### Page 1 of 161 

## CALIFORNIA:

Imperial County, Los Angeles

- South Coast Air Basin, Plumas County, San Joaquin Valley Area Designations

for the

2012 Primary Annual PM 2.5 

## National Ambient Air Quality Standard

Technical Support Document

1.0 Summary

In accordance with Section 107(d) of the Clean Air Act (CAA), the EPA must promulgate designations for all areas of the country. In particular, the EPA must identify those area s that are violating a National Ambient Air Quality Standard (NAAQS) or contributing to a violation of the NAAQS in a nearby area. The EPA must complete this process within 2 years of promulgating a new o r revised NAAQS, or may do so within 3 years

under c ircumstances not relevant to these designations. 1 

### This technical support document (TSD) describes the
areas in California the EPA is designat ing as nonattainment for the 2012 primary annual fine particle NAAQS (2012 annual PM 2.5 

### NAAQS). 2



### Under section 107(d), states are required to submit area designation recommendations to the EPA for the 2012
annual PM 2.5 

### NAAQS no later than 1 year following promulgation of the standard, or by December 13, 2013. In November 2013, California recommended that the counti es and partial counties identified in Table 1 be designated as “nonattainment” for the 2012 annual PM 2.5 

### NAAQS based on air quality data from 2010

- 2012. As also reflected in Table 1, California amended its recommendation for Plumas County on July 2, 2014 b ased on preliminary air quality data from 2011

- 2013. Tribes are also able to submit area designations under section 107(d), on a similar timeline as States, and designation of a State area may also impact Indian country. More information on tribal designat ions for the 2012 annual PM 2.5 

### NAAQS is below.

After considering these recommendations and based on the EPA’s technical analysis as described in this TSD, t he

EPA is designating the areas listed in Table 1 as nonattainment for the 2012 annual PM 2.5 

### standa rd. The EPA must

1 Section 107(d) of th e CAA requires the EP A to complete the initial designation process within 2 years of promulgation of a new or revised NAAQS, unless the Administrator has insufficient information to make initial designation decisions in the 2

- year time frame. In such circumstances, the EPA may take up to 1 additional year to make initial area designation decisions (i.e., no later than 3 years after promulgation of the standard).

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

.

### Page 2 of 161

designate an area nonattainment if it has an air 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 descriptions (e.g.,
county boundaries, townships and ranges) of these areas are found below in the supporting technical analysis for each area. As provided in CAA section 188(a), the EPA will initially classify all nonattainment areas as
“Moderate” nonattainment areas.

Table 1. Sta te ’s or Tribe’s Recommended Nonattainment Areas and EPA’s Nonattainment Areas for the
2012 annual PM 2.5 

### NAAQS

Area State’s/Tribe’s Recommended

Nonattainment Counties or Areas of Indian country EPA’s Nonattainment Counties or Areas of Indian country

Imperial County, CA Imperial County (partial) Imperial County (partial)

Los Angeles

- South Coast Air Basin a 

### Los Angeles County (partial) Orange County Riverside County (partial) San Bernardino County (partial) Los Angeles County (partial) Orange County Riverside County (partial) San Bernardino County (partial)

 Cahuilla Band of Mission Indians of the Cahuilla Reservation  did not submit recommendation  Cahuilla Band of Mission Indians of the Cahuilla Reservation

 Morongo Band of Mission Indians  did not submit recommendation  Morongo Band of Mission Indians

 Ramona Band of Cahuilla  did not submit recommendation  Ramona Band of Cahuilla

 San Manuel Band of Mission Indians  did not submit recommendation  San Manuel Band of Mission Indians

 Soboba Band of Luiseño Indians  did not submit recommendation  Soboba Band of Luiseño Indians

Plumas County, CA Plumas County (partial) Plumas County (partial)

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

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San Joaquin Valley, CA b 

### Fresno County Kern County (partial) Kings County Madera County Merced County San Joaquin County Stanislaus County Tulare County Fresno County Kern County (partial) Kings County Madera County Merced County San Joaquin County Stanislaus County Tulare County

 Big Sandy Rancheria of Western Mono Indians  did not submit recommendation  Big Sandy Rancheria of Western Mono Indians

 Cold Springs Rancheria of Mono Indians of California  did not submit recommendation  Cold Springs Rancheria of Mono Indians of California

 Northfork Ra n cheria of Mono Indians of California  did not submit recommendation  Northfork Ra n cheria of Mono Indians of California

 Picayune Rancheria of Chuckchansi Indians of California  did not submit recommendation  Picayune Rancheria of Chuckchansi Indians of California

 Santa Rosa Indian Community of the Santa
Rosa Rancheria  did not submit recommendation  Santa Rosa Indian Community of the Santa Rosa Rancheria

 Table Mountain Rancheria of California  did not submit recommendation  Table Mountain Rancheria of California

 Tule River Indian Tribe of the Tule River Reservation  did not submit recommendation  Tule River Indian Tribe of the Tule River Reservation

a Los Angeles

- South Coast Air Basin is a multi

- jurisdictional nonattainment area that includes areas of Indian country of Federally

- recognized tribes. Table 1 identifies the tribal lands that the EPA is designating as part of the nonattainment area. The areas of Indian country of each tribe that the EPA also is designating as part of the nonattainment area are discussed further in Section 3, Technical Analysis.

b San Joaquin Valley Air Basin is a multi

- jurisdictional nonattainment area that includes areas of Indian country of Federally

recognized tribes. Table 1 identifies the tribal lands that the EPA is designating as part of the nonattainment area. The areas of Indian country of each tribe that the EPA also is designating as part of the nonattainment area are discussed further in Section 3, Technical Analysis.



### In December 2013, the Pechanga Band of Luiseño Mission Indians of the Pechanga Reservation (Pechanga) recommended that their portion of Indian country be designated “unclassifiable” for the 2012 annual PM 2.5 

### NAAQS. On August 19, 2014, the EPA sent Pechanga a letter conveying our preliminary designations and informing Pechanga of the opportunity to provide additional information for the EPA to consider in its final designations. In October 2014, Pechanga responded by providing a revised recommendation of “attainment/unclassifiable” based on complete, certified 2011

- 2013 monitoring data. After con sidering Pechanga’s recommendations, t he EPA is designating Pechanga unclassifiable/attainment.

In its 2013 recommendation letter and additional July 2, 2014 recommendation , California recommended that the EPA designate as “unclassifiable/attainment” all o ther counties and partial counties not identified in the State’s/Tribe’s Recommendation Column of Table 1. For state lands, the EPA agrees with the State’s recommendation for nonattainment for the Los Angeles

- South Coast Air Basin, San Joaquin Valley, Impe rial County (partial), and Plumas County (partial). The EPA is designating the remainder of state lands in California
as unclassifiable/attainment based on the State’ s recommendation, ambient monitoring data collected during the

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2011

- 2013 period showing co mpliance with the 2012 annual PM 2.5 

### NAAQS, and the EPA’s assessment that those unclassifiable/attainment areas within the State are not likely to be contributing to nearby violations. 4

, 5



### The EPA has included the areas of Indian country of federally recogni zed tribes in the Los Angeles

- South Coast Air Basin and San Joaquin Valley listed in Table 1 in the nonattainment areas, based on the technical analysis
described in Section 3.0. All tribes are being designated in accordance with two guidance documents finalized in 2011 by the EPA’s Office of Air Quality, Planning, and Standards: Guidance to Regions for Working with Tribes during the National Ambient Air Quality Standard (NAAQS) Designations Process 6 

### and the Pol icy for Establishing Separate Air Quality Designations for Areas of Indian Country 7



### . The E PA is designating all of the Indian country of five tribes in the Los Angeles

- South Coast Air Basin and all of the Indian country of seven tribes in the San Joaquin V alley as part of these multi

- jurisdictional nonattainment areas. The EPA offered
consultation in accordance with EPA’s Policy on Coordination and Consultation with Indian Tribes 8



### .

2.0 Nonattainment Area Analyses and Boundary Determination

The EPA evaluated and determined the boundaries for each nonattainment area on a case

- by

- case basis considering the specific facts and circumstances unique to the area. In accordance with the CAA section 107(d),
the EPA is designating as nonattainment not only the area with the monitoring sites that violate the 2012 annual

PM 2.5 

### NAAQS, but also those nearby areas with emissions sources that contribute to the violation in the violating area. As described in EPA guidance 9 

### , after identifying each monitoring si te indicating a violation of the standard in an area, the EPA analyzed those areas with emissions contributing to that violating area by considering those counties in the entire metropolitan area (e.g., Core Based Statistical Area (CBSA) or Combined Statis tical Area (CSA)) in which the violating monitoring sites are located. The EPA also evaluated counties adjacent to the CBSA or CSA that have emissions sources with the potential to contribute to the violations. The EPA uses the CBSA or CSA as a starting po int for the contribution analysis because those areas are nearby for purposes of the

PM 2.5 

### NAAQS. Based upon relevant facts and circumstances in each area, the designated nonattainment area could be larger or smaller that the CBSA or CSA. The EPA’s analyti cal approach for nonattainment areas is described in section 3 of this technical support document.

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

. 5 EPA uses a designation category of "unclassifiable/ attainment" for areas that are monitoring attainment and for areas that
do not have monitor ing sites but which the EPA believes are likely attainment and does not emissions sources that are contributing to nearby violations based on the five factor analysis and other available information.

6 http://www.epa.gov/ttn/caaa/t1/memoranda/20120117naaqsguidance.pdf

7 http://www.epa.gov/ttn

/caaa/t1/memoranda/20120117indiancountry.pdf

8 http://www.epa.gov/tp/pdf/cons

- and

- coord

- with

- indian

- tribes

- policy.pdf

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

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3.0 Technical Analysis

In this technical analysis, the EPA used the latest data and information available to the EPA (and to the states and tribes through the PM 2.5 

### Designations Mapping Tool 10 

### and the EPA PM Designations Guidance and Data web

page 11 

### ) and/or data provided to the EPA by states or tribes. This technical analysis identifies the areas with monitoring sites that violate the 2012 annual PM 2.5 

### standa rd. The EPA evaluated these areas and other nearby areas with emissions sources or activities that potentially contribute to ambient fine particle concentrations at the violating monitors in the area based on the weight of evidence of the five factors reco mmended in EPA guidance and any other relevant information.

These five factors are:

Factor 1: Air Quality Data . The air quality data analysis involves examining available ambient PM 2.5 

### air quality monitoring data at, and in the proximity of, the violating monitoring locations. This includes reviewing the design values (DV) calculated for each monitoring location in the area based on air quality data for the most recent complete 3 consec utive calendar years of quality

- assured, certified air quality data in the EPA’s Air Quality System (AQS). In general, the EPA identifies violations using data from suitable Federal Reference Method (FRM), Federal Equivalent Method (FEM), and/or Approved R egional Method (ARM) monitors sited and operated in accordance with 40 CFR Part 58. 12 

### Procedures for using the air quality data to determine whether a violation has occurred are given in 40 CFR part 50 Appendix N, as revised by a final action published in t he Federal Register on January 15, 2013 (78 FR 3086). 13 

### In addition to reviewing data from violating monitor sites,
the EPA also assesses the air quality data from other monitoring locations to help ascertain the potential
contribution of sources in areas n earby to the violating monitoring sites. Examples include using chemical speciation data to help characterize contributing emissions sources and the determination of nearby contributions through analyses that differentiate local and regional source contrib utions.

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 carbon, crustal material (and/or individual tr ace metal compounds)), primary nitrate and primary sulfate, and precursor gaseous pollutants (e.g., SO 2 

### , NO X 

### , total VOC, and NH 3 

### ). Emissions data are generally derived from the most recent National Emissions Inventory (NEI) (i.e., 2011 NEI version 1) , and are given in tons per year. T he emission
estimates are based on the “2011ed” air quality modeling platform. 14 

### Although many emissions inventory components of the “2011ed” modeling platform derive from the 2011 NEIv1, there are some differences between

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

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

. 12 Suitable monitors include all FEM and/or ARMs except those specific continuous FEMs/ARMs used in the monitoring agency's network where the data are not of sufficient quality such that data are not to be compared to the NAAQS in accordance with 40 CFR part 58.10(b)(13) and approved by the EPA Regional Administrator per 40 CFR part 58.11(e).

13 As indicated in Appendix N to 40 CFR part 50, Interpretation of the National Ambient Air Quality Standards for PM 2.5 , section 3(a) indicates “Except as otherwise provi ded in this appendix, all valid FRM/FEM/ARM PM 2.5 mass concentration data produced by suitable monitors that are required to be submitted to AQS, or otherwise available to EPA, meeting the requirements of part 58 of this chapter including appendices A, C, and E shall be used in the DV (design value) calculations. Generally, EPA will only use such data if they have been certified by the reporting organization (as prescribed by § 58.15 of this chapter); however, data not certified by the reporting organizatio n can nevertheless be used, if the deadline for certification has passed and EPA judges the data to be complete and accurate.”

14 http://www.epa.gov/ttn/chief/emch/2011v6/outreach/2011v6_2018base_EmisMod_TSD_26feb2014.pdf

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the p latform inventories and the 2011NEIv1 emissions. There are also some differences in PM emissions between the 2011 NEIv1 and “2011ed” due to the meteorological adjustments made for certain sectors. In some cases, the EPA may also evaluate emissions informat ion from states, tribes, or other relevant sources that may not be reflected in the NEI. One example of “other information” could include an inventory or assessment of local/regional area sources that individually does not meet the current threshold for re porting to the NEI but collectively contributes to area PM 2.5 

### concentrations. Emissions data indicate the potential for a source to contribute to observed violations, making it useful in assessing boundaries of nonattainment areas.

Factor 3: Meteorology

. Evaluating meteorological data helps to determine the effect on the fate and transport of emissions contributing to PM 2.5 

### concentrations and to identify areas potentially contributing to the violations at monitoring sites. The Factor 3 analysis includes as sessing potential source

- receptor relationships in the area identified for evaluation using summaries of air trajectories, wind speed, wind direction, and other meteorological data, as available.

Factor 4: Geography/topography . The geography/topography analysis includes examining the physical features of the land that might define the airshed and, therefore, affect the formation and distribution of PM 2.5 

### over an area. Mountains or other physical features may influence the fate and transport of emissions and PM 2.5 

### concentrations. Additional analyses may consider topographical features that cause local stagnation episodes via inversions, such as valley

- type features that effectively “trap” air pollution, leading to periods of elevated PM 2.5 

### concentrations.

Factor 5: Jurisdictional boundaries . The analysis of jurisdictional boundaries identifies the governmental planning and organizational structure of an area that may be relevant for designations purposes. These jurisdictional boundaries provide insight int o how the governing air agencies conduct or might conduct air quality planning and enforcement in a potential nonattainment area. Examples of jurisdictional boundaries include counties, air districts, areas of Indian country, CBSA or CSA, metropolitan plan ning organizations (MPOs), and existing
nonattainment areas.

The EPA developed the area designations for all tribes in accordance with two guidance documents finalized in late 2011 by the EPA’s Office of Air Quality, Planning, and Standards: Guidance to R egions for Working with Tribes during the National Ambient Air Quality Standard (NAAQS) Designations Process 15 

### and the Policy for Establishing Separate Air Quality Designations for Areas of Indian Country 16 

### ; in addition to EPA Policy on
Consultation and Coor

dination with Indian Tribes. 17 

### In accordance with these documents, the EPA notified tribes
of the PM 2.5 

### designation process and their ability to consult with the EPA and provide designation recommendations in 2013 and again in May 2014. The EPA received rec ommendations from four tribes in 2013 : the Gila River Indian Community, the Pechanga Band of Luiseño Mission Indians (Pechanga)

, the Pala Band of Mission Indians, and the Salt River Pima Maricopa Indian Community. The EPA received a revised
recommendation from Pechanga in October 2014 and a request for consultation from the Soboba Band of Luiseño Indians (Soboba Band) in September 2014

. The EPA consulted with the Soboba Band in September and described the designations process, the history of the PM 2.5 

### standard in the southern California area, and implications of the nonattainment designation for the Soboba Band. On October 2, 2014, the EPA sent a response letter to the tribe summarizing this discussion. The EPA has carefully evaluated and considered all tribal recommendations and feedback in the development of these designations

.

15 http://www.epa.gov/ttn/caaa/t1/memoranda/20120117naaqsguidance.pdf

. 16 http://www.epa.gov/ttn/caaa/t1/memoranda/20120117indiancountry.pdf

. 17 http://www.epa.gov/tp/pdf/cons

- and

- coord

- with

- indian

- tribes

- policy.pdf

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3.1 Area Background and Overview

– Imperial County, CA

Figure 1 is a map of the EPA’s nonattainment boundary for Imperial County. The map shows the location and design values of ambient air quality monitoring locations, county and other jurisdictional boundaries, the EPA’s area of analysis, the State recommendation, and existing 2006 24

- hour PM 2.5 

### NAAQS nonattainment boundaries.

For purposes of the 2006 24

- hour PM 2.5 

### NAAQS, this area was designated nonattainment. The boundary for the nonattainment area for the 2006 24

- hour PM 2.5 

### NAAQS included a portion of Imperial County, CA. The boundary for the 2012 annual PM 2.5 

### NAAQS is a portion of Imperial County consistent with the existing 2006 24

- hour
PM 2.5 

### nonattainment area

.

The EPA must designate as nonattainment areas that violate the NAAQS and near by areas that contribute to the violation in the violating area. Monitor data in Imperial County show a violation of the 2012 PM 2.5 

### NAAQS, thus
the EPA needs to ascertain what areas contain sources of emissions that contribute to this violation. As shown in Figure 1a, the EPA evaluated each county located near the county with a violating monitoring site based on the
five fa ctors and other relevant information. In addition, because Imperial County itself includes a large geographic

area, the EPA has also evaluated whether the nonattainment area should encompass all of Imperial County or only

### Page 8 of 161

a smaller portion of such county i n order to include the sources of emissions and emissions activities that are contributing to the violations of the NAAQS at the violating monitor. The following sections describe this five
factor analysis process. While the factors are presented individua lly, they are not independent. The five factor analysis process carefully considers their interconnections and the dependence of each factor on one or more of
the others.

Factor 1: Air Quality Data

All data collected during the year are important when d etermining contributions to an annual standard such as the
2012 annual PM 2.5 

### NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings throughout the year, including days with monitored ambient concentrations below the level of the NAAQS. This is somewhat

different than the case for the 24

- hour PM 2.5 

### NAAQS, where compliance is often dependent on a few days over the level of the NAAQS, which are typically associated with episodic conditions. For the 2012 annual PM 2.5 

### NAAQS, the annual mean i s calculated as the mean of quarterly means. A high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3

- year DV. Although all data are important, seasonal or episodic emissions can provide insight as to relative contribut ors to measured PM 2.5 

### concentrations. For these reasons, for the Factor 1 air quality analysis, the EPA assessed and characterized air quality at, and in the proximity of, the violating monitoring site locations first, by evaluating trends and the spatial extent of measured concentrations at

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monitors in the area of analysis, and then, by identifying the conditions most associated with high average
concentration levels of PM 2.5 

### mass in the area of analysis.

In most cases, the EPA assessed air quality data o n a seasonal, or quarterly, basis. 18 

### The EPA also identified the spatial extent of these high PM 2.5 

### concentrations. The mass and composition at the design value location represents contributions from various emission sources including local, area

- wide (whic h may comprise nearby urban and rural areas) and regional sources. To determine the source mix (by mass) at the design value monitoring site, the EPA examined the chemical composition of the monitored PM 2.5 

### concentrations by pairing each violating

FRM/FEM/ ARM monitoring site with a collocated or nearby Chemical Speciation Network (CSN) monitoring site or sites. Then, the EPA contrasted the approximated mass composition at the design value monitoring site
with data collected at IMPROVE 19 

### and other monitoring locations whose data are representative of regional background. 20

, 21 

### This comparison of local/area

- wide chemical composition data to regional chemical composition data derives an “urban increment,” which helps differentiate the influence of more distant emis sions sources from

18 Although compliance with the annual NAAQS depends on contributions from all days of the year, examin ing data on a quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and mete o rology 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 represent seasonal variability. In these areas, air quality data may be aggregated and presented by those months that best correspo nd to the local “seasons” in these areas.

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

20 The “urban increment” analysis assesses and characterizes the increase in seasonal and annual average PM 2.5 mass and chemical constituents observed at violating monitoring site(s) relative to monitoring sites outside the area of analysis (whi ch represent background concentrations). Developing the urban increment invo lves pairing a violating FRM/FEM/ARM monitor with a collocated monitor or nearby monitor with speciation data. EPA made every effort to pair these data to represent the same temporal and spatial scales. However, in some cases, the paired violating and CSN “urban” monitoring locations were separated by some distance such that the included urban CSN site(s) reflect(s) a different mixture of emissions sources, which could lead to misinterpretations. To generally account for differences in PM 2.5 mass between th e 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 misl eading, or
in situations where non

- carbonaceous emissions are believed to be responsible for a local PM 2.5 concentration gradient, EPA used alternative analyses to reflect the mix of urban and rural sources contributing to the measured concentrations at vi olating monitoring sites.

21 The urban monitors were paired with any rural sites within a 150 mile radius of an urban site to calculate spatial means of the quarterly averages of each species. If there were no rural sites within 150 miles, then the nearest rural site was used alone. That rural mean was then subtracted from the quarterly mean of the urban site to get the increment. Negative values were simply replaced with zeros.

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the influence of closer emissions sources, thus representing the portion of the measured violation that is associated with nearby emission contributions. 22

, 23

, 24



### PM 2.5 

### Design Values and Total Mass Measurements

– The EPA examined ambient PM 2.5 

### air quality monitoring data represented by the DVs at the violating monitoring site and at other monitors in the area of analysis. The EPA calculated DVs based on air quality data for the most recent 3 consecutive calendar years of quality

- assured,

cer tified air quality data from suitable FEM/FRM/ARM monitoring sites in the EPA’s Air Quality System (AQS). For this designations analysis, the EPA used data for the 2011

- 2013 period (i.e., the 2013 design value ), which are the most recent years with fully

- certified air quality data. A monitor’s DV is the metric or statistic that indicates whether that monitor attains a specified air quality standard. The 2012 annual PM 2.5 

### NAAQS is met at a monitoring site when the 3

- year average annual mean concentrat ion is 12.0 micrograms per cubic meter (μg/m 3 

### ) or less (e.g., 12.1 μg/m 3 

### or greater is a violation). A DV is only valid if minimum data completeness criteria are met or when other regulatory data processing provisions are satisfied (See 40 CFR part 50 Appe ndix N). Table 2 identifies the current design value(s) (i.e., the 2013 DV) and the most recent two design values based on all monitoring sites in the area of analysis for the Imperial County nonattainment area. 25 

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

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

23 

### Hand, et. al. Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United

States: Report V, June 2011. Chapter 7

– Urban Excess in PM 2.5 

### Speciated Aerosol Concentrations, http://vista.cira.colostate.edu/improve/Publications/Reports/2011/PDF/Chapter7.pdf

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

25 In certain circumstances, one or more monitoring locations within a monito ring network may not meet the network technical requirements set forth in 40 CFR 58.11(e), which states, “ State and local governments must assess data from Class

III PM 2.5 FEM and ARM monitors operated within their network using the performance criteria de scribed in table C

- 4 to subpart C of part 53 of this chapter, for cases where the data are identified as not of sufficient comparability to a colloca ted FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to th e NAAQS. These assessments are required in the monitoring agency's annual monitoring network plan described in §58.10(b) for cases where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM ....”

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Table 2. Air Quality Data C ollected at Regulatory Monitors (all DV levels in μg/m 3 

### ) a



### County, State Monitor Site

ID Site Name State Rec
NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Imperial, CA 060250005 Calexico

- Ethel Yes 13.9 f 

### 14.1 f 

### 14.3 f


### Imperial, CA 060250007 Brawley Yes 7.1 7.1 7.5
Imperial, CA 060251003 El Centro Yes 7.4 7.2 7.4
La Paz, AZ N/A No No monitor
Riverside, CA 060651003 Riverside Yes e 

### 12 11.4 11.5
Riverside, CA 060652002 Indio No 7.3 7.2 7.7
Riverside, CA 060655001 Palm Springs No 6.2 6.2 6.4
Riverside, CA 060658001 Rubidoux Yes e 

### 14.2 c 

### 13. 4 c 

### 13. 2 c


### Riverside, CA 060658005 

### Mira Loma

- Van Buren Yes e 

### 1 5.9 c 

### 15. 2 c 

### 1 4.8 c 

### San Diego, CA 060730001 Chula Vista No 10.3 9.8 9.9
San Diego, CA 060730003 El Cajon No 11.8 10.6 c 

### 10.6 c 

### San Diego, CA 060731002 Escondido No 10.7 10.5 c 

### 10.7 c



### San Diego, CA 060731010 San Diego

- Downtown No 11.0 10.8 c 

### 10.8 c



### San Diego, CA 060731016 San Diego

- Kearny
Villa Road d 

### No 9.4 8.9 8.7

Yuma, AZ 040278011 Yuma Supersite No

- 7.8 7.8 b

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

b The listed design value is not valid due to completeness issues.

c This design value does not include data from Class III FEM monitors that EPA has approved as not eligible for comparison to the NAAQS per 40 CFR 58.11(e). Docume nts associated with this action available at www.regulations.gov

, Docket ID

No. EPA

- HQ

- OAR

- 2 012

- 0918.

d In February 2012, the San Diego

- Overland (Kearny Mesa) site (060730006) was relocated to the San Diego

- Kearny Villa Road site (060731016). The data listed for the San

- Diego

- Kearny Villa Road site is a combination of data from San Diego

Overland from 2009 through February 17, 2012, and from San

- Diego

- Kearny Villa Road from February 21, 2012 through

2013.

e State recommended nonattainment as part of a separate nonattainment area. See the section of this document titled “Area Background and Overview

– Los Angeles

- South Coast Air Basin, CA” for more information.

f Design value based on all valid data, including data in 2011 and 2012 that were submitted to, but are not currently in, AQS. EPA considers these data valid for use per 40 CFR Part 50 and 58 ( see Memo randum “Use of Data for Imperial County, CA
PM 2.5 Design Value Calculations ” ).



### In addition to the FEM/FRM/ARM monitoring sites identified in Table 2 whose collected data are used to calculate DVs, Imperial Irrigation District also operate s the nonregulatory monitoring locations identified in Table 2a. These monitors are run to evaluate the evolution of the impact of the water diversion from the Salton Sea on air quality and are not required per 40 CFR 58.20 to be compared to the NAAQS. Alt hough these nonregulatory monitors are not eligible for comparison to the NAAQS, the data collected may help define an appropriate boundary for areas with emissions sources or activities that potentially contribute to ambient fine particle
concentrations a t the violating monitor.

While the Salton Sea monitoring locations measure DVs below the 2012 annual PM 2.5 

### NAAQS , the daily and hourly data show episodic increases in PM 2.5 

### concentrations well above the NAAQS throughout the year that are likely associated with windblown dust and regional increases in PM 2.5 

### concentrations throughout Imperial County.

### Page 12 of 161

The episodic increases in PM 2.5 

### concentrations measured in this area suggests that the area surrounding the Salton Sea is likely a large contributing source of crustal material to PM 2.5 

### concentration in Imperial County. Generally, sources of crustal material in Imperial County contain a larger percentage of PM 2.5 

### than other areas, likely due to the unique soil characteristics in the area. Table 2a also shows an increase of 1.5 μg/m 3 

### from the 20 10

- 201 2 DV
to the 2011

- 2013 DV for the Salton City monitoring location. Annual means for the Salton City monitoring location also increase from 2010 to 2013, suggesting that PM 2.5 

### concentrations may be increasing i n the area surrounding the Salton Sea.

Table 2a. Air Quality Data Collected at Nonregulatory Monitors (all DV levels in μg/m 3 

### ) 26



### County, State Monitor Site

ID Site Name State Rec
NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Imperial, CA n/a Salton City No

- 7.1 a 

### 8.6 a 

### Imperial, CA n/a Naval Test Base No

- 5.0 a 

### 5.9 a 

### Imperial, CA n/a Sonny Bono No

- 6.5 a 

### 6.6 a 

### Imperial, CA n/a Bombay Beach No

- 6.0 a 

### 6.7 a

a The listed design value is not valid due to completeness issues.



### T he Figure 1 map, shown previously, identifies the Imperial County nonattainment area and monitoring locations
with 2011

- 2013 violating DVs. As indicated on the map, the one violating monitoring site within Imperial County
is located in the City of Calexico near the United States/Mexico bord er and the City of Mexicali. This monitor is approximately 0.7 miles from the border with Mexico. The data at the Calexico

- Ethel monitor show a slight increasing trend of 0.3 μg/m 3 

### from the 2009

- 2011 DV to the 2011

- 2013 DV . Further from the border with

red uced potential impacts from border

- related emissions sources are the Brawley and El Centro regulatory
monitors

. The monitors in the cities of El Centro and Brawley have a 7.4 μg/m 3 

### and 7.5 μg/m 3 

### 2011

- 2013 DV, respectively. These monitors are approximately 9and 22 miles from the border with Mexico, respectively. While
these monitors do not currently record violations of the 2012 annual PM 2.5 

### NAAQS, they, like the Salton Sea monitors, indicate that elevated PM 2.5 

### concentrations exist in Imperial County beyond areas closer to the United States/Mexico border. These elevated ambient levels likely reflect that there are sources PM 2.5 

### and PM 2.5 

### precursors at locations in portions of Imperial County that are contributing to the violations of the 2012 annual

PM 2.5 

### NAAQS at the Calexico

- Ethel monitor, in addition to contributions that are likely coming from beyond the
international border. Potential emission sources associated with these elevated concentrations will be discussed in Factor 2, and their potential contr ibution to measured violations at the Calexico

- Ethel monitor in Factor 3.

Seasonal variation can highlight those conditions most associated with high average concentration levels of PM 2.5



### . Figure 2 shows quarterly mean PM 2.5 

### concentrations for the most rec ent 3

- year period for the highest DV monitoring sites in each county within the area of analysis. Figure 2a shows quarterly mean PM 2.5 

### concentrations
for the most recent 3

- year period for all monitoring sites in Imperial County , allowing us to compare valu es over the past three years at monitors within the county . This type of graphical representation is particularly relevant when assessing air quality data for an annual standard, such as the 2012 annual PM 2.5 

### NAAQS, because, as previously stated, the annua l mean is calculated as the mean of quarterly means and a high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3

- year DV.

26 Data from CARB’s online Air Q uality and Meteorological Information System (AQMIS2), available at http://www.arb.ca.gov/aqmis2/aqmis2.php . Data pulled June 2, 2014.

### Page 13 of 161

Figure 2. Imperial County Area of Analysis Monitors

- PM 2.5 

### Quarterly Means for 2011

- 2013

Figure 2a. Imperial County Nonattainment Area Monitors

- PM 2.5 

### Quarterly Means for 2011

- 2013

As shown in Figure 2a

, at the design value monitor (Calexico

- Ethel) in Imperial County in 2012 and 2013, both
quarters two (Q2) and four (Q 4 ) measured higher PM 2.5 

### concentrations than other quarters of the year. Q2 is April through June and Q4 is October through December

. As discussed further in Factor 3, i ncreased concentrations and differences in meteorological patterns suggest that there are two different regimes of high PM 2.5 

### concentrations
that contribute to violation of the NAAQS during these quarters . Also, all quarters from 2011 quarter one (Q1) through 2013 Q 4 at Calexico Ethel measured quarterly averages above 12.0 μg/m 3 

### , with the sole exception of Q1 Imperial County Area of Analysis Monitors

– PM 2.5 

### Quarterly Means 2011

- 2013

Imperial County Nonattainment Area Monitors

– PM 2.5 

### Quarterly Means 2011

- 2013

### Page 14 of 161

in 2013, which measured a quarterly aver age of 10.4 μg/m 3



### . While the quarterly means and DVs for the Brawley and El Centro monitors are lower than the concentrations measured at Calexico Ethel, further evaluation of concentrations at these sites is warranted to determine whether sources in Imper ial County impacting these
monitors also contribute to violations at Calexico Ethel. As discussed further in F actors 2, 3, and 4, the areas surrounding these monitors contain numerous sources of PM 2.5 

### and PM 2.5 

### precursors along with meteorological
condition s during portions of the year which are conducive to transport of PM 2.5 

### to the Calexico Ethel monitoring
location.

PM 2.5 

### Composition Measurements

- To assess potential emissions contributions for each violating monitoring location, the EPA determined the v arious chemical species comprising total PM 2.5 

### to identify the chemical constituents over the analysis area, which can provide insight into the types of emission sources impacting the

monitored concentration. To best describe the PM 2.5 

### at the violating mon itoring location, the EPA first adjusted the chemical speciation measurement data from a monitoring location at or near the violating FRM monitoring site using the SANDWICH approach to account for the amount of PM 2.5 

### mass constituents retained in the FRM

measurement. 27

, 28

, 29

, 30 

### In particular, this approach accounts for losses in fine particle nitrate and increases in sulfate mass associated with particle bound water. Figure 3a illustrates the fraction of each PM 2.5 

### chemical
constituent at the Calexico Ethel mo nitoring site based on annual averages for the years 2010

- 2012.

27 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 results in reduced nitrates (relative to the amount measured by routine speciation networks), higher mass ass ociated with sulfates (reflecting water included in gravimetric FRM measurements) and a measure of organic carbonaceous mass derived from the difference
between measured PM 2.5 and its non

- carbon components. This characterization of PM 2.5 mass also reflects crustal material and other minor constituents. The resulting characterization provides a complete mass closure for the measured FRM PM 2.5

mass , which can be different than the data provided directly by the speciation measurements from the CSN network.

28 F rank, N. H., SANDWICH Material Balance Approach for PM 2.5 Data Analysis, National Air Monitoring Conference, Las Vegas, Nevada, November 6

- 9, 2006. http://www.epa.gov/ttn/amtic/files/2006conference/frank.pdf

. 29 Frank, N. H., The Chemical Composition of PM 2.5 to support PM Implementation, EPA State /Local/Tribal Training Workshop: PM 2.5 Final Rule Implementation and 2006 PM 2.5 Designation Process, Chicago IL, June 20

- 21, 2007, http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chemical_composition.pdf.

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

– 511.

### Page 15 of 161

Figure 3a. Imperial County Annual Average PM 2.5 

### Chemical Constituents (2010

- 2012)

Figure 3b shows annual and quarterly chemical composition profiles and illustrates any seasonal or episodic
contributors to PM 2.5 

### mass. This “increment analysis,” combined with the other factor analyses, can provide additional insight as to which sources or factors may contribute at a greater level. Simply stated, this analysis can help identify nearby sources of emissions that contribute to the violation at the violating monitoring site.

Figure 3b . Imperial County Annual and Quarterly Average PM 2.5 

### Species (2010

- 2012) a

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



### Figures 3a and 3b show that organic mass and crustal material are the predominant species on an annual mean basis, as well as for each quarter. S ulfates and elemental carbon also contribut e to measured PM 2.5 

### mass at Calexico Ethel throughout the year . Nitrates do not contribute to measured PM 2.5 

### mass in quarter two and three

, but they do increase in the winter months

, affecting Q1 and Q4 concentrations. There is also an increase in elemental carbon during Q1 and Q4. This suggests that biomass burning, combustion sources, and fugitive dust

### Page 16 of 161

sources such as agricultural sources, unpaved roads, and windbl own dust are large contributors to high annual

PM 2.5 

### concentrations within Imperial County. The speciation results for the days that exceed the 24

- hour standard show more of a contribution from organic matter and ammonium nitrate, which is different than w hat the annual mean speciation data show.

The EPA assessed seasonal and annual average PM 2.5 

### constituents at monitoring sites within the area relative to monitoring sites outside of the analysis area to account for the difference between regional backgroun d

concentrations of PM 2.5 

### , and the concentrations of PM 2.5 

### in the area of analysis, also known as the “urban increment.” This analysis differentiates between the influences of emissions from sources in nearby areas and in

more distant areas on the violatin g monitor. Estimating the urban increment in the area helps to illuminate the amount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in nearby areas, as opposed to impacts of more distant or regi onal sources of emissions. Figure 4a includes pie charts showing the annual and quarterly chemical mass constituents of the urban increment. The quarterly pie charts correspond to the high

- concentration quarters identified in Figure 2. Evaluating these hig h concentration quarters can help identify composition of PM 2.5 

### during these times. Note that in these charts, sulfates and nitrates have been adjusted to represent their mass in measured PM 2.5



### .

### Page 17 of 161

Figure 4a. Imperial County Urban Increment Analysis for 2010

- 2012.

### Page 18 of 161

Figure 4b . Imperial County Average Urban Increment Analysis for 2010

- 2012.

Imperial County has one monitoring site violating the NAAQS. T he Calexico

- Ethel monitor ing site measures increased PM 2.5 

### concentrations during Q2 and Q4

, and considering meteorology (discussed further in Factor 3),
this suggest s that there are two different regimes of high PM 2.5 

### concentrations that contribute to exceedances of the NAAQS during these quarters

.
The speciation analysis shows high percentages of both crustal material and organic mass suggest ing that the main contributing sources to PM 2.5 

### concentrations at Calexico

- Ethel are biomass burning

, other combustion

emissions , and fugitive dust emissions primarily associated with unpaved roads, agricult ural sources, and
windblown dust. In reviewing the urban increment analysis for the Imperial County DV monitor, the results are similar to the adjusted speciation measurements

.

Factor 2: Emissions and Emissions

- related D ata

In this designations process, for each area with a violating monitoring site, the EPA evaluated the emissions data from nearby areas using emissions related data for the relevant counties to assess each county’s potential

contribution to 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. The EPA examined emissions of identified sources or source categories of direct PM 2.5 

### , the major components of direct PM 2.5 

### (organic mass, elemental carbon, crustal material (and/or individual trace metal compounds)), primary nitrate and primary sulfate, and precursor gaseous pollutants (i.e., SO 2 

### , NO X 

### , total VOC, and NH 3 

### ). The EPA also considered the
distance of those sources of emissions from the violating monitoring site. While direct PM 2.5 

### emissions and its major carbonaceous components are generally associated with sources near violating PM 2.5 

### monitoring sites, the gaseous precursors tend to have a more regional inf luence (although the EPA is mindful of the potential local

NO X 

### and VOC emissions contributions to PM 2.5 

### from mobile and stationary sources) and transport from neighboring areas can contribute to higher PM 2.5 

### levels at the violating monitoring sites.

### Page 19 of 161

Emissions Data

For this factor, the EPA reviewed data from the 2011 National Emissions Inventory (NEI) version 1 (see http://www.epa.gov/ttn/chief/net/2011inventory.html ). For each county in the area of analysis, the EPA examined the magnitude of county

- level emissions reported in the NEI. These county

- level emissions represent the sum of emissions from the following general source categories: point sources, non

- point (i.e., area) sources, nonroad mobile, on

- road mobile, a nd fires. In some instances, non

- anthropogenic sources of emissions such as wildfires account for large portions of the emissions inventory data presented below. The EPA also looked at the geographic distribution of major point sources of the relevant poll utants. 31 

### Substantial emissions levels from sources in a nearby area indicate the potential for the area to contribute to monitored violations.

To further analyze area emissions data, the EPA also developed a summary of direct PM 2.5 

### , components of direct

P M 2.5 

### , and precursor pollutants, which is available at http://www.epa.gov/pmdesignations/2012standards/docs/nei2011v1pointnei2008v3county.xlsx

.

When considered with the urban increment analysis in Factor 1, evaluating the components of direct PM 2.5 

### and precursor gases can help identify specific sources or source types contributing to elevated concentrations at violating monitoring sites and thus assist in identifying appropriate area boundaries. In general, directly emitted particulate organic carbon (POC) and VOCs 32 

### contribute to PM 2.5 

### organic mass (OM); directly emitted EC
contributes to PM 2.5 

### EC; NO X 

### , NH 3 

### and directly emitted nitrate contribute to PM 2 .5 

### nitrate mass; SO 2 

### , NH 3 

### and directly emitted sulfate contribute to PM 2.5 

### sulfate mass; and directly emitted crustal material and metal oxides

contribute to PM 2.5 

### crustal matter. 33

, 34 

### The EPA believes that the quantities of those nearby emissions as potent ial
contributors to the PM 2.5 

### violating monitors are somewhat proportional to the PM 2.5 

### chemical constituents in the estimated urban increment. Thus, directly emitted POC is more important per ton than SO 2 

### , partially because POC emissions are already PM 2.5 

### whereas SO 2 

### must convert to PM 2.5 

### and not all of the emitted SO 2 

### undergoes this
conversion.

Table 3a provides a county

- level emissions summary (i.e., the sum of emissions from the following general source categories: point sources, non

- point (i.e., area) sources, nonroad mobile, on

- road mobile, and fires) of directly emitted PM 2.5 

### and precursor species for the county with the violating monitoring site and nearby counties considered for inclusion in the Imperial County, CA area. Table 3b summarizes the dire ctly emitted components
of PM 2.5 

### for the same counties in the area of analysis for the Imperial County, CA area. This information will be paired with the Urban Increment composition previously shown in Figures 4a and 4b.

31 For purposes of this designations effort, “major” point sour ces are those whose sum of PM precursor emissions (PM 2.5 +
NOx + SO 2 + VOC + NH 3 ) are greater than 500 tons per year based on NEI 2011v1

. 32 

### As previously mentioned, nearby VOCs are presumed to be a less important contributor to PM 2.5 

### OM than POC.

33 See , Seinfeld J. H. and Pandis S. N. (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change

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

34 USEPA Report (2004), The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003,

found at: http://www.epa.gov/airtrends/aqtrnd04/pm.html

.

### Page 20 of 161

Table 3a

. County

- Level Emissions of Directly Emitted PM 2.5 

### and Precursors (tons/year)

County , State Total

NH 3 

### Total

NOx Total

Direct
PM 2.5 

### Total

SO 2 

### Total

VOC Total

San Diego, CA 6,754 42,667 10,559 1,286 60,083 121,348
Riverside, CA 9,421 35,935 5,453 378 26,789 77,975 Imperial, CA 16,396 10,052 4,472 146 6,978 38,043
Yuma, AZ 2,835 6,303 1,855 86 5,950 17,029
La Paz, AZ 357 3,745 1,102 43 2,446 7,693

Table 3b . County

- Level Emissions for Components of Directly Emitted PM 2.5 

### (tons/year) 35



### County, State POM EC PSO 4 

### PNO 3 

### PCrustal Residual Total Direct
San Diego, CA 5,979 1,357 186 27 1,078 1,931 10,559 Riverside, CA 2,140 1,087 109 14 929 1,174 5,453 Imperial, CA 1,200 346 103 11 1,323 1,488 4,472 Yuma, AZ 453 198 13 2 629 560 1,855 La Paz, AZ 235 118 8 1 420 318 1,102

Table 3b breaks down the direct PM 2.5 

### emissions value from Table 3a into its components. These data will also be compared with the previously presented Urban Increment composition.

Using the previously described relationship between directly emitted and precursor gases and the measured mass to evaluate data presented in Tables 3a and 3b, the EPA identified the following components warranting
additional review: NH 3 

### , PM 2.5 

### , SO 2 

### , VOC, POM, PSO 4 

### , Crustal and Residual. The EPA then looked at the contribution of these constituents of interest from each of the counties included in the area of analysis as shown in

Tables 4 a

- h.

Table 4a. County

- Level NH 3 

### Emissions (tons/year)

Emissions in average tons/yr

County, State NH 3 Pct. Cumulative %

Imperial, CA 16,396 46 46

Riverside, CA 9,421 26 72
San Diego, CA 6,754 19 91

Yuma, AZ 2,835 8 99

La Paz, AZ 357 1 100

35 Data are based on the 2011 and 2 018 Emissions Modeling Platform Data Files and Summaries ( ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform ) available at: http://www.epa.gov/ ttn / chief/emch/index.html#2011 (accessed 02/26/14)

.

### Page 21 of 161

Table 4 b . County

- Level PM 2.5 

### Emissions (tons/year)

Emissions in average tons/yr

County, State PM 2.5 Pct. Cumulative %

San Diego, CA 10,559 45 45

Riverside, CA 5,453 23 68
Imperial, CA 4,472 19 87

Yuma, AZ 1,855 8 95

La Paz, AZ 1,102 5 100



### Table 4 c . County

- Level SO 2 

### Emissions (tons/year)

Emissions in average tons/yr

County, State SO 2 Pct. Cumulative %

San Diego, CA 1,286 66 66

Riverside, CA 378 19 86

Imperial, CA 146 8 93

Yuma, AZ 86 4 98

La Paz, AZ 43 2 100



### Table 4 d . County

- Level VOC Emissions (tons/year)

Emissions in average tons/yr

County, State VOC Pct. Cumulative %

San Diego, CA 60,083 59 59

Riverside, CA 26,789 26 85

Imperial, CA 6,978 7 92

Yuma, AZ 5,950 6 98

La Paz, AZ 2,446 2 100

### Page 22 of 161

Table 4 e . County

- Level POM Emissions (tons/year)

Emissions in average tons/yr

County, State POM Pct. Cumulative %

San Diego, CA 5,979 60 60

Riverside, CA 2,140 21 81
Imperial, CA 1,200 12 93

Yuma, AZ 453 5 98

La Paz, AZ 235 2 100



### Table 4 f . County

- Level PSO 4 

### Emissions (tons/year)

Emissions in average tons/yr

County, State PSO 4 Pct. Cumulative %

San Diego, CA 186 44 44

Riverside, CA 109 26 70

Imperial, CA 103 25 95

Yuma, AZ 13 3 98

La Paz, AZ 8 2 100



### Table 4 g . County

- Level Crustal Emissions (tons/year)

Emissions in average tons/yr

County, State Crustal Pct. Cumulative %

Imperial, CA 1,323 30 30
San Diego, CA 1,078 25 55

Riverside, CA 929 21 76

Yuma, AZ 629 14 90

La Paz, AZ 420 10 100



### Table 4 h . County

- Level Residual Emissions (tons/year)

Emissions in average tons/yr

County, State Residual Pct. Cumulative %

San Diego, CA 1,931 35 35

Imperial, CA 1,488 27 62

Riverside, CA 1,174 21 84

Yuma, AZ 560 10 94

La Paz, AZ 318 6 100



### The EPA also reviewed seasonal emissions for Imperial County, which show that all components (NH 3 

### , PM 2.5 

### , SO 2 

### , VOC, POM, PSO 4 

### , Crustal and Residual) had higher emissions in Q2 and Q3 than in Q1 and Q4. For example, direct PM 2.5 

### emissions were lowest in Q4, slightly higher in Q1, higher again in Q2, and highest in Q3. Crustal material emissions were lowest in Q1 and Q4, and highest in Q2 and Q3, which is consistent with the pollution rose analysis in Factor 3, showing that meteorological conditions occurring in Q2 and Q3 are conducive to the transport of crustal material to the Calexico Ethel monitoring location from various sources of crustal material present throughout the county. Also, NH 3 

### emissions increase in Q2 compared to other quarters in the

year.

### Page 23 of 161

In addition to reviewing county

- wide emissions of PM 2.5 

### and PM 2.5 

### precursors in the area of analysis, the EPA
also reviewed emissions from major point sources located in the area of analysis. The magnitude and location of these sources can help inform nonattainment boundaries. Table 5 provides facility

- level emissions of direct PM 2.5 

### , components of direct PM 2.5 

### , and precursor pollutants (give n in tons per year) from major point sources located in the area of analysis for the Imperial County, CA area. Table 5 also shows the distance from the facility to the DV monitor for the respective county.

Table 5 . NEI 2011 v1 Major Point Source Emissions (tons/year)

Distance 

### NEI 2011 v1 Emissions

- Tons/Year

County, State Facility Name (Facility ID) monitor (miles) NH 3 NOx PM 2.5 SO 2 VOC Total

San Diego, CA San Diego Intl

- Lindberg 100 n/a 1,156 21 125 165 1,468

Riverside, CA None n/a n/a n/a n/a n/a n/a n/a

Imperial, CA None n/a n/a n/a n/a n/a n/a n/a

Yuma, AZ None n/a n/a n/a n/a n/a n/a n/a

La Paz, AZ None n/a n/a n/a n/a n/a n/a n/a



### Figure 5 shows the major point source emissions (from the 2011 NEI in tons per year) in the area of analysis for Imperial County, CA and the relative distances of these sources from the violating monitoring location(s), as depicted by red dots. The actual distance from the point sources to the DV monitoring location is presented in Table 5). The distance from the violating monitoring location is particularly important for directly emitted 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. 36



### As indicated in Figure 5, there is one major point source located approximately 100 miles west of the monitor. The source is located on the other side of the coastal mountain range from the location of monitored violations in Imperial County, described further in Factor 4, below. Its larges t emissions (nearly 80% by weight) are of NO
x 

### , which is not observed at high concentrations in the speciation measurements, as discussed above in Factor 1. There are no major point sources in the remaining counties in the area of analysis. Thus, major poin t sources from all of the counties in the analysis area are unlikely to contribute to the violating monitor in Imperial County.

In addition to major point source emissions, the EPA further evaluated non

- point source emissions within Imperial County. The EP A examined the spatial and temporal extent of agricultural burning within Imperial County, which typically occurs in the winter and summer months. Historical data from 2006 and 2007 suggest that over 35, 000 acres of Bermuda grass and wheat fields are burne d annually. The highest number of acres burned occur in Q1 (specifically in January and February) and Q2 (specifically in May and June). 37 

### Agricultural burning occurs throughout Imperial County. As discussed in Factor 3, meteorological conditions during Q1 and Q2 are substantially different, with Q1 having a higher percentage of hours associated with stagnant conditions

36 Baker, K

. R. and K . M. Foley

. A nonlinear regression model e s timating single source concentrations of primary and secondarily formed PM 2.5

. Atmospheric Environment. 45 (2011) 3758

- 3767.

37 Agricultural Burning: Air Monitoring and Exposure Reduction in Imperial County, California: Final Report to U.S./Mexico Border Environmental Cooperation Commission Funded under Technical Assistance Agreement Number

TAA08

- 068. Environmental Health Investig ations Branch, California Department of Public Health and School of Public Health, San Diego State University. May 22, 2011.

### Page 24 of 161

and Q2 experiencing a higher number of hours of non

- stagnant conditions with winds coming from the north. Despite these differences, concent rations measured in both Q1 and Q2 are more likely to be influenced by emissions from agricultural burning than other times of the year when a lesser amount of acres are burned. The

combination of non

- stagnant meteorological conditions associated with wind s generally from the north and the high number of acres burned in Q2 may be a cause for the increased monitored PM 2.5 

### concentrations in Q2, shown in Figures 2 and 2a. The data at the Calexico

- Ethel monitor represent a combination of PM 2.5 

### concentrations fro m both non

- point sources located throughout Imperial County and emissions sources from the City of Mexicali to

the south

. The two other monitoring locations in Imperial County (Brawley and El Centro) are located north of the City of Calexico. While these m onitors do not currently record violations of the 2012 annual PM 2.5 

### NAAQS and are not as clearly influenced by emissions from Mexicali, they do represent contributions from non

- point emissions sources located throughout Imperial County. While not violating, the Brawley and El Centro monitors show elevated concentrations during the second and third quarters (see Factor 1). For example, Figure 2a in Factor 1 shows that quarterly means at Brawley and El Centro in 2013 were highest in Q2. As discussed further in Factor 3, various meteorological conditions that occur throughout the year are conducive to transport from sources located near the Brawley and El Centro monitoring locations, suggesting that these sources likely
contribute to elevated PM 2.5 

### concentrations at the violating Calexico

- Ethel monitoring location.

### Page 25 of 161

Figure 5. Major Point Source Emissions in the Area of Analysis for the Imperial County, CA Area

.

In addition to analyzing emissions from Imperial County and from other United St ates counties that are adjacent to Imperial County, CA, the EPA also looked at emissions from the City of Mexicali and the municipality (“municipio,” analogous to U.S. counties , hereafter referred to as the municipio/county ) of Mexicali, which are
located on the south side of the U.S.

– Mexico international border. While emissions from the Mexican side of the border are beyond the legal jurisdiction of the Clean Air Act, and thus are not in an area that the EPA could
include within the designated nonattainment area for the 2012 annual PM 2.5 

### NAAQS, the EPA nevertheless believes that the impacts of these sources should be acknowledged as a part of the nonattainment problem in Imperial County. Figure 5a shows a map of the Mexica li

- Calexico international area.

### Page 26 of 161

Figure 5a. Map of Border Region, City of Mexicali and Municipio/County of Mexicali

Figure 5b shows the locations of point source emissions in the metropolitan area of the City of Mexicali. Table 5a
shows the emissions from these sources.

### Page 27 of 161

Figure 5b. Location of Point Sources in the Urban Portion of Mexicali

Source: “2005 Mexicali Emissions Inventory: Final Report,” Figure 2

- 1, page 2

- 3



### Table 5a. 2005 Mexicali Point Source Emissions (Tons per Year)

NO x SO 2 VOC PM 2.5 Point Source Emissions 14,376 4,624 733 128 Source: “2005 Mexicali Emissions Inventory: Final Report,” Tables 2

- 2 and 2

- 3, page 2

- 9



### Emissions information for non

- point sources for the city of Mexicali was not available; information was available
from th e entire municipio/county of Mexicali and is presented in Table 5b. As shown in Figure 5a above, the municipio/county of Mexicali has an area of 5,300 square miles that includes the City of Mexicali and areas of Mexico that range from the U.S.

- Mexico bord er to the Sea of Cortez, which is approximately 120 miles to the
south. 38

38 See “2005 Mexicali Emissions Inventory: Final Report,” prepared for Western Governors’ Association, Denver, Colorado;

U.S. EPA Re gion 9, San Francisco, CA; and the Secretaria de Proteccion al Ambiente de Baja California, Mexicali, Baja California, Mexico.

### Page 28 of 161

Table 5b. 2005 Municipio/County of Mexicali Emissions Inventory Summary (Tons per Year) Source Type NO x SO 2 VOC CO PM 2.5 NH 3 CH 4 Point 14,376 4,624 732 4,116 128

Area 1,206 131 15,302 18,854 6,754 9,015 6,033

On

- Road Mobile 8,570 169 8,977 60,603 666 252 144

Nonroad Mobile 4,485 61 543 3,207 533 0 1.5

Total 28,637 4,985 25,554 86,780 8,081 9,268 6,179

Blanks indicate that pollutant emissions were not applicable or were not estimated due to a lack of data. Source: “2005 Mexicali Emissions Inventory: Final Report,” Table ES

- 1, page ES

- 3



### Table 5c shows a comparison of county

- level emissions for Imperial County and emissions in the adjacent municipio/county of Mexicali. Except for NH 3 

### , direct PM 2.5 

### emissions and precursors are 2 to 34 times as high in
the municipio/county of Mexicali as in Imperial.

Table 5c. 2011 Imperia l County and 2005 Municipio/County of Mexicali Emissions (Tons per Year)

NO x SO 2 VOC PM 2.5 NH 3 Imperial County, CA 10,052 146 6,978 4,472 16,396

Municipio/County of Mexicali, Baja California, Mexico 28,637 4,985 25,554 8,081 9,268



### In summary, the EPA’s analysis showed major point sources north of the U.S.

- Mexico border are not contributing

to violations of the 2012 annual PM 2.5 

### NA AQS in Imperial County, while non

- point sources throughout the county account for a large percentage of the emissions and are therefore likely contributing to violations of the

2012 annual PM 2.5 

### NA AQS. Crustal materia l and organic m ass are the highest identified components of directly

emitted PM 2.5 

### within Imperial County based on the speciated ambient PM 2.5 

### information presented in Factor 1, and are associated with emissions sources located throughout the county.

Popu lation density and degree of urbanization

In this part of the factor analysis, the EPA evaluated the population and vehicle use characteristics and trends of the area as indicators of the probable location and magnitude of non

- point source emissions. Rapid population growth in a county on the urban perimeter signifies increasing integrat ion with the core urban area, and indicates that it may be appropriate to include the county associated with area source and mobile source emissions as part of the nonattainment area. Table 6 shows the 2000 and 2010 population, population growth since 2000 , and population density for each county in the area.

### Page 29 of 161

Table 6 . Population Growth and Population Density

.

County , State Population

20 00 Population

2010 % Change

from 2000 Land

Area (Sq. Miles) Population

Density ( per Sq. Mile) % Cumulative
% San Diego,
CA 2,813,833 3,103,933 10 % 4,200 739 54 54 Riverside, CA 1,545,387 2,202,361 43 % 7,207 306 39 93 Imperial, CA 160,026 196,786 23 % 5,514 36 3 97 Yuma, AZ 142,361 174,667 23 % 4,175 42 3 100 La Paz, AZ 19,715 20,465 4 % 4,500 5 0 100

Total 4,681,322 5,698,212 Source: U.S. Census Bureau population estimates for 2000 and 2010

Imperial County, CA is low in both population and population density, compared to other counties in the area of analysis, as shown in Table 6. The county accounts for only 3% of the total population in that area. The population in Imperial County is not economically integrated with neighboring counties. The large population to the north in Riverside County is concentrated in the South Coast Air Basin, in the western portion of the county and to a lesser extent in the central portion, known as Coachella Valley, which forms the northern portion of the Salton Sea Air Basin (SSAB), of which the Imperial Valley forms the southern portion. In between these two portions lies the Sa lton Sea, which effectively splits the population in the SSAB between people who live in the north and are more or less associated with the greater Los Angeles metropolitan area on the west side of Banning

Pass

– the high mountain pass that separates the S SAB from the SCAB. Although Riverside County is experiencing the highest growth in the area of analysis, and one of the highest growth rates nationally, that growth is happening in areas that are disparate from the populated area of south central Imperial County, where
monitored violations of the 2012 PM 2.5 

### NAAQS are occurring.

The population in San Diego County, CA is concentrated along the Pacific Ocean Coast. San Diego County’s population is experiencing rapid growth, but that growth is occurring in t he western portion of that large (4,200 square miles) county, on the other side of a coastal range of mountains. Yuma and La Paz counties in Arizona, like San Diego County, are separate and distinct population centers that have little commuting to or from Imperial County. Both Arizona counties have low population and low population density. At a 4% increase from 2000 to 2010, La Paz is not experiencing rapid population growth. Although Yuma County is experiencing rapid growth, that growth is occurring in an d around Yuma City and is more associated with economic activity in Mexico than in Imperial County. The absence of contribution of emissions from VMT and little economic integration between these areas and Imperial County indicate these four counties are n ot one large, urban area.

In addition to population data for counties within the area of analysis, the EPA also notes that the Calexico

- Ethel monitoring location is 0.7 miles from the U.S.

– Mexico border and the City of Mexicali, which had a population of 689,775 in 2010.

### Page 30 of 161

Figure 6. 2010 County

- Level Population in the Area of Analysis for the Imperial Cou nty, CA Area.

Traffic and Vehicle Miles Travelled

High vehicle miles travelled (VMT) and/or a high number of commuters associated with a county is generally an indicator that the county is an integral part of an urban area. Mobile source emissions of N O
x 

### , VOC, and direct PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS in the area. In combination with the population/population density data and the location of main transportation arteries,

an assessmen t of VMT helps identify the probable location of nonpoint source emissions that contribute to violations in the area. Comparatively high VMT in a county outside of the CBSA or CSA signifies integration
with the core urban area contained within the CSA or C BSA, and indicates that a county with the high VMT may be appropriate to include in the nonattainment area because emissions from mobile sources in that county contribute to violations in the area. Table 7 shows 2011 VMT while Figure 7 overlays 2011 county

- level VMT with a map of the transportation arteries. VMT information reflects data from the Federal Highway
Administration.

### Page 31 of 161

Table 7. 2011 VMT for the Imperial County, CA Area

. County , State Total 2011 VMT Percent Cumulative % San Diego, CA 27,302,301,628 54 54 Riverside, CA 19,110,634,300 38 92 Imperial, CA 1,771,872,720 4 96 Yuma, AZ 1,453,430,294 3 98 La Paz, AZ 776,932,996 2 100
Total 50,415,171,938 http://www.census.gov/hhes/commuting/data/commuting.html

Figure 7. Overlay of 2011 County

- level VMT with Transportation Arteries.

According to Table 7 and Figure 7, Imperial County is shown as having the same pattern of isolation in terms of
its VMT as it had for its population. San Diego and Riverside counties in CA have larger VMT than any other counties in the area of analysis, a nd again as for population, this VMT is associated with the ocean

- facing western portion of San Diego County and for Riverside County, with the western portion of the county in SCAB and to a lesser extent the northern Coachella portion of SSAB, whose VMT i s associated with traffic within Coachella Valley as well as to and from SCAB.

According to Census Bureau statistics for commuter flow between counties for 2006

- 2010, Imperial County , CA
has 55,565 commuters. See: http://www.census.gov/population/metro/data/other.html . Of those commuters

### Page 32 of 161

residing in Imperial County, 51,171 or 92% travel to a workplace destination within the county, while 2% (1,231 commuters) travel to a workplace in Riv erside County and less than 2% (961 commuters) travel to a San Diego workplace.

In the reverse direction, commuters residing in the surrounding area of analysis do not appreciably commute into Imperial County for work. From Yuma County, AZ, 1,066 commut ers travel to Imperial for work, while from Riverside and San Diego counties only 665 and 792 commuters, respectively, travel to work in the county. There were no commuters who resided in La Paz County, AZ who travelled to work in Imperial County, CA.

Ta ble 7a presents statistics for border crossings at the two land ports of entry, Calexico and Calexico East, on the Imperial County portion of the U.S.

- Mexico border.

Table 7a. 2013 Border Crossing/Entry at Calexico and Calexico East, CA

Port Name Trucks Trains Train Passengers Buses Bus Passengers Personal

Vehicles Personal Vehicle

Passengers Pedestrians

Calexico, CA 0 0 0 0 0 4

, 112

, 348 7

, 132

, 134 4

, 398

, 916 Calexico East, CA 325

, 690 250 259 2571 103

, 690 3

, 198

, 849 5

, 915

, 717 717

, 009 Source: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of Transportation Statis tics, based on data from
the Department of Homeland Security, U.S. Customs and Border Protection, Office of Field Operations. Report created: Fri Jul 11 16:06:27 EDT 2014



### In summary for emissions

- related information, Imperial County is isolated from the surrounding counties in the area of analysis. Crustal material and organic m ass are the components that account for the largest percentage of directly

- emitted PM 2.5 

### within Imperial County, and are associated with emissions sources located throughout the county. North of the U.S

- Mexico border, there are no major point sources contributing to violations of the 2012

PM 2.5 

### NAAQS, and non

- point sources, population and VMT are isolated to Imperial County. The EPA does note that the Calexico Ethel monitoring site is in close proximity to the U.S

– Mexico border and the city of Mexicali,
which ha s a large population compared to Imperial County and is a known source of PM 2.5 

### emissions, which, in addition to the Imperial County emissions, likely impact the Calexico Ethel monitor, especially during times of stagnant conditions as discussed further in Factor 3.

Factor 3: Meteorology

EPA evaluated available meteorological data to determine how meteorological conditions, including, but not limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of direct ly emitted particulate matter and precursor emissions from sources in the area of analysis. The EPA used two primary tools for this assessment: wind roses and kernel density estimation (KDE). When considered in
combination with area PM 2.5 

### composition and c ounty

- level and facility emissions source location information, wind roses and KDE can help to identify nearby areas contributing to violations at violating monitoring sites.

Wind roses are graphic illustrations of the frequency of wind direction and wi nd speed. Wind direction can indicate the direction from which contributing emissions are transported; wind speed can indicate the force of the wind and thus the distance from which those emissions are transported. The EPA constructed wind roses from
hourl y observations of wind direction and wind speed using 2009

- 2012 data from National Weather Service

### Page 33 of 161

locations archived at the National Climate Data Center. 39 

### When developing these wind roses, the EPA also used wind observations collected at meteorological sampling stations collocated at air quality monitoring sites, where these data were available. Figure 8 shows wind roses that the EPA generated from data relevant in the Imperial County area.

Cli matic conditions in the Salton Sea Air Basin are governed by the large

- scale sinking and warming air in the subtropical high

- pressure center of the Pacific Ocean. The high pressure ridge blocks most mid

- latitude storms except in the winter when the high

- pr essure ridge is weakest and farther south. Similarly, the coastal mountains prevent the intrusion of any cool damp marine air from the coast. Because of the weakened storms and the mountainous barrier, the Salton Sea Air Basin has hot summers, mild winters , and little rainfall. The flat terrain of the Valley and the strong temperature differentials created by intense solar heating produces moderate winds and deep thermal convection.

39 ftp.ncdc.noaa.gov/pub/data/noaa or http://gis.ncdc.noaa.gov/map/viewer/#app=cdo&cfg=cdo&theme=hourly&layers=1&node=gis Qualit y assurance of the
National Weather Service data is described here: http://www1.ncdc.noaa.gov/pub/data/inventories/ish

- qc.pdf

.

### Page 34 of 161

Figure 8. Wind Roses in the Area of Analysis for the Imperial County A rea
.

As shown in Figure 8, there is a pattern across the CBSA of predominantly southwest to west winds on an annual average basis , mostly at mid

- level speeds of 2 to 8 meters per second.

In addition to wind roses, the EPA also generated kernel density e stimation (KDE) plots to represent HYSPLIT (HYbrid Single

- Particle Lagrangian Integrated Trajectory) backward trajectory frequency at violating monitoring

### Page 35 of 161

s ites. 40

, 41 

### These KDEs are graphical statistical estimations to determine the density of trajectory end points at a particular location represented by a grid cell. The EPA used KDEs to characterize and analyze the collection of individual HYSPLIT backward trajectories. 42 

### Higher density values, indicated by darker blue colors, indicate a greater frequency of o bserved trajectory endpoints within a particular grid cell. Figure 9 shows a HYSPLIT KDE plot for the Calexico

- Ethel air monitoring site (AQS number 060651103) in Imperial County

, summarized by calendar quarter for the 2010

- 2012 period.

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

nonattainme nt area boundaries. A HYSPLIT backward trajectory is usually depicted on a standard map as a single line, representing the centerline of an air parcel’s motion, extending in two dimensional (x,y) space from a starting point and regressing backward in time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining an air parcel’s point of origin on a day in which a short

- term standard, such as an 8

- hour standard or a 24

- hour standard, was exceeded. However, for an annual standard, such as the 2012 annual PM 2.5 NAAQS, every trajectory on every day is important. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT trajectori es may not be helpful as this process is likely to result in depicting air parcels originating in all directions from the violating monitoring site.

41 HYSPLIT

- Hybrid Single Particle Lagrangian Integrated Trajectory Model, http://www.arl.noaa.gov/HYSPLIT_info.php

42 The KDEs graphically represent the aggregate of HYSPLIT backward trajectories for the years 2010

- 2012, run every third day (beginning on the first day of monitoring), four times each day, and ending at four endpoint heights.

### Page 36 of 161

Figure s 9. Quarter 1

- Quarter 4: HYSPLIT Kernel Density Estimation Plots for the Calexico

- Ethel Monitoring Station

.

### Page 37 of 161

### Page 38 of 161

Figure 10. Pollution Roses for Calexico Ethel, 2011

- 2013

. PM 2.5 

### > 12 μ g/m 3 

### Binned by Wind Direction and Quarter.

Figure 10 shows the distribution of hourly PM 2.5 

### concentrations greater than 12 μ g/m 3 

### from the Calexico Ethel monitoring site binned by wind direction (e.g. direct N corresponds to hourly PM 2.5 

### concentrations from wind directions ranging from 349° to 11°, direct NNW corresponds to hourly PM 2.5 

### concentrations from wind directions ranging from 326° to 349°) for the years 2011

- 2013. These data are also separated by wind speed to highlight the
effect of wind s peeds over 3 miles per hour (mph) versus stagnant conditions, h ere characterized as instances
when wind speed s are less than 3 mph, on PM 2.5 

### concentrations throughout the different quarters of the year.

### Page 39 of 161

These values represent the percentage of hourly PM 2.5 

### values greater than 12 μg/m 3 

### for a given wind direction.
This analysis focuses on higher concentrations with respect to the annual NAAQS, which provides insight into the directionality of PM 2.5 

### concentrations on a subset of days throughout the year that may be contributing to violations of the annual NAAQS. For examp le, in Q2, 13% of all hourly values above 12 μg/m 3 

### are associated with wind speeds greater than 3 mph from the WNW wind direction. Generally , stagnant conditions are indicative of contributions from emission sources from locations closer to the monitor, w hile higher wind speeds indicate greater probability of contribution from emission sources from locations further away. For quarters two and four, shown to be the quarters with the highest average concentrations at the violating Calexico Ethel monitor (see Figure 2a in Factor 1), these data further support the conclusion that there are two different meteorological regimes resulting in different PM 2.5 

### sources contribut ing to violation s of the NAAQS during these quarters. 58% of all hourly PM 2.5 

### concentration s greater than 12 μ g/m 3 

### in quarter two are associated with wind speeds greater than 3 mph, while only 14% of the hourly PM 2.5 

### concentrations greater than 12 μ g/m 3 

### are associated with these same conditions in quarter four. Conversely, it appears that higher hourly concentrations measured in quarter four are more associated with stagnant conditions

- 86% of all hourly PM 2.5 

### concentrations greater than 12 μ g/m 3 

### measured in quarter four occurred when wind speeds were less than 3 mph. Also, when considering PM 2. 5 

### concentrations associated with wind speeds greater than 3 mph, 24 % of all hourly PM 2.5 

### concentrations greater

than 12 μ g/m 3 

### measured at the Calexico Ethel monitoring site in quarter two are from north west erly wind directions (i.e. >2 81 ° and < 78.8°), comp ared to only 6 % in quarter four. Figure 10 additionally shows in quarter 2
that the west

- northwest direction contains the highest percentage of hourly PM 2.5 

### concentrations greater than 12

μ g/m 3 

### at 13%. This indicates that there are greater contributions f rom sources in the west

- northwest direction to violations of the annual NAAQS during quarter two compared to quarter four.

Factor 4: Geography/topography

To evaluate the geography/topography factor, the EPA assessed physical features of the area of analysis that might define the airshed and thus affect the formation and distribution of PM 2.5 

### concentrations over the area.

Imperial Valley is located within the Salton Sea Air Basin along with the desert portion of Riverside County. Imperial County con sists of 4,175 square miles, bordering Mexico to the south, Riverside County to the north, San Diego County to the west, and the State of Arizona on the east. The Imperial Valley is a part of the larger Salton Trough. Also included in the Salton Trough is the western half of the Mexicali Valley and the Colorado River delta in Mexico. This trough is a very flat basin (see Figure 10 a) surrounded by mountains: the Peninsular Ranges to the west, the Chocolate, Orocopia

, and Cargo Muchacho Mountains to the east. Most of the trough is below sea level and is predominantly desert with agricultural land. Imperial Valley does not have any geographical or topographical barriers that limit air

- pollution transport within its airshed. There are no topographical barriers t o separate the EPA’s nonattainment area from the rest of the Imperial Valley. Outside of Imperial County, t he Peninsular Ranges to the west of Imperial County serve as a partial barrier to transport from San Diego County. The Chocolate, Orocopia

, and Cargo Muchacho Mountains mountain ranges within and to the east of Imperial County serve as a partial barrier to transport from Yuma County and La Paz County in Arizona.

### Page 40 of 161

Figure 10a

. Topographic Map in the Area of Analysis for Imperial County

.

### Page 41 of 161

Figure 10b

. Imagery in the Area of Analysis for Imperial County

.

Factor 5: Jurisdictional boundaries

In defining the boundaries of the Imperial County, CA nonattainment area, the EPA considered existing jurisdictional boundaries, which can provide easily identifiable and recognized boundaries for purposes of implementing the NAAQS. Existing jurisdictional boundaries often signify the state or local governmental organization with th e necessary legal authority for carrying out air quality planning and enforcement functions for
the nonattainment area. Examples of such jurisdictional boundaries include existing/prior nonattainment area boundaries for particulate matter, county lines, ai r district boundaries, township boundaries, areas covered by a metropolitan planning organization, state lines, and Reservation boundaries, if applicable. Where existing jurisdictional boundaries were not adequate or appropriate to describe the nonattainme nt area, the EPA considered other clearly defined and permanent landmarks or geographic coordinates for purposes of identifying the boundaries of the designated areas.

### Page 42 of 161

The EPA has previously established nonattainment boundaries for the 2006 24

- hour PM 2.5 

### NAAQS within Imperial County, and a larger partial

- county nonattainment area encompassing all of Imperial County except the portion east of the Chocolate Mountains for the 1997 24

- hour PM 10 

### NAAQS. The state has recommended the same boundary for the 2012 annual PM 2.5 

### NAAQS as exists for the 2006 24

- hr PM 2.5 

### NAAQS. The state did not provide a 5

- factor analysis to support their partial

- county recommended nonattainment area for Imperial County, CA, instead merely rel ying on the existing 2006 24

- hr PM 2.5 

### NAAQ S jurisdiction factor to recommend the same
area as nonattainment for the 2012 annual PM 2.5 

### NAAQS. [The 2006 24

- hr nonattainment boundary was chosen based on the assumption that emissions from the City of Mexicali from across the U.S.

– Mexico border influ ence exceedances of the 24

- hour NAAQS during stagnation conditions in the winter months of November through February.]

In Imperial County, CA, air quality is managed by the Imperial County Air Pollution Control District (ICAPCD or district). I C APCD has ju risdiction over control of stationary and non

- point sources in the county while the state retains authority over mobile sources and consumer products. In addition, the district is the lead agency in developing state implementation plans for any area within the county. SIPs are submitted by ICAPCD to the state for subsequent action and then the state submits them to the EPA for federal action.

Imperial County is a member of the Southern California Association of Governments (SCAG). According to its
website , “SCAG is the nation's largest metropolitan planning organization, representing six counties, 191 cities and more than 18 million residents.” SCAG covers most of southern California, with the exception of San Diego County, CA. The EPA deemed the area unde r SCAG jurisdiction to be inappropriately large with respect to developing a boundary for the Imperial County, CA nonattainment area. However, Imperial County is a member of SCAG and has the ability to perform certain transportation and transit related fun ctions in the county, such as addressing the requirements under the CAA of transportation conformity.

The Office of Management and Budget’s December 2009 delineations of core

- based statistical areas (CBSAs) lists Imperial County, CA as the El Centro metrop olitan statistical area (metro CBSA). In the listing, OMB did not include the metro CBSA in any larger adjacent combined statistical area (CSA). Directly to the north of the metro CBSA is one of the largest CSAs in the nation, the Los Angeles

- Long Beach

- Ri verside CSA (LA CSA). In not delineating the metro CBSA as part of the neighboring LA CSA, it is clear that the metro CBSA is not economically linked to the neighboring CSA, nor even to the neighboring metro CBSAs. To the west is the San Diego

- Carlsbad

- San Marcos metro CBSA, and to the east is the Yuma, AZ metro CBSA. This characterization is in line with the agricultural nature of Imperial County, CA, and is borne out by other factors above. The EPA has no indication that there is any large

- scale commuting pattern between Imperial County, CA and any other neighboring county, for example. The southern border of Imperial County, CA is also the international border
between the United States and Mexico.

Imperial County also includes portions of Indian country o f the following tribes: t he Quechan Tribe of the Fort Yuma Indian Reservation (Quechan) and the Torres Martinez Desert Cahuilla Indians (Torres Martinez). As
defined at 18 U.S.C. § 1151, “Indian country” refers to: “(a) all land within the limits of any In dian reservation under the jurisdiction of the United States Government, notwithstanding the issuance of any patent, and, including rights

- of

- way running through the reservation, (b) all dependent Indian communities within the borders of the
United States whether within the original or subsequently acquired territory thereof, and whether within or without the limits of a state, and (c) all Indian allotments, the Indian titles to which have not been extinguished, including rights

- of

- way running through the s ame.” The EPA recognizes the sovereignty of tribal governments, and has attempted to take the desires of the tribes into account in establishing appropriate nonattainment area

boundaries. We did not receive recommendations from either tribe.

### Page 43 of 161

Torres Martin ez is a federally recognized tribe that has non

- contiguous lands in both Imperial County and Riverside County. These portions of Indian country and the surrounding nonattainment areas are shown in Figure

1. We are designating the Torres Martinez reservati on land s as “unclassifiable/attainment

, ” consistent with the surrounding area.

Quechan is a federally recognized tribe that has contiguous lands spanning Imperial County, California and Yuma County, Arizona. These portions of Indian country and the surrou nding nonattainment areas are shown in Figure

1. W e are designating all areas of Quechan Indian country as “unclassifiable/attainment

, ” consistent with the surrounding area

.

The nonattainment area therefore does not include portions of Indian country.

Figure 11

. EPA’s Imperial County Nonattainment Area and Nearby Tribes

In summary for this factor, a nonattainment boundary consistent with the 2006 24

- hour PM 2.5 

### nonattainment area
covers both the area that violate s the NAAQS and near by areas that contribute to the violation

.

Conclusion for Imperial County Area

Based on the assessment of factors described above, both individually and in combination, the EPA has concluded that the following partial county should be included as part of the Imperial County nonattainment area because portions of the county are either violating the 2012 annual PM 2.5 

### NAAQS or contributing to a violation at a nearby

### Page 44 of 161

monitoring site: Imperial County (parti al). The area the EPA is designating nonattainment for the annual standard
is in the central

- southern portion of Imperial County, CA, and includes the City of Calexico. The EPA concurs with the state’s recommendation for this area, and is designating the same area as is included in the Imperial County nonatta inment area for the 2006 24

- hour PM 2.5 

### NAAQS

.

An evaluation of Factor 1 shows that the air quality monitoring sites in Imperial County indicate violations of the
2012 annual PM 2.5 

### NAAQS based on the 2013 DVs; therefore portions of the county are included in the
nonattainment area. Further evaluation of PM 2.5 

### chemical mass constituents at the Calexico Ethel monitoring station show that while organic mass comprises 58% of the annual average PM 2.5 

### concentrations in the area, crustal material comprises 21%, wh ich suggests that fugitive dust sources, including unpaved roads, agricultural sources, and windblown dust are also contributing sources to violations of the 2012 annual PM 2.5 

### NAAQS.
Factor 2 shows that the Imperial County nonattainment area includes sour ces of directly emitted PM 2.5 

### and/or

PM 2.5 

### precursors in the area that contribute to the violating monitor, and all areas in the nonattainment area contribute to the particulate matter concentrations which result in violations of the 2012 annual PM 2.5 

### NAAQ S through emissions from non

- point sources (e.g. area sources) and mobile sources. As discussed in Factor 1, both organic mass and crustal material are contributing emissions sources to violations of the 2012 annual PM 2.5 

### NAAQS and therefore, t he nonattain ment area includes both sources of organic mass and crustal material

.

Factor 3 suggests that during the spring months there are likely contribution s of emissions from the regions to the

west

- northwest and to the southeast of the Calexico Ethel monitoring location. S tagnant conditions during the winter months have the potential to cause more localized exceedances of the NAAQS, including influences from Mexico to the south of the violating Calexico

- Ethel monitoring site. Both of these time periods are associated with higher quarterly averages than other times of the year (see Factor 1), which further supports that there are two distinct meteorological regimes under which different source areas contribute to violations of the 2012 annual

PM 2.5 

### NAAQS.

A s discussed in Factor 4, Imperial County does not have any geographical or topographical barriers that limit air

pollution transport within the Imperial Valley portion of the county. Outside of Imperial Valley, t he Peninsular Ranges to the west of Imperial County does serve as a partial barrier to tr ansport from San Diego County, while

t he Chocolate, Orocopia

, and Cargo Muchacho Mountains mountain ranges within and to the east of Imperial County serve as a partial barrier to transport from Yuma County and L a Paz County in Arizona.

An assessment of Factor 5 supports the Imperial County nonattainment area due to the previous 2006 24

- hour

PM 2.5 

### nonattainment boundary designations and local air district jurisdiction, which encompasses a portion of Imperial Coun ty

.

Furthermore, g iven the uncertainty in the emissions of crustal sources outside the nonattainment boundary, the

EPA is designating Imperial County nonattainment for the 2012 annual PM 2.5 

### NAAQS pursuant to the same boundary as the 24

- hour PM 2.5 

### standard, including only a portion of Imperial County in the nonattainment area, based primarily on the jurisdiction factor.

Based on proximity to the border, emissions data from Mexico, population statistics, and wind patterns, the EPA
expects some contr ibution from sources in Mexicali to the violations at the Calexico Ethel site. In addition, based on speciated data, wind patterns, and land use, the EPA also expects a local contribution from within Imperial County. As described in the TSD, information av ailable indicates that biomass burning, combustion sources, and fugitive dust sources such as agricultural sources, unpaved roads, and windblown dust are large contributors to

### Page 45 of 161

high annual PM 2.5 

### concentr ations within Imperial County. S pecifically, wind patt erns indicate that part of the year is strongly impacted by sources to the north and northwest of Calexico Ethel. The EPA is designating using the same boundary as the 24

- hour PM 2.5 

### standard, based primarily on the jurisdiction factor.

### Page 46 of 161

3.2 Area Backgrou nd and Overview

– Los Angeles

- South Coast Air Basin, CA

Figure 1 is a map of the EPA’s nonattainment boundary for the Los Angeles

- South Coast Air Basin. The map shows the location and design values of ambient air quality monitoring locations, county and other jurisdictional boundaries, the State recommendation, and existing 1997 annual and 2006 24

- hour PM 2.5 

### NAAQS nonattainment
boundaries. Figure 1a shows the EPA’s area of analysis considered in this section, which includes the counties in the Los Angeles

- Long Beach

- Riverside combined statistical area (CSA) (Los Angeles, Orange, Riverside, and San Bernardino counties) as well as all adjacent counties (Imperial, Inyo, Kern, San Diego, and Ventura counties in California, Mojave and La Paz counties in Arizona , and Clark County, Nevada).

For purposes of the 1997 annual PM 2.5 

### NAAQS, this area was designated nonattainment. The boundary for the
nonattainment area for the 1997 annual PM 2.5 

### NAAQS included the entirety of Orange County and parts of Los Angeles, Sa n Bernardino and Riverside counties. For purposes of the 2006 24

- hour PM 2.5 

### NAAQS, this area was designated nonattainment. The boundary for the nonattainment area for the 2006 24

- hour PM 2.5 

### NAAQS included the entirety of Orange County and parts of Los Ange les, San Bernardino and Riverside counties. The boundary
for the 2012 annual PM 2.5 

### NAAQS is the same as the existing designated boundar ies for the 1997 annual and 2006

24

- hour PM 2.5 

### NAAQS. The Los Angeles

- South Coast Air Basin nonattainment area includes areas of Indian country of the following federally recognized tribes: the Cahuilla Band of Mission Indians of the Cahuilla Reservation, the Morongo Band of Mission Indians, the Ramona Band of Cahuilla, the San Manuel Band of Mission Indians, and the Soboba Band of Luiseño Indians.

### Page 47 of 161

The EPA must designate as nonattainment areas that violate the NAAQS and nearby areas that contribute to the violation in the violating area. Data from monitors located in Los Angeles, Riverside, and San Bernardino

c ounties show violations of the 2012 PM 2.5 

### NAAQS, therefore it is appropriate for these three counties or some portion of these three c ounties to be designated nonattainment. As shown in Figure 1a, the EPA evaluated each county with a violating monitor as well as those located near a county with a violating monitoring site based on
the five factors and ot her relevant information. The following sections describe this five factor analysis process. While the factors are presented individually, they are not independent. The five factor analysis process carefully considers their interconnections and the dependence of each factor on one or more of the others.

Factor 1: Air Quality Data

All data collected during the year are important when determining contributions to an ann ual standard such as the
2012 annual PM 2.5 

### NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings throughout the year, including days with monitored ambient concentrations below the level of the NAAQS. For the 2012
annual PM 2.5 

### NAAQS, th e annual mean is calculated as the mean of quarterly means. A high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3

- year DV. Although all data are important, seasonal or episodic emissions can provide insight as to rel ative contributors to measured PM 2.5 

### concentrations. For these reasons, for the Factor 1 air quality analysis, the EPA assessed and characterized air quality at, and in the proximity of, the violating monitoring site locations first, by evaluating trends a nd the spatial extent of measured

### Page 48 of 161

concentrations at monitors in the area of analysis, and then, by identifying the conditions most associated with high average concentration levels of PM 2.5 

### mass in the area of analysis.

In most cases, the EPA assessed air quality data on a seasonal, or quarterly, basis. 43 

### The EPA also identified the spatial extent of these high PM 2.5 

### concentrations. The mass and composition at the design value location represents contributions from various emission sources including local, area

- wide (which may comprise nearby urban and rural areas) and regional sources. To determine the source mix (by mass) at the design value monitoring site, the EPA examined the chemical composition of the monitored PM 2.5 

### concentrations by pairing each vio lating FRM/FEM/ARM monitoring site with a collocated or nearby Chemical Speciation Network (CSN) monitoring site or sites. Then, the EPA contrasted the approximated mass composition at the design value monitoring site
with data collected at IMPROVE 44 

### and ot her monitoring locations whose data are representative of regional background. 45

, 46 

### This comparison of local/area

- wide chemical composition data to regional chemical composition data derives an “urban increment,” which helps differentiate the influence of more distant emissions sources from

43 Although compliance with t he annual NAAQS depends on contributions from all days of the year, examin ing data on a quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and mete o rology and the resulting PM 2.5 mass and composition . In s ome areas of the country where there may be noticeable month

- to

- month variations in average PM 2.5 , the quarterly averages may not adequately represent seasonal variability. In these areas, air quality data may be aggregated and presented by those months th at best correspond to the local “seasons” in these areas.

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

45 The “urban increment” analysis assesses and ch aracterizes the increase in seasonal and annual average PM 2.5 mass and chemical constituents observed at violating monitoring site(s) relative to monitoring sites outside the area of analysis (whi ch represent background concentrations). Developing the urba n increment involves pairing a violating FRM/FEM/ARM monitor with a collocated monitor or nearby monitor with speciation data. EPA made every effort to pair these data to represent the same temporal and spatial scales. However, in some cases, the paired vi olating and CSN “urban” monitoring locations were separated by some distance such that the included urban CSN site(s) reflect(s) a different mixture of emissions sources, which could lead to misinterpretations. To generally account for differences in PM 2.5 mass between the violating site and the nearby CSN site(s), 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 appr oach may be misleading, or
in situations where non

- carbonaceous emissions are believed to be responsible for a local PM 2.5 concentration gradient, EPA used alternative analyses to reflect the mix of urban and rural sources contributing to the measured conc entrations at violating monitoring sites.

46 The urban monitors were paired with any rural sites within a 150 mile radius of an urban site to calculate spatial means of the quarterly averages of each species. If there were no rural sites within 150 miles, then the nearest rural site was used alone. That rural mean was then subtracted from the quarterly mean of the urban site to get the increment. Negative values were simply replaced with zeros.

### Page 49 of 161

the influence of closer emissions sources, thus representing the portion of the measured violation that is associated with nearby emission contributions. 47

, 48

, 49



### PM 2.5 

### Design Values and Total Mass Measurements

– The EPA examined ambient PM 2.5 

### air quality monitoring data represented by the DVs at the viol ating monitoring site and at other monitors in the area of analysis. The EPA calculated DVs based on air quality data for the most recent 3 consecutive calendar years of quality

- assured, certified air quality data from suitable FEM/FRM/ARM monitoring sites in the EPA’s Air Quality System (AQS). For this designations analysis, the EPA used data for the 2011

- 2013 period (i.e., the 2013 design value), which are the most recent years with fully

- certified air quality data. A monitor’s DV is the metric or statist ic that indicates whether that monitor attains a specified air quality standard. The 2012 annual PM 2.5 

### NAAQS is met at a monitoring site when the 3

- year average annual mean concentration is 12.0 micrograms per cubic meter (μg/m 3 

### ) or less (e.g., 12.1 μg/m 3 

### or greater is a violation). A DV is only valid if minimum data completeness criteria are met or when other regulatory data processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 2 identifies the current design value(s) (i.e., the 2013 D V) and the most recent two design values based on all monitoring sites in the area of analysis

. 50 

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

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

48 Hand, et. al. Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United

States: Report V, June 2011. Chapter 7

– Urban Excess in PM 2.5 Speciated Aerosol Concentrations, http://vista.cira.colostate.edu/improve/Publications/Reports/2011/PDF/Chapter7.pdf

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

50 In certain circumstances, one or more monitoring locations within a monito ring network may not meet the network technical requirements set forth in 40 CFR 58.11(e), which states, “ State and local governments must assess data from Class

III PM 2.5 FEM and ARM monitors operated within their network using the performance criteria de scribed in table C

- 4 to subpart C of part 53 of this chapter, for cases where the data are identified as not of sufficient comparability to a colloca ted FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to th e NAAQS. These assessments are required in the monitoring agency's annual monitoring network plan described in §58.10(b) for cases where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM ....”

### Page 50 of 161

Table 2. Air Quality Data collected at Regulatory Monitors (all DV levels in μg/m 3 

### ) a

County, State Monitor

Site ID Site Name State Rec NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV Los Angeles, CA 060370002 Azusa Yes 12 b 11.3 b 11.2 Los Angeles, CA 060371002 Burbank Yes 13. 7 c 12. 6 c 12. 5 c

Los Angeles, CA 060371103 Los Angeles

- Main St. Yes 13. 1 c 12.5 c 12.5 c Los Angeles, CA 060371201 Reseda Yes 10.6 10.3 10.2 Los Angeles, CA 060371302 Compton Yes 13.4 12.4 12.2 Los Angeles, CA 060371602 Pico Rivera #2 Yes 13.3 12.3 12 .0 Los Angeles, CA 060372005 Pasadena Yes 11.1 b 10.4 b 10.4 b

Los Angeles, CA 060374002 Long Beach (North) Yes 11.5 c 10. 6 c 10.9 c Los Angeles, CA 060374004 South Long Beach Yes 11.2 10. 6 c 1 0.8 c Los Angeles, CA 060379033 Lancaster No 6.9 b 6.2 b 6.1 b Orange, CA 060590007 Anaheim Yes 11. 1 c 10. 6 c 10. 6 c Orange, CA 060592022 Mission Viejo Yes 8.7 8.1 8.2
Riverside / San Diego, CA 060650009 Pechanga No 8.5 b 7.8 b 7 .7 Riverside, CA 060651003 Riverside Yes 12 11.4 11.5 Riverside, CA 060652002 Indio No 7.3 7.2 7.7 Riverside, CA 060655001 Palm Springs No 6.2 6.2 6.4 Riverside, CA 060658001 Rubidoux Yes 14.2 c 13. 4 c 13. 2 c

Riverside, CA 060658005 Mira Loma

- Van
Buren Yes 1 5.9 c 15. 2 c 14.8 c

San Bernardino, CA 060710025 Ontario Fire Station Yes 13.7 12.9 12.6

San Bernardino, CA 060710306 Victorville

- Park
Avenue No 7.7 b 6.9 b 6.8 b San Bernardino, CA 060712002 Fontana Yes 12.9 12.4 12.6 San Bernardino, CA 060718001 Big Bear Yes 8.9 8.3 8.7 San Bernardino, CA 060719004 San Bernardino Yes 12.1 11.7 11.8 Imperial, CA 060250005 Calexico Ethel Yes d 13.9 g 14.1 g 14.3 g Imperial, CA 060250007 Brawley Yes d 7.1 7.1 7.5 Imperial, CA 060251003 El Centro Yes d 7.4 7.2 7.4 Inyo, CA 060271003 Keeler No 7.3 7.3 7.5 Kern, CA 060290011 Mojave

- Poole No 5.3 b 5.7 b 7 .0 b

Kern, CA 060290014 Bakersfield

California Avenue Yes e 16.5 14.5 16.4 Kern, CA 060290015 Ridgecrest No 5.5 b 5.3 b 5.4 b Kern, CA 060290016 Bakersfield

- Planz Yes e 18.2 15.6 17.3 San Diego, CA 060730001 Chula Vista No 10.3 9.8 9.9 San Diego, CA 060730003 El Cajon No 11.8 10.6 c 10.6 c San Diego, CA 060731002 Escondido No 10.7 10.5 c 10.7 c

San Diego, CA 060731010 San Diego

- Downtown No 11.0 10.8 c 10.8 c

San Diego, CA 060731016 San Diego

- Kearny Villa Road f No 9.4 8.9 8. 7

Ventura, CA 061110007 Thousand Oaks No 9.3 8.9 9.1 Ventura, CA 061110009 Piru No 8.5 8.6 8.1 Ventura, CA 061111004 Ojai No na 9.5 b 9 .0 b Ventura, CA 061112002 Simi Valley No 9.3 8.9 9.1 Ventura, CA 061113001 El Rio No 9.2 8.7 9 .0 Clark, NV 320030298 Green Valley No 0 0 6.8 b Clark, NV 320030540 Jerome Mack No 9 7.8 8.1

### Page 51 of 161

County, State Monitor

Site ID Site Name State Rec NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV Clark, NV 320030561 Sunrise Acres No 7.7 7.9 8.8 Clark, NV 320031019 Jean No 3.7 4 4.6 Clark, NV 320032002 JD Smith No 7.3 b 8.3 b 9.5 b La Paz, AZ N/A No No Monitor Mojave, AZ N/A No No Monitor

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

b The listed design value is not valid due to completeness issues.

c This design value does not include data from Class III FEM monitors that the EPA has approved as not eligible for comparison to the NAAQS per 40 CFR 58.11(e). Docume nts associated with this action available at www .regulations.gov

, Docket ID No. EPA

- HQ

- OAR

- 2012

- 0918.

d State recommended nonattainment as part of a separate nonattainment area. See the section of this document titled “Area Background and Overview

– Imperial County, CA” for more information.

e State recommended nonattainment as part of a separate nonattainment area. See the section of this document titled “Area Background and Overview

– San Joaquin Valley, CA” for more information.

f In February 2012, the San Diego

- Overland (Kearny Mesa) site ( 060730006) was relocated to the San Diego

- Kearny Villa Road site (060731016). The data listed for the San

- Diego

- Kearny Villa Road site is a combination of data from San Diego

Overland from 2009 through February 17, 2012, and from San

- Diego

- Kearny Villa Roa d from February 21, 2012 through
2013.

g Design value based on all valid data, including data in 2011 and 2012 that were submitted to, but are not currently in, AQS. EPA considers these data valid for use per 40 CFR Part 50 and 58 (see Memorandum: “ Use of Data for Imperial County, CA
PM 2.5 Design Value Calculations ” )

.



### In addition to the FEM/FRM/ARM monitoring sites identified in Table 2 whose collected data are used to calculate DVs, additional nonregulatory monitors exist in the area of analysis, identifie d in Table 2a. The Temecula and Lake Elsinore sites were established for informational purposes, and the Agua Tibia, Joshua Tree and Lebec sites listed are IMPROVE sites established to evaluate visibility impacts on Class I areas. None of
these five monito rs are required per 40 CFR 58.20 to be compared to the NAAQS. Although these nonregulatory monitors are not eligible for comparison to the NAAQS, the data collected may help define an appropriate boundary for areas with emissions sources or activities that potentially contribute to ambient fine particle concentrations at the violating monitors.

Table 2a. Air Quality Data Collected at Special Purpose Monitors (all DV levels in μg/m 3 

### )

County, State Monitor Site
ID Site Name State Rec NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Riverside, CA 060650016 Temecula Y es

- - 8. 5 a

Riverside, CA 060659000 Agua Tibia N o 5.1 4.9 4. 7 a
Riverside, CA 060659000 Lake Elsinore Y es 11.6 10.9 10.6

San Bernardino, CA 060719002 Joshua Tree National
Monument

– Black Rock Canyon N o 3.8 3.5 3. 5 a

Los Angeles, CA 060379034 Lebec

- Peace Valley Rd Yes 3. 2 a 2. 7 a 2. 8 a

a One or more years of data are not complete.



### The Figure 1 a map, shown previously, identifies the Los Angeles

- South Coast Air Basin area of analysis and monitoring locations with 2011

- 2013 violating DVs. As indicated on the map, there are three violating monitoring
sites located in Los Angeles County, two violating monitoring sites in Riverside County, and two violating monitoring sites in San Bernardino County, and no monitoring s ites violating in Orange County.

### Page 52 of 161

Seasonal variation can highlight those conditions most associated with high average concentration levels of PM 2.5



### . Figure 2 shows quarterly mean PM 2.5 

### concentrations for the most recent 3

- year period for the highest DV

mon itoring sites in each county within the area of analysis . Figure 2a shows quarterly mean PM 2.5 

### concentrations
for the most recent 3

- year period for the highest DV monitoring sites in each county within only the Los Angeles

South Coast Air Basin nonattainment area , excluding sites from surrounding counties outside the nonattainment

area . This graphical representation is particularly relevant when assessing air quality data for an annual standard,
such as the 2012 annual PM 2.5 

### NAAQS, because, as previously stated, the annual mean is calculated as the mean of quarterly means and a high quarter can drive the mean for an entire year, which, in turn, can drive an elevated

3

- year DV.

Figure 2. Los Angeles

- South Coast Air Basin Area of Analysis Monitors

- PM 2.5 

### Quarterly Means for 20 11

- 201 3

Figure 2 a
. Los Angeles

- South Coast Air Basin Nonattainment Area Monitors

- PM 2.5 

### Quarterly Means for 20 11

- 201 3 0 5 10 15 20 25 30 35 40

'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 ) 

### Los Angeles

- South Coast Air Basin Area of Analysis PM 2.5 

### Quarterly



### Means 2011

- 2013

Calexico Ethel Keeler Bakersfield-Planz Anaheim
Mira Loma-Van Buren Escondido Simi Valley JD Smith Los Angeles (Main St.) Ontario Fire Station

0 5 10 15 20 25 30 35 40

'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 ) 

### Los Angeles

- South Coast Air Basin Nonattainment Area Monitors

-

PM 2.5 

### Quarterly Means 2011

- 2013

Anaheim Mira Loma-Van Buren Los Angeles (Main St.) Ontario Fire Station

### Page 53 of 161

As shown in Figure 2a

, in the Los Angele s

– South Coast Air Basin, quarter four tends to measure higher PM 2.5 

### concentrations than other quarters of the year.

PM 2.5 

### Composition Measurements

- To assess potential emissions contributions for each violating monitoring location, the EPA determined the various chemical species comprising total PM 2.5 

### to identify the chemical constituents over the analysis area, which can provide insight into the t ypes of emission sources impacting the
monitored concentration. To best describe the PM 2.5 

### at the violating monitoring location, the EPA first adjusted the chemical speciation measurement data from a monitoring location at or near the violating FRM monitor ing site using the SANDWICH approach to account for the amount of PM 2.5 

### mass constituents retained in the FRM

m easurement. 51

, 52

, 53

, 54 

### In particular, this approach accounts for losses in fine particle nitrate and increases in sulfate mass associated with partic le bound water. Figure 3a illustrates the fraction of each PM 2.5 

### chemical

constituent estimated at the Los Angeles Main St. monitoring site based on annual averages for the years 2010

- 2012. Figures 3a and Figure 3b shows that organic carbonaceous mass (OM) is the predominant species contributing over fifty percent of the total mass throughout the year. Sulfates are the second largest component in the annual mean, contributing 25 percent

, followed by crustal material (CM)

, contributing 9 percent. Nitrates

co ntribute most during quarter one ( Q1 ) and quarter four ( Q4 ), while sulfates contribute about thirty percent of

the total mass in quarter two ( Q2 ) and quarter three ( Q3 ). Crustal material (CM) contributes most during Q1 and Q2 and elemental carbon contribut e most in Q1 and Q4. This suggests that OM sources contribute at a greater level than any other species on an annual basis, while nitrates and sulfates also contribute annually and have even higher impacts in the colder and warmer months, respectively. Sou rces emitting CM also seem to contribute most in Q1 and Q2.

51 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 results in reduced nitrates (relative to the amount measured by routine speciatio n networks), higher mass associated with sulfates (reflecting water included in gravimetric FRM measurements) and a measure of organic carbonaceous mass derived from the difference
between measured PM 2.5 and its non

- carbon components. This characterization of PM 2.5 mass also reflects crustal material and other minor constituents. The resulting characterization provides a complete mass closure for the measured FRM PM 2.5

mass , which can be different than the data provided directly by the speciation measuremen ts from the CSN network.

52 Frank, N. H., SANDWICH Material Balance Approach for PM 2.5 Data Analysis, National Air Monitoring Conference, Las Vegas, Nevada, November 6

- 9, 2006. http://www.epa.gov/ttn/amtic/files/2006conference/frank.pdf

. 53 Frank, N. H., The Chemical Composition of PM 2.5 to support PM Implementation, EPA State /Local/Tribal Training Workshop: PM 2.5 Final Rule Implementation and 2006 PM 2.5 Designation Process, Chicago IL, June 20

- 21, 2007, http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chemical_composition.pdf.

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

– 511.

### Page 54 of 161

Figure 3 a
. Los Angeles

- South Coast Air Basin Annual Average PM 2.5 

### Chemical Constituents ( 2010

- 2012 )

Figure 3 b

. Los Angeles

- South Coast Air Basin Annual and Quarterly Average PM 2.5 

### Species ( 2010

- 2012 ) a

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



### The EPA assessed seasonal and annual average PM 2.5 

### constituents at monitoring sites within the nonattainment area relative to monitoring sites outside of the analysis area to account for the difference between regional background concentrations of PM 2.5 

### , and the concentrations of PM 2.5 

### in the area of analy sis, also known as the “urban increment.” This analysis differentiates between the influences of emissions from sources in nearby areas and in more distant areas on the violating monitor. Estimating the urban increment in the area helps to illuminate

the a mount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in nearby areas, as opposed to impacts of more distant or regional sources of emissions. Figure 4a includes pie charts showing the annual and quarterly chemical mass constituents of the urban increment. The quarterly pie charts correspond to the high

- concentration quarters identified in Figure 2. Evaluating these high

### Page 55 of 161

concentration quarters can help identify composition of PM 2.5 

### during these ti mes. Note that in these charts, sulfates and nitrates have been adjusted to represent their mass in measured PM 2.5



### .
Figure 4a

. Los Angeles

- South Coast Air Basin Urban Increment Analysis for 201 0

- 201 2.

### Page 56 of 161

Figure 4 b

. Los Angeles

- South Coast Air Basin Average Urban Increment Analysis for 2010

- 2012

.

The Los Angeles

– South Coast Air Basin has seven monitoring sites violating the NAAQS located in Los Angeles, Riverside, and San Bernardino c ounties . In addition, monitor s within the nonattainment area measure increased PM 2.5 

### concentrations during quarter four, with more inter

- annual variability in the other quarters of the

year.

In reviewing the urban increment analysis for the Los Angeles Main St. monitor, the results are similar to the adjusted speciation measurements, indicating a somewhat consistent source mix throughout the area. There is an increase in the contribution of OM indicating that it may have an additional contribution from a source closer to the speciati on monitoring site. This analysis points in general to contributions from direct PM 2.5 

### or PM 2.5 

### precursors from sources throughout the region, with an increased contribution of OM from sources closer to the speciation monitoring site.

Factor 2: Emissions a nd E missions

- related D ata

In th is designations process, for each area with a violating monitor ing site

, the EPA evaluate d the emissions data from nearby areas using emissions related data for the relevant counties to assess each county’s potential
contribu tion to PM 2.5 

### concentrations at the violating monitor ing site or monitor ing site s in the area under
evaluation. The EPA examined emissions of identified sources or source categories of direct PM 2.5 

### , the major components of direct PM 2.5 

### (organic mass , eleme ntal carbon, crustal material (and/or individual trace metal compounds)), primary nitrate and primary sulfate, and pr ecursor gaseous pollutants (i.e. , SO 2 

### , NO X 

### , total VOC,
and NH 3 

### ). The EPA also considered the distance of those sources of emissions from th e violating monitoring site.
While d irect PM 2.5 

### emissions and its major carbonaceous components are generally associated with sources near violating PM 2.5 

### monitoring sites, t he gaseous precursors tend to have a more regional influence (although the EPA
is mindful of the potential local NO X 

### and VOC emissions contributions to PM 2.5 

### from mobile and stationary sources) and transport from neighboring areas can contribute to higher PM 2.5 

### levels at the violating monitor ing
site s.

### Page 57 of 161

Emissions Data

For this factor, t he EPA reviewed data from the 2011 National Emissions Inventory (NEI) version 1 (see http://www.epa.gov/ttn/chief/net/2011inventory.html ). For each county in the area of analysis, the EPA examined the magnitude of county

- level emissions reported in the NEI. These county

- level emissions represent the sum of emissions from the following general source categories: point sources, non

- point (i.e., area) sources, nonroad mobile, on

- road mobile, and fires. In some instances, non

- anthropogenic sources of emissions such as wildfires account for large portions of the emissions inventory data presented below. The EPA also looked at the geographic distrib ution of major point sources of the relevant pol lutants. 55 

### Substantial emissions levels from

sources in a nearby area indicate the potential for the area to contribute to monitored violations.

To further analyze area emissions data, the EPA also developed a summary of direct PM 2.5 

### , components of direct

PM 2.5 

### , and precursor pollutants, which is available at http://www.epa.gov/pmdesignations/2012standards/docs/nei2011v1pointnei2008v3county.xlsx

.

When co nsidered with the urban increment analysis in Factor 1, evaluating the components of direct PM 2.5 

### and

precursor gases can help identify specific sources or source types contributing to elevated concentrations at violating monitor ing sites and thus assist i n identifying appropriate area boundaries. In general, directly emitted particulate organic carbon (POC) and VOCs 56 

### contribute to PM 2.5 

### organic mass (OM); directly emitted EC

contributes to PM 2.5 

### EC; NO X 

### , NH 3 

### and directly emitted nitrate contribute to PM 2.5 

### nitrate mass; SO 2 

### , NH 3 

### and directly emitted sulfate contribute to PM 2.5 

### sulfate mass; and directly emitted crustal material and metal oxides
contribute to PM 2.5 

### crustal matter. 57

, 58 

### The EPA believes that the quantities of those nearby emissions as potential
contributors to the PM 2.5 

### violating monitor s are somewhat proportional to the PM 2.5 

### chemical constituents in the estimated urban increment. Thus, directly emitted POC is more important per ton than SO 2 

### , partially because POC emissions are already PM 2.5 

### whereas SO 2 

### must conver t to PM 2.5 

### and not all of the emitted SO 2 

### undergoes this
conversion.

Table 3a provides a county

- level emissions summary (i.e., the sum of emissions from the following general source categories: point sources, non

- point (i.e., area) sources, nonroad mobile, on

- road mobile, and fires) of directly emitted PM 2.5 

### and precursor species for the county with the violating monitoring site and nearby counties
considered for inclusion in the Los Angeles

- South Coast Air Basin, CA area . Table 3b s ummarizes the directly emitted components of PM 2.5 

### for the same counties in the area of analysis for the Los Angeles

- South Coast Air Basin, CA area. This information will be paired with the Urban Increment composition previously shown in Figures 4a and 4b.

55 For purposes of this designations effort, “major” point sources are those whose sum of PM precursor emissions ( PM 2.5 +
NOx + SO 2 + VOC + NH 3 ) are greater than 500 tons per year based on NEI 2011v1

. 56 As previously mentioned, nearby VOC s are presumed to be a less important contributor to PM 2.5 OM than POC.

57 See , Seinfeld J. H. and Pandis S. N. (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change

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

58 USEPA Report (2004), The Particle Pollution Report: Current Unde rstanding of Air Quality and Emissions through 2003,

found at: http://www.epa.gov/airtrends/aqtrnd04/pm.html

.

### Page 58 of 161

Table 3a

. County

- Level Emissions of Directly Emitted PM 2.5 

### and Precursors (tons/year)

County , State Total

NH 3 Total NO x Total Direct
PM 2.5 Total SO 2 Total VOC Total

Los Angeles, CA 19,205 143,295 16,929 7,888 117,854 305,172

Kern , CA 26,732 41,723 12,303 3,060 47,172 130,990
San Bernardino, CA 12,974 67,255 11,665 1,934 35,543 129,371
San Diego , CA 6,754 42,667 10,559 1,286 60,083 121,348

Clark, NV 1,329 52,422 11,931 7,160 38,011 110,852
Orange, CA 6,958 31,637 4,004 387 38,273 81,260
Riverside, CA 9,421 35,935 5,453 378 26,789 77,975
Imperial , CA 16,396 10,051 4,472 146 6,978 38,043

Ventura , CA 3,604 11,652 2,084 291 13,937 31,568

Mohave, AZ 446 12,747 2,850 132 10,995 27,170

La Paz , AZ 357 3,745 1,102 43 2,446 7,693
Inyo , CA 1,468 932 922 199 1,505 5,026
Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.



### Table 3b . County

- Level Emissions for Components of Directly Emitted PM 2.5 

### (tons/year) 59

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

Los Angeles, CA 6,953 2,968 692 63 1,942 4,310 16,929

Kern , CA 6,863 1,862 324 65 1,204 1,987 12,303
Clark, NV 2,968 1,478 215 24 3,567 3,678 11,931
San Bernardino, CA 3,237 1,512 344 48 2,765 3,760 11,665 San Diego , CA 5,979 1,357 186 27 1,078 1,931 10,559
Riverside, CA 2,140 1,087 109 14 929 1,174 5,453 Imperial , CA 1,200 346 103 11 1,323 1,488 4,472 Orange, CA 1,891 718 96 15 418 867 4,004
Mohave, AZ 767 379 25 5 841 833 2,850

Ventura , CA 1,138 384 35 6 218 303 2,084

La Paz , AZ 235 118 8 1 420 318 1,102
Inyo , CA 201 69 21 3 303 325 922
Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.



### Table 3b breaks down the d irect PM 2.5 

### emissions value from Table 3a into its components. Th ese data will also be compared with the previously presented Urban Increment composition.

Using the previously described relationship between directly emitted and precursor gases and the measured mass to evaluate data presented in Tables 3a and 3b, the EPA identified the following components warranting
additional review: NH 3 

### , NO X 

### , PM 2.5 

### , SO 2 

### , VOC, POM, EC, PSO 4 

### , PNO 3 

### , Crustal and Residual. The E PA then

59 Data are based on the 2011 and 2018 Emissions Modeling Platform Data Files and Su mmaries ( ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform ) available at: http://www.epa.gov/ ttn / chief/emch/index.html#2011 (accessed 02/26/14)

.

### Page 59 of 161

looked at the contribution of these constituents of interest from each of the counties included in the are a of analysis as shown in Tables 4a

- k.

Table 4a. County

- Level NH 3 Emissions (tons/year)

Emissions in average tons/yr

County, State N H 3 Pct. Cumulative %

Kern , CA 26,732 25 25

Los Angeles, CA 19,205 18 43

Imperial , CA 16,396 16 59

San Bernardino, CA 12,974 12 71

Riverside, CA 9,421 9 80

Orange, CA 6,958 7 87
San Diego , CA 6,754 6 93

Ventura , CA 3,604 3 97

Inyo , CA 1,468 1 98

Clark, NV 1,329 1 99

Mohave, AZ 446 0 100

La Paz , AZ 357 0 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

Table 4 b . County

- Level NO X Emissions (tons/year)

Emissions in average tons/yr

County, State N O X Pct. Cumulative %

Los Angeles, CA 143,295 32 32

San Bernardino, CA 67,255 15 46

Clark , NV 52,422 12 58
San Diego

, CA 42,667 9 67

Kern , CA 41,723 9 77

Riverside, CA 35,935 8 84

Orange, CA 31,637 7 91

Mohave , AZ 12,747 3 94

Ventura , CA 11,652 3 97
Imperial , CA 10,051 2 99

La Paz , AZ 3,745 1 100

Inyo , CA 932 0 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

### Page 60 of 161

Table 4c . County

- Level PM 2.5 Emissions (tons/year)

Emissions in average tons/yr

County, State PM 2.5 Pct. Cumulative %

Los Angeles, CA 16,929 20 20

Kern , CA 12,303 15 35

Clark, NV 11,931 14 49

San Bernardino, CA 11,665 14 63
San Diego , CA 10,559 13 75

Riverside, CA 5,453 6 82
Imperial , CA 4,472 5 87

Orange, CA 4,004 5 92

Mohave , AZ 2,850 3 95

Ventura , CA 2,084 2 98

La Paz , AZ 1,102 1 99

Inyo , CA 922 1 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

Table 4d . County

- Level SO 2 Emissions (tons/year)

Emissions in average tons/yr

County, State SO 2 Pct. Cumulative %

Los Angeles, CA 7,888 34 34

Clark, NV 7,160 31 66

Kern , CA 3,060 13 79

San Bernardino, CA 1,934 8 88
San Diego , CA 1,286 6 93

Orange, CA 387 2 95

Riverside, CA 378 2 96

Ventura , CA 291 1 98

Inyo , CA 199 1 99
Imperial , CA 146 1 99

Mohave, AZ 132 1 100

La Paz , AZ 43 0 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

### Page 61 of 161

Table 4e . County

- Level VOC Emissions (tons/year)

Emissions in average tons/yr

County, State VOC Pct. Cumulative %

Los Angeles, CA 117,854 29 29

San Diego , CA 60,083 15 45

Kern , CA 47,172 12 56

Orange, CA 38,273 10 66

Clark, NV 38,011 10 75

San Bernardino, CA 35,543 9 84

Riverside, CA 26,789 7 91

Ventura , CA 13,937 3 95

Mohave, AZ 10,995 3 97
Imperial , CA 6,978 2 99

La Paz , AZ 2,446 1 100

Inyo , CA 1,505 0 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

Table 4 f . County

- Level POM Emissions (tons/year)

Emissions in average tons/yr

County, State POM Pct. Cumulative %

Los Angeles, CA 6,953 21 21

Kern , CA 6,863 20 41
San Diego , CA 5,979 18 59

San Bernardino, CA 3,237 10 69

Clark, NV 2,968 9 77

Riverside, CA 2,140 6 84

Orange, CA 1,891 6 89
Imperial , CA 1,200 4 93

Ventura , CA 1,138 3 96

Mohave, AZ 767 2 99

La Paz , AZ 235 1 99

Inyo , CA 201 1 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

### Page 62 of 161

Table 4g . County

- Level EC Emissions (tons/year)

Emissions in average tons/yr

County, State EC Pct. Cumulative %

Los Angeles, CA 2,968 24 24

Kern , CA 1,862 15 39
San Bernardino, CA 1,512 12 52

Clark, NV 1,478 12 64

San Diego , CA 1,357 11 75

Riverside, CA 1,087 9 84

Orange, CA 718 6 89

Ventura , CA 384 3 93

Mohave, AZ 379 3 96
Imperial , CA 346 3 98

La Paz , AZ 118 1 99

Inyo , CA 69 1 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

Table 4 h . County

- Level PSO 4 Emissions (tons/year)

Emissions in average tons/yr

County, State PSO 4 Pct. Cumulative %

Los Angeles, CA 692 32 32

San Bernardino, CA 344 16 48

Kern , CA 324 15 63

Clark, NV 215 10 73
San Diego , CA 186 9 82

Riverside, CA 109 5 87
Imperial , CA 103 5 91

Orange, CA 96 4 96

Ventura , CA 35 2 97

Mohave, AZ 25 1 99

Inyo , CA 21 1 100

La Paz , AZ 8 0 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

### Page 63 of 161

Table 4 i . County

- Level PNO 3 Emissions (tons/year)

Emissions in average tons/yr

County, State PNO 3 Pct. Cumulative %

Kern , CA 65 23 23

Los Angeles, CA 63 22 45

San Bernardino, CA 48 17 62
San Diego , CA 27 10 72

Clark, NV 24 9 80

Orange, CA 15 5 86

Riverside, CA 14 5 91

Imperial , CA 11 4 94

Ventura , CA 6 2 97

Mohave, AZ 5 2 98

Inyo , CA 3 1 99

La Paz , AZ 1 0 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

Table 4j . County

- Level Crustal Emissions (tons/year)
Emissions in average tons/yr
County, State Crustal Pct. Cumulative %

Clark, NV 3,567 24 24
San Bernardino, CA 2,765 18 42
Los Angeles, CA 1,942 13 55
Imperial , CA 1,323 9 64

Kern , CA 1,204 8 72
San Diego , CA 1,078 7 79
Riverside, CA 929 6 85

Mohave, AZ 841 6 91

La Paz , AZ 420 3 94
Orange, CA 418 3 97
Inyo , CA 303 2 99

Ventura , CA 218 1 100
Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.

### Page 64 of 161

Table 4 k . County

- Level Residual Emissions (tons/year)

Emissions in average tons/yr

County, State Residual Pct. Cumulative % Los Angeles, CA 4,310 21 21
San Bernardino, CA 3,760 18 38

Clark, NV 3,678 18 56

Kern , CA 1,987 9 65

San Diego, CA 1,931 9 75

Imperial , CA 1,488 7 82

Riverside , CA 1,174 6 87

Orange, CA 867 4 92

Mohave, AZ 833 4 95

Inyo , CA 325 2 97

La Paz , AZ 318 2 99

Ventura , CA 303 1 100 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.



### In addition to reviewing county

- wide emissions of PM 2.5 

### and PM 2.5 

### precursors in the area of analysis, the EPA also reviewed emissions from major point sources located in the area of analysis. The magnitude and locat ion of these sources can help inform nonattainment boundaries. Table 5 provides facility

- level emissions of direct PM 2.5 

### , components of direct PM 2.5 

### , and precursor pollutants (given in tons per year) from major point sources located in the area of analysis for the Los Angeles

- South Coast Air Basin, CA area. Table 5 also shows the distance from the facility to the Los Angeles

- South Coast Air Basin nonattainment area DV monitoring site (Mira Loma

- Van Buren). These major sources may be closer to another violat ing monitor within the Los Angeles

- South Coast
nonattainment area.

Table 5 . NEI 2011 v1 Major Point Source Emissions (tons/year)

Distance 

### NEI 2011 v1 Emissions

- Tons/Year

County , State Facility Name (Facility ID) monitor (m iles ) NH 3 NO
x PM 2.5 SO 2 VOC Total

Clark , NV REID

- GARDNER GENERATING

STATION 245 18 3,034 543 1,423 37 5,056

Clark , NV Mc Carran Intl 195 2,508 59 272 377 3,216

Clark , NV Lhoist North America and Granite Const. (Apex) 219 1,200 62 229 6 1,497

Clark , NV Nevada Power (Chuck Lenzie) 220 15 227 177 30 71 519

Kern , CA CALIFORNIA PORTLAND CEMENT CO. 85 2,204 168 790 6 3,168

Kern , CA NATIONAL CEMENT CO 92 817 224 13 14 1,067

Kern , CA CHEVRON USA INC 146 78 274 633 26 1,011

Kern , CA OCCIDENTAL OF ELK HILLS, INC. 144 8 372 1 9 261 651

Kern , CA CHEVRON USA INC 155 16 195 138 134 89 571

Kern , CA AERA ENERGY LLC 157 354 60 20 71 506

Los Angeles, CA LOS ANGELES INT AIRPORT 53 5,485 95 539 852 6,971

Los Angeles, CA EXXONMOBIL OIL CORPORATION 49 50 818 322 347 613 2,150

### Page 65 of 161 Distance 

### NEI 2011 v1 Emissions

- Tons/Year

County , State Facility Name (Facility ID) monitor (m iles ) NH 3 NO
x PM 2.5 SO 2 VOC Total

Los Angeles, CA BP WEST COAST PROD.LLC BP
CARSON REF. 45 164 584 312 524 483 2,067

Los Angeles, CA CHEVRON PRODUCTS CO. 53 48 649 177 379 541 1,794

Los Angeles, CA TESORO REFINING AND MARKETING

CO 45 38 584 153 215 235 1,225

Los Angeles, CA CONOCOPHILLIPS COMPANY 48 81 583 89 115 241 1,110

Los Angeles, CA CONOCOPHILLIPS COMPANY 45 1 331 53 287 91 764

Los Angeles, CA ULTRAMAR INC (NSR USE ONLY) 45 32 275 65 202 179 753

Los Angeles, CA SCE

- VINCENT SUBSTATION 49 604 0 0 0 604

Los Angeles, CA SCE

- ANTELOPE SUBSTATION 66 604 0 0 0 604

Los Angeles, CA ROBERTSONS READY MIX /

PALMDAL 49 508 14 522

Orange, CA JOHN WAYNE AIRPORT 31 580 16 63 95 753

San Bernardino, CA MITSUBISHI CEMENT 44 1,920 421 321 39 2,701

San Bernardino, CA CEMEX

- BLACK MOUNTAIN QUARRY 49 10 2,008 189 164 30 2,400

San Bernardino, CA SEARLES VALLEY MINERAL 122 5 1,865 178 160 33 2,241

San Bernardino, CA US ARMY NATIONAL TRAINING CTR. 99 0 1,272 213 30 128 1,643

San Bernardino, CA PG&E TOPOCK COMPRESSOR
STATION 178 1,204 14 0 125 1,344

San Bernardino, CA ACE COGENERATION CO 122 12 620 37 479 4 1,152

San Bernardino, CA TXI RIVERSIDE CEMENT COMPANY 43 870 221 14 15 1,120

San Bernardino, CA MAGTFTC MCAGCC TWENTYNINE
PALMS 84 1 719 45 68 222 1,055

San Bernardino, CA TETRA TECHNOLOGIES, INC.

- AMB 122 0 20 836 1 2 859

San Bernardino, CA ONTARIO INT. AIRPORT 7 550 10 57 80 697

San Diego , CA San Diego Intl

- Lindberg 89 1,156 21 125 165 1,468 Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.



### Figure 5 shows the major point source emissions (from the 2011 NEI in tons per year ) in the area of analysis for Los Angeles

- South Coast Air Basin, CA and the relative distances of these sources from the violating monitoring location( s), as depicted by red dots. The actual distance from the point sources to the Los Angeles

- South Coast Air Basin nonattainment area DV monitoring site (Mira Loma

- Van Buren) is presented in Table 5. The distance from the violating monitoring location is par ticularly important for directly emitted PM 2.5 

### . The influence of directly emitted PM 2.5 

### on ambient PM 2.5 

### diminishes more than that of gaseous precursors as a function of
distance. 60



### As indicated in Figure 5, there are 33 major point sources located in the area of analysis

.

60 Baker, K

. R. and K . M. Foley

. A nonlinear regression model es timating single source concentrations of primary and secondarily formed PM 2.5

. Atmospheric Environment. 45 (2011) 3758

- 3767.

### Page 66 of 161

Figure 5. Major Point Source Emissions in the Area of Analysis for the Los Angeles

- South Coast Air Basin,
CA Area

.

T he EPA’s analysis of relevant county

- level emissions and the geographic locations of the relevant pollutants
indicates that, of the twelve counties included in the area of analysis, Los Angeles, San Bernardino, Orange, Riverside, Kern, and San Diego counties in California, and Clark County in Nevada rank the highest for county

level emissions of directly emitted PM 2.5 

### and precursors. The Los Angeles

- South Coast Air Basin nonattainment area includes all of Orange County and portions of Los Angeles, San Bernardino, and Riverside counties . The northern portions Los Angeles County and the eastern portions of San Bernardino and Riverside counties are not
included in the nonattainment are a primarily due to topography, the geographic extent of the South Coast A ir

B asin, and jurisdiction

. These topographic and jurisdictional separations between the nonattainment area and

sources to the north in northern Los Angeles County and Kern County, to the south in San Diego County, and to
the east in San Bernardino and Riverside counties are described in further detail in Factor 4 and Factor 5

. Clark County, Nevada and its emission sources are a substantial distance from the Los Angeles

- South Coast Air Basin
nonattainment area and associated monitors , and mete o rological data indicates that the emissions in Clark County are less likely to be contr ibuting to violations at monitor ing locations in the Los Angeles

- South Coast area

.
Several counties in the area of analysis are also located substantial distances away from the Los Angeles

- South
Coast Air Basin nonattainment area and rank low in directly e mitted PM 2.5 

### and precursors relative to emissions
from other counties under evaluation , and meteorological data indicate that the emissions from these counties are less likely to be contributing to violations at the monitor locations in the Los Angeles

- Sou th Coast area . These

### Page 67 of 161

counties are La Paz and Mohave counties in Arizona, and Inyo County in California. While Ventura and Imperial counties are closer geographically to the nonattainment area, emissions of directly emitted PM 2.5 

### and precursors
from these t wo counties are 90% and 88% less, respectively, than emissions from Los Angeles County, and are even smaller when compared to emissions from the entire Los Angeles

- South Coast Air Basin nonattainment

area. Moreover, the EPA is separately designating a port ion of Imperial County as a separate nonattainment area
to address violations of the NAAQS that occur in that area.

Population density and degree of urbanization

In this part of the factor analysis, the EPA evaluated the population and vehicle use characteristics and trends of the area as indicators of the probable location and magnitude of non

- point source emissions. Rapid population growth in a county on the urban perimeter signifies increasing integrat ion with the core urban area, and indicates that it may be appropriate to include the county associated with area source and mobile source emissions as part
of the nonattainment area. Table 6 shows the 2000 and 2010 population , population growth since 2000 , and population density for each county in the area.

Table 6 . Population Growth and Population Density

.

County, State Population

2000 Population

2010 %

Change from 2000 Land Area

(Sq. Miles) Population

Density (per Sq. Mile) % Cumulative
%

Los Angeles, CA 9,519,338 9,825,761 3% 4,061 2,420 41 41 San Diego, CA 2,813,833 3,103,933 10% 4,200 739 13 53 Orange, CA 2,846,289 3,018,181 6% 789 3,823 12 66 Riverside, CA 1,545,387 2,202,361 43% 7,207 306 9 75 San Bernardino, CA 1,709,434 2,041,626 19% 20,052 102 8 83
Clark, NV 1,375,765 1,953,422 42% 7,910 247 8 91
Kern, CA 661,645 841,687 27% 8,141 103 3 95
Ventura, CA 753,197 825,378 10% 1,845 447 3 98
Mohave, AZ 155,032 200,380 29% 13,312 15 1 99 Imperial, CA 142,361 174,667 23% 4,175 42 1 100
La Paz, AZ 19,715 20,465 4% 4,500 5 0 100 Inyo, CA 17,945 18,531 3% 10,203 2 0 100

Total 21,559,941 24,226,392 Source: U.S. Census Bureau population estimates for 2000 and 2010
Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.



### Although most of southern California is experiencing rapid population growth, as seen in Table 6 and Figures 6 and 6a, the population growth in the Los Angeles

- South Coast, CA area is constrained by terrain.

Reviewing the list of co unties in Table 6, the county with the largest population is Los Angeles. The most populated portion of the county falls within the nonattainment area. San Diego County’s population grew by 10.3% between 2000 and 2010, but given the smaller population actu ally added a similar total number of people as Los Angeles County added in the same time period.

The entirety of Orange County, CA is in the Los Angeles

- South Coast Air Basin nonattainment area, proximate to violating monitors and although the smallest in terms of land area (789 square miles) among the counties in the area of analysis, Orange County is third in terms of population and is by a wide margin the first in terms of

### Page 68 of 161

p opulation density. The population of Orange County is integrated with the populations of the surrounding areas in Los Angeles, Riverside, and San Bernardino c ounties.

Riverside County, CA has an east

- west geographic orientation and is hundreds of miles lo ng in this direction. As such, the county has a widely diverse pattern of population distribution, with the western portion of the county, within the nonattainment area, reflecting population density that is fairly undifferentiated from that of the highly populated urban pattern of Orange County, which borders Riverside County to the west, while the central portion of the county is more rural in its population pattern, in the Coachella Valley, and the eastern portion has low population in terms of both popu lation density and total population.

San Bernardino County, CA is like Riverside County in that the portion of the county that is within the Los Angeles

- South Coast Air Basin nonattainment area is highly dense in terms of population while the rest of th e county is sparse in comparison. San Bernardino County is the largest county in terms of square miles in the
United States. The next

- largest county is more than a thousand square miles smaller (Coconino County, AZ).
Counties in the American Southwest are large for historic reasons having to do with how the nation was settled, and San Bernardino County is typical of that pattern. As such, the county covers a wide diversity of landscape, with the densely populated portion of the county in the southwest, with in the nonattainment area, more closely integrated to the surrounding areas of Los Angeles, Orange, and Riverside c ounties, and the desert wilderness areas in much of the rest of the county more separate.

After San Bernardino, Clark County, NV is listed next in Table 6 in terms of 2010 population. However, Clark County’s population is part of an adjacent combined statistical area (CSA) and is not economically integrated with the population in the Los Angeles CSA (for further discussion of statistical are as in the area of analysis, see factor 5 below).

Kern County, CA has a smaller but still appreciable population, over 800,000, and a high level of population growth (27%). Generally, due to commuting patterns, Kern County is not economically integrated with the population in the Los Angeles

- South Coast Air Basin nonattainment area. See traffic discussion below.

Ventura County, CA also has a smaller but still appreciable population, with over 800,000 people. And like Kern County, has a high level of pop ulation growth (10%). Although it too is outside of the nonattainment area, the population of Ventura County is economically integrated with the population in the South Coast Air Basin. The population is fairly continuous and consistent with that of the po rtion of Los Angeles County that borders Ventura County to the southeast, the portion of Los Angeles County (the southwestern portion) within the South Coast Air
Basin. Also see traffic discussion below.

Mohave and La Paz counties in Arizona and Imperial and Inyo counties in California have very small populations compared to the rest of the counties in the area of analysis. These populations are both remote and sparsely distributed within geographically very large counties.

In conclusion, the population that most impacts air quality at the violating monitors in the area of analysis are for the most part included in the nonattainment area, and populations outside the area are generally not as dense or not integrated with the population in the nonattainment area. Ventura County appears to also be economically integrated with the nonattainment area.

### Page 69 of 161

Figure 6. 2010 County

- Level Population in the Area of Analysis for the Los Angeles

- South Coast Air Basin, CA Area.

Figure 6 a. 2010 Tract

- Level Population in the Area of Analysis for the Los Angeles

- South Coast Air Basin, CA Area.

### Page 70 of 161

Traffic and Vehicle Miles Travelled

H igh vehicle miles travelled (VMT) and/or a high number of commuters associated with a county is generally an indicator that the county is an integral part of an urban area. Mobile source emissions of NO
x 

### , VOC, and direct PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS in the

area . In combination with the population/population density data an d the location of main transportation arteries, an assessment of VMT help s identify the probable location of nonpoint source emissions that contribute to
violations in the area

. Comparatively high VMT in a county outside of the CBSA or CSA signifies integr ation
with the core urban area contained within the CSA or CBSA , and indicate s that a county with the high VMT may be appropriate to include in the nonattainment area because emissions from mobile sources in that county
contribute to violations in the area

. Table 7 shows 2011 VMT while Figure 7 overlays 2011 county

- level VMT with a map of the transportation arteries. The VMT sh own is from the Federal Highway Administration.

Table 7. 2011 VMT for the Los Angeles

- South Coast Air Basin, CA Area

. County, State Total 2011 VMT Percent Cumulative %

Los Angeles, CA 76,095,823,872 37 37 San Diego, CA 27,302,301,628 13 51 Orange, CA 26,418,285,864 13 63 San Bernardino, CA 20,423,645,232 10 73 Riverside, CA 19,110,634,300 9 83
Clark, NV 14,587,496,972 7 90
Kern, CA 8,288,439,996 4 94
Ventura, CA 6,744,473,079 3 97
Mohave, AZ 2,587,178,738 1 98 Imperial, CA 1,771,872,720 1 99
La Paz, AZ 776,932,996 0 100 Inyo, CA 604,078,683 0 100

Total 204,711,164,080 

### http://www.census.gov/hhes/commuting/data/commuting.html

Bold = counties (whole or partial) within the Los Angeles

- South Coast nonattainment area.



### Reviewing the data in Table 7, Los Angeles County has by far the highest amount of VMT. This high level of traffic is a clear indication that Los Angeles County is contributing to violations of the 2012 PM2.5 NAAQS in
the South Coast Air Basin. According to Census Bureau statistics for commuter flow between counties for 2006

- 2010, Los Angeles County has 4.4 million commuters. See: http://www.census.gov/population/me tro/data/other.html . Of those, 4.0 million commuters, or approximately 92%, commute from their residence to a workplace within the county. Nearly half (approximately 45%) of the remaining 0.4 million commuters commute to a workplace in Orange County. The p opulation patterns, described above, further indicate that the commuting patterns in the area of analysis are likely confined to an area within the Los Angeles

- South Coast Air Basin nonattainment area. Likewise, the majority of the VMT in San Bernardino

an d Riverside counties, given their population densities, is expected to occur within the nonattainment area.

In contrast, other portions of these same counties and the rest of the counties in the area of analysis either have low overall VMT or are not integrated with the populated areas within the Los Angeles

- South Coast Air Basin
nonattainment area. Mohave and L a Paz counties in Arizona and Imperial and Inyo counties in CA are both small in terms of overall VMT as compared to the rest of the area of analysis. Together their VMT amounts to roughly 3% of the total VMT in the area of analysis. Given the relatively small populations in these counties and the

### Page 71 of 161

distances to any portion of the nonattainment area, and considering other factors such as topography and meteorology, it is unlikely that mobile source emissions from Mohave County, AZ, La Paz County, AZ, Imperia l County, CA, or Inyo County, CA are contributing to violations of the NAAQS at monitoring locations within the

nonattainment area

.
San Diego County, CA, Kern County, CA, and Clark County, NV are not economically integrated with any portion of the nonatt ainment area. Of 1.4 million commuters in San Diego, only 2% travel to the counties with in
the South Coast Air Basin ( i.e., Los Angeles, Orange, Riverside or San Bernardino counties ) . Of 894,892 commuters in Clark County, NV, only 0.5% (4,281 commuters) tr avel to work in any of the other counties in the area of analysis. Likewise, of 304,506 commuters in Kern County, less than 5% (14,146 commuters) travel to work in any other county in the area of analysis.

Arterial flow, as shown in Figure 7, that otherwis e might suggest that expanding a nonattainment area boundary is appropriate , is characteristic of the area’s ports (the international ports of Los Angeles and Long Beach) and the role the area plays as the point of origin for goods movement from oceangoing vessels to trucks for distribution in

the rest of the nation.

According to the same Census Bureau statistics for commuter flow between counties for 2006

- 2010, 18% of Ventura County’s commuters commuted from their residence to a workplace in Los Angeles or Orange counties.

See: http://www.census.gov/population/metro/data/other.html . This could indicate inclusion in the EPA’s Los Angeles

- South Coast Air Basin nonattainment area, if it wer e the only factor the EPA considered.

Figure 7. Overlay of 2011 County

- level VMT with Transportation Arteries.

### Page 72 of 161

In conclusion, emissions from directly emitted PM 2.5 

### and precursors are highest for the four counties included in the nonattainment area, as well as Kern and San Diego counties in California, and Clark County in Nevada. Clark County is geographically distant from the Los Angeles

- South Coast Air Basin nonatta inment area, while Kern and San Diego are topographically separated (see Factor 4). Emissions from Imperial and Ventura counties were small in comparison to the nonattainment area emissions. Major point sources in the nonattainment area contribute to
the m onitored violations, and due to topography and meteorology, it is unlikely that those outside of the Los Angeles

- South Coast Air Basin nonattainment area contribute to the monitored violations.

Factor 3: Meteorology

The EPA evaluated available meteorolo gical data to determine how meteorological conditions, including, but not limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of directly emitted particulate matter and precursor emissions from sources in the area of analysis

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

. When considered in
combination with area PM 2.5 

### composition and county

- level and facility emissions source location information,
wind roses and KDE can help to identify nearby areas contributing to violations at violating monitoring sites.

Wind roses are graphic illustrations of the frequency of wind direction and wind speed. Wind direction can
indicate the direction from which contrib uting emissions are transported; wind speed can indicate the force of the wind and thus the distance from which those emissions are transported. The EPA constructed w ind roses from hourly observations of wind direction and wind speed using 2009

- 2012 data f rom National Weather Service
locations archived at the National Climate Data Center. 61 

### When developing these wind roses, the EPA also used wind observations collected at meteorological sampling stations collocated at air quality monitoring sites , where
thes e data were available . Figure 8 shows wind roses that the EPA generated from data relevant in the Los Angeles

- South Coast Air Basin nonattainment area

.

61 ftp.ncdc.noaa.gov/pub/data/noaa or http://gis.ncdc.noaa.gov/map/viewer/#app=cdo&cfg=cdo&theme=hourly&layers=1&node=gis Quality assurance of the
National Weather Service data is described here: http://www1.ncdc.noaa.gov/pub/data/inventories/ish

- qc.pdf

.

### Page 73 of 161

Figure 8. Wind Roses in the Area of Analysis for Los Angeles

– South Coast Air Basin.

As shown in Figure 8, there is a pattern across the CBSA of predominantly westerly winds, mostly at mid

- level speeds of 2 to 6 meters per second, suggesting that potential emission sources in the west upwind direction should be considered for analysis. T he roses also suggest less contribution from the east, thus supporting the exclusion of counties to the east, including Clark Co unty, NV and Imp erial County, CA.

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

- Particle Lagrangian Integrated Trajectory) backward trajector y frequency at violating monitor ing

### Page 74 of 161

sites

. 62

, 63 

### These KDEs are graphical statistical estimations to determine the density of trajectory endpoints at a particular location represented by a grid cell. The EPA used KDEs to characterize and analyze the collection of individual HYSPLIT backward trajectories. 64 

### Higher density values, indicated by darker blue colors, indicate a greater frequency of observed trajectory end points within a particular grid cell.

Figures 9 shows a HYSPLIT KDE plot for the Los Angeles

– South Coast Air Basin summarized by calendar quarter fo r the 2010

- 2012 period. Figure 9a show s a HYSPLIT KDE plot for each calendar quarter for the Los Angele s Main St. air monitoring site (AQS number 060651103) in Los Angeles County. Figure 9b show s a HYSPLIT KDE plot for each calendar quarter for the Mira Loma

- Van Buren air monitoring site (AQS number 060658005) in Riverside County. The HYSPLIT KDE is weigh ted in westerly direction, indicating a greater frequency of trajectories passing over grid cells to the west and supporting the Los Angeles

- South Coast Air
Basin nonattainment boundaries

.

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

nona ttainment area boundaries. A HYSPLIT backward trajectory is usually depicted on a standard map as a single line, representing the centerline of an air parcel’s motion, extending in two dimensional (x,y) space from a starting point and regressing backward i n time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining an air parcel’s point of origin on a day in which a short

- term standard, such as an 8

- hour standard or a 24

- hour standard, was exceeded. However, for an annual standard, such as the 2012 annual PM 2.5 NAAQS, every trajectory on every day is important. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT trajectori es may not be helpful as this process is lik ely to result in depicting air parcels originating in all directions from the violating monitoring site.

63 HYSPLIT

- Hybrid Single Particle Lagrangian Integrated Trajectory Model, http://www.arl.noaa.gov/HYSPLIT_info.php

64 The KDEs graphically represent th e aggregate of HYSPLIT backward trajectories for the years 2010

- 2012, run every third day (beginning on the first day of monitoring), four times each day, and ending at four endpoint heights.

### Page 75 of 161

Figures 9a. Q1

- Q4: HYSPLIT Kernel Density Estimation Plots for the Los Angeles Air Monitor.

### Page 76 of 161

### Page 77 of 161

Figures 9b. Q1

- Q4: HYSPLIT Kernel Density Estimation Plots

– Mira Loma Air Monitor in Riverside County.

### Page 78 of 161

### Page 79 of 161

Factor 4: Geography/topography

To evaluate the geography/topography factor, the EPA assessed physical features of the a rea of analysis that might define the airshed and thus affect the formation and distribution of PM 2.5 

### concentrations over the area.

Unlike many areas of the Eastern U.S.

, topography can have a substantial impact on pollutant formation and transport in California, and thus can play a more important role in assessing what areas are contributing to monitored violations of the NAAQS

. California has historically been divided into fifteen distinct air basins. The
South Coa st Air Basin consists of Orange County and parts of Los Angeles, Riverside, and San Bernardino counties, and contain the same state lands as the EPA’s Los Angeles

- South Coast Air Basin nonattainment area. The South Coast Air Basin forms a low plain, border ed on the west by the Pacific Ocean, and surrounded on the other sides by mountains which channel and confine the airflow. The San Gabriel Mountains lie to the north; the San Bernardino Mountains lie to the north and east, the San Jacinto Mountains to the southeast and the Santa Ana
Mountains to the south

. While these mountain ranges do not preclude transport from other areas , they are nevertheless high enough of limit contribution from other areas

.

Figure 10. Topographic Map in the Area of Analysis for the Los Angeles

– South Coast Air Basin.

### Page 80 of 161

Factor 5: Jurisdictional boundaries

In defining the boundaries of the Los Angeles

- South Coast Air Basin nonattainment area, the EPA considered existing jurisdictional boundaries, which can provide easily identifiable and recognized boundaries for purposes of implementing the NAAQS. Existing jurisdictional boundaries often signify the state, local, or tribal governmental organizatio n with the necessary legal authority for carrying out air quality planning and enforcement functions for the area. Examples of such jurisdictional boundaries include existing/prior nonattainment area boundaries for particulate matter, county lines, air dis trict boundaries, township boundaries, areas covered by a metropolitan planning organization, state lines, and Reservation boundaries, if applicable. Where existing jurisdictional boundaries were not adequate or appropriate to describe the nonattainment ar ea, the EPA considered other clearly defined and permanent landmarks or geographic coordinates for purposes of identifying the boundaries of the designated areas.

The Los Angeles

- South Coast Air Basin nonattainment area has previously established nonattai nment area boundaries associated with both the 1997 annual and 2006 24

- hour PM 2.5 

### NAAQS. The boundary for the
nonattainment area for the 1997 annual PM 2.5 

### NAAQS and the 2006 24

- hour PM 2.5 

### NAAQS was the same and included the entirety of Orange County and pa rts of Los Angeles, San Bernardino and Riverside counties , and is the same boundary as the South Coast Air Basin . The state has recommended the same boundary for the 2012
annual PM 2.5 

### NAAQS.

The Los Angeles

- South Coast Air Basin nonattainment area compr ises a large western portion of the larger Los Angeles

- Long Beach

- Riverside combined statistical area (CSA). The CSA includes the entirety of Los Angeles, Orange, San Bernardino and Riverside counties, while the nonattainment area includes only portions o f some of these counties. Transportation planning is performed by the Southern California Association of Governments (SCAG), whose jurisdictional area includes Los Angeles, Orange, Riverside, San Bernardino, Imperial and

Ventura c ounties

. The EPA’s analysi s indicates that the excluded portions of Los Angeles, San Berna r dino, and
Riverside c ounties are less likely to be contributing to the violations in the area as a whole due to topography and meteorology. The nonattainment area for the 2012 PM 2.5 

### NAAQS is within the jurisdictional boundaries of the South Coast Air Quality Management District, and is also consistent with our analysis of the air quality data (Factor 1), meteorology (Factor 3), and topography (Factor 4).

The Los Angeles

- South Coast Air Basin nonattainment area also includes Indian country of the following tribes: the Cahuilla Band of Mission Indians of the Cahuilla Reservation, the Morongo Band of Mission Indians, the Ramona Band of Cahuilla, and the Sobob a Band of Luiseño Indians (Soboba Band) . As defined at 18 U.S.C. § 1151, “Indian country” refers to: “(a) all land within the limits of any Indian reservation under the jurisdiction of the United States Government, notwithstanding the issuance of any paten t, and, including rights

- of

- way running through the reservation, (b) all dependent Indian communities within the borders of the United States whether within the original or subsequently acquired territory thereof, and whether within or without the limits o f a state, and (c) all Indian allotments, the Indian titles to which have not been extinguished, including rights

- of

way running through the same.” The EPA recognizes the sovereignty of tribal governments, and has attempted to
take the desires of the tribe s into account in establishing appropriate nonattainment area boundaries. The EPA received a request for consultation from the Soboba Band in September 2014. The EPA consulted wi th the Soboba Band in September and described the designations process, the hi story of the PM 2.5 

### standard in the southern California area, and implications of the nonattainment designation for the Soboba Band. On October 2, 2014, the EPA sent a response letter to the tribe summarizing this discussion. The EPA received designation

re commendations from the Pechanga Band of Luiseño Mission Indians of the Pechanga Reservation (Pechanga).

### Page 81 of 161

We did not receive designation recommendations from any of the tribes within the Los Angeles

- South Coast Air
Basin nonattainment area.

The Santa Rosa B and of Cahuilla Indians (Santa Rosa Cahuilla) has contiguous areas of Indian country in both the Los Angeles

- South Coast Air Basin nonattainment area and in Riverside County. We are designating all of
Santa Rosa Cahuilla as “unclassifiable/attainment” cons istent with surrounding Riverside County.

Pechanga has contiguous areas of Indian country in both the Los Angeles

- South Coast Air Basin and San Diego County. In December 2013, Pechanga submitted a recommendation of “unclassifiable” based on a five

- factor

a nalysis which noted that the tribe did not yet have a complete design value from a monitor on tribal land s . In August 2014, the EPA shared its intended designations with states and tribes, and published a notice of availability and public comment period in the Federal Register. The EPA intended to designate Pechanga “unclassifiable” based on a technical analysis (available at www.regulations.gov , Docket ID No. EPA

- HQ

- OAR

2012

- 0918) indicating Pechanga was unlikely to be contributing to exceedances within the Los Angeles

- South
Coast Air Basin intended nonattainment area

, the lack of complete data from a monitor on tribal lands, Pechanga’s recommendation and tribal jurisdiction, and overall consideration of the five f actors of analysis

. In October 2014, Pechanga submitted a revised recommendation of “attainment/unclassifiable” based on complete,

certified 2011

- 2013 monitoring data. Considering there is now a monitor on tribal lands with a complete, attaining design val ue, in addition to the previous factors including the technical analysis indicating Pechanga is unlikely to contribute to exceedances elsewhere, tribal jurisdiction, and Pechanga’s revised recommendation, t he EPA is designating all of Pechanga’s Indian country as “unclassifiable /attainment

. ”

### Page 82 of 161

Figure 1 1

. EPA’s Nonattainment Boundaries for the Los Angeles

– South Coast Air Basin Area and Nearby
Tribes

.

Conclusion for Los Angeles

– South Coast Air Basin Area

Based on the assess ment of factors described above, both individually and in combination, the EPA has concluded that the following counties or portions of counties should be included as part of the Los Angeles

– South Coast

Air Basin nonattainment area because they are eithe r violating the 2012 annual PM 2.5 

### NAAQS or contributing to a violation in a nearby area: Los Angeles (partial), Orange, Riverside (partial), and San Bernardino (partial)
counties. The relevant portions of Los Angele s , Orange, Riverside, and San Bernardino will be the same as those previously designated nonattainment for the 2006 24

- hour and 1997 annual PM 2.5 

### NAAQS and follow the area contained within the South Coast Air Basin: the southwest portion of Los Angeles County, all of Orange County, the westernmost portion of Riverside, and the southwestern portion of San Bernardino County

. The EPA concurs
with the S tate’s recommendation for the designation and the boundaries for this area

.

An evaluation of information relevant to Factor 1 shows that the air quality monitoring sites in Los Angeles, Riverside, and San Bernardino counties indicate violations of the 2012 annual PM 2.5 

### NAAQS based on the 2013 DVs; therefore these counties or portions of counties are included in the nonat tainment area. Orange County is a nearby county that do es not have violating monitoring sites, but the EPA has concluded that emissions from this
county contribute to the particulate matter concentrations in violation of the 2012 annual PM 2.5 

### NAAQS through

emissions from point sources, non

- point sources ( i.e ., area sources), and from mobile source emissions.

### Page 83 of 161

Consideration of data relevant to Factor 2 indicates that Los Angeles and San Bernardino counties have among the highest emissions of directly emitted PM 2.5 

### and/or PM 2.5 

### precursors in the area, and all counties or portions of
counties in the nonattainment area have relatively large of amounts of these emissions that can contribute to the
violations of the 2012 annual PM 2.5 

### NAAQS through emissions from point sources, non

- point sources ( i.e

. , area sources), and mobile sources

. While Ventura is economically integrated with the nonattainment area and shares commuters with Orange County and Los Angeles County, Ventura does not appear to have s ubstantial emis sions
of PM 2.5 

### and/or PM 2.5 

### precursors

. Thus, evaluation of Factor 2 suggests that inclusion of Ventura is not necessary

for this area.

Factor 3 suggests that the greatest potential contribution of emissions is from the regions immediately to the west
of t he violating monitors, which provides further support for the nonattainment boundary for the Los Angeles

South Coast Air Basin

.

As discussed with respect to Factor 4, the topography of this area is particularly relevant to evaluation of the appropriate bo undarie s for this nonattainment area. T he Los Angeles

- South Coast Air Basin is bordered on the west by the Pacific Ocean, and surrounded on the other sides by mountains which channel and confine the
airflow

. The location of the mountain ranges that border the airshed indicate that it is appropriate to include only portions of Los Angeles, San Bernardino, and Riverside c ounties and all of Orange County within the designated
nonattainment area.

Finally, an assessment of information relevant to Factor 5 further supports the Los Angeles

- South Coast Air Basin nonattainment area due to previous historical nonattainment boundary designations for both PM 2.5 

### and

ozone and local air district jurisdiction.

Only portions of Los Angeles, Riverside, and San Bernar dino counties are included in the Los Angeles

- South
Coast Air Basin nonattainment area because of air quality data (Factor 1), meteorology (Factor 3), topography (Factor 4), and jurisdictional boundaries (Factor 5). The northeastern portion of the Los Ange les County, and the eastern portions of Riverside and San Bernardino do not have a violating monitoring site . Also, the portions of the counties excluded from the nonattainment are separated by topographical barrier s

. Considering topography and meteorology

, it is unlikely these excluded portions of Los Angeles, Riverside, and San Bernardino counties are contributing to the NAAQS violations at monitoring locations within the nonattainment area . The factors that the

EPA primarily relied upon when determining the appropriate boundary for the Los Angeles

- South Coast Air Basin nonattainment area were air quality data (Factor 1), meteorology (Factor 3) geography/topography (Factor 4), and jurisdiction (Factor 5).

Also, the EPA is includ ing the areas of Indian cou ntry of the following tribes as part of the Los Angeles

- South
Coast Air Basin nonattainment area for the 2012 annual PM 2.5 

### NAAQS: the Cahuilla Band of Mission Indians of the Cahuilla Reservation, the Morongo Band of Mission Indians, the Ramona Band of Cahuilla, and the S oboba
Band of Luiseño Indians.

### Page 84 of 161

3.3 Area Background and Overview

– Plumas County, CA

Figure 1 is a map of the EPA’s nonattainment boundary for the nonattainment area located in Plumas County, CA. The map shows the location and design values of ambient air quality monitoring locations, county and other jurisdictional boundaries and other information, such as 2006 24

- hour PM 2.5 

### NAAQS nonattainment boundaries. As shown in Figure 1a, the EPA’s area of analysis considere d in this section includes Plumas County (which does not include any core

- based statistical areas (CBSAs)), as well as all adjacent counties (Butte, Shasta, Tehama, Lassen, Sierra, and Yuba counties).

The EPA must designate as nonattainment areas that vi olate the NAAQS and nearby areas that contribute to the violation in the violating area. Data from a monitor located in Plumas County shows a violation of the 2012 PM 2.5 

### NAAQS, therefore a portion of this county is included in the nonattainment area. As ex plained below, the EPA is designating only a portion of Plumas County as nonattainment, based upon an analysis of various factors such as the location of emission sources and topography. As shown in Figure 1a, the EPA evaluated each county located
near the county with a violating monitoring site based on the five factors and other relevant information. The following sections describe this five factor analysis process. While the factors are presented individually, they are not independent. The five factor analysis process carefully considers their interconnections and the dependence of
each factor on one or more of the others.

### Page 85 of 161

Factor 1: Air Quality Data

All data coll ected during the year are important when determining contributions to an annual standard such as the
2012 annual PM 2.5 

### NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings throughout the year, including days with monitored ambient conc entrations below the level of the NAAQS. For the 2012
annual PM 2.5 

### NAAQS, the annual mean is calculated as the mean of quarterly means. A high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3

- year DV. Although all data are important, seasonal or episodic emissions can provide insight as to relative contributors to measured PM 2.5 

### concentrations. For these reasons, for the Factor 1 a ir q uality analysis, the EPA assesse d and characterize d air quality at, and in the proximi ty of, the violating monitor ing site locations first, by evaluating trends and the spatial extent of measured concentrations at monitors in the area of analysis, and then, by identifying the conditions most associated with high average concentration levels of PM 2.5 

### mass in the area of analysis.

In most cases, the EPA assessed air quality data on a seasonal, or quarterly, basis

. 65 

### The EPA also identifie d the spatial extent of these high PM 2.5 

### concentrations

. The mass and composition at the design value location

65 Although compliance with the annual NAAQS depends on contributions from all days of the year, examin ing data on a quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and mete o rology and the resulting PM 2.5 mass and composition . In some areas of the country where there may be noticeable month

- to

- month

### Page 86 of 161

represents contributions from various emission sources including local, area

- wide (which may comprise nearby urban and rural areas) and regional sources. To determine the source mix (by mass) at the des ign value monitoring site, the EPA examined the chemical composition of the monitored PM 2.5 

### concentrations by pairing each violating FRM/FEM/ARM monitoring site with a collocated or nearby Chemical Speciation Network (CSN) monitoring site or sites. Then, t he EPA contrasted the approximated mass composition at the design value monitoring site
with data collected at IMPROVE 66 

### and other monitoring locations whose data are representative o f regional background. 67

, 68 

### This comparison of local/area

- wide chemical comp osition data to regional chemical composition

data derives an “ urban increment, ” which helps differentiate the influence of more distant emissions sources from the influence of closer emissions sources, thus representing the portion of the measured violati on that is associated with nearby emission contributions. 69

, 70

, 71



### PM 2.5 

### Design Values and Total Mass Measurements

– The EPA examin ed ambient PM 2.5 

### air quality monitoring data represented by the DV s at the violating monitoring site and at other monitors in the area of analysis

. The
EPA calculate d DVs based on air quality data for the most recent 3 consecutive calendar years of quality

- assured, certified air quality data from suitable FEM/FRM/ARM monitoring sites in the EPA’s Air Quality System (AQS).

For thi s designations analysis, the EPA used data for the 2011

- 2013 period (i.e., the 2013 design value), which are the most recent years with fully

- certified air quality data. A monitor’s DV is the metric or statistic that indicates

variations in average PM 2.5 , the quarterly averages may not adequately represent seasonal variability. In these areas, air quality data may be aggregated and presented by those months that best correspond to the local “seasons” in these areas.

66 IMPROVE stands for Interagency Monit oring for Protected Visual Environments and is an aerosol monitoring network in mostly rural and remote areas.

67 The “urban increment” analysis assesses and characterizes the increase in seasonal and annual average PM 2.5 mass and

chemical constituents obse rved at violating monitoring site(s) relative to monitoring sites outside the area of analysis (which represent background concentrations). Developing the urban increment involves pairing a violating FRM/FEM/ARM monitor with a collocated monitor or nearby monitor with speciation data. EPA made every effort to pair these data to represent the same temporal and spatial scales. However, in some cases, the paired violating and CSN “urban” monitoring locations were separated by some distance such that the includ ed urban CSN site(s) reflect(s) a different mixture of emissions sources, which could lead to misinterpretations. To generally account for differences in PM 2.5 mass between the violating site and the nearby CSN site(s), EPA determined material balance of t he PM 2.5 composition at the violating site by assigning the extra

measured PM 2.5 mass to the carbon components of PM 2.5 . Where the general urban increment approach may be misleading, or

in situations where non

- carbonaceous emissions are believed to be resp onsible for a local PM 2.5 concentration gradient, EPA used alternative analyses to reflect the mix of urban and rural sources contributing to the measured concentrations at violat ing monitoring sites.

68 The urban monitors were paired with any rural sites within a 150 mile radius of an urban site to calculate spatial means of the quarterly averages of each species. If there were no rural sites within 150 miles, then the nearest rural site was used a lone. That rural mean was then subtracted from the quarterl y mean of the urban site to get the increment. Negative values were simply replaced with zeros.

69 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 ar ea, the “urban increment” represents the difference between local and other nearby emission sources in the vicinity of the violating monitoring location and more regional sources.

70 Hand, et. al. Spatial and Seasonal Patterns and Temporal Variability of Ha ze and its Constituents in the United

States: Report V, June 2011. Chapter 7

– Urban Excess in PM 2.5 Speciated Aerosol Concentrations, http://vista.cira.colostate.edu/improve/Publications/Reports/2011/PDF/Chapter7.pdf

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

.

### Page 87 of 161

whether that monitor attains a specified air quality standard. The 2012 annual PM 2.5 

### NAAQS is met at a monitoring site when the 3

- year average annual mean concentration is 12.0 micrograms per cubic meter (μg/m 3 

### ) or less (e.g., 12.1 μg/m 3 

### or greater is a violation). A DV is only valid if minimum data completeness criteria are met or when other regulatory data processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 2 identifies the current design value(s) (i.e., the 2013 DV) and the most recent two design values based on all monitoring sites in the area of analysis for the Plumas County nonattainment area. 72 

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

Table 2

. Air Quality Data C ollected at Regulatory Monitors ( all DV levels in μg/m 3 

### ) a

County, State Monitor Site

ID Site Name State Rec
NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Plumas, CA 060631006 Quincy No 9.4 9.1 10.2

Plumas, CA 060631010 c Portola North
Substation c Yes e 10.3 11.5 12.8

Butte, CA 060070008 d Chico

- East d No 10.1 9.5 10.1

Lassen, CA N/A No No monitor

Shasta, CA 060890004 Redding No 5.3 5.3 5.7

Shasta, CA 060893004 Redding

- Buckeye No 6.5 6.3 6.2

Shasta, CA 060893005 Redding

- Toyon No 4.0 4.3 4.9 b

Sierra, CA N/A No No monitor

Tehama, CA N/A No No monitor
Yuba, CA N/A No No monitor

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

b The listed design value is not valid due to completeness issues.

c In July 2013, the Portola

- Nevada Street site (AQS ID 060631009) was relocated to the Portola North Substation site (AQS ID 060631010). The data listed for Plumas County is a combination of data from Portola

- Nevada Street site from 2009 through June 2013, and from Portola North Substation from Ju ly 2013 through the remainder of 2013.

d In July 2012, the Chico

- Manzanita site (AQS ID 060070002) was relocated to the Chico East site (AQS ID 06007 0008). The data listed for Butte County is a combination of data from Chico

- Manzanita from 2009 through June 2012, and from Chico East from July 2012 through 2013.

e California amended its November 2013 recommendation for Plumas County on July 2, 2014, revising its recommendation to nonattainment for a portion of the county.



### In addition to the FEM/FRM/ARM monitoring sites identified in Table 2 whose collected data are used to calculate DVs, the Northern Sierra Air Pollution Control District , the Tehama County Air Pollution Control District, and the National Park Service also operate the nonregulatory monito ring locations identified in Table 2a. The Paradise, Gridley

, Chester , and Red Bluff sites were established for informational purposes, and the Lassen Volcanic National Park site is an IMPROVE site, established to evaluate visibility impacts on Class I are as. These

72 In certain circumstances, one or more monitoring locations within a monitoring network may not meet the network technical requirements set forth in 40 CFR 58 .11(e), which states, “ State and local governments must assess data from Class

III PM 2.5 FEM and ARM monitors operated within their network using the performance criteria described in table C

- 4 to subpart C of part 53 of this chapter, for cases where the d ata are identified as not of sufficient comparability to a collocated FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to the NAAQS. These assessments are required in the monitoring agency's annual monitorin g network plan described in §58.10(b) for cases where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM ....”

### Page 88 of 161

monitors are not required per 40 CFR 58.20 to be compared to the NAAQS. Although these nonregulatory monitors are not eligible for comparison to the NAAQS, the data collected may help define an appropriate boundary for areas with emissions source s or activities that potentially contribute to ambient fine particle concentrations at the violating monitor.

Within Plumas County, the Portola monitor is in the south of the county within the Portola Valley. The Quincy
monitor is further north near th e c enter of the county. The non regulatory Chester monitor is the northernmost

PM 2.5 

### monitor in Plumas County. The California Air Resources Board (CARB) recommendation states that the coefficient of divergence and other statistical analyses indicate emissions that affect readings at the Quincy monitor do not likely directly impact the Portola monitor. While the Chester non regulatory monitor indicates
elevated PM 2.5 

### concentrations, the aforementioned CARB statement that emissions impacting a monitor north of

Por tola do not likely impact the Port ola monitor likely applies in this area as well. The analysis of concentrations measured at the Portola monitor versus the Quincy monitor, coupled with topographic considerations (discussed further in Factor 4) and low mix ing height suggests emissions outside of Portola Valley do not contribute to the violating monitor.

Table 2a. Air Quality Data Collected at Nonregulatory Monitors (all DV levels in μg/m 3 

### )

County, State Monitor Site

ID Site Name State Rec NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Butte, CA 060072002 Paradise No 9.0 9.4 10.1

Butte, CA 060074001 Gridley No 10.6 10.0 10.4

Plumas, CA 060631007 Chester No 9.1 a 11.6 13.1

Shasta, CA 060893003 Lassen Volcanic
NP No 2.7 2.6 2.4

Tehama, CA 0 61030006 b Red Bluff No 10.5 a 9.8 a 8.1

a One or more years of data are not complete.

b This monitor is currently incorrectly coded as a regulatory monitor in AQS. Recent information indicates this is a non

- FEM instrument, and should be reported in AQS under the nonregulatory parameter code of 88502. EPA is working with the monitoring agency and CARB to resolve this issue.



### The Figure 1 map, shown above , identifies the Plumas County nonattainment area and the monitoring location
with a 2011

- 2013 violating DV. As indicated on th e map, the area has one violating monitoring site located in the city of Portola in Plumas County. The 2013 DV at Portola is the first time the DV for Plumas County has been
above 12 μg/m 3 

### since 2005. There is one other regulatory monitoring location in Pl umas County, in Quincy,
which is located north

- east of the city of Portola, which does not currently record violations of the PM 2.5 

### standard. Seasonal variation can highlight those conditions most associated with high average concentration levels of PM 2.5



### . Figure 2 shows quarterly mean PM 2.5 

### concentrations for the most recent 3

- year period for the highest DV monitoring sites and other, non

- violating, monitoring sites in each county within the area of analysis. This graphical representation is particularly r elevant when assessing air quality data for an annual standard, such as the
2012 annual PM 2.5 

### NAAQS, because, as previously stated, the annual mean is calculated as the mean of quarterly means and a high quarter can drive the mean for an entire year, which , in turn, can drive an elevated 3

- year DV.

### Page 89 of 161

Figure 2. Plumas County Area of Analysis Monitor

- PM 2 .5 

### Quarterly Means for 20 11

- 201 3

As shown, there is consistent seasonal temporal variation across the 3

- year period for the Portola Substation,
Chico

- East Ave, and Quincy

- N. Church Street monitors. Quarterly values across the period are relatively higher

in quarter one ( Q1 ) and quarter four ( Q4 ) (October

- March) for these monitors. This signature is most pronounced at the Portola North Substation monitor where quarterly means in Q1 and Q4, 2011

- 2013, range from 15.2

- 24.0 μg/m 3 

### whereas in quarter two ( Q2 ) and quarter three ( Q3 ) the mean concent rations range from 4.8

- 6.6 μg/m 3 

### . The Redding

- Health Department, Redding

- Toyon, and Redding

- Buckeye monitors do not exhibit this seasonality as strongly, though both the Redding

- Health Department and Redding

- Buckeye monitors peak in Q4 of 2011 and
trend similarly across the three

- year period.

PM 2.5 

### Composition Measurements

- To assess potential emissions contribution s for each violating monitor ing
location , the EPA determined the various chemical species comprising total PM 2.5 

### to identify the chemical

con stituents over the analysis area , which can provide insight into the types of emission sources impacting the
monitor ed concentration

. To best describe the PM 2.5 

### at the violating monitor ing location, the EPA first adjusted the chemical speciation measuremen t data from a monitoring location at or near the violating FRM monitoring
site using the SANDWICH approach to account for the amount of PM 2.5 

### mass constituents retained in the FRM 0 5 10 15 20 25

'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 ) 

### Plumas County Area of Analysis Monitors

- PM 2.5 

### Quarterly Means 2011

- 2013

Portola North Substation Chico - East Avenue Quincy-N. Church Street Redding - Health Dept Redding - Buckeye Redding - Toyon

### Page 90 of 161

measurement. 73

, 74

, 75

, 76 

### In particular, this approach accounts for losses in fine particle nitrate and increases in sulfate mass associated with particle bound water. Figure 3 a illustrates the fraction of each PM 2.5 

### chemical
constituent at the Portola North Substation monitoring s ite based on annual averages for the years 2010

- 2012.

Figure 3a. Plumas County Annual Average PM 2.5 

### Chemical Constituents ( 2010

- 2012 )

Figure 3b shows annual and quarterly chemical composition profiles and illustrates any seasonal or episodic
contributors to PM 2.5 

### mass. This “ increment analysis ,” combin ed with the other factor analyses, can provide additional insight as to which sources or factors may contribute at a greater level. Simply stated, t his analysis can help identify nearby sources of emissions that contribute to the violation at the violating monitoring site.

73 SANDWICH stands for measured S ulfate, A djusted N itrate, D erived W ater, I nferred C arbonaceous mass H ybrid Materia l Balance Approach.” The SANDWICH adjustment uses an FRM mass construction methodology that results in reduced nitrates (relative to the amount measured by routine speciation networks), higher mass associated with sulfates (reflecting water included in gra vimetric FRM measurements) and a measure of organic carbonaceous mass derived from the difference

between measured PM 2.5 and its non

- carbon components. This characterization of PM 2.5 mass also reflects crustal material

and other minor constituents. The res ulting characterization provides a complete mass closure for the measured FRM PM 2.5

mass , which can be different than the data provided directly by the speciation measurements from the CSN network.

74 Frank, N. H., SANDWICH Material Balance Approach for PM 2 .5 Data Analysis, National Air Monitoring Conference, Las Vegas, Nevada, November 6

- 9, 2006. http://www.epa.gov/ttn/amtic/files/2006conference/frank.pdf

. 75 Frank, N. H., The Chemical Composition of PM 2.5 to support PM Implementation, EPA State /Local/Tribal Training Workshop: PM 2.5 Final Rule Implementation and 2006 PM 2.5 Designation Process, Chicago IL, June 20

- 21, 2007, http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chemical_composition.pdf.

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

– 511.

### Page 91 of 161

Figure 3b. Plumas County Annual and Quarterly Average PM 2.5 

### Species ( 2010

- 2012 ) a

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



### Figures 3a and 3b show that organic mass (OM) accounts for over seventy

- five percent of the PM 2 .5 

### mass on an annual basis, and elemental carbon (EC), sulfates and crustal mass (CM) are the next largest contributors at 9.8%, 7.5% and 6.5% respectively. The percent contribution of OM is even larger (over 80%) during Q1 and Q4 when
the PM 2.5 

### concentrat ions are the highest. Sulfates and CM contribute about 15

- 25 percent during Q2 and Q3, but less than five percent during Q1 and Q4. The percent contributions of EC and nitrates to measured PM 2.5 

### mass are about ten percent under one percent, respectively, a nd are relatively constant throughout the year. This suggests that for the Q1 and Q4, which include the winter months, organic mass is the overwhelmingly predominant source
of PM 2.5 

### and , as discussed in Factor 2, is likely related to emissions from residen tial wood burning. A letter from the Northern Sierra Air Quality Management District, dated June 18, 2014, supports this conclusion. It cites the diurnal pattern of the PM 2.5 

### concentrations that peak around the two times people use their wood stoves most: from 5:00pm to 12:00am, and from 5:00 am to 8:00 am. 77 

### The CARB recommendation indicates that levoglucosan and PM 2.5 

### concentrations are correlated, affirming the source of high concentrations as emissions from wood burning.

The EPA assessed seasonal and annual average PM 2.5 

### constituents at monitoring sites within the area relative to monitoring sites outside of the analysis area to account for the difference between regional background
concentrations of PM 2.5 

### , and the concentrations of PM 2.5 

### in the area o f analysis, also known as the “urban increment.” This analysis differentiates between the influences of emissions from sources in nearby areas and in
more distant areas on the violating monitor

. Estimating the urban increment in the area helps to illuminat e the amount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in nearby areas, as opposed to impacts of more distant or regional sources of emissions. Figure 4a includes pie charts showing the ann ual and quarterly chemical mass co nstituents of the urban increment. The quarterly pie charts correspond to the high

- concentration quarters identified in Figure 2. Evaluating these high concentration quarters can help identify composition of PM 2.5 

### during t hese times. Note that in these charts, s ulfates and n itrates have been adjusted to represent their mass in measured PM 2.5



### .

77 L etter from Gretchen Bennitt, Executive Director, Northern Sier ra Air Quality Management District’s Board of Directors,

to Gwen Y o shimura, U.S. EPA Region IX. June 18, 2014 , pp. 1

- 4 & attach ment 2.

### Page 92 of 161

Figure 4a . Plumas County Urban Increment Analysis for 201 0

- 201 2.

### Page 93 of 161

Figure 4 b . Plumas County Average Urban Increment Analysis for 2010

- 2012

.

Plumas County has one regulatory monitoring site with a DV exceeding the NAAQS. In addition, the DV monitoring site as well as surrounding sites have clear seasonal peaks in ambient PM 2.5 

### concentrations in Q 1 and Q 4.

In reviewing the speciation data derived for the Portola North Substation monitor, organic mass is the predominant species throughout the year, though the percent contribution is largest in Q1 and Q4. Sulfates, CM and EC are small components of the measured PM 2.5 

### mass and nitrate is a negligible component of the measured

PM 2.5 

### mass throughout the year. This suggests that for the high concentration quarters, Q1 and Q4, which include the winter months, organic mass related to emissions from residential wood burning is the pre dominant
contributor to PM 2.5 

### mass measured by the FRM monitor at Portola. The results of the urban increment analysis

for the Portola monitor are similar to the adjusted speciation measurements, indicating a somewhat consistent source mix throughout the a rea. However, t here is a substantial inc rease in the contribution of OM in the annual mean and quarters 1 and 4, indicating that there is likely an additional contribution from a source closer to the DV monitoring site. This supports the conclusions from t he speciation data that residential wood burning is a dominant source of high PM 2.5 

### concentrations

.

Factor 2: Emissions and E missions

- related D ata

In th is designations process, for each area with a violating monitor ing site

, the EPA evaluate d the emissions data from nearby areas using emissions related data for the relevant counties to assess each county’s potential
contribution to PM 2.5 

### concentrations at the violating monitor ing site or monitor ing site s in the area under
evaluation. The EPA examined emissions of identified sources or source categories of direct PM 2.5 

### , the major components of direct PM 2.5 

### (organic mass , elemental carbon, crustal material (and/or individual trace metal compounds)), primary nitrate and primary sulfate, and p r ecursor gaseous pollutants (i.e. , SO 2 

### , NO X 

### , total VOC,
and NH 3 

### ). The EPA also considered the distance of those sources of emissions from the violating monitoring site.
While d irect PM 2.5 

### emissions and its major carbonaceous components are generally associ ated with sources near violating PM 2.5 

### monitoring sites, t he gaseous precursors tend to have a more regional influence (although the EPA is mindful of the potential local NO X 

### and VOC emissions contributions to PM 2.5 

### from mobile and stationary

### Page 94 of 161

sources) and transport from neighboring areas can contribute to higher PM 2.5 

### levels at the violating monitor ing
site s.

Emissions Data

For this factor, the EPA reviewed data from the 2011 National Emissions Inventory (NEI) version 1 (see http://www.epa.gov/ttn/chief/net/2011inventory.html ). For each county in the area of analysis, the EPA examined the magnitude of county

- level emissions reported in the NEI. These county

- lev el emissions represent the sum of emissions from the following general source categories: point sources, non

- point (i.e., area) sources, nonroad mobile, on

- road mobile, and fires. In some instances, non

- anthropogenic sources of emissions such as wildfires account for large portions of the emissions inventory data presented below. The EPA also looked at the geographic distrib ution of major point sources of the relevant pollutants. 78 

### Substantial emissions levels from

sources in a nearby area indicate the potential for the area to contribute to monitored violations.

To further analyze area emissions data, the EPA also developed a summary of direct PM 2.5 

### , components of direct

PM 2.5 

### , and precursor pollutants, which is available at http://www.epa.gov/pmdesignations/2012standards/docs/nei2011v1pointnei2008v3county.xlsx

.
When considered with the urban increment analysis in Facto r 1, evaluating the components of direct PM 2.5 

### and

precursor gases can help identify specific sources or source types contributing to elevated concentrations at violating monitor ing sites and thus assist in identifying appropriate area boundaries. In gener al, directly emitted particulate organic carbon (POC) and VOCs 79 

### 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 sulfat e contribute to PM 2.5 

### sulfate mass; and directly emitted crustal material and metal oxides
contribute to PM 2.5 

### crustal matter. 80

, 81 

### The EPA believes that the quantities of those nearby emissions as potential
contributors to the PM 2.5 

### violating monitor s are somewhat proportional to the PM 2.5 

### chemical constituents in the estimated urban increment. Thus, directly emitted POC is more important per ton than SO 2 

### , partially because POC emissions are already PM 2.5 

### whereas SO 2 

### must conver t to PM 2.5 

### and not all o f the emitted SO 2 

### undergoes this
conversion.

Table 3a provides a county

- level emissions summary (i.e., the sum of emissions from the following general source categories: point sources, non

- point (i.e., area) sources, nonroad mobile, on

- road mobile, and fi res) of directly emitted PM 2.5 

### and precursor species for the county with the violating monitoring site and nearby counties
considered for inclusion in the Plumas County, CA area . Table 3b summarizes the directly emitted components of

PM 2.5 

### for the same counties in the area of analysis for the Plumas County, CA area. This information will be paired with the Urban Increment composition previously shown in Figures 4a and 4b.

78 For purposes of this designations effort, “major” point sources are those whose sum of PM precursor emissions ( PM 2.5 + NOx + SO 2 + VOC + NH 3 ) are greater than 500 tons per year based on NEI 2011v1

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

80 See , Seinfeld J. H. and Pandis S. N. (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change

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

81 USEPA Report ( 2004), The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003,

found at: http://www.epa.gov/airtrends/aqtrnd04/pm.html

.

### Page 95 of 161

Table 3a

. County

- Level Emissions of Directly Emitted PM 2.5 

### and Precur sors (tons/year)

County , State Total NH 3 Total NOx Total Direct
PM 2.5 Total

SO 2 Total VOC Total

Shasta , CA 2,578 8,847 2,723 206 9,301 23,653

Butte , CA 2,471 6,847 2,886 181 10,195 22,580

Lassen , CA 1,863 1,961 2,320 237 6,646 13,026

Tehama , CA 1,500 4,346 1,632 134 4,762 12,375

Plumas , CA 1,191 2,138 2,013 200 6,291 11,833

Yuba , CA 940 2,181 1,881 146 5,966 11,114

Sierra , CA 485 324 1,200 87 3,789 5,885



### Table 3b . County

- Level Emissions for Components of Directly Emitted PM 2.5 

### (tons/year) 82

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

Butte , CA 1,880 287 27 15 243 435 2,886

Shasta , CA 1,872 333 44 19 148 308 2,723

Lassen , CA 1,714 226 30 15 94 241 2,320

Plumas , CA 1,588 216 16 16 53 124 2,013

Yuba , CA 1,414 196 12 14 84 160 1,881

Tehama , CA 1,126 204 16 11 102 174 1,632

Sierra , CA 931 118 12 5 24 109 1,200



### Table 3b breaks down the d irect PM 2.5 

### emissions value from Table 3a into its components. Th ese data will also be compared with the previously presented Urban Increment composition.

Using the previously described relationship between directly emitted and precursor gases and the measured mass to evaluate data presented in Tables 3a and 3b, the EPA identified the following components warranting
additional review: PM 2.5 

### , VO C, POM and Crustal. The EPA then looked at the contribution of these constituents of interest from each of the counties included in the area of analysis as shown in Tables 4a

- d.

Table 4a. County

- Level PM 2.5 Emissions (tons/year)

Emissions in average tons/yr

County, State PM 2.5 Pct. Cumulative %

Butte , CA 2,886 20 20

Shasta , CA 2,723 19 38

Lassen , CA 2,320 16 54

Plumas , CA 2,013 14 68

Yuba , CA 1,881 13 81

Tehama , CA 1,632 11 92

Sierra , CA 1,200 8 100

82 Data are based on the 2011 and 2018 Emissions Modeling Platform Data Files and Summaries ( ftp: //ftp.epa.gov/EmisInventory/2011v6/v1platform ) available at: http://www.epa.gov/ ttn / chief/emch/index.html#2011 (accessed 02/26/14)

.

### Page 96 of 161

Table 4 b . County

- Level VOC Emissions (tons/year)

Emissions in average tons/yr

County, State VOC Pct. Cumulative %

Butte , CA 10,195 22 22

Shasta , CA 9,301 20 42

Lassen , CA 6,646 14 56

Plumas , CA 6,291 13 69

Yuba , CA 5,966 13 82

Tehama , CA 4,762 10 92

Sierra , CA 3,789 8 100

Table 4 c . County

- Level POM Emissions (tons/year)

Emissions in average tons/yr

County, State POM Pct. Cumulative %

Butte , CA 1,880 18 18

Shasta , CA 1,872 18 36

Lassen , CA 1,714 16 52

Plumas , CA 1,588 15 67

Yuba , CA 1,414 13 80

Tehama , CA 1,126 11 91

Sierra , CA 931 9 100

Table 4 d . County

- Level Crustal Emissions (tons/year)

Emissions in average tons/yr

County, State SO 2 Pct. Cumulative %

Butte , CA 243 33 33

Shasta , CA 148 20 52

Tehama , CA 102 14 66

Lassen , CA 94 13 79

Yuba , CA 84 11 90

Plumas , CA 53 7 97

Sierra , CA 24 3 100



### Tables 4a

- d above show a pattern in county

- level emissions in the area of analysis, whereby the least emissions are from Sierra County and the most are in Butte and Shasta counties.

A letter from the Northern Sierra Air Quality Management District, dated June 18, 2014, cites wood burning for residential heating in wood stoves and fireplaces, open burn piles for yard waste disposal, and prescribed burning in nearby federal lands performed by the U.S. Forest Service and a r ailroad switching yard as the sources affecting

PM 2.5 

### concentrations in Portola. The letter also states that natural gas is not available in Portola but is available in other communities in the area, such as Quincy. 83 

### Lack of alternate fuel sources such as natural gas likely results in

83 L etter from Gretchen Bennitt, Executive Director, Northern Sierra Air Quality Management District’s Board of Directors,

to Gwen Y o shimura, U. S. EPA Region IX. June 18, 2014, p

. 1

- 4 and attachment 2.

### Page 97 of 161

more people using wood stoves for heating and cooking in Portola versus in communities that have access to
other fuel sources.

In addition to reviewing county

- wide emissions of PM 2.5 

### and PM 2.5 

### precursors in the area of analysis, the EPA also reviewed emissions from major point sources located in the area of analysis. The magnitude and location of these sources can help inform nonattainment boundaries.

There are no major point sources within t he area of analysis (Plumas, Butte, Lassen, Shasta, Sierra, Tehama, and Yuba counties), however, there are several biomass facilities and other lumber

- related sources of emissions in
these counties. As t here are no major point sources located in the area of analysis (as defined in a previous footnote, for purposes of this designations effort, “major” point sources are those whose sum of PM precursor emissions ( PM 2.5 

### + NOx + SO 2 

### + VOC + NH 3 

### ) are greater than 500 tons per year based on NEI 2011v1)

, major poi nt sources are unlikely to contribute to monitored violations of the 2012 PM 2.5 

### NAAQS at the Portola monitoring site.

In summary, the EPA’s analysis of relevant county

- level emissions and the geographic locations of the relevant pollutants showed that maj or point sources are not contributing to violations in Plumas County of the 2012 PM 2.5 

### NAAQS, and that the main type of source contributing to these violations is residential wood burning. While there are likely other areas where substantial amounts of woo d burning occur, the impacts tend to be highly localized due to rough terrain and winter meteorology, and are not expected to contribute to the violations in Portola, as
discussed further in Factors 3 and 4.

Population density and degree of urbanization

In this part of the factor analysis, the EPA evaluated the population and vehicle use characteristics and trends of the area as indicators of the probable location and magnitude of non

- point source emissions. Rapid population growth in a county on the urban perimeter signifies increasing integration with the core urban area, and indicates that it may be appropriate to include the county associated with area source and mobile source emissions as part

of the nonattainment area. Table 5 shows the 2000 and 2010 p opulation , population growth since 2000, and population density for each county in the area.

### Page 98 of 161

Table 5 . Population Growth and Population Density

.

County , State Population

20 00 Population

2010 % Change

from 2000 Land

Area (Sq. Miles) Population

Density ( per Sq. Mile) % Cumulative
%
Butte, CA 203,171 219,968 8 % 1,639 3,785
631 2,951 4,557 2,554 953 134 37 37 Shasta, CA 163,256 177,324 9 % 3,785 47 30 67 Yuba, CA 60,219 72,366 20 % 631 115 

### 12 79
Tehama, CA 56,039 63,666 14 % 2,951 22 

### 11 90
Lassen, CA 33,828 34,820 3 % 4,557 8 6 96 Plumas, CA 20,824 19,940

- 4 % 2,554 8 3 99 Sierra, CA 3,555 3,226

- 9 % 953 3 1 100

Total 540,892 591,310 Source: U.S. Census Bureau population estimates for 2000 and 2010



### As seen in Table 5

, Plumas County has a small population and it is declining. Plumas County and the adjacent county to the south, Sierra County, are the only two counties in the area of analysis that have declining populations. However, Lassen County to the north has a similarly small pop ulation density, with only 34,820 people in a county that is 4,557 square miles. These three counties have some of the smallest population densities in the nation. Although the adjacent Yuba County has a higher degree of population growth (20%), compared to all other counties in the area of analysis, and Butte, Shasta and Tehama counties also are experiencing population growth, it is not in a pattern that is associated with the population in Plumas County, which is spread throughout this large (2,554 squar e miles) county. Yuba County’s population, for example, is associated with Yuba City

Marysville in the central portion of the two

- county Sutter County and Yuba County area. Butte and Shasta counties do have much higher population than Plumas County suggest ing the potential for contribution from those counties, but those populations are much farther away and emission activity from those counties is less likely to contribute to the violations in Plumas County.

### Page 99 of 161

Figure 5 . 2010 County

- Level Population in the Area of Analysis for the Plumas County, CA Area.

### Page 100 of 161

As shown in Figure 5 and in more detail in Figure 5a, the population of Plumas County is small and sparse.

Traffic and Vehicle Miles Travelled

H igh vehicle miles travelled (VMT) and/or a high number of commuters associated with a county is generally an indicator that the county is an integral part of an urban area. Mobile source emissions of NO
x 

### , VOC, and direct PM may contribute to ambient partic ulate matter that contributes to monitored violations of the NAAQS in the

area . In combination with the population/population density data and the location of main transportation arteries, an assessment of VMT help s identify the probable location of nonpoi nt source emissions that contribute to
violations in the area

. Comparatively high VMT in a county outside of the CBSA or CSA signifies integration
with the core urban area contained within the CSA or CBSA , and indicate s that a county with the high VMT may be appropriate to include in the nonattainment area because emissions from mobile sources in that county
contribute to violations in the area. Table 6 shows 2011 VMT while Figure 6 overlays 2011 county

- level VMT with a map of the transportation arteries. V MT information shown below comes from the Federal Highway
Administration.

Table 6. 2011 VMT for the Plumas County, CA Area

. County , State Total 2011 VMT Percent Cumulative %
Shasta, CA 

### 2,423,021,028 34 34
Butte, CA 

### 1,803,224,860 25 59
Tehama, CA 

### 1,096,503,798 15 74 Yuba, CA 

### 779,782,024 11 85
Lassen, CA 

### 775,933,492 11 96
Plumas, CA 

### 216,739,350 3 99
Sierra, CA 

### 74,257,303 1 100
Total 7,169,461,855 http://www.census.gov/hhes/commuting/data/commuting.html

### Page 101 of 161

Figure 6 . Overlay of 2011 County

- level VMT with Transportation Arteries.

Figure 6 shows that there are no major transportation arteries within Plumas County. The county is isolated in terms of its population. According to Census Bureau statistics for commuter flow between counties for 2006

- 2010, Plumas County has 8,744 commuters. See: http:/ /www.census.gov/population/metro/data/other.html . Of these, 7,260, or 83%, commute to work within the county. Only 30 commuters commute to Plumas County from Shasta County; 220 from Lassen County; 81 from Sierra County, the closest county in the area of an alysis to the violating monitor in Plumas County; no commuters in Yuba County commute to Plumas County; 314 from Butte County, to the west; and again, no commuters in Glenn County commute to Plumas County.

In summary, with respect to emissions sources in the area of analysis for Plumas County, there are no major point sources. Emissions that impact monitored PM 2.5 

### concentrations are likely related to residential burning activities. The population in most of the area of analysis is sparse and widely distri buted, typical of relatively uninhabited areas of the American West. In this area, the EPA believes that most of the emissions that are contributing to the violations in Plumas County occur within the portion of the county that the EPA is designating as no nattainment.

Factor 3: Meteorology

The EPA evaluated available meteorological data to determine how meteorological conditions, including, but not limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of d irectly emitted particulate matter and precursor emissions from sources in the area of analysis

. The EPA generally use s wind roses for this analysis; however, there is no National Weather Service or comparable meteorological data
available within the narro w Portola Valley.

### Page 102 of 161

Prevailing winds in the Portola V alley are from the southwest during the day, with stagnant wind conditions at night. The average low temperature for the 6

- month period of October through March is 21.8°F with frost conditions experienced an average of 218 days per year

. 84 

### At night the ground cools, creating a shallow surface

based inversion, since air near the ground is cooler than that above. This, along with the valley ’s steep topography, inhibits mixing and confines pollutants to a rela tively shallow layer near the ground. The low sun

angle and climatologically dominant high pressure over Northern California, allow for little heat or wind to break
the inversion. As such, h igh PM 2.5 

### concentrations at the Portola monitor mostly occur in th e wintertime during evening through morning hours and are confined to the Portola Valley , supporting the limited portion of Plumas County as the appropriate nonattainment boundary

.

The EPA generated kernel density estimation (KDE) plots to represent HYSP LIT (HYbrid Single

- Particle Lagrangian Integrated Trajectory) backward trajectory frequency at violating monitoring sites. 85

, 86 

### These KDEs are graphical statistical estimations to determine the density of trajectory endpoints at a particular location

represe nted by a grid cell. The EPA used KDEs to characterize and analyze the collection of individual HYSPLIT backward trajectories. 87 

### Higher density values, indicated by darker blue colors, indicate a greater frequency of observed trajectory endpoints within a p articular grid cell. Figure 7 shows a HYSPLIT KDE plot for the Portola

Nevada Street air monitoring site (AQS ID 060631009) in Plumas County summarized by calendar quarter for the

2010

- 2012 period. These trajectories a re not conclusive due to the coarse grid resolution (40kmx40km) of the Eta Data Assimilation System ( EDAS ) data driving the HYSPLIT trajectories. T he valley containing Portola is at
most 20 km wide. The data a re not resolved enough to portray the wintertime stagnation that is a key feature of

local Portola meteorology.

84 L etter from Gretchen Bennitt, Executive Director, Northern Sierra Air Quality Management District’s Board of Directors,

to Gwen Y o shimura, U.S. EPA Region IX. June 18, 2014 , pp. 2

- 5.

85 In some past area designations efforts, EPA has used HYSPLIT backward trajectories to assist in determining nonattainment area boundaries. A HYSPLIT backward trajectory is usually depicted on a standard map as a single line, representing the centerl ine of an air parcel’s motion, extending in two dimensional (x,y) space from a starting point and regressing backward in time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining a n air parcel’s point of origin on a day in which a short

- term standard, such as an 8

- hour standard or a 24

- hour standard, was exceeded. However, for an annual standard, such as the 2012 annual PM 2.5 NAAQS, every trajectory on every day is important. Plotting a mass of individual daily (e. g., 365 individual back trajectories), or more frequent, HYSPLIT trajectories may not be helpful as this process is likely to result in depicting air parcels originating in all directions from the violat ing monitoring site.

86 HYSPLIT

- Hybrid Single Partic le Lagrangian Integrated Trajectory Model, 

### http://www.arl.noaa.gov/HYSPLIT_info.php

87 The KDEs graphically represent the aggregate of HYSPLIT backward trajectories for the years 2010

- 2012, run every third day (beginning on the first day of monitoring), f our times each day, and ending at four endpoint heights.

### Page 103 of 161

Figure s 7. Quarter 1

- Quarter 4: HYSPLIT Kernel Density Estimation Plots for the Portola

– Nevada Street Monitoring Station.

### Page 104 of 161

Factor 4: Geography/topography

To evaluate the geography/topography factor, the EPA assessed physical features of the area of analysis that might define the airshed and thus affect the formation and distribution of PM 2.5 

### concentrations over the area.

The town of Portola (elevation 4890 feet) lies in Plumas Count y in the northern Sierra Nevada Mountains near the southern border of the Cascade Range. The Middle Fork of the Feather River runs through the center of Portola (See Figure 8b ). The topography of Plumas County is highly variable, including mountain peaks (>7,000 ft) and valleys with extreme slopes and differences in altitude. This topography affects and directs airflow, caus ing

### Page 105 of 161

shallow vertical mixing, and crea ting areas of high pollutant concentrations by hindering dispersion. Th e Portola area is geographically isolated from other areas by rugged mountains, forming a bowl

- shaped valley. There is a distinct range with 6000

- 7000 ft peaks separating Portola from Quincy, 32 miles to the west

- northwest. 88 

### To the east the Feather River p rovides a narrow air corridor from the Portola Valley into the expansive Sierra Valley (See Figure 8b). While high PM 2.5 

### concentrations within the Portola Valley could enter the Sierra Valley, these concentrations would be quickly diluted limiting their ef fect on unmonitored concentrations in that area.



### Figure 8 . Topographical Map of Northern California (a) and of Plumas County. County boundaries and AQS stations are shown.

(a) Northern California

88 Id.

### Page 106 of 161



### (b) Plumas County

Factor 5: Jurisdictional boundaries

In defining the boundaries of the Plumas County nonattainment area, the EPA considered existing jurisdictional boundaries, which can provide easily identifiable and recognized boundaries for purposes of implementing the NAAQS , as well as recommendations from the state and local air district

. On June 18, 2014, Northern Sierra Air Quality Management District (the District) provided its recommendation to the EPA, recommending that the EPA designate the Portola Valley, following the existing s tate standard nonattainment area, as the federal
nonattainment area

. 89 

### The District’s recommendation was accompanied by a five

- factor analysis.

On July 2, 2014, CARB provided an additional recommendation to its November 2013 State recommendation.
The additional recommendation provided a recommendation for nonattainment for Plumas County, CA, based on complete air quality monitoring data for 2013 that was not available at the time of its initial recommendation. The
State’s letter recommended the same ar ea as the District recommended, and also included a five

- factor analysis in support of its recommended boundary. 90

89 Letter from Gretchen Bennitt, Executive Director, Northern Sierra Air Quality Management District to Gwen Yoshimura, U.S. EPA Region 9 Air Division Air Quality Analysis Office, dated June 18, 2014.

90 Letter from Richard Corey, Executive Officer, Air Resources Board, to Jared Blumenfeld, Regional Administrator, U.S. EPA Region 9, dated July 2, 2014.

### Page 107 of 161

Existing jurisdictional boundaries often signify the state or local governmental organization with the necessary legal authority for carrying out air quality planning and enforcement functions for the area. Examples of such jurisdictional boundaries include existing/prior nonattainment area boundaries for particulate matter, county lines, air district boundaries, township boundaries, areas cove red by a metropolitan planning organization, state lines, and Reservation boundaries, if applicable. Where existing jurisdictional boundaries were not adequate or appropriate to describe the nonattainment area, the EPA considered other clearly defined and permanent landmarks or geographic coordinates for purposes of identifying the boundaries of the designated areas.

The Plumas County nonattainment area is the same as the State’s recommended partial

- county area, which is currently designated by the State a s a PM 2.5 

### nonattainment area for the State of California’s PM 2.5 

### standard. The
State of California’s PM 2.5 

### standard is set at an annual arithmetic mean of 12 μg/m 3 

### . This area is defined by the

S tate’s hydrological “super planning watershed” areas in Plumas County, CA, as described below, and the Sierra

Plumas county line to the south

.
Plumas County’s air quality is managed by the Northern Sierra Air Quality Management District, which also manages the air quality of Sierra and Nevada Counties. Plumas Count y is in the northeastern part of California and is part of the Mountain Counties Air Basin. The Mountain Counties Air Basin includes nine counties: Plumas, Sierra, Nevada, Placer, El Dorado, Amador, Calaveras, Tuolumne and Mariposa. There is no metropolit an planning organization (MPO) for Plumas County or for the state

- recommended partial

- county area.

The Office of Management and Budget’s December 2009 delineations of core

- based statistical areas (CBSAs) does not consider Plumas County a CBSA. Plumas Count y is surrounded by six counties with a wide range of
CBSA/CSA delineations: Sierra County (not a CBSA), Lassen County (micro

- CBSA), Tehama County (micro

CBSA), Butte County (metro

- CBSA), Yuba County (part of Sacramento

- CSA), and Shasta County (metro

CBSA). This diversity of delineations is in keeping with the EPA’s assessment that Plumas County is not interconnected with any of the surrounding counties. Neither Plumas County itself, nor the county to the south, Sierra County, have populations that are larg e enough to be delineated as a micropolitan CBSA.

The State designated the nonattainment area as nonattainment for the State’s PM 2.5 

### ambient air quality standard in 2003. Since that time, air quality in the area has been managed by cooperation between fede ral, state and local authorities and businesses, regarding programs ranging from limits on open burning to requirements for EPA
certified wood

- burning appliances for home change of ownership, to an agreement with the railway not to idle their diesel engine s in Portola. See District recommendation, pages 3 and 4.

The State’s nonattainment area consists of one whole watershed, as defined by the State, and several partial watersheds. To the northeast, the entirety of the State’s “super planning watershed” # 5 5183301 (Humbug Valley) includes the city of Portola as well as all or part of the cities of Delleker, Iron Horse, Beckworth, Lake Davis, Mohawk Vista, C

- Road, Clio and Valley Ranch. To the south, the Plumas County portion of watershed # 55183302 (Sulpher Creek) includes the city of Whitehawk and the remainder of Valley Ranch. To the southwest, the Plumas County portion of watershed # 55183303 (Frazier Creek) includes another portion of Mohawk Vista
and the remainders of D

- Road and Clio and a portion of Gr aeagle. To the west, the Plumas County portion of watershed # 55183304 (Eureka Lake) includes the remainders of Graeagle and Mohawk Vista, Plumas Eureka and most of Johnsville. The remainder of the nonattainment area is unincorporated. See District recomme ndation Attachment 1, map of the Portola Valley State PM 2.5 

### Nonattainment Area; see also the attachment to the State’s recommendation, page 16.

The nonattainment area does not include portions of Indian country.

### Page 108 of 161

Conclusion for Plumas County Area

Based o n the assessment of factors described above, both individually and in combination, the EPA has concluded that the following partial county should be included as part of the Plumas County nonattainment area because portions of the county are either violatin g the 2012 annual PM 2.5 

### NAAQS or contributing to a violation at a nearby monitoring site: Plumas County (partial) , including Portola Valley and the city of Portola, and as defined by the existing state standard nonattainment area

. The EPA concurs with the state’s recommendation for this area.

An evaluation of design value and speciation information relevant to Factor 1 shows that the air quality monitoring sites in Plumas County indicate violations of the 2012 annual PM 2.5 

### NAAQS based on the 2013 DVs

, likel y primarily due to local sources of residential burning ; therefore a portion of the county is included in the

nonattainment area.

Information relevant to Factor 2 indicates that there are no large sources in the area of analysis. The likely source
contrib uting the most to the particulate matter concentrations which result in violations of the 2012 annual PM 2.5 

### NAAQS in Portola are residential burning activities, which tend to affect a very localized area when they occur in a topographically

- and meteorolog ically

- contained area such as the Portola Valley

.
Factor 3 suggests that the greatest potential contribution of emissions is from the area contained within the valley with the violating monitor due to the meteorological conditions of stagnant, wintertime inversions during the quarters with the highest PM 2.5 

### concentrations. Although there may be similar sources of emissions elsewhere in the area of analysis, geographic distance and topographical barriers in combination with this meteorology make it
less lik ely that those emissions contribute to the violations in the Portola Valley in Plumas County.

As discussed with respect to Factor 4, the topography of the region is highly variable, including mountain peaks (>7,000 ft) and valleys with extreme slopes and d ifferences in altitude. The Portola Valley area is geographically isolated from other areas by rugged mountains, forming a bowl

- shaped valley. This topography affects and directs airflow, causes shallow vertical mixing, and creates areas of high pollutant concentrations by hindering dispersion, especially in the Portola area . Finally, an assessment of information relevant to Factor 5 further supports the boundary of the Plumas County nonattainment area due to local air district jurisdiction and the existing State nonattainment area.

Only a portion of Plumas County is included in the Plumas County nonattainment area. The remainder of the
county does not have sources of emissions or emissions activity that are likely to be contributing to the violating
monitor in the Portola Valley . The factors that the EPA primarily relied upon when determining the appropriate boundary for the Plumas County nonattainment area were Air Quality Data (Factor 1), Meteorology (Factor 3), Geography/Topography (Factor 4), and Jurisdi ction (Factor 5).

### Page 109 of 161

3.4 Area Background and Overview

– San Joaquin Valley, CA

Figure 1 is a map of the EPA’s nonattainment boundary for the San Joaquin Valley. The map shows the location and design values of ambient air quality monitoring locations, county and other jurisdictional boundaries and existing 1997 annual and 2006 24

- hour PM 2.5 

### NAAQS nonattainment boundaries. As shown in Figure 1a, the EPA’s area of analysis considered in this section includes the counties in the San Joaquin Valley Air Basi n (consisting of San Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, and Tulare counties, as well as the western portion of Kern County), and all counties adjacent to the San Joaquin Valley Air Basin (Alameda, Amador, Calaveras, Contra Costa, Inyo, Los Angeles, Mariposa, Mono, Monterey, Sacramento, San Benito, San Bernardino, San Luis Obispo, Santa Barbara, Santa Clara, Tuolumne, and Ventura counties) as part of the area of analysis. The San Joaquin Valley Air Basin includes the Fresno

- Madera combined s tatistical area (CSA), and the entireties of the Stockton, Modesto, Merced, Hanford

- Corcoran, and Visalia

- Porterville metropolitan statistical areas (MSA). The entirety of Kern County forms the Bakersfield

- Delano MSA.

For purposes of the 1997 annual PM 2.5 

### NAAQS, this area was designated nonattainment. The boundary for the
nonattainment area for the 1997 annual PM 2.5 

### NAAQS included the entire counties of Fresno, Kings, Madera, Merced, San Joaquin, Stanislaus and Tulare and a portion of Kern County. For p urposes of the 2006 24

- hour

PM 2.5 

### NAAQS, this area was designated nonattainment. The boundary for the nonattainment area for the 2006 24

- hour PM 2.5 

### NAAQS also included the entire counties of Fresno, Kings, Madera, Merced, San Joaquin, Stanislaus
and Tulare and a portion of Kern County. The boundary for the 2012 annual PM 2.5 

### NAAQS is the same as the

nonattainment area boundary for the 1997 annual PM 2.5 

### NAAQS and the 2006 24

- hour PM 2.5 

### NAAQS. This nonattainment area includes areas of Indian country of the fol lowing federally recognized tribes: the Big Sandy Rancheria of Western Mono Indians, the Cold Springs Rancheria of Mono Indians of California, the Northfork Rancheria of Mono Indians of California, the Picayune Rancheria of Chuckchansi Indians of Californi a, the Santa Rosa Indian Community of the Santa Rosa Rancheria, the Table Mountain Rancheria of California, and the Tule
River Indian Tribe of the Tule River Reservation

.

### Page 110 of 161

The EPA must designate as nonattainment areas that violate the NAAQS and nearby are as that contribute to the violation in the violating area. Eight counties

- San Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, Tulare, and Kern counties, show a violation of the 2012 PM 2.5 

### NAAQS, therefore the first seven counties listed and a portion of Kern County are included in the nonattainment area. As shown in Figure 1a, the EPA evaluated each county as well as those counties located near the counties with a violating monitoring site based on the five

factors and other relevant information. The following sections describe this five factor analysis process. While the factors are presented individually, they are not independent. The five factor analysis process carefully considers their interconnections and the dependence of each factor on one or m ore of the others.

### Page 111 of 161

Factor 1: Air Quality Data

All data collected during the year are important when determining contributions to an annual standard such as the
2012 annual PM 2.5 

### NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings throughout the year, including days with monitored ambient concentrations below the level of the NAAQS. For the 2012
annual PM 2.5 

### NAAQS, the annual mean is calculated as the mean of quarterly means. A high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3

- year DV. Although all data are important, seasonal or episodic emissions can provide insight as to relative contributors to measured PM 2.5 

### conce ntrations. For these reasons, for the Factor 1 a ir q uality analysis, the EPA assesse d and characterize d air quality at, and in the proximity of, the violating monitor ing site locations first, by evaluating trends and the spatial extent of measured

concentr ations at monitors in the area of analysis, and then, by identifying the conditions most associated with high average concentration levels of PM 2.5 

### mass in the area of analysis.

In most cases, the EPA assessed air quality data on a seasonal, or quarterly, basis

. 91 

### The EPA also identifie d the spatial extent of these high PM 2.5 

### concentrations

. The mass and composition at the design value location

91 Although compliance with the annual NAAQS depends on contributions from all days of the year, e xamin ing data on a quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and mete o rology and the resulting PM 2.5 mass and composition . In some areas of the country where there may be noticeable month

- to

- month

### Page 112 of 161

represents contributions from various emission sources including local, area

- wide (which may comprise nearby urban and rural areas) and regional sources. To determine the source mix (by mass) at the design value monitoring site, the EPA examined the chemic al composition of the monitored PM 2.5 

### concentrations by pairing each violating FRM/FEM/ARM monitoring site with a collocated or nearby Chemical Speciation Network (CSN) monitoring site or sites. Then, the EPA contrasted the approximated mass composition at the design value monitoring site
with data collected at IMPROVE 92 

### and other monitoring locations whose data are representative o f regional background. 93

, 94 

### This comparison of local/area

- wide chemical composition data to regional chemical composition

data der ives an “ urban increment, ” which helps differentiate the influence of more distant emissions sources from the influence of closer emissions sources, thus representing the portion of the measured violation that is associated with nearby emission contributio ns. 95

, 96

, 97



### PM 2.5 

### Design Values and Total Mass Measurements

– The EPA examin ed ambient PM 2.5 

### air quality monitoring data represented by the DV s at the violating monitoring site and at other monitors in the area of analysis

. The
EPA calculate d DVs based on air quality data for the most recent 3 consecutive calendar years of quality

- assured, certified air quality data from suitable FEM/FRM/ARM monitoring sites in the EPA’s Air Quality System (AQS). For this designations analysis, the EPA used data f or the 2011

- 2013 period (i.e., the 2013 design value), which are the most recent years with fully

- certified air quality data. A monitor’s DV is the metric or statistic that indicates

variations in average PM 2.5 , the quarterly averages may not adequately represent seasonal variability. In these areas, air quality data may be aggregated and presented by those months that best correspond to the local “seasons” in these areas.

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

93 The “urban increment” analysis assesses and characterizes the increase in seasonal and annual average PM 2.5 mass an d chemical constituents observed at violating monitoring site(s) relative to monitoring sites outside the area of analysis (whi ch represent background concentrations). Developing the urban increment involves pairing a violating FRM/FEM/ARM monitor
with a c ollocated monitor or nearby monitor with speciation data. EPA made every effort to pair these data to represent the same temporal and spatial scales. However, in some cases, the paired violating and CSN “urban” monitoring locations were separated by some d istance such that the included urban CSN site(s) reflect(s) a different mixture of emissions sources, which could lead to misinterpretations. To generally account for differences in PM 2.5 mass between the violating site and the nearby CSN site(s), EPA dete rmined material balance of the PM 2.5 composition at the violating site by assigning the extra

measured PM 2.5 mass to the carbon components of PM 2.5 . Where the general urban increment approach may be misleading, or

in situations where non

- carbonaceous emiss ions are believed to be responsible for a local PM 2.5 concentration gradient, EPA used alternative analyses to reflect the mix of urban and rural sources contributing to the measured concentrations at violat ing monitoring sites.

94 The urban monitors were paired with any rural sites within a 150 mile radius of an urban site to calculate spatial means of the quarterly averages of each species. If there were no rural sites within 150 miles, then the nearest rural site was used a lone.

That rural mean was then subtracted from the quarterly mean of the urban site to get the increment. Negative values were simply replaced with zeros.

95 In most, but not all, cases, the violating design value monitoring site is located in an urban area. Where the violating

monitor i s not located in an urban area, the “urban increment” represents the difference between local and other nearby emission sources in the vicinity of the violating monitoring location and more regional sources.

96 Hand, et. al. Spatial and Seasonal Patterns an d Temporal Variability of Haze and its Constituents in the United

States: Report V, June 2011. Chapter 7

– Urban Excess in PM 2.5 Speciated Aerosol Concentrations, http://vista.cira.colostate.edu/improve/Publications/Reports/2011/PDF/Chapter7.pdf

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

.

### Page 113 of 161

whether that monitor attains a specified air quality standard. The 2012 a nnual PM 2.5 

### NAAQS is met at a monitoring site when the 3

- year average annual mean concentration is 12.0 micrograms per cubic meter (μg/m 3 

### ) or less (e.g., 12.1 μg/m 3 

### or greater is a violation). A DV is only valid if minimum data completeness criteria are

me t or when other regulatory data processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 2 identifies the current design value(s) (i.e., the 2013 DV) and the most recent two design values based on all monitoring sites in the area of analy sis for the San Joaquin Valley nonattainment area. 98 

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

Table 2

. Air Quality Data C ollected at Regulatory Monitors (all DV levels in μg/m 3 

### ) a

County, State Monitor

Site ID Site Name State Rec NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Fresno, CA 060190011 Fresno

– Garland d Yes 14.5 14.3 15.4

Fresno, CA 060192009 Tranquillity Yes

- 7.4 7.8

Fresno, CA 060195001 Clovis Yes 17 .0 16 .0 16.4

Fresno, CA 060195025 Fresno

- Pacific Yes 14.5 13.8 14.7

Kern, CA 060290011 Mojave

- Poole No 5.3 b 5.7 b 7 .0 b

Kern, CA 060290014 Bakersfield

California Avenue Yes 16.5 14.5 16.4

Kern, CA 060290015 Ridgecrest No 5.5 b 5.3 b 5.4 b

Kern, CA 060290016 Bakersfield

- Planz Yes 18.2 15.6 17.3

Kings, CA 060310004 Corcoran Yes 16.2 15.8 b 15 .0 b

Kings, CA 060311004 Hanford Yes 16.3 b 15.8 17 .0

Madera, CA 060392010 Madera

- City Yes 20.5 b 19 .0 18.1

Merced, CA 060470003 Merced

- Coffee Yes 18.2 14.3 13.3

Merced, CA 060472510 Merced

- M St Yes 11.7 10.4 11.1

San Joaquin, CA 060771002 HAZELTON

- HD, STOCKTON Yes 11.1 11.4 13.8

San Joaquin, CA 060772010 Manteca Yes 14.2 b 12.1 b 10.2

Stanislaus, CA 060990005 Modesto

- 14th Street Yes 13.2 b 12.9 13.6

Stanislaus, CA 060990006 Turlock Yes 15.3 14.9 15.7

Tulare, CA 061072002 Visalia

- N. Church Yes 15.2 14.8 16.6

Amador, CA N/A N/A No No monitor

Alameda, CA 060010007 Livermore

- Rincon No 8.2 7.3 7.6

Alameda, CA 060010009 Oakland East No 9 .0 9.1 10 .0

Alameda, CA 060010011 Oakland West No na 7 .0 b 9.9 b

98 In certain circumstances, one or more monitoring locations within a monitoring network may not meet the network technical requirements set forth in 40 CFR 58 .11(e), which states, “ State and local governments must assess data from Class

III PM 2.5 FEM and ARM monitors operated within their network using the performance criteria described in table C

- 4 to subpart C of part 53 of this chapter, for cases where the d ata are identified as not of sufficient comparability to a collocated FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to the NAAQS. These assessments are required in the monitoring agency's annual monitorin g network plan described in §58.10(b) for cases where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM ....”

### Page 114 of 161

County, State Monitor

Site ID Site Name State Rec NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Calaveras, CA 060090001 SAN ANDREAS

- GOLD STRIKE ROAD No 7.3 7.6 8.4

Contra Costa, CA 060130002 Concord No 7.8 7.2 7.4

Contra Costa, CA 060131004 San Pablo

- Rumrill No n/a 7.3 b 9.6

Inyo, CA 060271003 Keeler No 7.3 7.3 7.5

Los Angeles, CA 060370002 Azusa Yes c 12 .0 b 11.3 b 11.2

Los Angeles, CA 060371002 Burbank Yes c 13. 7 e 12. 6 e 12. 5 e

Los Angeles, CA 060371103 Los Angeles (Main St.) Yes c 13. 1 e 12.5 e 12.5 e

Los Angeles, CA 060371201 Reseda Yes c 10.6 10.3 10.2

Los Angeles, CA 060371302 Compton Yes c 13.4 12.4 12.2

Los Angeles, CA 060371602 Pico Rivera #2 Yes c 13.3 12.3 12 .0

Los Angeles, CA 060372005 Pasadena Yes c 11.1 b 10.4 b 10.4 b

Los Angeles, CA 060374002 Long Beach (North) Yes c 11.5 e 10. 6 e 10.9 e

Los Angeles, CA 060374004 South Long Beach Yes c 11.2 10. 6 e 1 0.8 e

Los Angeles, CA 060379033 Lancaster No 6.9 b 6.2 b 6.1 b

Mariposa, CA N/A N/A No No monitor

Mono, CA N/A N/A No No monitor

Monterey, CA 060531003 Salinas 3 No 6.1 6.1 6.1

Monterey, CA 060530002 Carmel Valley No n/a n/a 5.9 b

Monterey, CA 060530008 King City 2 No n/a n/a 6.3 b

Sacramento, CA 060670006 Sacramento

- Del
Paso Manor No 10 .0 9.5 10.4

Sacramento, CA 060670010 Sacramento

- 1309 T Street No 9.2 8.8 9.5

Sacramento, CA 060674001 Sacramento

Health Dept. No 9.2 8.7 9.3

San Benito, CA 060690002 Hollister 2 No 5.6 5.4 5.5

San Bernardino, CA 060710025 Ontario Fire Station Yes c 13.7 12.9 12.6

San Bernardino, CA 060710306 Victorville

- Park
Avenue No 7.7 b 6.9 b 6.8 b

San Bernardino, CA 060712002 Fontana Yes c 12.9 12.4 12.6

San Bernardino, CA 060718001 Big Bear Yes c 8.9 8.3 8.7

San Bernardino, CA 060719004 San Bernardino Yes c 12.1 11.7 11.8

San Luis Obispo, CA 060792004 Unocal

- Nipomo No 8.2 b 8.2 8.7

San Luis Obispo, CA 060792006 SAN LUIS OBISPO No 6.1 6.1 6.6

San Luis Obispo, CA 060792007 MESA CAL FIRE STATION 22 No 11 .0 b 10.5 b 11.3

San Luis Obispo, CA 060798001 ATASCADERO CITY FIRE DEPARTMENT No 7.7 6.6 7 .0

### Page 115 of 161

County, State Monitor

Site ID Site Name State Rec NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Santa Barbara, CA 060830011 Santa Barbara

National Guard Armory No 9.9 9.5 9.5

Santa Barbara, CA 060831008 RELOCATED FROM ARB SITE AT 500 S. BROADWAY No 6.9 7.2 7.6

Santa Clara, CA 060850002 Gilroy

- 9th St No 8.4 7.9 8 .0

Santa Clara, CA 060850005 San Jose

- Jackson St. No 9.6 9.3 10.3

Tuolumne, CA N/A N/A No No monitor

Ventura, CA 061110007 THOUSAND
OAKS No 9.3 8.9 9.1

Ventura, CA 061110009 PIRU No 8.5 8.6 8.1

Ventura, CA 061111004 OJAI No na 9.5 b 9 .0 b

Ventura, CA 061112002 SIMI VALLEY No 9.3 8.9 9.1

Ventura, CA 061113001 EL RIO No 9.2 8.7 9 .0

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

b The listed design value is not valid.

c State recommended nonattainment as part of a separate nonattainment area. See the section of this document titled: “Area Background and Overview” for the Los Angeles

- South Coast Air Basin.

d At the end of 2011, the Fresno

- First Street site (AQS ID 060190008) was relocated to the Fresno

- Garland site (AQS ID 060190011). The design values listed combine data from Fresno

- F irst Street through 2011 and data from Fresno

- Garland
from 2012 and 2013.

e This design value does not include data from Class III FEM monitors that the EPA has approved as not eligible for comparison to the NAAQS per 40 CFR 58.11(e). Docume nts associated with this action available at www.regulations.gov

, Docket ID No. EPA

- HQ

- OAR

- 2012

- 0918.



### In addition to the FEM/FRM/ARM monitoring sites identified in Table 2 whose collected data are used to calculate DVs, San Joaquin Valley Air Pollution Control District, California Air Resources Board , and the
National Parks Service also operate special pur pose monitoring locations identified in Table 2a. The Huron,
Lebec

- Beartrap Road, Yosemite NP

- Visitor Center

, Sequoia and Kings Canyon NP

- Ash Mountain

, and
Porterville sites were established for informational purposes, and the remaining sites in Table 2a a re IMPROVE sites, established to evaluate visibility impacts on Class I areas. These monitors are not required per 40 CFR 58.20 to be compared to the NAAQS. Although these nonregulatory monitors are not eligible for comparison to the NAAQS, the data collec ted may help define an appropriate boundary for areas with emissions sources or activities that potentially contribute to ambient fine particle concentrations at the violating monitors.

### Page 116 of 161

Table 2 a. Air Quality Data Collected at Nonregulatory Monitors (all DV levels in μg/m 3 

### )

County, State Monitor

Site ID Site Name State Rec NA? 09

- 11 DV 10

- 12 DV 11

- 13 DV

Fresno, CA 060192008 Huron Yes 14.4 a 13.6 a 13.6 a

Fresno, CA 060199000 Kaiser Yes 3.0 2.9 2.8 a

Inyo, CA 060270101 DEATH
VALLEY NM No 3.4 3.4 3.1 a

Kern, CA 060292009 Lebec

- Beartrap Rd Yes 6.9 a 7.5 a 7.8 a

Kern, CA 060299001 Lebec

- Peace Valley Rd Kern County Yes 4.6 4.6 4.5 a

Los Angeles, CA 060379034 Lebec

- Peace Valley Rd LA County Yes 3.2 a 2.7 a 2.8 a

Mariposa, CA 060430003 Yosemite NP

Turtleback Dome No 3.4 3.3 3.3 a

Mariposa, CA 060431001 YOSEMITE NP

Visitor Center No 10.3 a 9.8 a 10.2 a

Mono, CA 060519000 Hoover No 2.2 2.2 2.2 a
San Benito, CA 060690003 Pinnacles NM No 3.9 3.7 3.9 a

San Bernardino, CA 060719002 Joshua Tree NM

Black Rock Canyon No 3.8 3.5 3.5 a

Tulare, CA 061070009 Sequoia and Kings Canyon
NP

- Ash Mountain Yes 9.9 a 9.0 8.3 a

Tulare, CA 061071001 ASH MOUNTIAN, SEQUOIA NATIONAL
PARK Yes 6.4 6.4 6.2 a
Tulare, CA 061072010 Porterville Yes

- 15.4 a 16.4

a One or more years of data are not complete.



### The Figure 1 map, shown previously, identifies the San Joaquin Valley nonattainment area, the existing PM 2.5 

### nonattainment area boundaries, and monitoring locations with 2011

- 2013 violating DVs. As indicated on the map, there are 12 violating monitoring sites located within the San Joaquin Valley nonattainment area. Many sites
within the nonattainment area have persistently shown violation s for the 2009

- 2011, 2010

- 2012, and 2011

- 2013 DV periods. Within San Joaquin County, a monitor in the city of Stockton has violated for the 2011

- 2013 DV period while to the south of this monitor within the same county, the Mante ca monitor did not violate for the same DV period. Within Merced County , a monitor near the center of the city of Merced on M Street has not shown a
violation for the 2009

- 2011 or 2010

- 2012 DV periods

, while a monitor to the southeast on Coffee Avenue has persistently shown a violation for the 2009

- 2011, 2010

- 2012, and 2011

- 2013 DV periods. Within Fresno County

, all monitors persistently violate for the 2009

- 2011, 2010

- 2012, and 2011

- 2013 DV periods with the exception of a background monitor west of the cit y of Fresno near the city of Tranquillity which does not show a violation for
the 2010

- 2012 and 2011

- 2013 DV periods. Within the western portion of Kern C ounty that is included as part of the San Joaquin Valley nonattainment area, all monitors in the city of Bakersfield have persistently shown a
violation for the 2009

- 2011, 2010

- 2012, and 2011

- 2013 DV periods. Two monitors, located in the cities of Mojave and Ridgecrest, with non

- valid design values exist in the eastern portion of Kern C ounty that is not

### Page 117 of 161

in cluded as part of the San Joaquin Valley nonattainment area. All other counties within the remainder of the San Joaquin Valley nonattainment area, including Stanislaus, Madera, Kings, and Tulare counties, only contain
monitors that have shown a violation f or the 2011

- 2013 DV period. Within the area of analysis several violating monitors exist in Los Angeles and San Bernardino counties. However, the EPA is designating these areas nonattainment as part of the Los Angeles

- South Coast Air Basin nonattainment ar ea. (See the “Area Background

and Overview

- Los Angeles

- South Coast Air Basin, CA” section of this document.) No other monitors in the area of analysis have valid 2009

- 2011, 2010

- 2012, or 2011

- 2013 DVs that violat e the 2012 annual PM 2.5 

### NAAQS

.

Seasonal va riation can highlight those conditions most associated with high average concentration levels of PM 2.5



### . Figure 2 shows quarterly mean PM 2.5 

### concentrations for the most recent 3

- year period for the highest DV

m onitor ing site in the nonattainment area (Madera

- City), as well as from the highest DV m onitor ing site in each
county outside of the nonattainment area

, but within the area of analysis (i.e.

, th os e counties bordering the nonattainment area)

. For Kern County , which is being partially designated no nattainment as part of the San Joaquin Valley nonattainment area , one monitor (Mojave

- Poole) is also included in Figure 2 from the unclassifiable/attainment portion of the county. Figure 2a shows quarterly mean PM 2.5 

### concentrations for the most

recent 3

- ye ar period for all monitoring sites with in the San Joaquin Valley nonattainment area , and excludes sites from surrounding counties outside the nonattainment area

. In both figures t he design value monitor (Madera

- City)
for the nonattainment area is shown as a black line. This graphical representation is particularly relevant when assessing air quality data for an annual standard, such as the 2012 annual PM 2.5 

### NAAQS, because, as previously stated, the annual mean is calculated as the mean of quarterly means an d a high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3

- year DV.

### Page 118 of 161

Figure 2. San Joaquin Valley Area of Analysis Monitors

- PM 2.5 

### Quarterly Means for 20 11

- 201 3

Figure 2a. San Joaquin Valley Nonattainment Area Monitors

- PM 2.5 

### Quarterly Means for 20 11

- 201 3

### Page 119 of 161

As shown, quarter four ( Q4 ) of 2013 recorded the highest concentration average within the San Joaquin Valley followed by Q4 2011. Peak concentrations within t he San Joaquin Valley for the 3

- year period occurred in Q4 or quarter one ( Q1 )

.

PM 2.5 

### Composition Measurements

- To assess potential emissions contribution s for each violating monitor ing
location , the EPA determined the various chemical species comprising total PM 2.5 

### to identify the chemical

constituents over the analysis area , which can provide insight into the types of emission sources impacting the
monitor ed concentration

. To best describe the PM 2.5 

### at the violating monitor ing location, the E PA first adjusted the chemical speciation measurement data from a monitoring location at or near the violating FRM monitoring
site using the SANDWICH approach to account for the amount of PM 2.5 

### mass constituents retained in the FRM

measurement. 99

, 100

, 101

, 102 

### In p articular, this approach accounts for losses in fine particle nitrate and increases in
sulfate mass associated with particle bound water. Figure 3a illustrates the fraction of each PM 2.5 

### chemical constituent estimated at the Madera design value monitoring site using the speciation sampler from the nearby

Fresno

- Garland monitoring site based on annual averages for the years 201 0

- 2012.

99 SANDWICH stands for measured S ulfate, A djusted N itrate, D erived W ater, I nferred C arbonaceous mass H ybrid Materia l Balance Approach.” The SANDWICH adjustment uses an FRM mass construction methodology that results in reduced nitrates (relative to the amount measured by routine speciation networks), higher mass associated with sulfates (reflecting water included in gra vimetric FRM measurements) and a measure of organic carbonaceous mass derived from the difference

between measured PM 2.5 and its non

- carbon components. This characterization of PM 2.5 mass also reflects crustal material and other minor constituents. The resulting characterization provides a complete mass closure for the measured FRM PM 2.5

mass , which can be different than the data provided directly by the speciation measurements from t he CSN network.

100 Frank, N. H., SANDWICH Material Balance Approach for PM 2.5 Data Analysis, National Air Monitoring Conference, Las Vegas, Nevada, November 6

- 9, 2006. http://www.epa.gov/ttn/amtic/files/2006conference/frank.pdf

. 101 Frank, N. H., The Chemical Composition of PM 2.5 to support PM Implementation, EPA State /Local/Tribal Training Workshop: PM 2.5 Final Rule Implementation and 2006 PM 2.5 Designation Process, Chicago IL, June 20

- 21, 2007, http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chemical_composi tion.pdf.

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

– 511.

### Page 120 of 161

Figure 3a. San Joaquin Valley Annual Average PM 2.5 

### Che mical Constituents ( 2010

- 2012 )

Figure 3b shows annual and quarterly chemical composition profiles and illustrates any seasonal or episodic
contributors to PM 2.5 

### mass. This “ increment analysis ,” combin ed with the other factor analyses, can provide additional insight as to which sources or factors may contribute at a greater level. Simply stated, t his analysis can help identify nearby sources of emissions that contribute to the violation at the violating monitoring site s

.
Figure 3 b

. San Joaq uin Valley Annual and Quarterly Average PM 2.5 

### Species ( 2010

- 2012 ) a

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



### Figure 3b shows that organic carbonaceous mass (OM) is the predominant species contributing over fifty percent of the total mass throughout the year. Nitrates are the second largest component in the annual mean, contributing 21 percent followed by sulfates contributing 14 percent. Nitrates contribute most during quarter one ( Q1 ) and quarter four ( Q4 ) , when the total PM 2.5 

### concentration is highest, but not as much to quarter two ( Q2 ) and quarter three ( Q3 ) . Sulfates contribute about thirty percent o f the total mass in Q2 and Q3. Crustal material (CM) and

### Page 121 of 161

elemental carbon contribute only six percent each to the total mass on an annual average, although CM does contribute over ten percent of the total mass in Q2 and Q3. This suggests that OM sources co ntribute at a greater level than any other species on an annual basis, while nitrates and sulfates also contribute annually and have even higher impacts in the colder and warmer months, respectively. Sources emitting CM also seem to contribute most in Q2 a nd Q3. Based on speciation measurements at the Modesto, Visalia and Bakersfield

- California Avenue sites, the magnitude of the contribution of each species varies somewhat throughout the San Joaquin Valley, however, OM is consistently the largest contributo r, followed by nitrates and sulfates.

The EPA assessed seasonal and annual average PM 2.5 

### constituents at monitoring sites within the area relative to monitoring sites outside of the analysis area to account for the difference between regional background
concentrations of PM 2.5 

### , and the concentrations of PM 2.5 

### in the area of analysis, also known as the “urban increment.” This analysis differentiates between the influences of emissions from sources in nearby areas and in
more distant areas on the violating monitor

. Estimating the urban increment in the area helps to illuminate the amount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in nearby areas, as opposed to impacts of more distant or region al sources of emissions. Figure 4a includes pie charts showing the annual and quarterly chemical mass constituents of the urban increment estimated for the Madera design value monitoring site. Evaluating the average urban increment in each quarter can help identify composition of PM 2.5 

### at the design value site

. Note that in these charts, the individual components have been adjusted to represent their retained mass in FRM

- measured PM 2.5 

### as described for Figures 3a and 3b above

.

### Page 122 of 161

Figure 4a. San Joaquin Valley Urban Increment Analysis for 201 0

- 201 2.

9%

69% 20% 1% 1% 

## Annual Average Urban Increment

2010

- 2012 ( μ g/m 3 

## )

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

1.13

8.58 4.33 0.19 

## Q1 Average Urban Increment

2010

- 2012 ( μ g/m 3 

## )

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal 0.68

9.10 0.00 0.04 0.00 

## Q2 Average Urban Increment

2010

- 2012 ( μ g/m 3 

## )

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

1.45

8.79 0.00 0.07 0.60 

## Q3 Average Urban Increment

2010

- 2012 ( μ g/m 3 

## )

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal 1.24

7.59 5.35 0.28 0.13 

## Q4 Average Urban Increment

2010

- 2012 ( μ g/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

### Page 123 of 161

Figure 4 b

. San Joaquin Valley Average Urban Increment Analysis for 2010

- 2012

.

Each county in the San Joaquin Valley nonattainment area has one or more monitoring site with a 2011

- 2013 DV exceeding the NAAQS . Seasonal peaks in ambient PM 2.5 

### concentrations for monitoring sites within the San Joaquin Valley tend to occur in quarters four and one.

In reviewing the urban increment analysis for the San Joaquin Valley DV monitor, the results are similar to the adjusted speciation measurements, indicating a somewhat consistent source mix throughout the area. There is an increase in the contribution of OM indicating that it may have an additional contribution from a source closer to

the DV monitoring site. This analysis points in general to contributions from direct PM 2.5 

### or PM 2.5 

### precursors from sources throughout the region, with an increased contribution of OM from sources closer to the DV monitoring
site.

Factor 2: Emissions and E mi ssions

- related D ata

In th is designations process, for each area with a violating monitor ing site

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

contribution to PM 2.5 

### concentrations at the violating monitor ing site or monitor ing site s in the area under
evaluation

. Similar to the air quality analysis, these data were examined on a seasonal basis. The EPA examine d
emissions of identified sources or source categor ies 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 pr ecursor gaseous pollutants (i.e. , SO 2 

### , NO X 

### , total VOC, and NH 3 

### ). The EPA also considered t he

distance of those sources of emissions from the violating monitor ing site

. While d irect PM 2.5 

### emissions and its major carbonaceous components are generally associated with sources near violating PM 2.5 

### monitoring sites, t he

gaseous precursors tend to have a more regional influence (although the EPA is mindful of the potential local

NO X 

### and VOC emissions contributions to PM 2.5 

### from mobile and stationary sources) and transport from neighboring areas can contribute to higher PM 2 .5 

### levels at the violating monitor ing site s. 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

Annual Q1 Q2 Q3 Q4 μ g/m 3 

## Average Urban Increment 2010

- 2012 ( μ g/m 3 

## )

Sulfates Organic Mass Nitrates Elemental Carbon Crustal

### Page 124 of 161

Emissions Data

For this factor, the EPA reviewed data from the 2011 National Emissions Inventory ( NEI) version 1 (see http://www.epa.gov/ttn/chief/net/2011inventory.html ). For each county in the area of analysis, the EPA examined the magnitude of county

- level emissions reported in the NEI. These county

- level emissions represent the sum of emissions from the following gen eral source categories: point sources, non

- point (i.e., area) sources, nonroad mobile, on

- road mobile, and fires. In some instances, non

- anthropogenic sources of emissions such as wildfires account for large portions of the emissions inventory data present ed below. The EPA also looked at the geographic distrib ution of major point sources of the relevant pollutants. 103 

### Substantial emissions levels from

sources in a nearby area indicate the potential for the area to contribute to monitored violations.

To further analyze area emissions data, the EPA also developed a summary of direct PM 2.5 

### , components of direct

PM 2.5 

### , and precursor pollutants, which is available at http://www.epa.gov/pmdesignations/2012standards/docs/nei2011v1pointnei2008v3county.xlsx

.
When considered with the urban increment analysis in Factor 1, evaluating the components of direct PM 2.5 

### and

precursor gases can help identify specific sources or source types contributing to elevated concentrations at violating monitor ing sites and thus assist in identifying appropriate area boundaries. In general, directly emitted particulate organic carbon (POC) and VOCs 104 

### contribute to PM 2.5 

### organic ma ss (OM); directly emitted EC

contributes to PM 2.5 

### EC; NO X 

### , NH 3 

### and directly emitted nitrate contribute to PM 2.5 

### nitrate mass; SO 2 

### , NH 3 

### and directly emitted sulfate contribute to PM 2.5 

### sulfate mass; and directly emitted crustal material and metal oxides
contribute to PM 2.5 

### crustal matter. 105

, 106 

### The EPA believes that the quantities of those nearby emissions as potential contributors to the PM 2.5 

### violating monitor s are somewhat proportional to the PM 2.5 

### chemical constituents in the estimated urban increment. Thus, directly emitted POC is more important per ton than SO 2 

### , partially because POC emissions are already PM 2.5 

### whereas SO 2 

### must conver t to PM 2.5 

### and not all of the emitted

SO 2 

### undergoe s this conversion.

Table 3a provides a county

- level emissions summary (i.e., the sum of emissions from the following general source categories: point sources, non

- point (i.e., area) sources, nonroad mobile, on

- road mobile, and fires) of directly emitted P M 2.5 

### and precursor species for the county with the violating monitoring site and nearby counties
considered for inclusion in the San Joaquin Valley, CA area . Table 3b summarizes the directly emitted components of PM 2.5 

### for the same counties in the area of analysis for the San Joaquin Valley, CA area. This information will be paired with the Urban Increment composition previously shown in Figures 4a and 4b.

103 For purposes of this designations effort, “major” point sources are those whose sum of PM precursor emissions ( PM 2.5 + NOx + SO 2 + VOC + NH 3 ) are greater than 500 tons per year based on NEI 2011v1

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

105 See , Seinfeld J. H. and Pandis S. N. (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change

, 2nd edition, J. Wiley, New York. See also, Seinfeld J. H. and Pandis S. N. (1998) Atmospheric Chemistry and Physics: From

A ir Pollution to Climate Change , 1st edition, J. Wiley, New York.

106 USEPA Report (2004), The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003,

found at: http://www.epa.gov/airtrends/aqtrnd04/pm.html

.

### Page 125 of 161

Table 3a

. County

- Level Emissions of Directly Emitted PM 2.5 

### and Precursors (tons/year)

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

Los Angeles, CA 19,205 143,295 16,929 7,888 117,854 305,172
Tulare, CA 34,722 13,621 22,462 1,624 67,387 139,817
Kern, CA 26,732 41,723 12,303 3,060 47,172 130,990

San Bernardino, CA 12,974 67,255 11,665 1,934 35,543 129,371

Fresno, CA 20,412 24,503 4,465 657 20,780 70,817
San Joaquin, CA 26,692 21,199 2,551 1,256 13,696 65,394

Santa Clara, CA 2,969 24,824 4,996 747 26,637 60,172

Alameda, CA 2,002 29,385 4,755 1,057 21,260 58,460

Contra Costa, CA 2,411 20,752 5,928 5,516 17,536 52,143

Sacramento, CA 4,446 19,759 3,779 353 21,444 49,782
Stanislaus, CA 16,609 11,670 2,164 478 13,147 44,067
Merced, CA 17,829 10,967 1,666 138 7,039 37,639
Monterey, CA 4,222 12,236 2,800 472 12,784 32,514

Ventura, CA 3,604 11,652 2,084 291 13,937 31,568
Kings, CA 14,315 5,717 865 98 3,418 24,414

Santa Barbara, CA 1,517 9,943 1,487 544 10,808 24,299

Madera, CA 5,610 6,857 1,899 217 5,979 20,561

San Luis Obispo, CA 1,910 6,285 1,855 273 7,699 18,022

Tuolumne, CA 1,186 2,512 3,380 373 10,301 17,752
Mariposa, CA 1,153 939 3,430 263 10,340 16,126

Calaveras, CA 650 1,453 1,242 85 4,520 7,951

Amador, CA 392 1,650 1,258 72 2,868 6,240

San Benito, CA 941 2,752 464 24 1,596 5,777

Mono, CA 784 634 888 78 2,691 5,074
Inyo, CA 1,468 932 922 199 1,505 5,026
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 126 of 161

Table 3b . County

- Level Emissions for Components of Directly Emitted PM 2.5 

### (tons/year) 107

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

Tulare, CA 16,794 2,179 295 39 668 2,488 22,462 Los Angeles, CA 6,953 2,968 692 63 1,942 4,310 16,929
Kern, CA 6,863 1,862 324 65 1,204 1,987 12,303
San Bernardino, CA 3,237 1,512 344 48 2,765 3,760 11,665
Contra Costa, CA 2,406 794 354 53 941 1,381 5,928
Santa Clara, CA 1,851 585 115 16 1,031 1,398 4,996
Alameda, CA 1,403 632 135 12 971 1,603 4,755
Fresno, CA 1,986 621 95 14 764 985 4,465
Sacramento, CA 2,344 441 57 16 357 565 3,779 Mariposa, CA 2,667 331 39 8 52 335 3,430
Tuolumne, CA 2,514 315 47 15 139 349 3,380 Monterey, CA 1,621 356 51 13 318 441 2,800 San Joaquin, CA 1,039 381 87 9 456 579 2,551 Stanislaus, CA 976 280 62 7 348 491 2,164
Ventura, CA 1,138 384 35 6 218 303 2,084
Madera, CA 1,034 228 47 6 186 398 1,899 San Luis Obispo, CA 1,118 195 26 5 192 320 1,855
Merced, CA 714 279 18 5 321 328 1,666
Santa Barbara, CA 721 248 29 5 222 262 1,487
Amador, CA 852 116 30 10 59 191 1,258
Calaveras, CA 941 127 9 6 34 126 1,242 Inyo, CA 201 69 21 3 303 325 922
Mono, CA 647 85 12 2 36 106 888 Kings, CA 206 141 11 2 262 244 865
San Benito, CA 179 67 14 1 76 127 464
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.



### Table 3b breaks down the d irect PM 2.5 

### emissions value from Table 3a into its components. Th ese data will also be compared with the previously presented Urban Increment composition.

Using the previously described relationship between directly emitted and precursor gases and the measured mass to evaluate data presented in Tables 3a and 3b, the EPA identified the following components warranting
additional review: NH 3 

### , NOx, PM 2.5 

### , SO 2 

### , VOC, POM, PSO 4 

### , PNO 3 

### , Crustal and Residual. The EPA then looked
at the contribution of these constituents of interest from each of the counties included in the area of analysis as
shown in Tables 4a

-

j.

107 Data are based on the 2011 and 2018 Emissions Modeling Platform Data Files and Summaries ( ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform ) available at: http://www.epa.gov/ ttn / chief/emch/index.html#2011 ( accessed 02/26/14)

.

### Page 127 of 161

Table 4a. County

- Level NH 3 Emissions (tons/year)

Emissions in average tons/yr

County, State NH 3 Pct. Cumulative %

Tulare, CA 34,722 15 15

Kern, CA 26,732 12 27

San Joaquin, CA 26,692 12 39

Fresno, CA 20,412 9 48
Los Angeles , CA 19,205 9 57

Merced, CA 17,829 8 65

Stanislaus, CA 16,609 7 72

Kings, CA 14,315 6 79

San Bernardino , CA 12,974 6 84

Madera, CA 5,610 2 87

Sacramento , CA 4,446 2 89

Monterey , CA 4,222 2 91

Ventura , CA 3,604 2 92

Santa Clara , CA 2,969 1 94

Contra Costa , CA 2,411 1 95

Alameda , CA 2,002 1 96
San Luis Obispo , CA 1,910 1 96

Santa Barbara , CA 1,517 1 97

Inyo , CA 1,468 1 98

Tuolumne , CA 1,186 1 98
Mariposa , CA 1,153 1 99

San Benito , CA 941 0 99

Mono , CA 784 0 100

Calaveras , CA 650 0 100

Amador , CA 392 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 128 of 161

Table 4 b . County

- Level NOx Emissions (tons/year)

Emissions in average tons/yr

County, State NO X Pct. Cumulative %

Los Angeles , CA 143,295 29 29

San Bernardino , CA 67,255 14 43

Kern, CA 41,723 8 51

Alameda , CA 29,385 6 57

Santa Clara , CA 24,824 5 62

Fresno, CA 24,503 5 67

San Joaquin, CA 21,199 4 72

Contra Costa , CA 20,752 4 76

Sacramento , CA 19,759 4 80

Tulare, CA 13,621 3 82

Monterey , CA 12,236 2 85

Stanislaus, CA 11,670 2 87

Ventura , CA 11,652 2 90

Merced, CA 10,967 2 92

Santa Barbara , CA 9,943 2 94

Madera, CA 6,857 1 95
San Luis Obispo , CA 6,285 1 97

Kings, CA 5,717 1 98

San Benito , CA 2,752 1 98

Tuolumne , CA 2,512 1 99

Amador , CA 1,650 0 99

Calaveras , CA 1,453 0 99
Mariposa , CA 939 0 100

Inyo , CA 932 0 100

Mono , CA 634 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 129 of 161

Table 4 c . County

- Level PM 2.5 Emissions (tons/year)

Emissions in average tons/yr

County, State PM 2.5 Pct. Cumulative %

Tulare, CA 22,462 19 19
Los Angeles , CA 16,929 15 34

Kern, CA 12,303 11 44

San Bernardino , CA 11,665 10 55

Contra Costa , CA 5,928 5 60

Santa Clara , CA 4,996 4 64

Alameda , CA 4,755 4 68

Fresno, CA 4,465 4 72

Sacramento , CA 3,779 3 75

Mariposa , CA 3,430 3 78

Tuolumne , CA 3,380 3 81

Monterey , CA 2,800 2 83

San Joaquin, CA 2,551 2 86

Stanislaus, CA 2,164 2 87

Ventura , CA 2,084 2 89

Madera, CA 1,899 2 91
San Luis Obispo , CA 1,855 2 92

Merced, CA 1,666 1 94

Santa Barbara , CA 1,487 1 95

Amador , CA 1,258 1 96

Calaveras , CA 1,242 1 97

Inyo , CA 922 1 98

Mono , CA 888 1 99

Kings, CA 865 1 100

San Benito , CA 464 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 130 of 161

Table 4 d . County

- Level SO 2 Emissions (tons/year)

Emissions in average tons/yr

County, State SO 2 Pct. Cumulative %

Los Angeles , CA 7,888 28 28

Contra Costa , CA 5,516 20 48

Kern, CA 3,060 11 59

San Bernardino , CA 1,934 7 66

Tulare, CA 1,624 6 72

San Joaquin, CA 1,256 5 77

Alameda , CA 1,057 4 81

Santa Clara , CA 747 3 83

Fresno, CA 657 2 86

Santa Barbara , CA 544 2 88

Stanislaus, CA 478 2 89

Monterey , CA 472 2 91

Tuolumne, CA 373 1 92

Sacramento , CA 353 1 94

Ventura , CA 291 1 95

San Luis Obispo , CA 273 1 96
Mariposa , CA 263 1 97

Madera, CA 217 1 97

Inyo , CA 199 1 98

Merced, CA 138 0 99

Kings, CA 98 0 99

Calaveras , CA 85 0 99

Mono , CA 78 0 100

Amador , CA 72 0 100

San Benito , CA 24 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 131 of 161

Table 4 e . County

- Level VOC Emissions (tons/year)

Emissions in average tons/yr

County, State VOC Pct. Cumulative %

Los Angeles , CA 117,854 24 24

Tulare, CA 67,387 14 37

Kern, CA 47,172 9 47

San Bernardino , CA 35,543 7 54

Santa Clara , CA 26,637 5 59

Sacramento , CA 21,444 4 63

Alameda , CA 21,260 4 68

Fresno, CA 20,780 4 72

Contra Costa , CA 17,536 4 75

Ventura , CA 13,937 3 78

San Joaquin, CA 13,696 3 81

Stanislaus, CA 13,147 3 84

Monterey , CA 12,784 3 86

Santa Barbara , CA 10,808 2 88
Mariposa , CA 10,340 2 90

Tuolumne , CA 10,301 2 93
San Luis Obispo , CA 7,699 2 94

Merced, CA 7,039 1 95

Madera, CA 5,979 1 97

Calaveras , CA 4,520 1 98

Kings, CA 3,418 1 98

Amador , CA 2,868 1 99

Mono , CA 2,691 1 99

San Benito , CA 1,596 0 100

Inyo , CA 1,505 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 132 of 161

Table 4 f . County

- Level POM Emissions (tons/year)

Emissions in average tons/yr

County, State POM Pct. Cumulative %

Tulare, CA 16,794 28 28
Los Angeles , CA 6,953 12 39

Kern, CA 6,863 11 51

San Bernardino , CA 3,237 5 56

Mariposa , CA 2,667 4 60

Tuolumne , CA 2,514 4 65

Contra Costa , CA 2,406 4 69

Sacramento , CA 2,344 4 72

Fresno, CA 1,986 3 76

Santa Clara , CA 1,851 3 79

Monterey , CA 1,621 3 82

Alameda , CA 1,403 2 84

Ventura , CA 1,138 2 86 San Luis Obispo

, CA 1,118 2 88

San Joaquin, CA 1,039 2 89

Madera, CA 1,034 2 91

Stanislaus, CA 976 2 93

Calaveras , CA 941 2 94

Amador , CA 852 1 96

Santa Barbara , CA 721 1 97

Merced, CA 714 1 98

Mono , CA 647 1 99

Kings, CA 206 0 99

Inyo , CA 201 0 100

San Benito , CA 179 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 133 of 161

Table 4 g . County

- Level PSO 4 Emissions (tons/year)

Emissions in average tons/yr

County, State PSO 4 Pct. Cumulative %

Los Angeles , CA 692 24 24

Contra Costa , CA 354 12 36

San Bernardino , CA 344 12 47

Kern, CA 324 11 58

Tulare, CA 295 10 68

Alameda , CA 135 5 73

Santa Clara , CA 115 4 77

Fresno, CA 95 3 80

San Joaquin, CA 87 3 83

Stanislaus, CA 62 2 85

Sacramento , CA 57 2 87

Monterey , CA 51 2 89

Madera, CA 47 2 90

Tuolumne , CA 47 2 92
Mariposa , CA 39 1 93

Ventura , CA 35 1 94

Amador , CA 30 1 95

Santa Barbara , CA 29 1 96
San Luis Obispo , CA 26 1 97

Inyo , CA 21 1 98

Merced, CA 18 1 98

San Benito , CA 14 0 99

Mono , CA 12 0 99

Kings, CA 11 0 100

Calaveras , CA 9 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 134 of 161

Table 4 h . County

- Level PNO 3 Emissions (tons/year)

Emissions in average tons/yr

County, State PNO 3 Pct. Cumulative %

Kern, CA 65 15 15
Los Angeles , CA 63 15 30

Contra Costa , CA 53 12 42

San Bernardino , CA 48 11 53

Tulare, CA 39 9 62

Sacramento , CA 16 4 66

Santa Clara , CA 16 4 70

Tuolumne , CA 15 4 73

Fresno, CA 14 3 76

Monterey , CA 13 3 79

Alameda , CA 12 3 82

Amador , CA 10 2 85

San Joaquin, CA 9 2 87
Mariposa , CA 8 2 89

Stanislaus, CA 7 2 90

Ventura , CA 6 1 92

Madera, CA 6 1 93

Calaveras , CA 6 1 95

Merced, CA 5 1 96

Santa Barbara , CA 5 1 97
San Luis Obispo , CA 5 1 98

Inyo , CA 3 1 99

Mono , CA 2 0 99

Kings, CA 2 0 100

San Benito , CA 1 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 135 of 161

Table 4 i . County

- Level Crustal Emissions (tons/year)

Emissions in average tons/yr

County, State Crustal Pct. Cumulative %

San Bernardino , CA 2,765 20 20

Los Angeles , CA 1,942 14 34

Kern, CA 1,204 9 43

Santa Clara , CA 1,031 7 50

Alameda , CA 971 7 57

Contra Costa , CA 941 7 64

Fresno, CA 764 6 69

Tulare, CA 668 5 74

San Joaquin, CA 456 3 77

Sacramento , CA 357 3 80

Stanislaus, CA 348 3 83

Merced, CA 321 2 85

Monterey , CA 318 2 87

Inyo , CA 303 2 89

Kings, CA 262 2 91

Santa Barbara , CA 222 2 93

Ventura , CA 218 2 94
San Luis Obispo , CA 192 1 96

Madera, CA 186 1 97

Tuolumne , CA 139 1 98

San Benito , CA 76 1 99

Amador , CA 59 0 99
Mariposa , CA 52 0 99

Mono , CA 36 0 100

Calaveras , CA 34 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.

### Page 136 of 161

Table 4 j

. County

- Level Residual Emissions (tons/year)

Emissions in average tons/yr

County, State Residual Pct. Cumulative %

Los Angeles , CA 4,310 18 18

San Bernardino , CA 3,760 16 34

Tulare, CA 2,488 11 45

Kern, CA 1,987 8 54

Alameda , CA 1,603 7 60

Santa Clara , CA 1,398 6 66

Contra Costa , CA 1,381 6 72

Fresno, CA 985 4 77
San Joaquin, CA 579 2 79

Sacramento , CA 565 2 81

Stanislaus, CA 491 2 84

Monterey , CA 441 2 85

Madera, CA 398 2 87

Tuolumne , CA 349 1 89
Mariposa , CA 335 1 90

Merced, CA 328 1 91

Inyo , CA 325 1 93
San Luis Obispo , CA 320 1 94

Ventura , CA 303 1 95

Santa Barbara , CA 262 1 97

Kings, CA 244 1 98

Amador , CA 191 1 98

San Benito , CA 127 1 99

Calaveras , CA 126 1 100

Mono , CA 106 0 100
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.



### Table 3a shows county

- level emissions of directly

- emitted PM 2.5 

### and PM 2.5 

### precursors . Counties within the San Joaquin Valley nonattainment area are shown in bold, while bordering counties are shown in plain text. Of the
twenty

- five counties within and bordering the nonattainment area, the five with the highest emissions of directly

emitted PM 2.5 

### and PM 2.5 

### precursors are Los Angeles and San Bernardino counties to the south of the nonattainment area, Tulare and Kern counties which fall within the nonattainment area, and Alameda County to the west of San Joaquin Valley. As discussed in Factor 4, San Joaquin Valley is topographically separated from bordering counties by mountain ranges to the west, south and east. Emis sions from these bordering counties are
therefore less likely to contribute to monitored violations within the San Joaquin Valley.

In addition to reviewing county

- wide emissions of PM 2.5 

### and PM 2.5 

### precursors in the area of analysis, the EPA also reviewed emissions from major point sources located in the area of analysis. The magnitude and location of these sources can help inform nonattainment boundaries. Table 5 provides facility

- level emissions of direct PM 2.5 

### , components of direct PM 2.5 

### , and precursor pollutants (given in tons per year) from major point sources located in the area of analysis for the San Joaquin Valley, CA area. Table 5 also shows the distance from the facility to the

### Page 137 of 161

San Joaquin Valley nonattainment area DV monitorin g site (Madera

- City). These major sources may be closer to another violating monitor within the S an Joaquin Valley nonattainment area.

Table 5 . NEI 2011 v1 Major Point Source Emissions (tons/year)

Distance 

### NEI 2011 v1 Emissions

- Tons/Year

County , State Facility Name (Facility ID) monitor (m iles ) NH 3 NOx PM 2.5 SO 2 VOC Total

Alameda , CA Metropolitan Oakland In 131 941 20 97 156 1,215

Amador , CA SIERRAPINE LTD AMPINE DIVISION 107 83 414 0 263 760

Contra Costa , CA SHELL MARTINEZ REFINERY 136 0 1,036 832 1,160 1,228 4,256

Contra Costa , CA CHEVRON PRODUCTS COMPANY 146 128 835 590 367 1,008 2,929

Contra Costa , CA TESORO REFINING AND MARKETING 134 0 1,023 137 470 587 2,217

Contra Costa , CA PHILLIPS 66 CARBON PLANT 141 507 13 1,151 0 1,671

Contra Costa , CA PHILLIPS 66 COMPANY

- SAN FRAN 143 55 309 112 411 156 1,044

Kern, CA CALIFORNIA PORTLAND CEMENT CO. 164 2,204 168 790 6 3,168

Kern, CA NATIONAL CEMENT CO 164 817 224 13 14 1,067

Kern, CA CHEVRON USA INC 123 78 274 633 26 1,011

Kern, CA OCCIDENTAL OF ELK HILLS, INC. 120 8 372 1 9 261 651

Kern, CA CHEVRON USA INC 112 16 195 138 134 89 571

Kern, CA AERA ENERGY LLC 111 354 60 20 71 506

Los Angeles , CA LOS ANGELES INT AIRPORT 227 5,485 95 539 852 6,971

Los Angeles , CA EXXONMOBIL OIL CORPORATION 234 50 818 322 347 613 2,150

Los Angeles , CA BP WEST COAST PROD.LLC BP CARSON REF. 239 164 584 312 524 483 2,067

Los Angeles , CA CHEVRON PRODUCTS CO. 229 48 649 177 379 541 1,794

Los Angeles , CA TESORO REFINING AND MARKETING CO 241 38 584 153 215 235 1,225

Los Angeles , CA CONOCOPHILLIPS COMPANY 241 81 583 89 115 241 1,110

Los Angeles , CA CONOCOPHILLIPS COMPANY 240 1 331 53 287 91 764

Los Angeles , CA ULTRAMAR INC (NSR USE ONLY) 242 32 275 65 202 179 753

Los Angeles , CA SCE

- VINCENT SUBSTATION 202 604 0 0 0 604

Los Angeles , CA SCE

- ANTELOPE SUBSTATION 185 604 0 0 0 604

Los Angeles , CA ROBERTSONS READY MIX / PALMDAL 201 508 14 522

Sacramento , CA Sacramento Internationa l 147 586 11 66 90 752

Sacramento , CA KIEFER LANDFILL 125 376 69 14 16 136 610

San Bernardino , CA MITSUBISHI CEMENT 253 1,920 421 321 39 2,701

San Bernardino , CA CEMEX

- BLACK MOUNTAIN QUARRY 230 10 2,008 189 164 30 2,400

San Bernardino , CA SEARLES VALLEY MINERAL 169 5 1,865 178 160 33 2,241

San Bernardino

, CA US ARMY NATIONAL TRAINING CTR. 220 0 1,272 213 30 128 1,643

San Bernardino , CA PG&E TOPOCK COMPRESSOR STATION 347 1,204 14 0 125 1,344

San Bernardino , CA ACE COGENERATION CO 169 12 620 37 479 4 1,152

San Bernardino , CA TXI RIVERSIDE CEMENT COMPANY 222 870 221 14 15 1,120

San Bernardino , CA MAGTFTC MCAGCC TWENTYNINE PALMS 292 1 719 45 68 222 1,055

### Page 138 of 161 Distance 

### NEI 2011 v1 Emissions

- Tons/Year

County , State Facility Name (Facility ID) monitor (m iles ) NH 3 NOx PM 2.5 SO 2 VOC Total

San Bernardino , CA TETRA TECHNOLOGIES, INC.

– AMB 320 0 20 836 1 2 859

San Bernardino , CA ONTARIO INT. AIRPORT 242 550 10 57 80 697

San Joaquin, CA PILKINGTON NORTH AMERICA, INC 91 632 82 69 1 783

San Joaquin, CA OWENS

- BROCKWAY GLASS CONTAINER 96 404 21 226 0 652

Santa Clara , CA LEHIGH SOUTHWEST CEMENT COMPAN Y 116 1,755 17 492 71 2,335

Santa Clara , CA Norman Y. Mineta San Jo se 109 575 12 65 96 748

Stanislaus, CA GROVER LANDSCAPE SERVICES 83 10 1 1 0 624 636

Stanislaus, CA GALLO GLASS COMPANY 70 195 49 284 3 532
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.



### Figure 5 shows the major point source emissions (from the 2011 NEI in tons per year ) in the area of analysis for San Joaquin Valley, CA and the relative distances of these sources from the violating monitoring location(s), as depicted by red dots. The actual distance from the major point sources to the DV monitoring location is presented in Table 5). The distance from the violating monitoring location is particularly important for directly emitted

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. 108



### As listed in Table 5 and shown in Figure 5, there are 42 major point sources located in the area of analysis

. Major point sources located in air basins outside the San Joaquin Valley Air Basin ( consisting of San Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, and Tulare counties, as well as the western portion of Kern County ) are not expected to impact air quality within the San Joaquin Valley because of the topography of the air basin (see Factors 3 and 4 ) . This includes sources located in the eastern portion of Kern County, and in San Bernardino, Los Angeles, Santa Clara, Alameda, Contra Costa, Sacramento and Amador counties.

Of the counties bordering the nonattainment area, Ventura, Santa Barbara, San Lu is Obispo, Monterey, San Benito, Calaveras, Tuolumne, Mariposa, Mono and Inyo counties have no major point sources.

108 Baker, K

. R. and K . M. Foley

. A nonlinear regression model es timating single source concentrations of primary and secondarily formed PM 2.5

. Atmospheric Environment. 45 (2011) 3758

- 3767.

### Page 139 of 161

Figure 5. Major Point Source Emissions in the Area of Analysis for the San Joaquin Valley, CA Area

.

In summary, the EPA’s analysis of the geographic locations of the relevant pollutants showed that point sources
located outside of the San Joaquin Valley are typically over 100 miles from the nonattainment area’s DV monitor

and are located outside of the airshed for this area . Accordingly, th ese areas are less likely to contribute to the violations in the San Joaquin Valley

.
Population density and degree of urbanization

In this part of the factor analysis, the EPA evaluated the population and vehicle use characteristics and trends of
the area as indicators of the probable location and magnitude of non

- point source emissions. Rapid population growth in a county on the urban perimeter signifies increasing integration with the core urban area, and indicates that it may be appropriate to include th e county associated with area source and mobile source emissions as part
of the nonattainment area. Table 6 shows the 2000 and 2010 population , population growth since 2000, and population density for each county in the area.

### Page 140 of 161

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
% Los Angeles, CA 9,519,338 9,825,761 3% 4,061 2,420 41 41
San Bernardino , CA 1,709,434 2,041,626 19% 20,052 102 9 50 Santa Clara, CA 1,682,585 1,786,540 6% 1,291 1,384 8 57 Alameda, CA 1,443,741 1,513,586 5% 738 2,052 6 64 Sacramento, CA 1,223,499 1,422,316 16% 966 1,473 6 70 Contra Costa, CA 948,816 1,052,887 11% 720 1,462 4 74 Fresno, CA 799,407 932,696 17% 5,963 156 4 78 Kern, CA 661,645 841,687 27% 8,141 103 4 82 Ventura, CA 753,197 825,378 10% 1,845 447 3 85 San Joaquin, CA 563,598 687,516 22% 1,399 491 3 88 Stanislaus, CA 446,997 515,326 15% 1,494 345 2 90 Tulare, CA 368,021 443,218 20% 4,824 92 2 92 Santa Barbara, CA 399,347 424,403 6% 2,737 155 2 94 Monterey, CA 401,762 416,335 4% 3,322 125 2 95 San Luis Obispo, CA 246,681 269,954 9% 3,304 82 1 97 Merced, CA 210,554 256,877 22% 1,929 133 1 98 Kings, CA 129,461 152,301 18% 1,391 109 1 98 Madera, CA 123,109 151,177 23% 2,136 71 1 99 San Benito, CA 53,234 55,583 4% 1,389 40 0 99 Tuolumne, CA 54,501 55,162 1% 2,235 25 0 99 Calaveras, CA 40,554 45,488 12% 1,020 45 0 100 Amador, CA 35,100 37,829 8% 593 64 0 100 Inyo, CA 17,945 18,531 3% 10,203 2 0 100 Mariposa, CA 17,130 18,254 7% 1,451 13 0 100 Mono, CA 12,853 14,268 11% 3,044 5 0 100
Total 21,862,509 23,804,699 Source: U.S. Census Bureau population estimates for 2000 and 2010
Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.



### As with point sources discussed above, population in adjacent air basins is not expected to impact air quality in the San Joaquin Valley due to the topography and meteorology of the air basin (see Factors 3 and 4) . As Table 6 and Figure 6 show, there is a high degree of urbanization in the San Joaquin Valley A ir B asin, as there is in a number of counties adjacent to the air basin. Population g rowth within the eight counties included i n the San Joaquin Valley nonattainment area has been around twenty percent from 20 0 0 to 2010. While Figure 6 give s the appearance that there are populations immediately adjacent to the air basin , given the topography of the air basin, populations within and outside the air basin are separated by large distances and mountain ranges and thus are unlikely to reflect as much contribution to violations in the San Joaquin Valley

.

### Page 141 of 161

Figure 6. 2010 County

- Level Population in the Area of Analysis for the San Joaquin Valley, CA Area.

Figure 6a shows a closer view of the northern portion of the area of analysis. Tract

- level population data (trac t s as defined by the U.S. Census Bureau) show that the population in the EPA’s nonattainment area is fairly contained within the nonattainment area. Amador and Calaveras counties are shown to the northeast of San Joaquin Co unty, shown in the center of the figure. Amador and Calaveras counties would at first appear to be an exception, as if they both had large, contiguous populations with San Joaquin County, however, their populations are small (around 80,000 total for the tw o counties) and are not as likely to be contributing to violations of the 2012 PM 2.5 

### NAAQS in the San Joaquin Valley nonattainment area. See traffic discussion below. Otherwise, tract

- level population data shown in Figure 6a show a clear pattern of denser population in the central portion of the San Joaquin Valley , along the Highway 99 corridor.

### Page 142 of 161

Figure 6 a . 2010 Tract

- Level Population in the Northern Portion of the Area of Analysis for the San Joaquin Valley, CA Area.

Figure 6b shows similar tract level population data for the central portion of the area of analysis. As in Figure 6a, the trend of population clustered along the Highway 99 corridor continues in this central portion of the San Joaquin Valley.

### Page 143 of 161

Figure 6 b . 20 10 Tract

- Level Population in the Central Portion of the Area of Analysis for the San Joaquin Valley, CA Area.

Figure 6c shows similar tract level data as above for the north and central portions of the area of analysis. In the southern portion of San Joaq uin Valley, census tracts follow the curvature of the southern portion of the valley. There is a clear lack of population contiguity between population in the valley, centered in the city of Bakersfield in the western (San Joaquin Valley) portion of Kern C ounty and the more sparsely populated areas to the east, west and south. There is also a clear separation between the high density populations of Los Angeles County and the city of Bakersfield. See Factor 4 for topographical reasons for this separation.

### Page 144 of 161

Fi gure 6 c . 2010 Tract

- Level Population in the Southern Portion of the Area of Analysis for the San Joaquin Valley, CA Area.

Traffic and Vehicle Miles Travelled

H igh vehicle miles travelled (VMT) and/or a high number of commuters associated with a county is generally an indicator that the county is an integral part of an urban area. Mobile source emissions of NO
x 

### , VOC, and direct PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS in the

area . In combination w ith the population/population density data and the location of main transportation arteries, an assessment of VMT help s identify the probable location of nonpoint source emissions that contribute to
violations in the area

. Comparatively high VMT in a count y outside of the CBSA or CSA signifies integration
with the core urban area contained within the CSA or CBSA , and indicate s that a county with the high VMT may be appropriate to include in the nonattainment area because emissions from mobile sources in tha t county
contribute to violations in the area. Table 7 shows 2011 VMT while Figure 7 overlays 2011 county

- level VMT with a map of the transportation arteries. VMT information presented is from the Federal Highway
Administration.

### Page 145 of 161

Table 7

. 2011 VMT for the San Joaquin Valley, CA Area

. County , State Total 2011 VMT Percent Cumulative %
Los Angeles , CA 76,095,823,872 38 38

San Bernardino , CA 20,423,645,232 10 48
Santa Clara , CA 14,809,943,116 7 56
Alameda , CA 12,771,269,214 6 62
Sacramento , CA 10,784,648,568 5 68
Contra Costa , CA 8,414,684,420 4 72 Kern, CA 8,288,439,996 4 76 Fresno, CA 7,576,060,237 4 80

Ventura , CA 6,744,473,079 3 83 San Joaquin, CA 6,435,426,334 3 87 Monterey , CA 3,981,084,587 2 89 Stanislaus, CA 3,718,885,802 2 90 Tulare, CA 3,478,307,796 2 92
Santa Barbara , CA 3,441,832,537 2 94 San Luis Obispo , CA 2,656,494,099 1 95 Merced, CA 2,612,728,520 1 97 Madera, CA 1,821,119,189 1 97 Kings, CA 1,318,583,674 1 98 Inyo , CA 604,078,683 0 98
Tuolumne , CA 602,697,848 0 99
Amador , CA 593,212,816 0 99
San Benito , CA 569,721,719 0 99

Mono , CA 502,386,471 0 100
Calaveras , CA 501,779,972 0 100 Mariposa , CA 382,208,742 0 100

Total 199,129,536,523 http://www.census.gov/hhes/commuting/data/commuting.html

Bold = counties (whole or partial) within the San Joaquin Valley nonattainment area.



### Table 7 lists all the counties in the area of analysis, ranked by the level of VMT in each county. The data shown in the table can be characterized as having three regimes or groups of VMT levels, each separated from the next by a
factor of 10. With under one million VMT each, the counties listed at the bottom of the table (starting with Inyo County down to Mariposa County) can be discounted for discussion of this table. Together their VMT accounts for less than 2% of the VMT for the entire area of analysis

.

### Page 146 of 161

The group of counties with a mid

- range of VMT ( Contra Costa County to Kings County , as listed in Table 7 ) each have between one and ten million VMT. Kings County has 1.3 million VMT and Contra Costa County has 8.4
million VMT. Of these thirteen countie s, seven (Fresno, San Joaquin, Stanislaus, Tulare, Merced, Madera and Kings) are entirely within the nonattainment area. Kern County has 8.2 million VMT, the second highest of this middle group of counties. The western portion of Kern County has the bulk o f the VMT for the county and is the portion that is included in the nonattainment area.

The remaining counties in this mid

- range (Contra Costa, Ventura, Monterey, Santa Barbara and San Luis Obispo), as well as the counties listed with the highest VMT (Lo s Angeles, San Bernardino, Santa Clara, Alameda and Sacramento), are all in separate air basins and are not as likely to contribute to violations of the 2012 PM2.5 NAAQS in the San Joaquin Valley Air Basin. Los Angeles County is the highest ranked county i n Table 7 and is adjacent, to the south, of Kern County, the western portion of which includes the city of Bakersfield. According to
Census Bureau statistics for commuter flow between counties for 2006

- 2010, Los Angeles County has 4.4 million

commuters. Se e: http://www.census.gov/population/metro/data/other.html . Of those, a mere 728 commute to a workplace anywhere in Kern County. These 728 commuters are also a small group when compared to the total number of commuters in Kern County (304,506 commuters).

To the north of the EPA’s San Joaquin Valley nonattainment area, Sacramento County has 9,767 commuters , or about 1.6% of Sacramento’s commuters, who commute to workplaces in counties in the area. Other adjacent counties in the area of analysis have far fewer commuters traveling to counties in the nonattainment area, for example: Alameda County (990 commuters), Contra Costa (567 commuters). Regarding the counties with the
lowest VMT in Tab le 7, only a handful of commuters commute to workplace destinations within the San Joaquin Valley . Inyo County, for example has a total of seven commuters working in the air basin. Of Amador County’s 13,776 commuters, 588 travel to work in the air basin. Taken together, all commuters from outside the nonattainment area are a small fraction of the nearly 1.5 million commuters whose commute originate within
counties in the nonattainment area.

While individual s commuting into the San Joaquin Valley air basin might not result in contribution to violations
of the 2012 PM 2.5 

### NAAQS inside the air basin, there is a great deal of commerce in the form of goods movement from the ports of Los Angeles and Long Beach that is trucked through the San Joaquin Vall ey A ir B asin and no doubt contributes to such violations. However, these emissions occur both in the upstream (in terms of the flow of goods movement) greater Los Angeles area, that is, the South Coast Air Basin, which the EPA is designating

n onattainment as the Los Angeles

- South Coast Air Basin nonattainment area, as well as in the San Joaquin Valley

Air Basin. These two air basins are separated by a mountain range and a high

- elevation mountain pass. To the extent these emissions are occurring as a result of transportation routes that go through the San Joaquin Valley, those routes will be within the boundaries of the designated nonattainment area and the question of what controls may or may not be appropriate for such emissions will be evaluated in the co ntext of the subsequent development of the attainment plan for the area.

### Page 147 of 161

Figure 7. Overlay of 2011 County

- level VMT with Transportation Arteries.

In summary for “Factor 2: Emissions and E miss ions

- related D ata ,” the EPA’s nonattainment area for the 2012

PM 2.5 

### NAAQS include s the counties and the Kern

- County partial

- county area that violate and contribute to nearby
violations in those counties. The EPA’s nonattainment area comprises the entirety of the San Joaquin Valley Air
Basin. This air basin contains point and non

- point sources of PM 2.5 

### and precursor emissions, and the related population and VMT associated with other emissions activity that all contribute to the monitored vi olations in the
nonattainment area. Population growth within the air basin was around twenty percent from 2000 to 2010. The EPA analyzed the data relevant to c ounties in the area of analysis that are adjacent to the nonattainment area and has determined that there is less likely to be contribution from emissions in those adjacent are as for a number of reasons, but most particularly because they are occurring in separate airsheds and thus are less likely to contribute
to violations in the San Jo a quin Valley airshed

. In addition, the EPA is designating several of the adjacent

counties a s a separate nonattainment area due to their violations or contribution to other nearby violating areas in

a separate airshed. Populations in adjacent areas are not extensions of urban areas within the nonattainment area. Although Los Angeles to the south and Sacramento to the north of the nonattainment area represent large
urbanized areas near the San Joaquin Valley, neither area contributes relatively meanin g ful commuter traffic to counties within the San Joaquin Valley

. The EPA is designating the Los Ang eles

- South Coast Air Basin (consisting of Orange County and portions Los Angeles, Riverside, and San Bernardino counties) as a separate
nonattainment area based on violations in that airshed. The unique meteorology and topography discussed in

Factor s 3 and 4 below describes why it is less likely that the emissions from the Sacramento area are contributing to the violations throughout the San Joaquin Valley Air Basin.

### Page 148 of 161

Factor 3: Meteorology

The EPA evaluated available meteorological data to determine how meteorological conditions, including, but not limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of directly emitted particulate matter and pr ecursor emissions from sources in the area of analysis. The EPA used two primary tools for this assessment: wind roses and kernel density estimation (KDE). When considered in
combination with area PM 2.5 

### composition and county

- level and facility emissions s ource location information, wind roses and KDE can help to identify nearby areas contributing to violations at violating monitoring sites.

Wind roses are graphic illustrations of the frequency of wind direction and wind speed. Wind direction can
indicat e the direction from which contributing emissions are transported; wind speed can indicate the force of the wind and thus the distance from which those emissions are transported. The EPA constructed wind roses from hourly observations of wind direction and wind speed using 2009

- 2012 data from National Weather Service
locations archived at the National Climate Data Center. 109 

### When developing these wind roses, the EPA also used wind observations collected at meteorological sampling stations collocated at air qu ality monitoring sites, where these data were available. Figure 8 shows wind roses that the EPA generated from data relevant in the northern (top) and southern (bottom) San Joaquin Valley area.

As shown in Figure 8, wind flow in the San Joaquin Valley most frequently comes from the northwest , at low wind speeds between 2

- 6 meters per second. This is consistent with the geographic orientation of the San Joaquin Valley and its relationship to the Golden Gate (at the mouth of San Fran c isco Bay), the key ro ute for air flow between the Pacific Ocean and the Central Valley of California. These data suggest that potential emission sources in the northwest upwind direction should be considered for analysis.

109 ftp.ncdc.noaa.gov/pub/data/noaa or http://gis.ncdc.noaa.gov/map/viewer/#app=cdo&cfg=cdo&theme=hourly&layers=1&node=gis Quality assurance of the
National Weather Service data is described here: http://www1.ncdc.noaa.gov/pub/data/inventories/ish

- qc.pdf

.

### Page 149 of 161

Figure 8. Wind Roses in the Area of Anal ysis for the (a) N orthern and (b) S outhern San Joaquin Valley. (a) Northern San Joaquin Valley

(b) Southern San Joaquin Valley

### Page 150 of 161

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

- Particle Lagrangian Integrated Trajectory) backward trajectory frequency at violating monitoring
sites. 110

, 111 

### These KDEs are graphical statistical estimations to determine the density of trajectory endpoints at a particular location represented by a grid cell. The EPA used KDEs to characterize and analyze the collection of individual HYSPLIT backward trajectories

. 112 

### Higher density values, indicated by darker blue colors, indicate a greater frequency of observed trajectory endpoints within a particular grid cell. Figure 9 shows a HYSPLIT KDE plot for the San Joaquin Valley area summarized by calendar quarter for the 2010

- 2012 period. The HYSPLIT KDE is weighted in the west

- northwesterly direction, indicating a greater frequency of trajectories passing over grid cells to the west and northwest. Here we show three representative stations in the North (Modesto

- 14 th 

### Stre et Station), Central (Fresno

- Garland Station) and Southern (Bakersfield

- Planz Station) San Joaquin Valley.

For the Modesto

- 14 th 

### Street station, representing the northern San Joaquin Valley, the HYPSPLIT KDE plot suggest greatest potential contribution o f emissions from Modesto and the regions immediately to the northwest, Tracey and Stockton in all quarters, with additional contributions from Concord and Livermore in the Bay Area in quarters 2 and 3 (Figure 9a). For the Fresno

- Garland station, representi ng the central San Joaquin Valley, the HYPSPLIT KDE plots suggest greatest potential contribution of emissions from Fresno and Madera in all quarters, with additional contributions from regions to the northwest extending to Merced and Modesto in quarters 2 and 3 (Figure 9b). For the Bakersfield

- Planz station, representing the southern San Joaquin Valley, the HYPSPLIT KDE plot suggest greatest potential contribution of emissions from Bakersfield and regions just to the northwest for all quarters (Figure 9c).

In summary, for the violating San Joaquin Valley monitors, the HYPSPLIT KDE plots and wind roses suggest greatest potential contribution of emissions is from the regions immediately to the west

- northwest of the monitors. While there is interchange of air between San Joaquin Valley and the Sacramento Valley to the north and the San Francisco Bay Area to the northwest, San Joaquin Valley’s unique flow patterns make it meteorologically distinct from those areas. While there is no topographic barrier between t he San Joaquin Valley and the Sacramento Valley to the north, and at times there can be transport between them, generally the air flow from the Pacific Ocean through the Golden Gate toward the east tends to bifurcate where the two valleys meet, providing s ome degree of separation much of the time.

110 In some past area designations effort s, EPA has used HYSPLIT backward trajectories to assist in determining nonattainment area boundaries. A HYSPLIT backward trajectory is usually depicted on a standard map as a single line, representing the centerline of an air parcel’s motion, extending in two dimensional (x,y) space from a starting point and regressing backward in time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining a n 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 mo re frequent, HYSPLIT trajectories may not be helpful as this process is likely to result in depicting air parcels originating in all directions from the violat ing monitoring site.

111 HYSPLIT

- Hybrid Single Particle Lagrangian Integrated Trajectory Model, h ttp://www.arl.noaa.gov/HYSPLIT_info.php

112 The KDEs graphically represent the aggregate of HYSPLIT backward trajectories for the years 2010

- 2012, run every third day (beginning on the first day of monitoring), four times each day, and ending at four endpoi nt heights.

### Page 151 of 161

Figures 9a) HYSPLIT Kernel Density Estimation Plots for the northern San Joaquin Valley, Modesto

- 14 th 

### Street Station (AQS # 060990005)

Q1

Q2

### Page 152 of 161

Figures 9a) HYSPLIT Kernel Density Estimation Plots for the northern San Joaquin Valley, Modesto

- 14 th 

### Street Station (AQS # 060990005) (continued)

Q3

Q4

### Page 153 of 161

Figures 9b) HYSPLIT Kernel Density Estimation Plots for the Central San Joaquin Valley, Fresno

Garland Station (AQS # 060190011)

Q1

Q2

### Page 154 of 161

Figures 9b) HYSPLIT Kernel Density Estimation Plots for the Central San Joaquin Valley, Fresno

Garland Station (AQS # 060190011) (continued)

Q3

Q4

### Page 155 of 161

Figures 9c) HYSPLIT Kernel Density Estimation Plots for the southern San Joaquin Valley, Bakersfield

Planz Station (AQS # 060290016)

Q1

Q2

### Page 156 of 161

Figures 9c) HYSPLIT Kernel Density Estimation Plots for the southern San Joaquin Valley, Bakersfield

Planz Station (AQS # 060290016) (continued)

Q3

Q4

### Page 157 of 161

Factor 4: Geography/topography

To evaluate the geography/topography factor, the EPA assessed physical features of the a rea of analysis that might define the airshed and thus affect the formation and distribution of PM 2.5 

### concentrations over the area.

The San Joaquin Valley is the southern half of the large, flat Central Valley of Cali fornia, extending from the
Sacramento

- San Joaquin Delta in the north to the Tehachapi Mountains in the south, and from the various California coastal ranges (from the Diablo in th e north to the Santa Ynez in the south) in the west to the Sierra Nevada in the east (see Figure 10 ).

Except to the north, the San Joaquin Valley is surrounded on all sides by tall mountains. These ranges tend to restrict air flow and ventilation. Mountai ns also run through Kern C ounty , separating it into an eastern and western portion . As shown in Figure 10, this topography makes it unlikely that the small emissions in the eastern portion of the county contribute to the western part that has violating mon itors and is included in the nonattainment area. While there is no topographic barrier between the San Joaquin Valley and the Sacramento Valley to the north, air masses tend to bifurcate where the two valleys meet, providing a degree of separation much of the time. Additionally, periods of high PM 2.5 

### concentrations in the San Joaquin Valley are often associated with stagnation and low wind speeds, limiting the influence of air masses outside of the nonattainment

area boundary.

The San Joaquin Valley has lo ng suffered from some of the United States' worst air pollution. This pollution, exacerbated by stagnant weather, comes mainly from diesel

- and gasoline

- fueled vehicles, residential wood burning, and agricultural operations such as dairies and field

- tilling that occur widely throughout the counties in the nonattainment area . Consideration of this factor supports the boundary for the San Joaquin Valley nonattainment area

.

### Page 158 of 161



### Figure 10 . Topographical Map of San Joaquin Valley. County and nonattainment area for the
PM 2.5 

### NAAQS boundaries are shown.

### Page 159 of 161

Factor 5: Jurisdictional boundaries

In defining the boundaries of the San Joaquin Valley nonattainment area, the EPA considered existing jurisdictional boundaries, which can provide easily identifiable and recognized boundaries for purposes of implementing the NAAQS. Existing jurisdictional boundaries often signify the state, local, or tribal governmental organizatio n with the necessary legal authority for carrying out air quality planning and enforcement functions for
the nonattainment area. Examples of such jurisdictional boundaries include existing/prior nonattainment area boundaries for particulate matter, county lines, air district boundaries, township boundaries, areas covered by a metropolitan planning organization, state lines, and Reservation boundaries, if applicable. Where existing jurisdictional boundaries were not adequate or appropriate to describe the no nattainment area, t he EPA considered other clearly defined and permanent landmarks or geographic coordinates for purposes of identifying the boundaries of the designated areas.

The San Joaquin Valley area has previously established nonattainment boundaries associated with both the 1997
annual and 2006 24

- hour PM 2.5 

### NAAQS. The boundary for the nonattainment area for the 1997 annual PM 2.5 

### NAAQS and the 2006 24

- hour PM 2.5 

### NAAQS was the same and included the entire counties of Fresno, Kings, Madera, M erced, San Joaquin, Stanislaus and Tulare and a portion of Kern County. The state has recommended the same boundary for the 2012 annual PM 2.5 

### NAAQS.

The San Joaquin Valley nonattainment area includes the Fresno

- Madera combined statistical area (CSA), an d the entireties of the Stockton, Modesto, Merced, Hanford

- Corcoran, and Visalia

- Porterville metropolitan statistical areas (MSA). The entirety of Kern County forms the Bakersfield

- Delano MSA; however, only the western portion is being included as part of the San Joaquin Valley nonattainment area. The boundary for the San Joaquin Valley
nonattainment area i s the same as the jurisdictional boundary for the San Joaquin Valley Air Pollution Control District and the San Joaquin Valley Air Basin.

The boundary between western and eastern Kern County is a generally north

- south line bisecting the county along the Sierra Nevada mountain range. The eastern portion of Kern County is under the jurisdiction of the Kern County Air Pollution Control District . It contain s part of the Sierra Nevada mountain range and lands east of the Sierras, within the Mojave Desert Air Basin. The eastern portion of Kern County is less likely to be contributing to violating monitors in the San Joaquin Valley A ir B asin due to the lower p opulation and VMT discussed in

F actor 2, combined with the separation between the airsheds due to the mountain range discussed in Factor 4

.

San Joaquin Valley also includes Indian country of the followi ng tribes: the Big Sandy Rancheria of Western

Mono Indians, the Cold Springs Rancheria of Mono Indians of California, the Northfork Rancheria of Mono Indians of California, the Picayune Rancheria of Chuckchansi Indians of California, the Santa Rosa Indian Community of the Santa Rosa Rancheria, the Table Mo untain Rancheria of California, and the Tule River Indian Tribe of the Tule River Reservation. Indian country within the nonattainment areas are shown in Figure 1

. As defined at 18 U.S.C. 1151, “Indian country” refers to: “(a) all land within the limits of any Indian reservation under the jurisdiction of the United States Government, notwithstanding the issuance of any patent, and, including rights

- of

- way running through the reservation, (b) all dependent Indian communities within the borders of the
United States whether within the original or subsequently acquired territory thereof, and whether within or without the limits of a state, and (c) all Indian allotments, the Indian titles to which have not been extinguished, including rights

- of

- way running throug h the same.” The EPA recognizes the sovereignty of tribal governments, and has attempted to take the desires of the tribes into account in establishing appropriate nonattainment area
boundaries. The EPA did not receive recommendations from these tribes.

### Page 160 of 161



### Fi gure 1 1

. EPA’s Nonattainment Boundaries for the San Joaquin Valley Area and Nearby Tribes

.

Conclusion for San Joaquin Valley Area

Based on the assessment of factors described above, both individually and in combination, the EPA has concluded
that the following counties or portions of counties should be included as part of the San Joaquin Valley nonattainment area because they are either violating the 2012 annual PM 2.5 

### NAAQS or contributing to a violation in a nearby area: San Joaquin, Stanislaus, Merce d, Madera, Fresno, Kings, Tulare, and Kern (partial) counties.
The EPA concurs with the state’s recommendation for this area and notes that this is the same area that is included in the San Joaquin Valley nonattainment area for both the 2006 24

- hour and 19 97 annual PM 2.5 

### NAAQS.

An evaluation of Factor 1 shows that the air quality monitoring sites in San Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, Tulare, and Kern counties indicate violations of the 2012 annual PM 2.5 

### NAAQS based on the 2013 DVs; the refore these counties or portions of counties are included in the nonattainment area. Factor 2 shows that Tulare and Kern counties have among the highest emissions of directly emitted PM 2.5 

### and/or PM 2.5 

### precursors in the area, population growth from 2000 t o 2010 was around twenty percent for all counties in the nonattainment

area, and there is not relatively meaningful commuting between Sacramento and San Joaquin Valley. Factor 2
also shows that all counties in the nonattainment area contribute to the parti culate matter concentrations which
result in violations of the 2012 annual PM 2.5 

### NAAQS through emissions from point sources, non

- point sources (e.g. area sources), and mobile sources. Factor 3 suggests that the greatest potential contribution of emissions is from the regions immediately to the west

- northwest of the monitors. As discussed in Factor s 3 and 4, the San Joaquin Valley is surrounded on all sides by tall mountains, except to the north where generally the air flow from

### Page 161 of 161

the Pacific Ocean through the Golden Gate toward the east tends to bifurcate where the two valleys meet, providing some degree of separation between the San Joaquin Valley and the Sacramento Valley much of the time. Finally, an assessment of Factor 5 further supports the San Joaquin V alley nonattainment area due to previous nonattainment boundary designations and local air district jurisdiction.

Only a portion of Kern County is included in the San Joaquin Valley nonattainment area because of jurisdictional boundaries (Factor 5), and b ecause the sources in the eastern portion of Kern are less likely to be contributing

because they are relatively small and there is topographic separation between the air basins (Factor s 2 and 4)

. The
factors that the EPA primarily relied upon when determining the appropriate boundary for the San Joaquin Valley nonattainment area were Air Quality Data (Factor 1), Meteorology (Factor 3), Geography/Topography (Factor 4), and Jurisdiction (Factor 5).

Also, the EPA is including the areas of Indian count ry of the following tribes as part of the San Joaquin Valley
nonattainment area for the 2012 annual PM 2.5 

### NAAQS: the Big Sandy Rancheria of Western Mono Indians, the Cold Springs Rancheria of Mono Indians of California, the Northfork Rancheria of Mono Indi ans of California,

the Picayune Rancheria of Chuckchansi Indians of California, the Santa Rosa Indian Community of the Santa Rosa Rancheria, the Table Mountain Rancheria of California, and the Tule River Indian Tribe of the Tule River
Reservation.

### Appendix 1 :

Air Quality Monitoring Data Table

### Appendix 1: Page 1

Appendix 1: Air Quality Monitoring Data Table

State Nonattainment Area (if applicable) County AQS ID Site Name 2011

- 2013 DV

California Imperial Co, CA Imperial 060250005 Calexico

- Ethel 14.3 e



### California Imperial Co, CA Imperial 060250007 Brawley 7.5

California Imperial Co, CA Imperial 060251003 El Centro 7.4

California Los Angeles

- South Coast Air Basin Los Angeles 060370002 Azusa 11.2

California Los Angeles

- South Coast Air Basin Los Angeles 060371002 Burbank 12. 5 f



### California Los Angeles

- South Coast Air Basin Los Angeles 060371103 Los Angeles

- Main St. 1 2.5 f



### California Los Angeles

- South Coast Air Basin Los Angeles 060371201 Reseda 10.2

California Los Angeles

- South Coast Air Basin Los Angeles 060371302 Compton 12.2

California Los Angeles

- South Coast Air Basin Los Angeles 060371602 Pico Rivera #2 12.0

California Los Angeles

- South Coast Air Basin Los Angeles 060372005 Pasadena 10.4*

California Los Angeles

- South Coast Air Basin Los Angeles 060374002 Long Beach (North) 1 0.9 f



### California Los Angeles

- South Coast Air Basin Los Angeles 060374004 South Long Beach 1 0.8 f



### California Los Angeles

- South Coast Air Basin Orange 060590007 Anaheim 10. 6 f



### California Los Angeles

- South Coast Air Basin Orange 060592022 Mission Viejo 8.2

California Los Angeles

- South Coast Air Basin Riverside 060651003 Riverside 11.5

California Los Angeles

- South Coast Air Basin Riverside 060658001 Rubidoux 13. 2 f



### California Los Angeles

- South Coast Air Basin Riverside 060658005 Mira Loma

- Van Buren 1 4.8 f



### California Los Angeles

- South Coast Air Basin San Bernardino 060710025 Ontario Fire Station 12.6

California Los Angeles

- South Coast Air Basin San Bernardino 060712002 Fontana 12.6

California Los Angeles

- South Coast Air Basin San Bernardino 060718001 Big Bear 8.7

California Los Angeles

- South Coast Air Basin San Bernardino 060719004 San Bernardino 11.8

California Plumas County, CA Plumas 060631006 Quincy 10.2

California Plumas County, CA Plumas 060631010 a 

### Portola North Substation 12.8

California San Joaquin Valley, CA Fresno 060192009 Tranquillity 7.8

### Appendix 1: Page 2

State Nonattainment Area (if applicable) County AQS ID Site Name 2011

- 2013 DV

California San Joaquin Valley, CA Fresno 060195001 Clovis 16.4

California San Joaquin Valley, CA Fresno 060195025 Fresno

- Pacific 14.7

California San Joaquin Valley, CA Fresno 060190011 b 

### Fresno

- Garland 15.4

California San Joaquin Valley, CA Kern 060290014 Bakersfield

- California Avenue 16.4

California San Joaquin Valley, CA Kern 060290016 Bakersfield

- Planz 17.3

California San Joaquin Valley, CA Kings 060310004 Corcoran 15.0*

California San Joaquin Valley, CA Kings 060311004 Hanford 17.0

California San Joaquin Valley, CA Madera 060392010 Madera

- City 18.1

California San Joaquin Valley, CA Merced 060470003 Merced

- Coffee Ave 13.3

California San Joaquin Valley, CA Merced 060472510 Merced

- M Street 11.1

California San Joaquin Valley, CA San Joaquin 060771002 Stockton

- Hazelton 13.8

California San Joaquin Valley, CA San Joaquin 060772010 Manteca 10.2

California San Joaquin Valley, CA Stanislaus 060990005 Modesto

- 14th Street 13.6

California San Joaquin Valley, CA Stanislaus 060990006 Turlock 15.7

California San Joaquin Valley, CA Tulare 061072002 Visalia

- N. Church 16.6

California Alameda 060010007 Livermore

- Rincon 7.6

California Alameda 060010009 Oakland East 10.0

California Alameda 060010011 Oakland West 9.9*

California Butte 060070008 c 

### Chico

- East Avenue 10.1

California Calaveras 060090001 San Andreas

- Gold Strike Road 8.4

California Colusa 060111002 Colusa

- Sunrise Blvd 7.1

California Contra Costa 060130002 Concord 7.4

California Contra Costa 060131004 San Pablo

- Rumrill 9.6*

California Humboldt 060231002 Eureka

- I Street 6.2

California Humboldt 060231004 Eureka

- Jacobs 7.7*

### Appendix 1: Page 3

State Nonattainment Area (if applicable) County AQS ID Site Name 2011

- 2013 DV

California Humboldt 060231005 Eureka

- Humboldt Hill 7.4*

California Inyo 060271003 Keeler 7.5

California Kern 060290011 Mojave

- Poole 7.0*

California Kern 060290015 Ridgecrest 5.4 *

California Lake 060333001 Lakeport 3.8

California Los Angeles 060379033 Lancaster 6.1*

California Marin 060410001 San Rafael 9.5

California Mendocino 060450006 Ukia h

- Library 6.7*

California Mendocino 060452002 Willits Justice Center 8.8*

California Monterey 060530002 Carmel Valley 5.9*

California Monterey 060530008 King City 2 6.3*

California Monterey 060531003 Salinas 3 6.1

California Napa 060550003 Napa 12.7* g



### California Nevada 060570005 Grass Valley

- Litton Bldg 4.6

California Nevada 060571001 Truckee

- Fire Station 7.0

California Placer 060610003 Auburn

- 11645 Atwood 5.8*

California Placer 060610006 Roseville

- N Sunrise Blvd 7.5

California Riverside 060652002 Indio 7.7

California Riverside 060655001 Palm Springs 6.4

California Riverside / San Diego 060650009 Pechanga 7 .7

California Sacramento 060670006 Sacramento

- Del Paso Manor 10.4

California Sacramento 060670010 Sacramento

- 1309 T Street 9.5

California Sacramento 060674001 Sacramento

- Health Dept. 9.3

California San Benito 060690002 Hollister 2 5.5

California San Bernardino 060710306 Victorville 6.8*

### Appendix 1: Page 4

State Nonattainment Area (if applicable) County AQS ID Site Name 2011

- 2013 DV

California San Diego 060730001 Chula Vista 9.9

California San Diego 060730003 El Cajon 10.6 f



### California San Diego 060731002 Escondido 10.7 f



### California San Diego 060731006 Alpine 7.8*

California San Diego 060731008 Camp Pendleton 10.4*

California San Diego 060731010 San Diego

- Downtown 10.8 f



### California San Diego 060731016 d 

### San Diego

- Kearny Villa Road 8.7

California San Francisco 060750005 San Francisco

- Arkansas St. 9.2

California San Luis Obispo 060792004 Unocal

- Nipomo 8.7

California San Luis Obispo 060792006 San Luis Obispo 6.6

California San Luis Obispo 060792007 Mesa Cal Fire Station 22 11.3

California San Luis Obispo 060798001 Atascadero 7.0

California San Mateo 060811001 Redwood City 9.3

California Santa Barbara 060830011 Santa Barbara 9.5

California Santa Barbara 060831008 Santa Maria 7.6

California Santa Clara 060850002 Gilroy 8.0

California Santa Clara 060850005 San Jose 10.3

California Santa Cruz 060870007 Santa Cruz 4 6.3

California Shasta 060890004 Redding

– Health Dept 5.7

California Shasta 06089 3 004 Redding

- Buckeye 6.2

California Shasta 060893005 Redding

- Toyon 4.9*

California Siskiyou 060932001 Yreka 6.3

California Solano 060950004 Vallejo 9.6

California Sonoma 060970003 Santa Rosa 8.4

California Sutter 061010003 Yuba City 7.7

### Appendix 1: Page 5

State Nonattainment Area (if applicable) County AQS ID Site Name 2011

- 2013 DV

California Trinity 061050002 Weaverville 6.5*

California Ventura 061110007 Thousand Oaks 9.1

California Ventura 061110009 Piru 8.1

California Ventura 061111004 Ojai 9.0*

California Ventura 061112002 Simi Valley 9.1

California Ventura 061113001 El Rio 9.0

California Yolo 061131003 Woodland

- Gibson Road 7.2

a In July 2013, the Portola

- Nevada Street site (AQS ID 060631009) was relocated to the Portola North Substation site (AQS ID 060631010). The data listed for Plumas County is a combination of data from Portola

- Nevada Street site from 2009 through June 2013, and from Portola North Substation from July 2013 through
the remainder of 2013.

b In December 2011, the Fresno

- First Street site (AQS ID 060190008) was relocated to the Fresno

- Garland site (AQS ID 060190011). The design values listed
combine data from Fresno

- First Street through 2011 and data from Fresno

- Garland from 2012 and 2013.

c In July 2012, the Chico

- Manzanita site (AQS ID 060070002) was relocated to the Chico East site (AQS ID 060070008). The data listed for Butte County i s a combination of dat a from Chico

- Manzanita from 2009 through June 2012, and from Chico East from July 2012 through 2013.

d In February 2012, the San Diego

- Overland (Kearny Mesa) site (060730006) was relocated to the San Diego

- Kearny Villa Road site (060731016). The data list ed for
the San

- Diego

- Kearny Villa Road site is a combination of data from San Diego

- Overland from 2009 through February 17, 2012, and from San

- Diego

- Kearny Villa Road from February 21, 2012 through 2013.

e Design value based on all valid data, including data in 2011 and 2012 that were submitted to, but are not currently in, AQS. EPA considers these data valid for use per 40 CFR Part 50 and 58 (see Memorandum “Use of Data for Imperial County, CA PM2.5 Design Value Calculations”).

f This design value does not include data from Class III FEM monitors that EPA has approved as not eligible for comparison to t he NAAQS per 40 CFR 58.11(e).
Docume nts associated with this action available at www.regulations.gov

, Docket ID No. EPA

- HQ

- OAR

- 2012

- 0918.

g This design value is from a monitor that began operating on December 13, 2012.

* = Design Value (DV) does not meet data completeness requirements.

BOLD = DV monitor for the County
BOLD AND RED = DV monitor for the Nonattainment Area and the County
Grouped by Nonattainment Area (and within the area, alphabetically by County) , then Unclassifiable/Attainment Counties or partial counties

.