1

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27711 

April 15, 2011

MEMORANDUM

SUBJECT: PM2.5 Air Quality Analyses - Update 

FROM: Mark Schmidt, OAR/OAQPS/AQAD 

 

TO: PM NAAQS Review Docket EPA-OAQR-2007-0492

Overview

 

This memorandum documents updates to PM2.5 air quality analyses conducted for the review of 

the particulate matter (PM) national ambient air quality standards (NAAQS) previously presented in 

Schmidt et al., 2010.1 The purpose of these analyses was to inform staff conclusions on alternative PM2.5 

standards that are appropriate to consider in the current PM NAAQS review. 

Specific analyses conducted, including tasks, assumptions, caveats, and processing methodologies 

are described in more details below. In summary, these analyses address the following: 

 Analysis A – Evaluation of the spatial averaging provisions for the PM2.5 annual standard, 

specifically, a demographic analysis of the potential for disproportionate impacts on potentially 

vulnerable populations (i.e., environmental justice analysis) – clarification of Analysis 1 described 

in Schmidt et al., 2010. 

 Analysis B – County-level 24-hour design values versus annual design values, 2007-2009 – 

update of Analysis 4 described in Schmidt et al., 2010. 

 Analysis C – Assessment of areas not likely to meet current and alternative PM2.5 standards2 - 

update of Analysis 5 described in Schmidt et al., 2010. 

In Analyses B and C counties were identified and/or aggregated by U.S. geographic region (Figure 

1). These regional definitions are identical to regions considered in the Quantitative Health Risk 

Assessment for Particulate Matter (EPA-452/R-10-005)3

 and in previous PM NAAQS reviews. 

Additional data processing details are described in the analysis-specific descriptions below. 

 

1

 Schmidt M, Hassett-Sipple B, Rajan P (2010). PM2.5 Air Quality Analyses. Memorandum to PM NAAQS review docket 

EPA-HQ-OAR-2007-0492. July 22, 2010. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html . 2

 This assessment was not considered as a basis for staff conclusions presented in the Policy Assessment for the Review of the 

Particulate Matter National Ambient Air Quality Standards (April 2011), available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html 3

 Available http://www.epa.gov/ttn/naaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf 
2

Figure 1. Regional definitions used in analyses 

Analysis A – Evaluation of the spatial averaging provisions for the PM2.5 annual standard, specifically, a 

demographic analysis of the potential for disproportionate impacts on potentially vulnerable populations 

(i.e., environmental justice analysis)

This analysis focused on determining if the spatial averaging provisions as modified in 2006 could 

introduce inequities in protection for susceptible populations exposed to PM2.5. The current form of the 

annual PM2.5 standard includes provisions for spatial averaging if certain criteria are met (i.e., 40 CFR Pt. 

50 App. N, 2.0(b)); those criteria were not checked in the analysis described here. We evaluated whether 

persons with a lower socioeconomic status (SES) are more likely than the general population to live in 

areas in which the monitors recording the highest air quality values in an area are located. Data used in 

this analysis included demographic parameters measured at the Census Block or Census Block Group 

level including percent minority population, percent minority subgroup population, percent people living 

below poverty level, percent people 18-years old or younger, and percent people 65 years and older. 

The air quality data for this project originated from EPA’s Air Quality System (AQS) database, 

the official repository of NAAQS-comparable ambient measurements. Two sets of AQS data were 

extracted on May 9, 2010: 1) a file of raw 2000-2008, 24-hour duration (filter-based), monitor-level, 

federal reference method (FRM) data, and 2) a file of hourly (continuous) 2000-2008, monitor-level, 

federal equivalent method (FEM) data summarized in AQS to a 24-hour basis. The two monitor-level 

files were subsequently combined and then aggregated to a site basis by averaging by site-day. That is, in 

situations where there was more than one 24-hour average concentration reported for the same site 

location (i.e., collocated monitors) for the same day, the multiple 24-hour averages were averaged 

together. The following statistical metrics were computed from the site-level 24-hour average PM2.5 

concentrations: 

1) annual 98th percentile 24-hour average concentrations, 

2) 3-year average 98th percentile concentrations (24-hour design value estimates), 

3) annual means, and 

4) 3-year average annual means (annual design value estimates) 

The metrics were computed at the site level using the regular (non-seasonal) protocols specified in 

40CFR Part 50, Appendix N. A completeness criterion of a minimum of 11 samples per quarter for all 4 

quarters of each year (or all 12 quarters of a 3-year period for design value estimates) was imposed on all 

the site-based metrics. Appendix N provides additional options for validating the annual and 3-year 

metrics, depending on the metric type and level in relation to the associated NAAQS. To avoid the level 

and metric validation bias, an across-the-board 11 sample minimum per quarter criterion was imposed. 

Other (AK, HI, VI, PR)

So. CA

Northwest

Upper Midwest

Northeast

Industrial Midwest

Southeast

Southwest
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Also, Appendix N has a special 98th percentile computation protocol for sites authorized to sample 

seasonally (that is, to sample at a less stringent frequency in months when lower concentrations are 

expected). The seasonal calculation protocol was not used in these analyses; 98th percentile values were 

computed at all sites using the regular calculation protocol. Because of the possible differences in metric 

validation and 98th percentile calculation protocol options, the generated 3-year metrics are referred to as 

design value estimates. 

Air quality data from 2006-2008, summarized as 3-year annual PM2.5 DV estimates, were 

evaluated in order to identify the highest concentration site location for each urban area that contained at 

least two valid DV monitoring locations. Using 2000 Census block and block group information, the 

population demographics of the areas surrounding the high site location as characterized by buffer radii of 

0.5, 1.0, 2.0, and 3.0 miles, were compared to the population demographics for the overall urban area to 

determine if there were any SES-related differences. Core Based Statistical Area (CBSA) definitions 

were used to classify the urban areas. As described in the General Data Processing section above, an 

across-the-board data completeness criterion of 11 or more samples for each quarter of the 12-quarter 

period (2006-2008) was used to validate the annual DV estimates. 116 CBSAs, with sum population of 

almost 176 million, contained at least two valid annual DV locations for the period 2006-2008 and hence 

were used in the analysis. Table 1 lists the 116 CBSAs and Figure 2 maps them along with the 

corresponding high site locations. The high site buffer radii demographic attributes were determined by 

summarizing data for Census blocks whose centroids fell within the determined cut point distances. The 

overall CBSA attributes were based on a summarization of all Census blocks located in the county-based 

CBSA definitions. Some of the summarized Census population information (e.g., population below the 

poverty line, population of under age 18, and population of age 65 and over) were actually reported using 

Census block group as the lowest level, and for these variables, data were prorated to blocks according to 

total population. 

Table 2 shows summary results of the comparison between the four high site buffers and the 

overall CBSA area. These results are identical to those documented in Schmidt et al, 2010 except for 

results outlined in red. After Schmidt et al, 2010 was finalized, errors were discovered in the processing 

of the “Below Poverty Level” information. This memo (i.e., the Analysis A results) corrects the previous 

mistakes, but in no way changes the previous (and current) message which is: In general, the areas 

surrounding the high monitor of an area were observed to have higher percentages of minorities and 

people under the poverty level than the area as a whole. 
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Table 1. CBSAs Used in the Spatial Averaging Analysis. 

CBSA CBSA nam e

CBSA 

populat ion 

(1000's)

Num ber of 

sit es in CBSA CBSA n am e

CBSA 

populat ion 

(1000's)

Num ber of 

sit es in CBSA

10420 Akron, OH 695 3 Las Vegas-Paradise, NV 1,376 3

10740 Albuquerq ue, NM 730 3 Lexingt on-Fayet t e, KY 408 2

10900 Allent ow n-Bet h leh em -East on, PA-NJ 740 2 Lit t le Rock-Nort h Lit t le Rock-Conw ay, A 611 3

11260 Anch orage, AK 320 2 Logan, UT-ID 103 2

12060 At lant a-Sandy Springs-Mariet t a, GA 4,248 8 Los Angeles-Long Beach-San t a Ana, CA 12,366 10

12260 August a-Richm on d Coun t y, GA-SC 500 3 Louisville/Jef f erson Count y, KY-IN 1,162 7

12540 Bakersf ield, CA 662 4 Macon, GA 222 2

12580 Balt im ore-Tow so n, MD 2,553 7 Medf ord, OR 181 2

12940 Bat on Rouge, LA 706 5 Mem phis, TN-MS-AR 1,205 4

13820 Birm in gham -Hoover, AL 1,052 10 Miam i-Fo rt Laud erdale-Pom pan o Beac 5,008 8

14460 Bost on-Cam bridge-Quincy, MA-NH 4,391 10 Milw aukee-Waukesha-West Allis, WI 1,501 7

14500 Boulder, CO 291 2 Minn eap olis-St . Paul-Blo om ingt on, MN 2,969 9

14860 Bridgeport -St am f ord-Norw alk, CT 883 4 Mo bile, AL 400 2

15380 Buf f alo-Niagara Falls, NY 1,170 3 Nashville-Davidson -Murf reesboro-Fran 1,312 3

16580 Cham p aign -Urbana, IL 210 2 New Haven -Milf ord, CT 824 5

16620 Charlest o n, WV 310 2 New Orleans-Met airie-Kenner, LA 1,317 2

16700 Charlest o n-No rt h Ch arlest on, SC 549 2 New York-Nort hern New Jersey-Lo ng I 18,323 21

16740 Charlot t e-Gast onia-Concord, NC-SC 1,330 4 Ogden-Clearf ield, UT 443 3

16860 Chat t ano oga, TN-GA 477 3 Oklaho m a Cit y, OK 1,095 2

16980 Chicago-Naperville-Joliet , IL-IN-WI 9,098 27 Om ah a-Council Bluf f s, NE-IA 767 5

17140 Cincinnat i-Middlet ow n, OH-KY-IN 2,010 11 Orlando-Kissim m ee, FL 1,645 3

17300 Clarksville, TN-KY 232 2 Oxnard-Th ousand Oaks-Vent ura, CA 753 4

17460 Cleveland-Elyria-Ment o r, OH 2,148 9 Ph iladelphia-Cam d en-Wilm ingt on , PA- 5,687 12

17900 Colum bia, SC 647 3 Ph oenix-Mesa-Scot t sdale, AZ 3,252 8

17980 Colum bus, GA-AL 282 4 Pit t sburgh, PA 2,431 11

18140 Colum bus, OH 1,613 2 Po rt land-Vancouver-Beavert on, OR-WA 1,928 2

19100 Dallas-Fort Wort h-Arlingt o n, TX 5,162 6 Pro vidence-New Bedf ord-Fall River, RI 1,583 5

19340 Davenp ort -Mo line-Ro ck Island, IA-IL 376 4 Pro vo-Orem , UT 377 4

19380 Dayt on, OH 848 3 Rapid Cit y, SD 113 2

19740 Den ver-Aurora, CO 2,203 5 Richm ond, VA 1,097 4

19780 Des Moines-West Des Moines, IA 481 2 Riverside-San Bernardino-Ont ario, CA 3,255 9

19820 Det roit -Warren-Livo nia, MI 4,453 11 Sacram ent o--Arden-Arcade--Roseville, 1,797 5

20100 Dover, DE 127 2 St . Louis, MO-IL 2,721 9

20260 Dulut h, MN-WI 275 3 Salt Lake Cit y, UT 969 4

20940 El Cent ro, CA 142 3 San Diego-Carlsbad-San Marcos, CA 2,814 4

21340 El Paso , TX 680 3 San Francisco-Oakland -Frem o nt , CA 4,124 5

21660 Eugen e-Sp rin gf ield, OR 323 3 San Juan-Caguas-Guayn abo , PR 2,509 2

21780 Evansville, IN-KY 343 4 San Luis Obispo-Paso Robles, CA 247 2

22900 Fort Sm it h, AR-OK 273 2 Sant a Barbara-Sant a Maria-Golet a, CA 399 2

23420 Fresn o, CA 799 2 Savannah, GA 293 2

23540 Gainesville, FL 232 2 Seat t le-Taco m a-Bellevue, WA 3,044 2

24540 Greeley, CO 181 2 Sherid an, WY 27 2

24860 Greenville-Mauldin-Easley, SC 560 2 Sprin gf ield, MA 680 3

25180 Hagerst ow n-Mart insburg, MD-WV 223 2 Tam pa-St . Pet ersburg-Clearw at er, FL 2,396 4

25420 Harrisburg-Carlisle, PA 509 2 Terre Haut e, IN 171 2

26180 Honolulu, HI 876 3 Toledo, OH 659 2

26420 Houst on -Sugar Lan d-Bayt ow n, TX 4,715 3 Trent on-Ew ing, NJ 351 2

26580 Hunt ingt on-Ashland, WV-KY-OH 289 2 Truckee-Grass Valley, CA 92 2

26900 Indianap olis-Carm el, IN 1,525 5 Tucson , AZ 844 2

27260 Jacksonville, FL 1,123 2 Tulsa, OK 860 2

27540 Jasp er, IN 53 3 Virgin ia Beach-No rf o lk-New po rt New s 1,576 3

28060 Kalispell, MT 74 2 Washingt on-Arlin gt on-Alexandria, DC- 4,796 9

28140 Kansas Cit y, MO-KS 1,836 8 Weirt on-St eubenville, WV-OH 132 5

28700 Kingsp ort -Brist o l-Brist ol, TN-VA 298 2 Wh eeling, WV-OH 153 2

28940 Kno xville, TN 616 5 Wichit a, KS 571 4

29180 Laf ayet t e, LA 239 2 Winst on -Salem , NC 422 2

29340 Lake Charles, LA 194 2 Wo rcest er, MA 751 2

29740 Las Cruces, NM 175 2 Yo ungst ow n-Warren-Board m an, OH-PA 603 4

Total population of 116 CBSAs 175,761
Figure 2. Map of CBSAs Used in Spatial Averaging Analysis. 
Table 2. Summary Results: Comparison Between the Four High Site Buffers and the Overall C

Area counts (for 116 CBSAs) comparing SES variables within 0.5, 1.0, 2.0, and 3.0 mile radii buffers versus SES variables 

Population Variable Buffer 

Percentage of 

areas where 

variable 

percentage 

greater in high 

site buffer than in 

overall CBSA 

Percentage of 

areas where 

variable 

percentage not 

greater in high 

site buffer than in 

overall CBSA 

Mean difference 

in variable 

percentage in 

high site buffer 

minus variable 

percentage in 

overall CBSA 

Median difference 

in variable 

percentage in high 

site buffer minus 

variable 

percentage in 

overall CBSA 

Mean ra

variab

percenta

high site b

variab

percenta

overall C

Minority 

0.5 mile 58.9% 41.1% 8.9% 2.8% 1.57 

1.0 mile 67.2% 32.8% 11.6% 6.1% 1.6

2.0 miles 72.4% 27.6% 11.6% 7.8% 1.63.0 miles 76.7% 23.3% 11.4% 9.6% 1.59 

avg of 4 buffers 68.8% 31.2% 10.9% 6.6% 1.6

African American 

0.5 mile 49.1% 50.9% 6.5% -0.1% 1.81.0 mile 57.8% 42.2% 7.3% 1.1% 1.6

2.0 miles 58.6% 41.4% 8.2% 1.0% 1.62 3.0 miles 66.4% 33.6% 8.4% 3.1% 1.59 

avg of 4 buffers 58.0% 42.0% 7.6% 1.3% 1.6

Native American 

0.5 mile 52.7% 47.3% 0.3% 0.0% 1.5

1.0 mile 62.9% 37.1% 0.8% 0.1% 1.64 

2.0 miles 65.5% 34.5% 0.5% 0.1% 1.43.0 miles 66.4% 33.6% 0.5% 0.0% 1.3

avg of 4 buffers 61.9% 38.1% 0.5% 0.1% 1.4

Other and Multiracial 

0.5 mile 59.8% 40.2% 2.1% 0.4% 1.32 1.0 mile 58.6% 41.4% 3.5% 2.1% 1.4

2.0 miles 66.4% 33.6% 2.9% 1.0% 1.37 3.0 miles 66.4% 33.6% 2.5% 1.2% 1.32 

avg of 4 buffers 62.8% 37.2% 2.8% 1.2% 1.37 

Hispanic 

0.5 mile 55.4% 44.6% 3.9% 0.3% 1.50 

1.0 mile 60.3% 39.7% 5.8% 1.1% 1.77 

2.0 miles 66.4% 33.6% 5.3% 1.4% 1.63.0 miles 75.0% 25.0% 4.5% 1.1% 1.54 

avg of 4 buffers 64.3% 35.7% 4.9% 1.0% 1.62 

Below Poverty Line 

0.5 mile 75.0% 25.0% 9.2% 5.4% 1.81.0 mile 75.9% 24.1% 8.7% 6.1% 1.77 

2.0 miles 77.6% 22.4% 8.0% 7.3% 1.70 3.0 miles 76.7% 23.3% 6.8% 6.7% 1.59 

avg of 4 buffers 76.3% 23.7% 8.2% 6.4% 1.72 
Under age 18 

0.5 mile 48.2% 51.8% -1.6% -0.5% 0.94 

1.0 mile 50.9% 49.1% -0.2% 0.1% 0.99 

2.0 miles 51.7% 48.3% 0.0% 0.0% 1.00 3.0 miles 54.3% 45.7% 0.1% 0.2% 1.00 

avg of 4 buffers 51.3% 48.7% -0.4% -0.1% 0.9

Age 65 and over 

0.5 mile 53.6% 46.4% -0.5% 0.3% 0.97 1.0 mile 49.1% 50.9% 0.0% -0.2% 1.0

2.0 miles 52.6% 47.4% 0.2% 0.1% 1.00 3.0 miles 56.0% 44.0% 0.3% 0.3% 1.04 

avg of 4 buffers 52.8% 47.2% 0.0% 0.1% 1.0

Average percentage of people living within each buffer for each demographic category 

Buffer Minority African American Native American Other and Multiracial Hispanic Living Below Poverty Line UnCBSA 22.9 12.1 0.8 9.9 11.1 12.6 

within 0.5 mile 32.9 19.3 1.2 12.4 15.5 21.8 

within 1 mile 34.4 19.5 1.6 13.3 16.9 21.3 

within 2 miles 34.5 20.3 1.3 12.8 16.4 20.7 within 3 miles 34.2 20.5 1.3 12.4 15.5 19.5 
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Analysis B - county-level 24-hour DVs versus Annual DVs, 2007-2009.

Air quality data from 2007-2009 were evaluated to identify the distributions of the ratios of 98th

percentile DVs to annual mean DVs by geographic region. Data utilized in this analysis were extracted 

from AQS on 1/29/2011; these data served two primary purposes: 1) to generate official updated design 

values (not estimates) and supporting statistics for public dissemination (i.e., via the EPA Air Trends 

website, specifically at http://epa.gov/airtrends/values.html), and 2) to support Analysis B and C 

described in this memo. Site-level PM2.5 annual design values and 24-hour (98th percentile) design values 

were calculated using the protocols specified in 40 CFR Appendix N. County- level design values were 

derived by identifying the highest valid (i.e., complete) site-level design values in each monitored county, 

as consistent with Table 4 of the posted official design value Excel file). Figure 3 presents a scatter plot 

of annual and 24-hour DVs in counties across the U.S., color-coded by geographic region. This figure 

provides a visual perspective of whether the annual or 24-hour standard is likely to be the controlling 

standard for various combinations of standards. In Figure 3, the horizontal lines represent alternative 24-

hour standard levels (i.e., 35 or 30 μg/m3) with a 98th percentile form, averaged over three years, while the 

vertical lines represent alternative annual standard levels (i.e., 13, 12, or 11 μg/m3

), using an annual 

arithmetic mean averaged over three years. The diagonal lines that intercept the origin and the 

intersection of a suite of alternative standard levels (e.g., the “11/35” line) represents the point of 

demarcation between those counties where the 24-hour standard is the controlling standard (to the left of 

the diagonal line) and those areas where the annual standard level represents the controlling standard (to 

the right of the diagonal line). 

Figure 3. County-level 24-hour DVs versus Annual DVs, 2007-2009. 

 
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Analysis C – Assessment of potential PM2.5 non-attainment areas 

To provide some perspective on the implications of various alternative suites of annual and 24-

hour standards, we compared the most recent (2007-2009) official county level PM2.5 annual and 24-hour 

DVs to those alternative levels as well as to the current standards. As noted above, the input data for the 

design values were extracted from AQS on 1/19/2011, and all calculations were performed according to 

the 40CFR Part 50 Appendix N protocols. 

The percentage of counties and the population in those counties that would likely not meet the 

current and various alternative suites of standards are presented in Table 3. Results are summarized for 

the entire U.S. level as well as the specific geographic regions illustrated in Figure 1.
Table 3. Predicted Percent of Counties with Monitors (and percent of population in counties with m

Not Likely to Meet Current and Alternative Annual and 24-hour PM2.5 Standards

Region > All U.S. Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwest Southe

Califor

Total # of counties > 532 93 149 135 45 17 69 17 

Total population (x 1,000)> 184,180 44,345 40,271 37,512 7,694 8,962 20,821 22,66

Current Standards 

annual 

μg/m3 

24-

hour 

μg/m3 

Statistic Numbers of counties, populations, and percentages of total 

15 35 

# counties 35 0 1 5 0 1 19 8 

population 28,801 0 662 3,683 0 180 6,615 17,57

% # counties 7% 0% 1% 4% 0% 6% 28% 47% 

% population 16% 0% 2% 10% 0% 2% 32% 78% 

Alternative Standards 

annual 

μg/m3 

24-

hour 

μg/m3 

Statistic Numbers of counties, populations, and percentages of total 

12 35 

# counties 149 21 30 66 0 2 20 9 

population 76,579 14,936 10,318 23,998 0 218 6,634 20,39

% # counties 28% 23% 20% 49% 0% 12% 29% 53% 

% population 42% 34% 26% 64% 0% 2% 32% 90% 

11 35 

# counties 263 39 82 102 5 3 22 9 

population 107,447 22,952 21,224 31,346 1,491 3,290 6,668 20,39

% # counties 49% 42% 55% 76% 11% 18% 32% 53% 

% population 58% 52% 53% 84% 19% 37% 32% 90% 

13 30 

# counties 130 21 10 50 2 3 32 10 

population 78,286 13,688 6,152 23,692 1,627 393 12,210 20,41

% # counties 24% 23% 7% 37% 4% 18% 46% 59% 

% population 43% 31% 15% 63% 21% 4% 59% 90%