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Characterizing Ambient PM Concentrations and Processes

Characterizing Ambient PM Concentrations and Processes. Overview Temporal Patterns of Primary and Secondary PM Components Spatial Patterns. Compositional Patterns Natural PM Events: Dust and Smoke Methods and Tools Summary References.

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Characterizing Ambient PM Concentrations and Processes

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  1. Characterizing Ambient PM Concentrations and Processes • Overview • Temporal Patterns of Primary and Secondary PM Components • Spatial Patterns • Compositional Patterns • Natural PM Events: Dust and Smoke • Methods and Tools • Summary • References What are the temporal, spatial, chemical, and size characteristics of suspended particles and precursor gases? By understanding these characteristics, we can begin to understand the sources, transport properties, formation, and health effects of PM. PM Data Analysis Workbook: Characterizing PM

  2. Overview • Spatial and temporal analyses of PM data are the basis for improving our understanding of emissions and the dynamic atmospheric processes that influence particle formation and distribution. Goals of the data analyst performing these investigations can include • identifying possible important sources of PM and precursors. • determining chemical and physical processes that lead to high PM concentrations. • assessing efficacy of existing control strategies. • Analyses help one to develop a conceptual model of processes affecting PM concentrations. Questions the analyst could be addressing with the data include the following: • What is the chemical composition of PM, and how does the composition change with time and by site? • What are the statistical characteristics of pollutant concentrations, and how do they change from site to site and from time to time? • How do different pollutant concentrations vary in space and time relative to each other? • What spatial and temporal scales are represented by pollutant measurements at each site? • What local or regional sources influence a given measurement site? • How did meteorology, nearby precursor and PM emissions, and natural events influence both spatial and temporal characteristics of the PM data? • Many of the more detailed analyses discussed later in this workbook, such as source apportionment, are improved by a thorough understanding of spatial and temporal characteristics. For example, the analyst can point out key features in the data that need to be reproduced in modeling efforts used to assess control strategies or can identify key components of PM for source apportionment. Clearly, spatial and temporal characterization of the data is a fundamental part of all the workbook chapters. Solomon, 1994 PM Data Analysis Workbook: Characterizing PM

  3. Decision Matrix for Spatial and Temporal Analyses Decision matrix used to select analysis objectives for the characterization of PM. To use the matrix, find your analysis objective across the top. Follow this column down to see which technical topic areas at the left illustrate analyses pertaining to the objective. For each of these analysis objectives, go to the next page to see which data and data analysis tools might be needed to meet the objective. PM Data Analysis Workbook: Characterizing PM

  4. Decision Matrix for Spatial and Temporal Analyses For each of the analysis objectives that are of interest to you, follow down the column to see which data and data analysis tools might be needed. PM Data Analysis Workbook: Characterizing PM

  5. Temporal Patterns of Primary and Secondary PM Components • Diurnal patterns: explore the daily cycle of PM and its relationship to emissions and meteorology. • Day-of-week patterns: explore the weekly cycle of PM and its relationship to emissions. • Episodic patterns: explore differences between episodes of high PM concentrations and non-episodes. • Seasonal patterns: explore differences in seasonal PM concentrations and the causal factors. PM Data Analysis Workbook: Characterizing PM

  6. Diurnal Patterns: Overview • Since the ambient PM standard is expressed as a daily average (65 g/m3), most measurements of PM are 24-hr averages. Hourly values are not relevant for regulating compliance purposes. • When measurements with shorter averaging times than 24-hr are available, analysts have observed a significant diurnal pattern of PM at most locations. • The diurnal PM variation is due to the daily cycle of emissions, dispersion, and PM formation and removal processes. • The diurnal variation of PM is not well understood, mostly because of data limitations. However, the limited data can be used to suggest possible influences. PM Data Analysis Workbook: Characterizing PM

  7. Diurnal Patterns Nationwide • In this national summary plot, the PM2.5 concentrations peak during the 0600 to 0900 period and again in the 1800 to 2000 period. • Investigation of the data site-by-site showed the diurnal peaks in PM2.5 concentrations varies by location and by season. U.S. EPA, 2000 PM Data Analysis Workbook: Characterizing PM

  8. Diurnal Pattern of PM in an Urban Setting Figures prepared using Voyager. Note that the PM2.5 and PM10 monitors are sited ~1 mile apart. Husar, 1999 Concentration g/m3 Concentration g/m3 In the summer in New Haven, Connecticut (June 1997), the PM2.5 concentrations are nearly constant throughout the day while the PM10 concentrations peak during the day due to increases in the coarse particle fraction. In the winter (December 1997), the PM2.5 appears to have small peaks during the rush hours. In contrast, the PM10 concentration doubles during the day due to increases in coarse particle concentrations. PM Data Analysis Workbook: Characterizing PM

  9. Diurnal Pattern of PM in a Rural Setting Plots prepared using Voyager. Husar, 1999 In the summer in Liberty, Pennsylvania (June 1997), the PM2.5 and PM10 concentrations peak at night and decrease during the daytime. The daily cycle of nighttime boundary layer formation and daytime mixing height growth appear to drive PM concentrations. In the winter (December 1997), the PM2.5 and PM10 concentrations show a mild diurnal fluctuation because there is a smaller difference between daytime and nighttime inversion heights. PM Data Analysis Workbook: Characterizing PM

  10. Concentrations of PM2.5 organic and elemental carbon were highest during the nighttime in Fresno, California, during December 1995. At the more rural site of Chowchilla, California, much less diurnal variation in PM2.5 OC and EC was observed. Diurnal Pattern of PM2.5 Species Plots prepared using a spreadsheet. Eight 3-hr PM2.5 samples. Chow, 1998 PM Data Analysis Workbook: Characterizing PM

  11. Some Causes of Diurnal PM Variation Husar, 1999 In urban areas, during the afternoon, vertical mixing and horizontal transport tend to dilute concentrations. During the night and early morning, the emissions are trapped by poor ventilation. In the afternoon, vertical mixing may carry pollutants above topographical barriers. During the night and early morning, dispersion may be hampered by topography. PM Data Analysis Workbook: Characterizing PM

  12. Diurnal Patterns: Summary • When PM measurements are made on a <24-hr time-scale, daily cycles in concentration and composition are observed. These daily cycles are attributable to daily cycles in emissions, dispersion, and PM formation and removal processes. • Meteorological information is critical to a complete understanding of daily cycles in the meteorological data, including mixing height, temperature, relative humidity, and wind speed and direction changes with time of day. Mountain barriers and large bodies of water are also factors to be considered. PM Data Analysis Workbook: Characterizing PM

  13. Motivations for Continuous PM Measurements • Evaluate diurnal variation (human exposure/health effects, local vs. transport). • Reduce site visits (manpower) and network operating costs (laboratory analysis). • Identify need to increase FRM/FEM sampling frequency. • Evaluate real-time data to issue alerts or implement control strategy (e.g., burning bans, no-drive days). • Define zones of representativeness of monitoring sites and zones of influence of pollution sources. • Understand general atmospheric processes (the physics, chemistry, and sources of high particulate matter concentrations). • Boost data capture at key sites. • Promote the exchange and consistency of data between nations. • Assess performance of source-based models. • Provide input to receptor-based models. Sheehan, 1999 PM Data Analysis Workbook: Characterizing PM

  14. Day-of-Week Patterns in PM • There is a measurable weekly cycle of PM at most monitoring sites. • The weekly periodicity of PM is explicitly attributable to the weekly cycling of anthropogenic emission sources. • Hence, the weekly cycle can reveal features of PM emissions such as weekday peaks in concentration at industrial sites and weekend peaks at recreational sites. • At this time, the weekly cycle has been analyzed for PM10 but not for PM2.5. PM Data Analysis Workbook: Characterizing PM

  15. Weekly Pattern of PM2.5 in the Midwest At the industrial Midwest urban sites, the daily average PM2.5 concentrations (g/m3) were slightly higher on Wednesday, Friday, and Saturday than on other days of the week. The emissions cycles in these cities should be reviewed to better understand this pattern. At rural monitoring sites in the industrial Midwest, the overall PM2.5 concentrations are lower than at urban sites. Interestingly, the weekly cycle looks similar to the cycle observed at the urban sites. U.S. EPA, 2000. Plots were made using SAS where the box = 25th, 50th, and 75th percentiles and plus = mean. The numbers above the boxes indicate the number of samples used in the analysis. Data were retrieved from AIRS on July 12, 2000. PM Data Analysis Workbook: Characterizing PM

  16. Weekly Pattern of PM2.5 in West The 1999 PM2.5 data show very little day-of-week pattern at southern California urban sites. At rural southern California sites, PM2.5 concentrations were significantly lower than at the urban sites and little day-of-week pattern was observed. In previous studies of PM10 at Yosemite NP in California (not shown here), the highest PM10 concentrations occurred on Sundays. The monitoring site in Yosemite is near major recreational facilities that experience a large weekend influx of visitors. PM2.5 data could be investigated for similar trends. U.S. EPA, 2000. Plots were made using SAS where the box = 25th, 50th, and 75th percentiles and plus = mean. The numbers above the boxes indicate the number of samples used in the analysis. Data were retrieved from AIRS on July 12, 2000. PM Data Analysis Workbook: Characterizing PM

  17. Sample Size and Day-of-Week Analysis (1 of 3) • In order to assess day-of-week patterns or trends in air quality data, a sufficient number of samples are required. The actual data requirements will vary depending upon the analysis types and variability of the data, among other factors. • Statistically, decreasing the sample size increases the confidence interval (CI). In general, if the 95% CIs of two data subsets (e.g., weekend vs. weekday PM2.5 concentrations) do not overlap, then there is good evidence that the subset population means are different. Additional statistical tests, such as a t-test, can be used to assess this. • Graphically, box whisker plots with notches showing the 95% CI around the means can be used to take into account sample size. PM Data Analysis Workbook: Characterizing PM

  18. Mon. Sun. Sample Size and Day-of-Week Analysis (2 of 3) • In the plot, boxes are notched (narrowed) at the median and return to full width at the lower and upper CI values. PM2.5 concentrations (in g/m3 measured using a beta attenuation mass monitor - BAM) are plotted by day of week. Data include samples collected during the hours beginning at 0700 and 0800 at an urban California site from October through December 2000. • At this site, concentrations were statistically significantly higher on Fridays than on other days. Possible reasons for this day of week pattern should be investigated. Notched box plot figure prepared using SYSTAT statistical package. The box represents the 25th and 75th percentiles. Whiskers extend to data within 1.5 times the interquartile range. The boxes are notched (narrowed) at the median and return to full width at the lower and upper confidence interval values (95% here). If the intervals around two medians do not overlap, one can be confident at about the 95% level that the two population medians are different. Data are level 0. PM Data Analysis Workbook: Characterizing PM

  19. Sun. Mon. Sample Size and Day-of-Week Analysis (3 of 3) • Example day-of-week pattern (where Monday=1, Sunday=7) of ambient PM2.5 elemental carbon concentrations (g/m3) at an urban California site from October through December 2000. Data presented are for 0700 and 0800, representing the morning commute. • Note that Saturday and Sunday median 75th percentile and median concentrations are generally lower than Monday through Friday, but the difference in the medians is not statistically significant. Notched box plot prepared using SYSTAT. The box represents the 25th and 75th percentiles. Whiskers extend to data within 1.5 times the interquartile range. The boxes are notched (narrowed) at the median and return to full width at the lower and upper confidence interval values (95% here). If the intervals around two medians do not overlap, one can be confident at about the 95% level that the two population medians are different Data are level 0. PM Data Analysis Workbook: Characterizing PM

  20. Day-of-Week: Summary • Initial investigations of the PM2.5 concentration with day of week shows different results than those obtained for PM10. • Many of the sites, both urban and rural, show that PM2.5 concentrations can be uniform during the entire week. • We might expect urban centers to exhibit higher concentrations during the workweek and reduced values on weekends, consistent with activity patterns. Initial investigations of the data do not necessarily show this result. • At recreational locations, PM10 concentrations have been shown to peak during the weekend; PM2.5 concentrations should be investigated to see if similar patterns are observed. • It is important to have a sufficient number of measurements on weekends versus weekdays to assess this issue. Also, activity patterns and emissions should be compared to the ambient data for corroboration. PM Data Analysis Workbook: Characterizing PM

  21. Episodic Patterns in PM • Investigations of episodes of high PM concentrations are necessary in order to understand the meteorological conditions and possible PM and precursor sources that lead to the high concentrations. • Unlike ozone episodes which typically occur during the summer, episodes of high PM2.5 concentrations can occur during any time of year (e.g., winter wood smoke, summer photochemical event, etc.). Plot prepared using a spreadsheet. Hourly TEOM PM10 data in August 1995 overlaid with the 24-hr moving average. Poirot et al., 1999 PM Data Analysis Workbook: Characterizing PM

  22. Contribution of High PM Episodes on Long-term Means • At the urban sites of New York City and Washington, DC., episodes of high 24-hr PM2.5 concentrations do not influence the long-term mean concentrations as much as at the more rural sites illustrated here by the Underhill, Vermont, and Shenandoah National Park sites. • In this analysis using eastern United States data, the Burlington, Vermont, data show episodic characteristics in between the more urban and more rural site characteristics. Plot prepared using a spreadsheet. Plot of the percent of the long-term mean accounted for by the samples. Poirot, 1999 PM Data Analysis Workbook: Characterizing PM

  23. Seasonal Pattern of PM2.5 • The seasonal cycle results from changes in PM background levels, emissions, atmospheric dilution, and chemical reaction, formation, and removal processes. • Examining the seasonal cycles of PM2.5 mass and its elemental constituents can provide insights into these causal factors. • The season with the highest concentrations is a good candidate for PM2.5 control actions. Plot prepared using Voyager. Monthly average PM2.5 concentrations at two IMPROVE sites in 1988. Shenandoah NP PM2.5 concentrations vary over a wider range by season than at the urban site. Schichtel, 1999a PM Data Analysis Workbook: Characterizing PM

  24. Summer Daytime Winter Daytime Some Causes of Seasonal PM Variation Schichtel, 1999a PM primary and precursor emissions are dependent on seasonal energy consumption for heating and cooling, occurrence of fires, etc. Many of the gas-to-particle transformation rates are photochemically driven and peak in the summer. In urban areas, the winter mixing heights are low, trapping emissions. In the summer, intense vertical mixing raises the mixing heights which, in turn, tends to dilute the concentrations. PM Data Analysis Workbook: Characterizing PM

  25. PM10-PM2.5 Relationship in the Northeast and Southern California Plots prepared using Voyager. Monthly average PM concentrations at IMPROVE sites in the Northeast and southernCalifornia during 1988. Schichtel, 1999a In the northeastern United States, PM2.5 and PM10 concentrations peak in the summer with approximately 30% more mass in the summer than in the winter. In southern California, the PM2.5 concentrations peak in the winter with 2.5 times more mass than during the spring and summer. The PM10 peaks in the fall. PM Data Analysis Workbook: Characterizing PM

  26. Seasonal PM2.5 During 1999 • In Atlanta, Georgia, the 1999 PM2.5 concentrations were significantly higher in summer (e.g., August) than in winter (e.g., December). • In coastal Los Angeles, the summer PM2.5 concentrations were lower than during the winter. U.S.EPA, 2000 • In Chicago, Illinois, PM2.5 concentrations varied significantly from month to month with highest concentrations in January, May, and June. • In Newark, New Jersey, PM2.5 concentrations were higher during July and July than during the rest of the year. PM Data Analysis Workbook: Characterizing PM

  27. Seasonal PM2.5 Compositional Patterns (1 of 2) • At the more northerly (and humid) Mt. Rainier and Acadia sites, soil K levels are relatively low and exhibit a moderate seasonal variation. • Smoke K is higher on average than soil K at these northern sites, peaking in fall at Mt. Rainier and showing both a winter (wood stove?) and secondary summer peak at Acadia. Plots prepared using a spreadsheet. Poirot, 1998 PM Data Analysis Workbook: Characterizing PM

  28. Seasonal PM2.5 Compositional Patterns (2 of 2) Plots prepared using a spreadsheet. Poirot, 1998 • At Bryce Canyon, soil K and smoke K are comparable with soil K peaking in the spring and smoke K peaking a few months later. • Smoke K at the Everglades exhibits a seasonal pattern similar to Acadia (primary winter and secondary summer peaks) but with higher concentrations. Soil K at the Everglades exhibits an extreme summer peak, consistent with the periodic influence of Saharan dust exposure in the southeastern United States. PM Data Analysis Workbook: Characterizing PM

  29. Seasonal Pattern: Summary • Summertime photochemical production of secondary PM2.5 can be important at some sites. • Summertime PM concentrations can be high because of dust events (affecting mostly PM10) and secondary PM formation (affecting mostly PM2.5). • Wintertime PM2.5 concentrations can be high because of lower inversions and changes in emissions such as the use of wood-burning for home heating. • Because of the potentially different sources of PM2.5 on a seasonal basis, different controls may be appropriate, depending on when PM2.5 exceedances are observed. PM Data Analysis Workbook: Characterizing PM

  30. Spatial Patterns • Urban spatial patterns: explore PM2.5 concentrations in urban settings. • Urban/rural spatial patterns: explore the differences between urban and rural PM2.5 concentrations. • Elevational patterns: explore PM2.5 concentrations as a function of elevation (e.g., on a mountain) and altitude (e.g., measured above ground by aircraft). • Regional patterns: explore regional PM2.5 concentrations. • National/international patterns: explore PM2.5 concentrations across the nation and internationally and assess important transport phenomena. PM Data Analysis Workbook: Characterizing PM

  31. Urban PM Concentration Spatial Pattern • Urban areas contain sources of particulate matter that increase PMconcentrations and cause “hot spots” (areas with concentrations in excess of background concentrations) in the PM spatial pattern. • Urban PMconcentrations vary greatly from day to day. • Knowledge of urban concentrations aids in the siting of monitors. • The following examples are PM10; these examples can be replaced with suitable PM2.5 examples once adequate data are available. PM Data Analysis Workbook: Characterizing PM

  32. Daily Average PM10 Concentrations in Philadelphia, July 1995 PM10 concentration contour maps with monitoring stations superimposed Falke, 1999 • PM10 concentrations in Philadelphia exhibit large differences among sites from day to day. • Background concentrations on July 8, 1995, were 30 to 38 μg/m3. High concentrations (above 79 μg/m3) were only observed at one urban site. • On July 20, 1995, the background PM10 concentrations were 30-46 μg/m3 and multiple locations experienced high concentrations. PM Data Analysis Workbook: Characterizing PM

  33. Monthly & Seasonal Average PM10 in Philadelphia, Summer 1995 PM10 concentration contour maps with monitoring stations superimposed Falke, 1999 • The monthly and seasonal spatial pattern varies greatly in Philadelphia although the variation is generally less than that of the daily concentrations. • Local source-influenced sites are represented as “hotspots” in the spatial concentration maps. • The summer average concentrations tend to show less spatial variation than average concentrations for the single month of July. PM Data Analysis Workbook: Characterizing PM

  34. Urban/Rural Patterns • Diurnal PM concentrations at an urban site may be dominated by local emissions (e.g., traffic rush hours). • At a more rural site, PM concentration changes during the day may be driven more by meteorological changes (e.g., mixing height, wind speed). • Dominant PM sources may differ between urban and rural sites. For example, wind blown dust or transported PM may strongly influence a more rural site while fresh emissions from motor vehicles and nearby industrial sources may dominate an urban PM sample. Three-year averages of fine particle concentrations (in g/m3) for each of the sites in the IMPROVE network (mostly rural sites) in the United States for the 3-yr period, March 1988 through February 1991. Malm et al., 1994. PM Data Analysis Workbook: Characterizing PM

  35. Example Urban/Rural Differences (g/m3) • This figure shows a contrast between urban (Burlington, Vwemont) and rural (Whiteface Mountain and Underhill) PM10 sites by month. • The monthly mean concentrations are higher at the urban site where more emission sources are near the site. • During summer, concentrations are more similar among the sites. • During winter some of the differences between the Plot prepared using a spreadsheet. Poirot, 1999 concentrations are due to elevational differences. Burlington is at 63 m msl while Whiteface (630 m msl) and Underhill (400 m msl) may be above the mixed layer in winter. PM Data Analysis Workbook: Characterizing PM

  36. Dependence of PM on Elevation • An understanding of the dependence of PM2.5 on elevation is needed to assess the representativeness of a monitoring site to its surrounding areas. For example, a high elevation site outside the haze layer is not representative of nearby valley concentrations. • PM2.5 dependence on elevation is the result of the limited extent and intensity of vertical mixing, source elevation, and changes in the chemical and physical removal processes with height. • These causal factors vary both seasonally and diurnally; therefore, the PM2.5 dependence on elevation should also vary with season and time of day. A thick layer of polluted air trapped in a valley Photograph from Ahrens D.C. (1994) Meteorology Today. West Publishing Company, Minneapolis/St. Paul Schichtel, 1999a PM Data Analysis Workbook: Characterizing PM

  37. PM Elevation Dependence: Influence of the Seasonal Variation in Mixing Heights • During the summer, the afternoon mixing heights typically reach 1-3 km, and PM is relatively evenly distributed throughout this layer. • During the winter, mixing heights are lower, so the PM is distributed within only the first several hundred meters. • Above the mixing height, the PM concentrations normally decrease with altitude. Schichtel, 1999a PM Data Analysis Workbook: Characterizing PM

  38. Vertical Profile of the Light Scattering Coefficient in the Los Angeles Basin Mean morning and afternoon summer light scattering profiles • The light scattering coefficient, bscat, is largely dependent on particle concentrations between 0.1 - 1 mm. • The bscat is highest in the mixed layer, fairly uniform through the layer, and drops to low levels above the mixing layer at these sites. Schichtel, 1999a PM Data Analysis Workbook: Characterizing PM

  39. Seasonal PM2.5 Dependence on Elevation in the Appalachian Mountains • In August, the PM2.5 concentrations are independent of elevation to at least 1200 m. Above 1200 m, PM2.5 concentrations decrease. (Note that elevation is not the only variable here.) • In January, PM2.5 concentrations decrease between sites at 300 and 800 m by about 50% . PM2.5 concentrations are approximately constant from 800 m to 1200 m and decrease another ~50% from 1200 to 1700 m. Schichtel, 1999a PM Data Analysis Workbook: Characterizing PM

  40. Topographical Influence on PM • Mountains can restrict the horizontal flow of particles as a physical barrier. The mixing height can restrict the vertical mixing of particles. • Pollutants can be “trapped” in valleys depending upon the height of the surrounding mountains and the mixing height. When the mixing height is lower than the mountain top site, the elevated site may have low concentrations. • The analyst needs to know the physical and meteorological properties of the mountain sites in order to assess the data collected at that site. Schichtel, 1999a PM Data Analysis Workbook: Characterizing PM

  41. Spatial contouring of PM10 concentrations using topography Topography Sierra Nevada San Joaquin Valley South Coast Basin Incorporating Barriers in Mapping PM10 Schichtel, 1999a Because topography can significantly affect PM concentrations, it should be considered in preparing spatial contour maps of PM concentrations. As an example of the mountain-valley effect, the concentrations in the San Joaquin Valley and South Coast Basin are much higher than in the Sierra Nevada Mountains. PM Data Analysis Workbook: Characterizing PM

  42. Separation of sites by region (e.g., Southeast sites designated by ‘x’). Annual concentrations of five PM2.5 components for the 8 regions shown above. Regional Spatial Patterns • The composition and concentrations of rural PM vary by region (here, IMPROVE data for March 1996 - February 1997). • The main difference between the northwest and southwest is the relative concentrations of sulfate and organics: organics are larger in the northwest, while sulfate is larger in the southwest. The southwest also has a larger soil component. • Sulfate concentrations are larger in the east than in the west. • Organics are similar at all sites in this analysis. Adapted from Eldred et al., 1998, using a spreadsheet. PM Data Analysis Workbook: Characterizing PM

  43. International Spatial Patterns • Motivation: We share our air and emissions with other countries (e.g., Canada and Mexico). We therefore need to understand transboundary pollution transport. • Examples: Scientists have investigated many transboundary pollutant episodes including • Saharan and Asian dust storm impacts on PM in the United States (discussed later in this chapter). • The impact of point sources of specific PM on monitoring sites (such as particulate arsenic measured in Vermont from a Canadian smelter). • Impact of forest fires in Canada and the northeastern United States on monitoring sites (e.g., what do speciated PM2.5 samples tell us about fingerprints of forest fires?) • Relevance: To better understand the problems, we need to develop effective means of exchanging and merging air quality data. PM Data Analysis Workbook: Characterizing PM

  44. Example: Investigating PM at Northern U.S. Border • In late August 1995, a number of forest fires broke out in the northeastern United Statesand eastern Canada. High PM impacts were observed throughout the Northeast. High gaseous mercury measurements, CO, NOx, and isoprene were also observed during this time period; these species also appear to be associated with forest fires in this region. • Research into the fires using satellite photos, trajectory analyses, PM and VOC speciation data, and gaseous pollutant data can be used to discern detailed fingerprints of forest fire impacts on speciated PM2.5 and gaseous VOC and to recognize future forest fire influences during less “obvious” fire events. • These analyses also provide an example of how an analyst might investigate international spatial PM patterns and perform a PM episode analysis. Poirot et al., 1999 PM Data Analysis Workbook: Characterizing PM

  45. National Spatial Patterns • Annual PM2.5 concentration maps are useful in identifying potential non-attainment areas of the PM2.5 NAAQS (annual average of 15 μg/m3 ) • Monitoring data are used to estimate PM2.5 maps. • The limited number of PM2.5 monitoring data requires the application of surrogate data (i.e., PM10 and visibility) in the mapping process. PM Data Analysis Workbook: Characterizing PM

  46. Annual Average PM2.5 Concentrations (1994-1996) PM10-Aided PM2.5 Visibility-Aided PM2.5 Augmenting PM2.5 data with PM2.5 concentrations derived from visibility data Augmenting PM2.5 data with PM2.5 concentrations derived from PM10 data Annual average PM2.5 concentrations are above 15 μg/m3 in the San Joaquin Valley and South Coast Basin of California. Annual average PM2.5 concentrations in Pittsburgh, St. Louis, Roanoke, and an area stretching from New York City to Washington D.C. are above 15 μg/m3 in both maps. The visibility-aided estimates indicate a larger region above 15 μg/m3 along the eastern seaboard. Additional areas above 15 μg/m3 are shown with PM10-aided estimates including Atlanta and eastern Tennessee. Falke, 1999 PM Data Analysis Workbook: Characterizing PM

  47. PM2.5 Map Uncertainty Differences between visibility- and PM10-aided annual estimates Falke, 1999 • The PM10- and visibility-aided PM2.5 maps on the previous page have similar overall patterns but contain distinct differences in some areas. For instance, the visibility-aided PM2.5 concentrations are more than 5 μg/m3 higher than PM10-aided estimates in Texas, Michigan, and the eastern seaboard. • The differences between the two maps (shown here) is one indication of the uncertainty in the estimation of PM2.5 concentrations. Where the two maps are similar, the PM2.5 concentration estimates are more certain than in areas of large differences. PM Data Analysis Workbook: Characterizing PM

  48. These maps illustrate the regional differences in PM. The same control strategies may not be effective if applied on a national scale. The PM2.5 concentrations peak during the summer (Q3) in the eastern U.S. The PM2.5 concentrations peak in the winter (Q1) in populated regions of the southwest and in the San Joaquin Valley in California. Seasonal Maps of PM2.5 (1994-1996) Falke, 1999 PM Data Analysis Workbook: Characterizing PM

  49. More sulfate in the east • More nitrate in the west • Carbonaceous fraction is important everywhere Compositional Patterns PM2.5 Ambient Composition • Species Groups: explore the patterns in PM source types including soil dust, combustion, etc. • Detailed Species: explore the patterns in PM species including sulfate, nitrate, metals, etc. U.S. EPA, 1998 PM Data Analysis Workbook: Characterizing PM

  50. Seasonal variation in the chemical composition of PM2.5 at a rural site (Champaign) and urban site (Chicago) Champaign Other>40 Chicago PM Species Group Patterns • Seasonal variations are observed in PM2.5 species groups. • At both the rural site in Champaign, IL and the urban site in Chicago, sulfate concentrations are higher in the summer than in the winter. This increase corresponds to variations in ozone and other atmospheric oxidants that react with SO2 to form sulfates in the summer. • Crustal material accounted for a small percentage of the total mass at both sites. Fugitive dust from farming may have contributed to the large crustal fraction at the rural site in April. • Wintertime NaCl in Chicago is related to heavy high-speed traffic on salted roadways near the monitoring site. Adapted from Sweet and Gatz, 1998 PM Data Analysis Workbook: Characterizing PM

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