**Page 1 of 59**



## Cleveland and Canton-Massillon, Ohio Area Designati ons for the 2012 Primary Annual PM 2.5 

## National Ambient Air Quality Standard

Technical Support Document

1.0 Summary
**In accordance with Section 107(d) of the Clean Air Act (CAA), the EPA must promulgate designations for**  **all areas of the country. In particular, EPA must ident**  **ify those areas that are violating a National Ambie nt Air**  **Quality Standard (NAAQS) or contributing to a viola tion of the NAAQS in a nearby area. EPA must comple**  **te this process within 2 years of promulgating a new o**  **r revised NAAQS, or may do so within 3 years under circumstances not relevant to these designations.**  **1 This technical support document (TSD) describes th**  **e EPA’s intent to designate areas in Ohio as nonattainment**  **for the 2012 primary annual fine particle NAAQS (20 12**  **annual PM 2.5**  **NAAQS), except that the Cincinnati area is address ed in a separate TSD.**  **2**
**Under section 107(d), states are required to submit area designation recommendations to the EPA for th**  **e 2012 annual PM**  **2.5 NAAQS no later than 1 year following promulgation**  **of the standard, or by December 13, 2013. In December 2013, Ohio recommended that the counties i**  **dentified in Table 1 be designated as “nonattainmen t”**  **for the 2012 annual PM 2.5**  **NAAQS based on air quality data from 2011-2013.**
**After considering these recommendations, and based on EPA’s technical analysis as described in this TS**  **D, the EPA intends to designate the areas listed in Table**  **1 as nonattainment for the 2012 annual PM 2.5**  **standard. EPA must designate an area nonattainment if it has an a**  **ir quality monitoring site 3**  **that is violating the standard or if it has sources of emissions that are contributing to a**  **violation of the NAAQS in a nearby area. Legal des criptions**  **(e.g., county boundaries, townships, and ranges) of these areas are found below in the supporting tech**  **nical**

**1 Section 107(d) of the CAA requires the EPA to comp**  **lete the initial designation process within 2 years of promulgation of**  **a new or revised NAAQS, unless the Administrator ha s insufficient information to make initial designat**  **ion decisions in the 2-year time frame. In such circumstances, the EPA m**  **ay take up to 1 additional year to make initial are a designation**  **decisions (i.e., no later than 3 years after promul gation of the standard).** 

**2 On December 14, 2012, the EPA promulgated a revised**  **primary annual PM 2.5**  **NAAQS (78 FR 3086, January 15, 2013). In that action, the EPA revised the primary annual**  **PM 2.5**  **standard, strengthening it from 15.0 micrograms pe r cubic meter**  **(μg/m 3**  **) to 12.0 μg/m 3** 

**.**

**3 In accordance with 40 CFR 50 Appendix N, PM**  **2.5 measurements from the primary monitor and suitable**  **collocated PM 2.5**  **FRM, FEM or ARMs may be used in a “combined site da ta record” to establish a PM**  **2.5 design value to determine whether**  **the NAAQS is met or not met at a particular PM 2.5**  **monitoring site. Page 2 of 59** 

**analysis for each area. As provided in CAA section 188(a), the EPA will initially classify all nonatta**  **inment areas as “Moderate” nonattainment areas.** 

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

Area Ohio’s Recommended

Nonattainment Counties EPA’s Intended Nonattainment Counties

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

**Cleveland, OH Cuyahoga**  **Cuyahoga, Lake, 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 th is multi-state area is discussed in a separate**  **Technical Support Document.**

**In its recommendation letter, Ohio recommended that EPA designate as attainment all counties not ident**  **ified in the Ohio Recommendation column of Table 1. EPA agr**  **ees with Ohio’s recommendations except as to Lake County, Lorain County, and Summit County, and parts**  **of Wayne County and Warren County, as listed in Ta ble**  **1. EPA intends to designate the remainder of Ohio as unclassifiable/attainment based on ambient monit**  **oring data collected during the 2011-2013 period showing**  **compliance with the 2012 annual PM 2.5**  **NAAQS and the EPA’s assessment that areas within the State are no**  **t likely contributing to nearby violations. 4,5** 

**4 Unless a state or tribe has specifically identifie**  **d jurisdictional boundaries in its area recommendat ions, when determining**  **“remainder of the state,” EPA will use Federal Info rmation Processing Standard (FIPS) codes maintained**  **by the National Institute of Standards and Technology (NIST), which**  **are used to identify counties and county equivalen ts (e.g., parishes,**  **boroughs) of the United States and its unincorporat ed territories (e.g., American Samoa, Guam, Norther**  **n 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 attainm ent and for areas that**  **do not have monitoring sites but which the EPA beli eves are likely attainment and does not emissions s**  **ources that are contributing to nearby violations based on the five**  **factor analysis and other available information. Page 3 of 59** 

2.0 Nonattainment Area Analyses and Intended Bound ary Determination

**The EPA evaluated and determined the intended bound aries for each nonattainment area on a case-by-case**  **basis considering the specific facts and circumstances un**  **ique to the area. In accordance with the CAA sectio n 107(d),**  **EPA intends to designate as nonattainment not only the areas with the monitoring sites that violate th**  **e 2012 annual PM**  **2.5 NAAQS, but also those nearby areas with emissions**  **sources that contribute to the violations in the violating area. As described in EPA guidance**  **6 , after identifying each monitoring site indicating**  **a violation of the standard in an area, EPA analyzed those areas w**  **ith emissions contributing to that violating area b y**  **considering those counties in the entire metropolit an area (e.g., Core Based Statistical Area (CBSA) o**  **r Combined Statistical Area (CSA)) in which the viola**  **ting monitoring sites are located. The EPA also ev aluated**  **counties adjacent to the CBSA or CSA that have emis sions sources with the potential to contribute to t**  **he violations. EPA uses the CBSA or CSA as a starting**  **point for the contribution analysis because those areas are**  **nearby for purposes of the PM 2.5**  **NAAQS. Based upon relevant facts and circumstances in each area, the**  **designated nonattainment area could be larger or sm aller than the CBSA or CSA. EPA’s analytical approa**  **ch is described in section 3 of this technical support do**  **cument.**

3.0 Technical Analysis
**In this technical analysis, EPA used the latest dat a and information available to EPA (and to the stat**  **es and tribes through the PM**  **2.5 Designations Mapping Tool**  **7 and the EPA PM Designations Guidance and Data web**  **page 8**  **) and/or data provided to EPA by states or tribes. Th**  **is technical analysis identifies the areas with one or more**  **monitoring sites that violate the 2012 annual PM 2.5**  **standard. EPA evaluated these areas and other near by areas**  **with emissions sources or activities that potential ly contribute to ambient fine particle concentratio**  **ns 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 an alysis involves examining available ambient PM**  **2.5 air quality**  **monitoring data at, and in the proximity of, the vi olating monitoring locations. This includes reviewi**  **ng the design values (DV) calculated for each monitoring l**  **ocation in the area based on air quality data for t he most**  **recent complete 3 consecutive calendar years of qua lity-assured, certified air quality data in the EPA**  **’s Air Quality System (AQS). In general, EPA identifies vi**  **olations using data from suitable Federal Reference Method**  **(FRM), Federal Equivalent Method (FEM), and/or Appr oved Regional Method (ARM) monitors sited and** 

**6 EPA issued guidance on April 16, 2013, that identif**  **ied important factors that EPA intended to evaluate , in making a**  **recommendation for area designations and nonattainm ent boundaries for the 2012 annual PM**  **2.5 NAAQS. Available at**  http://www.epa.gov/pmdesignations/2012standards/doc s/april2013guidance.pdf.

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

**.**

**8 EPA’s PM Designations Guidance and Data web page c**  **an be found at** http://www.epa.gov/pmdesignations/2012standards/tec hinfo.htm

**. Page 4 of 59** 

**operated in accordance with 40 CFR Part 58. 9**  **Procedures for using the air quality data to deter mine 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 monit**  **or sites, EPA also assesses the air quality data from other m**  **onitoring locations to help ascertain the potential contribution of sources in areas nearby to the viol**  **ating monitoring sites. Examples include using chem ical**  **speciation data to help characterize contributing e missions sources and the determination of nearby**  **contributions through analyses that differentiate l ocal and regional source contributions.** 

**Factor 2: Emissions and emissions-related data. The emissions analysis examines identified sources of**  **direct PM**  **2.5 , the major components of direct PM**  **2.5 (primary organic carbon/organic mass, elemental ca**  **rbon, crustal material (and/or individual trace metal compounds))**  **, primary nitrate and primary sulfate, and precurso r gaseous**  **pollutants (e.g., SO 2**  **, NO X**  **, total VOC, and NH 3**  **. Emissions data are generally derived from the mos t 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 st**  **ates, tribes, or other relevant sources that may no t be**  **reflected in the NEI. One example of “other informa tion” 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 PM**  **2.5 concentrations. Emissions data indicate the potent**  **ial for a source to contribute to observed violations, making it useful**  **in assessing boundaries of nonattainment areas.**
**Factor 3: Meteorology. Evaluating meteorological da ta helps to determine the effect on the fate and tr**  **ansport of emissions contributing to PM**  **2.5 concentrations and to identify areas potentially c**  **ontributing to the violations at monitoring sites. The Factor 3 analysis includes**  **assessing potential source-receptor relationships i n the area**  **identified for evaluation using summaries of air tr ajectories, wind speed, wind direction, and other**  **meteorological data, as available.**
**Factor 4: Geography/topography. The geography/topog raphy analysis includes examining the physical feat**  **ures of the land that might define the airshed and, ther**  **efore, affect the formation and distribution of PM 2.5**  **over an area. Mountains or other physical features may infl**  **uence the fate and transport of emissions and PM 2.5**  **concentrations. Additional analyses may consider to pographical features that cause local stagnation ep**  **isodes via inversions, such as valley-type features that effec**  **tively “trap” air pollution, leading to periods of elevated PM**  **2.5 concentrations.** 
**Factor 5: Jurisdictional boundaries. The analysis o f jurisdictional boundaries identifies the governme**  **ntal planning and organizational structure of an area th**  **at may be relevant for designations purposes. These jurisdictional boundaries provide insight into how**  **the governing air agencies conduct or might conduct air** 

**9 Suitable monitors include all FEM and/or ARMs exce**  **pt those specific continuous FEMs/ARMs used in the monitoring**  **agency’s network where the data are not of sufficie nt quality such that data are not to be compared to**  **the NAAQS in accordance with 40 CFR part 58.10(b)(13) and approv**  **ed by the EPA Regional Administrator per 40 CFR par t 58.11(e).** 

**10 As indicated in Appendix N to 40 CFR part 50, Inte**  **rpretation of the National Ambient Air Quality Stan dards for PM**  **2.5 ,**  **section 3(a) indicates “Except as otherwise provide d in this appendix, all valid FRM/FEM/ARM PM**  **2.5 mass concentration**  **data produced by suitable monitors that are require d to be submitted to AQS, or otherwise available to**  **EPA, meeting the requirements of part 58 of this chapter including a**  **ppendices A, C, and E shall be used in the DV (desi gn value)**  **calculations. Generally, EPA will only use such dat a if they have been certified by the reporting orga**  **nization (as prescribed by § 58.15 of this chapter); however, data not cert**  **ified by the reporting organization can nevertheles s be used, if the**  **deadline for certification has passed and EPA judge s the data to be complete and accurate.”** **Page 5 of 59**

**quality planning and enforcement in a potential non attainment area. Examples of jurisdictional boundar**  **ies include counties, air districts, areas of Indian co**  **untry, CBSA or CSA, metropolitan planning organizat ions**  **(MPOs), and existing nonattainment areas.**

3.1 Area Background and Overview Canton-Massillon, OH Area
**Figure 1a is a map of EPA’s intended nonattainment boundary for the Canton-Massillon Area. The map sho**  **ws the location and design values of ambient air quali**  **ty monitoring locations, county boundaries and Ohio ’s**  **recommended nonattainment boundary.**
**Portions of this area were designated nonattainment for purposes of the 1997 annual PM**  **2.5 NAAQS. Stark**  **County was included within the boundary of the Cant on-Massillon area for the 1997 annual PM**  **2.5 NAAQS. In**  **addition, Summit County was included within the bou ndary of the Cleveland-Akron-Lorain Area for the 19**  **97 annual PM**  **2.5 NAAQS.** 
**Portions of this area were also designated nonattai nment for purposes of the 2006 24-hour PM**  **2.5 NAAQS. Stark**  **County was included within the boundary of the Cant on-Massillon area for the 2006 24-hour PM**  **2.5 NAAQS. In**  **addition, Summit County was included within the bou ndary of the Cleveland-Akron-Lorain Area for the 20**  **06 24-hour PM**  **2.5 NAAQS.** **Page 6 of 59**

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

**EPA must designate as nonattainment areas that viol ate the NAAQS and nearby areas that contribute to t**  **he violation in the violating area.**  **Stark County shows a violation of the 2012 PM 2.5**  **NAAQS, therefore this county is included in the nonattainment area. As shown in**  **Figure 1b, EPA evaluated each county without a viol ating**  **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 descr ibe this**  **five factor analysis process. While the factors are presented individually, they are not independent.**  **The five factor analysis process carefully considers their i**  **nterconnections and the dependence of each factor o n one or**  **more of the others. Page 7 of 59** 

Figure 1b. Area of Analysis for the Canton-Massillo n, OH Area

Factor 1: Air Quality Data
**All data collected during the year are important wh en determining contributions to an annual standard**  **such as the 2012 annual PM**  **2.5 NAAQS. Compliance with an annual NAAQS is dependen**  **t upon monitor readings throughout the year, including days with monitored**  **ambient concentrations below the level of the NAAQS . For**  **the 2012 annual PM 2.5**  **NAAQS, the annual mean is calculated as the mean o f quarterly means. A high quarter**  **can drive the mean for an entire year, which, in tu rn, can drive an elevated 3-year DV. Although all d**  **ata are important, seasonal or episodic emissions can provi**  **de insight as to relative contributors to measured PM**  **2.5 concentrations. For these reasons, for the Factor 1**  **air quality analysis, EPA assessed and characteriz ed air**  **quality at, and in the proximity of, the violating monitoring site locations first, by evaluating tren**  **ds and the spatial extent of measured concentrations at monito**  **rs in the area of analysis, and then, by identifyin g the**  **conditions most associated with high average concen tration levels of PM**  **2.5 mass in the area of analysis.** **Page 8 of 59**

**In most cases, EPA assessed air quality data on a s easonal, or quarterly, basis.**  **11 EPA also identified the spatial**  **extent of these high PM 2.5**  **concentrations. The mass and composition at the de sign value location represents**  **contributions from various emission sources includi ng local, area-wide (which may comprise nearby urba**  **n and rural areas) and regional sources. To determine the**  **source mix (by mass) at the design value monitorin g site,**  **EPA examined the chemical composition of the monito red PM**  **2.5 concentrations by pairing each violating**  **FRM/FEM/ARM monitoring site with a collocated or ne arby Chemical Speciation Network (CSN) monitoring**  **site or sites. Then, EPA contrasted the approximate d mass composition at the design value monitoring s**  **ite with data collected at IMPROVE**  **12 and other monitoring locations whose data are repr**  **esentative of regional background.**  **13** , **14 This comparison of local/area-wide chemical compos**  **ition data to regional chemical composition data derives an “urban increment,” whic**  **h helps differentiate the influence of more distant**

**11 Although compliance with the annual NAAQS depends**  **on contributions from all days of the year, examini ng data on a**  **quarterly or seasonal basis can inform the relation ship between the temporal variability of emissions**  **and meteorology and the resulting PM**  **2.5 mass and composition. In some areas of the country**  **where there may be noticeable month-to-month variations in average PM**  **2.5 , the quarterly averages may not adequately represe**  **nt seasonal variability. In these areas, air quality data may be aggregated and presented by tho**  **se months that best correspond to the local “season s” in these areas.** 

**12 IMPROVE stands for Interagency Monitoring for Prot**  **ected Visual Environments and is an aerosol monitor ing network**  **in mostly rural and remote areas.**

**13 The “urban increment” analysis assesses and charac**  **terizes the increase in seasonal and annual average PM**  **2.5 mass and**  **chemical constituents observed at violating monitor ing site(s) relative to monitoring sites outside th**  **e area of analysis (which represent background concentrations). Develo**  **ping the urban increment involves pairing a violati ng**  **FRM/FEM/ARM monitor with a collocated monitor or ne arby monitor with speciation data. EPA made every e**  **ffort to pair these data to represent the same temporal and spati**  **al scales. However, in some cases, the paired viola ting and CSN “urban”**  **monitoring locations were separated by some distanc e such that the included urban CSN site(s) reflect(**  **s) a different mixture of emissions sources, which could lead to m**  **isinterpretations. To generally account for differe nces in PM**  **2.5 mass**  **between the violating site and the nearby CSN site( s), EPA determined material balance of the PM**  **2.5 composition at the**  **violating site by assigning the extra measured PM 2.5**  **mass to the carbon components of PM 2.5**  **. Where the general urban increment approach may be misleading, or in situati**  **ons where non-carbonaceous emissions are believed t o be responsible**  **for a local PM 2.5**  **concentration gradient, EPA used alternative analys es to reflect the mix of urban and rural sources**  **contributing to the measured concentrations at viol ating monitoring sites.** 

**14 The urban monitors were paired with any rural site**  **s within a 150 mile radius of an urban site to calc ulate spatial means**  **of the quarterly averages of each species. If there were no rural sites within 150 miles, then the nea**  **rest rural site was used alone. That rural mean was then subtracted from the**  **quarterly mean of the urban site to get the increm ent. Negative values**  **were simply replaced with zeros. Page 9 of 59** 

**emissions sources from the influence of closer emis sions sources, thus representing the portion of the**  **measured violation that is associated with nearby emission c**  **ontributions. 15,16,17** 

**PM 2.5**  **Design Values and Total Mass Measurements - EPA ex amined ambient PM**  **2.5 air quality monitoring data**  **represented by the DVs at the violating monitoring site and at other monitors in the area of analysis.**  **EPA calculated DVs based on air quality data for the mo**  **st recent 3 consecutive calendar years of quality-a ssured,**  **certified air quality data from suitable FEM/FRM/AR M monitoring sites in the EPA’s Air Quality System**  **(AQS). For this designations analysis, EPA used dat a for the 2011-2013 period (i.e., the 2013 design v**  **alue), which are the most recent years with fully-certifie**  **d air quality data. A monitor’s DV is the metric or statistic**  **that indicates whether that monitor attains a speci fied air quality standard. The 2012 annual PM**  **2.5 NAAQS is**  **met at a monitoring site when the 3-year average an nual mean concentration is 12.0 micrograms per cubi**  **c meter (μg/m**  **3 ) or less (e.g., 12.1 μg/m**  **3 or greater is a violation). A DV is only valid if**  **minimum data completeness criteria are met or when other regulatory data proc**  **essing 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 additio**  **nal design values based on all monitoring sites in the area of**  **analysis for the Canton-Massillon intended nonatta inment** 

**area. 18**  **Where a county has more than one monitoring locati on, the county design value is indicated in red typ**  **e.**

Table 2. Air Quality Data collected at Regulatory M onitors (all DV levels in μg/m 3 ) a County, State Monitor Site ID State Rec NA? 09-11 D V 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 m**  **onitoring location and more regional sources.**

**16 Hand, et. al. Spatial and Seasonal Patterns and Te**  **mporal Variability of Haze and its Constituents in the United**  **States: Report V, June 2011. Chapter 7 – Urban Ex cess in PM**  **2.5 Speciated Aerosol Concentrations,**  http://vista.cira.colostate.edu/improve/Publication s/Reports/2011/PDF/Chapter7.pdf

**17 US EPA, Office of Air Quality Planning and Standar**  **ds, December 2004. (2004) Area Designations for 199 7 Fine**  **Particle (PM 2.5**  **) Standards, Technical Support Document for State a nd Tribal Air Quality Fine Particle (PM**  **2.5 )**  **Designations, Chapter 3, Urban Excess Methodology. Available at**  www.epa.gov/pmdesignations/1997standards/documents/ final/TSD/Ch3.pdf

**18 In certain circumstances, one or more monitoring l**  **ocations within a monitoring network may not meet t he network**  **technical requirements set forth in 40 CFR 58.11(e) , which states, “State and local governments must a**  **ssess data from Class III PM**  **2.5 FEM and ARM monitors operated within their network**  **using the performance criteria described in table C**  **4 to subpart C of part 53 of this chapter, for case s where the data are identified as not of sufficien**  **t comparability to a collocated FRM, and the monitoring agency requests**  **that the FEM or ARM data should not be used in comp arison to the**  **NAAQS. These assessments are required in the monito ring agency's annual monitoring network plan descri**  **bed in §58.10(b) for cases where the FEM or ARM is identif**  **ied as not of sufficient comparability to a colloca ted FRM....”** **Page 10 of 59**

County, State Monitor Site ID State Rec NA? 09-11 D V 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 locatio**  **n, the county design value is indicated in red type**

**.**

**The Figure 1 map, shown previously, identifies the Canton-Massillon intended nonattainment area, the M**  **SA boundary and monitoring locations with 2011-2013 vi**  **olating 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 m ost associated with high average concentration leve**  **ls of PM**  **2.5 . Figure 2 shows quarterly mean PM**  **2.5 concentrations for the most recent 3-year period f**  **or the highest DV monitoring sites in each county within the area of**  **analysis, as well as an additional non-violating mo nitoring**  **site in the Canton-Massillon metropolitan statistic al area (MSA). This graphical representation is par**  **ticularly relevant when assessing air quality data for an ann**  **ual standard, such as the 2012 annual PM 2.5**  **NAAQS, because, as previously stated, the annual mean is c**  **alculated as the mean of quarterly means and a high quarter**  **can drive the mean for an entire year, which, in tu rn, can drive an elevated 3-year DV.** **Page 11 of 59**

Figure 2. Canton-Massillon PM 2.5 Quarterly Means for 2011-2013

**For the Canton-Massillon MSA monitors, quarterly va lues across the 3-year period vary by 1-3 μg/m**  **3 at each**  **site, with higher concentrations typically occurrin g in Q1 and Q3. Annual peaks most often occur in Q**  **3, although one annual peak was recorded in Q1 and one**  **in Q4. This suggests that these quarters should b e**  **considered more closely when evaluating chemical co nstituent and urban increment data. ...** 

**PM 2.5**  **Composition Measurements - To assess potential emi ssions contributions for each violating monitoring**  **location, the EPA determined the various chemical s pecies comprising total PM**  **2.5 to identify the chemical**  **constituents over the analysis area, which can prov ide insight into the types of emission sources impa**  **cting the monitored concentration. To best describe the PM**  **2.5 at the violating monitoring location, EPA first adj**  **usted the chemical speciation measurement data from a monitor**  **ing location at or near the violating FRM monitorin g site**  **using the SANDWICH approach to account for the amou nt of PM**  **2.5 mass constituents retained in the FRM** 

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

'11 Q1 '11 Q2 '11 Q3 '11 Q4 '12 Q1 '12 Q2 '12 Q3 '12 Q4 '13 Q1 '13 Q2 '13 Q3 '13 Q4**PM 2.5** **Concentrations μg/m** **3** 

## Canton PM **2.5** 

## Quarterly Means 2011-2013

Stark - 391510017 Stark - 391510020**Page 12 of 59**

**measurement. 19,20,21,22**  **In particular, this approach accounts for losses i n fine particle nitrate and increases in**  **sulfate mass associated with particle bound water. Figure 3a illustrates the fraction of each PM**  **2.5 chemical**  **constituent at monitoring site 391510017 in Stark C ounty based on annual averages for the years 2010-2**  **012.**

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

4.3

5.0 1.6 0.9 1.1 

## Annual Average PM **2.5** 

## Chemical Constituents

**2010-2012 (μg/m 3**  

## )

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

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

**19 SANDWICH stands for measured**  S **ulfate,** A **djusted** N **itrate,** D **erived** W **ater,** I **nferred** C **arbonaceous mass** H **ybrid Material Balance Approach.” The SANDWICH adjustment**  **uses an FRM mass construction methodology that res ults in**  **reduced nitrates (relative to the amount measured b y routine speciation networks), higher mass associa**  **ted with sulfates (reflecting water included in gravimetric FRM measu**  **rements) and a measure of organic carbonaceous mass derived from**  **the difference between measured PM 2.5**  **and its non-carbon components. This characterizati on of PM**  **2.5 mass also reflects**  **crustal material and other minor constituents. The resulting characterization provides a complete mass**  **closure for the measured FRM PM**  **2.5 mass, which can be different than the data provide**  **d directly by the speciation measurements from the CSN network.** 

**20 Frank, N. H., SANDWICH Material Balance Approach f**  **or PM 2.5**  **Data Analysis, National Air Monitoring Conference, Las Vegas, Nevada, November 6-9, 2006.**  http://www.epa.gov/ttn/amtic/files/2006conference/f rank.pdf

**.**

**21 Frank, N. H., The Chemical Composition of PM**  **2.5 to support PM Implementation, EPA State /Local/Tri**  **bal Training Workshop: PM**  **2.5 Final Rule Implementation and 2006 PM**  **2.5 Designation Process, Chicago IL, June 20-21, 2007,**  http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chem ical_composition.pdf.

**22 Frank, N. H.**  Retained Nitrate, Hydrated Sulfates, and Carbonaceo us Mass in Federal Reference Method Fine Particulate Matter for Six Eastern U.S. Cities **. J. Air & Waste Manage. Assoc. 2006 56:500–511. Page 13 of 59** 

**additional insight as to which sources or factors m ay contribute at a greater level. Simply stated, th**  **is analysis can help identify nearby sources of emissions that**  **contribute to the violation at the violating monito ring site.** 

Figure 3b. Canton-Massillon Area Annual and Quarter ly Average PM 2.5 Species (2010-2012) a

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

Annual Q1 Q2 Q3 Q4 

## Annual and Quarterly Average PM **2.5** 

## Species **2010-2012 Adjusted to FRM Total PM 2.5** 

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

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

Annual Q1 Q2 Q3 Q4 

## Annual and Quarterly Average PM **2.5** 

## Species **2010-2012 Adjusted to FRM Total PM 2.5** 

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

**a** Adjusted to FRM Total PM 2.5 **indicates that the speciation profile and total ma ss depicted in this figure are the result of the**  **SANDWICH calculations from speciation measurements for the particular PM**  **2.5 monitoring site.** **Page 14 of 59**

**Figure 3b shows that sulfate and organic mass are t he predominant species, with an exception in Q1 whe**  **re 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 i mpact on the monitored values in the Canton-Massill**  **on area. EPA assessed seasonal and annual average PM**  **2.5 constituents at monitoring sites within the area re**  **lative to monitoring sites outside of the analysis area to ac**  **count for the difference between regional backgroun d**  **concentrations of PM 2.5**  **, and concentrations of PM 2.5**  **in the area of analysis, also known as the “urban increment.” This analysis differentiates between th**  **e influences of emissions from sources in nearby ar eas and in**  **more distant areas on the violating monitor. Estima ting the urban increment in the area helps to illum**  **inate the amount and type of particles at the violating monit**  **or that are most likely to be the result of sources of emissions**  **in nearby areas, as opposed to impacts of more dist ant or regional sources of emissions. Figure 4a inc**  **ludes pie charts showing the annual and quarterly chemical ma**  **ss constituents of the urban increment. The quarter ly pie**  **charts correspond to the high-concentration quarter s identified in Figure 2. Evaluating these high-con**  **centration quarters can help identify composition of PM**  **2.5 during these times. Note that in these charts, sul**  **fates and nitrates have been adjusted to represent their mass**  **in measured PM 2.5**  **.** Figure 4a. Canton-Massillon Area Urban Increment An alysis for 2010-2012.

2%

56% 26% 8% 8% 

### Annual Average Urban Increment **2010-2012 (μg/m3)**

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

0.08

2.27 2.11 0.10

0.06 

### Q1 Average Urban Increment **2010-2012 (μg/m3)**

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal 0.86

0.06 0.27 0.44 

### Q2 Average Urban Increment **2010-2012 (μg/m3)**

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal**Page 15 of 59** 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 Incr ement 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 monitori ng site with a DV exceeding the NAAQS. Stark County**  **ambient PM 2.5**  **concentrations are fairly consistent across the ye ar, with modestr seasonal peaks in in Q1 and Q3,**  **although one annual high was recorded in Q4. This s uggests that particular attention should be given t**  **o urban increment information for Q1 and Q2, although data**  **for Q4 also needs to be evaluated.**

**In reviewing the urban increment analysis for the C anton-Massillon DV monitor, organic mass consistent**  **ly represents the largest component. Sulfate appears t**  **o consistently have small urban contribution, altho ugh**  **conversely large regional contribution. Nitrate is the second highest urban increment component for Q**  **1 and Page 16 of 59** 

**Q4, but is not a factor inQ3, during which crustal and elemental carbon both comprise a higher percent**  **age. This analysis points to contribution from local nitrate**  **and direct PM 2.5**  **sources nearby the violating monitor and contribution from regional sulfate and nitrate sour**  **ces.**

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 re**  **lated data for the relevant counties to assess each county’s potential contribution to PM**  **2.5** 

### concentrations at the violating monitoring site or **monitoring sites in the area under evaluation. 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 pollutan**  **ts; therefore EPA is only analyzing annual emission s**  **data. (Although nitrate is much higher in the coole r months, NOx emissions have similar importance**  **all year due to their role in fostering the photoch emistry that causes secondary particulate matter.)** 

**EPA examined emissions of identified sources or sou rce categories of direct PM**  **2.5 , the major components of**  **direct PM 2.5**  **(organic mass, elemental carbon, crustal material (and/or individual trace metal compounds)),**  **primary nitrate and primary sulfate, and precursor gaseous pollutants (i.e., SO**  **2 , NO**  **X , total VOC, and NH**  **3 ).**  **EPA also considered the distance of those sources o f emissions from the violating monitoring site. Whi**  **le direct PM**  **2.5 emissions and its major carbonaceous components ar**  **e generally associated with sources near violating PM**  **2.5 monitoring sites, the gaseous precursors tend to h**  **ave a more regional influence (although the EPA is mindful of the potential for local NO**  **X and VOC emissions contributions to PM**  **2.5 from mobile and stationary**  **sources) and transport from neighboring areas can c ontribute to higher PM**  **2.5 levels at the violating monitoring**  **sites.**

Emissions Data
**For this factor, EPA reviewed data from the 2011 Na tional 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 th**  **e sum of emissions from the following general source categor**  **ies: 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 sourc**  **es of the relevant pollutants.**  **23 Significant emissions levels from sources in a nea**  **rby area indicate the potential for the area to contribute to monitored violations.** 
**To further analyze area emissions data, EPA also de veloped a summary of direct PM**  **2.5 , components of direct**  **PM 2.5**  **, and precursor pollutants, which is available at** http://www.epa.gov/pmdesignations/2012standards/doc s/nei2011v1county_nei2011v1point_nei2008v3_county. xlsx

**.**

**23 For purposes of this designations effort, “major”**  **point sources are those whose sum of PM precursor e missions (PM**  **2.5 +**  **NOx + SO 2**  **+ VOC + NH 3**  **) are greater than 500 tons per year based on NEI 2 011v1.** **Page 17 of 59**

**When considered with the urban increment analysis i n Factor 1, evaluating the components of direct PM**  **2.5 and**  **precursor gases can help identify specific sources or source types contributing to elevated concentrat**  **ions at violating monitoring sites and thus assist in ident**  **ifying appropriate area boundaries. In general, dir ectly emitted**  **particulate organic carbon (POC) and VOCs 24**  **contribute to PM 2.5**  **organic mass (OM); directly emitted EC contributes to PM**  **2.5 EC; NO**  **X , NH**  **3 and directly emitted nitrate contribute to PM**  **2.5 nitrate mass; SO**  **2 , NH**  **3 and**  **directly emitted sulfate contribute to PM 2.5**  **sulfate mass; and directly emitted crustal materia l and metal oxides**  **contribute to PM 2.5**  **crustal matter. 25,26**  **EPA believes that the quantities of those nearby e missions as potential**  **contributors to the PM 2.5**  **violating monitors are somewhat proportional to the PM**  **2.5 chemical constituents in the**  **estimated urban increment. Thus, directly emitted P OC is more important per ton than SO**  **2 , partially because**  **POC emissions are already PM 2.5**  **whereas SO 2**  **must convert to PM 2.5**  **and not all of the emitted SO 2**  **undergoes this conversion.** 
**Table 3a provides a county-level emissions summary (i.e., the sum of emissions from the following gene**  **ral source categories: point sources, non-point (i.e.,**  **area) sources, nonroad mobile, on-road mobile, and fires) of**  **directly emitted PM 2.5**  **and precursor species for the county with the viol ating monitoring site and nearby**  **counties considered for inclusion in the Canton-Mas sillon**  **area. Table 3b summarizes the directly emitted components of PM**  **2.5 for the same counties in the area of analysis for**  **the Canton-Massillon**  **area. This information will be considered in conjunction with**  **the urban increment composition information previou sly**  **shown in Figures 4a and 4b.**
Table 3a. County-Level Emissions of Directly Emitte d PM 2.5 and Precursors (tons/year)
County, State Total

NO X Total

SO 2 Total Direct

PM 2.5 Total

NH 3 Total

VOC Total

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

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

**25** See **, Seinfeld J. H. and Pandis S. N. (2006)** Atmospheric Chemistry and Physics: From Air Polluti on 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 Emission s through**  **2003, found at:** http://www.epa.gov/airtrends/aqtrnd04/pm.html

**. Page 18 of 59** 

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

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

**Table 3b breaks down the direct PM 2.5**  **emissions value from Table 3a into its components. These data will also**  **be compared with the previously presented Urban Inc rement composition.** 
**Using the previously described relationship between directly emitted and precursor gases and the measu**  **red mass to evaluate data presented in Tables 3a and 3b**  **, EPA identified the following components warrantin g**  **additional review: POM, NO X**  **, SO 2**  **, EC, and Pcrustal. EPA then looked at the contribution of these consti**  **tuents of interest from each of the counties included in t**  **he area of analysis, as shown in Tables 4a-e.**

Table 4a. County-Level Primary Organic Mass Emissio ns (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 Mode**  **ling 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 19 of 59** 

Table 4b. County-Level NO X Emissions County, State NO X Percent Cumulative % **Summit, OH 16,925 21% 21% Stark, OH 14,466 18% 39%**  **Jefferson, OH 12,593 16% 55% Mahoning OH 9,712 12% 67%**  **Wayne, OH 7,323 9% 76% Portage, OH 6,428 8% 84%**  **Tuscarawas, OH 4,399 5% 90% Columbiana, OH 4,160 5% 95%**  **Carroll, OH 1,649 2% 97% Holmes, OH 1,601 2% 99%**  **Harrison, OH 731 1% 100%**

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

Table 4d. County-Level Primary Elemental Carbon Emi ssions 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 20 of 59** 

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 P M**  **2.5 and PM**  **2.5 precursors in the area of analysis, EPA also**  **reviewed emissions from major point sources located in the area of analysis. The magnitude and locatio**  **n of these sources can help inform nonattainment boundar**  **ies. Table 5 provides facility-level emissions of d irect**  **PM 2.5**  **, components of direct PM 2.5**  **, and precursor pollutants (given in tons per year) from major point sources**  **located in the area of analysis for the Canton-Mass illon area. Table 5 also shows the distance from th**  **e facility to the DV monitor.** **Page 21 of 59**

Table 5. NEI 2011 v1 Point Source Emissions (tons/y ear)

County, State Facility Name (Facility ID) NH 3 NO X PM 2.5 SO 2 VOC Total
**Carroll, OH Tennessee Gas Pipline- Station 214 (0210000046)**  **22 662 4 0 23 689**

**Jefferson, OH Cardinal Power Plant (Cardinal Operating** 

**Company) (0641050002) 54 3 2,250 616 25,122 103 28,094** 

**Jefferson, OH W. H. Sammis Plant (0641160017)**  **44 1 7,544 6,916 4,153 141 18,756 Mahoning,**  **OH Youngstown Thermal (0250110024)**  **44 0 123 19 1,063 1 1,206**
**Stark, OH Alliance Casting Co. LLC (1576010014) 18 614 216 3 33 865** 
**Stark, OH Republic Engineered Products, Inc. (1576050694)**  **3 2 224 174 63 66 529**

**Stark, OH Marathon Petroleum Company LP - Canton** 

**Refinery (1576002006) 1 8 285 188 93 224 798** 

**Summit, OH Cargill, Incorporated - Salt Division (Akron, OH)** 

**(1677010027) 19 0 140 18 1,516 1 1,676** 

**Summit, OH Emerald Performance Materials, LLC** 

**(1677010029) 20 0 115 1 869 10 996** 
**Summit, OH City of Akron Steam Generating (167701075 7) 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 4,43 4 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 (fr om the 2011 NEI in tons per year) in the area of an**  **alysis for the Canton-Massillon area**  **and the relative distances of these sources from the violat**  **ing monitoring location, as depicted by red dot in Stark County. The actual dis**  **tance from the point sources to the DV monitoring l ocation is**  **presented in Table 5. The distance from the violati ng monitoring location is particularly important fo**  **r directly emitted PM**  **2.5 . The influence of directly emitted PM**  **2.5 on ambient PM**  **2.5 diminishes more than that of gaseous**  **precursors as a function of distance. 28** 
**As indicated in Table 5, there are 15 major point s ources located within the area of analysis – all wi**  **thin approximately 50 miles of the violating monitor. Ni**  **ne of these sources are in the intended nonattainme nt area.** 

**28 Baker, K. R. and K. M. Foley.**  A nonlinear regression model estimating single sour ce concentrations of primary and secondarily formed PM 2.5
. **Atmospheric Environment. 45 (2011) 3758-3767. Page 22 of 59** 

Figure 5. Major Point Source Emissions in the Area of Analysis for the Canton-Massillon Area.

**In summary, EPA’s evaluation of county-level emissi ons from Table 3a shows that Jefferson, Summit, Way**  **ne, 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 emi**  **ssions from sources located east of the monitor are unlike**  **ly to have a meaningful impact on the monitored vio lation.**  **Therefore, while Jefferson and Mahoning Counties ha ve significant emissions, these emissions are unlik**  **ely to have a meaningful impact on the monitored violation**  **recorded in Stark County. Columbiana County is al so**  **located east of Stark County, between Mahoning and Jefferson Counties and is therefore also unlikely t**  **o meaningfully contribute to the monitored violation**  **in Stark County.**
**Consideration of emissions in the eight other count ies shows that Stark is responsible for 22% of emis**  **sions and 26% of direct PM**  **2.5 emissions. Together, Stark, Summit, and Wayne Cou**  **nties are responsible for 72% of emissions and 66% of PM**  **2.5 emissions. In addition, when considering these ei**  **ght counties, Stark County contributes 19%, 27%, 2%, 24% and 36% to the POM, N**  **O X**  **, SO 2**  **, EC, and Pcrustal components, respectively, while Stark, Summit, and Wayne Counties together c**  **ontribute 53%, 72%, 89%, 66% and 80% to the POM, Page 23 of 59** 

**NO X**  **, SO 2**  **, EC, and Pcrustal components, respectively. Emiss ions in Portage, Tuscarawas, Holmes, Harrison**  **and Carroll Counties are relatively low compared to the other counties in the area of analysis. Of the**  **se counties, Portage’s emissions comprise 9% of the total emissi**  **ons in these eight other counties.**
**Of the major point sources listed in Table 5 that a re located in the eight counties not excluded for**  **meteorological reasons, the largest point source is in Wayne County. In addition, the two largest poi**  **nt sources in Wayne County account for 70% of the SO**  **2 emissions in the eight counties. Further, Wayne C**  **ounty is directly west of Stark County with point sources lo**  **cated only18-24 miles from the violating monitor. Given the**  **strong westerly component shown in the wind roses i n Figure 8, below, these point source emissions wou**  **ld be expected to contribute to PM**  **2.5 concentrations recorded at the Stark County monito**  **r. Taken together, 89% of the total emissions from major sources in these eig**  **ht counties come from facilities in Stark County, S ummit**  **County, and Wayne County. While Jefferson County h as the two largest sources in the full area of ana**  **lysis, this county has historically been addressed as part**  **of the Steubenville area and, as previously noted, meteorological considerations indicate that emissio**  **ns from Jefferson County are unlikely to contribute to the**  **monitored violation in Stark County, particularly w ith 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 mag**  **nitude of non-point source emissions. Rapid populat ion**  **growth in a county on the urban perimeter signifies increasing integration with the core urban area, a**  **nd indicates that it may be appropriate to include the**  **county associated with area source and mobile sour ce**  **emissions as part of the nonattainment area. Table 6 shows the 2000 and 2010 population, population gr**  **owth since 2000, and population density for each county**  **in the area.**
Table 6. Population Growth and Population Density.

County, State Population

2000 Population

2010 % Change

from 2000 Land Area

(Sq. Miles) Population

Density (per Sq. Mile) % Cumulative

% % Urban a

**Summit, OH 542,899 541,652 -0.2% 413 1,312 30% 30% 47% Stark, OH 378,098 375,417 -0.7% 576 652 21% 51% 22%**  **Mahoning OH 257,555 238,339 -7.5% 415 574 13% 65% 23% Portage, OH 152,061 161,403 6.1% 492 328 9% 74% 13%**  **Wayne, OH 111,564 114,480 2.6% 555 206 6% 80% 5% Columbiana, OH 112,075 107,820 -3.8% 532 202 6% 86% 6%**  **Tuscarawas, 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**

**a Source: Ohio Department of Development, Ohio County**  **Profiles: http://development.ohio.gov/reports/reports_countyt**  **rends_map.htm Page 24 of 59** 

**When considering the entire area of analysis, Summi t County has by far the greatest population, popula**  **tion density, and percent urban. Considering 2010 popu**  **lation and population density, Stark County is next most**  **populous and population-dense county, followed by M ahoning, Portage, Wayne and Columbiana counties.**  **When setting aside Mahoning, Columbiana, and Jeffer son counties – which are unlikely to contribute to**  **violations given the relation between available met eorological data – Summit and Stark counties togeth**  **er contain 67% of the total population of these counti**  **es. Population and population density are signific antly 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 nu mber of commuters associated with a county is gener**  **ally an indicator that the county is an integral part of**  **an urban area. Mobile source emissions of NO x**  **, VOC, and direct PM may contribute to ambient particulate mat**  **ter that contributes to monitored violations of the NAAQS**  **in the area. In combination with the population/pop ulation density data and the location of main trans**  **portation arteries, an assessment of VMT helps identify the p**  **robable location of nonpoint source emissions that Page 25 of 59** 

**contribute to violations in the area. Comparatively high VMT in a county outside of the CBSA or CSA si**  **gnifies integration with the core urban area contained with**  **in the CSA or CBSA, and indicates that a county wit h the**  **high VMT may be appropriate to include in the nonat tainment area because emissions from mobile sources**  **in that county contribute to violations in the area. T**  **able 7 shows 2011 VMT, while Figure 7 overlays 2011 county**  **level VMT with a map of the transportation arteries . The VMT used in this analysis was submitted by Oh**  **io.**

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 discus**  **sed below, however, meteorological considerations indic**  **ate that emissions from Mahoning County (as well as Jefferson and Columbiana counties) are unlikely to**  **contribute to the monitored violation in Stark Coun ty.**  **Considering the eight counties not excluded from th e intended area for meteorological considerations,**  **Summit and Stark have the highest VMT, together comprising**  **66% of the VMT in the eight counties. Portage Cou nty**  **has the next highest VMT , followed by Wayne Count y and Tuscarawas County.** **Page 26 of 59**

Figure 7. Overlay of 2011 County-level VMT with Tra nsportation Arteries.

**After evaluating meteorology, discussed in greater detail below under Factor 3, it becomes apparent th**  **at emissions from sources located east of the monitor**  **are unlikely to contribute meaningfully to the moni tored**  **violation. Therefore, while Jefferson and Mahoning counties consistently have significant emissions,**  **these emissions are unlikely to contribute meaningfully t**  **o 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 mon**  **itored violation in Stark County. Therefore emissions from**  **Stark, Summit, Wayne, Portage, Tuscarawas, Holmes, Carroll, and Harrison Counties merit closer analysi**  **s when determining contribution to the violation mo nitored**  **in Stark County.**
**Of these eight counties, Summit County has the high est total emissions, followed by Wayne and Stark, a**  **s well as the highest NO**  **X , VOC, primary organic matter, and elemental carbon**  **emissions. Wayne County has the highest SO**  **2 emissions and second highest overall total emissio**  **ns, largely due to SO 2**  **emissions from two point sources. When considering emissions from the count**  **ies that cannot be reasonably excluded for meteorol ogical**  **reasons, Stark, Summit, and Wayne counties together comprise 72% of the total emissions, 66% of the to**  **tal Page 27 of 59** 

**direct PM 2.5**  **emissions, 75% of the total population and 74% of the VMT. Although Portage County has higher**  **population density and VMT than Wayne County, and T uscarawas County VMT and population density are**  **similar to that of Wayne County, emissions in Wayne County are significantly higher than emissions in**  **either Portage or Tuscarawas counties, or in the counties**  **of Holmes, Carroll and Harrison. Additionally, Port age,**  **Holmes, and Harrison counties lack large singular p oint source contributors.** 
Factor 3: Meteorology **EPA evaluated available meteorological data to dete rmine how meteorological conditions, including, but**  **not limited to, weather, transport patterns, and stagna**  **tion conditions, could affect the fate and transpor t of directly**  **emitted particulate matter and precursor emissions from sources in the area of analysis. EPA used two**  **primary tools for this assessment: wind roses and kernel de**  **nsity estimation (KDE). When considered in combinat ion**  **with area PM 2.5**  **composition and county-level and facility emission s source location information, wind roses**  **and KDE can help to identify nearby areas contribut ing to violations at violating monitoring sites.** 

**Wind roses are graphic illustrations of the frequen cy of wind direction and wind speed. Wind direction**  **can indicate the direction from which contributing emis**  **sions are transported; wind speed can indicate the force of**  **the wind and thus the distance from which those emi ssions are transported. EPA constructed wind roses**  **from hourly observations of wind direction and wind spee**  **d using 2009-2012 data from National Weather Servic e**  **locations archived at the National Climate Data Cen ter.**  **29 When developing these wind roses, EPA also used**  **wind observations collected at meteorological sampl ing stations collocated at air quality monitoring s**  **ites, where these data were available. Figure 8 shows wind rose**  **s that EPA generated from data relevant to the Cant on**  **Massillon area.**

**29** ftp.ncdc.noaa.gov/pub/data/noaa **or** http://gis.ncdc.noaa.gov/map/viewer/#app=cdo&cfg=cd o&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 28 of 59**

Figure 8. Wind Roses in the Area of Analysis for Ca nton-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, s**  **uggesting that potential emission sources in the so uth**  **through-west upwind direction should be considered for analysis. Some northerly winds are seen, notabl**  **y in Summit County.** 
**In addition to wind roses, EPA also generated kerne l density estimation (KDE) plots to represent HYSPL**  **IT (HYbrid Single-Particle Lagrangian Integrated Traje**  **ctory) backward trajectory frequency at violating Page 29 of 59** 

**monitoring sites. 30,31**  **These KDEs are graphical statistical estimations t o determine the density of trajectory**  **endpoints at a particular location represented by a grid cell. The EPA used KDEs to characterize and a**  **nalyze the collection of individual HYSPLIT backward traje**  **ctories. 32**  **Higher density values, indicated by darker blue colors, indicate a greater frequency of observed tr**  **ajectory endpoints within a particular grid cell. F igure 9 shows**  **a HYSPLIT KDE plot for the Canton-Massillon area su mmarized by calendar quarter for the 2010-2012 peri**  **od. The HYSPLIT KDE is weighted in the south to westerl**  **y direction, indicating a greater frequency of traj ectories**  **passing over grid cells to the south and west. Neve rtheless, these plots also suggest that impact from**  **the north, notably including Summit County, also frequently oc**  **curs.** Figure 9. HYSPLIT Kernel Density Estimation Plots f or the Canton-Massillon Area.

**30 In some past initial area designations efforts, EP**  **A has used HYSPLIT backward trajectories to assist in determining**  **nonattainment area boundaries. A HYSPLIT backward t rajectory is usually depicted on a standard map as**  **a single line, representing the centerline of an air parcel’s moti**  **on, extending in two dimensional (x,y) space from a starting point and**  **regressing backward in time to a point of origin. B ackward trajectories may be an appropriate tool to**  **assist in determining an air parcel’s point of origin on a day in which a**  **short-term standard, such as an 8-hour standard or a 24-hour standard,**  **was exceeded. However, for an annual standard, such as the 2012 annual PM**  **2.5 NAAQS, every trajectory on every day is**  **important. Plotting a mass of individual daily (e.g ., 365 individual back trajectories), or more frequ**  **ent, HYSPLIT trajectories may not be helpful as this process is**  **likely to result in depicting air parcels originati ng in all directions from the**  **violating monitoring site.**

**31 HYSPLIT - Hybrid Single Particle Lagrangian Integra**  **ted Trajectory Model,** http://www.arl.noaa.gov/HYSPLIT_info.php

**32 The KDEs graphically represent the aggregate of H**  **YSPLIT backward trajectories for the years 2010-201 2, run every**  **third day (beginning on the first day of monitoring ), four times each day, and ending at four endpoint**  **heights. Page 30 of 59** 

**In summary, the HYSPLIT KDE plots and wind roses su ggest that emissions from Stark, Summit, Wayne,**  **Holmes and Tuscarawas counties (and to a lesser ext ent, Carroll County) have the greatest potential to**  **reach the violating Canton-Massillon monitor. Comparison of t**  **hese plots to the analogous plots for the Cleveland area**  **further suggests that Summit and Wayne counties con tribute more significantly to Stark County than to**  **the Cuyahoga County. Accordingly, placing Summit and Wa**  **yne counties in the Canton-Massillon area may be more appropriate than placing them in the Cleveland**  **area. The HYSPLIT KDE plots indicate relatively lo w**  **density values in Jefferson, Columbiana and Mahoni ng counties. These counties are therefore unlikely**  **to meaningfully impact the violation monitored in Star**  **k County. Of particular note, the Jefferson County point**  **sources shown in Figure 5 are located in the easter n portion of that county –along the Ohio River – an**  **d are therefore especially unlikely to have meaningful im**  **pact on the violating monitor in Stark County.**

Factor 4: Geography/topography

**To evaluate the geography/topography factor, EPA as sessed physical features of the area of analysis th**  **at might define the airshed and thus affect the formation an**  **d distribution of PM 2.5**  **concentrations over the area. The Canton-Massillon area does not have any geograp**  **hical or topographical barriers inhibiting air poll ution**  **transport around the area of analysis. Therefore, t his factor did not play a significant role in defin**  **ing the boundary of the intended area.** 

Factor 5: Jurisdictional boundaries

**In defining the boundaries of the intended Canton-M assillon nonattainment area, EPA considered existin**  **g jurisdictional boundaries, which can provide easily**  **identifiable and recognized boundaries for purpose s of**  **implementing the NAAQS. Existing jurisdictional bou ndaries often signify well recognized boundaries th**  **at the state can easily administer and for which the state**  **has the necessary legal authority for carrying out air quality**  **planning and enforcement functions. Examples of su ch jurisdictional boundaries include existing/prior** **Page 31 of 59**

**nonattainment area boundaries for particulate matte r, county lines, air district boundaries, township**  **boundaries, areas covered by a metropolitan planning organizati**  **on, state lines, and Reservation boundaries, if app licable.**  **Where existing jurisdictional boundaries were not a dequate or appropriate to describe the nonattainmen**  **t area, EPA considered other clearly defined and permanent**  **landmarks or geographic coordinates for purposes of identifying the boundaries of the intended designat**  **ed areas.**

**The Canton-Massillon Metropolitan Statistical Area consists of Stark and Carroll Counties. The Canton** 

**** **Massillon area also has previously established nona ttainment boundaries, consisting of Stark County, a**  **ssociated with the 2006 24-hour and 1997 annual PM**  **2.5 NAAQS. Summit and Portage counties were previously**  **designated nonattainment as part of the Cleveland a rea for both the 1997 and 2006 PM**  **2.5 NAAQS. The state**  **has recommended that only Stark County be included in the Canton-Massillon nonattainment area for the**  **2012 annual PM**  **2.5 NAAQS.** 

**In addition to counties, the Bureau of Census recog nizes legally defined county subdivisions, referred**  **to as “minor civil divisions.” For Ohio, these minor civi**  **l divisions consist of townships and cities. When considering**  **Wayne County’s contribution to the violation monito red in Stark County, major point source emissions w**  **ere of particular concern. Limiting the nonattainment por**  **tion of Wayne County to the following Bureau of Cen sus**  **defined minor civil divisions captures the three ma jor sources in Wayne County and establishes a conti**  **guous nonattainment area with clearly defined legal bound**  **aries: Baughman, Chippewa, Green, and Milton townsh ips**  **and the portion of Norton City located within Wayne County. This sub-county area consists of approxima**  **tely 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**  **preliminarily concluded that the following counties should be included as part of the Canton-Massillon**  **nonattainment area because they are either violatin g the 2012 annual PM**  **2.5 NAAQS or contributing to a**  **violation in a nearby area: Stark County, Summit C ounty, and Wayne County (partial). These are not th**  **e same boundaries that were promulgated for the Canton-Mas**  **sillon nonattainment area for the 2006 24-hour and 1997**  **annual PM 2.5**  **NAAQS.**

**The air quality monitoring site in Stark County indicates a violation of the 2012 annual PM**  **2.5 NAAQS based on**  **the 2011-2013 DV; therefore this county is included in the nonattainment area. Summit and Wayne count**  **ies are nearby counties that do not have violating moni**  **toring sites, but EPA has concluded that these area s**  **contribute to the particulate matter concentrations in violation of the 2012 annual PM**  **2.5 NAAQS** 

**Jefferson and Mahoning counties have among the high est emissions of directly emitted PM**  **2.5 and/or PM**  **2.5 precursors in the area, however, Jefferson and Maho**  **ning counties are frequently downwind of the violat ing**  **monitor, as is Columbiana County, and emissions fro m 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 NO**  **X , VOC,**  **primary organic matter, and elemental carbon emissi ons. Stark County has the highest total direct PM**  **2.5 Page 32 of 59** 

**emissions, followed by Wayne and Summit, as well as the highest crustal and second highest NO**  **X , VOC,**  **primary organic mass and elemental carbon. Wayne Co unty has the highest SO**  **2 emissions and second highest**  **overall total emissions, largely due to SO 2**  **emissions from two point sources. Including the p ortion of Wayne**  **County consisting of Baughman, Chippewa, Green, and Milton townships and the portion of Norton City**  **located within Wayne County captures the three majo r sources listed in Table 5 – the majority of the e**  **missions in Wayne County. Stark, Summit, and Wayne counties**  **together comprise 72% of the total emissions, 66% o f**  **the total direct PM 2.5**  **emissions, 75% of the total population and 74% of the VMT in the eight counties not**  **excluded for primarily meteorological reasons. Por tage County is most often somewhat downwind, being**  **located northeast of the Canton-Massillon monitor, and has relatively lower emissions as compared to S**  **tark, Summit, and Wayne Counties. Finally, although Carr**  **oll County is in the Canton-Massillon MSA, it has relatively lower emissions, population density and**  **VMT as compared to Stark, Summit, and Wayne countie s.**  **Accordingly, it is unlikely to contribute meaningfu lly to the violation in Stark County.** 

3.2 Area Background and Overview Cleveland
**Figure 1a 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 an d Ohio’s**  **recommended nonattainment boundary.**
**For purposes of the 1997 annual PM 2.5**  **NAAQS, portions of this area were designated nonatt ainment. The**  **boundary for the nonattainment area for the 1997 an nual PM**  **2.5 NAAQS included the entire counties of**  **Cuyahoga, Lake, Lorain, Medina, Portage, and Summit , and parts of Ashtabula County. For purposes of t**  **he 2006 24-hour PM**  **2.5 NAAQS, all of this area minus the portion of Ashta**  **bula County was designated nonattainment. That is, the boundary for the nonatt**  **ainment area for the 2006 24-hour PM 2.5**  **NAAQS included the entire counties of Cuyahoga, Lake, Lorain, Medi**  **na, Portage, and Summit Counties. Page 33 of 59** 

Figure 1a. EPA’s Intended Nonattainment Boundaries for the Cleveland, OH Area

**EPA must designate as nonattainment areas that viol ate the NAAQS and nearby areas that contribute to t**  **he violation in the violating area.**  **Cuyahoga County shows violations of the 2012 PM 2.5**  **NAAQS, therefore this county is included in the nonattainment area of ana**  **lysis. As shown in Figure 1b, EPA also evaluated ea ch**  **county without a violating monitoring site located in or near the combined statistical area (CSA) of t**  **he county with a violating monitoring site based on the five**  **factors and other relevant information, and determi ned that**  **Lake and Lorain counties contribute to the nearby v iolations in Cuyahoga County. The following section**  **s describe this five factor analysis process. While t**  **he factors are presented individually, they are not independent.**  **The five factor analysis process carefully consider s their interconnections and the dependence of each**  **factor on one or more of the others.** **Page 34 of 59**

Figure 1b. Area of Analysis for the Cleveland, OH A rea

Factor 1: Air Quality Data
**All data collected during the year are important wh en determining contributions to an annual standard**  **such as the 2012 annual PM**  **2.5 NAAQS. Compliance with an annual NAAQS is dependen**  **t upon monitor readings throughout the year, including days with monitored**  **ambient concentrations below the level of the NAAQS . For**  **the 2012 annual PM 2.5**  **NAAQS, the annual mean is calculated as the mean o f quarterly means. A high quarter**  **can drive the mean for an entire year, which, in tu rn, can drive an elevated 3-year DV. Although all d**  **ata are important, seasonal or episodic emissions can provi**  **de insight as to relative contributors to measured PM**  **2.5 concentrations. For these reasons, for the Factor 1**  **air quality analysis, EPA assessed and characteriz ed air**  **quality at, and in the proximity of, the violating monitoring site locations first, by evaluating tren**  **ds and the spatial extent of measured concentrations at monito**  **rs in the area of analysis, and then, by identifyin g the**  **conditions most associated with high average concen tration levels of PM**  **2.5 mass in the area of analysis.** **Page 35 of 59**

**In most cases, EPA assessed air quality data on a s easonal, or quarterly, basis.**  **33 EPA also identified the spatial**  **extent of these high PM 2.5**  **concentrations. The mass and composition at the de sign value location represents**  **contributions from various emission sources includi ng local, area-wide (which may comprise nearby urba**  **n and rural areas) and regional sources. To determine the**  **source mix (by mass) at the design value monitorin g site,**  **EPA examined the chemical composition of the monito red PM**  **2.5 concentrations by pairing each violating**  **FRM/FEM/ARM monitoring site with a collocated or ne arby Chemical Speciation Network (CSN) monitoring**  **site or sites. Then, EPA contrasted the approximate d mass composition at the design value monitoring s**  **ite with data collected at IMPROVE**  **34 and other monitoring locations whose data are repr**  **esentative of regional background.**  **35** , **36 This comparison of local/area-wide chemical compos**  **ition data to regional chemical composition data derives an “urban increment,” whic**  **h helps differentiate the influence of more distant**

**33 Although compliance with the annual NAAQS depends**  **on contributions from all days of the year, examini ng data on a**  **quarterly or seasonal basis can inform the relation ship between the temporal variability of emissions**  **and meteorology and the resulting PM**  **2.5 mass and composition. In some areas of the country**  **where there may be noticeable month-to-month variations in average PM**  **2.5 , the quarterly averages may not adequately represe**  **nt seasonal variability. In these areas, air quality data may be aggregated and presented by tho**  **se months that best correspond to the local “season s” in these areas.** 

**34 IMPROVE stands for Interagency Monitoring for Prot**  **ected Visual Environments and is an aerosol monitor ing network**  **in mostly rural and remote areas.**

**35 The “urban increment” analysis assesses and charac**  **terizes the increase in seasonal and annual average PM**  **2.5 mass and**  **chemical constituents observed at violating monitor ing site(s) relative to monitoring sites outside th**  **e area of analysis (which represent background concentrations). Develo**  **ping the urban increment involves pairing a violati ng**  **FRM/FEM/ARM monitor with a collocated monitor or ne arby monitor with speciation data. EPA made every e**  **ffort to pair these data to represent the same temporal and spati**  **al scales. However, in some cases, the paired viola ting and CSN “urban”**  **monitoring locations were separated by some distanc e such that the included urban CSN site(s) reflect(**  **s) a different mixture of emissions sources, which could lead to m**  **isinterpretations. To generally account for differe nces in PM**  **2.5 mass**  **between the violating site and the nearby CSN site( s), EPA determined material balance of the PM**  **2.5 composition at the**  **violating site by assigning the extra measured PM 2.5**  **mass to the carbon components of PM 2.5**  **. Where the general urban increment approach may be misleading, or in situati**  **ons where non-carbonaceous emissions are believed t o be responsible**  **for a local PM 2.5**  **concentration gradient, EPA used alternative analys es to reflect the mix of urban and rural sources**  **contributing to the measured concentrations at viol ating monitoring sites.** 

**36 The urban monitors were paired with any rural site**  **s within a 150 mile radius of an urban site to calc ulate spatial means**  **of the quarterly averages of each species. If there were no rural sites within 150 miles, then the nea**  **rest rural site was used alone. That rural mean was then subtracted from the**  **quarterly mean of the urban site to get the increm ent. Negative values**  **were simply replaced with zeroes. Page 36 of 59** 

**emissions sources from the influence of closer emis sions sources, thus representing the portion of the**  **measured violation that is associated with nearby emission c**  **ontributions. 37,38,39** 

**PM 2.5**  **Design Values and Total Mass Measurements - EPA ex amined ambient PM**  **2.5 air quality monitoring data**  **represented by the DVs at the violating monitoring site and at other monitors in the area of analysis.**  **EPA calculated DVs based on air quality data for the mo**  **st recent 3 consecutive calendar years of quality-a ssured,**  **certified air quality data from suitable FEM/FRM/AR M monitoring sites in the EPA’s Air Quality System**  **(AQS). For this designations analysis, EPA used dat a for the 2011-2013 period (i.e., the 2013 design v**  **alue), which are the most recent years with fully-certifie**  **d air quality data. A monitor’s DV is the metric or statistic**  **that indicates whether that monitor attains a speci fied air quality standard. The 2012 annual PM**  **2.5 NAAQS is**  **met at a monitoring site when the 3-year average an nual mean concentration is 12.0 micrograms per cubi**  **c meter (μg/m**  **3 ) or less (e.g., 12.1 μg/m**  **3 or greater is a violation). A DV is only valid if**  **minimum data completeness criteria are met or when other regulatory data proc**  **essing provisions are satisfied (See 40 CFR part 50 Appendix**  **N). Table 2 identifies the current design values (i .e., the 2013 DV) and the most recent two additiona**  **l design values based on all monitoring sites in the area of**  **analysis for the Cleveland area. 40**  **Where a county has more than one monitoring location, the county design val**  **ue is indicated in red type.**

Table 2. Air Quality Data collected at Regulatory M onitors (all DV levels in μg/m 3 ) a , County, State Monitor Site ID State Rec NA? 09-11 D V 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** 

**37 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 m**  **onitoring location and more regional sources.**

**38 Hand, et. al. Spatial and Seasonal Patterns and Te**  **mporal Variability of Haze and its Constituents in the United**  **States: Report V, June 2011. Chapter 7 – Urban Ex cess in PM**  **2.5 Speciated Aerosol Concentrations,**  http://vista.cira.colostate.edu/improve/Publication s/Reports/2011/PDF/Chapter7.pdf

**39 US EPA, Office of Air Quality Planning and Standar**  **ds, December 2004. (2004) Area Designations for 199 7 Fine**  **Particle (PM 2.5**  **) Standards, Technical Support Document for State a nd Tribal Air Quality Fine Particle (PM**  **2.5 )**  **Designations, Chapter 3, Urban Excess Methodology. Available at**  www.epa.gov/pmdesignations/1997standards/documents/ final/TSD/Ch3.pdf

**40 In certain circumstances, one or more monitoring l**  **ocations within a monitoring network may not meet t he network**  **technical requirements set forth in 40 CFR 58.11(e) , which states, “State and local governments must a**  **ssess data from Class III PM**  **2.5 FEM and ARM monitors operated within their network**  **using the performance criteria described in table C**  **4 to subpart C of part 53 of this chapter, for case s where the data are identified as not of sufficien**  **t comparability to a collocated FRM, and the monitoring agency requests**  **that the FEM or ARM data should not be used in comp arison to the**  **NAAQS. These assessments are required in the monito ring agency's annual monitoring network plan descri**  **bed in §58.10(b) for cases where the FEM or ARM is identif**  **ied as not of sufficient comparability to a colloca ted FRM....”** **Page 37 of 59**

County, State Monitor Site ID State Rec NA? 09-11 D V 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 locatio**  **n, the county design value is indicated in red type**

**. The Figure 1 map, shown previously, identifies the**  **Cleveland intended nonattainment area, the CSA boun dary,**  **and monitoring locations with 2011-2013 violating D Vs. 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 t here is no strong intra-annual trend other than low**  **er values in the fourth quarter. The monitors outside of the**  **central Cleveland area, including other monitors i n Cuyahoga**  **County, are not violating in the 2011-2013 time per iod.** 
**Seasonal variation can highlight those conditions m ost associated with high average concentration leve**  **ls of PM**  **2.5 . Figure 2 shows quarterly mean PM**  **2.5 concentrations for the most recent 3-year period f**  **or the highest DV monitoring sites and other, non-violating, monitori**  **ng sites in each county within the area of analysis . This**  **graphical representation is particularly relevant w hen assessing air quality data for an annual standa**  **rd, such as the 2012 annual PM**  **2.5 NAAQS, because, as previously stated, the annual m**  **ean is calculated as the mean of quarterly means and a high quarter can drive the me**  **an for an entire year, which, in turn, can drive an elevated 3-**  **year DV.**

Figure 2. Cleveland PM 2.5 Quarterly Means for 2011-2013**Page 38 of 59**

**As shown in Figure 2, there is a modest decline in monitored values from 2011 through 2013. Although**  **Q4 values are a little lower, there are no strong seas**  **onal trends**

**. PM**  **2.5 Composition Measurements - To assess potential emi**  **ssions contributions for each violating monitoring location, the EPA determined the various chemical s**  **pecies comprising total PM 2.5**  **to identify the chemical constituents over the analysis area, which can prov**  **ide insight into the types of emission sources impa cting the**  **monitored concentration. To best describe the PM 2.5**  **at the violating monitoring location, EPA first adj usted the**  **chemical speciation measurement data from a monitor ing location at or near the violating FRM monitorin**  **g site using the SANDWICH approach to account for the amou**  **nt of PM 2.5**  **mass constituents retained in the FRM**

0 2 4 6 8 10 12 14 16PM2.5 Concentrationsug/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 59**

**measurement. 41,42,43,44**  **In particular, this approach accounts for losses i n fine particle nitrate and increases in**  **sulfate mass associated with particle bound water. Figure 3a illustrates the fraction of each PM**  **2.5 chemical**  **constituent at the Cleveland 39-035-0060 monitoring site based on annual averages for the years 2011-2**  **013.**
Figure 3a. Cleveland-Akron-Lorain Annual Average PM 2.5 Chemical Constituents (2010-2012)



### 4.4

5.4 1.5 1.1 0.6 

## Annual Average PM **2.5** 

## Chemical Constituents

**2010-2012 (μg/m 3**  

## )

Sulfates Organic Mass
Nitrates
Elemental Carbon
Crustal

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

**41 SANDWICH stands for measured**  S **ulfate,** A **djusted** N **itrate,** D **erived** W **ater,** I **nferred** C **arbonaceous mass** H **ybrid Material Balance Approach.” The SANDWICH adjustment**  **uses an FRM mass construction methodology that res ults in**  **reduced nitrates (relative to the amount measured b y routine speciation networks), higher mass associa**  **ted with sulfates (reflecting water included in gravimetric FRM measu**  **rements) and a measure of organic carbonaceous mass derived from**  **the difference between measured PM 2.5**  **and its non-carbon components. This characterizati on of PM**  **2.5 mass also reflects**  **crustal material and other minor constituents. The resulting characterization provides a complete mass**  **closure for the measured FRM PM**  **2.5 mass, which can be different than the data provide**  **d directly by the speciation measurements from the CSN network.** 

**42 Frank, N. H., SANDWICH Material Balance Approach f**  **or PM 2.5**  **Data Analysis, National Air Monitoring Conference, Las Vegas, Nevada, November 6-9, 2006.**  http://www.epa.gov/ttn/amtic/files/2006conference/f rank.pdf

**.**

**43 Frank, N. H., The Chemical Composition of PM**  **2.5 to support PM Implementation, EPA State /Local/Tri**  **bal Training Workshop: PM**  **2.5 Final Rule Implementation and 2006 PM**  **2.5 Designation Process, Chicago IL, June 20-21, 2007,**  http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chem ical_composition.pdf.

**44 Frank, N. H.**  Retained Nitrate, Hydrated Sulfates, and Carbonaceo us 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 59** 

**additional insight as to which sources or factors m ay contribute at a greater level. Simply stated, th**  **is analysis can help identify nearby sources of emissions that**  **contribute to the violation at the violating monito ring site**  **s**

**. Figure 3b shows that sulfate and OM are the predomi**  **nant species overall, with an exception in Q1 when nitrate**  **is similar to sulfate levels. Crustal is a relativ ely small component, with the highest crustal conce**  **ntrations in Q2. This suggests that power plants, other station**  **ary sources and vehicles are the largest source of emissions.** 
Figure 3b. Cleveland Annual and Quarterly Average P M 2.5 Species (2010-2012) a

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

Annual Q1 Q2 Q3 Q4 

## Annual and Quarterly Average PM **2.5** 

## Species **2010-2012 Adjusted to FRM Total PM 2.5** 

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

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

Annual Q1 Q2 Q3 Q4 

## Annual and Quarterly Average PM **2.5** 

## Species **2010-2012 Adjusted to FRM Total PM 2.5** 

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

**a** Adjusted to FRM Total PM 2.5 **indicates that the speciation profile and total ma ss depicted in this figure are the result of the**  **urban increment calculation for the particular FRM monitor.** **Page 41 of 59**

**EPA assessed seasonal and annual average PM 2.5**  **constituents at monitoring sites within the area re lative to**  **monitoring sites outside of the analysis area to ac count for the difference between regional backgroun**  **d concentrations of PM**  **2.5 , and concentrations of PM**  **2.5 in the area of analysis, also known as the “urban**  **increment.” This analysis differentiates between th e influences of emissions from sources in nearby ar**  **eas and in more distant areas on the violating monitor. Estima**  **ting the urban increment in the area helps to illum inate the**  **amount and type of particles at the violating monit or that are most likely to be the result of sources**  **of emissions in nearby areas, as opposed to impacts of more dist**  **ant or regional sources of emissions. Figure 4a inc ludes pie**  **charts showing the annual and quarterly chemical ma ss constituents of the urban increment. The quarter**  **ly pie charts correspond to the 2011-2012 high-concentrati**  **on quarters identified in Figure 2. Evaluating thes e high**  **concentration quarters can help identify compositio n of PM**  **2.5 during these times. Note that in these charts,**  **sulfates and nitrates have been adjusted to represe nt their mass in measured PM**  **2.5 .**  Figure 4a. Cleveland Urban Increment Analysis for 2 010-2012.

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 59** 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 59**

Figure 4b. Cleveland Average Urban Increment Analys is for 2010-2012.

**As shown in Table 2, Cuyahoga County has three monitoring site with a DV exceedin**  **g the NAAQS. In addition, Figure 2 shows that Cuyahoga and adjoinin**  **g counties have experienced a modest decline in amb ient**  **concentrations since 2010. Although Q4 values are a little lower, there are no strong seasonal trends** 

**. In reviewing the urban increment analysis for the C**  **leveland CSA DV monitor, organic mass is the larges t**  **PM 2.5**  **chemical consistent and shows a large urban contri bution. While the sulfate component appears to have**  **small urban contribution and therefore large region al contribution, its values are higher than what wa**  **s shown for the Canton area, particularly for quarters 1, 2**  **and 4. Nitrate is the second highest urban increme nt**  **component for the quarter 1 peak, but is not a fact or in the quarter 3 peak season, during which perio**  **d 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 subst antial nearby contribution appears to result from o**  **ther 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, EPA evaluated the emissi**  **ons data from nearby areas using emissions related data for**  **the relevant counties to assess each county’s poten tial**  **contribution to PM 2.5**  **concentrations at the violating monitoring site or monitoring sites in the area under**  **evaluation. However, seasonal emissions trends are not significantly different, and the seasonal patt**  **erns are similar for all major emitted pollutants; therefore**  **EPA is only analyzing annual emissions data. (Alt hough**  **nitrate is much higher in the cooler months, NOx em issions have similar importance all year due to the**  **ir 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 59**

**EPA examined emissions of identified sources or sou rce categories of direct PM**  **2.5 , the major components of**  **direct PM 2.5**  **(organic mass, elemental carbon, crustal material (and/or individual trace metal compounds)),**  **primary nitrate and primary sulfate, and precursor gaseous pollutants (i.e., SO**  **2 , NO**  **X , total VOC, and NH**  **3 ).**  **EPA also considered the distance of those sources o f emissions from the violating monitoring site. Whi**  **le direct PM**  **2.5 emissions and its major carbonaceous components ar**  **e generally associated with sources near violating PM**  **2.5 monitoring sites, the gaseous precursors tend to h**  **ave a more regional influence (although the EPA is mindful of the potential for local NO**  **X and VOC emissions contributions to PM**  **2.5 from mobile and stationary**  **sources) and transport from neighboring areas can c ontribute to higher PM**  **2.5 levels at the violating monitoring**  **sites.**

Emissions Data
**For this factor, EPA reviewed data from the 2011 Na tional 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 th**  **e sum of emissions from the following general source categor**  **ies: 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 sourc**  **es of the relevant pollutants.**  **45 Significant emissions levels from sources in a nea**  **rby area indicate the potential for the area to contribute to monitored violations.** 
**To further analyze area emissions data, EPA also de veloped a summary of direct PM**  **2.5 , components of direct**  **PM 2.5**  **, and precursor pollutants, which is available at** http://www.epa.gov/pmdesignations/2012standards/doc s/nei2011v1pointnei2008v3county.xlsx

**. When considered with the urban increment analysis i**  **n Factor 1, evaluating the components of direct PM 2.5**  **and precursor gases can help identify specific sources**  **or source types contributing to elevated concentrat ions at**  **violating monitoring sites and thus assist in ident ifying appropriate area boundaries. In general, dir**  **ectly emitted particulate organic carbon (POC) and VOCs**  **46 contribute to PM**  **2.5 organic mass (OM); directly emitted EC**  **contributes to PM 2.5**  **EC; NO X**  **, NH 3**  **and directly emitted nitrate contribute to PM 2.5**  **nitrate mass; SO 2**  **, NH 3**  **and directly emitted sulfate contribute to PM**  **2.5 sulfate mass; and directly emitted crustal materia**  **l and metal oxides contribute to PM**  **2.5 crustal matter.**  **47,48 EPA believes that the quantities of those nearby e**  **missions as potential contributors to the PM**  **2.5 violating monitors are somewhat proportional to the**  **PM 2.5**  **chemical constituents in the estimated urban increment. Thus, directly emitted P**  **OC is more important per ton than SO 2**  **, partially because**

**45 For purposes of this designations effort, “major”**  **point sources are those whose sum of PM precursor e missions (PM**  **2.5 +**  **NOx + SO 2**  **+ VOC + NH 3**  **) are greater than 500 tons per year based on NEI 2 011v1.** 

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

**47** See **, Seinfeld J. H. and Pandis S. N. (2006)** Atmospheric Chemistry and Physics: From Air Polluti on 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.**

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

**. Page 45 of 59** 

**POC emissions are already PM 2.5**  **whereas SO 2**  **must convert to PM 2.5**  **and not all of the emitted SO 2**  **undergoes this conversion.** 
**Table 3a provides a county-level emissions summary (i.e., the sum of emissions from the following gene**  **ral source categories: point sources, non-point (i.e.,**  **area) sources, nonroad mobile, on-road mobile, and fires) of**  **directly emitted PM 2.5**  **and precursor species for the county with the viol ating monitoring site and nearby**  **counties considered for inclusion in the Cleveland area. Table 3b summarizes the directly emitted comp**  **onents of PM**  **2.5 for the same counties in the area of analysis for**  **the Cleveland area. This information will be considered in conjunction with the Urban Increment**  **composition information previously shown in Figures 4a**  **and 4b.**
Table 3a. County-Level Emissions of Directly Emitte d PM 2.5 and Precursors (tons/year) County, State Total NH 3 Total NOx Total Direct PM 2.5 Total

SO 2 Total VOC Total **Lake, OH 211 17,875 6,250 52,697 7,828 84,862 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**  **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** **Page 46 of 59**

Table 3b. County-Level Emissions for Components of Directly Emitted PM 2.5 (tons/year) 49
County, State POM EC PSO4 PNO3 Pcrustal Residual To tal Direct **Lake, OH 593 396 445 4 2,175 2,638 6,250 Cuyahoga, OH 2,166 946 184 17 648 871 4,831**  **Stark, OH 738 375 124 6 616 612 2,471 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 3b breaks down the direct PM 2.5**  **emissions value from Table 3a into its components. These data will also**  **be compared with the previously presented Urban Inc rement composition.** 
**Using the previously described relationship between directly emitted and precursor gases and the measu**  **red mass to evaluate data presented in Tables 3a and 3b**  **, EPA identified the following components warrantin g**  **additional review: For the Cleveland area, the co nstituents of interest are NOx, POM, EC, PNO3, SO2,**  **and PSO4. EPA then looked at the contribution of thes**  **e constituents of interest from each of the countie s included**  **in the area of analysis as shown in Tables 4a-f.**

Table 4a. County-Level NOx Emissions (tons/year) Emissions in average tons/yr County, State NOX Pct. Cumulative % **Cuyahoga, OH 34,631 27% 27% Lake, OH 17,875 14% 41%**  **Summit, OH 16,925 13% 54% Lorain, OH 14,793 12% 66%**  **Stark, OH 14,466 11% 77% bWayne, OH 7,323 6% 83%**  **Ashtabula, OH 6,878 5% 88% Portage, OH 6,428 5% 93%**  **Medina, OH 5,496 4% 98% Geauga, OH 2,837 2% 100%** 

Table 4b. County-Level POM Emissions Emissions in average tons/yr County, State POM Pct. Cumulative % **Cuyahoga, OH 2,166 32% 32%**

**49 Data are based on the 2011 and 2018 Emissions Mode**  **ling 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 59** 

**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 4c. 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 4d. 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%** 

Table 4e. County-Level SO2 Emissions Emissions in average tons/yr County, State SO2 Pct. Cumulative %**Page 48 of 59 Lake, OH 52,697 44% 44%**  **Lorain, OH 32,558 27% 71% Wayne, OH 18, 028 15% 86%**  **Cuyahoga, OH 6,916 6% 92% Summit, OH 4,310 4% 96%**  **Ashtabula, OH 3,765 3% 99% Stark, OH 566 <1%**  **Geauga, OH 261 <1% Portage, OH 170 <1%**  **Medina, OH 162 <1% 100%**

Table 4f. 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 P M**  **2.5 and PM**  **2.5 precursors in the area of analysis, EPA also**  **reviewed emissions from major point sources located in the area of analysis. The magnitude and locatio**  **n of these sources can help inform nonattainment boundar**  **ies. Table 5 provides facility-level emissions of d irect**  **PM 2.5**  **, components of direct PM 2.5**  **, and precursor pollutants (given in tons per year) from major point sources**  **located in the area of analysis for the Cleveland area. Table 5 also shows the distance from the faci**  **lity to the 2011-2013 DV monitor for the respective county.** 

Table 5. NEI 2011 v1 Point Source Emissions (tons/y ear)

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 59** 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 52 4 1 671**  **Cuyahoga, OH**  **ArcelorMittal Cleveland Inc. (1318001613) 1 11 1,16 5 553 723 94 2,546**  **Cuyahoga, OH**  **Cleveland Electric Illuminating Co., Lake Shore Pla nt** 

**(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 1 8,446 4,023 48,300 103 60,873** 
**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. (15760 50694) 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 (1677 010029) 49 0 115 1 869 10 996**  **Summit, OH City of Akron Steam Generating (16770107 57) 47 0 254 44 1,729 2 2,028** 
**Wayne, OH Department of Public Utilities, City of Orrville, O**  **hio**

**(0285010188) 70 0 1,902 745 13,038 5 15,690** 
**Wayne, OH Morton Salt, Inc. (0285020059) 57 0 195 5 0 4,434 2 4,681**  **Wayne, OH East Ohio Gas - Chippewa Station (0285000 366) 60 0 654 13 0 23 690** 

**Figure 5 shows the major point source emissions (fr om the 2011 NEI in tons per year) in the area of an**  **alysis for the Cleveland area and the distances of these sourc**  **es from the violating monitoring locations, as depi cted by red**  **dots in Cuyahoga County. The actual distance from t he point sources to the DV monitoring location is p**  **resented in Table 5). The distance from the violating monito**  **ring location is particularly important for directl y emitted**  **PM 2.5**  **. The influence of directly emitted PM 2.5**  **on ambient PM 2.5**  **diminishes more than that of gaseous precursors as a function of distance.**  **50**

**50 Baker, K. R. and K. M. Foley.**  A nonlinear regression model estimating single sour ce concentrations of primary and secondarily formed PM 2.5
. **Atmospheric Environment. 45 (2011) 3758-3767. Page 50 of 59** 

Figure 5. 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 rele**  **vant pollutants shows that about 59% of the directly emi**  **tted and precursor emissions, in the area of analys is, come**  **from Lake, Cuyahoga, and Lorain Counties, with the highest emissions from Lake County. As discussed m**  **ore fully under Factor 3, the winds are generally from**  **the south through west, further minimizing the impa ct of**  **Ashtabula, Portage and Geauga Counties on the viola ting monitors in Cleveland. Lake County, which is**  **adjacent to Cuyahoga County, emits 27% of the direc t PM**  **2.5 from the area of analysis and Cuyahoga County,**  **where the violating monitors are located, emits 21% of the direct PM**  **2.5 .**  **Although Stark County has recorded violations of th e 2013 PM**  **2.5 standard, it is in a separate metropolitan area**  **(the Canton-Massillon area), and this county has hi storically been designated as a separate nonattainm**  **ent area under both the 1997 annual PM**  **2.5 standard and the 2006 24-hour PM**  **2.5 standard as well as for various ozone**  **standards. Furthermore, emissions from Wayne and S ummit counties contribute more to Stark County than**  **to Cuyahoga County. Therefore, as discussed in the sec**  **tion of this document pertaining to the Canton-Mass illon**  **area, Stark and Summit County, as well as part of W ayne County are being included in the Canton**  **Massillonarea;not in the Cleveland area. Page 51 of 59** 

**The impact of PM 2.5**  **from Ashtabula, Geauga and Portage Counties is lim ited by location and emission levels.**  **Geauga is adjacent to Cuyahoga County, but only emi ts 3% of direct PM**  **2.5 and only 2% of the combined total**  **directly emitted and precursor emissions from the a rea of analysis. Ashtabula County only emits 5% of**  **both the total direct PM**  **2.5 and combined total directly emitted and precursor**  **emissions from the area of analysis and Portage County only emits 4% of both the total dire**  **ct PM 2.5**  **and combined total directly emitted and precursor emissions from the area of analysis. In addition, a**  **ll of these counties are east of Cuyahoga County, f urther**  **reducing their impact.**
**As seen in Tables 4a–f, Cuyahoga, Lake, Lorain, Sum mit, and Wayne counties all consistently ranked in**  **the highest percentages of directly emitted PM**  **2.5 and precursors. Cuyahoga County accounts for 27%**  **of the NOx emissions, 32% of POM emissions, 28% of EC emission**  **s, 39% of PNO3 emissions, 6% of SO2 emissions and 17% of PSO4 emissions in the area of analysis. Lak**  **e County contributes 14% of NOx, 9% of POM, 12% of EC, 9% of PNO3, 44% of SO2 and 41% of PSO4 emission**  **s in the area of analysis. Summit County contribut es**  **11% of NOx, 13% of POM, 13% of EC, 7% of PNO3, 4% o f 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 C ounty contributes 6% of NOx, 6% of POM, 6% of EC, 6**  **% of PNO3, 15% of SO2, and 10% of PSO4 emissions in t**  **he area of analysis. All of these counties, but La ke, are**  **upwind of the area of analysis. By contrast, Medin a, Portage, and Geauga counties consistently ranked**  **in the lowest percentages of directly emitted PM**  **2.5 and precursors within the area of analysis.** 
**As indicated in Figure 5, there are 22 point source s located within 70 miles of the violating monitor.**  **The largest source is in Lake County, with the next lar**  **gest sources found in Lorain and Wayne Counties. T he**  **source in Lake County is only 18 miles from the vio lating monitor and accounts for 71% of the total em**  **issions in Lake County. There are 6 point sources within 10**  **miles of the violating monitor and most of the oth ers are**  **about 30 or more miles away from the violating moni tor.**  **About 14% of the point source direct PM 2.5**  **and precursor emissions are from the point sources in W**  **ayne County. However, as discussed in the section on the**  **Canton-Massillon Nonattainment Area, Summit and the industrialized portion of Wayne Counties are inclu**  **ded as part of the intended Canton-Massillon Nonattainm**  **ent Area. As explained below, this is because thos e**  **counties contributed more to the violating monitor in Stark County than to the violating monitors in C**  **uyahoga County.** 
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 mag**  **nitude of non-point source emissions. Rapid populat ion**  **growth in a county on the urban perimeter signifies increasing integration with the core urban area, a**  **nd indicates that it may be appropriate to include the**  **county associated with area source and mobile sour ce**  **emissions as part of the nonattainment area. Table 6 shows the 2000 and 2010 population, population gr**  **owth 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 % **Cuyahoga, OH 1,393,978 1,278,466 -8.3% 458 2,788 38 38** **Page 52 of 59**

**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**  **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, populat ion density and is the largest percent urban. Cuya**  **hoga, Summit, Stark, Lorain, Lake and Medina Counties contain 86%**  **of the population in the area of analysis. Cuyaho ga, Summit and**  **Lake Counties have the highest population densities whereas Medina, Portage, Wayne, Ashtabula and Geau**  **ga have the lowest population densities. Cuyahoga has the high**  **est population at 1,278,466 and Summit County has t he 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 sq uare mile whereas Ashtabula, Wayne and Geauga Count**  **ies have population densities less than 250 per square mile.** 

Figure 6. 2010 County-Level Population in the Area of Analysis for the Cleveland Area.**Page 53 of 59**

Traffic and Vehicle Miles Travelled
**High vehicle miles travelled (VMT) and/or a high nu mber of commuters associated with a county is gener**  **ally an indicator that the county is an integral part of**  **an urban area. Mobile source emissions of NO x**  **, VOC, and direct PM may contribute to ambient particulate mat**  **ter that contributes to monitored violations of the NAAQS**  **in the area. In combination with the population/pop ulation density data and the location of main trans**  **portation arteries, an assessment of VMT helps identify the p**  **robable location of nonpoint source emissions that contribute to violations in the area. Comparatively**  **high VMT in a county outside of the CBSA or CSA si gnifies**  **integration with the core urban area contained with in the CSA or CBSA, and indicates that a county wit**  **h the high VMT may be appropriate to include in the nonat**  **tainment area because emissions from mobile sources in**  **that county contribute to violations in the area. T able 7 shows 2011 VMT while Figure 7 overlays 2011**  **county level VMT with a map of the transportation arteries**  **. The VMT used in this analysis was submitted by Oh io.** 

Table 7. 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** **Page 54 of 59**

**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 7. Overlay of 2011 County-level VMT with Tra nsportation Arteries

**.**

**Cuyahoga and Summit Counties have the highest and s econd 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, Ashta**  **bula, and Geauga Counties. As previously discussed, Star**  **k County, Summit County, and part of Wayne County a re**  **being considered in the evaluation of the Canton-Ma ssillon area instead of the Cleveland area because**  **they contribute more to the violation in Stark County th**  **an to the violations in Cuyahoga County. Of the co unties**  **being considered as part of the Cleveland area, the re 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 (2,787,828,5**  **81) and the rest gradually decreasing to Geauga County as t**  **he lowest (1,045,092,778). Page 55 of 59** 

Factor 3: Meteorology **EPA evaluated available meteorological data to dete rmine how meteorological conditions, including, but**  **not limited to, weather, transport patterns, and stagna**  **tion conditions, could affect the fate and transpor t of directly**  **emitted particulate matter and precursor emissions from sources in the area of analysis. EPA used two**  **primary tools for this assessment: wind roses and kernel de**  **nsity estimation (KDE). When considered in combinat ion**  **with area PM 2.5**  **composition and county-level and facility emission s source location information, wind roses**  **and KDE can help to identify nearby areas contribut ing to violations at violating monitoring sites.** 

**Wind roses are graphic illustrations of the frequen cy of wind direction and wind speed. Wind direction**  **can indicate the direction from which contributing emis**  **sions are transported; wind speed can indicate the force of**  **the wind and thus the distance from which those emi ssions are transported. EPA constructed wind roses**  **from hourly observations of wind direction and wind spee**  **d using 2009-2012 data from National Weather Servic e**  **locations archived at the National Climate Data Cen ter.**  **51 When developing these wind roses, EPA also used**  **wind observations collected at meteorological sampl ing stations collocated at air quality monitoring s**  **ites, where these data were available. Figure 8 shows wind rose**  **s that EPA generated from data relevant in the Clev eland** 

**area.**

**51** ftp.ncdc.noaa.gov/pub/data/noaa **or** http://gis.ncdc.noaa.gov/map/viewer/#app=cdo&cfg=cd o&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 59**

Figure 8. Wind Roses in the Area of Analysis for th e Cleveland Area.

**As shown in Figure 8, there is a pattern across the area of predominantly** **south to west winds, mostly at mid level speeds of 4 to 10 meters per second, suggesti**  **ng that potential emission sources in the south-thr ough-west**  **upwind direction should be considered for analysis.**
**In addition to wind roses, EPA also generated kerne l density estimation (KDE) plots to represent HYSPL**  **IT (HYbrid Single-Particle Lagrangian Integrated Traje**  **ctory) backward trajectory frequency at violating Page 57 of 59** 

**monitoring sites. 52,53**  **These KDEs are graphical statistical estimations t o determine the density of trajectory**  **endpoints at a particular location represented by a grid cell. The EPA used KDEs to characterize and a**  **nalyze the collection of individual HYSPLIT backward traje**  **ctories. 54**  **Higher density values, indicated by darker blue colors, indicate a greater frequency of observed tr**  **ajectory endpoints within a particular grid cell. F igure 9 shows**  **a HYSPLIT KDE plot for the Cleveland area summarize d by calendar quarter for the 2010-2012 period. The**  **HYSPLIT KDE is weighted in the west to southerly di rection, indicating a greater frequency of trajecto**  **ries passing over grid cells to the west and south.** 

Figure 9. HYSPLIT Kernel Density Estimation Plots f or the Cleveland Area.

**52 In some past initial area designations efforts, EP**  **A has used HYSPLIT backward trajectories to assist in determining**  **nonattainment area boundaries. A HYSPLIT backward t rajectory is usually depicted on a standard map as**  **a single line, representing the centerline of an air parcel’s moti**  **on, extending in two dimensional (x,y) space from a starting point and**  **regressing backward in time to a point of origin. B ackward trajectories may be an appropriate tool to**  **assist in determining an air parcel’s point of origin on a day in which a**  **short-term standard, such as an 8-hour standard or a 24-hour standard,**  **was exceeded. However, for an annual standard, such as the 2012 annual PM**  **2.5 NAAQS, every trajectory on every day is**  **important. Plotting a mass of individual daily (e.g ., 365 individual back trajectories), or more frequ**  **ent, HYSPLIT trajectories may not be helpful as this process is**  **likely to result in depicting air parcels originati ng in all directions from the**  **violating monitoring site.**

**53 HYSPLIT - Hybrid Single Particle Lagrangian Integra**  **ted Trajectory Model,** http://www.arl.noaa.gov/HYSPLIT_info.php

**54 The KDEs graphically represent the aggregate of H**  **YSPLIT backward trajectories for the years 2010-201 2, run every**  **third day (beginning on the first day of monitoring ), four times each day, and ending at four endpoint**  **heights. Page 58 of 59** 

**In summary, for the violating Cleveland monitor, th e HYPSPLIT KDE plots and wind roses suggest greates**  **t potential contribution of emissions from Cuyahoga,**  **Lorain, Summit, Wayne and Medina Counties. The HYSPLIT KDE plots indicate low density values in As**  **htabula County. As discussed in the emission discus sion,**  **Medina has a relatively small amount of relevant co ntributing emissions, and Wayne County are being**  **considered as part of the Canton-Massillon area non attainment area.** 

Factor 4: Geography/topography **To evaluate the geography/topography factor, EPA as sessed physical features of the area of analysis th**  **at might define the airshed and thus affect the formation an**  **d distribution of PM 2.5**  **concentrations over the area. The Cleveland area does not have any geographical o**  **r topographical barriers known to significantly lim it air**  **pollution transport around the area of analysis. Th erefore, this factor did not play a significant rol**  **e in defining the boundary of the intended area.** 

Factor 5: Jurisdictional boundaries **In defining the boundaries of the intended Clevelan d nonattainment area, EPA considered existing juris**  **dictional boundaries, which can provide easily identifiable a**  **nd recognized boundaries for purposes of implementi ng the**  **NAAQS. Existing jurisdictional boundaries often si gnify well recognized boundaries that the state can**  **easily administer and for which the state has the necessar**  **y legal authority for carrying out air quality plan ning and**  **enforcement functions. Examples of such jurisdicti onal boundaries include existing/prior nonattainmen**  **t area boundaries for particulate matter, county lines, ai**  **r district boundaries, township boundaries, areas c overed by a** **Page 59 of 59**

**metropolitan planning organization, state lines, an d Reservation boundaries, if applicable. Where exis**  **ting jurisdictional boundaries were not adequate or appr**  **opriate to describe the nonattainment area, EPA con sidered**  **other clearly defined and permanent landmarks or ge ographic coordinates for purposes of identifying th**  **e boundaries of the intended designated areas.** 

**The Cleveland area has previously established nonat tainment boundaries associated with the 1997 and 20**  **06 PM2.5 NAAQS. Ashtabula (partial county), Cuyahoga,**  **Lake, Lorain, Medina, Portage and Summit Counties were designated as nonattainment for the 1997 annua**  **l standard and Cuyahoga, Lake, Lorain, Medina, Port age**  **and Summit Counties were designated as nonattainmen t of the 2006 24-hour standard. Ohio recommended t**  **hat only Cuyahoga County be designated as nonattainment**  **of the 2012 PM2.5 annual PM2.5 NAAQS.**

**EPA is removing Ashtabula County and Portage Counti es from the 2012 nonattainment area because their**  **emissions are relatively low and they are not upwin d of Cleveland. Summit County and part of Wayne Co**  **unty are being considered as part of the Canton-Massillo**  **n area.**

Conclusion for Cleveland Area **Based on the assessment of factors described above, both individually and in combination, EPA has**  **preliminarily concluded that the following counties should be included as part of the Cleveland nonatt**  **ainment area because they are either violating the**  **2012 annual PM**  **2.5 NAAQS or contributing to a violation in a nearby**  **area: Cuyahoga, Lake, and Lorain Counties. These a re not**  **the same counties that were included in the Cleveland-Akron-Lorain nonattainment area for the 2**  **006 24-hour or the 1997 annual PM 2.5**  **NAAQS. The air quality monitoring sites in Cuyahoga County indicat**  **e violations of the 2012 annual PM 2.5**  **NAAQS based on the 2013 DVs; therefore this county is included in the**  **nonattainment area. Lake and Lorain 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 t**  **he 2012 annual PM 2.5**  **NAAQS through emissions from point sources and other]non-point sources (e.g., area sou**  **rces), and from mobile source emissions. Because of a**  **greater contribution to the violating monitor in St ark County than in Cuyahoga County, Summit and part**  **of Wayne County are being included as part of the Cant**  **on-Massillon Area. Lake, Cuyahoga, and Lorain Coun ties**  **have the highest emissions of directly emitted PM 2.5**  **and/or PM 2.5**  **precursors in the area and Lorain County is upwind of and contributes significantly to the viol**  **ating monitors in Cleveland.**
**Cuyahoga County has violating monitors and has been recommended as nonattainment by Ohio. Lake County**  **emits the greatest amount of directly emitted PM2.5 and precursors in the Cleveland area, including 44**  **% of the SO2 emissions and has a high population density.**  **The Eastlake Plant of the Cleveland Electric Illumi nating**  **company has the highest combined emissions of any s ource in the Cleveland area and is only 18 miles fr**  **om the design value monitor in Cleveland. Lorain County e**  **mits the third highest amount of directly emitted P M2.5**  **and precursors in the Cleveland area. It emits 32% of the SO2 emissions in the Cleveland area. Lake**  **County and Lorain Counties combined contribute over 75% of**  **the SO2 emissions in the Cleveland area and are therefore considerable contributors to the sulfate**  **urban increment, which is between 0.3-.4 ug/m 3**  **for quarters 1,2 and 4. The Avon Lake Power Plant emits the se**  **cond highest amount of combined emissions in the ar ea and**  **is only 19 miles from the violating design value mo nitor in Cleveland. Lorain County is southwest, an**  **d upwind, of Cleveland and is therefore a considerabl**  **e contributor to the violating monitors in Clevelan d. In**  **summary, Lake County has the highest combined emiss ions of any County and is adjacent to Cuyahoga Coun**  **ty, which has the violating monitors. The wind roses a**  **nd kernel densities indicate that Lorain is a consi derable**  **contributor to the violating monitors in Cleveland.**