Georgia's State Implementation Plan 

for 

The Macon PM2.5 Nonattainment Area 

June 12, 2009 

Prepared by: 

Georgia Department of Natural 

Resources 

Environmental Protection Division 

Air Protection Branch 

 PM2.5 Attainment SIP - Macon, GA 

Preface 

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

National Ambient Air Quality Standard (NAAQS) for PM2.5 in the Macon nonattainment 

area. The Macon nonattainment area includes all of Bibb County and a portion of 

Monroe County. Specifically, this SIP demonstrates attainment of the annual PM2.5

NAAQS, promulgated in 1997, in the Macon nonattainment area by April 5, 2010. 

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Table of Contents PM2.5 Attainment SIP - Macon, GA 

TABLE OF CONTENTS 

Preface................................................................................................................................. 3 

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

LIST OF APPENDICES..................................................................................................... 7 

LIST OF FIGURES ............................................................................................................ 8 

LIST OF TABLES............................................................................................................ 11 

Executive Summary.......................................................................................................... 15 

Section 1. Introduction..................................................................................................... 19 

1.1 FINE PARTICLES...................................................................................................... 19 

1.2 1997 NATIONAL AMBIENT AIR QUALITY STANDARDS FOR PM2.5 .......................... 19 

Section 2. Macon Nonattainment Area............................................................................ 21 

2.1 NONATTAINMENT AREA DESIGNATION .................................................................. 23 

2.2 PM2.5 MONITORING STATIONS................................................................................ 24 

2.3 CONCEPTUAL DESCRIPTION OF THE PM2.5 PROBLEM ............................................ 26 

2.3.1 Long-term trends of PM2.5............................................................................. 26

2.3.2 Spatial Trends of PM2.5 in Georgia................................................................ 35

2.3.3 Source Apportionment of PM2.5...................................................................... 39

2.3.4 Meteorology ..................................................................................................... 41

2.3.5 Sensitivities to Pollutant Emissions................................................................. 44

2.4 PM2.5 SOURCES AND PRECURSORS........................................................................ 48 

2.4.1 Sources of PM2.5............................................................................................ 48

2.4.2 Precursors....................................................................................................... 50

2.4.3 Technical Justification for Reversal of NOx Precursor Presumption ............ 52

Section 3. Attainment Demonstration Methods and Inputs............................................. 59 

3.1 ANALYSIS METHOD ................................................................................................. 59 

3.2 MODEL SELECTION .................................................................................................. 60 

3.2.1 Selection of Air Quality Model ........................................................................ 60

3.2.2 Selection of Meteorological Model.................................................................. 61

3.2.3 Selection of Emissions Processing System....................................................... 63

3.3 SELECTION OF THE BASE MODELING YEAR ............................................................. 64 

3.4 MODELING DOMAINS............................................................................................... 65 

3.4.1 Horizontal Modeling Domain.......................................................................... 65

3.4.2 Vertical Modeling Domain .............................................................................. 67

3.5 BASELINE EMISSIONS INVENTORY ........................................................................... 69 

3.5.1 Stationary Point Sources.................................................................................. 72

3.5.2 Stationary Area Sources .................................................................................. 73

3.5.3 Off-Road Mobile Sources................................................................................. 74

3.5.4 Highway Mobile Sources ................................................................................. 74

3.5.5 Biogenic Emission Sources.............................................................................. 74

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Table of Contents PM2.5 Attainment SIP - Macon, GA 

Section 4. Model Performance Evaluation ...................................................................... 75 

4.1 METEOROLOGICAL MODEL PERFORMANCE ............................................................. 75 

4.2 AIR QUALITY MODEL PERFORMANCE...................................................................... 80 

4.2.1 Modeling Performance Goals and Criteria..................................................... 80

4.2.2 Domain-Wide Model Performance .................................................................. 83

4.2.3 Model Performance within Georgia and in the Macon Nonattainment Area . 85

4.2.3.1 Model Performance Statistics....................................................................... 88

4.2.3.1.2 Model Performance Statistics for STN Sites.............................................. 92

4.2.4 Summary of the Performance Evaluation of the CMAQ Modeling ................. 94

Section 5. Control and Contingency Measures................................................................ 96 

5.1 FEDERAL CONTROL MEASURES FOR PM2.5 ATTAINMENT ...................................... 96 

5.1.1 Clean Air Interstate Rule ................................................................................. 96

5.1.2 Tier 2 Vehicle Standards.................................................................................. 99

5.1.3 Heavy-Duty Gasoline and Diesel Highway Vehicles Standards ................... 100

5.1.4 Large Nonroad Diesel Engines Rule ............................................................. 100

5.1.5 Nonroad Spark-Ignition Engines and Recreational Engines Standard......... 100

5.1.6 NOx SIP Call in Surrounding States.............................................................. 101

5.2 STATE CONTROL MEASURES FOR PM2.5 ATTAINMENT......................................... 101 

5.2.1 Multipollutant Control for Electric Utility Steam Generating Units, GA Rule 

391-3-1-.02(2)(sss).................................................................................................. 103

5.2.2 Heavy Duty Diesel Engines Rule (ooo) ......................................................... 104

5.3. SUMMARY OF SO2 EMISSIONS CHANGES ............................................................ 105 

5.4. CONTINGENCY MEASURES................................................................................... 106 

5.5 UNMODELED CONTROL MEASURES ...................................................................... 109 

5.5.1 GA rule 391-3-1-.02 (5), Open Burning ........................................................ 111

5.5.2 Middle Georgia Clean Air Coalition ............................................................ 111

5.5.3 Switch to Lower Sulfur Coal, Georgia Power Plant Scherer....................... 113

5.5.4 Switch to Natural Gas from Coal, Georgia Power Plant McDonough ........ 113

5.5.5 Multipollutant Rule, Early Implementation of Controls............................... 113

5.6 RACT AND RACM .............................................................................................. 113 

Section 6. Attainment Demonstration............................................................................ 116 

6.1 MODELED ATTAINMENT TEST ............................................................................... 116 

6.1.1 Determination of Baseline Design Values..................................................... 116

6.1.2 Relative Response Factor Calculation........................................................... 121

6.1.3. Future Year Quarterly Concentration Calculation ...................................... 122

6.1.4. Future-Year Annual Average Estimate......................................................... 123

6.2 SUPPLEMENTAL ANALYSIS .................................................................................... 123 

6.2.1 Additional Air Quality Modeling ................................................................... 124

6.2.2 Air Quality and Emission Trends Analysis.................................................... 128

6.2.3 Supplemental Analysis Conclusions .............................................................. 130

6.3 UNMONITORED AREA ANALYSIS ........................................................................... 130 

6.4 DATA ACCESS........................................................................................................ 132 

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Table of Contents PM2.5 Attainment SIP - Macon, GA 

Section 7. Other Clean Air Act Requirements............................................................... 133 

7.1 RACT/RACM REQUIREMENTS ............................................................................ 134 

7.2 REASONABLE FURTHER PROGRESS ....................................................................... 134 

7.3 ACTUAL EMISSIONS INVENTORY........................................................................... 134 

7.4 PERIODIC EMISSIONS INVENTORY......................................................................... 134 

7.5 PERMIT PROGRAM REQUIREMENTS....................................................................... 134 

7.6 OTHER MEASURES ................................................................................................ 135 

7.7 COMPLIANCE WITH SECTION 110(A)(2) (INFRASTRUCTURE ELEMENTS)............... 135 

7.8 EQUIVALENT TECHNIQUES.................................................................................... 135 

7.9 CONTINGENCY MEASURES.................................................................................... 136 

Section 8. 2009 Motor Vehicle Emissions Budget ........................................................ 137 

8.1 BACKGROUND....................................................................................................... 137 

8.2 ESTABLISHMENT OF 2009 MOTOR VEHICLE EMISSIONS BUDGETS FOR THE MACON 

PM2.5 NONATTAINMENT AREA................................................................................... 137 

Section 9. Summary and Conclusion............................................................................. 141 

Section 10. References................................................................................................... 143 

6 of 143 
List of Appendices PM2.5 Attainment SIP - Macon, 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 Macon 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 - Macon, GA 

LIST OF FIGURES 

Figure 2-1. PM2.5 Nonattainment Areas in Georgia..................................................................... 21 

Figure 2-2. Macon PM2.5 Nonattainment Area ........................................................................... 22 

Figure 2-3. Locations of PM2.5 Monitors in the Macon Nonattainment Area .................................... 25 

Figure 2-4. Trend of annual mean (arithmetic average of quarterly averages) PM2.5 concentrations at two 

FRM monitors (13-021-0007 and 13-021-0012) in the Macon NAA. ............................................... 27 

Figure 2-5. Trend of PM2.5 Design Values at two FRM monitors (13-021-0007 and 13-021-0012) in the 

Macon NAA. ........................................................................................................................ 28 

Figure 2-6. Quarterly averages of PM2.5 concentrations at 13-021-0007........................................... 29 

Figure 2-7. Quarterly averages of PM2.5 concentrations at 13-021-0012........................................... 30 

Figure 2-8. Trend of annual total and speciated PM2.5 concentrations (arithmetic average of daily 

averages) at the Allied Chemical speciation monitor (13-021-0007). ................................................ 31 

Figure 2-9. Quarterly averages of PM2.5 concentrations at the speciation site in the Macon NAA ......... 32 

(13-021-0007)....................................................................................................................... 32 

Figure 2-10. Quarterly averages of SO4 concentrations at the speciation site in the Macon NAA ........... 32 

(13-021-0007)....................................................................................................................... 32 

Figure 2-11. Quarterly averages of NO3 concentrations at the speciation site in the Macon NAA .......... 33 

(13-021-0007)....................................................................................................................... 33 

Figure 2-12. Quarterly averages of NH4 concentrations at the speciation site in the Macon NAA .......... 34 

(13-021-0007)....................................................................................................................... 34 

Figure 2-13. Quarterly averages of OC concentrations at the speciation site in the Macon NAA (13-021-

0007)................................................................................................................................... 34 

Figure 2-14. Quarterly averages of EC concentrations at the speciation site in the Macon NAA............. 35 

(13-021-0007)....................................................................................................................... 35 

Figure 2-15: 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). ....................................................................................... 36 

Figure 2-16: 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). ............................................................................. 36 

Figure 2-17: 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). ............................................................................. 37 

Figure 2-18. 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). ....................................................................................... 37 

8 of 143 
List of Figures PM2.5 Attainment SIP - Macon, GA 

Figure 2-19: Average factor contributions for 2003-04 at the Atlanta, Floyd County, and Macon speciation 

sites using PMF ..................................................................................................................... 40 

Figure 2-20: Average composition for all key PM2.5 bins in Macon. ............................................... 44 

Figure 2-21: Sensitivities of PM2.5 by pollutant and source category for the summer episode in Macon. 

ΔPM2.5 is based upon a 30% reduction of pollutant levels from 2009 levels...................................... 45 

Figure 2-22: Sensitivities of PM2.5 by pollutant and source category for the winter episode in Macon. 

ΔPM2.5 is based upon a 30% reduction of pollutant levels from 2009 levels...................................... 45 

Figure 2-23: Annual average emission sensitivities by pollutant and source category based on the average 

response of the summer and winter episodes in Macon. ................................................................. 46 

Figure 2-24: Annual average emission sensitivities by pollutant and source category based on the CART 

weighted response of the summer and winter episodes in Macon. .................................................... 47 

Figure 2-25. 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......... 52 

Figure 2-26. Reductions in EC, OC, and NO3 concentrations between 2002 & 2009 and 2009 & 2018 at 

Georgia nonattainment monitors. .............................................................................................. 54 

Figure 2-27. Annual Average PM2.5 Response to GA Statewide 50% NOx Emission Reduction in 2009 in 

the Georgia PM2.5 Nonattainment Areas. ................................................................................... 54 

Figure 2-28. Annual Average PM2.5 Response to Georgia Statewide 30% NOx Emission Reduction in 

2009 in the Georgia PM2.5 Nonattainment Areas. ........................................................................ 55 

Figure 2-29. 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................................................................................................ 57 

Figure 3-1. The MM5 and CMAQ 36-km Horizontal Domains ....................................................... 66 

Figure 3-2. VISTAS 12-km Modeling Domain ............................................................................ 66 

Figure 4-1: Monthly averaged Bias statistics for 2-m temperature and humidity ................................. 78 

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... 83 

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................... 84 

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............................... 84 

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. ......................................................... 84 

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 Macon PM2.5 nonattainment area (right). ............. 87 

Figure 4-7. Stacked bar plots of daily PM2.5 species concentration at 13-021-0007......................................... 95 

Figure 6-1. Grid cells for which the air quality metrics were applied in the Macon PM2.5 nonattainment 

area. .................................................................................................................................. 125 

Figure 6-2. Percentage of Cell-Days in the Macon Nonattainment Area within Concentration Categories 

for 2002 and 2009. ............................................................................................................... 126 

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List of Figures PM2.5 Attainment SIP - Macon, GA 

Figure 6-3. Annual PM2.5 Average Values for the Monitors in the Macon Nonattainment Area. ........ 128 

Figure 6-4. Modeled 2009 Annual Mean PM2.5 Concentration near the Macon Nonattainment Area on the 

VISTAS 12km Grid ............................................................................................................. 131 

10 of 143 
List of Tables PM2.5 Attainment SIP - Macon, GA 

LIST OF TABLES 

Table E-1. PM2.5 2002 Baseline Design Values and 2009 Future (Modeled) Design 

Values ............................................................................................................................... 18 

Table 1-1. 1997 NAAQS for PM2.5................................................................................ 19 

Table 2-1. 2001 – 2003 PM2.5 Design Values for Macon nonattainment area Monitors24 

Table 2-2. Macon PM2.5 Data Collection Sites................................................................ 25 

Table 2-3. Annual mean (arithmetic average of quarterly averages) PM2.5 

concentrations at two FRM monitors in the Macon NAA................................................ 28 

Table 2-4. PM2.5 Design Values at two FRM monitors (13-021-0007 and 13-021-0012) 

in the Macon NAA............................................................................................................ 29 

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

the nonattainment areas..................................................................................................... 38 

Table 2-6. Estimated primary and secondary percentage of PM2.5.................................. 38 

Table 2-7: Average factor contributions to PM2.5 (μg/m3) for 2003-04 at the Atlanta, 

Floyd County, and Macon speciation sites using PMF..................................................... 40 

Table 2-8: Summary of average input parameters for each CART classification Category 

in Macon. .......................................................................................................................... 42 

Table 2-9: Summary of average input parameters for key bins for CART classification 

(Class) categories 3 and 4 in Macon................................................................................. 43 

Table 2-10. Typical Base Year (2002) Emissions Inventories for Bibb and Monroe 

Counties............................................................................................................................ 48 

Table 2-11. Point Sources in the Macon Nonattainment Area That Are Title V Major 

Sources.............................................................................................................................. 49 

Table 2-12. Expected Change in NOx Emissions (%) in Georgia PM2.5 Nonattainment 

Areas. Reductions are negative........................................................................................ 53 

Table 3-1. Vertical Layer Definition For MM5 and CMAQ............................................ 68 

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

........................................................................................................................................... 69 

Table 3-3. 2009 BaseG4 Annual Emission Summary for Georgia Counties (tons) ........ 70 

Table 4-1: Mathematical Formulation of Statistical Metrics Used for Evaluating 

Mesoscale Meteorological Model Performance ............................................................... 77 

Table 4-2: Statistical Benchmarks for Mesoscale Meteorological Models...................... 77 

Table 4-3: Monthly averaged statistics for the region of the 12-km modeling domain in 

and around Georgia........................................................................................................... 79 

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List of Tables PM2.5 Attainment SIP - Macon, GA 

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

Species.............................................................................................................................. 81 

Table 4-5 Statistical Metric Calculations.......................................................................... 82 

Table 4-6. Ambient monitoring data available in 2002. .................................................. 86 

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

State of Georgia and in the Macon nonattainment area.................................................... 89 

Table 4-8. Monthly model performance statistics for PM2.5 at 13-021-0007................... 90 

Table 4-9. Monthly model performance statistics for PM2.5 at 13-021-0012................... 91 

Table 4-10. Annual model performance statistics for PM2.5 species and total PM2.5 at all 

STN monitors in the State of Georgia............................................................................... 92 

Table 4-11. Annual model performance statistics for PM2.5 at 13-021-0007................... 93 

Table 5-1. Federal PM2.5 Control Measures................................................................... 97 

Table 5-2. State PM2.5 Control Measures and Contingency Measures........................ 102 

Table 5-3. Georgia Multipollutant Rule SO2 Control Requirements and Construction 

Permit Numbers.............................................................................................................. 103 

Table 5-4. Changes in SO2 Emissions, 2002 to 2009, Bibb County (tons)................... 105 

Table 5-5. Changes in SO2 Emissions, 2002 to 2009, Monroe County (tons)*............ 105 

Table 5-6. Changes in SO2 Emissions, 2002 to 2009, Nonattainment Area (tons)....... 106 

Table 5-7. Changes in SO2 Emissions, 2002 to 2009, Statewide (tons) ....................... 106 

Table 5-8. Modeled Air Quality Benefit of Specific EGU Emissions Controls............ 108 

Table 5-9. Unmodeled Control Measures...................................................................... 110 

Table 5-10. Major Initiatives of the Middle Georgia Clean Air Coalition .................... 112 

Table 5-11 Macon Nonattainment Area Major Source SO2 Emissions (Actual) ........... 115 

Table 6-1. Quarterly Average PM2.5 Mass for the FRM Monitors in the Macon 

Nonattainment Area........................................................................................................ 117 

Table 6-2. Observed Quarterly and Annual Mean PM2.5 DVB (in μg/m3) for Monitors 

in the Macon Nonattainment Area.................................................................................. 117 

Table 6-3. DVB Quarterly Species Fractions (%) from the CAIR SMAT Tool ........... 120 

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

Macon Nonattainment Area............................................................................................ 120 

Table 6-5. USEPA’s Recommendation for Defining “Near” Cells............................... 121 

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

......................................................................................................................................... 122 

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

Macon Nonattainment Area............................................................................................ 122 

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List of Tables PM2.5 Attainment SIP - Macon, GA 

Table 6-8. Quarterly Mean PM2.5 Mass estimates for 2009......................................... 123 

Table 6-9. Estimated Annual Mean 2009 PM2.5 Mass Compared to the DVB............ 123 

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. ....................................................................................................................... 126 

Table 6-11. US EPA CAIR Modeling Results for Bibb County (highest monitor) ...... 127 

Table 6-12. Annual Average PM2.5 values (in μg/m3) in Macon for the past nine years.

......................................................................................................................................... 128 

Table 6-13. Three Year Design values (in μg/m3) for the Monitors in the Macon PM2.5 

Nonattainment Area........................................................................................................ 129 

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

EGUs in 2008 and 2009.................................................................................................. 129 

Table 7-1. General Plan Requirements for Nonattainment Areas ................................. 133 

Table 8-1. 2009 PM2.5 Motor Vehicle Emissions Budget, Macon Nonattainment Area

......................................................................................................................................... 138 

Table 8-2. Macon Nonattainment Area Total Projected 2009 PM2.5 Emissions, 

Tons/Year........................................................................................................................ 140 

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

......................................................................................................................................... 141 

Table 9-2. PM2.5 2009 Future (Modeled) Design Values.............................................. 142 

13 of 143 
Acronyms PM2.5 Attainment SIP - Macon, 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 

14 of 143 
Introduction PM2.5 Attainment SIP - Macon, 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 Macon Nonattainment Area 

(NAA). This SIP meets the requirements of EPA’s fine particle implementation rule, 40 

CFR 51 Subpart Z. The Macon 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 a 2001 - 2003 design value at the Allied Chemical monitor of 15.2 

μg/m3. This document does not address the 24-hour PM2.5 standard promulgated in 

December 2006, as nonattainment areas and requirements for this new standard have not 

been finalized. 

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; and 

• 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 Bibb County and a small portion of Monroe 

County that includes Plant Scherer, a coal-fired power plant. Land utilization in this area 

includes both urban (City of Macon) and suburban land uses. 

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

• Elemental carbon 

• Organic carbon 

• Sulfates 

• Nitrates 

• Crustal materials 

• Ammonia 

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. 

15 of 143 
Introduction PM2.5 Attainment SIP - Macon, GA 

The largest components measured in this nonattainment area are sulfates and organic 

carbon. Sulfate has made up approximately 30 percent of the ambient annual PM2.5 

measured since 2001 at the Allied Chemical monitor. 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. 

Organic carbon, like sulfates, comprised approximately 30 percent of the ambient annual 

PM2.5 measured from 2001 through 2005. This percentage increased over the next two 

years to approximately 45 percent in 2007. The sulfate percentage decreased to 

approximately 25 percent over the same period. 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. 

Elemental carbon, which is a component of direct PM2.5, has averaged approximately 

five percent of the ambient annual PM2.5. Nitrate concentrations are essentially the same 

as those of 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. Biogenic formation of organic carbon 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: 

 

• 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; and 

• NOx SIP call in surrounding states. 

The Clean Air Interstate Rule (CAIR) provides for reductions of both SO2 and NOx. The 

other federal measures provide for reductions of NOx and direct PM emissions. 

16 of 143 
Introduction PM2.5 Attainment SIP - Macon, GA 

Some measures were not modeled, but they are expected to achieve small reductions in 

ambient PM fine concentrations. These measures include: 

• Revisions to Georgia’s open burning rule to further restrict open burning in the 

Macon area during May through September; 

• Locally-developed and implemented air pollution control measures; 

• Switching of two EGUs to lower-sulfur coal; 

• Replacement of coal-fired capacity with natural-gas-fired combustion turbines at 

two EGUs; and 

• Planned, early start-up of SO2 scrubbers at two EGUs. 

These measures were not modeled because their expected reductions are small and 

because they are voluntary and/or difficult to model. 

For each nonattainment area the State must submit an attainment demonstration showing 

attainment with the annual PM2.5 standard as expeditiously as possible. Per 40 CFR 51.1007, 

the attainment demonstration must include: 

• Inventory data, 

• Modeling results, and 

• Emission reduction analyses. 

Air quality modeling conducted by Georgia EPD indicates that the nonattainment area 

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

projected SO2 emissions reductions from EGU sources within the State. 

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 tested by comparing 

predicted species concentrations to 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-1. 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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Introduction PM2.5 Attainment SIP - Macon, GA 

Table E-1. PM2.5 2002 Baseline Design Values and 2009 Future (Modeled) Design 

Values 

Federal 

Reference 

Monitor 

Site ID 2002 Annual Design 

Value (DVB) 

 (μg/m3) 

2009 Annual 

Design Value 

(DVF) 

 (μg/m3) 

Allied Chemical 130210007 15.2 14.3 

GA Forestry 

Commission 

130210012 13.3 12.5 

 As required by the EPA Clean Air Fine Particle Implementation Rule, sources of PM2.5 

and its precursors within the Macon Nonattainment Area 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 that was 

performed 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 5 years after the date of NAA 

designation. The Macon 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 Macon 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 Summary & Conclusion 

Section 10 References 

The appendices provide additional material to support the SIP narrative. 

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Introduction PM2.5 Attainment SIP - Macon, GA 

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. 

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 NAAQS for PM2.5 

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 US 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-

year average of the annual mean, which is, in turn, the average of one year’s four19 of 143 
Introduction PM2.5 Attainment SIP - Macon, GA 

calendar-quarter 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 3-year average of 

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

the 24-hour average PM2.5 concentration which 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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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Section 2. Macon 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 Macon nonattainment area is the southernmost of 

these areas and is located near the center of the state. 

 

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

The geographic boundaries of the Macon PM2.5 nonattainment area are shown in Figure 

2-2. The nonattainment area is made up of Bibb County, which contains the City of 

Macon, and a small portion of eastern Monroe County that includes Plant Scherer, a coal21 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

fired power plant. The portion of Monroe County is described as follows in 70FR965, 

dated January 5, 2005: 

From the point where Bibb and Monroe Counties meet at U.S. Hwy 23/Georgia Hwy 87 follow the 

Bibb/Monroe County line westward 150' from the U.S. Hwy 23/Georgia Hwy 87 centerline, proceed 

northward 150' west of and parallel to the U.S. Hwy 23/Georgia Hwy 87 centerline to 33 degrees, 04 

minutes, 30 seconds; proceed westward to 83 degrees, 49 minutes, 45 seconds; proceed due south to 150' 

north of the Georgia Hwy 18 centerline, proceed eastward 150' north of and parallel to the Georgia Hwy 

18 centerline to 1150' west of the U.S. Hwy 23/Georgia Hwy 87 centerline, proceed southward 1150' west 

of and parallel to the U.S. Hwy 23/Georgia Hwy 87 centerline to the Monroe/Bibb County line; then follow 

the Monroe/Bibb County line to 150' west of the U.S. Hwy 23/ Georgia Hwy 87 centerline. 

Land utilization in the nonattainment area includes both urban and suburban land uses. 

The population of the nonattainment area was approximately 154,000 in 2000 and is 

 

Figure 2-2. Macon PM2.5 Nonattainment Area 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

projected to grow no more than five percent by 2010. The nonattainment area 

designation, 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. 

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 is designated as 

nonattainment if it is deemed to contain sources that contribute to the nonattainment 

status of a county with a non-attaining design value. 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 Macon nonattainment area, for the PM2.5 NAAQS on January 5, 2005 

(70FR944), and amended the designations on April 5, 2005 (70FR19844). The Macon 

PM2.5 nonattainment area is made up of Bibb County and a small portion of Monroe 

County that contains a single coal-fired electric generation facility, Georgia Power’s 

Plant Scherer. The inclusion of the portion of Monroe County was based on high levels 

of SO2 emissions from Plant Scherer which contribute to ambient sulfate levels observed 

in Bibb County. Monroe County is located adjacent to and northwest of Bibb County. 

The designation of the Macon nonattainment area was based upon Georgia’s 

recommendations to EPA and upon EPA’s 9-factor analysis of counties in or adjacent to 

the Macon Metropolitan Statistical Area. 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; and 

• 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 one of the two nonattainment area monitors violated the 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

annual standard, as shown in Table 2-1 below. Both of the monitors are located in Bibb 

County, as shown in Figure 2-3. Details of the 9-factor analysis are included as 

Appendix A of this submittal.

Table 2-1. 2001 – 2003 PM2.5 Design Values for Macon nonattainment area Monitors 

Site name Monitor ID PM2.5 Design Value 

 (μg/m3) 

Allied Chemical 

(Bibb County) 

13-021-0007 15.2 

Georgia Forestry 

(Bibb County) 

13-021-0012 13.3 

2.2 PM2.5 Monitoring Stations

There are currently two ambient PM2.5 monitoring sites in the Macon nonattainment area, 

as shown in Table 2-2 and Figure 2.3. The Macon Allied Chemical and Macon Forestry 

sites are in Bibb County. Providing adequate coverage of the nonattainment area, data 

from both sites represent a neighborhood scale, which extends up to 4.0 kilometers from 

each monitor. Both of the sites are considered to be part of the State and Local Air 

Monitoring Stations (SLAMS) monitoring network, with the monitoring objective of 

sampling for population exposure. 

 

Two Federal Reference Method (FRM) monitors and one speciation monitor are located 

at the Macon-Allied Chemical site (13-021-0007). The FRM monitors were installed in 

accordance with 40 CFR 58. The first FRM monitor has been collecting data since 

February 2, 1999. Its sampling schedule was every three days until March 1, 2007, when 

the schedule was changed to daily sampling. The second FRM monitor has been 

collecting data since January 5, 2006. The speciation monitor has been collecting data 

since March 3, 2002. The sampling schedules of the second FRM monitor and the 

speciation monitor have been and remain every six days. 

One FRM monitor, which has been collecting data since February 11, 1999, is located at 

the Macon-Forestry site (13-021-0012). Its sampling schedule had been and remains 

every three days. There is no speciation monitor at the Macon-Forestry site. 

 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Table 2-2. Macon PM2.5 Data Collection Sites 

Site Name County Site ID 

Start Date – 

FRM Data 

Collection 

Start Date – 

speciation Data 

Collection 

Macon-Allied 

Chemical Bibb 13-021-0007 

Mon. 1 – Feb. 2, 

1999 

Mon. 2 – Jan. 5, 

2006 

March 3, 2002 

MaconForestry Bibb 13-021-

0012 February 11, 1999 Not Applicable 

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

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2.3 Conceptual Description of the PM2.5 Problem

This section evaluates ambient monitoring data obtained from the two FRM monitors and 

one speciation monitor located within the Macon 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 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 Macon 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 Macon NAA, there are two FRM monitors (13-021-0007 and 13-021-0012) that 

have been collecting data since 1999 and one speciation monitor (13-021-0007) that has 

been collecting data since 2002. For the purpose of long-term trend analysis presented 

here, 2007 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 2007. Overall, PM2.5 concentrations have decreased 

for this time period. 

The overall trend can be divided into four periods. The first period (1999 to 2002) shows 

relatively large decreases with each year except 2000 at 13-021-0007 when concentration 

changed little from 1999. The second period (2003) shows very little change in 

concentrations from 2002 at 13-021-0007. The third period (2004-2006) contains 

elevated concentrations compared to 2002 and 2003. The last period (2007) shows a 

significant decrease in concentrations compared to 2004-2006 concentrations. The actual 

values used to create Figure 2-4 are listed in Table 2-3. In 2004, the PM2.5 annual mean 

concentrations were 16.8 μg/m3

 at 13-021-0007 and 14.3 μg/m3 at 13-021-0012. In 

2007, the PM2.5 concentrations were 15.8 μg/m3

 at 13-021-0007 and 12.4 μg/m3 at 13-

021-0012. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Figure 2-4. Trend of annual mean (arithmetic average of quarterly averages) PM2.5 concentrations 

at two FRM monitors (13-021-0007 and 13-021-0012) in the Macon NAA. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Table 2-3. Annual mean (arithmetic average of quarterly averages) PM2.5 

concentrations at two FRM monitors in the Macon NAA. 

Year Monitor 

13-021-0007

(μg/m3) 

Monitor 

13-021-0012 

(μg/m3) 

1999 18.4 18.1 

2000 18.4 16.6 

2001 16.1 13.8 

2002 14.8 13.2 

2003 14.8 12.9 

2004 16.8 14.3 

2005 16.7 14.3 

2006 16.9 14.1 

2007 15.8 12.4 

Figure 2-5 shows changes of three-year design values from 2001 to 2007. Overall trends 

were similar to those of annual mean values (Figure 2-4) except design values increased 

steadily from 2003 to 2006. 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 two FRM monitors (13-021-0007 and 13-021-0012) in 

the Macon NAA. 

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Table 2-4. PM2.5 Design Values at two FRM monitors (13-021-0007 and 13-021-

0012) in the Macon NAA. 

DV Years 

13-021-0007 

(μg/m3) 

13-021-0012 

(μg/m3) 

99-01 17.6 16.1 

00-02 16.4 14.5 

01-03 15.2 13.3 

02-04 15.5 13.5 

03-05 16.1 13.9 

04-06 16.8 14.2 

05-07 16.5 13.6 

Figures 2-6 and Figure 2-7 show PM2.5 trends for each quarter from 1999 to 2007 at 13-

021-0007 and 13-021-0012, respectively. In general, PM2.5 concentrations were higher 

in the 2nd and 3rd quarters at both monitors. This is to be expected due to higher 

temperatures and more photochemistry during these quarters. Overall, the two sites 

showed similar patterns in their trends although quarterly PM2.5 concentrations at 13-

021-0012 are generally lower than those at 13-021-0007. 

Figure 2-6. Quarterly averages of PM2.5 concentrations at 13-021-0007 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Figure 2-7. Quarterly averages of PM2.5 concentrations at 13-021-0012 

Figure 2-8 shows changes of annual total and speciated PM2.5 concentrations (arithmetic 

average of daily averages) from 1999 to 2007 at the Allied Chemical speciation site. The 

speciation site started measuring PM2.5 species in 2002. With respect to contributions of 

each species to total PM2.5 concentrations, OC (5-7 μg/m3

) and SO4 (4-5 μg/m3) are the 

major constituents at the monitor. In general, PM2.5 species concentrations were 

consistent from 2002 to 2007 except for the recent increase of OC concentration and 

decrease of SO4 from 2005 to 2007. From 2002 to 2007, NH4 concentrations were 

around 1.3 μg/m3

. For the same period, NO3 and EC concentrations were approximately 

0.75 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. Finally, the OC and EC annual averages are missing 

data in the forth quarter of 2007 that is not missing from the total PM2.5 annual average. 

30 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Figure 2-8. Trend of annual total and speciated PM2.5 concentrations (arithmetic average of daily 

averages) at the Allied Chemical speciation monitor (13-021-0007). 

Figure 2-9 shows quarterly averages of PM2.5 concentrations at the speciation site in the 

Macon NAA (13-021-0007). Quarterly average PM2.5 concentration for the first quarter 

increased from 2005 to 2007. The second quarter values showed increase from 2005 to 

2006 then decreased from 2006 to 2007. The third quarter for the same time period 

displayed a steady decrease of PM2.5 concentrations. Contrary to the first quarter, the 

fourth quarter showed large decreases of PM2.5 concentrations. Overall, PM2.5 

concentrations were higher in the second and third quarters than in the first and fourth 

quarters at this site. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Figure 2-9. Quarterly averages of PM2.5 concentrations at the speciation site in the Macon NAA 

(13-021-0007) 

Figure 2-10 shows quarterly averages of SO4 concentrations at the speciation site in the 

Macon NAA (13-021-0007). SO4 concentrations decreased from 2006 to 2007 except the 

fourth quarter. The largest decrease of SO4 concentrations occurred in the third quarter 

from 2002 to 2003 and from 2005 to 2007. Compared to the quarterly PM2.5 trends, 

quarterly SO4 concentrations were relatively high during the second and third quarters 

and relatively lower during the first and fourth quarter. 

Figure 2-10. Quarterly averages of SO4 concentrations at the speciation site in the Macon NAA 

(13-021-0007) 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Figure 2-11 shows quarterly averages of NO3 concentrations at the speciation site in the 

Macon NAA (13-021-0007). Contrary to PM2.5 and SO4 trends, NO3 concentrations 

were low in the second and third quarters and high in the first and fourth quarters. From 

2006 to 2007, NO3 concentrations increased for the first quarter. In terms of absolute 

values, however, NO3 concentrations did not change dramatically. 

Figure 2-11. Quarterly averages of NO3 concentrations at the speciation site in the Macon NAA 

(13-021-0007)

Figure 2-12 shows quarterly averages of NH4 concentrations at the speciation site in the 

Macon NAA (13-021-0007). In an absolute sense, NH4 concentrations did not change 

much. Relatively large decreases of NH4 concentrations were in the first and third 

quarters from 2006 to 2007. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Figure 2-12. Quarterly averages of NH4 concentrations at the speciation site in the Macon NAA 

(13-021-0007) 

Figure 2-13 shows quarterly averages of OC concentrations at the speciation site in the 

Macon NAA (13-021-0007). In recent years, OC concentrations seemed to increase for 

all quarters although there is no OC concentration data available for the fourth quarter of 

2007 (as of September 2008). One distinctive case of OC trends is that a steady increase 

of OC concentrations for the third quarter from 2003 to 2007 and the third quarter ended 

up showing the highest OC concentrations in 2007. 

Figure 2-13. Quarterly averages of OC concentrations at the speciation site in the Macon NAA (13-

021-0007) 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Figure 2-14 shows quarterly averages of EC concentrations at the speciation site in the 

Macon NAA (13-021-0007). Overall, EC concentrations of the fourth quarter from 2004 

to 2006 were higher than those of other quarters. The EC concentration is missing in the 

fourth quarter of 2007. From 2005 to 2007, EC concentrations increased in the first and 

third quarter. In terms of absolute values, however, EC concentrations did not change 

dramatically. 

Figure 2-14. Quarterly averages of EC concentrations at the speciation site in the Macon NAA 

(13-021-0007) 

2.3.2 Spatial Trends of PM2.5 in Georgia

To analyze spatial patterns in PM2.5 concentration and composition, Georgia EPD used 

2003-04 data from the speciation 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 2-15 through 2-18, 

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 is an indication of the regional 

nature of PM2.5. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

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 2-15: 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 2-16: 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). 

36 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

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 2-17: 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 2-18. 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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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

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 speciation sites 

in the nonattainment areas 

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 Chattanooga and Atlanta (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. 

The Macon area also exhibits spatial gradients in PM2.5 concentrations, as observed by 

the difference in DVs between the Macon Allied Chemical (15.2 μg/m3

) and Macon 

Forestry (13.3 μg/m3) sites. The Macon Allied Chemical site is located in an 

urban/industrial area, while the Macon Forestry site is located roughly six miles northeast 

of the Macon Allied Chemical site in a fairly rural area. The difference in PM2.5 levels 

between the two sites is likely an indication of the local urban/industrial contribution. 

Also, as previously mentioned, the Macon Allied site exhibits fairly high concentrations 

of MMO, mainly calcium, which could be related to the local industry surrounding the 

site. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

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. 

Presented here are source-apportionment results for 2003-2004 (Figure 2-19 and Table 2-

7). These once again show the regional nature of PM2.5, given the similarities between 

the PMF results for the three sites. The major impacting factors at all sites are secondary 

sulfates, secondary nitrates, SOA, mobile sources and biomass burning. The Atlanta area, 

as expected, has higher levels of mobile-source PM2.5, compared to Floyd county and 

Macon. On the other hand, these latter two sites exhibit higher levels of soil dust, and the 

Macon site exhibits a relatively high contribution of Lime/Minerals PM2.5. This may be 

due to local sources in the industrial area where the site is located. 

These results are also 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 coal-burning EGUs) would reduce PM2.5 levels 

throughout the state. 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, though the Macon 

and Rome sites seem to be affected by local sources related to lime/minerals processing 

and soil dust re-suspension. More information on source apportionment is presented in 

Appendix B and Appendix C. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

Atlanta Floyd county Macon

ug/m3

Unspec.

SOA

Sec. Nitrate

Sec. Sulfate

Zn-rich

Cu-rich

Road-dust

Oil burning

Na-rich

Lime/Minerals

Soil

Biomass burning

Mobile-sources

Figure 2-19: Average factor contributions for 2003-04 at the Atlanta, Floyd County, and Macon 

speciation sites using PMF 

Table 2-7: Average factor contributions to PM2.5 (μg/m3) for 2003-04 at the Atlanta, 

Floyd County, and Macon speciation sites using PMF 

Factor Atlanta Floyd County Macon 

Mobile-source 2.78 1.61 2.12 

Biomass burning 1.06 1.31 1.50 

Soil 0.29 0.61 0.67 

Lime/Minerals 0.05 0.24 0.59 

Na-rich 0.43 0.42 0.33 

Oil 0.03 0.04 0.04 

Road-dust 0.12 0.13 0.11 

Cu-rich 0.07 0.03 0.02 

Zn-rich 0.03 0.03 0.03 

Sec. Sulfate 4.82 5.10 4.67 

Sec. Nitrate 0.99 1.05 0.74 

SOA 2.55 3.46 3.81 

Unspecified 2.32 2.97 1.81 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

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 the extinction coefficient. 

The categorical comparisons for PM2.5 in Macon consisted of four categories: <17.5 

(Category 1), 17.5-25 (Category 2), 25-30 (Category 3), and > 30 (Category 4) μg/m3

. A 

summary of the average input parameters for each CART classification category in 

Macon is contained in Table 2-8. 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: higher surface temperature, lower wind speeds, and lower amounts of rain. In 

addition, it can be seen that important upper-air meteorological parameters leading to 

higher PM2.5 concentrations include higher 850 mb temperatures, lower 850 mb wind 

speeds, and more stability in Atlanta. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Table 2-8: Summary of average input parameters for each CART classification 

Category in Macon. 

For Macon, there is one key bin (group of days with similar meteorological conditions) 

for each Category 3 and Category 4 concentration range (Table 2-9). Both are 

characterized, on average, by very low surface wind speeds, moderate relative humidity, 

and prior-day PM2.5 concentrations of about 30 μg/m-3 in the Atlanta area. Bin 19 

(Category 3) has higher temperatures and lower wind speeds aloft. Surface wind 

directions are from the southeast, and upper air wind directions are from the southwest. 

Bin 14 (Category 4) has cooler temperatures and greater stability. Surface wind directions 

are from the southwest, and upper air wind directions are from the northwest. Local 

conditions combined with a greater potential for pollutant transport from Atlanta appear 

to drive the higher PM2.5 concentrations for Bin 14. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Table 2-9: Summary of average input parameters for key bins for CART classification 

(Class) categories 3 and 4 in Macon. 

Figure 2-20 shows the average composition for all key PM2.5 bins in Macon. Consider 

the two key high PM2.5 bins for Macon (Bins 19 and 14). We noted earlier, that more 

stable conditions and a greater potential for pollutant transport from Atlanta appear to 

drive the higher PM2.5 concentrations for Bin 14. This is manifested by higher average 

sulfate concentrations for this bin, compared to Bin 19. On average, Bin 19 is dominated 

by organic matter, while Bin 14 has a greater proportion of sulfate. 

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

PM2.5 on previous day (Atlanta), very low wind speeds near the surface and low wind 

speeds aloft, west-northwesterly winds aloft and westerly winds near the surface, and 

stable conditions. The high PM2.5 classification categories are associated with 

higher SO4 and OM concentrations.

43 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Macon Key PM Bins: Component Averages

0.00

4.00

8.00

12.00

16.00

20.00

1 (158) 11 (49) 29 (28) 5 (29) 10 (27) 28 (14) 25 (12) 19 (5) 14 (14)

Class 1 Class 2 Class 3 Class 4

Bin No. (No. of Correctly Classified Days)

m3)

Figure 2-20: Average composition for all key PM2.5 bins in Macon. 

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). 

Figures 2-21 and 2-22 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 is Georgia EGU SO2 followed by Georgia non-EGU SO2, 

Georgia ground-level PC, Georgia point PC, and VISTAS NH3. In the wintertime, the 

most important anthropogenic source category is VISTAS NH3 followed by Georgia 

ground-level PC, Georgia EGU SO2, domain-wide VOCs, and Georgia fire PC. 

Avg. Conc. (μg/

EC

NO3

OM

SO4

44 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Macon, GA (130210007) 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 2-21: Sensitivities of PM2.5 by pollutant and source category for the summer episode in 

Macon. ΔPM2.5 is based upon a 30% reduction of pollutant levels from 2009 levels. 

Macon, GA (130210007) Winter

-1.60

-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 2-22: Sensitivities of PM2.5 by pollutant and source category for the winter episode in 

Macon. ΔPM2.5 is based upon a 30% reduction of pollutant levels from 2009 levels. 

45 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Figure 2-23 contains the annual average sensitivities for each pollutant and source 

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

Figure 2-24 contains the annual average sensitivities for each pollutant and source 

category based on the CART-weighted response of the summer and winter episodes in 

Macon. Both of these figures indicate that the most important anthropogenic source 

category is VISTAS NH3 followed by Georgia EGU SO2 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 costeffective ways to reduce NH3 emissions in Georgia. Therefore, control strategy 

development should focus on SO2 from EGUs and PC from ground-level sources. 

Macon, GA (130210007) 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 2-23: Annual average emission sensitivities by pollutant and source category based on the 

average response of the summer and winter episodes in Macon. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Macon, GA (130210007) 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 2-24: Annual average emission sensitivities by pollutant and source category based on the 

CART weighted response of the summer and winter episodes in Macon. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

2.4 PM2.5 Sources and Precursors 

This section of the SIP includes a discussion of sources of PM2.5 emissions and of 

significant PM2.5 precursors in the Macon Nonattainment Area. The technical 

justification is also presented for reversal of the presumption, under the PM 

Implementation Rule, that NOx is a significant PM2.5 precursor in Georgia. 

2.4.1 Sources of PM2.5

Sources of PM2.5 and PM2.5 precursors in the nonattainment area have been separated 

into the following five source classifications: 

• stationary point sources, 

• stationary area sources , 

• on-road mobile sources, 

• off-road mobile sources, and 

• biogenic sources 

Table 2-10 shows typical base year inventories of PM2.5 and its precursors for Bibb and 

Monroe Counties. Note that the Monroe County data is for the entire county. The 

method for generating the typical base year inventory is explained in Section 3.5. 

Table 2-10. Typical Base Year (2002) Emissions Inventories for Bibb and Monroe 

Counties 

Source Type County Typical 2002 Emissions (tons/year)* 

VOC NOx SO2 PM-2.5 NH3 

Point Bibb 2110.0 3608.6 4816.1 298.0 168.1 

Monroe 369.2 28542.0 89172.6 271.2 0.0 

Area Bibb 6426.7 740.6 1201.1 897.9 102.6 

Monroe 913.4 123.3 63.6 701.2 684.1 

Mobile Bibb 6320.2 5466.0 220.6 80.4 192.7 

Monroe 2221.8 3205.9 113.5 52.0 79.4 

Offroad Bibb 1164.6 1325.3 105.0 89.3 0.8 

Monroe 252.8 458.5 33.7 21.2 0.1 

Biogenic Bibb 8463.5 94.7 NA NA NA 

Monroe 15749.7 63.8 

Biogenic 24213 158 NA NA NA 

48 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

subtotal 

Anthropogenic 

subtotal 

19779 

43470 95726 2411 1228 

Total 43992 43629 95726 2411 1228 

* Source: VISTAS - Typical 2002 Base G2 Annual Emissions Summary for Georgia Counties 

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

(specific to pollutant) per year, with data provided at the facility level. Point sources are 

responsible for the large majority of VOC and SO2 emissions in the nonattainment area. 

Electric generating units (EGUs) and industrial sources are the major types of stationary 

point sources. 

There is one EGU source, Plant Scherer, located in the nonattainment area (in Monroe 

County). Plant Scherer has four coal-burning EGUs which contribute the majority of the 

SO2 and NOx emissions in the nonattainment area. Coal-burning power plants located 

outside of the nonattainment area but within the State boundaries are also important 

sources of SO2 emissions that affect ambient sulfate levels inside of the nonattainment 

area. Table 2-11 lists point sources inside the nonattainment area that are Title V major 

sources of one or more of the following pollutants: NOx, VOC, PM, and SO2. 

Table 2-11. Point Sources in the Macon Nonattainment Area That Are Title V Major 

Sources 

Major Source Facility Type County NOx VOC PM SO2 

Graphic Packaging Pulp and paper 

mill 

Bibb X X X X 

Southern Natural 

Gas 

Nat. gas 

compressor station 

Bibb X X 

Armstrong World 

Industries 

Ceiling tile 

manufacturer 

Bibb X X X 

YKK Zipper and 

fastener 

manufacturer 

Bibb X 

YKK Chestney Zipper and 

fastener 

manufacturer 

Bibb X X 

Georgia Power – 

Plant Scherer 

EGU Monroe X X X X 

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 

49 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

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. 

Emissions from large fires that occur in South Georgia, outside of the nonattainment area, 

can also have significant effects on ambient PM levels in the nonattainment area. 

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. 

Non-road (or off-road) mobile sources are pieces of equipment that can move but do not 

use the roadways. Examples are lawn mowers, construction equipment, railroad 

locomotives, and aircraft. The emissions from these sources, like stationary area sources, 

are estimated on a countywide level. A large non-road mobile source is Norfolk 

Southern's Brosnan rail yard. It is located in Bibb County, southwest of Interstate 16 and 

east of State Hwy 129, and is within two miles of EPD’s Allied Chemical PM2.5 

Reference Method monitor. The rail yard uses approximately 10 older-style switcher 

locomotives. These older style diesel-fired engines are designed to be operated 

continuously and may be an important source of soot (elemental carbon emitted as direct 

PM2.5). 

2.4.2 Precursors

The PM2.5 SIP must address both direct PM 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. 

40 CFR 51.1000 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. 

40 CFR 51.1000 defines a “PM2.5 attainment plan precursor” as SO2 and any other 

precursor which the State must evaluate for emissions reduction measures in the SIP. 40 

CFR 51.1002 states that, for control purposes, NOx is presumed to be a precursor 

whereas VOCs 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: 

50 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

• 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. 

Georgia EPD has designated SO2 to be the only attainment plan precursor in the Macon 

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 Macon nonattainment area. The 

technical justification for NOx insignificance is presented in the following subsection. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

2.4.3 Technical Justification for Reversal of NOx Precursor Presumption

The US 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 2-25 contains the annual average PM2.5 concentrations averaged across all FRM 

monitors (after applying the SANDWICH technique) in each nonattainment area. The 

measurements show 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. 

0

2

4

6

8

10

12

14

16

18

20

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 2-25. 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. 

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Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

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 2-12). 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 2-26). 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 2-26). 

Table 2-12. 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 

53 of 143 
Macon Nonattainment Area PM2.5 Attainment SIP - Macon, GA 

Georgia Nonattainment Area Concentration Differences:

(2002 DV)-(2009 DV) (Left) and (2009 DV)-(2018 DV) (Right)

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

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 2-26. Reductions in EC, OC, and NO3 concentrations between 2002 & 2009 and 2009 & 2018 at 

Georgia nonattainment monitors. 

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 

2-27), and 0.11 μg/m3 for 30% removal (Figure 2-28) 

Statewide 50% NOx Reduction

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

Δ PM2.5 (

μg/m3

)

Soils

EC

OC

H2O

NO3

NH4

SO4

Figure 2-27. Annual Average PM2.5 Response to GA Statewide 50% NOx Emission Reduction in 2009 in the 

Georgia PM2.5 Nonattainment Areas. 

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Figure 2-28. Annual Average PM2.5 Response to Georgia Statewide 30% 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 

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. 

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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 VOCs. 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 large state. This introduces additional uncertainty in the 

interpretation of the responses of organic carbon aerosol to large NOx reductions. 

In Figure 2-29, 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 2-31, 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. 

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Organic Carbon in June 2002 @ YRK

0

5

10

15

20

25

30

35

40

6/1/02 0:006/2/02 0:006/3/02 0:006/4/02 0:006/5/02 0:006/6/02 0:006/7/02 0:006/8/02 0:006/9/02 0:00

6/10/02 0:006/11/02 0:006/12/02 0:006/13/02 0:006/14/02 0:006/15/02 0:006/16/02 0:006/17/02 0:006/18/02 0:006/19/02 0:006/20/02 0:006/21/02 0:006/22/02 0:006/23/02 0:006/24/02 0:006/25/02 0:006/26/02 0:006/27/02 0:006/28/02 0:006/29/02 0:006/30/02 0:00

Date

Abs Concentration [ug/m3]

-4

-3

-2

-1

0

1

2

3

4Diff Concentration [ug/m3]

OC_02_Typical OC_09_Typical OC dSOMA dSOMB dPSOM dPOM

Figure 2-29. 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. 

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 

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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

. 

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. 

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Section 3. Attainment Demonstration Methods and Inputs 

The attainment modeling for the Macon 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 Macon 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 regionalscale photochemical, fine particulate matter, and regional haze regulatory modeling 

studies. 

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

system is an emissions modeling system that generates hourly gridded speciated emission 

inputs of mobile, nonroad mobile, area, point, fire, and biogenic emission sources for 

photochemical grid models. 

• Air Quality Model: The US EPA’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, an historical year is selected to model that 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, since the mandatory 

attainment date for the Macon 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 

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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. 

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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 decade, the 

USEPA 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 US EPA’s desire to develop an advanced modeling framework for “holistic” 

environmental modeling utilizing state-of-science representations of atmospheric processes in a 

high performance computing environment. The USEPA 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 oneatmospheric 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, 

• Simulates Cloud Physics, 

• Publicly available at no or low cost, 

• Output available in I/O API format, 

• Supports Four Dimensional Data Assimilation, and 

• 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 non-hydrostatic MM5, the 

sigma levels are defined according to the initial hydrostatically balanced reference state so that 

the sigma levels are also time-invariant. The gridded meteorological fields produced by MM5 

are directly compatible with the input requirements of “one atmosphere” air-quality models using 

this coordinate. MM5 fields can be easily used in other regional air quality models with different 

coordinate systems by performing a vertical interpolation, followed by a 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 threedimensional analyses performed at 12-hour intervals on the outermost grid mesh selected by the 

user. Surface fields are analyzed at three-hour intervals. A Cressman-based technique is used to 

analyze standard surface and radiosonde observations, using the National Meteorological 

Center's spectral analysis, as a first guess. The lateral boundary data are introduced using a 

relaxation technique applied in the outermost five rows and columns of the coarsest grid domain. 

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; and 

• 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 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. 

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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 US 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 US 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 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 5-year baseline period. This 

analysis is discussed in more detail in the project report in Appendix D. 

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• 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 US EPA was requiring states to provide 

emissions inventory data for the Comprehensive Emissions Reporting Rule (CERR), it 

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

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

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

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

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

representative, and pragmatic choice for modeling. All of the US 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 US 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 Macon PM2.5 nonattainment areas for 2002 and 

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

inventories developed by VISTAS for 2002 and 2009, respectively. The baseline emissions 

inventory for 2002 was developed for Georgia following the methods described in Appendix H. 

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

 Point 

County VOC NOx SO2 PM-10 PM-2.5 NH3 

Bibb 2110.0 3608.6 4816.1 563.4 298.0 168.1 

Monroe 369.2 28542.0 89172.6 627.1 271.2 0.0 

Statewide 34964.4 197376.8 571410.9 33077.3 22531.6 3699.2

 Non-road

County VOC NOx SO2 PM-10 PM-2.5 NH3

Bibb 1164.6 1325.3 105.0 94.2 89.3 0.8 

Monroe 252.8 458.5 33.7 22.8 21.2 0.1 

Statewide 85965.4 97961.4 9005.4 8617.9 8226.4 60.4

 Area

County VOC NOx SO2 PM-10 PM-2.5 NH3 

Bibb 6426.7 740.6 1201.1 6497.5 897.9 102.6 

Monroe 913.4 123.3 63.6 3251.8 701.2 684.1 

Statewide 333044.8 49987.4 60370.2 757656.1 159437.8 83066.0

 Mobile

County VOC NOx SO2 PM-10 PM-2.5 NH3 

Bibb 6320.2 5466.0 220.6 116.5 80.4 192.7 

Monroe 2221.8 3205.9 113.5 69.7 52.0 79.4 

Statewide 283420.6 307731.7 12183.5 7245.9 5167.8 10546.2

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Biogenics

County VOC NOx 

Bibb 8463.46 94.71 

Monroe 15749.66 63.84 

Statewide 1972795.40 24159.19

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

 

 Point

County VOC NOx SO2 PM-10 PM-2.5 NH3 

Bibb 2250.0 2383.5 758.0 282.4 156.3 165.2 

Monroe 323.2 19353.4 106690.4 628.0 355.5 3.1 

Statewide 36430.1 148849.7 462666.5 40993.6 29889.7 4648.8

 

 Non-road

County VOC NOx SO2 PM-10 PM-2.5 NH3

Bibb 1081.8 1112.0 17.8 81.7 77.1 0.9 

Monroe 270.4 402.7 5.5 19.8 18.4 0.1 

Statewide 67686.1 85732.9 2724.7 7521.1 7174.6 67.5

 Area

County VOC NOx SO2 PM-10 PM-2.5 NH3 

Bibb 6268.4 771.3 1204.6 7347.8 990.0 108.2 

Monroe 861.6 126.2 64.0 3649.3 744.1 765.5 

Statewide 306337.3 51925.0 60604.0 840244.9 169011.4 91439.1

 Mobile

County VOC NOx SO2 PM-10 PM-2.5 NH3 

Bibb 4292.3 3728.0 26.1 97.8 60.0 225.2 

Monroe 1568.6 2170.9 13.5 55.7 37.2 95.6 

Statewide 195125.2 209349.2 1585.0 6072.0 3840.1 12686.9

Biogenics

County VOC NOx 

Bibb 8463.46 94.71 

Monroe 15749.66 63.84 

Statewide 1972795.40 24159.19

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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 reduction 

factors used in the attainment demonstration test. 

 

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 Macon nonattainment area, as well as for 

the State, are in Appendix I. Below is an overview of the inventories used for each source 

classification. 

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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 US EPA’s Acid Rain program or 2002 hourly emissions data 

provided by stakeholders. These 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, 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 

inputs from 2001 (or 2000) were used in the normalizing calculations. 

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 leastcost means of meeting electric generation and capacity requirements while complying with 

specified constraints including air pollution regulations, transmission bottlenecks, and plantspecific 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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3.5.1.2 Other Industrial Point Sources 

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

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

The general approach for assembling future-year data was to use recently-updated growth and 

control data consistent with the USEPA’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 baseyear emissions inventory used in the attainment demonstration modeling. Growth factors 

supplied from the states or the USEPA’s CAIR emission projections were applied to project the 

controlled emissions to the appropriate year. In some cases, the USEPA’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. Appendix H provides a detailed discussion of 

the area source inventory. 

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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 

US 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 base year emissions inventories. 

 

For the source categories estimated using the US 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. 

3.5.4 Highway Mobile Sources

For on-road 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 UAM-BEIS3. The basis for the gridded land use data 

used by BEIS3 is the county land use data in the Biogenic Emissions Landcover Database 

version 3 (BELD3) provided by the USEPA. 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 US 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 Macon 

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). 

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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 depend upon the number of observation-prediction pairs and 

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 “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. 

 

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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

NovemberDecember

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

November

December

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 Georgia

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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 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 Macon PM2.5 nonattainment area. 

4.2 Air Quality Model Performance

The air quality model evaluation mostly focused on the FRM and speciation 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. speciation 

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” (e.g., 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. The bugle plots 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 statistical measures 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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Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA 

4.2.2 Domain-Wide Model Performance

 

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

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

concentrations and the performance statistics for all of the 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 shows generally good model performance for most species of PM2.5 and total PM2.5. The 

exception is the prediction of NO3 values, which most values fall outside the criteria level 

(Figure 4-2). There are a few months that 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 (Figure 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 

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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Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA 

STN VISTAS Sites CMAQ 12km - 2002 Monthly

-200.0

-150.0

-100.0

-50.0

0.0

50.0

100.0

150.0

200.0

0.0 4.0 8.0 12.0 16.0 20.0

Average Concentration (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). 

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). 

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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 Macon Nonattainment Area

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

as the Macon 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 

Macon nonattainment area contains two FRM monitors (13-021-0007 and 13-021-0012) and one 

STN monitor (13-021-0007). 

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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 near the GA PM2.5 non

and the Macon PM2.5 nonattainment area (right). 

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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 two FRM monitors in the Macon 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 Macon 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 US 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 two FRM sites in the Macon nonattainment area. As 

shown in Table 4-7, the model’s performance at two FRM monitors in the Macon nonattainment 

area is comparable with that of the all FRM monitors in Georgia. Exceptions are fractional bias, 

normalized mean bias, and mean bias. These bias statistics clearly indicate that the model tends 

to show more positive biases at the Macon FRM monitors than monitors across Georgia. 

For more in-depth examinations, the model performance statistics are broken out by month at 

each of the FRM monitors in the Macon nonattainment area to see if there is any performance 

issue during the base year (Table 4-8 and Table 4-9). It was noted that the model had significant 

under-prediction during summer. In addition, the model showed less significant underprediction in February. During months of under-prediction, errors were in similar magnitude, 

which indicates these errors were dominated by the model’s under-predictions. During the 

monthly performance evaluation, we found the model’s mean predictions were comparable to the 

mean-observed concentrations but the model could not replicate very high PM2.5 concentrations 

that were observed. For example, the model predicted mean PM2.5 concentration of 17.3 μg/m3

at 13-021-0012 in May while 43.2 μg/m3

 was observed at the same monitor for the same period. 

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 Macon 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 Macon nonattainment area. 

Statistic GA 13-021-0007 13-021-0012 

MFB (%) -5.7 -24.3 -11.6 

MFE (%) 30.2 35.5 32.1 

Ap -0.1 -0.2 -0.2 

NMB (%) -2.7 -19.2 -9.1 

NME (%) 27.4 28.5 28.9 

MNB (%) 4.5 -15.4 -4.3 

MNGE (%) 32.9 29.4 29.3 

MB -0.4 -2.8 -1.2 

MAGE 4.0 4.2 3.8 

Geometric Mean Bias -0.1 -0.3 -0.1 

Geometric Mean Error 0.3 0.4 0.3 

RMSE 5.3 5.8 5.5 

MaxOBS 52.0 41.0 43.2 

MaxPRE 48.0 33.3 33.1 

Mean Observations 14.5 14.7 13.3 

Mean Predictions 14.1 11.9 12.1 

Standard Deviation of 

Observations 6.6 6.6 6.2 

Standard Deviation of 

Predictions 6.9 6.0 5.7 

R2 0.5 0.5 0.3 

BF 1.0 0.8 1.0 

Number of Samples 3166 90 112 

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Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA 

Table 4-8. Monthly model performance statistics for PM2.5 at 13-021-0007. 

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

MFB (%) 12.2 -23.7 2.6 N/A -36.7 -61.1 -60.6 -44.2 -27.9 5.4 

MFE (%) 25.1 26.4 2.6 N/A 36.7 61.1 61.7 46.9 32.7 23.7 

Ap 0.1 -0.3 0.0 N/A -0.3 -0.3 -0.2 -0.2 -0.2 0.0 

NMB (%) 11.8 -20.4 2.6 N/A -31.0 -45.4 -46.0 -30.6 -16.0 6.1 

NME (%) 26.3 21.7 2.6 N/A 31.0 45.4 47.1 33.7 19.7 23.5 

MNB (%) 18.9 -19.3 2.6 N/A -31.0 -43.4 -41.9 -33.6 -19.0 10.1 

MNGE (%) 30.4 22.2 2.6 N/A 31.0 43.4 43.0 36.4 24.3 26.7 

MB 1.3 -3.0 0.2 N/A -7.3 -7.2 -7.2 -5.1 -3.0 0.9 

MAGE 2.9 3.2 0.2 N/A 7.3 7.2 7.4 5.6 3.7 3.4 

Geometric Mean Bias 0.1 -0.2 0.0 N/A -0.4 -0.7 -0.7 -0.5 -0.3 0.1 

Geometric Mean Error 0.3 0.3 0.0 N/A 0.4 0.7 0.7 0.5 0.4 0.2 

RMSE 3.8 4.2 0.2 N/A 7.3 9.4 9.2 6.1 4.9 4.0 

MaxOBS 16.4 30.0 7.0 N/A 23.4 29.3 25.5 27.7 41.0 21.1 

MaxPRE 17.5 20.1 7.2 N/A 16.1 20.9 19.8 21.5 33.3 20.5 

Mean Observations 11.1 14.6 7.0 N/A 23.4 15.9 15.7 16.7 18.6 14.3 

Mean Predictions 12.4 11.6 7.2 N/A 16.1 8.7 8.5 11.6 15.6 15.2 

Standard Deviation of 

Observations 3.5 7.2 N/A N/A N/A 8.1 5.7 5.5 10.7 4.6 

Standard Deviation of 

Predictions 3.4 5.4 N/A N/A N/A 5.7 4.6 6.3 10.3 4.4 

R2 0.2 0.8 N/A N/A N/A 0.4 0.1 0.7 0.9 0.3 

BF 1.2 0.8 1.0 N/A 0.7 0.6 0.6 0.7 0.8 1.1 

Number of Samples 9 10 1 N/A 1 9 11 10 10 10 

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Table 4-9. Monthly model performance statistics for PM2.5 at 13-021-0012. 

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

MFB (%) 31.0 -6.4 16.9 0.9 -42.1 -43.7 -54.4 -25.4 -14.4 6.0 

MFE (%) 33.4 18.9 19.2 25.2 42.1 43.7 56.8 32.5 34.6 15.2 

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

NMB (%) 34.2 -5.8 20.3 6.5 -38.6 -32.5 -40.2 -15.6 -4.3 6.2 

NME (%) 37.3 17.3 22.5 27.3 38.6 32.5 42.8 25.3 25.1 15.2 

MNB (%) 40.9 -3.7 20.5 6.3 -32.1 -33.2 -38.6 -18.5 -6.7 8.8 

MNGE (%) 43.2 18.7 22.7 26.0 32.1 33.2 41.1 27.0 29.6 17.4 

MB 3.3 -0.7 2.4 0.7 -7.1 -5.0 -6.2 -2.1 -0.7 0.9 

MAGE 3.6 2.2 2.7 2.8 7.1 5.0 6.6 3.4 4.1 2.1 

Geometric Mean Bias 0.3 -0.1 0.2 0.0 -0.4 -0.5 -0.6 -0.3 -0.2 0.1 

Geometric Mean Error 0.3 0.2 0.2 0.3 0.4 0.5 0.6 0.3 0.4 0.2 

RMSE 4.1 2.7 3.7 3.7 10.8 7.0 8.1 4.2 4.6 2.9 

MaxOBS 14.7 23.8 15.8 14.9 43.2 27.5 23.6 18.3 36.8 20.4 

MaxPRE 18.6 20.1 20.5 17.9 17.3 21.5 20.1 21.7 33.1 20.3 

Mean Observations 9.7 12.6 11.9 10.3 18.5 15.3 15.3 13.4 16.5 14.1 

Mean Predictions 13.0 11.9 14.3 11.0 11.4 10.3 9.2 11.3 15.8 14.9 

Standard Deviation of 

Observations 3.2 6.5 3.6 2.8 10.2 7.6 5.0 3.4 9.3 4.5 

Standard Deviation of 

Predictions 3.5 5.5 5.2 4.8 3.8 6.4 4.9 5.6 10.2 4.3 

R2 0.5 0.8 0.7 0.4 0.3 0.5 0.1 0.6 0.8 0.6 

BF 1.4 1.0 1.2 1.1 0.7 0.7 0.6 0.8 0.9 1.1 

Number of Samples 9 9 6 10 10 10 10 10 10 9 

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Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA 

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 Macon 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 4-10). 

Nitrate was underestimated in GA 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 4-11, similar tendencies can be 

seen at the STN monitor in the Macon nonattainment area. More detailed performance analyses 

with graphical measures can be found in the following sections. 

Table 4-10. 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 4-11. Annual model performance statistics for PM2.5 at 13-021-0007. 

Statistics SO4 NO3 NH4 OC EC PM2.5

MFB (%) -27.9 -114.1 -5.4 -39.6 -37.4 -34.1

MFE (%) 40.7 142.0 45.5 52.9 59.5 41.7

Ap -0.7 0.8 -0.7 0.5 -0.4 0.1

NMB (%) -30.8 -16.8 -18.9 -19.8 -36.9 -25.3

NME (%) 42.3 92.3 48.4 40.1 44.3 34.2

MNB (%) -18.3 -44.8 8.5 -25.5 50.3 -24.1

MNGE (%) 33.9 92.4 45.8 41.0 121.1 33.1

MB -1.8 -0.1 -0.3 -1.1 -0.2 -4.1

MAGE 2.5 0.6 0.7 2.3 0.2 5.5

Geometric Mean Bias -0.3 -2.4 -0.1 -0.4 -0.4 -0.4

Geometric Mean Error 0.4 2.7 0.5 0.6 0.7 0.4

RMSE 5.7 0.8 1.5 2.6 0.3 7.0

MaxOBS 39.9 1.9 10.7 9.8 1.3 28.7

MaxPRE 10.6 3.4 3.2 14.7 0.8 32.0

Mean Observations 5.8 0.6 1.5 5.8 0.5 16.2

Mean Predictions 4.0 0.5 1.2 4.6 0.3 12.1

Standard Deviation of 

Observations 6.9 0.4 1.9 1.8 0.3 6.0

Standard Deviation of 

Predictions 2.5 1.0 0.7 3.6 0.2 6.4

R2 0.5 0.5 0.5 0.7 0.4 0.3

BF 0.8 0.6 1.1 0.7 1.5 0.8

Number of Samples 30 30 30 28 28 29

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Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA 

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, Georgia EPD estimated several statistical 

measures for all FRM monitors and all STN monitors within Georgia and in the Macon 

nonattainment areas annually and monthly. Important findings from this statistical 

performance evaluation at the FRM monitors was the model’s performance at FRM 

monitors within Georgia was comparable to the performance at two FRM monitors 

collectively in the Macon nonattainment area although there were slight negative biases 

observed in the Macon nonattainment area. These under-predictions became more 

apparent during summer. Monthly performance analyses of mean concentrations and 

maximum concentrations revealed that these apparently negative biases were likely due 

to the model’s lack of ability to capture exceptionally high PM2.5 concentration cases. 

After examining the statistical performance measures, Georgia EPD found that the 

model’s performance is generally adequate across Georgia and acceptable for the Macon 

nonattainment area. 

Similar analyses were conducted for the STN monitors on total PM2.5 and all of 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 Macon 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 were available, each package included 

scatter plots for monitors within Georgia and in the Macon nonattainment area, time 

series plots for monitors in the Macon 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 Macon 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 Georgia 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 13-021-0007. However, these performance issues are tied to low EC 

concentrations and the same tendency was found in the model’s SOIL performance. As 

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Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA 

shown in Figure 4-7, SO4 and OMC were the major contributors to the Macon 

nonattainment areas. Therefore, Georgia EPD concluded that performance issues found 

in minor constituents of PM2.5 are not likely degrading the overall model performance. 

Figure 4-7. Stacked bar plots of daily PM2.5 species concentration at 13-021-0007. 

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 Macon 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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Control and Contingency Measures PM2.5 Attainment SIP - Macon, GA 

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. Those measures 

that had already been implemented through the end of 2002 (the year of the baseline 

emissions inventory) are not discussed in this SIP. This section identifies those control 

measures which 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. 

This section also describes other measures which 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 USEPA promulgated the “Rule To Reduce Interstate Transport of 

Fine Particulate Matter and Ozone (Clean Air Interstate Rule); 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 

Control Measure for Attainment Demonstration1

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 

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 vehicle 

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 of heavy duty diesel PM controls (see above) 

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Table 5-1. Federal PM2.5 Control Measures (continued)

Control Measure for Attainment Demonstration1

Control Measure – Benefits2

Measure Sources 

Affected 

Measure and Schedule 

Species Affected/ Reduced 

Measure and Schedule 

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 for attainment demonstration – after 2002, before 2009 – permanent and enforceable, SIP counts toward attainment demonstration (2) Control measure – continuing benefits – effective 2009 or later – SIP does not count toward attainment but will provide emissions reductions 

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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, 

it cannot be counted as a control measure for the attainment demonstration. Phase II caps 

for NOx and SO2 become effective in 2015, which is too late to count as a contingency 

measure. The USEPA 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 ((391-3-1-.02(13)) 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/yr and 149,140 tons/year, effective 

in 2010 and 2015, respectively. Adjacent CAIR states that have been identified as 

contributing significantly to nonattainment of the PM2.5 standard in the Macon 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 Macon 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 

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converters on vehicles resulting in higher emissions. Lower-sulfur gasoline is necessary 

to achieve the Tier 2 vehicle emission standards. 

5.1.3 Heavy-Duty Gasoline and Diesel Highway Vehicles Standards

New USEPA 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 USEPA 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 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. 

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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 2006. 

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 USEPA 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 Measure – Attainment Demonstration1

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 Desulf. for 7 EGUs3 starting in 

2008 - approximately 216,000 tons/yr reduced 

SO2 sources 

outside the nonattainment area 

Flue Gas Desulf. for Scherer Unit 3, December 31, 2011 

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 equipment turnover 

(1) Control measures for attainment demonstration – after 2002, before 2009 – permanent and enforceable, SIP counts toward attainment demonstratio(2) Control measures – continuing benefits – effective 2009 or later – SIP does not count toward attainment but will provide emissions reductions (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 for complying with the Clean Air 

Mercury rule. 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 northwest of the nonattainment area, 

ranging in approximate distance from 100 to 150 miles from Macon. The Title V permit 

of each affected facility has been amended to permit operation of the FGD controls. 

Controls on three more units located outside the nonattainment area will be implemented 

in 2009 and 2010. 

All of the SO2 controls for Plant Scherer, the EGU facility located within the 

nonattainment area, are required to be implemented in 2011 or later. Controls on 

Scherer Unit 3 will be discussed in Section 5.3, Contingency Measures. 

 

A separate Georgia rule, rule (uuu), places specific SO2 emissions limits on each of the 

EGUs addressed by rule (sss). Reductions will occur beginning in 2008 as a result of 

implementation of rule (sss). The emission limits required by rule (uuu) become 

effective January 1, 2012, through June 1, 2015, depending upon the unit. 

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 Conting. Construction Permit 

Number 

Permit 

Effective 

Date 

 Bowen Unit 

3 

Bartow 12/31/2008 X 4911-015-0011-V02-1 2/27/06 

 Bowen Unit 2/27/06

4 

Bartow 12/31/2008 X 4911-015-0011-V02-1

 Hammond 12/20/06

Unit 1 

Floyd 12/31/2008 X 4911-115-0003-V02-2

 Hammond 12/20/06

Unit 2 

Floyd 12/31/2008 X 4911-115-0003-V02-2

 Hammond 

Unit 3 

Floyd 12/31/2008 X 4911-115-0003-V02-212/20/06

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Plant/Unit County Required 

FGD 

operation 

date, per rule 

(sss) 1

Control4 Conting. Construction Permit 

Number 

Permit 

Effective 

Date 

 Hammond 12/20/06

Unit 4 

Floyd 12/31/2008 X 4911-115-0003-V02-2

 Wansley 1/1/2007

Unit 1 

Heard 12/31/2008 X 4911-149-0001-V02-0 (incorporated 

draft permit V01-9 

(permit to construct))

 Bowen Unit 

2 

Bartow 06/01/2009 NA2 4911-015-0011-V02-3 12/20/06 

 Wansley 

Unit 2 

Heard 12/31/2009 X 4911-149-0001-V02-0 

(incorporated draft 

permit V01-9 (permit 

to construct)) 

1/1/2007 

 Bowen Unit 

1 

Bartow 6/1/2010 NA2 4911-015-0011-V02-3 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 109,040 

tons/yr (based on modeled emissions projections, see Table 5-7). 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 17.1 percent of uncontrolled 

statewide SO2 emissions (109,040 tons/yr plus 527,580 tons/yr). 

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. The rule was expected to reduce NOx by 13 tons per day statewide and 8 tons 

per day in the 45 county Atlanta ozone control area in 2005. These emissions benefits 

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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 Bibb County, Monroe County (Plant Scherer), 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. Note that fire emissions are not affected by 

controls included in attainment modeling. 

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

EGU 

point 

NonEGU 

point 

Nonroad Area Mobile Fire Total 

2002* 4047 769 105 1200 220 1 6343 

2009 0 758 18 1203 26 1 2006 

Change -4047 -11 -87 3 -194 0 -4337 

* typical emissions 

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

EGU 

point 

NonEGU 

point 

Nonroad Area Mobile Fire Total 

2002** 89172 NA NA NA NA NA 89172 

2009 106690 NA NA NA NA NA 106690 

Change 17518 NA NA NA NA NA 17518 

* Plant Scherer EGUs only; emissions from other sources in partial county are 

 negligible 

** typical emissions 

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

EGU 

point 

NonEGU 

point 

Nonroad Area Mobile Fire Total 

2002* 93219 769 105 1200 220 1 95515 

2009 106690 758 18 1203 26 1 108696 

Change 13471 -11 -87 3 -194 0 13181 

* typical emissions 

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

EGU 

point 

NonEGU 

point 

Nonroad Area Mobile Fire Total 

2002* 515958 55453 9006 57555 12183 2815 652970 

2009 406918 55749 2725 57692 1585 2912 527580 

Change -109040 296 -6281 137 -10598 97 -125390

* typical emissions 

The total SO2 change in the nonattainment area is an increase of 13,181 tons. This is due 

to a projected increase in emissions at Plant Scherer. Reductions of SO2 will occur at 

Plant Scherer when controls are phased in starting in 2011. 

The total statewide SO2 emissions change is a decrease of 125,390 tons. The statewide 

change is more pertinent to demonstrating attainment in 2009. 

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 USEPA upon a determination by 

EPA that an area has either failed to demonstrate Reasonable Further Progress (RFP) 

toward attainment or 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 

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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 Macon, a quantity of emissions reductions 

equivalent to one year of the reductions needed to meet attainment will satisfy the 

requirement for contingency measures. However, since SO2 emissions actually increase 

within the Macon NAA prior to the attainment date, GAEPD will demonstrate an 

equivalent air quality benefit for the required continued improvement using modeled 

results to satisfy the contingency requirement. 

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 control measures are 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 2 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 

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 Macon nonattainment area is based largely on 

reductions of SO2 emissions from Georgia’s coal-fired EGUs, as required by Georgia 

rules 391-3-1-.02 (2) (sss) and (uuu). Contingency measures for Macon will be based on 

control of SO2 emissions from sources located both inside and outside of the 

nonattainment area, as the control of SO2 is a regional approach to attainment. 

To demonstrate adequate contingency measures, EPD is adopting an approach to show 

that the contingency measures will provide a minimum benefit equivalent to one year of 

the air quality benefit needed to bring Macon into attainment over the seven-year period 

from 2002 to 2009. The five-year baseline design value established for modeling at the 

highest monitor (Allied Chemical) is 15.7 ug/m3

. Therefore the total benefit needed to 

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come into attainment is 15.7 minus 15.0 (the standard) which equals 0.7 ug/m3

. Oneseventh (one year divided by seven years) of 0.7 equals 0.1 ug/m3

. 

Under EPD’s rule (sss), SO2 controls are required at Plant Scherer Unit 3 and at Plant 

Wansley Unit 2 during the contingency period. Plant Wansley is located outside of the 

nonattainment area, 148 kilometers from the DV monitor and is included because it will 

be controlled as part of Georgia’s regional approach to PM emissions and because “EPA 

believes that states could be able to justify considering not only all emissions in the 

nonattainment area, but also emissions within a distance that may be up to 200 kilometers 

from the nonattainment area” (72 FR 20636). It is important to note that since this is a 

modeled approach, all other sources of emissions outside (and within) the NAA are 

already included (modeled) in this analysis prior to determining the sensitivities of air 

quality to the individual EGU sources used for contingency. Therefore, a separate 

accounting of all sources of emissions located outside of the nonattainment area is not 

necessary as those emissions are included in the model. 

EPD has performed sensitivity analyses for the air quality benefits of SO2 reductions 

attributable to controls to be installed after 2009. The results for Scherer Unit 3 and 

Wansley Unit 2 are presented in Table 5-8. The sum of the expected air quality benefits 

is 0.161 ug/m3

, which exceeds the required one year benefit of 0.1 ug/m3. 

 Table 5-8. Modeled Air Quality Benefit of Specific EGU Emissions Controls. 

Emissions Unit Control date 

per rule (sss) 

SO2 Emission 

Reductions for 

Contingency 

(tons)1

Air Quality Benefit 

of Control 

Implementation 

(ug/m3)

2

Wansley 2 12/31/2009 36,421 0.070 

Scherer 3 12/31/2011 27,735 0.091 

Sum 0.161 

(1) Modeled emissions 

(2) Evaluated at the Macon Allied Chemical monitor (DV monitor) 

Modeled demonstration of attainment in 2010 does not depend upon the reduction from 

Plant Scherer Unit 3 or Plant Wansley Unit 2 controls. These control measures are 

required by Georgia rule 391-3-1-.02 (2) (sss). Therefore, no trigger mechanism is 

needed and no further action is needed from EPD or EPA for this rule to be implemented. 

These control measures therefore satisfy the contingency measure requirement. 

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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 

• Local measures 

• Switching of two EGUs to lower sulfur coal 

• Replacement of coal-fired capacity with natural-gas-fired combustion turbines at 

two EGUs 

• Planned early start-up of SO2 scrubbers at two EGUs 

• Rail yard emissions reductions 

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 5-9 and in the following 

text. 

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Table 5-9. 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 (5)

Vegetation, tires, 

municipal waste, 

construction 

debris, 

agriculture, etc. 

Restricts types of 

outdoor burning, 2005 

Direct PM, 

organic carbon 

Local measures (Middle 

Georgia Clean Air 

Coalition) 

School buses, 

freight trucks, 

passenger 

vehicles 

Bus retrofits, truck 

stop electrification, 

anti-idling campaign, 

commute alternatives 

– since 2004 

Direct PM, 

NOx 

Switch 2 coal-fired EGUs 

to lower sulfur coal 

Plant Scherer 

Units 1 and 2 

Switched to PRB coal 

in 2003 and 2004 

SO2 

Replace 2 coal-fired 

EGUs with natural-gasfired EGUs 

Plant 

McDonough 

Units 1 and 2 

Replace existing coalfired capacity with 

natural-gas-fired 

combustion turbines 

in 2010 and 2011 

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 start-up 

of SO2 scrubbers at 

Bowen Unit 2 and 

Wansley Unit 2 would 

achieve emissions 

reductions not 

credited in the 2009 

inventory 

SO2 

CMAQ2 funding for 

replacement of 

traditional dieselpowered rail yard 

switcher engines with 

gensets

Switcher engines 

in Macon rail 

yard 

Substitution of 

Gensets for traditional 

diesel-powered 

switchers, reduces 

emissions rate and 

idling time 

Direct PM, 

NOx 

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

(2) Congestion Mitigation and Air Quality federal grant 

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

Georgia’s Open Burning Rule established additional May through September burning 

restrictions in the Macon nonattainment area and surrounding counties beginning in 2005. 

The rule prohibits open burning with the exception of 12 types of burning activities. In 

addition, there is a smog season (May through September) burn ban that reduces the 

number of allowed exceptions to 6. 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 Middle Georgia Clean Air Coalition

On Earth Day of 2004 representatives of seven counties and thirteen cities in central 

Georgia signed a joint resolution to form the Middle Georgia Clean Air Coalition 

(MGCAC). The coalition member jurisdictions are as follows: 

• Counties – Bibb, Crawford, Houston, Jones, Monroe, Peach, and Twiggs 

• Cities – Macon, Warner Robins, Centerville, Perry, Payne City, Danville, Byron, 

Forsyth, Roberta, Gray, Jeffersonville, Culloden, and Fort Valley 

The MGCAC was formed to initiate and support local measures to improve air quality 

and public awareness of air quality issues. The coalition meets bimonthly. The meeting 

members consist of voting members, who are the 7 county commission chairpersons and 

the 13 mayors, and non-voting members. The daily operations are performed by the 

mobile source coordinator. There are 6 standing subcommittees as follows: 

• Open burning ban 

• School bus improvements 

• Public education and awareness 

• Commuter strategies 

• Truck stop electrification 

• Strategy for the future 

Funding for the MGCAC administration and its initiatives is secured in various ways. 

Member counties and cities provide funding in proportion to their respective populations. 

Grant money has been obtained from congressional earmark funding. The following 

table lists major initiatives sponsored by the MGCAC since its inception. 

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Table 5-10. Major Initiatives of the Middle Georgia Clean Air Coalition 

Category Initiative(s) 

Burn Ban Lobbied Georgia DNR to initiate a seasonal burn ban for all MGCAC 

counties each May through October. 

Public Education Created a web page and sponsored Earth Day celebrations. Middle 

Georgia CAN! (Clean Air Network) worked to educate the 

community on new rulings through television, printed collateral 

material and email marketing 

School Buses School bus study served as basis for award of a 2005 US EPA grant 

for $225,000 to retrofit 18 Bibb, Houston, and Monroe County buses 

with particulate filters. Eleven Bibb County buses have been 

retrofitted thus far. Additional federal funding that may be available 

for additional retrofits is awaiting approval by the Georgia Dept. of 

Transportation (GDOT). 

Commuter 

Strategies 

Partnered with GDOT and Middle Georgia Clean Cities Coalition 

(MGCCC) to perform a survey of commuter programs with area 

employers. Survey is now serving as the basis for the 

implementation of commuter strategies. Middle Georgia CAN! is an 

initiative of the MGCAC administered by MGCCC. Middle Georgia 

CAN! partnered with the Macon Transit Authority on its Customer 

Appreciation Day. Worked to educate the public on the 

environmental benefits of using public transit. 

Truck Stop 

Electrification 

Pursuing funding options for implementing Truck Stop Electrification 

(TSE) at a truck stop in Middle Georgia. 

Strategy For The 

Future 

MGCAC has published a comprehensive strategy as a guide to 

achieve and maintain attainment. 

 

Anti-Idling 

Resolution 

The MGCAC counties and cities have passed a resolution supporting 

anti-idling by all residents. 

 

Alternative Fuels 

Resolution 

The MGCAC approved a resolution for the development of policies 

in each city and county to support the purchase and use of alternative 

fuels in government fleets and school buses and to require requestfor-purchase decisions to favor suppliers meeting alternative fuel 

standards. 

Other Initiatives MGCAC is currently working to implement Landfill Gas to Energy 

Generation, Energy Savings Performance Contracts, Energy Star 

Programs, and the adoption of “green” Municipal Procurement 

Policies. 

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These initiatives are expected to reduce PM2.5 emissions and ambient concentrations in 

the Macon nonattainment area. 

5.5.3 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 inside the nonattainment area in Monroe County. Units 1 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 systemwide average was 1.12% over the same time period. 

5.5.4 Switch to Natural Gas from Coal, Georgia Power Plant McDonough 

Plant McDonough is located approximately 80 miles north of the nonattainment area, in 

Fulton County. The plant has two coal-fired units, Units 2 and 1. Units 2 and 1 are 

scheduled to be shut down in October 2010 and October 2011, respectively. They will be 

replaced by combined cycle combustion turbines, fired on natural gas. This will reduce 

sulfur dioxide emissions from the plant to almost zero. 

5.5.5 Multipollutant Rule, Early Implementation of Controls

Table 5-3 shows the compliance dates required by Georgia rule (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.

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 1 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 

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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 

5 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 Macon Annual PM 2.5 nonattainment area are 14.3 ug/m3

 for the Allied 

Chemical site and 12.5 ug/m3

 for the GA Forestry Commission site (see Section 6.1.4). 

As discussed below, the Macon 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 contribute to the attainment of 

the Macon NAA. Therefore the Macon NAA 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 the Macon 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. Table 5-11 below 

identifies the EGU and non-EGU sources of SO2 emissions in the Macon Nonattainment 

Area and serves to demonstrate the relative difference in the magnitude of emissions 

between EGU and non-EGU sources of SO2 emissions. 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 

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Georgia’s responses to the Clean Air Interstate Rule (CAIR), the Clean Air Mercury Rule 

(CAMR) (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). 

Table 5-11 Macon Nonattainment Area Major Source SO2 Emissions (Actual) 

Facility 2002 tpy 2009 tpy 

Georgia Power Plant Arkwright 1,938.84 Closed

Graphic Packaging 252.85 260.63

Brown & Williamson Tobacco Corporation 514.61 Closed

Georgia Power Plant Scherer 86,349.70 106,690.39

Planning for the Macon area was included in this rule by requiring all EGUs located at 

Georgia Power Plant Scherer, which is located within the Macon nonattainment area, to 

install FGD. The rule requires Scherer Unit 3 to begin operation of FGD in 2011, 

followed by an additional unit each year with all units completed by December 31, 2014. 

While 72 FR 20623 specifies that CAIR satisfies RACT for SO2 emissions, the timing of 

the installation and operation of controls at Plant Scherer will not advance the attainment 

date for Macon. EPD notes that the Multipollutant rule requires SO2, NOx and Hg 

controls on all large EGUs in the state. The controls at Plant Scherer 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. 

Additionally, EPD has long been active (prior to nonattainment designations) in the 

Macon area for both PM2.5 as well as ozone nonattainment planning to help develop 

proactive measures for improvement of air quality in the area. At the request of 

MGCAC, the State adopted May through September open burning restrictions (391-3-1-

.02(5)) effective January 9, 2005, for Bibb County and the surrounding contiguous 

Counties of Crawford, Houston, Peach, and Twiggs. Monroe and Jones Counties have 

previously been subject to open burning restrictions as part of the Atlanta Ozone 

nonattainment control area. The Coalition was also responsible for a number of 

voluntary local measures identified in Section 5.5.2. 

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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 Macon nonattainment area. As described in the US Environmental Protection 

Agency’s (USEPA’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 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 US 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 

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baseline inventory year). This works out to a 5-year weighted average, with the baseline 

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 nonattainment areas. This quarterly data was then averaged, using the weighted 

scheme suggested by the US 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 Macon 

Nonattainment Area 

AIRS ID County Site Name Year Q1 Q2 Q3 Q4 

13-021-0007 Bibb Macon Allied Chem. 2000 16.46 16.06 19.76 21.38 

 2001 13.93 17.07 19.64 13.81 

2002 12.63 16.63 16.97 12.92 

 2003 11.98 15.86 16.29 15.09 

 2004 14.60 16.42 19.19 16.93 

13-021-0012 Bibb Macon Forestry 2000 14.59 14.98 16.99 19.82 

 2001 12.28 15.71 15.54 11.50 

2002 11.32 14.71 15.05 11.55 

 2003 10.63 14.40 14.73 12.03 

 2004 12.41 13.71 17.36 13.72 

Table 6-2. Observed Quarterly and Annual Mean PM2.5 DVB (in μg/m3) for 

Monitors in the Macon Nonattainment Area 

AIRS ID County Site Name 

2000-2004 

Q1 

2000-2004 

Q2 

2000-2004 

Q3 

2000-2004 

Q4 

2000-2004 

DVB 

13-021-0007 Bibb Macon Allied Chem. 13.421 16.470 17.970 14.984 15.7 

13-021-0012 Bibb Macon Forestry 11.862 14.783 15.562 12.805 13.8 

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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 monitors. 

The Macon Allied Chemical site (13-021-0007) has a co-located speciation monitor, 

while the Macon Forestry site (13-021-0012) does not. The following sections will 

explain how these issues were overcome to produce the speciated values needed for this 

attainment demonstration. 

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 monitors. 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 US 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 

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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 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 US 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 US 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 

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). 

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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). 

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 

13-021-0007 Macon Allied Chem. 0.5 1 0.27826 0.032976 0.06925 0.496741 0.239901 0.023908 0.073688 0.063536 

2 0.26114 0.049597 0.039678 0.446611 0.309361 0.00062 0.080966 0.073166 

3 0.24701 0.083349 0.022585 0.263868 0.436105 0.000538 0.107878 0.085677 

4 0.26837 0.026577 0.052349 0.378028 0.33745 0.024161 0.097568 0.083867 

13-021-0012 Macon Forestry 0.5 1 0.27826 0.03698 0.077658 0.435637 0.269029 0.02681 0.082635 0.071251 

2 0.26114 0.056312 0.045049 0.371692 0.351244 0.000704 0.091928 0.083072 

3 0.24701 0.083349 0.022585 0.263868 0.436105 0.000538 0.107878 0.085677 

4 0.26837 0.029869 0.058833 0.300993 0.379245 0.027154 0.109652 0.094255 

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

Macon Nonattainment Area 

AIRS ID Site Name 

Blank 

Mass qtr 

FRM 

Mass 

Non-Blank 

Mass Crustal EC OC SO4 NO3 NH4 PBW 

13-021-0007 Macon Allied Chem. 0.5 1 13.42 12.92 0.43 0.89 6.42 3.10 0.31 0.95 0.82 

2 16.47 15.97 0.79 0.63 7.13 4.94 0.01 1.29 1.17 

3 17.97 17.47 1.46 0.39 4.61 7.62 0.01 1.88 1.50 

4 14.98 14.48 0.38 0.76 5.48 4.89 0.35 1.41 1.21 

13-021-0012 Macon Forestry 0.5 1 11.86 11.36 0.42 0.88 4.95 3.06 0.30 0.94 0.81 

2 14.78 14.28 0.80 0.64 5.31 5.02 0.01 1.31 1.19 

3 15.56 15.06 1.26 0.34 3.97 6.57 0.01 1.62 1.29 

4 12.80 12.30 0.37 0.72 3.70 4.67 0.33 1.35 1.16 

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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 US EPA's recommendations for 

defining “nearby” cells for grid systems having cells of various sizes. Since the modeling 

for the Georgia attainment demonstration was preformed at a 12-km grid resolution, a 

3x3 grid array was used. 

Table 6-5. USEPA’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 Macon 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: 

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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)) 

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

Area 

AIRS ID Site Name qtr 

Crustal 

RRF 

EC 

RRF 

OC 

RRF 

SO4 

RRF 

NO3 

RRF 

13-021-0007 Macon Allied Chem. 1 1.079 0.919 0.993 0.887 1.058 

2 1.103 0.860 0.977 0.818 0.820 

3 1.107 0.827 0.961 0.782 0.879 

4 1.097 0.830 0.988 0.881 0.988 

13-021-0012 Macon Forestry 1 1.081 0.923 0.993 0.886 1.069 

2 1.106 0.870 0.978 0.816 0.840 

3 1.110 0.835 0.961 0.778 0.973 

4 1.099 0.831 0.985 0.882 0.996 

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 

Macon PM2.5 nonattainment area are provided below in Table 6-7. 

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

the Macon Nonattainment Area 

AIRS ID Site Name qtr 

Crustal 

DVF 

EC 

DVF 

OC 

DVF 

SO4 

DVF 

NO3 

DVF 

NH4 

DVF 

PBW 

DVF 

13-021-0007 Macon Allied Chem. 1 0.460 0.822 6.375 2.749 0.327 0.860 0.729 

2 0.874 0.545 6.970 4.041 0.008 1.058 0.969 

3 1.611 0.326 4.428 5.956 0.008 1.473 1.240 

4 0.422 0.630 5.409 4.306 0.346 1.256 1.068 

13-021-0012 Macon Forestry 1 0.454 0.815 4.916 2.708 0.326 0.848 0.718 

2 0.890 0.560 5.191 4.092 0.008 1.071 0.981 

3 1.394 0.284 3.818 5.109 0.008 1.264 1.087 

4 0.404 0.601 3.649 4.117 0.333 1.201 1.024 

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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. 

13-021-0007 Macon Allied Chem. 12.320 14.465 15.043 13.437 13.816 

13-021-0012 Macon Forestry 10.786 12.793 12.964 11.329 11.968 

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 (Table 6-9). These 2009 annual PM2.5 

concentrations (DVFs) are then compared to the NAAQS (15.0 μg/m3

). Since the values 

at both FRM sites in the nonattainment areas are less than 15.0 μg/m3

, the test is passed. 

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 

13-021-0007 Macon Allied Chem. 15.7 13.8 0.5 14.3 

13-021-0012 Macon Forestry 13.8 12.0 0.5 12.5 

6.2 Supplemental Analysis 

The Attainment Guidance asserts that all attainment demonstrations should be 

accompanied by 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. 

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- 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 Macon 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 Macon 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 set ups 

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 US 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. 

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Because of the complexity of the model extraction for PM2.5, only the third metric is 

presented for supplemental analysis. In this analysis, all 365 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 Macon nonattainment area. Figure 6-1 

highlights the 13 cells that encompass the Macon nonattainment area. 

Figure 6-1. Grid cells for which the air quality metrics were applied in the Macon PM2.5 

nonattainment area.

The cell-days counts were binned based on concentration ranges of five 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 76.7% of cell-days fall in the 0 –15 range, 

a substantial increase from the 70.6% in 2002. Raw cell-days counts show a total of 287 

cell-days (20.6% 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). 

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Attainment Demonstration PM2.5 Attainment SIP - Macon, GA 

Figure 6-2 also shows a decrease in the number of cell-days in the 15 – 20 μg/m3 bin (141 

cell-days decrease) and the 20 – 25 μg/m3

 bin (131 cell-days decrease). In addition, the 

number of cell-days in the greater than 30 μg/m3

 bin decreased by 15 cell-days (71.4% 

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. 

Percentage changes of cell-days in MACON NAA 

from 2002 to 2009

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2002 2009

>30

25-30

20-25

15-20

10-15

5-10

0-5

Figure 6-2. Percentage of Cell-Days in the Macon 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 

0-5 459 511 52 11.3 

5-10 1315 1461 146 11.1 

10-15 1566 1655 89 5.7 

15-20 1007 866 -141 -14.0 

20-25 299 168 -131 -43.8 

25-30 65 65 0 0.0 

>30 21 6 -15 -71.4 

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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 Macon PM2.5 

nonattainment area is the US 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 US 

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 Bibb County (highest monitor) 

Future Year PM2.5 (μg/m3) 

2010 15.17 

2015 14.83 

The US EPA’s results were for the highest monitor in counties with more than one 

monitor. The US EPA’s modeling results predicts that the Macon 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 Macon nonattainment area would have modeled 

attainment of the annual PM2.5 standard by 2009 and 2010. 

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6.2.2 Air Quality and Emission Trends Analysis

Since measurements began in 1999, annual average concentrations of PM2.5 decreased 

steadily until 2002 at which time they leveled out thru 2003. Annual average PM2.5 

concentrations increased in 2004 and remained steady thru 2006. Significantly lower 

concentrations were measured in 2007. The Macon Forestry (13-021-0012) monitor has 

maintained values lower than the NAAQS for the past seven years. Table 6-12 provides 

the annual average data and with Figure 6-3 provides a graphical representation of the 

data. 

Table 6-12. Annual Average PM2.5 values (in μg/m3) in Macon for the past nine 

years. 

Year 

Macon Allied Chem. 

(13-021-0007) 

Macon Forestry 

(13-021-0012) 

1999 18.40 18.10 

2000 18.40 16.60 

2001 16.10 13.80 

2002 14.80 13.20 

2003 14.81 12.95 

2004 16.79 14.30 

2005 16.66 14.31 

2006 16.92 14.08 

2007 15.78 12.42 

Annual PM2.5 Concentrations in Macon

10.0

12.0

14.0

16.0

18.0

20.0

1999 2000 2001 2002 2003 2004 2005 2006 2007

PM2.5 Conc. (

μg/m3)

Macon Allied Chem. (13-021-0007) Macon Forestry (13-021-0012)

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

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When one takes into account the period of record, PM2.5 design values have improved 

over the last seven years at both monitors. It is also important to note the Macon Forestry 

(13-021-0012) monitor has maintained design values that meet the NAAQS over the past 

6 design value periods (See Table 6-13). 

Table 6-13. Three Year Design values (in μg/m3) for the Monitors in the Macon 

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 

13-021-0007 Macon Allied Chem. 17.6 16.4 15.2 15.5 16.1 16.8 16.5 

13-021-0012 Macon Forestry 16.2 14.5 13.3 13.5 13.9 14.2 13.6 

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 prior to the 

attainment year in the utility sector. As a result of CAIR and the Georgia Multipollutant 

Rule, multiple scrubbers will be installed by Georgia Power on some of their large coalfired power plants between 2007 and 2009. Table 6-14 lists the units in the state of 

Georgia and shows the date the controls did come or are expected to come on line and the 

estimated amount of yearly SO2 emissions reductions. In total, over 244,000 tons of SO2 

will be reduced annually in Georgia. This reduction represents a 43% 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 the Macon Allied 

Chemical site (13-021-0007) 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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(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. 

6.2.3 Supplemental Analysis Conclusions

After examining the totality of the modeling evidence, GA EPD is confident the Macon 

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 two FRM monitors 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 

anyway 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 been 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 

Macon 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 those calculated at the FRM 

monitors using SMAT and SANDWICH. However, this discrepancy between the 

absolute and relative (RRF) 2009 concentrations will have no impact on our conclusion 

that there are no unmonitored hotspots in the Macon area. 

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Attainment Demonstration PM2.5 Attainment SIP - Macon, GA 

Despite being confident our monitoring network is robust enough to cover the Macon 

nonattainment area, GA EPD has looked at preliminary results from the “beta” (draft) 

version of the “Modeled Attainment Test Software” (MATS) tool. The MATS tool has 

been developed by the US EPA to spatially interpolate data, adjust the spatial fields based 

on model output gradients, and multiply the fields by model calculated RRFs to complete 

the unmonitored area analysis. The MATS tool is currently being “beta tested” and peer 

reviewed, with the release of a guidance document for the tool still pending. Once the 

final version of the MATS tool has been released, after sufficient peer review and proper 

guidance documentation for the analysis of the results is provided, GA EPD will evaluate 

the MATS tool output.

Figure 6-4. Modeled 2009 Annual Mean PM2.5 Concentration near the Macon Nonattainment Area 

on the VISTAS 12km Grid 

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Attainment Demonstration PM2.5 Attainment SIP - Macon, GA 

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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Other Clean Air Act Requirements PM2.5 Attainment SIP - Macon, GA 

Section 7. Other Clean Air Act Requirements 

Section 172(c) of the CAA, as amended, contains the plan requirements for 

nonattainment areas in general. The Macon 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 7.8. 

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. 

Sections 5.3 and 7.9 

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Other Clean Air Act Requirements PM2.5 Attainment SIP - Macon, GA 

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 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 3 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 

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Other Clean Air Act Requirements PM2.5 Attainment SIP - Macon, GA 

the Administrator to which economic development should be targeted. Georgia has no 

such zones targeted for economic development identified by the Administrator. 

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. The Georgia EPD has met the permit program requirements for a 

PM2.5 nonattainment area. Georgia EPD is currently complying with the recently 

promulgated May 16, 2008, rulemaking including 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 limits 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 signed the final rule regarding the status of the elements of Section 110(a) 

Infrastructure Elements for 1997 8-hour PM2.5 NAAQS on October 3, 2008. Georgia 

was found to have a complete SIP revision on file at EPA that addresses these 

infrastructure elements for PM2.5. 

7.8 Equivalent Techniques

The Georgia EPD believes that the procedures for modeling, emissions inventory and 

planning follow the US EPA guidance and is not requesting approval for equivalent 

techniques, as envisioned under Section 172(c)(8). 

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Other Clean Air Act Requirements PM2.5 Attainment SIP - Macon, GA 

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 US EPA upon a 

determination by the US 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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2009 Motor Vehicle Emissions Budget PM2.5 Attainment SIP - Macon, GA 

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 Macon PM2.5 

Nonattainment Area

In preparation for this Macon PM2.5 Attainment Demonstration, EPD worked closely 

with the Georgia Department of Transportation (GDOT) and the Macon Area 

Transportation Study (MATS), the metropolitan planning organization for Macon, to 

develop estimates of mobile2

 source emissions for the Macon 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. 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. Two sets of 

emission factors were produced to properly reflect different summertime gasoline 

volatility limits in the Bibb County and the Monroe partial-county parts of the Macon 

nonattainment area. For both parts of the nonattainment area, 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. Monroe County emission factors reflect Georgia's seasonal low-RVP 

gasoline marketing requirement. 

The emission factors resulting from the MOBILE6.2 runs for the Bibb County part of the 

Macon nonattainment area were used with GDOT's link-based emissions estimation 

procedure to calculate 2009 tons per year emissions in the following manner: 

 

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 Bibb County and the Monroe partial-county parts of the Macon 

nonattainment area; updated VMT and speeds from the latest Macon travel demand model) 

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2009 Motor Vehicle Emissions Budget PM2.5 Attainment SIP - Macon, GA 

• For each of four times of day (a.m. peak, midday, p.m. peak, and night), the HPMSadjusted4

 2009 VMT from each link in the Macon 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 per day, 

then multiplied by 365 to get tons per year. 

For the Monroe partial-county area, an off-model technique was used to estimate daily 

VMT, which were then multiplied by Monroe emission factors reflecting the appropriate 

speeds to get daily emissions, and by 365 to get annual emissions. 

These mobile source inventories reflect up-to-date mobile modeling assumptions, 

including 2009 VMT projected from a travel demand model and 2009 emission factors 

from EPA's latest mobile source emission factor model. The same mobile source control 

rules reflected in Georgia's 8-hour ozone maintenance plan for Macon were modeled for 

this Macon PM2.5 Attainment Demonstration. 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 Bibb County and Monroe 

partial-county portions of the Macon PM2.5 nonattainment area. 

Table 8-1. 2009 PM2.5 Motor Vehicle Emissions Budget, 

Macon Nonattainment Area 

PM2.5 

(tons/year) 

2009 Mobile Emissions for Bibb County 75.5252 

2009 Mobile Emissions for Monroe Partial-County 0.0122 

Total, 2009 Motor Vehicle Emissions Budget 75.5374 

This Macon PM2.5 Attainment Demonstration hereby establishes a 2009 motor vehicle 

emissions budget of 75.5374 PM2.5 tons per year for the Macon PM2.5 nonattainment 

area. Interagency consultation among the relevant agencies occurred during the 

development of this MVEB and prior to the submittal of this Macon PM2.5 Attainment 

Demonstration. Full implementation plan documentation was provided to EPA, and 

EPA's stated concerns have been addressed. 

The MVEB established by this Macon PM2.5 Attainment Demonstration is based on new 

estimates of VMT and speeds from an updated travel demand model; on the latest version 

 

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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2009 Motor Vehicle Emissions Budget PM2.5 Attainment SIP - Macon, GA 

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. 

Since the Macon nonattainment area includes a partial-county area comprised of the 

portion of Monroe County identified in Section 2, to show the MVEB "together with all 

other emissions sources6

," area and non-road emissions for that portion of Monroe 

County were apportioned based on the ratio of the population of the nonattainment area 

to the total population of Monroe County. EPD consulted EPA's Office of Air Quality 

Planning & Standards (OAQPS) who concurred that population scaling is an appropriate 

method. The population of Monroe County from the year 2000 census was 21,757 

people. Linear extrapolation from U.S. Census Bureau population estimates7

 for 2000 

through 2007 indicates that by July 1, 2009, the population of Monroe County will be 

25,725 persons, or a growth of 18.238%. The Georgia Department of Transportation 

(GDOT) calculated that, based on Census 2000 and selecting the census blocks whose 

centers were inside the nonattainment area boundary, the population of the partial-county 

area in 2000 was 175 persons. Since the census blocks cover an area that is somewhat 

larger than the Monroe partial-county nonattainment area, this is a conservative (high) 

value. Adjusting for the 18.238% growth between the 2000 census and the 2009 

extrapolation, the population of the partial-county area by July 1, 2009, would be 207 

persons. Therefore, the partial-county area contains 0.805% of the Monroe County 

population and 0.805% was the scaling factor applied to emission inventory data to 

determine the emissions from the Monroe partial-county nonattainment area. The 

Monroe County emissions used for point sources (Plant Scherer is the only point source 

in the Monroe County inventory) and those used to factor the partial-county area and 

nonroad sources, were from the 2009 "best-and-final" modeling inventories developed by 

the VISTAS/ASIP contractors, as were the Bibb County point, area, and nonroad 

emissions. Total projected 2009 annual PM2.5 emissions for the Macon nonattainment 

area are shown in Table 8-2 below. 

 

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. See Appendix J, Exhibit 2, 

for more details on the registration distribution by age. 

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...." 

7 http://www.census.gov/popest/counties/tables/CO-EST2007-01-13.xls

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2009 Motor Vehicle Emissions Budget PM2.5 Attainment SIP - Macon, GA 

Table 8-2. Macon Nonattainment Area Total Projected 

2009 PM2.5 Emissions, Tons/Year 

 

Point Nonroad Area Mobile 

all Monroe 355.5300 18.4000 744.1305 --- 

partial-county factor --- 0.805% --- 

Monroe partial-county 355.5300 0.1481 5.9903 0.0122 

Bibb 156.3270 77.1000 989.9938 75.5252 total

Macon nonattainment area totals 511.8570 77.2481 995.9841 75.5374 1,660.6266 

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Summary and Conclusion PM2.5 Attainment SIP - Macon, GA 

Section 9. Summary and Conclusion 

This section summarizes and concludes the results of the attainment demonstration 

documented in this SIP revision. EPA designated the Macon area as nonattainment for 

the annual PM2.5 NAAQS on January 5, 2005, and confirmed the designation on April 

5, 2005. The Macon PM2.5 nonattainment area is made up of Bibb County and a small 

portion of Monroe County that contains a single coal-fired electric generation facility, 

Georgia Power’s Plant Scherer. The observed 2001 – 2003 PM2.5 design value at one of 

the two nonattainment area monitors violated the annual standard, as shown in Table 9-1 

below. 

Table 9-1. 2001 – 2003 PM2.5 Design Values for Macon Nonattainment Area 

Monitors 

Site name Monitor ID PM2.5 Design 

Value 

 (μg/m3) 

 Annual PM2.5

Standard 

 (μg/m3) 

Allied Chemical 

(Bibb Co.) 

13-021-0007 15.2 15.0 

GA Forestry 

(Bibb Co.) 

13-021-0012 13.3 15.0 

Speciated monitoring data from the Allied Chemical site have shown that the most 

significant components of ambient 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-

.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’s Attainment Modeling Guidance. The modeling was performed 

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Summary and Conclusion PM2.5 Attainment SIP - Macon, GA 

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 model: MM5 

• Emissions model: SMOKE 

• Air Quality model: 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. 

Table 9-2. PM2.5 2009 Future (Modeled) Design Values 

Federal 

Reference 

Monitor 

2009 Annual Design 

Value (DVF) 

 (μg/m3) 

PM2.5 Standard 

 (μg/m3) 

Allied Chemical 14.3 15.0 

GA Forestry 

Commission 

12.5 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 Macon 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 both FRM monitors located in the Macon Nonattainment 

Area. 

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References PM2.5 Attainment SIP - Macon, GA 

Section 10. References 

Boylan, J.W. and Russell, A.G. (2006) PM and Light Extinction Model Performance 

Metrics, Goals, and Criteria for Three-Dimensional Air Quality Models; Atmos. Environ., 

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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