1 MEMORANDUM

TO: Docket No. EPA-HQ-OAR-2012-0918 Air Quality Designations for the 2012 PM 2.5 Standard

FROM: Beth W. Palma Air Quality Policy Division, OAQPS

SUBJECT: Georgia Deferred Area - Air Qu ality Designations for the 2012 PM 2.5 National Ambient Air Quality Standard (SAN 5706)

This memorandum provides the rationale for th e U.S. Environmental Protection Agency’s
decision to use the additional time available to it under section 107( d)(1)(B) of the Clean Air Act (CAA) to obtain additional information and further evaluate air quality monitoring data before promulgating initial area designations for the 2012 primary annual fine particle National Ambient Air Quality Standard (2012 annual PM 2.5 NAAQS) 1 for certain areas in Georgia and relevant nearby counties in Alabama and South Carolina.

Upon promulgation of a NAAQS, section 107(d) of the CAA requires the EPA to subsequently promulgate area designations based on that NAAQS. Specifically, EPA must designate as
“nonattainment” those areas that are violating a NAAQS, or that are contributing to a violation of the NAAQS in a nearby area. By contrast, the EP A designates as “unclassifiable/attainment” those areas where air quality monitoring data in dicate attainment of the NAAQS, and for areas
that do not have monitors but which the EPA has r eason to believe are likely to be in attainment and are not contributing to nearby violations . Finally, the EPA rese rves the category of
“unclassifiable” for areas wher e the EPA cannot determine based on available information whether an area is meeting the NAAQS or contributing to a nearby violation.

As described in more detail in the memorandum, Data Quality Issues in Georgia Affecting Air Quality Designations for the 2012 PM 2.5 National Ambient Air Quality Standards , Georgia’s monitoring program has experienced data complete ness issues for several areas across the State. 2 Given these data completeness issues, for seve ral counties the EPA ca nnot calculate a valid design value for the 2011-2013 time period (the data that the EPA is usi ng to determine areas
that violate the 2012 annual PM 2.5 NAAQS). Without a valid design value, the EPA has
insufficient information to determine whether th ese areas are meeting or are not meeting the

1 On December 14, 2012, the EPA prom ulgated a revised primary annual PM 2.5 NAAQS (78 FR 3086, January 15, 2013). In that action, the EP A revised the primary annual PM 2.5 standard, strengthening it from 15.0 micrograms per cubic meter ( μ g/m 3 ) to 12.0 μ g/m 3

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2 Memorandum from Liz P. Naess, Gr oup Leader, Air Quality Analysis Gr oup, US EPA Office of Air Quality Planning and Standard, to EPA Docket EPA-HQ-OAR-2012-0918, Air Quality Designations for the 2012 PM 2.5 Standards, titled “Data Quality Issues in Georgia Affecting Air Quality Designations for the 2012 PM 2.5 National Ambient Air Quality Standard.”2 NAAQS. However, the EPA believes that forthcom ing monitoring data will likely result in the three years of complete and valid data needed to assess compliance with the standard and promulgate designations for the areas identifi ed below. Accordingly, the EPA is deferring designations for these areas, and using the addi tional time available unde r section 107(d)(1)(B) of the CAA to assess that forthcoming data, and to promulgate initial ar ea designations for the
identified areas.

Table 1a identifies the counties for which the EP A is exercising its discretionary authority to defer designations. Figure 1 displays a map of the areas being deferred. Counties being deferred either contain monitors with incomplete 2011 -2013 data, or are in the same Core Based Statistical Areas (CBSA) as such counties and woul d be most likely to cont ribute to a violation of the NAAQS should forthcoming data reveal such a violation. Accordingly, the EPA has determined that it currently lacks sufficient information to promulgate designations for these counties. The additional time afforded EPA by the decision to defer designations for these
counties will allow the EPA to collect and asse ss that forthcoming info rmation (including air quality monitoring data) before designati ons for these areas are promulgated.

Table 1a. Counties* for which the EPA is Deferring Designations

County with Incomplete Data Adjacent County Associated Area Name

Dougherty --- Albany
Cobb Fulton Gwinnett Paulding Bartow Clayton Cherokee
Coweta DeKalb Douglas Forsyth Henry Atlanta

Richmond Aiken (South Carolina) Columbia Augusta

Glynn --- Brunswick Muscogee Russell (Alabama) Columbus Chatham Effingham Savannah Lowndes Brooks Valdosta Washington --- Washington County

*Unless otherwise noted, the counties listed in this table are in Georgia.3 Figure 1. Map of Georgia Co unties (including Adjacent Co unties in Alabama and South Carolina) that the EPA is Deferring

This memorandum also provides information re garding certain counties nearby the deferred counties listed in Table 1a, whic h the EPA is not deferring designations for. Instead, the EPA is, for the time being, designating these counties “u nclassifiable/attainment ,” based on the EPA’s determination that these counties are less likely to contribute to nearby affected monitors than the counties that the EPA is deferring designations for within the same CBSA. Because these
unclassifiable/attainment countie s are relatively less likely to be contributing to a hypothetical NAAQS violations that may be revealed by fort hcoming monitoring data, there is comparatively less justification to defer designations for these counties.

In conducting the contribution analyses descri bed in this memorandum, the EPA focused only on
counties located within the same CBSA as an af fected monitor, because such counties would be most likely to contain nearby sour ces that would contribute to a ny hypothetical violation at that monitor. In the event that comple te data at any affected monitor ultimately reveals a violation of the NAAQS, the EPA intends to conduct the full five-factor analysis to analyze potential4 contribution, as described in the April 2013 me morandum issued for this designation process. 3 Such an analysis would reevalua te contribution from all counties within the viol ating monitor’s CBSA (including those currently be ing designated unclassif iable/attainment in this action), as well as from additional nearby areas with the potential to contribute to that violating monitor. As a result of any such future analyses, the boundari es of any future nonattainment areas may differ
from the boundaries of the areas for which th e EPA currently is deferring designations.

Table 1b shows information on the available, but incomplete, PM 2.5 annual average concentration data from 2012, 2013 and 2014 at the highest readi ng monitors in the Georgia areas for which the EPA is currently deferring designations. This da ta is not directly rele vant to the analyses contained in this memorandum, but serves as contex t to show that it is less likely that any of these areas is violating the 2012 PM 2.5 standard of 12.0 micrograms per cubic meter (μg/m 3 ), and that complete data collected after 2013 w ill demonstrate these areas all meet the NAAQS. Nonetheless, as a regulatory matter, at this time the EPA cannot determine that areas with affected monitors are attaining the standards, and is thus invoking its disc retionary right to defer designations for those areas.

Table 1b: PM 2.5 Annual Averages 2012-2014 for the Deferred Areas in Georgia

Area 2012 Annual Average (μg/m 3 ) 2013 Annual Average (μg/m 3 ) Preliminary 2014 Annual Average for

Q1-Q3 (μg/m 3 ) 4
Savannah 9.2 9.0 9.5 Atlanta 11.3 5 10.4 11.3 Albany 10.6 10.0 10.2 Brunswick 7.5 6 8.2 8.3 Valdosta 8.6 8.5 8.9
Columbus 10.4 9.7 10.4 Augusta 10.4 9.2 10.2 Washington 9.8 9.4 10.2

The EPA will work with Georgia, Alabama, and S outh Carolina to finalize the designations for these counties as soon as complete certified PM 2.5 monitoring data are available, which the EPA anticipates will allow for prompt promulgation of these designations. When complete air quality

3 Memorandum dated April 16, 2013, from Gina McCarthy, Assistant Administrator, to Regional Administrators, Regions 1-10, titled “Initial Area Designations for the 2012 Revised Primary Annual Fine Particle National Ambient Air Quality Standard.”

4 Preliminary 2014 data is based on uncertified data in AQS for th e first three quar ters of 2014.

5 This annual average contains a quarter with less than 50% of the required data.

6 This annual average contains a quarter with less than 50% of the required data.5 monitoring data are available and have been certified, the EPA i nvites the affected states to submit revised designation and boundary recommenda tions, as appropriate. If at that time the EPA believes that it is necessary to modify a state’s recommendation and to promulgate a designation different from the state’s recommenda tion, then the EPA will notify the state at least 120 days prior to promulgating the final desi gnation and the EPA will provide the state an opportunity to comment on the potential modification. Each state will then have an opportunity to respond to the EPA’s proposed designations a nd boundaries. Pursuant to section 107(d), the EPA cannot promulgate the designation for these ar eas less than 120 days from the date of the EPA’s announced intention to modify the recommendation. The EPA will promulgate the designations for these deferre d areas at a later date, in a separate final rule.

Approach

In determining the appropriate set of neighboring counties to include with the set of monitored counties for which the EPA is deferring designa tions, the EPA conducted a hypothetical analysis assuming that the monitors with the incomplete data were violating the 2012 standard of 12.0

μ g/m 3 . The EPA is proceeding under this assump tion under the precautionary principle that
because it is unclear whether there is a violation in this area due to data incompleteness, then for purposes of conducting the contribution analysis th e EPA will presume that there may be such a violation. The EPA’s contribution analysis for these hypothetical violations focused on counties
within the same CBSA as the affected monitor because these counties w ould be most likely to contain nearby sources that cont ribute to any violations. Spec ifically, the EPA evaluated all counties in the same CBSA as the county with the monitors in question, and assessed the relatively likelihood that these presumptively “nearby” counties could contribute to any hypothetical violation of the standa rd at the affected monitors. Us ing information similar to the information used for determining nonattainment area boundaries, 7 the EPA evaluated the information available for these CBSAs, including the more limited Air Quality Data, as well as emissions and emissions-related data; meteorology; and geography/topography. 8 One of the EPA’s primary boundary considerations for dete rmining whether to defer a final designation decision for these counties is whether the agency believes the emissions in those nearby counties are likely to contribute to the potential violations, and whether th e meteorological data indicate
that those emissions woul d be likely to impact an affected monitor. Where available evidence indicates that emissions in adjacent counties coul d likely have the potential to contribute to

7 Memorandum dated April 16, 2013, from Gina McCarthy, Assistant Administrator, to Regional Administrators, Regions 1-10, titled “Initial Area Designations for the 2012 Revised Primary Annual Fine Particle National Ambient Air Quality Standard.”

8 The EPA did not consider jurisdictional boundaries for purposes of deferred designations because jurisdictional boundaries are most helpful for nonattainment planning purposes, whereas these evaluations merely concern EPA’s best assessment of an appropriate dividing line between de ferred and unclassifiable/attainment counties. No areas in Georgia are being designated nonattainment pursuant to this analysis, although this would change in the future if forthcoming data reveal a NAAQS violation. This and other circumstances described in this memorandum and other deferral memoranda being released today further highlight critical differences between the considerations and analysis for designating areas nonattainment or unclassifiable, versus the process for deferring designations.6 monitors with data incompleteness, the EPA is deferring the designation for those counties as well as for the county with the affected monitor.



### Albany Area (Albany, GA CBSA)

Albany is located in a CBSA that includes five counties in Georgia (Baker, Dougherty, Lee, Terrell, and Worth). The most popul ated and industrialized por tion of the Albany area, where
most of the emissions and emissions activitie s occur, is primarily contained in Dougherty County. More information on the EPA’s evaluation of the emissions for the counties in the Albany CBSA is provided below. The Albany area does not have any geographical or topographical barriers meaningfully limiting air po llution transport within its air shed. Therefore, this factor did not play a mean ingful role in this evaluati on. The Albany CBSA is outlined in green in Figure 2, along with the location of point sources and ai r quality monitors.

Figure 2. Albany CBSA with Point Sources and Air Quality Monitor Location7 The Dougherty County monitor located in th e Albany area has incomplete 2011-2013 data. Based on incomplete data, the incomplete D ougherty County design value (DV) is 10.9 μg/m 3

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The EPA evaluated PM 2.5 and the precursor emissions, along w ith other related data from the counties in the Albany CBSA. The Albany area has th ree major point sources, all of which are in Dougherty County: MillerCoors, LLC, Procte r & Gamble Paper Products Co., and GA Power Company Plant Mitchell. Other sources in th e CBSA include county level emissions from nonpoint sources, nonroad mobile, on-road mobile, fires and local traffic. As can be seen in Table 2 below, Dougherty County has the largest SO 2 emissions (1,703 tpy) and NOx emissions (4,962 tpy) of any of the counties in the CBSA and a large amount of direct PM 2.5 emissions (2,183 tpy). Dougherty is the most populous county in the Albany CBSA with approximately 60 % of the population residing this county. A lthough the remainder of the CBSA accounts for 40 % of the populations in the CBSA, the areas outside of Dougherty are mainly rural. While the population density in Dougherty is 287 people per square mile, the population density in the remainder of the counties ranges from 10 to 80 people per squa re mile. The highest category of emissions from the other counties in the CBSA are total direct PM 2.5 , with Worth County having the highest level at (3,027 tpy). 9 A further evaluati on of the direct PM 2.5 emissions for Baker, Lee, Terrell and Worth counties indicates that the major ity of these emissions are contributed by fires, and most of the fire emissions are from prescribed fires.

Table 2. Summary Statistics for the Albany, GA CBSA 10

Dougherty Baker Lee Terrell Worth

Core urbanized county or outlying? Core 11 Outlying Outlying Outlying Outlying

SO 2 emissions (tpy) 1,703 138 116 105 145

PM 2.5 emissions (tpy) 2,183 1,958 1,718 1,741 3,027
NOx emissions (tpy) 4,962 704 1,246 933 1,625

NH 3 emissions (tpy) 302 560 340 423 829

VOC emissions (tpy) 4,157 1,458 1,708 1,451 2,592

9 The majority of these direct PM 2.5 emissions are from non-point sources and may be event-related. If a violation of the NAAQS occurs when three years of complete data be comes available, the poten tial impacts of these eventrelated emissions may be further evaluated.

10 Emissions from this table are generated from the 2011 National Emissions Inventory (NEI). Sulfur dioxide is denoted by SO 2 , oxides of nitrogen is denoted by NOx, ammonia is denoted by NH 3 and volatile organic compounds is denoted by VOC in this table.

11 In this document, the EPA’s reference to a county as a Core county is an indication of whether most of the urbanized area and population in is within a CBSA.8 Population 94,755 3,437 28,440 9,322 21,626

VMT (Millions) 956 59 278 157 310

VMT (% of CBSA) 54% 3% 16% 9% 18%

The EPA also evaluated the meteorology in the area by evaluating wind data collected at the monitor located in the Albany CBSA as shown in Figure 3. As shown, the winds blow from all directions at times throughout th e year, with a lower frequency fr om the northerly directions. These variable winds occur mostly at mid-level speeds of 2 to 6 meters per second. This indicates that emissions sources in Lee County to the north are less likely to contribute to the Albany monitor.

Figure 3. Wind Rose in the Area of Analysis for the Albany Area

Albany GA CBSA Conclusion

Based on the information summarized in Table 2 and the wind rose data, the EPA is concluding that of the five counties in the Albany CBSA, Dougherty County has th e greatest potential to9 contribute to potential future violations of the standard at the Dougherty County monitor currently lacking complete data. For all othe r counties in the Albany CBSA, the EPA believes that it is relatively less likely that emissions from these counties w ould contribute to any hypothetical violation of the 2012 annual PM 2.5 NAAQS.

With the exception of direct PM 2.5 and NH 3 emissions, Dougherty County has the most emissions of any county in the CBSA. The NH 3 emissions are most likely relate d to agricultural activities. The relative potential for particles traveling from agricultural activities is less likely than from industrial sources because of thei r proximity to the ground. More th an 50 percent of the direct
PM 2.5 emissions in the outlying counties (i.e., Baker, Lee, Terrell and Worth counties) in the Albany CBSA are due to prescribed fires. In ge neral, the carbonaceous fractions of direct emissions of PM 2.5 (i.e., particulate organic carbon and particulate el emental carbon) are an important “local” contributor to ambient PM 2.5 concentrations and are co mparatively less likely to travel great distances. In Baker, Lee, Terre ll, and Worth Counties – just as in other parts of southern Georgia and some nearby areas of s outheast Alabama and nor thern Florida – large amounts of direct PM 2.5 emissions are associated with prescr ibed and managed fires, which can be an annual activity. Although these emi ssion totals are higher than direct PM 2.5 emissions in other parts of the country, emissions associated w ith such fires are different than emissions from stationary and mobile sources. In general, these fires are only a llowed to occur during conditions and times of the year that minimize their air quality impact, and emissions from these prescribed fires therefore tend to have lower ambient PM 2.5 potential than is implied by their mass. Furthermore, most prescribed fires are low-in tensity fires performed in accordance with burn permits issued by the Georgia Forestry Commi ssion, which limit these fires to circumstances with “good smoke dispersion conditions.” Therefore, the potential for larg e quantities of smoke (direct PM 2.5 emissions) from Baker, Lee, Terrell a nd Worth counties to be transported long distances and to contribute to po tential violations at the monito r in Albany is relatively less likely.

Of particular importance to the EPA’s conclu sion are the populati on and emissions of SO 2 and
PM 2.5 in Baker, Lee, Terrell and Worth Counties, which are relatively lo w when compared to Dougherty County. Although the county-level NO x, VOC and ammonia emissions appear sizable in the Table 2 above, ambient PM 2.5 concentrations in the sout heastern U.S. tend to be impacted most significantly by emissions of direct PM 2.5 emissions and SO 2 emissions. Furthermore, although the outlying counties acco unt for 40% of the population in the Albany CBSA, the 60% located in Dougherty County are mo re densely concentrated and thus correlate more strongly with an increas ed likelihood of contribution. Em issions in Dougherty County, for the most part, are much higher than emissions in the other counties wi th the exceptions noted above. Additionally, the wind rose data suggest that emissions from Lee County are less likely to contribute at the Dougherty County monitor. Acco rdingly, the EPA is not deferring designations for Baker, Lee, Terrell, or Worth Counties, and is designating them unclassifiable/attainment based on the available data.10 

### Atlanta Area (Atlanta-Sandy Springs-Roswell, GA CBSA)

1.0 Atlanta Area Summary

Atlanta-Sandy Springs-Roswell, Georgia is a 29 -county CBSA in which the EPA has determined that designations for 12 full c ounties of this area should be deferred for the 2012 annual PM 2.5 NAAQS. Designations for the following countie s are being deferred due to existing PM 2.5 monitoring sites in these coun ties that have invalid desi gn values for 2013: Fulton, Cobb, Gwinnett and Paulding. In addition, the EPA has dete rmined that the following adjacent counties would have the potential to contribute to a viola ting monitor, if there we re one, at one of the invalid sites: Bartow, Cherokee, Clayton, Co weta, DeKalb, Douglas, Forsyth and Henry.

As shown in Figure 4, below, for 2013 in the At lanta CBSA, there are two attaining monitors with complete data and four monitors with incomplete data. The EPA focused much of its analysis on the Fire Station #8 monitor because it has historically been the highest reading monitor in Atlanta. The Fire Station #8 site is three miles down wind of Georgia Power Company’s Plant McDonough-Atkinson, and less than a mile away from a major rail hub. The only other monitor in the Atla nta area that was violating in 2012 was the Clayton County monitor. The Clayton County monitor currently ha s a complete, valid 2013 design value that is below the standard at 11.1 μg/m 3

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Most of the urbanized portion of the Atlanta CB SA is located within five central counties. Pockets of urbanization around these core coun ties exist in adjacent counties as well.

Due to data completeness issues, the EPA is unable at this time to calculate valid design values for four counties within the Atlanta CBS A – the counties of Cobb, Fulton, Gwinnett and Paulding. Without valid design values, the EPA has insufficient information to determine whether these areas are meeting the NAAQS. Ho wever, the EPA believes that forthcoming monitoring data for these counties will likely result in the three years of complete and valid data necessary to make such a dete rmination. Accordingly, the EPA has decided to defer designations for Cobb, Fulton, Gwinnett and Paulding Counties, pursuant to CAA s ection 107(d)(1)(B).

In determining whether to defer designations fo r other portions of the Atlanta CBSA, the EPA considered the relatively likelihood that any countie s in the CBSA could poten tially contribute to violations in Cobb, Fulton, Gwinne tt or Paulding Counties, in th e event that complete data subsequently shows that monitors in any of t hose four counties violates the standard. The EPA has determined that eight counties – Bartow , Clayton, Coweta, DeKalb, Cherokee, Henry, Forsyth, and Douglas – would be the counties most likely to contribute to any violation that may be identified in the future. Both Clayton a nd DeKalb counties have valid attaining 2011-2013 design values, but the designations for these co unties are being deferred because these counties have high emissions, are upwind of the deferred monitored counties, and overall would be relatively more likely to contri bute to a hypothetical violation at an affected monitor. Generally, these counties have similar f eatures with respect to high SO 2 and PM emissions, point sources, high population and VMT, relative to other counties in the CBSA.

Substantial emission reductions have been reali zed in the Atlanta area since the time the EPA evaluated the area for the 1997 PM 2.5 standards. This is evident by the smaller footprint of11 recently violating monitors for this area. Specifically, based on 2010-2012 data, only two monitors in the Atlanta area (one in Fulton C ounty and one in Clayton County) had violating design values. In 2005, when the designations for the 1997 annual PM 2.5 NAAQS were finalized, there were five monitors scattered throughout th e metropolitan area with design values violating the 1997 standards. In contrast, there were only two monitors in the area with 2012 design values exceeding the 2012 annual PM 2.5 NAAQS, and these two monito rs are located 14 miles apart.

The South DeKalb monitor (traditionally one of the higher reading monitors for this area) has complete data for the entire period as the data collected there was not impacted by a 2011 snow storm that impacted data completeness for other monitors in Georgia. For 2013, this monitor has a valid attaining design value of 10.5 μg.m 3 , well below the 2012 standard.

For purposes of this contribution analysis, the EPA has assumed that the four monitors with incomplete data – Fire Station #8, Cobb-Ke nnesaw, Gwinnett-Gwinnett Tech and PauldingYorkville – may be in violation of the standa rd. The EPA notes that Cobb-Kennesaw, GwinnettGwinnett Tech and Paulding-Yorkville monitors a ll had steadily declinin g design values from 2008 through 2011 and recorded valid 2011 design values of 12.3, 12.1 and 11.0 μg/m 3

, respectively. The EPA believes that Table 3 belo w and Figure 6 below, show that these three monitors were likely on a trajectory for r ecording attaining design values by the year 2011, notwithstanding the data complete ness issues that led to invalid design values for 2011, 2012 and 2013. Nevertheless, these counties lacked the complete data nece ssary to develop a valid 2011- 2013 design value, and are thus be ing deferred on that basis.

Figure 4: Map of the Atlanta Area.12 2.0 Deferred Boundary Determination

As noted above, the EPA’s starting point, for an ar ea that has monitors with incomplete data and no violating monitors, is the CBSA where one exists. The Atlanta ar ea had invalid data at several monitoring sites in 2011. For the period 2010-2012, Atlanta had va lid design values at five monitors, with two violating the standard (Clayton and Fulton Counties). For 2011-2013, the Area has only three valid design valu es, all attaining the standard; the remaining four monitors in operation have invalid data. Although the monito r readings have been trending downward, the EPA has determined that it lacks sufficient inform ation at this time to promulgate designations for the four counties with incomplete monitori ng data, as well for several nearby counties that could potentially contribute to the four count ies with data completeness issues. However, because the EPA believes that thes e data incompleteness issues can be rectified with forthcoming data, the EPA is deferring desi gnations for these counties.

3.0 Technical Analysis for the Atlanta CBSA

In this technical analysis, the EPA used the late st data and information available to the EPA (and to the states and tribes through the PM 2.5 Designations Mapping Tool 12 and the EPA PM Designations Guidance and Data web page 13 ) and/or data provided to the EPA by states or tribes. This technical analysis identifies the areas with emissions that could co ntribute to a potential violation of the 2012 annual PM 2.5 standard at the incomplete monitors. The EPA evaluated the relevant information for these areas and other n earby areas with emissions sources or activities that potentially contribute to am bient fine particle concentrations at the monitors with invalid 2013 design values in the area based on the using a weight of evidence of the five factors recommended in the EPA guidance.

12 The EPA’s PM 2.5 Designations Mapping Tool can be found at http://geoplatform2.epa.gov/PM_MAP/index.html

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13 The EPA’s PM Designations Guidance and Data web page can be found at http://www.epa.gov/pmdesignations/2012standards/techinfo.htm

.13 Figure 5: Map of EPA’s Deferre d Area for the Atlanta Area.

Figure 5 is a map of the boundary within which that the EPA is deferring designations for in the Atlanta Region. For purposes of the 1997 annual PM 2.5 NAAQS, portions of this area were designated nonattainment. The boundary for the nonattainment area for the 1997 annual PM 2.5 NAAQS included the entire counties of Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinnett, Hall, 14 Henry, Newton, Paulding, Rockdale, Spalding, Walton, and partia l counties of Heard and Putnam. 15

Air Quality Data

All data collected during the year are important when dete rmining contributions to an annual standard such as the 2012 annual PM 2.5 NAAQS. Compliance with an annual NAAQS depends on monitor readings throughout the year, including days with mon itored ambient concentrations

14 Hall County is not a part of the Atlanta CBSA.

15 Putnam County is not a part of the Atlanta CBSA.14 below the level of the NAAQS. For the 2012 annual PM 2.5 NAAQS, the annual mean is calculated as the mean of quarter ly means. A high quarter can driv e the mean for an entire year, which, in turn, can drive an el evated 3-year DV. Although all da ta are important, seasonal or episodic emissions can provide insight as to relative contributors to measured PM 2.5 concentrations. For these reasons, for this air quality analysis, the EPA assessed and characterized air quality at, and in the proximity of, the moni toring site locations with invalid data first, by evaluating trends and the spatial extent of measured concentrations at monitors in the area of analysis, and then, by identifying the conditions most associated with high average concentration levels of PM 2.5 mass in the area of analysis.

In most cases, the EPA assessed air quality data on a seasonal, or quarterly, basis. 16 The EPA also identified the spatial extent of these high PM 2.5 concentrations. The mass and composition at the design value location represen ts contributions from various emission sources including local, area-wide (which may comprise nearby urban an d rural areas) and regional sources. To determine the source mix (by mass) at the desi gn value monitoring site, the EPA examined the chemical composition of the monitored PM 2.5 concentrations by pair ing each violating FRM/FEM/ARM monitoring site with a collocat ed or nearby Chemical Speciation Network (CSN) monitoring site or sites. Then, the EPA contrasted the approximated mass composition at the design value monitoring site with data collected at IMPROVE 17 and other monitoring locations whose data are represen tative of regional background. 18,19 This comparison of

16 Although compliance with the annual NAAQS depends on cont ributions from all days of the year, examining data on a quarterly or seasonal basis can inform the relati onship between the temporal variability of emissions and meteorology and the resulting PM 2.5 mass and composition. In some areas of the country where there may be noticeable month-to-month va riations in average PM 2.5 , the quarterly averages may not adequately represent seasonal variability. In these areas, air quality data may be aggregated and presented by those months that best correspond to th e local “seasons” in these areas.

17 IMPROVE stands for Interagency Monitoring for Protected Visual Environments and is an aerosol monitoring network in mostly rural and remote areas.

18 The “urban increment” analysis assesses and character izes the increase in seasonal and annual average PM 2.5 mass and chemical constituents observed at violating monitoring site(s) relative to monitoring sites outside the area of analysis (which represent background concentrations). Developing the urban increment involves pairing a violating FRM/FEM/ARM monitor with a collocated monitor or nearby monitor with speciation data. The EPA made every effort to pair these data to represent the same temporal and spatial scales. However, in some cases, the paired violating and CSN “urban” monitoring locations were separated by some distance such that the included urban CSN site(s) reflect(s) a different mixture of emissions sources, which could lead to misinterpretations. To generally account for differences in PM 2.5 mass between the violating site and the nearby CSN site(s), the EPA determined material balance of the PM 2.5 composition at the violating site by assigning the extra measured PM 2.5 mass to the carbon components of PM 2.5 . Where the general urban increment approach may be misleading, or in situations where non-carbonaceous emissions are believed to be responsible for a local PM 2.5 concentration gradient, the EPA used alternative analyses to reflect the mix of urban and ru ral sources contributing to the measured concentrations at violating monitoring sites.

19 The urban monitors were paired with any rural sites within a 150 mile radius of an urban site to calculate spatial means of the quarterly averages of each sp ecies. If there were no rural sites w ithin 150 miles, then the nearest rural site was used alone. That rural mean was then subtracted from the quarterly mean of the urban site to get the increment. Negative values were simply replaced with zeros.15 local/area-wide chemical composition data to re gional chemical composition data derives an “urban increment,” which helps differentiate th e influence of more distant emissions sources from the influence of closer emissions sources , thus representing the portion of the measured potential violation that is associat ed with nearby emission contributions. 20,21,22

PM 2.5 Design Values and Total Mass Measurements – The EPA examined ambient PM 2.5 air quality monitoring data represented by the DVs at the monitoring sites with invalid data and at other monitors in the area of analysis. The EPA calculated DVs based on air quality data for the most recent 3 consecutive calendar years of air quality data from suitable FEM/FRM/ARM monitoring sites in the EPA’s Air Quality System (AQS). For this designations analysis, the EPA generally reviewed data for the 2011-2013 period (i.e., the 2013 design value), which are the most recent years with air quality data. A monitor’s DV is the metr ic or statistic that indicates whether that monitor atta ins a specified air quality standard. The 2012 annual PM 2.5 NAAQS is met at a monitoring site when the 3-year average annual mean concentration is 12.0 μg/m 3 or less (e.g., 12.1 μg/m 3 or greater is a violation). A DV is only valid if minimum data completeness criteria are met or when other regul atory data processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 3 identif ies the current design values (i.e., the 2013 DV) where available, and the re cent design values from 2008 to 2012 for all monitoring sites in the Atlanta CBSA. 23

There is currently not enough verifiable data from the monitors in the At lanta area to obtain an accurate determination of air quality in th e Atlanta CBSA. As noted in an August 8, 2014, memorandum from Gregg M. Worley, Chief of Re gion 4’s Air Toxics & Monitoring Branch, to R. Scott Davis, Chief of Region 4’s Air Planning Branch, the Atla nta area had less than 50% data completeness in the first quarter of 2011 and, therefore, does not meet the data handling requirements of 40 CFR Part 58, Appendix N, 4.1( c)(ii), for conducting data substitution during the period 2011 through 2013.

20 In most, but not all, cases, the violating design value monitoring site is located in an urban area. Where the violating monitor is not located in an urban area, the “urb an increment” represents the difference between local and other nearby emission sources in the vicinity of the violating monitoring location and more regional sources.

21 Hand, et. al. Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United States: Report V, June 2011. Chapter 7 – Urban Excess in PM 2.5 Speciated Aerosol Concentrations, http://vista.cira.colostate.edu/improve/ Publications/Reports/2011/PDF/Chapter7.pdf

22 US EPA, Office of Air Quality Planning and Standards, December 2004. (2004) Area Designations for 1997 Fine Particle (PM 2.5 ) Standards, Technical Support Document for St ate and Tribal Air Quality Fine Particle (PM 2.5 ) Designations, Chapter 3, Urban Excess Methodology. Available at www.epa.gov/pmdesignations/1997standards/documents/final/TSD/Ch3.pdf

23 In certain circumstances, one or more monitoring locations within a monitoring network may not meet the network technical requirements set forth in 40 CFR 58.11( e), which states, “State and local governments must assess data from Class III PM 2.5 FEM and ARM monitors operated within th eir network using the performance criteria described in table C-4 to subpart C of part 53 of this ch apter, for cases where the da ta are identified as not of sufficient comparability to a collocated FRM, and the monito ring agency requests that the FEM or ARM data should not be used in comparison to the NAAQS. These assessmen ts are required in the m onitoring agency's annual monitoring network plan described in §58.10(b) for cases where the FEM or ARM is iden tified as not of sufficient comparability to a collocated FRM....”16 Table 3. Air Quality Data Collected at Regulatory Monitors in the Atlanta CBSA (all DV levels in μg/m 3 ) a,b

County - Site name Monitor Site

ID 06-08

DV 07-09

DV 08-10

DV 09-11

DV 10-12

DV 11-13

DV

Clayton-Forest Park 13-063- 0091 15.3 13.6 12.9 12.6 12.3 11.1

DeKalb-South DeKalb 13-089- 0002 14.4 13.0 12.1 11.9 11.5 10.5

Fulton-Fire Station #8 13-121- 0039 NV NV NV 13.2 13.0 NV

Cobb-Kennesaw 13-067- 0003 15.3 13.5 12.3 NV NV NV

Gwinnett-Gwinnett Tech 13-135- 0002 14.5 12.7 12.1 NV NV NV

Paulding-Yorkville 13-223- 0003 13.5 12.2 11.0 NV NV NV

Cobb - Powder Springs 13-067- 0004 14.7 12.8 NV NV 11.1 -

DeKalb – Doraville 13-089- 2001 14.4 13.4 12.3 NV NV -

Fulton - E. Rivers Sch. 13-121- 0032 14.8 13.5 12.3 NV NV -

NV: Not valid

a This design value does not include data from Class III FEM monitors that are not eligible for comparison to the NAAQS, as approved by the EPA pursuant to 40 CFR 58.11(e).

b Site approved for shutdown during network plan review at the end of 2012.

The Figure 5 map, shown previously, identifi es the Atlanta CBSA boundary and monitoring locations with 2013 attaining DVs and invalid DVs; there are no valid violating DVs for 2013. The Fire Station #8 monitor has consistently b een the highest DV in the region, including for the 1997 PM designation, when valid data have been av ailable for the site. Th ere are a total of six monitors in the CBSA, and all but two have invalid DVs for 2013.

Figure 6, below, shows the history of design values for the monitoring data that is available for the Atlanta area since 2002. The Fire Stati on #8 monitor in Fulton County exceeded the 2012
PM 2.5 NAAQS in 2012, but for 2013 the design value is i nvalid due to incomplete data issues. In the years that Fire Station #8 site had valid desi gn values, the site reco rded the highest annual
PM 2.5 design values in the Atlanta area. (Note that the last three sites shown, Cobb-Powder Springs, DeKalb-Doraville and Fulton-E. Ri vers School all ceased operation in 2012 in accordance with Georgia’s approved monitoring plan.)17 Figure 6. Atlanta PM 2.5 Valid Design Values for 2002-2013

Seasonal variation can highlight those conditi ons most associated with high average concentration levels of PM 2.5 . Figure 7, below, shows quarterly mean PM 2.5 concentrations for the 3-year period 2010-2012 for the highest DV m onitoring site and the other, non-violating, monitoring sites in each county w ithin the area of analysis. This graphical repr esentation is particularly relevant when asse ssing air quality data for an annual standard, such as the 2012 annual PM 2.5 NAAQS, because, as previously stated, the annual mean is calculated as the mean of quarterly means and a high quart er can drive the mean for an entire year, which, in turn, can drive an elevated 3-year DV. 8 12 16 20

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013μg/m3

DV Year 

### Atlanta Area Valid Design Value Trends

2012 PM2.5 NAAQS

Clayton ‐ Forest Park

DeKalb ‐ South DeKalb

Fulton ‐ Firestation #8

Cobb ‐ Kennesaw

Gwinnett ‐ Gwinnett Tech
Paulding ‐ Yorkville

Cobb ‐ Powder Springs

DeKalb ‐ Doraville

Fulton ‐ E. Rivers Sch.18 Figure 7. Atlanta PM 2.5 Quarterly Means for 2010-2012

As context, Quarterly values across the period ha ve peaked in Q2. Overall, the quarterly means from year to year are also trending down at all of these sites.

PM 2.5 Composition Measurements - To assess pote ntial emissions contributions for each monitoring location with invali d data, the EPA determined the various chemical species comprising total PM 2.5 to identify the chemical constituents over the analysis area, which can provide insight into the types of emission sour ces impacting the monitored concentration. To best describe the PM 2.5 at the monitoring sites that were violating in 2012 (none of the three monitoring sites with valid 2013 design values are vi olating), the EPA first adjusted the chemical speciation measurement data from a monitoring lo cation at or near the violating FRM monitoring sites using the SANDWICH approach to account for the amount of PM 2.5 mass constituents 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0

'10 Q1 '10 Q2 '10 Q3 '10 Q4 '11 Q1 '11 Q2 '11 Q3 '11 Q4 '12 Q1 '12 Q2 '12 Q3 '12 Q4PM2.5Concentrationsμg/m3 

### Atlanta PM2.5 Quarterly Means 2010

‐ 2012

Clayton Fulton Dekalb Cobb19 retained in the FRM measurement. 24,25,26,27 In particular, this approa ch accounts for losses in fine particle nitrate and increases in sulfate mass associated with pa rticle bound water. Figure 4a, below, illustrates the fraction of each PM 2.5 chemical constituent at the Fulton (131210039) monitoring site based on annual av erages for the years 2010-2012.

Figure 8. Atlanta Annual Average PM 2.5 Chemical Constitu ents (2010-2012)

Figure 9 shows annual and quarterly chemical co mposition profiles and illustrates any seasonal or episodic contributors to PM 2.5 mass. This “increment analysis,” combined with the other

24 SANDWICH stands for measured S ulfate, A djusted N itrate, D erived W ater, I nferred C arbonaceous mass H ybrid Material Balance Approach.” The SA NDWICH adjustment uses an FRM mass construction methodology that results in reduced nitrates (relative to the amount measur ed by routine speciation networ ks), higher mass associated with sulfates (reflecting water incl uded in gravimetric FRM measurements ) and a measure of organic carbonaceous mass derived from the difference between measured PM 2.5 and its non-carbon components. This characterization of
PM 2.5 mass also reflects crustal material and other minor constituents. The resulting characterization provides a complete mass closure for the measured FRM PM 2.5 mass, which can be different than the data provided directly by the speciation measurements from the CSN network.

25 Frank, N. H., SANDWICH Material Balance Approach for PM 2.5 Data Analysis, National Air Monitoring Conference, Las Vegas, Nevada, November 6-9, 2006. http://www.epa.gov/ttn/amtic/file s/2006conference/frank.pdf

.

26 Frank, N. H., The Chemical Composition of PM 2.5 to support PM Implementation, the EPA State /Local/Tribal Training Workshop: PM 2.5 Final Rule Implementation and 2006 PM 2.5 Designation Process, Chicago IL, June 20-21, 2007, http://www.epa.gov/ttn/naaqs/pm/pres ents/pm2.5_chemical_composition.pdf

.

27 Frank, N. H. Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in Federal Reference Method Fine Particulate Matter for Six Eastern U.S. Cities . J. Air & Waste Manage. Assoc. 2006 56:500–511. 6.74

1.84 0.10 3.61 0.53 

### Annual Average PM2.5 Chemical Constituents
2010

‐ 2012 ( μ g/m3) for Fulton County

Organic Mass from DV monitor Elemental Carbon from DV monitor

Nitrates from SANDWICH Sulfates from SANDWICH

Crustal from SANDWICH20 relevant information, can provide additional insight as to which sources may contribute to observed PM levels. Simply stated, this analysis can help identify nearby sources of emissions that contribute to the violation at the violating monitoring site.

Figure 9. Atlanta Annual and Quarterly Average PM 2.5 Species (2010-2012) a

a Adjusted to FRM Total PM 2.5 indicates that the speciation profile and total mass depicted in this figure are the result of the urban increment calculation for the particular FRM monitor. 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

Annual Q1 Q2 Q3 Q4 Fulton County

‐ Annual and Quarterly Average PM 2.5 Species
2010

‐ 2012 Adjusted to FRM Total PM 2.5

Organic Mass Elemental Carbon Nitrates Sulfates Crustal

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Organic Mass Elemental Carbon Nitrates Sulfates Crustal Fulton County

‐ Annual and Quarterly Average PM 2.5 Species
2010

‐ 2012 Adjusted to FRM Total PM 2.5

Annual Q1 Q2 Q3 Q421 Figures 8 and 9 show that sulfate and OM are the predominant species overall. Crustal and elemental carbon comprise a small percentage in all four quarters. Sulf ate peaks in the third quarter. Nitrate is a relatively sm all component, but is slightly hi gher in the first quarter. It is more likely that the large sulfate component is caused by SOx emissions from large electric generating units in the area. High levels of or ganic mass are typically as sociated with mobile sources, wood or biomass burning a nd localized combustion sources. 28

The EPA assessed seasonal and annual average PM 2.5 constituents at monitoring sites within the area relative to monitoring sites ou tside of the analysis area to account for the difference between regional background con centrations of PM 2.5 , and the concentrations of PM 2.5 in the area of analysis, also known as the “u rban increment.” This analys is differentiates between the influences of emissions from sources in nearby areas and in more distant areas on the violating monitor. Estimating the urban increment in the area helps to illuminate the amount and type of particles at the violating monitor that are most lik ely to be the result of sources of emissions in nearby areas, as opposed to impacts of more dist ant or regional sources of emissions. Figure 10, below, includes pie charts showing the annual an d quarterly chemical ma ss constituents of the urban increment. Note that in these charts, sulfat es and nitrates have been adjusted to represent their mass in measured PM.

Figure 10. Atlanta CBSA Urba n Increment Analysis for 2010-2012.

28 The EPA Guidance Memorandum, “Initial Area Designations for the 2012 Revised Primary Annual Fire Particulate
National Ambient Air Quality Standard,” dated April 16, 2013, Attachment 3. 57% 40% 0% 3% 0% 

### Fulton

‐ Annual Average Urban Increment
2010

‐ 2012 ( μ g/m3)

Organic Mass

Elemental Carbon

Nitrates

Sulfates

Crustal22 Figure 11. Atlanta CBSA Average Urban Increment Analysis for 2010-2012. 1.27

1.36 0.01 0.20 0.02 

### Fulton Q

‐ 1 Average Urban

Increment
2010

‐ 2012 ( μ g/m3)

Organic Mass

Elemental
Carbon

Nitrates

Sulfates 1.53 0.88 0.00 0.00 0.00 

### Fulton Q

‐ 2 Average Urban

Increment
2010

‐ 2012 ( μ g/m3)

Organic Mass

Elemental Carbon

Nitrates

Sulfates

Crustal

1.49 1.01 0.00 0.09 0.00 

### Fulton Q

‐ 3 Average Urban

Increment
2010

‐ 2012 ( μ g/m3)

Organic Mass

Elemental
Carbon

Nitrates

Sulfates 2.17 1.24 0.03 0.00 0.04 

### Fulton Q

‐ 4 Average Urban

Increment
2010

‐ 2012 ( μ g/m3)

Organic Mass

Elemental Carbon

Nitrates

Sulfates

Crustal

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

Annual Q1 Q2 Q3 Q4 

### Fulton

‐ Average Urban Increment
2010

‐ 2012 ( μ g/m3)

Organic Mass Elemental Carbon Nitrates Sulfates Crustal23 For 2012, Clayton County and Fulton County each had one monitoring site with a DV exceeding the NAAQS. For 2013, the Clayton County design va lue is below the standard at 11.1 μg/m 3

, however, the Fulton County monitor has an invalid design value.

A comparison of the average urban increment to the total measured PM 2.5 for the Atlanta area indicates that the urban increment is less than one- third of the total. This indicates a meaningful contribution from regional sources. In addi tion, Fulton and adjoining counties have clear seasonal peaks in ambient PM 2.5 concentrations in quarters 1 and 4. Looking at speciated components of the urban increment for the Fult on County monitor, the organic mass component appears to have the largest urban contribution for quarters 2, 3 and 4. Elemental carbon is the second highest urban increment component and peaks during Q1. Carbonaceous mass (Organic Carbon + Elemental Carbon) is typically associ ated with mobile sources, wood or biomass combustion, and localized combustion sources. The relatively high elemental carbon component could be an indicator of contribu tion from diesel combustion sources in the area. This analysis points to contribution from both regional and local PM 2.5 sources.

Emissions and emissions-related data

In this designations process, for each area with a violating monitoring site or a monitoring site with invalid data, the EPA evaluated the emi ssions data from nearby areas using emissions related-data for the relevant counties to a ssess each county’s potential contribution to PM 2.5 concentrations at the monitori ng sites in the area under evaluati on. Similar to the air quality analysis, these data were examined on a seas onal basis. The EPA examined emissions of identified sources or sour ce categories of direct PM 2.5 , the major components of direct PM 2.5 (organic mass, elemental carbon, crustal materi al (and/or individual trace metal compounds)), primary nitrate and primary sulfate, a nd precursor gaseous pollutants (i.e., SO 2 , NO X , total VOC, and NH 3 ). The EPA also considered the distance of t hose sources of emissions from the violating monitoring site. While direct PM 2.5 emissions and its major carbonaceous components are generally associated with sources near violating PM 2.5 monitoring sites, the gaseous precursors tend to have a more regional influence (although the EPA is mindful of the potential local NOx and VOC emissions contributions to PM 2.5 from mobile and stationary sources) and transport from neighboring areas can contribute to higher PM 2.5 levels at the monitoring sites.

Emissions Data

For each county in the Atlanta CBSA, the EPA examined the magnitude of county-level emissions reported in the 2011 NEI version 1 (see http://www.epa.gov/ttn/chief /net/2011inventory.html ). These county-level emissions represent the sum of emissions from the following general source categories: point sources, non-point (i.e., area) sources, nonroad mobile, on-road mobile, and fires. The EPA also looked at the geographic distribution of major point sour ces of the relevant pollutants. 29 Substantial emissions levels from sources in a nearby area indicate th e potential for the area to contri bute to monitored violations.

29 For purposes of this designations effort, “major” poin t sources are those whose sum of PM precursor emissions (PM 2.5 + NOx + SO 2 + VOC + NH 3 ) are greater than 500 tons per year based on NEI 2011v1.24 To further analyze area emissions data, the EPA also developed a summary of direct PM 2.5 , components of direct PM 2.5 , and precursor pollutants, which is available at http://www.epa.gov/pmdesignations/2012standa rds/docs/nei2011v1pointnei2008v3county.xlsx

. When considered with the urban increment anal ysis above, evaluating the components of direct
PM 2.5 and precursor gases can help identify specific sources or source types contributing to elevated concentrations at potentially violating monitoring sites and thus assist in identifying appropriate area boundaries. In ge neral, directly emitted partic ulate organic carbon (POC) and
VOCs 30 contribute to PM 2.5 organic mass (OM); directly emitted EC contributes to PM 2.5 EC; NO X , NH 3 and directly emitted nitrate contribute to PM 2.5 nitrate mass; SO 2 , NH 3 and directly emitted sulfate contribute to PM 2.5 sulfate mass; and directly emitted crustal material and metal oxides contribute to PM 2.5 crustal matter. 31,32 The EPA believes that the quantities of those nearby emissions as potentia l contributors to the PM 2.5 monitors with invalid data are somewhat proportional to the PM 2.5 chemical constituents in the estimated urban increment. Thus, directly emitted POC is more important per ton than SO 2 , partially because POC emissions are already
PM 2.5 whereas SO 2 must convert to PM 2.5 and not all of the emitted SO 2 undergoes this conversion.

Table 4, below, provides a county-level emissions summary (i.e., the sum of emissions from the following general source categories: point s ources, non-point (i.e., area) sources, nonroad mobile, on-road mobile, and fires) of directly emitted PM 2.5 and precursor species for the county with the monitoring sites with invalid data a nd nearby counties in th e Atlanta CBSA. Table 5 summarizes the directly emitted components of PM 2.5 for the same counties in the Atlanta CBSA. This information will be paired with th e Urban Increment composition previously shown in Figures 10 and 11.

Table 4. County-Level Emissi ons of Directly Emitted PM 2.5 and Precursors in the Atlanta CBSA (tons/year)

County Total NH 3 Total NOx Total Direct
PM 2.5 Total SO 2 Total VOC Total

Coweta 277 10,545 1,615 47,614 3,053 63,104 Cobb 430 17,404 1,859 19,127 15,837 54,657 Fulton 706 24,444 2,614 607 20,237 48,608

Gwinnett 477 16,271 1,504 234 17,862 36,348 DeKalb 466 14,240 1,348 247 14,595 30,895 Bartow 1,200 13,988 2,147 6,716 4,085 28,137 Clayton 180 10,045 550 621 6,232 17,629

30 As previously mentioned, nearby VOCs are presumed to be a less important contributor to PM 2.5 OM than POC.

31 See , Seinfeld J. H. and Pandis S. N. (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change , 2nd edition, J. Wiley, New York. See also, Seinfeld J. H. and Pandis S. N. (1998) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change , 1st edition, J. Wiley, New York.

32 USEPA Report (2004), The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003, found at: http://www.epa.gov/airtrends/aqtrnd04/pm.html

.25 County Total NH 3 Total NOx Total Direct
PM 2.5 Total SO 2 Total VOC Total

Henry 187 7,477 580 76 4,448 12,769 Carroll 2,136 4,085 645 98 3,714 10,678 Cherokee 539 4,530 505 61 4,464 10,098 Heard 786 3,429 1,561 3,116 635 9,526 Forsyth 713 3,737 435 73 4,340 9,297 Newton 147 3,067 663 65 3,170 7,113 Douglas 104 3,006 294 38 2,812 6,254 Walton 583 2,556 480 25 2,482 6,125 Paulding 265 2,332 340 25 2,306 5,267 Barrow 549 2,172 351 25 1,927 5,024 Fayette 85 2,057 275 31 2,402 4,850 Rockdale 89 2,181 253 72 1,915 4,509 Spalding 178 1,642 405 36 1,780 4,042 Butts 69 1,505 256 26 856 2,713 Dawson 1,169 718 191 8 722 2,809 Haralson 397 1,452 320 19 1,678 3,866 Jasper 321 593 651 38 828 2,432 Lamar 694 847 247 10 762 2,559 Meriwether 223 1,691 948 70 1,294 4,226 Morgan 1,129 2,256 598 55 1,335 5,373 Pickens 1,212 1,017 389 12 1,088 3,717 Pike 279 574 384 17 808 2,062

Table 5. County-Level Emissions for Co mponents of Directly Emitted PM 2.5 in the Atlanta CBSA (tons/year) 33

County POM EC PSO4 PNO3 Crustal Residual Total Direct Fulton 1,111 628 50 9 299 517 2,614 Bartow 268 196 132 3 683 865 2,147 Cobb 582 344 62 3 381 487 1,859 Coweta 463 180 73 4 392 503 1,615 Heard 257 153 111 7 446 587 1,561 Gwinnett 708 440 17 3 130 206 1,504 DeKalb 611 337 19 3 170 208 1,348

33 Data are based on the 2011 and 2018 Emissions Modeling Platform Data Files and Summaries ( ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform ) available at: http://www.epa.gov/ttn/ch ief/emch/index.html#2011 (accessed 02/26/14).26 County POM EC PSO4 PNO3 Crustal Residual Total Direct
Meriwether 637 117 11 6 45 132 948
Newton 207 89 9 2 91 266 663 Jasper 449 76 11 5 24 86 651
Carroll 255 115 10 2 108 155 645 Morgan 334 95 8 4 54 103 598 Henry 200 178 11 2 87 102 580 Clayton 211 216 7 1 51 64 550
Cherokee 201 131 5 1 76 90 505
Walton 205 76 6 2 80 111 480 Forsyth 157 105 5 1 76 91 435 Spalding 192 56 5 1 60 91 405
Pickens 105 36 14 1 79 154 389
Pike 228 42 4 2 39 69 384
Barrow 129 60 4 1 50 107 351 Paulding 149 72 4 1 48 66 340
Haralson 159 55 4 1 35 65 320 Douglas 117 74 4 1 47 51 294 Fayette 99 57 3 1 55 61 275
Butts 115 55 3 1 35 47 256
Rockdale 91 60 3 1 46 52 253
Lamar 126 38 3 1 28 51 247
Dawson 94 28 2 1 27 40 191

Table 5 breaks down the direct PM 2.5 emissions value from Table 4 into its components. These data will also be compared with the prev iously presented Urban Increment composition.

Using the previously described relationship betw een directly emitted and precursor gases and the measured mass to evaluate data presented in Ta bles 4 and 5 the EPA identified the following components warranting additional review: POM, EC and SO 2 . The EPA then looked at the contribution of these constituents of interest fr om each of the counties included in the area of analysis as shown in Tables 6 a-c.27 Table 6a. County-Level [POM] Emissions (tons/year)

Emissions in average tons/yr

County POM Pct. Cumulative % Fulton 1,111 13% 13%
Gwinnett 708 8% 22%
Meriwether 637 8% 29%
DeKalb 611 7% 36%
Cobb 582 7% 43%
Coweta 463 5% 49% Jasper 449 5% 54% Morgan 334 4% 58%
Bartow 268 3% 61%
Heard 257 3% 64%
Carroll 255 3% 67%
Pike 228 3% 70% Clayton 211 2% 72%
Newton 207 2% 75%
Walton 205 2% 77%
Cherokee 201 2% 80% Henry 200 2% 82% Spalding 192 2% 84%
Haralson 159 2% 86% Forsyth 157 2% 88% Paulding 149 2% 90%
Barrow 129 2% 91%
Lamar 126 1% 93% Douglas 117 1% 94%
Butts 115 1% 95%
Pickens 105 1% 97% Fayette 99 1% 98%
Dawson 94 1% 99%
Rockdale 91 1% 100%28 Table 6b. County-Level [EC] Emissions Emissions in average tons/yr

County EC Pct. Cumulative %
Fulton 628 15% 15%
Gwinnett 440 11% 26%
Cobb 344 8% 34%
DeKalb 337 8% 43% Clayton 216 5% 48%
Bartow 196 5% 53%
Coweta 180 4% 57% Henry 178 4% 61%
Heard 153 4% 65%
Cherokee 131 3% 68%
Meriwether 117 3% 71%
Carroll 115 3% 74% Forsyth 105 3% 76% Morgan 95 2% 79%
Newton 89 2% 81%
Walton 76 2% 83% Jasper 76 2% 85% Douglas 74 2% 86% Paulding 72 2% 88%
Rockdale 60 1% 90%
Barrow 60 1% 91% Fayette 57 1% 92% Spalding 56 1% 94%
Butts 55 1% 95%
Haralson 55 1% 96%
Pike 42 1% 98%
Lamar 38 1% 98%
Pickens 36 1% 99%
Dawson 28 1% 100%29 Table 6c. County-Level [SO2] Emissions Emissions in average tons/yr

County SO2 Pct. Cumulative % Coweta 47,614 60% 60% Cobb 19,127 24% 84% Bartow 6,716 8% 93% Heard 3,116 4% 97% Clayton 621 1% 98%
Fulton 607 1% 98%
DeKalb 247 0% 99%
Gwinnett 234 0% 99%
Carroll 98 0% 99% Henry 76 0% 99% Forsyth 73 0% 99%
Rockdale 72 0% 99%
Meriwether 70 0% 99%
Newton 65 0% 99%
Cherokee 61 0% 100% Morgan 55 0% 100% Jasper 38 0% 100% Douglas 38 0% 100% Spalding 36 0% 100% Fayette 31 0% 100%
Butts 26 0% 100%
Barrow 25 0% 100% Paulding 25 0% 100%
Walton 25 0% 100%
Haralson 19 0% 100%
Pike 17 0% 100%
Pickens 12 0% 100%
Lamar 10 0% 100%
Dawson 8 0% 100%

In addition to reviewing c ounty-wide emissions of PM 2.5 and PM 2.5 precursors in the area of analysis, the EPA also reviewed emissions from major point sources lo cated in the area of analysis. The magnitude and lo cation of these sources can he lp inform boundaries. Table 7 provides facility-level emissions of direct PM 2.5 , components of direct PM 2.5 , and precursor pollutants (given in tons per ye ar) from major point sources located in the Atlanta CBSA. Table 7 also shows the distance from the facility to the closest PM 2.5 monitor with incomplete data.30 Table 7. NEI 2011 v1 Point Source Emissions (tons/year)

County Facility Name (Facility ID) Distance to NEI 2011 v1 Emissions - Tons/Year

monitor (miles) NH3 NOx PM 2.5 SO2 VOC Total

Bartow Chemical Products Corporation 31 1 23 25 557 2 607

Bartow Ga Power Company - Plant Bowen 36 15 8,371 1,161 5,889 188 15,623

Clayton The William B Hartsfield International Airport 11 - 4,316 90 537 788 5,731

Cobb Caraustar Industries Inc 12 - 176 44 564 15 799

Cobb Ga Power Company - Plant McDonough/Atkinson 3 14 3,162 424 18,307 27 21,934

Coweta Ga Power Company - Plant Yates 36 7 6,763 626 47,530 61 54,987

Fulton Owens Brockway Glass Container Inc. 9 1 350 84 214 3 654

Heard Ga Power Company - Plant Wansley 44 216 2,831 1,017 3,097 141 7,303

Henry Transcontinental Gas Pipe Line Company, LLC - Compressor Station 120 19 - 2,259 59 - 404 2,722

Morgan Georgia-Pacific Wood Products LLC (Madison Plywood) 60 - 207 139 16 199 562

Figure 12, below, shows the major point source em issions (from the 2011 NEI in tons per year) in the area of analysis for the Atlanta CBSA and the relative distances of these sources from the monitoring location(s) with invalid data, as depict ed by blue dots. The actual distance from the point sources to the PM monito ring location is presented in Ta ble 7). The distance from the monitoring location with invalid data is pa rticularly important for directly emitted PM 2.5 . The influence of directly emitted PM 2.5 on ambient PM 2.5 diminishes more than that of gaseous precursors as a function of distance. 34

As indicated in Figure 12, ther e are 12 point sources located w ithin 60 miles of the highest reading invalid monitor (i.e., Fire Stati on #8). Plant Wansley in Heard County had SO 2 and PM 2.5 emissions of 75,650 tpy and 3,823 tpy, resp ectively, at the time of the 1997 PM 2.5 designation. As context, Georgia Power installed emissi ons control technology beginning in 2002, and SO 2 and PM 2.5 emissions have decreased by 96 percent a nd 73 percent, respectively. Morgan County has one point source located 60 miles away from the Fire Station #8 monitor, and SO 2 and PM 2.5 emissions from the source are low.

34 Baker, K. R. and K. M. Foley. A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM 2.5

. Atmospheric Environment. 45 (2011) 3758-3767.31 Figure 12. Major Point Source Lo cations in the Atlanta Area

Population density and degree of urbanization

The EPA evaluated the population and vehicle use charac teristics and trends of the area as indicators of the probable lo cation and magnitude of non-point source emissions. Population levels and rapid population growth in a county on the urban perimeter signifies increasing integration with the core urban area, and indicates that it may be appropriate to examine the county associated with area sour ce and mobile source emissions fo r potential contribution to the invalid monitors. Table 8 shows the 2000 and 2010 population, population growth since 2000, and population density for each county in the Atlanta CBSA.

Table 8. Population Growth and Population Density.

County Population 2000 Population 2010 % Change

from 2000 Land Area (Sq. Miles) Population Density (per Sq.

Mile) % Cumul. % Fulton 816,006 926,119 13.5% 529 1,752 17% 17% Gwinnett 588,448 808,409 37.4% 433 1,868 15% 33% DeKalb 665,865 692,546 4.0% 268 2,582 13% 46% Cobb 607,751 689,695 13.5% 340 2,028 13% 59% Clayton 236,517 259,832 9.9% 143 1,822 5% 64% Cherokee 141,903 215,248 51.7% 424 508 4% 68%32 County Population 2000 Population 2010 % Change

from 2000 Land Area (Sq. Miles) Population Density (per Sq.

Mile) % Cumul. % Henry 119,341 205,283 72.0% 323 636 4% 72% Forsyth 98,407 176,820 79.7% 226 783 3% 75% Paulding 81,678 142,788 74.8% 313 456 3% 78% Douglas 92,174 132,646 43.9% 199 666 3% 80%
Coweta 89,215 127,906 43.4% 443 289 2% 83% Carroll 87,268 110,723 26.9% 499 222 2% 85% Fayette 91,263 107,064 17.3% 197 543 2% 87%
Bartow 76,019 100,161 31.8% 459 218 2% 89%
Newton 62,001 100,130 61.5% 276 362 2% 90% Rockdale 70,111 85,400 21.8% 131 654 2% 92% Walton 60,687 84,092 38.6% 329 255 2% 94%
Barrow 46,144 69,732 51.1% 162 430 1% 95% Spalding 58,417 64,111 9.7% 198 324 1% 96% Pickens 22,983 29,454 28.2% 232 127 1% 97% Haralson 25,690 28,786 12.1% 282 102 1% 97%
Butts 19,522 23,756 21.7% 187 127 <1% 98%
Dawson 15,999 22,304 39.4% 211 106 <1% 98% Meriwether 22,534 21,846 -3.1% 503 43 <1% 98%
Lamar 15,912 18,250 14.7% 185 99 <1% 99% Pike 13,688 17,905 30.8% 218 82 <1% 99% Morgan 15,457 17,901 15.8% 350 51 <1% 100% Jasper 11,426 13,900 21.7% 370 38 <1% 100% Heard 11,012 11,858 7.7% 296 40 <1% 100% Total 4,263,438 5,304,665 Source: U.S. Census Bureau population estimates for 2000 and 2010

The most densely populated counties are in and adjacent to the monitors with invalid data. Clayton, Cobb, DeKalb, Fulton and Gwinnett Countie s had the highest population density (over 1,700 persons per square mile). Barrow, Cherok ee, Coweta, Douglas, Forsyth, Henry, Newton and Paulding have seen substantial growth (over 40 percent) since 2000.33 Figure 13. 2010 County-Level Population in the Area of Analysis for the Atlanta CBSA Area.

Traffic and VMT

High VMT and/or a high number of commuters associated with a county is generally an indicator that the county is an integral part of an urban area. Mobile source emissions of NOx, VOC, and direct PM may contribute to ambient pa rticulate matter that contributes to potential monitored violations of the NAAQS in the area. In combination with the population/population density data and the location of main transportation arteries, an assessment of VMT helps identify the probable location of nonpoint source emissions that c ontribute to potential violations in the area. Table 9 shows 2011 VMT while Fi gure 8 overlays 2011 county-level VMT with a map of the transportation arteries. This data is from the Federal Highway Administration.34 Table 9. 2011 VMT by County for the Atlanta CBSA. County Total 2011 VMT Percent Cumulative % Fulton 12,221,928,709 22% 22% DeKalb 7,644,333,957 13% 35% Gwinnett 7,421,564,338 13% 48% Cobb 6,617,102,885 12% 60% Clayton 2,881,122,333 5% 65% Henry 2,214,706,881 4% 69% Cherokee 1,872,284,829 3% 72% Forsyth 1,689,888,438 3% 75% Bartow 1,651,438,196 3% 78% Douglas 1,587,530,332 3% 81% Coweta 1,459,781,612 3% 83% Carroll 1,249,251,642 2% 85% Rockdale 984,757,532 2% 87% Paulding 940,307,960 2% 89% Fayette 887,949,158 2% 90% Newton 869,539,158 2% 92% Barrow 703,578,903 1% 93% Walton 697,510,275 1% 94% Spalding 550,494,314 1% 95% Morgan 464,799,942 1% 96% Haralson 343,417,806 1% 97% Butts 333,055,273 1% 97% Pickens 315,317,952 1% 98% Meriwether 296,860,449 1% 98% Lamar 248,137,330 0% 99% Dawson 220,141,830 0% 99% Pike 167,900,073 0% 100% Jasper 135,538,407 0% 100% Heard 124,143,180 0% 100% Total 56,794,383,694 http://www.census.gov/hhes/commuting/data/commuting.html35 Figure 14. Overlay of 2011 County-level VM T with Transportation Arteries.

Cherokee, Clayton, Cobb, DeKalb, Fulton, Gwinnett, and Henry Counties had the largest VMT. More than half of all the VMT in the CBSA are in Cobb, DeKalb, Fulton, and Gwinnett. It is reasonable to infer that a substantial amount of the traffic is due to inter-county commuters, mainly from the inner core and highly urbanized counties, most of it adjacent to the monitors with invalid data.

Cherokee, Clayton, Cobb, DeKalb, Douglas, Fo rsyth, Fulton, Gwinnett, Henry, and Paulding Counties consistently ra nk highest in direct PM 2.5 /key precursor emissions (i.e., VMT and population). Bartow, Clayton, Cobb, Coweta , and Fulton Counties have large PM 2.5 precursor emissions from stationary sources in the CBS A. Carroll, Newton, and Rockdale Counties are mid-ranked in emissions and mid-ranked for population and VMT. Barrow, Butts, Dawson, Fayette, Haralson, Heard, Jasper, Lamar, Meri wether, Morgan, Pickens, Pike, Spalding, and Walton Counties have relatively low emissions and re latively low VMT/population counts, and lack large singular point source contributors.36 Meteorology

The EPA evaluated available meteorological data to determine how meteorological conditions, including, but not limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of directly emitted partic ulate matter and precursor emissions from sources in the area of analysis. The EPA used two pr imary tools for this assessment: wind roses and kernel density estimation (KDE). When c onsidered in combination with area PM 2.5 composition and county-level and facility emissions source location information, wind roses and KDE can help to identify nearby areas contributing to violations at violati ng monitoring sites.

Wind roses are graphic illustra tions of the frequency of wi nd direction and wind speed. Wind direction can indicate the dire ction from which contributing em issions are transported; wind speed can indicate the force of the wind and thus the distance from which those emissions are transported. The EPA constructed wind roses fr om hourly observations of wind direction and wind speed using 2009-2012 data fro m National Weather Service lo cations archived at the National Climate Data Center. 35 When developing these wind ro ses, the EPA also used wind observations collected at meteor ological sampling stations collo cated at air quality monitoring sites, where these data were available. Figure 15 shows wind roses that the EPA generated from data relevant in the Atlanta CBSA.

Figure 15. Wind Roses in the Atlanta Area.

35 ftp.ncdc.noaa.gov/pub/data/noaa or http://gis.ncdc.noaa.gov/map/viewer/#app= cdo&cfg=cdo&theme=hourly&layers=1&node=gis Quality assurance of the National Weather Service data is described here: http://www1.ncdc.noaa.gov/pub/data/inventories/ish-qc.pdf37 As shown in Figure 15, there is a pattern across the CBSA of predominantly northwest winds and a smaller component of east winds, mostly at mid-level speeds of 4 to 10 meters per second, suggesting that potential emission sources in th e north-through-west upwi nd direction and to a lesser extent in the east upwind direction have poten tial to contribute to PM levels at the invalid monitors.

In addition to wind roses, the EPA also genera ted KDE plots to represent the frequency of HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajecto ry) backward trajectories at monitoring sites with invalid data. 36,37 These KDEs are graphical statistical estimations to determine the density of trajectory endpoints at a particular location repr esented by a grid cell. The EPA used KDEs to characterize and anal yze the collection of individual HYSPLIT backward trajectories. 38 Higher density values, indicated by da rker blue colors, indicate a greater frequency of observed trajectory endpoints wi thin a particular grid cell. Figure 16 shows HYSPLIT KDE plots for the Atlanta area su mmarized by calendar quarter for the 2010-2012 period. The HYSPLIT KDE is weighted in the northwesterly directi on, indicating a greater frequency of trajectories passing over grid cells to the northwest , but the plots also indicate a high frequency of trajectories passing over the primary metro-Atlanta counties as well.

36 In some past initial area designations efforts, the EPA has used HYSPLIT backward trajectories to assist in determining nonattainment area boundaries. A HYSPLIT backward trajectory is usually depicted on a standard map as a single line, representing the centerline of an air par cel’s motion, extending in tw o dimensional (x,y) space from a starting point and regressing backward in time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining an air parcel’s point of origin on a day in which a short-term standard, such as an 8-hour standard or a 24-hour standard, was exceeded. However, for an annual standard, such as the 2012 annual PM 2.5 NAAQS, every trajectory on every day is important to consider. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT traj ectories may not be helpful as this process is likely to result in depicting air parcels originating in all directions from the violating monitoring site.

37 HYSPLIT - Hybrid Single Particle Lagr angian Integrated Trajectory Model, http://www.arl.noaa.gov/HYSPLIT_info.php

38 The KDEs graphically represent the aggregate of HYSPL IT backward trajectories for the years 2010-2012, run every third day (beginning on the first day of monitoring), four times each day, and ending at four endpoint heights.38 Figure 16. HYSPLIT Kernel Density Estimati on Plots for the Atlanta CBSA Area.3940 In summary, for the monitors with invalid da ta in the Atlanta Area, the HYPSPLIT KDE plots and wind roses suggest greatest po tential contribution of emissi ons from Clayton, Cobb, DeKalb, Douglas, and Paulding.

Geography/topography

To evaluate the geography/topograp hy factor, the EPA assessed physical features of the area of analysis that might define the airshed and t hus affect the formati on and distribution of PM 2.5 concentrations over the area. The Atlanta Area do es not have any geograp hical or topographical barriers meaningfully limiting air pollution transport w ithin its air shed. Theref ore, this factor did not play a meaningful role in this evaluation.

Conclusion for the Counties in the Atlant a CBSA for which the EPA is Deferring Designations

Speciation and urban increment data show high OM and sulfur content. Organic Compounds and SOx components are the most im portant portions of the total PM 2.5 mass throughout the year. Organic Compounds are predominately attribut ed to biogenic emissions sources. The predominant source of SOx and PM emission are point sources.

Based on the assessment of factors described ab ove, both individually and in combination, the EPA has preliminarily concluded that designations for the following counties in Georgia as a part41 of the Atlanta should be deferred because th ere are incomplete design values with the surrounding monitors and based on analysis of th e information above the EPA concludes that these counties contain emissions that would be relatively more likely to contribute to any potential violations: Bartow, Cherokee, Cl ayton, Cobb, Coweta, DeKalb, Douglas, Forsyth, Fulton, Gwinnett, Henry, and Paulding Counties. Thes e counties are also part of in the Atlanta nonattainment area for the 1997 annual PM 2.5 NAAQS.

Bartow County – This nearby county does not have a monitoring site, but has the second highest
PM 2.5 emissions in the CBSA with 2,147 tpy and third highest SO 2 emissions in the CBSA with 6,716 tpy. There are two point sources with substantial SO 2 emissions- Georgia Power Company’s Plant Bowen (5,889 tpy), and Chemi cal Products Corporation (557 tpy). Although all winds are relevant when designating areas for an annual NAAQS, here the predominant winds blow in the direction from Bartow C ounty, toward the monitors with invalid data.

Cherokee County – This nearby county does not have a monitoring site, but the meteorology analysis indicates a high freque ncy of winds blowing from Cher okee County toward monitors with invalid data. This county has more than 10,000 tons of PM 2.5 related emissions (5 th highest in the CBSA), including significan t population-related sources (i ncluding over 1.8 billion miles VMT).

Clayton County – This nearby county has an attaining monitoring site, but the EPA has concluded that the county woul d be relatively likely to co ntribute to particulate matter concentrations at monitors in the CBSA with invalid data in the Atlanta area, particularly in light of the high level of emissions from the Hartsfie ld-Jackson International Ai rport. Furthermore, the meteorology analysis indicate s that winds blow from Clayt on County toward monitors with invalid data during some time periods.

Cobb County – This county has a monitor with invalid data for 2011-2013, and is being deferred on that basis. Additionally, C obb County has the third highest PM 2.5 emissions in the CBSA with 1,859 tpy and the second highest SO 2 emissions in the CBSA with 19,127 tpy. There are two point sources w ith substantial SO 2 emissions: Georgia Power Company’s Plant McDonough/Atkinson (18,307 tpy), and Caraus tar Industries Inc (564 tpy).

Coweta County – This nearby county does not ha ve a monitoring site, but has the highest SO 2 emissions in the CBSA with 47,614 tpy, and fourth highest PM 2.5 emissions in the CBSA with 1,615 tpy. There is one point source with substantial SO 2 and PM 2.5 emissions- Georgia Power Company’s Plant Yates (47,530 a nd 626 tpy, respectively). The me teorology analysis indicates that winds blow from Coweta County toward monitors with invalid data during some time periods. The EPA has determined that a designa tion for Coweta should be deferred because of the relative likelihood that emissions from this large point source could to contribute to invalid monitors in the Atlanta CBSA, should those monitors subsequently show a violation.

DeKalb County – This nearby county has a valid attaining design value. However, the EPA has concluded that the county is less likely to co ntribute to particulate matter concentrations at monitors with invalid data through emissions from non-point sources (e .g., area sources) and from mobile source emissions. The count y has the seventh highest level of PM 2.5 emissions in42 the CBSA (1,348 tpy), and the second largest VMT (7.6 billion miles). The county is highly urbanized and has 13 percent of the CBSA population, and is 12 miles from the highest reading invalid monitor. The meteorology analysis i ndicates that winds blow from Dekalb County toward monitors with invalid data during some time periods.

Douglas County – This nearby county does not have a monitoring site, but the EPA has concluded that the county is relatively more lik ely to contribute to the particulate matter concentrations to monitors with invalid data through emissions from non-point sources (e.g., area sources) and from mobile source emissions. The County is the seventh fa stest population growth (43 percent). Although all winds are relevant when designating areas for an annual NAAQS, here the predominant winds blow in the direction fr om Douglas County, toward the highest reading invalid monitor.

Forsyth County – This nearby county does not have a monitoring site, but the EPA has concluded that the county likely contributes to the particulate ma tter concentrations in violation of the 2012 annual PM 2.5 NAAQS through emissions from non- point sources (e.g., area sources) and from mobile source emissions. The County is the fastest population gr owth (79 percent) and eighth largest VMT (over 1.6 billion miles).

Fulton County – This county has a monitor wi th invalid data for 2011-2013, and is being deferred on that basis. Furthermore, Plant Mc Donough/Atkinson power plan t is less than three miles from the Fulton County monitor. In addition, th ere is a major rail yard within a mile of the monitor. The county has the highest PM 2.5 emissions with 2,614 tpy. Th ere is one point sourceOwens Brockway Glass Container Inc ., which has a modest 84 tpy of SO 2 emissions. Although all winds are relevant when designating areas for an annual NAAQS, here the predominant winds blow in the direction from Fulton County, toward monitors with invalid data.

Gwinnett County – This county has a monitor w ith invalid data for 2011-2013, and is being deferred on that basis. Additionally, Gwinnett County has the sixth highest level of PM 2.5 emissions in the CBSA with 1,504 tpy and third la rgest VMT (7.4 billion m iles). The county is highly urbanized and has 15 pe rcent of the CBSA population.

Henry County – This nearby county does not have a monitoring site, but the EPA has concluded that the county likely contributes to the partic ulate matter concentrations at the incomplete monitors in the Atlanta CBSA through emissions from non-point sources (e.g., area sources) and from mobile sources The county is highly urbanize d as part of the Atlanta metropolitan area, has the third largest population growth (72 percent), and sixth larges t VMT (2.2 billion miles). The meteorology analysis indicates that winds blow fr om Henry County toward monitors with invalid data during some time periods.

Paulding County – This county has a monitor with invalid data for 2011-2013, and is being deferred on that basis. The county is highly urbanized as part of the Atlanta metropolitan area, and has the second largest populatio n growth (74 percent). Although all winds are relevant when designating areas for an annual NAAQS, here the pr edominant winds blow from the direction of Paulding County toward mon itors with invalid data.43 The EPA has made the determination not to include seven counties in the Atlanta Area CBSA in the boundary for the Atlanta Area that the EPA is deferring designations for the 2012 PM 2.5 NAAQS. Based on currently available informati on, the EPA believes that these counties would be relatively less likely to be contributing to a hypothetical NAAQS vi olation at any of the monitors with affected data. Accordingly, th e EPA believes there is comparatively less of a justification to defer designations for these counties at this tim e. Discussed as follows, these counties include: Barrow, Carroll, Fayette, He ard, Newton, Rockdale, Spalding and Walton.

Barrow County – There are low emissions; low popul ation; low VMT. This rural county has no major emission sources and is 44 miles away from the Fire Station #8 monitor. As context, SO 2 and PM emissions have decreased 80 percent and 50 percent, respec tively, since 2001.

Carroll County – There are low emissions; m oderate population growth; moderate VMT and commuters. The predominantly rural county ha s no major emission sources and is 27 miles away from the Fire Station # 8 monitor. As context, SO 2 and PM emissions have decreased 88 percent and 75 percent, respectively since 2001.

Fayette County – There are low emissions; m oderate population growth; moderate VMT and commuters in this county. The county is partia lly urbanized, has no major emission sources and it is 40 miles away from the Fire Station #8 monitor. As context, SO 2 and PM emissions have decreased 67 percent and 60 pe rcent, respectively, since 2001.

Heard County– A portion of this county was include d in the previous nona ttainment area in 2005 because of emissions from Plant Wans ley. As context, Plant Wansley had SO 2 scrubber controls installed in early 2008; th is achieved a 96 percent SO 2 reduction. Otherwise this county has low population; and low VMT.

Newton County – There are low emissions; moderate VMT and low commuters. The predominantly rural county contains no major emission sources and is 37 miles away from the Fire Station #8 monitor. As context, SO 2 and PM emissions have decreased 71 percent and 42 percent, respec tively, since 2001.

Rockdale County – There are relatively low em issions; moderate population growth; moderate VMT and low commuters. The County is partially urbanized, has no major emission sources and is 26 miles away from the Fire St ation #8 monitor. As context, SO 2 and PM emissions have decreased 68 percent and 67 pe rcent, respectively, since 2001.

Spalding County – There are low emissions; low population; and low VMT. This mostly rural county has no major emission sources and is 39 m iles away from the Fire Station #8 monitor. As context, SO 2 and PM emissions have decreased 80 pe rcent and 49 percent, respectively, since
2001.

Walton County – There are low emissions; low population; and low VMT. The mostly rural county has no major emission sources and it is 40 miles away from the Fire Station #8 monitor. As context, Walkton County is adjacent to De Kalb County (which has a valid attaining design44 value for 2011-2013), and SO 2 and PM emissions in Walton County have decreased 86 percent and 48 percent, respectively, since 2001.



### Brunswick Area

The Brunswick, Georgia CBSA is wholly contai ned in Georgia and includes three counties – Brantley, Glynn and McIntosh. The most populated a nd industrialized por tion of Brunswick is contained within Glynn County. More information on the EPA’s evaluation of the emissions for the counties in the CBSA is provided below. Th e Brunswick area does not have any geographical or topographical barriers meaningfully limiting air pollution transport within its air shed. Therefore, this factor did not play a meaningful role in this evaluation. The Brunswick CBSA is outlined in green in Figure 17 along with the loca tion of point sources and air quality monitors.

The Glynn County monitor located in the Alba ny area has incomplete 2011-2013 data. Based on incomplete data, the incomplete Glynn County DV is 8.2 μg/m 3

. The City of Brunswick, in Glynn County, Georgi a, has one major point source – Brunswick Cellulose Inc. Other sources include county le vel emissions from non-point sources, nonroad Figure 17. Brunswick CBSA with Point Sources and Air Quality Monitor Locations45 mobile, on-road mobile, fires and local traffic. Brantley and McIntosh Counties are the other counties in the Brunswick CBSA. The emissions in each of these counties are relatively lower than comparable emissions in Glynn County, partic ularly for the precursors that tend to drive
PM 2.5 problems in the southern part of Georgia. Additionally, most of the population in the CBSA is concentrated in Glynn County. Summary information on emissions and population can be found in the Table 10 below.

Table 10. Summary statistics fo r the Brunswick, Georgia CBSA

Glynn Brantley McIntosh Core urbanized county or outlying? Core Outlying Outlying

SO 2 emissions (tpy) 647 41 18

PM 2.5 emissions (tpy) 1,013 686 39 373

NOx emissions (tpy) 5,983 746 2,219

NH 3 emissions (tpy) 174 216 47

VOC emissions (tpy) 5,636 822 2,100

Population 79,863 18,487 14,287
Population (% of CBSA) 71% 16% 13%

VMT (Millions) 1,046 166 441

VMT (% of CBSA) 63% 10% 27%

The EPA also evaluated the meteorology in the area by evaluating wind da ta collected at two locations in the Brunswick CBSA as shown in Figur e 18 below. As shown in the figure, there are two airports near the Brunsw ick area, Brunswick Golden Is les Airport and Malcolm McKinnon Airport, with available surface level wind speed and direction data. The wind rose from each airport indicates that there is a pattern across th e CBSA of winds blowing from all directions, but with a higher frequency from the west-southwest, southeast and nor theast, mostly at low to midlevel speeds of 2 to 6 meters per second. The wind patterns su ggest that potential emission sources in Brantley and McIntosh counties, ge nerally in the west-southwest, southeast and northeast upwind directions should be considered for analysis.

39 The majority of these direct PM 2.5 emissions are from non-point sources and are event related. If a violation of the NAAQS occurs when three years of comp lete data becomes available, the potential impacts of these event-related emissions will be further evaluated for potential exclusion from the design value calculations.46 Figure 18. Wind Roses in the Brunswick Area

Brunswick, Georgia CBSA Conclusion WIND ROSE PLOT: Brunswick Golden Isle Airport 2009-2012

NORTH

SOUTH WEST EAST 2% 4% 6% 8% 10%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 29.24% DISPLAY: Wind Speed Direction (blowing from)

WIND ROSE PLOT: Malcolm McKinnon Airport 2009-2012

NORTH

SOUTH WEST EAST 2% 4% 6% 8% 10%

WIND SPEED (m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 14.30% DISPLAY: Wind Speed Direction (blowing from)47 The EPA is concluding that Glynn County in the Brunswick CBSA has the most likelihood of all the counties to contribute to a hypothetical violat ion of the standard at the Glynn County monitor that currently has incomplete data. The EPA identified Glynn County beca use the monitor with incomplete data is located in the southeast porti on of this county and Glynn County contains the majority of emissions, VMT, and is the populati on center of the CBSA. Th e outlying counties of Brantley and McIntosh have lo w population, and low VOC and SO 2 emissions in comparison to Glynn County. The direct PM 2.5 emissions from Brantley and Mc Intosh counties are primarily non-point source emissions largely from fire ev ents and road dust sources. The direct PM 2.5 emissions from these non-point sources are genera lly local in nature and are less likely to be transported large distances due to their close proximity to the ground. Accordingly, because of incomplete data at the Glynn County monitor in the Brunswick CBSA, the EPA is deferring a designation for Glynn County. Furthermore, the EP A is not deferring a designation for Brantley and McIntosh counties, and is designating these counties as un classifiable/attainment based on
the reduced likelihood that emissi ons from these counties would contribute to a hypothetical violation at the Glynn County monitor. Should such violation be identified in the future, the EPA will further evaluate Brantley and McIntosh counties for potential contribution to emissions at the Glynn County monitor.



### Augusta Area (Augusta, GA-SC CBSA)

Augusta is located in a bi-state CBSA that incl udes seven counties – five in Georgia (Burke, Columbia, McDuffie, Lincoln, and Richmond) and tw o in South Carolina (Aiken and Edgefield). The most populated and industria lized portion of the Augusta area where most of the emissions and emissions activities occur is primarily cont ained in three of the seven counties – Richmond and the southern region of Columbia County in Georgia, and the western region of Aiken County
in South Carolina. More information on the EPA’s evaluation of the emissi ons for the counties in the CBSA is provided below. The Augusta area does not have any geographical or topographical barriers meaningfully limiting air pollution transport w ithin its air shed. Theref ore, this factor did not play a meaningful role in this evaluation. The Augusta CBSA is outlined in green in Figure 19, along with the location of point sources and air quality monitors.48 The Richmond County monitor located in the Georgia portion of the Augusta area has incomplete 2011-2013 data. Based on incomplete data, the incomplete Richmond County DV is 10.5 μg/m 3 . As context, the monitor in Edgefield C ounty, South Carolina (also part of this CBSA) attains the 2012 annual PM 2.5 NAAQS with a DV of 9.3 μg/m 3

.
The EPA evaluated PM 2.5 and precursor emissions, along with other related data from the counties in the Augusta CBSA. As shown in Table 11, Richmond and Aiken Counties account for 98 percent of SO 2 emissions (45 and 53 percent, resp ectively) and 67 percent (26 and 41 percent, respec tively) of PM 2.5 emissions. Richmond, Columbia and Aiken Counties, together account for 85 percent of the population and 83 pe rcent of the VMT. Population within the CBSA is sharply divided between core and ou tlying counties. The largest outlying county (Edgefield County) accounts for only 5% of the CBSA population, and all four outlying counties combined comprise only 14% of the CBS A’s population. By contrast, even the smallest core county (Columbia County) comprises 22% of the CBSA population, and the core counties cumulatively comprise 85% of the CBSA’s population. Figure 19. Augusta CBSA with Point So urces and Air Quality Monitor Locations49 Table 11. Summary statistics for the Augusta, GA-SC CBSA

Aiken Columbia Richmond Burke Lincoln McDuffie Edgefield

State South
Carolina Georgia Georgia Georgia Georgia Georgia South
Carolina

Core
urbanized county or outlying? Core Core Core Outlying Outlying Outlying Outlying

SO 2 Emissions (tpy) 5,020 28 4,294 73 6 100 45

PM 2.5 emissions (tpy) 3,660 447 2,364 1,510 203 429 419

NOx
emissions (tpy) 7,144 3,758 10,110 1,414 405 1,655 664

NH 3 emissions (tpy) 2,605 118 1,798 672 72 138 434

VOC
emissions (tpy) 7,156 3,946 8,471 1,693 1,014 1,252 1,222

Population 160,622 124,965 201,056 23,350 7,966 21,852 26,970

Population (% of CBSA) 28% 22% 35% 4% 1% 4% 5%

VMT (Millions) 1,717 1,014 1,990 299 86 333 225

VMT (% of CBSA) 30% 18% 35% 5% 2% 6% 4%

The EPA also evaluated the meteorology in the area by evaluating wind data collected at three locations in the Augusta CBSA as shown in Figur e 20. As shown, the frequency and intensity of wind from any given direction is variable.50 Augusta GA-SC CBSA Conclusion

Based on the information summarized in Tabl e 11 and the wind rose data, the EPA has
concluded that of the five counties in the A ugusta CBSA, Columbia a nd Richmond Counties in Georgia, and Aiken County, South Carolina, are the most likely to contribute to potential
violations of the standard at the Richmond County monitor, which currently has incomplete data. The EPA identified Aiken County as a county with relatively higher potentia l to contribute to the
invalid monitor because of Aike n County’s high population, and the fact that nearly half of SO 2 and PM 2.5 emissions in the CBSA originate in Aiken County. Two point sources in Aiken County have combined SO 2 emissions greater than 5,000 tpy, and countywide direct PM 2.5 emissions exceed 3,600 tpy. While wind rose data may suggest a lesser contribution from Aiken County, there remains the potential for trans port of these emissions into Richmond County
because winds blow from the northeast at times and the point sources have elevated stack releases which may be transported longer distances.

Although Columbia County has somewhat lower em issions, this county is home to roughly onethird of the population of the CBSA, is expe riencing high population growth, and has VMT Figure 20. Augusta GA-S C CBSA Wind Rose Data51 comparative to Richmond and Aiken Counties. Wind rose data indicate the potential for transport of emissions from Columbia County to the affected monitor in Richmond County.

Accordingly, because of the affected mon itor in Richmond County, and the potential for
contribution to that monitor from Aiken Count y and Columbia County, th e EPA is deferring its decision on designations for these three counties.

However, the EPA is designating the remaini ng counties unclassifiable/attainment for the following reasons. Although Burke County has comparatively higher direct PM 2.5 emissions due to non-point sources than the othe r outlying counties, these are sti ll meaningfully lower than the
emissions from the core counties of Richmond and Aiken. These non-point source emissions are primarily from fire events, agricultural and road dust, and waste disposal sources. The direct

PM 2.5 emissions from these non-point sources are genera lly local in nature and are relatively less likely to be transported large distances. The distance from Burke County to the Richmond County monitor is large compared to the emi ssion sources in Richm ond, Columbia and Aiken Counties. The small population and low VMT of Burke County and the outlying counties of Lincoln, McDuffie, and Edgefield C ounties lead the EPA to believe that these counties would be relatively likely to be contri buting to any nearby violations that may be identified with forthcoming data. Accordingly, the EPA will not de fer designations for the counties of Burke, Lincoln, McDuffie, or Edgefield Counties, but will instead designate them
unclassifiable/attainment based on th e data available at this time.



### Columbus Area (Columbus, GA-AL CBSA)

C olumbus is in a bi-state CBSA that includes fi ve counties – four in Georgia (Chattahoochee, Harris, Marion, and Muscogee) and one in Al abama (Russell). The most populated and industrialized portion of the Columbus area is mo stly contained in two of the five counties – Muscogee County in Georgia, and Russell County in Alabama. The Columbus CBSA is outlined in green in Figure 21, along with the location of point sources and air quality monitors.52 The Muscogee County monitor lo cated in the Georgia portion of the Columbus area has incomplete 2011-2013 data. As context, the mon itor in Russell County, Alabama (also part of this CBSA) attains the 2012 annual PM 2.5 NAAQS with a DV of 11.2 μg/m 3 . As further context, based on incomplete data, the inco mplete Muscogee County DV is 10.8 μg/m 3

.
The EPA evaluated PM 2.5 and precursor emissions, along with other related data from the counties in the Columbus area. As shown in Table 3, Russell County alone accounts for 94 percent of SO 2 emissions and 52 percent of PM 2.5 emissions. The most populous area is located in the Georgia portion of the CBSA in Muscog ee County, which accounts for 64 percent of the population, followed by Russell County, Alabama with 18 percent. A similar pattern in VMT is also noted with Muscogee and Russell Countie s together accounting for 82 percent of the VMT (64 percent and 18 percent, respectively). By comparison, the outlyi ng Georgia counties of Chattahoochee, Harris, and Marion account for approximately 35 percent of PM 2.5 emissions and less than 5 percent of SO 2 emissions and have no major point sources. 40 Only 18 percent of the

40 For purposes of this designations analysis, “major” point sources are those whose sum of PM precursor emissions (PM 2.5 + NOx + SO 2 + VOC + NH 3 ) are greater than 500 tpy based on (NEI 2011v1. NOx is nitrogen oxides; VOC is vola tile organic compounds; and NH 3 is ammonia. The EPA notes that emissions from all source categories are relevant in the context of designations, but the presence or absence of major53 CBSA population resides in these ou tlying counties, which is also reflected in a low proportion of VMT (20 percent).

Table 12. Summary statistics for the Columbus, GA-AL CBSA

Muscogee Russell Chat tahoochee Harris Marion

State Georgia Alabama Georgia Georgia Georgia

Core
urbanized county or outlying? Core Core Outlying Outlying Outlying

SO 2 emissions (tpy) 84 3,487 51 43 29

PM 2.5 emissions (tpy) 729 3,083 714 809 554

NOx
emissions (tpy) 4,969 5,324 429 2,335 349

NH3
emissions (tpy) 144 583 68 109 572

VOC
emissions (tpy) 6,105 6,591 619 1,848 502

Population 190,484 53,238 11,131 32,146 8,742

Population (% of CBSA) 64% 18% 4% 11% 3%

VMT (Millions) 1,655 738 74 428 78

VMT (% of CBSA) 56% 25% 2% 14% 3%

The EPA also evaluated the meteorology in the area by evaluating wind data collected at three
locations in the Columbus CBSA as shown in Figure 22. The wind roses indicate that wind

point sources helps to identify nearby areas with emissi ons that are likely to be contributing to violations of the NAAQS.54 direction is less likely to originate from the s outhwest or from the nor theast. North-northwest winds are more frequently observed.

Figure 22. Wind Rose Data for Columbus GA-AL CBSA

Columbus GA-AL CBSA Conclusion

Based on the wind rose data and the inform ation summarized in Table 12, the EPA has
concluded that of the five counties in th e Columbus CBSA, Muscogee County, Georgia and Russell County, Alabama have relatively higher potenti al to contribute to pot ential violations of the standard at the Muscogee County monitor, cu rrently has incomplete data. The EPA identified Muscogee County because the monitor with incomple te data is located in this county. The EPA identified Russell County as a potentially c ontributing county because it accounts for the majority of emissions in the CBSA, despite the fact that its popul ation is less than one-third of Muscogee County’s. Wind rose data further indicate that fairly frequent wi nds from the southerly direction increase the potential for transport of these emissions from Russell County to Muscogee County. Because of incomplete data at the Muscogee County monitor in the Columbus CBSA, the EPA is deferring a de signation for Russell and Muscogee Counties.55 By contrast, the outlying counties of Chatta hoochee and Marion have low emissions, population,
and VMT. The wind rose data also indicate infr equent and low intensit y winds impacting the Muscogee County monitor from the southeasterly di rection (i.e., from thes e counties). The third outlying county, Harris County, has comparativ ely higher VMT than Chattahoochee or Marion County. However, Harris County nevertheless has re latively low emissions compared to the two
core counties. This information leads the EPA to believe that these Chattahoochee, Marion and Harris Counties are relatively less likely to contribut e to any potential violat ion that may exist at the Muscogee County, Georgia monitor. Accordingl y, the EPA will not defer designation for the Chattahoochee, Harris, or Marion Counties, a nd is instead at this time designating them
unclassifiable/attainment ba sed on the available data.



### Savannah Area (Savanna h, Georgia CBSA)

The Savannah, Georgia CBSA is wh olly contained in Georgia and includes three counties – Bryan, Chatham, and Effingham. Most of the most populated and industrialized portion of Savannah is contained within Ch atham County. More information on the EPA’s evaluation of the emissions for the counties in the CBSA is provi ded below. The Savannah area does not have any geographical or topographical barrie rs meaningfully limiting air pollution transport within its air shed. Therefore, this factor did not play a m eaningful role in this evaluation. The Savannah CBSA is outlined in green in Figure 8, along with the location of point sources and air quality monitors.56 Figure 23. Savannah, Georgia CBSA with Point Sources and Air Quality Monitor Locations

The Chatham County monitor has incomplete 2011- 2013 data that do not allow calculation of a valid 2011-2013 DV. Based on incomplete data, the incomplete Chatham County DV is 10.2 μg/m 3

.
In particular, the EPA evaluated PM 2.5 and SO 2 emissions and related data from the counties in the Savannah CBSA. As shown in Table 4, Chatham and Effingham Counties account for 99 percent of SO 2 emissions (69 and 30 percent, respectively) and 70 percent of PM 2.5 emissions (42 percent and 28 percent, respectively) . Bryan County has 30 percent of PM 2.5 emissions from nonpoint sources. Chatham County is the most populous area with 76 percent of the CBSA population followed by Effingham (15 percent) and Bryan (9 percen t). A similar pattern in VMT is also noted with Chatham County accounting for 73 percent of VMT and Effingham and Bryan Counties contributing 11 percent and 15 percent, respectively to VMT.57 Table 13. Summary statistics fo r the Savannah, Georgia CBSA

Chatham Effingham Bryan Core urbanized county or outlying? Core Outlying Outlying

SO 2 Emissions (tpy) 10,312 4,567 97
PM 2.5 emissions (tpy) 2,039 1,332 1,439 41

NOx emissions (tpy) 17,000 3,819 2,784

NH 3 emissions (tpy) 452 280 123

VOC emissions (tpy) 12,215 2,550 2,147

Population 265,998 52,420 30,412

Population (% of CBSA) 76% 15% 9%

VMT (Millions) 2,804 437 582

VMT (% of CBSA) 73% 11% 15%

The EPA also evaluated the meteorology in the area by evaluating wind da ta collected at two
locations in the Savannah CBSA as shown in Figure 24. The wind rose data shows that the frequency of wind direction a nd intensity is variable.

41 The majority of these direct PM 2.5 emissions are from non-point sources and are event related. If a violation of the NAAQS occurs when three years of comp lete data becomes available, the potential impacts of these event-related emissions will be further evaluated.58 Figure 24. Wind Rose Data for the Savannah, Georgia CBSA

Savannah Georgia CBSA Conclusion

Based on the wind rose data that shows consisten tly variable wind directi on and intensity and the information summarized in Table 13, the EPA is c oncluding that two of th e three counties in the Savannah CBSA are more likely to have the pote ntial to contribute to any violations of the standard at the Chatham County monitor, wh ich currently has incomplete data. The EPA identified Chatham County because the monitor w ith incomplete data is located in this county and Chatham County contributes th e majority of emissions, VMT, and is the population center of the CBSA. The EPA identified Effingham County as a potentially contri buting county because Effingham County has three point sources having combined SO 2 emissions greater than 4,500 tpy (30 percent of CBSA SO 2 emissions) and over 1,300 tpy of direct PM 2.5 emissions. In addition, the EPA’s analysis of meteorology indicat es potential transport of these emissions to
the PM 2.5 monitoring site in Chatham County.

By contrast, outlying Bryan County has low SO 2 emissions (97 tpy) in comparison to Chatham and Effingham Counties (respectively, 10,312 t py and 4,567 tpy). Bryan County also has the lowest population of any county in the CBSA. Although Bryan County does have a similar level
of direct PM 2.5 emissions as Effingham County, Bryan Count y is located a s ubstantial distance from the Chatham County monitor in Savannah and direct PM 2.5 emissions generally have more59 localized impacts. Accordingly, because of inco mplete data at the Chatham County monitor in the Savannah CBSA, and the potential for Effingha m County to contribute to that monitor, the EPA is deferring a designation for Chatham and Effingham Counties. However, the EPA is not deferring a designation for Bryan County and is instead designating this county as
unclassifiable/attainment ba sed on the available data.



### Valdosta Area (Valdosta, Georgia CBSA)

The Valdosta, Georgia CBSA is wholly contai ned in Georgia and incl udes four counties – Brooks, Echols, Lanier, and Lowndes. The Val dosta CBSA and surrounding counties are shown in Figure 25 along with the location of point sources a nd air quality monitors.

Figure 25. Valdosta CBSA Point Source and Air Quality Monitor Locations

The Lowndes County monitor has incomplete 201 1-2013 data that do not allow calculation of a valid 2011-2013 DV. Based on incomplete data, the incomplete Lowndes County DV is 8.9 μg/m 3

.60 In particular, the EPA evaluated PM 2.5 and SO 2 emissions and related data from the counties in the Valdosta CBSA. As shown in Table 5, Brooks and Lowndes Counties account for 96 percent of SO 2 emissions (20% and 76%, respectively) and 86% of PM 2.5 emissions (52 % and 34%, respectively). Lowndes County is the most popu lous area with 78% of the CBSA population, followed by Brooks (12%), Lanier (7%) and Echols (3 %). A similar pattern in VMT is also noted with Lowndes and Brooks Counties accounting fo r 93% (81% and 12%, respectively) to CBSA VMT, and Echols and Lanier togeth er contributing 7% of VMT.

Table 14. Summary statistics for the Valdosta, Georgia CBSA

Lowndes Brooks Echols Lanier

Core urbanized county or outlying? Core Outlying Outlying Outlying

SO 2 emissions (tpy) 784 209 18 22
PM 2.5 emissions (tpy) 1,941 2,959 309 478 NOx emissions (tpy) 6,255 1,596 321 408
NH 3 emissions (tpy) 273 867 56 159 VOC emissions (tpy) 6,307 2,260 633 723 Population 109,761 16,189 4,037 10,103 Population (% of CBSA) 78% 12% 3% 7%

VMT 1,324 195 39 80 VMT (% of CBSA) 81% 12% 2% 5%

The EPA also evaluated the meteorology in the area by evaluating wind data collected in the Valdosta CBSA as shown in Figure 26. These da ta indicate frequency of wind direction and intensity is variable.61 Figure 26. Wind Rose Data for the Valdosta, Georgia CBSA

Valdosta Georgia CBSA Conclusion

Based on the wind rose data and the information summarized in Table 14, the EPA is concluding
that of the four counties in the Valdosta CBSA, Lowndes and Brooks Counties are the most likely to potentially contribute to hypothetical viol ations of the standard at the Lowndes County monitor, which that currently has incomplete data. The EPA identified Lowndes County because the monitor with incomplete data is located in this county and Lowndes County contributes the majority of emissions, VMT, and is the popul ation center of the CBS A. The EPA identified Brooks County as a potentially contributing coun ty because it has a c ountywide total of over 2,900 tpy of direct PM 2.5 emissions from a major point sour ce and other non-point sources, and the EPA’s analysis of meteorology indicates pos sible transport of thes e emissions from the
west/southwest toward the PM 2.5 monitoring site in Lowndes C ounty. Consequently, because of incomplete data at the Lowndes County monito r in the Valdosta CBSA, and the potential for
contribution to that monitor from Lowndes and Brooks Counties, the EPA is deferring a designation for Lowndes and Brooks Counties. Although the meteorological information suggests possible transport from Lanier County, the county has low emissions, no major point sources, and low VMT relative to Lowndes and Brooks Counties. The same is true of Echols County, for which the wind rose data suggest ev en less likelihood of contribution from this easterly county to the affected monitor in Lo wndes County. This information leads the EPA to62 believe that Echols and Lanier C ounties would be less likely to cont ribute to a potential violation at the Lowndes County monitor. Accordingly, th e EPA is not deferring a designation for either
Echols or Lanier Counties and is at this time de signating them unclassifia ble/attainment based on
the available data.



### Washington County, GA

Washington County is a single county that is not a part of a larger CBSA. Due to a snow storm in 2011, the Washington County monitor has inco mplete 2011-2013 data that do not allow calculation of a valid 2011-2013 DV. 42 Based on incomplete data, the incomplete Washington County DV is 8.9 μg/m 3

.

Washington County, Georgia Conclusion

The EPA is deferring a designation for Washington County because the monitor with incomplete data is located in this county.

As discussed above, the EPA’s limited contributi on analyses in this deferral memorandum evaluate only those counties in the same CBSA as an affected monitor. Because Washington County is not part of a larger CBSA, the EPA is not at this time assessi ng the likelihood that any other areas could potentially co ntribute to a hypothetical viol ation at the Washington County monitor.

42 Memorandum from Liz P. Naess, Gr oup Leader, Air Quality Analysis Gr oup, US EPA Office of Air Quality Planning and Standards, to EPA Docket EPA-HQ-OAR-2012-0918, Air Quality Designations for the 2012 PM 2.5 Standards, titled “Data Quality Issues in Georgia Affecting Air Quality Designations for the 2012 PM 2.5 National Ambient Air Quality Standard.”