# Georgia's State Implementation Plan for
The Macon PM 2.5 

# Nonattainment Area

June 12, 2009

Prepared by:



# Georgia Department of Natural
Resources Environmental Protection Division Air Protection BranchPM2.5 Attainment SIP - Macon, GA

Preface

This document is Georgia’s State Implementatio n Plan (SIP) for attainment of the Federal National Ambient Air Quality Standard (NAAQS) for PM 2.5 in the Macon nonattainment
area. The Macon nonattainme nt area includes all of Bibb County and a portion of Monroe County. Specifically, this SIP de monstrates attainment of the annual PM 2.5 NAAQS, promulgated in 1997, in the Macon nonattainment area by April 5, 2010.

3 of 143Table 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 F INE P ARTICLES ...................................................................................................... 19
1.2 1997 N ATIONAL A MBIENT A IR Q UALITY S TANDARDS FOR PM 2.5 .......................... 19

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

2.1 N ONATTAINMENT A REA D ESIGNATION .................................................................. 23
2.2 PM 2.5 M ONITORING S TATIONS ................................................................................ 24
2.3 C ONCEPTUAL D ESCRIPTION OF THE PM2.5 P ROBLEM ............................................ 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 S OURCES AND P RECURSORS ........................................................................ 48 2.4.1 Sources of PM2.5 ............................................................................................ 48
2.4.2 Precursors ....................................................................................................... 50 2.4.3 Technical Justification for Re versal of NOx Precursor Presumption ............ 52

Section 3. Attainment Dem onstration Methods and Inputs ............................................. 59

3.1 A NALYSIS M ETHOD ................................................................................................. 59
3.2 M ODEL S ELECTION .................................................................................................. 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 S ELECTION OF THE B ASE M ODELING Y EAR ............................................................. 64
3.4 M ODELING D OMAINS ............................................................................................... 65 3.4.1 Horizontal Modeling Domain .......................................................................... 65 3.4.2 Vertical Modeling Domain .............................................................................. 67
3.5 B ASELINE E MISSIONS I NVENTORY ........................................................................... 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

4 of 143Table of Contents PM2.5 Attainment SIP - Macon, GA

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

4.1 M ETEOROLOGICAL M ODEL P ERFORMANCE ............................................................. 75
4.2 A IR Q UALITY M ODEL P ERFORMANCE ...................................................................... 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 Ev aluation of the CMAQ Modeling ................. 94

Section 5. Control a nd Contingency Measures ................................................................ 96

5.1 F EDERAL C ONTROL M EASURES FOR PM2.5 A TTAINMENT ...................................... 96
5.1.1 Clean Air Interstate Rule ................................................................................. 96
5.1.2 Tier 2 Vehicle Standards .................................................................................. 99 5.1.3 Heavy-Duty Gasoline and Dies el 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 S TATE C ONTROL M EASURES FOR PM2.5 A TTAINMENT ......................................... 101 5.2.1 Multipollutant Control fo r 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. S UMMARY OF SO2 E MISSIONS C HANGES ............................................................ 105
5.4. C ONTINGENCY M EASURES ................................................................................... 106
5.5 U NMODELED C ONTROL M EASURES ...................................................................... 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, Earl y Implementation of Controls ............................... 113
5.6 RACT AND RACM .............................................................................................. 113

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

6.1 M ODELED A TTAINMENT T EST ............................................................................... 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 S UPPLEMENTAL A NALYSIS .................................................................................... 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 U NMONITORED A REA A NALYSIS ........................................................................... 130
6.4 D ATA A CCESS ........................................................................................................ 132

5 of 143Table of Contents PM2.5 Attainment SIP - Macon, GA

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

7.1 RACT/RACM R EQUIREMENTS ............................................................................ 134
7.2 R EASONABLE F URTHER P ROGRESS ....................................................................... 134
7.3 A CTUAL E MISSIONS I NVENTORY ........................................................................... 134
7.4 P ERIODIC E MISSIONS I NVENTORY ......................................................................... 134
7.5 P ERMIT P ROGRAM R EQUIREMENTS ....................................................................... 134
7.6 O THER M EASURES ................................................................................................ 135
7.7 C OMPLIANCE WITH S ECTION 110( A )(2) (I NFRASTRUCTURE E LEMENTS )............... 135
7.8 E QUIVALENT T ECHNIQUES .................................................................................... 135
7.9 C ONTINGENCY M EASURES .................................................................................... 136

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

8.1 B ACKGROUND ....................................................................................................... 137
8.2 E STABLISHMENT OF 2009 M OTOR V EHICLE E MISSIONS B UDGETS FOR THE M ACON PM2.5 N ONATTAINMENT A REA ................................................................................... 137

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

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

6 of 143List 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 Visibil ity in the VISTAS Region

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

F Modeling Protocol For Associati on for Southeastern Integrated Planning (ASIP) Emissions and Ai r Quality Modeling to Address
8-Hour Ozone and PM2.5 Nonattainment in the Southeastern
United States"
G Technical Support Document for Association for Southeastern Integrated Planning (ASIP) Em issions 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) Require ments (SIP Infrastructure Elements): Communications dated 7-23-08 and 9-22-08

7 of 143List 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 concentr ations at the speciation site in the Macon NAA ......... 32

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

Figure 2-10. Quarterly averages of SO 4 concentrations at the speciation site in the Macon NAA ........... 32

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

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

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

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

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

Figure 2-13. Quarterly averages of OC concentratio ns at the speciation site in the Macon NAA (13-021- 0007)

............................................................................................................................... .... 34

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

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

Figure 2-15: Seasonal patterns in PM 2.5 concentrations and composition in Atlanta (Site ID 13-089-0002) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon). ....................................................................................... 36

Figure 2-16: Seasonal patterns in PM 2.5 concentrations and composition in Chattanooga (Site ID 47-065- 4002) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon). ............................................................................. 36

Figure 2-17: Seasonal patterns in PM 2.5 concentrations and composition in Floyd county (Site ID 13-115- 0005) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon). ............................................................................. 37

Figure 2-18. Seasonal patterns in PM 2.5 concentrations and composition in Macon (Site ID 13-021-0012) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon). ....................................................................................... 37

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

Figure 2-19: Average factor contributions for 2003-0 4 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.

Δ PM2.5 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.

Δ PM2.5 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 co ncentration 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 concentr ations for all VISTAS STN monitoring sites. .................. 84

Figure 4-4. VISTAS FR M Soccer Plots - Soccer plots depicti ng both the mean fractional error and

fractional bias for PM 2.5 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 PM 2.5 nonattainment areas (left) and the Macon PM 2.5 nonattainment area (right). ............. 87

Figure 4-7. Stacked bar plots of daily PM 2.5 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

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

LIST OF TABLES

Table E-1. PM2.5 2002 Baseline Design Valu es 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 Monitors 24

Table 2-2. Macon PM 2.5 Data Collection Sites ................................................................ 25

Table 2-3. Annual mean (arithmetic av erage 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 PM 2.5 composition during 2003-2004 for th e four speciation sites in
the nonattainment areas ..................................................................................................... 38

Table 2-6. Estimated primary a nd secondary percentage of PM 2.5 .................................. 38

Table 2-7: Average factor contributions to PM 2.5 ( μ g/m 3 ) 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 averag e input parameters for key bi ns for CART classification (Class) categories 3 and 4 in Macon. ................................................................................ 43

Table 2-10. Typical Base Y ear (2002) Emissions Invent ories for Bibb and Monroe
Counties ............................................................................................................................ 48

Table 2-11. Point Sources in the Macon N onattainment Area That Are Title V Major
Sources .............................................................................................................................. 49

Table 2-12. Expected Change in NOx Em issions (%) 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 Em ission Summary for Georgia Counties (tons)

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

Table 3-3. 2009 BaseG4 Annual Emissi on 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

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

Table 4-4. Established Model Performance Go als and Criteria for the PM2.5 Component Species .............................................................................................................................. 81

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

Table 4-6. Ambient monito ring data available in 2002. .................................................. 86

Table 4-7. Annual model performance statistics for PM 2.5 at all FRM monitors in the State of Georgia and in the Macon nonattainment area. ................................................... 89

Table 4-8. Monthly model performance statistics for PM 2.5 at 13-021-0007. .................. 90

Table 4-9. Monthly model performance statistics for PM 2.5 at 13-021-0012. .................. 91

Table 4-10. Annual model perf ormance statistics for PM 2.5 species and total PM 2.5 at all STN monitors in the State of Georgia. .............................................................................. 92

Table 4-11. Annual model perf ormance statistics for PM 2.5 at 13-021-0007. .................. 93

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

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

Table 5-3. Georgia Multipollutant Rule SO 2 Control Requirements and Construction
Permit Numbers .............................................................................................................. 103

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

Table 5-5. Changes in SO2 Emissi ons, 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 Emi ssions, 2002 to 2009, Statewide (tons) ....................... 106

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

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

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

Table 5-11 Macon Nonattain ment Area Major Source SO 2 Emissions (Actual) ........... 115

Table 6-1. Quarterly Average PM2.5 Ma ss for the FRM Monitors in the Macon
Nonattainment Area ........................................................................................................ 117

Table 6-2. Observed Quarterly and Annual Mean PM2.5 DVB (in μg/m 3 ) for Monitors
in the Macon Nonattainment Area .................................................................................. 117

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

Table 6-4. Quarterly PM2.5 Co mponent-Specific DVB (μg/m 3 ) for Monitors in the
Macon Nonattainment Area ............................................................................................ 120

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

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

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

Table 6-7. Quarterly Component Future Design Values (in μg/m 3 ) for Monitors in the
Macon Nonattainment Area ............................................................................................ 122

12 of 143List 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 Conc entration 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/m 3 ) in Macon for the past nine years.

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

Table 6-13. Three Year Design values (in μg/m 3 ) for the Monitors in the Macon PM2.5
Nonattainment Area. ....................................................................................................... 129

Table 6-14. Projected SO2 emission reducti ons from new controls on Georgia Power
EGUs in 2008 and 2009.................................................................................................. 129

Table 7-1. General Plan Requi rements 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. PM 2.5 2009 Future (Modeled) Design Values .............................................. 142

13 of 143Acronyms 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 availa ble control measure RACT Reasonably available control technology
RFP Reasonable further progress
RRF Relative response factor SANDWICH Sulfate, adj usted 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 Stat e and Tribal Association of the Southeast VOC Volatile organic compound
VMT Vehicle miles traveled

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

Executive Summary

This document is Georgia’s state implementa tion plan (SIP) for a ttainment of the 1997 PM2.5 National Ambient Air Quality Standa rds 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 ( μ g/m 3 ) or less. The nonattainment area designation was based upon a 2001 - 2003 design value at the Allied Chemical monitor of 15.2

μ g/m 3 . This document does not address the 24-hour PM2.5 standard promulgated in December 2006, as nonattainment areas and requir ements for this new standard have not
been finalized.

This SIP contains the elements require d 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 th e 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 Mac on) and suburban land uses.

PM2.5 is made up of a number of compone nt 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. Elementa l 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 143Introduction 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 mo nitor. Sulfates are formed primarily
from sulfur dioxide emissions from coal-fir ed 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 appr oximately 30 percent of the ambient annual PM2.5 measured from 2001 through 2005. This pe rcentage 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. Nitr ate 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 SO 2 (and, therefore, sulfates) from EGUs. Biogenic formation of organic carbon is not addressed by contro l measures in this SIP. Significant State measur es 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 re ductions in SO2 emissions fr om electric generating units (EGUs) and is the measure that will provide the greatest benefit towa rd demonstration of attainment in calendar year 2009. Fede ral 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) provi des for reductions of both SO2 and NOx. The other federal measures provide for reducti ons of NOx and direct PM emissions.

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

Some measures were not mode led, 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 re strict 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 na tural-gas-fired combustion turbines at two EGUs; and

• Planned, early start-up of SO 2 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 su bmit an attainment de monstration showing
attainment with the annual PM2.5 standard as expeditiously as po ssible. 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. Mode led attainment is based primarily upon projected SO2 emissions reductions fr om EGU sources within the State.

Since it is performed on the basis of indivi dual 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 concen trations to observed concentratio ns 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 hi storical baseline average a nnual concentration, known as the relative response factor (RRF), is cal culated 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 indi vidual species are then summed to obtain the overall 2009 modeled design value. This SM AT 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/m 3 , modeled attainment is demonstrated. Supplemental analyses (in addition to SMAT ) are presented that also support the
demonstration of attainment.

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

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

Federal
Reference
Monitor Site ID 2002 Annual Design Value (DVB) (μg/m 3 ) 2009 Annual Design Value (DVF) (μg/m 3 )

Allied Chemical 130210007 15.2 14.3

GA Forestry
Commission 130210012 13.3 12.5

As required by the EPA Clean Air Fine Part icle Implementation Rule, sources of PM2.5 and its precursors within the Macon Nonattain ment Area were evaluated to verify that they were meeting at least reasonably avai lable control measure (RACM) levels of emission controls. This evaluation and the results of sensitivity modeling that was performed indicate that no addi tional reductions are availabl e 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 Pr ogress (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 Demons tration Methods and Inputs
Section 4 Model Performance Evaluation Section 5 Control and Contingency Measures
Section 6 Attainment Demonstration Section 7 Other Clean Air Act Requirements Section 8 Motor Vehicle Emissions Budget Section 9 Summary & Conclusion
Section 10 References

The appendices provide additional mate rial to support th e SIP narrative.

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

Section 1. Introduction

1.1 Fine Particles

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

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

1.2 1997 National Ambient Air Quality Standards for PM 2.5

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

Table 1-1. 1997 NAAQS for PM2.5

Description Standard ( μ g/m 3 )
Annual arithmetic mean concentration 1 ≤ 15.0 24-hour average concentration, 98 th percentile 1 ≤ 65 [1] determined in accordance with Appendix N of 40 CFR Part 50

These standards were promulgated by US EPA on July 18, 1997 (62 FR 38711 and 40CFR50.7). A monitoring site (or group of spa tially-averaged sites) is in attainment of
the annual PM 2.5 standard when the annual standard design value is less than or equal to 15.0 micrograms per cubic meter ( μ g/m 3 ). The annual standard design value is the 3- year average of the annual mean, which is, in turn, the average of one year’s four19 of 143Introduction PM2.5 Attainment SIP - Macon, GA

calendar-quarter averages. A monitoring site (or group of spatially-ave raged sites) is in
attainment of the 24-hour PM 2.5 standard when the 24-hour standard design value is less than or equal to 65 μ g/m 3

. The 24-hour standard design valu e is the 3-year average of
annual 98 th percentile 24-hour averages. The annual 98 th percentile 24-hour average is the 24-hour average PM 2.5 concentration which is higher than 98 percent of the year’s valid 24-hour averages. Therefore, to achieve attainment of the 1997 PM 2.5 standards by a given date, the annual and 24-hour standard design values calculated from valid data collected for the previous three calendar ye ars 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 attain ment. More details on the calculation of design values and the interpretation of the PM 2.5 NAAQS can be found in Appendix N of
40 CFR Part 50.

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Section 2. Macon Nonattainment Area

There are currently four PM 2.5 nonattainment areas in Georgia: Chattanooga (which also includes parts of Alabama and Tenne ssee), Floyd County, Atlanta, and Macon. Figure 2-1 shows their locations. The Macon n onattainment 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 PM 2.5 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 143Macon 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 m eet at U.S. Hwy 23/Geo rgia 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 eastwa rd 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 nonattain ment area includes both urba n 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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projected to grow no more than five percent by 2010. The nonattainment area designation, major PM2.5 sources, PM2.5 precurso rs, 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 stat es and tribes, designates a county as
attainment/unclassifiable or nonattainment. In addition, a county is designated as
nonattainment if it is deemed to contain sour ces that contribute to the nonattainment status of a county with a non- attaining design value. Nona ttainment counties are grouped together into nonattainment areas base d on location within common metropolitan statistical areas (MSAs). EP A made initial designations of the nonattainment areas, including the Macon nonatta inment area, for the PM 2.5 NAAQS on January 5, 2005 (70FR944), and amended the designations on April 5, 2005 (70F R19844). The Macon

PM 2.5 nonattainment area is made up of Bi bb County and a small portion of Monroe County that contains a single coal-fired el ectric generation facility, Georgia Power’s Plant Scherer. The inclusion of the porti on of Monroe County was based on high levels
of SO 2 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 potentia lly included versus excluded from the nonattainment area;

• Factor 2: Air quality in potenti ally included vers us excluded areas;

• Factor 3: Population density and de gree of urbanization including commercial development in potentially incl uded 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 criteri a used by EPA in their analysis. The observed PM2.5 design value at one of the two nonattainment area monitors violated the

23 of 143Macon 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 no nattainment area Monitors

Site name Monitor ID PM 2.5 Design Value (μg/m 3 )

Allied Chemical (Bibb County) 13-021-0007 15.2

Georgia Forestry (Bibb County) 13-021-0012 13.3

2.2 PM 2.5 Monitoring Stations

There are currently two ambient PM 2.5 monitoring sites in th e 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. Provi ding 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 ne twork, with the mon itoring 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 mo nitor 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 samp ling. The second FRM monitor has been collecting data since January 5, 2006. The sp eciation monitor has b een 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 speciati on monitor at the Macon-Forestry site.

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

Table 2-2. Macon PM 2.5 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 tre nds in annual averages and quart erly averages are evaluated. For the speciation monitor, trends in the di fferent PM2.5 species measured are analyzed and compared to speciation data from ot her PM2.5 nonattainment areas in Georgia. Receptor modeling results are presented to provi de an insight into the relative amount of
ambient PM2.5 attributable to different emissi on source categories. CART analysis was performed to examine the relationship betw een meteorology and air quality. Lastly, sensitivity modeling was conducted to examin e 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 cont rol 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 purpos e of long-term trend analysis presented here, 2007 values were estimated after excludi ng exceptional events, as concurred with by EPA. Figure 2-4 shows changes of annua l PM2.5 concentrations (arithmetic average of quarterly averages) from 1999 to 2007. Over all, PM2.5 concentrations have decreased for this time period.

The overall trend can be divi ded 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 peri od (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 concen trations compared to 2004-2006 concentrations. The actual values used to create Figure 2-4 are listed in Table 2-3. In 20 04, the PM2.5 annual mean
concentrations were 16.8 μ g/m 3 at 13-021-0007 and 14.3 μ g/m 3 at 13-021-0012. In 2007, the PM2.5 concentrations were 15.8 μ g/m 3 at 13-021-0007 and 12.4 μ g/m 3 at 13-
021-0012.

26 of 143Macon 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.

27 of 143Macon 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 ( μ g/m 3 ) Monitor
13-021-0012 ( μ g/m 3 )
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 desi gn values from 2001 to 2007. Overall trends
were similar to those of annual mean values (Figure 2-4) except de sign values increased steadily from 2003 to 2006. The actual values us ed 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.

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

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 ( μ g/m 3 ) 13-021-0012 ( μ g/m 3 )

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 gene ral, PM2.5 concentrations were higher
in the 2 nd and 3 rd quarters at both monitors. This is to be expected due to higher temperatures and more photochemistry during these quarters. Over all, 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

29 of 143Macon 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 μ g/m 3 ) and SO 4 (4-5 μ g/m 3 ) are the major constituents at the m onitor. In general, PM2.5 sp ecies concentrations were consistent from 2002 to 2007 except for the re cent increase of OC concentration and
decrease of SO 4 from 2005 to 2007. From 2002 to 2007, NH 4 concentrations were
around 1.3 μ g/m 3 . For the same period, NO 3 and EC concentrations were approximately

0.75 μ g/m 3 . It should be noted that the total PM 2.5 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 tr ends 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 143Macon 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 c oncentrations at the sp eciation 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 th ird quarter for the same time period displayed a steady decrease of PM2.5 concentrat ions. 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 th an in the first and fourth quarters at this site.

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

Figure 2-9. Quarterly averages of PM2.5 concentrat ions at the speciation site in the Macon NAA (13-021-0007)

Figure 2-10 shows quarterly averages of SO 4 concentrations at the speciation site in the Macon NAA (13-021-0007). SO 4 concentrations decreased from 2006 to 2007 except the fourth quarter. The largest decrease of SO 4 concentrations occurred in the third quarter from 2002 to 2003 and from 2005 to 2007. Comp ared to the quarterly PM2.5 trends, quarterly SO 4 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 SO 4 concentrations at the speciation site in the Macon NAA (13-021-0007)

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Figure 2-11 shows quarterly averages of NO 3 concentrations at the speciation site in the Macon NAA (13-021-0007). Contrary to PM2.5 and SO 4 trends, NO 3 concentrations were low in the second and third quarters and hi gh in the first and fourth quarters. From 2006 to 2007, NO 3 concentrations increased for the fi rst quarter. In terms of absolute values, however, NO 3 concentrations did not change dramatically.

Figure 2-11. Quarterly averages of NO3 concentrat ions at the speciation site in the Macon NAA (13-021-0007)

Figure 2-12 shows quarterly averages of NH4 co ncentrations at the speciation site in the Macon NAA (13-021-0007). In an absolute se nse, NH4 concentrations did not change much. Relatively large decreases of NH4 c oncentrations were in the first and third quarters from 2006 to 2007.

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

Figure 2-12. Quarterly averages of NH4 concentrat ions at the speciation site in the Macon NAA (13-021-0007)

Figure 2-13 shows quarterly averages of OC co ncentrations at the speciation site in the Macon NAA (13-021-0007). In rece nt years, OC concentrations seemed to increase for all quarters although there is no OC concentratio n 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 th e third quarter from 2003 to 2007 and the third quarter ended up showing the highest OC c oncentrations 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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Figure 2-14 shows quarterly averag es 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 c oncentrations increased in the first and third quarter. In terms of absolute values , however, EC concentra tions 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 PM 2.5 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, Tenne ssee (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 fa irly similar seasonal trends, with PM 2.5 peaking in summertime. This peak is mainly due to elev ated sulfate (SO4) concentrations, as well as elevated organic matter (OM), some of wh ich 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 classi fied as “Other”). Sulfate and OC are by far
the two dominant PM 2.5 species, followed by ammonium (NH4), elemental carbon (EC), nitrate (NO3), major metal oxides (MMO = 2.20Al+2.49Si+1.63C a+2.42Fe+1.94Ti, as defined by the IMPROVE protoc ol. Nitrate levels are hi gher during wintertime when cooler temperatures favor ni trate particle formation. Ammonium levels follow those of
sulfate and nitrate as it binds with these anions . The similarities in seasonal patterns and composition of PM 2.5 in these four nonattainment area s is an indication of the regional
nature of PM 2.5

.

35 of 143Macon 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-Decug/m3 Other

OC

EC

MMO

NH4

NO3

SO4

Figure 2-15: Seasonal patterns in PM 2.5 concentrations and composition in Atlanta (Site ID 13-089- 0002) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon).

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0

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

OC

EC

MMO

NH4

NO3

SO4

Figure 2-16: Seasonal patterns in PM 2.5 concentrations and compositio n in Chattanooga (Site ID 47- 065-4002) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon).

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

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

OC

EC

MMO

NH4

NO3

SO4

Figure 2-17: Seasonal patterns in PM 2.5 concentrations and composition in Floyd county (Site ID 13- 115-0005) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon).

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0

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

OC

EC

MMO

NH4

NO3

SO4

Figure 2-18. Seasonal patterns in PM 2.5 concentrations and composit ion in Macon (Site ID 13-021- 0012) for 2003-04 (MMO – Major Metal Oxides; Other – PM 2.5 other than sulfate, nitrate, ammonium, MMO, elemental carbon and organic carbon).

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

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

Table 2-5. Average PM 2.5 composition during 2003-2004 for the four 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 se condary percentage of PM 2.5

Site % primary % secondary

Atlanta 40 60 Chattanooga 32 68 Floyd county 43 57

Macon 44 56

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

, respectively). The Floyd County monitor also exhibits higher levels of unspecified (“Other”) PM 2.5 , compared to the other three sites. Elemental carbon levels are highest in Atlanta (1.0 μ g/m 3 compared to 0.5-0.7 μ g/m 3 at the other three sites), the largest city of these four areas, and are likely an indicator of emissions from mobile sources and especially diesel traffic. As expected, concentrations of OC (partially secondary) and secondary ionic PM 2.5 (sulfate, nitrate, ammonium) are fa irly similar at all four sites, though the Chattanooga site exhibits somewhat higher levels of ammonium nitrate.

The Macon area also exhibits spatial gradients in PM 2.5 concentrations, as observed by
the difference in DVs between th e Macon Allied Chemical (15.2 μ g/m 3 ) and Macon Forestry (13.3 μ g/m 3 ) sites. The Macon Allied Chemical site is located in an urban/industrial area, while the Macon Forestry site is located roughl y six miles northeast of the Macon Allied Chemical site in a fairly rural area. The difference in PM 2.5 levels between the two sites is likely an indicati on 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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2.3.3 Source Apportionment of PM2.5

Receptor models are useful tools for identif ying the sources contributing to measured levels of pollutants. One of the most recentl y used source-apportionment models in recent years is the Positive Matrix Factorization (P MF) model. PMF is a factor analytic method that distinguishes correlation patterns among speciated PM 2.5 measurements in a given
location.

Presented here are source-apportionment re sults for 2003-2004 (Figure 2-19 and Table 2- 7). These once again show the regional natu re of PM2.5, given the similarities between the PMF results for the three sites. The major impacting factors at a ll 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 relativ ely high contribution of Lime/M inerals PM2.5. This may be
due to local sources in the industr ial area where the site is located.

These results are also useful for a preliminar y assessment of control strategies. Given the large contributions to PM2.5 from the seconda ry-sulfate factor, it is evident that controlling SO2 emissions (mainly from co al-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-categorie s 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.

39 of 143Macon 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 Maconug/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 cont ributions for 2003-04 at the At lanta, Floyd County, and Macon speciation sites using PMF

Table 2-7: Average factor contributions to PM 2.5 ( μ g/m 3 ) 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

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

2.3.4 Meteorology

ICF International performed a meteorological characteriza tion study designed to examine the relationships between meteorology, fine particulates, and regional haze at air quality monitoring sites throughout the Southeastern U.S. (Charact erization of Meteorology and Its Relationships to Fine Particulate Mass and Visibil ity 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 th at are important to atmospheric modeling. Separate CART anal yses 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 mo nitoring 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. Cl assification accuracy ranged from approximately 72 to 85 percent fo r visibility and from approximately 75 to 90 percent for PM2.5. In applying the CART technique, ICF International found that good classification for most sites was depende nt 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 c oncentrations. They also found that moisture is an important meteor ological parameter for good classification for both PM2.5 and visibility and that the more complex role of mo isture in determining light extinction—affecting both partic le formation and the contribu tion 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 M acon consisted of four categories: <17.5 (Category 1), 17.5-25 (Category 2), 25-30 (Category 3), and > 30 (Category 4) μ g/m 3 . 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 concentr ations 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 meteorologi cal parameters leading to higher PM2.5 concentrations include highe r 850 mb temperatures, lower 850 mb wind speeds, and more stability in Atlanta.

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

Table 2-8: Summary of average input para meters for each CART classification Category in Macon.

For Macon, there is one key bin (group of da ys with similar meteorological conditions) for each Category 3 and Category 4 concen tration range (Table 2-9). Both are characterized, on average, by very low surf ace wind speeds, moderate relative humidity, and prior-day PM2.5 concentrations of about 30 μ g/m -3 in the Atlanta area. Bin 19 (Category 3) has higher temperatures a nd lower wind speeds aloft. Surface wind
directions are from the south east, and upper air wind directi ons are from the southwest. Bin 14 (Category 4) has cooler temperatures and greater stab ility. 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.

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

Table 2-9: Summary of average input paramete rs 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 Bi n 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 associ ated 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 alof t and westerly winds near the surface, and stable conditions. The high PM2.5 classifi cation categories are associated with higher SO4 and OM concentrations.

43 of 143Macon 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 spec ific 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 sensitivitie s for each pollutant and source category for the summer and winter episodes, respectivel y. In the summertime, the most important anthropogenic source category is Georgi a 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 cat egory is VISTAS NH3 followed by Georgia ground-level PC, Georgia E GU SO2, domain-wide VOCs, and Georgia fire PC.Avg.Conc. (μg/ EC

NO3

OM

SO4

44 of 143Macon 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

S

O

2 _

E

GU SO2_nonEGU

N

O

x _

G

r o

u

n d NOx_Point NH 3 VOCs PC_G r ound

P

C

_Poin t

PC_ F

iresΔPM2.5(μg/m3) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Figure 2-21: Sensitivities of PM 2.5 by pollutant and source cate gory for the summer episode in

Macon. Δ PM2.5 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

SO

2_

E

GU

SO

2_

n on

EG

U

NO x_

Gr

o

u nd

NO x_

P oi

n t NH 3 VO Cs

PC

_Grou n d

PC

_P

oint PC

_F

i r e sΔPM2.5(μg/m3) 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 catego ry for the winter episode in

Macon. Δ PM2.5 is based upon a 30% reduction of pollutant levels from 2009 levels.

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

Figure 2-23 contains the annual average se nsitivities for each pollutant and source category based on the average response of th e summer and winter episodes in Macon. Figure 2-24 contains the annual average se nsitivities 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 th at the most important anthropogenic source category is VISTAS NH3 followed by Georgi a 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 sulf ates 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 EG Us 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 _n

on

EGU

NOx _ Grou n d NOx_Point NH 3 VOCs PC_ G

roun d

PC_P o int PC_ Fir

esΔPM2.5(μg/m3) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Figure 2-23: Annual average emission sensitivities by pollutant and source category based on the average response of the summer and winter episodes in Macon.

46 of 143Macon 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 _n

on

EGU

NOx _ Gro un

d NOx_Point NH3 VOCs PC_ Gr

ou

nd

PC_Point PC_ F

i r esΔPM2.5(μg/m3) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Figure 2-24: Annual average emission sensitivities by pollutant and source category based on the CART weighted response of the summer and winter episodes in Macon.

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

2.4 PM2.5 Sources and Precursors

This section of the SIP includes a discu ssion of sources of PM2.5 emissions and of significant PM2.5 precursors in the Macon Nonattainment Area. The technical justification is also presented for re versal of the presumption, under the PM Implementation Rule, that NOx is a si gnificant PM2.5 precursor in Georgia.

2.4.1 Sources of PM2.5

Sources of PM2.5 and PM2.5 precursors in the nonattainment area ha ve 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 inventor ies of PM2.5 and its precursors for Bibb and
Monroe Counties. Note that the Monroe County data is for th e 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

Bibb 2110.0 3608.6 4816.1 298.0 168.1 Point

Monroe 369.2 28542.0 89172.6 271.2 0.0

Bibb 6426.7 740.6 1201.1 897.9 102.6 Area

Monroe 913.4 123.3 63.6 701.2 684.1

Bibb 6320.2 5466.0 220.6 80.4 192.7 Mobile

Monroe 2221.8 3205.9 113.5 52.0 79.4

Bibb 1164.6 1325.3 105.0 89.3 0.8 Offroad

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 143Macon 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 a nd 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, lo cated 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 ar ea but within the State bo undaries are also important
sources of SO2 emissions that affect ambien t 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 No nattainment 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 whos e individual emissions are relatively small, but, due to the large number of these sources, the collectiv e emissions from the source

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

category could be significant (i.e ., dry cleaners, service stati ons, 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 th e nonattainment area, can also have significant effects on ambi ent PM levels in the nonattainment area.

On-road mobile sources are automobiles, bus es, 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, southwes t 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-fir ed 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 PM 2.5 SIP must address both direct PM and s econdary 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 nonattai nment area.

40 CFR 51.1000 defines a “PM 2.5 precursor” as any air polluta nt other than direct PM 2.5 that contributes to the formation of PM 2.5 . Emissions inventories required for attainment
demonstration must include emissions of all PM 2.5 precursors identified for the
nonattainment area.

40 CFR 51.1000 defines a “PM 2.5 attainment plan precursor” as SO2 and any other precursor which the State must evaluate for em issions 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 th e State to address VOCs or ammonia as an attainment plan precursor unless either the State or EPA provides a technical demonstration for a specific area showing that emissions from the State significantly contribute to PM [refer ence 40 CFR 51.1002 (3) and (4)]. 40 CFR 51.1002 (c) implies that all sources of precursors w ithin the State boundaries must be evaluated for emissions reduction measures. However, th e 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 143Macon 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 emissi ons 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 tec hnical 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. Geor gia 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.

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

2.4.3 Technical Justification for Rev ersal of NOx Precursor Presumption

The US EPA, in 72 FR 20586, requires states to “evaluate and consider control strategies for
sources of SO 2 , 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 ba sed 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, summar ized 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 th e analysis is presented in Appendix E.

2.4.3.1 Ambient Measurements

Ambient monitoring data in Georgia nonatt ainment areas were evaluated using FRM measurements and speciated data. The “ S ulfate, A djusted N itrate, D erived W ater, I nferred
C arbonaceous Mass H ybrid” (“SANDWICH”) material balan ce 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 PM 2.5 concentrations aver aged across all FRM monitors (after applying the SANDWICH technique) in each nonattainment area. The measurements show annual average nitrate conc entrations (plus ammonium and PBW associated with nitrate) collected on the FRM filters range from 0.27 to 0.33 μ g/m 3 which is only 1.7 to 2.5 percent of the total FRM mass. These are very small compared to sulfate and OC contributions.

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

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

EC

OC

PBW

NO3

NH4

SO4

Blank

Figure 2-25. 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 of 143Macon 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 fo r 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 c oncentrations are only reduced by 0.00 to 0.03

μ g/m3 across all nonattainment areas in Georgia (F igure 2-26). In addi tion, 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 Chang e 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 143Macon 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 0.45Clayton-0091Cobb-0003Cobb-0004DeKalb-0002DeKalb-2001Fulton-0032Fulton-0039Gwinnett-0002Hall-0003Paulding-0003Bibb-0007Bibb-0012Floyd-0005Hamilton-1011Hamilton-31-1Hamilton-4002Walker-0002

Monitor Locationug/m3 EC

OC

NO3

Figure 2-26. 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 predic ted PM2.5 reductions are approximately 1.2 μ g/m 3 for 100% statewide NOx removal, 0.4

 μ g/m 3 for 70% removal, 0.22

 μ g/m 3 for 50% removal (Figure 2-27), and 0.11

 μ g/m 3 for 30% removal (Figure 2-28)



### Statewide 50% NOx Reduction

-0.25

-0.20

-0.15

-0.10

-0.05

0.00ΔPM2.5 (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.

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

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 si nce this is an unreal istic 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 ambi ent conditions. They were not intended to be used to make pollutant predictions under the unrealistically low oxidizing a tmospheric conditions caused by zeroing-out all NOx within a stat e. Instead, GA EPD feels it is more appropriate to evaluate more realistic, yet aggressive, NOx reductions fr om sources across the state. GA EPD conducted a technical analysis looking at pot ential 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 reduc tion of approximately 47%. This scenario assumes the installation and ope ration of all possible controls through 2030. In addition, GA EPD evaluated aggressive upper bound NOx emi ssions 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 wors t, a statewide 50% NOx reduction run would be the most appropriate scenarios to use in the NOx insignificance demonstration. Modeling results show a maximum impact of 0.11 μ g/m 3 across all nonattainment monitors in the state for a 30% statewide NOx reduction run (monitors in downtown Atlanta showed a slight increase in PM2.5 formation) and a maximum impact of 0.22 μ g/m 3 across all nonattainment monitors in the state
for a 50% statewide NOx reduction run.

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

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 arom atic 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 ra tes under ambient atmospheric conditions and
that SOA formation rates have not been tested unde r 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 orga nic carbon aerosol to large NOx reductions.

In Figure 2-29, it can be seen that the CMAQ m odel (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 pr edictions in the early morning hours when the mixing height is low. It is during these same hours that th e model shows large OC sensitivities to NOx reductions. Note that in Figure 2-31, dSOMA (p ink) is the response of anthropogenic secondary organic aerosols, dSOMB (bold red) is the re sponse 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 aeros ols. 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 ar e likely too high. It should be noted that near the end of the month (06/27-06/30) when the m odel is performing well, that the OC sensitivity has been reduced by an order of magnitude . As a result, it very likely th at the OC responses presented earlier are highly overestimated and should not be used to quantify the impacts of NOx
reductions.

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

Organic Carbon in June 2002 @ YRK

0 5 10 15 20 25 30 35 406/1/02 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 0:006/10/02 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

DateAbs 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 co ncentrations, and modeling has lead GA EPD to conclude that NOx is an insigni ficant precursor to PM2.5 in Geor gia’s nonattainment areas. GA EPD feels it is inappropriate to evaluate the impacts of a 100% st atewide NOx sensitivity as part of a NOx insignificance demonstrati on 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 re ductions since the CMAQ modeling system does not properly simulate hourly OC temporal va riations 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 PM 2.5. Modeling results show a maximum impact of 0.11 μ g/m 3 across all nonattainment monitors in the state for a 30% statewide NOx reduction scenario and a maximum impact of 0.22 μ g/m 3 across all
nonattainment monitors in the state for a 50% statewide NOx reduction scenario.

EPA’s CAIR modeling used a significance level of 0.2 mg/m 3 to evaluate interstate impacts (e.g., Georgia emissions impacts on no n-Georgia PM2.5 monitors). Therefore, it would seem reasonable to assume that the significance thresh old 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 Signifi cant Deterioration (PSD) signifi cant 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 th reshold all sources in a state should be even greater than this rang e. At an absolute minimum, the significance threshold level should set at 0.3 μg/m 3

.

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/m 3 , 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 Demons tration Methods and Inputs

The attainment modeling for the Macon PM 2.5 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 Sout heast (VISTAS), and the

PM 2.5 and ozone (O 3 ) modeling being done by the Association of Southeastern Integrated Planning (ASIP). VISTAS and AS IP are run by the ten Southeas t states (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia). Since the regional haze and PM 2.5 modeling use annual simu lations and include an intermediate year that is the attain ment year required for the Macon PM 2.5 nonattainment area, GA EPD decided to use this modeling for its attainment demonstration. The sections below outline the methods and inputs used by VISTAS/ASIP for the regional modeling.

3.1 Analysis Method

The modeling analysis is a complex technical eval uation that begins by se lection of the modeling system. VISTAS decided to use the following modeling system:

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

• Emissions Model: The Sparse Matrix Oper ator 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-Atmosphe re” photochemical grid model capable of addressing O 3 , 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 PM 2.5 and poor visibility are observed throughout the region. Once the hist orical year is selected, mete orological inputs are developed using the meteorological model. Emission inventories are also deve loped for the historical year and processed through the emissions model. These inputs are used in th e air quality model to predict ozone, PM 2.5 and visibility, with the results compar ed to the historic data. The model performance is evaluated by comparing the modele d predicted data to the historic air quality
data.

Once model performance is deemed adequate, t ypical baseline and futu re-year emissions are processed through the emissions model. For this demonstration, the base line 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 PM 2.5 nonattainment area is April 5, 2010. The attainment date is set prior to the completion of the 2010 calendar year; therefore, the attainment of the NAAQS

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would have to be met by the end of 2009. Thes e emissions are processe d through the air quality model with the meteorological inputs. The air quality modeling results are used to determine a
relative reduction in future PM 2.5 , which is used in the attainment demonstration.

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

3.2 Model Selection

To ensure that a modeling study is defensible, care must be taken in the selection of the models
to be used. The models selected must be scie ntifically appropriate for the intended application and be freely accessible to all stakeholders. “S cientifically appropriate” means that the models address important physical and chemical phenomen a in sufficient detail, using peer-reviewed methods. “Freely accessible” means that model fo rmulations and coding are freely available for
review and that the models are available to stak eholders, and their consul tants, for execution and
verification at no or low cost.

The following sections outline the criteria for sel ecting 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 modeli ng 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 attainme nt 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 th e 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 ex ercise 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 co mmon platform. The original justification for the Models-3 development em erged from the challenges posed by the CAAA and the US EPA’s desire to develop an a dvanced 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 atmo spheric model is the ability to do oneatmospheric modeling. Since GA EPD will be usi ng the same modeling exercise for the ozone
and PM 2.5 attainment demonstration state implementa tion plans (SIPs), as well as the regional haze SIP, having a model that can handle both oz one and particulate matte r is essential. A number of features in CMAQ’s theoretical form ulation and technical implementation make the
model well suited for annual PM 2.5 modeling.

The configuration used for this modeling demonstra tion, 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 at mosphere 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 replicat e important meteorological phenomena in the region of study, and the model's ability to interface with the re st of the modeling systems, particularly the air quality model. With th ese issues in mind, the following criteria were established for the meteorological m odel 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 Bo undary Layer heights for AQ modeling.

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3.2.2.2 Overview of MM5

The non-hydrostatic MM5 model is a 3-dimensiona l, 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 ope rational and research models. In the non-hydrostatic MM5, the sigma levels are defined according to the initial hydrostatically balanced re ference state so that the sigma levels are also time-invariant. Th e gridded meteorological fields produced by MM5 are directly compatible with th e input requirements of “one atmo sphere” air-quality models using this coordinate. MM5 fields can be easily used in other regional air quality models with different coordinate systems by performi ng 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 formul ation for stable and neutral environments and a
modified first-order scheme fo r 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 conditio ns are specified for real-dat a cases from mesoscale threedimensional analyses performed at 12-hour interv als on the outermost grid mesh selected by the user. Surface fields are analyzed at three-hour intervals. A Cre ssman-based techni que is used to analyze standard surface and radiosonde obse rvations, 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 applicatio n history in regulatory modeling studies compared with RAMS or other m odels. Furthermore, in evaluations of these models in over 60 recent regional scale air quali ty application studies since 1995, it has generally been found that the MM5 model tends to produce somewhat better photoch emical model inputs
than alternative models.

The configuration used for this modeling demonstra tion, 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 processi ng system is that it accurately prepares emissions files in a format suitable for the phot ochemical grid model being used. The following
list includes clarification of this criterion and add itional desirable criteria for effective use of the system.

• File System Compatibility with the I/O API;

• File Portability;

• Ability to grid emissions on a Lambert Conformal projection;

• Report Capability;

• Graphical Analysis Capability;

• MOBILE6 Mobile Source Emissions;

• Biogenic Emissions Inventory Sy stem 3 Version 0.9 (BEIS-3);

• Ability to process emissions for the proposed domain in a reasonable amount of time;

• Ability to process control strategies;

• No- or low-cost for acquisition and maintenance; and

• Expandable to support other species and mechanisms.

3.2.3.2 Overview of SMOKE

The Sparse Matrix Operator Kernel Emissi ons (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 “f irst 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 fo rmatted 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 factor s 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 algor ithms. Indeed, SMOKE is the fastest emissions processing tool currently availa ble 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 us ed in previous systems. The processing is flexible because the processing steps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allocation have been separate d 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 ma jor features that make it an attractive component of the modeling system. The model supports a variet y of input formats from other emissions processing systems and models. It supports bot h gridded and county total land use scheme for biogenic emissions modeling. SMOKE can accommodate emissions files from up to 10 countries and any pollutant can be processed by the system.

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

3.3 Selection of the Base Modeling Year

A crucial step to SIP modeling is the selection of the period of time to model to represent current air quality conditions and to project changes in air quality in re sponse 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 ai r quality standards. The US EPA recommends that episode selection derive from four principal criteria:

• Simulate a variety of me teorological conditions;

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

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

• Model a sufficient number of days so that the modeled 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 preferab le, representative modeling year. These steps include the following:

• Representativeness of Meteorological Conditions: The VISTAS meteorological contractor (BAMS) identified important meteor ological 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 i nputs and model performance evaluation.
VISTAS used Classification and Regression Tree (CART) analyses to evaluate the candidate modeling years. The year 2002 was f ound 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 PM 2.5 concentrations in 2002 were representati ve 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 analys is, episode characterization analyses, collaborative investigations by VISTAS states (e.g., NCDAQ, GA EPD, FL DEP) intensively studied the availability of PM 2.5 , meteorological, and emissions data and representativeness of alternative baseline mode ling 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 Comprehe nsive 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 choi ce for modeling. All of the US EPA criteria for model year selection were directly considered in this proc ess together with many ot her considerations (e.g., timing of new emissions or aerometric data de liveries 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 CM AQ 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 VIST AS states, a one-way ne sted 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 primar ily defines the CMAQ vertical structure. The MM5 model employed a terrain-following coordina te 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-aver aging scheme was used to gene rate 19 vertical layers for CMAQ to reduce the computationa l cost of the CMAQ simulati ons. 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 performan ce 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 manda tory 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 fo r the baseline year 2002 and projected to 2009. The pollutants inventoried include volatile organic compounds (VOCs), NOx, PM 2.5 , coarse particulate (PM 10 ), ammonia (NH 3 ) and SO 2 . Tables 3-2 and 3-3 show the typical annual
emissions from the various source sectors for the Macon PM 2.5 nonattainment areas for 2002 and
2009. The 2002 BaseG2 and the 2009 BaseG4 emi ssion inventories are the latest emission inventories developed by VISTAS for 2002 and 2009, respectively. The baseline emissions inventory for 2002 was developed for Georgia follo wing the methods described in Appendix H.

Table 3-2. Typical 2002 BaseG2 Annual Emis sion 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 sour ce 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 industr ial 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 c ould be significant (i.e., dry cleaners, service stations, agricultural sources, fi re emissions, etc.). These type s of emissions are estimated on a countywide level.

Non-road (or off-road) mobile s ources are equipment that can move but do not use the roadways, i.e., lawn mowers, construction equipment, railr oad 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. Bioge nic sources are the na tural sources like trees , crops, grasses and natural decay of plants. The emissions from th ese 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 in ventory. 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 evalua ting the air quality model performance.

The second type of inventory is th e typical base-year inventory. Th is inventory is similar to the actual base year inventory, excep t 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 fi re 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 inventor y 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 co mplete year for which the standard must be attained. It is for the future base-year inventory that sensitivitie s will be evaluated to determine what additional controls beyond those control measures already in cluded in the future-y ear base inventory will need to be implemented to at tain and maintain the annual PM 2.5 standard.

In the sections that follow, a synopsis of the in ventories used for each source classification is
discussed. The detailed discussions of the em issions inventory develo pment 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 ove rview of the inventories used for each source
classification.

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3.5.1 Stationary Point Sources

Point source emissions are emissions from indivi dual sources having a fixe d location. Generally, these sources must have permits to operate, an d 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 a nd other industrial point sources, also called non-electric generating units (non-EGUs). Ap pendix 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 sour ces 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 SO 2 and NOx that can be used in air quality modeling. Emissions profiles are used to estimate emissions of other pollutants (VOC, carbon monoxide, NH 4 , PM 2.5 , soil) based on measured emissions of SO 2 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 E GUs 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 emi ssions inventory for EGU sources, each unit’s average CEM heat input for 2000 through 2004 was divided by the 2002 actua l 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-y ear period. If a unit was shut down for an entire year during the 2000 through 2004 period, the averag e of the years the un it 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-yea r emission inventories for the electric generating sector of the contiguous Un ited 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 pollu tants throughout the contiguous Un ited States for the entire electric power system. The dynamic nature of IP M enables projection of the behavior of the power system over a specified future period. Op timization logic in IPM determines the leastcost means of meeting electric generation an d capacity requirements while complying with specified constraints including air pollution re gulations, transmission bottlenecks, and plantspecific operational constraints. The versatility of IPM allows users to specify which constraints to exercise, and to populate IP M with their own datasets.

The IPM modeling runs took in to consideration both The Cl ean 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 us ed 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 st akeholder input on growth assumptions.

3.5.2 Stationary Area Sources

Stationary area sources are sources whose individua l emissions are relatively small, but due to the large number of these sources, the collectiv e emissions could be sign ificant (i.e., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by multiplying an emission factor by some known indi cator of collective activity, such as fuel usage, number of households, or population. Sta tionary area source emissions are estimated at the countywide level.

A portion of the area source 2002 base-year in ventory for Georgia wa s developed by the GA EPD and provided to the VISTAS/A SIP contractor. The VISTAS/A SIP contractor calculated the remaining portion of the area s ource inventory. The s ources 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 mode ling 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 activ ity in 2002 compared to longer-term averages. Development of a typical-year fire inventory provided the capabi lity 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 bur ned over a longer term period (thr ee or more years, as available from state records) to 2002. Based on these ra tios, the 2002 acreage was then scaled up or down to develop a typica l year inventory.

For categories other than wild la nd fires, the VISTAS/ASIP contract or generated the future baseyear emissions inventory used in the attain ment demonstration mode ling. Growth factors supplied from the states or the USEPA’s CAIR em ission 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 f iles. 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 equipm ent 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 emissi ons for 2002 were estimated using the
US EPA’s NONROAD2005c model. For the th ree source categories not included in the NONROAD model, i.e., aircraft engines, rail road 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 mode l, the model growth assumptions were used to create the 2009 futu re year inventory. The NONROAD model takes into consideration regulations affecting emi ssions from these source categories. For the commercial marine, railroad locomotives and ai rport 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 ro ads, the average speeds on the roads, the mix of vehicl es on the roads, any programs in place in an area to reduce emissions for motor vehicles (such as emi ssions inspection programs), and temperature.

The MOBILE model takes into consideration regulati ons that affect emissions from this sourcesector. 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 da ta reflective of that year. The 2002 vehicle miles traveled (VMT) estimates were obtained from the Georgia Depa rtment 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 mo st recent heavy-duty di esel 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 th e SMOKE-BEIS3 (Biogeni c Emission Inventory System 3 version 0.9) preprocessor. SMOKE-BEIS3 is a modified version of the Urban Airshed Model (UAM)-BEIS3 model. Modifications incl ude use of MM5 data, gridded land use data, and improved emissions characterization. The em ission factors that are used in SMOKE-BEIS3
are the same as the emission factors as in UAM-B EIS3. The basis for the gridded land use data used by BEIS3 is the county la nd use data in the Biogenic Emissions Landcover Database version 3 (BELD3) provided by th e 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 po rtions 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 ev aluating the performance of the air quality model. Before the air quality model can be fully evaluated, the meteorological modeling performance is needed to unders tand 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 performan ce 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 co nsiderable skill in replicating the state variables (e.g. temperature, mixing ratio, relative humidit y, wind speed and direction, cloud cover, and precipitation). The meteorologi cal modeling performance statisti cs fell within expected and acceptable ranges of error during the majority of the 2002 modeled year.

The meteorological modeling perfor mance for Georgia was very similar to the performance for the VISTAS/ASIP region for the 12-km modeli ng domain. Again, large-scale meteorological patterns were accurately predicted. The meteor ological 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 appli cations. Since MM5 algorithms and modules have undergone sign ificant peer review a nd testing, an “operati onal” evaluation of the modeling system is sufficient to serve as the ba sis of evaluating if the system is operating with sufficient reliability to be used in support of SI P 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 meteorologi cal model, this amounts to a statistical analysis of model predictions and surface observations.

GA EPD used the software package METSTA T developed by ENVIRON corporation for calculating model performance statistics. METS TAT computes domain-wide surface statistics for temperature, wind speed and direction, and hu midity. The metrics include: Bias Error (B), Gross Error (E) and Root Mean Square Error (RMSE), Systematic Root Mean Square Error (RMSE
s ), Unsystematic Root Mean Square Error (RMSE
u ) and Index Of Agreement (IOA).
Mathematical formulation of these variable s is provided in Tabl e 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 statis tics depend upon the number of obs ervation-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 m ean performance statistics for the 12-km grid resolution modeling simulation is provided in Appendix K.2. Mont hly-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 Statis tical Metrics Used for Evaluating Mesoscale Meteorological Model Performance

Metrics Formulation

Bias 

# ( ) ∑∑

= = − = J

j I

i i j i j OP IJ B

1 1 1

Gross Error 

## ∑∑

= = − = J

j I

i i j i j OP IJ E

1 1 1

Root Mean Square Error 

# ( ) 2/1

1 1 2 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− = 

## ∑∑

= = J

j I

i i j i j OP IJ RMSE

Systematic Root Mean Square Error 

# ( ) 2/1

1 1 2 ˆ 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− = 

## ∑∑

= = J

j I

i i j i j S OP IJ RMSE

Unsystematic Root Mean Square Error 

# ( ) 2/1

1 1 2 ˆ 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− = 

## ∑∑

= = J

j I

i i j i j u PP IJ RMSE

Index of Agreement ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

−+− −= 

## ∑∑

= = J

j I

i o i jo i j MOMP

RMSEIJ IOA

1 1 2

. 1

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

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Monthly Average Temperature Bias

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

J

anu a r y

F ebu rary Ma r ch April Ma y June J u ly August

S

e p t e m

b

er

O c t ober November

De

c emberBias (degree K)

Monthly Average Humidity Bias

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

Januar y

Feb ur

ar

y M arch Ap r il Ma y Jun e July Aug us

t

S

ep

tem b

er

O c t o ber Novembe r Dec embe rBias (g/kg)

Figure 4-1: Monthly averaged Bias stat istics for 2-m temperature and humidity

78 of 143Model Performance EvaluationPM2.5 Attainment SIP - Macon, GATable 4-3: Monthly averaged statisticsfor the region of the 12-km modeling domain in and around Georgia.79 of 143Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA

The MM5 simulation is able to capture mete orological conditions observed during the study period with an adequate level of accuracy. Pe rformance statistics for most days are within the benchmarks proposed by Emery (2001). Monthly-av erage statistics rev eal under prediction in humidity from May through November, and an over prediction in temperature during the summer months (May through September). The si mulation tends to over-predict daytime and nighttime temperature, and consistently over-predicts nighttime wind speed. However, the magnitude of these under-/over- predictions remains sm all. It should also be pointed out that air quality model performance evaluation serves as an additional check on how accurately the meteorological model was able to capture the observed atmosphe ric dynamics during a particular time period. In an unlikely event of unusuall y poor air quality model performance, it is reasonable to further investigate the pe rformance of 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 PM 2.5 nonattainment area.

4.2 Air Quality Model Performance

The air quality model evaluation mostly focuse d on the FRM and speciation monitors across the state of Georgia, due to the nature of the a ttainment test. Designa tions were based on FRM monitors, and calculations of fu ture design values are based on current design value information from these sites. Thus, model performance for these FRM sites s hould be evaluated. speciation
data was also utilized as this data is used to speciate the FR M 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 performanc e evaluation for the VISTAS/ASIP 12-km grid
domain will be discussed in the subsections to follow. For the full model performance evaluation for the 12-km grid domai n, please refer to Appendix K.3.

4.2.1 Modeling Performance Goals and Criteria

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

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Table 4-4. Established Model Performance Go als and Criteria for the PM2.5 Component Species

Mean Fractional
Bias Mean Fractional
Error Comment

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

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

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

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

An additional way to evaluate model performance statistics is to visua lize performance based on
these fractional bias and mean fractional errors via “soccer plots” (e .g. Figure 4-2) and “bugle plots” (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 th e “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 concentra tions 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 dem onstrated that greater emphasis should be placed on performance of those components with the greatest contribution to PM 2.5 mass (e.g. SO 4 and OC) and that greater bias and error could be accepted for com ponents with smaller contributions to total PM 2.5 mass (e.g. EC, NO 3 , and SOIL). The soccer plots and bugle plots have been included as suggested in the Attain ment Modeling Guidance.

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Table 4-5 Statistical Metric Calculations

Statistical
Measure Shorthand
Notation Mathematical Expression Notes

Accuracy of Paired
Peak A p

peak peak O OP

−

Coefficient of
Determination R 2


# ( ) 

# ( )

∑ ∑ ∑

= = =



### − − ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

−−

N

i N

i i i N

i i i



### OOPP OOPP

1 1 2 2 2

1


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

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

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



## ∑ ∑

=

= −

N

i i N

i ii

O OP

1 1 Reported as %

Root Mean Square
Error RMSE 

## ( ) 2 1

1 2 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− 

## ∑

= N

i ii OP N Reported as %

Mean Fractional
Error MFE 

## ∑

= + − N

i ii ii OP OP

N 1 2 Reported as %

Mean Absolute
Gross Error MAGE 

# ∑

= − N

i ii OP N 1 1

Mean Normalized
Gross Error MNGE 

# ∑

= − N

i i ii

O OP

N 1 1 Reported as %

Mean Biased MB 

## ( ) 

# ∑

= − N

i ii OP N 1 1 Reported as concentration

Mean Normalized
Bias MNB 

## ( ) 

## ∑

= − N

i i ii

O OP

N 1 1 Reported as %

Mean
Fractionalized Bias (Fractional Bias) MFB 

## ∑

= ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ − N

i ii ii OP OP

N 1 2 Reported as %

Normalized Mean
Bias NMB 

## ( )



# ∑ ∑

=

= −

N

i i N

i ii

O OP

1 1 Reported as %

Bias Factor BF 

## ∑

= ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ N

i i i O P

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

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4.2.2 Domain-Wide Model Performance

As a summary of model performance, soccer pl ots and bugle plots for the all of the VISTAS
STN and FRM monitors are included here. Plot s have been developed for the average monthly concentrations and the performance statistics for all of the PM 2.5 component species (SO 4 , NO 3 , NH 4 , OC, and EC) and reconstructed PM 2.5 total mass from the STN monitoring sites (Figures 4- 2 and 4-3), as well as the total FRM PM 2.5 mass (Figures 4-4 and 4-5).

The soccer plots for monthly average component performance for all the VISTAS/ASIP STN sites shows generally good model pe rformance for most species of PM 2.5 and total PM 2.5 . The exception is the prediction of NO 3 values, 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 presen ted in the image on the right in Figure 4-2. However, when the very low concentration of NO 3 is taken into consideration, as presented in the bugle plots (Figure 4-3), NO 3 performance largely falls within the criteria and goal levels. One can still note a general tendency for under-prediction in NO 3 , and other species (e.g. OC) in left hand image in Figure 4-3, which leads to a slight under prediction in total reconstructed

PM 2.5

.

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

STN VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0

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

Nitrate

Ammon.

Organics

EC

PM2.5

Criteria

Goal STN VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0
Fractional BiasMean Fractional Error Sulfate

Nitrate

Ammon.

Organics

EC

PM2.5

Criteria

Goal

Figure 4-2. VISTAS STN Soccer Plots - The image on th e 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 goal s (green box).

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STN VISTAS Sites CMAQ 12km - 2002 Monthly

-200.0 -150.0 -100.0 -50.0

0.0 50.0 100.0 150.0 200.0

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

Nitrate

Ammon.
Organics

EC

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

0.0 50.0 100.0 150.0 200.0

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

Nitrate

Ammon.

Organics

EC

PM2.5

Criteria

Goal

Figure 4-3. VISTAS STN Bugle Plots - The image on the left is a bugle plot of the mean fraction bias for particulate matter and its component concentrat ions 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 m onthly mean fraction bias value as compared to the model performance criteria (red lines) and mode ling performance goals (green lines).

FRM VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0

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

PM2.5

Criteria

Goal FRM VISTAS Sites CMAQ 12km - 2002 Monthly

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0
Fractional BiasMean Fractional Error

PM2.5

Criteria

Goal

Figure 4-4. VISTAS FRM Soccer Plots - Soccer plots depictin g both the mean fraction al error and fractional
bias for PM 2.5 concentration for all the VI STAS 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 goal s (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 ( μ g/m 3 )Mean Fractional Bias

PM2.5

(+) Criteria

(+) Goal

(-) Goal

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

0.0 50.0 100.0 150.0 200.0

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

PM2.5

Criteria

Goal

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

. The image on the right is a bugle plot of mean frac tion 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 pe rformance goals (green lines).

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

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

measurements.

4.2.3 Model Performance within Georgia and in the Macon Nonattainment Area

Statistical metrics and performance plots were de veloped 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) PM 2.5 and PM 10 Mass Networks, (2) EPA Speciati on Trends Network (STN) of PM 2.5 species, (3) Interagency Monitoring of Protected Visual Environments (IMPROVE), (4) Clean Ai r Status and Trends Network (CASTNET), and (5) Southeastern Aero sol Research and Characterization (SEARCH).
It should be noted that the STN monitor here re fers to the combination of the official STN monitors and the CSN (Chemical Speciation Netw ork) monitors. Table 4-6 and Figure 4-6. summarize the species data collected and loca tions 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 mo nitors (13-021-0007 and 13-021-0012) and one STN monitor (13-021-0007).

85 of 143Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA

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

Monitoring
Network Chemical Species Measured Sampling Frequency; Duration

FRM Only total fine particulate mass (PM 2.5 ) Varies; 24 hr
IMPROVE Speciated PM 2.5 and PM 10 1 in 3 days; 24 hr
STN Speciated PM 2.5 Varies; Varies
CASTNET Speciated PM 2.5 and ozone Hourly, Weekly; 1 hr, Week

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

86 of 143Model Performance EvaluationPM2.5 Attainment SIP - Macon, GAFigure 4-6. FRM, STN, IMPROVE, CASTNET, NADP, and SEARCH monitoring sites located in and near the GA PM2.5nonattainment areas (left)and the Macon PM2.5nonattainment area (right).87 of 143Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA

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 th e Macon nonattainment area to demonstrate the model’s ability to replicate total PM 2.5 mass at these sites. In addition, the same approach was taken to conduct model performa nce evaluation for the STN monitors within Georgia and for the Macon STN site on PM 2.5 component species and total PM 2.5 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, PM 2.5 , and Regional Haze (“

Attainment Guidance ”).

4.2.3.1.1 Model Performance Statistics for the FRM Sites

To examine the overall model performance, th e annual model performance statistics were estimated for all FRM sites in Georgia and two FR M 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 mon itors 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 FR M monitors than monitors across Georgia.

For more in-depth examinations, the model perf ormance statistics are br oken out by month at
each of the FRM monitors in the Macon nonattainm ent area to see if ther e is any performance issue during the base year (Table 4-8 and Table 4-9). It was no ted that the model had significant under-prediction during summer. In additi on, the model showed less significant underprediction in February. During months of under-prediction, erro rs 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 PM 2.5 concentrations that were observed. For example, the model predicted mean PM 2.5 concentration of 17.3 μ g/m 3 at 13-021-0012 in May while 43.2 μ g/m 3 was observed at the same monitor for the same period. More detailed performance evaluation was conduc ted with graphical meas ures, 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 st atistics with less numbers of samples indicates the overall performance of the model in the Macon n onattainment 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 PM 2.5 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

A p -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

Max OBS 52.0 41.0 43.2

Max PRE 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

R 2 0.5 0.5 0.3

BF 1.0 0.8 1.0 Number of Samples 3166 90 112

89 of 143Model Performance EvaluationPM2.5 Attainment SIP - Macon, GATable 4-8. Monthly model performance statistics for PM2.5at 13-021-0007.StatisticJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberMFB (%)12.2-23.72.6N/A-36.7-61.1-60.6-44.2-27.95.4-7.1-8.1MFE (%)25.126.42.6N/A36.761.161.746.932.723.713.730.2Ap0.1-0.30.0N/A-0.3-0.3-0.2-0.2-0.20.0-0.1-0.1NMB (%)11.8-20.42.6N/A-31.0-45.4-46.0-30.6-16.06.1-3.3-7.1NME (%)26.321.72.6N/A31.045.447.133.719.723.59.824.8MNB (%)18.9-19.32.6N/A-31.0-43.4-41.9-33.6-19.010.1-4.5-1.9MNGE (%)30.422.22.6N/A31.043.443.036.424.326.711.529.0MB1.3-3.00.2N/A-7.3-7.2-7.2-5.1-3.00.9-0.4-0.8MAGE2.93.20.2N/A7.37.27.45.63.73.41.32.7Geometric Mean Bias0.1-0.20.0N/A-0.4-0.7-0.7-0.5-0.30.1-0.1-0.1Geometric Mean Error0.30.30.0N/A0.40.70.70.50.40.20.10.3RMSE3.84.20.2N/A7.39.49.26.14.94.02.03.1MaxOBS16.430.07.0N/A23.429.325.527.741.021.120.222.2MaxPRE17.520.17.2N/A16.120.919.821.533.320.518.919.9Mean Observations11.114.67.0N/A23.415.915.716.718.614.313.410.9Mean Predictions12.411.67.2N/A16.18.78.511.615.615.212.910.1Standard Deviation ofObservations3.57.2N/AN/AN/A8.15.75.510.74.63.85.2Standard Deviation ofPredictions3.45.4N/AN/AN/A5.74.66.310.34.44.54.8R20.20.8N/AN/AN/A0.40.10.70.90.30.80.6BF1.20.81.0N/A0.70.60.60.70.81.11.01.0Number of Samples9101N/A191110101010990 of 143Model Performance EvaluationPM2.5 Attainment SIP - Macon, GATable 4-9. Monthly model performance statistics for PM2.5at 13-021-0012.StatisticsJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberMFB (%)31.0-6.416.90.9-42.1-43.7-54.4-25.4-14.46.08.53.3MFE (%)33.418.919.225.242.143.756.832.534.615.223.031.1Ap0.3-0.20.30.2-0.6-0.2-0.10.2-0.10.00.30.2NMB (%)34.2-5.820.36.5-38.6-32.5-40.2-15.6-4.36.215.67.7NME (%)37.317.322.527.338.632.542.825.325.115.223.429.9MNB (%)40.9-3.720.56.3-32.1-33.2-38.6-18.5-6.78.812.59.7MNGE (%)43.218.722.726.032.133.241.127.029.617.423.533.1MB3.3-0.72.40.7-7.1-5.0-6.2-2.1-0.70.91.80.8MAGE3.62.22.72.87.15.06.63.44.12.12.62.9Geometric Mean Bias0.3-0.10.20.0-0.4-0.5-0.6-0.3-0.20.10.10.0Geometric Mean Error0.30.20.20.30.40.50.60.30.40.20.20.3RMSE4.12.73.73.710.87.08.14.24.62.93.13.3MaxOBS14.723.815.814.943.227.523.618.336.820.415.515.7MaxPRE18.620.120.517.917.321.520.121.733.120.320.818.8Mean Observations9.712.611.910.318.515.315.313.416.514.111.39.8Mean Predictions13.011.914.311.011.410.39.211.315.814.913.010.6Standard Deviation ofObservations3.26.53.62.810.27.65.03.49.34.52.83.4Standard Deviation ofPredictions3.55.55.24.83.86.44.95.610.24.35.04.7R20.50.80.70.40.30.50.10.60.80.60.80.5BF1.41.01.21.10.70.70.60.80.91.11.11.1Number of Samples996101010101010991091 of 143Model 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 partic ulate matter components measured at the all STN sites in Georgia and separa tely at the STN site in the Macon nonattainment area. Since detailed graphical performance evalua tion was conducted for all available PM 2.5 species, we briefly present annual summary statis tics for these speci es and total PM 2.5 here.

Sulfate and total PM 2.5 had negative bias and er ror at the Georgia STN monitors (Table 4-10). Nitrate was underestimated in GA as previous ly 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 NO 3 to the total PM 2.5 is relatively small in Georgia. Also, we saw under pr edictions of OC concentr ations 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 f ound in the following sections.

Table 4-10. Annual model performance statistics for PM 2.5 species and total PM 2.5 at all STN monitors in the State of Georgia.

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

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

MB -0.7 0.1 0.1 -0.7 0.0 -2.3

MAGE 1.6 0.7 0.6 1.9 0.3 4.6
Geometric Mean Bias -0.2 -1.3 0.1 -0.3 0.0 -0.2
Geometric Mean Error 0.4 1.8 0.4 0.5 0.5 0.4

RMSE 2.6 1.1 0.8 2.4 0.4 5.9

Max OBS 39.9 5.0 10.7 13.6 3.5 40.9

Max PRE 15.9 7.8 5.2 14.7 3.4 32.5
Mean Observations 5.0 0.7 1.4 5.4 0.6 15.7

Mean Predictions 4.3 0.8 1.5 4.7 0.7 13.4
Standard Deviation of
Observations 3.3 0.6 0.9 2.1 0.5 7.0

Standard Deviation of
Predictions 2.8 1.3 0.8 3.1 0.6 6.6

R 2 0.5 0.3 0.4 0.4 0.6 0.5

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

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Table 4-11. Annual model performance statistics for PM 2.5 at 13-021-0007.

Statistics SO 4 NO 3 NH 4 OC EC PM 2.5 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

A p -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

Max OBS 39.9 1.9 10.7 9.8 1.3 28.7

Max PRE 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

R 2 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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4.2.4 Summary of the Performance Evaluation of the CMAQ Modeling

In general, the CMAQ modeling met the perf ormance “criteria level” for all major PM 2.5 constituents and the total PM 2.5 across the State of Georgia throughout the base year. As the first step of model perfor mance 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 monito rs was the model’s performance at FRM monitors within Georgia was comparable to the performance at two FRM monitors collectively in the M acon nonattainment area although there were slight negative biases
observed in the Macon nonattainment area. These under-predictions became more apparent during summer. Monthly performanc e 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 PM 2.5 concentration cases. After examining the statistical performan ce 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 PM 2.5 and all of measured

PM 2.5 species. The most significant findings we re that (1) the model’s sulfate and PM 2.5 performance were quite good and (2) NO 3 and OMC were somewhat under predicted. In addition, the model’s performance at the ST N 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 PM 2.5 species and total PM 2.5 concentrations were examined with these packages. Whenever da ta 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 nonattain ment 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. Th ese graphical packages helped Georgia EPD conduct the analysis in a systematic and cons istent manner. The following is a brief summary of what was found by this graphi cal analysis. The evaluation included speciated data available from IMPROVE, CASTNET, SEARCH, and STN as well as
total PM 2.5 from FRM monitors.

Overall, the model showed r easonable performance for PM 2.5 , SO4, NH4, and OC throughout the base year acro ss all monitors in Georgia. During summer, the model
showed a seasonal variation of NO 3 performance with under pred iction bias greater than –100% and moderate winter over prediction at CASTNET sites. The amount of error and bias in monthly time series was most likel y contributed by low concentration (< 1.0

μ g/m 3 ) cases. For EC, the model showed some over-prediction in Georgia while underpredicting at 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

94 of 143Model Performance Evaluation PM2.5 Attainment SIP - Macon, GA

shown in Figure 4-7, SO 4 and OMC were the major contributors to the Macon nonattainment areas. Therefore, Georgia EP D concluded that perf ormance issues found
in minor constituents of PM 2.5 are not likely degrading the ov erall model performance.

Figure 4-7. Stacked bar plots of daily PM 2.5 species concentration at 13-021-0007.

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

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

95 of 143Control and Contingency Measures PM2.5 Attainment SIP - Macon, GA

Section 5. Control and Contingency Measures

Control measures are used to reduce emissi ons of direct PM2.5 and PM2.5 precursors and are therefore of primary importance to de monstration of attainment. Those measures that had already been implemented through th e end of 2002 (the year of the baseline emissions inventory) are not disc ussed 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 a ttainment. 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 atta in 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 PM 2.5 levels. Finally, this section also discusses the implementation of RACT and RACM in the
nonattainment area.

5.1 Federal Control Measures for PM2.5 Attainment

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

• Clean Air Interstate Rule

• Tier 2 Vehicle Standards

• Heavy-duty Gasoline and Diesel Highway Vehicles Standards

• Ultra-low Sulfur Diesel Rule

• Large Nonroad Diesel Engines Rule

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

• NOx SIP call in surrounding states

These measures are described in Ta ble 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 limiti ng the emissions of NOx and sulfur dioxide (SO2) and a model rule for the states to use in developing th eir rules. The purpose of the CAIR is to reduce interstate transport of precursors to fine particulate and ozone.

96 of 143Control and Contingency MeasuresPM2.5 Attainment SIP - Macon, GATable 5-1. Federal PM2.5 Control MeasuresControl Measure for AttainmentDemonstration1Control Measure – ContinuingBenefits2MeasureSourcesAffectedMeasure andScheduleSpeciesAffected/ReducedMeasure andScheduleSpeciesReducedClean Air Interstate Rule(CAIR)40 CFR 96, Subparts AAA -HHHCoal-firedElectric Gen.Units, 28eastern statesand D.C.Regional cap on NOxand SO2 emissionsfrom EGU’s: NOXPhase 1 starts2009NOxRegional cap onNOX and SO2emissions fromEGU’s: SO2 Phase1 starts 2010SO2,NOxTier 2 vehicle standards40 CFR 80, 85 , and 86New PassengerVehiclesFleet average for NOxemissions per mile,effective 2004 through2007NOxContinuingvehicleturnoverNOxHeavyDuty EngineStandard (Gasoline andDiesel), second phase40 CFR 86, Subpart ANew gas anddiesel highwayvehicles 14001lbs or greaterEffective 2004 andlater – NOx and VOCcontrols;Effective 2007 andlater – PM controlsNOx, VOC,and PM (soot);PM limit of0.01 g/bhp/hrContinuing vehicleturnoverNOx, VOC, andPM (soot)Ultra Low Sulfur Diesel40 CFR 80, Subpart INew dieselhighwayvehicles 14001lbs or greaterLimits 80 % of pool ofhighway diesel tosulfur content of 15ppm or less; 2007 –2009; facilitatesproper operation ofheavy duty diesel PMcontrols (see above)PM (soot)Limits 100 % ofpool of highwaydiesel to 15 ppmsulfur or less; 2010on - facilitatesproper operation ofheavy duty dieselPM controls (seeabove)PM (soot)97 of 143Control and Contingency MeasuresPM2.5 Attainment SIP - Macon, GATable 5-1. Federal PM2.5Control Measures (continued)Control Measure for AttainmentDemonstration1Control Measure – ContinuingBenefits2MeasureSourcesAffectedMeasure andScheduleSpeciesAffected/ReducedMeasure andScheduleSpeciesand QtyReduced (tons)NOx, PM (soot)Nonroad Diesel Rule40 CFR 89New mobilesources: offroad, ships,locomotives2008 through 2014;Should reduce NOxand PM emissions byover 90 %NOx, PM(soot)Continuing vehicleturnoverVOC, NOx, COLarge Spark Ignition andRecreational VehicleRule40 CFR 90Forklifts,airline groundserviceequipment, allterrainvehicles, offhighwaymotorcycles,marine dieselenginesLarge spark ignitionequipment (forklifts,airline ground serviceequipment) – 2004;off-road vehicles –2006; marine engines- 2006VOC, NOx,COContinuingequipment turnoverNOx SIP Call inSurrounding Statesutility andindustrialboilers;internalcombustionenginesControls on utility andindustrial boilers andon internalcombustion engines in22 eastern states by2004NOx(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 counttoward attainment butwill provide emissions reductions98 of 143Control and Contingency Measures PM2.5 Attainment SIP - Macon, GA

The CAIR applies to (1) any stationary, fossil- fuel-fired boiler or stationary, fossil-fuelfired combustion turbine serving, at any tim e 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 un it first produces electric ity 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 out put capacity or 219,000 MWh, whichever is greater, to any utility power distribution system for sale.

This rule provides annual state caps for NOx and SO2 in two phases, with the Phase I caps starting in 2009 and 2010, respectively. Be cause the Phase I SO2 cap starts in 2010,
it cannot be counted as a control measure for the attainment demonstr ation. Phase II caps for NOx and SO2 become effective in 2015, whic h 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 St ates by over 70 percent and NOx emissions by over 60 percent from 2003 levels.

As part of the final rule, EPA determined that Georgia contributes significantly to
downwind PM2.5 nonattainment areas and/or in terferes with maintenance of the PM2.5 NAAQS (70 FR 25246-25250). Accordingly, a State CAIR rule ((391-3-1-.02(13)) has been promulgated that, for the most part, mi rrors 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 NO
x emissions from passenger vehicles. The standards require all passenger vehicles in a manufacturer’s fleet , including light-duty trucks and sport utility vehicles (SUVs), to meet an average standard of 0.07 grams of NOx per mile. Implementation began in 2004, and was completely phased in by 2007.
The Tier 2 standards will also cover passenger vehicles over 8,500 pounds gross vehicle weight rating (the larger pi ckup trucks and SUVs), which are not covered by the current Tier 1 regulations. For these vehicles, the st andards will be phased in beginning in 2008, with full compliance in 2009. The new standa rds require vehicles to be 77% to 95% cleaner than those on the road prior to implem entation of Tier 2. The Tier 2 rule also reduced the sulfur content of gasoline to 30 parts per million (ppm) starting in January of 2006. Sulfur occurs naturally in gasoline, bu t interferes with the operation of catalytic

99 of 143Control and Contingency Measures PM2.5 Attainment SIP - Macon, GA

converters on vehicles resulting in higher emi ssions. 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 be gan in 2007, will redu ce particulate matter from heavy-duty highway engines, and will also reduce highway diesel fuel sulfur content to 15 ppm to prevent damage to th e emission control devices. The total program is expected to achieve a 90% reduction in particulate matter (PM) emissions and a 95% reduction in NOx emissions for these new engi nes using low sulfur diesel, compared to older engines using diesel with higher sulfur content.

5.1.4 Large Nonroad Diesel Engines Rule

In May 2004, the USEPA promulgated new rule s 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 ru les also reduce the a llowable sulfur in nonroad diesel fuel by over 99%. Nonroad di esel 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 ru les would reduce NOx and PM emissions from large nonroad diesel engines by over 90%, comp ared to older engine s 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 unregul ated nonroad engines. The new standard will apply to all new engines sold in the Unite d States and imported after these standards began and apply to large spark-ignition e ngines (forklifts and airport ground service equipment), recreational vehicles (off-highway motorcycles and all-terrain-vehicles), and recreational marine diesel engines. The re gulation 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 sta ndard 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 e xhaust emissions standard was phased-in by 50% and for model years 2007 and later at 100% . Recreational marine diesel engines
over 37 kilowatts are used in yachts, cruisers, and other types of pleasure craft. Recreational marine engines contribute to oz one formation and PM levels, especially in marinas. Depending on the size of the e ngine, the standard began phasing in 2006.

When all of the nonroad spar k-ignition engines and recreat ional engines standards are fully implemented, an overall 72% reduc tion 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 fi nding of significant contribution of NOx emissions from certain states and published a rule that set ozone season NOx budgets for the purpose of reducing regional 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 an d 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 Ta ble 5-2 and in the following text.

101 of 143Control and Contingency MeasuresPM2.5 Attainment SIP - Macon, GATable 5-2. State PM2.5 Control Measures and Contingency MeasuresControl Measure – AttainmentDemonstration1Control Measure – ContinuingBenefits2MeasureSourcesAffectedMeasure andScheduleSpeciesAffected/ReducedMeasure andScheduleSpeciesand QtyReduced (tons)Multipollutant ruleAir Quality Control 391-3-1-.02 (2) (sss)Electric Gen.Units, CoalFired, GAFlue Gas Desulf. for 7EGUs3starting in2008 - approximately216,000 tons/yrreducedSO2sourcesoutside thenonattainmentareaFlue Gas Desulf.for Scherer Unit 3,December 31, 2011SO2source insidethe nonattainmentarea – approx.17,000 tons/yrreducedHeavy Duty Diesel EnginesruleAir Quality Control 391-3-1-.02 (2) (ooo)DieselVehicles14001 lbs orgreaterDiesel Vehicles 14001lbs or greater meetCARB standards;2005 and subsequentmodelsPM (soot)sources insidethe nonattainment areaContinuingequipment turnoverPM (soot) sourcesinside thenonattainmentarea(1) Control measures for attainment demonstration – after 2002, before 2009 – permanent and enforceable, SIP counts toward attainment demonstration(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 1102 of 143Control and Contingency Measures PM2.5 Attainment SIP - Macon, GA

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

This state rule requires flue gas desulfuri zation (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 SO 2 emissions from the affected emissions units by at least 95 percent and will reduce NO
x emissions by approximately 85 percent.

The control implementation date of the 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 lo cated 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 amende d 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 SO 2 controls for Plant Scherer, the EGU facility located within the nonattainment area, are required to be impl emented 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 Ju ne 1, 2015, depending upon the unit.

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

Plant/Unit County Required
FGD operation date, per rule (sss) 1 Control 4 Conting. Construction Permit
Number Permit
Effective
Date

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

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

12/20/06 Hammond
Unit 1 Floyd 12/31/2008 X 4911-115-0003-V02-2

12/20/06 Hammond
Unit 2 Floyd 12/31/2008 X 4911-115-0003-V02-2

Hammond
Unit 3 Floyd 12/31/2008 X 4911-115-0003-V02-2 12/20/06

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Plant/Unit County Required
FGD operation date, per rule (sss) 1 Control 4 Conting. Construction Permit
Number Permit
Effective
Date

12/20/06 Hammond
Unit 4 Floyd 12/31/2008 X 4911-115-0003-V02-2

1/1/2007 Wansley
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 NA 2 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 NA 2 4911-015-0011-V02-3 12/20/06

Scherer
Unit 3 Monroe 12/31/2011 X FGD control to be incorporated into permit renewal 3

(1) rule (sss) is not federally enforceable (2) control not needed to meet one year of emissions (3) current permit expires December 2010 and construction permit will be incorporated into permit
renewal (4) controls on these units are modeled in the attainment demonstration

The total estimated reduction of SO2 emi ssions from the seven EGUs by 2009 is 109,040 tons/yr (based on modeled emissions projectio ns, see Table 5-7). The estimated statewide level of SO2 emissions (from all sour ces) in 2009 is 527,580 t ons/yr after the EGU reductions. Therefore, the E GU 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 engine s (vehicle weight of 14001 lbs or more) to meet the California Air Resources Board (C ARB) emissions standards by 2005. These standards are essentially the same as the fe deral heavy duty engine standards (40 CFR 86, Subpart A) described in the federal contro l measures section. The CARB heavy duty diesel engine standards were adopted to en sure that the emission reductions would be
achieved in the event that U.S. EPA relaxed the federal requirements and did not require 2005 and 2006 model year engines to meet the Not-To-Exceed (NTE) test requirements. The CARB standards and Georgi a’s rule (ooo) were promul gated prior to the federal standards. The rule was expected to redu ce NOx by 13 tons per day statewide and 8 tons per day in the 45 county Atlanta ozone contro l 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 fede rally 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 e ngine standards. Also, the CARB engine requirements differ from EPA’s in that CA RB requires a 5 minute idle shutdown or the use of a clean-idle engine beginning with th e 2008 model year. The benefit of this 5- minute idle limit has not been quantified for Ge orgia. However, it is expected to reduce
NOx and PM emissions.

5.3. Summary of SO2 Emissions Changes

Control of SO2 sources is the primary mechan ism for attainment of the PM2.5 standard. Expected changes in SO2 emissions between th e typical baseline a nd 2009, including the effects of SO2 controls, are quantified by s ource category in the tables below. The changes for Bibb County, Monroe County (Pla nt Scherer), the nonattainment area, and the state are presented in Tables 5-4, 5-5, 5-6, and 5-7, respecti vely. 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 fr om 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 Emission s, 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 Plan t Scherer. Reductions of SO2 will occur at
Plant Scherer when controls ar e 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 de monstrating attainment in 2009.

5.4. Contingency Measures

Section 172(c)(9) of the Clean Ai r Act requires states to identify and include contingency
measures in their attainment demonstration plans. Contingency measures must take effect without significant fu rther action by the State or USEPA upon a determination by
EPA that an area has either failed to de monstrate Reasonable Further Progress (RFP)
toward attainment or attainment of the a pplicable 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 nonatt ainment areas must include in their SIPs contingency measures consiste nt 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 attain ment date. These contingency measures must be fully adopted rules or control measures th at 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 SI P 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 signifi cant further action by the State or by EPA. The contingency measures should consist of ot her 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 st ates to adopt “contingency measures equal to approximately one year’s wo rth of reductions necessary to achieve RFP for the area.” Because an RFP plan will not be required fo r Macon, a quantity of emissions reductions equivalent to one year of the reductions n eeded to meet attainment will satisfy the requirement for contingency measures. However, since SO 2 emissions actually increase within the Macon NAA prior to the attain ment date, GAEPD will demonstrate an equivalent air quality benefit for the requi red continued improvement using modeled results to satisfy the contingency requirement.

The purpose of these contingency measures “i s to provide a cushion while the plan is being revised to meet the missed milest one” (page 20643). Calendar year 2009 is the final year for which emissions control measures are to be included in an attainment demonstration for the attainment date of Apr il 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 implem ented 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 s ubmit a revision to the SIP. Contingency
measures will be those reductions that occur after the 2009 control y ear 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 SO 2 emissions from Georgia’s coal-fi red EGUs, as required by Georgia rules 391-3-1-.02 (2) (sss) and (uuu). Conti ngency measures for Macon will be based on
control of SO2 emissions from sources lo cated 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 equiva lent 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 desi gn value established for modeling at the highest monitor (Allied Chemical) is 15.7 ug/m 3 . Therefore the tota l benefit needed to

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come into attainment is 15.7 minus 15. 0 (the standard) which equals 0.7 ug/m 3 . Oneseventh (one year divided by seven years) of 0.7 equals 0.1 ug/m 3

.

Under EPD’s rule (sss), SO2 controls are re quired 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 em issions outside (and within) the NAA are already included (modeled) in this analysis pr ior to determining the sensitivities of air quality to the individual EGU sources used for contingency. Therefore, a separate accounting of all sources of emissions locate d 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/m 3 , which exceeds the required one year benefit of 0.1 ug/m 3

.

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

Emissions Unit Control date per rule (sss) SO 2 Emission
Reductions for Contingency (tons) 1 Air Quality Benefit
of Control Implementation (ug/m 3 ) 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 satisf y 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 ambi ent PM2.5 concentrations:

• Open burning rule

• Local measures

• Switching of two EGUs to lower sulfur coal

• Replacement of coal-fired capacity with na tural-gas-fired combustion turbines at
two EGUs

• Planned early start-up of SO 2 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 1 Measure 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-firedEGUs 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

CMAQ 2 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 establishe d additional May through September burning
restrictions in the Macon nonattainment ar ea and surrounding counties beginning in 2005. The rule prohibits open burning with the excep tion of 12 types of burni ng activities. In addition, there is a smog season (May th rough September) burn ban that reduces the number of allowed exceptions to 6. The ru le reduces emissions of elemental carbon and organic carbon year round. The ban furthe r 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 seve n 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, Centervill e, Perry, Payne City, Danville, Byron, Forsyth, Roberta, Gray, Jeffersonv ille, Culloden, and Fort Valley

The MGCAC was formed to initiate and suppor t local measures to improve air quality and public awareness of air quali ty issues. The coalition meets bimonthly. The meeting members consist of voting members, who ar e the 7 county commissi on chairpersons and the 13 mayors, and non-voting members. Th e daily operations ar e performed by the
mobile source coordinator. There ar e 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 in itiatives is secured in various ways. Member counties and cities provide funding in proportion to their re spective 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 ba n for all MGCAC counties each May through October.

Public Education Created a web page and sponsored Earth Day ce lebrations. Middle Georgia CAN! (Clean Air Netw ork) worked to educate the community on new rulings through television, prin ted 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, Hous ton, and Monroe County buses with particulate filters. Elev en 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 strategi es. Middle Georgia CAN! is an initiative of the MGCAC administered by MGCCC. Middle Georgia CAN! partnered with the Macon Tr ansit 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 implem enting Truck Stop Electrification (TSE) at a truck stop in Middle Georgia. Strategy For The
Future MGCAC has published a comprehens ive strategy as a guide to
achieve and maintain attainment.

Anti-Idling
Resolution The MGCAC counties and cities ha ve passed a resolution supporting anti-idling by all residents.

Alternative Fuels
Resolution The MGCAC approved a resolution fo r the development of policies in each city and county to support th e purchase and use of alternative fuels in government fleets and schoo l buses and to require requestfor-purchase decisions to favor s uppliers meeting alternative fuel
standards.

Other Initiatives MGCAC is currently working to implement Landfill Gas to Energy Generation, Energy Savings Perfor mance 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-fir ed 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 ar ea in Monroe County. Units 1 and 2 were switched to the PRB coal in July 2003 and Ja nuary 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 C oal, Georgia Power Plant McDonough

Plant McDonough is located approximately 80 m iles north of the nona ttainment 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 a nd 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 SO 2 controls on coal-fired EGUs. B ecause 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 thes e 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 st art-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 SO 2 emissions reductions beyond those credited in th e 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 sp ecific nonattainment area. The EPA has consistently interpreted RACM as a collec tion of measures that would advance the attainment date by at least 1 year. This requirement also applies to emissions reductions from existing stationary s ources 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 projecte d 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/m 3 or lower. The 2009 annual modeled Future Design Values (DVF) for the Macon Annual PM 2.5 nonattainment area are 14.3 ug/m 3 for the Allied Chemical site and 12.5 ug/m 3 for the GA Forestry Commissi on site (see Section 6.1.4). As discussed below, the Macon Nonattainment Area will attain primarily due to the EGU
SO 2 reductions resulting from the requirement s 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 em ission reductions that will c ontribute 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 St ate could not achieve significant emissions reductions by the beginning of 2008 due to the time needed to implement reasonable measures or other factors, then it could be concluded that reasonably available local
measures would not advance the attainment date.” The EPA promulgated the Clean Air Fine Particle Implementation Rule (72 FR 20586) on April 25, 2007. This rule defined the requirements for RACT and RACM and also reaffirmed the due date for submission of the SIP revision for fine particles as Ap ril 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, publ ic 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 th e 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 SO 2 , with the primary source of
SO 2 emissions within the State of Georgia (and, to a lesser extent, the Southeast) being coal-fired Electric Generating Units (EGUs) lo cated at power plants. Table 5-11 below
identifies the EGU and non-EGU sources of SO 2 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 SO 2 emissions. The supplemental analysis in the
attainment demonstration section of this document discusses EPD’s determination that
SO 2 is the significant contribu tor of PM2.5 emissions a nd that the control of SO 2 emissions from EGUs is the most effective strategy for significant PM2.5 air quality improvements. Using the results of this modeling analysis, evaluating Georgia’s other PM2.5 nonattainment areas (Atlanta, Chattanoog a, and Floyd County), and incorporating

114 of 143Control and Contingency Measures PM2.5 Attainment SIP - Macon, GA

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” (Multipollu tant Rule) effective March 14, 2007 (see Appendix L). This rule incl udes 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 SO 2 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 loca ted 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 satisfi es 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 SO 2 , NO
x and Hg controls on all large EGUs in the state. The controls at Plant Sche rer are being installed as “expeditiously as practicable” given the extent of the engin eering and construction
schedules involved in retrof itting all large EGUs within the State of Georgia.

Additionally, EPD has long been active (prior to nonattainment de signations) 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 bur ning 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 rest rictions as part of the Atlanta Ozone
nonattainment control area. The Coaliti on 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) Nati onal 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 necessa ry 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 an alysis (UAA) to the air quality modeling
results to determine if the annual PM2.5 NAA QS will be met. A dditional technical or corroboratory analyses may also be used as part of a “supplemental analysis” or a more stringent “weight of evidence” determination to supplement the modeled attainment test and to further support a demonstration of attainment of the NAAQS. The modeled attainment test, additional corroborative analyses and weight of evidence, and unmonitored area analysis are described in further detail in this section.

6.1 Modeled Attainment Test

The purpose of a modeled attainment test is to determine if control strategies currently being implemented or currently adopted with a future compliance date (“on the books”)
will lead to attainment of the NAAQS for PM2.5 by the attainment year of 2009. The modeling is applied in a rela tive sense, similar to the 8- hour ozone attainment test. However, the PM2.5 attainment test is more complicated and reflects the fact that PM2.5 is a mixture of different species. In the test, ambient PM2.5 is divided into major components, with a separate relative res ponse 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 (SMA T). The following subsections outline the process that was used to determine 2009 mode l projections of PM2.5 and compare them to the NAAQS. Complete calculations a nd results are contained in Appendix M.

6.1.1 Determination of Baseline Design Values

The first step in any attainment test proce ss is to determine the baseline design value (DVB). In the Attainment Guidance, the 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, a nd 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 calc ulation 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 calenda r quarters (Q1: January - March; Q2: April - June; etc.) as the NAAQS is calculated for a calendar year, and the quarters need to fit evenly within a year. Also, calculating the attainment test on a quarterly basis allows
states to examine the differences in PM2. 5 composition that occu r during the different

seasons.

Table 6-1 contains the 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 pr oduce 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 Ma ss 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/m 3 ) for
Monitors in the Maco n 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 w ith sulfates (SO4)’’ 2. Mass associated with nitrates (NO3); 3. Mass associated with ammonium (NH4); 4. Mass associated with organic carbon (OC); 5. Mass associated with elemental carbon (EC);
6. Mass associated with particle bound water (PBW); 7. Mass associated with “other” primary i norganic particulate matter (Crustal); and 8. And passively collected mass or the mass of the blank filter (Blank = 0.5 μ g/m 3 ).

The second part of the process is to use the quarterly mean PM2.5 DVBs (as calculated in Section 6.1.1.1) with speciated data to calculate the quarter ly 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 th e same components and do not retain all of the PM2.5 that is measured by routine speciation sa mplers 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 pa rticle-bound water associated with sulfates, nitrates and other hygroscopic species (positive sampling artifacts). This results in concentrations (and percent c ontributions to PM2.5 mass), wh ich may be different than
the ambient levels of some PM2.5 chemical constituents.

To resolve the differences between FRM and STN total mass, the US EPA has recommended using the S ulfate, A djusted N itrate, D erived W ater, I nferred C arbon
H ybrid (SANDWICH) material balance appr oach. With the SANDWHICH approach, nitrate mass is adjusted to account for volatilization based on hourly meteorology

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parameters. Subsequently, quarterly averag e 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 be tween the total PM2.5 mass as measured at the FRM monitor, and the calculated co mponent mass (i.e., OC from mass balance [OCMmb] = PM2.5 FRM - {[EC] + [SO4] + [NO3] + [NH4] + [PBW] + [Crustal] + [Blank]}).

6.1.1.2.2 Speciated Profiles

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

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

Of the four methodologies, the US EPA recommends using one of the spatial interpolation techniques to estimate species c oncentrations at FRM s ites 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 a nd 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 c ourse, we have used the speciated profiles from the Clean Air Interstate Rule (CAIR) SMAT tool, which is the predecessor for the MATS program, as an alternative.

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

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

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As a result of the CAIR SMAT process, qua rterly 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 con centrations. Table 6- 4 shows the quarterly DVBs for crustal, EC, OC, SO4, NO3, NH4 a nd PBW assuming a cons tant passive blank
mass of 0.5 μ g/m 3 . DON refers to degree of neutraliz ation of sulfate and ranges from 0.0 (sulfuric acid) to 0.375 (completely neutraliz ed in the form of ammonium sulfate).

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

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

13-021-0007 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/m 3 ) 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 res ponse 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 quarterl y average base-year concentra tion near the same monitor,

or:

X""Monitor Near""ion Concentrat Year Base Modeled Mean Quarterly X""Monitor Near""ion Concentrat Year Future Modeled Mean Quarterly

= RRF

Instead of focusing on the indivi dual cell containing the monitor, an array of cells that are
“near” a monitor are considered in the attain ment test. By sampling an array of cells from the modeling, the attainment test allows for variations in the model performance, as the peak concentrations may not occur in th e grid cell that contai ns the monitor, but rather nearby the monitor. Table 6-5 provides the US EPA's recommendations for defining “nearby” cells for grid systems havi ng cells of various sizes. Since the modeling for the Georgia attainment demonstration wa s 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 con centration for each component of PM2.5 is extracted from the base-year and future-y ear modeling output near the FRM monitoring sites for each day. The daily component c oncentrations from each cell of the 3x3 array are then averaged to develop a single m ean 24-hour average concentration for each component on each day in both the base and 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 divi ded by their respectiv e base-year quarterly mean component concentrations to develop qua rterly RRFs for each component of PM2.5. The quarterly
RRFs for the FRM monitors in the Macon PM 2.5 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 fo r 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 cal culate future quarterly mean concentration estimates (DVFs) for each component of PM2.5. To accomplish this, the quarterly component-specific DVBs from Table 6-4 are multiplied by the component-specific RRFs contained in Table 6-6. The quarterly component DVFs for the monitors in the Macon PM2.5 nonattainment area are provided below in Table 6-7.

Table 6-7. Quarterly Component Future Design Values (in μg/m 3 ) 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 es timate for each FRM site is
corrected for the 0.5 μ g/m 3 blank mass (Table 6-9). These 2009 annual PM2.5 concentrations (DVFs) are then compared to the NAAQS (15.0 μ g/m 3 ). Since the values
at both FRM sites in the nonattain ment areas are less than 15.0 μ g/m 3 , 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 additio nal analyses of air quality, emissions and meteorological data, and consider modeli ng outputs other than the results of the
attainment test. If the attainment test resu lts fall short of the standard, the results of corroboratory analyses may be used in a we ight of evid ence determination (WOE) to show that attainment is likely despite modeled results, which may be inconclusive.

The Attainment Guidance defines the guideline s for supplemental analysis/WOE for the
annual PM2.5 standard as follows:

- Site with a DVF less than 14.5 μ g/m 3 should submit basic supplemental analysis
to confirm the outcome of the model attainment test.
- Sites with a DVF between 14.5 and 15.5 μ g/m 3 should submit a weight of
evidence demonstration to aggregate supplemental analysis to support the model
attainment demonstration.

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- Sites with a DVF greater than or equal to 15.5 μ g/m 3 should consider additional control measure to ensure attainment, as more qualitative analysis is unlikely to
demonstrate attainment.

Because all Macon PM2.5 nonattainment monitors have DVFs lower than 14.5 μ g/m 3

, the following supplemental analysis to corr oborate 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, an d air quality and emissions trends as part of a supplemental analysis for the Ma con PM2.5 nonattainment area.

6.2.1 Additional Air Quality Modeling

The Attainment Guidance suggests several additional modeling exercises that can be performed as part of supplemental/WOE an alysis. Suggestions include completing additional analysis on the air quality modeling preformed, or 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 μ g/m 3 within the nonattainment

area; 2. Percent change in number of grid cells > 15 μ g/m 3 within the nonattainment area; 3. Percent change in grid cell-hours (days) > 15 μ g/m 3 within the nonattainment area; and 4. Percent change in maximum modeled 24-hour PM2.5 concentrations within the
nonattainment area.

As the US EPA notes in the Attainment Gu idance, care should be taken in interpreting
absolute metrics if the model evaluation s hows a large under predic tion or over prediction of PM2.5 concentrations, because under (over) prediction of observed concentrations will make it artificially easy (hard) to show progress towards absolute attainment levels.

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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 conc entrations were tallied from both the 2002 baseline and the 2009 attainment year modeling run for each day. Data was extracted for only the grid cells that contai ned a portion of the Macon nona ttainment area. Figure 6-1 highlights the 13 cells that encomp ass the Macon nonattainment area.

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

The cell-days counts were binned ba sed on concentration ranges of five μ g/m 3 intervals. Figure 6-2 presents the cell-da ys count results graphically and shows an increase in the number of cell-days below 15 μ g/m 3 . By 2009 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 μ g/m 3 range to the less than

15 μ g/m 3 range between 2002 and 2009 (Table 6-10).

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Figure 6-2 also shows a decrease in th e number of cell-days in the 15 – 20 μ g/m 3 bin (141 cell-days decrease) and the 20 – 25 μ g/m 3 bin (131 cell-days decrea se). In addition, the number of cell-days in the greater than 30 μ g/m 3 bin decreased by 15 cell-days (71.4% decrease) from 2002 to 2009. Overall, the re sults from the air quality modeling metric are encouraging as shown by the substantial increase in the number of cell-days below 15

μ g/m 3

.

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 ch ange) within the bins are noted by red (blue) coloration.

Concentration [ μ g/m 3 ] 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 studi es. Other modeling studies may use different physical and chemical modeling options for their meteorological and air quality modeling runs, which would provide a sensitivity of these different options.

An air quality modeling exercise that 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 demonstratio n are: 1) the mete orology was for 2001, 2) the DVB was the weighted design va lues for the 1999-2003 period and 3) the modeling results were for 2010. The DVF was calculated using the CAIR SMAT tool, so methodologies between the CAIR DVF and the values presented in Section 6.1 are the same. These modeling results ar e listed in Table 6-11 below.

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

Future Year PM2.5 (μg/m 3 )
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 CA IR modeling did not account for all the EGU controls that will be installed by 2009. If the CAIR modeling had properly accounted for these controls, it is highly likely that 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 av erage concentrations of PM2.5 decreased steadily until 2002 at which time they levele d 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 gra phical representation of the
data.

Table 6-12. Annual Average PM2.5 values (in μg/m 3 ) 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 2007PM2.5 Conc. (μg/m3)

Macon Allied Chem. (13-021-0007) Macon Forestry (13-021-0012)

Figure 6-3. Annual PM2.5 Averag e 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/m 3 ) 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 not e that there are still significant sulfur dioxide (SO2) emission re ductions 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. Ta ble 6-14 lists the units in the state of Georgia and shows the date the controls did co me or are expected to come on line and the estimated amount of yearly SO2 emissions reductions. In total, ove r 244,000 tons of SO2 will be reduced annually in Georgia. This reduction represents a 43% reduction in statewide SO2 emissions from point sources comp ared 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 signifi cant reductions of the observed PM2.5 concentrations and push the annual average PM2.5 concentr ation at the Macon Allied Chemical site (13-021-0007) below the NAAQS.

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

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

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

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

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

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

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

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

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

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

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

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

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(1) Dates prior to 2009 are actual field in stallation 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 modeli ng evidence, GA EPD is confident the Macon
nonattainment area should reach attainment status by the 2009 deadline. PM2.5 annual average concentrations should c ontinue to decrease due to substantial sulf ate reductions anticipated in the state over the next two y ears. GA EPD feels these reductions will more than likely allow the PM2.5 nonattainme nt areas to achieve the NAAQS by 2009.

6.3 Unmonitored Area Analysis

The modeled attainment test does not address fu ture air quality at locations where there is not a PM2.5 monitor nearby. To guard against the possibility that ai r quality levels could
exceed the standard in areas with limited monitoring, Section 3.4 of the Attainment
Guidance suggests that additional review is “n ecessary, particularly in nonattainment
areas where the ozone or PM 2.5 monitoring network just meets or minimally exceeds the size of the network required to report data to Air Quality System (AQS).” This review is intended to ensure that a control strategy lead s to reductions in PM2.5 and its constituent pollutants at other locations that could have baseline (and future) design values exceeding the NAAQS were a monitor deployed there. The test is called an “unmonitored area analysis.” The purpose of the analysis is to use a combination of model output and ambient data to identify areas that might exceed the NAAQS if monitors were located
there.

GA EPD currently operates tw o FRM monitors in the nonattainment county and believes that the density of this monitoring networ k relieves the necessity of applying this additional analysis. However, GA EPD decided to apply some additional analysis anyway and looked at 2009 PM2.5 concentratio ns directly from the model (did not apply SMAT or SANDWICH to results). A ma p of each PM2.5 monitor and its position relative to the 12-km modeling gr id is provided in Figure 6-4. As can been seen by the figure, the spatial coverage of the monito rs, 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 Fi gure 6-4. From this figure, it can be seen that there are no significant concentrat ion gradients across the area of interest. Therefore, it can be concluded that there ar e 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 concentratio ns will have no impact on our conclusion
that there are no unmonitored ho tspots in the Macon area.

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Despite being confident our monitoring network is robust enough to cover the Macon nonattainment area, GA EPD has looked at prel iminary results from the “beta” (draft)
version of the “Modeled Attainment Test So ftware” (MATS) tool. The MATS tool has been developed by the US EPA to spatially inte rpolate 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 doc ument for the tool still pending. Once the
final version of the MATS tool has been rel eased, after sufficient peer review and proper guidance documentation for the analysis of th e 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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6.4 Data Access

The modeling input and output files are very larg e 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 ac cess to these files pleas e contact the Georgia
EPD Air Protection Branch’s Data & Modeling Unit at 404-362-4851.

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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 Mac on PM2.5 nonattainment area must meet these requirements, which are listed in Table 7-1 and summarized below.

Table 7-1. General Plan Requi rements for Nonattainment Areas

Federal
CAA Description Found Where in Plan?

172(c)(1) Provide for the implementation of RACM as expeditiously as practicable and shall provide for attainment of the primary NAAQS Section 5.5

172(c)(2) Provide for Reasonable Further Progress Section 7.2 172(c)(3) Provisions shall include a comprehensive current inventory of actual emissions from all sources of the relevant pollutant or pollutants in such area, including periodic revisions as determined necessary. Section 7.3, Appendix I.

172(c)(4) Plan shall expressly identify and quantify the emissions of any such pollutant(s)
which will be allowed in accordance from section 173(a)(1)(B) from the construction and operation of major new or modified stationary sources in each such area. Emissions quantified for this purpose will
be consistent with the achievement of RFP
and will not interfere with attainment of the applicable NAAQS. Section 7.5

172(c)(5) Plan shall require permits for the construction of new or modified major stationary sources as applicable. Section 7.5

172(c)(6) Plan shall incl ude enforceable emission
limitations and such other control measures, means or techniques as well as schedules and timetables for compliance as deemed necessary to provide for attainment of the applicable NAAQS. Section 5

172(c)(7) Shall meet the applicable provisions of section 110(a)(2). Section 7.7

172(c)(8) Administrator may allow the use of equivalent modeling, emission inventory, and planning procedures. Section 7.8.

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

133 of 143Other Clean Air Act Requirements PM 2.5 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 (R ACM) 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 designati on, the State is not required to submit a separate RFP plan. Compliance with the emission reduction
measures in the attainment demonstration and State implementation plan will meet the requirements for achieving reasonable further progress for the area.” This SIP demonstrates attainment of the NAAQS within five years of designation, so no RFP plan is required.

7.3 Actual Emissions Inventory

Section 172(c)(3) requires the development of statewide inventorie s of emissions of direct PM2.5 and PM2.5 precursors. Such 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 thr ough the public hearing process with the full attainment demonstration, which will include any updates or revisions that are necessary
since the CERR submittal.

7.4 Periodic Emissions Inventory

Section 172(c)(3) requires periodic 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 id entification and quantification of emissions that will be allowed under the provisions of section 173(a)(1)(B), pertaining to zones identified by

134 of 143Other Clean Air Act Requirements PM 2.5 Attainment SIP - Macon, GA

the Administrator to which economic devel opment should be targeted. Georgia has no such zones targeted for economic devel opment identified by the Administrator.

Section 172(c)(5) requires a permit program cons istent with the requirements of Section 173. Georgia EPD updated rules for nonattain ment new source review (NNSR) effective July 25, 2007. These provisions can be found at 391-3-1-.03(8) of the Georgia Rules for Air Quality Control. 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 incl uding 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 SI Ps to include enforceable limits and other control measures, along with schedules for compliance as needed to demonstrate
attainment. Section 5 of this document discu sses in detail the Federal and State measures that are necessary for attainment.

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

Section 172(c)(7) requires nonatta inment SIPs to meet the applicable provisions of Section 110(a)(2). Georgia EPD has submitted a letter dated July 23, 2008, to Mr. James I. Palmer, Jr., Regional Administrator for EP A Region 4, certifying that its existing SIP contains provisions that a ddress the requirements for th e 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 sectio n 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 el ements 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 procedur es for modeling, emissions inventory and planning follow the US EPA guidance and is not requesting appr oval for equivalent techniques, as envisione d under Section 172(c)(8).

135 of 143Other Clean Air Act Requirements PM 2.5 Attainment SIP - Macon, GA

7.9 Contingency Measures

The Clean Air Act requires States containi ng nonattainment areas to adopt contingency
measures that will take effect without fu rther action by the Stat e 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 a pplicable NAAQS, as described in Section 172(c)(9). These measures ar e discussed in Section 5.3.

136 of 1432009 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 stra tegy SIP revision. As such, it establishes a motor vehicle emissions budget (MVEB) for the nonattainment area. A motor vehicle emissions budget is described in EP A's transportation conformity rule 1 as "...the implementation plan's estimate of future [m otor vehicle] emissions." Such budgets establish caps on motor vehicle emissions; proj ected emissions from transportation plans and programs must be equal to or less th an these caps for a positive conformity
determination to be made.

8.2 Establishment of 2009 Motor Vehicle Emissions Budgets for the Macon PM2.5
Nonattainment Area

In preparation for this Macon PM2.5 Attain ment Demonstration, EPD worked closely with the Georgia Department of Tr ansportation (GDOT) and the Macon Area Transportation Study (MATS), the metropolitan planning organization for Macon, to develop estimates of mobile 2 source emissions for the Macon nonattainment area.
Mobile source inventories for 2009 were develo ped 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. Th e 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 f actor model was used to calculate 2009 emission factors with all proposed 2009 mobile source control rule s in place. Two sets of emission factors were produced to properl y 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 nonatt ainment 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 20 09 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 fr om the latest Macon travel demand model)

137 of 1432009 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 HPMSadjusted 4 2009 VMT from each link in the Macon travel demand model were multiplied by the 2009 MOBILE6.2 emission f actor 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 convert ed 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 trav el demand model and 2009 emission factors
from EPA's latest mobile source emission fact or model. The same mobile source control rules reflected in Georgia's 8-hour ozone ma intenance 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 i nventories for the Bibb County and Monroe partial-county portions of the Macon PM2.5 nonattainment area.

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

PM2.5 (tons/year) 2009 Mobile Emissions for Bibb County 75.5252
2009 Mobile Emissions for M onroe Partial-County 0.0122 Total, 2009 Motor Vehicle Emissions Budget 75.5374

This Macon PM2.5 Attainment Demonstra tion hereby establishes a 2009 motor vehicle emissions budget of 75.5374 PM2.5 tons per year for the Macon PM2.5 nonattainment area. Interagency consulta tion among the relevant agen cies 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 A ttainment 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).

138 of 1432009 Motor Vehicle Emissions Budget PM2.5 Attainment SIP - Macon, GA

of EPA's motor vehicle emi ssion factor model; and on a registration distribution by age developed using registration data 5 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 Secti on 2, to show the MVEB "together with all
other emissions sources 6 ," 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 populati on of Monroe County. EPD consu lted EPA's Office of Air Quality Planning & Standards (OAQPS) who concurred that population scaling is an appropriate method. The population of Monroe Count y from the year 2000 census was 21,757 people. Linear extrapolation from U.S. Census Bureau population estimates 7 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%. Th e Georgia Department of Transportation (GDOT) calculated that, based on Census 2000 and selecting the census blocks whose
centers were inside the nonatta inment area boundary, the popul ation 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 nonattain ment area, this is a conservative (high) value. Adjusting for the 18.238% grow th 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 ar ea 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 sour ces (Plant Scherer is the only point source in the Monroe County inventory) and those us ed 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 conformi ty rule requires that the "motor vehicle emissions budget(s), when considered together with all other emissions sources, is consistent with applicable requirements for...attainment...."

7 http://www.census.gov/ popest/counties/tables/CO-EST2007-01-13.xls

139 of 1432009 Motor Vehicle Emissions Budget PM2.5 Attainment SIP - Macon, GA

Table 8-2. Macon Nonattainme nt 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

140 of 143Summary 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 PM 2.5 Design
Value (μg/m 3 ) Annual PM 2.5 Standard (μg/m 3 )

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 su lfates and organic carbon. Sulfates are formed primarily from sulfur dioxide emissi ons from coal-fired el ectric generating units (EGUs), including EGUs located inside the nonattainment area and other more distant
EGUs located both inside a nd outside of Georgia.

Control measures that may be credited in the SIP toward attainment are those that have been or will be implemented after 2002 but before 2009 (the modeled attainment year). Because of the large contribution of sulfat es to the ambient PM2.5 problem in the nonattainment area, the most important measur es for achieving attainment are those that
reduce SO2 emissions from coal-fired EGUs. Georgia’s Multipollutant Rule (391-3-1- .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 proj ected 2009 statewide SO2 emissions (744,000 tons/yr) if no controls were assumed. Other modeled state and federal control measures, as well as unm odeled measures, will contribute to PM2.5
reduction to a smaller extent.

Models were used to demonstrate attainme nt of the PM2.5 standard and the modeling was performed per EPA’s Attainment Modeli ng Guidance. The modeling was performed

141 of 143Summary and Conclusion PM2.5 Attainment SIP - Macon, GA

in conjunction with the re gional haze modeling being done by VISTAS and the PM 2.5 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 wa s developed for the baseline year 2002 and projected to 2009. The pollutants inventoried include VOC, NOx, PM 2.5 , coarse particulate (PM 10 ), NH 3 , and SO 2 . The projection to 2009 acc ounted for control measures expected to be implemented.

The CMAQ modeling performance was meas ured by accepted methods. Overall, GA EPD believes that the model pred ictions can be used to form the scientific foundation for proposed control options because its performa nce is well within the limits of acceptable performance criteria es tablished in the Attainment Modeling Guidance.

To demonstrate attainment, the models were us ed 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/m 3 , modeled attainment is demonstrated.

Table 9-2. PM 2.5 2009 Future (Model ed) Design Values

Federal
Reference
Monitor 2009 Annual Design Value (DVF) (μg/m 3 ) PM 2.5 Standard (μg/m 3 )

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 Ap ril 5, 2010. This demonstration is based upon implementation of the control measures de scribed previously in this narrative and
will demonstrate attainment at both FRM m onitors located in the Macon Nonattainment
Area.

142 of 143References 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 Comm ission (TNRCC), ENVIRON Inte rnational Corporation, Novato, CA.

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