# 1 UNITED STATES ENVIRONMENTAL PROTECTION AGENCY Office of Air Quality Planning and Standards Research Triangle Park, NC 27711



# April 15, 2011

MEMORANDUM

SUBJECT: PM 2.5 

# 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 PM 2.5 

# air quality analyses conducted for the review of the particulate matter (PM) national ambient air qua lity standards (NAAQS) previously presented in Schmidt et al., 2010. 1 

# The purpose of these analyses was to in form staff conclusions on alternative PM 2.5 

# standards that are appropriate to consid er in the current PM NAAQS review.

Specific analyses conducted, including tasks, a ssumptions, caveats, and processing methodologies

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

 

# Analysis A – Evaluation of the sp atial averaging provisions for the PM 2.5 

# annual standard, specifically, a demographic analys is of the potential for dispr oportionate impacts on potentially vulnerable populations (i.e., environmental justice an alysis) – clarifi cation of Analysis 1 described in Schmidt et al., 2010.

 

# Analysis B – County-level 24- hour design values versus an nual design values, 2007-2009 – update of Analysis 4 described in Schmidt et al., 2010.

 

# Analysis C – Assessment of areas not likel y to meet current and alternative PM 2.5 

# standards 2



# - 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 iden tical 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). PM 2.5 

## 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 sp atial averaging provisions for the PM 2.5 

# annual standard, specifically, a demographic analysis of the potential for dispropor tionate impacts on potentia lly vulnerable populations (i.e., environmental justice analysis)

This analysis focused on determining if the spat ial averaging provisions as modified in 2006 could introduce inequities in protection for susceptible populations exposed to PM 2.5 

# . The current form of the

annual PM 2.5 

# 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 desc ribed here. We evaluated whether persons with a lower socioeconomic status (SES) are more likely than the genera l population to live in

areas in which the monitors recordin g the highest air quality values in an area are located. Data used in this analysis included demographi c parameters measured at the Ce nsus 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 per cent people 65 years and older.

The air quality data for this pr oject originated from EPA’s Air Quality System (AQS) database, the official repository of NAAQS-c omparable 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 repor ted 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 PM 2.5 

# concentrations:

1) annual 98 th 

# percentile 24-hour aver age concentrations, 2) 3-year average 98 th 

# 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 usin g the regular (non-seasonal ) protocols specified in 40CFR Part 50, Appendix N. A comp leteness criterion of a minimum of 11 samples per quarter for all 4 quarters of each year (or all 12 qua rters of a 3-year period for design value estimates) was imposed on all the site-based metrics. Appendix N provides add itional options for validating the annual and 3-year metrics, depending on the metric type and level in re lation to the associated NAA QS. To avoid the level and metric validation bias, an acros s-the-board 11 sample minimum pe r quarter criterion was imposed. Other (AK, HI, VI, PR) 

# So. CA Northwest Upper Midwest Northeast

Industrial Midwest Southeast

Southwest

# 3 Also, Appendix N has a special 98 th 

# percentile computation protocol for sites authorized to sample seasonally (that is, to sample at a less stringent frequenc y in months when lower concentrations are expected). The seasonal calculation protoc ol was not used in these analyses; 98 th 

# percentile values were computed at all sites using the regular calculation prot ocol. Because of the possible differences in metric

validation and 98 th 

# percentile calculation protoc ol options, the generated 3-year metrics are referred to as design value estimates

.

Air quality data from 2006-2008, su mmarized as 3-year annual PM 2.5 

# DV estimates, were evaluated in order to identify the hi ghest concentration site location for each urban area that contained at least two valid DV monitoring locat ions. 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 populatio n demographics for the overall urban area to determine if there were any SES-re lated differences. Core Based St atistical Area (CBSA) definitions were used to classify the urban areas. As descri bed 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 a nnual 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. Th e high site buffer radii demographi c 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 Cens us 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 va riables, data were prorated to blocks according to total population.

Table 2 shows summary results of the comparis on 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 Schm idt et al, 2010 was finalized, errors were discovered in the processing of the “Below Poverty Level” inform ation. This memo (i.e., the Analys is A results) corre cts the previous mistakes, but in no way changes th e previous (and current) message which is: In general, the areas surrounding the high monitor of an area were observe d to have higher percentages of minorities and people under the poverty level th an the area as a whole.

# 4 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, C A 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 Bea c 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, M N 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-Fra n 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-W A 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-P A 603 4

Total population of 116 CBSAs 175,7615Figure 2. Map of CBSAs Used inSpatial Averaging Analysis.6Table 2. Summary Results: Comparison Between theFour High Site Buffers and the Overall CBSA AreaArea counts (for 116 CBSAs) comparing SES variables within 0.5,1.0, 2.0, and 3.0 mile radii buffers versus SES variables across overall CBSAPopulation VariableBufferPercentage ofareas wherevariablepercentagegreater in highsite buffer than inoverall CBSAPercentage ofareas wherevariablepercentage notgreater in highsite buffer than inoverall CBSAMean differencein variablepercentage inhigh site bufferminus variablepercentage inoverall CBSAMedian differencein variablepercentage in highsite buffer minusvariablepercentage inoverall CBSAMean ratio ofvariablepercentage inhigh site buffer /variablepercentage inoverall CBSAMedian ratio ofvariablepercentage inhigh site buffer /variablepercentage inoverall CBSAMinority0.5 mile 58.9% 41.1% 8.9% 2.8% 1.57 1.161.0 mile 67.2% 32.8% 11.6% 6.1% 1.66 1.372.0 miles 72.4% 27.6% 11.6% 7.8% 1.63 1.503.0 miles 76.7% 23.3% 11.4% 9.6% 1.59 1.47avg of 4 buffers 68.8% 31.2% 10.9% 6.6% 1.61 1.38African American0.5 mile49.1%50.9%6.5%-0.1%1.830.901.0 mile57.8%42.2%7.3%1.1%1.661.162.0 miles58.6%41.4%8.2%1.0%1.621.323.0 miles66.4%33.6%8.4%3.1%1.591.52avg of 4 buffers58.0%42.0%7.6%1.3%1.681.23Native American0.5 mile 52.7% 47.3% 0.3% 0.0% 1.51 1.031.0 mile 62.9% 37.1% 0.8% 0.1% 1.64 1.212.0 miles 65.5% 34.5% 0.5% 0.1% 1.43 1.143.0 miles 66.4% 33.6% 0.5% 0.0% 1.33 1.11avg of 4 buffers 61.9% 38.1% 0.5% 0.1% 1.48 1.12Other and Multiracial0.5 mile59.8%40.2%2.1%0.4%1.321.101.0 mile58.6%41.4%3.5%2.1%1.461.252.0 miles66.4%33.6%2.9%1.0%1.371.193.0 miles66.4%33.6%2.5%1.2%1.321.27avg of 4 buffers62.8%37.2%2.8%1.2%1.371.20Hispanic0.5 mile 55.4% 44.6% 3.9% 0.3% 1.50 1.091.0 mile 60.3% 39.7% 5.8% 1.1% 1.77 1.282.0 miles 66.4% 33.6% 5.3% 1.4% 1.68 1.323.0 miles 75.0% 25.0% 4.5% 1.1% 1.54 1.33avg of 4 buffers 64.3% 35.7% 4.9% 1.0% 1.62 1.26Below Poverty Line0.5 mile75.0%25.0%9.2%5.4%1.811.471.0 mile75.9%24.1%8.7%6.1%1.771.512.0 miles77.6%22.4%8.0%7.3%1.701.603.0 miles76.7%23.3%6.8%6.7%1.591.55avg of 4 buffers76.3%23.7%8.2%6.4%1.721.537Under age 180.5 mile 48.2% 51.8% -1.6% -0.5% 0.94 0.981.0 mile 50.9% 49.1% -0.2% 0.1% 0.99 1.012.0 miles 51.7% 48.3% 0.0% 0.0% 1.00 1.023.0 miles 54.3% 45.7% 0.1% 0.2% 1.00 1.01avg of 4 buffers 51.3% 48.7% -0.4% -0.1% 0.98 1.01Age 65 and over0.5 mile53.6%46.4%-0.5%0.3%0.971.031.0 mile49.1%50.9%0.0%-0.2%1.010.982.0 miles52.6%47.4%0.2%0.1%1.001.013.0 miles56.0%44.0%0.3%0.3%1.041.03avg of 4 buffers52.8%47.2%0.0%0.1%1.011.01Average percentage of people living within each buffer for each demographic categoryBufferMinorityAfricanAmericanNativeAmericanOther andMultiracialHispanicLiving BelowPoverty LineUnder Age 18Age 65 andOverCBSA 22.9 12.1 0.8 9.9 11.1 12.6 27.3 12within 0.5 mile 32.9 19.3 1.2 12.4 15.5 21.8 26.6 12within 1 mile 34.4 19.5 1.6 13.3 16.9 21.3 27.1 12within 2 miles 34.5 20.3 1.3 12.8 16.4 20.7 27.3 12.2within 3 miles 34.2 20.5 1.3 12.4 15.5 19.5 27.3 12.4

# 8 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 98 th


# 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 primar y purposes: 1) to generate official updated design values (not estimates) and supporting statistics fo r public dissemination (i.e., via the EPA Air Trends website, specifically at http://e pa.gov/airtrends/values.html), and 2) to support Analysis B and C

described in this memo. Site-level PM 2.5 

# annual design values and 24-hour (98 th 

# 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 desi gn 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/m 3 

# ) with a 98 th 

# percentile form, averaged over three years, while the vertical lines represent a lternative annual standard levels (i.e., 13, 12, or 11 μg/m 3 

# ), using an annual arithmetic mean averaged over three years. The diagonal lines that interc ept 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 stan dard is the controlling st andard (to the left of the diagonal line) and those areas where the annual st andard level represents th e controlling standard (to the right of the diagonal line).

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

# 9 Analysis C – Assessment of potential PM 2.5 

# non-attainment areas

To provide some perspective on the implications of various alternative suites of annual and 24- hour standards, we compared the most re cent (2007-2009) offi cial county level PM 2.5 

# annual and 24-hour

DVs to those alternative levels as well as to the curre nt standards. As noted a bove, 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 th e 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 1.10Table 3. Predicted Percent of Counties with Monitors(and percent of population in counties with monitors)Not Likely to Meet Current andAlternative Annual and 24-hour PM2.5StandardsRegion >All U.S.NortheastSoutheastIndustrialMidwestUpperMidwestSouthwestNorthwestSouthernCaliforniaOutlyingareasTotal # of counties > 532 93 149 135 45 17 69 17 7Total population (x 1,000)> 184,180 44,345 40,271 37,512 7,694 8,962 20,821 22,663 1,913Current Standardsannualμg/m324-hourμg/m3StatisticNumbers of counties, populations, and percentages of total15 35# counties 35 0 1 5 0 1 19 8 1population 28,801 0 662 3,683 0 180 6,615 17,579 83% # counties 7% 0% 1% 4% 0% 6% 28% 47% 14%% population 16% 0% 2% 10% 0% 2% 32% 78% 4%Alternative Standardsannualμg/m324-hourμg/m3StatisticNumbers of counties, populations, and percentages of total1235# counties149213066022091population76,57914,93610,31823,99802186,63420,39383% # counties28%23%20%49%0%12%29%53%14%% population42%34%26%64%0%2%32%90%4%11 35# counties 263 39 82 102 5 3 22 9 1population 107,447 22,952 21,224 31,346 1,491 3,290 6,668 20,393 83% # counties 49% 42% 55% 76% 11% 18% 32% 53% 14%% population 58% 52% 53% 84% 19% 37% 32% 90% 4%1330# counties1302110502332102population78,28613,6886,15223,6921,62739312,21020,411114% # counties24%23%7%37%4%18%46%59%29%% population43%31%15%63%21%4%59%90%6%