Page 1 of 60 

## Cleveland and Canton-Massillon, Oh io Area Designations for the 2012 Primary Annual PM 2.5 

## National Ambient Air Quality Standard Technical Support Document

1.0 Summary
In accordance with Section 107(d) of the Clean Air Ac t (CAA), the EPA must promulgate designations for all areas of the country. In particular, the EPA must identif y those areas that are violating a National Ambient Air Quality Standard (NAAQS) or contributing to a viol ation of the NAAQS in a nearby area. The EPA must complete this process within 2 years of promulgating a new or revised NAAQS, or may do so within 3 years under circumstances not relevant to these designations. 1 This technical support document (TSD) describes the EPA’s intent to designate areas in Ohio as nonattainme nt for the 2012 primary annual fine particle NAAQS (2012 annual PM 2.5 NAAQS), except that the Cincinnati area is addressed in a separate TSD. 2
Under section 107(d), states are required to submit area designation recommendations to the EPA for the 2012 annual PM 2.5 NAAQS no later than 1 year following promulgation of the standard, or by December 13, 2013. In December 2013, Ohio recommended that the counties iden tified in Table 1 be designated as “nonattainment” for the 2012 annual PM 2.5 NAAQS based on air quality data from 2011-2013.
On August 19, 2014, the EPA sent Ohio a letter conveying our preliminary designations and informing Ohio of the opportunity to provide additional information for the EPA to consider in its final designations. On September 29, 2014, Ohio responded by providing additional analyses related to boundaries for the Cleveland, Canton-Massillon, and Cincinnati-Hamilton areas. After considering Ohio’s recommendations and additional analyses, and based on the EPA’s technical analysis as described in this TSD, the EPA is designating the areas listed in Table 1 as nonattainment for the 2012 annual PM 2.5 standard. The EPA must designate an area nonattainment if it has an air quality monitoring site 3 that is violating the standard or if it has sources of

1 Section 107(d) of the CAA requires the EPA to complete the initial designation process within 2 years of promulgation of a new or revised NAAQS, unless the Administrator has insuffici ent information to make initial designation decisions in the 2-year time frame. In such circumstan ces, the EPA may take up to 1 additional year to make initial area designation decisions (i.e., no later than 3 years after promulgation of the standard).

2 On December 14, 2012, the EPA promulgated a revised primary annual PM 2.5 NAAQS (78 FR 3086, January 15, 2013). In that action, the EPA 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

.

3 In accordance with 40 CFR 50 Appendix N, PM 2.5 measurements from the primary monitor and suitable collocated PM 2.5 FRM, FEM or ARMs may be used in a “combined site data record” to establish a PM 2.5 design value to determine whether the NAAQS is met or not met at a particular PM 2.5 monitoring site.Page 2 of 60 emissions that are contributing to a violation of the NAAQS in a nearby area. Legal descriptions (e.g., county boundaries, townships, and ranges) of these areas are found below in the supporting technical analysis for each area. As provided in CAA section 188(a), the EPA will initially classify all nonattainment areas as “Moderate” nonattainment areas.

Table 1. Ohio’s Recommended Nonattainment Areas and the EPA’s Final Designated Nonattainment Areas for the 2012 annual PM 2.5 NAAQS

Area Ohio’s Recommended Nonattainment Counties EPA’s Final Designated Nonattainment Counties

Canton-Massillon, OH Stark Stark, Summit, Wayne (partial)

Cleveland, OH Cuyahoga Cuyahoga, Lorain

Cincinnati-Hamilton, OH-KY Butler, Clermont, Hamilton OH: Butler, Clermont, Hamilton, Warren (partial)
KY: Boone (partial), Campbell (partial), Kenton (partial)

*Cincinnati-Hamilton is a multi-state nonattainment ar ea composed of counties and/or partial counties in Ohio and Kentucky. The technical analysis fo r this multi-state area is discussed in a separate Technical Support Document.

In its recommendation letter, Ohio recommended that the EPA designate as attainment all counties not identified in the Ohio Recommendation column of Ta ble 1. The EPA agrees with Ohio’s recommendations except as to Lorain County, Summit County, and parts of Wayne County and Warren County, as listed in Table 1. The EPA is designating the remainder of Ohio as unclassifiable/attainment based on ambient monitoring data collected during the 2011-2013 pe riod showing compliance with the 2012 annual PM 2.5 NAAQS and the EPA’s assessment that areas within the Stat e are not likely contributing to nearby violations. 4,5

4 Unless a state or tribe has specifically identified jurisdicti onal boundaries in its area recommendations, when determining “remainder of the state,” EPA will use Federal Information Pro cessing Standard (FIPS) codes maintained by the National Institute of Standards and Technology (NIST), which are used to identify counties and county equivalents (e.g., parishes, boroughs) of the United States and its unincorporated territori es (e.g., American Samoa, Gu am, Northern Mariana Islands, Puerto Rico, and the US Virgin Islands). Available on EPA’s Envirofacts website at http://www.epa.gov/enviro fw/html/codes/state.html

.

5 EPA uses a designation category of “unclassifiable/ attainment ” for areas that are monitoring attainment and for areas that do not have monitoring sites but which the EPA believes are likely attainment and does not emissions sources that are contributing to nearby violations based on the five factor analysis and other available information.Page 3 of 60 2.0 Nonattainment Area Analyses and Final Boundary Determination

The EPA evaluated and determined the final bounda ries for each nonattainment area on a case-by-case basis considering the specific facts and circumstances unique to the area. In accordance w ith the CAA section 107(d), the EPA is designating as nonattainment not only the areas with the monitoring sites that violate the 2012 annual PM 2.5 NAAQS, but also those nearby areas with emissions sources that contribute to the violations in the violating area. As described in EPA guidance 6 , after identifying each monitoring site indicating a violation of the standard in an area, the EPA an alyzed those areas with emissions contributing to that violating area by considering those counties in the entire metropolitan area (e.g., Core Based Statistical Area (CBSA) or Combined Statistical Area (CSA)) in which the violating m onitoring sites are located. The EPA also evaluated counties adjacent to the CBSA or CSA that have emissi ons sources with the potential to contribute to the violations. The EPA uses the CBSA or CSA as a starting point for the contribution analysis because those areas are nearby for purposes of the PM 2.5 NAAQS. Based upon relevant facts and circumstances in each area, the designated nonattainment area could be larger or smaller than the CBSA or CSA. EPA’s analytical approach is described in section 3 of this technical support document.

3.0 Technical Analysis
In this technical analysis, EPA used the latest data an d information available to the EPA (and to the states and tribes through the PM 2.5 Designations Mapping Tool 7 and the EPA PM Designations Guidance and Data web
page 8 ) and/or data provided to the EPA by states or tribes or produced by EPA in response to data provided by states or tribes. This technical analysis identifies the ar eas with one or more monitoring sites that violate the 2012 annual PM 2.5 NAAQS. The EPA evaluated these areas and other nearby areas with emissions sources or activities that potentially contribute to ambient fine particle concentrations at the violating monitors in the area based on the weight of evidence of the five factor s recommended in EPA guidance and any other relevant information.

These five factors are:
Factor 1: Air Quality Data. The air quality data analysis involves examining available ambient PM 2.5 air quality monitoring data at, and in the proximity of, the viola ting monitoring locations. This includes reviewing the design values (DV) calculated for each monitoring locati on in the area based on air quality data for the most recent complete 3 consecutive calendar years of quality -assured, certified air quality data in the EPA’s Air Quality System (AQS). In general, the EPA identifies violations using data from suitable Federal Reference Method (FRM), Federal Equivalent Method (FEM), and/ or Approved Regional Met hod (ARM) monitors sited

6 The EPA issued guidance on April 16, 2013, which identified important factors that the EPA intended to evaluate, in making recommendations for area designations and nonattainment boundaries for the 2012 annual PM 2.5 NAAQS. Available at http://www.epa.gov/pmdesignations/2012standards/docs/april2013guidance.pdf.

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

.

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

.Page 4 of 60 and operated in accordance with 40 CFR Part 58. 9 Procedures for using the air quality data to determine whether a violation has occurred are given in 40 CFR part 50 A ppendix N, as revised by a final action published in the Federal Register on January 15, 2013 (78 FR 3086). 10 In addition to reviewing data from violating monitor sites, the EPA also assesses the air quality data from other m onitoring locations to help ascertain the potential contribution of sources in areas nearby to the violatin g monitoring sites. Examples include using chemical speciation data to help characterize contributing emissions sources and the determination of nearby contributions through analyses that differentia te local and regional source contributions.

Factor 2: Emissions and emissions-related data. The emis sions analysis examines identified sources of direct PM 2.5 ; the major components of direct PM 2.5 , e.g., primary organic carbon (POC) and PM 2.5 organic mass (POM), elemental carbon (EC), crustal material (and/or individual trace metal compounds), primary nitrate and primary sulfate; and precursor gaseous pollutants (e.g., sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), total volatile organic compounds (VOC), and ammonia (NH 3 ). Emissions data are generally derived from the most recent National Emissions Inventory (NEI) (i.e., 2011 NEI version 1) , and are given in tons per year. In some cases, EPA may also evaluate emissions information from states, tribes, or other relevant sources that may not be reflected in the NEI. One example of “other info rmation” could include an inventory or assessment of local/regional area sources that individually does not m eet the current threshold fo r reporting to the NEI but collectively contributes to area PM 2.5 concentrations. Emissions data indicate the potential for a source to contribute to observed violations, making it usef ul in assessing boundaries of nonattainment areas.
Factor 3: Meteorology. Evaluating meteorological data help s to determine the effect on the fate and transport of emissions contributing to PM 2.5 concentrations and to identify areas potentially contributing to the violations at monitoring sites. The Factor 3 analysis includes assessing potential source-receptor relationships in the area identified for evaluation using summaries of air tr ajectories, wind speed, wind direction, and other meteorological data, as available.
Factor 4: Geography/topography. The geography/topograp hy analysis includes examining the physical features of the land that might define the airshed and, ther efore, affect the formation and distribution of PM 2.5 over an area. Mountains or other physical features may influence the fate and transport of emissions and PM 2.5 concentrations. Additional analyses may consider topogra phical features that cause local stagnation episodes via inversions, such as valley-type features that effectively “trap” air pollution, leading to periods of elevated PM 2.5 concentrations.
Factor 5: Jurisdictional boundaries. The analysis of jurisdictional boundaries identifies the governmental planning and organizational structure of an area th at may be relevant for designations purposes. These

9 Suitable monitors include all FEM and/or ARMs except th ose specific continuous FEMs/ARMs used in the monitoring agency’s network where the data are not of sufficient quality such that data are not to be compared to the NAAQS in accordance with 40 CFR part 58 .10(b)(13) and approv ed by the EPA Regional Administra tor per 40 CFR part 58.11(e).

10 As indicated in Appendix N to 40 CFR part 50, Interpretation of the National Ambient Air Quality Standards for PM 2.5 , section 3(a) indicates “Except as otherwise provided in this appendix, all valid FRM/FEM/ARM PM 2.5 mass concentration data produced by suitable monitors that are required to be submitted to AQS, or otherwise available to EPA, meeting the requirements of part 58 of this chapter including appendic es A, C, and E shall be used in the DV (design value) calculations. Generally, the EPA will only use such data if they have been certified by the reporting organization (as prescribed by § 58.15 of this chapter); however, data not certified by the reporting organization can nevertheless be used, if the deadline for certification has passed and the EPA judges the data to be complete and accurate.”Page 5 of 60 jurisdictional boundaries provide insight into how th e governing air agencies conduct or might conduct air quality planning and enforcement in a potential nonattai nment area. Examples of jurisdictional boundaries include counties, air districts, areas of Indian country, CBSA or CSA, metropolitan planning organizations (MPOs), and existing nonattainment areas.

3.1 Area Background and Overview fo r the Canton-Massillon, Ohio Area
Figure 1a is a map of EPA’s final nonattainment bounda ry for the Canton-Massillon Area. The map shows the location and design values of ambient air quality monitoring locations, county boundaries and Ohio’s recommended nonattainment boundary.
Portions of this area were designated nonattainment for purposes of the 1997 annual and 2006 24-hour PM 2.5 NAAQS. Stark County was included within the bounda ry of the Canton-Massillon area for both the 1997 annual and 2006 24-hour PM 2.5 NAAQS. In addition, Summit County was included within the boundary of the Cleveland-Akron-Lorain Area for the 1997 annual and 2006 24-hour PM 2.5 NAAQS.
Figure 1a. EPA’s Final Nonattainment Bounda ries for the Canton-Massillon, Ohio AreaPage 6 of 60 EPA must designate as nonattainment areas that violat e the NAAQS and nearby areas that contribute to the violation in the violating area. Stark County shows a violation of the 2012 PM 2.5 NAAQS, therefore this county is included in the nonattainment area. As shown in Fi gure 1b, EPA evaluated each county without a violating monitoring site located in or near the me tropolitan statistical area (MSA) of the county with a violating monitoring site based on the five factors and other rele vant information. The following sections describe this five factor analysis process. While the factors are pres ented individually, they are not independent. The five factor analysis process carefully considers their interc onnections and the dependence of each factor on one or more of the others.
Figure 1b. Area of Analysis for the Canton-Massillon, Ohio Area

Factor 1: Air Quality Data
All data collected during the year are important when de termining contributions to an annual standard such as the 2012 annual PM 2.5 NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings throughout the year, including days with monitored ambient concentrati ons below the level of the NAAQS. For the 2012 annual PM 2.5 NAAQS, the annual mean is calculated as the mean of quarterly means. A high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3-year DV. Although all data arePage 7 of 60 important, seasonal or episodic emissions can provide insight as to relative contributors to measured PM 2.5 concentrations. For these reasons, for the Factor 1 air quality analysis, the EPA assessed and characterized air quality at, and in the proximity of, the violating monitori ng site locations first, by evaluating trends and the spatial extent of measured concentrations at monitors in the area of analysis, a nd 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. 11 The EPA also identified the spatial extent of these high PM 2.5 concentrations. The mass and composition at the design value location represents contributions from various emission sources including local, area-wide (which may comprise nearby urban and rural areas) and regiona l sources. To determine the source mix (by mass) at the design value monitoring site, the EPA examined the chemical composition of the monitored PM 2.5 concentrations by pairing each violating FRM/FEM/ARM monitoring site with a collocated 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 12 and other monitoring locations whose data are representative of regional background. 13
, 14 This comparison of local/area-wide chemical composition data to regional chemical composition data derives an “urban in crement,” which helps differentiate the influence of

11 Although compliance with the annual NAAQS depends on contri butions from all days of the year, examining data on a quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and meteorology and the resulting PM 2.5 mass and composition. In some areas of the coun try where there may be noticeable month-to-month variations 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 th at best correspond to the local “seasons” in these areas.

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

13 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 dist ance 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 alternativ e analyses to reflect the mix of urban and rural sources contributing to the measured conc entrations at violating monitoring sites.

14 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 species. If there were no rural sites within 150 m iles, then the nearest rural site was used alone. That rural mean was then subtracted from the quarterly mean of the urban s ite to get the increment. Negative values were simply replaced with zeros.Page 8 of 60 more distant emissions sources from the influence of closer emissions sources, thus representing the portion of the measured violation that is associated with nearby emission contributions. 15,16,17

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 violating monitoring s ite 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 qualityassured, certified air quality data from suitable FEM/F RM/ARM monitoring sites in the EPA’s Air Quality System (AQS). For this designations analysis, the EPA used data for the 2011-2013 period (i.e., the 2013 design value), which are the most recent years with fully-certif ied air quality data. A monitor’s DV is the metric or statistic that indicates whether th at monitor attains a specified air quality standard. The 2012 annual PM 2.5 NAAQS is met at a monitoring site when the 3-year av erage annual mean concentration is 12.0 micrograms per cubic meter (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 regulatory da ta processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 2 identifies the current design va lue(s) (i.e., the 2013 DV) and the most recent two additional design values based on all monitoring sites in the area of analysis for the Canton-Massillon final nonattainment area. 18 Where a county has more than one monitoring location, the county design value is indicated in red type.
Table 2. Air Quality Data collected at Re gulatory Monitors (all DV levels in μg/m 3 ) a County, State Monitor Site ID State Rec NA? 09-11 DV 10-12 DV 11-13 DV
Carroll, OH N/A No No monitor Columbiana, OH N/A No No monitor Harrison, OH N/A No No monitor Holmes, OH N/A No No monitor

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

16 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

17 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

18 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 lo cal governments must assess data from Class III PM 2.5 FEM and ARM monitors operated within their network using the performance criteria described in table C4 to subpart C of part 53 of this chapter, for cases where th e data are identified as not of sufficient comparability to a collocated FRM, and the monitoring agency requests that the FEM or ARM data shou ld not be used in comparison to the NAAQS. These assessments are required in the monitoring agency's annual monitoring network plan described in §58.10(b) for cases where the FEM or AR M is identified as not of sufficient comparability to a collocated FRM....”Page 9 of 60 County, State Monitor Site ID State Rec NA? 09-11 DV 10-12 DV 11-13 DV
Jefferson, OH 390810017 No 12.5 12.2 11.6 Jefferson, OH 390811001 No 11.8 11.4 10.8 Mahoning, OH 390990005 No 11.4 11.2 10.7 Mahoning, OH 390990014 No 11.8 11.3 10.4 Portage, OH 391330002 No 10.9 10.3 9.5 Stark, OH 391510017 Yes 13.4 13.0 12.1 Stark, OH 391510020 Yes 12.3 11.8 10.8 Summit, OH 391530017 No 12.6 12.0 11.0 Summit, OH 391530023 No 11.7 11.2 10.4 Tuscarawas, OH N/A No No monitor Wayne, OH N/A No No monitor

a Where a county has more than one monitoring location, the county design value is indicated in red type.

The Figure 1 map, shown previously, identifies the Canton-Massillon final nonattainment area, the MSA boundary and monitoring locations with 2011-2013 viol ating DVs. As indicated on the map, there is one violating monitor located in the city of Canton in Stark County.
Seasonal variation can highlight tho se conditions most associated with high average concentration levels of PM 2.5 . Figure 2 shows quarterly mean PM 2.5 concentrations for the most recent 3-year period for the highest DV monitoring sites in each county within the area of anal ysis, as well as an additional non-violating monitoring site in the Canton-Massillon metropolitan statistical area (MSA). This graphical representation is particularly relevant when assessing air quality data for an annual standard, such as the 2012 annual PM 2.5 NAAQS, because, as previously stated, the annu al mean is calculated as the mean of quarterly means and a high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3-year DV.Page 10 of 60 Figure 2. Canton-Massillon PM 2.5 Quarterly Means for 2011-2013

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0

'11 Q1 '11 Q2 '11 Q3 '11 Q4 '12 Q1 '12 Q2 '12 Q3 '12 Q4 '13 Q1 '13 Q2 '13 Q3 '13 Q4PM2.5Concentrationsμg/m3 

## Canton PM 2.5 

## Quarterly Means 2011

‐ 2013

Stark Jefferson Mahoning Portage Summit

For the monitors in the Canton-Massillon area of analysis , quarterly values across the 3-year period vary by 1-4
μ g/m 3 at each site. Annual peaks most often occur in Q1 and Q3, although one annual peak was recorded in Q4. This suggests that these quarters should be consider ed more closely when evaluating chemical constituent and urban increment data.

PM 2.5 Composition Measurements - To assess potential emissi ons contributions for each violating monitoring location, 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 sources impacting the monitored concentration. To best describe the PM 2.5 at the violating monitoring location, the EPA first adjusted the chemical speciation measurement data from a monito ring location at or near the violating FRM monitoring site using the SANDWICH approach to account for the amount of PM 2.5 mass constituents retained in the FRMPage 11 of 60 measurement. 19,20,21,22 In particular, this approach accounts for lo sses in fine particle nitrate and increases in sulfate mass associated with particle bound wate r. Figure 3a illustrates the fraction of each PM 2.5 chemical constituent at monitoring site 391510017 in Stark Count y based on annual averages for the years 2010-2012.

Figure 3a. Canton-Massillon Annual Average PM 2.5 Chemical Constituents (2010-2012)

4.3

5.0 1.6 0.9 1.1 

## Annual Average PM 2.5 

## Chemical Constituents 2010

‐ 2012 (μg/m 3 

## )

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

Figure 3b shows annual and quarterly chemical compos ition profiles and illustrates any seasonal or episodic contributors to PM 2.5 mass. This “increment analysis,” combined with the other factor analyses, can provide

19 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 SANDWI CH adjustment uses an FRM mass construction methodology that results in reduced nitrates (relative to the amount measured by routine speciation networks), higher mass associated with sulfates (reflecting water included 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.

20 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 /files/2006conference/frank.pdf

.

21 Frank, N. H., The Chemical Composition of PM 2.5 to support PM Implementation, 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/prese nts/pm2.5_chemical_composition.pdf.

22 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.Page 12 of 60 additional insight as to which sources or factors may contri bute at a greater level. Simply stated, this analysis can help identify nearby sources of emissions that contribut e to the violation at the violating monitoring site.
Figure 3b. Canton-Massillon Area Annual and Quarterly Average PM 2.5 Species (2010-2012) a

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

Annual Q1 Q2 Q3 Q4 

### Annual and Quarterly Average PM 2.5 

### Species 2010

‐ 2012 Adjusted to FRM Total PM 2.5

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

0% 20% 40% 60% 80% 100%

Annual Q1 Q2 Q3 Q4 

## Annual and Quarterly Average PM 2.5 

## Species 2010

‐ 2012 Adjusted to FRM Total PM 2.5

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

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 SANDWICH calculations from speciation measurements for the particular PM 2.5 monitoring site.
Figure 3b shows that sulfate and organic mass are the predominant species, with an exception in Q1 where nitrates comprise a fraction comparable to sulfates and organic mass. Crustal and elemental carbon are relatively small components, contributing from 6-12% and 5-7%, respectively. Figure 3b suggests that sulfate, organic mass and nitrate sources have the highest impact on the monitored values in the Canton-Massillon area.Page 13 of 60 EPA assessed seasonal and annual average PM 2.5 constituents at monitoring sites within the area relative to monitoring sites outside of the analysis area to account for the difference between regional background concentrations of PM 2.5 , and concentrations of PM 2.5 in the area of analysis, also known as the “urban increment.” This analysis differentiates between the in fluences of emissions from sources in nearby areas and in more distant areas on the violating monitor. Estimating th e urban increment in the area helps to illuminate the amount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in nearby areas, as opposed to impacts of more distant or regional sources of emissions. Figure 4a includes pie charts showing the annual and quarterly chemical mass c onstituents of the urban increment. The quarterly pie charts correspond to the high-concentration quarters id entified in Figure 2. Evaluating these high-concentration quarters can help identify composition of PM 2.5 during these times. Note that in these charts, sulfates and nitrates have been adjusted to represent their mass in measured PM 2.5 . Figure 4a. Canton-Massillon Area Urba n Increment Analysis for 2010-2012.

2%

56% 26% 8% 8% 

### Annual Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

0.08

2.27 2.11 0.10 0.06 

### Q1 Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal 0.86

0.06 0.27 0.44 

### Q2 Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
CrustalPage 14 of 60 1.90 0.51 0.36 

### Q3 Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal 0.09

1.84 1.06 0.12 0.13 

### Q4 Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

Figure 4b. Canton-Massillon Area Average Urban Increment Analysis for 2010-2012.

0.00 1.00 2.00 3.00 4.00 5.00

Annual Q1 Q2 Q3 Q4μg/m3 

## Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

As shown in Figure 2, Stark County has one monitori ng site with a DV exceeding the NAAQS. Stark County ambient PM 2.5 concentrations are fairly consistent across the y ear, with modest seasonal peaks in in Q1 and Q3, although one annual high was recorded in Q4. This suggest s that particular attention should be given to urban increment information for Q1 and Q2, although data for Q4 also needs to be evaluated.

In reviewing the urban increment analysis for the Canton-Massillon DV monitor, organic mass consistently represents the largest component. Sulfate appears to c onsistently have small ur ban contribution, although conversely large regional contribution. Nitrate is th e second highest urban increment component for Q1 andPage 15 of 60 Q4, but is not a factor inQ3, during which crustal a nd elemental carbon both comprise a higher percentage. This analysis points to contribution from local nitrate and direct PM 2.5 sources nearby the violating monitor and contribution from regional sulfate and nitrate sources.

Factor 2: Emissions and emissions-related data

In this designations process, for each area with a viol ating monitoring site, EPA evaluated the emissions data from nearby areas using emissions related data for th e relevant counties to assess each county’s potential contribution to PM 2.5 concentrations at the violating monitoring site or monitoring sites in the area under evaluation. Similar to the air quality analysis, these data were examined on a seasonal basis. However, seasonal emission trends are not significantly different, and the seasonal patterns are similar for all major emitted pollutants; therefore EPA only analyzed annual emissions data. (Although nitrate is much higher in the cooler months, NOx emissions have similar importance all year due to their role in fostering the photochemistry that causes secondary particulate matter.)

EPA examined emissions of identified sources or source categories of direct PM 2.5 , the major components of direct PM 2.5 (organic mass, elemental carbon, crustal ma terial (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 ). EPA also considered the distance of those sources of em issions 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 for local NO X 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 violating monitoring sites.

Emissions Data
For this factor, EPA reviewed data from the 2011 National Emissions Inventory (NEI) version 1 (see http://www.epa.gov/ttn/chief/net/2011inventory.html ). For each county in the area of analysis, EPA examined the magnitude of county-level emissions reported in th e NEI. 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. EPA also looked at the geographic distribution of major point sources of the relevant pollutants. 23 Substantial emissions levels from sources in a nearby area indicate the potential for the area to contribute to monitored violations.
To further analyze area emissions data, 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/2012standards/d ocs/nei2011v1county_nei2011v 1point_nei2008v3_county. xlsx

.

23 For purposes of this designations effort, “major” point 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.Page 16 of 60 When considered with the urban increment analysis in Factor 1, evaluating the components of direct PM 2.5 and precursor gases can help identify specific sources or sour ce types contributing to elevated concentrations at violating monitoring sites and thus assist in identifying appropriate area boundaries. In general, directly emitted POC and VOCs 24 contribute to POM; directly emitted EC contributes to PM 2.5 EC; NO X , NH 3 and directly emitted nitrate contribute to PM 2.5 nitrate mass (PNO 3 ); SO 2 , NH 3 and directly emitted sulfate contribute to PM 2.5 sulfate mass (PSO 4 ); and directly emitted crustal material and metal oxides contribute to PM 2.5 crustal matter (Pcrustal). 25,26 EPA believes that the quantities of those nearby emissions as potential contributors to the PM 2.5 violating monitors 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 3a provides a county-level emissions summary (i .e., the sum of emissions from the following general source categories: point sources, non-point (i.e., area) s ources, nonroad mobile, on-road mobile, and fires) of directly emitted PM 2.5 and precursor species for the county with the violating monitoring site and nearby counties considered for inclusion in the Canton-Massillon area. Table 3b summarizes the directly emitted components of PM 2.5 for the same counties in the area of analysis for the Canton-Massillon area. This information will be considered in conjunction with the urban increment composition information previously shown in Figures 4a and 4b.
Table 3a. County-Level Emissions of Directly Emitted PM 2.5 and Precursors (tons/year)
County, State Total NO X Total SO 2 Total Direct PM 2.5 Total NH 3 Total VOC Total

Jefferson, OH 12,593 29,761 10,072 234 2,522 55,181 Summit, OH 16,925 4,310 1,760 495 13,110 36,601 Wayne, OH 7,323 18,028 2,080 3,868 4,637 35,937 Stark, OH 14,466 566 2,471 2,159 12,424 32,085 Mahoning OH 9,712 1,481 1,031 738 7,430 20,392 Portage, OH 6,428 170 858 473 5,604 13,533 Tuscarawas, OH 4,399 2,482 819 1,138 4,176 13,014 Columbiana, OH 4,160 173 677 2,024 3,963 10,996 Holmes, OH 1,601 100 656 2,261 2,199 6,817 Carroll, OH 1,649 70 428 436 1,562 4,146 Harrison, OH 731 99 495 220 1,184 2,730

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

25 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.

26 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

.Page 17 of 60 Table 3b. County-Level Emissions for Components of Directly Emitted PM 2.5 (tons/year) 27

County, State POM EC PSO 4 PNO 3 Pcrustal Residual Total Direct PM 2.5 Jefferson, OH 598 412 780 6 3,721 4,555 10,072 Stark, OH 738 375 124 6 616 612 2,471 Wayne, OH 418 186 106 3 598 769 2,080 Summit, OH 857 451 36 3 179 234 1,760 Mahoning OH 428 254 26 3 135 184 1,031 Portage, OH 390 201 15 2 103 147 858 Tuscarawas, OH 387 161 24 5 97 145 819 Columbiana, OH 328 124 12 2 88 123 677 Holmes, OH 400 67 7 1 84 96 656 Harrison, OH 380 49 4 1 20 41 495 Carroll, OH 250 47 9 2 35 86 428

Table 3b breaks down the direct PM 2.5 emissions value from Table 3a into its components. These data will also be compared with the previously presented Urban Increment composition.
Using the previously described relationship between directly emitted and precursor gases and the measured mass to evaluate data presented in Tables 3a a nd 3b, EPA identified the following components warranting additional review: POM, NO X , SO 2 , EC, and Pcrustal. EPA then looked at the cont ribution of these constituents of interest from each of the counties included in the area of analysis, as shown in Tables 4a-e.
Table 4a. County-Level Primary Or ganic Mass Emissions (tons/year) County, State POM Percent Cumulative % Summit, OH 857 17% 17% Stark, OH 738 14% 31% Jefferson, OH 598 12% 42% Mahoning OH 428 8% 51% Wayne, OH 418 8% 59% Holmes, OH 400 8% 66% Portage, OH 390 8% 74% Tuscarawas, OH 387 7% 81% Harrison, OH 380 7% 89% Columbiana, OH 328 6% 95% Carroll, OH 250 5% 100%

27 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/chief/emch/index.html#2011 (accessed 02/26/14).Page 18 of 60 Table 4b. County-Level NO X Emissions County, State NO X Percent Cumulative % Summit, OH 16,925 21% 21% Stark, OH 14,466 18% 39% Jefferson, OH 12,593 16% 55% Mahoning OH 9,712 12% 67% Wayne, OH 7,323 9% 76% Portage, OH 6,428 8% 84% Tuscarawas, OH 4,399 5% 90% Columbiana, OH 4,160 5% 95% Carroll, OH 1,649 2% 97% Holmes, OH 1,601 2% 99% Harrison, OH 731 1% 100%

Table 4c. County-Level SO 2 Emissions County, State SO 2 Percent Cumulative % Jefferson, OH 29,761 52% 52% Wayne, OH 18,028 31% 83% Summit, OH 4,310 8% 91% Tuscarawas, OH 2,482 4% 95% Mahoning OH 1,481 3% 98% Stark, OH 566 1% 99% Columbiana, OH 173 0% 99% Portage, OH 170 0% 100% Holmes, OH 100 0% 100% Harrison, OH 99 0% 100% Carroll, OH 70 0% 100%

Table 4d. County-Level Primar y Elemental Carbon Emissions County, State EC Percent Cumulative % Summit, OH 451 19% 19% Jefferson, OH 412 18% 37% Stark, OH 375 16% 53% Mahoning OH 254 11% 64% Portage, OH 201 9% 73% Wayne, OH 186 8% 81% Tuscarawas, OH 161 7% 88% Columbiana, OH 124 5% 93% Holmes, OH 67 3% 96% Harrison, OH 49 2% 98% Carroll, OH 47 2% 100%Page 19 of 60 Table 4e. County-Level Primary Crustal Emissions County, State Pcrustal Percent Cumulative % Jefferson, OH 3,721 66% 66% Stark, OH 616 11% 76% Wayne, OH 598 11% 87% Summit, OH 179 3% 90% Mahoning OH 135 2% 92% Portage, OH 103 2% 94% Tuscarawas, OH 97 2% 96% Columbiana, OH 88 2% 98% Holmes, OH 84 1% 99% Carroll, OH 35 1% 100% Harrison, OH 20 0% 100%

In addition to reviewing c ounty-wide emissions of PM 2.5 and PM 2.5 precursors in the area of analysis, EPA also reviewed emissions from major point sources located in the area of analysis. The magnitude and location of these sources can help inform nonattainment boundaries. Ta ble 5 provides facility-level emissions of direct PM 2.5 , components of direct PM 2.5 , and precursor pollutants (given in tons per year) from major point sources located in the area of analysis for the Canton-Massillon ar ea. Table 5 also shows the di stance from the facility to the DV monitor.Page 20 of 60 Table 5. NEI 2011 v1 Point Source Emissions (tons/year)

County,

State Facility Name (Facility ID) NH 3 NO X PM 2.5 SO 2 VOC Total
Carroll, OH Tennessee Gas Pipline- Station 214 (0210000046) 22 662 4 0 23 689
Jefferson, OH Cardinal Power Plant (Cardinal Operating Company) (0641050002) 54 3 2,250 616 25,122 103 28,094
Jefferson, OH W. H. Sammis Plant (0641160017) 44 1 7,544 6,916 4,153 141 18,756 Mahoning, OH Youngstown Thermal (0250110024) 44 0 123 19 1,063 1 1,206
Stark, OH Alliance Casting Co. LLC (1576010014) 18 614 216 3 33 865
Stark, OH Republic Engineered Products, Inc. (1576050694) 3 2 224 174 63 66 529

Stark, OH Marathon Petroleum Company LP - Canton Refinery (1576002006) 1 8 285 188 93 224 798

Summit, OH Cargill, Incorporated - Salt Division (Akron, OH) (1677010027) 19 0 140 18 1,516 1 1,676

Summit, OH Emerald Performance Materials, LLC (1677010029) 20 0 115 1 869 10 996

Summit, OH City of Akron Steam Generating (1677010757) 21 0 254 44 1,729 2 2,028
Tuscarawas, OH Dover Municipal Light Plant (0679010146) 19 0 278 27 1,396 1 1,702
Tuscarawas, OH The Belden Brick Company (0679000118) 23 39 46 957 4 1,045

Wayne, OH Department of Public Utilities, City of Orrville, Ohio (0285010188) 20 0 1,902 745 13,038 5 15,690
Wayne, OH Morton Salt, Inc. (0285020059) 24 0 195 50 4,434 2 4,681
Wayne, OH East Ohio Gas - Chippewa Station (0285000366) 18 654 13 0 23 690 NEI 2011 v1 Emissions - Tons/Year Distance to Monitor (miles)

Figure 5 shows the major point source emissions (from the 2011 NEI in tons per year) in the area of analysis for the Canton-Massillon area and the relative distances of these sources from th e violating monitoring location, as depicted by the red dot in Stark County. The actual distance from the point sources to the DV monitoring location is presented in Table 5. The distance from the vi olating monitoring location is particularly 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. 28
As indicated in Table 5, there are 15 major point sour ces located within the area of analysis – all within approximately 50 miles of the violating monitor. Three of these sources are in Stark County, three of these sources are in Summit County, and three of these sources ar e in the northeastern quadrant of Wayne County.

28 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.Page 21 of 60 Figure 5. Major Point Source Emissions in the Ar ea of Analysis for the Canton-Massillon Area.

In summary, EPA’s evaluation of county-level emissions from Table 3a shows that Jefferson, Summit, Wayne, Stark and Mahoning Counties account for 78% of the total emissions in the area of analysis. However, after evaluating meteorology, discussed in greater detail belo w under Factor 3, it becomes apparent that emissions from sources located east of the monitor are unlikely to have a meaningful im pact on the monitored violation in Stark County. Therefore, while Jefferson and Mahoning Counties have substantial emissions, these emissions are unlikely to have a meaningful impact on the monito red violation recorded in Stark County. Columbiana County is also located east of Stark County – betwee n Mahoning and Jefferson Counties – and is therefore also unlikely to meaningfully affect the monitored violation in Stark County. It should be noted that the major point sources in Summit County are in the southern portion of th e county, closer to the violating monitor in Stark County than the violating monitors in Cuyahoga County (part of Cleveland).
Consideration of emissions in the eight other counties s hows that Stark is responsible for 22% of emissions and 26% of direct PM 2.5 emissions. Together, Stark, Summit, and Wayne Counties are responsible for 72% of emissions and 66% of PM 2.5 emissions. In addition, among these eight counties, Stark County accounts for 19%, 27%, 2%, 24% and 36% to the POM, NO X , SO 2 , EC, and Pcrustal components, respectively. When Stark County is combined with Summit and Wayne County, those three counties account for 53%, 72%, 89%, 66%Page 22 of 60 and 80% to the POM, NO X , SO 2 , EC, and Pcrustal components, respectively. Emissions in Portage, Tuscarawas, Holmes, Harrison and Carroll Counties are rela tively low compared to the other counties in the area of analysis. Of these counties, Portage’s emissions comprise 9% of the total emissions in these eight other counties.
Of the major point sources listed in Table 5 that are lo cated in the eight counties not excluded for primarily meteorological reasons, the largest point source is in Wa yne County. In addition, the two largest point sources in Wayne County account for 70% of the SO 2 emissions in the eight counties. Further, Wayne County is directly west of Stark County with point sources loca ted only18-24 miles from the violating monitor. Given the strong westerly component shown in the wind roses in Figure 8, below, these point source emissions would be expected to contribute to PM 2.5 concentrations recorded at the Stark County monitor. Taken together, 89% of the total emissions from major sources in these eight counties come from facilities in Stark County, Summit County, and Wayne County. While Jefferson County has the two largest sources in the full area of analysis, this county has historically been addressed as part of the Steubenville area and, as previously noted, meteorological considerations indicate that emissions from Jeffers on County are unlikely to contribute to the monitored violation in Stark County, particularly with the point source being located near the eastern border of the county, along the Ohio River.
Population density and degree of urbanization
In this part of the factor analysis, EPA evaluated the population and vehicle use characteristics and trends of the area as indicators of the probable location and magnit ude of non-point source emissions. Rapid population growth in a county on the urban perimeter signifies in creasing integration with the core urban area, and indicates that it may be appropriate to include the county associated with area source and mobile source emissions as part of the nonattainment area. Table 6 shows the 2000 and 2010 population, population growth since 2000, and population density for each county in the area.
Table 6. Population Growth and Population Density.

County, State Population 2000 Population 2010 % Change from 2000 Land Area (Sq. Miles) Population Density (per Sq.

Mile) % Cumulative % % Urban a

Summit, OH 542,899 541,652 -0.2% 413 1,312 30% 30% 47% Stark, OH 378,098 375,417 -0.7% 576 652 21% 51% 22% Mahoning OH 257,555 238,339 -7.5% 415 574 13% 65% 23% Portage, OH 152,061 161,403 6.1% 492 328 9% 74% 13% Wayne, OH 111,564 114,480 2.6% 555 206 6% 80% 5% Columbiana, OH 112,075 107,820 -3.8% 532 202 6% 86% 6% Tuscarawas, O H 90,914 92,565 1.8% 568 163 5% 91% 5% Jefferson, OH 73,894 69,593 -5.8% 410 170 4% 95% 4% Holmes, OH 38,943 42,448 9.0% 423 100 2% 98% <1% Carroll, OH 28,836 28,800 -0.1% 395 73 2% 99% 1% Harrison, OH 15,856 15,857 0.0% 404 39 1% 100% <1% Total 1,802,695 1,788,374 Source: U.S. Census Bureau population estimates for 2000 and 2010Page 23 of 60 a Source: Ohio Department of Development, Ohio County Profiles: http://development.ohio.gov/reports/reports_countytrends_map.htm

When considering the entire area of analysis, Summit County has by far the greatest population, population density, and percent urban. Consider ing 2010 population and population dens ity, Stark County is next most populous and population-dense county, followed by Mahoni ng, Portage, Wayne and Columbiana counties. When setting aside Mahoning, Columbiana, and Jeffers on counties – which are unlikely to contribute to violations when considering available meteorological data – Summit and Stark counties together contain 67% of the total population of these counties. Population and population density are substantially lower in the other six counties.

Figure 6. 2010 County-Level Population in the A rea of Analysis for the Canton-Massillon Area.

Traffic and Vehicle Miles Travelled
High vehicle miles travelled (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 NO x , VOC, and direct PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQSPage 24 of 60 in the area. In combination with the population/populati on density data and the loca tion of main transportation arteries, an assessment of VMT helps identify the probable location of nonpoint source emissions that contribute to violations in the area. Comparatively high VMT in a county outside of the CBSA or CSA signifies integration with the core urban area contained within the CSA or CBSA, and indicates that a county with the high VMT may be appropriate to incl ude in the nonattainment area becau se emissions from mobile sources in that county contribute to violations in the area. Tabl e 7 shows 2011 VMT, while Figure 7 overlays 2011 countylevel VMT with a map of the transportation arteries. Th e VMT used in this analysis was submitted by Ohio.

Table 7. 2011 VMT for the Canton-Massillon Area

. County, State Total 2012 VMT % Cumulative % Summit, OH 6,250,389,061 31% 31% Stark, OH 3,838,738,336 19% 50% Mahoning OH 2,893,842,592 14% 65% Portage, OH 2,128,490,347 11% 75% Wayne, OH 1,192,145,098 6% 81% Tuscarawas, OH 1,122,381,268 6% 87% Columbiana, OH 1,092,970,892 5% 92% Jefferson, OH 731,496,924 4% 96% Holmes, OH 316,674,316 2% 98% Carroll, OH 265,377,054 1% 99% Harrison, OH 215,789,852 1% 100% Total 20,048,295,740 http://www.census.gov/hhes/commuting/data/commuting.html
Summit County has by far the highest VMT, followed by Stark, Mahoning, and Portage Counties. As discussed below, however, meteorological considerations indi cate that emissions from Mahoning County (as well as Jefferson and Columbiana counties) are unlikely to cont ribute to the monitored violation in Stark County. Considering those eight counties not excluded from the final nonattainment area for primarily meteorological considerations, Summit and Stark have the highest VMT, together comprising 66% of the VMT in the eight counties. Portage County has the next highest VMT, followed by Wayne County and Tuscarawas County.Page 25 of 60 Figure 7. Overlay of 2011 County-level VMT with Transportation Arteries.

After evaluating meteorology, discussed in greater deta il below under Factor 3, it becomes apparent that emissions from sources located east of the monitor are un likely to contribute meaningfully to the monitored violation. Therefore, while Jeffe rson and Mahoning counties consistently have substantial emissions, these emissions are unlikely to contribute meaningfully to the monitored violation recorded in Stark County. For similar meteorological reasons, Columbiana County – al so located east of Stark County, but with fewer emissions than Jefferson or Mahoning counties – is also unlikely to contribute meaningfully to the monitored violation in Stark County. Therefore emissions from Stark, Summit, Wayne, Portage, Tuscarawas, Holmes, Carroll, and Harrison Counties merit closer analysis when determining contribution to the violation monitored in Stark County.
Of these eight counties, Summit County has the highest total emissions, followed by Wayne and Stark, as well as the highest NO X , VOC, primary organic matter, and elemen tal carbon emissions. Wayne County has the highest SO 2 emissions and second highest overall total emissions, largely due to SO 2 emissions from two point sources. When considering emissions from the counti es that cannot be reasonably excluded for meteorological reasons, Stark, Summit, and Wayne counties together co mprise 72% of the total emissions, 66% of the totalPage 26 of 60 direct PM 2.5 emissions, 75% of the total population and 74% of the VMT. Although Portage County has higher population density and VMT than Wayne County, a nd Tuscarawas County VMT and population density are similar to that of Wayne County, emissions in Wayne C ounty are substantially higher than emissions in either Portage or Tuscarawas counties, or in the counties of Holmes, Carroll and Harrison. Additionally, Portage, Holmes, and Harrison counties lack large singular point source contributors.
Factor 3: Meteorology The EPA evaluated available meteorological data to determine how meteorological conditions, including, but not limited to, weather, transport patte rns, and stagnation conditions, could affect the fate and transport of directly emitted particulate matter and precursor emissions from sources in the area of analysis. The EPA initially used two primary tools for this assessment: wind roses and kernel density estimation (KDE). When considered 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 n earby areas contributing to violations at violating monitoring sites. In response to comments from Ohio, and as discussed below, the EPA additionally considered information based on trajectory analyses of a s ubstantial set of days with relatively high PM 2.5 concentrations., .

Wind roses are graphic illustrations of the frequency of wind direction and wind speed. Wind direction can indicate the direction from which contributing emissions are transported; wind speed can indicate the force of the wind and thus the distance from which those emissi ons are transported. EPA constructed wind roses from hourly observations of wind direction and wind speed using 2009-2012 data from National Weather Service locations archived at the National Climate Data Center. 29 When developing these wind roses, EPA also used wind observations collected at meteorological sampling sta tions collocated at air quality monitoring sites, where these data were available. Figure 8 shows wind roses that EPA generated from data relevant to the CantonMassillon area.

29 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.pdfPage 27 of 60 Figure 8. Wind Roses in the Area of Analysis for Canton-Massillon Area.

As shown in Figure 8, there is a pattern across the area of predominantly west and s outhwesterly winds, mostly at mid-level speeds of 4 to 10 meters per second. Alth ough the previous section shows that Stark, Summit, and Wayne Counties are responsible for 72% of emissions and 66% of PM 2.5 emissions, the predominant winds described above suggest that potential emission sources in the south-through-west upwind direction should also be considered for analysis. Some northerly winds are seen , notably at the sites in southern Summit County.
In addition to wind roses, the EPA also generated kern el density estimation (KDE) plots to represent HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Traj ectory) backward trajectory frequency at violatingPage 28 of 60 monitoring sites. 30,31 These KDEs are graphical statistical estimati ons to determine the density of trajectory endpoints at a particular location represented by a grid cell. The EPA used KDEs to characterize and analyze the collection of individual HYSPLIT backward trajectories 32 . Higher density values, indicated by darker blue colors, indicate a greater frequency of observed trajectory endpoints within a particular grid cell. Figure 9 shows a HYSPLIT KDE plot for the Canton-Massillon area summarized by calendar quarter for the 2010-2012 period. The HYSPLIT KDE is weighted in the south, west, and north directions in quarters 1 - 3, and weighted more in the south and west in quarter 4, indicating a greater fre quency of trajectories passing over grid cells from those directions. The highest densities outside of Stark Co unty occur in Summit, Wayne, Holmes and Tuscarawas Counties. EPA examined these KDEs on a quarterly basis in order to capture the seasonal variability in wind patterns which could then be paired with the seasona l variability in PM2.5 concentration and composition. Finally, the wind-concentration-composition pairings ar e used to identify the key emitted pollutants and the nearby areas that are responsible for the urban increment and th e PM2.5 violation.

Figure 9. HYSPLIT Kernel Density Estimati on Plots for the Canton-Massillon Area.

30 In some past initial area designations efforts, the EPA has us ed 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 parcel’s motion, extending in two dimensional (x,y) space from a starting point and regressing backward in time to a point of origin. Backward tr ajectories 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. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT trajectories may not be helpful as this process is likely to re sult in depicting air parcels originating in all directions from the violating monitoring site.

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

32 The KDEs graphically represent the aggregate of HYSPLIT 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.Page 29 of 60 In summary, the HYSPLIT KDE plots and wind roses suggest that emissions from Stark, Summit, Wayne, Holmes and Tuscarawas Counties (and to a lesser extent, Carroll County) have the greatest potential to reach the violating Canton-Massillon monitor. Comparison of th ese plots to the analogous plots for the Cleveland area further suggests that Wayne County emissions have a greater potential to contribute to Stark County than to the Cuyahoga County, and that Summit C ounty emissions have potential to c ontribute to both areas. However, consideration of the high density of trajectories in the southern portion of Summit County along its border with Stark County, in conjunction with the location of the point sources in Summit County, also in the southern portion of the county, as shown in Figure 5, suggests that much of the emissions from Summit County are slightly more prone to influence concentrations in Ca nton than Cleveland. Accordingly, this data indicates placing Summit and Wayne Counties in the Canton-Massillon ar ea is more appropriate than placing them in the Cleveland area. The HYSPLIT KDE plots indicate relativ ely low density values in Jefferson, Columbiana and Mahoning counties. These counties are therefore unlikely to meaningfully impact the violation monitored in Stark County. Of particular note, the Jefferson County poi nt sources shown in Figure 5 are located in the more distant eastern portion of that county –along the Ohio Ri ver – and are therefore especially unlikely to have meaningful impact on the violating monitor in Stark County.Page 30 of 60 As an attachment to its September 29, 2014 letter, Ohio provided the EPA HYSPLIT modeling for selected days showing back trajectories from the Stark County monitor passing over areas primarily to the northwest through southeast of the violating monitor, with no tr ajectories passing over Summit County. Ohio cited this modeling as supporting an attainment designation fo r Summit County. However, the HYSPLIT modeling provided by Ohio was limited in that it only considered trajectories for the four highest concentration days and the 24-hour exceedance days for the 2010-2012 time peri od. For the 2012 annual NAAQS, the monitoring data for the entire calendar year (including on days that are below the level of the NAAQS) are part of the mathematical calculation of whether or not a monitor is nonattainment. Therefore, wind directions during periods that do not have high ambient levels are still rele vant. Nevertheless, it is also informative to evaluate the days with monitored concentrations exceeding the level of the annual standard (here, 12.0 μg/m 3 ) because those days tend to have a majority of the mass observed at the monitor and are the days that make a larger contribution towards a violation. Therefore, to fu rther evaluate questions raised by HYSPLIT modeling provided by Ohio, the EPA analyzed all of the days with concentrations exceeding 12.0 μg/m 3 during the 2011- 2013 time period at both the Canton a nd Cleveland design monitors using the HYSPLIT model to simulate back trajectories. 33 Of the days with concentrations exceeding the value of annual standard at the Canton monitor, 37% of the days have trajectories passing over Summit County and 43% of the days have trajectories passing over the northeast quarter of Wayne Count y. This modeling supports the c onclusion that emissions originating in Summit County and the northeast quarter of Wayne County contribute to the violation of the NAAQS at the Stark County monitor.
The wind data suggests potential contribution from Hom es and Tuscarawas counties to the west and south. These counties, however, have less emissions than Su mmit and Wayne, especially for organic carbon which is the largest component of the urban increment. These coun ties each have 7-8% of the total POC for the area of analysis. When the frequency of pollutant transport, d escribed by the KDEs, are examined together with the lower quantity of emissions in Homes and Tuscarawas coun ties, the potential for those emissions to contribute to the PM2.5 violation is judged to be small. This is especially true for Holmes whose emissions are more distant from the violating monitor.

Factor 4: Geography/topography

To evaluate the geography/topography factor, EPA assessed physical features of the area of analysis that might define the airshed and thus affect the formation and distribution of PM 2.5 concentrations over the area. The Canton-Massillon area does not have any geographical or topographical barriers inhibiting air pollution transport around the area of analysis. Therefore, this f actor did not play a substantial role in defining the boundary of the Canton-Massill on final nonattainment area.

33 A file containing HYSPLIT modeling for days with concentrations exceeding 12.0 μg/m 3 during the 2011-2013 time period at the Canton design monitor is available in docket EPA-HQ-OAR-2012-0918.Page 31 of 60 Factor 5: Jurisdictional boundaries

In defining the boundaries of the Canton-Massillon fi nal nonattainment area, EPA considered existing jurisdictional boundaries, which can provide easily identifiable and recognized boundaries for purposes of implementing the NAAQS. Existing jurisdictional boundaries often signify well recognized boundaries that the state can easily administer and for which the state has the necessary legal authority for carrying out air quality planning and enforcement functions. Examples of su ch jurisdictional boundaries include existing/prior nonattainment area boundaries for particulate matter, county lines, air district boundaries, township boundaries, areas covered by a metropolitan planning organization, st ate lines, and Reservation boundaries, if applicable. Where existing jurisdictional boundaries were not adequate or appropriate to describe the nonattainment area, EPA considered other clearly defined and permanent landmarks or geographic coordinates for purposes of identifying the boundaries of the final designated areas.

The Canton-Massillon Metropolitan Statistical Area cons ists of Stark and Carroll Counties. The CantonMassillon area also has previously established nonattain ment boundaries, consisting of Stark County, associated with the 2006 24-hour and 1997 annual PM 2.5 NAAQS. Summit and Portage counties were previously designated nonattainment as part of the Cleveland area for both the 1997 and 2006 PM 2.5 NAAQS. The state has recommended that only Stark County be included in the Canton-Massillon nonattainment area for the 2012 annual PM 2.5 NAAQS; however, Ohio also in its September 2014 response to EPA’s preliminary designation decision stated that Summit County is better suited in the Canton-Massillon nonattainment area as opposed to the Cleveland area in which it has historically been designated.

In addition to counties, the Bureau of Census rec ognizes legally defined county subdivisions, referred to as “minor civil divisions.” For Ohio, these minor civil divisi ons consist of townships and cities. When considering Wayne County’s contribution to the violation monitored in Stark County, major point source emissions were of particular concern. Limiting the nonattainment portio n of Wayne County to the following Bureau of Census defined minor civil divisions captures the three majo r sources in Wayne County and establishes a contiguous nonattainment area with clearly defi ned legal boundaries: Baughman, Chi ppewa, Green, and Milton townships and the portion of Norton City located within Wayne County. This sub-county area consists of approximately the northeast quarter of the county.

Conclusion for the Canton-Massillon Area

Based on the assessment of factors descr ibed above, both individually and in combination, EPA has concluded that the following counties should be included as part of the Canton-Massillon nonattainment area because they are either violating the 2012 annual PM 2.5 NAAQS or contributing to a violation in a nearby area: Stark County, Summit County, and Wayne County (partial).

The air quality monitoring site in Stark County indicates a violation of the 2012 annual PM 2.5 NAAQS based on the 2011-2013 DV; therefore this county is included in the nonattainment area. Summit and Wayne counties are nearby counties that do not have violating m onitoring sites, but EPA has concluded that these areas contribute to the particulate matter concentr ations in violation of the 2012 annual PM 2.5 NAAQS at the monitor in Stark County.Page 32 of 60 Jefferson and Mahoning counties have among the highest emissions of directly emitted PM 2.5 and/or PM 2.5 precursors in the area, however, Jefferson and Mahoni ng counties are frequently downwind of the violating monitor, as is Columbiana County, and emissions from th ese counties are unlikely to have a meaningful impact on the concentrations at the violating monitor. Of the eight other counties in the area of analysis, Summit County has the highest total emissions, followed by Wayne and Stark, as well as the highest NO X , VOC, primary organic matter, and elemental carbon emissions. Stark County has the highest total direct PM 2.5 emissions, followed by Wayne and Summit, as we ll as the highest crustal and second highest NO X , VOC, primary organic mass and elemental carbon. Wayne County has the highest SO 2 emissions and second highest overall total emissions, largely due to SO 2 emissions from two point sources. Including the portion of Wayne County consisting of Baughman, Ch ippewa, Green, and Milton townships and the portion of Norton City located within Wayne County captures the three major sources listed in Table 5 – the majority of the emissions in Wayne County. Stark, Summit, and Wayne counties t ogether comprise 72% of the total emissions, 66% of the total direct PM 2.5 emissions, 75% of the total population and 74% of the VMT in the eight counties not excluded for primarily meteorological reasons. Portag e County is most often somewhat downwind, being located northeast of the Canton-Massillon monitor, and h as relatively lower emissions as compared to Stark, Summit, and Wayne Counties. Although Holmes and Tuscarawas Counties are identified to be upwind, EPA concludes that the combination of transport potential w ith their lower emissions deems their impact to be negligible. Finally, although Carroll County is in th e Canton-Massillon MSA, it has relatively lower emissions, population density and VMT as compared to Stark, Summit, and Wayne counties. Accordingly, it is unlikely to contribute meaningfully to the violation in Stark County.
As discussed above, the EPA has concluded that emissi ons from Summit County contribute to violations of the 2012 annual PM 2.5 NAAQS in the Canton-Massillon area. However, as discussed below in section 3.2, the EPA has concluded that emissions from Summit County also contribute to violations of the 2012 annual PM 2.5 NAAQS in the Cleveland area. The CAA does not requi re the EPA to establish overlapping boundaries for the Canton-Massillon and Cleveland nonattainment areas that both include Summit County. Rather, CAA section 107(d) merely requires the EPA to designate Su mmit County as nonattainment because Summit County “contributes to ambient air quality in an area that does not meet” the 2012 annual PM 2.5 NAAQS. In this instance, the EPA believes it is more appropriate to include the entirety of Summit County within a single nonattainment area, in light of the benefits that flow from using a single set of jurisdictional boundaries to provide clarity to the planning and enforcem ent functions for nonattainment areas.

In the EPA’s 120-day letter to Ohio, the EPA indicated its intent to designate Summit County nonattainment as part of the Canton-Massillon area. In response, Ohio ma intained that Summit County “should not be designated nonattainment,” but agreed in the alternative that Summ it County is “best suited” in the Canton-Massillon area.

As described with respect to the Canton-Massillon area above, and the Cleveland area below, the EPA believes that Summit County is more appropriately included in the Canton-Massillon nonattainment area than the Cleveland area. The major point sources in Summit County ar e in the southern portion of the county, closer to the violating monitor in Canton than to the violating monito r in Cleveland. In addition, kernel density plots for Canton show a high density of trajectories covering the southern portion of Summit County. For these reasons, the EPA is designating Summit County as part of the Canton-Massillon area even though Summit County also contributes to the Cleveland area.Page 33 of 60 3.2 Area Background and Ov erview for the Cleveland Area
Figure 9a is a map of EPA’s inte nded nonattainment boundary for the Cleveland nonattainment area. The map shows the location and design values of ambient air qua lity monitoring locations, c ounty boundaries and Ohio’s recommended nonattainment boundary.
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 Cuyahoga, Lake, Lorain, Medina, Portage, and Summit, and parts of Ashtabula County. For purposes of the 2006 24-hour PM 2.5 NAAQS, all of this area minus the portion of Ashtabula County was designated nonattainment. That is, the boundary for the nonattainment area for the 2006 24-hour PM 2.5 NAAQS included the entire counties of Cuyahoga, Lake, Lorain, Medina, Portage, and Summit Counties.

Figure 9a. EPA’s Final Nonattainment Boundaries for the Cl eveland, OH AreaPage 34 of 60 EPA must designate as nonattainment areas that violat e the NAAQS and nearby areas that contribute to the violation in the violating area. Cuyahoga County shows violations of the 2012 PM 2.5 NAAQS, therefore this county is included in the nonattainment area of analys is. As shown in Figure 9b, EPA also evaluated each county without a violating monitoring site located in or n ear the combined statistical area (CSA) of the county with a violating monitoring site based on the five factor s and other relevant informa tion, and determined that Lorain County contributes to the nearby violations in Cuyahoga County. The following sections describe this five factor analysis process. While the factors are pres ented individually, they are not independent. The five factor analysis process carefully considers their interc onnections and the dependence of each factor on one or more of the others.
Figure 9b. Area of Analysis for the Cleveland, OH Area

Factor 1: Air Quality Data
All data collected during the year are important when de termining contributions to an annual standard such as the 2012 annual PM 2.5 NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings throughout the year, including days with monitored ambient concentrati ons below the level of the NAAQS. For the 2012 annual PM 2.5 NAAQS, the annual mean is calculated as the mean of quarterly means. A high quarterPage 35 of 60 can drive the mean for an entire year, which, in turn, can drive an elevated 3-year DV. Although all data are important, seasonal or episodic emissions can provide insight as to relative contributors to measured PM 2.5 concentrations. For these reasons, for the Factor 1 air quality analysis, the EPA assessed and characterized air quality at, and in the proximity of, the violating monitori ng site locations first, by evaluating trends and the spatial extent of measured concentrations at monitors in the area of analysis, a nd 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. 34 The EPA also identified the spatial extent of these high PM 2.5 concentrations. The mass and composition at the design value location represents contributions from various emission sources including local, area-wide (which may comprise nearby urban and rural areas) and regiona l sources. To determine the source mix (by mass) at the design value monitoring site, the EPA examined the chemical composition of the monitored PM 2.5 concentrations by pairing each violating FRM/FEM/ARM monitoring site with a collocated 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 35 and other monitoring locations whose data are representative of regional background. 36
, 37 This comparison of local/area-wide chemical composition data to regional chemical composition data derives an “urban in crement,” which helps differentiate the influence of

34 Although compliance with the annual NAAQS depends on contri butions from all days of the year, examining data on a quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and meteorology and the resulting PM 2.5 mass and composition. In some areas of the coun try where there may be noticeable month-to-month variations 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 th at best correspond to the local “seasons” in these areas.

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

36 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 dist ance 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 alternativ e analyses to reflect the mix of urban and rural sources contributing to the measured conc entrations at violating monitoring sites.

37 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 species. If there were no rural sites within 150 m iles, then the nearest rural site was used alone. That rural mean was then subtracted from the quarterly mean of the urban s ite to get the increment. Negative values were simply replaced with zeroes.Page 36 of 60 more distant emissions sources from the influence of closer emissions sources, thus representing the portion of the measured violation that is associated with nearby emission contributions. 38,39,40

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 violating monitoring s ite 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 qualityassured, certified air quality data from suitable FEM/F RM/ARM monitoring sites in the EPA’s Air Quality System (AQS). For this designations analysis, the EPA used data for the 2011-2013 period (i.e., the 2013 design value), which are the most recent years with fully-certif ied air quality data. A monitor’s DV is the metric or statistic that indicates whether th at monitor attains a specified air quality standard. The 2012 annual PM 2.5 NAAQS is met at a monitoring site when the 3-year av erage annual mean concentration is 12.0 micrograms per cubic meter (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 regulatory da ta processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 8 identifies the current design values (i.e., the 2013 DV) and the most recent two additional design values based on all monitoring sit es in the area of analysis for the Cleveland area. 41 Where a county has more than one monitoring location, the county design value is indicated in red type.

Table 8. Air Quality Data collected at Re gulatory Monitors (all DV levels in μg/m 3 ) a , County, State Monitor Site ID State Rec NA? 09-11 DV 10-12 DV 11-13 DV
Ashland, OH N/A No No monitor Ashtabula, OH N/A No No monitor Cuyahoga, OH 390350034 Yes 10.4 10.1 9.6

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

39 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

40 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

41 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 lo cal governments must assess data from Class III PM 2.5 FEM and ARM monitors operated within their network using the performance criteria described in table C4 to subpart C of part 53 of this chapter, for cases where th e data are identified as not of sufficient comparability to a collocated FRM, and the monitoring agency requests that the FEM or ARM data shou ld not be used in comparison to the NAAQS. These assessments are required in the monitoring agency's annual monitoring network plan described in §58.10(b) for cases where the FEM or AR M is identified as not of sufficient comparability to a collocated FRM....”Page 37 of 60 County, State Monitor Site ID State Rec NA? 09-11 DV 10-12 DV 11-13 DV
Cuyahoga, OH 390350038 Yes 13.1 13.0 12.4 Cuyahoga, OH 390350045 Yes 12.3 12.2 11.5 Cuyahoga, OH 390350060 Yes 12.8 13.0 12.5 Cuyahoga, OH 390350065 Yes 12.7 12.7 12.1 Cuyahoga, OH 390351002 Yes 10.9 10.5 9.7 Erie, OH N/A No No monitor Geauga, OH N/A No No monitor Huron, OH N/A No No monitor Lake, OH 390850007 No 10.1 9.6 9.0 Lorain, OH 390933002 No 9.9 9.7 9.2 Medina, OH N/A No No monitor Portage, OH 391330002 No 10.9 10.3 9.5 Stark, OH 391510017 No 13.4 13.0 12.1 Stark, OH 391510020 No 12.3 11.8 10.8 Summit, OH 391530017 No 12.6 12.0 11.0 Summit, OH 391530023 No 11.7 11.2 10.4 Trumbull, OH No 11.3 NV 10.6 9.9 Wayne, OH N/A No No monitor

a Where a county has more than one monitoring location, the county design value is indicated in red type.
The Figure 9 map, shown previously, identifies the Cl eveland intended nonattainment area, the CSA boundary, and monitoring locations with 2011-2013 violati ng DVs. As indicated on the map, there are 3 violating monitoring locations located in Cuyahoga County in the cen tral Cleveland area, an industrialized area. There has been a modest decline in monito red values and there is no strong intra- annual trend other than lower values in the fourth quarter. The monitors outside of the cen tral Cleveland area, including other monitors in Cuyahoga County, are not violating in the 2011-2013 time period.
Seasonal variation can highlight tho se conditions most associated with high average concentration levels of PM 2.5 . Figure 10 shows quarterly mean PM 2.5 concentrations for the most rece nt 3-year period for the highest DV monitoring sites and other, non-violating, monitoring sites in each county within the area of analysis. This graphical representation is particularly relevant when assessing air quality data for an annual standard, such as the 2012 annual PM 2.5 NAAQS, because, as previously stated, the a nnual mean is calculated as the mean of quarterly means and a high quarter can drive the mean for an entire year, which, in tu rn, can drive an elevated 3- year DV.Page 38 of 60 Figure 10. Cleveland PM 2.5 Quarterly Means for 2011-2013

As shown in Figure 10, there is a modest decline in monitored values from 2011 through 2013. Although Q4 values are a little lower, there are no strong seasonal trends.

PM 2.5 Composition Measurements - To assess potential emissi ons contributions for each violating monitoring location, 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 sources impacting the monitored concentration. To best describe the PM 2.5 at the violating monitoring location, the EPA first adjusted the chemical speciation measurement data from a monito ring location at or near the violating FRM monitoring site using the SANDWICH approach to account for the amount of PM 2.5 mass constituents retained in the FRM 0 2 4 6 8 10 12 14 16PM2.5Concentrationsug/m3

'11Q1 '11Q2 '11Q3 '11Q4 '12Q1 '12Q2 '12Q3 '12Q4 '13Q1'13Q2'13Q3'13Q4



## Cleveland

‐ Akron

‐ Lorain PM2.5 Quarterly Means 2011

‐ 2013

Cuyahoga Lake Lorain Portage Summit TrumbullPage 39 of 60 measurement. 42,43,44,45 In particular, this approach accounts for lo sses in fine particle nitrate and increases in sulfate mass associated with particle bound wate r. Figure 11aillustrates the fraction of each PM 2.5 chemical constituent at the Cleveland 39-035-0060 monitoring s ite based on annual averages for the years 2011-2013.
Figure 11a. Cleveland-Akron-Lorain Annual Average PM 2.5 Chemical Constituents (2010-2012)



### 4.4

5.4 1.5 1.1 0.6 

## Annual Average PM 2.5 

## Chemical Constituents 2010

‐ 2012 (μg/m 3 

## )

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

Figure 11b shows annual and quarterly chemical compos ition profiles and illustrates any seasonal or episodic contributors to PM 2.5 mass. This “increment analysis,” combined with the other factor analyses, can provide

42 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 SANDWI CH adjustment uses an FRM mass construction methodology that results in reduced nitrates (relative to the amount measured by routine speciation networks), higher mass associated with sulfates (reflecting water included 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.

43 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 /files/2006conference/frank.pdf

.

44 Frank, N. H., The Chemical Composition of PM 2.5 to support PM Implementation, 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/prese nts/pm2.5_chemical_composition.pdf.

45 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.Page 40 of 60 additional insight as to which sources or factors may contri bute at a greater level. Simply stated, this analysis can help identify nearby sources of emissions that contri bute to the violation at the violating monitoring site s

. Figure 11b shows that sulfate and organic mass are the predominant species overall, with an exception in Q1 when nitrate is similar to sulfate levels. Crustal is a relatively small component, with the highest crustal concentrations in Q2. This suggests that power plan ts, other stationary sources and vehicles are the largest source of emissions.
Figure 11b. Cleveland Annual and Quarterly Average PM 2.5 Species (2010-2012) a

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

Annual Q1 Q2 Q3 Q4 

## Annual and Quarterly Average PM 2.5 

## Species 2010

‐ 2012 Adjusted to FRM Total PM 2.5

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

0% 20% 40% 60% 80% 100%

Annual Q1 Q2 Q3 Q4 

## Annual and Quarterly Average PM 2.5 

## Species 2010

‐ 2012 Adjusted to FRM Total PM 2.5

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

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.Page 41 of 60 The EPA assessed seasonal and annual average PM 2.5 constituents at monitoring sites within the area relative to monitoring sites outside of the analysis area to account for the difference between regional background concentrations of PM 2.5 , and concentrations of PM 2.5 in the area of analysis, also known as the “urban increment.” This analysis differentiates between the in fluences of emissions from sources in nearby areas and in more distant areas on the violating monitor. Estimating th e urban increment in the area helps to illuminate the amount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in nearby areas, as opposed to impacts of more distant or regional sources of emissions. Figure 12a includes pie charts showing the annual and quarterly chemical mass c onstituents of the urban increment. The quarterly pie charts correspond to the 2011-2012 high-concentration qua rters identified in Figure 10. Evaluating these high concentration quarters can help identify composition of PM 2.5 during these times. Note that in these charts, sulfates and nitrates have been adjusted to represent their mass in measured PM 2.5 . Figure 12a. Cleveland Urban Increment Analysis for 2010-2012.

8%

64% 10% 16% 2% 

## Annual Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

0.42

3.29 1.28 0.09 

## Q1 Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
CrustalPage 42 of 60 0.37

2.17 0.03

0.79 0.15 

## Q2 Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

0.17

1.93 1.19 0.32 

## Q3 Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

0.32

2.57 0.33 0.41 0.12 

## Q4 Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass
Nitrates
Elemental Carbon
CrustalPage 43 of 60 Figure 12b. Cleveland Average Urban Increment Analysis for 2010-2012.

As shown in Table 8, Cuyahoga County has three monitoring sit es with a DV exceeding the NAAQS. In addition, Figure 10 shows that Cuyahoga and adjoining c ounties have experienced a modest decline in ambient concentrations since 2010. Although Q4 values are a little lower, there are no strong seasonal trends.
In reviewing the urban increment analysis for the Cl eveland CSA DV monitor, organic mass is the largest PM 2.5 chemical consistent and shows a large urban contri bution. While the sulfate component appears to have small urban contribution and therefore large regional c ontribution, its values are higher than what was shown for the Canton-Massillon area, particularly for quarte rs 1, 2 and 4. Nitrate is the second highest urban increment component for the quarter 1 peak, but is not a factor in the quarter 3 peak season, during which period both carbon components comprise high percentages of the total. The majority of the contributions come from carbonaceous sources. As shown in the next secti on, these include direct emission POC, EC as well as VOC. In addition, the urban increment shows that a substantial nearby contribution appears to result from other PM2.5 precursors – including NOx and SO2.

Factor 2: Emissions and emissions-related data In this designations process, for each area with a viol ating monitoring site, the EPA evaluated the emissions data from nearby areas using emissions related data fo r the relevant counties to assess each county’s potential contribution to PM 2.5 concentrations at the violating monitoring site or monitoring sites in the area under evaluation. However, seasonal emissions trends are not significantly different, and the seasonal patterns are similar for all major emitted pollutants; therefore the EPA is only analyzing annual emissions data. (Although nitrate is much higher in the cooler months, NOx emissions have similar importance all year due to their role in fostering the photochemistry that causes secondary particulate matter.) 0.00 1.00 2.00 3.00 4.00 5.00 6.00

Annual Q1 Q2 Q3 Q4μg/m3 

## Average Urban Increment 2010

‐ 2012 (μg/m3)

Sulfates Organic Mass Nitrates Elemental Carbon CrustalPage 44 of 60 The EPA examined emissions of identified sources or source categories of direct PM 2.5 , the major components of direct PM 2.5 (organic mass, elemental carbon, crustal material (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 those sources of em issions 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 for local NO X 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 violating monitoring sites.

Emissions Data
For this factor, the EPA reviewed data fro m the 2011 National Emissions Inventory (NEI) version 1 (see http://www.epa.gov/ttn/chief/net/2011inventory.html ). For each county in the area of analysis, the EPA examined the magnitude of county-level emissions repor ted in the NEI. These county-level emissions represent the sum of emissions from the following general source cat egories: point sources, non-point (i.e., area) sources, nonroad mobile, on-road mobile, and fires. EPA also looked at the geographic distribution of major point sources of the relevant pollutants. 46 Substantial emissions levels from sources in a nearby area indicate the potential for the area to contribute to monitored violations.
The 2011 NEI emissions information has been partia lly replaced by newer emission information for the Eastlake Power Plant in Lake County. The EPA’s pr oposed boundary for the Cleveland nonattainment area included Lake County; the only county to the east of Cuyah oga County proposed for incl usion. As explained in the initial TSD, meteorological information generally m itigated against including east of Cuyahoga County in the Cleveland area. However, the EPA’s initial emissi ons information for Lake County showed substantial emissions of direct PM 2.5 and precursors, particularly from the East lake Power Plant. EPA determined that these Lake County emissions were sufficient to overc ome the countervailing meteorological information, and thus to justify inclusion of Lake County as a contributor to the ambient air quality in Cleveland. However, in Ohio’s September 29, 2014, comments on the EPA’s d esignation recommendations, Ohio explained that the two biggest units at Eastlake have been shut down and th at this shut down is permanent and enforceable. This shutdown has resulted in the following reductions (in tons per year) since 2013: NH 3 – 8.56, NO x – 6,204.50, PM 2.5 – 3992.53, SO 2 – 43,264.40, and VOC – 51.20. These updated figures call into question the validity of EPA’s initial rationale for including Lake County in the Cleveland area. For these reasons, and as further discussed below, the EPA no longer believes it is appropriate to include Lake County in the Cleveland area. The appropriate emission tables included in the technical supporting document with the EPA’s August 19, 2014, designation recommendations have therefore been revised to reflect these reductions.
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/2012standar ds/docs/nei2011v1pointnei2008v3county.xlsx

.

46 For purposes of this designations effort, “major” point 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.Page 45 of 60 When considered with the urban increment analysis in Factor 1, evaluating the components of direct PM 2.5 and precursor gases can help identify specific sources or sour ce types contributing to elevated concentrations at violating monitoring sites and thus assist in identifying appropriate area boundaries. In general, directly emitted POC and VOCs 47 contribute to POM; directly emitted EC contributes to PM 2.5 EC; NO X , NH 3 and directly emitted nitrate contribute to PM 2.5 nitrate mass (PNO 3 ); SO 2 , NH 3 and directly emitted sulfate contribute to PM 2.5 sulfate mass (PSO 4 ); and directly emitted crustal material and metal oxides contribute to PM 2.5 crustal matter (Pcrustal). 48,49 The EPA believes that the quantities of those nearby emissions as potential contributors to the PM 2.5 violating monitors 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 9a provides a county-level emissions summary (i .e., the sum of emissions from the following general source categories: point sources, non-point (i.e., area) s ources, nonroad mobile, on-road mobile, and fires) of directly emitted PM 2.5 and precursor species for the county with the violating monitoring site and nearby counties considered for inclusion in the Cleveland ar ea. Table 9b summarizes the directly emitted components of PM 2.5 for the same counties in the area of analysis for the Cleveland area. This information will be considered in conjunction with the Urban Increment co mposition information previously shown in Figures 12a and 12b.
Table 9a. County-Level Emissions of Directly Emitted PM 2.5 and Precursors (tons/year) County, State Total NH 3 Total NOx Total Direct PM 2.5 Total SO 2 Total VOC Total Cuyahoga, OH 1,097 34,631 4,831 6,916 31,734 79,209

Lorain, OH 565 14,793 1,997 32,558 9,302 59,215 Summit, OH 495 16,925 1,760 4,310 13,110 36,601 Wayne, OH 3,868 7,323 2,080 18,028 4,637 35,937

Stark, OH 2,159 14,466 2,471 566 12,424 32,085 Lake, OH 202 11,671 2257 9,433 7,777 31,340 Ashtabula, OH 737 6,878 1,237 3,765 7,894 20,511

Portage, OH 473 6,428 858 170 5,604 13,533 Medina, OH 366 5,496 867 162 4,834 11,724 Geauga, OH 345 2,837 612 261 3,473 7,528

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

48 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.

49 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

.Page 46 of 60 Table 9b. County-Level Emissions for Components of Directly Emitted PM 2.5 (tons/year) 50

County, State POM EC PSO 4 PNO 3 Pcrustal Residual Total Direct

Cuyahoga, OH 2,166 946 184 17 648 871 4,831

Stark, OH 738 375 124 6 616 612 2,471 Lake, OH 593 396 445 4 352 467 2257 Wayne, OH 418 186 106 3 598 769 2,080 Lorain, OH 525 317 101 4 458 591 1,997 Summit, OH 857 451 36 3 179 234 1,760 Ashtabula, OH 404 194 48 3 245 343 1,237

Medina, OH 407 178 12 1 117 152 867 Portage, OH 390 201 15 2 103 147 858 Geauga, OH 329 112 11 1 57 101 612

Table 9b breaks down the direct PM 2.5 emissions value from Table 9a into its components. These data will also be compared with the previously presented Urban Increment composition.
Using the previously described relationship between directly emitted and precursor gases and the measured mass to evaluate data presented in Tables 9a and 3b, the EPA identified the following components warranting additional review: For the Cleveland area, the constituents of interest are NOx, POM, EC, PNO 3 , SO 2 , and PSO 4 . The EPA then looked at the contribution of these constituents of interest from each of the counties included in the area of analysis as shown in Tables 10a-f.
Table10a. County-Level NOx Emissions (tons/year) Emissions in average tons/yr County, State NOX Pct. Cumulative % Cuyahoga, OH 34,631 27% 29%

Summit, OH 16,925 14% 41% Lorain, OH 14,793 12% 55% Stark, OH 14,466 12% 67% Lake, OH 11,671 10% 76% Wayne, OH 7,323 6% 82% Ashtabula, OH 6,878 6% 88%

Portage, OH 6,428 5% 93% Medina, OH 5,496 5% 98% Geauga, OH 2,837 2% 100%

50 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/chief/emch/index.html#2011 (accessed 02/26/14).Page 47 of 60 Table 10b. County-Level POM Emissions Emissions in average tons/yr County, State POM Pct. Cumulative % Cuyahoga, OH 2,166 32% 32%

Summit, OH 857 13% 44%

Stark, OH 738 11% 55% Lake, OH 593 9% 64% Lorain, OH 525 8% 71% Wayne, OH 418 6% 78% Medina, OH 407 6% 84% Ashtabula, OH 404 6% 89%

Portage, OH 390 6% 95% Geauga, OH 329 5% 100%

Table 10c. County-Level Elemental Carbon Emissions Emissions in average tons/yr County, State EC Pct. Cumulative % Cuyahoga, OH 946 28% 28%

Summit, OH 451 13% 42%

Lake, OH 396 12% 53% Stark, OH 375 11% 65% Lorain, OH 317 9% 74% Portage, OH 201 6% 80% Ashtabula, OH 194 6% 86%

Wayne, OH 186 6% 91% Medina, OH 178 5% 97% Geauga, OH 112 3% 100%

Table 10d. County-Level PNO3 Emissions Emissions in average tons/yr County, State PNO3 Pct. Cumulative % Cuyahoga, OH 17 39% 39%

Stark, OH 6 13% 52% Lorain, OH 4 9% 62% Lake, OH 4 9% 70% Summit, OH 3 7% 77% Ashtabula, OH 3 6% 83%

Wayne, OH 3 6% 89% Portage, OH 2 5% 94% Medina, OH 1 3% 97% Geauga, OH 1 3% 100%Page 48 of 60 Table 10e. County-Level SO2 Emissions Emissions in average tons/yr County, State SO2 Pct. Cumulative %

Lorain, OH 32,558 43% 43% Wayne, OH 18, 028 24% 66%

Lake, OH 9,433 12% 79% Cuyahoga, OH 6,916 9% 88%

Summit, OH 4,310 6% 94% Ashtabula, OH 3,765 5% 98%

Stark, OH 566 <1% 99% Geauga, OH 261 <1% Portage, OH 170 <1% Medina, OH 162 <1% 100%

Table 10f. County-Level PSO4 Emissions Emissions in average tons/yr County, State PSO4 Pct. Cumulative %

Lake, OH 445 41% 41% Cuyahoga, OH 184 17% 58% Stark, OH 124 11% 70% Wayne, OH 106 10% 79% Lorain, OH 101 9% 89% Ashtabula, OH 48 4% 93%

Summit, OH 36 3% 96% Portage, OH 15 1% 98% Medina, OH 12 1% 99% Geauga, OH 11 1% 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 located in the area of analysis. The magnitude and location of these sources can help inform nonattainment boundaries. Ta ble 11 provides facility-level emissions of direct PM 2.5 , components of direct PM 2.5 , and precursor pollutants (given in tons per year) from major point sources located in the area of an alysis for the Cleveland area. Table 11 also shows the distance from the facility to the 2011-2013 DV monitor for the respective county.

Table 11. NEI 2011 v1 Point Source Emissions (tons/year)

Distance NEI 2011 v1 Emissions - Tons/Year County,

State Facility Name (Facility ID) monitor (miles) NH3 NOx PM2.5 SO2 VOC Total
Ashtabula, OH Millennium Inorganic Chemicals, Inc. - Plant 2 (0204010193) 55 1 193 62 8 1,697 1,962Page 49 of 60 Distance NEI 2011 v1 Emissions - Tons/Year County,

State Facility Name (Facility ID) monitor (miles) NH3 NOx PM2.5 SO2 VOC Total
Ashtabula, OH Millennium Inorganic Chemicals, Inc. Plant #I (0204010200) 56 1 40 11 14 733 799
Ashtabula, OH FirstEnergy Generation Corp., Ashtabula Plant (0204010000) 56 0 1,148 317 3,454 17 4,937 Cuyahoga, OH DiGeronimo Aggregates LLC (1318270383) 9 0 69 78 524 1 671 Cuyahoga, OH ArcelorMittal Cleveland Inc. (1318001613) 1 11 1,165 553 723 94 2,546 Cuyahoga, OH Cleveland Electric Illuminating Co., Lake Shore Plant (1318000245) 4 0 771 66 1,942 11 2,791 Cuyahoga, OH Cleveland Thermal LLC (1318000246) 1 0 252 69 930 2 1,253 Cuyahoga, OH The Medical Center Company (1318003059) 3 0 204 38 2,133 4 2,379 Cuyahoga, OH Cleveland-Hopkins Intl (XXXXXXXXXX) 10 0 599 21 75 137 833

Lake, OH Carmeuse Lime, Inc - Grand River Operations (0243030257) 28 0 520 18 891 3 1,432

Lake, OH PAINESVILLE MUNICIPAL ELECTRIC PLANT (0243110008) 28 0 509 150 2,745 2 3,406

Lake, OH CLEVELAND ELECTRIC ILLUMINATING CO., EASTLAKE PLANT (0243160009) 18 0 920 10 11,588 8 12,547
Lorain, OH Avon Lake Power Plant (0247030013) 19 1 4,659 394 32,041 30 37,125 Stark, OH Alliance Casting Co. LLC (1576010014) 49 0 614 216 3 33 865 Stark, OH Republic Engineered Products, Inc. (1576050694) 50 2.2 224 174 63 66 529
Stark, OH Marathon Petroleum Company LP - Canton Refinery (1576002006) 50 8 285 188 93 224 798

Summit, OH Cargill, Incorporated - Salt Division (Akron, OH) (1677010027) 30 0 140 18 1,516 1 1,676
Summit, OH Emerald Performance Materials, LLC (1677010029) 49 0 115 1 869 10 996 Summit, OH City of Akron Steam Generating (1677010757) 47 0 254 44 1,729 2 2,028
Wayne, OH Department of Public Utilities, City of Orrville, Ohio (0285010188) 70 0 1,902 745 13,038 5 15,690
Wayne, OH Morton Salt, Inc. (0285020059) 57 0 195 50 4,434 2 4,681 Wayne, OH East Ohio Gas - Chippewa St ation (0285000366) 60 0 654 13 0 23 690

Figure 13 shows the major point source emissions (from the 2011 NEI in tons per year) in the area of analysis for the Cleveland area and the distances of these sources fro m the violating monitoring locations, as depicted by red dots in Cuyahoga County. The actual distance from the point sources to the DV monitoring location is presented in Table 11). The distance from the violating mon itoring location is particularly 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. 51

51 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.Page 50 of 60 Figure 13. Major Point Source Emissions in the Area of Analysis for the Cleveland Area.

In summary, EPA’s analysis of releva nt county-level emissions and the ge ographic locations of the relevant pollutants shows that about 42% of the directly emitted and precursor emissions, in the area of analysis, come from Cuyahoga and Lorain Counties. As discussed more fully under Factor 3, the winds are generally from the south through west, further minimizing the impact of Ashtabula, Portage, Geauga, and Lake Counties on the violating monitors in Cleveland.
Although Stark County has recorded violations of the 2012 PM 2.5 standard, it is in a separate metropolitan area (the Canton-Massillon area), and this county has histori cally been designated as a separate nonattainment area under both the 1997 annual PM 2.5 standard and the 2006 24-hour PM 2.5 standard as well as for various ozone standards. Furthermore, emissions from Wayne Count y and Summit County contribute more to Stark County than to Cuyahoga County. For this reason, as well as the reasons discussed in section 3.1 above of this document for the Canton-Massillon area, Stark County, Summit County as well as part of Wayne County, are being included in the Canton-Massillon nonattainment area and not in the Cleveland area.Page 51 of 60 The impact of PM 2.5 from Ashtabula, Lake, Geauga, and Portag e Counties is limited by location and emission levels. Geauga is adjacent to Cuyahoga County, but only emits 3% of direct PM 2.5 and only 2% of the combined total directly emitted and precursor emissi ons from the area of analysis. Ashtabula County only emits 7% of the total direct PM 2.5 and 6% of the combined total direc tly emitted and precursor emissions from the area of analysis and Portage County only emits 4% of both the total direct PM 2.5 and combined total directly emitted and precursor emissions from the area of analys is. In addition, all of these counties are east of Cuyahoga County, further reducing their impact.
As seen in Tables 10a–f, Cuyahoga, Lorain, Summit, and Wa yne counties all consistently ranked in the highest percentages of directly emitted PM 2.5 and precursors. Cuyahoga County accounts for 29% of the NOx emissions, 32% of POM emissions, 28% of EC emissions , 39% of PNO3 emissions, 9% of SO2 emissions and 17% of PSO4 emissions in the area of analysis. Summit County contributes 14% of NOx, 13% of POM, 13% of EC, 7% of PNO3, 6% of SO2 and 3% of PSO4 emissi ons in the area of analysis. Lorain County contributes 12% of NOx, 8% of POM, 9% of EC, 9% of PNO3, 27% of SO2, and 9% of PSO4 emissions in the area of analysis. Wayne County contributes 6% of NOx, 6% of POM, 6% of EC, 6% of PNO3, 24% of SO2, and 10% of PSO4 emissions in the area of analysis. All of these counties are upwind of the area of analysis. By contrast, Medina, Portage, and Geauga counties consistently ranked in the lowest percentages of directly emitted PM 2.5 and precursors within the area of analysis.
As indicated in Figure 13, there are 22 point sources locat ed within 70 miles of the violating monitor. The largest sources are in Cuyahoga, Lorain and Wayne Counti es. There are 6 point sources within 10 miles of the violating monitor and most of the others are about 30 or more miles away from the violating monitor. About 21% of the point source direct PM 2.5 and precursor emissions are from the point sources in Wayne County (mostly SO2 and some NOx). However, as discussed in the section on the Canton-Massillon nonattainment area, relevant meteorological information suggests that Wayne County sources contribute more to the violating monitor in Stark County than to the violating monitors in Cuyahoga County. Therefore, as explained in the Canton-Massillon section of this TSD, EPA is designating relevant portions of Wayne County as part of the final Canton-Massillon nonattainment area.

Population density and degree of urbanization
In this part of the factor analysis, the EPA evaluated the population and vehicle use characteristics and trends of the area as indicators of the probable location and magn itude of non-point source emissions. Rapid population growth in a county on the urban perimeter signifies in creasing integration with the core urban area, and indicates that it may be appropriate to include the county associated with area source and mobile source emissions as part of the nonattainment area. Table 12 shows the 2000 and 2010 population, population growth since 2000, and population density for each county in the area.
Table 12. Population Growth and Population Density.

County,

State Population 2000 Population 2010 % Change from 2000 Land Area (Sq.

Miles) Population Density (per Sq.

Mile) % Cumulative % Cuyahoga, OH 1,393,978 1,278,466 -8.3% 458 2,788 38 38 Summit, OH 542,899 541,652 -0.2% 413 1,312 16 54 Stark, OH 378,098 375,417 -0.7% 576 652 11 65Page 52 of 60 Lorain, OH 284,664 301,533 5.9% 493 612 9 74 Lake, OH 227,511 230,054 1.1% 228 1,008 7 81 Medina, OH 151,095 172,656 14.2% 422 409 5 86 Portage, OH 152,061 161,403 6.1% 492 328 5 91 Wayne, OH 111,564 114,480 2.6% 555 206 3 94 Ashtabula, OH 102,728 101,425 -1.3% 702 144 3 97 Geauga, OH 90,895 93,398 2.8% 404 231 3 100 Total 3,435,493 3,370,393 Source: U.S. Census Bureau population estimates for 2000 and 2010

Cuyahoga County has the largest population, population de nsity and is the largest percent urban. Cuyahoga, Summit, Stark, Lorain, Lake and Medina Counties cont ain 86% of the population in the area of analysis. Cuyahoga, Summit and Lake Counties have the highest population densities whereas Medina, Portage, Wayne, Ashtabula and Geauga have the low est population densities. Cuyahoga h as the highest population at 1,278,466 and Summit County has the next highest population at 541,652. These two counties c ontain 54% of the total population in the area of analysis. Medina, Portage, Wa yne, Ashtabula, and Geauga Counties contain less than 20% of the total population. Cuya hoga, Summit and Lake Counties ha ve population densities over 1000 per square mile whereas Ashtabula, Wa yne and Geauga Counties have populatio n densities less than 250 per square mile.Page 53 of 60 Figure 14. 2010 County-Level Population in the Area of Analysis for the Cleveland Area.

Traffic and Vehicle Miles Travelled
High vehicle miles travelled (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 NO x , VOC, and direct PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS in the area. In combination with the population/populati on density data and the loca tion of main transportation arteries, an assessment of VMT helps identify the probable location of nonpoint source emissions that contribute to violations in the area. Comparatively high VMT in a county outside of the CBSA or CSA signifies integration with the core urban area contained within the CSA or CBSA, and indicates that a county with the high VMT may be appropriate to incl ude in the nonattainment area becau se emissions from mobile sources in that county contribute to violations in the area. Ta ble 13 shows 2011 VMT while Figure 15 overlays 2011 county-level VMT with a map of the transportation arteri es. The VMT used in this analysis was submitted by Ohio.Page 54 of 60 Table 13. 2011 VMT for the Cleveland Area

. County, State Total 2011 VMT Percent Cumulative % Cuyahoga, OH 8,534,134,941 27 27 Summit, OH 6,250,389,061 20 47 Stark, OH 3,838,738,336 12 60 Lorain, OH 2,787,828,581 9 69 Lake, OH 2,461,578,196 8 77 Portage, OH 2,128,490,347 7 83 Medina, OH 1,774,539,943 6 89 Wayne, OH 1,192,145,098 4 93 Ashtabula, OH 1,137,086,286 4 97 Geauga, OH 1,045, 092,778 3 100 Total 31,150,023,566 http://www.census.gov/hhes/co mmuting/data/commuting.html
Figure 15. Overlay of 2011 County-level VMT with Transportation Arteries

.

Cuyahoga and Summit Counties have the highest and second highest VMT and account for 47% of the total VMT in the area of analysis. These are followed by Star k, Lorain, Lake, Portage, Medina, Wayne, Ashtabula, and Geauga Counties. As previously discussed, St ark County, Summit County, and Wayne County werePage 55 of 60 considered in the evaluation of the Canton-Massillon ar ea instead of the Cleveland area because of EPA’s belief regarding their relative contributions to Stark Count y versus Cuyahoga County. Of the counties being considered as part of the Cleveland area, there is a big gap between the VMT for Cuyahoga County (8,534,134,941) versus the remaining counties, with Lora in County having the next highest (2,787,828,581) and the rest gradually decreasing to Geauga County as the lowest (1,045,092,778).
Factor 3: Meteorology EPA evaluated available meteorological data to dete rmine how meteorological conditions, including, but not limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of directly emitted particulate matter and precursor emissions from sources in the area of analysis. The EPA used two primary tools for this assessment: wind roses and kernel density estimation (KDE). When considered in combination with area PM 2.5 composition and county-level and facilit y emissions source location information, wind roses and KDE can help to identify nearby areas contribu ting to violations at violating monitoring sites.

Wind roses are graphic illustrations of the frequency of wind direction and wind speed. Wind direction can indicate the direction from which contributing emissions are transported; wind speed can indicate the force of the wind and thus the distance from which those emissi ons are transported. The EPA constructed wind roses from hourly observations of wind direction and wind speed using 2009-2012 data from National Weather Service locations archived at the National Climate Data Center. 52 When developing these wind roses, the EPA also used wind observations collected at meteorological sampling stations collocated at air quality monitoring sites, where these data were available. Figure 16 shows wi nd roses that the EPA generated from data relevant in the Cleveland area.

52 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.pdfPage 56 of 60 Figure 16. Wind Roses in the Area of Analysis for the Cleveland Area.

As shown in Figure 16, there is a pattern across the area of predomin antly south to west winds, mostly at midlevel speeds of 4 to 10 meters per second, suggesting th at potential emission sources in the south-through-west upwind direction should be considered for analysis.
In addition to wind roses, the EPA also generated kern el density estimation (KDE) plots to represent HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Traj ectory) backward trajectory frequency at violatingPage 57 of 60 monitoring sites. 53,54 These KDEs are graphical statistical estimati ons to determine the density of trajectory endpoints at a particular location represented by a grid cell. The EPA used KDEs to characterize and analyze the collection of individual HYSPLIT backward trajectories. 55 Higher density values, indicated by darker blue colors, indicate a greater frequency of observed trajectory endpoints within a particular grid cell. Figure 17 shows a HYSPLIT KDE plot for the Cleveland area su mmarized by calendar quarter for the 2010-2012 period. The HYSPLIT KDE is weighted in the west to southerl y direction when considering only land-based regions, indicating a greater frequency of trajectories passing over grid cells to the west a nd south. The high density values to the north are over Lake Erie.

EPA examined these KDEs on a quarterly basis in orde r to capture the seasonal variability in wind patterns which could then be paired with th e seasonal variability in PM2.5 concentra tion and composition. Finally, the wind-concentration-composition pairings are used to identify the key emitted pollutants and the nearby areas that are responsible for the urban increment and the PM2.5 violation.

Figure 17. HYSPLIT Kernel Density Estimation Plots for the Cleveland Area.

53 In some past initial area designations efforts, the EPA has us ed 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 parcel’s motion, extending in two dimensional (x,y) space from a starting point and regressing backward in time to a point of origin. Backward tr ajectories 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. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT trajectories may not be helpful as this process is likely to re sult in depicting air parcels originating in all directions from the violating monitoring site.

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

55 The KDEs graphically represent the aggregate of HYSPLIT 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.Page 58 of 60 In summary, for the violating Cleveland monitor, the HYPSPLIT KDE plots and wind roses suggest greatest potential contribution of emissions from Cuyahoga, Lorain , Summit, Wayne and Medina Counties. By contrast, the counties generally downwind and to the east of Cuyah oga County – Ashtabula, Portage, Geauga, and Lake Counties – show relatively little potential contribution, particularly in light of their relatively small emissions. The HYSPLIT KDE plots further indicate low density valu es in Ashtabula County, Portage, Geauga, and Lake Counties. As discussed in the emission discussion, Medina has a relatively small amount of relevant emissions. Because of distance, the emissions from Wayne County are judged to have little influence on the violating monitor in Cleveland. In fact, EPA has concluded that it is contributing to the violating monitor in Stark County, because of its proximity together with sufficient potential to transport its emissions to that monitor. Thus it is being considered as part of the Canton-M assillon area nonattainment area. Regarding Summit County, EPA has determined that Summit contributes to the vi olating Cleveland monitor. This is based on the combination of its emissions, transport potential a nd distance. However, it has also been identified as contributing to the violating monitor in Stark County. Moreover, because the violating monitor in Stark County has a lower design value and thus a smaller urban increm ent, the contribution from Summit represents a larger portion of the Stark County urban increment. Therefore, like Wayne, it is being considered as part of the Canton-Massillon area nonattainment area.

Factor 4: Geography/topography To evaluate the geography/topography factor, the EPA assess ed physical features of the area of analysis that might define the airshed and thus aff ect the formation and distribution of PM 2.5 concentrations over the area. The Cleveland area does not have any geographical or topographical barriers known to substantial limit air pollution transport around the area of analysis. Therefore, th is factor did not play a substantial role in defining the boundary of the final Cleveland nonattainment area.

Factor 5: Jurisdictional boundaries In defining the boundaries of the final Cleveland nonattai nment area, the EPA considered existing jurisdictional boundaries, which can provide easily identifiable and recognized boundaries for purposes of implementing the NAAQS. Existing jurisdictional boundaries often signify well recognized boundaries that the state can easily administer and for which the state has the necessary le gal authority for carrying out air quality planning and enforcement functions. Examples of such jurisdictiona l boundaries include existing/prior nonattainment areaPage 59 of 60 boundaries for particulate matter, county lines, air dist rict boundaries, township boundaries, areas covered by a metropolitan planning organization, state lines, and Reservation boundaries, if applicable. Where existing jurisdictional boundaries were not adeq uate or appropriate to describe the nonattainment area, EPA considered other clearly defined and permanent landmarks or ge ographic coordinates for pu rposes of identifying the boundaries of the intended designated areas.

The Cleveland area has previously established nonattain ment boundaries associated with the 1997 and 2006 PM2.5 NAAQS. Ashtabula (partial county), Cuyahoga, Lake, Lorain, Medina, Portage and Summit Counties were designated as nonattainment for the 1997 annual sta ndard and Cuyahoga, Lake, Lorain, Medina, Portage and Summit Counties were designated as nonattainment of the 2006 24-hour standard. Ohio recommended that only Cuyahoga County be designa ted as nonattainment of the 2012 PM2.5 annual PM2.5 NAAQS.

The EPA is not including Ashtabula County and Portage Counties in the 2012 nonattainment area because their emissions are relatively low and they are not upwind of Cleveland.

Conclusion for Cleveland Area Based on the assessment of factors described above, bo th individually and in combination, the EPA has concluded that the following counties should be includ ed as part of the Cleveland nonattainment area because they are either violating the 2012 annual PM 2.5 NAAQS or contributing to a violation in a nearby area: Cuyahoga and Lorain Counties. These are not the same counties that were included in the Cleveland-AkronLorain nonattainment area for the 2006 24-hour or the 1997 annual PM 2.5 NAAQS. The air quality monitoring sites in Cuyahoga County indicate violations of the 2012 annual PM 2.5 NAAQS based on the 2013 DVs; therefore this county is included in the nonattainment ar ea. Lorain County is an adjacent county that does not have violating monitoring sites, but EPA has concluded that this county contributes to the particulate matter concentrations in violation of the 2012 annual PM 2.5 NAAQS through emissions from point sources and nonpoint sources (e.g., area sources), and from mobile source emissions. Cuyahoga and Lorain Counties have the highest emissions of directly emitted PM 2.5 and/or PM 2.5 precursors in the area and Lorain County is upwind of and contributes to the violating monitors in Cleveland.
Cuyahoga County has violating monitors and was re commended as nonattainment by Ohio. Lorain County emits the second greatest amount of directly emitted PM 2.5 and precursors in the Cleveland area, emitting 43% of the SO 2 emissions in the Cleveland area. This county is therefore a considerable contributor to the sulfate urban increment, which is between 0.3-0.4 ug/m 3 for quarters 1, 2 and 4. The Avon Lake Power Plant emits the highest amount of combined emissions in the area and is only 19 miles from the violating design value monitor in Cleveland. Lorain County is southwest, and upwind, of Cleveland and is therefore a considerable contributor to the violating monitors in Cleveland. Also, the wind roses and kernel densities indicate that emissions in Lorain County are prone to contribut e to the violating monitors in Cleveland.
This discussion, in conjunction with the review above for the Canton-Massillon area, indicates that Summit County contributes to violations in both the Canton-Mass illon and Cleveland areas. EPA believes that it would be inappropriate to designate Summit County as part of both nonattainment areas. Therefore, for reasons discussed above in the review for the Canton-Massillon area, EPA is designating Summit County as part of the Canton-Massillon area.Page 60 of 60 As discussed above, emissions fro m Summit County contribute to violations of the 2012 annual PM 2.5 NAAQS in the Canton-Massillon area and the Cleveland area. The CAA does not require the EPA to establish overlapping boundaries for the Canton- Massillon and Cleveland nonattainme nt areas that both include Summit County. Rather, CAA section 107(d) merely requir es the EPA to designate Summit County as nonattainment because Summit County “contributes to ambient air quality in an area that does not meet” the 2012 annual PM 2.5 NAAQS. In this instance, the EPA believes it is mo re appropriate to include the entirety of Summit County within a single nonattainment area, in light of the benefits that flow from using a single set of jurisdictional boundaries to provide clarity to the planning and enforcement functions for nonattainment areas. With respect to Summit County, in the EPA’s 120-day lette r to Ohio the EPA indicated its intent to designate Summit County nonattainment as part of the Canton-Massillon area. In res ponse, Ohio maintained that Summit County “should not be designated nonattainment,” but agr eed in the alternative that Summit County is “best suited” in the Canton-Massillon area.