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, 20071.0 Overview of 8-hour Ozone Modeling/Analysis Project

1.1. Policy Overview of the Ozone Nati onal 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 pp m 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 remande d the 8-hour ozone NAAQS back to the USEPA. In 2001, the USEPA proposed a resp onse 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 oz one 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 mu ch 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 fo r an eight-hour peri od (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 cons ecutive 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) ba sed on these monitored exceedances and adjacent areas that could be contri buting 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 particul ar pollutant were formally designated as nonattainment for that pollutant. Five categories to define the degree of non attainment were created for the 1-hour ozone NAAQS (section 181 of th e 1990 CAAA). In order of severity, these categories were classified as marginal, modera te, serious, severe and extreme. The highest monitor design value in a nonattainment area was us ed 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 unde r section 172 of the 1990 CAAA (subpart 1) as“basic” and would have up to 5 years from de signation to attain the NAAQS, or the area could be designated under section 181 (sub part 2) and classified as one of the five categories listed above with attainment dates base d on the classification. Areas w ith a 1-hour ozone design value greater than 0.121 ppm were classi fied under subpart 2, and all othe r 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 ar eas to achieve clean air earlier than the Clean Air Act (CAA) schedule. This EAC area has at tained 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 wi ll focus solely on the Atlanta 8-hour ozone nonattainment area. This area includes 20 countie s (Barrow, Cherokee, Clayton , Cobb, Coweta, DeKalb , Douglas, Fayette, Forsyth , Fulton, Gwinnett, Henry, Pauldi ng, Rockdale, Bartow, Carroll , Hall, Newton, Spalding, Walton).

The Atlanta area has been classified as marg inal 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 formati on, 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 gr oups that are affected by overall air qua lity and that impact the air shed of the affected states. It is imperative to include groups of “sta keholders” from industry, government, and the private sector represented du ring the modeling/analysis project. As each group involved brings its own pe rspective, knowledge, and experi ence to the modeling process, the ability to model and develop strategies for ozone reduction is gr eatly enhanced. The following organizations are invited to part icipate 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 participa ting agencies, organiza tions, and universities will be utilized in determining projected emi ssions and control strategies. All data and information will be reviewed and evaluated by th e GA EPD. All stakeholders are invited to contribute emissions projections and control strategy information.

Georgia EPD will coordinate with various stat es and other parties as regional modeling is initiated to address the new ozone standards. Various other regional modeling applications, suchas Southeastern States Air Resource Managers’ seasonal oz one modeling and Visibility Improvement and Tribal Associ ation of the Southeast (VISTAS) regional haze and fine particulate matter (PM 2.5 ) 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 stak eholders is an integral part of completing the modeling/analysis project. Stakeholders n eed 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 resolv e 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 at tainment 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 se veral 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 sour ce 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 me thodologies, an issue paper stati ng the need for modification will be developed and circulated to a ll organizations participating in the study. The issue paper will be discussed at the next schedul ed technical coordination meeti ng. 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 cont rol strategy standpoint is the selection of the future year by which attainment will be mode led. The future modeling year has been chosen to meet the schedule previously put forth. The time line set by th e CAA requires attainment of the 8-hour ozone NAAQS in a mode rate 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 coinci de with future year modeling for the PM 2.5 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 descript ion 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 m odel grid specifications.

• Section 6 discusses the emi ssion inventory development.

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

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

• Section 9 discusses how the cont rol strategies will be designed.

• Section 10 focuses on the model attainme nt 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-atta inment area consists of 20 counties (Barrow, Cherokee, Clayton , Cobb, Coweta, DeKalb , Douglas, Fa yette, Forsyth , Fulton, Gwinnett, Henry, Paulding, Rockdale, Bartow, Carroll , Hall, Newton, Spalding, Walton) in which 11 ozone monitors are located (Figure 2-1). Ozone desi gn-values (DV; three year average of the 4 th highest reading per year) for the 2001-2003 period, which wa s used in designating the Atlanta 20 county area as non-attainment, show that 10 out of th e 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 th e Atlanta 20-county non-attainment areaTable 2-1 : 2001-2003 ozone design values for At lanta 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 concen trations throughout the Atlanta non-attainment area, the yearly 4 th highest 8-hour ozone concentration and number of exceedances of the NAAQS (expressed as 85 ppb) fr om 1996-2006 were investigated (T able 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 Conf ederate Avenue site. This site exhibited the highest ozone concentration (4 th highest value) in six out of the eleven years analyzed, and recorded the highest number of exceedances in se ven 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 4 th highest reading per year) (Table 2-4), also indicates that the C onfederate Avenue site consistently exhibits the highest DV within the non-attainment area. Howeve r, 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 s ubset of sites within the non-attainment area.Table 2-2 : Yearly 4 th highest 8-hour ozone concentrati ons (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 de sign-values (ppm) for the 11 Atlanta non-attainment area sites during 1998-2006 (e.g, the 1998 DV is base d on data from 1996-1998). Bold red numbers indicate maximum per year. Bold black (and re d) 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 0.079Spatial patterns in ozone DVs outside of the A tlanta non-attainment area we re also investigated, to assess whether ozone levels are impact ed 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 si te) DVs for the period of 2001-2006 are shown in Figure 2-2. Area-average and area-maximum (si ngle 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 leve ls in Georgia. A sharp decline in both ozone levels and the number of exceedances are observe d at all areas from 2001 to 2005, followed by a slight increase in the 2006 DV. In addition to this regional patte rn, a local one is evid ent in Atlanta, as expressed by higher ozone levels and more freque nt 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 compar ison between an average of 11 monitor for the Atlanta area and a single site in Macon. Ho wever, 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 emissi ons of ozone precursors in the Atlanta area. In fact, previous work has demonstrated that em issions of ozone precursors in Atlanta also contribute to ozone formation at Mac on and other regions of the state.60.0 65.0 70.0 75.0 80.0 85.0 90.0 95.0 100.0

Atlant a M a c o

n

C o lu

m

bus Augu s ta At h

e

n s

S a v an

na

h

S.

C

o a s ta

l G

A S. Central GA N. GA Mo u nta

i nsAverage 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

Atlant a M acon

C o lumbus Augu s t a At h

ens Sav a nna h

S

. C

o astal GA

S .

Cent r al G A

N

. GA M

o

u nta

i nsHighest 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-si te 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

Atl

a nta Maco n

Columbu s Augusta Athe n s

Savanna h

S .

Coastal GA

S .

C

e

n t r a l G

A

N

. GA M

ountainsDays 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

Atlan t a Macon

C

o lu

m

bus Augusta Athens

S

a v a nnah

S. C o astal GA S. C e ntral GA

N.

G

A

M

o unt a insDays 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 ar ea-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 NO x emissions (both EGU and mobile-sources) and in the number of “ozone conducive days” are presented for 1996-2005 (2006 meteorological a nd vehicle miles traveled data were not available by the time of this analysis). State total EGU NO x emissions were obtained from the Clean Air Markets Division (CAMD) website ( http://www.epa.gov/airmarkets/ ). Approximate 13-county highway mobile source emissions i nventories were calculated by GA EPD using a modified draft version of EPA's National M obile Inventory Model (NMIM20050311) into which "historic" 1994 through 2004 aver age annual daily vehicle miles traveled (VMT) from GeorgiaDepartment of Transportation were importe d (The MOBILE6.2.03 emission factors produced by NMIM reflect default MOBILE6 speed distributi ons). Trends in meteorology were quantified based on the count of “ozone conducive days” pe r ozone season. An “ozone conducive day” was defined here as a day in which the maximum daily temperature was above the 75 th percentile, and both the average relative-humidity a nd average wind-speed were below the 50 th percentile for the period studied (1996-2005, using data fr om the National weathe r Service station in Atlanta). These parameters were chosen to re present meteorological c onditions conducive to ozone formation, i.e. hot, dry and stagnant cond itions. The use of relati ve (percentile based) rather than absolute thres holds (e.g., temperature above 90 ° F) allows for an easy inter-annual comparison incorporating several meteorologica l parameters. Different thresholds were investigated, resulting in a change in the actua l number of ozone conducive days per year, but this had little effect on the observed trend. Em issions data show a strong decline in EGU NO x emissions in Georgia (60% decrease from 1998 to 2005) due to the installation of Selective Catalytic Reduction (SCR) devices at several power plants (B owen Units 1-4, Hammond Unit 4, and Wansley Units 1-2) and other controls (such as the use of Po wder River Basin coal and overfire air). On-road mobile source emissions also exhibit a downward tre nd (32% decrease from 1998 to 2005), mainly due to fleet turnover. In a ddition, there seems to be a substantial amount of variability in the meteorological condition be tween years, with an ove rall decrease in the count of “ozone conducive days” in the last se veral ozone seasons. The combination of these reductions in emissions and more-favorable mete orology in the last several years can help in explaining the downward tre nd in ozone design values.

0 100 200 300 400 500 600 700

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005NOx emissions (TPD)

0 5 10 15 20 25 30 35Number of "ozone conducive" days

EGU NOx (TPD) Mobile-source NOx (TPD) Count of "O3 conducive" days
Figure 2-4 : Inter-annual trends in NO x emissions (total GA EGU and 13-county Atlanta on-road mobile-source emissions) and count of “ozone conducive days” for 1996-20052.2. Conditions Leading to Eleveted Ozone in the Atlanta Area

To further study the factors contri buting to ozone formation in A tlanta and to the occurrence of exeedances of the ozone NAAQS, data from the Photochemical Assessment Monitoring Stations (PAMS) network was used. The Atlanta PAMS networ k consists of four sites: Yorkville (type 1, upwind/background site), Tucker (type 2, maximu m 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 dail y 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) NO x and VOC (sum of 56 PAMS VOC species) concentrations, prev ious day ozone concentrations , and meteorological conditions (maximum daily temperature, average daytime wind speed and relative hu midity). 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 as sociated with elevated previous day ozone, indicating a buildup of ozone duri ng episodes. As expected, elevat ed ozone is also associated with elevated temperatures, low wind speeds and low relative humidity, as these meteorological conditions are known to be c onducive of ozone formation.

To examine the relative importa nce of precursors of ozone (NO x and VOCs) to ozone formations, trends in these precursors were an alyzed. Elevated ozone at the South-DeKalb, Tucker and Conyers are typically associ ated with elevated levels of both NO x and VOCs. However, lower ratios of VOC/NO x are typically associated with elevated ozone, possibly indicating ‘NO x -limited’ conditions (ozone formation is limited by the amount of available NO x ). However, this analysis is a descriptive one, a nd it is difficult to conclude on the relative importance on NO x 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 NO x 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-qua lity measurements to de termine 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 as sess 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 tr ends in the relative effectiveness of VOC vs. NOx controls. The calculations are carrie d 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 measurem ents of ozone, nitric oxide (NO), and either NOx (NO + NO 2 ) or NO y (the sum of all oxidized nitr ogen species) to compute the extent of reaction. The extent of reaction is a di mensionless 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 NO x , 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 O 3 <65ppb 65ppb ≤ O 3 < 85ppb 85ppb ≤ O 3 < 105ppb O 3 ≥ 105ppb Number of days 155 112 36 13 O 3 (ppb) 48.3 73.1 90.8 116.2 Previous day O 3 (ppb) 54.7 70.8 79.4 85.2 Morning NO x (ppb) 17.7 20.9 34.0 32.7 Mid-day NO x (ppb) 7.5 6.0 8.0 10.7 Evening NO x (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/NO x (ppbC/ppb) 4.6 4.2 5.3 3.6 Midday VOC/NO x (ppbC/ppb) 7.6 8.0 5.1 5.4 Evening VOC/NO x (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 NO x , VOCs and meteorological conditions associated with various ozone levels at the South-DeKalb PAMS site (o zone concentrations reported as daily maximum 8-hour ozone; VOC reported are th e 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 O 3 <65ppb 65ppb ≤ O 3 < 85ppb 85ppb ≤ O 3 < 105ppb O 3 ≥ 105ppb Number of days 272 146 43 15 O 3 (ppb) 45.5 73.3 91.9 113.7 Previous day O 3 (ppb) 51.7 69.0 80.0 88.2 Morning NO x (ppb) 26.4 45.3 71.5 75.4 Mid-day NO x (ppb) 5.7 5.2 5.1 6.5 Evening NO x (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/NO x (ppbC/ppb) 3.9 2.9 2.0 1.9 Midday VOC/NO x (ppbC/ppb) 7.9 8.2 8.3 7.4 Evening VOC/NO x (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 54.6Table 2-7 : Concentrations of NO x , 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 O 3 <65ppb 65ppb ≤ O 3 < 85ppb 85ppb ≤ O 3 < 105ppb O 3 ≥ 105ppb Number of days 146 64 29 11 O 3 (ppb) 47.4 72.9 91.9 110.0 Previous day O 3 (ppb) 52.5 69.6 78.8 98.9 Morning NO x (ppb) 6.7 10.7 17.1 25.4 Mid-day NO x (ppb) 2.5 3.4 4.6 5.9 Evening NO x (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/NO x (ppbC/ppb) 6.6 6.0 4.8 4.5 Midday VOC/NO x (ppbC/ppb) 10.1 10.5 9.7 8.2 Evening VOC/NO x (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 NO x , VOCs and meteorological conditions associated with various ozone levels at the Yorkville PAMS site (ozone concentratio ns 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 O 3 <65ppb 65ppb ≤ O 3 < 85ppb 85ppb ≤ O 3 < 105ppb O 3 ≥ 105ppb Number of days 58 39 2 0 O 3 (ppb) 48.6 73.7 86.0 - Previous day O 3 (ppb) 50.4 66.3 71.0 - Morning NO x (ppb) 2.0 2.4 2.0 - Mid-day NO x (ppb) 1.4 1.5 1.0 - Evening NO x (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/NO x (ppbC/ppb) 11.0 13.0 6.9 - Midday VOC/NO x (ppbC/ppb) 14.5 11.8 12.2 - Evening VOC/NO x (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 NO x limitation 0.9 – 1.0 Strongly indicative of NO x limitation

Ozone M.A.P.P.E.R. should be used as a qualitati ve indicator of control preferences, rather than quantitatively as an estimator of control require ments because: (1) the computational algorithms are simple representations of ozone formati on in smog chambers and do not incorporate many physical processes that are im portant in affecting ozone c oncentrations in the ambient environment, and (2) bias and imprecision of me asurements 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 s ites 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 calculat e extent of reaction beca use it is considered to be more advanced and comprehensive.

NO x data were available at all four PAMS sites (along with ozone and NO), however NO y data were only partially available (for 2000-2005 at South DeKalb and Tucker, 1998 at Conyers, not available for Yorkville). For cons istency across site s and years, NO x data (rather than NO y ) 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 reacti on (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 va lues in the afternoon and valu es 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 NO x and VOC controls may be efficien t for reducing ozone levels. Also evident is that the Yorkville and Conyers sites are more NO x -limited compared to the two ‘type 2’ (maximum precursor) sites, Tucker and South-DeKalb.

To further analyze the relative importance of NO x 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 averag e 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 NO x -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 implemente d. While the data in Figure 2-5 seem to0.0 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 dayExtent 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 dayExtent 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 dayExtent 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 dayExtent 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 dayExtent 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 concentrat ion (<65ppb, 65-85ppb, 85-105ppb, >105ppb) for (a) Yorkville, (b) Tucker, (c) S outh-DeKalb, and (d) Conyers.indicate that Atlanta ozone is ‘transitional’ to NO x -limitation, data in Figures 1-6 clearly indicate that days of high-ozone are NO x -limited. Hence, NO x controls are likely to be substantially more efficient than VOC controls in reducing maxi mum 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 sensi tivity analysis, showing a much gr eater sensitivity of elevated 8- hour ozone concentrations to NO x controls than VOC contro ls, 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 provid e 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 emi ssions 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 an thropogenic precursors are lower compared to weekdays, because major sources of ozone precurs ors, such as mobile sources and industrial sources, are less active on weekends. Data collected at the four Atlanta PA MS sites (Table 2-10) indicate that NO x 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 evap oration, remain constant irresp ective of the day of the week. For both precursors, the weeke nd/weekday differences are mo re evident at the maximum precursor sites (Tucker and South-DeKalb) th an at the upwind (Yor kville) and downwind (Conyers) sites.

Table 2-10 : Weekday/weekend differences in daytime (6am-7pm) NO x 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) NO x (ppb) VOC (TNMOC, ppbC) Site Weekday Weekend % change W eekday 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, forthe period of 1997-2005, as well as for 3-year pe riods within that range. The Confederate Avenue site is of special intere st 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 dow ntown Atlanta). These results indicate that ozone in Atlanta is typically lower on the week ends compared to weekdays, which can serve as an additional indication to Atlanta ozone being NO x -limited. Of the five sites investigated, Confederate Avenue and South-DeKalb seem the least NO x -limited, which is expected given their location in the middle of th e core urban area (Confederate Av enue) 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-ye ar periods (1997-1999, 2000-2002, 2002-2005) seems to indicate that Atlanta ozone may ha ve shifted from being VOC-limited/NO x -inhibited in the earlier period (1997-1999), as expressed by highe r ozone concentrations during the weekends (especially in the Confederate Ave nue and South-DeKalb sites) to NO x -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 NO x emissions in the region (Figure 2-4), and hence the declin e in availability of NO x 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. SouthDeKalb Tucker Conyers YorkvilleAverage 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. SouthDeKalb Tucker Conyers YorkvilleAverage 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. SouthDeKalb Tucker Conyers YorkvilleAverage 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. SouthDeKalb Tucker Conyers YorkvilleAverage of max 8-hr O3(ppb) Weekdays Weekend

Figure 2-5 : Weekdays/weekend differences in ozone c oncentrations 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, th ere 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 likelyto introduce a change in (local) ozone concentrat ions. In addition, it has been shown that the Atlanta non-attainment area is generally NO x -limited, though may be VOC-limited or transitional in some cases. Analysis of data coll ected at the four PAMS sites throughout Atlanta indicates that lower VOC/NO x ratios are associated with elev ated daily maximum 8-hour ozone concentrations. Analysis of ozone, NO and NO x data using the ozone M.A.P.P.E.R. program indicated that ozone formation in A tlanta is generally transitional to NO x -limited, but when the results were binned according to the daily ma ximum 8-hour ozone, ozone formation seemed strongly NO x -limited on days of elevated ozone. An anal ysis of weekend/weekday differences in ozone concentration also indicate a generally NO x -limited regime, with ozone formation becoming more NO x -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 midw eek concentrations compared to weekends, with stronger weekly patterns in later years (1995-1999 compared to 1986-1990), concluding that the availability of NO x drives weekly ozone cycle in Atlanta. Analyzing various aircraft measurements, Sillman et al. (1997) conc lude that Atlanta ozone is mostly NO x -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 th ese various analyses s upport the control of NO x emissions as part of a state-policy for re ducing 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 whic h 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 c onditions leading to el evated 8-hour ozone concentrations. Additionally, episodes should be se lected that contain days close (within ± 0.01 ppm) to the baseline design valu e (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 prec ursor measurements. Finally, a sufficient number of days should be selected to ensure robust at tainment tests at violating monitoring sites.

In addition to these primary crite ria, 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 consideratio ns include selecting episodes maximizing the number of days and s ites observing a viola tion, selecting episodes which include weekends, and the selection of epis odes 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 storag e technologies, and aided by regional modeling efforts, GA EPD decided to model an entire “o zone season” for both sensitivity and attainment test purposes, rather than selec ting 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 criteri a, such as modeling a sufficien t number of days to ensure a robust modeled attainment test, representing a myri ad of meteorological c onditions that influence ozone concentrations, an d modeling of weekends.

Efforts were made to determine the most appr opriate 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 indica te 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 NO x emissions during that period, compared to 2002, as shown in Figure 2-4. This figurealso displays a count of “ozone conducive days ” (defined as days in which the maximum daily temperature was above the 75 th percentile, and both the averag e relative-humidity and average wind-speed were below the 50 th percentile, for the 1996-2005 pe riod), 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 numbe r of days in which each monitoring site observed a value within 0.01 pp m of the (site) design value was analyzed for the 2000-2004 period (Table 3-1). The 2002 ozone season exhibits th e 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 maximu m 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 ba sed 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 gr id cell and the site grid-cell itself (Tables 2-1 and 2-2 in the Attainment Test section). Both these cases indica te 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 meteorol ogical parameters associated with each bin. Also provided is frequency of occurrence of th e 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 O 3 (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 hos ts eleven ozone monitors, five NOx sites, four PAMS sites, two SEARCH sites, numerous meteorologi cal 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. Additio nally, the selection of 2002 as the base case season also fulfills the secondary cr iteria that states give prefer ential treatment of previously modeled episodes. Through GA EPD’s collaboration with the Visibility Improvement, State and Tribal Association of the S outheast Regional Planning Orga nization (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 PM 2.5 , the modelingcan still be utilized for ozone. The VISTAS m odeling employs “one atmosphere” modeling, or modeling of all atmospheric constituents incl uding particulate matter (PM) and ozone. This modeling is done in parallel to capture interactions between various compounds. Since ozone, along with PM 2.5 and regional haze, is being modeled as pa rt of the VISTAS modeling efforts, its data can easily be extracted from the modeling results.

The episode selection process has considered mete orological variability, oz one 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 configur ation of regional mete orological, emissions processing, and photochemical ai r 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 brie fly in this section. Although th e configuration of each of the modeling components has been sel ected as the culmination of in tensive study by VISTAS, there remains the possibility that certain algorithms a nd parameter settings may still be updated prior to the running of the final annual 2002 base cas e simulation and subsequent model performance testing.

The GA EPD modeling team will remain in cl ose contact with the VISTAS, as well as other RPO regional modeling initiatives through out the Atlanta attainment modeling study, to determine appropriate refinements to the mode l codes, input databases, and post-modeling analysis procedures. No table limitations of the m odels, relevant to their intended purpose in this attainment modeling analysis, will al so 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/Nat ional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorologi cal Model (MM5) is a nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale photochemical, fine particulate matter, and re gional haze regulatory modeling studies.

• SMOKE : The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that ge nerates 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, visibi lity, and acid depositi on 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 (NCA R) 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 th rough enhancements provided by a broad user community (e.g., Chen and Dudhia, 2001; Stau ffer and Seaman, 1990, 1991; Xiu and Pleim, 2000). The MM5 modeling system is routinely employed in forecasting projects as well asrefined investigations of severe weather. Utiliz ation of MM5 within air quality applications is also a common practice. In recent years, th e MM5 modeling system ha s been successfully applied in continental scale annual simulations for the year s 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 establis hed performance record compar ed with other applicationsoriented prognostic models, MM5 has been select ed as the preferred meteorological model for this effort. This section provi des 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, prognos tic model that has been used widely in regional air quality model appl ications (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 mesoscal e studies, including cycl ogenesis, polar lows, cold-air damming, coastal fronts, severe thunder storms, 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 fo r three-dimensional wind components (u – zonal wind component, v – meridional wind comp onent, 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 st eep terrain. The model uses an efficient semi-implicit temporal integra tion scheme and has a nested-grid capability that can use up to ten different domains of arbitr ary horizontal and vertical resolution. The interfaces of the nested grids can be either one-w ay 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-dimensiona lized pressure, or “sigma,” vertical coordinate similar to that used in many operation al and research models. In the non-hydrostatic MM5 (Dudhia, 1993), the sigma levels are defi ned according to the in itial hydrostaticallybalanced reference state so that the sigma le vels 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 m odels with different coordinate systems (e.g., Comprehensive Air Quality Model with Exte nsions - CAMx) by performing a vertical interpolation, followed by a mass-conservation re-adjustment.

Distinct planetary boundary layer (PBL) para meterizations are available for air-quality applications, both of which repr esent 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 insola tion, atmospheric path length, water vapor, cloud cover, and longwave radiation. The surface physical properties of albedo, roughness length, moisture availability, emissivity and thermal in ertia are defined as functions of land-use for numerous categories via a look- up table. One scheme uses a first-order eddy diffusivityformulation for stable and neutra l environments and a modified first-order scheme for unstable regimes. The other scheme uses a prognostic equation for the second-or der turbulent kinetic energy while diagnosing the other key boundary layer terms.

Initial and lateral bounda ry 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-ho ur 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 st ate-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 applic ations 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 sy stem in regulatory air quality application studies have been widely reported in the litera ture (e.g., Emery et al., 1999; Tesche et al., 2000, 2003), and many studies have involved comparisons with othe r prognostic models such as the Regional Atmospheric Modeling System (RAMS) and the Systems Application International Mesoscale Model. The MM5 enjoys a far richer applic ation history in regul atory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 recent regional scale air quality application st udies 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 modeli ng, Barons Advanced Meteorological Systems, LLC (BAMS) (Olerud and Sims, 2003), MM5 wa s 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 us ed 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 nests4.2.3 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 modeli ng exercise. Some of the plots and statistics to be generated include:

• Spatial plots of model predictions with th e 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 te mperature, mixing ratio, wind speeds, wind direction, and cloud cover. These will in clude 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 observ ations with modeled winds

4.2.4 Meteorological Data

Meteorological data are being generated us ing the MM5 prognostic meteorological model by BAMS. BAMS is operating the MM5 at 5-da y increments for 2002 on the 36 km and 12 km grid with a 14-day spin up period for th e 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 da ta to be visualized on the model fields via Package for Analysis and Visualization of Enviro nmental 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 statis tics are only calculated over cells that MM5 deems to be land since the CPC analyses are derive d primarily from rain gauges.

For aloft analyses, standard sounding obser vations from the National Center for Environmental Predictions (NCEP) ds353.4 arch ive are processed. These observations are quality controlled and used to produce model/ observation skewT sounding plots for selected sites. Additionally, the observ ations are integrated into sigma levels that match the MM5 specifications and subsequently can be statistical ly analyzed for performance at sigma levels 9, 17, and 22 (~500m, ~1600m, ~3400m, respectivel y). Qualitative pr ofiler plots showingmodel/observed hourly winds are also created base d upon the data stored at the Forecast Systems Lab.

4.3 SMOKE Emissions Modeling System

The SMOKE Emissions Processing System Prot otype was originally developed at the Micro-computing Center of North Carolin a (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 sour ces, its purpose is to provide an efficient, modern tool for converti ng emissions inventory da ta into the formatted emission files required by an air quality simulati on model. For mobile sources, SMOKE actually simulates emissions rates based on input mob ile-source activity data, emission factors and outputs from transportation tr avel-demand models.

SMOKE was originally designed to allow emi ssions data processing methods to utilize emergent high-performance-computing as applie d to sparse-matrix algorithms. Indeed, SMOKE is the fastest emissions processing tool curr ently available to the air quality modeling community. The sparse matrix approach ut ilized throughout SMOKE permits both rapid and flexible processing of emissions data. The pro cessing 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 step s of temporal projection, controls, chemical speciation, temporal allocation, a nd 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, fi re and point source emission processing and includes biogenic emissions modeling thr ough a rewrite of the Biogenic Emission Inventory System, version 3 (BEIS3). SMOKE has been availabl e 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 fo r 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 ma jor features that make it an attractive component of the modeling system (Seppanen, 2003). The model suppor ts a variety of input formats from other emissions processing systems and models in cluding the Inventory Data Analyzer (IDA), Emissions Modeling System – 2003 (EMS), and th e 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 comp ared with other emissions processing software• Run-time memory allocation, eliminating a ny need to recompile the programs for different inventories, grids, or chemical mechanisms

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

A number of science features have been inco rporated 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 em issions 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 solv ent 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 f ile 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 Qualit y Assurance (QA) capabilities

• Improved temporal allocation

The Carolina Environmental Program at the University of North Carolin a is continuing model development activities with SMOKE. The emissions modeling will employ the SMOKE version 2.0, released on September 30, 2003. The SMOKE ex ecutables, 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 Mode ls-3 development emerged from the challenges posed by the 1990 CAAA and USEPA’s desire to develop an advanced modeling framework for“holistic” environmental modeli ng utilizing state-of-science re presentations of atmospheric processes in a high performance computing environment (Ching et al., 1998). USEPA completed the initial stage of development w ith Models-3 and released the CMAQ model in mid 1999 as the initial operating science mode l under the Models-3 framework (Byun et al., 1998b).

CMAQ consists of a core Chemical Transpor t Model (CTM) and seve ral 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 m odules 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 th e CMAS center to support the coordination, update and distributi on of the Models-3 system.

Another reason for choosing CMAQ as the atmo spheric model is the ability to do oneatmospheric modeling. Since GA EPD will be us ing the same modeling exercise for both the ozone and PM 2.5 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 appro ach has been adapted to dynamically represent the PM size distribution using th ree 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 compositio n 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 (SO 2 ) 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 proce sses, CMAQ uses the RADM/Regional Particulate Model approach. Particle dry de position 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). Th e 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-Xi u Land-Surface Model (LSM) and the ACM PBL scheme will be used, and the recently update d CMAQ MCIPv2.3 would process the MM5 data using the "pass through" option. The Carbon B ond version 4 (CB4) gas-phase, RADM aqueous-phase, and AERO3/ISORROPIA aerosol chemistr y 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 evaluate d three photochemical mechanisms: CB4, CB4- 2002 and SAPRC99. CB4-2002 produced nearly iden tical 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. Comparis ons of CB4 and SAPRC99 found they produced mostly similar but different model performance. However, no one mechanism performed better than any other mechanism across al l species, sites, and periods. The testing only evaluated the mechanism’s base case performance, not their re sponse 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 (IC s) will be used along with a ~15 day spin up period to eliminate any significant influenc e of the ICs. The CMAQ Boundary Conditions (BCs) for the initial simulations will be ba sed on seasonal averages of 3-hour 2001 GEOSCHEM global simulation model output. VISTAS and other Re gional Planning Organizations (RPOs) are finding a 2002 GEOS-CHE M 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 inhere nt limitations owing to the necessary simplifications and approximations made in fo rmulating 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 conf igurations performed better. The PX_ACM LSM_PBL configuration was selected because it was consistently near the top performing configuration in the southeastern US across va riables and locations and was never the worstperforming configuration. Howeve r, there are numerous limitations in the MM5 with the LSM and PBL treatment being some of the most impo rtant. The MM5 PX_ACM frequently predicts very low PBL heights that can appear as "holes" in the spatial distributi on of PBL heights that do not appear physically realistic and may affect air quality modeling. Although the MM5 PX_ACM configuration model performance in th e southeastern US mo stly 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 ittries to organize the flow fields. However, th e 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 newl y-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 wh ich would require additional SMOKE runs or potential SMOKE modifications or alternat e modeling methods, NCDAQ and SCDHEC will immediately notify stakeholders and make reco mmendations for resolving the issues. Upon receipt of technical direction from the stakeholde rs, appropriate corrective action will be taken.

Features are continuing to be developed in th e 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 assistin 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 limita tions in the model itself include the nitrate formation chemistry. Testing found the CMAQ nitrate performance suspect with winter overestimations and summer underestimations. Ot her science limitations in the current version of CMAQ include inadequate trea tment of sea salt and the assumption that all secondary PM is in the fine mode. Lack of any two-way grid ne sting limits the ability of the model to properly resolve point source plumes or urban photochemi stry. 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 categor ies: 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, exerci sing, and evaluating the MM5 model for the 2002 season consist of various fi xed 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 th roughout 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 S eaman (1990). These 3-hr synoptic-scale initialization data include th e horizontal wind components (u and v), temperature, and relative humidity at the standard pressure levels, plus sea-leve l pressure and ground temperature. Here, ground temperature repr esents 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, wh ere necessary, filtered to smooth out irregularities.

• Clouds and Precipitation : While the non-hydrostatic MM5 treats cloud formation and precipitation directly through e xplicit, resolved-scale, and pa rameterized sub-grid scale processes, the model does not require preci pitation or cl oud input. The potential for precipitation and cloud formation enters th rough the thermodynamic and cloud processes formulations in the model. The only precip itation-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 as similation 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 fi elds that will be generated by the CMAQ MCIP2.3 processing of the BAMS MM5 output

• Three-dimensional hourly emi ssions generated by SMOKE

• Initial conditions and boundary conditions (IC/BC)

• Topographic information

• Land use categories

• Photolysis rates generated by the CMAQ JPROC processor5.0 Grid Specifications and Modeling Domains

This chapter summarizes the model domain definitions including the model domain, resolution, map projections and nesting sc hemes 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. Th e 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 ne sted 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 sh ows 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 a nd the high resolution, ne sted 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 gr id definition for the 36 km continental domain. The RPO unified grid consists of a Lambert-Conformal map pr ojection using the map projections parameters listed in Table 5-1.

Table 5-1: RPO Unified Grid Definition.
PARAMETER VALUE projection Lambert-conformal

1 st

true latitude (alpha) 33 degrees

2 nd true latitude (beta) 45 degrees

x center - 97 degrees
y center 40 degrees

The MM5 36 km grid includes 164 cells in th e 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 th e MM5 coarse grid is also nested in the Eta grid, there is a possibi lity 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 doma in. 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 he re exceeds the USEPA sugg estion of at least 5 grid cell buffer at each boundary.

Table 5-2 lists the number of rows and column s 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 me sh defined at the vertices of the grid cells while “cross” refers to the grid mesh defined by the gr id cell centers. Thus, the dimension of the dot mesh is equal to the cross mesh plus one. Fi nally, 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 vert ical 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 su rface to the 100 mb. Table 5-3 lists the layerdefinitions for both MM5 and for CMAQ. A layer averaging scheme is adopted for CMAQ to reduce the computational cost of the CMAQ simula tions. The effects of layer averaging were evaluated in conjunction with the VISTAS modeli ng effort and were found to have a relatively minor effect on the model performance metric s when both the 34-layer and a 19-layer CMAQ models were compared to ambient monitoring data.

Table 5-3: Vertical Layer Defi nition For MM5 Simulations (Left Most Columns), And Approach For Reducing CMAQ Layers By Collapsing Mu ltiple 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 06.0 Development of Emissions Inventories

There are five different emissi on inventory source classificati ons; stationary point sources area sources, off-road mobile sour ces, 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 ar ea sources are those sources whose emissions are relatively small but due to the large number of th ese sources, the collective emissions could be significant (i.e., dry cleaners, serv ice stations, etc.) Off-road m obile sources are equipment that can move but do not use the roadways, i.e., la wn mowers, construction equipment, railroad locomotives, aircrafts, etc. On-road mobile s ources 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 form atted 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. Th e biogenic emissions are also estimated within the SMOKE system, using the USEPA’s BEIS model, with modeling meteorology.

In addition to the various source classificati ons, there are also various types of emission inventories. The first is the actua l base year inventory. This inve ntory is the base year emissions that correspond to the meteorologi cal data, for this modeling effo rt is 2002. These emissions are used for evaluating the air quality model performance.

The second type of inventory is the typical base year inventor y. 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 inventor y is an inventory developed fo r some future year for which attainment of the ozone standard is needed. For this modeling project, th e future year inventory will be 2009, the last complete year for which the sta ndard must be attained. It is the future year base inventory that control strate gies and sensitivities are applied to determine what controls, to which source classifications, must be made in or der to attain and mainta in 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 fo r splitting the point source inve ntory is that the EGU sources account for the majority of the point source NO x emissions and hour specific data is available for these sources through the USEPA’s acid rain databa se. Using this more refined data will helpimprove the air quality modeling performance. Annual emissions will be used for the non-EGU

sources.
All point sources will be spat ially allocated in the domain based on the stationary source geographic coordinates. If a poi nt source is missing its latitude/l ongitude 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, NO x , SO 2 , and heat input-based hour-speci fic profiles were developed and applied to NO x , SO 2 , 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 st akeholders, this data will be substituted for USEPA CEM-based emissi ons and distributions.

To temporally allocate the remaining EGU point sources, the NO x , SO 2 , and heat input data will be collected from the 2002 CEM datase ts, 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 suppl ied emissions data to calculate hourly EGU emissions by unit.

Typical Base Year Inventory

Since the NO x emissions from EGU sources are a significant part of the emissions inventory, a typical base year emissions inventory was develope d for these sources to avoid anomalies in emissions due to variability in meteorology, economic and outage factors in 2002. This approach is consistent w ith the USEPA’s modeling guidance.

To develop a typical year 2002 emissions i nventory for EGU sources, for each unit the average CEM heat input for 2000 through 2004 wa s divided by the 2002 actual heat input to generate a unit specific normalizing factor. Th is 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 monitori ng 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 wa s used. If a unit was shutdown in 2002, but not permanently shutdown, the emissions and h eat inputs 2001 (or 2000) were used in the normalizing calculations.

Future Base Year Inventory

As part of the VISTAS modeling, VIST AS and the Midwest Regional Planning Organization (MRPO) contracted with ICF Resour ces, L.L.C., to generate future year emission inventory for the electric generating sector of the contiguous US using the IPM. IPM is adynamic linear optimization model that can be used to examine air polluti on control policies for various pollutants throughout th e 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 op timization logic determines the least-cost means of meeting electric generation and capacity requirements while complying with specified constraints including air pollution regulations, transmissi on 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 th e 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 appli cations 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 anal ysis is USEPA’s National Electric Energy Data System Database, with changes based upon the comments and technical directions from VISTAS and MRPO’s stakeholders. Th e changes focused on existing installations of NO x , SO 2 and particulate matter controls, NO x emission rates, SO 2 emission limits, capacity of existing units, heat rate and un it 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 se lected years within the study horizon known as run years. Each run year represents se veral calendar years in the study horizon, and all calendar years within the study horizon are mappe d 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 de veloping the projections.

iii) The Duke Power and Progress Energy cont rol technology investment strategies for complying with North Carolina’s Clean Smokest acks 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 CF R Parts 51 et. al., Supplemental Proposal for the Rule to Reduce Interstate Transport of Fine Particul ate 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 Ca rolina, Tennessee, Texas, Virginia, West Virginia, Wisconsin are the states affected by the CAIR SO 2 and the CAIR annual NO x policies starting 2010. Connecticut is affected by an ozone season NO x policy. The CAIR plants affected by the annual NO x policy are capped at 1.6 m illion tons starting 2010 and 1.33 million tons starting 2015. The power plants affected by the CAIR SO 2 policy have to surrender two Title IV SO 2 allowances for every ton of SO 2 emitted starting 2010 and three Title IV SO 2 allowances for every ton of SO 2 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-E GU category will use annual emissions, which will be temporally allocated to month, day, and hour using source catego ry code (SCC) based allocation factors. These factor s will be based on the cross-refe rence 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 (CE RR) 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 y ear 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 st akeholder review to en sure credibility. To assemble growth/control data needed for the fi nal 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 Ec onomic 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 NO x Control Measures Adopted by States and Nonattainment Areas for 1999 NEI Ba se Case Emissions Projection Calculations

. The report identified and compile d a listing of the VOC and NO x 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 seve ral counties in Virginia), Louisville, and northern Kentucky.Emission reductions requirements from NO x Reasonably Available Control Technology (RACT) in 1-hour Ozone SIP areas were impl emented 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.

NO x SIP Call

For non-EGU sources, Phase I of the NO x SIP call applies to larg e industrial boilers and turbines, and cement kilns. States in the VISTAS region affected by the NO x SIP call have developed rules for the control of NO x 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 oz one season allowances.

The Phase II rule applies to large internal combustion engines, which are primarily used in pipeline transmission service at compressor stations. For th e 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 co mbustion 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 SO 2 as a result of the Industrial Boiler/Process Heater maximum achievable contro l 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 demonstr ation is utilizing one a tmosphere 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 initia l compliance date of 2002 or great er, since effects from MACT with earlier compliance date s should already be accounted for in the 2002 base year inventory.

The future year base inve ntory does not include the NO x co-benefit effects of the Gas Turbines or stationary Reciprocating Intern al Combustion Engines MACT regulations, which USEPA estimates to be small compared to the overall inventory.

Petroleum Refinery Initiative

Three refineries in the VISTAS region ar e affected by two October 2003 Clean Air Act settlements under the USEPA Petroleum Refinery Init iative. The refineries are: (1) the Chevron refinery in Pascagoula, Mississi ppi; (2) the Ergon refi nery in Vicksburg, Mi ssissippi; and (3) the Ergon refinery in Newell, West Virginia. Alt hough these sources are not within Georgia, the expected emission reductions will be accounted for in the 2009 modeling.NO x RACT in 8-hour Ozone SIP

GA EPD will make every effort to include NO x 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 ru le, 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 relative ly 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 i ndicator of collective activity, such as fuel usage, number of household or population. Thus, a variety of activity leve l data is collected, including, US Census economic data, forestry and agriculture agency data, and other data sources. Stationary area source emissi ons are estimated on the county level.

Actual Base Year Inventory

In Georgia, the area source inventory was cal culated 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 modeli ng domain, the state supplied data or the CERR data was used.

Area source categories estimated by GA EPD we re 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 appro aches were used to calculate area source emissions. Emission factors may be grouped as per capita emission factors; commodity consumption-related emission factors; and leve l-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 collectiv e activity for each source categor y 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 ar e gathered from various sources (government reports, census, trade groups, employment da ta, direct surveys, etc.) as appropriate.For the animal husbandry and fertilizer a pplication 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 a nd therefore, do not effect the 8-hour ozone attainment modeli ng demonstration.

For wildland fires, i.e., wildfires and pr escribed 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 th e fire emissions, rather than relying on standard distribution profiles. Data will be obtained through consultation with stake holders that participate in the VISTAS Fire Special Interest Work Group. The fi re data will be spli t into two groups, small fires estimated on a county level a nd treated as an area source; and la rge fires that will be treated as a point source.

Typical Base Year Inventory

The actual base year inventory will serve as th e typical base year in ventory for all area source categories except for wildland fires. Fo r this source category, development of a typical year fire inventory provided th e 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 Fi re Special Interest Work Group was consulted and decided to use State level ratio s 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 deve lop 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 appr oach 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 th e 2002 base year inventory based on existing controls reported for the 2002 base year inventory.

• Controls (including control efficiency, rule e ffectiveness and rule pe netration) provided by the States or originally developed for us e in estimating projected emissions for the USEPA’s Heavy Duty Diesel rulemaking emission projections and used in the CAIRprojections were then used to calculate cont rolled 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 c ontrolled emissions to the approp riate 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 e ither 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 loco motives, 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 USEP A’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 inve ntory will be used for both the actual and typical base year emissions i nventories. All non-road mobile source emissions, except for aircraft engines, commercial ma rine vessels and railroad locomo tives, were estimated using the USEPA NONROAD2005 model. Th is model predicts the emissions for non-road equipment based upon the year inputte d into the model.

For railroad locomotive emissions, em ission 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 airpor ts 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 Dispersi on Modeling System version 4.2, wh en there is sufficient detail to employ it.

Commercial marine emissions are es timated by procedures described in Commercial Marine Activity for Deep Sea Ports in the United States (EPA420-R-99-020)

. Future Base Year InventoryFor the source categories estimated using the USEPA NONROAD model, the model was used to create a future base year inventory. The NONROAD model take s 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 a nd after controls) for 1996 and 2010 will be obtained from the USEPA’s Clean Air Inters tate Rule Technical Support Document. Straight-line interpolations between 1996 a nd 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 thos e vehicles that travel on the roadways. Emissions from motor vehicles occur throughout th e day while the vehicle is in motion, at idle, parked, and during refueling. Each of these emissi ons sources needs to be estimated in order to properly reflect the total emissions from this s ource category. In simplest terms emissions from highway mobile sources are calculated by multiplyi ng 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 devel op the on-road mobile source emissions estimates for carbon monoxide (CO), NO x , PM, and VOC. Key inputs for the MOBILE model include information on the age of vehicles on th e roads, the average speed of those vehicles, what types of road those vehicles are trave ling on, and any control programs (e.g., emissions inspection programs). Inputs are combined with gridded, day-specific temperature data to calculate the gridded, temporali zed emission estimates. Of not e, whereas on-network, link-level emissions estimates are spatially allocated ba sed on link location and s ubsequently summed to the grid cell level, off-network, county-level emissions estimates are spatially allocated based on a combination of the Federal Highway Admi nistration version 2.0 hi ghway 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 th e manner in which the vehicle is operated. MOBILE 6.2 default speed assumptions we re used for this modeling effort.

6.4.2 Vehicle Age Distribution

Georgia-specific vehicle ag e 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 (modele d) meteorology data to calculate temperature. Sp atial 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 Atlant a 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 Insp ection 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 catalyt ic converter tampering on 25- year-old through 1995 model year ve hicles; 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 gasoli ne’s volatility. Geor gia 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 hist orical fuel survey da ta. In 2002, Georgia's gasoline marketing rule limited average gasoline su lfur levels in covere d 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 bi ogenic emissions model, the Biogenic Emissions Inventory System, has recently b een developed and tested by USEPA over two separate modeling domains/episodes. This versi on of the model (BEIS-3, v0.9) contains several changes over BEIS-2, including the following:

• Vegetation input data -- are now base d 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 sunl it/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 cas e 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 signa l 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 conforma l 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 th e most up to date available. The temporal profiles used to disaggregate the annual emissions to the appropria te month, day and hour ar e the latest available profiles provided with the SMOKE model with th e exception of the EGU profiles, which will be developed based on CEM data.

For each model-ready emissions inventory, separa te 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-ro ad 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 liste d here describe the combined efforts to be employed by VISTAS and GA EPD. The VI STAS 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 re asonableness. Additionally, the State agencies will review the mode ling outputs for reasonableness.

7.1 Quality Assurance Objectives

In December 2002, the USEPA published exte nsive 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 scientifica lly sound, robust, and defensible. The new USEPA guidance suggests that a QAPP should include the following elements:

• A systematic planning process including id entification of assessments and related performance criteria

• Peer reviewed theory and equations

• A carefully designed life-cycle developm ent 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 ac curate and appropriate for the problem

• Output data that can be used to help inform decision-making

• Documentation of any changes from th e original quality assurance plan

Moreover, the USEPA guidance specifies that different levels of QAPP may be required depending on the intended application of th e model, with a modeling study designed for regulatory purposes requir ing 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 applica tion 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 mo deling 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 do cument for modeling studies.

7.2 Emissions Model Inputs and Outputs

Emissions QA and Quality Control (QC) are th e most critical steps in performing air quality modeling studies. Emissions processi ng can be time consuming and involves complex manipulation of many different types of large da tasets. If errors are made and rigorous QAmeasures are not in place, these errors may re main 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 fr om 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 appr opriate appraisal of the emissions data and estimates for the VISTAS States. Other tasks wi ll include any additiona l translation from mass emissions files into the emissions modeling input file structure necessary for modeling. The VISTAS Study Team will supplement these activ ities with QA checks on the intermediate and model output files using internal and public domain visualiz ation and diagnostic packages.

This multistep emissions QA/QC approach in cludes the initial emissions QA/QC by the Emissions Gatekeeper described above, as we ll 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 happ en; it is the process of looking for glaring faults in the model input and output data (I/O) and determinin g whether the input data are producing the desired results. Scru tiny of the I/O using standard st atistical analyses can reveal problems in the data and/or the model setup. Us ing 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 da ta 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/he r 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 mode ling 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, orga nize 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 periodi c reviews/audits of the data and modeling process. Outside reviewers will consist of peers, co-workers no t working directly on the inventory in question, state inventory contac ts and stakeholders.

7.2.3 SMOKE Log Files

Each of the programs that make up SMOKE pr oduces 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 (co llectively called "flags ") that occurred during model execution. Generated as text files, the logs are named acco rding to the program that created them and the emissions source modeled by the simulation, and th e names include identifiers that distinguish the simulation from all others. The logs are usua lly 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 SM OKE. 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. C ontrolled by configuration files, Smkreport can create text reports at every step in the em issions 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 assi gned to different temporal, spatial, and chemical profiles; normalize emissions by popul ation; and report the amount of emissions allocated to each vertical layer per model-hour. Smkreport also allows the ta rgeted 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 (spa tial 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 repor t biogenic emissions totals, whereas Smkreport can create reports only for an thropogenic emissions sources.

7.2.5 Visualization Tools

Visualization is an important part of the QA/ QC procedure. Viewi ng bar charts and pie charts of the data verifies that more populous urban counties have greater emissions then the rural counties. Additionally, the P AVE visualization tool is used to graphically view the data to make sure that the data appears reason able 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 mode ling results to ensure that the modeling results look reasonable.

7.2.6 Document Tracking

In order to keep track of the details of modeling, certa in 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 "Meteoro logical Gatekeeper" will be tasked with providing an independent review and quality assurance of th e meteorological modeling and related datasets developed by the VISTAS meteorological mode ling contractor (BAMS) and used subsequently by the emissions and air qu ality 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 cas e of meteorology, the Gatekeeper’s independent QA analysis of the MM5 meteorolog ical datasets serves to prov ide direct assistance to the emissions and air quality modeling team as it undertakes to ratify the SMOKE model outputs and to diagnose CMAQ model perfor mance 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 Meteor ological Gatekeeper will be responsible for ensuring and maintaining th e integrity of the data files uploaded to the project website. This website, hosted by UCR (Uni versity California – Riverside), serves as the repository of data for the ENVIRON/UCR/Al pine modeling centers and for the VISTAS Technical Analysis Workgroup participants. In performing the Gatekeep er quality assurance activity, one of the first steps is to conduct an independent op erational 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, a nd 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 ex ercise MCIP version 2.3 to read the MM5 outputs from BAMS and produce binary input files fo r the CCTM to provide the complete set of parameters necessary in the emission s processing and air quality modeling.

In summary, the quality assurance plan fo r 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 MM 5 files with MCIP v2.3 using the predetermined MCIP options. The MCIP f iles will then be compared with those provided by BAMS to verify that identic al results from the MCIP processing were obtained.

• Horizontal and vertical plots of temperat ure, pressure, precipitation, modeled flow patterns, PBL heights, etc. will be cr eated 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 simu lation as an independent QA and evaluation of the database.

• Plots constructed by the VISTAS Gatek eeper 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 a nd output data include the following:

• Verification that correct c onfiguration and science options are used in compiling and running each model of the CMAQ mode ling 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 cri tical 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 ve rification that the correct scien ce options were specified in the model itself and that the correct input files we re used when running the model. For the CMAQ model, a system of naming conventions was empl oyed which uses environment variables in the compile and run scripts that gua rantee that correct inputs and science options are used. A redundant naming system is also used so that th e 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 file s are located. This is accomplished by using the environment variables in the scri pts 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 defi nition “$MECH = CB4,” and this va riable is hard coded into the script for the executable name, the output file name, and the output directory name. Thisprocedure 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 or iginal runs scripts and directory structure that were used in perf orming a model simulation). For example, if a simulation is performed with the SAPRC mechanis m, instead of editing the original scripts to specify “$MECH = SAPRC,” a parall el directory structure with a new set of scripts to perform the SAPRC simulations will be created. This prov ides 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 pr ovides a record of i nput file names, warni ng messages, etc., that will be archived.

Post-processing QA of the CMAQ output files si milar 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, erro rs in the emissions inpu ts 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 replicat e observed concentrations of ozone and precursors, and

• How accurately the model characterizes th e 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 “dia gnostic evaluation.” Sinc e 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 eval uate model performance. Working with the knowledge that many states involved with the VI STAS regional haze work would want to apply some of the work to their individual SIPs for 8 hour ozone and PM 2.5 , plans were put in place to perform exhaustive analysis of all atmospheri c constituents, including ozone. GA EPD intends to build off the modeling efforts with VISTAS; therefore, the model pe rformance 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, PM 2.5 and Regional Haze , VISTAS will compile a suite of me trics 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 fulf ill the requirements in the 8-hour ozone guidance (addition of average peak pred iction accuracy), and to bett er accommodate other modeling groups with their comparison of mo deling efforts. A list of metr ics for calculation on a routine basis using the UCR analysis package is list ed in Table 8-1. The metrics calculated in conjunction with VISTAS will include the examin ation of various atmospheric constituents, including ozone.

Typically, the statistical metrics are calcul ated 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 com puted for the appropriate averaging times: 1 hr for ozone, and gas-phase precursors su ch as NO, nitrogen dioxide (NO 2 ), CO, SO 2 , 8-hour for ozone, and 24-hour for sulfate, nitrate, PM 2.5 , and other aerosol species. These results will then be averaged over annual, mont hly, and seasonal periods for di splay, further analysis, and reporting. Should it become necessary as part of model performance dia gnosis, the statistics willbe aggregated in other ways, e.g., (a) day vs. ni ght, (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 statistic s for 1-hour and 8-hour ozone will be reported. The statistics for the pollutants and precursors wi ll be reviewed internal ly for reasonableness.

Table 8-1: Statistical Metrics Statistical Measure Shorthand Notation Mathematical Expression Notes

Accuracy of Paired Peak A p

peak peak O OP

−

Coefficient of Determination r 2 

# ( ) 

# ( )
∑ ∑ ∑

= = =



### − − ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

−−

N

i N

i i i N

i i i



### OOPP OOPP

1 1 2 2 2

1 

### )()( P i = prediction at time and location i; O i = observation at time and location i;
P = arithmetic average of P i , i = 1, 2, . . ., N; O = arithmetic average of O i , i = 1, 2, . . ., N; Normalized Mean Error NME



## ∑ ∑

=

= −

N

i i N

i ii

O OP

1 1 Reported as %

Root Mean Square Error RMSE 

## ( ) 2 1

1 2 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− 

## ∑

= N

i ii OP N Reported as %

Fractional Gross Error F E 

## ∑

= + − N

i ii ii OP OP
N 1 2 Reported as %

Mean Absolute Gross Error MAGE 

# ∑

= − N

i ii OP N 1 1

Mean Normalized Gross Error MNGE 

# ∑

= − N

i i ii

O OP
N 1 1 Reported as %

Mean Biased MB 

## ( ) 

# ∑

= − N

i ii OP N 1 1 Reported as concentration

Mean Normalized Bias MNB 

## ( ) 

## ∑

= − N

i i ii

O OP
N 1 1 Reported as %

Mean Fractionalized Bias (Fractional Bias) MFB 

## ∑

= ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ − N

i ii ii OP OP
N 1 2 Reported as %

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 ev aluation will utilize numerous graphical displays to facilitate quantitative and qualita tive comparisons between CMAQ predictions and measurements. Together with the statistical metr ics 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 t ools are intended to depict the model’s ability to predict the observed gaseous species, su ch 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 c oncentration maps (i.e., tile plots)

• Concentration scatter plots stratified by station, by time, and by network

• Bias and error strati fied by concentration

• Bias and error stratified by time

• Histogram plots of the statistical metr ics, 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 hour ly 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 Testin g (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 improve ment wherein a variety of diagnostic probing tools and sensitivity testing methods are used to id entify, analyze, and then attempt to remove the causes of inadequate model performance. This is invariably the most technically-challengingand time consuming phase of a modeling study. The annual CMAQ model base case simulations are expected to present some performance cha llenges that may necessitate focused diagnostic and sensitivity testing in order fo r them to be resolved. It is hoped that these diagnostic and/or sensitivity tests can be adequa tely carried out within the re sources and schedule. Where practical, diagnostic or sensitivit y analyses, if needed, could be performed on selected episodes within the annual cycle, there by avoiding the time-consuming task of running CMAQ for the full 2002 period. Below, the types of diagnostic and sensitivity te sting methods that might be employed in diagnosing inadequate model perf ormance and devising appropriate methods for improving the model response are identified.

8.2.1 Traditional Sensitivity Testing

Model sensitivity experiments are useful in th ree distinct phases, or “levels”, of an air quality modeling study and all will be used as appropriate. These levels are:

• Level I : Model algorithm evaluatio n and configuration testing

• Level II : Model performance testing, uncer tainty 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, give n that open community nature of CMAQ and the frequent science upd ates to the model and supporti ng 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 comp ensatory 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 refine ment and data collection programs

The merits of performing Level II se nsitivity testing will depend upon whether performance problems are encountered. In addi tion, 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 po ssible 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 (HNO 3 ) chemistry

• Modified ammonia and HNO 3 deposition velocities

Note that in a few cases [e.g., ver tical grid structure, ammonium (NH 4 ) emissions estimates], some sensitivity experimentation has already been carried out by VISTAS. To the extent that this information can help guide the future diagno stics analyses, this earlier work will be used.

Level III sensitivity analyses have two main purpo ses. First, they facilitate the emissions control scenario identificati on 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 me thod has its strong point s and they will be employed where needed. The second purpose of Leve l III sensitivity analyses is to help quantify the estimated reliability of the air quality mode l 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 a nd other aerosol sensitivities to SO 2 emissions

• Ozone, sulfate, nitrate, ammonium and othe r aerosol sensitivities to elevated point source NO x emissions

• Ozone, sulfate, nitrate, ammonium and ot her aerosol sensitivities to ground level NO x emissions

• Sulfate, nitrate, ammonium and other aerosol sensitivities to ammonia

The need to perform sensitivity experimentati on (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 diagnos tic experiments will hinge on the availability resources and sufficient time to carry out the anal yses. 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 bot h gas and aerosol species are used in the model performance evaluation. Table 8-2 summa rizes ambient monitoring networks used to collect data for Air Quality model performance evaluation. Data have been compiled for all networks listed except the Photochemical A ssessment 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 oz one 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 PM 2.5 and PM 10 (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 PM 2.5 , 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, NO 2 , O 3 , SO 2 , PM 2.5 , PM 10 , 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 PM 2.5 (FRM Mass, OC, BC, SO 4 , NO 3 , NH 4 , Elem.); 24-hr PM coarse (SO 4 , NO 3 , NH 4 , elements); Hourly PM 2.5 (Mass, SO 4 , NO 3 , NH 4 , EC, TC); and Hourly
gases (O 3 , NO, NO 2 , NO y , HNO 3 , SO 2 , CO) 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 PM 2.5 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; SO 4 , NO 3 , HNO 3 , NH 4 , SO 2 ), O 3 , meteorological data Hourly http://www2.nature.nps.gov/ard/gas/netdata1.ht

m9.0 Control Strategy

It is important to remember that photochemi cal models are tools; they do not make decisions. The results from photochemical models ar e one of several pieces of information that decision-makers must consider when adopting cont rol 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 se ction 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 desi gn a series of simulations using across-theboard reductions of VOC, NO x , or VOC and NO x combined can be run. The purpose of these simulations is to evaluate the relative effectiveness of VOC and NO x 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 decision s 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 fa shion that reduces both peak ozone concentrations and population exposure. Emission controls that affect both VOC and NO x should be sorted separa tely from VOC or NO x 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 subs ets which focus on VOC only, NO x only, and combinations of VOC and NO x 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 stra tegy on air quality and popul ation exposure. The results of these analyses will be summarized both in tabular and graphical form to allow systematic comparison and cont rast of all strategies.

To assist decision makers in fully unde rstanding 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, populat ion exposure and peak oz one 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 cont rol 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 cont rol strategies identified by the pr eceding steps, will be modeled until attainment is reached.

A “frozen” future year dataset will be avai lable 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 a nd 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 demonstr ation 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 re ductions 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 de termine attainment. These site-specific ratios are called relative redu ction 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, th en additional weight of evidence is needed to corroborate a positive attainment de monstration. If the predicted future design values are below 82 ppb, the area is determined to clearly atta in 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 mo nitored at site "x," in units of ppb. Currently the USEPA recommends calculating the DVB by averaging the three design value periods to include the ba seline inventory year. In othe r words, the DVB is based on a 5-year weighted average centere d 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 epis ode) 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 superim posed grid system. Table 10-1 provides the USEPA recommendations for defi ning “nearby” cells for grid systems having cells of various sizes.Table 10-1: USEPA R ecommendations 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 perf orm 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 th rough 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 enti re 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 m eet 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 te st, the USEPA 8-hour Ozone Guidance suggests additional measures should be take n to further support or refute the attainment test results. This corroboratory evidence is referred to as supplementa l 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 attainme nt (DVF < 82 ppb), then basic supplemental analysis will be performed to further support the te st’s findings. If either the attainment or screening tests are greater than 87 ppb, it is doub tful that the more qualitative arguments made in a weight of evidence determination can be suffi ciently convincing to conclude that the NAAQS for ozone will be attained.

For DVFs between 82 to 87 ppb, a weight of ev idence determination will be preformed to supplement the conclusion that the area is expect ed to attain the NAAQS. The end product of a weight of evidence determination is a document wh ich 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.11.0 References

Anthes, R.A. and T.T. Warner. 1978. “The Deve lopment of Mesoscale Models Suitable for Air Pollution and Other Mesome teorological Studies,” Mon. Wea. Rev. , Vol. 106, pp, 1045- 1078.

Anthes, R.A., Hsie, E.-Y. and Kuo, Y.-H., 1987. Description of the Penn State/NCAR Mesoscale Model Version 4 (MM4). NCAR/TN-282+STR, National Center for Atmospheric Research.

Byun, D.W., and J.K.S. Ching, 1999. “Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) M odeling System,” EPA/600/R-99/030.

Byun, D.W., J. Young, G. Gipson, J. Godowitch, F. Binkowski, S. Roselle, B. Benjey, J. Pleim, J.K.S. Ching, J. Novak, C. Coats, T. Od man, A. Hanna, K. Alapaty, R. Mathur, J. McHenry, U. Shankar, S. Fine, A. Xiu and C. Jang (1998a). Description of the Model-3 Community Multiscale Air Quality (CMAQ) model. Proceedings of the American Meteorological Society 78 th Annual Meeting, Phoenix, AZ, Jan 11-16, 1998: 264-268.

Byun, D.W., J. Young, G. Gipson, J. Schere, J. Godowitch, J. Pleim, F. Binkowski, S. Roselle, B. Benjey, J. Ching, J. Novak, and S. LeDu c. “An urban air quality simulation with the Community Multi-Scale Air Quality (CMAQ) modeling system.” (1998b). Preprint, Second Urban Environment Symposium, 13th Conference on Biometeorology and Aerobiology, November 2-6, 1998, Albuque rque, NM. American Meteorological Society, Boston, 36-39.

Clark, Richard D., R. Walker Jr., D. O'Donnell, E. Hohman, and D. Unruh, 2003. Low level jets over the mid-Atlantic region during NE-OPS 1999–2002. Paper No. 6.7 presented at the Fifth Conference on Atmospheric Chemistry: Gases, Aerosols, and Clouds (American Meteorological Society Annual Meeti ng), Long Beach, CA, February 9-13.

Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569-585.

Ching, J.K.S., D.W. Byun, J. Young, F. Binkowsk., J. Pleim, S. Roselle, J. Godowitch, W. Benjey., and G. Gipson, 1998: Science featur es in Models-3 Community multiscale air quality system. Proceedings of the Amer ican Meteorological Society 78th Annual Meeting, Phoenix, AZ, Jan. 11-16, 1998: 269-273.

Coats, C. J., A.H. Hanna, D. Hwang, and D.W. Byun, 1995: Model engineering concepts for air quality models in an integrated environmenta l modeling system. Transactions of Air and Waste Management Association Specialt y Conference on Regional Photochemical Measurement and Modeling Studies, Nov. 8-12, San Diego, CA. 1993, 213-223.

Coats, C.J, 1995: The EDSS/Models-3 I/ O applications programming interface. http://envpro.ncsc.org/products/ioapi/index.html MCNC Env. Programs, RTP NC.Dennis, R.L., D.W. Byun, J.H. Novak, K.J. Ga lluppi, C.J. Coats, and M.A. Vouk, 1996: The next generation of integrated air quality modeling: EPA's Models-3. Atmospheric Environment, 30, No. 12, 1925-1938.

Dudhia, J. 1993. "A Non-hydrostatic Versi on of the Penn State/NCAR Mesoscale Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front,” Mon. Wea. Rev. , Vol. 121. pp. 1493-1513.

Emery, C. et al., 1999. “Ozone Modeling for the Kansas City Nonatta inment Area: Final Protocol,” prepared for the Kansas Department of Health and Environment, prepared by ENVIRON International Corpor ation and Alpine Geophysics.

ENVIRON, Alpine Geophysics, UC Riverside and UC Davis. 2003a. “VISTAS Emissions and Air Quality Modeling – Phase I Task 4a/b Report: Review of Model Sensitivity Simulations and Recommendations of Initial CMAQ Model Configurations and Sensitivity Tests.” Revised Draft July 25.

EPA, 2001. “Guidance for Demonstrating Atta inment of Air Quality Goals for PM2.5 and Regional Haze,” Draft Report, U.S. Environm ental Protection Agency, Research Triangle Park, NC.

EPA, 2002. “Guidance for Quality Assurance Project Plans (G-5),” U.S. Environmental Protection Agency, Office of Environmen tal Information, Washington, DC 20460.

EPA, 2005. “Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-hr Ozone NAAQS.” Draft Fi nal (February 2005), U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, N.C.

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994. "A Description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5).” NCAR Tech. Note, NCAR TN-398-STR, 138
pp.

Houyoux M. R., and J. M. Vukovich, 1999. “Update s to the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System and Inte gration with Models-3,” The Emissions Inventory: Regional Strategies for the Futu re, Air and Waste Management Association, 26-28 October, Raleigh, NC.

ICF Consulting/SAI, 2005. “Analysis of the Re lationships between Meteorology and Ozone for Several Southern Cities” (Report 05-078).

Johnson, M., Meteorological Mode ling Protocol: Iowa Department of Natural Resources (IDNR) 2002 Annual MM5 Application, 2003.

McNally, D. E., and T W. Tesche, 2003. “Annual A pplication of MM5 to the Continental United States,” prepared for the U. S. Environmen tal Protection Agency, Office of Air Quality Planning and Standards, prepared by Alpine Geophysics, LLC, Arvada, CO.Olerud, Donald, K. Alapaty, and N. Wheeler, 2000: Meteorological Modeling of 1996 for the United States with MM5. Prepared for Normal Possiel, Client Representative, U.S. EPA/OAQPS. EPA Task Order Number CAA689805.

Olerud, D and A. Sims. 2003. “MM5 Sensitivity in Support of VISTAS (Visibility Improvement – State and Tribal Association): Task 2e De liverables.” Baron Advanced Meteorological Systems, LLC, Research Tria ngle Park, NC. (available at: http://www.baronams.com/projects/VISTAS/#reports). December 4.

Pleim, J., Gipson, G., Roselle, S., Young, J., 2003, New features of th e 2003 release of the CMAQ model. 2nd Annual CMAS Models-3 Conference, Research Triangle Park, NC.

Seaman, N.L. 2000. “Meteorological M odeling for Air Quality Assessments,” Atmos. Environ

., Vol. 34, No. 12-14, 2231-2260.

Seaman, N.L., D.R. Stauffer, and T.W. Tesche . 1992. "The SARMAP Meteorological Model: A Four-Dimensional Data Assimilation Techni que Used to Simulate Mesobeta-Scale Meteorology During a High-Ozone Episode in California,” International Specialty Conference on Tropospheric Ozone Nonattain ment and Design Value Issues, U.S. EPA/AWMA, 27-30 October, Boston, MA.

Seaman, N. L., et al., 1997. "The Use of the San Joaquin Valley Meteorological Model in Preparation of a Field Program in the Sout h Coast Air Basin and Surrounding Regions of Southern California: Volume II -- Numerical Modeling Studies for the Development and Application of a Guidance T echnique to Support of the Design of the 1997 Southern California Ozone Study Field Pr ogram,” prepared for Alpine Geophysics, LLC and the California Air Resources Board, prepar ed by the Department of Meteorology, Pennsylvania State University, University Park, PA.

Seppanen, C., 2003. “Status of SMOKE,” 2003 Models-3 Users’ Workshop October 27-29, Research Triangle Park, NC.

Stauffer, D.R. and N.L. Seaman, 1990. “Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part I: Expe riments with Synoptic Data.” Mon. Wea. Rev., 118, 1250-1277.

Stauffer, D.R., N.L. Seaman and F.S. Binkowski, 1991. “Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part II: Effects of Data Assimilation Within the Planetary Boundary Layer.” Mon. Wea. Rev., 119, 734-754.

Tesche, T. W., et al., 2000. “Ozone Modeling Protocol for the Penins ular Florida Ozone Study (Version 1.0),” prepared for the Florida Department of Environmental Protection, prepared by Alpine Geophysic s, LLC, Ft. Wright, KY.

Tesche, T. W., et al., 2003. “Peninsular Florida Ozone Study Final Report,” prepared for the Florida Department of Environmental Protec tion, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.Xiu, A., and J.E. Pleim, 2000: Development of a land surface model. Part I: Application in a mesoscale meteorology model. Journa l of Applied Meteorology, 40, 192-209.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 Advan ced Meteorological, LLC BCON Boundary Condition Processor BCs Boundary Conditions BEIS3 Biogenic Emission I nventory 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 HNO 3 Nitric Acid I/O input/output I/O API Input/Output Applications Programming Interface IC/BC Initial Condition/Boundary ConditionICON 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-chlo ride-sodium-water inorganic aerosol in thermodynamic e quilibrium 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-Chemi stry Interface Processor MCIP2.3 Meteorological-Chemistry Interface Processor (ver. 2.3) MM5 Mesoscale Meteorological Mode l, 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 f actor 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 NH 4 Ammonium NMC National Meteorological Center NO Nitric Oxide NO 2 Nitrogen Dioxide NO x Oxides of NitrogenNO x 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 NO x , one of the precursors of ozone. NO y Total Available Nitrogen NWS National Weather Service O 3 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 PM 2.5 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 Univ ersity/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 Ava ilable 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 Op erator Kernel Emissions SMVGEAR Sparse Matrix Vectorized Gear solver SO 2 Sulfur Dioxide SOA Secondary Organic Aerosol SORGAM Secondary Organic Aerosol Model STN Speciation Trends NetworkT 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 Califor nia 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 Stat e 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