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CALIFORNIA:

Imperial County, Los Angeles-South Coast Air Basin, Plumas County, San Joaquin 

Valley Area Designations 

for the 

2012 Primary Annual PM2.5 National Ambient Air Quality Standard

Technical Support Document

1.0 Summary

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

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

Quality Standard (NAAQS) or contributing to a violation of the NAAQS in a nearby area. The EPA must 

complete this process within 2 years of promulgating a new or revised NAAQS, or may do so within 3 years 

under circumstances not relevant to these designations.1 This technical support document (TSD) describes the

areas in California the EPA is designating as nonattainment for the 2012 primary annual fine particle NAAQS 

(2012 annual PM2.5 NAAQS).2

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

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

November 2013, California recommended that the counties and partial counties identified in Table 1 be 

designated as “nonattainment” for the 2012 annual PM2.5 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 based 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 designations for the 2012 annual PM2.5 NAAQS is below. 

After considering these recommendations and based on the EPA’s technical analysis as described in this TSD, the 

EPA is designating the areas listed in Table 1 as nonattainment for the 2012 annual PM2.5 standard. The EPA must 

 

1 Section 107(d) of the CAA requires the EPA to complete the initial designation process within 2 years of promulgation of a 

new or revised NAAQS, unless the Administrator has insufficient information to make initial designation decisions in the 2-

year time frame. In such circumstances, the EPA may take up to 1 additional year to make initial area designation decisions 

(i.e., no later than 3 years after promulgation of the standard).

2 On December 14, 2012, the EPA promulgated a revised primary annual PM2.5 NAAQS (78 FR 3086, January 15, 2013). In 

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

(μg/m3) to 12.0 μg/m3.
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designate an area nonattainment if it has an air quality monitoring site3that 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. State’s or Tribe’s Recommended Nonattainment Areas and EPA’s Nonattainment Areas for the 

2012 annual PM2.5 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 Basina

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, PM2.5 measurements from the primary monitor and suitable collocated PM2.5 

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

the NAAQS is met or not met at a particular PM2.5 monitoring site.
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San Joaquin Valley, CAb

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 Rancheria of 

Mono Indians of California

 did not submit 

recommendation

 Northfork Rancheria 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 Federallyrecognized 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 PM2.5

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 considering 

Pechanga’s recommendations, the 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 other 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, Imperial 

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 compliance with the 2012 annual PM2.5 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 recognized 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 Process6and the Policy for 

Establishing Separate Air Quality Designations for Areas of Indian Country7. The EPA 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 Valley as part of these multi-jurisdictional nonattainment areas. The EPA offered 

consultation in accordance with EPA’s Policy on Coordination and Consultation with Indian Tribes8.

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 

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

area. As described in EPA guidance9, after identifying each monitoring site indicating a violation of the standard 

in an area, the EPA analyzed those areas with emissions contributing to that violating area by considering those 

counties in the entire metropolitan area (e.g., Core Based Statistical Area (CBSA) or Combined Statistical Area 

(CSA)) in which the violating monitoring sites are located. The EPA also evaluated counties adjacent to the 

CBSA or CSA that have emissions sources with the potential to contribute to the violations. The EPA uses the 

CBSA or CSA as a starting point for the contribution analysis because those areas are nearby for purposes of the 

PM2.5 NAAQS. Based upon relevant facts and circumstances in each area, the designated nonattainment area 

could be larger or smaller that the CBSA or CSA. The EPA’s analytical 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 counties and county equivalents (e.g., parishes, 

boroughs) of the United States and its unincorporated territories (e.g., American Samoa, Guam, Northern Mariana Islands, 

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

http://www.epa.gov/envirofw/html/codes/state.html. 

5 EPA uses a designation category of "unclassifiable/ attainment" for areas that are monitoring attainment and for areas that 

do not have monitoring sites but which the EPA believes are likely attainment and does not emissions sources that are 

contributing to nearby violations based on the five factor analysis and other available information.

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 identified important factors that EPA intended to evaluate, in making a 

recommendation for area designations and nonattainment boundaries for the 2012 annual PM2.5 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 PM2.5 Designations Mapping Tool10 and the EPA PM Designations Guidance and Data web 

page11) 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 PM2.5 standard. The EPA evaluated these areas and other nearby 

areas with emissions sources or activities that potentially contribute to ambient fine particle concentrations at the 

violating monitors in the area based on the weight of evidence of the five factors recommended in EPA guidance 

and any other relevant information.

These five factors are:

Factor 1: Air Quality Data. The air quality data analysis involves examining available ambient PM2.5 air quality 

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

values (DV) calculated for each monitoring location in the area based on air quality data for the most recent 

complete 3 consecutive calendar years of quality-assured, certified air quality data in the EPA’s Air Quality 

System (AQS). In general, the EPA identifies violations using data from suitable Federal Reference Method 

(FRM), Federal Equivalent Method (FEM), and/or Approved Regional Method (ARM) monitors sited 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 the 

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 nearby to the violating monitoring sites. Examples include using chemical 

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

through analyses that differentiate local and regional source contributions. 

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

PM2.5, the major components of direct PM2.5 (primary organic carbon/organic mass, elemental carbon, crustal 

material (and/or individual trace metal compounds)), primary nitrate and primary sulfate, and precursor gaseous 

pollutants (e.g., SO2, NOX, total VOC, and NH3). Emissions data are generally derived from the most recent 

National Emissions Inventory (NEI) (i.e., 2011 NEI version 1), and are given in tons per year. The 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 PM2.5 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 PM2.5, 

section 3(a) indicates “Except as otherwise provided in this appendix, all valid FRM/FEM/ARM PM2.5 mass concentration 

data produced by suitable monitors that are required to be submitted to AQS, or otherwise available to EPA, meeting the 

requirements of part 58 of this chapter including appendices A, C, and E shall be used in the DV (design value) calculations. 

Generally, EPA will only use such data if they have been certified by the reporting organization (as prescribed by § 58.15 of

this chapter); however, data not certified by the reporting organization can nevertheless be used, if the deadline for 

certification has passed and 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 platform 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 information from states, tribes, or other relevant sources that may not be 

reflected in the NEI. One example of “other information” could include an inventory or assessment of 

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

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

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

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

emissions contributing to PM2.5 concentrations and to identify areas potentially contributing to the violations at 

monitoring sites. The Factor 3 analysis includes assessing potential source-receptor relationships in the area 

identified for evaluation using summaries of air trajectories, wind speed, wind direction, and other meteorological 

data, as available.

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

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

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

Additional analyses may consider topographical features that cause local stagnation episodes via inversions, such 

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

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

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

boundaries provide insight into how the governing air agencies conduct or might conduct air quality planning and 

enforcement in a potential nonattainment area. Examples of jurisdictional boundaries include counties, air 

districts, areas of Indian country, CBSA or CSA, metropolitan planning organizations (MPOs), and existing 

nonattainment areas.

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 Regions for Working with 

Tribes during the National Ambient Air Quality Standard (NAAQS) Designations Process15 and the Policy for 

Establishing Separate Air Quality Designations for Areas of Indian Country16; in addition to EPA Policy on 

Consultation and Coordination with Indian Tribes.17 In accordance with these documents, the EPA notified tribes 

of the PM2.5 designation process and their ability to consult with the EPA and provide designation 

recommendations in 2013 and again in May 2014. The EPA received recommendations 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 PM2.5 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 PM2.5 NAAQS nonattainment boundaries.

For purposes of the 2006 24-hour PM2.5 NAAQS, this area was designated nonattainment. The boundary for the 

nonattainment area for the 2006 24-hour PM2.5 NAAQS included a portion of Imperial County, CA. The boundary 

for the 2012 annual PM2.5 NAAQS is a portion of Imperial County consistent with the existing 2006 24-hour 

PM2.5 nonattainment area.

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

violation in the violating area. Monitor data in Imperial County show a violation of the 2012 PM2.5 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 factors 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 
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a smaller portion of such county in 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 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 annual standard such as the 

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

the year, including days with monitored ambient concentrations below the level of the NAAQS. This is somewhat 

different than the case for the 24-hour PM2.5 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 PM2.5

NAAQS, the annual mean is calculated as the mean of quarterly means. A high quarter can drive the mean for an 

entire year, which, in turn, can drive an elevated 3-year DV. Although all data are important, seasonal or episodic 

emissions can provide insight as to relative contributors to measured PM2.5 concentrations. For these reasons, for 

the Factor 1 air quality analysis, the EPA assessed and characterized air quality at, and in the proximity of, the 

violating monitoring site locations first, by evaluating trends and the spatial extent of measured concentrations at 
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monitors in the area of analysis, and then, by identifying the conditions most associated with high average 

concentration levels of PM2.5 mass in the area of analysis. 

In most cases, the EPA assessed air quality data on a seasonal, or quarterly, basis.18 The EPA also identified the 

spatial extent of these high PM2.5 concentrations. The mass and composition at the design value location 

represents contributions from various emission sources including local, area-wide (which may comprise nearby 

urban and rural areas) and regional sources. To determine the source mix (by mass) at the design value monitoring 

site, the EPA examined the chemical composition of the monitored PM2.5 concentrations by pairing each violating 

FRM/FEM/ARM monitoring site with a collocated or nearby Chemical Speciation Network (CSN) monitoring 

site or sites. Then, the EPA contrasted the approximated mass composition at the design value monitoring site 

with data collected at IMPROVE19 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 emissions sources from 

 

18 Although compliance with the annual NAAQS depends on contributions from all days of the year, examining data on a 

quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and meteorology and the 

resulting PM2.5 mass and composition. In some areas of the country where there may be noticeable month-to-month 

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

quality data may be aggregated and presented by those months that best correspond to the local “seasons” in these areas. 

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 PM2.5 mass and 

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

represent background concentrations). Developing the urban increment involves pairing a violating FRM/FEM/ARM monitor 

with a collocated monitor or nearby monitor with speciation data. EPA made every effort to pair these data to represent the 

same temporal and spatial scales. However, in some cases, the paired violating and CSN “urban” monitoring locations were 

separated by some distance such that the included urban CSN site(s) reflect(s) a different mixture of emissions sources, 

which could lead to misinterpretations. To generally account for differences in PM2.5 mass between the violating site and the 

nearby CSN site(s), EPA determined material balance of the PM2.5 composition at the violating site by assigning the extra 

measured PM2.5 mass to the carbon components of PM2.5. Where the general urban increment approach may be misleading, or 

in situations where non-carbonaceous emissions are believed to be responsible for a local PM2.5 concentration gradient, EPA 

used alternative analyses to reflect the mix of urban and rural sources contributing to the measured concentrations at violating 

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

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

data represented by the DVs at the violating monitoring site and at other monitors in the area of analysis. The

EPA calculated DVs based on air quality data for the most recent 3 consecutive calendar years of 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 statistic that indicates 

whether that monitor attains a specified air quality standard. The 2012 annual PM2.5 NAAQS is met at a 

monitoring site when the 3-year average annual mean concentration is 12.0 micrograms per cubic meter (μg/m3) 

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

met or when other regulatory data processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 2 

identifies the current design value(s) (i.e., the 2013 DV) and the most recent two 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 located in an urban area. Where the violating 

monitor is not located in an urban area, the “urban increment” represents the difference between local and other nearby 

emission sources in the vicinity of the violating monitoring location and more regional sources.

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 PM2.5 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 

(PM2.5) Standards, Technical Support Document for State and Tribal Air Quality Fine Particle (PM2.5) Designations, Chapter 

3, Urban Excess Methodology. Available at www.epa.gov/pmdesignations/1997standards/documents/final/TSD/Ch3.pdf

25 In certain circumstances, one or more monitoring locations within a monitoring network may not meet the network 

technical requirements set forth in 40 CFR 58.11(e), which states, “State and local governments must assess data from Class 

III PM2.5 FEM and ARM monitors operated within their network using the performance criteria described in table 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 collocated 

FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to the NAAQS. 

These assessments are required in the monitoring agency's annual monitoring network plan described in §58.10(b) for cases 

where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM....”
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Table 2. Air Quality Data Collected at Regulatory Monitors (all DV levels in μg/m3)

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 14.1f 14.3f

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 Yese 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 Yese 14.2c 13.4

c 13.2c

Riverside, CA 060658005 Mira Loma-Van Buren Yese 15.9 c 15.2

c 14.8 c

San Diego, CA 060730001 Chula Vista No 10.3 9.8 9.9

San Diego, CA 060730003 El Cajon No 11.8 10.6c 10.6c

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

San Diego, CA 060731010 San Diego -

Downtown No 11.0 10.8c 10.8c

San Diego, CA 060731016 San Diego - Kearny 

Villa Roadd No 9.4 8.9 8.7

Yuma, AZ 040278011 Yuma Supersite No - 7.8 7.8b

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). Documents associated with this action available at www.regulations.gov, Docket ID 

No. EPA-HQ-OAR-2012-0918.

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 DiegoOverland 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 Memorandum “Use of Data for Imperial County, CA 

PM2.5 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 operates 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. 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 monitor.

While the Salton Sea monitoring locations measure DVs below the 2012 annual PM2.5 NAAQS, the daily and 

hourly data show episodic increases in PM2.5 concentrations well above the NAAQS throughout the year that are 

likely associated with windblown dust and regional increases in PM2.5 concentrationsthroughout Imperial County. 
Page 12 of 161

The episodic increases in PM2.5 concentrations measured in this area suggests that the area surrounding the Salton 

Sea is likely a large contributing source of crustal material to PM2.5 concentration in Imperial County. Generally, 

sources of crustal material in Imperial County contain a larger percentage of PM2.5 than other areas, likely due to 

the unique soil characteristics in the area. Table 2a also shows an increase of 1.5 μg/m3from the 2010-2012 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 PM2.5 concentrations may be increasing in the area 

surrounding the Salton Sea.

Table 2a. Air Quality Data Collected at Nonregulatory Monitors (all DV levels in μg/m3)

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 - 7.1a 8.6a

Imperial, CA n/a Naval Test Base No - 5.0a 5.9a

Imperial, CA n/a Sonny Bono No - 6.5a 6.6a

Imperial, CA n/a Bombay Beach No - 6.0a 6.7a

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

The 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 border 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/m3from the 2009-2011 DV to the 2011-2013 DV. Further from the border with 

reduced 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/m3and 7.5 μg/m3 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 PM2.5 NAAQS, they, like the Salton Sea 

monitors, indicate that elevated PM2.5 concentrations exist in Imperial County beyond areas closer to the United 

States/Mexico border. These elevated ambient levels likely reflect that there are sources PM2.5 and PM2.5 

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

PM2.5 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 contribution 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 PM2.5. 

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

monitoring sites in each county within the area of analysis. Figure 2a shows quarterly mean PM2.5 concentrations 

for the most recent 3-year period for all monitoring sites in Imperial County, allowing us to compare values 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 PM2.5 NAAQS, because, as 

previously stated, the annual mean is calculated as the mean of quarterly means and a high quarter can drive the 

mean for an entire year, which, in turn, can drive an elevated 3-year DV. 

 

26 Data from CARB’s online Air Quality 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 - PM2.5 Quarterly Means for 2011-2013 

Figure 2a. Imperial County Nonattainment Area Monitors - PM2.5 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 (Q4) measured higher PM2.5 concentrations than other quarters of the year. Q2 is April 

through June and Q4 is October through December. As discussed further in Factor 3, increased concentrations and 

differences in meteorological patterns suggest that there are two different regimes of high PM2.5 concentrations 

that contribute to violation of the NAAQS during these quarters. Also, all quarters from 2011 quarter one (Q1) 

through 2013 Q4 at Calexico Ethel measured quarterly averages above 12.0 μg/m3, with the sole exception of Q1

Imperial County Area of Analysis Monitors –

PM2.5 Quarterly Means 2011-2013

Imperial County Nonattainment Area Monitors –

PM2.5 Quarterly Means 2011-2013
Page 14 of 161

in 2013, which measured a quarterly average of 10.4 μg/m3. 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 Imperial County impacting these 

monitors also contribute to violations at Calexico Ethel. As discussed further in Factors 2, 3, and 4, the areas 

surrounding these monitors contain numerous sources of PM2.5and PM2.5 precursors along with meteorological 

conditions during portions of the year which are conducive to transport of PM2.5 to the Calexico Ethel monitoring 

location.

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

location, the EPA determined the various chemical species comprising total PM2.5 to identify the chemical 

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

monitored concentration. To best describe the PM2.5 at the violating monitoring location, the EPA first adjusted 

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

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

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 PM2.5 chemical 

constituent at the Calexico Ethel monitoring site based on annual averages for the years 2010-2012. 

 

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

Balance Approach.” The SANDWICH adjustment uses an FRM mass construction methodology that results in reduced 

nitrates (relative to the amount measured by routine speciation networks), higher mass associated with sulfates (reflecting 

water included in gravimetric FRM measurements) and a measure of organic carbonaceous mass derived from the difference 

between measured PM2.5 and its non-carbon components. This characterization of PM2.5 mass also reflects crustal material 

and other minor constituents. The resulting characterization provides a complete mass closure for the measured FRM PM2.5

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

28 Frank, N. H., SANDWICH Material Balance Approach for PM2.5 Data Analysis, National Air Monitoring Conference, Las 

Vegas, Nevada, November 6-9, 2006. http://www.epa.gov/ttn/amtic/files/2006conference/frank.pdf.

29 Frank, N. H., The Chemical Composition of PM2.5 to support PM Implementation, EPA State /Local/Tribal Training 

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

http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chemical_composition.pdf.

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 PM2.5 Chemical Constituents (2010-2012) 

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

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

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 PM2.5 Species (2010-2012)a

aAdjusted to FRM Total PM2.5 indicates that the speciation profile and total mass depicted in this figure are the result of the urban increment 

calculation for the particular FRM monitor.

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. Sulfates and elemental carbon also contribute to measured PM2.5 mass at 

Calexico Ethel throughout the year. Nitrates do not contribute to measured PM2.5 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 windblown dust are large contributors to high annual

PM2.5 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 what the annual 

mean speciation data show.

The EPA assessed seasonal and annual average PM2.5 constituents at monitoring sites within the area relative to 

monitoring sites outside of the analysis area to account for the difference between regional background 

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

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

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

amount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in 

nearby areas, as opposed to impacts of more distant or regional sources of emissions. Figure 4a includes pie charts 

showing the annual and quarterly chemical mass constituents of the urban increment. The quarterly pie charts 

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

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

been adjusted to represent their mass in measured PM2.5.
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. The Calexico-Ethel monitoring site measures 

increased PM2.5 concentrations during Q2 and Q4, and considering meteorology (discussed further in Factor 3), 

this suggests that there are two different regimes of high PM2.5 concentrations that contribute to exceedances of 

the NAAQS during these quarters. 

The speciation analysis shows high percentages of both crustal material and organic mass suggesting that the

main contributing sources to PM2.5 concentrations at Calexico-Ethel are biomass burning, other combustion 

emissions, and fugitive dust emissions primarily associated with unpaved roads, agricultural 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 Data

In this designations process, for each area with a violating monitoring site, the EPA evaluated the emissions data 

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

contribution to PM2.5 concentrations at the violating monitoring site or monitoring sites in the area under 

evaluation. 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 PM2.5, the major components of direct PM2.5 (organic 

mass, elemental carbon, crustal material (and/or individual trace metal compounds)), primary nitrate and primary 

sulfate, and precursor gaseous pollutants (i.e., SO2, NOX, total VOC, and NH3). The EPA also considered the 

distance of those sources of emissions from the violating monitoring site. While direct PM2.5 emissions and its 

major carbonaceous components are generally associated with sources near violating PM2.5 monitoring sites, the 

gaseous precursors tend to have a more regional influence (although the EPA is mindful of the potential local 

NOX and VOC emissions contributions to PM2.5 from mobile and stationary sources) and transport from 

neighboring areas can contribute to higher PM2.5 levels at the violating monitoring sites. 
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, 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 distribution of major point sources of the relevant pollutants.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 PM2.5, components of direct 

PM2.5, 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 PM2.5 and 

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

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

particulate organic carbon (POC) and VOCs32 contribute to PM2.5 organic mass (OM); directly emitted EC 

contributes to PM2.5 EC; NOX, NH3 and directly emitted nitrate contribute to PM2.5 nitrate mass; SO2, NH3 and 

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

contribute to PM2.5 crustal matter. 33,34 The EPA believes that the quantities of those nearby emissions as potential 

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

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

emissions are already PM2.5 whereas SO2 must convert to PM2.5 and not all of the emitted SO2 undergoes this 

conversion.

Table 3a provides a county-level emissions summary (i.e., the sum of emissions from the following general 

source categories: point sources, non-point (i.e., area) sources, nonroad mobile, on-road mobile, and fires) of 

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

considered for inclusion in the Imperial County, CA area. Table 3b summarizes the directly emitted components 

of PM2.5 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 sources are those whose sum of PM precursor emissions (PM2.5 + 

NOx + SO2 + VOC + NH3) are greater than 500 tons per year based on NEI 2011v1.

32 As previously mentioned, nearby VOCs are presumed to be a less important contributor to PM2.5 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 Climate 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 PM2.5 and Precursors (tons/year)

County, State Total 

NH3

Total 

NOx

Total 

Direct 

PM2.5

Total 

SO2

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 PM2.5 (tons/year) 35

County, State POM EC PSO4 PNO3 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 PM2.5 emissions value from Table 3a into its components. These data will also be 

compared with the previously presented Urban Increment composition.

Using the previously described relationship between directly emitted and precursor gases and the measured mass 

to evaluate data presented in Tables 3a and 3b, the EPA identified the following components warranting 

additional review: NH3, PM2.5, SO2, VOC, POM, PSO4, 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-h.

 Table 4a. County-Level NH3 Emissions (tons/year)

Emissions in average tons/yr

County, State NH3 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 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 21 of 161

 Table 4b. County-Level PM2.5 Emissions (tons/year)

Emissions in average tons/yr

County, State PM2.5 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 4c. County-Level SO2 Emissions (tons/year)

Emissions in average tons/yr

County, State SO2 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 4d. 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 4e. 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 4f. County-Level PSO4 Emissions (tons/year)

Emissions in average tons/yr

County, State PSO4 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 4g. 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 4h. 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 (NH3, PM2.5, 

SO2, VOC, POM, PSO4, Crustal and Residual) had higher emissions in Q2 and Q3 than in Q1 and Q4. For 

example, direct PM2.5 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, NH3 emissions increase in Q2 compared to other quarters in the 

year.
Page 23 of 161

In addition to reviewing county-wide emissions of PM2.5 and PM2.5 precursors in the area of analysis, the EPA 

also reviewed emissions from major point sources located in the area of analysis. The magnitude and location of 

these sources can help inform nonattainment boundaries. Table 5 provides facility-level emissions of direct PM2.5, 

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

the area of analysis for the 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) NH3 NOx PM2.5 SO2 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 PM2.5. 

The influence of directly emitted PM2.5 on ambient PM2.5 diminishes more than that of gaseous precursors as a 

function of distance.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 largest emissions (nearly 80% by weight) are of NOx, 

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 point 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 EPA 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 burned 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 estimating single source concentrations of primary and 

secondarily formed PM2.5. 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 Investigations 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, concentrations 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 winds generally from the north and the 

high number of acres burned in Q2 may be a cause for the increased monitored PM2.5 concentrations in Q2, shown 

in Figures 2 and 2a.The data at the Calexico-Ethel monitor represent a combination of PM2.5 concentrations from 

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 monitors do not currently record violations of the 2012 annual PM2.5 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 PM2.5 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 States 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 PM2.5 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 Mexicali-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)

NOx SO2 VOC PM2.5

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 the 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 border 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 Region 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 NOx SO2 VOC CO PM2.5 NH3 CH4

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 NH3, direct PM2.5 emissions and precursors are 2 to 34 times as high in 

the municipio/county of Mexicali as in Imperial.

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

NOx SO2 VOC PM2.5 NH3

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 PM2.5 NAAQS 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 PM2.5 NAAQS. Crustal material and organic mass are the highest identified components of directlyemitted PM2.5 within Imperial County based on the speciated ambient PM2.5 information presented in Factor 1, and 

are associated with emissions sources located throughout the county. 

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 6 shows the 2000 and 2010 population, population growth since 2000, and 

population density for each county in the area. 
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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

%

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 Salton 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 SSAB 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 PM2.5 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 the 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 and 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 not 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.
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Figure 6. 2010 County-Level Population in the Area of Analysis for the Imperial County, 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 NOx, VOC, and direct 

PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS in the 

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

an assessment of VMT helps identify the probable location of nonpoint source emissions that contribute to 

violations in the area. Comparatively high VMT in a county outside of the CBSA or CSA signifies integration 

with the core urban area contained within the CSA or CBSA, and indicates that a county with the high VMT may 

be appropriate to include in the nonattainment area because emissions from mobile sources in that county 

contribute to violations in the area. Table 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.
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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, and 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 is 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 
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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 Riverside 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 commuters 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. 

Table 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 Statistics, 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 mass are the components that account for the largest percentage of 

directly-emitted PM2.5 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 

PM2.5 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 has a large population compared to Imperial County and is a known source of PM2.5 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 directly 

emitted particulate matter and precursor emissions from sources in the area of analysis. The EPA used two 

primary tools for this assessment: wind roses and kernel density estimation (KDE). When considered in 

combination with area PM2.5 composition and county-level and facility emissions source location information, 

wind roses and KDE can help to identify nearby areas contributing to violations at violating monitoring sites. 

Wind roses are graphic illustrations of the frequency of wind direction and wind speed. Wind direction can 

indicate the direction from which contributing emissions are transported; wind speed can indicate the force of the 

wind and thus the distance from which those emissions are transported. The EPA constructed wind roses from 

hourly observations of wind direction and wind speed using 2009-2012 data from National Weather Service 
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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.

Climatic 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-pressure 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 Quality 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 Area.

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 estimation (KDE) plots to represent HYSPLIT 

(HYbrid Single-Particle Lagrangian Integrated Trajectory) backward trajectory frequency at violating monitoring
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sites.40,41 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.42 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 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 

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

representing the centerline of an air parcel’s motion, extending in two dimensional (x,y) space from a starting point and 

regressing backward in time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining an 

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

exceeded. However, for an annual standard, such as the 2012 annual PM2.5 NAAQS, every trajectory on every day is 

important. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT trajectories 

may not be helpful as this process is likely to result in depicting air parcels originating in all directions from the violating 

monitoring site.

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. 
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Figures 9. Quarter 1-Quarter 4: HYSPLIT Kernel Density Estimation Plots for the Calexico-Ethel 

Monitoring Station. 
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Figure 10. Pollution Roses for Calexico Ethel, 2011-2013. PM2.5 > 12 μg/m3 Binned by Wind Direction and 

Quarter.

Figure 10 shows the distribution of hourly PM2.5 concentrations greater than 12 μg/m3from the Calexico Ethel 

monitoring site binned by wind direction (e.g. direct N corresponds to hourly PM2.5 concentrations from wind 

directions ranging from 349° to 11°, direct NNW corresponds to hourly PM2.5 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 speeds over 3 miles per hour (mph) versus stagnant conditions, here characterized as instances 

when wind speeds are less than 3 mph, on PM2.5 concentrations throughout the different quarters of the year. 
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These values represent the percentage of hourly PM2.5 values greater than 12 μg/m3 for a given wind direction.

This analysis focuses on higher concentrations with respect to the annual NAAQS, which provides insight into the 

directionality of PM2.5 concentrations on a subset of days throughout the year that may be contributing to 

violations of the annual NAAQS. For example, in Q2, 13% of all hourly values above 12 μg/m3 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, while 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 PM2.5 sources contributing to violations of the NAAQS during these quarters. 58% 

of all hourly PM2.5 concentrations greater than 12 μg/m3 in quarter two are associated with wind speeds greater 

than 3 mph, while only 14% of the hourly PM2.5 concentrations greater than 12 μg/m3 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 PM2.5 concentrations greater than 12 μg/m3

measured in quarter four occurred when wind speeds were less than 3 mph. Also, when considering PM2.5 

concentrations associated with wind speeds greater than 3 mph, 24% of all hourly PM2.5 concentrations greater 

than 12 μg/m3 measured at the Calexico Ethel monitoring site in quarter two are from northwesterly wind 

directions (i.e. >281° and <78.8°), compared to only 6% in quarter four. Figure 10 additionally shows in quarter 2 

that the west-northwest direction contains the highest percentage of hourly PM2.5 concentrations greater than 12 

μg/m3at 13%. This indicates that there are greater contributions from 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 PM2.5 concentrations over the area. 

Imperial Valley is located within the Salton Sea Air Basin along with the desert portion of Riverside County. 

Imperial County consists 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 10a) 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 to separate the EPA’s nonattainment area from the rest of the Imperial Valley. Outside of 

Imperial County, the 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.
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Figure 10a. Topographic Map in the Area of Analysis for Imperial County.
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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 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 nonattainment area, the EPA considered 

other clearly defined and permanent landmarks or geographic coordinates for purposes of identifying the 

boundaries of the designated areas.
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The EPA has previously established nonattainment boundaries for the 2006 24-hour PM2.5 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 PM10 NAAQS. The state has recommended the 

same boundary for the 2012 annual PM2.5 NAAQS as exists for the 2006 24-hr PM2.5 NAAQS. The state did not 

provide a 5-factor analysis to support their partial-county recommended nonattainment area for Imperial County, 

CA, instead merely relying on the existing 2006 24-hr PM2.5 NAAQS jurisdiction factor to recommend the same 

area as nonattainment for the 2012 annual PM2.5 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 influence 

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). ICAPCD has jurisdiction 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 under 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 functions 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 metropolitan 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-Riverside 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 of the following tribes: the 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 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 through 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. We did not receive recommendations from either tribe.
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Torres Martinez 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 reservation lands 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 surrounding nonattainment areas are shown in Figure 

1. We 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 PM2.5 nonattainment area

covers both the area that violates the NAAQS and nearby 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 PM2.5 NAAQS or contributing to a violation at a nearby 
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monitoring site: Imperial County (partial). 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 nonattainment area for the 2006 24-hour PM2.5 NAAQS.

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

2012 annual PM2.5 NAAQS based on the 2013 DVs; therefore portions of the county are included in the 

nonattainment area. Further evaluation of PM2.5 chemical mass constituents at the Calexico Ethel monitoring 

station show that while organic mass comprises 58% of the annual average PM2.5 concentrations in the area, 

crustal material comprises 21%, which suggests that fugitive dust sources, including unpaved roads, agricultural 

sources, and windblown dust are also contributing sources to violations of the 2012 annual PM2.5 NAAQS. 

Factor 2 shows that the Imperial County nonattainment area includes sources of directly emitted PM2.5 and/or 

PM2.5 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 PM2.5 NAAQS 

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 PM2.5

NAAQS and therefore, the nonattainment area includes both sources of organic mass and crustal material.

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

west-northwest and to the southeast of the Calexico Ethel monitoring location. Stagnant 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 

PM2.5 NAAQS.

As discussed in Factor 4, Imperial County does not have any geographical or topographical barriers that limit airpollution transport within the Imperial Valley portion of the county. Outside of Imperial Valley, the Peninsular 

Ranges to the west of Imperial County does serve as a partial barrier to transport from San Diego County, while 

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.

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

PM2.5 nonattainment boundary designations and local air district jurisdiction, which encompasses a portion of 

Imperial County. 

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

EPA is designating Imperial County nonattainment for the 2012 annual PM2.5 NAAQS pursuant to the same 

boundary as the 24-hour PM2.5 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 contribution 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 available indicates that biomass burning, combustion sources, and 

fugitive dust sources such as agricultural sources, unpaved roads, and windblown dust are large contributors to 
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high annual PM2.5 concentrations within Imperial County. Specifically, wind patterns 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 PM2.5 standard, based primarily on the jurisdiction factor.
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3.2 Area Background 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 PM2.5 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 PM2.5 NAAQS, this area was designated nonattainment. The boundary for the 

nonattainment area for the 1997 annual PM2.5 NAAQS included the entirety of Orange County and parts of Los 

Angeles, San Bernardino and Riverside counties. For purposes of the 2006 24-hour PM2.5 NAAQS, this area was 

designated nonattainment. The boundary for the nonattainment area for the 2006 24-hour PM2.5 NAAQS included 

the entirety of Orange County and parts of Los Angeles, San Bernardino and Riverside counties. The boundary 

for the 2012 annual PM2.5 NAAQS is the same as the existing designated boundaries for the 1997 annual and 2006 

24-hour PM2.5 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.
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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 

counties show violations of the 2012 PM2.5 NAAQS, therefore it is appropriate for these three counties or some 

portion of these three counties 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 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.

Factor 1: Air Quality Data

All data collected during the year are important when determining contributions to an annual standard such as the 

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

the year, including days with monitored ambient concentrations below the level of the NAAQS. For the 2012 

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

mean for an entire year, which, in turn, can drive an elevated 3-year DV. Although all data are important, seasonal 

or episodic emissions can provide insight as to relative contributors to measured PM2.5 concentrations. For these 

reasons, for the Factor 1 air quality analysis, the EPA assessed and characterized air quality at, and in the 

proximity of, the violating monitoring site locations first, by evaluating trends and the spatial extent of measured 
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 PM2.5 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 PM2.5 concentrations. The mass and composition at the design value location 

represents contributions from various emission sources including local, area-wide (which may comprise nearby 

urban and rural areas) and regional sources. To determine the source mix (by mass) at the design value monitoring 

site, the EPA examined the chemical composition of the monitored PM2.5 concentrations by pairing each violating 

FRM/FEM/ARM monitoring site with a collocated or nearby Chemical Speciation Network (CSN) monitoring 

site or sites. Then, the EPA contrasted the approximated mass composition at the design value monitoring site 

with data collected at IMPROVE44 and other 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 the annual NAAQS depends on contributions from all days of the year, examining data on a 

quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and meteorology and the 

resulting PM2.5 mass and composition. In some areas of the country where there may be noticeable month-to-month 

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

quality data may be aggregated and presented by those months that best correspond to the local “seasons” in these areas. 

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 characterizes the increase in seasonal and annual average PM2.5 mass and 

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

represent background concentrations). Developing the urban increment involves pairing a violating FRM/FEM/ARM monitor 

with a collocated monitor or nearby monitor with speciation data. EPA made every effort to pair these data to represent the 

same temporal and spatial scales. However, in some cases, the paired violating and CSN “urban” monitoring locations were 

separated by some distance such that the included urban CSN site(s) reflect(s) a different mixture of emissions sources, 

which could lead to misinterpretations. To generally account for differences in PM2.5 mass between the violating site and the 

nearby CSN site(s), EPA determined material balance of the PM2.5 composition at the violating site by assigning the extra 

measured PM2.5 mass to the carbon components of PM2.5. Where the general urban increment approach may be misleading, or 

in situations where non-carbonaceous emissions are believed to be responsible for a local PM2.5 concentration gradient, EPA 

used alternative analyses to reflect the mix of urban and rural sources contributing to the measured concentrations 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

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

data represented by the DVs at the violating monitoring site and at other monitors in the area of analysis. The 

EPA calculated DVs based on air quality data for the most recent 3 consecutive calendar years of 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 statistic that indicates 

whether that monitor attains a specified air quality standard. The 2012 annual PM2.5 NAAQS is met at a 

monitoring site when the 3-year average annual mean concentration is 12.0 micrograms per cubic meter (μg/m3) 

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

met or when other regulatory data processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 2 

identifies the current design value(s) (i.e., the 2013 DV) and the most recent two 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 monitoring site is located in an urban area. Where the violating 

monitor is not located in an urban area, the “urban increment” represents the difference between local and other nearby 

emission sources in the vicinity of the violating monitoring location and more regional sources.

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 PM2.5 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 

(PM2.5) Standards, Technical Support Document for State and Tribal Air Quality Fine Particle (PM2.5) Designations, Chapter 

3, Urban Excess Methodology. Available at www.epa.gov/pmdesignations/1997standards/documents/final/TSD/Ch3.pdf

50 In certain circumstances, one or more monitoring locations within a monitoring network may not meet the network 

technical requirements set forth in 40 CFR 58.11(e), which states, “State and local governments must assess data from Class 

III PM2.5 FEM and ARM monitors operated within their network using the performance criteria described in table 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 collocated 

FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to the NAAQS. 

These assessments are required in the monitoring agency's annual monitoring network plan described in §58.10(b) for cases 

where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM....”
Page 50 of 161

Table 2. Air Quality Data collected at Regulatory Monitors (all DV levels in μg/m3)

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 12.6c 12.5c

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 10.8 c

Los Angeles, CA 060379033 Lancaster No 6.9 b 6.2 b 6.1 b

Orange, CA 060590007 Anaheim Yes 11.1

c 10.6c 10.6c

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 13.2c

Riverside, CA 060658005

Mira Loma - Van 

Buren Yes 15.9 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 Yesd 13.9g 14.1g 14.3g

Imperial, CA 060250007 Brawley Yesd 7.1 7.1 7.5

Imperial, CA 060251003 El Centro Yesd 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

BakersfieldCalifornia Avenue Yese 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 Yese 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 10.6c 10.6 c

San Diego, CA 060731002 Escondido No 10.7 10.5c 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). Documents 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 DiegoOverland from 2009 through February 17, 2012, and from San-Diego-Kearny Villa Road 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 

PM2.5 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, identified 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 monitors 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/m3)

County, State Monitor Site 

ID Site Name State Rec NA? 09-11 DV 10-12 DV 11-13 DV

Riverside, CA 060650016 Temecula Yes - - 8.5

a

Riverside, CA 060659000 Agua Tibia No 5.1 4.9 4.7

a

Riverside, CA 060659000 Lake Elsinore Yes 11.6 10.9 10.6

San Bernardino, CA 060719002

Joshua Tree 

National 

Monument – Black 

Rock Canyon

No 3.8 3.5 3.5

a

Los Angeles, CA 060379034 Lebec-Peace 

Valley Rd Yes 3.2

a 2.7a 2.8a

a One or more years of data are not complete.

The Figure 1a 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 sites violating in Orange County. 
Page 52 of 161

Seasonal variation can highlight those conditions most associated with high average concentration levels of PM2.5. 

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

monitoring sites in each county within the area of analysis. Figure 2a shows quarterly mean PM2.5 concentrations 

for the most recent 3-year period for the highest DV monitoring sites in each county within only the Los AngelesSouth 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 PM2.5 NAAQS, because, as previously stated, the annual mean is calculated as the mean 

of quarterly means and a high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 

3-year DV. 

Figure 2. Los Angeles-South Coast Air Basin Area of Analysis Monitors - PM2.5 Quarterly Means for 2011-

2013 

Figure 2a. Los Angeles-South Coast Air Basin Nonattainment Area Monitors - PM2.5 Quarterly Means for 

2011-2013 

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

PM2.5 Concentrations (μg/m3

)

Los Angeles - South Coast Air Basin Area of Analysis PM2.5 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

PM2.5 Concentrations (μg/m3

)

Los Angeles - South Coast Air Basin Nonattainment Area Monitors -

PM2.5 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 Angeles – South Coast Air Basin, quarter four tends to measure higher PM2.5 

concentrations than other quarters of the year.

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

location, the EPA determined the various chemical species comprising total PM2.5 to identify the chemical 

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

monitored concentration. To best describe the PM2.5 at the violating monitoring location, the EPA first adjusted 

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

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

measurement.51,52,53,54 In particular, this approach accounts for losses in fine particle nitrate and increases in 

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

constituent 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 

contribute 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 contribute 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. Sources emitting CM also seem to contribute most 

in Q1 and Q2.

 

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

Balance Approach.” The SANDWICH adjustment uses an FRM mass construction methodology that results in reduced 

nitrates (relative to the amount measured by routine speciation networks), higher mass associated with sulfates (reflecting 

water included in gravimetric FRM measurements) and a measure of organic carbonaceous mass derived from the difference 

between measured PM2.5 and its non-carbon components. This characterization of PM2.5 mass also reflects crustal material 

and other minor constituents. The resulting characterization provides a complete mass closure for the measured FRM PM2.5

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

52 Frank, N. H., SANDWICH Material Balance Approach for PM2.5 Data Analysis, National Air Monitoring Conference, Las 

Vegas, Nevada, November 6-9, 2006. http://www.epa.gov/ttn/amtic/files/2006conference/frank.pdf.

53 Frank, N. H., The Chemical Composition of PM2.5 to support PM Implementation, EPA State /Local/Tribal Training 

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

http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chemical_composition.pdf.

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 3a. Los Angeles-South Coast Air Basin Annual Average PM2.5 Chemical Constituents (2010-2012)

Figure 3b. Los Angeles-South Coast Air Basin Annual and Quarterly Average PM2.5 Species (2010-2012)

a

a Adjusted to FRM Total PM2.5 indicates that the speciation profile and total mass depicted in this figure are the result of the urban 

increment calculation for the particular FRM monitor.

The EPA assessed seasonal and annual average PM2.5 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 PM2.5, and the concentrations of PM2.5 in the area of analysis, also known as the 

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

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

the amount and type of particles at the violating monitor that are most likely to be the result of sources of 

emissions in nearby areas, as opposed to impacts of more distant or regional sources of emissions. Figure 4a 

includes pie charts showing the annual and quarterly chemical mass constituents of the urban increment. The 

quarterly pie charts correspond to the high-concentration quarters identified in Figure 2. Evaluating these high 
Page 55 of 161

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

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

Figure 4a. Los Angeles-South Coast Air Basin Urban Increment Analysis for 2010-2012.
Page 56 of 161

Figure 4b. 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 counties. In addition, monitors within the nonattainment area measure 

increased PM2.5 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 speciation monitoring site. This analysis points in general to contributions from direct PM2.5 or PM2.5 

precursors from sources throughout the region, with an increased contribution of OM from sources closer to the 

speciation monitoring site.

Factor 2: Emissions and Emissions-related Data

In this designations process, for each area with a violating monitoring site, the EPA evaluated the emissions data 

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

contribution to PM2.5 concentrations at the violating monitoring site or monitoring sites in the area under 

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

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

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

and NH3). The EPA also considered the distance of those sources of emissions from the violating monitoring site. 

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

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

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

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

sites. 
Page 57 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, 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 distribution of major point sources of the relevant pollutants.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 PM2.5, components of direct 

PM2.5, 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 PM2.5 and 

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

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

particulate organic carbon (POC) and VOCs56 contribute to PM2.5 organic mass (OM); directly emitted EC 

contributes to PM2.5 EC; NOX, NH3 and directly emitted nitrate contribute to PM2.5 nitrate mass; SO2, NH3 and 

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

contribute to PM2.5 crustal matter.57,58 The EPA believes that the quantities of those nearby emissions as potential

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

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

emissions are already PM2.5 whereas SO2 must convert to PM2.5 and not all of the emitted SO2 undergoes this 

conversion. 

Table 3a provides a county-level emissions summary (i.e., the sum of emissions from the following general 

source categories: point sources, non-point (i.e., area) sources, nonroad mobile, on-road mobile, and fires) of 

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

considered for inclusion in the Los Angeles-South Coast Air Basin, CA area. Table 3b summarizes the directly 

emitted components of PM2.5 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 (PM2.5 + 

NOx + SO2 + VOC + NH3) are greater than 500 tons per year based on NEI 2011v1.

56 As previously mentioned, nearby VOCs are presumed to be a less important contributor to PM2.5 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 Understanding 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 PM2.5 and Precursors (tons/year)

County, State Total 

NH3

Total NOx

Total Direct 

PM2.5

Total SO2 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 PM2.5 (tons/year) 59

County, State POM EC PSO4 PNO3 PCrustal 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 direct PM2.5 emissions value from Table 3a into its components. These data will also be 

compared with the previously presented Urban Increment composition.

Using the previously described relationship between directly emitted and precursor gases and the measured mass 

to evaluate data presented in Tables 3a and 3b, the EPA identified the following components warranting 

additional review: NH3, NOX, PM2.5, SO2, VOC, POM, EC, PSO4, PNO3, Crustal and Residual. The EPA then 

 

59 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 59 of 161

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-k. 

Table 4a. County-Level NH3 Emissions (tons/year)

Emissions in average tons/yr

County, State NH3 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 4b. County-Level NOX Emissions (tons/year)

Emissions in average tons/yr

County, State NOX 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 PM2.5 Emissions (tons/year)

Emissions in average tons/yr

County, State PM2.5 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 SO2 Emissions (tons/year)

Emissions in average tons/yr

County, State SO2 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.
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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 4f. 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 4h. County-Level PSO4 Emissions (tons/year)

Emissions in average tons/yr

County, State PSO4 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 4i. County-Level PNO3 Emissions (tons/year)

Emissions in average tons/yr

County, State PNO3 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 4k. 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 PM2.5 and PM2.5 precursors in the area of analysis, the EPA 

also reviewed emissions from major point sources located in the area of analysis. The magnitude and location of 

these sources can help inform nonattainment boundaries. Table 5 provides facility-level emissions of direct PM2.5, 

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

the area of analysis for the 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 violating 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 

(miles)

NH3 NOx PM2.5 SO2 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 

(miles)

NH3 NOx PM2.5 SO2 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 particularly important for directly emitted PM2.5. The influence of 

directly emitted PM2.5 on ambient PM2.5 diminishes more than that of gaseous precursors as a function of 

distance.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 estimating single source concentrations of primary and 

secondarily formed PM2.5. 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.

The 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 countylevel emissions of directly emitted PM2.5 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 area primarily due to topography, the geographic extent of the South Coast Air 

Basin, 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 meteorological data indicates that the emissions in Clark County 

are less likely to be contributing to violations at monitoring 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 emitted PM2.5 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-South 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 PM2.5 and precursors 

from these two 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 portion 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 integration with the core urban area, and indicates 

that it may be appropriate to include the county associated with area source and mobile source emissions as part 

of the nonattainment area. Table 6 shows the 2000 and 2010 population, population growth since 2000, and 

population density for each county in the area.

 Table 6. Population Growth and Population Density.

County, State

Population 

2000

Population 

2010

% 

Change 

from 2000

Land Area 

(Sq. Miles)

Population 

Density (per 

Sq. Mile) %

Cumulative

%

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 counties 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 actually 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

population density. The population of Orange County is integrated with the populations of the surrounding areas 

in Los Angeles, Riverside, and San Bernardino counties.

Riverside County, CA has an east-west geographic orientation and is hundreds of miles long 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 population 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 the 

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, within the nonattainment area, more closely 

integrated to the surrounding areas of Los Angeles, Orange, and Riverside counties, 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 areas 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 population 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 portion 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 6a. 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

High vehicle miles travelled (VMT) and/or a high number of commuters associated with a county is generally an 

indicator that the county is an integral part of an urban area. Mobile source emissions of NOx, VOC, and direct 

PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS in the 

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

an assessment of VMT helps identify the probable location of nonpoint source emissions that contribute to 

violations in the area. Comparatively high VMT in a county outside of the CBSA or CSA signifies integration 

with the core urban area contained within the CSA or CBSA, and indicates that a county with the high VMT may 

be appropriate to include in the nonattainment area because emissions from mobile sources in that county 

contribute to violations in the area. Table 7 shows 2011 VMT while Figure 7 overlays 2011 county-level VMT 

with a map of the transportation arteries. The VMT shown 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/metro/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 population 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 

and 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 La 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, Imperial 

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 nonattainment area. Of 1.4 million commuters in San Diego, only 2% travel to the counties within 

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) travel 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 otherwise 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 were 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 PM2.5 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 nonattainment 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 monitored 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 meteorological data to determine how meteorological conditions, including, but not 

limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of directly 

emitted particulate matter and precursor emissions from sources in the area of analysis. The EPA used two 

primary tools for this assessment: wind roses and kernel density estimation (KDE). When considered in 

combination with area PM2.5 composition and county-level and facility emissions source location information, 

wind roses and KDE can help to identify nearby areas contributing to violations at violating monitoring sites. 

Wind roses are graphic illustrations of the frequency of wind direction and wind speed. Wind direction can 

indicate the direction from which contributing emissions are transported; wind speed can indicate the force of the 

wind and thus the distance from which those emissions are transported. The EPA constructed wind roses from 

hourly observations of wind direction and wind speed using 2009-2012 data from National Weather Service 

locations archived at the National Climate Data Center.61 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 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. The roses also suggest less contribution from the east, thus supporting the exclusion of

counties to the east, including Clark County, NV and Imperial 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 trajectory frequency at violating monitoring 
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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 endpoints within a particular grid cell.

Figures 9 shows a HYSPLIT KDE plot for the Los Angeles – South Coast Air Basin summarized by calendar 

quarter for the 2010-2012 period. Figure 9a shows a HYSPLIT KDE plot for each calendar quarter for the Los 

Angeles Main St. air monitoring site (AQS number 060651103) in Los Angeles County. Figure 9b shows 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 weighted 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 

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

representing the centerline of an air parcel’s motion, extending in two dimensional (x,y) space from a starting point and 

regressing backward in time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining an 

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

exceeded. However, for an annual standard, such as the 2012 annual PM2.5 NAAQS, every trajectory on every day is 

important. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT trajectories 

may not be helpful as this process is likely to result in depicting air parcels originating in all directions from the violating 

monitoring site.

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

64 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. 
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Figures 9a. Q1-Q4: HYSPLIT Kernel Density Estimation Plots for the Los Angeles Air Monitor.
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Figures 9b. Q1-Q4: HYSPLIT Kernel Density Estimation Plots – Mira Loma Air Monitor in Riverside 

County.
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Factor 4: Geography/topography

To evaluate the geography/topography factor, the EPA assessed physical features of the area of analysis that 

might define the airshed and thus affect the formation and distribution of PM2.5 concentrations over the area. 

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 Coast 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, bordered 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.
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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 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 covered by a metropolitan planning organization, state lines, and Reservation boundaries, if applicable. 

Where existing jurisdictional boundaries were not adequate or appropriate to describe the nonattainment area, 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 nonattainment area 

boundaries associated with both the 1997 annual and 2006 24-hour PM2.5 NAAQS. The boundary for the 

nonattainment area for the 1997 annual PM2.5 NAAQS and the 2006 24-hour PM2.5 NAAQS was the same and 

included the entirety of Orange County and parts 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 PM2.5 NAAQS.

The Los Angeles-South Coast Air Basin nonattainment area comprises 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 of 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 counties. The EPA’s analysis indicates that the excluded portions of Los Angeles, San Bernardino, and 

Riverside counties 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 PM2.5 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 Soboba 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 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-ofway running through 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 

received a request for consultation from the Soboba Band in September 2014. The EPA consulted with the 

Soboba Band in September and described the designations process, the history of the PM2.5 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 

recommendations from the Pechanga Band of Luiseño Mission Indians of the Pechanga Reservation (Pechanga). 
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We did not receive designation recommendations from any of the tribes within the Los Angeles-South Coast Air 

Basin nonattainment area. 

The Santa Rosa Band 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” consistent 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 

analysis which noted that the tribe did not yet have a complete design value from a monitor on tribal lands. 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-OAR2012-0918) 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 factors 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 value, 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, the EPA is 

designating all of Pechanga’s Indian country as “unclassifiable/attainment.”
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Figure 11. 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 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 Los Angeles – South Coast 

Air Basin nonattainment area because they are either violating the 2012 annual PM2.5 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 Angeles, Orange, Riverside, and San Bernardino will be the same as those 

previously designated nonattainment for the 2006 24-hour and 1997 annual PM2.5 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 State’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 PM2.5 NAAQS based on the 2013 

DVs; therefore these counties or portions of counties are included in the nonattainment area. Orange County is a 

nearby county that does 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 PM2.5 NAAQS through

emissions from point sources, non-point sources (i.e., area sources), and from mobile source emissions.
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Consideration of data relevant to Factor 2 indicates that Los Angeles and San Bernardino counties have among 

the highest emissions of directly emitted PM2.5 and/or PM2.5 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 PM2.5 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 substantial emissions 

of PM2.5 and/or PM2.5 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 the violating monitors, which provides further support for the nonattainment boundary for the Los AngelesSouth Coast Air Basin. 

As discussed with respect to Factor 4, the topography of this area is particularly relevant to evaluation of the 

appropriate boundaries for this nonattainment area. The 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 counties 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 PM2.5 and 

ozone and local air district jurisdiction. 

Only portions of Los Angeles, Riverside, and San Bernardino 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 Angeles 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 barriers. 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 including the areas of Indian country of the following tribes as part of the Los Angeles- South 

Coast Air Basin nonattainment area for the 2012 annual PM2.5 NAAQS: the Cahuilla Band of Mission Indians of 

the Cahuilla Reservation, the Morongo Band of Mission Indians, the Ramona Band of Cahuilla, and the Soboba 

Band of Luiseño Indians.
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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 PM2.5 NAAQS nonattainment boundaries.

As shown in Figure 1a, the EPA’s area of analysis considered 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 violate 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 PM2.5

NAAQS, therefore a portion of this county is included in the nonattainment area. As explained 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.
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Factor 1: Air Quality Data

All data collected during the year are important when determining contributions to an annual standard such as the 

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

the year, including days with monitored ambient concentrations below the level of the NAAQS. For the 2012 

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

mean for an entire year, which, in turn, can drive an elevated 3-year DV. Although all data are important, seasonal 

or episodic emissions can provide insight as to relative contributors to measured PM2.5 concentrations. For these 

reasons, for the Factor 1 air quality analysis, the EPA assessed and characterized air quality at, and in the 

proximity of, the violating monitoring site locations first, by evaluating trends and the spatial extent of measured 

concentrations at monitors in the area of analysis, and then, by identifying the conditions most associated with 

high average concentration levels of PM2.5 mass in the area of analysis. 

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

65 The EPA also identified the 

spatial extent of these high PM2.5 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, examining data on a 

quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and meteorology and the 

resulting PM2.5 mass and composition. In some areas of the country where there may be noticeable month-to-month 
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 design value monitoring 

site, the EPA examined the chemical composition of the monitored PM2.5 concentrations by pairing each violating 

FRM/FEM/ARM monitoring site with a collocated or nearby Chemical Speciation Network (CSN) monitoring 

site or sites. Then, the EPA contrasted the approximated mass composition at the design value monitoring site

with data collected at IMPROVE66 and other monitoring locations whose data are representative of regional 

background.67,68 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 

the influence of closer emissions sources, thus representing the portion of the measured violation that is associated 

with nearby emission contributions.69,70,71

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

data represented by the DVs at the violating monitoring site and at other monitors in the area of analysis. The 

EPA calculated DVs based on air quality data for the most recent 3 consecutive calendar years of 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 statistic that indicates 

 

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

quality data may be aggregated and presented by those months that best correspond to the local “seasons” in these areas. 

66 IMPROVE stands for Interagency Monitoring 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 PM2.5 mass and 

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

represent background concentrations). Developing the urban increment involves pairing a violating FRM/FEM/ARM monitor 

with a collocated monitor or nearby monitor with speciation data. EPA made every effort to pair these data to represent the 

same temporal and spatial scales. However, in some cases, the paired violating and CSN “urban” monitoring locations were 

separated by some distance such that the included urban CSN site(s) reflect(s) a different mixture of emissions sources, 

which could lead to misinterpretations. To generally account for differences in PM2.5 mass between the violating site and the 

nearby CSN site(s), EPA determined material balance of the PM2.5 composition at the violating site by assigning the extra 

measured PM2.5 mass to the carbon components of PM2.5. Where the general urban increment approach may be misleading, or 

in situations where non-carbonaceous emissions are believed to be responsible for a local PM2.5 concentration gradient, EPA 

used alternative analyses to reflect the mix of urban and rural sources contributing to the measured concentrations at violating 

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 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.

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 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.

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

States: Report V, June 2011. Chapter 7 – Urban Excess in PM2.5 Speciated Aerosol Concentrations, 

http://vista.cira.colostate.edu/improve/Publications/Reports/2011/PDF/Chapter7.pdf.

71 US EPA, Office of Air Quality Planning and Standards, December 2004. (2004) Area Designations for 1997 Fine Particle 

(PM2.5) Standards, Technical Support Document for State and Tribal Air Quality Fine Particle (PM2.5) Designations, Chapter 

3, Urban Excess Methodology. Available at www.epa.gov/pmdesignations/1997standards/documents/final/TSD/Ch3.pdf.
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whether that monitor attains a specified air quality standard. The 2012 annual PM2.5 NAAQS is met at a 

monitoring site when the 3-year average annual mean concentration is 12.0 micrograms per cubic meter (μg/m3) 

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

met or when other regulatory data processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 2 

identifies the current design value(s) (i.e., the 2013 DV) and the most recent two 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 Collected at Regulatory Monitors (all DV levels in μg/m3)

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 060631010c

Portola North 

Substationc Yese

10.3 11.5 12.8

Butte, CA 060070008d Chico-Eastd 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 4.9b

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 July 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 060070008). 

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 monitoring 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 areas. 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 PM2.5 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 data are identified as not of sufficient comparability to a collocated 

FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to the NAAQS. 

These assessments are required in the monitoring agency's annual monitoring network plan described in §58.10(b) for cases 

where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM....”
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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 sources 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 the center of the county. The nonregulatory Chester monitor is the northernmost 

PM2.5 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 nonregulatory monitor indicates 

elevated PM2.5 concentrations, the aforementioned CARB statement that emissions impacting a monitor north of 

Portola do not likely impact the Portola 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 mixing 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/m3)

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 9.1a 11.6 13.1

Shasta, CA 060893003 Lassen Volcanic 

NP No 2.7 2.6 2.4

Tehama, CA 061030006 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 the 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/m3since 2005. There is one other regulatory monitoring location in Plumas County, in Quincy, 

which is located north-east of the city of Portola, which does not currently record violations of the PM2.5 standard.

Seasonal variation can highlight those conditions most associated with high average concentration levels of PM2.5.

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

monitoring sites and other, non-violating, monitoring sites in each county within the area of analysis. This 

graphical representation is particularly relevant when assessing air quality data for an annual standard, such as the 

2012 annual PM2.5 NAAQS, because, as previously stated, the annual mean is calculated as the mean of quarterly 

means and a high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3-year DV. 
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Figure 2. Plumas County Area of Analysis Monitor - PM2.5 Quarterly Means for 2011-2013 

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/m3 whereas in quarter two (Q2) and quarter three (Q3) the mean concentrations range from 4.8-6.6 μg/m3. 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.

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

location, the EPA determined the various chemical species comprising total PM2.5 to identify the chemical 

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

monitored concentration. To best describe the PM2.5 at the violating monitoring location, the EPA first adjusted 

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

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

0

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

PM2.5 Concentrations (μg/m3

)

Plumas County Area of Analysis Monitors -

PM2.5 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 3a illustrates the fraction of each PM2.5 chemical 

constituent at the Portola North Substation monitoring site based on annual averages for the years 2010-2012. 

Figure 3a. Plumas County Annual Average PM2.5 Chemical Constituents (2010-2012)

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

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

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. 

 

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

Balance Approach.” The SANDWICH adjustment uses an FRM mass construction methodology that results in reduced 

nitrates (relative to the amount measured by routine speciation networks), higher mass associated with sulfates (reflecting 

water included in gravimetric FRM measurements) and a measure of organic carbonaceous mass derived from the difference 

between measured PM2.5 and its non-carbon components. This characterization of PM2.5 mass also reflects crustal material 

and other minor constituents. The resulting characterization provides a complete mass closure for the measured FRM PM2.5

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

74 Frank, N. H., SANDWICH Material Balance Approach for PM2.5 Data Analysis, National Air Monitoring Conference, Las 

Vegas, Nevada, November 6-9, 2006. http://www.epa.gov/ttn/amtic/files/2006conference/frank.pdf.

75 Frank, N. H., The Chemical Composition of PM2.5 to support PM Implementation, EPA State /Local/Tribal Training 

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

http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chemical_composition.pdf.

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 PM2.5 Species (2010-2012)

a

a Adjusted to FRM Total PM2.5 indicates that the speciation profile and total mass depicted in this figure are the result of the 

urban increment calculation for the particular FRM monitor.

Figures 3a and 3b show that organic mass (OM) accounts for over seventy-five percent of the PM2.5 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 PM2.5 concentrations 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 PM2.5 mass are 

about ten percent under one percent, respectively, and 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 PM2.5 and, as discussed in Factor 2, is likely related to emissions from residential 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 PM2.5 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 PM2.5 concentrations are correlated, affirming the source of high concentrations as emissions 

from wood burning.

The EPA assessed seasonal and annual average PM2.5 constituents at monitoring sites within the area relative to 

monitoring sites outside of the analysis area to account for the difference between regional background 

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

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

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

amount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in 

nearby areas, as opposed to impacts of more distant or regional sources of emissions. Figure 4a includes pie charts 

showing the annual and quarterly chemical mass constituents of the urban increment. The quarterly pie charts 

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

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

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

 

77 Letter from Gretchen Bennitt, Executive Director, Northern Sierra Air Quality Management District’s Board of Directors, 

to Gwen Yoshimura, U.S. EPA Region IX. June 18, 2014, pp. 1-4 & attachment 2.
Page 92 of 161

Figure 4a. Plumas County Urban Increment Analysis for 2010-2012.

91%

7%

0% 1% 1%

Annual Average Urban Increment 

2010-2012 (μg/m3)

Organic Mass

Elemental Carbon

Nitrates

Sulfates

Crustal

92%

7% 0% 1% 0%

Q1 Average Urban Increment 

2010-2012 (μg/m3)

Organic Mass

Elemental Carbon

Nitrates

Sulfates

Crustal

85%

11%

0% 2% 2%

Q2 Average Urban Increment 

2010-2012 (μg/m3)

Organic Mass

Elemental Carbon

Nitrates

Sulfates

Crustal

49%

8% 0%

7%

36%

Q3 Average Urban Increment 

2010-2012 (μg/m3)

Organic Mass

Elemental Carbon

Nitrates

Sulfates

Crustal

94%

6% 0% 0% 0%

Q4 Average Urban Increment 

2010-2012 (μg/m3)

Organic Mass

Elemental Carbon

Nitrates

Sulfates

Crustal
Page 93 of 161

Figure 4b. 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 PM2.5 concentrations in Q1 and 

Q4.

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 PM2.5 mass and nitrate is a negligible component of the measured 

PM2.5 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 predominant 

contributor to PM2.5 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 area. However, there is a substantial increase 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 the speciation data that residential wood burning is a 

dominant source of high PM2.5 concentrations.

Factor 2: Emissions and Emissions-related Data

In this designations process, for each area with a violating monitoring site, the EPA evaluated the emissions data 

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

contribution to PM2.5 concentrations at the violating monitoring site or monitoring sites in the area under 

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

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

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

and NH3). The EPA also considered the distance of those sources of emissions from the violating monitoring site. 

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

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

is mindful of the potential local NOX and VOC emissions contributions to PM2.5 from mobile and stationary 
Page 94 of 161

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

sites. 

Emissions Data

For this factor, the EPA reviewed data from the 2011 National Emissions Inventory (NEI) version 1 (see 

http://www.epa.gov/ttn/chief/net/2011inventory.html). For each county in the area of analysis, the EPA examined 

the magnitude of county-level emissions reported in the NEI. These county-level emissions represent the sum of 

emissions from the following general source categories: point sources, non-point (i.e., area) sources, nonroad 

mobile, on-road mobile, and fires. 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 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 PM2.5, components of direct 

PM2.5, 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 PM2.5 and 

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

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

particulate organic carbon (POC) and VOCs79 contribute to PM2.5 organic mass (OM); directly emitted EC 

contributes to PM2.5 EC; NOX, NH3 and directly emitted nitrate contribute to PM2.5 nitrate mass; SO2, NH3 and 

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

contribute to PM2.5 crustal matter.80,81 The EPA believes that the quantities of those nearby emissions as potential 

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

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

emissions are already PM2.5 whereas SO2 must convert to PM2.5 and not all of the emitted SO2 undergoes this 

conversion. 

Table 3a provides a county-level emissions summary (i.e., the sum of emissions from the following general 

source categories: point sources, non-point (i.e., area) sources, nonroad mobile, on-road mobile, and fires) of 

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

considered for inclusion in the Plumas County, CA area. Table 3b summarizes the directly emitted components of 

PM2.5 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 (PM2.5 + 

NOx + SO2 + VOC + NH3) 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 PM2.5 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 PM2.5 and Precursors (tons/year)

County, State Total NH3 Total NOx Total Direct 

PM2.5

Total 

SO2

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 PM2.5 (tons/year) 82

County, State POM EC PSO4 PNO3 PCrustal 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 direct PM2.5 emissions value from Table 3a into its components. These data will also be 

compared with the previously presented Urban Increment composition.

Using the previously described relationship between directly emitted and precursor gases and the measured mass 

to evaluate data presented in Tables 3a and 3b, the EPA identified the following components warranting 

additional review: PM2.5, VOC, 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 PM2.5 Emissions (tons/year)

Emissions in average tons/yr

County, State PM2.5 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 4b. 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 4c. 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 4d. County-Level Crustal Emissions (tons/year)

Emissions in average tons/yr

County, State SO2 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 railroad switching yard as the sources affecting 

PM2.5 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 Letter from Gretchen Bennitt, Executive Director, Northern Sierra Air Quality Management District’s Board of Directors, 

to Gwen Yoshimura, 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 PM2.5 and PM2.5 precursors in the area of analysis, the EPA 

also reviewed emissions from major point sources located in the area of analysis. The magnitude and location of 

these sources can help inform nonattainment boundaries. 

There are no major point sources within the 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 there 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 (PM2.5 + NOx + SO2 + VOC + NH3) are greater than 500 tons per year based on NEI 2011v1), major 

point sources are unlikely to contribute to monitored violations of the 2012 PM2.5 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 major point sources are not contributing to violations in Plumas County of the 2012 PM2.5 

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 wood 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 population, 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 

2000

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 population 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 square miles) county. Yuba County’s population, for example, is associated with Yuba CityMarysville 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 suggesting 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

High vehicle miles travelled (VMT) and/or a high number of commuters associated with a county is generally an 

indicator that the county is an integral part of an urban area. Mobile source emissions of NOx, VOC, and direct 

PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS in the 

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

an assessment of VMT helps identify the probable location of nonpoint source emissions that contribute to 

violations in the area. Comparatively high VMT in a county outside of the CBSA or CSA signifies integration 

with the core urban area contained within the CSA or CBSA, and indicates that a county with the high VMT may 

be appropriate to include in the nonattainment area because emissions from mobile sources in that county 

contribute to violations in the area. Table 6 shows 2011 VMT while Figure 6 overlays 2011 county-level VMT 

with a map of the transportation arteries. VMT 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 analysis 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 PM2.5 concentrations are likely related to residential burning activities. 

The population in most of the area of analysis is sparse and widely distributed, 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 nonattainment.

Factor 3: Meteorology

The EPA evaluated available meteorological data to determine how meteorological conditions, including, but not 

limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of directly 

emitted particulate matter and precursor emissions from sources in the area of analysis. The EPA generally uses

wind roses for this analysis; however, there is no National Weather Service or comparable meteorological data 

available within the narrow Portola Valley.
Page 102 of 161

Prevailing winds in the Portola Valley 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 surfacebased 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 relatively shallow layer near the ground. The low sunangle and climatologically dominant high pressure over Northern California, allow for little heat or wind to break 

the inversion. As such, high PM2.5 concentrations at the Portola monitor mostly occur in the 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 HYSPLIT (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 

represented 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 particular grid cell. Figure 7 shows a HYSPLIT KDE plot for the PortolaNevada Street air monitoring site (AQS ID 060631009) in Plumas County summarized by calendar quarter for the 

2010-2012 period. These trajectories are not conclusive due to the coarse grid resolution (40kmx40km) of the Eta 

Data Assimilation System (EDAS) data driving the HYSPLIT trajectories. The valley containing Portola is at 

most 20 km wide. The data are not resolved enough to portray the wintertime stagnation that is a key feature of 

local Portola meteorology.

 

84 Letter from Gretchen Bennitt, Executive Director, Northern Sierra Air Quality Management District’s Board of Directors, 

to Gwen Yoshimura, 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 centerline of an air parcel’s motion, extending in two dimensional (x,y) space from a starting point and 

regressing backward in time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining an 

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

exceeded. However, for an annual standard, such as the 2012 annual PM2.5 NAAQS, every trajectory on every day is 

important. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT trajectories 

may not be helpful as this process is likely to result in depicting air parcels originating in all directions from the violating 

monitoring site.

86 HYSPLIT - Hybrid Single Particle 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), four times each day, and ending at four endpoint heights. 
Page 103 of 161

Figures 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 PM2.5 concentrations over the area. 

The town of Portola (elevation 4890 feet) lies in Plumas County 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, causing
Page 105 of 161

shallow vertical mixing, and creating areas of high pollutant concentrations by hindering dispersion. The 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 provides a narrow air corridor from the Portola Valley into the expansive Sierra Valley (See 

Figure 8b). While high PM2.5 concentrations within the Portola Valley could enter the Sierra Valley, these 

concentrations would be quickly diluted limiting their effect 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 state 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 area as the District recommended, and also included a five-factor analysis in 

support of its recommended boundary.90

 

89Letter 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.

90Letter 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 covered by a metropolitan planning organization, state lines, 

and Reservation boundaries, if applicable. Where existing jurisdictional boundaries were not adequate or 

appropriate to describe the nonattainment area, 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 as a PM2.5 nonattainment area for the State of California’s PM2.5 standard. The 

State of California’s PM2.5 standard is set at an annual arithmetic mean of 12 μg/m3. This area is defined by the 

State’s hydrological “super planning watershed” areas in Plumas County, CA, as described below, and the SierraPlumas 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 County 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 

metropolitan 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 County is surrounded by six counties with a wide range of 

CBSA/CSA delineations: Sierra County (not a CBSA), Lassen County (micro-CBSA), Tehama County (microCBSA), Butte County (metro-CBSA), Yuba County (part of Sacramento-CSA), and Shasta County (metroCBSA). 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 large enough to be delineated as a micropolitan CBSA.

The State designated the nonattainment area as nonattainment for the State’s PM2.5 ambient air quality standard in 

2003. Since that time, air quality in the area has been managed by cooperation between federal, 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 engines 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” # 55183301 (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 Graeagle. 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 recommendation 

Attachment 1, map of the Portola Valley State PM2.5 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 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 Plumas County nonattainment area because 

portions of the county are either violating the 2012 annual PM2.5 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 PM2.5 NAAQS based on the 2013 DVs, 

likely 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 

contributing the most to the particulate matter concentrations which result in violations of the 2012 annual PM2.5 

NAAQS in Portola are residential burning activities, which tend to affect a very localized area when they occur in 

a topographically- and meteorologically-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 PM2.5 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 likely 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 differences 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 Jurisdiction (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 PM2.5 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 Basin 

(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 statistical 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 PM2.5 NAAQS, this area was designated nonattainment. The boundary for the 

nonattainment area for the 1997 annual PM2.5 NAAQS included the entire counties of Fresno, Kings, Madera, 

Merced, San Joaquin, Stanislaus and Tulare and a portion of Kern County. For purposes of the 2006 24-hour 

PM2.5 NAAQS, this area was designated nonattainment. The boundary for the nonattainment area for the 2006 24-

hour PM2.5 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 PM2.5 NAAQS is the same as the 

nonattainment area boundary for the 1997 annual PM2.5 NAAQS and the 2006 24-hour PM2.5 NAAQS. This 

nonattainment area includes areas of Indian country of the following 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 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.
Page 110 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. Eight counties - San Joaquin, Stanislaus, Merced, Madera, Fresno, Kings, Tulare, 

and Kern counties, show a violation of the 2012 PM2.5 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 more 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 PM2.5 NAAQS. Compliance with an annual NAAQS is dependent upon monitor readings throughout 

the year, including days with monitored ambient concentrations below the level of the NAAQS. For the 2012 

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

mean for an entire year, which, in turn, can drive an elevated 3-year DV. Although all data are important, seasonal 

or episodic emissions can provide insight as to relative contributors to measured PM2.5 concentrations. For these 

reasons, for the Factor 1 air quality analysis, the EPA assessed and characterized air quality at, and in the 

proximity of, the violating monitoring site locations first, by evaluating trends and the spatial extent of measured 

concentrations at monitors in the area of analysis, and then, by identifying the conditions most associated with 

high average concentration levels of PM2.5 mass in the area of analysis. 

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

91 The EPA also identified the 

spatial extent of these high PM2.5 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, examining data on a 

quarterly or seasonal basis can inform the relationship between the temporal variability of emissions and meteorology and the 

resulting PM2.5 mass and composition. In some areas of the country where there may be noticeable month-to-month
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 chemical composition of the monitored PM2.5 concentrations by pairing each violating 

FRM/FEM/ARM monitoring site with a collocated or nearby Chemical Speciation Network (CSN) monitoring 

site or sites. Then, the EPA contrasted the approximated mass composition at the design value monitoring site

with data collected at IMPROVE92 and other monitoring locations whose data are representative of regional 

background.93,94 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 

the influence of closer emissions sources, thus representing the portion of the measured violation that is associated 

with nearby emission contributions.95,96,97

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

data represented by the DVs at the violating monitoring site and at other monitors in the area of analysis. The 

EPA calculated DVs based on air quality data for the most recent 3 consecutive calendar years of 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 statistic that indicates 

 

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

quality data may be aggregated and presented by those months that best correspond to the local “seasons” in these areas. 

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 PM2.5 mass and 

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

represent background concentrations). Developing the urban increment involves pairing a violating FRM/FEM/ARM monitor 

with a collocated monitor or nearby monitor with speciation data. EPA made every effort to pair these data to represent the 

same temporal and spatial scales. However, in some cases, the paired violating and CSN “urban” monitoring locations were 

separated by some distance such that the included urban CSN site(s) reflect(s) a different mixture of emissions sources, 

which could lead to misinterpretations. To generally account for differences in PM2.5 mass between the violating site and the 

nearby CSN site(s), EPA determined material balance of the PM2.5 composition at the violating site by assigning the extra 

measured PM2.5 mass to the carbon components of PM2.5. Where the general urban increment approach may be misleading, or 

in situations where non-carbonaceous emissions are believed to be responsible for a local PM2.5 concentration gradient, EPA 

used alternative analyses to reflect the mix of urban and rural sources contributing to the measured concentrations at violating 

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 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.

95 In most, but not all, cases, the violating design value monitoring site is located in an urban area. Where the violating 

monitor is not located in an urban area, the “urban increment” represents the difference between local and other nearby 

emission sources in the vicinity of the violating monitoring location and more regional sources.

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

States: Report V, June 2011. Chapter 7 – Urban Excess in PM2.5 Speciated Aerosol Concentrations, 

http://vista.cira.colostate.edu/improve/Publications/Reports/2011/PDF/Chapter7.pdf.

97 US EPA, Office of Air Quality Planning and Standards, December 2004. (2004) Area Designations for 1997 Fine Particle 

(PM2.5) Standards, Technical Support Document for State and Tribal Air Quality Fine Particle (PM2.5) Designations, Chapter 

3, Urban Excess Methodology. Available at www.epa.gov/pmdesignations/1997standards/documents/final/TSD/Ch3.pdf.
Page 113 of 161

whether that monitor attains a specified air quality standard. The 2012 annual PM2.5 NAAQS is met at a 

monitoring site when the 3-year average annual mean concentration is 12.0 micrograms per cubic meter (μg/m3) 

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

met or when other regulatory data processing provisions are satisfied (See 40 CFR part 50 Appendix N). Table 2 

identifies the current design value(s) (i.e., the 2013 DV) and the most recent two design values based on all 

monitoring sites in the area of analysis 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 Collected at Regulatory Monitors (all DV levels in μg/m3)

a

County, State

Monitor 

Site ID Site Name

State Rec 

NA? 09-11 DV 10-12 DV 11-13 DV

Fresno, CA 060190011 Fresno – Garlandd 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 5.3b 5.7 b 7.0 b

Kern, CA 060290014 BakersfieldCalifornia 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 PM2.5 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 data are identified as not of sufficient comparability to a collocated 

FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to the NAAQS. 

These assessments are required in the monitoring agency's annual monitoring network plan described in §58.10(b) for cases 

where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM....”
Page 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 Yesc 12.0 b 11.3 b 11.2

Los Angeles, CA 060371002 Burbank Yesc 13.7

e 12.6e 12.5e

Los Angeles, CA 060371103 Los Angeles (Main St.)

Yesc

13.1

e 12.5e 12.5e

Los Angeles, CA 060371201 Reseda Yesc

10.6 10.3 10.2

Los Angeles, CA 060371302 Compton Yesc

13.4 12.4 12.2

Los Angeles, CA 060371602 Pico Rivera #2 Yesc

13.3 12.3 12.0

Los Angeles, CA 060372005 Pasadena Yesc

11.1 b 10.4 b 10.4 b

Los Angeles, CA 060374002 Long Beach (North)

Yesc

11.5e 10.6

e 10.9e

Los Angeles, CA 060374004 South Long Beach Yesc

11.2 10.6

e 10.8e

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 SacramentoHealth 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 Yesc 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 Yesc

12.9 12.4 12.6

San Bernardino, CA 060718001 Big Bear Yesc

8.9 8.3 8.7

San Bernardino, CA 060719004 San Bernardino Yesc

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 BarbaraNational 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-First 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). Documents 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 purpose 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 are 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 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.
Page 116 of 161

Table 2a. Air Quality Data Collected at Nonregulatory Monitors (all DV levels in μg/m3)

County, State

Monitor 

Site ID Site Name

State Rec 

NA? 09-11 DV 10-12 DV 11-13 DV

Fresno, CA 060192008 Huron Yes 14.4a 13.6a 13.6a

Fresno, CA 060199000 Kaiser Yes 3.0 2.9 2.8a

Inyo, CA 060270101

DEATH 

VALLEY NM No 3.4 3.4 3.1a

Kern, CA 060292009

Lebec-Beartrap 

Rd Yes 6.9a 7.5a 7.8a

Kern, CA 060299001

Lebec-Peace 

Valley Rd Kern 

County Yes 4.6 4.6 4.5a

Los Angeles, CA 060379034

Lebec-Peace 

Valley Rd LA 

County Yes 3.2a 2.7a 2.8a

Mariposa, CA 060430003

Yosemite NPTurtleback Dome No 3.4 3.3 3.3a

Mariposa, CA 060431001

YOSEMITE NPVisitor Center No 10.3a 9.8a 10.2a

Mono, CA 060519000 Hoover No 2.2 2.2 2.2a

San Benito, CA 060690003 Pinnacles NM No 3.9 3.7 3.9a

San Bernardino, CA 060719002

Joshua Tree NMBlack Rock 

Canyon No 3.8 3.5 3.5a

Tulare, CA 061070009

Sequoia and 

Kings Canyon 

NP- Ash 

Mountain Yes 9.9a 9.0 8.3a

Tulare, CA 061071001

ASH 

MOUNTIAN, 

SEQUOIA 

NATIONAL 

PARK Yes 6.4 6.4 6.2a

Tulare, CA 061072010 Porterville Yes - 15.4a 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 PM2.5 

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 violations 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 Manteca 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 city 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 County 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 County that is not 
Page 117 of 161

included 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 for 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 area. (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 violate the 2012 annual PM2.5 NAAQS.

Seasonal variation can highlight those conditions most associated with high average concentration levels of PM2.5. 

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

monitoring site in the nonattainment area (Madera-City), as well as from the highest DV monitoring site in each 

county outside of the nonattainment area, but within the area of analysis (i.e., those counties bordering the 

nonattainment area). For Kern County, which is being partially designated nonattainment 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 PM2.5 concentrations for the most 

recent 3-year period for all monitoring sites within the San Joaquin Valley nonattainment area, and excludes sites 

from surrounding counties outside the nonattainment area. In both figures the 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 PM2.5 NAAQS, because, as previously 

stated, the annual mean is calculated as the mean of quarterly means and a high quarter can drive the mean for an 

entire year, which, in turn, can drive an elevated 3-year DV.
Page 118 of 161

Figure 2. San Joaquin Valley Area of Analysis Monitors - PM2.5 Quarterly Means for 2011-2013

Figure 2a. San Joaquin Valley Nonattainment Area Monitors - PM2.5 Quarterly Means for 2011-2013
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 the San Joaquin Valley for the 3-year period occurred in Q4 or 

quarter one (Q1).

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

location, the EPA determined the various chemical species comprising total PM2.5 to identify the chemical 

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

monitored concentration. To best describe the PM2.5 at the violating monitoring location, the EPA first adjusted 

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

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

measurement.99,100,101,102 In particular, this approach accounts for losses in fine particle nitrate and increases in 

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

constituent 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 2010-2012. 

 

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

Balance Approach.” The SANDWICH adjustment uses an FRM mass construction methodology that results in reduced 

nitrates (relative to the amount measured by routine speciation networks), higher mass associated with sulfates (reflecting 

water included in gravimetric FRM measurements) and a measure of organic carbonaceous mass derived from the difference 

between measured PM2.5 and its non-carbon components. This characterization of PM2.5 mass also reflects crustal material 

and other minor constituents. The resulting characterization provides a complete mass closure for the measured FRM PM2.5

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

100 Frank, N. H., SANDWICH Material Balance Approach for PM2.5 Data Analysis, National Air Monitoring Conference, Las 

Vegas, Nevada, November 6-9, 2006. http://www.epa.gov/ttn/amtic/files/2006conference/frank.pdf.

101 Frank, N. H., The Chemical Composition of PM2.5 to support PM Implementation, EPA State /Local/Tribal Training 

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

http://www.epa.gov/ttn/naaqs/pm/presents/pm2.5_chemical_composition.pdf.

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 PM2.5 Chemical Constituents (2010-2012)

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

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

additional insight as to which sources or factors may contribute at a greater level. Simply stated, this analysis can 

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

Figure 3b. San Joaquin Valley Annual and Quarterly Average PM2.5 Species (2010-2012)

a

aAdjusted to FRM Total PM2.5 indicates that the speciation profile and total mass depicted in this figure are the result of the 

urban increment calculation for the particular FRM monitor.

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 PM2.5 concentration is highest, but not as much to quarter two (Q2) and quarter 

three (Q3). Sulfates contribute about thirty percent of 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 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. Sources emitting CM also seem to contribute most 

in Q2 and 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 contributor, followed by nitrates and sulfates.

The EPA assessed seasonal and annual average PM2.5 constituents at monitoring sites within the area relative to 

monitoring sites outside of the analysis area to account for the difference between regional background 

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

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

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

amount and type of particles at the violating monitor that are most likely to be the result of sources of emissions in 

nearby areas, as opposed to impacts of more distant or regional sources of emissions. Figure 4a includes pie charts 

showing the annual and quarterly chemical mass constituents of the urban increment estimated for the Madera 

design value monitoring site. Evaluating the average urban increment in each quarter can help identify 

composition of PM2.5 at the design value site. Note that in these charts, the individual components have been 

adjusted to represent their retained mass in FRM-measured PM2.5 as described for Figures 3a and 3b above.
Page 122 of 161

Figure 4a. San Joaquin Valley Urban Increment Analysis for 2010-2012.

9%

69%

20%

1% 1%

Annual Average Urban Increment 

2010-2012 (μg/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

1.13

8.58

4.33

0.19

Q1 Average Urban Increment 

2010-2012 (μg/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

0.68

9.10

0.00

0.04 0.00

Q2 Average Urban Increment 

2010-2012 (μg/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

1.45

8.79

0.00

0.07 0.60

Q3 Average Urban Increment 

2010-2012 (μg/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal

1.24

7.59

5.35

0.28 0.13

Q4 Average Urban Increment

2010-2012 (μg/m3)

Sulfates

Organic Mass

Nitrates

Elemental Carbon

Crustal
Page 123 of 161

Figure 4b. 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 PM2.5 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 PM2.5 or PM2.5 precursors from 

sources throughout the region, with an increased contribution of OM from sources closer to the DV monitoring 

site.

Factor 2: Emissions and Emissions-related Data

In this designations process, for each area with a violating monitoring site, the EPA evaluated the emissions data 

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

contribution to PM2.5 concentrations at the violating monitoring site or monitoring sites in the area under 

evaluation. 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 PM2.5, the major components of direct PM2.5 (organic 

mass, elemental carbon, crustal material (and/or individual trace metal compounds)), primary nitrate and primary 

sulfate, and precursor gaseous pollutants (i.e., SO2, NOX, total VOC, and NH3). The EPA also considered the 

distance of those sources of emissions from the violating monitoring site. While direct PM2.5 emissions and its 

major carbonaceous components are generally associated with sources near violating PM2.5 monitoring sites, the 

gaseous precursors tend to have a more regional influence (although the EPA is mindful of the potential local 

NOX and VOC emissions contributions to PM2.5 from mobile and stationary sources) and transport from 

neighboring areas can contribute to higher PM2.5 levels at the violating monitoring sites. 

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

Annual Q1 Q2 Q3 Q4

μg/m3

Average Urban Increment 2010-2012 (μg/m3)

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 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 distribution 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 PM2.5, components of direct 

PM2.5, 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 PM2.5 and 

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

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

particulate organic carbon (POC) and VOCs104 contribute to PM2.5 organic mass (OM); directly emitted EC 

contributes to PM2.5 EC; NOX, NH3 and directly emitted nitrate contribute to PM2.5 nitrate mass; SO2, NH3 and 

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

contribute to PM2.5 crustal matter. 105,106 The EPA believes that the quantities of those nearby emissions as 

potential contributors to the PM2.5 violating monitors are somewhat proportional to the PM2.5 chemical 

constituents in the estimated urban increment. Thus, directly emitted POC is more important per ton than SO2, 

partially because POC emissions are already PM2.5 whereas SO2 must convert to PM2.5 and not all of the emitted 

SO2 undergoes this conversion. 

Table 3a provides a county-level emissions summary (i.e., the sum of emissions from the following general 

source categories: point sources, non-point (i.e., area) sources, nonroad mobile, on-road mobile, and fires) of 

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

considered for inclusion in the San Joaquin Valley, CA area. Table 3b summarizes the directly emitted 

components of PM2.5 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 (PM2.5 + 

NOx + SO2 + VOC + NH3) 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 PM2.5 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 

Air 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 PM2.5 and Precursors (tons/year)

County, State Total NH3 Total NOx Total Direct 

PM2.5

Total SO2 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 PM2.5 (tons/year) 107

County, State POM EC PSO4 PNO3 PCrustal 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 direct PM2.5 emissions value from Table 3a into its components. These data will also be 

compared with the previously presented Urban Increment composition.

Using the previously described relationship between directly emitted and precursor gases and the measured mass 

to evaluate data presented in Tables 3a and 3b, the EPA identified the following components warranting 

additional review: NH3, NOx, PM2.5, SO2, VOC, POM, PSO4, PNO3, 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 NH3 Emissions (tons/year)

Emissions in average tons/yr

County, State NH3 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 4b. County-Level NOx Emissions (tons/year)

Emissions in average tons/yr

County, State NOX 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 4c. County-Level PM2.5 Emissions (tons/year)

Emissions in average tons/yr

County, State PM2.5 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 4d. County-Level SO2 Emissions (tons/year)

Emissions in average tons/yr

County, State SO2 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 4e. 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 4f. 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 4g. County-Level PSO4 Emissions (tons/year)

Emissions in average tons/yr

County, State PSO4 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 4h. County-Level PNO3 Emissions (tons/year)

Emissions in average tons/yr

County, State PNO3 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 4i. 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 4j. 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 PM2.5 and PM2.5 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 directlyemitted PM2.5 and PM2.5 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. Emissions 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 PM2.5 and PM2.5 precursors in the area of analysis, the EPA 

also reviewed emissions from major point sources located in the area of analysis. The magnitude and location of 

these sources can help inform nonattainment boundaries. Table 5 provides facility-level emissions of direct PM2.5, 

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

the area of analysis for the 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 monitoring site (Madera-City). These major sources may be closer to 

another violating monitor within the San 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 

(miles)

NH3 NOx PM2.5 SO2 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 International 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 

(miles)

NH3 NOx PM2.5 SO2 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 COMPANY 116 1,755 17 492 71 2,335

Santa Clara, CA Norman Y. Mineta San Jose 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 

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

as a function of distance.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 Luis 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 estimating single source concentrations of primary and 

secondarily formed PM2.5. Atmospheric Environment. 45 (2011) 3758-3767.
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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, these 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 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.
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 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 Air Basin, as there is in a 

number of counties adjacent to the air basin. Population growth within the eight counties included in the San 

Joaquin Valley nonattainment area has been around twenty percent from 2000 to 2010. While Figure 6 gives 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.
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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 (tracts 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 County, 

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 two counties) and are not as likely to be contributing to violations of the 2012 PM2.5 

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.
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Figure 6a. 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.
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Figure 6b. 2010 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 Joaquin 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 County 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.
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Figure 6c. 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

High vehicle miles travelled (VMT) and/or a high number of commuters associated with a county is generally an 

indicator that the county is an integral part of an urban area. Mobile source emissions of NOx, VOC, and direct 

PM may contribute to ambient particulate matter that contributes to monitored violations of the NAAQS in the 

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

an assessment of VMT helps identify the probable location of nonpoint source emissions that contribute to 

violations in the area. Comparatively high VMT in a county outside of the CBSA or CSA signifies integration 

with the core urban area contained within the CSA or CBSA, and indicates that a county with the high VMT may 

be appropriate to include in the nonattainment area because emissions from mobile sources in that county 

contribute to violations in the area. Table 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 counties, 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 of 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 (Los 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 in 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. See: 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 Table 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 individuals commuting into the San Joaquin Valley air basin might not result in contribution to violations 

of the 2012 PM2.5 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 Valley Air Basin 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

nonattainment 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 context of the subsequent development 

of the attainment plan for the area.
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Figure 7. Overlay of 2011 County-level VMT with Transportation Arteries.

In summary for “Factor 2: Emissions and Emissions-related Data,” the EPA’s nonattainment area for the 2012 

PM2.5 NAAQS includes 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 PM2.5 and precursor emissions, and the related 

population and VMT associated with other emissions activity that all contribute to the monitored violations 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 counties 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 areas 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 Joaquin Valley airshed. In addition, the EPA is designating several of the adjacent 

counties as 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 meaningful commuter traffic to 

counties within the San Joaquin Valley. The EPA is designating the Los Angeles-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 

Factors 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.
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Factor 3: Meteorology

The EPA evaluated available meteorological data to determine how meteorological conditions, including, but not 

limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of directly 

emitted particulate matter and precursor emissions from sources in the area of analysis. The EPA used two 

primary tools for this assessment: wind roses and kernel density estimation (KDE). When considered in 

combination with area PM2.5 composition and county-level and facility emissions source location information, 

wind roses and KDE can help to identify nearby areas contributing to violations at violating monitoring sites. 

Wind roses are graphic illustrations of the frequency of wind direction and wind speed. Wind direction can 

indicate the direction from which contributing emissions are transported; wind speed can indicate the force of the 

wind and thus the distance from which those emissions are transported. The EPA constructed wind roses from 

hourly observations of wind direction and wind speed using 2009-2012 data from National Weather Service 

locations archived at the National Climate Data Center.109 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 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 Francisco Bay), the key route 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 Analysis for the (a) Northern and (b) Southern 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-14th Street 

Station), Central (Fresno-Garland Station) and Southern (Bakersfield-Planz Station) San Joaquin Valley. 

For the Modesto-14th Street station, representing the northern San Joaquin Valley, the HYPSPLIT KDE plot 

suggest greatest potential contribution of 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, representing 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 the 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 some 

degree of separation much of the time. 

 

110 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 centerline of an air parcel’s motion, extending in two dimensional (x,y) space from a starting point and 

regressing backward in time to a point of origin. Backward trajectories may be an appropriate tool to assist in determining an 

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

exceeded. However, for an annual standard, such as the 2012 annual PM2.5 NAAQS, every trajectory on every day is 

important. Plotting a mass of individual daily (e.g., 365 individual back trajectories), or more frequent, HYSPLIT trajectories 

may not be helpful as this process is likely to result in depicting air parcels originating in all directions from the violating 

monitoring site.

111 HYSPLIT - Hybrid Single Particle Lagrangian Integrated Trajectory Model, http://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 endpoint heights. 
Page 151 of 161

Figures 9a) HYSPLIT Kernel Density Estimation Plots for the northern San Joaquin Valley, Modesto-14th

Street Station (AQS # 060990005)

Q1

Q2
Page 152 of 161

Figures 9a) HYSPLIT Kernel Density Estimation Plots for the northern San Joaquin Valley, Modesto-14th

Street Station (AQS # 060990005) (continued)

Q3

Q4
Page 153 of 161

Figures 9b) HYSPLIT Kernel Density Estimation Plots for the Central San Joaquin Valley, FresnoGarland Station (AQS # 060190011)

Q1

Q2
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Figures 9b) HYSPLIT Kernel Density Estimation Plots for the Central San Joaquin Valley, FresnoGarland Station (AQS # 060190011) (continued)

Q3

Q4
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Figures 9c) HYSPLIT Kernel Density Estimation Plots for the southern San Joaquin Valley, BakersfieldPlanz Station (AQS # 060290016)

Q1

Q2
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Figures 9c) HYSPLIT Kernel Density Estimation Plots for the southern San Joaquin Valley, BakersfieldPlanz 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 area of analysis that 

might define the airshed and thus affect the formation and distribution of PM2.5 concentrations over the area. 

The San Joaquin Valley is the southern half of the large, flat Central Valley of California, 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 the 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. Mountains also run through Kern County, 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 monitors 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 PM2.5 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 long 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 

PM2.5 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 

organization 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 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 San Joaquin Valley area has previously established nonattainment boundaries associated with both the 1997 

annual and 2006 24-hour PM2.5 NAAQS. The boundary for the nonattainment area for the 1997 annual PM2.5 

NAAQS and the 2006 24-hour PM2.5 NAAQS was the same and included the entire counties of Fresno, Kings, 

Madera, Merced, San Joaquin, Stanislaus and Tulare and a portion of Kern County. The state has recommended 

the same boundary for the 2012 annual PM2.5 NAAQS.

The San Joaquin Valley nonattainment area includes the Fresno-Madera combined statistical 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; 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 is 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 contains 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 Air Basin due to the lower population and VMT discussed in 

Factor 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 following 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 Mountain 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 through 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

Figure 11. 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 PM2.5 NAAQS or contributing to a violation 

in a nearby area: San Joaquin, Stanislaus, Merced, 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 1997 annual PM2.5 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 PM2.5 NAAQS based on the 2013 

DVs; therefore 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 PM2.5 and/or PM2.5 precursors in 

the area, population growth from 2000 to 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 particulate matter concentrations which 

result in violations of the 2012 annual PM2.5 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 Factors 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 Valley 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 because 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 (Factors 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 country of the following tribes as part of the San Joaquin Valley 

nonattainment area for the 2012 annual PM2.5 NAAQS: 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 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 14.3e

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. 12.5f

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) 10.9f

California Los Angeles-South Coast Air Basin Los Angeles 060374004 South Long Beach 10.8f

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 14.8f

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 060631010a 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 060190011bFresno - 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 060070008c 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 Ukiah-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 10.6f

California San Diego 060731002 Escondido 10.7f

California San Diego 060731006 Alpine 7.8*

California San Diego 060731008 Camp Pendleton 10.4*

California San Diego 060731010 San Diego - Downtown 10.8f

California San Diego 060731016dSan 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 060893004 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 is a 

combination of data 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 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 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 the NAAQS per 40 CFR 58.11(e).

Documents 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.