UFPs PM Constituents

Transcription

1 The maximum annual average PM 10 concentration calculated from 24-h FRM data over these three years was 85 µg/m 3 in Stanfield, AZ (AQS monitor ID: ) during Stanfield is a small agricultural town (2007 population = 1074) approximately 64 km south of Phoenix and is in a region heavily influenced by windblown dust. Many of the maximum 24-h and 1-h avg PM 10 concentrations in Table 3-10 exceed 1,000 µg/m 3, but these represent rare events given the much lower 99th percentiles. Exceptional events were not removed from this data set and are responsible for at least some of the higher concentrations observed. The distribution of the 24-h FRM and FEM PM 10 data was similar across the three years ( ) investigated. Summer (June-August) had the highest mean and median relative to other seasons, consistent with PM 2.5 observations. Of the 15 CSAs/CBSAs investigated, the highest mean of 24-h PM 10 concentrations was reported for Phoenix (52 µg/m 3 ), considerably higher than the means for the other CSAs/CBSAs investigated. The lowest was reported for Boston (17 µg/m 3 ) with New York, Philadelphia and Seattle only slightly higher (19 µg/m 3 ). On average using the data for PM 2.5 in Table 3-8 and PM 10 in Table 3-10, the distribution between fine and coarse PM varies substantially by location. A larger fraction of PM mass is present in the thoracic coarse mode in Phoenix and Denver (3-yr mean PM 2.5 /PM 10 ratios of 0.19 and 0.32, respectively). In contrast, a larger fraction is present in the fine mode in Philadelphia (0.74), New York (0.68) and Pittsburgh (0.67). Comparisons of PM 2.5 to PM 10 as reported to AQS should be used with caution, however, since PM 2.5 concentrations are reported for local conditions while PM 10 concentrations are converted to STP before reporting. Nevertheless, these findings are consistent with those in Table 3-9 for PM in the subset of 6 cities with available co-located lowvolume PM data that have been properly adjusted for temperature and pressure. These findings are also consistent with those reported in the 2004 PM AQCD (U.S. EPA, 2004, ) where ratios of PM 2.5 to PM 10 were observed to be highest in the northeast (0.70), southeast (0.70), and industrial Midwest (0.70) and lower in the upper Midwest (0.53), northwest (0.50), southern California (0.47) and southwest (0.38). UFPs Little is known about the spatiotemporal distribution or composition of UFPs on a regional scale. New particle formation has been observed in environments ranging from relatively unpolluted marine and continental environments to polluted urban areas as an ongoing background process and during nucleation events (Kulmala et al., 2004, ). During nucleation events, which may last for several hours, UFP number concentrations can exceed 10 4 per cm 3 over distances of several hundred kilometers (Kulmala et al., 2004, ; Qian et al., 2007, ). These events occur throughout the year on 5-40% of days, depending on location (Qian et al., 2007, ). Cloud condensation nuclei, with diameters between ~10 and ~100 nm have been monitored for several years at a number of nonurban sites in the U.S. ( Average particle number counts at these sites in the U.S. range from several hundred to several thousand per cm 3. The particles are formed by nucleation of atmospheric gases with additional contribution from primary emissions in these environments (Pierce and Adams, 2009, ). In an urban setting, a large percentage of UFPs come from combustion-related emissions from mobile sources (Sioutas et al., 2005, ). UFP number concentrations drop off quickly with distance from the roadway (Levy et al., 2003, ; Reponen et al., 2003, ; Zhu et al., 2005, ), and therefore concentrations can be highly heterogeneous in the near-road environment depending on traffic, meteorological and topographic conditions (Baldauf et al., 2008, ). Studies characterizing spatial variability in UFPs are currently limited to a handful of close proximity locations and therefore are discussed in Sections and in the context of urban- and neighborhood-scale variability. Further elaboration on the composition of UFPs is included below. PM Constituents Only PM 2.5 is collected routinely at CSN network sites so the majority of this section on PM constituents is devoted to PM 2.5 composition. PM and UFP composition is discussed to the extent possible below. Figure 3-12 through Figure 3-16 contain U.S. concentration maps for OC, EC, SO 2 4, NO - 3, and NH + 4 mass from PM 2.5 measurements taken as part of the CSN network for the period Data used in these figures are as reported to AQS: no correction was applied to OC for non-carbon mass and NO - 3 represents total particulate nitrate. Figure 3-12 shows regions of December

2 high PM 2.5 OC mass concentration with annual average concentrations greater than 5 µg/m 3 in the western and the southeastern U.S. Concentrations at the western monitors peak in the fall and winter while those in the Southeast peak anywhere from spring through fall. The central and northeastern portions of the U.S. generally contain lower measured OC. Bell et al. (2007, ) present a similar map for estimated organic carbon mass (OCM) from calculated by multiplying the blank corrected OC measurement by 1.4 to account for non-carbon mass. There are a range of estimates in the literature for suggested scaling factors (Turpin and Lim, 2001, ), depending predominantly on how highly oxygenated the aerosol is (Pang et al., 2006, ). Fresh PM, more common in urban regions, has undergone limited chemical transformation. As the aerosol is transported to rural regions, it becomes more oxygenated. Turpin and Lim (2001, ) recommended ratios of 1.6 ± 0.2 for urban and 2.1 ± 0.2 for non-urban aerosols. Estimates range from 1.6 to 2.6 for rural IMPROVE monitors (El-Zanan et al., 2005, ). Therefore, applying one correction factor of 1.4 across the entire U.S. will lead to an underestimate of the OCM in rural regions. Therefore, the OC data in Figure 3-12 is presented as measured with a national blank correction, but no adjustment to OCM. Figure 3-13 contains a similar map for PM 2.5 EC mass concentration that exhibits smaller seasonal variability than OC, particularly in the eastern half of the U.S. There are isolated monitors spread throughout the country that measure high annual average EC concentrations. These EC hot spots are primarily associated with larger metropolitan areas such as Los Angeles, Pittsburgh, and New York, but El Paso, TX, also reported high annual average EC concentrations (driven by a wintertime average concentration greater than 2 µg/m 3 ). In a similar analysis for EC by Bell et al. (2007, ) for data, there were also high wintertime EC concentrations in eastern Kentucky and western Montana. These particular locations do not stand out in the data in Figure Figure 3-14 contains a map for PM 2.5 SO 4 2 mass concentration which shows that SO 4 2 is more prevalent in the eastern U.S. owing to the strong west-to-east gradient in SO 2 emissions. This gradient is magnified in the summer months when more sunlight is available for photochemical formation of SO 4 2. In contrast, PM 2.5 NO 3 mass concentration in Figure 3-15 is highest in the west, particularly in California. There are also elevated concentrations of NO 3 in the upper Midwest. The seasonal plots show generally higher NO 3 in the wintertime as a result of temperature driven partitioning. Exceptions exist in Los Angeles and Riverside where high NO 3 readings appear yearround. The PM 2.5 NH 4 + mass concentration maps in Figure 3-16 shows spatial patterns related to both SO 4 2 and NO 3 resulting from its presence in both (NH 4 ) 2 SO 4 and NH 4 NO 3. Figure A-31 through Figure A-36 in Annex A show similar U.S. concentration maps for PM 2.5 Cu, Fe, Ni, Pb, Se and V mass concentrations as measured by XRF. There is considerably less seasonal variation in the concentration profile for these metals than OC or the ions. For the 15 metropolitan areas identified earlier, the contribution of the major component classes to total PM 2.5 mass was derived using the measured sulfate, adjusted NO 3, derived water, inferred carbonaceous mass approach (SANDWICH) (Frank, 2006, ). This approach uses the measured FRM PM 2.5 mass and co-located CSN chemical constituents to perform a mass balancebased estimation of the PM 2.5 mass fraction attributed to SO 4 2, NO 3, EC, OCM, and crustal material. SO 4 2 and NO 3 include associated NH 4 + mass and estimated particle-bound water. Furthermore, NO 3 is assumed to be fully neutralized as NH 4 NO 3 and has been adjusted to represent the amount retained by the FRM monitor. EC is taken as measured, and the crustal component is derived from common oxides contained in the Earth s crust (Pettijohn, 1957, ), but can also include significant anthropogenic contributions, such as coal fly ash that are unrelated to soil resuspension. Finally, OCM is estimated using mass balance by subtracting the sum of all other constituents from the FRM PM 2.5 mass. The SANDWICH method takes into account passive collection of semi-volatile or handling-related mass on the FRM filters in the mass balance calculation. The magnitude of this artifact is assigned a nominal value of 0.5 µg/m 3, which is derived from limited analysis of FRM field blanks. Other constituents such as salt and other metallic oxides, however, are not included in these calculations and therefore the OCM fraction estimated by mass balance represents an upper bound on the FRM retained OCM. The calculations and assumptions that go into the SANDWICH method are discussed in detail in Frank (2006, )with further information available on EPA s AirExplorer web site December

3 Figure Three-yr avg 24-h PM 2.5 OC concentrations measured at CSN sites across the U.S., December

4 Figure Three-yr avg 24-h PM 2.5 EC concentrations measured at CSN sites across the U.S., December

5 Figure Three-yr avg 24-h PM 2.5 SO 4 2 concentrations measured at CSN sites across the U.S., December

6 Figure Three-yr avg 24-h PM 2.5 NO 3 concentrations measured at CSN sites across the U.S., December

7 Figure Three-yr avg 24-h PM 2.5 NH 4 + concentrations measured at CSN sites across the U.S., December

8 Figure 3-17 shows the PM 2.5 compositional breakdown for the 15 CSAs/CBSAs. All available monitoring sites with co-located FRM PM 2.5 and CSN speciation data reporting in all four seasons for at least one calendar year from were included. Furthermore, each season was required to contain five reported values for mass and the major PM 2.5 constituents. This resulted in a varying number of sites (ranging from one to seven, as indicated in the caption to Figure 3-17) used to create the averages shown in the figure. Variability in PM 2.5 composition within each CSA/CBSA where multiple monitors were available and trends in composition over time are discussed in subsequent sections. Figure Three-yr avg PM 2.5 speciation estimates for derived using the SANDWICH method. For the following 15 CSAs/CBSAs (with the number of sites per CSA/CBSA listed in parenthesis): Atlanta, GA (1); Birmingham, AL (3); Boston, MA (4); Chicago, IL (7); Denver, CO (2); Detroit, MI (4); Houston, TX (1); Los Angeles, CA (1); New York City, NY (7); Philadelphia, PA (6); Phoenix, AZ (2); Pittsburgh, PA (4); Riverside, CA (1); Seattle, WA (4); and St. Louis, MO (3). SO 4 2 and NO 3 estimates include NH 4 + and particle bound water and the circle area is scaled in proportion to FRM PM 2.5 mass as indicated in the legend. On an annual average basis, SO 4 2 is a dominant PM component in the eastern U.S. cities. For the presented cities, this includes everything east of Houston where the SO 4 2 fraction of PM 2.5 ranges from 42% in Chicago to 56% in Pittsburgh on an annual average basis. OCM is the next largest component in the east ranging from 27% in Pittsburgh to 42% in Birmingham. In the west, OCM is the largest constituent on an annual basis, ranging from 34% in Los Angeles to 58% in Seattle. SO 4 2, NO 3 and crustal material are also important in many of the included western cities. In the west, fractional SO 4 2 ranges from 18% in Denver to 32% in Los Angeles while fractional NO 3 is relatively large in Riverside (22%), Los Angeles (19%) and Denver (15%) and less important on an annual basis in Phoenix (1%) and Seattle (2%). Crustal material is particularly prevalent in Phoenix (28%). EC makes up a smaller fraction of the PM 2.5 (4-11%), but it is consistently present in all included cities regardless of region. December

9 Figure Seasonally-stratified 3-yr avg PM 2.5 speciation estimates for derived using the SANDWICH method. For the following 15 CSAs/CBSAs: Atlanta, GA; Birmingham, AL; Boston, MA; Chicago, IL; Denver, CO; Detroit, MI; Houston, TX; Los Angeles, CA; New York City, NY; Philadelphia, PA; Phoenix, AZ; Pittsburgh, PA; Riverside, CA; Seattle, WA; and St. Louis, MO. SO 4 2 and NO 3 estimates include NH 4 + and particle bound water and the circle area is scaled in proportion to FRM PM 2.5 mass as indicated in the legend. The seasonal variation in PM 2.5 composition across the 15 CSAs/CBSAs is shown in Figure 3-18 where the seasons are defined as before. SO 4 2 dominates in most metropolitan areas in the summertime, while NO 3 becomes important in the colder wintertime months. Notable exceptions include Denver, Phoenix, Riverside, and Seattle where summertime SO 4 2 makes up a smaller fraction of the PM 2.5 mass compared with other regions. Likewise, NO 3 is less pronounced in the wintertime in Atlanta, Birmingham, Houston, Phoenix, and Seattle compared with other regions. Los Angeles and Riverside exhibit elevated NO 3 from fall through spring. Crustal material is a substantial summertime component in Houston (26%), and is generally low elsewhere in the East in all seasons. In the West, crustal material represents a substantial component year-round in Phoenix and Denver. The only PM size fraction routinely collected at CSN network sites is PM 2.5, resulting in less available information on speciated PM Edgerton et al. (2005, ; 2009, ) published speciated measurements for PM 2.5 and PM obtained using dichotomous samplers from four locations included in the Southeastern Aerosol Research and Characterization (SEARCH) study: Yorkville, GA, Centreville, AL, Birmingham, AL and Atlanta, GA. Samples were collected between 1999 and 2003 on a 1-in-3 day or 1-in-6 day schedule, depending on site. Speciated measurements for both PM 2.5 and PM included SO 4 2, NO 3 -, NH 4 +, and major metal oxides (MMO). In addition, OC and either black carbon (BC) or EC were reported for PM 2.5 over the entire study period and for PM for a subset of samples extending from April 2003 to April For the Atlanta and Birmingham SEARCH sites, the annual average NO 3 - mass fraction was approximately equal for PM 2.5 (5.6% and 5.0%, respectively, for Atlanta and Birmingham) and PM (4.9% and 3.3%). Likewise, the OC mass fraction was approximately equal for PM 2.5 (26% and 26%) and PM (24% and 27%). MMO contributed an order of magnitude smaller mass fraction to PM 2.5 (2.6% and 4.7%) than PM (38% and 35%). In contrast, SO 4 2 contributed an order of December

10 magnitude greater mass fraction to PM 2.5 (25.1% and 24.1%) than PM (2.8 and 2.1%). BC also contributed a larger mass fraction of PM 2.5 (8.6% and 10.5%) than EC did for PM (2.9% and 2.4%). Based on these findings, MMO are present primarily in the thoracic coarse mode, while SO 4 2 and EC/BC are present primarily in the fine mode. NO 3 and OC are present in both modes in approximately equal mass fractions. These results are specific to Atlanta and Birmingham and may not represent other geographic regions. Information about the composition of ambient UFPs directly emitted by sources is still sparse compared to that for the larger size modes. However, their composition is expected to reflect that of their sources. As noted in Section 3.3 (and references therein), particle number emissions from motor vehicles are dominantly in the UF size range. The composition of gasoline vehicle emissions consists mainly of a mix of OC, EC and small quantities of trace metals and sulfates, with OC constituting anywhere from 26-88% of PM. Diesel PM is generally comprised of an EC and trace metal ash core onto which organic material and nucleation-mode SO 4 2 condense. With the introduction of new diesel emissions standards in 2007, total emissions have decreased dramatically, particularly for carbon. In areas where atmospheric nucleation is the dominant source of UFPs, sulfate along with ammonium, and secondary organic compounds are the likely major components of UFPs. In a study conducted at several urban sites in Southern California, Cass et al. (2000, ) found that the composition of UFPs ranged from 32-67% OC, % EC, 1-18% SO 4 2-, 0-19% NO 3, 0-9% NH 4 +, 1-26% metal oxides, 0-2% Na, and 0-2% Cl. Thus carbon, in various forms, was found to be the major contributor to the mass of UFPs. However, ammonium was found to contribute 33% of the mass of UFPs at one site in Riverside. Fe was the most abundant metal found in the UFPs. Chung et al. (2001, ) found that carbon was the major component of the mass of UFPs in a study conducted during January of 1999 in Bakersfield, CA. However, in the study of Chung et al. (2001, ), the contribution of carbonaceous species (OC and EC; typically 20-30%) was much lower than that found in the cities in Southern California. They found that Ca was the dominant cation, accounting for about 20% of the mass of UFPs in their samples. Sizable contributions from Si (0-4%) and Al (6-14%) were also found. MOUDIs are used to collect sizesegregated filter samples in the UFP compositional analyses described above. Coarse particle bounce is a concern when using MOUDIs and further studies, including scanning electron microscopy, may be needed to quantify the effect of this sampling artifact on UFP compositional analyses. Herner et al. (2005, ) reported a gradual increase in OC mass fraction as particle size decreases from 1 µm (20% OC) to 100 nm (80% OC) in the San Joaquin Valley of California. Sardar et al. (2005, ) found OC to be the major component of UFPs at four locations in California, with higher OC mass fraction in the wintertime relative to summertime. EC and SO 4 2 were also present in the UFP samples, but at much smaller mass fractions. EC was present year-round, whereas SO 4 2 had a summertime increase. More detailed chemical characterization of the OC fraction of ambient UFPs is extremely limited, but recent studies have identified specific organic molecular markers affiliated with motor vehicle emissions including hopanes and PAHs (Fine et al., 2004, ; Ning et al., 2007, ; Phuleria et al., 2007, ). As noted in the 2004 PM AQCD (U.S. EPA, 2004, ), primary biological aerosol particles (PBAP), which include microorganisms, fragments of living things, and organic compounds of miscellaneous origin in surface deposits on filters, are not distinguishable in analyses of total OC. A clear distinction should be made between PBAP and primary OC that is produced by organisms (e.g., waxes coating the surfaces of organisms) and precursors to secondary OC such as isoprene and terpenes. Indeed, the fields of view of many photomicrographs of PM samples obtained by scanning electron microscopy are often dominated by large numbers of pollen spores, plant and insect fragments, and microorganisms. Bioaerosols such as pollen, fungal spores, and most bacteria are expected to be found mainly in the coarse size fraction (see Figure 3-2 for an illustrative example of a pollen particle). However, allergens from pollens can also be found in respirable particles (Edgerton et al., 2009, ; Taylor, 2002, ). Matthias-Maser et al. (2000, ) summarized information about the size distribution of PBAP in and around Mainz, Germany in what is perhaps the most complete study of this sort. Matthias-Maser found that PBAP constituted up to 30% of total particle number and volume in the approximate size range from µm on an annual basis. Additionally, whereas the contribution of PBAP to the total aerosol volume did not change appreciably with season, the contribution of PBAP to total particle number ranged from about 10% in December and March to about 25% in June and October. Bauer et al. (2008, ) measured contributions of fungal spores to OC at an urban and a suburban site in Vienna, Austria in spring and summer. Fungal spores at the suburban site contributed on average 10% to OC in PM 10 December

11 A.2.3. Speciation Figure A-127. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Atlanta, GA. December 2009 A-213

12 Figure A-128. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Birmingham, AL. December 2009 A-214

13 Figure A-129. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Boston, MA. December 2009 A-215

14 Figure A-130. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Chicago, IL. December 2009 A-216

15 Figure A-131. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter, derived using the SANDWICH method in Denver, CO. December 2009 A-217

16 Figure A-132. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Detroit, MI. December 2009 A-218

17 Figure A-133. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Houston, TX. December 2009 A-219

18 Figure A-134. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Los Angeles, CA. December 2009 A-220

19 Figure A-135. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in New York, NY. December 2009 A-221

20 Figure A-136. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Philadelphia, PA. December 2009 A-222

21 Figure A-137. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Phoenix, AZ. December 2009 A-223

22 Figure A-138. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Pittsburgh, PA. December 2009 A-224

23 Figure A-139. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Riverside, CA. December 2009 A-225

24 Figure A-140. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Seattle, WA. December 2009 A-226

25 Figure A-141. Seasonally averaged PM 2.5 speciation data for for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in St. Louis, MO. December 2009 A-227

26 Figure A-142. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Atlanta, GA, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. Figure A-143. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Birmingham, AL, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. December 2009 A-228

27 Figure A-144. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Boston, MA, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. Figure A-145. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Chicago, IL, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. December 2009 A-229

28 Figure A-146. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Denver, CO, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. Figure A-147. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Detroit, MI, The gray line represents the difference in OCM calculated using material balance and blank corrected OC x 1.4. December 2009 A-230

29 Figure A-148. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Houston, TX, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. Figure A-149. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Los Angeles, CA, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. December 2009 A-231

30 Figure A-150. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for New York, NY, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. Figure A-151. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Philadelphia, PA, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. December 2009 A-232

31 Figure A-152. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Phoenix, AZ, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. Figure A-153. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Pittsburgh, PA, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. December 2009 A-233

32 Figure A-154. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Riverside, CA, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. Figure A-155. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for Seattle, WA, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. December 2009 A-234

33 Figure A-156. Seasonal patterns in PM 2.5 chemical composition from city-wide monthly average values for St. Louis, MO, The gray line represents the difference in OCM calculated using material balance and blank corrected OC 1.4. A.2.4. Diel Trends Figure A-157. Diel plots generated from all available hourly FRM-like PM 2.5 data, stratified by weekday (left) and weekend (right), in Atlanta, GA. Included are the number of monitor days (N) and the median, mean, 5th, 10th, 90th and 95th percentiles for each hour. December 2009 A-235