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Cleveland and Canton-Massillon, Ohio Area Designations for the 

2012 Primary Annual PM2.5 National Ambient Air Quality Standard 

Technical Support Document 

1.0 Summary 

In accordance with Section 107(d) of the Clean Air Act (CAA), the EPA must promulgate designations for all 

areas of the country. In particular, the EPA must identify those areas that are violating a National Ambient Air 

Quality Standard (NAAQS) or contributing to a violation 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 nonattainment for the 2012 primary annual fine particle NAAQS 

(2012 annual PM2.5 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 PM2.5 NAAQS no later than 1 year following promulgation of the standard, or by December 13, 2013. In 

December 2013, Ohio recommended that the counties identified in Table 1 be designated as “nonattainment” 

for the 2012 annual PM2.5 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 PM2.5 standard. The EPA must designate an area 

nonattainment if it has an air quality monitoring site3

 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 insufficient information to make initial designation decisions in the 

2-year time frame. In such circumstances, 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 PM2.5 NAAQS (78 FR 3086, January 15, 2013). 

In that action, the EPA revised the primary annual PM2.5 standard, strengthening it from 15.0 micrograms per cubic meter 

(μg/m3

) to 12.0 μg/m3. 

3

 In accordance with 40 CFR 50 Appendix N, PM2.5 measurements from the primary monitor and suitable collocated PM2.5 

FRM, FEM or ARMs may be used in a “combined site data record” to establish a PM2.5 design value to determine whether 

the NAAQS is met or not met at a particular PM2.5 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 PM2.5 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 area composed of counties and/or partial counties 

in Ohio and Kentucky. The technical analysis for 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 Table 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 period showing compliance with the 2012 annual PM2.5

NAAQS and the EPA’s assessment that areas within the State are not likely contributing to nearby violations.4,5

 

                                                            

4

 Unless a state or tribe has specifically identified jurisdictional boundaries in its area recommendations, when determining 

“remainder of the state,” EPA will use Federal Information Processing 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 territories (e.g., American Samoa, Guam, Northern Mariana Islands, 

Puerto Rico, and the US Virgin Islands). Available on EPA’s Envirofacts website at 

http://www.epa.gov/envirofw/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. 
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2.0 Nonattainment Area Analyses and Final Boundary Determination 

The EPA evaluated and determined the final boundaries for each nonattainment area on a case-by-case basis 

considering the specific facts and circumstances unique to the area. In accordance with the CAA section 107(d), 

the EPA is designating as nonattainment not only the areas with the monitoring sites that violate the 2012 

annual PM2.5 NAAQS, but also those nearby areas with emissions sources that contribute to the violations in the 

violating area. As described in EPA guidance6

, after identifying each monitoring site indicating a violation of 

the standard in an area, the EPA analyzed 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 monitoring sites are located. The EPA also evaluated 

counties adjacent to the CBSA or CSA that have emissions 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 PM2.5 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 and information available to the EPA (and to the states and 

tribes through the PM2.5 Designations Mapping Tool7

 and the EPA PM Designations Guidance and Data web 

page8

) 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 areas with one or more monitoring sites that violate the 

2012 annual PM2.5 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 factors 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 PM2.5 air quality 

monitoring data at, and in the proximity of, the violating monitoring locations. This includes reviewing the 

design values (DV) calculated for each monitoring location 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 Method (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 PM2.5 NAAQS. 

Available at http://www.epa.gov/pmdesignations/2012standards/docs/april2013guidance.pdf. 

7

 The EPA’s PM2.5 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. 
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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 Appendix 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 monitoring locations to help ascertain the potential 

contribution of sources in areas nearby to the violating monitoring sites. Examples include using chemical 

speciation data to help characterize contributing emissions sources and the determination of nearby 

contributions through analyses that differentiate local and regional source contributions. 

Factor 2: Emissions and emissions-related data. The emissions analysis examines identified sources of direct 

PM2.5; the major components of direct PM2.5, e.g., primary organic carbon (POC) and PM2.5 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 (SO2), nitrogen oxides (NOx), total 

volatile organic compounds (VOC), and ammonia (NH3). 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 information” could include an inventory or assessment of 

local/regional area sources that individually does not meet the current threshold for reporting to the NEI but 

collectively contributes to area PM2.5 concentrations. Emissions data indicate the potential for a source to 

contribute to observed violations, making it useful in assessing boundaries of nonattainment areas. 

Factor 3: Meteorology. Evaluating meteorological data helps to determine the effect on the fate and transport of 

emissions contributing to PM2.5 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 trajectories, wind speed, wind direction, and other 

meteorological data, as available.

Factor 4: Geography/topography. The geography/topography analysis includes examining the physical features 

of the land that might define the airshed and, therefore, affect the formation and distribution of PM2.5 over an 

area. Mountains or other physical features may influence the fate and transport of emissions and PM2.5

concentrations. Additional analyses may consider topographical features that cause local stagnation episodes via 

inversions, such as valley-type features that effectively “trap” air pollution, leading to periods of elevated PM2.5

concentrations. 

Factor 5: Jurisdictional boundaries. The analysis of jurisdictional boundaries identifies the governmental 

planning and organizational structure of an area that may be relevant for designations purposes. These 

                                                            

9

 Suitable monitors include all FEM and/or ARMs except those 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 approved by the EPA Regional Administrator 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 PM2.5, 

section 3(a) indicates “Except as otherwise provided in this appendix, all valid FRM/FEM/ARM PM2.5 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 appendices 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 the governing air agencies conduct or might conduct air 

quality planning and enforcement in a potential nonattainment 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 for the Canton-Massillon, Ohio Area

Figure 1a is a map of EPA’s final nonattainment boundary 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 PM2.5

NAAQS. Stark County was included within the boundary of the Canton-Massillon area for both the 1997 

annual and 2006 24-hour PM2.5 NAAQS. In addition, Summit County was included within the boundary of the 

Cleveland-Akron-Lorain Area for the 1997 annual and 2006 24-hour PM2.5 NAAQS. 

Figure 1a. EPA’s Final Nonattainment Boundaries for the Canton-Massillon, Ohio Area 

   
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EPA must designate as nonattainment areas that violate the NAAQS and nearby areas that contribute to the 

violation in the violating area. Stark County shows a violation of the 2012 PM2.5 NAAQS, therefore this county 

is included in the nonattainment area. As shown in Figure 1b, EPA evaluated each county without a violating 

monitoring site located in or near the metropolitan statistical area (MSA) of the county with a violating 

monitoring site based on the five factors and other relevant information. The following sections describe this 

five factor analysis process. While the factors are presented individually, they are not independent. The five 

factor analysis process carefully considers their interconnections 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 determining contributions to an annual standard such as 

the 2012 annual PM2.5 NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings 

throughout the year, including days with monitored ambient concentrations below the level of the NAAQS. For 

the 2012 annual PM2.5 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 are 
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important, seasonal or episodic emissions can provide insight as to relative contributors to measured PM2.5

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 monitoring site locations first, by evaluating trends and the 

spatial extent of measured concentrations at monitors in the area of analysis, and then, by identifying the 

conditions most associated with high average concentration levels of PM2.5 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 PM2.5 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 regional sources. To determine the source mix (by mass) at the design value 

monitoring site, the EPA examined the chemical composition of the monitored PM2.5 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 IMPROVE12 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 increment,” which helps differentiate the influence of 

                                                            

11 Although compliance with the annual NAAQS depends on contributions 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 PM2.5 mass and composition. In some areas of the country where there may be noticeable month-to-month 

variations in average PM2.5, the quarterly averages may not adequately represent seasonal variability. In these areas, air 

quality data may be aggregated and presented by those months that best correspond to 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 characterizes the increase in seasonal and annual average PM2.5 mass and 

chemical constituents observed at violating monitoring site(s) relative to monitoring sites outside the area of analysis 

(which represent background concentrations). Developing the urban increment involves pairing a violating 

FRM/FEM/ARM monitor with a collocated monitor or nearby monitor with speciation data. The EPA made every effort to 

pair these data to represent the same temporal and spatial scales. However, in some cases, the paired violating and CSN 

“urban” monitoring locations were separated by some distance such that the included urban CSN site(s) reflect(s) a 

different mixture of emissions sources, which could lead to misinterpretations. To generally account for differences in 

PM2.5 mass between the violating site and the nearby CSN site(s), the EPA determined material balance of the PM2.5

composition at the violating site by assigning the extra measured PM2.5 mass to the carbon components of PM2.5. Where the 

general urban increment approach may be misleading, or in situations where non-carbonaceous emissions are believed to 

be responsible for a local PM2.5 concentration gradient, the EPA used alternative analyses to reflect the mix of urban and 

rural sources contributing to the measured concentrations 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 miles, then the nearest rural site was used 

alone. That rural mean was then subtracted from the quarterly mean of the urban site to get the increment. Negative values 

were simply replaced with zeros. 
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 

PM2.5 Design Values and Total Mass Measurements – The EPA examined ambient PM2.5 air quality monitoring 

data represented by the DVs at the violating monitoring site 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/FRM/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-certified air quality data. A monitor’s DV is the metric or 

statistic that indicates whether that monitor attains a specified air quality standard. The 2012 annual PM2.5

NAAQS is met at a monitoring site when the 3-year average annual mean concentration is 12.0 micrograms per 

cubic meter (μg/m3

) or less (e.g., 12.1 μg/m3 or greater is a violation). A DV is only valid if minimum data 

completeness criteria are met or when other regulatory data processing provisions are satisfied (See 40 CFR part 

50 Appendix N). Table 2 identifies the current design value(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 Regulatory Monitors (all DV levels in μg/m3

)

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 monitoring site is located in an urban area. Where the violating 

monitor is not located in an urban area, the “urban increment” 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 PM2.5 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 (PM2.5) Standards, Technical Support Document for State and Tribal Air Quality Fine Particle (PM2.5) 

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 local governments must assess data from 

Class III PM2.5 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 the data are identified as not of sufficient comparability to a 

collocated FRM, and the monitoring agency requests that the FEM or ARM data should 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 ARM is identified as not of sufficient comparability to a collocated FRM....” 
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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 violating DVs. As indicated on the map, there is one 

violating monitor located in the city of Canton in Stark County. 

Seasonal variation can highlight those conditions most associated with high average concentration levels of 

PM2.5. Figure 2 shows quarterly mean PM2.5 concentrations for the most recent 3-year period for the highest DV 

monitoring sites in each county within the area of analysis, 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 PM2.5 NAAQS, 

because, as previously stated, the annual 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 PM2.5 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 Q4

PM2.5 Concentrations

μg/m3

Canton PM2.5 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 

μg/m3

 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 considered more closely when evaluating chemical constituent 

and urban increment data. 

PM2.5 Composition Measurements - To assess potential emissions contributions for each violating monitoring 

location, the EPA determined the various chemical species comprising total PM2.5 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 PM2.5 at the violating monitoring location, the EPA first adjusted 

the chemical speciation measurement data from a monitoring location at or near the violating FRM monitoring 

site using the SANDWICH approach to account for the amount of PM2.5 mass constituents retained in the FRM 
Page 11 of 60 

measurement.19,20,21,22 In particular, this approach accounts for losses in fine particle nitrate and increases in 

sulfate mass associated with particle bound water. Figure 3a illustrates the fraction of each PM2.5 chemical 

constituent at monitoring site 391510017 in Stark County based on annual averages for the years 2010-2012. 

Figure 3a. Canton-Massillon Annual Average PM2.5 Chemical Constituents (2010-2012) 

4.3

5.0

1.6

0.9

1.1

Annual Average PM2.5 Chemical Constituents

2010‐2012 (μg/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

Figure 3b shows annual and quarterly chemical composition profiles and illustrates any seasonal or episodic 

contributors to PM2.5 mass. This “increment analysis,” combined with the other factor analyses, can provide 

                                                            

19 SANDWICH stands for measured Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous mass Hybrid 

Material Balance Approach.” The SANDWICH 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 PM2.5 and its non-carbon components. This characterization of PM2.5 mass also reflects 

crustal material and other minor constituents. The resulting characterization provides a complete mass closure for the 

measured FRM PM2.5 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 PM2.5 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 PM2.5 to support PM Implementation, EPA State /Local/Tribal Training 

Workshop: PM2.5 Final Rule Implementation and 2006 PM2.5 Designation Process, Chicago IL, June 20-21, 2007, 

http://www.epa.gov/ttn/naaqs/pm/presents/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 contribute at a greater level. Simply stated, this analysis 

can help identify nearby sources of emissions that contribute to the violation at the violating monitoring site. 

Figure 3b. Canton-Massillon Area Annual and Quarterly Average PM2.5 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 PM2.5 Species

2010‐2012 Adjusted to FRM Total PM2.5

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

0%

20%

40%

60%

80%

100%

Annual Q1 Q2 Q3 Q4

Annual and Quarterly Average PM2.5 Species

2010‐2012 Adjusted to FRM Total PM2.5

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

a

Adjusted to FRM Total PM2.5 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 PM2.5 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 PM2.5 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 PM2.5, and concentrations of PM2.5 in the area of analysis, also known as the “urban 

increment.” This analysis differentiates between the influences of emissions from sources in nearby areas and in 

more distant areas on the violating monitor. Estimating the urban increment in the area helps to illuminate the 

amount and type of particles at the violating monitor that are most 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 constituents of the urban increment. The quarterly pie 

charts correspond to the high-concentration quarters identified in Figure 2. Evaluating these high-concentration 

quarters can help identify composition of PM2.5 during these times. Note that in these charts, sulfates and 

nitrates have been adjusted to represent their mass in measured PM2.5. 

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

2%

26% 56%

8%

8%

Annual Average Urban Increment

2010‐2012 (μg/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

0.08

2.11 2.27

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

Crustal
Page 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 Q4

μg/m3

Average Urban Increment 2010‐2012 (μg/m3)

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

As shown in Figure 2, Stark County has one monitoring site with a DV exceeding the NAAQS. Stark County 

ambient PM2.5 concentrations are fairly consistent across the year, with modest seasonal peaks in in Q1 and Q3, 

although one annual high was recorded in Q4. This suggests 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 consistently have small urban contribution, although 

conversely large regional contribution. Nitrate is the second highest urban increment component for Q1 and 
Page 15 of 60 

Q4, but is not a factor inQ3, during which crustal and elemental carbon both comprise a higher percentage. This 

analysis points to contribution from local nitrate and direct PM2.5 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 violating monitoring site, EPA evaluated the emissions data 

from nearby areas using emissions related data for the relevant counties to assess each county’s potential 

contribution to PM2.5 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 PM2.5, the major components of 

direct PM2.5 (organic mass, elemental carbon, crustal material (and/or individual trace metal compounds)), 

primary nitrate and primary sulfate, and precursor gaseous pollutants (i.e., SO2, NOX, total VOC, and NH3). 

EPA also considered the distance of those sources of emissions from the violating monitoring site. While direct 

PM2.5 emissions and its major carbonaceous components are generally associated with sources near violating 

PM2.5 monitoring sites, the gaseous precursors tend to have a more regional influence (although the EPA is 

mindful of the potential for local NOX and VOC emissions contributions to PM2.5 from mobile and stationary 

sources) and transport from neighboring areas can contribute to higher PM2.5 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 the 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 PM2.5, components of direct 

PM2.5, and precursor pollutants, which is available at 

http://www.epa.gov/pmdesignations/2012standards/docs/nei2011v1county_nei2011v1point_nei2008v3_county.

xlsx. 

                                                            

23 For purposes of this designations effort, “major” point sources are those whose sum of PM precursor emissions (PM2.5 + 

NOx + SO2 + VOC + NH3) 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 PM2.5 and 

precursor gases can help identify specific sources or source types contributing to elevated concentrations at 

violating monitoring sites and thus assist in identifying appropriate area boundaries. In general, directly emitted 

POC and VOCs24 contribute to POM; directly emitted EC contributes to PM2.5 EC; NOX, NH3 and directly 

emitted nitrate contribute to PM2.5 nitrate mass (PNO3); SO2, NH3 and directly emitted sulfate contribute to 

PM2.5 sulfate mass (PSO4); and directly emitted crustal material and metal oxides contribute to PM2.5 crustal 

matter (Pcrustal). 25,26 EPA believes that the quantities of those nearby emissions as potential contributors to the 

PM2.5 violating monitors are somewhat proportional to the PM2.5 chemical constituents in the estimated urban 

increment. Thus, directly emitted POC is more important per ton than SO2, partially because POC emissions are 

already PM2.5 whereas SO2 must convert to PM2.5 and not all of the emitted SO2 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) sources, nonroad mobile, on-road mobile, and fires) of 

directly emitted PM2.5 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 PM2.5 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 PM2.5 and Precursors (tons/year)  

County, State Total

NOX

Total 

SO2

Total Direct 

PM2.5

Total

NH3

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 PM2.5 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 PM2.5 (tons/year) 27

County, State POM EC PSO4 PNO3 Pcrustal Residual

Total Direct 

PM2.5

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 PM2.5 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 and 3b, EPA identified the following components warranting 

additional review: POM, NOX, SO2, EC, and Pcrustal. 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 4a-e.

Table 4a. County-Level Primary Organic 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 NOX Emissions 

County, State NOX 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 SO2 Emissions 

County, State SO2 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 Primary 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 county-wide emissions of PM2.5 and PM2.5 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. Table 5 provides facility-level emissions of direct 

PM2.5, components of direct PM2.5, and precursor pollutants (given in tons per year) from major point sources 

located in the area of analysis for the Canton-Massillon area. Table 5 also shows the distance 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) NH3 NOX PM2.5 SO2 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 the 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 violating monitoring location is particularly important 

for directly emitted PM2.5. The influence of directly emitted PM2.5 on ambient PM2.5 diminishes more than that 

of gaseous precursors as a function of distance.28 

As indicated in Table 5, there are 15 major point sources 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 are 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 PM2.5. Atmospheric Environment. 45 (2011) 3758-3767. 
Page 21 of 60 

Figure 5. Major Point Source Emissions in the Area 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 below under Factor 3, it becomes apparent that emissions 

from sources located east of the monitor are unlikely to have a meaningful impact 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 monitored violation recorded in Stark County. Columbiana 

County is also located east of Stark County – between 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 the 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 shows that Stark is responsible for 22% of emissions and 

26% of direct PM2.5 emissions. Together, Stark, Summit, and Wayne Counties are responsible for 72% of 

emissions and 66% of PM2.5 emissions. In addition, among these eight counties, Stark County accounts for 

19%, 27%, 2%, 24% and 36% to the POM, NOX, SO2, 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, NOX, SO2, EC, and Pcrustal components, respectively. Emissions in Portage, 

Tuscarawas, Holmes, Harrison and Carroll Counties are relatively 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 located in the eight counties not excluded for primarily 

meteorological reasons, the largest point source is in Wayne County. In addition, the two largest point sources 

in Wayne County account for 70% of the SO2 emissions in the eight counties. Further, Wayne County is 

directly west of Stark County with point sources located 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 PM2.5 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 Jefferson 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 magnitude of non-point source emissions. Rapid population 

growth in a county on the urban perimeter signifies increasing integration with the core urban area, and 

indicates that it may be appropriate to 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 

%

% 

Urbana

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, OH 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 2010 
Page 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. Considering 2010 population and population density, Stark County is next most 

populous and population-dense county, followed by Mahoning, Portage, Wayne and Columbiana counties. 

When setting aside Mahoning, Columbiana, and Jefferson 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 Area 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 NOx, VOC, and 

direct PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS 
Page 24 of 60 

in the area. In combination with the population/population density data and the location of main transportation 

arteries, an assessment of VMT helps identify the probable location of nonpoint source emissions that 

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 include in the nonattainment area because emissions from mobile sources in 

that county contribute to violations in the area. Table 7 shows 2011 VMT, while Figure 7 overlays 2011 countylevel VMT with a map of the transportation arteries. The 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 indicate that emissions from Mahoning County (as well as 

Jefferson and Columbiana counties) are unlikely to contribute 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 detail below under Factor 3, it becomes apparent that 

emissions from sources located east of the monitor are unlikely to contribute meaningfully to the monitored 

violation. Therefore, while Jefferson 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 – also 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 NOX, VOC, primary organic matter, and elemental carbon emissions. Wayne County has the 

highest SO2 emissions and second highest overall total emissions, largely due to SO2 emissions from two point 

sources. When considering emissions from the counties that cannot be reasonably excluded for meteorological 

reasons, Stark, Summit, and Wayne counties together comprise 72% of the total emissions, 66% of the total 
Page 26 of 60 

direct PM2.5 emissions, 75% of the total population and 74% of the VMT. Although Portage County has higher 

population density and VMT than Wayne County, and Tuscarawas County VMT and population density are 

similar to that of Wayne County, emissions in Wayne County 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 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 

initially used two primary tools for this assessment: wind roses and kernel density estimation (KDE). When 

considered in combination with area PM2.5 composition and county-level and facility emissions source location 

information, wind roses and KDE can help to identify nearby areas contributing to violations at violating 

monitoring sites. In response to comments from Ohio, and as discussed below, the EPA additionally considered 

information based on trajectory analyses of a substantial set of days with relatively high PM2.5 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 emissions 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 stations 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.pdf 
Page 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 southwesterly winds, mostly 

at mid-level speeds of 4 to 10 meters per second. Although the previous section shows that Stark, Summit, and 

Wayne Counties are responsible for 72% of emissions and 66% of PM2.5 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 kernel density estimation (KDE) plots to represent HYSPLIT 

(HYbrid Single-Particle Lagrangian Integrated Trajectory) backward trajectory frequency at violating 
Page 28 of 60 

monitoring sites.30,31 These KDEs are graphical statistical estimations 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 trajectories32. 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 frequency of trajectories passing over grid cells from those 

directions. The highest densities outside of Stark County 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 seasonal variability in PM2.5 concentration 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 9. HYSPLIT Kernel Density Estimation Plots for the Canton-Massillon Area. 

                                                            

30 In some past initial area designations efforts, the EPA has used HYSPLIT backward trajectories to assist in determining 

nonattainment area boundaries. A HYSPLIT backward trajectory is usually depicted on a standard map as a single line, 

representing the centerline of an air 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 trajectories may be an appropriate tool to assist in determining 

an air parcel’s point of origin on a day in which a short-term standard, such as an 8-hour standard or a 24-hour standard, 

was exceeded. However, for an annual standard, such as the 2012 annual PM2.5 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 result in depicting air parcels originating in all directions from the 

violating monitoring site. 

31 HYSPLIT - Hybrid Single Particle Lagrangian 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 these 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 County emissions have potential to contribute 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 Canton than Cleveland. Accordingly, this data indicates 

placing Summit and Wayne Counties in the Canton-Massillon area is more appropriate than placing them in the 

Cleveland area. The HYSPLIT KDE plots indicate relatively 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 point sources shown in Figure 5 are located in the more 

distant eastern portion of that county –along the Ohio River – 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 trajectories passing over Summit County. Ohio cited this 

modeling as supporting an attainment designation for 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 period. 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 relevant. Nevertheless, it is also informative to evaluate 

the days with monitored concentrations exceeding the level of the annual standard (here, 12.0 μg/m3

) 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 further evaluate questions raised by HYSPLIT modeling 

provided by Ohio, the EPA analyzed all of the days with concentrations exceeding 12.0 μg/m3

 during the 2011-

2013 time period at both the Canton and 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 County. This modeling supports the conclusion 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 Homes and Tuscarawas counties to the west and south. 

These counties, however, have less emissions than Summit and Wayne, especially for organic carbon which is 

the largest component of the urban increment. These counties each have 7-8% of the total POC for the area of 

analysis. When the frequency of pollutant transport, described by the KDEs, are examined together with the 

lower quantity of emissions in Homes and Tuscarawas counties, 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 PM2.5 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 factor did not play a substantial role in defining the 

boundary of the Canton-Massillon final nonattainment area. 

 

                                                            

33 A file containing HYSPLIT modeling for days with concentrations exceeding 12.0 μg/m3

 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 final 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 such 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, state 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 consists of Stark and Carroll Counties. The CantonMassillon area also has previously established nonattainment boundaries, consisting of Stark County, associated 

with the 2006 24-hour and 1997 annual PM2.5 NAAQS. Summit and Portage counties were previously 

designated nonattainment as part of the Cleveland area for both the 1997 and 2006 PM2.5 NAAQS. The state 

has recommended that only Stark County be included in the Canton-Massillon nonattainment area for the 2012 

annual PM2.5 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 recognizes legally defined county subdivisions, referred to as 

“minor civil divisions.” For Ohio, these minor civil divisions 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 portion of Wayne County to the following Bureau of Census 

defined minor civil divisions captures the three major sources in Wayne County and establishes a contiguous 

nonattainment area with clearly defined legal boundaries: Baughman, Chippewa, 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 described 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 PM2.5 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 PM2.5 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 monitoring sites, but EPA has concluded that these areas 

contribute to the particulate matter concentrations in violation of the 2012 annual PM2.5 NAAQS at the monitor 

in Stark County. 
Page 32 of 60 

Jefferson and Mahoning counties have among the highest emissions of directly emitted PM2.5 and/or PM2.5

precursors in the area, however, Jefferson and Mahoning counties are frequently downwind of the violating 

monitor, as is Columbiana County, and emissions from these 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 NOX, VOC, 

primary organic matter, and elemental carbon emissions. Stark County has the highest total direct PM2.5

emissions, followed by Wayne and Summit, as well as the highest crustal and second highest NOX, VOC, 

primary organic mass and elemental carbon. Wayne County has the highest SO2 emissions and second highest 

overall total emissions, largely due to SO2 emissions from two point sources. Including the portion of Wayne 

County consisting of Baughman, Chippewa, 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 together comprise 72% of the total emissions, 66% of 

the total direct PM2.5 emissions, 75% of the total population and 74% of the VMT in the eight counties not 

excluded for primarily meteorological reasons. Portage County is most often somewhat downwind, being 

located northeast of the Canton-Massillon monitor, and has 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 with their lower emissions deems their impact to be 

negligible. Finally, although Carroll County is in the 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 emissions from Summit County contribute to violations of the 

2012 annual PM2.5 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 PM2.5

NAAQS in the Cleveland area. The CAA does not require 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 Summit County as nonattainment because Summit County 

“contributes to ambient air quality in an area that does not meet” the 2012 annual PM2.5 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 enforcement 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 maintained that Summit County “should not be designated 

nonattainment,” but agreed in the alternative that Summit 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 are in the southern portion of the county, closer to 

the violating monitor in Canton than to the violating monitor 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 Overview for the Cleveland Area

Figure 9a is a map of EPA’s intended nonattainment boundary for the Cleveland nonattainment area. The map 

shows the location and design values of ambient air quality monitoring locations, county boundaries and Ohio’s 

recommended nonattainment boundary. 

For purposes of the 1997 annual PM2.5 NAAQS, portions of this area were designated nonattainment. The 

boundary for the nonattainment area for the 1997 annual PM2.5 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 PM2.5 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 PM2.5 NAAQS included 

the entire counties of Cuyahoga, Lake, Lorain, Medina, Portage, and Summit Counties. 

Figure 9a. EPA’s Final Nonattainment Boundaries for the Cleveland, OH Area 
Page 34 of 60 

EPA must designate as nonattainment areas that violate the NAAQS and nearby areas that contribute to the 

violation in the violating area. Cuyahoga County shows violations of the 2012 PM2.5 NAAQS, therefore this 

county is included in the nonattainment area of analysis. As shown in Figure 9b, EPA also evaluated each 

county without a violating monitoring site located in or near the combined statistical area (CSA) of the county 

with a violating monitoring site based on the five factors and other relevant information, 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 presented individually, they are not independent. The five 

factor analysis process carefully considers their interconnections 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 determining contributions to an annual standard such as 

the 2012 annual PM2.5 NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings 

throughout the year, including days with monitored ambient concentrations below the level of the NAAQS. For 

the 2012 annual PM2.5 NAAQS, the annual mean is calculated as the mean of quarterly means. A high quarter 
Page 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 PM2.5

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 monitoring site locations first, by evaluating trends and the 

spatial extent of measured concentrations at monitors in the area of analysis, and then, by identifying the 

conditions most associated with high average concentration levels of PM2.5 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 PM2.5 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 regional sources. To determine the source mix (by mass) at the design value 

monitoring site, the EPA examined the chemical composition of the monitored PM2.5 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 IMPROVE35 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 increment,” which helps differentiate the influence of 

                                                            

34 Although compliance with the annual NAAQS depends on contributions 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 PM2.5 mass and composition. In some areas of the country where there may be noticeable month-to-month 

variations in average PM2.5, the quarterly averages may not adequately represent seasonal variability. In these areas, air 

quality data may be aggregated and presented by those months that best correspond to 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 characterizes the increase in seasonal and annual average PM2.5 mass and 

chemical constituents observed at violating monitoring site(s) relative to monitoring sites outside the area of analysis 

(which represent background concentrations). Developing the urban increment involves pairing a violating 

FRM/FEM/ARM monitor with a collocated monitor or nearby monitor with speciation data. The EPA made every effort to 

pair these data to represent the same temporal and spatial scales. However, in some cases, the paired violating and CSN 

“urban” monitoring locations were separated by some distance such that the included urban CSN site(s) reflect(s) a 

different mixture of emissions sources, which could lead to misinterpretations. To generally account for differences in 

PM2.5 mass between the violating site and the nearby CSN site(s), the EPA determined material balance of the PM2.5

composition at the violating site by assigning the extra measured PM2.5 mass to the carbon components of PM2.5. Where the 

general urban increment approach may be misleading, or in situations where non-carbonaceous emissions are believed to 

be responsible for a local PM2.5 concentration gradient, the EPA used alternative analyses to reflect the mix of urban and 

rural sources contributing to the measured concentrations 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 miles, then the nearest rural site was used 

alone. That rural mean was then subtracted from the quarterly mean of the urban site to get the increment. Negative values 

were simply replaced with 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 

PM2.5 Design Values and Total Mass Measurements – The EPA examined ambient PM2.5 air quality monitoring 

data represented by the DVs at the violating monitoring site 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/FRM/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-certified air quality data. A monitor’s DV is the metric or 

statistic that indicates whether that monitor attains a specified air quality standard. The 2012 annual PM2.5

NAAQS is met at a monitoring site when the 3-year average annual mean concentration is 12.0 micrograms per 

cubic meter (μg/m3

) or less (e.g., 12.1 μg/m3 or greater is a violation). A DV is only valid if minimum data 

completeness criteria are met or when other regulatory data 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 sites 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 Regulatory Monitors (all DV levels in μg/m3

)

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 monitoring site is located in an urban area. Where the violating 

monitor is not located in an urban area, the “urban increment” 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 PM2.5 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 (PM2.5) Standards, Technical Support Document for State and Tribal Air Quality Fine Particle (PM2.5) 

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 local governments must assess data from 

Class III PM2.5 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 the data are identified as not of sufficient comparability to a 

collocated FRM, and the monitoring agency requests that the FEM or ARM data should 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 ARM 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 Cleveland intended nonattainment area, the CSA boundary, 

and monitoring locations with 2011-2013 violating DVs. As indicated on the map, there are 3 violating 

monitoring locations located in Cuyahoga County in the central Cleveland area, an industrialized area. There 

has been a modest decline in monitored values and there is no strong intra-annual trend other than lower values 

in the fourth quarter. The monitors outside of the central Cleveland area, including other monitors in Cuyahoga 

County, are not violating in the 2011-2013 time period.

Seasonal variation can highlight those conditions most associated with high average concentration levels of 

PM2.5. Figure 10 shows quarterly mean PM2.5 concentrations for the most recent 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 PM2.5 NAAQS, because, as previously stated, the annual 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 38 of 60 

Figure 10. Cleveland PM2.5 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. 

PM2.5 Composition Measurements - To assess potential emissions contributions for each violating monitoring 

location, the EPA determined the various chemical species comprising total PM2.5 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 PM2.5 at the violating monitoring location, the EPA first adjusted 

the chemical speciation measurement data from a monitoring location at or near the violating FRM monitoring 

site using the SANDWICH approach to account for the amount of PM2.5 mass constituents retained in the FRM 

0

2

4

6

8

10

12

14

16

PM2.5 Concentrations

ug/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 Trumbull
Page 39 of 60 

measurement.42,43,44,45 In particular, this approach accounts for losses in fine particle nitrate and increases in 

sulfate mass associated with particle bound water. Figure 11aillustrates the fraction of each PM2.5 chemical 

constituent at the Cleveland 39-035-0060 monitoring site based on annual averages for the years 2011-2013. 

Figure 11a. Cleveland-Akron-Lorain Annual Average PM2.5 Chemical Constituents (2010-2012) 

4.4

5.4

1.5

1.1

0.6

Annual Average PM2.5 Chemical Constituents

2010‐2012 (μg/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

Figure 11b shows annual and quarterly chemical composition profiles and illustrates any seasonal or episodic 

contributors to PM2.5 mass. This “increment analysis,” combined with the other factor analyses, can provide 

                                                            

42 SANDWICH stands for measured Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous mass Hybrid 

Material Balance Approach.” The SANDWICH 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 PM2.5 and its non-carbon components. This characterization of PM2.5 mass also reflects 

crustal material and other minor constituents. The resulting characterization provides a complete mass closure for the 

measured FRM PM2.5 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 PM2.5 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 PM2.5 to support PM Implementation, EPA State /Local/Tribal Training 

Workshop: PM2.5 Final Rule Implementation and 2006 PM2.5 Designation Process, Chicago IL, June 20-21, 2007, 

http://www.epa.gov/ttn/naaqs/pm/presents/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 contribute at a greater level. Simply stated, this analysis 

can help identify nearby sources of emissions that contribute to the violation at the violating monitoring sites. 

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 plants, other stationary sources and vehicles are the largest 

source of emissions. 

Figure 11b. Cleveland Annual and Quarterly Average PM2.5 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 PM2.5 Species

2010‐2012 Adjusted to FRM Total PM2.5

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

0%

20%

40%

60%

80%

100%

Annual Q1 Q2 Q3 Q4

Annual and Quarterly Average PM2.5 Species

2010‐2012 Adjusted to FRM Total PM2.5

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

a

Adjusted to FRM Total PM2.5 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 PM2.5 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 PM2.5, and concentrations of PM2.5 in the area of analysis, also known as the “urban 

increment.” This analysis differentiates between the influences of emissions from sources in nearby areas and in 

more distant areas on the violating monitor. Estimating the urban increment in the area helps to illuminate the 

amount and type of particles at the violating monitor that are most 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 constituents of the urban increment. The quarterly pie 

charts correspond to the 2011-2012 high-concentration quarters identified in Figure 10. Evaluating these high 

concentration quarters can help identify composition of PM2.5 during these times. Note that in these charts, 

sulfates and nitrates have been adjusted to represent their mass in measured PM2.5. 

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

Crustal
Page 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

Crustal
Page 43 of 60 

Figure 12b. Cleveland Average Urban Increment Analysis for 2010-2012. 

As shown in Table 8, Cuyahoga County has three monitoring sites with a DV exceeding the NAAQS. In 

addition, Figure 10 shows that Cuyahoga and adjoining counties 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 Cleveland CSA DV monitor, organic mass is the largest 

PM2.5 chemical consistent and shows a large urban contribution. While the sulfate component appears to have 

small urban contribution and therefore large regional contribution, its values are higher than what was shown 

for the Canton-Massillon area, particularly for quarters 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 section, 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 violating monitoring site, the EPA evaluated the emissions 

data from nearby areas using emissions related data for the relevant counties to assess each county’s potential 

contribution to PM2.5 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 Q4

μg/m3

Average Urban Increment 2010‐2012 (μg/m3)

Sulfates Organic Mass Nitrates Elemental Carbon Crustal
Page 44 of 60 

The EPA examined emissions of identified sources or source categories of direct PM2.5, the major components 

of direct PM2.5 (organic mass, elemental carbon, crustal material (and/or individual trace metal compounds)), 

primary nitrate and primary sulfate, and precursor gaseous pollutants (i.e., SO2, NOX, total VOC, and NH3). The 

EPA also considered the distance of those sources of emissions from the violating monitoring site. While direct 

PM2.5 emissions and its major carbonaceous components are generally associated with sources near violating 

PM2.5 monitoring sites, the gaseous precursors tend to have a more regional influence (although the EPA is 

mindful of the potential for local NOX and VOC emissions contributions to PM2.5 from mobile and stationary 

sources) and transport from neighboring areas can contribute to higher PM2.5 levels at the violating monitoring 

sites. 

Emissions Data 

For this factor, the 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, the EPA

examined the magnitude of county-level emissions reported in the 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.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 partially replaced by newer emission information for the 

Eastlake Power Plant in Lake County. The EPA’s proposed boundary for the Cleveland nonattainment area 

included Lake County; the only county to the east of Cuyahoga County proposed for inclusion. As explained in 

the initial TSD, meteorological information generally mitigated against including east of Cuyahoga County in 

the Cleveland area. However, the EPA’s initial emissions information for Lake County showed substantial 

emissions of direct PM2.5 and precursors, particularly from the Eastlake Power Plant. EPA determined that 

these Lake County emissions were sufficient to overcome 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 designation recommendations, Ohio explained that the 

two biggest units at Eastlake have been shut down and that this shut down is permanent and enforceable. This 

shutdown has resulted in the following reductions (in tons per year) since 2013: NH3 – 8.56, NOx – 6,204.50, 

PM2.5 – 3992.53, SO2 – 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 PM2.5, components of 

direct PM2.5, and precursor pollutants, which is available at 

http://www.epa.gov/pmdesignations/2012standards/docs/nei2011v1pointnei2008v3county.xlsx. 

                                                            

46 For purposes of this designations effort, “major” point sources are those whose sum of PM precursor emissions (PM2.5 + 

NOx + SO2 + VOC + NH3) 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 PM2.5 and 

precursor gases can help identify specific sources or source types contributing to elevated concentrations at 

violating monitoring sites and thus assist in identifying appropriate area boundaries. In general, directly emitted 

POC and VOCs47 contribute to POM; directly emitted EC contributes to PM2.5 EC; NOX, NH3 and directly 

emitted nitrate contribute to PM2.5 nitrate mass (PNO3); SO2, NH3 and directly emitted sulfate contribute to 

PM2.5 sulfate mass (PSO4); and directly emitted crustal material and metal oxides contribute to PM2.5 crustal 

matter (Pcrustal). 48,49 The EPA believes that the quantities of those nearby emissions as potential contributors to 

the PM2.5 violating monitors are somewhat proportional to the PM2.5 chemical constituents in the estimated 

urban increment. Thus, directly emitted POC is more important per ton than SO2, partially because POC 

emissions are already PM2.5 whereas SO2 must convert to PM2.5 and not all of the emitted SO2 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) sources, nonroad mobile, on-road mobile, and fires) of 

directly emitted PM2.5 and precursor species for the county with the violating monitoring site and nearby 

counties considered for inclusion in the Cleveland area. Table 9b summarizes the directly emitted components 

of PM2.5 for the same counties in the area of analysis for the Cleveland area. This information will be 

considered in conjunction with the Urban Increment composition information previously shown in Figures 12a 

and 12b. 

Table 9a. County-Level Emissions of Directly Emitted PM2.5 and Precursors (tons/year)

County, State Total NH3 Total NOx Total Direct 

PM2.5

Total 

SO2 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 PM2.5 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 PM2.5 (tons/year) 50

County, State POM EC PSO4 PNO3 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 PM2.5 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, PNO3, SO2, and 

PSO4. 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 county-wide emissions of PM2.5 and PM2.5 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. Table 11 provides facility-level emissions of direct 

PM2.5, components of direct PM2.5, and precursor pollutants (given in tons per year) from major point sources 

located in the area of analysis 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 1,962 
Page 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 Station (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 from 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 monitoring location is particularly important for directly 

emitted PM2.5. The influence of directly emitted PM2.5 on ambient PM2.5 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 PM2.5. 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 relevant county-level emissions and the geographic 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 PM2.5 standard, it is in a separate metropolitan area 

(the Canton-Massillon area), and this county has historically been designated as a separate nonattainment area 

under both the 1997 annual PM2.5 standard and the 2006 24-hour PM2.5 standard as well as for various ozone 

standards. Furthermore, emissions from Wayne County 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 PM2.5 from Ashtabula, Lake, Geauga, and Portage Counties is limited by location and emission 

levels. Geauga is adjacent to Cuyahoga County, but only emits 3% of direct PM2.5 and only 2% of the 

combined total directly emitted and precursor emissions from the area of analysis. Ashtabula County only 

emits 7% of the total direct PM2.5 and 6% of the combined total directly emitted and precursor emissions from 

the area of analysis and Portage County only emits 4% of both the total direct PM2.5 and combined total directly 

emitted and precursor emissions from the area of analysis. 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 Wayne counties all consistently ranked in the highest 

percentages of directly emitted PM2.5 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 emissions 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 PM2.5

and precursors within the area of analysis. 

As indicated in Figure 13, there are 22 point sources located within 70 miles of the violating monitor. The 

largest sources are in Cuyahoga, Lorain and Wayne Counties. 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 PM2.5 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 magnitude of non-point source emissions. Rapid population 

growth in a county on the urban perimeter signifies increasing integration with the core urban area, and 

indicates that it may be appropriate to 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 65 
Page 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 density and is the largest percent urban. Cuyahoga, 

Summit, Stark, Lorain, Lake and Medina Counties contain 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 lowest population densities. Cuyahoga has the highest population at 1,278,466 

and Summit County has the next highest population at 541,652. These two counties contain 54% of the total 

population in the area of analysis. Medina, Portage, Wayne, Ashtabula, and Geauga Counties contain less than 

20% of the total population. Cuyahoga, Summit and Lake Counties have population densities over 1000 per 

square mile whereas Ashtabula, Wayne and Geauga Counties have population 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 NOx, 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/population density data and the location 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 include in the nonattainment area because emissions from mobile sources in 

that county contribute to violations in the area. Table 13 shows 2011 VMT while Figure 15 overlays 2011 

county-level VMT with a map of the transportation arteries. 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/commuting/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 Stark, Lorain, Lake, Portage, Medina, Wayne, Ashtabula, 

and Geauga Counties. As previously discussed, Stark County, Summit County, and Wayne County were 
Page 55 of 60 

considered in the evaluation of the Canton-Massillon area instead of the Cleveland area because of EPA’s belief 

regarding their relative contributions to Stark County 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 Lorain 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 determine 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 PM2.5 composition and county-level and facility emissions source location information, 

wind roses and KDE can help to identify nearby areas contributing to violations at 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 emissions 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 wind 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.pdf 
Page 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 predominantly south to west winds, mostly at midlevel speeds of 4 to 10 meters per second, suggesting that potential emission sources in the south-through-west 

upwind direction should be considered for analysis. 

In addition to wind roses, the EPA also generated kernel density estimation (KDE) plots to represent HYSPLIT 

(HYbrid Single-Particle Lagrangian Integrated Trajectory) backward trajectory frequency at violating 
Page 57 of 60 

monitoring sites.53,54 These KDEs are graphical statistical estimations 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 summarized by calendar quarter for the 2010-2012 period. 

The HYSPLIT KDE is weighted in the west to southerly direction when considering only land-based regions, 

indicating a greater frequency of trajectories passing over grid cells to the west and south. The high density 

values to the north are over Lake Erie. 

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 seasonal variability in PM2.5 concentration 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 used HYSPLIT backward trajectories to assist in determining 

nonattainment area boundaries. A HYSPLIT backward trajectory is usually depicted on a standard map as a single line, 

representing the centerline of an air 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 trajectories may be an appropriate tool to assist in determining 

an air parcel’s point of origin on a day in which a short-term standard, such as an 8-hour standard or a 24-hour standard, 

was exceeded. However, for an annual standard, such as the 2012 annual PM2.5 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 result in depicting air parcels originating in all directions from the 

violating monitoring site. 

54 HYSPLIT - Hybrid Single Particle Lagrangian 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 Cuyahoga 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 values 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-Massillon area nonattainment area. Regarding Summit County, 

EPA has determined that Summit contributes to the violating Cleveland monitor. This is based on the 

combination of its emissions, transport potential and 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 increment, 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 assessed physical features of the area of analysis that 

might define the airshed and thus affect the formation and distribution of PM2.5 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, this 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 nonattainment 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 legal authority for carrying out air quality planning and 

enforcement functions. Examples of such jurisdictional boundaries include existing/prior nonattainment area 
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boundaries for particulate matter, county lines, air district boundaries, township boundaries, areas covered by a 

metropolitan planning organization, state 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 intended designated areas. 

The Cleveland area has previously established nonattainment 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 standard 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 designated 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, both individually and in combination, the EPA has 

concluded that the following counties should be included as part of the Cleveland nonattainment area because 

they are either violating the 2012 annual PM2.5 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 PM2.5 NAAQS. The air quality monitoring 

sites in Cuyahoga County indicate violations of the 2012 annual PM2.5 NAAQS based on the 2013 DVs; 

therefore this county is included in the nonattainment area. 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 PM2.5 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 PM2.5 and/or PM2.5 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 recommended as nonattainment by Ohio. Lorain County 

emits the second greatest amount of directly emitted PM2.5 and precursors in the Cleveland area, emitting 43% 

of the SO2 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/m3

 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 contribute 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-Massillon 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. 
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As discussed above, emissions from Summit County contribute to violations of the 2012 annual PM2.5 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 nonattainment areas that both include Summit 

County. Rather, CAA section 107(d) merely requires 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 

PM2.5 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 enforcement functions for nonattainment areas. 

With respect to Summit County, 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 maintained that Summit 

County “should not be designated nonattainment,” but agreed in the alternative that Summit County is “best 

suited” in the Canton-Massillon area.