovative controls, as well as for major upgrades to existing controls. • The other is the uncertainty of cost-effectiveness data. DAQ believes that selection of any ranked RACT candidate demands site-specific knowledge, including accurate actual cost data, instead of data estimated through regulatory models. Based on its research, DAQ believes available data are outdated or do not reflect current supply-demand pressures that have significantly increased costs in recent years.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 119 9.3.b.1. Background Because of the quantity of emissions from industrial boilers and the potential to maximize environmental benefit from their control, this RACT analysis focuses only on them. These may be located at source categories such as pe troleum refining, carbon-black production, coal-fired electricity generation, coal-tar distillation, coking and chemicals production. Some boilers may combust only coal; some may have the capacity to burn coal; some burn fuel oil—typically, No. 2 low-sulfur distillate; and natural gas; and some burn natural gas as the primary fuel.But some industrial boilers may also burn process off-gases, such as refinery waste gas or cokeoven gas. For example, Mountain State Carbon LLC (formerly, Wheeling Pittsburgh Steel Corp.), in Follansbee, which is in the Weirton-Steubenville interstate PM 2.5 non-attainment area, has four industrial boilers that burn coke-oven gas. Boiler sizes range from 85-92 million British thermal units per hour (MMBTU/hr). Upstream of the boilers in a separate processing unit, that fuel is essentially stripped of various organic fractions and sulfur. However, the gaseous feed would contain traces of organics, hydrogen sulfide and carbonyl sulfide. The boilers also may burn natural gas, as required. In varying quantities, these and all other industrial boilers in the non-attainment areas emit PM 2.5 , SO 2 and NO x . However, coal is the only fuel that this RACT analysis addresses for all three pollutants. Several reasons exist why. • Boilers that combust No. 6 residual fuel oil, which may be associated with coal-fired or natural-gas-fired industrial boilers, do so infrequently: at startup or doing some malfunction or limited maintenance period. Thus any PM 2.5 , SO 2 and NO xemissions from these units would be infrequent, at best. However, because controls might differ for the particulate and SO 2 emissions from those suggested for coal-fired boilers, DAQ did examine controls for those two pollutants from oil combustion. • Other oil-fired boilers use No. 2 low-sulfur distillate fuel oil that in most, if not all, cases has a weight percent of sulfur of 0.25 or less. Because of the fuel characteristics, required control of SO 2 and PM 2.5 from these units is highly unlikely. If, though, NO xcontrols were needed for boilers burning this residual fuel oil, options would include those listed, analyzed and selected in Chap. 6 of the ICI RACT analysis, as well as shown in Table 9.3b-6. However, DAQ notes that Table 4.4 of a Feb. 2007 technical support document—“Identificati on and Evaluation of Candidate Control Measures: Final Technical Support Document. Prepared for Seth Barna, Ozone Transport Commission (OTC), Washington, D.C. Herndon, Va. Feb. 28.”—lists No. 2 distillate as a compliance option for boilers having inputs between 25-250 MMBTU/hr. But no industrial boilers listed in West Virginia’s most recent emissions inventory that fire No. 2 distillate fuel oil as the primary fuel exceed heat-input rates of 250 MMBTU/hr. • For boilers that fire natural gas primarily, if controls were needed, they would be for NO x , not PM 2.5 and/or SO 2 because of the fuel’s characteristics. Therefore, DAQ examined only NO x controls for such boilers.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 120 • Because of their size and fuel-firing rates, boilers that burn hydrogen or process gas would not be significant sources of PM 2.5 , SO 2 or NO x . Therefore, DAQ performed no RACT analyses on these types of units. DAQ notes, though, that the NO xcontrols examined in Chap. 6 of the ICI RACT analysis could apply to these boilers. While all these types of industrial boilers do emit PM 2.5 , they are not the principal sources of stationary-source emissions in the non-attainment areas that contribute directly or indirectly to non-attainment. But regardless of the location of the source of those emissions, direct PM 2.5 emissions have more significance in their ability to contribute to non-attainment than SO 2 , which does so indirectly through atmospheric or secondary formation of sulfates; or NO x , which does so through atmospheric formation of nitrates.
9.3.b.2. Final Ranking of RACT Candidates To determine RACT candidates that might apply to each of the three pollutants of concern emitted from the EGU category, DAQ followed EPA guidance published in the federal government’s PM 2.5 Implementation Rule. Among several things, that guidance required evaluations of technical and economic feasibilities of each candidate technology. For technical feasibility, DAQ compared existing controls at the West Virginia facilities to those expected or potentially used with any coal-fired or oil-fired utility boiler. Cost effectiveness of controls, presented as dollars per ton of respective pollutant removed ($/ton), described the principal test for determining if any control, for any pollutant, was economically feasible. Chapters 4-6 of the ICI RACT analysis cont ain, respectively, the evaluation of potential RACT controls for PM 2.5 , SO 2 and NO x . Based on the technical and economic analyses recorded in those chapters, the following tables, which DAQ considers to be the summary of its key findings, list the DAQ’s final ranking of RACT candidates for industrial boilers. • Table 9.3.b-2, RACT Candidates for Coal-fired Industrial Boilers for PM 2.5 • Table 9.3.b-3, RACT Candidates for Oil-fired Industrial Boilers for PM 2.5 • Table 9.3.b-4, RACT Candidates for Coal-fired Industrial Boilers for SO 2 • Table 9.3.b-5, RACT Candidates for Oil-fired Industrial Boilers for SO 2 • Table 9.3.b-6, RACT Candidates for Industrial Boilers for NO xWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 121 Table 9.3.b-2. Final Ranking of RACT Candidates for PM 2.5 from Coal-fired Industrial Boilers in PM 2.5 Nonattainment Areas Control Scenario Control Technology/Enhancement Pre-combustion Coal washing

Post-combustion FF ESP / wet or dry Venturi scrubber

Wet scrubber Multiclone/variable-throat venturi Gas conditioning A Addition downstream of existing control of ESP or FF Addition downstream of smaller technologies such as COHPAC A Addition of particle agglomerator upstream of ESP or FF A Notes: ESP = electrostatic precipitator; FF = fabric f ilter; and COHPAC = compact hybrid particulate collector A Some enhancements might not be considered RA CT, if they were the only technology implemented.

Table 9.3.b-3. Final Ranking of RACT Candidates for PM 2.5 from Oil-fired Industrial Boilers in PM 2.5 Nonattainment Areas Control Scenario Control Technology/Enhancement Pre-combustion Fuel switching from high-sulfur (S) fuel to lower-S fuel
Post-combustion ESP / dry Venturi scrubber

Wet scrubberWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 122 Table 9.3.b-4. Final Ranking of RACT Candidates for SO 2 from Coal-fired Industrial Boilers in PM 2.5 Nonattainment Areas Control Scenario Control Technology/Enhancement Pre-combustion Fuel switching: higher-S coal to lower-S coal Coal washing

Combustion FSI Improved/intelligent sootblowing A
Post-combustion FGD (wet) FGD (SDA)

DSI/duct Notes: S = sulfur; FSI = furnace sorbent injection; FGD = flue gas desulfuization (also known as wet scrubbing) A By itself, this enhancement cannot be considered RA CT. However, it might give an incremental pollutant reduction that, when combined with other enhancements for either PM or NO x , may give significantly reduced NO x . The slight increase in SO 2 removal is incidental to the primary purpose of improved/intelligent sootblowing: to reduce boiler generated NO x .

Table 9.3.b-5. Final Ranking of RACT Candidates for SO 2 from Oil-fired Industrial Boilers in PM 2.5 Nonattainment Areas Control Scenario Control Technology/Enhancement Pre-combustion Fuel switching: higher-S coal to lower-S oil
Post-combustion FGD (wet)WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 123 Table 9.3.b-6. Final Ranking of RACT Candidates for NO xfrom Industrial Boilers in PM 2.5 Nonattainment Areas Control Scenario Control Technology/Enhancement Combustion LNB WSI OT + WSI LNB + OFA Coal reburn LNB + FGR Natural gas reburn OT/staged combustion B Boiler tuning B Improved/intelligent sootblowing B Low-excess-air firing/oxygen trimB Switching to lower-fuel-bound-nitrogen fuel

Post-combustion SCR SNCR Notes: LNB = low-NO x burner; WSI = water/steam injection; OT = oxygen trim, which may also be known as staged combustion; OFA = overfire air; SCR = se lective catalytic reduction; SNCR = selective noncatalytic reduction A These applications may find use with large, medium or small boilers fired with coal, oil or natural gas. B By themselves, these enhancements cannot be consider ed RACT. However, they or any others that might give incremental pollutant reduction are meant to be used with each other, as appropriate, as well as NO x control technologies such as LNB + OFA, reburn, SCR and SNCR; SO 2 scrubbers; or boilers controlled with just ESPs.
9.3.b.3. Why Controls Implementation Could Be Delayed If additional controls or major upgrades were warranted, DAQ believes they could not be installed and operational by January 2009. Thus, the attainment date could be not advanced by one year. At least five major obstacles prevent realistic timely implementation by 2009 of any RACT candidate involving retrofit of any major air-pollution-control device or major upgrades to any existing technologies—even if an owner/operator initiated the design/procure/fabricate/install/accept process immediately.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 124 1. While, in the strictest sense, add-on technologies as well as enhancements or major upgrades may be technically feasible and/or cost effective, whether any occur depends on site-specific factors. DAQ believes regulatory estimates cannot accurately or completely describe these factors for a particular facility, nor the costs owners/operators could incur.Nor can regulatory estimates reasonably predict the amount of time the entire add-ontechnology/major-upgrade process may take from decision to final acceptance. 2. Because owners/operators compete simultaneously among themselves for those vendors who have the capacity to supply equipment and services—and they compete against the power-generation industry and its technology programs (see point 3, which follows), it is unwise to presume that equipment vendors can and will accept new capital projects. 3. Materials and labor such as boilermakers and steelworkers, for example, to install equipment may not be available. For example, as respondents to a LADCO/MRPO draft RACT document for industrial boilers said: “Because the ICI boiler owners and operators are competing for resources (labor and materials) with the EGU category, there are likely to be impacts and delays in engineering, materials procurement, etc., going forward.” 4. At least one federal program, the Clean Air Interstate Rule or CAIR, injects negative impacts and delays. Besides the competition noted in point 2, above, DAQ believes that because of CAIR and the NO x SIP Call, costs of some controls have increased significantly in the past few years. Likely, those costs will not plateau until the CAIR and PM 2.5 programs are fully completed. 5. If vendors can be found who will commit to supplying the equipment, based on the technical and economic analyses performed in or referred to by this document, it appears that it could take several years—from decision, to procurement, to design, to fabrication, then installation and through acceptance testing—to implement major add-on technologies at an existing site. Major upgrades may take less time, though DAQ has no estimates of how much less.

9.3.b.4. Why RACT Selection Is Site Specific and Costs May Be Underestimated If any new technology or major upgrade is required, based on technical feasibility and cost effectiveness, DAQ notes two significant c oncerns about the ranked RACT candidates, particularly those requiring significant capital expense, and their selection: 1. Regardless of what candidate measure is selected or combination of measures are selected, final choice at any facility, for any boiler, depends on site-specific factors.Primarily, those include available physical space and compatibility with existing airpollution-control technologies. But among other things, site-specific factors also include combustion processes and conditions, as well as fuels used. 2. Regardless of the estimated costs presented in the ICI RACT Analysis, they may underestimate the real-world expenditures that could or would occur. Underestimation could come from out-of-date data that need adjustment for inflation or for increases dueWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 125 to environmental-regulations-induced supply-demand pressures. Underestimation could also come from use of regulatory models or other means, rather than actual data, used to derive these costs. Underestimation may also come from using inadequate retrofit multipliers when approximating costs.

9.4. Evaluation of RACM for Purposes of Achieving Attainment In accordance with 40 CFR §51.1010, as part of the SIP revision, the state is required to submit a demonstration that it has adopted all RACM, including RACT, necessary to demonstrate attainment as expeditiously as practicable and to meet any RFP requirements. In evaluating a particular measure or set of measures, that section of the rule directs the state to consider the cumulative impact of implementing the available measures and potential reasonably available control measures that, if considered collectively, would advance the attainment date by one year or more. The DAQ evaluation of the control measures described in detail in Section 6 demonstrates that the cumulative impact of these previously adopted and on-going measures is sufficient to demonstrate, by April 2010, attainment of the 1997 PM 2.5 NAAQS. Thus, DAQ finds that no additional measures are necessary to demonstrate attainment as expeditiously as practicable or to meet any RFP requirements. Furthermore, the time required to implement new regulatory programs in West Virginia exceeds one year. DAQ reviewed and analyzed the list of potential control measures identified by EPA in the PM 2.5 Implementation Rule. The summary of the results of the analysis, provided below in Tables 9.4-1 through 3, shows that there are no additional measures beyond those already on the books that could be adopted and implemented by January 1, 2008. For these reasons listed above, DAQ finds there are no potential reasonably available control measures that would advance the attainment date by one year or more.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 126 Table 9.4-1. Analysis of Stationary Source Control Measures to Determine Reasonably Available Control Measures (RACM)

Stationary Source MeasuresImplementation by January 2008?Enforceable?Technologically Feasible?Economically Feasible?Adverse Impacts?Reductions >0.1 tpd?RACM: Yes/No?

Stationary diesel engine retrofit, rebuild or replacement, with catalyzed particulate filter No Yes Yes – No – No
New or upgraded emission control requirements for direct PM 2.5 emissions at stationary sources (e.g., installation or improved performance of control devices such as a baghouse or electrostatic precipitator; revised opacity standard; improved compliance monitoring methods) No Yes – – No Yes No

Improved capture of particulate emissions to increase the amount of PM 2.5 ducted to control devices, and to minimize the amount of PM 2.5 emitted to the atmosphere, for example through roof monitors No Yes – – No – No

New or upgraded emission controls for PM 2.5 precursors at stationary sources (e.g., SO 2 controls such as wet scrubbers or dry scrubbers, or reduced sulfur content in fuel; desulfurization of coke oven gas at coke ovens; improved sulfur recovery at refineries; increasing the recovery efficiency at sulfuric acid plants) No Yes – – – Yes No

Energy efficiency measures to reduce fuel consumption and associated pollutant emissions (either from local sources or distant power providers) No No Yes – No – No

Measures to reduce fugitive dust from industrial sites No Yes Yes – No No NoWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 127 Table 9.4-2. Analysis of Mobile Source Control Measures to Determine Reasonably Available Control Measures ( RACM)

Mobile Source MeasuresImplementation by January 2008?Enforceable?Technologically Feasible?Economically Feasible?Adverse Impacts?Reductions >0.1 tpd?RACM: Yes/No?

Onroad diesel engine retrofits for school buses, trucks, transit buses using EPA-verified technologies No Yes Yes – No – No
Nonroad diesel engine retrofit, rebuild or replacement, with catalyzed particle filter No No Yes – No – No
Diesel idling programs for trucks, locomotive, and other mobile sources No No Yes Yes No – No
Transportation control measures (including those listed in section 108(f) of the CAA, as well as other TCMs), other transportation demand management and transportation systems management strategies No Yes YesVariesVaries Yes No

Programs to reduce emissions or accelerate retirement of high emitting vehicles, boats, and lawn and garden equipment No No Yes – No – No

Emissions testing and repair/maintenance programs for onroad vehicles No Yes Yes – No Yes No
Emissions testing and repair/maintenance programs for nonroad heavy-duty vehicles and equipment No No Yes – No – No
Programs to expand use of clean burning fuels No No Yes Yes No – No Low emissions specifications for equipment or fuel used for large construction contracts, industrial facilities, ship yard, airports, and public or private vehicle fleets No No Yes – No – No

Opacity or other emissions standards for “grossemitting” diesel equipment or vessels No No Yes – No Yes NoWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 128 Table 9.4-3. Analysis of Area Source Control Measures to Determine Reasonably Available Control Measures (RACM )

Area Source MeasuresImplementation by January 2008?Enforceable?Technologically Feasible?Economically Feasible?Adverse Impacts?Reductions >0.1 tpd?RACM: Yes/No?

New open burning regulations and/or measures to improve program effectiveness such as programs to reduce or eliminate burning of land clearing vegetation No Yes Yes Yes No No No

Programs to reduce emissions from woodstoves and fireplaces including outreach programs, curtailments during days with expected high ambient levels of PM 2.5 , and programs to encourage replacement of woodstoves when houses are sold No No Yes – No No No

Controls on emissions from charbroiling or other commercial cooking operations No No Yes – No – No
Reduced solvent usage or solvent substitution (particularly for organic compounds with 7 carbon atoms or more, such as toluene, xylene, and trimethyl benzene) No Yes – – No – NoWV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 129 9.5. Summary
9.5.a. RACM Determination West Virginia will continue to implement the RACM already adopted and described in Section 6. The analysis given above, which meets the applicable statutory requirements set forth at Section 172(c)(1) of the Clean Air Act and the applicable regulatory requirements set forth at 40 CFR §51.1010, demonstrates that these measures are sufficient to allow the Parkersburg area to demonstrate attainment, by April 2010, of the 1997 PM 2.5 NAAQS.
The state also finds that: • there are no additional measures necessary to demonstrate attainment as expeditiously as practicable; • there are no additional measures to meet any RFP requirements; and • there are no additional and potential measures that, if considered collectively, would advance the attainment date by one year or more. 9.5.b. RACT Applicability
For each PM 2.5 nonattainment area, 40 CFR §51.1010 notes that a state must submit a SIP revision that demonstrates all RACM, including RA CT for stationary sources, have been adopted that are necessary to demonstrate attainment as expeditiously as practicable.
That section of the PM 2.5 Implementation Rule goes on to state that the potential measures that, considering technical and economic feasibility, are reasonably available must be adopted as RACM if, considered collectively, those measures would advance the attainment date by one year or more. As discussed in Section 8.1.c., West Virginia has determined that there are no additional control measures that could be adopted by January 1, 2008. Further, DAQ expects existing measures and those planned for implementation in 2009 to enable the Parkersburg area to demonstrate compliance with the 1997 PM 2.5 NAAQS by the April 2010 attainment date. Therefore, DAQ finds that no further actions on RACT are warranted.WV PM 2.5 SIP for the Parkersburg Nonattainment Area Page 130 10.0. MID-COURSE REVIEW [40 CFR §51.1011] The proposed attainment date is within five (5) years of the date of designation, therefore, pursuant to 40 CFR 51.1011 no mid-course review is required.

11.0. PUBLIC PARTICIPATION A public notice was published in the West Virginia State Register , the Charleston Gazette , the Charleston Daily Mailand the Parkersburg News on June 13, 2008, announcing the opening of a 30-day public comment period. The public notice also announced that a Public Hearing will be held on July 15 at the Wood-Washington-Wirt Interstate Planning Commission, 531 Market Street in Parkersburg, beginning at 6:00 p.m. The public notice, along with a copy of the Proposed SIP, including all appendices, was posted on the DAQ website at http://www.wvdep.org/item.cfm?ssid=8&ss1id=376

. For documentation of the public participation process see Appendix Q.

12.0 CONCLUSION The State of West Virginia requests that the United States Environmental Protection Agency act in a timely manner to approve this attainment demonstration for the Parkersburg Nonattainment Area, with a projected attainment date of April 2010, as a revision to the state implementation plan (SIP).