Georgia's State Implementation Plan 

for the 

Chattanooga PM2.5 Nonattainment Area 

For Catoosa and Walker Counties 

October 16, 2009 

Prepared by: 

Georgia Department of Natural Resources 

Environmental Protection Division 

Air Protection Branch 
Table of Contents PM2.5 Attainment SIP 

 – Catoosa and Walker Counties, GA 

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Table of Contents PM2.5 Attainment SIP 

 – Catoosa and Walker Counties, GA 

PREFACE 

This document is Georgia’s portion of the State Implementation Plan (SIP) for attainment 

of the Federal National Ambient Air Quality Standard (NAAQS) for PM2.5 in the 

Chattanooga nonattainment area. This area consists 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 

Georgia Air Protection Branch, the Chattanooga-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 PM2.5 NAAQS State 

Implementation Plan (SIP) for each jurisdiction. Specifically, this SIP demonstrates 

attainment of the annual PM2.5 NAAQS, promulgated in 1997, in the Chattanooga 

nonattainment area by April 5, 2010. 

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Table of Contents PM2.5 Attainment SIP 

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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 PM2.5 ................................ 21

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

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

2.2 PM2.5 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 Reversal 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 and 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 Utility 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 - Early 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 for the Georgia Part 

of the Chattanooga PM2.5 Nonattainment Area. .................................................. 156

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

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

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 Of 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 Visibility in the VISTAS Region 

E Insignificance of NH3, VOCs, and NOx to PM2.5 Attainment in 

Georgia Nonattainment Areas 

F Modeling Protocol For Association for Southeastern Integrated 

Planning (ASIP) Emissions and Air 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) Emissions 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) Requirements (SIP Infrastructure 

Elements): Communications dated 7-23-08 and 9-22-08 

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List 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 (arithmetic 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 PM2.5 concentrations at 13-295-0002 ....................... 31 

Figure 2-7. Quarterly averages of PM2.5 concentrations at 47-065-0031 ....................... 32 

Figure 2-8. Quarterly averages of PM2.5 concentrations at 47-065-1011 ....................... 32 

Figure 2-9. Quarterly averages of PM2.5 concentrations at 47-065-4002 ....................... 33 

Figure 2-10. Trend of annual total and speciated PM2.5 concentrations (arithmetic 

average of daily averages) at the Rossville PM2.5 Speciation monitor (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 concentrations at the Rossville PM2.5 Speciation 

site in the Chattanooga NAA (13-295-0002).................................................................... 35 

Figure 2-13. Quarterly averages of NO3 concentrations at the Rossville PM2.5 Speciation 

site in the Chattanooga NAA (13-295-0002).................................................................... 36 

Figure 2-14. Quarterly averages of NH4 concentrations at the Rossville PM2.5 Speciation 

site in the Chattanooga NAA (13-295-0002).................................................................... 37 

Figure 2-15. Quarterly averages of OC concentrations at the Rossville PM2.5 Speciation 

site in the Chattanooga NAA (13-295-0002).................................................................... 38 

Figure 2-16. Quarterly averages of EC concentrations at the Rossville PM2.5 Speciation 

site in the Chattanooga NAA (13-295-0002).................................................................... 39 

Figure 2-17. Trend of annual total and speciated 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 

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List of Figures PM2.5 Attainment SIP 

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Figure 2-24. Seasonal patterns in PM2.5 concentrations and composition in Atlanta (Site 

ID 13-089-0002) for 2003-04 (MMO – Major Metal Oxides; Other – PM2.5 other than 

sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon)..................... 46 

Figure 2-25. Seasonal patterns in PM2.5 concentrations and composition in Chattanooga 

(Site ID 47-065-4002) for 2003-04 (MMO – Major Metal Oxides; Other – PM2.5 other 

than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon). ............ 46 

Figure 2-26. Seasonal patterns in PM2.5 concentrations and composition in Floyd county (Site 

ID 13-115-0005) for 2003-04 (MMO – Major Metal Oxides; Other – PM2.5 other than sulfate, 

nitrate, ammonium, MMO, elemental carbon and organic carbon). ..................................... 47 

Figure 2-27. Seasonal patterns in PM2.5 concentrations and composition in Macon (Site 

ID 13-021-0012) for 2003-04 (MMO – Major Metal Oxides; Other – PM2.5 other than 

sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon)..................... 47 

Figure 2-28. Average factor contributions to PM2.5 (μg/m3) for 2002-06 at the 

Chattanooga STN site using PMF..................................................................................... 49 

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

Figure 2-30. Sensitivities of PM2.5 by pollutant and source category for the summer 

episode in Chattanooga. ΔPM2.5 is based upon a 30% reduction of pollutant levels from 

2009 levels. ....................................................................................................................... 54 

Figure 2-31. Sensitivities of PM2.5 by pollutant and source category for the winter 

episode in Chattanooga. ΔPM2.5 is based upon a 30% reduction of pollutant levels from 

2009 levels. ....................................................................................................................... 55 

Figure 2-32. Annual average emission sensitivities by pollutant and source category 

based on the average response of the summer and winter episodes in Chattanooga........ 56 

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. ..................................................................................................................... 57 

Figure 2-34. SANDWICH Speciated Concentrations 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 Response 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 modeled 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 

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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 STN Soccer Plots ............................................................................ 93 

Figure 4-3. VISTAS STN Bugle Plots.............................................................................. 94 

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

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................ 95 

Figure 4-6. FRM, STN, IMPROVE, CASTNET, NADP, and SEARCH monitoring sites 

located in and near the GA PM2.5 nonattainment areas (left) and the Chattanooga PM2.5

nonattainment area (right)................................................................................................. 97 

Figure 4-7. Stacked bar plots of daily PM2.5 species concentration at 47-065-4002...... 108 

Figure 5-1. Chattanooga Contingency Area of Influence............................................... 124 

Figure 6-1. Grid cells for which the air quality 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 Chattanooga compared to statewide EGU SO2 

emissions from Georgia and neighboring states. ............................................................ 150 

Figure 6-5. Modeled 2009 Annual Mean PM2.5 Concentration near the Chattanooga 

Nonattainment Area on the VISTAS 12km Grid............................................................ 152 

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List of Tables PM2.5 Attainment SIP 

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LIST OF TABLES 

Table E-1. Annual PM2.5 Design Values ......................................................................... 16 

Table E-2. Projected SO2 Emission Reductions from New Controls on Georgia Power 

EGUs in 2008 and 2009.................................................................................................... 19 

Table E-3. PM2.5 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 PM2.5 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 PM2.5 composition during 2003-2004 for the four STN sites in the 

non-attainment areas (μg/m3)............................................................................................ 48 

Table 2-6. Estimated primary and secondary percentage of PM2.5.................................. 48 

Table 2-7. Summary of average input parameters for each CART classification Category 

in Chattanooga. ................................................................................................................. 51 

Table 2-8. Summary of average input parameters for key bins for CART classification 

(Class) categories 3 and 4 in Chattanooga........................................................................ 52 

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

Areas. Reductions are negative........................................................................................ 61 

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

Table 3-2. Typical 2002 BaseG2 Annual 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 for 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 PM2.5 Component 

Species .............................................................................................................................. 91 

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Table 4-5. Statistical Metric Calculations......................................................................... 92 

Table 4-6. Ambient monitoring data available in 2002. .................................................. 96 

Table 4-7. Annual model performance statistics for PM2.5 at all FRM monitors in the 

State of Georgia and in the Chattanooga nonattainment area........................................... 99 

Table 4-8. Monthly model performance statistics for PM2.5 at 13-295-0002................. 100 

Table 4-9. Monthly model performance statistics for PM2.5 at 47-065-0031................. 101 

Table 4-10. Monthly model performance statistics for PM2.5 at 47-065-1011............... 102 

Table 4-11. Monthly model performance statistics for PM2.5 at 47-065-4002............... 103 

Table 4-12. Annual model performance statistics for PM2.5 species and total PM2.5 at all 

STN monitors in the State of Georgia............................................................................. 105 

Table 4-13. Annual model performance statistics for PM2.5 at 47-065-4002. ................ 106 

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

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

Table 5-3. Georgia Multipollutant Rule SO2 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 Emissions, 2002 to 2009, Statewide (tons) ....................... 120 

Table 5-8. Predicted changes in SO2 emissions inside the Georgia Portion of the 

Chattanooga Nonattainment Area, 2002 to 2009............................................................ 122 

Table 5-9. Predicted changes in SO2 emissions 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 Chattanooga Area of Influence........................... 126 

Table 5-12. 2012 SO2 Emissions For Chattanooga 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 generating units outside the Chattanooga 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 Quarterly and Annual Mean PM2.5 DVB (in μg/m3

) for Monitors 

in the Chattanooga Nonattainment Area......................................................................... 135 

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

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Table 6-4. Quarterly PM2.5 Component-Specific DVB (μg/m3) for Monitors in the 

Chattanooga Nonattainment Area................................................................................... 139 

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

Table 6-6. Quarterly Component RRFs for Monitors in the Chattanooga Nonattainment 

Area................................................................................................................................. 141 

Table 6-7. Quarterly Component Future Design Values (in μg/m3) 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 Concentration 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 Results 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 μg/m3) for the Monitors in the Chattanooga 

PM2.5 Nonattainment Area. ........................................................................................... 149 

Table 6-14. Projected SO2 emission reductions from new controls on Georgia Power 

EGUs in 2008 and 2009.................................................................................................. 149 

Table 7-1. General Plan Requirements 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. PM2.5 2009 Future (Modeled) Design Values .............................................. 161 

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Acronyms PM2.5 Attainment SIP 

 – Catoosa and Walker Counties, GA 

Acronyms 

ASIP Association of Southeastern Integrated Planning 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CMAQ Community Multiscale 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 available 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 Kernel Emissions modeling system 

SOA Secondary Organic Aerosols 

STN Speciated Trends Network 

VISTAS Visibility Improvement State and Tribal Association of the Southeast 

VOC Volatile organic compound 

VMT Vehicle miles traveled 

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Executive Summary PM2.5 Attainment SIP 

- Catoosa and Walker Counties, GA 

Executive Summary 

This document is Georgia’s state implementation plan (SIP) for attainment of the 1997 

PM2.5 National Ambient Air Quality Standards 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 concentration of 15.0 micrograms per cubic meter (μg/m3) or 

less. The nonattainment area designation was based upon 2001 - 2003 design values at 

the following monitors: 

Table E-1. Annual PM2.5 Design Values 

PM2.5 Design Value (μg/m3) 

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 measures 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 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 identifier 01-071-9503-1. Although Jackson 

County is not contiguous to the rest of the nonattainment area, a coal-fired power plant 

located in this census tract is a major contributor to the PM2.5 problem. Land utilization 

in this area includes both urban (City of Chattanooga) and suburban land uses. 

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PM2.5 is made up of a number of component 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 emission. 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 monitor. 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. Atmospheric 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 formed 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. Nitrates are measured at essentially the same 

percentage as elemental carbon, and ammonia is only slightly higher. 

The control measures needed to reach attainment 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 SO2 (and, therefore, sulfates) from 

EGUs. As mentioned above, biogenic formation 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 measures include the following: 

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• Clean Air Interstate Rule 

• Tier 2 Vehicle Standards 

Diesel Highway Vehicles Standards 

ule 

es and Recreational Engines Standard 

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

ther federal measures provide for reductions 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 detail 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 standard as expeditiously as possible. Per 

• Modeling results, and 

ion analyses. 

Air quality modeling conducted by Georgia EPD indicates that the 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 are expected to achieve reductions in 

P

• Georgia’s open burning rule that restricts 

• Planned early start-up of SO2 scrubbers at two EGUs (at Plant Bowen – Unit 2 

and at Plant Wansley – Unit 2). 

measures are discussed in more

b

For each nonattainment area, the State must sub

sh

40 CFR 51.1007, the attainment demonstration must include: 

• Inventory data, 

• Emission reduct

w

projected SO2 emissions reductions from EGU sources within the state. Georgia Power’s 

projected SO2 emission reductions are listed in Table E-2 below. 

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Table E-2. Projected SO2 Emission Reductions from New Controls on Georgia 

Power EGUs in 2008 and 2009. 

Facility Unit Control Date1 2007 SO2 

Emissions2

(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 dates. Dates 2009 or later are company-provided 

forecast installation dates. 

(2) Calculated from continuous emissions monitor data.

Since it is performed on the basis of individual PM species (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). Then a historical baseline emissions inventory (based on 

“typical” emissions and meteorology) and the 2009 projected emissions inventory are 

input to the model to find PM2.5 species concentrations for both years. The ratio of the 

future predicted average annual concentration to the historical baseline average annual 

concentration, known as the relative response factor (RRF), is calculated for each species. 

The 2002 observed design value (baseline design 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 modeled values for the individual species are then 

summed to obtain the overall 2009 modeled design value. This SMAT process is 

repeated for each federal reference monitor 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/m3

, modeled attainment is demonstrated. 

Supplemental analyses (in addition to SMAT) are presented that also support the 

demonstration of attainment. 

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Table E-3. PM2.5 2002 Baseline Design Values and 2009 Future (Modeled) Design 

Values 

Federal 

Reference 

Monitor 

Site ID 2001-2003 Annual 

Design Value (DVF) 

(μg/m3)

2009 Annual 

Design Value 

(DVF) 

 (μg/m3) 

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 Particle 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 controls. 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 states to submit a Reasonable Further Progress (RFP) plan for 

any PM2.5 SIP that targets attainment more than five years after the date of 

nonattainment designation. The Chattanooga Nonattainment 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 Demonstration 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 material to support the SIP narrative. 

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Section 1. Introduction 

1.1 Fine Particles

Fine particles in the atmosphere, which may be solid or liquid, are comprised of a 

complex mixture of components. Common constituents include: sulfate (SO4); nitrate 

(NO3); ammonium (NH4); elemental carbon; a great variety of organic compounds; and 

inorganic material (including metals, dust, sea salt, and other trace elements) generally 

referred to as “crustal” material, although it may contain material 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 PM2.5). 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 liquid particles (e.g., elemental carbon from diesel 

engines or fire activities, or condensable organic particles from gasoline engines). 

“Secondary” particles (e.g., sulfate and nitrate) form in the atmosphere from emissions of 

gaseous precursors (e.g., SO2 and NOx) that subsequently undergo chemical reactions to 

form particulate matter. Secondary organic 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 PM2.5 based 

on evidence from numerous health studies demonstrating that serious health effects are 

associated with exposures to elevated levels of PM2.5. Epidemiological studies have 

shown statistically significant correlations between elevated PM2.5 levels and premature 

mortality. Other important effects associated with PM2.5 exposure include aggravation 

of respiratory and cardiovascular disease, changes in lung function and increased 

respiratory symptoms, as well as new evidence for more subtle indicators of 

cardiovascular health. Individuals particularly sensitive to PM2.5 exposure include older 

adults, people with heart and lung disease, and children. [Source: Preamble to Clean Air 

Fine Particle Implementation Rule – Final Rule, April 25, 2007.] 

1.2 1997 National Ambient Air Quality Standards for PM2.5 

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

Table 1-1, 1997 PM2.5 NAAQS 

Description Standard (μg/m3

) 

Annual arithmetic mean concentration1 ≤ 15.0 

24-hour average concentration, 98th percentile1 ≤ 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 spatially-averaged sites) is in attainment of 

the annual PM2.5 standard when the annual standard design value is less than or equal to 

15.0 micrograms per cubic meter (μg/m3).

 The annual standard design value is the 3-

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year average of the annual mean, which is in turn the average of one year’s four calendarquarter averages. A monitoring site (or group of spatially-averaged sites) is in attainment 

of the 24-hour PM2.5 standard when the 24-hour standard design value is less than or 

equal to 65 μg/m3.

 The 24-hour standard design value is the three-year average of annual 

98th percentile 24-hour averages. The annual 98th percentile 24-hour average is the 24-

hour average PM2.5 concentration that is higher than 98 percent of the year’s valid 24-

hour averages. Therefore, to achieve attainment of the 1997 PM2.5 standards by a given 

date, the annual and 24-hour standard design values 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 achieve 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 PM2.5 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 Tennessee), Floyd County, Atlanta, and Macon. 

Figure 2.1 shows their locations. 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 PM2.5 nonattainment area 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 portion 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 

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County is not contiguous to the rest of the nonattainment area, a coal-fired power plant 

located in this census tract is a major contributor to the PM2.5 problem. 

Land utilization in Catoosa and Walker Counties includes both urban, suburban and rural 

land uses. The population of Catoosa County 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 monitoring stations, major PM2.5 sources, 

PM2.5 precursors, the conceptual description of the PM2.5 problem, and ambient PM2.5 

trends are described in this section. 

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2.1 Nonattainment Area Designation

EPA, based on data submissions from states 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 sources that contribute to the nonattainment 

status of a county. Nonattainment counties are grouped together into nonattainment areas 

based on location within common metropolitan statistical areas (MSAs). EPA made 

initial designations of the nonattainment areas, including the Chattanooga nonattainment 

area, for the PM2.5 NAAQS on January 5, 2005 (70FR944), and amended the 

designations on April 5, 2005 (70FR19844). The Georgia portion of the Chattanooga 

PM2.5 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 Metropolitan Statistical Area. The designation 

of Catoosa County as part of the Chattanooga 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 Counties. The nine factors are as follows: 

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

nonattainment area 

 

• Factor 2: Air quality in potentially included versus excluded areas 

• Factor 3: Population density and degree of urbanization including 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 criteria 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 PM2.5 Monitoring Stations

Georgia currently has one ambient PM2.5 monitoring site in the Chattanooga, TennesseeGeorgia nonattainment area, as shown in Table 2-2 and Figure 2.3. The Rossville-Maple 

Street site is in Walker County. As part 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, which 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 objective of sampling for population 

exposure. 

 

One PM2.5 Federal Reference Method (FRM) monitor and one PM2.5 speciation monitor 

are located at the Rossville-Maple Street site (13-295-0002). The PM2.5 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 construction of the Rossville Health Department 

sampling location, this monitor was moved from the Rossville Health Department site to 

its current location on Maple Street. The sampler was temporarily shut down on June 11, 

2007, and restarted on November 14, 2007. During this time, EPD actively searched and 

found a location to set up the monitoring equipment within the required distance to keep 

the same AQS identification. The PM2.5 speciation monitor has been collecting data 

since March 23, 2005. The sampling schedule of the PM2.5 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 

PM2.5 Design 

Value 

 (μg/m3) 

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 PM2.5 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 speciation monitor sites located within the 

Chattanooga nonattainment area. The evaluations include long-term trends of PM2.5 

levels obtained from the FRM monitors and the speciation monitor. Both trends in 

annual averages and quarterly averages are evaluated. For the speciation monitor, trends 

in the different PM2.5 species measured are analyzed and are compared to speciation data 

from other PM2.5 nonattainment areas in Georgia. Receptor modeling results are 

presented to provide an insight into the relative amount of ambient PM2.5 attributable to 

different emission source categories. CART analysis was performed to examine the 

relationship between meteorology and air quality. Lastly, sensitivity modeling was 

conducted to examine the response in predicted 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 recent 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 (13-295-0002, 47-065-0031, 47-

065-1011, and 47-065-4002) that have been collecting data since as early as 1999 and 

two PM2.5 Speciation monitors, 13-295-0002 and 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 estimated after excluding exceptional events, 

as concurred with by EPA. Figure 2-4 shows changes of annual PM2.5 concentrations 

(arithmetic average of quarterly averages) from 1999 to 2008. Overall, PM2.5 

concentrations have decreased for this time 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. The 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 concentrations compared with 2002, and the PM2.5 

concentrations fluctuated a bit within the second period. For the period (2005-2007), 13-

295-0002 and 47-065-4002 showed a steady decrease of PM2.5 concentrations from year 

to year, while 47-065-0031 and 47-065-1011 showed an increase in PM2.5 concentration 

in 2007. Generally, 47-065-1011 showed the lowest PM2.5 concentrations in the region 

from 2002 to 2007. For the last 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 Table 2-3. In 2005, the PM2.5 annual mean concentrations were 

16.4 μg/m3, 16.1 μg/m3, 13.8 μg/m3, and 15.5 μg/m3 at 13-295-0002, 47-065-0031, 47-

065-1011, and 47-065-4002. In 2007, the PM2.5 annual mean concentrations were 13.8 

μg/m3

, 14.9 μg/m3, 14.2 μg/m3, and 15.0 μg/m3 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 monitor (13-295-0002). In 2008 the lowest 

annual mean was recorded at the Soddy-Daisy monitor. 

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Figure 2-4. Trend of annual mean (arithmetic 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

(μg/m3) 

Monitor 

47-065-0031

(μg/m3) 

Monitor 

47-065-1011

(μg/m3) 

Monitor 

47-065-4002

(μg/m3) 

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 design values from 2001 to 2008. In general, 

design values decreased steadily at all monitors 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 are 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

(μg/m3

) 

47-065-0031

(μg/m3) 

47-065-1011

(μg/m3) 

47-065-4002

(μg/m3) 

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, PM2.5 concentrations were higher 

in the 2nd and 3rd quarters than the 1st and 4th quarter. This is to be expected due to higher 

temperatures and more photochemistry during these quarters. 

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

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

Figure 2-8. Quarterly averages of PM2.5 concentrations at 47-065-1011 

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

Figure 2-10 shows changes of annual total 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 Speciation site started measuring PM2.5 

species in 2005. With respect to contributions of each species to total PM2.5 

concentrations, OC (ca. 5.0 μg/m3) and SO4 (3.7-5.6 μg/m3) 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 concentration and the recent increase of NO3. For 

this time period, SO4 decreased from 5.6 μg/m3 to 3.7 μg/m3

 and NO3 increased from 0.6 

μg/m3 to 1.0 μg/m3. From 2005 to 2007, NH4 concentrations were around 1.5 μg/m3. 

For the same period, EC concentrations were approximately 0.6 μg/m3

. 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 sixth day. Therefore, unusually high or low 

values on a specific day would be weighted more by the individual species trends than by 

the total PM2.5 trend. In addition, there is always 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, for relocation (within a mile from the 

original location) and restarted November 14, 2007, which may have impacted quarterly 

trends in the fourth quarter between 2006 and 2007. 

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Figure 2-10. Trend of annual total and speciated PM2.5 concentrations (arithmetic 

average of daily averages) at the Rossville 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 concentration for the first 

quarter changed little from 2002 to 2007, except 2003. The second quarter values 

decreased steadily from 2005 to 2007. The third quarter and the fourth quarter showed 

large decreases from 2005 to 2006. There was 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 this site is much longer than the speciation data that follows 

since the speciation monitor did not start operation until 2005.) 

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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 concentrations decreased from 2005 to 2006 except 

the first quarter. Like the PM2.5 trends, quarterly 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 third quarters than the first and fourth quarters. From 2005 

to 2007, NO3 concentrations increased for the 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 concentrations 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 first quarter showed a large increase of OC 

concentration while the second and fourth quarter showed opposite trends. Note that 

there is no OC measurement available 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 concentrations at the Rossville PM2.5 

Speciation site in the Chattanooga 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 little 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 total 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 concentrations, OC (4.4-5.4 μg/m3) and SO4 (2.8-5.4 μg/m3) 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 SO4 in 2004 as well as SO4, NH4, and NO3 from 2001 to 2002. 

From 2006 to 2007, SO4 concentration increased from 4.0 μg/m3 to 4.9 μg/m3

 and OC 

increased from 4.9 μg/m3 to 5.3 μg/m3

. From 2001 to 2007, NH4 concentrations were 

around 1.6 μg/m3. For the same period, EC concentrations were approximately 0.71 

μg/m3

. It should be noted that 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 sixth day. Therefore, unusually high or low values on a specific day would be 

weighted more by the individual species trends than by the total PM2.5 trend. In 

addition, there is always an unidentified amount of speciated PM2.5 (not shown) than 

could be increasing or decreasing. 

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Figure 2-17. Trend of annual total and speciated 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 and fourth quarters showed steady decrease of 

PM2.5 concentrations from 2005 to 2007 while the third quarter displayed the opposite 

trend for the same period. Overall, PM2.5 concentrations 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 quarter. 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 NO3 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 concentrations 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 these two groups showed large year-to-year 

variations. From 2006 to 2007, NH4 concentrations 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 the first quarter from 2003 to 2004 and in the 

third quarter from 2002 to 2004. There was large increase of OC concentrations from 

2006 to 2007 in the first quarter and from 2004 to 2005 in the third quarter. 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 averages of EC concentrations at 47-065-4002. Overall, EC 

concentration in the fourth quarter 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 concentrations increased for all quarters. From 

2004 to 2006, EC concentration decreased in the 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 PM2.5 concentration and composition, GA EPD used 2003-

04 data from the Speciation Trends Network (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 13-115-0005), 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 PM2.5 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 PM2.5 species, followed by 

ammonium (NH4), elemental carbon (EC), nitrate (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 cooler temperatures favor nitrate particle 

formation. Ammonium levels follow those of sulfate and nitrate, as it binds with these 

anions. The similarities in seasonal patterns and composition of PM2.5 in these four 

nonattainment areas are an indication of the regional nature of PM2.5. 

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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-Dec

ug/m3

Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-24. Seasonal patterns in PM2.5 concentrations and composition in Atlanta (Site 

ID 13-089-0002) for 2003-04 (MMO – Major Metal Oxides; Other – PM2.5 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-Dec

ug/m3

Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-25. Seasonal patterns in PM2.5 concentrations and composition in 

Chattanooga (Site ID 47-065-4002) for 2003-04 (MMO – Major Metal Oxides; Other 

– PM2.5 other than sulfate, nitrate, ammonium, 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-Dec

ug/m3

Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-26. Seasonal patterns in PM2.5 concentrations and composition in Floyd county 

(Site ID 13-115-0005) for 2003-04 (MMO – Major Metal Oxides; Other – PM2.5 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-Dec

ug/m3

Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-27. Seasonal patterns in PM2.5 concentrations and composition in Macon 

(Site ID 13-021-0012) for 2003-04 (MMO – Major Metal Oxides; Other – PM2.5

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

carbon). 

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Assessing the relative contribution of primary and secondary species is not trivial, since 

OC is part primary and part secondary. Also, 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 possible to estimate that about 40% of PM2.5 in the 

region is primary, with the remaining fraction being secondary (Table 2.6). 

Table 2-5. Average PM2.5 composition during 2003-2004 for the four STN sites in 

the non-attainment areas (μg/m3) 

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 PM2.5 

Site % primary % secondary 

Atlanta 40 60 

Chattanooga 32 68 

Floyd county 43 57 

Macon 44 56 

Despite the regional nature of PM2.5 and the high fraction of secondary components, there 

is clear evidence of impacts of local sources of primary PM2.5 at some of the sites. Both 

the Macon and Floyd County monitors show higher levels of crustal elements (MMO, 

Table 2-5), compared to Atlanta and Chattanooga (1.47, 1.05, 0.62 and 0.55 μg/m3, 

respectively). The Floyd County monitor also exhibits higher levels of unspecified 

(“Other”) PM2.5, compared to the other three sites. Elemental carbon levels are highest in 

Atlanta (1.0 μg/m3

 compared to 0.5-0.7 μg/m3 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, concentrations of OC (partially secondary) and 

secondary ionic PM2.5 (sulfate, nitrate, ammonium) are fairly similar at all four sites, 

though the Chattanooga site exhibits somewhat higher levels of ammonium nitrate. 

Fairly minor spatial gradients are observed in the Chattanooga NAA, with the 2002 DV 

for the Georgia site (15.5 μg/m3

 at 13-295-0002) being in between the DVs of the two 

Tennessee Hamilton County sites (16.1 μg/m3 at 47-065-0031; 15.2 μg/m3 at 47-065-

4002). 

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2.3.3 Source Apportionment of PM2.5

Receptor models are useful tools for identifying the sources contributing to measured 

levels of pollutants. One of the most recently used source-apportionment models in 

recent years is the Positive Matrix Factorization (PMF) model. PMF is a factor analytic 

method that distinguishes correlation patterns among speciated PM2.5 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. These results are useful for a preliminary 

assessment of control strategies. Given the large contributions to PM2.5 from the 

secondary-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 useful. Unfortunately, these are not yet well 

understood, and limited available knowledge suggests 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 photochemical modeling study and was found to 

be inefficient (see Section 2.4.3). Contributions 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 PM2.5 (μg/m3) for 2002-06 at the 

Chattanooga STN site using PMF 

 

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2.3.4 Meteorology 

ICF International performed a meteorological characterization study designed to examine 

the relationships between meteorology, fine particulates, and regional haze at air quality 

monitoring sites throughout the Southeastern U.S. (Characterization 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 Tree (CART) statistical analysis was used to examine 

relationships between meteorology, fine particulates, and visibility that are important to 

atmospheric modeling. Separate CART analyses were conducted for visibility (as 

defined by the extinction coefficient) and fine particulates (particulate 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 bins) with similar air 

quality (visibility or PM2.5) and meteorological characteristics. Classification accuracy 

ranged from approximately 72 to 85 percent for 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 dependent 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 meteorological 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 extinction—may account for the overall better classification 

accuracy for PM2.5 compared to extinction coefficient. 

The categorical comparisons for PM2.5 in Chattanooga consisted of four categories: 

<17.5 (Category 1), 17.5-25 (Category 2), 25-32.5 (Category 3), and > 32.5 (Category 4) 

μg/m3, respectively. A summary of the average input parameters for each CART 

classification category in Chattanooga is contained 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, Birmingham, and Knoxville; lower wind speeds; 

southerly winds; and lower amounts of rain. In addition, it can be seen that important 

upper-air meteorological parameters leading 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 parameters for each CART classification 

Category in Chattanooga. 

For Chattanooga, there are three 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 μg/m3

) and higher surface 

temperatures. The Category 4 bins (28) are characterized by high prior-day PM2.5 

concentrations in Birmingham, Atlanta, and Knoxville (> 44 μg/m3

). All bins are 

characterized by low wind speeds at the surface 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) categories 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 organic matter, while Bins 21 and 28 have a 

greater proportion of sulfate. 

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In summary, high PM2.5 for Chattanooga is associated with moderate temperatures, high 

PM2.5 on previous day from upwind urban areas (Birmingham, Atlanta, and Knoxville), 

very low wind speeds near the surface and low wind speeds aloft, south-southwesterly 

winds near the surface, and stable conditions. The high PM2.5 classification categories 

are associated with higher SO4 and OM concentrations. 

 

Figure 2-29. Average composition for 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 episode (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 specific subset of counties 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, respectively. In the summertime, the most important 

anthropogenic source category impacting Chattanooga is EGU SO2 followed by nonEGU SO2 and ground-level PC. In the wintertime, the most important anthropogenic 

source category is VISTAS NH3 followed 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

SO2_EGU

SO2_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

ΔPM2.5 (

μg/m

3

)

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. ΔPM2.5 is based upon a 30% reduction of pollutant levels 

from 2009 levels. 

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Chattanooga, TN (470650031) Winter

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

SO2_EGU

SO2_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

ΔPM2.5 (

μg/m

3

)

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. ΔPM2.5 is based upon a 30% reduction of pollutant levels 

from 2009 levels. 

Figure 2-32 contains the annual average sensitivities for each pollutant and source 

category based on the average response of the summer and winter episodes in 

Chattanooga. Figure 2-33 contains the annual average sensitivities for each pollutant and 

source category based on the CART weighted 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-level PC. However, it should be noted NH3 and SO2 

(both of which are gases at ambient temperatures) react to form ammonium 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 should 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_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

ΔPM2.5 (

μg/m

3

)

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 summer 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_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

ΔPM2.5 (

μg/m

3

)

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 discussion of sources of PM2.5 emissions, direct 

PM2.5, and PM2.5 precursors in and near Chattanooga nonattainment area. 

2.4.1 Sources of PM2.5

The primary sources of direct PM2.5 emissions within the boundaries of the area are a 

coal-burning power plant, other point sources, mobile sources, and biogenic sources. 

Sources of PM2.5 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 direct 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 contribute to direct PM 

emissions in the non-attainment area, their reported actual emissions in the CERR show 

that the facility emits 70 tons-per-year on a normal basis. 

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2.4.2 Precursors

The PM2.5 SIP must address both direct PM emissions and secondary PM. As discussed 

previously, secondary PM is formed from emissions of PM precursors. Therefore, it is 

critical to identify the precursors that are relevant to a specific nonattainment area. 

40CFR51.1000 defines a “PM2.5 precursor” as any air pollutant other than direct PM2.5 

that contributes to the formation of PM2.5. Emissions inventories required for attainment 

demonstration must include emissions of all PM2.5 precursors identified for the 

nonattainment area. 

40CFR51.1000 defines a “PM2.5 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 the 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 sources of precursors within the State boundaries must be 

evaluated for emissions reduction measures. However, the preamble to the final rule, 72 

FR 20636, gives EPA’s policies on the geographic 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 emissions throughout the state 

must be evaluated for potential reduction measures. 

The major sources of SO2 emissions in the Catoosa and Walker County portion of the 

non-attainment area come primarily from one industrial source and mobile sources. SO2

emissions from combined industrial and mobile sources within the area are significantly 

less than those from regional coal-fired electric 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 EPD 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 Reversal of NOx Precursor Presumption

The U.S. EPA, in 72 FR 20586, requires states to “evaluate and consider control 

strategies for sources of SO2, direct PM2.5, and NOx emissions in all nonattainment 

areas.” However, the rule does allow states to make technical demonstrations to reverse 

the inclusion of NOx as precursor of PM2.5, or deem the precursor insignificant, based 

on geographical regions, emissions, etc. This then eliminates the need to examine NOx 

control measures for purposes of reducing PM2.5 concentrations. Georgia EPD’s 

analysis, summarized below demonstrates that 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 nonattainment areas were evaluated using FRM 

measurements and speciated data. The “Sulfate, Adjusted Nitrate, Derived Water, 

Inferred Carbonaceous Mass Hybrid” (“SANDWICH”) material balance approach was 

used to speciate the FRM total PM2.5 mass into its components - SO4, NO3, NH4, PBW, 

OC, EC, and crustals. Figure 2-34 contains the annual average PM2.5 concentrations 

averaged across all FRM monitors in each nonattainment area. The measurements show 

that annual average nitrate concentrations (plus ammonium and PBW associated with 

nitrate) collected on the FRM filters range from 0.27 to 0.33 μg/m3, which is only 1.7 to 

2.5 percent of the total FRM mass. These are very small compared to sulfate and OC 

contributions. 

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0

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Atlanta

Macon 

Rome 

Chatta. 

Atlanta

Macon 

Rome 

Chatta. 

Atlanta

Macon 

Rome 

Chatta. 

Atlanta

Macon 

Rome 

Chatta. 

Atlanta

Macon 

Rome 

Chatta. 

 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 Concentrations 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 (SMAT) 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 μg/m3 across all nonattainment areas in Georgia (Figure 2-35). 

In addition, there is a reduction in OC. However, much of the reductions in OC are due 

to reductions in primary emissions of OC and 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

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Clayton-0091

Cobb-0003Cobb-0004

DeKalb-0002DeKalb-2001

Fulton-0032Fulton-0039

Gwinnett-0002

Hall-0003

Paulding-0003

Bibb-0007Bibb-0012

Floyd-0005

Hamilton-1011

Hamilton-31-1

Hamilton-4002

Walker-0002

Monitor Location

ug/m3

EC

OC

NO3

Figure 2-35. Reductions in EC, OC, and 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 maximum predicted PM2.5 reductions are 

approximately 1.2 μg/m3 for 100% statewide NOx removal, 0.4 μg/m3 for 70% removal, 

0.22 μg/m3 for 50% removal (Figure 2-36), and 0.11 μg/m3 for 30% removal (Figure 2-

37). 

Statewide 50% NOx Reduction

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

DeKalb-2001

Fulton-0032Fulton-0039

Clayton-0091

Hall-0003

Gwinnett-0002

Cobb-0003

DeKalb-0002

Paulding-0003

Bibb-0007Bibb-0012

Floyd-0005

Hamilton-4002

Walker-0002

Δ PM2.5 (

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 inappropriate to apply a 100% statewide NOx emission reduction to 

evaluate the significance of NOx emissions on ambient PM2.5 since this is an unrealistic 

control scenario leading to unrealistic atmospheric conditions. In addition, the CB-IV 

chemical mechanism and SOA module used in the CMAQ modeling were developed to 

accurately predict pollutant concentrations under typical ambient conditions. They were 

not intended to be used to make pollutant predictions under the unrealistically low 

oxidizing atmospheric conditions caused by zeroing-out all NOx within a state. Instead, 

GA EPD feels it is more appropriate to evaluate more realistic, yet aggressive, NOx 

reductions from sources across the state. GA EPD conducted a technical analysis looking 

at potential statewide reductions 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

0.04

DeKalb-2001

Fulton-0032Fulton-0039

Clayton-0091

Hall-0003

Gwinnett-0002

Cobb-0003

DeKalb-0002

Paulding-0003

Bibb-0007Bibb-0012

Floyd-0005

Hamilton-4002

Walker-0002

Δ PM2.5 (

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 all 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 nonattainment areas is 2015. Assuming all the 

controls listed for 2030 are achievable by 2014 and replacing the 2030 on-road mobile 

fleet turnover with 2014 on-road mobile emissions, results in a 31% statewide reduction 

in NOx emissions. As a result, GA EPD feels 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 

μg/m3

 across all nonattainment monitors in the state for a 30% statewide NOx reduction 

run (monitors in downtown Atlanta showed a slight increase in PM2.5 formation) and a 

maximum impact of 0.22 μg/m3

 across all nonattainment monitors in the state for a 50% 

statewide NOx reduction run. 

2.4.3.4 CMAQ Model Performance for Organic Carbon 

OC modeling is still developing 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 isoprene, 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 was developed based on SOA formation rates under 

ambient atmospheric conditions, and that SOA formation rates have not been tested under 

the artificially-low oxidant levels that result from zeroing out all NOx emissions in a 

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large state. This introduces additional uncertainty 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 compared to OC observations (black line). 

The model predicts unrealistically high OC predictions in the early morning hours when 

the mixing height is low. It is during these 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 aerosols, 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 insignificant precursor to PM2.5 in Georgia’s 

nonattainment areas. GA EPD feels it is inappropriate 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 the predicted changes in OC resulting from 

NOx reductions since the CMAQ modeling system does not properly simulate hourly OC 

temporal variations and was not designed to predict concentrations under extreme 

reductions in precursor emissions 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 PM2.5. Modeling results 

show a maximum impact of 0.11 μg/m3

 across all nonattainment monitors in the state for 

a 30% statewide NOx reduction scenario and a maximum impact of 0.22 μg/m3 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/m3

 to evaluate interstate 

impacts (e.g., Georgia emissions impacts on non-Georgia PM2.5 monitors). Therefore, it 

would seem reasonable to assume that the significance threshold level for a state on itself, 

for the purpose of reversal of the NOx precursor presumption, should be higher than a 

state on a neighboring state. The proposed Prevention of Significant Deterioration (PSD) 

significant impact levels (SILs) 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 threshold level should set at 

0.3 μg/m3

. 

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Since both the 30% statewide NOx reduction and 50% statewide NOx reduction 

modeling scenarios result in a reduction of PM2.5 impact of less 0.3 μg/m3, EPD has 

clearly demonstrated that NOx is insignificant as a precursor to PM2.5 in the Georgia 

nonattainment areas. 

Organic Carbon in June 2002 @ YRK

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nc

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[ug

/m

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Dif

f 

Co

nc

ent

rati

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[ug

/m

3]

OC_02_Typical OC_09_Typical OC dSOMA dSOMB dPSOM dPOM

Figure 2-38. Hourly measured and modeled 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. 

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Section 3. Attainment Demonstration Methods and Inputs 

The attainment modeling for the Chattanooga PM2.5 nonattainment area was performed in 

conjunction with the regional haze modeling being done by the Southeast regional 

planning organization (RPO), Visibility Improvement State and Tribal Association of the 

Southeast (VISTAS), and the PM2.5 and ozone (O3) modeling being done by the 

Association of Southeastern Integrated Planning (ASIP). VISTAS and ASIP are run by 

the ten Southeast states (Alabama, Florida, Georgia, Kentucky, Mississippi, North 

Carolina, South Carolina, Tennessee, Virginia and West Virginia). Since the regional 

haze and PM2.5 modeling use annual simulations and include an intermediate year that is 

the attainment year required for the Chattanooga PM2.5 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) Mesoscale Meteorological Model (MM5) is 

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

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

modeling studies. 

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

modeling system is an emissions modeling system that generates hourly gridded 

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

capable of addressing O3, particulate matter, visibility, 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 conditions in the Southeast when high ozone, high PM2.5 and poor 

visibility are observed throughout the region. Once the historical year is selected, 

meteorological inputs are developed using the meteorological model. Emission 

inventories are also developed for the historical year and processed through the emissions 

model. These inputs are used in the air quality model to predict ozone, PM2.5 and 

visibility, with the results compared to the historic data. The model performance is 

evaluated by comparing the modeled predicted data to the historic air quality data. 

Once model performance is deemed adequate, typical baseline and future year emissions 

are processed through the emissions model. For this demonstration, the baseline year 

was 2002, which corresponds with the same year as the historic meteorology used in the 

modeling. The attainment future year GA EPD is using for this demonstration is 2009, 

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since the mandatory attainment date for the Chattanooga PM2.5 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 air quality model with the meteorological 

inputs. The air quality modeling results are used to determine a relative reduction in 

future PM2.5, which is used in the attainment demonstration. 

The complete modeling protocol used for this 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 methods. Freely accessible means that model 

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

to stakeholders, and their consultants, for execution and verification at no or low cost. 

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

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

the modeling system used for this modeling attainment demonstration. 

3.2.1 Selection of Air Quality Model

3.2.1.1 Criteria 

 

For an air quality model to qualify as a candidate for use in an attainment demonstration, 

a state needs to show that it meets several general criteria: 

• The model has received a scientific peer review. 

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

• Databases needed to perform the analysis are available and adequate. 

• Available past appropriate performance evaluations have shown the model is not 

biased toward underestimates or overestimates. 

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

• 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 modeling 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-Atmosphere” air quality modeling system 

capable of addressing ozone, fine particulate matter, visibility and acid deposition within 

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

from the challenges posed by the CAAA and the U.S. EPA’s desire to develop an 

advanced modeling framework for “holistic” environmental modeling utilizing state-ofscience representations of atmospheric processes in a high performance computing 

environment. The U.S. EPA completed the initial stage of development with Models-3 

and released the CMAQ model in mid-1999 as the initial operating science model under 

the Models-3 framework. 

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 PM2.5 attainment demonstration state implementation 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 PM2.5 modeling. 

The configuration used for this modeling demonstration, 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 objective, diagnostic, or prognostic analysis, 

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

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

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

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

interface with the rest of the modeling systems, particularly the air quality model. With 

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

to be used in this study: 

• Non-Hydrostatic Formulation 

• Reasonably current, peer reviewed formulation 

• 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 Boundary Layer heights for AQ modeling 

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3.2.2.2 Overview of MM5 

The non-hydrostatic MM5 model is a 3-dimensional, limited-area, primitive equation, 

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

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

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

scientists for a variety of mesoscale studies. 

 

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

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

meteorological fields produced by MM5 are directly compatible with the input 

requirements of “one atmosphere” air-quality models using this coordinate. MM5 fields 

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

by performing a vertical interpolation, followed by a mass-conservation readjustment. 

Two planetary boundary layer parameterizations are available for air-quality applications, 

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

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

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

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

diagnosing the other key boundary layer terms. 

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

three-dimensional analyses performed at 12-hour intervals on the outermost grid mesh 

selected by the user. Surface fields are analyzed at three-hour intervals. A Cressmanbased technique is used to analyze standard surface and radiosonde observations, using 

the National Meteorological Center's spectral analysis, as a first guess. The lateral 

boundary data are introduced using a relaxation technique applied in the outermost five 

rows and columns of the coarsest grid domain. 

Results of detailed performance evaluations of the MM5 modeling system in regulatory 

air quality application studies have been widely reported in the literature (e.g., Emery et 

al., 1999; Tesche et al., 2000, 2003) and many have involved comparisons with other 

prognostic models such as the Regional Atmospheric Modeling System (RAMS) and the 

Systems Application International Mesoscale Model. The MM5 enjoys a far richer 

application history in regulatory modeling studies compared with RAMS or other models. 

Furthermore, in evaluations of these models in over 60 recent regional scale air quality 

application studies since 1995, it has generally been found that the MM5 model tends to 

produce somewhat better photochemical model inputs than alternative models. 

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

description of the MM5 model, can be found in 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 processing system is that it accurately prepares 

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

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

effective use of the system. 

• File System Compatibility with the I/O API 

• File Portability 

• Ability to grid emissions on a Lambert Conformal projection 

• Report Capability 

• Graphical Analysis Capability 

• MOBILE6 Mobile Source Emissions 

• Biogenic Emissions Inventory System 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 Micro-Computing Center of North Carolina. As with most 

emissions models, SMOKE is principally an emission processing system and not a true 

emissions modeling system in which emissions estimates are simulated from “first 

principles.” This means that, with the exception of mobile and biogenic sources, its 

purpose is to provide an efficient, modern tool for converting emissions inventory data 

into the formatted emission files required by an air quality simulation model. For mobile 

sources, SMOKE actually simulates emissions rates based on input mobile-source 

activity data, emission factors and estimates of vehicle miles traveled. 

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

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

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

modeling community. The sparse matrix approach utilized throughout SMOKE permits 

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

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

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

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

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

steps are merged together at a final stage of processing. 

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

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

processing systems and models. It supports both gridded and county total land use 

scheme for biogenic emissions modeling. SMOKE can accommodate emissions files 

from up to 10 countries and any pollutant can be processed by the system. 

For additional information about the SMOKE model please refer to 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 changes 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 modeling attainment with air quality standards. 

The U.S. EPA recommends that episode selection derive from four principal criteria: 

• Simulate a variety of meteorological conditions; 

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

baseline design value or visibility impairment; 

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

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

each monitor violating the NAAQS is based on multiple days. 

VISTAS adopted a logical, stepwise approach in implementing the Attainment Modeling 

Guidance in order to identify the most preferable, representative 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 relevant to the evaluation of candidate annual 

modeling episodes. 

• Initial Episode Typing: At the time of selection in 2003, meteorological and air 

quality data were available for 2002 for model inputs and model performance 

evaluation. VISTAS used Classification and Regression Tree (CART) analyses to 

evaluate the candidate modeling years. The year 2002 was found to be 

representative of conditions in the other years. Subsequently, 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 PM2.5 concentrations 

in 2002 were representative of the five-year baseline period. This analysis is 

discussed in more detail in the 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 PM2.5, meteorological, and 

emissions data and representativeness of alternative baseline modeling periods 

from a regulatory standpoint. Additionally, 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 advantage of the 2002 inventory. 

• Years to be used by other RPOs: VISTAS also considered what years other RPOs 

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

modeling year. 

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

the selection of Calendar Year 2002 (1 January through 31 December) as the most 

current, representative, and pragmatic choice for modeling. All of the U.S. EPA criteria 

for model year selection were directly considered in this process together with many 

other considerations (e.g., timing of new emissions or aerometric data deliveries by the 

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 grid mode. This allowed the larger outer 

domains to feed concentration data to the inner nested domain. The horizontal coarse 

grid modeling domain boundaries were determined through a national effort to develop a 

common grid projection and boundary. A smaller 12-km grid modeling domain was 

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

particulate matter nonattainment areas. Processing time was also a factor in choosing a 

smaller 12-km grid modeling domain. 

The coarse 36-km horizontal grid domain covers the continental United States. This 

domain was used as the outer grid domain for MM5 modeling with the CMAQ domain 

nested within the MM5 domain. Figure 3-1 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 the VISTAS states, a one-way nested high 

resolution (12-km grid resolution) was used. 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 modeling primarily defines the CMAQ vertical 

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

pressure, using 34 layers that extend from the surface to the 100 mb. 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 averaging 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-layer 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 provisions 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 that were designated nonattainment 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 inventoried include volatile organic 

compounds (VOCs), NOx, PM2.5, coarse particulate (PM10), ammonia (NH3) and SO2. 

Tables 3-2 and 3-3 show the typical annual emissions from the various source sectors for 

the Chattanooga PM2.5 nonattainment area for 2002 and 2009. The baseline emissions 

inventory for 2002 was developed for Georgia following the methods described in 

Appendix H. The 2002 BaseG2 and the 2009 BaseG4 emission inventories are the latest 

emission inventories developed by VISTAS 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

Nonattainment 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 

Nonattainment 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

Nonattainment 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

Nonattainment 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 

Nonattainment 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

Nonattainment 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 

Nonattainment 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

Nonattainment 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

Nonattainment 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 

Nonattainment Area Totals 54,370.35 564.2 

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

area sources, off-road and on-road mobile sources, and biogenic sources. 

Stationary point sources are those sources that emit greater than a specified tonnage per 

year, with data provided at the facility level. Electric generating utilities and industrial 

sources are the major categories for stationary point sources. Stationary area sources are 

those sources whose individual emissions are relatively small, but due to the large 

number of these sources, the collective emissions from the source category could be 

significant (i.e., dry cleaners, service stations, agricultural sources, fire emissions, etc.). 

These types of emissions are estimated 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 equipment, railroad locomotives, aircraft, etc. 

The emissions from these sources, like stationary 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, and are summed to the countywide level. 

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

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

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

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

emissions that correspond to the meteorological data used, which for this modeling effort 

is data from 2002. These emissions are used for evaluating the air quality model 

performance. The second type of inventory is the typical base-year inventory. This 

inventory is similar to the actual base-year inventory, except that for sources whose 

emissions change significantly from year to year, a more typical emission value is used. 

In this modeling effort, typical emissions were developed for the electric generating units 

(EGUs) and the fire emissions. The air quality modeling runs using the typical base year 

inventory are used to calculate relative response factors 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 standard is needed. For this modeling project, the future year 

inventory will be 2009, the last complete year for which the standard must be attained. It 

is for the future base-year inventory that sensitivities will be evaluated to determine what 

additional controls beyond those control measures already included in the future-year 

base inventory will need to be implemented to attain and maintain the annual PM2.5

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 sources emitting at least 100 tons-per-year (tpy) 

of a criteria pollutant are inventoried annually. Smaller sources have been inventoried 

less frequently. The point source emissions data can be grouped as EGU sources and 

other industrial point sources, also called non-electric generating units (non-EGUs). 

Appendix H documents the point source modeling inventory development in more detail. 

3.5.1.1 Electric Generating Units 

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

monitoring (CEM) data reported to the U.S. EPA’s Acid Rain program or 2002 hourly 

emissions data provided by stakeholders. This data provide hourly emissions profiles for 

SO2 and NOx that can be used in air quality modeling. Emissions profiles are used to 

estimate emissions of other pollutants (VOC, carbon monoxide, NH4, PM2.5, and soil) 

based on measured emissions of SO2 and NOx. 

Emissions from EGUs vary daily and seasonally 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 variability in meteorology, 

economic and outage factors in 2002. This approach is consistent with the Attainment 

Modeling Guidance. To develop a typical-year 2002 emissions inventory for EGU 

sources, each unit’s average CEM heat input for 2000 through 2004 was divided by the 

2002 actual heat input to generate a unit-specific normalizing factor. This normalizing 

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

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 years the unit was operational was used. If 

a unit was shut down in 2002, but not permanently 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 contiguous United States using the Integrated 

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

examine air pollution control policies for various pollutants throughout the contiguous 

United States for the entire electric power system. The dynamic nature of IPM enables 

projection of the behavior of the power system over a specified future period. 

Optimization logic in IPM determines the least-cost means of meeting electric generation 

and capacity requirements while complying with specified constraints including air 

pollution regulations, transmission bottlenecks, and plant-specific operational constraints. 

The versatility of IPM allows users to specify which constraints to exercise, and to 

populate IPM with their own datasets. 

The IPM modeling runs took into consideration 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 

Georgia Power SGM1 2442 13802 165 73 2127 11134 323 192 

– 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 

Georgia Power SG01 10960 38741 445 193 2430 1890 1566 933 

– 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 category uses annual emissions as reported 

under the CERR for the year 2002. These emissions 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 year 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 individual emissions are relatively small, but 

due to the large number of these sources, the collective emissions could be significant 

(i.e., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are 

estimated by multiplying an emission factor by some known indicator of collective 

activity, such as fuel usage, number of households, or population. Stationary area source 

emissions are estimated at the countywide level. 

A portion of the area source 2002 base-year inventory for Georgia was developed by the 

GA EPD and provided to the VISTAS/ASIP contractor. The VISTAS/ASIP contractor 

calculated the remaining portion of the area source inventory. The sources estimated by 

the contractor include emissions from animal 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 will serve as the typical base-year inventory for all area 

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

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

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

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

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

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

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

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

year inventory. 

For categories other than wild land fires, the VISTAS/ASIP contractor 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 controlled emissions to the appropriate 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 either the states or the CAIR growth factor 

files. Table 3-5 shows the top five categories of area source emissions in the Georgia 

nonattainment counties, separately 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 equipment that can move but do not use the 

roadways, such as construction equipment, aircraft, railroad locomotives, lawn and 

garden equipment, etc. For the majority of the non-road mobile sources, the emissions 

for 2002 were estimated using the U.S. EPA’s NONROAD2005c model. For the three 

source categories not included in the NONROAD model, i.e., aircraft engines, railroad 

locomotives, and commercial marine, more traditional methods of estimating 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 consideration 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 available, state-specific growth factors were 

used. Appendix H provides a detailed discussion of the nonroad mobile source 

inventory. 

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 – Catoosa and Walker Counties, GA 

3.5.4 Highway Mobile Sources

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

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

average speeds on the roads, the mix of vehicles on the roads, any programs in place in 

an area to reduce emissions for motor vehicles (such as emissions inspection programs), 

and temperature. 

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

source sector. The same MOBILE run is used to represent the actual- and typical-year 

emissions for onroad vehicles using input data reflective of 2002. The MOBILE model is 

then run for the 2009 inventory using input data reflective of that year. The 2002 vehicle 

miles traveled (VMT) estimates were obtained from the Georgia Department of 

Transportation (GDOT). The VMT for 2009 were projected by the VISTAS contractors 

based on linear growth as derived from the 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 the 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 model. Modifications include use of MM5 

data, gridded land use data, and improved emissions characterization. The emission 

factors that are used in SMOKE-BEIS3 are the same as the emission factors 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 scheme, 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 the 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 directly 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 modeling 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 skill in replicating the state variables (e.g. 

temperature, mixing ratio, relative humidity, wind speed and direction, cloud cover, and 

precipitation). The meteorological modeling performance statistics fell within expected and 

acceptable ranges of error during the majority of the 2002 modeled year. 

The meteorological modeling performance for Georgia was very similar to the performance 

for the VISTAS/ASIP region for the 12-km modeling domain. Again, large-scale 

meteorological patterns were accurately predicted. The meteorological model demonstrated 

substantial skills throughout the entire year and was especially skillful during the 

summertime season from May through September. 

U.S. EPA guidance (EPA, 2007) recognizes the complexity and importance of evaluating 

meteorological modeling results for air quality applications. Since MM5 algorithms and 

modules have undergone significant peer review and testing, 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 quantities. In the case of a prognostic meteorological model, this amounts 

to a statistical analysis of model predictions and surface observations. 

GA EPD used the software package METSTAT developed by ENVIRON Corporation for 

calculating model performance statistics. METSTAT computes domain-wide surface 

statistics for temperature, wind speed and direction, and humidity. The metrics include: Bias 

Error (B), Gross Error (E) and Root Mean Square Error (RMSE), Systematic Root Mean 

Square Error (RMSEs), Unsystematic Root Mean Square Error (RMSEu) 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 statistics depends upon the number of observationprediction pairs, and they generally improve with 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 

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“pass/fail” test for a model, but only as a means of assessing general confidence in 

meteorological model output. 

A graphical summary of the daily and hourly mean performance statistics 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 Table 4-3. Statistics have been computed at 

monitoring stations that are located in and around Georgia. 

 

Table 4-1. Mathematical Formulation of Statistical 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

1 2

⎥

⎦

⎤ ⎢

⎣

⎡ = ∑∑ − = =

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

S j OP

IJ

RMSE

Unsystematic Root Mean Square Error ( )

2/1

1 1

2 ˆ 1 ⎥

⎦

⎤ ⎢

⎣

⎡ = ∑∑ − = =

J

j

I

i

i

j

i

u j 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 

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Monthly Average Temperature Bias

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

January

Feburary

March

April

May

June

July

August

September

October

November

December

Bias (degree K)

Monthly Average Humidity Bias

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

January

Feburary

March

April

May

June

July

August

September

October

NovemberDecember

Bias (g/kg)

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

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

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The MM5 simulation is able to capture meteorological conditions observed during the study 

period with an adequate level of accuracy. Performance statistics for most days are within the 

benchmarks proposed by Emery (2001). Monthly average statistics reveal under prediction in 

humidity from May through November, and an over prediction in temperature during the 

summer months (May through September). The simulation tends to over-predict daytime and 

nighttime temperature, and consistently over-predicts nighttime wind speed. However, the 

magnitude of these under/over predictions remains small. 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 atmospheric dynamics during a particular 

time period. In an unlikely event of unusually poor air quality model performance, it is 

reasonable to further investigate the performance 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 PM2.5 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 were 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 sites should be evaluated. STN data was also 

utilized as this data is used to speciate the 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 described in Section 6.1 and Appendix G. 

Only a brief summary of the model performance 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 domain, please refer to Appendix K.3. 

4.2.1 Modeling Performance Goals and Criteria

In 2004, VISTAS/ASIP established model performance “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 Guidance (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 are included for FRM and STN monitors. 

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Table 4-4. Established Model Performance Goals and Criteria for the PM2.5 Component 

Species 

Mean Fractional 

Bias 

Mean Fractional 

Error 

Comment 

<15 percent <35 percent Goal for PM2.5 model performance based on ozone 

model performance, considered “excellent 

performance” 

<30 percent <50 percent Goal for PM2.5 model performance, considered good 

performance; hereafter, it is called “(performance) 

goal level” 

<60 percent <75 percent Criteria for PM2.5 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 triggers 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 visualize 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 named because the dotted lines resemble a soccer goal. 

The soccer plot is useful as both bias and error are shown on a single plot. As bias and error 

approach zero, the points are plotted 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 demonstrated that greater emphasis should be placed on performance 

of those components with the greatest contribution to PM2.5 mass (e.g. SO4 and OC) and that 

greater bias and error could be accepted for components with smaller contributions to total PM2.5

mass (e.g. EC, NO3, and SOIL). The soccer plots and bugle plots have been included as 

suggested in the Attainment Modeling Guidance. 

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Table 4-5. Statistical Metric Calculations 

Statistical Measure Shorthand 

Notation 

Mathematical Expression Notes 

Accuracy of Paired 

Peak 

 Ap

peak

peak

O

− OP

Coefficient of 

Determination 

 R2

( )( )

∑ ∑

∑

= =

=

− −

⎥

⎦

⎤ ⎢

⎣

⎡ −−

N

i

N

i

i i

N

i

i i

OOPP

OOPP

1 1

2 2

2

1

)()(

Pi= prediction at time and 

 location i; 

Oi = observation at time and 

 location i; 

P = arithmetic average of Pi, 

 i = 1, 2, . . ., N; 

O = arithmetic average of Oi, 

 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

1 2

⎥

⎦

⎤ ⎢

⎣

⎡ ∑ − =

N

i

OP ii 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

OP ii N 1

1

Mean Normalized 

Gross Error 

MNGE 

∑=

N −

i i

ii

O

OP

N 1

1 Reported as % 

Mean Biased MB ∑( ) =

−

N

i

OP ii 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) 

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4.2.2 Domain-Wide Model Performance

 

As a summary of model performance, soccer plots and bugle plots for all of the VISTAS STN 

and FRM monitors are included here. Plots have been developed for the average monthly 

concentrations and the performance statistics for all of the PM2.5 component species (SO4, NO3, 

NH4, OC, and EC) and reconstructed PM2.5 total mass from the STN monitoring sites (Figures 4-

2 and 4-3), as well as the total FRM PM2.5 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 performance for most species of PM2.5 and total PM2.5. The 

exception is the prediction of NO3 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 presented in the image on the right in Figure 4-2. 

However, when the very low concentration of NO3 is taken into consideration, as presented in 

the bugle plots (Figures 4-3), NO3 performance largely falls within the criteria and goal levels. 

One can still note a general tendency for under prediction in NO3, 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 

PM2.5. 

Monthly total PM2.5 performance at all the VISTAS/ASIP FRM monitors largely falls within 

goal level thresholds, with only three months falling just outside goal level (Figure 4-4). Figure 

4-5 suggests a slight negative bias in PM2.5 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 Bias

Mean 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 Bias

Mean 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 fractional error and fractional bias 

for component concentration for all VISTAS STN monitoring sites. The image on the right is a 

zoomed view of the soccer plot on the left to better depict the area inside the criteria/goal lines. 

Each point represents a monthly value as compared to the model performance criteria (red box) 

and modeling performance goals (green box). 

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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 (μg/m3)

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 (μg/m3)

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 mean fraction bias for particulate matter and its 

component concentrations for all VISTAS STN monitoring sites. The image 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 monthly mean fraction bias value as compared to 

the model performance criteria (red lines) and 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 Bias

Mean 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 Bias

Mean Fractional Error 

PM2.5

Criteria

Goal

Figure 4-4. VISTAS FRM Soccer Plots 

Soccer plots depicting both the mean fractional error and fractional bias for PM2.5 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 depict the area inside the criteria/goal lines. Each point represents 

a monthly value as compared to the model performance criteria (red box) and modeling 

performance goals (green box). 

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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 (μg/m3)

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 (μg/m3)

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 particulate matter for all the 

VISTAS FRM Monitoring sites. Each point represents a monthly mean fraction bias value as 

compared to the model performance criteria (red lines) and modeling performance goals (green 

lines). 

The overall tendency of the model was that the model had some difficulties in predicting NO3. 

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 NO3 are generally found in low 

concentrations across the southeast, and the model usually has difficulties representing any 

compound with low ambient concentrations. The bugle plots are more encouraging with NO3

performance, as these plots take into account for absolute concentrations of PM2.5 components 

when evaluating performance. The bugle plots show all components and total PM2.5 falling 

within criteria levels, if not goal levels, of model performance. The under-predictions of NO3

and OC account for the slight negative bias in the modeled PM2.5 mass compared to the 

measurements. 

4.2.3 Model Performance within Georgia and in the Chattanooga nonattainment area

Statistical metrics and performance plots were developed for the entire state of Georgia as well 

as the Chattanooga nonattainment area. For Georgia, the primary monitoring networks available 

to evaluate the CMAQ modeling were: (1) EPA Federal Reference Method (FRM) PM2.5 and 

PM10 Mass Networks, (2) EPA Speciation Trends Network (STN) of PM2.5 species, (3) 

Interagency Monitoring of Protected Visual Environments (IMPROVE), (4) Clean Air Status and 

Trends Network (CASTNET), and (5) Southeastern 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 data collected and locations of the monitoring sites for the 

FRM, IMPROVE, STN, CASTNET, and SEARCH monitoring 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-located STN monitor (47-065-4002).

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

Monitoring 

Network Chemical Species Measured Sampling Frequency; 

Duration 

FRM Only total fine particulate mass (PM2.5) Varies; 24 hr 

IMPROVE Speciated PM2.5 and PM10 1 in 3 days; 24 hr 

STN Speciated PM2.5 Varies; Varies 

CASTNET Speciated PM2.5 and ozone Hourly, Weekly; 1 hr, 

Week 

SEARCH 

24-hr PM25 (FRM Mass, OC, BC, SO4, 

NO3, NH4, EC); 24-hr PM coarse (SO4, 

NO3, NH4, elements); Hourly PM2.5

(Mass, SO4, NO3, NH4, EC, TC); and 

Hourly gases (O3, NO, NO2, NOy, HNO3, 

SO2, CO) 

Daily, Hourly; 

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Figure 4-6. FRM, STN, IMPROVE, CASTNET, NADP, and SEARCH monitoring sites located in and n

nonattainment areas (left) and the Chattanooga PM2.5 nonattainment area (right). 

NOTE: 47-065-0032 was excluded because it has only one measurement in 2002

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4.2.3.1 Model performance statistics

Model performance statistics were calculated collectively for the FRM monitors within Georgia, 

as well as individually for the four FRM monitors in the Chattanooga nonattainment area to 

demonstrate the model’s ability to replicate total PM2.5 mass at these sites. In addition, the same 

approach was taken to conduct model performance evaluation for the STN monitors within 

Georgia and for the Chattanooga STN site on PM2.5 component species and total PM2.5 basis for 

the entire base year. The statistical measures 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, PM2.5, and Regional 

Haze (“Attainment Guidance”). 

4.2.3.1.1 Model Performance Statistics for the FRM Sites 

To examine the overall model performance, the annual model performance statistics were 

estimated for all FRM sites in Georgia and the FRM site in the Chattanooga nonattainment area. 

As shown in Table 4-7, the model’s performance at four FRM monitors in the Chattanooga 

nonattainment area is comparable with that of the all FRM monitors in Georgia. Exceptions are 

fractional bias, normalized mean bias, and mean bias for TN monitors but their mean predictions 

were close to observations. These bias statistics and the comparison of mean values indicate that 

the model tends to show more negative biases at the Chattanooga FRM monitors than monitors 

across Georgia for low concentration cases. 

For more in-depth examinations, the model performance 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 summer followed by over-prediction in October 

for all monitors and in November at some monitors in Tennessee. In addition, the model showed 

less significant under-prediction in February. Overall, however, all of the sites showed model 

performance well within the “criteria” level except at 47-065-1011 in January and at 47-065-

4002 in March when the model performance was very close to the “criteria” 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 PM2.5 concentrations that were observed except in a few cases. For example, the model 

predicted peak PM2.5 concentration of 23.6 μg/m3

 at 47-065-4002 in October while 14.0 μg/m3

was observed at the same monitor for the same period. A more detailed performance evaluation 

was conducted with graphical measures and the results 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 samples indicates the overall performance of the 

model in the Chattanooga nonattainment area can be equal or better than the model’s overall 

performance across Georgia. 

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Table 4-7. Annual model performance statistics for PM2.5 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 

Ap -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 

MaxOBS 52.0 15.0 15.4 14.0 14.6 

MaxPRE 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 

R2 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 

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Table 4-8. Monthly model performance statistics for PM2.5 at 13-295-0002. 

Statistics January February March April May June July August September October No

MFB (%) 33.0 2.7 -5.2 -15.8 -44.4 -32.0 -23.1 -3.7 -11.0 5.4 

MFE (%) 33.0 28.6 34.1 22.4 47.1 32.2 30.9 15.4 29.2 21.3 

Ap 0.6 0.3 0.4 -0.1 -0.3 -0.2 0.0 0.0 -0.3 0.1 

NMB (%) 40.9 11.2 -2.1 -12.7 -35.9 -28.7 -21.9 -6.6 -7.7 7.2 

NME (%) 40.9 29.4 33.5 18.5 38.9 28.8 30.4 14.1 24.8 21.8 

MNB (%) 45.2 8.2 2.1 -12.9 -33.1 -25.2 -17.4 -1.8 -4.6 9.2 

MNGE (%) 45.2 27.8 33.3 20.1 35.9 25.4 25.9 14.2 25.4 23.1 

MB 4.5 1.3 -0.3 -1.5 -5.5 -4.1 -3.8 -1.3 -1.2 1.1 

MAGE 4.5 3.3 4.1 2.2 6.0 4.1 5.3 2.7 3.7 3.2 

Geometric Mean Bias 0.3 0.0 -0.1 -0.2 -0.5 -0.3 -0.2 0.0 -0.1 0.1 

Geometric Mean Error 0.3 0.3 0.3 0.2 0.5 0.3 0.3 0.2 0.3 0.2 

RMSE 5.6 4.1 5.0 2.5 7.9 5.7 6.8 4.1 4.8 4.0 

MaxOBS 12.0 20.9 17.5 24.8 25.1 25.2 25.6 31.6 34.3 20.7 

MaxPRE 18.6 27.5 24.0 21.3 18.2 21.0 25.5 32.0 25.1 22.8 

Mean Observations 11.0 11.3 12.3 12.1 15.5 14.4 17.5 19.4 15.0 14.7 

Mean Predictions 15.4 12.6 12.0 10.6 9.9 10.2 13.7 18.1 13.8 15.8 

Standard Deviation of 

Observations 1.7 6.0 4.6 7.3 5.6 6.4 6.0 10.7 8.6 4.7 

Standard Deviation of 

Predictions 3.0 9.2 6.9 6.6 4.7 4.6 4.9 9.7 7.4 5.3 

R2 0.1 0.9 0.3 0.9 0.1 0.6 0.2 0.8 0.7 0.4 

BF 1.5 1.1 1.0 0.9 0.7 0.7 0.8 1.0 1.0 1.1 

Number of Samples 4 5 5 5 7 10 9 4 9 10 

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Table 4-9. Monthly model performance statistics for PM2.5 at 47-065-0031. 

Statistics January February March April May June July August September October No

MFB (%) 10.0 22.0 -4.1 -27.4 -34.9 -31.0 -38.5 -21.1 -14.2 13.2 

MFE (%) 12.9 22.0 29.2 27.4 35.6 31.0 38.5 28.2 33.5 23.9 

Ap 0.1 0.2 0.4 -0.1 -0.2 -0.3 -0.3 -0.2 0.1 0.7 

NMB (%) 11.0 24.7 -0.3 -19.5 -27.2 -28.6 -32.6 -22.6 -13.6 20.6 

NME (%) 14.4 24.7 29.9 19.5 27.7 28.6 32.6 26.9 28.2 30.6 

MNB (%) 12.1 24.7 1.4 -22.8 -26.9 -25.3 -31.3 -16.6 -5.7 17.4 

MNGE (%) 14.9 24.7 28.4 22.8 27.6 25.3 31.3 24.4 30.6 27.2 

MB 1.5 5.4 0.0 -2.5 -4.7 -4.9 -6.5 -5.0 -2.5 1.9 

MAGE 2.0 5.4 3.6 2.5 4.8 4.9 6.5 6.0 5.2 2.8 

Geometric Mean Bias 0.1 0.2 0.0 -0.3 -0.4 -0.3 -0.4 -0.2 -0.2 0.1 

Geometric Mean Error 0.1 0.2 0.3 0.3 0.4 0.3 0.4 0.3 0.4 0.2 

RMSE 3.0 5.4 4.5 2.8 6.2 6.7 7.5 7.4 6.6 3.9 

MaxOBS 16.9 22.0 16.9 15.7 31.0 30.4 29.4 41.4 31.6 15.5 

MaxPRE 18.3 27.4 23.3 14.5 24.1 20.2 19.4 31.8 33.6 25.7 

Mean Observations 14.0 22.0 11.9 12.9 17.4 17.3 19.9 22.2 18.3 9.2 

Mean Predictions 15.6 27.4 11.9 10.3 12.7 12.3 13.4 17.2 15.8 11.1 

Standard Deviation of 

Observations 2.6 N/A 4.3 4.0 6.2 7.6 6.9 9.8 8.3 3.5 

Standard Deviation of 

Predictions 3.1 N/A 6.6 5.8 5.6 4.9 4.8 6.1 6.7 6.3 

R2 0.2 N/A 0.4 1.0 0.6 0.6 0.7 0.7 0.5 0.8 

BF 1.1 1.2 1.0 0.8 0.7 0.7 0.7 0.8 0.9 1.2 

Number of Samples 3 1 5 2 28 10 7 31 27 10 

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 Table 4-10. Monthly model performance statistics for PM2.5 at 47-065-1011. 

Statistics January February March April May June July August September October No

MFB (%) 59.6 -13.0 -10.4 -21.1 -22.8 -29.6 -26.5 -13.3 -31.8 34.9 

MFE (%) 59.6 26.7 20.5 24.7 38.4 32.4 27.6 20.7 50.1 38.9 

Ap 0.8 0.2 -0.2 -0.1 -0.2 -0.2 -0.1 0.0 -0.2 0.4 

NMB (%) 84.9 -7.6 -8.5 -15.7 -24.2 -26.8 -23.9 -12.9 -17.0 43.6 

NME (%) 84.9 27.0 23.3 20.0 34.7 28.8 24.6 17.9 32.2 48.1 

MNB (%) 84.9 -7.9 -6.9 -16.8 -14.9 -24.1 -22.4 -10.6 -16.8 47.6 

MNGE (%) 84.9 23.6 18.6 20.7 32.6 27.3 23.6 18.6 38.7 51.3 

MB 6.9 -0.7 -1.1 -2.0 -3.1 -4.9 -5.7 -2.8 -2.7 4.2 

MAGE 6.9 2.5 3.0 2.6 4.5 5.2 5.9 3.8 5.1 4.7 

Geometric Mean Bias 0.6 -0.1 -0.1 -0.2 -0.2 -0.3 -0.3 -0.1 -0.4 0.4 

Geometric Mean Error 0.6 0.3 0.2 0.3 0.4 0.3 0.3 0.2 0.6 0.4 

RMSE 6.9 3.2 4.3 3.3 5.6 6.3 6.8 4.9 6.0 5.4 

MaxOBS 8.1 12.9 30.5 21.1 20.0 32.7 36.6 39.2 30.2 18.9 

MaxPRE 15.0 15.4 23.9 19.7 16.0 25.5 34.0 40.9 24.7 27.2 

Mean Observations 8.1 9.2 12.9 12.9 13.0 18.2 23.9 21.5 15.8 9.7 

Mean Predictions 15.0 8.5 11.8 10.8 9.8 13.3 18.2 18.7 13.1 13.9 

Standard Deviation of 

Observations N/A 2.7 7.8 4.0 5.1 6.8 6.7 9.7 9.0 3.8 

Standard Deviation of 

Predictions N/A 4.4 7.3 4.5 2.9 4.8 5.2 9.5 8.5 5.6 

R2 N/A 0.4 0.7 0.7 0.1 0.7 0.7 0.8 0.6 0.6 

BF 1.8 0.9 0.9 0.8 0.9 0.8 0.8 0.9 0.8 1.5 

Number of Samples 1 7 9 29 8 30 23 9 10 10 

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Table 4-11. Monthly model performance statistics for PM2.5 at 47-065-4002. 

Statistics January February March April May June July August September October No

MFB (%) 20.1 -25.9 -56.8 -7.4 -25.2 -34.5 -25.0 1.1 2.3 29.4 

MFE (%) 44.3 40.1 56.8 30.2 30.7 35.2 31.6 17.0 16.9 34.6 

Ap -0.2 -0.3 -0.2 0.2 -0.3 -0.2 -0.3 -0.2 -0.1 0.7 

NMB (%) 11.4 -28.5 -40.8 -4.0 -25.0 -30.1 -23.5 -4.3 -1.4 40.6 

NME (%) 38.8 36.3 40.8 32.8 28.4 30.7 28.3 17.2 12.7 46.1 

MNB (%) 5.1 -16.0 -40.5 -0.6 -19.8 -27.9 -19.7 3.0 6.1 38.8 

MNGE (%) 27.4 33.7 40.5 29.5 25.9 28.6 27.1 17.2 18.9 43.4 

MB 1.5 -3.4 -5.0 -0.4 -4.0 -5.5 -5.0 -0.8 -0.3 4.0 

MAGE 5.1 4.3 5.0 3.2 4.6 5.6 6.0 3.3 2.3 4.5 

Geometric Mean Bias 0.0 -0.3 -0.6 -0.1 -0.3 -0.4 -0.3 0.0 0.0 0.3 

Geometric Mean Error 0.3 0.4 0.6 0.3 0.3 0.4 0.3 0.2 0.2 0.4 

RMSE 7.0 6.2 6.1 4.2 5.6 6.9 7.0 4.4 3.4 5.6 

MaxOBS 27.6 27.0 16.5 14.7 23.5 32.7 45.0 33.9 28.9 14.0 

MaxPRE 21.7 18.1 13.0 17.2 16.7 26.9 32.8 28.4 27.3 23.6 

Mean Observations 13.1 11.8 12.3 9.7 16.1 18.3 21.3 19.0 18.5 9.9 

Mean Predictions 14.6 8.5 7.3 9.3 12.1 12.8 16.3 18.2 18.3 13.9 

Standard Deviation of 

Observations 8.1 7.6 3.6 2.8 5.5 6.8 8.0 10.5 8.6 2.8 

Standard Deviation of 

Predictions 5.6 4.1 4.0 4.0 3.1 4.9 5.3 8.1 7.5 5.8 

R2 0.2 0.5 0.2 0.0 0.5 0.6 0.6 0.8 0.8 0.6 

BF 0.9 0.8 0.6 1.0 0.8 0.7 0.8 1.0 1.1 1.4 

Number of Samples 10 10 5 9 10 28 30 10 8 10 

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4.2.3.1.2 Model Performance Statistics for STN Sites

The model performance was evaluated for particulate matter components measured at the all 

STN sites in Georgia and separately at the STN site in the Chattanooga nonattainment area. 

Since detailed graphical performance evaluation was conducted for all available PM2.5 species, 

we briefly present annual summary statistics for these species and total PM2.5 here. 

Sulfate and total PM2.5 had negative bias and error at the Georgia STN monitors (Table 4-12). 

Nitrate was underestimated in Georgia as previously seen throughout the model performance 

evaluation for the entire VISTAS 12-km modeling domain. However, this is not a significant 

issue for the overall model performance because the contribution of NO3 to the total PM2.5 is 

relatively small in Georgia. Also, 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 Chattanooga nonattainment area except all of PM2.5 components 

showed negative biases. More detailed performance analyses with graphical measures can be 

found in the following sections. 

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Table 4-12. Annual model performance statistics for PM2.5 species and total PM2.5 at all 

STN monitors in the State of Georgia. 

Statistics SO4 NO3 NH4 OC EC PM2.5

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 

Ap -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 

MaxOBS 39.9 5.0 10.7 13.6 3.5 40.9 

MaxPRE 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 

R2 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 PM2.5 at 47-065-4002. 

Statistics SO4 NO3 NH4 OC EC PM2.5

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 

Ap -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 

MaxOBS 14.3 3.5 4.9 9.5 1.6 39.6 

MaxPRE 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 

R2 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 evaluation of the CMAQ modeling

In general, the CMAQ modeling met the performance “criteria level” for all major PM2.5

constituents and the total PM2.5 across the State of Georgia throughout the base year. As 

the first step of model performance evaluation, 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. Important findings from this 

statistical performance evaluation at the 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 nonattainment area although there were 

slightly more biases observed in the Chattanooga nonattainment area. These biases 

became more apparent during summer and winter. After examining the statistical 

performance measures, GA EPD found that the model’s performance is generally 

adequate across Georgia and acceptable for the Chattanooga nonattainment area. 

Similar analyses were conducted for the STN monitors on total PM2.5 and all of the 

measured PM2.5 species. The most significant findings were that (1) the model’s sulfate 

and PM2.5 performance were quite good, and (2) NO3 and OMC were somewhat under 

predicted. In addition, the model’s performance 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 PM2.5 species and total PM2.5 concentrations were 

examined with these packages. Whenever data was available, each package included 

scatter plots for monitors within Georgia and in the Chattanooga nonattainment area, time 

series plots for monitors in the Chattanooga nonattainment area, bar plots of fractional 

bias and fractional error for monitors within Georgia, bar plots of fractional bias and 

fractional error for monitors in the Chattanooga nonattainment area, bugle plots for 

monitors within Georgia, and soccer plots for monitors within Georgia. In addition, more 

graphical measures were examined if necessary. 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 graphical analysis. The evaluation included 

speciated data available from IMPROVE, CASTNET, SEARCH, and STN as well as 

total PM2.5 from FRM monitors. 

Overall, the model showed reasonable performance for PM2.5, SO4, NH4, and OC 

throughout the base year across all monitors in Georgia. During summer, the model 

showed a seasonal variation of NO3 performance with under-prediction bias greater than 

–100% and moderate winter over-prediction at CASTNET sites. The amount of error and 

bias in monthly time series was most likely contributed by low concentration (< 1.0 

μg/m3

) 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, SO4 and OMC were the major contributors to the Chattanooga 

nonattainment areas. Therefore, GA EPD concluded that performance issues found in 

minor constituents of PM2.5 are not likely degrading the overall model performance. 

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Figure 4-7. Stacked bar plots of daily PM2.5 species concentration at 47-065-4002. 

Detailed graphical analyses led GA EPD to investigate a few exceptional performance 

issues for SO4, OMC, and NH4. 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 the Chattanooga nonattainment area. 

When the performance is looked at as a function 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 performance is well within the limits of 

acceptable performance criteria established 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 Ozone, PM2.5 and Regional Haze. U.S. 

Environmental Protection Agency, Research 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 Document for VISTAS Emission and Air Quality 

Modeling to Support Regional Haze State Implementation 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 emissions of direct PM2.5 and PM2.5 precursors 

and are, therefore, of primary importance to demonstration of attainment. This section 

identifies those control measures that are included as inputs in the generation of the 2009 

emissions inventory and are, therefore, relied 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 provide small reductions to ambient PM2.5 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 and 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 and 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 states to use in developing their rules. The 

purpose of the CAIR is to reduce interstate transport of precursors to fine particulate and 

ozone. 

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Table 5-1. Federal PM2.5 Control Measures and Contingency Measures 

Control Measure1 Control Measure 

Benefits2

Measure Sources 

Affected 

Measure and Schedule 

Species Affected/ Reduced 

Measure and Schedule 

Clean Air Interstate Rule (CAIR) 40 CFR 96, Subparts AAA - HHH 

Coal-fired Electric Gen. Units, 28 eastern states and D.C. 

Regional cap on NOx and SO2 emissions from EGU’s: NOX Phase 1 starts 2009. 

NOx. Regional cap on 

NOX and SO2 emissions from EGU’s: SO2 Phase 1 starts 2010 (SO2

Tier 2 vehicle standards 40 CFR 80, 85, and 86

New passenger vehicles. 

Fleet average for NOx emissions per mile, effective 2004 through 2007. 

NOx. Continuing vehicl

turnover. 

Heavy Duty Engine Standard (Gasoline and Diesel), second phase 40 CFR 86, Subpart A

New gas and diesel highway vehicles 14001 lbs or greater. 

Effective 2004 and later – NOx and VOC controls; Effective 2007 and later – PM controls. 

NOx, VOC, and PM (soot); PM limit of 0.01 g/bhp/hr. 

Continuing vehicle turnover. 

Ultra Low Sulfur Diesel 40 CFR 80, Subpart I

New diesel highway vehicles 14001 lbs or greater. 

Limits 80 % of pool of highway diesel to sulfur content of 15 ppm or less; 2007 – 2009; facilitates proper operation of heavy duty diesel PM controls (see above). 

PM (soot). Limits 100 % of 

pool of highway diesel to 15 ppm sulfur or less; 2010 on - facilitates proper operation oheavy duty diesel PM controls (see above). 

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Nonroad Diesel Rule 40 CFR 89

New mobile sources: offroad, ships, locomotives. 

2008 through 2014; Should reduce NOx and PM emissions by over 90%. 

NOx, PM (soot). 

Continuing vehicle turnover. 

Large Spark Ignition and Recreational Vehicle Rule 40 CFR 90 

Forklifts, airline ground service equipment, allterrain vehicles, offhighway motorcycles, marine diesel engines. 

Large spark ignition equipment (forklifts, airline ground service equipment) – 2004; off-road vehicles – 2006; marine engines – 2006. 

VOC, NOx, CO. 

Continuing equipment turnover. 

NOx SIP Call in Surrounding States 

Utility and industrial boilers; internal combustion engines. 

Controls on utility and industrial boilers and on internal combustion engines in 22 eastern states by 2004. 

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, take effect without significant further action by State or EPA. 

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The CAIR applies to (1) any stationary, fossil-fuel-fired boiler or stationary, fossil-fuelfired combustion turbine serving at any time, since the start-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 cogeneration unit during the 12-

month period starting on the date that the unit first produces electricity 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 and supplying in any calendar year more than 

one-third of the unit's potential electric output 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. Because the Phase I SO2 cap starts in 2010, 

this can only be counted as a contingency measure and is discussed later. Phase II caps 

for NOx and SO2 become effective in 2015, which is too late to count 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 program. When fully implemented, the CAIR will 

reduce SO2 emissions in the eastern United States 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 interferes with maintenance of the PM2.5 

NAAQS (70 FR 25246-25250). Accordingly, a State CAIR rule has been promulgated 

that, for the most part, mirrors the provisions of the federal rule. Georgia will have 

statewide SO2 caps of 213,057 tons/year and 149,140 tons/year, effective in 2010 and 

2015, respectively. Adjacent CAIR states, which have been identified as contributing 

significantly to nonattainment of the PM2.5 standard in the Chattanooga PM2.5 

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 NOx 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 passenger vehicles over 8,500 pounds gross vehicle 

weight rating (the larger pickup trucks and SUVs), which are not covered by the current 

Tier 1 regulations. For these vehicles, the standards will be phased in beginning in 2008, 

with full compliance in 2009. The new standards 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, but interferes with the operation of catalytic 

converters on vehicles resulting in higher emissions. 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 reduce 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 began 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 the 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 engines 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 diesel engines, such 

as those used in construction, agricultural, and industrial equipment, to be phased in 

between 2008 and 2014. The nonroad diesel rules also reduce the allowable sulfur in 

nonroad diesel fuel by over 99%. Nonroad diesel fuel currently averages about 3,400 

ppm of sulfur. The rule limits nonroad diesel 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 engines (forklifts and airport ground service 

equipment), recreational vehicles (off-highway motorcycles and all-terrain-vehicles), and 

recreational marine diesel engines. The regulation 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 standard was implemented in 2004 and Tier 2 started 

in 2007. Like the large spark-ignition engines, recreational vehicles contribute to ozone 

formation and ambient CO and PM levels. For the off-highway motorcycles and allterrain-vehicles, model year 2006, the new exhaust 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 ozone formation and PM levels, especially in 

marinas. Depending on the size of the engine, the standard began phasing-in in 2006. 

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When all of the nonroad spark-ignition engines and recreational engines standards are 

fully implemented, an overall 72% reduction in HC, 80% reduction in NOx, and 56% 

reduction in CO emissions are 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 finding 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 transport of ozone (63 FR 57356). This rule, referred to 

as the NOx SIP Call, called for ozone season controls to be put on utility 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 state 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 budgets 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 generation of the 2009 

emissions inventory: 

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

• Heavy-Duty Diesel Engines Rule 

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

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Table 5-2. State PM2.5 Control Measures and Contingency Measures 

Control Measure1 Control Measure – 

Benefits2

Measure Sources 

Affected 

Measure and Schedule 

Species Affected/ Reduced 

Measure and Schedule 

Multipollutant rule Air Quality Control 391-3-1-.02 (2) (sss) 

Electric Gen. Units, CoalFired, GA 

Flue Gas Desulfurization for 

7 EGUs3 starting in 2008 - approximately 194,000 tons/yr reduced. 

SO2 sources 

outside the nonattainment area. 

Flue Gas Desulfurization for Bowen Unit 2, June 1, 2009, Wansley Unit 2, December 31, 2009, and Bowen Unit 1, June 1, 2010. 

Heavy Duty Diesel Engines rule Air Quality Control 391-3-1-.02 (2) (ooo) 

Diesel Vehicles 14001 lbs or greater. 

Diesel Vehicles 14001 lbs or greater meet CARB standards; 2005 and subsequent models. 

PM (soot) sources inside the nonattainment area. 

Continuing vehicle turnover 

(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. 

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5.2.1 Multipollutant Control for Electric Utility 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 Georgia. It was enacted in 2007 for the purposes 

of lowering ozone and PM2.5 concentrations and reducing mercury deposition. The 

controls will reduce SO2 emissions from the affected emissions units by at least 95 

percent and will reduce NOx emissions by approximately 85 percent. 

The control implementation date of the rule varies by EGU, starting on December 31, 

2008. Controls are required on seven of the units prior to the attainment demonstration 

year (see Table 5-3). These units are located south of the nonattainment area. Four of 

these units are located at Plant Hammond, which is located approximately 25 miles south 

of the nonattainment area. Two of the units are 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 located approximately 80 miles south of the 

Chattanooga nonattainment area. The Title V permit of each affected facility has been 

amended to permit operation of the FGD controls. 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 places 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 emission 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 SO2 Control Requirements and 

Construction Permit Numbers 

Plant/Unit County Required 

FGD 

operation 

date, per rule 

(sss) 1

Control4 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

Wansley Unit 1/1/2007

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 NA2 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 NA2 4911-015-0011-V-02-3 12/20/06 

Scherer Unit 3 Monroe 12/31/2011 X FGD control to be 

incorporated into permit 

renewal3

(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 permit will be incorporated into permit renewal 

(4) controls on these units are modeled in the attainment demonstration

The total estimated reduction of SO2 emissions from the seven EGUs by 2009 is 216,412 

tons/yr. The estimated statewide 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 weight of 14001 lbs or more) to 

meet the California Air Resources Board (CARB) emissions standards by 2005. These 

standards are essentially the same as the federal heavy-duty engine standards (40 CFR 

86,Subpart A) described in the federal control measures section. The CARB heavy duty 

diesel engine standards were adopted to ensure 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 NTE test requirements. Manufacturers were 

required federally to begin testing the engines to the NTE requirements with the 2007 

model year. Georgia has kept the CARB standards in place to prevent a delay in the state 

receiving cleaner engines in the event that EPA 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 the use of a clean-idle engine beginning with 

the 2008 model year. The benefit of this 5-minute 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 mechanism 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 source 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 demonstrating attainment in 2009. 

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5.4. Contingency Measures

Section 172(c)(9) of the Clean Air Act requires states to identify and include contingency 

measures in their attainment demonstration plans. Contingency measures must take 

effect without significant further action by the State or U.S. EPA upon determination by 

EPA that an area has either failed to demonstrate 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 attain the standards by its attainment date. These 

contingency measures must be fully adopted rules 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 contingency measures, specify a schedule for 

implementation, and indicate that the measures will be implemented without significant 

further action by the State or by EPA. The contingency measures should consist of other 

control measures for the area that are not included 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 designation, 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 rule requires states to adopt “contingency measures equal 

to approximately one year’s worth of reductions 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 reductions needed to meet attainment will satisfy 

the requirement for contingency measures. 

The purpose of these contingency measures “is to provide a cushion while the plan is 

being revised to meet the missed milestone” (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 understanding, based on evaluation of 

the language of the CAA and from discussions with EPA Region 4, that these 

contingency measures are to be implemented approximately within two calendar years 

following the final emissions control year in the attainment demonstration. This time 

period takes into consideration the time required for EPA to make a finding of failure to 

attain the NAAQS and the requirement to submit 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 

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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 Chattanooga NAA is based largely on statewide 

reductions of SO2 emissions from Georgia’s coal-fired 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 Chattanooga NAA, there are no specific requirements in this plan 

for SO2 reductions within the Chattanooga nonattainment area prior to January 1, 2009. 

However, due to federal fuel requirements there 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 emissions 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 emissions 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 

Total 1267.2 1,278.9 11.7

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

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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 located outside of the nonattainment area and 

located in Georgia. 

5.4.1 Region for Contingency Measures

The application of RFP procedures to calculate 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 impacting the PM2.5 concentrations in the 

non-attainment area. “EPA believes 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 200 

kilometers from the nonattainment area” (72 FR 20636). Given that there are no coalfired EGUs located within the Georgia portion 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 contingency 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 limits discussed above” (72 FR 20638). For the 

Chattanooga NAA’s contingency measures, GA EPD has defined an area that includes 

counties within, and bisected, by an arc from the monitoring location in Rossville, 

Georgia, to the nearest significant source of SO2 emissions relied upon for reduction to 

demonstrate attainment, Georgia Power’s Plant Bowen. There are two counties not 

included in this analysis, Forsyth and Polk counties, both of which are bisected at the 

counties’ border by the circle 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-attainment 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 more 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. 

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Figure 5-1. Chattanooga Contingency Area of Influence 

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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 demonstration 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 influence to demonstrate contingency emission 

reductions. 

Table 5-10 displays the 2002 baseline SO2 emissions, by source sector, for the counties 

located within the area of influence for the 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 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 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 control year SO2 emissions, by source sector, 

for the counties located within the area of influence for the Chattanooga non-attainment 

area and includes the nonattainment counties of Catoosa 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. 

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 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 emissions, by source sector, for the counties 

located within the area of influence for the 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 additional reduction of 

72,982.2 tons of SO2 in the area of influence during the period of time for contingency 

measures to be evaluated. 

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 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 influence 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 For 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

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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 occur 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 within 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 subject to an emission 

reduction requirement that will reflect the new controls and be required by Georgia Rule 

.02(2)(uuu). The controls and limit will effect 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 generating 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 measure 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 needed and no further action is needed from 

EPD or EPA for this rule to be implemented. The Plant Bowen control measure, 

therefore, satisfies the contingency measure requirement. 

5.5 Unmodeled Control Measures

The following unmodeled measures, some of which are voluntary actions, should provide 

additional reductions to ambient PM2.5 concentrations: 

• Open burning rule; 

• Switching of two EGUs to lower sulfur coal; and 

• Planned early start-up of SO2 scrubbers at two EGUs. 

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Because the measures are voluntary and/or the benefits are relatively small, credits for 

these measures have not been taken in the projected emissions inventory 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 Measure 1 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 restrictions 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 carbon 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 and 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 July 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 - Early Implementation of Controls

Table 5-3 shows the compliance dates required by Georgia Rule .02(2)(sss) for 

installation and operation of SO2 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 with these controls are not credited in the 

projected 2009 inventory used for modeled attainment demonstration. However, to 

ensure compliance with the rule, Georgia Power has planned for nominal start-up dates 

that are six months prior to the required dates. If the start-ups occur early as planned, 

then the scrubbers at Bowen Unit 2 and Wansley Unit 2 would achieve 2009 SO2

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 contribute to attainment as 

“expeditiously as practicable” in the specific nonattainment area. The EPA has 

consistently interpreted RACM as a collection of measures that would advance the 

attainment date by at least one year. This requirement also applies to emissions 

reductions from existing stationary sources through the adoption of reasonably available 

control technology (RACT). “Reasonably available” 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/m3

 or lower. The 2009 annual modeled Future Design 

Values (DVF) for the Chattanooga Annual PM 2.5 nonattainment area are 13.6 ug/m3

 for 

the Riverside site, 14.4 ug/m3

 for the Maxwell Road/East Ridge site, 12.3 ug/m3 for the 

Soddy-Daisy High School site, and 13.9 ug/m3

 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 Federal 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 Chattanooga area. Therefore the Chattanooga 

nonattainment area is subject to a limited RACT/RACM analysis. 

EPA has stated (72 FR 20617) that “If the State 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 time from the promulgation of the rule to the 

SIP due date, it would be difficult for states to devise, adopt, and implement RACM in 

time to advance the attainment date. EPD concurs that either, a case-by-case RACT 

analysis and permitting, or new RACT/RACM rule development, including the analysis, 

stakeholder process, rule development, public comment, and administrative 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 the 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 nonattainment area. This 

led to a determination that the primary component of ambient monitored PM2.5 is the 

precursor SO2, with the primary source of SO2 emissions within the State of Georgia 

(and, to a lesser extent, the Southeast) being 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 SO2 is the significant 

contributor of PM2.5 emissions and that the control of SO2 emissions from EGUs is the 

most effective strategy for significant PM2.5 air quality improvements. Using the results 

of this modeling analysis, evaluating Georgia’s other PM2.5 nonattainment areas 

(Atlanta, Chattanooga, and Floyd County), and incorporating Georgia’s responses to the 

Clean Air Interstate Rule (CAIR), the Clean 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) “Multipollutant Control for Electric 

Utility Steam Generating Units” (Multipollutant 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 requiring all EGUs located 

at Georgia Power Plant Bowen and Plant Hammond, both of which are located just 

outside the Chattanooga nonattainment area, to install 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 satisfies RACT for SO2 emissions, it also 

specifies that “after consulting with power companies, the State may conclude that 

establishing additional ‘beyond CAIR’ emission control requirements 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 controls at both Plant Bowen 

and Plant Hammond will not advance the attainment date for Chattanooga. EPD notes 

that the Multipollutant rule requires SO2, NOx and Hg controls on all large EGUs in the 

state. The controls at both Plant Bowen and Plant Hammond are being installed as 

“expeditiously as practicable” given the extent of the engineering and construction 

schedules involved in retrofitting all large EGUs within the State of Georgia. 

There is only one major point source in 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 contribute 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) National 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 Quality Goals for Ozone, PM2.5, and Regional 

Haze (“Attainment Guidance”), an attainment demonstration 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 components 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 analysis (UAA) to the air quality modeling 

results to determine if the annual PM2.5 NAAQS will be met. Additional technical or 

corroboratory analyses may also be used as part of a “supplemental analysis” or a more 

stringent “weight of evidence” determination to supplement the modeled attainment test 

and to further support a demonstration of attainment of the NAAQS. 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 attainment of the NAAQS for PM2.5 by the attainment year of 2009. The 

modeling is applied in a relative sense, similar to the 8-hour ozone attainment test. 

However, the PM2.5 attainment test is more complicated and reflects the fact that PM2.5 

is a mixture of different species. In the test, ambient PM2.5 is divided into major 

components, with a separate relative response factor (RRF) and future design value 

(DVF) calculated for each of the PM2.5 components. 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 model projections of PM2.5 and compare them 

to the NAAQS. Complete calculations and results are contained in Appendix M. 

6.1.1 Determination of Baseline Design Values 

The first step in any attainment test process 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) monitor in the PM2.5 nonattainment areas. 

Concentrations are calculated based on calendar 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 occur during the different 

seasons. 

Table 6-1 contains the quarterly average PM2.5 concentration for the FRM monitors in 

the Chattanooga nonattainment area. This quarterly 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 Quarterly and Annual Mean PM2.5 DVB (in μg/m3) 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 

13-295-0002 Walker Rossville 13.163 16.592 19.486 13.361 15.7 

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

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

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

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 divide 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 inorganic particulate matter (Crustal); and 

8. And passively collected mass or the mass of the blank filter (Blank = 0.5 μg/m3

). 

The second part of the process is to use the 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 the 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-located Speciation Trends Network (STN) 

speciation monitors. 

The Hamilton Riverside site (47-065-4002) has a co-located STN monitor prior to 2005, 

while the other sites (13-295-0002, 47-065-0031, and 47-065-1011) do not. The 

following sections will explain how these issues were overcome to produce the speciated 

values needed for this attainment 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 components and do not retain all of the PM2.5 

that is measured by routine speciation samplers 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) and includes particle-bound water associated with sulfates, 

nitrates and other hygroscopic species (positive sampling artifacts). This results in 

concentrations (and percent contributions to PM2.5 mass), which 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 Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbon 

Hybrid (SANDWICH) material balance approach. With the SANDWHICH approach, 

nitrate mass is adjusted to account for volatilization based on hourly meteorology 

parameters. Subsequently, quarterly average nitrate, sulfate, elemental carbon, and 

crustal mass can be calculated, as well as the Degree of Neutralization (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 water can be calculated 

from the previously obtained DON, sulfate, nitrate, and ammonium values. Finally, 

organic carbon is calculated by taking the difference between the total PM2.5 mass as 

measured at the FRM monitor, and the calculated component mass (i.e. OC from mass 

balance ([OCMmb]) = PM2.5FRM - {[EC] + [SO4] + [NO3] + [NH4] + [PBW] + 

[Crustal] + [Blank]}). 

6.1.1.2.2 Speciated Profiles 

While the SANDWICH method reconciles the 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 Guidance 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 create a spatial field using ambient speciation 

data; and 

4. Use of interpolation techniques to create 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 concentrations at FRM sites that do not have 

speciation data (numbers 3 and 4 above). To assist in this task, EPA is developing a 

software tool called “Modeled Attainment Test Software” (or MATS) that will perform 

the spatial analysis of options number 3 and 4 described above. However, the PM2.5 

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portion of the MATS tool had not been released at the time this analysis was performed. 

In trying to pursue the US EPA recommended course, 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 both the Interagency Monitoring of Protected 

Visual Environments (IMPROVE) and the US EPA’s Speciation Network (ESPN) to 

derive mean concentrations for six PM2.5 components. Quarterly average concentrations 

between Jan 2002 to December 2002 were retained 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 crustal material (which includes the five 

trace elements aluminum, calcium, iron, silicon, and titanium). The major species for 

IMPROVE are the same except for NH4, which is not routinely measured in the 

IMPROVE protocol. 

The quarterly averaged species concentrations at the IMPROVE and ESPN monitors 

were used to interpolate concentrations at the PM2.5 FRM monitoring sites using a 

technique called Voronoi Neighbor Averaging (VNA). 

As a result of the CAIR SMAT process, quarterly 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 constant passive blank 

mass of 0.5 μg/m3

. DON refers to degree of neutralization of sulfate and ranges from 0.0 

(sulfuric acid) to 0.375 (completely neutralized in the form of ammonium sulfate). 

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Table 6-3. DVB Quarterly Species Fractions (%) 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 (μg/m3) 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-year concentration near the same monitor, 

or: 

Quarterly Mean Year Base Modeled Concentrat X""Monitor Near""ion 

Quarterly Mean Year Future Modeled Concentrat X""Monitor Near""ion RRF =

Instead of focusing on the individual 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 the 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 Recommendation 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 concentration 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 concentrations from each cell of the 3x3 array 

are then averaged to develop a single mean 24-hour average concentration for each 

component on each day in both the base and future 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 respective base year quarterly mean component 

concentrations to develop quarterly RRFs for each component of PM2.5. The quarterly 

RRFs for the FRM monitors in the Chattanooga PM2.5 nonattainment area are presented 

below in Table 6-6. In lieu 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 RRFs 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 calculate 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 μg/m3) 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 average PM2.5 estimate for each FRM site is 

corrected for the 0.5 μg/m3

 blank mass. These 2009 annual PM2.5 concentrations 

(DVFs) are then compared to the NAAQS (15.0 μg/m3

) in Table 6-9 below. Since the 

values at both FRM sites in the nonattainment areas are less than 15.0 μg/m3

, 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 additional analyses of air quality, emissions and 

meteorological data, and consider modeling outputs other than the results of the 

attainment test. If the attainment test results fall short of the standard, the results of 

corroboratory analyses may be used in a weight of evidence determination (WOE) to 

show that attainment is likely despite modeled results, which may be inconclusive. 

The Attainment Guidance defines the guidelines for supplemental analysis/WOE for the 

annual PM2.5 standard as follows: 

- Site with a DVF less than 14.5 μg/m3

 should submit basic supplemental analysis 

to confirm the outcome of the model attainment test. 

- Sites with a DVF between 14.5 and 15.5 μg/m3

 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 μg/m3

 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 

μg/m3, the following supplemental analysis to corroborate modeling results should be 

adequate to complete the attainment demonstration, rather than a WOE analysis to show 

attainment. In the following sections, we explore refinements to the attainment test, 

additional modeling studies, and air quality and emissions trends as part of a 

supplemental analysis for the Chattanooga 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 analysis. Suggestions include completing 

additional analysis on the air quality modeling preformed, or modeling 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 μg/m3

 within the nonattainment 

area; 

2. Percent change in number of grid cells > 15 μg/m3

 within the nonattainment area; 

3. Percent change in grid cell-hours (days) > 15 μg/m3

 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 Guidance, care should be taken in interpreting 

absolute metrics if the model evaluation shows a large under prediction 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 concentrations 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 contained a portion of the Chattanooga nonattainment area. 

Figure 6-1 highlights the 37 cells that encompass the Chattanooga nonattainment area. 

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Figure 6-1. Grid cells for which the air quality metrics were applied in the 

Chattanooga PM2.5 nonattainment area.

The cell-days counts were binned based on concentration ranges of 5 μg/m3 intervals. 

Figure 6-2 presents the cell-days count results graphically and shows an increase in the 

number of cell-days below 15 μg/m3

. 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 μg/m3 range to the less than 

15 μg/m3 range between 2002 and 2009 (Table 6-10). 

Figure 6-2 also shows a decrease in the number of cell-days in the 15 – 20 μg/m3 bin (429 

cell-days decrease), the 20 – 25 μg/m3

 bin (375 cell-days decrease), the 25 – 30 μg/m3

bin (216 cell-days decrease), and the 30 – 35 μg/m3

 bin (65 cell-days decrease). In 

addition, the number of cell-days greater than 35 μg/m3

 bin decreased to zero cell-days 

(100.0% decrease) from 2002 to 2009. Overall, the results from the air quality modeling 

metric are encouraging as shown by the substantial increase in the number of cell-days 

below 15 μg/m3

. 

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Figure 6-2. Percentage of Cell-Days in the Chattanooga Nonattainment Area within 

Concentration Categories for 2002 and 2009. 

Table 6-10. Number of Cell-Days within Concentration Bins. Increases (decreases) 

in the number of cell-days (and percent change) within the bins are noted by red 

(blue) coloration. 

 

Concentration 

[μg/m3

] 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 studies. 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 contained results for the Chattanooga PM2.5 

nonattainment area is the U.S. EPA’s modeling for the Clean Air Interstate Rule (CAIR). 

The Technical Support Document for the final CAIR, March 2005, provided modeling 

results with and without the implementation for the CAIR. Differences between the U.S. 

EPA’s modeling and the attainment demonstration are: 1) the meteorology was for 2001, 

2) the DVB was the weighted design values for the 1999-2003 period and 3) the 

modeling results were for 2010. The DVF was calculated using the CAIR SMAT tool, so 

methodologies between the CAIR DVF and the values presented in Section 6.1 are the 

same. These modeling results are listed in Table 6-11 below. 

Table 6-11. US EPA CAIR Modeling Results for Walker County, GA and Hamilton 

County, TN (highest monitors) 

Future Year Walker County, GA 

PM2.5 (μg/m3) 

Hamilton County, TN 

PM2.5 (μg/m3) 

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 CAIR 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 the 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 average 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 measured 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 provides a graphical representation 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) 

(μg/m3) 

TN Hamilton 

Maxwell 

(47-065-0031) 

(μg/m3) 

TN Hamilton 

Soddy Daisy 

(47-065-1011) 

(μg/m3) 

TN Hamilton 

Riverside 

(47-065-4002) 

(μg/m3) 

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 all the Chattanooga monitors have maintained 

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design values that were close to or below the NAAQS over the past five design value 

periods (see Table 6-13). 

Table 6-13. Three Year Design values (in μg/m3) 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 note that there are still 

significant sulfur dioxide (SO2) emission reductions that are expected between now and 

the attainment year in the utility sector. As a result of the Georgia Multipollutant Rule, 

multiple scrubbers will be installed 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 total, over 216,400 tons of SO2 will be reduced 

annually in Georgia. This reduction represents a 38% reduction 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 reductions from new controls on Georgia 

Power EGUs in 2008 and 2009. 

Facility Unit Control Date1 2007 SO2 

Emissions2

(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 (based 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 plotted in the same figure to allow a 

comparison between emission changes and changes in PM2.5 concentrations. The 2009 

PM2.5 concentrations are from the model projections and the 2008 values are calculated 

by interpolating between the 2007 observations and the 2009 projections. SO2 emissions 

in Georgia are higher in 2003-2007 compared to 2002, as are some PM2.5 

concentrations. However, PM2.5 concentrations start to trend downward in 2006 due to 

emission reductions in neighboring states. The large emission reduction in Georgia 

between 2007 and 2009 are projected to be more than enough to bring Chattanooga into 

attainment. It should be noted that the year-to-year variability in PM2.5 concentrations 

are due to a combination of emission changes 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

1,000,000

19992000200120022003200420052006200720082009

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

PM2.5 (

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 interpolated 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 modeling 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 future air quality at locations where there is 

not a PM2.5 monitor nearby. To guard against the possibility that air quality levels could 

exceed the standard in areas with limited monitoring, Section 3.4 of the Attainment 

Guidance suggests that additional review is “necessary, particularly in nonattainment 

areas where the ozone or PM2.5 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 leads 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 network relieves the necessity of applying this 

additional analysis. However, GA EPD decided to apply some additional analysis and 

looked at 2009 PM2.5 concentrations directly from the model (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 seen 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 higher. 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 gradients across the area of interest. Therefore, it 

can be concluded that there are no unmonitored hotspots that need to be addressed. It 

should be noted that the 2009 absolute modeled PM2.5 annual concentrations (no SMAT 

or SANDWICH) are lower than that calculated at the FRM monitor using SMAT and 

SANDWICH. However, this discrepancy between 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 and air quality models and any other files 

used to develop the modeling. To request access 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 amended, contains the plan requirements for 

nonattainment areas in general. The Chattanooga 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 include 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 (RACM) to demonstrate attainment as 

expeditiously as practicable. A subset of RACM is reasonably available control 

technology (RACT), which 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 designation, 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 inventories of emissions of 

direct PM2.5 and PM2.5 precursors. Such inventories 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 through 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 inventory 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 identification 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 development should be targeted. Georgia has no such 

zones targeted for economic development identified by the Administrator. 

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Section 172(c)(5) requires a permit program consistent with the requirements of Section 

173. Georgia EPD updated rules for nonattainment 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 the permit program requirements for a PM2.5 

nonattainment area. GA EPD is currently complying with the federal nonattainment new 

source review regulations as promulgated 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 discusses 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 nonattainment 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 EPA 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 requirements for Section 110(a)(2)(G). A copy of 

these letters can be found in Appendix N. EPA agreed with EPD’s certification 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 envisioned under Section 172(c)(8). 

7.9 Contingency Measures

The Clean Air Act requires States containing nonattainment areas to adopt contingency 

measures that will take effect 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 applicable NAAQS, as described in Section 

172(c)(9). These measures are 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 strategy SIP revision. As such, it establishes a 

motor vehicle emissions budget (MVEB) for the nonattainment area. A motor vehicle 

emissions budget is described in EPA's transportation conformity rule1

 as "...the 

implementation plan's estimate of future [motor vehicle] emissions." Such budgets 

establish caps on motor vehicle emissions; projected emissions from transportation plans 

and programs must be equal to or less than these caps for a positive conformity 

determination to be made. 

8.2 Establishment of 2009 Motor Vehicle Emissions 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 Conservation; the Chattanooga-Hamilton County Air 

Pollution Control Bureau; and the Chattanooga-Hamilton County/North Georgia 

Transportation Planning Organization (TPO), the metropolitan planning organization for 

the Chattanooga area; to develop the best possible estimates of mobile2

 source emissions 

for the Chattanooga nonattainment area. Mobile source inventories for 2009 were 

developed using the latest available planning assumptions,3

 the most recent travel 

demand model, and EPA's latest motor vehicle emission factor model, MOBILE6.2.03. 

To establish a separate motor vehicle emissions budget for the Georgia part of the 

Chattanooga nonattainment area (Catoosa and 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 (all of Catoosa County, 

part of Walker) and an off-model methodology for the remainder of Walker County (see 

Appendix J). The methodology used to calculate 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 factor model was used to calculate 2009 

emission factors with all proposed 2009 mobile 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 Tennessee, 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-year emissions in the following manner: 

• For each of four times of day (a.m. peak, midday, p.m. peak, and night), the HPMSadjusted4

 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-flow 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 converted from grams-per-day to tons-perday, then multiplied by 365 to get tons-per-year. 

For the part of Walker County not included in the 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 emissions modeling methodology, along with the 

MOBILE6.2 input files, emission factors, and other documentation, are in Appendix J. 

Table 8-1 sums the calculated emissions inventories 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/year) 

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. Interagency consultation among the relevant 

agencies occurred during the development of this 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 Demonstration 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 emission factor model; and on a 

registration distribution by age developed using registration data5

 obtained from R.L. 

Polk & Company. 

To show the MVEB "together with all other emissions sources6

," point, area, and nonroad emissions from the 2009 "best-and-final" modeling inventories developed by the 

VISTAS/ASIP contractors were used. Total projected 2009 annual PM2.5 emissions for 

the Georgia part of the Chattanooga nonattainment area are shown 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 conformity 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, and 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 PM2.5 Design 

Value 

 (μg/m3) 

PM2.5

Standard 

 (μg/m3) 

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-Maple Street site in Georgia and the Siskin 

Drive/UTC site in Tennessee have shown that the most significant components of PM2.5 

are sulfates and organic carbon. Sulfates are formed primarily from sulfur dioxide 

emissions from coal-fired electric generating 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 sulfates to the ambient PM2.5 problem in the 

nonattainment area, the most important measures 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 achieve the needed reductions by reducing 

SO2 emissions from EGUs. The total estimated reduction of SO2 emissions from seven 

EGUs between 2002 and 2009 is equivalent to 29.1 percent of projected 2009 statewide 

SO2 emissions (744,000 tons/yr) if no controls were assumed. Other modeled state and 

federal control measures, as well as unmodeled 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 being done by VISTAS and the PM2.5 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 pollutants inventoried include VOC, NOx, PM2.5, coarse 

particulate (PM10), NH3, and SO2. The projection to 2009 accounted for control measures 

expected to be implemented.

The CMAQ modeling performance was measured 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 performance is well within the limits of acceptable 

performance criteria established 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 SMAT 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/m3

, modeled attainment is demonstrated. 

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Table 9-2. PM2.5 2009 Future (Modeled) Design Values 

Federal 

Reference 

Monitor 

Monitor ID 2009 Annual 

Design Value 

(DVF) 

 (μg/m3) 

PM2.5 Standard 

 (μg/m3) 

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 demonstrates that the Chattanooga 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 162 
References PM2.5 Attainment SIP 

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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., 

Volume 40, pp. 4946-4959. 

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 Ozone, PM2.5 and Regional Haze. U.S. 

Environmental Protection Agency, Research Triangle Park, NC. EPA-454/B-07-002. 

April. 

IMPROVE, 1995. IMPROVE Data Guide - A Guide 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 Document for VISTAS Emission and Air Quality 

Modeling to Support Regional Haze State Implementation Plans (Draft Final Report). 

ENVIRON International Corporation, Novato, California. November 14. 

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