The Atlanta Eight-Hour Ozone Attainment Demonstration 

Appendix N 

Modeling Protocol for the Atlanta, GA,

8-hour Ozone Nonattainment Area 
Modeling Protocol 

for the 

Atlanta, GA 

8-hour Ozone 

Nonattainment Area 

Prepared by: 

Georgia Department of Natural Resources 

Environmental Protection Division 

Air Protection Branch 

July 9, 2007 
1.0 Overview of 8-hour Ozone Modeling/Analysis Project 

1.1. Policy Overview of the Ozone National Ambient Air Quality Standard 

On July 16, 1997, the U. S. Environmental Protection Agency (USEPA) revised the 

national ambient air quality standard (NAAQS) for ground-level ozone. The 1-hour ozone 

NAAQS of 0.12 parts per million (ppm) averaged over a 1-hour period (40 CFR 50.9) was 

modified to an 8-hour ozone NAAQS of 0.08 ppm averaged over an 8-hour period (40 CFR 

50.10). The USEPA was subsequently sued over the new NAAQS, and in May 1999, the U. S. 

Court of Appeals for the D. C. Circuit remanded the 8-hour ozone NAAQS back to the USEPA. 

In 2001, the USEPA proposed a response to the D.C. Circuit court’s remand and reaffirmed the 

8-hour ozone NAAQS, which became effective in 2003. On April 30, 2004, the USEPA then 

made nonattainment designations for the 8-hour ozone NAAQS with an effective date of June 

15, 2004, based on air quality data from 2001 through 2003. The designations under the 8-hour 

ozone NAAQS began the implementation process. 

The new 8-hour ozone NAAQS is considered much more stringent than the previous 

NAAQS. The 1-hour ozone NAAQS was remanded one year from the effective date of the 

nonattainment designations. An exceedance of the 8-hour ozone NAAQS occurs when a monitor 

measures ozone above 0.084 ppm on average for an eight-hour period (per the rounding 

convention). A violation of this NAAQS occurs when the average of the annual fourth-highest 

daily maximum 8-hour ozone values over three consecutive years is greater than or equal to 

0.085 ppm. This three-year average is termed the design value for the monitor. The USEPA 

designates nonattainment areas (Figure 1-1) based on these monitored exceedances and adjacent 

areas that could be contributing to the violation. 
Figure 1-1: Map indicates the US EPA’s final 8-hour ozone non-attainment designations in 

Georgia. 

1.2. Designations 

Since the passage of the 1977 Clean Air Act Amendments (CAAA), areas of the country 

that violated the ambient NAAQS for a particular pollutant were formally designated as nonattainment for that pollutant. Five categories to define the degree of nonattainment were created 

for the 1-hour ozone NAAQS (section 181 of the 1990 CAAA). In order of severity, these 

categories were classified as marginal, moderate, serious, severe and extreme. The highest 

monitor design value in a nonattainment area was used to determine its classification, with the 

classification ultimately defining the attainment dates. 

With the implementation of the 8-hour ozone NAAQS, there are two subcategories of 

designation. An area could be designated under section 172 of the 1990 CAAA (subpart 1) as 
“basic” and would have up to 5 years from designation to attain the NAAQS, or the area could 

be designated under section 181 (subpart 2) and classified as one of the five categories listed 

above with attainment dates based on the classification. Areas with a 1-hour ozone design value 

greater than 0.121 ppm were classified under subpart 2, and all other areas were classified under 

subpart 1. 

The Chattanooga area subject to designation entered into an Early Action Compact 

(EAC) that made provisions for states and local areas to achieve clean air earlier than the Clean 

Air Act (CAA) schedule. This EAC area has attained the 8-hour ozone NAAQS. Additionally, 

the Murray and Macon non-attainment areas in Georgia have also attained the 8-hour ozone 

NAAQS. Therefore, this modeling protocol will focus solely on the Atlanta 8-hour ozone nonattainment area. This area includes 20 counties (Barrow, Cherokee, Clayton , Cobb, Coweta, 

DeKalb , Douglas, Fayette, Forsyth , Fulton, Gwinnett, Henry, Paulding, Rockdale, Bartow, 

Carroll , Hall, Newton, Spalding, Walton). 

The Atlanta area has been classified as marginal under subpart 2 by the US EPA, but will 

be bumped up to moderate due to violations of the 8-hour ozone NAAQS. The Georgia 

Environmental Protection Division (GA EPD) is committed to developing a comprehensive air 

quality control strategy to attain the 8-hour ozone NAAQS in the Atlanta area. 

1.3. Participating Organizations 

From the conceptual model of ozone formation, it is obvious that ozone is both a local 

and regional problem, which causes SIP modeling to be a substantial undertaking that relies on 

the interaction of many groups that are affected by overall air quality and that impact the air shed 

of the affected states. It is imperative to include groups of “stakeholders” from industry, 

government, and the private sector represented during the modeling/analysis project. As each 

group involved brings its own perspective, knowledge, and experience to the modeling process, 

the ability to model and develop strategies for ozone reduction is greatly enhanced. The 

following organizations are invited to participate in developing the ozone SIP: 

• GA EPD 

• USEPA Region 4 

• USEPA Office of Air Quality Planning and Standards 

• USEPA Office of Research and Development 

• Georgia Department of Transportation 

• Georgia Power 

• Georgia Institute of Technology 

• Representatives from city and county governments in the nonattainment areas 

Data and available expertise from participating agencies, organizations, and universities 

will be utilized in determining projected emissions and control strategies. All data and 

information will be reviewed and evaluated by the GA EPD. All stakeholders are invited to 

contribute emissions projections and control strategy information. 

Georgia EPD will coordinate with various states and other parties as regional modeling is 

initiated to address the new ozone standards. Various other regional modeling applications, such 
as Southeastern States Air Resource Managers’ seasonal ozone modeling and Visibility 

Improvement and Tribal Association of the Southeast (VISTAS) regional haze and fine 

particulate matter (PM2.5) modeling, will also be considered. These interactions should provide a 

forum for discussing the latest improvements and refinements to air quality modeling. 

1.3.1. Communication 

Communication between the stakeholders is an integral part of completing the 

modeling/analysis project. Stakeholders need the opportunity to review and comment on 

documentation, control strategies, and modeling analysis. GA EPD will host periodic technical 

coordination meetings on the SIP process for their respective portions of the Atlanta nonattainment area. Consultation meetings on control strategy development and contingency plans 

will be held as necessary during the process. In general, as issues arise among the participants, 

special studies will be defined to help resolve all pertinent issues. Documentation will be 

developed concerning these issues, including methods of resolution and any remaining 

uncertainties, which will be submitted as part of each state's SIP. 

Due to the far-reaching effects of the ozone attainment demonstration, it is important that 

all interested parties are kept informed on the progress of the modeling. Industries or 

organizations not directly represented on a modeling committee can monitor progress through 

the VISTAS website. GA EPD will also host several public meetings and focus groups with 

potentially impacted parties in order to get the most objective and comprehensive input in the 

development of the final control strategies. 

1.3.2. Protocol Modification Procedures 

The model configuration, as well as the source of input data and evaluation process, will 

be determined at the beginning of the process. In the event that the protocol needs to be revised 

to incorporate new tools or methodologies, an issue paper stating the need for modification will 

be developed and circulated to all organizations participating in the study. The issue paper will 

be discussed at the next scheduled technical coordination meeting. The revised protocol would 

then be developed and submitted to the USEPA for their review. 

1.4. Schedule 

GA EPD will follow the schedule outlined by the CAA. Using a 2009 modeling year, 

attainment will be demonstrated by at least June 15, 2010 or as expeditiously as practicable. 

1.5. Selection of Future Year 

A key decision from both a modeling and control strategy standpoint is the selection of 

the future year by which attainment will be modeled. The future modeling year has been chosen 

to meet the schedule previously put forth. The time line set by the CAA requires attainment of 

the 8-hour ozone NAAQS in a moderate area be met by June 15, 2010. Since this date is set 

prior to the completion of the 2010 ozone season, attainment of the NAAQS would have to be 

met by at least the end of the 2009 ozone season. GA EPD plans to use 2009 as the future year 

for attainment modeling, as it would coincide with future year modeling for the PM2.5 SIP and 

VISTAS regional haze modeling effort. 
1.6. Organization of Air Quality Modeling Protocol 

The remainder of the protocol documentation is broken down into nine additional sections 

as follows: 

• Section 2 provides a conceptual description of ozone formation in Atlanta. 

• Section 3 presents details of the ozone episode selection process. 

• Section 4 details the models that will be used during this modeling project. 

• Section 5 describes the model grid specifications. 

• Section 6 discusses the emission inventory development. 

• Section 7 lays out the quality assurance plan and procedures. 

• Section 8 details the tools and procedures for model performance evaluation. 

• Section 9 discusses how the control strategies will be designed. 

• Section 10 focuses on the model attainment test and supplemental analyses. 

• Section 11 lists the references. 

• Appendix follow the final section. 
2.0 Conceptual Description of the Ozone Problem in Atlanta 

The Atlanta 8-hour ozone non-attainment area consists of 20 counties (Barrow, Cherokee, 

Clayton , Cobb, Coweta, DeKalb , Douglas, Fayette, Forsyth , Fulton, Gwinnett, Henry, 

Paulding, Rockdale, Bartow, Carroll , Hall, Newton, Spalding, Walton) in which 11 ozone 

monitors are located (Figure 2-1). Ozone design-values (DV; three year average of the 4th highest 

reading per year) for the 2001-2003 period, which was used in designating the Atlanta 20 county 

area as non-attainment, show that 10 out of the 11 sites exceed the 8-hour ozone NAAQS (0.08 

ppm, expressed as 85 ppb) (Table 2-1). 

Cherokee

13-121-0055 

(Conf. Ave)

13-089-3001 

(Tucker)

13-089-0002 

(S.DeKalb)

13-223-0003 

(Yorkville)

13-067-0003 

(Kennesaw)

13-247-0001 

(Conyers)

13-077-0002 

(Newnan)

13-113-0001 

(Fayetville ) 13-151-0002 

(McDonough )

13-097-0004 

(Douglasville)

13-135-0002 

(Lawrenceville )

13-121-0055 

(Conf. Ave)

13-089-3001 

(Tucker)

13-089-0002 

(S.DeKalb)

13-223-0003 

(Yorkville)

13-067-0003 

(Kennesaw)

13-247-0001 

(Conyers)

13-077-0002 

(Newnan)

13-113-0001 

(Fayetville ) 13-151-0002 

(McDonough )

13-097-0004 

(Douglasville)

13-135-0002 

(Lawrenceville )

13-121-0055 

(Conf. Ave)

13-089-3001 

(Tucker)

13-089-0002 

(S.DeKalb)

13-223-0003 

(Yorkville)

13-067-0003 

(Kennesaw)

13-247-0001 

(Conyers)

13-077-0002 

(Newnan)

13-113-0001 

(Fayetville ) 13-151-0002 

(McDonough )

13-097-0004 

(Douglasville)

13-135-0002 

(Lawrenceville )

Cherokee

Figure 2-1: Location of ozone monitors within the Atlanta 20-county non-attainment area 
Table 2-1: 2001-2003 ozone design values for Atlanta non-attainment area sites 

Site name AIRS ID 2001-2003 DV (ppm) 

Confederate Ave. 13-121-0055 0.091 

Conyers 13-247-0001 0.089 

Douglasville 13-097-0004 0.091 

Fayetville 13-113-0001 0.083 

Kennesaw 13-067-0003 0.090 

Lawrenceville 13-135-0002 0.085 

McDonough 13-151-0002 0.089 

Newnan 13-077-0002 0.087 

South-DeKalb 13-089-0002 0.087 

Tucker 13-089-3001 0.089 

Yorkville 13-223-0003 0.089 

2.1. Review and Assessment of Trends in Ozone Levels in Atlanta and Georgia

To analyze the spatial patterns in ozone concentrations throughout the Atlanta non-attainment 

area, the yearly 4th highest 8-hour ozone concentration and number of exceedances of the 

NAAQS (expressed as 85 ppb) from 1996-2006 were investigated (Table 2-2, 2-3). These data 

indicate that peak 8-hour ozone concentrations typically occur at the center (urban core) of the 

non-attainment area, represented here by the Confederate Avenue site. This site exhibited the 

highest ozone concentration (4th highest value) in six out of the eleven years analyzed, and 

recorded the highest number of exceedances in seven out of the eleven years. Other sites in 

which peak concentrations and number of exceedances were recorded are the Conyers, 

Douglasville, Lawrenceville, and McDonough sites, all representing suburban sites, downwind 

of peak precursor emissions. Analyzing ozone DVs (three year average of the 4th highest reading 

per year) (Table 2-4), also indicates that the Confederate Avenue site consistently exhibits the 

highest DV within the non-attainment area. However, despite most violations occurring at the 

Confederate Avenue site near downtown Atlanta, it is important to note that all of the 11 sites 

within the non-attainment area continuously record violations of the ozone NAAQS, hence the 

problem is not limited to a specific site or subset of sites within the non-attainment area. 
Table 2-2: Yearly 4th highest 8-hour ozone concentrations (ppm) at the 11 Atlanta nonattainment area sites during 1996-2006. Bold red numbers indicate maximum per year. 

Site name 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 

Conf. Ave. 0.110 0.104 0.126 0.124 0.113 0.084 0.100 0.091 0.089 0.092 0.092 

Conyers 0.098 0.110 0.113 0.123 0.099 0.091 0.099 0.078 0.087 0.088 0.099

Douglasville N/A 0.090 0.110 0.105 0.097 0.092 0.098 0.085 0.080 0.089 0.095 

Fayetville N/A N/A 0.111 0.113 0.100 0.084 0.088 0.077 0.084 0.086 0.090 

Kennesaw N/A N/A N/A 0.095 0.107 0.088 0.100 0.084 0.073 0.081 0.093 

Lawrenceville 0.089 0.086 0.111 0.103 0.100 0.080 0.089 0.088 0.092 0.082 0.096 

McDonough N/A N/A N/A 0.126 0.111 0.086 0.099 0.082 0.085 0.089 0.095 

Newnan N/A N/A N/A 0.108 0.096 0.085 0.099 0.077 0.083 0.078 0.086 

South DeKalb 0.102 0.092 0.112 0.112 0.107 0.085 0.095 0.083 0.084 0.087 0.096 

Tucker 0.100 0.085 0.111 0.111 0.107 0.088 0.090 0.091 0.088 0.084 0.094 

Yorkville 0.092 0.086 0.104 0.103 0.088 0.085 0.099 0.083 0.073 0.082 0.084 

Table 2-3: Number of exceedances of the ozone NAAQS (expressed as 85 ppb) at the 11 Atlanta 

non-attainment area sites during 1996-2006. Bold red numbers indicate maximum per year. 

Site name 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 

Conf. Ave. 21 24 41 54 22 3 15 4 7 7 11

Conyers 15 24 38 43 11 6 12 1 6 4 10 

Douglasville N/A 9 36 37 13 6 21 4 1 8 8 

Fayetville N/A N/A 34 30 9 3 6 0 3 4 6 

Kennesaw N/A N/A N/A 8 17 5 12 3 0 2 11 

Lawrenceville 9 6 27 16 12 2 7 6 4 1 8 

McDonough N/A N/A N/A 34 22 4 11 0 5 6 9 

Newnan N/A N/A N/A 33 11 4 9 0 2 0 4 

South DeKalb 15 8 21 28 16 4 10 2 3 7 10 

Tucker 15 4 19 27 16 7 11 5 4 3 8 

Yorkville 10 5 26 23 6 4 11 2 0 2 3 

Table 2-4: Ending year 8-hour ozone design-values (ppm) for the 11 Atlanta non-attainment area 

sites during 1998-2006 (e.g, the 1998 DV is based on data from 1996-1998). Bold red numbers 

indicate maximum per year. Bold black (and red) numbers indicate an exeedance of the NAAQS 

(expresses as 85 ppb). 

Site name 1998 1999 2000 2001 2002 2003 2004 2005 2006 

Conf. Ave. 0.113 0.118 0.121 0.107 0.099 0.091 0.093 0.090 0.091 

Conyers 0.107 0.115 0.111 0.104 0.096 0.089 0.088 0.084 0.091 

Douglasville N/A 0.101 0.104 0.098 0.095 0.091 0.087 0.084 0.088 

Fayetville N/A N/A 0.108 0.099 0.090 0.083 0.083 0.082 0.086 

Kennesaw N/A N/A N/A 0.096 0.098 0.090 0.085 0.079 0.082 

Lawrenceville 0.095 0.100 0.104 0.094 0.089 0.085 0.089 0.087 0.090 

McDonough N/A N/A N/A 0.107 0.098 0.089 0.088 0.085 0.089 

Newnan N/A N/A N/A 0.096 0.093 0.087 0.086 0.079 0.082 

South DeKalb 0.102 0.105 0.110 0.101 0.095 0.087 0.087 0.084 0.089 

Tucker 0.098 0.102 0.109 0.102 0.095 0.089 0.089 0.087 0.088 

Yorkville 0.094 0.097 0.098 0.092 0.090 0.089 0.085 0.079 0.079 
Spatial patterns in ozone DVs outside of the Atlanta non-attainment area were also investigated, 

to assess whether ozone levels are impacted by regional factors (such as emissions, 

meteorology), or exhibit a local phenomenon to Atlanta. For this purpose, 1999-2006 ozone DVs 

from all monitoring-sites throughout Georgia were grouped to the following regions (only sites 

with continuous ozone records for 1999-2006 were used): 

1. Atlanta: 11 sites listed in Table 2-2 

2. Macon: Macon south-east monitor (AIRS ID 13-021-0012) 

3. Columbus: Columbus Airport (13-215-0008) and Columbus Crime lab (13-215-1003) 

monitors 

4. Augusta: Augusta 13-245-0091 monitor 

5. Savannah: Savannah 12-051-0021 monitor 

6. South Coastal Georgia: Brunswick monitor (12-127-0006) 

7. South Central Georgia: Leslie monitor (13-261-1001) 

8. North Georgia Mountains: Fort mountain (13-213-0003) and Dawsonville (13-085-

0001) monitors 

The area-average and area-maximum (single site) DVs for the period of 2001-2006 are shown in 

Figure 2-2. Area-average and area-maximum (single site) count of daily exceedances of the 

NAAQS (expressed as 85 ppb) are shown in Figure 2-3. These data seem to exhibit both regional 

and local patterns in ozone levels in Georgia. A sharp decline in both ozone levels and the 

number of exceedances are observed at all areas from 2001 to 2005, followed by a slight increase 

in the 2006 DV. In addition to this regional pattern, a local one is evident in Atlanta, as 

expressed by higher ozone levels and more frequent exceedances compared to any other area of 

the state. The relatively minor differences in ozone levels between Atlanta and Macon as 

observed in Figure 2-2(a) are due to the comparison between an average of 11 monitor for the 

Atlanta area and a single site in Macon. However, when the maximum DV and number of 

exeedances in the Atlanta area are compared to the Macon monitor, larger differences are 

observed. The increment in ozone levels in Atlanta, compared to other areas in the state, can be 

interpreted as a local contribution caused by emissions of ozone precursors in the Atlanta area. In 

fact, previous work has demonstrated that emissions of ozone precursors in Atlanta also 

contribute to ozone formation at Macon and other regions of the state. 
60.0

65.0

70.0

75.0

80.0

85.0

90.0

95.0

100.0

Atlanta

Macon

Columbus

Augusta

Athens

Savannah

S. Coastal GA

S. Central GA

N. GA Mountains

Average O3 DV per geographical area (ppb)

1999-2001 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

60.0

70.0

80.0

90.0

100.0

110.0

Atlanta

Macon

Columbus

Augusta

Athens

Savannah

S. Coastal GA

S. Central GA

N. GA Mountains

Highest O3 DV per geographical area (ppb)

1999-2001 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

(a) 

(b) 

Figure 2-2: (a) Area-average and (b) single-site area-maximum ozone DVs for 2001-2006. 
0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

Atlanta

Macon

Columbus

Augusta

Athens

Savannah

S. Coastal GA

S. Central GA

N. GA Mountains 

Days per year in which max 8-hr O3 >= 85ppb 

(average per area)

1999-2001 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Atlanta

Macon

Columbus

Augusta

Athens

Savannah

S. Coastal GA

S. Central GA

N. GA Mountains 

Days per year in which max 8-hr O3 >= 85ppb 

(maximum in single site per area)

1999-2001 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

(a) 

(b) 

Figure 2-3: (a) Area-average and (b) single-site area-maximum number of exceedances of the 

ozone NAAQS level (expressed as 85 ppb) in 2001-2006. 

The strong (generally) downward trend in ozone DVs throughout the state is of interest, and is 

likely due to a combination of both a decrease in emissions and variations in meteorological 

conditions leading to ozone formation. This can be inferred from Figure 2-4, in which variations 

in NOx emissions (both EGU and mobile-sources) and in the number of “ozone conducive days” 

are presented for 1996-2005 (2006 meteorological and vehicle miles traveled data were not 

available by the time of this analysis). State total EGU NOx emissions were obtained from the 

Clean Air Markets Division (CAMD) website (http://www.epa.gov/airmarkets/). Approximate 

13-county highway mobile source emissions inventories were calculated by GA EPD using a 

modified draft version of EPA's National Mobile Inventory Model (NMIM20050311) into which 

"historic" 1994 through 2004 average annual daily vehicle miles traveled (VMT) from Georgia 
Department of Transportation were imported (The MOBILE6.2.03 emission factors produced by 

NMIM reflect default MOBILE6 speed distributions). Trends in meteorology were quantified 

based on the count of “ozone conducive days” per ozone season. An “ozone conducive day” was 

defined here as a day in which the maximum daily temperature was above the 75th percentile, 

and both the average relative-humidity and average wind-speed were below the 50th percentile 

for the period studied (1996-2005, using data from the National weather Service station in 

Atlanta). These parameters were chosen to represent meteorological conditions conducive to 

ozone formation, i.e. hot, dry and stagnant conditions. The use of relative (percentile based) 

rather than absolute thresholds (e.g., temperature above 90°F) allows for an easy inter-annual 

comparison incorporating several meteorological parameters. Different thresholds were 

investigated, resulting in a change in the actual number of ozone conducive days per year, but 

this had little effect on the observed trend. Emissions data show a strong decline in EGU NOx

emissions in Georgia (60% decrease from 1998 to 2005) due to the installation of Selective 

Catalytic Reduction (SCR) devices at several power plants (Bowen Units 1-4, Hammond Unit 4, 

and Wansley Units 1-2) and other controls (such as the use of Powder River Basin coal and overfire air). On-road mobile source emissions also exhibit a downward trend (32% decrease from 

1998 to 2005), mainly due to fleet turnover. In addition, there seems to be a substantial amount 

of variability in the meteorological condition between years, with an overall decrease in the 

count of “ozone conducive days” in the last several ozone seasons. The combination of these 

reductions in emissions and more-favorable meteorology in the last several years can help in 

explaining the downward trend in ozone design values. 

0

100

200

300

400

500

600

700

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

NOx emissions (TPD)

0

5

10

15

20

25

30

35

Number of "ozone conducive" days

EGU NOx (TPD) Mobile-source NOx (TPD) Count of "O3 conducive" days

Figure 2-4: Inter-annual trends in NOx emissions (total GA EGU and 13-county Atlanta on-road 

mobile-source emissions) and count of “ozone conducive days” for 1996-2005 
2.2. Conditions Leading to Eleveted Ozone in the Atlanta Area 

To further study the factors contributing to ozone formation in Atlanta and to the occurrence of 

exeedances of the ozone NAAQS, data from the Photochemical Assessment Monitoring Stations 

(PAMS) network was used. The Atlanta PAMS network consists of four sites: Yorkville (type 1, 

upwind/background site), Tucker (type 2, maximum precursor location), South-DeKalb (type 2, 

maximum precursor location) and Conyers (type 3, maximum ozone site). Data from the four 

PAMS sites were binned, according to the daily maximum 8-hour ozone concentration, into the 

following bins: <65 ppb, 65-85 ppb, 85-105 ppb, and >105 ppb. Parameters investigated were 

morning (6-10am), midday (10am-4pm), and evening (4-7pm) NOx and VOC (sum of 56 PAMS 

VOC species) concentrations, previous day ozone concentrations, and meteorological conditions 

(maximum daily temperature, average daytime wind speed and relative humidity). Data from 

1997-2003 were analyzed, for days in which the above-mentioned parameters were available 

(Tables 2-5, 2-6, 2-7, and 2-8). Elevated ozone at both the ‘type 2’ sites (Tucker and SouthDeKalb) and the ‘type 3’ site (Conyers) are associated with elevated previous day ozone, 

indicating a buildup of ozone during episodes. As expected, elevated ozone is also associated 

with elevated temperatures, low wind speeds and low relative humidity, as these meteorological 

conditions are known to be conducive of ozone formation. 

To examine the relative importance of precursors of ozone (NOx and VOCs) to ozone 

formations, trends in these precursors were analyzed. Elevated ozone at the South-DeKalb, 

Tucker and Conyers are typically associated with elevated levels of both NOx and VOCs. 

However, lower ratios of VOC/NOx are typically associated with elevated ozone, possibly 

indicating ‘NOx-limited’ conditions (ozone formation is limited by the amount of available NOx). 

However, this analysis is a descriptive one, and it is difficult to conclude on the relative 

importance on NOx or VOC based on these data alone. 

2.2.1. Analysis of PAMS data in Atlanta using Ozone M.A.P.P.E.R. 

To further examine the relative importance of NOx and VOC to ozone formation, used the Ozone 

M.A.P.P.E.R. program (Blanchard et al., 1995, 1996). Ozone M.A.P.P.E.R. is a Windows-based 

program that analyses ozone and other air-quality measurements to determine if ground-level 

ozone formation is limited by the availability of the pre-cursor pollutants: VOCs or NOx. Ozone 

M.A.P.P.E.R. can be used to: (1) qualitatively assess the preferential effectiveness of controlling 

VOC or NOx emissions, (2) compare control strategy assessments obtained from the application 

of grid-based photochemical air quality models, and (3) evaluate trends in the relative 

effectiveness of VOC vs. NOx controls. The calculations are carried out according to the 

equations of the Integrated Empirical Rate (IER) model or the Smog Production (SP) algorithm 

(Blanchard et al., 1995, 1996). These equations use measurements of ozone, nitric oxide (NO), 

and either NOx (NO + NO2) or NOy (the sum of all oxidized nitrogen species) to compute the 

extent of reaction. The extent of reaction is a dimensionless parameter ranging from zero to one. 

As the extent of reaction approaches one, ozone production ceases because virtually all of the 

NOx has reacted. Table 1-9 contains a summary of how the extent of reaction is typically 

interpreted. 
Table 2-5: Concentrations of NOx, VOCs and meteorological conditions associated with various 

ozone levels at the Tucker PAMS site (ozone concentrations reported as daily maximum 8-hour 

ozone; VOC reported are the sum of 56 PAMS target species; morning refers to 6-10am; midday 

refers to 10am-4pm; evening refers to 4-7pm; daytime referred to as 6am-7pm) 

Parameter O3

<65ppb 

65ppb ≤ O3

< 85ppb 

85ppb ≤ O3

< 105ppb 

O3 ≥

105ppb 

Number of days 155 112 36 13 

O3 (ppb) 48.3 73.1 90.8 116.2 

Previous day O3 (ppb) 54.7 70.8 79.4 85.2 

Morning NOx (ppb) 17.7 20.9 34.0 32.7 

Mid-day NOx (ppb) 7.5 6.0 8.0 10.7 

Evening NOx (ppb) 11.5 10.7 13.5 15.9 

Morning VOC (ppbC) 58.8 70.9 95.6 107.0 

Midday VOC (ppbC) 35.5 34.0 36.6 47.0 

Evening VOC (ppbC) 49.5 45.9 59.8 63.1 

Morning VOC/NOx (ppbC/ppb) 4.6 4.2 5.3 3.6 

Midday VOC/NOx (ppbC/ppb) 7.6 8.0 5.1 5.4 

Evening VOC/NOx (ppbC/ppb) 5.4 5.3 4.5 4.7 

Daytime wind speed (m/s) 1.52 1.44 1.21 1.15 

Max. Temperature (°C) 28.5 31.1 32.2 33.1 

Daytime relative humidity (%) 73.5 58.7 53.9 48.5 

Table 2-6: Concentrations of NOx, VOCs and meteorological conditions associated with various 

ozone levels at the South-DeKalb PAMS site (ozone concentrations reported as daily maximum 

8-hour ozone; VOC reported are the sum of 56 PAMS target species; morning refers to 6-10am; 

midday refers to 10am-4pm; evening refers to 4-7pm; daytime referred to as 6am-7pm) 

Parameter O3

<65ppb 

65ppb ≤ O3

< 85ppb 

85ppb ≤ O3

< 105ppb 

O3 ≥

105ppb 

Number of days 272 146 43 15 

O3 (ppb) 45.5 73.3 91.9 113.7 

Previous day O3 (ppb) 51.7 69.0 80.0 88.2 

Morning NOx (ppb) 26.4 45.3 71.5 75.4 

Mid-day NOx (ppb) 5.7 5.2 5.1 6.5 

Evening NOx (ppb) 10.5 14.1 13.8 16.8 

Morning VOC (ppbC) 52.0 83.9 117.7 108.7 

Midday VOC (ppbC) 25.6 25.5 28.8 34.0 

Evening VOC (ppbC) 40.4 43.4 47.6 55.6 

Morning VOC/NOx (ppbC/ppb) 3.9 2.9 2.0 1.9 

Midday VOC/NOx (ppbC/ppb) 7.9 8.2 8.3 7.4 

Evening VOC/NOx (ppbC/ppb) 9.0 7.2 7.5 7.8 

Daytime wind speed (m/s) 1.44 1.26 1.12 1.07 

Max. Temperature (°C) 29.3 31.7 32.9 34.8 

Daytime relative humidity (%) 74.4 61.2 58.2 54.6 
Table 2-7: Concentrations of NOx, VOCs and meteorological conditions associated with various 

ozone levels at the Conyers PAMS site (ozone concentrations reported as daily maximum 8-hour 

ozone; VOC reported are the sum of 56 PAMS target species; morning refers to 6-10am; midday 

refers to 10am-4pm; evening refers to 4-7pm; daytime referred to as 6am-7pm) 

Parameter O3

<65ppb 

65ppb ≤ O3

< 85ppb 

85ppb ≤ O3

< 105ppb 

O3 ≥

105ppb 

Number of days 146 64 29 11 

O3 (ppb) 47.4 72.9 91.9 110.0 

Previous day O3 (ppb) 52.5 69.6 78.8 98.9 

Morning NOx (ppb) 6.7 10.7 17.1 25.4 

Mid-day NOx (ppb) 2.5 3.4 4.6 5.9 

Evening NOx (ppb) 3.0 3.5 5.6 6.9 

Morning VOC (ppbC) 27.8 44.6 56.2 100.8 

Midday VOC (ppbC) 17.6 22.7 27.5 31.6 

Evening VOC (ppbC) 24.9 32.8 37.3 52.8 

Morning VOC/NOx (ppbC/ppb) 6.6 6.0 4.8 4.5 

Midday VOC/NOx (ppbC/ppb) 10.1 10.5 9.7 8.2 

Evening VOC/NOx (ppbC/ppb) 13.2 16.5 12.2 13.0 

Daytime wind speed (m/s) 1.21 1.39 1.43 1.03 

Max. Temperature (°C) 29.2 31.1 32.8 33.2 

Daytime relative humidity (%) 73.5 62.0 59.4 60.7 

Table 2-8: Concentrations of NOx, VOCs and meteorological conditions associated with various 

ozone levels at the Yorkville PAMS site (ozone concentrations reported as daily maximum 8-

hour ozone; VOC reported are the sum of 56 PAMS target species; morning refers to 6-10am; 

midday refers to 10am-4pm; evening refers to 4-7pm; daytime referred to as 6am-7pm) 

Parameter O3

<65ppb 

65ppb ≤ O3

< 85ppb 

85ppb ≤ O3

< 105ppb 

O3 ≥

105ppb 

Number of days 58 39 2 0 

O3 (ppb) 48.6 73.7 86.0 - 

Previous day O3 (ppb) 50.4 66.3 71.0 - 

Morning NOx (ppb) 2.0 2.4 2.0 - 

Mid-day NOx (ppb) 1.4 1.5 1.0 - 

Evening NOx (ppb) 1.8 1.9 1.2 - 

Morning VOC (ppbC) 16.7 19.8 14.3 - 

Midday VOC (ppbC) 16.7 14.1 12.2 - 

Evening VOC (ppbC) 21.0 25.0 26.4 - 

Morning VOC/NOx (ppbC/ppb) 11.0 13.0 6.9 - 

Midday VOC/NOx (ppbC/ppb) 14.5 11.8 12.2 - 

Evening VOC/NOx (ppbC/ppb) 15.9 19.7 24.3 - 

Daytime wind speed (m/s) 2.65 2.38 3.13 - 

Max. Temperature (°C) 28.4 30.0 31.4 - 

Daytime relative humidity (%) 77.3 63.7 73.0 - 
Table 2-9: Interpretation of Extent of Reaction 

Extent of Reaction Interpretation 

0.0 – 0.6 Strongly indicative of VOC limitation 

0.6 – 0.9 Transitional to NOx limitation 

0.9 – 1.0 Strongly indicative of NOx limitation 

Ozone M.A.P.P.E.R. should be used as a qualitative indicator of control preferences, rather than 

quantitatively as an estimator of control requirements because: (1) the computational algorithms 

are simple representations of ozone formation in smog chambers and do not incorporate many 

physical processes that are important in affecting ozone concentrations in the ambient 

environment, and (2) bias and imprecision of measurements typically result in uncertainties on 

the order of 0.1 in the extent of reaction. 

2.2.1.1 Application to Atlanta 

PAMS data was available for four PAMS sites in Atlanta for the ozone seasons (MaySeptember) of 1997-2005. Ozone M.A.P.P.E.R. was applied to each of these PAMS sites using 

the Smog Production (SP) algorithm. The Smog Production algorithm was chosen over the 

Integrated Empirical Rate (IER) model to calculate extent of reaction because it is considered to 

be more advanced and comprehensive. 

NOx data were available at all four PAMS sites (along with ozone and NO), however NOy data 

were only partially available (for 2000-2005 at South DeKalb and Tucker, 1998 at Conyers, not 

available for Yorkville). For consistency across sites and years, NOx data (rather than NOy) were 

used in the analysis. 

2.2.1.2 Summary of Ozone M.A.P.P.E.R. Results 

Average diurnal patterns in the extent of reaction (EOR) for the four PAMS sites for the period 

of 1997-2005 are shown in Figure 2-5. The extent of reaction clearly follows a diurnal pattern 

similar to ozone, with peak values in the afternoon and values approaching zero during the 

nighttime. Based on the average diurnal patterns of the EOR, formation of ozone during the 

afternoon (peak ozone) hours is in the ‘transitional’ range (EOR in the range of 0.6-0.9), 

indicating that both NOx and VOC controls may be efficient for reducing ozone levels. Also 

evident is that the Yorkville and Conyers sites are more NOx-limited compared to the two ‘type 

2’ (maximum precursor) sites, Tucker and South-DeKalb. 

To further analyze the relative importance of NOx and VOCs to the formation of ozone, EOR 

results were binned according to daily maximum 8-hour ozone concentrations (in the ranges of 

<65 ppb, 65-85 ppb, 85-105 ppb, and >105 ppb). The average diurnal patterns associated with 

each bin at each site were calculated, and are presented in Figures 2-6. These figures clearly 

demonstrate that ozone formation becomes more NOx-limited with elevated daily maximum 8-

hour ozone concentrations. This is of special importance, as the 8-hour ozone NAAQS is a 

percentile/maximum based standard (4 highest concentration per year), hence in order to bring 

the region into attainment of the NAAQS, controls that are most efficient in reducing peak ozone 

concentrations should be adopted and implemented. While the data in Figure 2-5 seem to 
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Time of day

Extent of reaction

Conyers South-DeKalb Tucker Yorkville

Figure 2-5: Average diurnal pattern in extent of reaction for the four PAMS sites for 1997-2005 

a) Yorkville

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time of day

Extent of reaction

O3<65ppb 65ppb<O3<85ppb

85ppb<O3<105ppb O3>105ppb

b) Tucker

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time of day

Extent of reaction

O3<65ppb 65ppb<O3<85ppb

85ppb<O3<105ppb O3>105ppb

c) South-DeKalb

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time of day

Extent of reaction

O3<65ppb 65ppb<O3<85ppb

85ppb<O3<105ppb O3>105ppb

d) Conyers

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time of day

Extent of reaction

O3<65ppb 65ppb<O3<85ppb

85ppb<O3<105ppb O3>105ppb

Figure 2-6: Average diurnal pattern in extent of reaction for 1997-2005 binned by the daily 

maximum 8-hour ozone concentration (<65ppb, 65-85ppb, 85-105ppb, >105ppb) for (a) 

Yorkville, (b) Tucker, (c) South-DeKalb, and (d) Conyers. 
indicate that Atlanta ozone is ‘transitional’ to NOx-limitation, data in Figures 1-6 clearly indicate 

that days of high-ozone are NOx-limited. Hence, NOx controls are likely to be substantially more 

efficient than VOC controls in reducing maximum 8-hour ozone concentrations. The apparent 

discrepancy between the trends in Figure 2-5 and 2-6 are due to the distribution of days between 

the four ozone bins. In all four PAMS sites, the vast majority of days (72%-75%) are categorized 

in the low ozone bin (<65 ppb), while only 4.4%-8.6% of days are categorized in the top two 

bins (>85 ppb). 

2.2.1.3 Ozone M.A.P.P.E.R. Conclusions 

The application of Ozone M.A.P.P.E.R. to the four PAMS sites in Atlanta clearly indicates that 

the control of NOx emissions is preferred to VOC emission controls. This is especially true for 

the days of elevated 8-hour ozone concentrations. These results are consistent with results from 

an air-quality model based sensitivity analysis, showing a much greater sensitivity of elevated 8-

hour ozone concentrations to NOx controls than VOC controls, both at core-urban and 

suburban/rural sites. 

2.2.2 Weekday/weekend trends in ozone concentrations 

The weekly cycles of ozone are of interest because they provide information about the response 

of ozone to changes in anthropogenic emissions from weekdays to weekends. While it is not 

feasible to conduct ambient experiments with long-term changes in precursor emissions, the 

response of ozone to changes in precursor emissions that occur on weekends has ramifications 

toward its response to long-term strategies to control ozone through reductions in precursor 

emissions. During weekends, the emissions of anthropogenic precursors are lower compared to 

weekdays, because major sources of ozone precursors, such as mobile sources and industrial 

sources, are less active on weekends. Data collected at the four Atlanta PAMS sites (Table 2-10) 

indicate that NOx concentrations are substantially lower on weekends relative to weekdays, while 

differences in VOC concentrations (expressed here as total non-methane organic compounds, 

TMNOC) are less pronounced. This is due to the fact that some major VOC sources, such as 

biogenic emissions and fuel evaporation, remain constant irrespective of the day of the week. 

For both precursors, the weekend/weekday differences are more evident at the maximum 

precursor sites (Tucker and South-DeKalb) than at the upwind (Yorkville) and downwind 

(Conyers) sites. 

Table 2-10: Weekday/weekend differences in daytime (6am-7pm) NOx and VOC (TNMOC) 

concentrations at the four Atlanta PAMS sites during 1997-2005 (only days in which both NOx 

and TNMOC measurements were available are included) 

NOx (ppb) VOC (TNMOC, ppbC) Site Weekday Weekend % change Weekday Weekend % change 

South-DeKalb 18.9 10.6 -43.8 65.1 49.3 -24.2 

Tucker 15.4 7.6 -50.5 72.6 51.6 -28.9 

Conyers 6.2 4.1 -34.7 34.5 31.3 -9.3 

Yorkville 3.1 2.1 -31.3 24.5 24.6 0.3 

Changes in weekdays/weekend daily maximum 8-hour ozone concentrations are presented in 

Figure 2-7, for all four Atlanta PAMS sites, as well as the Atlanta Confederate Avenue site, for 
the period of 1997-2005, as well as for 3-year periods within that range. The Confederate 

Avenue site is of special interest as its DV has consistently been the highest of all the Atlanta 

monitors throughout the 1998-2005 period, and due to its location in the center of the nonattainment area (about 2 miles south-east of downtown Atlanta). These results indicate that 

ozone in Atlanta is typically lower on the weekends compared to weekdays, which can serve as 

an additional indication to Atlanta ozone being NOx-limited. Of the five sites investigated, 

Confederate Avenue and South-DeKalb seem the least NOx-limited, which is expected given 

their location in the middle of the core urban area (Confederate Avenue) and adjacent to a major 

highway (South-DeKalb, located about 0.8 miles away from I-285). Splitting the dataset of 

ozone concentrations into three 3-year periods (1997-1999, 2000-2002, 2002-2005) seems to 

indicate that Atlanta ozone may have shifted from being VOC-limited/NOx-inhibited in the 

earlier period (1997-1999), as expressed by higher ozone concentrations during the weekends 

(especially in the Confederate Avenue and South-DeKalb sites) to NOx-limited in the latter 

period (2003-2005), as expressed by lower ozone concentrations during the weekends. These 

results are consistent with the overall trend of reduction in NOx emissions in the region (Figure 

2-4), and hence the decline in availability of NOx for ozone formation. 

b) 1997-1999

52.0

54.0

56.0

58.0

60.0

62.0

64.0

66.0

68.0

70.0

72.0

Conf.

Ave.

SouthDeKalbTucker Conyers Yorkville

Average of max 8-hr O3 

(ppb)

Weekdays Weekend

d) 2003-2005

44.0

46.0

48.0

50.0

52.0

54.0

56.0

Conf.

Ave.

SouthDeKalbTucker Conyers Yorkville

Average of max 8-hr O3 

(ppb)

Weekdays Weekend

a) 1997-2005

51.0

52.0

53.0

54.0

55.0

56.0

57.0

58.0

59.0

60.0

61.0

Conf.

Ave.

SouthDeKalbTucker Conyers Yorkville

Average of max 8-hr O3 

(ppb)

Weekdays Weekend

c) 2000-2002

48.0

50.0

52.0

54.0

56.0

58.0

60.0

Conf.

Ave.

SouthDeKalbTucker Conyers Yorkville

Average of max 8-hr O3 

(ppb)

Weekdays Weekend

Figure 2-5: Weekdays/weekend differences in ozone concentrations at the Confederate Avenue 

site and the four Atlanta PAMS sites for (a) 1997-2005, (b) 1997-1999, (c) 2000-2002, and (d) 

2003-2005. 

2.3 Conclusions 

Various analyses presented in this section suggest that despite a regional trend in ozone levels 

throughout the state of Georgia, there is a local pattern of ozone formation in the Atlanta area 

and that reductions in emissions of ozone precursors in the Atlanta non-attainment area are likely 
to introduce a change in (local) ozone concentrations. In addition, it has been shown that the 

Atlanta non-attainment area is generally NOx-limited, though may be VOC-limited or 

transitional in some cases. Analysis of data collected at the four PAMS sites throughout Atlanta 

indicates that lower VOC/NOx ratios are associated with elevated daily maximum 8-hour ozone 

concentrations. Analysis of ozone, NO and NOx data using the ozone M.A.P.P.E.R. program 

indicated that ozone formation in Atlanta is generally transitional to NOx-limited, but when the 

results were binned according to the daily maximum 8-hour ozone, ozone formation seemed 

strongly NOx-limited on days of elevated ozone. An analysis of weekend/weekday differences in 

ozone concentration also indicate a generally NOx-limited regime, with ozone formation 

becoming more NOx-limited in recent years compared to the late 1990’s. 

These findings are generally in agreement with several studies of the trends and limiting factors 

for ozone formation in Atlanta. Pun et al. (2003) investigated the weekly patterns of ozone in 

Atlanta and found elevated midweek concentrations compared to weekends, with stronger 

weekly patterns in later years (1995-1999 compared to 1986-1990), concluding that the 

availability of NOx drives weekly ozone cycle in Atlanta. Analyzing various aircraft 

measurements, Sillman et al. (1997) conclude that Atlanta ozone is mostly NOx-limited, using 

various indicator ratios. 

The findings presented here are also consistent with results from an air-quality model based 

sensitivity analysis. The integration of these various analyses support the control of NOx

emissions as part of a state-policy for reducing ozone levels in the Atlanta area. 
3.0 Episode Selection 

A crucial step to SIP modeling is the selection of episodes to model. Several considerations need 

to be weighed before settling on not only which days to model, but how many days for each 

episode. This section details the guidance and process by which episodes were selected for the 

Atlanta 8-hour ozone SIP modeling package. 

3.1. Overview of US EPA Guidance on Ozone 

The US EPA’s September 2006 draft final guidance, Guidance on the Use of Models and Other 

Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5 and Regional 

Haze, (referred to as Attainment Guidance) sets out specific criteria for the selection of episodes 

to model for attainment of the 8-hour ozone NAAQS. First, episodes should include days 

encompassing a variety of meteorological conditions leading to elevated 8-hour ozone 

concentrations. Additionally, episodes should be selected that contain days close (within ± 0.01 

ppm) to the baseline design value (DVB). It is also recommended choosing episodes for which 

there are extensive air quality and meteorology measurements, including measurements aloft, 

measurements of indicator species and/or precursor measurements. Finally, a sufficient number 

of days should be selected to ensure robust attainment tests at violating monitoring sites. 

In addition to these primary criteria, the US EPA also suggests a set of secondary criteria that 

may be used in the selection of episodes. This set of criteria allows states to give preference to 

previously modeled episodes. This is a very valuable consideration, as the US EPA points out, 

since it can save modeling resources and effort. Additional considerations include selecting 

episodes maximizing the number of days and sites observing a violation, selecting episodes 

which include weekends, and the selection of episodes meeting primary and secondary criteria in 

other non-attainment areas, when participating in regional modeling. Using these criteria laid 

out by the US EPA, the data available was systematically examined to determine the best 

episodes for modeling. 

3.2. Episode Selection 

With the advances in computing and storage technologies, and aided by regional modeling 

efforts, GA EPD decided to model an entire “ozone season” for both sensitivity and attainment 

test purposes, rather than selecting individual episodes, which might be less representative of the 

longer-term trends in ozone formation. Modeling an entire “ozone season” increases the 

likelihood of covering several criteria, such as modeling a sufficient number of days to ensure a 

robust modeled attainment test, representing a myriad of meteorological conditions that influence 

ozone concentrations, and modeling of weekends. 

Efforts were made to determine the most appropriate ozone season to model. The selection 

process started with an examination of the 8-hour ozone data for the 1996 through 2006 seasons, 

to determine which season may yield the most days to be included for study. Table 2-2 and Table 

2-3 in the “Conceptual Description” section of this protocol indicate that out the of most recent 

six years, the 2002 season exhibited the most exceedances of the ozone NAAQS. More 

exeedances were recorded during the 1998-2000 ozone seasons, however, this is due, in part, to 

higher NOx emissions during that period, compared to 2002, as shown in Figure 2-4. This figure 
also displays a count of “ozone conducive days” (defined as days in which the maximum daily 

temperature was above the 75th percentile, and both the average relative-humidity and average 

wind-speed were below the 50th percentile, for the 1996-2005 period), which indicates that 

meteorological conditions during 2002 (22 ozone conducive days) were as ozone conducive as 

during the 1998 and 1999 seasons (22 and 21 days, respectively), and only slightly less 

conducive than during the 2000 period (26 days). 

Following the second primary criteria, the number of days in which each monitoring site 

observed a value within 0.01 ppm of the (site) design value was analyzed for the 2000-2004 

period (Table 3-1). The 2002 ozone season exhibits the most days of ozone concentrations within 

0.01 ppm of the DVB, indicating that modeling 2002 as the baseline modeling year would 

include many days that are representative of the conditions leading to the DVB level. 

Table 3-1: Frequency of occurrence of daily maximum 8-hr ozone concentrations within 10 ppb 

of the DVB (5-year weighted average surrounding each year reported here) at the Atlanta nonattainment area monitors during 2000-2004 

DVBs used Site name 

2000 2001 2002 2003 2004

# days 

2000 

# days 

2001 

# days 

2002 

# days 

2003 

# days 

2004 

Conf. Ave. 109.0 99.2 94.6 91.8 91.6 6 3 11 6 7 

McDonough N/A 98.4 92.1 87.6 87.8 N/A 2 9 6 8 

Douglasville 99.2 95.1 91.6 88.0 86.7 8 5 23 6 6 

Kennesaw N/A 95.2 91.5 85.2 82.4 N/A 5 16 7 4 

Tucker 102.2 95.5 91.4 89.0 88.6 6 7 11 8 6 

Conyers 104.1 96.6 91.2 87.2 87.8 5 3 12 4 9 

S.DeKalb 102.4 94.8 90.2 86.5 87.0 5 4 12 5 7 

Newnan N/A 92.2 88.8 84.2 82.6 N/A 4 8 3 7 

Lawrenceville 96.2 89.8 88.3 87.5 89.0 7 4 14 7 8 

Yorkville 93.6 90.5 88.2 84.4 81.3 7 6 17 7 5 

Fayetville 99.2 90.8 85.5 82.7 84.0 1 8 13 5 11 

The 2002 ozone season was further evaluated to ensure that it encompasses a variety of 

meteorological conditions, as required for baseline modeling. Based on findings from a 

Classification and Regression Tree (CART) analysis for Atlanta (ICF consulting, 2005), several 

ozone formation conditions are represented in the 2002 ozone season. Table 3-2 contains a 

summary of the various CART bins composed of days of elevated ozone concentrations (bins 15, 

19, 21, 26, 25, 8, 16, 9, 14). Moreover, the days used for RRF calculations represent most of 

these meteorological conditions, as demonstrated using modeling results for the Confederate 

Avenue site. These days were determined based on the 2002 modeling, using a threshold of 

85ppb for inclusion in RRF calculations. Reported here are the number of RRF days using both 

the 3x3 array surrounding the site grid cell and the site grid-cell itself (Tables 2-1 and 2-2 in the 

Attainment Test section). Both these cases indicate that the requirement of including at least 10 

days for RRF calculations was met (21 days using the 3x3 array, 12 days using the site grid cell). 
Table 3-2: Frequency of occurrence of CART bins during the 2002 Atlanta ozone season, along 

with the maximum 8-hour ozone and key meteorological parameters associated with each bin. 

Also provided is frequency of occurrence of the various bins among days used for calculating 

RRFs for the Confederate Avenue site. (RH – noon time relative-humidity; WS - 7:00AM4:00PM average wind-speed; DT900 – difference between 900mb and surface morning 

temperatures, WS850 – wind-speed at 850mb). The full CART analysis includes additional 

meteorological parameters not reported here (ICF, 2005). 

Bin # of 

days 

Avg. of 

max 8-hr 

O3 (ppb) 

Max. 

Temp. 

(°C) 

RH (%) WS 

(m/s) 

DT900 

(°C) 

WS850 

(m/s) 

RRF 

days 

(array) 

RRF 

days 

(cell) 

15 3 110.6 32.2 40.7 2.8 6.1 4.0 2 2 

19 1 108.1 33.3 50.0 2.5 5.2 3.1 1 1 

21 1 107.9 29.4 48.0 3.5 6.6 1.5 

26 2 104.0 33.6 46.0 2.8 7.6 5.1 2 2 

25 2 102.6 32.0 43.5 1.8 3.9 5.4 2 1 

8 3 89.4 33.7 44.7 4.0 5.0 5.3 1 1 

16 8 89.2 33.0 45.1 4.3 4.0 5.7 2 

9 6 88.9 31.5 50.3 3.1 1.2 3.8 1 

14 15 85.4 32.0 44.2 3.9 2.7 5.3 4 2 

12 4 78.7 28.0 50.5 4.9 2.7 7.7 1 

29 1 77.5 30.6 65.0 1.0 4.6 1.5 

7 7 76.6 32.6 46.9 4.6 1.5 6.9 2 1 

31 1 74.5 31.1 63.0 5.7 0.0 3.6 

18 3 74.2 30.7 65.0 2.6 -0.5 4.1 1 1 

5 10 70.9 26.5 47.5 3.6 4.3 4.1 

6 7 70.2 32.9 50.6 3.8 2.6 5.5 2 1 

2 5 67.8 31.6 55.4 3.0 2.6 6.9 

10 4 59.6 29.7 65.5 2.6 0.2 4.6 

3 6 57.5 32.1 57.7 3.1 0.3 5.6 

11 8 48.5 27.7 69.0 3.8 -0.7 9.6 

13 2 46.6 25.3 90.5 3.7 -5.0 11.3 

1 54 46.2 25.9 69.7 4.2 -1.0 8.9 

The US EPA also recommends selecting episodes for which there are extensive air quality and 

meteorological measurements. The Atlanta area hosts eleven ozone monitors, five NOx sites, 

four PAMS sites, two SEARCH sites, numerous meteorological stations with surface 

measurements, and a National Weather Service site with aloft measurements. The data from all 

these sites are regularly available. Hence, this criterion is fulfilled regardless of which ozone 

season is modeled. 

It is also important to factor in secondary criteria suggested by US EPA. As 2002 serves as the 

base case inventory year for model performance evaluations (using the 2002 NEI), the use of the 

2002 as the baseline-modeling year is preferable, as this would result in fewer emission estimates 

and fewer air quality model simulations. Additionally, the selection of 2002 as the base case 

season also fulfills the secondary criteria that states give preferential treatment of previously 

modeled episodes. Through GA EPD’s collaboration with the Visibility Improvement, State and 

Tribal Association of the Southeast Regional Planning Organization (VISTAS RPO), the 2002 

calendar year has already been modeled as the base-case year for regional haze SIPs. Though 

VISTAS modeling was originally geared mainly towards regional haze and PM2.5, the modeling 
can still be utilized for ozone. The VISTAS modeling employs “one atmosphere” modeling, or 

modeling of all atmospheric constituents including particulate matter (PM) and ozone. This 

modeling is done in parallel to capture interactions between various compounds. Since ozone, 

along with PM2.5 and regional haze, is being modeled as part of the VISTAS modeling efforts, its 

data can easily be extracted from the modeling results. 

The episode selection process has considered meteorological variability, ozone concentrations, as 

well as regional collaboration efforts. In summary, GA EPD believes 2002 meets all the 

requirements to be used as the baseline modeling year.
4.0 Models and Modeling Configurations

GA EPD intends to utilize the same configuration of regional meteorological, emissions 

processing, and photochemical air quality models used by the VISTAS PM/regional haze 

modeling study. The underlying science behind each component of the overall modeling system 

are identified and discussed briefly in this section. Although the configuration of each of the 

modeling components has been selected as the culmination of intensive study by VISTAS, there 

remains the possibility that certain algorithms and parameter settings may still be updated prior 

to the running of the final annual 2002 base case simulation and subsequent model performance 

testing. 

The GA EPD modeling team will remain in close contact with the VISTAS, as well as 

other RPO regional modeling initiatives throughout the Atlanta attainment modeling study, to 

determine appropriate refinements to the model codes, input databases, and post-modeling 

analysis procedures. Notable limitations of the models, relevant to their intended purpose in this 

attainment modeling analysis, will also be evaluated in detail. 

4.1. Recommended Models 

Based on extensive research of available documentation of the VISTAS Regional Haze 

modeling analysis, it has been determined by GA EPD that the Atlanta attainment modeling 

should utilize the following suite of models: 

• MM5: The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5) is a nonhydrostatic, 

prognostic meteorological model routinely used for urban- and regional-scale 

photochemical, fine particulate matter, and regional haze regulatory modeling studies. 

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

is an emissions modeling system that generates hourly gridded speciated emission 

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

photochemical grid models. 

• CMAQ: USEPA’s Models-3/ Community Multiscale Air Quality (CMAQ) 

modeling system is a “One-Atmosphere” photochemical grid model capable of 

addressing ozone, particulate matter, visibility, and acid deposition at regional scale 

for periods up to one year. 

4.2. MM5 – Mesoscale Prognostic Model 

Over the past decade, researchers at the Pennsylvania State University (PSU) and the 

National Center for Atmospheric Research (NCAR) have collaborated in the refinement and 

extension of the PSU Mesoscale Meteorological Model leading to the current version of the 

system, MM5 (version 3.6, MPP). Originally developed in the 1970s at PSU and first 

documented by Anthes and Warner (1978), the MM5 modeling system has maintained its 

status as a state-of-the-science model through enhancements provided by a broad user 

community (e.g., Chen and Dudhia, 2001; Stauffer and Seaman, 1990, 1991; Xiu and Pleim, 

2000). The MM5 modeling system is routinely employed in forecasting projects as well as 
refined investigations of severe weather. Utilization of MM5 within air quality applications is 

also a common practice. In recent years, the MM5 modeling system has been successfully 

applied in continental scale annual simulations for the years 1996 (Olerud et al., 2000), 2001 

(McNally and Tesche, 2003), and 2002 (Johnson, 2003). Due to its ongoing scientific 

development worldwide, extensive historical applications, broad user community support, 

public availability, and established performance record compared with other applicationsoriented prognostic models, MM5 has been selected as the preferred meteorological model for 

this effort. This section provides an overview of the MM5 and its data input requirements. 

4.2.1 MM5 Overview 

The non-hydrostatic MM5 model (Dudhia, 1993; Grell et al., 1994) is a threedimensional, limited-area, primitive equation, prognostic model that has been used widely in 

regional air quality model applications (Seaman, 2000). The basic model has been under 

continuous development, improvement, testing, and has been openly peer-reviewed for more 

than 20 years (Anthes and Warner, 1978; Anthes et al., 1987). It has been used world-wide by 

hundreds of scientists for a variety of mesoscale studies, including cyclogenesis, polar lows, 

cold-air damming, coastal fronts, severe thunderstorms, tropical storms, subtropical easterly 

jets, mesoscale convective complexes, desert mixed layers, urban-scale modeling, air quality 

studies, frontal weather, lake-effect snows, sea-breezes, orographically induced flows, and 

operational mesoscale forecasting. 

MM5 is based on the prognostic equations for three-dimensional wind components (u – 

zonal wind component, v – meridional wind component, and w – vertical wind component), 

temperature, water vapor mixing ratio, and the perturbation pressure. Use of a constant 

reference-state pressure increases the accuracy of the calculations near steep terrain. The model 

uses an efficient semi-implicit temporal integration scheme and has a nested-grid capability that 

can use up to ten different domains of arbitrary horizontal and vertical resolution. The 

interfaces of the nested grids can be either one-way or two-way interactive. The model is also 

capable of using a hydrostatic option, if desired, for coarse-grid applications. 

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

coordinate similar to that used in many operational and research models. In the non-hydrostatic 

MM5 (Dudhia, 1993), the sigma levels are defined according to the initial hydrostaticallybalanced reference state so that the sigma levels are also time-invariant. The gridded 

meteorological fields produced by MM5 are directly compatible with the input requirements of 

“one atmosphere” air-quality models using this coordinate (e.g., CMAQ). MM5 fields can be 

easily used in other regional air quality models with different coordinate systems (e.g., 

Comprehensive Air Quality Model with Extensions - CAMx) by performing a vertical 

interpolation, followed by a mass-conservation re-adjustment. 

Distinct planetary boundary layer (PBL) parameterizations are available for air-quality 

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

momentum. These parameterizations employ various surface energy budget equations to 

estimate ground temperature based on the insolation, atmospheric path length, water vapor, cloud 

cover, and longwave radiation. The surface physical properties of albedo, roughness length, 

moisture availability, emissivity and thermal inertia are defined as functions of land-use for 

numerous categories via a look-up table. One scheme uses a first-order eddy diffusivity 
formulation for stable and neutral environments and a modified first-order scheme for unstable 

regimes. The other scheme uses a prognostic equation for the second-order turbulent kinetic 

energy while diagnosing the other key boundary layer terms. 

Initial and lateral boundary conditions are specified from mesoscale three-dimensional 

analyses performed at 12-hour intervals on the outermost grid mesh selected by the user. 

Additional surface fields are analyzed at three-hour intervals. A Cressman-based technique is 

used to analyze standard surface and radiosonde observations, using the National Meteorological 

Center's (NMC) spectral analysis as a first guess. The lateral boundary data are introduced into 

MM5 using a relaxation technique applied in the outermost five rows and columns of the most 

coarse grid domain. 

A major feature of the MM5 is its use of state-of-science methods for Four Dimensional 

Data Assimilation (FDDA). The theory underlying this approach and details on how it has been 

applied in a variety of applications throughout the country are described in depth elsewhere 

(Stauffer and Seaman, 1990, 1991; Seaman et al., 1992, 1997). 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 

studies have involved comparisons with other prognostic models such as the Regional 

Atmospheric Modeling System (RAMS) and the Systems Application International Mesoscale 

Model. The MM5 enjoys a far richer application history in regulatory modeling studies 

compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 

recent regional scale air quality application studies since 1995, it has generally been found that 

the MM5 model tends to produce somewhat better photochemical model inputs than alternative 

models. For these and other reasons set forth in the MM5 modeling protocol developed by the 

contractor performing the meteorological modeling, Barons Advanced Meteorological Systems, 

LLC (BAMS) (Olerud and Sims, 2003), MM5 was selected as the meteorological modeling 

system for this study. 

4.2.2 MM5 Configuration 

Based on the extensive sensitivity testing carried out by Olerud and Sims (2003), the 

MM5 (version 3.6, MMP) configuration to be used by BAMS modelers will consist of the 

following: 

• Nested 36/12 kilometer (km) grids, with 34 vertical layers 

• Two way nesting, no feedback 

• Initialization and boundary conditions from Eta analysis fields 

• Pleim-Xiu (PX) soil model 

• Asymmetric Convective Mixing (ACM) PBL model 

• Kain-Fritsch 2 cumulus parameterization 

• Mixed phase (Reisner 1) cloud microphysics 

• Rapid Radiative Transfer Model radiation 

• Snow effect turned on 

• ETA model sea surface temperature 

• 24-category United States Geological Survey (USGS) vegetation datasets 

• Thermal roughness by the Garratt method 

• Standard FDDA analysis nudging on 36 km and 12 km grid nests 
4.2.3 MM5 Evaluation 

The MM5 modeling results will be evaluated using plots and statistical analyses to 

determine if the model performance is adequate for the air quality modeling exercise. Some of 

the plots and statistics to be generated include: 

• Spatial plots of model predictions with the appropriate observations overlaid. These 

will provide a visual to determine how well such meteorological parameters as 

temperature, mixing ratios, and winds are being captured by the model. 

• Graphical statistical plots for surface temperature, mixing ratio, wind speeds, wind 

direction, and cloud cover. These will include time series of modeled/observed 

means, bias/error, and index of agreement. 

• Daily accumulated precipitation plots of modeled versus observed. 

• Tabular statistics for temperature, winds, mixing ratio, and cloud cover for various 

domains. 

• Comparison of satellite versus modeled cloud images. 

• Comparison of surface analysis maps to the MM5 pressure/wind maps 

• Comparison of profiler observations with modeled winds 

4.2.4 Meteorological Data 

Meteorological data are being generated using the MM5 prognostic meteorological model 

by BAMS. BAMS is operating the MM5 at 5-day increments for 2002 on the 36 km and 12 km 

grid with a 14-day spin up period for the end of December 2001. The meteorological 

observations to be used for statistics come primarily from University Corporation for 

Atmospheric Research’s (UCAR’s) ds472.0 archive. These data are quality controlled and 

converted to NetCDF format, thus allowing the data to be visualized on the model fields via 

Package for Analysis and Visualization of Environmental data (PAVE). Due to the unreliability 

in precipitation values in the UCAR dataset, precipitation statistics are calculated from the 24-h 

gridded accumulations available from the Climate Prediction Center (CPC). However, these 

fields undergo grid transformation to match our 36-km and 12-km domains from their original 

0.25- degree resolution. The statistics are only calculated over cells that MM5 deems to be land 

since the CPC analyses are derived primarily from rain gauges. 

For aloft analyses, standard sounding observations from the National Center for 

Environmental Predictions (NCEP) ds353.4 archive are processed. These observations are 

quality controlled and used to produce model/observation skewT sounding plots for selected 

sites. Additionally, the observations are integrated into sigma levels that match the MM5 

specifications and subsequently can be statistically analyzed for performance at sigma levels 9, 

17, and 22 (~500m, ~1600m, ~3400m, respectively). Qualitative profiler plots showing 
model/observed hourly winds are also created based upon the data stored at the Forecast Systems 

Lab. 

4.3 SMOKE Emissions Modeling System 

The SMOKE Emissions Processing System Prototype was originally developed at the 

Micro-computing Center of North Carolina (Coats, 1995; Houyoux and Vukovich, 1999). As 

with most “emissions models,” SMOKE is principally an emission processing system and not a 

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

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

provide an efficient, modern tool for converting emissions inventory data into the formatted 

emission files required by an air quality simulation model. For mobile sources, SMOKE actually 

simulates emissions rates based on input mobile-source activity data, emission factors and 

outputs from transportation travel-demand models. 

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

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

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

community. The sparse matrix approach utilized throughout SMOKE permits both rapid and 

flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series 

of matrix calculations instead of less efficient algorithms used in previous systems. The 

processing is flexible because the processing steps of temporal projection, controls, chemical 

speciation, temporal allocation, and spatial allocation have been separated into independent 

operations wherever possible. The results from these steps are merged together at a final stage of 

processing. 

SMOKE supports area, mobile, fire and point source emission processing and includes 

biogenic emissions modeling through a rewrite of the Biogenic Emission Inventory System, 

version 3 (BEIS3). SMOKE has been available since 1996, and it has been used for emissions 

processing in a number of regional air quality modeling applications. In 1998 and 1999, 

SMOKE was redesigned and improved with the support of the USEPA for use with USEPA's 

Models-3/CMAQ. The primary purposes of the SMOKE redesign were support of: (a) 

emissions processing with user-selected chemical mechanisms and (b) emissions processing for 

reactivity assessments. 

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

modeling system (Seppanen, 2003). The model supports a variety of input formats from other 

emissions processing systems and models including the Inventory Data Analyzer (IDA), 

Emissions Modeling System – 2003 (EMS), and the Emissions Preprocessor System 2.x (EPS). 

It supports both gridded and county total land use scheme for biogenic emissions modeling. 

Although it is not necessary for our purposes, SMOKE can accommodate emissions files from 

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

Recent computational improvements to SMOKE include: 

• Enhanced disk space requirements compared with other emissions processing 

software 
• Run-time memory allocation, eliminating any need to recompile the programs for 

different inventories, grids, or chemical mechanisms 

• Updated Input/Output Applications Programming Interface (I/O API) libraries 

A number of science features have been incorporated into the latest version of SMOKE 

(version 2.0), including: 

• Any chemical mechanism can be used to partition pollutants to model species, as 

long as the appropriate input data are supplied 

• Integration with the MOBILE6.2 on-road mobile source emissions model 

including link based processing 

• Support of plume-in-grid processing 

• Integration of the BEIS3 emissions factors in SMOKE 

Notable features of SMOKE from an applications standpoint include: 

• Improved control strategy input formats and designs 

• Control strategies can include changes in the reactivity of emitted pollutants, a 

useful capability, for example, when a solvent is changed in an industrial process 

• No third party software is required to run SMOKE, although some input file 

preparation may require other software 

• Integration with Models-3 file formats and settings 

• Improved data file formats 

• Support of various air quality model emissions input formats (e.g., CMAQ, 

MAQSIP, UAMIV, UAM-V, REMSAD and CAMx) 

• Enhanced quality assurance pre- and post-processing 

• Fully integrated with Models-3, which will provide the SMOKE Tool for 

SMOKE input file preparation 

• Enhanced treatment of growth and control factors 

• Improved emissions reporting and Quality Assurance (QA) capabilities 

• Improved temporal allocation 

The Carolina Environmental Program at the University of North Carolina is continuing model 

development activities with SMOKE. The emissions modeling will employ the SMOKE version 

2.0, released on September 30, 2003. The SMOKE executables, scripts and databases may be 

downloaded through the Community Modeling and Analysis (CMAS) center’s Model 

Clearinghouse. 

4.4 CMAQ Modeling System 

4.4.1 CMAQ Overview 

For more than a decade, USEPA has been developing the Models-3 CMAQ modeling 

system with the overarching aim of producing a “One-Atmosphere” air quality modeling system 

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

common platform (Dennis et al., 1996; Byun et al., 1998a; Byun and Ching, 1999; Pleim et al., 

2003). The original justification for the Models-3 development emerged from the challenges 

posed by the 1990 CAAA and USEPA’s desire to develop an advanced modeling framework for 
“holistic” environmental modeling utilizing state-of-science representations of atmospheric 

processes in a high performance computing environment (Ching et al., 1998). 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 (Byun et al., 

1998b). 

CMAQ consists of a core Chemical Transport Model (CTM) and several pre-processors 

including the Meteorological-Chemistry Interface Processor (MCIP), initial and boundary 

conditions processors (ICON and BCON), and a photolysis rates processor (JPROC). USEPA 

is continuing to improve and develop new modules for the CMAQ model and typically provides 

a new release each year. In the past, USEPA has also provided patches for CMAQ as errors are 

discovered and corrected. More recently, USEPA has funded the CMAS center to support the 

coordination, update and distribution of the Models-3 system. 

Another reason for choosing CMAQ as the atmospheric model is the ability to do oneatmospheric modeling. Since GA EPD will be using the same modeling exercise for both the 

ozone and PM2.5 attainment demonstrations SIPs, as well as the regional haze SIP, having a 

model that can handle both ozone and particulate matter is essential. A number of features in 

CMAQ’s theoretical formulation and technical implementation make the model well-suited for 

annual PM modeling. In CMAQ, the model approach has been adapted to dynamically represent 

the PM size distribution using three log-normal modes (2 fine and 1 coarse). Transfer of mass 

between the aerosol and gas phases is assumed to be in equilibrium and all secondary aerosols 

(sulfate, nitrate, secondary organic aerosols) are assumed to be in the fine modes. The 

thermodynamics of inorganic aerosol composition are treated using the ISORROPIA module. 

Aerosol composition is coupled to mass transfer between the aerosol and gas phases. For 

aqueous phase chemistry, the Regional Acid Deposition Model (RADM) is currently employed. 

This scheme includes oxidation of sulfur dioxide (SO2) to sulfate by ozone, hydrogen peroxide, 

oxygen catalyzed by metals and radicals. The impact of clouds on the PM size distribution is 

treated empirically. For wet deposition processes, CMAQ uses the RADM/Regional Particulate 

Model approach. Particle dry deposition is included as well. CMAQ contains three options for 

treating secondary organic aerosol (SOA), latest being the Secondary Organic Aerosol Model 

(SORGAM) that was updated in August 2003 to be a reversible semi-volatile scheme whereby 

VOCs can be converted to condensable gases that can then form SOA and then evaporate back 

into condensable gases depending on atmospheric conditions. 

4.4.2 CMAQ Configuration 

GA EPD propose to run CMAQ (version 4.5). The model would be set up and exercised 

on a nested 36/12 km grid domain, employing one-way grid nesting. That is, boundary 

conditions for the 12 km grid simulation are extracted from the 36 km run using the CMAQ 

BCON processor. A total of 19 vertical layers would be implemented, extending up to a region 

top of 100 mb (approximately 15 km above ground level). 

The Piecewise Parabolic Method advection solver would be used along with the spatially 

varying (Smagorinsky) horizontal diffusion approach and K-theory for vertical diffusion. MM5 

meteorological output based on the Pleim-Xiu Land-Surface Model (LSM) and the ACM PBL 

scheme will be used, and the recently updated CMAQ MCIPv2.3 would process the MM5 data 

using the "pass through" option. The Carbon Bond version 4 (CB4) gas-phase, RADM aqueous-
phase, and AERO3/ISORROPIA aerosol chemistry schemes will be used. Treatment of 

reversible secondary organic aerosols would be simulated by the SORGAM implementation in 

CMAQ (version 4.4). 

Testing completed with VISTAS evaluated three photochemical mechanisms: CB4, CB4-

2002 and SAPRC99. CB4-2002 produced nearly identical results as CB4 but took much longer 

to run since it is only implemented in the slower SMVGEAR chemistry solver, compared to CB4 

that is also implemented in the faster Euler Backward Iterative chemistry solver. Thus, CB4-

2002 was dropped from consideration. Comparisons of CB4 and SAPRC99 found they produced 

mostly similar but different model performance. However, no one mechanism performed better 

than any other mechanism across all species, sites, and periods. The testing only evaluated the 

mechanism’s base case performance, not their response to emission reductions. Given that CB4 

runs twice as fast as SAPRC99, the CB4 mechanism was chosen for use. 

4.4.3 Initial and Boundary Condition Data 

The CMAQ default Initial Concentrations (ICs) will be used along with a ~15 day spin 

up period to eliminate any significant influence of the ICs. The CMAQ Boundary Conditions 

(BCs) for the initial simulations will be based on seasonal averages of 3-hour 2001 GEOSCHEM global simulation model output. VISTAS and other Regional Planning Organizations 

(RPOs) are finding a 2002 GEOS-CHEM simulation that would be used to define days specific 

high time resolved (e.g., 3-hourly) CMAQ BCs. 

4.5 Model Limitations 

All mathematical models possess inherent limitations owing to the necessary 

simplifications and approximations made in formulating the governing equations, implementing 

them for numerical solution on fast computers, and in supplying them with input datasets and 

parameters that are themselves approximations of the full state of the atmosphere and emissions 

processes. The more important limitations of the various modeling systems to be employed are 

noted in this section. 

4.5.1 MM5 

Four different configurations of the MM5 LSM and PBL were evaluated. Depending on 

the meteorological variable (e.g., winds, temperature, moisture) and location (e.g., mountains, 

coastal, east, west) different LSM_PBL configurations performed better. The PX_ACM 

LSM_PBL configuration was selected because it was consistently near the top performing 

configuration in the southeastern US across variables and locations and was never the worstperforming configuration. However, there are numerous limitations in the MM5 with the LSM 

and PBL treatment being some of the most important. The MM5 PX_ACM frequently predicts 

very low PBL heights that can appear as "holes" in the spatial distribution of PBL heights that do 

not appear physically realistic and may affect air quality modeling. Although the MM5 

PX_ACM configuration model performance in the southeastern US mostly met performance 

benchmarks, the performance was much worse in the western US. In addition, there is a 

stochastic component of real world meteorology that is not captured by MM5. For example, for 

some ozone episodes stagnation is an important attribute that MM5 fails to simulate well as it 
tries to organize the flow fields. However, the MM5 model represents approximately 20 years of 

development by various researchers. 

4.5.2 SMOKE 

In early testing, a number of undocumented features of the SMOKE 1.5b version 

necessitated re-runs of the emissions processing software to overcome errors and/or ambiguities 

in source documentation and QA reporting. It is unclear whether similar conditions will be 

encountered with the SMOKE 2.0 release. As a full software release, rather than a "beta" 

version, SMOKE 2.0 is expected to be more robust and more fully-documented than the 

SMOKE 1.5b release. However, with any newly-released software system, there is the 

potential for errors and/or ambiguities to affect the emissions modeling schedule. Should 

problems arise or issues be encountered which would require additional SMOKE runs or 

potential SMOKE modifications or alternate modeling methods, NCDAQ and SCDHEC will 

immediately notify stakeholders and make recommendations for resolving the issues. Upon 

receipt of technical direction from the stakeholders, appropriate corrective action will be taken. 

Features are continuing to be developed in the SMOKE emissions model. As it is not as 

mature as some other emission models (e.g., EMS, EPS, etc.), SMOKE does not include as many 

features. NCDAQ and SCDHEC will keep abreast of SMOKE development activities to identify 

new features that will assist in the emissions modeling. 

4.5.3 CMAQ 

Like all air quality models, a major limitation of CMAQ is the input for emissions, 

meteorological, and IC/BC data. Key science limitations in the model itself include the nitrate 

formation chemistry. Testing found the CMAQ nitrate performance suspect with winter 

overestimations and summer underestimations. Other science limitations in the current version 

of CMAQ include inadequate treatment of sea salt and the assumption that all secondary PM is 

in the fine mode. Lack of any two-way grid nesting limits the ability of the model to properly 

resolve point source plumes or urban photochemistry. Other limitations of CMAQ include its 

computational requirements, such as the need for excessive disk space. 

4.6 Model Input Requirements 

Each of the modeling system components has significant database requirements. These 

data needs fall into two categories: those required for model setup and operation, and those 

required for model evaluation testing. The main input data base requirements for the 

meteorological, emissions, and air quality models are identified in the following section. 

4.6.1 MM5 

The databases required for setting up, exercising, and evaluating the MM5 model for the 

2002 season consist of various fixed and variable inputs. 

• Topography: High resolution (e.g., 30 sec to 5 min) topographic information derived 

from the Geophysical Data Center global datasets from the NCAR terrain databases are 

available for prescribing terrain elevations throughout the 36 km and 12 km grid domain. 
• Vegetation Type and Land Use: Vegetation type and land use information on the 36 km 

grid may be developed using the PSU/NCAR 10 min. (~18.5 km) databases while for the 

12 km grids, the USGS data are available. 

• Atmospheric Data: Initial and boundary conditions to the MM5 may be developed from 

operationally analyzed fields derived from the NCEP ETA (40 km resolution) following 

the procedures outlined by Stauffer and Seaman (1990). These 3-hr synoptic-scale 

initialization data include the horizontal wind components (u and v), temperature, and 

relative humidity at the standard pressure levels, plus sea-level pressure and ground 

temperature. Here, ground temperature represents surface temperature over land and seasurface temperature over water. 

 

• Water Temperature: Water temperatures required on both 36 km and 12 km grids can be 

derived from the ETA skin temperature variable. These temperatures are bi-linearly 

interpolated to each model domain and, where necessary, filtered to smooth out 

irregularities. 

• Clouds and Precipitation: While the non-hydrostatic MM5 treats cloud formation and 

precipitation directly through explicit, resolved-scale, and parameterized sub-grid scale 

processes, the model does not require precipitation or cloud input. The potential for 

precipitation and cloud formation enters through the thermodynamic and cloud processes 

formulations in the model. The only precipitation-related input required is the initial 

mixing ratio field that is developed from the National Weather Service (NWS) and NMC 

datasets previously discussed. 

• Multi-Scale FDDA: The standard "multi-scale" data assimilation strategy to be used on 

the 36 km and 12 km grids will objectively analyze three-dimensional fields produced 

every 3 hours from the NWS rawinsonde wind, temperature, and mixing ratio data, and 

similar analyses are generated every three hours from the available NWS surface data. 

4.6.2 SMOKE 

The databases required to set up and operate SMOKE are as follows: 

• Area source emissions in IDA format 

• Off-road mobile source emissions in IDA format 

• Stationary point source emissions in IDA format 

• Utility emissions 

o Continuous Emissions Monitoring (CEM) emissions, day specific for actual 

2002 

o 5-year average CEM emissions, day specific for typical 2002 

o Based on Integrated Planning Model (IPM) modeling for future year 

• Wildfire emissions 

o Day specific for actual 2002 

o Multi-year average for typical year 2002 and future year 

• On-road motor vehicle activity data 
• MOBILE6.2 input parameters 

Also required for annual modeling are data files specific for: 

• Temporal allocation 

• Spatial allocation 

• Speciation 

4.6.3 CMAQ 

The CMAQ CTM requires the following inputs: 

 

• Three-dimensional hourly meteorological fields that will be generated by the CMAQ 

MCIP2.3 processing of the BAMS MM5 output 

• Three-dimensional hourly emissions generated by SMOKE 

• Initial conditions and boundary conditions (IC/BC) 

• Topographic information 

• Land use categories 

• Photolysis rates generated by the CMAQ JPROC processor 
5.0 Grid Specifications and Modeling Domains 

This chapter summarizes the model domain definitions including the model domain, 

resolution, map projections and nesting schemes for high resolution sub-domains. 

5.1 Horizontal Modeling Domain 

A coarse grid continental US domain with a 36 km horizontal grid resolution will be used 

as the outer grid domain for MM5 modeling. The CMAQ domain is nested within the MM5 36 

km domain. Figure 5-1 shows the MM5 horizontal domain as the outer most, blue grid with the 

CMAQ 36 km domain nested in the MM5 domain. To achieve finer spatial resolution in the 

VISTAS states, GA EPD will also use a one-way nested high resolution grid with a 12 km grid 

resolution. Figure 5-2 shows the 36 km CMAQ continental grid and the high resolution, nested 

12-km grid in the VISTAS states. Figure 5-3 shows in more detail the 12 km grid for the 

VISTAS region. 

Figure 5-1: The MM5 horizontal domain is the outer most, blue grid, with the CMAQ 36 km 

domain nested in the MM5 domain. 
Figure 5-2: The 36 km CMAQ continental grid and the high resolution, nested 12-km grid over 

the VISTAS states. 

Figure 5-3: A more detailed view of the 12 km grid over the VISTAS region. 
Both MM5 and CMAQ employ the RPO unified grid definition for the 36 km continental 

domain. The RPO unified grid consists of a Lambert-Conformal map projection using the map 

projections parameters listed in Table 5-1. 

Table 5-1: RPO Unified Grid Definition.

PARAMETER VALUE 

projection Lambert-conformal 

1st true latitude (alpha) 33 degrees 

2nd true latitude (beta) 45 degrees 

x center - 97 degrees 

y center 40 degrees 

The MM5 36 km grid includes 164 cells in the east-west dimension by 128 cells in the 

north-south dimension. The CMAQ 36 km grid includes 148 cells in the east-west dimension 

and 112 cells in the north-south dimension. Since the MM5 coarse grid is also nested in the Eta 

grid, there is a possibility of boundary effects near the MM5 boundary that occur as the Eta 

meteorological variables are being simulated by MM5 and must come into dynamic balance with 

MM5’s algorithms. Thus, a larger MM5 domain was selected to provide a buffer of 8 to 9 grid 

cells around each boundary of the CMAQ 36 km domain. This is designed to eliminate any 

errors in the meteorology from boundary effects in the MM5 simulation at the interface of the 

MM5 and Eta grids. The buffer region used here exceeds the USEPA suggestion of at least 5 

grid cell buffer at each boundary. 

Table 5-2 lists the number of rows and columns and the definition of the X and Y origin 

(i.e., the southwest corner) for the 36 km and 12 km grids for both MM5 and CMAQ. Note that 

the CMAQ grid is rotated 90 degrees relative to the MM5 grid, so rows and columns are 

reversed. In Table 5-2 “Dot” refers to the grid mesh defined at the vertices of the grid cells while 

“cross” refers to the grid mesh defined by the grid cell centers. Thus, the dimension of the dot 

mesh is equal to the cross mesh plus one. Finally, note that the grid definition for the CMAQ 

MCIP and CMAQ Chemical Transport Model (CCTM) are identical. 

Table 5-2: Grid Definitions for MM5 and CMAQ. 

MODEL COLUMNS DOT 

(CROSS)

ROWS DOT 

(CROSS)

XORIGIN YORIGIN

MM5 36km 129 (128) 165 (164) -2952000 -2304000 

CMAQ 36km 149 (148) 113 (112) -2736000 -2088000 

MM5 12km 190 (189) 181 (180) 7200 -1656000 

CMAQ 12km 169 (168) 178 (177) 108000 -1620000 

5.2 Vertical Modeling Domain 

The CMAQ vertical structure is primarily defined by the vertical grid used in the MM5 

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

pressure, using 34 layers that extend from the surface to the 100 mb. Table 5-3 lists the layer 
definitions for both MM5 and for CMAQ. A layer averaging scheme is adopted for CMAQ to 

reduce the computational cost of the CMAQ simulations. The effects of layer averaging were 

evaluated in conjunction with the VISTAS modeling effort and were found to have a relatively 

minor effect on the model performance metrics when both the 34-layer and a 19-layer CMAQ 

models were compared to ambient monitoring data. 

Table 5-3: Vertical Layer Definition For MM5 Simulations (Left Most Columns), And Approach 

For Reducing CMAQ Layers By Collapsing Multiple MM5 Layers (Right Columns). 

MM5 CMAQ 19L

Layer Sigma Pres. 

(mb) 

Height (m) Depth 

(m) 

Layer Sigma Pres 

.(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 
6.0 Development of Emissions Inventories 

There are five different emission inventory source classifications; stationary point sources 

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

point sources are those sources that emit greater than a specified tonnage per year and the data is 

provided at the facility level. Stationary area sources are those sources whose emissions are 

relatively small but due to the large number of these sources, the collective emissions could be 

significant (i.e., dry cleaners, service stations, etc.) Off-road mobile sources are equipment that 

can move but do not use the roadways, i.e., lawn mowers, construction equipment, railroad 

locomotives, aircrafts, etc. On-road mobile sources are automobiles, trucks, and motorcycles 

that use the roadway system. Biogenic sources are emissions from natural sources, such as trees, 

crops, grasses and natural decay of plants. 

Emission estimates for stationary point and area sources, as well as for off-road mobile 

sources are calculated and formatted for processing through the SMOKE emissions processing 

system, which formats the data into air quality model ready files. On-road mobile source 

emissions are estimated within the SMOKE system, which uses the USEPA’s MOBILE6.2 

model, with modeling meteorology and various mobile inputs. The biogenic emissions are also 

estimated within the SMOKE system, using the USEPA’s BEIS model, with modeling 

meteorology. 

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

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

that correspond to the meteorological data, for this modeling effort is 2002. These emissions are 

used for evaluating the air quality model performance. 

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

to the actual base year, however for sources that may have significant changes from year to year 

a more typical emission value is used. In this modeling effort, typical emissions were developed 

for the electric generating units and the wildland fire emissions. The air quality modeling results 

using these emissions are used in calculating the relative reduction factors used in the attainment 

demonstration test. 

The future year base inventory is an inventory developed for some future year for which 

attainment of the ozone standard is needed. For this modeling project, the future year inventory 

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

base inventory that control strategies and sensitivities are applied to determine what controls, to 

which source classifications, must be made in order to attain and maintain the ozone standard. 

In the sections that follow, the inventories used for each source classifications are 

discussed. 

6.1 Point Source Emissions 

The point source emissions will be separated into electric generating units (EGU) and 

non-EGU categories. The reason for splitting the point source inventory is that the EGU sources 

account for the majority of the point source NOx emissions and hour specific data is available for 

these sources through the USEPA’s acid rain database. Using this more refined data will help 
improve the air quality modeling performance. Annual emissions will be used for the non-EGU 

sources. 

All point sources will be spatially allocated in the domain based on the stationary source 

geographic coordinates. If a point source is missing its latitude/longitude coordinates, the source 

will be placed in the center of its respective county. 

6.1.1 Electric Generating Units 

Actual Base Year Inventory

For EGU sources with USEPA reported 2002 CEM data or with 2002 hourly emissions 

provided by stakeholders, actual hourly data will be used. For the sources where USEPA CEM 

data are utilized, NOx, SO2, and heat input-based hour-specific profiles were developed and 

applied to NOx, SO2, and all other emissions, respectively. The annual emission values that have 

been provided will be maintained, but will be distributed using hourly to annual profiles. For 

sources where hour-specific data was provided by stakeholders, this data will be substituted for 

USEPA CEM-based emissions and distributions. 

To temporally allocate the remaining EGU point sources, the NOx, SO2, and heat input 

data will be collected from the 2002 CEM datasets, and used to develop unit-level temporal 

distributions. The hourly, day of week, and monthly specific temporal profiles will be used in 

conjunction with the emissions inventory supplied emissions data to calculate hourly EGU 

emissions by unit. 

Typical Base Year Inventory

Since the NOx emissions from EGU sources are a significant part of the emissions 

inventory, a typical base year emissions inventory was developed for these sources to avoid 

anomalies in emissions due to variability in meteorology, economic and outage factors in 2002. 

This approach is consistent with the USEPA’s modeling guidance. 

To develop a typical year 2002 emissions inventory for EGU sources, for each unit the 

average CEM heat input for 2000 through 2004 was divided by the 2002 actual heat input to 

generate a unit specific normalizing factor. This normalizing factor was then multiplied by the 

2002 actual emissions. The heat inputs for the period 2000 through 2004 were used since the 

modeling current design values use monitoring data from this same 5-year period. 

If a unit was shutdown for an entire year during the 2000 through 2004 period, the 

average of the years the unit was operational was used. If a unit was shutdown in 2002, but not 

permanently shutdown, the emissions and heat inputs 2001 (or 2000) were used in the 

normalizing calculations. 

Future Base Year Inventory

As part of the VISTAS modeling, VISTAS and the Midwest Regional Planning 

Organization (MRPO) contracted with ICF Resources, L.L.C., to generate future year emission 

inventory for the electric generating sector of the contiguous US using the IPM. IPM is a 
dynamic linear optimization model that can be used to examine air pollution control policies for 

various pollutants throughout the contiguous US for the entire electric power system. The 

dynamic nature of IPM enables the projection of the behavior of the power system over a 

specified future period. The optimization logic determines the least-cost means of meeting 

electric generation and capacity requirements while complying with specified constraints 

including air pollution regulations, transmission bottlenecks, and plant-specific operational 

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

populate IPM with their own datasets. 

Since the modeling is based on the USEPA’s prior analyses for which detailed public 

documentation is available, a summary of only the incremental changes that were proposed by 

VISTAS and MRPO as part of this analysis are presented here. 

The VISTAS analysis is based on the USEPA modeling applications using IPM 

(V.2.1.6). As per the analytical needs of VISTAS and MRPO, the following changes were made 

to the underlying assumptions in the USEPA Base Case (V2.1.6): 

i) The underlying database in the VISTAS analysis is USEPA’s National Electric Energy 

Data System Database, with changes based upon the comments and technical directions 

from VISTAS and MRPO’s stakeholders. The changes focused on existing installations 

of NOx, SO2 and particulate matter controls, NOx emission rates, SO2 emission limits, 

capacity of existing units, heat rate and unit identifications of selected units in the 

VISTAS and MRPO regions. 

ii) The analysis covers the period between 2007 and 2030. To make the model size and run 

time tractable, IPM is run for a number of selected years within the study horizon known 

as run years. Each run year represents several calendar years in the study horizon, and all 

calendar years within the study horizon are mapped to their representative run years. 

Although results are only reported for the run years, IPM takes into account all years in 

the study horizon while developing the projections. 

iii) The Duke Power and Progress Energy control technology investment strategies for 

complying with North Carolina’s Clean Smokestacks Rule were explicitly hardwired in 

the analysis. 

iv) The USEPA’s Clean Air Interstate Rule (CAIR) rule implemented as part of this analysis 

is broadly consistent with the USEPA 40 CFR Parts 51 et. al., Supplemental Proposal for 

the Rule to Reduce Interstate Transport of Fine Particulate Matter and Ozone, proposed 

on June 10, 2004. Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, 

Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusetts, 

Michigan, Minnesota, Mississippi, Missouri, New Jersey, New York, North Carolina, 

Ohio, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, West Virginia, 

Wisconsin are the states affected by the CAIR SO2 and the CAIR annual NOx policies 

starting 2010. Connecticut is affected by an ozone season NOx policy. The CAIR plants 

affected by the annual NOx policy are capped at 1.6 million tons starting 2010 and 1.33 

million tons starting 2015. The power plants affected by the CAIR SO2 policy have to 

surrender two Title IV SO2 allowances for every ton of SO2 emitted starting 2010 and 

three Title IV SO2 allowances for every ton of SO2 emitted starting 2015. 
6.1.2 Non-Electric Generating Units 

2002 Base Year Inventory

For the non-EGU sources, the same inventory will be used for both the actual and typical 

base year emissions inventories. The non-EGU category will use annual emissions, which will 

be temporally allocated to month, day, and hour using source category code (SCC) based 

allocation factors. These factors will be based on the cross-reference and profile data supplied 

with the SMOKE 2.0 version. 

The non-EGU sources annual emissions will be the 2002 VISTAS inventory based on the 

2002 Consolidated Emissions Reporting Rule (CERR) submitted data for all states in the 

modeling domain unless a state or RPO provides updated data. 

Future Year Base Inventory

The general approach for assembling future year data is to use recently updated growth 

and control data consistent with USEPA’s CAIR analyses, supplement these data with available 

stakeholder input, and provide the results for stakeholder review to ensure credibility. To 

assemble growth/control data needed for the final 2009 inventories, the VISTAS contractor will 

perform the following activities: 

• Use the final 2002 VISTAS inventory as the starting point for the future base year 

inventory. 

• Obtain, review, and apply the most current growth factors developed by USEPA, based 

on forecasts from an updated Regional Economic Models, Inc. model (version 5.5) and 

the latest Annual Energy Outlook published by the Department of Energy. 

• Obtain, review, and apply any State-specific or sector-specific growth factors submitted 

by stakeholders. 

• Obtain information regarding sources that have shut down after 2002 and work with the 

states to determine if these sources should be removed from the future year inventory. 

• Obtain, review, and apply control assumptions that are expected to be in place by 2009. 

Controls Applied to the Non-EGU Inventory

1-hour Ozone SIP

Information about control programs for the 1-hour ozone non-attainment areas came from 

the report by E.H. Pechan and Associates entitled VOC and NOx Control Measures Adopted by 

States and Nonattainment Areas for 1999 NEI Base Case Emissions Projection Calculations. 

The report identified and compiled a listing of the VOC and NOx control measure programs 

expected to be implemented after 1999, as well as an estimate of their influence on projected 

emissions. Five nonattainment areas in the VISTAS region were included: Atlanta, 

Birmingham, metro Washington DC (including several counties in Virginia), Louisville, and 

northern Kentucky. 
Emission reductions requirements from NOx Reasonably Available Control Technology 

(RACT) in 1-hour Ozone SIP areas were implemented prior to 1999. These reductions should 

already be accounted for in the VISTAS 2002 inventory since the 2002 inventory was based on 

2002 actual emissions submitted by the States. 

NOx SIP Call

For non-EGU sources, Phase I of the NOx SIP call applies to large industrial boilers and 

turbines, and cement kilns. States in the VISTAS region affected by the NOx SIP call have 

developed rules for the control of NOx emissions that have been approved by the USEPA. The 

VISTAS contractor has reviewed the available state rules and guidance documents to determine 

the affected sources and ozone season allowances. 

The Phase II rule applies to large internal combustion engines, which are primarily used 

in pipeline transmission service at compressor stations. For the states in the VISTAS region, 

affected units were identified using the same methodology as was used by USEPA in the 

proposed Phase II rule (i.e., a large internal combustion engine is one that emitted, on average, 

more than 1 ton per day during 2002). The final rule reflects a control level of 82 percent for 

natural gas-fired internal combustion engines and 90 percent for diesel or dual fuel categories. 

Therefore, these control levels were applied to the identified sources. 

Maximum Achievable Control Technology Regulations

The USEPA anticipates reductions in PM and SO2 as a result of the Industrial 

Boiler/Process Heater maximum achievable control technology (MACT) standard. The methods 

used to account for these reductions are the same as those used for the Interstate Air Quality 

Transport Rule. Since the attainment demonstration is utilizing one atmosphere modeling, the 

reductions for these pollutants were accounted for. 

MACT requirements were also applied, as documented in the USEPA report entitled 

Control Packet Development and Data Sources, dated July 14, 2004. The point source MACTs 

and associated emission reductions were designed from Federal Register notices and discussions 

with USEPA’s Emission Standards Division staff. Emission reductions will be applied only for 

MACT standards with an initial compliance date of 2002 or greater, since effects from MACT 

with earlier compliance dates should already be accounted for in the 2002 base year inventory. 

The future year base inventory does not include the NOx co-benefit effects of the Gas 

Turbines or stationary Reciprocating Internal Combustion Engines MACT regulations, which 

USEPA estimates to be small compared to the overall inventory. 

Petroleum Refinery Initiative

Three refineries in the VISTAS region are affected by two October 2003 Clean Air Act 

settlements under the USEPA Petroleum Refinery Initiative. The refineries are: (1) the Chevron 

refinery in Pascagoula, Mississippi; (2) the Ergon refinery in Vicksburg, Mississippi; and (3) the 

Ergon refinery in Newell, West Virginia. Although these sources are not within Georgia, the 

expected emission reductions will be accounted for in the 2009 modeling. 
NOx RACT in 8-hour Ozone SIP

GA EPD will make every effort to include NOx RACT controls for 8-hour ozone 

nonattainment areas in the VISTAS region. However, since cost is a factor of consideration in a 

RACT determination, it may not be known at the time of the final modeling which sources will 

be subject to actual controls. 

Clean Air Interstate Rule

As stated in the preamble to the CAIR rule, the rule would not require or assume 

additional emission reductions from non-EGU boilers and turbines. 

6.2 Stationary Area Source Emissions 

Stationary area sources include sources whose emissions are relatively small but due to 

the large number of these sources, the collective emissions could be significant (i.e., combustion 

of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by 

multiplying an emission factor by some known indicator of collective activity, such as fuel 

usage, number of household or population. Thus, a variety of activity level data is collected, 

including, US Census economic data, forestry and agriculture agency data, and other data 

sources. Stationary area source emissions are estimated on the county level. 

Actual Base Year Inventory

In Georgia, the area source inventory was calculated by the VISTAS contractor based on 

the 2002 CERR submittal. The sources estimated by the contractor included emissions from 

animal husbandry, wildland fires, and particulate matter from paved and unpaved roads. For the 

other states within the modeling domain, the state supplied data or the CERR data was used. 

Area source categories estimated by GA EPD were identified from a list in the USEPA 

guidance document EPA-450/4-91-016, Procedures for the Preparation of Emission Inventories 

of Carbon Monoxide and Precursors of Ozone, and from the Emission Inventory Improvement 

Program (EIIP) technical reports. 

In general, emission factor estimation approaches were used to calculate area source 

emissions. Emission factors may be grouped as per capita emission factors; commodity 

consumption-related emission factors; and level-of-activity based emission factors. The 

emission factors were obtained from the Procedures for the Preparation of Emission Inventories 

of Carbon Monoxide and Precursors of Ozone, from the EIIP technical reports, or the USEPA's 

AP-42, Compilation of Air Pollutant Emission Factors, Fifth Edition. 

The emissions from area sources were estimated by multiplying an emission factor by the 

appropriate indicator of collective activity for each source category within the inventory area. 

An indicator is any parameter associated with the activity level of a source that can be correlated 

with the air pollutant emissions from that source, such as fuel usage, number of households, or 

population. The values of these indicators are gathered from various sources (government 

reports, census, trade groups, employment data, direct surveys, etc.) as appropriate. 
For the animal husbandry and fertilizer application emissions, the Carnegie Mellon 

University ammonia model was used. For paved and unpaved roads particulate matter 

emissions, emissions developed by the USEPA as part of their 2002 National Emissions 

Inventory development effort was used. 

Wind blown dust and sea salt emissions were not included in the inventory. These source 

categories are sources of particulate matter and therefore, do not effect the 8-hour ozone 

attainment modeling demonstration. 

For wildland fires, i.e., wildfires and prescribed burns, monthly estimates of fire 

emissions, which include burn acreage and biomass loading information will be used. 

Depending on the completeness and quality of the data, attempts will be made to calculate spatial 

and temporal distributions of the fire emissions, rather than relying on standard distribution 

profiles. Data will be obtained through consultation with stakeholders that participate in the 

VISTAS Fire Special Interest Work Group. The fire data will be split into two groups, small 

fires estimated on a county level and treated as an area source; and large fires that will be treated 

as a point source. 

Typical Base Year Inventory

The actual base year inventory will serve as the typical base year inventory for all area 

source categories except for wildland fires. For this source category, development of a typical 

year fire inventory provided the capability of using a comparable dataset for both the base year 

and future years. Thus, fire emissions would remain the same for air quality modeling in both 

the base and any future years. The VISTAS Fire Special Interest Work Group was consulted and 

decided to use State level ratios of acres over a longer term record (three or more years) 

developed for each fire type relative to 2002. The 2002 acreage was then scaled up or down 

based on these ratios to develop a typical year inventory. 

Future Year Base Inventory

The VISTAS contractor generated the future base year emissions inventory used in the 

attainment demonstration modeling. The general approach used to calculate the future base year 

emissions for stationary area sources was as follows: 

• Use the final 2002 VISTAS base year inventory as the starting point for the future base 

year inventory. 

• Obtain any State specific growth factors and/or future controls from the States to use in 

developing the projections. 

• Back calculate uncontrolled emissions for the 2002 base year inventory based on existing 

controls reported for the 2002 base year inventory. 

• Controls (including control efficiency, rule effectiveness and rule penetration) provided 

by the States or originally developed for use in estimating projected emissions for the 

USEPA’s Heavy Duty Diesel rulemaking emission projections and used in the CAIR 
projections were then used to calculate controlled emissions. State submitted controls 

had precedence over the USEPA developed controls. 

• Growth factors supplied from the States or the USEPA’s CAIR emission projections were 

then applied to project the controlled emissions to the appropriate year. In some cases, 

the USEPA’s Economic Growth and Analysis System Version 5 growth factors were 

used if no growth factor was available from either the States or the CAIR growth factor 

files. 

6.3 Non-Road Mobile Source Emissions 

Non-road mobile sources are equipment that can move but do not use the roadways, such 

as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, etc. For 

the non-road mobile source inventory, the list of sources to inventory came from the USEPA’s 

NONROAD2005 model and the USEPA guidance document, Procedures for Emission Inventory 

Preparation Volume IV: Mobile Sources. For the majority of the non-road mobile sources, the 

emissions can be estimated using the USEPA’s NONROAD model. For the three source 

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

commercial marine, more traditional methods of estimating the emissions were used. 

2002 Base Year Inventory

For the non-road mobile sources, the same inventory will be used for both the actual and 

typical base year emissions inventories. All non-road mobile source emissions, except for 

aircraft engines, commercial marine vessels and railroad locomotives, were estimated using the 

USEPA NONROAD2005 model. This model predicts the emissions for non-road equipment 

based upon the year inputted into the model. 

For railroad locomotive emissions, emission factors were supplied by the Procedures for 

Emission Inventory Preparation Volume IV: Mobile Sources document, which were then 

multiplied by a variety of different activity levels (i.e., gallons of fuel per county for railroad 

locomotive engines). Refinements could be made using information from Development of 

Railroad Emission Inventory Methodologies (SR2004-06-02) from the Southeastern States Air 

Resource Managers, Inc. 

Aircraft emissions at airports were calculated by VISTAS contractors using landing and 

take off data from Federal data sources. These will be reviewed and refined as appropriate for 

the Atlanta airport. Emissions are calculated using the Federal Aviation Administration’s 

(FAA’s) Emissions and Dispersion Modeling System version 4.2, when there is sufficient detail 

to employ it. 

Commercial marine emissions are estimated by procedures described in Commercial 

Marine Activity for Deep Sea Ports in the United States (EPA420-R-99-020). 

Future Base Year Inventory
For the source categories estimated using the USEPA NONROAD model, the model was 

used to create a future base year inventory. The NONROAD model takes into consideration 

rules that are in effect that could impact the emissions from these source categories. 

For the commercial marine, railroad locomotives and the remaining airport emissions, the 

VISTAS contractor will project the future base year emissions using the following guidelines: 

• Use the final 2002 VISTAS inventory as the starting point for the future base year 

inventory. 

• Detailed inventory data (both before and after controls) for 1996 and 2010 will be 

obtained from the USEPA’s Clean Air Interstate Rule Technical Support Document. 

Straight-line interpolations between 1996 and 2010 will be used to create a combined 

growth and control factor. This is done at the State-County-SCC-Pollutant level of 

detail. 

• Obtain, review and apply any State-specific growth factors submitted. 

• Apply adjustments to account for additional emission reductions do the low sulfur 

non-road diesel fuels. 

6.4 On-Road Mobile Source Emissions 

Highway mobile sources are considered those vehicles that travel on the roadways. 

Emissions from motor vehicles occur throughout the day while the vehicle is in motion, at idle, 

parked, and during refueling. Each of these emissions sources needs to be estimated in order to 

properly reflect the total emissions from this source category. In simplest terms emissions from 

highway mobile sources are calculated by multiplying an activity level, in this case 2002 daily 

vehicle miles traveled (VMT) as provided by the Georgia Department of Transportation 

(GDOT), by an emission factor. 

The USEPA developed the MOBILE model to estimate emission factors based on 

information on the way vehicles are driven in a particular area. The newest version of the 

MOBILE model, MOBILE6.2, was used to develop the on-road mobile source emissions 

estimates for carbon monoxide (CO), NOx, PM, and VOC. Key inputs for the MOBILE model 

include information on the age of vehicles on the roads, the average speed of those vehicles, 

what types of road those vehicles are traveling on, and any control programs (e.g., emissions 

inspection programs). Inputs are combined with gridded, day-specific temperature data to 

calculate the gridded, temporalized emission estimates. Of note, whereas on-network, link-level 

emissions estimates are spatially allocated based on link location and subsequently summed to 

the grid cell level, off-network, county-level emissions estimates are spatially allocated based on 

a combination of the Federal Highway Administration version 2.0 highway networks and 

population. For the Georgia 36/12 km modeling, no link-based data has yet been used. The 

MOBILE6 emissions factors are based on day-specific temperatures predicted by the 

meteorological model. 
6.4.1 Speed Assumptions 

Emissions from motor vehicles vary with the manner in which the vehicle is operated. 

MOBILE 6.2 default speed assumptions were used for this modeling effort. 

6.4.2 Vehicle Age Distribution 

Georgia-specific vehicle age distributions were used for this modeling effort. 

6.4.3 Vehicle Mix Assumptions 

MOBILE 6.2 default vehicle mix assumptions were used for this modeling effort. 

6.4.4 Temperature Assumptions 

MOBILE6 in the SMOKE emissions model uses the gridded (modeled) meteorology data 

to calculate temperature. Spatial and temporal temperature averaging were implemented to 

minimize the SMOKE (mobile) run times. 

6.4.5 Vehicle Inspection and Maintenance Program Assumptions 

In the 13 counties of the former Atlanta 1-hour ozone nonattainment area (Cherokee 

(13057), Clayton (13063), Cobb (13067), Coweta (13077), DeKalb (13089), Douglas (13097), 

Fayette (13113), Forsyth (13117), Fulton (13121), Gwinnett (13135), Henry (13151), Paulding 

(13223), and Rockdale (13247)) there is an annual enhanced vehicle inspection and maintenance 

(I/M) program (see Rules for Enhanced Inspection and Maintenance, Chapter 391-3-20, 

http://www.gaepd.org/Files_PDF/rules/rules_exist/391-3-20.pdf), with onboard diagnostics 

system checks on 1996 and newer model year cars and light trucks; 2-mode ASM tests on 25-

year-old through 1995 model year vehicles; a check for catalytic converter tampering on 25-

year-old through 1995 model year vehicles; a gas cap pressure test on all subject vehicles; and 

technician training and certification. 

6.4.6 RVP and Gasoline Sulfur Assumptions 

Reid Vapor Pressure (RVP) reflects a gasoline’s volatility. Georgia EPD has adopted a 

Phase II RVP of 7.0 psi during June 1 -September 15 as a control measure for the counties 

covered by Georgia's gasoline marketing rule (see Rules for Air Quality Control, Chapter 391-3-

1-.02(2)(bbb), Gasoline Marketing, http://www.gaepd.org/Files_PDF/rules/rules_exist/391-3-

1.pdf). The remaining areas in the state have an RVP of 9.0 psi during June-September. For 

other months, RVPs vary and are based on historical fuel survey data. In 2002, Georgia's 

gasoline marketing rule limited average gasoline sulfur levels in covered counties to 150 parts 

per million from June 1 -September 15. 

6.4.7 VMT Assumptions 

Mobile source emissions are calculated by multiplying emission factors by daily VMT. 

In this modeling exercise, 2002 daily VMT provided by GDOT were used. 
6.5 Biogenic Source Emissions 

A revised version of a commonly used biogenic emissions model, the Biogenic 

Emissions Inventory System, has recently been developed and tested by USEPA over two 

separate modeling domains/episodes. This version of the model (BEIS-3, v0.9) contains several 

changes over BEIS-2, including the following: 

• Vegetation input data -- are now based on a 1-km Biogenic Emissions Landuse 

Database (BELD3) vegetation data base, 

• Emission factors – many updates including some recent North American Research 

Strategy for Tropospheric Ozone (NARSTO) modifications, 

• Environmental algorithm -- includes a sunlit/shaded leaf solar radiation model. 

A series of sensitivity modeling simulations has been completed and concluded that the 

more recent BEIS-3 methodology will impact base case model ozone predictions in most parts of 

the US. The preliminary tests have also shown that the newer biogenic emissions do not appear 

to have a large effect on: 1) the control signal response, 2) relative reduction factors resulting 

from a projected emissions change, or 3) overall regional model performance in the eastern US. 

For this particular application of BEIS-3, version 0.9 as currently incorporated in the 

SMOKE processor will be used. This means that: 1) soil nitric oxide (NO) emissions shall be 

prepared without the input of specific soil moisture and precipitation data and 2) methanol 

emissions will not be modeled explicitly. Otherwise, the modeling should be identical to a 

BEIS-3 (v1.0) application. 

The BELD-3 landuse data on a Lambert conformal grid at 1-km resolution have already 

been developed, are available, and will be used to estimate biogenic emissions in this study. The 

BEIS model also requires as input hourly, gridded temperature and solar radiation data to 

estimate biogenic emissions, and these data will be derived from the MM5 predictions. 

6.6 Development of Modeling Inventories 

The SMOKE emissions model will be used to create the air quality model ready files. 

The chemical speciation method used is the CB4 mechanism. The gridding surrogates are based 

off the 2000 census data and are the most up to date available. The temporal profiles used to 

disaggregate the annual emissions to the appropriate month, day and hour are the latest available 

profiles provided with the SMOKE model with the exception of the EGU profiles, which will be 

developed based on CEM data. 

For each model-ready emissions inventory, separate air quality model-ready files will be 

created for the EGU point sources, non-EGU point sources, area sources, dust, low-level fires, 

elevated fires, non-road mobile sources, on-road mobile sources, and biogenic emissions. 
7.0 Quality Assurance Plan 

This section discusses the Quality Assurance (QA) procedures that will be used in the SIP 

modeling. The QA procedures listed here describe the combined efforts to be employed by 

VISTAS and GA EPD. The VISTAS contractors will perform QA on modeling inputs and 

outputs for the modeling region as a whole. GA EPD will perform QA on their respective 

emission inventories, as well as look at near state data for reasonableness. Additionally, the 

State agencies will review the modeling outputs for reasonableness. 

7.1 Quality Assurance Objectives 

In December 2002, the USEPA published extensive guidance on developing a Quality 

Assurance Project Plan (QAPP) for modeling studies (EPA, 2002). The objective of a QAPP is 

to ensure that a modeling study is scientifically sound, robust, and defensible. The new USEPA 

guidance suggests that a QAPP should include the following elements: 

 

• A systematic planning process including identification of assessments and related 

performance criteria 

• Peer reviewed theory and equations 

• A carefully designed life-cycle development process that minimizes errors 

• Clear documentation of assumptions, theory, and parameterization that is detailed 

enough so others can fully understand the model output 

• Input data and parameters that are accurate and appropriate for the problem 

• Output data that can be used to help inform decision-making 

• Documentation of any changes from the original quality assurance plan 

Moreover, the USEPA guidance specifies that different levels of QAPP may be required 

depending on the intended application of the model, with a modeling study designed for 

regulatory purposes requiring the highest level of quality assurance. 

The QAPP also provides a valuable resource for project management. It can be used to 

document data sources and assumptions used in the modeling study, and it can be used to guide 

project personnel through the data processing and model application process to ensure that 

choices are consistent with the project objectives. 

The guidance document also addresses model development, coding and selection of 

models, and model performance requirements. VISTAS and GA EPD modeling are using an 

existing USEPA sponsored model hence our QAPP will focus primarily on documenting data 

sources and QA of data processing performed by the model team. QA objectives for specific 

aspects of the project are discussed below, and these will be incorporated into a QAPP that 

conforms to the USEPA guidance document for modeling studies. 

7.2 Emissions Model Inputs and Outputs 

Emissions QA and Quality Control (QC) are the most critical steps in performing air 

quality modeling studies. Emissions processing can be time consuming and involves complex 

manipulation of many different types of large datasets. If errors are made and rigorous QA 
measures are not in place, these errors may remain undetected, resulting in delays and wasted 

time and resources.

7.2.1 VISTAS QA Effort 

As part of the VISTAS QA effort, an "Emissions Gatekeeper" function will be 

implemented. The role of this Gatekeeper is to perform quality assurance activities on the 

following emissions inventory data: 

• Emissions inventory data obtained from the VISTAS emissions inventory 

contractors 

• The emission inventory to be used for modeling outside of the states in the 

VISTAS region. 

Specifically, the Emissions Gatekeeper will review the content and format of the 

provided emission inventories, ensuring an appropriate appraisal of the emissions data and 

estimates for the VISTAS States. Other tasks will include any additional translation from mass 

emissions files into the emissions modeling input file structure necessary for modeling. The 

VISTAS Study Team will supplement these activities with QA checks on the intermediate and 

model output files using internal and public domain visualization and diagnostic packages. 

This multistep emissions QA/QC approach includes the initial emissions QA/QC by the 

Emissions Gatekeeper described above, as well as QA/QC by the Emissions Modeler during the 

processing of emissions, and then additional QA/QC by the air quality modeler of the processed 

model ready emission files. This multistep process, with three separate groups involved in the 

QA/QC of the emissions, is much more likely to catch any errors prior to the air quality model 

simulations. 

7.2.2 Emissions Modeling QA/QC 

Modeling QA involves performing data quality checks, assuring simulation accuracy, and 

recognizing and identifying problems as they happen; it is the process of looking for glaring 

faults in the model input and output data (I/O) and determining whether the input data are 

producing the desired results. Scrutiny of the I/O using standard statistical analyses can reveal 

problems in the data and/or the model setup. Using a standard approach for analyzing emissions 

model I/O establishes reference points to use when scrutinizing the data. Seeking these 

indicators of correct model performance allows QA personnel to determine the accuracy of the 

simulations and whether faults in the data or model configuration exist. 

QA documentation will include records of model configuration, details about data files, 

simulation records, and final report generation. After finishing each QA step, the modeler will 

record the result and his/her initials on a QA checklist. 

Data formats will be confirmed using the SMOKE manual to check text files and using 

PAVE to check binary netCDF files, such as the meteorology inputs. Sanity checks look for 

glaring errors in the file contents and ensure that the data make sense in the context of how they 

will be used and relative to similar or reference datasets. 
Lead modelers will oversee the entire modeling process, perform the majority of the 

SMOKE modeling, and receive and archive input and output data. Secondary modelers will 

perform some of the SMOKE modeling, organize the SMOKE QA reports into emissions 

summaries for data QA and reporting, and will generate custom QA summaries and reports for 

troubleshooting any problems encountered during the modeling process. 

Outside reviewers will be solicited from outside the emissions modeling team on a 

volunteer basis to conduct periodic reviews/audits of the data and modeling process. Outside 

reviewers will consist of peers, co-workers not working directly on the inventory in question, 

state inventory contacts and stakeholders. 

7.2.3 SMOKE Log Files 

Each of the programs that make up SMOKE produces a log at run time. Stored in a 

single directory for each unique simulation, the logs contain information about the configuration 

of SMOKE, the names and locations of the input and output files used in the simulation, and any 

warnings, notes, or errors (collectively called "flags") that occurred during model execution. 

Generated as text files, the logs are named according to the program that created them and the 

emissions source modeled by the simulation, and the names include identifiers that distinguish 

the simulation from all others. The logs are usually the first source of information consulted in 

determining whether a simulation completed as expected or for troubleshooting suspected 

problems. 

7.2.4 SMOKE QA Reports 

Two types of QA reports are generated by SMOKE. One set is created by the program 

Smkmerge and the other by the program Smkreport. While both programs allow users to 

configure the content of the reports, Smkreport is a more powerful reporting program that was 

designed specifically as a QA tool. Controlled by configuration files, Smkreport can create text 

reports at every step in the emissions generation process. In addition to creating reports from 

information drawn from the intermediate SMOKE data matrices (e.g., the temporal matrix), 

Smkreport can summarize the amount of emissions assigned to different temporal, spatial, and 

chemical profiles; normalize emissions by population; and report the amount of emissions 

allocated to each vertical layer per model-hour. Smkreport also allows the targeted reporting of 

emissions at specific sources, plants, grid cells, or subdomains. 

The program Smkmerge creates either state- or county-level reports at each of the major 

steps in the emissions generation process (spatial allocation, temporal allocation, chemical 

allocation or speciation, and merging). Although Smkmerge cannot create as many different 

report types as Smkreport, Smkmerge does have the ability to report biogenic emissions totals, 

whereas Smkreport can create reports only for anthropogenic emissions sources. 

7.2.5 Visualization Tools 

Visualization is an important part of the QA/QC procedure. Viewing bar charts and pie 

charts of the data verifies that more populous urban counties have greater emissions then the 

rural counties. Additionally, the PAVE visualization tool is used to graphically view the data to 

make sure that the data appears reasonable both spatially and temporally. 
Visualization tools will be used to assist in the QA process for the emissions data both 

before and after being processed through the SMOKE emissions model. The air quality data will 

also employ visualization tools to view the modeling results to ensure that the modeling results 

look reasonable. 

7.2.6 Document Tracking 

 In order to keep track of the details of modeling, certain notes and files will be 

maintained. Notes will be kept of files produced on desktop computers as to origin and purpose. 

These notes may be maintained in a logbook or by using the file properties summary tag 

available for files in the Windows operating system. Files in the workstation will be similarly 

tracked. It may be useful to maintain a log within directories for this purpose. 

7.3 Meteorological Model Outputs 

As part of the VISTAS QA effort, a "Meteorological Gatekeeper" will be tasked with 

providing an independent review and quality assurance of the meteorological modeling and 

related datasets developed by the VISTAS meteorological modeling contractor (BAMS) and 

used subsequently by the emissions and air quality modeling teams. This Gatekeeper QA 

review ensures that any potential problems with the datasets (should they exist) are identified 

and corrected in a timely manner. In the case of meteorology, the Gatekeeper’s independent 

QA analysis of the MM5 meteorological datasets serves to provide direct assistance to the 

emissions and air quality modeling team as it undertakes to ratify the SMOKE model outputs 

and to diagnose CMAQ model performance and sensitivity analyses. 

 

In addition to having personal responsibility for the quality and chain of custody of the 

meteorological datasets supplied by other VISTAS contractors, the Meteorological Gatekeeper 

will be responsible for ensuring and maintaining the integrity of the data files uploaded to the 

project website. This website, hosted by UCR (University California – Riverside), serves as the 

repository of data for the ENVIRON/UCR/Alpine modeling centers and for the VISTAS 

Technical Analysis Workgroup participants. In performing the Gatekeeper quality assurance 

activity, one of the first steps is to conduct an independent operational evaluation on the MM5 

model results at 36 km and 12 km grid scale. This evaluation covers surface and aloft wind 

direction, temperature, mixing ratio, precipitation, and PBL depths on a continental scale 

(36 km) and subregional scale (12 km) basis. 

The Gatekeeper will also perform supplemental, ad hoc analysis of pertinent MM5 fields 

(e.g., PBL depths) where that might be useful to the emissions and air quality modeling teams. 

Another task of the Gatekeeper will be to exercise MCIP version 2.3 to read the MM5 outputs 

from BAMS and produce binary input files for the CCTM to provide the complete set of 

parameters necessary in the emissions processing and air quality modeling. 

In summary, the quality assurance plan for the meteorological data will include the 

following elements: 

• Upon receiving the MM5 and MCIP 2.3 output files from BAMS, GA EPD will 

verify the integrity of the file transfer (i.e., no missing and/or corrupted files). 
• Since the CMAQ modeling domain is a subset of the MM5 domain, GA EPD 

will verify that the modeling domain and vertical layer structures in the MCIP 

files are identical to the CMAQ modeling domain. 

• Several days of the MM5 output will be selected and the meteorological 

modeling team will reprocess the MM5 files with MCIP v2.3 using the 

predetermined MCIP options. The MCIP files will then be compared with those 

provided by BAMS to verify that identical results from the MCIP processing 

were obtained. 

• Horizontal and vertical plots of temperature, pressure, precipitation, modeled flow 

patterns, PBL heights, etc. will be created to assess whether the MCIP output 

fields are reasonable. 

• The VISTAS 2002 MM5 simulation will be evaluated using the same surface 

observations, sub-domains and procedures as used to evaluate the Western 

Regional Air Partnership 2002 MM5 simulation as an independent QA and 

evaluation of the database. 

• Plots constructed by the VISTAS Gatekeeper will be made available on the 

VISTAS website for viewing and download 

(http://pah.cert.ucr.edu/vistas/vistas2/index.shtml). 

7.4 Air Quality Model Inputs and Outputs 

Key aspects of QA for the CMAQ input and output data include the following: 

• Verification that correct configuration and science options are used in compiling 

and running each model of the CMAQ modeling system, where these include the 

MCIP, JPROC, ICON, BCON and the CCTM. 

• Verification that correct input datasets are used when running each model. 

• Evaluation of CCTM results to verify that model output is reasonable and 

consistent with general expectations. 

• Processing of ambient monitoring data for use in the model performance 

evaluation. 

• Evaluation of the CCTM results against concurrent observations. 

• Backup and archiving of critical model input data. 

The most critical element in the QA plan for CMAQ simulations is the QA/QC of the 

meteorological and emissions input files. The major QA issue specifically associated with the 

air quality model simulations is verification that the correct science options were specified in the 

model itself and that the correct input files were used when running the model. For the CMAQ 

model, a system of naming conventions was employed which uses environment variables in the 

compile and run scripts that guarantee that correct inputs and science options are used. A 

redundant naming system is also used so that the name of key science options or inputs is 

included in the name of CMAQ executable program, in the name of the CMAQ output files, and 

in the name of the directory in which the files are located. This is accomplished by using the 

environment variables in the scripts to specify the names and locations of key input files. For 

example, if a model simulation is performed using the CB4 mechanism, all compile and run 

scripts contain the variable definition “$MECH = CB4,” and this variable is hard coded into the 

script for the executable name, the output file name, and the output directory name. This 
procedure produces long file/directory names but it effectively prevents mistakes or makes 

mistakes readily apparent if they do occur. 

A second key QA procedure is to never “recycle” run scripts (i.e., the original runs scripts 

and directory structure that were used in performing a model simulation). For example, if a 

simulation is performed with the SAPRC mechanism, instead of editing the original scripts to 

specify “$MECH = SAPRC,” a parallel directory structure with a new set of scripts to perform 

the SAPRC simulations will be created. This provides a permanent archive of the scripts that 

were used in performing model simulations. In addition, output from the model simulation will 

be directed to a log file that provides a record of input file names, warning messages, etc., that 

will be archived. 

Post-processing QA of the CMAQ output files similar to that described for the emissions 

processing will be performed. Animated graphics interchange format (GIF) files using PAVE 

will be generated to search for unexpected patterns in the CMAQ output files. In the case of 

model sensitivity studies, the animated GIFs will be prepared as difference plots for the 

sensitivity case minus the base case. Often, errors in the emissions inputs can be discovered by 

viewing the animated GIFs. Finally, 24-hour average plots for each day of the CMAQ 

simulations will be produced. This provides a summary that can be useful for more quickly 

comparing various model simulations. 
8.0 Model Performance Evaluation 

USEPA guidance (EPA, 2005, pg. 85; EPA, 2001, pg 227) suggests that model 

performance be evaluated based on two components: 

• How well the model is able to replicate observed concentrations of ozone and 

precursors, and 

• How accurately the model characterizes the sensitivity of changes in component 

concentrations to changes in emissions. 

Each component suggests a different type of evaluation procedure, with the first being 

“operational evaluation,” and the second being “diagnostic evaluation.” Since the attainment test 

is a relative test, it is not as necessary to exactly duplicate ozone concentrations. As a result, 

there is now more emphasis placed on the diagnostic model evaluation. 

This section outlines the method used to evaluate model performance. Working with the 

knowledge that many states involved with the VISTAS regional haze work would want to apply 

some of the work to their individual SIPs for 8 hour ozone and PM2.5, plans were put in place to 

perform exhaustive analysis of all atmospheric constituents, including ozone. GA EPD intends 

to build off the modeling efforts with VISTAS; therefore, the model performance evaluation will 

be an extension of VISTAS efforts. 

8.1 Model Evaluation Tools (Operational Evaluation) 

8.1.1 Statistical Performance Metrics 

In compliance with USEPA’s September 2006 guidance document Guidance on the Use 

of Models and Other Analyses for Demonstrating Attainment of the Air Quality Goals for Ozone, 

PM2.5 and Regional Haze, VISTAS will compile a suite of metrics for use in evaluating model 

performance. The standard set of statistical performance measures suggested by USEPA for 

evaluating ozone models includes: normalized bias, normalized gross (unsigned) error, fractional 

bias, fractional gross error, and fractional bias in standard deviations. Several other measures 

will be included in the final report to fulfill the requirements in the 8-hour ozone guidance 

(addition of average peak prediction accuracy), and to better accommodate other modeling 

groups with their comparison of modeling efforts. A list of metrics for calculation on a routine 

basis using the UCR analysis package is listed in Table 8-1. The metrics calculated in 

conjunction with VISTAS will include the examination of various atmospheric constituents, 

including ozone. 

Typically, the statistical metrics are calculated at each monitoring site across the full 

computational domain for all simulation days. During the VISTAS CMAQ evaluation, the gasphase and aerosol statistical measures shown in Table 8-1 will be computed for the full 36 km 

and 12 km domains, as well as for the individual RPOs and on other sub-domains as appropriate. 

Temporally, the statistical measures will be computed for the appropriate averaging times: 1 hr 

for ozone, and gas-phase precursors such as NO, nitrogen dioxide (NO2), CO, SO2, 8-hour for 

ozone, and 24-hour for sulfate, nitrate, PM2.5, and other aerosol species. These results will then 

be averaged over annual, monthly, and seasonal periods for display, further analysis, and 

reporting. Should it become necessary as part of model performance diagnosis, the statistics will 
be aggregated in other ways, e.g., (a) day vs. night, (b) weekday vs. weekend, (c) precipitation 

vs. non-precipitation days, (d) month of the year, and (e) the 20% haziest/cleanest days, in order 

to help elucidate model performance problems. For the purposes of the 8-hour ozone SIP for the 

Metrolina nonattainment area, only the statistics for 1-hour and 8-hour ozone will be reported. 

The statistics for the pollutants and precursors will be reviewed internally for reasonableness. 

Table 8-1: Statistical Metrics 

Statistical 

Measure 

Shorthand 

Notation 

Mathematical Expression Notes 

Accuracy of 

Paired Peak 

Ap

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

)()(

Pi= prediction at time and 

location i; 

Oi = observation at time and 

location i; 

P = arithmetic average of 

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

O = arithmetic average of 

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

Normalized 

Mean Error 

NME 

∑

∑

=

=

−

N

i

i

N

i

ii

O

OP

1

1

Reported as % 

Root Mean 

Square Error 

RMSE 

( ) 2

1

1

1 2

⎥

⎦

⎤ ⎢

⎣

⎡ ∑ − =

N

i

OP ii N

Reported as % 

Fractional Gross 

Error 

FE

∑= +

− N

i ii

ii

OP

OP

N 1

2 Reported as % 

Mean Absolute 

Gross Error 

MAGE ∑=

−

N

i

OP ii N 1

1

Mean 

Normalized 

Gross Error 

MNGE 

∑=

N −

i i

ii

O

OP

N 1

1 Reported as % 

Mean Biased MB ∑( ) =

−

N

i

OP ii N 1

1 Reported as concentration 

Mean 

Normalized Bias 

MNB ( ) ∑=

− N

i i

ii

O

OP

N 1

1 Reported as % 

Mean 

Fractionalized 

Bias (Fractional 

Bias) 

MFB 

∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

− N

i ii

ii

OP

OP

N 1

2

Reported as % 

Table 8-1: Statistical Metrics (Continued) 
Statistical 

Measure 

Shorthand 

Notation 

Mathematical Expression Notes 

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) 

8.1.2 Graphical Representations 

The core operational air quality model evaluation will utilize numerous graphical 

displays to facilitate quantitative and qualitative comparisons between CMAQ predictions and 

measurements. Together with the statistical metrics listed in Table 8-1, the graphical procedures 

are intended to help: (a) identify obviously flawed model simulations, (b) guide the 

implementation of any performance improvements in the 2002 model input files in a logical, 

defensible manner, and (c) to help elucidate the similarities and differences between the 

alternative CMAQ simulations. These graphical tools are intended to depict the model’s ability 

to predict the observed gaseous species, such as ozone, and fine particulate species 

concentrations. The core graphical displays to be considered for use in model performance 

evaluation include the following: 

• Spatial mean concentration time series plots 

• Time series plots at monitoring locations 

• Ground-level gas-phase and particulate concentration maps (i.e., tile plots) 

• Concentration scatter plots stratified by station, by time, and by network 

• Bias and error stratified by concentration 

• Bias and error stratified by time 

• Histogram plots of the statistical metrics, stratified by day, by pollutant, by 

subregion (e.g., 12 km vs. 36 km, by RPO), and by monitoring network 

• Quantile - Quantile (Q-Q) plots 

• Animations of predicted hourly ozone concentration 

These graphical displays will be generated, where appropriate, for the full annual cycle as well as 

for monthly and seasonal periods. 

8.2 Model Performance Testing (Diagnostic Evaluation) 

Rarely does a modeling team find that the first simulation satisfactorily meets all (or even 

most) model performance expectations. Based on experience, initial simulations that “look very 

good” usually do so as the result of compensating errors. The norm is to engage in a logical, 

documented process of model performance improvement wherein a variety of diagnostic probing 

tools and sensitivity testing methods are used to identify, analyze, and then attempt to remove the 

causes of inadequate model performance. This is invariably the most technically-challenging 
and time consuming phase of a modeling study. The annual CMAQ model base case simulations 

are expected to present some performance challenges that may necessitate focused diagnostic 

and sensitivity testing in order for them to be resolved. It is hoped that these diagnostic and/or 

sensitivity tests can be adequately carried out within the resources and schedule. Where 

practical, diagnostic or sensitivity analyses, if needed, could be performed on selected episodes 

within the annual cycle, thereby avoiding the time-consuming task of running CMAQ for the full 

2002 period. Below, the types of diagnostic and sensitivity testing methods that might be 

employed in diagnosing inadequate model performance and devising appropriate methods for 

improving the model response are identified. 

8.2.1 Traditional Sensitivity Testing 

Model sensitivity experiments are useful in three distinct phases, or “levels”, of an air 

quality modeling study and all will be used as appropriate. These levels are: 

• Level I: Model algorithm evaluation and configuration testing 

• Level II: Model performance testing, uncertainty analysis and compensatory 

error diagnosis 

• Level III: Investigation of model output response (e.g., ozone, aerosol, 

deposition) to changes in precursors as part of emissions control scenario 

analyses. 

The Level I and Level II cover the aspect of operational evaluation, while Level III covers 

diagnostic evaluation. 

The Level I sensitivity tests with CMAQ have already been completed in the initial 

VISTAS configuration and diagnostic analyses. However, given that open community nature of 

CMAQ and the frequent science updates to the model and supporting data bases, it is possible 

that some additional configuration sensitivity testing will be necessary. 

Potential Level II sensitivity analyses might be helpful in accomplishing the following 

tasks: 

• To reveal internal inconsistencies in the model 

• To provide a basis for compensatory error analysis 

• To reveal the parameters (or inputs) that dominate (or do not dominate) the 

model’s operation 

• To reveal propagation of errors through the model 

• To provide guidance for model refinement and data collection programs 

The merits of performing Level II sensitivity testing will depend upon whether 

performance problems are encountered. In addition, the number of tests possible, should 

performance difficulties arise, will be limited by the available schedule and resources. From past 

experience with CMAQ and other models, it is possible to identify examples of sensitivity runs 

that could be useful in model performance improvement exercises with the annual 2002 CMAQ 

simulation. These include: 
• Modified biogenic emissions estimates 

• Modified on-road motor vehicle emissions 

• Modified air quality model vertical grid structure 

• Modified boundary conditions 

• Modified fire emissions 

• Modified EGU emissions 

• Modified ammonia emission estimates 

• Modified aerosol/Nitric Pentoxide/Nitric acid (HNO3) chemistry 

• Modified ammonia and HNO3 deposition velocities 

Note that in a few cases [e.g., vertical grid structure, ammonium (NH4) emissions estimates], 

some sensitivity experimentation has already been carried out by VISTAS. To the extent that 

this information can help guide the future diagnostics analyses, this earlier work will be used. 

Level III sensitivity analyses have two main purposes. First, they facilitate the emissions 

control scenario identification and evaluation processes. Currently, four complimentary 

sensitivity “tools” can be used in regional photochemical models depending upon the platform 

being used. These methods include: (a) traditional or “brute force” testing, (b) Decoupled Direct 

Method, (c) Ozone Source Apportionment Technology and PM Source Apportionment 

Technology, and (d) Process Analysis. Each method has its strong points and they will be 

employed where needed. The second purpose of Level III sensitivity analyses is to help quantify 

the estimated reliability of the air quality model in simulating the atmosphere’s response to 

significant emissions changes. 

Examples of Level III monthly or annual sensitivity runs for Phase II might include: 

• Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to SO2 emissions 

• Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to elevated point 

source NOx emissions 

• Ozone, sulfate, nitrate, ammonium and other aerosol sensitivities to ground level 

NOx emissions 

• Sulfate, nitrate, ammonium and other aerosol sensitivities to ammonia 

The need to perform sensitivity experimentation (Levels I, II, or III) will depend on the 

outcome of operational performance evaluations. If such a need arises, the ability to actually 

carry out selected sensitivity and/or diagnostic experiments will hinge on the availability 

resources and sufficient time to carry out the analyses. Clearly, selection of the specific analysis 

method will depend upon the nature of the technical question(s) being addressed at the time. 

8.3 Air Quality and Ozone Column Data 

Data from ambient monitoring networks for both gas and aerosol species are used in the 

model performance evaluation. Table 8-2 summarizes ambient monitoring networks used to 

collect data for Air Quality model performance evaluation. Data have been compiled for all 

networks listed except the Photochemical Assessment Monitoring Stations (PAMS) and PM 

Super-sites. 
Additional data used in the air quality modeling include the Total Ozone Mapping 

Spectrometer (TOMS). TOMS data provides ozone column data, is available for 24-hour 

average, and is obtained from http://toms.gsfc.nasa.gov/eptoms/ep.html. The TOMS data is used 

in the CMAQ radiation model to calculate photolysis rates. 

Table 8-2: Overview of Ambient Data Monitoring Networks. 

Monitoring Network Chemical Species Measured Sampling 

Period Data Availability/Source 

The Interagency 

Monitoring of 

Protected Visual 

Environments 

(IMPROVE) 

Speciated PM2.5 and PM10 (see 

species mappings) 

1 in 3 days; 

24 hr 

average 

http://vista.cira.colostate.edu/improve/Data/IMP

ROVE/improve_data.htm 

Clean Air Status and 

Trends 

Network 

(CASTNET) 

Speciated PM2.5, Ozone (see 

species mappings) 

Approximat

ely 1-week 

average 

http://www.epa.gov/castnet/data.html 

National Atmospheric 

Deposition 

Program (NADP) 

Wet deposition (hydrogen 

(acidity as pH), sulfate, 

nitrate, ammonium, chloride, 

and base cations (such as 

calcium, magnesium, 

potassium and sodium)), 

Mercury 

1-week 

average http://nadp.sws.uiuc.edu/ 

Air Quality System 

(AQS) Aka 

Aerometric 

Information Retrieval 

System (AIRS) 

CO, NO2, O3, SO2, PM2.5, 

PM10, Pb 

Typically 

hourly 

average 

http://www.epa.gov/air/data/ 

Speciation Trends 

Network (STN) Speciated PM 24-hour 

average http://www.epa.gov/ttn/amtic/amticpm.html 

Southeastern Aerosol 

Research and 

Characterization 

(SEARCH) 

24-hr PM2.5 (FRM Mass, OC, 

BC, SO4, NO3, NH4, Elem.); 

24-hr PM coarse (SO4, NO3, 

NH4, elements); Hourly 

PM2.5 (Mass, SO4, NO3, 

NH4, EC, TC); and Hourly 

gases 

(O3, NO, NO2, NOy, HNO3, 

SO2, CO) 

Hourly or 

24hour 

average, 

depending 

on 

parameter. 

Electric Power Research Institute (EPRI), 

Southern Company, and other companies. 

http://www.atmospheric-research.com 

USEPA Particulate 

Matter Supersites Speciated PM2.5 http://www.epa.gov/ttn/amtic/supersites.html 

Photochemical 

Assessment 

Monitoring Stations 

(PAMS) 

Varies for each of 4 station 

types. http://www.epa.gov/ttn/amtic/pamsmain.html 

National Park Service 

Gaseous Pollutant 

Monitoring Network 

Acid deposition (Dry; SO4, 

NO3, HNO3, NH4, SO2), O3, 

meteorological data 

Hourly http://www2.nature.nps.gov/ard/gas/netdata1.ht

m 
9.0 Control Strategy 

It is important to remember that photochemical models are tools; they do not make 

decisions. The results from photochemical models are one of several pieces of information that 

decision-makers must consider when adopting control strategies. To ensure that the modeling 

analyses provide information that meets the needs of the decision makers, it is imperative that the 

air quality modelers and decision makers agree upon the type and amount of information that is 

needed to meet the study objectives. This section outlines the process behind developing and 

evaluating emission control strategies to be employed for the 8-hour ozone SIP. 

9.1 Control Strategy Design 

9.1.1 Emission Sensitivity Test 

To begin the process of control strategy design a series of simulations using across-theboard reductions of VOC, NOx, or VOC and NOx combined can be run. The purpose of these 

simulations is to evaluate the relative effectiveness of VOC and NOx controls. 

Errors in emission estimates can lead to errors in control decisions. Important sources in 

future year inventories can be simulated at the lower and upper bounds of their estimated 

accuracy. In doing this, GA EPD can find out if changes, within the known accuracy of the 

emission estimates, can lead to different decisions for control strategies. Once the future year 

inventories are assembled, the sources with the highest uncertainty can be identified. These 

sources could include biogenic emissions, motor vehicle exhaust, and gasoline evaporation. 

VOC speciation profiles can also be included in these sensitivity tests.

9.1.2 Ranking Control Strategies 

Control strategies should be implemented in an ordered fashion that reduces both peak 

ozone concentrations and population exposure. Emission controls that affect both VOC and NOx

should be sorted separately from VOC or NOx only controls. Estimates of control levels that are 

expected in future years should be made. An attempt to reduce population exposure to a 

minimum each year while reducing peak ozone, can be made by looking at all potentially 

available controls and at subsets which focus on VOC only, NOx only, and combinations of VOC 

and NOx control. 

9.2 Control Strategy Evaluation 

Selection of candidate control strategies will take into consideration the results of the 

combination of analyses described in the previous sections. Once candidate control strategies are 

identified, the strategies may be simulated. If needed, an analysis will be performed to 

investigate the predicted impact of each strategy on air quality and population exposure. The 

results of these analyses will be summarized both in tabular and graphical form to allow 

systematic comparison and contrast of all strategies. 

To assist decision makers in fully understanding the impact of proposed control 

strategies, the following products may be prepared as a part of the control strategy evaluation: 
1. Peak Ozone Tables 

2. Peak Ozone Spatial Plots 

3. Difference Plots 

4. Population Exposure Tables and Histograms 

5. Change in predicted future design values 

Each of these products will compare future year base simulations with one or more 

control simulations. An attempt should be made to minimize population exposure as controls are 

introduced. To assist in this effort, population exposure and peak ozone statistics can be 

organized by future year and control strategy. Upon completion of this evaluation, a final 

control strategy will be selected for detailed evaluation. 

9.3 Identification of Control Strategy Scenarios 

A designated subcommittee will select the control strategy scenarios to be modeled for 

demonstrating attainment. The control strategy selection process will follow the current USEPA 

guidance, and will incorporate our present understanding of ozone formation on an urban and 

regional scale. 

Mandated controls will be modeled first. If attainment of the ozone NAAQS is not 

shown, additional alternative control strategies identified by the preceding steps, will be modeled 

until attainment is reached. 

A “frozen” future year dataset will be available for use in testing alternative control 

strategies. This will consist of a set of model input and output files for each episode. Anyone 

with access to the model (e.g., power companies and universities) can use these files as long as 

they do not change future base case emission inventories, meteorology, growth factors, or 

mandated controls. Alternative controls can be modeled in addition to controls strategies 

modeled by the states. 
10.0 Demonstration of Attainment 

This section summarizes the procedures that will be used to demonstrate attainment of 

the 8-hour ozone NAAQS. An attainment demonstration consists of (a) analyses which estimate 

whether selected emissions reductions will result in ambient concentrations that meet the 

NAAQS, and (b) an identified set of measures that will result in the required emissions 

reductions. Determining necessary emission reductions may be done by relying exclusively on 

results obtained with air quality models. These include the outcomes of the modeled attainment 

test plus a screening test to estimate whether a proposed emission reduction suffices to meet the 

NAAQS. 

10.1 Ozone Model Attainment Test 

The modeled attainment test is the practice of using an air quality model to simulate 

current and future air quality. For the 8-hour ozone NAAQS, the model estimates are used in a 

“relative” rather than “absolute” sense. That is, the ratios of the model’s future to current 

predictions at each ozone monitor are used to determine attainment. These site-specific ratios 

are called relative reduction factors. Future ozone design values are estimated at existing 

monitors by multiplying the monitor-specific modeling current design value by the modeled 

relative reduction factor “near” each monitor. If the resulting predicted site-specific future 

design values are greater than 87 ppb, the modeled attainment test is failed. If the predicted 

future design values are between 82 and 87 ppb, then additional weight of evidence is needed to 

corroborate a positive attainment demonstration. If the predicted future design values are below 

82 ppb, the area is determined to clearly attain the standard. Equation 10.1 describes the 

modeled attainment test, applied near monitoring site “x.” 

(DVF) = (RRF) (DVB) Equation 10.1 

(DVB) = the modeling baseline design value monitored at site "x," in units of ppb. 

Currently the USEPA recommends calculating the DVB by averaging the three design 

value periods to include the baseline inventory year. In other words, the DVB is based on 

a 5-year weighted average centered on the baseline inventory year. 

(RRF) = the ratio of the future 8-hr daily maximum concentration predicted near a 

monitor (averaged over each day of the episode) to the current 8-hr daily maximum 

concentration predicted near the monitor (averaged over each day of the episode). 

(DVF) = the estimated future design value for the time attainment is required, ppb. 

It is important to consider an array of cells “near” a monitor rather that focusing on the 

individual cell containing the monitor. This diminishes the likelihood of quirks in the test’s 

results resulting from geometry of the superimposed grid system. Table 10-1 provides the 

USEPA recommendations for defining “nearby” cells for grid systems having cells of various 

sizes. 
Table 10-1: USEPA Recommendations for Defining nearby Cells 

Size of Cell, Size of the Array of Nearby Cells, 

(km) (unit less) 

<5 7 x 7 

>5-8 5 x 5 

>8-15 3 x 3 

>15 1 x 1 

10.2 Screening Test 

Per the USEPA Guidance, the states will perform an analysis of unmonitored areas to 

determine if attainment of the ozone standard is expected in these areas. The unmonitored area 

analysis, or screening test, will be achieved through the use of the USEPA’s Model Attainment 

Test Software (MATS). This tool will allow for spatial interpolation of baseline monitoring data, 

which will provide modeling current DVBs for an entire area and not just at monitoring sites. 

This field is then paired with the modeling results in MATS to produce DVFs for an entire 

geographic area. This final gradient adjusted spatial field can then be examined for any 

unmonitored areas that area predicted not to meet the ozone NAAQS. Any violation suggested 

by this analysis then will then be carefully evaluated. 

10.3 Corroborative Analysis 

After the completion of the attainment test, the USEPA 8-hour Ozone Guidance suggests 

additional measures should be taken to further support or refute the attainment test results. This 

corroboratory evidence is referred to as supplemental analysis when used to further support an 

attainment demonstration. A weight of evidence determination can be used to conclude that 

attainment is likely, especially when the predicted future design values are between 82 and 

87 ppb. Analysis can include a wide variety of tests and analyses, including the application and 

results of air quality models, observed air quality trends and estimated emissions trends, and the 

outcome of observational models. 

Should the area, clearly demonstrate attainment (DVF < 82 ppb), then basic supplemental 

analysis will be performed to further support the test’s findings. If either the attainment or 

screening tests are greater than 87 ppb, it is doubtful that the more qualitative arguments made in 

a weight of evidence determination can be sufficiently convincing to conclude that the NAAQS 

for ozone will be attained. 

For DVFs between 82 to 87 ppb, a weight of evidence determination will be preformed to 

supplement the conclusion that the area is expected to attain the NAAQS. The end product of a 

weight of evidence determination is a document which describes analyses performed, data used, 

key assumptions and outcomes of each analysis, and why the State believes that the evidence, 

viewed as a whole, supports a conclusion that the area will attain the NAAQS for ozone. 
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Appendix A: Acronyms Used 

ACM Asymmetric Convective Mixing 

AERO3/ISORROPIA Aerosol Chemistry Scheme for CMAQ 

AIRS Aerometric Information Retrieval System 

AQS Air Quality System 

BAMS Barons Advanced Meteorological, LLC 

BCON Boundary Condition Processor 

BCs Boundary Conditions 

BEIS3 Biogenic Emission Inventory System, version 3 

BELD3 Biogenic Emissions Landuse Database 

CAA Clean Air Act 

CAAA Clean Air Act Amendments 

CAIR Clean Air Interstate Rule 

CAMx Comprehensive Air Quality Model with Extensions 

CASNET Clean Air Status and Trends Network 

CB4 Carbon Bond Version 4 

CB4-2002 Carbon Bond Version 4 - 2002 update 

CCTM CMAQ Chemical Transport Model 

CEM Continuous Emissions Monitoring 

CERR Consolidated Emissions Reporting Rule 

CLT Charlotte-Douglas International Airport 

CMAQ Community Multiscale Air Quality 

CMAS Community Modeling and Analysis 

CO Carbon monoxide 

CPC Climate Prediction Center 

CTM Chemical Transport Model 

DVB Current Design Value 

DVF Future Design Value 

EAC Early Action Compact 

EGU Electric Generating Unit 

EIIP Emissions Inventory Improvement Program 

EMS Emissions Modeling System 

EPA Environmental Protection Agency, see also USEPA 

EPS Emissions Preprocessing System 

ETA NCEP meteorological model named for the vertical 

coordinate system used in the model. 

FAA Federal Aviation Administration 

FAY Fayetteville 

FDDA Four Dimensional Data Assimilation 

FRM Federal Reference Method 

GIFs Graphics Interchange Format 

GSO Greensboro 

HKY Hickory 

HNO3 Nitric Acid 

I/O input/output 

I/O API Input/Output Applications Programming Interface 

IC/BC Initial Condition/Boundary Condition 
ICON Initial Condition Processor 

ICs Initial Conditions 

IDA Inventory Data Analyzer 

IMPROVE Integrated Monitoring of Protected Visual Environments 

IPM Integrated Planning Model 

ISORROPIA Inorganic Aerosol Thermodynamics/Partitioning: Model 

that calculates the composition and phase state of an 

ammonia-sulfate-nitrate-chloride-sodium-water inorganic 

aerosol in thermodynamic equilibrium with gas phase 

precursors. 

JPROC Photolysis Rate Processor 

km kilometer 

LSM Land Surface Model 

MACT Maximum Achievable Control Technology 

MATS USEPA’s Model Attainment Test Software 

mb millibar, Measure of atmospheric pressure 

MCIP Meteorological-Chemistry Interface Processor 

MCIP2.3 Meteorological-Chemistry Interface Processor (ver. 2.3) 

MM5 Mesoscale Meteorological Model, 5 refers to the version 

number 

MM5 PX_ACM MM5 use of the Pleim-Xiu Soil Model and Asymmetric 

Convective Mixing PBL model 

MOBILE6.2 USEPA vehicle emission factor model, which is a software 

tool for predicting gram per mile emissions of 

hydrocarbons, carbon monoxide, oxides of nitrogen, carbon 

dioxide, particulate matter, and toxics from cars, trucks, 

and motorcycles under various conditions. 

mph miles per hour 

MPP Massively Parallel Processors 

MRPO Midwest Regional Planning Organization 

MSA Metropolitan Statistical Area 

NAAQS National Ambient Air Quality Standard 

NADP National Atmospheric Deposition Program 

NARSTO North American Research Strategy for Tropospheric Ozone 

NCAR National Center for Atmospheric Research 

NCEP National Center for Environmental Predictions 

NEI National Emissions Inventory 

NH4 Ammonium 

NMC National Meteorological Center 

NO Nitric Oxide 

NO2 Nitrogen Dioxide 

NOx Oxides of Nitrogen 
NOx SIP Call Finding of Significant Contribution and Rulemaking for 

Certain States in the Ozone Transport Assessment Group 

Region for Purposes of Reducing Regional Transport of 

Ozone. Rule designed to mitigate significant transport of 

NOx, one of the precursors of ozone. 

NOy Total Available Nitrogen 

NWS National Weather Service 

O3 Ozone 

OBDII Onboard diagnostics 

PAMS Photochemical Assessment Monitoring Stations 

PAVE Package for Analysis and Visualization of Environmental 

data 

PBL Planetary Boundary Layer 

PM Particulate Matter 

PM2.5 Particulate Matter with a diameter less than 2.5 μm 

ppb parts per billion 

ppm parts per million 

PSU Pennsylvania State University 

PSU/NCAR Pennsylvania State University/National Center for 

Atmospheric Research 

PX Pleim-Xiu 

PX_ACM LSM_PBL MM5 configuration of Pleim-Xiu Land Surface Model, 

Asymmetric Convective Mixing PBL model 

QA Quality Assurance 

QAPP Quality Assurance Project Plan 

QC Quality Control 

Q-Q plots Quantile-Quantile plots 

RACT Reasonably Available Control Technology 

RADM Regional Acid Deposition Model 

RAMS Regional Atmospheric Modeling System 

RDU Raleigh-Durham International Airport 

REMSAD Regional Modeling System for Aerosols and Deposition 

RPO Regional Planning Organization 

RRF Relative Reduction Factor 

RVP Reid Vapor Pressure 

SAPRC99 Photochemical Mechanism in CMAQ 

SCBAQ South Carolina Bureau of Air Quality 

SCC Source Classification Code 

SCDHEC South Carolina Department of Health and Environmental 

Control 

SEARCH Southeastern Aerosol Research and Characterization 

SIP State Implementation Plan 

SMOKE Sparse Matrix Operator Kernel Emissions 

SMVGEAR Sparse Matrix Vectorized Gear solver 

SO2 Sulfur Dioxide 

SOA Secondary Organic Aerosol 

SORGAM Secondary Organic Aerosol Model 

STN Speciation Trends Network 
T Temperature 

TOMS Total Ozone Mapping Spectrometer 

u, v, w Three Dimensional Wind Components in spherical 

coordinates : u = E/W; v = N/S; w = vertical 

UAMIV Urban Airshed Model - Version 4 

UAM-V Urban Airshed Model - Version 5 

UCAR University Corporation for Atmospheric Research 

UCR University of California at Riverside and Davis 

US United States 

USEPA United States Environmental Protection Agency 

USGS United States Geological Service 

UTC Universal Time or Greenwich Mean Time 

VISTAS Visibility Improvement State and Tribal Association of the 

Southeast 

VMT Vehicle Miles Traveled 

VOCs Volatile Organic Compounds 

WRF Weather Research Forecast 

Z Zulu Time, or UTC (Universal Time), or Greenwich Mean 

Time