# Georgia's State Implementation Plan
for the Chattanooga PM 2.5 

# Nonattainment Area
For Catoosa and Walker Counties

October 16, 2009

Prepared by:

Georgia Department of Natural Resources
Environmental Protection Division
Air Protection BranchTable of Contents PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

This page was intentionally left blankTable of Contents PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

PREFACE

This document is Georgia’s portion of the St ate Implementation Plan (SIP) for attainment of the Federal National Ambient Air Quality Standard (NAAQS) for PM 2.5 in the Chattanooga nonattainment area. This area cons ists of Catoosa and Walker Counties in Georgia, Hamilton County in Tennessee, a nd the portion of Jackson County in Alabama
that is described as U.S. Census 2000 block group identifier 01–071-9503–1. The Georgia Air Protection Branch, the Cha ttanooga-Hamilton County Air Pollution Control Bureau, the Tennessee Division of Air Pollution Control, and the Alabama Department of Environmental Management have collaborated to develop a PM 2.5 NAAQS State Implementation Plan (SIP) for each jurisdic tion. Specifically, this SIP demonstrates
attainment of the annual PM 2.5 NAAQS, promulgated in 1997, in the Chattanooga nonattainment area by April 5, 2010.

3 of 162Table of Contents PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

TABLE OF CONTENTS

PREFACE .......................................................................................................................... 3

TABLE OF CONTENTS................................................................................................... 4

LIST OF APPENDICES ................................................................................................... 8

LIST OF FIGURES........................................................................................................... 9

LIST OF TABLES ........................................................................................................... 12

Executive Summary ......................................................................................................... 16

Section 1. Introduction ................................................................................................... 21

1.1 Fine Particles ........................................................................................................ 21

1.2 1997 National Ambient Air Quality Standards for PM 2.5 ................................ 21

Section 2. Chattanooga Nonattainment Area ................................................................ 23

2.1 Nonattainment Area Designation ....................................................................... 25

2.2 PM 2.5 Monitoring Stations................................................................................... 26

2.3 Conceptual Description of the PM2.5 Problem................................................. 28 2.3.1 Long-term trends of PM2.5............................................................................. 28 2.3.2 Spatial Trends of PM2.5 in Georgia ................................................................ 45 2.3.3 Source Apportionment of PM2.5..................................................................... 49 2.3.4 Meteorology..................................................................................................... 50
2.3.5 Sensitivities to Pollutant Emissions................................................................. 53

2.4 PM2.5 Sources and Precursors........................................................................... 57
2.4.1 Sources of PM2.5............................................................................................ 57
2.4.2 Precursors........................................................................................................ 58
2.4.3 Technical Justification for Revers al of NOx Precursor Presumption............. 59
2.4.3.1 Ambient Measurements ........................................................................... 59 2.4.3.2 Future-Year Attainment Modeling .......................................................... 60
2.4.3.3 Statewide NOx Sensitivities ................................................................ 62 2.4.3.4 CMAQ Model Performance for Organic Carbon ................................ 63
2.4.3.5 Conclusion ........................................................................................... 64

Section 3. Attainment Demonstration Methods and Inputs .......................................... 66

3.1 Analysis Method.................................................................................................... 66

3.2 Model Selection ..................................................................................................... 67 3.2.1 Selection of Air Quality Model........................................................................ 67
3.2.1.1 Criteria ...................................................................................................... 67 3.2.1.2 Overview of CMAQ ................................................................................. 67

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3.2.2 Selection of Meteorological Model ................................................................. 68
3.2.2.1 Criteria ...................................................................................................... 68
3.2.2.2 Overview of MM5 .................................................................................... 69 3.2.3 Selection of Emissions Processing System...................................................... 70
3.2.3.1 Criteria ...................................................................................................... 70
3.2.3.2 Overview of SMOKE ............................................................................... 70

3.3 Selection of the Base Modeling Year................................................................... 71

3.4 Modeling Domains ................................................................................................ 72 3.4.1 Horizontal Modeling Domain.......................................................................... 72 3.4.2 Vertical Modeling Domain .............................................................................. 74

3.5 Baseline Emissions Inventory .............................................................................. 76 3.5.1 Stationary Point Sources.................................................................................. 80 3.5.1.1 Electric Generating Units.......................................................................... 80
3.5.1.2 Other Industrial Point Sources.................................................................. 82 3.5.2 Stationary Area Sources................................................................................... 83
3.5.3 Off-Road Mobile Sources................................................................................ 84 3.5.4 Highway Mobile Sources................................................................................. 85 3.5.5 Biogenic Emission Sources.............................................................................. 85

Section 4. Model Performance Evaluation .................................................................... 86

4.1 Meteorological Model Performance.................................................................... 86

4.2 Air Quality Model Performance.......................................................................... 90 4.2.1 Modeling Performance Goals and Criteria ...................................................... 90
4.2.2 Domain-Wide Model Performance.................................................................. 93 4.2.3 Model Performance within Georgia a nd in the Chattanooga nonattainment area

............................................................................................................................... .... 95 4.2.3.1 Model performance statistics ........................................................................ 98
4.2.3.1.2 Model Performance Statistics for STN Sites ........................................... 104 4.2.4 Summary of the performance evaluation of the CMAQ modeling................ 107

Section 5. Control and Contingency Measures ........................................................... 110

5.1 Federal Control Measures for PM2.5 Attainment........................................... 110
5.1.1 Clean Air Interstate Rule ............................................................................... 110
Ultra Low Sulfur Diesel.................................................................................. 111
5.1.2 Tier 2 Vehicle Standards................................................................................ 113 5.1.3 Heavy-Duty Gasoline and Diesel Highway Vehicles Standards ................... 114 5.1.4 Large Nonroad Diesel Engines Rule.............................................................. 114 5.1.5 Nonroad Spark-Ignition Engines and Recreational Engines Standard .......... 114 5.1.6 NOx SIP Call in Surrounding States.............................................................. 115

5.2 State Control Measures for PM2.5 Attainment ............................................... 115 5.2.1 Multipollutant Control for Electric Ut ility Steam Generating Units, GA rule 391-3-1-.02(2)(sss).................................................................................................. 117 5.2.2 Heavy Duty Diesel Engines Rule (ooo)......................................................... 119

5.3. Summary of SO2 Emissions Changes ............................................................. 119

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5.4. Contingency Measures...................................................................................... 121 5.4.1 Region for Contingency Measures................................................................. 123 5.4.2 Area of Influence Emission Reductions for Contingency ............................. 125 5.4.3 Adopted Control Measures for Contingency ................................................ 128

5.5 Unmodeled Control Measures .......................................................................... 128
Switch 2 coal-fired EGUs to lower sulfur coal............................................... 129 5.5.1 GA Rule 391-3-1-.02(5) Open Burning........................................................ 129 5.5.2 Switch to Lower Sulfur Coal, Georgia Power Plant Scherer........................ 129 5.5.3 Multipollutant Rule - Earl y Implementation of Controls ............................. 130

5.6 RACT and RACM ............................................................................................. 130

Section 6. Attainment Demonstration .......................................................................... 133

6.1 Modeled Attainment Test................................................................................... 133 6.1.1 Determination of Baseline Design Values..................................................... 133 6.1.1.1 Mean PM2.5 Baseline Design Values .................................................... 134 6.1.1.2 Speciated Baseline Conditions............................................................... 135
6.1.1.2.1 SANDWICH.................................................................................... 136 6.1.1.2.2 Speciated Profiles............................................................................. 136 6.1.2 Relative Response Factor Calculation ........................................................... 139 6.1.3 Future Year Quarterly Concentration Calculation......................................... 141 6.1.4 Future Year Annual Average Estimate.......................................................... 142

6.2 Supplemental Analysis........................................................................................ 143 6.2.1 Additional Air Quality Modeling................................................................... 143 6.2.1.1 Analysis of Modeled Cell-Day Concentrations ...................................... 144 6.2.1.2 EPA CAIR Modeling.............................................................................. 147 6.2.2 Air Quality and Emission Trends Analysis ................................................... 147 6.2.3 Supplemental Analysis Conclusions.............................................................. 151

6.3 Unmonitored Area Analysis............................................................................... 151

6.4 Data Access.......................................................................................................... 152

Section 7.0 Other Clean Air Act Requirements .......................................................... 153

7.1 RACT/RACM Requirements............................................................................ 154

7.2 Reasonable Further Progress............................................................................ 154

7.3 Actual Emissions Inventory .............................................................................. 154

7.4 Periodic Emissions Inventory ........................................................................... 154

7.5 Permit Program Requirements ........................................................................ 154

7.6 Other Measures.................................................................................................. 155

7.7 Compliance with Section 110(a)(2 ) (Infrastructure Elements)...................... 155

7.8 Equivalent Techniques ...................................................................................... 155

7.9 Contingency Measures....................................................................................... 155

Section 8. 2009 Motor Vehicle Emissions Budget....................................................... 156

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8.1 Background ........................................................................................................ 156

8.2 Establishment of 2009 Motor Vehicle Emissions Budgets fo r the Georgia Part of the Chattanooga PM2.5 Nonattainment Area. .................................................. 156

Section 9. Summary and Conclusion ........................................................................... 159

Section 10. References ................................................................................................... 162

7 of 162Of List of Appendices PM2.5 Attainment SIP - Floyd County, GA

LIST OF APPENDICES

Appendix Title

A Justification for Modification to State Recommendations, PM2.5 Nonattainment Areas, State of Georgia

B PM2.5 Source Apportionment at Georgia STN Sites Using
Positive Matrix Factorization

C PM2.5 Source Apportionment in Georgia

D Characterization of Meteorology and Its Relationships to Fine
Particulate Mass and Visibil ity in the VISTAS Region

E Insignificance of NH 3 , VOCs, and NOx to PM2.5 Attainment in Georgia Nonattainment Areas

F Modeling Protocol For Associati on for Southeastern Integrated Planning (ASIP) Emissions and Ai r Quality Modeling to Address
8-Hour Ozone and PM2.5 Nonattainment in the Southeastern
United States"

G Technical Support Document for Association for Southeastern Integrated Planning (ASIP) Emi ssions and Air Quality Modeling to Support PM2.5 State Implementation Plans

H Documentation of the Base G2 and Best and Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS

I Emissions Inventories – State of Georgia

J Mobile Source Emissions Modeling for Chattanooga PM2.5 Attainment Demonstration Motor Vehicle Emissions Budgets

K Model Performance Evaluation

L Multipollutant Control for Electric Utility Steam Generating Units [Georgia Rule 391-3-1-.02(2)(sss)]

M Attainment Demonstration Calculations and Results

N CAA Section 110(a)(2) Require ments (SIP Infrastructure Elements): Communications dated 7-23-08 and 9-22-08

8 of 162List of Figures PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

LIST OF FIGURES

Figure 2-1. PM2.5 Nonattainment Areas in Georgia........................................................ 23

Figure 2-2. Chattanooga PM2.5 Nonattainment Area ..................................................... 24

Figure 2-3. Locations of PM2.5 Monitors in the Chattanooga Nonattainment Area ...... 27

Figure 2-4. Trend of annual mean (arithme tic average of quarterly averages) PM2.5
concentrations at FRM monitors (13-295-0002) in the Chattanooga NAA. .................... 29

Figure 2-5. Trend of PM2.5 Design Values at the FRM monitors in the Chattanooga
NAA............................................................................................................................ ...... 30

Figure 2-6. Quarterly averages of PM 2.5 concentrations at 13-295-0002 ....................... 31

Figure 2-7. Quarterly averages of PM 2.5 concentrations at 47-065-0031 ....................... 32

Figure 2-8. Quarterly averages of PM 2.5 concentrations at 47-065-1011 ....................... 32

Figure 2-9. Quarterly averages of PM 2.5 concentrations at 47-065-4002 ....................... 33

Figure 2-10. Trend of annual total and sp eciated PM2.5 concentrations (arithmetic average of daily averages) at the Rossv ille PM2.5 Speciation m onitor (13-295-0002). .. 34

Figure 2-11. Quarterly averages of PM2.5 concentrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002).................................................. 35

Figure 2-12. Quarterly averages of SO4 con centrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002).................................................................... 35

Figure 2-13. Quarterly averages of NO3 con centrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002).................................................................... 36

Figure 2-14. Quarterly averages of NH4 con centrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002).................................................................... 37

Figure 2-15. Quarterly averages of OC con centrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002).................................................................... 38

Figure 2-16. Quarterly averages of EC con centrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002).................................................................... 39

Figure 2-17. Trend of annual total and sp eciated PM2.5 concentrations (arithmetic average of daily averages) at 47-065-4002....................................................................... 40

Figure 2-18. Quarterly averages of PM2.5 concentrations at 47-065-4002 ..................... 41

Figure 2-19. Quarterly averages of SO4 concentrations at 47-065-4002 ......................... 41

Figure 2-20. Quarterly averages of NO3 concentrations at 47-065-4002 ........................ 42

Figure 2-21. Quarterly averages of NH4 concentrations at 47-065-4002 ........................ 43

Figure 2-22. Quarterly averages of OC concentrations at 47-065-4002........................... 44

Figure 2-23. Quarterly averages of EC concentrations at 47-065-4002 ........................... 45

9 of 162List of Figures PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 2-24. Seasonal patterns in PM 2.5 concentrations and com position in Atlanta (Site ID 13-089-0002) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, el emental carbon and organic carbon)..................... 46

Figure 2-25. Seasonal patterns in PM 2.5 concentrations and co mposition in Chattanooga (Site ID 47-065-4002) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, am monium, MMO, elemental car bon and organic carbon). ............ 46

Figure 2-26. Seasonal patterns in PM 2.5 concentrations and composition in Floyd county (Site ID 13-115-0005) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon). ..................................... 47

Figure 2-27. Seasonal patterns in PM 2.5 concentrations and co mposition in Macon (Site ID 13-021-0012) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, el emental carbon and organic carbon)..................... 47

Figure 2-28. Average factor contributions to PM 2.5 ( μ g/m 3 ) for 2002-06 at the Chattanooga STN site using PMF..................................................................................... 49

Figure 2-29. Average composition for al l key PM2.5 bins in Chattanooga. .................... 53

Figure 2-30. Sensitivities of PM2.5 by pollu tant and source category for the summer episode in Chattanooga. Δ PM2.5 is based upon a 30% reducti on of pollutant levels from
2009 levels. ....................................................................................................................... 54

Figure 2-31. Sensitivities of PM2.5 by pollu tant and source cate gory for the winter episode in Chattanooga. Δ PM2.5 is based upon a 30% reducti on of pollutant levels from
2009 levels. ....................................................................................................................... 55

Figure 2-32. Annual average emission sens itivities by pollutant and source category based on the average response of the summe r and winter episodes in Chattanooga........ 56

Figure 2-33. Annual average emission sens itivities by pollutant and source category based on the CART weighted response of the summer and winter episodes in Chattanooga. ..................................................................................................................... 57

Figure 2-34. SANDWICH Speciated Concentr ations of Quarterly and Annual Average 2002 PM2.5 Base Design Values (DVB) averaged over FRM Monitors in each Georgia
PM2.5 Nonattainment Area. ............................................................................................. 60

Figure 2-35. Reductions in EC, OC, and NO3 concentrations between 2002 & 2009 and 2009 & 2018 at Georgia nonattainment monitors............................................................. 61

Figure 2-36. Annual Average PM2.5 Respons e to GA Statewide 50% NOx Emission Reduction in 2009 in the Georgia PM2.5 Nonattainment Areas. ..................................... 62

Figure 2-37. Annual Average PM2.5 Response to GA Statewide 30% NOx Emission Reduction in 2009 in the Georgia PM2.5 Nonattainment Areas. ..................................... 63

Figure 2-38. Hourly measured and modele d OC concentrations and the difference between the base and sensitivity concentrations of the four components of OC to 100%
statewide NOx emissions reductions at the Yorkville SEARCH monitor........................ 65

Figure 3-1. The MM5 and CMAQ 36-km Horizontal Domains....................................... 73

10 of 162List of Figures PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 3-2. VISTAS 12-km Modeling Domain................................................................ 73

Figure 4-1. Monthly averaged Bias statistics for 2-m temperature and humidity ............ 88

Figure 4-2. VISTAS ST N Soccer Plots ............................................................................ 93

Figure 4-3. VISTAS ST N Bugle Plots.............................................................................. 94

Figure 4-4. VISTAS FRM Soccer Plots ........................................................................... 94

Figure 4-5. VISTAS FRM Bugle Pl ots - The image on the left is a bugle plot of the mean fraction bias for particulate matter for all the VISTAS FRM Monitoring sites................ 95

Figure 4-6. FRM, STN, IM PROVE, CASTNET, NADP, and SEARCH monitoring sites
located in and near the GA PM 2.5 nonattainment areas (lef t) and the Chattanooga PM 2.5 nonattainment area (right)................................................................................................. 97

Figure 4-7. Stacked bar plots of daily PM 2.5 species concentration at 47-065-4002...... 108

Figure 5-1. Chattanooga Continge ncy Area of Influence............................................... 124

Figure 6-1. Grid cells for which the air quali ty metrics were applied in the Chattanooga
PM2.5 nonattainment area. ............................................................................................. 145

Figure 6-2. Percentage of Cell-Days in the Chattanooga Nonattainment Area within Concentration Categories for 2002 and 2009. ................................................................ 146

Figure 6-3. Annual PM2.5 Average Values for the Monitors in the Chattanooga
Nonattainment Area........................................................................................................ 148

Figure 6-4. PM2.5 monitoring trends in Cha ttanooga compared to statewide EGU SO2 emissions from Georgia and neighboring states. ............................................................ 150

Figure 6-5. Modeled 2009 Annual Mean PM 2.5 Concentration near the Chattanooga
Nonattainment Area on the VISTAS 12km Grid............................................................ 152

11 of 162List of Tables PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

LIST OF TABLES

Table E-1. Annual PM 2.5 Design Values ......................................................................... 16

Table E-2. Projected SO2 Emission Reductio ns from New Controls on Georgia Power
EGUs in 2008 and 2009.................................................................................................... 19

Table E-3. PM 2.5 2002 Baseline Design Values and 2009 Future (Modeled) Design
Values ............................................................................................................................... 20

Table 1-1, 1997 PM2.5 NAAQS....................................................................................... 21

Table 2-1. 2001 – 2003 PM2.5 Design Values for Chattanooga Nonattainment Area
Monitors............................................................................................................................ 26

Table 2-2. Rossville PM 2.5 Data Collection Site.............................................................. 27

Table 2-3. Annual Mean (Arithmetic Average of Quarterly Averages) PM2.5 Concentrations at the FRM Monitor in the Chattanooga NAA. ....................................... 29

Table 2-4. PM2.5 Design Values at the FRM monitors in the Chattanooga NAA........... 30

Table 2-5. Average PM 2.5 composition during 2003-2004 for the four STN sites in the non-attainment areas ( μ g/m 3 )............................................................................................ 48

Table 2-6. Estimated primary a nd secondary percentage of PM 2.5 .................................. 48

Table 2-7. Summary of average input paramete rs for each CART classification Category in Chattanooga. ................................................................................................................. 51

Table 2-8. Summary of average input paramete rs for key bins for CART classification (Class) categories 3 and 4 in Chattanooga........................................................................ 52

Table 2-9. Expected Change in NOx Em issions (%) in Georgia PM2.5 Nonattainment Areas. Reductions are negative........................................................................................ 61

Table 3-1. Vertical Layer De finition For MM5 and CMAQ............................................ 75

Table 3-2. Typical 2002 BaseG2 A nnual Emission Summary for Chattanooga Nonattainment Counties (tons) ......................................................................................... 76

Table 3-3. 2009 BaseG4 Annual Emission Summary for Chattanooga Nonattainment Counties (tons).................................................................................................................. 78

Table 3-4. 2002 and 2009 Georgia EGU Emissions........................................................ 82

Table 3-5. 2002 and 2009 Georgia Area Source Emissions ............................................ 84

Table 4-1. Mathematical Formulation of Statistical Metrics Used for Evaluating Mesoscale Meteorological Model Performance ............................................................... 87

Table 4-2. Statistical Benchmarks fo r Mesoscale Meteorological Models ...................... 87

Table 4-3. Monthly averaged statistics for the region of the 12-km modeling domain in and around Georgia........................................................................................................... 89

Table 4-4. Established Model Performance Goals and Criteria for the PM 2.5 Component Species .............................................................................................................................. 91

12 of 162List of Tables PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Table 4-5. Statistical Me tric Calculations......................................................................... 92

Table 4-6. Ambient monitori ng data available in 2002. .................................................. 96

Table 4-7. Annual model performance statistics for PM 2.5 at all FRM monitors in the State of Georgia and in the Ch attanooga nonattainment area........................................... 99

Table 4-8. Monthly model performance statistics for PM 2.5 at 13-295-0002................. 100

Table 4-9. Monthly model performance statistics for PM 2.5 at 47-065-0031................. 101

Table 4-10. Monthly model performance statistics for PM 2.5 at 47-065-1011............... 102

Table 4-11. Monthly model performance statistics for PM 2.5 at 47-065-4002............... 103

Table 4-12. Annual model perf ormance statistics for PM 2.5 species and total PM 2.5 at all STN monitors in the State of Georgia............................................................................. 105

Table 4-13. Annual model perf ormance statistics for PM 2.5 at 47-065-4002. ................ 106

Table 5-1. Federal PM2.5 Control Meas ures and Contingency Measures .................... 111

Table 5-2. State PM2.5 Control Meas ures and Contingency Measures ........................ 116

Table 5-3. Georgia Multipollutant Rule SO 2 Control Requirements and Construction
Permit Numbers .............................................................................................................. 118

Table 5-4. Changes in SO2 Emissions , 2002 to 2009, Catoosa County (tons).............. 120

Table 5-5. Changes in SO2 Emissions , 2002 to 2009, Walker County (tons)*............. 120

Table 5-6. Changes in SO2 Emissions, 2002 to 2009, Nonattainment Area (tons)....... 120

Table 5-7. Changes in SO2 Emissi ons, 2002 to 2009, Statewide (tons) ....................... 120

Table 5-8. Predicted changes in SO2 emi ssions inside the Georgia Portion of the Chattanooga Nonattainment Area, 2002 to 2009............................................................ 122

Table 5-9. Predicted changes in SO2 emi ssions inside the Georgia Portion of the Chattanooga Nonattainment Area, 2009 to 2012 (Contingency).................................... 122

Table 5-10. 2002 Baseline SO2 Emissions For Chattanooga Area of Influence............ 125

Table 5-11. 2009 SO2 Emissions For Ch attanooga Area of Influence........................... 126

Table 5-12. 2012 SO2 Emissions For Ch attanooga Area of Influence........................... 127

Table 5-13. Total SO2 Emissions For Chattanooga Area of Influence .......................... 127

Table 5-14. SO2 emissions reductions expected from control and contingency measures at coal-fired electric gene rating units outside the Chatta nooga Nonattainment Area. ... 128

Table 5-15. Unmodeled Control Measures.................................................................... 129

Table 6-1. Quarterly Average PM2.5 Mass for the FRM Monitors in the Chattanooga
Nonattainment Area........................................................................................................ 134

Table 6-2. Observed Quarterl y and Annual Mean PM2.5 DVB (in μ g/m 3 ) for Monitors in the Chattanooga Nonattainment Area......................................................................... 135

Table 6-3. DVB Quarterly Species Fractions (%) from the CAIR SMAT Tool ........... 138

13 of 162List of Tables PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Table 6-4. Quarterly PM2.5 Component-Specific DVB ( μ g/m 3 ) for Monitors in the Chattanooga Nonattainment Area................................................................................... 139

Table 6-5. US EPA’s Recommenda tion for Defining “Near” Cells.............................. 140

Table 6-6. Quarterly Compone nt RRFs for Monitors in th e Chattanooga Nonattainment
Area................................................................................................................................. 141

Table 6-7. Quarterly Component Future Design Values (in μ g/m 3 ) for Monitors in the Chattanooga Nonattainment Area................................................................................... 142

Table 6-8. Quarterly Mean PM2.5 Mass estimates for 2009......................................... 142

Table 6-9. Estimated Annual Mean 2009 PM2.5 Mass Compared to the DVB............ 143

Table 6-10. Number of Cell-Days within Conc entration Bins. Increases (decreases) in the number of cell-days (and percent change) within the bins are noted by red (blue)
coloration. ....................................................................................................................... 146

Table 6-11. US EPA CAIR Modeling Resu lts for Walker County, GA and Hamilton County, TN (highest monitors)....................................................................................... 147

Table 6-12. Annual Average PM2.5 values in Chattanooga for the past 9 years.......... 148

Table 6-13. Three Year Design values (in μ g/m 3 ) for the Monitors in the Chattanooga
PM2.5 Nonattainment Area. ........................................................................................... 149

Table 6-14. Projected SO2 emission reducti ons from new controls on Georgia Power
EGUs in 2008 and 2009.................................................................................................. 149

Table 7-1. General Plan Require ments for Nonattainment Areas ................................. 153

Table 8-1. 2009 PM2.5 Motor Vehicle Emissions Budget for Georgia Part of Chattanooga Nonattainment Area................................................................................... 157

Table 8-2. Georgia Part of Chattanooga Nonattainment Area Total Projected 2009 PM2.5 Emissions, Tons/Year ......................................................................................... 158

Table 9-1. 2001 – 2003 PM2.5 Design Values for Chattanooga Nonattainment Area
Monitors.......................................................................................................................... 159

Table 9-2. PM 2.5 2009 Future (Modeled) Design Values .............................................. 161

14 of 162Acronyms PM2.5 Attainment SIP – Catoosa and Walker Counties, GA



## Acronyms

ASIP Association of South eastern Integrated Planning

CAA Clean Air Act

CAIR Clean Air Interstate Rule CMAQ Community Multisc ale Air Quality model

DON Degree of neutralization (of sulfate, in the context of this document)

DVB Baseline design value

DVF Future design value

EC Elemental carbon

EGU Electric generating unit

EI Emissions inventory

EPD Environmental Protection Division

FGD Flue gas desulfurization

FRM Federal Reference Monitor

MGCAC Middle Georgia Clean Air Coalition

MM5 Mesoscale Meteorological Model

MVEB Motor vehicle emissions budget

NAA Nonattainment area NAAQS National Ambient Air Quality Standards

OC Organic carbon

OM Organic matter

PBW Particle bound water

PM2.5 Fine particulate matter (2.5 microns or less in diameter)

RACM Reasonably available control measure

RACT Reasonably availa ble control technology

RFP Reasonable further progress RRF Relative response factor

SANDWICH Sulfate, adjusted nitrate, derived water, inferred carbon hybrid

SCR Selective catalytic reduction

SIP State implementation plan

SMAT Speciated Modeled Attainment Test

SMOKE Sparse Matrix Operator Ke rnel Emissions modeling system

SOA Secondary Organic Aerosols

STN Speciated Trends Network

VISTAS Visibility Improvement State a nd Tribal Association of the Southeast

VOC Volatile organic compound

VMT Vehicle miles traveled

15 of 162Executive Summary PM2.5 Attainment SIP - Catoosa and Walker Counties, GA

Executive Summary

This document is Georgia’s state implementatio n plan (SIP) for attainment of the 1997 PM2.5 National Ambient Air Quality Standard s in the Chattanooga Nonattainment Area (NAA) for Catoosa and Walker Counties. This SIP meets the requirements of EPA’s fine particulate implementation rule, 40 CFR 51 Subpart Z. The Chattanooga NAA has been determined to be out of attainment with the annual National Ambient Air Quality Standard (NAAQS) for PM2.5 (particulate matter with a diameter of 2.5 microns or less, also called fine particulate matter). The annual air quality standard for PM2.5 is a 3-year

annual arithmetic mean c oncentration of 15.0 micrograms per cubic meter ( μ g/m 3 ) or less. The nonattainment area designation was based upon 2001 - 2003 design values at the following monitors:

Table E-1. Annual PM 2.5 Design Values

PM 2.5 Design Value ( μ g/m 3 )

Federal Reference Monitor 2001-2003

Siskin Drive/UTC, Tennessee (470654002) 15.2

Maxwell Road/East Ridge, Tennessee (470650031) 16.1

Soddy-Daisy High School, Tennessee (470651011) 14.1

Rossville –Maple St., Georgia (132950002) 15.5

This SIP contains the elements required by 40 CFR 51 Subpart Z, including the following:

• Identification and description of the nonattainment area, including major PM2.5
emissions sources

• Identification of PM2.5 control measur es needed to reach attainment

• Description of modeling that demonstrates attainment of the annual standard by April 5, 2010

These elements are summarized in the following paragraphs.

The nonattainment area is made up of Ca toosa and Walker Counties in Georgia, Hamilton County in Tennessee, and the portion of Jackson County in Alabama that is
described as U.S. Census 2000 block identifier 01-071-9503-1. Although Jackson County is not contiguous to th e rest of the nonattainment ar ea, a coal-fired power plant located in this census tract is a major contributor to the PM 2.5 problem. Land utilization in this area includes both urban (City of Chattanooga) and suburban land uses.

16 of 162Executive Summary PM2.5 Attainment SIP - Catoosa and Walker Counties, GA

PM2.5 is made up of a number of compone nt species, including the following:

• Elemental carbon (EC)

• Organic carbon (OC)

• Sulfates (SO4)

• Nitrates (NO3)

• Crustal materials

• Ammonia (NH4)

Sulfates, nitrates, and ammonia are mostly secondary PM2.5. Secondary PM2.5 is not emitted directly into the atmosphere, but rather formed by chemical reactions of precursor species subsequent to their emi ssion. Elemental carbon and crustal materials (soils) are primary PM2.5, which are directly emitted into the atmosphere. Organic carbon can be a mixture of secondary and primary PM2.5.

The largest components measured in this nonattainment area are sulfates and organic carbon. Sulfate has made up between 20-to -30 percent of the ambient annual PM2.5
measured since 2000 at the Rossville –Maple Street m onitor. Sulfates are formed primarily from sulfur dioxide emissions from coal-fired electric generating units (EGUs) located both inside and outside of Georgia.

Organic carbon comprised approximately 30 percent of the ambient annual PM2.5 measured from 2001 through 2005. Atmosp heric organic carbon is produced by vegetation (biogenic sources), emissions of volatile organic chemicals used in commercial and industrial applications, and combustion of wood and fossil fuels. The bulk of organic carbon is believed to be form ed from biogenic sources. The biogenic formation process is not well understood.

Elemental carbon, which is a component of direct PM2.5, has averaged approximately five percent of the ambient annual PM2.5. Nitr ates are measured at essentially the same percentage as elemental carbon, and ammonia is only slightly higher.

The control measures needed to reach attain ment include both state and federal measures and must address both primary PM2.5 and sulfur dioxide (the precursor to sulfate formation). The attainment demonstration may take credit for measures implemented after 2002 (the baseline year) and before 2009 (the year in which modeling demonstrates attainment).

Control measures for attainment focus on control of SO 2 (and, therefore, sulfates) from EGUs. As mentioned above, biogenic forma tion of organic carbon is not well understood and, therefore, is not addressed by control measures in this SIP. Significant State measures include Georgia Rule 391-3-1-.02(2)(sss), also known as the Multipollutant Rule, and the Heavy-Duty Diesel Engines Rule. The Multipollutant Rule requires significant reductions in SO2 emissions from electric generating units (EGUs) and is the measure that will provide the greatest benefit toward demonstration of attainment in calendar year 2009. Federal measur es include the following:

17 of 162Executive Summary PM2.5 Attainment SIP - Catoosa and Walker Counties, GA

• Clean Air Interstate Rule

• Tier 2 Vehicle Standards
Diesel Highway Vehicles Standards
ule

es a nd Recreational Engines Standard

The l des for reductions of both SO2 and NOx. The

ther federal measures provide for reducti ons of NOx and direct PM emissions.

ambient
M fine concentrations. These measures include:

open burning in Catoosa and Walker Counties during the period of May through September; and

These deta il in Section 5. They were not modeled

ecause they are voluntary and/or difficult to model.

mit an attainment demonstration

owing attainment with the annual PM2.5 stan dard as expeditiously as possible. Per

• Modeling results, and ion analyses.

Air quality modeling conducted by Georgia EP D indicates that th e nonattainment area

ill attain the annual NAAQS in 2009. Modeled attainment is based primarily upon

• Heavy-duty Gasoline and

• Ultra-low Sulfur Diesel R

• Large Nonroad Diesel Engines Rule

• Nonroad spark ignition Engin

• NOx SIP call in surrounding states

C ean Air Interstate Rule (CAIR) provi

o

Some measures were not modeled but they ar e expected to achieve reductions in

P

• Georgia’s open burning rule that restricts

• Planned early start-up of SO 2 scrubbers at two EGUs (at Plant Bowen – Unit 2 and at Plant Wansley – Unit 2).

measures are discussed in more
b

or each nonattainment area, the State mu st sub F
sh 40 CFR 51.1007, the attainment demonstration must include:

• Inventory data,

• Emission reduct

w projected SO2 emissions reductions from EGU s ources within the state. Georgia Power’s projected SO2 emission reductions ar e listed in Table E-2 below.

18 of 162Executive Summary PM2.5 Attainment SIP - Catoosa and Walker Counties, GA

Table E-2. Projected SO2 Emission Reduc tions from New Controls on Georgia
Power EGUs in 2008 and 2009.

Facility Unit Control Date 1 2007 SO2
Emissions 2 (tons) Estimated
2008 (tons) Estimated
2009 (tons) 2008
Reductions (tons) 2009
Reductions (tons)

Bowen 1 5/30/2010 42,967.2 42,967.2 42,967.2 0.0 0.0

2 5/30/09 39,888.2 39,888. 2 17,783.5 0.0 22,104.7

3 5/01/08 64,745.5 23,740. 0 3,237.3 41,005.5 61,508.2

4 12/31/08 49,240.0 49,240. 0 2,462.0 0.0 46,778.0

Hammond 1 5/30/08 6,028.2 2, 687.6 301.4 3,340.6 5,726.8

2 5/30/08 6,047.4 2,696. 1 302.4 3,351.3 5,745.0

3 5/30/08 6,147.9 2,740. 9 307.4 3,407.0 5,840.5

4 5/30/08 29,585.3 13,190. 1 1,479.3 16,395.2 28,106.0
Wansley 1 12/31/08 42,739.9 42,739.9 2,137.0 0.0 40,602.9

2 5/30/09 51,105.7 51,105. 7 22,784.6 0.0 28321.1

TOTAL (tons) 338,495.3 270,995.8 93,762.0 67,499.5 244,733.3

(1) Dates prior to 2009 are actual field installation da tes. Dates 2009 or later are company-provided
forecast installation dates. (2) Calculated from continuous emissions monitor data.

Since it is performed on the ba sis of individual PM specie s (sulfate, nitrate, organic carbon, etc.), the attainment demonstration test for PM2.5 is referred to as the Speciated Modeled Attainment Test (SMAT). In brief, the air quality model is optimized to produce predicted species concentrations that closely match observed concentrations in the baseline year (2002). Th en a historical baseline em issions inventory (based on “typical” emissions and meteorology) and the 2009 projected emi ssions inventory are input to the model to find PM2.5 species concen trations for both years. The ratio of the future predicted average annual concentration to the historical ba seline average annual concentration, known as the relative response fact or (RRF), is calculate d for each species. The 2002 observed design value (baseline desi gn value, or DVB) for each species is then multiplied by the species RRF to obtain the 2009 modeled design value (future design value, or DVF) for the species. The modele d values for the individual species are then
summed to obtain the overall 2009 modeled de sign value. This SMAT process is repeated for each federal reference m onitor in the nonattainment area.

The 2009-modeled PM2.5 design values are provided in Table E-3. Since each of the modeled design values is less than 15.0 μg/m 3 , modeled attainment is demonstrated. Supplemental analyses (in addition to SMAT) are presented that also support the
demonstration of attainment.

19 of 162Executive Summary PM2.5 Attainment SIP - Catoosa and Walker Counties, GA

Table E-3. PM 2.5 2002 Baseline Design Values and 2009 Future (Modeled) Design

Values

Federal
Reference
Monitor Site ID 2001-2003 Annual Design Value (DVF) (μg/m 3 ) 2009 Annual Design Value (DVF) (μg/m 3 )

Siskin Drive/UTC,

Tennessee 470654002 15.2 13.6

Maxwell Road/East Ridge,

Tennessee 470650031 16.1 14.4

Soddy-Daisy High School,

Tennessee 470651011 14.1 12.3

Rossville-Maple St., Georgia 132950002 15.5 13.9

As required by the EPA Clean Air Fine Partic le Implementation Rule, sources of PM2.5 and its precursors within Catoosa and Walker Counties of the Chattanooga NAA were evaluated to verify that they were meeting at least reasonably available control measure (RACM) levels of emission co ntrols. This evaluation and the results of sensitivity modeling indicate that no additional reductions are available from local sources that would result in advancement of the attainment date by more than one year.

Federal regulations require st ates to submit a Reasonable Further Progress (RFP) plan for any PM2.5 SIP that targets attainment more th an five years after the date of nonattainment designation. The Chattanooga Nona ttainment Area is targeting attainment five years after designation, so no RFP plan is required.

The SIP narrative is organized as follows:

Section 1 Introduction Section 2 Chattanooga Nonattainment Area
Section 3 Attainment Demons tration Methods and Inputs
Section 4 Model Performance Evaluation Section 5 Control and Contingency Measures

Section 6 Attainment Demonstration Section 7 Other Clean Air Act Requirements Section 8 Motor Vehicle Emissions Budget
Section 9 References

The appendices provide additional mate rial to support the SIP narrative.

20 of 162Introduction PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Section 1. Introduction

1.1 Fine Particles

Fine particles in the atmosphere, which ma y be solid or liquid, are comprised of a complex mixture of components. Comm on constituents include: sulfate (SO 4 ); nitrate (NO 3 ); ammonium (NH 4 ); elemental carbon; a great va riety of organic compounds; and inorganic material (including metals, dust, sea salt, and other trace elements) generally
referred to as “crustal” mate rial, although it may contain ma terial from other sources. Airborne particles generally less than or equal to 2.5 micrometers in diameter are considered to be “fine particles” (also referred to as PM 2.5 ). 2.5 micrometers is less than one-seventh of the average width of a human hair. “Primary” or “direct” particles are emitted directly into the air as solid or liqui d particles (e.g., elementa l carbon from diesel engines or fire activities, or condensable organic partic les from gasoline engines). “Secondary” particles (e.g., sulfate and nitrate) form in the atmosphere from emissions of gaseous precursors (e.g., SO 2 and NO

x ) that subsequently undergo chemical reactions to form particulate matter. Secondary orga nic aerosols (SOA) are another form of secondary particles that consist of VOCs that have been converted from the gas phase to the particle phase.

The EPA established National Ambient Air Quality Standards (NAAQS) for PM 2.5 based
on evidence from numerous hea lth studies demonstrating that serious health effects are associated with exposures to elevated levels of PM 2.5 . Epidemiological studies have shown statistically significant co rrelations between elevated PM 2.5 levels and premature mortality. Other important effects associated with PM 2.5 exposure include aggravation of respiratory and cardiovascular diseas e, changes in lung function and increased respiratory symptoms, as well as new ev idence for more subtle indicators of
cardiovascular health. Individuals particularly sensitive to PM 2.5 exposure include older adults, people with heart and lung disease, and children. [Source: Preamble to Clean Air Fine Particle Implementation Ru le – Final Rule, April 25, 2007.]

1.2 1997 National Ambient Air Quality Standards for PM 2.5

The 1997 NAAQS for PM2.5 are listed in the following table:

Table 1-1, 1997 PM2.5 NAAQS Description Standard ( μ g/m 3 )
Annual arithmetic mean concentration 1 ≤ 15.0 24-hour average concentration, 98 th percentile 1 ≤ 65

(1) Determined in accordance with Appendix N of 40 CFR Part 50.

These standards were promulgated by U.S. EPA on July 18, 1997 (62 FR 38711 and 40CFR50.7). A monitoring site (or group of spa tially-averaged sites) is in attainment of
the annual PM 2.5 standard when the annual standard design value is less than or equal to 15.0 micrograms per cubic meter ( μ g/m 3)

. The annual standard design value is the 3-

21 of 162Introduction PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

year average of the annual mean, which is in turn the averag e of one year’s four calendarquarter averages. A monitoring s ite (or group of spat ially-averaged sites) is in attainment
of the 24-hour PM 2.5 standard when the 24-hour standard design value is less than or equal to 65 μ g/m 3

. The 24-hour standard design value is the three-year average of annual
98 th percentile 24-hour aver ages. The annual 98 th percentile 24-hour average is the 24- hour average PM 2.5 concentration that is higher than 98 percent of the year’s valid 24- hour averages. Therefore, to ach ieve attainment of the 1997 PM 2.5 standards by a given date, the annual and 24-hour standard design va lues calculated from valid data collected for the previous three calendar years must be less than or equal to their respective
standards. For a nonattainment area to achie ve attainment, all monitoring sites in the
nonattainment area must be in attainment. More details on the calculation of design values and the interpretation of the PM 2.5 NAAQS can be found in Appendix N of 40
CFR Part 50.

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Section 2. Chattanooga Nonattainment Area

There are currently four PM2.5 nonattainment areas in Georgia: Chattanooga (which also includes parts of Alabama and Tenne ssee), Floyd County, Atla nta, and Macon. Figure 2.1 shows their loca tions. The Chattanooga nonattainment area is the
northernmost of these areas and is located near the Tennessee and Alabama State lines.

Figure 2-1. PM2.5 Nonattainment Areas in Georgia

The geographic boundaries of the Chattanooga PM 2.5 nonattainment ar ea are shown in Figure 2.2. The portion of the nonattainment area located in Georgia is made up of
Catoosa and Walker Counties. Hamilton County in Tennessee and the porti on of Jackson County in Alabama that is described as U.S. Census 2000 block identifier 01-071-9503-1 are the portions of the non-attainment area in Tennessee and Alabama. Although Jackson

23 of 162Attainment Demonstration Methods & Inputs PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

County is not contiguous to th e rest of the nonattainment ar ea, a coal-fired power plant
located in this census tract is a major contributor to the PM 2.5 problem.

Land utilization in Catoosa and Walker C ounties includes both urban, suburban and rural land uses. The population of Catoosa C ounty was 53,282 in 2000 and is estimated at 62,016 in 2006, an increase of 16.5 percent. The population of Walker County is estimated to increase 5.8 percent from 61,053 in 2000 to 64,606 in 2006.

Figure 2-2. Chattanooga PM2.5 Nonattainment Area

The nonattainment area designation, PM2.5 mo nitoring stations, major PM2.5 sources, PM2.5 precursors, the conceptual descripti on of the PM2.5 problem, and ambient PM2.5
trends are described in this section.

24 of 162Attainment Demonstration Methods & Inputs PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

2.1 Nonattainment Area Designation

EPA, based on data submissions from stat es and tribes, designates a county as
attainment/unclassifiable or nonattainment. In addition, a county may be designated as
nonattainment if it is deemed to contain so urces that contribute to the nonattainment status of a county. Nonattain ment counties are grouped togeth er into nonattainment areas based on location within common metropolita n statistical areas (MSAs). EPA made initial designations of the nonattainment ar eas, including the Chattanooga nonattainment area, for the PM 2.5 NAAQS on January 5, 2005 (7 0FR944), and amended the designations on April 5, 2005 (70FR19844). The Georgia portion of the Chattanooga

PM 2.5 nonattainment area is made up of Catoosa and Walker Counties.

The designation of Walker County as a part of the Chattanooga nonattainment area was based upon Georgia’s recommendations to EPA and upon EPA’s 9-factor analysis of counties in or adjacent to the Chattanooga Me tropolitan Statistical Area. The designation of Catoosa County as part of the Chatta nooga nonattainment area was based upon EPA’s 9-factor analysis with specific emphasis on Catoosa County being located between violating monitors in Hamilton and Walker C ounties. The nine factors are as follows:

• Factor 1: Emissions in areas potentially included versus excluded from the
nonattainment area

• Factor 2: Air quality in potenti ally included versus excluded areas

• Factor 3: Population density and degr ee of urbanization in cluding commercial development in potentially included versus excluded areas

• Factor 4: Traffic and commuting patterns

• Factor 5: Expected growth

• Factor 6: Meteorology

• Factor 7: Geography/topography

• Factor 8: Jurisdictional boundaries

• Factor 9: Level of control of emission sources

Factors 1 through 5 were the primary criteri a used by EPA in their analysis. The observed PM2.5 design value at three of the four nonattainment area monitors violated the annual standard, as shown in Table 2-1 below. Three of the monitors are located in Hamilton County, Tennessee and one is located in Walker County, Georgia. Details of the 9-factor analysis are included as Appendix A of this submittal.

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2.2 PM 2.5 Monitoring Stations

Georgia currently has one ambient PM 2.5 monitoring site in th e Chattanooga, TennesseeGeorgia nonattainment area, as shown in Table 2-2 and Figure 2.3. The Rossville-Maple Street site is in Walker County. As pa rt of the Memorandum of Agreement with Chattanooga Hamilton County and the State of Tennessee, this monitor provides adequate coverage of Georgia’s portion of the nonattainment area. Data from this monitor represents a neighborhood scale, whic h extends up to 4.0 kilometers from the monitor. The site is considered to be part of the State and Local Air Monitoring Stations (SLAMS) monitoring network, with the monitoring objectiv e of sampling for population

exposure.

One PM 2.5 Federal Reference Method (FRM) monitor and one PM 2.5 speciation monitor are located at the Rossville-Maple Street site (13-295-0002). The PM 2.5 FRM monitor
was installed in accordance with 40 CFR 58 and has been collecting data since January 1, 2000. Its sampling schedule was every six days until January 3, 2007, when it started sampling every three days. Due to construc tion of the Rossville Health Department sampling location, this monitor was moved from the Rossville Health Department site to its current location on Maple St reet. The sampler was tempor arily shut down on June 11, 2007, and restarted on November 14, 2007. Duri ng this time, EPD actively searched and found a location to set up the monitoring equi pment within the requi red distance to keep the same AQS identification. The PM 2.5 speciation monitor has been collecting data since March 23, 2005. The sampling schedule of the PM 2.5 speciation monitor has been and remains every six days.

Table 2-1. 2001 – 2003 PM2.5 Design Values for Chattanooga Nonattainment Area
Monitors

Site name Monitor ID 2001-2003

PM 2.5 Design

Value (μg/m 3 )

Siskin Drive/UTC, Tennessee 470654002 15.2

Maxwell Road/East Ridge,

Tennessee 470650031 16.1

Soddy-Daisy High School,

Tennessee 470651011 14.1

Rossville – Maple St., Georgia 132950002 15.5

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Table 2-2. Rossville PM 2.5 Data Collection Site

Site Name County Site ID Start Date –

FRM Data
Collection Start Date – Speciation Data
Collection
RossvilleMaple Street Walker 13-295-0002 Jan. 1, 2000 March 23, 2005

Figure 2-3. Locations of PM2.5 Monitors in the Chattanooga Nonattainment Area

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2.3 Conceptual Description of the PM2.5 Problem

This section evaluates ambient monitoring data obtained from the four Federal Reference Method or FRM monitors and two specia tion monitor sites located within the Chattanooga nonattainment area. The evaluatio ns include long-term trends of PM2.5
levels obtained from the FRM monitors and th e speciation monitor. Both trends in annual averages and quarterly av erages are evaluated. For the speciation monitor, trends in the different PM2.5 species measured are an alyzed and are compared to speciation data
from other PM2.5 nonattainment areas in Ge orgia. Receptor modeling results are presented to provide an insight into the relative amount of am bient PM2.5 attributable to different emission source categories. CART analysis was performed to examine the relationship between meteorology and air qua lity. Lastly, sensitivity modeling was conducted to examine the response in predicte d ambient PM2.5 levels to reductions from different source categories. All of these analyses can be used to identify the key contributors to PM2.5 levels in the Chattanooga area, along with rece nt trends in their impact, and formulate effective control measure approaches for reducing ambient PM2.5
levels.

2.3.1 Long-term trends of PM2.5

In the Chattanooga NAA, there are four FRM monitors (1 3-295-0002, 47-065-0031, 47- 065-1011, and 47-065-4002) that have been coll ecting data since as early as 1999 and two PM2.5 Speciation monitors, 13-295-0002 a nd 47-065-4002, that have been collecting data since 2005 and 2001, respectively. For the purpose of long-term trend analysis presented here, 2007 and 2008 values were estim ated after excluding exceptional events, as concurred with by EPA. Figure 2-4 shows changes of annual PM2.5 concentrations (arithmetic average of quarterly aver ages) from 1999 to 2008. Overall, PM2.5
concentrations have decreased for this tim e period at all monitors except 47-065-1011
that showed a fluctuation from 2002 to 2008.

The overall trend can be divided into three periods. Th e first period (1999 to 2002) shows relatively large decreases with each year at all monitors. The second period (2003 to 2005) shows elevated PM2.5 concentra tions compared with 2002, and the PM2.5
concentrations fluctuated a bit within th e second period. For the period (2005-2007), 13- 295-0002 and 47-065-4002 showed a steady decreas e of PM2.5 concentrations from year to year, while 47-065-0031 and 47-065-1011 show ed an increase in PM2.5 concentration in 2007. Generally, 47-065-1011 showed the lowe st PM2.5 concentrations in the region
from 2002 to 2007. For the la st period (2006-2008), all monitors showed a steady decrease of PM2.5 concentrations from year to year. The actual values used to create Figure 2-4 are listed in Tabl e 2-3. In 2005, the PM2.5 annual mean concentrations were

16.4 μ g/m 3 , 16.1 μ g/m 3 , 13.8 μ g/m 3 , and 15.5 μ g/m 3 at 13-295-0002, 47-065-0031, 47- 065-1011, and 47-065-4002. In 2007, the PM2.5 a nnual mean concentr ations were 13.8

μ g/m 3 , 14.9 μ g/m 3 , 14.2 μ g/m 3 , and 15.0 μ g/m 3 at 13-295-0002, 47-065-0031, 47-065- 1011, and 47-065-4002. The most air quality improvement from 2005 to 2007 in the Chattanooga NAA occurred at the Georgia m onitor (13-295-0002). In 2008 the lowest annual mean was recorded at the Soddy-Daisy monitor.

28 of 162Attainment Demonstration Methods & Inputs PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 2-4. Trend of annual mean (arithme tic average of quarterly averages) PM2.5
concentrations at FRM monitors (13-295-0002) in the Chattanooga NAA.

Table 2-3. Annual Mean (Arithmetic Average of Quarterly Averages) PM2.5 Concentrations at the FRM Monitor in the Chattanooga NAA.

Year Monitor
13-295-0002 ( μ g/m 3 ) Monitor
47-065-0031 ( μ g/m 3 ) Monitor
47-065-1011 ( μ g/m 3 ) Monitor
47-065-4002 ( μ g/m 3 )

1999

- 21.2
- 18.9

2000 18.5 19.0
- 17.4

2001 15.9 16.7
- 16.1

2002 15.0 15.1 13.9 14.7

2003 15.9 16.5 14.3 14.9

2004 14.9 15.6 13.3 14.5

2005 16.4 16.1 13.8 15.5

2006 14.4 14.3 13.1 15.0

2007 13.8 14.9 14.2 15.0

2008 12.5 12.3 11.4 12.7

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Figure 2-5 shows changes of three-year desi gn values from 2001 to 2008. In general, design values decreased steadily at all m onitors except during 2005. Figure 2-5 also shows decrease of design values from 2006 to 2008 at all monitors. The actual values used to make Figure 2-5 ar e listed in Table 2-4.

Figure 2-5. Trend of PM2.5 Design Values at the FRM monitors in the Chattanooga
NAA.

Table 2-4. PM2.5 Design Values at the FRM monitors in the Chattanooga NAA.

DV Years 13-295-0002 ( μ g/m 3 ) 47-065-0031 ( μ g/m 3 ) 47-065-1011 ( μ g/m 3 ) 47-065-4002 ( μ g/m 3 )

99-01 17.0 18.9
- 17.5

00-02 16.3 16.9 13.9 16.1

01-03 15.5 16.1 14.1 15.2

02-04 15.2 15.7 13.8 14.7

03-05 15.8 16.1 13.8 15.0

04-06 15.2 15.3 13.4 15.0

05-07 14.9 15.1 13.7 15.2

06-08 13.5 13.8 12.9 14.2

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Figures 2-6 through 2-9 shows quarterly PM2.5 trends from 1999 to 2007 at each of FRM monitors in the Chattanooga NAA. There are monitor-to-monitor variations in each quarter over the analysis periods . In general, however, PM 2.5 concentrations were higher
in the 2 nd and 3 rd quarters than the 1 st and 4 th quarter. This is to be expected due to higher temperatures and more photochemi stry during these quarters.

Figure 2-6. Quarterly averages of PM2.5 concentrations at 13-295-0002

31 of 162Attainment Demonstration Methods & Inputs PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 2-7. Quarterly averages of PM2.5 concentrations at 47-065-0031

Figure 2-8. Quarterly averages of PM 2.5 concentrations at 47-065-1011

32 of 162Attainment Demonstration Methods & Inputs PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 2-9. Quarterly averages of PM2.5 concentrations at 47-065-4002

Figure 2-10 shows changes of annual tota l and speciated PM2.5 concentrations (arithmetic average of daily averages ) from 1999 to 2007 at the Rossville PM2.5 Speciation site (13-295-0002). The PM2.5 Sp eciation site started measuring PM2.5 species in 2005. With respect to contri butions of each species to total PM2.5 concentrations, OC (ca. 5.0 μ g/m 3 ) and SO4 (3.7-5.6 μ g/m 3 ) are the major constituents at the monitor. In general, PM2.5 species concentrations were consistent from 2005 to 2007 except for the recent decrease of SO4 concentr ation and the recent increase of NO3. For this time period, SO4 decreased from 5.6 μ g/m 3 to 3.7 μ g/m 3 and NO3 increased from 0.6

μ g/m 3 to 1.0 μ g/m 3 . From 2005 to 2007, NH4 concentrations were around 1.5 μ g/m 3

. For the same period, EC concen trations were approximately 0.6 μ g/m 3 . It should be noted that this site only has 3-year data from 2005 to 2007. Also, the total PM2.5 trends may not necessarily track with the trends of the summed individual species because the total PM2.5 annual averages are based on measurements taken daily whereas the speciated components were measured every si xth day. Therefore, unusually high or low values on a specific day would be weighted more by the indivi dual species trends than by
the total PM2.5 trend. In addition, there is al ways an unidentified amount of speciated PM2.5 (not shown) that could be increasing or decreasing. In addition, the Rossville PM2.5 Speciation site stopped June 11, 2007, fo r relocation (within a mile from the original location) and rest arted November 14, 2007, which may have impacted quarterly trends in the fourth quarter between 2006 and 2007.

33 of 162Attainment Demonstration Methods & Inputs PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 2-10. Trend of annual total and sp eciated PM2.5 concentrations (arithmetic average of daily averages) at the Ro ssville PM2.5 Speciation monitor (13-295-0002).

Figure 2-11 shows quarterly averages of PM2.5 concentrations at the Rossville PM2.5 Speciation site (13-295-0002). Quarterly average PM2.5 c oncentration for the first quarter changed little from 2002 to 2007, except 2003. The second quarter values decreased steadily from 2005 to 2007. The thir d quarter and the fourth quarter showed large decreases from 2005 to 2006. There wa s an increase of PM2.5 concentration from 2006 to 2007 for the fourth quarter. Overall, PM2.5 concentrations were higher in the second and third quarters than in the first and fourth quarters at this site. (Note that the
record of total PM2.5 data at th is site is much longer than th e speciation data that follows since the speciation monitor did not start operation until 2005.)

34 of 162Attainment Demonstration Methods & Inputs PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 2-11. Quarterly averages of PM2.5 concentrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002)

Figure 2-12 shows quarterly averages of SO4 concentrations at the Rossville PM2.5 Speciation site (13-295-0002). SO4 concentrat ions decreased from 2005 to 2006 except the first quarter. Like the PM2.5 trends, quarter ly SO4 concentrations were higher during the second and third quarters than the first and fourth quarter.

Figure 2-12. Quarterly averages of SO4 concentrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002)

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Figure 2-13 shows quarterly averages of NO3 concentrations at the Rossville PM2.5 Speciation site (13-295-0002). Contrary to PM2.5 and SO4 trends, NO3 concentrations
were lower in the second and th ird quarters than the first an d fourth quarters. From 2005 to 2007, NO3 concentrations increased for th e first quarter while the fourth quarter showed large fluctuation from 2005 to 2007. In terms of absolute values, however, NO3 concentrations did not change significantly.

Figure 2-13. Quarterly averages of NO3 concentrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002)

Figure 2-14 shows quarterly averages of NH4 concentrations at the Rossville PM2.5 Speciation site (13-295-0002). In an absolute sense, NH4 concentrations did not change much except for the increase from 2005 to 2006 in the first quarter. In general, NH4 concentrations were steady in each quarter at this monitor.

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Figure 2-14. Quarterly averages of NH4 concentrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002)

Figure 2-15 shows quarterly averages of OC concentrat ions at the Rossville PM2.5 Speciation site (13-295-0002). From 2005 to 2006, OC concentrations increased except for the third quarter. From 2006 to 2007, the firs t quarter showed a large increase of OC concentration while the second and fourth qua rter showed opposite trends. Note that
there is no OC measurement availabl e in the third quarter of 2007.

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Figure 2-15. Quarterly averages of OC concentrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002)

Figure 2-16 shows quarterly averages of EC concentrat ions at the Rossville PM2.5 Speciation site in the Cha ttanooga NAA (13-295-0002). For the first and fourth quarter,
EC concentration trends seem to be similar to OC trends. For the second and third quarter, EC concentrations changed l ittle in an absolute sense.

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Figure 2-16. Quarterly averages of EC concentrations at the Rossville PM2.5 Speciation site in the Chattanooga NAA (13-295-0002)

Figure 2-17 shows changes of annual tota l and speciated PM2.5 concentrations (arithmetic average of daily averages) from 1999 to 2007 at the PM2.5 Speciation site in the Tennessee side of Chattanooga NAA (47- 065-4002). This PM2.5 Speciation site started measuring PM2.5 species in 2001. With respect to contributions of each species

to total PM2.5 concen trations, OC (4.4-5.4 μ g/m 3 ) and SO4 (2.8-5.4 μ g/m 3 ) are the major constituents at the monitor. In general, PM2.5 species concentrations were consistent from 2001 to 2007 except for the increase of SO4, NO3, and OC from 2006 to 2007 and large changes of OC and SO 4 in 2004 as well as SO4, NH4, and NO3 from 2001 to 2002. From 2006 to 2007, SO4 concentration increased from 4.0 μ g/m 3 to 4.9 μ g/m 3 and OC

increased from 4.9 μ g/m 3 to 5.3 μ g/m 3 . From 2001 to 2007, NH4 concentrations were

around 1.6 μ g/m 3 . For the same period, EC concentrations were approximately 0.71

μ g/m 3 . It should be noted that the total PM2.5 trends may not necessarily track with the trends of the summed individual species be cause the total PM2.5 annual averages are based on measurements taken daily whereas the speciated components were measured every sixth day. Therefore, unusually high or low values on a specific day would be weighted more by the individu al species trends than by the total PM2.5 trend. In addition, there is always an unidentified am ount of speciated PM2.5 (not shown) than could be increasing or decreasing.

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Figure 2-17. Trend of annual total and sp eciated PM2.5 concentrations (arithmetic average of daily averages) at 47-065-4002

Figure 2-18 shows quarterly averages of PM2.5 concentrations at 47-065-4002. Quarterly average PM2.5 concentration for the first quarter was relatively steady compared with other quarters. The second a nd fourth quarters showed steady decrease of
PM2.5 concentrations from 2005 to 2007 while th e third quarter displayed the opposite trend for the same period. Overall, PM2.5 con centrations were higher in the second and third quarters than the first and fourth quarters at this site.

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Figure 2-18. Quarterly averages of PM2.5 concentrations at 47-065-4002

Figure 2-19 shows quarterly averages of SO4 concentrations at 47-065-4002. SO4
concentrations decreased from 2005 to 2006 in all quarters. Then, SO4 concentrations increased from 2006 to 2007 except the first qua rter. Compared to the quarterly PM2.5 trends, quarterly SO4 concentrations were higher during the second and third quarters than the first and fourth quarter.

Figure 2-19. Quarterly averages of SO4 concentrations at 47-065-4002

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Figure 2-20 shows quarterly averages of NO 3 concentrations at 47-065-4002. Contrary to PM2.5 and SO4 trends, NO3 concentrations were lower in the second and third quarters than the first and fourth quarters. From 2005 to 2007, NO3 concentrations increased for the fourth quarter while the first quarter showed a large increase from 2006 to 2007 followed by a large decrease of NO3 concentrations from 2005 to 2006. In terms of absolute values, however, NO3 concentr ations did not change significantly.

Figure 2-20. Quarterly averages of NO3 concentrations at 47-065-4002

Figure 2-21 shows quarterly averages of NH4 concentrations at 47-065-4002. Overall, NH4 concentrations were higher in the second and third quarters than the first and fourth quarters even though differences between thes e two groups showed large year-to-year variations. From 2006 to 2007, NH4 concentra tions largely increased while the first quarter showed little change.

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Figure 2-21. Quarterly averages of NH4 concentrations at 47-065-4002

Figure 2-22 shows quarterly averages of OC concentrations at 47-065-4002. OC
concentrations decreased about 35 % in th e first quarter from 2003 to 2004 and in the third quarter from 2002 to 2004. There was la rge increase of OC concentrations from 2006 to 2007 in the first quarter and from 2004 to 2005 in the third qua rter. Overall, OC concentrations were comparable throughout all quarters in 2007.

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Figure 2-22. Quarterly averages of OC concentrations at 47-065-4002

Figure 2-23 shows quarterly aver ages of EC concentrations at 47-065-4002. Overall, EC concentration in the fourth quart er was higher than or similar to EC concentration in other quarters from 2002 to 2006, except in 2003 when the first quarter showed the highest concentration. From 2006 to 2007, EC concentr ations increased for all quarters. From 2004 to 2006, EC concentration decreased in th e fourth quarter while it showed steady increase in the first quarter.

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Figure 2-23. Quarterly averages of EC concentrations at 47-065-4002

2.3.2 Spatial Trends of PM2.5 in Georgia

To analyze spatial patterns in PM 2.5 concentration and composition, GA EPD used 2003- 04 data from the Speciation Trends Networ k (STN) sites in each of the four nonattainment areas: Atlanta (Site ID 13- 089-0002), Chattanooga, Tennessee (Site ID 47- 065-4002), Floyd County (Site ID 13-115-00 05), and Macon (Site ID 13-021-0007) (Figures 2-24 through 2-27, Table 2-5). All four areas exhibit fairly similar seasonal trends, with PM 2.5 peaking in summertime. This peak is mainly due to elevated sulfate (SO4) concentrations, as well as elevated organic matter (OM), some of which is classified here under the “Other” category (OC is a measure of organic carbon, while other constituents of organic matter cannot be measured directly, and hence classified as “Other”). Sulfate and OC are by far the two dominant PM 2.5 species, followed by ammonium (NH4), elemental carbon (EC), ni trate (NO3), major metal oxides (MMO = 2.20Al+2.49Si+1.63Ca+2.42Fe+1.94Ti, as defined by the IMPROVE protocol). Nitrate levels are higher during wintertime, when c ooler temperatures favor nitrate particle
formation. Ammonium levels follow those of su lfate and nitrate, as it binds with these anions. The similarities in seasonal patterns and composition of PM 2.5 in these four
nonattainment areas are an indicatio n of the regional nature of PM 2.5

.

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0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0

Jan-Mar Apr-Jun Jul-Sep Oct-Decug/m3 Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-24. Seasonal patterns in PM 2.5 concentrations and composition in Atlanta (Site

ID 13-089-0002) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, el emental carbon and organic carbon).

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0

Jan-Mar Apr-Jun Jul-Sep Oct-Decug/m3 Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-25. Seasonal patterns in PM 2.5 concentrations and composition in Chattanooga (Site ID 47-065-4002) for 2003-04 (MMO – Major Me tal Oxides; Other

– PM 2.5 other than sulfate, nitrate, a mmonium, MMO, elemental carbon and organic carbon).

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0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0 22.0

Jan-Mar Apr-Jun Jul-Sep Oct-Decug/m3 Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-26. Seasonal patterns in PM 2.5 concentrations and composition in Floyd county

(Site ID 13-115-0005) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon).

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0

Jan-Mar Apr-Jun Jul-Sep Oct-Decug/m3 Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-27. Seasonal patterns in PM 2.5 concentrations and composition in Macon

(Site ID 13-021-0012) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5

other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon).

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Assessing the relative contributi on of primary and secondary species is not trivial, since OC is part primary and part secondary. Al so, an assumption needs to be made for the ratio of OM/OC. Assuming an OM/OC ratio of 1.4 and that roughly 50% of the OC is secondary (on an annual basis), it is po ssible to estimate that about 40% of PM 2.5 in the region is primary, with the remaining fraction being secondary (Table 2.6).

Table 2-5. Average PM 2.5 composition during 2003-2004 for the four STN sites in the non-attainment areas ( μ g/m 3 )

Site SO4 NO3 NH4 MMO EC OC Other

Atlanta 4.56 0.83 1.35 0.55 0.96 5.16 2.15

Chattanooga 4.96 1.10 1.70 0.62 0.71 4.92 1.13 Floyd county 4.93 0.88 1.46 1.05 0.50 5.01 3.23

Macon 4.50 0.67 1.26 1.47 0.68 5.51 2.34

Table 2-6. Estimated primary and secondary percentage of PM 2.5

Site % primary % secondary

Atlanta 40 60 Chattanooga 32 68 Floyd county 43 57

Macon 44 56

Despite the regional nature of PM 2.5 and the high fraction of secondary components, there is clear evidence of impacts of local sources of primary PM 2.5 at some of the sites. Both the Macon and Floyd County monitors show hi gher levels of crustal elements (MMO, Table 2-5), compared to Atlanta and Chattanooga (1.47, 1.05, 0.62 and 0.55 μ g/m 3

, respectively). The Floyd Count y monitor also exhibits hi gher levels of unspecified (“Other”) PM 2.5 , compared to the other three sites. Elemental carbon levels are highest in Atlanta (1.0 μ g/m 3 compared to 0.5-0.7 μ g/m 3 at the other three sites), the largest city of these four areas and are likely an indicator of emissions from mobile sources and especially diesel traffic. As expected, c oncentrations of OC (p artially secondary) and secondary ionic PM 2.5 (sulfate, nitrate, ammonium) are fairly similar at all four sites, though the Chattanooga site exhibits somewhat higher levels of ammonium nitrate. Fairly minor spatial gradie nts are observed in the Ch attanooga NAA, with the 2002 DV for the Georgia site (15.5 μ g/m 3 at 13-295-0002) being in be tween the DVs of the two Tennessee Hamilton County sites (16.1 μ g/m 3 at 47-065-0031; 15.2 μ g/m 3 at 47-065- 4002).

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2.3.3 Source Apportionment of PM2.5

Receptor models are useful tools for identif ying the sources contributing to measured levels of pollutants. One of the most r ecently used source-apportionment models in recent years is the Positive Matrix Factorizati on (PMF) model. PMF is a factor analytic method that distinguishes correla tion patterns among speciated PM 2.5 measurements in a given location.

A standalone PMF analysis was conducted for the Hamilton County STN monitor (47- 065-4002) using data from 2002-2006 (Figure 2-28). Details of this analysis can be found in Appendix B and Appendix C. Thes e results are useful for a preliminary assessment of control strategies. Given the large contributions to PM2.5 from thesecondary-sulfate factor, it is evident that controlling SO2 emissions (mainly from coalburning EGUs) would reduce PM2.5 levels in the Chattanooga MSA. Controlling emissions of SOA precursors would also be us eful. Unfortunately, these are not yet well understood, and limited available knowledge su ggests biogenic sources as the primary SOA precursors. Reducing primary PM2.5 may be best achieved by emission reduction
measures for mobile-source emissions and biomass burning. The efficiency of NOx controls on PM2.5 was addressed via a photoc hemical modeling study and was found to be inefficient (see Section 2.4.3). Contributio ns from all other source-categories were small in general.

1.11 0.65

0.46

0.64

0.48

0.21

0.24

6.27 0.20 4.39 0.59

Mobile-sources
Biomass-burning

Soil

Zn-rich

Fe-rich

Na-rich

Ca-rich

Sec. Sulfate

Sec. Nitrate

SOA
Unspecified

Figure 2-28. Average factor contributions to PM 2.5 ( μ g/m 3 ) for 2002-06 at the Chattanooga STN site using PMF

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2.3.4 Meteorology

ICF International performed a meteorological characteriza tion study designed to examine the relationships between meteorology, fine pa rticulates, and regiona l haze at air quality monitoring sites throughout the Southeastern U.S. (Character ization of Meteorology and Its Relationships to Fine Particulate Mass and Visibility in the VISTAS Region, December 5, 2006). This study is one of the technical analyses conducted by VISTAS to support regulatory planning for regional haze and PM2.5 nonattainment issues for the southeastern states. The study is presented as Appendix D.

Classification and Regression Tr ee (CART) statistical analysis was used to examine relationships between meteorology, fine particulates, and visibi lity that are important to atmospheric modeling. Separate CART an alyses were conducted for visibility (as defined by the extinction coefficient) and fine particulates (parti culate matter with a diameter less than 2.5 microns, PM2.5). PM2.5 analyses were conducted for the IMPROVE, STN, and SEARCH monitoring sites. CART was applied for each site and was used to classify days for the period 2000- 2004 into groups (or bi ns) with similar air quality (visibility or PM2.5) and meteorologica l characteristics. Cl assification accuracy ranged from approximately 72 to 85 percent fo r visibility and from approximately 75 to 90 percent for PM2.5. In applying the CART technique, ICF International found that good classification for most sites was depe ndent on information about prior day air quality. This indicates that regional buildup and transport of fine particles and related precursor species influences regional visibility and PM2.5 concentrations. They also found that moisture is an important meteorol ogical parameter for good classification for both PM2.5 and visibility and that the more complex role of moisture in determining light extinction—affecting both particle formation and the contribution of sulfate and nitrate particle species to light ex tinction—may account for the ove rall better classification accuracy for PM2.5 compared to extinction coefficient.

The categorical comparisons for PM2.5 in Ch attanooga consisted of four categories: <17.5 (Category 1), 17.5-25 (Categor y 2), 25-32.5 (Category 3), and > 32.5 (Category 4)

μ g/m 3 , respectively. A summary of the aver age input parameters for each CART classification category in Chattanooga is contai ned in Table 2-7. From this table, it can be seen that important surface parameters leading to higher PM2.5 concentrations include higher PM2.5 the day before in Atlanta, Bi rmingham, and Knoxville; lower wind speeds; southerly winds; and lower amounts of rain. In addition, it can be seen that important upper-air meteorological parameters leadi ng to higher PM2.5 concentrations include lower 850 mb wind speeds and more stability in Atlanta.

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Table 2-7. Summary of average input para meters for each CART classification Category in Chattanooga.

For Chattanooga, there are thr ee key bins (group of days with similar meteorological conditions) for the Category 3 concentration range and one key bin for the Category 4 concentration range (Table 2-8). Category 3 bins (22, 19, and 21) are characterized by high prior-day PM2.5 concentrations in Birmingham (> 30 μ g/m 3 ) and higher surface temperatures. The Category 4 bins (28) are characterized by high prior-day PM2.5 concentrations in Birmingham, Atlanta, and Knoxville (> 44 μ g/m 3 ). All bins are characterized by low wind speeds at the surf ace and aloft and surface winds from the
south/southwest.

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Table 2-8. Summary of average input parameters for key bins for CART classification (Class) cate gories 3 and 4 in Chattanooga.

Figure 2-29 shows the average composition for all key PM2.5 bins in Chattanooga. Consider the four key high PM2.5 bins for Chattanooga (Bins 22, 19, 21, and 28). On average, Bins 22 and 19 are dominated by or ganic matter, while Bins 21 and 28 have a greater proportion of sulfate.

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In summary, high PM2.5 for Chattanooga is associ ated with moderate temperatures, high PM2.5 on previous day from upwind urban ar eas (Birmingham, Atlanta, and Knoxville), very low wind speeds near the surface and lo w wind speeds aloft, south-southwesterly winds near the surface, and stable conditi ons. The high PM2.5 classification categories are associated with higher SO4 and OM concentrations.

Figure 2-29. Average composition fo r all key PM2.5 bins in Chattanooga.

2.3.5 Sensitivities to Pollutant Emissions

Georgia Tech performed sensitivity studies to examine the impacts of reductions of emissions of various pollutants on annual PM2.5 concentrations using CMAQv4.4_SOAmods on the VISTAS 12 km modeling domain. A summer episode (June 1 – July 10, 2002) and winter epis ode (November 19 – December 19, 2002) were simulated with VISTAS 2009 BaseD emissions. Each sensitivity consisted of reducing the emissions or boundary conditions by 30%. Most sensitivities were performed on a state-by-state basis or RPO-by-RPO basis. However, sensitivities for ground-level NOx and primary carbon were performed for a spec ific subset of coun ties submitted by each state. Below is a list of the specific sensitivities that were performed:

• EGU Point SO2 (10 states + 3 RPOs);

• Non-EGU Point SO2 (10 states + 3 RPOs);

• Boundary SO2 and SO4 (12 km domain);

• Point NOx (10 states + 3 RPOs) ;

• Ground-level NOx (10 states + 3 RPOs) ;

• Ground-level Primary Carbon (10 states + 3 RPOs);

• Point Primary Carbon (10 states + 3 RPOs);

• Fire Primary Carbon (10 states + 3 RPOs);

• NH3 (4 RPOs); and

• Anthropogenic and Biogenic VOCs (domain-wide).

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Figures 2-30 and 2-31 contain the sensitivities for each pollutant and source category for the summer and winter episodes, respectivel y. In the summertime, the most important anthropogenic source category impacting Chattanooga is EGU SO2 followed by nonEGU SO2 and ground-level PC. In the winter time, the most important anthropogenic source category is VISTAS NH3 followe d by Georgia and Tennessee ground-level PC, Georgia EGU SO2, and domainwide VOCs.



## Chattanooga, TN (470650031) Summer

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

S

O2

_EGU

SO2 _ n

o nEGU

NOx_Gro u nd

NOx_ P

o int NH3 VOCs

PC _ Gr

oun d

P

C

_Poi n t

P

C

_FiresΔPM2.5(μg/m3) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Figure 2-30. Sensitivities of PM2.5 by pollutant and source category for the summer episode in Chattanooga. Δ PM2.5 is based upon a 30% reduc tion of pollutant levels
from 2009 levels.

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### Chattanooga, TN (47 0650031) Winter

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

S

O

2_

E

GU

S

O

2_

no

nE

G

U

N

O

x _G r o

u nd

NOx_Po i nt N H3 VOCs

P

C_

G

roun d

P

C

_P

oi

n t

P

C

_F

i resΔPM2.5(μg/m3) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Figure 2-31. Sensitivities of PM2.5 by pollutant and source category for the winter episode in Chattanooga. Δ PM2.5 is based upon a 30% reduc tion of pollutant levels
from 2009 levels.

Figure 2-32 contains the annual average se nsitivities for each pollutant and source category based on the average response of the summer and wint er episodes in Chattanooga. Figure 2-33 contains the annual average sensitivities for each pollutant and source category based on the CART weight ed response of the summer and winter episodes in Chattanooga. Both of these figures indicate that the most important anthropogenic source category is VISTAS NH3 followed by Georgia EGU SO2 and Tennessee and Georgia ground-leve l PC. However, it should be noted NH3 and SO2 (both of which are gases at ambient temperatur es) react to form amm onium sulfates in the atmosphere (which are fine particulates at ambient temperatures). Thus, reduction of
either NH3 or SO2 emissions should result in a lowering of the other. Furthermore, there are currently no known cost-effective ways to reduce NH3 emissions in Georgia. Therefore, control strategy development s hould focus on SO2 from EGUs and PC from ground-level sources.

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### Chattanooga, TN (470650031) Annual

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2 _ EGU
SO2 _n

on

EGU

NOx _ Grou n d NOx_Point NH 3 VOCs PC_ G

roun d

PC_P o int PC_ Fir

esΔPM2.5(μg/m3) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Figure 2-32. Annual average emission sensitivities by pollutant and source category based on the average response of the su mmer and winter episodes in Chattanooga.

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### Chattanooga, TN (470650031) Annual

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

SO2 _ EGU
SO2 _n

on

EGU

NOx _ Gro un

d NOx_Point NH3 VOCs PC_ Gr

ou

nd

PC_Point PC_ F

i r esΔPM2.5(μg/m3) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Figure 2-33. Annual average emission sensitivities by pollutant and source category based on the CART weighted response of the summer and winter episodes in Chattanooga.

2.4 PM2.5 Sources and Precursors

This section of the SIP includes a discus sion of sources of PM2.5 emissions, direct PM2.5, and PM2.5 precursors in and n ear Chattanooga nonattainment area.

2.4.1 Sources of PM2.5

The primary sources of direct PM 2.5 emissions within the boundaries of the area are a coal-burning power plant, other point source s, mobile sources, and biogenic sources.
Sources of PM 2.5 located outside the boundaries of the area are coal-burning power plants and biogenic sources. These coal-burning power plants include TVA’s Widows Creek
Fossil Plant within the non-attainment area and Georgia Power’s Plant Bowen in Bartow County, Georgia, just outside the Chattanooga nonattainment area.

There is only one major point source of di rect PM in the Georgia portion of the nonattainment area. In Walker County, Crystal Springs Print Works has a potential to emit direct PM2.5 as a major source, but while the facility does cont ribute to direct PM emissions in the non-attainment area, their re ported actual emissions in the CERR show that the facility emits 70 t ons-per-year on a normal basis.

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2.4.2 Precursors

The PM 2.5 SIP must address both direct PM emissi ons and secondary PM. As discussed previously, secondary PM is formed from emi ssions of PM precursors. Therefore, it is critical to identify the precursor s that are relevant to a spec ific nonattainment area.

40CFR51.1000 defines a “PM 2.5 precursor” as any air pollutant other than direct PM 2.5 that contributes to the formation of PM 2.5 . Emissions inventories required for attainment
demonstration must include emissions of all PM 2.5 precursors identified for the
nonattainment area.

40CFR51.1000 defines a “PM 2.5 attainment plan precursor” as SO2 and any other precursor that the State must evaluate for emissions reduction measures in the SIP. 40CFR51.1002 states that, for control purposes , NOx is presumed to be a precursor whereas VOC and ammonia are presumed not to be precursors. The regulation allows any of these presumptions to be reversed by either the State or EPA by means of a technical demonstration. The regulation does not require th e State to address VOCs or ammonia as an attainment plan precursor unless either the State or EPA provides a technical demonstration for a specific area showing that emissions from the State significantly contribute to PM (reference 40 CFR 51.1002(3) and (4).) 40 CFR 51.1002(c) implies that all source s of precursors within the State boundaries must be
evaluated for emissions reduction measures. Ho wever, the preamble to the final rule, 72 FR 20636, gives EPA’s policies on the geogra phic ranges to use for evaluation of emissions reduction measures for RFP plans:

• Direct PM2.5 sources inside the nonattainment area only

• SO2 sources within 200 km

• NOx and VOC sources within 100 km

• Ammonia no policy

Therefore, it is EPD’s interpretation that sources of SO2 emissi ons throughout the state must be evaluated for potential reduction measures.

The major sources of SO 2 emissions in the Catoosa and Walker County portion of the non-attainment area come primarily from one industrial source and mobile sources. SO 2 emissions from combined industrial and mobile sources within the ar ea are significantly less than those from regional coal-fired electr ic utilities. The one non-EGU industrial point source that contributes SO2 emissions to the nonattainment area is Crystal Springs Print Works in Walker County with approximately 295 tons of emissions per year.

Georgia EPD has designated SO2 to be the only attainment plan precursor in the Georgia portion of the SIP. Georgia EP D is not providing a technical demonstration to show that VOC or Ammonia significantly contributes to PM to overcome EPA’s presumption that they are not to be regulated as precursors. Georgia EPD has determined that NOx is not a significant precursor for PM2.5 formation in the Chattanooga nonattainment area.

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2.4.3 Technical Justification for Rev ersal of NOx Precursor Presumption

The U.S. EPA, in 72 FR 20586, requires states to “evalua te and consider control strategies for sources of SO 2 , direct PM2.5, and NOx emi ssions in all nonattainment areas.” However, the rule does allow states to make technical dem onstrations to reverse the inclusion of NOx as precursor of PM2.5, or deem the precursor insignificant, based on geographical regions, emissions , etc. This then eliminat es the need to examine NOx control measures for purposes of reduc ing PM2.5 concentrati ons. Georgia EPD’s analysis, summarized below demonstrates th at NOx is an insignificant PM2.5 precursor in each of Georgia’s four nonattainment areas : Atlanta, Macon, Rome, and Chattanooga. The full text of the analysis is presented in Appendix E.

2.4.3.1 Ambient Measurements Ambient monitoring data in Georgia nonatt ainment areas were evaluated using FRM measurements and speciated data. The “ S ulfate, A djusted N itrate, D erived W ater,
I nferred C arbonaceous Mass H ybrid” (“SANDWICH”) materi al balance approach was used to speciate the FRM to tal PM2.5 mass into its compon ents - SO4, NO3, NH4, PBW, OC, EC, and crustals. Figure 2-34 contains the annual av erage PM2.5 concentrations averaged across all FRM monito rs in each nonattainment area. The measurements show that annual average nitrate co ncentrations (plus ammonium and PBW associated with nitrate) collected on the FRM filters range from 0.27 to 0.33 μ g/m 3 , which is only 1.7 to 2.5 percent of the tota l FRM mass. These are very sma ll compared to sulfate and OC
contributions.

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0 2 4 6 8 10 12 14 16 18 20AtlantaMaconRomeChatta.AtlantaMaconRomeChatta.AtlantaMaconRomeChatta.AtlantaMaconRomeChatta.AtlantaMaconRomeChatta.

Quarter 1 Quarter 2 Quarter 3 Quarter 4 Annual Avg.Concentration (μg/m3) Crust.

EC

OC

PBW

NO3

NH4

SO4

Blank

Figure 2-34. SANDWICH Speciated Con centrations of Quarterly and Annual Average 2002 PM2.5 Base Design Values (DVB) averaged over FRM Monitors in each Georgia PM2.5 Nonattainment Area.

2.4.3.2 Future-Year Attainment Modeling Attainment demonstration modeling using on-the-books, future-year emissions and the Speciated Modeled Attainment Test (SMA T) accounted for statewide NOx emission
reductions of 24% between 2002 and 2009 and 30% between 2009 and 2018 (Table 2-9). Despite these large emission reductions, the annual average NO3 concentrations are only reduced by 0.00 to 0.03 μ g/m 3 across all nonattainment areas in Georgia (Figure 2-35). In addition, there is a reduction in OC. Howe ver, much of the reductions in OC are due to reductions in primary emissions of OC a nd not due to reductions of NOx emissions. This is evident by the associated reductions in EC concentrations since primary emissions of OC and EC usually change together (Figure 2-35).

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Table 2-9. Expected Change in NOx Emissions (%) in Georgia PM2.5 Nonattainment Areas. Reductions are negative.

Change in NOx emissions (%)
2002 to 2009 Change in NOx emissions (%)
2009 to 2018 Nonattain.

Area Point Nonroad
mobile Area Onroad
mobile Total Point Nonroad
mobile Area Onroad
mobile Total

Atlanta -40.8 -11.4 5.2 -33.7 -30.5 -48.1 -24.3 10.8 -54.3 -40.3

Macon -32.4 -15.1 3.9 -32.0 -30.9 2.7 -27.8 9.4 -48.3 -8.6

Rome -16.8 -15.7 4.1 -28.9 -18.1 -35.2 -26.8 8.2 -46.1 -34.4 Chattanooga -70.9 -12.7 2.7 -34.2 -48.8 5.2 -21.4 7.1 -50.5 -23.7

GA Statewide -24.6 -12.5 3.9 -32.0 -24.1 -15.6 -24.7 6.9 -51.2 -29.8

Georgia Nonattainment Area Concentration Differences: (2002 DV)-(2009 DV) (Left) and (2009 DV)-(2018 DV) (Right)

-0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45Clayton-0091Cobb-0003Cobb-0004DeKalb-0002DeKalb-2001Fulton-0032Fulton-0039Gwinnett-0002Hall-0003Paulding-0003Bibb-0007Bibb-0012Floyd-0005Hamilton-1011Hamilton-31-1Hamilton-4002Walker-0002

Monitor Locationug/m3 EC

OC

NO3

Figure 2-35. Reductions in EC, OC, a nd NO3 concentrations between 2002 & 2009 and 2009 & 2018 at Georgia nonattainment monitors.

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2.4.3.3 Statewide NOx Sensitivities

Sensitivities to statewide NOx emission reduction (30%, 50%, 70%, and 100%) were modeled for 2009 in Georgia. The ma ximum predicted PM2.5 reductions are approximately 1.2 μ g/m 3 for 100% statewide NOx removal, 0.4

 μ g/m 3 for 70% removal,

0.22

 μ g/m 3 for 50% removal (Figure 2-36), and 0.11

 μ g/m 3 for 30% removal (Figure 2- 37).



### Statewide 50% NOx Reduction

-0.25

-0.20

-0.15

-0.10

-0.05

0.00DeKalb-2001Fulton-0032Fulton-0039Clayton-0091Hall-0003Gwinnett-0002Cobb-0003DeKalb-0002Paulding-0003Bibb-0007Bibb-0012Floyd-0005Hamilton-4002Walker-0002ΔPM2.5 (μg/m3) Soils

EC

OC

H2O

NO3

NH4

SO4

Figure 2-36. Annual Average PM2.5 Response to GA Statewide 50% NOx Emission
Reduction in 2009 in the Georgia PM2.5 Nonattainment Areas.

GA EPD feels it is inappropria te to apply a 100% statewid e NOx emission reduction to evaluate the significance of NOx emissions on ambient PM2.5 si nce this is an unrealistic control scenario leading to unrealistic atmospheric conditio ns. In addition, the CB-IV
chemical mechanism and SOA module used in the CMAQ modeling were developed to accurately predict pollutant concentrations und er typical ambient conditions. They were not intended to be used to make polluta nt predictions under th e unrealistically low oxidizing atmospheric conditions caused by zeroing-out all NOx within a state. Instead, GA EPD feels it is more appropriate to eval uate more realistic, yet aggressive, NOx
reductions from sources across th e state. GA EPD conducted a technical analysis looking at potential statewide reduc tions in mobile (on-road and non-road), point, and area sources and has concluded that a very aggressive upper bound on statewide emission
reductions would result in a NOx reduction of approximately 47%. This scenario

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### Statewide 30% NOx Reduction

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04DeKalb-2001Fulton-0032Fulton-0039Clayton-0091Hall-0003Gwinnett-0002Cobb-0003DeKalb-0002Paulding-0003Bibb-0007Bibb-0012Floyd-0005Hamilton-4002Walker-0002ΔPM2.5 (μg/m3) Soils

EC

OC

H2O

NO3

NH4

SO4

Figure 2-37. Annual Average PM2.5 Response to GA Statewide 30% NOx Emission Reduction in 2009 in the Georgia PM2.5 Nonattainment Areas.

assumes the installation and operation of a ll possible controls through 2030. In addition, GA EPD evaluated aggressive upper bound NOx emissions reductions for 2014 since the
latest attainment date for annual PM2.5 nona ttainment areas is 2015. Assuming all the controls listed for 2030 are achievable by 2014 and replacing the 2030 on-road mobile
fleet turnover with 201 4 on-road mobile emissions, results in a 31% statewide reduction in NOx emissions. As a result, GA EPD f eels that a statewide 30% or, at worst, a
statewide 50% NOx reduction run would be the most appropriate scenarios to use in the NOx insignificance demonstration. Modeling results show a maximum impact of 0.11

μ g/m 3 across all nonattainment monitors in the state for a 30% statewide NOx reduction run (monitors in dow ntown Atlanta showed a slight increase in PM2.5 formation) and a maximum impact of 0.22 μ g/m 3 across all nonattainment monito rs in the state for a 50%
statewide NOx reduction run.

2.4.3.4 CMAQ Model Performance for Organic Carbon

OC modeling is still devel oping and does not include many important pathways. Recent smog chamber studies at Caltech have shown that reducing NOx emissions can lead to higher SOA production from isopr ene, monoterpenes, and aromatic VOC. None of these pathways are currently included in the model. Furthermore, it should be noted that the SOA module used in CMAQ wa s developed based on S OA formation rates under ambient atmospheric conditions, and that SOA fo rmation rates have not been tested under the artificially-low oxidant levels that re sult from zeroing out all NOx emissions in a

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large state. This introduces additional uncertain ty in the interpretation of the responses of organic carbon aerosol to large NOx reductions.

In Figure 2-38, it can be seen that the CMAQ model (dark blue and light blue lines) has poor hourly OC model performance when comp ared to OC observati ons (black line). The model predicts unrealistical ly high OC predictions in the early morning hours when the mixing height is low. It is during th ese same hours that the model shows large OC sensitivities to NOx reductions. Note that in Figure 2-40, dSOMA (pink) is the response of anthropogenic secondary organic aerosol s, dSOMB (bold red) is the response of biogenic secondary organic aerosols, dPSOM (green) is the response of polymerized secondary organic aerosols, and dPOM (bold black) is the response of primary organic aerosols. Because large dSOMB responses to NOx reductions occur during the same periods that the model is performing poorly, it can be concluded that these responses are likely too high. It should be noted that near the end of the month (06/27-06/30) when the model is performing well, that the OC sensitivity has been reduced by an order of magnitude. As a result, it very likely that the OC responses presented earlier are highly
overestimated and should not be used to quantify the impacts of NOx reductions.

2.4.3.5 Conclusion

An analysis of emission trends, ambient data concentrations, and modeling has lead GA EPD to conclude that NOx is an insigni ficant precursor to PM2.5 in Georgia’s nonattainment areas. GA EPD feels it is inappr opriate to evaluate the impacts of a 100% statewide NOx sensitivity as part of a NOx insignificance demonstration since this level of control is not realistically achievable from a NOx control standpoint. In addition, caution should be used when interpreting th e predicted changes in OC resulting from NOx reductions since the CMAQ modeling syst em does not properly simulate hourly OC temporal variations and wa s not designed to predict co ncentrations under extreme reductions in precursor emi ssions and oxidant levels.

GA EPD feels that a 30% or, at worst, a 50% statewide NOx reduction scenario would be more appropriate to evaluate the effect of NOx reductions on PM 2.5. Modeling results show a maximum impact of 0.11 μ g/m 3 across all nonattainment monitors in the state for
a 30% statewide NOx reduction scenar io and a maximum impact of 0.22 μ g/m 3 across all
nonattainment monitors in the state for a 50% statewide NOx reduction scenario.

EPA’s CAIR modeling used a significance level of 0.2 mg/m 3 to evaluate interstate impacts (e.g., Georgia emissions impacts on non -Georgia PM2.5 monitors). Therefore, it would seem reasonable to assume that the signi ficance threshold level fo r a state on itself, for the purpose of reversal of the NOx pr ecursor presumption, should be higher than a state on a neighboring state. The proposed Pr evention of Significant Deterioration (PSD) significant impact levels (SIL s) for direct PM2.5 emissions from a single stationary source range from 0.3 to 1.0 μg/m3 on an annual average (70 FR 54154). From this, it would follow that the significance threshold all sources in a state should be even greater than this range. At an absolute minimum, the significance threshol d level should set at 0.3 μg/m 3

.

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Since both the 30% statewide NOx reduc tion and 50% statewide NOx reduction modeling scenarios result in a reduction of PM2.5 impact of less 0.3 μg/m 3 , EPD has clearly demonstrated that NOx is insignifi cant as a precursor to PM2.5 in the Georgia
nonattainment areas.

Organic Carbon in June 2002 @ YRK

0 5 10 15 20 25 30 35 40

6/1/
02
0:00 6/2/
02
0:00 6/3/
02
0:00 6/4/
02
0:00 6/5/
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0:00 6/26 /02
0:00 6/27 /02
0:00 6/28 /02
0:00 6/29 /02
0:00 6/30 /02
0:00

Date Ab

s Co

nc ent rati

on [ug /m 3] -4

-3

-2

-1

0

1

2

3

4 Di f
f Co

nc ent rati

on [ug /m 3] OC_02_Typical OC_09_Typical OC dSOMA dSOMB dPSOM dPOM

Figure 2-38. Hourly measured and modele d OC concentrations and the difference

between the base and sensitivity concentrat ions of the four components of OC to

100% statewide NOx emissions reduction s at the Yorkville SEARCH monitor.

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Section 3. Attainment Demons tration Methods and Inputs

The attainment modeling for the Chattanooga PM 2.5 nonattainment area was performed in conjunction with the regional haze mode ling being done by the Southeast regional planning organization (RPO), Visibility Improvement State and Tribal Association of the Southeast (VISTAS), and the PM 2.5 and ozone (O 3 ) modeling being done by the Association of Southeastern Integrated Pla nning (ASIP). VISTAS and ASIP are run by the ten Southeast states (Alabama, Florid a, Georgia, Kentuc ky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia). Since the regional
haze and PM 2.5 modeling use annual simulations and incl ude an intermediate year that is the attainment year required for the Chattanooga PM 2.5 nonattainment area, GA EPD decided to use this 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 decided to use the following modeling system:

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

• Emissions Model: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeli ng system that generates hourly gridded speciated emission inputs of mobile, nonroad mobile, area, point, fire, and biogenic emission sources for photochemical grid models.

• Air Quality Model: The USEPA’s Models -3/ Community Multiscale Air Quality (CMAQ) modeling system is a “One-A tmosphere” photochemical grid model capable of addressing O 3 , particulate matter, visibi lity, and acid deposition at regional scale for periods up to one year.

Additionally, a historical year is selected to model. This year represents typical meteorological conditio ns in the Southeast when high ozone, high PM 2.5 and poor visibility are observed thr oughout the region. Once the hist orical year is selected, meteorological inputs are de veloped using the meteorological model. Emission inventories are also developed for the historical year and processed through the emissions model. These inputs are used in th e air quality model to predict ozone, PM 2.5 and visibility, with the results compared to th e historic data. The model performance is evaluated by comparing the modeled predicte d data to the historic air quality data.

Once model performance is deemed adequate, ty pical baseline and future year emissions are processed through the emissions model. For this demonstrati on, the baseline year was 2002, which corresponds with the same year as the historic meteorology used in the modeling. The attainment future year GA EP D is using for this demonstration is 2009,

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since the mandatory attainment date for the Chattanooga PM 2.5 nonattainment area is April 5, 2010. The attainment date is set prior to the completion of the 2010 calendar year; therefore the attainment of the NAAQS would have to be met by the end of 2009. These emissions are processed through the ai r 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 at tainment demonstration.

The complete modeling protocol used for th is analysis can be found in Appendix F.

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 me thods. Freely accessible means that model formulations and coding are freely available for review and that the models are available to stakeholders, and their consultants, for execution and verification at no or low cost.

The following sections outline the criteria for selecting a modeling system that is both defensible and capable of meeting the study's goa ls. These criteria we re used in selecting the modeling system used for this modeling attainment demonstration.

3.2.1 Selection of Air Quality Model

3.2.1.1 Criteria

For an air quality model to qualify as a candida te 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 ev aluations have show n the model is not
biased toward underestimates or overestimates.

• A protocol on methods and procedures to be followed has been established.

• The developer of the model must be willing to make the source code available to users for free or for a reasonable cost, and the model cannot otherwise be proprietary.

3.2.1.2 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 the attainment mode ling exercise started. For more than a

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decade, the U.S. EPA has been developing the Models-3 CMAQ modeling system with the overarching aim of producing a “One-A tmosphere” air quality modeling system capable of addressing ozone, fine particulate matter, visibility and acid deposition within a common platform. The original justifica tion for the Models-3 development emerged from the challenges posed by the CAAA and the U.S. EP A’s desire to develop an advanced modeling framework for “holistic” environmental modeling utilizing state-ofscience representations of atmospheric pr ocesses in a high performance computing environment. The U.S. EPA completed the in itial 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.

Another reason for choosing CMAQ as the atmospheric model is the ability to do 1- atmospheric modeling. Since GA EPD will be using the same modeling exercise for the
ozone and PM 2.5 attainment demonstration state imple mentation plans (SIPs), as well as the regional haze SIP, having a model that can handle both ozone and particulate matter
is essential. A number of features in CMAQ’s theoretical formulation and technical implementation make the model well suited for annual PM 2.5 modeling.

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

3.2.2 Selection of Meteorological Model

3.2.2.1 Criteria

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

• Non-Hydrostatic Formulation

• Reasonably current, peer reviewed formulation

• Cloud Physics Simulation

• Public availability at no or low cost

• Output available in I/O API format

• Supports Four Dimensional Data Assimilation

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

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3.2.2.2 Overview of MM5

The non-hydrostatic MM5 model is a 3-dime nsional, limited-area, primitive equation, prognostic model that has been us ed widely in regional air quality model applications.
The basic model has been under continuous development, improvement, testing, and open peer-review for more than 20 years an d has been used worldwide by hundreds of scientists for a variety of mesoscale studies.

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

Two planetary boundary layer para meterizations are available fo r air-quality applications, both of which represent sub-grid -scale turbulent fluxes of heat, moisture and momentum. One scheme uses a first-order eddy diffusi vity formulation for stable and neutral
environments and a modified first-order sche me 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 condi tions are specified for real -data cases from mesoscale three-dimensional analyses performed at 12- hour intervals on the outermost grid mesh selected by the user. Surface fields are analyzed at three- hour intervals. A Cressmanbased technique is used to analyze standard surface and radiosonde observations, using the National Meteorological Center's spectral analysis, as a first guess. The lateral boundary data are introduced us ing a relaxation technique a pplied in the outermost five rows and columns of the coarsest grid domain.

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 th e Regional Atmospheric Modeli ng System (RAMS) and the Systems Application International Mesoscal e Model. The MM5 enjoys a far richer application history in regulator y 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 th at the MM5 model tends to produce somewhat better photochemical m odel inputs than a lternative models.

The configuration used for this modeling de monstration, as well as a more detailed description of the MM5 model, can be found in Appendix G.

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3.2.3 Selection of Emissions Processing System

3.2.3.1 Criteria

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

• File System Compatibility with the I/O API

• File Portability

• Ability to grid emissions on a Lambert Conformal projection

• Report Capability

• Graphical Analysis Capability

• MOBILE6 Mobile Source Emissions

• Biogenic Emissions Inventory Sy stem 3 version 0.9 (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

3.2.3.2 Overview of SMOKE

The Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System was originally developed at the Mi cro-Computing Center of Nort h 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 ex ception of mobile and biogenic sources, its purpose is to provide an effici ent, modern tool for convert ing 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 f actors and estimates of vehicle miles traveled.

SMOKE was originally designed to allow emi ssions data processing methods to utilize emergent high-performance-computing as app lied to sparse-matrix algorithms. Indeed, SMOKE is the fastest emissions processing to ol currently available to the air quality modeling community. The sparse matrix a pproach utilized throughout SMOKE permits both rapid and flexible proces sing of emissions data. The processing is rapid because
SMOKE utilizes a series of matr ix calculations instead of less efficient algorithms used in previous systems. The processing is flexib le because the processing steps of temporal

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projection, controls, chemical speciation, temporal allocation , and spatial allocation have been separated into independent operations wh erever possible. The results from these steps are merged together at a final stage of processing.

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

For additional information about the SM OKE model please refer to Appendix G.

3.3 Selection of the Base 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 ch anges in air quality in response to changes in emissions. The U.S. EPA’s April 2007 Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze (Attainment Modeling Guidance) identifies specific criteria to consider when selecting one or more episodes for modeli ng attainment with air quality standards. The U.S. EPA recommends that episode selec tion derive from four principal criteria:

• Simulate a variety of me teorological conditions;

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

• Model periods for which extensive air qua lity/meteorological databases exist; and

• Model a sufficient number of days so that the modeled a ttainment 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 pref erable, representative modeling year.

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 releva nt 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 for model inputs and model performance
evaluation. VISTAS used Cl assification and Regression Tree (CART) analyses to evaluate the candidate modeling year s. The year 2002 was found to be representative of conditions in the other years. Subse quently, these analyses were repeated with the meteorological and air quality monitoring data for 2000 to 2004

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to evaluate how well the 2002 modeling year represented the full 2000-2004 baseline period. This analysis confirmed that visibility and PM 2.5 concentrations in 2002 were representative of the five-yea r baseline period. This analysis is
discussed in more detail in th e project report in Appendix D.

• Data Availability: In parallel with the CART analysis, episode characterization analyses, collaborative investigations by VISTAS states (e.g., NCDAQ, GA EPD, FL DEP) intensively studied the availability of PM 2.5 , meteorological, and emissions data and representativeness of alternative baseline modeling periods from a regulatory standpoint. Additiona lly, because 2002 was the year that the USEPA was requiring states to provide emissions inventory data for the Comprehensive Emissions Reporting Rule (CERR), it made sense to use 2002 as the modeling year to take a dvantage of the 2002 inventory.

• Years to be used by other RPOs: VISTAS also considered what years other RPOs would be modeling, and several had alr eady chosen calendar year 2002 as the modeling year.

After a lengthy process of integrated studies, the episode selection process culminated in
the selection of Calendar Year 2002 (1 January throug h 31 December) as the most current, representative, and pragmatic choice fo r modeling. All of th e U.S. EPA criteria for model year selection were directly considered in this process together with many other considerations (e.g., tim ing of new emissions or aerom etric data deliveries by the U.S. EPA or the states to the modeling teams).

3.4 Modeling Domains

3.4.1 Horizontal Modeling Domain

The CMAQ model was run in one-way nested gr id mode. This allowed the larger outer
domains to feed concentration data to the inner nested domain. Th e horizontal coarse grid modeling domain boundaries were determined through a national ef fort 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, su ch as ozone and fine particulate matter nonattainment areas. Pro cessing time was also a factor in choosing a smaller 12-km grid modeling domain.

The coarse 36-km horizontal gr id domain covers the continental United States. This domain was used as the outer grid doma in for MM5 modeling with the CMAQ domain nested within the MM5 domain. Figure 3- 1 shows the MM5 horizontal domain as the outermost, blue grid with the CMAQ 36-km domain nested in the MM5 domain.

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Figure 3-1. The MM5 and CMAQ 36-km Horizontal Domains

To achieve finer spatial resolution in th e VISTAS states, a one-way nested high resolution (12-km grid resolution) was use d. Figure 3-2 shows the 12-km grid modeling domain for the VISTAS region. This is the modeling domain on which the attainment
test results are based.

Figure 3-2. VISTAS 12-km Modeling Domain

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3.4.2 Vertical Modeling Domain

The vertical grid used in the MM5 mode ling primarily defines the CMAQ vertical structure. The MM5 model employed a terra in-following coordinate system defined by pressure, using 34 layers that extend from th e surface to the 100 mb. Table 3-1 lists the layer definitions for both MM5 and for CMAQ. A layer-averaging scheme was used to generate 19 vertical layers for CMAQ to reduce the computational cost of the CMAQ simulations. The effects of layer averagi ng were evaluated in conjunction with the modeling effort and were found to have a relatively minor effect on the model performance metrics when both the 34-la yer and a 19-layer CMAQ models were compared to ambient monitoring data.

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Table 3-1. Vertical Layer Definition For MM5 and CMAQ

MM5 Simulation CMAQ 19 Layers
Layer Sigma Pressure (mb) Height (m) Depth (m) Layer Sigma Pressure (mb) Height (m) Depth (m)
34 0.000 100 14662 1841 19 0.000 100 14662 6536

33 0.050 145 12822 1466 0.050 145

32 0.100 190 11356 1228 0.100 190

31 0.150 235 10127 1062 0.150 235

30 0.200 280 9066 939 0.200 280

29 0.250 325 8127 843 18 0.250 325 8127 2966

28 0.300 370 7284 767 0.300 370

27 0.350 415 6517 704 0.350 415

26 0.400 460 5812 652 0.400 460

25 0.450 505 5160 607 17 0.450 505 5160 1712

24 0.500 550 4553 569 0.500 550

23 0.550 595 3984 536 0.550 595

22 0.600 640 3448 506 16 0.600 640 3448 986

21 0.650 685 2942 480 0.650 685

20 0.700 730 2462 367 15 0.700 730 2462 633

19 0.740 766 2095 266 0.740 766

18 0.770 793 1828 259 14 0.770 793 1828 428

17 0.800 820 1569 169 0.800 820

16 0.820 838 1400 166 13 0.820 838 1400 329

15 0.840 856 1235 163 0.840 856

14 0.860 874 1071 160 12 0.860 874 1071 160

13 0.880 892 911 158 11 0.880 892 911 158

12 0.900 910 753 78 10 0.900 910 753 155

11 0.910 919 675 77 0.910 919

10 0.920 928 598 77 9 0.920 928 598 153

9 0.930 937 521 76 0.930 937

8 0.940 946 445 76 8 0.940 946 445 76

7 0.950 955 369 75 7 0.950 955 369 75

6 0.960 964 294 74 6 0.960 964 294 74

5 0.970 973 220 74 5 0.970 973 220 74

4 0.980 982 146 37 4 0.980 982 146 37

3 0.985 986.5 109 37 3 0.985 986.5 109 37

2 0.990 991 73 36 2 0.990 991 73 36

1 0.995 995.5 36 36 1 0.995 995.5 36 36

0 1.000 1000 0 0 0 1.000 1000 0 0

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3.5 Baseline Emissions Inventory

The CAA amendments revised many of the prov isions of the CAA related to attainment of the NAAQS and the protection of visibility in mandatory Federal Class I areas (certain national parks and wilderness areas). These revisions established new emission inventory requirements applicable to certain areas th at were designated nona ttainment for certain pollutants. In the case of particulate matter, the emission inventory provisions are in the general provisions under Section 172(c)(3). An inventory was developed for the baseline year 2002 and projected to 2009. The pollutants inventorie d include volatile organic compounds (VOCs), NOx, PM 2.5 , coarse particulate (PM 10 ), ammonia (NH 3 ) and SO 2

. Tables 3-2 and 3-3 show the typical annual em issions from the various source sectors for the Chattanooga PM 2.5 nonattainment area for 2002 and 2 009. The baseline emissions inventory for 2002 was developed for Geor gia following the methods described in Appendix H. The 2002 BaseG2 and the 2009 Base G4 emission inventories are the latest emission inventories developed by VI STAS for 2002 and 2009, respectively.

Table 3-2. Typical 2002 BaseG2 Annual Emission Summary for Chattanooga Nonattainment Counties (tons)

Point County VOC NOx SO2 PM-10 PM-2.5 NH3 Walker (GA) 14.8 45.7 203.1 2.9 1.7 0.0 Catoosa (GA) 0.0 0.0 0.0 0.0 0.0 0.0 Hamilton (TN) 1937.4 2855.7 1720.9 1017.1 566.7 17.3 Jackson (AL) 255.6 27,027.3 45,581.6 1773.9 976.2 2.2 Statewide (GA) 34,964.4 197,376.8 571,410.9 33,077.3 22,531.6 3699.2

N onattainment Area Totals 2,207.8 29,928.7 47,505.6 2,793.9 1,544.6 19.5

Non-Road County VOC NOx SO2 PM-10 PM-2.5 NH3 Walker (GA) 207.6 269.1 21.0 19.9 19.2 0.3 Catoosa (GA) 401.9 671.4 50.8 39.9 37.4 0.2 Hamilton (TN) 2900.3 6427.7 539.0 311.5 292.0 2.1 Jackson (AL) 1603.6 1418.4 166.8 94.8 88.2 0.6 Statewide (GA) 85,965.4 97,961.4 9005.4 8617.9 8226.4 60.4

N onattainment Area Totals 5,113.4 8,786.6 777.6 466.1 436.8 3.2

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Area County VOC NOx SO2 PM-10 PM-2.5 NH3 Walker (GA) 2596.2 417.4 763.9 6218.5 1040.6 765.9 Catoosa (GA) 1699.1 165.4 272.8 4655.2 688.7 468.6 Hamilton (TN) 7213.7 811.6 507.9 6191.5 1588.0 173.3 Jackson (AL) 3325.1 331.8 578.0 7980.2 1290.8 1281.9 Statewide (GA) 333,044.8 49,987.4 603,70.2 757,656.1 159,437.8 83,066.0

N onattainment Area Totals 14,834.1 1,726.2 2,122.6 2,5045.4 4,608.1 2,689.7

Mobile County VOC NOx SO2 PM-10 PM-2.5 NH3 Walker (GA) 1947.7 1698.7 71.1 40.6 29.3 55.6 Catoosa (GA) 2303.6 2377.1 92.1 51.9 37.2 73.7 Hamilton (TN) 11,053.9 11,609.7 461.0 255.4 183.3 356.7 Jackson (AL) 1826.7 2195.5 98.8 57.0 41.4 77.4 Statewide (GA) 283,420.6 307,731.7 12,183.5 7245.9 5167.8 10,546.2

N onattainment Area Totals 17,131.9 17,881.0 723.0 404.9 291.2 563.4

Biogenics County VOC NOx Walker (GA) 16,578.04 112.72 Catoosa (GA) 5650.24 59.20 Hamilton (TN) 11,074.86 90.19 Jackson (AL) 21,067.21 302.09 Statewide (GA) 1,972,795.40 24,159.19

N onattainment Area Totals 54,370.35 564.20

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Table 3-3. 2009 BaseG4 Annual Emission Summary for Chattanooga Nonattainment Counties (tons)

Point County VOC NOx SO2 PM-10 PM-2.5 NH3 Walker (GA) 17.1 45.9 195.4 1.8 1.1 0.0 Catoosa (GA) 0.0 0.0 0.0 0.0 0.0 0.0 Hamilton (TN) 1723.1 2919.8 1695.6 851.3 498.3 17.3 Jackson (AL) 251.1 5663.0 46,577.6 2134.2 1155.5 7.7 Statewide (GA) 36,430.1 148,849.7 462,666.5 40,993.6 29,889.7 4648.8

N onattainment Area Totals 1,991.3 8,628.7 48,468.6 2,987.3 1,654.9 25.0

Non-Road County VOC NOx SO2 PM-10 PM-2.5 NH3 Walker (GA) 142.9 199.9 3.5 15.3 14.7 0.3 Catoosa (GA) 470.2 583.9 8.5 35.4 33.1 0.3 Hamilton (TN) 2369.4 5601.9 222.7 259.3 242.8 2.4 Jackson (AL) 1277.3 1287.0 78.9 75.0 69.7 0.6 Statewide (GA) 67,686.1 85,732.9 2724.7 7521.1 7174.6 67.5

N onattainment Area Totals 4,259.8 7,672.7 313.6 385.0 360.3 3.6

Area County VOC NOx SO2 PM-10 PM-2.5 NH3 Walker (GA) 2452.0 437.8 766.9 7023.9 1133.4 847.3 Catoosa (GA) 1586.3 172.6 273.6 5257.2 745.1 521.2 Hamilton (TN) 7522.2 817.5 541.8 6678.2 1661.1 173.8 Jackson (AL) 2356.1 340.3 533.7 8350.2 1337.2 1409.1 Statewide (GA) 306,337.3 51,925.0 60,604.0 840,244.9 169,011.4 91,439.1

N onattainment Area Totals 13,916.6 1,768.2 2,116.0 2,7309.5 4,876.8 2,951.4

Mobile County VOC NOx SO2 PM-10 PM-2.5 NH3 Walker (GA) 1336.4 1194.4 8.4 33.1 21.3 65.7 Catoosa (GA) 1586.1 1620.7 10.9 42.6 27.2 87.1 Hamilton (TN) 7138.7 7513.9 54.0 204.8 130.6 415.2 Jackson (AL) 1106.5 1436.5 11.6 45.9 29.8 88.3 Statewide (GA) 195,125.2 209,349.2 1585.0 6072.0 3840.1 12,686.9

N onattainment Area Totals 11,167.7 11,765.5 84.9 326.4 208.9 656.3

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Biogenics County VOC NOx Walker (GA) 16,578.04 112.72 Catoosa (GA) 5650.24 59.20 Hamilton (TN) 11,074.86 90.19 Jackson (AL) 21,067.21 302.09 Statewide (GA) 1,972,795.40 24,159.19

N onattainment Area Totals 54,370.35 564.2

There are five different emission inventory so urce classifications: stationary point and area sources, off-road and on-road mobile sources, and biogenic sources.

Stationary point sources are those sources th at emit greater than a specified tonnage per year, with data provided at the facility leve l. Electric generating utilities and industrial sources are the major categories for stationary point sources. Stationary area sources are
those sources whose individual emissions ar e relatively small, but due to the large number of these sources, the collective em issions from the source category could be significant (i.e., dry cleaners, se rvice stations, agricultural sour ces, fire emissions, etc.). These types of emissions are es timated on a countywide level.

Non-road (or off-road) mobile sources are equipment that can move but do not use the roadways, i.e., lawn mowers, construction equi pment, railroad locomotives, aircraft, etc. The emissions from these sources, like st ationary area sources, are estimated on a countywide level. On-road mobile sources are automobiles, buses, trucks, and motorcycles that use the roadway system. The emissions from these sources are estimated by vehicle type and road type, an d are summed to the countywide level. Biogenic sources are the natural sources like trees, crops, grasses an d natural decay of plants. The emissions from these sour ces are estimated on a countywide level.

In addition to the various sour ce classifications, there are al so various types of emission inventories. The first is the actual base-yea r inventory. This inve ntory is the base-year emissions that correspond to the meteorological data used, which for this modeling effort
is data from 2002. These emissions are us ed for evaluating the air quality model performance. The second type of inventory is the typical base-year inventory. This inventory is similar to the actual base-yea r inventory, except that for sources whose emissions change significantly from year to ye ar, a more typical emission value is used. In this modeling effort, typical emissions were developed for the elec tric generating units (EGUs) and the fire emissions. The air quality modeling runs using th e typical base year inventory are used to calcu late relative response factor s used in the attainment
demonstration test.

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The future-year base inventory is an inventory developed for some future year for which attainment of the air quality st andard is needed. Fo r this modeling project, the future year inventory will be 2009, the last co mplete year for which the sta ndard must be attained. It is for the future base-year inve ntory that sensitivities will be evaluated to determine what additional controls beyond thos e control measures already included in the future-year base inventory will need to be implemen ted to attain and maintain the annual PM 2.5 standard.

In the sections that follow, a synopsis of the inventories used for each source
classification is discussed. The detailed discussions of the emissions inventory development can be found in Appendix H and emission summaries by county for the Chattanooga nonattainment area, as well as for the State, are in Appendix I. Below is an
overview of the inventories used for each source classification.

3.5.1 Stationary Point Sources

Point source emissions are emissions from individual sources having a fixed location. Generally, these sources must have permits to operate, and their emissions are inventoried on a regular schedule. Large so urces emitting at least 100 tons-per-year (tpy) of a criteria pollutant are inventoried annually. Smalle r sources have been inventoried less frequently. The point source emissions data can be grouped as EGU sources and other industrial point sources, also called non-electric generating units (non-EGUs). Appendix H documents the point source modeli ng inventory development in more detail.

3.5.1.1 Electric Generating Units

The actual base-year inventory for the E GU sources used 2002 continuous emissions monitoring (CEM) data reporte d to the U.S. EPA’s Acid Rain program or 2002 hourly emissions data provided by stakeholders. This data provide hourly em issions profiles for
SO 2 and NOx that can be used in air quality modeling. Emissions profiles are used to estimate emissions of other po llutants (VOC, carbon monoxide, NH 4 , PM 2.5 , and soil)
based on measured emissions of SO 2 and NOx.

Emissions from EGUs vary daily and seasonall y as a function of variability in energy demand and utilization and outage schedules. Since emission from EGUs represents a significant portion of the emission inventory, a typical base-year emissions inventory was developed to avoid anomalies in future-year emissions due to vari ability in meteorology, economic and outage factors in 2002. This appr oach is consistent with the Attainment Modeling Guidance. To develop a typica l-year 2002 emissions inventory for EGU sources, each unit’s average CEM heat input for 2000 through 2004 was divided by the 2002 actual heat input to generate a unit-sp ecific normalizing factor. This normalizing factor was then multiplied by the 2002 actual emissions. The heat inputs for the period 2000 through 2004 were used because the modeling current design values use monitored data from this same 5-year period. If a unit was shut down for an entire year during the 2000 through 2004 period, the average of the ye ars the unit was operatio nal was used. If a unit was shut down in 2002, but not permanen tly shut down, the emissions and heat

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inputs from 2001 (or 2000) were used in the normalizing calculations. Table 3-4 shows emissions used in modeling from Georgia EGUs for 2002 and 2009.

As part of the air quality modeling, VISTAS, in cooperation with the other eastern RPOs, contracted with ICF Resources, L.L.C., to generate future year emission inventories for the electric generating sector of the con tiguous United States using the Integrated Planning Model (IPM). IPM is a dynamic linear optimization model that can be used to examine air pollution control policies for various pollutants throughout the contiguous United States for the entire electric power sy stem. 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 w ith specified constraints including air pollution regulations, transmission bottlenecks, and plant-specific opera tional constraints. The versatility of IPM allows users to specify which constraints to exercise, and to populate IPM with their own datasets.

The IPM modeling runs took into considerat ion both The Clean Air Interstate Rule (CAIR) implementation and Georgia’s Multi- pollutant Rule requirements for Georgia

Power.

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Table 3-4. 2002 and 2009 Georgia EGU Emissions

Facility Unit 2002
NOx (tons) 2002
SO2 (tons) 2002
PM10 (tons) 2002
PM2.5 (tons) 2009
NOx (tons) 2009
SO2 (tons) 2009
PM10 (tons) 2009
PM2.5 (tons)

SG01 5557 33050 422 183 1944 39036 542 323

SG02 6171 35266 1095 474 1958 39310 1338 797

SG03 12114 42900 1666 721 2484 2469 2034 1212 Georgia Power
–Plant Bowen

SG04 12022 42634 1936 838 2621 2534 2412 1438

SG01 5705 10387 171 74 5131 11901 162 83

SG02 4534 13941 228 99 4476 14408 195 100

SG03 7296 21211 776 336 7055 22293 1221 623 Georgia Power
– Plant Branch

SG04 7762 22342 825 357 6900 21802 1194 610

SG01 1788 3933 139 60 1759 259 394 201

SG02 1917 4092 149 64 1769 260 396 202

SG03 1930 4262 150 65 1751 257 391 200 Georgia Power
– Plant
Hammond

SG04 3488 18089 147 64 1387 1082 200 102

SGM1 2442 13802 165 73 2127 11134 323 192 Georgia Power
– Plant McDonough SGM2 2346 13631 161 70 2191 11469 333 198

SG01 7999 25286 201 87 4758 26264 256 153

SG02 7344 24966 235 102 4724 26075 297 177

SG03 4669 18149 38 16 5024 27735 16 5 Georgia Power
– Plant
Scherer

SG04 8529 20771 153 66 4822 26616 56 18

SG01 10960 38741 445 193 2430 1890 1566 933 Georgia Power
– Plant Wansley SG02 9800 36421 665 288 2342 37848 889 530

SG01 973 233 22 10 1414 663 42 25

SG02 1059 3991 249 108 1415 5938 182 108

SG03 874 3237 206 89 1562 6556 201 119

SG04 1175 4842 111 48 1559 6256 230 137

SG05 1214 5009 114 50 1593 6394 236 141

SG06 2469 15175 51 23 3010 15753 149 89 Georgia Power
– Plant Yates

SG07 2297 14024 34 16 3040 15910 594 354

3.5.1.2 Other Industrial Point Sources

For the non-EGU sources, the same inventory is used for both the actual and typical baseyear emissions inventories. The non-EGU cat egory uses annual emissions as reported under the CERR for the year 2002. These emissi ons are temporally allocated to month, day, and hour using source category code (SCC)-based allocation factors.

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

3.5.2 Stationary Area Sources

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

A portion of the area source 2002 base-year inve ntory for Georgia was developed by the GA EPD and provided to the VISTAS/ASIP co ntractor. The VISTAS/ASIP contractor calculated the remaining portion of the area so urce inventory. The sources estimated by
the contractor include emissions from anim al husbandry, wildland fires, and particulate matter from paved and unpaved roads. For the other states within the modeling domain, either state-supplied data or data reported under CERR for 2002 was used.

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

For categories other than wild land fires, the VISTAS/ASIP cont ractor generated the future base-year emissions inventory used in the attainment demonstration modeling. Growth factors supplied from the states or the U.S. EPA’s CAIR emission projections were applied to project the c ontrolled emissions to the approp riate year. In some cases, the U.S. EPA’s Economic Growth and Analysis System Version 5 growth factors were used if no growth factor was available from e ither the states or the CAIR growth factor files. Table 3-5 shows the top five categor ies of area source emi ssions in the Georgia nonattainment counties, separa tely and combined. Appendix H provides a more detailed discussion of the area source inventory.

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Table 3-5. 2002 and 2009 Georgia Area Source Emissions

County TIER 1 Name
Area Source 2002
PM10 (tons) 2002
PM2.5 (tons) 2009
PM10 (tons) 2009
PM2.5 (tons)
Fuel Combustion Industrial 38 25 38 25
Fuel Combustion Other 98 98 80 80
Other Industrial Processes 182 36 210 42 Waste Disposal and Recycling 80 74 90 83 Catoosa

Micellaneous 4241 442 4822 501
Fuel Combustion Industrial 110 71 111 71
Fuel Combustion Other 145 145 118 118
Other Industrial Processes 182 36 210 42 Waste Disposal and Recycling 99 91 111 103 Walker

Micellaneous 5554 577 6315 653
Fuel Combustion Industrial 148 96 149 96
Fuel Combustion Other 243 243 198 198
Other Industrial Processes 364 72 420 84 Waste Disposal and Recycling 179 165 201 186 Nonattainment
Totals

Miscellaneous 9795 1019 11137 1154

3.5.3 Off-Road Mobile Sources

Off-road (or non-road) mobile sources are eq uipment that can move but do not use the roadways, such as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, etc. For the majority of the non-road mobile sources, the emissions for 2002 were estimated using the U.S. EP A’s NONROAD2005c model. For the three source categories not included in the NONROAD model, i.e., aircraft engines, railroad locomotives, and commercial marine, more traditional methods of estimating the emissions were used. The same inventory is used for both the actual and typical baseyear emissions inventories.

For the source categories estimated using the U.S. EPA’s NONROAD model, the model growth assumptions were used to create the 2009 future year inventory. The NONROAD
model takes into considera tion regulations affecting emissions from these source categories. For the commercial marine, railroad locomotives and airport emissions, the VISTAS/ASIP contractor calculated the future growth in emissions using detailed inventory data (both before and after controls) for 1996 and 2010, obtained from the CAIR Technical Support Document. When ava ilable, state-specific growth factors were used. Appendix H provides a detailed di scussion of the nonroad mobile source inventory.

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3.5.4 Highway Mobile Sources

For onroad vehicles, the newe st version of the MOBILE m odel, MOBILE6.2, was used. Key inputs for MOBILE include information on the age of ve hicles on the roads, the average speeds on the roads, the mix of vehicles on the roads, any programs in place in
an area to reduce emissions for motor vehicl es (such as emissions inspection programs), and temperature.

The MOBILE model takes into consideration regul ations that affect emissions from this
source sector. The same MOBILE run is used to represent the actual- and typical-year emissions for onroad vehicles using input da ta reflective of 2002. The MOBILE model is then run for the 2009 inventory using input data reflective of that year. The 2002 vehicle miles traveled (VMT) estimates were obt ained from the Georgia Department of Transportation (GDOT). The VMT for 2009 we re projected by the VISTAS contractors based on linear growth as derived from th e VMT data assembled by the U.S. EPA for their most recent heavy-duty diesel rulemaking. For a detailed discussion about the mobile source inventory development used in the attainment demonstration modeling, please refer to Appendix H. For details on th e mobile source emissions modeling used to
calculate the motor vehicle emissions budget, see Section 8 and Appendix J.

3.5.5 Biogenic Emission Sources

Biogenic emissions were prepared with the SMOKE-BEIS3 (Biogenic Emission Inventory System 3 Version 0.9) preprocessor. SMOKE-BEIS3 is a modified version of the Urban Airshed Model (UAM)-BEIS3 mode l. Modifications include use of MM5 data, gridded land use data, and improved emissions char acterization. The emission
factors that are used in SMOKE-BEIS3 are th e same as the emission factors as in UAMBEIS3. The basis for the gridded land use data used by BEIS3 is the county land use data in the Biogenic Emissions Landcover Database Version 3 (BELD3) provided by the U.S. EPA. A separate land classification sche me, based upon satellite (AVHRR, 1 km spatial resolution) and census information aided in defining the forest, agriculture, and urban portions of each county.

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Section 4. Model Performance Evaluation

There are many aspects of model performance. This section will focus primarily on the methods and techniques recommended by the U.S. EPA for evaluating the performance of the air quality model. Before th e air quality model can be fully evaluated, the meteorological modeling performance is needed to understand potential biases and errors that may be passed from the meteorological model direct ly into the air quality model. A complete discussion of the meteorological and air quality model performance evaluations for VISTAS, Georgia, and Chattanooga can be found in Appendix K.

4.1 Meteorological Model Performance

Generally speaking, the meteorological modelin g performance was quite good at both the 36- km and 12-km grid resolutions. Synoptic features were routinely accurately predicted and the meteorological model showed considerable sk ill in replicating the state variables (e.g. temperature, mixing ratio, relative humidity, wind speed and direction, cloud cover, and precipitation). The meteorologi cal modeling performance statistics fell within expected and acceptable ranges of error during the majority of the 2002 modeled year.

The meteorological modeling perf ormance for Georgia was very similar to the performance for the VISTAS/ASIP region for the 12-k m modeling domain. Again, large-scale meteorological patterns were ac curately predicted. The meteor ological model demonstrated
substantial skills thro ughout the entire year and was especially skillful during the summertime season from May through September.

U.S. EPA guidance (EPA, 2007) recognizes the complexity a nd importance of evaluating meteorological modeling results for air quality appl ications. Since MM5 algorithms and modules have undergone si gnificant peer review and testin g, an “operational” evaluation of the modeling system is sufficient to serve as the basis of evaluating if the system is operating with sufficient reliability to be used in support of SIP development. An operational evaluation can be defined as the process of evaluating how accurately a modeling simulation estimates observed quan tities. In the case of a prognostic meteorologi cal model, this amounts to a statistical analysis of model predictions and surface observations.

GA EPD used the software package METSTA T developed by ENVIRON Corporation for calculating model performance statistics. METSTAT com putes domain-wide surface statistics for temperature, wind speed and direction, and humidity. The metrics include: Bias Error (B), Gross Error (E) and Root Mean S quare Error (RMSE), Systematic Root Mean Square Error (RMSE
s ), Unsystematic Root Mean Square Error (RMSE
u ) and Index Of Agreement (IOA). Mathematical formulation of these variables is provided in Table 4-1. The surface meteorological data for this analysis was obtained from the CISL Research Data Archive ( http://dss.ucar.edu/datasets/ds472.0 ).

While reviewing statistics, the reader is cautioned that summary statistics are useful in making only a general assessment about the adequacy of meteorological fields. For example, daily-mean performance statistics are likely to conceal important hour-to-hour variations. Also, the credibility of summary statis tics depends upon the number of observationprediction pairs, and they generally improve w ith larger sampling sizes and longer averaging periods. This is because the sensitivity of these summary statistics to extreme values is high in case of smaller sample sizes. These and other concerns have lead U.S. EPA to recommend that benchmarks proposed in Table 4-2 (Emery et al ., 2001) should not be used in a

86 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

“pass/fail” test for a model, but only as a means of asse ssing general confidence in meteorological model output.

A graphical summary of the daily and hourly me an performance statistic s for the 12-km grid resolution modeling simulation is provided in Appendix K.2. Monthly averaged statistics have also been provided in Figure 4-1 and Tabl e 4-3. Statistics have been computed at monitoring stations that are located in and around Georgia.

Table 4-1. Mathematical Formulation of St atistical Metrics Used for Evaluating Mesoscale Meteorological Model Performance

Metrics Formulation

Bias 

# ( ) ∑∑

= = − = J

j I

i i j i j OP IJ B

1 1 1

Gross Error 

## ∑∑

= = − = J

j I

i i j i j OP IJ E

1 1 1

Root Mean Square Error 

# ( ) 2/1

1 1 2 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− = 

## ∑∑

= = J

j I

i i j i j OP IJ RMSE

Systematic Root Mean Square Error 

# ( ) 2/1

1 1 2 ˆ 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− = 

## ∑∑

= = J

j I

i i j i j S OP IJ RMSE

Unsystematic Root Mean Square Error 

# ( ) 2/1

1 1 2 ˆ 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− = 

## ∑∑

= = J

j I

i i j i j u PP IJ RMSE

Index of Agreement ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

−+− −= 

## ∑∑

= = J

j I

i o i jo i j MOMP

RMSEIJ IOA

1 1 2

. 1

Table 4-2. Statistical Benchmarks for Mesoscale Meteorological Models
Statistical Measure Benchmark Wind Speed Bias (m/s) < ± 0.5 Wind Speed Total RMSE (m/s) 2.0 Wind Speed Index of Agreement 0.6 Wind Direction Gross Error (degree) 30.0 Wind Direction Bias (degree) < ± 10.0 Temperature Bias (Kelvin) < ± 0.5 Temperature Gross Error (degree) 2.0 Temperature Index of Agreement 0.8 Humidity Bias (g/kg) < ± 1.0 Humidity Gross Error (g/kg) 2.0 Humidity Index of Agreement 0.6

87 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

Monthly Average Temperature Bias

-0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60

J

anuar y

Feburary
M a rc

h Apr il May
J u

ne J u ly August Septe mber October Nov e mber

D

e cem berBias (degree K)

Monthly Average Humidity Bias

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

J

anuar y

Feb u rary Ma r ch April Ma y June July August Sep t ember October November

De

c emberBias (g/kg)

Figure 4-1. Monthly averaged Bias statistics for 2-m temperature and humidity

88 of 162Model Performance EvaluationPM2.5 Attainment SIP - Chattanooga, GATable 4-3. Monthly averaged statisticsfor the region of the 12-km modeling domain in and around Georgia.89 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

The MM5 simulation is able to capture mete orological conditions observed during the study period with an adequate level of accuracy. Pe rformance statistics for most days are within the benchmarks proposed by Emery (2001). Monthly average statistics rev eal under prediction in humidity from May through November, and an over prediction in temperature during the summer months (May through September). The si mulation tends to over-predict daytime and nighttime temperature, and consistently over- predicts nighttime wind speed. However, the magnitude of these under/over predictions remains sm all. It should also be pointed out that air quality model performance evaluation serves as an additional check on how accurately the meteorological model was able to capture the observed atmosphe ric dynamics during a particular time period. In an unlikely event of unusuall y poor air quality model performance, it is reasonable to further investigate the pe rformance of the meteorological model.

Overall, the GA EPD believes that the meteorological model performance is adequate for this modeling exercise and should produce credible inputs for the air quality modeling for the attainment demonstration for the Chattanooga PM 2.5 nonattainment area.

4.2 Air Quality Model Performance

The air quality model evaluation mostly focused on the FRM and STN monitors across the state of Georgia, due to the nature of the attainment test. Designations we re based on FRM monitors, and calculations of future design values are based on current design value information from these sites. Thus, model performance for these FRM si tes should be evaluate d. STN data was also utilized as this data is used to speciate th e FRM data so component-based relative response
factors can be calculated for each FRM monitoring site. More detailed information on the attainment test process is describe d in Section 6.1 and Appendix G.

Only a brief summary of the model performanc e evaluation for the VISTAS/ASIP 12-km grid
domain will be discussed in the subsections to follow. For the full model performance evaluation for the 12-km grid domai n, please refer to Appendix K.3.

4.2.1 Modeling Performance Goals and Criteria

In 2004, VISTAS/ASIP established model performa nce “goal levels” and “criteria levels” for components of fine particle mass (Table 4-4) based on previous model performance for ozone and fine particles. The Attainment Modeling Gu idance (EPA, 2007) for fine particulate matter at the time noted that PM models might not be able to achieve the same level of performance as
ozone models. VISTAS’ evaluation considered several statistical performance measures and graphical performance measures. Fractional bias and mean fractional error were selected as the most appropriate metrics to summarize model performance (Boylan and Russell, 2006); other
metrics were also calculated and ar e included for FRM and STN monitors.

90 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

Table 4-4. Established Model Performa nce Goals and Criteria for the PM 2.5 Component Species

Mean Fractional
Bias Mean Fractional

Error Comment

< 15 percent < 35 percent Goal for PM 2.5 model performance based on ozone model performance, considered “excellent performance”

< 30 percent < 50 percent Goal for PM 2.5 model performance, considered good performance; hereafter, it is called “(performance) goal level”

< 60 percent < 75 percent Criteria for PM 2.5 model performance, considered average performance; hereafter, it is called “(performance) criteria level”. Exceeding this level of performance indicates fundamental concerns with the modeling system and trigge rs diagnostic evaluation.

The statistical measures were calculated for all of the component species of particulate matter. For convenience, these statistical measures or metrics, along with a variety of additional statistical measures, are summarized in Table 4-5.

An additional way to evaluate model performance statistics is to visua lize performance based on
these fractional bias and mean fractional errors via “soccer plots” (e .g. Figure 4-2) and “bugle plots” (Figure 4-3). The soccer plot is so name d because the dotted lines resemble a soccer goal. The soccer plot is useful as both bias and erro r are shown on a single plot. As bias and error approach zero, the points are plot ted closer to or within the “performance criteria level,” the “performance goal level,” and/or the “excellent performance level,” represented here by the
dashed boxes.

The bugle plot, named for the shape formed by the criteria and goal lines, are shaped as such because the goal and criteria lines are adjusted based on the average concentration of the observed species concentrations and the modeled species concentrations. As the average concentration becomes smaller, the criteria and goal lines become larger to adjust for the model’s poor ability to predict at low concentrations and for the over-sensitivity of normalized-statisticalmeasures-to-low-concentration cases.

The analysis of bugle plots dem onstrated that greater emphasis should be placed on performance of those components with the greatest contribution to PM 2.5 mass (e.g. SO 4 and OC) and that greater bias and error could be accepted for com ponents with smaller contributions to total PM 2.5 mass (e.g. EC, NO 3 , and SOIL). The soccer plots and bugle plots have been included as suggested in the Attain ment Modeling Guidance.

91 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

Table 4-5. Statistical Metric Calculations
Statistical Measure Shorthand
Notation Mathematical Expression Notes

Accuracy of Paired
Peak A p

peak peak
O OP

−

Coefficient of
Determination R 2



# ( ) 

# ( )

∑ ∑ ∑

= = =



### − − ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

−−

N

i N

i i i N

i i i



### OOPP OOPP

1 1 2 2 2

1


### )()( P i = prediction at time and location i;
O i = observation at time and location i;

P = arithmetic average of P i , i = 1, 2, . . ., N;

O = arithmetic average of O i , i = 1, 2, . . ., N;
Normalized Mean

Error NME



## ∑ ∑

=

= −

N

i i N

i ii

O OP

1 1 Reported as %

Root Mean Square

Error RMSE 

## ( ) 2 1

1 2 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− 

## ∑

= N

i ii OP N Reported as %

Mean Fractional

Error MFE 

## ∑

= + − N

i ii ii OP OP

N 1 2 Reported as %

Mean Absolute
Gross Error MAGE 

# ∑

= − N

i ii OP N 1 1

Mean Normalized
Gross Error MNGE 

# ∑

= − N

i i ii

O OP

N 1 1 Reported as %

Mean Biased MB 

## ( ) 

# ∑

= − N

i ii OP N 1 1 Reported as concentration

Mean Normalized
Bias MNB 

## ( ) 

## ∑

= − N

i i ii

O OP

N 1 1 Reported as %

Mean
Fractionalized Bias (Fractional Bias) MFB 

## ∑

= ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ − N

i ii ii OP OP

N 1 2 Reported as %

Normalized Mean
Bias NMB 

## ( )



# ∑ ∑

=

= −

N

i i N

i ii

O OP

1 1 Reported as %

Bias Factor BF 

## ∑

= ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ N

i i i O P

N 1 1 Reported as BF:1 or 1:BF or in fractional notation (BF/1 or 1/BF)

92 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

4.2.2 Domain-Wide Model Performance

As a summary of model performance, soccer pl ots and bugle plots for all of the VISTAS STN
and FRM monitors are included here. Plots ha ve 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 4- 2 and 4-3), as well as the total FRM PM 2.5 mass (Figures 4-4 and 4-5).

The soccer plots for monthly average component performance for all the VISTAS/ASIP STN sites show generally good model pe rformance for most species of PM 2.5 and total PM 2.5 . The exception is the prediction of NO 3 values, for which most values fall outside the criteria level (Figure 4-2). There are a few months in which the model’s performance falls in the criteria level, which is better seen in the zoomed view pr esented in the image on the right in Figure 4-2. However, when the very low concentration of NO 3 is taken into consideration, as presented in the bugle plots (Figures 4-3), NO 3 performance largely falls within the criteria and goal levels. One can still note a general te ndency for under prediction in NO 3 , and other species (e.g. OC) in the left hand image in Figure 4-3, which leads to a slight under-prediction in total reconstructed

PM 2.5

.

Monthly total PM 2.5 performance at all the VISTAS/ASIP FRM monitors largely falls within goal level thresholds, with only three months falling just outside goal le vel (Figure 4-4). Figure 4-5 suggests a slight negative bias in PM 2.5 prediction for most of the year, with mean fractional error values remaining within goal levels across the base year.

STN VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0

-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional BiasMean Fractional Error Sulfate

Nitrate

Ammon.

Organics

EC

PM2.5

Criteria

Goal STN VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0
Fractional BiasMean Fractional Error Sulfate

Nitrate

Ammon.

Organics

EC

PM2.5

Criteria

Goal

Figure 4-2. VISTAS STN Soccer Plots The image on the left is a soccer plot depicting both the mean fr actional error and fractional bias for component concentration for al l VISTAS STN monitoring sites. The image on the right is a zoomed view of the soccer plot on the left to bett er depict the area inside the criteria/goal lines. Each point represents a monthly value as compared to the mode l performance criteria (red box) and modeling performance goals (green box).

93 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

STN VISTAS Sites CMAQ 12km - 2002 Monthly

-200.0 -150.0 -100.0 -50.0

0.0 50.0 100.0 150.0 200.0

0.0 4.0 8.0 12.0 16.0 20.0 Average Concentration ( μ g/m 3 )Mean Fractional Bias Sulfate

Nitrate

Ammon.
Organics

EC

PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria STN VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 50.0 100.0 150.0 200.0

0.0 4.0 8.0 12.0 16.0 20.0 Average Concentration ( μ g/m 3 )Mean Fractional Error Sulfate

Nitrate

Ammon.

Organics

EC

PM2.5

Criteria

Goal

Figure 4-3. VISTAS STN Bugle Plots The image on the left is a bugle plot of the m ean fraction bias for particulate matter and its component concentrations for all VISTAS STN m onitoring sites. The im age on the right is a bugle plot of mean fraction error for particulate matter and its component species for all VISTAS STN monitoring sites. Each point represents a m onthly mean fraction bias value as compared to the model performance criteria (red lines) a nd modeling performance goals (green lines).

FRM VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0

-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional BiasMean Fractional Error

PM2.5

Criteria

Goal FRM VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0
Fractional BiasMean Fractional Error

PM2.5

Criteria

Goal

Figure 4-4. VISTAS FRM Soccer Plots Soccer plots depicting both the mean fr actional error and fractional bias for PM 2.5 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 de pict 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).

94 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

FRM VISTAS Sites CMAQ 12km - 2002 Monthly

-200.0 -150.0 -100.0 -50.0

0.0 50.0 100.0 150.0 200.0

0.0 4.0 8.0 12.0 16.0 20.0 Average Concentration ( μ g/m 3 )Mean Fractional Bias

PM2.5

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria FRM VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 50.0 100.0 150.0 200.0

0.0 4.0 8.0 12.0 16.0 20.0 Average Concentration ( μ g/m 3 )Mean Fractional Error

PM2.5

Criteria

Goal

Figure 4-5. VISTAS FRM Bugle Plots - The image on the left is a bugle plot of the mean fraction bias for particulate matter for all the VISTAS FRM Monitoring sites. The image on the right is a bugle plot of mean fraction error for partic ulate matter for all the VISTAS FRM Monitoring sites. Each point repr esents a monthly mean fraction bias value as compared to the model performance criteria (r ed lines) and modeling pe rformance goals (green lines).

The overall tendency of the model was that the model had some difficulties in predicting NO 3

. For example, the monthly average values tend to fall outside the criteria levels in the soccer plots. Part of this under prediction lies in the fact that NO 3 are generally found in low concentrations across the southeast, and the model usually has difficu lties representing any compound with low ambient conc entrations. The bugle plots are more encouraging with NO 3 performance, as these plots take into account for absolute concentrations of PM 2.5 components when evaluating performance. The bugle plots show all components and total PM 2.5 falling within criteria levels, if not goal levels, of model performance. The under-predictions of NO 3 and OC account for the slight negative bias in the modeled PM 2.5 mass compared to the

measurements.

4.2.3 Model Performance within Georgia and in the Chattanooga nonattainment area

Statistical metrics and performance plots were de veloped for the entire state of Georgia as well as the Chattanooga nonattainment area. For Georgi a, the primary monitori ng networks available to evaluate the CMAQ modeling were: (1 ) EPA Federal Reference Method (FRM) PM 2.5 and

PM 10 Mass Networks, (2) EPA Speciat ion Trends Network (STN) of PM 2.5 species, (3) Interagency Monitoring of Protect ed Visual Environments (IMPR OVE), (4) Clean Air Status and Trends Network (CASTNET), and (5) Southeaste rn Aerosol Research and Characterization (SEARCH). It should be noted that the STN monitor here refers to the combination of the official STN monitors and the CSN (Chemical Speciation Network) monitors Table 4-6 and Figure 4-6 summarize the species da ta collected and locations of the monitoring sites for the FRM, IMPROVE, STN, CASTNET, and SEARCH monito ring networks used in this evaluation. The Chattanooga nonattainment area contains four FRM monitors (13-295-0002, 47-065-0031, 47-065-1011, and 47-065-4002) and a co-l ocated STN monitor (47-065-4002).

95 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

Table 4-6. Ambient monitoring data available in 2002.
Monitoring

Network Chemical Species Measured Sampling Frequency;
Duration

FRM Only total fine particulate mass (PM 2.5 ) Varies; 24 hr

IMPROVE Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr

STN Speciated PM 2.5 Varies; Varies

CASTNET Speciated PM 2.5 and ozone Hourly, Weekly; 1 hr,
Week

SEARCH 24-hr PM 25 (FRM Mass, OC, BC, SO 4 , NO 3 , NH 4 , EC); 24-hr PM coarse (SO 4 , NO 3 , NH 4 , elements); Hourly PM 2.5 (Mass, SO 4 , NO 3 , NH 4 , EC, TC); and Hourly gases (O 3 , NO, NO 2 , NO y , HNO 3 , SO 2 , CO) Daily, Hourly;

96 of 162Model Performance EvaluationPM2.5 Attainment SIP - Chattanooga, GAFigure 4-6. FRM, STN, IMPROVE, CASTNET, NADP, and SEARCH monitoring sites located in and near the GA PM2.5nonattainment areas (left) and the Chattanooga PM2.5nonattainment area (right).NOTE: 47-065-0032 was excluded becauseit has only one measurement in 200297 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

4.2.3.1 Model performance statistics

Model performance statistics were calculated coll ectively for the FRM monitors within Georgia, as well as individually for th e four FRM monitors in the Chattanooga nonattainment area to demonstrate the model’s ability to replicate total PM 2.5 mass at these sites. In addition, the same approach was taken to conduct model performa nce evaluation for the STN monitors within Georgia and for the Chattanooga STN site on PM 2.5 component species and total PM 2.5 basis for the entire base year. The statistical measur es selected were based on the recommendations
outlined in Section 18.4.1 of the U.S. EPA’ 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 (“ Attainment Guidance ”).

4.2.3.1.1 Model Performance Statistics for the FRM Sites

To examine the overall model performance, th e annual model performa nce statistics were estimated for all FRM sites in Georgia and the FR M site in the Chattano oga nonattainment area. As shown in Table 4-7, the model’s performa nce at four FRM monitors in the Chattanooga nonattainment area is comparable with that of th e all FRM monitors in Georgia. Exceptions are fractional bias, normalized mean bias, and mean bi as for TN monitors but their mean predictions
were close to observations. These bias statistics and the comparison of m ean values indicate that the model tends to show more negative biases at the Chattanooga FRM m onitors than monitors across Georgia for low concentration cases.

For more in-depth examinations, the model perfo rmance statistics are broken out by month at each of the FRM monitors in the Chattanooga nonattainment area to see if there is any performance issue during the base year (Table 4-8 through Table 4- 11). It was noted that the model had significant under-prediction during su mmer followed by over-prediction in October
for all monitors and in November at some monito rs in Tennessee. In a ddition, the model showed less significant under-p rediction in February. Overall, however, all of the sites showed model performance well within the “criteria” level except at 47-0 65-1011 in January and at 47-065- 4002 in March when the model performance was very close to the “c riteria” level.

During the monthly performance evaluation, we found the model’s mean predictions were comparable to the mean observed concentrations. In most cases, the model could replicate very high PM 2.5 concentrations that were observed except in a few cases. For example, the model predicted peak PM 2.5 concentration of 23.6 μ g/m 3 at 47-065-4002 in October while 14.0 μ g/m 3 was observed at the same monitor for the same pe riod. A more detailed performance evaluation was conducted with graphical measures and the re sults are presented in Appendix K.3. Given the number of samples, however, these discrepancies may not be significant. In fact, other comparable statistics with less numbers of sa mples indicates the over all performance of the model in the Chattanooga nonattainment area can be equal or better than the model’s overall performance across Georgia.

98 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

Table 4-7. Annual model performance statistics for PM 2.5 at all FRM monitors in the State of Georgia and in the Chattanooga nonattainment area.

Statistics GA 13-295-0002 47- 065-0031 47-065-1011 47-065-4002 MFB (%) -5.7 -7.6 -9.7 -11.6 -10.4 MFE (%) 30.2 32.0 28.6 34.7 31.5

A p -0.1 0.0 0.0 0.2 0.0 NMB (%) -2.7 -7.4 -8.8 -8.7 -9.6 NME (%) 27.4 29.6 26.4 30.9 26.4 MNB (%) 4.5 0.4 -3.6 -3.1 -5.5 MNGE (%) 32.9 31.2 26.8 32.5 27.2

MB -0.4 -1.1 -1.4 -1.2 -1.4

MAGE 4.0 4.4 4.1 4.3 3.8

Geometric Mean Bias -0.1 5.9 5.7 5.5 5.2

Geometric Mean Error 0.3 32.0 33.2 30.2 31.3

RMSE 5.3 32.2 32.1 36.4 31.9

Max OBS 52.0 15.0 15.4 14.0 14.6

Max PRE 48.0 13.9 14.0 12.8 13.2

Mean Observations 14.5 13.5 14.0 12.6 13.1

Mean Predictions 14.1 12.4 12.6 11.1 11.4

Standard Deviation of
Observations 6.6 6.8 6.8 6.3 7.1

Standard Deviation of
Predictions 6.9 6.5 6.5 6.8 6.5

R 2 0.5 0.4 0.4 0.4 0.5

BF 1.0 1.0 1.0 1.0 0.9 Number of Samples 3166 55 50 56 106

99 of 162Model Performance EvaluationPM2.5 Attainment SIP - Chattanooga, GATable 4-8. Monthly model performance statistics for PM2.5at 13-295-0002.StatisticsJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberMFB (%)33.02.7-5.2-15.8-44.4-32.0-23.1-3.7-11.05.4-11.4-11.5MFE (%)33.028.634.122.447.132.230.915.429.221.330.828.8Ap0.60.30.4-0.1-0.3-0.20.00.0-0.30.1-0.20.0NMB (%)40.911.2-2.1-12.7-35.9-28.7-21.9-6.6-7.77.2-8.5-9.4NME (%)40.929.433.518.538.928.830.414.124.821.827.524.9MNB (%)45.28.22.1-12.9-33.1-25.2-17.4-1.8-4.69.2-5.5-6.7MNGE (%)45.227.833.320.135.925.425.914.225.423.128.426.9MB4.51.3-0.3-1.5-5.5-4.1-3.8-1.3-1.21.1-1.0-1.3MAGE4.53.34.12.26.04.15.32.73.73.23.43.4Geometric Mean Bias0.30.0-0.1-0.2-0.5-0.3-0.20.0-0.10.1-0.1-0.1Geometric Mean Error0.30.30.30.20.50.30.30.20.30.20.30.3RMSE5.64.15.02.57.95.76.84.14.84.04.04.0MaxOBS12.020.917.524.825.125.225.631.634.320.721.127.0MaxPRE18.627.524.021.318.221.025.532.025.122.816.625.7Mean Observations11.011.312.312.115.514.417.519.415.014.712.313.8Mean Predictions15.412.612.010.69.910.213.718.113.815.811.212.5Standard Deviation ofObservations1.76.04.67.35.66.46.010.78.64.73.86.2Standard Deviation ofPredictions3.09.26.96.64.74.64.99.77.45.33.96.1R20.10.90.30.90.10.60.20.80.70.40.20.6BF1.51.11.00.90.70.70.81.01.01.10.90.9Number of Samples45557109491099100 of 162Model Performance EvaluationPM2.5 Attainment SIP - Chattanooga, GATable 4-9. Monthly model performance statistics for PM2.5at 47-065-0031.StatisticsJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberMFB (%)10.022.0-4.1-27.4-34.9-31.0-38.5-21.1-14.213.2-6.6-15.5MFE (%)12.922.029.227.435.631.038.528.233.523.927.939.8Ap0.10.20.4-0.1-0.2-0.3-0.3-0.20.10.70.0-0.1NMB (%)11.024.7-0.3-19.5-27.2-28.6-32.6-22.6-13.620.6-4.8-14.9NME (%)14.424.729.919.527.728.632.626.928.230.623.031.2MNB (%)12.124.71.4-22.8-26.9-25.3-31.3-16.6-5.717.4-0.9-4.3MNGE (%)14.924.728.422.827.625.331.324.430.627.226.136.6MB1.55.40.0-2.5-4.7-4.9-6.5-5.0-2.51.9-0.6-2.1MAGE2.05.43.62.54.84.96.56.05.22.82.94.4Geometric Mean Bias0.10.20.0-0.3-0.4-0.3-0.4-0.2-0.20.1-0.1-0.2Geometric Mean Error0.10.20.30.30.40.30.40.30.40.20.30.4RMSE3.05.44.52.86.26.77.57.46.63.93.55.4MaxOBS16.922.016.915.731.030.429.441.431.615.523.926.5MaxPRE18.327.423.314.524.120.219.431.833.625.724.223.7Mean Observations14.022.011.912.917.417.319.922.218.39.212.714.0Mean Predictions15.627.411.910.312.712.313.417.215.811.112.011.9Standard Deviation ofObservations2.6N/A4.34.06.27.66.99.88.33.55.16.1Standard Deviation ofPredictions3.1N/A6.65.85.64.94.86.16.76.35.25.5R20.2N/A0.41.00.60.60.70.70.50.80.60.4BF1.11.21.00.80.70.70.70.80.91.21.01.0Number of Samples3152281073127103026101 of 162Model Performance EvaluationPM2.5 Attainment SIP - Chattanooga, GATable 4-10. Monthly model performance statistics for PM2.5at 47-065-1011.StatisticsJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberMFB (%)59.6-13.0-10.4-21.1-22.8-29.6-26.5-13.3-31.834.98.2-36.5MFE (%)59.626.720.524.738.432.427.620.750.138.933.773.1Ap0.80.2-0.2-0.1-0.2-0.2-0.10.0-0.20.40.70.2NMB (%)84.9-7.6-8.5-15.7-24.2-26.8-23.9-12.9-17.043.619.8-16.8NME (%)84.927.023.320.034.728.824.617.932.248.141.854.1MNB (%)84.9-7.9-6.9-16.8-14.9-24.1-22.4-10.6-16.847.617.3-9.7MNGE (%)84.923.618.620.732.627.323.618.638.751.338.958.0MB6.9-0.7-1.1-2.0-3.1-4.9-5.7-2.8-2.74.22.0-1.5MAGE6.92.53.02.64.55.25.93.85.14.74.14.8Geometric Mean Bias0.6-0.1-0.1-0.2-0.2-0.3-0.3-0.1-0.40.40.1-0.6Geometric Mean Error0.60.30.20.30.40.30.30.20.60.40.30.9RMSE6.93.24.33.35.66.36.84.96.05.45.75.9MaxOBS8.112.930.521.120.032.736.639.230.218.915.915.5MaxPRE15.015.423.919.716.025.534.040.924.727.227.618.1Mean Observations8.19.212.912.913.018.223.921.515.89.79.98.9Mean Predictions15.08.511.810.89.813.318.218.713.113.911.87.4Standard Deviation ofObservationsN/A2.77.84.05.16.86.79.79.03.83.74.0Standard Deviation ofPredictionsN/A4.47.34.52.94.85.29.58.55.67.45.9R2N/A0.40.70.70.10.70.70.80.60.60.40.1BF1.80.90.90.80.90.80.80.90.81.51.20.9Number of Samples17929830239101099102 of 162Model Performance EvaluationPM2.5 Attainment SIP - Chattanooga, GATable 4-11. Monthly model performance statistics for PM2.5at 47-065-4002.StatisticsJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberMFB (%)20.1-25.9-56.8-7.4-25.2-34.5-25.01.12.329.448.90.9MFE (%)44.340.156.830.230.735.231.617.016.934.653.961.8Ap-0.2-0.3-0.20.2-0.3-0.2-0.3-0.2-0.10.70.50.0NMB (%)11.4-28.5-40.8-4.0-25.0-30.1-23.5-4.3-1.440.670.1-13.8NME (%)38.836.340.832.828.430.728.317.212.746.173.532.9MNB (%)5.1-16.0-40.5-0.6-19.8-27.9-19.73.06.138.874.4155.9MNGE (%)27.433.740.529.525.928.627.117.218.943.478.8199.4MB1.5-3.4-5.0-0.4-4.0-5.5-5.0-0.8-0.34.07.4-1.4MAGE5.14.35.03.24.65.66.03.32.34.57.83.3Geometric Mean Bias0.0-0.3-0.6-0.1-0.3-0.4-0.30.00.00.30.50.1Geometric Mean Error0.30.40.60.30.30.40.30.20.20.40.60.8RMSE7.06.26.14.25.66.97.04.43.45.68.44.1MaxOBS27.627.016.514.723.532.745.033.928.914.015.417.7MaxPRE21.718.113.017.216.726.932.828.427.323.623.117.8Mean Observations13.111.812.39.716.118.321.319.018.59.910.69.9Mean Predictions14.68.57.39.312.112.816.318.218.313.918.08.6Standard Deviation ofObservations8.17.63.62.85.56.88.010.58.62.82.96.0Standard Deviation ofPredictions5.64.14.04.03.14.95.38.17.55.85.26.2R20.20.50.20.00.50.60.60.80.80.60.40.6BF0.90.80.61.00.80.70.81.01.11.41.72.6Number of Samples10105910283010810108103 of 162Model Performance Evaluation PM2.5 Attainment SIP - Chattanooga, GA

4.2.3.1.2 Model Performance Statistics for STN Sites

The model performance was evaluated for partic ulate matter components measured at the all STN sites in Georgia and separa tely at the STN site in the Chattanooga nonattainment area. Since detailed graphical performance eval uation was conducted for all available PM 2.5 species, we briefly present annual summary stat istics for these species and total PM 2.5 here.

Sulfate and total PM 2.5 had negative bias and error at the Ge orgia STN monitors (Table 4-12). Nitrate was underestimated in Georgia as prev iously seen throughout the model performance
evaluation for the entire VISTAS 12-km modeling domain. Howeve r, this is not a significant
issue for the overall m odel performance because the contribution of NO 3 to the total PM 2.5 is relatively small in Georgia. Al so, we saw under-predictions of OC concentrations in Georgia. This issue was closely examined in Appendix K.3. From Table 4-13, similar tendencies can be
seen at the STN monitor in the Chatta nooga nonattainment ar ea except all of PM 2.5 components showed negative biases. More detailed performa nce analyses with graphical measures can be found in the following sections.

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Table 4-12. Annual model performance statistics for PM 2.5 species and total PM 2.5 at all STN monitors in the State of Georgia.

Statistics SO 4 NO 3 NH 4 OC EC PM 2.5 MFB (%) -17.1 -68.2 12.0 -26.5 -2.4 -18.4 MFE (%) 34.3 110.6 42.1 42.7 46.6 34.2

A p -0.6 0.6 -0.5 0.1 0.0 -0.2 NMB (%) -13.7 9.4 8.9 -13.4 3.6 -14.9 NME (%) 31.5 95.5 41.5 35.0 42.4 29.4 MNB (%) -7.6 0.1 37.9 -11.3 28.2 -10.5 MNGE (%) 32.7 101.2 61.6 40.4 65.6 30.5

MB -0.7 0.1 0.1 -0.7 0.0 -2.3

MAGE 1.6 0.7 0.6 1.9 0.3 4.6

Geometric Mean Bias -0.2 -1.3 0.1 -0.3 0.0 -0.2

Geometric Mean Error 0.4 1.8 0.4 0.5 0.5 0.4

RMSE 2.6 1.1 0.8 2.4 0.4 5.9

Max OBS 39.9 5.0 10.7 13.6 3.5 40.9

Max PRE 15.9 7.8 5.2 14.7 3.4 32.5

Mean Observations 5.0 0.7 1.4 5.4 0.6 15.7

Mean Predictions 4.3 0.8 1.5 4.7 0.7 13.4

Standard Deviation of
Observations 3.3 0.6 0.9 2.1 0.5 7.0

Standard Deviation of
Predictions 2.8 1.3 0.8 3.1 0.6 6.6

R 2 0.5 0.3 0.4 0.4 0.6 0.5

BF 0.9 1.0 1.4 0.9 1.3 0.9 Number of Samples 400 400 400 316 316 395

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Table 4-13. Annual model performance statistics for PM 2.5 at 47-065-4002.

Statistics SO 4 NO 3 NH 4 OC EC PM 2.5 MFB (%) -17.0 -86.2 -12.5 -55.5 -23.5 -31.6 MFE (%) 37.6 118.1 37.4 59.8 40.2 40.6

A p -0.1 0.1 -0.2 0.4 0.1 -0.2 NMB (%) -11.6 -22.4 -13.2 -36.8 -18.8 -27.6 NME (%) 33.0 77.0 33.3 44.2 35.9 33.9 MNB (%) -7.6 -31.2 -0.9 -38.5 -13.8 -21.8 MNGE (%) 33.7 82.8 37.3 43.9 35.2 32.2

MB -0.6 -0.2 -0.2 -1.9 -0.1 -4.8

MAGE 1.7 0.7 0.6 2.3 0.2 5.9
Geometric Mean Bias -0.2 -1.5 -0.1 -0.6 -0.2 -0.3
Geometric Mean Error 0.4 1.8 0.4 0.7 0.4 0.4

RMSE 2.2 0.9 0.8 2.8 0.3 8.0

Max OBS 14.3 3.5 4.9 9.5 1.6 39.6

Max PRE 12.9 4.0 3.9 13.4 1.7 32.5
Mean Observations 5.2 1.0 1.7 5.3 0.7 17.3

Mean Predictions 4.6 0.7 1.5 3.3 0.5 12.5
Standard Deviation of
Observations 2.9 0.7 0.9 2.1 0.3 8.0
Standard Deviation of
Predictions 3.0 1.0 0.7 2.5 0.3 6.3

R 2 0.5 0.2 0.3 0.4 0.3 0.4

BF 0.9 0.7 1.0 0.6 0.9 0.8 Number of Samples 56 56 56 55 55 56

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4.2.4 Summary of the performance e valuation of the CMAQ modeling

In general, the CMAQ modeling met the perf ormance “criteria level” for all major PM 2.5 constituents and the total PM 2.5 across the State of Georgia throughout the base year. As the first step of model performance evalua tion, GA EPD estimated several statistical measures for all FRM monitors and all STN monitors within Georgia and in the Chattanooga nonattainment area annually and monthly. Impor tant findings from this statistical performance evaluation at th e FRM monitors were that the model’s performance at FRM monitors within Georgia was comparable to the performance at two FRM monitors collectively in the Chattanooga nonattainme nt area although there were slightly more biases observ ed in the Chattanooga nonattai nment area. These biases became more apparent during summer and wi nter. After examining the statistical performance measures, GA EPD found that the model’s performance is generally adequate across Georgia and acceptable fo r the Chattanooga nonattainment area.

Similar analyses were conducted fo r the STN monitors on total PM 2.5 and all of the
measured PM 2.5 species. The most significant findings were that (1) the model’s sulfate
and PM 2.5 performance were quite good, and (2) NO 3 and OMC were somewhat under predicted. In addition, the model’s perfor mance at the STN monitor in the Chattanooga
nonattainment area was similar to that of the STN monitors within Georgia.

During the graphical performance analyses, GA EPD examined a set of various graphical measures as a “package.” Each PM 2.5 species and total PM 2.5 concentrations were examined with these packages. Whenever data was available, each package included scatter plots for monitors w ithin Georgia and in the Chat tanooga nonattainment area, time series plots for monitors in the Chattanooga nonattainment area, ba r plots of fractional
bias and fractional error for monitors within Georgia, bar plots of fractional bias and
fractional error for monitors in the Ch attanooga nonattainment area, bugle plots for monitors within Georgia, and soccer plots for mo nitors within Georgia. In addition, more graphical measures were examined if necessa ry. These graphical packages helped GA EPD conduct the analysis in a systematic and consistent manner. The following is a brief summary of what was found by this graphi cal analysis. The evaluation included speciated data available from IMPROVE, CASTNET, SEARCH, and STN as well as
total PM 2.5 from FRM monitors.

Overall, the model showed r easonable performance for PM 2.5 , SO 4 , NH 4 , and OC throughout the base year acro ss all monitors in Georgia. During summer, the model
showed a seasonal variation of NO 3 performance with under-predi ction bias greater than –100% and moderate winter over-prediction at CASTNET sites. The amount of error and bias in monthly time series was most likel y contributed by low concentration (< 1.0

μ g/m 3 ) cases. For EC, the model showed some over-prediction in Georgia while underpredicting at 47-065-4002. However, these performance issues are tied to low EC concentrations and the same tendency was found in the model’s SOIL performance. As shown in Figure 4-7, SO 4 and OMC were the major cont ributors to the Chattanooga nonattainment areas. Therefore, GA EPD conc luded that performan ce issues found in
minor constituents of PM 2.5 are not likely degrading the overall model performance.

107 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 4-7. Stacked bar plots of daily PM 2.5 species concentration at 47-065-4002.

Detailed graphical analyses led GA EPD to investigate a few exceptional performance
issues for SO 4 , OMC, and NH 4 . These analyses helped GA EPD to narrow down the possible causes of the performance issues. After reviewing these exceptional cases more closely, GA EPD concluded that these issues do not preclude the use of the model for the purpose of supporting this SIP for th e Chattanooga nonattainment area.

When the performance is looked at as a functio n of concentration, as in the bugle plots, the performance metrics indicate acceptable model performance for all species. Overall, GA EPD believes that the model predictions can be used to form the scientific foundation for proposed control options because its pe rformance is well within the limits of acceptable performance criteria establishe d in the Attainment Modeling Guidance.

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References:

Emery, C., E. Tai, and G. Yarwood, 2001: Enhanced meteorological modeling and performance evaluation for two Texas episodes, A report prepared for the Texas Natural Resources Conservation Commission (TNRCC) , ENVIRON International Corporation, Novato, CA.

Mass, C. F., D. Ovens, K. Westrick, B. A. Colle, 2002, Does increasing horizontal resolution produce more skillful forecast. Bulletin of American Meteorological Society, 83, 407-430.

EPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Oz one, PM2.5 and Regional Haze. U.S. Environmental Protection Agency, Resear ch Triangle Park, NC. EPA-454/B-07-002. April.

Boylan, J.W. and Russell, A.G. (2006) PM and Light Extinction Model Performance Metrics, Goals, and Criteria for Three-Dimensional Air Quality Models; Atmos. Environ.

, Volume 40, pp. 4946-4959.

Morris, R.E., B. Koo, B. Wang, G. Stella, D. McNally, C. Loomis, C-J. Chien, and G. Tonnesen. 2007. Technical Support Docume nt for VISTAS Emission and Air Quality Modeling to Support Regional Haze State Impl ementation Plans (Draft Final Report). ENVIRON International Corporation, Novato, California. November 14.

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Section 5. Control and Contingency Measures

Control measures are used to reduce emissi ons of direct PM2.5 and PM2.5 precursors and are, therefore, of primary importance to demonstration of attainment. This section
identifies those control measur es that are included as input s in the generation of the 2009 emissions inventory and are, therefore, re lied upon to demonstrate attainment. These measures have been or will be implemented after 2002 but before 2009 (the modeled attainment year).

Contingency measures, required by Section 172(c)(9) of the CAA, are additional control measures to be implemented in the event that an area fails to attain the standard by its
attainment date. These measures must be ready to be implemented quickly without significant further action by the State or by EPA and should achieve a quantity of emissions reductions approximately equal to one year’s worth of the reductions identified to meet the original attainment date.

This section also describes other measures that were not relied upon for attainment demonstration but which should provi de small reductions to ambient PM 2.5 levels. Finally, this section also discusses the implementation of RACT and RACM in the
nonattainment area.

5.1 Federal Control Measures for PM2.5 Attainment

The following federal control measures were included in the generation of the 2009 emissions inventory:

• Clean Air Interstate Rule

• Tier 2 Vehicle Standards

• Heavy-duty Gasoline and Diesel Highway Vehicles Standards

• Ultra-Low Sulfur Diesel Rule

• Large Nonroad Diesel Engines Rule

• Non-road Spark Ignition Engines a nd Recreational Engines Standard

• NOx SIP call in surrounding states

These measures are described in Table 5.1 and in the following text.

5.1.1 Clean Air Interstate Rule

On May 12, 2005, the U.S. EPA promulgated the “Rule To Reduce Interstate Transport
of Fine Particulate Matter a nd Ozone (Clean Air Interstate Rule or CAIR); Revisions to Acid Rain Program; Revisions to the NOX SIP Call,” referred to as CAIR. This rule established the requirement for States to adopt rules limiting the emissions of NOx and sulfur dioxide (SO2) and a model rule for the st ates to use in devel oping their rules. The purpose of the CAIR is to reduce interstate tran sport of precursors to fine particulate and

ozone.

110 of 162Control and Contingency MeasuresPM2.5 Attainment SIP– Catoosa and Walker Counties, GATable 5-1. Federal PM2.5 Control Measures and Contingency MeasuresControl Measure1Control Measure – ContinuingBenefits2MeasureSourcesAffectedMeasure andScheduleSpeciesAffected/ReducedMeasure andScheduleSpeciesReducedClean Air Interstate Rule(CAIR)40 CFR 96, Subparts AAA -HHHCoal-firedElectric Gen.Units, 28eastern statesand D.C.Regional cap on NOxand SO2 emissionsfrom EGU’s: NOXPhase 1 starts 2009.NOx.Regional cap onNOX and SO2emissions fromEGU’s: SO2 Phase1 starts 2010 (SO2).SO2,NOxTier 2 vehicle standards40 CFR 80, 85, and 86Newpassengervehicles.Fleet average for NOxemissions per mile,effective 2004 through2007.NOx.Continuing vehicleturnover.NOx.HeavyDuty EngineStandard (Gasoline andDiesel), second phase40 CFR 86, Subpart ANew gas anddiesel highwayvehicles 14001lbs or greater.Effective 2004 andlater – NOx and VOCcontrols;Effective 2007 andlater – PM controls.NOx, VOC, andPM (soot); PMlimit of 0.01g/bhp/hr.Continuing vehicleturnover.NOx, VOC, andelemental carbon.Ultra Low Sulfur Diesel40 CFR 80, Subpart INew dieselhighwayvehicles 14001lbs or greater.Limits 80 % of pool ofhighway diesel tosulfur content of 15ppm or less; 2007 –2009; facilitatesproper operation ofheavy duty diesel PMcontrols (see above).PM (soot).Limits 100 % ofpool of highwaydiesel to 15 ppmsulfur or less; 2010on - facilitatesproper operation ofheavy duty dieselPM controls (seeabove).PM (soot).111 of 162Control and Contingency MeasuresPM2.5 Attainment SIP– Catoosa and Walker Counties, GANonroad Diesel Rule40 CFR 89New mobilesources: offroad, ships,locomotives.2008 through 2014;Should reduce NOxand PM emissions byover 90%.NOx, PM(soot).Continuing vehicleturnover.NOx, PM (soot).Large Spark Ignition andRecreational VehicleRule40 CFR 90Forklifts,airline groundserviceequipment, allterrainvehicles, offhighwaymotorcycles,marine dieselengines.Large spark ignitionequipment (forklifts,airline ground serviceequipment) – 2004;off-road vehicles –2006; marine engines– 2006.VOC, NOx,CO.Continuingequipmentturnover.VOC, NOx, CO.NOx SIP Call inSurrounding StatesUtility andindustrialboilers;internalcombustionengines.Controls on utility andindustrial boilers andon internalcombustion engines in22 eastern states by2004.NOx.(1) Control measure – after 2002, before 2009 – permanent and enforceable, SIP counts toward attainment demonstration.(2) Contingency measure – 2002 or later – SIP does not count toward attainment, takeeffect without significant further actionby State or EPA.112 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

The CAIR applies to (1) any stationary, fossil- fuel-fired boiler or stationary, fossil-fuelfired combustion turbine servi ng at any time, since the st art-up of a unit's combustion chamber, a generator with a nameplate capacity of more than 25 MWe producing electricity for sale and (2) for a unit that qualifies as a cogenera tion unit during the 12- month period starting on the date that the un it first produces electrici ty and continues to qualify as a cogeneration unit, a cogeneration unit serving at any time, a generator with nameplate capacity of more than 25 MWe a nd supplying in any calendar year more than one-third of the unit's potential electric out put capacity or 219,000 MWh, whichever is greater, to any utility power distribution system for sale.

This rule provides annual state caps for NOx and SO2 in two phases, with the Phase I caps starting in 2009 and 2010, respectively. Be cause the Phase I SO2 cap starts in 2010, this can only be counted as a contingency meas ure and is discussed later. Phase II caps for NOx and SO2 become effective in 2015, whic h is too late to c ount as a contingency measure. The U.S. EPA is allowing the caps to be met through a cap–and-trade program if a state chooses to participate in the progr am. When fully implemented, the CAIR will
reduce SO2 emissions in the eastern United St ates by over 70 percent and NOx emissions by over 60 percent from 2003 levels.

As part of the final rule, EPA determined that Georgia contributes significantly to downwind PM2.5 nonattainment areas and/or in terferes with maintenance of the PM2.5 NAAQS (70 FR 25246-25250). Accordingly, a St ate CAIR rule has been promulgated that, for the most part, mirrors the provision s of the federal rule. Georgia will have
statewide SO 2 caps of 213,057 tons/year and 149,140 tons/year, effective in 2010 and 2015, respectively. Adjacent CAIR states, wh ich have been identified as contributing significantly to nonatt ainment of the PM 2.5 standard in the Chattanooga PM 2.5 nonattainment area, are Alabama, Florida, North Carolina, South Carolina, and
Tennessee. These states will also have caps on SO2 that will reduce the ambient
concentration of sulfates in the Chattanooga nonattainment area.

5.1.2 Tier 2 Vehicle Standards

Federal Tier 2 vehicle standards will reduce NO

x emissions from passenger vehicles. The standards require all passenger vehicles in a manufacturer’s fleet, including light-duty trucks and sport utility vehicles (SUVs), to meet an average standard of 0.07 grams of NOx per mile. Implementation began in 2004, and was completely phased in by 2007. The Tier 2 standards will also cover pa ssenger vehicles over 8,500 pounds gross vehicle weight rating (the larger pi ckup trucks and SUVs), which are not covered by the current Tier 1 regulations. Fo r these vehicles, the standards will be phased in beginning in 2008, with full compliance in 2009. The new standa rds require vehicles to be 77% to 95% cleaner than those on the road prior to implementation of Tier 2. The Tier 2 rule also reduced the sulfur content of gasoline to 30 parts per million (ppm) starting in January of 2006. Sulfur occurs naturally in gasoline, bu t interferes with the operation of catalytic converters on vehicles resulting in higher em issions. Lower-sulfur gasoline is necessary
to achieve the Tier 2 vehicle emission standards.

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5.1.3 Heavy-Duty Gasoline and Diesel Highway Vehicles Standards

New U.S. EPA standards designed to redu ce NOx and VOC emissions from heavy-duty gasoline and diesel highway vehicles began to take effect in 2004. A second phase of standards and testing procedures, which be gan in 2007, will reduce particulate matter from heavy-duty highway engines, and will also reduce highway diesel fuel sulfur content to 15 ppm to prevent damage to th e emission control devices. The total program is expected to achieve a 90% reduction in particulate matter (PM) emissions and a 95% reduction in NOx emissions for these new engi nes using low sulfur diesel, compared to older engines using diesel with higher sulfur content.

5.1.4 Large Nonroad Diesel Engines Rule

In May 2004, the U.S. EPA promulgated new rules for large nonroad di esel engines, such as those used in construction, agricultural, and industrial equipment, to be phased in
between 2008 and 2014. The non road diesel rules also reduce the allowable sulfur in nonroad diesel fuel by over 99%. Nonroad diesel fuel curre ntly averages about 3,400 ppm of sulfur. The rule limits nonroad di esel sulfur content to 500 ppm in 2006 and 15 ppm in 2010. The combined engine and fuel rules would reduce NOx and PM emissions from large nonroad diesel engines by over 90 %, compared to older engines using diesel with higher sulfur content.

5.1.5 Nonroad Spark-Ignition Engines and Recreational Engines Standard

The new standard, effective in July 2003, regulates NOx, hydrocarbons (HC) and carbon monoxide (CO) for groups of previously unregulated nonroad engines. The new standard will apply to all new engines sold in the United States and imported after these standards began and apply to large spark-ignition en gines (forklifts and airport ground service equipment), recreational vehicles (off-highway motorcycles and all-te rrain-vehicles), and recreational marine diesel engines. The re gulation varies based upon the type of engine
or vehicle.

The large spark-ignition engines contribute to ozone formation and ambient CO and PM
levels in urban areas. Tier 1 of this sta ndard was implemented in 2004 and Tier 2 started in 2007. Like the large spark- ignition engines, recr eational vehicles co ntribute to ozone
formation and ambient CO and PM levels. For the off-highway motorcycles and allterrain-vehicles, model year 2006, the new e xhaust emissions standard was phased-in by 50% and for model years 2007 and later at 100% . Recreational marine diesel engines
over 37 kilowatts are used in yachts, cruisers, and other types of pleasure craft. Recreational marine engines contribute to oz one formation and PM levels, especially in marinas. Depending on the size of the engi ne, the standard began phasing-in in 2006.

114 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

When all of the nonroad spark-ignition engi nes and recreational engines standards are fully implemented, an overall 72% reduction in HC, 80% reduction in NOx, and 56%
reduction in CO emissions ar e expected by 2020. These controls will help reduce ambient concentrations of ozone, CO, and fine PM.

5.1.6 NOx SIP Call in Surrounding States

In October 1998, the U.S. EPA made a fi nding of significant contribution of NOx emissions from certain states and published a rule that set ozone season NOx budgets for the purpose of reducing regional tr ansport of ozone (63 FR 57356). This rule, referred to as the NOx SIP Call, called for ozone season c ontrols to be put on ut ility and industrial boilers, as well as internal combustion engines in 22 states in the Eastern United States. A NOx emissions budget was set for each stat e and the states were required to develop
rules that would allow the state to meet their budget. A NOx trading program was established, allowing sources to buy credits to meet their NOx budget as opposed to actually installing controls. The emission bud gets were to be met by the beginning of
2004.

5.2 State Control Measures for PM2.5 Attainment

The following State control measures are included in the ge neration of the 2009 emissions inventory:

• Multipollutant Rule for coal-fired electric generating units (EGUs)

• Heavy-Duty Diesel Engines Rule

These measures are described in Ta ble 5-2 and in the following text.

115 of 162Control and Contingency MeasuresPM2.5 Attainment SIP– Catoosa and Walker Counties, GATable 5-2. State PM2.5 Control Measures and Contingency MeasuresControl Measure1Control Measure – ContinuingBenefits2MeasureSourcesAffectedMeasure andScheduleSpeciesAffected/ReducedMeasure andScheduleSpeciesand QtyReduced (tons)Multipollutant ruleAir Quality Control 391-3-1-.02 (2) (sss)Electric Gen.Units, CoalFired, GAFlue GasDesulfurization for7 EGUs3starting in2008 - approximately194,000 tons/yrreduced.SO2sourcesoutside thenonattainmentarea.Flue GasDesulfurization forBowen Unit 2, June1, 2009, WansleyUnit 2, December31, 2009, andBowen Unit 1, June1, 2010.SO2source insidethe nonattainmentarea – approx.127,263 tons/yrreduced.Heavy Duty Diesel EnginesruleAir Quality Control 391-3-1-.02 (2) (ooo)DieselVehicles14001 lbs orgreater.Diesel Vehicles 14001lbs or greater meetCARB standards;2005 and subsequentmodels.PM (soot)sources insidethenonattainmentarea.Continuing vehicleturnoverNOx, VOC, andelemental carbon(1) Control measure – after 2002, before 2009 – permanent and enforceable, SIP counts toward attainment demonstration.(2) Contingency measure – 2002 through 2011 – SIP does not count toward attainment, take effect without significant further action by State or EPA.(3) Bowen Units 3 and 4, Hammond Units 1 – 4, and Wansley Unit 1.116 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

5.2.1 Multipollutant Control for Electric Utili ty Steam Generating Units, GA rule 391-3-1-.02(2)(sss)

This state rule requires flue gas desulfurization (FGD) and selective catalytic reduction (SCR) controls on coal-fired EGUs in Georgi a. It was enacted in 2007 for the purposes of lowering ozone and PM2.5 concentrations and reducing mercury deposition. The
controls will reduce SO 2 emissions from the affected emissions units by at least 95 percent and will reduce NO

x emissions by approximately 85 percent.

The control implementation date of the ru le varies by EGU, starting on December 31, 2008. Controls are required on seven of the units prior to the atta inment demonstration year (see Table 5-3). These units ar e located south of the nonattainment area. Four of these units are located at Plant Hammond, wh ich is located approximately 25 miles south
of the nonattainment area. Two of the units ar e located at Plant Bowen, which is located approximately 35 miles south-southeast of the nonattainment area. The seventh unit is located at Plant Wansley, which is locat ed approximately 80 miles south of the Chattanooga nonattainment area. The Title V pe rmit of each affected facility has been amended to permit operation of the FGD contro ls. Controls on three more units will be implemented in 2009 and 2010.

Controls on two additional units at Plant Bowen will be discussed in Section 5.3, Contingency Measures.

A separate Georgia rule, .02(2)(uuu), will pl aces specific SO2 emissions limits on each of the EGUs addressed by Rule .02(2)(sss). Although reductions will occur beginning in 2008 as a result of implementation of Rule . 02(2)(sss), the emissi on limits required by Rule .02(2)(uuu) becomes effective January 1, 2010, through June 1, 2015, depending upon the unit.

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Table 5-3. Georgia Multipollutant Rule SO 2 Control Requirements and
Construction Permit Numbers

Plant/Unit County Required
FGD operation date, per rule (sss) 1 Control 4 Contingency Construction Permit

Number Permit
Effective

Date

Bowen Unit 3 Bartow 12/31/2008 X 4911-015-0011-V-02-1 2/27/06

Bowen Unit 4 Bartow 12/31/2008 X 4911-015-0011-V-02-1 2/27/06

Hammond
Unit 1 Floyd 12/31/2008 X 4911-115-0003-V-02-2 12/20/06

Hammond
Unit 2 Floyd 12/31/2008 X 4911-115-0003-V-02-2 12/20/06

Hammond
Unit 3 Floyd 12/31/2008 X 4911-115-0003-V-02-2 12/20/06

Hammond
Unit 4 Floyd 12/31/2008 X 4911-115-0003-V-02-2 12/20/06

1/1/2007 Wansley Unit

1 Heard 12/31/2008 X 4911-149-0001-V-02-0 (incorporated draft permit V01-9 (permit to construct))
Bowen Unit 2 Bartow 06/01/2009 NA 2 4911-015-0011-V-02-3 12/20/06

Wansley Unit

2 Heard 12/31/2009 X 4911-149-0001-V-02-0 (incorporated draft permit V01-9 (permit to construct)) 1/1/2007

Bowen Unit 1 Bartow 6/1/2010 NA 2 4911-015-0011-V-02-3 12/20/06

Scherer Unit 3 Monroe 12/31/2011 X FGD control to be incorporated into permit
renewal 3
(1) rule (sss) is not federally enforceable (2) control not needed to meet one year of emissions (3) current permit expires December 2010 and construction pe rmit will be incorporated into permit renewal (4) controls on these units are modele d in the attainment demonstration

The total estimated reduction of SO2 emissions from the seven EGUs by 2009 is 216,412 tons/yr. The estimated stat ewide level of SO2 emissions (from all sources) in 2009 is 527,580 tons/yr after the EGU reductions. Therefore, the EGU reductions are equivalent to 29.1 percent of uncontrolled statewide SO2 emissions (216,412 tons/yr plus 527,580 tons/yr).

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5.2.2 Heavy Duty Diesel Engines Rule (ooo)

This rule requires heavy-duty diesel engines (vehicle weig ht of 14001 lbs or more) to meet the California Air Resources Board (C ARB) emissions standards by 2005. These standards are essentially the same as the fe deral heavy-duty engi ne standards (40 CFR 86,Subpart A) described in the federal contro l measures section. The CARB heavy duty diesel engine standards were adopted to en sure that the emission reductions would be
achieved in the event that U.S. EPA relaxed the federal requirements and did not require 2005 and 2006 model year engines to meet the Not-To-Exceed (NTE) test requirements. The CARB standards and Georgia’s rule (ooo) were promulgated prior to the federal
standards. These emissions benefits would potentially double in 2006 if the
manufacturers continued to not meet the N TE test requirements. Manufacturers were required federally to begin testing the engi nes to the NTE requirements with the 2007 model year. Georgia has kept the CARB standard s in place to prevent a delay in the state receiving cleaner engines in the event that EP A were to relax or delay the federal 2007 or 2010 engine standards. Also, the CARB engine requirements differ from EPA’s in that CARB requires a 5-minute idle shutdown or th e use of a clean-idle engine beginning with the 2008 model year. The benefit of this 5-mi nute idle limit has not been quantified for Georgia. However, it is expected to reduce NOx and PM emissions.

5.3. Summary of SO2 Emissions Changes

Control of SO2 sources is the primary mechan ism for attainment of the PM2.5 standard. Expected changes in SO2 emissions between the typical baseline and 2009, including the effects of SO2 controls, are quantified by so urce category in the tables below. The changes for Catoosa and Walker Counties, the nonattainment area, and the state are presented in Tables 5-4, 5-5, 5-6, and 5-7, respectively. Reductions in emissions are shown as negative numbers.

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Table 5-4. Changes in SO2 Emissions, 2002 to 2009, Catoosa County (tons)

EGU point NonEGU point Nonroad Area Mobile Total

2002* 0.0 0.0 50.8 272.8 92.1 415.7

2009 0.0 0.0 8.5 273.6 10.9 293.0 Change 0.0 0.0 -42.3 0.8 -81.2 -122.7

* typical emissions

Table 5-5. Changes in SO2 Emissions, 2002 to 2009, Walker County (tons)*

EGU point NonEGU point Nonroad Area Mobile Total

2002* 0.0 203.1 21.0 763.9 71.1 1059.1

2009 0.0 195.4 3.5 766.9 8.4 974.2 Change 0.0 -7.7 -17.5 3.0 -62.7 -84.9

* typical emissions

Table 5-6. Changes in SO2 Emissions, 2002 to 2009, Nonattainment Area (tons)

EGU point NonEGU point Nonroad Area Mobile Total

2002* 0.0 203.1 71.8 1036.7 163.2 1474.8

2009 0.0 195.4 12.0 1040.5 19.3 1267.2 Change 0.0 -7.7 -59.8 3.8 -143.9 -207.6

* typical emissions

Table 5-7. Changes in SO2 Emissions, 2002 to 2009, Statewide (tons)

EGU point NonEGU point Nonroad Area Mobile Total

2002* 515958 55453 9006 57555 12183 652970

2009 406918 55749 2725 57692 1585 527580 Change -109040 296 -6281 137 -10598 -125390

* typical emissions

The total statewide SO2 emissions change is a decrease of 125,390 tons. The statewide change is more pertinent to de monstrating attainment in 2009.

120 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

5.4. Contingency Measures

Section 172(c)(9) of the Clean Ai r Act requires states to id entify and include contingency
measures in their attainment demonstration plans. Contingency measures must take effect without significant fu rther action by the State or U.S. EPA upon determination by
EPA that an area has either failed to dem onstrate Reasonable Further Progress (RFP)
toward attainment or failed to demonstrate attainment of the applicable NAAQS.

The preamble of the Clean Air Fine Particle Implementation Rule (72 FR 20586) states the following (page 20642): “Under subpart 1 of the CAA, all PM2.5 nonattainment areas must include in their SIPs contingency measures consistent with section 172(c)(9). Contingency measures are additional control measures to be implemented in the event
that an area fails to meet RFP or fails to atta in the standards by its attainment date. These contingency measures must be fully adopted ru les or control measures that are ready to be implemented quickly upon failure to meet RFP or failure of the area to meet the standard by its attainment date . The preamble to the proposal stated that the SIP should contain trigger mechanisms for the con tingency measures, specify a schedule for implementation, and indicate that the measur es will be implemented without significant further action by the State or by EPA. The c ontingency measures should consist of other
control measures for the area that are not incl uded in the control strategy for the SIP.”

40 CFR 51.1009(b) states the following: “If the State submits to EPA an attainment demonstration and State implementation plan for an area which demonstrates that it will attain the PM NAAQS within five years of the date of designati on, the State is not required to submit a separate RFP plan. Compliance with the emission reduction
measures in the attainment demonstration and State implementation plan will meet the requirements for achieving reasonable further progress for the area.” The preamble (page 20643) to the April 2007 final ru le requires states to adopt “contingency measures equal to approximately one year’s worth of reducti ons necessary to achieve RFP for the area.” Because an RFP plan will not be required for Chattanooga, a quantity of emissions reductions equivalent to one year of the reduc tions needed to meet attainment will satisfy the requirement for contingency measures.

The purpose of these contingency measures “i s to provide a cushion while the plan is being revised to meet the missed milest one” (page 20643). Calendar year 2009 is the final year for which emissions data is to be included in an attainment demonstration for the attainment date of April 5, 2010. It is EPD’s understand ing, based on evaluation of the language of the CAA and from discussions with EPA Region 4, that these contingency measures are to be implemente d approximately within two calendar years following the final emissions control year in the attainment demonstration. This time period takes into consideration the time requi red for EPA to make a finding of failure to attain the NAAQS and the requirement to s ubmit a revision to the SIP. Contingency
measures will be those reductions that occur after the 2009 control year and prior to the submittal of a revised SIP if this area fails to attain. Therefore, contingency measures for

121 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

the purpose of this plan are those measures that will be implemented any time during the calendar years 2010 and 2011.

Demonstration of attainment in the Ch attanooga NAA is based largely on statewide
reductions of SO 2 emissions from Georgia’s coal-fir ed EGUs, as required by Georgia rules 391-3-1-.02(2)(sss) and ( uuu). Since there are no coal -fired EGUs located in the Georgia portion of the Chatta nooga NAA, there are no specific requirements in this plan for SO2 reductions within the Chattanooga nonatta inment area prior to January 1, 2009. However, due to federal fuel requirements ther e is a small reduction of 207.9 tons of SO2 emissions occurring over seven years (2002 to 2009). Table 5-8 below illustrates the emissions for the Chattanooga, Georgia NAA for the attainment period:

Table 5-8. Predicted changes in SO2 emi ssions inside the Georgia Portion of the Chattanooga Nonattainment Area, 2002 to 2009.

Sources 2002 SO2
Emissions (tpy) 1 2009 SO2 Emissions (tpy) 1 Change (tpy)

Point 203.1 195.4 -7.7
Area 1,036.7 1,040.5 -3.8
Mobile 163.2 19.3 -143.9
Nonroad 71.8 12.0 -59.8

Total 1,474.8 1267.2 -207.6

Table 5-9 below illustrates the emission trends during the contingency period. This trend demonstrates a slight increase in emissions (12 tons of SO2), mostly due to typical growth factors applied to point and area sources.

Table 5-9. Predicted changes in SO2 emi ssions inside the Georgia Portion of the Chattanooga Nonattainment Area, 2009 to 2012 (Contingency).

Sources 2009 SO2
Emissions (tpy) 1 2012 SO2
Emissions (tpy) 1 Change (tpy)

Point 195.4 205.9 10.5

Area 1,040.5 1,052.1 11.6
Mobile 19.3 18.6 -0.7
Nonroad 12.0 2.3 -9.4

11.7 Total 1267.2 1,278.9

(1) Source: VISTAS 2002, 2009, and 2012 inventories.

122 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

Since there are no coal-fired EGUs, or other large sources of SO2 located in the Georgia portion of the Chattanooga NAA, and there are no federal measures that result in additional emission reductions, contingency measures for Chattanooga will be based on
control of SO2 emissions from sources lo cated outside of the nonattainment area and located in Georgia.

5.4.1 Region for Contingency Measures

The application of RFP procedures to calcu late the emission reductions necessary for Contingency will also apply to determining the geographical area from which the
emission reductions will occur. Because of the regional nature of PM2.5, sources of SO2 outside of the non-attainment area may be im pacting the PM2.5 concentrations in the
non-attainment area. “EPA belie ves that states could be able to justify considering not only all emissions in the NAA, but also emission within a distance that may be up to 200kilometers from the nonattainment area” (72 FR 20636). Given that there are no coalfired EGUs located within the Georgia porti on of the NAA, and that there are no other large sources of SO2 within the Georgia portion of the NAA, GA EPD will be required to go beyond the non-attainment area for its cont ingency measure emission reductions and rely on additional reductions from EGUs.

EPA has concluded “Since the demonstrations of impact are best done by states, in conjunction with their attainment planning, EPA intends to allow States to justify the area to be included, within the distance lim its discussed above” (7 2 FR 20638). For the Chattanooga NAA’s contingency measures, GA EPD has defined an area that includes counties within, and bisected, by an arc fr om the monitoring location in Rossville, Georgia, to the nearest significant source of SO2 emissions relied upon for reduction to demonstrate attainment, Georgia Power’s Pl ant Bowen. There are two counties not included in this analysis, Fors yth and Polk counties, both of which are bisected at the counties’ border by the circ le boundary, and would not be representative of a uniform

area.

Figure 5-1 shows the area for which contingency measures are being considered with respect to the Chattanooga non-a ttainment area. The circle represents a 99.4 km radius from the Rossville, Georgia, monitor to Plant Bowen. GA EPD notes that this method of determining the area from the monitor is mo re stringent that the typical method of determining the area from the border of the NAA. All net emissions from the Georgia counties within the circle were considered in the analysis. For simplicity, these counties
will be referred to as the “area of influence” for the Chattanooga non-attainment area.

123 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

Figure 5-1. Chattanooga Contingency Area of Influence

124 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

5.4.2 Area of Influence Emission Reductions for Contingency

The guidance from the preamble for the PM2. 5 Implementation rule requiring a quantity of emission reductions equivalent to one year of the reductions needed to meet attainment cannot be applied within the NAA for this dem onstration since there is actually a slight increase in SO2 emissions within the 7-year period. Therefore, EPD will evaluate all
sources of emission within the area of infl uence to demonstrate contingency emission
reductions.

Table 5-10 displays the 2002 baseline SO2 em issions, by source sector, for the counties
located within the area of influence for th e Chattanooga non-attainment area and includes
the nonattainment counties of Catoosa and Walker. The total 2002 baseline SO2 emissions, for the area of influence, are 205,018.1 tons per year.

Table 5-10. 2002 Baseline SO2 Emission s For Chattanooga Area of Influence County EGU
Point
Sources

(tpy) NonEGU
Point
Sources

(tpy) Non-road
Mobile
Sources

(tpy) Area
Sources

(tpy) Highway
Mobile
Sources

(tpy) Total
SO2
Emissions

(tpy)

Bartow 153,848.8 1752.1 105.9 1006.8 180.8 156,894.4

Catoosa 0.0 0.0 50.8 272.8 92.1 415.7 Chattooga 0.0 1507.7 11.0 492.6 39.3 2050.6
Cherokee 0.0 3.5 135.9 490.2 176.6 806.2
Dade 0.0 0.0 29.6 101.0 48.8 179.4
Fannin 0.0 0.0 24.9 111.4 36.4 172.7 Floyd 30,376.2 4868.9 81.2 979.5 128.1 36,433.9
Gilmer 0.0 0.0 16.6 323.5 51.2 391.3
Gordon 0.0 0.0 75.4 992.5 105.1 1173.0 Murray 0.0 0.0 35.6 727.8 58.8 822.2
Pickens 0.0 0.0 20.6 152.0 40.0 212.6
Walker 0.0 203.1 21.0 763.9 71.1 1059.1
Whitfield 0.0 1524.5 97.2 2608.9 176.4 4407.0

Total 184,225 9859.8 705.7 9022.9 1204.7 205,018.1

Table 5-11 displays the 2009 attainment cont rol year SO2 emissions, by source sector,
for the counties locate d within the area of influence for the Chattanooga non-attainment
area and includes the n onattainment counties of Catoos a and Walker. The total 2009 SO2 emissions, for the area of influence, are 104,409 tons. This demonstrates a reduction of 100,609.1 tons of SO2 in the area of influence during the attainment period.

125 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

Table 5-11. 2009 SO2 Emissions For Chattanooga Area of Influence County EGU
Point
Sources

(tpy) Non-EGU
Point
Sources

(tpy) Non-road
Mobile
Sources

(tpy) Area
Sources

(tpy) Highway
Mobile
Sources

(tpy) Total
SO2
Emissions

(tpy)

Bartow 83,349.3 1866.9 17.8 1009.0 25.5 86,268.5

Catoosa 0.0 0.0 8.2 273.6 10.9 292.7 Chattooga 0.0 1451.1 1.8 493.6 4.7 1951.2
Cherokee 0.0 3.7 22.7 491.9 25 543.3
Dade 0.0 0.0 4.9 101.4 5.7 112
Fannin 0.0 0.0 4.1 112.4 4.3 120.8 Floyd 1857.9 5006.5 13.5 981.8 15.5 7875.2
Gilmer 0.0 0.0 2.8 323.6 6.1 332.5
Gordon 0.0 0.0 12.6 994.6 12.7 1019.9 Murray 0.0 0.0 5.9 730.6 7.0 743.5
Pickens 0.0 0.0 3.4 152.1 5.5 161.0
Walker 0.0 195.4 3.5 766.9 8.4 974.2
Whitfield 0.0 1363.4 16.5 2613.0 21.3 4014.2

Total 85,207.2 9887 117.7 9044.5 152.6 104,409

Table 5-12 displays the 2012 year SO2 emi ssions, by source sector, for the counties
located within the area of influence for th e Chattanooga non-attainment area and includes
the nonattainment counties of Catoosa and Walker. The total 2012 SO2 emissions, for the area of influence, are 31,426.8 tons. This demonstrates an a dditional redu ction of 72,982.2 tons of SO2 in the area of influe nce during the period of time for contingency
measures to be evaluated.

126 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

Table 5-12. 2012 SO2 Emissions For Chattanooga Area of Influence County EGU
Point
Sources

(tpy) NonEGU
Point
Sources

(tpy) Non-road
Mobile
Sources

(tpy) Area
Sources

(tpy) Highway
Mobile
Sources

(tpy) Total
SO2
Emissions

(tpy)

Bartow 9540.0 1883.0 3.6 1020.5 24.8 12,471.9

Catoosa 0.0 0.0 1.8 276.8 10.5 289.1 Chattooga 0.0 1528.5 0.3 499.1 4.5 2032.4
Cherokee 0.0 3.9 2.8 496.9 24.3 527.9
Dade 0.0 0.0 1.3 102.5 5.4 109.2
Fannin 0.0 0.0 0.5 113. 4.1 117.6 Floyd 2190.0 5357.0 3.0 993.1 15.2 8558.3
Gilmer 0.0 0.0 0.4 327.0 5.8 333.2
Gordon 0.0 0.0 2.9 1005.8 12.3 1021.0 Murray 0.0 0.0 1.4 738.4 6.6 746.4
Pickens 0.0 0.0 0.4 153.7 5.2 159.3
Walker 0.0 205.9 0.5 775.3 8.1 989.8
Whitfield 0.0 1404.0 4.0 2642.1 20.6 4070.7

Total 11730 10382.3 22.9 9144.2 147.4 31,426.8

Table 5-13 summarized the previous area of in fluence emissions for the attainment period and the contingency period. The emission reductions the area of influence, which includes the NAA counties is 100,609.1 tons of SO2 during the attainment period. One seventh of this value is 14,372.7 tons of SO2. The emission reductions for the area during the contingency period is 71,982.2 tons of SO2, which is far in excess of one years worth of emission for RFP of 14,372.7.

Table 5-13. Total SO2 Emissions Fo r Chattanooga Area of Influence

Area 2002 (tpy) 2009 (tpy) 2012 (tpy) Attainment
Reductions
2002-2009 (tpy) Contingency
Reductions
2009-2012 (tpy)
Catoosa and
Walker Counties 1474.8 1266.9 1278.9 207.9 - 12.0

Outside
Nonattainment
Area 203,543.3 103,142.1 31,147.9 100,401.2 71,994.2

Total Area
Combined 205,018.1 104,231.1 31,426.8 100,609.1 71,982.2

127 of 162Control and Contingency Measures PM2. 5 Attainment SIP – Catoosa and Walker Counties, GA

5.4.3 Adopted Control Measures for Contingency

Evaluation of the emissions values in Tables 5-10, 5-11, and 5-12 demonstrate that the majority of the SO2 emission reductions occu r from EGU point sources. The emission reductions prior to attainment were contributions from both EGU reduction and federal fuel programs. The emission reductions during the contingency period were overwhelmingly EGU SO2 reductions.

The EGU Point Source located within the area of influence as previously discussed is Georgia Power’s Plant Bowen. Units 1 & 2 of Georgia Power’s Plant Bowen will start operating SO2 controls no later than June of 2010, which is with in the contingency measure time period. The Title V permit of this facility has been amended to permit construction and operation of the FGD controls and the facility is required to submit CAM plans as soon as the units come online. The facility will be su bject to an emission reduction requirement that will reflect the ne w controls and be required by Georgia Rule .02(2)(uuu). The controls and limit will eff ect emissions reductions of 73,809.3 tons, as shown in Table 5-14 during the contingency period.

Table 5-14. SO2 emissions reductions expected from control and contingency measures at coal-fired electric gene rating units outside the Chattanooga
Nonattainment Area.

Plant/
Unit County 2002
baseline

(tpy) 2009
emissions (tpy) 2012
emissions (tpy) Attainment
Reductions
2002--2009 Contingency
Reductions
2009-2012

Georgia
Power Plant
Bowen. Unit
1 and Unit 2 Bartow 153,848.8 83,349.3 9,540.0 70,499.5 73,809.3

The Georgia Power Plant Bowen SO2 control m easure is required by Georgia rule 391-3- 1-.02(2)(uuu) and will take affect regardless of the attainment status of the Chattanooga NAA. Therefore, no trigger mechanism is n eeded and no further action is needed from EPD or EPA for this rule to be implemen ted. The Plant Bowen control measure, therefore, satisfies the con tingency measure requirement.

5.5 Unmodeled Control Measures

The following unmodeled measures, some of wh ich are voluntary act ions, should provide
additional reductions to ambi ent PM2.5 concentrations:

• Open burning rule;

• Switching of two EGUs to lower sulfur coal; and

• Planned early start-up of SO 2 scrubbers at two EGUs.

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Because the measures are voluntary and/or th e benefits are relatively small, credits for
these measures have not been taken in the projected emissions inve ntory for attainment demonstration modeling. These measures are described in Table 5-15 and in the following text.

Table 5-15. Unmodeled Control Measures

Unmodeled Control Measure 1 Measure Sources
Affected

Measure and
Schedule Species
Affected/

Reduced Open burning rule Air Quality Control 391- 3-1-.02(2)(5) Vegetation, tires, municipal waste,
construction debris, agriculture, etc. Restricts types of outdoor burning,
2005. Direct PM, organic carbon.

Switch 2 coal-fired EGUs to lower sulfur coal Plant Scherer
Units 1 and 2 Switched to PRB coal
in 2003 and 2004 SO2

Multipollutant rule, early implementation of
controls Air Quality Control 391- 3-1-.02(2)(sss) Control start-up nominally 6
months before regulatory date. Planned early startup of SO2 scrubbers
at Bowen Unit 2 and Wansley Unit 2 would
achieve emissions
reductions not
credited in the 2009 inventory. SO2.

(1) Unmodeled measure that becomes effective after 2002 but before 2009.

5.5.1 GA Rule 391-3-1-.02(5) Open Burning

Georgia’s Open Burning Rule placed additional restrictio ns on open burning during the months of May through September in Catoosa and Walker Counties. The rule prohibits open burning with the exception of 12 types of burning activities. During May through September the number of allowed exceptions is reduced to six. The rule reduces emissions of elemental carbon and organic carb on year round. The ban further reduces emissions of elemental carbon and organic carbon during the spring and summer months, when PM2.5 is typically higher.

5.5.2 Switch to Lower Sulfur Coal, Georgia Power Plant Scherer

Two of Plant Scherer’s coal-fired units were voluntarily switched to Powder River Basin (PRB) coal, which is lower in both sulfur a nd BTU value than Appalachian coal. Plant Scherer is located south of Atlanta, inside the Macon PM2.5 nonattainment area. Units 1

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and 2 were switched to the PRB coal in Ju ly 2003 and January 2004, respectively. The sulfur levels in the PRB coal, as measured in 2007, were 0.285%, whereas the Georgia Power system-wide average was 1.12% over the same time period. Based on 2007 monitoring data, the estimated unmodeled emissions savings is approximately 54,000 tons of SO2 at each unit. These savings were not modeled because the switch to PRB coal was not a Federal or State requirement.

5.5.3 Multipollutant Rule - Earl y Implementation of Controls

Table 5-3 shows the compliance dates requ ired by Georgia Rule .02(2)(sss) for
installation and operation of SO 2 controls on coal-fired EGUs. Because the start-ups of scrubbers on Bowen Unit 2 and Wansley Unit 2 are not required until June and December of 2009, SO2 emissions reductions associated wi th these controls ar e not credited in the projected 2009 inventory used for modeled a ttainment demonstration. However, to ensure compliance with the rule, Georgia Po wer has planned for nominal start-up dates that are six months prior to the required date s. If the start-ups occur early as planned, then the scrubbers at Bowen Unit 2 and Wansley Unit 2 would achieve 2009 SO 2 emissions reductions beyond those credited in the modeled attainment demonstration. The quantity of unmodeled 2009 emissions reductions would be approximately 16,900 tons and 21,600 tons, respectively.

5.6 RACT and RACM

Section 172(c)(1) of the CAA requires SIPS to provide for the implementation of all reasonably available control measures (RACM) that cont ribute to attainment as “expeditiously as practicabl e” in the specific nonattainment area. The EPA has consistently interpreted RACM as a collec tion of measures that would advance the attainment date by at least one year. This requirement also applies to emissions reductions from existing stationary sources th rough the adoption of reasonably available control technology (RACT). “Reasonably availabl e” allows the state to consider both the cost and benefits of applying the measure and whether the measure can be readily and effectively implemented without undue administrative burden.

The Implementation Rule (72 FR 20612, 20617) defines a process by which a limited RACT and RACM analysis can be conducted for areas that are projected to attain within five years of designation as a result of existing national measures and are projected to have a design value of 14.5 ug/m 3 or lower. The 2009 annual modeled Future Design Values (DVF) for the Chattanooga Annual PM 2.5 nonattainment area are 13.6 ug/m 3 for the Riverside site, 14.4 ug/m 3 for the Maxwell Road/East Ridge site, 12.3 ug/m 3 for the Soddy-Daisy High School site, and 13.9 ug/m 3 for the Rossville-Maple St. site (see Section 6.1). As discussed below, the Chattanooga Nonattainment Area will attain primarily due to the EGU SO2 reductions resulting from the requirements of Georgia Rule 391-3-1-.02(2)(sss). Additionally, the Fe deral measures defined in Section 5.1 of this document will also result in local and regional emission reductions that will

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contribute to the attainment of the Cha ttanooga area. Ther efore the Chattanooga nonattainment area is subject to a limited RACT/RACM analysis.

EPA has stated (72 FR 20617) that “If the St ate could not achieve significant emissions reductions by the beginning of 2008 due to the time needed to implement reasonable measures or other factors, then it could be concluded that reasonably available local
measures would not advance the attainment date.” The EPA promulgated the Clean Air Fine Particle Implementation Rule (72 FR 20586) on April 25, 2007. This rule defined the requirements for RACT and RACM and also reaffirmed the due date for submission of the SIP revision for fine particles as April 5, 2008. EPA also acknowledges in the Implementation Rule that, due to the short tim e from the promulgation of the rule to the SIP due date, it would be diffi cult for states to devise, adopt, and implement RACM in
time to advance the attainment date. EP D concurs that either, a case-by-case RACT analysis and permitting, or new RACT/RACM rule development, including the analysis, stakeholder process, rule development, pub lic comment, and admini strative rule adoption procedures take far in excess of a year. Therefore, given the time constraints, EPD concludes that RACM, however limited, is not “reasonable” and is “impractical” to implement as “expeditiously as practicable.”

However, EPD had previously (prior to th e promulgation of the implementation rule) extensively evaluated the nature and sources of fine particulate matter emissions in the
state and in Catoosa and Walker Counties in the Chattanooga nonattain ment area. This
led to a determination that the primary component of ambi ent monitored PM2.5 is the precursor SO 2 , with the primary source of SO 2 emissions within the State of Georgia (and, to a lesser extent, the Southeast) bein g coal-fired Electric Generating Units (EGUs) located at power plants. The supplemental analysis in the attainment demonstration
section of this document discusses EPD’s determination that SO 2 is the significant
contributor of PM2.5 emissions and that the control of SO 2 emissions from EGUs is the most effective strategy for significant PM2.5 air quality improvements. Using the results of this modeling analysis, evaluating Ge orgia’s other PM2.5 nonattainment areas (Atlanta, Chattanooga, and Floyd County), and incorporating Georgia’ s responses to the Clean Air Interstate Rule (CAIR), the Cl ean Air Mercury Rule (also called CAMR, which is currently vacated), and the Clean Air Visibility Rule (CAVR), the State of Georgia adopted State Rule 391-3-1-.02(2)(sss) “Multipollu tant Control for Electric Utility Steam Generating Units” (Multipollu tant Rule) effective March 14, 2007 (see Appendix L). This rule includes a schedule that identifies the dates by which specific EGUs are to install flue gas desulfurization (FGD).

Planning for the Chattanooga area was included in this rule by requi ring all EGUs located at Georgia Power Plant Bowen and Plant Hammond, both of which are located just outside the Chattanooga nonattainment area, to in stall FGD. The rule requires all units at Plant Hammond and Bowen Units 3 and 4 to begin operation of FGD in 2008, followed by Bowen Units 2 and 1 in 2009 and 2010, respectively. While 72 FR 20623 includes a presumption that compliance with CAIR sa tisfies RACT for SO2 emissions, it also specifies that “after consulting with power companies, the State may conclude that establishing additional ‘beyond CAIR’ emission control requir ements on specific sources

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in nonattainment areas is warranted to provide for attainment as expeditiously as practicable. The timing of the installation and operation of c ontrols at both Plant Bowen
and Plant Hammond will not adva nce the attain ment date for Chattanooga. EPD notes that the Multipollutant rule requires SO 2 , NO
x and Hg controls on all large EGUs in the
state. The controls at both Plant Bowe n and Plant Hammond are being installed as “expeditiously as practicable” given the ex tent of the engineering and construction
schedules involved in retrofitt ing all large EGUs within the State of Georgia.

There is only one major point source in Ge orgia portion of the nonattainment area. In Walker County, Crystal Springs Print Works has a potential to emit direct PM2.5 as a major source, but while the facility does co ntribute to direct PM emissions in the nonattainment area, their reported actual emissions in the CERR show that the facility emits only 70 tons-per-year on a normal basis.

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Section 6. Attainment Demonstration

This chapter details the procedures that were used to demonstrate attainment of the annual fine particulate matter (PM2.5) Nati onal Ambient Air Quality Standard (NAAQS) for the Chattanooga nonattainment area. As described in the U.S. Environmental Protection Agency’s (U.S. EPA’s) Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Qua lity Goals for Ozone, PM2.5, and Regional

Haze (“ Attainment Guidance ”), an attainment demonstrati on consists of (a) analyses
which 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 co mponents may be determined by relying on
results obtained with air quality models.

Section 3.0 of the Attainment Guidance recommends applying both a modeled attainment test and a subsequent unmonitored area an alysis (UAA) to the air quality modeling
results to determine if the annual PM2.5 NAA QS will be met. A dditional 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. The modeled attainment test, additional corroborative analyses and weight of evidence, and unmonitored area analysis are described in further detail in this section.

6.1 Modeled Attainment Test

The purpose of a modeled attainment test is to determine if control strategies currently being implemented or currently adopted with a future compliance date (“on the books”)
will lead to attainme nt of the NAAQS for PM2.5 by the attainment year of 2009. The modeling is applied in a rela tive sense, similar to the 8- hour ozone attainment test. However, the PM2.5 attainment test is more complicated and reflects the fact that PM2.5 is a mixture of different species. In th e test, ambient PM2.5 is divided into major components, with a separate relative respon se factor (RRF) and future design value (DVF) calculated for each of the PM2.5 com ponents. Since the attainment test is calculated on a per species basis, the attainment test for PM2.5 is referred to as the Speciated Modeled Attainment Test (SMAT) . The following subsections outline the process that was used to determine 2009 mode l projections of PM2.5 and compare them to the NAAQS. Complete calculations a nd results are contained in Appendix M.

6.1.1 Determination of Baseline Design Values

The first step in any attainment test proce ss is to determine the baseline design value (DVB). In the Attainment Guidance, the U.S. EPA recommends using a DVB that is the average of the three design value periods that straddle the baseline inventory year (e.g., the average of the 2000-2002, 2001-2003, and 2002-2004 design value periods for a 2002 baseline inventory year). This works out to a 5-year weighted average, with the baseline

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year having the heaviest weight (e .g. {[2000] + 2*[2001] + 3*[2002] + 2*[2003] + [2004]}/9).

For the SMAT process, a mean PM2.5 DVB is determined, as well as component specific DVB for each quarter. The following section will detail the calculation of baseline design values needed for the PM2.5 attainment test.

6.1.1.1 Mean PM2.5 Baseline Design Values

To begin the SMAT process, a mean PM2.5 DVB is calculated on a quarterly basis for each Federal Reference Method (FRM) mon itor in the PM2.5 nonattainment areas.
Concentrations are calculated based on calenda r quarters (Q1: January - March; Q2: April - June; etc.) as the NAAQS is calculated for a calendar year, and the quarters need to fit evenly within a year. Also, calculating the attainment test on a quarterly basis allows
states to examine the differences in PM2. 5 composition that occu r during the different

seasons.

Table 6-1 contains the quarter ly average PM2.5 concentration for the FRM monitors in the Chattanooga nonattainment area. This qua rterly data was then averaged, using the weighted scheme suggested by the U.S. EPA, to produce a 5-year weighted DVB. Table 6-2 presents the final mean PM2.5 DVBs for the nonattainment areas.

Table 6-1. Quarterly Average PM2.5 Mass for the FRM Monitors in the Chattanooga Nonattainment Area

AIRS ID County Site Name Year Q1 Q2 Q3 Q4

13-295-0002 Walker Rossville 2000 18.34 16.86 19.26 19.65

2001 13.31 17.76 18.19 12.94

2002 11.57 16.23 20.57 10.98

2003 13.48 16.67 20.04 13.83

2004 11.84 14.91 17.93 14.13

47-065-0031 Hamilton Maxwell 2000 17.06 18.35 18.89 21.57

2001 14.02 16.25 19.12 17.23

2002 13.76 15.66 19.97 11.17

2003 15.81 17.81 18.58 13.63

2004 12.81 16.69 18.86 14.24

47-065-4002 Hamilton Riverside 2000 14.62 16.15 18.81 19.91

2001 13.42 16.55 19.18 15.38

2002 12.20 14.52 19.52 12.68

2003 12.61 15.69 18.45 12.77

2004 11.90 15.57 18.13 12.50

47-065-1011 Hamilton Soddy Daisy 2000
-- -- -- -- 2001
-- -- -- -- 2002 13.70 14.37 18.28 9.35

2003 11.64 16.01 18.56 10.89

2004 9.92 15.12 16.20 11.94

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Table 6-2. Observed Quarterl y and Annual Mean PM2.5 DVB (in μ g/m 3 ) for Monitors in the Chattanooga Nonattainment Area

AIRS ID County Site Name 2000-2004 Q1 2000-2004 Q2 2000-2004 Q3 2000-2004 Q4 2000-2004

DVB

15.7 13-295-0002 Walker Rossville 13 .163 16.592 19.486 13.361

16.3 47-065-0031 Hamilton Maxwell 14.533 16.682 19.230 14.560

15.3 47-065-4002 Hamilton Riverside 12.801 15.529 18.972 14.083

14.0 47-065-1011 Hamilton Soddy Daisy 12.709 14.910 18.123 10.064

6.1.1.2 Speciated Baseline Conditions

The monitored attainment test for PM2.5 utilizes both PM2.5 and individual PM2.5 component species. A separate RRF is calculated for each PM2.5 species. In order to perform the recommended modeled attainment test, States should di vide observed mass
concentrations of PM2.5 into seven components (plus passive mass):

1. Mass associated with sulfates (SO4); 2. Mass associated with nitrates (NO3); 3. Mass associated with ammonium (NH4); 4. Mass associated with organic carbon (OC); 5. Mass associated with elemental carbon (EC);
6. Mass associated with particle bound water (PBW); 7. Mass associated with “other” primary i norganic particulate matter (Crustal); and 8. And passively collected mass or the mass of the blank filter (Blank = 0.5 μ g/m 3 ).

The second part of the process is to use th e quarterly mean PM2.5 DVBs (as calculated in Section 6.1.1.1) with speciated data to calculate the quarterly mean concentrations of these seven components at the FRM sites. This need to speciate th e FRM data presents
two issues:

1. FRM measurements and speciated PM2. 5 measurements do not always measure the same mass; and 2. Not all FRM monitoring sites have co-l ocated Speciation Trends Network (STN) speciation monitors.

The Hamilton Riverside site (47-065-4002) has a co-located STN mon itor prior to 2005, while the other sites (13-295-0002, 47- 065-0031, and 47-065-1011) do not. The following sections will explai n how these issues were overcome to produce the speciated
values needed for this at tainment demonstration.

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6.1.1.2.1 SANDWICH

As the Attainment Guidance notes, recent data analyses (Frank, 2006) have noted that the FRM monitors do not measure the same comp onents and do not retain all of the PM2.5 that is measured by routine speciation samp lers and, therefore, cannot be directly compared to speciation measurements from STN. By design, the FRM mass
measurement does not retain all ammonium nitrate and other semi-volatile materials (negative sampling artifacts) an d includes particle-bound water associated with sulfates, nitrates and other hygroscopic species (positive sampling arti facts). This results in concentrations (and percent c ontributions to PM2.5 mass), wh ich may be different than
the ambient levels of some PM2.5 chemical constituents.

To resolve the differences between FRM and STN total mass, the U.S. EPA has recommended using the S ulfate, A djusted N itrate, D erived W ater, I nferred C arbon

H ybrid (SANDWICH) material balance appr oach. With the SANDWHICH approach, nitrate mass is adjusted to account for volatilization based on hourly meteorology parameters. Subsequently, quarterly averag e nitrate, sulfate, elemental carbon, and crustal mass can be calculated, as well as the Degree of Neut ralization (DON) of sulfates. Quarterly average NH4 can then be calculated from the adjusted nitrate mass, sulfate mass, and the DON of sulfate. Next, the mass of particle-bound wate r can be calculated from the previously obtained DON, sulfate, nitrate, and ammonium values. Finally, organic carbon is calculated by taking the difference betwee n the total PM2.5 mass as measured at the FRM monitor, and the calcu lated component mass (i.e. OC from mass balance ([OCMmb]) = PM2.5 FRM - {[EC] + [SO4] + [NO3] + [NH4] + [PBW] + [Crustal] + [Blank]}).

6.1.1.2.2 Speciated Profiles

While the SANDWICH method reconciles th e differences between FRM and STN, a lingering issue is not all FRM monitoring sites have co-located STN monitors to provide speciated data. The U.S. EPA Attainment Gu idance suggests four measures that can be taken to resolve the lack of speciated data:

1. Use of concurrent data from a nearby speciated monitor; 2. Use of representative data (from a different time period); 3. Use of interpolation techniques to creat e a spatial field using ambient speciation data; and 4. Use of interpolation techniques to cr eate spatial fields, and gridded modeling outputs to adjust the species concentrations.

Of the four methodologies, the U.S. EPA recommends using one of the spatial interpolation techniques to estimate species c oncentrations at FRM sites that do not have speciation data (numbers 3 and 4 above). To assist in th is task, EPA is developing a
software tool called “Modeled Attainment Test So ftware” (or MATS) that will perform the spatial analysis of options number 3 an d 4 described above. However, the PM2.5

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portion of the MATS tool had not been released at the time th is analysis was performed. In trying to pursue the US EPA recommended c ourse, we have used the speciated profiles from the Clean Air Interstate Rule (CAIR) SMAT tool, which is the predecessor for the MATS program, as an alternative.

The CAIR SMAT tool uses data from bot h the Interagency Monitoring of Protected Visual Environments (IMPROVE) and the US EPA’s Speciation Network (ESPN) to
derive mean concentrations for six PM2.5 co mponents. Quarterly average concentrations
between Jan 2002 to December 2002 were retain ed for sites that had at least 11 monitored values per quarter for each of the major PM2.5 species. Major species for ESPN include EC, OC, NH4, SO4, NO3, and cr ustal material (which includes the five trace elements aluminum, calcium, iron, sili con, and titanium). The major species for IMPROVE are the same except for NH4, whic h is not routinely measured in the IMPROVE protocol.

The quarterly averaged species concentra tions at the IMPROVE and ESPN monitors were used to interpolate concentrations at the PM2.5 FRM monitoring sites using a technique called Voronoi Ne ighbor Averaging (VNA).

As a result of the CAIR SMAT process, quart erly species fractions were generated for each FRM site, which are presented in Table 6-3 below. These fractions were then applied to Observed Quarterly Mean PM2.5 values calculated in Section 6.1.1.1 to determine quarterly component specific concentrations. Table 6-4 shows the quarterly DVBs for crustal, EC, OC, SO4, NO3, NH4 and PBW assuming a cons tant passive blank

mass of 0.5 μ g/m 3 . DON refers to degree of neutraliz ation of sulfate and ranges from 0.0 (sulfuric acid) to 0.375 (completely neutraliz ed in the form of ammonium sulfate).

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Table 6-3. DVB Quarterly Species Frac tions (%) from the CAIR SMAT Tool

AIRS ID Site Name Blank
Mass qtr DON Frac.
Crustal Frac.
EC Frac.
OC Frac.
SO4 Frac.
NO3 Frac.
NH4 Frac.
H2O

13-295-0002 Rossville 0.5 1 0.29045 0.02710 0.04426 0.51994 0.24387 0.01987 0.07659 0.06836

2 0.30979 0.06611 0.04005 0.40717 0.29941 0.00064 0.09294 0.09368

3 0.29350 0.09316 0.03138 0.28500 0.37263 0.00050 0.10951 0.10781

4 0.30657 0.03343 0.05603 0.29729 0.34971 0.04066 0.11900 0.10388

47-065-0031 Maxwell 0.5 1 0.29045 0.02563 0.04187 0.54588 0.23070 0.01880 0.07246 0.06467

2 0.30979 0.07419 0.04494 0.33473 0.33600 0.00071 0.10430 0.10513

3 0.29350 0.09636 0.03246 0.26043 0.38544 0.00052 0.11328 0.11152

4 0.30657 0.03038 0.05091 0.36140 0.31780 0.03695 0.10814 0.09440

47-065-4002 Riverside 0.5 1 0.29045 0.02563 0.04187 0.54588 0.23070 0.01880 0.07246 0.06467

2 0.30979 0.07419 0.04494 0.33473 0.33600 0.00071 0.10430 0.10513

3 0.29350 0.09636 0.03246 0.26043 0.38544 0.00052 0.11328 0.11152

4 0.30657 0.03038 0.05091 0.36140 0.31780 0.03695 0.10814 0.09440

47-065-1011 Soddy Daisy 0.5 1 0.29045 0.02563 0.04187 0.54588 0.23070 0.01880 0.07246 0.06467

2 0.30979 0.07419 0.04494 0.33473 0.33600 0.00071 0.10430 0.10513

3 0.29350 0.09636 0.03246 0.26043 0.38544 0.00052 0.11328 0.11152

4 0.30657 0.03038 0.05091 0.36140 0.31780 0.03695 0.10814 0.09440

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Table 6-4. Quarterly PM2.5 Component-Specific DVB ( μ g/m 3 ) for Monitors in the Chattanooga Nonattainment Area

AIRS ID Site Name Blank
Mass qtr FRM
Mass Non-Blank
Mass Crustal EC OC SO4 NO3 NH4 PBW

13-295-0002 Rossville 0.5 1 13.16 12.66 0.34 0.56 6.58 3.09 0.25 0.97 0.87

2 16.59 16.09 1.06 0.64 6.55 4.82 0.01 1.50 1.51

3 19.49 18.99 1.77 0.60 5.41 7.07 0.01 2.08 2.05

4 13.36 12.86 0.43 0.72 3.82 4.50 0.52 1.53 1.34

47-065-0031 Maxwell 0.5 1 14.53 14.03 0.36 0.59 7.66 3.24 0.26 1.02 0.91

2 16.68 16.18 1.20 0.73 5.42 5.44 0.01 1.69 1.70

3 19.23 18.73 1.80 0.61 4.88 7.22 0.01 2.12 2.09

4 14.56 14.06 0.43 0.72 5.08 4.47 0.52 1.52 1.33

47-065-4002 Riverside 0.5 1 12.80 12.30 0.32 0.51 6.71 2.84 0.23 0.89 0.80

2 15.53 15.03 1.12 0.68 5.03 5.05 0.01 1.57 1.58

3 18.97 18.47 1.78 0.60 4.81 7.12 0.01 2.09 2.06

4 14.08 13.58 0.41 0.69 4.91 4.32 0.50 1.47 1.28

47-065-1011 Soddy Daisy 0.5 1 12.71 12.21 0.31 0.51 6.66 2.82 0.23 0.88 0.79

2 14.91 14.41 1.07 0.65 4.82 4.84 0.01 1.50 1.51

3 18.12 17.62 1.70 0.57 4.59 6.79 0.01 2.00 1.97

4 10.06 9.56 0.29 0.49 3.46 3.04 0.35 1.03 0.90

6.1.2 Relative Response Factor Calculation

The next step in the SMAT process is to use base-year and future -year modeling results to estimate a relative response factor ( RRF) for each component of PM2.5 for each quarter. Simply put, the RRF is the quarterly average future-year concentration near a monitor divided by the quarterly average base-y ear concentration near the same monitor,

or:

X""Monitor Near""ion Concentrat Year Base Modeled Mean Quarterly X""Monitor Near""ion Concentrat Year Future Modeled Mean Quarterly

= RRF

Instead of focusing on the indi vidual cell containing the monitor, an array of cells that are “near” a monitor are considered in the attainment test. By sampling an array of cells from the modeling, the attainment test allows for variations in the model performance, as the peak concentrations may not occur in th e grid cell that contains the monitor, but rather nearby the monitor. Table 6-5 provides the U.S. EPA's recommendations for defining “nearby” cells for grid systems having cells of various sizes. Since the modeling

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for the Georgia attainment demonstration was preformed at a 12-km grid resolution, a 3x3 grid array was used.

Table 6-5. US EPA’s Recommendat ion for Defining “Near” Cells



### Size of Cell (km) Size of the Array of “Nearby” Cells

< 5 7 x 7

>5-8 5 x 5

>8-15 3 x 3

>15 1 x 1

For the PM2.5 SMAT, the RRF is calculated for each component, for each quarter. To accomplish this step, 24-hour averaged concen tration for each component of PM2.5 is extracted from the base-year and future-year modeling output near the FRM monitoring sites for each day. The daily component c oncentrations from each cell of the 3x3 array are then averaged to develop a single m ean 24-hour average concentration for each component on each day in both the base and fu ture years. These mean daily component concentrations are then averaged for each quarter to develop base-and future-year quarterly mean component concentration. The future-year quarterly mean component concentrations are then divided by their resp ective base year quarterly mean component concentrations to develop quarterly RRFs fo r each component of PM2.5. The quarterly
RRFs for the FRM monitors in the Chattanooga PM2.5 nonatta inment area are presented
below in Table 6-6. In lie u of using RRFs, the estimated future mass of NH4 and PBW will be determined by the estimated future mass of SO4 and NO3, as was done in the CAIR SMAT tool, per the following equations:

NH4 = DON * SO4 + 0.29*NO3

PBW =(-0.002618) + (0.980314*NH4) + (-0 .260011* NO3) + (-0.000784* SO4) + (- 0.159452* (NH4^2)) + (-0.356957* NO3* NH4) + (0.153894* (NO3^2)) + (0.212891* SO4* NH4) + 0.0444366* SO4* NO3) + (-0.048352* (SO4^2))

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Table 6-6. Quarterly Component RRF s for Monitors in the Chattanooga
Nonattainment Area

AIRS ID Site Name qtr Crustal

RRF EC

RRF OC

RRF SO4

RRF NO3

RRF

13-295-0002 Rossville 1 1.069 0.858 0.983 0.915 1.021

2 1.088 0.802 0.970 0.810 0.751

3 1.086 0.788 0.964 0.703 0.740

4 1.085 0.791 0.978 0.880 0.922

47-065-0031 Maxwell 1 1.064 0.852 0.981 0.911 1.007

2 1.085 0.801 0.971 0.810 0.735

3 1.080 0.786 0.964 0.699 0.684

4 1.079 0.792 0.978 0.880 0.906

47-065-4002 Riverside 1 1.064 0.850 0.982 0.909 1.016

2 1.074 0.796 0.970 0.805 0.745

3 1.069 0.783 0.962 0.694 0.711

4 1.077 0.791 0.980 0.878 0.920

47-065-1011 Soddy Daisy 1 1.064 0.857 0.986 0.910 1.026

2 1.065 0.800 0.969 0.804 0.761

3 1.065 0.787 0.960 0.694 0.746

4 1.077 0.796 0.983 0.878 0.938

6.1.3 Future Year Quarterly Concentration Calculation

The next step in the SMAT process is to cal culate future quarterly mean concentration estimates (DVFs) for each component of PM2.5. To accomplish this, the quarterly component-specific DVBs from Table 6-4 are multiplied by the component-specific RRFs contained in Table 6-6. The quarterly component DVFs for the monitors in the Chattanooga PM2.5 nonattainment area are provided below in Table 6-7.

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Table 6-7. Quarterly Component Future Design Values (in μ g/m 3 ) for Monitors in the Chattanooga Nonattainment Area

AIRS ID Site Name qtr Crustal

DVF EC

DVF OC

DVF SO4

DVF NO3

DVF NH4

DVF PBW
DVF

13-295-0002 Rossville 1 0.367 0.481 6.475 2.825 0.257 0.895 0.791

2 1.158 0.517 6.357 3.903 0.008 1.211 1.214

3 1.920 0.469 5.215 4.970 0.007 1.461 1.433

4 0.466 0.570 3.738 3.958 0.482 1.353 1.174

47-065-0031 Maxwell 1 0.383 0.501 7.515 2.951 0.266 0.934 0.825

2 1.303 0.582 5.262 4.402 0.008 1.366 1.374

3 1.950 0.478 4.704 5.044 0.007 1.482 1.454

4 0.461 0.567 4.970 3.933 0.471 1.342 1.167

47-065-4002 Riverside 1 0.335 0.438 6.597 2.580 0.235 0.817 0.723

2 1.197 0.538 4.882 4.066 0.008 1.262 1.266

3 1.904 0.469 4.630 4.943 0.007 1.453 1.425

4 0.445 0.547 4.811 3.789 0.462 1.295 1.124

47-065-1011 Soddy Daisy 1 0.333 0.438 6.568 2.564 0.236 0.813 0.719

2 1.139 0.518 4.675 3.891 0.008 1.208 1.210

3 1.808 0.451 4.404 4.712 0.007 1.385 1.358

4 0.313 0.387 3.397 2.669 0.332 0.914 0.795

6.1.4 Future Year Annual Average Estimate

The next step in the SMAT process is to sum the quarterly mean components from Table 6-7 to get annual mean PM2.5 values for each quarter. The quarterly mean PM2.5 values are then averaged to produce a “non-blank” future year annual average PM2.5 estimate for each FRM site in the nonattainment area (Table 6-8).

Table 6-8. Quarterly Mean PM2.5 Mass estimates for 2009

AIRS ID Site Name Non-Blank Mass Q1 Non-Blank Mass Q2 Non-Blank Mass Q3 Non-Blank Mass Q4 Non-Blank Mass Avg. 13-295-0002 Rossville 12.090 14.368 15.474 11.742 13.418
47-065-0031 Maxwell 13.374 14.297 15.119 12.910 13.925
47-065-4002 Riverside 11.725 13.219 14.830 12.473 13.062
47-065-1011 Soddy Daisy 11.670 12.649 14.123 8.807 11.812

Finally, the “non-blank” future-year annual av erage PM2.5 estimate for each FRM site is

corrected for the 0.5 μ g/m 3 blank mass. These 2009 a nnual PM2.5 concentrations (DVFs) are then compared to the NAAQS (15.0 μ g/m 3 ) in Table 6-9 below. Since the
values at both FRM sites in the nona ttainment areas are less than 15.0 μ g/m 3 , the test is passed.

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Table 6-9. Estimated Annual Mean 2009 PM2.5 Mass Compared to the DVB

AIRS ID Site Name 2002
Annual

DVB 2009
Non-Blank

Mass Blank

Mass 2009
Annual

DVF
13-295-0002 Rossville 15.7 13.4 0.5 13.9
47-065-0031 Maxwell 16.3 13.9 0.5 14.4
47-065-4002 Riverside 15.3 13.1 0.5 13.6
47-065-1011 Soddy Daisy 14.0 11.8 0.5 12.3

6.2 Supplemental Analysis

The Attainment Guidance asserts that all attainment demonstrations should be accompanied by a supplemental analysis that further supports the modeling conclusions. This supplemental analysis can include additio nal analyses of air quality, emissions and meteorological data, and consider modeli ng outputs other than the results of the
attainment test. If the attainment test resu lts fall short of the standard, the results of corroboratory analyses may be used in a we ight of evid ence determination (WOE) to show that attainment is likely despite modeled results, which may be inconclusive.

The Attainment Guidance defines the guideline s for supplemental analysis/WOE for the
annual PM2.5 standard as follows:

- Site with a DVF less than 14.5 μ g/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 μ g/m 3 should submit a weight of
evidence demonstration to aggregate supplemental analysis to support the model
attainment demonstration. - Sites with a DVF greater than or equal to 15.5 μ g/m 3 should consider additional control measure to ensure attainment, as more qualitative analysis is unlikely to
demonstrate attainment.

Because all Chattanooga PM2.5 nonattainment monitors have DVFs lower than 14.5

μ g/m 3 , the following supplemental analysis to corroborate modeling results should be adequate to complete the attainment demonstr ation, rather than a WOE analysis to show attainment. In the following sections, we explore refinements to the attainment test, additional modeling studies, an d air quality and emissions trends as part of a supplemental analysis for the Chat tanooga PM2.5 nonattainment area.

6.2.1 Additional Air Quality Modeling

The Attainment Guidance suggests several additional modeling exercises that can be performed as part of supplemental/WOE an alysis. Suggestions include completing additional analysis on the air quality modeling preformed, or modeli ng alternative setups

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and emissions as part of sensitivity. Each of theses items will be discussed in the following sections.

6.2.1.1 Analysis of Modeled Cell-Day Concentrations

In Section 7.0 of the Attainment Guidance, various aspects of air quality models, modeled performance, and uncertainties associated with the length of modeled episodes and
limited observational datasets are described. Section 7.1 suggests that some types of “absolute” modeling results may be used to assess general progress towards attainment from the baseline inventory to the projected future inventory. The Attainment Guidance goes on to describe several metrics that can be considered as part of this type of additional analysis, which include:

1. Percent change in total amount of PM2.5 > 15 μ g/m 3 within the nonattainment

area; 2. Percent change in number of grid cells > 15 μ g/m 3 within the nonattainment area; 3. Percent change in grid cell-hours (days) > 15 μ g/m 3 within the nonattainment area; and 4. Percent change in maximum modeled 24-hour PM2.5 concentrations within the
nonattainment area.

As the U.S. EPA notes in the Attainment Gu idance, care should be taken in interpreting
absolute metrics if the model evaluation s hows a large under predic tion or over prediction of PM2.5 concentrations, because under (over) prediction of observed concentrations will make it artificially easy (hard) to show progress towards absolute attainment levels.

Because of the complexity of the model extraction for PM2.5, only the third metric is presented for supplemental analysis. In this analysis, all 364 days were used. The celldays counts of absolute modeled PM2.5 conc entrations were tallied from both the 2002 baseline and the 2009 attainment year modeling run for each day. Data was extracted for only the grid cells that cont ained a portion of the Chat tanooga nonattainment area. Figure 6-1 highlights the 37 cells that enco mpass the Chattanooga nonattainment area.

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Figure 6-1. Grid cells for which the ai r quality metrics were applied in the Chattanooga PM2.5 nonattainment area.

The cell-days counts were binned based on concentration ranges of 5 μ g/m 3 intervals. Figure 6-2 presents the cell-da ys count results graphically and shows an increase in the number of cell-days below 15 μ g/m 3 . By 2009 75.3% of cell-days fall in the 0 –15 range,
a substantial increase from the 67.0% in 2002. Raw cell-days counts show a total of 1119 cell-days (25.2% decrease) shifted from the greater than 15 μ g/m 3 range to the less than

15 μ g/m 3 range between 2002 and 2009 (Table 6-10).

Figure 6-2 also shows a decrease in th e number of cell-days in the 15 – 20 μ g/m 3 bin (429 cell-days decrease), the 20 – 25 μ g/m 3 bin (375 cell-days decrease), the 25 – 30 μ g/m 3
bin (216 cell-days decrease), and the 30 – 35 μ g/m 3 bin (65 cell-days decrease). In addition, the number of cel l-days greater than 35 μ g/m 3 bin decreased to zero cell-days (100.0% decrease) from 2002 to 2009. Overal l, the results from the air quality modeling metric are encouraging as shown by the substa ntial increase in the number of cell-days

below 15 μ g/m 3

.

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Figure 6-2. Percentage of Cell-Days in th e Chattanooga Nonattainment Area within Concentration Categories for 2002 and 2009.

Table 6-10. Number of Cell-Days within Co ncentration Bins. Increases (decreases)

in the number of cell-days (and percent chan ge) within the bins are noted by red (blue) coloration.

Concentration
[ μ g/m 3 ] 2002 2009 Difference % Difference

0-5 1202 1393 191 15.9

5-10 4079 4533 454 11.1

10-15 3747 4221 474 12.7

15-20 2568 2139 -429 -16.7

20-25 1251 876 -375 -30.0

25-30 471 255 -216 -45.9

30-35 116 51 -65 -56.0

> 35 34 0 -34 -100.0

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6.2.1.2 EPA CAIR Modeling

One way to acquire modeling sensitivity runs is to examine the modeling results from other Regional Planning Organizations or from EPA modeling studi es. Other modeling studies may use different physical and chemical modeling options for their meteorological and air quality modeling runs, which would provide a sensitivity of these different options.

An air quality modeling exercise that co ntained results for the Chattanooga PM2.5 nonattainment area is the U.S. EPA’s modeling fo r the Clean Air Interstate Rule (CAIR). The Technical Support Document for the final CAIR, March 2005, provided modeling results with and without the implementation fo r the CAIR. Differences between the U.S. EPA’s modeling and the attainment demonstratio n are: 1) the mete orology was for 2001, 2) the DVB was the weighted design va lues 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 6.1 are the same. These modeling results ar e listed in Table 6-11 below.

Table 6-11. US EPA CAIR Modeling Resu lts for Walker County, GA and Hamilton County, TN (highest monitors)

Future Year Walker County, GA PM2.5 ( μ g/m 3 ) Hamilton County, TN PM2.5 ( μ g/m 3 )

2010 14.22 15.57

2015 13.65 14.94

The U.S. EPA’s results were for the highest monitor in counties with more than one monitor. The U.S. EPA’s modeling results predicts that the Chattanooga nonattainment area would be slightly above the annual PM2.5 standard by 2010 and slightly below the annual PM2.5 standard by 2015. Although the 2010 projection is above the PM2.5 standard, it should be noted that the CA IR modeling did not account for all the EGU controls that will be installed by 2009. If the CAIR modeling had properly accounted for these controls, it is highly likely that th e Chattanooga nonattainment area would have modeled attainment of the annual PM2.5 standard by 2009 and 2010.

6.2.2 Air Quality and Emission Trends Analysis

Since measurements began in 1999, annual av erage concentrations of PM2.5 decreased steadily until 2002. Annual average PM2.5 concentrations increased in 2003 and remained steady thru 2005. At the Rossville site in Georgia (13-295-0002), significant
decreases of concentrations were measur ed in 2006 and 2007. In 2007 and 2008, all
monitors have maintained values lower than the NAAQS. Table 6-12 provides the annual average data and with Figure 6-3 provi des a graphical repres entation of the data.

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Table 6-12. Annual Average PM2.5 values in Chattanooga for the past 9 years.

Year GA Walker
Rossville (13-295-0002) ( μ g/m 3 ) TN Hamilton

Maxwell (47-065-0031) ( μ g/m 3 ) TN Hamilton Soddy Daisy (47-065-1011) ( μ g/m 3 ) TN Hamilton
Riverside (47-065-4002) ( μ g/m 3 )

1999 N/A 21.2 N/A 18.9 2000 18.5 19.0 N/A 17.4 2001 15.9 16.7 N/A 16.1 2002 15.0 15.1 13.9 14.7 2003 15.9 16.5 14.3 14.9 2004 14.9 15.6 13.3 14.5 2005 16.4 16.1 13.8 15.5 2006 14.4 14.3 13.1 15.0 2007 13.8 14.9 14.2 15.0 2008 12.48 12.3 11.4 12.7

Figure 6-3. Annual PM2.5 Average Values for the Monitors in the Chattanooga
Nonattainment Area.

When one takes into account the period of record, PM2.5 design values improved since 2001. It is also important to note that a ll the Chattanooga monitors have maintained

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design values that were close to or below the NAAQS over the pa st five design value periods (see Table 6-13).

Table 6-13. Three Year Design values (in μ g/m 3 ) for the Monitors in the Chattanooga PM2.5 Nonattainment Area.

AIRS ID Site Name 99-01

DV 00-02

DV 01-03

DV 02-04

DV 03-05

DV 04-06

DV 05-07

DV 06-08

DV

13-295-0002 Rossville 17.0 16.3 15.5 15.2 15.8 15.2 14.9 13.5

47-065-0031 Maxwell 18.9 16.9 16.1 15.7 16.1 15.3 15.1 13.8

47-065-1011 Soddy Daisy 13.9 14.1 13.8 13.8 13.4 13.7 12.9

47-065-4002 Riverside 17.5 16.1 15.2 14.7 15.0 15.0 15.2 14.2

When evaluating the trends in air quality values it is important to not e that there are still significant sulfur dioxide (SO2) emission reduc tions that are expected between now and the attainment year in the utili ty sector. As a result of th e Georgia Multipollutant Rule, multiple scrubbers will be inst alled by Georgia Power on some of their large coal-fired power plants between 2007 and 2009. Table 6- 14 lists the units in the state of Georgia and shows the year the controls are expected to come online and the estimated amount of yearly SO2 emissions reductions. In tota l, over 216,400 tons of SO2 will be reduced annually in Georgia. This reduction repr esents a 38% reducti on in statewide SO2 emissions from point sources compared to 2002 levels (571,410.9 tons/year). As sulfates are one of the larger contributors to total PM2.5 in Georgia, this substantial reduction in SO2 should result in significant reductions of the observed PM2.5 concentrations and push the annual average PM2.5 concentration at all the Chattanooga monitors below the NAAQS.

Table 6-14. Projected SO2 emission reduc tions from new controls on Georgia
Power EGUs in 2008 and 2009.

Facility Unit Control Date 1 2007 SO2
Emissions 2 (tons) Estimated
2008 (tons) Estimated
2009 (tons) 2008
Reductions (tons) 2009
Reductions (tons)

Bowen 1 5/30/2010 42,967.2 42,967.2 42,967.2 0.0 0.0

2 5/30/09 39,888.2 39,888. 2 17,783.5 0.0 22,104.7

3 5/01/08 64,745.5 23,740. 0 3,237.3 41,005.5 61,508.2

4 12/31/08 49,240.0 49,240. 0 2,462.0 0.0 46,778.0

Hammond 1 5/30/08 6,028.2 2, 687.6 301.4 3,340.6 5,726.8

2 5/30/08 6,047.4 2,696. 1 302.4 3,351.3 5,745.0

3 5/30/08 6,147.9 2,740. 9 307.4 3,407.0 5,840.5

4 5/30/08 29,585.3 13,190. 1 1,479.3 16,395.2 28,106.0
Wansley 1 12/31/08 42,739.9 42,739.9 2,137.0 0.0 40,602.9

2 5/30/09 51,105.7 51,105. 7 22,784.6 0.0 28321.1

TOTAL (tons) 338,495.3 270,995.8 93,762.0 67,499.5 244,733.3

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Statewide EGU emission trends for SO2 (bas ed on CEMS data) are plotted for Georgia and the neighboring states in Figure 6-4. The 2008 and 2009 SO2 emissions for Georgia
are estimated based on the emission reductions discussed in Table 6-14. The annual average PM2.5 concentrations in Chattanooga are pl otted in the same figure to allow a comparison between emission changes and chan ges in PM2.5 concentrations. The 2009 PM2.5 concentrations are from the model pr ojections and the 2008 va lues are calculated by interpolating between the 2007 observations and the 2009 projections. SO2 emissions in Georgia are higher in 2003-2007 comp ared to 2002, as are some PM2.5 concentrations. However, PM2.5 concentra tions start to trend downward in 2006 due to emission reductions in neighbor ing states. The large emi ssion reduction in Georgia between 2007 and 2009 are projected to be mo re than enough to bring Chattanooga into
attainment. It should be noted that the year-to-year variab ility in PM2.5 concentrations
are due to a combination of emission ch anges and meteorological fluctuations.



## Monitoring Trends in Chattanooga

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,00019992000200120022003200420052006200720082009 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0PM2.5 (μg/m3)

Figure 6-4. PM2.5 monitoring trends in Chattanooga compared to statewide EGU SO2 emissions from Georgia and neighboring states.

Note: The 2009 design value is modeled projection and 2008 value is inte rpolated from 2007 and 2009. Also, 13-295- 0002 is the Rossville monitor, 47-065-0031 is the Maxwell monitor, 47-065-1011 is the Soddy Daisy monitor, and 47-
065-4002 is the Riverside monitor.SO2 Emissions(Tons/Year)

SO2-GA SO2-AL SO2-FL SO2-NC SOX-SC

SO2-TN 13-295-0002 47-065-4002 47-065-0031 47-065-1011

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6.2.3 Supplemental Analysis Conclusions

After examining the totality of the mode ling evidence, GA EPD is confident the Chattanooga nonattainment area should reach attainment status by the 2009 deadline. PM2.5 annual average concentrations should continue to decrease due to substantial sulfate reductions anticipated in the state over the next two years. GA EPD feels these reductions will more than likely allow the PM2.5 nonattainment areas to achieve the NAAQS by 2009.

6.3 Unmonitored Area Analysis

The modeled attainment test does not address fu ture air quality at locations where there is not a PM2.5 monitor nearby. To guard against the possibility that ai r quality levels could
exceed the standard in areas with limited monitoring, Section 3.4 of the Attainment
Guidance suggests that additional review is “n ecessary, particularly in nonattainment
areas where the ozone or PM 2.5 monitoring network just meets or minimally exceeds the size of the network required to report data to Air Quality System (AQS).” This review is intended to ensure that a control strategy lead s to reductions in PM2.5 and its constituent pollutants at other locations that could have baseline (and future) design values exceeding the NAAQS were a monitor deployed there. The test is called an “unmonitored area analysis.” 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.

GA EPD currently operates one FRM monitor in the nonattainment county and believes that the density of this monitoring networ k relieves the necessity of applying this additional analysis. However, GA EPD decide d to apply some additional analysis and
looked at 2009 PM2.5 concentrat ions directly from the mode l (did not apply SMAT or SANDWICH to results). A map of each PM2.5 monitor and its position relative to the 12-km modeling grid is provided in Figure 6-4. As can be s een by the figure, the spatial coverage of the monitors, and their resulting “nearby” 3x3 arrays, covers the majority of
the urban areas where PM2.5 tends to be hi gher. In addition, the absolute annual modeled PM2.5 concentrations for each grid cell in the vicinity of the Chattanooga nonattainment area have been added to Figure 6-4. From this figure, it can be seen that there are no significant concentration gradient s across the area of inte rest. Therefore, it
can be concluded that there are no unmonitored hotspots that need to be addressed. It
should be noted that the 2009 absolute mode led PM2.5 annual concentrations (no SMAT or SANDWICH) are lower than that calcul ated at the FRM monitor using SMAT and SANDWICH. However, this discrepancy betw een the absolute and relative (RRF) 2009 concentration will have no impact on our conclusion that there are no unmonitored hotspots in the Chattanooga area.

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Figure 6-5. Modeled 2009 Annual Mean PM2.5 Concentration near the Chattanooga Nonattainment Area on the VISTAS 12km Grid

6.4 Data Access

The modeling input and output files are very large and it would not be reasonable to
submit all of these files with the SIP attainment demonstration. These include all files used to process the emissions, meteorology an d air quality models and any other files used to develop the modeling. To request ac cess to these files please contact the Georgia
EPD Air Protection Branch’s Data & Modeling Unit at 404-362-4851.

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Section 7.0 Other Clean Air Act Requirements

Section 172(c) of the CAA, as amende d, contains the plan requirements for nonattainment areas in general. The Cha ttanooga PM2.5 nonattainment area must meet these requirements, which are listed in Table 7-1 and summarized below.

Table 7-1. General Plan Requirements for Nonattainment Areas

Federal
CAA Description Found Where in Plan?

172(c)(1) Provide for the implementation of RACM as expeditiously as practicable and shall provide for attainment of the primary NAAQS Section 5.5

172(c)(2) Provide for Reasonable Further Progress Section 7.2 172(c)(3) Provisions shall include a comprehensive current inventory of actual emissions from all sources of the relevant pollutant or pollutants in such area, including periodic revisions as determined necessary. Section 7.3, Appendix I.

172(c)(4) Plan shall expressly identify and quantify the emissions of any such pollutant(s)
which will be allowed in accordance from section 173(a)(1)(B) from the construction and operation of major new or modified stationary sources in each such area. Emissions quantified for this purpose will
be consistent with the achievement of RFP
and will not interfere with attainment of the applicable NAAQS. Section 7.5

172(c)(5) Plan shall require permits for the construction of new or modified major stationary sources as applicable. Section 7.5

172(c)(6) Plan shall incl ude enforceable emission
limitations and such other control measures, means or techniques as well as schedules and timetables for compliance as deemed necessary to provide for attainment of the applicable NAAQS. Section 5

172(c)(7) Shall meet the applicable provisions of section 110(a)(2). Section 7.7

172(c)(8) Administrator may allow the use of equivalent modeling, emission inventory, and planning procedures. Section 7.9.

172(c)(9) Plan shall provide for the implementation of specific measures to be undertaken if
the area fails to make RFP or to attain the applicable NAAQS. Such measures shall be included in the plan revision as contingency measures to take effect in any such case without further action by the
State/Administrator. Section 5.3

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7.1 RACT/RACM Requirements

Section 172(c)(1) of the CAA requires SIPs to provide for the implementation of all reasonably available control measures (R ACM) to demonstrate attainment as expeditiously as practicable. A subset of RACM is reasonabl y available control technology (RACT), whic h relates specifically to stationary point sources. RACM and
RACT are discussed in Section 5.5.

7.2 Reasonable Further Progress

Reasonable further progress (RFP) is a set of control measures with milestones that demonstrates that an attainment plan is being implemented on schedule. However, 40 CFR 51.1009(b) states the following: “If the State submits to EPA an attainment demonstration and State implementation plan for an area which demonstrates that it will attain the PM NAAQS within five years of the date of designati on, the State is not required to submit a separate RFP plan. Compliance with the emission reduction
measures in the attainment demonstration and State implementation plan will meet the requirements for achieving reasonable further progress for the area.” This SIP demonstrates attainment of the NAAQS within five years of designation, so no RFP plan is required.

7.3 Actual Emissions Inventory

Section 172(c)(3) requires the development of statewide inventorie s of emissions of direct PM2.5 and PM2.5 precursors. Such in ventories were due to EPA three years after designation of the PM2.5 nonattainment areas , or by April 5, 2008. The Georgia EPD met this requirement through the submittal of the 2002 emission inventories under the Consolidated Emission Reporting Rule (CERR). The final emission inventories used in the attainment demonstration will go thr ough the public hearing process with the full attainment demonstration, which will include any updates or revisions that are necessary
since the CERR submittal.

7.4 Periodic Emissions Inventory

Section 172(c)(3) requires periodic inventor y submittals. The Georgia EPD met this requirement by submitting the CERR inventory to EPA on June 1, 2007.

7.5 Permit Program Requirements

Section 172(c)(4) requires the id entification and quantification of emissions that will be allowed under the provisions of Section 173(a) (1)(B), pertaining to zones identified by the Administer to which economic developmen t should be targeted. Georgia has no such zones targeted for economic developm ent identified by the Administrator.

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Section 172(c)(5) requires a permit program cons istent with the requirements of Section 173. Georgia EPD updated rules for nonattain ment new source review (NNSR) effective July 25, 2007. These provisions can be found at 391-3-1-.03(8) of the Georgia Rules for Air Quality Control. GA EPD has met th e permit program requirements for a PM2.5 nonattainment area. GA EPD is currently comp lying with the federa l nonattainment new source review regulations as promul gated on May 16, 2008. This includes implementation of nonattainment NSR under 40 CFR 51, Appendix S, until Georgia has
amended its nonattainment New Source Review requirements consistent with this rulemaking.

7.6 Other Measures

Section 172(c)(6) requires the nonattainment SIPs to include enforceable limitation and other control measures, along with schedules for compliance as needed to demonstrate
attainment. Section 5 of this document discu sses in detail the Federal and State measures that are necessary for attainment.

7.7 Compliance with Section 110(a )(2) (Infrastructure Elements)

Section 172(c)(7) requires nonatta inment SIPs to meet the applicable provisions of Section 110(a)(2). Georgia EPD has submitted a letter dated July 23, 2008, to Mr. James I. Palmer, Jr., Regional Administrator for EP A Region 4, certifying that its existing SIP contains provisions that address the requirements for the 1997 PM2.5 NAAQS for Section 110(a)(2). Georgia EPD submitted supplemental information in a letter dated September 22, 2008, regarding the requirement s for Section 110(a)(2)(G). A copy of
these letters can be found in Appendix N. EPA agreed with EPD’s cer tification and published a finding of completeness in 73 FR 62902 on October 22, 2008.

7.8 Equivalent Techniques

The GA EPD believes that the procedures for modeling, emissions inventory and planning follow the U.S. EPA guidance and is not requesting approval for equivalent techniques, as envisione d under Section 172(c)(8).

7.9 Contingency Measures

The Clean Air Act requires States containi ng nonattainment areas to adopt contingency
measures that will take e ffect without further action by the State or U.S. EPA upon a determination by the U.S. EPA that an area failed to demonstrate Reasonable Further Progress (RFP) or to timely attain the a pplicable NAAQS, as described in Section 172(c)(9). These measures ar e discussed in Section 5.3.

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Section 8. 2009 Motor Vehicle Emissions Budget

8.1 Background

An attainment demonstration is a control stra tegy SIP revision. As such, it establishes a motor vehicle emissions budget (MVEB) for the nonattainment area. A motor vehicle emissions budget is described in EP A's transportation conformity rule 1 as "...the implementation plan's estimate of future [m otor vehicle] emissions." Such budgets establish caps on motor vehicle emissions; proj ected emissions from transportation plans and programs must be equal to or less th an these caps for a positive conformity
determination to be made.

8.2 Establishment of 2009 Motor Vehicle Em issions Budgets for the Georgia Part of the Chattanooga PM2.5 Nonattainment Area.

In preparation for this Chattanooga PM2. 5 Attainment Demonstration, EPD worked closely with the Georgia Department of Transportation (GDOT); the Tennessee Department of Environment and Conserva tion; the Chattanooga-Hamilton County Air Pollution Control Bureau; and the Cha ttanooga-Hamilton County/North Georgia Transportation Planning Organization (TPO), the metropolitan pla nning organization for the Chattanooga area; to develop th e best possible estimates of mobile 2 source emissions for the Chattanooga nonattainme nt area. Mobile source inventories for 2009 were developed using the latest av ailable planning assumptions, 3 the most recent travel demand model, and EPA's latest motor vehicl e emission factor model, MOBILE6.2.03.

To establish a separate motor vehicle em issions budget for the Georgia part of the Chattanooga nonattainment area (Catoosa a nd Walker Counties), mobile source emissions inventories were developed using a link-based emissions estimation procedure for the Georgia areas included in the TPO's travel demand model (a ll of Catoosa County, part of Walker) and an off-model methodology for the remainder of Walker County (see Appendix J). The methodology used to calcula te the highway mobile source emissions on which the 2009 MVEB is based is discussed below and, in greater detail, in Appendix J.

The MOBILE6.2 motor vehicle emission f actor model was used to calculate 2009 emission factors with all proposed 2009 mob ile source control rules in place. The
emission factors reflect all federal controls , e.g., the Federal Motor Vehicle Control Program including Tier 1 and (beginning with 2006 models) Tier 2 tailpipe standards; and the National Low Emission Vehicle program.

1 40 CFR Part 93.124(a)

2 The term "mobile" is used to describe emissions from on-road motor vehicles.

3 (e.g., separate emission factors for the Tenness ee, Georgia, and Alabama parts of the Chattanooga nonattainment area; updated VMT and speeds from the latest Chattanooga travel demand model)

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The emission factors resulting from the MOBILE6.2 runs for the Georgia part of the Chattanooga nonattainment area were used with a link-based emissions estimation procedure to calculate 2009 tons-per-y ear emissions in the following manner:

• For each of four times of day (a.m. peak, midday, p.m. peak, and night), the HPMSadjusted 4 2009 VMT from each Georgia link in the Chattanooga travel demand model were multiplied by the 2009 MOBILE6.2 emission factor at the average speed closest to the congested-fl ow speed on that link.

• Emissions from all the links and all four time periods were summed together to get grams-per-day inventories, which were c onverted from grams-per-day to tons-perday, then multiplied by 365 to get tons-per-year.

For the part of Walker Count y not included in th e travel demand model, the "donut" area, an off-model technique was used to estimate daily VMT, which were then multiplied by emission factors reflecting the appropriate speeds to get daily emissions, and by 365 to get annual emissions.

These mobile source inventories reflect the most up-to-date mobile modeling assumptions, including 2009 VMT projected from a state-of-the-art travel demand model
and 2009 emission factors from EPA's latest mobile source emission factor model.
Details of the mobile source emissi ons modeling methodology, along with the MOBILE6.2 input files, emissi on factors, and other documen tation, are in Appendix J.

Table 8-1 sums the calculated emissions inve ntories for the Catoosa and Walker County portions of the Chattanooga PM2.5 nonattainment area.

Table 8-1. 2009 PM2.5 Motor Vehicle Emissions Budget

for Georgia Part of Chattanooga Nonattainment Area

PM2.5 (tons/ y ear) 2009 Mobile Emissions for Catoosa County 31.8891 2009 Mobile Emissions for TPO Portion of Walker County 7.5862 2009 Mobile Emissions for Walker County Donut Area 16.7573 Total, 2009 Motor Vehicle Emissions Budget 56.2326

This Chattanooga PM2.5 Attainment Demonstration hereby establishes a 2009 motor vehicle emissions budget of 56.2326 PM2.5 tons per year for the Georgia part of the Chattanooga PM2.5 nonattainment area. In teragency consultation among the relevant agencies occurred during the development of th is MVEB and prior to the submittal of this Chattanooga PM2.5 Attainment Demonstration. Full implementation plan documentation was provided to EPA, and EPA's stated concerns have been addressed.

4 The VMT from the travel demand model were adjusted based on the VMT estimates that GDOT develops for the Highway Performance Monitoring System (HPMS).

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The MVEB established by this Chattanooga PM2.5 Attainment Demo nstration is based on new estimates of VMT and speeds from an updated, state-of-the-art travel demand model; on the latest version of EPA's motor vehicle emi ssion factor model; and on a registration distribution by age de veloped using registration data 5 obtained from R.L. Polk & Company.

To show the MVEB "together with all other emissions sources 6 ," point, area, and nonroad emissions from the 2009 "best-and-fina l" modeling inventor ies developed by the
VISTAS/ASIP contractors were used. Tota l projected 2009 annual PM2.5 emissions for the Georgia part of the Cha ttanooga nonattainment area are s hown in Table 8-2 below.

Table 8-2. Georgia Part of Chattanooga Nonattainment Area Total Projected 2009 PM2.5 Emissions, Tons/Year

Point Nonroad Area Mobile

Catoosa 0.0000 33.1000 745.0521 31.8891

Walker 1.0601 14.7000 1,133.3721 24.3435 total
Georgia part of Chattanooga nonattainment area totals 1.0601 47.8000 1,878.4242 56.2326 1,983.5169

5 Registration data was from R. L. Polk & Co.'s National Vehicle Population Profile ® current as of
October 2002 and from R. L. Polk & Co.'s TIPNet ® current as of March 2003.

6 Section 93.118(e)(4)(iv) of the transportation conformi ty rule requires that the "motor vehicle emissions budget(s), when considered together with all other emissions sources, is consistent with applicable requirements for...attainment...."

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Section 9. Summary and Conclusion

This section summarizes and concludes the results of the modeled attainment test documented in this SIP revision. EPA designated the Chattanooga area as nonattainment for the PM2.5 NAAQS on January 5, 2005, a nd confirmed the designation on April 5, 2005. The Chattanooga PM2.5 nonattainment area is made up of Catoosa and Walker Counties in Georgia, Hamilton County in Tennessee, and the portion of Jackson County
in Alabama that is described as U.S. Census 2000 block group identifier 01–071-9503–1. The observed PM2.5 design value at three of the four nonattainment area monitors violated the annual standard, as shown in Table 9-1 below.

Table 9-1. 2001 – 2003 PM2.5 Design Values for Chattanooga Nonattainment Area
Monitors

Site name Monitor ID PM 2.5 Design

Value (μg/m 3 ) PM 2.5 Standard (μg/m 3 )

Siskin Drive/UTC,

Tennessee 470654002 15.2 15.0

Maxwell
Road/East Ridge,

Tennessee 470650031 16.1 15.0

Soddy-Daisy High School,

Tennessee 470651011 14.1 15.0

RossvilleMaple St., Georgia 132950002 15.5 15.0

Speciated monitoring data from the Rossville-Map le Street site in Georgia and the Siskin
Drive/UTC site in Tennessee have shown th at the most significant components of PM2.5 are sulfates and organic carbon. Sulfates are formed primarily from sulfur dioxide emissions from coal-fired electric genera ting units (EGUs), including EGUs located
inside the nonattainment area and other more distant EGUs located both inside and outside of Georgia.

Control measures that may be credited in the SIP toward attainment are those that have been or will be implemented after 2002 but before 2009 (the modeled attainment year). Because of the large contribution of sulfat es to the ambient PM2.5 problem in the nonattainment area, the most important measur es for achieving attainment are those that reduce SO2 emissions from coal-fired EGUs. Georgia’s Multipollutant Rule [391-3-1-

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.02(2)(sss)], enacted in 2007, is expected to ach ieve the needed reductions by reducing
SO2 emissions from EGUs. The total estimat ed reduction of SO2 emissions from seven EGUs between 2002 and 2009 is equivalent to 29.1 percent of pr ojected 2009 statewide SO2 emissions (744,000 tons/yr) if no controls were assumed. Other modeled state and federal control measures, as well as unm odeled measures, will contribute to PM2.5
reduction to a smaller extent.

Models were used to demonstrate attainment of the PM2.5 standard and the modeling was performed per EPA guidance. The modeling was performed in conjunction with the regional haze modeling bei ng done by VISTAS and the PM 2.5 and ozone modeling being done by ASIP. The models used were as follows:

• Meteorology: MM5

• Emissions: SMOKE

• Air Quality: CMAQ

An emissions inventory for the modeling was developed for the baseline year 2002 and projected to 2009. The pollutant s inventoried include VOC, NOx, PM 2.5 , coarse particulate (PM 10 ), NH 3 , and SO 2 . The projection to 2009 accounted for control measures expected to be implemented.

The CMAQ modeling performance was meas ured by accepted methods. Overall, GA EPD believes that the model predictions can be used to form the scientific foundation for proposed control options because its performan ce is well within the limits of acceptable performance criteria es tablished in the Attainment Modeling Guidance.

To demonstrate attainment, the models were used to perform the SMAT test at both of
the federal reference monitor sites. The SM AT test projects future concentrations of individual PM species and then sums the results to project total PM2.5. The 2009- modeled PM2.5 design values are provided in Table 9-2. Since each of the modeled design values is less than 15.0 μg/m 3 , modeled attainment is demonstrated.

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Table 9-2. PM 2.5 2009 Future (Modeled) Design Values

Federal
Reference
Monitor Monitor ID 2009 Annual Design Value (DVF) (μg/m 3 ) PM 2.5 Standard (μg/m 3 )

Siskin Drive/UTC,

Tennessee 470654002 13.6 15.0

Maxwell
Road/East Ridge,

Tennessee 470650031 14.4 15.0

Soddy-Daisy High School,

Tennessee 470651011 12.3 15.0

RossvilleMaple St., Georgia 132950002 13.9 15.0

In conclusion, this revision to Georgia’s State Implementation Plan, in accordance with regulatory requirements and guidance provided by EPA, provides a comprehensive technical analysis that de monstrates that the Chatta nooga PM2.5 Nonattainment Area will attain the annual PM2.5 NAAQS on or before April 5, 2010. This demonstration is based upon implementation of the control measures described previously in this narrative and will demonstrate attainment at the FRM monitors located in the Chattanooga
Nonattainment Area.

161 of 162References PM2.5 Attainment SIP – Catoosa and Walker Counties, GA

Section 10. References

Boylan, J.W. and Russell, A.G. (2006) PM and Light Extinction Model Performance Metrics, Goals, and Criteria for Three-Dimensional Air Quality Models; Atmos. Environ.

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Emery, C., E. Tai, and G. Yarwood, 2001: Enhanced meteorological modeling and performance evaluation for two Texas episodes, A report prepared for the Texas Natural Resources Conservation Commission (TNRCC) , ENVIRON International Corporation, Novato, CA.

EPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Oz one, PM2.5 and Regional Haze. U.S. Environmental Protection Agency, Resear ch Triangle Park, NC. EPA-454/B-07-002. April.

IMPROVE, 1995. IMPROVE Data Guide - A Guid e To Interpret Data. University of California Davis. Prepared for National Park Service, Air Quality Research Division, Fort Collins, CO. Data located at <http://vista.cira.colostate.edu/ improve/Data/IMPROVE/improve_data.htm>.

Mass, C. F., D. Ovens, K. Westrick, B. A. Colle, 2002, Does increasing horizontal resolution produce more skillful forecast. Bulletin of American Meteorological Society, 83, 407-430.

Morris, R.E., B. Koo, B. Wang, G. Stella, D. McNally, C. Loomis, C-J. Chien, and G. Tonnesen. 2007. Technical Support Docume nt for VISTAS Emission and Air Quality Modeling to Support Regional Haze State Impl ementation Plans (Draft Final Report). ENVIRON International Corporation, Novato, California. November 14.

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