Lake Michigan Air Directors Consortium

Conceptual Model of PM2.5 Episodes in the Midwest

LADCO PM Data Analysis Workgroup January 2009

ii

Table of Contents

Executive Summary ..............................................................................................1 Introduction ...........................................................................................................2 Current Conditions and Trends .............................................................................2 Temporal and Spatial Patterns of PM2.5...............................................................7 PM2.5 Composition.............................................................................................12 Source Apportionment.........................................................................................16 PM2.5 Sensitivity to Changes in Precursor Concentrations ................................18 Meteorological Analyses .....................................................................................20 Wind Roses.........................................................................................................20 CART Analysis ....................................................................................................23 Trajectory Analysis..............................................................................................29 Synoptic Meteorology..........................................................................................32 Conclusions.........................................................................................................35 Control Recommendations..................................................................................36 References..........................................................................................................36 Supplemental Material.........................................................................................38

Figures Fig. 1 98 th Percentile PM2.5 Concentrations, 2005­2007.....................................4 Fig. 2 Trends in 98 th Percentiles, LADCO States, 1999­2007 ..............................4 Fig. 3 Linear Least Squares and Theil Trends of PM2.5 Values Greater than the 90 th Percentile at Wisconsin SE Headquarters Site (Milwaukee)....................................................................................................5 Fig. 4 Theil Trends in 98 th Percentile Values , 1999­2007, at PM2.5 Sites...........5 Fig. 5 Frequency of Elevated PM2.5 Concentrations by City ...............................8 Fig. 6 Frequency of Elevated PM2.5 Concentrations by Month (Southern Cities) ...................................................................................................9 Fig. 7 Frequency of Elevated PM2.5 Concentrations by Month (Northern Cities)..................................................................................................10 Fig. 8 Day­of­Week Variation in High PM2.5 Concentrations (Northern Cities)..................................................................................................11 Fig. 9 PM2.5 Composition on Episode and Nonepisode Days in Urban Areas (2005).............................................................................................12 Fig. 10 Ratio of Bulk Species Concentrations on High Days to All Days at Allen Park, MI.........................................................................................13 Fig. 11 PM2.5 Composition on Summer 2005 Episode and Nonepisode Days................................................................................................14

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Fig. 12 PM2.5 Composition on Winter 2005 Episode and Nonepisode Days.................................................................................................. 14 Fig. 13 Urban­Rural Comparison of Major PM2.5 Components ........................... 15 Fig. 14 PMF Contributions at (a) Allen Park, MI, and (b) Dearborn, MI ............... 17 Fig. 15 Ratios of Metal Concentrations on High Days to All Days ....................... 18 Fig. 16 Locations of Ammonia Monitoring Sites ................................................... 19 Fig. 17 Isopleths of mean predicted PM2.5 mass concentrations from SCAPE model results for Mayville, Wisconsin .............................................. 20 Fig. 18 Seasonal Wind Roses for Indianapolis..................................................... 21 Fig. 19 Episode­Day Wind Roses for 8 Urban Areas ........................................... 22 Fig. 20 Chicago CART Tree ................................................................................. 27 Fig. 21 Distribution of Chicago Episode Days among CART Nodes .................... 28 Fig. 22 Timeline of Chicago Episodes, by Node................................................... 28 Fig. 23 Trends in Chicago High­Concentration Nodes ......................................... 29 Fig. 24 Back Trajectory Analysis of High Sulfate Source Regions and High Nitrate Source Regions.......................................................................... 31 Fig. 25 September 4, 2004 – Surface Weather Map ............................................ 33 Fig. 26 AIRNow PM2.5 Maps – September 5 & 6, 2004 ...................................... 34 Fig. 27 Sulfate & Nitrate Concentrations – Indianapolis, IN – September 2­6, 2004............................................................................................. 35

Tables Table 1 Annual Trend in Pm2.5 Values above 90 th Percentile (ug/m3/year). ....... 6 Table 2 Valid Monitoring Days and High Pm2.5 Days, by Urban Area................. 7 Table 3 Coherence of High PM2.5 Days across the Detroit Network................... 11 Table 4 Variables included in CART Analysis ...................................................... 24

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A Conceptual Model of PM2.5 Episodes in the Midwest Draft Report of the LADCO PM Data Analysis Workgroup 1

Executive Summary

Nonattainment of the 24­hr PM2.5 ambient air quality standard is a widespread problem across the LADCO states, with 57 of 126 monitors exceeding the standard in 2005­ 2007. This study examined ambient PM2.5 and meteorological data from 1999­2007 along with several draft or published studies of LADCO projects for clues to the nature of elevated PM2.5 episodes in the LADCO 5­state region. Despite the varied analyses, the results were remarkably consistent. PM2.5 episodes generally occur across broad geographic areas involving multiple cities and states. The composition of PM2.5 indicates that regional sources are the primary contributors during episodes. Ammonium sulfate is always elevated during episodes regardless of season. Wintertime PM2.5 during episodes often is strongly enriched in ammonium nitrate, especially at the northern sites in the region. Organic carbon is elevated during both summer and winter episodes, although to a lesser degree than sulfate and nitrate. In contrast, components of PM2.5 that are typically associated with industrial sources (metals and crustal species) are not significantly enriched during episodes.

High daily concentrations are driven by specific meteorological conditions, not by a sudden increase in emissions from sources. Episodes are characterized by stagnant air masses accompanied by high pressure, slow wind speeds, high relative humidity, and southerly winds. The longer these conditions persist, the higher concentration build up, until a new weather system arrives with cleaner air.

While PM2.5 concentrations have declined across the region since 1999, meteorologically adjusted trends indicate that these changes may be driven more by year­to­year variations in meteorology than by changes in emissions. Sensitivity analyses indicate that reductions in SO2 emissions would be effective at reducing PM2.5 concentrations year­round across the region. Wintertime decreases of both ammonia and NOx would be effective, with most sites responding slightly more to ammonia reductions than to NOx. Organic carbon reductions would also be effective both regionally and locally.

1 Contributors: Bill Adamski, WDNR; Michele Boner, IDEM; Brian Callahan, IDEM; Michael Compher, USEPA R5; Jim Haywood, MDEQ; Cynthia Hodges, MDEQ; Donna Kenski, LADCO; Sam Rubens, Akron AQMD; Bart Sponseller, WDNR.,

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Introduction

In September 2006, U.S. Environmental Protection Agency (EPA) lowered the 24­hour PM2.5 National Ambient Air Quality Standard (NAAQS) from 65 ug/m3 to 35 ug/m3. Unlike the annual standard, which averages all measurements of PM2.5 over the entire year, the form of the daily standard is a 98 th percentile. Its target is extreme events, or episodes, in which concentrations are significantly higher than average. As states begin to plan how they can meet the tighter standard, information about PM2.5 episodes becomes increasingly important. This report is an effort to collect and organize information about PM2.5 episodes in the Midwest in order to improve our understanding of the behavior of PM2.5 and the factors that are most influential to the development of high concentrations. In this report, we summarize current concentrations and trends, spatial and temporal variability, composition, urban­rural differences, source contributions, and meteorological factors associated with PM episodes. The analyses focus on major urban areas in the LADCO region. The message that emerges from these various analyses is surprisingly consistent. With the exception of a handful of sites in the region that are close to large industrial facilities, PM2.5 episodes in the Midwest are largely a function of meteorological conditions that occur on a regional scale. Episodic concentrations generally occur across broad geographic regions, involve multiple cities and states, and are characterized by similar meteorology and similar PM2.5 composition. Thus efforts to lower concentrations during these meteorological conditions will be most effective if they target the regional pollutants that lead to ammonium sulfate, ammonium nitrate, and organic carbon particle formation.

Current Conditions and Trends

To set the stage for the analyses to follow, the current values of the 98 th percentile PM2.5 concentrations for 2005­2007 are shown in Fig. 1. All points in red or purple are sites that are currently exceeding the 24­hr PM2.5 NAAQS. These nonattainment sites total 57 of 126 monitors reporting complete data for the period. All of our major urban areas except Minneapolis fail to meet the standard, as well as a number of sites that fall outside the urban centers, especially in Indiana and Ohio. Nonattainment of the daily NAAQS is clearly a widespread regional problem. Bringing these areas into compliance with the standard will require equally widespread and wide­ranging control measures.

Trends in PM2.5 concentrations were examined several ways. States began measuring PM2.5 in 1999 to meet the requirements of the new PM2.5 standard promulgated in 1997, so the sampling record is only 8 or 9 years for most sites. Establishing statistically significant trends for a dataset that includes a high degree of variability, as most ambient air measurements do, requires a long period of measurements. Trends

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presented here should be considered a preliminary assessment; definitive statements on long­term trends require a more comprehensive data record. However, the data so far are encouraging, in both the downward direction of the trend and in the consistency among sites.

Figure 2 shows trends in the 98 th percentile values at a subset of sites in the region that have recorded data for the entire 9 years from 1999 to 2007. Concentrations have declined steadily, except for a sharp increase in 2005 and a slight uptick in 2007. Trends at individual sites were calculated for the 98 th percentile and for all values higher than the 90 th percentile. The 98 th percentile trends were highly unstable and not a reliable indicator of true data trends. Trends developed from values higher than the 90 th

percentile, in contrast, were very stable and remarkably consistent from site to site. Figure 3 shows the trend at one site in Wisconsin as an example. Change in PM concentration over time was calculated from both a linear regression (blue line in Fig. 3) and a Theil regression (red line in Fig. 3), which is the nonparametric equivalent of linear regression. The nonparametric test is preferable here because it is less sensitive to outliers and does not assume that the data are normally distributed. Trends were very similar for both calculations. Fig. 4 shows the direction and magnitude of trends for all the sites. Trends were downward at all sites and varied in magnitude from ­0.03 ug/m3/yr at Chiwaukee Prairie in Wisconsin to ­1.57 ug/m3/yr at Wyandotte in Michigan. The average decline was ­0.51 ug/m3/year and 21 of the 55 sites examined had statistically significant trends, as indicated in Table 1. Trend plots for all sites are included in the supplemental material. The consistency in direction of trends around the region strongly points to similar forces at work on all sites, rather than local influences.

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Fig. 1 98 th Percentile PM2.5 Concentrations, 2005­2007

Figure 2 Trends in 98 th Percentiles, LADCO States, 1999­2007

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Figure 3. Linear Least Squares and Theil Trends of PM2.5 Values Greater than the 90 th

Percentile at Wisconsin SE Headquarters Site (Milwaukee)

Fig. 4 Theil Trends in 98 th Percentile Values , 1999­2007, at PM2.5 Sites

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Table 1 Annual Trend in Pm2.5 Values above 90 th Percentile (ug/m3/year). Highlighted cells are statistically significant.

Site

Annual change in 90%ile PM2.5 (ug/m3) Site

Annual change in 90%ile PM2.5 (ug/m3)

IL­SE Police Sta. ­0.89 MI­Fort Street ­1.10 IL­Mayfair Pump Sta ­0.39 MI­Linwood ­0.76 IL­Blue Island ­0.31 MI­Dearborn ­0.86 IL­Summit ­0.36 MI­Wyandotte ­1.57 IL­Northbrook ­0.30 MN­Richfield ­1.12 IL­Granite City ­0.06 MN­Minneapolis City Hall ­0.83 IL­Wood River ­0.05 MN­St Paul, Red Rock Rd ­0.89 IN­New Albany (Louisville) ­0.37

MN­St Paul, Ramsey Hlth Ctr ­0.56

IN­Franklin Sch. ­0.22 MN­St. Paul, 6th St. ­0.44 IN­Gary, Ivanhoe Sch. ­0.62 MO­Arnold Tenbrook ­0.46 IN­Hammond­Purdue ­0.27 MO­W. Alton ­0.37 IN­Hammond, Clark HS ­0.50 MO­Clayton ­0.43 IN­Indianapolis, Mann Rd 0 MO­STL­Blair St ­0.76 IN­Washington Park ­0.36 MO­STL­Margaretta ­0.61 IN­Indianapolis, Lawrence North HS ­0.21 MO­STL­2nd & Mound ­0.47 IN­School 90 ­0.20 OH­Cleveland, St. Tikhon ­1.07

IN­School 15 ­0.31 OH­Cleveland, E. 14th&Orange ­0.90

IN­Indiana Dunes ­0.26 OH­Cincinnati, HCDOES ­0.21 IN­Porter Cty Water Plant ­0.32 OH­Cincinnati, Norwood ­0.61 KY­Shepherdsville ­0.73 OH­Cincinnati, St. Bernard ­0.44 KY­Louisville, 37th&Southern ­0.86 WI­Chiwaukee Prairie ­0.03 KY­Wyandotte Park ­0.73 WI­Milw. Hlth Ctr ­0.51 KY­Covington ­0.36 WI­DNR SER HQ ­0.41 MI­New Haven ­0.25 WI­Milw., Virginia FS ­0.70 MI­Oak Park ­0.57 WI­FAA, Milwaukee ­0.41 MI­Port Huron ­0.19 WI­Fire Dept, Milw. ­0.39 MI­Allen Park ­0.78 WI­Waukesha ­0.36 Average decrease regionwide = ­0.51 ug/m3

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Temporal and Spatial Patterns of PM2.5

In order to examine temporal and spatial patterns of PM2.5 events, a dataset of PM2.5 high days was developed following the methodology proposed by Turner (2008). First the dataset of all 24­hour federal reference method PM2.5 measurements was reduced to valid days, which are defined as days when a minimum of 70% of sites in the metro region reported valid observations. The total valid days in each of the metro areas analyzed here are reported in Table 2. Next a set of city­wide high days was developed, consisting of valid monitoring days on which a minimum of 60% of all FRM PM2.5 concentrations in the urban area were greater than a threshold value. This analysis examined threshold values of 25, 30, and 35 ug/m3. Table 2 lists the number of days over the 30 ug/m3 threshold.

The frequency of elevated concentrations by city is shown in Fig. 5 for the 3 thresholds. Spatial patterns were consistent regardless of which threshold was chosen. In general, the eastern sites in the study region (Cleveland, Detroit, Cincinnati, Louisville, Indianapolis) had the most days of high PM2.5 at all increments and the highest percentage of high PM days. The eastern cities may be influenced more by their proximity to emissions from industrial sources in the Ohio River Valley, which largely forms the southern boundary of the region.

Table 2 Valid Monitoring Days and High Pm2.5 Days, by Urban Area

Valid Monitoring Days, 99­07

Metro­wide high PM2.5

days

% Valid Days Classified as

High

24­hr PM2.5 Design Value, 05­07 (ug/m3)

Cleveland 943 60 6.4 42 Cincinnati 1033 61 5.9 41 Louisville 886 53 6.0 40 Indianapolis 1070 59 5.5 40 St Louis 1033 52 5.0 39 Detroit 991 73 7.4 43 Gary IN 1073 42 3.9 40 Chicago 1033 58 5.6 40 Milwaukee 1081 43 4.0 41 Minneapolis 974 15 1.5 27 (a) Source: US EPA (www.epa.gov/air/airtrends/values.html)

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Ten Midwestern Metro Areas

Total # of Days During 1999­2007 Metro­Wide Ave 24 Hr PM2.5 Concentrations

> 35 ug/m3, > 30 ug/m3, > 25 ug/m3

40 40 18

29 17

6

34 32 30 18

73 83

43

72

42 15

75 67 70 43

164 166

105

133

77

42

135 145 145

102

0

30

60

90

120

150

180

Cleveland

, OH

Detroit, M

I

Gary, IN

Chicago, IL

Milw

aukee,

WI

Mpls­St.

Paul, M

N

Cincinn

ati,

OH

Louisv

ille,

KY

Indianapolis,

IN

St. L

ouis,

MO

Metro Area

# of Calendar Days

Total Days: (99­07): Metro Daily Av PM2.5 > 35 ug/m3 Total Days: (99­07): Metro Daily Av PM2.5 > 30 ug/m3 Total Days: (99­07): Metro Daily Av PM2.5 > 25 ug/m3

Figure 5. Frequency of Elevated PM2.5 Concentrations by City

Seasonal patterns of high PM2.5 concentrations also exhibit some geographic differences, as shown in Figs. 6 (southern cities) and 7 (northern cities). Metro areas in the south and central portions of the LADCO region experience the greatest number of days with high PM2.5 levels during the warm months of June through September. These cities are likely to experience the warmest temperatures of the urban areas examined here. Warm temperatures and high humidity promote the formation of secondary particulates, especially sulfates, which typically peak in warm weather. Urban areas in the northern parts of the LADCO region exhibit a bimodal distribution of high PM2.5 concentrations, with peaks in both the winter and summer (Fig. 7). Cooler temperatures in these cities promote secondary nitrate formation in the winter (see following section on PM2.5 composition).

Day­of­week differences are shown for the northern cities in Fig. 8. When present, day­ of­week differences can indicate the influence of industrial sources, which often operate on a distinct weekly schedule. Traffic volumes can also influence these weekday differences. In the cities shown in Fig. 8, Monday and Sunday had the fewest high­ PM2.5 days, but only by a small margin. Assuming that the meteorological conditions favoring high PM2.5 occur uniformly on all days of the week, these data suggest that the relationship between known source emission patterns and high PM2.5 events is weak. The southern cities showed even fewer differences.

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A measure of how coherent PM2.5 measurements are across each urban area was developed by looking at the number of days per site above the 35 ug/m3 threshold that are also defined as metro­wide episode days, expressed as a percent. A high percentage (80­100%) indicates that most of a site’s high PM2.5 days occurred when other sites in the metro area also experienced high concentrations, indicating that the driving factor behind the elevated concentrations is occurring on a relatively large scale. Lower percentages indicate that a site’s high PM2.5 days were occurring at times when other nearby sites did not experience high concentrations, and possibly indicates the influence of nearby local sources. For most sites, these percentages were above 80%. The coherence measure is given for Detroit sites in Table 3. Dearborn, a site frequently identified as strongly influenced by local sources, has a coherence measure of 73%; all other sites are above 80%. Results for other cities are given in the supplemental material. In these cities as well, sites with coherence measures below 80% were typically industrial (e.g., 2 sites in Granite City, IL, St. Tikhon in Cleveland) or very near highways (e.g., Ramsey Health Center in Minneapolis, Mayfair Pumping Station in Chicago).

Cleveland, Cincinnati, Louisville, Indianapolis, St. Louis ("Southern / Central" Midwest Metro Areas): 1999­2007

Combined Total # of Days Per Month of Year Metro­Wide Ave 24 Hr PM2.5 Concentrations

> 35 ug/m3, > 30 ug/m3, > 25 ug/m3

7 12 4 1 6

24 30 22

29

11 6 2

21 16 10 4

20

52 62

50 50

23 13

7

54

33 40

17

38

93

118 114

97

39

21 27

0

20

40

60

80

100

120

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month of Year 1999 ­ 2007

# of Calendar Days

Total Days: Month of Yr (99­07): Metro Daily Av PM2.5 > 35 ug/m3 Total Days: Month of Yr (99­07): Metro Daily Av PM2.5 > 30 ug/m3 Total Days: Month of Yr (99­07): Metro Daily Av PM2.5 > 25 ug/m3

Figure 6. Frequency of Elevated PM2.5 Concentrations by Month (Southern and Central Cities)

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Detroit, Gary, Chicago, Milwaukee, Minneapolis­St. Paul ("Northern" Midwest Metro Areas): 1999­2007 Combined Total # of Days Per Month of Year Metro­Wide Ave 24 Hr PM2.5 Concentrations

> 35 ug/m3, > 30 ug/m3, > 25 ug/m3

8

18

8 4 2

17

4

14 11 4

10 10

22

35

25

6

16

29

13 20

29

15 18 27

46

64 57

20

31

52

42 45

55

27 31

53

0

20

40

60

80

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month of Year 1999 ­ 2007

# of Calendar Days

Total Days: Month of Yr (99­07): Metro Daily Av PM2.5 > 35 ug/m3 Total Days: Month of Yr (99­07): Metro Daily Av PM2.5 > 30 ug/m3 Total Days: Month of Yr (99­07): Metro Daily Av PM2.5 > 25 ug/m3

Figure 7. Frequency of Elevated PM2.5 Concentrations by Month (Northern Cities)

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Detroit, Gary, Chicago, Milwaukee, Minneapolis­St.Paul ("Northern" Midwest Metro Areas): 1999­2007

Combined Total # of Days Per Day of Week Metro­Wide Ave 24 Hr PM2.5 Concentrations

> 35 ug/m 3 , > 30 ug/m 3 , > 25 ug/m 3

12 11 19 20 18 17 12

27 34

48 39 44 40

28

66 73

99

76 81 81

49

0

20

40

60

80

100

Mon Tue Wed Thu Fri Sat Sun Day of Week 1999 ­ 2007

# of Calendar Days

Total Days: Day of Week (99­07): Metro Daily Av PM2.5 > 35 ug/m3: Total Days: Day of Week (99­07): Metro Daily Av PM2.5 > 30 ug/m3: Total Days: Day of Week (99­07): Metro Daily Av PM2.5 > 25 ug/m3:

Figure 8. Day­of­Week Variation in High PM2.5 Concentrations (Northern Cities)

Table 3. Coherence of High PM2.5 Days across the Detroit Network

AQS ID Site Name Days > 35 ug/m3

Days > 35 ug/m3 on metro­ wide episodes

Coherence Measure (%)

261630001 Allen Park 33 33 100 261630015 6921 West Fort 41 43 95 261630016 6050 Linwood 37 37 100 261630019 E Seven Mile Rd 26 29 90 261630025 38707 Seven Mile Rd 26 28 93 261630033 Dearborn 52 71 73 261630036 3625 Biddle Ave 29 36 81 261630038 Newberry 7 7 100 261630039 2000 W. Lafayette 3 3 100

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PM2.5 Composition

Data from the Speciation Trends Network (STN) were examined to compare the composition of PM2.5 on episode days to nonepisode days. Mass was reconstructed according to protocols developed by the IMPROVE program. Total PM2.5 mass on episode days is generally about twice the mass on nonepisode days. Of that, 40 to 50% is ammonium sulfate. Organic carbon accounts for the next highest proportion of mass (25­30%), and ammonium nitrate makes up most of the rest (Fig. 9). The relative proportions of these species on episode days are different from nonepisode days. Ammonium sulfate increases most (between 2.5 and 3.5 times its nonepisode concentrations). Ammonium nitrate increases by a factor of 1.5 to 2.5, while organic carbon increases by 1.5 to 2. Other components of PM2.5 (elemental carbon, soil) are only modestly higher, usually enriched by less than 1.5 times the nonepisode concentrations (Fig.10). These findings were consistent for each urban area examined.

The composition data were also examined by season; see Figs. 11 and 12 for summer (Jul­Sep) and winter (Jan­Mar) plots, respectively. Ammonium sulfate dominates summer PM2.5 at all sites, contributing half to two­thirds of the mass on episode days, usually at least double its mass on nonepisode days. Organic carbon concentrations are also significant, but

Fig. 9 PM2.5 Composition on Episode and Nonepisode Days in Urban Areas (2005)

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increase only slightly on summer episode days compared to nonepisode days, which is somewhat surprising given that the warm, humid conditions that promote conversion of SO2 to sulfate also favor secondary formation of organic carbon particles. Ammonium nitrate is negligible in the summer, and crustal material and elemental carbon increase proportionally to total mass on episode days. During the winter, ammonium nitrate plays a much more important role, generally contributing as much or more to PM2.5 mass than ammonium sulfate. Comparing episode to nonepisode days, ammonium nitrate and ammonium sulfate are about equally enriched during the winter. Organic carbon contributes significant mass in both summer and winter, although the increase from nonepisode days to episode days is smaller than for sulfate or nitrate.

Differences among the urban areas were slight, with some cities’ particles dominated more by sulfate and others more by nitrate. Overall, the compositional similarities among the urban areas support the idea that high PM2.5 episodes are often regional events, influenced by regional sources of sulfate, nitrate, and organic carbon. More detailed results for individual cities, by year and season, are given in the supplemental material.

Fig. 10 Ratio of Bulk Species Concentrations on High Days to All Days at Allen Park, MI (source: Wade 2008).

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Fig. 11. PM2.5 Composition on Summer 2005 Episode and Nonepisode Days

Fig. 12. PM2.5 Composition on Winter 2005 Episode and Nonepisode Days

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Fig 13. Urban­Rural Comparison of Major PM2.5 Components

Urban and rural PM2.5 composition was compared in another analysis with similar results. For each urban area studied, a rural monitor was identified to serve as an indicator of the regional background concentrations of PM2.5 entering the city. The difference between this rural background monitor and the urban monitor concentrations for each PM2.5 component species was calculated and plotted to estimate the urban source contribution to PM2.5. Figure 13 shows results for Indianapolis. Results for other cities are given in the supplemental material. For each species, the lighter part of the bar indicates the rural background concentration and the darker part of the bar indicates the urban contribution added to the background. Soil and elemental carbon make negligible contributions to total mass (although elemental carbon has significant health impacts so it cannot be ignored). Nitrate concentrations are actually higher at the rural background site than in the urban area. The urban area contributes about 10% of the total sulfate, and about 25% of organic carbon. These proportions do not change significantly from episode to nonepisode days.

These comparisons are another indication that control measures for the 24­hour NAAQS would probably be most effective if they targeted regional ammonium sulfate, ammonium nitrate, and organic carbon, because the background concentrations of those species are elevated before they even enter the urban areas. The comparatively small urban increment added to the high background concentrations is often enough to push concentrations above the 35 ug/m3 standard. Controlling local urban sources of organic carbon could also be effective, since OC dominates the small urban increment.

Indiana Urban/Rural Speciations 2006/2007

­2 0 2 4 6 8 10 12

EC Nitrate OC Soil Sulfate EC Nitrate OC Soil Sulfate

Episode Nonepisode

Darker bars are urban speciated monitor (Washington Park), Lighter bars are rural speciated monitor (Mechanicsburg) average

Con

c., ug/m3

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Source Apportionment

Sonoma Technology conducted a source apportionment of Speciation Trends Network (STN) data for six sites, two each in Detroit, Cleveland, and Chicago (Wade, 2008). Using Positive Matrix Factorization, source contributions were estimated for each of the six sites for all data available through 2006. Source estimates for high PM2.5 days were then compared with estimates for average days, for the entire data record and also by season. High days were split into two subsets, those with PM2.5 between 30 and 35 ug/m3, and those with PM2.5 greater than 35 ug/m3. STN composition (bulk species and metals) was also compared on high days and all days.

Figure 14 shows the PMF contributions for Allen Park, a suburban Detroit site, and Dearborn, a site a few miles away in a heavily industrial area of Detroit. The Allen Park PMF results indicate that the sources contributing most on high days are secondary sulfates and secondary nitrates, just as shown in the previous sections. The other source categories identified in the PMF modeling increased only slightly on high PM2.5 days. These results imply that regional sources of ammonium sulfate and ammonium nitrate are the most influential factors on high concentration days at Allen Park. In contrast, the Dearborn PMF results show less influence of sulfate and nitrate on high days (compared to Allen Park) and much more influence from local sources. In particular, contributions for a local zinc source and a local steel source approximately triple on high days.

The metals data for Dearborn are shown in Fig 15 as a ratio of high day concentration to average day concentration. Ratios greater than 1 indicate species that are enriched on high days. Especially for the highest days (>35 ug/m3), metals concentrations are usually 2.5 to 3 times higher than concentrations on average days. Dearborn is strongly influenced by local sources that contribute disproportionate amounts of several metal species, unlike its neighboring site at Allen Park, which shows impacts mostly from regional sulfate and nitrate. Results for the other cities and sites were similar to Allen Park, in that industrial sources contributed proportionally less on high­concentration days than on average days.

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(a) Allen Park

(b) Dearborn

Fig. 14 PMF Contributions at (a) Allen Park, MI, and (b) Dearborn, MI (source: Wade 2008).

0

5

10

15

20

25

30

35

40

45

All Days 30­35 >35

Concentration Range of Total PM2.5 (µg/m 3 )

Con

centratio

n (µg/m 3 )

Ind. Lead Steel Ind. Zinc Soil OM EC Sec. SO4 Sec. NO3

0

5

10

15

20

25

30

35

40

45

All Days 30­35 >35

Concentration Range of Total PM2.5 (µg/m 3 )

Con

centratio

n (µg/m 3 )

Mixed Ind. Steel Ind. Zn Soil OM EC Sec. Sulfate Sec. NO3

18

Fig. 15 Ratios of Metal Concentrations on High Days to All Days (source: Wade 2008).

PM2.5 Sensitivity to Changes in Precursor Concentrations

Reducing ambient PM2.5 can be accomplished by reducing concentrations of its precursor gases SO2, NOx, and NH3, but the relationship between particulates and gases is complex and nonlinear. Blanchard (2008) conducted a sensitivity study to analyze the response of PM2.5 to changes in ambient concentrations of sulfate, nitric acid, and ammonia at 15 sites in the LADCO/Cenrap ammonia monitoring network (Fig. 16). At these sites, the mean predicted PM2.5 mass decreased by:

• 0.8 to 3.6 ug/m3 in response to modeled 50% reductions of sulfate • 0.4 to 1.8 ug/m3 in response to modeled 50% reductions of total nitrate • 0.4 to 2.6 ug/m3 in response to modeled 50% reductions of total ammonia

Combined reductions of sulfate and total nitrate were approximately additive, whereas combined reductions of total nitrate and total ammonia were not. For example, Fig. 17 shows the sensitivity of PM2.5 to sulfate, nitrate, and ammonia reductions for the Mayville, Wisconsin, site. Complete results for all sites are given in Blanchard (2008).

Reductions were seasonally and geographically sensitive. Sulfate reductions are most effective in the summer, while nitrate and ammonia reductions are most effective in the winter. At Bondville IL and sites to the east, ammonia reductions are more effective than nitrate. At Mayville WI and sites to the west, nitrate reductions are as effective or more effective than reductions of ammonia. The geographic differences arise because of the varying concentrations of precursor gases across the region. Ammonia

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concentrations tend to be highest in the north and west, and consequently PM2.5 is less sensitive to changes in ammonia concentrations there; similarly, nitrate concentrations tend to be highest in the south and east of the region, reducing the sensitivity of PM2.5 to changes in nitrate concentration there. These results suggest that sulfate controls would be effective across the Midwest. Ammonia and nitrate controls would have varying effectiveness, depending on the existing concentrations of precursors in a particular location. This study did not address the potential for transported precursors to impact PM2.5 concentrations in distant regions.

Sites

IMPROVE

Meteorological

Midwest Network

STN

Allen Park MI

Athens OH

Pleasant Green MO Mammoth Cave KY

Lake Sugema IA

Reserve KS

Bondville IL

Great River Bluffs MN

Blue Mounds MN

Mayville WI

Cincinnati OH

Indianapolis IN

Holdenville OK

Seiling OK Cherokee Nation OK

Quaker City OH

Fig. 16 Locations of Ammonia Monitoring Sites (LADCO/Cenrap sites shown as red diamonds; other sites used for comparison of speciated PM and meteorological measurements). Source: Blanchard 2008.

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Fig 17. Isopleths of mean predicted PM2.5 mass concentrations from SCAPE model results for Mayville, Wisconsin. Ammonia concentrations were fixed at current levels (left) and sulfate concentrations were fixed at current levels (right). Source: Blanchard 2008.

Meteorological Analyses

Previous analyses have shown that high PM2.5 concentrations are often associated with specific meteorological conditions. This association was explored in several ways: 1) wind roses, 2) CART analysis, 3) back trajectory analysis, and 4) synoptic conditions analysis. Each of these is described below, and complete results for each urban area are in the supplemental material.

Wind Roses

Wind roses were developed from National Weather Service observations made at local airports in each urban area. The Lakes Environmental program, WRPlot View, was used to generate the plots for annual and seasonal summaries of wind speed and direction. Seasonal roses for Indianapolis are shown in Fig. 18. Winds are predominantly from the west and south most of the year, with stronger and more varied winds present in the spring. Wind roses were also generated for the predefined set of episode days described earlier. This plot for Indianapolis is shown in Fig. 19 along with the episode day roses for the other urban areas.

The episode rose for Indianapolis is distinguished by southerly winds at slower speeds than are typical for most of the year. It shares these characteristics with most of the

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other urban areas examined. This similarity among widely separated sites is a possible indication that the episode days are driven by regional meteorology more than local emission conditions. Minneapolis and St. Louis, the westernmost sites, have a more southeasterly component to episode winds, which is consistent with regional flow from the more industrialized areas east of them.

Figure 18. Seasonal Wind Roses for Indianapolis

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Figure 19 Episode­Day Wind Roses for 8 Urban Areas

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CART Analysis

Another way to quantify the relationship between multiple meteorological variables and PM2.5 is Classification and Regression Tree (CART) analysis. This technique, also known as binary recursive partitioning, was developed in 1984 by Breiman and Friedman. It has several advantages as a tool for data mining and predictive modeling. The tree produced represents a model or decision tree in which each node (branch) is determined by splitting the dataset on the basis of the one variable that results in the best separation as defined by values of the dependent variable (in this case, PM2.5 concentration). The splitting rule is expressed in natural language – for example, is temperature less than 75ºF – so the output trees are easy to interpret. At every branch, every variable is tested for its usefulness in further splitting. This exhaustive search for splitters can make CART computationally intensive.

A CART analysis (regression tree) was applied to the 1999­2007 PM2.5 and meteorology data for the 8 targeted Midwestern urban areas. The purpose was twofold: (1) to categorize specific PM2.5­conducive conditions for each city, and (2) assess PM2.5 trends, using the CART bins as meteorologically adjusted results. The application of the regression tree was straightforward, using CART software from Salford Systems. Emphasis was on finding trees that were able to distinguish the extreme PM2.5 days and also several subsets of moderately high PM2.5 days. Low PM2.5 conditions were of less interest. The model was constrained to include at least 200 days in each terminal node, in order to have a more robust distribution of days across the years. Trees were developed using a randomly selected 80% subset of the data (the learning subset) and tested using the remaining 20% (the test subset).

The average daily 24­hour concentration at these monitors, by city, was used as the dependent variable. Meteorological variables included temperature; dewpoint; pressure; relative humidity; solar radiation; cloud cover; morning and afternoon mixing height; wind direction (as north­south and east­west component vectors); wind speed; lake breeze indicator where relevant; day of week; temperature increase or decrease from previous day; pressure increase or decrease from previous day; previous­day temperature, pressure, wind speed, wind direction, and ozone; and 2­day and 3­day average wind speed and temperature. The years from 1999­2007 were modeled for each city. Trends in PM2.5 concentrations were then examined by comparing the change in average bin concentrations in an effort to control for the effect of meteorological variability.

PM2.5 data were extracted from EPA’s Air Quality System for the Consolidated Metropolitan Statistical Area (CMSA) associated with each city. Meteorological data

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were collected from National Weather Service TDL hourly observation tapes. In each city the primary airport data were used to represent daily conditions for all monitors. Meteorological and air quality variables used in the model are listed in Table 4.

Table 4 Variables included in CART analysis Meteorological parameter Variable name Solar radiation, MJ/m2/day solar Cloud cover, % clouds Mixing height, morning (12Z) and afternoon (00Z), m ammixht, pmmixht Conditions aloft: morning and afternoon (12Z, 0Z) temperature at 850 and 700 mb (deg C); wind speed at 850 and 700 mb (knots); wind direction at 850 and 700 mb (u, v components)

am700temp, am850temp, am700ws, am850ws, am700_s_wn, am700_w_wn (>0 is wind from south or west, <0 is wind from north or east)

Daily total precipitation (in.) precipitatioa Maximum daily, mean morning, mean afternoon, and previous day’s mean temperature (deg F)

maxtemp, tem5_7am, tem2_4pm, lagmeantem

Mean daily, early morning, morning, afternoon, and evening dew point (deg F)

meandp, dp_earlyam, dp_am, dp_pm, dp_eve

Mean daily relative humidity (%) meanrh Mean daily, early morning, morning, afternoon, and previous day’s pressure (mb)

meanpress, press_earl, press_am, press_pm, lagmeanpre

Mean daily, morning, afternoon, and previous day’s wind speed (mph)

meanws, ws5_7am, ws2_4pm, lagws

Mean daily, morning, afternoon, and previous day’s wind direction (u,v components)

mean_s_wn, mean_w_wn, southwn_am, westwn_am, southwn_pm, westwn_pm, lag_s_wn, lag_w_wn wn (>0 is wind from south or west, <0 is wind from north or east)

Lake breeze (present or absent) lakebreeze (0 is absent, 1 is present) Maximum 8­hr ozone (ppm) maxoz Stability (difference of afternoon surface and morning 850 mb temperatures, deg C)

stability (lower values=more stable)

Temperature change from previous day (deg F) tempchange (negative=decreasing temp) Pressure change from previous day (mb) presschang (negative=decreasing pressure) Mean 2­day and 3­day temperature (deg F) tem2day, tem3day Mean 2­day and 3­day wind speed (mph) ws2day, ws3day Day of week weekday (1=Sunday) Unless otherwise noted, early morning=1­5 am, morning =5­7 am, afternoon=2­4 pm, and evening=8­10 pm.

The regression tree for Chicago is presented in Figure 20 (complete trees for each city are presented in the supplemental material). The splitting criteria for each node are given within the blue boxes. If the condition is true (e.g., for the top box in Fig. 20, maximum ozone is less than 0.064 ppm), follow the left branch, otherwise follow the right branch. Terminal nodes (red boxes) give an average concentration and standard deviation of all the PM2.5 concentrations that fall into that node. For example, days in

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terminal node 13 are all characterized by ozone concentrations greater than 0.080 ppm, morning dewpoints greater than 60°F, and morning wind speeds aloft less than 9.5 knots. One characteristic of the CART methodology is that variables can be used multiple times in the decision tree, as ozone is here. In Chicago, of the 25,389 site­days analyzed (in the 80% learning subset), 248 met the Node 13 meteorological criteria and they had an average PM2.5 concentration of 35.8 µg/m3. This node represents summer episodes with a strong photochemical component.

Regional similarities were apparent when comparing the relative importance of meteorological variables from city to city. Extended 3­day periods of slow wind speeds were the most important factor in high PM2.5 across the region. Ozone was important in summer episodes, especially in Chicago and St. Louis. High relative humidity and dewpoints were important year­round. Southerly wind flow was important in most cities, although St. Louis episodes have a more easterly component. Temperature, mixing height, stability, and winds aloft were other variables that ranked high in importance. Not all of these variables appear as splitters in every tree; the relative importance of each variable is assessed based on its importance over all possible nodes and splits. In any one node, only one variable will be the best splitter although another may be a close second best (a good surrogate). The second­best variable may be a good surrogate for numerous splits without ever being selected as the best primary splitter. Its usefulness as a surrogate for multiple splits leads to its higher importance.

Once the model was established for each city, the distribution of PM2.5 concentrations among all nodes was examined with a series of box plots as shown in Fig. 21 for Chicago. This figure is based on the entire sample, not the 80% learning sample. Node 13 is typical of summer episode days and is characterized by high ozone, high morning dewpoints, and low wind speeds aloft. Node 5 is typical of winter episodes and is characterized by high humidity and prolonged low wind speeds and temperatures.

Another useful plot for examining the CART results is shown in Fig. 22. This timeline shows the identified episode days by time, color­coded by node. It is easy to see which nodes (i.e., which meteorological conditions) occur most frequently and what the range of concentrations is during those events. For example, node 5 is the most frequent, occurring several times each winter.

The groups of meteorologically similar days identified by the CART model were examined next for trends over the 1999­2007 period. Because each node shares similar characteristics, any change in concentration over the period is assumed to be due to changes in emissions rather than to changes in meteorology. Only high concentration nodes (>20 ug/m3) were examined. Figure 23 shows the trends for the

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Chicago nodes; trends for other cities are given in the supplemental material. In most cities, trends were flat or slightly downward. Occasional strong trends (for example, Chicago nodes 6 and 13) can usually be attributed to nodes that have fewer days than average, and consequently more unstable trends.

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Fig. 20 Chicago CART Tree

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Figure 21. Distribution of Chicago Episode Days among CART Nodes. Boxes are labeled with the % episode days in the node (>30% in red), the total number of days in the node (>30% in red), and the number of episode days in the node (>100 in red). Box width is scaled to total days in node.

Figure 22. Timeline of Chicago Episodes, by Node

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Figure 23. Trends in Chicago High­Concentration Nodes

Trajectory Analysis

Back trajectories are another way to explore the meteorology associated with high concentrations of PM2.5. A trajectory tracks the position of a parcel of air as it is transported by the wind. By tracking air parcels sampled at a monitor back in time, we gain information about where the air originated and what sources it passed over on its path to the monitor. Collecting trajectories for many samples and looking at them together as an ensemble can reveal patterns over time that indicate which source regions influence a monitor at particular times of the year or, in this case, during high PM2.5 concentration events.

Back trajectories were calculated using HYSPLIT Version 4 (NOAA, 2008) for samples collected at speciation monitors in the eight urban areas from 2000­2007. Hourly endpoints from the back trajectories were plotted using ARCGIS. Each endpoint (1 per hour, 72 per trajectory) has a concentration associated with it that corresponds to the measured species recorded at STN monitor on the trajectory start date. No attempt is made to distribute concentrations along the trajectory. Each hourly endpoint of a trajectory shares the same concentration as the start date. The ARCGIS Spatial Analyst extension was used to grid this concentration data for PM2.5 and its component

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species using a grid size of approximately 20 km and an inverse distance weighting algorithm. These gridded concentrations are shown for ammonium sulfate and nitrate in Fig. 24. The plots are displayed in increments of standard deviation from the mean to better distinguish areas of higher concentration. Darker red colors indicate higher concentrations and darker blue colors indicate lower concentrations.

Distinct patterns emerge from this analysis. On days when sulfate concentrations were high, air masses were most likely to travel through the Ohio River Valley, the Pittsburgh area, central West Virginia, central Tennessee, central Virginia, or eastern Virginia and North Carolina. These areas have high numbers of coal­burning power plants that emit SO2. In contrast, on days when nitrate concentrations were high, air masses were most likely to come from west of the LADCO region, passing through Illinois, southwestern Minnesota, Iowa, and states further west. These regions coincide with areas of high ammonia emissions from agriculture. For both sulfate and nitrate, the back trajectory analysis reveals areas that are distant from the monitors but that likely contribute significantly to elevated concentrations of these PM2.5 components.

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Fig. 24 Back Trajectory Analysis of High Sulfate Source Regions (top) and High Nitrate Source Regions (bottom). Darker red colors indicate higher concentrations and darker blue colors indicate lower concentrations.

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Synoptic Meteorology

The large­scale synoptic conditions during four region­wide episodes were analyzed in detail as part of this analysis. The four episodes were September 2­6, 2004; January 28­February 7, 2005; June 23­30, 2005, and December 17­22, 2007. Each of these episodes was accompanied by elevated PM2.5 concentrations in all 8 of the urban areas examined in this report. Although they occurred at different times of the year and the PM2.5 composition consequently varied among the episodes, there were notable similarities in meteorology. In particular, each was characterized by a high pressure system that tended to persist longer than usual, creating stagnant conditions and often strong inversions that allowed pollutant concentrations to build up under the limited mixing height. These high pressure systems also tend to slowly pull warmer, moist air from the southeast (the Ohio River Valley and further southeast). Suppressed atmospheric mixing and warm moist air constitute an ideal recipe for promoting both sulfate formation in the summer and nitrate formation in the winter. The September 2004 episode is discussed in more detail below; complete descriptions of this and the other three episodes are given in the supplemental material.

In early September 2004, a combination of meteorological factors resulted in a late summer Midwestern fine particle episode that caused elevated fine particle levels from states along the Mississippi River to Ohio. On September 1, a strong Canadian surface high pressure (1029 mb) system moved southeastward from Ontario toward New England nudging tropical storm Gaston into the North Atlantic. Surface temperatures were in the mid 70s to low 80s ºF throughout the Midwest and winds were light and variable. A surface high over the Oklahoma panhandle associated with a weak ridge situated over the Plain states induced warm air advection from the Southwest leading to temperatures exceeding 90 ºF by the end of the multi­day episode. A surface low over southern Alberta hinged a stationary front extending from the center of the low to central Wisconsin. During the next two days, the Canadian surface high continued sliding to the southeast. Winds remained light and variable. By Friday, September 3, the surface high was situated just off the New England coast and the surface low was located near the Manitoba­Ontario border. The 500 mb ridge remained over the central Canadian provinces and continued tilting westward. In addition, Frances, a category 3 hurricane with winds of ~ 125 mph, pushed westward into the Bahamas.

This placed the Midwest solidly in the surface low’s warm sector. Stagnating Midwestern surface conditions, enhanced by the presence of Hurricane Frances, led to increasing fine particle concentrations throughout the Midwest. Average 24­hr fine particle concentrations ranging from the mid­20s µg/m 3 to nearly 50 µg/m 3 in the Midwest. Hurricane Frances acted as a blocking mechanism and inhibited the forward

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progression of weather systems over North America. Surface high temperatures remained in the low to mid­80s ºF in the Midwest for the remainder of the episode and very little precipitation fell. During the same period, 850 mb temperatures averaged between 57 ­ 64 ºF and winds were light and variable at that level.

On September 4, the surface high was located over southern Ohio, and the ridge, having moved eastward, was firmly centered over the Great Lakes (Figure 25). The surface low retrograded northwestward into northern Manitoba province. The associated stationary front sagged along the US­Canadian border just north of the Great Lakes. Hurricane Frances, now a category 2 hurricane, was located over Grand Bahama Island off the Florida coast. It made landfall during the late night hours of September 4. The presence of Frances prevented the surface high from proceeding further eastward. The high over Ohio slowly advected warm, moist subtropical air into the Midwest. Dew points climbed into the mid­to­upper 60s ºF throughout the region. On this date, several Midwestern FRM monitoring sites measured fine particle concentrations in the USG range. Concentrations ranged from 24.0 µg/m3 in Des Moines, IA to as high as 43.3 µg/m3 in Indianapolis, IN.

Figure 25 September 4, 2004 – Surface Weather Map

NOAA Daily Weather Map ­ http://www.hpc.ncep.noaa.gov/dailywxmap/

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Further intensification of the fine particle episode occurred on September 5 (Figure 26). Once again monitors throughout the region measured elevated concentrations as warm, humid conditions prevailed. For most sites, September 5 was the day with the peak calendar concentrations during this episode. The synoptic surface high pressure system gave way to the surface low. With the northwestward progression of Hurricane Frances across the Florida peninsula, the surface high rapidly shifted northeastward to eastern Quebec while the surface low in Canada progressed to the southern end of Hudson Bay.

Figure 24 AIRNow PM2.5 Maps – September 5 & 6, 2004

Note, at this time, the USG concentration for PM2.5 was 40.5 µg/m 3 and the USG 8­hour ozone concentration was 85 ppb.

On September 6, the surface low retrograded southwestward into western Ontario, however the associated cold front progressed eastward into Wisconsin and Illinois bringing heavy rains to the Plain states, Minnesota and Iowa. The 500 mb ridge, followed by a trough over the central U.S, shifted eastward over New England and the Mid­Atlantic states. In the Midwest, maximum surface temperatures reached the low­to­ mid 80s ºF and low temperatures remained in the mid­60s ºF throughout the region. Hurricane Frances crossed the Florida peninsula and entered the northeastern Gulf of Mexico as a tropical storm. That evening Frances made a final landfall in the Florida panhandle. The storm continued moving northwestward until the morning of September 7, where the cold front nudged the weakening tropical system northeastward. The cold front not only helped shift tropical depression Frances eastward but it ushered in another Canadian high pressure system bringing cleaner air to the Midwest.

Sulfate and nitrate concentrations were measured in Indianapolis during the episode (Figure 27). Nitrate concentrations rarely exceeded 2 µg/m3, while sulfate

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concentrations averaged above 10 µg/m3 and peaked at 24.2 µg/m3 on September 5. Sulfate was clearly driving the elevated PM2.5 concentrations during this episode.

Figure 27

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Conclusions

• Nonattainment of the 24­hour PM2.5 NAAQS is a widespread problem, with 57 of 126 monitors in LADCO states exceeding the standard in 2005­2007. Most of the nonattainment monitors are in urban areas.

• Since measurement of PM2.5 began in 1999, concentrations on the highest 90% days have fallen by about 0.5 ug/m3/year. Trends are consistently downward at all monitors with long term records. Meteorologically­adjusted trends (based on the CART methodology), however, are relatively flat, suggesting that the actual trends are driven more by year­to­year variations in meteorology (i.e., occurrence of meteorologically conducive episodes) than by changes in emissions.

• Episodes of elevated concentrations generally occur across broad geographic areas, involving multiple cities and states. PM2.5 composition during an episode is similar across a city or affected area and is driven primarily by higher levels of ammonium sulfate during all seasons at all sites. Higher levels of ammonium nitrate is an important component during the winter, especially at northern sites in the LADCO region. Organic carbon is present in significant concentrations during both summer and winter episodes.

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• High daily concentrations depend on specific meteorological conditions: stagnant air masses with high pressure, slow wind speeds, high relative humidity, and southerly winds. The longer these conditions persist, the higher concentrations become, until a change in weather patterns brings less polluted air into the region.

• Local pollution sources can also be important contributors on high PM2.5 days in heavily industrial locations. .

Possible Approaches for Decreasing PM2.5 Concentrations during Episode Conditions

• Regional reductions in SO2 will be effective year­round and at all sites. Based on LADCO’s latest regional emissions inventory, the largest sources of SO2 emissions are electrical generating units (EGUs) (i.e., about 80%) and other point sources, such as industrial boilers, refineries, and cement plants (i.e., about 15%).

• Analysis of Midwest NH3 data show that during winter months and on a high winter PM2.5 day, “…the sensitivity to nitrate is greater than the sensitivity to sulfate…” and furthermore, “;;;decreases in ammonia yield lower predicted PM2.5 than decreases in total nitrate at the majority of sites.” This suggests that regional reductions in NH3 in the winter will be effective.

• Although less effective than NH3 reductions, regional reductions in NOx will be most effective in the winter and more effective at northern sites. Extending the NOx reductions to the summer would also produce benefits for ozone.

• Regional reductions in organic carbon mass will be effective year­round. Because a significant portion of the urban PM2.5 increment (i.e., mass added in the urban area above the regional background) consists of organic carbon, local controls on OC will also be effective. Although recent studies indicate that biogenic sources contribute anywhere from 15% – 30% to total OC in the summer (and a lesser percentage in the winter), there remains a large fraction of OC that is controllable.

REFERENCES

Blanchard, C.L., and S. Tanenbaum, Analysis of Inorganic Particulate Matter Formation in the Midwestern United States, final report for LADCO (Dec. 2008).

Breiman, L., J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, Pacific Grove, CA: Wadsworth (1984).

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Lewandowski, M.,et al., Primary and Secondary Contributions to Ambient PM in the Midwestern United States, Environmental Science and Technology, 42 (9), pp 3303– 3309 (March 2008)

NOAA, HYSPLIT Version 4, publicly available at http://www.arl.noaa.gov/ready/hysplit4.html. (Feb. 2008)

Wade, K., Analysis of High PM2.5 Days, Draft report to LADCO (July 2008)

Steinberg, D. and P. Colla, CART—Classification and Regression Trees, San Diego, CA, Salford Systems (1997)

Turner, J., A Conceptual Model for Ambient Fine Particulate Matter over Southeast Michigan: High Concentration Days, Draft Final Report prepared for Southeast Michigan Council of Governments (April 2008)