Risk and Exposure Assessment to Support the
Review of the SC>2 Primary National Ambient Air
Quality Standards: Second Draft
-------
EPA-452/P-09-003
March 2009
Risk and Exposure Assessment to Support the
Review of the SC>2 Primary National Ambient Air
Quality Standards: Second Draft
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, North Carolina
11
-------
Disclaimer
This draft document has been prepared by staff from the Ambient Standards Group, Office of Air
Quality Planning and Standards, U.S. Environmental Protection Agency. Any opinions,
findings, conclusions, or recommendations are those of the authors and do not necessarily reflect
the views of the EPA. This document is being circulated to obtain review and comment from the
Clean Air Scientific Advisory Committee (CASAC) and the general public. Comments on this
draft document should be addressed to Michael Stewart, U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, C504-06, Research Triangle Park, North Carolina
27711 (email: stewart.michael@epa.gov)
in
-------
1 Table of Contents
2
3 1.0 INTRODUCTION 1
4 1.1 History 5
5 1.1.1 History of the SO2NAAQS 5
6 1.1.2 Health Evidence from the Previous Review 6
7 1.1.3 Assessment from Previous Review 7
8 1.2 Scope of the Risk and Exposure Assessment for the Current Review 9
9 1.2.1 Overview of the Second Draft Assessment 9
10 1.2.2 Species of Sulfur Oxides Included in Analyses 11
11 2.0 OVERVIEW OF HUMAN EXPOSURE 12
12 2.1 Background 13
13 2.2 Sources of SO2 13
14 2.3 Ambient levels of SO2 14
15 2.4 Relationship of personal Exposure to Ambient Concentrations 15
16 3.0 AT RISK POPULATIONS 18
17 3.1 Overview 18
18 3.2 Susceptibility: Pre-existing Disease 19
19 3.3 Susceptibility: Genetics 19
20 3.4 Susceptibility: Age 20
21 3.5 Vulnerability 20
22 3.6 Number of Susceptible or Vulnerable Individuals 21
23 4.0 HEALTH EFFECTS 22
24 4.1 Introduction 22
25 4.2 Short-Term Peak ( 1-hour, Generally 24-Hour) SO2 Exposure and Respiratory Health Effects 27
32 4.3.1 Respiratory Symptoms 28
33 4.3.2 ED Visits and Hospitalizations for All Respiratory Causes 29
March 2009 iv Draft - Do Not Quote or Cite
-------
1 4.3.3 Emergency Department Visits and Hospitalizations for Asthma 31
2 5.0 IDENTIFICATION OF POTENTIAL ALTERNATIVE STANDARDS FOR ANALYSIS
3 33
4 5.1 Introduction 33
5 5.2 Indictator 33
6 5.3 Averaging Time 33
7 5.4 Form 36
8 5.5 Level 37
9 6.0 OVERVIEW OF RISK CHARACTERIZATION AND EXPOSURE ASSESSMENT 46
10 6.1 Introduction 46
11 6.2 Potential Health Effect Benchmark Levels 47
12 6.3 Approach for Assessing Exposure and Risk Associated with 5-Minute Peak So2 Exposures 48
13 6.4 Approach for Estimating 5-Minute Peak So2 Concentrations 50
14 6.5 Approach for Simulating the current and alternative standards 53
15 6.5.1 Adjustment of Ambient Air Quality 54
16 6.5.2 Adjustment of Potential Health Effect Benchmark Levels 60
17 7.0 AMBIENT AIR QUALITY AND BENCHMARK HEALTH RISK
18 CHARACTERIZATION FOR 5-MINUTE PEAK SO2 EXPOSURES 63
19 7.1 Overview 63
20 7.2 Approach 67
21 7.2.1 Air Quality Data Screening 67
22 7.2.2 Site Characteristics of Ambient SO2 Monitors 73
23 7.2.3 Statistical model to estimate 5-minute maximum SO2 concentrations 81
24 7.2.4 Locations to Evaluate the Current and Potential Alternative Standard Scenarios 96
25 7.2.5 Air Quality Concentration Metrics 102
26 7.3 Results 106
27 7.3.1 Measured 5-minute Maximum and Measured 1-Hour SO2 Concentrations at Ambient Monitors -As Is Air
28 Quality 106
29 7.3.2 Measured 1-Hour and Modeled 5-minute Maximum SC>2 Concentrations at All Ambient Monitors -As Is
30 Air Quality 112
31 7.3.3 Modeled 1-Hour and Modeled 5-minute Maximum SC>2 Concentrations at Ambient Monitors in 40 Counties
32 -Air Quality Adjusted to Just Meet the Current and Potential Alternative Standards 118
3 3 7.4 Uncertainty Analysis 134
34 7.4.1 Air Quality Data 135
35 7.4.2 Measurement Technique for Ambient SC>2 136
36 7.4.3 Temporal Representation 137
March 2009 v Draft - Do Not Quote or Cite
-------
1 7.4.4 Spatial Representation 140
2 7.4.5 Air Quality Adjustment Procedure 141
3 7.4.6 Statistical Model Used for Estimating 5-minute SO2 Concentrations 144
4 7.4.7 Ambient Monitor to Exposure Representation 155
5 7.4.8 Health Benchmark 155
6 7.5 Key Observations 156
7 8.0 EXPOSURE ANALYSIS 159
8 8.1 Overview 159
9 8.2 Overview of Human Exposure Modeling using APEX 160
10 8.3 Characterization of study areas 163
11 8.3.1 Study Area Selection 163
12 8.3.2 Study Area Descriptions 165
13 8.3.3 Time Period of Analysis 168
14 8.3.4 Populations Analyzed 168
15 8.4 Characterization of Ambient Hourly Air Quality Data Using AERMOD 168
16 8.4.1 Overview 168
17 8.4.2 General Model Inputs 169
18 8.4.3 Stationary Sources Emissions Preparation 171
19 8.4.4 Receptor Locations 180
20 8.4.5 Modeled Air Quality Evaluation 180
21 8.5 Simulated Population 193
22 8.5.1 Characterizing Ventilation Rates 194
23 8.6 Construction of Longitudinal Activity Sequences 195
24 8.7 Calculating Microenvironmental Concentrations 196
25 8.7.1 Approach for Estimating 5-Minute Maximum SC>2 Concentrations 197
26 8.7.2 Microenvironments Modeled 198
27 8.7.3 Microenvironment Descriptions 199
28 8.8 Exposure Measures and Health Risk Characterization 202
29 8.8.1 Adjustment for Just Meeting the Current and Alternative Standards 203
30 8.9 Exposure Modeling and Health Risk Characterization Results 205
31 8.9.1 Asthmatic Exposures to 5-minute Daily Maximum SC>2 in Greene County 205
32 8.9.2 Asthmatic Exposures to 5-minute Daily Maximum SO2 in St. Louis 207
33 8.10 Representativeness of Exposure Results 216
34 8.10.1 Introduction 216
35 8.10.2 Time spent outdoors 216
36 8.10.3 Asthma Prevalence 218
37 8.11 Uncertainty Analysis 220
38 8.11.1 Dispersion Modeling Uncertainties 221
39 8.11.2 Exposure Modeling Uncertainties 226
March 2009 vi Draft - Do Not Quote or Cite
-------
1 9.0 HEALTH RISK ASSESSMENT FOR LUNG FUNCTION RESPONSES IN
2 ASTHMATICS ASSOCIATED WITH 5-MINUTE PEAK EXPOSURES 248
3 9.1 Introdution 248
4 9.2 Development of Approach for 5-minute Lung Function Risk Assessment 249
5 9.2.1 General Approach 250
6 9.2.2 Exposure Estimates 255
7 9.2.3 Exposure-Response Functions 256
8 9.3 Lung Function Risk Estimates 265
9 9.3 Characterizing Uncertainty and Variability 277
10 9.4 Key Observations 281
11 10.0 EVIDENCE-AND EXPOSURE/RISK-BASED CONSIDERATIONS RELATED TO
12 THE PRIMARY SO2 NAAQS 283
13 10.1 Introduction 283
14 10.2 General Approach 284
15 10.3 Adequacy of the Current 24-hour Standard 287
16 10.3.1 Introduction 287
17 10.3.2 Evidence-based considerations 287
18 10.3.3 Air Quality, exposure and risk-based considerations 289
19 10.4 Adequacy of the Current Annual Standard 297
20 10.4.1 Introduction 297
21 10.4.2 Evidence-based considerations 298
22 10.4.3 Risk-based considerations 299
23 10.4.4 Conclusions regarding the adequacy of the current annual standard 300
24 10.5 potential Alternative Standards 300
25 10.5.1 Indicator 300
26 10.5.2 Averaging Time 301
27 10.5.3 Form 311
28 10.5.4 Level 313
March 2009 vii Draft - Do Not Quote or Cite
-------
1 List of Tables
2
3 Table 3-1 Factors Potentially Contributing to Susceptibility or Vulnerability to Air Pollution in
4 General (modified from Table 4-1 in the ISA) 18
5 Table 4-1. Weight of Evidence for Causal Determination 23
6 Table 7-1. Summary of all available 5-minute and 1-hour SO2 ambient monitoring data, years
7 1997-2007, pre-screened 68
8 Table 7-2. Analytical data sets generated using the continuous-5, max-5, and 1-hour ambient
9 SC>2 monitoring data, following screening 69
10 Table 7-3. Counts of complete and incomplete site-years of 1-hour SO2 ambient monitoring data
11 for 1997-2006 72
12 Table 7-4. Comparison of prediction errors and model variance parameters for the four models
13 evaluated 95
14 Table 7-5. Prediction errors of the statistical model used in estimating 5-minute maximum SO2
15 concentrations above benchmark levels 96
16 Table 7-6. Counties selected for evaluation of air quality adjusted to just meeting the current and
17 potential alternative SC>2 standards 99
18 Table 7-7. The co-occurrence of daily 5-minute maximum and 1-hour daily maximum SC>2
19 concentrations using measured ambient monitoring data 103
20 Table 7-8. Example of how the probability of exceeding a 300 ppb 5-minute benchmark would
21 be calculated given 1-hour daily maximum SO2 concentration bins 105
22 Table 7-9. Percent of days having a modeled daily 5-minute maximum SC>2 concentration above
23 the potential health effect benchmark levels given air quality as is and air quality adjusted to
24 just meeting the current and each of the potential alternative standards 122
25 Table 7-10. Mean number of modeled daily 5-minute maximum concentrations above 100 ppb
26 per year in 40 selected counties given 2001-2006 air quality as is and air quality adjusted to
27 just meet the current and alternative standards 130
28 Table 7-11. Mean number of modeled daily 5-minute maximum concentrations above 200 ppb
29 per year in 40 selected counties given 2001-2006 air quality as is and that adjusted to just
30 meet the current and alternative standards 131
31 Table 7-12. Mean number of modeled daily 5-minute maximum concentrations above 300 ppb
32 per year in 40 selected counties given 2001-2006 air quality as is and that adjusted to just
33 meet the current and alternative standards 132
34 Table 7-13. Mean number of modeled daily 5-minute maximum concentrations above 400 ppb
35 per year in 40 selected counties given 2001-2006 air quality as is and that adjusted to just
36 meet the current and alternative standards 133
37 Table 7-14. Summary of qualitative uncertainty analysis for the air quality and health risk
38 characterization 135
39 Table 7-15. The percent of site-years with a difference in the number of modeled exceedances
40 using a 100 model simulation run versus a 20 model simulation run 151
41 Table 8-1. Surface stations for the 862 study areas 170
42 Table 8-2. Upper air stations for the SC>2 study areas 170
43 Table 8-3. Seasonal monthly assignments 171
44 Table 8-4. NLCD2001 land use characterization 174
45 Table 8-5. Summary of NEI emission estimates and total emissions used for dispersion
46 modeling in Greene County and St. Louis modeling domains 178
March 2009 viii Draft - Do Not Quote or Cite
-------
1 Table 8-6. Asthma prevalence rates by age and gender used in Greene County and St. Louis
2 modeling domains 194
3 Table 8-7. Population modeled in Greene County and St. Louis modeling domains 194
4 Table 8-8. List of microenvironments modeled and calculation methods used 198
5 Table 8-9. Geometric means (GM) and standard deviations (GSD) for air exchange rates by A/C
6 type and temperature range 200
7 Table 8-10. Final parameter estimates of SO2 deposition distributions in several indoor
8 microenvironments modeled in APEX 201
9 Table 8-11. Exposure concentrations and adjusted potential health effect benchmark levels used
10 by APEX to simulate just meeting the current and potential alternative standards in the
11 Greene County and St Louis modeling domains 204
12 Table 8-12. States used to define five regions of the U.S. and characterize CHAD data diaries.
13 217
14 Table 8-13. Time spent outdoors for children ages 5-17 using CHAD diaries 218
15 Table 8-14. Asthma prevalence rates for children in four regions of the U.S 219
16 Table 8-15. Asthma prevalence rates for adults in five regions of the U.S 219
17 Table 8-16. Summary of qualitative uncertainty analysis for the exposure assessment 221
18 Table 8-17. Number of multiple exceedances of potential health effect benchmark levels within
19 an hour 239
20 Table 9-1. Example Calculation of the Number of Asthmatics in St. Louis Engaged in Moderate
21 or Greater Exertion Estimated to Experience At Least One Lung Function Response (Defined
22 as an Increase in sRaw > 100%) Associated with Exposure to SO2 Concentrations Just
23 Meeting a 99th percentile, 1-Hour 100 ppb Standard 253
24 Table 9-2. Example Calculation of Number of Occurrences of Lung Function Response
25 (Defined as an Increase in sRaw > 100%), Among Asthmatics in St. Louis Engaged in
26 Moderate or Greater Exertion Associated with Exposure to SOi Concentrations that Just Meet
27 a 99th Percentile 1-Hour, 100 ppb Standard 255
28 Table 9-3. Percentage of Asthmatic Individuals in Controlled Human Exposure Studies
29 Experiencing SO2-Induced Decrements in Lung Function 258
30 Table 9-4. Number of Asthmatics Engaged in Moderate or Greater Exertion Estimated to
31 Experience At Least One Lung Function Response Associated with Exposure to SO2 Under
32 Alternative Air Quality Scenarios 267
33 Table 9-5. Percent of Asthmatics Engaged in Moderate or Greater Exertion Estimated to
34 Experience At Least One Lung Function Response Associated with Exposure to SO2
35 Concentrations Under Alternative Air Quality Scenarios 268
36 Table 9-6. Number of Occurrences (In Hundreds) of a Lung Function Response Among
37 Asthmatics Engaged in Moderate or Greater Exertion Associated with Exposure to SO2
38 Concentratons Under Alternative Air Quality Scenarios 269
39 Table 9-7. Number of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated
40 to Experience At Least One Lung Function Response Associated with Exposure to SO2 Under
41 Alternative Air Quality Scenarios 270
42 Table 9-8. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to
43 Experience At Least One Lung Function Response Associated with Exposure to SO2
44 Concentrations Under Alternative Air Quality Scenarios 271
45 Table 9-9. Number of Occurrences (In Hundreds) of a Lung Function Response Among
46 Asthmatic Children Engaged in Moderate or Greater Exertion Associated with Exposure to
47 SO2 Concentratons Under Alternative Air Quality Scenarios 272
March 2009 ix Draft - Do Not Quote or Cite
-------
1 Table 9-10. Characterization of Key Uncertainties in the Lung Function Response Health Risk
2 Assessment for St. Louis and Greene County, Missouri 279
3 Table 10-1 Ratios of 99th percentile 5-minute maximums to 99th percentile 24-hour average and
4 1-hour daily maximum 862 concentrations for monitors reporting measured 5-minute data
5 from years 2004-2006 304
6 Table 10-2. 99th percentile 24-hour average SC>2 concentrations for 2004 given just meeting the
7 alternative 1-hour daily maximum standards analyzed in the risk assessment 306
8 Table 10-3. 2nd highest 24-hour average 862 concentrations (i.e. the current 24-hour standard)
9 for 2004 given just meeting the alternative 1-hour daily maximum standards analyzed in the
10 risk assessment 308
11 Table 10-4. Annual average SO2 concentrations for 2004 given just meeting the alternative 1-
12 hour daily maximum standards analyzed in the risk assessment 309
13 Table 10-5. SO2 concentrations (ppm) corresponding to the 2nd-9th daily maximum and 98th/99th
14 percentile forms (2004-2006) 313
March 2009 x Draft - Do Not Quote or Cite
-------
1 List of Figures
2
3 Figure 1-1. Overview of the analyses described in this document and their interconnections 3
4 Figure 5-1. Effect estimates for U.S. all respiratory ED visit studies and associated 98th and 99th
5 percentile 1-hour daily maximum SC>2 levels 38
6 Figure 5-2. 24-hour effect estimates for U.S. asthma ED visit studies and associated 98th and 99th
7 percentile 1-hour daily maximum SC>2 levels 39
8 Figure 5-3. 1-hour effect estimates for U.S. asthma ED visit studies and associated 98th and 99th
9 percentile 1-hour daily maximum SC>2 levels 40
10 Figure 5-4. 24-hour effect estimates for U.S. hospitalization studies and associated 98th and 99th
11 percentile 1-hour daily maximum SC>2 levels 41
12 Figure 5-5. Effect estimates for Canadian ED visits and hospitalization studies and associated 98th
13 and 99th percentile 1-hour daily maximum SC>2 levels 42
14 Figure 6-1. Overview of analyses addressing exposures and risks associated with 5-minute peak
15 SO2 exposures. All three outputs are calculated considering current air quality, air quality just
16 meeting the current standards, and air quality just meeting potential alternative standards. Note:
17 this schematic was modified from Figure 1-1 47
18 Figure 6-2. Example of an hourly time-series of measured 1-hour and measured 5-minute
19 maximum SO2 concentrations 51
20 Figure 6-3. Comparison of measured daily maximum SO2 concentration percentiles in Beaver
21 County, PA for a high concentration year (1992) versus a low concentration year (2007) at two
22 ambient monitors 56
23 Figure 6-4. Distributions of hourly SO2 concentrations at five ambient monitors in Cuyahoga
24 County, as is (top) and air quality adjusted to just meet the current 24-hour SO2 standard
25 (bottom), Year 2001 58
26 Figure 6-5. Comparison of adjusted ambient monitoring concentrations or adjusted benchmark
27 level (dashed line) to simulate just meeting the current annual average standard at one ambient
28 monitor in Cuyahoga County for year 2002 61
29 Figure 6-6. Comparison of the upper percentile modeled 5-minute daily maximum SO2 for where
30 1-hour ambient SO2 concentrations were adjusted and the benchmark level was adjusted to
31 simulate just meeting the current annual standard at one ambient monitor in Cuyahoga County
32 for year 2002. The complete distributions are provided in Figure 6-4 62
33 Figure 7-1. Distribution of site-years of data considering monitoring objectives and scale: monitors
34 that reported 5-minute maximum SO2 concentrations (top) and the broader SO2 monitoring
35 network (bottom) 76
36 Figure 7-2. Distribution of site-years of data considering land-use and setting: monitors that
37 reported 5-minute maximum SO2 concentrations (top) and the broader SO2 monitoring network
38 (bottom) 77
39 Figure 7-3. The percent of total SO2 emissions of sources located within 20 km of ambient
40 monitors: monitors reporting 5-minute maximum SO2 concentrations (top) and the broader SO2
41 monitoring network (bottom) 79
42 Figure 7-4. Distribution of the population residing within a 5 km radius of ambient monitors:
43 monitors reporting 5-minute maximum SO2 concentrations and the broader SO2 monitoring
44 network 81
45 Figure 7-5. Comparison of hourly and 5-minute concentration COVs and GSDs at sixteen monitors
46 reporting all twelve 5-minute SO2 concentrations over multiple years of monitoring 84
March 2009 xi Draft - Do Not Quote or Cite
-------
1 Figure 7-6. Cumulative density functions (CDFs) of hourly COVs (top) and GSDs (bottom) at
2 ambient monitors: monitors reporting 5-minute maximum SC>2 concentrations and the broader
3 SC>2 monitoring network 86
4 Figure 7-7. Peak-to-mean ratio (PMR) distributions for three COV and GSD variability bins and
5 seven 1-hour SC>2 concentration stratifications 89
6 Figure 7-8. Distribution of total SC>2 emissions (tpy) within 20 km of monitors by COV (left) and
7 GSD (right) concentration variability bins: monitors reporting 5-minute maximum SO2
8 concentrations (top) and the broader 862 monitoring network (bottom) 91
9 Figure 7-9. Percent of monitors within each concentration variability bin where the monitoring
10 objective was source-oriented: monitors reporting 5-minute maximum SC>2 concentrations (solid)
11 and the broader SO2 monitoring network (slotted) 92
12 Figure 7-10. The number of measured daily 5-minute maximum SC>2 concentrations above
13 potential health effect benchmark levels per year at 98 monitors given the annual average SC>2
14 concentration, 1997-2007 air quality as is 107
15 Figure 7-11. Probability of daily 5-minute maximum SO2 concentrations above potential health
16 effect benchmark levels given 24-hour average SC>2 concentrations, 1997-2007 air quality as is.
17 110
18 Figure 7-12. Probability of daily 5-minute maximum SC>2 concentrations above potential health
19 effect benchmark levels given 1-hour daily maximum SC>2 concentrations, 1997-2007 air quality
20 as is Ill
21 Figure 7-13. The number of modeled daily 5-minute maximum SC>2 concentrations above potential
22 health effect benchmark levels per year at 809 ambient monitors given the annual average SC>2
23 concentration, 1997-2006 air quality as is 113
24 Figure 7-14. Probability of daily 5-minute maximum 862 concentrations above potential health
25 effect benchmark levels given 24-hour average SC>2 concentrations, 1997-2006 air quality as is.
26 116
27 Figure 7-15. Probability of daily 5-minute maximum 862 concentrations above potential health
28 effect benchmark levels given 1-hour daily maximum SC>2 concentrations, 1997-2006 air quality
29 as is 117
30 Figure 7-16. The number of modeled daily 5-minute maximum SC>2 concentrations above potential
31 health effect benchmark levels per year at 128 ambient monitors in 40 selected counties given
32 the annual average 862 concentration, 2001-2006 air quality adjusted to just meet the current
33 NAAQS 119
34 Figure 7-17. The number of modeled daily 5-minute maximum SC>2 concentrations above 200 ppb
35 per year at 128 ambient monitors in 40 selected counties given the annual average 862
36 concentration, 2001-2006 air quality as is and that adjusted to just the current and four potential
37 alternative standards 121
38 Figure 7-18. The number of modeled daily 5-minute maximum SC>2 concentrations above 100 ppb
39 per year given the 99th and 98th percentile forms at a 1-hour daily maximum level of 200 ppb,
40 using the 40-county air quality data set 123
41 Figure 7-19. Probability of daily 5-minute maximum SC>2 concentrations above potential health
42 effect benchmark levels given 1-hour daily maximum SC>2 concentrations, 2001-2006 air quality
43 as is and that adjusted to just meet the current NAAQS 126
44 Figure 7-20. Probability of daily 5-minute maximum 862 concentrations above potential health
45 effect benchmark levels given 1-hour daily maximum SC>2 concentrations, 2001-2006 air quality
46 adjusted to just meet the current and each of the potential alternative standards 127
March 2009 xii Draft - Do Not Quote or Cite
-------
1 Figure 7-21. Temporal trends in the number of ambient monitors in operation per year for monitors
2 reporting both 5-minute and 1-hour SC>2 concentrations 139
3 Figure 7-22. Temporal trends in the coefficient of variability (COV) for 5-minute maximum and 1-
4 hour concentrations at the 98 monitors that reported both 5-minute and 1-hour 862
5 concentrations 139
6 Figure 7-23. Comparison of measured daily maximum SC>2 concentration percentiles in Allegheny
7 County PA for one high concentration year (1998) versus a low concentration years (2007) at
8 five ambient monitors 144
9 Figure 7-24. Distributions of annual average peak-to-mean ratios (PMRs) derived from the 98
10 monitors reporting both 5-minute maximum and 1-hour SC>2 concentrations, Years 1997 through
11 2007 145
12 Figure 7-25. Example histogram of peak-to-mean ratios (PMRs) compared with four fitted
13 distributions derived from monitors reporting the 5-minute maximum and 1-hour SC>2
14 concentrations: monitors with medium level variability and 1-hour concentrations between 75
15 andlSOppb 147
16 Figure 7-26. Example of a measured peak-to-mean ratio (PMRs) distribution with the percentiles of
17 a fitted lognormal distribution 147
18 Figure 7-27. Comparison of observed and predicted number of daily benchmark exceedances in a
19 year at monitors reporting 5-minute maximum SC>2 concentrations 149
20 Figure 7-28. Distributions of the calculated difference between estimated concentration
21 exceedances using a single 100 model simulation run and those estimated using ten independent
22 20 model simulation runs 151
23 Figure 7-29. 95% prediction intervals for the number of modeled daily 5-minute maximum SC>2
24 concentrations in a year above potential health effect benchmark levels by each monitor, Years
25 2001 through 2006 for 40 selected counties, air quality data as is 153
26 Figure 7-30. 95% prediction intervals for the number of modeled daily 5-minute maximum SC>2
27 concentrations in a year above potential health effect benchmark levels by each county, Years
28 2001 through 2006 for 40 selected counties, air quality data as is 154
29 Figure 8-1. General process flow used for SC>2 exposure assessment 161
30 Figure 8-2. Modeling domain for Greene County MO, along with identified emissions sources, air
31 quality receptors, ambient monitors, and meteorological station 166
32 Figure 8-3. Three county modeling domain for St. Louis, MO, along with identified emissions
33 sources, air quality receptors, ambient monitors, and meteorological station 167
34 Figure 8-4. Derived best-fit non-point area source diurnal emission profile for the St. Louis domain,
35 compared to other possible profiles 179
36 Figure 8-5. Derived best-fit non-point area source diurnal emission profile for the Greene County
37 domain, compared to other possible profiles 179
38 Figure 8-6. Comparison of measured ambient monitor SO2 concentration distribution and diurnal
39 profile with the modeled monitor receptor and receptors within 4 km of monitors 290770026 and
40 29077032 in Greene County, Mo 186
41 Figure 8-7. Comparison of measured ambient monitor SO2 concentration distribution and diurnal
42 profile with the modeled monitor receptor and receptors within 4 km of monitors 290770040 and
43 29077041 in Greene County, Mo 187
44 Figure 8-8. Comparison of measured ambient monitor SO2 concentration distribution and diurnal
45 profile with the modeled monitor receptor and receptors within 4 km of monitor 290770037 in
46 Greene County, Mo 188
March 2009 xiii Draft - Do Not Quote or Cite
-------
1 Figure 8-9. Comparison of measured ambient monitor SC>2 concentration distribution and diurnal
2 profile with the modeled monitor receptor and receptors within 4 km of monitors 291890004 and
3 291890006 in St Louis, Mo 189
4 Figure 8-10. Comparison of measured ambient monitor SO2 concentration distribution and diurnal
5 profile with the modeled monitor receptor and receptors within 4 km of monitors 291893001 and
6 291895001 in StLouis, Mo 190
7 Figure 8-11. Comparison of measured ambient monitor SO2 concentration distribution and diurnal
8 profile with the modeled monitor receptor an d receptors within 4 km of monitors 291897003
9 and 295100007 in StLouis, Mo 191
10 Figure 8-12. Comparison of measured ambient monitor SO2 concentration distribution and diurnal
11 profile with the modeled monitor receptor and receptors within 4 km of monitor 295100086 in St
12 Louis, Mo 192
13 Figure 8-13. Number of all asthmatics (top) and asthmatic children (bottom) with 5-minute daily
14 maximum SC>2 exposures above selected exposure levels in Greene County, year 2002 air quality
15 as is and adjusted to just meeting the current and potential alternative standards 208
16 Figure 8-14. Percent of all asthmatics (top) and asthmatic children (bottom) with 5-minute daily
17 maximum SC>2 exposures above selected exposure levels in Greene County, year 2002 air quality
18 as is and adjusted to just meeting the current and potential alternative standards 209
19 Figure 8-15. Number person days all asthmatics (top) and asthmatic children (bottom) experience
20 5-minute daily maximum 862 exposures above selected exposure levels in Greene County, year
21 2002 air quality as is and adjusted to just meeting the current and potential alternative standards.
22 210
23 Figure 8-16. Number of all asthmatics (top) and asthmatic children (bottom) with 5-minute daily
24 maximum 862 exposures above selected exposure levels in St. Louis, year 2002 air quality as is
25 and adjusted to just meeting the current and potential alternative standards 212
26 Figure 8-17. Percent of all asthmatics (top) and asthmatic children (bottom) with 5-minute daily
27 maximum 862 exposures above selected exposure levels in St. Louis, year 2002 air quality as is
28 and adjusted to just meeting the current and potential alternative standards 213
29 Figure 8-18. Number person days all asthmatics (top) and asthmatic children (bottom) experience
30 5-minute daily maximum SC>2 exposures above selected exposure levels in St. Louis, year 2002
31 air quality as is and adjusted to just meeting the current and potential alternative standards.... 214
32 Figure 8-19. The frequency of estimated exposure level exceedances in indoor, outdoor, and
33 vehicle microenvironments given as is air quality (top), air quality adjusted to just meeting the
34 current standard (middle) and that adjusted to just meeting a 99th percentile 1-hour daily
35 maximum standard level of 150 ppb (bottom) in St. Louis 215
36 Figure 8-20. Example comparison of estimated geometric mean and geometric standard deviations
37 of AER (h"1) for homes with air conditioning in several cities 234
38 Figure 8-21. Example of boot strap simulation results used in evaluating random sampling variation
39 of AER(h-l) distributions (data from cities outside California) 236
40 Figure 8-22. Example of boot strap simulation results used in evaluating random sampling
41 variation of AER (h-1) distributions (data from cities outside California) 236
42 Figure 8-23. Duration of time spent outdoors (in minutes) using all CHAD events 242
43 Figure 8-24. Percent of asthmatic children above given exposure level for two APEX simulations:
44 one using multiple peak concentrations in an hour, the other assuming a single peak
45 concentration 245
46 Figure 8-25. Percent of asthmatic children above given exposure level for two APEX simulations:
47 one using multiple peak concentrations in an hour, the other assuming a single peak
March 2009 xiv Draft - Do Not Quote or Cite
-------
1 concentration. Continuous 5-minute monitoring data (ID 42007005, year 2005) were used as the
2 air quality input 245
3 Figure 8-26. Frequency of exposure exceedances indoors for two APEX simulations: one using
4 multiple peak concentrations in an hour, the other assuming a single peak concentration.
5 Continuous 5-minute monitoring data (ID 42007005, year 2002) were used as the air quality
6 input 246
7 Figure 9-1. Major Components of 5-Minute Peak Lung Function Health Risk Assessment Based on
8 Controlled Human Exposure Studies 251
9 Figure 9-2. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw > 100%
10 for 5-Minute Exposures of Asthmatics Under Moderate or Greater Exertion 262
11 Figure 9-3. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw > 200%
12 for 5-Minute Exposures of Asthmatics Under Moderate or Greater Exertion 263
13 Figure 9-4. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEV1 > 20%
14 for 5-Minute Exposures of Asthmatics Under Moderate or Greater Exertion 263
15 Figure 9-5. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEV1 > 20%
16 for 5-Minute Exposures of Asthmatics Under Moderate or Greater Exertion 264
17 Figure 9-6. Legend for Figures 9-7 and 9-8 Showing Total and Contribution of Risk Attributable to
18 SO2 Exposure Ranges 274
19 Figure 9-7. Estimated Annual Number of Occurrences of Lung Function Response (Defined as >
20 100% increase in sRaw) for Asthmatics and Asthmatic Children Associated with Short-Term (5-
21 minute) Exposures to SC>2 Concentrations Associated with Alternative Air Quality Scenarios -
22 Total and Contribution of 5-Minute SC>2 Exposure Ranges 275
23 Figure 9-8. Estimated Percent of of Asthmatics and Asthmatic Children Experiencing One or More
24 Lung Function Responses (Defined as > 100% increase in sRaw) Per Year Associated with
25 Short-Term (5-minute) Exposures to SO2 Concentrations Associated with Alternative Air
26 Quality Scenarios - Total and Contribution of 5-Minute SO2 Exposure Ranges 276
27
March 2009 xv Draft - Do Not Quote or Cite
-------
1
2 List of Acronyms/Abbreviations
3
4 A/C Air conditioning
5 AER Air exchange rate
6 AERMOD American Meteorological Society (AMS)/EPA Regulatory Model
7 AHS American Housing Survey
8 APEX EPA's Air Pollutants Exposure model, version 4
9 ANOVA One-way analysis of variance
10 ANPR Advanced Notice of Proposed Rulemaking
11 AQS EPA's Air Quality System
12 AQCD Air Quality Criteria Document
13 AS Asthma symptoms
14 BRFSS Behavioral Risk Factor Surveillance System
15 C Cough
16 CAA Clean Air Act
17 CAMD EPA's Clean Air Markets Division
18 CASAC Clean Air Scientific Advisory Committee
19 CDC Centers for Disease Control
20 CHAD EPA's Consolidated Human Activity Database
21 Clev/Cinn Cleveland and Cincinnati, Ohio
22 CMSA Consolidated metropolitan statistical area
23 CO Carbon monoxide
24 COPD Chronic Obstructive Pulmonary Disease
25 COV Coefficient of Variation
26 C-R Concentration-Response
27 CTPP Census Transportation Planning Package
28 EDR Emergency department visits for respiratory disease
29 EDA Emergency department visits for asthma
30 ED AC Emergency department visits for asthma - children
31 EPA Environmental Protection Agency
32 HAAC Hospital admissions for asthma - children
33 ER Emergency room
34 EPA United States Environmental Protection Agency
35 EOC Exposure of Concern
36 FEVi Forced expiratory volume in the first second
37 GM Geometric mean
38 GSD Geometric standard deviation
39 GST Glutathione S-transferase (e.g., GSTM1, GSTP1, GSTT1)
40 ID Identification
41 ISA Integrated Science Assessment
42 ISH Integrated Surface Hourly Database
43 km Kilometer
44 L95 Lower limit of the 95th confidence interval
45 LOEL Lowest Observed Effect Level
46 m Meter
March 2009
xvi
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
max
ME
med
min
MSA
NAAQS
NAICS
NCEA
NEI
NEM
NCDC
NHAPS
NHIS
NO2
NOX
NWS
NYC
NYDOH
03
OAQPS
OR
ORD
ORIS
POC
ppb
PEN
PM
ppm
PRB
PROX
PVMRM
REA
RECS
RIU
RR
SAS
SB
SES
SIC
SD
Se
S02
S03
SO4"
sox
sRaw
tpy
Maximum
Microenvironment
Median
Minimum
Metropolitan statistical area
National Ambient Air Quality Standards
North American Industrial Classification System
National Center for Environmental Assessment
National Emissions Inventory
NAAQS Exposure Model
National Climatic Data Center
National Human Activity Pattern Study
National Health Interview Survey
Nitrogen dioxide
Oxides of nitrogen
National Weather Service
New York City
New York Department of Health
Ozone
Office of Air Quality Planning and Standards
Odds ratio
Office of Research and Development
Office of Regulatory Information Systems identification code
Parameter occurrence code
Parts per billion
Penetration factor
Particulate matter
Parts per million
Policy-Relevant Background
Proximity factor
Plume Volume Molar Ratio Method
Risk and Exposure Assessment
Residential Energy Consumption Survey
Rescue inhaler use
Relative risk
Statistical Analysis Software
Shortness of breath
Social-economic status
Standard Industrial Code
Standard deviation
Standard error
Sulfur dioxide
Sulfur trioxide
Sulfate
Oxides of Sulfur
Specific airway resistance
Tons per year
March 2009
xvn
Draft - Do Not Quote or Cite
-------
1 TRIM EPA's Total Risk Integrated Methodology
2 U95 Upper limit of the 95th confidence interval
3 UARG Utility Air Regulatory Group
4 US DOT United States Department of Transportation
5 US EPA United States Environmental Protection Agency
6 USGS United States Geological Survey
March 2009 xviii Draft - Do Not Quote or Cite
-------
i 1.0 INTRODUCTION
2 The U.S. Environmental Protection Agency (EPA) is presently conducting a review of
3 the primary, health based national ambient air quality standards (NAAQS) for sulfur dioxide
4 (802). Sections 108 and 109 of the Clean Air Act (The Act) govern the establishment and
5 periodic review of the NAAQS. These standards are established for pollutants that may
6 reasonably be anticipated to endanger public health and welfare, and whose presence in the
7 ambient air results from numerous or diverse mobile or stationary sources. The NAAQS are to
8 be based on air quality criteria, which are to accurately reflect the latest scientific knowledge
9 useful in indicating the kind and extent of identifiable effects on public health or welfare that
10 may be expected from the presence of the pollutant in ambient air. The EPA Administrator is to
11 promulgate and periodically review, at five-year intervals, primary (health-based) and secondary
12 (welfare-based) NAAQS for such pollutants. Based on periodic reviews of the air quality criteria
13 and standards, the Administrator is to make revisions in the criteria and standards and
14 promulgate any new standards as may be appropriate. The Act also requires that an independent
15 scientific review committee advise the Administrator as part of this NAAQS review process, a
16 function now performed by the Clean Air Scientific Advisory Committee (CASAC).
17 The first step in the SC>2 NAAQS review was the development of an integrated review
18 plan. This plan presented the schedule for the review, the process for conducting the review, and
19 the key policy-relevant science issues that would guide the review. The final integrated review
20 plan was informed by input from CASAC, outside scientists, and the public. The integrated
21 review plan for this review of the SC>2 primary NAAQS was presented in the Integrated Review
22 Plan for the Primary National Ambient Air Quality Standard for Sulfur Dioxide (EPA, 2007a).
23 This document was made available to the public on October 9, 2007 and can found at:
24 http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_pd.html.
25 The second step in this review was a science assessment. A concise synthesis of the most
26 policy-relevant science was compiled into an Integrated Science Assessment (ISA). The ISA
27 was supported by a series of annexes that contained more detailed information about the
28 scientific literature. The final ISA to support this review of the SO2 primary NAAQS was
29 presented in the Integrated Science Assessment for Oxides of Sulfur - Health Criteria, henceforth
30 referred to as the ISA (EPA, 2008a). This document was made available to the public in
March 2009 1 Draft - Do Not Quote or Cite
-------
1 September 2008 and can found at:
2 http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_pd.html.
3 The third step in this primary 862 NAAQS review is a risk and exposure assessment
4 (REA) that describes exposures and characterizes risks associated with 862 emissions from
5 anthropogenic sources. The plan for conducting the risk and exposure assessment to support the
6 SC>2 primary NAAQS review was presented in the Sulfur Dioxide Health Assessment Plan:
7 Scope and Methods for Exposure and Risk Assessment, henceforth referred to as the Health
8 Assessment Plan (EPA, 2008b). This document was made available to the public in November
9 2007 and can be found at: http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_pd.html. The
10 first draft SC>2 REA was informed by comments from the public and CAS AC on the Health
11 Assessment Plan, as well as the 1st and 2nd drafts of the ISA for SOX. The first draft SO2 REA
12 developed estimates of human exposures and risks associated with recent ambient levels of 862
13 and levels that just met the current SO2 standards. This first draft REA document was made
14 available to the public in July 2008 and can be found at:
15 http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_rea.html
16 The second draft SC>2 REA is this document and it has been informed by comments from
17 CAS AC and the public on the first draft REA, as well as findings and conclusions contained in
18 the final ISA. This document develops estimates of human exposures and risks associated with:
19 (1) recent ambient levels of 862, (2) levels that just meet the current 862 standards, and (3)
20 levels that just meet potential alternative standards: defined in terms of indicator, averaging time,
21 form, and level. This document also contains a policy assessment that will address the adequacy
22 of the current SO2 NAAQS and of potential alternative standards. More specifically, this policy
23 assessment considers epidemiological, human exposure, and animal toxicological evidence
24 presented in the ISA (EPA, 2008a), as well as the air quality, exposure, and risk characterization
25 results presented in this document, as they relate to the adequacy of the current SC>2 NAAQS and
26 potential alternative primary SC>2 standards (see Figure 1-1). This 2nd draft REA is to be
27 followed by a final REA. The final REA will be informed by comments from CASAC and the
28 public on the 2nd draft REA, as well as findings and conclusions contained in the final ISA.
29 The final step in the review of the SC>2 NAAQS will be the rulemaking process. This
30 process will be informed by the risk and exposure information contained in the final REA, as
March 2009 2 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
well the scientific evidence described in the final ISA. The rulemaking process will also take
into account CASAC advice and recommendations, as well as public comment on any policy
options under consideration. Notably, EPA is now under a consent decree to complete its review
of the SC>2 primary NAAQS by issuing a proposed rule no later than November 16, 2009 and a
final rule by June 2, 2010.
Evaluate Health Evidence in ISA
Characterization of human
clinical results
Characterization of
epidemiology
5-10 minute exposures
I ^
ą cor
-hour
concentrations
Identification of
potential 5-minute
health benchmark
values
i i
Quality Exposure
alysis analysis
t t
Identify potential
alternative standards
Estimation of
exposure-response
function
i
^ Quantitative risk analysis
///
i *//
t
1 /
Output 1: Number of
times per year SO2
concentrations at
ambient monitors
exceed 5-minute potential
benchmark values
Output 2: The probability
of a daily 5-minute
exceedance of
benchmark values given
a particular 24-hour
average, or 1-hour daily
maximum SO2
concentration.
Output: Estimates of
times per year asthmatics
at elevated ventilation
rates experience SO2
concentrations
exceeding 5-minute
potential benchmark
values
Output: Number and
percentage of exposed
asthmatics that would
experience moderate or greater
lung function decrements
Risk-based considerations to
inform standard setting
Characterization
of toxicology
exposures of
minutes to hours
Evidence based
considerations to inform
standard setting
Figure 1-1. Overview of the analyses described in this document and their interconnections
As mentioned above, an initial step in the review process was the development of an
integrated review plan. This plan identified policy relevant questions that would guide the review
of the SC>2 NAAQS. These questions are particularly important for the REA because they
provide a context for both evaluating health effects evidence presented in the ISA, as well as for
selecting the appropriate analyses for assessing exposure and risks associated with current
March 2009
Draft - Do Not Quote or Cite
-------
1 ambient SC>2 levels, and levels that just meet the current standards. These policy relevant
2 questions are:
3
4 Has new information altered/substantiated the scientific support for the occurrence of
5 health effects following short- and/or long-term exposure to levels of SOX found in the
6 ambient air?
7 Does new information impact conclusions from the previous review regarding the effects
8 of SOX on susceptible populations?
9 At what levels of SOX exposure do health effects of concern occur?
10 Has new information altered conclusions from previous reviews regarding the plausibility
11 of adverse health effects caused by SOX exposure?
12 To what extent have important uncertainties identified in the last review been reduced
13 and/or have new uncertainties emerged?
14 What are the air quality relationships between short-term and longer-term exposures
15 toSOx?
16 Additional questions will become relevant if the evidence suggests that revision of the
17 current standard might be appropriate. These questions are:
18 Is there evidence for the occurrence of adverse health effects at levels of SOX different
19 than those observed previously? If so, at what levels and what are the important
20 uncertainties associated with that evidence?
21 Do exposure estimates suggest that levels of concern for SOx-induced health effects will
22 occur with current ambient levels of SO2, or with levels that just meet the current, or
23 potential alternative standards? If so, are these exposures of sufficient magnitude such
24 that the health effects might reasonably be judged to be important from a public health
25 perspective? What are the important uncertainties associated with these exposure
26 estimates?
27 Do the evidence, the air quality assessment, and the risk/exposure assessment provide
28 support for considering different standard indicators, averaging times, or forms?
March 2009 4 Draft - Do Not Quote or Cite
-------
1 What range of levels is supported by the evidence, the air quality assessment, and
2 risk/exposure assessment? What are the uncertainties and limitations in the evidence and
3 assessments?
4 1.1 HISTORY
5 1.1.1 History of the SO2 NAAQS
6 The first SO2 NAAQS was established in 1971. At that time, a 24-hour standard of 0.14
7 ppm, not to be exceeded more than one time per year, and an annual standard of 0.03 ppm were
8 judged to be both adequate and necessary to protect public health. The most recent review of the
9 SC>2 NAAQS was completed in 1996 and focused on the question of whether an additional short-
10 term standard (e.g., 5-minute) was necessary to protect against short-term, peak exposures.
11 Based on the scientific evidence, the Administrator judged that repeated exposures to 5-minute
12 peak 862 levels (> 600 ppb) could pose a risk of significant health effects for asthmatic
13 individuals at elevated ventilation rates. The Administrator also concluded that the likely
14 frequency of such effects should be a consideration in assessing the overall public health risks.
15 Based upon an exposure analysis conducted by EPA, the Administrator concluded that exposure
16 of asthmatics to SO2 levels that could reliably elicit adverse health effects was likely to be a rare
17 event when viewed in the context of the entire population of asthmatics and therefore, did not
18 pose a broad public health problem for which a NAAQS would be appropriate. On May 22,
19 1996, EPA's final decision not to promulgate a 5-minute standard and to retain the existing 24-
20 hour and annual standards was announced in the Federal Register (61 FR 25566).
21 The American Lung Association and the Environmental Defense Fund challenged EPA's
22 decision not to establish a 5-minute standard. On January 30, 1998, the Court of Appeals for the
23 District of Columbia found that EPA had failed to adequately explain its determination that no
24 revision to the SO2 NAAQS was appropriate and remanded the decision back to EPA for further
25 explanation. Specifically, the court required EPA to provide additional rationale to support the
26 Agency judgment that 5-minute peaks of SO2 do not pose a public health problem from a
27 national perspective even though those peaks would likely cause adverse health impacts in a
28 subset of asthmatics. In response, EPA has collected and analyzed additional air quality data
29 focused on 5-minute concentrations of SO2. These air quality analyses conducted since the last
March 2009 5 Draft - Do Not Quote or Cite
-------
1 review will help inform the current review, which will answer the issues raised in the Court's
2 remand of the Agency' s 1 ast deci si on.
3 1.1.2 Health Evidence from the Previous Review
4 The 1982 Air Quality Criteria Document (AQCD) for Particulate Matter and Sulfur
5 Oxides (EPA, 1982), and its subsequent addenda and supplement (EPA, 1986b, 1994a) presented
6 an evaluation of SO2 associated health effects primarily drawn from epidemiological and human
7 clinical studies. In general, these documents identified adverse health effects that were likely
8 associated with both short- (generally hours to days), and long-term (months to years) exposures
9 to SC>2 at concentrations present in the ambient mixture of air pollutants. Moreover, these
10 documents presented evidence for bronchoconstriction and respiratory symptoms in exercising
11 asthmatics following controlled exposures to 5-10 minutes peak concentrations of 862.
12 Evidence drawn from epidemiological studies supported a likely association between 24-
13 hour average 862 concentrations and daily mortality, aggravation of bronchitis, and small,
14 reversible declines in children's lung function (EPA 1982, 1994a). In addition, a few
15 epidemiological studies found an association between respiratory symptoms and illnesses and
16 annual average SO2 concentrations (EPA 1982, 1994a). However, it was noted that most of
17 these epidemiological studies were conducted in years and cities where parti culate matter (PM)
18 counts were also quite high, thus making it difficult to quantitatively determine whether the
19 observed associations were the result of SC>2, PM, or a combination of both pollutants.
20 Evidence drawn from clinical studies exposing exercising asthmatics to <1000 ppb 862
21 for 5-10 minutes found that these types of 862 exposures evoked health effects that were similar
22 to those asthmatics would experience from other commonly encountered stimuli (e.g. exercise,
23 cold/dry air, psychological stress, etc. (EPA, 1994a). That is, there was an acute-phase response
24 characterized by bronchoconstriction and/or respiratory symptoms that occurred within 5-10
25 minutes of exposure but then subsided on its own within 1 to 2 hours. This acute-phase response
26 was followed by a short refractory period where the individual was relatively insensitive to
27 additional SO2 challenges. Notably, the SO2-induced acute-phase response was found to be
28 ameliorated by the inhalation of beta-agonist aerosol medications, and to occur without an
29 additional, often more severe, late-phase inflammatory response.
March 2009 6 Draft - Do Not Quote or Cite
-------
1 The 1994 supplement to the AQCD noted that of particular concern was the subset of
2 asthmatics in these clinical studies that appeared to be hyperresponsive (i.e. those experiencing
3 greater-than-average bronchoconstriction or respiratory symptoms at a given 862 concentration).
4 Thus, for a given concentration of 862, EPA estimated the number of asthmatics likely to
5 experience bronchoconstriction (and/or symptoms) of a sufficient magnitude to be considered a
6 health concern. At 600 to 1000 ppb SO2, EPA estimated that more than 25% of mild to moderate
7 exercising asthmatics would likely experience decrements in lung function distinctly exceeding
8 typical daily variations in lung function, or the response to commonly encountered stimuli (EPA,
9 1994a). Furthermore, the AQCD concluded that the severity of effects experienced at 600-1000
10 ppb was likely to be of sufficient concern to cause a cessation of activity, medication use, and/or
11 the possible seeking of medical attention. In contrast, at 200 - 500 ppb 862, it was estimated
12 that at most 10 - 20% of mild to moderate exercising asthmatics were likely to experience lung
13 function decrements larger than those associated with typical daily activity, or the response to
14 commonly encountered stimuli (EPA, 1994a).
15 1.1.3 Assessment from Previous Review
16 The risk and exposure assessment from the previous review of the SC>2 NAAQS
17 qualitatively evaluated both the existing 24-hour (0.14 ppm) and annual standards (0.03 ppm),
18 but primarily focused on whether an additional standard was necessary to protect against short-
19 term (e.g., 5-minute) peak exposures. Based on the human clinical data mentioned above, it was
20 judged that exposures to 5-minute 862 levels at or above 600 ppb could pose an immediate
21 significant health risk for a substantial proportion of asthmatics at elevated ventilation rates (e.g.,
22 while exercising). Thus, EPA analyzed existing ambient monitoring data to estimate the
23 frequency of 5-minute peak concentrations above 500, 600, and 700 ppb, the number of repeated
24 exceedances of these concentrations, and the sequential occurrences of peak concentrations
25 within a given day (SAI, 1996). The results of this analysis indicated that in the vicinity of local
26 sources, several locations in the U.S. had a substantial number of 5-minute peak concentrations
27 . at or above 600 ppb.
28 In addition to the ambient air quality analysis, the previous review also included several
29 annual exposure analyses that in general, combined 862 emission estimates from utility and non-
30 utility sources with exposure modeling to estimate the probability of exposure to short-term peak
March 2009 7 Draft - Do Not Quote or Cite
-------
1 SC>2 concentrations. The first such analysis conducted by the Agency estimated the number of 5-
2 minute exposures > 500 ppb associated with four selected coal-fired power utilities (EPA,
3 1986a). An expanded analysis sponsored by the Utility Air Regulatory Group (UARG)
4 considered the frequency of short-term exposure events that might result from the nationwide
5 operation of all power utility boilers (Burton et al., 1987). Additionally, the probability of peak
6 concentrations surrounding non-utility sources was the focus of another study conducted by the
7 Agency (Stoeckenius et al., 1990). The resultant combined exposure estimates based on these
8 early analyses indicated that between 0.7 and 1.8 percent of the total asthmatic population
9 potentially could be exposed one or more times annually, while outdoors at exercise, to 5-minute
10 SC>2 concentrations > 500 ppb. It also was noted that the frequency of 5-minute exposures above
11 the health effect benchmark of 600 ppb, while not part of the analysis, would be anticipated to be
12 lower.
13 In addition to the early analyses mentioned above, two other analyses were considered in
14 the prior review. The first was an exposure assessment sponsored by the UARG (Rosenbaum et
15 al., 1992) that focused on emissions from fossil-fueled power plants. That study accounted for
16 the anticipated reductions in SC>2 emissions after implementation of the acid deposition
17 provisions (Title IV) of the 1990 Clean Air Act Amendments. This UARG-sponsored analysis
18 predicted that these emission reductions would result in a 42% reduction in the number of 5-
19 minute exposures to 500 ppb for asthmatic individuals (reducing the number of asthmatics
20 exposed from 68,000 down to 40,000) in comparison with the earlier Burton et al. (1987)
21 analysis. The second was a new exposure analysis submitted by the National Mining
22 Association (Sciences International, Inc. 1995) that reevaluated non-utility sources. In this
23 analysis, revised exposure estimates were provided for four of the seven non-utility source
24 categories by incorporating new emissions data and using less conservative modeling
25 assumptions in comparison to those used for the earlier Stoeckenius et al. (1990) non-utility
26 analysis. Significantly fewer exposure events (i.e., occurrence of 5-minute 500 ppb or greater
27 exposures) were estimated in this industry-sponsored revised analysis, decreasing the range of
28 estimated exposures for these four sources by an order of magnitude (i.e., from 73,000-259,000
29 short-term exposure events in the original analysis to 7,900-23,100 in the revised analysis).
March 2009 8 Draft - Do Not Quote or Cite
-------
l 1.2 SCOPE OF THE RISK AND EXPOSURE ASSESSMENT FOR THE
2 CURRENT REVIEW
3 1.2.1 Overview of the Second Draft Assessment
4 The second draft REA describes exposure and risks associated with recent ambient levels
5 of 862, levels that just meet the current 862 standards, and levels that just meet potential
6 alternative standards. This second draft REA also contains a policy discussion regarding the
7 adequacy of the current SO2 NAAQS, and potential alternative primary standards. A concise
8 overview of the information, analyses, and policy discussion contained in this document is
9 presented below.
10 Chapters 2-4 evaluate background information presented in the ISA that is relevant for
11 conducting an exposure and risk assessment. This includes information on 1) human exposure to
12 SC>2, 2) at-risk populations, and 3) health effects associated with short- and long-term exposures
13 to SC>2. Chapter 5 presents in terms of indicator, averaging time, form, and level the potential
14 alternative standards that will be used in the exposure and risk chapters of the document.
15 Specifically, these potential alternative standards are 99th percentile 1-hour daily maximum 862
16 levels of 50, 100, 150, 200, and 250 ppb, and a 98th percentile 1-hour daily maximum SO2 level
17 of 200 ppb. Chapter 5 also describes the rationale for the selection of these potential alternative
18 standards. In brief, the rationale takes into consideration both human exposure and
19 epidemiological evidence from the ISA, as well as a qualitative analysis conducted by staff
20 characterizing 98th and 99th percentile 1-hour daily maximum SC>2 levels in cities and time
21 periods corresponding to key U.S. and Canadian hospitalization and ED visit studies for all
22 respiratory causes and asthma (key studies are identified in Table 5-5 of the ISA). Chapter 6 is
23 an overview of the technical analyses that will be presented in the subsequent chapters of this
24 document. This chapter will also present rationale for the selection of specific health benchmark
25 values derived from the human exposure literature.
26 Chapters 7-9 present the analytical portion of the document. Staff considered both
27 evidence of bronchoconstriction and respiratory symptoms from human exposure studies, as well
28 as CASAC advice on the first draft REA, and judged it appropriate to conduct a series of three
29 analyses to estimate risks associated with 5-minute 862 exposures ranging from 100-400 ppb in
30 exercising asthmatics (see Figure 1-1 and Chapter 6). Chapter 7 presents an air quality
31 characterization that uses monitored and statistically estimated 5-minute ambient SC>2
March 2009 9 Draft - Do Not Quote or Cite
-------
1 concentrations as a surrogate for exposure. This analysis estimates the number of times per year
2 measured or statistically estimated 5-minute peak 862 concentrations meet or exceed the
3 potential health benchmark values of 100, 200, 300 and 400 ppb This air quality analysis is done
4 under scenarios reflecting current air quality, air quality simulated to just meet the current
5 standards, and air quality simulated to just meet potential alternative standards (i.e. 99th
6 percentile 1-hour daily maximum SO2 levels of 50, 100, 150, 200 and 250 ppb and an 98th
7 percentile 1-hour daily maximum SC>2 level of 200 ppb). Chapter 8 presents results from
8 exposure analysis case studies conducted in St Louis and Greene Counties Missouri. Notably,
9 EPA is also attempting to extend its exposure and risk analyses to Alleghany County
10 (Pittsburgh), Pennsylvania and Cuyahoga County (Cleveland), Ohio. However, as of this date
11 we are still working to rectify technical issues involving disparities between dispersion model
12 predicted 862 concentrations and measured 862 concentrations at fixed site monitors. If EPA is
13 successful in resolving these technical issues, additional exposure and risk estimates for these
14 areas will be included in the presentation to the CAS AC Panel at the April 16-17 meeting. In
15 this document, analyses conducted in St. Louis and Greene Counties provides estimates of the
16 number and percent of asthmatics residing within 20 kilometers (km) of major SC>2 sources
17 experiencing 5-minute exposures to 100, 200, 300, and 400 ppb SO2, while at elevated
18 ventilation rates under the air quality scenarios mentioned above (i.e. recent air quality, and air
19 quality adjusted to just meet the current and alternative standards). Chapter 9 is a quantitative
20 risk assessment that produces health risk estimates for the number and percent of exposed
21 asthmatics (as determined by the exposure analysis; see Figure 1-1) that would experience
22 moderate or greater lung function responses under the air quality scenarios previously described.
23 In addition to the technical analyses presented in Chapters 7-9, Chapter 10 integrates the
24 scientific evidence and the air quality, exposure, and risk information as they pertain to
25 informing decisions about the primary SC>2 NAAQS. More specifically, Chapter 10 considers
26 the epidemiological, human exposure, and animal toxicological evidence presented in the ISA
27 (EPA, 2008a), as well as the air quality, exposure, and risk characterization results presented in
28 this document, as they relate to the adequacy of the current 862 NAAQS and potential
29 alternative primary 862 standards.
March 2009 10 Draft - Do Not Quote or Cite
-------
1 1.2.2 Species of Sulfur Oxides Included in Analyses
2 The sulfur oxides include multiple gaseous (e.g., SC>2, SOs) and particulate (e.g., sulfate)
3 species. In considering what species of sulfur oxides are relevant to the current review of the
4 SC>2 NAAQS, we note that the health effects associated with particulate species of sulfur oxides
5 have been considered within the context of the Agency's review of the primary NAAQS for
6 particulate matter (PM). In the most recent review of the NAAQS for PM, it was determined that
7 size-fractionated particle mass, rather than particle composition, remains the most appropriate
8 approach for addressing ambient PM. This conclusion will be re-assessed in the parallel review
9 of the PM NAAQS; however, at present it would be redundant to also consider effects of
10 particulate sulfate in this review. Therefore, the current review of the SC>2 NAAQS will focus on
11 gaseous species of sulfur oxides and will not consider health effects directly associated with
12 particulate sulfur oxide species. Additionally, of the gaseous species, EPA has historically
13 determined it appropriate to specify the indicator of the standard in terms of 862 because other
14 gaseous sulfur oxides (e.g. SO3) are likely to be found at concentrations many orders of
15 magnitude lower than SC>2 in the atmosphere, and because most all of the health effects and
16 exposure information is for SC>2. The ISA has again found this to be the case, and therefore this
17 REA will use SC>2 as a surrogate for all gaseous sulfur oxides.
March 2009 11 Draft - Do Not Quote or Cite
-------
i 2.0 OVERVIEW OF HUMAN EXPOSURE
2 In order to help inform the air quality, exposure, and risk analyses presented in Chapters
3 7-9, staff has briefly summarized relevant human exposure information from the ISA. After
4 defining the concept of "integrated exposure," this chapter discusses major sources of SC>2
5 emissions. Characterizing these SC>2 sources helps identify the most relevant locations for
6 conducting air quality, exposure, and health risk analyses. This Chapter then discusses ambient
7 levels of 862 associated with 1-hour, 24-hour, and annual averaging times. 862 concentrations
8 associated with these averaging times are relevant to the air quality, exposure, and risk analyses
9 because the current 862 standards have 24-hour and annual averaging times, and EPA is
10 considering potential alternative 1-hour averaging time standards (see section 5.3). In addition,
11 this chapter contains a general description of the monitors reporting 5-minute SO2
12 concentrations, as well a broad characterization of ambient 5-minute SCh levels (a more thorough
13 discussion of these topics be found in Chapters 6 and 7). This discussion is particularly relevant
14 to the analyses described in this document because the potential health effect benchmark levels
15 and the outputs of the air quality, exposure, and risk assessments are presented with respect to a
16 5-minute averaging time (see section 6.2). More specifically, as previously described in section
17 1.2.1, outputs of the air quality analysis presented in Chapter 7 include the number of measured,
18 or statistically estimated (see Chapter 6) 5-minute SO2 concentrations that exceed 5-minute
19 potential health effect benchmark levels. Similarly, the output of the exposure analysis in
20 Chapter 8 is the number of exercising asthmatics exposed to 5-minute SC>2 concentrations above
21 benchmark levels. Outputs of the exposure analysis (i.e. the number of exercising asthmatics
22 exposed to 5-minute SC>2 concentrations above benchmark levels) are then used as inputs into the
23 quantitative risk assessment in Chapter 9 to estimate the number and percent of exposed
24 exercising asthmatics expected to experience a moderate or greater lung function response (see
25 Figure 6-1).
26 In addition to providing information relevant to the air quality, exposure, and risk
27 analyses presented in this document, this Chapter also provides information relevant to the
28 Chapter 4 health discussion and the Chapter 10 policy assessment. That is, the current chapter
29 highlights uncertainties involved with using ambient SC>2 concentrations as a surrogate for
30 personal exposure in epidemiological studies, as well as the ISA's conclusions on this topic.
March 2009 12 Draft - Do Not Quote or Cite
-------
l 2.1 BACKGROUND
2 The integrated exposure of a person to a given pollutant is the sum of the exposures over
3 all time intervals for all environments in which the individual spends time. People spend
4 different amounts of time in different microenvironments and each microenvironment is
5 characterized by different pollutant concentrations. There is a large amount of variability in the
6 time that different individuals spend in different microenvironments, but on average people
7 spend the majority of their time (about 87%) indoors. Most of this time spent indoors is spent at
8 home with less time spent in an office/workplace or other indoor locations (ISA, Figure 2-21).
9 In addition, people spend on average about 8% of their time outdoors and 6% of their time in
10 vehicles. A potential consequence of multiple sources of exposure or microenvironments is the
11 exposure misclassification that may result when total human exposure is not disaggregated
12 between these various microenvironments. In epidemiological studies that rely on ambient
13 pollutant levels as a surrogate for exposure to ambient 862, such misclassification may obscure
14 the true relationship between ambient air pollutant exposures and health outcomes.
15 In addition to accounting for the times spent in different microenvironments, it is also
16 important to note the duration of exposure experienced. This is important because health effects
17 caused by long-term, low-level exposures may differ from those caused by relatively higher
18 shorter-term exposures.
19 2.2 SOURCES OF SO2
20 In order to estimate risks associated with SC>2 exposure, principle sources of the pollutant
21 must first be characterized because the majority of human exposures are likely to result from the
22 release of emissions from these sources. Anthropogenic SC>2 emissions originate chiefly from
23 point sources, with fossil fuel combustion at electric utilities (-66%) and other industrial
24 facilities (-29%) accounting for the majority of total emissions (ISA, section 2.1). Other
25 anthropogenic sources of SO2 include both the extraction of metal from ore as well as the
26 burning of high sulfur containing fuels by locomotives, large ships, and non-road diesel
27 equipment. Notably, almost the entire sulfur content of fuel is released as SC>2 or SOs during
28 combustion. Thus, based on the sulfur content in fuel stocks, oxides of sulfur emissions can be
29 calculated to a higher degree of accuracy than can emissions for other pollutants such as PM and
30 NO2 (ISA, section 2.1).
March 2009 13 Draft - Do Not Quote or Cite
-------
1 The largest natural sources of 862 are volcanoes and wildfires. Although 862 constitutes
2 a relatively minor fraction (0.005% by volume) of total volcanic emissions, concentrations in
3 volcanic plumes can be in the range of several to tens of ppm (thousands of ppb). Volcanic
4 sources of SC>2 in the U.S. are limited to the Pacific Northwest, Alaska, and Hawaii. Emissions
5 of SC>2 can also result from burning vegetation. The amount of SC>2 released from burning
6 vegetation is generally in the range of 1 to 2% of the biomass burned and is the result of sulfur
7 from amino acids being released as SC>2 during combustion.
8 2.3 AMBIENT LEVELS OF SO2
9 Since the integrated exposure to a pollutant is the sum of the exposures over all time
10 intervals for all environments in which the individual spends time, understanding the temporal
11 and spatial patterns of 862 levels across the U. S is an important component of conducting air
12 quality, exposure, and risk analyses. 862 emissions and ambient concentrations follow a strong
13 east to west gradient due to the large numbers of coal-fired electric generating units in the Ohio
14 River Valley and upper Southeast regions. In the 12 CMSAs that had at least 4 SO2 regulatory
15 monitors from 2003-2005, 24-hour average concentrations in the continental U.S. ranged from a
16 reported low of ~1 ppb in Riverside, CA and San Francisco, CA to a high of-12 ppb in
17 Pittsburgh, PA and Steubenville, OH (ISA, section 2.4.4). In addition, inside CMSAs from
18 2003-2005, the annual average SO2 concentration was 4 ppb (ISA, Table 2-8). However, spikes
19 in hourly concentrations occurred; the mean 1-hour maximum concentration was 130 ppb, with a
20 maximum value of greater than 700 ppb (ISA, Table 2-8).
21 In addition to considering 1-hour, 24-hour, and annual SO2 levels in this document,
22 examining the temporal and spatial patterns of 5-minute peaks of SO2 is also important given
23 that human clinical studies have demonstrated exposure to these peaks can result in adverse
24 respiratory effects in exercising asthmatics (see Chapter 4). Although the total number of SO2
25 monitors across the continuous U.S. can vary from year to year, in 2006 there were
26 approximately 500 SO2 monitors in the NAAQS monitoring network (ISA, section 2.5.2). State
27 and local agencies responsible for these monitors are required to report 1-hour average SO2
28 concentrations to the EPA Air Quality System (AQS). However, a small number of sites, only
29 98 total from 1997 to 2007, and not the same sites in all yearsvoluntarily reported 5-minute
30 block average data to AQS (ISA, section 2.5.2). Of these, 16 reported all twelve 5-minute
31 averages in each hour for at least part of the time between 1997 and 2007. The remainder
March 2009 14 Draft - Do Not Quote or Cite
-------
1 reported only the maximum 5-minute average in each hour. When maximum 5-minute
2 concentrations were reported, the absolute highest concentration over the ten-year period
3 exceeded 4000 ppb), but for all individual monitors, the 99th percentile was below 200 ppb (ISA,
4 section 2.5.2). Medians from these monitors reported data ranged from 1 ppb to 8 ppb, and the
5 average for each maximum 5-minute level ranged from 3 ppb to 17 ppb. Delaware,
6 Pennsylvania, Louisiana, and West Virginia had mean values for maximum 5-minute data
7 exceeding 10 ppb (ISA, section 2.5.2). Among aggregated within-state data for the 16 monitors
8 from which all 5-minute average intervals were reported, the median values ranged from 1 ppb to
9 5 ppb, and the means ranged from 3 ppb to 11 ppb (ISA, section 2.5.2). The highest reported
10 concentration was 921 ppb, but the 99th percentile values for aggregated within-state data were
11 all below 90 ppb (ISA, section 2.5.2).
12 EPA has generally conducted NAAQS risk assessments that focus on the risks associated
13 with levels of a pollutant that are in excess of policy relevant background (PRB). Policy relevant
14 background levels are defined as concentrations of a pollutant that would occur in the U. S. in the
15 absence of anthropogenic emissions in continental North America (defined here as the United
16 States, Canada, and Mexico). However, throughout much of the United States, 862 PRB levels
17 are estimated to be at most 30 parts per trillion and contribute less than 1% to present day 862
18 concentrations (ISA, section 2.5.3). We note that in the Pacific Northwest and Hawaii, PRB
19 concentrations can be considerably higher due to geothermal activity (e.g. volcanoes); in these
20 areas, PRB can account for 70-80% of total SO2 concentrations (ISA, section 2.5.3). Since we
21 do not plan on conducting SC>2 risk assessments in areas with high background SC>2 levels due to
22 natural sources, and the contribution of PRB is negligible in all other areas, EPA is addressing
23 the risks associated with monitored and/or modeled ambient SC>2 levels without regard to PRB
24 levels.
25 2.4 RELATIONSHIP OF PERSONAL EXPOSURE TO AMBIENT
26 CONCENTRATIONS
27 To help inform the evaluation of the epidemiological evidence in Chapter 4 and the
28 evidence-based considerations presented in Chapter 10, this section discusses the relationship of
29 personal SC>2 exposure to ambient SC>2 concentrations. Many epidemiological studies rely on
30 measures of ambient SC>2 concentrations as surrogates for personal exposure to ambient SC>2.
31 Thus, it is important to consider the potential sources of error that are associated with using SC>2
March 2009 15 Draft - Do Not Quote or Cite
-------
1 measured by ambient monitors as a surrogate for personal exposure to ambient 862. Key aspects
2 related to this issue include: (1) ambient and personal sampling issues, (2) the spatial variability
3 of ambient SO2 concentrations, and (3) the relationship between ambient concentrations and
4 personal exposures as influenced by exposure factors (e.g. indoor sources).
5 Only a limited number of studies have focused on the relationship between personal
6 exposure and ambient concentrations of SC>2, in part because ambient SC>2 levels have declined
7 markedly over the past few decades. Indoor and outdoor SC>2 concentrations are often below
8 detection limits for personal samplers1 and in these situations, the ISA notes that associations
9 between ambient concentrations and personal exposures are inadequately characterized (ISA,
10 section 2.6.3.2). However, in studies with personal measurements above detection limits, the
11 ISA states that a reasonably strong association was observed between personal SO2 exposure and
12 ambient concentrations (Brauer et al., 1989; Sarnat et al., 2006; described in ISA section 2.6.3.2).
13 In addition, the ISA notes that no study has examined the relationship between concentrations
14 measured at ambient monitors and the community average exposure: a relationship that is more
15 relevant than that of ambient concentration to personal exposure for the maj ority of the
16 epidemiological studies presented in the ISA (ISA, section 5.3).
17 Because epidemiological studies rely on ambient SC>2 measurements at fixed site
18 monitors, there is concern about the extent to which instrument error could influence the results
19 of these studies. That is, the 862 monitoring network was designed and put into place when 862
20 concentrations were considerably higher, and thus, well within the standard monitor's limits of
21 detection. However, SC>2 concentrations have fallen considerably over the years and are
22 currently at, or very near these monitors' lower limit of detection (~3 ppb). This introduces a
23 degree of uncertainty because as monitors approach their detection limits there can be greater
24 error in their measurements. The ISA states that it is unclear how uncertainties in measured SC>2
25 concentrations will change the effect estimates of epidemiological studies relying on these
26 monitors (ISA, section 2.6.4.1). As an additional matter, staff notes that the lower detection limit
27 of these monitors is not considered problematic with respect to attaining the standards because
28 the current 24-hour and annual standards, as well as the potential alternative 1-hour maximum
29 standards, are all well within the detection limits of the SC>2 NAAQS monitoring network.
1 The lower limit of detection of personal samplers is -60 ppb for 1-hour and ~5 ppb for 24-hour. A discussion of
personal sampler detection limits can be found in section 2.6.2 of the ISA.
March 2009 16 Draft - Do Not Quote or Cite
-------
1 Uncertainty in epidemiological studies is also associated with the spatial and temporal
2 variation of 862 across communities. The ISA finds that site-to-site correlations of 862
3 concentrations among monitors in U.S. cities ranges from very low to very high (ISA, section
4 2.6.4.1; ISA, Table 2-9). This suggests that at any given time, SC>2 concentrations at individual
5 monitoring sites may not highly correlate with the average SC>2 concentration in the community.
6 This could be the result of local sources (e.g. power plants) causing an uneven spatial
7 distribution of SC>2, monitors being sited to represent concentrations near local sources, or effects
8 related to terrain or weather (ISA, section 2.6.4.1). However, this type of error is not thought to
9 bias epidemiological conclusions in a positive direction because it generally tends to reduce,
10 rather than increase, the effect estimate (ISA, section 2.6.4.1).
11 In epidemiological studies, since people spend most of their time indoors, there is also
12 uncertainty in the relationship between ambient concentrations measured by local monitors and
13 actual personal exposure related to ambient sources. That is, the presence of indoor or
14 nonambient sources of SC>2 could complicate the interpretation of associations between personal
15 exposure and ambient SC>2 in exposure studies. Sources of indoor SC>2 are associated with the
16 use of sulfur-containing fuels, with higher levels expected when emissions are poorly vented. In
17 the U.S., the contribution of indoor sources is not thought to be a major contributor to overall
18 SC>2 exposure because the only known indoor source is kerosene heaters and their use is not
19 thought to be widespread (ISA, section 2.6.4.1).
20 As described above, the ISA finds that there is some error in epidemiological studies
21 associated with ambient SC>2 concentrations being used as a surrogate for personal exposure.
22 However, the ISA concludes that positive effect estimates in SC>2 community time-series or panel
23 epidemiological studies would likely be stronger and less uncertain if these errors had been take
24 into account (ISA, section 2.6.4.4.).
25
March 2009 17 Draft - Do Not Quote or Cite
-------
1
3.0 AT RISK POPULATIONS
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
3.1 OVERVIEW
The risk of an adverse health effect following exposure to a pollutant is dependent on a
number of factors, such as the individual's personal attributes (age, gender, preexisting health
conditions) and the toxic properties of the pollutant (e.g., as indicated by dose- or concentration-
response relationships). Individuals in potentially sensitive groups are of concern, as they may
experience adverse effects from lower levels of a pollutant compared to the general population or
experience a greater impact with the same level of exposure. The term susceptibility generally
encompasses innate (e.g. genetic) or acquired (age or disease) factors that make individuals more
likely to experience pollutant-related health effects. In addition, some population groups are
described as being particularly vulnerable to pollution-related health effects because their air
pollution exposures are higher than those of the general population. Table 3-1 presents a list of
factors that could potentially lead to increased susceptibility or vulnerability to air pollution in
general. However it should be noted that currently, only a subset of these factors has been shown
to lead to increased susceptibility or vulnerability to SC>2 specifically. Those groups identified in
the ISA to be potentially susceptible and/or vulnerable to SC>2 exposure are described in greater
detail below.
Table 3-1 Factors Potentially Contributing to Susceptibility or Vulnerability to Air
Pollution in General (modified from Table 4-1 in the ISA)
Susceptibility Factors
Pre-existing disease (e.g. asthma)
Genetic factors
Age
Gender
Race
Ethnicity
Obesity
Adverse birth outcomes (e.g. low birth
rate)
Vulnerability Factors
Increased activity patterns
Limited air conditioner use
Increased exertion level
Work environment (e.g. outdoor workers)
Lower SES
Lower education level
Residential location (e.g. living near sources)
Geographic location (e.g. east vs. west)
18
March 2009
18
Draft - Do Not Quote or Cite
-------
l 3.2 SUSCEPTIBILITY: PRE-EXISTING DISEASE
2 Both recent epidemiological and human clinical studies have strengthened the prior
3 conclusion that individuals with pre-existing respiratory disease are likely more susceptible to
4 the effects of 862 than the general public (ISA, section 4.2.1.1). Epidemiological studies have
5 reported associations between ambient 862 concentrations and a range of respiratory symptoms
6 in individuals with respiratory disease. Additionally, numerous controlled human exposure
7 studies have found that exercising asthmatics are more responsive to the respiratory effects of
8 SC>2 than healthy, non-asthmatic individuals. Specifically, clinical studies have demonstrated
9 that in non-asthmatics, SO2-attributible decrements in lung function have generally not been
10 shown at concentrations <1000 ppb. In contrast, increases in respiratory symptoms and/or
11 decrements in lung function have been shown in a significant proportion of exercising mild and
12 moderate asthmatics following 5-10 minute exposures to 862 concentrations as low as 200-600
13 ppb (ISA, section 4.2.1.1).
14 The ISA also examined the possible effects of pre-existing cardiovascular disease (CVD)
15 on SC>2 susceptibility. The ISA found that results from a limited number of epidemiological
16 studies provided inconsistent evidence that individuals with pre-existing CVD were more
17 susceptible than the general public to adverse health effects associated with ambient SC>2
18 exposure (ISA, section 4.2.1.2). Moreover, results from a single human clinical study found no
19 evidence to suggest that patients with stable angina were more susceptible to SCV related health
20 effects than healthy individuals. Overall, the ISA found the evidence for an association between
21 pre-existing CVD and increased susceptibility to 862 related health effects to be inconclusive
22 (ISA, section 4.2.1.2).
23 3.3 SUSCEPTIBILITY: GENETICS
24 The ISA noted that a consensus now exists among scientists that the potential association
25 between genetic factors and increased susceptibility to ambient air pollution merits serious
26 consideration. Several criteria must be satisfied in selecting and establishing useful links
27 between polymorphisms in candidate genes and adverse respiratory effects. First, the product of
28 the candidate gene must be significantly involved in the pathogenesis of the effect of interest,
29 which is often a complex trait with many determinants. Second, polymorphisms in the gene must
30 produce a functional change in either the protein product or in the level of expression of the
March 2009 19 Draft - Do Not Quote or Cite
-------
1 protein. Third, in epidemiological studies, the issue of confounding by other genes or
2 environmental exposures must be carefully considered (ISA section 4.2.2).
3 While many studies have examined the association between genetic polymorphisms and
4 susceptibility to air pollution in general, only one study has specifically examined the effects of
5 SC>2 exposure on genetically distinct subpopulations. Winterton et al. (2001) found a significant
6 association between SO2-induced decrements in Forced Expiratory Volume in the first second
7 (FEVi) and the homozygous wild-type allele in the promoter region of Tumor Necrosis Factor-a
8 (TNF- a; AA, position -308). However, the ISA concluded that the overall body of evidence was
9 too limited to reach a conclusion regarding the effects of 862 exposure on genetically distinct
10 subpopulations at this time.
11 3.4 SUSCEPTIBILITY: AGE
12 The ISA identifies children (i.e., <18 years of age) and older adults (i.e. >65 years of age)
13 as groups that are potentially more susceptible than the general population to the health effects
14 associated with SO2 exposure. In children, the developing lung is highly susceptible to damage
15 from environmental toxicants as it continues to develop through adolescence. The basis for
16 increased susceptibility in the elderly is unknown, but one hypothesis is that it may be related to
17 changes in antioxidant defenses in the fluid lining the respiratory tract. The ISA found a number
18 of epidemiological studies that observed increased respiratory symptoms in children associated
19 with increasing 862 exposures. In addition, several studies have reported that the excess risk
20 estimates for ED visits and hospitalizations for all respiratory causes, and to a lesser extent
21 asthma, associated with a 10-ppb increase in 24-hour average 862 concentrations were higher for
22 children and older adults than for all ages together (ISA, section 4.2.3). However, the ISA also
23 notes that results from human exposure studies do not suggest that adolescents are more
24 susceptible than adults to the respiratory effects of SC>2 (ISA, section 4.2.3). Overall, the ISA
25 states that compared to the general population, there is limited evidence to suggest that children
26 and older adults are more susceptible to the adverse respiratory effects of ambient SC>2 (ISA,
27 section 4.2.3).
28 3.5 VULNERABILITY
29 Indoor and personal SC>2 concentrations are generally much lower than outdoor ambient
30 concentrations. Therefore, people who spend most of their time indoors are generally less
March 2009 20 Draft - Do Not Quote or Cite
-------
1 vulnerable to 862 related health effects than those who spend a significant amount of time
2 outdoors. In addition, human clinical studies have demonstrated that decrements in lung
3 function and respiratory symptoms occur at significantly lower SO2 exposure levels in exercising
4 asthmatics compared to resting asthmatics. Thus, individuals who spend a significant amount of
5 time outdoors at elevated ventilation rates (e.g. while playing, or working) are expected to have
6 increased vulnerability and therefore be at greater risk of experiencing SO2-related health effects.
7 In addition to individuals who spend extended periods of time outdoors, the ISA also
8 describes evidence that vulnerability to 862 exposure is associated with lower socioeconomic
9 status (SES) (ISA section 4.2.5). Finkelstein et al. (2003) found that among people with below-
10 median income, the relative risk for above-median exposure to 862 was 1.18 (95% CI: 1.11,
11 1.26); the corresponding relative risk among subjects with above-median income was 1.03 (95%
12 CI: 0.83, 1.28). However, the ISA concludes that overall, the evidence is too limited to reach a
13 conclusion regarding SES and exposure to SC>2 (ISA section 4.2.5).
14 3.6 NUMBER OF SUSCEPTIBLE OR VULNERABLE INDIVIDUALS
15 Large proportions of the U.S. population are likely to be at increased risk for SC>2-related
16 health effects due to the potential susceptibilities and vulnerabilities mentioned above. In the
17 United States, approximately 10% of adults and 13% of children have been diagnosed with
18 asthma. Notably, the prevalence and severity of asthma is higher among certain ethnic or racial
19 groups such as Puerto Ricans, American Indians, Alaskan Natives, and African Americans (ISA
20 for NOX, section 4.4). Furthermore, a higher prevalence of asthma among persons of lower SES
21 and an excess burden of asthma hospitalizations and mortality in minority and inner-city
22 communities have been observed. In addition, population groups based on age comprise
23 substantial segments of individuals that may be potentially at risk for SC>2-related health impacts.
24 Based on U.S. census data from 2000, about 72.3 million (26%) of the U.S. population are under
25 18 years of age, 18.3 million (7.4%) are under 5 years of age, and 35 million (12%) are 65 years
26 of age or older. There is also concern for the large segment of the population that is potentially
27 vulnerable to SCVrelated health effects because of increased time spent outdoors at elevated
28 ventilation rates (e.g. those who work or play outdoors). Overall, the considerable size of the
29 population groups at risk indicates that exposure to ambient 862 could have a significant impact
30 on public health in the United States.
March 2009 21 Draft - Do Not Quote or Cite
-------
i 4.0 HEALTH EFFECTS
2 4.1 INTRODUCTION
3 The ISA along with its annexes integrates newly available epidemiological, human
4 clinical, and animal toxicological evidence with consideration of key findings and conclusions
5 from prior reviews to draw conclusions about the relationship between short- and long-term
6 exposure to SC>2 and numerous human health endpoints. For these health effects, the ISA
7 characterizes judgments about causality with a hierarchy (for discussion see ISA section 1.3.7)
8 that contains the following five levels:
9 Sufficient to infer a causal relationship
10 Sufficient to infer a likely causal relationship (i.e., more likely than not)
11 Suggestive but not sufficient to infer a causal relationship
12 Inadequate to infer the presence or absence of a causal relationship
13 Suggestive of no causal relationship
14 The ISA notes that these judgments about causality are informed by a series of aspects of
15 causality that are based on those set forth by Sir Austin Bradford Hill in 1965 (ISA section
16 1.3.6). These aspects include strength of the observed association, availability of experimental
17 evidence, consistency of the observed association, biological plausibility, coherence of the
18 evidence, temporal relationship of the observed association, and the presence of an exposure-
19 response relationship. A summary of each of the five levels of the hierarchy is provided in Table
20 1-2 of the ISA, which has also been included below (Table 4-1).
March 2009 22 Draft - Do Not Quote or Cite
-------
Table 4-1. Weight of Evidence for Causal Determination
Sufficient to infer a
causal relationship
Sufficient to infer a
likely causal
relationship (i.e.,
more likely than iiot).
Suggestive, but not
sufficient to infer a
causal relationship
Inadequate to infer the
presence or absence of
a causal relationship
Suggestive of no causal
relationship
Evidence is sufficient to conclude that there is a causal relationship between
relevant pollutant exposure and the outcome. Causality is supported when an
association has been observed between the pollutant and the outcome in
studies in which chance, bias, and confounding could be ruled out with
reasonable confidence. That is. human clinical studies provide the strongest
evidence for causality. Causality is also supported by findings from
epidemiologic "natural experiments" or observational studies supported by
other lines of evidence. Generally, determination is based on multiple studies
from more than one research group.
Evidence is sufficient to conclude that there is a likely causal association
between relevant pollutant exposures and the outcome. That is. an association
has been observed between the pollutant and the outcome in studies in which
chance, bias and confounding are minimized, but uncertainties remain. For
example, observational studies show associations but confounding and other
issues are difficult to address and/or other lines of evidence (human clinical.
animal, or mechanism of action information) are limited or inconsistent.
Generally, determination is based on multiple studies from more than one
research group.
Evidence is suggestive of an association between relevant pollutant exposures
and the outcome, but is weakened because chance, bias and confounding
cannot be ruled out. For example, at least one high-quality study shows an
association, while the results of other studies are inconsistent.
The available studies are inadequate to infer the presence or absence of a
causal relationship. That is. studies are of insufficient quality, consistency or
statistical power to permit a conclusion regarding the presence or absence of
an association between relevant pollutant exposure and the outcome. For
example, studies which fail to control for confounding or which have
inadequate exposure assessment, fall into this category.
The available studies are suggestive of no causal relationship. That is. several
adequate studies, examining relationships between relevant population
exposures and outcomes, and considering sensitive subpopiilatious. are
mutually consistent in not showing an association between exposure and the
outcome at any level of exposures. In addition, the possibility of a small
elevation in risk at the levels of exposure studied can never be excluded.
1
2 For the purpose of characterizing SO2-related health risks in the risk and exposure
3 analyses, we have focused on health endpoints for which the ISA concludes that the available
4 evidence is sufficient to infer a causal relationship. The ISA concludes that there is sufficient
5 evidence to infer a causal relationship between respiratory morbidity and short-term exposure to
6 SO2 (ISA, section 5.2). This conclusion is based on the consistency, coherence, and plausibility
7 of findings observed in controlled human exposure studies examining SC>2 exposures of 5-10
8 minutes, epidemiological studies mostly using 24-hour average exposures, and animal
9 toxicological studies using exposures of minutes to hours (ISA, section 5.2). The evidence for
10 causal associations between SC>2 exposure and other health endpoints is judged to be less
11 convincing, at most suggestive but not sufficient to infer a causal relationship, and therefore will
March 2009
23
Draft - Do Not Quote or Cite
-------
1 not be discussed in this chapter, but may be considered as part of the policy discussion in
2 Chapter 10. Key conclusions reached in the ISA are listed below:
3
4 Sufficient to infer a causal relationship:
5 o Short-Term Exposure to SO2 and Respiratory Morbidity
6 Suggestive but not sufficient to infer a causal relationship:
7 o Short-Term Exposure to SC>2 and Mortality
8 Inadequate to infer the presence or absence of a causal relationship
9 o Short-Term Exposure to SC>2 and Cardiovascular Morbidity;
10 o Long-Term Exposure to SC>2 and Respiratory Morbidity;
11 o Long-Term Exposure to SC>2 and Mortality ;
12 o Long-Term Exposure to SC>2 and Other Morbidity;
13 A more detailed summary of these conclusions can be found in Table 5-3 of the ISA.
14 4.2 SHORT-TERM PEAK (<1-HOUR, GENERALLY 5-10 MINUTES) SO2
15 EXPOSURES AND RESPIRATORY HEALTH EFFECTS
16 4.2.1 Overview
17 The ISA concludes that there is sufficient evidence to infer a causal relationship between
18 respiratory morbidity and short-term (5-minutes to 24-hours) exposure to 862 (ISA, section 5.2).
19 In large part, this determination is based on controlled human exposure studies demonstrating a
20 relationship between short-term peak SC>2 exposures and adverse effects on the respiratory
21 system in exercising asthmatics. Since the last review, several human clinical studies providing
22 evidence of SO2-induced decrements in lung function and increases in respiratory symptoms
23 among exercising asthmatics have been published (ISA section 3.1.3). In addition, based in part
24 on recent guidance from the American Thoracic Society (ATS) regarding what constitutes an
25 adverse health effect of air pollution (ATS, 2000), the ISA also reviewed and analyzed key older
26 studies along with those published since the last review. In their official statement, the ATS
27 concluded that an air pollution-induced shift in a population distribution of a given health-related
28 endpoint (e.g., lung function) should be considered adverse, even if this shift does not result in
29 the immediate occurrence of illness in any one individual in the population (ATS 2000). The
30 ATS also recommended that transient loss in lung function with accompanying respiratory
March 2009 24 Draft - Do Not Quote or Cite
-------
1 symptoms attributable to air pollution should be considered adverse. However, the ISA cautions
2 that symptom perception is highly variable among asthmatics even during severe episodes of
3 asthmatic bronchoconstriction, and that an asymptomatic decrease in lung function may pose a
4 significant health risk to asthmatic individuals as it is less likely that these individuals will seek
5 treatment (ISA section 3.1.3). As in previous reviews, the ISA also concludes that at
6 concentrations below 1000 ppb, healthy individuals are relatively insensitive to the respiratory
7 effects of short-term peak SC>2 exposures (ISA, sections 3.1.3.1 and 3.1.3.2).
8 4.2.2 Respiratory Symptoms
9 The 1994 Supplement to the Second Addendum described multiple studies that evaluated
10 respiratory symptoms (e.g. cough, wheeze, or chest tightness) following controlled exposures of
11 asthmatic subjects to 862. Linn et al. (1983) reported that relative to exposure to clean air,
12 exposure to SO2 levels as low as 400 ppb for 5 minutes in exercising asthmatics resulted in a
13 statistically significant increase in an overall respiratory symptoms score that included wheeze,
14 chest tightness, cough, and substernal irritation. In an additional study, Linn et al. (1987)
15 observed that 43% of asthmatics exhibited respiratory symptoms following exposure to 600 ppb
16 SC>2 during a 10-minute period of exercise; this study also found that exposure to SC>2
17 concentrations of 400 ppb resulted in 15% of study subjects experiencing respiratory symptoms
18 (ISA, 3.1.3.1). In addition, Balmes et al. (1987) reported that 7 out of 8 asthmatic adults at
19 elevated ventilation rates developed respiratory symptoms following a 3-minute exposure via
20 mouthpiece to 500 ppb 862 (ISA section 3.1.3.1). However, it should be noted that studies
21 utilizing a mouthpiece exposure system cannot be directly compared to studies involving freely
22 breathing subjects, as nasal absorption of SC>2 is bypassed during oral breathing, thus allowing a
23 greater fraction of inhaled SC>2 to reach the tracheobronchial airways. As a result, individuals
24 exposed to SC>2 through a mouthpiece are likely to experience greater respiratory effects from a
25 given SC>2 exposure.
26 Controlled human exposure studies published since the 1994 Supplement to the Second
27 Addendum have provided additional evidence of short-term peak 862 exposures resulting in
28 respiratory symptoms in asthmatics at elevated ventilation rates (ISA, section 3.1.3.1). In a study
29 conducted by Gong et al. (1995), unmedicated SO2-sensitive asthmatics were exposed to 0-, 500-
30 and 1000 ppb SC>2 for 10 minutes, while performing different levels of exercise (light, medium,
31 or heavy). The authors found that respiratory symptoms increased with increasing SC>2
March 2009 25 Draft - Do Not Quote or Cite
-------
1 concentrations. Moreover, they found that exposure to 500 ppb 862 during light exercise evoked
2 a more severe symptomatic response than heavy exercise in clean air. In addition, Trenga et al.
3 (1999) observed a correlation between decreases in FEVi and increases in respiratory symptoms
4 following 10-minute exposures to 500 ppb SC^by mouthpiece.
5 4.2.3 Lung function
6 The ISA notes that it has been clearly established that subjects with asthma are more
7 sensitive to the respiratory effects of SC>2 exposure than healthy individuals (ISA, section
8 3.1.3.2). Asthmatic individuals exposed to SC>2 concentrations as low as 200-300 ppb for 5-10
9 min during exercise have been shown to experience moderate or greater bronchoconstriction,
10 measured as an increase in specific airway resistance (sRaw) of > 100% or decrease in FEVi of >
11 15% after correction for exercise-induced responses in clean air (Bethel et al., 1983; Linn et al.,
12 1983, 1984, 1987; 1988; 1990; Magnussen et al., 1990; Roger et al., 1985). Moreover, the ISA
13 finds that among asthmatics, both the magnitude of SC>2-induced lung function decrements and
14 the percent of individuals affected have been shown to increase with increasing 5- to 10-minute
15 SC>2 exposures in the range of 200 to 1000 ppb.
16 The ISA finds supporting evidence for SCVinduced decrements in lung function from
17 more recently published studies. Gong et al. (1995) found that increasing 862 concentrations
18 resulted in both a decrease in FEVi, as well as an increase in sRaw. This same study found that
19 increasing the concentration of 862 had a greater effect on sRaw and FEVi than increasing the
20 level of exercise. In a separate study, following a 10-minute exposure to 500 ppb 862 by
21 mouthpiece (see caveat in section 4.2.2), Trenga et al. (1999) observed that 25 out of 47
22 exercising adult asthmatics experienced a > 8% decrease in FEVi versus baseline (mean decrease
23 = 17.2%).
24 4.2.4 Decrements in Lung Function in the Presence of Respiratory Symptoms
25 SO2-induced decrements in lung function (increased sRaw and decreased FEVi) have
26 frequently been associated with increases in respiratory symptoms among asthmatics at elevated
27 ventilation rates (Balmes et al., 1987; Gong et al., 1995; Linn et al., 1983b; 1987; 1988; 1990).
28 For example, Linn et al. (1987) exposed 40 asthmatics during 10-minute periods of exercise to 0,
29 200, 400, and 600 ppb SO2 and the individual results were made available to EPA (Smith, 1994).
30 In brief, this study found that after adjusting for effects of exercise in clean air, exposure to 600
March 2009 26 Draft - Do Not Quote or Cite
-------
1 ppb SC>2 resulted in 21 of the 40 subjects experiencing moderate or greater decrements in lung
2 function. Of these 21 responders, 14 (67%) also experienced mild to severe respiratory
3 symptoms. In the same study, 14 asthmatics experienced moderate or greater decrements in lung
4 function in response to 400 ppb SC>2, five of whom (36%) also experienced mild to moderate
5 respiratory symptoms. In addition, five asthmatics experienced moderate or greater decrements
6 in lung function in response to 200 ppb SC>2 (i.e. the lowest concentration tested), one of whom
7 (20%) also experienced mild respiratory symptoms.
8 4.2.5 Medication as an Effect Modifier
9 The ISA reports that quick-relief and long-term-control asthma medications have been
10 shown to provide varying degrees of protection against SCVinduced bronchoconstriction in mild
11 and moderate asthmatics (ISA section 3.1.3.2 and Annex Table D-l). More specifically, while no
12 therapy has been shown to completely eliminate SO2-induced respiratory effects in exercising
13 asthmatics, some short- and long-acting asthma medications are capable of significantly reducing
14 SC>2-induced bronchoconstriction (Gong et al., 1996; 2001; Koenig et al., 1987; Linn et al.,
15 1990). However, the ISA notes that asthma is often poorly controlled even among severe
16 asthmatics due to inadequate drug therapy or poor compliance among those who are on regular
17 medication (Rabe et al., 2004). Moreover, the ISA also notes that mild asthmatics, who
18 constitute the majority of asthmatic individuals, are much less likely to use asthma medication
19 than asthmatics with more severe disease (O'Byrne, 2007; Rabe et al., 2004). Therefore, the ISA
20 finds that it is reasonable to conclude that all asthmatics (i.e. mild, moderate, and severe), are at
21 high risk of experiencing adverse respiratory effects from SO2 exposure (ISA section 3.1.3.2).
22 4.3 SHORT-TERM (> 1-HOUR, GENERALLY 24-HOUR) SO2 EXPOSURE
23 AND RESPIRATORY HEALTH EFFECTS
24 In addition to the human clinical evidence described above (section 4.2), the ISA also
25 bases its causal determination for an association between exposure to short-term (5-minutes to
26 24-hour) SO2 and respiratory morbidity on results from epidemiological studies. More
27 specifically, this section will focus on the epidemiological results presented in the ISA with
28 regard to respiratory symptoms, as well as hospitalization and ED visits for all respiratory causes
29 and asthma. This is because the ISA emphasizes that epidemiological results from these studies
30 provide "supporting evidence" for its determination of causality (ISA section 5.2).
March 2009 27 Draft - Do Not Quote or Cite
-------
1 4.3.1 Respiratory Symptoms
2 The ISA finds that the strongest epidemiological evidence for an association between
3 short-term 862 concentrations and respiratory symptoms was in children, and comes from two
4 large U.S. multi-city studies: the National Cooperative Inner-City Asthma Study (NCICAS;
5 Mortimer et al., 2002; ISA section 3.1.4.1.), and the Childhood Asthma Management Program
6 (CAMP; Schildcrout et al., 2006; ISA section 3.1.4.1). Both of these studies found significant
7 associations between the level of SC>2 concentration and the risk of respiratory symptoms in
8 asthmatic children (Mortimer et al., 2002; Schildcrout et al., 2006;). However, it should be noted
9 that the Harvard Six Cities Study (Schwartz et al., 1994) suggested that the association between
10 SC>2 and respiratory symptoms in children could be confounded by PMi0; the authors found that
11 the effect of 862 was substantially diminished after adjustment for PMio in copollutant models
12 (ISA, section 3.1.4.1). These key studies are discussed in more detail below.
13 The National Cooperative Inner-City Asthma Study (NCICAS, Mortimer et al. 2002)
14 included asthmatic children (n = 846) from eight U.S. urban areas and examined the relationship
15 between respiratory symptoms and summertime air pollution levels. The strongest associations
16 were found between morning symptoms (e.g. morning cough) and the median 3-hour average
17 SC>2 concentrations during morning hours (8 a.m. to 11 a.m.)- following a 1- to 2-day lag (ISA,
18 Figure 3-2). Three hour average concentrations in the morning hours ranged from 17 ppb in
19 Detroit to 37 ppb in East Harlem, NY. This relationship remained robust and statistically
20 significant in multi-pollutant models with ozone (63), and nitrogen dioxide (NO2). When PMio
21 was also added to the model, the effect estimate was similar although no longer statistically
22 significant (ISA, Figure 3-2), but the ISA notes that this loss of statistical significance could have
23 been the result of reduced statistical power (only three of eight cities were included in this
24 analysis) or collinearity resulting from adjustment of multiple pollutants (ISA, section 3.1.4.1).
25 The Childhood Asthma Management Program (CAMP, Schildcrout et al. 2006) examined
26 the association between ambient air pollution and asthma exacerbations in children (n = 990)
27 from eight North American cities. The median 24-hour average SC>2 concentrations (collected in
28 seven of the eight study locations) ranged from 2.2 ppb in San Diego to 7.4 ppb in St. Louis. All
29 lag structures were positively associated with an increased risk of asthma symptoms, but only the
30 3-day moving average was statistically significant (ISA, Figure 3-3). In joint-pollutant models
31 with carbon monoxide (CO) and NC>2, the 3-day moving average effect estimates remained
March 2009 28 Draft - Do Not Quote or Cite
-------
1 robust and statistically significant. In a joint-pollutant model with PMio, the 3-day moving
2 average effect estimate remained robust, but was no longer statistically significant (ISA Figure 3-
3 3).
4 A longitudinal study of 1,844 schoolchildren during the summer months from the
5 Harvard Six Cities Study suggested that the association between SC>2 and respiratory symptoms
6 may potentially be confounded by PMio (Schwartz et al., 1994). It should be noted that unlike
7 the NCICAS and CAMP studies, this study was not limited to asthmatic children. The median
8 24-hour average 862 concentration during this period was 4.1 ppb (10th-90th percentile: 0.8,
9 17.9; maximum 81.9). 862 concentrations were found to be associated with cough incidence and
10 lower respiratory tract symptoms. However, the effect of 862 was substantially reduced and no
11 longer statistically significant after adjustment for PMi0. PMio had the strongest association with
12 respiratory symptoms, and the effect of PMio remained robust in copollutant models. Because
13 PMio concentrations were correlated strongly to SO2-derived sulfate particles (r = 0.80), the
14 reduced SO2 effect estimate may indicate that for PMio dominated by fine sulfate particles, PMio
15 has a slightly stronger association than SC>2 to cough incidence and lower respiratory symptoms
16 (ISA, section 3.1.4.1.1).
17 In addition to epidemiological studies examining the relationship between ambient 862
18 concentrations and respiratory symptoms in children, the ISA also describes studies that looked
19 for associations between 862 levels and respiratory symptoms in adults (ISA, section 3.1.4.1).
20 The ISA notes that compared to the number of epidemiological studies examining the association
21 between SC>2 exposure and respiratory symptoms in children, fewer studies examined this
22 association in adults. Moreover, results in adults were mixed; some studies demonstrated positive
23 associations while others showed no relationship at ambient SC>2 levels (ISA, section 3.1.4.1).
24 4.3.2 ED Visits and Hospitalizations for All Respiratory Causes
25 Respiratory causes for ED and hospitalization visits typically include asthma, pneumonia,
26 bronchitis, emphysema, upper and lower respiratory infections, as well as other minor categories.
27 Overall, the ISA concludes that these studies provide evidence to support an association between
28 ambient 862 concentrations and ED visits and hospitalizations for all respiratory causes (ISA,
29 section 3.1.4.6). The ISA also finds that when analyses are restricted by age, the results among
30 children (0-14 years) and older adults (65+ years) are mainly positive, but not always statistically
31 significant (ISA, section 3.1.4.6). When all age groups are combined, the ISA finds that the
March 2009 29 Draft - Do Not Quote or Cite
-------
1 results of studies are mainly positive; however, the excess risk estimates are generally smaller
2 compared to children and older adults (ISA, Figure 3-6). Results from key epidemiological
3 studies conducted in the U.S. and Canada are described below, and a more detailed discussion of
4 both the U.S. and international epidemiological literature can be found in the ISA (ISA, section
5 3.1.4.6).
6 Wilson et al., (2005) examined the association between SC>2 levels and ED visits for all
7 respiratory causes in Portland, ME (54,000 ED visits) and Manchester, NH (30,000 ED visits).
8 The authors found a negative association in Portland when analyses were limited to children. In
9 Portland, they found a positive and statistically significant 9% (95% CI: 5, 14) excess risk per 10
10 ppb increase in 24-hour average 862 in adults. Largest effects were observed among the elderly,
11 with a 16% (95% CI: 7, 26) excess risk per 10 ppb increase in 24-hour average SC>2. When all
12 ages were combined, a positive and statistically significant 7% (95% CI: 3, 12) excess risk per 10
13 ppb increase in 24-hour average SC>2 was observed in Portland. No relationship was observed
14 between SC>2 concentrations and ED visits for all respiratory causes in Manchester in the
15 analyses of all ages or any age-stratified group.
16 Schwartz (1995) conducted a study in New Haven, CT and Tacoma, WA evaluating the
17 relationship between hospital admissions for all respiratory causes (n ~ 8,800 in New Haven and
18 n ~ 4,600 in Tacoma) and ambient 862 concentrations in older adults (65+ years). The average
19 24-hour 862 concentration was 29.8 ppb in New Haven and 16.8 ppb in Tacoma. This study
20 found positive associations between hospitalizations and SO2, with a 2% (95% CI: 1, 3) excess
21 risk in New Haven and 3% (95% CI: 1, 6) excess risk in Tacoma per 10 ppb increase in 24-hour
22 average SC>2. Notably, the effect estimate for New Haven remained robust and statistically
23 significant in two-pollutant models with PMio, but in Tacoma was substantially reduced and no
24 longer statistically significant (ISA, Figure 3-8). Additional evidence for an association between
25 SC>2 exposure and hospital admissions for all respiratory causes in older adults was found in two
26 studies conducted in Vancouver, BC. Fung et al., (2006) and Yang et al., (2003) both found
27 positive associations between hospitalizations and 24-hour average 862 concentrations in older
28 adults.
29 Peel et al., (2005) investigated the relationship between 1-hour maximum SC>2
30 concentrations and respiratory ED visits (n ~ 480,000) for all ages in Atlanta, GA. The mean 1-
31 hour maximum SO2 concentration was 16.5 ppb. A weak and statistically non-significant
March 2009 30 Draft - Do Not Quote or Cite
-------
1 relationship was observed for respiratory ED visits. Specifically, Peel et al., (2005) found an
2 excess risk of 1.6% (95% CI: -0.6, 3.8) per 40 ppb increase in 1-hour maximum SO2. Tolbert et
3 al (2007) recently reanalyzed the data from this study along with four additional years of data
and found similar results
5 4.3.3 Emergency Department Visits and Hospitalizations for Asthma
6 The ISA also finds evidence of an association between SO2 levels and ED visits and
7 hospitalizations for asthma. The document notes that most of the effect estimates associated with
8 asthma ED visits are positive (suggesting an association with ambient 802), although few are
9 statistically significant (ISA, section 3.1.4.6). In an analysis encompassing all ages, Wilson et
10 al., (2005) found a statistically significant positive association between asthma ED visits and
1 1 862, with an 1 1% (95% CI: 2, 20) excess risk per 10 ppb increase in 24-hour average 862 in
12 Portland, ME. In Manchester NH, the authors found a positive, although not statistically
13 significant association with a 6% (95% CI: -4, 17) excess risk per 10 ppb increase in 24-hour
14 average SC>2. Ito et al., (2007) also examined the association between SC>2 and asthma ED visits
15 in all ages. This study was conducted in New York City and found a 6% (95% CI: 3, 10) excess
16 risk per 10 ppb increase in 24-hour average SC>2 in all year analyses. Multipollutant analyses
17 were conducted in data limited to the warm season only. While the 862 effect estimate was
18 robust and remained statistically significant after adjustment for PM2.s, 63, and CO in two-
19 pollutant models, it was found to diminish to null when adjusting for NC>2. Peel et al., (2005)
20 also examined the association between asthma ED visits and ambient 862. This study was
21 conducted in Atlanta and found a null association between ED visits for asthma and 1-hour
22 maximum SO2 levels. In addition to these ED studies, a hospital admissions study conducted by
23 the New York Department of Health (NY DOH, 2006) found a statistically significant 10% (95%
24 CI: 5, 15) excess risk for asthma hospital admissions per 10 ppb increase in 24-hour average SO2
25 for residents of the Bronx, but a null association for those living in Manhattan.
26 In three Ohio cities, Jaffe et al., (2003) examined the association between 862
27 concentrations and asthma ED visits among asthmatics, aged 5-34 years. The mean 24-hour
28 average 862 concentrations were 14 ppb in Cincinnati, 15 ppb in Cleveland, and 4 ppb in
29 Columbus. A statistically significant association was observed in the multeity analysis. The
30 authors found an excess risk of 6% (95% CI: 1, 1 1) per 10 ppb increase in 24-hour average SO2.
31 In the city-stratified analyses, statistically significant associations were observed for Cincinnati
March 2009 3 1 Draft - Do Not Quote or Cite
-------
1 (17% [95% CI: 5, 31]), but not in Cleveland (3% [95% [CI -4, 11]) or Columbus (13% [95% CI:
2 -14,49]).
3 Lin et al., (2004b) conducted a case-control study of children aged 0-14 years in Bronx
4 County, NY. The authors examined the potential association between daily ambient SO2
5 concentrations (categorized into quartiles of both average and maximum levels) and cases
6 admitted into the hospital for asthma, or controls who were admitted for reasons other than
7 asthma. The results of this study demonstrated that cases were exposed to higher daily average
8 concentrations of 862 than controls. When the highest exposure quartile (>20 ppb, 24-h average
9 862) was compared with the lowest (2.9-9.4 ppb, 24-h average SCh), the odds ratios (ORs) were
10 strongest when a 3-day lag was employed (OR 2.16 [95% CI: 1.77, 2.65]). However, the results
11 were positive and statistically significant for all lag days examined. Lin et al., (2005) observed a
12 weak positive association between hospitalizations for asthma and SC>2 among girls, and a null
13 association for boys in a Toronto, ON study (mean 24-h average SO2 of 5.36 ppb [SD 5.90]). In
14 addition to these hospitalization studies, Wilson et al. (2005) found a positive, but not
15 statistically significant 5% (95% CI -12, 25) excess risk per 10 ppb increase in 24-hour average
16 SO2 for asthma ED visits in Portland, ME, and a positive, but not statistically significant 20%
17 (95% CI -3, 49) excess risk in Manchester, NH among children aged 0-14 years.
March 2009 32 Draft - Do Not Quote or Cite
-------
i 5.0 IDENTIFICATION OF POTENTIAL ALTERNATIVE
2 STANDARDS FOR ANALYSIS
3 5.1 INTRODUCTION
4 The primary goals of the SC>2 risk and exposure assessment described in this draft
5 document are to estimate short-term exposures and potential human health risks associated with
6 1) recent levels of ambient SO2; 2) SC>2 levels associated with just meeting the current standards;
7 and 3) 862 levels associated with just meeting potential alternative standards. This section
8 identifies potential alternative standards to be included in the quantitative analyses discussed in
9 Chapters 7 through 9. The potential alternative standards to be analyzed are defined in terms of
10 indicator, averaging time, form, and level and this chapter provides the rationale that was used
11 for their selection
12 5.2 INDICTATOR
13 The SOX include multiple gaseous (e.g., SO2, SO3) and particulate (e.g., sulfate) species.
14 In considering the appropriateness of different indicators, we note that the health effects
15 associated with particulate species of SOX have been considered within the context of the health
16 effects of ambient particles in the Agency's review of the PM NAAQS. Thus, as discussed in
17 the integrated review plan (2007a), the current review of the SC>2 NAAQS is focused on the
18 gaseous species of SOX and will not consider health effects directly associated with particulate
19 species of SOX. Of the gaseous species, EPA has historically determined it appropriate to specify
20 the indicator of the standard in terms of 862 because other gaseous sulfur oxides (e.g., 863) are
21 likely to be found at concentrations many orders of magnitude lower than SO2 in the atmosphere,
22 and because most all of the health effects evidence and exposure information is related to SC>2.
23 The final ISA has again found this to be the case and therefore, staff finds that SC>2 remains the
24 most appropriate indicator for the alternative standards to be analyzed in this document.
25 5.3 AVERAGING TIME
26 Staff finds that the most robust evidence for SC>2-induced respiratory morbidity exists for
27 exposure durations < 1-hour. The strongest evidence for this finding comes from controlled
28 human exposure studies that have consistently demonstrated that exposure to SC>2 for 5-10
29 minutes can result in significant bronchoconstriction and/or respiratory symptoms in exercising
March 2009 33 Draft - Do Not Quote or Cite
-------
1 asthmatics (see section 4.2). In fact, the ISA describes the controlled human exposure studies as
2 being the "definitive evidence" for its causal determination between 862 exposure and short-
3 term respiratory morbidity (ISA, section 5.2). In addition to these controlled human exposure
4 studies, there is a relatively small body of epidemiological evidence describing positive
5 associations between 1-hour maximum SC>2 levels and respiratory symptoms as well as hospital
6 admissions and ED visits for all respiratory causes and asthma (ISA, Tables 5.4 and 5.5). In
7 addition to the 1-hour epidemiological evidence, there is a considerably larger body of
8 epidemiological studies reporting associations between 24-hour average 862 levels and
9 respiratory symptoms, as well as hospitalizations and ED visits; however, the ISA notes that it is
10 possible that associations observed in these 24-hour studies are being driven, at least in part, by
11 short-term peaks of duration < 24-hours of SO2. More specifically, when describing
12 epidemiological studies observing associations between ambient SC>2 and respiratory symptoms,
13 the ISA states "that it is possible that these associations are determined in large part by peak
14 exposures within a 24-hour period" (ISA, section 5.2). The ISA also states that the respiratory
15 effects following peak SC>2 exposures in controlled human exposure studies provides a basis for a
16 progression of respiratory morbidity that could result in increased ED visits and hospital
17 admissions (ISA, section 5.2). It should also be noted that epidemiological studies conducted in
18 Paris, France (Dab et al., 1996) and in Manhattan and Bronx, NY (NY DOH, 2006) used both
19 24-hour average and 1-hour daily maximum air quality levels and found similar effect estimates
20 with regard to hospital admissions for all respiratory causes (Dab et al., 1996) and asthma ED
21 visits (NY DOH, 2006). Finally, in addition to the controlled human exposure and
22 epidemiological evidence, the ISA describes key toxicological studies with exposures ranging
23 from minutes to hours resulting in decrements in lung function, airway inflammation, and/or
24 hyperresponsiveness in laboratory animals (ISA, Table 5-2).
25 The scientific evidence described above suggests that at a minimum, averaging time(s)
26 selected for further risk and exposure analyses should address respiratory effects associated with
27 SC>2 exposures of < 1-hour. We note that analyses conducted in the ISA demonstrate that at
28 monitors measuring all twelve 5-minute SC>2 levels in an hour (n=16), there is a high Pearson
29 correlation between the 5-minute maximum level and the corresponding 1-hour average SC>2
30 concentration, with only one monitor observing a correlation < 0.9 (ISA, section 2.5.2; ISA,
31 Table 2-12). Thus, for the purpose of conducting quantitative exposure and risk analyses staff
March 2009 34 Draft - Do Not Quote or Cite
-------
1 concluded that the focus should be on potential alternative 862 standards with an averaging time
2 of 1-hour. Staff believes that alternative standards with an averaging time of 1-hour will limit
3 both 5-minute peak concentrations within an hour, as well as other peak SO2 concentrations (> 1-
4 hour) that are likely in part, driving the respiratory outcomes described in epidemiological
5 studies.
6 Staff also considered examining alternative 5-minute standards in the risk and exposure
7 assessment, but concluded for several reasons that such an analysis would be of questionable
8 utility in the decision-making process. We note that EPA historically conducts air quality,
9 exposure, and risk analyses of alternative standards by adjusting measured, not modeled air
10 quality data. This is an issue in evaluating alternative 5-minute standards for 862 because there
11 were, and continue to be relatively few locations reporting 5-minute SO2 concentrations. As
12 described in Appendix A, from 1997-2007, there were a total of 98 monitors in 13 states and the
13 District of Columbia measuring maximum 5-minute SCh concentrations in an hour. In
14 comparison, there were 933 monitors in 49 states, the District of Columbia, Puerto Rico and the
15 Virgin Islands measuring 1-hour SC>2 concentrations. Moreover, it is important to consider that
16 those monitors reporting 5-minute concentrations do not represent data from a dedicated 5-
17 minute monitoring network, but rather a voluntary submission of 5-minute values from monitors
18 placed for the purpose of evaluating attainment of 24-hour and annual average 862 NAAQS.
19 Thus, staff has little confidence that this limited set of data, from monitors sited for a different
20 purpose, can provide the input required for a comprehensive air quality, exposure, and risk
21 analysis of a much shorter averaging time standard. In fact, given the spatial heterogeneity of 5-
22 minute peaks, and the aforementioned issues with monitor siting, staff is not confident (based on
23 5-minute monitoring data alone) that even in the 13 locations reporting 5-minute concentrations,
24 that those reported values adequately reflect the extent to which 5-minute peaks are occurring in
25 those areas.
26 While we have chosen to evaluate alternative 1-hour averaging time standards in the air
27 quality, exposure, and risk chapters of this document, it does not preclude the possibility of
28 considering 5-minute standards as part of the policy assessment discussion in Chapter 10, or
29 during the rulemaking process. Consideration of potential alternative 5-minute standards could
30 be based on evidence-based considerations, drawn from the discussion of the scientific evidence
31 related to 5-10 minute exposures from the ISA, and presented below in Chapter 10.
March 2009 3 5 Draft - Do Not Quote or Cite
-------
l 5.4 FORM
2 In evaluating alternative forms of primary standards to be analyzed in the risk and
3 exposure chapters, staff recognizes that it is important to have a form that: (1) reflects the health
4 risks posed by elevated 862 concentrations and (2) achieves a balance between limiting the
5 occurrence of peak concentrations and providing a stable and robust regulatory target.
6 Consistent with judgments made in recent reviews of the PM (71FR61144) and Oj (73 FR
7 16436) NAAQS, staff judges that a concentration-based form for the SC>2 standard would better
8 reflect the health risks and would provide greater stability than a form based on expected
9 exceedances. This is because a concentration-based form gives proportionally greater weight to
10 1-hour daily maximum values when concentrations are well above the level of the standard than
11 to 1-hour daily maximum values when the concentrations are just above the level of the standard.
12 In contrast, an expected exceedance form would give the same weight to a 1-hour daily
13 maximum concentration that just exceeds the level of the standard as to a 1-hour daily maximum
14 concentration that greatly exceeds the level of the standard. Therefore, a concentration-based
15 form better reflects the continuum of health risks posed by increasing SC>2 concentrations (i.e. the
16 percentage of asthmatics affected and the severity of the response increases with increasing SC>2
17 concentrations). The most recent review of the PM NAAQS (completed in 2006) judged that
18 using a 98th percentile form averaged over 3 years provides an appropriate balance between
19 limiting the occurrence of peak concentrations and providing a stable regulatory target (71 FR
20 61144). In that review, staff also considered other forms within the range of the 95th to the 99th
21 percentiles. In making recommendations regarding the form, staff considered the impact on risk
22 of different forms, the year-to-year stability in the air quality statistic, and the extent to which
23 different forms of the standard would allow different numbers of days per year to be above the
24 level of the standard in areas that achieve the standard. Based on these considerations, staff
25 recommended either a 98th percentile form or a 99th percentile form. We have made similar
26 judgments in identifying an appropriate range of forms for potential alternative 1-hour daily
27 maximum 862 standards. As a result of these judgments, we have determined it appropriate here
28 to consider 98th and 99th percentile 862 concentrations averaged over 3 years. We have judged
29 that the 98th and 99th percentile, when combined with the range of alternatives identified for the
30 level of a new 1-hour standard (see below), will likely offer a sufficient range of options to
March 2009 36 Draft - Do Not Quote or Cite
-------
1 balance the objective of providing a stable regulatory target against the objective of limiting the
2 occurrence of peak 5-minute to 1-hour 862 concentrations.
3 5.5 LEVEL
4 When considering the appropriate range of levels for alternative 1-hour daily maximum
5 standards to analyze in the exposure and risk assessments described in this document, staff
6 examined both the controlled human exposure and epidemiological evidence evaluated in the
7 ISA. Controlled human exposure evidence demonstrates that there is a continuum of SO2-related
8 health effects following 5-10 minute peak SC>2 exposures in exercising asthmatics. That is, the
9 ISA finds that the percentage of asthmatics affected and the severity of the response increases
10 with increasing SC>2 concentrations. At concentrations ranging from 200 ppb-300 ppb, 5-30%
11 percent of exercising asthmatics are likely to experience moderate or greater bronchoconstriction
12 (ISA, Table 3-1). At concentrations > 400 ppb, moderate or greater bronchoconstriction occurs
13 in 20-60% of exercising asthmatics, and compared to exposures at 200-300 ppb, a larger
14 percentage of subjects experience severe bronchoconstriction (ISA, Table 3-1). Moreover, at
15 concentrations > 400 ppb, moderate or greater bronchoconstriction was frequently accompanied
16 with respiratory symptoms (ISA, Table3-l).
17 In addition to the controlled human exposure evidence, we also considered the
18 epidemiological evidence, as well as an air quality analysis conducted by staff characterizing 1-
19 hour daily maximum 862 air quality levels in cities and time periods corresponding to key U.S.
20 and Canadian ED visit and hospital admission studies for all respiratory causes and asthma2 (key
21 studies are identified in Table 5-5 of the ISA). Figures 5-1 to 5-5 show standardized effect
22 estimates and the 98th and 99th percentile 1-hour daily maximum SO2 levels for locations and
23 time periods corresponding to these key U.S. (Figures 5-1 to 5-4) and Canadian3 (Figure 5-5)
Authors of relevant U.S. and Canadian studies were contacted and air quality statistics from the study monitor that
recorded the highest SO2 levels were requested. In some cases, U.S. authors provided the AQS monitor IDs used in
their studies and the statistics from the highest reporting monitor were calculated by EPA. In cases where U.S.
authors were unable to provide the requested data (Schwartz 1995, Schwartz 1996, and Jaffe 2005), EPA identified
the maximum reporting monitor from all monitors located in the study area and calculated the 98th and 99th
percentile statistics (see Thompson 2009).
3 The Canadian statistics presented in Figure 5-5 were calculated from a data set provided by Dr. Richard Burnett
and were used for all relevant single city studies on which he was an author. Note that air quality statistics presented
for Canadian studies are likely not directly comparable to those presented for U.S. studies. This is because SO2
concentrations presented for Canadian studies represent the 98th and 99th percentile 1-hour daily maximum SO2
concentrations across a given city, rather than concentrations from the single monitor that recorded the highest 98th
and 99th percentile SO2 levels in a given city (see Thompson, 2009).
March 2009 37 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
10
11
12
studies. In general, staff finds that the results presented in these figures demonstrate that most of
these epidemiological studies show positive, although frequently not statistically significant
associations with SO2. Furthermore, we find that Figures 5-1 to 5-5 demonstrate that positive
effect estimates, including some that are statistically significant, are found in locations that span
a broad range of 98th and 99th percentile 1-hour daily maximum SC>2 concentrations (98th
percentile range: 19-401 ppb; 99th percentile range: 21-457 ppb). Thus, staff finds it appropriate
to utilize the 1-hour daily maximum air quality data presented in these figures to help inform
both the upper and lower ranges of alternative 862 standards to be analyzed in the air quality,
exposure, and risk assessments described in this REA.
24-hour effect estimates
A
1-hour effect estimates
A
Si
S.
15-
10-
-5-
-10-
^
Wilson 2005
Manchester
1-hr99: 69
1-hr98: 59
EDRC
EDRA
Legend:
EDRE
EDRW
Wilson 2005
Portland
1-hr99: 47
1-hr98: 36
EDRE
EDRW
EDRA
EDRC
EDRA ED visits for all respiratory causes- all ages
EDRC: ED vis ts for all respiratory causes- children
EDRW: ED visits for all respiratory causes- ages 15-64
EDRE: ED visits for all respiratory causes-ages 65+
( \
Peel 2005
Atlanta
1-hr99: 81
1-hr98: 70
EDRA
EDRA
\
I
Tolbert 2007
Atlanta
1-hr99: 76
1-hr98: 62
Figure 5-1. Effect estimates for U.S. all respiratory ED visit studies and associated 98th and 99th
percentile 1-hour daily maximum SO2 levels.
March 2009
38
Draft - Do Not Quote or Cite
-------
24-hour effect estimates
.ŧ
K 40
^
| Wilson 2005
Manchester
1-hr99: 69
1-hr98:59
EDAC
EDAA
EDAE
EDAW
Wilson 2005
Portland
1-hr99' 47
1-hr98:36
EDAA
-
EDAE
EDAC
EDAW
NYDOH 2006
Manhattan
1-hr99: 80
1-hr98: 62
ED/
EDAA
\A
r
Bronx
1-hr99: 78
1-hr98:65
Jaffe 2003
Columbus
1-hr 99: 51
1-hr98:42
Cincinnati
EDAJ
1-hr99:457
1-hr98:401
EDAJ
m
ED/
\J
Cleveland
1-hr99:211
1-hrĢ
8: 175
,_
Ito 2007
New York
1-hr99:82
1-hr 98: 71
EDAA
f
Legend:
EDAA: ED visits for asthma- all ages
EDAC: ED visits for asthma- children
EDAW: ED visits for asthma- ages 15-64
EDAE: ED visits for asthma- ages 65+
EDAJ: ED visits for asthma- ages 5-34
3 Figure 5-2. 24-hour effect estimates for U.S. asthma ED visit studies and associated 98th and 99th
4 percentile 1-hour daily maximum SO2 levels.
March 2009
39
Draft - Do Not Quote or Cite
-------
1-hour effect estimates
Peel 2005
EDAA
NYDOH 2006
EDAA
EDAA
1 -5
a
Legend:
EDAA: ED visits for asthma- all ages
2 Figure 5-3.1 -hour effect estimates for U.S. asthma ED visit studies and associated 98th and 99th
3 percentile 1-hour daily maximum SO2 levels.
March 2009
40
Draft - Do Not Quote or Cite
-------
24-hour effect estimates
35 -1-=
30 -
25 -
20 -
10 -
5 -
-5 -
--in -
Schwartz 1995
Tacoma
1-hr99: 100
1-hr98: 89
New Haven
1-hr99: 150
1-hr98: 126
HARE
HARE
1
1
Schwartz 1996
Cleveland
1-hr 99' 170
1-hr98: 150
HARE
f
Sheppard 2003
Seattle
1-hr99:84
1-hr98: 70
HAAS
Linn 2004
HA
AL
Bronx
1-hr98:93
Legend:
HARE: Hospital admissions for all respiratory causes- ages 65+
HAAS: Hospital admissions for asthma- ages <65
HAAL: Hospital admissions for asthma- ages 0-14
1
2
3
4
5
Figure 5-4. 24-hour effect estimates for U.S. hospitalization studies and associated 98th and 99th
percentile 1-hour daily maximum SO2 levels.4
1 There were no key U.S. hospitalization studies with 1-hour effect estimates identified in Table 5-5 of the ISA
March 2009
41
Draft - Do Not Quote or Cite
-------
24-hour effect estimates
A
1-hour effect estimate
A
24-hour effect estimates
Yang 2003
HARY
HARE
Burnett 1997
HARA
Linn 2003
HAAG
HAAB
Legend:
HARY: Hospital admissions for all respiratory causes- ages <3
HARE: Hospital admissions for all respiratory causes- ages 65+
HARA: Hospital admissions for all respiratory causes- all ages
HAAB: Hospital admissions for asthma- boys ages 6-12
HAAG: Hospital admissions for asthma- girls ages 6-12
Figure 5-5. Effect estimates for Canadian ED visits and hospitalization studies and associated 98
ith
nth
4 and 99 percentile 1-hour daily maximum SO2 levels.
5
6 The highest 99th percentile 1-hour daily maximum air quality levels were found in
7 analyses conducted in the cities of Cincinnati (Figure 5-2), Cleveland (Figures 5-2 and 5-4) and
8 New Haven (Figure 5-4). These studies showed positive associations5 with respiratory-related
9 hospital admissions or ED visits during time periods when 98th and 99th percentile 1-hour daily
10 maximum SO2 concentrations ranged from 126 ppb to 457 ppb. Notably, this range of 1-hour
11 daily maximum SO2 levels overlaps considerably with 5-10 minute SO2 concentrations (> 200
12 ppb) that have consistently been shown in controlled human exposure studies to result in lung
13 function responses in exercising asthmatics. Of particular concern are the air quality levels that
14 were found in Cincinnati (Jaffe et al., 2003). The 98th and 99th percentile 1-hour daily maximum
15 SC>2 concentrations were in excess of 400 ppb. Notably, levels > 400 ppb have consistently been
' Results in Cincinnati (Jaffe et al., 2003) and New Haven (Schwartz et al., 1996) were statistically significant.
March 2009
42
Draft - Do Not Quote or Cite
-------
1 shown in human exposure studies to result in moderate or greater bronchoconstriction in the
2 presence of respiratory symptoms in a considerable percentage of exercising asthmatics. As a
3 result, staff judges that the upper bound of alternative standard levels to be analyzed should be
4 250 ppb. We find that it is reasonable to suggest that a 98th or 99th percentile 1-hour daily
5 maximum standard at this level will both substantially limit the number of days when the 1-hour
6 daily maximum SC>2 concentration is > 200 ppb, while also potentially limiting the number of 5-
7 10 minute SO2 peaks > 400 ppb.
8 In identifying the lower end of the range of alternative standards to be analyzed, staff
9 again considered controlled human exposure and epidemiological evidence. However, with
10 regard to the controlled human exposure evidence, several additional factors were considered.
11 First, it is important to consider that the subjects in human exposure studies do not necessarily
12 represent the most SC>2 sensitive asthmatics; that is, these studies included mild and moderate,
13 but not severe asthmatics. Also while human clinical studies have been conducted in
14 adolescents, younger children have not been included in these exposure studies, and thus, it is
15 possible asthmatic children represent a population that is more sensitive to the respiratory effects
16 of SC>2 than the individuals who have been examined to date. Moreover, it is important to
17 consider that 5-30% of asthmatics who engaged in moderate or greater exertion experienced
18 bronchoconstriction following exposure to 200-300 ppb SC>2, which is the lowest level tested in
19 breathing chamber studies (ISA, Table 3-1)6. Thus, it is highly likely that a subset of the
20 asthmatic population would also experience bronchoconstriction following exposure to levels
21 lower than 200 ppb.
22 In addition to the consideration of controlled human exposure evidence mentioned above,
23 we note that Figure 5-5 contains epidemiological analyses observing associations between
24 ambient SC>2 concentrations and hospital admissions in Canadian cities where 1-hour daily
25 maximum 862 levels were < 41 ppb. More specifically, positive associations between 862 and
26 hospital admissions were found in Toronto, (Burnett al., 1997) and Vancouver (Yang et. al.,
27 2003) when 99th percentile 1-hour daily maximum SC>2 levels were approximately 21 ppb and 41
28 ppb, respectively. Moreover, in a U.S. study, Delfino et al., (2003) observed an association
6 The ISA cites one chamber study with intermittent exercise where healthy and asthmatic children were exposed to
100 ppb SO2 in a mixture with ozone and sulfuric acid. The ISA notes that compared to exposure to filtered air,
exposure to the pollutant mix did not result in statistically significant changes in lung function or respiratory
symptoms (ISA section 3.1.3.4)
March 2009 43 Draft - Do Not Quote or Cite
-------
1 between ambient SC>2 and respiratory symptoms in Hispanic children when the maximum 1-hour
2 SC>2 concentration in Los Angeles was 26 ppb (ISA Table 5-4). However, it should be noted that
3 the association reported in the Vancouver study was not statistically significant in either single,
4 or multipollutant models with Os, and the study did not examine the potential for confounding by
5 PM (Figure 5-5; ISA Table 5-5). In addition, while the association observed in the Toronto study
6 was statistically significant in a single pollutant model, the effect estimate was substantially
7 diminished and no longer statistically significant in a multi-pollutant model with PMi0 (ISA,
8 Table 5-5). Finally, the epidemiological study conducted in Los Angeles (Delfmo et al., 2003;
9 ISA, Table 5-4) was very small (n=22), and did not examine potential confounding by co-
10 pollutants. Thus, staff finds that this evidence alone is not sufficient to warrant inclusion of
11 alternative 1-hour daily maximum standards at levels below 50 ppb in the risk and exposure
12 assessments.
13 In contrast to the epidemiological evidence in cities and over time frames when 1-hour
14 daily maximum SC>2 concentrations were < 46 ppb, staff finds relatively stronger evidence of an
15 association between SC>2 and hospital admissions and ED visits in cities and over time frames
16 when 98th and 99th percentile 1-hour daily maximum SC>2 concentrations ranged from 47 to 100
17 ppb (Figures 5-1 to 5-5). More specifically, the majority of epidemiological studies in this range
18 observed positive associations between ambient 862 levels and increased hospital admissions
19 and increased ED visits for all respiratory causes or asthma. Moreover, although most of these
20 positive effect estimates were not statistically significant, there were some statistically significant
21 results in single pollutant models (Portland, Wilson, 1995; Bronx, NYDOH, 2006; NYC, Ito,
22 2006; and Schwartz, 1995), as well as limited evidence of statistically significant associations in
23 multi-pollutant models with PM7 (Bronx, NYDOH, 2006; NYC, Ito, 2006; New Haven,
24 Schwartz 1995). Given these epidemiological and air quality results, as well as the
25 considerations mentioned above regarding the controlled human exposure evidence, staff
26 concluded it was appropriate to examine a range of alternative standards in the air quality,
27 exposure, and risk analyses that includes a level of 50 ppb as the lower bound. Staff believes
28 that a 98th or 99th percentile 1-hour daily maximum standard at this level would both limit the
7 In the NYDOH study (2006), the Bronx positive effect estimate remained statistically significant in the presence of
PM2 5 In Ito et al., (2006), the NYC positive effect estimate was statistically significant in the presence of PM2 5
during the warm season. In Schwartz et al., (1995), the positive effect estimate in New Haven remained statistically
significant in the presence of PM10.
March 2009 44 Draft - Do Not Quote or Cite
-------
1 number of days when 1-hour daily maximum SC>2 levels are > 50 ppb, while also limiting 5-10
2 minute peaks of 862 > 100 ppb. Moreover, we note that a level of 50 ppb is substantially below
3 the 98th and 99th percentile 1-hour daily maximum SC>2 levels observed in the Bronx during the
4 NYDOH analysis and in NYC during the period analyzed by Ito et al., (2006): two studies where
5 the SC>2 effect estimate remained robust and statistically significant in multi-pollutant models
6 with PM2.5 (ISA, Table 5-5).
March 2009 45 Draft - Do Not Quote or Cite
-------
i 6.0 OVERVIEW OF RISK CHARACTERIZATION AND
2 EXPOSURE ASSESSMENT
3 6.1 INTRODUCTION
4 The assessments presented in the subsequent chapters of this document will characterize
5 short-term exposures (i.e., 5-minutes) and potential health risks associated with: (1) recent
6 ambient levels of 862, (2) levels associated with just meeting the current 862 NAAQS, and (3)
7 levels associated with just meeting potential alternative standards (see chapter 5 of this document
8 for the discussion of potential alternative standards). To characterize health risks, we will
9 employ three approaches (Figure 6-1). With each approach, we will characterize health risks
10 associated with the air quality scenarios mentioned above (i.e., recent air quality unadjusted, air
11 quality adjusted to simulate just meeting the current standards, and air quality adjusted to
12 simulate just meeting potential alternative standards). In the first approach, SC>2 air quality levels
13 are compared to potential health effect benchmark values (see section 6.2) derived from the
14 controlled human exposure literature (Chapter 7). In the second approach, modeled estimates of
15 human exposure are compared to the same potential health effect benchmark values derived from
16 the human exposure literature (Chapter 8). In the third approach, outputs from the exposure
17 analysis are combined with exposure-response functions derived from the human clinical
18 literature to estimate the number and percent of exposed asthmatics that would experience
19 moderate or greater lung function responses under the different air quality scenarios (Chapter 9).
20 A more detailed overview of each of these approaches to characterizing potential health risks is
21 provided below (section 6.3), and each approach is described in more detail in their respective
22 chapters and associated appendices. In addition, this chapter also describes important
23 methodologies used throughout these analyses. That is, estimation of 5-minute 862
24 concentrations from 1-hour data (section 6.4) and adjustment of recent air quality to simulate just
25 meeting the current, as well as potential alternative SO2 standards (section 6.5).
March 2009 46 Draft - Do Not Quote or Cite
-------
Evaluation of Human Clinical
Evidence in the ISA
5-10 minute exposures in
exercising asthmatics
Identification of
potential 5-minute
health benchmark
values: 100- 400 ppb
\
Air Quality Analysis in
locations across U.S.
Informs location
selection
Exposure Analysis: modeling exercise
that assesses the likelihood an asthmatic
will be at moderate or greater exertion
while experiencing a 5-minute SO2
concentration above a benchmark level
Quantitative Risk Analysis:
combines outputs of the
exposure analysis with estimated
exposure-response function
Outputl: Number of times per year
SO2 concentrations at ambient
monitors exceed 5-minute potential
benchmark values
Output 2: The probability of a daily
5-minute exceedance of benchmark
values given a particular 24-hour
average, or 1-hour daily maximum
SO2 concentration.
Output: Estimates the number of
times per year asthmatics at elevated
ventilation rates experience SO2
concentrations exceeding 5-minute
potential benchmark values
Output: Estimates the number and
percentage of exposed asthmatics
that would experience moderate or
greater lung function decrements
2 Figure 6-1. Overview of analyses addressing exposures and risks associated with 5-minute peak
3 SO2 exposures. All three outputs are calculated considering current air quality, air
4 quality just meeting the current standards, and air quality just meeting potential
5 alternative standards. Note: this schematic was modified from Figure 1-1.
7 6.2 POTENTIAL HEALTH EFFECT BENCHMARK LEVELS
8 Potential health benchmark values to be used in the air quality, exposure, and risk
9 analyses are derived solely from the human exposure literature. This is primarily because
10 concentrations used in clinical studies represent actual personal exposures rather than
11 concentrations measured at fixed site ambient monitors. In addition, human exposure studies can
12 examine the health effects of 862 in the absence of co-pollutants that can confound results in
13 epidemiological analyses; thus, health effects observed in clinical studies can confidently be
14 attributed to a defined exposure level of SC>2.
15 The ISA presents human exposure evidence demonstrating decrements in lung function
16 in 5-30% of exercising asthmatics exposed to 200-300 ppb SC>2 for 5-10 minutes. However, it is
17 important to note: (1) subjects in human exposure studies do not include individuals who may be
18 most susceptible to the respiratory effects of 862, (e.g. severe asthmatics and children) and (2)
March 2009
47
Draft - Do Not Quote or Cite
-------
1 given that 5-30% of exercising asthmatics experienced bronchoconstriction following exposure
2 to 200 -300 ppb SC>2 (the lowest levels tested in free-breathing chamber studies), it is likely that a
3 percentage of asthmatics would also experience bronchoconstriction following exposure to levels
4 lower than 200 ppb. Considering this information, staff finds it appropriate to examine potential
5 5-minute benchmark values in the range of 100- 400 ppb. The lower end of the range considers
6 the factors mentioned above, while the upper end of the range recognizes that 400 ppb represents
7 the lowest concentration at which statistically significant decrements in lung function are seen in
8 conjunction with statistically significant respiratory symptoms. Moreover, we note that this
9 range of benchmark values is in general agreement with consensus CAS AC comments on the
10 first draft REA.
11 6.3 APPROACH FOR ASSESSING EXPOSURE AND RISK
12 ASSOCIATED WITH 5-MINUTE PEAK SO2 EXPOSURES
13 In the air quality characterization, we have compared SC>2 air quality with the potential
14 health effect benchmark levels for SC>2. Scenario-driven air quality analyses were performed
15 using ambient SC>2 concentrations for the years 1997 though 2006. All U.S. monitoring sites
16 where 1-hour 862 data have been collected are represented by this analysis and, as such, the
17 results generated are considered a broad characterization of national air quality and potential
18 human exposures that might be associated with these concentrations. An advantage of this
19 approach is its relative simplicity; however, there is uncertainty associated with the assumption
20 that SC>2 air quality can serve as an adequate indication of exposure to ambient SC>2. Actual
21 exposures will be influenced by factors not considered by this approach, such as the spatial and
22 temporal variability in human activities.
23 In the second approach, we have used an inhalation exposure model to generate estimates
24 of personal exposures. Estimates of personal exposure have also been compared to the potential
25 SC>2 health benchmark levels as was done in the air quality characterization. This results in
26 estimates of the number of individuals that are likely to experience exposures exceeding these
27 benchmark levels. For this exposure analysis, a probabilistic approach was used to model
28 individual exposures considering the time people spend in different microenvironments and the
29 variable SC>2 concentrations that occur within these microenvironments across time, space, and
30 microenvironment type. The model also accounts for activities that individuals perform within
31 the microenvironments, allowing for estimation of exposures that coincide with varying activity
March 2009 48 Draft - Do Not Quote or Cite
-------
1 levels. As such, this approach to assessing exposures was more resource intensive than
2 evaluating ambient air quality; therefore, at this time staff has included the analysis of two
3 specific locations in the U.S. (Greene County, MO. and St. Louis, MO.)8. Although the
4 geographic scope of this analysis is restricted, the approach provides realistic estimates of SO2
5 exposures, particularly those exposures associated with important emission sources of SO2 and
6 serves to complement the broad air quality characterization.
7 For the characterization of risks in both the air quality analysis and the exposure
8 modeling analysis described above, staff has used a range of short-term potential health effect
9 benchmarks. The levels of potential benchmarks are based on SO2 exposure levels that have
10 been associated with respiratory symptoms and decrements in lung function in exercising
11 asthmatics during controlled human exposure studies (ISA, section 5.2; see above section 4.2 for
12 discussion). Benchmark values of 100, 200, 300, and 400 ppb have been compared to both SO2
13 air quality (measured and modeled 5-minute concentrations) and to estimates of SO2 exposure.
14 In characterizing the SO2 air quality using ambient monitors, the output of the analysis is an
15 estimate of the number of times per year specific locations experience 5-minute daily maximum
16 levels of SO2 that exceed a particular benchmark. When personal exposures are simulated, the
17 output of the analysis is an estimate of the number of individuals at risk for experiencing daily
18 maximum 5-minute levels of SO2 of ambient origin that exceed a particular benchmark. An
19 advantage of using potential health effect benchmark levels to characterize health risks is that the
20 effects observed in controlled human exposure studies clearly result from SO2 exposure. This is
21 in contrast to health effects associated with SO2 in epidemiologic studies, which may also be
22 associated with pollutants that co-occur with SO2 in the ambient air. Thus, when using
23 epidemiologic studies as the basis for risk characterization, the unique contribution of SO2 to a
24 particular health effect may be difficult to quantify. A disadvantage of the potential benchmark
25 approach is that the magnitude of the SO2 effect on respiratory morbidity can vary considerably
26 from individual to individual and not all asthmatics would be expected to respond to the same
In the document titled Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient
Air Quality Standard: First Draft, staff presented the results of an exposure analysis for Greene County (or
Springfield, MO.) and several other source-based modeling domains. Based on CASAC comments received on that
exposure analysis, we have refined our approach and applied those refinements to the Greene County analysis
presented in this document and completed the exposure assessment in St. Louis which had been started at the time of
the earlier draft.
March 2009 49 Draft - Do Not Quote or Cite
-------
1 levels of SC>2 exposure. Therefore, the public health impacts of SCVinduced respiratory
2 morbidity are difficult to quantify.
3 The third approach is a quantitative risk assessment combining outputs from the exposure
4 analysis with estimated exposure-response functions based on data from controlled human
5 exposure studies. This analysis estimates the percentage, and number of asthmatics likely to
6 experience a given decrement in lung function associated with recent air quality and SC>2 levels
7 adjusted to simulate just meeting the current, and potential alternative standards.
8 6.4 APPROACH FOR ESTIMATING 5-MINUTE PEAK SO2
9 CONCENTRATIONS
10 Health effects evaluated in this REA include those associated with 5-10 minute peak
11 concentrations of SO2. While there are 98 ambient monitors that have reported 5-minute SO2
12 concentrations some time during 1997-2007, the spatial and temporal representation is limited to
13 a few states and often only a few years of monitoring. Most of these monitors report the 5-
14 minute maximum SC>2 concentration occurring within an hour, though there were a few that
15 reported all twelve continuous 5-minute SC>2 concentrations measured within the hour. The
16 ambient monitors reporting continuous 862 values are limited to fewer locations and number of
17 monitoring years, with sixteen monitors deployed within six US states and Washington DC, ten
18 of which operated only during one year. The overwhelming majority of the SC>2 ambient
19 monitoring data are for 1-hour average concentrations (upwards to 935 monitors), comprising a
20 broad monitoring network that includes most U.S. states and territories. Because the health
21 effects of greatest interest were associated with short-term exposures (5-10 minutes) and a
22 greater number of monitors and monitor-years were available for the 5-minute maximum SC>2
23 concentrations than 10 minute concentrations, a model was developed to estimate 5-minute
24 maximum SC>2 concentrations from the comprehensive 1-hour SC>2 ambient monitoring data.
25 Staff first reviewed the air quality characterization conducted in the prior 862 NAAQS
26 review and supplementary analyses, where the relationship between the maximum 5-minute 862
27 concentration and the 1-hour average SO2 concentration, or peak-to-mean ratios (PMRs) were
28 initially evaluated and used to approximate 5-minute maximum SC>2 concentrations (EPA,
29 1986a; EPA, 1994b; SAI, 1995; Thompson, 2000). While the relationship between the two
30 metrics is not expected to be linear, the temporal patterns in the two averaging times are
31 consistent. Five-minute maximum SC>2 concentrations are often much greater than that of the
March 2009 50 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
corresponding 1-hour 862 concentrations, and observed increases in a given 1-hour 862
concentration often coincide with increases in the 5-minute maximum SC>2 concentration. As an
example of this pattern, the time-series of 1-hour average and 5-minute maximum SO2
concentrations measured at an ambient monitor across a 3-day period in 2005 is illustrated in
Figure 6-2.
March 10-12, 2005 - ID 290770037
300
o
m
o
o
CM
o
2 were measured) and then used to estimate the occurrence of peak 5-minute SC>2
concentrations given a 1-hour ambient 862 concentration, generally as follows:
March 2009
51
Draft - Do Not Quote or Cite
-------
1 Cmax_5 =PMRxCl_hour equation (6-1)
2 where,
3 Cmax-s = estimated 5-minute maximum 862 concentration (ppb)
4 PMR = peak-to-mean ratio (PMR)
5 Ci-hour = measured 1-hour average SO2 concentration
6
7 At the time of the last NAAQS review, there were very few monitors reporting 5-minute
8 SC>2 data. In fact, distributions of PMRs from ambient monitors surrounding a single coal-fired
9 power utility served as the primary source used in estimating 5-minute peak concentrations used
10 in the exposure analyses (EPA, 1994b). As mentioned above, the PMRs were determined to be
11 approximately two in these earlier studies; however, the ratio can vary depending on a several
12 factors. It has been shown that there can be increased variability in the ratio with decreasing 1-
13 hour average SO2 concentrations, that is, there is a greater likelihood of values greater than two
14 at low hourly average concentrations than expected at high hourly average concentrations (EPA,
15 1986a). It has also been argued that the occurrence of short-term peak concentrations at ambient
16 monitors may be influenced by particular SC>2 emission sources (EPA, 1994b). Different sources
17 may have variable emission amounts, temporal operating patterns (e.g., seasonal, time-of-day),
18 facility maintenance, and other physical parameters (e.g., stack height, area terrain) that could
19 contribute to variability in 5-minute maximum 862 concentrations. In addition, a sensitivity
20 analysis conducted for copper-smelters determined that distance from the source was inversely
21 proportional to the PMR in all three of the 1-hour mean stratifications evaluated (i.e., < 0.04
22 ppm, 0.04 to < 0.15 ppm, and >0.15 ppm), with the highest 1-hour category having the lowest
23 range of PMR (Sciences International, 1995).9
24 There are some data available for the current SC>2 monitoring network regarding the type
25 of sources that may be near the ambient monitors, the magnitude of emissions, the temporal
26 variation in emissions, and distance from specific sources; however, staff determined that there
27 was no practical way to define every ambient monitor as being exclusively influenced by a single
28 source or a defined mix of sources. Given other conditions that may vary within a specific
29 source category (monitor-to-source distances, local meteorology, operating conditions, etc.), staff
9 In that analysis, normalized 1-hour SO2 concentrations were obtained by dividing by the maximum hourly
concentration.
March 2009 52 Draft - Do Not Quote or Cite
-------
1 also determined that there was no practical way to use such data quantitatively in the
2 construction of the PMR statistical model and apply such a model to the 1-hour 862 ambient
3 monitor data.
4 In recognizing the limited geographic span of the monitors reporting the 5-minute
5 maximum SC>2 concentrations and the overall uncertainty regarding the amount of influence of a
6 specific source on any given monitor, staff developed an approach based on hourly SC>2
7 concentration levels and the variability observed at the monitors reporting both the 5-minute
8 maximum 1-hour average 862 concentrations. The main assumption in the approach is that the
9 temporal and spatial pattern in 862 source emissions is influenced by the type of source(s)
10 present, its operating conditions, and that the emission pattern(s) is reflected in the ambient 862
11 concentration distribution measured at the monitor. Thus, measures of concentration level and
12 associated variability at each monitor were used as a surrogate for the variability in the source
13 characteristics that may impact concentrations at a particular monitor. Each monitor reporting 5-
14 minute maximum SC>2 concentrations was categorized based on the coefficient of variation
15 (COV) of 1-hour average SC>2 concentrations and then used to estimate distribution of PMRs for
16 range of 1-hour 862 concentrations. This approach is detailed in section 7.2.3.
17 6.5 APPROACH FOR SIMULATING THE CURRENT AND
18 ALTERNATIVE STANDARDS
19 A primary goal of this draft of the risk and exposure assessments is to evaluate the ability
20 of the current SC>2 standards (0.03 ppm annual average, 0.14 ppm 24-hour average) and potential
21 alternative standards (99th percentile 1-hour daily maximum SC>2 levels of 50, 100, 150, 200, and
22 250 ppb, and 98th percentile 1-hour daily maximum SC>2 levels: 200 ppb; see chapter 5 of this
23 document) to protect public health. In order to evaluate the ability of a specific standard to
24 protect public health, ambient 862 concentrations need to be adjusted such that they simulate
25 levels of 862 that just meet that standard. Such adjustments allow comparisons of the level of
26 public health protection that could be associated with just meeting the current and potential
27 alternative standards.
28 All areas of the United States currently have ambient SC>2 levels below the current annual
29 standard (EPA, 2007c). One site in Northampton County, Pa., measured concentrations above
30 the level of the 24-hour standard in 2006. Therefore, in order to evaluate whether the current
31 standards adequately protect public health, nearly all SC>2 concentrations need to be adjusted
March 2009 53 Draft - Do Not Quote or Cite
-------
1 upwards for all areas included in our assessment in order to simulate levels of 862 that would
2 just meet the current standard levels. Similarly, to simulate a potential standard that is below
3 current air quality levels, those current levels must be adjusted downward.
4 This procedure for adjusting ambient concentrations was necessary to provide insight into
5 the degree of exposure and risk which would be associated with an increase in ambient SC>2
6 levels such that the levels were just at or near the current standards in the areas analyzed. Staff
7 recognizes that it is extremely unlikely that SC>2 concentrations in any of the selected areas where
8 concentrations have been adjusted would rise to meet the current NAAQS and that there is
9 considerable uncertainty associated with the simulation of conditions that would just meet the
10 current standards. Nevertheless, this procedure was necessary to assess the ability of the current
11 standards, not current ambient SO2 concentrations, to protect public health. This process of
12 adjusting air quality to simulate just meeting a specific standard is described in more detail
13 below.
14 6.5.1 Adjustment of Ambient Air Quality
15 Ambient SC>2 concentrations were characterized in Chapter 7 by considering air quality as
16 is and several hypothetical air quality scenarios. Each of the hypothetical air quality scenarios
17 had an ambient concentration target, derived from the form and level of the current NAAQS or
18 from potential alternative standards. An overview of the approach used to adjust the ambient air
19 quality is provided in the following, with additional details in the approach and application
20 provided in section 7.2.4.
21 In developing a simulation approach to adjust air quality to meet a particular standard
22 level, policy-relevant background (PRB) levels in the U.S. were first considered. As described in
23 section 2.3, PRB is well below concentrations that might cause potential health effects at most
24 locations. Policy-relevant background will not be considered separately in any characterization
25 of health risk associated with as is air quality or air quality just meeting the current standards. In
26 monitoring locations where PRB is expected to be of particular importance however (e.g.,
27 Hawaii County, HI) data will be noted as under possible influence of natural rather than
28 anthropogenic sources and will not be used in analyses simulating air quality that would just
29 meet the current or potential alternative standards.
30 While annual average concentrations have declined significantly over the time period of
31 analysis, the variability in the concentrations (both the 5-minute and 1-hour SO2 concentrations)
March 2009 54 Draft - Do Not Quote or Cite
-------
1 have remained relatively constant. This trend is apparent when considering the air quality data
2 collectively (section 7.4.3) and when considering individual locations (Rizzo, 2009). As an
3 example, Figure 6-3 compares the pattern in daily maximum SO2 1-hour concentration
4 percentiles at the two ambient monitors in Beaver County PA that were in operation as far back
5 as 1978 and are currently part of the monitoring network. Staff selected a recent year of data to
6 constitute a low concentration year along with an historical year of data (1992) constituting a
7 high concentration year (2007), each of the years were common to both monitors. As shown in
8 the figure, the relationships between the low and high concentration years at each of the daily
9 maximum concentration percentiles are mostly linear, with R2 values above 0.98. Where
10 deviation from linearity did occur in many of the comparisons performed, it occurred primarily
11 at the extreme upper or lower portions of the distribution, often times at the maximum daily
12 maximum or the minimum daily maximum 1-hour SC>2 concentration (Rizzo, 2009). In addition,
13 the absolute values for simple linear regression intercepts were typically 1-3 ppb (Rizzo, 2008).
14 This indicates that the rate of decrease in ambient air quality concentrations at the mean value for
15 the monitors evaluated is consistent with the rate of change at the lower and upper daily
16 maximum 1-hour concentration percentiles. This evaluation provides support for the use of a
17 proportional approach to adjust current ambient concentrations to represent air quality under both
18 the current and alternative standard scenarios.
19
March 2009 55 Draft - Do Not Quote or Cite
-------
High Year: 1992 Low Year: 2007
0.00
I
0.05 0.10 0.15
i i i
0.20 0.25 0.30
i i i
1
2
3
4
5
9
10
11
12
13
14
15
16
17
18
0.20 -
a.
CL
o
<§ °-15
OJ
Q_
(T3
O
0.10 -
0.05 -
o.oo -
420Q700Q2
RA2: 0.99
L
-------
1 where,
2 FJJ = Adjustment factor derived from either the 24-hour or the annual
3 average concentrations at monitors in location / for yeary (unitless)
4 S = concentration values allowed that would just meet the current NAAQS
5 (either 144 ppb for 24-hour or 30.4 ppb for annual average)
6 Cmax,ij = the maximum 2nd highest daily mean SC>2 concentration at a monitor in
7 county /' and yeary or the maximum annual average SC>2 concentration
8 at a monitor in location /' and yeary (ppb)
9
10 In these cases where staff simulated a proportional adjustment in ambient SC>2
11 concentrations using equation (6-2), it was assumed that the current temporal and spatial
12 distribution of air concentrations (as characterized by the current air quality data) is maintained
13 and increased 862 emissions contribute to increased 862 concentrations, with the highest
14 monitor (in terms of annual averages) being adjusted so that it just meets either the current 0.03
15 ppm annual average standard or the 0.14 ppm 24-hour standard, whichever is the controlling
16 standard.10 Values for each air quality adjustment factor used for each location evaluated in the
17 air quality and risk characterization are given in Appendix A (section A.3). For each county and
18 calendar year, all the hourly SC>2 concentrations in a count were multiplied by the same constant
19 value F to make the highest annual mean equal to 30.4 ppb or the 2nd highest 24-hour average
20 equal to 144 ppb for that location and year.
21 For example, of five monitors measuring hourly 862 in Cuyahoga County for year 2001
22 (Figure 6-4, top), the maximum annual average concentration was 7.5 ppb (ID 390350060),
23 giving an adjustment factor ofF = 30.4/7.5 = 4.06 for that year. The 2nd highest 24-hour SO2
24 concentration was 35.5 (ID 390350038) giving an adjustment factor of F = 144/35.5 = 4.05 for
25 year 2001. Because the adjustment factor derived from the 24-hour concentration was lower,
26 4.05 was selected as the factor to adjust air quality to just meet the current standard. All 1-hour
27 concentrations measured at all monitoring sites in Cuyahoga County were multiplied by 4.05,
10 The controlling standard by definition would be the standard that allows air quality to just meet either the annual
concentration level of 30.4 ppb (i.e., the annual standard is the controlling standard) or the 2nd highest 24-hour
concentration level of 144 ppb (i.e, the 24-hour standard is the controlling standard). The factor selected is derived
from a single monitor within each county (even if there is more than one monitor in the county) for a given year. A
different (or the same) monitor in each county could be used to derive the factor for other years; the only
requirement for selection is that it be the lowest factor, whether derived from the annual or 24-hour standard level.
March 2009 57 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
10
11
12
13
resulting in an upward scaling of hourly 862 concentrations for that year. Therefore, one
monitoring site in Cuyahoga County for year 2001 would have an 2nd highest 24-hour average
concentration of 144 ppb, while all other monitoring sites would have a 2nd highest 24-hour
average concentration below that value, although still proportionally scaled up by 4.05 (Figure (
4, bottom). Then, using the adjusted hourly concentrations to simulate just meeting the current
standard, the metrics of interest (i.e., annual mean SC>2 concentration, 24-hour average SC>2
concentration, 1-hour daily maximum SC>2 concentration, and the number of potential health
effect benchmark exceedances) were estimated for each site-year.
90 -
80 -
Ģ
I 70-
5 60-
Ģ
ŧ 50 -,
2 40 J
| 30-
O
20-
10-
Cumulative Percentile
joj.ucria>--jo3
-------
1
2
3
4
5
6
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Proportional adjustment factors were also derived considering the form, averaging time,
and levels of the potential alternative standards under consideration. Discussion regarding the
staff selection of each of these components is provided in chapter 5 of this document. The 98th
and 99th percentile 1-hour daily maximum SO2 concentrations averaged across three years of
monitoring were used in calculating the adjustment factors at each of five standard levels as
follows:
Fm = S,
equation (6-3)
s max,?
where,
Si
Ca
= SO2 concentration adjustment factor (unitless) in location /' given alternative
standard percentile form k and standard level / across a 3-year period
= Standard level / (i.e., 50, 100, 150, 200, and 250 ppb 1-hour SO2 concentration
(ppb))
= Selected percentile k (i.e., 98th or 99th) 1-hour daily maximum SO2
concentration at a monitor in location /' (ppb) for each yeary
As described above for adjustments made in simulating just meeting the current
standards, it was assumed that the current temporal and spatial distribution of air concentrations
(as characterized by the current SO2 air quality data) is maintained and increased SO2 emissions
contribute to increased SO2 concentrations, with the highest monitor (in terms of the 3-year
average at the 98th or 99th percentile) being adjusted so that it just meets the level of the
particular 1-hour alternative standard. Since the alternative standard levels range from 50 ppb
through 250 ppb, both proportional upward and downward adjustments were made to the 1-hour
ambient SO2 concentrations. The values for each air quality adjustment factor used for each
location evaluated in the air quality and risk characterization are given in Appendix A (section
A.3). Due to the form of the alternative standards, the expected utility of such an analysis, and
the limited time available to conduct the analysis, only the more recent air quality data were used
(i.e., years 2001-2006). The 1-hour ambient SO2 concentrations were adjusted in a similar
manner described above for just meeting the current standard, however, due to the form of these
March 2009
59
Draft - Do Not Quote or Cite
-------
1 standards, only one factor was derived for two 3-year periods (i.e., 2001-2003, 2004-2006),
2 rather than one factor for each calendar year.
3 6.5.2 Adjustment of Potential Health Effect Benchmark Levels
4 Rather than proportionally modify the air quality concentrations used for input to the
5 exposure modeling described in Chapter 8, staff applied a proportional adjustment to the
6 potential health effect benchmark levels. The benchmark levels were adjusted rather than the air
7 quality to reduce the processing time associated the modeling of several thousands of receptors
8 in each of the large exposure modeling domains. In addition, because the adjustment procedure
9 is proportional, the application of an adjustment of the selected benchmark level (i.e., division by
10 the adjustment factor) is mathematically equivalent to a proportional adjustment of the air quality
11 concentrations (i.e., multiplication by the adjustment factor). The same proportional approach
12 used in the air quality adjustment described above was used in the exposure modeling to scale
13 the benchmark levels to simulate just meeting the current and potential alternative standards. For
14 example, an adjustment factor of 5.10 was determined for Cuyahoga County for year 2002 to
15 simulate ambient concentrations just meeting the current standard, based on a 2nd highest 24-hour
16 average SC>2 concentration of 28.2 ppb observed at an ambient monitor for that year (see
17 Appendix A, section A.3). Therefore, the 5-minute potential health effect benchmark levels of
18 100, 200, 300, and 400 ppb were proportionally adjusted downward to 19.6, 39.2, 58.8, and 78.4
19 ppb, respectively for year 2002.
20 A comparison of the two procedures is presented in Figure 6-5 where air quality is
21 adjusted to simulate just meeting the current annual standard (i.e., the controlling standard in this
22 example) and where the benchmark is adjusted to simulate air quality that just meets the current
23 standard with using the as is air quality. This example used the distribution of hourly SC>2
24 concentrations measured at one ambient monitor (ID 390350045) within the Cuyahoga County
25 modeling domain for year 2002. Both the adjusted and unadjusted 1-hour SC>2 concentrations
26 were input to the statistical model used to estimate 5-minute daily maximum 862 concentrations.
27 If one were interested in the number of exceedances of 5-minute daily maximum SC>2
28 concentrations of 400 ppb under the current standard scenario for example, this would be
29 equivalent to counting the number of exceedances of 5-minute daily maximum SO2
30 concentrations of 78.4 ppb using the as is air quality.
March 2009 60 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
For additional clarity, the same ambient air quality data are presented in Figure 6-6, only
with expansion of the highest percentiles on the graph to allow for the visualization of the
number of exceedances. In using the air quality adjusted to just meet the current standard, i.e.,
the as is air quality was adjusted upwards by a factor 5.10, there were 14 exceedances of a daily
maximum 5-minute concentration of 400 ppb.11 When considering the as is air quality without
adjustment but with a downward adjustment of the benchmark by the same factor of 5.10, there
are the same number of exceedances. This benchmark adjustment procedure was applied by
staff in each of the exposure modeling domains to simulate just meeting the current and
alternative standards. Additional details regarding derivation of the adjusted benchmark levels
used in the exposure modeling are provided in chapter 8 of this document.
MODELED CUYAHOGA COUNTY 5-Minute Daily Max (2002)
As Is and Adjusted to Just Meet the Current Standard (CS)
nuu -
90 -
0,80-
I 70-
50 -
Ŧ 40 -
iso-
°20-
10-
n
>
r
/
"~T~~
1
I
Lj
fy
t 7
\ /
IS
k
I/
ifr-
*--"*
S
-**--
/
/
i
j* *
./
>
--** '
>ŧ*^
vŧ ;-ŧ v
~*~*
* 390350045
. 390350045_cs
^^ Adjust AQ up
-Adjust Benchmark Down
100 200 300 400 500
5-minute Daily Maximum SO2 (ppb)
600
700
Figure 6-5. Comparison of adjusted ambient monitoring concentrations or adjusted benchmark
level (dashed line) to simulate just meeting the current annual average standard at one
ambient monitor in Cuyahoga County for year 2002.
11 Only 12 points are observed in Figure 6-3 however, three peak concentrations were identical within each of the
simulations.
March 2009
61
Draft - Do Not Quote or Cite
-------
o
a.
s
3
E
3
O
00
99
98
97 -
96
QE;
1
If
M
1 ^'
1 1
1
[O 1
U i
f
* 1 "
1
i
o°l
o' i
4
ŧ
>
4
/
>
o 390350045
* 390350045_cs
Adjust AQ up
Adjust Benchmark Down
2
3
4
5
6
7
50 100 150 200 250 300 350 400 450 500 550 600 650 700 750
5-minute Daily Maximum SO2 (ppb)
Figure 6-6. Comparison of the upper percentile modeled 5-minute daily maximum SO2 for where
1-hour ambient SO2 concentrations were adjusted and the benchmark level was
adjusted to simulate just meeting the current annual standard at one ambient monitor
in Cuyahoga County for year 2002. The complete distributions are provided in Figure 6-
4.
March 2009
62
Draft - Do Not Quote or Cite
-------
i 7.0 AMBIENT AIR QUALITY AND BENCHMARK HEALTH RISK
2 CHARACTERIZATION FOR 5-MINUTE PEAK SO2 EXPOSURES
3 7.1 OVERVIEW
4 Ambient monitoring data for each of the years 1997 through 2007 were used in this
5 analysis to characterize SO2 air quality across the U.S. Measured air quality, as well as
6 additional SO2 concentrations derived from the measured air quality data, were used as an
7 indicator of potential human exposure. While an ambient monitor measures SC>2 concentrations
8 at a stationary location, the monitor may well represent the concentrations to which persons
9 residing nearby are exposed. The quality of the extrapolation of ambient monitor concentration
10 to personal exposure depends upon the spatial distribution of important emission sources, the
11 siting of the ambient monitors, local meteorological conditions, and consideration of places that
12 persons visit. It is within this context that the approach for characterizing ambient 862 air
13 quality was designed by staff.
14 As previously mentioned, the ISA finds the evidence for an association between
15 respiratory morbidity and SC>2 exposure to be "sufficient to infer a causal relationship" (ISA
16 section 5.2). The ISA states that the "definitive evidence" for this conclusion comes from the
17 results of human exposure studies demonstrating decrements in lung function and/or respiratory
18 symptoms in exercising asthmatics following exposure to 862 levels as low as 200 to 300 ppb
19 for 5-10 minutes (section 5.2). Accordingly, 5-minute potential health effect benchmark levels
20 ranging from 100-400 ppb were derived from the human exposure literature (see section 6.2 for
21 benchmark level rationale) and compared to measured and statistically modeled 5-minute
22 ambient concentrations. A broad analysis is first presented that evaluates the potential health
23 risk at all ambient monitors, and then for more detailed analyses, at monitors located within
24 selected U.S. counties (see section 7.2.4). Both the number of the 5-minute benchmark
25 exceedances in a year and the probability of benchmark exceedances given 1-hour daily
26 maximum or 24-hour average concentrations were estimated.
27 All ambient monitors report hourly 862 concentrations; a subset of those report 5-minute
28 maximum 862 concentrations as well, with a subset of these reporting continuous 5-minute 862
29 concentrations. Because there were essentially two distinct sample averaging times reported for
30 the available ambient monitoring data (i.e., ambient monitors reporting 1-hour SC>2 concentration
March 2009 63 Draft - Do Not Quote or Cite
-------
1 measurements alone and monitors reporting both 5-minute and 1-hour average 862
2 concentrations), the data used in the analyses were separated by staff as follows. The first set of
3 ambient air quality data is from monitors reporting both 5-minute and 1-hour 862 concentrations.
4 Staff (1) analyzed the ambient monitoring data for trends in 1-hour and 5-minute 862
5 concentrations, (2) counted the number of measured daily 5-minute maximum SO2
6 concentrations above the potential health effect benchmark levels given the annual average SC>2
7 concentrations, (3) estimated the probability of benchmark exceedances given the 24-hour
8 average and 1-hour daily maximum SC>2 concentrations, and (4) developed a statistical model to
9 estimate 5-minute maximum SC>2 concentrations from 1-hour SC>2 concentrations (see section
10 7.2.3). The second set of ambient data was comprised of 1-hour 862 concentrations from the
11 broader 862 monitoring network; therefore this set also includes 1-hour 862 concentrations from
12 those monitors where 5-minute 862 data were reported, though the vast majority of the 1-hour
13 data were from monitors that did not report 5-minute concentration measurements. Staff applied
14 the statistical model that related 5-minute to 1-hour SC>2 measurements to this second set of
15 ambient monitoring data to estimate 5-minute maximum SC>2 concentrations. As was done with
16 the 5-minute SC>2 ambient measurement data, staff evaluated trends in SC>2 concentrations,
17 counted the number of statistically modeled potential health effect benchmark exceedances in a
18 day using the same longer-term averaging times, and estimated the probability of peak
19 concentrations associated with 1-hour daily maximum and 24-hour average 862 concentrations.
20 Staff considered three scenarios in this REA to characterize the ambient 862 air quality.
21 The first scenario involved an evaluation of the combined 5-minute and 1-hour SC>2
22 measurement data as they were reported, representing the conditions at the time of monitoring
23 (termed in this assessment "as is"). The second scenario also considered the as is air quality;
24 however in this scenario staff used the statistically modeled 5-minute SC>2 concentrations based
25 on the 1-hour SC>2 measurements. This second scenario expands the geographic scope of the 5-
26 minute air quality characterization in using the broader SC>2 monitoring network. The third
27 scenario considered ambient 1-hour 862 concentrations simulated to just meeting the current
28 NAAQS12 and each of the potential alternative 1-hour daily maximum standard levels of 50, 100,
12 For consistency, the concentration units in this chapter are reported as ppb, even though NAAQS have units of
ppm. Just meeting the current NAAQS levels could either be meeting a 30 ppb annual average or the 140 ppb 24-
March 2009 64 Draft - Do Not Quote or Cite
-------
1 150, 200 and 250 ppb (see chapter 5 for details). The data used in this third scenario were
2 limited to the most recent and comprehensive ambient monitoring data available (i.e., 2001-
3 2006).13 Due to the form of the alternative standards considered here (98th and 99th percentiles of
4 the 1-hour daily maximum concentrations averaged over 3 years), the recent ambient monitoring
5 data set was evaluated using two three-year groups, 2001-2003 and 2004-2006.14 To summarize,
6 the first scenario is the only scenario that used entirely 1-hour and 5-minute SC>2 measurement
7 data. The second and third scenarios are common in that they both used a simulation procedure
8 to estimate 5-minute concentrations from measured 1-hour SC>2 concentrations, while the third
9 scenario also included an adjustment of the 1-hour SC>2 concentrations to just meet a particular
10 standard level.
11 Staff expected there would be variability in the number of persons living within close
12 proximity of each monitor (both the 5-minute and 1-hour 862 monitors) given the particular
13 siting characteristics of the ambient monitors (e.g., either source- or population-oriented
14 monitoring objectives). Therefore, we separated some of the air quality results within each
15 scenario by using the population density surrounding each ambient monitor. First, each monitor
16 was characterized by having one of three population densities (i.e., low, medium, and high),
17 groupings defined by the three characteristic regions of the population distribution generated
18 from the broader SC>2 monitoring network. Then, staff counted the number of 5-minute
19 benchmark exceedances per year at each monitor, either measured or estimated depending on the
20 scenario considered, and aggregated the monitors by the population density group. Rather than
21 count the total number of 5-minute 862 concentrations above a particular benchmark, staff
22 calculated the number of times in a year the daily 5-minute maximum SO2 concentration
23 exceeded a benchmark.15
hour average concentration (one allowed exceedance), whichever is the controlling standard at that ambient monitor
(see section 6.5 and section 7.2.4).
13 At the time of the initial data download from the AQS data mart, many of the monitors did not have complete
years of data available for 2007, therefore the most recent data for most monitors was from 2006. These data are a
subset of the broader ambient monitoring data set.
14 A number of 3-year groups are within 2001-2006 (e.g., 2001-2003, 2002-2004, etc.) and a number of years of
monitoring data are outside the 2001-2006 time frame that could have been used in an extended 3-year grouping of
2001-2006 air quality (e.g., 2000-2002). For convenience, the upper and lower groupings were chosen by staff to
represent 3-year air quality within the 6-year period when considering just meeting the potential alternative
standards.
15 In the first draft SO2 REA, as well as the early draft NO2 RE As, all benchmark exceedances for any hour of the
day were reported. The use of the daily maximum exceedance was selected in the final NO2 REA as well in this
March 2009 65 Draft - Do Not Quote or Cite
-------
1 Because many of the SC>2 ambient monitoring sites used in this analysis are targeting
2 public health monitoring objectives and the monitoring results are separated by population
3 density groups, staff considers the results a broad characterization of national air quality and
4 potential human exposures that might be associated with these scenario-driven concentrations.
5 One of the outputs of this air quality characterization is an estimate of the number of times per
6 year a monitor experienced daily 5-minute maximum levels of SC>2 above those that may cause
7 adverse health effects in susceptible individuals (i.e., benchmark level exceedances). These
8 counts are a useful metric in comparing one monitor or location to another and in identifying
9 where and when frequent benchmark exceedances occur. The 1st draft SC>2 REA however
10 indicated that the relationship between the annual average 862 concentration and the number of
11 5-minute benchmark exceedances was generally weak; therefore comparison of the number of
12 exceedances in a year with the annual average 862 concentration is of limited use. This absence
13 of a strong relationship highlights the ineffectiveness of long-term averaged concentrations in
14 controlling short-term peak concentrations. In addition, while it was shown in the 1st draft SC>2
15 REA that the number of 5-minute maximum concentrations had an improved relationship with
16 24-hour average concentrations,16 it was also shown that the number of peak concentrations was
17 variable given a specific 24-hour average concentration. Often times the number of 5-minute
18 maximum SC>2 concentrations above benchmark levels was zero for a wide range of 24-hour
19 average 862 concentrations, while in other instances is could as many as five within the same
20 range. In recognizing that there is variability in the number of 5-minute peak 862 concentrations
21 associated with concentrations of longer-term averaging times, that a daily maximum 5-minute
22 SO2 concentration was the metric of interest, and that the potential alternative standards
23 investigated use 1-hour daily maximum SC>2 concentrations, staff decided that a more
24 appropriate comparison would be between the frequency of peak SC>2 concentrations and a given
25 1-hour daily maximum SC>2 concentration. Thus, the second output of this air quality
26 characterization is presented as the probability of a benchmark exceedance given a daily
27 maximum 1-hour SC>2 concentration. For comparison, the probability of a 5-minute benchmark
draft SO2 REA to improve the temporal perspective for the metric (i.e, the number of daily maximum exceedances
also gives the number of days in a year with an exceedance of a selected benchmark).
16 In the first draft SO2 REA, multiple exceedances within a day (if any) were counted. In this draft there is only one
possible exceedance per day.
March 2009 66 Draft - Do Not Quote or Cite
-------
1 exceedance given a 24-hour average concentration is also provided to offer additional
2 perspective on this averaging time.
3 7.2 APPROACH
4 There were five broad steps to characterize the SC>2 air quality. The first step involved
5 compiling and screening the ambient air quality data collected since 1997 to ensure consistency
6 with the 862 NAAQS requirements and for usefulness in this air quality characterization. Next,
7 due to potential variable influence of 862 emission sources on ambient monitor concentrations,
8 the monitors from each of the two data sets (i.e., combined 5-minute and 1-hour, broader 1-hour
9 only) were categorized and evaluated according to their monitoring site attributes, including land
10 use characteristics, location type, monitoring objective, distance to emissions sources, and
11 population density. In addition, the variability in 5-minute and 1-hour SC>2 concentrations was
12 evaluated and used to categorize each ambient monitor for use in development and application of
13 the 5-minute maximum 862 statistical model. Then, criteria based on the measured
14 concentrations proximity to the level of the current standards and the number of exceedances of
15 potential health effect benchmark levels were used to identify specific locations for focused
16 analysis. These locations served as the geographic centers of the current and potential alternative
17 standard analyses. And finally, air quality metrics of interest (i.e., the number and probability of
18 potential health effect benchmark exceedances) were calculated using the air quality data from
19 each scenario.
20 The following provides an overview of the five steps used to characterize air quality and
21 summarizes key portions of the analysis. Briefly, the five steps include: 1) an air quality data
22 screening; 2) evaluation of site characteristics of ambient 862 monitors; 3) development of a
23 statistical model to estimate 5-minute maximum 862 concentrations; 4) selection of locations to
24 evaluate the current and potential alternative standard scenarios; and 5) generation of air quality
25 metrics. Details regarding the ambient monitors used for characterizing air quality and
26 associated descriptive meta-data are provided in Appendix A-l.
27 7.2.1 Air Quality Data Screening
28 SC>2 air quality data and associated documentation from the years 1997 through 2007
29 were downloaded from EPA's Air Quality System for this analysis (EPA, 2007c, d). Data
30 obtained were used as reported; there were no substitutions performed for any missing or zero
March 2009 67 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
concentration data. The available SC>2 ambient monitoring data, collected over either 5-minute
or 1-hour averaging times, are summarized in Table 7-1. The 5-minute SC>2 monitoring data
existed in either one of two forms; the single highest 5-minute concentration occurring in a 1-
hour period (referred to here as max-5 data set), or all twelve 5-minute concentrations within a 1-
hour period (referred to here as continuous-5 data set).
Table 7-1. Summary of all available 5-minute and 1-hour SO2 ambient monitoring
data, years 1997-2007, pre-screened.
Sample Type
Max-5
Continuous-5
1-hour
Number of
Monitors
104
16
935
Number of
States1
13 + DC
6 + DC
49 + DC, PR, VI
Years in
Operation
1997-2007
1999-2007
1997-2007
Number of
Measurements
3,457,057
3,328,725
47,206,918
Notes:
1 DC=District of Columbia, PR=Puerto Rico, VI=Virgin Islands.
Staff evaluated the data for inconsistencies and duplication. The reported measurement
units varied within each of the data sets, therefore the staff converted all concentrations to parts
per billion (ppb). Next staff screened each of the three data sets listed in Table 7-1 for where
monitor IDs had multiple parameter occurrence codes (POCs) and identical monitoring times;
this could indicate that SC>2 concentrations were measured simultaneously at a given location.
These duplicate measures could either result from co-location of ambient monitors (i.e., more
than one instrument) or from duplicate reporting of ambient concentrations (i.e., the 5-minute
maximum concentration in the max-5 data set is the same as the maximum 5-minute
concentration reported from the continuous-5 data set). As a result of this evaluation and
additional concentration level screening (see below), staff constructed several data sets for
analysis in this REA and summarized in Table 7-2 and is described below.
March 2009
68
Draft - Do Not Quote or Cite
-------
Table 7-2. Analytical data sets generated using the continuous-5, max-5, and 1-
hour ambient 862 monitoring data, following screening.
Sample Type
Max-5
Continuous-5
with 1-hour1
1-hour
Within Set
Duplicates
(n)
300,438
0
0
Available
Data
(n)
3,156,619
283,2022
47,188,640
Combined Set
Duplicates
(n)
29,058
258,457
Final
Combined
Max-5 Data
(n)
3,410,763
Final
Combined
1-hour
(n)
47,21 3, 3853
Final
Combined
Max-5 Si-
hour
(n)
2,367,686
Notes:
1 1-hour concentrations from continuous-5 data were calculated from all 5-minute values within the hour.
2 The number of 5-minute maximum samples.
3 There were a total of 24,745 unique 1 -hour values added from the continuous-5 monitors.
1
2 1. Simultaneously reported/measured ambient SO2 data
3 Two data sets were constructed that had multiple 5-minute 862 measurements collected
4 at the same monitoring location and time for:
5 max-5 duplicates (i.e., simultaneous measurements of 5-minute maximum SO2
6 concentrations from co-located max-5 monitors; n=300,438)
7 max-5 and continuous-5 duplicates (i.e., simultaneous 5-minute maximum SO2
8 concentrations reported in max-5 and continuous-5 datasets; n=29,058)
9 A third data set was constructed that had simultaneous 1-hour SC>2 measurements
10 collected at the same monitoring location and time for:
11 1-hour duplicates (i.e., from 1-hour SC>2 monitors and from averaging the continuous-
12 5 monitors; n=258,457)
13 Each of these duplicate data sets were used for quality assurance purposes only, the
14 evaluation of which is presented in Appendix A-2. The duplicate values were not used in the
15 statistical model development or for any other 5-minute 862 concentration analysis.
March 2009
69
Draft - Do Not Quote or Cite
-------
1 2. Combined 5-minute and 1-hour ambient SO2 data
2 A complete set of 5-minute maximum SC>2 concentrations,17 generated from the max-5
3 data set and from the maximum 5-minute concentrations reported by the continuous-5 monitors,
4 was then combined with their corresponding measured 1-hour 862 concentrations (see below).
5 Then, the combined data were screened for validity, recognizing that the combined max-5 and 1-
6 hour SO2 data set may have certain anomalies (e.g., 5-minute maximum SO2 concentrations < 1-
7 hour mean SC>2 concentration). A value of 1 was selected as the lower bound peak-to-mean ratio
8 (PMR), accepting the possibility that the 5-minute maximum concentrations (and all other 5-
9 minute concentrations within the same hour) may be identical to the 1-hour average
10 concentration. A PMR of <12 was selected as the upper bound since it would be a mathematical
11 impossibility to generate a value at or above that given there are twelve 5-minute measurements
12 within any 1-hour period.18 This screening resulted in a total of nearly 2.4 million values
13 comprising the combined 5-minute maximum and 1-hour 862 concentration data set. Staff used
14 this data set to develop a statistical model (section 7.2.3) and in characterizing the measured 5-
15 minute maximum ambient air quality. Details on the monitors used and site attributes (e.g.,
16 latitude, longitude, operating years, monitoring objective) are provided in Appendix A-l.
17 3. Broader 1-hour ambient SO2 data
18 This data set was comprised of all 1-hour SC>2 data, whether obtained from the 1-hour
19 ambient monitoring data set or from averaging 5-minute concentrations from the continuous-5
20 data set. The raw 1-hour data from a total of 935 ambient monitors were first screened for
21 negative concentrations (n=3,555) and for where concentrations were less than 0.1 ppb
22 (n=14,723). The refined 1-hour data were then combined with the 1-hour average concentrations
23 obtained from the continuous 5-monitors. Staff retained the 1-hour average concentrations from
24 the continuous-5 monitors where duplicate values existed. This was done to better maintain the
25 relationship between the 5-minute maximum and 1-hour SC>2 concentrations. Staff removed
26 duplicate 1-hour values identified at each monitoring location originating from the 1-hour and
17 A single 5 -minute and 1 -hour SO2 concentration was used in each of data set 2 and 3. The criteria for selection of
a particular value was first based on whether the 1-hour concentration was calculated from the continuous-5 data
(where present) followed by the monitor ID POC that had the greatest overall number of samples.
18 As the 5-minute maximum concentration approaches infinity, the other 11 concentrations measured in the hour
comparatively tend towards zero, giving a maximum PMR = Peak/Mean = Cm^/IXC,^ + (Cothers-> 0) x 11)/12] <
12.
March 2009 70 Draft - Do Not Quote or Cite
-------
1 continuous-5 monitors for separate analysis (Appendix A-2). The remaining 1-hour SC>2 data set
2 (with duplicate 1-hour values removed) was then combined with the complete 5-minute
3 maximum data set described above (with duplicate 5-minute maximum 862 values removed).
4 Staff used this data set in developing the statistical model to estimate 5-minute maximum SCh
5 concentrations (section 7.2.3).
6 Additional screening of the 1-hour SC>2 data set was performed using a 75%
7 completeness criterion. For a monitor to have a valid year of data, first, valid days were selected
8 as those with at least 18 hours of data. Then, each monitor was required to have 75% of each
9 calendar quarter with complete days (either 68 or 69 days per quartile). This 75% completeness
10 criterion was applied to the available monitoring data to generate 4,692 valid site-years of data
11 obtained from 809 ambient monitors. The number of valid monitoring site-years available as a
12 result of this screening is presented in Table 7-3, effectively encompassing ambient 862
13 monitoring in 48 US States, Washington DC, Puerto Rico and the US Virgin Islands over years
14 1997 through 2006.19 This data set was used in the second data air quality characterization
15 scenario that considered the measured as is 1-hour SC>2 concentrations with statistically modeled
16 5-minute maximum concentrations. Details on the monitors used and site attributes (e.g.,
17 latitude, longitude, operating years, monitoring objective) are provided in Appendix A-l.
19 Based on the version date of the files downloaded from EPA's AQS data mart (6/20/2007), all 1-hour SO2 data
from 2007 were less than complete. In addition, two monitors located in Hawaii County, HI were identified in the
1st draft REA as having concentrations influenced by natural sources. Therefore, monitor IDs 150010005 and
150010007, while meeting the completeness criteria, were removed from the valid 1-hour SO2 data set due to the
influence of volcanic activity on measured SO2 concentrations at these locations. Alaska had no SO2 monitors
during the period of analysis.
March 2009 71 Draft - Do Not Quote or Cite
-------
Table 7-3. Counts of complete and incomplete site-years of 1-hour SO2 ambient
monitoring data for 1997-2006.
State
Abbr.
AL
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
Ml
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
Rl
SC
SD
Code
01
04
05
06
08
09
10
11
12
13
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
44
45
46
Number of Site-Years
Complete
36
44
17
308
33
69
27
10
223
65
31
17
235
276
110
28
104
57
25
10
102
84
74
25
166
121
9
16
63
117
56
229
61
155
309
59
0
398
21
90
7
Incomplete
15
24
14
136
13
18
16
1
76
34
19
10
30
80
33
27
42
11
18
7
33
28
23
11
40
50
13
6
26
21
24
72
29
45
74
32
4
97
2
34
4
Percent
Valid
71
65
55
69
72
79
63
91
75
66
62
63
89
78
77
51
71
84
58
59
76
75
76
69
81
71
41
73
71
85
70
76
68
78
81
65
0
80
91
73
64
Number of Valid
Monitors per year
Minimum
1
1
1
7
1
6
2
1
3
5
2
1
18
13
8
2
2
5
1
1
6
5
5
1
11
2
1
1
3
12
3
21
4
10
28
3
0
33
2
5
1
Maximum
5
6
2
41
6
12
4
1
28
9
4
3
30
34
14
4
13
6
7
3
15
15
12
4
21
18
2
4
11
14
9
24
9
18
35
9
0
51
3
11
3
March 2009
72
Draft - Do Not Quote or Cite
-------
State
Abbr.
TN
TX
UT
VT
VA
WA
WV
Wl
WY
PR
VI
Code
47
48
49
50
51
53
54
55
56
72
78
Total or
Average1
Number of Site-Years
Complete
175
172
33
11
94
18
203
39
3
33
24
4692
Incomplete
70
71
14
4
28
24
28
18
8
32
23
1612
Percent
Valid
71
71
70
73
77
43
88
68
27
51
51
68
Number of Valid
Monitors per year
Minimum
12
10
3
1
8
1
14
2
1
1
1
6
Maximum
23
21
4
2
11
7
25
7
1
6
5
12
Notes:
1 Complete and incomplete site years are summed. The percent valid site-years
and the monitors in operation per year generating valid data are averaged.
1 7.2.2 Site Characteristics of Ambient SO2 Monitors
2 The siting of the monitors is of particular importance, recognizing that proximity to local
3 sources could have an influence on the measured 862 concentration data and subsequent
4 interpretation of the air quality characterization. Staff evaluated the attributes of monitors within
5 each of the two data sets; the first data set comprised of monitors that reported 5-minute
6 maximum SC>2 concentrations, and the second generated from monitors within the broader SC>2
7 monitoring network and having valid 1-hour SC>2 concentrations. Two points are worthy of
8 mention for this analysis; the first being the number of monitors and the second being the
9 potential for differences in types of sources influencing each monitor. While there is overlap in
10 the measurement of 5-minute maximum and its associated 1-hour 862 concentration at some
11 locations (n=98), the remainder of 862 monitors with valid data (n=711) are sited in other
12 locations where 5-minute 862 measurements have not been reported. Staff evaluated the
13 ambient monitor attributes within each data set because there may be influential attributes in the
14 subset of data used to develop the statistical model (i.e., monitors reporting 5-minute maximum
15 SC>2 concentrations) that are not applicable to the broader SC>2 monitoring network.
16 First staff evaluated the specific monitoring site characteristics provided in AQS,
17 including the monitoring objective, measurement scale, and predominant land-use. Additional
18 features such as proximity to SC>2 emission sources and the population residing within various
March 2009
73
Draft - Do Not Quote or Cite
-------
1 distances of each monitor were estimated using monitoring site and emission source geographic
2 coordinates and U.S Census data. Each of these attributes is summarized here to provide
3 perspective on the attributes of where 5-minute maximum 862 concentrations were reported
4 versus the attributes of the broader SC>2 monitoring network. A more thorough discussion of the
5 ambient monitoring network is provided in Chapter 2. Individual monitor site characteristics are
6 given in Appendix A-l.
7 The monitoring objective meta-data field describes the nature of the monitor in terms of
8 its attempt to generally characterize health effects, the presence of point sources, regional
9 transport, or welfare effects. In recognizing that there were variable numbers of ambient
10 monitors in operation and variation in the number of valid site-years available for each data set,
11 the monitoring objectives were weighted by the number of site-years. This was done to provide
12 perspective on the air quality characterization results that are based on the total site-years of data
13 available, not just the number of ambient monitors. In addition, the monitors can have more than
14 one objective. Where multiple objectives were designated, staff selected a single objective to
15 characterize each monitor using the following order: population exposure, source-oriented, high
16 concentration, general/background, unknown. All other objectives (whether known or indicated
17 as "none") were grouped by staff into an "Other" category. Figure 7-1 summarizes the
18 monitoring objectives for each monitoring data set. Each of the data sets had a large proportion
19 of site-years that would target public health objectives through the population exposure and
20 highest concentration categories, though the monitors in the broader 862 monitoring network
21 had a greater percentage than the monitors reporting both 5-minute maximum and 1-hour 862
22 concentrations. The monitors reporting 5-minute concentrations had approximately twice the
23 percentage of site-years from source-oriented monitors when compared with the broader SO2
24 monitoring network.
25 Similarly, the overall measurement scale of the monitors used for the air quality
26 characterization in each location was evaluated based on the weighting of valid site-years of
27 data. The measurement scale represents the air volumes associated with the monitoring area
28 dimensions. While a monitor can have multiple objectives, each monitor typically has only one
29 measurement scale. Figure 7-1 summarizes the measurement scales for the monitoring site-years
30 comprising each data set. Both data sets had their greatest proportion of monitoring site-years
March 2009 74 Draft - Do Not Quote or Cite
-------
1 associated with neighborhood measurement scales (500 m to 4 km), though monitors recording
2 1-hour concentrations had about 22 percentage points greater than the monitors reporting 5-
3 minute maximum concentrations. Furthermore, monitors reporting 5-minute values had a larger
4 proportion of site-years of data characterized at an urban (4 to 50 km) and regional scale (50 km
5 to 1,000 km) compared with the broader SC>2 monitoring network.
6 The land-use meta-data indicate the prevalent land-use within 1A mile of the monitoring
7 site. Figure 7-2 summarizes the land-use surrounding monitors that reported 5-minute
8 maximum concentrations and the broader 1-hour SC>2 monitoring network. Over half of the site-
9 years are from residential and industrial areas and are of similar proportions for both data sets
10 considered. The greatest difference in the surrounding land-use was for the number of site-years
11 associated with monitors sited in agricultural and commercial areas. The monitors reporting 5-
12 minute maximum 862 concentrations had about 10 percentage points more site-years from
13 monitors within agricultural areas and 10 percentage points less in commercial areas when
14 compared to the respective land use of the broader SC>2 monitoring network.
15 The setting is a general description of the environment within which the site is located.
16 Figure 7-2 summarizes the setting of each data set. For monitors reporting 5-minute
17 concentrations, the greatest proportion of site-years is from ambient monitors with a rural setting
18 (49%). Most of the site-years in the broader SC>2 monitoring network were from monitors within
19 a suburban setting (40%).
March 2009 75 Draft - Do Not Quote or Cite
-------
Objective
i
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
2
o
+j
'E
o
^
-------
1
2
3
4
5
6
1
B
o
+j
'E
o
c
If)
9
10
11
12
13
14
15
16
17
18
19
20
21
22
B
o
'E
o
<
Figui
Land-Use
UNKNOWN
2%
RESIDENTIAL
33%
AGRICULTURAL
27%
COMMERCIAL
13%
INDUSTRIAL
20%
DESERT
FOREST
5%
0%
MOBILE
1%
UNKNOWN
1%
AGRICULTURAL
13%
RESIDENTIAL
36%
COMMERCIAL
23%
FOREST
3%
INDUSTRIAL
23%
Setting
URBAN AND
CENTER CITY
21%
SUBURBAN
30%
URBAN AND
CENTER CITY
28%
RURAL
49%
UNKNOWN
1%
RURAL
31%
SUBURBAN
40%
Figure 7-2. Distribution of site-years of data considering land-use and setting: monitors that reported 5-minute maximum SO2
concentrations (top) and the broader SO2 monitoring network (bottom).
March 2009
7T)raft - Do Not Quote or Cite
-------
1 Stationary sources (in particular, power generating utilities using fossil fuels) are the
2 largest contributor to 862 emissions in the U.S. (ISA). First, staff determined the distances,
3 amounts of, and types of stationary source emissions associated with each of the ambient 862
4 monitors. Then, staff selected the sources in close proximity of each monitor to identify whether
5 there are differences in the distribution of emission sources that could affect the monitored
6 concentrations. Stationary sources emitting > 5 tons per year (tpy) SC>2 and within 20 km of each
7 monitor were identified using data from the 2002 National Emissions Inventory (NEI).20 Details
8 on the number of sources, the distribution of emissions, and the method for determining the
9 distances to each individual ambient monitor are provided in Appendix A-l.
10 The total SC>2 source emissions within 20 km of every monitor were summed by their
11 source descriptions; the top eight source types were selected for evaluation followed by a
12 summing of all other remaining source types in a final source description group ("other"). These
13 emission results are presented in Figure 7-3 for the monitors reporting 5-minute maximum SC>2
14 concentrations and for the broader SC>2 monitoring network. A comparison of the sources
15 located within 20 km of monitors comprising both data sets indicates strong similarity in the
16 types of sources present. Approximately 70% of the stationary source emissions local to
17 monitors comprising either data set originate from fossil fuel power generation.21 Similarity in
18 emission contributions from several other source categories is also evident (e.g., petroleum
19 refineries, iron and steel mills, cement manufacturing). One of the largest distinctions between
20 the sources surrounding the two data sets is the emission contribution from primary smelters.
21 There were greater source emissions from smelters located within 20 km of the monitors
22 reporting 5-minute maximum SC>2 concentrations (8.8%) than within 20 km of the broader SC>2
23 monitoring network (1.1%). A second difference between the two sets of data existed in the
24 emission contribution from a combined power generation, transmission and distribution
25 description; this source category contributes approximately 11% to emissions proximal monitors
26 in the broader SC>2 monitoring network compared with only 2% at monitors measuring 5-minute
27 SC>2 concentrations.
20 2002 National Emissions Inventory Data & Documentation. Office of Air Quality Planning and Standards,
Research Triangle Park, NC. Available at: http://www.epa.gov/ttn/chief/net/2002inventory.html.
21 This emission category was summed from fossil fuel power generation (NEI code 221112) and hydroelectric
utilities (NEI code 221111). Hydroelectric utility SO2 emissions arise from power generating facility operations that
require fossil fuel combustion (e.g., diesel-fueled backup generators).
March 2009 78 Draft - Do Not Quote or Cite
-------
5-minute SO2 Monitors
Cement
Manufacturing
1%
Flour Milling
2% ~"\
Power Generation, \^
Transmission and
Distribution
2%
Iron and Steel Mills
2%
Petroleum
Refineries^
7%
Other
Petroleum/Coal
Products
Manufacturing
1%
Others
8%
Fossil Fuel Power
Generation
68%
Primary
Smelting/Refining
9%
All S02 Monitors Cement
Manufacturing
1%
Flour Milling
1%
Power Generation,
Transmission and
Distribution
11%
Iron and Steel Mi
2%
Petroleum / /
Refineries^ /
5% /
[
Primary
Smelting/Refining
1%
Other
Petroleum/Coal
Products
Manufacturing
1%
Others
9%
Fossil Fuel Power
Generation
69%
2
3
4
5
Figure 7-3. The percent of total SO2 emissions of sources located within 20 km of ambient
monitors: monitors reporting 5-minute maximum SO2 concentrations (top) and the
broader SO2 monitoring network (bottom).
March 2009
79
Draft - Do Not Quote or Cite
-------
1 And finally, the population residing within four buffer distances of each ambient monitor
2 was estimated using Arc View. First, staff obtained block group population data from the US
3 Census and converted the location of each block group polygon to single central point. Then
4 buffers were created around each monitor location at progressive 5 km distances to a final buffer
5 distance of 20 km. The total population was estimated by summing the population of all block
6 group centroids that fell within the monitor buffers. An example of the population distribution
7 represented by the monitors comprising each data set is given by Figure 7-4, with the population
8 within each of the buffer distances given in Appendix A-l. In general, the shape of the
9 population distribution was similar for each data set, though as a whole, the monitors reporting
10 5-minute 862 concentrations tended to be sited in locations with lower population density when
11 considering any of the population buffers. Staff created population density groups of low, mid,
12 and high to categorize all ambient monitors using the population distribution within 5 km, by
13 generally apportioning each data set into three equal sample size groupings. The low-population
14 density group included those monitors with populations under 10,000 persons. Mid-population
15 density included those monitors with between 10,000 and 50,000 persons, while the high-
16 population density group was assigned to monitors with greater than 50,000 persons within a 5
17 km buffer. These population density groups of low, medium, and high were used in separating
18 some of the air quality characterization results.
19
March 2009 80 Draft - Do Not Quote or Cite
-------
All SO2 Monitors (5 km)
5-minute SO2 Monitors (5 km)
O
1E+0 1E+1 1E+2 1E+3 1E+4 1E+5
Population Residing Within 5 km
1E+6
1E+7
1
2 Figure 7-4. Distribution of the population residing within a 5 km radius of ambient monitors:
3 monitors reporting 5-minute maximum SO2 concentrations and the broader SO2
4 monitoring network.
5 7.2.3 Statistical model to estimate 5-minute maximum SOi concentrations
6 As described earlier, staff noted there were a limited number of ambient monitors that
7 reported 5-minute maximum SC>2 concentrations. The majority of the SC>2 monitoring network
8 reports 1-hour average 862 concentrations. Staff developed a statistical model to extend the 5-
9 minute 862 air quality characterization to locations where 5-minute concentrations were not
10 reported. The statistical model was briefly introduced in section 6.4; this section details the
11 development of the statistical model designed to estimate 5-minute maximum SO2
12 concentrations from 1-hour SC>2 concentrations, using the combined 5-minute maximum and 1-
13 hour SC>2 measurement data set.
14 Fundamental to the model are the peak-to-mean ratios or PMRs. Peak-to-mean ratios are
15 derived by dividing the 5-minute maximum SC>2 concentration by the 1-hour average SC>2
16 concentration. These derived PMRs can be useful in estimating 5-minute maximum SC>2
17 concentrations when only the 1-hour concentration is known. The PMRs derived from
18 monitoring data can be variable however, and are likely dependent on local source emissions,
19 site meteorology, and other influential factors. Each of these factors will have variable influence
March 2009
81
Draft - Do Not Quote or Cite
-------
1 on the measured concentrations at ambient monitors. Therefore, to develop a useful tool for
2 extrapolating from the measured data, at a minimum, the approach needed to account for
3 variation in ambient concentration. It is within this context that the statistical model was
4 developed.
5 Staff selected the variability in SO2 concentrations at each individual ambient monitor as
6 a collective surrogate for source emissions, source types, and/or distance to sources in
7 developing a purposeful application of the statistical model to the broader 1-hour SC>2
8 measurement data. Many of the meta-data described in section 7.2.2, while useful for
9 qualitatively describing the SC>2 monitoring network, were not considered robust in quantifying
10 how sources could influence monitored concentrations, particularly when many monitor
11 characteristics are unknown, missing, or potentially mischaracterized. In addition, while
12 individual source types, emissions, and distances to the monitors are presented as quantitative
13 measures, use of this data can be problematic knowing that 1) source characteristics can change
14 over time, 2) it is largely unknown what source(s) influence many of the ambient monitors and
15 by how much, 3) there is uncertainty in source emission estimates, and 4) even similar source
16 types will not have the same emission characteristics. There may be several ways to extrapolate
17 between the two data sets, however staff decided that the measured concentrations had the most
18 to offer in efficiently designing the linkage between the statistical model and the broader SC>2
19 ambient monitoring data set given the strong relationships between averaging times,
20 concentration variability, and the frequency of peak concentrations. Where possible, staff
21 compared the relevant monitor attributes described in section 7.2.2 with selected variability
22 metrics used in developing and applying the statistical model.
23 The purpose of the first analysis that follows is to determine an appropriate variable to
24 reasonably connect the statistical model derived from 5-minute and 1-hour concentrations to any
25 1-hour SC>2 concentration data set where there are no 5-minute SC>2 measurements. Staff first
26 evaluated variability metrics associated with 5-minute and 1-hour SC>2 ambient monitoring
27 concentrations as a basis for linking the statistical model to 1-hour concentrations. Next, staff
28 generated distributions of PMRs for use in estimating 5-minute concentrations. Then the
29 statistical model was applied to where 5-minute measurements were reported and evaluated
30 using cross-validation.
March 2009 82 Draft - Do Not Quote or Cite
-------
1 7.2.3.1 Relationship Between 5-minute and 1-hour SO2 Concentrations
2 Because the statistical model employs 5-minute and 1-hour SO2 concentrations, staff
3 evaluated the relationship between the concentrations for the two averaging times. The monitors
4 reporting all twelve 5-minute concentrations within the hour were used for this analysis (n=16).
5 First, all of the continuous-5 minute data available for each monitor were averaged to generate a
6 single 5-minute mean concentration (both in an arithmetic and geometric mean form) and their
7 respective standard deviations, yielding a total of 16 monitor-specific 5-minute SC>2 values.22
8 Staff performed a second calculation to generate similar statistics using the continuous-5 data,
9 though a 1-hour averaging time was of interest. To obtain the 1-hour statistics, the twelve 5-
10 minute SC>2 concentrations were averaged to generate 1-hour mean SC>2 concentrations for each
11 monitor, which were then averaged to generate a single 1-hour mean 862 concentration (both in
12 an arithmetic and geometric mean form) and their corresponding standard deviations, yielding a
13 total of 16 monitor-specific 1-hour 862 values.
14 Staff selected the coefficient of variation (COV)23 and geometric standard deviation
15 (GSD) as metrics used to compare concentration variability in both 1-hour and 5-minute
16 averaging times, each of which are illustrated in Figure 7-5. As expected, a strong direct linear
17 relationship exists between the variability in 5-minute and 1-hour SC>2 concentrations at each
18 monitor. Even with the limited geographic representation (these monitors are from only six U.S.
19 States and Washington DC), there is a wide range in the observed concentration variability for
20 both the 5-minute and associated hourly measurements (i.e., COVs range from about 75 - 300%,
21 GSDs range from about 1.7 - 3.7). In general, this analysis demonstrates that variability in 5-
22 minute 862 concentrations is directly related to the variability in 1-hour 862 concentrations, and
23 these measures of variability may be used as to describe the potential variability in
24 concentrations measured at any ambient SC>2 monitor, similarly for either the 1-hour or 5-minute
25 measured concentrations. Note that there is a difference in the slope of the two lines, indicating
26 that there is not a direct linear relationship between the COV and GSD. This means that in
27 characterizing the variability at any ambient monitor, an identified COV (e.g., either low or high
28 COV) does not necessarily correspond to the same GSD characterization.
22 Each of the 16 continuous-5 monitors was characterized by four statistics, arithmetic and geometric means and
their respective standard deviations.
23 The COV used here is calculated by dividing the standard deviation by the arithmetic mean, then multiplying by
100.
March 2009 83 Draft - Do Not Quote or Cite
-------
Ambient Monitor 5-minute COV (%)
50 100 150 200 250 300
350
1
2
3
4
5
9
10
11
12
13
14
15
16
17
18
19
20
300
y = 0.75x + 20
R2 = 0.98
0
0
1234
Ambient Monitor 5-minute GSD
Figure 7-5. Comparison of hourly and 5-minute concentration COVs and GSDs at sixteen
monitors reporting all twelve 5-minute SO2 concentrations over multiple years of
monitoring.
Next, staff compared the variability in 1-hour SC>2 concentrations using data from the
monitors reporting 5-minute maximum SC>2 concentrations the broader SC>2 monitoring network.
The objective of this evaluation was to determine if the observed hourly concentration variability
was similar for the two sets of data. Four statistics were generated for each ambient monitor
using the 1-hour SO2 concentrations, with the variability at each represented by its COV and
GSD. Figure 7-6 illustrates the cumulative density functions (CDFs) for the hourly COVs and
GSDs at each of the 98 monitors that reported 5-minute maximum SO2 concentrations (i.e., the
data set used for developing the statistical model) and the 809 monitors from the broader SO2
monitoring network (i.e., the final 1-hour SO2 data set with complete years). While the subset of
monitors reporting the 5-minute maximum SO2 concentrations exhibit greater variability in
hourly concentration at most percentiles of the distribution, the overall shape and span of the
distribution is very similar to monitors within the broader SO2 monitoring network using either
variability metric. The similarity in variability distributions could indicate that the monitor
proximity to sources, the magnitude and temporal profile of source emissions, and the types of
sources affecting concentrations at either set of data (i.e., the monitors reporting 5-minute SO2
March 2009
84
Draft - Do Not Quote or Cite
-------
1 concentrations versus the broader SC>2 monitoring network) are similar. This, combined with the
2 meta-data evaluation and the source type, distance, and emissions analysis that indicated similar
3 source type emission proportions between the two sets of ambient monitoring data (7.2.2),
4 provides support for using concentration variability as a variable to extrapolate information from
5 the 5-minute SC>2 monitors to the 1-hour SC>2 monitors.
March 2009 85 Draft - Do Not Quote or Cite
-------
100% -f
o>
100% -F
All SO2 Monitors (n=809)
- - - 5-Minute Monitors (n=98)
50
100
150 200 250 300 350
Ambient Monitor Hourly COV
400 450
500
All SO2 Monitors (n=809)
- - - 5-minute SO2 Monitors (n=98)
0.5
1.5
5.5
6.5
2 2.5 3 3.5 4 4.5
_ Ambient Monitor Hourly GSD
3 Figure 7-6. Cumulative density functions (CDFs) of hourly COVs (top) and GSDs (bottom) at
4 ambient monitors: monitors reporting 5-minute maximum SO2 concentrations and the
5 broader SO2 monitoring network.
6 7.2.3.2 Development of Peak-to-Mean Ratio (PMR) Distributions
1 A key variable in the statistical model to estimate the 5-minute maximum SC>2
8 concentrations where only 1-hour average SC>2 concentrations were measured is the peak-to-
9 mean ratio (PMR). Peak-to-mean ratios are obtained by dividing the 5-minute maximum SC>2
10 concentration occurring within an hour by the 1-hour SC>2 concentration. The use of a PMR or
March 2009
86
Draft - Do Not Quote or Cite
-------
1 distributions of PMRs in estimating 5-minute maximum SC>2 concentrations is not new to the
2 current NAAQS review. Both individual PMRs and distributions of PMRs were used in the
3 previous NAAQS review in characterizing 5-minute 862 air quality (Thrall et al, 1982; EPA,
4 1986a; 1994b; Thompson 2000) and in estimating human exposures to 5-minute SC>2
5 concentrations (Burton et al. 1987; EPA, 1986a, 1994b; Stoeckenius et al. 1990; Rosenbaum et
6 al., 1992; Sciences International, 1995). In this review, staff generated distributions of PMRs to
7 characterize 5-minute SC>2 air quality and in estimating 5-minute SC>2 exposures (chapter 8). The
8 distributions of PMRs used here build upon recent PMR analyses conducted by Thompson
9 (2000).24 In the current PMR analysis, staff developed several distributions of PMRs using more
10 recent 5-minute 862 monitoring data (through 2007) and used concentration level and variability
11 as categorical variables in defining the distributions of PMRs.
12 Concentration variability has been identified as a potential attribute in characterizing
13 sources affecting concentrations measured at the ambient monitors (section 7.2.3.1). Instead of
14 designing a continuous function from the variability distribution, staff chose to use categorical
15 variables to describe the monitors comprising each data set. The approach involved the creation
16 of variability bins, such that PMR data from several monitors would comprise each bin. Staff
17 decided this approach would better balance the potential number of PMRs available in
18 generating the distributions of PMR given the variable number of samples collected and years of
19 monitoring at monitors that reported the 5-minute maximum 862 concentrations (Appendix A-
20 2). Using the hourly COV or GSD distributions in Figure 7-6, staff assigned one of three COV
21 or GSD bins to each of the 98 monitors reporting the 5-minute maximum SC>2 concentrations: for
22 COV, the bins were defined as low (COV < 100%), mid (100% < COV < 200%), and high (COV
23 > 200%). These three COV bins were selected to capture the upper and lower tails of the
24 variability distribution and a mid-range area.25 Similarly and based on the same percentile
25 ranges selected for the COV, three GSD bins were selected as follows: low (GSD < 2.17), mid
26 (2.17 < GSD < 2.94), and high (GSD > 2.94.
27 In addition, the level of the 1-hour mean SO2 concentration has been identified as an
28 important consideration in defining an appropriate PMR distribution to use in estimating 5-
24 In the Thompson (2000) analysis, a single distribution of PMRs was employed based on 6 ratio bins and assumed
independence between the ratio and the 1-hour SO2 concentration.
25 For monitors reporting the 5-minute maximum SO2 concentrations, these groupings corresponded to
approximately the 25th and the 84th percentile of the variability distribution.
March 2009 87 Draft - Do Not Quote or Cite
-------
1 minute maximum SC>2 concentrations (EPA, 1986a). Therefore, staff further stratified the PMRs
2 by seven 1-hour mean concentration ranges: 1-hour mean < 5 ppb, 5 < 1-hour mean < 10 ppb, 10
3 < 1-hour mean < 25 ppb, 25 < 1-hour mean < 75 ppb, 75 < 1-hour mean < 150 ppb, 150 < 1-
4 hour mean < 250 ppb, and 1-hour mean > 250 ppb. Staff selected these 1-hour concentration
5 stratifications to maximize any observed differences in the PMR distributions within a given
6 variability and concentration bin and to limit the total possible number of PMR distributions for
7 computational manageability. While PMR distributions were generated for 1-hour SC>2
8 concentrations < 5 ppb, it should be noted that any estimated 5-minute maximum SC>2
9 concentration would be below that of the lowest potential health effect benchmark level of 100
10 ppb.
11 Based on the concentration variability and 1-hour concentration bins, staff generated a
12 total of 19 separate PMR distributions.26 Due to the large number of PMRs available for several
13 of the variability and concentration bins, all of the empirical data were summarized into
14 distributions using the cumulative percentiles ranging from 0 to 100, by increments of 1. Figure
15 7-7 illustrates two patterns in the PMR distributions when comparing the different stratification
16 bins. First, the monitors with the highest COVs or GSDs contain the highest PMRs at each of
17 the percentiles of the distribution (bottom graph of each variability bin in Figure 7-7) when
18 compared with monitors from the other two variability bins (top and middle graphs), while the
19 mid-range variability bins (middle graph) had a greater proportion of higher PMRs than the low
20 variability bin (top graph). These distinctions in the PMR distributions are consistent with the
21 results illustrated in Figure 7-5, that is, the variability in the hourly average concentrations is
22 directly related to the variability in the short-term concentrations.
26 Although there were a total of 21 PMR distributions possible (i.e., 3 x 7), the COV < 100% and GSD <2.17
categories had only three 1-hour concentrations above 150 ppb. Therefore, the two highest concentration bins do
not have a distribution, and concentrations > 75 ppb constituted the highest concentration bin in the low COV or low
GSD bins. All PMR distributions are provided in Appendix A-3.
March 2009 88 Draft - Do Not Quote or Cite
-------
100 -
90 ---
o
COV Bins
COV < 100%
1-hour < 5 ppb
5 < 1-hour < 10 ppb
10 < 1-hour < 25 ppb
25 < 1-hour< 75 ppb
1-hour > 75 ppb
GSD Bins
GSD < 2.17
1-hour < 5 ppb
5 < 1-hour< 10 ppb
10 < 1-hour < 25 ppb
25 < 1-hour< 75 ppb
1-hour > 75 ppb
100 -
O
1-hour < 5 ppb
5< 1-hour < 10 ppb
10 < 1-hour < 25 ppb
25 < 1-hour < 75 ppb
75 < 1-hour < 150 ppb
150 < 1-hour < 250 ppb
1-hour > 250 ppb
100 250 ppb
2
3
4
100 -
90 ^r^^7,->
50 - - -f - -
30--
20
10
COV > 200%
1-hour < 5 ppb
5 < 1-hour < 10 ppb
10 < 1-hour < 25 ppb
25 < 1-hour < 75 ppb
75 < 1-hour < 150 ppb
150 < 1-hour < 250 ppb
1-hour > 250 ppb
/I
Iff
II
1
1
~tffc
//
77
<^^
^^1
1
1
1
1
1
1
1
1
1
1
1
1
1
1 1 1
1 1 1
1 1 1
1 1 1
GSD > 2.94%
1-hour < 5 ppb
5 < 1-hour< 10 ppb
25 < 1-hour < 75 ppb
75 < 1-hour < 150 ppb
1-hour > 250 ppb
i i i
10
12 1
10
Peak to Mean Ratio (PMR) Peak to Mean Ratio (PMR)
Figure 7-7. Peak-to-mean ratio (PMR) distributions for three COV and GSD
variability bins and seven 1-hour SO2 concentration stratifications.
March 2009
89
Draft - Do Not Quote or Cite
-------
1
2 Second, differences were observed in the PMR distributions within each variability bin when
3 stratified by 1-hour SC>2 concentration. This is most evident in the highest variability bin
4 (bottom graph of Figure 7-7); the highest 1-hour concentration category (> 250 ppb) had lower
5 PMRs at each of the distribution percentiles compared with the PMR distributions derived for the
6 lower concentration categories, most prevalent at the upper percentiles of the distribution. In
7 fact, the maximum PMRs for the > 250 ppb concentration bin were only 5.4 and 3.6 for the COV
8 and GSD high variability bin, respectively, compared with maximum PMRs of about 11.5 at
9 many of the other concentration bins. Again, this inverse relationship between the PMR and
10 concentration level has been shown by other researchers (EPA, 1986a). The stratification of
11 PMRs by the 1-hour concentration was done to avoid applying high PMRs calculated from low
12 hourly concentrations to high hourly concentrations. The observed patterns in the PMR
13 distributions support the staff selection of variability bins and 1-hour concentration stratifications
14 in controlling for the aberrant assignment of PMRs to particular 1-hour concentrations.
15 Staff compared the assigned concentration variability bin at each monitor with two
16 ambient monitoring site characteristics described in section 7.2.2. First, the total emissions
17 within 20 km of each monitor were compared with the assigned concentration variability bin.
18 This comparison was performed using the monitors reporting 5-minute maximum SC>2
19 concentrations and the broader SC>2 monitoring network (Figure 7-8). A pattern of increased
20 emissions associated with increasing concentration variability bin is common with both the COV
21 and GSD bins, though more prominent in the COV bins. In general, this indicates the variability
22 bins may be useful as a surrogate for local source emission characteristics.
March 2009 90 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
9
10
11
12
13
14
15
= 2
Ģ .-i 200000
COV Bins
GSD Bins
Q-o" 150000
w (0
.2- 100000^
E*-
LU O 50000-
ra c
+! ^
c>i
JL
~ Ŧ/) 250000
.*; o
3 - ;
._. = 2000001
Ss ;
ow
3; ^ 100000 ^
iTi g SOOOOi
^ ;
o° 0
n
H
n
D
n
D
1
+
'
:
]
+
i
J
n n
n
B
n
n []
1 -T-
a -H-
D I
n
B 1 +
1 1 _i_ ^_
Low
Mid
High
Low
Mid
High
Figure 7-8. Distribution of total SO2 emissions (tpy) within 20 km of monitors by COV (left) and
GSD (right) concentration variability bins: monitors reporting 5-minute maximum SO2
concentrations (top) and the broader SO2 monitoring network (bottom).
The second ambient monitoring site characteristic compared with the selected
concentration variability bins was the monitoring objective, principally when it was noted as
source-oriented. Staff calculated the percent of monitors in each variability bin that were
identified as source-oriented using the two sets of data; the set comprised of monitors that
reported 5-minute maximum 862 concentrations and those within the broader 862 monitoring
network. In general, there is an increasing percent of monitors characterized as source oriented
in the higher concentration variability bins when using either the COV or GSD metrics (Figure
7-9), though the pattern is more consistent with the COV metric than with the GSD metric. This
comparison also indicates that the concentration variability metric may be useful as a surrogate
for local source emission characteristics.
March 2009
91
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
O
o
11
12 JT
20
o
+j
c
30
25
20
COV (5-Minute SO2 Monitors)
QGSD (5-Minute SO2 Monitors
D COV (All SO2 Monitors)
mGSD(AII SO2 Monitors)
15 -:-:-:-:
Low
Mid
Variability Bin
High
Figure 7-9. Percent of monitors within each concentration variability bin where the monitoring
objective was source-oriented: monitors reporting 5-minute maximum SO2
concentrations (solid) and the broader SO2 monitoring network (slotted).
6 7.2.3.3 Application of Peak to Mean Ratios (PMRs)
1 As described above regarding the monitors reporting 5-minute maximum 862
8 concentrations, staff characterized the monitors within the broader SC>2 monitoring network
9 (n=809) were also characterized by their respective hourly concentration variability and placed in
10 one of the three COV bins (COV < 100%, 100% < COV < 200%, and COV > 200%) and GSD
11 bins (GSD < 2.17, 2.17 < GSD < 2.94, and GSD > 2.94). Based on the monitor's assigned
12 concentration variability bin (either from the COV or GSD, not mixed) and the 1-hour SO2
13 concentration, PMRs can be randomly sampled27 from the appropriate PMR distribution to
14 estimate a 5-minute maximum SO2 concentrations using the following equation:
15
27 The random sampling was based selection of a value from a uniform distribution {0,100}, whereas that value was
used to select the PMR from the corresponding distribution percentile value.
March 2009
92
Draft - Do Not Quote or Cite
-------
1 Cmax_5 = PMRV xC^_hour equation (7-1)
2
3 where,
4 Cmax-5 = estimated 5-minute maximum SO2 concentration (ppb) for each hour
5 PMRy = peak-to-mean ratio (PMR) randomly sampled from the /' concentration
6 variability andy 1-hour mean SC>2 concentration distribution
7 Cij-hour= measured 1-hour average SC>2 concentration at an /' concentration
8 variability monitor
9
10 As a result of this calculation, every 1-hour ambient SCh concentration has an estimated
11 5-minute maximum 862 concentration.28 These data were then summarized using the output
12 metrics described in section 7.2.5.
13 7.2.3.4 Evaluation of Statistical Model Performance
14 Staff evaluated the performance of the statistical model using cross-validation (Stone,
15 1974). Details of the evaluation are provided in Langstaff (2009). Briefly, PMR distributions
16 were estimated using 97 of the 98 monitors that reported both the 1-hour and 5-minute maximum
17 SC>2 concentrations. All ambient monitors were characterized using the same variability bins and
18 1-hour concentrations were characterized by the same stratifications described in section 7.2.3.2.
19 Then staff used the newly constructed PMR distributions to predict the 5-minute maximum 862
20 concentrations at the monitor not included in developing the PMR distributions using equation 7-
21 1. This modeling was performed 98 times, i.e., removing every single monitor (one monitor at a
22 time), generating new PMR distributions, and predicting 5-minute maximum SC>2 concentrations
23 at the removed monitor. Staff then compared the predicted and measured daily 5-minute
24 maximum SC>2 concentrations to generate a distribution of model prediction errors (e.g., median
25 errors, median absolute errors) and general model statistics (i.e., the root mean square error or
26 RMSEs, and R2, a measure of the amount of variance explained by the model).
27 Four statistical models were evaluated: the two variability bins (COV and GSD) using all
28 percentiles of the PMR distributions and the two variability bins without the minimum and
29 maximum percentiles of the PMR distributions. The models were evaluated at the benchmark
28 When the 1-hour SO2 concentration was > 0, otherwise the 5-minute maximum SO2 concentration was estimated
as zero).
March 2009 93 Draft - Do Not Quote or Cite
-------
1 concentration levels as well as at selected percentiles in the 5-minute SC>2 concentration
2 distribution. In comparing the model predictions, the model using variability bins defined by the
3 COV and excluding the minimum and maximum percentiles had the lowest prediction errors
4 (e.g., see Table 7-4).29 Based on these results, staff used this COV model (excluding the 0th and
5 100th percentiles of the PMR distribution) to estimate 5-minute maximum SC>2 concentrations
6 from 1-hour SC>2 concentrations.
29 Table 7-4 presents a few of the prediction error statistics used to compare each of the models, though several other
prediction errors were evaluated (e.g., the 75th and 99th). Each of these were consistent with results presented, that is
the alt. COV model had the lowest error when compared with the other models evaluated. See Langstaff (2009) for
the additional percentile comparisons for each of the models.
March 2009 94 Draft - Do Not Quote or Cite
-------
Table 7-4. Comparison of prediction errors and model variance parameters for
the four models evaluated.
Benchmark Level
(ppb)
100
200
300
400
Model1
cov
alt. COV
GSD
alt. GSD
COV
alt. COV
GSD
alt. GSD
COV
alt. COV
GSD
alt. GSD
COV
alt. COV
GSD
alt. GSD
Median
Prediction Error2
2.6
0.4
2.5
0.3
1
0.1
1.3
0.4
0.6
0
0.6
0.1
0.3
0
0.3
0
RMSE
18.9
14.1
24.8
19.8
10.7
8.6
12.8
10.2
6.5
5.6
8.2
7.1
4.5
3.9
6
5.5
R2
0.72
0.81
0.48
0.63
0.66
0.74
0.49
0.64
0.73
0.78
0.55
0.64
0.76
0.8
0.55
0.61
Notes:
1 The "alt." abbreviation denotes the alternative model was used: the minimum and
maximum percentiles of the PMR distributions were not used.
2 The absolute value of the prediction differences is calculated (predicted minus the
observed number of exceedances in a year), generating a distribution of prediction
errors. The value reported here is the (50th percentile) of that distribution.
1
2 Staff performed additional evaluations using the prediction errors associated with the
3 statistical model. Additional percentiles of the prediction error distribution were calculated to
4 estimate the magnitude and direction of the model bias. Table 7-5 summarizes the prediction
5 errors for each benchmark level. When considering paired percentiles (e.g., the 25th and the 75th
6 or prediction intervals) and the 50th percentile as a pivot point (there is no bias here), there
7 appears to be over-estimation bias at each of the benchmark levels. For example, there is a
8 greater overestimation of the 400 ppb benchmark level at the 95th percentile (i.e., 5 exceedances),
9 than compared with the under estimation at the 5th percentile (i.e., 1 exceedance). However,
10 there is good agreement in the predicted versus observed number of exceedances, whereas 90%
March 2009
95
Draft - Do Not Quote or Cite
-------
1 of the predicted exceedances of 400 ppb were within -1 to 5 exceedances per year. There is a
2 wider range in the prediction intervals at the lower benchmark levels, partly a function of the
3 greater number of exceedances at the lower benchmark levels rather than the degree of
4 agreement (Table 7-5). At the extreme ends of the distribution for each of the benchmarks, the
5 agreement between the predicted and observed exceedances widens, indicating that for some
6 site-years (approximately 2%), the number of exceedances can be over- or under-estimated by 20
7 to 50 days in a year.
Table 7-5. Prediction errors of the statistical model used in estimating 5-minute
maximum SO2 concentrations above benchmark levels.
Percentile
1
5
25
50
75
95
99
Prediction Error at Benchmark Level1
100
-31
-15
-1
0
7
32
48
200
-17
-7
0
0
1
20
43
300
-18
-3
0
0
1
10
26
400
-19
-1
0
0
0
5
14
Observed
Predicted
Mean Number of Benchmark Exceedances2
100
148
150
200
81
100
300
69
67
400
56
45
Notes:
1 The percentiles are based on the distribution of predicted minus the
observed values for each benchmark. Units are the number of
exceedances per year.
2 This is the average of all site-years. Units are the number of
exceedances per year.
9 7.2.4 Locations to Evaluate the Current and Potential Alternative Standard
10 Scenarios
11 As discussed in section 6.4, staff needed to adjust the air quality concentrations to allow
12 for comparison of the level of public health protection that could be associated with just meeting
13 the current and potential alternative standards. A proportional approach was selected based on
14 the mostly linear relationship between older high concentration years of air quality when
15 compared with recent low concentration years at several locations (Rizzo, 2009). Staff limited
16 the analysis to particular locations using designated geographic boundaries (not just the monitors
March 2009
96
Draft - Do Not Quote or Cite
-------
1 themselves). Counties were used to define the locations of interest in these alternative air quality
2 standard scenarios. Use of a county is consistent with current policies on the designation of
3 appropriate boundaries of non-attainment areas (Meyers, 1983). Further, to maintain a
4 computationally manageable data set given the number of air quality scenarios (i.e., eight) and
5 potential health effect benchmark levels investigated (i.e., four), staff used the recent ambient
6 monitoring data, specifically years 2001 through 2006.30
7 The first criterion used to select locations was based on monitors that had a high number
8 of daily 5-minute maximum SC>2 concentrations at or above the potential health effect
9 benchmark levels. Ambient monitors located in two counties in Missouri (Iron and Jefferson)
10 had the most frequently measured daily 5-minute maximum 862 concentrations above the
11 potential health effect benchmarks (see Appendix A-5). While there were limited data available
12 from these ambient monitors (4 and 2 years did not met the completeness criteria out of 8 total
13 site-years for each of Jefferson and Iron counties, respectively), it was decided by staff that lack
14 of a complete year should not preclude their use in this focused analysis given the high number
15 of measured daily 5-minute maximum SC>2 concentrations at these monitors. All other
16 monitoring data used in this focused analysis were selected from where 1-hour ambient
17 monitoring met the completeness criteria described in section 7.2.1.
18 Staff selected an additional 38 counties based on the relationship of the ambient SC>2
19 concentrations within the county to the current annual and daily NAAQS to expand the number
20 of counties investigated to a total of 40.31 Mean multiplicative factors were calculated using the
21 form and level of each standard (annual average or 2nd highest 24-hour concentration) in
22 comparison with the measured SO2 concentrations. First, for each county (/') and year (/), 24-
23 hour and annual SC>2 concentration adjustment factors (F) were derived by the following
24 equation:
25
26 Fj]=S/Cm^ equation (7-2)
27 where,
30 As described in the section 7.2.1, at the time the 1-hour concentrations were downloaded, none of the monitors
had a complete year of data for 2007. All data from 2007 were excluded from the 1-hour monitor simulations.
31 In the 1st draft SO2 REA, a total of 20 counties were selected for the current standard scenario only.
March 2009 97 Draft - Do Not Quote or Cite
-------
1 FJJ = Adjustment factor derived from either the 24-hour or the annual
2 average concentrations at monitors in county /' for yeary (unitless)
3 S = concentration values allowed that would just meet the current
4 standards (either 144 ppb for 24-hour or 30.4 ppb for annual average)
5 Cmax,tj = 2nd highest daily mean SO2 concentration at a monitor in county /' and
6 for yeary or the maximum annual average SC>2 concentration at a
7 monitor in county /' and for yeary (ppb)
8
9 Two initial criteria to be met for selection included having at least two monitors operating
10 in the county for at least five of the six possible years of monitoring.32 The mean daily and mean
11 annual factor for each county was calculated by averaging the site-years available at each
12 monitor, with the selection of the lowest mean factor retained to characterize the county. Each
13 county was then ranked in ascending order based on this selected mean factor. The 38 counties
14 were selected from the top 38 values, that is, those counties having the lowest mean adjustment
15 factors. The 40 counties selected and the mean factors used to select each location given the
16 above selection criteria are provided in Table 7-6.
32 In the 1st draft SO2 REA, having at least three monitors for all six years of the monitoring period was required.
These earlier criteria were relaxed to allow for additional locations that may have ambient concentrations close to
the current and daily standard levels.
March 2009 98 Draft - Do Not Quote or Cite
-------
Table 7-6. Counties selected for evaluation of air quality adjusted to just meeting the current
and potential alternative SO2 standards.
State
Arizona
Delaware
Florida
Iowa
Illinois
Indiana
Michigan
Missouri
New Hampshire
New Jersey
New York
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Virginia
US Virgin Islands
West Virginia
County1
Gila
New Castle
Hillsborough
Linn
Muscatine
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Wayne
Greene
Iron3
Jefferson3
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
St Croix
Brooke
Hancock
Monongalia
Wayne
Mean Factor
3.44
2.80
3.81
3.58
3.46
3.78
3.39
4.38
2.60
4.41
4.80
3.13
4.47
5.49
3.53
2.98
3.90
3.81
3.09
4.19
3.17
4.51
2.99
3.13
4.61
2.65
2.39
3.26
1.74
3.19
1.86
4.08
3.45
4.38
4.80
4.60
2.32
2.32
2.93
3.07
Closest Standard2
A
D
D
D
D
D
D
D
D
D
D
D
D
A
D
D
A
A
A
D
D
A
D
D
A
D
D
A
D
A
D
D
D
D
A
D
A
A
D
D
Notes:
1 Listed counties were selected based on lowest mean concentration adjustment factor,
derived from at least 2 monitors per year for years 2001 -2006.
2 Ambient concentrations were closest to either the annual (A) or daily (D) NAAQS level.
3 County selected based on frequent 5-minute benchmark level exceedances.
March 2009
99
Draft - Do Not Quote or Cite
-------
1 Following the selection of the 40 counties, staff retained the adjustment factors calculated
2 for each monitoring site-year (not the mean factor that was used for the county selection) to
3 simulate air quality just meeting the current standard (either the daily or annual factor, whichever
4 was lower). These adjustment factors are given in Appendix A, Table A.4-1. When simulating a
5 proportional adjustment in ambient SO2 concentrations using the factors generated by equation
6 (7-2), staff assumed that the current temporal and spatial distribution of air concentrations (as
7 characterized by the current air quality data) was maintained and that increased SC>2 emissions
8 would contribute to increased SC>2 concentrations. For the daily averages, the 2nd highest
9 monitor concentration would be adjusted so that it meets the current 140 ppb, 24-hour average
10 standard (144 ppb considering the rounding convention). For the annual average concentration,
11 the maximum monitor concentration would be adjusted so that it meets the current 30 ppb,
12 annual average standard (30.4 ppb considering the rounding convention). For each county and
13 calendar year, all the hourly ambient monitoring concentrations in the county were multiplied by
14 the same constant value F (whichever adjustment value was lower) determined for that county
15 and year. For example, of the seven monitors measuring SC>2 in Allegheny County, PA for year
16 2003, the 2nd highest 24-hour mean concentration was 64.6 ppb, giving an adjustment factor of
17 F daily = 144/64.6 = 2.23 for that year. This is lower than the adjustment factor calculated
18 considering the maximum annual average concentration for that year (Fannual = 30.4/11.9 = 2.54).
19 All hourly concentrations measured at all monitoring sites in that county would then be
20 multiplied by 2.23, resulting in an upward scaling of all hourly 862 concentrations for that year.
21 Therefore, one monitoring site in Allegheny County, Pa. for year 2003 would have the 2nd
22 highest 24-hour average concentration at 144 ppb, while all other monitoring sites would have
23 their 2nd highest daily average concentrations below that value, although still proportionally
24 scaled up by 2.23. Using the adjusted hourly concentrations to simulate just meeting the current
25 standard (either the daily or annual average standard), 5-minute maximum SC>2 concentrations
26 were estimated using equation (7-1). Then, air quality characterization metrics of interest were
27 estimated for each monitoring site and year as described in section 7.2.5.
28 Similarly, staff generated air quality adjustment factors for evaluating the potential
29 alternative standards described in chapter 5. The 98th and 99th percentile 1-hour daily maximum
March 2009 100 Draft - Do Not Quote or Cite
-------
1
2
SC>2 concentrations averaged across three years of monitoring were used in calculating the
adjustment factors at each of five standard levels as follows:
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
f 3
Fm = S,
v y
equation (7-3)
where,
Fjki = SO2 concentration adjustment factor (unitless) in county /' given alternative
standard percentile form k and standard level / across a 3-year period
Si = Standard level / (i.e., 50, 100, 150, 200, and 250 ppb 1-hour SO2 concentration
(ppb))
djk = Selected percentile k (i.e., 98th or 99th) 1-hour daily maximum 862
concentration at a monitor in county /' (ppb) for each yeary
As described above for adjustments made in simulating just meeting the current
standards, staff assumed that the current temporal and spatial distribution of air concentrations
(as characterized by the current SC>2 air quality data) is maintained and increased SC>2 emissions
contribute to increased SC>2 concentrations, with the highest monitor (in terms of the 3-year
average at the 98th or 99th percentile) being adjusted so that it just meets the level of the
particular 1-hour alternative standard. Since the alternative standard levels range from 50 ppb
through 250 ppb, both proportional upward and downward adjustments were made to the 1-hour
ambient 862 concentrations. The values for each percentile (i.e., 98th or 99th) 1-hour daily
maximum SO2 concentration averaged over three years at a monitor in the selected counties are
given in Appendix A, Table A.4-2. Staff adjusted the 1-hour ambient SC>2 air quality in a similar
manner described above for just meeting the current standard, however, due to the form of these
standards, only one factor was derived for two 3-year periods (i.e., 2001-2003, 2004-2006) and
applied to the individual years within the 3-year group, rather than one factor for each calendar
year as was done with just meeting the current standard. Using the adjusted hourly
concentrations to simulate just meeting each of the potential alternative standards, staff estimated
March 2009
101
Draft - Do Not Quote or Cite
-------
1 5-minute maximum SC>2 concentrations using equation (7-1). Then, air quality characterization
2 metrics of interest were estimated for each site and year as described in section 7.2.5.
3 7.2.5 Air Quality Concentration Metrics
4 For each of the three air quality characterization scenarios considered, several
5 concentration metrics were calculated; these included the annual average, 24-hour, and 1-hour
6 daily maximum SO2 concentrations for each site-year of data and the number of exceedances of
7 the potential health effect benchmark levels. The numbers of daily maximum 5-minute
8 concentration exceedances were counted (i.e., either 1 or none per day) rather than total number
9 of exceedances (i.e., which confounds numbers of exceedances and days with exceedances). To
10 characterize the relationship between the number of 5-minute benchmark exceedances and the
11 ambient concentrations, staff generated two types of analyses given the different concentration
12 averaging times.
13 The first analysis compares the annual average 862 concentration and the number of
14 daily 5-minute maximum SC>2 concentrations above the benchmark levels in a year. The output
15 of this is the number of days per year a monitor had a measured or modeled exceedance, given
16 an annual average SO2 concentration. In general, these results are graphically depicted in this
17 REA, though most of the individual results displayed in the figures are provided in Appendix A-
18 5. When considering the 40 counties used for detailed analysis, the results are presented at the
19 county-level, some of which had multiple ambient monitors. Therefore, the results for the
20 monitors within counties were aggregated to generate mean values representing the central
21 tendency of the county's concentrations and numbers of benchmark exceedances.
22 The second analysis provides the probability of potential health effect benchmark
23 exceedances associated with concentrations of short-term averaging times. It was proposed in
24 chapter 5 that the 1-hour daily maximum SC>2 concentration would be of an appropriate
25 averaging time in controlling the number of daily 5-minute maximum SC>2 concentrations. Staff
26 analyzed such a relationship using the measured 5-minute and 1-hour ambient SC>2
27 concentrations to determine if this indeed was the case. A tally was made every time a daily 5-
28 minute maximum SC>2 concentration occurred during the same hour of the day as the 1-hour
29 daily maximum 862 concentration. The results of this analysis, separated by benchmark
30 exceedance level, are given in Table 7-7. The co-occurrence of daily 5-minute maximum and 1-
March 2009 102 Draft - Do Not Quote or Cite
-------
1 hour daily maximum SC>2 concentrations is greater than 70% at each of the benchmark levels
2 indicating a strong relationship between the two concentrations.
Table 7-7. The co-occurrence of daily 5-minute maximum and 1-hour daily
maximum SO2 concentrations using measured ambient monitoring data.
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Concentration/Level
All concentrations
> 100 ppb
> 200 ppb
> 300 ppb
> 400 ppb
Co-occurring 5-
minute and 1-hour
daily maximums
(n)
106,115
6,192
2,030
1,067
700
Total 5-minute
Daily
Maximums
(n)
130,296
8,817
2,793
1,476
961
Percent
Co-occurring
(%)
81.4
70.2
72.7
72.3
72.8
Given the form of the current standard (i.e., the 24-hour average) and the potential
alternative standards (1-hour daily maximum) and the frequency of 5-minute 862 benchmark
exceedances (i.e., either one or none per concentration), the probability of the exceedance can be
calculated for any of the air quality scenarios considered (using either measured or modeled daily
5-minute maximum SC>2 concentrations). First, concentration data (24-hour average or 1-hour
daily maximum) were categorized by using concentration midpoints, separated by 10 ppb bins.
For example a concentration of 53 ppb would be included in the 50 ppb bin, while a
concentration of 55 ppb would fall within the 60 ppb bin. Then, the presence or absence of a
daily 5-minute benchmark exceedance given the number of values in each concentration bin (that
originate from all monitoring concentrations in the range of the bin) was used to estimate the
probability of an exceedance. For example, if there were 237 exceedances of the 300 ppb
benchmark level out of 1,265 instances of a 24-hour average binned concentration of 90 ppb, the
probability of a 300 ppb benchmark exceedance would be 237/1,265 x 100 = 19% given a 24-
hour concentration of around 90 ppb.
As mentioned earlier, these probability results were separated into one of three
population density groups; either low (< 10,000 persons within 5 km), mid (10,001 to < 50,000
persons within 5 km), or high (> 50,000 persons within 5 km). Staff hypothesized that there may
be different exceedance probabilities in dense population areas compared with locations having
fewer residents given the siting characteristics of the monitors with regard to the presence of
emission sources. This separation of the monitoring results by the surrounding population
March 2009
103
Draft - Do Not Quote or Cite
-------
1 should be useful in appropriately characterizing the air quality because the monitoring data are
2 used as indicator of potential human exposure; the results from monitors sited within greater
3 population densities should be more representative of potential population exposure.
4 In constructing the probability curves, staff noted there were fewer samples with
5 increasing concentrations (either 1-hour daily maximum or 24-hour average). Having too few
6 samples generated instability in the calculated probabilities at the highest 1-hour or 24-hour
7 concentrations. For example, there were very few measured 1-hour daily maximum SC>2
8 concentrations above the 130 ppb bin considering the high population density group (Table 7-8).
9 A total of 116 1-hour daily maximum SC>2 concentrations out of 26,983 were scattered across the
10 bins of 140 through 620 ppb, either indicating the presence or absence of a 300 ppb benchmark
11 exceedance. There are increasing probabilities of benchmark exceedances with increasing 1-
12 hour daily maximum 862 concentration starting at 100 ppb; however, at certain higher
13 concentrations (shaded area of Table 7-8) there are lower estimated probabilities of exceedances
14 than the preceding lower 1-hour daily maximum SC>2 concentration. If using the probability data
15 alone in Table 7-8, this would imply that at 1-hour daily maximum concentrations of about 210 -
16 230 ppb, the likelihood of an exceedance is less than that when considering 1-hour daily
17 maximum concentrations between 190 - 200 ppb. This is likely not the case, and in this
18 instance, the estimated probabilities are more a function of the sample sizes (no more than 3
19 samples per bin in this case) rather than the 1-hour daily maximum 862 concentrations.
20 Therefore, in viewing the occurrence of this issue at small sample sizes, staff selected
21 concentration bins having at least thirty 1-hour daily maximum (or 24-hour average)
22 concentrations (whether it was all, none, or a mixture of exceedances) for inclusion in the
23 probability curves.
March 2009 104 Draft - Do Not Quote or Cite
-------
Table 7-8. Example of how the probability of exceeding a 300 ppb 5-minute
benchmark would be calculated given 1-hour daily maximum SO2 concentration
bins.
Daily Maximum
1-hour bin
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
Number of times:
With no
exceedances
71
45
43
34
17
15
11
10
8
1
1
1
1
2
0
0
0
0
Withl
exceedance
0
2
1
1
1
2
4
2
3
4
3
0
2
0
2
2
4
2
Probability of
Exceedance
(%)
0
4
2
3
6
12
27
17
27
80
75
0
67
0
100
100
100
100
March 2009
105
Draft - Do Not Quote or Cite
-------
l 7.3 RESULTS
2 7.3.1 Measured 5-minute Maximum and Measured 1-Hour SOi Concentrations at
3 Ambient Monitors - As Is Air Quality
4 In this first scenario staff analyzed the as is air quality, solely based on the 862 ambient
5 monitor measurements. Ambient monitoring data were evaluated at the 98 locations where both
6 the hourly and 5-minute maximum SC>2 concentrations were reported for years 1997 through
7 2007. Due to the large size of the data set (i.e., 471 site-years), staff summarized the number of
8 potential health effect benchmark exceedances in a series of figures. This analysis centered on
9 the relationship between various concentration averaging times and the daily 5-minute maximum
10 SC>2 concentration exceedances. Descriptive statistics for the measured daily 5-minute maximum
11 and the 1-hour 862 concentrations are provided in Appendix A-5 and in the SOX ISA section
12 2.5.2, the latter of which includes additional discussion of the spatial and temporal variability of
13 the 5-minute maximum and continuous 5-minute SC>2 concentrations.
14 First, staff evaluated the occurrence of the daily 5-minute maximum SO2 concentration
15 exceedances in a year. Figure 7-10 compares the number of daily 5-minute maximum SC>2
16 concentrations above the potential health effect benchmark levels along with the corresponding
17 annual average SC>2 concentration from each max-5 monitor. Overall, the frequency of daily 5-
18 minute maximum SC>2 concentrations above the potential health effect benchmark levels in a
19 year is low. Given the data in Table 7-6, no more than 7% of total measured days had a daily 5-
20 minute maximum 862 concentrations above the 100 ppb benchmark, while approximately 2%,
21 1%, and 0.7% of days had a daily 5-minute maximum 862 concentrations above the 200, 300,
22 and 400 ppb levels, respectively. None of the monitors in this data set had annual average SO2
23 concentrations above the current NAAQS of 30 ppb. However, several of the monitors in
24 several years frequently had daily 5-minute maximum SC>2 concentrations above the potential
25 health effect benchmark levels. Many of those monitors where frequent exceedances occurred
26 had annual average SC>2 concentrations between 5 and 15 ppb, with little to no correlation
27 between the annual average 862 concentration and the number of daily 5-minute maximum 862
28 concentrations above the potential health effect benchmark levels. The data are useful in
29 determining the number of days in a year a particular monitor had a daily maximum exceedance
30 of a selected benchmark level, however from a practical perspective, the annual average
March 2009 106 Draft - Do Not Quote or Cite
-------
1 concentration would be ineffective at controlling daily 5-minute maximum SC>2 concentrations
2 given the observed weak relationships.
4
5
6
1
8
9
10
re
a)
0)
a)
a.
"55
a)
a
Ŧ
o
"55
a)
o
CM
O
E
3
E
'S
re
c
I
IO
a
"6
a>
E
250
225
200
175
150
125
100
75
50
25
0
250
225
200
175
150
125
100
75
50
25
0
> 100 ppb
o
o
o
o
08
0°° 0 °
o o o
° o°° ° ° ° o CD
° 300 ppb
%} 9
0 0
0
e 015
o""/" . 0<)
Ŧtf*>^ ° °°°° °°
-
-
-
_
-
> 200 ppb
o°9o °°
0 ° 0
0
o
o
o ° oo
Op o °
-- "&-J^M-ŧ°- J&*-°
1 1 1 1 1 1 1 1
> 400 ppb
tp ° °
Q °
00 °
° 0 0 Oo
51015202530350
Annual Mean SO2 Concentration (ppb)
5101520253035
Annual Mean SO2 Concentration (ppb)
Figure 7-10. The number of measured daily 5-minute maximum SO2 concentrations above
potential health effect benchmark levels per year at 98 monitors given the annual
average SO2 concentration, 1997-2007 air quality as is. The level of the annual average
SO2 NAAQS of 30 ppb is indicated by the dashed line.
The probability of potential health effect benchmark exceedances was estimated using the
combined 5-minute maximum and 1-hour measurement data set and considering the 24-hour
average and 1-hour daily maximum 862 concentrations. Figure 7-11 presents the probability
March 2009
107
Draft - Do Not Quote or Cite
-------
1 results using the 24-hour average SC>2 concentrations, separated by the three population densities.
2 There is an increasing probability of daily 5-minute maximum 862 concentration exceedances
3 with increasing 24-hour average concentrations at each of the potential health effect benchmark
4 levels and each of the population density groups. Some deviation from increasing probability
5 occurs near the end of the curves in the monitors cited within mid-population density areas, and
6 as discussed earlier, it is likely a function of the small sample size rather than 24-hour SC>2
7 concentrations. Monitors sited in the low-population density area exhibit a steeper slope on each
8 of the probability curves when compared with the other density groups, indicating a greater
9 probability of exceedance given the same 24-hour concentration. For example, the probability of
10 exceeding a daily 5-minute maximum concentration of 200 ppb is 30% at the low-population
11 density monitors given a 24-hour average concentration of about 20 ppb, while at the mid- and
12 high-population density monitors the probability is only about 14% and 3%, respectively. There
13 is a small probability (about 10%) of exceedance of the 300 and 400 ppb in the high-population
14 density areas given a 24-hour average concentration of about 40 ppb, though at monitors sited in
15 the low-population areas this probability is between 50% and 60%. At a 24-hour average
16 concentration of approximately 60 ppb, it is estimated that the probability of a daily 5-minute
17 maximum above 100 ppb is at or near 100% considering any of the population density groups.
18 Figure 7-12 presents similar relationships using the 5-minute and 1-hour ambient
19 measurement data, only the probabilities are associated with the 1-hour daily maximum 862
20 concentrations. A pattern of increasing probability with increasing 1-hour daily maximum 862
21 concentration is present considering each of the benchmark levels and population densities,
22 along with steeper slopes noted for the low-population density group when compared with the
23 higher population density groups. Note that while there is uncertainty regarding an extrapolation
24 beyond the measured 1-hour daily maximum SC>2 concentrations, one can be assured that the
25 probability of an exceedance of a daily 5-minute maximum SC>2 concentration of 400 ppb is
26 100% given a 1-hour daily maximum SC>2 concentration of 400 ppb (and so on for the other 5-
27 minute benchmark/1-hour daily maximum SC>2 concentration combinations).33 The shape of the
33 Technically, if all 5-minute concentrations were exactly 400 ppb, the 1-hour average concentration would be 400
ppb and the 5-minute maximum would not actually exceed 400 ppb. However, note that probability of exceeding
the 100 or 200 ppb benchmarks approaches 100% at less than a 1-hour daily maximum of 100 an 200 ppb,
respectively (Figure 7-11).
March 2009 108 Draft - Do Not Quote or Cite
-------
1 curves beyond the measured data to the peak level though can only be informed by additional
2 measurement or modeling, the latter being performed in subsequent sections.
March 2009 109 Draft - Do Not Quote or Cite
-------
100 -F
Low
Population
Density
> 1 00 ppb
> 200 ppb
> 300 ppb
> 400 ppb
Mid
Population
Density
High
Population
Density
> 100 ppb
> 200 ppb
> 300 ppb
- > 400 ppb
40
60
80
100
120
140
> 1 00 ppb
> 200 ppb
> 300 ppb
> 400 ppb
. 24-hour SO2 Concentration (ppb)
2 Figure 7-11. Probability of daily 5-minute maximum SO2 concentrations above potential health
3 effect benchmark levels given 24-hour average SO2 concentrations, 1997-2007 air
4 quality as is. Both the 5-minute maximum concentrations and 1-hour concentrations
5 were from measurements collected at 98 ambient monitors and then separated by
6 population density within 5 km of monitors.
March 2009
110
Draft - Do Not Quote or Cite
-------
Low
Population
Density
> 100 ppb
> 200 ppb
> 300 ppb
- - - > 400 ppb
Mid
Population
Density
High
Population
Density
100 150 200 250 300
Daily Maximum 1-hour SO2 Concentration (ppb)
350
> 100 ppb
> 200 ppb
> 300 ppb
- - - > 400 ppb
> 100 ppb
- > 200 ppb
- > 300 ppb
> 400 ppb
400
2
3
4
5
6
1
Figure 7-12. Probability of daily 5-minute maximum SO2 concentrations above potential health
effect benchmark levels given 1-hour daily maximum SO2 concentrations, 1997-2007 air
quality as is. Both the 5-minute maximum concentrations and 1-hour concentrations
were from measurements collected at 98 ambient monitors and then separated by
population density within 5 km of monitors.
March 2009
111
Draft - Do Not Quote or Cite
-------
1 7.3.2 Measured 1-Hour and Modeled 5-minute Maximum SOi Concentrations at All
2 Ambient Monitors - As Is Air Quality
3 Results for this second scenario analyzing the as is air quality are based on a combination
4 of measurement and modeled data. As described in section 7.2.3, a statistical model was applied
5 to 1-hour ambient 862 measurements to estimate 5-minute maximum 862 concentrations. This
6 was done because there are a greater number monitors in the broader 862 monitoring network
7 compared to subset of monitors reporting the 5-minute maximum SC>2 concentrations. This
8 larger monitoring data set included 809 ambient monitors in operation at some time during the
9 years 1997 through 2006 that met the completeness criteria described in section 7.2.1. This data
10 set includes 4,692 site-years of data, and combined with the estimated 5-minute SC>2
11 concentrations using the measured 1-hour values, allowed for a comprehensive characterization
12 of the hourly and 5-minute 862 air quality at ambient monitors located across the U.S.
13 Descriptive statistics for the measured 1-hour 862 concentrations are provided in the SOX ISA
14 section 2.5.1 including additional discussion of the spatial and temporal variability in 1-hour 862
15 concentrations.
16 Twenty separate simulations were performed to estimate the 5-minute maximum SC>2
17 concentration associated with each 1-hour measurement. The individual simulation results were
18 combined to generate a mean estimate for the number of daily 5-minute benchmark exceedances.
19 The modeled (5-minute maximum) and measurement (1-hour) data were analyzed in a similar
20 manner as performed on the measured 5-minute maximum and 1-hour SC>2 concentrations
21 described in section 7.3.1. The results provided in this section were generated using the modeled
22 daily 5-minute maximums and the measured hourly 862 concentrations at 1-hour, 24-hour, and
23 annual averaging times.
24
March 2009 112 Draft -Do Not Quote or Cite
-------
0 51015202530350 51015202530
Annual Arithmetic Mean SO2 (ppb) Annual Arithmetic Mean SO2 (ppb)
2 Figure 7-13. The number of modeled daily 5-minute maximum SO2 concentrations
3 above potential health effect benchmark levels per year at 809 ambient
4 monitors given the annual average SO2 concentration, 1997-2006 air
5 quality as is. The level of the annual average SO2 NAAQS of 30 ppb is
6 indicated by the dashed line.
7
8 The occurrence of the modeled daily 5-minute maximum SC>2 concentrations was first
9 evaluated with regard to the annual average SO2 concentrations calculated from the 1-hour
10 measurements. Figure 7-13 compares the number of daily 5-minute maximum SC>2
11 concentrations above the potential health effect benchmark levels with the annual average SC>2
12 concentration from each monitoring site-year of data. Fewer than 5% of total days had an
13 estimated daily 5-minute maximum SC>2 concentration above the 100 ppb benchmark, while
March 2009
113
Draft -Do Not Quote or Cite
-------
1 approximately 1%, 0.5%, and 0.2% of days had a 5-minute peak above the 200, 300, and 400
2 ppb levels, respectively. None of the monitors in this data set had annual average 862
3 concentrations at or above the level of the current annual NAAQS (30 ppb). However as
4 described above, several of the monitors in several years had estimated 5-minute SC>2
5 concentrations above the potential health effect benchmark levels. Many of those monitors
6 where frequent exceedances occurred had annual average SC>2 concentrations between 10 and 20
7 ppb, with a pattern of increasing numbers of benchmark exceedances with increasing annual
8 average concentrations, most prominent at the 100 ppb benchmark level, though progressively
9 less of a relationship present at each the subsequent benchmark levels.
10 Figure 7-14 presents the probability of benchmark exceedances using the modeled daily
11 5-minute maximum SO2 concentrations, exhibiting patterns similar to the measured daily 5-
12 minute maximum results (Figure 7-11). Again, low-population density probability curves are
13 steeper than both the higher population density curves at each to the benchmark levels
14 considered. The modeled probability curves though are slightly steeper than was observed with
15 the measurement data when considering the 100 ppb benchmark level. For example, at a 24-
16 hour average concentration of about 20 ppb, the probabilities for the low-, mid-, and high-
17 population densities using all measurement data is 60, 50, and 20%, respectively (Figure 7-11).
18 At the same 24-hour average and benchmark concentrations, the probability of an exceedance is
19 70, 60 and 30% when considering the modeled 5-minute maximum concentrations (Figure 7-14).
20 At the higher benchmark levels however (e.g., 300 and 400 ppb), the slopes appear to be
21 consistent between the measured and modeled 5-minute maximum data, where comparable 24-
22 hour average concentrations exist. In using the broader SC>2 monitoring network to estimate
23 daily 5-minute maximum SC>2 concentrations, there is insight as to the potential shape of each
24 probability curve at greater 24-hour average concentrations. The upper range of 24-hour
25 concentrations extends to around 70 - 100 ppb, while at the monitors reporting 5-minute
26 maximum concentrations the maximum 24-hour average concentrations extend to at most
27 between 50 and 60 ppb.
28 Similar patterns in the probability curves are exhibited when considering a 1-hour daily
29 maximum concentration (Figure 7-15). In comparing these short-term probability curves with
30 corresponding 24-hour probability curves (Figure 7-14), the overall slopes using the 1-hour daily
31 maximums are steeper. This means that changes in 1-hour daily maximum SC>2 concentration
March 2009 114 Draft -Do Not Quote or Cite
-------
1 (either up or down) will effectively result in larger changes in the probability of exceedances
2 when compared with the 24-hour average probability curves, given a similar concentration shift.
3 Again, a wider range of 1-hour daily maximum concentrations is observed in using the broader
4 monitoring network when compared with the results using the monitors reporting the 5-minute
5 maximum SC>2 concentrations (see Figure 7-12), giving greater ability to discern the probability
6 of benchmark exceedances at higher 1-hour daily maximum SC>2 concentrations.
March 2009 115 Draft -Do Not Quote or Cite
-------
1
2
3
4
5
6
Low
Population
Density
100 ppb
200 ppb
> 300 ppb
- - - > 400 ppb
Mid
Population
Density
100 ppb
200 ppb
> 300 ppb
> 400 ppb
High
Population
Density
100 ppb
200 ppb
> 300 ppb
> 400 ppb
30
60 90
24-hour SO2 Concentration (ppb)
120
150
Figure 7-14. Probability of daily 5-minute maximum SO2 concentrations above potential health
effect benchmark levels given 24-hour average SO2 concentrations, 1997-2006 air
quality as is. The 5-minute maximum concentrations were modeled from 1-hour
measurements collected at 809 ambient monitors and then separated by population
density within 5 km of monitors.
March 2009
116
Draft -Do Not Quote or Cite
-------
S m
o
I1
ra
o
Ģ
1 8
II
Ģ8
s.
a.
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
100 -\
1 8
II
8.
a.
90 --
80
70
60
50
40
30
20 :
10 --
Low
Population
Density
> 100 ppb
> 200 ppb
> 300 ppb
- - - > 400 ppb
Mid
Population
Density
> 100 ppb
> 200 ppb
> 300 ppb
- - - > 400 ppb
> 100 ppb
> 200 ppb
> 300 ppb
- - - > 400 ppb
100 150 200 250 300
Daily Maximum 1-hour SO2 Concentration (ppb)
350
High
Population
Density
400
3 Figure 7-15. Probability of daily 5-minute maximum SO2 concentrations above potential health
4 effect benchmark levels given 1-hour daily maximum SO2 concentrations, 1997-2006 air
5 quality as is. The 5-minute maximum concentrations were modeled from 1-hour
6 measurements collected at 809 ambient monitors and then separated by population
7 density within 5 km of monitors.
March 2009
117
Draft -Do Not Quote or Cite
-------
1 7.3.3 Modeled 1-Hour and Modeled 5-minute Maximum SOi Concentrations at Ambient
2 Monitors in 40 Counties - Air Quality Adjusted to Just Meet the Current and Potential
3 Alternative Standards
4 Staff selected forty counties for detailed analyses that included an evaluation of ambient
5 concentration distributions and the estimated numbers of exceedances of the potential health
6 effect benchmark levels using as is air quality and air quality adjusted to just meeting the current
7 and alternative standards. The counties were selected using criteria discussed in section 7.2.4; 38
8 counties having 1-hour ambient monitor 862 concentrations nearest the current NAAQS levels,
9 and two counties having a high frequency of measured daily 5-minute maximum 862
10 concentrations above the potential health effect benchmark levels. The 1-hour SO2 measurement
11 data were from 128 ambient monitors and totaled 610 site-years of monitoring, a subset of data
12 from the broader SC>2 monitoring network (see section 7.3.2). Staff evaluated multiple
13 alternative air quality scenarios by first adjusting the 1-hour ambient monitoring concentrations
14 (section 7.4). Then, staff performed twenty simulations to estimate the 5-minute maximum SC>2
15 concentration associated with each 1-hour adjusted concentration using the statistical model
16 described in section 7.2.3. These simulation results were combined to generate a mean estimate
17 for each of the metrics of interest (e.g., the number of daily 5-minute maximum SC>2
18 concentrations > 200 ppb) selected here as the best estimate from the twenty simulations.
19 First staff evaluated the relationship between the short-term peak concentrations and the
20 level of the current annual SC>2 NAAQS in the selected counties. Figure 7-16 presents the
21 number of 5-minute daily maximum SO2 concentrations above the potential health effect
22 benchmark levels along with the corresponding annual average concentration from each site-year
23 using air quality adjusted to just meeting the current SO2 standards. None of the monitors in the
24 selected counties had annual average concentrations above the level of the current NAAQS (30
25 ppb) by design34, however there are many more site-years with modeled daily 5-minute
26 maximum 862 concentrations above the potential health effect benchmark levels. There are a
27 decreasing number of exceedances with increasing benchmark concentrations, though there is a
28 greater proportion of monitors with exceedances when considering concentrations adjusted to
29 just meeting the current standard than when using the as is air quality (e.g., see Figure 7-13).
30 With the concentration adjustment procedure, there is a stronger relationship between the annual
34 The current annual SO2 NAAQS is 30 ppb. Concentrations of up to 30.4 ppb are possible due to a rounding
convention. This is why there are several data points just to the right of the dashed line in Figure 7-16.
March 2009 118 Draft-Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
average concentrations and the number of benchmark exceedances than observed previously
with the as is air quality however, the strength of that relationship weakens with increasing
benchmark levels.
Ŧ 350
Ģ 325
0) 300
(/> 275
0) 250
i 225
j"1ŧ
a)
CM
O
E
1
re
§
a)
"3
c
I
IO
a
5
>_
a)
E
200
175
"S 150
"o 125
5 100
2 ŧ
§ ŧ
^ 25
0
350
325
300
275
250
225
200
175
150
125
100
75
50
25
> 100 ppb
> 300 ppb
> 200 ppb
> 400 ppb
5 10 15 20 25 30
Annual Arithmetic Mean SO2 (ppb)
35 0 5 10 15 20 25 30
Annual Arithmetic Mean SO2 (ppb)
Figure 7-16. The number of modeled daily 5-minute maximum SO2 concentrations above potential
health effect benchmark levels per year at 128 ambient monitors in 40 selected counties
given the annual average SO2 concentration, 2001-2006 air quality adjusted to just meet
the current NAAQS. The level of the annual average SO2 NAAQS of 30 ppb is indicated
by the dashed line.
March 2009
119
Draft-Do Not Quote or Cite
-------
1 Similar relationships are present between the annual average 862 concentrations and the
2 number of benchmark exceedances when considering the potential alternative standards. Each of
3 the potential alternative standards had a unique adjustment factor to simulate the alternative air
4 quality. Based on the direction (either >1 or <1) and magnitude of the adjustment factor used,
5 the estimated number of 5-minute benchmark exceedances was within the range of results
6 generated using the as is air quality or the air quality adjusted to just meet the current standard.
7 For example, a comparison of the annual average SC>2 concentrations and number of daily 5-
8 minute maximum exceedances of 200 ppb is presented in Figure 7-17 for six air quality
9 scenarios: four of the 99th percentile 1-hour daily maximum potential alternative standards (i.e.,
10 the 100, 150, 200, and 250 ppb); the air quality adjusted to just meet the current standards; and
11 as is air quality.
12 Clearly, in using the air quality adjustment procedure combined with the statistical model
13 to estimate 5-minute maximum SC>2 concentrations, the current standard air quality scenario
14 allows for the greatest estimated number of potential health effect benchmark exceedances in a
15 year (Figure 7-17). However, at a minimum the annual standard does provide protection against
16 annual average concentrations above the level of the current standard. While there were fewer 5-
17 minute benchmark exceedances using the 1-hour daily maximum forms of a potential standard,
18 two of the levels (1-hour daily maximums of 200 and 250 ppb) did not prevent annual average
19 concentrations from exceeding the current annual standard (Figure 7-17). High annual average
20 concentrations become less of an issue when considering the lower levels of the 1-hour daily
21 maximum standards. Even though the 99th percentile 1-hour daily maximum standards of 100 or
22 150 ppb allow for greater annual average concentrations than when considering as is air quality,
23 all but one site-year are below the level of the current annual standard and there are fewer
24 estimated daily 5-minute benchmark exceedances. These results further demonstrate the stronger
25 relationship 5-minute peak concentrations have with 1-hour SC>2 concentrations than with annual
26 average concentrations.
March 2009 120 Draft-Do Not Quote or Cite
-------
1
2
3
4
5
6
ra
o
0)
o
Q.
Q.
Q.
O
O
01
O
&
E
3
E
'S
ra
o
E
IO
Q
"o
CD
E
350
300
250
100
50
0
350
300
250
200
100
501
th
99 percentile
Daily 1-hour
maximum of 100 ppb
th
99 percentile
Daily 1-hour
maximum of 200 ppb
99th percentile
Daily 1-hour
maximum of 150 ppb
99th percentile
Daily 1-hour
maximum of 250 ppb
fHodBBBaiyiBrcai*0''
-------
Table 7-9. Percent of days having a modeled daily 5-minute maximum SO2
concentration above the potential health effect benchmark levels given air quality
as is and air quality adjusted to just meeting the current and each of the potential
alternative standards.
Air Quality
Scenario1
As Is
CS
99-50
99-100
99-150
99-200
99-250
98-200
Percent of Days With Daily 5-minute Maximum SO2
Concentrations Above Benchmark Levels
> 100 ppb
9.1
41.0
0.7
4.5
10.6
17.2
23.6
22.5
> 200 ppb
2.4
17.2
0.0
0.7
2.2
4.5
7.4
6.9
> 300 ppb
0.9
9.1
0.0
0.2
0.7
1.6
2.9
2.6
> 400 ppb
0.5
5.3
0.0
0.0
0.3
0.7
1.3
1.2
Notes:
1 As is air quality is unadjusted; CS is air quality adjusted to just meet the
current standard; x-y are the xth percentile form of a 1 -hour daily maximum
level of y.
1
2 Staff summarized the frequency of daily 5-minute maximum SC>2 concentrations within
3 the 40-county data set for additional comparisons of the air quality scenarios. Table 7-9 presents
4 the percent of all days above each of the benchmark levels considering each of the air quality
5 scenarios. Again, the scenario where air quality just meets the current standard has the greatest
6 percent of days with benchmark exceedances. With each progressive decrease in the 1-hour
7 daily maximum SC>2 concentration levels of the potential alternative standards, there are fewer
8 days with benchmark exceedances. The percent of days with benchmark exceedances using as is
9 air quality was between a potential 1-hour daily maximum alternative standard level of 100 -
10 150 ppb (99th percentile form).
11 Staff also evaluated two forms of the potential alternative standards: the 99th and 98th
12 percentile forms, each having a 1-hour daily maximum level of 200 ppb. For example, Figure 7-
13 18 indicates that nearly all site-years have a greater number of daily 5-minute maximum SO2
14 concentrations above 100 ppb given the 98th percentile form when compared with a 99th
15 percentile form at the same level. This is expected given the number of allowable 1-hour SC>2
16 concentrations above the 200 ppb level for each of the percentile forms. On average, the 98th
17 percentile form allowed for approximately 46, 68, 84, and 86% more benchmark exceedances
March 2009
122
Draft-Do Not Quote or Cite
-------
1
2
3
considering the 100, 200, 300, and 400 ppb benchmark levels, respectively when compared with
the 99th percentile form.
50
100
150
200
250
300
350
4
5
6
7
Number of 5-minute Daily Maximum SO2 Above 100 ppb in a Year
-99th %tile 1-hour Daily Maximum 200 ppb Standard
Figure 7-18. The number of modeled daily 5-minute maximum SO2 concentrations
above 100 ppb per year given the 99th and 98th percentile forms at a 1-hour daily
maximum level of 200 ppb, using the 40-county air quality data set.
March 2009
123
Draft-Do Not Quote or Cite
-------
1 Staff approximated the probability of potential health effect benchmark exceedances
2 given the adjusted air quality scenarios and short-term averaging times. Again, patterns in the
3 curves were consistent with what was observed and described previously; monitors within low-
4 population density areas had steeper probability curves compared with those in higher population
5 density areas. Further, there were similarities in the shape and the steepness of the curves when
6 comparing the adjusted air quality probability curves with the curves developed from the
7 corresponding as is air quality. For example, Figure 7-19 presents the probability of 5-minute
8 benchmark exceedances using as is air quality and air quality adjusted to just meet the current
9 standard, given 1-hour daily maximum 862 concentrations. In general, all of the corresponding
10 100 and 200 ppb probability curves for all of the air quality scenarios follow a similar pattern.
11 However, the estimated probabilities of exceeding the 300 and 400 ppb benchmark levels using
12 the adjusted air quality were lower than when using the as is air quality given a similar 1-hour
13 daily maximum concentration, most notable at monitors sited in low- and high population
14 density areas. This is likely a function of the non-linear form of the statistical model used to
15 estimate the 5-minute maximum SC>2 concentrations, the proportional adjustment procedure to
16 simulate alternative standards, and the air quality characterization metric.
17 When adjusting the 1-hour 862 concentrations upwards using a proportional factor, a
18 corresponding proportional increase in the number of exceedances does not necessarily follow.
19 The statistical model uses multiple distributions of PMRs, not linearly related to 1-hour 862
20 concentrations. Certainly, the total number of days in a year with benchmark exceedances will
21 increase with an upward adjustment of air quality, and does so as observed in Figure 7-17.
22 However, the greatest proportion of monitoring days within any of the air quality scenarios is
23 comprised of days without an exceedance. The frequency of exceedances of the higher
24 benchmarks is already very low; the few added days with estimated exceedances of 300 or 400
25 ppb using the simulated air quality is not proportional to the universal increase in hourly
26 concentrations applied to all 1-hour concentrations, therefore the probability curves tend to be
27 less steep with the upward 1-hour concentration adjustments. Furthermore, days already having
28 an exceedance are only counted once, that is, if there were an exceedance on a given day using
29 the as is air quality, it is likely that the same day would also have an exceedance using the
30 adjusted air quality, only it is associated with a greater 1-hour (or 24-hour average)
31 concentration. Again, the 1-hour concentrations are increased without proportional increases in
March 2009 124 Draft-Do Not Quote or Cite
-------
1 the probability of exceedances when comparing the two scenarios. Conversely, it could also be
2 argued that there may be an increased probability of daily 5-minute exceedances of 300 and 400
3 ppb when using air quality with a relatively low concentration distribution (such as with the as is
4 air quality) compared with a distribution of higher concentrations (such as with the current
5 standard scenario). However, it should be noted that the total number of benchmark level
6 exceedances in a year (and the absence of exceedances at the same high 1-hour daily maximum
7 concentration) under either of these scenarios would be very few, with far fewer numbers of
8 exceedances associated with the relatively low concentration air quality.
9 This discussion of probability curves can be extended to each of the potential alternative
10 standards. For example, Figure 7-20 illustrates a range in each of the probability curves given
11 each of the adjusted air quality scenarios and using monitors sited within high-population density
12 areas. The 100 and 200 ppb benchmark level probability curves exhibit a narrow range across
13 each of the adjusted air quality scenarios. While the estimated 300 and 400 ppb probability
14 curves are wider than the 100 and 200 ppb curves, there is still agreement in the estimated
15 probabilities at many of the 1-hour daily maximum SC>2 values. The range in probability curves
16 tended to be widest at the lowest probabilities/1-hour daily maximum 862 concentrations within
17 a given benchmark, likely indicating an increased uncertainty in the relationship between
18 exceedance of the daily 5-minute maximum SC>2 concentrations of 300 and 400 ppb and 1-hour
19 daily maximum 862 concentrations less than 130 ppb and 180 ppb, respectively.
March 2009 125 Draft-Do Not Quote or Cite
-------
0,
II
in -3
^8
5 o
S^
o
2
o.
100 -|
90
80
70
60
50
40
30
20
10
X
n
>
2
1
Q.
0
100 i
90
80
70
60
50
40
30
20
10
0
100
I
E 80
x
70 -:
11 6°
2 UJ 40
o
2
a.
30 -:
20 --
10 -:
Low
Population
Density
100ppb CS
200 ppb CS
300 ppb CS
400 ppb CS
100 ppb AS IS
200 ppb AS IS
300 ppb AS IS
400 ppb AS IS
Mid
Population
Density
100 ppb CS
200 ppb CS
> 300 ppb CS
- - - > 400 ppb CS
> 100 ppb AS IS
e > 200 ppb AS IS
300 ppb AS IS
a~ > 400 ppb AS IS
High
Population
Density
100 ppb CS
200 ppb CS
> 300 ppb CS
- - - > 400 ppb CS
> 100 ppb AS IS
e> 200 ppb AS IS
--e-->300 ppb AS IS
---o-- >400 ppb AS IS
100 150 200 250 300
Daily Maximum 1-hour SO2 Concentration (ppb)
350
400
2 Figure 7-19. Probability of daily 5-minute maximum SO2 concentrations above potential health
3 effect benchmark levels given 1-hour daily maximum SO2 concentrations, 2001-2006 air
4 quality as is and that adjusted to just meet the current NAAQS. The 5-minute maximum
5 concentrations were modeled from 1-hour measurements collected at 128 ambient
6 monitors from 40 selected counties and then separated by population density within 5
7 km of monitors.
March 2009
126
Draft-Do Not Quote or Cite
-------
100
E
D
E
'x
re
0)
"3
c
E
ID
're
Q
"o
Ģ
re
.a
E
a.
0)
o
c
re
0)
8
X
in
90
80
70
60
50
40
30
20
10
0
1
2
3
4
5
6
1
- > 300 ppb
- - > 400 ppb
50 100 150 200 250 300 350
Daily Maximum 1-hour SO2 Concentration (ppb)
400
Figure 7-20. Probability of daily 5-minute maximum SO2 concentrations above potential health
effect benchmark levels given 1-hour daily maximum SO2 concentrations, 2001-2006 air
quality adjusted to just meet the current and each of the potential alternative standards.
The 5-minute maximum concentrations were modeled from 1-hour measurements
collected at 128 ambient monitors from 40 selected counties, high-population density
within 5 km of monitors.
March 2009
127
Draft-Do Not Quote or Cite
-------
1 While there are similarities in the probability of daily 5-minute maximum benchmark
2 exceedances for each of the potential alternative standard scenarios given either the 1-hour daily
3 maximum or 24-hour average SO2 concentrations, there are large differences in the total number
4 of exceedances given a particular county and air quality scenario. Table 7-10 presents the mean
5 number of days in a year where the daily 5-minute maximum SC>2 concentration was above 100
6 ppb in each of the 40 selected counties and for all air quality scenarios. In considering the
7 highest potential alternative standard levels of 200 and 250 ppb (1-hour daily maximum) and the
8 current standard air quality, counties such as Hudson NJ, Tulsa OK, and Wayne WV were
9 estimated to have the greatest number of benchmark exceedances. On average it is estimated
10 that between 100 and 200 days of the year there would be daily 5-minute maximum SO2
11 concentrations above 100 ppb in these counties. Most of the other locations though had fewer
12 than 100 exceedances in a year, particularly when considering the two potential alternative 1-
13 hour daily maximum standards. Air quality simulating just meeting the current standard was
14 associated with the greatest number of estimated exceedances at most locations. This consistent
15 pattern was observed with each of the benchmark levels (see below) again indicating the limited
16 influence the current standard has on the estimated number of 5-minute benchmark exceedances.
17 In addition, the number of exceedances using a 98th percentile 1-hour daily maximum alternative
18 standard level of 200 ppb was similar to the 99th percentile using a 250 ppb 1-hour concentration
19 level considering any of the 5-minute benchmarks. Decreases in the potential alternative
20 standard level corresponded with decreases in the number of exceedances. Most counties have
21 fewer mean estimated 5-minute benchmark exceedances of 100 ppb using air quality adjusted to
22 just meeting the 99th percentile daily 1-hour maximum concentration of 100 ppb, than estimated
23 using the as is air quality.
24 There were fewer estimated exceedances of 200 ppb given the 1-hour daily maximum
25 potential alternative standards than compared with the current standard scenario (Table 7-11).
26 Most counties had fewer than forty days with 5-minute maximum SO2 concentrations above 200
27 ppb, even at the 250 ppb 1-hour daily maximum level; though the number of exceedances was
28 typically double that using air quality simulating just meeting the current standard. With
29 progressive decreases in the 1-hour daily maximum standard level, the number of days with 5-
30 minute maximum SO2 concentrations also decreases. In most counties, the estimated number of
31 exceedances using as is air quality was within that estimated using 1-hour daily maximum
March 2009 128 Draft-Do Not Quote or Cite
-------
1 standard levels of 100 and 200 ppb (approximately 10-20 per year). The 99th percentile 1-hour
2 daily maximum concentration level of 50 was associated with the fewest days with 5-minute
3 maximum SO2 concentrations above 200 ppb. On average most locations had zero exceendaces
4 of the 200 ppb benchmark level.
5 Similar results are presented for each the 300 ppb (Table 7-12) and the 400 ppb (Table 7-
6 13) 5-minute benchmark levels, though the difference in the number of exceedances between the
7 current standard and the other air quality scenarios is much greater than was observed for the
8 lower benchmark levels. Most counties had a 5-fold (or greater) number of days with daily 5-
9 minute maximum SC>2 concentrations above 300 or 400 ppb when considering air quality just
10 meeting the current standard compared with air quality adjusted to just meet the 99th percentile 1-
11 hour daily maximum level of 250 ppb. The number of exceedances given as is air quality was
12 still within the range of values estimated using the potential standard levels of 100 and 200 ppb;
13 in most counties it was fewer than 10 days per year. Most counties did not have any estimated
14 daily 5-minute maximum SC>2 concentrations above 400 ppb given a 99th percentile 1-hour daily
15 maximum of 100 ppb, while 75% of the counties had 1 or fewer exceedances of 300 ppb
16 considering this same potential alternative standard.
17
18
19
March 2009 129 Draft-Do Not Quote or Cite
-------
Table 7-10. Mean number of modeled daily 5-minute maximum concentrations
above 100 ppb per year in 40 selected counties given 2001-2006 air quality as is
and air quality adjusted to just meet the current and alternative standards.
State
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
Ml
MO
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
VI
County
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Iron
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
St Croix
As
Is
119
21
22
24
42
47
58
17
27
29
34
29
20
65
70
46
3
2
8
38
60
16
44
51
26
30
76
14
63
25
62
11
75
24
0
76
78
39
30
8
CS
234
123
127
166
139
211
122
186
184
103
123
134
92
108
150
118
145
117
124
172
163
203
164
198
202
159
194
130
110
185
116
144
201
132
109
220
207
172
201
67
99tn Percentile 1-hour Daily Maximum
Potential Alternative Standard
50
9
1
3
1
6
8
8
3
2
8
9
2
8
9
6
7
1
1
2
6
13
2
3
3
4
1
2
2
3
2
3
3
2
3
1
3
2
3
4
1
100
36
8
12
11
17
24
23
20
12
25
26
18
24
30
22
31
20
16
28
18
34
23
20
23
43
8
11
25
17
21
19
13
20
19
17
25
21
15
33
4
150
63
19
23
25
30
43
37
41
27
42
41
40
37
40
37
52
62
51
71
33
52
55
41
51
93
22
30
56
33
53
42
26
49
40
54
62
52
26
83
11
200
89
34
37
42
43
62
50
64
44
56
54
62
47
48
50
68
111
98
115
50
68
93
61
81
133
41
55
87
48
88
63
39
74
58
98
101
86
38
138
20
250
111
50
50
60
54
81
63
91
63
68
68
80
59
55
61
81
161
141
155
70
83
122
80
110
162
65
83
114
62
125
83
53
94
75
143
140
118
50
180
30
98th
Percentile
200
107
46
53
61
64
83
61
93
68
66
65
76
57
54
61
76
150
122
137
65
75
129
73
96
154
58
79
127
62
110
75
57
100
68
129
135
110
54
166
37
March 2009
130
Draft-Do Not Quote or Cite
-------
Table 7-11. Mean number of modeled daily 5-minute maximum concentrations
above 200 ppb per year in 40 selected counties given 2001-2006 air quality as is
and that adjusted to just meet the current and alternative standards.
State
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
Ml
MO
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
VI
County
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Iron
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
St Croix
As
Is
55
4
6
5
17
17
28
2
6
10
14
5
6
44
38
14
0
0
0
15
29
1
11
11
2
5
17
2
25
3
19
2
21
5
0
16
17
15
3
2
CS
171
38
50
66
75
117
70
80
90
53
57
61
47
77
99
68
31
22
32
88
86
85
71
96
112
52
88
40
52
66
54
35
121
71
21
96
96
63
71
24
99tn Percentile 1-hour Daily Maximum
Potential Alternative Standard
50
0
0
1
0
1
1
1
0
0
2
1
0
1
0
0
1
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
100
9
1
3
1
6
7
8
3
2
8
9
2
8
9
6
7
1
1
2
6
13
2
3
3
5
1
2
3
3
2
3
3
2
3
1
3
2
3
4
1
150
22
4
7
5
11
16
16
10
6
17
18
9
16
21
14
18
7
6
11
11
24
10
10
12
19
3
5
10
9
10
10
7
9
10
6
12
9
9
16
3
200
36
8
12
11
17
24
22
20
12
25
26
18
24
29
22
30
20
15
27
18
34
23
20
24
42
8
11
25
17
21
20
13
21
19
17
26
21
15
33
4
250
49
13
17
17
23
33
30
31
19
34
34
29
31
36
29
42
39
31
48
25
43
38
30
37
69
14
20
40
25
36
31
20
35
29
34
43
36
21
58
7
98th
Percentile
200
47
12
18
18
29
34
29
31
21
33
32
25
30
34
31
37
34
24
38
24
38
38
26
31
62
12
18
41
25
29
26
21
39
25
28
40
32
22
48
10
March 2009
131
Draft-Do Not Quote or Cite
-------
Table 7-12. Mean number of modeled daily 5-minute maximum concentrations
above 300 ppb per year in 40 selected counties given 2001-2006 air quality as is
and that adjusted to just meet the current and alternative standards.
State
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
Ml
MO
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
VI
County
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Iron
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
St Croix
As
Is
31
1
3
1
9
8
16
0
2
5
6
1
2
33
24
5
0
0
0
9
17
0
3
2
0
1
6
1
11
1
7
0
7
1
0
5
4
7
1
0
cs
130
17
27
35
50
75
47
42
49
35
39
32
32
61
72
46
7
5
9
52
59
39
41
51
60
21
42
16
31
28
28
19
83
43
5
45
48
36
31
11
99tn Percentile 1-hour Daily Maximum
Potential Alternative Standard
50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
100
2
0
1
0
2
3
3
1
1
4
4
0
3
1
1
3
0
0
0
2
5
0
0
1
1
0
0
1
1
0
0
1
0
1
0
1
0
1
1
0
150
9
1
3
1
6
8
7
3
2
8
9
2
7
9
6
7
1
1
2
6
13
2
2
3
4
1
2
3
3
2
3
3
2
3
1
4
2
3
4
1
200
18
3
6
4
9
13
13
8
5
14
15
6
13
17
11
14
4
3
7
10
20
7
7
8
12
2
4
7
7
7
7
6
6
7
4
8
6
6
10
2
250
27
5
8
7
13
18
18
13
8
19
20
12
19
24
17
22
10
8
16
13
27
13
13
15
26
4
7
15
11
13
13
9
12
13
9
16
12
11
21
3
98th
Percentile
200
25
5
9
7
17
19
17
13
9
19
19
10
18
23
18
19
9
6
11
12
24
13
10
12
22
4
6
14
11
10
10
10
15
10
7
15
10
12
16
4
March 2009
132
Draft-Do Not Quote or Cite
-------
Table 7-13. Mean number of modeled daily 5-minute maximum concentrations
above 400 ppb per year in 40 selected counties given 2001-2006 air quality as is
and that adjusted to just meet the current and alternative standards.
State
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
Ml
MO
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
VI
County
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Iron
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
St Croix
As
Is
18
0
2
0
6
5
10
0
1
2
3
0
1
25
16
2
0
0
0
6
10
0
1
1
0
0
3
0
5
0
3
0
3
1
0
2
1
3
0
0
cs
102
9
17
21
36
52
34
23
30
24
28
18
23
50
54
31
2
1
2
34
44
19
25
30
30
10
22
7
19
13
15
12
58
27
1
24
25
25
14
6
99tn Percentile 1-hour Daily Maximum
Potential Alternative Standard
50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
100
0
0
1
0
1
1
1
0
0
2
2
0
1
0
0
1
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
150
3
0
2
0
4
4
4
1
1
5
5
1
4
3
2
3
0
0
0
3
7
1
1
1
1
0
1
1
1
1
1
1
1
1
0
1
0
1
1
0
200
9
1
3
1
6
8
8
3
2
9
9
2
8
9
6
7
1
1
2
6
13
2
3
3
4
1
2
3
3
2
3
3
2
3
1
3
2
3
4
1
250
15
2
5
3
8
11
11
6
3
12
13
5
12
15
10
12
3
2
5
9
18
5
6
6
10
2
3
6
6
5
5
5
5
6
3
7
5
5
8
2
98th
Percentile
200
14
2
5
3
10
12
12
6
4
12
12
4
11
13
11
10
3
2
3
8
15
5
4
5
8
1
3
5
6
4
4
5
6
5
2
7
4
6
6
2
March 2009
133
Draft-Do Not Quote or Cite
-------
2 7.4 UNCERTAINTY ANALYSIS
3 The approach for evaluating uncertainty was adapted from guidelines outlining how to
4 conduct a qualitative uncertainty characterization (WHO, 2008). First, the key sources of the
5 assessment that contribute to uncertainty are identified, and the rationale for why they are
6 included is discussed. Second, a qualitative characterization follows for the types and
7 components of uncertainty, resulting in a summary describing, for each source of uncertainty, the
8 level and direction of influence the uncertainty may have on the air quality characterization
9 results.
10 The overall characterization of uncertainty is qualitatively evaluated by considering the
11 degree of severity of the uncertainty, implied by the relationship between the source of the
12 uncertainty and the output of the air quality characterization. To the extent possible, an appraisal
13 of the knowledge base (e.g., the accuracy of the data used, acknowledgement of data gaps) and
14 evaluation of the decisions made (e.g., selection of particular model forms) is also included in
15 this uncertainty rating. The characterization is subjectively scaled by staff using a designation of
16 low, medium, and high. Briefly, a low level of uncertainty suggests large changes within the
17 source of uncertainty would have only a small effect on the results, there is completeness and
18 scientific consistency in the knowledge base, and decisions made regarding the particular source
19 of uncertainty would be widely accepted. A designation of medium implies that a change within
20 the source of uncertainty would likely have a proportional effect on the results; there may be
21 limited scientific backing, and limited selection of inputs or models to choose from. A
22 characterization of high implies that a small change in the source would have a large effect on
23 results, there may be inconsistencies present in the scientific support, and assumptions made
24 would be considered unusual and restrictive by others.
25 The bias direction indicates how the source of uncertainty has been judged to influence
26 estimated concentrations, either the concentrations are likely over- or under-estimated. In the
27 instance where two or more types or components of uncertainty are present that potentially offset
28 the direction of influence, the bias has been judged as both. An unknown bias has been assigned
29 where there was no evidence reviewed to judge the direction of uncertainty bias associated with
30 the source. Table 7-14 provides a summary of the sources of uncertainty identified in the air
31 quality characterization, the level of uncertainty, and the overall judged bias of each. A
March 2009 134 Draft-Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
discussion regarding each of these sources of uncertainty and how conclusions were drawn is
given in the sections that follow.
Table 7-14. Summary of qualitative uncertainty analysis for the air quality and
health risk characterization.
Source
Air Quality Data
Ambient
Measurement
Temporal
Representation
Spatial
Representation
Air Quality
Adjustment
Statistical Model
using PMRs
Ambient as
Indicate rot
Exposure
Health
Benchmarks
Type
Database quality
Interference
Scale
Missing data
Years evaluated
Scale
Proportional approach used
Spatial scale
Data Screening
Temporal Variation in PMRs
Distribution form of PMRs
Accuracy
Reproducibility
Scale
Averaging time
Susceptibility
Concentration/
Exceedance
Bias Direction
Both
Both
None - Unknown
Both
Unknown
Unknown
Unknown
Over
Both
Unknown
Unknown
Both
Both
Over
Unknown
Under
Characterization
of Uncertainty
Low
Medium
Medium
Low
Low
Medium - High
Moderate
Low - Moderate
Low
Low
Low
Low - Moderate
Low
Moderate
Low
Moderate
7.4.1 Air Quality Data
One basic assumption is that the AQS SC>2 air quality data used are quality assured
already. Methods exist for ensuring the precision and accuracy of the ambient monitoring data
(e.g., EPA, 1983). Reported concentrations contain only valid measures, since values with
quality limitations are not entered to the system, removed following determination of being of
lower quality or flagged. There is likely no selective bias in retention of data that is not of
reasonable quality if the data are in error; it is assumed that selection of high concentration poor
quality data would be just as likely as low concentration data of poor quality. Given the numbers
of measurements used for this analysis, it is likely that even if a few low quality data are present
in the data set, they would not have any significant effect on the results presented here. In
addition, a quantitative analysis of available simultaneous measures in Appendix A-3 indicated
little to no bias in measured concentrations or in the selection of one particular simultaneous
measurement over another. There are no alternative data sets available that are as
March 2009
135
Draft-Do Not Quote or Cite
-------
1 comprehensive, and where monitoring data are available that are not included in the AQS, it is
2 expected that given the same methods and quality assurances, they would be complementary to
3 the data existing in the AQS. Therefore, the air quality data and database used likely contributes
4 minimally to the uncertainty level, there is low uncertainty in the knowledge base, and the
5 uncertainty in the subjectivity of choices is also considered low.
6 Temporally, some of the ambient monitoring data used in this analysis had both the 5-
7 minute maximum and 1-hour concentrations reported and appropriately accounted for variability
8 in concentrations that are commonly observed for SC>2. When employing the completeness
9 criteria discussed in section 7.2, data were assured as representative of either a valid day or year.
10 In addition, having more than one ambient monitor accounted for some of the spatial variability
11 in selected counties. However, the degree of representation of the monitoring data used in this
12 analysis can be evaluated from several perspectives, one of which is how well the temporal and
13 spatial variability are represented. In particular, missing 5-minute maximum or hourly
14 measurements at a monitor may introduce bias (if there are specific periods within a day, month
15 or a year that influence measured values) and reduce certainty in the estimations. Furthermore,
16 the spatial representativeness will be poor if the monitoring network is not dense enough to
17 resolve the spatial variability (causing increased uncertainty) or if the monitors are not
18 effectively distributed to reflect population exposure (causing a bias). Additional uncertainty
19 regarding temporal and spatial representation by the monitors is expanded below.
20 7.4.2 Measurement Technique for Ambient SOi
21 One source of uncertainty in 862 air quality data is due to interference with other
22 compounds. The ISA notes several sources of positive and negative interference that could
23 increase the uncertainty in the measurement of ambient SC>2 concentrations (ISA, sections 2.3.1
24 and 2.3.2). Many of the identified sources (e.g., poly cyclic aromatic hydrocarbons, stray light,
25 collisional quenching) were described as having limited impact on SC>2 measurement due to the
26 presence of instrument controls that prevent the interference. The actual impact on any
27 individual monitor though is unknown; the presence of either negative or positive interference,
28 and the degree of interference contributed by one or the other, has not been quantified for any
29 ambient monitor. In addition, it is not known whether there is a concentration dependence on the
30 amount of interference. This may be an important uncertainty in considering the air quality
31 concentrations adjusted to just meet the current and potential alternative standards. While
March 2009 136 Draft - Do Not Quote or Cite
-------
1 reported ambient monitoring concentrations could be either over- or under-estimated, it is
2 probably minimal given instrument controls. The uncertainty is characterized as medium given
3 the limited quantitative evidence to judge the degree of bias at any individual ambient monitor.
4 7.4.3 Temporal Representation
5 Data are valid 5-minute and 1-hour average SC>2 measurements and are of the same
6 temporal scale as identified potential health effect benchmarks where 5-minute measurements
7 were reported. There are frequent missing values within a given valid year that may reduce the
8 degree of certainty in concentration distributions and model estimations; however, given the
9 level of the benchmark concentrations and the low frequency of benchmark exceedances, it is
10 likely of negligible consequence. Bias may be introduced if some seasons, day-types (e.g.,
11 weekday/weekend), or times of the day (e.g., nighttime or daytime) are not equally represented.
12 This type of bias may be present in the combined 5-minute and 1-hour measurement data set
13 because all of the available data were used without considering the standard 75% completeness
14 criteria. Staff elected to use all of the data rather than further reducing the already limited
15 number of samples and locations represented by the 5-minute SC>2 measurement data. The 5-
16 minute measurement data set did undergo screening that improved the quality of the data set,
17 including the removal of duplicate reporting/measurements, concentrations < 0.1 ppb, and any
18 concentrations resulting in technically impossible PMRs. For the analyses performed using the
19 broader 862 monitoring network and the 40-county analysis, a valid year of ambient monitoring
20 was based on 75 percent complete hours/day and days/quarter, and having all four complete
21 quarters/year. Therefore, these potential biases resultant from missing data are likely to have
22 been removed from these data sets.
23 Data were not interpolated in the analysis; missing data were not substituted with
24 estimated values and concentrations reported as zero were used as is. For the missing data, it is
25 assumed here that missing values are not systematic, i.e., high concentrations would be absent as
26 well low concentrations in equal proportions. There are methods available that can account for
27 time-of-day, day-of-week, and seasonal variation in ambient monitoring concentrations.
28 However, if a method were selected it would have to not simply interpolate the data, but
29 accurately estimate the probability of peak 1-hour SO2 concentrations that could occur outside
30 the predictive range of the method. It was judged that if such a method was available or one was
31 developed to substitute data, it would likely add to a similar level of uncertainty as not choosing
March 2009 137 Draft - Do Not Quote or Cite
-------
1 to substitute the missing values. Again, this can be viewed as having a limited effect on
2 uncertainty because using the validity criteria should select for the most representative and
3 complete ambient monitoring data sets possible. In using the concentrations reported as zero,
4 there is likely a negligible effect on uncertainty in the analyses resulting from not estimating any
5 extremely low (e.g., <1 ppb) 1-hour concentrations, since the concentrations of interest are well
6 above 1 ppb.
7 There may be bias and added uncertainty if the years and concentrations vary
8 significantly between monitors and across the two averaging times. When using older
9 monitoring data, the assumption is that the sources present at that time have similar emissions
10 and emission profiles as the current sources, adding uncertainty to results if this is not the case.
11 Monitoring sites across the U.S. have changed over time, with a trend of decreasing number of
12 monitors most evident for those reporting the 5-minute maximum SC>2 concentrations (Figure 7-
13 21). Five-minute SC>2 concentrations have been reported in fewer monitors than the 1-hour SC>2
14 concentrations; generally only a few years of data exist for 5-minute SC>2 concentrations
15 (Appendix A, Table A.l-1). This is the reason why, given the limited number of measurements,
16 all of the 5-minute maximum 862 data were used in developing the statistical relationships and
17 for the model evaluation without meeting 75% completeness criteria. In addition, the use of the
18 older ambient monitoring data (e.g., pre-2001) in some of the analyses here carries the
19 assumption that the sources present at that time are the same as current sources, potentially
20 adding to uncertainty if this is were not the case. The variability in monitoring concentrations
21 (both the 1-hour and 5-minute maximum 802) does not change significantly across most
22 monitoring years (i.e., years 1997 though 2004) and have a comparable range between the two
23 averaging times (Figure 7-22). There is some compression in the range of COVs considering
24 some of the more recent years of data, most notable for year 2007, possibly affected by the
25 reduction in the number of ambient monitors in operation rather than a reduction in the temporal
26 variability in 5-minute or 1-hour concentrations at particular monitors. Furthermore, the
27 selection of a subset of the recent air quality (2001-2006) for detailed analyses may reduce the
28 potential impact from changes in national- or location-specific source influences and is judged to
29 have a minimal bias in representing air quality concentrations for those selected years.
30 Therefore, due to the limited variation in temporal trends in COV for both 5-minute and 1-hour
March 2009 13 8 Draft - Do Not Quote or Cite
-------
1
2
3
SC>2, the overall impact to uncertainty is expected to be low for analyses performed using the
monitoring data set that spans several years.
o>
60
E
*
CO
0) '
f.*\
30
20
10
5
6
7
11122222222
99900000000
99900000000
78901 234567
Year
Figure 7-21. Temporal trends in the number of ambient monitors in operation per year for
monitors reporting both 5-minute and 1-hour SO2 concentrations
5-Minute Maximum SO2
1-hourSO2
500
400
gsoo
8 200
100
0
T T
nnnL
r T
\ r~i
T^T^VV
111222
999000
999000
78901 2
TTT
222
000
000
345
T
2
0
0
6
r
1
9
-
-
-
-
-
j
-r T
1 1 I 1 1 rji Jl 1 I I
TT???TTTT^
3
2 11122222222
0 99900000000
0 99900000000
7 78901234567
Year
Year
9
10
11
12
Figure 7-22. Temporal trends in the coefficient of variability (COV) for 5-minute maximum and 1
hour concentrations at the 98 monitors that reported both 5-minute and 1-hour SO2
concentrations
March 2009
139
Draft - Do Not Quote or Cite
-------
1 7.4.4 Spatial Representation
2 The ambient monitoring data are assumed to be spatially representative of the locations
3 analyzed in this REA because the monitors are used in determining whether areas meet or do not
4 meet the NAAQS. However, relative to the physical area, staff recognizes there are only a small
5 number of monitors, particularly when considering the set of monitors that reported 5-minute
6 maximum SC>2. When considering 1-hour ambient monitoring at the county level, data were
7 assumed to be spatially representative of those particular locations analyzed here. This includes
8 areas between the 1-hour ambient monitors that may or may not be influenced by similar local
9 sources of 862. For these reasons, the uncertainty due to the spatial networks may be moderate,
10 although the 862 monitoring network design should have addressed these issues within the
11 available resources and other monitoring constraints. Portions of the air quality characterization
12 used all monitors meeting the 75 percent completeness criteria, without taking into account the
13 monitoring objectives, scale, or land use. Thus, there may be a further lack of spatial
14 representation and contribution to uncertainty due to either the inclusion or exclusion of monitors
15 that are near local source emissions of SC>2 resultant from the validity screening. Bias will
16 depend on ambient monitoring objectives, monitoring scale, and whether there is large variability
17 in monitoring surface, i.e., areas of differing terrain that are not adequately represented by the
18 current distribution of monitors. The direction of this bias is largely unknown due to the
19 differences in the true representativeness of the network and the particular terrain in each
20 location.
21 In addition, because the monitors reporting 5-minute concentrations are not part of a
22 designed 5-minute SC>2 monitoring network but are entirely voluntary, it is largely unknown
23 what the direction of bias and magnitude of uncertainty may be for the results generated using
24 these monitors. In comparing the emission sources in close proximity to the monitors reporting
25 5-minute maximum versus the broader SC>2 monitoring network , similar distributions were
26 observed in the types of sources and the total emissions potentially impacting both sets of data.
27 This could indicate that the relationships derived from monitors reporting 5-minute 862
28 concentrations and applied to the broader monitoring network does not add to uncertainty when
29 considering the ambient monitoring data wholly. In comparing individual monitors, there are
30 varying numbers and types of sources within given distances of each monitor, each potentially
31 contributing in varying proportions to the measured SC>2 concentrations at each monitor. There
March 2009 140 Draft - Do Not Quote or Cite
-------
1 could be added uncertainty in extrapolating relationships derived from any one monitor and
2 applied to other ambient monitors with dissimilar source types and total emissions. However,
3 the method of applying both concentration level and variability measures to each hourly
4 concentration at each monitor should have accounted for some of the variability anticipated by
5 the presence differing source types and emissions. Additional discussion on the use of the
6 monitors reporting 5-minute data is given below in section 7.4.6. Based on the similarity in the
7 emission sources surrounding the group of monitors in each data set, it is judged that there is at
8 most a moderate uncertainty associated with the spatial representation of the monitors reporting
9 5-minute SC>2 concentrations with unknown bias.
10 7.4.5 Air Quality Adjustment Procedure
11 There is uncertainty in the air quality adjustment procedures due to the uncertainty of the
12 true relationship between the adjusted concentrations that are simulating a hypothetical scenario
13 and the as is air quality. The adjustment factors used for the current and the potential alternative
14 standards each assumed that all hourly concentrations will change proportionately at each
15 ambient monitoring site. Two principal uncertainties are discussed, namely uncertainty
16 regarding the proportional approach used and the universal application of the approach to all
17 ambient monitors within each location.
18 Different sources have different temporal emission profiles, so that equally applied
19 changes to the concentrations at the ambient monitors to simulate hypothetical changes in
20 emissions may not correspond well within all portions of the concentration distribution. When
21 adjusting concentrations upward to just meeting the current standard, the proportional adjustment
22 used an equivalent multiplicative factor derived from the annual mean or daily mean
23 concentration and equally applied that factor to all portions of the concentration distribution, i.e.,
24 the upper tails were treated the same as the area of central tendency. This may not necessarily
25 reflect changes in an overall emissions profile that may result from, for example, an increase in
26 the number of sources in a location. It is possible that while the mean concentration measured at
27 an ambient monitor may increase with an increase in the source emissions affecting
28 concentrations measured at the monitor, the tails of the hourly concentration distribution might
29 not have the same proportional increase. The increase could be greater or it could be less than
30 that observed at the mean, dependent largely on the type of sources and inherent operating
31 conditions. Adjusting the ambient concentrations upwards to simulate the potential alternative
March 2009 141 Draft - Do Not Quote or Cite
-------
1 standards also carries a similar level of uncertainty although the multiplicative factors were
2 derived from the upper percentiles of the 1-hour daily maximum 862 concentrations, rather than
3 the mean, and then applied to the 1-hour SO2 concentrations equally. If there are deviations from
4 proportionality, the magnitude of the bias is likely related to the magnitude of the concentration
5 adjustment. There is likely greater uncertainty in the results for evaluating the current and the
6 250 ppb 99th percentile alternative standards which have the highest adjustment factors, than
7 when considering the 50 ppb and 100 ppb 99th percentile alternative standard which have the
8 lowest adjustment factors.
9 In each of these instances of adjusting the concentrations upwards, one could argue that
10 there may be an associated over-estimation in the concentrations at the upper tails of the
11 distributions, possibly leading to over-estimation in the numbers of exceedances of benchmark
12 levels. An analysis was performed on monitors within seven counties used in the air quality
13 characterization to investigate how distributions of hourly nitrogen dioxide concentrations have
14 changed over time (Rizzo, 2009). The analysis indicates that a proportional approach can be
15 appropriate in simulating higher concentrations at most monitoring sites, since historically, SC>2
16 concentrations have decreased linearly across the entire concentration distribution at each of the
17 monitoring sites and counties evaluated.
18 At some of monitoring sites analyzed however, there were features not consistent with a
19 completely proportional relationship, including deviation from linearity primarily at the
20 maximum or minimum percentile concentrations, some indication of curvilinear relationships,
21 and the presence of either a positive or negative regression intercept (Rizzo, 2009). Where
22 multiple monitors were present in a location, there tended to be a mixture of each of these
23 conditions including proportionality (e.g., see Figure 7-23). Not all of the counties analyzed as
24 part of the air quality characterization were included in the evaluation. It was also assumed that
25 the analysis conducted for the seven counties would reflect what may be observed at the other
26 counties if evaluated for trends in concentration over long periods of time. Further, there is
27 uncertainty in adjusting concentrations upwards or downwards considering assumptions
28 regarding future source emission scenarios and how these would relate to observed trends in
29 current and historical air quality. The uncertainty about future source emission control scenarios
30 is largely unknown.
March 2009 142 Draft - Do Not Quote or Cite
-------
1 Universal application of the proportional simulation approach for each of the counties
2 and within each county was done for consistency and was designed to preserve the inherent
3 variability in the concentration distribution. There is uncertainty regarding emission changes
4 that would affect the concentrations at the design monitor having the highest concentration (e.g.,
5 the highest annual mean, 98th or 99th percentile 1-hour concentration) that may not necessarily
6 affect other lower concentration sites proportionately. This could result in either over- or under-
7 estimations in the number of exceedances at lower concentration sites within a county where the
8 current or alternative standard scenarios were evaluated. For example, Figure 7-23 shows the
9 daily maximum 1-hour SC>2 concentration percentiles for five ambient monitors in Allegheny
10 County PA, where each of the ambient monitors were in operation for years 1998 and 2007.
11 While all five of the monitors generally demonstrate features of proportionality, the difference in
12 regression slopes indicates that the rate of change in the concentration distribution was not equal
13 when comparing these monitors for these two monitoring years.
14 Given the limited deviations in linearity and proportionality at each monitor site that may
15 result in both over- or under-estimations in concentrations following either an adjustment
16 upwards or downwards and the limited time and resources available to develop a new universal
17 approach that addresses each of the observed deviations, staff judged the proportional approach
18 used to simulate just meeting the current and alternative standards as adequate and appropriate
19 for the scenario considered. The uncertainty is judged moderate, given the differences in the rate
20 of change in concentrations over time and limited deviations from linearity/proportionality at
21 individual monitoring sites, each of which could add to either an over- or underestimation bias.
March 2009 143 Draft - Do Not Quote or Cite
-------
High Year: 1998 Low Year: 2007
0.00 0.05 0.10 0.15 0.20
j i i i i i i
Q.
Q.
O
O
0>
Q_
420030064
RA2: 0.96
420030002
RA2: 0.99
420030067
R"2: 0.97
420030010
RA2: 0.99
420030021
RA2: 0.97
) 00 005 010 015 0.20
) 00
-. High Year Percentile Cone, (ppm)
2 Figure 7-23. Comparison of measured daily maximum SO2 concentration
3 percentiles in Allegheny County PA for one high concentration year
4 (1998) versus a low concentration years (2007) at five ambient monitors.
5 7.4.6 Statistical Model Used for Estimating 5-minute SOi Concentrations
6 Four components of uncertainty were identified regarding the statistical model: the
7 screening of the PMR data, the temporal representation of data used in the statistical model
8 development, the accuracy of the model in predicting daily 5-minute maximum concentrations
9 and the reproducibility of the model predictions.
10 Staff identified data for removal from the combined 5-minute and 1-hour ambient
11 measurement data set using the PMR. The calculation of PMRs less than 1 implies the 5-minute
12 peak is less than the 1-hour average, a physical impossibility, and values >12 are a mathematical
13 impossibility. The 5-minute ambient monitoring data were screened for values outside of these
14 bounds35, increasing confidence in the relevance of PMRs used for development of the statistical
15 model. While a total of 40,665 data points were excluded from the data set using the PMR
16 criterion, this comprised less than 2% of the data available to develop the PMR relationship. It is
It is possible to have a PMR equal to 12. The value is achievable with one 5-minute concentration above zero and
the other eleven 5-minute values reporting concentrations of zero. Data used in developing the statistical
relationship were screened for the values with a PMR equal to 12 however since it could not be used by the
AERMOD/APEX modeling. It is of little consequence because the distributions chosen in estimating the 5-minute
concentrations included the 1st through the 99th percentiles, not the minimum and maximum values.
March 2009
144
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
assumed that the criterion used for the data removal is not biased, only directed towards
identifying unrealistic 5-minute and 1-hour concentration combinations. Therefore, given the
few data removed from further analysis and recognizing there would be reduced uncertainty
offered in using a data set comprised of PMRs with realistic bounds, the uncertainty associated
with the screening of the 5-minute data is low.
The use of all screened 5-minute maximum SC>2 data (1997 to 2007) in developing
distributions of PMRs assumes that the source emissions present at that time of measurement are
similar to other year source emissions, possibly adding to uncertainty in estimated exceedances
in areas where source emissions have or have not changed over time. However, as noted with
the concentration variability, the PMRs do not have any apparent trend with monitoring year and
have averaged around 1.6 using the 5-minute maximum measurement data (Figure 7-24). This
indicates that the use of the older ambient monitoring data in developing the statistical model
may have a negligible impact on the predicted concentrations.
8
7-
i5
2
11122222222
99900000000
99900000000
78901 234567
Year
Figure 7-24. Distributions of annual average peak-to-mean ratios (PMRs) derived from the 98
monitors reporting both 5-minute maximum and 1-hour SO2 concentrations, Years 1997
through 2007.
The PMRs distributions for each COV and concentration bin were represented by a non-
parametric form condensed to single percentiles, with each value from the distribution having an
equal probability of selection. While there may be other distribution forms that could be
alternatively selected, staff judged that use of a fitted distribution would not improve the
representation of the true population of PMRs compared with a non-parametric form, and that
there would likely be no reduction in the uncertainty of estimated number of exceedances if
March 2009
145
Draft - Do Not Quote or Cite
-------
1 using a parameterized distribution. While many of the PMR distributions were similar to a
2 lognormal distribution (for example Figure 7-25), 93 of 95 possible statistical tests performed
3 indicated the distributions were statistically distinct (p<0.01) from any of the tested forms (i.e.,
4 normal, lognormal, Weibull, gamma, and exponential) (see Figure 7-26 as an example). The
5 PMRs derived from monitors having the greatest COV (all concentration bins) and those derived
6 from the lowest concentration bins (all COV bins) were most common in exhibiting atypical
7 distribution forms. While there is uncertainty associated with the use of the empirically-derived
8 data in representing the true population of PMRs, assuming a fitted distribution would not be
9 without its own uncertainties. For example, using a lognormal distribution may underestimate
10 the observed frequency of certain values of PMRs while overestimating others. For PMR
11 distributions that are of similar form with the lognormal distribution, it is likely that the small
12 variation in PMRs selected from a fitted lognormal distribution would have only limited impact
13 to the estimated 5-minute maximum SC>2 concentrations. For distributions exhibiting no
14 similarities to any parametric distribution, experimental justification criteria would need to be
15 developed in selecting the most appropriate form of the distribution, likely requiring multiple test
16 iterations, potentially yielding distributions with more uncertainty than those of a non-parametric
17 form (WHO, 2008). Each of these additional evaluations and iterations would require time and
18 resources not available to staff. Furthermore, the sample sizes for many of the PMR
19 distributions used are well above 1,000 (only 5 of the 19 distributions had fewer than 1,000),
20 with all above 100 samples, providing support that the true distribution may be well-represented
21 by the non-parametric form. Each of these factors mentioned (uncertainty in the form of the
22 distribution, limits on time and resources available, and numbers of samples available) were
23 considered and it was decided by staff that the non-parametric distribution derived from the
24 measurement data would be appropriate.
March 2009 146 Draft - Do Not Quote or Cite
-------
C(Mjin=b COWconcbin=4
2
3
4
5
6
7
30
25-
20"
" 15-
10
0"T
1 .00
3.75
E.50
PMR
9.25
12.00
Curves:
NormalCMu=1.6692 Signa=0.5724)
Lognormal(Theta=0 Shape=.28 Scale=.47)
Exponent iaH The ta=0 Scale=1.67)
Ueibu11(Theta=0 Shape=2.7 Scale=1.9)
GanmaCTheta=0 Shape=11.5 Scale=0.15)
Figure 7-25. Example histogram of peak-to-mean ratios (PMRs) compared with four fitted
distributions derived from monitors reporting the 5-minute maximum and 1-hour SO2
concentrations: monitors with medium level variability and 1-hour concentrations
between 75 and 150 ppb.
COVbin=c COVconcbin=2
10
8
P
M G
R
III I I
0.001 90
I
99
99.9 99.99
Lognormal Percentiles CSigma=0.904401 )
99.999
Figure 7-26. Example of a measured peak-to-mean ratio (PMRs) distribution with the percentiles
of a fitted lognormal distribution.
March 2009
147
Draft - Do Not Quote or Cite
-------
1 The accuracy in the predicted daily 5-minute maximum 862 concentrations above each of
2 the benchmark levels was evaluated using measured concentrations. The results indicated that
3 on average, the statistical model performed well in estimating of these short-term peak
4 concentrations (section 7.2.3.4). However, the evaluation indicated the estimated number of
5 daily 5-minute maximum SC>2 concentrations above the any of the benchmark levels could be
6 either over- or under-estimated by as many as 20 to 50 days in the year. These model prediction
7 errors were limited to several site-years from a few monitors. Figure 7-27 presents the model
8 predicted and observed number of benchmark exceedances at each of the benchmark levels.
9 While there is generally uniform agreement between the predicted and observed values at the
10 100 ppb benchmark, there is some deviation in the agreement at the highest and lowest number
11 of exceedances for the 200, 300, and 400 ppb benchmark levels. For example, there were a few
12 site-years without any observed daily 5-minute maximum SC>2 concentrations above 400 ppb,
13 although the statistical model predicted between 2-15 days in a year. This could indicate that a
14 few of the site-years may have moderate overestimations in the estimated number of daily 5-
15 minute maximum SC>2 concentrations, where the estimated number of exceedances is 15 or less.
16 In addition, site-years with the greatest number of observed exceedances of 400 ppb (about 50
17 per year) were consistently underestimated by about 30%. This could imply that when the
18 estimated number of daily 5-minute maximum 862 concentrations above 400 ppb is at 40 per
19 year, the underestimate may be as large as 15 days per year. Neither of these situations appeared
20 related to a source type; additional monitors sited in the same area impacted by similar source
21 types had good agreement between the observed and predicted concentrations. At the monitor
22 with the greatest number of measured benchmark exceedances (ID 290930030) and largest over-
23 prediction error, complex terrain may be an influential factor. A nearby monitor (ID 290930031)
24 sited in open, flat terrain and close to the major source of emissions (about 1.7 km) had small
25 prediction errors. The other monitor (ID 290930030) is about 4.6 km from the same primary
26 smelter, but located at the base of a ridge running between the source and the monitoring site.
27 These results suggests that when considering any individual monitor, there may be factors not
28 accounted for by the statistical model that are important in estimating benchmark exceedances.
29 Based on the results of the model evaluation, the greatest uncertainty in the number of
30 benchmark exceedances for individual monitors is likely at the lower and upper tails of the
31 prediction distribution. However, in evaluating the estimated number of benchmark exceedances
March 2009 148 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
9
10
11
12
at most of the other monitoring site-years (i.e., about 90% of the data set) and at all of the
benchmark levels, the uncertainty in the predicted number of exceedances as a whole is likely
low.
re
&
re
(0
a
o
X
(0
E
<1>
.a
_>
're
0)
.a
E
D
C
0)
a!
0 20 40 60 80 100 120 140 160 180
0 10 20 30 40 50 60 70 80 90 100 110
10 20 30 40 50 60 70 80
Observed number of daily benchmark exceedances in a year
Figure 7-27. Comparison of observed and predicted number of daily benchmark exceedances in a
year at monitors reporting 5-minute maximum SO2 concentrations.
Reproducibility in the model estimates was evaluated by performing multiple model runs.
For the sake of efficiency, a limit of 20 simulations per air quality scenario was selected as
sufficient to generate stable estimates of 5-minute concentrations where the statistical modeling
was performed. This determination was based on a series of sensitivity runs conducted using the
40-county as is air quality data set. First, ten independent model runs were performed using 20
March 2009
149
Draft - Do Not Quote or Cite
-------
1 simulations in each run. A simulation is where each monitor has all of its years of 1-hour data
2 used in estimating 5-minute maximum concentrations; this is repeated 20 times and used to
3 generate a mean number of daily 5-minute maximum SO2 concentration exceedances for each
4 year. This model run was repeated 10 times to generate the 10 runs of 20 simulations. Then an
5 additional model run was performed, only this time it was comprised of 100 simulations using
6 the same air quality data set. Estimated mean exceedances from this larger model simulation run
7 were compared with the 10 independent 20 model simulation runs to evaluate differences
8 between the runs. The absolute difference in the number of exceedances between the single 100
9 simulation run and each of the ten 20 simulation runs for each site-year (n=610) was calculated.
10 The distribution of the differences in the estimated number of exceedances of the potential health
11 effect benchmark levels is provided in Figure 7-28. For each of the potential benchmark levels,
12 there is little to no difference in using a 100 model simulation versus a 20 model simulation per
13 run. All of the differences in the number of exceedances are centered at zero, with a very few
14 site-years exhibiting prediction differences greater than one. There may be a small
15 overestimation bias in estimating daily 5-minute maximum concentrations above 100 ppb when
16 using a 20 model simulation run compared with a 100 model simulation run, however the
17 difference for most of the site years is at most 1 exceedance. Of the 610 site-years simulated in
18 each run, over 70% were within at least one exceedance when considering the 100 ppb level,
19 while about 95% were within at least two estimated exceedances (Table 7-15). At the higher
20 benchmark levels the agreement improves, with nearly 98% of all model run site-years with a
21 two or fewer exceedance difference between the 100 and 20 model simulation runs. Run-to-run
22 variability is also limited, indicating that a single independent 20 model simulation run would
23 consistently estimate the mean number of benchmark exceedances.
March 2009 150 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
4
0)
^m
1
c
0)
Ģ
.-
^^
^^
0)
fc
'&
0)
"3
"o
<
^^^
0)
o
E
o
o
re
D)
_c
CO
3
CO
c
<8
0)
0)
o
X
(1)
JĢ
E
o
c
0)
.Q
c
3
C
.2
5
3
E
CO
^^
o
o
o
CM
re
o
c
re
c
3
i_
|
J5
^3
g
^B
>
4-
3
2-
1
°
~1
-2-
-3-
-4-
n n n
n n n n
T T T D
III +
PPU7
T T T
-L -L -L n
n n n n
n n
>
4
3
2
1 -
0
-2
-3
-4
n n n n
n n n n
^ ^ ^ i
n n a n
an n
H O O >l
100 ppb
D n D n D a
n n n n n n
D T T T T T
| | | | |
T u u u P u
T T T T T
n -L -L -L -L -L
n n n n n a
D n D a
300 ppb
n
n n n n n n
n n n n n n
\- -*- I I- -I -H
n a a a a a
a a a a a
a
c c v o Q Hn
> 200 ppb
n
n n
n n
a n
n n
n n
a
n n n n
nnnnnnnn
nnnnnnnn
annanann
nnnnnnnn
n
> 400 ppb
20 simulation model run number
Figure 7-28. Distributions of the calculated difference between estimated concentration
exceedances using a single 100 model simulation run and those estimated using ten
independent 20 model simulation runs. Box represents the inner quartile range (IQR, or
the 25th to 75th percentile), + indicates the mean, whiskers are 1.5 times the IQR.
n n n n n n n
nnnnnnnnnn
nnnannaanD
ana ana
n
1 23456789 10
1
8
9
10
11
Table 7-15. The percent of site-years with a difference in the number of modeled
exceedances using a 100 model simulation run versus a 20 model simulation run.
Difference in
Estimated Yearly
Exceedances
<1
<2
Potential Health Effect Benchmark Level Compared
> 100 ppb
71.5-73.8
94.6 - 97.5
> 200 ppb
82.6 - 85.6
97.9-99.3
> 300 ppb
91.1 -93.1
99 - 99.8
> 400 ppb
93.8-95.4
99.7-100
In further analyzing the reproducibility of the statistical model, ninety-five percent
prediction intervals (95-PI) about the median number of daily 5-minute maximum benchmark
exceedances were generated for each monitor in the forty counties selected for detailed analysis,
using the model simulations for the as is air quality scenario, for each year simulated, and for
March 2009
151
Draft - Do Not Quote or Cite
-------
1 each potential health effect benchmark level. Percentile distributions were calculated from the
2 100 model simulation run using the number of estimated exceedances at each monitor, with the
3 2.5th, 50th, and 97.5th percentile values retained. First, the estimated median peak values were
4 ranked for each site-year and used to represent the central tendency. Then, the 2.5th and 97.5th
5 percentile predictions were used to construct the 95-PI about the ranked median estimates (i.e.,
6 97.5th value minus 2.5th value = 95th prediction interval).
7 Figure 7-29 presents the results of this sensitivity analysis for the number of days with 5-
8 minute benchmark exceedances per site-year in the selected counties when using the as is air
9 quality data. The 95-PI spans about 15 and is generally consistent across a wide range of
10 estimated number of potential health effect benchmark exceedances and for each benchmark
11 level, indicating little bias in the estimation procedure at any individual site-year. When a few
12 exceedances are estimated (e.g., 7 or less in a year), the 95-PI tends to include an estimate of
13 zero, suggesting that when a monitor contains this few estimated mean or median number of
14 exceedances, the certainty in the prediction may be limited. This is evident when considering the
15 lowest percentiles of the distribution, that of course varies with each given benchmark level. For
16 example, just over 15 percent of site-years have a 95-PI that includes zero exceedances of the
17 100 ppb benchmark. Even though there may have been a positive number (say 3 or 4) of mean
18 or median estimated exceedances for a percentage of these site-years, zero exceedances is still a
19 reasonable prediction. Compared to the benchmark of 100 ppb, far fewer monitors have mean
20 exceedances of 300 ppb greater than zero, with around 80 percent either having a 95-PI that
21 includes zero or a mean estimate of zero exceedances. Where the estimated exceedances of any
22 of the benchmarks in a year are greater than or equal to about 10, the 95-PI excludes a value of
23 zero, indicating greater certainty about the estimated mean or median number of exceedances
24 being different from zero. This is most evident in the number of estimated days with 5-minute
25 maximum concentrations above 100 ppb, where most of the site-years (about 80%) likely have
26 about 10 exceedances in a year. The other benchmark levels also have 95-PI that does not
27 include zero exceedances, albeit at a lower percentage of the total site-years.
28 Similarly, Figure 7-30 presents the 95-PI generated at the county level. These intervals
29 were generated using the mean estimates of each benchmark percentile (i.e., the 2.5th, the 50th,
30 and the 97.5th) by county for each year. The 95-PI have a smaller range than when considering
March 2009 152 Draft - Do Not Quote or Cite
-------
1
2
4
5
6
individual site-years, spanning approximately 10 exceedances, suggesting a mean prediction of
about 5 or more exceedances likely excludes the possibility of zero estimated exceedances.
100
90
70
0) 60
0_
0) 50
^ 40
O
0)
20
10:
0
100
90
> 100 ppb
PLCDT
' p50 * p97.5
Ŧf
*:*
> 200 ppb
PLOT " p2.5 ' p50 * p97.5
-Ģ 80
0)
O 70
0)
0_
>
60
JS 40
1 30
3
O 20
10
0
> 300 ppb
PLOT - p2.5
p50 * p97.5
> 400 ppb
PLOT -p2.5 -p50 *p97.5
0 50 100 150 200 2500 50 100 150 200 250
Number of 5-minute Daily Maximum SO2 Above Benchmark Level
Figure 7-29. 95% prediction intervals for the number of modeled daily 5-minute maximum SO2
concentrations in a year above potential health effect benchmark levels by each
monitor, Years 2001 through 2006 for 40 selected counties, air quality data as is.
March 2009
153
Draft - Do Not Quote or Cite
-------
2
O
4
5
6
7
100
90
= 8°
^
^^
f ^
^^
Q_
(1)
:= 40
re
3 30
E
3 20
0
10-
0"
100
90
5 80
m :
0 70
ĢŦ;
I50
re 40
E so
3 :
O 20
10
0
*/?*****'*
J|r
I/I > 100 ppb
J|
*
fŧ
| PLOT ' p2.5 ' p50 * p97.5
111111
iff*'* ' +
E
K
ŧ:t
r
g
* > 300 ppb
Ml-
ar
ŧ
ŧ
Ģ
i
PLOT - p2.5 ' p50 * p97.5
-
-
-
-
-
-
-
.
-
:
"
-
-
-
-
-
.
"
%$***
$f*
*//
iW-fr
1!*
Ģ* > 200 ppb
§.
ŧ
M
ŧ
V
PLOT ' p2.5 ' p50 * p97.5
X X^H-1'- +
f
f
m
Hfr
1 > 400 ppb
ŧ
$
PLOT - p2.5 ' p50 * p97.5
0 50 100 150 200 250 0 50 100 150 200
Mean Number of 5-minute Daily Maximum SO2 above Benchmark Level
Figure 7-30. 95% prediction intervals for the number of modeled daily 5-minute
maximum SO2 concentrations in a year above potential health effect
benchmark levels by each county, Years 2001 through 2006 for 40
selected counties, air quality data as is.
250
March 2009
154
Draft - Do Not Quote or Cite
-------
2 7.4.7 Ambient Monitor to Exposure Representation
3 Human exposure is characterized by contact of a pollutant with a person, and as such, the
4 air quality characterization assumes that the ambient monitoring concentrations can serve as a
5 surrogate for exposure. The ISA reports that personal exposure measurements (PEM) are of
6 limited use since ambient concentrations are typically below the detection limit of the personal
7 samplers. There is no method to quantitatively assess the relationship between 5-minute ambient
8 monitoring data and 5-minute personal exposures, particularly since personal exposures are time-
9 averaged over hours or days, and never by 5-minute averages. Therefore the fraction of actual 5-
10 minute maximum personal exposure concentrations attributed to 5-minute maximum ambient is
11 unknown and thus adds to uncertainty when using ambient as an indicator of human exposure.
12 An evaluation in the ISA indicates the relationship between longer-term averaged
13 ambient monitoring concentrations and personal exposures is strong, particularly when ambient
14 concentrations are above the limit of detection. The strength of the relationship between
15 personal and ambient concentrations is supported further by the limited presence of indoor
16 sources of SC>2; much of an individuals' personal exposure is of ambient origin. However, SC>2
17 personal exposure concentrations are reportedly a small fraction of ambient concentrations. This
18 is because local outdoor SC>2 concentrations are typically half that of the ambient monitoring SC>2
19 concentrations, and indoor concentrations about half that of the local outdoor 862 concentrations
20 (ISA). Therefore, while the relationship between personal exposures and ambient is strong, the
21 use of monitoring data as a surrogate for exposure would likely lead to an overestimate in the
22 number of peak concentrations those individuals might encounter. While the magnitude of the
23 uncertainty about the true relationship between actual human exposure and any given ambient
24 monitor short-term concentration exceedance is unknown, it is likely to be a moderate level
25 given the large difference between the longer-term PEM and the ambient concentrations.
26 7.4.8 Health Benchmark
27 The choice of potential health effect benchmarks, and the use of those benchmarks to
28 assess risks, can add to uncertainty in the risk assessment results. For example, the potential
29 health effect benchmarks used were from studies where volunteers were exposed to SC>2 for
30 varying lengths of time. Typically, the 862 exposure durations in the controlled human studies
31 were between 5 and 10 minutes. This may add to uncertainty in the characterization of risk using
March 2009 155 Draft - Do Not Quote or Cite
-------
1 the air quality because the potential health effect benchmark levels were compared to
2 concentration exceedances over a 5-minute time period. If there were a difference in the
3 response rate for a given concentration level and averaging time, the use of a 5-minute averaging
4 time could either over- or under-estimate risks. The true dose-response relationship may be
5 dependent on both the combined concentration level and the exposure duration, that is, it is
6 possible that a particular response rate observed at a 10-minute exposure level of concentration x
1 may be similar to that of a 5-minute exposure level equal to or greater than concentration x. In
8 this hypothetical scenario, if benchmarks were derived from 10 minute exposures and applied in
9 the evaluation of 5-minute ambient concentrations, the potential risk may well be over-estimated.
10 However, the ISA did not distinguish between health effects observed following either 5- or 10-
11 minute exposures. Therefore the potential bias is unknown, and given similarity in studies using
12 either 5- or 10-minute exposures, the overall uncertainty is judged low.
13 In addition, the human exposure studies evaluated airways responsiveness in mild to
14 moderate asthmatics. For ethical reasons, adults with severe asthma and younger asthmatics.
15 Severe asthmatics and/or asthmatic children may be more susceptible than mild asthmatic adults
16 to the effects of 862 exposure. Therefore, the potential health effect benchmarks based on these
17 studies could underestimate risks in populations with greater susceptibility. This assumption
18 likely adds a moderate level of uncertainty to the relevance of the particular health effect
19 benchmark levels.
20
21 7.5 KEY OBSERVATIONS
22 Presented below are key observations resulting from the SO2 air quality characterization:
23 For unadjusted as is air quality at ambient monitors measuring 5-minute
24 maximum concentrations, nearly 70% of the 471 site-years analyzed had at least
25 one daily 5-minute maximum concentration above 100 ppb and over 100 site-
26 years had > 25 days with a daily 5-minute maximum concentration above 100
27 ppb. Less than half (44%) of the site-years had at least one daily 5-minute
28 maximum concentration above 200 ppb and only 36 site-years had > 25 days with
29 a daily 5-minute maximum concentration above 200 ppb. Approximately 25%
30 and 17% of the 471 site-years analyzed had at least one daily 5-minute maximum
31 concentration above 300 and 400 ppb, respectively, with 23 and 12 site-years
March 2009 156 Draft - Do Not Quote or Cite
-------
1 having > 25 days with a daily 5-minute maximum concentration above 300 and
2 400 ppb, respectively (Appendix A, Table A.5-1).
3 For any of the air quality scenarios considered, the probability of exceeding the 5-
4 minute maximum benchmark levels was consistently greater at monitors sited in
5 low-population density areas compared with high-population density areas. In
6 addition, the probability of any 5-minute benchmark exceedance was consistently
7 related to either a 24-hour average or 1-hour daily maximum concentration.
8 For unadjusted air quality in the 40 counties selected for detailed analysis, most
9 counties are estimated to have, on average, fewer than 50 days per year where the
10 daily 5-minute maximum ambient SC>2 concentrations are > 100 ppb. Most
11 counties are estimated to have, on average, 25 days per year with daily 5-minute
12 maximum ambient SC>2 concentrations > 200 ppb. Very few counties are
13 estimated to have more than ten days with 5-minute maximum SC>2 concentrations
14 > 300 ppb, while nearly half did not have any days with 5-minute maximum SC>2
15 concentrations > 400 ppb (Tables 7-10 to 7-13).
16 When air quality is adjusted to simulate just meeting the current annual standard
17 in the 40 counties selected for detailed analysis, a hypothetical scenario requiring
18 air quality to be adjusted upward, all locations evaluated are estimated to have
19 multiple days per year where 5-minute maximum ambient SC>2 concentrations are
20 > 100 ppb. Most counties are estimated to have, on average, 100 days or more
21 per year with 5-minute maximum ambient SC>2 concentrations > 100 ppb, while
22 eight of the forty counties are estimated to have 200 days or more per year with 5-
23 minute maximum ambient SC>2 concentrations > 100 ppb. Fewer benchmark
24 exceedances are estimated to occur with higher benchmark levels. For example,
25 only five counties are estimated to have 60 or more days per year with 5-minute
26 maximum ambient SC>2 concentrations that exceed 300 ppb (Table 7-12) and only
27 four counties are estimated to 50 or more days per year with 5-minute maximum
28 ambient SC>2 concentrations that exceed 400 ppb.
29 In all counties, potential alternative standard levels of 100 and 150 ppb are
30 estimated to result in fewer days per year than the current standards and the
31 potential alternative standard levels of 200 and 250 ppb with daily 5-minute
March 2009 157 Draft - Do Not Quote or Cite
-------
1 maximum SC>2 concentrations > 300 and > 400 ppb (Tables 7-12 and 7-13).
2 When considering the potential 1-hour daily maximum potential alternative
3 standard levels of 100 and 200 ppb, corresponding annual average SO2
4 concentrations were typically between 3 and 15 ppb, similar to a range of
5 concentrations using unadjusted air quality (Appendix A). When considering the
6 potential alternative standard levels of 200 and 250 ppb, corresponding annual
7 average SC>2 concentrations were typically between 10 and 30 ppb, similar to the
8 range of concentrations observed when using adjusted air quality that just meets
9 the current annual standard.
March 2009 158 Draft - Do Not Quote or Cite
-------
i 8.0 EXPOSURE ANALYSIS
2 8.1 OVERVIEW
3 This section documents the methodology and data staff used in the inhalation exposure
4 assessment and associated health risk characterization for SO2 conducted in support of the
5 current review of the SC>2 primary NAAQS. Two important components of the analysis include
6 the approach for estimating temporally and spatially variable SO2 concentrations and simulating
7 human contact with these pollutant concentrations. The approach was designed to better reflect
8 exposures that may occur near SC>2 emission sources, not necessarily reflected by the existing
9 ambient monitoring data alone.
10 Staff used a combined air quality and exposure modeling approach to generate estimates
11 of 5-minute maximum, 24-hour, and annual average SC>2 exposures within Greene County, MO.
12 and portions of the St. Louis Metropolitan Statistical Area (MSA) for the year 2002. AERMOD,
13 an EPA recommended dispersion model, was used to estimate 1-hour ambient SC>2
14 concentrations using emissions estimates from stationary, non-point, and port sources. The Air
15 Pollutants Exposure (APEX) model, EPA's human exposure model, was used to estimate
16 population exposures using the census block level hourly SC>2 concentrations estimated by
17 AERMOD. Staff used the person-based exposure profiles to generate the number of 5-minutes
18 daily maximum exposure events in an entire year.
19 Exposure and potential health risk were characterized considering recent air quality
20 conditions (as is), for air quality adjusted to just meet the current SO2 standards (0.030 ppm,
21 annual average; 0.14 ppm, 24-hour average), and for just meeting potential alternative standards
22 (see Chapter 5). Specifically, the number of times an individual experienced a daily maximum
23 5-minute exposure concentration in excess of 100 ppb through 800 ppb was estimated. The
24 exposures for each individual were estimated over an entire year therefore, multiple occurrences
25 of exceedances were estimated, giving the number of days per year with an exceedance of the
26 potential health effect benchmark levels.
27 The approaches used for assessing exposures in Greene County and St. Louis are
28 described below. Additional model input data and supporting discussion of APEX modeling are
29 provided in Appendix B. Briefly, the discussion in this Chapter includes the following:
March 2009 159 Draft - Do Not Quote or Cite
-------
1 description of the inhalation exposure model and associated input data used for Green
2 County and St. Louis;
3 evaluation of estimated SC>2 air quality concentrations and exposures; and
4 assessment of the quality and limitations of the input data for supporting the goals of
5 the SC>2 NAAQS exposure and risk characterization.
6 The overall flow of the exposure modeling process performed for this SC>2 NAAQS
7 review is illustrated in Figure 8-1. Several models were used in addition to APEX and
8 AERMOD including emission factors and meteorological processing, as well as a number of data
9 bases and literature sources to populate the model input parameters. Each of these are described
10 within this Chapter, supplemented with additional details in Appendix B.
ll 8.2 OVERVIEW OF HUMAN EXPOSURE MODELING USING APEX
12 The EPA has developed the APEX model for estimating human population exposure to
13 criteria and air toxic pollutants. APEX serves as the human inhalation exposure model within
14 the Total Risk Integrated Methodology (TRIM) framework (EPA 2009a; 2009b). APEX was
15 recently used to estimate population exposures in 12 urban areas for the Os NAAQS review
16 (EPA, 2007d; 2007e) and in estimating population NC>2 exposures in Atlanta as part of the NC>2
17 NAAQS review (EPA, 2008d).
18 APEX is a probabilistic model designed to account for sources of variability that affect
19 people's exposures. APEX simulates the movement of individuals through time and space and
20 estimates their exposure to a given pollutant in indoor, outdoor, and in-vehicle
21 microenvironments. The model stochastically generates a sample of simulated individuals using
22 census-derived probability distributions for demographic characteristics. The population
23 demographics are drawn from the year 2000 Census at the tract, block-group, or block level, and
24 a national commuting database based on 2000 census data provides home-to-work commuting
25 flows. Any number of simulated individuals can be modeled, and collectively they approximate
26 a random sampling of people residing in a particular study area.
27
March 2009 160 Draft - Do Not Quote or Cite
-------
Upper Air
Meteorological
Data
(NOAA
Radiosonde
Database)
Surface
Meteorological
Data
(Integrated
Surface Hourly
Database]
L
AERMET/
AERSURFACE
/"major Stationary
Source
Emissions
-------
1 Daily activity patterns for individuals in a study area, an input to APEX, are obtained
2 from detailed diaries that are compiled in the Consolidated Human Activity Database (CHAD)
3 (McCurdy et al., 2000; EPA, 2002). The diaries are used to construct a sequence of activity
4 events for simulated individuals consistent with their demographic characteristics, day type, and
5 season of the year, as defined by ambient temperature regimes (Graham and McCurdy, 2004).
6 The time-location-activity diaries input to APEX contain information regarding an individuals'
7 age, gender, race, employment status, occupation, day-of-week, daily maximum hourly average
8 temperature, the location, start time, duration, and type of each activity performed. Much of this
9 information is used to best match the activity diary with the generated personal profile, using
10 age, gender, employment status, day of week, and temperature as first-order characteristics. The
11 approach is designed to capture the important attributes contributing to an individuals' behavior,
12 and of likely importance in this assessment (i.e., time spent outdoors) (Graham and McCurdy,
13 2004). Furthermore, these diary selection criteria give credence to the use of the variable data
14 that comprise CHAD (e.g., data collected were from different seasons, different states of origin,
15 etc.).
16 APEX has a flexible approach for modeling microenvironmental concentrations, where
17 the user can define the microenvironments to be modeled and their characteristics. Typical
18 indoor microenvironments include residences, schools, and offices. Outdoor microenvironments
19 include for example near roadways, at bus stops, and playgrounds. Inside cars, trucks, and mass
20 transit vehicles are microenvironments which are classified separately from indoors and
21 outdoors. APEX probabilistically calculates the concentration in the microenvironment
22 associated with each event in an individual's activity pattern and sums the event-specific
23 exposures within each hour to obtain a continuous series of hourly exposures spanning the time
24 period of interest. The estimated microenvironmental concentrations account for the
25 contribution of ambient (outdoor) pollutant concentration and influential factors such as the
26 penetration rate into indoor microenvironments, air exchange rates, decay/deposition rates,
27 proximity to important outdoor sources, and indoor source emissions. Each of these influential
28 factors are dependent on the microenvironment modeled, available data to define model inputs,
29 and estimation method selected by the model user. And, because the modeled individuals
30 represent a random sample of the population of interest, the distribution of modeled individual
31 exposures can be extrapolated to the larger population within the modeling domain.
March 2009 162 Draft - Do Not Quote or Cite
-------
1 The exposure modeling simulations can be summarized by five steps, each of which is
2 detailed in the subsequent sections of this document. Briefly, the five steps are as follows:
3 1. Characterize the study area. APEX selects the census blocks within a study
4 area - and thus identifies the potentially exposed population - based on user-
5 defined criteria and availability of air quality and meteorological data for the area.
6 2. Generate simulated individuals. APEX stochastically generates a sample of
7 hypothetical individuals based on the demographic data for the study area and
8 estimates anthropometric and physiological parameters for the simulated
9 individuals.
10 3. Construct a sequence of activity events. APEX constructs an exposure event
11 sequence spanning the period of the simulation for each of the simulated
12 individuals using time-location-activity pattern data.
13 4. Calculate 5-minute and hourly concentrations in microenvironments. APEX
14 users define microenvironments that people in the study area would visit by
15 assigning location codes in the activity pattern to the user-specified
16 microenvironments. The model calculates 5-minute and hourly concentrations of
17 a pollutant in each of these microenvironments for the period of simulation, based
18 on the user-provided microenvironment descriptions, the hourly air quality data,
19 and peak-to-mean ratios (PMRs; see section 7.2.3). Microenvironmental
20 concentrations are calculated for each of the simulated individuals.
21 5. Estimate exposures. APEX estimates a concentration for each exposure event
22 based on the microenvironment occupied during the event. In this assessment,
23 APEX estimated 5-minute exposures. These exposures can also be averaged by
24 clock hour to produce a sequence of hourly average exposures spanning the
25 specified exposure period. The values may be further aggregated to produce
26 daily, monthly, and annual average exposure values.
27
28 8.3 CHARACTERIZATION OF STUDY AREAS
29 8.3.1 Study Area Selection
30 The selection of areas to include in the exposure analysis takes into consideration the
31 availability of ambient monitoring, the presence of significant and diverse SC>2 emission sources,
32 population demographics, and results of the ambient air quality characterization. Although it
33 could be useful to characterize 862 exposures nationwide, because the exposure modeling
34 approach is both time and labor intensive, a regional and source-oriented approach was selected
35 to make the analysis tractable and with the goal of focusing on areas most likely to have elevated
36 SO2 peak concentrations and with sufficient data to conduct the analysis.
March 2009 163 Draft - Do Not Quote or Cite
-------
1 A broad study area was first identified based on the results of a preliminary screening of
2 the 5-minute ambient 862 monitoring data that were available. The state of Missouri was one of
3 only a few states reporting both 5-minute maximum and continuous 5-minute SC>2 ambient
4 monitoring data (14 total monitors), as well as having over thirty monitors in operation at some
5 time during the period from 1997 to 2007 that measured 1-hour SC>2 concentrations. In addition,
6 the air quality characterization described in Chapter 7 estimated frequent exceedances above the
7 potential health effect benchmark levels at several of the 1-hour ambient monitors. In a ranking
8 of estimated 862 emissions reported in the National Emissions Inventory (NEI), Missouri ranked
9 7th out of all U.S. states for the number of stacks with annual emissions greater than 1,000 tons.
10 These stack emissions were associated with a variety source types such as electrical power
11 generating units, chemical manufacturing, cement processing, smelters, and emissions associated
12 with port operations.
13 In the 1st draft SC>2 REA, several modeling domains were characterized within the
14 selected state of Missouri to assess the feasibility of the modeling methods. These modeling
15 domains were defined as areas within 20 km of a major point source of SC>2 emission. While
16 modeled air quality and exposure results were generated for several of these domains in the 1st
17 draft REA, changes in the methodology used in this 2nd draft REA precluded additional analysis
18 for most of the domains. Staff judged the availability of relevant ambient monitoring data within
19 the model domain as essential in evaluating model performance, increasing confidence in the
20 predicted air quality and exposure modeling results. For example, when comparing the modeled
21 air quality to ambient monitoring data in Greene County in the 1st draft REA, it was judged by
22 staff that non-point source emissions may contribute to a large proportion of measured ambient
23 concentrations. Addressing non-point source emissions then added a layer to the already
24 complex modeling performed, further limiting the potential number of locations analyzed.
25 Second, to assess the impact of potential alternative standards, baseline conditions (as is) need to
26 be known, again requiring ambient monitoring data. Because Greene County had a number of
27 ambient monitors and most of the model input data were already well-defined, it was selected for
28 additional modeling in the 2nd draft REA. Further, staff decided that modeling a large urban area
29 would be advantageous in combining both large emission sources and large potentially exposed
30 populations. Modeling for St. Louis, MO was already underway at the time of the 1st draft REA,
March 2009 164 Draft - Do Not Quote or Cite
-------
1 therefore it was decided that modeling in this domain should be continued and expanded for
2 other sources for this 2nd draft RE A.
3 8.3.2 Study Area Descriptions
4 8.3.2.1 Greene County
5 The greater Springfield, MO, Metropolitan Statistical Area (MSA) consists of five
6 counties in southwestern Missouri including Christian, Dallas, Greene, Polk, and Webster
7 counties. The only city in the region with a population greater than 150,000 is Springfield, in
8 Greene County. Greene County has a total area of approximately 678 mi2 (1,756 km2). Due to
9 the complexity of the air quality and exposure modeling to be performed in this exposure
10 assessment and the focus on receptors within 20 km of stationary sources, the modeling domain
11 was limited to Greene County (see Figure 8-2). The Springfield-Branson Regional Airport
12 (WBAN 13995) served as the source of meteorological data used in the Greene County modeling
13 domain.
14 8.3.2.2 St. Louis Area
15 The greater St. Louis Metropolitan Statistical Area (MSA) is the 18th largest MSA in the
16 United States and includes the independent City of St. Louis; the Missouri counties of St. Louis,
17 St. Charles, Jefferson, Franklin, Lincoln, Warren, and Washington; as well as the Illinois
18 counties of Madison, St. Clair, Macoupin, Clinton, Monroe, Jersey, Bond, and Calhoun. The
19 total MSA has an area of approximately 8,846 mi2 (22,911 km2). Due to the complexity of the
20 air quality and exposure modeling performed in this exposure assessment and the focus on
21 receptors within 20 km of stationary sources, staff limited the modeling domain to three counties
22 directly surrounding the city of St. Louis: St. Louis City, St. Louis County, and St. Charles
23 County (see Figure 8-3). These three counties comprise much of the urban center of the St.
24 Louis MSA, with a combined population of 1,151,094 (2000 Census), which is approximately 45
25 percent of the Greater St. Louis MSA population.
26
27
28
29
30
March 2009 165 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
::; . ..-... :
. .- .... -,'* .:. .'.. -^:.:. .
'
Lawrence County
0 3.5 7
I i i i I
14 Kilometers
ster County
<^p Surface Meteorological Station (WBAN = 13995)
A Point Sources
Monitors (Monitor IDs)
^ 290770026
)( 290770032
^ 290770037
^ 290770040
J( 290770041
Census Block Receptors
Area Sources
Christian County
Figure 8-2. Modeling domain for Greene County MO, along with identified emissions sources, air
quality receptors, ambient monitors, and meteorological station.
The St. Louis modeling domain reported on here was assembled from three separate
modeling domains described in the 1st Draft SC>2 REA, aggregated to utilize the most reliable
hourly meteorological data available (St. Louis International-Lambert Field; WBAN 13994). It
was then reduced to just the three counties of the urban core. Figure 8-3 shows the modeling
domain for the greater St. Louis, MO area.
March 2009
166
Draft - Do Not Quote or Cite
-------
1
2
Calhoun County f "." 5
Surface Meteorological Station (WBAN = 13994)
A in-State Point Sources
\7 Cross-Border Point Sources
Monitors (Monitor IDs)
291890004
291890006
291893001
291895001
291897003
295100007
295100086
Port Area Sources
Census Block Receptors (Subset ID)
1
2
3
Area Sources
20
40 Kilometers
i i i I
I St. Francois County *-\ ^^^^V.
Figure 8-3. Three county modeling domain for St. Louis, MO, along with identified emissions sources, air quality receptors, ambient
monitors, and meteorological station.
March 2009
167
Draft - Do Not Quote or Cite
-------
1 8.3.3 Time Period of Analysis
2 Calendar year 2002 was simulated for both modeling domains to characterize the most
3 recent year of emissions data available for the study locations. Year 2002 temperature and
4 precipitation used in the dispersion modeling was compared with 30-year climate normal period
5 data from 1978 through 2007. For Greene County, 2002 was similar to the 30-year normal (56.2
6 °F compared to 56.3 °F) though drier than the 30-year normal (37.8 in. compared to 40.2 in.).
7 For St. Louis, 2002 was warmer on average than the 30-year normal (57.9 °F compared to 56.8
8 °F) and received an annual rainfall total that was similar with the 30-year normal (40.9 in.
9 compared to 39.1 in.). See Appendix B, Attachment 1 for further details.
10 8.3.4 Populations Analyzed
11 The exposure assessment included the total population residing in each modeled area and
12 population subgroups that were considered more susceptible as identified in the ISA. These
13 population subgroups include:
14 Asthmatic children (5-18 years in age)
15 All Asthmatics (all ages)
16 In addition, based on the observed responses in the human clinical trials, all asthmatic
17 exposures were characterized only when the individual was at moderate or greater exertion levels
18 during the exposure events.
19
20 8.4 CHARACTERIZATION OF AMBIENT HOURLY AIR QUALITY DATA
21 USING AERMOD
22 8.4.1 Overview
23 Air quality data used for input to APEX were generated using AERMOD, a steady-state,
24 Gaussian plume model (EPA, 2004). For both modeling domains, the following steps were
25 performed.
26 1 Collect and analyze general input parameters. Meteorological data, processing
27 methodologies used to derive input meteorological fields (e.g., temperature, wind
28 speed, precipitation), and information on surface characteristics and land use are
29 needed to help determine pollutant dispersion characteristics, atmospheric
30 stability and mixing heights.
31 2. Define sources and estimate emissions. The emission sources modeled included:
March 2009 168 Draft - Do Not Quote or Cite
-------
1 a. Major stationary emission sources within the domain,
2 b. Major stationary emission sources outside the domain (cross-border
3 stacks)
4 c. Non-point source area emissions,
5 d. Emissions from ports, and
6 e. Background sources not otherwise captured.
7 However, note that not all source categories were present in both modeling
8 domains.
9 3. Define air quality receptor locations. Two sets of receptors were identified for
10 the dispersion modeling, including ambient monitoring locations (where
11 available) and census block centroids.
12 4. Estimate concentrations at receptors. Full annual time series of hourly
13 concentration were estimated for 2002 by summing concentration contributions
14 from each of the emission sources at each of the defined air quality receptors.
15 Estimated hourly concentrations output from AERMOD were then used as input to the
16 APEX model to estimate population exposure concentrations. Details regarding both modeling
17 approaches and input data used are provided below. Supplemental information regarding model
18 inputs and methodology is provided in Appendix B.
19 8.4.2 General Model Inputs
20 8.4.2.1 Meteorological Inputs
21 All meteorological data used for the AERMOD dispersion model simulations were processed
22 with the AERMET meteorological preprocessor, version 06341. The National Weather Service
23 (NWS) served as the source of input meteorological data for AERMOD. Tables 8-1 and 8-2 list
24 the surface and upper air NWS stations chosen for the two areas. A potential concern related to
25 the use of NWS meteorological data is the often high incidence of calms and variable wind
26 conditions reported for the Automated Surface Observing Stations (ASOS) in use at most NWS
27 stations. A variable wind observation may include wind speeds up to 6 knots, but the wind
28 direction is reported as missing. The AERMOD model currently cannot simulate dispersion
29 under these conditions. To reduce the number of calms and missing winds in the surface data for
30 each of the four stations, archived one-minute winds for the ASOS stations were used to
31 calculate hourly average wind speed and directions, which were used to supplement the standard
32 archive of winds reported for each station in the Integrated Surface Hourly (ISH) database.
33 Details regarding this procedure are described in Appendix B, Attachment 1.
March 2009 169 Draft - Do Not Quote or Cite
-------
Table 8-1. Surface stations for the SO2 study areas.
Area
Greene
County
St. Louis
Station
Springfield-
Branson
Regional
AP
Lambert-St.
Louis
International
AP
Identifier
SGF
STL
WMO
(WBAN)
724400
(13995)
724340
(13994)
Latitude1
37.23528
38.7525
Longitude1
-93.40028
-90.37361
Elevation
(m)
387
161
Time
Zone2
6
6
Notes:
1 Latitude and longitude are the best approximation coordinates of the meteorological towers.
2 Time zone is the offset from UTC/GMT to LSI in hours.
Table 8-2. Upper air stations for the SO2 study areas.
Area
Greene
County
St. Louis
Station
Springfield-
Branson
Regional
AP
Lincoln-
Logan
County AP,
IL
Identifier
SGF
ILX
WMO
(WBAN)
724400
(13995)
724340 (4833)
Latitude
37.23
40.15
Longitude
-93.40
-89.33
Elevation
(m)
394
178
Time
Zone1
6
6
Notes:
1 Time zone is the offset from UTC/GMT to LST in hours.
4 8.4.2.2 Surface Characteristics and Land Use Analysis
5 The AERSURFACE tool (US EPA, 2008e) was used to determine surface characteristics
6 (albedo, Bowen ratio, and surface roughness) for input to AERMET. Surface characteristics
7 were calculated for the location of the ASOS meteorological towers, approximated by using
8 aerial photos and the station history from the National Climatic Data Center (NCDC). A draft
9 version of AERSURFACE (08256) that utilizes 2001 National Land Cover Data (NLCD) was
10 used to determine the surface characteristics for this application since the 2001 land cover data
11 will be more representative of the meteorological data period than the 1992 NLCD data
12 supported by the current version of AERSURFACE available on EPA's SCRAM website. All
13 stations were considered at an airport. Monthly seasonal assignments were defined as shown in
14 Table 8-3 and because the AERSURFACE default seasonal assignments were not used, the
15 surface characteristics were output by month.
March 2009
170
Draft - Do Not Quote or Cite
-------
Table 8-3. Seasonal monthly assignments.
Station
SGF
STL
Winter (no snow)
December, January,
February, March
December, January,
February
Spring
April, May
March, April, May
Summer
June, July, August
June, July, August
Autumn
September,
October,
November
September,
October,
November
Seasonal definitions
Winter (no snow)
Spring
Summer
Autumn
Late autumn after frost and harvest, or winter with no snow
Transitional spring with partial green coverage or short annuals
Midsummer with lush vegetation
Autumn with unharvested cropland
2 8.4.3 Stationary Sources Emissions Preparation
3 8.4.3.1 Emitting Sources and Locations
4 Point Sources
5 Point sources at major facilities were identified and paired to a representative surface
6 meteorological station. Any stacks listed as in the same location with identical release
7 parameters within a certain resolution (typically to the nearest integer value) were aggregated
8 into a single stack to simplify modeling but retain all emissions. For this analysis, major
9 facilities were defined as those with an SC>2 emission total exceeding 1,000 tpy in 2002. Within
10 such facilities, every stack emitting more than one tpy was included in the modeling inventory.
11 This process resulted in the identification of 11 (combined) stacks in Greene County and 38
12 (combined) stacks in St. Louis. Additionally, 45 (combined) stacks were identified across the
13 state border that could influence concentrations in St. Louis. These cross-border stacks were
14 modeled the same as the within-state stacks. The locations of all emitting stacks were corrected
15 based on GIS analysis. This was necessary because many stacks in the NEI are assigned the
16 same location, which often corresponds to a location in the facility - such as the front office -
17 rather than the actual stack locations. To correct for this, stack locations were reassigned
18 manually with the MicrosoftŪ Live MapsŪ Virtual EarthŪ tool to visually match stacks from
19 the NEI database to their locations within the facilities using stack heights as a guide to stack
20 identification. All release heights and other stack parameters were taken from the values listed in
21 the NEI. Table B-3-1 (in Appendix B) lists all stacks in both domains.
22
March 2009
171
Draft - Do Not Quote or Cite
-------
1 Port-Related Sources
2 Only the St. Louis domain has relevant port emissions. The Port of St. Louis is one of
3 the Nation's largest inland river ports. Activity from this port was modeled as fourteen area
4 sources along the waterfront. All port-related emission sources were considered as non-point
5 area emissions with boundaries based on GIS analysis of aerial photographic images. A release
6 height of 5.0 m with a plume initial vertical standard deviation (
-------
1 City and St. Louis County - were subdivided into regular grid cells. St. Louis County grid cells
2 were 5 km by 5 km; St. Louis City grid cells were 1 km by 1 km, more closely approximating the
3 smaller and denser census tracts in that region. All county-wide non-point source emissions
4 were spatially allocated uniformly to the grid cells. St. Charles County was modeled as a single
5 area source, with edges approximating the full county boundaries.
6 The release parameters for the St. Louis domain varied according to the urban and rural
7 designation of individual grid cells. Rural grid cells have a release height of 10 m and initial
8 dispersion length of 4.67 m. Urban grid cells have a release height of 20 m and initial dispersion
9 length of 9.34m.
10 Backgroun d Sources
11 For the Greene County domain, background sources were assembled to account for any
12 emissions not otherwise included. These were comprised of any point sources in facilities not
13 meeting the 1,000 tpy selection criteria and any residual non-point sources, as well as on-road
14 and non-road mobile sources. In addition, all emission sources in neighboring Christian County
15 were modeled as a rural, county-wide non-point area source with uniform density. Both
16 background sources were characterized as county-wide polygon rural area sources with release
17 heights of 10.0 m and initial dispersion length of 4.67 m.
18 For the St. Louis domain, emissions from residual point sources, on-road mobile sources,
19 and non-road mobile sources were combined with the county-wide non-point sources as
20 described above. Thus, no separate background sources were simulated.
21 8.4.3.2 Urban vs. Rural Designations
22 AERMOD regulatory default settings were employed in each model domain. Therefore,
23 no chemical decay is assumed for rural sources, while urban sources experience a 4-hour half-
24 life. For urban sources, additional dispersion is simulated at night to account for increased
25 surface heating within an urban area under stable atmospheric conditions. The magnitude of this
26 effect is weakly proportional to the urban area population.
27 Point Sources
28 Urban or rural designations for point sources were made according to EPA guidance based on
29 the land use within 3 km of the source. The 2001 NLCD database was used to make this
30 determination. Table 8-4 lists the land use categories in the 2001 NLCD.
March 2009 173 Draft - Do Not Quote or Cite
-------
Table 8-4. NLCD2001 land use characterization.
Category
11
12
21
22
23
24
31
32
41
42
43
51
52
71
72
Land Use Type
Open Water
Perennial Ice/Snow
Developed, Open Space
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Barren Land (Rock/Sand/Clay)
Unconsolidated Shore1
Deciduous Forest
Evergreen Forest
Mixed Forest
Dwarf Scrub
Shrub/Scrub
Grassland/Herbaceous
Sedge/Herbaceous
Category
73
74
81
82
90
91
92
93
94
95
96
97
98
99
Land Use Type
Lichens
Moss
Pasture/Hay
Cultivated Crops
Woody Wetlands
Palustrine Forested Wetland1
Palustrine Scrub/Shrub Wetland1
Estuarine Forested Wetland1
Estuarine Scrub/Shrub Wetland1
Emergent Herbaceous Wetlands
Palustrine Emergent Wetland (Persistent) 1
Estuarine Emergent Wetland1
Palustrine Aquatic Bed1
Estuarine Aquatic Bed1
Notes:
1 Coastal NLCD class only.
4
5
6
9
10
11
12
13
14
15
Each stack where more than half the land use within 3 km fell into categories 21-24 were
designated as urban. These categories are consistent with those considered developed by
AERSURFACE.
37
Non-Point Sources
Non-point area sources were defined as rural or urban using a similar methodology as
that for the point sources. As noted in the 2008 AERMOD Implementation Guide38, in some
cases, a population density is more appropriate than a land use characterization. Therefore, non-
point area sources were evaluated from both a land use and population density perspective.
In Greene County, area sources were defined as corresponding to the census tract
boundaries. Each tract was then considered urban or rural by considering both the population
density and land use fraction from NLCD2001. If the population density was greater than 750/
km2 or the developed land use categories 22-24 throughout the tract was greater than 50 percent,
31 AERSURFACE User's Guide, U.S. EPA, OAQPS, Research Triangle Park, NC, EPA-454/B-08-001, January
2008.
38 AERMOD IMPLEMENTATION GUIDE, AERMOD Implementation Workgroup, US EPA, OAQPS, Air Quality
Assessment Division, Research Triangle Park, NC, Revised January 9, 2008,
March 2009
174
Draft - Do Not Quote or Cite
-------
1 the tract was designated as urban. In addition, if a tract was surrounded by urban tracts it was
2 designated as urban, since the emissions from such a tract would likely be subject to urban
3 dispersion conditions.
4 As explained above, for the St. Louis domain, the counties with the greatest non-point
5 emissions - St. Louis City and St. Louis County - were subdivided into regular grid cells, while
6 St. Charles County was represented as a polygon area source with its political boundaries. The
7 urban or rural designation was then assigned to each based on population density. St. Charles
8 County and all but eleven of the 5 km grid cells in St. Louis County were designated rural; the
9 remaining cells in St. Louis County and all of St. Louis City were designated urban.
10 Port-Related Sources
11 Only the St. Louis domain has relevant port emissions. The fourteen port-related non-
12 point area sources described above were designated urban, given their location in the urban core
13 along the waterfront and their associated industrial activities.
14 Backgroun d Sources
15 Background area sources for Greene County were classified with the same procedures for
16 non-point area sources. Both Greene and Christian counties were designated rural.
17 8.4.3.3 Source Terrain Characterization
18 All corrected locations for the final list of major facility stacks in St. Louis and Green
19 County domains were processed with a pre-release version of the AERMAP terrain
20 preprocessing tool. This version is functionally equivalent to the current release version of the
21 tool (version 08280). In particular, this updated version allows use of 1 arc-second terrain data
22 from the USGS Seamless Server39 which allows for more highly resolved values of the source
23 and receptor heights as well as the hill height scales.
24 Terrain height information for point sources was processed through AERMAP with input
25 data taken from the USGS server. For all area sources (non-point and background source types),
26 the outputs from AERMAP were modified. In these cases, rather than using a single point to
27 represent these large areas, the terrain height for each vertex of the area was estimated with
28 AERMAP. The terrain height for the entire source polygon was then characterized as the
29 average terrain height from all vertices.
39 http://seamless.usgs.gov/index.php
March 2009 175 Draft - Do Not Quote or Cite
-------
1 8.4.3.4 Emissions Data Sources
2 Point Sources
3 Data for the parameterization of major facility point sources in the two modeling domains
4 comes primarily from three sources: the 2002 NEI (EPA, 2007f), Clean Air Markets Division
5 (CAMD) Unit Level Emissions Database (EPA, 2007g), and temporal emission profile
6 information contained in the EMS-HAP (version 3.0) emissions model.40 The NEI database
7 contains stack locations, emissions release parameters (i.e., height, diameter, exit temperature,
8 exit velocity), and annual SO2 emissions. The CAMD database has information on hourly SO2
9 emission rates for all the electric generating units in the US, where the units are the boilers or
10 equivalent, each of which can have multiple stacks.41 These two databases generally contain
11 complimentary information, and were first evaluated for matching facility data. However,
12 CAMD lacks SO2 emissions data for facilities other than electric-generating units. To convert
13 annual total emissions data from the NEI into hourly temporal profiles required for AERMOD, a
14 three tiered prioritization was used, as follows.
15 1. CAMD hourly concentrations to create relative temporal profiles.
16 2. EMS-HAP seasonal and diurnal temporal profiles for source categorization codes
17 (SCCs).
18 3. Flat profiles.
19 Details of these processes are as follows:
20 Tier 1: CAMD to NEI Emissions Alignment and Scaling
21 Of the 94 major facility stacks within the model domains identified above (11 in Greene
22 County and 45 cross-border and 38 within-state in the St. Louis domain), 35 (11 in Greene
23 County and 7 cross-border and 17 in-state in the St. Louis domain) were able to be matched
24 directly to sources within the CAMD database. Stack matching was based on the facility name,
25 Office of Regulatory Information Systems (ORIS) identification code (when provided) and
26 facility total SO2 emissions. For these stacks the relative hourly profiles were derived from the
27 hourly values in the CAMD database, and the annual emissions totals were taken from the NEI.
40 http ://www. epa. gov/ttn/chief/emch/proj ection/emshapS 0 .html
41 The CAMD database also contains hourly NO2 emission data for both electric generating units and other types of
industrial facilities. In the case of facilities for which CAMD has hourly NO2 data but not SO2 data, SO2 relative
temporal profiles could be approximated by NO2 temporal profiles. However, there were no such cases for MO
facilities.
March 2009 176 Draft - Do Not Quote or Cite
-------
1 That is, hourly emissions in the CAMD database were scaled to match the NEI annual total
2 emissions.
3 Tier 2: EMS-HAP to NEI Emissions Profiling
4 Of the 94 major facility stacks within the two MO domains, 38 stacks (all of which are
5 cross-border stacks in the St. Louis domain) could not be matched to a stack in the in the CAMD
6 database, but had SCC values that corresponded to SCCs that have temporal profiles included in
7 the EMS-HAP emissions model. In these cases, the SCC-specific seasonal and hourly variation
8 (SEASHR) values from the EMS-HAP model were used to characterize the temporal profiles of
9 emissions for each hour of a typical day by season and day type.
10 Tier 3: Other Emissions Profiling
11 Of the 94 major facility stacks within the two MO model domains, 21 (all from the St.
12 Louis in-state domain) could not be matched to a stack in CAMD database, or to profiles in the
13 EMS-HAP model by SCC code. In these cases, aflat profile of emissions was assumed. That is,
14 emissions were assumed to be constant for all hours of every day, but with an annual total that
15 equals the values from the NEI. A summary of the point source emissions used for the two
16 modeling domains is given in Table 8-5. Appendix B, Table B-3-1 contains all 94 stacks within
17 the modeling domains and the data source used to determine their emissions profiles.
18 Nearly all of the point sources in both domains were accounted for directly in the
19 dispersion modeling. Table 8-5 shows the point source contribution captured directly within each
20 modeling domain.
21 Port-Related Sources
22 Ports were the only non-road sector explicitly simulated in either modeling domain. Only
23 the St. Louis domain had port emissions. All relevant port emissions were directly captured,
24 comprising 51 percent of the total non-road emissions for the domain. Emission profiles for
25 port-related activity were taken from the EMS-HAP model for sectors matching the modeled
26 activity. Table 8-5 shows the port source contribution modeled directly within each modeling
27 domain and compares it to the total non-road emissions.
28 Non-Point and Background Sources
29 Non-point polygon area sources were developed to capture non-point
30 commercial/institutional and industrial emissions within the domains, as specified in the NEI.
March 2009 177 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
For the St. Louis domain, all non-point emissions were included either in gridded area sources
over St. Louis City and St. Louis County or a polygon area source over St. Charles County, as
described above. For the Greene County domain, commercial/institutional and industrial non-
point area source polygons were created to represent the individual census tracts within the
county that captured approximately 87 percent of the relevant emissions countywide from the
NEI. Other non-point sources, as well as on-road mobile and non-road mobile sources were
included in the background source
Because non-point area source and background area source temporal profiles are
unknown, profiles were derived that provided a best-fit match between the model predictions and
monitor data. Figures 8-4 and 8-5 show the diurnal emissions profiles derived for both the St.
Louis and Greene County domains compared to other profiles derived from commonly used
emissions models. Table 8-5 shows the non-port source contribution modeled directly within
each modeling domain and compares it to the total non-point emissions.42
Table 8-5. Summary of NEI emission estimates and total emissions used for dispersion modeling
in Greene County and St. Louis modeling domains.
Modeling
Domain
Greene Co.
St. Louis
Point Sources
NEI
Emissions
(tpy)
9,255
70,016
Modeled
Emissions
(tpy)
9,047
68,656
(%)
98%
98%
Area Sources
NEI
Emissions
(tpy)
2,055
15,137
Modeled
Emissions
(tpy)
1,781
15,137
(%)
87%
100
%
Non-road Sources
NEI
Emissions
(tpy)
N/A
3,058
Modeled
Emissions
(tpy)
N/A
1,559
(%)
N/A
51%
17
18
19
Table 8-5 does not have the relevant background contribution for each domain. This is because the total
background in each domain includes not only the counties in the modeling domain (three in the St. Louis domain
and one in the Greene County domain), but also adjacent counties that could influence concentrations within the
modeling domain. In those cases, the total countywide emissions are included in the background. Thus, directly
expressing those values would be confusing and are thus omitted.
March 2009
178
Draft - Do Not Quote or Cite
-------
1
2
3
4
----EMS-HAP: Industrial
----SMOKE
- -*- EMS-HAP: Commercial/Institutional
Monitors
12
Hour of the Day
18
24
Figure 8-4. Derived best-fit non-point area source diurnal emission profile for the St. Louis
domain, compared to other possible profiles.
0.12
5
6
7
0.1
i/)
i/)
0)
-EMS-HAP: Industrial
-SMOKE
- EMS-HAP: Commercial/Institutional
Best-fit to Monitors
I 0.08
12
Hour of the Day
18
24
Figure 8-5. Derived best-fit non-point area source diurnal emission profile for the Greene County
domain, compared to other possible profiles.
March 2009
179
Draft - Do Not Quote or Cite
-------
1
2 8.4.4 Receptor Locations
3 Two sets of receptors were chosen to represent the locations of interest within each of the
4 modeling domains. The first set was selected to represent the locations of the residential
5 population of the modeling domain. These receptors were US Census block centroids in the
6 Greene County and St. Louis modeling domains, (Figures 8-2 and 8-3, respectively), that lie
7 within 20 km (12 miles) of any of the major facility stacks.43 Each of these receptors was
8 modeled at ground level. A total of 17,703 receptors were selected in the St. Louis domain and a
9 total of 5,359 receptors were selected in the Greene County domain.
10 The second set of receptors included the locations of the available ambient SO2 monitors.
11 These receptors were used in evaluating the dispersion model performance. In Greene County,
12 there were five ambient monitors with valid ambient monitoring concentrations (Figure 8-2).
13 Within the three St. Louis counties, there were seven monitors (Figure 8-3).
14 8.4.5 Modeled Air Quality Evaluation
15 The hourly 862 concentrations estimated from each of the sources within a modeling
16 domain were combined at each receptor. These concentration predictions were then compared
17 with the measured concentrations at ambient SC>2 monitors. Rather than compare concentrations
18 estimated at a single modeled receptor point to the ambient monitor concentrations, a distribution
19 of concentrations was developed for the predicted concentrations for all receptors within a 4 km
20 distance of the monitors. Further, instead of a comparison of central tendency values (mean or
21 median), the modeled and measurement concentration distributions were used for comparison.
22 As an initial comparison of modeled versus measured air quality, all modeled receptors
23 within 4 km of each ambient monitor location were used to generate a prediction envelope.44
24 This envelope was constructed based on selected percentiles from the modeled concentration
25 distribution at each receptor for comparison to the ambient monitor concentration distribution.
43
The block centroids used for this analysis are actually population-weighted locations reported in the ESRI data
base. They were derived from geocoded addresses within the block taken from the Acxiom Corporation InfoBase
household database (Skuta and Wombold 2008; ESRI 2008). These centroids differ from the "internal points"
reported by the US Census, which are often referred to as centroids because they are designed to represent the
approximate geographic center of the block.
44 500 m to 4 km is the area of representation of a neighborhood-scale monitor, according to EPA guidance.
March 2009 180 Draft - Do Not Quote or Cite
-------
1 The 2.5th and 97.5th percentiles from all monitor distribution percentiles45 were selected to create
2 the lower and upper bounds of the envelope. The full 1-hour distributions for the ambient
3 measurement data, the modeled monitor receptor,46 and the prediction envelope were compared
4 using their respective cumulative density functions (CDFs). When illustrating the cumulative
5 percentiles, only concentrations above the 80th percentile 1-hour concentration were shown
6 because over 80% of the 1-hour SC>2 concentrations were less than 5 ppb (concentrations
7 generally not of interest).
8 A second comparison between the modeled and monitored data was performed to
9 evaluate the diurnal variation in 862 concentrations. AERMOD receptor concentrations during
10 each hour-of-the-day were averaged (i.e., 365 values for hour 1, 365 values for hour 2, and so
11 on) to generate an annual average SO2 concentration for each hour at each modeled receptor.
12 Prediction envelopes were constructed similar to that described above from modeled receptors
13 located within 4 km of each ambient monitor. The measured ambient monitoring data was also
14 averaged to generate the diurnal profile. Then, annual averaged concentrations for the ambient
15 measurement data, the modeled monitor receptor, and the prediction envelope were plotted by
16 hour-of-the-day for comparison.
17 8.4.5.1 Greene County Modeled Air Quality Evaluation
18 For Greene County, there were five monitors used for comparison with the AERMOD
19 concentration estimates. The distribution of the modeled 1-hour 862 concentrations estimated
20 for the monitor receptor, the receptor envelope (i.e., all receptors within 4 km of monitor
21 receptor), and the hourly concentration distribution measured at each ambient monitor are
22 provided in Figures 8-6 to 8-8. Data used to generate the figures is provided in Appendix B.
23 When considering the total hourly distribution or CDFs, most of the monitor
24 concentration distributions are completely bounded by the modeled distributions. At some of the
25 upper percentiles of the distributions, the deviations were of varying direction (over- or under-
26 prediction) and magnitude. For example, monitor ID 290770026 (Figure 8-6) exhibits higher
45 As an example, suppose there are 1,000 receptors surrounding a monitor, each receptor containing 8,760 hourly
values used to create a concentration distribution. Then say the 73rd percentile concentration prediction is to be
estimated for each receptor. The lower bound of the 73rd percentile of the modeled receptors would represented by
the 2.5th percentile of all the calculated 73rd percentile concentration predictions, i.e., the 25th highest 73rd percentile
concentration prediction across the 1,000 73rd percentile values generated from all of the receptors. Note that at any
given percentile along either of the envelope bounds as well as at the central tendency distribution (the receptor 50th
percentile), the concentration from a different receptor may be used.
46 The modeled monitor is the modeled air quality at the ambient monitoring location.
March 2009 181 Draft - Do Not Quote or Cite
-------
1 measured concentrations at the upper percentiles of the distribution that extend above the
2 AERMOD prediction envelope, however the deviation occurred beyond the 99th percentile
3 (maximum observed =114 ppb, AERMOD P97.5 = 101 ppb). At monitor ID 290770032 (Figure
4 8-6), the measured concentrations fall below the prediction envelope, beginning just above the
5 95th percentile 1-hour concentration.
6 Even though ambient monitors 290770040 and 290770041 (Figure 8-2) are located
7 approximately 150 m from one another, they exhibited very different measured concentrations at
8 the extreme upper percentiles (Figure 8-7). The greatest difference is in comparing the
9 maximum observed concentrations; 203 ppb versus 33 ppb. The AERMOD predictions followed
10 a similar pattern at the upper percentiles, i.e., the modeled concentrations for the monitor
11 location were greater (50 to 100%) at monitor ID 290770040 when compared with 290770041,
12 but not nearly as great as a difference noted at the maximum measured concentrations. The
13 AERMOD prediction envelope was similar for both of these monitors, encompassing the
14 ambient measured concentrations from the 80th through the 99th percentiles for both, while
15 completely enveloping all 1-hour concentrations at monitor ID 290770041.
16 The pattern in the AERMOD modeled concentrations at the monitor location and the
17 ambient measurement concentration distribution for monitor ID 290770037 is nearly identical
18 and the only difference observed is that the measured concentrations are greater at each of the
19 upper percentiles. Much of the measured distribution falls within the AERMOD prediction
20 envelope, with deviation occurring at the maximum concentration.
21 The diurnal pattern observed at each of the ambient monitors is represented well by the
22 modeled concentrations; in general concentrations are elevated during the midday hours and
23 lowest during the late-night and early-morning hours. In addition, most of the measured
24 concentrations fall within the AERMOD prediction envelopes at all hours of the day, with a few
25 exceptions. For example, all observed concentrations for monitor ID 290770032 are below that
26 of the upper AERMOD prediction envelope, though at monitor ID 290770026, measured
27 concentrations are above those modeled during the early-morning and late-night hours (Figure 8-
28 6). Much of the deviation during these hours-of-the-day is likely a result of the concentrations at
29 or below the 80th percentile, where measured concentrations were always greater than any of the
30 predicted concentrations at corresponding percentiles of the distribution. While the prediction
31 envelopes encompassed the diurnal pattern observed at monitor IDs 290770040 and 290770041
March 2009 182 Draft - Do Not Quote or Cite
-------
1 (Figure 8-7), the modeled concentrations at the monitor location results were not equally
2 representative. The diurnal pattern and magnitude of concentrations was well reproduced at
3 monitor ID 290770041, while modeled concentrations at the monitor location during the midday
4 and evening hours were greater than the measured concentrations at monitor ID 290770040.
5 8.4.5.2 St. Louis Modeled Air Quality Evaluation
6 For St. Louis, there were seven monitors used for comparison with the AERMOD
7 concentration estimates. The distribution of the modeled 1-hour SC>2 concentrations estimated
8 for the monitor receptor, the receptor envelope (i.e., all receptors within 4 km of monitor
9 receptor), and the hourly concentration distribution measured at each ambient monitor are
10 provided in Figures 8-9 to 8-12. Data used to generate the figures is provided in Appendix B.
11 There are distinct differences in the comparison of modeled versus measured
12 concentration distributions at ambient monitoring locations in St. Louis when compared with
13 Greene County. Most noticeable is the width of the prediction envelopes; St. Louis prediction
14 envelopes were not as wide as those generated for Greene County. This indicates that, in
15 comparison with the Greene County modeling domain, there is less spatial variability in the
16 concentrations modeled at receptors surrounding the ambient monitoring locations in St. Louis.
17 This is likely a result of the emission source contributions; four of five ambient monitors in
18 Green County were primarily influenced by point sources, while most of the concentration
19 contribution in St. Louis monitors was from area source emissions.
20 The modeled concentrations at the monitor locations and ambient measured concentration
21 distributions showed better overall agreement at the St. Louis monitors, though many of the
22 measured concentrations are outside of the prediction envelopes. For example, at monitor ID
23 291890006 all measured concentrations up to the 99th percentile fell below the prediction
24 envelope (Figure 8-9) (the maximum was within). Note however that the difference in the
25 measured concentrations was only about 1 ppb when compared with concentrations at any of the
26 envelope percentiles and at most 2 ppb when compared with the modeled concentrations at the
27 monitor receptor. In addition, because most of these under-predictions occur at concentrations
28 well below levels of interest, it is not of great consequence. At the upper percentiles, many of
29 the ambient concentrations fell within the prediction envelopes; 6 of 7 monitors at the maximum
30 percentile were within, 3 of 7 monitors at the 99th percentile were within, and 4 of 7 monitors at
31 the 95th percentile were within the prediction envelopes. Where measured upper percentile
March 2009 183 Draft - Do Not Quote or Cite
-------
1 concentrations were outside of the prediction envelopes, it was consistently beneath the 2.5th
2 prediction, possibly indicating some over-prediction bias at these monitors at certain percentiles
3 of the distribution. When comparing the AERMOD monitor concentrations with the measured
4 ambient concentrations between the 80th and 99th percentile of the distribution, most of the
5 predicted values were greater than the measured concentrations. The magnitude of this over-
6 prediction ranged from about 1 to 2 ppb, although one monitor had a 7 ppb difference at the 99th
7 percentile. Predictions at the maximum concentrations were more balanced; 4 of the 7 monitors
8 had over-predictions, while all predictions (under or over) were approximately with 10 to 35 ppb
9 of the measured concentrations.
10 The diurnal pattern was reproduced at the St. Louis monitoring locations, with some of
11 the prediction envelopes encompassing much of the measured ambient concentrations (e.g.,
12 Figure 8-9, monitor ID 291890004; Figure 8-11 monitor ID 291897003). Again where deviation
13 did occur at a few of the monitors, the contribution of the lower concentrations (i.e., mostly those
14 beneath the 90th percentile) likely played a role in the magnitude of the disagreement. This can
15 be seen at monitor ID 291890006 (Figure 8-10) where most (99%) of the predicted
16 concentrations are consistently above the measure concentrations by 1 to 2 ppb. It is not
17 surprising to see that the difference in comparing the measured versus modeled diurnal profile at
18 every hour-of-the-day is also between 1 to 2 ppb.
19 8.4.4.3. Using unadjusted AERMOD predicted SO2 concentrations
20 The SC>2 concentrations estimated using AERMOD do not have significant bias, save for
21 small overestimation primarily observed at the lowest concentrations and some difficulty in
22 reproducing the maximum concentrations. Most ambient monitoring concentrations fell within
23 the modeled prediction envelopes constructed of modeled receptors surrounding the monitor. In
24 generating the modeled air quality, staff made judgments in appropriately modifying model
25 inputs including an adjustment of the area source temporal emission profile to improve the
26 comparison of the model predictions with the measurement data. Staff went through several
27 iterations of evaluating the model performance in each modeling domain following model input
28 adjustments to obtain the current modeled air quality results. Given the time and resources to
29 perform this assessment, the good agreement in the model-to-monitor comparisons, the degree of
30 confidence in the dispersion modeling system, the spatial representation of the monitors
31 compared with receptors modeled, and the number of comparisons available, staff did not
March 2009 184 Draft - Do Not Quote or Cite
-------
1 perform any further adjustments to the modeled concentrations to improve the relationship
2 between modeled versus measured concentration at each monitor. Additional details on the
3 staffs reasoning are provided in section 8.11.
March 2009 185 Draft - Do Not Quote or Cite
-------
Monitor ID 290770026
Monitor ID 290770026
100 --
1
2
3
Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
- - AERMOD P2.5
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
20
30 40 50 60 70 80 90
1-hour SO2 Concentration (ppb)
Monitor ID 290770032
100 110 120
12
Hour of the Day
Monitor ID 290770032
24
100 --
AERMOD P2.5
AERMOD P97.5
Ambient Monitor
AERMOD Monito
Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
80
20 30 40 50
1-hour SO2 Concentration (ppb)
12
Hour of the Day
18
24
Figure 8-6. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the modeled monitor
receptor and receptors within 4 km of monitors 290770026 and 29077032 in Greene County, Mo.
March 2009
186
Draft - Do Not Quote or Cite
-------
Monitor ID 290770040
Monitor ID 290770040
100
1
2
3
Ŧ 95
c
V
o
<5
Q.
<1>
^
ss
D
3
O
85
80
Ambient Monitor
AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
40
60 80 100 120 140 160
1-hour SO2 Concentration (ppb)
Monitor ID 290770041
180 200
100 --
Cumulative Percentile (%)
ŧ 00 CO CO
D cn o cn
J I- - ^ 1
'f
f
1
^
X
f
h
^_
Ŧ
f
*
^ Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
AERMODP97.5
Ambient Monitor
AERMOD Monitor
12
Hour of the Day
Monitor ID 290770041
ppb
AERMOD P2.5
AERMOD P97.5
-Ambient Monitor
-AERMOD Monitor
s \
1 0 20
24
30 40 50 60 70 80 90 100 110 120 0 6 12
1-hour SO2 Concentration (ppb) Hour of the Day
Figure 8-7. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the modeled monitor
receptor and receptors within 4 km of monitors 290770040 and 29077041 in Greene County, Mo.
24
March 2009
187
Draft - Do Not Quote or Cite
-------
Monitor ID 290770037
Monitor ID 290770037
2
3
4
100 --
Ŧ 95
&
90 -!
85 -:
o
80
Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
7 --
6 --
5 --
AERMOD P2.5
AERMOD P97.5
-Ambient Monitor
-AERMOD Monitor
\
10 20 30
40 50 60 70 80 90 100 110 120 130 140 150 0 6 12
1 -hour SO2 Concentration (ppb) Hour of the Dav
Figure 8-8. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the modeled monitor
receptor and receptors within 4 km of monitor 290770037 in Greene County, Mo.
24
March 2009
188
Draft - Do Not Quote or Cite
-------
Monitor ID 291890004
Monitor ID 291890004
100 --
c
Ol
o
Ģ
Ol
3
3
O
^ Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
85 ---
80
100 --
_aj 95 --
I
ff
^90 +
3
I 85
80
20
30 40 50 60 70 80
1-hour SO2 Concentration (ppb)
Monitor ID 291890006
^ Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
.a
35
c
o
c
01
u
c
o
O
2 ---
- AERMOD P2.5
- -AERMODP97.5
Ambient Monitor
AERMOD Monitor
3 X
90 100 110
12
Hour of the Day
Monitor ID 291890006
18
24
.Q
Ģ
u
8 3 +
O
1 --
- - AERMOD P2.5
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
20
18
30 40 50 60 70 80 90 100 0 6 12
| 1-hour SO, Concentration (ppb) Hourof the Day
2 Figure 8-9. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the modeled monitor
3 receptor and receptors within 4 km of monitors 291890004 and 291890006 in St Louis, Mo.
24
March 2009
189
Draft - Do Not Quote or Cite
-------
1
2
3
4
Monitor ID 291893001
Monitor ID 291893001
100
95 --
O
80
90 --
85 I '
Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
- - AERMOD P2.5
-AERMODP97.5
Ambient Monitor
AERMOD Monitor
20
30 40 50 60 70
1-hour SO2 Concentration (ppb)
Monitor ID 291895001
80
90
100
12
Hour of the Day
Monitor ID 291895001
18
24
100
O
Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
AERMOD P97.5
max = 545 ppb
2
1 --
- - AERMOD P2.5
-AERMODP97.5
Ambient Monitor
AERMOD Monitor
25 50 75 100 125 150
1-hour SO2 Concentration (ppb)
175
200
12
Hour of the Day
18
24
Figure 8-10. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with
the modeled monitor receptor and receptors within 4 km of monitors 291893001 and 291895001 in St Louis, Mo.
March 2009
190
Draft - Do Not Quote or Cite
-------
1
2
3
4
Monitor ID 291897003
Monitor ID 291897003
100 1 1
95 --
90 ---
80
85 --J
Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
20 30
40 50 60 70 80 90 100
1-hour SO2 Concentration (ppb)
Monitor ID 295100007
110 120 130 140
100 1 '--
O
Ambient Monitor
- - AERMOD P2.5
AERMOD P97.5
AERMOD Monitor
80
20 30
40 50 60 70 80 90 100
1-hour SO2 Concentration (ppb)
110 120 130 140
c
<
1 --
- - AERMOD P2.5
-AERMOD P97.5
Ambient Monitor
AERMOD Monitor
12
Hour of the Day
Monitor ID 295100007
18
24
- - AERMOD P2.5
-AERMOD P97.5
Ambient Monitor
AERMOD Monitor
12
Hour of the Day
24
Figure 8-11. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with
the modeled monitor receptor an d receptors within 4 km of monitors 291897003 and 295100007 in St Louis, Mo.
March 2009
191
Draft - Do Not Quote or Cite
-------
Monitor ID 295100086
Monitor ID 295100086
1
2
3
4
100 1
c
01
Ģ
>
1
3
I
o
95 --
90 --
85
80
Ambient Monitor
- AERMOD P2.5
- AERMOD P97.5
AERMOD Monitor
10 20 30 40 50 60 70 80 90 100
1-hour SO2 Concentration (ppb)
10
.a
Q.
_g
C
o
c
01
u
c
o
O
O
c
ro
3
c
<
110 120 130 140
9 --
8 --
7 --
6 --
5 --
4 ---r
3
2 --
1 --
- - AERMOD P2.5
-AERMODP97.5
Ambient Monitor
AERMOD Monitor
12
Hour of the Day
18
24
Figure 8-12. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with
the modeled monitor receptor and receptors within 4 km of monitor 295100086 in St Louis, Mo.
March 2009
192
Draft - Do Not Quote or Cite
-------
l 8.5 SIMULATED POPULATION
2 The population subgroups included in this exposure assessment are asthmatics and
3 asthmatic children. Evaluating exposures of the exposure of this group with APEX requires the
4 estimation of children's asthma prevalence rates. The proportion of the population of children
5 characterized as being asthmatic was estimated by statistics on asthma prevalence rates recently
6 used in the NAAQS review for O3 (US EPA, 2007d). See Appendix B, Attachment 2 for details
7 in the derivation. Specifically, an analysis of data provided in the National Health Interview
8 Survey (NHIS) for 2003 (CDC, 2007) generated age and gender specific asthma prevalence rates
9 for children ages 0-17. Adult asthma prevalence rates were estimated by gender and for each
10 particular modeling domain based on Missouri regional data (MO DOH, 2002). Table 8-6
11 provides a summary of the asthma prevalence used in the exposure analysis, stratified by age and
12 gender.
13 The total population simulated within the two modeling domains was approximately 1.4
14 million persons, of which there was a total simulated population of about 130,000 asthmatics.
15 The model simulated over 360,000 children ages 5 through 17, of which there were nearly
16 50,000 asthmatics. The individual populations for each modeling domain and subpopulation of
17 interest are provided in Table 8-7. For comparison, the MO Department of Health (2003) reports
18 the following 2003 asthma prevalence rates by county for all ages as follows; Greene (10.2%),
19 St. Charles (8.8%); St. Louis (5.8%), and St. Louis City (16.4%) which amounts to a county
20 population-weighted value of 8.8%, similar to the 9.3% modeled here using APEX.
March 2009 193 Draft Do Not Quote or Cite
-------
Table 8-6. Asthma prevalence rates by age and gender used in Greene County
and St. Louis modeling domains.
Modeling
Domain
(Region)
Greene Co.
and St. Louis
(Midwest)
Greene Co.
St. Louis
Age1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
>17
>17
Asthma Prevalence (%)2
Females
7.0
7.1
7.3
7.5
8.1
9.5
9.2
9.0
8.6
11.0
16.2
19.6
21.2
17.0
14.0
13.3
14.0
16.5
10.7
9.3
Males
3.1
6.3
10.8
15.8
21.6
17.8
12.8
12.1
12.8
14.7
17.7
19.0
19.5
16.9
16.8
18.0
20.1
23.7
6.1
5.3
Notes:
1 Ages 0-17 from the National Health Interview Survey
(NHIS) for 2003 (CDC, 2007); ages >17 from (MO DOH,
2002).
Table 8-7. Population modeled in Greene County and St. Louis modeling
domains.
Modeling
Domain
Green Co.
St. Louis
Population
All Ages
224,145
1,151,094
Children (5 -18)
54,373
308,939
Asthmatic Population
All Ages
21,948
105,456
Children (5 -18)
7,285
41,714
3 8.5.1 Characterizing Ventilation Rates
4 Human activities are variable over time, a wide range of activities are possible even
5 within a single hour of the day. The type of activity an individual performs, such as sleeping or
6 jogging, will influence their breathing rate. The ISA indicates that adverse lung function
7 responses associated with short-term peak exposures at levels below 1,000 ppb occurs with
March 2009
194
Draft Do Not Quote or Cite
-------
1 moderate to heavy exertion levels. Therefore, ventilation rates needed to be defined to further
2 characterize exposures of interest. The target ventilation for adults (both a mix of males and
3 females) experiencing effects from 5-10 minute SC>2 exposures from most of the controlled
4 human exposure studies was between 40-50 L/min. Since there were limited controlled human
5 exposure study data available for asthmatic children, the ventilation targets needed to be
6 adjusted. As was done in the Os NAAQS review (EPA, 2007d), target ventilation rates were
7 normalized to body surface area (BSA) to allow for such an extrapolation from adults to
8 children. The resulting normalization yields an equivalent ventilation rate (EVR). Since BSA
9 was not measured in the controlled human exposure studies and the data were reported as
10 grouped, median estimates for males (1.94 m2) and females (1.69 m2) were obtained from EPA
11 (1997) and averaged to normalize the target ventilation rates. Therefore, an EVR = 40/1.81 = 22
12 L/min-m2 was used to characterize the minimum target ventilation rate of interest. Individuals at
13 or above an EVR of 22 L/min-m2 (children or adult) would be characterized as performing
14 activities at or above a moderate ventilation rate.
15 8.6 CONSTRUCTION OF LONGITUDINAL ACTIVITY SEQUENCES
16 Exposure models use human activity pattern data to predict and estimate exposure to
17 pollutants. Different human activities, such as spending time outdoors, indoors, or driving, will
18 result in varying pollutant exposure concentrations. To accurately model individuals and their
19 exposure to pollutants, it is critical to understand their daily activities. EPA's CHAD provides
20 data for where people spend time and the activities performed. Typical time-activity pattern data
21 available for inhalation exposure modeling consist of a sequence of location/activity
22 combinations spanning 24-hours, with 1 to 3 diary-days for any single study individual.
23 The exposure assessment performed here requires information on activity patterns over a
24 full year. Long-term multi-day activity patterns were estimated from single days by combining
25 the daily records using an algorithm that represents the day-to-day correlation of activities for
26 individuals. The algorithm first uses cluster analysis to divide the daily activity pattern records
27 into groups that are similar, and then select a single daily record from each group. This limited
28 number of daily patterns is then used to construct a long-term sequence for a simulated
29 individual, based on empirically-derived transition probabilities. This approach is intermediate
30 between an assumption of no day-to-day correlation (i.e., re-selection of diaries for each time
31 period) and perfect correlation (i.e., selection of a single daily record to represent all days).
March 2009 195 Draft Do Not Quote or Cite
-------
1 Details regarding the algorithm and supporting evaluations are provided in Appendix B,
2 Attachments 3 and 4.
3 8.7 CALCULATING MICROENVIRONMENTAL CONCENTRATIONS
4 Probabilistic algorithms are used to estimate the pollutant concentration associated with
5 each exposure event. The estimated pollutant concentrations account for temporal and spatial
6 variability in ambient (outdoor) pollutant concentration and factors affecting indoor
7 microenvironment, such as a penetration, air exchange rate, and pollutant decay or deposition
8 rate. APEX calculates air concentrations in the various microenvironments visited by the
9 simulated person by using the ambient air data estimated for the relevant blocks/receptors, the
10 user-specified algorithm, and input parameters specific to each microenvironment. The method
11 used by APEX to estimate the microenvironmental concentration depends on the
12 microenvironment, the data available for input to the algorithm, and the estimation method
13 selected by the user. The current version of APEX calculates hourly concentrations in all the
14 microenvironments at each hour of the simulation for each of the simulated individuals using one
15 of two methods: by mass balance or a transfer factors method. Details regarding the algorithms
16 used for estimating specific microenvironments and associated input data derivations are
17 provided in Appendix B.
18 Briefly, the mass balance method simulates an enclosed microenvironment as a well-
19 mixed volume in which the air concentration is spatially uniform at any specific time. The
20 concentration of an air pollutant in such a microenvironment is estimated using the following
21 processes:
22 Inflow of air into the microenvironment
23 Outflow of air from the microenvironment
24 Removal of a pollutant from the microenvironment due to deposition, filtration, and
25 chemical degradation
26 Emissions from sources of a pollutant inside the microenvironment.
27 A transfer factors approach is simpler than the mass balance model, however, most
28 parameters are derived from distributions rather than single values to account for observed
29 variability. It does not calculate concentration in a microenvironment from the concentration in
30 the previous hour as is done by the mass balance method, and the transfer factors approach
March 2009 196 Draft Do Not Quote or Cite
-------
1 contains only two parameters. A proximity factor is used to account for proximity of the
2 microenvironment to sources or sinks of pollution, or other systematic differences between
3 concentrations just outside the microenvironment and the ambient concentrations (at the
4 measurements site or modeled receptor). The second parameter, a penetration factor, quantifies
5 the amount of outdoor pollutant penetrates into the microenvironment.
6 8.7.1 Approach for Estimating 5-Minute Maximum SOi Concentrations
7 The 5-minute peak concentrations were estimated probabilistically considering the
8 empirically-derived PMR CDFs developed from recent 5-minute ambient monitoring data (see
9 section 7.2). Thus for every 1-hr concentration estimated at each receptor, an associated 5-
10 minute maximum SC>2 concentration was generated.
11 The approach is designed to generate the maximum 5-minute 862 concentrations to use
12 in evaluating exceedances of the potential health effects benchmarks. In general, it is not an
13 objective to estimate each of the other eleven 5-minute concentrations within the hour with a
14 high degree of certainty. While the occurrence of multiple peak concentrations above
15 benchmark levels within an hour is possible, the potential health effect benchmark levels are
16 related to single peak exposures within a day. The APEX model originally used 1-hour ambient
17 SC>2 concentrations as input prior to the calculation of microenvironmental concentrations. The
18 current APEX model now can use ambient concentrations of almost any time step, including
19 down to 5-minutes. The file size was an issue with this approach however, since each of the
20 thousands of receptor files generated by AERMOD would be increase by a factor of twelve,
21 creating both disk space and processing difficulties. An algorithm was incorporated into the
22 flexible time-step APEX model to estimate the 5-minute maximum SC>2 concentrations real-time
23 using the 1-hour SO2 concentration, an appropriate PMR (section 7.2), and equation 7-1. The
24 additional eleven 5-minute concentrations within an hour at each receptor were approximated
25 using the following:
nC-P
26 X = equation (8-1)
n-l
27 where,
28 X = 5-minute concentration in each of non-peak concentration periods in the
29 hour at a receptor (ppb)
30 C = 1-hr mean concentration estimated at a receptor (ppb)
March 2009 197 Draft Do Not Quote or Cite
-------
1
2
3
4
5
6
7
10
11
12
13
14
15
16
17
P = estimated peak concentration at a receptor (ppb) estimated
probabilistically using equation 7-1.
n = number of time steps within the hour (12)
In addition to the level of the maximum concentration, the actual time of when the
contact occurs with a person is also of importance. There is no reason to expect a temporal
relationship of the peak concentrations within the hour, thus clock times for peak values were
estimated randomly (i.e., any one of the 12 possible time periods within the hour). The PMR
assignment also assumes a standard frequency during any hour of the day.
8.7.2 Microenvironments Modeled
In APEX, microenvironments represent the exposure locations for simulated individuals.
For exposures to be estimated accurately, it is important to have realistic microenvironments that
match closely to the locations where actual people spend time on a daily basis. As discussed
above, the two methods available in APEX for calculating pollutant levels within
microenvironments were mass balance or a transfer factors approach. Table 8-8 lists the
microenvironments used in this study, the calculation method used, and the type of parameters
used to calculate the microenvironment concentrations.
Table 8-8. List of microenvironments modeled and calculation methods used.
Microenvironment
Indoors - Residence
Indoors - Bars and restaurants
Indoors - Schools
Indoors - Day-care centers
Indoors - Office
Indoors- Shopping
Indoors - Other
Outdoors - Near road
Outdoors - Public garage - parking lot
Outdoors - Other
In-vehicle - Cars and Trucks
In-vehicle - Mass Transit (bus, subway,
train)
Calculation
Method
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Factors
Factors
Factors
Factors
Factors
Parameter Types
used1
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
PR
PR
None
PE and PR
PE and PR
1 AER=air exchange rate, DE=decay-deposition rate, PR=proximity factor,
PE=penetration factor
18
March 2009
198
Draft Do Not Quote or Cite
-------
1 8.7.3 Microenvironment Descriptions
2 8.7.3.1 Microenvironment 1: Indoor-Residence
3 The Indoor-Residence microenvironment uses several variables that affect SC>2 exposure:
4 whether or not air conditioning is present, the average outdoor temperature, the 862 removal
5 rate, and an indoor concentration source.
6 A ir con dition ing prevalen ce rates
1 Since the selection of an air exchange rate distribution is conditioned on the presence or
8 absence of an air-conditioner, for each modeled area the air conditioning status of the residential
9 microenvironments is simulated randomly using the probability that a residence has an air
10 conditioner. A value of 95.5% was calculated to represent the air conditioning prevalence rate in
11 both Greene County and St. Louis, using the data and survey weights for St. Louis, MO.
12 obtained from the American Housing Survey of 2003 (AHS, 2003a; 2003b).
13 A ir exch ange rates
14 Air exchange rate data for the indoor residential microenvironment were the same used in
15 APEX for the most recent O3 NAAQS review (EPA, 2007d; see Appendix B, Attachment 5).
16 Briefly, data were reviewed, compiled and evaluated from the extant literature to generate
17 location-specific AER distributions categorized by influential factors, namely temperature and
18 presence of air conditioning. In general, lognormal distributions provided the best fit, and are
19 defined by a geometric mean (GM) and standard deviation (GSD). To avoid unusually extreme
20 simulated AER values, bounds of 0.1 and 10 were selected for minimum and maximum AER,
21 respectively.
22 Briefly, AER data were reviewed, compiled, and evaluated from the extant literature to
23 generate location-specific AER distributions categorized by influential factors, namely, location,
24 temperature, and presence of A/C. The AER data obtained was limited in the number of
25 samples, particularly when considering these influential factors. When categorizing by
26 temperature, a range of temperatures was used to maintain a reasonable number of samples
27 within each category to allow for some variability within the category, while still allowing for
28 differences across categories. Several distribution forms were investigated (i.e., exponential,
29 log-normal, normal, and Weibull) and in general, lognormal distributions provided the best fit.
30 Fitted lognormal distributions were defined by a geometric mean (GM) and standard deviation
March 2009 199 Draft Do Not Quote or Cite
-------
1
2
3
4
5
6
7
10
11
12
13
14
15
16
17
18
19
(GSD). Because no fitted distribution was available specifically for St. Louis or Greene County,
distributions were selected from other locations thought to have similar characteristics,
qualitatively considering factors that might influence AERs including the age composition of
housing stock, construction methods, and other meteorological variables not explicitly treated in
the analysis, such as humidity and wind speed patterns. To avoid unusually extreme simulated
AER values, bounds of 0.1 and 10 were selected for minimum and maximum AER, respectively.
Table 8-9 summarizes the distributions used by A/C prevalence and temperature categories. See
Appendix B, Attachment 5 for additional details.
Table 8-9. Geometric means (GM) and standard deviations (GSD) for air
exchange rates by A/C type and temperature range.
A/C Type1
Central or
Room A/C
No A/C
Temp
(°C)
<=10
10-20
20-25
25-30
>30
<=10
10-20
>20
N
179
338
253
219
24
61
87
44
GM
0.9185
0.5636
0.4676
0.4235
0.5667
0.9258
0.7333
1.3782
GSD
1.8589
1 .9396
2.2011
2.0373
1 .9447
2.0836
2.3299
2.2757
Notes:
1 All distributions derived from data reported in non-California cities. See
Appendix B, Attachment 5 for details in the data used and distribution
derivation.
SO2 Removal Rate
Staff estimated distributions of indoor 862 deposition rates by applying a Monte Carlo
sampling approach to configurations of indoor microenvironments of interest. The relative
composition of particular surface materials (e.g., painted wall board, wall paper, wool carpet,
synthetic carpet, synthetic floor covering, cloth) within various sized buildings were
probabilistically modeled to estimate 1,000 SO2 deposition rates that in turn were used to
parameterize lognormal distributions (Table 8-10). The modeling was fundamentally based on a
review of 862 deposition conducted by Grontoft and Raychaudhuri (2004) for a variety of
building material surfaces under differing conditions of relative humidity. Details on the data
used and derivation of removal rates are provided in Appendix B, section 4.
March 2009
200
Draft Do Not Quote or Cite
-------
Table 8-10. Final parameter estimates of SO2 deposition distributions in several
indoor microenvironments modeled in APEX.
1
2
4
5
6
7
8
9
10
11
12
13
14
15
16
Microenv-
ironment
Residence
Office
School/
Day Care
Center
Restaurant
Other
Indoors
Heating or Air Conditioning in Use
Geom.
Mean
(hr'1)
3.14
3.99
4.02
2.36
2.82
Geom.
Stand.
Dev.
(hr'1)
1.11
1.04
1.02
1.28
1.21
Lower
Limit
(hr1)
2.20
3.63
3.90
1.64
1.71
Upper
Limit
(hr1)
5.34
4.37
4.21
4.17
4.12
Air Conditioning Not in Use
(Summertime Ambient Morning
Relative Humidity of 90%)
Geom.
Mean
(hr1)
13.41
N/A
N/A
N/A
N/A
Geom.
Stand.
Dev.
(hr1)
1.11
N/A
N/A
N/A
N/A
Lower
Limit
(hr1)
10.31
N/A
N/A
N/A
N/A
Upper
Limit
(hr1)
26.96
N/A
N/A
N/A
N/A
Notes:
N/A not applicable, assumed by staff to always have A/C in operation.
8.7.3.2 Microenvironments 2-7: All Other Indoor Microenvironments
The remaining five indoor microenvironments, which represent Bars and Restaurants,
Schools, Day Care Centers, Office, Shopping, and the broadly defined Other Indoor
microenvironments, were all modeled using the same data and functions. An air exchange rate
distribution (GM = 1.109, GSD = 3.015, Min = 0.07, Max = 13.8) was based on an indoor air
quality study (Persily et al, 2005). This is the same distribution in APEX used for the most
recent Os NAAQS review (EPA, 2007d). See Appendix B, Attachment 5 for details in the data
used and derivation. The SO2 removal rates were estimated as explained in section 8.7.3.1, and
described in more detail in Appendix B, section 4. The resulting lognormal distributions are
presented in Table 8-10. These microevironments all assumed to all have air-conditioning.
8.7.3.3 Microenvironments 8-10: Outdoor Microenvironments
All outdoor microenvironmental concentrations are well represented by the modeled
concentrations. Therefore, both the penetration factor and proximity factor for this
microenvironment were set to 1.
17 8.7.3.4 Microenvironments 11 and 12: In Vehicle- Cars and Trucks, and Mass Transit
18 There were no available measurement data for SO2 penetration factors, therefore the
19 penetration factors used were developed from NO2 data provided in Chan and Chung (2003) and
March 2009
201
Draft Do Not Quote or Cite
-------
1 used in the recent NOX NAAQS review (EPA, 2008d). Inside-vehicle and outdoor NC>2
2 concentrations were measured with for three ventilation conditions, air-recirculation, fresh air
3 intake, and with windows. Mean values range from about 0.6 to just over 1.0, with higher values
4 associated with increased ventilation (i.e., window open). A uniform distribution U{0.6, 1.0}
5 was selected for the penetration factor for Inside-Cars/Trucks due to the limited data available to
6 describe a more formal distribution and the lack of data available to reasonably assign potentially
7 influential characteristics such as use of vehicle ventilation systems for each location. Mass
8 transit systems, due to the frequent opening and closing of doors, was assigned a uniform
9 distribution U{0.8, 1.0} based on the reported mean values for fresh-air intake (0.796) and open
10 windows (1.032) on urban streets.
ll 8.8 EXPOSURE MEASURES AND HEALTH RISK CHARACTERIZATION
12 APEX calculates exposure as a time-series of exposure concentrations that a simulated
13 individual experiences during the simulation period. APEX calculates exposure by identifying
14 concentrations in the microenvironments visited by the person according to the composite diary.
15 In this manner, a time-series of event exposures are found. Then, the time-step exposure
16 concentration at any clock hour during the simulation period is calculated using the following
17 equation:
N
V c t
Z_^ ^ time- step (j) (j)
r^ 7=1
18 <-; - equation (8-2)
19
20 where,
21 d = Time-step exposure concentration at clock hour/of the simulation
22 period (ppm)
23 N = Number of events (i.e., microenvironments visited) in time-step/'
24 of the simulation period.
25 Ctime_stepU} = Time-step concentration in microenvironmenty (ppm)
26 t(j) = Time spent in microenvironmenty (minutes)
27 T = Length of time-step (or 5 minutes in this analysis)
28
29 From the time-step exposures, APEX calculates time-series of 5-minute, 1-hour, 24-hour,
30 and annual average exposure concentrations that a simulated individual would experience during
31 the simulation period. APEX then statistically summarizes and tabulates the 5-minute time-step
32 (or daily, or annual average) exposures. From this, APEX can calculate two general types of
March 2009 202 Draft Do Not Quote or Cite
-------
1 exposure estimates: counts of the estimated number of people whose exposure exceeded a
2 specified 862 concentration level 1 or more times in a year and the number of times per year that
3 they are so exposed; the latter metric is in terms of person-occurrences or person-days. The
4 former highlights the number of individuals whose exposure exceeded at least one or more times
5 per modeling period the health effect benchmark level of interest. APEX can also report counts
6 of individuals with multiple exposures. This person-occurrences measure estimates the number
7 of times per season that individuals are exposed to the exposure indicator of interest and then
8 accumulates these estimates for the entire population residing in an area.
9 In this exposure assessment, APEX tabulates and displays the two measures for
10 exposures above levels ranging from 0 to 800 ppb by 50 ppb increments for all exposures. These
11 results are tabulated for the population and subpopulations of interest.
12 8.8.1 Adjustment for Just Meeting the Current and Alternative Standards
13 We used a different approach to simulate just meeting the current and alternative
14 standards than was used in the Air Quality Characterization (see section 7.2.4). In this case,
15 instead of adjusting upward47 the air quality concentrations, to reduce computer processing time,
16 we adjusted the health effect benchmark levels by the same factors described for each specific
17 modeling domain and simulated year (Table 8-11). Since it is a proportional adjustment, the end
18 effect of adjusting concentrations upwards versus adjusting benchmark levels downward within
19 the model is the same. The same follows for where as is concentrations were in excess of an
20 alternative standard level (e.g., 50 ppb for the 99th percentile averaged over three years), only the
21 associated benchmarks are adjusted upwards (i.e., a higher threshold concentration that would
22 simulate lower exposures).
47 To evaluate the current and most of the alternative standards proposed, ambient concentrations were lower than air
quality that would just meet the standards.
March 2009 203 Draft Do Not Quote or Cite
-------
Table 8-11. Exposure concentrations and adjusted potential health effect benchmark levels used by APEX to
simulate just meeting the current and potential alternative standards in the Greene County and St Louis modeling
domains.
Modeling
Domain
Greene
County
St. Louis
Form1
98
99
99
99
99
99
CS
AS IS
98
98
98
98
98
99
99
99
99
99
CS
AS IS
Level2
200
50
100
150
200
250
50
100
150
200
250
50
100
150
200
250
Exposure Concentrations and Adjusted Potential Health Effect Benchmark Levels (ppb)3
50
20.3
94.3
47.2
31.4
23.6
18.9
14.4
50
53
26.5
17.7
13.3
10.6
63.3
31.7
21.1
15.8
12.7
8
50
100
40.5
188.7
94.3
62.9
47.2
37.7
28.8
100
106
53
35.3
26.5
21.2
126.7
63.3
42.2
31.7
25.3
16
100
150
60.8
283
141.5
94.3
70.8
56.6
43.2
150
159
79.5
53
39.8
31.8
190
95
63.3
47.5
38
24
150
200
81
377.3
188.7
125.8
94.3
75.5
57.6
200
212
106
70.7
53
42.4
253.3
126.7
84.4
63.3
50.7
32
200
250
101.3
471.7
235.8
157.2
117.9
94.3
72
250
265
132.5
88.3
66.3
53
316.7
158.3
105.6
79.2
63.3
40
250
300
121.5
566
283
188.7
141.5
113.2
86.4
300
318
159
106
79.5
63.6
380
190
126.7
95
76
48
300
350
141.8
660.3
330.2
220.1
165.1
132.1
100.8
350
371
185.5
123.7
92.8
74.2
443.3
221.7
147.8
110.8
88.7
56
350
400
162
754.7
377.3
251.6
188.7
150.9
115.2
400
424
212
141.3
106
84.8
506.7
253.3
168.9
126.7
101.3
64
400
450
182.3
849
424.5
283
212.3
169.8
129.6
450
477
238.5
159
119.3
95.4
570
285
190
142.5
114
72
450
500
202.5
943.3
471.7
314.4
235.8
188.7
144
500
530
265
176.7
132.5
106
633.3
316.7
211.1
158.3
126.7
80
500
550
222.8
1037.7
518.8
345.9
259.4
207.5
158.3
550
583
291.5
194.3
145.8
116.6
696.7
348.3
232.2
174.2
139.3
88
550
600
243
1132
566
377.3
283
226.4
172.7
600
636
318
212
159
127.2
760
380
253.3
190
152
96
600
650
263.3
1226.3
613.2
408.8
306.6
245.3
187.1
650
689
344.5
229.7
172.3
137.8
823.3
411.7
274.4
205.8
164.7
104
650
700
283.5
1320.7
660.3
440.2
330.2
264.1
201.5
700
742
371
247.3
185.5
148.4
886.7
443.3
295.6
221.7
177.3
112
700
750
303.8
1415
707.5
471.7
353.8
283
215.9
750
795
397.5
265
198.8
159
950
475
316.7
237.5
190
120
750
800
324
1509.3
754.7
503.1
377.3
301.9
230.3
800
848
424
282.7
212
169.6
1013.3
506.7
337.8
253.3
202.7
128
800
Notes:
1 The form of the standard used to adjust the air quality. 98 is the 98th percentile 1 -hour daily maximum alternative standard, 99 is the 99th percentile 1-hour
daily maximum alternative standard, CS is either the current annual average or 24-hour NAAQS (whichever had the lowest factor), AS IS is unadjusted air
quality.
2 The level of the potential alternative standards, i.e., the 1-hour daily maximum at the noted percentile of the distribution.
3 Exposure levels were defined in 50 ppb increments from 0 through 800 ppb even though the selected potential health effect benchmark levels were 100
to 400 ppb in 100 ppb increments.
March 2009
204
Draft Do Not Quote or Cite
-------
l 8.9 EXPOSURE MODELING AND HEALTH RISK CHARACTERIZATION
2 RESULTS
3 Exposure results are presented for simulated asthmatic populations residing in the two
4 modeling domains in Missouri. Five-minute daily maximum SC>2 exposures were estimated for
5 each day for year 2002. These short-term exposures were evaluated for all asthmatics and
6 asthmatic children when the exposure corresponded with moderate or greater activity levels. The
7 number of daily maximum 5-minute SC>2 exposures that were at or above any level from 0
8 through 800 ppb in 50 ppb increments was estimated by APEX. Therefore, depending on the
9 concentration level, an individual would have at most one exceedance of a particular level per
10 day, or 365 per year, provided that the person was at a moderate (or higher) exertion level while
11 exposed.
12 Multiple air quality scenarios were evaluated, including unadjusted air quality (termed as
13 is), air quality adjusted to just meet the current NAAQS, and air quality adjusted to just meet
14 several potential alternative 1-hr daily maximum standards. Exposure results are presented in a
15 series of figures that allow for simultaneous comparison of exposures associated with each air
16 quality scenario. Four types of results are provided for each modeling domain: (1) the number of
17 persons in the simulated subpopulation exposed at or above selected levels 1 or more times in a
18 year, (2) the percent of the simulated subpopulation exposed at or above selected levels 1 or
19 more times in a year, (3) the total number of days in a year the simulated subpopulation is
20 exposed (or person days) at or above selected levels, and (4) the percent of time associated with
21 the exposures at or above the selected levels. Tables summarizing all of the exposure results for
22 each modeling domain, air quality scenario, exposure level, and subpopulation are provided in
23 Appendix B, section 4.
24 8.9.1 Asthmatic Exposures to 5-minute Daily Maximum SOi in Greene County
25 When considering the lowest 5-minute benchmark level of 100 ppb, approximately one
26 thousand asthmatics are estimated to be exposed at least once in the year 2002 while at moderate
27 or greater exertion and when considering the current standard air quality scenario (top of Figure
28 8-13). Each of the potential alternative 1-hr standard air quality scenarios as well as the as is air
29 quality scenario result in fewer asthmatics exposed when compared with the current standard
30 scenario, and progressively fewer persons were exposed with decreases in the 1-hour daily
31 maximum concentration levels of the potential alternative standards. The 99th percentile 1-hour
March 2009 205 Draft Do Not Quote or Cite
-------
1 daily maximum standard levels of 50 and 100 ppb produced the same number of persons with 5-
2 minute daily maximum exposures at or above 100 ppb as the as is air quality (i.e., 13). With
3 progressive increases in exposure level, there were corresponding decreases in the number of
4 individuals exposed. None of the asthmatics had a 5-minute daily maximum exposure above 100
5 ppb when considering the as is air quality scenario. Asthmatic children exhibited similar
6 patterns in the estimated number of exposures at each of the exposure levels, though comprising
7 a large proportion of the total asthmatics exposed (bottom of Figure 8-13).
8 The difference between all asthmatics and asthmatic children is best demonstrated by
9 comparing the percent of the subpopulation exposed. Asthmatic children have nearly double the
10 percentage of the subpopulation exposed at any of the benchmark levels considered when
11 compared with that of all asthmatics (Figure 8-14). For example, approximately 1% of asthmatic
12 children experience at least one 5-minute daily maximum exposure at or above 200 ppb in a year
13 in considering the current standard scenario, while approximately 0.6% of all asthmatics
14 experienced a similar exposure. As observed with the numbers of persons exposed, a lower
15 estimated percent of persons was exposed at the higher benchmark levels, though again, the
16 current standard scenario contains the greatest percent of asthmatics exposed when compared
17 with the other air quality scenarios.
18 The number of person days or occurrences of exposures is greater than the number of
19 persons exposed, indicating that some of the simulated asthmatics had more than one 5-minute
20 daily maximum exposure above selected benchmark levels (Figure 8-15). For example, when
21 considering all asthmatics and the current standard scenario, there were approximately 22 person
22 days with exposures at or above 300 ppb. This corresponds with the 18 asthmatics estimated to
23 experience at least one 5-minute daily maximum SC>2 concentration above this level, indicating
24 that a number of persons had experienced at least 2 benchmark exceedances in the year. For
25 both subpopulations considered, there were no estimated exposures above 300 ppb when
26 considering the 99th percentile 1-hour daily maximum alternative standard level of 200 ppb.
27 Staff evaluated the microenvironments where the peak exposures frequently occurred.
28 There were very few persons exposed considering the as is air quality, though 99% or greater
29 experienced their 5-minute daily maximum exposure in an outdoor microenvironment (i.e.,
30 outdoors or outdoors near-roads) when considering any of the benchmark levels. For the current
31 standard air quality scenario, approximately 7% of persons were exposed to the 100 ppb
March 2009 206 Draft Do Not Quote or Cite
-------
1 benchmark level indoors (i.e., primarily in the persons residence), though with increasing
2 benchmark level (e.g., 300 ppb) the percent of persons with any benchmark exceedances indoors
3 approached zero (i.e., > 99% occurred outdoors). The inside vehicle microenvironment also
4 comprised a small percent of the cases where the exposures above selected levels occurred; at
5 most 2% of benchmark exceedances occurred inside vehicles at some of the lowest benchmark
6 levels.
7 8.9.2 Asthmatic Exposures to 5-minute Daily Maximum SOi in St. Louis
8 The patterns in the number of persons (either asthmatics or asthmatic children) exposed
9 in St. Louis were different to that observed in Greene County; a greater number of persons were
10 estimated exposed in St. Louis at each of the corresponding benchmark levels and air quality
11 scenarios (Figure 8-16). For example, nearly 80,000 asthmatics were estimated to experience at
12 least one 5-minute daily maximum SO2 concentration at or above 100 ppb when considering the
13 current standard scenario compared to the one thousand asthmatics estimated in Greene County
14 (section 8.9.1). In addition, there were more persons exposed to the higher benchmark levels in
15 St. Louis compared with Greene County. For example, none of the asthmatics were exposed to a
16 5-minute daily maximum SC>2 concentration above 450 ppb in Greene County considering any of
17 the air quality scenarios. In St. Louis many of the air quality scenarios had persons with
18 exceedances of 450 ppb; the estimated number of persons experiencing at least one 5-minute
19 daily maximum 862 concentration above 450 ppb ranged from a low of 16 (the 99th percentile 1-
20 hour daily maximum standard level of 100 ppb) to over 10,000 (the current standard air quality
21 scenario). Note though, in considering the as is air quality scenario, none of the asthmatics in St.
22 Louis had exceedances of a 450 ppb exposure level.
23 There were also differences in the estimated percent of asthmatics and asthmatic children
24 exposed to concentrations above the benchmark levels in St. Louis when compared with Greene
25 County. For example, over 40% of asthmatic children were estimated to experience a 5-minute
26 daily maximum exposure above 300 ppb in St. Louis considering the current standard air quality
27 scenario, while less than 1% of asthmatic children in Greene County experienced a similar
28 exposure (Figure 8-17). Just as observed with the Greene County estimates though, there were
March 2009 207 Draft Do Not Quote or Cite
-------
1E+3 a
<
<
s
I_
0)
SI
E
3
re
a>
reM
s > o5
i 3 uj
Ŧ 0) 1-
.Ģ2 > o>
PO*;
Ŧ= Ģ 8
E < 2:
"S o i_
+* o
- re. a)
1E+2
1E+1
Q. O
X S
LJJ
0)
O
1E+0
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
1E+3
2
3
4
5
6
1
re
0)
w ą;
o Ģ
Q. 0)
UJ E 1E+1
1E+0
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
Figure 8-13. Number of all asthmatics (top) and asthmatic children (bottom) with 5-minute daily
maximum SO2 exposures above selected exposure levels in Greene County, year 2002
air quality as is and adjusted to just meeting the current and potential alternative
standards.
March 2009
208
Draft Do Not Quote or Cite
-------
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum Exposure SO2 Level (ppb)
4
5
6
7
re
I 1 J J J J I L L L I I
\
-K X K******
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
Figure 8-14. Percent of all asthmatics (top) and asthmatic children (bottom) with 5-minute daily
maximum SO2 exposures above selected exposure levels in Greene County, year 2002
air quality as is and adjusted to just meeting the current and potential alternative
standards.
March 2009
209
Draft Do Not Quote or Cite
-------
1E+4
°§
CO L.
Q) O
^_
CA
^
^
(A
re
Q
c
o
(A
0)
Q.
M-
0
0)
J2
E
3
^
(A
O
Q.
X
HI
0)
C
o
^^
(A
re
0)
^j
re
0)
o
c
0)
'C
0)
Q.
X
LJ
0
0)
4-1
s
0)
c
o
^
re
r-
5
"3
0)
0)
0
2
^<
c
Q
t
0)
X
LJ
1E+3
1E+2
1E+1
1E+0
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
1E+4
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
3 Figure 8-15. Number person days all asthmatics (top) and asthmatic children (bottom) experience
4 5-minute daily maximum SO2 exposures above selected exposure levels in Greene
5 County, year 2002 air quality as is and adjusted to just meeting the current and
6 potential alternative standards.
March 2009
210
Draft Do Not Quote or Cite
-------
1 decreases in the percent of persons exposed with decreases in the 1-hour daily maximum level of
2 the potential alternative standards. For example, less than 3% of asthmatic children were
3 estimated to have at least one 5-minute daily maximum exposure above 300 ppb when
4 considering a 99th percentile 1-hour daily maximum standard level of 150 ppb.
5 The discussion regarding the patterns observed in the number of persons exposed in St.
6 Louis can be extended to the number of person days (i.e., both a greater number and at higher
7 benchmark levels when compared with Greene County). In addition, St. Louis had a greater
8 number of persons with multiple exceedances when compared with Greene County (Figure 8-
9 18). For example, given the 22 person days at or above 300 ppb in Greene County experienced
10 by the 18 asthmatics considering the current standard air quality, on average this amounts to
11 approximately 1.2 exposures per year. In contrast, approximately 26,000 asthmatics had nearly
12 50,000 person days at the same benchmark level and air quality scenario in St. Louis; on average
13 each person is estimated to experience 1.9 exceedances in a year.
14 Staff also evaluated the microenvironments where the peak exposures occurred in St.
15 Louis, and again, there were differences when compared with the exposures in Greene County.
16 In St. Louis, there were a greater percentage of benchmark exceedances within indoor and inside
17 vehicle microenvironments, although overall still comprising a small percentage of where the
18 exceedances were occurring. At the 100 ppb benchmark level, approximately 10% of the
19 exposures occur within indoor microenvironments (i.e., principally inside residences) and about
20 5% occur inside vehicles considering as is air quality (Figure 8-19). The percentage increases
21 when considering air quality adjusted to just meeting the current standard, with approximately
22 30% of benchmark exceedances of 100 ppb occurring indoors and 20% occurring inside
23 vehicles. Just beyond the benchmark level of 400 ppb, nearly all of the exceedances occur
24 outdoors when considering the as is air quality, while indoor microenvironments still contribute
25 to around 10% of exceedances up to a 5-minute daily maximum exposure level of 800 ppb. For
26 comparison, air quality adjusted to just meet a 99th percentile 1-hour daily maximum standard
27 level of 150 ppb is also shown, and falls within the range of values provided by the as is and
28 current standard scenarios.
March 2009 211 Draft Do Not Quote or Cite
-------
*- re
re a,
Q) =
ra _
Ģ >
t
fl)
Ŧ Q) 1-
.Ģ2 > a)
-5 o *:
c ^ S
E < S:
w o >-
rf * O
- re Q)
^ fl) -4-i
< ^ re
o5 a-^
|ĢE
3 0)
^5
1E+5
1E+4
1E+3
1E+2
1E+1
1E+0
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
1E+5
4
5
6
ra
0)
1E+0
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
Figure 8-16. Number of all asthmatics (top) and asthmatic children (bottom) with 5-minute daily
maximum SO2 exposures above selected exposure levels in St. Louis, year 2002 air
quality as is and adjusted to just meeting the current and potential alternative
standards.
March 2009
212
Draft Do Not Quote or Cite
-------
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
100% -F
4
5
6
7
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
Figure 8-17. Percent of all asthmatics (top) and asthmatic children (bottom) with 5-minute daily
maximum SO2 exposures above selected exposure levels in St. Louis, year 2002 air
quality as is and adjusted to just meeting the current and potential alternative
standards.
March 2009
213
Draft Do Not Quote or Cite
-------
1E+6
2
3
4
5
6
7
1E+0
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
1E+6
1E+0
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5- Minute Daily Maximum SO2 Exposure Level (ppb)
Figure 8-18. Number person days all asthmatics (top) and asthmatic children (bottom) experience
5-minute daily maximum SO2 exposures above selected exposure levels in St. Louis,
year 2002 air quality as is and adjusted to just meeting the current and potential
alternative standards.
March 2009
214
Draft Do Not Quote or Cite
-------
Air Quality As Is
S> S?
3 ' '
x E
m c
n
uu%
90%
i
50%
30%
i
no/, .
i i i
i i i
<"* * ^
i i i
i i i
i i i
i i i
i i i
i i i
i i i
i i i
i i i
i i i
i i i
^^i "
-T ;--;..,
*~
k- - -i
'
>--
/
k
X
k-.J
h.-,
P
L=-M
i i i
i i i
i i i
i i i
Indoors
Outdoors
* Vehicles
i i i
i i i
i i i
i i i
i i i
i i
i i
L. -L -L A
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5-Minute Daily Maximum SO2 Exposure (ppb)
Air Quality Adjusted to Just Meet the Current Standard
100% -
90%
S! s?
^ I I I I I I
T ~* - g i ^11
*~.L ^ ^ ^ ^ ~ T ^^T "*" 9 ^^ Ŧ-^
T ~ ~ + ~ ' *--* --A-- *-- -^ -. A-. i- ..^
i
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5-Minute Daily Maximum SO2 Exposure (ppb)
Air Quality Adjusted to Just Meet the 99th %ile 150 ppb Standard
fĨ
iiiir r r r~
Indoors
Outdoors
- A- Vehicles
I 1 J J J I L L L L I I l_
1 1_ * '
t ŧ -t^ - T^ - -
II
100 150 200 250 300 350 400 450 500 550 600 650 700 750 800
5-Minute Daily Maximum SO2 Exposure (ppb)
j
4 Figure 8-19. The frequency of estimated exposure level exceedances in indoor, outdoor, and
5 vehicle microenvironments given as is air quality (top), air quality adjusted to just
6 meeting the current standard (middle) and that adjusted to just meeting a 99th
7 percentile 1-hour daily maximum standard level of 150 ppb (bottom) in St. Louis.
March 2008
215
Draft Do Not Quote or Cite
-------
l 8.10 REPRESENTATIVENESS OF EXPOSURE RESULTS
2 8.10.1 Introduction
3 Due to time and resource constraints the exposure assessment evaluating the current and
4 alternative standards was only applied to the two locations in Missouri. A natural question is
5 how representative are the estimates from this assessment of exposures in Greene County and St.
6 Louis to other areas in the United States with elevated peak 862 concentrations. To address this
7 question, additional data were compiled and analyzed to provide perspective on how
8 representative the exposure modeling results might be for other areas. Because most estimated
9 exceedances were associated with the outdoor microenvironments, this analysis and discussion is
10 centered on time spent outdoors to allow for comparison of the two modeling domains with
11 several other broad regions. In addition, the distribution of asthma prevalence rates in the U.S. is
12 also discussed.
13 8.10.2 Time spent outdoors
14 The time spent outdoors by children age 5-17 was calculated from CHAD-Master48 for
15 five regions of the country. The U.S. states used in the air quality characterization (Chapter 7)
16 were of interest, which already includes Missouri (representing the two exposure modeling
17 domains). Staff analyzed the outdoor time by broad geographic regions because it was thought
18 that the regional climate would have influence on each population. In addition, most of the
19 location descriptors are already broadly defined to protect the identity of persons in CHAD; finer
20 spatial scale such as at a city-level is uncommon. Table 8-12 has the States used to identify
21 CHAD diaries available to populate a data set for each of the five regions. Staff further
22 separated the diaries by time-of-year (school year versus summer)49 and the day-of-week
23 (weekdays versus weekends), both important factors influencing time spent outdoors (Graham
24 and McCurdy, 2004). Summer days were not separated by day of week; staff assumed that the
25 variation in outdoor time during the summer would not be greatly influenced by this factor for
26 children. The results for time spent outdoors in each region are given in the attached Table 8-13.
27
48 Currently available through EPA at mccurdy.tom@epa.gov.
49 A traditional school year was considered (months of September-May); summer months included June-August.
March 2009 216 Draft - Do Not Quote or Cite
-------
Table 8-12. States used to define five regions of the U.S. and characterize CHAD
data diaries.
Region
Mid-Atlantic (MA)
Midwest (MW)
Northeast (NE)
Southeast (SE)
Southwest (SW)
States
New York, New Jersey, Delaware, Maryland, District of Columbia, Virginia,
Virginia, Pennsylvania
West
Ohio, Iowa, Missouri, Illinois, Indiana, Kentucky
Maine, New Hampshire, Vermont, Massachusetts, Connecticut, Rhode Island
North Carolina, South Carolina, Georgia, Florida, Tennessee, Alabama,
Mississippi, Arkansas, Louisiana
Nevada, Utah, Colorado, Arizona, New Mexico, Texas, Oklahoma
1
2 Participation rates for the selected time of year and day of week groupings were similar
3 for each of the regions. In general, a smaller percent of children spend time outdoors during the
4 school year (about 45-50%) compared to the summer (about 70-77%). There was no apparent
5 pattern in the day-of week participation rates considering the school year days. However,
6 children did spend more time outdoors on weekend days compared to weekdays at all percentiles
7 of the distribution and within all regions. In addition, children consistently spent more time
8 outdoors during summer days within all regions. There were few differences in outdoor time
9 when comparing each of the regions. Children in Northeastern States had the widest range in the
10 distributions for time spent outdoors. In this region of the U.S., children spent the least amount
11 of time outdoors during the school-year days-of-the-week and the greatest amount of time
12 outdoors on average during the summer. Based on this analysis, it is not expected that the results
13 generated for the two Missouri modeling domains would be largely different from results
14 generated in most areas of the U.S. when considering time spent outdoors, though there may be
15 differences in exposures estimated in Northeastern states.50 Depending on when the peak
16 exposure events occur in the year, the exposures estimated in these states may be lower or
17 higher.
18
19
20
50 Note however that all of the Northeastern data have the fewest number of person days available, in particular the
summer days (n=23).
March 2009 217 Draft - Do Not Quote or Cite
-------
Table 8-13. Time spent outdoors for children ages 5-17 using CHAD diaries.
Region
MA
MW
NE
SE
SW
Time of
Year
school
summer
school
summer
school
summer
school
summer
school
summer
Day of
Week
weekdays
weekends
all
weekdays
weekends
all
weekdays
weekends
all
weekdays
weekends
all
weekdays
weekends
all
Doers1
(n)
400
317
474
336
258
154
70
54
23
641
593
244
253
232
273
(%)
45
43
71
42
41
71
48
43
77
49
52
70
46
50
76
Time S
Mean
113
158
193
109
152
193
106
148
217
120
157
185
119
162
187
SD
97
159
140
92
131
180
89
128
148
98
126
147
106
142
137
Min
1
2
5
2
1
5
2
15
30
2
1
5
1
7
2
pent Outdoors
Med
90
120
165
88
116
143
75
115
175
95
123
150
90
120
150
P95
301
365
462
300
422
565
290
480
465
325
404
480
315
405
450
minutes)
Max
700
1440
1210
550
870
1250
335
574
635
555
810
935
650
1390
840
GM
73
105
146
73
102
131
66
105
172
84
112
135
80
116
136
GSD
3.0
2.7
2.3
2.7
2.7
2.6
3.1
2.4
2.1
2.6
2.5
2.4
2.8
2.4
2.5
Notes:
1 Doers are those engaged in the particular activity, in this case those children that had at least 1 minute of
outdoor time recorded in their CHAD time-location-activity diary. The participation rate (%) was estimated by
the total number of persons in each subgroup (not included). The n indicates the person-days of diaries used
to calculate the outdoor time statistics.
2 8.10.3 Asthma Prevalence
3 Staff compared regional asthma prevalence statistics for children <18 years in age and all
4 persons. For children, the estimated age-adjusted percents of ever having asthma are presented
5 in Table 8-13 using data from Dey et al. (2004). There are similar prevalence rates for asthmatic
6 children in three of the four regions of the U.S. (Midwest, South, and West), suggesting that
7 exposure analyses conducted in these broader regions may result in similar distributions in the
8 percent of asthmatics exposed to the two Missouri modeling domains used in this assessment.
9 The Northeastern U.S. has a higher percentage of asthmatic children. This suggests that there
10 may be a greater percentage of peak exposures to asthmatic children in the Northeast than
11 compared with the percent modeled in St. Louis or Greene County, holding all other influential
12 variables are constant (e.g., time spent outdoors, a similar air quality distribution).
13 Staff weighted the BRFSS 2002 state-level adult asthma prevalence rates (self-reported)
14 to generate prevalence rates for five U.S regions (Table 8-15).51 Similar rates (between 7.6-
http://www.cdc.gov/asthma/brfss/02/current/tableCl.htm. Regions were mapped using Table 8-12.
March 2009
218
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
7.9%) were estimated for three of the five regions (Mid-Atlantic, Midwest, and the Southwest),
suggesting that exposure analyses conducted in these broader regions may result in similar
distributions in the percent of asthmatics exposed to the two Missouri modeling domains used in
this assessment. Consistent with that observed for asthmatic children, the Northeastern U.S. has
the greatest percent of asthmatic adults. The Southeastern states on average were estimated to
have the lowest adult asthma prevalence. This suggests that there may be a greater percentage of
peak exposures to asthmatic adults in the Northeast and a lower percentage of peak exposures in
the Southeast when compared with the percent modeled in St. Louis or Greene County, holding
all other influential variables are constant (e.g., time spent outdoors, a similar air quality
distribution).
Table 8-14. Asthma prevalence rates for children in four regions of the U.S.
Region
Northeast
Midwest
South
West
Asthma Prevalence1
(%)
15.2
11.6
11.9
11.1
Notes:
1 prevalence is based on the question, "Has
a doctor or other health professional ever
told you that [child's name] had asthma?'"
(Deyetal.,2004)
Table 8-15. Asthma prevalence rates for adults in five regions of the U.S.
Region1
Mid-Atlantic
Midwest
Northeast
Southeast
Southwest
Asthma Prevalence2
(%)
7.9
7.7
8.9
6.9
7.6
Notes:
1 Table 8-12 was used in mapping the states to regions.
2 state level data obtained from
http://www.cdc.sov/asthma/brfss/02/current/tableCl.htm.
March 2009
219
Draft - Do Not Quote or Cite
-------
l 8.11 UNCERTAINTY ANALYSIS
2 The methods and the model used in this exposure assessment conform to the most
3 contemporary modeling methodologies available. APEX is a powerful and flexible model that
4 allows for the realistic estimation of air pollutant exposure to individuals. Since it is based on
5 human time-location-activity diaries and accounts for the most important variables known to
6 affect exposure (where people are located and what they are doing), it has the ability to
7 effectively approximate actual exposure conditions. In addition, the input data selected were the
8 best available data to generate the exposure results. However, there are constraints and
9 uncertainties with the modeling approaches and the input data that limit the realism and accuracy
10 of the modeling results.
11 Uncertainties and assumptions associated with 862 specific model inputs, their
12 utilization, and application are discussed in the following sections. Analyses for certain
13 components of APEX performed previously in other NAAQS reviews (see EPA, 2007d;
14 Langstaff, 2007) that are relevant to the SC>2 NAAQS review are only summarized below. This
15 includes a sensitivity analyses performed on the CHAD data base using Os exposures and an
16 analysis of the air exchange rate data.
17 Following the same general approach described in section 7.8 and adapted from WHO
18 (2008), a qualitative analysis of the components contributing to uncertainty in the exposure
19 results was performed. This includes an identification of the important uncertainties, an
20 indication of the potential bias direction, and a scaling of the uncertainty using low, medium, and
21 high categories. Even though uncertainties in AERMOD concentrations predictions are an
22 APEX input uncertainty, they are addressed separately here for clarity. Table 8-16 summarizes
23 the results of the qualitative uncertainty analysis conducted by staff for the SC>2 exposure
24 assessment.
25
March 2009 220 Draft - Do Not Quote or Cite
-------
Table 8-16. Summary of qualitative uncertainty analysis for the exposure
assessment.
Source
AERMOD
Inputs and
Algorithms
APEX
Inputs and
Algorithms
Type
Algorithms
Meteorological data
Area source emission
profiles, temporal and
spatial
Population data base
Commuting data base
CHAD data base
Longitudinal profile
Meteorological data
Air exchange rates
A/C prevalence
Indoor decay distribution
Multiple peaks
Asthma prevalence rate
Concentration/
Exceedance
Bias Direction
unknown
unknown
both
both
both
both
both
both
unknown
none
unknown
under
unknown
Characterization
of Uncertainty
Low
Low- Medium
Medium
Low
Low - Medium
Low - High
Low
Low
Medium
Low
Low
Low - Medium
Low
2 8.11.1 Dispersion Modeling Uncertainties
3 Air quality data used in the exposure modeling was determined through use of EPA's
4 recommended regulatory air dispersion model, AERMOD (version 07026 (EPA, 2004)), with
5 meteorological data and emissions data discussed above. Parameterization of meteorology and
6 emissions in the model were made in as accurate a manner as possible to ensure best
7 representation of air quality for exposure modeling. Thus, the resulting air quality values are
8 likely free of systematic errors to the best approximation available through application of
9 modeled data.
10 An analysis of uncertainty associated with application of a model is generally broken
11 down into two main categories of uncertainty: 1) model algorithms, and 2) model inputs. While
12 it is convenient to discuss uncertainties in this context, it is also important to recognize that there
13 is some interdependence between the two in the sense that an increase in the complexity of
14 model algorithms may entail an increase in the potential uncertainty associated with model
15 inputs.
March 2009
221
Draft - Do Not Quote or Cite
-------
1 8.11.1.1 AERMOD Algorithms
2 The AERMOD model was promulgated by in 2006 as a "refined" dispersion model for
3 near-field applications (with plume transport distances nominally up to 50 kilometers), based on
4 a demonstration that the model produces largely unbiased estimates of ambient concentrations
5 across a range of source characteristics, as well as a wide range of meteorological conditions and
6 topographic settings (Perry, etal., 2005; EPA, 2003). While a majority of the 17 field study
7 databases used in evaluating the performance of AERMOD are associated with elevated plumes
8 from stationary sources (typically power plants), a number of evaluations included low-level
9 releases. Moreover, the range of dispersion conditions represented by these evaluation studies
10 provides some confidence that the fundamental dispersion formulations within the model will
11 provide robust performance in other settings.
12 AERMOD is a steady-state, straight-line plume model, which implies limitations on the
13 model's ability to simulate certain aspects of plume dispersion. For example, AERMOD treats
14 each hour of simulation as independent, with no memory of plume impacts from one hour to the
15 next. As a result, AERMOD may not adequately treat dispersion under conditions of
16 atmospheric stagnation or recirculation when emissions may build up within a region over
17 several hours. This could lead to a bias toward underprediction by AERMOD during such
18 periods. On the other hand, AERMOD assumes that each plume may impact the entire domain
19 for each hour, regardless of whether the actual transport time for a particular source-receptor
20 combination exceeds an hour. While these assumptions imply some degree of physically
21 unrealistic behavior when considering the impacts of an individual plume simulation, their
22 importance in terms of overall uncertainty will vary depending upon the application. The degree
23 of uncertainty attributable to these basic model assumptions is likely to be more significant for
24 individual plume simulations than for a cumulative analysis based on a large inventory. This
25 question deserves further investigation to better define the limits and capabilities of a modeling
26 system such as AERMOD for large scale exposure assessments such as this. The evidence
27 provided by the model-to-monitor comparisons presented in section 8.4.5 is encouraging as to
28 the viability of the approach in this application when adequate meteorological and other inputs
29 are available. However, each modeling domain and inventory will present its own challenges
30 and will require a separate assessment based on the specifics of the application.
March 2009 222 Draft - Do Not Quote or Cite
-------
1 One of the improvements in the AERMOD model formulations relative to the ISCST
2 model which it replaced is a more refined treatment of enhanced turbulence and other boundary
3 layer processes associated with the nighttime heat island influence in urban areas. The
4 magnitude of the urban influence in AERMOD is scaled based on the urban population specified
5 by the user. Since the sensitivity of AERMOD model concentrations to the user-specified
6 population is roughly proportional to population to the l/4th power, this is not a significant
7 source of uncertainty. The population areas of interest for this application are also well-defined,
8 reducing any uncertainty associated with specification of population or with defining the extent
9 of the modeling domain to be treated as urban.
10 While the AERMOD model algorithms are not considered to be a significant source of
11 uncertainty for this assessment, the representativeness of modeled concentrations for any
12 application are strongly dependent on the quality and representativeness of the model inputs.
13 The main categories of model inputs that may contribute to bias and uncertainty are emission
14 estimates and meteorological data. These issues are addressed in the following sections.
15 8.11.1.2 Meteorological Inputs
16 Details regarding the representativeness of the meteorological data inputs for AERMOD
17 are addressed separately in section 8.4.2. One of the main issues associated with
18 representativeness is the sensitivity of the AERMOD model to the surface roughness of the
19 meteorological tower site used to process the meteorological data for use in AERMOD relative
20 to the surface roughness across the full domain of sources. This issue has been shown to be
21 more significant for low-level sources due to the importance of mechanical shear-stress induced
22 turbulence on dispersion for such sources. A previous application of the AERMOD model to
23 support the REA for the NO2 NAAQS review (EPA, 2008d) provided an opportunity for a direct
24 assessment of this issue by comparing AERMOD modeled concentrations based on processed
25 meteorological data from the Atlanta Hartsfield airport (ATL) with concentrations based on
26 processed meteorological data from a SEARCH monitoring station located on Jefferson Street
27 (1ST) near Georgia Tech. The ATL data were representative of an open exposure, low
28 roughness, site typical for an airport meteorological station. The 1ST data were representative of
29 a higher roughness exposure more typical of many locations within an urban area. Surface
30 roughness lengths were generally about an order of magnitude higher at the 1ST site relative to
31 the ATL site. A comparison of AERMOD modeled concentrations for the mobile source NOX
March 2009 223 Draft - Do Not Quote or Cite
-------
1 inventory, representing near ground-level emissions, showed relatively good agreement in
2 modeled concentrations based on the two sets of meteorological inputs, at least for the peak of
3 the concentration distribution at four monitor locations across the modeling domain. This
4 suggests that the sensitivity of AERMOD model results to variations in surface roughness may
5 be less significant than commonly believed, provided that meteorological data inputs are
6 processed with surface characteristics appropriate for the meteorological site.
7 8.11.1.3 Area source temporal and spatial emission profiles
8 Details regarding the modeling of non-point and background area sources in AERMOD
9 are addressed in Section 8.4.3. In the case of SO2, the area source emissions category for
10 AERMOD represents a cumulative approximation of several small point sources, such as small
11 commercial/industrial boilers, which are too small to be represented as individual sources within
12 the existing emissions inventories. Given the lack of detailed information regarding the location
13 and release characteristics of these small emission sources, estimated emissions are typically
14 aggregated at a county level within the emission inventories. Given these limitations in terms the
15 emission inventory, two of the main uncertainties associated with modeling these sources are the
16 temporal and spatial profiles used in simulating their releases. Lacking detailed location
17 information, the emissions are assumed to be uniformly distributed across a specified area,
18 typically at a county or census tract level since the emissions are aggregated at the county level
19 and allocated spatially using population as one of the surrogates. An additional uncertainty
20 associated with the area source category for SO2 emissions is the likelihood that the actual
21 emissions may be associated with buoyancy that cannot be explicitly treated the area source
22 algorithm within the dispersion model. At best, the anticipated effect of plume buoyancy can be
23 reflected on average through the release height assigned to the area source.
24 As discussed in Section 8.4.3, all emissions in the regions of interest were simulated,
25 either through their representative group (point sources, port-related sources, or other non-point
26 area sources) or through cumulative background sources. Staff obtained emission strengths from
27 the 2002 National Emissions Inventory (NET) however, only annual total emissions at the county
28 level are provided. To better parameterize these emissions for the hourly, census block-level
29 dispersion modeling conducted here, we relied on additional data and an algorithm to optimize
30 model performance based on model-to-monitor comparisons.
March 2009 224 Draft - Do Not Quote or Cite
-------
1 Additional data on the spatial distribution of non-point emissions was used to allocate
2 county-wide to census tract values for the Greene County domain. Staff used the spatial
3 allocation factors (SAFs) from EPA's EMS-HAP database. Emissions within each modeled tract
4 were simulated as uniform over the tract, while emissions outside the modeled tracts and other
5 residual emissions were characterized as uniform over an entire county. The performance
6 obtained by using tract-level sources in Greene County was verified by model-monitor
7 comparisons. In the St. Louis area, model performance evaluations using factors from the EMS-
8 HAP database made it apparent that some factors were mischaracterizing the allocations. Thus,
9 in the St. Louis area, spatial bias was avoided by modeling emissions with a uniform density
10 throughout each of the counties of interest. In both cases, using spatially uniform emissions
11 resolved to the finest level the input data allows eliminates spatial bias and reduces overall
12 uncertainty.
13 Unlike point sources, where the temporal profile was based largely on direct observations
14 via the CAMD database, these non-point emission profiles are based on generalized emissions
15 surrogates and may not well represent specific source or local group of sources. Model
16 performance evaluations of diurnal profiles showed that temporal factors based on these models
17 inadequately represented the true, aggregate, temporal release profile. Unlike spatial allocations,
18 however, uniformly distributing the emissions in time resulted in significantly worse model
19 performance than using these sample profiles. In order to account for these uncertainties in the
20 temporal profiles of area source emissions, an algorithm was developed to determine the optimal
21 temporal emission release profile in each area. Examination of the diurnal profiles of modeled
22 and monitored concentrations with uniform and with EMS-HAP emission profiles for monitors
23 in locations dominated by area sources showed that, while monitored concentrations increased
24 during the daytime, modeled concentrations actually decreased. An examination of the
25 dispersion characteristics showed that increased dilution during the daytime overcame the small
26 increase in emission strength predicted using emissions models such as EMS-HAP that lack
27 locally specific information. Thus, it is reasonable to conclude that emissions in the St. Louis
28 and Greene County areas should show a more pronounced diurnal cycle than is reflected in the
29 standardized temporal profiles.
30 To determine the most representative average non-point area source emission profile
31 across each modeling domain, we first selected monitors where ambient concentrations were
March 2009 225 Draft - Do Not Quote or Cite
-------
1 expected to be primarily influenced by area sources. Due to their locations relative to sources,
2 all but one monitor (290770032) in Greene County indicated ambient concentrations were
3 primarily influenced by point source emissions. In St. Louis, all seven ambient monitors
4 (291890004, 291890006, 291893001, 291895001, 291897003, 295100007, and 295100086)
5 indicated significant influence from area source emissions. Next, simulations were conducted
6 with all sources modeled in detail - except area sources, which were modeled with uniform
7 emission profiles. A weighting function was then determined based on the modeled error for
8 each hour of the day at the one Greene County monitor and as an average of the errors at the
9 seven individual St. Louis area monitors. In both cases, the error function was defined as the
10 ratio of the total observed concentration, minus the total concentration due to all non-point
11 sources, to the concentration predicted by the non-point sources alone. This diurnal error
12 function was then normalized such that its average value is unity. Finally, a corrected non-point
13 emission profile was determined by combining this normalized weighting function with the
14 uniform emission profile.
15 This method of determining an appropriate, local, non-point source emission profile has
16 the advantage of preserving total emissions reflected in the emission inventory while deducing
17 what the actual temporal emission profile from these local sources should be, based on the
18 observed trends in each region. Essentially, it derives an emission profile that best agrees with
19 observations when coupled with local meteorology and dispersion. This is justified given the
20 lack of detail regarding emission characteristics of local area sources. Because there is large
21 uncertainty in the emission characteristics in the sources being modeled, this approach
22 effectively mitigates the effect of that uncertainty on the modeling results by application of a
23 systematic approach to minimize discrepancies between predicted and observed values.
24 8.11.2 Exposure Modeling Uncertainties
25 8.11.2.1 Population Data Base
26 The population and commuting data are drawn from U.S. Census data from the year
27 2000. This is a high quality data source for nationwide population data in the U.S. however, the
28 data do have limitations. The Census used random sampling techniques instead of attempting to
29 reach all households in the U.S., as it has in the past. While the sampling techniques are well
30 established and trusted, they may introduce uncertainty to exposure results. The Census has a
31 quality section (http://www.census.gov/quality/) that discusses these and other issues with
March 2009 226 Draft - Do Not Quote or Cite
-------
1 Census data. It is likely the bias within this data would not affect the results in any particular
2 direction, and given the use of the sampled demographics to represent the simulated population,
3 it is expected that the uncertainty in the exposure results from this source is low.
4 8.11.2.2 Commuting Data Base
5 Commuting pattern data were derived from the 2000 U.S. Census. The commuting data
6 address only home-to-work travel. A few simplifying assumptions needed to be made to allow
7 for practical use of this data base to reflect a simulated individual's commute. First, there were a
8 few commuter identifications that necessitated a restriction of their movement from a home
9 block to a work block. This is not to suggest that they never travelled on roads, only that their
10 home and work blocks were the same. This includes the population not employed outside the
11 home, individuals indicated as commuting within their home block, and individuals that
12 commute over 120 km a day. This could lead to either over- or under-estimations in exposure if
13 they were in fact to visit a block with either higher or lower SC>2 concentrations. Given that the
14 number of individuals who meet these conditions is likely a small fraction of the total population
15 and that the bias is likely in either direction, the overall uncertainty is considered low.
16 Second, although several of the APEX microenvironments account for time spent in
17 travel, the travel is assumed to always occur in basically a composite of the home and work
18 block. No other provision is made for the possibility of passing through other census blocks
19 during travel. This could also contribute to bias in either direction, dependent on the number of
20 blocks the simulated individual would actually traverse and the spatial variability of the
21 concentration across different blocks. This could potentially affect a large portion of the
22 population, since we expect that at the block level, many persons would have a commute transect
23 that included more than two blocks, although the actual number of persons and the number of
24 blocks per commute and the spatial variability across blocks has not been quantified. In
25 addition, the commuting route (i.e., which roads individuals are traveling on during the
26 commute) is not accounted for. This may bias the exposure results in either direction, with some
27 individual under-estimated and others over-estimated.
28 Furthermore, the estimation of block-to-block commuter flows relied on the assumption
29 that the frequency of commuting to a workplace block within a tract is proportional to the
30 amount of commercial and industrial land in the block. This assumption may introduce a bias in
31 overestimating exposures if 1) the blocks with greater commercial/industrial land density also
March 2009 227 Draft - Do Not Quote or Cite
-------
1 have greater concentrations when compared with lower density commercial/industrial density
2 blocks, and 2) most persons commute to lower commercial/industrial density blocks. It should
3 also be noted that recent surveys, notably the National Household Transportation Survey
4 (NHTS), have found that most trips taken and most VMT accrued by households are non-work
5 trips, particularly social/recreational and shopping-related travel (Hu and Reuscher, 2004). This
6 constitutes an unquantified source of uncertainty that is not be addressed by the Census
7 commuter dataset. However, because most benchmark exceedances occur outdoors the overall
8 impact to uncertainty is likely low.
9 8.11.2.3 Human Time-Location-Activity Pattern Data
10 The CHAD time-location activity diaries used are the most comprehensive source of such
11 data and realistically represent where individuals are located and what they are doing. The
12 diaries are sequential records of each persons activities performed and microenvironments
13 visited. There are however, uncertainties in the exposure results as a result of the CHAD diaries
14 used for simulating individuals. First, much of the data used to generate the daily diaries were
15 collected in surveys conducted over 20 years ago. While the trends in people's daily activities
16 may not have changed much over the years, it is certainly possible that some differences do exist
17 such as the amount of time spent outdoors, time spent performing activities at a particular level
18 of exertion, and the microenvironments where moderate or greater exertion is likely to occur. It
19 would be extremely difficult to determine real differences in the distribution of these factors that
20 may influence 862 exposure. Much of the data available to test such differences is survey-based.
21 The survey methods used to collect data twenty years ago are not consistent with survey methods
22 used today. If there are observed differences, it is likely an affect of the survey methods rather
23 than changes in population activities.
24 Second, the CHAD data are taken from numerous surveys that were performed for
25 different purposes. Some of these surveys collected only a single diary-day while others went on
26 for several days. Some of the studies were designed to not be representative of the U.S.
27 population, although a large portion of the data is from National surveys. In addition, study
28 collection periods occur at different times of the year, possibly resulting in seasonal differences.
29 This could add uncertainty to the results if there are characteristics of the survey population that
30 are distinct from the simulated population.
March 2009 228 Draft - Do Not Quote or Cite
-------
1 The CHAD diaries that are selected from APEX to represent the Greene County and St.
2 Louis population are not all from these cities, the State of Missouri, or from the Midwest, albeit
3 some of the diaries may be. As stated above, most of the diaries are from National surveys,
4 therefore there are diaries from locations other than Greene County and St. Louis that are used to
5 simulate the modeled population. A few of the limitations associated with the use of diaries
6 from different locations or seasons are corrected by the approaches used in the exposure
7 modeling. For example, diaries used are weighted by population demographics (i.e., age and
8 gender) for a particular location and temperature is used as a classification variable to account
9 for its affect on human activities.
10 A sensitivity analysis was recently performed to evaluate the affect of using different
11 CHAD studies has on APEX results for the recent Oj NAAQS review (see Langstaff (2007) and
12 EPA (2007d)). Briefly, O3 exposure results were generated using APEX with all of the CHAD
13 diaries and compared with results generated from running APEX using only the CHAD diaries
14 from the National Human Activity Pattern Study (NHAPS), a nationally representative study in
15 CHAD. There was agreement between the APEX exposure results for the 12 metropolitan areas
16 evaluated (one of which was St. Louis), whether all of CHAD or only the NHAPS component of
17 CHAD is used. The absolute difference in percent of persons above a particular concentration
18 level ranged from -1% to about 4%, indicating that the exposure model results are not being
19 overly influenced by any single study in CHAD. It is likely that similar results would be
20 obtained here for SO2 exposures, although it remains uncertain due to different averaging times
21 (5-minute vs. 8-hour average). This is not to suggest that the uncertainty is low in using all of
22 the CHAD data to represent the Greene County and St. Louis areas, but that similar results would
23 be obtained in using the diaries available, so long as the population was appropriately stratified
24 and certain characteristics influencing exposure were considered.
25 In addition, due to limitations in the data summaries output from the current version of
26 APEX, certain exposure data could only be output for the entire population modeled (i.e., all
27 persons - includes asthmatics and healthy persons of all ages) rather than the particular
28 subpopulation. The exposure results for time spent in microenvironments at or above a potential
29 health effect benchmark level was estimated from the total persons simulated (not just
30 asthmatics) and is assumed by staff to be representative of the asthmatic population in the
31 modeling results. This is a reasonable modeling assumption because the asthmatic population
March 2009 229 Draft - Do Not Quote or Cite
-------
1 does not have its microenvironmental concentrations and activities estimated any differently
2 from those of the total population. There is however uncertainty in the use of all CHAD diaries
3 in simulating all individuals without considering the health status of the surveyed population and
4 the simulated population if health status affects an important element of how persons are
5 exposed. In this assessment it was shown that the most important microenvironments for
6 contacting the 5-minute peak concentration were those that were outdoors. Therefore, if there is
7 a difference in the time spent outdoors (e.g., total time, time-of-day) between asthmatics and
8 healthy individuals, there may be a greater uncertainty in the estimated number of asthamtics
9 exposed than if there were no difference.
10 This assumption of modeling asthmatics similarly to healthy individuals (i.e., using the
11 same time-location-activity profiles) is supported by the findings of van Gent et al. (2007), at
12 least when considering children 7-10 years in age. These researchers used three different
13 activity-level measurement techniques; an accelerometer recording 1-minute time intervals, a
14 written diary considering 15-minute time blocks, and a categorical scale of activity level. Based
15 on analysis of 5-days of monitoring, van Gent et al. (2007) showed no difference in the activity
16 data collection methods used as well as no difference between asthmatic children and healthy
17 children when comparing their activity levels. Activity level is directly correlated with time
18 spent outdoors.
19 There is also the possibility that information regarding bad air quality may affect the
20 activities performed by the asthmatic population. There has been some research regarding
21 significant "averting behavior" i.e., there is a reduction in time spent outdoors when the
22 individual is informed of the potential for bad air quality days (e.g., Bresnahan, et al. 1997;
23 Mansfield, 2005), though one study reviewed by staff reported no effect on outdoor time (e.g.,
24 Yen et. al. 2004). Of the limited studies reviewed by staff, most were focused on the population
25 response to ozone (or smog) air pollution alerts. The strength of the relationship between ozone
26 air quality and the occurrence of short-term 862 pollution events modeled here is not known at
27 this time. In addition, being informed of the potential air pollution event was an important factor
28 in whether there was a reported reduction in outdoor time for either asthmatic children or non-
29 asthmatic children (Mansfield et al., 2005). Parents of asthmatic children checked air quality
30 alerts more frequently than parents of non-asthmatic children and, though reported as statistically
31 significant, only about 25% of parents of asthmatics checked the air quality on a daily basis
March 2009 230 Draft - Do Not Quote or Cite
-------
1 (Mansfield et. al., 2005). Therefore, if there is averting behavior in response to air pollution
2 events, it is largely uncertain the degree to which an asthmatics 862 exposure would be altered.
3 It is likely that there would be a reduction in the estimated number of asthmatics exposed if there
4 were a strong relationship between an ozone air pollution event and an SC>2 air pollution event
5 and the frequency of averting behavior was accounted for by APEX, though the reduction is
6 likely to be small given the apparently limited degree of awareness.
7 8.11.2.4 Longitudinal Profile
8 APEX creates seasonal or annual sequences of daily activities for a simulated individual
9 by sampling human activity data from more than one subject. Each simulated person essentially
10 becomes a composite of several actual people in the underlying activity data. Certain aspects of
11 the personal profiles are held constant, though in reality they change as an individual ages. This
12 is only important for simulations with long timeframes, particularly when simulating young
13 children (e.g., over a year or more).
14 The cluster algorithm used in constructing longitudinal profiles was evaluated against a
15 sequence of available multiday diaries sets collected as part of the Harvard Southern California
16 Chronic Ozone Exposure Study (Xue et al. 2005, Geyh et al. 2000). Briefly, the activity pattern
17 records were characterized according to time spent in each of 5 aggregate microenvironments:
18 indoors-home, indoors-school, indoors-other, outdoors, and in-transit. The predicted value for
19 each stratum was compared to the value for the corresponding stratum in the actual diary data
20 using a mean normalized bias statistic. See Appendix B, Attachment 3 and 4 for details. The
21 evaluation indicated the cluster algorithm can replicate the observed sequential diary data, with
22 some exceptions. The predicted time-in-microenvironment averages matched well with the
23 observed values. For combinations of microenvironment/age/gender/season, the normalized bias
24 ranges from -35% to +41%. Sixty percent of the predicted averages have bias between -9% and
25 +9%, and the mean bias across any microenvironment ranges from -9% to +4%. Although, on
26 occasion there were large differences in replicating variance across persons and within-person
27 variance subsets, about two-thirds of the predictions for each case were within 30% of the
28 observed time spent in each microenvironment.
29 The longitudinal approach used in the exposure assessment was an intermediate between
30 random selection of diaries (a new diary used for every day for each person in the year) and
31 perfect correlation (same diary used for every day for each person in the year). The cluster
March 2009 231 Draft - Do Not Quote or Cite
-------
1 algorithm used here was previously compared with two other algorithms, one that used random
2 sampling and the other employing diversity (D) and autocorrelation (A) statistics (see EPA,
3 2007g for details on this latter algorithm). The number of persons with at least one or more
4 exposure to a given Os concentration was about 30% less when using the cluster algorithm than
5 when using random sampling, while the number of multiple exposures for those persons exposed
6 was greater using the cluster algorithm (by about 50%). The algorithm employing the D and A
1 statistics exhibited similar patterns, although were lower in magnitude when compared with
8 random sampling (about 5% fewer persons with one or more exposures, about 15% greater
9 multiple exposures). These exposure results using the cluster algorithm in APEX appeared to be
10 the result of a greater correlation of diaries selected in comparison with the other two algorithms.
11 This outcome conforms to an expectation of correlation between the daily activities of
12 individuals. While the evaluation was performed using 8-hour Os as the exposure output it is
13 expected that similar results would be obtained for 5-minute SC>2 exposures. That is, the
14 characteristics of the diaries that contribute greatly to any pollutant exposure above a given
15 threshold (e.g., time spent outdoors, vehicle driving time, time spent indoors) are likely a strong
16 component in developing each longitudinal profile. Given these results and that the REA is not
17 necessarily focused on health effects resulting from multi-day exposures, the particular
18 longitudinal approach used likely contributes minimally to uncertainty. See Appendix B,
19 Attachments 3 and 4 for further details in the cluster algorithm and the evaluations performed.
20 8.11.2.5 Meteorological Data
21 Meteorological data are taken directly from monitoring stations in the assessment areas.
22 It is assumed that most of the data used are error free and have undergone required quality
23 assurance review. One strength of these data is that it is relatively easy to see significant errors if
24 they appear in the data. Because general climactic conditions are known for the simulated area,
25 it would have been apparent upon review if there were outliers in the dataset, and at this time
26 none were identified. If there were a bias in the data, it would be expected to be limited in
27 extend and randomly occurring, therefore contributing to both under and over-estimations
28 equally to a marginal degree. To reduce the number of calms and missing winds in the 1-hour
29 MET data, archived one-minute winds for the ASOS stations in each model domain were used to
30 calculate hourly average wind speed and directions. This approach reduces the number of
31 estimated zero concentrations that would be output by AERMOD if not supplemented by the
March 2009 232 Draft - Do Not Quote or Cite
-------
1 additional wind data, thus preventing a downward bias in the predicted 1-hour 862
2 concentrations.
3 There are limitations in the use of the meteorological data in APEX. APEX only uses
4 one temperature value per day in selecting an appropriate CHAD diary and indoor
5 microenvironment air exchange rate. Because the model does not represent hour-to-hour
6 variations in meteorological conditions throughout the day, there may be uncertainty in some of
7 the exposure estimates for indoor microenvironments (see the next section).
8 8.11.2.6 Air Exchange Rates (AER)
9 The residential air exchange rate (AER) distributions used to estimate indoor exposures
10 contribute to uncertainty in the exposure results. Three components of the AER analyzed
11 previously by EPA (2007d) include 1) the extrapolation of air exchange rate distributions
12 between-CMSAs, 2) analysis of within-CMSA uncertainty due to sampling variation, and 3) the
13 uncertainty associated with estimating daily AER distributions from AER measurements with
14 different averaging times. The results of those previous investigations are briefly summarized
15 here. See Appendix B, Attachments 6 and 7 for details in the data used to generate the AER and
16 the uncertainty analyses performed.
17 Extrapolation of AER among locations
18 Air exchange rate (AER) distributions were assigned in the APEX model, as described in
19 the indoors-residential microenvironment. Because location-specific AER data for St. Louis and
20 Greene County were not available and that there were no AER data from cities thought to have
21 similar influential characteristics affecting AER52, staff constructed an aggregate distribution of
22 the available AER data from cities outside California to represent the distribution of AERs in St.
23 Louis and Greene County (see Appendix B, Attachment 6).
24 In the absence of location-specific data for the microenvironments modeled by APEX
25 within each model domain, only limited uncertainty analyses were performed. To assess the
26 uncertainty associated with deriving AERs from one city and applying those to another city,
27 between-location uncertainty was evaluated by examining the variation of the geometric means
28 and standard deviations across several cities and originating from several different studies. The
29 evaluation showed a relatively wide variation across different cities in their AER geometric
52 Such potential influential factors would include age, composition of housing stock, construction methods used,
and other meteorological variables not explicitly treated in the analysis, such as humidity and wind speed patterns.
March 2009 233 Draft - Do Not Quote or Cite
-------
1 means and standard deviations, stratified by air-conditioning status, and temperature range. For
2 example, Figure 8-20 Illustrates the GM and GSD of AERs estimated for several cities in the
3 U.S. where A/C was present and within the temperature range of 20-25 °C. The wide range in
4 GM and GSD pairs implies that the modeling results may be very different if the matching of
5 modeled location to a particular study location was changed. For example, the SC>2 exposure
6 estimates may be sensitive to use of an alternative distribution, say those in New York City,
7 compared with results generated using the aggregate non-California AER distributions. It is
8 possible though that the true distribution could be more similar to the selected distribution from
9 all non-California cities than that of the specific locations given the population of available AER
10 data. It is unclear as to the direction of bias given the limited number of data available for
11 comparison. It is possible that the impact to the number of exceedances is low, given that most
12 of the exceedances occurred outdoors for most of the air quality scenarios evaluated.
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
4.0
3.5
3.0
F
5 ? n
CD ^-u
o
G
D
B E
0.5
I
1.5
Geometric Mean
2.0
2.5
3.0
13
14
15
16
17
AAAHouston BBBLosAngdes C C CLosAngeles-Avol DDDLosAngdes-RTOPA
EEELosAngeles-Wilsonl984 F F FNewYorkCity GGGNewYorkCity-RlOPA HHHNewYorkCity-TEACH
1 1 iRedBluff J J JResearchTrianglePark
Figure 8-20. Example comparison of estimated geometric mean and geometric standard
deviations of AER (h~1) for homes with air conditioning in several cities.
March 2009
234
Draft - Do Not Quote or Cite
-------
1 Within location uncertainty
2 There is also variation in AERs within studies for the same location (e.g., Outside
3 California data), but this is much smaller than the observed variation across different CMSAs.
4 This finding tends to support the approach of combining different studies for a CMS A, where
5 data were available. The within-city uncertainty was assessed by using a bootstrap distribution
6 to estimate the effects of sampling variation on the fitted geometric means and standard
7 deviations for the non-California data used to represent the St. Louis and Greene County AERs.
8 These bootstrap distributions assess the uncertainty due to random sampling variation. They do
9 not address other uncertainties such as the lack of representativeness of the available study data
10 or the variation in the lengths of the AER monitoring periods. Because only the GM and GSD
11 were used, the bootstrap analyses does not account for uncertainties about the true distributional
12 shape, which may not necessarily be lognormal.
13 One-thousand bootstrap samples were randomly generated for each AER subset (of size
14 N), producing a set of 1,000 geometric mean (GM) and geometric standard deviation (GSD)
15 pairs. The analysis of the non-California city data used to represent Greene County and St. Louis
16 indicated that the GSD uncertainty for a given AER temperature group tended to have a range
17 within ą0.3 fitted GSD (hr"1), with smaller intervals surrounding the GM (i.e, about ą0.10 fitted
18 GM (hr"1) (Figure 8-21). Broader ranges were generated from the bootstrap simulation for AER
19 distributions used for Greene County and St. Louis homes without A/C (Figure 8-22), although
20 both still within ą0.5 of the fitted GM and GSD values. See Appendix B, Attachment 6 for
21 further details.
March 2009 23 5 Draft - Do Not Quote or Cite
-------
Geometric mean and standard deviation of air exchange rate
1 bootstrapped distributions for different cities
City: Outside California
Air Conditioner Type: Central or Room A/'C
Temperature Range: 20-25 Degrees Celsius
4.0
3.5
1 3.0-
.g 2.5
Ģ 2.0
C
1.5
1.0
|
1
'$'
1 1 1 1 1 1 1
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data ++~Original Data
2 Figure 8-21. Example of boot strap simulation results used in evaluating random sampling
3 variation of AER (h-1) distributions (data from cities outside California).
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Outside California
Air Conditioner Type: No A/C
Temperature Range: >20 Degrees Celsius
4.0
3.5
I 3.0-
0.5
\^
1.0
n
1.5
Geometric Mean
T^
2.0
^^
2.5
6
7
Boolsliappecl Dala II Original Dalti
Figure 8-22. Example of boot strap simulation results used in evaluating random
sampling variation of AER (h-1) distributions (data from cities outside California).
March 2009
236
Draft - Do Not Quote or Cite
-------
1 Variation in AER measurement averaging times
2 Although the averaging periods for the air exchange rates in the study data varied from
3 one day to seven days, the analyses did not take the measurement duration into account and
4 treated the data as if they were a set of statistically independent daily averages. To investigate
5 the uncertainty of this assumption, correlations between consecutive 24-hour air exchange rates
6 measured at the same house were investigated using data from the Research Triangle Park Panel
7 Study (Appendix B, Attachment 7). The results showed extremely strong correlations, providing
8 support for the simplified approach of treating multi-day averaging periods as if they were 24-
9 hour averages.
10 8.11.2.7 Air Conditioning Prevalence
11 Because the selection of an air exchange rate distribution is conditioned on the presence
12 or absence of an air-conditioner, the air conditioning status of the residential microenvironment
13 was simulated randomly using the probability that a residence has an air conditioner, i.e., the
14 residential air conditioner prevalence rate. For this study we used location-specific data for St.
15 Louis (AHS, 2003a; 2003b) and applied that data to Greene County as well. EPA (2007d)
16 details the specification of uncertainty estimates in the form of confidence intervals for the air
17 conditioner prevalence rate, and compares these with prevalence rates and confidence intervals
18 developed from the Residential Energy Consumption Survey (RECS) of 2001 for several
19 aggregate geographic subdivision (e.g., states, multi-state Census divisions and regions) (EIA,
20 2001).
21 Briefly, Air conditioning prevalence rates were 95.5% for St. Louis, with reported
22 standard errors of 1.7% (AHS, 2003a; 2003b). Estimated 95% confidence intervals were also
23 small and span approximately 6.5 percentage points (AHS, 2003a; 2003b). The RECS
24 prevalence estimate for Census Divisions was 92% (ranging between 86.4% and 98.4%), while
25 the Census Region prevalence estimate was 83.6% (ranging between 80.0% and 87.2%). This
26 suggests that the air conditioning prevalence used, while likely being representative of a city in
27 Missouri, may be overestimated for non-urban locations. The magnitude of uncertainty
28 associated with the estimated A/C prevalence and the impact to estimated exposures is likely
29 low.
March 2009 237 Draft - Do Not Quote or Cite
-------
1 8.11.2.8 Indoor Decay
2 There may be uncertainty added to the exposure results when considering the estimated
3 parameters, the form (i.e., lognormal) and limits (limited by the bounds of the measurement data)
4 of the distribution used to represent indoor decay. The data used to develop the distribution were
5 obtained from a review of several studies that analyzed SC>2 removal for a variety of building
6 material surfaces (Grontoft and Raychaudhuri, 2004). Potential influential factors such as
7 humidity and air exchange rate were accounted for in developing and applying the removal
8 distributions within the indoor microenvironments. The distributions were based on a large
9 empirical data base and likely well represent expected 862 removal within indoor
10 microenvironments.
11 However, several assumptions were made to characterize the likely materials used within
12 a simulated indoor microenvironment, some of which were data-based, others in the absence of
13 supporting data, were based solely on professional judgment. Staff performed a Monte Carlo
14 simulation using the removal data and 1,000 simulated indoor rooms of buildings to effectively
15 generate a distribution of SC>2 removal rates, weighted by the approximated room configurations
16 and proportion of materials present. There are many assumptions staff made that could be
17 modified with newly available data, particularly where inputs were based on professional
18 judgment. It is largely unknown what the current direction of bias is in the absence of new or
19 refined input data. While some of the assumptions used may add to uncertainty, the magnitude
20 of the uncertainty is likely low given the relative contribution of the indoor microenvironments
21 to exposure concentrations above the potential health effect benchmark levels.
22 8.11.2.9 Occurrence of Multiple Exceedances Within an Hour
23 The statistical model described in section 7.2 was used within APEX to estimate a single
24 5-minute maximum SC>2 concentration for every hour. However, multiple short-term peak
25 concentrations above selected levels are possible within any hour. Analysis of the 5-minute
26 continuous monitoring data indicates that multiple occurrences of 5-minute concentrations above
27 the 100, 200, 300, and 400 ppb within the same hour can be common. Using the continuous
28 monitoring data obtained from years 1997-2007, multiple peak concentrations (i.e., 2 or more) at
29 or above 400 ppb within the same hour occurred with a 61% frequency (Table 8-17). The
30 frequency of multiple exceedances was similar for the lower 5-minute SC>2 concentration levels,
31 where 63, 56, and 53% of the time there were two or more exceedances within the same hour at
March 2009 23 8 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
the 100, 200, and 300 ppb benchmark levels, respectively. These results may suggest that a
single peak approach for estimating the 5-minute daily maximum 862 concentrations alone as a
surrogate for all possible peak exposure events may lead to an underestimate in the number of
potential exposures.
Table 8-17. Number of multiple exceedances of potential health effect benchmark
levels within an hour.
Number of Exceedances of
5-minute SO2 in 1-hour1
1
2
3
4
5
6
7
8
9
10
11
12
Total
Number of Hours with Multiple 5-minute SO2
> 100 ppb
1248
658
411
257
242
153
125
89
64
49
50
73
3419
> 200 ppb
267
122
78
35
28
25
14
11
6
6
3
5
600
> 300 ppb
76
31
21
10
6
4
5
2
3
1
0
1
160
> 400 ppb
26
20
7
5
4
1
1
1
1
1
0
0
67
Notes:
1 The analysis is based on the 16 monitors reporting all 5-minute SO2 concentrations in an
hour (n=3, 328,725).
In using the data in Table 8-17 alone, the potential underestimation bias may be
somewhat overstated however, particularly when considering the benchmark levels of 200, 300,
and 400 ppb. A detailed analysis of the multiple exceedances by monitor indicated that one of
the monitors (ID 420070005) was highly influential in generating the values in Table 8-17,
contributing greatly to the multiple peak occurrences at the higher benchmark levels. This
Beaver, PA urban-scale monitor is identified as population-based, within a rural setting, and
having agricultural land use (Appendix A). Five of eight of the sources located within 20 km of
this monitor had SC>2 emissions <250 tpy, one smelter emitting about 7,000 tpy was within 2.5
km, and two power generating facilities located approximately 3.4 and 7.5 km from the monitor
had SC>2 emissions of 3,000 and 30,000 tpy, respectively. Of the number of hours having
multiple exceedances, monitor 420070005 contributed to 61, 73, and 80% of the hours with
multiple peaks >200, >300, and >400 ppb, respectively. Following removal of this monitor from
March 2009
239
Draft - Do Not Quote or Cite
-------
1 the full data set, the occurrence of multiple exceedances of each the 200, 300, and 400 ppb
2 benchmark lowered to approximately 40% of all hours having co-occuring peaks.
3 This suggests there may be added uncertainty in the exposure results if the continuous
4 monitoring data were used to design an approach for estimating multiple exceedances within an
5 hour. These data were only from 16 ambient monitors, each having a limited number of
6 monitoring years. The analyses above indicated that one of the monitors contributed to most of
7 the hours with multiple peak concentrations. How this one monitor (as well as any other monitor
8 having multiple exceedances) reflects what may occur at the APEX modeled receptors in St.
9 Louis and Greene County (or other different locations) is largely unknown. There is no simple
10 extrapolation possible using the continuous monitoring data because the time of the peak (and
11 hence multiple peak) concentrations modeled are not known with respect to the simulated
12 individuals' time spent outdoors.
13 The PMR statistical model is based on both concentration and variability measures,
14 implemented by APEX in estimating a single maximum 5-minute SC>2 concentration for every
15 hour at every receptor. This is based on known concentration and variability relationships
16 described in section 7.2. While APEX can model all twelve 5-minute concentrations, staff chose
17 to normalize the eleven remaining 5-minute concentrations within an hour to the 1-hour mean
18 concentration. This decision was based largely on the size of the air quality files used (thousands
19 of receptors across a year) that already required a time consuming post-processing step prior to
20 input in APEX and ultimately, the run time associated with the exposure model simulations.
21 Estimating the 5-minute maximum SC>2 concentrations and the other 11 concentrations within
22 APEX was more efficient than pre-processing all twelve 5-minute SC>2 concentrations.
23 There is bias in having all eleven other 5-minute SC>2 concentrations normalized to the
24 mean; the exposure simulation could miss a persons exposure that might have occurred if in fact
25 there are multiple peak concentrations within the same hour (a likely event given the continuous
26 monitoring data, roughly between 40-60%). The CHAD time-location-activity diaries used in
27 APEX are fixed, that is, the modeled time spent outdoors is based on the actual time (and
28 amount) recorded by the surveyed individual. APEX models exposure on a minute-by-minute
29 basis; if most persons spend time outdoors for a short time (e.g., 5-minutes), then it is possible
30 that persons are not realistically encountering peak concentrations given the normalization of the
March 2009 240 Draft - Do Not Quote or Cite
-------
1 eleven 5-minute 862 concentrations. Therefore, staff analyzed outdoor activities in the CHAD
2 diaries used by APEX to determine the duration of time spent outdoors for each outdoor event.
3 Figure 8-23 illustrates the distribution of time spent outdoors, given activity outdoor
4 events defined by clock-hour increments (already part of the CHAD design). Thirty-five percent
5 of all outdoor events are for the entire hour; if the event corresponds with the same hour as a
6 simulated peak concentration, there would be no underestimation bias associated with exposure
7 occurring during these events. Therefore, occurrence of multiple peaks within an hour is
8 potentially not an issue for 35% of the peak exposure events that occur outdoors. However, at
9 each of the other outdoor events, there is a probability of underestimating the exposure, given by
10 the duration of the event divided by 60 minutes. For example, approximately 15% of outdoor
11 events were 30 minutes. If these outdoor events occurred at the time where there was a second
12 estimated peak concentration in the same hour, there is a 50% chance that the exposure is
13 missed. The probability of missing a potential exposure increases with decreasing duration of
14 the outdoor event and, given the data in Figure 8-23, this could be a frequent occurrence (i.e.,
15 about 65% of outdoor events may have some probability of missing an exposure). This analysis
16 does not account for multiple outdoor events that may increase an individual's chance of a daily
17 maximum exposure exceedance, regardless of the event duration. It also assumes the each of the
18 outdoor events evaluated have an equal probability of occurring at the time of the peak
19 concentration, which may or may not be the case. In addition, the outdoor time distribution is
20 based on all of the CHAD diary days, potentially not the same distribution of diaries that were
21 used in the APEX exposure simulations.
22 A better method to determine the potential number of missing exposures is to model the
23 exposures using two input data sets: air quality with all continuous 5-minute measurements, and
24 air quality having the measured 5-minute maximum and the eleven other 5-minute
25 concentrations within the hour normalized to the 1-hour mean. Staff constructed a data set using
26 measurements from the continuous-5 ambient monitoring. While there were two monitors
27 reporting continuous 5-minute measurements in Greene County (monitor IDs 290770037 and
28 290770026), there were only two years with exceedances of the 200 ppb benchmark level, and
29 no exceedances of the 300 or 400 ppb benchmarks. To explore the maximum effect of multiple
30 peak concentrations within an hour, staff used two years of data from monitor ID 420070005,
March 2009 241 Draft - Do Not Quote or Cite
-------
1 noted above as having the greatest number of benchmark exceedances in a year (years 2002 and
2 2005 were selected).
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
t 15
5 3D 35
out doort i rre
Figure 8-23. Duration of time spent outdoors (in minutes) using all CHAD events
First, staff replaced missing concentrations (approximately 5% of each year) using the
time-of-day monthly averaged 862 concentration. This data set served as the multiple peak air
quality data set to be tested; all measured 5-minute concentrations were used as is. Next, staff
constructed a similar data set, only this second data set had the maximum measured 5-minute
concentration retained and all other eleven 5-minute concentrations within the hour were
normalized using the 1-hour mean. This single peak data set reflects what was being modeled by
APEX. Each of the data sets were used as the air quality input to APEX simulation, controlling
for all model sampling, the algorithms used, microenvironments modeled, and persons
simulated. The only difference in the two runs was the air quality input. Fifty thousand persons
were simulated using APEX, 13% of which were asthmatic children. Figure 8-24 illustrates the
percent of asthmatic children exposed to selected 5-minute maximum concentrations for each of
the two scenarios; a multiple peak scenario and a single maximum peak concentration, using two
site-years of continuous monitoring data with the greatest number of benchmark exceedances.
As expected, there are more asthmatic children exposed when considering the occurrence of
March 2009
242
Draft - Do Not Quote or Cite
-------
1 multiple peaks in an hour. The difference in the percent of asthmatic children exposed at each of
2 the benchmark levels is small, about 2-5 percentage points differ between the two simulations.
3 However, considering the percent difference in the numbers of persons exposed at most of the
4 benchmarks levels, the simulations using the single peak air quality method had between 20-35%
5 fewer persons exposed than the multiple peak simulation. Similar results were generated in
6 simulations using the site-year with the 2nd highest number of exceedances only the
7 underestimation using the single peak method was about 15-30% (Figure 8-25). Based on these
8 analyses, at most the estimated number of persons exposed in St. Louis and Greene County are
9 underestimated by 35% when using a single peak method. The actual amount of underestimation
10 is likely smaller given that these results were generated using site-years of monitoring data
11 having the greatest numbers of exceedances and contributing significantly to the high frequency
12 of multiple peak exceedances.
13 The location where exposures occur may also be influenced by the presence or absence of
14 multiple peak concentrations. In particular, the modeled indoor 5-minute maximum
15 concentrations may be markedly diluted if the indoor air exchange rate is low and all eleven
16 other 5-minute values within the same hour are normalized to the 1-hour mean concentration.
17 APEX estimates all microenvironmental concentrations using a mass balance method for 5-
18 minute time-steps (equation 8-2) that accounts for estimated microenvironmental concentrations
19 from the previous time-step (EPA, 2009b). While dilution of the indoor air is not an unusual
20 circumstance considering the physical process modeled, it is possible that the number of
21 exposure events from indoor sources is underestimated when the prior time-step concentration is
22 artificially reduced.
23 Staff evaluated the microenvironments where peak exposures occurred, by aggregating
24 the time 5-minute exposures occurred into three broad microenvironmental groups: indoors,
25 outdoors, and in-vehicles. A comparison of the APEX simulations using the two air quality
26 input simulations (i.e., multiple peak versus single peak, monitor 420070005 - year 2002) and
27 considering how often peak exposures occur indoors is presented in Figure 8-26. The
28 differences in the percent of indoor exposure exceedances are consistent with the design of the
29 model and the particular input data used. For exposures less than the 400 ppb level, a greater
30 percent of the overall exposures occur indoors using the single peak method than compared with
31 the multiple peak data set. For exposures at or above the 400 ppb level, a smaller percent of the
March 2009 243 Draft - Do Not Quote or Cite
-------
1 overall exposures occur indoors using the single peak method than compared with the multiple
2 peak data set. In fact, the multiple peak simulation had indoor peak exposures at levels not
3 observed using the single peak method. This is likely a function of the normalized
4 concentrations, that when used in the mass balance equation as the prior time-step
5 microenvironmental concentration, the microenvironmental concentration at time t is less than
6 what would be expected.
7 While this analysis and its findings are encouraging, context is needed to assign relevance
8 to the current exposure analyses in St. Louis and Greene County. As stated earlier, the data set
9 used had the greatest number of benchmark exceedances, designed by staff to observe the effect
10 that multiple peaks within the hour has on estimated exposures. The observed differences in the
11 contribution from the indoor microenvironment may be more appropriately applied in
12 discussions regarding air quality scenarios with high concentrations distributions (e.g., air quality
13 adjusted to just meeting the current standard, Figure 8-19). While the differences in the highest
14 benchmarks exceedances are likely of greatest interest when investigating the possibility of
15 missing exposure events, it should be noted that the greatest proportion of exposure events still
16 occur outdoors (in this simulation, >70% of exposures above 400 ppb occurred outdoors). In
17 addition, the differences observed at the lower benchmarks indicated the role of indoor exposures
18 was fairly similar. At most the difference was four percentage points, with the multiple peak
19 simulation having a consistently lower contribution of exceedances from indoor exposures.
March 2009 244 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
5-minute Max with Normalized 11
Multiple Peak Data Set
Percent Difference in Exposures
I
H
UJ Q.
W 6
8O)
C
O CO
Q. m
E i
81
I*
|*
5 S
#1
-40%
5-minute SO2 Exposure Level (ppb)
Figure 8-24. Percent of asthmatic children above given exposure level for two APEX simulations:
one using multiple peak concentrations in an hour, the other assuming a single peak
concentration. Continuous 5-minute monitoring data (ID 42007005, year 2002) were used as the
air quality input.
70%
5 10 60% - -
O g 50% 4-
| 1 40% +
o | 30% +
20% - -
10% 4-
0%
-- -5%
-- -10%
^5-minute Max with Normalized 11
CZI 5-minute Max with Measured 11
Percent Difference in Exposures
f] ^
i i n i ^~i
0%
K
^
Ģ
w i
o o
4- -15% Ģ .E
0) CO
Q. in
+ -20% - |
g|
-25% | i
5 E
s?l
-30%
-35%
888888888888888
5-minute SO2 Exposure Level (ppb)
Figure 8-25. Percent of asthmatic children above given exposure level for two APEX simulations:
one using multiple peak concentrations in an hour, the other assuming a single peak
concentration. Continuous 5-minute monitoring data (ID 42007005, year 2005) were
used as the air quality input.
March 2009
245
Draft - Do Not Quote or Cite
-------
1
2
ou -
(/)
8
a
c oc;
i/>
8
1 9n
X
LLI
Ŧ<| c
3
I/)
R
^ -in
5
S1
Ģ =;
1
Ģ
n
-i
-,
5-minute Max with Normalized 11
D Multiple Peak Data Set
1
I llrfl-n
o
o
o
o
CM
o
LO
CM
O
O
CO
O
LO
CO
O
O
o
LO
o
o
o
LO
LO
o
o
CD
o
LO
CD
o
O
o
LO
o
O
oo
SO2 Exposure Level (ppb)
4 Figure 8-26. Frequency of exposure exceedances indoors for two APEX simulations: one using
5 multiple peak concentrations in an hour, the other assuming a single peak
6 concentration. Continuous 5-minute monitoring data (ID 42007005, year 2002) were
7 used as the air quality input.
10 8.11.2.10 Asthma Prevalence Rate
11 The best estimate of asthma prevalence used in this analysis was generated using a
12 comprehensive and widely used data set (CDC, 2007). Staff judged that variability in the asthma
13 prevalence based on age was an important attribute to represent in simulating SC>2 exposures, one
14 of the principal reasons for selection of the particular data set. There are however limitations in
15 using the data that may add to uncertainty in the generated exposure results. The percent of
16 asthmatics simulated by APEX using a combined regional (children by age) and local (adults all
17 ages) prevalence was comparable with an independent estimate of the percent of asthmatics
18 within the four counties modeled (9.3% versus 8.8% of the population, respectively). Therefore,
19 the uncertainty in the overall total percent of asthmatics exposed is likely low, particularly in
20 Greene County. In Greene County, 9.8% of the simulated population was asthmatic and
21 compares well with the 10.2% asthma prevelance reported by MO DOH (2003). However, the
22 asthma prevalence across the three county domain in St. Louis was variable, with St. Louis City
23 County having a high estimated prevalence rate (16.4%) and St Louis County having a much
March 2009
246
Draft - Do Not Quote or Cite
-------
1 lower prevalence rate (5.8%). This variable distribution was not represented in the exposure
2 modeling simulation; all children and adults in each of the counties used the data summarized in
3 Table 8-6. Therefore in St. Louis County, the asthma prevalence may have been underestimated,
4 while in St. Louis County the asthma prevalence may have been overestimated. This may add to
5 uncertainly in the total number of asthmatics exposed in St. Louis (not the percent of asthmatics
6 exposed), though the direction of bias is largely unknown because individual county level
7 exposures are not output by the model.
March 2009 247 Draft - Do Not Quote or Cite
-------
i 9.0 HEALTH RISK ASSESSMENT FOR LUNG FUNCTION
2 RESPONSES IN ASTHMATICS ASSOCIATED WITH 5-MINUTE
3 PEAK EXPOSURES
4 9.1 INTRODUTION
5 In the previous review, it was clearly established that subjects with asthma are more
6 sensitive to the respiratory effects of 862 exposure than healthy individuals (ISA, section
7 3.1.3.2). As discussed above in section 4.2, asthmatics exposed to 862 concentrations as low as
8 200-300 ppb for 5-10 minutes during exercise have been shown to experience significant
9 bronchoconstriction, measured as an increase in sRaw (>100%) or decrease in FEVi (>15%)
10 after correction for exercise-induced responses in clean air. These studies exposed asthmatic
11 volunteers to SC>2 in the absence of other pollutants that often confound associations in the
12 epidemiological literature. Therefore, these controlled human exposure studies provide direct
13 evidence of a causal relationship between exposure to SC>2 and respiratory health effects. Staff
14 judges the controlled human exposure evidence presented in the ISA with respect to lung
15 function effects in exercising asthmatic subjects as providing an appropriate basis for conducting
16 a quantitative risk assessment for this health endpoint and exposure scenario.
17 A brief description of the approach used to conduct this health risk assessment is
18 presented below. More detailed discussion of the approach can be found in the risk assessment
19 technical support document, included as Appendix C to this document. The goals of this SC>2
20 risk assessment are: (1) to develop health risk estimates of the number and percent of the
21 asthmatic population that would experience moderate or greater lung function decrements in
22 response to 5-minute daily maximum peak exposures while engaged in moderate or greater
23 exertion for several air quality scenarios (described below); (2) to develop a better understanding
24 of the influence of various inputs and assumptions on the risk estimates; and (3) to gain insights
25 into the risk levels and patterns of risk reductions associated with meeting several alternative 1-
26 hour daily maximum SC>2 standards. Health risks have been estimated for the following three
27 scenarios: (1) recent ambient levels of SC>2, (2) air quality adjusted to simulate just meeting the
28 current 24-hour standard, and (3) air quality adjusted to simulate just meeting several alternative
29 1-hour standards.
March 2009 248 Draft - Do Not Quote or Cite
-------
1 As discussed in Chapter 8, the geographic scope of the assessment includes selected
2 locations encompassing a variety of 862 emission source types in two areas within the state of
3 Missouri (i.e., Greene County and St. Louis). These areas were identified based on the results of
4 a preliminary screening of the 5-minute ambient SC>2 monitoring data that were available. The
5 state of Missouri was one of only a few states having both 5-minute maximum and continuous 5-
6 minute SC>2 ambient monitoring, as well as having over 30 1-hour SC>2 monitors in operation at
7 some time during the period from 1997 to 2007. In addition, the air quality characterization,
8 described in Chapter 7, estimated frequent exceedances above the potential health effect
9 benchmark levels at several of the 1-hour ambient monitors. In a ranking of estimated SC>2
10 emissions reported in the National Emissions Inventory (NEI), Missouri ranked 7th for the
11 number of stacks with > 1000 tpy SOX emissions out of all U.S. states. These stack emissions
12 were associated with a variety of source types such as electrical power generating units, chemical
13 manufacturing, cement processing, and smelters. For all these reasons, the current SC>2 lung
14 function risk assessment focuses on Missouri and, within Missouri, on those areas within 20 km
15 of a major point source of SC>2 emissions in Greene County and the St. Louis area.
16 9.2 DEVELOPMENT OF APPROACH FOR 5-MINUTE LUNG FUNCTION
17 RISK ASSESSMENT
18 The lung function risk assessment is based on the health effects information evaluated in
19 the ISA and discussed above in Chapter 4. The basic structure of the risk assessment reflects the
20 fact that we have available controlled human exposure study data from several studies involving
21 volunteer asthmatic subjects who were exposed to SC>2 concentrations at specified exposure
22 levels while engaged in moderate or greater exertion for 5- or 10-minute exposures. As
23 discussed in the ISA (section 3.1.3.5), among asthmatics, both the magnitude of SO2-induced
24 lung function decrements and the percent of individuals affected have been shown to increase
25 with increasing 5- to 10-minute SC>2 exposures in the range of 200 to 1,000 ppb. Therefore, for
26 the SC>2 lung function risk assessment we have developed probabilistic exposure-response
27 relationships based on these data. The analysis was based on the combined data set consisting of
28 all available individual data that describe the relationship between a measure of personal
29 exposure to SC>2 and measures of lung function recorded in these studies. For the purposes of
30 this risk assessment, all of the individual data, including both 5- and 10-minute exposure
31 duration, were combined and treated as representing 5-minute responses. These probabilistic
March 2009 249 Draft - Do Not Quote or Cite
-------
1 exposure-response relationships were then combined with 5-minute daily maximum peak
2 exposure estimates for mild and moderate asthmatics engaged in moderate or greater exertion
3 associated with the various air quality scenarios mentioned above. A more detailed description
4 of the exposure assessment that was the source of the estimated daily maximum 5-minute peak
5 exposures under moderate or greater exertion is provided above in Chapter 8.
6 9.2.1 General Approach
7 The major components of the lung function health risk assessment are illustrated in
8 Figure 9-1. As shown in Figure 9-1, under the lung function risk assessment, exposure estimates
9 for mild and moderate asthmatics for a number of different air quality scenarios (i.e., recent year
10 of air quality, just meeting the current 24-hour standard, just meeting alternative standards) are
11 combined with probabilistic exposure-response relationships derived using a combined data base
12 consisting of data from several controlled human exposure studies to develop risk estimates. The
13 air quality and exposure analysis components that are integral to this risk assessment are
14 discussed in greater detail in Chapters 7 and 8 of this document and in the Exposure Assessment
15 TSD. Only the air quality and exposure aspects affecting the scope of the lung function risk
16 assessment are briefly discussed in section 9.2.2. A description of the overall approach to
17 estimating the exposure-response relationship is included in section 9.2.3 below.
18 Two types of risk measures were generated for the lung function risk assessment. The
19 first type included estimates of the number and percentage of all asthmatics (or asthmatic
20 children) experiencing one or more occurrences of a defined lung function response associated
21 with 5-minute exposures to SO2 while engaged in moderate or greater exertion under a given air
22 quality scenario. The second type of risk measure generated for each defined lung function
23 response is the number of occurrences of the lung function response in asthmatics (or asthmatic
24 children) in a year associated with 5-minute exposures under moderate or greater exertion under
25 a given air quality scenario. Since asthmatic school age children are a subset of all asthmatics,
26 the risk estimates presented for these two groups should not be combined.
27 To obtain risk estimates associated with 862 concentrations under different scenarios, we
28 estimated expected risk given the personal exposures associated with 862 concentrations under
29 each scenario - i.e., associated with
March 2009 250 Draft - Do Not Quote or Cite
-------
Air Quality
Ambient Modeling
for Selected Areas
Air Quality
Adjustment
Procedures
Recent
("As Is")
Ambient
SO2
Levels
Current and
Alternative
Proposed
Standards
Exposure
Exposure
Model
Exposure Estimates
Associated with:
Recent Air Quality
Current Standard
Alternative
Standards
Exposure-Response
Controlled Human
Exposure Studies
Probabilistic
Exposure -
Response
Relationships
Health
Risk
Model
Risk Estimates:
Recent Air
Quality
Current
Standard
Alternative
Standards
1
2
Figure 9-1. Major Components of 5-Minute Peak Lung Function Health Risk Assessment Based on Controlled Human Exposure Studies
March 2009
251
Draft Do Not Quote or Cite
-------
1 "as is" ambient 862 concentrations representing a recent year,
2 SC>2 air quality levels simulating just meeting the current 24-hour and annual standards,
3 and
4 SC>2 air quality levels simulating just meeting specified alternative 1-hour standards.
5 Note that, in contrast to the headcount risk estimates calculated for the 63 health risk
6 assessment, the headcount risk estimates calculated for the 862 health risk assessment do not
7 subtract out risk given the personal exposures associated with estimated policy-relevant
8 background ambient SO2 concentrations. This is because policy-relevant background SO2
9 concentrations are estimated to be at most 30 parts per trillion and they contribute less than 1%
10 to present day SC>2 ambient concentrations (ISA, section 2.4.6).
11 The first measure of risk (i.e., the number or percent of individuals in the designated
12 population to experience at least one lung function response in a year) is calculated as follows:
13 1) From the exposure modeling described in Chapter 8, we obtain the number of
14 individuals exposed at least once to x ppb SC>2 or higher, for x = 0, 50, 100, etc.;
15
16 2) We then calculate the number of individuals exposed at least once to 862
17 concentrations within each 862 exposure bin defined above;
18
19 3) We then multiply the number of individuals in each exposure bin by the response
20 probability corresponding to the midpoint of the exposure bin; and
21
22 4) We sum the results across all of the bins.
23
24 Because response probabilities are calculated for each of several percentiles of a
25 probabilistic exposure-response distribution, estimated numbers of individuals with at least one
26 SO2-related lung function response are similarly percentile-specific. For example, the kth
27 percentile number of individuals, Y^ associated with SC>2 concentrations under a given air quality
28 scenario is:
n
29 Yk=^NIJx(Rk e^ (equation 9-1)
30 where:
31 Cj = (the midpoint of) the jth category of personal exposure to SC>2, given "as is" ambient
32 SC>2 concentrations;
March 2009 252 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
M, = the number of individuals who highest exposure is to QJ ppb 862, given ambient
SC>2 concentrations under the specified air quality scenario.
RRk | ej = the kth percentile response rate at 862 concentration e/, and
n = the number of intervals (categories) of SO2 personal exposure concentration.
The kth percentile estimate of the total number responding is then calculated by multiplying the
kth percentile risk by the number of people in the relevant population. An example is given in
Table 9-1, for the median (i.e., 50th percentile) risk estimate using personal exposures associated
with a 99th percentile 100 ppb 1-hour daily maximum SC>2 standard for asthmatics in the St.
Louis modeling domain. We note that this calculation assumes that individuals who do not
respond at the highest 862 concentration to which they are exposed will not respond to any lower
SC>2 concentrations to which they are exposed.
Table 9-1. Example Calculation of the Number of Asthmatics in St. Louis
Engaged in Moderate or Greater Exertion Estimated to Experience At Least One
Lung Function Response (Defined as an Increase in sRaw > 100%) Associated
with Exposure to SO2 Concentrations Just Meeting a 99th percentile, 1-Hour 100
ppb Standard
SO2 Exposure Bin
Lower
Bound
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
Upper
Bound
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
Midpoint
(D
0.025
0.075
0.125
0.175
0.225
0.275
0.325
0.375
0.425
0.475
0.525
0.575
0.625
0.675
0.725
0.775
Number of
Asthmatics with
At Least One
Exposure in Bin
(2)
53711
34236
9835
3059
929
368
145
84
31
22
8
0
0
8
0
0
Probability of
Response at Midpoint
SO2 Level
(3)
0.00406
0.02334
0.05162
0.08563
0.12300
0.16220
0.20210
0.24190
0.28060
0.31830
0.35430
0.38850
0.42090
0.45150
0.46600
0.49380
Estimated Number of
Asthmatics Experiencing
at Least One Lung
Function Response
=(2) x (3)
218
799
508
262
114
60
29
20
9
7
3
0
0
4
0
0
Total: 102436 Total: 2032
14
15
16
17
March 2009
253
Draft - Do Not Quote or Cite
-------
1 The second type of risk measure, the number of occurrences of a defined lung function
2 response in the designated population (i.e., asthmatics or asthmatic children) in a year associated
3 with SO2 concentrations under a given air quality scenario is calculated as follows:
4 1) From the exposure modeling described in Chapter 8, we obtain the number of
5 exposure occurrences among the population at and above each benchmark level (i.e.,
6 0,ppb, 50 ppb, 100 ppb, etc.);
7
8 2) We then calculate the number of exposure occurrences within each 50 ppb exposure
9 "bin" (e.g., < 50 ppb, 50-100 ppb, etc.)53;
10
11 3) We then multiply the number of occurrences in each exposure bin by the response
12 probability corresponding to the midpoint of the exposure bin; and
13
14 4) We sum the results across all of the bins.
15
16 Similar to the first type of risk measure discussed above, because response probabilities
17 are calculated for each of several percentiles of a probabilistic exposure-response distribution,
18 estimated numbers of occurrences are similarly percentile-specific. The kth percentile number of
19 occurrences, Ok, associated with SC>2 concentrations under a given air quality scenario is:
20
21 Ok=^N].x(Rk\e].) (equation 9-2)
;=i
22
23 where:
24
25 Cj = (the midpoint of) the jth category of personal exposure to SO2;
26
27 NJ = the number of exposures to Cj ppb SO2, given ambient SO2 concentrations under the
28 specified air quality scenario;
29
30 Rk ej = the kth percentile response probability at SC>2 concentration e/, and
31
32 n = the number of intervals (categories) of SC>2 personal exposure concentration.
33
34 An example calculation is given in Table 9-2.
35
53 The final exposure bin was from 0.75 to 0.8 ppm SO2. In at least one of the alternative standard scenarios, there
were a few individuals whose exposure was greater than 0.8 ppm. For anyone whose exposure exceeded 0.8 ppm,
we assumed a final bin from 0.8 to 0.85 ppm, and assigned them the midpoint value of that bin, 0.825 ppm. This
will result in a slight downward bias in the estimate of risk.
March 2009 254 Draft - Do Not Quote or Cite
-------
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Table 9-2. Example Calculation of Number of Occurrences of Lung Function
Response (Defined as an Increase in sRaw > 100%), Among Asthmatics in St.
Louis Engaged in Moderate or Greater Exertion Associated with Exposure to SO2
Concentrations that Just Meet a 99th Percentile 1-Hour, 100 ppb Standard
SO2 Exposure Bin
Lower
Bound
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
Upper
Bound
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
Midpoint
(D
0.025
0.075
0.125
0.175
0.225
0.275
0.325
0.375
0.425
0.475
0.525
0.575
0.625
0.675
0.725
0.775
Number of
Exposures
(2)
16519000
136621
15760
3826
1051
413
175
83
31
24
8
0
0
8
0
0
Probability of
Response at Midpoint
SO2 Level
(3)
0.00406
0.02334
0.05162
0.08563
0.12300
0.16220
0.20210
0.24190
0.28060
0.31830
0.35430
0.38850
0.42090
0.45150
0.46600
0.49380
Expected Number of
Occurrences of Lung
Function Response
=(2) x (3)
67067
3189
814
328
129
67
35
20
9
8
3
0
0
4
0
0
Expected Number
Total Number of Exposures: 16677000 of Occurrences: 71672
9.2.2 Exposure Estimates
As noted above, exposure estimates used in the lung function risk assessment were
obtained from running the APEX exposure model for the population of individuals with asthma
for selected locations encompassing a variety of 862 emission source types within two areas in
the state of Missouri (i.e., St. Louis and Greene County). Chapter 8 provides additional details
about the inputs and methodology used to estimate 5-minute daily maximum peak SC>2 exposures
while engaged in moderate or greater exertion for the asthmatic population in these two areas.
These 5-minute exposure estimates for asthmatic children and adult asthmatics have been
combined separately with probabilistic exposure-response relationships for lung function
response associated with 5-minute 862 exposures. Only the highest 5-minute peak exposure
(with moderate or greater exertion) on each day has been considered in the lung function risk
assessment, since the controlled human exposure studies have shown an acute-phase response
March 2009
255
Draft - Do Not Quote or Cite
-------
1 that was followed by a short refractory period where the individual was relatively insensitive to
2 additional 862 challenges.
3 As described in section 8.8.1, instead of adjusting upward54 the air quality concentrations
4 to simulate just meeting the current SC>2 standards and potential alternative 1-hr daily maximum
5 standards, to reduce computer processing time, the exposure assessment simulated exposures
6 associated with just meeting various standards by adjusted the health effect benchmark levels by
7 the same factors described for each specific modeling domain and simulated year (see Table 8-
8 11). Since it is a proportional adjustment, the end effect of adjusting concentrations upwards
9 versus adjusting benchmark levels downward within the model is the same. The same follows
10 for where as is concentrations were in excess of an alternative standard level (e.g., 50 ppb for the
11 99th percentile averaged over three years), only the associated benchmarks are adjusted upwards
12 (i.e., a higher threshold concentration that would simulate lower exposures).
13 9.2.3 Exposure-Response Functions
14 Similar to the approach used in the ozone lung function risk assessment (Abt Associates,
15 2007), we have used a Bayesian Markov Chain Monte Carlo approach to estimate probabilistic
16 exposure-response relationships for lung function decrements associated with 5-minute daily
17 maximum peak exposures while engaged in moderate or greater exertion using the WinBUGS
18 software (Spiegelhalter et al., 1996).55 The combined data set includes all available individual
19 data from controlled human exposure studies of mild-to-moderate asthmatic individuals exposed
20 for 5- or 10-minutes while engaged in moderate or greater exertion. As noted above, for the
21 purposes of this risk assessment, all of the individual response data, including both 5- and 10-
22 minute exposure durations, have been combined and treated as representing 5-minute responses.
23 Table 9-2 summarizes the available controlled human exposure data that have been used to
24 develop the probabilistic exposure-response relationships for the lung function risk assessment.
25 The combined data set from Linn et al. (1987, 1988, 1990), Bethel et al. (1983, 1985),
26 Roger et al. (1985), and Kehrl et al. (1987), summarized in Table 9-2, provide data with which to
27 estimate exposure-response relationships between responses defined in terms of sRaw and 5-
54 To evaluate the current and most of the alternative 1-hr standards analyzed, "as is" ambient concentrations were
lower than air quality that would just meet the standards.
55 See Gleman et al. (1995) or Gilks et al. (1996) for an explanation of these methods.
March 2009 256 Draft - Do Not Quote or Cite
-------
1 minute exposures to SO2 at levels of 200, 250, 300, 400, 500, 600, and 1,000 ppb.56 As noted
2 above, two definitions of response have been used: (1) an increase in sRaw > 100% and (2) an
3 increase in sRaw > 200%.
4 Likewise, the combined data set from Linn et al. (1987, 1988, 1990), summarized in
5 Table 9-3, provide data with which to estimate exposure-response relationships between
6 responses defined in terms of FEVi and 5-minute exposures to SC>2 at levels of 200, 300, 400,
7 and 600 ppb. As noted above, two definitions of response have been used: a decrease in FEVi >
8 15% and a decrease in FEVi > 20%.
9 Before being used to estimate exposure-response relationships for 5-minute exposures,
10 the data from these controlled human exposure studies were corrected for the effect of exercising
11 in clean air to remove any systematic bias that might be present in the data attributable to an
12 exercise effect and this correction is reflected in the summary of the response data provided in
13 Table 9-3.57 Generally, this correction for exercise in clean air is small relative to the total
14 effects measures in the SO2-exposed cases.
15 We considered two different functional forms for the exposure-response functions: a 2-
16 parameter logistic model and a probit model. In particular, we used the data in Table 9-3 to
17 estimate the logistic function,
18
19 y(x; /?, y} = (equation 9-3)
S\ > f>l / Q_|_e/fl+7*ln(V)\ v n '
20 and the probit function,
.. /?+/ln(i)
21 yfrp,Y) = -- jV'2/2<# (equation 9-4)
56 Data from Magnussen et al. (1990) were not used in the estimation of sRaw exposure-response functions because
exposures in this study were conducted using a mouthpiece rather than a chamber.
57 Corrections were subject-specific. A correction was made by subtracting the subject's percent change (in FEVi
or sRaw) under the no-SO2 protocol from his or her percent change (in FEVi or sRaw) under the given SO2 protocol,
and rounding the result to the nearest integer. For example, if a subject's percent change in sRaw under the no-SO2
protocol was 110.12% and his percent change in sRaw under the 0.6 ppm SO2 protocol was 185.92%, then his
percent change in sRaw due to SO2 is 185.92% -110.12% = 75.8%, which rounds to 76%.
March 2009 257 Draft - Do Not Quote or Cite
-------
Table 9-3. Percentage of Asthmatic Individuals in Controlled Human Exposure Studies Experiencing SO2-lnduced
Decrements in Lung Function.
S02
Level
(PPB)
200
250
300
400
500
Exposure
Duration
10 min
10 min
5 min
5 min
10 min
10 min
10 min
10 min
10 min
10 min
10 min
5 min
10 min
10 min
No. of
Subjects
40
40
19
9
28
20
21
20
21
40
40
10
28
45
Ventilation
(L/min)
-40
-40
-50-60
-80-90
-40
-50
-50
-50
-50
-40
-40
-50-60
-40
-30
Lung
Funct.
sRaw
FEV-,
sRaw
sRaw
sRaw
sRaw
sRaw
FEV1
FEV-,
sRaw
FEV-,
sRaw
sRaw
sRaw
Cumulative Percentage of
Responders
(Number of Subjects)1
> 100% t
> 15% 4-
5% (2)
13% (5)
32% (6)
22% (2)
4%(1)
10% (2)
33% (7)
15% (3)
24% (5)
23% (9)
30% (12)
60% (6)
18% (5)
36% (16)
sRaw
> 200% t
FEVi
> 20% 4-
0
5% (2)
16% (3)
0
0
5%(1)
10% (2)
0
14% (3)
8% (3)
23% (9)
40% (4)
4%(1)
16% (7)
> 300% t
> 30% 4-
0
3%(1)
0
0
0
5%(1)
0
0
10% (2)
3%(1)
13% (5)
20% (2)
4%(1)
13% (6)
Reference
Limetal. (1987)2
Limetal. (1987)
Bethel etal. (1985)
Roger etal. (1985)
Limetal. (1988)3
Limetal. (1990)3
Limetal. (1988)
Limetal. (1990)
Limetal. (1987)
Limetal. (1987)
Bethel etal. (1983)
Roger etal. (1985)
Magnussen et al.
(1990)4
Respiratory Symptoms:
Supporting Studies
Limited evidence of SO2-induced
increases in respiratory symptoms
in some asthmatics: Linn et al.
(1983b; 1984; 1987; 1988; 1990),
Schacter et al. (1984)
Stronger evidence with some
statistically significant increases in
respiratory symptoms: Balmes
et al. (1987)4, Gong et al. (1995),
Linn etal. (1983b; 1987), Roger
etal. (1985)
March 2009
258
Draft - Do Not Quote or Cite
-------
S02
Level
(PPB)
600
1,000
Exposure
Duration
10 min
10 min
10 min
10 min
10 min
10 min
10 min
10 min
No. of
Subjects
40
20
21
40
20
21
28
10
Ventilation
(L/min)
-40
-50
-50
-40
-50
-50
-40
-40
Lung
Funct.
sRaw
sRaw
sRaw
FEV-,
FEV-,
FEV-,
sRaw
sRaw
Cumulative Percentage of
Responders
(Number of Subjects)1
> 100% t
> 15% 4-
35% (14)
60% (12)
62% (13)
53% (21)
55% (11)
43% (9)
50% (14)
60% (6)
sRaw
> 200% t
FEVi
> 20% 4-
28% (11)
35% (7)
29% (6)
48% (19)
55% (11)
33% (7)
25% (7)
20% (2)
> 300% t
> 30% 4-
18% (7)
10% (2)
14% (3)
20% (8)
5%(1)
14% (3)
14% (4)
0
Reference
Linn etal. (1987)
Linn etal. (1988)
Linn etal. (1990)
Linn etal. (1987)
Linn etal. (1988)
Linn etal. (1990)
Roger etal. (1985)
Kehrl etal. (1987)
Respiratory Symptoms:
Supporting Studies
Clear and consistent increases in
SO2-induced respiratory
symptoms: Linnet al.( 1984; 1987;
1988; 1990), Gong et al. (1995),
Horstman etal. (1988)
'Data presented from all references from which individual data were available. Percentage of individuals who experienced greater than or equal to a 100, 200, or 300% increase
in specific airway resistance (sRaw), or a 15, 20, or 30% decrease in FEVi. Lung function decrements are adjusted for effects of exercise in clean air (calculated as the difference
between the percent change relative to baseline with excersise/SO2 and the percent change relative to baseline with excerise/clean air). Quality control of data was performed by
two EPA staff scientists.
2Responses of mild and moderate asthmatics reported in Linn et al. (1987) have been combined. Data reported only for the first 10 min period of exercise in the first round of
exposures.
3Analysis includes data from only mild (1988) and moderate (1990) asthmatics who were not receiving supplemental medication.
4One subject was not exposed to 1,000 ppb due to excessive wheezing and chest tightness experienced at 500 ppb. For this subject, the values used for 500 ppb were also used
for 1 ,000 ppb under the assumptions that the response at 1 ,000 ppb would be equal to or greater than the response at 500 ppb. .
'indicates studies in which exposures were conducted using a mouthpiece rather than a chamber.
1 Source: ISA, Table 3-1 (EPA, 2008c, p.3-10).
March 2009
259
Draft - Do Not Quote or Cite
-------
1 for each of the four lung function responses defined above, where x denotes the 862
2 concentration (in ppm) to which the individual is exposed, ln(x) is the natural logarithm
3 of x, y denotes the corresponding probability of response (increase in sRaw > 100% or >
4 200% or decrease in FEVi > 15% or > 20%), and ft and y are the two parameters whose
5 values are estimated. 58
6 We assumed that the number of responses, sf, out of TV, subjects exposed to a
7 given SC>2 concentration, xt, has a binomial distribution with response probability given
8 by equation (9-3) when we assume the logistic model and equation (9-4) when we
9 assume the probit model. The likelihood function is therefore
10
11 L(fl,y;datd) = Yl ' y(Xi; ArX'tl-X*,-; #r)f'~* (equation 9-5)
12 In some of the controlled human exposure studies, subjects were exposed to a
13 given 862 concentration more than once. However, because there were insufficient data
14 to estimate subject-specific response probabilities, we assumed a single response
15 probability (for a given definition of response) for all individuals and treated the repeated
16 exposures for a single subject as independent exposures in the binomial distribution.
17 For each model, we derived a Bayesian posterior distribution using this binomial
18 likelihood function in combination with uniform prior distributions for each of the
19 unknown parameters.59 We used 4,000 iterations as the "burn-in" period followed by
20 10,000 iterations, a number sufficient to ensure convergence of the resulting posterior
21 distribution. Each iteration corresponds to a set of values for the parameters of the
22 logistic or probit exposure-response function.
23 For any 862 concentration, x, we could then derive the nih percentile response
24 value, for any Ŧ, by evaluating the exposure-response function at x using each of the
25 18,000 sets of parameter values. The resulting median (50th percentile) exposure-
26 response functions based on the 2-parameter logistic and probit models are shown
58 For ease of exposition, the same two Greek letters are used to indicate two unknown parameters in the
logistic and probit models; this does not imply, however, that the values of these two parameters are the
same in the two models.
59 We used the following uniform prior distributions for the 2-parameter logistic model: (3 ~ U(-10, 0); and
y ~ U(-10,0); we used the following normal prior distributions for the probit model: (3 ~ N(0, 1000); and y ~
N(0,1000).
March 2009 260 Draft - Do Not Quote or Cite
-------
1 together, along with the data used to estimate these functions, for increases in sRaw >
2 100% and > 200% and decreases in FEVi > 15% and > 20% in Figures 9-2, 9-3, 9-4, and
3 9-5, respectively. The 2.5th percentile, median, and 97.5th percentile curves, along with
4 the response data to which they were fit, are shown separately for each of the eight
5 combinations of (four) response definitions and (two) exposure-response models in
6 Appendix C.
7 We note that there were only limited data with which to estimate the logistic and
8 probit exposure-response functions, and that the logistic and probit models both appear to
9 fit the data equally well. We also note that since the data being fit has already been
10 corrected to account for the lung function response due to exercise in clean air, then the
11 response must by definition be zero associated with 0 ppm SO2 exposure. As one
12 observes in Figures 9-2 through 9-5 there is very little difference in the exposure-
13 response relationship between the two models, particularly at concentrations at and below
14 about 0.6 ppm. Since nearly all of the risk is attributable to exposures below the 0.6 ppm
15 level, we have chosen to use the 2-parameter logistic exposure-response functions to
16 develop the risk estimates associated with exposure to 862 under the different air quality
17 scenarios considered.
March 2009 261 Draft - Do Not Quote or Cite
-------
100%
Response Rate
The data
probit
2-parameter logistic
0.4 0.6
SO2 Concentration (ppm)
1
2 Figure 9-2. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw
3 > 100% for 5-Minute Exposures of Asthmatics Under Moderate or Greater Exertion*
A The data
probit
2-parameter logistic
0.2 0.4 0.6
SO2 Concentration (ppm)
0.8
March 2009
262
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
Figure 9-3. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw
> 200% for 5-Minute Exposures of Asthmatics Under Moderate or Greater
Exertion*
'Derived using method described in text based on all of the individual response data from Linn et al.
(1987), Linn et al. (1988), Linn et al. (1990), Bethel et al. (1983), Bethel et al. (1985), Roger et al. (1985),
and Kehrletal. (1987).
100%
90% -
80% -
70% -
60% -
Response Rate
The data
probit
2-parameter logistic
11
12
13
0.2 0.4 0.6
SO2 Concentration (ppm)
0.8
Figure 9-4. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEV1
> 20% for 5-Minute Exposures of Asthmatics Under Moderate or Greater Exertion*
March 2009
263
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
100%
90% -
80% -
70% -
0)
Ŧ 60% -
0)
Ģ 50%
A The data
pro bit
2-parameter logistic
0.2 0.4 0.6
SO2 Concentration (ppm)
0.8
Figure 9-5. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEV1
> 20% for 5-Minute Exposures of Asthmatics Under Moderate or Greater
Exertion*
'Derived using method described in text based on all of the individual response data from Linn et al.
(1987), Linn et al. (1988), and Linn et al. (1990).
March 2009
264
Draft - Do Not Quote or Cite
-------
l 9.3 LUNG FUNCTION RISK ESTIMATES
2 In this section, we present and discuss risk estimates associated with several air
3 quality scenarios, including a recent year of air quality as represented by 2002 monitoring
4 data. In addition, risk estimates are presented for several hypothetical scenarios,
5 equivalent to adjusting air quality upward to simulate just meeting the current annual 862
6 24-hour standard and to adjusting air quality (either up or down) to simulate just meeting
7 potential alternative 98th and 99th percentile daily maximum 1-h standards. As
8 discussed previously in Chapter 5, potential alternative 1-h standards with levels set at
9 50, 100, 150, 200, and 250 ppb have been included in the risk assessment. Only selected
10 risk estimates are presented in this section and additional risk estimates are presented in
11 Appendix C. Throughout this section and Appendix C the uncertainty surrounding risk
12 estimates resulting from the statistical uncertainty in the 862 exposure-response
13 relationships due to sampling error is characterized by ninety-five percent credible
14 intervals around estimates of occurrences, number of asthmatics experiencing one or
15 more lung function responses, and percent of total incidence that is SO2-related.
16 Risk estimates for selected lung function responses for all asthmatics and
17 asthmatic children associated with 5-minute exposures to ambient SC>2 concentrations
18 while engaged in moderate or greater exertion are presented in Tables 9-4 through 9-9.
19 Tables 9-4 through 9-6 are for all asthmatics and Tables 9-7 through 9-9 are for asthmatic
20 children. Each table includes risk estimates for both Greene County and St. Louis,
21 Missouri. As discussed in section 9.2.3, the risk assessment included two types of lung
22 function responses (i.e., sRaw and FEVi) and two levels of response for each type of lung
23 function response (> 100 and 200% increase for sRaw and > 15 and 20% decrease for
24 FEVi). Risk estimates using sRaw as the measure of lung function response are included
25 in this section and additional risk estimates using FEVi as the indicator of lung function
26 response are included in Tables 4-3, 4-4, 4-7, and 4-8 in Appendix C.
27 Tables 9-4 and 9-5 summarize the estimated number and percent of asthmatics
28 that would experience 1 or more lung function responses in a year, where lung function
29 response was defined as > 100% and > 200% increase in sRaw, in all asthmatics
30 associated with ambient 5-minute 862 exposures estimated to occur under "as is" air
31 quality (i.e., air quality based on 2002 monitored and modeled SO2 air quality data) and
March 2009 265 Draft - Do Not Quote or Cite
-------
1 under air quality representing just meeting the current 862 standards and several
2 alternative 1-hour daily maximum 862 standards. Tables 9-7 and 9-8 present the same
3 types of estimates for asthmatic children. The median estimates are presented in each
4 cell of the table with the 95% credible intervals based on statistical uncertainty
5 surrounding the SC>2 coefficient in the exposure-response relationship shown in
6 parentheses below the median estimates.
7 Tables 9-6 and 9-9 summarize the estimated number of occurrences of two
8 defined levels of lung function response (> 100% and > 200% increase in sRaw) in all
9 asthmatics and in asthmatic children, respectively, associated with ambient 5-minute 862
10 exposures estimated to occur under "as is" air quality (i.e., air quality based on 2002
11 monitored and modeled SO2 air quality data) and under air quality representing just
12 meeting the current SC>2 standards and several alternative 1-hour daily maximum SC>2
13 standards.
14 The current primary SC>2 standards include a 24-hour standard set at 0.14 parts per
15 million (ppm), not to be exceeded more than once per year, and an annual standard set at
16 0.03 ppm, calculated as the arithmetic mean of hourly averages. In St. Louis, 862
17 concentrations that are predicted to occur if the current standards were just met are
18 substantially higher than "as is" air quality (based on 2002 monitoring and modeling
19 data) and also substantially higher than they would be under any of the alternative 1-hr
20 standards considered in this analysis. Consequently, the levels of response that would be
21 seen if the current standard were just met are well above the levels that would be seen
22 under the "as is" air quality scenario or under any of the alternative 1-hr standards - for
23 asthmatics and for asthmatic children, and for all four definitions of lung function
24 response. We also note that the only standard resulting in decreases in lung function
25 responses relative to the "as is" scenario is the 50 ppb, 99th percentile 1-hr daily
26 maximum standard.
March 2009 266 Draft - Do Not Quote or Cite
-------
Table 9-4. Number of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung
Function Response Associated with Exposure to SO2 Under Alternative Air Quality Scenarios*
Location
"As is" SO2
Concentrations**
SO2
Concentrations
that Just Meet
the Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with
Levels (in ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene
County,
MO
St. Louis,
MO
90
(20 - 390)
1010
(340-3010)
210
(80 - 620)
13460
(9740-18510)
80
(20 - 380)
730
(220 - 2490)
90
(20 - 390)
1990
(860 - 4690)
100
(20 - 420)
3650
(1900-7100)
120
(30 - 460)
5520
(3230 - 9490)
160
(50 - 520)
7500
(4770-11850)
140
(40 - 500)
7050
(4410-11320)
Response = Increase in sRaw >= 200%
Greene
County,
MO
St. Louis,
MO
30
(0-210)
330
(70-1520)
70
(20-310)
5520
(3400 - 8960)
30
(0-210)
230
(40-1290)
30
(0-210)
670
(210-2270)
30
(0 - 220)
1280
(510-3360)
40
(10-240)
2010
(940 - 4470)
50
(10-270)
2830
(1470-5590)
50
(10-260)
2640
(1340-5330)
*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical
uncertainty surrounding the SO2 coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest ten.
**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual
standard set at 0.03 ppm, calculated as the arithmetic mean of hourly averages.
March 2009
267
Do Not Quote or Cite
-------
Table 9-5. Percent of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung
Function Response Associated with Exposure to SO2 Concentrations Under Alternative Air Quality Scenarios*
Location
"As is" SO2
Concentrations**
SO2
Concentrations
that Just Meet
the Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels
(in ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene
County,
MO
St. Louis,
MO
0.4%
(0.1% -1.8%)
1%
(0.3% - 2.9%)
1%
(0.4% - 2.9%)
13.1%
(9.5% -
18.1%)
0.4%
(0.1% -1.8%)
0.7%
(0.2% - 2.4%)
0.4%
(0.1% -1.8%)
1 .9%
(0.8% - 4.6%)
0.5%
(0.1% -2%)
3.6%
(1 .9% - 6.9%)
0.6%
(0.2% -2.1%)
5.4%
(3.2% - 9.3%)
0.7%
(0.2% - 2.4%)
7.3%
(4.7% - 1 1 .6%)
0.7%
(0.2% - 2.3%)
6.9%
(4.3% -11.1%)
Response = Increase in sRaw >= 200%
Greene
County,
MO
St. Louis,
MO
0.1%
(0% - 1 %)
0.3%
(0.1% -1.5%)
0.3%
(0.1% -1.5%)
5.4%
(3.3% - 8.7%)
0.1%
(0%-1%)
0.2%
(0% - 1 .3%)
0.1%
(0%-1%)
0.7%
(0.2% - 2.2%)
0.2%
(0%-1%)
1 .3%
(0.5% - 3.3%)
0.2%
(0%-1.1%)
2%
(0.9% - 4.4%)
0.2%
(0% - 1 .3%)
2.8%
(1 .4% - 5.5%)
0.2%
(0% - 1 .2%)
2.6%
(1 .3% - 5.2%)
*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical
uncertainty surrounding the SO2 coefficient in the 2-parameter logistic exposure-response function. Percents are rounded to the nearest tenth.
**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard
set at 0.03 ppm, calculated as the arithmetic mean of hourly averages.
March 2009
268
Do Not Quote or Cite
-------
Table 9-6. Number of Occurrences (In Hundreds) of a Lung Function Response Among Asthmatics Engaged in Moderate or
Greater Exertion Associated with Exposure to 362 Concentratons Under Alternative Air Quality Scenarios*
Location
"As is" SO2
Concentrations**
SO2
Concentrations
that Just Meet
the Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with
Levels (in ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene
County, MO
St. Louis, MO
125
(24 - 572)
657
(128-2985)
127
(25 - 577)
1672
(663 - 4740)
125
(24 - 572)
652
(125-2975)
125
(24 - 572)
686
(141 -3041)
125
(24 - 573)
762
(176-3184)
126
(24 - 573)
880
(234 - 3398)
126
(24 - 575)
1036
(315-3673)
126
(24 - 574)
997
(295 - 3604)
Response = Increase in sRaw >= 200%
Greene
County, MO
St. Louis, MO
38
(4-310)
201
(21 -1614)
39
(4-312)
560
(165-2407)
38
(4-310)
199
(20-1609)
38
(4-310)
211
(24-1639)
38
(4-310)
237
(32-1703)
38
(4-310)
278
(47-1799)
39
(4-311)
332
(68-1923)
39
(4-311)
319
(63-1892)
*Numbers are median (50th percentile) numbers of occurrences. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty
surrounding the SO2 coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest whole number.
**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SC>2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual
standard set at 0.03 ppm, calculated as the arithmetic mean of hourly averages.
March 2009
269
Do Not Quote or Cite
-------
Table 9-7. Number of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One
Lung Function Response Associated with Exposure to SO2 Under Alternative Air Quality Scenarios*
Location
"As is" SO2
Concentrations**
SO2
Concentrations
that Just Meet
the Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with
Levels (in ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene
County,
MO
St. Louis,
MO
30
(10-130)
590
(220-1570)
110
(40 - 270)
8020
(6080 - 1 0370)
30
(10-130)
400
(130-1210)
30
(10-140)
1220
(560 - 2620)
40
(10-150)
2240
(1240-4010)
50
(20-180)
3370
(2090 - 5350)
70
(30-210)
4560
(3060 - 6680)
60
(20 - 200)
4290
(2840 - 6390)
Response = Increase in sRaw >= 200%
Greene
County,
MO
St. Louis,
MO
10
(0 - 70)
190
(50 - 780)
40
(10-130)
3380
(2190-5070)
10
(0 - 70)
130
(30-610)
10
(0 - 70)
410
(140-1240)
10
(0 - 80)
800
(340- 1870)
20
(0 - 90)
1250
(620 - 2500)
20
(10- 110)
1750
(970-3140)
20
(10- 100)
1640
(890 - 3000)
*Numbers are median (50th percentile) numbers of asthmatic children. Numbers in parentheses below the median are 95% credible intervals based on statistical
uncertainty surrounding the SO2 coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest ten.
**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual
standard set at 0.03 ppm, calculated as the arithmetic mean of hourly averages.
March 2009
270
Do Not Quote or Cite
-------
Table 9-8. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One
Lung Function Response Associated with Exposure to SO2 Concentrations Under Alternative Air Quality Scenarios*
Location
"As is" SO2
Concentrations**
SO2
Concentrations
that Just Meet
the Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with
Levels (in ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene
County, MO
St. Louis,
MO
0.4%
(0.1% -1.8%)
1 .4%
(0.5% - 3.8%)
1 .4%
(0.6% - 3.7%)
19.2%
(14.6%-
24.9%)
0.4%
(0.1% -1.8%)
0.9%
(0.3% - 2.9%)
0.4%
(0.1% -1.9%)
2.9%
(1.3% -6. 3%)
0.5%
(0.1% -2.1%)
5.4%
(3% - 9.6%)
0.7%
(0.2% - 2.4%)
8.1%
(5% -12. 8%)
1%
(0.3% - 2.9%)
10.9%
(7. 3% -16%)
0.9%
(0.3% - 2.7%)
10.3%
(6.8% -15.3%)
Response = Increase in sRaw >= 200%
Greene
County, MO
St. Louis,
MO
0.1%
(0%-1%)
0.5%
(0.1% -1.9%)
0.5%
(0.1% -1.8%)
8.1%
(5. 3% -12. 2%)
0.1%
(0%- 1%)
0.3%
(0.1% -1.5%)
0.1%
(0% - 1 %)
1%
(0.3% - 3%)
0.2%
(0%- 1.1%)
1 .9%
(0.8% - 4.5%)
0.2%
(0% - 1 .3%)
3%
(1 .5% - 6%)
0.3%
(0.1% -1.5%)
4.2%
(2.3% - 7.5%)
0.3%
(0.1%- 1.4%)
3.9%
(2.1% -7.2%)
*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical
uncertainty surrounding the SC>2 coefficient in the 2-parameter logistic exposure-response function. Percents are rounded to the nearest tenth.
**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SC>2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual
standard set at 0.03 ppm, calculated as the arithmetic mean of hourly averages.
March 2009
271
Do Not Quote or Cite
-------
Table 9-9. Number of Occurrences (In Hundreds) of a Lung Function Response Among Asthmatic Children Engaged in
Moderate or Greater Exertion Associated with Exposure to SO2 Concentratons Under Alternative Air Quality Scenarios*
Location
"As is" SO2
Concentrations**
SO2
Concentrations
that Just Meet
the Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards,
with Levels (in ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene
County, MO
St. Louis, MO
71
(13-324)
417
(81 -1893)
72
(14-327)
1179
(484 - 3209)
71
(13-324)
413
(80-1885)
71
(14-324)
439
(91 -1935)
71
(14-324)
497
(118-2043)
71
(14-325)
586
(162-2206)
71
(14-325)
704
(222-2413)
71
(14-325)
674
(207-2361)
Response = Increase in sRaw >= 200%
Greene
County, MO
St. Louis, MO
22
(2-175)
128
(13-1023)
22
(2-177)
397
(122-1618)
22
(2-175)
126
(13-1019)
22
(2-175)
135
(15- 1042)
22
(2-175)
155
(22- 1091)
22
(2-176)
186
(33- 1164)
22
(2- 176)
227
(49- 1257)
22
(2- 176)
217
(45- 1234)
*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical
uncertainty surrounding the SC>2 coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest whole number.
**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual
standard set at 0.03 ppm, calculated as the arithmetic mean of hourly averages.
March 2009
272
Do Not Quote or Cite
-------
As an illustration of the changes in the number of occurrences of sRaw increases > 100%
in all asthmatics across the range of standards analyzed in the St. Louis modeling domain, under
the current 862 standards the median estimate is 117,900. These estimated occurrences decrease
for increasingly more stringent alternative 1-hour standards with the 50 ppb, 99th percentile daily
maximium 1-hour standard, the most stringent alternative standard analyzed, reducing the
median estimated number of occurrences of this lung function response to 41,300. The pattern
of reductions observed for all asthmatics is similar to that observed in asthmatic children.
The estimated occurrences of sRaw responses are much lower in Greene County due both
to a smaller population as well as fewer exposure occurrences of elevated 5-minute 862
concentrations. We also note that the differences in estimated occurrences of lung function
responses associated with all of the air quality scenarios analyzed are much smaller for Greene
County than in St. Louis. The minimal differences observed in Greene County among the air
quality scenarios analyzed is due to the relatively small differences in the distribution of
exposures while engaged in moderate or greater exertion among the air quality scenarios
analyzed.
Figures 9-7 (a) and (b) show the contribution of different exposure "bins" or intervals to
the total estimated occurrences of SCVrelated lung function responses to 5-minute 862
exposures for asthmatics and asthmatic children, respectively, in the St. Louis modeling domain
using > 100% increases in sRaw as the indicator of lung function response. Figures 9-8 (a) and
(b) show the percent of asthmatics and asthmatic children, respectively, engaged in moderate or
greater exertion in St. Louis, MO estimated to experience at least one lung function response in a
year, defined as an increase in sRaw > 100%, attributable to exposure to SC>2 in each exposure
"bin." Figure 9-6 displays the legend for Figures 9-7 and 9-8 indicating the exposure bins used
in the figures. Similar figures are included in Appendix C for lung function responses defined in
terms of > 15% and > 20% decrements in FEVi for both asthmatics and asthmatic children.
As one observes in Figures 9-8 (a) and (b), for total occurrences of lung function
response the pattern of the contribution of exposures from different concentration intervals is
very similar. Of course the magnitude of occurrences is smaller for asthmatic children since they
are a subset of all asthmatics. For the two most stringent alternative standards, nearly all of the
SO2-related risk is attributable to exposures in the lowest exposure interval (i.e., < 50 ppb), and
for the remaining alternative 1-hr standards most of the SO2-related risk is attributable to
March 2009 273 Draft - Do Not Quote or Cite
-------
Q Attributable to 500 ppb<=S02
E Attributable to 450 ppb<=S02<500 ppb
D Attributable to 400 ppb<=S02<450 ppb
C Attributable to 350 ppb<=S02<400 ppb
Attribuable to 300 ppb<=S02<350 ppb
H Attributable to 250 ppb<=S02<300 ppb
E3 Attributable to 200 ppb<=S02<250 ppb
B Attributable to 150 ppb<=S02<200 ppb
D Attributable to 100 ppb<=S02<150 ppb
D Attributable to 50 ppb<=S02<100 ppb
Attributable to S02<50 ppb
Figure 9-6. Legend for Figures 9-7 and 9-8 Showing Total and Contribution of
Risk Attributable to SO2 Exposure Ranges.
March 2009
274
Draft - Do Not Quote or Cite
-------
600
a) Asthmatics
600
500 -
C 3OO -
100 -
b) Asthmatic children
Figure 9-7. Estimated Annual Number of Occurrences of Lung Function Response (Defined as >
100% increase in sRaw) for Asthmatics and Asthmatic Children Associated with Short-
Term (5-minute) Exposures to SO2 Concentrations Associated with Alternative Air
Quality Scenarios - Total and Contribution of 5-Minute SO2 Exposure Ranges (see
Figure 9-6 for legend).
March 2009
275
Draft - Do Not Quote or Cite
-------
30% -
J2 re
V)
3
o
in
o>
o>
o>
o>
o>
o>
C!
o>
o>
in
C!
o>
o>
a
o>
a) Asthmatics
30% -
= 25% -
O
in
o>
I 20% -
3.
O)
1 15% H
Q.
U)
V
a:
g 10% H
5% -
0%
++
**
^
2
w
3
O
o
in
o>
o>
o
o
o>
o>
o>
O)
o
o
C!
o>
O)
s
O)
o
o
g
O)
b) Asthmatic children
Figure 9-8. Estimated Percent of of Asthmatics and Asthmatic Children Experiencing One or More
Lung Function Responses (Defined as > 100% increase in sRaw) Per Year Associated
with Short-Term (5-minute) Exposures to SO2 Concentrations Associated with
Alternative Air Quality Scenarios - Total and Contribution of 5-Minute SO2 Exposure
Ranges (see Figure 9-6 for legend).
March 2009
276
Draft - Do Not Quote or Cite
-------
1 exposures < 100 ppb.
2 We note that, while in several air quality scenarios the great majority of occurrences of
3 lung function response are in the lowest exposure bin, the numbers of individuals with at least
4 one lung function response attributable to exposures in that lowest bin are typically quite small.
5 This is because the calculation of numbers of individuals with at least one lung function response
6 uses individuals' highest exposure only. While individuals may be exposed mostly to low SC>2
7 concentrations, many are exposed at least occasionally to higher levels. Thus, the percentage of
8 individuals in a designated population with at least one lung function response associated with
9 SC>2 concentrations in the lowest bin is likely to be very small, since most individuals are
10 exposed at least once to higher 862 levels. For example, defining lung function response as an
11 increase in sRaw > 100%, under a scenario in which SO2 concentrations just meet an alternative
12 1-hour 99th percentile 100 ppb standard, about 93 percent of occurrences of lung function
13 response among asthmatics in St. Louis are associated with SC>2 exposures in the lowest
14 exposure bin (0 - 50 ppb). However, the lowest SC>2 exposure bin accounts for only about 0.2
15 percent of asthmatics estimated to experience at least 1 SCVrelated lung function response. For
16 this very small percent of the population, the lowest exposure bin represents their highest 862
17 exposures under moderate exertion in a year. Thus Figure 9-7(a) shows virtually all of the
18 occurrences among asthmatics in St. Louis associated with the lowest 862 exposure bin;
19 however, Figure 9-8 (a) shows a relatively small proportion of asthmatics in St. Louis
20 experiencing at least one response to be experiencing those responses because of exposures in
21 that lowest exposure bin.
22 Finally, we observe that the risks are greater for asthmatic children than all asthmatics in
23 terms of percentage of the population experiencing lung function responses in terms of
24 population responding 1 or more times per year.
25 9.3 CHARACTERIZING UNCERTAINTY AND VARIABILITY
26 An important issue associated with any population health risk assessment is the
27 characterization of uncertainty and variability (see section 7.4 for definition of uncertainty and
28 variability). Our approach to characterizing uncertainty includes both qualitative and
29 quantitative elements. From a quantitative perspective, the statistical uncertainty surrounding the
30 estimated SO2 exposure-response relationships due to sampling error are reflected in the credible
31 intervals that have been provided for the risk estimates in this document. Following the same
March 2009 277 Draft - Do Not Quote or Cite
-------
1 general approach described in section 7.4 and 8.11, the approach for qualitatively evaluating
2 uncertainty was adapted from guidelines outlining how to conduct a qualitative uncertainty
3 characterization (WHO, 2008). This includes an identification of the important uncertainties, an
4 indication of the potential bias direction, and a scaling of the uncertainty using low, medium, and
5 high categories.
6 The bias direction indicates how the source of uncertainty was judged to influence
7 estimated lung function responses associated with SC>2 5-minute exposures, either the estimated
8 number or percent of asthmatics experiencing 1 or more lung function responses and total
9 occurrences are likely "over-" or "under-estimated". In the instance where two or more types or
10 components of uncertainty result in offsetting direction of influence, the bias was judged as
11 "both." An "unknown" bias was assigned where there was no evidence reviewed to judge the
12 uncertainty associated with the source. Table 9-10 provides a summary of the sources of
13 uncertainty identified in the health risk assessment, the level of uncertainty, and the overall
14 judged bias of each. A brief summary discussion regarding those sources of uncertainty not
15 already examined in Chapters 7 and 8 is included in the comments section of Table 9-10.
16 The 5-minute daily maximum exposure estimates for asthmatics and asthmatic children
17 while engaged in moderate or greater exertion is an important input to the lung function response
18 risk assessment. A qualitative characterization of uncertainties associated with the exposure
19 model and the inputs to the exposure model are summarized in Table 8-13 and discussed in
20 section 8.11.
21 With respect to variability, the lung function risk assessment incorporates some of the
22 variability in key inputs to the analysis by its use of location-specific inputs for the exposure
23 analysis (e.g., location specific population data, air exchange rates, air quality, and temperature
24 data). The extent to which there may be variability in exposure-response relationships for the
25 populations included in the risk assessment residing in different geographic areas is currently
26 unknown. Temporal variability also is more difficult to address, because the risk assessment
27 focuses on some unspecified time in the future. To minimize the degree to which values of
28 inputs to the analysis may be different from the values of those inputs at that unspecified time,
29 we have used the most current inputs available.
March 2009 278 Draft - Do Not Quote or Cite
-------
Table 9-10. Characterization of Key Uncertainties in the Lung Function Response Health Risk Assessment for St.
Louis and Greene County, Missouri
Uncertainty
AERMOD Inputs
and Algorithms
Exposure Model
(APEX) Inputs and
Algorithms
Spatial
representation
Air quality
adjustment
Causality
Use of 2-parameter
logistic model to
estimate
probabilistic
exposure-response
relationships
Use of 5- and 10-
minute lung
function response
data to estimate 5-
minute lung
function risk
estimates
Use of exposure-
response data from
studies of
mild/moderate
asthmatics to
Direction of Bias
Unknown
Unknown
Unknown
Unknown
None
Overestimate
Overestimate
Underestimate
Level of
of Uncertainty
Low-Medium
Medium
Medium - High
Medium
Low
Low - within
range of data
Medium -for
levels well
below 200 ppb
Low
Medium
Comments
See Table 8-13 and section 8.12.1
See Table 8-13 and section 8.12.2
See discussion in section 7.4.4
See discussion in section 7.4.5
The SO2-related lung function responses have been observed in controlled human
exposure studies and, thus there is little uncertainty that SO2 exposures are
responsible for the observed lung function responses.
It was necessary to estimate responses at SO2 levels both within the range of
exposure levels tested (i.e., 200 to 1 ,000 pppb) as well as below the lowest
exposure levels used in free-breathing controlled human exposure studies (i.e.,
below 200 ppb). We have developed probabilistic exposure-response relationships
using two different functional forms (i.e., probit and 2-parameter logistic). As shown
in Figures 9-2 through 9-5, the two functional forms result in very similar probabilistic
exposure-response relationships for the four health response definitions, and
therefore, we used only the logistic model to estimate risks. For the risks
attributable to exposure levels below 200 ppb, there is greater uncertainty.
The 5-minute lung function risk estimates are based on a combined data set from
several controlled human exposure studies, most of which evaluated responses
associated with 1 0-minute exposures. However, since some studies which
evaluated responses after 5-minute exposures found responses occurring as early
as 5-minutes after exposure, we are using all of the 5- and 1 0-minute exposure data
to represent responses associated with 5-minute exposures. We do not believe that
this approach appreciably impacts the risk estimates.
The data set that was used to estimate exposure-response relationships included
mild and/or moderate asthmatics. There is uncertainty with regard to how well the
population of mild and moderate asthmatics included in the series of SO2 controlled
human exposure studies represent the distribution of mild and moderate asthmatics
in the U.S. population. As indicated in the ISA (p. 3-9), the subjects studied
March 2009
279
Draft - Do Not Quote or Cite
-------
Uncertainty
Direction of Bias
Level of
of Uncertainty
Comments
represent all
asthmatics
represent the responses "among groups of relatively healthy asthmatics and cannot
necessarily be extrapolated to the most sensitive asthmatics in the population who
are likely more susceptible to the respiratory effects of exposure to SO2."
Reproducibility of
SO2-induced lung
function response
Unknown
Low
The risk assessment assumes that the SO2-induced responses for individuals are
reproducible. We note that this assumption has some support in that one study
(Linn et al., 1987) exposed the same subjects on two occasions to 0.6 ppm and the
authors reported a high degree of correlation (r > 0.7 for mild asthmatics and r > 0.8
for moderate asthmatics, p < 0.001), while observing much lower and nonsignificant
correlations (r = 0.0 - 0.4) for the lung function response observed in the clean air
with exercise exposures.
Use of adult
asthmatic lung
function response
data to estimate
exposure-response
relationships for
asthmatic children
Unknown
Low to Medium
Because the vast majority of controlled human exposure studies investigating lung
function responses were conducted with adult subjects, the risk assessment relies
on data from adult asthmatic subjects to estimate exposure-response relationships
that have been applied to all asthmatic individuals, including children. The ISA
(section 3.1.3.5) indicates that there is a strong body of evidence that suggests
adolescents may experience many of the same respiratory effects at similar SO2
levels, but recognizes that these studies administered SO2 via inhalation through a
mouthpiece rather than an exposure chamber. This technique bypasses nasal
aborption of SO2 and can result in an increase in lung SO2 uptake. Therefore, the
uncertainty is greater in the risk estimates for asthmatic children.
Exposure history
Unknown
Low
The risk assessment assumes that the SO2-induced response on any given day is
independent of previous SO2 exposures. For some pollutants (e.g., ozone) prior
exposure history can lead to both enhanced and diminished lung function responses
depending on the pattern of exposure. This type of information is not available for
SO2 lung function responses.
Assumed no
interaction effect of
other co-pollutants
on SO2-related
lung function
responses
Underestimate
Low to Medium
Because the controlled human exposure studies used in the risk assessment
involved only SO2 exposures, it is assumed that estimates of SO2-induced health
responses are not affected by the presence of other pollutants (e.g., PM25, O3,
N02).
March 2009
280
Draft - Do Not Quote or Cite
-------
l 9.4 KEY OBSERVATIONS
2 Presented below are key observations related to the risk assessment for lung function
3 responses in asthmatics and asthmatic children associated with 5-minute exposures to 862 while
4 engaged in moderate or greater exertion:
5 Lung function responses estimated to result from 5-minute exposures to 862 were
6 estimated for two areas in Missouri (i.e., Greene County and St. Louis) for 2002 air
7 quality and for air quality adjusted to simulate just meeting the current suite of annual
8 and 24-hour SO2 standards and just meeting several alternative 1-hour daily maximum
9 SC>2 standards. While we would expect some differences in estimated SCVrelated lung
10 function responses across different locations due to differences in populations, asthma
11 prevalence rates, location and types of SC>2 sources, and various factors affecting
12 exposure, we believe that the risk estimates do provide a useful perspective on the likely
13 overall magnitude and pattern of lung function responses associated with various 862 air
14 quality scenarios in areas within the U.S. that have significant SC>2 point and area
15 sources.
16
17 Only the 50 ppb/99th percentile daily maximum 1-hr standard is estimated to reduce risks
18 in one of the two modeling domains (i.e., St. Louis) relative to the "as is" air quality
19 scenario.
20
21 With respect to total occurrences of lung function responses, where response is defined as
22 > 100% increase in sRaw, most of the estimated risk for the St. Louis modeling domain
23 for the potential 1-hr standard alternatives analyzed is due to exposures at and below 100
24 ppb and for the most stringent standards analyzed (i.e., 50 and 100 ppb), most of the
25 estimated risk is due to exposures at and below 50 ppb.
26
27 In terms of estimated percentage of asthmatics or asthmatic children experiencing 1 or
28 more lung function responses, risks are greater for asthmatic children and risks are
29 attributed to a broader range of exposure intervals, as high as 500 ppb, for some of the
30 standards considered in the assessment.
31
32 Important uncertainties and limitations associated with the risk assessment which were
33 discussed above in section 9.3 and which should be kept in mind as one considers the
34 quantitative risk estimates include:
35 - uncertainties affecting the exposure estimates which are described in section 8.11 and
36 which are an important input to the risk assessment;
37 - uncertainties related to how changes in population, activity patterns, air quality, and
38 other factors over time might impact the exposure estimates which are an important
39 input to the risk assessment;
40 - uncertainties associated with the air quality adjustment procedure that was used
41 to simulate just meeting the current annual and several alternative 1-h daily maximum
42 standards;
March 2009 281 Draft - Do Not Quote or Cite
-------
1 - statistical uncertainty due to sampling error which is characterized in the
2 assessment;
3 - uncertainty about the shape of the exposure-response relationship for lung function
4 responses at levels well below 200 ppb, the lowest level examined in free-breathing
5 single pollutant controlled human exposure studies;
6 - uncertainty with respect to how well the estimated exposure-response relationships
7 reflect asthmatics with more severe disease than those tested in chamber studies;
8 - uncertainty about whether the presence of other pollutants in the ambient air would
9 enhance the SCVrelated responses observed in the controlled human exposure studies;
10 - uncertainty about the extent to which the risk estimates presented for the two
11 modeled areas in Missouri are representative of other locations in the U.S. with
12 significant SO2 point and area sources.
March 2009 282 Draft - Do Not Quote or Cite
-------
i 10.0 EVIDENCE-AND EXPOSURE/RISK-BASED
2 CONSIDERATIONS RELATED TO THE PRIMARY SO2 NAAQS
3 10.1 INTRODUCTION
4 This chapter considers the scientific evidence in the ISA (EPA, 2008a) and the air
5 quality, exposure and risk characterization results presented in this document as they relate to the
6 adequacy of the current SC>2 primary NAAQS and potential alternative primary SC>2 standards.
7 The available scientific evidence includes epidemiologic, controlled human exposure, and animal
8 toxicological studies. The 862 air quality, exposure, and risk analyses described in Chapters 7-9
9 of this document include characterization of air quality, exposure, and health risks associated
10 with recent 862 concentrations and with 862 concentrations adjusted to simulate scenarios just
11 meeting the current suite of standards and potential alternative 1-hour standards. In considering
12 the scientific evidence and the exposure- and risk-based information, we have also considered
13 relevant uncertainties. Section 10.2 of this Chapter presents our general approach to considering
14 the adequacy of the current standards and potential alternative standards. Sections 10.3 and 10.4
15 focus on evidence- and exposure-/risk-based considerations related to the adequacy of the current
16 24-hour and annual standards respectively, while section 10.5 focuses on such considerations
17 related to potential alternative standards (in terms of the indicator, averaging time, form, and
18 level).
19 These considerations are intended to inform the Agency's policy assessment of a range of
20 options with regard to the SC>2 NAAQS. We note that the final decision on retaining or revising
21 the current SC>2 primary standard, taking into account the Agency's policy assessment, is largely
22 a public health policy judgment. A final decision will draw upon scientific information and
23 analyses about health effects, population exposure and risks, and policy judgments about the
24 appropriate response to the range of uncertainties that are inherent in the scientific evidence and
25 analyses. Our approach to informing these judgments, discussed more fully below, is based on a
26 recognition that the available health effects evidence reflects a continuum consisting of ambient
27 levels at which scientists generally agree that health effects are likely to occur through lower
28 levels at which the likelihood and magnitude of the response become increasingly uncertain.
29 This approach is consistent with the requirements of the NAAQS provisions of the Act and with
30 how EPA and the courts have historically interpreted the Act. These provisions require the
March 2009 283 Draft - Do Not Quote or Cite
-------
1 Administrator to establish primary standards that, in the Administrator's judgment, are requisite
2 to protect public health with an adequate margin of safety. In so doing, the Administrator seeks
3 to establish standards that are neither more nor less stringent than necessary for this purpose.
4 The Act does not require that primary standards be set at a zero-risk level but rather at a level
5 that avoids unacceptable risks to public health, including the health of at risk populations.
6 10.2 GENERAL APPROACH
7 This section describes the general approach that staff is taking to inform decisions
8 regarding the need to retain or revise the current SC>2 NAAQS. The current standards, a 24-hour
9 average of 0.14 ppm, not to be exceeded more than one time per year, and an annual average of
10 0.03 ppm were retained by the Administrator in the most recent review completed in 1996 (61
11 FR 25566). The decision to retain the 24-hour standard was largely based on an assessment of
12 epidemiological studies that supported a likely association between 24-hour average 862
13 exposure and daily mortality, aggravation of bronchitis, and small, reversible declines in
14 children's lung function (EPA 1982, 1994a). Similarly, the decision to retain the annual standard
15 (see section 10.4) was largely based on an assessment of epidemiological studies finding an
16 association between respiratory symptoms/illnesses and annual average SC>2 concentrations (EPA
17 1982, 1994a).
18 The previous review of the SC>2 NAAQS also questioned whether an additional short-
19 term standard (e.g., 5-minute) was necessary to protect against short-term peak 862 exposures.
20 Based on the scientific evidence, the Administrator judged that repeated exposures to 5-minute
21 peak levels > 600 ppb could pose a risk of significant health effects for asthmatic individuals at
22 elevated ventilation rates (61 FR 25566). The Administrator also concluded that the likely
23 frequency of such effects should be a consideration in assessing the overall public health risks.
24 Based upon an exposure analysis conducted by EPA (see section 1.1.3), the Administrator
25 concluded that exposure of asthmatics to SC>2 levels that could reliably elicit adverse health
26 effects was likely to be a rare event when viewed in the context of the entire population of
27 asthmatics, and therefore did not pose a broad public health problem for which a NAAQS would
28 be appropriate (61 FR 25566). On May 22, 1996, EPA published its final decision to retain the
29 existing 24-hour and annual standards and not to promulgate a 5-minute standard (61 FR 25566).
30 The decision not to set a 5-minute standard was ultimately challenged by the American Lung
31 Association and remanded back to EPA for further explanation on January 30,1998 by the DC
March 2009 284 Draft - Do Not Quote or Cite
-------
1 Court of Appeals (see section 1.1.1). Specifically, the court required EPA to provide additional
2 rationale to support the Agency judgment that 5-minute peaks of 862 do not pose a public health
3 problem when viewed from a national prospective even though those peaks would likely cause
4 adverse health effects in a subset of exposed asthmatics.
5 To inform the range of options that the Agency will consider in the current review of the
6 primary SC>2 NAAQS, the general approach we have adopted builds upon the approaches used in
7 reviews of other criteria pollutants, including the most recent reviews of the Pb, Os, and PM
8 NAAQS (EPA, 2007i; EPA, 2007e; EPA, 2005). As in these other reviews, we consider the
9 implications of placing more or less weight or emphasis on different aspects of the scientific
10 evidence and the exposure/risk-based information, recognizing that the weight to be given to
11 various elements of the evidence and exposure/risk information is part of the public health policy
12 judgments that the Administrator will make in reaching decisions on the standard.
13 A series of general questions frames our approach to considering the scientific evidence
14 and exposure/risk-based information. First, our consideration of the scientific evidence and
15 exposure/risk-based information with regard to the adequacy of the current standard is framed by
16 the following questions:
17
18 To what extent does evidence and exposure/risk-based information that has become
19 available since the last review reinforce or call into question evidence for SCVassociated
20 effects that were identified in the last review?
21
22 To what extent has evidence for different health effects and/or sensitive populations
23 become available since the last review?
24
25 To what extent have uncertainties identified in the last review been reduced and/or have
26 new uncertainties emerged?
27
28 To what extent does evidence and exposure/risk-based information that has become
29 available since the last review reinforce or call into question any of the basic elements of
30 the current standard?
31
32 To the extent that the available evidence and exposure/risk-based information suggests it
33 may be appropriate to consider revision of the current standards, we consider that evidence and
34 information with regard to its support for consideration of a standard that is either more or less
35 protective than the current standard. This evaluation is framed by the following questions:
March 2009 285 Draft - Do Not Quote or Cite
-------
1
2 Is there evidence that associations, especially causal or likely causal associations,
3 extend to ambient 862 concentrations as low as, or lower than, the concentrations that
4 have previously been associated with health effects? If so, what are the important
5 uncertainties associated with that evidence?
6
1 Are exposures above benchmark levels and/or health risks estimated to occur in areas
8 that meet the current standard? If so, are the estimated exposures and health risks
9 important from a public health perspective? What are the important uncertainties
10 associated with the estimated risks?
11
12
13 To the extent that there is support for consideration of a revised standard, we then
14 consider the specific elements of the standard (indicator for gaseous SOx, averaging time, form,
15 and level) within the context of the currently available information. In so doing, we address the
16 following questions:
17
18 Does the evidence provide support for considering a different indicator for gaseous
19 SOX?
20
21 Does the evidence provide support for considering different averaging times?
22
23 What ranges of levels and forms of alternative standards are supported by the evidence,
24 and what are the associated uncertainties and limitations?
25
26 To what extent do specific averaging times, levels, and forms of alternative standards
27 reduce the estimated exposures above benchmark levels and risks attributable to SO2, and
28 what are the uncertainties associated with the estimated exposure and risk reductions?
29
30 The following discussion addresses the questions outlined above and presents staffs
31 conclusions regarding the scientific evidence and the exposure-/risk-based information
32 specifically as they relate to the current and potential alternative standards. This discussion is
33 intended to inform the Agency's consideration of policy options that will be presented during the
34 rulemaking process, together with the scientific support for such options. Sections 10.3 and 10.4
35 consider the adequacy of the current standards while section 10.5 considers potential alternative
36 standards in terms of indicator, averaging time, form, and level. Each of these sections considers
March 2009 286 Draft - Do Not Quote or Cite
-------
1 key conclusions as well as the uncertainties associated with the evidence and exposure/risk
2 analyses.
3 10.3 ADEQUACY OF THE CURRENT 24-HOUR STANDARD
4 10.3.1 Introduction
5 In the last review of the SC>2 NAAQS, retention of the 24-hour standard was based
6 largely on epidemiological studies conducted in London in the 1950's and 1960's. The results of
7 those studies suggested an association between 24-hour average levels of 862 and increased daily
8 mortality and aggravation of bronchitis when in the presence of elevated levels of PM (53
9 FR14927). Additional epidemiological evidence suggested that elevated SO2 levels were
10 associated with the possibility of small, reversible declines in children's lung function (53
11 FR14927). However, it was noted that in the locations where these epidemiological studies were
12 conducted, high SC>2 levels were usually accompanied by high levels of PM- thus making it
13 difficult to disentangle the individual contribution each pollutant had on these health outcomes. It
14 was also noted that rather than 24-hour average 862 levels, the health effects observed in these
15 studies may have been related, at least in part, to the occurrence of shorter-term peaks of 862
16 within a 24-hour period (53 FR14927).
17 10.3.2 Evidence-based considerations
18 In discussing the adequacy of the current 24-hour NAAQS, we first note the conclusions
19 presented in the ISA with regard to short-term SC>2 effects on mortality and respiratory
20 morbidity. As mentioned above, the previous review described positive associations between
21 SC>2 and mortality, when in the presence of elevated PM levels. In this review, based on
22 numerous studies conducted since the last review, the ISA characterizes the evidence of an
23 association between short-term (> 1-hour) 862 levels and mortality as being "suggestive of a
24 causal relationship." The ISA consistently finds positive associations between short-term 862
25 levels and mortality, but these effect estimates are generally diminished in multi-pollutant
26 models- an indication that results may be confounded by the presence of co-pollutants (ISA
27 Table 5-3).
28 With respect to respiratory morbidity, the previous review of the SC>2 NAAQS described
29 possible associations between 24-hour SC>2 levels and small reversible declines in children's lung
30 function and aggravation of bronchitis. The current ISA concludes that there is sufficient
March 2009 287 Draft - Do Not Quote or Cite
-------
1 evidence to infer "a causal relationship between respiratory morbidity and short-term exposure to
2 SC>2 (ISA section 5.2)." The ISA states that the strongest evidence for this judgment is from
3 human exposure studies demonstrating increased respiratory symptoms and decreased lung
4 function in exercising asthmatics exposed for 5-10 minutes to > 200 ppb SC>2 (ISA section 5.2).
5 Supporting this conclusion is a larger body of epidemiological studies published since the last
6 review observing associations between >l-hour SC>2 concentrations and respiratory symptoms,
7 ED visits, and hospital admissions (ISA section 5.2).
8 In considering the adequacy of the current 24-hour standard, we further note that many
9 epidemiological studies demonstrating positive associations between ambient 862 and
10 respiratory symptoms, ED visits, and hospitalizations were conducted in areas where 862
11 concentrations met the level of the current 24-hour (as well as the annual; see section 10.4)
12 NAAQS. With regard to these epidemiological studies, we note that the ISA characterizes the
13 evidence for respiratory effects as consistent and coherent. The evidence is consistent in that
14 associations are reported in studies conducted in numerous locations and with a variety of
15 methodological approaches (ISA, section 5.2). It is coherent in the sense that respiratory
16 symptoms results from short-term (> 1-hour) epidemiological studies are generally in agreement
17 with respiratory symptom results from controlled human exposure studies of 5-10 minutes.
18 These results are also coherent in that the respiratory effects observed in controlled human
19 exposure studies of 5-10 minutes provides a basis for a progression of respiratory morbidity that
20 could lead to the ED visits and hospitalizations observed in epidemiological studies (ISA section
21 5.2). Moreover, the ISA states that several of the more precise effect estimates in these studies
22 are statistically significant (ISA, section 5.2).
23 However, it should be noted that interpretation of the epidemiological literature is
24 complicated by the fact that SC>2 is but one component of a complex mixture of pollutants
25 present in the ambient air. The matter is further complicated by the fact that 862 is a precursor
26 to sulfate, which can be a principle component of PM. Ultimately, this uncertainty calls into
27 question the extent to which effect estimates from epidemiological studies reflect the
28 independent contributions of 862 to the adverse respiratory outcomes assessed in these studies.
29 In order to provide some perspective on this uncertainty, the ISA evaluates epidemiological
30 studies that employ multi-pollutant models. The ISA concludes that these analyses indicate that
31 although copollutant adjustment has varying degrees of influence on SC>2 effect estimates, the
March 2009 288 Draft - Do Not Quote or Cite
-------
1 effect of SO2 on respiratory health outcomes appears to be generally robust and independent of
2 the effects of gaseous copollutants, including NO2 and 63 (ISA, section 5.2). With respect to
3 PMio, evidence of an independent SO2 effect on respiratory health is less consistent, with about
4 half of the positive ED visit and hospitalization results becoming negative (although result were
5 not statistically significantly negative) after inclusion of PMio in regression models (ISA section
6 3.1.4.6). In epidemiological studies of respiratory symptoms, inclusion of PMio in multipllutant
7 models often resulted in the SC>2 effect estimate losing statistical significance (although the effect
8 estimate may have remained positive, and relatively unchanged; ISA section 3.1.4.1). The ISA
9 also concludes that SCVeffect estimates generally remained robust in the limited number of
10 studies that included PM2.5 and/or PMi0-2.5 in multipolutant models (ISA section 3.1.4.6). Taken
11 together, the ISA finds studies employing multi-pollutant models do suggest that SO2 has an
12 independent effect on respiratory morbidity outcomes (ISA, section 5.2). Thus, the results of
13 experimental and epidemiological studies form a plausible and coherent data set that supports a
14 relationship between SO2 exposures and respiratory morbidity endpoints.
15 10.3.3 Air Quality, exposure and risk-based considerations
16 In addition to the evidence-based considerations described above, staff has considered the
17 extent to which exposure- and risk-based information can inform decisions regarding the
18 adequacy of the current 24-hour 862 standard, taking into account key uncertainties associated
19 with the estimated exposures and risks. For this review, we have employed three approaches. In
20 the first approach, 862 air quality levels were used as a surrogate for exposure. In the second
21 approach, modeled estimates of human exposure were developed for all asthmatics and asthmatic
22 children living in Greene County, MO and the St. Louis modeling domain in MO. Notably, this
23 second approach considers time spent in different microenvironments, as well as time spent at
24 elevated ventilation rates. In each of the first two approaches, health risks have been
25 characterized by comparing estimates of air quality or exposure to potential 5-minute health
26 effect benchmarks These benchmarks are based on controlled human exposure studies
27 involving known SO2 exposure levels and corresponding decrements in lung function, and/or
28 increases in respiratory symptoms in asthmatics at elevated ventilation rates (e.g. while
29 exercising; see section 6.2 for further discussion of benchmark levels). In addition to these
30 analyses, staff also conducted a quantitative risk assessment for lung function responses
31 associated with 5-minute exposures to characterize SO2-related health risks. This assessment
March 2009 289 Draft - Do Not Quote or Cite
-------
1 combined outputs from the exposure analysis with estimated exposure-response functions
2 derived from the controlled human exposure literature to estimate the number, and percent of
3 exposed asthmatics that would experience moderate or greater lung function responses per year
4 and to estimate the total number of occurrences of these lung function responses per year
5 (Chapter 9).
6 In making judgments as to whether SO2-induced effects should be regarded as adverse to
7 the health of individuals, staff has relied upon the guidelines published by the American
8 Thoracic Society (ATS) (ATS 2000) and conclusions from the ISA. The ATS notes that an air
9 pollution-induced shift in a population distribution of a given health-related endpoint (e.g., lung
10 function) should be considered adverse, even if this shift does not result in the immediate
11 occurrence of illness in any one individual in the population (ATS 2000). The ATS also
12 recommends that transient loss in lung function with accompanying respiratory symptoms
13 attributable to air pollution be considered adverse. However, the ISA cautions that symptom
14 perception is highly variable among asthmatics even during severe episodes of asthmatic
15 bronchoconstriction, and that an asymptomatic decrease in lung function may pose a significant
16 health risk to asthmatic individuals as it is less likely that these individuals will seek treatment
17 (ISA section 3.1.3). In addition, regarding decrements in lung function and respiratory
18 symptoms, we note the following:
19 5-30% of exercising asthmatics will experience moderate or greater lung function
20 decrements (i.e. > 100% increase in sRaw and/or a > 15% decrease in FEVi)
21 following exposure to 200- 300 ppb SC>2 for 5-10 minutes (ISA, section 3.1).
22
23 20-60% of exercising asthmatics will experience moderate or greater lung
24 function decrements (i.e > 100% increase in sRaw and/or a > 15% decrease in
25 FEVi) following exposure to 400- 1000 ppb SO2 for 5-10 minutes (ISA, Table 5-
26 3).
27
28 At concentrations > 400 ppb, moderate or greater statistically significant
29 decrements in lung function are frequently associated with respiratory symptoms
30 (ISA, section 3.1).
31
32 Over 20 million people in the U.S. have asthma (ISA, for NOx, Table 4.4-1).
33
34 Overall, based on the ATS guidance mentioned above and the potential size of the population
35 affected, staff suggests that the Agency may want to consider moderate or greater SO2-induced
March 2009 290 Draft - Do Not Quote or Cite
-------
1 lung function decrements (i.e. lung function responses) and/or related increases in respiratory
2 symptoms as representing adverse health effects for the asthmatic population.
3 10.3.3.1 Key Uncertainties
4 The way in which exposure and risk results will inform ultimate decisions regarding the
5 SC>2 standard will depend upon the weight placed on each of the analyses when uncertainties
6 associated with those analyses are taken into consideration. Sources of uncertainty associated
7 with each of the analyses (air quality, exposure, and quantitative risk) are briefly presented below
8 and are described in more detail in Chapters 7-9 of this document. Although we are discussing
9 these uncertainties within the context of the adequacy of the 24-hour standard, they apply equally
10 to consideration of the annual, as well as alternative 1-hour standards.
11 Air Quality Analysis
12 A number of key uncertainties should be considered when interpreting air quality results
13 with regard to decisions on the standards. Such uncertainties are highlighted below, and these, as
14 well as other sources of uncertainty are discussed in greater depth in section 7.4 of this
15 document.
16 In order to simulate just meeting the current standards, and many of the alternative 1-hour
17 standards, an upward adjustment of recent ambient SO2 concentrations was required. We
18 note that this adjustment does not reflect a judgment that levels of 862 are likely to
19 increase under the current standard or any of the potential alternative standards under
20 consideration. Rather, these adjustments reflect the fact that the current standard, as well
21 as some of the alternatives under consideration, could allow for such increases in ambient
22 SC>2 concentrations. In adjusting air quality to simulate just meeting these standards, we
23 have assumed that the overall shape of the distribution of SC>2 concentrations would not
24 change. While we believe this is a reasonable assumption in the absence of evidence
25 supporting a different distribution and we note that available analyses support this
26 approach (Rizzo, 2008), we recognize this as an important uncertainty. It may be an
27 especially important uncertainty for those scenarios where considerable upward
28 adjustment is required to simulate just meeting one or more of the standards.
29
30 In recognizing the limited geographic span of monitors reporting 5-minute maximum SC>2
31 levels, staff developed an approach to statistically estimate 5-minute 862 concentrations
32 from 1-hour average SC>2 concentrations. This method uses monitors that reported both
33 5-minute and 1-hour average SC>2 concentrations and the associated variability in their
34 measurements (from the years 1997-2007) as a surrogate for variability in source
35 characteristics that may impact 862 levels at a given monitor. As a result, this method
36 assumes that source emissions present at the time of the measurement are similar to more
37 recent source emissions. This could add uncertainty in the number of estimated
38 exceedances in areas where source emissions have changed. However, peak-to-mean
March 2009 291 Draft - Do Not Quote or Cite
-------
1 ratios (PMRs) do not show any apparent trend with monitoring year and have averaged
2 around 1.6 when considering the 5-minute measurement data over the period analyzed.
3 This indicates that the use of the older ambient monitoring data in developing the
4 statistical model used for estimating 5-minute concentrations may have a negligible
5 impact on the predicted concentrations. In addition, there is uncertainty in the extent to
6 which the relationships used to estimate 5-minute maximum concentrations from
7 measured 1-hour average concentrations reflects the actual relationship in the locations
8 and over the time periods of interest. However, we note general agreement between
9 analyses restricted to the subset monitors where 5-minute maximum levels were actually
10 reported, and corresponding analyses where statistically estimated 5-minute maximum
11 concentrations were derived from monitors reporting 1-hour concentrations (e.g. see
12 Figures 7-12 and 7-15). Thus, measured and modeled results in the air quality analysis
13 appear to be in general agreement.
14
15 The human exposure studies that form the basis for potential health effect benchmark
16 levels include mild and moderate asthmatics. For ethical reasons, more severely affected
17 asthmatics are not included in these analyses. This is important because severe asthmatics
18 may be more susceptible than mild or moderate asthmatics to the respiratory effects of
19 SO2 exposure. Therefore, the potential health effect benchmarks based on these studies
20 could underestimate risks in populations with greater susceptibility. Although approaches
21 to classifying asthma severity differ, some estimates indicate that over half of asthmatics
22 could be classified as moderate or severe (Fuhlbrigge et al., 2002; Stout et al., 2006).
23
24 St Louis and Green Counties Exposure Analysis
25 A number of key uncertainties should be considered when interpreting the St. Louis and
26 Greene County exposure results with regard to decisions on the standards. Such uncertainties are
27 highlighted below, and these, as well as other sources of uncertainty are also discussed in greater
28 depth in section 8.11 of this document.
29 Details regarding the modeling of non-point and background area sources in AERMOD
30 are addressed in section 8.4.3. In brief, two of the main uncertainties associated with
31 these sources are the temporal and spatial profiles used in simulating their releases. Staff
32 obtained emission strengths from the 2002 National Emissions Inventory (NEI).
33 However, only annual total emissions at the county level are provided. Thus, to better
34 parameterize these emissions for the hourly, census block-level dispersion modeling, we
35 relied on additional data and an optimization algorithm (described in section 8.4.3).
36 Overall, the uncertainties associated with the temporal and spatial profiles of SC>2
37 emissions is characterized as a medium uncertainty and is thought to bias concentrations
38 of SC>2 in both directions (see Table 8-13).
39
40 Commuting pattern data were derived from the 2000 U.S. Census. The commuting data
41 addresses only home-to-work travel. A few simplifying assumptions needed to be made
42 to allow for practical use of this data base to reflect a simulated individual's commute.
43 First, there were a few commuter identifications that necessitated a restriction of their
44 movement from a home block to a work block. This is not to suggest that they never
March 2009 292 Draft - Do Not Quote or Cite
-------
1 travelled on roads, only that their home and work blocks were the same. This includes
2 the population not employed outside the home, individuals indicated as commuting
3 within their home block, and individuals that commute over 120 km a day. This could
4 lead to either over- or under-estimations in exposure if they were in fact to visit a block
5 with either higher or lower SC>2 concentrations. Given that the number of individuals
6 who meet these conditions is likely a small fraction of the total population, and that the
7 bias with respect to SC>2 exposures above health benchmark levels is likely in either
8 direction, the overall uncertainty is considered low.
9
10 Although several of the APEX microenvironments account for time spent in travel, the
11 travel is assumed to always occur in basically a composite of the home and work block.
12 No other provision is made for the possibility of passing through other census blocks
13 during travel. This could contribute to bias in SC>2 exposures above benchmark levels in
14 either direction. In addition, the commuting route (i.e., which roads individuals are
15 traveling on during the commute) is not accounted for and this may also contribute to
16 bias in either direction.
17
18 The best estimate of asthma prevalence was generated using a comprehensive data set
19 that provides variability in asthma prevalence rates based on age (CDC, 2007). However,
20 it is possible that this data overestimates asthma prevalence rates in some areas while
21 underestimating it in others. It is unknown how this uncertainty would bias exposure
22 results.
23
24
25 St Louis and Green Counties Quantitative Risk Analysis
26 A number of key uncertainties should be considered when interpreting the St. Louis and
27 Greene County quantitative risk estimated for lung function responses with regard to decisions
28 on the standards. Such uncertainties are highlighted below, and these, as well as other sources of
29 uncertainty are also discussed in greater depth in section 9.3 of this document.
30 It was necessary to estimate responses at 862 levels below the lowest exposure levels
31 used in the controlled human exposure studies (i.e., below 200 ppb). We have developed
32 probabilistic exposure-response relationships using two different functional forms (i.e.,
33 probit and 2-parameter logistic), but nonetheless there remains greater uncertainty in
34 responses below 0.2 ppm because of the lack of experimental data.
35
36 The risk assessment assumes that the SO2-induced responses for individuals are
37 reproducible. We note that this assumption has some support in that one study (Linn et
38 al., 1987) exposed the same subjects on two occasions to 600 ppb and the authors
39 reported a high degree of correlation while observing a much lower correlation for the
40 lung function response observed in the clean air with exercise exposure.
41
42 Because the vast majority of controlled human exposure studies investigating lung
43 function responses were conducted with adult subjects, the risk assessment relies on data
44 from adult asthmatic subjects to estimate exposure-response relationships that have been
March 2009 293 Draft - Do Not Quote or Cite
-------
1 applied to all asthmatic individuals, including children. The ISA (section 3.1.3.5)
2 indicates that there is a strong body of evidence that suggests adolescents may experience
3 many of the same respiratory effects at similar SC>2 levels, but recognizes that these
4 studies administered SO2 via inhalation through a mouthpiece (which can result in an
5 increase in lung 862 uptake) rather than an exposure chamber. Therefore, the uncertainty
6 is greater in the risk estimates for asthmatic children.
7
8 Because the controlled human exposure studies used in the risk assessment involved only
9 SC>2 exposures, it is assumed that estimates of SO2-induced health responses are not
10 affected by the presence of other pollutants (e.g., PM2.5, 63, NC>2).
11
12 10.3.3.2 Assessment Results
13 As previously mentioned, the ISA finds the evidence for an association between
14 respiratory morbidity and SC>2 exposure to be "sufficient to infer a causal relationship" (ISA
15 section 5.2) and that the "definitive evidence" for this conclusion comes from the results of
16 controlled human exposure studies demonstrating decrements in lung function and/or respiratory
17 symptoms in exercising asthmatics (ISA, section 5.2). Accordingly, the exposure and risk
18 analyses presented in this document focused on exposures and risks associated with 5-minute
19 peaks of 862 in excess of potential health effect benchmark values derived from the human
20 exposure literature. These potential health effect benchmark levels span a range of 100 to 400
21 ppb. In brief, the 400 ppb benchmark represents the lowest exposure level in free breathing
22 chamber studies at which moderate or greater lung function responses are consistently
23 accompanied by respiratory symptoms in exercising asthmatics. The 100 ppb benchmark takes
24 into consideration that the LOEL for moderate or greater lung function decrements in exercising
25 asthmatics participating in free breathing chamber studies is 200 ppb, but that the asthmatics
26 participating in these studies might not represent the most 862 sensitive population (e.g. severe
27 asthmatics are excluded from these studies). Additional discussion concerning the selection of
28 the potential health effect benchmark levels can be found in section 6.2 of this document.
29 Air Quality Assessment
30 The results of our air quality assessment provide additional perspective on the public
31 health impacts of exposure to ambient levels of SC>2. In considering these results, we first note
32 that the benchmark values derived from the controlled human exposure literature are associated
33 with a 5-minute averaging time, but very few monitors in the U.S. report measured 5-minute
34 concentrations since it is not required. As a result, staff developed a statistical relationship to
March 2009 294 Draft - Do Not Quote or Cite
-------
1 estimate the highest 5-minute level in an hour, given a reported 1-hour average 862
2 concentration (see section 7.2.3). Thus, many of the outputs of the air quality analysis are
3 presented with respect to statistically estimated 5-minute concentrations in excess of potential
4 health effect benchmark values. Results of these analyses, as they relate to the adequacy of the
5 current standards, are discussed below.
6 A key output of the air quality analysis (i.e. where air quality serves as a surrogate for
7 exposure) is the predicted number of statistically estimated 5-minute daily maximum SC>2
8 concentrations above benchmark levels given air quality simulated to just meet the level of the
9 current 24-hour or annual 862 standards, whichever is controlling for a given county. Under this
10 scenario, in 40 counties selected for detailed analysis, we note that the predicted mean number of
11 5-minute daily maximum concentrations > 400 ppb ranges from 1-102 days per year, with most
12 counties in this analysis experiencing a mean of at least 20 days per year when 5-minute daily
13 SC>2 concentrations exceed 400 ppb (Table 7-13). In addition, the predicted mean number of 5-
14 minute daily maximum concentrations >200 ppb ranges from 22-171 days per year, with about
15 half of the counties in this analysis experiencing > 70 days per year when 5-minute daily
16 maximum 862 concentrations exceed 400 ppb (Table 7-11).
17 Exposure Assessment
18 When considering the Missouri exposure results as they relate to the adequacy of the
19 current standard, we focus on the number of asthmatics at elevated ventilation rates estimated to
20 experience at least one benchmark exceedence given air quality that is adjusted upward to
21 simulate just meeting the current 24-hour standard (i.e. the controlling standard in St. Louis). We
22 note that in these analyses, if SC>2 concentrations are such that the St Louis area just meets the
23 current standard, approximately 13% (-14,000) of asthmatics would be estimated to experience
24 at least one SC>2 exposure concentration greater than or equal to the 400 ppb benchmark level
25 while at elevated ventilation rates (Figure 8-17). Similarly, approximately 46% (-48,000) of
26 asthmatics would be expected to experience at least one 862 exposure concentration greater than
27 or equal to the 200 ppb benchmark level while at elevated ventilation rates. When the St. Louis
28 results are restricted to asthmatic children at elevated ventilation rates, approximately 25%
29 (-10,000) and 73% (-31,000) of these children would be estimated to experience at least one
30 SC>2 exposure concentration greater than or equal to the 400 ppb and 200 ppb benchmark levels,
31 respectively (Figure 8-17).
March 2009 295 Draft - Do Not Quote or Cite
-------
1 In Greene County, results of the exposure analysis indicate that when air quality is
2 adjusted to just meet the current 24-hour standard, considerably fewer asthmatics are exposed to
3 SC>2 concentrations in excess of benchmark levels. An estimated <0.1% (13) and 0.6% (139) of
4 asthmatics at elevated ventilation rates would be expected to experience at least one SC>2
5 exposure concentration greater than or equal to the 400 ppb and 200 ppb benchmarks
6 respectively (Figure 8-14). When the Greene County results are restricted to asthmatic children
7 at elevated ventilation rates, approximately <0.1% (4) and 0.9% (72) of these children would be
8 estimated to experience at least one 862 exposure concentration greater than or equal to the 400
9 ppb and 200 ppb benchmark levels respectively (Figure 8-14).
10 Risk results
11 When considering the St. Louis risk results as they relate to the adequacy of the current
12 standard, we note the percent of asthmatics at elevated ventilation rates likely to experience at
13 least one lung function response given air quality that is adjusted upward to simulate just
14 meeting the current standards. Under this scenario, 13.1% (-13,000) of exposed asthmatics at
15 elevated ventilation rates are estimated to experience at least one moderate lung function
16 response (defined as an increase in sRaw > 100% (Table 9-5)). Furthermore, 5.4% (-5,500) of
17 exposed asthmatics at elevated ventilation rates are estimated to experience at least one large
18 lung function response (defined as an increase in sRaw > 200% (Table 9-5)). We also note that
19 estimates from this analysis indicate that the percentage of exposed asthmatic children in the St.
20 Louis modeling domain estimated to experience at least one moderate or large lung function
21 response is somewhat greater than the percentage for the asthmatic population as a whole (Table
22 9-8).
23 When considering the Greene County risk results, as in the exposure analysis, we note
24 that compared to St. Louis, there are far fewer exposures in Greene County, and therefore, there
25 are far fewer numbers of asthmatics predicted to have a given lung function response. That is, in
26 Greene County 1% (210) of exposed asthmatics at elevated ventilation rates are estimated to
27 experience at least one moderate lung function response (Table 9-5). Furthermore, 0.3% (70) of
28 exposed asthmatics at elevated ventilation rates are estimated to experience at least one large
29 lung function response (Table 9-5). Similar to St. Louis, the percentage of exposed asthmatic
30 children estimated to experience at least one moderate or large lung function response is greater
31 than the percentage for the asthmatic population as a whole (Table 9-8).
March 2009 296 Draft - Do Not Quote or Cite
-------
1 10.3.3.3 Conclusions regarding the adequacy of the 24-hour standard
2 As noted above, several lines of scientific evidence are relevant to consider when making
3 a decision regarding the adequacy of the current 24-hour standard to protect the public health.
4 These include causality judgments made in the ISA, as well as the human exposure and
5 epidemiological evidence supporting those judgments. In particular, we note that numerous
6 epidemiological studies reporting positive associations between ambient SC>2 and respiratory
7 morbidity endpoints were conducted in locations that met the current 24-hour standard. To the
8 extent that these considerations are emphasized, the adequacy of the current standard to protect
9 the public health would clearly be called into question. This suggests consideration of a revised
10 24-hour standard and/or that an additional shorter-averaging time standard may be needed to
11 provide additional health protection for sensitive groups, including asthmatics and individuals
12 who spend time outdoors at elevated ventilation rates. Moreover, this conclusion also suggests
13 that an alternative SC>2 standard(s) should protect against health effects ranging from lung
14 function responses and increased respiratory symptoms following 5-10 minute peak SC>2
15 exposures, to increased respiratory symptoms and respiratory-related ED visits and hospital
16 admissions associated with SC>2 exposures >l-hour.
17 In examining the exposure- and risk-based information with regard to the adequacy of the
18 current 24-hour 862 standard to protect the public health, we note that the results described
19 above (and in more detail in Chapters 7-9) indicate risks associated with air quality adjusted
20 upward to simulate just meeting the current standard that can reasonably be judged important
21 from a public health perspective. Therefore, exposure- and risk-based considerations reinforce
22 the scientific evidence in supporting the conclusion that consideration should be given to
23 revising the current 24-hour standard and/or setting a new shorter averaging time standard (e.g.
24 1-hour) to provide increased public health protection, especially for sensitive groups (e.g.
25 asthmatics), from SO2-related adverse health effects.
26 10.4 ADEQUACY OF THE CURRENT ANNUAL STANDARD
27 10.4.1 Introduction
28 In the last review of the SC>2 NAAQS, retention of the annual standard was largely based
29 on an assessment of qualitative evidence gathered from a limited number of epidemiological
30 studies. The strongest evidence for an association between annual SC>2 concentrations and
31 adverse health effects in the 1982 AQCD was from a study conducted by Lunn et al (1967). The
March 2009 297 Draft - Do Not Quote or Cite
-------
1 authors found that among children a likely association existed between chronic upper and lower
2 respiratory tract illnesses and annual 862 levels of 70 -100 ppb in the presence of 230-301 ug/m3
3 black smoke. Three additional studies described in the 1986 Second Addendum also suggested
4 that long-term exposure to SC>2 was associated with adverse respiratory effects. Notably, studies
5 conducted by Chapman et al. (1985) and Dodge et al. (1985) found associations between long-
6 term SC>2 concentrations (with or without high particle concentrations) and cough in children and
7 young adults. However, it was noted that there was considerable uncertainty associated with
8 these studies because they were conducted in locations subject to high, short-term peak 862
9 concentrations (i.e. locations near point sources); therefore it was difficult to discern whether this
10 increase in cough was the result of long-term, low level 862 exposure, or repeated short-term
11 peak SO2 exposures.
12 It was concluded in the last review that there was no quantitative rationale to support a
13 specific range for an annual standard (EPA, 1994b). However, it was also found that while no
14 single epidemiological study provided clear quantitative conclusions, there appeared to be some
15 consistency across studies indicating the possibility of respiratory effects associated with long-
16 term exposure to 862 just above the level of the existing annual standard (EPA, 1994b). In
17 addition, air quality analyses conducted during the last review indicated that the short-term
18 standards being considered (1-hour and/or 24-hour) could not by themselves prevent long-term
19 concentrations of 862 from exceeding the level of the existing annual standard in several large
20 urban areas. Ultimately, both the scientific evidence and the air quality analyses were used by the
21 Administrator to conclude that retaining the existing annual standard was requisite to protect
22 human health.
23 10.4.2 Evidence-based considerations
24 The ISA presents numerous studies published since the last review examining possible
25 associations between long-term SC>2 exposure and mortality and morbidity outcomes. This
26 includes discussion of additional epidemiological studies examining possible associations
27 between long-term 862 exposure and respiratory effects in children (in part, the basis for
28 retaining the annual standard in the last review; see section 10.4.1). In addition, the ISA presents
29 results from epidemiological and animal toxicological studies published since the last review
30 examining possible associations between long-term ambient SC>2 concentrations and adverse
March 2009 298 Draft - Do Not Quote or Cite
-------
1 respiratory, cardiovascular, and birth outcomes, as well as carcinogenesis. The current ISA also
2 discusses the possible association between long-term 862 exposure and mortality.
3 As an initial consideration with regard to the adequacy of the current annual standard,
4 staff notes that the evidence relating long-term (weeks to years) SO2 exposure to adverse health
5 effects (respiratory morbidity, carcinogenesis, adverse prenatal and neonatal outcomes, and
6 mortality) is judged to be "inadequate to infer the presence or absence of a causal relationship"
7 (ISA, Table 5-3). That is, the ISA finds this health evidence to be of insufficient quantity,
8 quality, consistency, or statistical power to make a determination as to whether 862 is truly
9 associated with these health endpoints (ISA Table 1-2). With respect specifically to respiratory
10 morbidity in children, the ISA presents recent epidemiological evidence of an association with
11 long-term exposure to SO2 (ISA section 3.4.2). However, the ISA finds the strength of these
12 epidemiological studies to be limited because of 1) variability in results across studies with
13 respect to specific respiratory morbidity endpoints, 2) high correlations between long-term
14 average SC>2 and co-pollutant concentrations, particularly PM, and 3) a lack of evaluation of
15 potential confounding (ISA section 3.4.2.1). In addition, the ISA finds that results from animal
16 toxicological studies do not provide strong biological plausibility for an association between
17 long-term ambient 862 concentrations and respiratory morbidity (ISA, Table 5-3). Thus, the
18 current evidence does not provide support for an annual 862 standard for purposes of protecting
19 against long-term health effects.
20 We also note that many epidemiological studies demonstrating positive associations
21 between 1- to 24-hour ambient SC>2 concentrations and respiratory symptoms, ED visits, and
22 hospitalizations, were conducted in areas where ambient SC>2 concentrations were well below the
23 current annual NAAQS. This evidence suggests that the current annual standard is not providing
24 adequate protection against health effects associated with shorter-term SC>2 concentrations.
25 10.4.3 Risk-based considerations
26 Results of the risk characterization based on the air quality assessment provide additional
27 insight into the adequacy of the current annual standard. Analyses in this document describe the
28 extent to which the current annual standard provides protection against 5-minute peaks of 862 in
29 excess of potential health effect benchmark levels. Figure 7-10 counts the number of measured
30 5-minute daily maximum SC>2 concentrations above the 100 -400 ppb benchmark levels for a
31 given annual average SC>2 concentration. None of the monitors in this data set contained annual
March 2009 299 Draft - Do Not Quote or Cite
-------
1 average 862 concentrations above the current NAAQS, but several of the monitors in several of
2 the years frequently reported 5-minute daily maximum concentrations above the potential health
3 effect benchmark levels. Many of these monitors where frequent exceedances are reported had
4 annual average SC>2 concentrations between 5 and 15 ppb, with little to no correlation between
5 the annual average SC>2 concentration and the number of 5-minute daily maximum
6 concentrations above potential health effect benchmark levels. This suggests that the annual
7 standard adds little in the way of protection against 5-minute peaks of SC>2 (see section 7.3.1).
8 10.4.4 Conclusions regarding the adequacy of the current annual standard
9 As noted, the ISA concludes that the evidence relating long-term (weeks to years) 862
10 exposure to adverse health effects (respiratory morbidity, carcinogenesis, adverse prenatal and
11 neonatal outcomes, and mortality) is "inadequate to infer the presence or absence of a causal
12 relationship" (ISA, Table 5-3). The ISA also reports that many epidemiological studies
13 demonstrating positive associations between short-term (>l-hour) SC>2 concentrations and
14 respiratory symptoms, as well as ED visits and hospitalizations, were conducted in areas where
15 annual ambient SC>2 concentrations were well below the level of the current annual NAAQS. In
16 addition, analyses conducted in this REA suggest that the current annual standard is not
17 providing protection against 5-10 minute peaks of 862. Taken together, the scientific evidence
18 and the risk and exposure information suggest that the current annual 862 standard does not
19 provide adequate protection from the health effects associated with shorter-term exposures to
20 862 and that consideration should be given to revoking the annual standard in conjunction with
21 setting an appropriate short-term standard(s).
22 10.5 POTENTIAL ALTERNATIVE STANDARDS
23 10.5.1 Indicator
24 In the last review, EPA focused on 862 as the most appropriate indicator for ambient
25 SOX. This was in large part because other gaseous sulfur oxides (e.g. 863) are likely to be found
26 at concentrations many orders of magnitude lower than SO2 in the atmosphere, and because most
27 all of the health effects and exposure information was for SC>2. The current ISA has again found
28 this to the case, and while the presence of gaseous SOX species other than SC>2 has been
29 recognized, no alternative to SC>2 has been advanced as being a more appropriate surrogate for
30 ambient gaseous SOX. Importantly, controlled human exposure studies and animal toxicology
March 2009 300 Draft - Do Not Quote or Cite
-------
1 studies provide specific evidence for health effects following exposure to 862. Epidemiological
2 studies also typically report levels of 862, as opposed to other gaseous SOX. Because emissions
3 that lead to the formation of SO2 generally also lead to the formation of other SOX oxidation
4 products, measures leading to reductions in population exposures to SC>2 can generally be
5 expected to lead to reductions in population exposures to other gaseous SOX. Therefore, meeting
6 an SC>2 standard that protects the public health can also be expected to provide some degree of
7 protection against potential health effects that may be independently associated with other
8 gaseous SOX even though such effects are not discernable from currently available studies
9 indexed by 862 alone. Given these key points, staff judges that the available evidence supports
10 the retention of SC>2 as the indicator in the current review.
11 10.5.2 Averaging Time
12 The current 24-hour and annual averaging times for the primary SC>2 NAAQS were
13 originally set in 1971. As previously described, (section 10.3.1) the 24-hour NAAQS was based
14 on epidemiological studies that observed associations between 24-hour average SC>2 levels and
15 adverse respiratory effects and daily mortality (EPA 1982, 1994b). The annual standard was
16 supported by a few epidemiological studies that found an association between adverse
17 respiratory effects and annual average 862 concentrations (EPA 1982, 1994b). Based on
18 currently available evidence, the issue of averaging time is being reconsidered in the current
19 review. In order to inform these judgments, staff has considered causality judgments from the
20 ISA, results from experimental and epidemiological studies, and 862 air quality correlations.
21 These considerations are described in more detail below.
22 10.5.2.1 Evidence-based considerations
23 As an initial consideration regarding the most appropriate averaging time (e.g., short-
24 term, long-term, or a combination of both) for alternative SC>2 standard(s), we note that the ISA
25 finds evidence relating long-term (weeks to years) SC>2 exposures to adverse health effects to be
26 "inadequate to infer the presence or absence of a causal relationship" (ISA, Table 5-3). In
27 contrast, the ISA judges evidence relating short-term (5-minutes to 24-hours) 862 exposure to
28 respiratory morbidity to be "sufficient to infer a causal relationship" and short-term exposure to
29 SC>2 and mortality to be "suggestive of a causal relationship" (ISA, Table 5-3). Taken together,
March 2009 301 Draft - Do Not Quote or Cite
-------
1 these judgments most directly support standard averaging time(s) that focus protection on 862
2 exposures from 5-minutes to 24-hours.
3 In considering the level of support available for specific short-term averaging times, we
4 first note the strength of evidence from human exposure and epidemiological studies. Human
5 clinical studies exposed exercising asthmatics to 5-10 minute peak concentrations of SC>2 and
6 consistently found decrements in lung function and/or respiratory symptoms. Importantly, the
7 ISA describes the controlled human exposure studies as being the "definitive evidence" for its
8 causal association between short-term (5-minutes to 24-hours) 862 exposure and respiratory
9 morbidity (ISA section 5.2). Supporting the human clinical evidence is a relatively small body
10 of epidemiological evidence describing positive associations between 1-hour maximum 862
11 levels and respiratory symptoms as well as hospital admissions and ED visits for all respiratory
12 causes and asthma (ISA tables 5.4 and 5.5). In addition to the 1-hour epidemiological evidence,
13 there is a considerably larger body of epidemiological studies reporting associations between 24-
14 hour average SC>2 levels and respiratory symptoms, as well as hospitalizations and ED visits for
15 all respiratory causes and asthma. However, as in the last review, there remains considerable
16 uncertainty as to whether these associations are due to 24-hour average 862 exposures, or
17 exposure (or multiple exposures) to short-term peaks of 862 within a 24-hour period. That is, the
18 ISA notes that it is possible that associations observed in these 24-hour studies are being driven,
19 at least in part, by short-term peaks of 862. More specifically, when describing epidemiological
20 studies observing associations between ambient SO2 and respiratory symptoms, the ISA states
21 "that it is possible that these associations are determined in large part by peak exposures within a
22 24-hour period" (ISA, section 5.2). The ISA also states that the respiratory effects following 5-
23 10 minute SC>2 exposures in controlled human exposure studies provides a basis for a progression
24 of respiratory morbidity that could result in increased ED visits and hospital admissions (ISA,
25 section 5.2).
26 The controlled human exposure evidence described above provides support for an
27 averaging time that protects against 5-10 minute peak exposures. Results from the
28 epidemiological evidence provides support for both 1-hour and 24-hour averaging times.
29 However, it is worth noting that the effects observed in epidemiological studies may least be in
30 part, especially in 24-hour epidemiological studies, due to shorter-term peaks of SC>2. Overall,
31 the evidence mentioned above suggests that a primary concern with regard to averaging time is
March 2009 3 02 Draft - Do Not Quote or Cite
-------
1 the level of protection provided against 5-10 minute peak 862 exposures. Additionally, even
2 though there is a greater degree of uncertainty, the evidence described above also suggests it
3 would be appropriate to consider the ability of averaging times under consideration to protect
4 against both 1-hour daily maximum and 24-hour average SC>2 concentrations.
5 10.5.2.2 Risk-based considerations
6 The shortest averaging time for a current primary SC>2 standard is 24-hours. It is
7 therefore instructive to evaluate the potential for a standard based on 24-hour average SC>2
8 concentrations to provide protection against 5-minute peak SC>2 exposures. Table 10-1 reports
9 the ratio between 99th percentile 5-minute maximum and 99th percentile 24-hour average SC>2
10 concentrations for 42 monitors reporting measured 5-minute data from 2004-2006. Across this
11 set of monitors in 2004, ratios of 99th percentile 5-minute maximum to 99th percentile 24-hour
12 average SO2 concentrations spanned a range of 2.0 to 14.1, with an average ratio of 6.7 (Table
13 10-1). These results suggests that a standard based on 24-hour average SC>2 concentrations
14 would not likely be an effective or efficient approach for addressing 5-minute peak SC>2
15 concentrations. That is, using a 24-hour average standard to address 5-minute peaks would
16 likely result in over controlling in some areas, while under controlling in others. In addition, it is
17 important to note that this analysis also suggests that a 5-minute standard would not likely be an
18 effective or efficient means for controlling 24-hour average SC>2 concentrations.
19 Table 10-1 also reports the ratios between 99th percentile 5-minute maximum and 99th
20 percentile 1-hour daily maximum 862 levels from this set of monitors. Compared to the ratios
21 discussed above (5-minute maximum to 24-hour average), there is far less variability between 5-
22 minute maximum and 1-hour daily maximum ratios. More specifically, 39 of the 42 monitors
23 had 99th percentile 5-minute maximum to 99th percentile 1-hour daily maximum ratios in the
24 range of 1.4 to 2.5 (Table 10-1). The remaining 3 monitors had ratios of 3.6, 4.2 and 4.6
25 respectively. Overall, this relatively narrow range of ratios suggests that a standard with a 1-
26 hour averaging time would be more efficient and effective at limiting 5-minute peaks of SC>2
27 than a standard with a 24-hour averaging time. In addition, these results also suggest that a 5-
28 minute standard could be a relatively effective means of controlling 1-hour daily maximum 862
29 concentrations.
March 2009 303 Draft - Do Not Quote or Cite
-------
Table 10-1 Ratios of 99th percentile 5-minute maximums to 99th percentile 24-hour
average and 1-hour daily maximum SO2 concentrations for monitors reporting
measured 5-minute data from years 2004-200660
Monitor ID
110010041
191770005
290930030
290930031
370670022
120890005
190330018
190450019
191390016
191390017
191390020
191630015
191770006
291630002
380130002
380150003
380590002
380590003
540990003
540990004
540990005
541071002
051190007
051390006
080310002
290770026
290770037
290990004
291370001
301110084
380070002
380130004
380171004
380250003
380530002
380530104
380530111
380570004
380650002
381050103
381050105
420070005
# of years
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5-minute max: 24-
hour average
3.8
4.1
2.9
3.4
5.5
9.4
8.2
11.2
6.9
9.8
6.2
4.5
3.1
7
8.4
4.8
5.6
8.4
2
5.9
5.3
8.1
4.7
12
5.5
6.6
8.1
14.1
2.4
5.8
6.3
6.1
4.3
5.1
4
7.9
11.6
7.5
7.3
9.7
6.4
10.5
5-minute max: 1-hour
daily maximum
.4
.7
.2
.6
.6
2.2
2
3.6
1.5
2.2
.8
.5
.3
.8
.9
.6
.9
.9
.4
2
2
1.6
2.2
2.3
1.7
1.7
2.2
2.5
.3
.6
2.1
.8
.6
.6
.4
4.2
4.6
2.3
1.9
2.5
2.4
2
60 5-minute maximum, 1-hour daily maximum, and 24-hour average 99th percentile values were calculated for each
year a given monitor was in operation from 2004-2006. If a monitor was in operation for multiple years over that
span, 99th percentile values were calculates for each year, averaged, and then the appropriate ratio was determined.
March 2009
304
Draft - Do Not Quote or Cite
-------
1 As noted, the strongest evidence for SCVinduced respiratory effects is associated with an
2 averaging time of 5-10 minutes. Furthermore, as mentioned above epidemiological studies of
3 respiratory symptoms and ED visits and hospitalizations for all respiratory causes and asthma
4 provide evidence for SO2-associated respiratory effects at averaging times ranging from 1 to 24-
5 hours. Notably, there is a greater degree of uncertainty associated with the epidemiological
6 literature because: (1) results of these studies are generally positive, but often not statistically
7 significant in single pollutant models; (2) only a limited subset of these studies investigated
8 potential confounding by co-pollutants; and (3) it is very possible that associations observed in
9 these studies are being driven at least in part, by short-term peaks of 862 within a 24-hour period.
10 Despite these uncertainties, since the majority of the epidemiological literature is associated with
11 a 24-hour averaging time, staff finds that it would be instructive to evaluate the potential of the
12 1-hour daily maximum standards analyzed in this REA to provide protection against 24-hour
13 average SO2 exposures. The 99th percentile 24-hour average SC>2 concentrations in cities where
14 key U.S. ED visit and hospitalization studies (for all respiratory causes and asthma) were
15 conducted ranged from 16 ppb to 115 ppb (Thomson, 2009). Moreover, effect estimates that
16 remained statistically significant in multipollutant models with PM were found in cities with 99th
17 percentile 24-hour average 862 concentrations ranging from approximately 36 ppb to 64 ppb.
18 Table 10-2 suggests that a 99th percentile 1-hour daily maximum standard set at a level of 50-
19 100 ppb would limit 99th percentile 24-hour average 862 concentrations observed in
20 epidemiological studies where statistically significant results were observed in multi-pollutant
21 models with PM. That is, given a 50 ppb 99th percentile 1-hour daily maximum standard, none
22 of the 39 counties analyzed would be expected to have 24-hour average SC>2 concentrations > 36
23 ppb; and, given a 100 ppb 99th percentile 1-hour daily maximum standard, only 5 of the 39
24 counties (Linn, Union, Bronx, Fairfax, and Wayne) included in this analysis would be estimated
25 to have 99th percentile 24-hour average 862 concentrations > 36 ppb. This analysis was also
26 done for the years 2005 and 2006 and similar results were found (Appendix D).
March 2009 305 Draft - Do Not Quote or Cite
-------
ith
Table 10-2. 99 percentile 24-hour average SO2 concentrations for 2004 given just
meeting the alternative 1-hour daily maximum standards analyzed in the risk
assessment (note: concentrations in ppb).
State
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
MI
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
VI
County
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
St Croix
99th percentile
50
6
12
10
12
7
8
9
12
10
21
17
17
12
9
17
19
18
23
13
14
17
10
12
16
12
10
11
11
15
15
17
8
9
23
12
15
10
30
14
100
12
23
20
24
13
15
18
24
19
42
34
33
24
18
33
38
36
47
27
27
34
19
24
32
23
20
23
22
31
31
34
16
17
46
24
29
20
59
27
150
18
35
30
36
20
23
27
36
29
64
51
50
36
27
50
57
54
70
40
41
51
29
36
47
35
30
34
33
46
46
51
24
26
69
37
44
30
89
41
200
25
47
40
48
27
31
36
48
39
85
68
66
48
36
66
76
72
93
54
54
67
39
48
63
47
40
45
44
62
61
68
32
35
92
49
58
40
119
54
250
31
59
50
60
33
39
45
60
48
106
85
83
60
45
83
95
90
117
67
68
84
48
61
79
59
51
56
56
77
77
85
39
44
116
61
73
50
149
68
98th percentile
200
32
56
55
56
38
41
41
62
48
98
76
74
62
51
79
96
89
107
65
61
80
47
55
72
60
49
72
56
71
71
81
46
41
103
62
69
51
133
101
1
2
3
4
As an additional matter, we note that a 99th percentile 1-hour daily maximum standard at
a level of 50-150 ppb could have the effect of maintaining 862 concentrations below the level of
the current 24-hour and annual standards (see Table 10-3). That is, under these alternative
March 2009
306
Draft - Do Not Quote or Cite
-------
1 standard scenarios (using 2004 air quality data), there would be no counties in this analysis with
2 a 2nd highest 24-hour average greater than 140 ppb. Similarly, under these alternative standard
3 scenarios, there would be no counties in this analysis with an annual SO2 concentration in excess
4 of the current annual standard (0.03 ppm; see Table 10-4). These analyses were also done with
5 air quality from the years 2005 and 2006 and similar results were found (Appendix D).
6
March 2009 307 Draft - Do Not Quote or Cite
-------
Table 10-3. 2 highest 24-hour average SO2 concentrations (i.e. the current 24-hour
standard) for 2004 given just meeting the alternative 1-hour daily maximum standards
analyzed in the risk assessment (note: concentrations in ppb).61
State
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
MI
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
VI
County
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
St Croix
99th percentile levels
50
7
12
11
14
10
8
11
15
10
28
17
19
17
11
18
21
19
25
21
15
19
13
17
19
13
10
15
13
16
17
19
10
13
26
18
17
12
33
17
100
14
38
23
28
19
17
21
29
20
57
38
38
34
22
37
43
38
51
42
31
38
27
35
38
28
21
30
27
31
34
38
21
25
52
36
35
24
67
34
150
21
57
34
42
29
25
32
44
30
85
57
56
51
33
55
64
57
76
63
46
58
40
52
57
42
31
45
40
50
50
57
31
38
78
54
52
35
100
51
200
27
76
45
55
39
34
43
58
40
113
75
75
67
45
74
86
77
102
83
61
77
54
70
76
56
42
60
54
67
67
76
42
50
104
72
69
47
134
68
250
34
95
57
69
48
42
53
73
50
142
94
94
84
56
92
107
96
127
104
77
96
67
87
95
70
52
75
67
84
84
95
52
63
130
90
86
59
167
85
98th percentile
level
200
36
91
63
65
55
44
48
76
50
130
86
84
87
63
88
109
95
117
100
69
91
65
79
87
71
51
96
68
77
78
90
60
59
117
91
82
60
150
126
61 99th percentile 1-hour daily maximum concentrations were determined for each monitor in a given county for
years 2004-2006. These concentrations were averaged, and the monitor with the highest average in a given county
was determined. Based on this highest average, all monitors in a given county were adjusted to just meet the
potential alternative standards defined above, and for each of the years, the 2nd highest 24-hour maximum
concentration was identified. Iron County did not meet completeness criteria for all years and is therefore not part
of this analysis.
March 2009
308
Draft - Do Not Quote or Cite
-------
Table 10-4. Annual average SO2 concentrations for 2004 given just meeting the
alternative 1-hour daily maximum standards analyzed in the risk assessment (note:
concentrations in ppb).62
State
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
MI
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
VI
County
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
St Croix
99th percentile
50
1.7
2.0
1.6
1.8
0.8
1.8
1.5
2.0
1.5
1.8
2.5
2.5
2.0
1.5
2.2
6.4
6.4
7.6
2.6
3.1
3.9
2.3
2.6
3.9
2.9
2.5
4.6
2.3
4.3
3.0
3.5
2.1
1.3
7.7
4.8
4.0
2.2
6.1
1.2
100
3.4
4.0
3.2
3.6
1.6
3.6
2.9
4.1
3.1
3.5
5.0
5.1
4.1
2.9
4.4
12.8
12.7
15.1
5.3
6.1
7.7
4.7
5.1
7.8
5.8
5.1
9.1
4.5
8.7
6.1
7.0
4.2
2.6
15.5
9.6
8.0
4.3
12.2
2.4
150
5.1
6.0
4.7
5.4
2.3
5.3
4.4
6.1
4.6
5.3
7.5
7.6
6.1
4.4
6.5
19.3
19.1
22.7
7.9
9.2
11.6
7.0
7.7
11.7
8.7
7.6
13.7
6.7
13.0
9.1
10.4
6.3
3.9
23.2
14.3
12.0
6.5
18.3
3.7
200
6.8
7.9
6.3
7.2
3.1
7.1
5.9
8.2
6.1
7.0
10.0
10.2
8.2
5.8
8.7
25.7
25.4
30.2
10.5
12.2
15.5
9.3
10.2
15.5
11.6
10.1
18.3
9.0
17.4
12.1
13.9
8.4
5.3
30.9
19.1
16.1
8.7
24.4
4.9
250
8.5
9.9
7.9
9.0
3.9
8.9
7.3
10.2
7.7
8.8
12.5
12.7
10.2
7.3
10.9
32.1
31.8
37.8
13.2
15.3
19.3
11.6
12.8
19.4
14.5
12.7
22.8
11.2
21.7
15.2
17.4
10.4
6.6
38.6
23.9
20.1
10.9
30.6
6.1
98th percentile
200
9.0
9.5
8.7
8.5
4.4
9.4
6.7
10.7
7.6
8.1
11.2
11.3
10.6
8.3
10.4
32.5
31.4
34.8
12.7
13.8
18.4
11.2
11.5
17.7
14.8
12.3
29.1
11.3
20.0
14.0
16.5
12.0
6.2
34.6
24.2
19.1
11.1
27.4
9.1
62 99th percentile 1-hour daily maximum concentrations were determined for each monitor in a given county for
years 2004-2006. These concentrations were averaged, and the monitor with the highest average in a given county
was determined. Based on this highest average, all monitors in a given county were adjusted to just meet the
potential alternative standards defined above, and for each of the years, the annual concentration was calculated.
Iron county did not meet completeness criteria for all years and is therefore not part of this analysis.
March 2009
309
Draft - Do Not Quote or Cite
-------
1 10.5.2.3 Conclusions regarding averaging time
2 Staff finds that the scientific evidence and the air quality analyses most support an
3 averaging time of 1-hour. We first note that the ISA finds a causal relationship between ambient
4 SO2 exposure and respiratory morbidity, and that this conclusion is based on both human
5 exposure studies of 5-10 minutes, as well as supporting evidence from epidemiological studies
6 ranging from 1 to 24-hours. This evidence suggests that an appropriate averaging time should
7 protect against SC>2 concentrations ranging from 5-minutes to 24-hours. Importantly, the air
8 quality analyses presented above demonstrate that it is likely that an alternative 99th percentile
9 (see form discussion in 10.5.3) 1-hour daily maximum standard at an appropriate level (see level
10 discussion in 10.5.4) will substantially diminish: (1) 5-10 minute peaks of SC>2 shown in human
11 exposure studies to result in respiratory symptoms and/or decrements in lung function in
12 exercising asthmatics, (2) 99th percentile 1-hour daily maximum air quality concentrations in
13 cities observing positive effect estimates in epidemiological studies of hospital admissions and
14 ED visits for all respiratory causes and asthma, and (3) 99th percentile 24-hour average air
15 quality concentrations found in U.S. cities where ED visit and hospitalization studies (for all
16 respiratory causes and asthma) observed statistically significant associations in multi-pollutant
17 models with PM (i.e. 99th percentile 24-hour average SC>2 concentration > 36 ppb). Taken
18 together, staff concludes that a 1-hour daily maximum standard, set at an appropriate level,
19 would likely provide protection against the range of health outcomes associated with averaging
20 times from 5-minutes to 24-hours.
21 We note that based solely on the controlled human exposure evidence, staff also
22 considered a 5-minute averaging time. Staffs initial view does not favor such an approach. It is
23 legitimate to consider the stability of the design of pollution control programs in considering the
24 elements of a NAAQS, since more stable programs are more effective, and hence result in
25 enhanced public safety. Here, staff has concerns about the stability of a 5-minute averaging time
26 and standard. Specific concerns relate to the number of monitors needed and the placement of
27 such monitors given the temporal and spatial heterogeneity of 5-minute 862 concentrations.
28 Moreover, staff is concerned that compared to longer averaging times (e.g. 1-hour, 24-hour),
29 year to year variation in 5-minute SO2 concentrations is likely to be substantially more
30 temporally and spatially diverse. Consequently, staff initially judges that a 5-minute averaging
31 time would not provide a stable regulatory target and therefore, is not the preferred approach to
March 2009 310 Draft - Do Not Quote or Cite
-------
1 provide adequate public health protection. However, as noted above, staff's provisional view is
2 that a 1-hour averaging time, given an appropriate form (see 10.5.3) and level (see 10.5.4), will
3 adequately control 5-minute 862 exposures (see 10.5.4.2) and provide a more stable regulatory
4 target than setting a 5-minute standard.
5 10.5.3 Form
6 When evaluating alternative forms in conjunction with specific levels, staff considers the
7 adequacy of the public health protection provided by the combination of level and form to be the
8 foremost consideration. In addition, we recognize that it is important to have a form that is
9 reasonably stable and relatively insulated from the impacts of extreme meteorological events. A
10 standard set with a high degree of instability could have the effect of reducing public health
11 protection because shifting in and out of attainment due to meteorological conditions could
12 disrupt an area's ongoing implementation plans and associated control programs.
13 Controlled human exposure evidence demonstrates that there is a continuum of SC>2
14 related health effects in exercising asthmatics following 5-10 minute peak SC>2 exposures. That
15 is, the ISA finds that the percentage of asthmatics affected and the severity of the response
16 increases with increasing SC>2 concentrations. Therefore, as noted in Chapter 5 and consistent
17 with recent reviews of the Os and PM NAAQS, we focus this review on concentration-based
18 forms averaged over 3 years. This is because a concentration-based form gives proportionally
19 greater weight to 1-hour daily maximum values when concentrations are well above the level of
20 the standard than to 1-hour daily maximum values when the concentrations are just above the
21 level of the standard. In contrast, an expected exceedance form would give the same weight to a
22 1-hour daily maximum concentration that just exceeds the level of the standard as to a 1-hour
23 daily maximum concentration that greatly exceeds the level of the standard. Therefore, a
24 concentration-based form better reflects the continuum of health risks posed by increasing SC>2
25 concentrations (i.e. the percentage of asthmatics affected and the severity of the response
26 increases with increasing 862 concentrations). Concentration-based forms also provide greater
27 regulatory stability than a form based on allowing only a single expected exceedance.
28 In considering specific concentration-based forms, we recognize the importance to
29 minimize the number of days per year that an area could exceed the standard level and still attain
30 the standard. Given this, we have focused on 98th and 99th percentile forms averaged over 3
31 years. With regard to these alternative forms, staff notes in most locations analyzed, the 99th
March 2009 311 Draft - Do Not Quote or Cite
-------
1 percentile form of a 1-hour daily maximum standard would correspond to the 4th highest daily
2 maximum concentration in a year, while a 98th percentile form would correspond approximately
3 to the 7th to 8th highest daily maximum concentration in a year (Table 10-5; see Thompson, 2009
4 for methods). Staff also notes that the air quality analyses indicate that a 99th percentile form
5 could be appreciably more effective at limiting 5-minute peak SC>2 concentrations than a 98th
6 percentile form. For example, in all but one of the 40 counties selected for detailed analyses, a
7 99th percentile 1-hour daily maximum standard at 200 ppb allows less days per year of 5-minute
8 daily maximum 862 concentrations > 400 ppb than the corresponding 98th percentile standard
9 (see Table 7-11).63 Taken together, staff finds provisionally that the scientific evidence and the
10 risk and exposure analyses suggest consideration be given primarily to a 1-hour daily maximum
11 standard with a 99th percentile form.
63 In Alleghany County, both a 98th and 99th percentile 1-hour daily maximum standard at 200 ppb would be
estimated to allow 2 days of 5-minute daily maximum SO2 concentrations > 400 ppb.
March 2009 312 Draft - Do Not Quote or Cite
-------
nd nth
Table 10-5. SO2 concentrations (ppm) corresponding to the 2-9 daily maximum
and 98th/99th percentile forms (2004-2006)
COUNTY
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Iron
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
St Croix
STATE
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
Ml
MO
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
VI
SO2 Daily Maximums
2nd
0.362
0.169
0.129
0.163
0.213
0.208
0.256
0.152
0.152
0.107
0.154
0.138
0.096
**
0.503
0.164
0.064
0.071
0.082
0.108
0.165
0.093
0.192
0.170
0.108
0.127
0.302
0.186
0.265
0.120
0.208
0.118
0.237
0.152
0.049
0.213
0.183
0.225
**
0.156
3m
0.329
0.150
0.121
0.149
0.190
0.186
0.232
0.116
0.132
0.098
0.146
0.131
0.088
**
0.428
0.161
0.062
0.065
0.073
0.107
0.153
0.087
0.186
0.161
0.094
0.117
0.250
0.172
0.244
0.108
0.200
0.096
0.218
0.140
0.045
0.178
0.170
0.217
**
0.126
4th
0.276
0.147
0.117
0.144
0.173
0.170
0.215
0.107
0.125
0.096
0.135
0.127
0.081
**
0.413
0.151
0.059
0.057
0.070
0.101
0.129
0.080
0.175
0.150
0.081
0.111
0.227
0.146
0.226
0.102
0.194
0.085
0.208
0.129
0.041
0.158
0.159
0.178
**
0.086
5tn
0.260
0.132
0.112
0.138
0.165
0.157
0.201
0.105
0.120
0.094
0.132
0.121
0.073
**
0.338
0.144
0.056
0.053
0.066
0.098
0.125
0.075
0.167
0.141
0.076
0.102
0.216
0.143
0.220
0.096
0.188
0.081
0.190
0.121
0.039
0.143
0.147
0.170
**
0.077
6th
0.247
0.129
0.109
0.131
0.152
0.145
0.195
0.099
0.113
0.103
0.128
0.120
0.065
**
0.312
0.141
0.052
0.049
0.063
0.088
0.123
0.073
0.164
0.140
0.074
0.096
0.200
0.124
0.190
0.091
0.182
0.078
0.164
0.116
0.039
0.138
0.145
0.163
**
0.054
7th
0.228
0.123
0.095
0.127
0.133
0.140
0.185
0.095
0.110
0.088
0.122
0.116
0.064
**
0.293
0.132
0.050
0.047
0.062
0.087
0.119
0.070
0.148
0.136
0.071
0.092
0.189
0.099
0.182
0.090
0.175
0.077
0.153
0.113
0.038
0.134
0.139
0.152
**
0.049
8th
0.219
0.123
0.090
0.123
0.129
0.129
0.184
0.088
0.102
0.097
0.120
0.114
0.063
**
0.280
0.127
0.048
0.047
0.060
0.084
0.114
0.067
0.146
0.133
0.070
0.089
0.188
0.092
0.180
0.089
0.167
0.072
0.145
0.109
0.037
0.125
0.134
0.141
**
0.046
9th
0.211
0.117
0.085
0.120
0.123
0.123
0.179
0.085
0.099
0.090
0.119
0.106
0.059
**
0.266
0.125
0.047
0.045
0.060
0.079
0.111
0.066
0.142
0.127
0.066
0.088
0.179
0.086
0.177
0.085
0.162
0.070
0.143
0.105
0.035
0.120
0.131
0.135
**
0.044
Percentiles
99th
0.276
0.147
0.117
0.144
0.153
0.170
*
0.107
0.125
0.096
0.135
0.081
**
0.346
0.151
0.059
0.057
0.063
0.101
0.129
0.077
0.175
0.150
0.081
0.111
0.130
0.146
0.226
0.102
0.194
0.085
0.208
0.129
0.041
0.158
0.159
0.168
**
0.050
98th
0.219
0.123
0.090
0.123
0.127
0.130
*
0.091
0.102
0.080
0.120
0.063
**
0.250
0.127
0.048
0.047
0.056
0.084
0.114
0.067
0.146
0.133
0.070
0.089
0.106
0.092
0.180
0.089
0.169
0.072
0.145
0.109
0.037
0.125
0.134
0.139
**
0.036
1 10.5.4 Level
2 In considering alternative standard levels that would provide greater protection than that
3 afforded by the current standard against SCVrelated adverse health effects, staff has taken into
4 account scientific evidence from both experimental and epidemiological studies, as well as the
March 2009
313
Draft - Do Not Quote or Cite
-------
1 uncertainties and limitations in that evidence. In particular, we have considered the extent to
2 which controlled human exposure studies provide evidence for a lowest-observed-effects level
3 (LOEL) and the extent to which epidemiological studies provide evidence for potential effect
4 thresholds and/or for positive associations that extend down to the lower levels of SC>2
5 concentrations observed in studies. We note that the scientific evidence can provide insights into
6 alternative standard levels only within the context of specific averaging times and forms.
7 Therefore, while this section considers the evidence as it relates to alternative levels, such
8 considerations assume particular averaging times and forms (see sections 10.5.2 and 10.5.3).
9 10.5.4.1 Evidence-based consideration
10 Human Clinical
11 Controlled human exposure results demonstrate that there is a continuum of SC>2 related
12 health effects following 5-10 minute peak 862 exposures in exercising asthmatics. That is, the
13 ISA finds that the percentage of asthmatics affected and the severity of the response increases
14 with increasing SC>2 concentrations. At concentrations ranging from 200 - 300 ppb, the lowest
15 levels tested in free breathing chamber studies, 5-30% percent of exercising asthmatics are likely
16 to experience moderate or greater bronchoconstriction. At concentrations > 400 ppb, moderate
17 or greater bronchoconstriction occurs in 20-60% of exercising asthmatics, and compared to
18 exposures at 200- 300 ppb, a larger percentage of subjects experience severe
19 bronchoconstriction. At concentrations > 400 ppb, statistically significant moderate or greater
20 bronchoconstriction is frequently accompanied by respiratory symptoms.
21 With regard to the controlled human exposure evidence as it relates to alternative
22 standard levels, several additional factors must be considered. First, it is important to note that
23 the subjects in human exposure studies do not represent the most 862 sensitive asthmatics; that
24 is, these studies included mostly mild and some moderate, but not severe asthmatics. Also,
25 children have not been included in free-breathing controlled human exposure studies, and thus, it
26 is possible asthmatic children represent a population that is more sensitive to the respiratory
27 effects of SC>2 than the individuals who have been examined to date. Moreover, it is important to
28 consider that 5-30% of asthmatics who engaged in moderate or greater exertion experienced
29 bronchoconstriction following exposure to 200- 300 ppb SC>2, which is the lowest level tested in
30 free-breathing chamber studies. Thus, it is likely that a subset of the asthmatic population would
31 also experience bronchoconstriction following exposure to levels lower than 200 ppb. We also
March 2009 314 Draft - Do Not Quote or Cite
-------
1 note this effect could have important public health implications due to the large size of the
2 asthmatic population in the United States (see section 3.6).
3 Epidemiological
4 When evaluating the epidemiologic literature for its potential to inform decisions on
5 standard level, we note that the ISA describes the evidence for the presence of an effects
6 threshold as inconclusive (ISA, section 4.1.3). While some epidemiological studies found the
7 concentration-response relationship to be linear, other studies found a marked increase in SC>2
8 related respiratory effects at higher SC>2 concentrations (ISA, section 4.1.3). However, the ISA
9 urges caution when interpreting studies finding a marked increase in respiratory effects at higher
10 SC>2 concentrations because these results were based on a few potentially influential data points
11 (i.e., 24-hour 862 concentrations above the 90th percentile; ISA, section 4.1.3). In the absence of
12 a clear threshold, our discussion of alternative standard levels will focus on the range of 1-hour
13 daily maximum SC>2 concentrations observed in cities where key U.S. and Canadian
14 epidemiological studies of ED visits and hospitalizations were conducted.
15 Figures 5-1 to 5-5 (see Chapter 5) show standardized effect estimates and the 98th and
16 99th percentile concentrations of 1-hour daily maximum SC>2 levels for locations and time periods
17 for which key U.S. (Figures 5-1 to 5-4) and Canadian (Figure 5-5) ED visit and hospitalization
18 studies were conducted. The highest 99th percentile 1-hour daily maximum air quality levels
19 were found in analyses conducted in the cities of Cincinnati (Figure 5-2), Cleveland (Figures 5-2
20 and 5-4), and New Haven (Figure 5-4). These studies showed positive associations with
21 respiratory-related hospital admissions or ED visits during time periods when 99th percentile 1-
22 hour daily maximum SC>2 concentrations ranged from 150 to 457 ppb. Notably, this range of 1-
23 hour daily maximum SC>2 levels overlaps considerably with 5-10 minute SC>2 concentrations (>
24 200 ppb) that have consistently been shown in controlled human exposure studies to result in
25 decrements in lung function in exercising asthmatics. Of particular concern are the air quality
26 levels that were found in Cincinnati. The 98th and 99th percentile 1-hour daily maximum SC>2
27 concentrations were in excess of 400 ppb. Notably, levels > 400 ppb have consistently been
28 shown in human clinical studies with 5-10 minute exposures to result in statistically significant
29 moderate or greater bronchoconstriction in the presence of respiratory symptoms in a
30 considerable percentage of exercising asthmatics.
March 2009 315 Draft - Do Not Quote or Cite
-------
1 With regard to the lowest levels of 862 observed in cities where positive effect estimates
2 were observed in epidemiological studies, we first note that Figure 5-5 contains epidemiological
3 studies reporting associations between ambient SO2 concentrations and hospital admissions in
4 Canadian cities where 99th percentile, 1-hour daily maximum SC>2 levels were < 46 ppb.
5 Specifically, 99th percentile, 1-hour daily maximum SC>2 levels for hospital admission studies
6 conducted in Toronto (Burnett et al., 1997) and Vancouver (Yang et al., 2003) were
7 approximately 21 ppb, and 41 ppb, respectively. Moreover, in a U.S. analysis, Delfino et al.
8 (2003) reported an association between ambient 862 and respiratory symptoms in Hispanic
9 children when the maximum 1-hour SC>2 concentration in Los Angeles was 26 ppb (ISA Table 5-
10 4). However, it should be noted that the Vancouver study was not statistically significant in
11 either single, or multipollutant models with O3, and that the study did not examine the potential
12 for confounding by PM (Figure 5-5; ISA, Table 5-5). In addition, while the Toronto study was
13 statistically significant in a single pollutant model, the effect estimate was substantially
14 diminished and no longer statistically significant in a multi-pollutant model with PMio (ISA,
15 Table 5-5). Finally, the epidemiological study conducted in Los Angeles (Delfino et al., 2003;
16 ISA, Table 5-4) was very small (n=22), and did not examine potential confounding by co-
17 pollutants. Thus, staff finds that this evidence considered by itself is not sufficient to warrant
18 consideration of alternative 1-hour daily maximum standards at levels below 50 ppb.
19 In contrast to the epidemiological evidence in cities where 99th percentile 1-hour daily
20 maximum SC>2 concentrations were < 47 ppb, staff finds relatively stronger evidence of an
21 association between SC>2 and hospital admissions and ED visits for all respiratory causes and
22 asthma in cities where 99th percentile 1-hour daily maximum SC>2 concentrations were > 47 ppb
23 (Figures 5-1 to 5-5). More specifically, the majority of epidemiological studies in this range
24 observed positive associations between ambient SC>2 levels and hospital admissions and ED
25 visits for all respiratory causes or asthma. Moreover, although most of these positive effect
26 estimates were not statistically significant, there were some statistically significant results in
27 single pollutant models (Portland, Wilson, 1995; Bronx, NYDOH, 2006; NYC, Ito, 2006; and
28 Schwartz, 1995), as well as limited evidence of statistical significance in multi-pollutant models
29 with PM (Bronx, NYDOH, 2006; NYC, Ito, 2006; New Haven, Schwartz 1995).
March 2009 316 Draft - Do Not Quote or Cite
-------
1 10.5.4.2 Exposure- and risk-based considerations
2 Staff s consideration of exposure- and risk-based information as it relates to alternative
3 levels for the primary 862 NAAQS builds upon our conclusions, discussed above in sections
4 10.3.3.3 and 10.4.4, that the overall body of scientific evidence clearly calls into question the
5 adequacy of the current standards to protect the public health. Therefore, we have judged it
6 appropriate to consider a range of alternative levels that would improve upon the level of
7 protection provided by the current standard. As noted in Chapter 5, this range of levels (50- 250
8 ppb) is based on results from controlled human exposure and epidemiologic studies. When
9 considering this range of levels given recent air quality, we note that all of the potential
10 alternative 1-hour daily maximum 862 standard levels would be estimated to result in counties in
11 the U.S. with air quality above the level of the given alternative standard (Table 10-6;
12 Thompson, 2008). In contrast, given recent air quality, all counties in the U.S. meet the current
13 24-hour and annual standards. Thus, to varying extents, all potential alternative 1-hour daily
14 maximum standards would represent increased protection against ambient SC>2 concentrations
15 compared to the current standards.
16
March 2009 317 Draft - Do Not Quote or Cite
-------
1 Table 10-6. Percent of counties that may be above the level of alternative standards (based on years 2004-2006)
Alternative Standards and
Levels (ppb)
Number of counties with
monitors
Percent of counties, total and by region not likely to meet a given standard
Total Counties
(population in
millions)
211 (96.5)
3 year 99th percentile daily 1-hour max:
250
200
150
100
50
1 (0.4)
3 (0.8)
10(2.4)
22(13.5)
54 (43.5)
3 year 98th percentile daily 1-hour max:
200
1 (0.4)
Northeast
52
0
0
2
8
38
0
Southeast
40
0
3
5
13
55
0
Industrial
Midwest
75
1
4
20
47
81
1
Upper
Midwest
19
0
0
5
5
37
0
Southwest
7
14
14
14
14
14
14
Northwest
9
0
0
0
0
22
0
Southern
CA
6
0
0
0
0
0
0
Outside
Regions
3
33
33
33
33
33
33
March 2009
DraftHDo Not Quote or Cite
-------
1 The results of the air quality analyses are presented in Chapter 7 of this document. The
2 outputs of these analyses include estimates of the number of daily exposures greater than or
3 equal to benchmark levels. These estimates are based on as-is air quality and air quality that has
4 been adjusted to simulate just meeting the current and potential alternative standards. In
5 considering the results presented Chapter 7, we note the following key points with respect to
6 exceedences of the 200 and 400-ppb benchmark levels. We highlight these benchmark levels
7 because (1) 400 ppb represents the lowest concentration in human exposure studies where
8 statistically significant moderate or greater lung function decrements are frequently accompanied
9 by respiratory symptoms, (2) 200 ppb is the LOEL for moderate or greater decrements in lung
10 function in free-breathing human exposure studies, and (3) taken together, the human exposure
11 evidence suggests that the 1-hour daily maximum SC>2 standard level should be set low enough
12 to significantly reduce the number of 5-10 minute peaks in excess of 200 ppb, and thus provide
13 even greater protection against peaks in excess of 400 ppb.
14
15 When air quality is simulated to just meet the current standards in 40 counties selected
16 for detailed analysis, all locations are estimated to have between 21-171 days per year
17 where 5-minute daily maximum SC>2 concentrations exceed 200 ppb. Moreover, most
18 counties are estimated to have > 50 days per year where 5-minute daily maximum SC>2
19 concentrations exceed 200 ppb (Table 7-11).
20
21 When air quality is simulated to just meeting the current standards in the 40 counties
22 selected for detailed analysis, all locations are estimated to have between 1-102 days per
23 year when 5-minute daily maximum SC>2 concentrations exceed 400 ppb. Moreover,
24 most counties will have > 20 days per year where 5-minute daily maximum SC>2
25 concentrations exceed 400 ppb (Table 7-13)
26
27 In all counties selected for detailed analysis, simulating just meeting the 99th percentile
28 alternative standard levels of 50, 100 and 150 ppb results in fewer estimated days per
29 year when 5-minute daily maximum SC>2 concentrations exceed the 200 and 400 ppb
30 benchmarks compared to just meeting the current standards (Tables 7-11 and 7-13).
31
32 In all counties selected for detailed analysis, compared to the current standards,
33 simulating just meeting the 99th percentile alternative standard level of 200 ppb results in
34 fewer estimated days per year when 5-minute daily maximum SC>2 concentrations exceed
35 200 ppb, and in all but three counties, results in fewer exceedences of the 400 ppb
36 benchmark (under the current, and 200 ppb alternative standards, Bronx, Union, and
37 Fairfax Counties would have the same number of exceedences of the 400 ppb benchmark
38 (Tables 7-11 and 7-13).
39
March 2009 319 Draft Do Not Quote or Cite
-------
1 In Bronx, Union, Hudson, and Fairfax Counties simulating just meeting the 99th
2 percentile 1-hour daily maximum standard of 250 ppb results in more exceedences of the
3 200 ppb benchmark than just meeting the current standard. Similarly, this 250 ppb
4 alternative standard results in these counties experiencing more exceedences of the 400
5 ppb benchmark than just meeting the current standard (Tables 7-11 and 7-13).
6
7 In all counties selected for detailed analysis, a 99th percentile 1-hour daily maximum
8 standard of 50 ppb would be estimated to result in at most 2 days/year where 5-minute
9 maximum SC>2 concentrations were > 200 ppb, and 0 days/year where 5-minute
10 maximum SC>2 concentrations were > 400 ppb (Tables 7-11 and 7-13).
11
12 In all counties selected for detailed analysis, a 99th percentile 1-hour daily maximum
13 standard of 100 ppb would be estimated to result in at most 13 days/year where 5-minute
14 maximum 862 concentrations were > 200 ppb, and at most 2 days/year where 5-minute
15 maximum SC>2 concentrations were > 400 ppb (Tables 7-11 and 7-13).
16
17 In all counties selected for detailed analysis, a 99th percentile 1-hour daily maximum
18 standard of 150 ppb would be estimated to result in at most 24 days/year where 5-minute
19 maximum SC>2 concentrations were > 200 ppb, and at most 7 days/year where 5-minute
20 maximum SC>2 concentrations were > 400 ppb (Tables 7-11 and 7-13).
21
22 The results of the St Louis and Greene County exposure assessments are presented in
23 Chapter 8 of this document. In Figures 8-13 through 8-17, we present estimates of the percent
24 and number of asthmatics in St. Louis and Greene Counties expected to experience 862 exposure
25 concentrations at or above our potential health benchmark levels for the year 2002, given
26 unadjusted air quality and air quality that has been adjusted to simulate just meeting the current
27 and potential alternative standards. In considering the results presented in those figures, we note
28 the following key points (values in parentheses represent the numbers of asthmatics exposed):
29 In St. Louis under the current standards, about 46% (-48,000) and 13% (-14,000) of
30 asthmatics at elevated ventilation rates would be estimated to experience at least one
31 exposure greater than or equal to the 200 ppb and 400 ppb benchmarks, respectively
32 (Figures 8-16 and 8-17).
33
34 In St. Louis, 99th percentile 1-hour daily maximum standards of 50 ppb, 100 ppb, 150
35 ppb, and 200 ppb would be estimated to result in < 1% (69), 1.5% (-1,600), 6.4%
36 (-7,000) and 13.7% (-14,000), respectively, of asthmatics at elevated ventilation rates
37 experiencing at least one exposure greater than or equal to the 200 ppb benchmark
38 respectively (Figures 8-16 and 8-17).
39
40 In St. Louis, 99th percentile 1-hour daily maximum standards of 50 ppb, 100 ppb and 150
41 ppb would be estimated to result in <1% of asthmatics at elevated ventilation rates
42 experiencing at least one exposure greater than or equal to the 400 ppb benchmark
43 (Figure 8-17).
March 2009 320 Draft Do Not Quote or Cite
-------
1 In St. Louis, a 99th percentile 1-hour daily maximum standard of 200 ppb would be
2 estimated to result in 1.5% (-1,600) of asthmatics at elevated ventilation rates
3 experiencing at least one exposure greater than or equal to the 400 ppb benchmark
4 (Figures 8-16 and 8-17).
5
6 When results are restricted to asthmatic children, similar trends are found with respect to
7 exceedences of the 200 ppb and 400 ppb benchmark levels under the current and 99th
8 percentile 1-hour daily maximum standards (Figure 8-17).
9
10 In Greene County, although all of the alternative 99th percentile 1-hour daily maximum
11 standards are estimated to be more protective than the current standards, <1% of
12 asthmatics at elevated ventilation rates are estimated to experience at least one exposure
13 greater than or equal to the 200 ppb and 400 ppb benchmarks under all air quality
14 scenarios. At most, 139 and 13 asthmatics at elevated ventilation rates would be expected
15 to experience at least one exposure over the 200 or 400 ppb benchmark respectively
16 (Figures 8-13 and 8-14).
17
18 In Greene County, under the current standards, about 1.0% (72) and <1% (<10) of
19 asthmatic children at elevated ventilation rates would be estimated to experience at least
20 one exposure greater than or equal to the 200 ppb and 400 ppb benchmarks, respectively
21 (Figures 8-13 and 8-14).
22
23 In Greene County, under all of the 1-hour daily maximum alternative standards, <35
24 asthmatic children at elevated ventilation rates would be estimated to experience at least
25 one exposure greater than or equal to the 200 ppb benchmark (Figure 8-13).
26
27 The results of the St Louis and Greene County risk assessment are presented in Chapter 9
28 of this document. In Tables 9-4 and 9-5 we present estimates of the number and percent of
29 exposed asthmatics likely to experience moderate or greater lung function responses. In
30 considering the results from these tables we note the following key points (values in parentheses
31 represent the numbers of asthmatics):
32 In the St. Louis modeling domain the median percentage of exposed asthmatics at
33 elevated ventilation rates estimated to experience at least one moderate decrement in lung
34 function defined as a > 100% increase in sRaw is 13.1% (-13,500) under the current
35 standards.
36
37 In the St. Louis modeling domain the median percentage of exposed asthmatics at
38 elevated ventilation rates estimated to experience at least one > 100% increase in sRaw is
39 0.7% (730), 1.9% (-2,000), 3.6 % (-3,700), and 5.4% (-5,500) given a 50 ppb, 100 ppb,
40 150 ppb, and 200 ppb 99th percentile 1-hour daily maximum standard, respectively.
41
42 In the St. Louis modeling domain the median percentage of exposed asthmatics at
43 elevated ventilation rates estimated to experience at least one large decrement in lung
March 2009 321 Draft Do Not Quote or Cite
-------
1 function defined as a > 200% increase in sRaw is 5.4% (-5,500) under the current
2 standard.
O
4 In the St. Louis modeling domain the median percentage of exposed asthmatics at
5 elevated ventilation rates estimated to experience at least one > 200% increase in sRaw is
6 0.2% (230), 0.7% (670) 1.3% (-1,300) and 2% (-2,000) given a 50 ppb, 100 ppb, 150
7 ppb, and 200 ppb 99th percentile 1-hour daily maximum standard respectively.
8
9 Similar trends are found in the St. Louis modeling domain with respect to the median
10 percentages of exposed asthmatic children at elevated ventilation rates expected to
11 experience at least one > 100% or > 200% increase in sRaw.
12
13 Similar trends are found in the St. Louis modeling domain in exposed asthmatics and
14 asthmatic children at elevated ventilation rates when moderate or large lung function
15 decrements are defined in terms of FEVi rather than sRaw (Table 9-8 and Appendix C
16 Tables 4-7, 4-8, 4-11, and 4-12).
17
18 In Greene County under the current standard the median percentage of exposed
19 asthmatics at elevated ventilation rates estimated to experience at least one > 100%
20 increase in sRaw is 1% (210).
21
22 In Greene County the median percentage of exposed asthmatics at elevated ventilation
23 rates estimated to experience at least one > 100% increase in sRaw is 0.4% (80), 0.4%
24 (90), 0.5 % (100), and 0.6% (120) given a 50 ppb, 100 ppb, 150 ppb, and 200 ppb 99th
25 percentile 1-hour daily maximum standard, respectively.
26
27 In Greene County under the current standard the median percentage of exposed
28 asthmatics at elevated ventilation rates estimated to experience at least one > 200%
29 increase in sRaw is 0.3% (70).
30
31 In Greene County, using a 2-Parameter Logistic function, the median percentage of
32 exposed asthmatics at elevated ventilation rates estimated to experience at least one >
33 200% increase in sRaw is 0.1% (30), 0.1% (30), 0.2 % (30), and 0.2% (40) given a 50
34 ppb, 100 ppb, 150 ppb, and 200 ppb 99th percentile 1-hour daily maximum standard,
35 respectively.
36 10.5.4.3 Conclusions regarding level
1>1 Taken together, staff provisionally concludes that the evidence and exposure and risk
38 information reasonably support a 1-hour daily maximum standard within a range of 50- 150 ppb.
39 Controlled human exposure evidence has consistently demonstrated increases in respiratory
40 symptoms and/or decrements in lung function in exercising asthmatics following 5-10 minute
41 SC>2 exposures > 200 ppb. At concentrations > 400 ppb, human exposure studies have
42 demonstrated that decrements in lung function are frequently accompanied with respiratory
March 2009 322 Draft Do Not Quote or Cite
-------
1 symptoms. Suggestive evidence for health effects associated with this range of 862
2 concentrations can also be found in epidemiological studies. That is, our air quality analysis
3 based on key U.S. and Canadian hospital admission and ED visit studies identified in the ISA
4 indicates positive effect estimates have been observed in cities where 99th percentile 1-hour daily
5 maximum SC>2 concentrations were also between 200- 400 ppb. At a minimum, these multiple
6 lines of evidence suggest that a 99th percentile 1-hour daily maximum standard should be lower
7 than 200 ppb. Moreover, given that the definitive evidence for the ISA's causal determination is
8 the controlled human exposure evidence, staff believes that the level of a 1-hour daily maximum
9 SC>2 standard should be low enough to significantly limit the number of 5-minute peaks > 200
10 ppb. Thus, we note results from the air quality and risk analyses indicate that a 1-hour daily
11 maximum standard ranging from 50- 150 ppb would provide substantial protection against 5-
12 minute peaks > 200 ppb. For a given county in our detailed air quality analysis, it is estimated
13 that air quality just meeting a 1-hour daily maximum standard set at a level of 50 ppb, 100 ppb,
14 or 150 ppb would result in at most 2, 13, or 24 days per year respectively, when 5-minute daily
15 maximum SC>2 concentrations would be > 200 ppb (Table 7-11). We also note that results from
16 the St. Louis exposure analysis indicate that air quality just meeting a 50 ppb, 100 ppb, or 150
17 ppb 1-hour daily maximum standard, respectively, would result in a corresponding < 0.1% (69),
18 1.5% (-1,600), or 6.4% (-7,000) of asthmatics at elevated ventilation rates experiencing at least
19 one 5-minute daily maximum exposure > 200 ppb. In the Greene County exposure analysis, it is
20 estimated that < 10 exercising asthmatics would be expected to experience an SO2 exposure >
21 200 ppb given a 1-hour daily maximum standard set at 50, 100, or 150 ppb. Finally, results of
22 the St. Louis quantitative lung function response risk assessment estimate that the median
23 percentage of asthmatics at elevated ventilation rates estimated to experience at least one > 100%
24 increase in sRaw (a moderate lung function decrement) is 0.7% (730), 1.9% (-2,000) and 3.6 %
25 (-3,700) given air quality just meeting a 50 ppb, 100 ppb, and 150 ppb 99th percentile 1-hour
26 daily maximum standard, respectively.
27 In further informing this range of 862 standard levels, we again consider the
28 epidemiological evidence, as well as the air quality analysis conducted by staff characterizing
29 99th percentile 1-hour daily maximum SC>2 concentrations in cities corresponding to key U.S. and
30 Canadian epidemiological studies (identified in the ISA). We first note that there are important
31 sources of uncertainty associated with the results of the epidemiological literature (see section
March 2009 323 Draft Do Not Quote or Cite
-------
1 10.5.2.2). However, we also note that the ISA does conclude that SO2 likely has an independent
2 effect on the respiratory outcomes observed in these studies (ISA, section 5.2). As previously
3 described, positive associations between ambient SO2 and hospital admissions and ED visits for
4 all respiratory causes and asthma were observed in cities where 99th percentile 1-hour daily
5 maximum SO2 concentrations were > 47 ppb. Notably, this range includes three studies where
6 statistically significant results in multi-pollutant models with PM were observed. Studies
7 conducted in NYC, NY (Ito et al., 2007), Bronx, NY (NYDOH, 2006), and New Haven, CT
8 (Schwartz et al., 1995) observed statistically significant results in multi-pollutant models with
9 PM when 99th percentile 1-hour daily maximum SO2 concentrations were 82 ppb, 78 ppb, and
10 150 ppb, respectively. However, in Tacoma, WA (Schwartz et al., 1995), when the 99th
11 percentile 1-hour daily maximum SO2 concentration was 100 ppb, the positive SO2 effect
12 estimate was substantially reduced and no longer statistically significant in a multi-pollutant
13 model with PM. In addition, we note that several other studies reported positive and sometimes
14 statistically significant SO2 effect estimates in cities where 99th percentile 1-hour SO2
15 concentrations ranged from 70-100 ppb (Figures 5-1 to 5-5). Thus, if the epidemiological results
16 are emphasized, they could reasonably support a 99th percentile 1-hour daily maximum standard
17 in the lower end of the standard range, that is, from 70 ppb -100 ppb. Moreover, if emphasis is
18 placed on the 99thpercentile 24-hour average SC>2 concentrations observed in cities where the
19 epidemiological studies mentioned above were conducted, we note that a 50 ppb (and to a lesser
20 extent the 100 ppb) 99th percentile 1-hour daily maximum standard would likely provide
21 protection against the range of 99th percentile 24-hour average SO2 concentrations in cities where
22 statistically significant associations in multi-pollutant models with PM were observed (i.e. > 36
23 ppb; see section 10.5.2.2).
24 In addition to using the epidemiological results, uncertainties in the controlled human
25 exposure evidence could also be used to further inform consideration of standards within this 50
26 -150 ppb range of alternative standard levels. For example, if emphasis is placed on the
27 uncertainty that the participants in human exposure studies do not represent the most SO2
28 sensitive individuals, consideration could be given to a 1-hour daily maximum standard level that
29 provides increased protection against peaks < 200 ppb to provide a margin of safety for these
30 SO2 sensitive individuals. In this case, we would note that in the St. Louis exposure analysis,
31 compared to a standard level of 100 or 150 ppb, a 1-hour daily maximum standard of 50 ppb
March 2009 324 Draft Do Not Quote or Cite
-------
1 would likely result in substantially fewer asthmatics at elevated ventilation rates experiencing
2 SC>2 concentrations > 100 ppb. That is, given a 50 ppb, 100 ppb, and 150 ppb 1-hour daily
3 maximum standard, 1.5% (-1,600), 13.7%, (-14,000) and 31% (-33,000) of asthmatics at
4 elevated ventilation rates would be expected to experience at least one SC>2 concentrations > 100
5 ppb, respectively.
6 On the other hand, if emphasis is placed on the uncertainties associated with the
7 controlled human exposure results at the lowest levels, it could be argued that the upper end of
8 this range of alternative standard levels could be sufficient to protect public health. That is, the
9 ISA notes that while decrements in lung function have been demonstrated to occur in free
10 breathing chamber studies as low as 200 ppb, it also notes that results of chamber studies
11 exposing exercising asthmatics to 200-300 ppb are not statistically significant, nor are they
12 frequently accompanied with respiratory symptoms ( ISA, section 4.1.1). Thus, an argument
13 could be made that a 99th percentile 1-hour daily maximum SC>2 standard should predominantly
14 provide protection against 5-10 minute SC>2 concentrations > 400 ppb (i.e. the lowest
15 concentration in free-breathing chamber studies where statistically significant respiratory effects
16 have been observed). In this instance, staff notes that results of the St. Louis exposure analysis
17 indicate that a 99th percentile 1-hour daily maximum standard at levels ranging from 100-150
18 ppb, would be estimated to result in <1% (-80) and <1% (-500) of asthmatics at elevated
19 ventilation rates experiencing at least one exposure greater than or equal to the 400 ppb
20 benchmark, respectively (Figure 8-17).
21 Given the above considerations, staffs initial views are that selecting a level from within
22 this range will be based on the relative weight given to different types of information from the
23 exposure and risk assessment, as well as to the evidence, and the uncertainties associated with
24 the evidence and assessments. Finally, staff recognizes that the particular level selected will also
25 have implications for retaining or revoking the current 24-hour and/or annual standards. That is,
26 if the alternative standard selected would be expected to prevent ambient 862 concentrations
27 from exceeding the levels of the current standards, it could reasonably be suggested that the
28 current NAAQS be revoked. However, if the alternative standard selected is not expected to
29 prevent ambient SC>2 concentrations from exceeding the levels of the current standards, it would
30 be appropriate to consider retaining the current NAAQS.
31
March 2009 325 Draft Do Not Quote or Cite
-------
1 References
2
3 Abt Associates, 2007. Ozone Health Risk Assessment for Selected Urban Areas. EPA 452/R-
4 07-009. Prepared for Office of Air Quality Planning and Standards, U.S. EPA, Research
5 Triangle Park, NC. July 2007. Available electronically on the internet at:
6 http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_pr_td.html.
7
8 AHS. (2003a). American Housing Survey for 2003. Available at:
9 http://www.census.gov/hhes/www/housing/ahs/ahs.html.
10
11 AHS. (2003b). Source and Accuracy Statement for the 2003 AHS-N Data Chart. Available at:
12 http ://www. census. gov/hhes/www/housing/ahs/03 dtchrt/source .html.
13
14 American Thoracic Society. 2000. What Constitutes an Adverse Health Effect of Air Pollution?
15 Am. J. Respir. Crit. Care Med. 161: 665-673.
16
17 Balmes JR, Fine JM, Sheppard D. (1987). Symptomatic Bronchoconstriction After Short-Term
18 Inhalation of Sulfur Dioxide. Am Rev Respir Dis 136:1117-21.
19
20 Bethel RA; Epstein J; Sheppard D; Nadel JA; Boushey HA. (1983) Sulfur dioxide-Induced
21 Bronchoconstriction in Freely Breathing, Exercising, Asthmatic Subjects. Am Rev Respir
22 Dis. 128:987-90.
23
24 Bethel RA; Sheppard D; Geffroy B; Tarn E; Nadel JA; Boushey HA. (1985) Effect of 0.25 ppm
25 Sulfur Dioxide on Airway Resistance in Freely Breathing, Heavily Exercising, Asthmatic
26 Subjects. Am Rev Respir Dis. 131:659-61.
27
28 Bresnahan BW, Dickie M, Gerking S. (1997). Averting behavior and urban air pollution. Land
29 Economics. 73(3):340-357.
30
31 Burnett, R. T.; Cakmak, S.; Brook, J. R.; Krewski, D. (1997a) The Role of Paniculate Size and
32 Chemistry in the Association Between Summertime Ambient Air Pollution and
33 Hospitalization for Cardiorespiratory Diseases. Environ. Health Perspect. 105: 614-620.
34
35 Burnett, R. T.; Smith-Doiron, M.; Stieb, D.; Cakmak, S.; Brook, J. R. (1999) Effects of
36 Particulate and Gaseous Air Pollution on Cardiorespiratory Hospitalizations. Arch. Environ.
37 Health 54: 130-139.
38
39 Burnett, R. T.; Smith-Doiron, M.; Stieb, D.; Raizenne, M. E.; Brook, J. R.; Dales, R. E.; Leech,
40 J. A.; Cakmak, S.; Krewski, D. (2001) Association Between Ozone and ospitalization for
41 acute respiratory diseases in children less than 2 years of age. Am. J. Epidemiol. 153: 444-
42 452.
March 2009 326 Draft Do Not Quote or Cite
-------
1 Burton CS, Stockenius TE, Stocking TS, Carr EL, Austin BS, Roberson RL (1987). Assessment
2 of Exposures of Exercising Asthmatics to Short-term SO2 Levels as a Result of Emissions
3 from U.S. Fossil-fueled Power Plants. Systems Applications Inc., San Rafael, CA.
4 Publication No. 87/176, September 23, 1987.
5
6 Cakmak, S.; Dales, R. E.; Judek, S. 2006. Respiratory Health Effects of Air Pollution Gases:
7 Modification by Education and Income. Arch. Environ. Occup. Health 61: 5-10.
8
9 CDC. (2007). National Center for Health Statistics. National Health Interview Survey (NHIS)
10 Public Use Data Release (2003). Available at:
11 http://www.cdc.gov/nchs/about/major/nhis/quest_data_related_1997_forward.htm.
12
13 Chan AT and Chung MW. (2003). Indoor-Outdoor Air Quality Relationships in Vehicle: Effect
14 of Driving Environment and Ventilation Modes. Atmos Environ. 37:3795-3808.
15
16 Chappmann(1985).
17
18 EPA. (2004). AERMOD: Description of Model Formulation. Office of Air Quality Planning
19 and Standards, Research Triangle Park, NC. EPA-454/R-03-004. Available at:
20 http://www.epa.gov/scramOO l/7thconf/aermod/aermod_mfd.pdf
21
22 Chang MC, Sioutas C, Kim S, Gong H, Jr., Linn WS. (2000). Reduction of Nitrate Losses from
23 Filter and Impactor Samplers by Means of Concentration Enrichment. Atmos Environ 34:85-
24 98.
25
26 Delfmo, R. J.; Gone, H.; Linn, W. S.; Pellizzari, E. D.; Hu, Y. (2003a) Asthma Symptoms in
27 Hispanic Children and Daily Ambient Exposures to Toxic and Criteria Air Pollutants.
28 Environ. Health Perspect. Ill: 647-656.
29
30 Dey AN, Schiller JS, Tai DA. (2004). Summary Health Statistics for U.S. Children: National
31 Health Interview Survey, 2002. National Center for Health Statistics. Vital Health Stat
32 10(221).
33
34 Dodge, R. etal. (1985). A Longitudinal Study of Children Exposed to Sulfur Oxides. Am. J.
35 Epidemiology 121:720-736.
36
37 EIA. (2001). Residential Energy Consumption Survey of 2001. Energy Information
38 Administration. Available at:
39 http://www.eia.doe.gov/emeu/recs/recs2001 he/200 ltblhp.html.
40
41
March 2009 327 Draft Do Not Quote or Cite
-------
1
2
3 EPA. (1982). Air Quality Criteria for Particulate Matter and Sulfur Oxides. In: Agency USEP,
4 editor. Research Triangle Park, NC: Office of Health and Environmental Assessment.
5
6 EPA. (1986a).Review of the National Ambient Air Quality Standards for Sulfur Oxides: Updated
7 Assessment of Scientific and Technical Information, addendum to the 1982 OAQPS staff
8 paper. Research Triangle Park, NC: Office of Air Quality Planning and Standards, Strategies
9 and Air Standards Division; report no.
10 EPA/450/5-86-13. Available from: NTIS, Springfield, VA; PB87-200259/XAB.
11
12 EPA. (1986b). Second Addendum to Air Quality Criteria for Particulate Matter and Sulfur
13 Oxides (1982): Assessment of Newly Available Health Effects Information. In: Agency
14 USEP A, editor. Research Triangle Park, NC: Office of Health and Environmental
15 Assessment.
16
17 EPA. (1986c). An Analysis of Short-Term Sulfur Dioxide Population Exposures in the Vicinity
18 of Utility Power Plants. US EPA Office of Air Quality Planning and Standards, Strategies
19 and Air Standards Division, Research Triangle Park, NC. September 12, 1986.
20
21 EPA. (1994a). Supplement to the Second Addendum (1986) to Air Quality Criteria for
22 Particulate Matter and Sulfur Oxides (1982). In: Agency USEP, editor, report no. EPA-
23 600/FP-93/002 ed. Research Triangle Park, NC: Office of Health and Environmental
24 Assessment, Environmental Criteria and Assessment Office.
25
26 EPA. (1994b). Review of the National Ambient Air Quality Standards for Sulfur Oxides:
27 Assessment of Scientific and Technical Information, Supplement to the 1986 OAQPS Staff
28 Paper Addendum. Research Triangle Park, NC: Office of Air Quality Planning and
29 Standards; report no. EPA-452/R-94/013.
30
31 EPA. (2002). Consolidated Human Activities Database (CHAD) Users Guide. Database and
32 documentation available at: http://www.epa.gov/chadnet 1 /.
33
34 EPA. (2003). AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003. U.S.
35 Environmental Protection Agency, Research Triangle Park, NC. Available at:
36 http://www.epa.gov/scram001/7thconf/aermod/aermod mep.pdf.
37
38 EPA. (2004). AERMOD: Description of Model Formulation. Office of Air Quality Planning
39 and Standards. EPA-454/R-03-004. Available at:
40 http://www.epa.gov/scram001/7thconf/aermod/aermod mfd.pdf
March 2009 328 Draft Do Not Quote or Cite
-------
1 EPA. (2005). Review of the National Ambient Air Quality Standards for Particulate Matter:
2 Policy Assessment of Scientific and Technical Information - OAQPS Staff Paper. Research
3 Triangle Park, NC: Office of Air Quality Planning and Standards; report no. EPA-452/R-05-
4 005a. Available at:
5 http://www.epa.gov/ttn/naaqs/standards/pm/data/pmstaffpaper_20051221.pdf
6
7 EPA. (2007a). SO2 NAAQS Review Plan - Final. In: Agency USEP, editor. Research Triangle
8 Park, NC: National Center for Environmental Assessment.
9
10 EPA. (2007b). Sulfur Dioxide Health Assessment Plan: Scope and Methods for Exposure and
11 Risk Assessment. In: Agency USEP, editor. Research Triangle Park, NC: Office of Air
12 Quality Planning and Standards, Research Triangle Park.
13 Available at: http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_pd.html.
14
15 EPA. (2007c). US EPA Air Quality System (AQS). Download Detailed AQS Data. Available
16 at: http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm.
17
18 EPA. (2007d). Ozone Population Exposure Analysis for Selected Urban Areas (July 2007).
19 Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-07-
20 010. Available at: http://epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html.
21
22 EPA. (2007e). Review of the National Ambient Air Quality Standards for Ozone: Assessment of
23 Scientific and Technical Information, OAQPS Staff paper. Office of Air Quality Planning
24 and Standards, Research Triangle Park, NC. EPA-452/R-07-007a. Available at:
25 http://epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_sp.html.
26
27 EPA. (2007f). 2002 National Emissions Inventory Data & Documentation. Office of Air
28 Quality Planning and Standards, Research Triangle Park, NC. Available at:
29 http ://www. epa. gov/ttn/chief/net/2002inventory. html.
30
31 EPA. (2007g). Clean Air Markets - Data and Maps. Office of Atmospheric Programs,
32 Washington, DC. Emissions Prepackaged Data Sets. Available at:
33 http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=emissions. wizard.
34
35 EPA. (2007h). Sulfur Dioxide NAAQS Review - Analyses and Data Sets. Available at:
36 http://staging.epa.gov/ttn/analysis/so2.htm.
March 2009 329 Draft Do Not Quote or Cite
-------
2 EPA. (20071). Review of the National Ambient Air Quality Standards for Lead: Policy
3 Assessment of Scientific and Technical Information - OAQPS Staff Paper. Office of Air
4 Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-07-013.
5 Available at:
6 http://www.epa.gov/ttn/naaqs/standards/pm/data/pmstaffpaper_20051221.pdf
7
8 EPA. (2008a). Integrated Science Assessment (ISA) for Sulfur Oxides - Health Criteria (Final
9 Report). EPA/600/R-08/047F. September 2008. Available online at:
10 http://cfpub. epa.gov/ncea/cfm/recordisplay. cfm?deid= 198843.
11
12 EPA. (2008b). Risk and Exposure Assessment to Support the Review of the SO2 Primary
13 National Ambient Air Quality Standard: First Draft. EPA-452/P-08-003. June 2008.
14 Available online at: http://www.epa.gov/ttn/naaqs/standards/so2/s so2 cr rea.html.
15
16 EPA. (2008c). Sulfur Dioxide Health Assessment Plan: Scope and Methods for Exposure and
17 Risk Assessment. In: Agency USEPA editor. Research Triangle Park, NC: Office of Air
18 Quality Planning and Standards, Research Triangle Park. Available at:
19 http://www.epa.gOv/ttn/naaqs/standards/so2/s so2 cr_pd.html.
20
21 EPA. (2008d). Risk and Exposure Assessment to Support the Review of the NO2 Primary
22 National Ambient Air Quality Standard. EPA-452/R-08-008a. Available at:
23 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121 NO2 REA fmal.pdf.
24
25 EPA. (2008e). AERSURFACE User's Guide. EPA-454/B-08-001. U.S. Environmental
26 Protection Agency, Research Triangle Park, NC. Available at:
27 http://www.epa.gov/scram001/7thconf/aermod/aersurface_userguide.pdf
28
29 EPA. (2009a). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model
30 Documentation (TRIM.Expo / APEX, Version 4.3) Volume I: User's Guide. Office of Air
31 Quality Planning and Standards, Research Triangle Park, NC. January 2009. Available at:
32 http ://www. epa. gov/ttn/fera/human_apex.html.
33
34 EPA. (2009b). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model
35 Documentation (TRIM.Expo / APEX, Version 4.3) Volume II: Technical Support Document.
36 Office of Air Quality Planning and Standards, Research Triangle Park, NC. January 2009.
37 Available at: http://www.epa.gov/ttn/fera/human_apex.html.
38
39 ESRI. (2008). ESRI Demographic Update Methodology: 2008/2013 (page 8). Available at:
40 http://www.esri.com/library/whitepapers/pdfs/demographic-update-methodologv-2008.pdf
41
March 2009 330 Draft Do Not Quote or Cite
-------
1 Fung, K. Y.; Khan, S.; Krewski, D.; Chen, Y. (2006) Association between air pollution and
2 multiple respiratory hospitalizations among the elderly in Vancouver, Canada. Inhalation
3 Toxicol. 18: 1005-1011.
4
5 Gelman et al. (1995). Bayesian Data Analysis. Chapman and Hall, New York.
6
7 Geyh AS, Xue J, Ozkaynak H, Spengler JD. (2000). The Harvard Southern California chronic
8 ozone exposure study: Assessing ozone exposure of grade-school-age children in two
9 Southern California communities. Environ Health Persp. 108:265-270.
10
11 Gilks, W.R., Richardson, S., and Spiegelhalter, DJ. (Eds). (1996). Markov Chain Monte Carlo
12 in Practice. Chapman and Hall, London, UK.
13
14 Gong H, Jr., Lachenbruch PA, Harber P, Linn WS. (1995). Comparative Short-term Health
15 Responses to Sulfur Dioxide Exposure and Other Common Stresses in a Panel of Asthmatics.
16 Toxicol Ind Health 11:467-87.
17
18 Graham, SE and McCurdy T. (2004). Developing Meaningful Cohorts for Human Exposure
19 models. J Expos Anal Environ Epidemiol. 14(1)23-43.
20
21 Grontoft T and MR Raychaudhuri. 2004. Compilation of Tables of Surface Deposition
22 Velocities for O3, NO2 and SO2 to a Range of Indoor Surfaces. Atmos Environ. 38:533-
23 544.
24
25 Horstman D, Roger LJ, Kehrl H, Hazucha M. (1986). Airway Sensitivity of Asthmatics to Sulfur
26 Dioxide. Toxicol Ind Health 2:289-98.
27
28 Hu PS and Reuscher TR. (2004). Summary of travel trends. 2001 National Household Travel
29 Survey. US Department of Transportation, Federal Highway Administration. Available at:
30 http://nhts.ornl.gov/2001/pub/STT.pdf
31
32 Ito, K. (2007) Characterization of PM2.5, Gaseous Pollutants, and Meteorological Interactions in
33 the Context of Time-Series Health Effects Models. J. Expos. Sci. and Environ. Epid. 17: S45-
34 S60.
35
36 Jaffe, D. H.; Singer, M. E.; Rimm, A. A. (2003) Air Pollution and Emergency Department Visits
37 for Asthma Among Ohio Medicaid Recipients, 1991-1996. Environ. Res. 91: 21-28.
38
39 Kindzierski WB, Ranganathan HKS. (2006). Indoor and outdoor SO2 in a Community Near Oil
40 Sand Extraction and Production Facilities in Northern Alberta. Journal of Environmental
41 Engineering and Science 5:S121-S129.
42
March 2009 331 Draft Do Not Quote or Cite
-------
1 Langstaff JE. (2007). OAQPS Staff Memorandum to Ozone NAAQS Review Docket (OAR-
2 2005-0172). Subject: Analysis of Uncertainty in Ozone Population Exposure Modeling.
3 [January 31,2007]. Available at:
4 http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html.
5
6 Langstaff J. (2009). Analysis of Uncertainty in the Statistical Model for Estimating 5-minute
7 Maximum Concentrations of SO2. Sulfur Dioxide NAAQS Review Docket. (OAR-2005-
8 0172.
9
10 Lin, M.; Chen, Y.; Burnett, R. T.; Villeneuve, P. J.; Krewski, D. (2003) Effect of Short-Term
11 Exposure to Gaseous Pollution on Asthma Hospitalizations in Children: a bi-directional
12 Case-Crossover Analysis. J. Epidemiol. Community Health 57: 50-55.
13
14 Lin, M.; Chen, Y.; Villeneuve, P. J.; Burnett, R. T.; Lemyre, L.; Hertzman, C.; McGrail, K. M.;
15 Krewski, D. (2004a) Gaseous Air Pollutants and Asthma Hopitalization of Chiildren with
16 low household income in Vancouver, British Columbia, Canada. Am. J. Epidemiol. 159:
17 294-303.
18
19 Lin, S.; Hwang, S.-A.; Pantea, C.; Kielb, C.; Fitzgerald, E. (2004b) Childhood Asthma
20 hospitalizations and ambient air sulfur dioxide concentrations in Bronx County, New York.
21 Arch. Environ. Health 59: 266-275.
22
23 Lin M; Stieb DM; Chen Y. (2005) Coarse Particulate Matter and Hospitalization for Respiratory
24 Infections in Children Younger Than 15 Years in Toronto: A Case-Crossover Analysis.
25 Pediatrics. 116:e235-240.
26
27 Linn WS, Venet TG, Shamoo DA, Valencia LM, Anzar UT, Spier CE, Hackney JD. (1983).
28 Respiratory Effects of Sulfur Dioxide in Heavily Exercising Asthmatics. A dose-Response
29 Study. Am Rev Respir Dis 127:278-83.
30
31 Linn WS, Avol EL, Shamoo DA, Venet TG, Anderson KR, Whynot JD, Hackney JD. (1984).
32 Asthmatics' Responses to 6-hr Sulfur Dioxide Exposures on Two Successive Days. Arch
3 3 Environ Health 39:313-9.
March 2009 332 Draft Do Not Quote or Cite
-------
1 Linn WS, Avol EL, Peng RC, Shamoo DA, Hackney JD. (1987). Replicated Dose-Response
2 Study of Sulfur Dioxide Effects in Normal, Atopic, and Asthmatic Volunteers. Am Rev
3 RespirDis 136:1127-34.
4
5 Linn WS, Avol EL, Shamoo DA, Peng RC, Spier CE, Smith MN, Hackney JD. (1988). Effect of
6 Metaproterenol Sulfate on Mild Asthmatics' Response to Sulfur Dioxide Exposure and
7 Exercise. Arch Environ Health 43:399-406.
8
9 Linn WS, Shamoo DA, Peng RC, Clark KW, Avol EL, Hackney JD. (1990). Responses to Sulfur
10 Dioxide and Exercise by Medication-Dependent Asthmatics: Effect of Varying Medication
11 Levels. Arch Environ Health 45:24-30.
12
13 Luginaah, I. N.; Fung, K. Y.; Gorey, K. M.; Webster, G.; Wills, C. (2005) Association of
14 Ambient Air Pollution with Respiratory Hospitalization in a Government Designated "Area
15 of Concern": the case of Windsor, Ontario. Environ. Health Perspect. 113: 290-296.
16
17 Magnussen H, Jorres R, Wagner HM, von Nieding G. 1990. Relationship Between the Airway
18 Response to Inhaled Sulfur Dioxide, Isocapnic Hyperventilation, and Histamine in Asthmatic
19 Subjects. Int Arch Occup Environ Health 62:485-91.
20
21 Mansfield C, Van Houtven G, Johnson FR, Crawford-Brown D. (2005). Behavioral Reactions
22 to Ozone Alerts: What Do They Tell Us About Willingness-to-Pay for Children's Health?
23 Research Update - 6/17/05.
24
25 Meyers S. (1983). Section 107 Designation Policy Summary. Memo to the Directors of Air and
26 Waste Management Division, Regions II-IV, VI-VIII, X and Director of Air Management
27 Division Regions I, V, and IX. US EPA Office of Air Quality Planning and Standards. April
28 21, 1983.
29
30 McCurdy, T, Glen G, Smith L, Lakkadi Y. (2000). The National Exposure Research
31 Laboratory's Consolidated Human Activity Database. J Exp Anal Environ Epidemiol. 10:
32 566-578.
33
34 MO Department of Health. 2002. Health Risk Behaviors of Adult Missourians, 2002 Annual
35 Report, Behavioral Risk Factor Surveillance System. Division of Community and Public
36 Health, Bureau of Health Informatics, Available at:
37 http://www.dhss.mo.gov/BRFSS/2002AnnualReport.pdf.
38
39 MO DOH. 2003. Health and Preventive Practices Status Report from Missouri County-level
40 Study - Community Data Profiles. Available at:
41 http://www.dhss.mo.gov/CommunityDataProfiles/
March 2009 333 Draft Do Not Quote or Cite
-------
1 Mortimer, K. M.; Neas, L. M.; Dockery, D. W.; Redline, S.; Tager, I. B. (2002) The Effect of Air
2 pollution on Inner-city Children with Asthma. Eur. Respir. J. 19: 699-705.
3
4 Neas, L. M.; Dockery, D. W.; Koutrakis, P.; Tollerud, D. J.; Speizer, F. E. (1995) The
5 Association of Ambient Air Pollution with Twice Daily Peak Expiratory Flow Rate
6 Measurements in Children. Am. J. Epidemiol. 141: 111-122.
7
8 Peel, J. L.; Tolbert, P. E.; Klein, M.; Metzger, K. B.; Flanders, W. D.; Knox, T.; Mulholland, J.
9 A.; Ryan, P. B.; Frumkin, H. (2005) Ambient Air Pollution and Respiratory Emergency
10 Department Visits. Epidemiology 16: 164-174.
11
12 Perry SG, Cimorelli AJ, Paine RJ, Erode RW, Weil JC, Venkatram A, Wilson RB, Lee RF,
13 Peters WD. (2005). AERMOD: A Dispersion Model for Industrial Source Applications.
14 Part II: Model Performance against 17 Field Study Databases. J. Appl. Meteor. 44:694-708.
15
16 Persily, A, Gorfain J, Brunner G. (2005). Ventilation design and performance in U.S. office
17 buildings. ASHRAE Journal. April 2005, 30-35.
18
19 Rizzo, M. (2009). Investigation of How Distributions of Hourly Sulfur Dioxide Concentrations
20 Have Changed Over Time in Six Cities. Sulfur Dioxide NAAQS Review Docket. (EPA-
21 HQ-OAR-2007-0352.
22
23 Robinson JP, Wiley JA, Piazza T, Garrett K, Cirksena K. (1989). Activity Patterns of California
24 Residents and their Implications for Potential Exposure to Pollution. California Air
25 Resources Board, Sacramento, CA. CARB-A6-177-33.
26
27 Roger LJ, Kehrl HR, Hazucha M, Horstman DH. (1985). Bronchoconstriction in Asthmatics
28 Exposed to Sulfur Dioxide During Repeated Exercise. J Appl Physiol 59:784-91.
29
30 Rosenbaum AS, Hudischewskyj AB, Roberson RL, Burton CS (1992). Estimates of Future
31 Exposures of Exercising Asthmatics to Short-term Elevated SO2 Concentrations Resulting
32 from Emissions of U.S. Fossil-fueled Power Plants: Effects of the 1990 Amendments to the
33 Clean Air Act and a 5-Minute Average Ambient SO2 Standard. Publication No. SYSAPP-
34 92/016. April 23, 1992. Docket No. A-84-25, IV-K-37. \
35
36 Schachter EN; Witek TJ, Jr.; Beck GJ; Hosein HB; Colice G; Leaderer BP; Cain W. (1984)
37 Airway Effects of Low Concentrations of Sulfur Dioxide: Dose-Response Characteristics.
38 Arch Environ Health. 39:34-42.
March 2009 334 Draft Do Not Quote or Cite
-------
1 Schildcrout, J. S.; Sheppard, L.; Lumley, T.; Slaughter, J. C.; Koenig, J. Q.; Shapiro, G. G.
2 (2006) Ambient Air Pollution and Asthma Exacerbations in Children: An Eight-City
3 Analysis. Am. J. Epidemiol. 164: 505-517.
4
5 Schwartz, J.; Dockery, D. W.; Neas, L. M.; Wypij, D.; Ware, J. H.; Spengler, J. D.; Koutrakis,
6 P.; Speizer, F. E.; Ferris, B. G., Jr. (1994) Acute Effects of Summer Air Pollution on
7 Respiratory Symptom Reporting in Children. Am. J. Respir. Crit. Care Med. 150: 1234-1242.
8
9 Schwartz, J. (1995) Short Term Fluctuations in Air Pollution and Hospital Admissions of the
10 Elderly for Respiratory Disease. Thorax 50: 531-538.
11
12 Schwartz J. (1996) Air Pollution and Hospital Admissions for Respiratory Disease.
13 Epidemiology, 7: 20-28.
14
15 Science International (1995). Estimate of the Nationwide Exercising Asthmatic Exposure
16 Frequency to Short-term Peak Sulfur Dioxide Concentrations in the Vicinity of Non-Utility
17 Sources. Prepared for National Mining Association by Sciences International, Inc.,
18 Alexandria VA. April 1995. Docket No. A-84-25, VIII-D-71.
19
20
21 Sheppard reanalysis (2003). Ambient Air Pollution and Nonelderly Asthma Hospital
22 Admissions in Seattle, Washington, 1987-1994. In: Revised analyses of time-series studies of
23 air pollution and health. Special report. Boston, MA: Health Effects Institute; pp. 227-230.
24
25 Sheppard L, Slaughter JC, Schildcrout J, Liu LJ, Lumley T. (2005). Exposure and Measurement
26 Contributions to Estimates of Acute Air Pollution Effects. J Expo Anal Environ Epidemiol
27 15:366-76.
28
29 Skuta and Wombold. (2008). The Secret Life of Polygons: Understanding the relative accuracy
30 of user-defined areas. Available at: http://www.esri.com/news/arcuser/1008/polygons.html.
31
32 Smith E. (1994). Subject Data Supplied by the Researchers for the Recent Controlled Human
33 Studies Analyzed in the Staff Paper Supplement and Accompanying Memorandum.
34 Memorandum to Docket No. A-84-25, Item IV-B-5
35
36 Spier CE, Little DE, Trim SC, Johnson TR, Linn WS, Hackney JD. (1992). Activity Patterns in
37 Elementary and High school Students Exposed to Oxidant Pollution. J Expo Anal Environ
38 Epidemiol. 2:277-293.
39
40 Spiegelhalter, D.; Thomas, A.; Best, N.; and Gilks, W. (1996). Bugs .5 Bayesian inference using
41 Gibbs sampling. Manual, version ii. MRC Biostatistics Unit, Institute of Public Health.
42 Cambridge, U.K.
43
March 2009 335 Draft Do Not Quote or Cite
-------
1 Stieb, D. M.; Beveridge, R. C.; Brook, J. R.; Smith-Doiron, M.; Burnett, R. T.; Dales, R. E.;
2 Beaulieu, S.; Judek, S.; Mamedov, A. Air pollution, Aeroallergens and Cardiorespiratory
3 Emergency Department Visits in Saint John, Canada. 2000 . J Exposure Anal. Environ.
4 Epidemiol. 10: 461-477.
5
6 Stoeckenius TE, Garelick B, Austin BS, O'Connor K, Pehling JR (1990). Estimates of
7 Nationwide Asthmatic Exposures to Short-term Sulfur Dioxide Concentrations in the
8 Vicinity of Non-Utility Sources. Systems Applications Inc., San Rafael, CA. Publication No.
9 SYSAPP-90/129, December 6, 1990.
10
11 Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of
12 the Royal Statistical Society. Ser.B, 36:111-147.
13
14 Thompson, R (2000). Preliminary Analysis of 5-Minute Maximum Ambient SO2
15 Concentrations. OAQPS. 12/21/00. Office of Air Quality Planning and Standards, U.S.
16 Environmental Protection Agency, Research Triangle Park, NC. Available at:
17 http ://www. epa. gov/ttn/amtic/so2data.html.
18
19 Thompson, R (2009). Air Quality Statistics for Cities Referenced in Key U.S. and Canadian
20 Hospital Admission and Emergency Department Visits for All Respiratory Causes and
21 Asthma. Office of Air Quality Planning and Standards, U.S. Environmental Protection
22 Agency, Research Triangle Park, NC. Available at:
23 http://www.epa.gOv/ttn/naaqs/standards/so2/s so2 cr rea.html.
24
25 Thrall AD, Langstaff JE, Liu MK, Burton CS. (1982). On the Variability in Peak Sulfur
26 Dioxide Concentrations Contained in Longer-Term Averages: An Empirical Study of an
27 Isolated PowerPlant. Systems Applications Inc.
28
29 Tolbert, P. E.; Klein, M.; Peel, J. L.; Sarnat, S. E.; Sarnat, J. A. (2007) Multipollutant Modeling
30 Issues in a Study of Ambient Air Quality and Emergency Department Visits in Atlanta. J.
31 Exposure Sci. Environ. Epidemiol.:
32
33 Trenga CA, Koenig JQ, Williams PV. (1999). Sulphur Dioxide Sensitivity and Plasma
34 Antioxidants in Adult Subjects with Asthma. Occup Environ Med 56:544-7.
35
36 van Gent, R, van der Ent K, van Essen-Zandvliet LEM, Rovers MM, Kimpen ILL, de Meer G,
37 Klijn PHC. (2007). No difference in physical activity in (un)diagnosed asthma and healthy
38 controls. PediatricPulmonology. 42:1018-1023.
39
40 WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on
41 characterizing and communicating uncertainty in exposure assessment. Available at:
42 http://www.who.int/ipcs/methods/harmonization/areas/exposure/en/.
43
March 2009 336 Draft Do Not Quote or Cite
-------
1 Xue J, Liu SV, Ozkaynak H, Spengler J. (2005). Parameter evaluation and model validation of
2 ozone exposure assessment using Harvard Southern California Chronic Ozone Exposure
3 Study Data. J. Air & Waste Manage Assoc. 55:1508-1515.
4
5 Yang, Q.; Chen, Y.; Shi, Y.; Burnett, R. T.; McGrail, K. M.; Krewski, D. (2003) Association
6 Between Ozone and Respiratory Admissions Among Children and the Elderly in Vancouver,
7 Canada. Inhalation Toxicol. 15: 1297-1308.
8
9 Yen ST, Shaw WD, Eiswerth ME. (2004). Asthma patients' activities and air pollution: a
10 semiparametric censored regression analysis. Review of Economics of the Household.
11 2(1)1569-7152.
12
13 Zeger SL, Thomas D, Dominici F, Samet JM, Schwartz J, Dockery D, Cohen A. (2000).
14 Exposure Measurement Error in Time-Series Studies of Air Pollution? Official statement of
15 the American Thoracic Society: concepts and consequences. Environ Health Perspect.
16 108:877-685.
17
March 2009 337 Draft Do Not Quote or Cite
-------
i Appendix A: Supplement to the SO2 Air Quality
2 Characterization
o
4 This appendix contains supplementary information on the SC>2 ambient monitoring data
5 used in the air quality characterization described in Chapter 7 of the SC>2 REA. Included in this
6 appendix are spatial and temporal attributes important for understanding the relationship between
7 the ambient monitor and those sources affecting air quality measurements.
8 In section A. 1, important spatial characteristics described include the physical locations
9 of the ambient monitors (e.g., U.S. states, counties, territories, and cities). Temporal attributes of
10 interest include, for example, the number of samples collected, sample averaging times, and
11 years of monitoring data available. Attributes of the monitors that reported both the 5-minute
12 maximum and the 1-hour SC>2 concentrations are given in Tables A. 1-1 and A. 1-2, while the
13 supplemental characteristics of the broader ambient monitoring network are given in Table A. 1-3
14 and A. 1-4. The method for calculating the proximity of the ambient monitors follows, along
15 with the distance and emission results summarized in Table A. 1-5.
16 Section A.2 details the analyses performed on simultaneous concentrations, some of
17 which are the result of co-located monitoring instruments, others the result of duplicate
18 reporting. Simultaneous measurements were identified by staff using monitor IDs and multiple
19 concentrations present given the hour-of-day on each available date. Staff estimated a relative
20 percent difference between the simultaneous measurements at each monitor.
21 Section A-3 has the tables summarizing the COV and GSD peak-to-mean ratio (PMRs).
22 Section A-4 has tables summarizing the individual factors used in adjusting ambient air quality
23 to just meet the current and potential alternative SC>2 air quality standards. Section A-5
24 summarizes measured 1-hour and 5-minute maximum concentrations at the 98 monitors
25 reporting both averaging times.
26
27
28
A-l
-------
i A-1 Spatial and Temporal Attributes of Ambient SO2 Monitors
A-2
-------
Table A.1-1. Meta-data for ambient monitors reporting 5-minute maximum and corresponding 1-hour SO2
concentrations.
State
AR
AR
AR
CO
DC
DE
FL
IA
IA
IA
IA
IA
IA
IA
IA
IA
LA
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
County
Pulaski
Pulaski
Union
Denver
District of Columbia
New Castle
Nassau
Cerro Gordo
Clinton
Muscatine
Muscatine
Muscatine
Scott
Van Buren
Van Buren
Wood bury
West Baton Rouge
Buchanan
Buchanan
Greene
Greene
Iron
Iron
Jefferson
Jefferson
Jefferson
Jefferson
Monroe
Pike
Saint Charles
Mionitor ID
051190007
051191002
051390006
080310002
110010041
100031008
120890005
190330018
190450019
191390016
191390017
191390020
191630015
191770005
191770006
191930018
221210001
290210009
290210011
290770026
290770037
290930030
290930031
290990004
290990014
290990017
290990018
291370001
291630002
291830010
Latitude
34.756111
34.830556
33.215
39.75119
38.897222
39.577778
30.658333
43.16944
41.823283
41.419429
41.387969
41.407796
41.530011
40.689167
40.695078
42.399444
30.501944
39.731389
39.731389
37.128333
37.11
37.466389
37.519444
38.2633
38.267222
38.252778
38.297694
39.473056
39.3726
38.579167
Longitude
-92.275833
-92.259444
-92.668889
-104.98762
-76.952778
-75.611111
-81.463333
-93.202426
-90.211982
-91.070975
-91.054504
-91 .062646
-90.587611
-91 .994444
-92.006318
-96.355833
-91.209722
-94.8775
-94.868333
-93.261667
-93.251944
-90.69
-90.7125
-90.3785
-90.379444
-90.393333
-90.384333
-91.789167
-90.9144
-90.841111
Objective
POP
HIC
UNK
HIC
POP
UNK
HIC
UNK
UNK
UNK
UNK
UNK
HIC
UNK
GEN
POP
HIC
GEN
GEN
POP
POP
SRC
UNK
POP
OTH
UNK
HIC
UNK
HIC
UNK
Setting2
URB
RUR
URB
URB
URB
RUR
SUB
SUB
URB
URB
SUB
SUB
URB
RUR
RUR
URB
SUB
URB
URB
SUB
RUR
RUR
RUR
RUR
RUR
SUB
SUB
RUR
RUR
RUR
Land Use3
COM
FOR
COM
COM
RES
AGR
IND
RES
IND
RES
IND
IND
RES
FOR
FOR
RES
COM
IND
IND
RES
RES
RES
AGR
IND
RES
RES
RES
UNK
RES
AGR
Scale4
NEI
NEI
NEI
NEI
NEI
MID
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
(m)
4
4
4
5
2
4
3
4
4
4
3
3
3
2
3
3
3
4
4
2
3
4
5
5
3
3
Years
n
6
5
11
10
6
2
4
5
5
5
5
5
5
4
2
2
4
4
4
11
11
8
8
4
5
4
3
11
3
2
First
2002
1997
1997
1997
2000
1997
2002
2001
2001
2001
2001
2001
2001
2001
2004
2001
1997
1997
2000
1997
1997
1997
1997
2004
1997
1998
2001
1997
2005
1997
Last
2007
2001
2007
2006
2007
1998
2005
2005
2005
2005
2005
2005
2005
2004
2005
2002
2000
2000
2003
2007
2007
2004
2004
2007
2001
2001
2003
2007
2007
1998
A-3
-------
State
MO
MT
MT
MT
MT
MT
MT
MT
NC
NC
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
PA
PA
PA
PA
County
Saint Charles
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Forsyth
New Hanover
Billings
Billings
Burke
Burke
Burleigh
Cass
Cass
Dunn
McKenzie
McKenzie
McKenzie
Mercer
Mercer
Morton
Morton
Oliver
Steele
Williams
Williams
Allegheny
Allegheny
Allegheny
Allegheny
Mionitor ID
291831002
301110066
301110079
301110080
301110082
301110083
301110084
301112008
370670022
371290006
380070002
380070003
380130002
380130004
380150003
380171003
380171004
380250003
380530002
380530104
380530111
380570001
380570004
380590002
380590003
380650002
380910001
381050103
381050105
420030002
420030021
420030031
420030032
Latitude
38.8725
45.788318
45.769439
45.777149
45.783889
45.795278
45.831453
45.786389
36.110556
34.268403
46.8943
46.9619
48.9904
48.64193
46.825425
46.910278
46.933754
47.3132
47.5812
47.575278
47.605556
47.258853
47.298611
46.84175
46.873075
47.185833
47.599703
48.408834
48.392644
40.500556
40.413611
40.443333
40.414444
Longitude
-90.226389
-108.459536
-108.574292
-108.47436
-108.515
-108.455833
-108.449964
-108.523056
-80.226667
-77.956529
-103.37853
-103.356699
-102.7815
-102.4018
-100.76821
-96.795
-96.85535
-102.5273
-103.2995
-103.968889
-104.017222
-101.783035
-101.766944
-100.870059
-100.905039
-101.428056
-97.899009
-102.90765
-102.910233
-80.071944
-79.941389
-79.990556
-79.942222
Objective
UNK
SRC
POP
UNK
POP
SRC
POP
UNK
POP
GEN
GEN
HIC
SRC
REG
POP
POP
POP
GEN
GEN
SRC
SRC
POP
POP
SRC
SRC
SRC
GEN
SRC
SRC
POP
POP
POP
UNK
Setting2
RUR
RUR
SUB
RUR
URB
SUB
SUB
URB
URB
RUR
RUR
RUR
RUR
RUR
SUB
SUB
SUB
RUR
RUR
RUR
RUR
SUB
RUR
SUB
SUB
RUR
RUR
RUR
RUR
SUB
SUB
URB
SUB
Land Use3
AGR
RES
COM
AGR
COM
AGR
RES
RES
RES
IND
AGR
IND
AGR
AGR
RES
RES
AGR
AGR
AGR
AGR
IND
RES
AGR
IND
IND
AGR
AGR
IND
IND
RES
RES
COM
RES
Scale4
NEI
NEI
NEI
NEI
URB
REG
URB
REG
REG
URB
URB
URB
REG
REG
URB
URB
NEI
URB
NEI
NEI
URB
REG
URB
URB
NEI
NEI
NEI
Height
(m)
2
3.5
4.5
4
3
4
4.5
3
3
3
12.2
4
4
4
4
4
3
4
4
3
3
5
4
4
4
3
3
4
4
6
6
13
5
Years
n
4
7
4
5
3
5
4
1
8
4
10
1
7
5
3
2
10
11
9
10
10
3
9
9
8
11
4
6
6
3
4
3
3
First
1997
1997
1997
1997
2001
1999
2003
1997
1997
1999
1998
1997
1999
2003
2005
1997
1998
1997
1997
1998
1998
1997
1999
1997
1998
1997
1997
2002
2002
1997
1997
1997
1997
Last
2000
2003
2003
2001
2003
2003
2006
1997
2004
2002
2007
1997
2005
2007
2007
1998
2007
2007
2007
2007
2007
1999
2007
2005
2005
2007
2000
2007
2007
1999
2002
1999
1999
A-4
-------
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
UT
WV
WV
WV
County
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Berks
Cambria
Erie
Philadelphia
Philadelphia
Philadelphia
Warren
Warren
Washington
Washington
Washington
Barnwell
Charleston
Charleston
Georgetown
Greenville
Lexington
Oconee
Richland
Richland
Richland
Salt Lake
Wayne
Wayne
Wayne
Mionitor ID
420030064
420030067
420030116
420031301
420033003
420033004
420070002
420070005
420110009
420210011
420490003
421010022
421010048
421010136
421230003
421230004
421250005
421250200
421255001
450110001
450190003
450190046
450430006
450450008
450630008
450730001
450790007
450790021
450791003
490352004
540990002
540990003
540990004
Latitude
40.323611
40.381944
40.473611
40.4025
40.318056
40.305
40.56252
40.684722
40.320278
40.309722
42.14175
39.916667
39.991389
39.9275
41.857222
41 .844722
40.146667
40.170556
40.445278
33.320344
32.882289
32.941023
33.362014
34.838814
34.051017
34.805261
34.093959
33.81468
34.024497
40.736389
38.39186
38.390278
38.380278
Longitude
-79.868333
-80.185556
-80.077222
-79.860278
-79.881111
-79.888889
-80.503948
-80.359722
-75.926667
-78.915
-80.038611
-75.188889
-75.080833
-75.222778
-79.1375
-79.169722
-79.902222
-80.261389
-80.420833
-81 .465537
-79.977538
-79.657187
-79.294251
-82.402918
-81.15495
-83.2377
-80.962304
-80.781135
-81.036248
-112.210278
-82.583923
-82.585833
-82.583889
Objective
POP
GEN
POP
HIC
POP
UNK
REG
POP
HIC
HIC
HIC
HIC
UNK
POP
HIC
HIC
POP
POP
REG
SRC
POP
SRC
SRC
POP
SRC
REG
OTH
GEN
POP
HIC
POP
HIC
HIC
Setting2
SUB
RUR
SUB
SUB
SUB
SUB
RUR
RUR
SUB
URB
SUB
URB
RUR
URB
SUB
RUR
SUB
SUB
RUR
RUR
URB
RUR
URB
URB
SUB
RUR
SUB
RUR
URB
RUR
RUR
RUR
RUR
Land Use3
RES
RES
RES
RES
IND
RES
AGR
AGR
RES
COM
COM
IND
RES
RES
RES
FOR
COM
RES
AGR
FOR
COM
FOR
IND
COM
COM
FOR
COM
FOR
COM
IND
IND
RES
RES
Scale4
NEI
NEI
NEI
NEI
REG
URB
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
REG
URB
NEI
REG
NEI
NEI
NEI
REG
NEI
URB
MID
NEI
NEI
NEI
Height
(m)
8
9
5
9
5
8
3
3
4
12
4
7
5
4
4
4
2
4
4
3.1
4.3
4
2.13
4
3.35
4.3
3
4.42
4
4
3
3
Years
n
4
3
4
3
4
3
2
8
3
3
3
5
3
7
2
2
3
3
2
3
3
3
3
3
2
3
3
3
2
2
1
4
4
First
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2000
2000
2000
2000
2000
2001
2000
2000
2000
2001
1997
2002
2002
2002
Last
2002
1999
2002
1999
2002
1999
1998
2007
1999
1999
1999
2001
1999
2003
1998
1998
1999
1999
1998
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
1998
2002
2005
2005
A-5
-------
1
2
State
WV
wv
County
Wayne
Wood
Mionitor ID
540990005
541071002
Latitude
38.372222
39.323533
Longitude
-82.588889
-81.552367
Objective
HIC
POP
Setting2
RUR
SUB
Land Use3
RES
IND
Scale4
NEI
URB
Height
(m)
3
4
Years
n
4
5
First
2002
2001
Last
2005
2005
Notes:
1 Objectives are POP=Population Exposure; HIC=Highest Concentration; SRC=Source Oriented; GEN=General/Background; REG=Regional
Transport; OTH=Other; UNK=Unknown
2 Settings are R=Rural; U=Urban and Center City; S=Suburban
3 Land Uses are AGR=Agricultural; COM=Commercial; IND=lndustrial; FOR=Forest; RES=Residential; UNK=Unknown
4 Scales are NEI=Neighborhood; MID=Middle; URB=URBAN; REG=Regional
A-6
-------
1 Table A.1-2. Population density, concentration variability, and total SO2 emissions associated with ambient
2 monitors reporting 5-minute maximum and corresponding 1-hour SO2 concentrations.
State
AR
AR
AR
CO
DE
DC
FL
IA
IA
IA
IA
IA
IA
IA
IA
IA
LA
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
County
Pulaski
Pulaski
Union
Denver
New Castle
District of Columbia
Nassau
Cerro Gordo
Clinton
Muscatine
Muscatine
Muscatine
Scott
Van Buren
Van Buren
Wood bury
West Baton Rouge
Buchanan
Buchanan
Greene
Greene
Iron
Iron
Jefferson
Jefferson
Jefferson
Jefferson
Monroe
Pike
Saint Charles
Saint Charles
Monitor ID
051190007
051191002
051390006
080310002
100031008
110010041
120890005
190330018
190450019
191390016
191390017
191390020
191630015
191770005
191770006
191930018
221210001
290210009
290210011
290770026
290770037
290930030
290930031
290990004
290990014
290990017
290990018
291370001
291630002
291830010
291831002
Population Residing Within:
5km
67784
45800
21877
189782
5386
216129
17963
21247
24561
20360
11109
20360
90863
994
994
4449
21249
23253
28224
41036
21784
1121
0
15049
11967
19711
12258
0
645
2637
4587
10km
178348
109372
29073
574752
80025
813665
21386
30341
37638
27101
27101
27101
201277
2252
2252
44815
137455
72613
75073
146752
110681
1121
3799
33379
35082
36471
41709
1439
2077
6349
95765
15km
270266
230200
32652
1158644
192989
1461563
38521
39284
42404
31886
31696
31886
268535
3764
3764
92956
239718
87121
86317
224445
210953
4507
6585
64516
61963
60199
79196
2093
6916
34541
273147
20km
334649
310362
36340
1608099
391157
2029936
48316
45105
45947
40248
36604
40290
293627
6984
6984
112802
366741
93365
93365
256158
254437
8447
8436
124301
125932
116882
170110
5612
11249
90953
431484
Analysis Bins
Population1
hi
mid
mid
hi
low
hi
mid
mid
mid
mid
mid
mid
hi
low
low
low
mid
mid
mid
mid
mid
low
low
mid
mid
mid
mid
low
low
low
low
cov2
a
a
b
b
b
a
c
c
b
b
b
c
b
b
a
b
b
c
b
c
c
c
c
c
c
c
c
a
b
b
b
GSD3
a
a
a
b
b
a
b
c
c
b
b
c
c
b
b
c
b
b
b
b
b
c
b
c
b
b
b
a
b
b
b
Emissions
(tpy)4
20
20
2527
26354
39757
18325
5050
10737
9388
31137
31054
31054
9415
36833
31242
3563
3563
9206
9206
43340
43340
55725
55725
55725
32468
13495
47610
67735
A-7
-------
State
MT
MT
MT
MT
MT
MT
MT
NC
NC
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
PA
PA
PA
PA
PA
County
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Forsyth
New Hanover
Billings
Billings
Burke
Burke
Burleigh
Cass
Cass
Dunn
McKenzie
McKenzie
McKenzie
Mercer
Mercer
Morton
Morton
Oliver
Steele
Williams
Williams
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Monitor ID
301110066
301110079
301110080
301110082
301110083
301110084
301112008
370670022
371290006
380070002
380070003
380130002
380130004
380150003
380171003
380171004
380250003
380530002
380530104
380530111
380570001
380570004
380590002
380590003
380650002
380910001
381050103
381050105
420030002
420030021
420030031
420030032
420030064
Population Residing Within:
5 km
27389
61645
33774
58256
27620
22577
61335
61669
17957
0
0
0
655
49591
48975
2118
0
0
0
0
3280
3280
17925
10305
0
0
0
0
83332
170777
183843
174072
64846
10km
79644
89282
86065
94753
76641
59919
95574
170320
83529
0
888
0
655
67377
134561
91149
0
596
521
0
3280
4428
67959
31348
0
934
1259
1259
277442
560187
580429
558904
201143
15km
98733
102887
1 04825
103200
98733
97912
103200
258102
145330
1887
1887
0
655
83082
144878
145789
0
596
521
2283
5902
5902
75685
75685
2057
934
1259
1259
651551
921490
877668
922097
520438
20km
107178
114640
108399
1 06046
109475
110980
1 06046
325974
1 70260
1887
1887
625
655
84415
154455
148002
537
596
2283
5771
6465
7455
84415
82584
2670
934
1827
1827
961378
1142754
1145039
1144558
943781
Analysis Bins
Population1
mid
hi
mid
hi
mid
mid
hi
hi
mid
low
low
low
low
mid
mid
low
low
low
low
low
low
low
mid
mid
low
low
low
low
hi
hi
hi
hi
hi
cov2
b
b
b
b
b
b
b
b
c
a
a
b
b
b
b
a
a
a
b
c
b
b
c
b
b
a
c
b
b
b
a
b
b
GSD3
c
a
b
a
b
b
b
b
c
a
a
b
b
a
a
b
a
a
a
a
b
a
c
b
b
a
b
c
b
b
b
b
c
Emissions
(tpy)4
5480
5480
5480
5480
5480
15298
5480
3945
30020
283
283
426
4592
771
756
5
210
823
91617
91617
4592
4592
28565
1605
1605
1964
52447
46957
52447
11490
A-8
-------
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
UT
WV
WV
WV
WV
County
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Berks
Cambria
Erie
Philadelphia
Philadelphia
Philadelphia
Warren
Warren
Washington
Washington
Washington
Barnwell
Charleston
Charleston
Georgetown
Greenville
Lexington
Oconee
Richland
Richland
Richland
Salt Lake
Wayne
Wayne
Wayne
Wayne
Monitor ID
420030067
420030116
420031301
420033003
420033004
420070002
420070005
420110009
420210011
420490003
421010022
421010048
421010136
421230003
421230004
421250005
421250200
421255001
450110001
450190003
450190046
450430006
450450008
450630008
450730001
450790007
450790021
450791003
490352004
540990002
540990003
540990004
540990005
Population Residing Within:
5 km
13277
96820
115432
55221
38588
3434
17292
121330
50440
81199
316944
262592
382995
14142
13965
31276
32125
1359
0
40872
1103
10567
70221
42208
0
35872
1666
87097
0
17320
17320
16553
13314
10km
86792
331624
411867
202092
170065
28961
77240
203799
79710
150626
985213
1102727
985957
19940
18884
68512
52910
15854
4022
132716
1103
18215
173012
131361
2260
121006
4643
213836
4074
62645
59989
54251
48330
15km
324154
704601
766188
509708
461433
68617
143738
250610
102905
190212
1726387
1938877
1718068
25715
28805
111222
83324
43364
13647
273298
9529
22467
284047
257820
11136
255135
13324
300874
35159
124477
123349
122072
114824
20km
610975
996267
1088115
944188
904760
120780
224631
309553
124592
209983
2446142
2607877
2381173
32490
33523
183285
118188
126091
21554
364953
22255
34357
379022
355854
26182
353072
33098
396116
124394
178576
177744
179815
173807
Analysis Bins
Population1
mid
hi
hi
hi
mid
low
mid
hi
hi
hi
hi
hi
hi
mid
mid
mid
mid
low
low
mid
low
mid
hi
mid
low
mid
low
hi
low
mid
mid
mid
mid
cov2
a
b
b
b
b
b
b
a
a
b
a
b
b
b
b
a
b
b
a
b
b
b
a
b
a
a
b
a
a
a
b
b
b
GSD3
b
b
b
c
b
b
c
b
b
b
b
b
b
b
c
b
b
b
a
b
a
b
a
b
a
a
a
a
a
b
b
b
b
Emissions
(tpy)4
1167
1964
52100
11490
11501
187257
41385
14817
16779
4122
18834
6214
21700
4890
4890
8484
7
2566
65
34934
40841
1067
10433
5
613
40492
12935
3735
10172
10172
10172
10172
A-9
-------
State
WV
County
Wood
Monitor ID
541071002
Population Residing Within:
5 km
24917
10km
70324
15km
1 04458
20km
128127
Analysis Bins
Population1
mid
COV2
b
GSD3
b
Emissions
(tpy)4
48124
Notes:
1 Population bins: low (<1 0,000); mid (10,001 to 50,000); hi (>50,000) using population within 5 km of ambient monitor.
2 COV bins: a (<100%); b (>100 to <200); c (>200).
3 GSD bins: a (<2.17); b (>2.17 to <2.94); c (>2.94).
4 Sum of emissions within 20 km radius of ambient monitor based on 2002 NEI.
A-10
-------
1
2
Table A.1-3. Meta-data for ambient monitors in the broader SO2 monitoring network.
State
AL
AL
AL
AL
AL
AL
AL
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AR
AR
AR
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
County
Colbert
Jackson
Jefferson
Lawrence
MOB
MOB
Montgomery
Gila
Gila
Maricopa
Maricopa
Maricopa
Pima
Final
Pulaski
Pulaski
Union
Alameda
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Imperial
Los Angeles
Los Angeles
Los Angeles
Monitor ID
010330044
010710020
010731003
010790003
010970028
010972005
011011002
040070009
040071001
040130019
040133002
040133003
040191011
040212001
051190007
051191002
051390006
060010010
060130002
060130006
060130010
060131001
060131002
060131003
060131004
060132001
060133001
060250005
060371002
060371103
060374002
Latitude
34.690556
34.876944
33.485556
34.589571
30.958333
30.474674
32.40712
33.399135
33.006179
33.48385
33.45793
33.47968
32.208333
32.600479
34.756111
34.830556
33.215
37.7603
37.936
37.9478
38.0313
38.055556
38.010556
37.964167
37.96028
38.013056
38.029167
32.676111
34.17605
34.06659
33.82376
Longitude
-87.821389
-85.720833
-86.915
-87.109445
-88.028333
-88.14114
-86.256367
-110.858896
-110.785797
-112.14257
-112.04601
-111.91721
-110.872222
-110.633598
-92.275833
-92.259444
-92.668889
-122.1925
-122.0262
-122.3651
-122.1318
-122.219722
-121.641389
-122.339167
-122.35667
-122.133611
-121.902222
-115.483333
-118.31712
-118.22688
-118.18921
Objective1
UNK
LINK
HIC
UNK
HIC
POP
HIC
SRC
SRC
UNK
HIC
POP
POP
POP
POP
HIC
UNK
POP
SRC
UNK
POP
SRC
UNK
UNK
POP
UNK
HIC
UNK
UNK
UNK
POP
Setting2
RUR
RUR
SUB
RUR
SUB
RUR
SUB
URB
URB
SUB
URB
SUB
SUB
SUB
URB
RUR
URB
SUB
SUB
URB
URB
SUB
RUR
URB
URB
URB
URB
SUB
URB
URB
SUB
Land Use3
AGR
AGR
RES
AGR
IND
AGR
COM
RES
IND
RES
RES
RES
RES
RES
COM
FOR
COM
RES
RES
IND
COM
IND
AGR
COM
COM
RES
RES
RES
COM
RES
RES
Scale4
NEI
URB
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
4
4
4
1
6
4
11.3
5.8
5
4
4
4
4
8.3
8.5
7
7
6
20
9
7
5
11
7
Years
n
9
9
9
2
3
3
1
7
7
1
9
7
9
4
5
5
7
1
9
9
1
8
9
4
3
9
9
6
7
6
9
First
1997
1997
1997
1998
1997
2002
1997
1999
1999
1998
1997
1998
1998
1998
2002
1997
1997
2002
1997
1997
2002
1997
1997
1998
2003
1997
1997
1999
1998
1997
1997
Last
2005
2005
2006
1999
1999
2004
1997
2005
2005
1998
2006
2006
2006
2005
2006
2001
2006
2002
2005
2005
2002
2004
2005
2001
2005
2005
2005
2005
2005
2005
2005
A-ll
-------
State
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
County
Los Angeles
Los Angeles
Orange
Riverside
Sacramento
Sacramento
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Diego
San Diego
San Diego
San Francisco
San Luis Obispo
San Luis Obispo
San Luis Obispo
San Luis Obispo
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Monitor ID
060375001
060375005
060591003
060658001
060670002
060670006
060710012
060710014
060710306
060711234
060712002
060714001
060730001
060731007
060732007
060750005
060791005
060792001
060792004
060794002
060830008
060831012
060831013
060831015
060831016
060831019
060831020
060831025
060831026
060831027
060832004
060832011
060834003
Latitude
33.92288
33.9508
33.67464
33.99958
38.712778
38.614167
34.426111
34.5125
34.51
35.763889
34.10002
34.418056
32.631231
32.709172
32.552164
37.766
35.043889
35.125
35.022222
35.028333
34.462222
34.451944
34.725556
34.478056
34.477778
34.475278
34.415278
34.489722
34.479444
34.469167
34.6375
34.445278
34.596111
Longitude
-118.37026
-118.43043
-117.92568
-117.41601
-121.38
-121.366944
-117.563056
-117.33
-117.330556
-117.396111
-117.49201
-117.284722
-117.059075
-117.153975
-116.937772
-122.3991
-120.580278
-120.633333
-120.569444
-120.387222
-120.024444
-120.457778
-120.427778
-120.210833
-120.205556
-120.188889
-119.878611
-120.045833
-120.0325
-120.039444
-120.456389
-119.827778
-120.630278
Objective1
POP
UPW
UNK
POP
UNK
HIC
UNK
UNK
UNK
OTH
POP
UNK
POP
POP
POP
UNK
UNK
UNK
UNK
POP
POP
UNK
UNK
UNK
UNK
UNK
UNK
UNK
UNK
UNK
POP
POP
UNK
Setting2
URB
SUB
SUB
SUB
SUB
SUB
RUR
SUB
SUB
RUR
SUB
SUB
SUB
URB
RUR
URB
RUR
SUB
RUR
RUR
RUR
RUR
RUR
RUR
RUR
RUR
RUR
RUR
RUR
RUR
URB
SUB
RUR
Land Use3
COM
RES
RES
RES
RES
RES
COM
RES
RES
DES
IND
RES
RES
COM
MOB
IND
COM
RES
IND
RES
UNK
AGR
AGR
AGR
AGR
AGR
AGR
AGR
AGR
AGR
COM
RES
AGR
Scale4
NEI
NEI
MID
NEI
NEI
NEI
NEI
NEI
NEI
NEI
REG
REG
REG
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
2
4
6
7
5
5
4
4
1
5
7
5
5
4
5
4
4
4
Years
n
7
1
9
7
7
9
1
3
7
8
5
1
9
8
8
9
5
5
9
7
9
1
9
1
1
1
6
9
2
2
9
9
8
First
1997
2005
1997
1997
1997
1997
1997
1997
2000
1998
1997
1997
1997
1997
1997
1997
1997
1997
1997
2000
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
Last
2003
2005
2005
2005
2006
2006
1997
1999
2006
2006
2005
1997
2005
2004
2004
2005
2001
2002
2006
2006
2005
1997
2005
1997
1997
1997
2005
2005
1998
1998
2005
2005
2005
A-12
-------
State
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
CO
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
DE
DE
DE
DE
DE
DE
DC
County
Santa Cruz
Solano
Solano
Ventura
Adams
Adams
Denver
El Paso
El Paso
El Paso
El Paso
Fairfield
Fairfield
Fairfield
Fairfield
Fairfield
Hartford
Hartford
Hartford
New Haven
New Haven
New Haven
New Haven
New London
Tolland
New Castle
New Castle
New Castle
New Castle
New Castle
New Castle
District of
Columbia
Monitor ID
060870003
060950001
060950004
061113001
080010007
080013001
080310002
080416001
080416004
080416011
080416018
090010012
090010017
090011123
090012124
090019003
090031005
090031018
090032006
090090027
090091003
090091123
090092123
090110007
090130003
100031003
100031007
100031008
100031013
100032002
100032004
110010041
Latitude
37.011944
38.052222
38.1027
34.255
39.8
39.83818
39.75119
38.633611
38.921389
38.846667
38.811389
41.195
41.003611
41.399167
41.063056
41.118333
42.015833
41.760833
41.7425
41.301111
41.310556
41.310833
41.550556
41.361111
41.73
39.761111
39.551111
39.577778
39.773889
39.757778
39.739444
38.897222
Longitude
-122.193333
-122.144722
-122.2382
-119.1425
-104.910833
-104.94984
-104.98762
-104.715556
-104.8125
-104.827222
-104.751389
-73.163333
-73.585
-73.443056
-73.528889
-73.336667
-72.518056
-72.670833
-72.634444
-72.902778
-72.915556
-72.916944
-73.043611
-72.08
-72.213611
-75.491944
-75.730833
-75.611111
-75.496389
-75.546389
-75.558056
-76.952778
Objective1
UNK
UNK
UNK
HIC
POP
POP
HIC
UNK
UNK
UNK
UNK
HIC
UNK
UNK
HIC
POP
POP
POP
HIC
POP
UNK
HIC
POP
UNK
UNK
HIC
UNK
UNK
POP
POP
UNK
POP
Setting2
RUR
URB
URB
RUR
URB
RUR
URB
RUR
URB
URB
URB
URB
SUB
SUB
URB
RUR
RUR
URB
SUB
URB
SUB
URB
URB
SUB
SUB
SUB
RUR
RUR
SUB
URB
URB
URB
Land Use3
RES
COM
COM
RES
RES
AGR
COM
IND
RES
RES
COM
RES
RES
RES
RES
FOR
AGR
COM
IND
COM
IND
RES
MOB
RES
COM
RES
AGR
AGR
RES
COM
COM
RES
Scale4
NEI
NEI
NEI
NEI
NEI
NEI
NEI
REG
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
6
8
4
4
4
5
4
4
3
3
3
3
3
3
3
9
3.67
5
5
5
3
3
4
6
Years
n
9
1
9
7
2
9
8
4
3
4
3
9
1
9
8
8
2
1
9
1
1
7
9
2
2
6
3
8
2
2
6
10
First
1997
1997
1997
1997
2002
1997
1997
1997
1997
1997
1998
1997
1997
1997
1997
1998
1997
1997
1997
2005
1997
1997
1997
1997
1997
1997
2002
1997
2004
1997
2000
1997
Last
2006
1997
2005
2003
2003
2005
2006
2000
1999
2000
2000
2005
1997
2005
2004
2005
1998
1997
2005
2005
1997
2003
2005
1998
1998
2002
2006
2006
2006
1998
2006
2006
A-13
-------
State
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
GA
GA
County
B reward
Duval
Duval
Duval
Duval
Escambia
Escambia
Hamilton
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Manatee
Miami-Dade
Nassau
Nassau
Orange
Palm Beach
Pinellas
Pinellas
Pinellas
Pinellas
Polk
Polk
Putnam
Sarasota
Sarasota
Sarasota
Baldwin
Bartow
Monitor ID
120110010
120310032
120310080
120310081
120310097
120330004
120330022
120470015
120570021
120570053
120570081
120570095
120570109
120571035
120574004
120813002
120860019
120890005
120890009
120952002
120993004
121030023
121033002
121035002
121035003
121050010
121052006
121071008
121151002
121151005
121151006
130090001
130150002
Latitude
26.128611
30.356111
30.308889
30.422222
30.367222
30.525
30.544722
30.411111
27.947222
27.886389
27.739722
27.9225
27.856389
27.928056
27.9925
27.632778
25.8975
30.658333
30.686389
28.599444
26.369722
27.863333
27.871389
28.09
28.141667
27.856111
27.896944
29.6875
27.299722
27.306944
27.350278
33.153258
34.103333
Longitude
-80.167222
-81.635556
-81 .6525
-81.621111
-81.594167
-87.204167
-87.216111
-82.783611
-82.453333
-82.481389
-82.465278
-82.401389
-82.383667
-82.454722
-82.125833
-82.546111
-80.38
-81.463333
-81 .4475
-81.363056
-80.074444
-82.623333
-82.691667
-82.700833
-82.739722
-82.017778
-81.960278
-81 .656667
-82.524444
-82.570556
-82.48
-83.235807
-84.915278
Objective1
HIC
HIC
HIC
HIC
POP
POP
HIC
UNK
HIC
POP
UNK
HIC
POP
POP
HIC
POP
POP
HIC
HIC
HIC
HIC
POP
HIC
HIC
HIC
HIC
HIC
HIC
HIC
POP
POP
SRC
POP
Setting2
SUB
SUB
SUB
SUB
SUB
SUB
SUB
RUR
RUR
SUB
UNK
SUB
SUB
SUB
SUB
RUR
UNK
SUB
SUB
URB
SUB
RUR
SUB
RUR
SUB
RUR
SUB
RUR
SUB
SUB
SUB
RUR
SUB
Land Use3
RES
COM
COM
RES
COM
IND
COM
IND
RES
RES
UNK
COM
COM
RES
RES
IND
UNK
IND
RES
COM
COM
IND
COM
RES
RES
IND
IND
IND
RES
RES
RES
RES
AGR
Scale4
NEI
NEI
MID
MID
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
REG
Height
4
3
3
4
5
4
6
3
2
4
4
4
3
4
4
4
4
2
4
4
10
4
3
4
4
2
4
5
4
5
5
5
Years
n
8
8
8
8
8
9
8
10
3
9
8
9
9
9
6
5
7
8
1
9
6
9
9
9
7
8
6
10
1
4
4
3
5
First
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2000
1999
1997
1997
1997
1997
1997
1997
1997
1997
1999
1997
1997
1997
1997
1997
2000
1998
1997
Last
2005
2004
2005
2005
2005
2005
2005
2006
1999
2005
2005
2005
2005
2005
2005
2004
2003
2006
1997
2005
2002
2005
2005
2005
2005
2004
2002
2006
1997
2000
2003
2006
2004
A-14
-------
State
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
HI
HI
HI
HI
ID
ID
ID
ID
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
County
Bibb
Chatham
Chatham
Chatham
Dougherty
Fannin
Floyd
Fulton
Fulton
Glynn
Muscogee
Richmond
Honolulu
Honolulu
Honolulu
Honolulu
Bannock
Caribou
Caribou
Power
Adams
Champaign
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Monitor ID
130210012
130510019
130510021
130511002
130950006
131110091
131150003
131210048
131210055
131270006
132150008
132450003
150030010
150030011
150031001
150031006
160050004
160290003
160290031
160770011
170010006
170190004
170310050
170310059
170310063
170310064
170310076
170311018
170311601
170312001
170314002
170314201
170318003
Latitude
32.805244
32.093889
32.06905
32.090278
31.567778
34.985556
34.261113
33.779189
33.720428
31.16953
32.521099
33.393611
21.329167
21.337222
21.310278
21.3475
42.916389
42.661298
42.695278
42.9125
39.93301
40.123796
41.70757
41.6875
41.876969
41.790787
41.7514
41.773889
41.66812
41.662109
41.855243
42.139996
41.631389
Longitude
-83.543628
-81.151111
-81 .048949
-81.130556
-84.102778
-84.375278
-85.323018
-84.395843
-84.357449
-81 .496046
-84.944695
-82.006389
-158.093333
-158.119167
-157.858056
-158.113333
-112.515833
-111.591443
-111.593889
-112.535556
-91 .404237
-88.229531
-87.568574
-87.536111
-87.63433
-87.601646
-87.713488
-87.815278
-87.99057
-87.696467
-87.75247
-87.799227
-87.568056
Objective1
POP
HIC
SRC
POP
HIC
POP
POP
HIC
POP
POP
POP
POP
SRC
SRC
POP
UNK
HIC
POP
SRC
SRC
POP
POP
POP
HIC
POP
POP
POP
HIC
POP
HIC
POP
POP
POP
Setting2
RUR
SUB
SUB
URB
SUB
URB
RUR
URB
SUB
SUB
SUB
SUB
RUR
RUR
URB
RUR
RUR
URB
RUR
RUR
URB
SUB
SUB
SUB
URB
SUB
SUB
SUB
SUB
SUB
SUB
SUB
SUB
Land Use3
IND
IND
COM
IND
RES
IND
RES
COM
COM
RES
RES
IND
IND
COM
COM
IND
IND
RES
DES
IND
COM
RES
IND
IND
MOB
RES
RES
IND
RES
IND
RES
RES
RES
Scale4
URB
URB
NEI
NEI
MID
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
MIC
NEI
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
NEI
NEI
URB
NEI
Height
4
4
10
5
4
3
4
5
5
8
4
4
4
10
3
3
4
9
5
8
10
3
15
4
4
4
9
4
8
4
Years
n
3
1
6
3
1
9
10
8
10
1
2
3
9
6
7
9
9
3
4
1
10
4
10
4
10
1
3
8
10
7
10
2
6
First
1998
2000
1998
2004
1998
1997
1997
1999
1997
1999
1999
1997
1997
2000
1998
1997
1997
1999
2002
2004
1997
1997
1997
1997
1997
1997
2004
1997
1997
1997
1997
2004
1997
Last
2003
2000
2006
2006
1998
2006
2006
2006
2006
1999
2005
2001
2005
2005
2004
2005
2005
2001
2005
2004
2006
2000
2006
2000
2006
1997
2006
2004
2006
2003
2006
2005
2002
A-15
-------
State
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
County
DuPage
La Salle
Macon
Macoupin
Madison
Madison
Madison
Madison
Madison
Peoria
Randolph
Rock Island
Saint Clair
Saint Clair
Saint Clair
Sangamon
Tazewell
Wabash
Wabash
Will
Daviess
Dearborn
Floyd
Floyd
Floyd
Fountain
Gibson
Gibson
Hendricks
Hendricks
Hendricks
Jasper
Jasper
Monitor ID
170436001
170990007
171150013
171170002
171190008
171190017
171191010
171193007
171193009
171430024
171570001
171610003
171630010
171631010
171631011
171670006
171790004
171850001
171851001
171970013
180270002
180290004
180430004
180430007
180431004
180450001
180510001
180510002
180630001
180630002
180630003
180730002
180730003
Latitude
41.813049
41.293015
39.866834
39.396075
38.890186
38.701944
38.828303
38.860669
38.865984
40.68742
38.176278
41.511944
38.612034
38.592192
38.235
39.800614
40.55646
38.397222
38.369444
41.459963
38.572778
39.092778
38.367778
38.273333
38.308056
39.964167
38.361389
38.392778
39.876944
39.863361
39.880833
41.187778
41.135833
Longitude
-88.072827
-89.049425
-88.925594
-89.809739
-90.148031
-90.149167
-90.058433
-90.105851
-90.070571
-89.606943
-89.788459
-90.514167
-90.160477
-90.165081
-89.841944
-89.591225
-89.654028
-87.773611
-87.834444
-88.182019
-87.214722
-84.855
-85.833056
-85.836389
-85.834167
-87.421389
-87.748611
-87.748333
-86.473889
-86.47075
-86.542194
-87.053333
-86.987778
Objective1
POP
SRC
POP
POP
SRC
HIC
SRC
POP
SRC
POP
GEN
HIC
POP
HIC
SRC
SRC
SRC
HIC
HIC
SRC
HIC
HIC
HIC
SRC
POP
HIC
HIC
HIC
HIC
HIC
HIC
HIC
HIC
Setting2
SUB
SUB
SUB
RUR
SUB
URB
SUB
SUB
SUB
SUB
RUR
URB
SUB
SUB
RUR
SUB
SUB
URB
RUR
RUR
RUR
SUB
RUR
RUR
SUB
RUR
RUR
RUR
RUR
SUB
SUB
RUR
RUR
Land Use3
AGR
IND
IND
AGR
IND
MOB
IND
IND
COM
COM
IND
COM
IND
IND
IND
IND
IND
MOB
AGR
IND
AGR
COM
AGR
RES
RES
AGR
AGR
AGR
IND
COM
COM
AGR
AGR
Scale4
NEI
NEI
NEI
REG
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
URB
Height
14
5
5
5
15
3
5
10
7
5
5
8
5
7
5
8
6
2
2
13
2
5
5
4
5
2
5
9
3
3
Years
n
4
1
10
10
6
4
10
10
9
10
10
4
10
6
5
10
10
5
6
10
9
9
5
5
10
6
5
5
2
2
2
10
6
First
1997
2006
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2004
2004
2004
1997
1997
Last
2000
2006
2006
2006
2002
2000
2006
2006
2006
2006
2006
2000
2006
2002
2001
2006
2006
2005
2005
2006
2005
2005
2005
2005
2006
2005
2005
2004
2005
2005
2005
2006
2002
A-16
-------
State
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IA
IA
IA
IA
IA
County
Jefferson
Lake
Lake
LaPorte
LaPorte
Marion
Marion
Marion
Marion
Marion
Morgan
Perry
Perry
Pike
Porter
Porter
Porter
Spencer
Spencer
Sullivan
Vanderburgh
Vanderburgh
Vigo
Vigo
Warrick
Warrick
Wayne
Wayne
Cerro Gordo
Clinton
Clinton
Clinton
Lee
Monitor ID
180770004
180890022
180892008
180910005
180910007
180970042
180970054
180970057
180970072
180970073
181091001
181230006
181230007
181250005
181270011
181270017
181270023
181470002
181470010
181530004
181630012
181631002
181670018
181671014
181730002
181731001
181770006
181770007
190330018
190450018
190450019
190450020
191110006
Latitude
38.776667
41.606667
41.639444
41.716944
41.679722
39.646254
39.730278
39.749019
39.768056
39.789167
39.515
37.99433
37.983773
38.519167
41.633889
41.621944
41.616667
37.9825
37.95536
39.099444
38.021667
37.9025
39.486111
39.514722
37.9375
37.938056
39.812222
39.795833
43.16944
41.824722
41.823283
41.845833
40.392222
Longitude
-85.407222
-87.304722
-87.493611
-86.9075
-86.852778
-86.248784
-86.196111
-86.186314
-86.16
-86.060833
-86.391667
-86.763457
-86.772202
-87.249722
-87.101389
-87.116389
-87.145833
-86.96638
-87.0318
-87.470556
-87.569444
-87.671389
-87.401389
-87.407778
-87.314167
-87.345833
-84.89
-84.880833
-93.202426
-90.212778
-90.211982
-90.216389
-91.4
Objective1
HIC
UNK
HIC
HIC
HIC
POP
HIC
HIC
POP
POP
HIC
UNK
UNK
HIC
HIC
HIC
HIC
HIC
HIC
HIC
POP
UNK
POP
HIC
HIC
HIC
HIC
HIC
UNK
UNK
UNK
HIC
UNK
Setting2
SUB
URB
SUB
URB
RUR
RUR
URB
URB
URB
URB
SUB
RUR
RUR
RUR
RUR
RUR
SUB
RUR
RUR
RUR
URB
RUR
URB
RUR
RUR
RUR
SUB
RUR
SUB
SUB
URB
SUB
URB
Land Use3
COM
IND
COM
IND
RES
AGR
IND
RES
COM
RES
RES
IND
IND
AGR
IND
IND
IND
AGR
AGR
AGR
COM
AGR
RES
COM
IND
IND
IND
IND
RES
RES
IND
COM
IND
Scale4
NEI
NEI
NEI
NEI
URB
NEI
NEI
MID
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
MID
URB
Height
5
5
4
3
4
9
4
3
5
2
4
4
5
5
2
5
9
5
5
4
4
5
9
4
4
7
5
Years
n
8
8
9
10
6
10
1
10
4
9
2
5
5
8
10
6
6
5
4
7
10
10
10
8
5
4
10
10
9
1
10
1
2
First
1997
1998
1997
1997
1997
1997
1997
1997
1997
1997
1997
1998
1998
1997
1997
1997
1997
1997
2002
1997
1997
1997
1997
1997
1997
1997
1997
1997
1998
1997
1997
1997
1997
Last
2004
2005
2006
2006
2002
2006
1997
2006
2000
2005
2005
2003
2003
2005
2006
2002
2002
2001
2005
2005
2006
2006
2006
2005
2006
2002
2006
2006
2006
1997
2006
1997
1998
A-17
-------
State
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
KS
KS
KS
KS
KS
KS
KS
KS
KS
KY
KY
KY
KY
KY
KY
KY
County
Lee
Linn
Linn
Linn
Linn
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Scott
Scott
Van Buren
Van Buren
Van Buren
Woodbury
Linn
Montgomery
Pawnee
Sedgwick
Sumner
Trego
Wyandotte
Wyandotte
Wyandotte
Boyd
Boyd
Boyd
Campbell
Campbell
Daviess
Fayette
Monitor ID
191111007
191130028
191130029
191130031
191130032
191130034
191130038
191130039
191390016
191390017
191390020
191630015
191630017
191770004
191770005
191770006
191930018
201070002
201250006
201450001
201730010
201910002
201950001
202090001
202090020
202090021
210190015
210190017
210191003
210370003
210371001
210590005
210670012
Latitude
40.5825
41.910556
41.974722
41.983333
41.964722
41.971111
41.941111
41.934167
41.419429
41.387969
41.407796
41.530011
41.467236
40.711111
40.689167
40.695078
42.399444
38.135833
37.046944
38.17625
37.701111
37.476944
38.770278
39.113056
39.151389
39.1175
38.465833
38.459167
38.388611
39.065556
39.108611
37.780833
38.065
Longitude
-91.4275
-91.651944
-91 .666667
-91.662778
-91 .664722
-91 .645278
-91.633889
-91.6825
-91.070975
-91 .054504
-91 .062646
-90.587611
-90.688451
-91.975278
-91.994444
-92.006318
-96.355833
-94.731944
-95.613333
-99.108028
-97.313889
-97.366389
-99.763611
-94.624444
-94.6175
-94.635556
-82.621111
-82.640556
-82.6025
-84.451944
-84.476111
-87.075556
-84.5
Objective1
UNK
HIC
HIC
SRC
UNK
UNK
SRC
SRC
UNK
UNK
UNK
HIC
UNK
HIC
UNK
GEN
POP
REG
POP
POP
POP
REG
GEN
HIC
POP
POP
POP
POP
POP
POP
POP
POP
POP
Setting2
RUR
SUB
URB
URB
URB
URB
SUB
URB
URB
SUB
SUB
URB
RUR
RUR
RUR
RUR
URB
RUR
URB
SUB
URB
RUR
RUR
URB
URB
URB
URB
SUB
SUB
SUB
URB
SUB
SUB
Land Use3
IND
COM
COM
RES
RES
RES
IND
IND
RES
IND
IND
RES
IND
FOR
FOR
FOR
RES
AGR
RES
RES
RES
RES
AGR
COM
IND
RES
RES
RES
IND
RES
RES
COM
RES
Scale4
NEI
NEI
MID
MID
NEI
NEI
REG
NEI
NEI
NEI
REG
REG
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
15
8
16
4
4.5
3
4
4
4
4
3
3
3
3
4
4
3
4
4
4
15
9
4
4
3
5
4
4
4
4
Years
n
2
5
9
10
2
2
8
1
10
10
10
8
1
2
4
2
1
6
7
1
1
3
3
2
1
4
3
4
3
6
3
9
9
First
1998
1997
1997
1997
1998
1998
1999
2001
1997
1997
1997
1997
1997
1997
2000
2005
2002
1999
1998
1997
1997
2001
2002
1997
1997
2000
1997
2002
1997
2000
1997
1997
1997
Last
2000
2001
2006
2006
1999
1999
2006
2001
2006
2006
2006
2005
1997
1998
2003
2006
2002
2004
2005
1997
1997
2005
2005
1998
1997
2005
2000
2005
1999
2005
1999
2005
2005
A-18
-------
State
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
LA
LA
LA
LA
LA
LA
ME
ME
ME
ME
ME
ME
ME
ME
ME
MD
MD
MD
MD
County
Greenup
Hancock
Henderson
Henderson
Jefferson
Jefferson
Jefferson
Livingston
McCracken
McCracken
McCracken
Warren
Bossier
Calcasieu
East Baton
Rouge
Ouachita
St. Bernard
West Baton
Rouge
Androscoggin
Aroostook
Aroostook
Aroostook
Aroostook
Aroostook
Cumberland
Cumberland
Oxford
Allegany
Anne Arundel
Baltimore
Baltimore (City)
Monitor ID
210890007
210910012
211010013
211010014
211110032
211110051
211111041
211390004
211450001
211451024
211451026
212270008
220150008
220190008
220330009
220730004
220870002
221210001
230010011
230030009
230030012
230031003
230031013
230031018
230050014
230050027
230172007
240010006
240032002
240053001
245100018
Latitude
38.548333
37.938889
37.858889
37.871389
38.1825
38.060833
38.23163
37.070833
37.131667
37.058056
37.040833
37.036667
32.53626
30.261667
30.46198
32.509713
29.981944
30.501944
44.089406
47.351667
47.354444
47.351667
46.123889
46.660899
43.659722
43.661944
44.543056
39.649722
39.159722
39.310833
39.314167
Longitude
-82.731667
-86.896944
-87.575278
-87.463333
-85.861667
-85.896111
-85.82672
-88.334167
-88.813333
-88.5725
-88.541111
-86.250556
-93.74891
-93.284167
-91.17922
-92.046093
-89.998611
-91.209722
-70.214219
-68.303611
-68.314167
-68.311389
-67.829722
-67.902066
-70.261389
-70.265833
-70.545833
-78.762778
-76.511667
-76.474444
-76.613333
Objective1
POP
POP
POP
POP
HIC
POP
POP
HIC
HIC
POP
POP
POP
POP
POP
HIC
GEN
SRC
HIC
HIC
UNK
LINK
UNK
UNK
SRC
HIC
HIC
UNK
POP
POP
POP
POP
Setting2
SUB
RUR
SUB
RUR
SUB
SUB
SUB
RUR
RUR
SUB
SUB
RUR
URB
RUR
URB
URB
SUB
SUB
URB
SUB
URB
SUB
URB
RUR
URB
URB
SUB
URB
SUB
SUB
URB
Land Use3
RES
RES
RES
COM
RES
RES
IND
AGR
IND
COM
RES
RES
COM
IND
COM
IND
RES
COM
COM
RES
IND
RES
COM
IND
COM
IND
IND
COM
RES
RES
RES
Scale4
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
4
4
4
4
4
4
5
4
5
4
6
4
3
5
5
4
2
2
4
1
9
3
4
4
4
4
5
5
5
4
Years
n
9
7
5
2
3
10
8
9
3
6
2
3
10
10
10
10
7
10
4
1
1
1
1
1
1
7
8
1
4
2
2
First
1997
1998
1997
2004
1997
1997
1997
1997
1997
2000
1997
2003
1997
1997
1997
1997
1998
1997
1997
1997
1997
1997
1997
2004
1997
2000
1997
1997
1999
2004
1997
Last
2005
2004
2001
2005
2001
2006
2006
2005
1999
2005
1998
2005
2006
2006
2006
2006
2004
2006
2002
1997
1997
1997
1997
2004
1997
2006
2004
1997
2002
2005
1998
A-19
-------
State
MD
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
County
Baltimore (City)
Bristol
Essex
Essex
Essex
Essex
Hampden
Hampden
Hampshire
Middlesex
Middlesex
Suffolk
Suffolk
Suffolk
Suffolk
Suffolk
Suffolk
Suffolk
Worcester
Worcester
Delta
Genesee
Genesee
Kent
Macomb
Missaukee
St. Clair
Schoolcraft
Wayne
Wayne
Wayne
Wayne
Wayne
Monitor ID
245100036
250051004
250090005
250091004
250091005
250095004
250130016
250131009
250154002
250171701
250174003
250250002
250250019
250250020
250250021
250250040
250250042
250251003
250270020
250270023
260410902
260490021
260492001
260810020
260991003
261130001
261470005
261530001
261630001
261630005
261630015
261630016
261630019
Latitude
39.265
41.683279
42.709444
42.515556
42.525
42.772222
42.108581
42.085556
42.298279
42.474444
42.383611
42.348873
42.316394
42.309417
42.377833
42.340251
42.3294
42.401667
42.267222
42.263877
45.796667
43.047224
43.168336
42.984173
42.51334
44.310555
42.953336
46.288877
42.22862
42.267231
42.302786
42.357808
42.43084
Longitude
-76.536667
-71.169171
-71.146389
-70.931389
-70.934167
-71.061111
-72.590614
-72.579722
-72.333904
-71.111111
-71.213889
-71.097163
-70.967773
-71.055573
-71.027138
-71.03835
-71.0825
-71.031111
-71.798889
-71.794186
-87.089444
-83.670159
-83.461541
-85.671339
-83.005971
-84.891865
-82.456229
-85.950227
-83.2082
-83.132086
-83.10653
-83.096033
-83.000138
Objective1
HIC
HIC
HIC
UNK
UNK
OTH
POP
HIC
OTH
UNK
POP
HIC
OTH
OTH
HIC
POP
POP
POP
HIC
POP
UNK
POP
GEN
POP
POP
GEN
HIC
GEN
POP
HIC
HIC
POP
POP
Setting2
URB
SUB
URB
SUB
SUB
SUB
URB
SUB
RUR
SUB
RUR
URB
RUR
URB
URB
URB
URB
SUB
URB
URB
RUR
URB
RUR
URB
SUB
RUR
SUB
RUR
SUB
SUB
URB
URB
SUB
Land Use3
RES
COM
RES
RES
RES
RES
COM
RES
FOR
RES
AGR
COM
RES
COM
RES
IND
COM
RES
COM
COM
IND
RES
AGR
IND
RES
FOR
RES
FOR
COM
IND
COM
RES
RES
Scale4
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
5
5
4
9
4
5
5
4
5
5
5
4
4
5
4
3
4
4
5
3
4
4
4
4
4
4
Years
n
1
9
5
1
1
3
9
3
9
2
2
8
9
8
9
9
5
3
4
3
7
8
1
8
10
1
10
1
1
4
9
9
7
First
1997
1997
1997
1997
1997
1997
1997
1997
1998
1997
1997
1997
1997
1997
1997
1997
2001
1997
1998
2004
1997
1997
2004
1997
1997
2003
1997
2005
1997
1997
1997
1997
1997
Last
1997
2006
2001
1997
1997
2000
2006
1999
2006
1999
1998
2006
2005
2005
2005
2005
2006
1999
2002
2006
2003
2006
2004
2005
2006
2003
2006
2005
1997
2000
2006
2006
2006
A-20
-------
State
Ml
Ml
Ml
Ml
Ml
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MS
MS
MS
MS
MO
MO
MO
MO
MO
MO
MO
County
Wayne
Wayne
Wayne
Wayne
Wayne
Anoka
Carlton
Dakota
Dakota
Dakota
Dakota
Dakota
Hennepin
Hennepin
Koochiching
Ramsey
Sherburne
Sherburne
Sherburne
Sherburne
Washington
Wright
Harrison
Hinds
Jackson
Lee
Buchanan
Buchanan
Clay
Greene
Greene
Greene
Greene
Monitor ID
261630025
261630027
261630033
261630062
261630092
270031002
270176316
270370020
270370423
270370439
270370441
270370442
270530954
270530957
270711240
271230864
271410003
271410011
271410012
271410013
271630436
271710007
280470007
280490018
280590006
280810004
290210009
290210011
290470025
290770026
290770032
290770037
290770040
Latitude
42.423063
42.292231
42.306674
42.340833
42.296111
45.13768
46.733611
44.76323
44.77553
44.748039
44.7468
44.73857
44.980995
45.021111
48.605278
44.991944
45.420278
45.394444
45.394444
45.369444
44.84737
45.329167
30.446806
32.296806
30.378425
34.263333
39.731389
39.731389
39.183889
37.128333
37.205278
37.11
37.108889
Longitude
-83.426263
-83.106807
-83.148754
-83.0625
-83.116944
-93.20772
-92.418889
-93.03255
-93.06299
-93.043266
-93.02611
-93.00496
-93.273719
-93.281944
-93.402222
-93.183056
-93.871667
-93.8975
-93.885
-93.898056
-92.9954
-93.835833
-89.029139
-90.188306
-88.533985
-88.759722
-94.8775
-94.868333
-94.4975
-93.261667
-93.283333
-93.251944
-93.252778
Objective1
POP
HIC
HIC
POP
HIC
POP
SRC
UNK
UNK
UNK
UNK
UNK
HIC
HIC
UNK
POP
UNK
UNK
UNK
UNK
UNK
UNK
HIC
POP
POP
UNK
GEN
GEN
POP
POP
UNK
POP
SRC
Setting2
SUB
URB
SUB
URB
URB
SUB
RUR
RUR
RUR
RUR
RUR
RUR
URB
URB
URB
SUB
RUR
RUR
URB
RUR
SUB
RUR
SUB
URB
URB
SUB
URB
URB
SUB
SUB
URB
RUR
SUB
Land Use3
COM
IND
IND
RES
RES
RES
AGR
IND
IND
IND
IND
AGR
COM
IND
IND
RES
AGR
IND
MOB
IND
IND
AGR
RES
COM
COM
COM
IND
IND
RES
RES
RES
RES
RES
Scale4
NEI
MID
MID
NEI
MID
URB
NEI
NEI
NEI
NEI
MID
NEI
NEI
NEI
NEI
MID
NEI
NEI
NEI
NEI
NEI
NEI
Height
4
3
5
5
7
4.57
3
3
3.66
4
3
3.5
3
10
10
6
4.88
4
4
4
3
3
4
3
3
4
Years
n
1
3
2
1
1
4
2
8
9
1
7
6
7
6
2
6
1
2
1
2
9
1
8
9
7
1
3
1
5
10
10
10
4
First
1997
1997
1997
1997
1997
2003
2001
1997
1997
1999
2000
2001
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2001
1997
1997
1997
1997
2003
Last
1997
1999
1998
1997
1997
2006
2002
2006
2006
1999
2006
2006
2006
2002
1999
2002
1997
1998
1997
1998
2006
1997
2004
2005
2006
1997
1999
2001
2001
2006
2006
2006
2006
A-21
-------
State
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MT
MT
MT
MT
MT
MT
MT
MT
County
Greene
Iron
Iron
Jackson
Jefferson
Jefferson
Jefferson
Jefferson
Monroe
Pike
Platte
Saint Charles
Saint Charles
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
St. Louis City
St. Louis City
St. Louis City
St. Louis City
Cascade
Cascade
Jefferson
Jefferson
Jefferson
Lewis and Clark
Lewis and Clark
Rosebud
Monitor ID
290770041
290930030
290930031
290950034
290990004
290990014
290990017
290990018
291370001
291630002
291650023
291830010
291831002
291890001
291890004
291890006
291890014
291893001
291895001
291897002
291897003
295100007
295100072
295100080
295100086
300132000
300132001
300430903
300430911
300430913
300490702
300490703
300870700
Latitude
37.108611
37.466389
37.519444
39.104722
38.2633
38.267222
38.252778
38.297694
39.473056
39.3726
39.3
38.579167
38.8725
38.521667
38.5325
38.613611
38.7109
38.641389
38.766111
38.727222
38.720917
38.5425
38.624167
38.682778
38.672222
47.532222
47.53
46.557679
46.548056
46.534722
46.583333
46.593889
45.886944
Longitude
-93.272222
-90.69
-90.7125
-94.570556
-90.3785
-90.379444
-90.393333
-90.384333
-91.789167
-90.9144
-94.7
-90.841111
-90.226389
-90.343611
-90.382778
-90.495833
-90.4759
-90.345833
-90.285833
-90.379444
-90.367028
-90.263611
-90.198611
-90.246667
-90.238889
-111.271111
-111.283611
-111.918098
-111.873333
-111.861389
-1 1 1 .934444
-111.92
-106.628056
Objective1
SRC
SRC
UNK
LINK
POP
OTH
UNK
HIC
UNK
HIC
UNK
UNK
UNK
POP
POP
UNK
HIC
UNK
UNK
POP
POP
HIC
POP
UNK
POP
SRC
SRC
UNK
UNK
UNK
UNK
UNK
UNK
Setting2
SUB
RUR
RUR
URB
RUR
RUR
SUB
SUB
RUR
RUR
SUB
RUR
RUR
SUB
SUB
RUR
RUR
SUB
SUB
SUB
SUB
URB
URB
URB
URB
SUB
SUB
RUR
RUR
RUR
RUR
RUR
SUB
Land Use3
RES
RES
AGR
COM
IND
RES
RES
RES
UNK
RES
MOB
AGR
AGR
RES
RES
RES
RES
COM
COM
RES
RES
RES
COM
RES
RES
AGR
IND
AGR
AGR
AGR
AGR
RES
RES
Scale4
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
4
2
3
4
5
5
3
3
3
2
3
3
4
3
4
2
4
4
4
14
4
4
3
3.5
4
4
3
3
4
Years
n
4
7
6
9
3
4
2
1
10
1
8
1
3
1
6
8
1
10
8
4
2
10
4
3
7
3
5
4
4
4
4
4
4
First
2003
1997
1997
1997
2004
1997
1999
2002
1997
2006
1997
1997
1997
1997
1999
1997
2006
1997
1997
1997
2002
1997
1997
1997
2000
1997
2001
1997
1997
1997
1997
1997
1998
Last
2006
2003
2003
2006
2006
2000
2000
2002
2006
2006
2004
1997
1999
1997
2004
2004
2006
2006
2004
2000
2003
2006
2000
1999
2006
1999
2005
2000
2000
2000
2000
2000
2001
A-22
-------
State
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
NE
NE
NE
NE
NV
NV
NV
NV
NH
NH
NH
NH
NH
NH
NH
NH
County
Rosebud
Rosebud
Rosebud
Rosebud
Rosebud
Rosebud
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Douglas
Douglas
Douglas
Douglas
Clark
Clark
Clark
Clark
Cheshire
Coos
Coos
Coos
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Monitor ID
300870701
300870702
300870760
300870761
300870762
300870763
301110016
301110066
301110079
301110080
301110082
301110083
301110084
301111065
301112005
301112006
301112007
310550048
310550050
310550053
310550055
320030022
320030078
320030539
320030601
330050007
330070019
330070022
330071007
330110016
330110019
330110020
330111009
Latitude
45.901944
45.863889
45.668056
45.603056
45.648333
45.976667
45.656389
45.788318
45.769439
45.777149
45.783889
45.795278
45.831453
45.801944
45.803889
45.81
45.832778
41.323889
41.332778
41.297778
41.362433
36.390775
35.46505
36.144444
35.978889
42.930556
44.488611
44.458333
44.596667
42.992778
43.000556
43.000556
42.764444
Longitude
-106.637778
-106.557778
-106.518889
-106.464167
-106.556667
-106.660556
-108.765833
-108.459536
-108.574292
-108.47436
-108.515
-108.455833
-108.449964
-108.426111
-108.445556
-108.413056
-108.377778
-95.942778
-95.956389
-95.9375
-95.976112
-114.90681
-114.919615
-115.085556
-114.844167
-72.277778
-71.180278
-71.154167
-71.516667
-71.459444
-71 .468056
-71 .468056
-71.4675
Objective1
UNK
UNK
SRC
SRC
OTH
UNK
UNK
SRC
POP
UNK
POP
SRC
POP
UNK
UNK
OTH
OTH
HIC
HIC
POP
HIC
REG
REG
POP
POP
UNK
POP
UNK
POP
HIC
UNK
UNK
HIC
Setting2
RUR
RUR
RUR
RUR
RUR
RUR
RUR
RUR
SUB
RUR
URB
SUB
SUB
SUB
SUB
SUB
RUR
URB
URB
URB
SUB
RUR
RUR
SUB
SUB
URB
UNK
RUR
URB
URB
URB
URB
URB
Land Use3
AGR
AGR
FOR
FOR
FOR
IND
AGR
RES
COM
AGR
COM
AGR
RES
RES
IND
AGR
RES
RES
RES
IND
RES
IND
DES
MOB
COM
COM
UNK
IND
IND
COM
COM
COM
COM
Scale4
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
REG
URB
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
5
5
4
3
4
3.5
4.5
4
3
4
4.5
4
4
3
3
5
6
4
8
3.5
4
3.5
4
4
5
5
5
5
3
Years
n
3
2
5
5
5
1
9
10
2
4
2
3
3
9
9
8
9
1
2
4
2
5
2
8
1
7
5
1
4
1
1
5
3
First
1997
1997
1998
1997
1998
1997
1997
1997
2002
1997
2002
2000
2004
1997
1997
1997
1997
1997
2002
2002
2005
1998
2001
1998
2002
1997
1997
1997
1997
1997
2000
2002
1997
Last
1999
2000
2003
2003
2003
1997
2005
2006
2003
2000
2003
2002
2006
2005
2005
2004
2005
1997
2003
2006
2006
2002
2002
2005
2002
2003
2001
1997
2001
1997
2000
2006
2001
A-23
-------
State
NH
NH
NH
NH
NH
NH
NH
NH
NH
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NM
NM
NM
NM
NM
NM
NM
NM
NM
County
Hillsborough
Merrimack
Merrimack
Merrimack
Merrimack
Rockingham
Rockingham
Rockingham
Sullivan
Atlantic
Bergen
Burlington
Camden
Camden
Cumberland
Essex
Essex
Gloucester
Hudson
Hudson
Middlesex
Morris
Union
Union
Dona Ana
Dona Ana
Eddy
Grant
Grant
Hidalgo
San Juan
San Juan
San Juan
Monitor ID
330111010
330130007
330131003
330131006
330131007
330150009
330150014
330150015
330190003
340010005
340035001
340051001
340070003
340071001
340110007
340130011
340130016
340150002
340170006
340171002
340232003
340273001
340390003
340390004
350130008
350130017
350151004
350170001
350171003
350230005
350450008
350450009
350450017
Latitude
42.701944
43.206944
43.177222
43.132444
43.218491
43.078056
43.075278
43.0825
43.364444
39.53024
40.88237
40.07806
39.92304
39.68425
39.42227
40.726667
40.722222
39.80034
40.67025
40.73169
40.50888
40.78763
40.66245
40.64144
31.930556
31.795833
32.855556
32.759444
32.691944
31.783333
36.735833
36.742222
36.752778
Longitude
-71.445
-71.534167
-71 .4625
-71.45827
-71 .45827
-70.762778
-70.748056
-70.761944
-72.338333
-74.46069
-74.04217
-74.85772
-75.09762
-74.86149
-75.0252
-74.144167
-74.146944
-75.21212
-74.12608
-74.06657
-74.2682
-74.6763
-74.21474
-74.20836
-106.630556
-106.5575
-104.411389
-108.131389
-108.124444
-108.497222
-108.238333
-107.976944
-108.716667
Objective1
UNK
UNK
UNK
OTH
OTH
UNK
POP
POP
UNK
UNK
POP
HIC
POP
GEN
UNK
UNK
POP
UNK
POP
HIC
HIC
UNK
POP
HIC
UNK
SRC
SRC
UNK
SRC
UNK
UNK
SRC
UNK
Setting2
SUB
URB
RUR
SUB
URB
SUB
URB
SUB
URB
RUR
URB
URB
SUB
RUR
RUR
URB
URB
RUR
URB
URB
URB
RUR
URB
SUB
RUR
SUB
URB
SUB
RUR
RUR
RUR
RUR
RUR
Land Use3
IND
COM
RES
RES
COM
COM
RES
COM
RES
RES
COM
COM
RES
COM
IND
IND
IND
AGR
COM
COM
COM
AGR
COM
IND
AGR
COM
COM
IND
IND
UNK
DES
IND
UNK
Scale4
MIC
NEI
NEI
NEI
URB
NEI
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
NEI
NEI
MID
NEI
URB
NEI
NEI
NEI
Height
5
3
3
9
3
2
4
4
4
4
5
4
4
4
5
4
5
4
5
5
5
4
2
4
3
3
Years
n
6
6
7
4
1
3
3
1
5
8
9
9
8
9
9
2
1
9
9
8
9
9
9
9
6
9
9
5
5
5
6
3
1
First
1997
1997
1997
2003
2005
1997
2004
2002
1997
1997
1997
1997
1997
1997
1997
1997
2002
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1999
1997
1997
1997
1997
Last
2002
2003
2003
2006
2005
2000
2006
2002
2001
2005
2005
2005
2005
2005
2005
1998
2002
2005
2005
2005
2005
2005
2005
2005
2002
2005
2005
2001
2005
2001
2002
2005
1997
A-24
-------
State
NM
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
County
San Juan
Albany
Bronx
Bronx
Bronx
Bronx
Chautauqua
Chautauqua
Chautauqua
Chemung
Erie
Erie
Erie
Essex
Franklin
Hamilton
Herkimer
Kings
Kings
Madison
Monroe
Monroe
Monroe
Nassau
New York
New York
Niagara
Onondaga
Putnam
Queens
Queens
Rensselaer
Rensselaer
Monitor ID
350451005
360010012
360050073
360050080
360050083
360050110
360130005
360130006
360130011
360150003
360290005
360294002
360298001
360310003
360330004
360410005
360430005
360470011
360470076
360530006
360551004
360551007
360556001
360590005
360610010
360610056
360632008
360671015
360790005
360810097
360810124
360830004
360831005
Latitude
36.796667
42.68069
40.811389
40.83608
40.86586
40.81616
42.29073
42.49945
42.29073
42.11105
42.87684
42.99549
42.818889
44.39309
44.434309
43.44957
43.68578
40.73277
40.67185
42.73046
43.16545
43.146198
43.161
40.74316
40.739444
40.75917
43.08216
43.05238
41.44151
40.75527
40.7362
42.78187
42.72444
Longitude
-108.4725
-73.75689
-73.91
-73.92021
-73.88075
-73.90207
-79.58958
-79.31888
-79.58658
-76.80249
-78.80988
-78.90157
-78.840833
-73.85892
-74.24601
-74.51625
-74.98538
-73.94722
-73.97824
-75.78443
-77.55479
-77.54813
-77.60357
-73.58549
-73.986111
-73.96651
-79.00099
-76.0592
-73.70762
-73.75861
-73.82317
-73.46361
-73.43166
Objective1
UNK
HIC
UNK
HIC
UNK
OTH
POP
HIC
POP
UNK
POP
HIC
HIC
GEN
GEN
POP
POP
HIC
POP
POP
POP
POP
HIC
UNK
HIC
HIC
POP
POP
UNK
GEN
POP
OTH
GEN
Setting2
UNK
RUR
URB
URB
URB
URB
URB
URB
RUR
URB
URB
SUB
URB
RUR
RUR
RUR
RUR
URB
URB
RUR
SUB
URB
URB
SUB
URB
URB
SUB
SUB
RUR
URB
SUB
RUR
RUR
Land Use3
UNK
AGR
RES
RES
COM
RES
IND
IND
AGR
COM
RES
IND
IND
FOR
COM
COM
FOR
IND
RES
AGR
RES
RES
COM
COM
RES
COM
IND
COM
FOR
RES
RES
FOR
FOR
Scale4
NEI
MID
NEI
REG
NEI
NEI
NEI
NEI
URB
REG
NEI
REG
NEI
NEI
NEI
NEI
MID
NEI
NEI
Height
9
5
13
12
5
4
4
4
4
4
4
4
5
4
13
11
4
12
5
38
10
4
5
12
5
Years
n
7
10
2
3
6
5
4
7
10
9
10
10
2
10
2
10
8
1
2
10
7
2
7
9
2
8
8
10
10
3
5
3
3
First
1997
1997
1997
1997
2001
2000
1997
2000
1997
1998
1997
1997
1997
1997
2005
1997
1997
1998
1997
1997
1997
2005
1997
1997
1997
1997
1999
1997
1997
1999
2002
2002
1998
Last
2005
2006
1998
1999
2006
2006
2000
2006
2006
2006
2006
2006
1998
2006
2006
2006
2006
1998
1999
2006
2003
2006
2003
2006
1999
2006
2006
2006
2006
2001
2006
2004
2000
A-25
-------
State
NY
NY
NY
NY
NY
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
ND
ND
ND
ND
ND
ND
ND
County
Richmond
Schenectady
Suffolk
Suffolk
Ulster
Alexander
Beaufort
Beaufort
Beaufort
Chatham
Cumberland
Davie
Duplin
Edgecombe
Forsyth
Johnston
Lincoln
Martin
Mecklenburg
Mecklenburg
New Hanover
New Hanover
Northampton
Person
Pitt
Swain
Billings
Billings
Burke
Burke
Burleigh
Cass
Cass
Monitor ID
360850067
360930003
361030002
361030009
361111005
370030003
370130003
370130004
370130006
370370004
370511003
370590002
370610002
370650099
370670022
371010002
371090004
371170001
371190034
371190041
371290002
371290006
371310002
371450003
371470099
371730002
380070002
380070111
380130002
380130004
380150003
380171003
380171004
Latitude
40.59733
42.79963
40.74529
40.8275
42.1438
35.903611
35.3575
35.377241
35.377778
35.757222
34.968889
35.809289
34.954823
35.988333
36.110556
35.590833
35.438556
35.81069
35.248611
35.2401
34.364167
34.268403
36.48438
36.306965
35.583333
35.435509
46.8943
47.296667
48.9904
48.64193
46.825425
46.910278
46.933754
Longitude
-74.12619
-73.94019
-73.41919
-73.05694
-74.49414
-81.184167
-76.779722
-76.748997
-76.766944
-79.159722
-78.9625
-80.559115
-77.960781
-77.582778
-80.226667
-78.461944
-81 .27675
-76.89782
-80.766389
-80.785683
-77.838611
-77.956529
-77.61998
-79.09197
-77.598889
-83.443697
-103.37853
-103.095556
-102.7815
-102.4018
-100.76821
-96.795
-96.85535
Objective1
POP
POP
HIC
UNK
POP
GEN
SRC
HIC
SRC
GEN
POP
GEN
GEN
GEN
POP
GEN
GEN
GEN
POP
POP
POP
GEN
SRC
GEN
GEN
GEN
GEN
HIC
SRC
REG
POP
POP
POP
Setting2
SUB
SUB
SUB
SUB
RUR
SUB
RUR
RUR
RUR
RUR
SUB
SUB
URB
RUR
URB
RUR
RUR
RUR
SUB
URB
RUR
RUR
RUR
RUR
RUR
SUB
RUR
RUR
RUR
RUR
SUB
SUB
SUB
Land Use3
RES
RES
IND
RES
COM
COM
IND
FOR
IND
AGR
COM
IND
RES
AGR
RES
AGR
RES
AGR
RES
RES
AGR
IND
COM
AGR
COM
RES
AGR
IND
AGR
AGR
RES
RES
AGR
Scale4
NEI
NEI
NEI
URB
URB
NEI
NEI
NEI
MIC
NEI
NEI
REG
NEI
URB
NEI
URB
NEI
NEI
URB
URB
URB
URB
REG
NEI
REG
NEI
REG
REG
URB
URB
URB
Height
20
5
5
5
3
3
3
4
3
5
5
5
3
3
4
12.2
4
4
4
4
4
3
Years
n
3
10
2
7
9
2
3
2
5
2
2
2
1
2
8
1
2
2
2
6
1
10
2
2
2
2
5
1
6
3
1
1
8
First
1997
1997
1997
2000
1997
1999
1997
1997
2001
1998
1999
1997
1999
1999
1997
1999
1997
1998
1997
2000
2005
1997
1997
1998
1997
1998
2000
1997
2000
2004
2006
1997
1999
Last
1999
2006
1998
2006
2006
2003
1999
1998
2006
2001
2006
2000
1999
2004
2004
1999
2000
2001
1998
2006
2005
2006
2000
2004
2000
2004
2006
1997
2005
2006
2006
1997
2006
A-26
-------
State
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
County
Dunn
McKenzie
McKenzie
McKenzie
McLean
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Morton
Morton
Oliver
Steele
Williams
Williams
Adams
Allen
Ashtabula
Belmont
Butler
Butler
Clark
Clermont
Columbiana
Columbiana
Columbiana
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Monitor ID
380250003
380530002
380530104
380530111
380550113
380570001
380570004
380570102
380570118
380570123
380570124
380590002
380590003
380650002
380910001
381050103
381050105
390010001
390030002
390071001
390133002
390170004
390171004
390230003
390250021
390290016
390290022
390292001
390350038
390350045
390350060
390350065
390356001
Latitude
47.3132
47.5812
47.575278
47.605556
47.606667
47.258853
47.298611
47.325
47.371667
47.385725
47.400619
46.84175
46.873075
47.185833
47.599703
48.408834
48.392644
38.795
40.772222
41.959444
39.968056
39.383333
39.53
39.855556
38.961273
40.634722
40.635
40.620278
41.476944
41.471667
41.493955
41.446389
41.504722
Longitude
-102.5273
-103.2995
-103.968889
-104.017222
-102.036389
-101.783035
-101.766944
-101.765833
-101.780833
-101.862917
-101.92865
-100.870059
-100.905039
-101.428056
-97.899009
-102.90765
-102.910233
-83.535278
-84.051944
-80.5725
-80.7475
-84.544167
-84.3925
-83.9975
-84.09445
-80.546389
-80.546667
-80.580833
-81.681944
-81.657222
-81.678542
-81.661944
-81.623889
Objective1
GEN
GEN
SRC
SRC
POP
POP
POP
SRC
SRC
SRC
SRC
SRC
SRC
SRC
GEN
SRC
SRC
POP
POP
POP
POP
POP
POP
POP
HIC
POP
POP
POP
HIC
POP
POP
HIC
POP
Setting2
RUR
RUR
RUR
RUR
RUR
SUB
RUR
RUR
RUR
RUR
RUR
SUB
SUB
RUR
RUR
RUR
RUR
SUB
UNK
SUB
SUB
SUB
SUB
RUR
URB
SUB
SUB
URB
URB
URB
URB
URB
SUB
Land Use3
AGR
AGR
AGR
IND
AGR
RES
AGR
IND
IND
IND
IND
IND
IND
AGR
AGR
IND
IND
RES
AGR
RES
IND
COM
COM
AGR
RES
RES
COM
COM
IND
IND
COM
RES
COM
Scale4
REG
REG
URB
URB
URB
NEI
URB
URB
URB
URB
URB
NEI
NEI
URB
REG
URB
URB
NEI
URB
URB
NEI
NEI
NEI
NEI
URB
NEI
MIC
NEI
NEI
NEI
NEI
NEI
NEI
Height
4
4
3
3
3
5
4
3
3
4
4
4
4
3
3
4
4
5
6
8
6
7
4
4
5
7
6
20
4
4
4
5
6
Years
n
10
6
9
7
6
1
8
10
10
10
10
8
6
9
2
9
9
10
10
10
7
10
10
10
8
1
5
1
9
10
10
9
6
First
1997
1997
1998
2000
1998
1997
1999
1997
1997
1997
1997
1997
1999
1997
1997
1997
1997
1997
1997
1997
2000
1997
1997
1997
1997
1997
2002
1998
1997
1997
1997
1998
1997
Last
2006
2006
2006
2006
2003
1997
2006
2006
2006
2006
2006
2004
2004
2006
1999
2006
2005
2006
2006
2006
2006
2006
2006
2006
2004
1997
2006
1998
2006
2006
2006
2006
2002
A-27
-------
State
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OK
OK
OK
County
Franklin
Franklin
Gallia
Hamilton
Hamilton
Jefferson
Jefferson
Jefferson
Lake
Lake
Lawrence
Lorain
Lorain
Lorain
Lucas
Lucas
Mahoning
Mahoning
Meigs
Montgomery
Morgan
Morgan
Scioto
Scioto
Scioto
Stark
Summit
Summit
Tuscarawas
Tuscarawas
Cherokee
Kay
Kay
Monitor ID
390490004
390490034
390530002
390610010
390612003
390810016
390810017
390811001
390850003
390853002
390870006
390930017
390930026
390931003
390950008
390950024
390990009
390990013
391051001
391130025
391150003
391150004
391450013
391450020
391450022
391510016
391530017
391530022
391570003
391570006
400219002
400710602
400719003
Latitude
39.992222
40.0025
38.944167
39.214931
39.228889
40.362778
40.366104
40.321944
41.673056
41.7225
38.520278
41.368056
41.471667
41.365833
41.663333
41.644167
41.098333
41.096111
39.037778
39.758333
39.631667
39.634221
38.754167
38.609048
38.588034
40.827778
41.063333
41.080278
40.516389
40.511416
35.85408
36.705328
36.662778
Longitude
-83.041667
-82.994444
-82.112222
-84.690723
-84.448889
-80.615556
-80.615002
-80.606389
-81.4225
-81.241944
-82.666667
-82.110556
-82.143611
-82.108333
-83.476667
-83.546667
-80.651944
-80.658611
-82.045556
-84.2
-81 .673056
-81.670038
-82.9175
-82.822911
-82.834973
-81.378611
-81.468611
-81.516389
-81 .476389
-81.639149
-94.985964
-97.087656
-97.074444
Objective1
HIC
POP
POP
POP
HIC
POP
HIC
HIC
UNK
HIC
POP
POP
POP
HIC
HIC
POP
HIC
GEN
POP
HIC
HIC
SRC
HIC
HIC
UPW
HIC
HIC
POP
POP
POP
REG
UNK
POP
Setting2
SUB
URB
SUB
RUR
SUB
URB
URB
URB
SUB
SUB
SUB
URB
SUB
URB
URB
URB
URB
URB
SUB
URB
RUR
RUR
SUB
RUR
RUR
SUB
SUB
URB
URB
RUR
RUR
URB
RUR
Land Use3
COM
COM
RES
IND
IND
COM
COM
IND
RES
COM
RES
COM
IND
COM
IND
IND
COM
RES
RES
COM
AGR
AGR
IND
FOR
IND
RES
IND
COM
IND
RES
RES
RES
RES
Scale4
NEI
NEI
NEI
NEI
NEI
NEI
NEI
MID
NEI
MID
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
URB
NEI
URB
URB
MID
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
NEI
Height
5
4
10
5
3
10
3
6
5
16
8
6
5
9
8
8
6
6
4
3
5
4
10
4
4
5
4
3
5
10
3
4
3
Years
n
3
9
5
9
1
4
3
6
10
10
9
3
6
3
7
8
3
7
10
7
9
1
10
2
2
7
10
10
6
3
4
8
2
First
1997
1997
2002
1998
1997
1999
2004
1998
1997
1997
1998
2001
1997
1997
1998
1999
1997
2000
1997
1997
1997
2006
1997
2005
2005
1997
1997
1997
1997
2004
2001
1997
2002
Last
1999
2006
2006
2006
1997
2002
2006
2003
2006
2006
2006
2003
2002
1999
2006
2006
1999
2006
2006
2003
2005
2006
2006
2006
2006
2003
2006
2006
2002
2006
2005
2005
2003
A-28
-------
State
OK
OK
OK
OK
OK
OK
OK
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Kay
Mayes
Muskogee
Oklahoma
Oklahoma
Ottawa
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Beaver
Berks
Berks
Blair
Bucks
Cambria
Centre
Dauphin
Delaware
Delaware
Monitor ID
400719010
400979014
401010167
401090025
401091037
401159004
401430175
401430235
401430501
420030002
420030010
420030021
420030031
420030032
420030064
420030067
420030116
420031301
420033003
420033004
420070002
420070004
420070005
420070014
420110009
420110100
420130801
420170012
420210011
420270100
420430401
420450002
420450109
Latitude
36.956222
36.228408
35.793134
35.553056
35.614131
36.922222
36.149877
36.126945
36.16127
40.500556
40.445577
40.413611
40.443333
40.414444
40.323611
40.381944
40.473611
40.4025
40.318056
40.305
40.56252
40.635575
40.684722
40.747796
40.320278
40.335278
40.535278
40.107222
40.309722
40.811389
40.245
39.835556
39.818715
Longitude
-97.03135
-95.249943
-95.302235
-97.623611
-97.475083
-94.838889
-96.011664
-95.998941
-96.015784
-80.071944
-80.016155
-79.941389
-79.990556
-79.942222
-79.868333
-80.185556
-80.077222
-79.860278
-79.881111
-79.888889
-80.503948
-80.230605
-80.359722
-80.316442
-75.926667
-75.922778
-78.370833
-74.882222
-78.915
-77.877028
-76.844722
-75.3725
-75.413973
Objective1
GEN
GEN
SRC
POP
POP
UNK
UNK
SRC
UNK
POP
POP
POP
POP
UNK
POP
GEN
POP
HIC
POP
UNK
REG
HIC
POP
POP
HIC
UNK
POP
POP
HIC
POP
HIC
HIC
UNK
Setting2
RUR
RUR
RUR
SUB
SUB
RUR
SUB
URB
URB
SUB
URB
SUB
URB
SUB
SUB
RUR
SUB
SUB
SUB
SUB
RUR
URB
RUR
URB
SUB
URB
SUB
SUB
URB
RUR
RUR
URB
URB
Land Use3
AGR
AGR
COM
RES
RES
RES
IND
IND
COM
RES
COM
RES
COM
RES
RES
RES
RES
RES
IND
RES
AGR
IND
AGR
RES
RES
COM
IND
RES
COM
AGR
COM
IND
IND
Scale4
NEI
NEI
NEI
URB
URB
NEI
NEI
MID
NEI
URB
NEI
NEI
NEI
NEI
NEI
NEI
REG
NEI
URB
URB
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
3
3
5
4
4
4
4
6
4
6
13
5
8
9
5
9
5
8
3
4
3
4
4
4
6
2
12
3
4
4
Years
n
2
1
9
4
2
3
9
9
6
8
9
7
3
2
10
9
7
4
7
4
10
2
10
10
9
2
10
10
10
3
10
10
3
First
2004
2005
1997
1999
2004
2001
1997
1997
2000
1997
1998
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2004
1997
1997
1997
Last
2005
2005
2005
2002
2005
2004
2005
2005
2005
2006
2006
2006
1999
1998
2006
2006
2005
2000
2005
2000
2006
1998
2006
2006
2005
1998
2006
2006
2006
2006
2006
2006
1999
A-29
-------
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
Rl
County
Erie
Indiana
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Lycoming
Lycoming
Mercer
Montgomery
Northampton
Northampton
Northampton
Perry
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Schuylkill
Warren
Warren
Washington
Washington
Washington
Westmoreland
York
Providence
Monitor ID
420490003
420630004
420692006
420710007
420730015
420770004
420791101
420810100
420810403
420850100
420910013
420950025
420950100
420958000
420990301
421010004
421010022
421010024
421010027
421010029
421010047
421010048
421010055
421010136
421070003
421230003
421230004
421250005
421250200
421255001
421290008
421330008
440070012
Latitude
42.14175
40.56333
41.442778
40.046667
40.995848
40.611944
41.265556
41.2508
41.246111
41.215014
40.112222
40.628056
40.676667
40.692224
40.456944
40.008889
39.916667
40.076389
40.010556
39.957222
39.944722
39.991389
39.922517
39.9275
40.820556
41.857222
41.844722
40.146667
40.170556
40.445278
40.304694
39.965278
41.825556
Longitude
-80.038611
-78.919972
-75.623056
-76.283333
-80.346442
-75.4325
-75.846389
-76.9238
-76.989722
-80.484779
-75.309167
-75.341111
-75.216667
-75.237156
-77.165556
-75.097778
-75.188889
-75.011944
-75.151944
-75.173056
-75.166111
-75.080833
-75.186783
-75.222778
-76.212222
-79.1375
-79.169722
-79.902222
-80.261389
-80.420833
-79.505667
-76.699444
-71 .405278
Objective1
HIC
POP
HIC
HIC
POP
POP
POP
POP
POP
POP
POP
POP
UNK
POP
GEN
POP
HIC
UNK
UNK
POP
POP
UNK
POP
POP
POP
HIC
HIC
POP
POP
REG
POP
HIC
POP
Setting2
SUB
RUR
SUB
SUB
SUB
SUB
SUB
URB
URB
URB
SUB
SUB
SUB
SUB
RUR
URB
URB
SUB
URB
URB
URB
RUR
URB
URB
RUR
SUB
RUR
SUB
SUB
RUR
SUB
SUB
URB
Land Use3
COM
COM
RES
IND
IND
COM
RES
RES
COM
COM
RES
COM
IND
RES
UNK
RES
IND
IND
MOB
COM
RES
RES
RES
RES
RES
RES
FOR
COM
RES
AGR
COM
RES
COM
Scale4
NEI
NEI
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
NEI
NEI
NEI
REG
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
REG
URB
NEI
NEI
Height
4
3
4
4
4
3
4
3.5
8
3
4
3
3
4
4
7
7
4
5
11
4
5
4
4
4
4
4
2
4
4
4
4
20
Years
n
10
2
10
10
10
10
9
5
4
9
10
9
2
7
10
8
2
2
2
8
2
2
1
7
9
10
10
10
10
10
9
10
10
First
1997
2005
1997
1997
1997
1997
1997
2002
1997
1997
1997
1998
1997
2000
1997
1997
1997
1997
1997
1997
1997
1997
2005
1997
1998
1997
1997
1997
1997
1997
1998
1997
1997
Last
2006
2006
2006
2006
2006
2006
2005
2006
2000
2006
2006
2006
1998
2006
2006
2004
1998
1998
1998
2004
1998
1998
2005
2004
2006
2006
2006
2006
2006
2006
2006
2006
2006
A-30
-------
State
Rl
Rl
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SD
SD
SD
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
County
Providence
Providence
Aiken
Barnwell
Charleston
Charleston
Georgetown
Greenville
Greenville
Lexington
Oconee
Orangeburg
Richland
Richland
Richland
Richland
Custer
Jackson
Minnehaha
Anderson
Blount
Blount
Blount
Bradley
Coffee
Davidson
Hawkins
Humphreys
McMinn
Montgomery
Montgomery
Polk
Polk
Monitor ID
440071005
440071009
450030003
450110001
450190003
450190046
450430006
450450008
450450009
450630008
450730001
450750003
450790007
450790021
450791003
450791006
460330132
460710001
460990007
470010028
470090002
470090006
470090101
470110102
470310004
470370011
470730002
470850020
471070101
471250006
471250106
471390003
471390007
Latitude
41.878333
41.823611
33.342226
33.320344
32.882289
32.941023
33.362014
34.838814
34.899141
34.051017
34.805261
33.29959
34.093959
33.81468
34.024497
33.817902
43.5578
43.74561
43.537626
36.027778
35.775
35.768056
35.63149
35.283164
35.582222
36.205
36.366944
36.051944
35.29733
36.520056
36.504529
35.026111
34.988333
Longitude
-71.378889
-71.411667
-81.788731
-81.465537
-79.977538
-79.657187
-79.294251
-82.402918
-82.31307
-81.15495
-83.2377
-80.442218
-80.962304
-80.781135
-81.036248
-80.826596
-103.4839
-101.941218
-96.682001
-84.151389
-83.965833
-83.976667
-83.943512
-84.759371
-86.015556
-86.744722
-82.977778
-87.965
-84.75076
-87.394167
-87.396675
-84.384722
-84.371667
Objective1
HIC
HIC
HIC
SRC
POP
SRC
SRC
POP
WEL
SRC
REG
SRC
OTH
GEN
POP
GEN
REG
GEN
POP
UNK
HIC
HIC
GEN
UNK
UNK
POP
UNK
UNK
HIC
UNK
HIC
POP
POP
Setting2
URB
URB
SUB
RUR
URB
RUR
URB
URB
SUB
SUB
RUR
RUR
SUB
RUR
URB
RUR
RUR
RUR
URB
SUB
RUR
SUB
RUR
URB
RUR
URB
RUR
RUR
SUB
RUR
RUR
SUB
URB
Land Use3
RES
COM
RES
FOR
COM
FOR
IND
COM
RES
COM
FOR
FOR
COM
FOR
COM
FOR
FOR
AGR
RES
RES
COM
RES
FOR
RES
AGR
RES
AGR
AGR
AGR
IND
RES
COM
COM
Scale4
NEI
NEI
URB
URB
NEI
REG
NEI
NEI
NEI
NEI
REG
NEI
NEI
URB
MID
MIC
REG
REG
NEI
MID
MID
REG
NEI
NEI
NEI
MID
NEI
NEI
Height
6
3
4.02
3.1
4.3
4
2.13
4
4
3.35
4.3
3.2
3
4.42
4
5
3.35
3
4
3
4
4
10
4
13
1
4
4
3
4
8
1
Years
n
1
10
2
10
10
8
7
9
2
9
9
1
7
4
10
2
2
2
3
8
8
8
1
8
1
10
6
8
8
10
10
9
9
First
1997
1997
1997
1997
1997
1997
1997
1997
2005
1997
1997
2003
1999
2002
1997
1997
2005
2005
2004
1997
1997
1997
1999
1997
1998
1997
1998
1997
1997
1997
1997
1997
1997
Last
1997
2006
1998
2006
2006
2006
2006
2006
2006
2006
2006
2003
2006
2005
2006
1999
2006
2006
2006
2006
2006
2006
1999
2006
1998
2006
2004
2006
2005
2006
2006
2005
2005
A-31
-------
State
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
County
Polk
Polk
Roane
Shelby
Shelby
Shelby
Shelby
Stewart
Sullivan
Sullivan
Sumner
Cameron
Dallas
Ellis
Ellis
Ellis
El Paso
El Paso
El Paso
Galveston
Galveston
Gregg
Harris
Harris
Harris
Harris
Harris
Harris
Harris
Jefferson
Jefferson
Jefferson
Kaufman
Monitor ID
471390008
471390009
471450009
471570034
471570043
471570046
471571034
471610007
471630007
471630009
471651002
480610006
481130069
481390015
481390016
481390017
481410037
481410053
481410058
481670005
481671002
481830001
482010046
482010051
482010059
482010062
482010070
482011035
482011050
482450009
482450011
482450020
482570005
Latitude
34.995833
34.989722
35.947222
35.0434
35.087778
35.272778
35.087222
36.389722
36.534804
36.513971
36.341667
25.892509
32.819952
32.436944
32.482222
32.473611
31.768281
31.758504
31.893928
29.385236
29.398611
32.37871
29.8275
29.623611
29.705833
29.625833
29.735129
29.733713
29.583032
30.036446
29.89403
30.06607
32.564969
Longitude
-84.368333
-84.383889
-84.522222
-90.0136
-90.025278
-89.961389
-90.133611
-87.633333
-82.517078
-82.560968
-86.398333
-97.493824
-96.860082
-97.025
-97.026944
-97.0425
-106.501253
-106.501023
-106.425813
-94.931526
-94.933333
-94.711834
-95.283611
-95.473611
-95.281111
-95.2675
-95.315583
-95.257591
-95.015535
-94.071073
-93.987898
-94.077383
-96.31766
Objective1
UNK
UNK
UNK
HIC
HIC
POP
UNK
OTH
HIC
HIC
OTH
HIC
POP
HIC
GEN
OTH
POP
HIC
POP
HIC
HIC
GEN
POP
SRC
HIC
POP
GEN
POP
HIC
HIC
SRC
SRC
HIC
Setting2
RUR
RUR
SUB
SUB
SUB
SUB
RUR
RUR
SUB
RUR
RUR
URB
URB
SUB
SUB
RUR
URB
URB
URB
URB
SUB
RUR
SUB
SUB
SUB
SUB
SUB
SUB
SUB
SUB
URB
URB
SUB
Land Use3
RES
IND
RES
RES
COM
IND
AGR
AGR
RES
RES
AGR
COM
COM
AGR
AGR
RES
COM
COM
RES
RES
RES
RES
RES
RES
RES
RES
RES
IND
RES
RES
IND
IND
COM
Scale4
NEI
NEI
URB
MID
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
MID
NEI
NEI
NEI
NEI
Height
3
4
4
3
3
3
3
3
3
3
6
4
4
3
5
5
5
4
4
4
6
5
11
6
11
6.31
4
5
5
Years
n
3
3
6
4
2
10
10
7
9
10
7
3
10
9
7
1
9
8
5
2
7
6
8
8
1
9
6
9
5
10
10
8
6
First
1998
1997
1998
2002
1997
1997
1997
1997
1998
1997
1997
1998
1997
1998
1998
2005
1998
1999
2001
2005
1997
2000
1997
1997
1997
1997
2001
1997
2002
1997
1997
1998
2001
Last
2000
2000
2005
2005
1998
2006
2006
2005
2006
2006
2004
2000
2006
2006
2006
2005
2006
2006
2005
2006
2003
2005
2006
2006
1997
2006
2006
2006
2006
2006
2006
2006
2006
A-32
-------
State
TX
TX
TX
UT
UT
UT
UT
UT
UT
VT
VT
VT
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
WA
WA
WA
WA
WA
WA
WA
County
Nueces
Nueces
Nueces
Cache
Davis
Davis
Salt Lake
Salt Lake
Salt Lake
Chittenden
Chittenden
Rutland
Charles
Fairfax
Fairfax
Fairfax
Fairfax
Fairfax
Madison
Roanoke
Rockingham
Rockingham
Alexandria City
Hampton City
Norfolk City
Richmond City
Clallam
Clallam
King
King
Pierce
Pierce
Skagit
Monitor ID
483550025
483550026
483550032
490050004
490110001
490110004
490350012
490351001
490352004
500070003
500070014
500210002
510360002
510590005
510590018
510591004
510591005
510595001
511130003
511611004
511650002
511650003
515100009
516500004
517100023
517600024
530090010
530090012
530330057
530330080
530530021
530530031
530570012
Latitude
27.76534
27.832409
27.804482
41.731111
40.886389
40.902967
40.8075
40.708611
40.736389
44.478889
44.4762
43.608056
37.343294
38.893889
38.7425
38.868056
38.837517
38.931944
38.521944
37.285556
38.389444
38.47732
38.810833
37.003333
36.850278
37.562778
48.113333
48.0975
47.563333
47.568333
47.281111
47.2656
48.493611
Longitude
-97.434272
-97.555381
-97.431553
-111.8375
-1 1 1 .882222
-111.884467
-111.921111
-112.094722
-112.210278
-73.211944
-73.2106
-72.982778
-77.260034
-77.465278
-77.0775
-77.143056
-77.163231
-77.198889
-78.436111
-79.884167
-78.914167
-78.81904
-77.044722
-76.399167
-76.257778
-77.465278
-123.399167
-123.425556
-122.3406
-122.308056
-122.374167
-122.3858
-122.551944
Objective1
POP
HIC
POP
POP
POP
POP
UNK
HIC
HIC
HIC
POP
POP
HIC
POP
UNK
UNK
POP
UNK
UNK
HIC
POP
POP
POP
HIC
POP
HIC
UNK
UNK
HIC
POP
HIC
POP
UNK
Setting2
URB
URB
SUB
URB
SUB
SUB
SUB
SUB
RUR
URB
URB
URB
SUB
RUR
SUB
SUB
SUB
SUB
RUR
SUB
RUR
SUB
URB
SUB
URB
URB
SUB
SUB
SUB
URB
SUB
SUB
SUB
Land Use3
RES
RES
RES
COM
COM
RES
IND
RES
IND
COM
COM
COM
RES
AGR
RES
COM
RES
RES
FOR
RES
AGR
COM
RES
RES
COM
COM
RES
RES
IND
RES
RES
IND
RES
Scale4
NEI
NEI
NEI
NEI
NEI
MID
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
Height
4
6
4
4
3
4
4
6
4
4
5
4
4
11
4
4
7
6
10
4
5
5
4
5
11
5
5
5
5
Years
n
9
8
8
3
6
2
6
9
7
3
1
7
10
9
1
4
4
9
3
10
6
2
10
10
8
8
1
5
2
4
1
1
1
First
1997
1998
1998
2003
1997
2004
1999
1997
1997
1997
2004
1997
1997
1997
1997
1997
2003
1998
2000
1997
1998
2005
1997
1997
1997
1999
1997
1999
1997
2001
1997
1997
1997
Last
2005
2005
2005
2005
2002
2005
2004
2005
2003
1999
2004
2005
2006
2006
1997
2000
2006
2006
2003
2006
2003
2006
2006
2006
2004
2006
1997
2004
1998
2004
1997
1997
1997
A-33
-------
State
WA
WA
WA
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
Wl
Wl
Wl
Wl
County
Skagit
Snohomish
What com
Brooke
Brooke
Cabell
Greenbrier
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Kanawha
Kanawha
Kanawha
Marshall
Monongalia
Monongalia
Monongalia
Ohio
Wayne
Wayne
Wayne
Wayne
Wood
Brown
Dane
FOR
Marathon
Monitor ID
530571003
530610016
530730011
540090005
540090007
540110006
540250001
540290005
540290007
540290008
540290009
540290011
540290014
540290015
540290016
540291004
540390004
540390010
540392002
540511002
540610003
540610004
540610005
540690007
540990002
540990003
540990004
540990005
541071002
550090005
550250041
550410007
550730005
Latitude
48.486111
47.983333
48.750278
40.341023
40.389655
38.424133
37.819444
40.529021
40.460138
40.61572
40.427372
40.394583
40.43552
40.618353
40.411944
40.421539
38.343889
38.3456
38.416944
39.915961
39.649367
39.633056
39.648333
40.12043
38.39186
38.390278
38.380278
38.372222
39.323533
44.516667
43.100833
45.56498
45.028333
Longitude
-122.549444
-122.209722
-122.482778
-80.596635
-80.586235
-82.4259
-80.5125
-80.576067
-80.576567
-80.56
-80.592318
-80.612017
-80.600579
-80.540616
-80.601667
-80.580717
-81.619444
-81.628317
-81 .846389
-80.733858
-79.920867
-79.957222
-79.957778
-80.699265
-82.583923
-82.585833
-82.583889
-82.588889
-81.552367
-87.993889
-89.357222
-88.80859
-89.652222
Objective1
UNK
UNK
UNK
POP
POP
POP
UNK
POP
POP
POP
POP
POP
POP
POP
HIC
HIC
POP
POP
HIC
POP
POP
UNK
UNK
HIC
POP
HIC
HIC
HIC
POP
POP
POP
GEN
HIC
Setting2
RUR
URB
URB
SUB
RUR
SUB
RUR
SUB
RUR
SUB
SUB
SUB
SUB
URB
SUB
SUB
SUB
URB
SUB
SUB
SUB
SUB
SUB
SUB
RUR
RUR
RUR
RUR
SUB
URB
URB
RUR
RUR
Land Use3
IND
COM
IND
IND
RES
COM
AGR
RES
RES
RES
RES
RES
RES
RES
RES
RES
COM
COM
IND
RES
COM
RES
RES
RES
IND
RES
RES
RES
IND
RES
RES
FOR
FOR
Scale4
NEI
NEI
NEI
URB
URB
NEI
NEI
NEI
MID
URB
NEI
NEI
URB
NEI
URB
URB
URB
NEI
NEI
NEI
NEI
NEI
URB
NEI
NEI
REG
MID
Height
3
4
12
4
4
13.6
4
4
5
4
4
3
8
13
4
4
4.6
10.7
8
4
3
3
3
4
11
5
6
5
Years
n
1
1
1
10
10
10
1
10
10
10
10
10
7
10
7
10
2
6
1
10
10
4
9
6
6
8
8
8
10
7
2
2
3
First
1997
1997
1998
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2001
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2004
1997
Last
1997
1997
1998
2006
2006
2006
1997
2006
2006
2006
2006
2006
2003
2006
2003
2006
1998
2006
1997
2006
2006
2000
2005
2002
2002
2005
2005
2005
2006
2005
1998
2005
1999
A-34
-------
State
Wl
Wl
Wl
Wl
Wl
Wl
Wl
WY
PR
PR
PR
PR
PR
PR
PR
PR
PR
VI
VI
VI
VI
VI
County
Milwaukee
Milwaukee
Milwaukee
Oneida
Sauk
Vilas
Wood
Campbell
Barceloneta
Bayamon
Bayamon
Catano
Catano
Catano
Catano
Guayama
Salinas
St Croix
St Croix
St Croix
St Croix
St Croix
Monitor ID
550790007
550790026
550790041
550850996
551110007
551250001
551410016
560050857
720170003
720210004
720210006
720330004
720330007
720330008
720330009
720570009
721230001
780100006
780100011
780100013
780100014
780100015
Latitude
43.047222
43.061111
43.075278
45.645278
43.435556
46.048056
44.3825
44.277222
18.436111
18.412778
18.416667
18.430556
18.444722
18.440028
18.449964
17.966844
17.963002
17.706944
17.719167
17.7225
17.734444
17.741667
Longitude
-87.920278
-87.9125
-87.884444
-89.4125
-89.680278
-89.653611
-89.819167
-105.375
-66.580556
-66.132778
-66.150833
-66.142222
-66.116111
-66.127076
-66.149043
-66.188014
-66.254749
-64.780556
-64.775
-64.776667
-64.783333
-64.751944
Objective1
POP
POP
HIC
UNK
GEN
GEN
POP
SRC
UNK
HIC
POP
UNK
POP
POP
POP
SRC
SRC
HIC
HIC
POP
POP
SRC
Setting2
URB
URB
URB
URB
RUR
RUR
URB
RUR
RUR
SUB
SUB
SUB
URB
URB
URB
RUR
RUR
RUR
RUR
SUB
RUR
RUR
Land Use3
COM
COM
RES
IND
FOR
FOR
RES
IND
RES
IND
IND
RES
RES
COM
RES
COM
AGR
IND
IND
RES
AGR
AGR
Scale4
NEI
NEI
NEI
REG
REG
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
NEI
Height
7
9
7
6
6
15
7
4
3
3
4
2
4
4
4
4
4
Years
n
4
4
4
9
1
1
2
3
5
6
7
7
1
1
1
4
1
5
5
5
5
4
First
1997
2002
1997
1997
2003
2003
1998
2002
1997
1997
1997
1997
2002
2005
2005
2002
2004
1998
1997
1999
1999
2000
Last
2000
2005
2001
2005
2003
2003
1999
2004
2005
2004
2005
2005
2002
2005
2005
2005
2004
2004
2004
2004
2004
2004
Notes:
1 Objectives are POP=Population Exposure; HIC=Highest Concentration; SRC=Source Oriented; GEN=General/Background; REG=Regional
Transport; OTH=Other; UNK=Unknown; UPW=Upwind Background; WEL=Welfare Related Impacts
2 Settings are R=Rural; U=Urban and Center City; S=Suburban
3 Land Uses are AGR=Agricultural; COM=Commercial; IND=lndustrial; FOR=Forest; RES=Residential; UNK=Unknown; DES=Desert; MOB=Mobile.
4 Scales are NEI=Neighborhood; MID=Middle; URB=URBAN; REG=Regional; MIC=Micro
A-35
-------
1
2
3
Table A. 1-4. Population density, concentration variability, and total SO2 emissions associated with ambient
monitors in the broader SO2 monitoring network.
State
AL
AL
AL
AL
AL
AL
AL
AR
AR
AR
AZ
AZ
AZ
AZ
AZ
AZ
AZ
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
County
Colbert
Jackson
Jefferson
Lawrence
Mobile
Mobile
Montgomery
Pulaski
Pulaski
Union
Gila
Gila
Maricopa
Maricopa
Maricopa
Pima
Pinal
Alameda
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Imperial
Los Angeles
Los Angeles
Monitor ID
010330044
010710020
010731003
010790003
010970028
010972005
011011002
051190007
051191002
051390006
040070009
040071001
040130019
040133002
040133003
040191011
040212001
060010010
060130002
060130006
060130010
060131001
060131002
060131003
060131004
060132001
060133001
060250005
060371002
060371103
Population Residing within:
5km
2195
1902
76802
3952
5966
3017
45389
67784
45800
21877
7801
1359
197458
144581
91955
111215
4375
236320
136288
119088
29809
53051
4033
146336
125350
34743
64019
27033
167653
378843
10km
7954
8137
196682
28674
7758
18106
1 56786
178348
109372
29073
14076
1359
613618
490123
340325
354473
7679
532827
303088
231479
123220
181259
39708
256417
233220
1 55226
152758
31895
827729
1618324
15km
25394
19317
344386
73092
17087
52682
213606
270266
230200
32652
17280
3098
1036233
980730
829051
561487
9577
841443
445297
471471
403137
321500
117118
420619
433669
433934
303597
56234
2001363
3027507
20km
62838
29686
489181
91057
39111
111608
259730
334649
310362
36340
17633
5401
1447648
1612687
1518806
639921
10125
1342267
598861
968983
685185
610171
173196
856435
876585
807706
478310
84405
3286038
4530714
Analysis Bins
Population1
low
low
hi
low
low
low
mod
hi
mod
mod
low
low
hi
hi
hi
hi
low
hi
hi
hi
mod
hi
low
hi
hi
mod
hi
mod
hi
hi
cov2
c
c
b
b
c
b
a
a
a
b
b
c
a
a
a
a
c
a
b
b
a
b
a
a
a
a
b
b
a
a
GSD3
a
b
b
b
c
a
a
a
a
a
b
c
b
a
a
a
a
a
a
a
a
a
a
a
a
a
a
c
a
a
Emissions
(tpy)4
50041
45357
6478
8937
66130
1187
3650
20
20
2527
18438
186
185
180
3119
369
15056
5032
17834
19592
79
5032
5032
17834
8105
7
51
551
A-36
-------
State
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
County
Los Angeles
Los Angeles
Los Angeles
Orange
Riverside
Sacramento
Sacramento
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Diego
San Diego
San Diego
San Francisco
San Luis Obispo
San Luis Obispo
San Luis Obispo
San Luis Obispo
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Monitor ID
060374002
060375001
060375005
060591003
060658001
060670002
060670006
060710012
060710014
060710306
060711234
060712002
060714001
060730001
060731007
060732007
060750005
060791005
060792001
060792004
060794002
060830008
060831012
060831013
060831015
060831016
060831019
060831020
060831025
060831026
060831027
060832004
060832011
Population Residing within:
5km
240505
276378
94836
200253
78757
92433
132584
6720
58937
59772
0
89732
40799
168237
169117
9376
433367
4725
39236
2135
0
655
0
6617
0
0
0
39222
655
655
655
38688
55496
10km
913176
890302
652173
744882
360234
328190
472019
17620
114149
114149
0
314392
114888
528890
616102
15849
827164
56677
55657
34056
51508
1678
0
41576
0
0
0
71015
1678
1678
1678
49356
105491
15km
1850549
2071144
1628468
1303743
734267
645533
866437
29756
193928
193046
1911
650533
174610
866015
1097387
218480
1227784
85064
61709
113260
95245
17486
960
59590
2391
4034
4689
117832
11216
15659
13618
58271
170865
20km
3218392
3561110
2848126
1829713
1141466
916197
1180898
69717
224008
224008
1911
1142460
219525
1177835
1449106
452120
1729715
152491
121393
162669
141786
67965
3201
89777
17826
17826
17826
170206
56132
63963
62298
59279
181894
Analysis Bins
Population1
hi
hi
hi
hi
hi
hi
hi
low
hi
hi
low
hi
mod
hi
hi
low
hi
low
mod
low
low
low
low
low
low
low
low
mod
low
low
low
mod
hi
cov2
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
c
a
b
b
a
a
a
a
a
a
a
a
a
b
a
a
GSD3
b
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
b
a
c
b
a
a
a
a
a
a
a
a
a
a
a
a
Emissions
(tpy)4
5869
6282
2304
68
299
5
58
8
251
251
290
203
32
21
34
21
399
3755
3755
3755
3755
118
1109
1109
18
18
18
118
118
118
118
1109
118
A-37
-------
State
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
CO
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
DC
DE
DE
DE
DE
DE
DE
County
Santa Barbara
Santa Cruz
Solano
Solano
Ventura
Adams
Adams
Denver
El Paso
El Paso
El Paso
El Paso
Fairfield
Fairfield
Fairfield
Fairfield
Fairfield
Hartford
Hartford
Hartford
New Haven
New Haven
New Haven
New Haven
New London
Tolland
District of Columbia
New Castle
New Castle
New Castle
New Castle
New Castle
New Castle
Monitor ID
060834003
060870003
060950001
060950004
061113001
080010007
080013001
080310002
080416001
080416004
080416011
080416018
090010012
090010017
090011123
090012124
090019003
090031005
090031018
090032006
090090027
090091003
090091123
090092123
090110007
090130003
110010041
100031003
100031007
100031008
100031013
100032002
100032004
Population Residing within:
5km
0
0
27872
102003
47248
45071
81896
189782
0
84979
97849
93065
164887
30184
72689
121109
28181
33414
152497
91965
140329
156879
154781
104191
58457
23441
216129
68790
14297
5386
79498
111236
111609
10km
0
6016
130319
166693
227525
360261
334611
574752
24520
242841
288563
266008
291072
188214
126452
209567
151905
147625
329646
333744
290735
293853
292598
189838
97870
47285
813665
223079
67478
80025
221315
245832
245173
15km
8430
51831
359105
247861
401656
903964
784343
1 1 58644
54194
368203
407401
388801
393358
330125
191805
343909
313449
319902
523045
510929
389117
414381
417546
276310
141173
78649
1461563
369450
178295
192989
386624
400217
411000
20km
51692
124792
620107
374613
427503
1344766
1205604
1608099
111518
430076
448545
438812
528453
672435
277225
476656
546288
484462
693079
671515
529118
552021
557442
447334
182476
115317
2029936
603736
274942
391157
618604
624587
600168
Analysis Bins
Population1
low
low
mod
hi
mod
mod
hi
hi
low
hi
hi
hi
hi
mod
hi
hi
mod
mod
hi
hi
hi
hi
hi
hi
hi
mod
hi
hi
mod
low
hi
hi
hi
cov2
a
a
a
a
a
b
b
b
b
a
b
a
b
b
a
b
b
a
a
a
b
b
b
a
a
a
a
b
b
b
b
b
b
GSD3
a
a
a
a
b
b
b
b
b
a
b
a
b
b
b
b
b
b
b
b
b
b
b
b
a
a
a
b
b
b
b
b
b
Emissions
(tpy)4
1109
722
17821
17763
19
24028
23817
26354
5010
8547
8547
8537
4671
757
766
5039
1268
113
83
4761
5085
5085
430
3898
18325
33133
34382
39757
33133
28868
59518
A-38
-------
State
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
GA
GA
County
Broward
Duval
Duval
Duval
Duval
Escambia
Escambia
Hamilton
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Manatee
Miami-Dade
Nassau
Nassau
Orange
Palm Beach
Pinellas
Pinellas
Pinellas
Pinellas
Polk
Polk
Putnam
Sarasota
Sarasota
Sarasota
Baldwin
Bartow
Monitor ID
120110010
120310032
120310080
120310081
120310097
120330004
120330022
120470015
120570021
120570053
120570081
120570095
120570109
120571035
120574004
120813002
120860019
120890005
120890009
120952002
120993004
121030023
121033002
121035002
121035003
121050010
121052006
121071008
121151002
121151005
121151006
130090001
130150002
Population Residing within:
5km
173204
81831
70468
23862
59980
43464
32534
582
90125
54303
5101
28554
11493
63839
32134
2043
54755
17963
8627
85060
54596
40222
74280
58164
48341
1499
8128
10853
78620
28895
65360
7410
1628
10km
475485
270954
288474
152805
225163
1 33022
122295
1733
287073
140247
24672
192630
81649
244436
66598
18810
283528
21386
18803
389159
222249
180398
310490
184586
174960
21899
49090
21601
1 80672
140026
1 88269
22059
15879
15km
953527
439516
506828
305323
418997
233520
223566
6459
539627
307460
48751
493886
287436
463185
149341
82190
685044
38521
27645
808816
446441
488170
633807
401002
304905
60024
125120
35511
237782
244918
295631
44230
50084
20km
1459284
620929
704506
463770
600591
303319
291695
12479
762352
668911
228142
719140
509661
764479
346648
281383
1386189
48316
59574
1031221
718156
901428
907997
655181
492683
142707
198136
44711
332704
356779
386824
50761
91503
Analysis Bins
Population1
hi
hi
hi
mod
hi
mod
mod
low
hi
hi
low
mod
mod
hi
mod
low
hi
mod
low
hi
hi
mod
hi
hi
mod
low
low
mod
hi
mod
hi
low
low
cov2
c
b
b
c
b
b
c
b
c
b
b
c
c
b
b
b
a
c
b
b
b
b
b
b
b
b
b
b
b
b
b
c
c
GSD3
a
b
b
b
b
b
b
a
b
b
b
b
b
b
a
b
a
c
b
a
a
c
c
b
b
a
b
b
b
a
a
b
a
Emissions
(tpy)4
19178
38010
38015
38001
38010
43573
43573
2264
89751
89830
122051
65362
65352
89751
8617
365
235
5050
5050
46
235
24819
24813
30797
30797
21475
21989
29894
143
73950
162418
A-39
-------
State
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
HI
HI
HI
HI
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
County
Bibb
Chatham
Chatham
Chatham
Dougherty
Fannin
Floyd
Fulton
Fulton
Glynn
Muscogee
Richmond
Honolulu
Honolulu
Honolulu
Honolulu
Cerro Gordo
Clinton
Clinton
Clinton
Lee
Lee
Linn
Linn
Linn
Linn
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Scott
Monitor ID
130210012
130510019
130510021
130511002
130950006
131110091
131150003
131210048
131210055
131270006
132150008
132450003
150030010
150030011
150031001
150031006
190330018
190450018
190450019
190450020
191110006
191111007
191130028
191130029
191130031
191130032
191130034
191130038
191130039
191390016
191390017
191390020
191630015
Population Residing within:
5km
5430
24119
47852
40337
28572
3943
2671
139962
103612
22992
63822
30694
24951
16119
197479
16676
21247
24561
24561
25544
11675
1202
9112
72325
76896
66674
63548
30007
30134
20360
11109
20360
90863
10km
38736
107149
121273
113925
73138
9432
22348
429736
409533
38643
167389
124609
89592
58440
344436
66976
30341
37638
37638
36227
18308
11474
77687
146914
148919
131315
146044
108042
106631
27101
27101
27101
201277
15km
102539
188444
183343
186077
101552
19045
46960
806001
779857
61789
234866
206847
181585
160177
483321
180191
39284
42404
42404
41370
24246
20995
143283
168250
170320
169310
170320
163636
160903
31886
31696
31886
268535
20km
1 53254
220328
220814
222588
117779
24026
74655
1253530
1209013
67649
254253
298992
344307
277456
672198
300444
45105
45947
45947
48214
25010
34036
189856
179312
179312
183904
185547
180807
180968
40248
36604
40290
293627
Analysis Bins
Population1
low
mod
mod
mod
mod
low
low
hi
hi
mod
hi
mod
mod
mod
hi
mod
mod
mod
mod
mod
mod
low
low
hi
hi
hi
hi
mod
mod
mod
mod
mod
hi
cov2
b
b
b
c
b
c
c
b
b
b
b
b
a
a
b
b
c
b
b
b
b
b
b
b
c
b
c
c
b
c
b
c
b
GSD3
a
b
b
b
a
b
b
b
b
a
a
a
a
a
a
a
c
b
c
b
c
c
a
b
b
a
b
c
a
c
b
c
c
Emissions
(tpy)4
2694
19069
19069
19069
6773
1900
32455
30375
30375
2464
6960
20025
15617
15617
3130
15617
10737
9388
9388
9388
29
208
15400
15400
15400
15400
15400
15400
15400
31137
31054
31054
9415
A-40
-------
State
IA
IA
IA
IA
IA
ID
ID
ID
ID
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
County
Scott
Van Buren
Van Buren
Van Buren
Wood bury
Bannock
Caribou
Caribou
Power
Adams
Champaign
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
DuPage
La Salle
Macon
Macoupin
Madison
Madison
Madison
Madison
Madison
Peoria
Randolph
Monitor ID
191630017
191770004
191770005
191770006
191930018
160050004
160290003
160290031
160770011
170010006
170190004
170310050
170310059
170310063
170310064
170310076
170311018
170311601
170312001
170314002
170314201
170318003
170436001
170990007
171150013
171170002
171190008
171190017
171191010
171193007
171193009
171430024
171570001
Population Residing within:
5km
3486
0
994
994
4449
16523
0
0
7702
40173
91239
162765
67237
307232
299183
289574
113572
23495
138992
406933
63731
111959
83416
4862
54806
0
36580
37113
9382
32393
27788
76341
5095
10km
43003
2252
2252
2252
44815
57823
1351
604
50773
49711
126127
649556
496359
1205813
965573
1034471
617444
167647
604707
1482581
232428
456791
401929
26956
92426
5005
84254
201161
70816
71861
69631
167513
10038
15km
159186
3764
3764
3764
92956
64147
3211
3211
64147
54168
134689
1310508
1055079
2476802
1758392
2000564
1657665
466741
1 380464
2777797
627873
1004517
787802
37974
103292
16518
152472
536687
176153
172196
136629
232727
16360
20km
245960
7809
6984
6984
112802
69313
4218
3211
69313
64300
152309
1997666
1759830
3318024
2786664
2971446
3102521
1000711
2117578
3752141
1254146
1682955
1266818
63052
112667
19043
330907
950679
323143
353090
273179
269180
29336
Analysis Bins
Population1
low
low
low
low
low
mod
low
low
low
mod
hi
hi
hi
hi
hi
hi
hi
mod
hi
hi
hi
hi
hi
low
hi
low
mod
mod
low
mod
mod
hi
low
cov2
c
a
b
a
b
b
c
c
b
b
b
b
b
b
b
a
b
b
b
b
b
b
b
c
b
b
b
b
b
b
b
b
c
GSD3
a
b
b
b
b
c
b
c
a
b
b
b
b
b
b
b
b
b
b
b
b
b
b
c
b
a
b
b
b
b
b
c
b
Emissions
(tpy)4
14841
36833
1609
12572
12572
1609
3859
362
42308
36403
23944
50763
33488
24023
45681
39578
24553
659
30075
35837
3561
13757
67657
35077
26719
72660
72512
73334
26296
A-41
-------
State
IL
IL
IL
IL
IL
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
County
Rock Island
Saint Clair
Saint Clair
Saint Clair
Sangamon
Tazewell
Wabash
Wabash
Will
Daviess
Dearborn
Floyd
Floyd
Floyd
Fountain
Gibson
Gibson
Hendricks
Hendricks
Hendricks
Jasper
Jasper
Jefferson
Lake
Lake
LaPorte
LaPorte
Marion
Marion
Marion
Marion
Marion
Morgan
Monitor ID
171610003
171630010
171631010
171631011
171670006
171790004
171850001
171851001
171970013
180270002
180290004
180430004
180430007
180431004
180450001
180510001
180510002
180630001
180630002
180630003
180730002
180730003
180770004
180890022
180892008
180910005
180910007
180970042
180970054
180970057
180970072
180970073
181091001
Population Residing within:
5km
87160
48405
49630
1148
41165
32800
8738
1069
12237
905
11932
17205
65510
45432
788
792
6276
4657
7481
1776
991
1688
11228
40318
97669
28928
29106
19283
53595
79478
115856
100599
4178
10km
228445
274406
269778
9915
123641
50160
9493
10899
66320
9377
21347
86512
228353
169258
2536
10900
9493
29661
31567
11450
8080
4551
22061
152401
293157
42982
54698
109791
301941
349455
380088
357454
26279
15km
275180
621019
593969
18231
154447
99136
13312
11617
171777
21937
69595
201325
408246
351938
9505
18174
16779
66108
79685
41400
16959
12127
32050
292371
745205
60818
82651
306701
612446
640054
684608
585925
53331
20km
296786
999843
973751
27769
171401
194767
27993
21643
249868
32380
151228
363262
607160
532952
19361
30700
29981
183728
205437
79693
28865
20725
36387
500754
1339901
97304
112181
564512
863127
909257
922620
880596
105208
Analysis Bins
Population1
hi
mod
mod
low
mod
mod
low
low
mod
low
mod
mod
hi
mod
low
low
low
low
low
low
low
low
mod
mod
hi
mod
mod
mod
hi
hi
hi
hi
low
cov2
b
b
b
c
c
c
c
c
b
b
b
b
b
c
c
c
c
c
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
GSD3
a
b
b
b
b
b
b
b
b
c
b
c
b
b
b
b
c
b
b
b
a
b
b
b
b
b
b
b
b
b
b
b
c
Emissions
(tpy)4
9449
13346
13346
26296
10849
73270
127357
127357
46347
65217
151052
52000
67211
66977
55655
127357
127357
147
27494
27494
38198
50716
36590
12499
9198
51880
51077
51077
51096
50949
18019
A-42
-------
State
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
KS
KS
KS
KS
KS
KS
KS
KS
KS
KY
KY
KY
KY
KY
KY
KY
County
Perry
Perry
Pike
Porter
Porter
Porter
Spencer
Spencer
Sullivan
Vanderburgh
Vanderburgh
Vigo
Vigo
Warrick
Warrick
Wayne
Wayne
Linn
Montgomery
Pawnee
Sedgwick
Sumner
Trego
Wyandotte
Wyandotte
Wyandotte
Boyd
Boyd
Boyd
Campbell
Campbell
Daviess
Fayette
Monitor ID
181230006
181230007
181250005
181270011
181270017
181270023
181470002
181470010
181530004
181630012
181631002
181670018
181671014
181730002
181731001
181770006
181770007
201070002
201250006
201450001
201730010
201910002
201950001
202090001
202090020
202090021
210190015
210190017
210191003
210370003
210371001
210590005
210670012
Population Residing within:
5km
6348
6153
3991
12202
14162
13645
1935
2483
1735
45373
1289
50963
25046
2200
11943
34483
31811
1728
9331
5329
102842
1476
0
63756
41751
61336
31077
34804
14960
67933
153388
25889
92980
10km
13158
15700
7372
44110
59080
79678
4701
5934
8313
141869
30177
82314
72089
27584
28798
51601
48948
3741
14142
6038
276624
13125
0
288005
237368
271585
78140
79205
58723
285451
421973
70609
195446
15km
20298
19228
12598
101993
118122
136098
13255
14936
15494
184521
123286
98561
100022
60538
80348
59606
59606
4705
17807
6038
380868
56924
578
588511
491118
571758
124766
119732
117154
616440
754366
81162
267016
20km
30372
29270
29314
210946
223900
256849
32146
32405
25746
225094
201383
1 1 5726
1 1 8986
123354
155370
71062
72278
6412
21677
6038
426333
120034
578
868652
742170
840225
179511
161810
181371
910551
1016145
92902
309266
Analysis Bins
Population1
low
low
low
mod
mod
mod
low
low
low
mod
low
hi
mod
low
mod
mod
mod
low
low
low
hi
low
low
hi
mod
hi
mod
mod
mod
hi
hi
mod
hi
cov2
c
b
b
b
b
b
b
b
a
b
b
b
b
b
b
b
b
b
b
a
a
b
a
b
b
b
b
b
b
b
b
b
a
GSD3
c
c
a
b
b
b
b
b
a
b
b
b
c
b
b
c
c
a
b
a
a
a
a
a
a
a
b
b
b
b
b
b
b
Emissions
(tpy)4
56262
56262
65217
39173
29995
29975
109391
60394
27810
9032
9032
65055
65055
109088
109088
12892
12892
1873
806
806
19433
19433
19427
11909
11933
10172
74986
5111
60963
626
A-43
-------
State
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
LA
LA
LA
LA
LA
LA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
MA
County
Greenup
Hancock
Henderson
Henderson
Jefferson
Jefferson
Jefferson
Livingston
McCracken
McCracken
McCracken
Warren
Bossier
Calcasieu
East Baton Rouge
Ouachita
St. Bernard
West Baton Rouge
Bristol
Essex
Essex
Essex
Essex
Hampden
Hampden
Hampshire
Middlesex
Middlesex
Suffolk
Suffolk
Suffolk
Suffolk
Suffolk
Monitor ID
210890007
210910012
211010013
211010014
211110032
211110051
211111041
211390004
211450001
211451024
211451026
212270008
220150008
220190008
220330009
220730004
220870002
221210001
250051004
250090005
250091004
250091005
250095004
250130016
250131009
250154002
250171701
250174003
250250002
250250019
250250020
250250021
250250040
Population Residing within:
5km
19411
3345
21591
2594
49825
13446
81560
1695
1336
17904
9706
1865
43077
12932
76518
24260
97021
21249
89767
125952
123377
109921
57974
136483
127283
5182
109401
164954
486825
6913
320320
243006
261273
10km
45899
4280
35051
30452
208276
52332
281755
8508
15733
48907
42285
8137
149478
68406
193981
87999
407863
137455
169077
225058
309194
314258
128881
296109
278577
23547
512228
629764
1141656
437626
899106
887256
962956
15km
85066
20931
126144
135741
375535
121743
485759
15337
28279
63098
62036
23083
247738
137949
321486
116037
672107
239718
221707
376322
545716
523212
316108
450050
447646
50329
1210094
1334022
1582622
1118549
1461574
1488386
1475999
20km
109294
39607
202537
194289
586335
257453
676730
31298
64951
83436
82624
68407
295731
154942
408305
131643
856519
366741
372963
598605
906225
870238
422519
532663
541476
123102
1773702
1860034
1955479
1681211
1895175
1966520
1921168
Analysis Bins
Population1
mod
low
mod
low
mod
mod
hi
low
low
mod
low
low
mod
mod
hi
mod
hi
mod
hi
hi
hi
hi
hi
hi
hi
low
hi
hi
hi
low
hi
hi
hi
cov2
b
b
b
b
b
b
b
b
b
b
b
a
a
b
c
a
b
b
b
b
a
b
a
a
b
a
b
b
a
a
a
a
b
GSD3
b
b
b
b
b
b
b
b
b
a
b
a
a
b
b
a
b
b
b
b
b
b
a
b
b
b
b
b
b
a
a
a
a
Emissions
(tpy)4
4806
109458
9026
1 09476
86910
39110
68947
1775
61380
1760
1760
52
153
53630
39378
2166
7543
31242
44817
1626
20202
20170
1235
7360
2065
859
7670
7254
7999
7791
8024
7921
7952
A-44
-------
State
MA
MA
MA
MA
MD
MD
MD
MD
MD
ME
ME
ME
ME
ME
ME
ME
ME
ME
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
County
Suffolk
Suffolk
Worcester
Worcester
Allegany
Anne Arundel
Baltimore
Baltimore (City)
Baltimore (City)
Androscoggin
Aroostook
Aroostook
Aroostook
Aroostook
Aroostook
Cumberland
Cumberland
Oxford
Delta
Genesee
Genesee
Kent
Macomb
Missaukee
St. Clair
Schoolcraft
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Monitor ID
250250042
250251003
250270020
250270023
240010006
240032002
240053001
245100018
245100036
230010011
230030009
230030012
230031003
230031013
230031018
230050014
230050027
230172007
260410902
260490021
260492001
260810020
260991003
261130001
261470005
261530001
261630001
261630005
261630015
261630016
261630019
261630025
261630027
Population Residing within:
5km
441455
260061
155688
151851
28416
40618
99648
360916
105632
46561
3403
3403
3403
6476
2387
65123
67865
5903
7503
94710
4058
122533
116002
0
32599
0
151437
86804
98193
203577
210099
81534
79205
10km
1048879
829040
248143
252264
49750
1 34276
383980
823207
490543
61938
4534
4534
4534
6476
8245
122951
124508
10118
26225
227235
17555
294283
549258
2308
64545
0
338726
350207
423093
654802
695836
280589
384693
15km
1536036
1436251
316330
318317
66814
372761
785111
1195508
1004531
83767
6561
6561
6561
10298
15656
151066
153138
12717
28725
323367
47495
453477
1171414
7840
82832
0
682793
804947
975303
1283000
1189529
668415
915619
20km
1941989
1951612
404489
403312
79171
829885
1155009
1472306
1351499
101615
9030
9030
9030
11213
21187
187005
190157
17231
31746
388490
126929
553989
1769656
14456
98014
1389
1135095
1386398
1647773
1934280
1756001
1150319
1574294
Analysis Bins
Population1
hi
hi
hi
hi
mod
mod
hi
hi
hi
mod
low
low
low
low
low
hi
hi
low
low
hi
low
hi
hi
low
mod
low
hi
hi
hi
hi
hi
hi
hi
cov2
a
b
a
a
a
b
b
a
a
b
b
b
c
b
b
b
b
a
a
b
b
a
b
a
b
b
b
b
b
b
b
b
b
GSD3
a
b
a
a
a
b
b
a
b
b
b
b
b
b
b
b
b
a
a
b
b
a
b
a
c
b
b
b
c
b
b
b
c
Emissions
(tpy)4
7987
22045
690
690
1363
64947
97428
65129
97338
283
90
90
90
48
772
3201
3201
499
4222
166
127
541
718
58
1572
64065
64412
34236
34225
31238
81
64407
A-45
-------
State
Ml
Ml
Ml
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
County
Wayne
Wayne
Wayne
Anoka
Carlton
Dakota
Dakota
Dakota
Dakota
Dakota
Hennepin
Hennepin
Koochiching
Ramsey
Sherburne
Sherburne
Sherburne
Sherburne
Washington
Wright
Buchanan
Buchanan
Clay
Greene
Greene
Greene
Greene
Greene
Iron
Iron
Jackson
Jefferson
Jefferson
Monitor ID
261630033
261630062
261630092
270031002
270176316
270370020
270370423
270370439
270370441
270370442
270530954
270530957
270711240
271230864
271410003
271410011
271410012
271410013
271630436
271710007
290210009
290210011
290470025
290770026
290770032
290770037
290770040
290770041
290930030
290930031
290950034
290990004
290990014
Population Residing within:
5km
150194
123532
96517
57502
9236
3854
8572
1487
1487
2705
224357
157024
6444
112909
5629
5629
5629
0
46665
5377
23253
28224
40627
41036
96594
21784
18988
24455
1121
0
84236
15049
11967
10km
544634
491879
429048
226660
17582
64533
101147
55052
36683
24656
608888
542309
8075
599029
7667
9806
9806
10957
149177
28368
72613
75073
163217
146752
180831
110681
109888
120781
1121
3799
310816
33379
35082
15km
1115397
1104610
973432
496686
28511
221239
265053
218201
191183
153752
1082178
1022041
8923
1 052764
35016
29985
29774
33889
354337
39511
87121
86317
366686
224445
208384
210953
210953
213312
4507
6585
605775
64516
61963
20km
1730610
1743263
1618949
903982
56009
432974
574966
411081
384938
332905
1517123
1489863
10210
1510602
50427
51661
50884
58410
679510
77671
93365
93365
617013
256158
244406
254437
254437
256766
8447
8436
921037
124301
125932
Analysis Bins
Population1
hi
hi
hi
hi
low
low
low
low
low
low
hi
hi
low
hi
low
low
low
low
mod
low
mod
mod
mod
mod
hi
mod
mod
mod
low
low
hi
mod
mod
cov2
b
b
b
b
b
b
b
b
b
b
b
b
c
b
a
b
a
a
b
a
c
b
b
c
a
c
b
a
c
c
c
c
c
GSD3
b
b
b
a
a
b
a
a
a
a
b
a
a
a
a
a
a
a
b
a
b
b
a
b
a
b
a
a
c
b
b
c
b
Emissions
(tpy)4
34236
34225
64407
13324
362
9155
13685
8949
8639
5567
21921
18443
67
20773
26742
26742
26742
26742
11441
26794
3563
3563
25233
9206
9206
9206
9206
9206
43340
43340
19433
55725
55725
A-46
-------
State
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MS
MS
MS
MS
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
County
Jefferson
Jefferson
Monroe
Pike
Platte
Saint Charles
Saint Charles
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
St. Louis City
St. Louis City
St. Louis City
St. Louis City
Harrison
Hinds
Jackson
Lee
Cascade
Cascade
Jefferson
Jefferson
Jefferson
Lewis and Clark
Lewis and Clark
Rosebud
Rosebud
Rosebud
Monitor ID
290990017
290990018
291370001
291630002
291650023
291830010
291831002
291890001
291890004
291890006
291890014
291893001
291895001
291897002
291897003
295100007
295100072
295100080
295100086
280470007
280490018
280590006
280810004
300132000
300132001
300430903
300430911
300430913
300490702
300490703
300870700
300870701
300870702
Population Residing within:
5km
19711
12258
0
645
2159
2637
4587
95190
61422
68741
48016
1 1 7492
108578
82790
88786
107568
101305
1 54740
145966
18607
54986
39463
24421
40281
42971
1767
0
0
10126
7706
2353
2353
0
10km
36471
41709
1439
2077
36438
6349
95765
327257
315539
235858
223506
487564
358731
336688
383007
375790
393971
463092
473923
88520
171385
49647
44442
64778
64778
25076
11616
6845
38881
31421
2353
2353
2353
15km
60199
79196
2093
6916
113990
34541
273147
630767
647834
488837
550275
929037
617042
729925
764342
678820
726063
861774
857733
139495
273630
65034
61390
68296
70181
47509
36425
27041
49340
48723
2353
3131
2353
20km
116882
170110
5612
11249
238276
90953
431484
966432
1020228
927852
1005593
1305061
941386
1170973
1192267
979578
1097105
1168442
1177204
181694
332464
75787
74867
70181
70181
49340
49340
47509
49340
49340
3131
3131
3131
Analysis Bins
Population1
mod
mod
low
low
low
low
low
hi
hi
hi
mod
hi
hi
hi
hi
hi
hi
hi
hi
mod
hi
mod
mod
mod
mod
low
low
low
mod
low
low
low
low
cov2
c
c
a
b
a
b
b
b
b
b
b
b
b
b
b
b
b
b
b
c
b
b
a
b
c
b
c
c
c
b
b
b
b
GSD3
b
b
a
b
a
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
a
b
a
b
b
b
b
b
c
c
a
a
a
Emissions
(tpy)4
55725
32468
13495
11030
47610
67735
24466
22816
190
265
10737
66892
697
7262
24933
13346
13502
13486
25071
256
34318
702
702
234
234
234
234
234
16735
16735
16735
A-47
-------
State
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
County
Rosebud
Rosebud
Rosebud
Rosebud
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Alexander
Beaufort
Beaufort
Beaufort
Chatham
Cumberland
Davie
Duplin
Edgecombe
Forsyth
Johnston
Lincoln
Martin
Mecklenburg
Mecklenburg
New Hanover
New Hanover
Northampton
Monitor ID
300870760
300870761
300870762
300870763
301110016
301110066
301110079
301110080
301110082
301110083
301110084
301111065
301112005
301112006
301112007
370030003
370130003
370130004
370130006
370370004
370511003
370590002
370610002
370650099
370670022
371010002
371090004
371170001
371190034
371190041
371290002
371290006
371310002
Population Residing within:
5km
0
0
0
0
8526
27389
61645
33774
58256
27620
22577
13350
24420
11205
5391
7574
1085
0
0
4146
32970
4799
850
0
61669
9854
10568
573
90874
105796
2584
17957
12284
10km
0
643
0
1536
9747
79644
89282
86065
94753
76641
59919
59574
68288
46767
26316
16738
1762
1762
1762
12138
108671
16224
6058
11321
170320
32163
32515
5282
276915
295729
20636
83529
29917
15km
643
3524
2928
3131
14953
98733
102887
104825
103200
98733
97912
97912
97912
86788
69446
40689
5519
6616
6616
23134
203822
44277
12866
25673
258102
67759
62768
14427
474624
494494
67021
145330
38134
20km
2928
3524
2928
3131
39121
107178
114640
108399
106046
109475
110980
110980
109475
110980
1 04067
80547
8488
8488
8488
72477
280713
93569
29813
51492
325974
129979
125735
26518
629520
647110
127088
170260
46966
Analysis Bins
Population1
low
low
low
low
low
mod
hi
mod
hi
mod
mod
mod
mod
mod
low
low
low
low
low
low
mod
low
low
low
hi
low
mod
low
hi
hi
low
mod
mod
cov2
c
b
a
a
b
b
b
b
b
b
b
b
b
b
b
a
a
b
b
a
a
a
a
a
b
a
a
a
b
b
b
b
a
GSD3
a
a
a
a
c
c
a
b
a
b
b
b
b
c
c
a
a
a
b
a
a
a
a
a
b
a
a
a
a
b
a
b
a
Emissions
(tpy)4
16735
5480
5480
5480
5480
5480
15298
5480
5480
15298
15298
4730
4730
4730
474
1477
7795
414
325
3945
29
10
3426
1030
821
29923
30020
2416
A-48
-------
State
NC
NC
NC
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
NE
NE
NE
NE
NH
NH
County
Person
Pitt
Swain
Billings
Billings
Burke
Burke
Burleigh
Cass
Cass
Dunn
McKenzie
McKenzie
McKenzie
McLean
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Morton
Morton
Oliver
Steele
Williams
Williams
Douglas
Douglas
Douglas
Douglas
Cheshire
Coos
Monitor ID
371450003
371470099
371730002
380070002
380070111
380130002
380130004
380150003
380171003
380171004
380250003
380530002
380530104
380530111
380550113
380570001
380570004
380570102
380570118
380570123
380570124
380590002
380590003
380650002
380910001
381050103
381050105
310550048
310550050
310550053
310550055
330050007
330070019
Population Residing within:
5km
2620
5860
3268
0
0
0
655
49591
48975
2118
0
0
0
0
0
3280
3280
1574
0
0
557
17925
10305
0
0
0
0
50168
45166
82663
13902
16719
9280
10km
8081
10688
8992
0
0
0
655
67377
134561
91149
0
596
521
0
632
3280
4428
4428
1574
557
557
67959
31348
0
934
1259
1259
209209
187855
264396
109385
30003
13603
15km
24203
23742
15036
1887
0
0
655
83082
144878
145789
0
596
521
2283
698
5902
5902
5902
6898
3837
557
75685
75685
2057
934
1259
1259
371395
367828
424100
299381
39998
14203
20km
41995
72588
18230
1887
0
625
655
84415
154455
148002
537
596
2283
5771
698
6465
7455
7455
7455
5981
3903
84415
82584
2670
934
1827
1827
532173
525602
578351
473231
53389
14928
Analysis Bins
Population1
low
low
low
low
low
low
low
mod
mod
low
low
low
low
low
low
low
low
low
low
low
low
mod
mod
low
low
low
low
hi
mod
hi
mod
mod
low
cov2
b
a
a
a
b
b
b
b
b
a
a
a
c
c
b
b
b
b
b
b
b
c
b
b
a
b
b
c
b
c
b
a
b
GSD3
b
a
a
a
a
b
b
a
a
b
a
a
a
a
a
b
a
b
b
b
b
c
b
b
a
a
c
b
a
b
a
b
c
Emissions
(tpy)4
96752
28
283
426
4592
771
756
5
210
823
91617
91617
91617
91617
91617
91617
4592
4592
28565
1605
1605
31850
31850
31850
11535
81
638
A-49
-------
State
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NM
NM
NM
County
Coos
Coos
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Merrimack
Merrimack
Merrimack
Merrimack
Rockingham
Rockingham
Rockingham
Sullivan
Atlantic
Bergen
Burlington
Camden
Camden
Cumberland
Essex
Essex
Gloucester
Hudson
Hudson
Middlesex
Morris
Union
Union
Dona Ana
Dona Ana
Eddy
Monitor ID
330070022
330071007
330110016
330110019
330110020
330111009
330111010
330130007
330131003
330131006
330131007
330150009
330150014
330150015
330190003
340010005
340035001
340051001
340070003
340071001
340110007
340130011
340130016
340150002
340170006
340171002
340232003
340273001
340390003
340390004
350130008
350130017
350151004
Population Residing within:
5km
8360
2438
107911
104650
104650
72131
37423
27595
8787
8066
9351
25227
25984
25227
11339
6123
209619
71953
193686
8015
26454
209592
200779
32432
158136
343775
95281
13515
221266
194256
10195
40832
12050
10km
12552
2438
145660
140235
140235
130360
145620
54309
45710
35862
43118
48762
48762
48762
17306
33910
973093
261206
806251
46392
77939
1133321
1136145
107924
930071
1754575
371119
60394
868022
750485
49347
158545
12050
15km
14928
6025
196209
189502
189502
219169
333540
75576
74945
104656
73240
88743
78775
92738
34644
71617
3404473
561157
1761045
121996
109030
2811759
2763272
537340
3370494
5021807
839280
181888
1790660
1727936
114220
258940
14785
20km
14928
8364
270491
266391
266391
438168
467236
101847
138179
218207
98696
157669
148875
152363
48414
160179
5751193
1133142
2534030
262931
160091
5933785
5837087
1392192
5894707
8159098
1615249
361716
3314852
3277263
181522
387481
16465
Analysis Bins
Population1
low
low
hi
hi
hi
hi
mod
mod
low
low
low
mod
mod
mod
mod
low
hi
hi
hi
low
mod
hi
hi
mod
hi
hi
hi
mod
hi
hi
mod
mod
mod
cov2
b
c
b
b
b
a
b
b
c
b
b
b
b
b
a
a
a
b
b
a
a
a
b
b
a
a
a
b
a
a
a
c
b
GSD3
b
b
b
b
b
a
b
b
c
c
b
b
b
b
a
a
b
b
b
b
b
b
b
b
b
b
b
b
b
a
a
b
b
Emissions
(tpy)4
638
18
30806
30806
30806
454
772
30833
30833
30833
30833
13706
13706
13706
220
27848
15099
10733
17
646
27424
27638
26452
27538
29856
1675
38
23181
23146
37
574
4233
A-50
-------
State
NM
NM
NM
NM
NM
NM
NM
NV
NV
NV
NV
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
County
Grant
Grant
Hidalgo
San Juan
San Juan
San Juan
San Juan
Clark
Clark
Clark
Clark
Albany
Bronx
Bronx
Bronx
Bronx
Chautauqua
Chautauqua
Chautauqua
Chemung
Erie
Erie
Erie
Essex
Franklin
Hamilton
Herkimer
Kings
Kings
Madison
Monroe
Monroe
Monroe
Monitor ID
350170001
350171003
350230005
350450008
350450009
350450017
350451005
320030022
320030078
320030539
320030601
360010012
360050073
360050080
360050083
360050110
360130005
360130006
360130011
360150003
360290005
360294002
360298001
360310003
360330004
360410005
360430005
360470011
360470076
360530006
360551004
360551007
360556001
Population Residing within:
5km
4292
1429
0
22921
2930
0
491
0
0
226197
13570
108841
1215989
1278526
1162835
1205886
3605
14144
3605
41915
150194
80118
66153
492
0
0
2043
1301071
1173879
806
149439
129608
222716
10km
6951
5721
0
41258
18431
6492
2247
0
0
557934
22316
255221
3522226
3040232
2294809
344471 1
6928
29535
6928
68619
458758
328976
237799
2054
2880
0
2043
3958499
3316779
4985
384621
381741
407438
15km
21790
9904
0
51483
32213
10898
11772
0
2836
933583
71616
371301
5762144
5159927
4245952
5621679
15645
39906
15645
82014
680793
575596
503575
7005
5697
454
2043
6872002
5595972
7448
579436
570995
582031
20km
23982
24316
0
68906
58595
10936
16909
10778
2836
1236711
97845
484970
8036800
7489995
6315293
7878863
22519
47684
22519
101244
839570
768392
729503
10934
11358
2054
2043
8807020
7596057
17313
665760
669909
678777
Analysis Bins
Population1
low
low
low
mod
low
low
low
low
low
hi
mod
hi
hi
hi
hi
hi
low
mod
low
mod
hi
hi
hi
low
low
low
low
hi
hi
low
hi
hi
hi
cov2
c
c
c
b
b
b
b
a
a
a
a
b
a
a
a
a
b
c
b
a
b
c
b
b
b
b
b
a
a
b
b
b
b
GSD3
b
b
c
a
a
b
c
a
a
a
a
b
b
b
b
a
c
b
c
a
b
c
b
b
b
c
c
b
b
b
b
a
b
Emissions
(tpy)4
263
263
17344
585
50191
178
362
27101
26825
6659
26965
52177
404
40734
41722
40659
29050
28686
50379
50379
50379
A-51
-------
State
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
County
Nassau
New York
New York
Niagara
Onondaga
Putnam
Queens
Queens
Rensselaer
Rensselaer
Richmond
Schenectady
Suffolk
Suffolk
Ulster
Adams
Allen
Ashtabula
Belmont
Butler
Butler
Clark
Clermont
Columbiana
Columbiana
Columbiana
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Franklin
Franklin
Monitor ID
360590005
360610010
360610056
360632008
360671015
360790005
360810097
360810124
360830004
360831005
360850067
360930003
361030002
361030009
361111005
390010001
390030002
390071001
390133002
390170004
390171004
390230003
390250021
390290016
390290022
390292001
390350038
390350045
390350060
390350065
390356001
390490004
390490034
Population Residing within:
5km
172837
1062324
1289280
60505
56156
15437
378415
823992
1987
1222
282277
100404
80740
101641
755
4630
15401
11409
17529
68823
47209
19786
7297
21336
21336
25779
136697
151001
116933
132176
191842
133697
157233
10km
677944
3421130
3673609
96530
207136
57790
1589364
2441512
5975
6357
653196
157970
526254
341308
1541
6792
67353
17288
41346
163124
96458
66337
20144
46769
46769
43920
547523
564795
512974
562942
529243
467572
482749
15km
1424915
6487922
6607580
176040
329787
111398
3438261
5839274
22806
19071
2026407
233426
950326
551178
7851
15822
90874
23848
95392
276076
152032
175311
53435
67377
67377
64319
932680
962245
907112
968826
883601
806703
868013
20km
2365352
8988411
8980807
348603
395331
223357
7138176
8419326
1 1 8285
69278
4371801
383092
1417428
802861
10684
22444
114512
42433
120821
487924
287701
410155
96496
101068
101068
92597
1214114
1221356
1201852
1244026
1165619
1042146
1090438
Analysis Bins
Population1
hi
hi
hi
hi
hi
mod
hi
hi
low
low
hi
hi
hi
hi
low
low
mod
mod
mod
hi
mod
mod
low
mod
mod
mod
hi
hi
hi
hi
hi
hi
hi
cov2
b
a
a
b
a
b
a
b
b
b
a
a
a
a
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
GSD3
b
b
a
a
a
c
b
b
c
c
b
a
b
b
c
b
b
b
c
b
b
b
b
b
c
b
b
b
b
b
b
b
b
Emissions
(tpy)4
1806
28873
29021
40748
3280
8183
8043
379
188
24733
96
1404
7344
19670
3977
8655
138904
9979
13912
2034
91822
186262
1 86262
179205
7403
7403
7403
7403
74869
450
450
A-52
-------
State
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OK
OK
OK
OK
OK
County
Gallia
Hamilton
Hamilton
Jefferson
Jefferson
Jefferson
Lake
Lake
Lawrence
Lorain
Lorain
Lorain
Lucas
Lucas
Mahoning
Mahoning
Meigs
Montgomery
Morgan
Morgan
Scioto
Scioto
Scioto
Stark
Summit
Summit
Tuscarawas
Tuscarawas
Cherokee
Kay
Kay
Kay
Mayes
Monitor ID
390530002
390610010
390612003
390810016
390810017
390811001
390850003
390853002
390870006
390930017
390930026
390931003
390950008
390950024
390990009
390990013
391051001
391130025
391150003
391150004
391450013
391450020
391450022
391510016
391530017
391530022
391570003
391570006
400219002
400710602
400719003
400719010
400979014
Population Residing within:
5km
1087
15310
71390
28019
30069
21833
48791
40430
26563
58361
54867
58580
62606
134960
79207
78376
5440
123978
1122
1122
15699
4530
3469
99075
104817
140332
26914
2710
993
25029
6614
1123
1947
10km
13134
124569
325799
70995
71838
53684
145694
92415
71376
129195
114602
124277
205665
319708
210961
214611
15029
304826
3168
3168
47369
11216
12081
208779
292059
329963
40238
15439
22584
31461
29697
3516
14224
15km
30170
345879
683493
96550
96408
91514
238216
141902
94538
249878
202571
251182
356815
466184
293714
294367
21812
511565
9162
9871
61292
45697
40548
291216
470747
454363
61526
38518
28182
31461
32746
16273
26265
20km
49474
632705
1079723
122094
122094
119322
407417
209471
131453
362235
298148
365323
487567
528531
378289
375287
31834
645130
22426
24252
77940
87756
82103
350367
574282
570258
85938
72765
36130
36740
35459
20121
29243
Analysis Bins
Population1
low
mod
hi
mod
mod
mod
mod
mod
mod
hi
hi
hi
hi
hi
hi
hi
low
hi
low
low
mod
low
low
hi
hi
hi
mod
low
low
mod
low
low
low
cov2
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
c
c
b
b
b
a
b
b
b
b
c
b
b
b
a
GSD3
b
c
b
c
c
c
b
c
b
b
b
b
b
b
b
b
b
b
c
b
b
c
c
b
c
b
b
b
a
b
b
a
b
Emissions
(tpy)4
190311
92654
7257
223185
223185
78071
72266
4799
11400
495
53
495
37337
37450
21074
21074
190311
9652
115526
115526
4351
4351
1269
11053
11053
2579
2556
7003
7003
19079
A-53
-------
State
OK
OK
OK
OK
OK
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Muskogee
Oklahoma
Oklahoma
Ottawa
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Beaver
Berks
Berks
Blair
Bucks
Cambria
Centre
Dauphin
Delaware
Delaware
Erie
Indiana
Monitor ID
401010167
401090025
401091037
401159004
401430175
401430235
401430501
420030002
420030010
420030021
420030031
420030032
420030064
420030067
420030116
420031301
420033003
420033004
420070002
420070004
420070005
420070014
420110009
420110100
420130801
420170012
420210011
420270100
420430401
420450002
420450109
420490003
420630004
Population Residing within:
5km
5633
78654
46197
6272
53094
65020
46840
83332
168140
170777
183843
174072
64846
13277
96820
1 1 5432
55221
38588
3434
35152
17292
36335
121330
118553
44392
85719
50440
60659
86638
74840
59762
81199
1110
10km
39252
254952
141934
22614
207546
235972
187023
277442
536314
560187
580429
558904
201143
86792
331624
411867
202092
170065
28961
104660
77240
82468
203799
202746
72996
324327
79710
76595
219394
237232
209503
1 50626
8662
15km
56271
384825
258441
29716
357175
405434
333482
651551
842237
921490
877668
922097
520438
324154
704601
766188
509708
461433
68617
203430
143738
134467
250610
254794
94779
638218
102905
96267
324647
510590
446058
190212
23057
20km
64455
552894
459371
37508
485641
515780
468989
961378
1114184
1142754
1145039
1144558
943781
610975
996267
1088115
944188
904760
120780
317823
224631
220614
309553
310286
124536
1212911
124592
107078
384070
1091830
812243
209983
57759
Analysis Bins
Population1
low
hi
mod
low
hi
hi
mod
hi
hi
hi
hi
hi
hi
mod
hi
hi
hi
mod
low
mod
mod
mod
hi
hi
mod
hi
hi
hi
hi
hi
hi
hi
low
cov2
b
a
a
b
b
b
b
b
a
b
a
b
b
a
b
b
b
b
b
a
b
b
b
a
b
a
b
a
a
a
a
a
b
GSD3
b
a
a
a
c
c
b
b
b
b
b
b
b
b
b
b
c
b
b
a
c
b
b
b
b
b
b
b
b
b
b
a
b
Emissions
(tpy)4
30011
182
182
62
9377
9377
9377
1964
4688
52447
46957
52447
11490
1167
1964
52100
11490
11501
187257
41170
41385
44003
14817
14774
441
15117
16779
4359
857
38833
38470
4122
14389
A-54
-------
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PR
PR
PR
County
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Lycoming
Lycoming
Mercer
Montgomery
Northampton
Northampton
Northampton
Perry
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Schuylkill
Warren
Warren
Washington
Washington
Washington
Westmoreland
York
Barceloneta
Bayamon
Bayamon
Monitor ID
420692006
420710007
420730015
420770004
420791101
420810100
420810403
420850100
420910013
420950025
420950100
420958000
420990301
421010004
421010022
421010024
421010027
421010029
421010047
421010048
421010055
421010136
421070003
421230003
421230004
421250005
421250200
421255001
421290008
421330008
720170003
720210004
720210006
Population Residing within:
5km
68522
97205
40803
133092
68639
15088
41897
40443
91275
79756
71422
71626
6450
400078
316944
197076
472813
484661
410380
262592
341893
382995
19152
14142
13965
31276
32125
1359
35656
85574
29823
192976
208167
10km
144913
1 74296
57962
298181
157363
60400
69102
69465
239337
173911
118395
133639
13169
1147634
985213
588104
1348135
1229942
1153434
1102727
1 020004
985957
30388
19940
18884
68512
52910
15854
82661
156166
83433
679576
587003
15km
189515
254789
81815
395772
215050
83910
80935
96468
706445
398867
209567
228524
26326
1971579
1726387
1351349
2026206
1999611
1989848
1938877
1774411
1718068
59370
25715
28805
111222
83324
43364
148990
216656
134176
1 002864
956783
20km
246604
344292
118770
501878
265123
108961
103969
184589
1623890
513651
317220
330629
49400
2631448
2446142
2063868
2632847
2574304
2573573
2607877
2476647
2381173
100508
32490
33523
183285
118188
126091
213978
284208
243828
1292141
1256603
Analysis Bins
Population1
hi
hi
mod
hi
hi
mod
mod
mod
hi
hi
hi
hi
low
hi
hi
hi
hi
hi
hi
hi
hi
hi
mod
mod
mod
mod
mod
low
mod
hi
mod
hi
hi
cov2
a
b
b
a
a
b
a
b
a
a
a
b
b
b
a
b
a
b
a
b
a
b
a
b
b
a
a
b
a
b
b
b
b
GSD3
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
b
c
a
b
b
b
b
a
b
b
Emissions
(tpy)4
66
375
28854
9143
467
83
83
28
4794
12167
32680
32714
6228
18834
1663
6246
17550
17536
6214
18848
21700
4987
4890
4890
8484
7
2566
72
80487
A-55
-------
State
PR
PR
PR
PR
PR
PR
Rl
Rl
Rl
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SD
SD
SD
TN
TN
TN
TN
TN
TN
TN
County
Catano
Catano
Catano
Catano
Guayama
Salinas
Providence
Providence
Providence
Aiken
Barnwell
Charleston
Charleston
Georgetown
Greenville
Greenville
Lexington
Oconee
Orangeburg
Richland
Richland
Richland
Richland
Custer
Jackson
Minnehaha
Anderson
Blount
Blount
Blount
Bradley
Coffee
Davidson
Monitor ID
720330004
720330007
720330008
720330009
720570009
721230001
440070012
440071005
440071009
450030003
450110001
450190003
450190046
450430006
450450008
450450009
450630008
450730001
450750003
450790007
450790021
450791003
450791006
460330132
460710001
460990007
470010028
470090002
470090006
470090101
470110102
470310004
470370011
Population Residing within:
5km
154575
95500
99778
110439
12086
20645
223521
148802
226940
752
0
40872
1103
10567
70221
56686
42208
0
2904
35872
1666
87097
1666
0
0
65647
11872
28887
36020
0
2540
1286
77459
10km
583552
576841
594607
457427
49373
31312
487990
390751
493584
6505
4022
132716
1103
18215
173012
151862
131361
2260
7856
121006
4643
213836
5435
0
0
119287
59225
70731
72290
12650
11940
9718
228349
15km
958456
983702
972270
883511
90444
68199
638092
615465
646894
18533
13647
273298
9529
22467
284047
279293
257820
11136
14446
255135
13324
300874
15920
3940
0
138918
153931
105939
104178
44702
46188
23113
410925
20km
1233122
1219701
1238188
1164315
174005
174332
816597
809993
821476
55485
21554
364953
22255
34357
379022
356410
355854
26182
24656
353072
33098
396116
47548
4686
0
147218
292415
198408
189214
81010
84762
35158
583532
Analysis Bins
Population1
hi
hi
hi
hi
mod
mod
hi
hi
hi
low
low
mod
low
mod
hi
hi
mod
low
low
mod
low
hi
low
low
low
hi
mod
mod
mod
low
low
low
hi
cov2
b
b
b
b
a
b
a
b
a
a
a
b
b
b
a
b
b
a
b
a
b
b
b
b
a
b
c
b
b
b
b
b
a
GSD3
b
b
b
b
a
b
b
b
b
a
a
b
a
b
b
a
b
a
a
a
b
a
a
a
a
a
b
c
b
a
a
a
b
Emissions
(tpy)4
2228
2265
2253
21498
65
34934
40841
1067
1082
10433
5
7166
613
40492
12935
42894
496
44761
4263
4263
4263
5437
8019
A-56
-------
State
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
County
Hawkins
Humphreys
McMinn
Montgomery
Montgomery
Polk
Polk
Polk
Polk
Roane
Shelby
Shelby
Shelby
Shelby
Stewart
Sullivan
Sullivan
Sumner
Cameron
Dallas
Ellis
Ellis
Ellis
El Paso
El Paso
El Paso
Galveston
Galveston
Gregg
Harris
Harris
Harris
Harris
Monitor ID
470730002
470850020
471070101
471250006
471250106
471390003
471390007
471390008
471390009
471450009
471570034
471570043
471570046
471571034
471610007
471630007
471630009
471651002
480610006
481130069
481390015
481390016
481390017
481410037
481410053
481410058
481670005
481671002
481830001
482010046
482010051
482010059
482010062
Population Residing within:
5km
6748
2474
2540
21032
16569
1613
2491
2491
2491
8848
74216
94449
18782
886
787
28689
28254
5070
70071
93552
6089
7883
5723
56009
49083
78658
37427
38619
1349
65125
123431
151412
73770
10km
14441
6672
11940
79399
74449
9042
9432
9432
10239
21677
277713
325228
113964
97506
4566
78826
77403
38555
151247
455917
13876
18193
17592
1 82473
163206
126481
62491
65658
17138
350122
372470
475338
352818
15km
22457
13621
37322
112883
109087
14124
19726
17401
17235
37175
497847
534950
273306
277857
8854
112565
117095
53602
160048
991123
35210
68740
50332
337222
325118
299419
98724
98768
52116
756166
896497
902121
695432
20km
39857
23460
84929
139621
138438
24537
24026
25902
24026
57683
695164
751299
473443
484234
20362
1 53445
151856
119241
167993
1609774
113413
191352
1 52699
522824
519008
524259
182464
196215
105781
1283440
1380154
1392348
1108749
Analysis Bins
Population1
low
low
low
mod
mod
low
low
low
low
low
hi
hi
mod
low
low
mod
mod
low
hi
hi
low
low
low
hi
mod
hi
mod
mod
low
hi
hi
hi
hi
cov2
c
c
b
a
a
b
c
a
b
c
a
a
b
b
b
b
b
c
a
a
b
b
c
b
b
b
b
b
c
b
b
b
b
GSD3
c
b
a
a
a
a
b
a
a
a
a
b
a
b
a
c
c
b
a
a
b
c
b
b
b
a
b
b
b
a
a
c
b
Emissions
(tpy)4
35493
111597
5501
1330
1330
1900
1900
1900
1900
77881
21675
21675
3945
21847
16682
30097
30156
34373
307
7972
7972
7972
574
574
614
7976
7976
66443
17583
26
25608
25677
A-57
-------
State
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
UT
UT
UT
UT
UT
UT
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VI
VI
VI
County
Harris
Harris
Harris
Jefferson
Jefferson
Jefferson
Kaufman
Nueces
Nueces
Nueces
Cache
Davis
Davis
Salt Lake
Salt Lake
Salt Lake
Charles
Fairfax
Fairfax
Fairfax
Fairfax
Fairfax
Madison
Roanoke
Rockingham
Rockingham
Alexandria City
Hampton City
Norfolk City
Richmond City
St Croix
St Croix
St Croix
Monitor ID
482010070
482011035
482011050
482450009
482450011
482450020
482570005
483550025
483550026
483550032
490050004
490110001
490110004
490350012
490351001
490352004
510360002
510590005
510590018
510591004
510591005
510595001
511130003
511611004
511650002
511650003
515100009
516500004
517100023
517600024
780100006
780100011
780100013
Population Residing within:
5km
153479
99581
23794
33143
13164
35739
6583
99888
16215
48320
49600
56718
52464
57910
31709
0
3370
34561
87725
215952
203219
80603
1316
33161
17897
13821
137533
73011
124263
109306
0
0
0
10km
511407
451485
83705
87386
93985
101563
9190
1 86846
28033
128230
64094
82741
83909
1 83684
1 07346
4074
32169
183637
293189
660586
670880
358173
4823
123148
58020
47577
622283
182507
379455
309672
0
0
0
15km
991134
891195
224120
182005
121116
177284
28396
231717
92841
228351
80592
178141
154925
370433
260423
35159
76679
408647
730360
1410007
1238334
1098236
13930
197615
76316
71219
1320784
356676
703082
524083
0
0
0
20km
1610993
1287766
405297
237033
140687
223336
43226
280479
177008
272861
86020
311810
295333
630857
522228
124394
176978
687195
1388941
2092422
1844099
2041931
28417
235072
85276
92912
1894197
601943
871632
656099
0
0
0
Analysis Bins
Population1
hi
hi
mod
mod
mod
mod
low
hi
mod
mod
mod
hi
hi
hi
mod
low
low
mod
hi
hi
hi
hi
low
mod
mod
mod
hi
hi
hi
hi
low
low
low
COV2
b
b
a
b
b
b
a
b
b
b
a
b
b
b
b
b
b
a
a
a
a
a
b
a
a
a
b
b
b
b
b
b
c
GSD3
b
b
a
c
c
c
a
b
a
b
a
b
a
a
a
a
b
b
b
a
a
a
c
a
a
a
b
b
b
b
c
c
c
Emissions
(tpy)4
24501
25635
11195
13807
26962
1362
7954
8056
7954
5
2807
2807
2807
5832
3735
86717
156
18204
18303
18405
17221
7
677
277
235
18293
4274
36499
2675
A-58
-------
State
VI
VI
VT
VT
VT
WA
WA
WA
WA
WA
WA
WA
WA
WA
WA
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
WV
WV
WV
WV
WV
WV
WV
County
St Croix
St Croix
Chittenden
Chittenden
Rutland
Clallam
Clallam
King
King
Pierce
Pierce
Skagit
Skagit
Snohomish
What com
Brown
Dane
Forest
Marathon
Milwaukee
Milwaukee
Milwaukee
Oneida
Sauk
Vilas
Wood
Brooke
Brooke
Cabell
Greenbrier
Hancock
Hancock
Hancock
Monitor ID
780100014
780100015
500070003
500070014
500210002
530090010
530090012
530330057
530330080
530530021
530530031
530570012
530571003
530610016
530730011
550090005
550250041
550410007
550730005
550790007
550790026
550790041
550850996
551110007
551250001
551410016
540090005
540090007
540110006
540250001
540290005
540290007
540290008
Population Residing within:
5km
0
0
50990
54166
21330
17871
20830
131605
116769
68072
55628
3580
1733
46071
60525
79060
79610
1330
5095
248317
214859
137816
8351
2743
934
19525
25010
30794
50835
2158
6006
14924
24095
10km
0
0
89229
87471
30052
26073
27014
394412
423064
250876
275358
22573
21622
152230
83632
158940
189421
3913
42173
606921
572784
455868
17018
15039
934
33790
64711
70187
88879
9273
23418
44311
49351
15km
0
0
110853
110853
35316
30255
30036
730218
811856
548806
555755
32120
32120
303720
111425
201226
306132
5514
61417
865925
834939
765734
17018
24240
8639
43315
92813
95823
125923
18280
77160
83167
63727
20km
0
0
133530
133749
46525
37672
36843
1093083
1157199
839357
820805
70660
75069
432356
126291
215144
353861
6669
93151
1037293
1014161
964876
23821
43368
10755
50360
118070
120385
164495
23902
125873
128126
91485
Analysis Bins
Population1
low
low
hi
hi
mod
mod
mod
hi
hi
hi
hi
low
low
mod
hi
hi
hi
low
low
hi
hi
hi
low
low
low
mod
mod
mod
hi
low
low
mod
mod
cov2
c
c
b
a
b
b
b
a
b
b
b
b
b
a
a
b
b
b
c
b
b
b
c
b
a
c
b
b
b
a
b
b
b
GSD3
c
c
a
a
c
b
a
a
a
b
a
b
b
a
a
b
b
a
a
b
a
b
c
a
a
b
b
b
b
a
b
b
b
Emissions
(tpy)4
6
6
756
756
1203
1203
538
538
8951
8951
381
4391
23888
9049
5
12120
15753
15753
15753
2304
63
14245
78071
223185
7504
176554
148520
186262
A-59
-------
State
WV
wv
WV
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
WY
County
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Kanawha
Kanawha
Kanawha
Marshall
Monongalia
Monongalia
Monongalia
Ohio
Wayne
Wayne
Wayne
Wayne
Wood
Campbell
Monitor ID
540290009
540290011
540290014
540290015
540290016
540291004
540390004
540390010
540392002
540511002
540610003
540610004
540610005
540690007
540990002
540990003
540990004
540990005
541071002
560050857
Population Residing within:
5km
20946
31890
22857
20793
19278
24761
46977
48231
21694
13403
43902
44079
46591
20818
17320
17320
16553
13314
24917
3288
10km
61117
76198
58620
45848
70483
63677
80511
83340
61059
32048
65672
63708
61019
60048
62645
59989
54251
48330
70324
11413
15km
90717
95992
89998
65851
96151
91977
120631
123101
111812
55054
80405
80385
77800
91967
124477
123349
122072
114824
104458
23902
20km
1 1 5283
115162
120724
102031
114992
115615
164476
172217
164912
95735
98315
98966
99544
126981
178576
177744
179815
173807
128127
25752
Analysis Bins
Population1
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
mod
low
COV2
b
b
b
b
b
b
b
b
b
b
b
b
b
b
a
b
b
b
b
b
GSD3
b
b
b
b
b
b
b
b
b
c
b
b
b
b
b
b
b
b
b
b
Emissions
(tpy)4
148404
223185
148520
1 86262
169771
169771
6115
6115
113491
138904
91984
97887
96396
74781
10172
10172
10172
10172
48124
10106
Notes:
1 Population bins: low (<1 0,000); mid (10,001 to 50,000); hi (>50,000) using population within 5 km of ambient monitor.
2 COV bins: a (<100%); b (>100 to <200); c (>200).
3 GSD bins: a (<2.17); b (>2.17 to <2.94); c (>2.94).
4 Sum of emissions within 20 km radius of ambient monitor based on 2002 NEI.
A-60
-------
1 A.1.2 Analysis of SOi Emission Sources Surrounding Ambient Monitors
2 Distances of the 5-minute and 1-hour ambient monitoring sites to stationary sources
3 emitting SC>2 were estimated using data from the 2002 National Emissions Inventory1 (NEI).
4 The NEI database reports emissions of SO2 in tons per year (tpy) for 98,667 unique emission
5 sources at various points of release. The release locations were all taken from the latitude
6 longitude values within the NEI. First, all SC>2 emissions were summed for identical latitude and
7 longitude entries while retaining source codes for the emissions (e.g., Standard Industrial Code
8 (SIC), or North American Industrial Classification System (NAICS)). Therefore, any facility
9 containing similar emission processes were summed at the stack location, resulting in 32,521
10 observations. These data were then screened for sources with emissions greater than 5 tpy,
11 yielding 6,104 unique 862 emission sources. Locations of these stationary source emissions
12 were compared with ambient monitoring locations using the following formula:
13
14 d = arccos(sin(toj) x sin(to2) + cos(latl) x cos(lat2) x cos(lon2 - lon^ ))xr
15 where
16 d = distance (kilometers)
17 latj = latitude of a monitor (radians)
18 Iat2 = latitude of source emission (radians)
19 lon} = longitude of monitor (radians)
20 Ion2 = longitude of source emission (radians)
21 r = approximate radius of the earth (or 6,371 km)
22
23 Location data for monitors and sources provided in the AQS and NEI data bases were
24 given in units of degrees therefore, these were first converted to radians by dividing by 180/Ti.
25 For each monitor, source emissions within 20 km of the monitor were retained.
26 Table A. 1-5 contains the summary of the distance of stationary source emissions to each
27 of the monitors in the broader SC>2 monitoring network. There were varying numbers of sources
28 emitting >5 tpy of SC>2 and located within a 20 km radius for many of the monitors. Some of the
29 monitors are point-source oriented, that is, sited to measure ambient concentrations potentially
1 2002 National Emissions Inventory Data & Documentation. Office of Air Quality Planning and Standards,
Research Triangle Park, NC. Available at: http://www.epa.gov/ttn/chief/net/2002inventory.html.
A-61
-------
1 influenced by a specific single sources (e.g., Missouri monitor IDs 290210009, 290210011,
2 290930030), or by several sources (e.g., Pennsylvania monitor IDs 420030021, 420030031) of
3 varying emission strength. A few of the monitors contained no source emissions >5 tpy (e.g.,
4 Iowa monitor IDs 191770005, 191770006).
A-62
-------
Table A.1-5. Distance of ambient SO2 monitors (all used in analysis) to stationary sources emitting > 5 tons of SO2
per year, within a 20 kilometer distance of monitoring site, and SO2 emissions associated with those stationary
sources.
monid
010330044
010710020
010731003
010790003
010970028
010972005
011011002
040070009
040071001
040130019
040133002
040133003
040191011
040212001
051190007
051191002
051390006
060010010
060130002
060130006
060130010
060131001
060131002
060131003
060131004
060132001
060133001
060250005
060371002
060371103
060374002
060375001
n
3
3
43
5
10
9
4
0
2
8
9
9
1
0
1
1
6
7
15
9
15
13
3
9
9
15
16
1
3
15
32
31
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
16680
15119
151
1787
6613
132
913
9219
23
21
20
3119
20
20
421
53
1004
559
1189
1507
26
559
559
1189
507
7
17
37
183
203
std
28821
25004
227
3416
13057
154
1183
10723
19
19
19
689
66
2007
789
1977
2036
21
789
789
1977
1104
7
36
313
342
min
30
98
5
6
14
5
180
1637
10
10
6
3119
20
20
8
5
6
5
6
6
6
5
5
6
6
7
10
7
5
5
p2.5
30
98
5
6
14
5
180
1637
10
10
6
3119
20
20
8
5
6
5
6
6
6
5
5
6
6
7
10
7
5
5
p50
51
1276
38
58
214
72
403
9219
19
14
14
3119
20
20
22
14
58
38
419
793
25
38
38
419
48
7
17
29
46
61
p97.5
49960
43983
786
7852
38917
440
2663
16801
69
69
69
3119
20
20
1689
187
7009
1829
7009
7009
48
1829
1829
7009
4337
7
24
119
1503
1503
p100
49960
43983
982
7852
38917
440
2663
16801
69
69
69
3119
20
20
1689
187
7009
1829
7009
7009
48
1829
1829
7009
4337
7
24
119
1503
1503
Distance of monitor to SO2 emission source (km)1
mean
6.0
5.7
11.4
8.4
7.5
7.7
12.7
0.0
1.3
11.0
10.8
12.5
6.1
0.0
6.3
13.7
7.7
8.9
13.5
13.0
8.3
10.1
11.7
12.6
12.8
8.8
9.8
18.0
6.8
13.8
10.4
13.4
std
0.7
2.4
5.5
1.7
5.8
2.1
7.2
0.0
0.5
3.6
6.6
4.7
0.0
0.0
0.0
0.0
4.2
5.4
2.8
6.4
5.6
5.9
1.4
4.8
5.7
5.4
6.1
0.0
2.1
5.1
5.2
5.9
min
5.5
3.1
1.1
5.5
1.4
4.3
4.5
0.0
0.9
5.6
1.9
5.5
6.1
0.0
6.3
13.7
1.9
1.2
9.6
2.5
1.6
0.2
10.1
5.4
4.1
2.3
0.7
18.0
4.7
6.3
4.1
3.7
p2.5
5.5
3.1
1.2
5.5
1.4
4.3
4.5
0.0
0.9
5.6
1.9
5.5
6.1
0.0
6.3
13.7
1.9
1.2
9.6
2.5
1.6
0.2
10.1
5.4
4.1
2.3
0.7
18.0
4.7
6.3
4.1
3.7
p50
5.9
6.2
13.1
8.6
6.1
7.2
13.2
0.0
1.3
10.2
11.2
12.4
6.1
0.0
6.3
13.7
8.8
9.0
13.3
15.0
6.4
9.9
12.4
12.2
13.5
6.7
11.4
18.0
6.9
12.5
9.3
16.4
p97.5
6.8
7.8
16.8
9.8
19.1
10.1
19.9
0.0
1.6
16.9
19.2
18.5
6.1
0.0
6.3
13.7
11.7
16.8
17.8
19.3
19.7
19.8
12.7
19.0
19.1
19.9
18.6
18.0
8.8
19.8
19.5
19.6
p100
6.8
7.8
19.8
9.8
19.1
10.1
19.9
0.0
1.6
16.9
19.2
18.5
6.1
0.0
6.3
13.7
11.7
16.8
17.8
19.3
19.7
19.8
12.7
19.0
19.1
19.9
18.6
18.0
8.8
19.8
19.5
19.6
A-63
-------
monid
060375005
060591003
060658001
060670002
060670006
060710012
060710014
060710306
060711234
060712002
060714001
060730001
060731007
060732007
060750005
060791005
060792001
060792004
060794002
060830008
060831012
060831013
060831015
060831016
060831019
060831020
060831025
060831026
060831027
060832004
060832011
060834003
060870003
060950001
060950004
061113001
n
12
7
4
1
1
1
2
2
3
2
1
1
3
1
6
7
7
7
7
3
2
2
1
1
1
3
3
3
3
2
3
2
1
13
12
2
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
192
10
75
5
58
8
126
126
97
102
32
21
11
21
66
536
536
536
536
39
554
554
18
18
18
39
39
39
39
554
39
554
722
1371
1480
9
std
332
5
76
132
132
85
112
9
83
1369
1369
1369
1369
43
357
357
43
43
43
43
357
43
357
2071
2124
3
min
6
5
17
5
58
8
32
32
6
22
32
21
5
21
5
6
6
6
6
10
302
302
18
18
18
10
10
10
10
302
10
302
722
6
6
7
p2.5
6
5
17
5
58
8
32
32
6
22
32
21
5
21
5
6
6
6
6
10
302
302
18
18
18
10
10
10
10
302
10
302
722
6
6
7
p50
33
7
50
5
58
8
126
126
110
102
32
21
7
21
39
24
24
24
24
18
554
554
18
18
18
18
18
18
18
554
18
554
722
790
791
9
P97.5
1119
18
181
5
58
8
219
219
175
181
32
21
21
21
224
3642
3642
3642
3642
89
807
807
18
18
18
89
89
89
89
807
89
807
722
7009
7009
11
p100
1119
18
181
5
58
8
219
219
175
181
32
21
21
21
224
3642
3642
3642
3642
89
807
807
18
18
18
89
89
89
89
807
89
807
722
7009
7009
11
Distance of monitor to SO2 emission source (km)1
mean
9.1
13.9
16.8
14.8
9.7
11.9
8.0
8.1
4.9
13.2
6.5
4.0
12.9
16.4
13.3
1.0
10.5
2.5
18.4
9.7
16.5
14.1
15.6
15.2
13.7
7.3
10.9
9.9
10.3
4.2
10.4
16.4
0.8
7.1
13.0
10.4
std
5.9
5.7
4.7
0.0
0.0
0.0
3.0
3.3
5.8
2.9
0.0
0.0
1.3
0.0
6.2
0.2
0.2
0.1
0.1
6.2
0.1
0.1
0.0
0.0
0.0
8.2
8.9
8.0
7.7
0.1
8.4
0.1
0.0
3.3
4.9
5.1
min
2.3
5.3
9.8
14.8
9.7
11.9
5.9
5.7
1.3
11.2
6.5
4.0
11.8
16.4
1.8
0.8
10.2
2.3
18.3
2.8
16.4
14.1
15.6
15.2
13.7
2.0
0.8
0.9
1.7
4.2
3.9
16.4
0.8
2.1
5.5
6.8
p2.5
2.3
5.3
9.8
14.8
9.7
11.9
5.9
5.7
1.3
11.2
6.5
4.0
11.8
16.4
1.8
0.8
10.2
2.3
18.3
2.8
16.4
14.1
15.6
15.2
13.7
2.0
0.8
0.9
1.7
4.2
3.9
16.4
0.8
2.1
5.5
6.8
p50
6.0
15.6
18.8
14.8
9.7
11.9
8.0
8.1
1.9
13.2
6.5
4.0
12.5
16.4
15.2
1.0
10.4
2.6
18.5
11.3
16.5
14.1
15.6
15.2
13.7
3.2
14.2
12.6
12.8
4.2
7.5
16.4
0.8
7.5
13.6
10.4
P97.5
19.8
19.7
19.6
14.8
9.7
11.9
10.1
10.4
11.7
15.3
6.5
4.0
14.4
16.4
18.3
1.5
10.9
2.7
18.5
14.9
16.5
14.2
15.6
15.2
13.7
16.7
17.7
16.2
16.4
4.2
20.0
16.5
0.8
13.8
19.6
14.0
p100
19.8
19.7
19.6
14.8
9.7
11.9
10.1
10.4
11.7
15.3
6.5
4.0
14.4
16.4
18.3
1.5
10.9
2.7
18.5
14.9
16.5
14.2
15.6
15.2
13.7
16.7
17.7
16.2
16.4
4.2
20.0
16.5
0.8
13.8
19.6
14.0
A-64
-------
monid
080010007
080013001
080310002
080416001
080416004
080416011
080416018
090010012
090010017
090011123
090012124
090019003
090031005
090031018
090032006
090090027
090091003
090091123
090092123
090110007
090130003
100031003
100031007
100031008
100031013
100032002
100032004
110010041
120110010
120310032
120310080
120310081
120310097
120330004
120330022
120470015
n
24
20
24
3
3
3
2
11
3
0
4
10
28
7
6
8
9
9
5
6
0
34
11
24
34
36
39
13
8
14
15
13
14
6
6
3
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
1001
1191
1098
1670
2849
2849
4268
425
252
192
504
45
16
14
595
565
565
86
650
975
3126
1657
975
802
1526
1410
2397
2715
2534
2923
2715
7262
7262
755
std
3352
3657
3356
2857
4920
4920
6026
1198
423
366
1257
106
9
7
1388
1302
1302
96
1088
1619
6528
4554
1619
1272
3681
4437
6653
5784
5617
5965
5784
14101
14101
1268
min
8
8
6
7
7
7
7
5
5
5
5
5
5
5
5
5
5
9
7
5
15
5
5
5
5
7
17
5
5
5
5
6
6
18
p2.5
8
8
6
7
7
7
7
5
5
5
5
5
5
5
5
5
5
9
7
5
15
5
5
5
5
7
17
5
5
5
5
6
6
18
p50
25
28
28
34
10
10
4268
21
11
10
10
12
15
15
32
43
43
28
110
112
103
60
112
97
116
24
41
287
257
317
287
330
330
27
P97.5
15958
15958
15958
4969
8530
8530
8530
4024
741
741
4024
522
30
25
4012
4012
4012
198
2755
6720
19923
19923
6720
5051
19923
16141
18861
20908
20908
20908
20908
35417
35417
2218
p100
15958
15958
15958
4969
8530
8530
8530
4024
741
741
4024
522
30
25
4012
4012
4012
198
2755
6720
19923
19923
6720
5051
19923
16141
18861
20908
20908
20908
20908
35417
35417
2218
Distance of monitor to SO2 emission source (km)1
mean
9.8
8.3
9.2
6.2
13.0
9.9
6.8
6.1
9.6
0.0
7.4
13.2
14.2
7.7
4.5
6.3
7.4
7.3
9.0
8.4
0.0
9.0
10.5
10.9
9.1
10.2
10.9
11.7
11.0
9.0
12.0
7.9
9.2
9.3
7.6
3.0
std
6.1
5.8
4.5
8.6
3.9
7.6
0.5
4.9
6.4
0.0
6.0
5.1
3.6
6.3
5.1
7.3
8.2
8.2
5.7
3.0
0.0
5.2
2.2
6.9
4.8
6.2
6.2
6.5
6.1
4.2
4.7
4.6
4.7
4.2
4.4
0.5
min
2.4
1.6
3.9
0.8
10.7
2.5
6.5
2.1
5.7
0.0
2.3
4.0
3.0
1.9
0.5
1.0
0.7
0.8
0.8
3.3
0.0
1.5
9.1
2.2
2.8
1.1
1.3
0.6
5.1
1.3
1.1
1.3
3.1
4.9
2.4
2.6
p2.5
2.4
1.6
3.9
0.8
10.7
2.5
6.5
2.1
5.7
0.0
2.3
4.0
3.0
1.9
0.5
1.0
0.7
0.8
0.8
3.3
0.0
1.5
9.1
2.2
2.8
1.1
1.3
0.6
5.1
1.3
1.1
1.3
3.1
4.9
2.4
2.6
p50
8.2
5.9
7.0
1.8
10.9
9.6
6.8
4.8
6.2
0.0
6.5
14.0
14.2
3.7
1.8
3.1
2.6
2.7
8.9
8.7
0.0
8.1
9.8
13.9
8.3
9.4
11.1
11.5
7.5
9.1
13.3
6.5
7.7
8.9
8.4
2.7
P97.5
19.7
19.8
19.5
16.1
17.6
17.7
7.2
19.7
17.0
0.0
14.4
19.5
19.9
18.4
11.4
18.6
19.7
19.7
15.2
12.7
0.0
19.8
16.2
19.7
18.9
19.7
19.8
19.8
19.2
18.5
19.7
15.5
19.5
14.6
12.3
3.6
p100
19.7
19.8
19.5
16.1
17.6
17.7
7.2
19.7
17.0
0.0
14.4
19.5
19.9
18.4
11.4
18.6
19.7
19.7
15.2
12.7
0.0
19.8
16.2
19.7
18.9
19.7
19.8
19.8
19.2
18.5
19.7
15.5
19.5
14.6
12.3
3.6
A-65
-------
monid
120570021
120570053
120570081
120570095
120570109
120571035
120574004
120813002
120860019
120890005
120890009
120952002
120993004
121030023
121033002
121035002
121035003
121050010
121052006
121071008
121151002
121151005
121151006
130090001
130150002
130210012
130510019
130510021
130511002
130950006
131110091
131150003
131210048
131210055
131270006
132150008
n
18
19
18
17
16
18
3
5
7
4
4
5
6
7
6
2
2
9
13
3
0
0
2
2
4
11
14
14
14
4
1
8
7
7
3
4
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
4986
4728
6781
3845
4084
4986
2872
73
34
1262
1262
9
39
3546
4136
15398
15398
2386
1691
9965
71
36975
40604
245
1362
1362
1362
1693
1900
4057
4339
4339
821
1740
std
11445
11180
13097
11285
11610
11445
4949
93
45
1594
1594
4
38
7041
7521
21767
21767
2929
2627
12565
90
52282
80047
468
2664
2664
2664
2220
9625
10445
10445
948
3214
min
6
6
6
6
6
6
11
6
5
11
11
5
5
6
23
7
7
6
6
12
7
6
21
6
8
8
8
5
1900
5
68
68
14
8
p2.5
6
6
6
6
6
6
11
6
5
11
11
5
5
6
23
7
7
6
6
12
7
6
21
6
8
8
8
5
1900
5
68
68
14
8
p50
341
104
1116
61
83
341
19
9
12
765
765
10
32
104
156
15398
15398
1210
230
5799
71
36975
862
17
235
235
235
932
1900
101
169
169
586
197
P97.5
47103
47103
47103
47103
47103
47103
8587
208
130
3509
3509
14
103
18822
18822
30790
30790
8587
8587
24083
135
73943
160673
1576
7969
7969
7969
4905
1900
27594
27993
27993
1865
6559
p100
47103
47103
47103
47103
47103
47103
8587
208
130
3509
3509
14
103
18822
18822
30790
30790
8587
8587
24083
135
73943
160673
1576
7969
7969
7969
4905
1900
27594
27993
27993
1865
6559
Distance of monitor to SO2 emission source (km)1
mean
11.6
10.8
14.4
10.1
9.9
10.6
14.2
7.2
9.6
4.5
4.2
13.8
12.0
7.4
10.3
13.6
9.8
10.4
11.9
5.8
0.0
0.0
15.8
11.3
10.4
10.1
7.1
6.8
6.2
6.3
1.6
1.4
10.3
15.1
3.6
12.5
std
6.5
3.5
4.5
6.2
4.0
5.8
8.5
8.3
5.8
5.0
4.6
2.8
3.1
6.4
4.2
0.1
4.0
3.1
6.2
3.4
0.0
0.0
0.8
5.4
5.2
5.2
4.1
4.4
3.4
5.3
0.0
0.4
2.1
3.3
2.8
2.6
min
1.4
5.9
8.1
2.5
6.7
1.6
4.4
0.7
2.6
1.1
1.1
10.1
7.0
2.3
3.5
13.5
7.0
3.7
2.7
2.6
0.0
0.0
15.2
7.5
2.5
1.5
0.4
1.4
1.6
2.2
1.6
1.1
8.4
8.0
1.8
10.1
p2.5
1.4
5.9
8.1
2.5
6.7
1.6
4.4
0.7
2.6
1.1
1.1
10.1
7.0
2.3
3.5
13.5
7.0
3.7
2.7
2.6
0.0
0.0
15.2
7.5
2.5
1.5
0.4
1.4
1.6
2.2
1.6
1.1
8.4
8.0
1.8
10.1
p50
14.3
12.1
15.1
14.2
7.4
12.9
18.9
2.2
6.9
2.5
2.4
13.6
12.0
3.7
10.0
13.6
9.8
10.8
13.7
5.6
0.0
0.0
15.8
11.3
13.0
8.8
6.6
7.2
6.6
4.4
1.6
1.2
9.2
15.6
2.2
12.4
P97.5
18.2
17.3
19.6
19.3
19.9
16.3
19.3
16.5
19.1
12.0
11.0
17.6
16.7
19.6
15.4
13.7
12.6
14.4
19.9
9.3
0.0
0.0
16.4
15.1
13.0
19.9
12.0
14.0
10.0
14.1
1.6
2.3
14.0
18.1
6.8
15.1
p100
18.2
17.3
19.6
19.3
19.9
16.3
19.3
16.5
19.1
12.0
11.0
17.6
16.7
19.6
15.4
13.7
12.6
14.4
19.9
9.3
0.0
0.0
16.4
15.1
13.0
19.9
12.0
14.0
10.0
14.1
1.6
2.3
14.0
18.1
6.8
15.1
A-66
-------
monid
132450003
150030010
150030011
150031001
150031006
160050004
160290003
160290031
160770011
170010006
170190004
170310050
170310059
170310063
170310064
170310076
170311018
170311601
170312001
170314002
170314201
170318003
170436001
170990007
171150013
171170002
171190008
171190017
171191010
171193007
171193009
171430024
171570001
171610003
171630010
171631010
n
15
7
7
3
7
2
13
13
2
4
3
47
40
23
50
36
26
12
43
25
4
36
12
4
11
0
15
40
28
28
26
10
2
10
30
30
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
1335
2231
2231
1043
2231
804
967
967
804
965
121
900
910
1041
1015
930
924
3807
920
982
165
835
2986
890
1251
4510
877
954
2595
2789
7333
13148
945
445
445
std
2379
2339
2339
1509
2339
606
2904
2904
606
614
182
1775
1928
1800
1902
1976
1721
5540
1807
1738
230
1797
5690
1527
2596
11972
2339
2564
8875
9193
11752
18554
1612
1152
1152
min
8
79
79
6
79
376
7
7
376
392
10
5
5
5
5
5
5
7
5
5
7
5
6
6
22
6
6
6
6
6
5
28
7
6
6
p2.5
8
79
79
6
79
376
7
7
376
392
10
5
5
5
5
5
5
7
5
5
7
5
6
6
22
6
6
6
6
6
5
28
7
6
6
p50
545
1566
1566
350
1566
804
33
33
804
817
21
65
65
17
51
26
16
1090
64
17
77
70
17
189
164
111
117
183
214
247
67
13148
169
68
68
P97.5
8275
6978
6978
2774
6978
1233
10544
10544
1233
1834
331
5951
7381
6229
6229
8443
6229
15934
6229
6229
498
8443
15934
3178
8032
45960
9663
12063
45960
45960
35748
26268
4963
6250
6250
p100
8275
6978
6978
2774
6978
1233
10544
10544
1233
1834
331
8443
8443
6229
8443
8443
6229
15934
8443
6229
498
8443
15934
3178
8032
45960
12063
12063
45960
45960
35748
26268
4963
6250
6250
Distance of monitor to SO2 emission source (km)1
mean
8.0
5.0
5.7
10.1
6.1
1.3
2.9
1.4
0.8
4.8
1.8
11.0
7.5
11.0
14.6
13.2
10.7
14.1
16.5
9.0
18.0
8.8
16.5
7.2
3.3
0.0
10.1
9.5
10.5
12.2
12.4
13.2
6.4
11.4
9.3
10.4
std
1.4
4.6
5.3
8.2
4.8
0.1
0.4
1.1
0.1
4.5
0.9
5.0
5.2
6.8
4.1
4.0
6.8
6.4
3.2
3.1
3.0
2.4
5.1
6.1
2.3
0.0
3.7
6.6
6.8
6.9
7.5
5.8
0.4
5.6
4.1
4.3
min
4.7
2.5
2.2
0.7
1.8
1.2
2.7
0.8
0.7
1.9
0.8
2.1
1.5
0.9
3.9
4.9
0.5
4.0
3.4
3.9
13.4
2.9
1.5
0.5
1.8
0.0
3.2
0.5
0.7
2.4
2.9
1.3
6.0
2.3
1.3
1.1
p2.5
4.7
2.5
2.2
0.7
1.8
1.2
2.7
0.8
0.7
1.9
0.8
3.4
1.5
0.9
6.4
4.9
0.5
4.0
8.4
3.9
13.4
2.9
1.5
0.5
1.8
0.0
3.2
0.7
0.7
2.4
2.9
1.3
6.0
2.3
1.3
1.1
p50
8.2
3.3
4.1
13.8
5.0
1.3
2.8
1.2
0.8
2.9
2.3
10.2
5.8
9.3
16.4
13.3
11.6
18.5
17.7
9.5
19.4
8.4
18.1
6.7
3.2
0.0
9.5
11.2
15.6
16.1
14.7
15.5
6.4
12.3
9.6
11.7
P97.5
10.0
15.3
17.5
15.7
16.5
1.4
4.3
4.9
0.8
11.5
2.4
19.7
19.3
19.7
19.9
19.7
19.8
19.3
19.3
18.5
19.7
14.7
19.8
14.8
9.9
0.0
19.7
19.0
18.4
18.9
19.8
18.8
6.7
17.2
18.5
19.4
p100
10.0
15.3
17.5
15.7
16.5
1.4
4.3
4.9
0.8
11.5
2.4
19.8
19.5
19.7
19.9
19.7
19.8
19.3
19.9
18.5
19.7
14.7
19.8
14.8
9.9
0.0
19.7
19.6
18.4
18.9
19.8
18.8
6.7
17.2
18.5
19.4
A-67
-------
monid
171631011
171670006
171790004
171850001
171851001
171970013
180270002
180290004
180430004
180430007
180431004
180450001
180510001
180510002
180630001
180630002
180630003
180730002
180730003
180770004
180890022
180892008
180910005
180910007
180970042
180970054
180970057
180970072
180970073
181091001
181230006
181230007
181250005
181270011
181270017
181270023
n
2
5
6
3
3
19
6
7
8
10
9
3
3
3
0
1
0
4
4
2
50
39
3
2
22
20
20
21
20
3
8
8
6
23
22
21
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
13148
2170
12212
42452
42452
2439
10869
21579
6500
6721
7442
18552
42452
42452
147
6874
6874
19099
1014
938
4166
4599
2358
2554
2554
2433
2547
6006
7033
7033
10869
1703
1363
1427
std
18554
3169
13311
25439
25439
6269
16456
32930
10778
10131
10470
32099
25439
25439
1422
1422
1297
1502
1945
4640
6476
6820
7138
7138
6980
7141
9709
17145
17145
16456
2266
1612
1623
min
28
9
22
27097
27097
6
9
174
12
12
12
10
27097
27097
147
6085
6085
18182
5
5
20
20
5
5
5
5
5
242
7
7
9
20
20
23
p2.5
28
9
22
27097
27097
6
9
174
12
12
12
10
27097
27097
147
6085
6085
18182
6
5
20
20
5
5
5
5
5
242
7
7
9
20
20
23
p50
13148
202
10290
28443
28443
37
2241
1574
484
516
798
28
28443
28443
147
6204
6204
19099
188
72
3301
4599
36
23
23
19
18
561
38
38
2241
1062
1029
1062
P97.5
26268
7210
35748
71817
71817
25224
41536
85699
23995
23995
23995
55617
71817
71817
147
9002
9002
20016
5951
8443
9178
9178
30896
30896
30896
30896
30896
17216
49028
49028
41536
9178
6318
6318
p100
26268
7210
35748
71817
71817
25224
41536
85699
23995
23995
23995
55617
71817
71817
147
9002
9002
20016
6318
8443
9178
9178
30896
30896
30896
30896
30896
17216
49028
49028
41536
9178
6318
6318
Distance of monitor to SO2 emission source (km)1
mean
4.0
7.3
5.4
2.9
5.9
6.6
6.3
4.2
13.4
9.2
10.0
9.8
2.0
2.9
0.0
19.2
0.0
4.3
10.2
4.3
14.1
6.4
9.1
6.0
11.2
3.3
4.2
6.9
13.7
4.3
7.7
6.8
3.0
6.7
5.4
4.1
std
0.6
3.6
5.2
0.1
0.1
4.8
0.6
4.1
3.1
6.4
3.5
8.7
0.1
0.0
0.0
0.0
0.0
1.0
1.2
0.1
4.0
4.1
9.7
0.8
2.9
2.3
2.0
3.5
2.3
2.4
4.3
4.2
4.7
6.2
5.7
4.4
min
3.6
4.9
0.8
2.8
5.8
1.1
5.8
1.2
8.8
1.1
5.0
4.5
1.8
2.9
0.0
19.2
0.0
3.5
9.5
4.3
0.8
1.6
0.4
5.4
7.8
0.9
0.9
0.8
6.2
2.1
2.8
2.1
0.9
2.2
2.0
1.1
p2.5
3.6
4.9
0.8
2.8
5.8
1.1
5.8
1.2
8.8
1.1
5.0
4.5
1.8
2.9
0.0
19.2
0.0
3.5
9.5
4.3
1.8
1.6
0.4
5.4
7.8
0.9
0.9
0.8
6.2
2.1
2.8
2.1
0.9
2.2
2.0
1.1
p50
4.0
5.6
3.6
2.9
5.8
5.2
6.0
3.4
12.3
7.3
9.8
5.1
2.0
2.9
0.0
19.2
0.0
4.0
9.7
4.3
14.6
5.6
7.3
6.0
11.0
2.4
4.3
6.6
14.5
4.0
7.0
5.7
1.1
3.6
2.6
2.4
P97.5
4.4
13.5
13.8
3.1
6.0
18.6
7.3
12.8
17.7
19.9
14.7
19.8
2.1
3.0
0.0
19.2
0.0
5.8
12.1
4.4
19.8
17.6
19.6
6.5
17.0
9.2
9.8
18.7
15.3
6.9
14.3
13.1
12.7
18.7
17.8
14.6
p100
4.4
13.5
13.8
3.1
6.0
18.6
7.3
12.8
17.7
19.9
14.7
19.8
2.1
3.0
0.0
19.2
0.0
5.8
12.1
4.4
19.9
17.6
19.6
6.5
17.0
9.2
9.8
18.7
15.3
6.9
14.3
13.1
12.7
18.7
17.8
14.6
A-68
-------
monid
181470002
181470010
181530004
181630012
181631002
181670018
181671014
181730002
181731001
181770006
181770007
190330018
190450018
190450019
190450020
191110006
191111007
191130028
191130029
191130031
191130032
191130034
191130038
191130039
191390016
191390017
191390020
191630015
191630017
191770004
191770005
191770006
191930018
201070002
201250006
201450001
n
7
4
3
5
5
6
6
8
8
2
2
4
2
2
2
1
2
7
7
7
7
7
7
7
5
4
4
7
7
0
0
0
4
0
4
0
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
15627
15099
9270
1806
1806
10842
10842
13636
13636
6446
6446
2684
4694
4694
4694
29
104
2200
2200
2200
2200
2200
2200
2200
6227
7763
7763
1345
2120
9208
468
std
24405
25616
8089
2589
2589
25028
25028
16457
16457
9089
9089
3305
839
839
839
105
2428
2428
2428
2428
2428
2428
2428
6934
6956
6956
1810
3515
10818
464
min
7
20
10
5
5
12
12
50
50
19
19
20
4101
4101
4101
29
29
12
12
12
12
12
12
12
83
463
463
17
17
15
11
p2.5
7
20
10
5
5
12
12
50
50
19
19
20
4101
4101
4101
29
29
12
12
12
12
12
12
12
83
463
463
17
17
15
11
p50
66
3589
12846
382
382
417
417
3559
3559
6446
6446
1934
4694
4694
4694
29
104
1954
1954
1954
1954
1954
1954
1954
3790
7345
7345
336
303
7845
428
P97.5
53196
53196
14955
6004
6004
61901
61901
41049
41049
12873
12873
6850
5287
5287
5287
29
179
5480
5480
5480
5480
5480
5480
5480
15901
15901
15901
4963
8983
21127
1006
p100
53196
53196
14955
6004
6004
61901
61901
41049
41049
12873
12873
6850
5287
5287
5287
29
179
5480
5480
5480
5480
5480
5480
5480
15901
15901
15901
4963
8983
21127
1006
Distance of monitor to SO2 emission source (km)1
mean
13.0
12.3
12.1
13.1
8.5
6.8
6.8
2.9
3.0
2.1
3.2
3.9
1.4
1.3
3.4
3.7
13.3
5.8
3.8
4.3
3.5
3.6
3.9
4.6
8.7
3.8
4.9
9.5
9.6
0.0
0.0
0.0
6.4
0.0
5.8
0.0
std
3.6
6.6
6.4
7.7
5.3
3.6
5.9
0.4
0.5
1.4
2.5
3.7
0.9
1.0
0.6
0.0
6.3
2.4
3.1
3.2
2.7
2.5
1.9
3.0
6.9
3.6
4.4
5.0
4.2
0.0
0.0
0.0
4.3
0.0
9.3
0.0
min
8.0
3.3
4.8
3.1
3.4
5.0
1.9
2.5
2.5
1.1
1.4
0.4
0.7
0.6
3.0
3.7
8.8
2.8
0.5
0.5
0.6
0.2
0.6
1.1
2.4
0.6
0.9
1.1
1.1
0.0
0.0
0.0
0.7
0.0
0.5
0.0
p2.5
8.0
3.3
4.8
3.1
3.4
5.0
1.9
2.5
2.5
1.1
1.4
0.4
0.7
0.6
3.0
3.7
8.8
2.8
0.5
0.5
0.6
0.2
0.6
1.1
2.4
0.6
0.9
1.1
1.1
0.0
0.0
0.0
0.7
0.0
0.5
0.0
p50
15.0
14.0
14.7
18.0
9.5
5.5
5.5
3.0
2.9
2.1
3.2
3.2
1.4
1.3
3.4
3.7
13.3
6.7
4.0
4.7
3.1
2.9
4.2
4.2
7.4
3.1
4.0
11.7
11.2
0.0
0.0
0.0
7.1
0.0
1.6
0.0
P97.5
16.6
17.9
16.8
19.6
16.5
14.1
17.3
3.3
3.7
3.1
5.0
8.8
2.0
2.0
3.8
3.7
17.7
8.8
9.2
9.3
8.8
7.4
6.2
10.3
19.2
8.5
10.4
15.1
13.6
0.0
0.0
0.0
10.7
0.0
19.7
0.0
p100
16.6
17.9
16.8
19.6
16.5
14.1
17.3
3.3
3.7
3.1
5.0
8.8
2.0
2.0
3.8
3.7
17.7
8.8
9.2
9.3
8.8
7.4
6.2
10.3
19.2
8.5
10.4
15.1
13.6
0.0
0.0
0.0
10.7
0.0
19.7
0.0
A-69
-------
monid
201730010
201910002
201950001
202090001
202090020
202090021
210190015
210190017
210191003
210370003
210371001
210590005
210670012
210890007
210910012
211010013
211010014
211110032
211110051
211111041
211390004
211450001
211451024
211451026
212270008
220150008
220190008
220330009
220730004
220870002
221210001
230010011
230030009
230030012
230031003
230031013
n
3
3
0
14
14
13
9
10
8
11
11
4
3
5
9
4
10
14
12
11
4
7
3
3
1
2
16
28
1
18
28
9
1
1
1
3
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
269
269
1388
1388
1494
1323
1193
1271
6817
465
15241
209
961
12162
2256
10948
6208
3259
6268
444
8769
587
587
52
77
3352
1406
2166
419
1116
31
90
90
90
16
std
448
448
2341
2341
2402
2058
1983
2194
20950
664
25506
316
1147
22226
2755
15581
8948
5326
9779
869
13010
1005
1005
21
5531
3913
846
3650
41
17
min
6
6
6
6
6
25
25
25
12
12
26
12
25
7
5
5
38
38
12
6
174
6
6
52
62
6
6
2166
8
6
5
90
90
90
5
p2.5
6
6
6
6
6
25
25
25
12
12
26
12
25
7
5
5
38
38
12
6
174
6
6
52
62
6
6
2166
8
6
5
90
90
90
5
p50
15
15
34
34
40
401
343
343
268
213
3871
42
401
38
1508
2980
516
168
234
11
7435
7
7
52
77
184
45
2166
52
33
23
90
90
90
7
P97.5
785
785
7625
7625
7625
6285
6285
6285
69953
1848
53196
573
2589
53196
6004
41049
23995
14977
23995
1747
37077
1747
1747
52
91
18851
18680
2166
3009
18680
140
90
90
90
36
p100
785
785
7625
7625
7625
6285
6285
6285
69953
1848
53196
573
2589
53196
6004
41049
23995
14977
23995
1747
37077
1747
1747
52
91
18851
18680
2166
3009
18680
140
90
90
90
36
Distance of monitor to SO2 emission source (km)1
mean
11.4
16.3
0.0
9.2
9.0
8.6
12.3
12.8
9.3
12.0
8.5
7.4
3.2
10.9
10.4
10.2
12.9
11.4
10.6
9.1
8.0
7.5
18.2
15.3
19.1
8.7
7.6
5.8
10.1
8.8
5.4
6.6
1.9
1.0
1.3
4.7
std
3.1
2.4
0.0
5.9
6.1
5.5
5.5
5.4
5.4
3.0
3.4
6.8
2.2
6.2
5.1
5.5
1.4
5.6
7.5
7.3
6.9
3.4
2.1
2.2
0.0
0.1
6.1
5.6
0.0
4.2
4.7
4.3
0.0
0.0
0.0
4.5
min
9.0
13.6
0.0
3.5
0.6
3.4
1.6
2.9
1.3
8.1
4.2
2.2
1.2
5.1
1.2
2.0
11.5
2.4
1.6
1.3
3.1
2.0
15.8
12.7
19.1
8.6
1.2
1.5
10.1
0.5
2.4
1.3
1.9
1.0
1.3
1.4
p2.5
9.0
13.6
0.0
3.5
0.6
3.4
1.6
2.9
1.3
8.1
4.2
2.2
1.2
5.1
1.2
2.0
11.5
2.4
1.6
1.3
3.1
2.0
15.8
12.7
19.1
8.6
1.2
1.5
10.1
0.5
2.4
1.3
1.9
1.0
1.3
1.4
p50
10.2
17.3
0.0
7.1
7.7
6.6
14.6
13.8
9.9
10.8
7.5
5.5
2.7
7.6
10.6
12.7
12.8
13.7
14.6
7.7
5.4
9.4
19.3
16.5
19.1
8.7
5.8
3.2
10.1
7.8
3.4
6.5
1.9
1.0
1.3
2.8
P97.5
14.9
18.0
0.0
19.8
18.9
19.1
17.7
19.5
15.4
17.8
15.5
16.5
5.6
19.8
18.9
13.3
16.6
18.3
18.7
19.3
17.9
11.2
19.5
16.7
19.1
8.8
16.7
20.0
10.1
19.0
18.1
13.3
1.9
1.0
1.3
9.9
p100
14.9
18.0
0.0
19.8
18.9
19.1
17.7
19.5
15.4
17.8
15.5
16.5
5.6
19.8
18.9
13.3
16.6
18.3
18.7
19.3
17.9
11.2
19.5
16.7
19.1
8.8
16.7
20.0
10.1
19.0
18.1
13.3
1.9
1.0
1.3
9.9
A-70
-------
monid
230031018
230050014
230050027
230172007
240010006
240032002
240053001
245100018
245100036
250051004
250090005
250091004
250091005
250095004
250130016
250131009
250154002
250171701
250174003
250250002
250250019
250250020
250250021
250250040
250250042
250251003
250270020
250270023
260410902
260490021
260492001
260810020
260991003
261130001
261470005
261530001
n
4
12
12
2
2
20
22
21
21
24
25
23
22
14
34
32
12
55
57
62
50
58
58
59
60
58
28
28
3
4
2
9
3
1
3
0
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
193
267
267
249
681
3247
4429
3101
4635
1867
65
878
917
88
216
65
72
139
127
129
156
138
137
135
133
380
25
25
1407
42
64
60
239
58
524
std
233
628
628
344
685
9622
11101
9402
11331
8085
148
3071
3137
197
907
148
113
678
663
639
710
660
660
654
649
1952
35
35
1264
24
79
96
287
431
min
7
5
5
6
197
5
5
5
5
6
6
5
5
8
5
5
6
5
5
5
5
5
5
5
5
5
6
6
671
7
7
9
10
58
31
p2.5
7
5
5
6
197
5
5
5
5
6
6
5
5
8
5
5
6
5
5
5
5
5
5
5
5
5
6
6
671
7
7
9
10
58
31
p50
133
16
16
249
681
21
27
22
22
31
26
16
16
25
14
13
29
15
13
14
14
15
14
14
14
15
12
12
685
48
64
12
148
58
715
P97.5
499
2091
2091
492
1166
39974
39974
39974
39974
39593
762
14132
14132
762
5282
671
363
640
460
640
640
640
640
640
640
5007
178
178
2867
63
120
280
560
58
826
p100
499
2091
2091
492
1166
39974
39974
39974
39974
39593
762
14132
14132
762
5282
671
363
5007
5007
5007
5007
5007
5007
5007
5007
14132
178
178
2867
63
120
280
560
58
826
Distance of monitor to SO2 emission source (km)1
mean
8.5
6.1
6.0
1.0
8.9
11.9
11.9
9.1
6.6
7.5
9.6
11.3
11.2
8.6
7.6
8.4
15.8
13.3
12.2
9.6
12.0
10.0
10.6
10.2
9.4
11.0
5.0
5.1
2.5
10.9
19.0
10.5
14.0
10.3
8.7
0.0
std
5.6
4.8
4.7
0.1
4.0
4.5
3.3
4.6
3.3
6.7
6.6
6.3
6.3
4.2
5.2
4.7
3.4
4.6
4.0
6.1
3.8
5.0
4.7
5.3
5.8
4.6
5.9
5.8
1.5
4.5
0.4
5.6
3.4
0.0
5.9
0.0
min
0.3
1.2
0.8
1.0
6.0
2.7
4.6
1.4
1.6
0.1
0.3
0.8
0.7
0.7
0.5
1.7
9.1
0.4
0.6
0.7
0.7
1.1
1.8
1.0
0.5
1.0
0.1
0.6
0.8
4.2
18.8
4.3
10.2
10.3
3.8
0.0
p2.5
0.3
1.2
0.8
1.0
6.0
2.7
4.6
1.4
1.6
0.1
0.3
0.8
0.7
0.7
0.5
1.7
9.1
2.9
5.6
1.1
4.2
3.0
3.4
1.4
0.7
2.1
0.1
0.6
0.8
4.2
18.8
4.3
10.2
10.3
3.8
0.0
p50
10.3
5.0
4.8
1.0
8.9
13.3
12.1
7.6
6.8
3.8
9.2
12.8
11.9
10.1
7.4
7.4
16.8
15.0
12.4
8.6
12.0
9.1
9.3
9.5
9.1
10.4
2.8
2.9
3.3
13.1
19.0
10.6
15.2
10.3
6.9
0.0
P97.5
13.0
16.8
16.6
1.1
11.7
19.9
19.2
16.7
16.0
18.9
19.9
20.0
18.6
14.7
19.2
18.9
19.7
19.4
19.5
19.5
18.1
19.2
19.5
19.5
19.1
19.3
19.5
19.1
3.4
13.1
19.3
19.4
16.7
10.3
15.2
0.0
p100
13.0
16.8
16.6
1.1
11.7
19.9
19.2
16.7
16.0
18.9
19.9
20.0
18.6
14.7
19.2
18.9
19.7
20.0
19.7
19.7
18.4
19.2
20.0
19.8
19.3
19.4
19.5
19.1
3.4
13.1
19.3
19.4
16.7
10.3
15.2
0.0
A-71
-------
monid
261630001
261630005
261630015
261630016
261630019
261630025
261630027
261630033
261630062
261630092
270031002
270176316
270370020
270370423
270370439
270370441
270370442
270530954
270530957
270711240
271230864
271410003
271410011
271410012
271410013
271630436
271710007
280470007
280490018
280590006
280810004
290210009
290210011
290470025
290770026
290770032
n
36
34
32
31
23
6
33
32
31
33
10
5
15
17
14
12
11
24
21
1
27
1
1
1
1
21
2
2
5
7
0
1
1
15
4
4
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
1780
1894
1070
1104
1358
13
1952
1070
1104
1952
1332
72
610
805
639
720
506
913
878
67
769
26742
26742
26742
26742
545
13397
12535
51
4903
3563
3563
1682
2302
2302
std
5390
5529
2436
2469
2828
14
5605
2436
2469
5605
4067
84
1015
1227
1047
1114
873
2729
2877
2540
997
18873
17718
45
10049
2364
2728
2728
min
5
5
5
5
10
5
5
5
5
5
5
5
9
9
9
9
9
5
5
67
5
26742
26742
26742
26742
7
52
6
15
12
3563
3563
6
5
5
p2.5
5
5
5
5
10
5
5
5
5
5
5
5
9
9
9
9
9
5
5
67
5
26742
26742
26742
26742
7
52
6
15
12
3563
3563
6
5
5
p50
109
117
117
121
121
9
121
117
121
121
11
26
104
205
79
79
54
48
12
67
46
26742
26742
26742
26742
104
13397
12535
30
96
3563
3563
105
1772
1772
P97.5
30171
30171
8913
8913
8913
42
30171
8913
8913
30171
12904
190
3071
3821
3071
3071
2869
12904
12904
67
12904
26742
26742
26742
26742
3821
26742
25064
128
27207
3563
3563
7625
5657
5657
p100
30171
30171
8913
8913
8913
42
30171
8913
8913
30171
12904
190
3071
3821
3071
3071
2869
12904
12904
67
12904
26742
26742
26742
26742
3821
26742
25064
128
27207
3563
3563
7625
5657
5657
Distance of monitor to SO2 emission source (km)1
mean
10.9
6.1
5.5
9.0
17.3
14.8
5.5
5.0
9.0
5.4
14.3
13.7
11.9
11.6
12.5
11.6
12.2
10.9
10.7
0.3
12.0
4.9
1.7
1.8
1.1
11.1
11.8
6.5
7.3
7.0
0.0
0.7
0.9
11.9
8.2
7.8
std
4.0
5.4
4.2
2.7
4.5
2.4
5.2
4.5
2.9
5.1
4.4
6.8
6.1
5.5
5.8
5.7
5.5
5.8
5.3
0.0
4.8
0.0
0.0
0.0
0.0
5.6
6.5
7.9
5.4
4.9
0.0
0.0
0.0
4.8
4.5
3.9
min
5.4
1.2
1.5
3.6
3.7
11.2
0.4
0.4
3.1
0.9
4.7
2.2
0.9
0.4
2.6
1.6
2.3
0.6
0.9
0.3
3.9
4.9
1.7
1.8
1.1
0.9
7.2
0.9
3.2
3.3
0.0
0.7
0.9
2.8
2.3
3.0
p2.5
5.4
1.2
1.5
3.6
3.7
11.2
0.4
0.4
3.1
0.9
4.7
2.2
0.9
0.4
2.6
1.6
2.3
0.6
0.9
0.3
3.9
4.9
1.7
1.8
1.1
0.9
7.2
0.9
3.2
3.3
0.0
0.7
0.9
2.8
2.3
3.0
p50
9.6
4.4
3.8
8.6
18.9
15.2
3.9
4.2
8.5
3.0
15.5
16.4
12.4
12.4
13.1
12.6
13.8
12.2
10.9
0.3
12.6
4.9
1.7
1.8
1.1
11.4
11.8
6.5
6.0
5.4
0.0
0.7
0.9
10.8
9.3
8.5
P97.5
20.0
19.0
17.9
17.0
19.8
17.8
19.7
15.8
17.2
19.9
18.9
19.7
19.6
18.8
20.0
19.0
18.8
19.0
18.3
0.3
19.7
4.9
1.7
1.8
1.1
18.4
16.3
12.1
16.6
17.3
0.0
0.7
0.9
18.2
11.8
11.0
p100
20.0
19.0
17.9
17.0
19.8
17.8
19.7
15.8
17.2
19.9
18.9
19.7
19.6
18.8
20.0
19.0
18.8
19.0
18.3
0.3
19.7
4.9
1.7
1.8
1.1
18.4
16.3
12.1
16.6
17.3
0.0
0.7
0.9
18.2
11.8
11.0
A-72
-------
monid
290770037
290770040
290770041
290930030
290930031
290950034
290990004
290990014
290990017
290990018
291370001
291630002
291650023
291830010
291831002
291890001
291890004
291890006
291890014
291893001
291895001
291897002
291897003
295100007
295100072
295100080
295100086
300132000
300132001
300430903
300430911
300430913
300490702
300490703
300870700
300870701
n
4
4
4
1
1
14
5
5
5
4
0
2
4
1
15
14
9
7
8
29
35
14
18
19
30
34
32
2
2
1
1
1
1
1
1
1
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
2302
2302
2302
43340
43340
1388
11145
11145
11145
8117
6747
2757
47610
4516
1748
2535
27
33
370
1911
50
403
1312
445
397
421
351
351
234
234
234
234
234
16735
16735
std
2728
2728
2728
2341
10277
10277
10277
8927
934
3602
11970
4547
5610
48
47
1164
7823
75
1461
3936
1152
1088
1118
481
481
min
5
5
5
43340
43340
6
243
243
243
243
6087
19
47610
6
8
8
6
6
6
6
6
6
8
6
6
6
11
11
234
234
234
234
234
16735
16735
p2.5
5
5
5
43340
43340
6
243
243
243
243
6087
19
47610
6
8
8
6
6
6
6
6
6
8
6
6
6
11
11
234
234
234
234
234
16735
16735
p50
1772
1772
1772
43340
43340
34
15223
15223
15223
7889
6747
1693
47610
136
35
13
8
10
60
111
16
37
50
68
61
68
351
351
234
234
234
234
234
16735
16735
P97.5
5657
5657
5657
43340
43340
7625
23258
23258
23258
16447
7408
7625
47610
45960
16447
16447
136
136
6250
45960
277
6250
16447
6250
6250
6250
691
691
234
234
234
234
234
16735
16735
p100
5657
5657
5657
43340
43340
7625
23258
23258
23258
16447
7408
7625
47610
45960
16447
16447
136
136
6250
45960
277
6250
16447
6250
6250
6250
691
691
234
234
234
234
234
16735
16735
Distance of monitor to SO2 emission source (km)1
mean
9.2
9.2
8.6
1.7
4.6
8.7
9.7
9.8
10.2
8.3
0.0
7.3
17.8
1.7
12.6
14.8
14.0
14.7
11.9
15.2
15.1
13.2
14.1
12.5
8.8
10.7
9.8
4.1
4.1
3.3
4.5
4.9
6.2
7.3
19.8
19.0
std
6.1
6.2
5.5
0.0
0.0
4.9
7.4
7.4
7.1
6.6
0.0
6.6
1.3
0.0
3.4
4.4
3.1
4.6
6.2
4.2
3.1
5.7
5.4
6.0
3.8
4.3
3.9
3.6
4.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
min
0.6
0.5
1.2
1.7
4.6
1.4
0.2
0.7
1.6
1.4
0.0
2.7
16.0
1.7
4.3
6.4
9.8
8.4
3.2
5.1
6.7
3.9
3.5
0.5
2.0
0.4
1.7
1.5
0.7
3.3
4.5
4.9
6.2
7.3
19.8
19.0
p2.5
0.6
0.5
1.2
1.7
4.6
1.4
0.2
0.7
1.6
1.4
0.0
2.7
16.0
1.7
4.3
6.4
9.8
8.4
3.2
5.1
6.7
3.9
3.5
0.5
2.0
0.4
1.7
1.5
0.7
3.3
4.5
4.9
6.2
7.3
19.8
19.0
p50
11.0
11.0
9.7
1.7
4.6
8.1
11.4
11.9
10.6
8.2
0.0
7.3
18.1
1.7
13.5
15.9
15.2
15.7
11.3
16.0
15.9
14.6
16.2
14.0
9.7
10.5
10.0
4.1
4.1
3.3
4.5
4.9
6.2
7.3
19.8
19.0
P97.5
14.0
14.1
13.8
1.7
4.6
15.4
17.1
17.5
17.3
15.3
0.0
12.0
19.1
1.7
17.3
19.7
18.2
19.9
19.7
20.0
20.0
20.0
19.4
19.6
19.2
19.7
18.6
6.7
7.5
3.3
4.5
4.9
6.2
7.3
19.8
19.0
p100
14.0
14.1
13.8
1.7
4.6
15.4
17.1
17.5
17.3
15.3
0.0
12.0
19.1
1.7
17.3
19.7
18.2
19.9
19.7
20.0
20.0
20.0
19.4
19.6
19.2
19.7
18.6
6.7
7.5
3.3
4.5
4.9
6.2
7.3
19.8
19.0
A-73
-------
monid
300870702
300870760
300870761
300870762
300870763
301110016
301110066
301110079
301110080
301110082
301110083
301110084
301111065
301112005
301112006
301112007
310550048
310550050
310550053
310550055
320030022
320030078
320030539
320030601
330050007
330070019
330070022
330071007
330110016
330110019
330110020
330111009
330111010
330130007
330131003
330131006
n
1
0
0
0
1
0
4
4
4
4
4
6
4
4
6
6
5
5
5
3
4
0
0
0
1
1
1
2
3
3
3
11
16
4
4
4
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
16735
16735
1370
1370
1370
1370
1370
2550
1370
1370
2550
2550
6370
6370
6370
3845
45
81
638
638
9
10269
10269
10269
41
48
7708
7708
7708
std
1322
1322
1322
1322
1322
2627
1322
1322
2627
2627
9218
9218
9218
6637
27
4
10386
10386
10386
42
42
9906
9906
9906
min
16735
16735
75
75
75
75
75
75
75
75
75
75
6
6
6
6
16
81
638
638
6
149
149
149
6
6
41
41
41
p2.5
16735
16735
75
75
75
75
75
75
75
75
75
75
6
6
6
6
16
81
638
638
6
149
149
149
6
6
41
41
41
p50
16735
16735
1135
1135
1135
1135
1135
1976
1135
1135
1976
1976
58
58
58
20
44
81
638
638
9
9754
9754
9754
20
38
4945
4945
4945
P97.5
16735
16735
3135
3135
3135
3135
3135
7415
3135
3135
7415
7415
20257
20257
20257
11509
75
81
638
638
12
20902
20902
20902
149
149
20902
20902
20902
p100
16735
16735
3135
3135
3135
3135
3135
7415
3135
3135
7415
7415
20257
20257
20257
11509
75
81
638
638
12
20902
20902
20902
149
149
20902
20902
20902
Distance of monitor to SO2 emission source (km)1
mean
19.8
0.0
0.0
0.0
15.2
0.0
3.1
7.8
2.4
3.4
3.4
10.3
4.7
4.1
10.1
11.4
12.7
13.4
11.3
13.0
3.9
0.0
0.0
0.0
0.3
1.7
2.3
0.6
17.3
17.0
17.0
12.7
13.0
7.3
7.7
8.2
std
0.0
0.0
0.0
0.0
0.0
0.0
0.5
3.0
1.8
2.7
0.7
6.6
2.7
1.8
7.2
3.9
7.5
7.5
5.7
7.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
1.3
2.3
2.3
6.0
3.0
3.9
5.4
8.8
min
19.8
0.0
0.0
0.0
15.2
0.0
2.6
5.8
0.9
1.7
2.7
3.1
0.7
1.5
1.1
4.7
0.5
1.0
3.3
4.7
3.8
0.0
0.0
0.0
0.3
1.7
2.3
0.6
16.5
15.7
15.7
4.4
7.2
1.4
4.0
1.3
p2.5
19.8
0.0
0.0
0.0
15.2
0.0
2.6
5.8
0.9
1.7
2.7
3.1
0.7
1.5
1.1
4.7
0.5
1.0
3.3
4.7
3.8
0.0
0.0
0.0
0.3
1.7
2.3
0.6
16.5
15.7
15.7
4.4
7.2
1.4
4.0
1.3
p50
19.8
0.0
0.0
0.0
15.2
0.0
3.1
6.7
1.9
2.3
3.4
7.4
5.7
4.6
7.6
11.2
13.6
14.7
10.6
16.1
3.9
0.0
0.0
0.0
0.3
1.7
2.3
0.6
16.6
15.7
15.7
14.7
12.0
9.0
5.6
5.8
P97.5
19.8
0.0
0.0
0.0
15.2
0.0
3.7
12.2
5.0
7.3
4.4
18.6
6.7
5.7
18.8
15.3
19.3
19.6
18.0
18.2
3.9
0.0
0.0
0.0
0.3
1.7
2.3
0.7
18.8
19.6
19.6
19.0
19.0
9.6
15.4
19.8
p100
19.8
0.0
0.0
0.0
15.2
0.0
3.7
12.2
5.0
7.3
4.4
18.6
6.7
5.7
18.8
15.3
19.3
19.6
18.0
18.2
3.9
0.0
0.0
0.0
0.3
1.7
2.3
0.7
18.8
19.6
19.6
19.0
19.0
9.6
15.4
19.8
A-74
-------
monid
330131007
330150009
330150014
330150015
330190003
340010005
340035001
340051001
340070003
340071001
340110007
340130011
340130016
340150002
340170006
340171002
340232003
340273001
340390003
340390004
350130008
350130017
350151004
350170001
350171003
350230005
350450008
350450009
350450017
350451005
360010012
360050073
360050080
360050083
360050110
360130005
n
4
9
9
9
2
0
61
21
60
2
4
59
61
50
59
71
21
2
38
38
1
13
4
1
1
0
7
2
0
8
9
68
66
56
67
0
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
7708
1523
1523
1523
110
457
719
179
8
161
465
453
529
467
421
80
19
610
609
37
44
1058
263
263
2478
293
6274
40
399
406
119
402
std
9906
2990
2990
2990
81
2442
3104
644
1
198
2471
2431
1281
2471
2267
206
8
3074
3075
92
973
2496
378
10983
46
2309
2344
355
2326
min
41
6
6
6
53
6
5
5
8
28
5
5
5
5
5
6
13
5
5
37
5
168
263
263
11
25
11
7
5
5
6
5
p2.5
41
6
6
6
53
6
5
5
8
28
6
6
6
5
5
6
13
5
5
37
5
168
263
263
11
25
11
7
6
6
6
6
p50
4945
52
52
52
110
22
35
25
8
81
25
25
44
25
18
16
19
19
19
37
11
983
263
263
2554
293
2630
20
22
18
19
21
P97.5
20902
8057
8057
8057
168
2302
14266
2378
9
456
1845
1845
4450
1845
2302
958
25
18958
18958
37
345
2099
263
263
5919
560
32847
153
2302
2302
1129
2302
p100
20902
8057
8057
8057
168
18958
14266
4450
9
456
18958
18958
6720
18958
18958
958
25
18958
18958
37
345
2099
263
263
5919
560
32847
153
18958
18958
2302
18958
Distance of monitor to SO2 emission source (km)1
mean
9.3
9.0
9.6
8.9
2.5
0.0
14.8
10.7
9.7
10.2
7.5
13.1
13.4
13.2
13.0
11.9
8.6
17.7
11.5
11.2
17.9
14.8
8.6
6.1
1.5
0.0
17.2
3.3
0.0
6.1
10.8
10.0
10.6
11.2
10.1
0.0
std
2.1
6.9
7.0
7.1
1.7
0.0
3.7
6.7
3.4
0.5
6.6
4.9
5.0
3.7
4.6
5.0
4.6
3.1
5.3
5.6
0.0
4.0
8.4
0.0
0.0
0.0
3.5
2.0
0.0
3.8
5.2
4.9
5.0
5.6
4.9
0.0
min
7.5
2.0
1.0
1.9
1.3
0.0
2.2
1.5
2.0
9.9
1.8
1.6
1.8
2.1
2.0
0.8
1.8
15.5
2.3
0.7
17.9
1.7
0.9
6.1
1.5
0.0
11.9
2.0
0.0
3.2
3.5
3.4
1.8
1.6
2.7
0.0
p2.5
7.5
2.0
1.0
1.9
1.3
0.0
5.2
1.5
2.8
9.9
1.8
2.2
2.7
4.6
3.2
0.8
1.8
15.5
2.3
0.7
17.9
1.7
0.9
6.1
1.5
0.0
11.9
2.0
0.0
3.2
3.5
3.4
3.0
1.8
2.8
0.0
p50
8.6
4.4
5.5
4.1
2.5
0.0
15.7
12.3
9.6
10.2
5.7
14.2
14.3
12.9
13.5
11.6
9.2
17.7
12.4
12.1
17.9
15.7
8.7
6.1
1.5
0.0
19.2
3.3
0.0
3.5
9.0
9.1
9.6
11.3
9.0
0.0
P97.5
12.3
19.2
19.9
19.5
3.7
0.0
19.7
19.9
17.2
10.5
16.8
19.2
19.8
19.2
19.9
19.7
15.8
19.8
20.0
19.9
17.9
17.7
16.1
6.1
1.5
0.0
19.3
4.7
0.0
11.9
18.0
19.2
19.5
19.6
19.2
0.0
p100
12.3
19.2
19.9
19.5
3.7
0.0
19.9
19.9
19.9
10.5
16.8
19.4
19.9
19.7
19.9
19.8
15.8
19.8
20.0
19.9
17.9
17.7
16.1
6.1
1.5
0.0
19.3
4.7
0.0
11.9
18.0
19.7
19.9
19.6
19.7
0.0
A-75
-------
monid
360130006
360130011
360150003
360290005
360294002
360298001
360310003
360330004
360410005
360430005
360470011
360470076
360530006
360551004
360551007
360556001
360590005
360610010
360610056
360632008
360671015
360790005
360810097
360810124
360830004
360831005
360850067
360930003
361030002
361030009
361111005
370030003
370130003
370130004
370130006
370370004
n
1
0
2
10
16
9
0
0
0
0
77
67
0
4
4
4
12
77
76
13
4
0
60
66
3
2
48
4
9
10
0
0
1
1
1
4
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
52177
202
4073
2608
4518
377
428
12595
12595
12595
151
375
382
3134
820
136
122
126
94
515
24
156
734
4730
4730
4730
119
std
270
12273
9706
12932
2178
2333
14519
14519
14519
301
2178
2192
10777
1602
358
342
106
124
2737
26
344
2013
71
min
52177
11
8
8
8
5
5
8
8
8
6
5
5
8
8
5
5
10
6
5
6
6
11
4730
4730
4730
12
p2.5
52177
11
8
8
8
5
5
8
8
8
6
5
5
8
8
6
6
10
6
6
6
6
11
4730
4730
4730
12
p50
52177
202
182
166
247
18
17
11988
11988
11988
26
17
18
118
24
22
21
153
94
17
14
19
42
4730
4730
4730
148
P97.5
52177
393
38999
38999
38999
2302
2302
26395
26395
26395
1057
2302
2302
38999
3223
1129
1129
217
182
1845
62
1057
6453
4730
4730
4730
165
p100
52177
393
38999
38999
38999
18958
18958
26395
26395
26395
1057
18958
18958
38999
3223
2302
2302
217
182
18958
62
1057
6453
4730
4730
4730
165
Distance of monitor to SO2 emission source (km)1
mean
2.0
0.0
10.2
10.2
10.4
13.5
0.0
0.0
0.0
0.0
10.3
11.6
0.0
11.0
11.3
10.5
11.8
10.4
9.9
9.3
5.9
0.0
14.8
12.5
18.4
17.6
14.0
9.5
9.3
11.3
0.0
0.0
2.2
2.7
1.1
17.2
std
0.0
0.0
13.6
4.7
6.2
5.6
0.0
0.0
0.0
0.0
5.5
4.8
0.0
4.2
4.1
6.8
4.8
5.4
5.4
7.3
4.3
0.0
4.0
4.0
1.8
1.6
4.0
6.6
5.8
5.7
0.0
0.0
0.0
0.0
0.0
3.7
min
2.0
0.0
0.6
2.5
1.6
4.6
0.0
0.0
0.0
0.0
0.7
2.3
0.0
7.6
6.4
5.2
1.9
0.3
0.3
0.3
1.9
0.0
2.9
2.1
16.3
16.5
5.5
2.0
1.9
2.0
0.0
0.0
2.2
2.7
1.1
11.8
p2.5
2.0
0.0
0.6
2.5
1.6
4.6
0.0
0.0
0.0
0.0
1.9
3.1
0.0
7.6
6.4
5.2
1.9
1.4
1.4
0.3
1.9
0.0
5.0
2.3
16.3
16.5
6.2
2.0
1.9
2.0
0.0
0.0
2.2
2.7
1.1
11.8
p50
2.0
0.0
10.2
11.1
12.3
14.7
0.0
0.0
0.0
0.0
10.8
11.5
0.0
10.0
11.9
8.5
11.8
11.1
10.6
12.2
5.2
0.0
15.5
12.4
19.3
17.6
14.2
9.7
7.3
11.9
0.0
0.0
2.2
2.7
1.1
18.6
P97.5
2.0
0.0
19.9
15.4
18.3
19.0
0.0
0.0
0.0
0.0
19.2
19.4
0.0
16.5
15.0
19.8
19.1
19.4
19.9
19.8
11.5
0.0
19.9
19.5
19.6
18.8
19.6
16.5
18.2
19.3
0.0
0.0
2.2
2.7
1.1
19.9
p100
2.0
0.0
19.9
15.4
18.3
19.0
0.0
0.0
0.0
0.0
19.7
19.9
0.0
16.5
15.0
19.8
19.1
19.6
19.9
19.8
11.5
0.0
20.0
20.0
19.6
18.8
19.9
16.5
18.2
19.3
0.0
0.0
2.2
2.7
1.1
19.9
A-76
-------
monid
370511003
370590002
370610002
370650099
370670022
371010002
371090004
371170001
371190034
371190041
371290002
371290006
371310002
371450003
371470099
371730002
380070002
380070111
380130002
380130004
380150003
380171003
380171004
380250003
380530002
380530104
380530111
380550113
380570001
380570004
380570102
380570118
380570123
380570124
380590002
380590003
n
5
4
5
1
9
2
1
2
12
12
9
12
3
3
2
0
1
0
0
1
1
3
2
1
1
0
2
0
2
2
2
2
2
2
1
1
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
295
1949
83
325
438
15
10
1713
86
68
3325
2502
805
32251
14
283
426
4592
257
378
5
210
411
45808
45808
45808
45808
45808
45808
4592
4592
std
264
3658
132
848
4
2329
121
103
6800
5987
759
54874
3
226
119
522
55924
55924
55924
55924
55924
55924
min
17
13
6
325
5
12
10
66
5
5
6
6
16
5
12
283
426
4592
15
294
5
210
42
6264
6264
6264
6264
6264
6264
4592
4592
p2.5
17
13
6
325
5
12
10
66
5
5
6
6
16
5
12
283
426
4592
15
294
5
210
42
6264
6264
6264
6264
6264
6264
4592
4592
p50
173
175
36
325
46
15
10
1713
11
11
313
50
871
1136
14
283
426
4592
294
378
5
210
411
45808
45808
45808
45808
45808
45808
4592
4592
P97.5
675
7432
317
325
2591
17
10
3360
320
320
20865
20865
1529
95610
16
283
426
4592
462
462
5
210
781
85352
85352
85352
85352
85352
85352
4592
4592
p100
675
7432
317
325
2591
17
10
3360
320
320
20865
20865
1529
95610
16
283
426
4592
462
462
5
210
781
85352
85352
85352
85352
85352
85352
4592
4592
Distance of monitor to SO2 emission source (km)1
mean
15.8
15.3
12.3
16.1
6.3
10.3
10.7
6.6
13.3
12.7
14.5
6.9
4.2
18.8
1.3
0.0
11.4
0.0
0.0
18.6
9.8
7.7
9.0
13.9
17.3
0.0
16.1
0.0
2.5
2.7
5.4
10.7
14.3
18.6
2.6
5.1
std
2.5
4.3
4.9
0.0
5.7
7.5
0.0
7.8
4.7
5.0
4.9
4.8
1.8
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.9
1.1
0.0
0.0
0.0
0.1
0.0
2.6
2.0
2.3
2.2
1.4
1.0
0.0
0.0
min
11.5
10.4
4.1
16.1
1.2
5.0
10.7
1.1
6.3
6.3
2.3
0.6
2.1
18.4
1.3
0.0
11.4
0.0
0.0
18.6
9.8
3.0
8.2
13.9
17.3
0.0
16.1
0.0
0.7
1.3
3.8
9.1
13.3
17.9
2.6
5.1
p2.5
11.5
10.4
4.1
16.1
1.2
5.0
10.7
1.1
6.3
6.3
2.3
0.6
2.1
18.4
1.3
0.0
11.4
0.0
0.0
18.6
9.8
3.0
8.2
13.9
17.3
0.0
16.1
0.0
0.7
1.3
3.8
9.1
13.3
17.9
2.6
5.1
p50
16.5
15.6
13.1
16.1
3.9
10.3
10.7
6.6
12.8
12.2
15.4
7.1
5.1
18.7
1.3
0.0
11.4
0.0
0.0
18.6
9.8
4.6
9.0
13.9
17.3
0.0
16.1
0.0
2.5
2.7
5.4
10.7
14.3
18.6
2.6
5.1
P97.5
17.9
19.6
17.0
16.1
17.8
15.6
10.7
12.2
19.8
19.8
19.0
14.5
5.3
19.3
1.3
0.0
11.4
0.0
0.0
18.6
9.8
15.7
9.7
13.9
17.3
0.0
16.2
0.0
4.3
4.1
7.0
12.2
15.3
19.3
2.6
5.1
p100
17.9
19.6
17.0
16.1
17.8
15.6
10.7
12.2
19.8
19.8
19.0
14.5
5.3
19.3
1.3
0.0
11.4
0.0
0.0
18.6
9.8
15.7
9.7
13.9
17.3
0.0
16.2
0.0
4.3
4.1
7.0
12.2
15.3
19.3
2.6
5.1
A-77
-------
monid
380650002
380910001
381050103
381050105
390010001
390030002
390071001
390133002
390170004
390171004
390230003
390250021
390290016
390290022
390292001
390350038
390350045
390350060
390350065
390356001
390490004
390490034
390530002
390610010
390612003
390810016
390810017
390811001
390850003
390853002
390870006
390930017
390930026
390931003
390950008
390950024
n
1
0
1
1
1
9
5
5
11
9
4
6
9
9
8
10
10
10
10
13
6
6
6
10
11
17
17
13
6
3
8
3
2
3
9
10
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
28565
1605
1605
19670
442
1731
27781
907
1546
509
15304
20696
20696
22401
740
740
740
740
5759
75
75
31718
9265
660
13129
13129
6005
12044
1600
1425
165
27
165
4149
3745
std
535
3761
23029
1265
2186
349
28111
19955
19955
20621
916
916
916
916
16867
74
74
26583
26865
817
20063
20063
15392
24426
2615
2178
241
29
241
4513
4443
min
28565
1605
1605
19670
16
12
795
56
56
105
26
18
18
18
15
15
15
15
8
5
5
9
12
12
10
10
10
8
18
25
6
6
6
204
113
p2.5
28565
1605
1605
19670
16
12
795
56
56
105
26
18
18
18
15
15
15
15
8
5
5
9
12
12
10
10
10
8
18
25
6
6
6
204
113
p50
28565
1605
1605
19670
45
34
35454
233
309
492
145
24766
24766
25596
382
382
382
382
382
64
64
29551
537
268
361
361
234
2390
163
343
47
27
47
3712
2406
P97.5
28565
1605
1605
19670
1469
8458
56009
3998
6275
946
69953
59928
59928
59928
2453
2453
2453
2453
61629
192
192
74452
85699
2164
59928
59928
53414
61629
4618
6285
442
47
442
13581
13581
p100
28565
1605
1605
19670
1469
8458
56009
3998
6275
946
69953
59928
59928
59928
2453
2453
2453
2453
61629
192
192
74452
85699
2164
59928
59928
53414
61629
4618
6285
442
47
442
13581
13581
Distance of monitor to SO2 emission source (km)1
mean
8.5
0.0
2.8
1.8
11.4
8.5
17.3
14.5
14.7
6.5
12.2
15.0
12.7
12.7
11.4
9.8
10.1
10.4
9.8
13.8
8.7
9.5
7.0
16.1
8.7
9.5
9.6
4.9
9.1
5.3
13.7
11.4
3.3
11.6
8.1
11.4
std
0.0
0.0
0.0
0.0
0.0
0.4
0.6
5.1
6.9
6.5
6.1
2.7
3.6
3.6
4.1
4.9
5.5
5.7
4.3
7.1
3.4
3.0
7.4
3.0
5.5
7.1
6.9
5.6
4.2
6.0
6.0
2.2
0.5
2.1
5.5
6.4
min
8.5
0.0
2.8
1.8
11.4
7.9
16.6
6.0
0.9
1.7
5.8
12.7
7.2
7.2
4.6
1.9
1.2
1.0
2.0
1.7
2.9
3.4
1.0
8.6
0.4
1.7
2.0
0.3
5.6
1.1
2.2
8.9
3.0
9.2
2.5
3.9
p2.5
8.5
0.0
2.8
1.8
11.4
7.9
16.6
6.0
0.9
1.7
5.8
12.7
7.2
7.2
4.6
1.9
1.2
1.0
2.0
1.7
2.9
3.4
1.0
8.6
0.4
1.7
2.0
0.3
5.6
1.1
2.2
8.9
3.0
9.2
2.5
3.9
p50
8.5
0.0
2.8
1.8
11.4
8.3
17.2
15.8
18.5
3.3
12.0
14.1
13.5
13.6
10.8
11.7
10.4
13.3
9.8
16.8
9.2
10.4
3.6
16.8
8.0
5.6
5.9
2.9
7.4
2.6
15.5
12.5
3.3
12.5
4.5
9.5
P97.5
8.5
0.0
2.8
1.8
11.4
9.3
18.2
19.8
19.3
19.8
19.2
18.7
18.1
18.2
19.3
14.3
15.8
15.5
14.5
20.0
12.9
11.5
16.5
19.7
19.4
19.0
18.6
18.0
15.2
12.3
19.3
12.8
3.6
13.1
14.6
18.6
p100
8.5
0.0
2.8
1.8
11.4
9.3
18.2
19.8
19.3
19.8
19.2
18.7
18.1
18.2
19.3
14.3
15.8
15.5
14.5
20.0
12.9
11.5
16.5
19.7
19.4
19.0
18.6
18.0
15.2
12.3
19.3
12.8
3.6
13.1
14.6
18.6
A-78
-------
monid
390990009
390990013
391051001
391130025
391150003
391150004
391450013
391450020
391450022
391510016
391530017
391530022
391570003
391570006
400219002
400710602
400719003
400719010
400979014
401010167
401090025
401091037
401159004
401430175
401430235
401430501
420030002
420030010
420030021
420030031
420030032
420030064
420030067
420030116
420031301
420033003
n
10
10
6
6
2
2
0
3
3
7
4
4
7
6
0
2
2
0
6
8
2
2
1
10
10
10
19
55
64
62
64
54
16
19
57
54
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
2107
2107
31718
1609
57763
57763
1450
1450
181
2763
2763
368
426
3502
3502
3180
3751
91
91
62
938
938
938
103
85
819
757
819
213
73
103
914
213
std
5350
5350
26583
2326
38696
38696
1306
1306
213
2244
2244
741
795
457
457
5200
4529
110
110
1088
1088
1088
137
101
5274
5327
5274
741
105
137
5587
741
min
6
6
9
105
30401
30401
25
25
10
863
863
15
15
3178
3178
173
23
13
13
62
9
9
9
7
5
5
5
5
5
7
7
5
5
p2.5
6
6
9
105
30401
30401
25
25
10
863
863
15
15
3178
3178
173
23
13
13
62
9
9
9
7
7
7
7
7
6
7
7
7
6
p50
353
353
29551
753
57763
57763
1737
1737
43
2091
2091
38
38
3502
3502
713
1130
91
91
62
263
263
263
30
49
47
46
47
52
29
30
47
52
P97.5
17244
17244
74452
6275
85125
85125
2589
2589
510
6009
6009
2017
2017
3825
3825
13428
9866
169
169
62
2729
2729
2729
468
407
5395
468
5395
1164
407
468
5395
1164
p100
17244
17244
74452
6275
85125
85125
2589
2589
510
6009
6009
2017
2017
3825
3825
13428
9866
169
169
62
2729
2729
2729
468
468
42018
42018
42018
5395
407
468
42018
5395
Distance of monitor to SO2 emission source (km)1
mean
12.4
12.4
13.6
13.4
4.8
5.1
0.0
9.6
8.4
6.6
5.0
3.9
12.0
6.4
0.0
3.4
1.8
0.0
4.7
5.9
8.7
8.8
5.2
11.8
10.7
12.6
7.4
14.2
11.7
13.9
11.7
6.0
15.1
7.4
9.9
5.6
std
7.3
7.5
2.2
5.4
0.2
0.3
0.0
6.9
7.5
1.5
2.4
0.7
6.4
6.1
0.0
2.3
2.0
0.0
1.3
4.2
4.5
7.9
0.0
6.9
6.9
6.8
5.9
5.6
3.3
5.1
3.3
5.2
3.5
5.1
4.6
5.4
min
2.0
1.7
11.6
7.3
4.6
4.9
0.0
4.6
2.8
4.5
1.4
3.0
0.6
0.4
0.0
1.8
0.4
0.0
2.7
3.7
5.6
3.2
5.2
1.4
1.5
2.7
0.6
2.5
3.2
1.3
3.1
2.0
6.1
2.1
1.1
1.0
p2.5
2.0
1.7
11.6
7.3
4.6
4.9
0.0
4.6
2.8
4.5
1.4
3.0
0.6
0.4
0.0
1.8
0.4
0.0
2.7
3.7
5.6
3.2
5.2
1.4
1.5
2.7
0.6
2.5
4.8
1.4
4.7
2.0
6.1
2.1
1.1
1.0
p50
15.6
15.8
13.0
13.4
4.8
5.1
0.0
6.7
5.4
5.9
6.0
4.1
13.3
5.3
0.0
3.4
1.8
0.0
5.5
3.7
8.7
8.8
5.2
13.9
13.4
14.2
8.6
15.5
13.1
14.4
13.2
3.1
15.7
7.7
11.0
2.3
P97.5
19.6
19.6
17.8
19.4
4.9
5.3
0.0
17.5
16.9
8.7
6.6
4.6
18.6
14.2
0.0
5.0
3.2
0.0
5.7
15.8
11.9
14.4
5.2
18.3
18.1
19.2
18.1
20.0
18.0
18.7
18.1
17.9
19.7
17.0
17.5
17.8
p100
19.6
19.6
17.8
19.4
4.9
5.3
0.0
17.5
16.9
8.7
6.6
4.6
18.6
14.2
0.0
5.0
3.2
0.0
5.7
15.8
11.9
14.4
5.2
18.3
18.1
19.2
18.1
20.0
18.7
19.8
18.7
18.2
19.7
17.0
17.8
17.8
A-79
-------
monid
420033004
420070002
420070004
420070005
420070014
420110009
420110100
420130801
420170012
420210011
420270100
420430401
420450002
420450109
420490003
420630004
420692006
420710007
420730015
420770004
420791101
420810100
420810403
420850100
420910013
420950025
420950100
420958000
420990301
421010004
421010022
421010024
421010027
421010029
421010047
421010048
n
55
10
7
8
10
13
12
1
22
4
4
8
57
45
5
3
5
5
9
13
4
3
3
2
28
18
15
16
0
61
66
36
63
67
65
60
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
209
18726
5881
5173
4400
1140
1231
441
687
4195
1090
107
681
855
824
4796
13
75
3206
703
117
28
28
14
171
676
2179
2045
102
285
46
99
262
270
104
std
735
19819
11104
10474
9400
3818
3973
3033
5171
1267
99
1415
1553
1068
5156
5
109
8423
1041
160
28
28
4
704
1020
5602
5439
316
1022
77
311
1007
1022
318
min
5
18
9
9
8
14
14
441
5
34
53
10
5
5
10
1497
6
6
6
7
9
6
6
11
5
7
7
7
5
5
5
5
5
5
5
p2.5
6
18
9
9
8
14
14
441
5
34
53
10
5
5
10
1497
6
6
6
7
9
6
6
11
5
7
7
7
6
5
5
6
5
5
6
p50
49
15912
118
157
157
37
34
441
27
3004
834
78
47
91
228
2154
15
23
28
120
53
18
18
14
15
86
120
86
20
26
13
20
24
26
22
P97.5
1164
59928
30312
30312
30312
13841
13841
441
14266
10738
2638
313
5051
5051
2398
10738
18
264
25551
2888
351
59
59
17
3753
2888
22057
22057
560
4450
407
560
4450
4450
560
p100
5395
59928
30312
30312
30312
13841
13841
441
14266
10738
2638
313
6720
6720
2398
10738
18
264
25551
2888
351
59
59
17
3753
2888
22057
22057
2378
6720
407
2378
6720
6720
2378
Distance of monitor to SO2 emission source (km)1
mean
5.9
13.0
14.5
9.6
12.0
9.8
8.7
1.3
11.1
8.5
10.4
5.4
13.6
12.4
3.1
18.4
10.9
3.7
12.5
12.5
12.3
11.3
15.8
10.8
15.3
13.1
10.4
10.1
0.0
10.5
8.0
13.0
9.8
8.3
7.9
10.4
std
6.0
3.2
5.1
5.6
3.1
7.1
6.3
0.0
6.5
7.4
6.2
4.0
5.5
6.4
1.9
1.4
7.4
3.7
5.6
5.8
3.4
0.7
1.1
11.8
4.5
4.3
5.5
5.9
0.0
5.2
5.6
3.8
4.6
4.7
4.5
4.9
min
0.6
9.2
7.4
2.5
7.1
1.3
1.5
1.3
1.2
1.5
2.3
0.8
1.3
0.5
1.2
17.0
2.1
0.6
0.6
0.3
7.8
10.6
14.9
2.4
1.4
4.0
2.5
0.6
0.0
1.0
0.9
6.3
0.8
1.1
0.6
0.9
p2.5
0.7
9.2
7.4
2.5
7.1
1.3
1.5
1.3
1.2
1.5
2.3
0.8
1.9
1.6
1.2
17.0
2.1
0.6
0.6
0.3
7.8
10.6
14.9
2.4
1.4
4.0
2.5
0.6
0.0
1.3
1.0
6.3
1.7
1.8
0.8
1.7
p50
3.3
11.4
16.0
8.8
12.0
10.3
7.5
1.3
12.4
8.9
11.4
3.7
15.8
13.3
2.6
18.4
8.2
2.7
13.2
12.0
12.9
11.2
15.4
10.8
16.2
14.1
10.7
9.1
0.0
10.9
7.0
12.6
11.0
6.8
6.4
10.7
P97.5
18.8
18.6
19.8
17.1
17.2
19.8
17.2
1.3
19.6
14.9
16.6
12.1
19.8
19.9
5.4
19.8
19.6
10.1
18.0
19.3
15.8
12.0
16.9
19.1
20.0
19.7
19.3
18.8
0.0
19.2
19.4
19.9
19.7
18.9
17.6
18.6
p100
18.8
18.6
19.8
17.1
17.2
19.8
17.2
1.3
19.6
14.9
16.6
12.1
19.8
20.0
5.4
19.8
19.6
10.1
18.0
19.3
15.8
12.0
16.9
19.1
20.0
19.7
19.3
18.8
0.0
19.7
20.0
19.9
19.7
19.6
17.9
19.2
A-80
-------
monid
421010055
421010136
421070003
421230003
421230004
421250005
421250200
421255001
421290008
421330008
440070012
440071005
440071009
450030003
450110001
450190003
450190046
450430006
450450008
450450009
450630008
450730001
450750003
450790007
450790021
450791003
450791006
460330132
460710001
460990007
470010028
470090002
470090006
470090101
470110102
470310004
n
66
68
6
2
2
33
1
8
3
9
54
55
55
13
1
16
0
7
12
13
11
1
5
10
8
13
10
0
0
1
8
3
3
3
2
0
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
286
319
831
2445
2445
257
7
321
24
8943
41
41
41
1654
65
2183
5834
89
83
948
5
1433
61
5061
995
4289
496
5595
1421
1421
1421
2719
std
1022
1042
687
659
659
945
439
9
22698
90
89
89
2599
6339
14038
136
132
2944
1913
103
12720
2730
11350
14808
2325
2325
2325
3687
min
5
5
8
1979
1979
5
7
7
16
14
5
5
5
8
65
6
6
6
6
5
5
5
5
7
5
7
496
7
6
6
6
112
p2.5
5
5
8
1979
1979
5
7
7
16
14
5
5
5
8
65
6
6
6
6
5
5
5
5
7
5
7
496
7
6
6
6
112
p50
26
27
674
2445
2445
47
7
82
22
171
13
13
13
549
65
28
24
20
19
9
5
211
18
89
52
89
496
34
153
153
153
2719
P97.5
4450
4450
1743
2911
2911
5395
7
1017
34
68932
392
392
392
8275
65
25544
37622
411
411
9820
5
4088
343
36378
9820
36378
496
42188
4104
4104
4104
5326
p100
6720
6720
1743
2911
2911
5395
7
1017
34
68932
521
521
521
8275
65
25544
37622
411
411
9820
5
4088
343
36378
9820
36378
496
42188
4104
4104
4104
5326
Distance of monitor to SO2 emission source (km)1
mean
7.9
8.8
10.4
4.0
3.0
15.7
1.1
15.9
9.8
9.3
8.4
9.1
8.6
15.3
13.2
7.2
0.0
4.6
11.7
10.1
11.5
14.9
8.5
14.0
14.7
10.9
17.5
0.0
0.0
17.5
12.2
5.7
5.4
12.1
2.5
0.0
std
5.4
5.4
7.4
1.2
1.6
4.7
0.0
4.1
1.4
5.8
5.8
5.5
6.0
1.5
0.0
5.0
0.0
4.3
4.5
5.7
5.4
0.0
5.1
4.1
1.2
5.9
3.3
0.0
0.0
0.0
6.5
5.7
5.3
6.9
1.2
0.0
min
1.3
1.1
3.3
3.2
1.9
1.1
1.1
9.3
8.7
0.8
0.3
0.9
0.1
11.4
13.2
1.1
0.0
0.2
2.1
4.0
0.5
14.9
3.4
6.4
12.3
1.4
8.2
0.0
0.0
17.5
0.9
0.7
1.4
4.2
1.6
0.0
p2.5
1.4
1.4
3.3
3.2
1.9
1.1
1.1
9.3
8.7
0.8
0.4
1.0
0.4
11.4
13.2
1.1
0.0
0.2
2.1
4.0
0.5
14.9
3.4
6.4
12.3
1.4
8.2
0.0
0.0
17.5
0.9
0.7
1.4
4.2
1.6
0.0
p50
6.8
9.3
8.8
4.0
3.0
17.5
1.1
17.2
9.3
10.1
5.9
8.4
6.3
15.3
13.2
6.2
0.0
3.4
10.7
5.4
13.0
14.9
9.6
15.9
15.3
10.9
18.9
0.0
0.0
17.5
12.8
4.5
3.3
15.4
2.5
0.0
P97.5
18.8
18.7
19.2
4.9
4.1
18.7
1.1
19.7
11.5
17.7
18.9
18.5
19.5
17.5
13.2
16.3
0.0
13.2
17.4
17.3
19.2
14.9
15.8
18.7
15.6
18.5
19.1
0.0
0.0
17.5
18.8
11.9
11.3
16.7
3.4
0.0
p100
20.0
19.8
19.2
4.9
4.1
18.7
1.1
19.7
11.5
17.7
19.0
19.0
19.9
17.5
13.2
16.3
0.0
13.2
17.4
17.3
19.2
14.9
15.8
18.7
15.6
18.5
19.1
0.0
0.0
17.5
18.8
11.9
11.3
16.7
3.4
0.0
A-81
-------
monid
470370011
470730002
470850020
471070101
471250006
471250106
471390003
471390007
471390008
471390009
471450009
471570034
471570043
471570046
471571034
471610007
471630007
471630009
471651002
480610006
481130069
481390015
481390016
481390017
481410037
481410053
481410058
481670005
481671002
481830001
482010046
482010051
482010059
482010062
482010070
482011035
n
9
3
6
3
6
6
1
1
1
1
4
18
18
2
19
3
10
12
4
0
9
12
12
12
13
13
16
43
43
5
29
2
38
37
31
39
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
891
11831
18599
1834
222
222
1900
1900
1900
1900
19470
1204
1204
1973
1150
5561
3010
2513
8593
34
664
664
664
44
44
38
185
185
13289
606
13
674
694
790
657
std
2248
10420
44191
3024
401
401
22311
2391
2391
2640
2336
5107
5303
4935
10129
25
993
993
993
92
92
83
611
611
12287
1182
8
1486
1503
1622
1470
min
9
6
12
64
8
8
1900
1900
1900
1900
9
5
5
106
5
21
22
13
88
9
13
13
13
5
5
5
5
5
6
6
7
6
6
6
6
p2.5
9
6
12
64
8
8
1900
1900
1900
1900
9
5
5
106
5
21
22
13
88
9
13
13
13
5
5
5
6
6
6
6
7
6
6
6
6
p50
60
15822
281
112
35
35
1900
1900
1900
1900
19188
32
32
1973
35
6580
495
286
7029
18
57
57
57
11
11
12
22
22
19024
161
13
48
49
161
46
P97.5
6842
19666
108788
5326
1025
1025
1900
1900
1900
1900
39495
6540
6540
3839
6540
10081
16855
16855
20226
69
3003
3003
3003
345
345
345
1937
1937
24837
5097
18
6968
6968
6968
6968
p100
6842
19666
108788
5326
1025
1025
1900
1900
1900
1900
39495
6540
6540
3839
6540
10081
16855
16855
20226
69
3003
3003
3003
345
345
345
3599
3599
24837
5097
18
6968
6968
6968
6968
Distance of monitor to SO2 emission source (km)1
mean
10.4
2.9
3.2
7.6
6.2
7.1
3.1
1.6
1.4
1.0
10.9
11.4
9.6
6.0
3.5
1.8
3.7
5.7
4.2
0.0
12.1
9.5
9.0
9.6
9.7
9.7
13.9
2.3
3.6
18.9
12.8
19.1
10.3
14.8
10.7
8.6
std
3.6
2.1
1.8
10.2
6.9
7.3
0.0
0.0
0.0
0.0
6.7
2.2
1.7
6.7
5.6
0.2
2.6
6.0
1.8
0.0
5.7
5.8
6.3
6.9
1.8
1.6
2.3
1.3
1.1
0.5
3.1
0.6
5.9
3.8
5.3
5.4
min
5.6
1.7
1.6
0.5
1.0
1.5
3.1
1.6
1.4
1.0
5.3
4.8
5.3
1.3
0.5
1.7
1.7
2.0
2.9
0.0
2.0
2.3
2.9
1.9
4.5
5.1
9.5
1.2
2.5
18.6
6.2
18.7
1.8
7.8
2.2
1.6
p2.5
5.6
1.7
1.6
0.5
1.0
1.5
3.1
1.6
1.4
1.0
5.3
4.8
5.3
1.3
0.5
1.7
1.7
2.0
2.9
0.0
2.0
2.3
2.9
1.9
4.5
5.1
9.5
1.3
2.5
18.6
6.2
18.7
1.8
7.8
2.2
1.6
p50
10.7
1.7
2.6
3.0
2.5
3.5
3.1
1.6
1.4
1.0
9.5
11.8
10.0
6.0
0.7
1.7
2.6
2.7
3.5
0.0
12.9
9.4
6.1
7.6
10.0
9.9
14.7
2.0
3.3
18.7
13.1
19.1
8.5
15.7
8.7
7.7
P97.5
17.6
5.2
6.3
19.3
15.0
16.3
3.1
1.6
1.4
1.0
19.1
15.3
11.4
10.8
18.0
1.9
10.7
18.7
6.9
0.0
20.0
16.6
17.4
18.6
12.0
11.9
16.0
3.3
4.6
19.9
19.6
19.5
19.5
20.0
19.5
17.6
p100
17.6
5.2
6.3
19.3
15.0
16.3
3.1
1.6
1.4
1.0
19.1
15.3
11.4
10.8
18.0
1.9
10.7
18.7
6.9
0.0
20.0
16.6
17.4
18.6
12.0
11.9
16.0
9.5
9.5
19.9
19.6
19.5
19.5
20.0
19.5
17.6
A-82
-------
monid
482011050
482450009
482450011
482450020
482570005
483550025
483550026
483550032
490050004
490110001
490110004
490350012
490351001
490352004
500070003
500070014
500210002
510360002
510590005
510590018
510591004
510591005
510595001
511130003
511611004
511650002
511650003
515100009
516500004
517100023
517600024
530090010
530090012
530330057
530330080
530530021
n
46
16
27
8
0
17
19
17
1
6
6
6
7
3
1
1
0
18
5
10
11
13
11
1
8
7
6
11
15
21
14
1
1
5
5
3
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
243
863
999
170
468
424
468
5
468
468
468
833
1245
6
6
4818
31
1820
1664
1416
1566
7
85
40
39
1663
285
1738
191
756
756
241
241
179
std
1028
2732
2362
306
1086
1032
1086
500
500
500
1006
1415
17274
46
5043
4813
4435
4837
117
36
40
4813
505
7026
363
301
301
213
min
6
6
6
6
6
6
6
5
8
8
8
8
8
6
6
7
8
8
7
7
6
7
5
8
5
7
6
5
6
756
756
63
63
11
p2.5
7
6
6
6
6
6
6
5
8
8
8
8
8
6
6
7
8
8
7
7
6
7
5
8
5
7
6
5
6
756
756
63
63
11
p50
36
80
45
64
43
43
43
5
366
366
366
712
939
6
6
35
11
74
59
59
24
7
34
32
25
59
92
85
16
756
756
117
117
109
P97.5
829
11064
11064
908
3955
3955
3955
5
1332
1332
1332
2788
2788
6
6
73839
114
16141
16141
16141
16141
7
341
108
108
16141
1983
32344
1148
756
756
771
771
419
p100
6968
11064
11064
908
3955
3955
3955
5
1332
1332
1332
2788
2788
6
6
73839
114
16141
16141
16141
16141
7
341
108
108
16141
1983
32344
1148
756
756
771
771
419
Distance of monitor to SO2 emission source (km)1
mean
16.5
14.8
9.0
10.8
0.0
6.7
10.0
3.9
1.8
8.2
9.7
4.9
13.0
9.8
1.6
1.9
0.0
12.1
17.2
13.5
10.9
13.6
14.8
10.8
9.3
12.3
11.4
9.6
11.1
8.3
9.4
5.6
5.3
4.0
5.0
3.2
std
3.9
6.8
5.3
8.1
0.0
3.0
3.3
4.1
0.0
5.8
6.0
3.7
6.5
8.0
0.0
0.0
0.0
7.2
1.6
3.9
3.5
4.3
4.4
0.0
5.5
5.1
5.4
5.1
4.9
3.4
5.8
0.0
0.0
6.0
4.2
1.1
min
5.0
0.4
2.8
1.8
0.0
4.2
4.6
0.4
1.8
1.5
2.3
0.6
2.1
2.4
1.6
1.9
0.0
2.0
15.0
8.4
3.7
4.6
5.1
10.8
2.9
5.1
6.3
1.1
4.0
3.6
1.2
5.6
5.3
0.6
2.5
2.1
p2.5
5.3
0.4
2.8
1.8
0.0
4.2
4.6
0.4
1.8
1.5
2.3
0.6
2.1
2.4
1.6
1.9
0.0
2.0
15.0
8.4
3.7
4.6
5.1
10.8
2.9
5.1
6.3
1.1
4.0
3.6
1.2
5.6
5.3
0.6
2.5
2.1
p50
17.9
18.7
7.0
11.3
0.0
5.2
11.0
1.7
1.8
8.1
9.8
4.5
13.0
8.9
1.6
1.9
0.0
13.6
17.3
15.7
11.2
13.8
16.0
10.8
9.7
13.9
10.3
8.6
11.3
8.3
10.3
5.6
5.3
1.3
3.1
3.2
P97.5
19.1
19.7
18.1
19.9
0.0
16.4
13.6
16.0
1.8
17.7
19.2
8.9
19.6
18.3
1.6
1.9
0.0
19.9
19.4
17.5
16.3
19.0
19.8
10.8
19.1
17.8
17.9
17.9
17.9
18.8
20.0
5.6
5.3
14.7
12.5
4.3
p100
19.9
19.7
18.1
19.9
0.0
16.4
13.6
16.0
1.8
17.7
19.2
8.9
19.6
18.3
1.6
1.9
0.0
19.9
19.4
17.5
16.3
19.0
19.8
10.8
19.1
17.8
17.9
17.9
17.9
18.8
20.0
5.6
5.3
14.7
12.5
4.3
A-83
-------
monid
530530031
530570012
530571003
530610016
530730011
540090005
540090007
540110006
540250001
540290005
540290007
540290008
540290009
540290011
540290014
540290015
540290016
540291004
540390004
540390010
540392002
540511002
540610003
540610004
540610005
540690007
540990002
540990003
540990004
540990005
541071002
550090005
550250041
550410007
550730005
550790007
n
3
4
4
2
9
13
17
5
0
8
16
9
15
17
16
9
16
16
4
4
5
5
2
4
3
2
8
8
8
8
11
7
7
1
3
9
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
179
2238
2238
191
488
6005
13129
1501
22069
9282
20696
9894
13129
9282
20696
10611
10611
1529
1529
22698
27781
45992
24472
32132
37391
1271
1271
1271
1271
4375
3413
1293
5
4040
1750
std
213
2630
2630
194
695
15392
20063
2677
20983
17668
19955
18112
20063
17668
19955
17732
17732
1146
1146
47491
23029
63840
44468
51128
22660
2194
2194
2194
2194
9095
5045
2743
6715
4858
min
11
21
21
53
8
10
10
124
18
10
18
10
10
10
18
10
10
854
854
750
795
850
850
850
21367
25
25
25
25
7
9
7
5
24
5
p2.5
11
21
21
53
8
10
10
124
18
10
18
10
10
10
18
10
10
854
854
750
795
850
850
850
21367
25
25
25
25
7
9
7
5
24
5
p50
109
1793
1793
191
349
234
361
401
25596
238
24766
243
361
238
24766
302
302
1008
1008
1009
35454
45992
2952
4412
37391
343
343
343
343
1517
850
71
5
303
28
P97.5
419
5345
5345
328
2286
53414
59928
6285
59928
59928
59928
59928
59928
59928
59928
59928
59928
3245
3245
107633
56009
91134
91134
91134
53414
6285
6285
6285
6285
31006
13470
7417
5
11792
14686
p100
419
5345
5345
328
2286
53414
59928
6285
59928
59928
59928
59928
59928
59928
59928
59928
59928
3245
3245
107633
56009
91134
91134
91134
53414
6285
6285
6285
6285
31006
13470
7417
5
11792
14686
Distance of monitor to SO2 emission source (km)1
mean
1.8
2.2
1.7
0.5
16.9
5.3
10.7
13.2
0.0
9.3
13.1
12.1
11.0
10.7
11.8
12.1
10.8
11.5
10.2
9.7
9.1
10.1
4.6
11.8
9.2
13.9
9.7
9.6
9.6
9.5
8.5
4.2
7.4
8.3
10.7
6.5
std
0.9
0.8
0.6
0.1
6.2
5.3
5.3
7.1
0.0
5.3
3.8
4.2
3.5
5.2
4.0
3.5
4.3
3.9
4.3
4.6
5.6
4.7
1.4
8.9
9.7
1.8
5.5
5.5
6.0
6.4
5.4
3.4
4.7
0.0
9.2
3.4
min
1.2
1.3
1.1
0.4
0.5
0.9
3.9
0.5
0.0
4.7
4.8
6.3
1.0
3.2
1.5
7.1
1.1
1.8
6.0
5.2
2.3
2.2
3.6
0.8
1.0
12.7
1.7
1.5
1.0
0.9
2.7
1.1
2.8
8.3
0.1
1.8
p2.5
1.2
1.3
1.1
0.4
0.5
0.9
3.9
0.5
0.0
4.7
4.8
6.3
1.0
3.2
1.5
7.1
1.1
1.8
6.0
5.2
2.3
2.2
3.6
0.8
1.0
12.7
1.7
1.5
1.0
0.9
2.7
1.1
2.8
8.3
0.1
1.8
p50
1.3
2.3
1.7
0.5
19.3
2.7
8.3
16.2
0.0
7.5
13.1
11.2
12.0
8.8
11.1
12.4
10.6
11.8
10.0
9.8
6.7
11.4
4.6
13.5
6.7
13.9
10.6
10.7
11.3
11.4
8.8
3.1
5.2
8.3
15.8
5.9
P97.5
2.8
3.1
2.4
0.6
19.7
16.8
18.8
17.2
0.0
17.6
18.3
19.8
17.7
18.8
19.4
18.2
18.3
19.8
14.8
14.0
15.5
15.0
5.6
19.4
19.9
15.2
16.0
15.8
15.8
16.2
17.0
9.7
14.7
8.3
16.2
12.9
p100
2.8
3.1
2.4
0.6
19.7
16.8
18.8
17.2
0.0
17.6
18.3
19.8
17.7
18.8
19.4
18.2
18.3
19.8
14.8
14.0
15.5
15.0
5.6
19.4
19.9
15.2
16.0
15.8
15.8
16.2
17.0
9.7
14.7
8.3
16.2
12.9
A-84
-------
monid
550790026
550790041
550850996
551110007
551250001
551410016
560050857
n
9
9
2
2
0
6
4
SO2 emissions (tpy) from sources within 20 km of monitor1
mean
1750
1750
1152
31
2374
2527
std
4858
4858
1617
35
2368
3868
min
5
5
9
7
6
23
p2.5
5
5
9
7
6
23
p50
28
28
1152
31
2032
896
P97.5
14686
14686
2295
56
5782
8291
p100
14686
14686
2295
56
5782
8291
Distance of monitor to SO2 emission source (km)1
mean
7.6
10.1
0.9
14.7
0.0
5.3
4.6
std
3.0
3.0
0.1
7.4
0.0
2.6
6.5
min
3.5
5.9
0.9
9.5
0.0
2.3
1.1
p2.5
3.5
5.9
0.9
9.5
0.0
2.3
1.1
p50
7.5
10.2
0.9
14.7
0.0
4.9
1.6
P97.5
12.8
14.5
1.0
19.9
0.0
9.8
14.4
p100
12.8
14.5
1.0
19.9
0.0
9.8
14.4
Notes:
1 Mean, std , min, p2.5, p50, p97.5, max are the arithmetic average, standard deviation, minimum, 2.5th, 50th, 97.5th percentiles, and maximum distances
and emissions.
2 There were no emissions above 5 tpy for sources located within 20 km of the monitors sited in Puerto Rico and the Virgin Islands.
A-85
-------
A.2 Analysis Duplicate SO2 Values at Ambient Monitor Locations
During the screening of each of the ambient monitoring data sets, it became evident that
simultaneous measurements were present. Staff analyzed the duplicate SO2 measurements to
discern if there were any differences in the reported/measured values because ultimately only
one value would be selected for use in each of the final screened data sets. Staff was not
interested in whether multiple monitors were present at a particular monitoring site or if there
were duplicate reporting of SC>2 concentrations, only to determine that the selection of a
particular value used in the final data sets were not biased.
In selecting which of the duplicate concentrations to use for final REA data sets, staff
made the following judgements. First, the ambient monitor POC containing the greatest number
of samples was used to populate the max-5 data set. Second, where continous-5 measurements
were available and coincided with max-5 measurements, staff selected the 5-minute maximum
SC>2 concentration from the continuous-5 data set. And finally, where continuous-5 data were
available and used to estimate a 1-hour average SC>2 concentration that coincided with a reported
1-hour ambient monitor concentration, the continuous-5 1-hour average concentration was used.
Staff designed the following analyses to explore the effect the selection of one concentration
over another may have on the final data set used.
Staff calculated the relative percent difference (RPD) for each duplicate concentration,
considering measurements within the 5-max data set (n=300,438), duplicate reporting between
the continuous-5 and the max-5 data sets (n=29,058), and duplicate values between the 1-hour
and the continuous-5 data sets (n=258,457), separately. We anticipated that small fluctuations in
concentration between the duplicate data would have a greater influence on the RPD at lower
concentrations than at higher concentrations. Therefore, staff separated the duplicate values into
concentration groups for this analysis. Two groups were constructed; one with concentrations <
10 ppb and the other conrtaining concentrations > 10 ppb. The following formula calculates the
RPD for each duplicate value:
RPD = ^ ^ x 200 equation A.2-1
(Q+C2)
where,
RPD = Relative percent difference (%)
A-86
-------
C2
First 862 concentration value
Second 862 concentration value
Depending on the difference in concentration, the value for the calculated RPD could be
as low as -200 or as high +200, indicating the maximum difference between any two values,
while an RPD of zero indicates no difference. The sign of the value can also indicate the
direction of bias when comparing the first concentration to the second.
In the first comparison (i.e., the within max-5 duplicates), Ci was selected as the ambient
monitor containing the overall greater sample size/duration. Table A.2-1 summarizes the
distribution of RPDs for where duplicate values of 862 concentrations were less than 10 ppb
within the max-5 monitoring data set. On average, there were relatively small differences in the
duplicate values reported at each of the monitoring locations. Most duplicate values were within
+1-61% of one another, although some were noted at or above 100% (absolute difference). In
considering that these maximum 5-minute SC>2 concentrations are well below that of potential
interest in the exposure and risk analysis, this degree of agreement between the two values at
these concentration levels is acceptable.
Table A.2-1. Distribution of the relative percent difference (RPD) between
duplicate 5-minute maximum SO2 values at max-5 monitors, where
concentrations were < 10 ppb.
Monitor ID
290210009
290210011
290930030
290930031
290990004
290990014
290990017
290990018
291630002
n
25868
22247
54904
48417
22788
33245
21460
17025
11528
Relative Percent Difference (%)1
mean
0
-7
8
-14
-8
-12
2
2
-3
std
34
22
34
29
27
29
30
25
34
min
-196
-143
-181
-122
-120
-133
-120
-156
-164
P5
-50
-40
-40
-67
-50
-67
-50
-40
-40
p50
0
0
0
0
0
0
0
0
0
p95
67
18
67
67
67
29
67
67
67
max
100
67
100
67
100
67
120
100
67
Notes:
1 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum,
5th, median, 95th, and maximum, respectively.
When considering duplicate values > 10 ppb, the RPD was much lower at each of the
monitors (Table A.2-2). Most of the RPDs are within +/-10%, indicating excellent agreement
among the duplicate values. A small negative bias may exist with selection of the monitor with
A-87
-------
the greatest number of samples as the base monitor, but on average the difference was typically
less than 3%.
Table A.2-2. Distribution of the relative percent difference (RPD) between
duplicate 5-minute maximum SO2 values at max-5 monitors, where
concentrations were > 10 ppb.
Monitor ID
290210009
290210011
290930030
290930031
290990004
290990014
290990017
290990018
291630002
n
2333
2344
8068
7652
8627
4973
5138
2626
1195
Relative Percent Difference (%)1
mean
-2
0
-1
-3
-1
2
-1
0
-6
std
6
3
6
6
4
16
7
6
32
min
-133
-66
-120
-134
-100
-17
-137
-81
-137
P5
-10
-6
-9
-13
-7
-8
-11
-7
-133
p50
0
0
0
-2
0
0
0
0
0
p95
6
5
4
0
5
9
10
10
11
max
18
18
24
10
20
184
32
32
29
Notes:
1 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum,
5th, median, 95th, and maximum, respectively.
Staff also analyzed data where the max-5 sampling times corresponded with the
continuous-5 monitoring at the same location. Of the 29,058 duplicate measurement values, only
312 contained different values among the two sample types (i.e, a non-zero RPD). This indicates
that the majority of the data are duplicate values reported in each of the two data sets. Since
there were very few samples with RPDs deviating from zero (i.e., 1.1%), the following analysis
included only the samples that had a non-zero difference and at any concentration levels. The
distribution for the RPD given these monitors and duplicate monitoring events is provided in
Table A.2-3. On average there may be a small positive bias in selecting the continuous-5
monitoring concentrations where differences existed, however given that there were only 1% of
samples that differed among the two data sets, the overall impact to the below estimation
procedure is determined as negligible. In addition, selection of the continuous-5 measurement
preserves the relationship between the actual 5-minute maximum and the calculated 1-hour
concentration derived from the multiple 5-minute measurements that occurred within the hour,
not adding to uncertainty regarding the true relationship between the 1-hour and 5-minute
maximum concentrations.
A-88
-------
Table A.2-3. Distribution of the relative percent difference (RPD) between
simultaneous 5-minute SO2 maximum values in the max-5 and continuous-5 data
sets, where concentrations > 0 ppb.
Monitor ID
301110066
301110079
301110082
301110083
n1
76
149
47
40
Relative Percent Difference (%) 2
mean
26
27
25
78
std
57
48
52
64
min
-143
-178
-67
-120
P5
-117
-67
-67
-53
p50
16
29
29
67
p95
133
67
67
160
max
160
164
186
160
Notes:
1 This distribution is for the number of samples where the RPD was non-zero. The majority of the
duplicate measures (n=28,746) were identical.
2 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum, 5th,
median, 95th, and maximum, respectively.
In the last comparison (i.e., the 1-hour concentration duplicates), the 1-hour
concentration from the continous-5 ambient monitors was selected as Ci in equation A.2-1.
Table A.2-4 summarizes the distribution of RPDs for where duplicate measurements of SO2
concentrations were less than 10 ppb within the max-5 monitoring data set. While nearly 20%
had no difference between the duplicate values, on average, there were greater differences in the
duplicate 1-hour values at most of the monitors than was observed for the 5-minute duplicates.
Nearly 20% of the concentrations were noted at or above 100% one another (absolute
difference), however all of these were due to where reported values were zero at the 1-hour
monitor and concentrations of 1 ppb were reported for the continuous-5 monitor. This factor
contributes to the observed positive bias at most of the monitors, however in considering that
these 1-hour SO2 concentrations are below that of potential interest in the exposure and risk
analysis, this degree of limited agreement between the two data sets at these concentration levels
should be acceptable.
Table A.2-4. Distribution of the relative percent difference (RPD) between
duplicate 1-hour SO2 values in the continuous-5 and 1-hour data sets, where
concentrations were < 10 ppb.
Monitor ID
110010041
120890005
290770026
290770037
301110066
301110079
301110082
301110083
371290006
n
2049
25163
24286
24822
6640
7906
7930
4757
27954
Relative Percent Difference (%)1
mean
0
88
91
41
24
119
69
82
-45
std
7
99
99
80
62
95
92
96
83
min
-34
-175
-105
-46
-100
-133
-165
-105
-193
P5
-12
-11
-9
-13
-13
-9
-13
-9
-133
p50
0
15
15
0
0
200
12
15
-59
p95
12
200
200
200
200
200
200
200
200
max
45
200
200
200
200
200
200
200
200
A-89
-------
Monitor ID
420030021
420030064
420030116
420033003
420070005
540990002
541071002
n
4594
5174
4231
4640
30386
6592
23864
Relative Percent Difference (%)1
mean
34
20
3
23
63
1
1
std
81
71
25
69
91
10
11
min
-175
-172
-61
-67
-133
-40
-156
P5
-18
-29
-18
-23
-10
-13
-13
p50
2
-4
0
-1
6
0
0
p95
200
200
19
200
200
19
17
max
200
200
200
200
200
90
200
Notes:
1 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum,
5th, median, 95th, and maximum, respectively.
When considering duplicate 1-hour concentrations > 10 ppb, the RPD was much lower at
each of these same monitors (Table A.2-5). Most RPD distributions were within +/-5%,
indicating excellent agreement among the duplicate 1-hour values at concentrations above 10
ppb. A very small positive bias may exist with selection of the continuous-5 monitor data for use
in the air quality characterization when compared with the reported 1-hour concentrations, but on
average, the difference was typically less than 1% when considering concentrations above 10
ppb.
Table A.2-5. Distribution of the relative percent difference (RPD) between
duplicate 1-hour SO2 values in the continuous-5 and 1-hour data sets, where
concentrations were > 10 ppb.
Monitor ID
110010041
120890005
290770026
290770037
301110066
301110079
301110082
301110083
371290006
420030021
420030064
420030116
420033003
420070005
540990002
541071002
n
202
2400
1906
1373
1616
71
176
85
3747
1852
2892
1145
2625
15034
2062
10283
Relative Percent Difference (%)1
mean
0
0
0
0
0
0
0
1
1
1
-2
0
-1
0
0
0
std
2
4
2
2
5
3
2
3
25
14
2
9
5
2
2
2
min
-5
-90
-10
-5
-50
-6
-5
-4
-108
-59
-10
-34
-36
-23
-5
-87
P5
-4
-3
-3
-3
-3
-4
-3
-3
-15
-4
-6
-4
-5
-3
-3
-3
p50
0
0
0
0
0
-1
0
1
-2
0
-2
0
-1
0
0
0
p95
4
3
3
3
4
4
4
5
12
4
0
4
2
3
4
3
max
5
34
7
7
173
6
6
20
186
200
11
200
187
73
10
65
Notes:
1 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum,
5th, median, 95th, and maximum, respectively.
A-90
-------
A.3 Peak-To-Mean Ratio Distributions
Peak-to-mean ratios (PMR) were calculated using the measured values for each the 5-
minute maximum and 1-hour SC>2 concentrations. PMRs were seperated into 19 groups2 based
on the observed variability (3 bins) and concentrations ranges (7 bins) in measured 1-hour
ambient monitor concentrations (n=2,367,686). Table A.3-1 summarizes the PMR distributions
used for estimating 5-minute maximum concentrations from 1-hour measurements where
ambient monitors were characterized by the 1-hour coefficient of variation (COV). These are the
PMR distributions used in the statistical modeling of 5-minute maximum SC>2 concentrations in
the air quality characterization and in the exposure modeling. Table A.3-2 summarizes the PMR
distributions used for estimating 5-minute maximum SC>2 concentrations from 1-hour
measurements where ambient monitors were characterized by the 1-hour coefficient of variation
(GSD). Peak-to-mean ratios estimated by categorizing the ambient monitors by GSD were used
only in evaluating an alternative method of estimating 5-minute SC>2 concentrations.
2 Although there are 21 PMR distributions possible (i.e., 3 x 7), the COV < 100% and GSD <2.17 categories had
only three 1-hour concentrations above 150 ppb. Therefore, the two highest concentration bins do not have a
distribution, and concentrations > 75 ppb constituted the highest concentration bin in the low COV or low GSD bins.
A-91
-------
Table A.3-1. Distribution of 5-minute maximum peak to 1-hour mean SO2 concentration ratios (PMRs) using ambient
monitors categorized by 1-hour coefficient of variation (COV) and 1-hour mean concentration.
Concbin1
Pet2 -0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-10
-11
-12
-13
-14
-15
-16
-17
-18
-19
-20
-21
-22
-23
-24
-25
-26
-27
-28
-29
GSD<100%
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.04
1.11
1.11
1.11
1.11
1.13
1.13
1.13
1.13
1.13
1.13
1.14
2
1.00
1.00
1.00
1.00
1.00
1.05
1.06
1.06
1.06
1.07
1.07
1.08
1.08
1.08
1.08
1.08
1.08
1.09
1.09
1.09
1.09
1.10
1.10
1.10
1.10
1.10
1.11
1.12
1.13
1.13
3
1.00
1.03
1.03
1.04
1.04
1.05
1.06
1.06
1.07
1.07
1.07
1.07
1.08
1.08
1.09
1.09
1.10
1.10
1.11
1.11
1.11
1.12
1.12
1.12
1.13
1.13
1.13
1.14
1.14
1.15
4
1.01
1.02
1.02
1.04
1.04
1.05
1.06
1.07
1.08
1.09
1.09
1.10
1.10
1.11
1.11
1.11
1.12
1.13
1.14
1.15
1.15
1.16
1.17
1.17
1.18
1.18
1.19
1.19
1.20
1.20
100% 200 %
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.06
1.11
1.11
1.13
1.13
1.14
1.14
1.14
1.14
1.17
1.17
1.17
1.17
1.17
1.18
1.20
1.20
2
1.00
1.00
1.06
1.08
1.08
1.09
1.10
1.11
1.14
1.15
1.17
1.18
1.20
1.20
1.22
1.24
1.26
1.27
1.30
1.30
1.33
1.34
1.36
1.38
1.40
1.42
1.44
1.46
1.50
1.50
3
1.00
1.12
1.17
1.21
1.24
1.26
1.29
1.31
1.33
1.36
1.38
1.40
1.42
1.44
1.46
1.48
1.50
1.52
1.53
1.55
1.57
1.59
1.61
1.62
1.64
1.66
1.68
1.70
1.71
1.73
4
1.00
1.14
1.18
1.21
1.24
1.26
1.28
1.30
1.32
1.33
1.35
1.37
1.38
1.40
1.42
1.43
1.45
1.46
1.48
1.50
1.51
1.53
1.54
1.56
1.57
1.59
1.60
1.62
1.64
1.65
5
1.08
1.18
1.21
1.22
1.25
1.28
1.31
1.34
1.37
1.40
1.42
1.44
1.47
1.49
1.51
1.54
1.57
1.59
1.60
1.64
1.65
1.68
1.72
1.75
1.76
1.78
1.80
1.81
1.83
1.87
6
1.13
1.25
1.28
1.29
1.30
1.33
1.34
1.37
1.38
1.43
1.47
1.48
1.50
1.51
1.53
1.54
1.57
1.58
1.59
1.59
1.61
1.61
1.63
1.64
1.64
1.67
1.69
1.71
1.73
1.73
A-92
-------
Concbin1
-30
-31
-32
-33
-34
-35
-36
-37
-38
-39
-40
-41
-42
-43
-44
-45
-46
-47
-48
-49
-50
-51
-52
-53
-54
-55
-56
-57
-58
-59
-60
-61
-62
GSD<100%
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.05
1.11
1.20
1.25
1.25
1
1.14
1.14
1.14
1.14
1.14
1.15
1.17
1.17
1.17
1.17
1.17
1.17
1.17
1.17
1.17
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.22
1.22
1.24
1.25
1.25
1.25
2
1.13
1.14
1.14
1.15
1.15
1.16
1.17
1.17
1.17
1.18
1.18
1.18
1.18
1.19
1.20
1.20
1.20
1.20
1.20
1.21
1.21
1.22
1.23
1.24
1.25
1.25
1.25
1.27
1.27
1.27
1.29
1.29
1.30
3
1.15
1.16
1.16
1.16
1.17
1.17
1.18
1.18
1.19
1.19
1.19
1.20
1.21
1.21
1.22
1.22
1.23
1.23
1.24
1.24
1.25
1.25
1.26
1.27
1.27
1.28
1.28
1.29
1.30
1.31
1.31
1.32
1.32
4
1.23
1.23
1.23
1.24
1.24
1.24
1.24
1.25
1.26
1.29
1.29
1.29
1.30
1.30
1.31
1.31
1.32
1.32
1.34
1.35
1.35
1.36
1.37
1.39
1.40
1.41
1.42
1.42
1.45
1.45
1.46
1.46
1.47
100% 200 %
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.04
1.11
1.17
1.25
1.25
1
1.20
1.20
1.20
1.20
1.20
1.20
1.22
1.24
1.25
1.27
1.29
1.29
1.33
1.33
1.33
1.34
1.38
1.40
1.40
1.40
1.40
1.43
1.44
1.50
1.50
1.50
1.56
1.57
1.60
1.60
1.63
1.67
1.67
2
1.53
1.55
1.57
1.60
1.62
1.64
1.67
1.69
1.71
1.74
1.76
1.80
1.82
1.84
1.87
1.90
1.92
1.94
2.00
2.00
2.03
2.07
2.09
2.11
2.15
2.18
2.20
2.24
2.27
2.30
2.34
2.38
2.41
3
1.75
1.76
1.78
1.80
1.81
1.83
1.85
1.87
1.88
1.90
1.92
1.94
1.96
1.98
2.00
2.02
2.04
2.06
2.08
2.10
2.12
2.14
2.17
2.19
2.21
2.24
2.26
2.29
2.31
2.34
2.37
2.39
2.42
4
1.67
1.69
1.70
1.73
1.74
1.77
1.78
1.80
1.82
1.84
1.86
1.88
1.90
1.93
1.95
1.97
1.99
2.01
2.04
2.06
2.09
2.12
2.15
2.18
2.21
2.24
2.27
2.30
2.34
2.36
2.40
2.44
2.48
5
1.90
1.91
1.93
1.96
1.97
1.99
2.02
2.05
2.08
2.10
2.14
2.16
2.18
2.20
2.21
2.23
2.24
2.26
2.28
2.30
2.31
2.34
2.36
2.38
2.41
2.43
2.44
2.47
2.50
2.53
2.57
2.60
2.62
6
1.76
1.77
1.78
1.79
1.79
1.80
1.81
1.82
1.82
1.83
1.84
1.84
1.87
1.89
1.91
1.91
1.93
1.94
1.96
1.96
1.97
1.97
1.98
2.01
2.02
2.04
2.06
2.08
2.09
2.13
2.14
2.15
2.17
A-93
-------
Concbin1
-63
-64
-65
-66
-67
-68
-69
-70
-71
-72
-73
-74
-75
-76
-77
-78
-79
-80
-81
-82
-83
-84
-85
-86
-87
-88
-89
-90
-91
-92
-93
-94
-95
GSD<100%
0
1.25
1.25
1.30
1.33
1.33
1.33
1.33
1.33
1.43
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.58
1.67
1.75
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
1
1.29
1.29
1.29
1.33
1.33
1.33
1.33
1.33
1.33
1.38
1.38
1.40
1.40
1.40
1.40
1.40
1.43
1.44
1.50
1.50
1.50
1.55
1.57
1.60
1.60
1.63
1.67
1.71
1.78
1.80
1.86
2.00
2.00
2
1.30
1.31
1.31
1.33
1.33
1.35
1.36
1.36
1.38
1.39
1.40
1.40
1.42
1.43
1.45
1.46
1.48
1.50
1.50
1.53
1.55
1.58
1.60
1.63
1.65
1.69
1.71
1.75
1.80
1.83
1.90
1.95
2.05
3
1.33
1.34
1.35
1.36
1.37
1.38
1.39
1.40
1.42
1.42
1.44
1.44
1.46
1.47
1.48
1.50
1.52
1.52
1.54
1.57
1.59
1.61
1.64
1.67
1.70
1.72
1.76
1.80
1.85
1.89
1.96
2.02
2.10
4
1.52
1.54
1.55
1.57
1.58
1.58
1.60
1.64
1.64
1.65
1.65
1.65
1.66
1.67
1.68
1.69
1.70
1.71
1.74
1.75
1.77
1.77
1.82
1.85
1.86
1.88
1.91
1.98
2.10
2.25
2.26
2.30
2.50
100% 200 %
0
1.25
1.33
1.33
1.33
1.33
1.42
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.60
1.67
1.71
1.85
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.44
2.67
3.00
3.00
3.75
1
1.74
1.78
1.80
1.83
1.86
1.89
2.00
2.00
2.00
2.10
2.14
2.18
2.22
2.29
2.34
2.40
2.46
2.56
2.60
2.67
2.78
2.83
3.00
3.00
3.17
3.29
3.40
3.56
3.68
3.86
4.00
4.29
4.57
2
2.45
2.50
2.53
2.56
2.60
2.64
2.69
2.73
2.77
2.82
2.87
2.92
2.96
3.00
3.07
3.13
3.18
3.25
3.31
3.38
3.46
3.54
3.62
3.70
3.80
3.90
4.00
4.12
4.25
4.39
4.56
4.76
5.00
3
2.45
2.48
2.51
2.54
2.57
2.61
2.64
2.68
2.72
2.76
2.80
2.84
2.89
2.93
2.97
3.03
3.09
3.14
3.20
3.26
3.33
3.41
3.48
3.57
3.67
3.77
3.90
4.04
4.18
4.35
4.55
4.77
5.03
4
2.52
2.56
2.60
2.66
2.71
2.76
2.80
2.85
2.89
2.95
3.01
3.06
3.11
3.16
3.22
3.30
3.35
3.41
3.47
3.57
3.65
3.72
3.80
3.90
4.00
4.10
4.21
4.35
4.44
4.62
4.82
5.03
5.24
5
2.64
2.67
2.70
2.73
2.77
2.80
2.84
2.88
2.90
2.93
2.97
2.99
3.02
3.06
3.10
3.16
3.19
3.24
3.26
3.32
3.38
3.42
3.49
3.55
3.62
3.69
3.80
3.88
3.94
4.07
4.18
4.28
4.40
6
2.17
2.19
2.21
2.24
2.27
2.28
2.30
2.31
2.33
2.33
2.35
2.37
2.41
2.44
2.49
2.52
2.53
2.55
2.57
2.60
2.64
2.65
2.67
2.70
2.71
2.74
2.82
2.84
2.94
2.98
3.03
3.09
3.13
A-94
-------
Concbin1
-96
-97
-98
-99
-100
n3
GSD<100%
0
2.33
2.67
3.00
4.00
11.67
352735
1
2.14
2.29
2.50
2.89
10.60
74053
2
2.14
2.27
2.50
2.91
10.08
42876
3
2.22
2.37
2.63
3.02
6.81
6895
4
2.53
2.56
3.12
3.61
6.10
147
100% 200 %
0
4.75
6.00
10.00
10.00
11.75
475572
1
4.88
5.29
5.86
6.86
11.50
55341
2
5.27
5.69
6.30
7.27
11.93
35502
3
5.37
5.80
6.51
7.50
11.45
20077
4
5.48
5.94
6.48
7.29
11.39
4019
5
4.48
4.63
5.06
5.36
6.48
989
6
3.33
3.38
3.48
3.70
5.39
341
Notes:
1 1-hour SO2 concentration bins are: 0 = 1-hour mean < 5 ppb; 2 = 5 < 1-hour mean < 10 ppb; 2 = 10 < 1-hour mean < 25 ppb; 3 = 25 < 1-hour mean < 75
ppb; 4 = 75 < 1-hour mean < 150 ppb ; 5 = 150 < 1-hour mean 250 ppb; 6 = 1-hour mean > 250 ppb.
pet -x indicates the percentile of the distribution.
3 n is the number of 5-minute maximum and 1-hour SO2 measurements used to develop distribution.
A-95
-------
Table A.3-2. Distribution of 5-minute maximum peak to 1-hour mean SO2 concentration ratios (PMRs) using ambient
monitors categorized by 1-hour geometric standard deviation (GSD) and 1-hour mean concentration.
Concbin1
Pet2 -0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-10
-11
-12
-13
-14
-15
-16
-17
-18
-19
-20
-21
-22
-23
-24
-25
-26
-27
-28
-29
GSD < 2.1 7
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.07
1.11
1.11
1.11
1.13
1.13
1.13
1.14
2
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.04
1.06
1.06
1.07
1.07
1.07
1.08
1.08
1.08
1.08
1.09
1.09
1.09
1.09
1.10
1.10
1.10
1.10
1.10
1.11
1.12
1.13
1.14
3
1.00
1.03
1.04
1.04
1.06
1.07
1.08
1.10
1.11
1.12
1.13
1.14
1.14
1.15
1.16
1.17
1.19
1.19
1.20
1.21
1.22
1.23
1.24
1.25
1.26
1.27
1.28
1.29
1.30
1.30
4
1.01
1.07
1.17
1.21
1.22
1.24
1.25
1.27
1.29
1.30
1.30
1.31
1.32
1.32
1.33
1.34
1.35
1.36
1.36
1.37
1.40
1.43
1.44
1.44
1.46
1.47
1.48
1.49
1.50
1.50
2.17 < GSD < 2.94
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.05
1.10
1.11
1.11
1.11
1.13
1.13
1.13
1.13
1.14
1.14
1.14
1.14
1.14
1.17
1.17
1.17
1.17
2
1.00
1.00
1.00
1.00
1.05
1.06
1.06
1.07
1.07
1.08
1.08
1.08
1.09
1.09
1.10
1.10
1.10
1.10
1.11
1.12
1.13
1.13
1.14
1.15
1.15
1.16
1.17
1.17
1.18
1.18
3
1.00
1.00
1.03
1.04
1.05
1.06
1.07
1.08
1.08
1.09
1.10
1.11
1.12
1.12
1.13
1.14
1.15
1.15
1.16
1.17
1.17
1.18
1.19
1.20
1.21
1.21
1.22
1.23
1.24
1.25
4
1.00
1.05
1.08
1.10
1.12
1.14
1.16
1.17
1.18
1.20
1.21
1.23
1.24
1.25
1.26
1.28
1.29
1.30
1.31
1.32
1.34
1.35
1.36
1.38
1.39
1.40
1.41
1.42
1.43
1.44
5
1.05
1.13
1.21
1.23
1.26
1.28
1.29
1.31
1.32
1.34
1.35
1.38
1.39
1.40
1.42
1.43
1.44
1.46
1.47
1.49
1.50
1.52
1.53
1.55
1.57
1.57
1.59
1.60
1.61
1.64
6
1.13
1.19
1.26
1.28
1.29
1.30
1.33
1.34
1.35
1.37
1.38
1.43
1.45
1.46
1.47
1.48
1.50
1.51
1.52
1.53
1.54
1.54
1.56
1.58
1.58
1.58
1.59
1.59
1.61
1.63
GSD > 2.94
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.03
1.05
1.07
1.09
1.10
1.11
1.11
1.13
1.13
1.13
1.14
1.14
1.14
1.15
1.16
1.17
1.17
1.17
1.18
1.19
1.20
1.20
1.20
2
1.00
1.00
1.04
1.06
1.07
1.08
1.08
1.09
1.10
1.10
1.11
1.12
1.13
1.14
1.15
1.16
1.17
1.18
1.18
1.19
1.20
1.20
1.21
1.23
1.24
1.25
1.25
1.27
1.27
1.29
3
1.00
1.05
1.07
1.08
1.09
1.10
1.12
1.13
1.14
1.15
1.16
1.17
1.18
1.19
1.20
1.21
1.22
1.23
1.24
1.25
1.26
1.27
1.29
1.30
1.31
1.32
1.33
1.34
1.35
1.37
4
1.00
1.08
1.10
1.12
1.14
1.15
1.17
1.18
1.19
1.20
1.21
1.22
1.24
1.25
1.26
1.27
1.29
1.30
1.31
1.32
1.33
1.34
1.35
1.36
1.37
1.39
1.40
1.41
1.42
1.43
5
1.07
1.14
1.17
1.20
1.21
1.21
1.22
1.23
1.25
1.27
1.28
1.30
1.32
1.35
1.36
1.38
1.39
1.42
1.43
1.44
1.46
1.49
1.50
1.53
1.56
1.58
1.60
1.62
1.64
1.65
6
1.02
1.14
1.16
1.18
1.24
1.25
1.29
1.30
1.31
1.33
1.36
1.37
1.38
1.45
1.46
1.46
1.47
1.49
1.51
1.54
1.55
1.57
1.58
1.60
1.61
1.64
1.64
1.64
1.68
1.68
A-96
-------
Concbin1
-30
-31
-32
-33
-34
-35
-36
-37
-38
-39
-40
-41
-42
-43
-44
-45
-46
-47
-48
-49
-50
-51
-52
-53
-54
-55
-56
-57
-58
-59
-60
-61
-62
GSD<2.17
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.07
1.14
1.22
1.25
1
1.14
1.14
1.14
1.14
1.15
1.17
1.17
1.17
1.17
1.17
1.17
1.17
1.18
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.22
1.25
1.25
1.27
1.29
1.29
1.32
1.33
2
1.14
1.15
1.16
1.17
1.17
1.18
1.18
1.18
1.20
1.20
1.20
1.20
1.21
1.22
1.23
1.25
1.25
1.27
1.27
1.27
1.29
1.30
1.30
1.31
1.32
1.33
1.35
1.36
1.37
1.38
1.40
1.40
1.42
3
1.31
1.32
1.33
1.34
1.35
1.36
1.37
1.39
1.40
1.40
1.42
1.43
1.44
1.44
1.46
1.47
1.48
1.50
1.51
1.52
1.54
1.56
1.57
1.59
1.61
1.63
1.65
1.67
1.69
1.71
1.72
1.74
1.75
4
1.52
1.53
1.55
1.57
1.58
1.59
1.60
1.60
1.62
1.63
1.63
1.64
1.65
1.66
1.67
1.70
1.70
1.71
1.72
1.74
1.74
1.75
1.76
1.76
1.77
1.78
1.80
1.81
1.81
1.83
1.83
1.84
1.91
2.17 2.94
0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.03
1.05
1.11
1.11
1.11
1.15
1.18
1.21
1.25
1.25
1.25
1.25
1.29
1.33
1.33
1.33
1.33
1.38
1.43
1.43
1.48
1.50
1.50
1.50
1.50
1
1.20
1.20
1.22
1.23
1.25
1.25
1.26
1.28
1.29
1.29
1.32
1.33
1.33
1.33
1.35
1.37
1.38
1.40
1.40
1.40
1.41
1.43
1.44
1.47
1.50
1.50
1.50
1.54
1.57
1.58
1.60
1.60
1.63
2
1.30
1.30
1.32
1.33
1.34
1.36
1.36
1.38
1.39
1.40
1.42
1.43
1.45
1.46
1.47
1.50
1.50
1.53
1.54
1.56
1.58
1.59
1.61
1.63
1.65
1.67
1.69
1.71
1.73
1.76
1.79
1.81
1.83
3
1.38
1.39
1.40
1.42
1.43
1.44
1.46
1.47
1.49
1.50
1.52
1.53
1.55
1.56
1.58
1.59
1.61
1.63
1.64
1.66
1.68
1.69
1.71
1.73
1.75
1.77
1.79
1.80
1.82
1.84
1.86
1.88
1.90
4
1.45
1.46
1.47
1.48
1.50
1.51
1.52
1.53
1.54
1.56
1.57
1.58
1.60
1.61
1.62
1.64
1.65
1.67
1.68
1.69
1.71
1.72
1.74
1.77
1.78
1.80
1.82
1.84
1.86
1.88
1.91
1.93
1.96
5
1.67
1.71
1.73
1.75
1.76
1.77
1.80
1.82
1.85
1.90
1.92
1.94
1.96
1.99
2.02
2.03
2.08
2.12
2.16
2.17
2.21
2.22
2.24
2.26
2.28
2.31
2.37
2.38
2.41
2.43
2.47
2.50
2.52
6
1.74
1.76
1.77
1.79
1.81
1.83
1.83
1.84
1.90
1.91
1.91
1.93
1.96
1.96
1.96
1.97
1.98
1.98
2.00
2.01
2.03
2.04
2.06
2.08
2.09
2.11
2.14
2.15
2.16
2.18
2.19
2.20
2.26
A-97
-------
Concbin1
-63
-64
-65
-66
-67
-68
-69
-70
-71
-72
-73
-74
-75
-76
-77
-78
-79
-80
-81
-82
-83
-84
-85
-86
-87
-88
-89
-90
-91
-92
-93
-94
-95
GSD<2.17
0
1.25
1.29
1.33
1.33
1.33
1.33
1.36
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.67
1.75
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.25
2.50
1
1.33
1.33
1.33
1.38
1.40
1.40
1.40
1.40
1.40
1.43
1.43
1.49
1.50
1.50
1.56
1.57
1.60
1.60
1.63
1.67
1.71
1.77
1.80
1.83
1.86
1.97
2.00
2.00
2.14
2.20
2.29
2.40
2.56
2
1.44
1.45
1.47
1.50
1.50
1.53
1.55
1.58
1.60
1.62
1.64
1.67
1.69
1.71
1.73
1.77
1.80
1.83
1.87
1.91
1.94
2.00
2.00
2.09
2.13
2.20
2.27
2.33
2.42
2.54
2.64
2.79
2.93
3
1.78
1.80
1.81
1.84
1.87
1.89
1.91
1.94
1.97
2.00
2.02
2.04
2.07
2.10
2.12
2.15
2.19
2.22
2.27
2.30
2.36
2.43
2.48
2.56
2.63
2.69
2.79
2.88
2.97
3.08
3.24
3.50
3.61
4
1.93
1.93
1.99
2.00
2.05
2.08
2.09
2.11
2.15
2.18
2.19
2.23
2.26
2.27
2.28
2.31
2.40
2.46
2.47
2.47
2.49
2.53
2.68
2.74
2.78
2.81
2.85
2.96
3.06
3.24
3.39
3.55
3.68
2.17 2.94
0
1.50
1.54
1.62
1.67
1.67
1.71
1.80
1.86
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.29
2.50
2.50
2.75
3.00
3.00
3.33
3.33
4.00
4.67
5.00
5.50
10.00
10.00
1
1.67
1.67
1.70
1.72
1.76
1.80
1.80
1.83
1.86
1.89
1.98
2.00
2.00
2.03
2.13
2.17
2.20
2.24
2.31
2.37
2.40
2.50
2.57
2.63
2.73
2.83
2.94
3.00
3.18
3.33
3.50
3.71
4.00
2
1.86
1.89
1.92
1.94
1.99
2.00
2.03
2.07
2.10
2.14
2.18
2.21
2.25
2.29
2.33
2.38
2.43
2.49
2.54
2.60
2.65
2.71
2.79
2.87
2.94
3.00
3.10
3.20
3.31
3.46
3.61
3.77
4.00
3
1.92
1.95
1.97
2.00
2.02
2.04
2.07
2.10
2.13
2.15
2.18
2.21
2.25
2.28
2.32
2.36
2.40
2.44
2.48
2.53
2.58
2.63
2.69
2.75
2.82
2.89
2.96
3.06
3.16
3.28
3.41
3.57
3.78
4
1.98
2.01
2.04
2.07
2.10
2.14
2.18
2.21
2.24
2.29
2.33
2.37
2.42
2.48
2.54
2.60
2.68
2.77
2.85
2.95
3.05
3.13
3.24
3.35
3.47
3.62
3.73
3.86
4.03
4.22
4.41
4.65
4.94
5
2.56
2.59
2.62
2.63
2.67
2.70
2.72
2.77
2.81
2.85
2.90
2.93
2.98
3.01
3.03
3.09
3.12
3.17
3.21
3.25
3.30
3.35
3.39
3.46
3.54
3.59
3.68
3.78
3.88
3.98
4.10
4.18
4.35
6
2.28
2.29
2.31
2.31
2.33
2.35
2.36
2.37
2.39
2.41
2.44
2.50
2.51
2.53
2.53
2.54
2.56
2.58
2.60
2.64
2.65
2.69
2.71
2.73
2.81
2.84
2.84
2.94
2.97
3.02
3.06
3.12
3.16
A-98
-------
Concbin1
-96
-97
-98
-99
-100
n3
GSD<2.17
0
3.00
3.00
3.50
4.00
11.75
456580
1
2.71
3.00
3.29
4.00
11.57
54454
2
3.13
3.40
3.85
4.68
11.94
16117
3
3.91
4.23
4.71
5.58
10.14
1925
4
3.93
4.13
4.49
5.09
6.10
150
2.17 2.94
0
10.00
10.00
10.00
10.00
11.67
297365
1
4.25
4.67
5.22
6.20
11.67
63582
2
4.23
4.57
5.04
5.91
11.93
55615
3
4.07
4.48
5.06
6.19
11.45
28545
4
5.23
5.59
6.11
6.89
11.39
3952
5
4.44
4.58
4.89
5.45
6.63
759
6
3.27
3.33
3.35
3.51
3.62
224
Notes:
1 1-hour SO2 concentration bins are: 0 = 1-hour mean < 5 ppb; 2 = 5 < 1-hour mean < 10 ppb; 2 = 10 < 1-hour mean < 25 ppb; 3 = 25 < 1-hour mean < 75
ppb; 4 = 75 < 1-hour mean < 150 ppb ; 5 = 150 < 1-hour mean 250 ppb; 6 = 1-hour mean > 250 ppb.
pet -x indicates the percentile of the distribution.
3 n is the number of 5-minute maximum and 1-hour SO2 measurements used to develop distribution.
A-99
-------
A.4 Factors Used in Adjusting Air Quality to Just Meet the Current and Potential
Alternative SO2 Air Quality Standards
The adjustment factors used for simulating just meeting particular forms and levels of
SO2 standards are described here in two sections. This was done given the difference in how the
adjustment factors were derived and applied to each of the air quality scenarios and given the
number of factors generated for the potential alternative standards. The first section includes the
factors used for adjusting air quality to just meet the current standards (either the 24-hour or
annual average), while the second section note the concentrations used in deriving the factors
applied to simulate just meeting potential alternative standards.
A.4.1 Adjustment factors for just meeting the current standard
Both annual and daily adjustment factors were calculated for all selected counties in
evaluating the current annual and daily standards however, the lowest value of the two was
selected for use in adjusting concentrations (see REA section 7.2.4). The adjustment factors for
each county, year, and the standard from which the factors were derived is given in Table A.4-1.
In addion, the coefficient of variation (i.e., COV) was used as a measure to indicate the
variability associated with each of the calculated factors when considering all of the monitors in
a county. Within a given year, the COV generally indicates the extent of spatial variability in
ambient concentrations, considering the number of monitors in operation. Variation in the COV
across different years can indicate the temporal variability in a county however, year-to-year
differences in the number and location of ambient monitors may confound this comparison.
Lower COVs indicate similarity in that concentration metric in the county, while higher values
indicate less homogeneity in concentrations (whether spatially or temporally).
Table A.4-1. Adjustment factors used in simulating air quality just meeting the
current SO2 NAAQS in selected counties by year.
State
Abbreviation
AZ
AZ
AZ
AZ
AZ
AZ
DE
DE
County
Gila
Gila
Gila
Gila
Gila
Gila
New Castle
New Castle
Year
2001
2002
2003
2004
2005
2006
2001
2002
Monitors
(n)
2
2
2
2
2
2
4
4
Adjustment
Factor
3.12
3.53
3.82
3.04
3.33
4.40
3.38
2.67
COV
4
5
12
21
5
1
16
9
Closest
Standard1
D
A
A
A
D
D
D
D
A-100
-------
State
Abbreviation
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IA
IA
County
New Castle
New Castle
New Castle
New Castle
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Madison
Madison
Madison
Madison
Madison
Madison
Wabash
Wabash
Wabash
Wabash
Wabash
Wabash
Floyd
Floyd
Floyd
Floyd
Floyd
Floyd
Gibson
Gibson
Gibson
Gibson
Gibson
Gibson
Lake
Lake
Lake
Lake
Lake
Lake
Vigo
Vigo
Vigo
Vigo
Vigo
Vigo
Linn
Linn
Year
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
Monitors
(n)
5
4
4
4
7
7
6
6
6
6
4
4
3
3
3
3
2
2
2
2
2
2
3
3
3
2
3
3
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
5
3
Adjustment
Factor
2.75
2.58
2.73
2.68
3.14
3.09
3.09
4.95
4.40
4.19
3.51
2.88
3.60
3.61
4.19
4.90
3.25
3.33
2.95
3.98
3.80
3.01
3.98
4.85
4.14
5.04
3.98
3.64
2.34
2.68
1.17
2.99
4.78
1.67
4.87
4.43
4.94
4.39
3.39
8.12
2.47
4.65
4.06
5.28
4.57
6.97
3.53
4.70
cov
9
13
11
14
13
16
19
32
25
29
7
12
6
18
11
16
1
3
5
1
7
5
2
6
5
6
11
5
6
19
13
10
3
16
0
17
7
14
16
0
16
18
13
1
5
5
18
5
Closest
Standard1
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
A
A
D
D
D
D
D
D
D
D
D
D
D
D
A
D
A
A
D
D
D
D
D
A-101
-------
State
Abbreviation
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
Ml
Ml
Ml
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
NH
NH
NH
NH
NH
NH
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
County
Linn
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Greene
Iron
Iron
Iron
Iron
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Merrimack
Merrimack
Merrimack
Merrimack
Merrimack
Merrimack
Hudson
Hudson
Hudson
Hudson
Hudson
Hudson
Union
Union
Union
Union
Year
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
Monitors
(n)
3
3
3
3
3
3
3
3
3
3
6
3
3
3
3
3
3
5
5
5
5
5
2
2
2
2
3
1
1
1
1
1
2
3
3
2
2
2
2
1
2
2
2
2
2
2
2
2
Adjustment
Factor
3.45
2.29
3.41
4.10
4.20
3.87
4.09
2.78
2.90
2.94
3.21
2.97
3.30
2.99
3.35
2.95
3.57
3.47
5.12
5.29
4.87
4.46
2.26
2.11
2.44
7.96
5.74
3.89
5.65
1.87
2.13
1.93
3.07
3.71
3.31
2.59
2.70
2.51
3.39
5.26
3.52
3.67
3.67
6.25
3.71
3.52
3.70
3.99
cov
5
10
9
35
12
11
11
16
17
10
9
15
5
12
7
13
17
32
26
29
34
19
0
2
2
22
10
0
0
0
0
0
21
18
10
17
18
28
6
0
6
4
1
5
7
11
8
8
Closest
Standard1
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
A
D
D
D
D
D
D
D
D
D
D
D
D
A
A
A
A
A
D
A
A
A
A
A-102
-------
State
Abbreviation
NJ
NJ
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OH
OK
OK
OK
OK
OK
OK
PA
PA
PA
PA
County
Union
Union
Bronx
Bronx
Bronx
Bronx
Bronx
Bronx
Chautauqua
Chautauqua
Chautauqua
Chautauqua
Chautauqua
Chautauqua
Erie
Erie
Erie
Erie
Erie
Erie
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Lake
Lake
Lake
Lake
Lake
Lake
Summit
Summit
Summit
Summit
Summit
Summit
Tulsa
Tulsa
Tulsa
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Year
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
Monitors
(n)
2
2
1
2
2
2
1
2
3
2
2
2
2
2
2
2
2
2
2
2
5
5
5
4
4
4
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
4
7
5
7
7
Adjustment
Factor
4.12
7.98
2.95
3.04
2.82
2.96
3.26
3.44
1.85
2.34
2.30
3.42
5.78
9.47
2.66
2.01
1.85
3.65
4.14
4.72
4.05
5.10
3.98
4.54
3.43
4.25
3.78
3.34
2.79
3.05
1.87
2.51
3.25
2.39
2.65
2.75
3.76
3.79
4.16
4.51
3.65
4.07
4.57
5.69
2.72
2.80
2.23
2.81
cov
7
4
0
3
1
3
0
6
12
18
13
16
11
2
13
16
16
20
14
17
6
11
5
11
6
8
8
15
10
13
13
16
3
8
2
11
14
9
10
2
6
3
4
59
5
4
5
6
Closest
Standard1
A
D
A
A
D
A
A
A
D
D
D
D
D
D
D
D
D
D
D
D
D
A
D
D
D
D
A
A
D
D
D
D
D
D
D
D
A
D
A
D
D
D
A
D
D
A
D
D
A-103
-------
State
Abbreviation
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TX
TX
TX
TX
County
Allegheny
Allegheny
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Northampton
Northampton
Northampton
Northampton
Northampton
Northampton
Warren
Warren
Warren
Warren
Warren
Warren
Washington
Washington
Washington
Washington
Washington
Washington
Blount
Blount
Blount
Blount
Blount
Blount
Shelby
Shelby
Shelby
Shelby
Shelby
Shelby
Sullivan
Sullivan
Sullivan
Sullivan
Sullivan
Sullivan
Jefferson
Jefferson
Jefferson
Jefferson
Year
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
Monitors
(n)
7
6
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
2
2
2
2
2
2
3
3
3
3
4
3
2
2
2
2
2
2
3
3
3
3
Adjustment
Factor
2.17
2.97
2.01
1.91
1.73
2.64
2.42
2.67
2.15
5.01
3.73
2.28
3.55
0.98
1.66
1.45
1.40
2.37
1.91
1.68
2.95
3.11
2.99
3.42
3.07
3.48
1.62
2.05
1.88
2.22
1.61
1.79
3.47
4.79
3.75
4.46
3.90
4.12
2.95
3.26
3.28
3.33
3.72
3.33
2.68
4.82
4.30
4.47
cov
7
8
5
6
6
6
7
8
28
0
18
21
3
19
11
15
11
15
17
19
6
6
8
2
5
6
18
10
12
1
7
10
19
20
21
20
46
44
8
10
4
3
4
3
8
4
4
13
Closest
Standard1
D
D
D
D
D
A
A
D
A
A
A
A
A
D
D
D
D
D
D
D
A
A
A
A
D
A
D
D
D
D
D
D
D
D
D
D
D
D
A
D
D
D
D
D
D
D
D
D
A-104
-------
State
Abbreviation
TX
TX
VA
VA
VA
VA
VA
VA
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
VI
VI
VI
VI
VI
VI
County
Jefferson
Jefferson
Fairfax
Fairfax
Fairfax
Fairfax
Fairfax
Fairfax
Brooke
Brooke
Brooke
Brooke
Brooke
Brooke
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Monongalia
Monongalia
Monongalia
Monongalia
Monongalia
Monongalia
Wayne
Wayne
Wayne
Wayne
Wayne
St Croix
St Croix
St Croix
St Croix
St Croix
St Croix
Year
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2006
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2006
Monitors
(n)
3
3
2
2
3
3
3
3
2
2
2
2
2
2
9
9
9
7
7
7
3
2
2
2
2
2
4
4
4
3
3
5
5
5
5
5
5
Adjustment
Factor
5.67
4.31
4.50
4.49
4.89
4.80
4.79
5.35
2.13
2.49
2.63
2.02
2.16
2.50
2.20
2.38
2.30
2.38
2.22
2.34
2.37
2.22
3.26
3.25
3.13
3.20
2.85
3.31
3.41
2.87
2.02
3.41
3.46
3.66
3.26
9.25
4.59
cov
7
4
18
14
15
19
19
18
5
4
3
6
5
8
3
3
3
4
5
4
3
2
1
1
3
1
4
3
7
9
11
83
64
66
56
15
25
Closest
Standard1
D
D
A
A
A
A
A
A
A
A
A
A
A
A
A
D
D
A
A
A
D
D
D
D
A
D
D
A
D
D
D
D
D
D
D
D
D
Notes:
1 Ambient SO2 concentrations were closest to either the annual (A) or daily (D) NAAQS level.
A-105
-------
A.4.2 Adjustment factors for just meeting the potential alternative standards
Five potential alternative standards (i.e., 50, 100, 150, 200, and 250 ppb daily maximum
1-hour) given a 99th percentile form and one alternative standard (200 ppb daily maximum 1-
hour) given a 98th percentile form were selected for evaluation (for details, see REA chapter 5).
Adjustment factors were derived for each of two 3-year groups of recent air quality (i.e., 2001-
2003 and 2004-2006). For the sake of brevity, only the maximum 3-year averaged
concentrations for each of the percentile forms are provided in Table A.4-2, rather than all of the
adjustment factors. The actual adjustment factors used in simulating air quality can be derived
for each of the concentration levels by dividing by the county concentration for each year goup.
For example, the adjustement factor applied to the 2002 hourly mean concentrations in New
Castle DE to simulate just meeting a 99th percentile daily maximum 1-hour of 100 ppb is
100/164 = 0.61. That is to say, to meet this particular standard, the hourly concentrations need to
be adjusted downward by a factor of 0.61. The COV is also used to represent the temporal
variability over the three years of monitoring (where such data exist).
Table A.4-2. Concentrations used in developing adjustment factors when
simulating air quality just meeting potential alternative SO2 NAAQS in selected
counties by year.
State
Abbreviation
AZ
AZ
DE
DE
FL
FL
IA
IA
IA
IA
IL
IL
IL
IL
IN
IN
IN
County
Gila
Gila
New Castle
New Castle
Hillsbo rough
Hillsbo rough
Linn
Linn
Muscatine
Muscatine
Madison
Madison
Wabash
Wabash
Floyd
Floyd
Gibson
Year Group
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
98th Percentile
Years
(n)
3
2
2
3
3
2
3
3
3
3
3
3
1
1
3
3
2
Cone
(ppb)
226
222
138
123
117
93
82
96
92
120
110
123
139
131
124
129
185
COV
(%)
10
6
5
20
12
8
21
17
13
10
22
5
17
14
12
99th Percentile
Years
(n)
3
2
2
3
3
2
3
3
3
3
3
3
1
1
1
3
2
Cone
(ppb)
260
294
164
147
146
128
105
111
113
135
144
144
216
187
151
170
235
COV
(%)
10
1
0
31
2
8
12
27
9
8
24
7
6
19
A-106
-------
State
Abbreviation
IN
IN
IN
IN
IN
Ml
Ml
MO
MO
MO
MO
MO
MO
NH
NH
NJ
NJ
NJ
NJ
NY
NY
NY
NY
NY
NY
OH
OH
OH
OH
OH
OH
OH
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
County
Gibson
Lake
Lake
Vigo
Vigo
Wayne
Wayne
Greene
Greene
Iron
Iron
Jefferson
Jefferson
Merrimack
Merrimack
Hudson
Hudson
Union
Union
Bronx
Bronx
Chautauqua
Chautauqua
Erie
Erie
Cuyahoga
Cuyahoga1
Cuyahoga1
Lake
Lake
Summit
Summit
Tulsa
Tulsa
Allegheny
Allegheny
Beaver
Beaver
Northampton
Northampton
Warren
Warren
Washington
Washington
Blount
Blount
Shelby
Year Group
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
98th Percentile
Years
(n)
1
3
2
3
2
2
3
3
3
3
1
1
3
3
3
2
2
3
2
2
2
3
3
3
3
2
3
3
3
3
3
3
3
2
1
2
3
3
3
3
3
3
3
3
1
3
3
Cone
(ppb)
199
68
87
114
110
102
115
81
63
289
20
230
244
110
127
54
51
52
49
64
59
238
84
206
114
76
67
67
129
146
131
133
63
82
149
144
200
188
55
92
218
180
99
89
189
168
70
cov
(%)
5
1
7
8
3
2
13
29
20
10
30
2
9
3
13
10
1
7
2
47
10
33
1
8
18
10
5
12
9
22
32
16
28
6
9
41
6
22
10
10
5
29
99th Percentile
Years
(n)
1
2
2
3
2
2
3
3
3
3
1
1
3
3
3
2
2
3
2
2
2
3
3
3
3
2
3
3
3
3
3
3
2
1
2
3
3
3
3
3
3
3
3
1
3
3
Cone
(ppb)
226
84
113
159
136
126
128
94
81
341
22
234
346
125
151
61
65
57
60
71
68
285
101
225
129
101
80
145
175
148
150
76
93
164
183
245
228
65
146
270
226
111
102
204
194
101
COV
(%)
52
3
25
2
4
2
13
25
9
16
34
9
1
1
7
9
7
2
12
54
8
24
1
9
4
9
12
13
7
33
36
31
8
3
65
12
15
11
11
6
35
A-107
-------
State
Abbreviation
TN
TN
TN
TN
TX
TX
VA
VA
VI
VI
WV
WV
WV
WV
WV
WV
WV
WV
County
Shelby1
Shelby1
Sullivan
Sullivan
Jefferson
Jefferson
Fairfax
Fairfax
St Croix
St Croix
Brooke
Brooke
Hancock
Hancock
Monongalia
Monongalia
Wayne
Wayne
Year Group
2004-2006
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
2001-2003
2004-2006
98th Percentile
Years
(n)
3
3
3
3
3
3
3
3
2
1
3
3
3
3
3
2
3
2
Cone
(ppb)
72
72
157
145
92
109
38
37
103
70
154
125
182
134
163
148
93
67
cov
(%)
35
2
13
7
20
49
15
8
6
20
8
17
24
22
3
7
11
99th Percentile
Years
(n)
3
3
3
3
3
3
3
2
1
3
3
3
3
3
2
3
2
Cone
(ppb)
85
195
208
103
129
48
41
126
130
180
158
217
159
218
188
109
75
COV
(%)
33
19
17
16
46
24
11
18
17
19
23
19
26
16
14
0
Notes:
1 Two monitors in the county had the same average 98th percentile daily 1-hour maximum
concentrations. Concentrations, monitoring years, and COVs for both monitors are indicated.
A-108
-------
A.5 Supplementary Results Tables for 5-minute Measurement Data
A-109
-------
Table A.5-1. Annual average SO2 concentrations and number of measured 5-minute daily maximum SO2
concentrations above potential health effect benchmark levels. Data used were from 98 monitors that reported both
the 5-minute maximum and 1-hour SO2 concentrations for years 1997 through 2007.
State
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
AR
CO
CO
CO
CO
CO
CO
CO
CO
County
Pulaski
Pulaski
Pulaski
Pulaski
Pulaski
Pulaski
Pulaski
Pulaski
Pulaski
Pulaski
Pulaski
Union
Union
Union
Union
Union
Union
Union
Union
Union
Union
Union
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Monitor ID
051190007
051190007
051190007
051190007
051190007
051190007
051191002
051191002
051191002
051191002
051191002
051390006
051390006
051390006
051390006
051390006
051390006
051390006
051390006
051390006
051390006
051390006
080310002
080310002
080310002
080310002
080310002
080310002
080310002
080310002
Year
2002
2003
2004
2005
2006
2007
1997
1998
1999
2000
2001
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
2000
2001
2002
2003
2004
Days
(n)
339
365
359
350
365
90
365
329
275
352
364
365
313
275
357
364
275
364
334
249
365
90
365
360
156
137
360
365
362
337
Hours
(n)
7138
7799
7687
6702
8356
2062
6607
5997
3833
5596
6529
7624
6766
5101
5792
7474
6296
7239
4267
4922
8364
2061
7014
4311
1626
2434
5575
6830
6250
4412
Annual Hourly (ppb)
Mean
2.76
2.47
2.08
1.91
3.2
2.88
2.33
1.62
2.31
2.38
2.28
5.27
6.4
5.39
6.21
3.09
2.92
2.14
2.15
2.36
2.89
2.99
6.77
7.37
6.77
6.53
6.63
5.36
3.83
3.68
std
1.43
1.3
1.61
1.17
1.13
1.12
1.5
1.3
1.51
1.63
1.18
11.3
7.45
6.94
10.95
3.86
2.27
5.13
2.74
2.58
2.19
1.3
9.36
9.45
8.21
8.62
8.85
7.27
4.62
4.09
GM
2.44
2.18
1.69
1.65
3.03
2.71
1.99
1.35
1.85
1.77
2.02
3.28
5.14
3.66
3.76
2.28
2.5
1.59
1.63
1.94
2.61
2.81
3.75
4.29
4.01
3.84
3.84
3.11
2.54
2.48
GSD
1.65
1.64
1.84
1.69
1.39
1.39
1.74
1.74
2.04
2.44
1.63
2.15
1.73
2.44
2.29
2.06
1.65
1.88
1.89
1.76
1.49
1.39
2.86
2.79
2.76
2.69
2.75
2.67
2.34
2.31
Number of 5-minute Daily Maximum
> 100 ppb
1
0
0
0
0
0
0
0
0
0
0
30
17
12
44
5
1
2
3
2
1
0
23
18
3
4
8
6
1
0
> 200 ppb
0
0
0
0
0
0
0
0
0
0
0
11
3
1
7
1
0
2
2
1
1
0
0
2
0
0
0
0
0
0
> 300 ppb
0
0
0
0
0
0
0
0
0
0
0
5
1
0
2
1
0
2
0
0
1
0
0
0
0
0
0
0
0
0
> 400 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
A-110
-------
State
CO
CO
DE
DE
DC
DC
DC
DC
DC
DC
FL
FL
FL
FL
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
County
Denver
Denver
New Castle
New Castle
District of Columbia
District of Columbia
District of Columbia
District of Columbia
District of Columbia
District of Columbia
Nassau
Nassau
Nassau
Nassau
Cerro Gordo
Cerro Gordo
Cerro Gordo
Cerro Gordo
Cerro Gordo
Clinton
Clinton
Clinton
Clinton
Clinton
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Monitor ID
080310002
080310002
100031008
100031008
110010041
110010041
110010041
110010041
110010041
110010041
120890005
120890005
120890005
120890005
190330018
190330018
190330018
190330018
190330018
190450019
190450019
190450019
190450019
190450019
191390016
191390016
191390016
191390016
191390016
191390017
191390017
191390017
191390017
191390017
Year
2005
2006
1997
1998
2000
2001
2002
2003
2004
2007
2002
2003
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
Days
(n)
337
349
330
257
160
358
365
181
119
268
357
365
275
175
38
254
296
366
173
70
345
333
353
177
91
365
353
365
181
83
364
365
363
181
Hours
(n)
3599
6199
7490
4898
3731
7774
8365
4267
2765
6394
8415
8662
6507
4120
513
3325
5032
8141
3528
1276
6516
5939
7093
3323
1733
7391
6570
6664
3629
1373
7242
7586
7322
3441
Annual Hourly (ppb)
Mean
3.92
3.38
10.29
8.86
8.64
7
6.89
8.63
7.88
5.05
6.39
3.44
3.2
4.06
1.22
1.16
1.88
0.8
0.69
2.14
3.29
2.89
2.83
3.99
3.27
4.07
3.87
3.92
4.22
2.14
3.12
3.93
3.56
3.16
std
4.2
3.62
17.99
14.99
6.17
6.51
5.62
5.92
5.51
3.74
15.33
8.95
7.18
10.16
3.38
3.83
7.57
2.84
1.49
1.69
3.37
3.2
3.06
4.31
4.61
5.36
7.01
5.67
7.55
1.86
3.82
4.26
3.92
4.14
GM
2.57
2.33
5.23
4.35
7.25
4.83
5.29
7.28
6.3
4.24
2.65
1.6
1.68
1.65
0.44
0.33
0.27
0.23
0.31
1.52
1.96
1.68
1.67
2.35
1.89
2.78
2.21
2.43
2.34
1.42
2.05
2.69
2.24
2.03
GSD
2.42
2.26
2.86
3.03
1.77
2.45
2.11
1.75
2.06
1.76
2.95
2.5
2.37
2.71
3.16
4.07
4.83
3.4
3.16
2.54
3.02
3.14
3.12
3.11
2.93
2.39
2.86
2.7
2.79
2.66
2.62
2.51
2.79
2.59
Number of 5-minute Daily Maximum
> 100 ppb
0
1
103
64
1
3
1
5
1
1
69
26
11
26
0
0
4
0
0
0
3
4
3
2
0
4
4
5
9
0
3
4
2
4
> 200 ppb
0
0
33
16
0
1
0
1
0
1
23
5
5
4
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
> 300 ppb
0
0
1
2
0
1
0
1
0
1
6
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
> 400 ppb
0
0
0
0
0
0
0
1
0
0
2
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
A-lll
-------
State
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
LA
LA
LA
LA
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
County
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Scott
Scott
Scott
Scott
Scott
Van Buren
Van Buren
Van Buren
Van Buren
Van Buren
Van Buren
Wood bury
Wood bury
West Baton Rouge
West Baton Rouge
West Baton Rouge
West Baton Rouge
Buchanan
Buchanan
Buchanan
Buchanan
Buchanan
Buchanan
Buchanan
Buchanan
Greene
Greene
Greene
Greene
Monitor ID
191390020
191390020
191390020
191390020
191390020
191630015
191630015
191630015
191630015
191630015
191770005
191770005
191770005
191770005
191770006
191770006
191930018
191930018
221210001
221210001
221210001
221210001
290210009
290210009
290210009
290210009
290210011
290210011
290210011
290210011
290770026
290770026
290770026
290770026
Year
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2004
2005
2001
2002
1997
1998
1999
2000
1997
1998
1999
2000
2000
2001
2002
2003
1997
1998
1999
2000
Days
(n)
92
363
365
366
181
85
364
364
336
177
65
353
358
305
53
181
85
280
277
353
354
361
361
364
362
264
72
329
331
253
339
350
362
366
Hours
(n)
1909
7682
7695
7757
3931
1345
7505
7451
6696
3436
597
6350
7118
5011
877
3349
1578
3875
4966
7566
7272
7360
8484
8161
7415
5297
1672
6412
6457
5141
4763
5810
7242
8721
Annual Hourly (ppb)
Mean
5.36
5.27
5.31
7.36
5.55
1.15
2.28
2.09
2.11
2.56
0.9
1.03
1.1
0.88
0.85
0.9
1.32
1.5
7.04
7.52
6.4
7.3
8.3
7.06
2.77
2.37
5.27
3.7
4.01
4.06
4.32
5.73
4.09
4.97
std
9.76
10.27
11.2
15.39
13.61
2.13
3.17
2.68
2.65
3.05
0.92
0.92
0.91
1.45
0.94
0.79
2.28
2.94
12.51
10.67
9.59
11.13
31.64
24.17
3.07
3.04
8.53
5.3
7.33
7.04
9.65
11.66
7.53
10.21
GM
2.04
2.22
2.11
3.02
2.02
0.45
0.87
1.02
1.06
1.17
0.64
0.72
0.78
0.5
0.55
0.69
0.77
0.7
3.94
5.03
4.01
4.51
2.77
2.8
2.08
1.81
3.45
2.52
2.52
2.59
2.02
2.35
2.22
2.41
GSD
3.95
3.61
3.71
3.2
3.64
4
4.7
3.79
3.68
4.17
2.33
2.48
2.48
2.87
2.53
2.09
2.45
3.14
2.65
2.29
2.44
2.46
2.8
2.64
2
1.88
2.15
2.15
2.23
2.25
2.69
3.07
2.5
2.67
Number of 5-minute Daily Maximum
> 100 ppb
1
31
42
60
27
0
0
0
0
0
0
0
0
0
0
0
0
0
42
50
55
76
94
92
3
7
8
6
21
13
20
39
13
52
> 200 ppb
0
1
5
14
12
0
0
0
0
0
0
0
0
0
0
0
0
0
13
18
12
26
79
67
0
0
0
0
0
0
2
1
1
1
> 300 ppb
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
4
2
1
7
57
44
0
0
0
0
0
0
0
0
0
0
> 400 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
39
19
0
0
0
0
0
0
0
0
0
0
A-112
-------
State
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
County
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Greene
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Iron
Monitor ID
290770026
290770026
290770026
290770026
290770026
290770026
290770026
290770037
290770037
290770037
290770037
290770037
290770037
290770037
290770037
290770037
290770037
290770037
290930030
290930030
290930030
290930030
290930030
290930030
290930030
290930030
290930031
290930031
290930031
290930031
290930031
290930031
290930031
290930031
Year
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
2000
2001
2002
2003
2004
1997
1998
1999
2000
2001
2002
2003
2004
Days
(n)
365
360
362
274
365
365
272
356
361
363
341
355
335
363
274
365
365
272
365
365
356
324
356
354
363
90
352
363
341
332
365
365
350
91
Hours
(n)
8304
7054
7935
6574
8756
8753
6520
6559
8134
8554
5318
6707
6373
8179
6575
8760
8745
6496
8575
8475
6546
4071
5388
7960
6963
1846
6177
7991
7918
5170
8426
8665
8230
2172
Annual Hourly (ppb)
Mean
4.52
4.28
3.5
3.21
2.95
3.15
3.2
4.98
4.27
3.13
6.36
4.04
4
3.32
2.71
3.05
3.26
2.42
8.24
7.9
9.33
14.3
9.32
6.95
7.58
2.47
8.09
7.56
8.41
8.27
6.62
6.32
6.6
3.82
std
9.62
9.08
6.16
6.41
5.94
6.77
7.07
14.73
7.37
7.72
17.9
10.65
9.68
6.96
4.79
6.06
8.44
6.03
26.43
25.09
28.07
46.11
32.18
23.55
23.2
2.56
24.57
22.94
25.99
24.93
23.42
18.53
21.05
2.74
GM
2.17
1.94
2.02
1.64
1.58
1.58
1.59
1.89
2.76
1.72
2.13
1.91
2.15
1.93
1.79
1.93
1.57
1.37
3.12
2.73
3.29
3.2
2.37
2.2
2.69
1.76
2.92
3.03
3.93
2.81
2.47
3.19
2.89
3.2
GSD
2.63
2.72
2.36
2.45
2.35
2.42
2.43
2.78
2.18
2.23
3.04
2.49
2.27
2.21
2.05
2.11
2.38
2.08
2.89
2.99
3.09
3.95
3.41
2.98
2.94
2.11
3.17
2.94
2.63
3.21
2.79
2.35
2.64
1.74
Number of 5-minute Daily Maximum
> 100 ppb
36
27
5
3
5
8
9
52
30
31
46
37
40
19
13
20
37
16
93
85
83
95
88
99
99
0
77
88
92
86
95
88
88
0
> 200 ppb
0
0
0
0
0
0
0
21
2
3
23
9
11
1
0
1
4
0
78
70
74
77
74
73
81
0
55
57
54
53
60
54
54
0
> 300 ppb
0
0
0
0
0
0
0
8
0
0
3
2
1
0
0
0
0
0
63
62
63
69
64
58
64
0
37
37
37
35
40
28
39
0
> 400 ppb
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
54
52
49
55
56
52
48
0
27
22
23
23
22
19
23
0
A-113
-------
State
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
County
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Monroe
Monroe
Monroe
Monroe
Monroe
Monroe
Monroe
Monroe
Monroe
Monroe
Monroe
Pike
Pike
Pike
Saint Charles
Saint Charles
Saint Charles
Saint Charles
Monitor ID
290990004
290990004
290990004
290990004
290990014
290990014
290990014
290990014
290990014
290990017
290990017
290990017
290990017
290990018
290990018
290990018
291370001
291370001
291370001
291370001
291370001
291370001
291370001
291370001
291370001
291370001
291370001
291630002
291630002
291630002
291830010
291830010
291831002
291831002
Year
2004
2005
2006
2007
1997
1998
1999
2000
2001
1998
1999
2000
2001
2001
2002
2003
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2005
2006
2007
1997
1998
1997
1998
Days
(n)
346
351
343
90
359
365
363
361
132
289
360
355
74
219
352
272
364
364
365
366
309
321
336
316
348
338
51
311
348
68
365
230
365
362
Hours
(n)
8033
7144
6524
2125
7174
7770
7591
6588
2433
5721
7289
7153
1044
3492
6305
6009
8280
8411
8714
8617
4346
5358
5948
5123
6518
6169
526
4879
6469
1019
8152
4810
8514
8122
Annual Hourly (ppb)
Mean
10.32
11.41
13.12
6.31
8.38
4.57
4.6
3.87
3.15
7.37
8.65
6.06
7.72
5.33
5.51
4.41
2.92
2.35
3.58
2.93
1.78
1.81
1.82
2.29
2.03
1.73
1.86
4.37
3.94
3.08
4.35
4.32
5.72
6.31
std
22.63
24.87
27.2
11.92
19
9.67
9.49
7.06
5.64
18.87
22.19
16.54
16.53
11.74
14.84
10.38
2.86
2.25
2.36
2.06
1.44
1.48
1.48
2.31
1.81
1.26
2
5.43
4.67
3.69
7.95
5.69
6.95
7.9
GM
4.78
4.62
4.3
3.08
4.14
2.62
2.48
2.36
1.95
3.47
3.8
2.87
3.69
2.53
2.59
2.4
2.38
1.86
3.13
2.54
1.47
1.48
1.51
1.77
1.63
1.47
1.48
2.89
2.78
2.09
2.6
2.77
3.65
4.02
GSD
2.96
3.34
4.02
2.88
2.79
2.48
2.57
2.35
2.25
2.87
3.01
2.77
3.02
2.84
2.75
2.54
1.79
1.87
1.63
1.65
1.74
1.75
1.73
1.91
1.81
1.68
1.8
2.33
2.2
2.24
2.45
2.38
2.5
2.5
Number of 5-minute Daily Maximum
> 100 ppb
106
118
134
21
87
37
32
28
7
59
90
59
13
34
56
27
0
0
0
0
0
0
0
0
0
0
0
5
3
0
5
1
23
25
> 200 ppb
41
68
78
8
54
23
19
7
1
33
57
40
9
18
36
18
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
> 300 ppb
26
47
53
4
31
13
11
4
0
22
42
26
5
9
24
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
> 400 ppb
13
28
41
1
23
6
5
2
0
16
29
17
3
6
18
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
A-114
-------
State
MO
MO
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
NC
NC
NC
County
Saint Charles
Saint Charles
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Forsyth
Forsyth
Forsyth
Monitor ID
291831002
291831002
301110066
301110066
301110066
301110066
301110066
301110066
301110066
301110079
301110079
301110079
301110079
301110080
301110080
301110080
301110080
301110080
301110082
301110082
301110082
301110083
301110083
301110083
301110083
301110083
301110084
301110084
301110084
301110084
301112008
370670022
370670022
370670022
Year
1999
2000
1997
1998
1999
2000
2001
2002
2003
1997
2001
2002
2003
1997
1998
1999
2000
2001
2001
2002
2003
1999
2000
2001
2002
2003
2003
2004
2005
2006
1997
1997
1998
1999
Days
(n)
363
331
362
357
352
355
365
364
347
180
55
353
350
363
358
350
360
150
169
365
361
112
341
357
360
166
99
294
291
273
177
362
364
352
Hours
(n)
7969
6421
6873
7198
5767
6099
6872
8347
5691
3166
837
8034
5107
5433
5371
5588
5999
2015
2605
8212
5173
2087
3845
5604
6847
1641
759
2465
2577
1983
2579
7822
7122
6428
Annual Hourly (ppb)
Mean
5.61
4.6
8.06
7.14
7.75
7.72
7.77
6.81
7.37
3.84
4.64
1.9
3.02
7.54
6.85
6.36
6.22
5.55
4.19
2.32
2.93
8.07
4.68
4.36
2.31
2.29
2.99
3.48
2.96
2.75
3.96
7.06
6.98
5.85
std
7.24
5.45
10.76
9.49
9.65
10.26
10.46
11.61
9.92
4.06
3.71
1.91
2.55
10.11
9.12
7.81
7.65
6.3
4.62
2.77
3.25
8.01
5.36
5.59
3.21
3.08
4.51
5.45
4.98
4.56
4.57
6.91
7.54
5.92
GM
3.58
3.01
4.4
4
4.31
4.25
4.13
3.46
4.06
2.65
3.43
1.48
2.3
4.29
3.98
3.79
3.68
3.54
2.87
1.7
2.11
5.01
3
2.71
1.65
1.62
1.99
2.14
1.79
1.71
2.65
5.13
4.72
4.13
GSD
2.5
2.42
3
2.9
2.99
2.97
3.06
3.04
2.93
2.28
2.23
1.83
2.06
2.86
2.79
2.75
2.74
2.56
2.32
1.99
2.11
2.81
2.49
2.51
1.98
1.99
2.19
2.37
2.28
2.23
2.35
2.2
2.48
2.29
Number of 5-minute Daily Maximum
> 100 ppb
17
5
45
42
34
66
56
52
39
1
0
0
0
59
38
47
59
12
1
0
1
4
10
11
1
0
0
2
2
1
2
10
13
3
> 200 ppb
2
0
7
5
5
7
4
4
2
0
0
0
0
11
14
7
10
2
0
0
1
0
1
1
0
0
0
0
0
0
0
0
1
0
> 300 ppb
0
0
1
1
0
2
0
2
0
0
0
0
0
3
6
4
1
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
> 400 ppb
0
0
1
0
0
1
0
1
0
0
0
0
0
0
0
2
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
A-115
-------
State
NC
NC
NC
NC
NC
NC
NC
NC
NC
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
County
Forsyth
Forsyth
Forsyth
Forsyth
Forsyth
New Hanover
New Hanover
New Hanover
New Hanover
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burleigh
Burleigh
Monitor ID
370670022
370670022
370670022
370670022
370670022
371290006
371290006
371290006
371290006
380070002
380070002
380070002
380070002
380070002
380070002
380070002
380070002
380070002
380070002
380070003
380130002
380130002
380130002
380130002
380130002
380130002
380130002
380130004
380130004
380130004
380130004
380130004
380150003
380150003
Year
2000
2001
2002
2003
2004
1999
2000
2001
2002
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1999
2000
2001
2002
2003
2004
2005
2003
2004
2005
2006
2007
2005
2006
Days
(n)
266
361
362
363
259
360
335
358
352
143
276
248
283
275
26
164
128
106
43
167
297
347
338
346
353
340
263
63
315
244
302
99
60
294
Hours
(n)
5203
7634
7022
8075
4710
8208
7980
8168
8028
1940
3216
2724
2860
3113
341
1256
835
418
221
2657
3852
5268
5653
5367
6328
5229
3098
882
3198
2238
3152
1227
683
3686
Annual Hourly (ppb)
Mean
5.52
5.12
6.12
5.87
5.56
4.1
4.67
5.71
6.44
1.31
1.38
1.42
1.37
1.43
1.48
1.24
1.44
1.53
1.5
1.72
2.79
2.96
2.72
2.64
2.6
2.77
2.88
2.89
2.76
2.47
2.27
3.8
3.4
2.33
std
5.58
5.64
8.19
6.19
8.21
8.34
8.92
13.73
13.85
1.04
1.04
1.1
1.12
1.11
0.87
0.85
0.92
1.25
1.26
1.52
4.61
5.77
4.97
4.72
4.77
5.03
4.99
3.99
3.59
3.18
3.16
5.18
2.97
2.6
GM
3.77
3.46
3.87
4.17
3.37
1.92
2.13
2.08
2.61
1.16
1.21
1.24
1.2
1.26
1.32
1.13
1.27
1.29
1.29
1.43
1.65
1.77
1.62
1.58
1.62
1.65
1.67
1.84
1.83
1.72
1.59
2.27
2.47
1.68
GSD
2.39
2.38
2.51
2.24
2.55
2.73
2.87
3.09
3.12
1.48
1.53
1.56
1.51
1.53
1.54
1.41
1.55
1.64
1.6
1.7
2.31
2.27
2.25
2.24
2.16
2.26
2.33
2.26
2.21
2.09
2.02
2.53
2.2
2.04
Number of 5-minute Daily Maximum
> 100 ppb
1
5
15
11
6
54
76
109
127
0
0
0
0
0
0
0
0
0
0
0
3
7
3
4
7
6
4
0
0
0
1
1
0
0
> 200 ppb
0
0
3
0
1
8
6
54
39
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
> 300 ppb
0
0
0
0
0
4
3
10
7
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
> 400 ppb
0
0
0
0
0
3
0
3
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
A-116
-------
State
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
County
Burleigh
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Dunn
Dunn
Dunn
Dunn
Dunn
Dunn
Dunn
Dunn
Dunn
Dunn
Dunn
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
Monitor ID
380150003
380171003
380171003
380171004
380171004
380171004
380171004
380171004
380171004
380171004
380171004
380171004
380171004
380250003
380250003
380250003
380250003
380250003
380250003
380250003
380250003
380250003
380250003
380250003
380530002
380530002
380530002
380530002
380530002
380530002
380530002
380530002
380530002
380530104
Year
2007
1997
1998
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
2001
2002
2003
2004
2005
2006
2007
1998
Days
(n)
97
206
132
162
246
213
203
274
200
256
146
358
116
224
242
323
353
276
334
355
347
183
262
79
238
144
108
262
305
303
225
276
73
224
Hours
(n)
947
2254
2943
2501
3325
1868
1686
2476
1297
3140
928
7385
2256
3313
2688
5099
7455
3575
4484
7289
6019
1314
2213
667
2552
1989
754
3361
5345
4614
2515
2896
511
1525
Annual Hourly (ppb)
Mean
3.77
1.74
1.88
1.11
1.32
1.37
1.34
1.12
1.25
1.21
1.24
0.39
0.55
1.38
1.78
1.5
1.4
1.6
1.31
1.5
1.34
1.48
1.53
1.65
1.5
1.66
1.31
1.23
1.5
1.4
1.29
1.28
1.64
2.38
std
4.32
2.31
1.83
0.43
0.75
0.84
0.93
0.43
0.82
0.6
0.68
0.42
0.74
1.14
2.07
1.56
1.44
1.48
1.09
1.28
1.13
1.53
1.57
1.5
1.23
1.57
0.84
0.77
1.29
1.19
0.82
0.85
1.34
4.92
GM
2.49
1.32
1.5
1.07
1.2
1.23
1.2
1.08
1.15
1.13
1.15
0.28
0.33
1.2
1.39
1.26
1.2
1.34
1.16
1.29
1.17
1.23
1.26
1.37
1.28
1.36
1.18
1.13
1.28
1.22
1.17
1.16
1.38
1.59
GSD
2.36
1.79
1.8
1.27
1.46
1.5
1.49
1.27
1.41
1.37
1.41
2.19
2.6
1.54
1.79
1.62
1.55
1.66
1.48
1.58
1.51
1.62
1.65
1.69
1.61
1.7
1.47
1.4
1.6
1.55
1.46
1.45
1.67
2.04
Number of 5-minute Daily Maximum
> 100 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
> 200 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
> 300 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
> 400 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
A-117
-------
State
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
County
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Morton
Morton
Morton
Monitor ID
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530111
380530111
380530111
380530111
380530111
380530111
380530111
380530111
380530111
380530111
380570001
380570001
380570001
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380590002
380590002
380590002
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
Days
(n)
240
294
283
236
293
271
245
234
71
258
294
329
336
297
288
308
296
304
78
243
319
14
334
362
338
336
351
344
273
301
107
346
290
359
Hours
(n)
1500
2755
2281
1526
2333
2231
1900
1827
764
2063
2379
2805
3183
2255
2243
2857
2790
2896
722
2824
4735
320
5584
7348
4647
3701
5555
4678
3037
2755
1133
6547
4696
6837
Annual Hourly (ppb)
Mean
2.3
1.96
1.68
1.9
1.98
1.34
1.32
1.32
1.44
3.11
2.36
2.68
1.81
1.87
2.03
1.82
1.39
1.35
1.61
2.93
3.33
5.18
2.6
2.29
2.9
2.65
2.21
2.62
2.43
2.77
2.48
9.31
9.3
7.7
std
3.7
4.07
1.75
4.04
5.29
1.34
2.32
1.78
1.13
7.34
5.4
8.27
2.09
3.52
3.84
5.94
3.28
2.43
1.89
4.29
6.47
3.12
3.94
3.8
5.34
4.59
3.11
3.57
3.25
3.37
3.44
20.26
22.47
16.99
GM
1.66
1.44
1.38
1.34
1.3
1.19
1.14
1.14
1.26
1.8
1.56
1.65
1.4
1.38
1.44
1.27
1.14
1.16
1.3
1.87
2.09
4.43
1.66
1.55
1.76
1.73
1.55
1.73
1.68
1.86
1.7
2.93
2.78
2.53
GSD
1.97
1.85
1.72
1.83
1.84
1.49
1.46
1.46
1.56
2.29
2.02
2.1
1.81
1.8
1.87
1.72
1.5
1.48
1.69
2.26
2.28
1.73
2.2
2.06
2.26
2.17
2.01
2.19
2.08
2.21
2.1
3.67
3.75
3.55
Number of 5-minute Daily Maximum
> 100 ppb
3
5
1
9
15
1
2
4
0
7
7
7
0
8
7
3
5
4
1
0
5
0
3
3
8
2
1
1
0
0
0
102
75
90
> 200 ppb
3
2
0
2
3
0
0
1
0
2
2
5
0
3
2
1
2
1
1
0
2
0
1
1
0
1
0
0
0
0
0
19
8
4
> 300 ppb
1
1
0
0
1
0
0
0
0
0
1
4
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
> 400 ppb
0
1
0
0
0
0
0
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
A-118
-------
State
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
County
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Oliver
Oliver
Oliver
Oliver
Oliver
Oliver
Oliver
Oliver
Oliver
Oliver
Oliver
Steele
Steele
Steele
Steele
Williams
Williams
Williams
Williams
Williams
Monitor ID
380590002
380590002
380590002
380590002
380590002
380590002
380590003
380590003
380590003
380590003
380590003
380590003
380590003
380590003
380650002
380650002
380650002
380650002
380650002
380650002
380650002
380650002
380650002
380650002
380650002
380910001
380910001
380910001
380910001
381050103
381050103
381050103
381050103
381050103
Year
2000
2001
2002
2003
2004
2005
1998
1999
2000
2001
2002
2003
2004
2005
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
2000
2002
2003
2004
2005
2006
Days
(n)
363
346
355
365
363
111
95
353
351
357
342
364
344
106
244
319
349
351
214
350
357
354
275
325
101
216
202
152
83
319
339
348
301
322
Hours
(n)
7964
5947
6258
8033
7532
1450
1924
6522
5984
6345
5245
7991
6338
1012
2356
4175
4856
4765
2404
4482
6953
6138
2443
3369
780
3134
2804
1845
805
2724
3323
3438
2331
2976
Annual Hourly (ppb)
Mean
6.47
7.48
6.26
6.25
6.74
4.85
3.71
5.06
4.71
4.94
4.41
3.55
4.44
3.84
4.28
3.92
3.47
3.14
3.42
2.71
2.37
2.76
3.86
2.85
4.12
1.41
2.22
1.25
1.11
3.18
2.48
2.52
3.51
1.88
std
14.58
13.57
12.03
13.66
13.2
6.08
7.47
8.84
8.04
8.17
7.53
6.34
7.03
5.1
7.23
7.23
6.94
5.54
5.86
4.75
5.58
5.16
6.7
4.32
6.99
0.74
2.1
0.79
0.4
7.56
3.71
5.21
8
2.32
GM
2.22
2.81
2.49
2.33
2.62
2.7
2.01
2.48
2.44
2.54
2.35
1.96
2.5
2.42
2.3
2.1
1.93
1.89
1.96
1.69
1.47
1.65
2.05
1.77
2.35
1.28
1.72
1.14
1.07
1.68
1.64
1.62
1.85
1.4
GSD
3.31
3.5
3.25
3.18
3.29
2.82
2.48
2.88
2.74
2.81
2.68
2.49
2.59
2.39
2.63
2.58
2.42
2.32
2.42
2.21
2.05
2.24
2.62
2.28
2.53
1.5
1.91
1.42
1.26
2.36
2.13
2.12
2.45
1.87
Number of 5-minute Daily Maximum
> 100 ppb
73
66
59
82
76
1
8
41
24
27
26
27
24
1
7
12
15
8
1
4
10
7
6
1
2
0
0
0
0
8
3
5
20
0
> 200 ppb
3
2
1
3
2
0
0
2
0
1
1
0
0
0
0
1
1
0
0
0
1
1
2
0
0
0
0
0
0
3
0
3
3
0
> 300 ppb
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
1
0
> 400 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
A-119
-------
State
ND
ND
ND
ND
ND
ND
ND
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Williams
Williams
Williams
Williams
Williams
Williams
Williams
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Monitor ID
381050103
381050105
381050105
381050105
381050105
381050105
381050105
420030002
420030002
420030002
420030021
420030021
420030021
420030021
420030031
420030031
420030031
420030032
420030032
420030032
420030064
420030064
420030064
420030064
420030067
420030067
420030067
420030116
420030116
420030116
420030116
420031301
420031301
420031301
Year
2007
2002
2003
2004
2005
2006
2007
1997
1998
1999
1997
1998
1999
2002
1997
1998
1999
1997
1998
1999
1997
1998
1999
2002
1997
1998
1999
1997
1998
1999
2002
1997
1998
1999
Days
(n)
86
302
342
346
349
262
24
357
3
325
355
3
362
313
362
3
360
364
3
210
361
3
355
350
364
3
257
361
3
299
232
363
3
363
Hours
(n)
834
2843
3523
4129
4492
2938
263
7821
72
6986
7830
72
8279
7291
8000
68
7443
7951
60
4326
7526
71
7232
8239
8231
72
5891
7767
70
5684
5403
7663
70
8161
Annual Hourly (ppb)
Mean
3.35
6.77
5.67
5.64
6.79
3.74
3.59
12.57
43.18
11.04
18.11
10.22
9
7.32
10.98
11.38
8.98
15.4
35.2
8.18
11.9
20.11
12.11
10.9
10.43
17.01
10.05
13.26
17
12.12
7
9.37
12.66
9.64
std
4.62
10.88
9.39
10.64
13
6.66
5.63
15.05
32.27
11.16
18.87
8.23
7.94
7.33
9.63
9.36
7.84
19.34
20.65
7.8
13.08
7.99
14.34
13.26
11.13
12.54
8.81
17.76
11.04
16.01
7.96
9.8
6.88
9.62
GM
2.07
2.93
2.55
2.55
2.49
1.91
1.99
7.69
31.63
7.36
11.07
7.48
6.64
4.49
8.05
8.2
6.43
9.39
27.51
5.66
7.16
18.41
7.35
5.91
6.69
12.63
7.35
8.33
12.59
7.82
4.56
6.25
11.29
6.57
GSD
2.4
3.34
3.12
3.1
3.46
2.62
2.53
2.68
2.43
2.53
2.93
2.27
2.2
2.85
2.24
2.3
2.33
2.73
2.26
2.41
2.86
1.56
2.78
3.15
2.62
2.25
2.22
2.6
2.46
2.54
2.5
2.48
1.58
2.44
Number of 5-minute Daily Maximum
> 100 ppb
0
35
13
19
52
14
1
70
3
31
87
0
3
3
12
0
1
84
2
2
17
0
18
18
12
0
1
60
0
50
3
21
0
21
> 200 ppb
0
4
1
2
12
1
0
8
1
2
19
0
0
0
1
0
0
15
0
0
2
0
3
5
2
0
0
19
0
26
0
4
0
3
> 300 ppb
0
1
0
2
1
0
0
2
0
0
5
0
0
0
0
0
0
6
0
0
0
0
2
1
1
0
0
12
0
13
0
1
0
1
> 400 ppb
0
0
0
1
0
0
0
0
0
0
2
0
0
0
0
0
0
4
0
0
0
0
1
0
1
0
0
8
0
8
0
1
0
1
A-120
-------
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Berks
Berks
Berks
Cambria
Cambria
Cambria
Erie
Erie
Erie
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Monitor ID
420033003
420033003
420033003
420033003
420033004
420033004
420033004
420070002
420070002
420070005
420070005
420070005
420070005
420070005
420070005
420070005
420070005
420110009
420110009
420110009
420210011
420210011
420210011
420490003
420490003
420490003
421010022
421010022
421010022
421010022
421010022
421010048
421010048
421010048
Year
1997
1998
1999
2002
1997
1998
1999
1997
1998
1997
1998
2002
2003
2004
2005
2006
2007
1997
1998
1999
1997
1998
1999
1997
1998
1999
1997
1998
1999
2000
2001
1997
1998
1999
Days
(n)
356
2
350
316
362
3
361
351
270
359
277
361
365
364
362
361
324
350
365
119
361
356
120
363
363
120
364
363
137
179
98
365
356
178
Hours
(n)
7422
45
6998
7363
7461
66
7408
7889
6205
7447
6388
8491
8706
8656
8578
8457
7556
7805
8641
2790
8129
7908
2835
8169
8416
2778
8297
8065
2665
3630
2094
8456
7285
3939
Annual Hourly (ppb)
Mean
11.8
11.47
13.59
12.66
9.18
13.12
8.55
11.83
12.96
16.57
16.14
14.24
10.79
11.59
12.57
9.26
9.79
8.66
8.93
9.22
9.76
8.78
9.74
9.76
10.57
11.48
8.56
7.3
7.79
7.63
7.53
8.88
6.27
6.08
std
13.86
6.31
19.91
18.25
9.66
6.01
9.09
15.38
16.48
25.11
26.85
26.51
17.07
17.68
18.18
18.5
13.98
8.87
7.56
8.38
9.15
9.69
7.99
11.22
13.5
15.12
8.74
7.04
8.26
6.88
7.17
18.38
6.03
6.57
GM
7.01
9.35
7.86
6.32
6.17
11.71
5.79
6.83
7.8
8.65
8.36
5.28
4.38
5.55
6.82
3.49
4.94
5.87
7.11
6.86
6.72
5.65
7.61
6.68
7.09
7.46
5.63
4.82
4.76
5.05
5.16
4.96
4.21
3.95
GSD
2.85
2.09
2.86
3.29
2.47
1.65
2.47
2.84
2.71
3.14
3.01
4.12
3.83
3.39
3.04
3.78
3.26
2.44
1.92
2.17
2.47
2.62
1.99
2.33
2.35
2.48
2.57
2.56
2.79
2.62
2.44
2.74
2.48
2.53
Number of 5-minute Daily Maximum
> 100 ppb
27
0
37
53
12
0
6
91
74
98
92
113
75
74
75
71
45
35
33
9
8
16
1
60
60
26
7
2
4
1
0
59
0
1
> 200 ppb
1
0
2
8
2
0
3
11
6
39
39
49
16
22
26
30
12
4
3
1
0
1
0
9
12
7
1
0
1
0
0
40
0
1
> 300 ppb
0
0
2
5
0
0
2
1
2
17
21
23
3
10
12
11
4
0
0
0
0
0
0
1
1
3
0
0
0
0
0
27
0
0
> 400 ppb
0
0
2
3
0
0
0
1
0
11
13
13
2
3
7
5
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
23
0
0
A-121
-------
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
County
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Warren
Warren
Warren
Warren
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Barnwell
Barnwell
Barnwell
Charleston
Charleston
Charleston
Charleston
Charleston
Charleston
Georgetown
Georgetown
Georgetown
Greenville
Greenville
Greenville
Monitor ID
421010136
421010136
421010136
421010136
421010136
421010136
421010136
421230003
421230003
421230004
421230004
421250005
421250005
421250005
421250200
421250200
421250200
421255001
421255001
450110001
450110001
450110001
450190003
450190003
450190003
450190046
450190046
450190046
450430006
450430006
450430006
450450008
450450008
450450008
Year
1997
1998
1999
2000
2001
2002
2003
1997
1998
1997
1998
1997
1998
1999
1997
1998
1999
1997
1998
2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
Days
(n)
360
339
337
351
266
359
119
346
89
355
89
364
362
120
364
365
120
365
277
100
267
202
114
344
201
100
269
189
71
241
140
113
356
212
Hours
(n)
7532
6491
7144
7044
5149
7271
2585
7157
2126
7022
1966
8374
8540
2821
8369
8656
2829
8425
6559
789
2625
2544
1703
4806
3509
1252
3497
2927
604
2218
1169
1987
6418
4679
Annual Hourly (ppb)
Mean
4.99
5.25
5.63
5.76
6.77
5.38
6.74
10.53
7.62
17.14
13.97
8.95
8.88
8.32
10.52
10.46
10.15
12.71
13.46
3.95
2.72
2.11
6.24
4.16
2.85
4.61
2.64
2.34
4.92
4.76
2.5
4.84
4.24
3.06
std
5.52
5.52
6.04
5.97
7.43
5.7
6.71
11.59
7.38
28.18
21.76
8.41
7.78
7.68
11.23
10.49
9.81
15.24
13.09
2.83
2.61
1.72
5.36
4.12
3.49
3.9
2.6
2.89
4.35
6.11
4.33
3.75
3.86
2.8
GM
3.29
3.5
3.71
3.74
4.38
3.57
4.62
6.64
5.41
7.47
6.8
6.45
6.68
6.36
6.99
7.18
7
8.39
10.28
3.39
2.13
1.67
4.77
2.95
1.97
3.71
1.99
1.68
3.97
3.13
1.67
3.95
3.18
2.29
GSD
2.43
2.44
2.48
2.54
2.55
2.47
2.42
2.68
2.26
3.66
3.18
2.25
2.14
2.02
2.45
2.37
2.4
2.36
1.97
1.66
1.93
1.88
2.02
2.22
2.16
1.84
2
2.02
1.82
2.33
2.08
1.82
2.1
2.09
Number of 5-minute Daily Maximum
> 100 ppb
1
2
2
0
2
3
2
26
0
148
30
7
4
1
17
15
3
57
42
0
1
0
1
1
0
0
0
0
0
3
1
0
3
1
> 200 ppb
0
0
1
0
0
0
0
3
0
44
6
0
0
0
0
1
1
5
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
> 300 ppb
0
0
0
0
0
0
0
0
0
14
2
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
> 400 ppb
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
A-122
-------
State
SC
sc
SC
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
UT
UT
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
County
Lexington
Lexington
Oconee
Oconee
Oconee
Richland
Richland
Richland
Richland
Richland
Richland
Richland
Richland
Salt Lake
Salt Lake
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wood
Wood
Wood
Wood
Wood
Monitor ID
450630008
450630008
450730001
450730001
450730001
450790007
450790007
450790007
450790021
450790021
450790021
450791003
450791003
490352004
490352004
540990002
540990003
540990003
540990003
540990003
540990004
540990004
540990004
540990004
540990005
540990005
540990005
540990005
541071002
541071002
541071002
541071002
541071002
Year
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2001
2002
1997
1998
2002
2002
2003
2004
2005
2002
2003
2004
2005
2002
2003
2004
2005
2001
2002
2003
2004
2005
Days
(n)
263
211
89
288
188
110
365
210
109
283
202
193
212
335
354
365
361
362
366
365
362
365
366
363
365
365
366
365
92
365
365
366
266
Hours
(n)
3941
4242
1218
4304
3063
1808
6419
4335
911
2700
2505
3346
4323
4524
5792
8711
7417
8057
8659
8141
8560
8570
8673
8586
8283
7927
8681
8453
2152
8648
8641
8581
6219
Annual Hourly (ppb)
Mean
4.2
4.5
3.85
2.9
1.82
4.48
3.88
2.95
4.43
3.73
2.94
3.14
2.87
2.31
1.94
7.49
8.48
8.76
9.21
9.58
9.21
8.53
7.22
7.67
8.44
8.31
7.03
6.68
7.76
9.9
9.48
10.88
8.34
std
7.8
8.74
2.87
2.1
1.52
2.81
3.47
2.71
5.47
4.89
4.85
2.8
2.8
2.5
1.66
7.14
9.1
9.73
9.46
11.8
9.18
9.77
6.66
6.39
9.75
11.03
5.92
5.52
12.51
11.29
12.26
13.25
12.71
GM
2.37
2.33
3.26
2.35
1.43
3.86
2.99
2.23
3.4
2.64
1.92
2.46
2.16
1.76
1.58
5.13
5.21
5.56
6.38
5.96
6.37
5.84
5.36
5.97
5.38
5.02
5.25
4.89
4.04
6.21
5.8
7
4.07
GSD
2.44
2.61
1.7
1.89
1.95
1.69
2.02
2.04
1.85
2.1
2.16
1.96
2.04
1.94
1.78
2.42
2.75
2.58
2.31
2.61
2.37
2.35
2.12
2
2.58
2.7
2.16
2.26
3.04
2.63
2.61
2.55
3.23
Number of 5-minute Daily Maximum
> 100 ppb
26
22
0
0
0
0
0
0
0
0
0
0
0
6
0
1
7
8
5
6
22
26
6
7
67
52
2
4
9
42
53
57
42
> 200 ppb
3
3
0
0
0
0
0
0
0
0
0
0
0
1
0
0
2
0
1
0
1
4
0
0
3
20
0
1
3
7
9
13
12
> 300 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
3
0
0
0
5
0
0
2
1
2
3
1
> 400 ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
1
0
0
0
0
0
0
1
0
0
1
1
A-123
-------
Appendix B: Supplement to the SO2 Exposure Assessment
B-l
-------
B-1 OVERVIEW
This appendix contains supplemental descriptions of the methods and data used in the
SC>2 exposure assessment, as well as detailed results from the exposure analyses performed.
First, a broad description of the exposure modeling approach is described (section B-2),
applicable to the two exposure modeling domains conducted to date: Greene County, Mo. and St.
Louis, MO. Supplementary input data used in AERMOD are provided in section B-3, as well as
the model predictions and ambient monitor measurements in each modeling domain. Section B-
4 has additional input and output data for APEX. A series of Attachments follow, further
documenting some of the data sources and modeling approaches used, as well as previously
conducted uncertainty analyses on selected input parameters in APEX.
B-2
-------
B-2 HUMAN EXPOSURE MODELING USING APEX
The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program
designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and
consolidated metropolitan levels. APEX, also known as TREVI.Expo, is the human inhalation
exposure module of EPA's Total Risk Integrated Methodology (TRIM) model framework (US
EPA, 1999), a modeling system with multimedia capabilities for assessing human health and
ecological risks from hazardous and criteria air pollutants. It is developed to support evaluations
with a scientifically sound, flexible, and user-friendly methodology. Additional information on
the TRIM modeling system, as well as downloads of the APEX Model, user's guide, and other
supporting documentation, are on EPA's Technology Transfer Network (TTN) at
http://www.epa.gov/ttn/fera.
B-2.1 History
APEX was derived from the National Ambient Air Quality Standards (NAAQS)
Exposure Model (NEM) series of models, developed to estimate exposure to the criteria
pollutants (e.g., carbon monoxide (CO), ozone Os). In 1979, EPA began by assembling a
database of human activity patterns that could be used to estimate exposures to indoor and
outdoor pollutants (Roddin et al., 1979). These data were then combined with measured outdoor
concentrations in NEM to estimate exposures to CO (Biller et al., 1981; Johnson and Paul,
1983). In 1988, OAQPS began to incorporate probabilistic elements into the NEM methodology
and use activity pattern data based on various human activity diary studies to create an early
version of probabilistic NEM for Os (i.e., pNEM/Os). In 1991, a probabilistic version of NEM
was extended to CO (pNEM/CO) that included a one-compartment mass-balance model to
estimate CO concentrations in indoor microenvironments. The application of this model to
Denver, Colorado has been documented in Johnson et al. (1992). Additional enhancements to
pNEM/Os in the early- to mid-1990's allowed for probabilistic exposure assessments in nine
urban areas for the general population, outdoor children, and outdoor workers (Johnson et al.,
1996a; 1996b; 1996c). Between 1999 and 2001, updated versions of pNEM/CO (versions 2.0
and 2.1) were developed that relied on activity diary data from EPA's Consolidated Human
Activities Database (CHAD) and enhanced algorithms for simulating gas stove usage, estimating
alveolar ventilation rate (a measure of human respiration), and modeling home-to-work
commuting patterns.
B-3
-------
The first version of APEX was essentially identical to pNEM/CO (version 2.0) except
that it was capable of running on a PC instead of a mainframe. The next version, APEX2, was
substantially different, particularly in the use of a personal profile approach (i.e., simulation of
individuals) rather than a cohort simulation (i.e., groups of similar persons). APEX3 introduced
a number of new features including automatic site selection from national databases, a series of
new output tables providing summary exposure and dose statistics, and a thoroughly reorganized
method of describing microenvironments and their parameters. Most of the spatial and temporal
constraints of pNEM and APEX1 were removed or relaxed by version 3.
The version of APEX used in this exposure assessment is APEX4.3, described in the
APEX User's Guide and the APEX Technical Support Document (US EPA, 2009a; 2009b) and
referred to here as the APEX User's Guide and TSD. This latest version has the added flexibility
of addressing user defined exposure timesteps within an hour.
B-2.2 APEX Model Overview A microenvironment is a three-
APEX estimates human exposure to criteria and toxic dimensional space in which human
contact with an environmental
air pollutants at the local, urban, or consolidated metropolitan pollutant takes place and which can
area levels using a stochastic, microenvironmental approach. "Q treated as a well-Characterized,
relatively homogeneous location
The model randomly selects data for a sample of hypothetical wjfh respect to pollutant
individuals from an actual population database and simulates concentrations for a specified time
period.
each hypothetical individual's movements through time and
space (e.g., at home, in vehicles) to estimate their exposure to a pollutant. APEX simulates
commuting, and thus exposures that occur at home and work locations, for individuals who work
in different areas than they live.
APEX can be conceptualized as a simulated field study that would involve selecting an
actual sample of specific individuals who live in (or work and live in) a geographic area and then
continuously monitoring their activities and subsequent inhalation exposure to a specific air
pollutant during a specific period of time.
The main differences between APEX and an actual field study are that in APEX:
The sample of individuals is a virtual sample, not actual persons. However, the
population of individuals appropriately balanced according to various demographic
B-4
-------
variables and census data using their relative frequencies, in order to obtain a
representative sample (to the extent possible) of the actual people in the study area
The activity patterns of the sampled individuals (e.g., the specification of indoor and
other microenvironments visited and the time spent in each) are assumed by the
model to be comparable to individuals with similar demographic characteristics,
according to activity data such as diaries compiled in EPA's Consolidated Human
Activity Database (or CHAD; US EPA, 2002; McCurdy et al., 2000)
The pollutant exposure concentrations are estimated by the model using a set of user-
input ambient outdoor concentrations (either modeled or measured) and information
on the behavior of the pollutant in various microenvironments;
Variation in ambient air quality levels can be simulated by either adjusting air quality
concentrations to just meet alternative ambient standards, or by reducing source
emissions and obtaining resulting air quality modeling outputs that reflect these
potential emission reductions, and
The model accounts for the most significant factors contributing to inhalation
exposure - the temporal and spatial distribution of people and pollutant
concentrations throughout the study area and among microenvironments - while also
allowing the flexibility to adjust some of these factors for alternative scenarios and
sensitivity analyses.
APEX is designed to simulate human population exposure to criteria and air toxic
pollutants at local, urban, and regional scales. The user specifies the geographic area to be
modeled and the number of individuals to be simulated to represent this population. APEX then
generates a personal profile for each simulated person that specifies various parameter values
required by the model. The model next uses diary-derived time/activity data matched to each
personal profile to generate an exposure event sequence (also referred to as activity pattern or
diary) for the modeled individual that spans a specified time period, such as one year. Each
event in the sequence specifies a start time, exposure duration, geographic location,
microenvironment, and activity performed. Probabilistic algorithms are used to estimate the
pollutant concentration associated with each exposure event. The estimated pollutant
concentrations account for the effects of ambient (outdoor) pollutant concentration, penetration
factors, air exchange rates, decay/deposition rates, and proximity to emission sources, depending
B-5
-------
on the microenvironment, available data, and estimation method selected by the user. Because
the modeled individuals represent a random sample of the population of interest, the distribution
of modeled individual exposures can be extrapolated to the larger population. Additional
discussion regarding the five basic exposure modeling steps noted in the SCh REA are described
in sections that follow.
B-2.2.1 Study Area Characterization
The APEX study area has traditionally been on the scale of a city or slightly larger
metropolitan area, although it is now possible to model larger areas such as combined statistical
areas (CSAs). In the exposure analyses performed as part of this NAAQS review, the study area
is defined by either a single or a few counties. The demographic data used by the model to
create personal profiles is provided at the census block level. For each block the model requires
demographic information representing the distribution of age, gender, race, and work status
within the study population. Each block has a location specified by latitude and longitude for
some representative point (e.g., geographic center). The current release of APEX includes input
files that already contain this demographic and location data for all census tracts, block groups,
and blocks in the 50 United States, based on the 2000 Census. In this assessment, exposures
were evaluated at the block level.
Air Quality Data
Air quality data can be input to the model as measured data from an ambient monitor or
that generated by air quality modeling. This exposure analysis used modeled air quality data,
whereas the principal emission sources included both mobile and stationary sources as well as
fugitive emissions. Air quality data used for input to APEX were generated using AERMOD, a
steady-state, Gaussian plume model (US EPA, 2004). The following steps were performed using
AERMOD.
In APEX, the ambient air quality data are assigned to geographic areas called districts.
The districts are used to assign pollutant concentrations to the blocks/tracts and
microenvironments being modeled. The ambient air quality data are provided by the user as
hourly time series for each district. As with blocks/tracts, each district has a representative
location (latitude and longitude). APEX calculates the distance from each block/tract to each
district center, and assigns the block/tract to the nearest district, provided the block/tract
representative location point (e.g., geographic center) is in the district. Each block/tract can be
B-6
-------
assigned to only one district. In this assessment the district was synonymous with the receptor
modeled in the dispersion modeling.
Meteorological Data
Ambient temperatures are input to APEX for different sites (locations). As with districts,
APEX calculates the distance from each block to each temperature site and assigns each block to
the nearest site. Hourly temperature data are from the National Climatic Data Center Surface
Airways Hourly TD-3280 dataset (NCDC Surface Weather Observations). Daily average and 1-
hour maxima are computed from these hourly data.
There are two files that are used to provide meteorological data to APEX. One file, the
meteorological station location file, contains the locations of meteorological data recordings
expressed in latitude and longitude coordinates. This file also contains start and end dates for the
data recording periods. The temperature data file contains the data from the locations in the
temperature zone location file. This file contains hourly temperature readings for the period
being modeled for the meteorological stations in and around the study area.
B-2.2.2 Generate Simulated Individuals
APEX stochastically generates a user-specified number of simulated persons to represent
the population in the study area. Each simulated person is represented by a personal profile, a
summary of personal attributes that define the individual. APEX generates the simulated person
or profile by probabilistically selecting values for a set of profile variables (Table B-2-1). The
profile variables could include:
Demographic variables, generated based on the census data;
Physical variables, generated based on sets of distribution data;
Other daily varying variables, generated based on literature-derived distribution data
that change daily during the simulation period.
APEX first selects demographic and physical attributes for each specified individual, and
then follows the individual over time and calculates his or her time series of exposure.
B-7
-------
Table B-2-1. Examples of profile variables in APEX.
Variable
Type
Demographic
Physical
Profile Variables
Age
Gender
Home block
Work tract
Employment status
Air conditioner
Gas Stove
Description
Age (years)
Male or Female
Block in which a simulated person lives
Tract in which a simulated person works
Indicates employment outside home
Indicates presence of air conditioning at home
Indicates presence of gas stove at home
Population Demographics
APEX takes population characteristics into account to develop accurate representations of
study area demographics. Specifically, population counts by area and employment probability
estimates are used to develop representative profiles of hypothetical individuals for the
simulation.
APEX is flexible in the resolution of population data provided. As long as the data are
available, any resolution can be used (e.g., county, census tract, census block). For this
application of the model, census block level data were used. Block-level population counts come
from the 2000 Census of Population and Housing Summary File 1 (SF-1). This file contains the
100-percent data, which is the information compiled from the questions asked of all people and
about every housing unit.
As part of the population demographics inputs, it is important to integrate working
patterns into the assessment. In the 2000 U.S. Census, estimates of employment were developed
by census information (US Census Bureau, 2007). The employment statistics are broken down
by gender and age group, so that each gender/age group combination is given an employment
probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are:
16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75.
Children under 16 years of age were assumed to be not employed.
Since this analysis was conducted at the census block level, block level employment
probabilities were required. It was assumed that the employment probabilities for a census tract
apply uniformly to the constituent census blocks.
B-8
-------
Commuting
In addition to using estimates of employment by tract, APEX also incorporates home-to-
work commuting data. Commuting data were originally derived from the 2000 Census and were
collected as part of the Census Transportation Planning Package (CTPP) (US DOT, 2007). The
data used contain counts of individuals commuting from home to work locations at a number of
geographic scales. These data were processed to calculate fractions for each tract-to-tract flow to
create the national commuting data distributed with APEX. This database contains commuting
data for each of the 50 states and Washington, D.C.
Commuting within the Home Tract
The APEX data set does not differentiate people that work at home from those that
commute within their home tract.
Commuting Distance Cutoff
A preliminary data analysis of the home-work counts showed that a graph of log(flows)
versus log(distance) had a near-constant slope out to a distance of around 120 kilometers.
Beyond that distance, the relationship also had a fairly constant slope but it was flatter, meaning
that flows were not as sensitive to distance. A simple interpretation of this result is that up to
120 km, the majority of the flow was due to persons traveling back and forth daily, and the
numbers of such persons decrease rapidly with increasing distance. Beyond 120 km, the
majority of the flow is comprosed of persons who stay at the workplace for extended times, in
which case the separation distance is not as crucial in determining the flow.
To apply the home-work data to commuting patterns in APEX, a simple rule was chosen.
It was assumed that all persons in home-work flows up to 120 km are daily commuters, and no
persons in more widely separated flows commute daily. This meant that the list of destinations
for each home tract was restricted to only those work tracts that are within 120 km of the home
tract. When the same cutoff was performed on the 1990 census data, it resulted in 4.75% of the
home-work pairs in the nationwide database being eliminated, representing 1.3% of the workers.
The assumption is that this 1.3% of workers do not commute from home to work on a daily
basis. It is expected that the cutoff reduced the 2000 data by similar amounts.
Eliminated Records
A number of tract-to-tract pairs were eliminated from the database for various reasons. A
fair number of tract-to-tract pairs represented workers who either worked outside of the U.S.
B-9
-------
(9,631 tract pairs with 107,595 workers) or worked in an unknown location (120,830 tract pairs
with 8,940,163 workers). An additional 515 workers in the commuting database whose data
were missing from the original files, possibly due to privacy concerns or errors, were also
deleted.
Commuting outside the study area
APEX allows for some flexibility in the treatment of persons in the modeled population
who commute to destinations outside the study area. By specifying "KeepLeavers = No" in the
simulation control parameters file, people who work inside the study area but live outside of it
are not modeled, nor are people who live in the study area but work outside of it. By specifying
"KeepLeavers = Yes," these commuters are modeled. This triggers the use of two additional
parameters, called LeaverMult and LeaverAdd. While a commuter is at work, if the workplace is
outside the study area, then the ambient concentration is assumed to be related to the average
concentration over all air districts at the same point in time, and is calculated as:
Ambient Concentration = LeaverMult xavg(t) +Leaver Add equation (B-l)
where:
Ambient Concentration = Calculated ambient air concentrations for locations outside
of the study area (ppm or ppm)
LeaverMult = Multiplicative factor for city-wide average concentration,
applied when working outside study area
avg(t) = Average ambient air concentration over all air districts in
study area, for time t (ppm or ppm)
LeaverAdd = Additive term applied when working outside study area
All microenvironmental concentrations for locations outside of the study area are
determined from this ambient concentration by the same function as applies inside the study
area.
Block-level commuting
For census block simulations, APEX requires block-level commuting file. A special
software preprocesser was created to generate these files for APEX on the basis of the tract-level
B-10
-------
commuting data and finely-resolved land use data. The software calculates commuting flows
between census blocks for the employed population according equation (B-2).
Flowblock =Flow tractxFpopxFland equat.on (B_2)
where:
Flow biock = flow of working population between a home block and a work block.
Flow tmct = flow of working population between a home tract and a work tract.
F pop = fraction of home tract's working population residing in the home block.
F land = fraction of work tract's commercial/industrial land area in the work
block
Thus, it is assumed that the frequency of commuting to a workplace block within a tract
is proportional to the amount of commercial and industrial land in the block.
Profile Functions
A Profile Functions file contains settings used to generate results for variables related to
simulated individuals. While certain settings for individuals are generated automatically by
APEX based on other input files, including demographic characteristics, others can be specified
using this file. For example, the file may contain settings for determining whether the profiled
individual's residence has an air conditioner, a gas stove, etc. As an example, the Profile
Functions file contains fractions indicating the prevalence of air conditioning in the cities
modeled in this assessment (Figure B-2-1). APEX uses these fractions to stochastically generate
air conditioning status for each individual. The derivation of particular data used in specific
microenvironments is provided below.
AC_Home
! Has air conditioning at home
TABLE
INPUT 1 PROBABILITY 2 "A/C probabilities"
0.850.15
RESULT INTEGER 2 "Yes/No"
12
#
Figure B-2-1. Example of a profile function file for A/C prevalence.
B-ll
-------
B-2.2.3 Longitudinal Activity Pattern Sequences
Exposure models use human activity pattern data to predict and estimate exposure to
pollutants. Different human activities, such as spending time outdoors, indoors, or driving, will
have varying pollutant exposure concentrations. To accurately model individuals and their
exposure to pollutants, it is critical to understand their daily activities.
The Consolidated Human Activity Database (CHAD) provides data for where people
spend time and the activities performed. CHAD was designed to provide a basis for conducting
multi-route, multi-media exposure assessments (McCurdy et al., 2000). The data contained
within CHAD come from multiple activity pattern surveys with varied structures (Table B-2-2),
however the surveys have commonality in containing daily diaries of human activities and
personal attributes (e.g., age and gender).
There are four CHAD-related input files used in APEX. Two of these files can be
downloaded directly from the CHADNet (http://www.epa.gov/chadnetl), and adjusted to fit into
the APEX framework. These are the human activity diaries file and the personal data file, and
are discussed below. A third input file contains metabolic information for different activities
listed in the diary file, these are not used in this exposure analysis. The fourth input file maps
five-digit location codes used in the diary file to APEX microenvironments; this file is discussed
in the section describing microenvironmental calculations (Section B-2.2.4.4).
Personal Information file
Personal attribute data are contained in the CHAD questionnaire file that is distributed
with APEX. This file also has information for each day individuals have diaries. The different
variables in this file are:
The study, person, and diary day identifiers
Day of week
Gender
Employment status
Age in years
Maximum temperature in degrees Celsius for this diary day
Mean temperature in degrees Celsius for this diary day
Occupation code
B-12
-------
Time, in minutes, during this diary day for which no data are included in the database
Diary Events file
The human activity diary data are contained in the events file that is distributed with
APEX. This file contains the activities for the nearly 23,000 people with intervals ranging from
one minute to one hour. An individuals' diary varies in length from one to 15 days. This file
contains the following variables:
The study, person, and diary day identifiers
Start time of this activity
Number of minutes for this activity
Activity code (a record of what the individual was doing)
Location code (a record of where the individual was)
B-13
-------
Table B-2-2. Summary of activity pattern studies used in CHAD.
Study Name
Baltimore
California
Adolescents
and Adults
(GARB)
California
Children
(GARB)
Cincinnati
(EPRI)
Denver
(EPA)
Los Angeles:
Elementary
School
Children
Los Angeles:
High School
Adolescents
National:
NHAPS-Air
National:
NHAPS-
Water
Washington,
D.C. (EPA)
Location
A single
building in
Baltimore
California
California
Cincinnati
MSA
Denver
MSA
Los
Angeles
Los
Angeles
National
National
Wash. DC
MSA
Study
time
period
01/1997-
02/1997,
07/1 998-
08/1 998
1 0/1 987-
09/1 988
04/1 989-
02/1 990
03/1 985-
04/1985,
08/1 985
11/1982-
02/1 983
10/1989
09/1 990-
10/1990
09/1 992-
10/1994
09/1 992-
10/1994
11/1982-
02/1 983
Ages
72-93
12-17
18-94
0-11
0-86
18-70
10-12
13-17
0-93
0-93
18-98
Persons
26
181
1,552
1,200
888
432
17
19
4,326
4,332
639
Person
-days
292
181
1,552
1,200
2,587
791
51
42
4,326
4,332
639
Diary type
/study
design
Diary
Recall
/Random
Recall
/Random
Diary
/Random
Diary
/Random
Diary
Diary
Recall
/Random
Recall
/Random
Diary
/Random
Reference
Williams et al. (2000)
Robinson et al.
(1989);
Wiley etal. (1991 a)
Wiley etal. (1991 b)
Johnson (1989)
Johnson (1984);
Aklandetal. (1985)
Spier etal. (1992)
Spier etal. (1992)
Klepeisetal. (1996);
Tsang and Klepeis
(1996)
Klepeisetal. (1996);
Tsang and Klepeis
(1996)
Hartwell etal. (1984);
Aklandetal. (1985)
Construction of Longitudinal Activity Sequences
Typical time-activity pattern data available for inhalation exposure modeling consist of a
sequence of location/activity combinations spanning a 24-hour duration, with 1 to 3 diary-days
for any single individual. Exposure modeling requires information on activity patterns over
longer periods of time, e.g., a full year. For example, even for pollutant health effects with short
averaging times (e.g., SO2 5-minute average concentration) it may be desirable to know the
frequency of exceedances of a concentration over a long period of time (e.g., the annual number
of exceedances of a 24-hour average SC>2 concentration of 100 ppb for each simulated
individual).
B-14
-------
Long-term multi-day activity patterns can be estimated from single days by combining
the daily records in various ways, and the method used for combining them will influence the
variability of the long-term activity patterns across the simulated population. This in turn will
influence the ability of the model to accurately represent either long-term average high-end
exposures, or the number of individuals exposed multiple times to short-term high-end
concentrations.
A common approach for constructing long-term activity patterns from short-term records
is to re-select a daily activity pattern from the pool of data for each day, with the implicit
assumption that there is no correlation between activities from day to day for the simulated
individual. This approach tends to result in long-term activity patterns that are very similar
across the simulated population. Thus, the resulting exposure estimates are likely to
underestimate the variability across the population, and therefore, underestimate the high-end
exposure concentrations or the frequency of exceedances.
A contrasting approach is to select a single activity pattern (or a single pattern for each
season and/or weekday-weekend) to represent a simulated individual's activities over the
duration of the exposure assessment. This approach has the implicit assumption that an
individual's day-to-day activities are perfectly correlated. This approach tends to result in long-
term activity patterns that are very different across the simulated population, and therefore may
over-estimate the variability across the population.
Cluster-Markov Algorithm
A new algorithm has been developed and incorporated into APEX to represent the day-
to-day correlation of activities for individuals. The algorithms first use cluster analysis to divide
the daily activity pattern records into groups that are similar, and then select a single daily record
from each group. This limited number of daily patterns is then used to construct a long-term
sequence for a simulated individual, based on empirically-derived transition probabilities. This
approach is intermediate between the assumption of no day-to-day correlation (i.e., re-selection
for each time period) and perfect correlation (i.e., selection of a single daily record to represent
all days).
The steps in the algorithm are as follows.
1. For each demographic group (age, gender, employment status), temperature range, and
day-of-week combination, the associated time-activity records are partitioned into 3
B-15
-------
groups using cluster analysis. The clustering criterion is a vector of 5 values: the time
spent in each of 5 microenvironment categories (indoors - residence; indoors - other
building; outdoors - near road; outdoors - away from road; in vehicle).
2. For each simulated individual, a single time-activity record is randomly selected from
each cluster.
3. A Markov process determines the probability of a given time-activity pattern occurring
on a given day based on the time-activity pattern of the previous day and cluster-to-
cluster transition probabilities. The cluster-to-cluster transition probabilities are
estimated from the available multi-day time-activity records. If insufficient multi-day
time-activity records are available for a demographic group, season, day-of-week
combination, then the cluster-to-cluster transition probabilities are estimated from the
frequency of time-activity records in each cluster in the CHAD data base.
Details regarding the Cluster-Markov algorithm and supporting evaluations are provided in
Attachments 3 and 4.
B-2.2.4 Calculating Microenvironmental Concentrations
Probabilistic algorithms estimate the pollutant concentration associated with each
exposure event. The estimated pollutant concentrations account for the effects of ambient
(outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate,
and proximity to microenvironments can use the transfer factors method while the others use the
mass balance emission sources, depending on the microenvironment, available data, and the
estimation method selected by the user.
APEX calculates air concentrations in the various microenvironments visited by the
simulated person by using the ambient air data for the relevant blocks, the user-specified
estimation method, and input parameters specific to each microenvironment. APEX calculates
hourly concentrations in all the microenvironments at each hour of the simulation for each of the
simulated individuals using one of two methods: by mass balance or a transfer factors method.
Mass Balance Model
The mass balance method simulates an enclosed microenvironment as a well-mixed
volume in which the air concentration is spatially uniform at any specific time. The following
processes are used estimate the concentration of an air pollutant in such a microenvironment:
Inflow of air into the microenvironment
B-16
-------
Outflow of air from the microenvironment
Removal of a pollutant from the microenvironment due to deposition, filtration, and
chemical degradation
Pollutant emissions inside the microenvironment.
Table B-2-3 lists the parameters required by the mass balance method to calculate
concentrations in a microenvironment. A proximity factor (/proximity) is used to account for
differences in ambient concentrations between the geographic location represented by the
ambient air quality data (e.g., a regional fixed-site monitor or modeled concentration) and the
geographic location of the microenvironment (e.g., near a roadway). This factor could take a
value either greater than or less than 1. Emission source (ES) represents the emission rate for the
emission source and concentration source (CS) is the mean air concentration resulting from the
source. Removal is defined as the removal rate of a pollutant from a microenvironment due to
deposition, filtration, and chemical reaction. The air exchange rate (Rair exchange) is expressed in
air changes per hour.
Table B-2-3. Mass balance model parameters.
Variable
' proximity
CS
" removal
p
" air exchange
V
Definition
Proximity factor
Concentration source
Removal rate due to deposition,
filtration, and chemical reaction
Air exchange rate
Volume of microenvironment
Units
unitless
ppb
1/hr
1/hr
mj
Value Range
' proximity "
CS>0
"removal "
" air exchange U
V>0
The mass balance equation for a pollutant in a microenvironment is described by:
dt
where:
dCME(t)
ACm
A(^out
- = ACm - ACout - ACremoval + ACsour
equation
(B-3)
Change in concentration in a microenvironment at time t (ppb),
Rate of change in microenvironmental concentration due to influx
of air (ppb/hour),
Rate of change in microenvironmental concentration due to outflux
of air (ppb/hour),
B-17
-------
A ^removal
Lą ^ source
Rate of change in microenvironmental concentration due to
removal processes (ppb/hour), and
Rate of change in microenvironmental concentration due to an
emission source inside the microenvironment (ppb/hour).
Within the timestep selected, each of the rates of change, A. Cm, A.Cout, A.Cremovai, and
AC'source, is assumed to be constant. At each timestep of the simulation period, APEX estimates
the equilibrium, ending, and mean concentrations using a series of equations that account for
concentration changes expected to occur due to these physical processes. Details regarding these
equations are provided in the APEX TSD (US EPA, 2009b). The calculation continues to the
next timestep by using the end concentration for the previous timestep as the initial
microenvironmental concentration. A brief description of the input parameters estimates used
for microenvironments using the mass balance approach is provided below.
Factors Model
The factors method is simpler than the mass balance method. It does not calculate
concentration in a microenvironment from the concentration in the previous hour and it has
fewer parameters. Table B-2-4 lists the parameters required by the factors method to calculate
concentrations in a microenvironment without emissions sources.
Table B-2-4. Factors model parameters.
Variable
' proximity
i penetration
Definition
Proximity factor
Penetration factor
Units
unitless
unitless
Value Range
' proximity "
U l penetration *
The factors method uses the following equation to calculate the timestep concentration in
a microenvironment from the user-provided hourly air quality data:
equation (B-4)
^timestep _^ r r
ME ambient J proximity J penetration
where:
i timestep
^ambient
Jproximity
J penetration
Timestep concentration in a microenvironment (ppb)
Timestep concentration in ambient environment (ppb)
Proximity factor (unitless)
Penetration factor (unitless)
B-18
-------
The ambient NC>2 concentrations are from the air quality data input file. The proximity
factor is a unitless parameter that represents the proximity of the microenvironment to a
monitoring station. The penetration factor is a unitless parameter that represents the fraction of
pollutant entering a microenvironment from outside the microenvironment via air exchange. The
development of the specific proximity and penetration factors used in this analysis are discussed
below for each microenvironment using this approach.
Microenvironments Modeled
In APEX, microenvironments represent the exposure locations for simulated individuals.
For exposures to be estimated accurately, it is important to have realistic microenvironments that
match closely to the locations where actual people spend time on a daily basis. As discussed
above, the two methods available in APEX for calculating pollutant levels within
microenvironments are: 1) factors and 2) mass balance. A list of microenvironments used in this
study, the calculation method used, and the parameters used to calculate the microenvironment
concentrations can be found in Table B-2-5.
Each of the microenvironments is designed to simulate an environment in which people
spend time during the day. CHAD locations are linked to the different microenvironments in the
Microenvironment Mapping File (see below). There are many more CHAD locations than
microenvironment locations (there are 113 CHAD codes versus 12 microenvironments in this
assessment), therefore most of the microenvironments have multiple CHAD locations mapped to
them.
B-19
-------
Table B-2-5. List of microenvironments and calculation methods used.
Microenvironment
No.
1
2
3
4
5
6
7
8
9
10
11
12
0
Name
Indoors - Residence
Indoors - Bars and restaurants
Indoors -Schools
Indoors - Day-care centers
Indoors -Office
Indoors -Shopping
Indoors -Other
Outdoors - Near road
Outdoors - Public garage - parking lot
Outdoors - Other
In-vehicle - Cars and Trucks
In-vehicle - Mass Transit (bus, subway, train)
Not modeled
Calculation
Method
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Factors
Factors
Factors
Factors
Factors
Parameter
Types used 1
AERandDE
AERand DE
AERandDE
AERand DE
AERandDE
AERandDE
AERandDE
PR
PR
None
PE and PR
PE and PR
1 AER=air exchange rate, DE=decay-deposition rate, PR=proximity factor, PE=penetration
factor
Mapping of APEX Microenvironments to CHAD Diaries
The Microenvironment Mapping file matches the APEX Microenvironments to CHAD
Location codes. Table B-2-6 gives the mapping used for the APEX simulations.
Table B-2-6. Mapping of CHAD activity locations to APEX microenvironments.
CHAD LOG.
U
X
30000
30010
30020
30100
30120
30121
30122
30123
30124
30125
30126
30127
30128
30129
30130
30131
30132
30133
30134
30135
Description
Uncertain of correct code
No data
Residence, general
Your residence
Other residence
Residence, indoor
Your residence, indoor
. . . , kitchen
. . . , living room or family room
. . . , dining room
. . . , bathroom
. . . , bedroom
. . . , study or office
. . . , basement
. . . , utility or laundry room
. . . , other indoor
Other residence, indoor
. . . , kitchen
. . . , living room or family room
. . . , dining room
. . . , bathroom
. . . , bedroom
APEX
-1
-1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
micro
Unknown
Unknown
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
Indoors -Residence
B-20
-------
30136 ..., study or office = 1
30137 ..., basement = 1
30138 ..., utility or laundry room = 1
30139 ..., other indoor = 1
30200 Residence, outdoor = 10
30210 Your residence, outdoor = 10
30211 ..., pool or spa = 10
30219 ..., other outdoor = 10
30220 Other residence, outdoor = 10
30221 ..., pool or spa = 10
30229 ..., other outdoor = 10
30300 Residential garage or carport = 7
30310 ..., indoor = 7
30320 ..., outdoor = 10
30330 Your garage or carport = 1
30331 ..., indoor = 1
30332 ..., outdoor = 10
30340 Other residential garage or carport = 1
30341 ..., indoor = 1
30342 ..., outdoor = 10
30400 Residence, none of the above = 1
31000 Travel, general = 11
31100 Motorized travel = 11
31110 Car = 11
31120 Truck = 11
31121 Truck (pickup or van) = 11
31122 Truck (not pickup or van) = 11
31130 Motorcycle or moped = 8
31140 Bus = 12
31150 Train or subway = 12
31160 Airplane = 0
31170 Boat = 10
31171 Boat, motorized = 10
31172 Boat, other = 10
31200 Non-motorized travel = 10
31210 Walk = 10
31220 Bicycle or inline skates/skateboard = 10
31230 In stroller or carried by adult = 10
31300 Waiting for travel = 10
31310 , bus or train stop = 8
31320 ..., indoors = 7
31900 Travel, other = 11
31910 ..., other vehicle = 11
32000 Non-residence indoor, general = 7
32100 Office building/ bank/ post office = 5
32200 Industrial/ factory/ warehouse = 5
32300 Grocery store/ convenience store = 6
32400 Shopping mall/ non-grocery store = 6
32500 Bar/ night club/ bowling alley = 2
32510 Bar or night club = 2
32520 Bowling alley = 2
32600 Repair shop = 7
32610 Auto repair shop/ gas station = 7
32620 Other repair shop = 7
32700 Indoor gym /health club = 7
32800 Childcare facility = 4
32810 ..., house = 1
32820 ..., commercial = 4
32900 Large public building = 7
32910 Auditorium/ arena/ concert hall = 7
32920 Library/ courtroom/ museum/ theater = 7
33100 Laundromat = 7
33200 Hospital/ medical care facility = 7
33300 Barber/ hair dresser/ beauty parlor = 7
33400 Indoors, moving among locations = 7
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Indoors-Other
Indoors-Other
Outdoors-Other
Indoors-Residence
Indoors-Residence
Outdoors-Other
Indoors-Residence
Indoors-Residence
Outdoors-Other
Indoors-Residence
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
Outdoors-Near_Road
In Vehicle-Mass_Transit
In Vehicle-Mass_Transit
Zero_concentration
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Near_Road
Indoors-Other
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
Indoors-Other
Indoors-Office
Indoors-Office
Indoors-Shopping
Indoors-Shopping
Indoors-Bars_and_Restaurants
Indoors-Bars_and_Restaurants
Indoors-Bars_and_Restaurants
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Day_Care_Centers
Indoors-Residence
Indoors-Day_Care_Centers
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
B-21
-------
33500
33600
33700
33800
33900
34100
34200
34300
35000
35100
35110
35200
35210
35220
35300
35400
35500
35600
35610
35620
35700
35800
35810
35820
35900
36100
36200
36300
School
Restaurant
Church
Hotel/ motel
Dry cleaners
Indoor parking garage
Laboratory
Indoor, none of the above
Non-residence outdoor, general
Sidewalk, street
Within 10 yards of street
Outdoor public parking lot /garage
. . . , public garage
. . . , parking lot
Service station/ gas station
Construction site
Amusement park
Playground
. . . , school grounds
. . . , public or park
Stadium or amphitheater
Park/ golf course
Park
Golf course
Pool/ river/ lake
Outdoor restaurant/ picnic
Farm
Outdoor, none of the above
3
2
7
7
7
7
7
7
10
8
8
9
9
9
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Indoors- Schools
Indoors-Bars and Restaurants
Indoors -Other
Indoors -Other
Indoors -Other
Indoors -Other
Indoors -Other
Indoors -Other
Outdoors -Other
Outdoors-Near Road
Outdoors-Near Road
Outdoors-Public Garage-Parking
Outdoors-Public Garage-Parking
Outdoors-Public Garage-Parking
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
Outdoors -Other
B-2.2.5 Exposure Calculations
APEX calculates exposure as a time series of exposure concentrations that a simulated
individual experiences during the simulation period. APEX determines the exposure using
hourly ambient air concentrations, calculated concentrations in each microenvironment based on
these ambient air concentrations (and indoor sources if present), and the minutes spent in a
sequence of microenvironments visited according to the composite diary. The hourly exposure
concentration at any clock hour during the simulation period is determined using the following
equation:
r~< timestep >
-ME(j) l (j)
7=1
where:
c,
N
T timestep
T
equation
(B-5)
= Hourly exposure concentration at clock hour /' of the simulation period
(ppb)
= Number of events (i.e., microenvironments visited) in clock hour /' of the
simulation period.
= Timestep concentration in microenvironment y (ppm)
= Time spent in microenvironment y (minutes)
B-22
-------
T = Length of timestep (minutes)
From the timestep exposures, APEX calculates time series of 1-hour, 8-hour and daily average
exposures that a simulated individual would experience during the simulation period. APEX then
statistically summarizes and tabulates the timestep, hourly, 8-hour, and daily exposures. Note
that if the APEX timestep is greater than an hour, the 1-hour and 8-hour exposures are not
calculated and the corresponding tables are not produced. Exposures are calculated
independently for all pollutants in the simulation.
From the timestep exposures, APEX can calculate the time-series of 1-hour, 8-hour, and
daily average exposures that a simulated individual would experience during the simulation
period. APEX then statistically summarizes and tabulates the timestep (or hourly, daily, annual
average) exposures. In this analysis, the exposure indicator is 5-minute exposures above
potential health effect benchmark levels. From this, APEX can calculate two general types of
exposure estimates: counts of the estimated number of people exposed to a specified SO2
concentration level and the number of times per year that they are so exposed; the latter metric is
typically expressed in terms of person-occurrences or person-days. The former highlights the
number of individuals exposed at least one or more times per modeling period to the health
effect benchmark level of interest. APEX can also report counts of individuals with multiple
exposures. This person-occurrences measure estimates the number of times per season that
individuals are exposed to the exposure indicator of interest and then accumulates these
estimates for the entire population residing in an area.
APEX tabulates and displays the two measures for exposures above levels ranging from
any number of benchmark levels, by any increment (e.g., 0 to 800 ppb by 50 ppb increments for
5-minute exposures). These exposure results are tabulated for the population and subpopulations
of interest.
Exposure Model Output
All of the output files written by APEX are ASCII text files. Table B-2-7 lists each of the
output data files written for these simulations and provides descriptions of their content.
Additional output files that can produced by APEX are given in Table 5-1 of the APEX User's
Guide, and include hourly exposure, ventilation, and energy expenditures, and even detailed
event-level information, if desired. The names and locations, as well as the output table levels
B-23
-------
(e.g., output percentiles, cut-points), for these output files are specified by the user in the
simulation control parameters file.
Table B-2-7. Example of APEX output files.
Output File Type
Log
Profile Summary
Microenvironment
Summary
Sites
Output Tables
Description
The Log file contains the record of the APEX model simulation as it progresses.
If the simulation completes successfully, the log file indicates the input files and
parameter settings used for the simulation and reports on a number of different
factors. If the simulation ends prematurely, the log file contains error messages
describing the critical errors that caused the simulation to end.
The Profile Summary file provides a summary of each individual modeled in the
simulation.
The Microenvironment Summary file provides a summary of the time and
exposure by microenvironment for each individual modeled in the simulation.
The Sites file lists the tracts, districts, and zones in the study area, and identifies
the mapping between them.
The Output Tables file contains a series of tables summarizing the results of the
simulation. The percentiles and cut-off points used in these tables are defined
in the simulation control parameters file.
B-24
-------
B-3 Supplemental AERMOD Dispersion Modeling Data
B-25
-------
B-3.1 AERMOD Input data
Table B-3-1. Emission parameters by stack for all major facility stacks in Missouri.
Stack ID
5049
5050
5051
5054
5063
5064
5066
5068
5069
5070
5073
5074
City
LABADIE
LABADIE
LABADIE
LABADIE
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
Facility Name
AMERENUE-LABADIE
PLANT
AMERENUE-LABADIE
PLANT
AMERENUE-LABADIE
PLANT
AMERENUE-LABADIE
PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
NEI Site ID
NEI7514
NEI 7514
NEI 7514
NEI 7514
NEI 7525
NEI 7525
NEI 7525
NEI 7525
NEI 7525
NEI 7525
NEI 7525
NEI 7525
UTMX
(m)1
688,392
688,357
688,461
688,442
476,842
476,853
476,913
476,884
476,890
476,918
476,919
UTMY
(m)1
4,270,394
4,270,439
4,270,338
4,270,322
4,106,944
4,106,922
4,106,929
4,106,932
4,106,922
4,106,919
4,106,930
S02
Emissions
(tpy)
10,970
14,753
14,285
7,602
1,137
1,433
757
159
660
567
218
255
Stack
Height
(m)
213
213
213
213
107
107
61
61
61
61
60
60
Exit
Temp.
(K)
444
444
444
444
422
422
422
422
422
422
422
422
Stack
Diam.
(m)
6.2
6.2
8.8
8.8
2.5
2.5
3.7
3.7
3.7
3.7
3.7
3.7
Exit
Velocity
(mis)
28
28
28
28
15
15
6
6
5
5
6
6
Profile
Method2
Tier 1
Tier 1
Tier 1
Tier 1
Tier 2
TieM
Tier 1
Tier 1
TieM
Tier 1
Tier 1
Tier 1
B-26
-------
Stack ID
5076
5077
5084
5113
5114
5115
5131
5141
5145
5147
5148
5149
5244
5245
City
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
WEST
ALTON
WEST
ALTON
WEST
ALTON
HERCU-
LANEUM
HERCU-
LANEUM
HERCU-
LANEUM
FESTUS
FESTUS
FESTUS
STE. GENE-
VIE VE
STE. GENE-
VIE VE
Facility Name
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-JAMES
RIVER POWER PLANT
CITY UTILITIES OF
SPRINGFIELD
MISSOURI-
SOUTHWEST POWER
PLANT
AMERENUE-SIOUX
PLANT
AMERENUE-SIOUX
PLANT
AMERENUE-SIOUX
PLANT
DOE RUN COMPANY-
HERCULANEUM
SMELTER
DOE RUN COMPANY-
HERCULANEUM
SMELTER
DOE RUN COMPANY-
HERCULANEUM
SMELTER
AMERENUE-RUSH
ISLAND PLANT
AMERENUE-RUSH
ISLAND PLANT
AMERENUE-RUSH
ISLAND PLANT
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
NEI Site ID
NEI 7525
NEI 7525
NEI 12640
NEI 7516
NEI 7516
NEI 7516
NEI 34412
NEI 34412
NEI 34412
NEI 12618
NEI 12618
NEI 12618
NEI
M01 860001
NEI
MO1 860001
UTMX
(m)1
476,952
477,050
476,992
465,416
735,034
735,027
734,948
729,589
729,543
729,537
739,910
739,893
739,931
757,358
757,384
UTMY
(m)1
4,106,940
4,106,880
4,106,881
4,111,816
4,310,876
4,310,819
4,310,864
4,238,084
4,237,936
4,237,973
4,223,934
4,223,827
4,223,869
4,207,065
4,207,015
S02
Emissions
(tpy)
219
252
3,390
24,932
21,025
2
2
2
15,219
2
10,511
12,744
62
89
Stack
Height
(m)
60
60
117
183
183
65
3
9
168
76
213
213
23
23
Exit
Temp.
(K)
422
422
397
427
427
436
295
287
350
577
405
405
519
469
Stack
Diam.
(m)
3.7
3.7
3.4
5.8
5.8
1.4
0.0
0.3
6.1
1.5
8.8
8.8
3.2
3.4
Exit
Velocity
(mis)
6
6
21
29
29
15
0
6
18
9
25
25
4
6
Profile
Method2
Tier 1
Tier 1
Tier 2
TieM
TieM
TieM
Tier 2
TierS
Tier 2
Tier 1
Tier 1
Tier 1
TierS
TierS
B-27
-------
Stack ID
5246
5247
5248
5261
5262
5263
5264
5265
5267
5270
5271
5276
5277
5278
5279
5293
City
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
VIE VE
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
Facility Name
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
AMERENUE-
MERAMEC PLANT
AMERENUE-
MERAMEC PLANT
AMERENUE-
MERAMEC PLANT
AMERENUE-
MERAMEC PLANT
ANHEUSER-BUSCH
INC-ST LOUIS
NEI Site ID
NEI
M01 860001
NEI
MO1 860001
NEI
MO1 860001
NEI
M01 860001
NEI
MO1 860001
NEI
M01 860001
NEI
MO1 860001
NEI
M01 860001
NEI
MO1 860001
NEI
M01 860001
NEI
MO1 860001
NEI 7515
NEI 7515
NEI 7515
NEI 7515
NEI 34732
UTMX
(m)1
757,697
757,666
757,697
757,561
757,735
757,727
757,550
757,524
757,633
757,627
757,540
732,584
732,631
732,677
732,714
742,736
UTMY
(m)1
4,206,939
4,206,950
4,206,981
4,206,988
4,206,971
4,206,997
4,206,964
4,206,924
4,206,999
4,206,989
4,206,931
4,253,799
4,253,790
4,253,784
4,253,779
4,275,786
S02
Emissions
(tpy)
103
106
105
1,290
1,394
1,505
67
77
2
1
1,199
5,195
6,463
2,359
2,430
2
Stack
Height
(m)
23
23
23
35
35
35
35
35
20
20
35
107
107
76
76
30
Exit
Temp.
(K)
469
469
469
343
343
344
346
346
367
362
343
463
447
436
436
371
Stack
Diam.
(m)
3.4
3.4
3.4
1.7
1.7
1.7
2.1
2.1
1.1
1.2
1.7
4.9
4.3
3.4
3.2
1.2
Exit
Velocity
(mis)
6
6
6
11
11
13
9
9
15
11
11
33
31
27
27
3
Profile
Method2
TierS
TierS
TierS
TierS
TierS
TierS
TierS
TierS
Tier 2
TierS
TierS
Tier 1
Tier 1
Tier 1
Tier 1
Tier 2
B-28
-------
Stack ID
5295
5296
5297
5298
5299
5302
5304
City
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
Facility Name
ANHEUSER-BUSCH
INC-ST LOUIS
ANHEUSER-BUSCH
INC-ST LOUIS
ANHEUSER-BUSCH
INC-ST LOUIS
ANHEUSER-BUSCH
INC-ST LOUIS
ANHEUSER-BUSCH
INC-ST LOUIS
ANHEUSER-BUSCH
INC-ST LOUIS
ANHEUSER-BUSCH
INC-ST LOUIS
NEI Site ID
NEI 34732
NEI 34732
NEI 34732
NEI 34732
NEI 34732
NEI 34732
NEI 34732
UTMX
(m)1
742,775
742,750
742,781
742,800
742,759
742,739
742,711
UTMY
(m)1
4,275,743
4,275,704
4,275,753
4,275,764
4,275,714
4,275,677
4,275,740
S02
Emissions
(tpy)
176
256
249
158
3,066
2,339
4
Stack
Height
(m)
69
69
69
69
69
69
22
Exit
Temp.
(K)
450
450
450
450
461
439
486
Stack
Diam.
(m)
3.0
3.0
3.0
3.0
3.0
3.0
1.2
Exit
Velocity
(mis)
6
6
6
6
6
6
9
Profile
Method2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Notes:
1 UTM Zone 15 values in all cases.
2 Three methods were possible to convert annual total emissions data from the NEI into hourly temporal profiles required for AERMOD, based on availability of
data:
Tier 1 : CAMD hourly concentrations to create relative temporal profiles.
Tier 2: EMS-HAP seasonal and diurnal temporal profiles for source categorization codes (SCCs).
Tier 3: Flat profiles
B-29
-------
Table B-3-2. Emission parameters by stack for all major cross-border facility stacks in the St. Louis scenario.
Stack ID
1
2
3
4
5
9
10
11
City
East
Alton
East
Alton
Baldwin
Baldwin
Baldwin
Hartford
Hartford
Hartford
Facility Name
DYNEGY
MIDWEST
GENERATION
INC
DYNEGY
MIDWEST
GENERATION
INC
DYNEGY
MIDWEST
GENERATION
INC
DYNEGY
MIDWEST
GENERATION
INC
DYNEGY
MIDWEST
GENERATION
INC
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
NEI Site ID
NEI52119
NEI52119
NEI52781
NEI52781
NEI52781
NEI52159
NEI52159
NEI52159
UTMX
(m)1
748,654
748,654
775,316
775,316
775,316
753,003
752,783
752,886
UTM Y
(m)1
4,305,518
4,305,518
4,233,202
4,233,202
4,233,202
4,302,381
4,302,408
4,302,285
SO2
Emissions
(tpy)
1536.2
5725.8
9931.4
9053
7283
131.95786
907.24
132.9
Stack
Height
(m)
76.2
106.7
184.4
184.4
184.4
33.5
19.9
24.1
Exit
Temp.
(K)
427.6
416.5
425.4
428.7
424.8
533.2
502.0
519.3
Stack
Diam.
(m)
5.2
4.6
5.9
5.9
5.9
1.5
1.1
2.1
Exit
Velocity
(m/s)
8.5
34.6
39.7
38.3
38.4
3.1
6.5
7.0
Profile
Method2
TieM
TieM
TieM
TieM
TieM
Tier 2
Tier 2
Tier 2
B-30
-------
Stack ID
12
13
14
15
16
17
18
19
20
City
Hartford
Hartford
Hartford
Hartford
Hartford
Hartford
Sauget
Sauget
Roxana
Facility Name
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
Premcor
Refining Group
(prev. Clark Oil
and Refining
Corp.)
BIG RIVER
ZINC CORP
BIG RIVER
ZINC CORP
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
NEI Site ID
NEI52159
NEI52159
NEI52159
NEI52159
NEI52159
NEI52159
NEI53013
NEI53013
NEI55835
UTMX
(m)1
752,883
753,003
752,886
752,783
753,003
752,783
746,429
746,429
755,000
UTMY
(m)1
4,302,377
4,302,381
4,302,285
4,302,408
4,302,381
4,302,408
4,276,339
4,276,339
4,302,599
S02
Emissions
(tpy)
106.67
79
66.43
4.90219
171.36006
7.42
1.34
1377.28
15.38
Stack
Height
(m)
24.4
36.0
16.8
35.4
30.5
30.7
21.3
25.9
106.7
Exit
Temp.
(K)
533.2
533.2
677.6
570.4
533.2
513.2
317.6
422.0
472.0
Stack
Diam.
(m)
1.8
1.2
1.8
1.5
1.5
1.7
0.7
0.9
4.6
Exit
Velocity
(mis)
2.6
3.1
6.2
7.8
4.1
11.4
10.6
41.3
11.4
Profile
Method2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
B-31
-------
Stack ID
21
22
23
24
25
26
27
28
29
30
31
32
City
Roxana
Roxana
Roxana
Roxana
Roxana
Roxana
Roxana
Roxana
Roxana
Roxana
Roxana
Roxana
Facility Name
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
NEI Site ID
NEI55835
NEI55835
NEI55835
NEI55835
NEI55835
NEI55835
NEI55835
NEI55835
NEI55835
NEI55835
NEI55835
NEI55835
UTMX
(m)1
755,000
755,188
754,405
754,997
754,505
754,994
755,084
754,658
754,994
755,000
754,658
755,231
UTMY
(m)1
4,302,599
4,302,550
4,303,105
4,302,691
4,302,984
4,302,783
4,303,003
4,302,515
4,302,783
4,302,599
4,302,515
4,302,561
S02
Emissions
(tpy)
7.27
1.2
1.25
1.45
1.53
3.39
1.15
385.25
3.24
16.73
11677.82
212.41
Stack
Height
(m)
106.7
45.7
56.4
61.0
95.1
40.2
45.7
36.9
45.7
106.7
10.1
45.7
Exit
Temp.
(K)
463.7
628.2
432.6
672.0
483.7
491.5
699.8
754.8
431.5
483.7
293.7
699.8
Stack
Diam.
(m)
4.6
2.3
2.4
3.7
4.3
2.1
2.3
3.4
3.0
4.6
0.1
2.4
Exit
Velocity
(mis)
0.3
7.9
6.7
6.7
0.3
13.2
7.0
5.9
15.9
0.3
0.1
8.8
Profile
Method2
Tier 2
Tierl
Tier 2
Tier 2
Tier 2
Tier 2
Tierl
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
B-32
-------
Stack ID
33
34
35
36
37
38
39
40
41
42
City
Roxana
Roxana
Roxana
Granite
City
Granite
City
Granite
City
Granite
City
Granite
City
Granite
City
Granite
City
Facility Name
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
ConocoPhillips
Co. (prev.
Phillips 66 Co.)
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NEI Site ID
NEI55835
NEI55835
NEI55835
NEI55848
NEI55848
NEI55848
NEI55848
NEI55848
NEI55848
NEI55848
UTMX
(m)1
755,231
753,801
755,231
747,795
750,041
749,778
749,897
749,970
750,041
749,847
UTMY
(m)1
4,302,561
4,303,085
4,302,561
4,286,723
4,286,824
4,286,692
4,286,788
4,286,761
4,286,824
4,286,849
S02
Emissions
(tpy)
206.96
110.6
108.6
61.88
506.7
228.47
421.58883
375.19
351.93
264.95442
Stack
Height
(m)
45.7
38.1
45.7
30.5
46.3
24.5
68.6
15.4
46.3
61.0
Exit
Temp.
(K)
672.0
792.0
672.0
616.5
441.5
372.0
460.9
453.7
441.5
460.9
Stack
Diam.
(m)
2.4
2.2
2.4
2.1
2.1
1.5
4.3
0.9
2.1
3.4
Exit
Velocity
(mis)
8.2
5.4
4.3
17.9
10.6
6.2
4.5
9.9
1.2
3.1
Profile
Method2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
B-33
-------
Stack ID
43
44
46
47
50
51
City
Granite
City
Granite
City
Granite
City
Granite
City
Granite
City
Granite
City
Facility Name
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NATIONAL
STEEL CORP -
GRANITE CITY
DIV
NEI Site ID
NEI55848
NEI55848
NEI55848
NEI55848
NEI55848
NEI55848
UTMX
(m)1
750,280
749,828
747,842
750,180
748,053
750,255
UTMY
(m)1
4,286,925
4,286,663
4,286,755
4,286,983
4,287,055
4,286,924
S02
Emissions
(tpy)
923.52
501.19
85.86
20.99
8
959.82
Stack
Height
(m)
43.5
43.5
30.5
24.9
19.2
43.5
Exit
Temp.
(K)
538.7
538.7
616.5
335.9
323.7
538.7
Stack
Diam.
(m)
2.0
2.0
2.1
1.5
2.1
2.0
Exit
Velocity
(mis)
9.2
9.2
17.9
8.9
13.1
9.2
Profile
Method2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Notes:
1 UTM Zone 15 values in all cases.
2 Three methods were possible to convert annual total emissions data from the NEI into hourly temporal profiles required for AERMOD, based on
availability of data:
Tier 1 : CAMD hourly concentrations to create relative temporal profiles.
Tier 2: EMS-HAP seasonal and diurnal temporal profiles for source categorization codes (SCCs).
Tier 3: Flat profiles
B-34
-------
1
2
B-3.2 AERMOD Air Quality Evaluation Data
Table B-3-2. Measured ambient monitor SO2 concentration distributions and the
modeled monitor receptor and receptors within 4 km of the ambient monitors in
Greene County for year 2002.
Ambient
Monitor ID
290770026
290770032
290770037
290770040
290770041
Receptor(s)1
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
Percentile Concentration (ppb)
100
29
48
101
114
48
30
41
62
28
42
35
53
106
144
115
34
53
106
203
116
31
52
108
33
73
99
6
12
46
46
22
10
12
14
8
14
5
13
55
49
42
5
13
55
18
45
5
14
56
9
23
95
2
4
18
16
11
5
6
8
6
8
2
3
21
8
5
2
3
21
6
6
2
3
22
3
4
90
1
2
8
7
6
3
4
6
5
6
1
2
8
4
3
1
2
8
3
3
1
2
8
2
2
80
0
1
2
3
2
2
3
5
4
5
0
1
2
2
1
0
1
2
2
1
0
1
2
1
1
70
0
1
1
2
1
1
2
4
4
4
0
0
1
2
1
0
0
1
1
1
0
0
1
1
1
60
0
0
1
1
1
1
2
3
3
3
0
0
1
2
1
0
0
1
1
1
0
0
1
1
1
50
0
0
1
1
1
1
2
3
3
3
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
25
0
0
0
1
0
0
1
2
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Notes:
1 AERMOD concentrations are for the given percentile (p2.5 = 2.5th; p50 = 50th; p97.5 = 97.5th) of the
modeled distribution of all modeled air quality receptors within 4 km of ambient monitor. AERMOD
monitor is the concentration prediction at the ambient monitor location using AERMOD.
B-35
-------
Table B-3-3. Measured ambient monitor SO2 concentration diurnal profile and the
modeled monitor receptor and receptors within 4 km of the ambient monitors in
Greene County for year 2002.
Monitor ID
290770026
290770032
Hour
of
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Annual Average SO2 Concentration (ppb) at Given Receptor
AERMOD
P2.5
0.2
0.2
0.2
0.1
0.1
0.1
0.2
0.5
0.7
0.8
0.7
0.5
0.6
0.6
0.5
0.6
0.6
0.5
0.3
0.2
0.2
0.2
0.2
0.2
1.5
1.4
1.4
1.3
1.1
1.1
1.4
1.9
1.7
1.5
1.3
1.2
1.1
1.1
1.0
1.1
1.4
1.6
1.8
AERMOD
P50
0.4
0.4
0.4
0.3
0.3
0.3
0.7
1.5
1.5
1.6
1.4
1.2
1.1
1.0
1.0
1.0
1.3
1.2
0.9
0.5
0.5
0.5
0.4
0.4
2.3
2.1
2.2
2.0
1.7
1.8
2.0
2.2
2.3
2.3
2.2
2.2
2.1
2.0
2.0
2.1
2.4
2.4
2.4
AERMOD
P97.5
1.6
1.8
2.0
1.6
1.5
1.7
2.3
4.2
5.6
5.6
6.0
6.3
6.1
6.0
5.8
5.1
4.6
3.7
2.3
1.7
1.7
1.9
1.9
1.9
3.2
3.0
3.4
2.8
2.4
2.5
2.5
2.7
3.4
3.9
4.0
4.1
4.3
4.1
4.0
4.1
3.6
3.3
3.2
Ambient
Monitor
2.7
2.4
2.5
2.5
2.9
2.9
3.6
4.2
4.6
5.3
5.0
4.7
4.5
4.1
3.7
3.8
3.7
2.9
2.6
3.0
2.8
3.0
2.9
2.9
2.8
2.6
2.5
2.5
2.3
2.2
2.3
2.7
3.0
3.3
3.2
3.2
3.3
3.2
3.2
3.1
3.2
3.1
3.1
AERMOD
Monitor
1.2
1.0
0.9
0.9
0.8
0.9
1.4
2.8
3.6
3.4
3.7
3.2
2.8
2.6
2.6
2.5
2.9
2.9
2.4
1.4
1.4
1.4
1.1
1.1
3.0
2.9
2.8
2.7
2.5
2.4
2.5
2.9
3.2
3.6
3.5
3.6
3.6
3.6
3.5
3.4
3.5
3.5
3.4
B-36
-------
Monitor ID
290770037
290770040
Hour
of
Day
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Annual Average SO2 Concentration (ppb) at Given Receptor
AERMOD
P2.5
1.7
1.8
1.7
1.6
1.4
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.4
0.6
0.8
0.7
0.6
0.6
0.6
0.5
0.5
0.5
0.4
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.4
0.6
0.8
0.7
0.5
0.6
0.6
0.5
0.5
0.5
0.4
AERMOD
P50
2.5
2.4
2.6
2.5
2.4
0.2
0.2
0.2
0.2
0.2
0.2
0.4
1.0
1.2
1.5
1.4
1.3
1.3
1.2
1.2
1.2
1.2
1.0
0.5
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.4
1.0
1.2
1.5
1.4
1.3
1.3
1.2
1.2
1.2
1.1
1.0
AERMOD
P97.5
3.2
3.3
3.6
3.4
3.2
1.4
1.5
1.7
1.5
1.4
1.6
2.4
4.5
6.2
6.4
7.0
6.9
7.0
7.2
6.5
5.7
5.2
4.2
2.5
1.4
1.5
1.7
1.8
1.6
1.4
1.5
1.7
1.5
1.4
1.6
2.4
4.5
6.2
6.4
7.0
6.9
7.0
7.2
6.5
5.7
5.2
4.2
Ambient
Monitor
3.1
3.1
3.1
3.1
2.9
1.6
1.5
1.5
1.9
1.9
1.8
1.9
2.3
3.1
3.8
4.1
4.8
5.0
5.2
5.3
4.9
4.2
3.0
2.2
2.2
1.9
1.8
1.9
2.1
1.0
0.8
1.0
1.0
1.0
1.0
1.0
1.2
1.6
2.2
2.4
2.9
2.4
3.0
2.8
2.2
1.8
1.8
AERMOD
Monitor
3.4
3.3
3.4
3.4
3.1
0.5
0.5
0.5
0.4
0.3
0.3
0.3
1.0
1.9
3.7
4.6
5.4
5.0
4.6
4.3
3.1
2.4
1.9
0.7
0.5
0.5
0.6
0.5
0.5
0.5
0.5
0.5
0.4
0.3
0.3
0.3
0.8
1.8
3.7
4.8
6.2
5.9
4.8
4.6
3.2
2.6
1.8
B-37
-------
Monitor ID
290770041
Hour
of
Day
19
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Annual Average SO2 Concentration (ppb) at Given Receptor
AERMOD
P2.5
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.5
0.6
0.7
0.6
0.4
0.5
0.4
0.4
0.4
0.4
0.4
0.3
0.2
0.2
0.2
0.2
0.2
AERMOD
P50
0.5
0.3
0.3
0.3
0.2
0.2
0.3
0.3
0.3
0.2
0.2
0.2
0.6
1.2
1.4
1.7
1.5
1.4
1.4
1.3
1.2
1.3
1.2
1.1
0.5
0.3
0.3
0.4
0.3
0.3
AERMOD
P97.5
2.5
1.4
1.5
1.7
1.8
1.6
1.5
1.7
1.9
1.5
1.4
1.7
2.4
4.9
6.2
6.5
7.2
7.0
7.4
7.9
6.6
6.2
5.4
4.2
2.6
1.5
1.6
1.8
1.9
1.8
Ambient
Monitor
1.2
1.0
1.0
0.9
0.9
1.0
0.6
0.6
0.6
0.5
0.4
0.6
0.6
0.8
1.1
1.4
1.5
1.5
1.6
1.3
1.1
1.0
0.9
0.7
0.6
0.6
0.7
0.7
0.6
0.7
AERMOD
Monitor
0.8
0.5
0.5
0.6
0.5
0.5
0.6
0.5
0.6
0.5
0.4
0.4
0.6
1.5
1.7
1.8
2.4
2.1
2.4
2.0
2.1
2.3
2.1
1.8
0.8
0.6
0.6
0.7
0.6
0.6
B-38
-------
Table B-3-4. Measured ambient monitor SO2 concentration distributions and the
modeled monitor receptor and receptors within 4 km of the ambient monitors in
St. Louis for year 2002.
Ambient
Monitor ID
291890004
291890006
291893001
291895001
291897003
295100007
295100086
Receptor(s)1
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
AERMOD P2.5
AERMOD P50
AERMOD P97.5
Ambient Monitor
AERMOD Monitor
Percentile Concentration (ppb)
100
60
69
103
99
67
48
55
94
85
73
58
75
91
80
71
97
168
545
158
191
67
89
138
91
99
71
93
137
64
100
71
91
124
86
111
99
20
22
25
24
22
19
20
20
18
20
24
26
30
24
25
32
38
51
23
40
25
28
32
24
27
26
31
43
25
32
29
32
36
30
31
95
11
12
14
13
11
10
11
12
9
11
13
14
17
12
14
13
14
15
12
14
11
12
13
12
12
13
18
23
14
17
15
18
22
16
17
90
7
8
9
8
7
7
7
8
6
8
9
10
12
8
10
8
9
10
8
9
7
8
9
8
8
8
11
16
10
11
11
13
16
11
13
80
4
5
5
5
4
4
5
5
3
5
5
6
8
5
6
5
6
6
5
6
4
5
5
5
5
4
7
10
6
7
7
9
11
7
8
70
3
3
4
3
3
3
3
4
2
3
4
4
6
4
4
4
4
4
4
4
3
3
4
4
4
3
5
8
5
6
5
6
8
5
6
60
2
2
3
2
2
2
2
3
1
3
3
3
5
3
3
3
3
3
3
3
2
3
3
3
3
2
4
6
3
5
4
5
6
4
5
50
2
2
2
1
2
2
2
2
1
2
2
2
3
2
2
2
2
2
2
2
2
2
2
2
2
2
3
5
2
4
3
4
5
3
4
25
1
1
1
0
1
1
1
1
0
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
2
3
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Notes:
1 AERMOD concentrations are for the given percentile (p2.5 = 2.5th; p50 = 50th; p97.5 = 97.5th) of the
modeled distribution of all modeled air quality receptors within 4 km of ambient monitor. AERMOD
monitor is the concentration prediction at the ambient monitor location using AERMOD.
B-39
-------
Table B-3-5. Measured ambient monitor SO2 concentration diurnal profile and the
modeled monitor receptor and receptors within 4 km of the ambient monitors in
St. Louis for year 2002.
Ambient
Monitor ID
291890004
291890006
Hour
of
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Annual Average SO2 Concentration (ppb) at Given Receptor
AERMOD
P2.5
2.0
1.7
1.5
1.5
1.4
1.4
1.8
2.7
4.0
4.6
4.8
4.6
4.4
4.1
3.9
3.7
3.8
3.5
3.1
3.0
2.7
2.4
2.1
2.1
2.4
2.1
1.7
1.8
1.6
1.5
2.0
2.5
4.2
4.4
4.8
4.3
4.2
4.1
3.9
3.7
3.8
3.4
3.0
AERMOD
P50
2.6
2.2
1.9
1.9
1.8
1.8
2.2
3.2
4.5
5.0
5.0
5.2
4.9
4.7
4.3
4.2
4.4
4.2
3.6
3.5
3.4
2.9
2.7
2.6
2.6
2.3
1.9
2.0
1.8
1.6
2.0
2.7
4.3
4.6
4.9
4.5
4.3
4.2
4.0
3.8
4.0
3.7
3.3
AERMOD
P97.5
3.2
2.7
2.3
2.3
2.1
2.6
2.6
4.0
4.8
5.4
5.3
5.6
5.2
5.0
4.6
4.4
4.7
4.6
4.0
3.7
3.7
3.3
3.3
3.0
2.9
2.6
2.1
2.3
1.9
1.7
2.1
2.7
4.4
4.8
5.3
4.8
4.7
4.6
4.4
4.2
4.6
4.5
3.8
Ambient
Monitor
2.4
2.1
1.8
1.8
1.8
2.0
2.4
3.3
4.1
4.6
4.6
4.5
4.1
4.3
4.0
3.6
3.8
3.8
3.4
2.9
2.8
2.9
2.9
2.5
1.6
1.6
1.5
1.2
1.2
1.2
1.4
1.9
2.7
3.4
3.5
3.8
3.5
2.9
2.6
2.9
2.7
2.6
2.4
AERMOD
Monitor
2.2
1.8
1.7
1.6
1.7
1.8
2.0
3.0
4.2
4.7
4.8
4.8
4.5
4.2
4.0
3.7
3.9
3.5
3.1
3.1
2.9
2.6
2.4
2.2
2.6
2.5
2.0
2.1
1.8
1.5
2.0
2.5
4.3
4.7
5.1
4.6
4.6
4.6
4.3
4.2
4.3
4.1
3.4
B-40
-------
291893001
291895001
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
2.9
2.8
2.5
2.3
2.2
3.0
2.8
2.3
2.5
2.1
2.2
2.6
3.2
4.5
5.2
5.3
5.3
5.0
4.6
4.5
4.3
4.5
4.3
3.9
3.6
3.6
3.3
3.2
3.0
3.2
3.1
2.8
2.8
2.4
2.5
2.6
3.2
4.5
5.3
5.5
5.6
4.6
4.6
4.4
4.1
4.1
4.5
3.2
3.3
3.1
3.1
3.0
2.7
2.8
2.4
3.6
3.2
2.7
2.8
2.5
2.6
3.0
3.7
5.1
5.6
5.8
5.8
5.4
5.1
4.8
4.7
4.8
4.8
4.3
3.9
4.0
3.8
3.8
3.7
3.7
3.7
3.2
3.3
3.0
3.6
3.2
4.3
5.5
6.0
5.9
5.7
5.3
5.1
4.8
4.6
4.5
4.8
4.2
4.1
4.3
3.4
3.4
3.0
3.2
2.8
4.2
3.9
3.3
3.4
2.8
3.1
3.7
4.6
6.5
7.8
8.1
8.2
7.7
7.8
7.3
7.1
6.4
6.0
5.3
4.7
4.6
4.7
4.5
4.4
4.9
5.1
3.8
4.3
3.9
4.9
3.9
5.3
6.1
6.6
6.1
6.0
5.7
5.3
5.0
4.9
4.8
5.3
5.3
5.5
5.6
2.4
2.0
2.0
1.9
1.8
2.2
2.2
2.0
1.7
1.8
2.3
2.8
3.7
4.5
4.9
4.8
4.7
4.4
4.5
4.1
3.8
3.8
4.1
3.7
3.5
3.4
3.2
3.0
2.7
3.1
2.9
2.7
2.7
2.7
2.8
3.2
4.0
4.6
4.7
5.2
5.1
4.6
4.1
4.1
3.9
4.1
3.9
3.7
3.2
3.5
3.0
3.1
2.8
2.8
2.7
3.5
3.2
2.7
2.8
2.5
2.7
3.1
3.6
5.0
5.7
5.8
5.8
5.3
5.0
4.8
4.7
4.7
4.8
4.2
3.9
4.0
3.7
3.9
3.6
3.8
3.5
3.2
3.2
3.3
3.8
3.0
4.7
5.4
6.0
6.0
6.0
5.3
5.2
4.9
4.6
4.7
4.9
3.7
4.0
3.9
B-41
-------
291897003
295100007
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
3.5
3.7
3.8
2.3
2.6
2.1
2.2
1.7
2.1
1.8
2.9
4.1
4.9
4.8
4.5
4.3
4.0
3.9
3.6
3.7
3.2
3.1
2.8
2.8
3.4
2.7
3.0
2.2
2.1
1.6
1.7
1.4
1.6
2.9
3.6
5.0
5.1
5.1
4.9
4.6
4.5
4.0
4.0
4.4
4.2
3.6
3.5
3.2
2.7
2.6
4.4
4.4
4.3
2.8
3.0
2.6
2.5
2.2
2.5
2.1
3.4
4.4
5.3
5.2
5.0
4.7
4.4
4.4
4.1
4.3
4.2
3.8
3.2
3.4
3.7
3.3
3.4
3.7
3.4
3.0
3.1
3.0
3.1
4.5
5.2
6.6
6.8
7.1
7.0
6.9
6.8
6.3
6.2
6.2
6.0
5.7
5.1
4.8
4.4
4.0
5.6
6.3
5.1
3.3
3.2
2.9
3.0
2.7
3.2
2.6
3.9
5.0
5.8
5.6
5.6
5.1
4.8
4.7
4.4
4.6
4.7
4.5
4.0
3.9
4.0
4.2
3.8
5.8
5.6
5.3
5.2
5.1
5.2
7.6
7.8
8.4
8.2
8.5
8.2
8.1
8.1
7.6
7.8
8.2
8.7
10.0
7.4
7.1
6.6
5.9
3.3
3.2
2.9
2.6
2.4
2.5
2.4
2.3
2.5
3.2
4.1
4.7
4.8
5.1
4.7
4.5
4.4
4.2
3.8
3.7
3.9
3.6
3.5
3.3
3.1
3.1
2.9
3.4
3.2
3.2
3.0
2.9
3.1
3.7
4.1
5.2
5.7
5.5
4.9
4.7
4.6
4.4
4.2
4.2
3.9
3.8
4.2
4.1
4.0
4.1
4.7
5.6
4.3
2.7
3.2
2.7
2.4
2.2
2.5
2.1
3.2
4.5
5.4
5.4
5.1
4.9
4.6
4.6
4.3
4.5
4.4
3.8
3.2
3.2
3.9
3.6
3.6
4.0
3.5
3.3
3.3
3.1
3.2
4.6
5.2
6.6
7.0
7.5
7.2
7.1
7.1
6.6
6.6
6.6
6.3
6.5
5.2
4.9
4.5
4.0
B-42
-------
295100086
24
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
2.3
3.8
3.3
3.1
3.0
3.0
2.3
3.7
4.2
5.8
6.7
6.6
6.5
6.2
6.1
5.7
5.6
5.3
5.2
5.0
4.7
4.6
4.3
4.1
3.8
3.9
4.9
4.7
4.3
4.0
3.8
3.9
4.4
5.5
7.0
8.1
8.0
7.8
7.7
7.5
7.1
7.2
7.2
7.2
6.5
6.2
6.3
5.6
5.4
5.1
5.8
6.5
6.7
5.5
5.6
5.8
5.9
7.0
7.0
8.0
8.4
8.4
8.3
8.1
7.9
7.4
7.5
7.9
8.5
9.2
8.0
7.7
7.9
7.3
6.9
3.7
4.3
4.2
3.7
3.9
3.9
4.3
4.4
5.4
6.3
6.0
6.0
5.4
5.1
4.9
4.6
4.7
4.9
4.4
4.6
4.7
4.9
4.8
4.3
4.2
3.9
4.8
4.4
4.1
4.0
3.6
4.0
4.3
5.3
6.9
8.1
8.0
7.8
7.6
7.4
7.1
7.2
6.9
7.0
6.2
6.0
6.1
5.1
5.1
4.8
B-43
-------
B-4 SUPPLEMENTAL APEX EXPOSURE MODELING
DATA
B-4.1 APEX Input Data Distributions for SO2 deposition
In recognizing the relationship between SO2 deposition rate and various surface
types within indoor microenvironments and that the presence of these surfaces would
vary in proportions dependent on the microenvironment, staff estimated the APEX input
SC>2 deposition rate distributions using a Monte Carlo sampling approach. First, 1,000
different hypothetical indoor microenvironments were simulated, each with a different
ratio of wall area to floor area and furniture area to floor area. Based on these ratios,
surface area to volume ratios were estimated in each sample indoor microenvironment.
Then, surface area to volume ratios were used to convert the deposition velocities to
deposition rates in hr"1 by dividing the velocities by the surface area to volume ratio and
then making an appropriate unit conversion. And finally, the deposition rate for each
surface type was combined using a weighted average to estimate an effective deposition
rate, as follows:
A A
r\ , r\ * ceiling j^. % furntiture
^ floor ' ^ceiling . ' ^furniture .
Deff = - floor - ^ equation (B-6)
1 ceiling furniture
A A
^ floor ^ floor
where D denotes deposition rate, A denotes area of the indoor microenvironment,
and Deff is the effective deposition rate. If more than one surface type is present in the
sample indoor microenvironment (e.g. both carpet and non-carpeted floors), these values
were first averaged using the fraction of the room that contains each. Details regarding
the data used for estimating the SC>2 deposition rate within simulated indoor
microenvironments are provided in the following sections.
B-4.1. 1 Surface deposition data and surface type mapping
Staff obtained SC>2 deposition velocities from a literature review conducted by
Grantoft and Raychaudhuri (2004). These authors categorized the data by several
relative humidities and considering several different surface types. Staff mapped the
B-44
-------
surface classes reported in Grantoft and Raychaudhuri (2004) to surface types typically
found within indoor microenvironments (Table B-4-1).
Table B-4-1. Classification of SO2 deposition data for several
microenvironmental surfaces.
Surface
Category
Floor
Ceiling
Wall
Furniture
Surface Type
Carpet
Floor
Ceiling Tile
Ceiling Wallboard
Wallpaper
Wall Wallboard
Wood paneling
Furniture
Surface Class1
Average of the wool
and synthetic carpet
values
Synthetic Floor
Covering - medium
worn
Coarse composite
panels
Treated gypsum
wallboard
Wall paper
Treated gypsum
wallboard
Surface treated wood
work and wall boards
Cloth
Deposition in cm/s1
50%
Relative
Humidity
0.0625
0.007
0.14
0.048
0.036
0.048
0.014
0.019
70%
Relative
Humidity
0.075
0.015
0.15
0.16
0.043
0.16
0.047
0.023
90%
Relative
Humidity
0.117
0.032
0.18
0.27
0.068
0.27
0.078
0.036
Notes:
1 Obtained from Table 6 of Grantoft and Raychaudhuri (2004).
B-4.1.2 Indoor Microenvironment Configurations
Because the configuration of rooms within a building will affect the wall area to
floor area ratio, staff first estimated the areas of several indoor microenvironments. Staff
had to make several assumptions due to the limited availability of data. The first broad
assumption was that a single room within the indoor microenvironment could represent
all potential rooms within the particular building type. Secondly, staff assumed all rooms
were square to calculate the area distributions. Additional assumptions specific to the
type of indoor microenvironment are provided below, along with the estimated indoor
microenvironment area distributions.
B-45
-------
Residential area distributions
In residences, the American Housing Survey (AHS, 2008) provides a matrix that
gives the number of survey homes within a given total square footage and a given
number of rooms category. Staff converted the data to probabilities using the total
number of homes in each category (Table B-4-2). In calculating the room area using
these distributions, a series of two independent random numbers were used to select a
square footage category and then to find the number of rooms within that square footage
category, accounting for the inherent correlation of the number of rooms in a given
building with the total square footage. Staff derived a representative room area by
dividing the square footage by the number of rooms.
Table B-4-2. Distributions used to calculate a representative room size in
an indoor residential microenvironment.
Cumulative
probability
for number
of rooms
within
each
square
footage
class
1
Cumulative
probability
for each
square
footage
class *
Rooms
1
2
3
4
5
6
7
8
9
10
Square Footage
250
0.01
750
0.10
1250
0.35
1750
0.60
2250
0.77
2750
1.00
0.07
0.13
0.40
0.64
0.81
0.90
0.97
0.99
0.99
1.00
0.00
0.00
0.07
0.47
0.80
0.94
0.98
0.99
1.00
0.00
0.00
0.01
0.13
0.54
0.86
0.96
0.99
1.00
0.00
0.00
0.00
0.04
0.28
0.65
0.90
0.98
0.99
1.00
0.00
0.00
0.00
0.02
0.15
0.41
0.71
0.92
0.98
1.00
0.00
0.00
0.00
0.01
0.08
0.23
0.46
0.71
0.87
1.00
Non-residential area distributions
An office can contain many different rooms, each with either one or two
occupants (usually a smaller office) or a collection of cubicles (usually a larger office).
B-46
-------
Staff used the Building Assessment Survey and Evaluation study (BASE; US EPA,
2008a) to generate representative office areas for simulated buildings. The BASE data
provided the mean, standard deviation, minimum, and maximum of the total square
footage and the number of people per square meter of occupied space (Table B-4-3).1
Based on this, staff represented the data as a normal distribution and set the lower and
upper limits using the minimum and maximum observations. BASE (US EPA, 2008a)
also provides the average number of occupants in private or semi-private work areas
(40%) compared to shared space (60%).2 Staff assumed that the private and semi-private
offices have an average of two people in each and the shared spaces have an average of
six people in each. In calculating the area, two independent random numbers were used
to select the total floor area and the number of occupants in that floor area. The total
square footage of the office was then divided by the number of rooms to obtain the
representative office area.
For schools, the distribution of the total building square footage is available from
the Commercial Building Energy Consumption Survey (CBECS; US DOE, 2003);
however, information on the number of rooms in each square footage class is not
available. As an alternate data source, information was available on the range of the
square footage of a typical school classroom (600 to 1,400 square feet) to generate a
uniform distribution bounded by these extremes (NCBG, 2008; US Army Corps of
Engineers, 2002). For restaurants and other buildings, staff assumed that the entire
building was one room; therefore, the CBECS (US DOE, 2003) provided data for this
building category to estimate square footage distributions (Table B-4-3).
Table B-4-3. Distributions used to calculate representative room size for
non-residential microenvironments.
Microenvironment
Office, Building size
(ft2)
Office, number of
people per m2.
Parameter
1a
16,632
4.0
Parameter
2b
8,035
1.5
Parameter
3C
4,612
1.5
Parameter
4d
69,530
8.5
Distribution
Type
Normal
Normal
http://www.epa.gov/iaq/base/pdfs/test_space_characteristics/tc-0.pdf
http://www.epa. gov/iaq/base/pdfs/test_space_characteristics/tc-l.pdf
B-47
-------
School (ft2)
Restaurant (ft2)
Other Buildings (ft2)
600
5,340
3,750
1,400
31
24
N/A
668
750
N/A
42,699
18,796
Uniform
Lognormal
Lognormal
Notes:
a Mean for normal, geometric mean for lognormal, lower limit for uniform distribution.
b Standard deviation for normal, geometric standard deviation for lognormal, upper limit for
uniform.
c Minimum value for normal and lognormal.
d Maximum value for normal and lognormal.
Additional specifications
Two additional specifications were required to calculate the room volumes and
surface areas: the ceiling heights and surface area of furniture within the rooms. Table B-
4-4 provides the data values and sources used to estimate each of these variables.
Table B-4-4. Ceiling heights and furniture surface area to floor ratios for
simulated indoor microenvironments.
Indoor
Microenvironment
Residence
Office
School
Restaurant
Other Buildings
Ceiling Height3
8ft
10ft
10ft
10ft
10ft
Furniture Surface Area
to Floor Ratio
2D
4C
4C
4C
4C
Notes:
3 Assumed by staff.
b Thatcher et al. (2002) and Singer et al. (2002).
c The surface area to volume ratio was assumed higher in the commercial
microenvironments than in residences. A value of 4 was selected since it
kept the range of total surface area to volume ratio within a typical range of 2
to 4 (Lawrence Berkeley National Laboratory., 2003).
B-4.1.3 Surface type probabilities
Following the calculation of the basic dimensions of the simulated room, staff
performed additional probabilistic sampling to specify the surface types present. In some
microenvironments, it is possible that only a single surface type be present (e.g., a public
access building likely contains only hard floors and no carpet). However, in other cases,
a typical building may have multiple surface types (e.g., a residence may have a mixture
B-48
-------
of both hard floor and carpet). Thus, in each microenvironment, staff estimated a
probability of occurrence for each surface type. If more than one surface type is possible
at the same time, then staff also approximated the fraction of each. Table B-4-5
summarizes both the probabilities and fractions assumed by staff for each
microenvironment.
B-4.1.4 Final SOi deposition distributions
Following the estimation of the room dimensions and surface types within each
simulated indoor microenvironment, an effective deposition rate was estimated for all
1,000 sample buildings. The geometric mean and geometric standard deviation were
calculated across all 1,000 samples and used to parameterize a lognormal distribution
(Table B-4-6). In applying these to the relative humidity conditions in the study areas,
staff assumed that the relative humidity is below 50% when the air conditioning or
heating unit is on. If the building has no air conditioner, the ambient summer humidity
was used (90 % in the morning, 50% in the afternoon). Staff also assumed that all non-
residential buildings had air-conditioning.
As far as mapping to the APEX microenvironments, residences, offices, and
restaurants are explicitely modeled microenvironments. The daycare microenvironment
used the school deposition distribution, while other indoor microenvironments (i.e.,
shopping or other) used the other building deposition distribution.
B-49
-------
Table B-4-5. Probability of occurrence and fractional quantity for surface types in indoor micronenvironments.
Indoor
Microenvironment
Residence
Office
School
Restaurant
Other Buildings
Floor
Carpet
P = 1
F =
N{0.52, 0.23}a
P = 1
F = 5/6 if hard
floor present0
P = 0C
P = 0.1d
P = 0.1d
Hard floor
P = 1
F =
1 - fraction
carpeted3
P = 0.34
F= 1/6 if hard
floor is present13
P = 1C
P = 0.9d
P = 0.9d
Ceiling
Wallboard
P = 1C
P = 0C
P = 0C
P = 0.55d
P = 0.19d
Ceiling Tile
P = 0C
P = 1C
P = 1C
P = 0.45d
P = 0.81d
Wall
Wallboard
P = 1
F=5/6
if wallpaper is
present0
P = 1
F is adjusted if
wallpaper and/or
wood paneling is
present0
P = 1C
P = 1
F is adjusted if
wallpaper and/or
wood paneling is
present0
P = 1
F is adjusted if
wallpaper and/or
wood paneling is
present0
Wallpaper
P = 0.225
F= 1/6 if
wallpaper is
presentd
P = 0.11
F= 1/6 if
wallpaper is
present13
P = 0C
P = 0.09
F = 1/2if
wallpaper is
presentd
P = 0.09
F = 1/2if
wallpaper is
presentd
Wood Paneling
P = 0C
P = 0.13
F = 1/6 if wood
paneling is
present13
P = 0C
P = 0.25
F = 1/10 If wood
paneling is
presentd
P = 0.045
F=1/10 if wood
paneling is
presentd
Notes:
a US EPA, 2008b.
b BASE study, Table 4 (US EPA, 2008a); the fraction of 1/6 is based on professional judgment.
c Assumed by staff.
d Source Ranking Database (SRD, US EPA, 2004b). The fraction of buildings value in the database was used to specify a probability each surface type
occurs in the microenvironment. SRD names were matched to the APEX environments. Most categories in the SRD have the same fraction of building
values. To map to the necessary surface types: Carpet - Networx represented carpet; Ceiling tile represented ceiling tile; vinyl coated wallpaper
represented wallpaper; and Hardwood plywood paneling represented wood paneling. Fractions were assumed by staff. Then, probabilities in the
remaining surface types were calculated assuming either only one type could be present or multiple types could be present.
B-50
-------
Table B-4-6. Final parameter estimates of SO2 deposition distributions in several
indoor microenvironments modeled in APEX.
Microenv-
ironment
Residence
Office
School
Restaurant
Other
Buildings
Heating or Air Conditioning in Use
Geom.
Mean
(hr-1)
3.14
3.99
4.02
2.36
2.82
Geom.
Stand.
Dev.
(hr-1)
1.11
1.04
1.02
1.28
1.21
Lower
Limit
(hr-1)
2.20
3.63
3.90
1.64
1.71
Upper
Limit
(hr-1)
5.34
4.37
4.21
4.17
4.12
Air Conditioning Not in Use
(Summertime Ambient Morning
Relative Humidity of 90%)
Geom.
Mean
(hr-1)
13.41
N/A
N/A
N/A
N/A
Geom
Stand.
Dev.
(hr-1)
1.11
N/A
N/A
N/A
N/A
Lower
Limit
(hr-1)
10.31
N/A
N/A
N/A
N/A
Upper
Limit
(hr-1)
26.96
N/A
N/A
N/A
N/A
Notes:
N/A not applicable, assumed by staff to always have A/C in operation.
B-51
-------
B-4.2 APEX Exposure Output
Table B.4-7. APEX estimated exposures in Greene county (AS IS).
Exposure
level
o:
so :
100 :
150 (
200 (
250 (
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
o :
50 :
100'
150 (
200 (
250 (
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
Person
days
2 18000
0 9
8
821000
9 3
#
persons Gi
21262
193
13
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7280
108
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
oup
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
Populatio n
Fraction
0.97
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-52
-------
Table B.4-8. APEX estimated exposures in Greene county (Current Standard).
Exposure
level
o:
50'
100
150 i
200
250 (
300 :
350 :
400
450^
500 (
550 (
600 (
650 (
700 (
750 (
800 (
0
50 (
100
150 :
200
250 t
300
350'
400^
450^
500 (
550 (
600 (
650 (
700 (
750 (
800 (
Person
days
2 18000
5 98
6 59
1 1
9 7
0
2
8
3
821000
3 93
0 36
2 3
1 2
9
o
5
#
persons Gi
21262
4322
982
323
139
67
18
13
13
4
0
0
0
0
0
0
0
7280
2609
569
188
72
40
9
4
4
4
0
0
0
0
0
0
0
oup
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
Populatio n
Fraction
0.97
0.20
0.04
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.36
0.08
0.03
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-53
-------
Table B.4-9. APEX estimated exposures in Greene County (99th %ile 50 ppb).
Exposure
level
o:
50
100 (
150 (
200 (
250 (
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
0
50'
100 (
150 (
200 (
250 (
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
Person
days
2 18000
8
821000
#
persons Gi
21262
13
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7280
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
oup
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
Populatio n
Fraction
0.97
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-54
-------
Table B.4-10. APEX estimated exposures in Greene County (99th %ille 100 ppb).
Exposure
level
o:
50 :
100 :
150 (
200 (
250 (
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
o:
50 :
100 (
150 (
200 (
250 (
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
Person
days
2 18000
5 9
8
821000
2 9
#
persons Gi
21262
229
13
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7280
139
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
oup
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
Populatio n
Fraction
0.97
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-55
-------
Table B.4-11. APEX estimated exposures in Greene County (99th %ille 150 ppb).
Exposure
level
o:
50 :
100 :
150 :
200 (
250 (
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
o:
50"
100 (
150'
200 L
250 (
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
Person
days
2 18000
3 27
3 9
8
821000
9 8
7
#
persons Gi
21262
811
103
13
9
0
0
0
0
0
0
0
0
0
0
0
0
7280
466
49
4
4
0
0
0
0
0
0
0
0
0
0
0
0
oup
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
Populatio n
Fraction
0.97
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.06
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-56
-------
Table B.4-12. APEX estimated exposures in Greene County (99
Exposure
level
o:
50 :
100 :
150 (
200 :
250 :
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
o:
50 :
100 :
150 ;
200 (
250^
300 (
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
Person
days
2 18000
7 79
5 9
4
8
3
821000
7 57
2 9
4
#
persons Gi
21262
1600
229
72
13
13
0
0
0
0
0
0
0
0
0
0
0
7280
955
139
45
4
4
0
0
0
0
0
0
0
0
0
0
0
oup
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
Populatio n
Fraction
0.97
0.07
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.13
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
th %ille 200 ppb)
B-57
-------
Table B.4-13. APEX estimated exposures in Greene County (99th %ille 250 ppb).
Exposure
level
o:
50 <:
100'
150 :
200 (
250 :
300 :
350^
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
o:
50 :
100^
150 :
200 L
250'
300^
350^
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
Person
days
2 18000
9 18
8 0
0 2
o
6
8
o
5
821000
2 01
5 7
1 7
0
#
persons Gi
21262
2726
484
143
54
13
13
4
0
0
0
0
0
0
0
0
0
7280
1659
256
76
31
4
4
4
0
0
0
0
0
0
0
0
0
oup
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
Populatio n
Fraction
0.97
0.12
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.23
0.04
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-58
-------
Table B.4-14. APEX estimated exposures in Greene County (98th %ille 200 ppb).
Exposure
level
o:
50 <:
100 (
150 :
200 <:
250 :
300 :
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
o:
50 :
100 :
150 I
200:
250'
300^
350 (
400 (
450 (
500 (
550 (
600 (
650 (
700 (
750 (
800 (
Person
days
2 18000
1 38
3 2
6 1
5
8
o
5
821000
6 54
9 0
5
7
#
persons Gi
21262
2304
386
117
40
13
13
0
0
0
0
0
0
0
0
0
0
7280
1390
220
58
22
4
4
0
0
0
0
0
0
0
0
0
0
oup
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMA,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD
Populatio n
Fraction
0.97
0.10
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.19
0.03
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-59
-------
Table B.4-15. APEX estimated exposures in St. Louis (AS IS).
Exposure
level Gr
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
oup
) MTS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
#
persons
102436
24405
3866
789
229
69
23
8
8
0
0
0
0
0
0
0
0
41714
16938
2776
575
160
39
16
0
0
0
0
0
0
0
0
0
0
Person
days
6677000
44100
4631
896
244
69
23
8
8
0
0
0
0
0
0
0
0
0 560000
32800
3357
651
176
38
15
0
0
0
0
0
0
0
0
0
0
Population
fraction
0.97
0.23
0.04
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.41
0.07
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-60
-------
Table B.4-16. APEX estimated exposures in St. Louis (Current Standard).
Exposure
level Gr
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
01
501
100
150
200
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
oup
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD ^
DMTS,ASTHMACHILD,MOD :
DMTS,ASTHMACHILD,MOD ;
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
#
persons
102436
93692
79422
63016
48211
36315
26363
19278
14181
10448
7853
5880
4431
3336
2631
1985
1556
41714
41607
0 319 (
6 287:
0 504
24386
18254
13539
9991
7547
5658
4237
3204
2376
1909
1426
1111
Person
days
16677000
2889400
793000
316400
153990
84540
49440
31700
20719
14242
10060
7229
5343
3972
3099
2253
1747
10560000
2158300
.0 2800
3 9310
1 6260
63570
36830
23507
15304
10636
7420
5295
3901
2851
2231
1609
1240
Population
fraction
0.97
0.89
0.75
0.60
0.46
0.34
0.25
0.18
0.13
0.10
0.07
0.06
0.04
0.03
0.02
0.02
0.01
1.00
1.00
0.97
0.87
0.73
0.58
0.44
0.32
0.24
0.18
0.14
0.10
0.08
0.06
0.05
0.03
0.03
B-61
-------
Table B.4-17. APEX estimated exposures in St. Louis (99th %ile 50 ppb).
Exposure
level Gr
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
oup
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
#
persons
102436
14488
1595
298
69
16
8
0
0
0
0
0
0
0
0
0
0
41714
10229
1135
214
39
8
0
0
0
0
0
0
0
0
0
0
0
Person
days
16677000
21379
1794
328
69
15
8
0
0
0
0
0
0
0
0
0
0
10560000
15835
1272
237
38
8
0
0
0
0
0
0
0
0
0
0
0
Population
fraction
0.97
0.14
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.25
0.03
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-62
-------
ith
Table B.4-18. APEX estimated exposures in St. Louis (99 %ile 100 ppb)
Exposure
level Gr
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
01
50
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
oup
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD :
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
#
persons
102436
48725
14488
4654
1595
666
298
153
69
38
16
8
8
8
0
0
0
41714
0 703
10229
3349
1135
491
214
99
39
31
8
0
0
0
0
0
0
Person
days
16677000
158000
21379
5619
1794
742
328
152
69
38
15
8
8
8
0
0
0
10560000
1 9350
15835
4100
1272
551
237
99
38
31
8
0
0
0
0
0
0
Population
fraction
0.97
0.46
0.14
0.04
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.74
0.25
0.08
0.03
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-63
-------
Table B.4-19. APEX estimated exposures in St. Louis (99th %ile 150 ppb).
Exposure
level Gr
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
01
50
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
oup
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD :
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
#
persons
102436
68830
33447
14488
6702
3212
1595
844
521
298
198
130
69
38
23
16
8
41714
8 024:
22721
10229
4843
2323
1135
621
376
214
138
76
39
31
16
8
0
Person
days
16677000
429400
73000
21379
8403
3817
1794
958
582
328
198
130
69
38
23
15
8
10560000
2 5900
54890
15835
6177
2767
1272
705
422
237
137
76
38
31
15
8
0
Population
fraction
0.97
0.65
0.32
0.14
0.06
0.03
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.91
0.54
0.25
0.12
0.06
0.03
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
B-64
-------
Table B.4-20. APEX estimated exposures in St. Louis (99th %ile 200 ppb).
Exposure
level Gr
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
oup
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
#
persons
102436
79775
48725
27030
14488
8097
4654
2707
1595
1050
666
428
298
214
153
107
69
41714
40388
30703
18690
10229
5856
3349
1947
1135
773
491
314
214
145
99
61
39
Person
days
16677000
813700
158000
51270
21379
10427
5619
3198
1794
1180
742
458
328
229
152
107
69
10560000
618700
119350
38210
15835
7718
4100
2292
1272
857
551
336
237
160
99
61
38
Population
fraction
0.97
0.76
0.46
0.26
0.14
0.08
0.04
0.03
0.02
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.97
0.74
0.45
0.25
0.14
0.08
0.05
0.03
0.02
0.01
0.01
0.01
0.00
0.00
0.00
0.00
B-65
-------
Table B.4-21. APEX estimated exposures in St. Louis (99th %ile 250 ppb).
Exposure
level Gr
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
01
50
100
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
oup
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMACHILD,MOD
DMTS,ASTHMACHILD,MOD '
DMTS,ASTHMACHILD,MOD :
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
#
persons
102436
85784
60235
39121
23681
14488
9180
5750
3696
2452
1595
1150
751
574
405
298
229
41714
1 147'
5 351 :
25834
16477
10229
6686
4138
2637
1786
1135
849
536
422
298
214
160
Person
days
16677000
1276000
278550
97390
42330
21379
12037
7061
4416
2843
1794
1287
858
643
435
328
244
10560000
ŧ6 7000
1 0680
73310
31530
15835
8975
5166
3173
2070
1272
941
613
475
321
237
176
Population
fraction
0.97
0.81
0.57
0.37
0.22
0.14
0.09
0.05
0.04
0.02
0.02
0.01
0.01
0.01
0.00
0.00
0.00
1.00
0.99
0.85
0.62
0.39
0.25
0.16
0.10
0.06
0.04
0.03
0.02
0.01
0.01
0.01
0.01
0.00
B-66
-------
Table B.4-21. APEX estimated exposures in St. Louis (98th %ile 200 ppb).
Exposure
level Gr
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
01
501
1001
1501
2001
2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
8001
oup
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMA,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
)M TS,ASTHMACHILD,MOD
#
persons
102436
84633
57867
36682
21576
12925
8014
5022
3174
2023
1387
913
666
474
314
229
198
41714
41070
34529
24576
15085
9168
5774
3648
2285
1464
1011
675
491
352
222
160
138
Person
days
16677000
1159900
249490
85910
37060
18498
10304
6041
3772
2299
1539
1035
742
512
344
252
198
10560000
880000
188770
64600
27517
13677
7596
4446
2721
1655
1110
759
551
375
245
183
137
Population
fraction
0.97
0.80
0.55
0.35
0.20
0.12
0.08
0.05
0.03
0.02
0.01
0.01
0.01
0.00
0.00
0.00
0.00
1.00
0.98
0.83
0.59
0.36
0.22
0.14
0.09
0.05
0.04
0.02
0.02
0.01
0.01
0.01
0.00
0.00
B-67
-------
B-5 REFERENCES
AHS. (2008). The American Housing Survey. U.S. Department of Housing and Urban
Development, http://www.census.gov/prod/2008pubs/hl50-07.pdf. Last accessed December
17, 2008.
Akland GG, Hartwell TD, Johnson TR, Whitmore RW. (1985). Measuring human exposure to
carbon monoxide in Washington, D. C. and Denver, Colorado during the winter of 1982-83.
Environ Sci Technol. 19:911-918.
Biller WF, Feagans TB, Johnson TR, Duggan GM, Paul RA, McCurdy T, Thomas HC. (1981).
A general model for estimating exposure associated with alternative NAAQS. Paper No. 81-
18.4 in Proceedings of the 74th Annual Meeting of the Air Pollution Control Association,
Philadelphia, PA.
Grontoft T, and Raychaudhuri MR. (2004). Compilation of tables of surface deposition
velocities for Os, NC>2 and SC>2 to a range of indoor surfaces. Atmos Environ. 38:533-544.
Hartwell TD, Clayton CA, Ritchie RM, Whitmore RW, Zelon HS, Jones SM, Whitehurst DA.
(1984). Study of Carbon Monoxide Exposure of Residents of Washington, DC and Denver,
Colorado. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of
Research and Development, Environmental Monitoring Systems Laboratory. EPA-600/4-84-
031.
Johnson T, Capel J, Mozier J, McCoy M. (1996b). Estimation of Ozone Exposures
Experienced by Outdoor Children in Nine Urban Areas Using a Probabilistic Version of
NEM. Prepared for the Air Quality Management Division under Contract No. 68-DO-30094,
April.
Johnson T, Capel J, McCoy M, Mozier J. (1996c). Estimation of Ozone Exposures Experienced
by Outdoor Workers in Nine Urban Areas Using a Probabilistic Version of NEM. Prepared
for the Air Quality Management Division under Contract No. 68-DO-30094, April.
Johnson T, Capel J, McCoy M. (1996a). Estimation of Ozone Exposures Experienced by Urban
Residents Using a Probabilistic Version of NEM and 1990 Population Data. Prepared by IT
Air Quality Services for the Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina, September.
Johnson T, Capel J, Olaguer E, Wijnberg L. (1992). Estimation of Ozone Exposures
Experienced by Residents of ROMNET Domain Using a Probabilistic Version of NEM.
Prepared by IT Air Quality Services for the Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.
Johnson T. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado.
Research Triangle Park, NC: U.S. Environmental Protection Agency, Environmental
Monitoring Systems Laboratory. EPA-600/4-84-014.
Johnson T. (1989). Human Activity Patterns in Cincinnati, Ohio. Palo Alto, CA: Electric
Power Research Institute. EPRI EN-6204.
Johnson TR and Paul RA. (1983). The NAAQS Exposure Model (NEM) Applied to Carbon
Monoxide. EPA-450/5-83-003. Prepared for the U.S. Environmental Agency by PEDCo
Environmental Inc., Durham, N.C. under Contract No. 68-02-3390. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina.
B-68
-------
Klepeis NE, Tsang AM, Behar JV. (1996). Analysis of the National Human Activity Pattern
Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment. Washington, DC:
U.S. Environmental Protection Agency, Office of Research and Development. EPA/600/R-
96/074.
Lawrence Berkeley National Laboratory (2003). Modeling shelter-in-place including sorption
on indoor surfaces. Available at:
http://repositories.cdhb.org/cgi/viewcontent.cgi?article=1450&context=lbnl.
McCurdy T, Glen G, Smith L, Lakkadi Y. (2000). The National Exposure Research
Laboratory's Consolidated Human Activity Database, JExp Anal Environ Epidemiol. 10:
566-578.
NCBG (2008). Design Capacity in the Chicago Public Schools. The Neighborhood Capital
Budget Group. Available at: http://www.ncbg.org/schools/design_capacity.htm .
Roddin MF, Ellis HT, Siddiqee WM. (1979). Background Data for Human Activity Patterns,
Vols. 1, 2. Draft Final Report. Prepared for Strategies and Air Standards Division, Office of
Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research
Triangle Park, N.C.
Singer, BC, AT Hodgson, and WW Nazaroff. (2002). Effect of sorption on exposures to
organic gases from environmental tobacco smoke. LBNL report number 49576. January
2002.
Spier CE, Little DE, Trim SC, Johnson TR, Linn WS, Hackney JD. (1992). Activity patterns in
elementary and high school students exposed to oxidant pollution. J Expo Anal Environ
Epidemiol. 2:277-293.
Thatcher, TL, Lai ACK, Moreno-Jackson R, Sextro RG, and Nazaroff WW. (2002). Effects of
room furnishings and air speed on particle deposition rates indoors. Atmos Environ.
36:1811-1819.
Tsang AM and Klepeis NE. (1996). Descriptive Statistics Tables from a Detailed Analysis of
the National Human Activity Pattern Survey (NHAPS) Data. U.S. Environmental Protection
Agency. EPA/600/R-96/148.
US Army Corps of Engineers. (2002). Unified Facilities Criteria (Ufc) Design: Child
Development Centers. Report no. UFC 4-740-14, (Table 2.6). Available at:
http ://www. wbdg. org/ccb/DOD/UFC/ufc_4_740_l 4.pdf.
US Census Bureau. (2007). Employment Status: 2000- Supplemental Tables. Available at:
http://www.census.gov/population/www/cen2000/phc-t28.html.
US DOE (2003). Commercial Building Energy Consumption Survey. U. S. Department of
Energy. Available at:
http://www.eia.doe.gov/emeu/cbecs/cbecs2003/public_use_2003/cbecs_pudata2003.html.
Last accessed December 17, 2008.
US DOT. (2007). Part 3-The Journey To Work files. Bureau of Transportation Statistics
(BTS). Available at: http://transtats.bts.gov/.
US EPA. (1999). Total Risk Integrated Methodology. Website:
http://www.epa.gov/ttnatw01/urban/trim/trimpg.html.
US EPA. (2002). Consolidated Human Activities Database (CHAD) Users Guide. Database
and documentation available at: http://www.epa.gov/chadnetl/.
US EPA. (2004a). AERMOD: Description of Model Formulation. Office of Air Quality
Planning and Standards. EPA-454/R-03-004. Available at:
http://www.epa.gov/scramOO l/7thconf/aermod/aermod_mfd.pdf
US EPA. (2004b). Source Ranking Database. Available at:
http://www.epa.gov/opptintr/exposure/pubs/srddl.htm. Last accessed September 30, 2008.
B-69
-------
US EPA. (2008a). Building Assessment Survey and Evaluation (BASE) Study.
http://www.epa.gov/iaq/base/summarized_data.html. Last accessed December 17, 2008.
US EPA. (2008b). The Approach Used for Estimating Changes in Children's IQ from Lead
Dust Generated During Renovation, Repair, and Painting in Residences and Child-Occupied
Facilities: Appendix A. Inputs for the Residential and Child-occupied Facilities (COFs) RRP
Models. Office of Pollution Prevent!on and Toxics. March 28, 2008.
US EPA. (2009a). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model
Documentation (TRIM.Expo / APEX, Version 4.3) Volume I: User's Guide. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. January 2009. Available at:
http ://www. epa.gov/ttn/fera/human_apex.html.
US EPA. (2009b). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model
Documentation (TRIM.Expo / APEX, Version 4.3) Volume II: Technical Support Document.
Office of Air Quality Planning and Standards, Research Triangle Park, NC. January 2009.
Available at: http://www.epa.gov/ttn/fera/human_apex.html.
Wiley JA, Robinson JP, Cheng Y-T, Piazza T, Stork L, Pladsen K. (1991b). Study of Children's
Activity Patterns: Final Report. California Air Resources Board, Sacramento, CA. ARB-R-
93/489.
Wiley JA, Robinson JP, Piazza T, Garrett K, Cirksena K, Cheng Y-T, Martin G. (1991a).
Activity Patterns of California Residents: Final Report. California Air Resources Board,
Sacramento, CA. ARB/R93/487. Available from: NTIS, Springfield, VA., PB94-108719.
Williams R, Suggs J, Creason J, Rodes C, Lawless P, Kwok R, Zweidinger R, Sheldon L.
(2000). The 1998 Baltimore particulate matter epidemiology-exposure study: Part 2.
Personal exposure associated with an elderly population. J Expo Anal Environ Epidemiol.
10(6):533-543.
B-70
-------
ATTACHMENT 1: TECHNICAL MEMORANDUM ON
METEOROLOGICAL DATA PREPARATION FOR AERMOD
FOR SO2 REA FOR GREENE COUNTY AND ST. LOUIS
MODELING DOMAINS, YEAR 2002
B-71
-------
ATTACHMENT 2. TECHNICAL MEMORANDUM ON THE
ANALYSIS OF NHIS ASTHMA PREVALENCE DATA
B-72
-------
INTERNATIONAL
DRAFT MEMORANDUM
To: John Langstaff
From: Jonathan Cohen, Arlene Rosenbaum
Date: September 30, 2005
EPA 68D01052, Work Assignment 3-08. Analysis of NHIS Asthma Prevalence
Ke: Data
This memorandum describes our analysis of children's asthma prevalence data from the National
Health Interview Survey (NHIS) for 2003. Asthma prevalence rates for children aged 0 to 17
years were calculated for each age, gender, and region. The regions defined by NHIS are
"Midwest," "Northeast," "South," and "West." For this project, asthma prevalence was defined
as the probability of a Yes response to the question CASHMEV: "Ever been told that... had
asthma?" among those that responded Yes or No to this question. The responses were weighted
to take into account the complex survey design of the NHIS survey. Standard errors and
confidence intervals for the prevalence were calculated using a logistic model, taking into
account the survey design. Prevalence curves showing the variation of asthma prevalence
against age for a given gender and region were plotted. A scatterplot smoothing technique using
the LOESS smoother was applied to smooth the prevalence curves and compute the standard
errors and confidence intervals for the smoothed prevalence estimates. Logistic analysis of the
prevalence curves shows statistically significant differences in prevalence by gender and by
region. Therefore we did not combine the prevalence rates for different genders or regions.
Logistic Models
NHIS survey data for 2003 were provided by EPA. One obvious approach to calculate
prevalence rates and their uncertainties for a given gender, region, and age is to calculate the
proportion of Yes responses among the Yes and No responses for that demographic group,
weighting each response by the survey weight. Although that approach was initially used, two
problems are that the distributions of the estimated prevalence rates are not well approximated by
normal distributions, and that the estimated confidence intervals based on the normal
approximation often extend outside the [0, 1] interval. A better approach is to use a logistic
transformation and fit a model of the form:
Prob (asthma) = exp(beta) / (1 + exp(beta)),
B-73
-------
where beta may depend on the explanatory variables for age, gender, or region. This is
equivalent to the model:
Beta = logit (prob (asthma)} = log { prob (asthma) / [1 - prob (asthma)] }.
The distribution of the estimated values of beta is more closely approximated by a normal
distribution than the distribution of the corresponding estimates of prob (asthma). By applying a
logit transformation to the confidence intervals for beta, the corresponding confidence intervals
for prob (asthma) will always be inside [0, 1]. Another advantage of the logistic modeling is that
it can be used to compare alternative statistical models, such as models where the prevalence
probability depends upon age, region, and gender, or on age and region but not gender.
A variety of logistic models for asthma prevalence were fit and compared, where the transformed
probability variable beta is a given function of age, gender, and region. SAS's
SURVEYLOGISTIC procedure was used to fit the logistic models, taking into account the NHIS
survey weights and survey design (stratification and clustering).
The following Table G-l lists the models fitted and their log-likelihood goodness-of-fit
measures. 16 models were fitted. The Strata column lists the four possible stratifications: no
stratification, by gender, by region, by region and gender. For example, "4. region, gender"
means that separate prevalence estimates were made for each combination of region and gender.
As another example, "2. gender" means that separate prevalence estimates were made for each
gender, so that for each gender, the prevalence is assumed to be the same for each region. The
prevalence estimates are independently calculated for each stratum.
Table G-l. Alternative logistic models for asthma prevalence.
Model
1
2
3
4
5
6
7
8
9
10
11
12
13
Description
1 . logit(prob) = linear in age
1 . logit(prob) = linear in age
1 . logit(prob) = linear in age
1 . logit(prob) = linear in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
4. logit(prob) = f(age)
Strata
1. none
2. gender
3. region
4. region, gender
1. none
2. gender
3. region
4. region, gender
1. none
2. gender
3. region
4. region, gender
1. none
- 2 Log Likelihood
54168194.62
53974657.17
54048602.57
53837594.97
53958021.20
53758240.99
53818198.13
53593569.84
53849072.76
53639181.24
53694710.66
53441122.98
53610093.48
DF
2
4
8
16
3
6
12
24
4
8
16
32
18
B-74
-------
Model
14
15
16
Description
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
Strata
2. gender
3. region
4. region, gender
- 2 Log Likelihood
53226610.02
53099749.33
52380000.19
DF
36
72
144
The Description column describes how beta depends upon the age:
Linear in age: Beta = a + |3 x age, where a and |3 vary with the strata.
Quadratic in age: Beta = a + |3 x age + y x age2 where a |3 and y vary with the
strata.
Cubic in age: Beta = a + |3 x age + y x age2 + 5 x age3 where a |3, y, and 5 vary with the
strata.
f(age) Beta = arbitrary function of age, with different functions for different strata
The category f(age) is equivalent to making age one of the stratification variables, and is also
equivalent to making beta a polynomial of degree 16 in age (since the maximum age for children
is 17), with coefficients that may vary with the strata.
The fitted models are listed in order of complexity, where the simplest model (1) is an
unstratified linear model in age and the most complex model (16) has a prevalence that is an
arbitrary function of age, gender, and region. Model 16 is equivalent to calculating independent
prevalence estimates for each of the 144 combinations of age, gender, and region.
Table G-l also includes the -2 Log Likelihood, a goodness-of-fit measure, and the degrees of
freedom, DF, which is the total number of estimated parameters. Two models can be compared
using their -2 Log Likelihood values; lower values are preferred. If the first model is a special
case of the second model, then the approximate statistical significance of the first model is
estimated by comparing the difference in the -2 Log Likelihood values with a chi-squared
random variable with r degrees of freedom, where r is the difference in the DF. This is a
likelihood ratio test. For all pairs of models from Table G-l, all the differences are at least
70,000 and the likelihood ratios are all extremely statistically significant at levels well below 5
percent. Therefore the model 16 is clearly preferred and was used to model the prevalences.
The SURVEYLOGISTIC model predictions are tabulated in Table G-2 below and plotted in
Figures 1 and 3 below. Also shown in Table G-2 and in Figures 2 and 4 are results for smoothed
curves calculated using a LOESS scatterplot smoother, as discussed below.
The SURVEYLOGISTIC procedure produces estimates of the beta values and their 95 %
confidence intervals for each combination of age, region, and gender. Applying the inverse logit
transformation,
Prob (asthma) = exp( beta) / (1 + exp(beta)),
converted the beta values and 95 % confidence intervals into predictions and 95 % confidence
intervals for the prevalence, as shown in Table G-2 and Figures 1 and 3. The standard error for
the prevalence was estimated as
B-75
-------
Std Error (Prob (asthma)} = Std Error (beta) x exp(- beta) / (1 + exp(beta) )2,
which follows from the delta method (a first order Taylor series approximation).
Loess Smoother
The estimated prevalence curves shows that the prevalence is not a smooth function of age. The
linear, quadratic, and cubic functions of age modeled by SURVEYLOGISTIC were one strategy
for smoothing the curves, but they did not provide a good fit to the data. One reason for this
might be due to the attempt to fit a global regression curve to all the age groups, which means
that the predictions for age A are affected by data for very different ages. We instead chose to
use a local regression approach that separately fits a regression curve to each age A and its
neighboring ages, giving a regression weight of 1 to the age A, and lower weights to the
neighboring ages using a tri-weight function:
Weight = {1 - [ |age - A / q ]3}, where | age - A| <= q.
The parameter q defines the number of points in the neighborhood of the age a. Instead of calling
q the smoothing parameter, SAS defines the smoothing parameter as the proportion of points in
each neighborhood. We fitted a quadratic function of age to each age neighborhood, separately
for each gender and region combination. We fitted these local regression curves to the beta
values, the logits of the asthma prevalence estimates, and then converted them back to estimated
prevalence rates by applying the inverse logit function exp(beta) / (1 + exp(beta)). In addition to
the tri-weight variable, each beta value was assigned a weight of
1 / [std error (beta)]2, to account for their uncertainties.
The SAS LOESS procedure was applied to estimate smoothed curves for beta, the logit of the
prevalence, as a function of age, separately for each region and gender. We fitted curves using
the choices 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 for the smoothing parameter in an effort to
determine the optimum choice based on various regression diagnostics.3,4
3 Two outlier cases were adjusted to avoid wild variations in the "smoothed" curves: For the West region, males,
age 0, there were 97 children surveyed that all gave No answers to the asthma question, leading to an estimated
value of -15.2029 for beta with a standard error of 0.14. For the Northeast region, females, age 0, there were 29
children surveyed that all gave No answers to the asthma question, leading to an estimated value of -15.2029 for
beta with a standard error of 0.19. In both cases the raw probability of asthma equals zero, so the corresponding
estimated beta would be negative infinity, but SAS's software gives -15.2029 instead. To reduce the impact of these
outlier cases, we replaced their estimated standard errors by 4, which is approximately four times the maximum
standard error for all other region, gender, and age combinations.
4 With only 18 points, a smoothing parameter of 0.2 cannot be used because the weight function assigns zero
weights to all ages except age A, and a quadratic model cannot be uniquely fitted to a single value. A smoothing
parameter of 0.3 also cannot be used because that choice assigns a neighborhood of 5 points only (0.3 x 18 = 5,
rounded down), of which the two outside ages have assigned weight zero, making the local quadratic model fit
exactly at every point except for the end points (ages 0, 1, 16 and 17). Usually one uses a smoothing parameter
below one so that not all the data are used for the local regression at a given x value.
B-76
-------
Quantities predicted in these smoothing parameter tests were the predicted value, standard error,
confidence interval lower bound and confidence interval upper bound for the betas, and the
corresponding values for the prevalence rates.
The polygonal curves joining values for different ages show the predicted values with vertical
lines indicating the confidence intervals in Figures 3 and 4 for smoothing parameters 0 (i.e., no
smoothing) and 0.5, respectively. Note that the confidence intervals are not symmetric about the
predicted values because of the inverse logit transformation.
Note that in our application of LOESS, we used weights of 1 / [std error (beta)]2, so that a2 = 1
for this application. The LOESS procedure estimates a2 from the weighted sum of squares. Since
in our application we assume a2 = 1, we multiplied the estimated standard errors by 1 /
estimated a, and adjusted the widths of the confidence intervals by the same factor.
Additionally, because the true value of a equals 1, the best choices of smoothing parameter
should give residual standard errors close to one. Using this criterion the best choice varies with
gender and region between smoothing parameters 0.4 (3 cases), 0.5 (2 cases), 0.6 (1 case), and
0.7 (1 case).
As a further regression diagnostic the residual errors from the LOESS model were divided by
std error (beta) to make their variances approximately constant. These approximately studentized
residuals, 'student,' should be approximately normally distributed with a mean of zero and a
variance of a2 = 1. To test this assumption, normal probability plots of the residuals were
created for each smoothing parameter, combining all the studentized residuals across genders,
regions, and ages. The plots for smoothing parameters seem to be equally straight for each
smoothing parameter.
The final regression diagnostic is a plot of the studentized residuals against the smoothed beta
values. Ideally there should be no obvious pattern and an average studentized residual close to
zero. The plots indeed showed no unusual patterns, and the results for smoothing parameters 0.5
and 0.6 seem to showed a fitted LOESS close to the studentized residual equals zero line.
The regression diagnostics suggested the choice of smoothing parameter as 0.4 or 0.5. Normal
probability plots did not suggest any preferred choices. The plots of residuals against smoothed
predictions suggest the choices of 0.5 or 0.6. We therefore chose the final value of 0.5. These
predictions, standard errors, and confidence intervals are presented in tabular form below as
Table G-2.
B-77
-------
Figure 1. Raw asthma prevalence rates by age and gender tor each region
region = Mi dwes t
I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I
10 11 12 13 14 15 16 17
Male
Figure 1. Raw asthma prevalence rates by age and gender tor each region
region=Northeast
11 12
14 15 16 17
gender
B-78
-------
Figure 1. Raw asthma prevalence rates by age and gender for each region
I I I I 11 I 11 I I I I I I I I 11 I I I I I I I I 11 I I I I I I I I I I I I I I I I I
8 9 10 11 12 13 14 15 16 17
Male
Figure 1. Raw asthma prevalence rates by age and gender tor each region
regi on=We st
gender
Male
B-79
-------
Figure 2. Smoothed asthma prevalence rates by age for each region and gender
11
13 14
' i '
15
~
17
Male
Figure 2. Smoothed asthma prevalence rates by age for each region and gender
region=Northeast
gender
Fema1e
B-80
-------
Figure 2. Smoothed asthma prevalence rates by age for each region and gender
Male
Figure 2. Smoothed asthma prevalence rates by age for each region and gender
regi on=We st
gender
Fema1e
B-81
-------
Figure 3. Raw asthma prevalence rates and confidence intervals
0 1
1 I ' ' ' ' I
11
13 14
17
Male
Figure 3. Raw asthma prevalence rates and confidence intervals
region=Northeast
10 11 12 13 14 15 16 17
gender
age
Fema1e
B-82
-------
Figure 3. Raw asthma prevalence rates and confidence intervals
regi on = South
Male
Figure 3. Raw asthma prevalence rates and confidence intervals
regi on=We st
13 14
17
gender
age
Fema1e
B-83
-------
Rgure 4. Smoothed asthma prevalence rates and confidence intervals
region = Mi dwes t
1 I ' ' ' ' I
11
13 14
17
Male
Rgure 4. Smoothed asthma prevalence rates and confidence intervals
region=Northeast
11
14 15 16 17
gender
age
Fema1e
Male
B-84
-------
Rgure 4. Smoothed asthma prevalence rates and confidence intervals
1 I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I
9 10 11 12 13 14 15 16 17
Male
Rgure 4. Smoothed asthma prevalence rates and confidence intervals
regi on=We st
p r e v
0.32-
0.30-
0 . 2 E
0.26-
0.24-
0.22
0.20-
0.18-
0.16-
0.14-
0.12-
0.10-
0 . 0 Ģ
0.06-
0.04-
0.02-
0 00-
11 12
14 15 16 17
gender
age
Fema1e
B-85
-------
Table G-2. Raw and smoothed prevalence rates, with confidence intervals, by region,
gender, and age.
Obs
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Region
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Gender
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
e male
Age
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
Smoothed
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.04161
0.06956
0.10790
0.07078
0.05469
0.07324
0.06094
0.07542
0.09049
0.08100
0.08463
0.09540
0.14869
0.09210
0.04757
0.09032
0.10444
0.08612
0.09836
0.11040
0.10916
0.16190
0.27341
0.19597
0.10055
0.21214
0.22388
Std
Error
0.02965
0.03574
0.04254
0.01995
0.02578
0.01778
0.03474
0.01944
0.03407
0.02163
0.03917
0.02613
0.08250
0.02854
0.02927
0.02563
0.03638
0.02181
0.04283
0.02709
0.04859
0.03486
0.06817
0.03920
0.04780
0.03957
0.05905
95 % Conf
Interval -
Lower
Bound
0.01001
0.02143
0.04840
0.03736
0.02131
0.04228
0.01936
0.04205
0.04233
0.04417
0.03317
0.05106
0.04643
0.04534
0.01389
0.04728
0.05160
0.04842
0.04062
0.06298
0.04400
0.09838
0.16112
0.12296
0.03816
0.13724
0.12907
95 % Conf
Interval -
Upper
Bound
0.15717
0.20330
0.22336
0.13008
0.13325
0.12395
0.17579
0.13163
0.18298
0.14393
0.19942
0.17131
0.38520
0.17808
0.15051
0.16571
0.19997
0.14857
0.21943
0.18643
0.24600
0.25484
0.42437
0.29763
0.23952
0.31309
0.35959
B-86
-------
Obs
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
Region
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest F
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Gender
e male
e male
e male
e male
e male
e male
e male
e male
e male
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
Age
13
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.16966
0.10511
0.14020
0.12026
0.13341
0.13299
0.14040
0.17497
0.16478
0.06419
0.03134
0.02824
0.06250
0.05102
0.10780
0.18650
0.15821
0.24649
0.21572
0.11609
0.17822
0.14158
0.12788
0.09726
0.12145
0.16718
0.12757
0.13406
0.14718
0.13986
Std
Error
0.03371
0.04233
0.02603
0.03805
0.02266
0.03933
0.02235
0.04786
0.04037
0.03612
0.01537
0.01694
0.01751
0.02343
0.02078
0.04864
0.02705
0.05823
0.03661
0.04818
0.03525
0.05280
0.02799
0.03614
0.02642
0.05814
0.02700
0.04783
0.02976
0.04422
95 % Conf
Interval -
Lower
Bound
0.10716
0.04637
0.09164
0.06327
0.09056
0.07288
0.09764
0.09970
0.09320
0.02068
0.01042
0.00859
0.03321
0.02040
0.06960
0.10898
0.10696
0.15035
0.14543
0.04973
0.11280
0.06576
0.07751
0.04588
0.07391
0.08134
0.07864
0.06458
0.09254
0.07331
95 % Conf
Interval -
Upper
Bound
0.25807
0.22104
0.20857
0.21670
0.19226
0.23037
0.19777
0.28884
0.27468
0.18227
0.09046
0.08879
0.11457
0.12189
0.16328
0.30057
0.22775
0.37686
0.30774
0.24793
0.27003
0.27873
0.20375
0.19448
0.19317
0.31276
0.20031
0.25769
0.22603
0.25050
B-87
-------
Obs
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
Region
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Midwest IS
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast
Northeast ;
Gender
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
lale
Te male
Fe male
Fe male
Te male
Te male
Fe male
Fe male
Te male
Te male
Fe male
Fe male
Te male
Te male
Te male
Fe male
Age
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.17728
0.23907
0.18961
0.13660
0.19487
0.18501
0.16939
0.16673
0.16795
0.14583
0.17953
0.24965
0.20116
0.21152
0.23741
0.00000
0.06807
0.12262
0.07219
0.07217
0.07522
0.08550
0.07709
0.08704
0.08171
0.07597
0.11603
0.19149
0.16106
0.22034
Std
Error
0.02996
0.05031
0.03044
0.04784
0.03078
0.04498
0.02841
0.05094
0.02631
0.04241
0.02561
0.06037
0.03048
0.06481
0.05816
0.00000
0.06565
0.07443
0.03765
0.03707
0.02212
0.03991
0.02021
0.03804
0.02252
0.03754
0.03012
0.06960
0.03737
0.07076
95 % Conf
Interval -
Lower
Bound
0.12020
0.15449
0.13100
0.06668
0.13541
0.11230
0.11528
0.08886
0.11734
0.08054
0.12951
0.15033
0.14187
0.11131
0.13243
0.00000
0.00670
0.03476
0.02088
0.02561
0.03764
0.03324
0.04162
0.03596
0.04269
0.02801
0.06258
0.08937
0.09219
0.11195
95 % Conf
Interval -
Upper
Bound
0.25366
0.35075
0.26639
0.25946
0.27221
0.28945
0.24195
0.29104
0.23459
0.24967
0.24347
0.38489
0.27721
0.36490
0.38835
0.00000
0.44174
0.35164
0.22109
0.18713
0.14468
0.20269
0.13840
0.19592
0.15080
0.18998
0.20515
0.36372
0.26629
0.38783
B-88
-------
Obs
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
Region
Northeast
Northeast
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast ;
Northeast ;
Northeast
Northeast
Northeast
Northeast ;
Gender
Te male
Te male
Te male
Fe male
Fe male
Te male
Te male
Fe male
Fe male
Te male
Te male
Fe male
Fe male
Te male
Te male
Te male
Fe male
Fe male
Te male
Te male
Fe male
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Age
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.18503
0.11002
0.17054
0.17541
0.14457
0.12980
0.13487
0.15128
0.14072
0.11890
0.16615
0.22638
0.17374
0.15807
0.15137
0.07460
0.14564
0.13603
0.14601
0.19074
0.15662
0.03904
0.04768
0.05533
0.04564
0.05525
0.05161
0.03842
0.06766
0.07436
Std
Error
0.04087
0.05128
0.04039
0.07488
0.03538
0.04964
0.03098
0.05287
0.03068
0.04426
0.03375
0.06285
0.03402
0.05513
0.02946
0.03409
0.02761
0.05328
0.03095
0.07382
0.05374
0.03829
0.03299
0.03425
0.01831
0.03119
0.01505
0.02923
0.01784
0.02906
95 % Conf
Interval -
Lower
Bound
0.10844
0.04241
0.09628
0.07159
0.08042
0.05930
0.07799
0.07366
0.08367
0.05568
0.10211
0.12650
0.10861
0.07694
0.09519
0.02971
0.09279
0.06081
0.08805
0.08451
0.06784
0.00547
0.00991
0.01596
0.01850
0.01781
0.02680
0.00840
0.03734
0.03393
95 % Conf
Interval -
Upper
Bound
0.29764
0.25654
0.28407
0.36981
0.24618
0.26087
0.22319
0.28547
0.22704
0.23597
0.25877
0.37158
0.26626
0.29719
0.23220
0.17506
0.22127
0.27686
0.23241
0.37568
0.32151
0.23095
0.20023
0.17461
0.10821
0.15872
0.09709
0.15853
0.11955
0.15522
B-89
-------
Obs
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
Region
Northeast
Northeast
Northeast
Northeast ]
Northeast ]
Northeast
Northeast
Northeast ]
Northeast ]
Northeast
Northeast
Northeast ]
Northeast ]
Northeast
Northeast
Northeast
Northeast ]
Northeast ]
Northeast
Northeast
Northeast ]
Northeast ]
Northeast
Northeast
Northeast ]
Northeast ]
Northeast
South Fe
South Fe
South Fe
Gender
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
Vlale
male 0
male 0
male 1
Age
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.09964
0.17601
0.14854
0.23271
0.20731
0.13074
0.22820
0.33970
0.22240
0.13761
0.21238
0.21785
0.17652
0.11448
0.16617
0.17736
0.18279
0.19837
0.17078
0.16201
0.17033
0.11894
0.18246
0.24306
0.20406
0.22559
0.24185
0.02459
0.03407
0.08869
Std
Error
0.02330
0.04519
0.02948
0.09319
0.04235
0.05195
0.04524
0.08456
0.04298
0.05024
0.04071
0.06659
0.03731
0.05849
0.03516
0.05489
0.03589
0.05450
0.03078
0.04973
0.02889
0.04584
0.02858
0.05798
0.03216
0.06980
0.06066
0.01116
0.01282
0.03373
95 % Conf
Interval -
Lower
Bound
0.05859
0.10393
0.09428
0.09832
0.12875
0.05785
0.14338
0.19726
0.14157
0.06507
0.13589
0.11464
0.10824
0.04005
0.10200
0.09349
0.11611
0.11222
0.11288
0.08618
0.11547
0.05417
0.12740
0.14759
0.14187
0.11748
0.13291
0.01002
0.01465
0.04118
95 % Conf
Interval -
Upper
Bound
0.16441
0.28234
0.22623
0.45756
0.31640
0.26922
0.34311
0.51855
0.33157
0.26785
0.31617
0.37465
0.27460
0.28601
0.25907
0.31067
0.27581
0.32635
0.25000
0.28386
0.24408
0.24139
0.25438
0.37326
0.28447
0.38930
0.39898
0.05906
0.07723
0.18067
B-90
-------
Obs
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
Region
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
South Fe
Gender
male 1
male 2
male 2
male 3
male 3
male 4
male 4
male 5
male 5
male 6
male 6
male 7
male 7
male 8
male 8
male 9
male 9
male
male
male
male
male
male
male
male
male
male
male
male
male
Age
10
10
11
11
12
12
13
13
14
14
15
15
16
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.05182
0.05097
0.07110
0.08717
0.08759
0.11010
0.09897
0.09409
0.11870
0.15318
0.12150
0.09608
0.11192
0.09955
0.09287
0.07477
0.09117
0.10602
0.10821
0.14411
0.13237
0.12646
0.12346
0.11376
0.09653
0.02915
0.09469
0.11985
0.09988
0.14183
Std
Error
0.01167
0.02373
0.01386
0.03240
0.01718
0.03209
0.01914
0.02943
0.02157
0.04317
0.02282
0.03538
0.02171
0.03288
0.01897
0.02719
0.01786
0.03214
0.02026
0.04267
0.02251
0.02981
0.02004
0.03270
0.01717
0.01339
0.01619
0.03357
0.01586
0.03685
95 % Conf
Interval -
Lower
Bound
0.03127
0.02012
0.04584
0.04122
0.05624
0.06113
0.06387
0.05015
0.07855
0.08611
0.07925
0.04565
0.07204
0.05111
0.05850
0.03606
0.05855
0.05750
0.07077
0.07875
0.08989
0.07860
0.08543
0.06365
0.06458
0.01174
0.06436
0.06801
0.06978
0.08366
95 % Conf
Interval -
Upper
Bound
0.08472
0.12319
0.10869
0.17500
0.13394
0.19035
0.15025
0.16968
0.17548
0.25777
0.18182
0.19105
0.16985
0.18493
0.14436
0.14864
0.13929
0.18732
0.16201
0.24907
0.19071
0.19723
0.17519
0.19510
0.14190
0.07054
0.13721
0.20259
0.14099
0.23028
B-91
-------
Obs
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
Region
South Fe
South Fe
South Fe
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
Gender
male
male
male
a
a
3
3
a
a
3
3
a
a
3
3
3
a
a
3
3
a
a
3
3
a
a
3
3
3
a
Age
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.11501
0.13141
0.14466
0.01164
0.04132
0.10465
0.06981
0.11644
0.10189
0.10794
0.12852
0.08480
0.14393
0.22243
0.16450
0.13908
0.16386
0.10695
0.13329
0.13660
0.13818
0.15978
0.16839
0.21482
0.17848
0.15078
0.16247
0.13727
0.14480
0.14136
Std
Error
0.01620
0.03007
0.02946
0.00852
0.01867
0.03216
0.01623
0.03486
0.01672
0.03253
0.02139
0.02973
0.02379
0.04227
0.02373
0.03392
0.02460
0.04272
0.02322
0.03841
0.02276
0.03742
0.02450
0.04702
0.02453
0.03440
0.02224
0.03260
0.01976
0.03119
95 % Conf
Interval -
Lower
Bound
0.08365
0.08280
0.09067
0.00275
0.01487
0.05629
0.04125
0.06353
0.07024
0.05874
0.08793
0.04190
0.09861
0.15052
0.11821
0.08485
0.11613
0.04747
0.08951
0.07712
0.09484
0.09920
0.12062
0.13676
0.13021
0.09492
0.11881
0.08489
0.10610
0.09049
95 % Conf
Interval -
Upper
Bound
0.15612
0.20226
0.22291
0.04790
0.10956
0.18635
0.11576
0.20382
0.14557
0.19005
0.18405
0.16410
0.20534
0.31592
0.22430
0.21964
0.22617
0.22347
0.19392
0.23049
0.19702
0.24720
0.23012
0.32086
0.23972
0.23112
0.21820
0.21438
0.19453
0.21409
B-92
-------
Obs
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
Region
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
South Mai
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
Gender
3
3
3
a
a
3
3
a
a
male
male
male
male
male
male
male
male
male
male
male
male
male
male
male
male
male
male
male
male
male
Age
13
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.14318
0.16110
0.15339
0.16172
0.15088
0.15836
0.14038
0.11156
0.12247
0.00983
0.01318
0.02367
0.03105
0.08097
0.05440
0.07528
0.07444
0.09263
0.07696
0.01976
0.07737
0.15792
0.07298
0.06955
0.08146
0.07753
0.09062
0.13440
0.10215
0.06573
Std
Error
0.01928
0.03444
0.01875
0.03519
0.01746
0.03879
0.01773
0.02737
0.02596
0.00990
0.00987
0.01862
0.01312
0.03759
0.01482
0.03851
0.01842
0.03196
0.02064
0.01347
0.02123
0.07301
0.01985
0.02567
0.01987
0.02825
0.01994
0.04481
0.02347
0.03719
95 % Conf
Interval -
Lower
Bound
0.10537
0.10438
0.11612
0.10394
0.11598
0.09614
0.10533
0.06810
0.07537
0.00135
0.00248
0.00497
0.01204
0.03170
0.02948
0.02679
0.04257
0.04621
0.04194
0.00513
0.04157
0.06009
0.03947
0.03321
0.04691
0.03731
0.05507
0.06802
0.06061
0.02102
95 % Conf
Interval -
Upper
Bound
0.19165
0.24037
0.19992
0.24291
0.19398
0.24974
0.18467
0.17746
0.19286
0.06802
0.06700
0.10522
0.07769
0.19166
0.09825
0.19404
0.12701
0.17703
0.13701
0.07302
0.13949
0.35487
0.13107
0.13989
0.13776
0.15417
0.14558
0.24832
0.16709
0.18736
B-93
-------
Obs
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
Region
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Fe
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
Gender
male
male
male
male
male
male
male
male
male
male
male
male
male
male
male
Age
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.12152
0.15354
0.12719
0.10120
0.13054
0.14759
0.11968
0.08748
0.11063
0.10099
0.11236
0.12538
0.12224
0.14672
0.14371
0.00000
0.03075
0.05457
0.04584
0.07833
0.06254
0.05897
0.07844
0.07267
0.09122
0.19732
0.11262
0.13335
0.12119
0.08881
Std
Error
0.02660
0.04584
0.02688
0.03594
0.02498
0.04125
0.02369
0.03284
0.02132
0.03841
0.02051
0.04343
0.02210
0.04582
0.03992
0.00000
0.02534
0.02662
0.01889
0.02789
0.01442
0.02530
0.01913
0.03354
0.02482
0.10033
0.02937
0.04859
0.02916
0.03493
95 % Conf
Interval -
Lower
Bound
0.07376
0.08329
0.07852
0.04934
0.08440
0.08346
0.07629
0.04105
0.07145
0.04674
0.07428
0.06188
0.08108
0.07743
0.07558
0.00000
0.00437
0.02056
0.01729
0.03833
0.03627
0.02500
0.04398
0.02870
0.04765
0.06632
0.06021
0.06322
0.06799
0.04015
95 % Conf
Interval -
Upper
Bound
0.19374
0.26584
0.19950
0.19631
0.19650
0.24769
0.18284
0.17675
0.16744
0.20471
0.16645
0.23755
0.18021
0.26052
0.25621
0.00000
0.18642
0.13695
0.11595
0.15342
0.10573
0.13281
0.13607
0.17208
0.16763
0.45969
0.20092
0.25970
0.20680
0.18508
B-94
-------
Obs
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
Region
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
West Male
Gender
Age
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Prevalence
0.12691
0.15183
0.13161
0.17199
0.15079
0.12897
0.16356
0.19469
0.16965
0.13214
0.17494
0.19947
0.16217
0.10759
0.16487
0.18459
0.17018
0.19757
0.17888
0.18078
0.19218
Std
Error
0.02806
0.05484
0.02705
0.05164
0.02837
0.03747
0.02584
0.04002
0.02623
0.04542
0.02738
0.04814
0.02773
0.03838
0.02644
0.05348
0.02480
0.04862
0.02540
0.04735
0.04291
95 % Conf
Interval -
Lower
Bound
0.07464
0.07210
0.08037
0.09260
0.09590
0.07151
0.11192
0.12785
0.11699
0.06547
0.12002
0.12127
0.10747
0.05220
0.11214
0.10138
0.11996
0.11892
0.12718
0.10548
0.11118
95 % Conf
Interval -
Upper
Bound
0.20758
0.29200
0.20811
0.29715
0.22915
0.22159
0.23279
0.28505
0.23956
0.24865
0.24792
0.31029
0.23732
0.20880
0.23582
0.31235
0.23578
0.30993
0.24569
0.29227
0.31153
B-95
-------
ATTACHMENT 3: TECHNICAL MEMORANDUM ON
LONGITUDINAL DIARY CONSTRUCTION APPROACH
B-96
-------
INTERNATIONAL
TECHNICAL MEMORANDUM
TO: Stephen Graham and John Langstaff, US EPA
FROM: Arlene Rosenbaum
DATE: February 29, 2008
SUBJECT: The Cluster-Markov algorithm in APEX
Background
The goals of population exposure assessment generally include an accurate estimate of
both the average exposure concentration and the high end of the exposure distribution. One of
the factors influencing the number of exposures at the high end of the concentration distribution
is time-activity patterns that differ from the average, e.g., a disproportionate amount of time
spent near roadways. Whether a model represents these exposure scenarios well depends on
whether the treatment of activity pattern data accurately characterizes differences among
individuals.
Human time-activity data for population exposure models are generally derived from
demographic surveys of individuals' daily activities, the amount of time spent engaged in those
activities, and the ME locations where the activities occur. Typical time-activity pattern data
available for inhalation exposure modeling consist of a sequence of location/activity
combinations spanning a 24-hour duration, with 1 to 3 records for any single individual. But
modeling assessments of exposure to air pollutants typically require information on activity
patterns over long periods of time, e.g., a full year. For example, even for pollutant health
effects with short averaging times (e.g., ozone 8-hour average) it may be desirable to know the
frequency of exceedances of a threshold concentration over a long period of time (e.g., the
annual number of exceedances of an 8-hour average ozone concentration of 0.07 ppm for each
simulated individual).
Long-term activity patterns can be estimated from daily ones by combining the daily
records in various ways, and the method used for combining them will influence the variability
of the long-term activity patterns across the simulated population. This in turn will influence the
ability of the model to accurately represent either long-term average high-end exposures, or the
number of individuals exposed multiple times to short-term high-end concentrations.
A common approach for constructing long-term activity patterns from short-term records
is to re-select a daily activity pattern from the pool of data for each day, with the implicit
assumption that there is no correlation between activities from day to day for the simulated
individual. This approach tends to result in long-term activity patterns that are very similar
across the simulated population. Thus, the resulting exposure estimates are likely to
B-97
-------
underestimate the variability across the population, and therefore, underestimate the high-end
concentrations.
A contrasting approach is to select a single activity pattern (or a single pattern for each
season and/or weekday-weekend) to represent a simulated individual's activities over the
modeling period. This approach has the implicit assumption that an individual's day to day
activities are perfectly correlated. This approach tends to result in long-term activity patterns
that are very different across the simulated population, and therefore may over-estimate the
variability across the population.
The Cluster-Markov Algorithm
Recently, a new algorithm has been developed and incorporated into APEX that attempts
to more realistically represent the day-to-day correlation of activities for individuals. The
algorithms first use cluster analysis to divide the daily activity pattern records into groups that
are similar, and then select a single daily record from each group. This limited number of daily
patterns is then used to construct a long-term sequence for a simulated individual, based on
empirically-derived transition probabilities. This approach is intermediate between the
assumption of no day-to-day correlation (i.e., re-selection for each time period) and perfect
correlation (i.e., selection of a single daily record to represent all days).
The steps in the algorithm are as follows.
For each demographic group (age, gender, employment status), temperature range,
and day-of-week combination, the associated time-activity records are partitioned into
3 groups using cluster analysis. The clustering criterion is a vector of 5 values: the
time spent in each of 5 microenvironment categories (indoors - residence; indoors -
other building; outdoors - near road; outdoors - away from road; in vehicle).
For each simulated individual, a single time-activity record is randomly selected from
each cluster.
Next the Markov process determines the probability of a given time-activity pattern
occurring on a given day based on the time-activity pattern of the previous day and
cluster-to-cluster transition probabilities. The cluster-to-cluster transition
probabilities are estimated from the available multi-day time-activity records. (If
insufficient multi-day time-activity records are available for a demographic group,
season, day-of-week combination, then the cluster-to-cluster transition probabilities
are estimated from the frequency of time-activity records in each cluster in the CHAD
data base.).
Figure 1 illustrates the Cluster-Markov algorithm in flow chart format.
B-98
-------
CHAD Data Base
Demographic
Group 1
Weekday
Season 1
Demographic
Group 1
Weekday
Season 2
Demographic
Group 1
Weekend
TRANSITION
PROBABILITIES
Annual Time-Activity Sequence
Figure 1. Flow chart of Cluster-Markov algorithm used for constructing longitudinal time-activity diaries.
B-99
-------
Evaluation of modeled diary profiles versus observed diary profiles
The Cluster-Markov algorithm is also incorporated into the Hazardous Air
Pollutant Exposure Model (HAPEM). Rosebaum and Cohen (2004) incorporated the
algorithm in HAPEM and tested modeled longitudinal profiles with multi-day diary data
sets collected as part of the Harvard Southern California Chronic Ozone Exposure Study
(Xue et al. 2005, Geyh et al. 2000). In this study, 224 children in ages between 7 and 12
yr were followed for 1 year from June 1995 to May 1996, for 6 consecutive days each
month. The subjects resided in two separate areas of San Bernardino County: urban
Upland CA, and the small mountain towns of Lake Arrowhead, Crestline, and Running
Springs, CA.
For purposes of clustering the activity pattern records were characterized
according to time spent in each of 5 aggregate microenvironments: indoors-home,
indoors-school, indoors-other, outdoors, and in-transit. For purposes of defining diary
pools and for clustering and calculating transition probabilities the activity pattern
records were divided by day type (i.e., weekday, weekend), season (i.e., summer or ozone
season, non-summer or non-ozone season), age (7-10 and 11-12), and gender.
Week-long sequences (Wednesday through Tuesday) for each of 100 people in
each age/gender group for each season were simulated. To evaluate the algorithm the
following statistics were calculated for the predicted multi-day activity patterns and
compared them with the actual multi-day diary data.
For each age/gender group for each season, the average time in each
microenvironment
For each simulated person-week and microenvironment, the average of the
within-person variance across all simulated persons. (The within-person
variance was defined as the variance of the total time per day spent in the
microenvironment across the week.)
For each simulated person-week the variance across persons of the mean time
spent in each microenvironment.
In each case the predicted statistic for the stratum was compared to the statistic for
the corresponding stratum in the actual diary data. The mean normalized bias for the
statistic, which is a common performance measure used in dispersion model performance
and was also calculated as follows.
(predicted - observed)
j
N i observed
The predicted time-in-microenvironment averages matched well with the
observed values. For combinations of microenvironment/age/gender/season the
normalized bias ranges from -35% to +41%. Sixty percent of the predicted averages
have bias between -9% and +9%, and the mean bias across any microenvironment ranges
from -9% to +4%. Fourteen predictions have positive bias and 23 have negative bias.
B-100
-------
For the variance across persons for the average time spent in each
microenvironment, the bias ranged from -40% to +120% for any
microenvironment/age/gender/season. Sixty-five percent of the predicted variances had
bias between -22% and +24%. The mean normalized bias across any microenvironment
ranged from -10% to +28%. Eighteen predictions had positive bias and 20 had negative
bias.
For the within-person variance for time spent in each microenvironment, the bias
ranged from -47% to +150% for any microenvironment/age/gender/season. Seventy
percent of the predicted variances had bias between -25% and +30%. The mean
normalized bias across any microenvironment ranged from -11% to +47%. Twenty-eight
predictions had positive bias and 12 had negative bias, suggesting some tendency for
overprediction of this variance measure.
The overall conclusion was that the proposed algorithm appeared to be able to
replicate the observed data reasonably well. Although some discrepancies were rather
large for some of the "variance across persons" and "within-person variance" subsets,
about two-thirds of the predictions for each case were within 30% of the observed value.
A detailed description of the evaluation using HAPEM is presented in Attachment 4.
Comparison of Cluster-Markov approach with other algorithms
As part of the application of APEX in support of US EPA's recent review of the
ozone NAAQS several sensitivity analyses were conducted (US EPA, 2007). One of
these was to make parallel simulations using each of the three algorithms for constructing
multi-day time-activity sequences that are incorporated into APEX.
Table 1 presents the results for the number of persons in Atlanta population
groups with moderate exertion exposed to 8-hour average concentrations exceeding 0.07
ppm. The results show that the predictions made with alternative algorithm Cluster-
Markov algorithm are substantially different from those made with simple re-sampling or
with the Diversity-Autocorrelation algorithm ("base case"). Note that for the cluster
algorithm approximately 30% of the individuals with 1 or more exposure have 3 or more
exposures. The corresponding values for the other algorithms range from about 13% to
21%.
Table 2 presents the results for the mean and standard deviation of number of
days/person with 8-hour average exposures exceeding 0.07 ppm with moderate or greater
exertion. The results show that although the mean for the Cluster-Markov algorithm is
similar to the other approaches, the standard deviation is substantially higher, i.e., the
Cluster-Markov algorithm results in substantially higher inter-individual variability.
B-101
-------
Table 1. Sensitivity to longitudinal diary algorithm: 2002 simulated counts of Atlanta
general population and children (ages 5-18) with any or three or more 8-hour ozone
exposures above 0.07 ppm concomitant with moderate or greater exertion (after US EPA
2007).
Population
Group
General
Population
Children (5-18)
One or more exposures
Simple
re-sampling
979,533
411,429
Diversity-
Autocorrelation
939,663
(-4%)
389,372
(-5%)
Cluster-
Markov
668,004
(-32%)
295,004
(-28%)
Three or more exposures
Simple
re-sampling
124,687
71,174
Diversity-
Autocorrelation
144,470
(+16%)
83,377
(+17%)
Cluster-
Markov
188,509
(+51%)
94,216
(+32%)
Table 2. Sensitivity to longitudinal diary algorithm: 2002 days per person with 8-hour
ozone exposures above 0.07 ppm concomitant with moderate or greater exertion for
Atlanta general population and children (ages 5-18) (after US EPA 2007).
Population
Group
General
Population
Children (5-1 8)
Mean Days/Person
Simple
re-sampling
0.332
0.746
Base case
0.335
(+1%)
0.755
(+1%)
Cluster-
Markov
0.342
(+3%)
0.758
(+2%)
Standard Deviation
Simple re-
sampling
0.757
1.077
Base case
0.802
(+6%)
1.171
(+9%)
Cluster-
Markov
1.197
(+58%)
1.652
(+53%)
References
Geyh AS, Xue J, Ozkaynak H, Spengler JD. (2000). The Harvard Southern California
chronic ozone exposure study: Assessing ozone exposure of grade-school-age
children in two Southern California communities. Environ Health Persp. 108:265-
270.
Rosenbaum AS and Cohen JP. (2004). Evaluation of a multi-day activity pattern
algorithm for creating longitudinal activity patterns. Memorandum prepared for Ted
Palma, US EPA OAQPS, by ICF International.
US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. EPA-
452/R-07-010. Available at:
http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007-01_o3_exposure_tsd.pdf
Xue J, Liu SV, Ozkaynak H, Spengler J. (2005). Parameter evaluation and model
validation of ozone exposure assessment using Harvard Southern California Chronic
Ozone Exposure Study Data. J. Air & Waste Manage Assoc. 55:1508-1515.
B-102
-------
ATTACHMENT 4: TECHNICAL MEMORANDUM ON
THE EVALUATION CLUSTER-MARKOV ALGORITHM
B-103
-------
INTERNATIONAL
TECHNICAL MEMORANDUM
TO: Ted Palma, US EPA
FROM: Arlene Rosenbaum and Jonathan Cohen, ICF Consulting
DATE: November 4, 2004
SUBJECT: Evaluation of a multi-day activity pattern algorithm for creating longitudinal
activity patterns.
BACKGROUND
In previous work ICF reviewed the HAPEM4 modeling approach for developing
annual average activity patterns from the CHAD database and recommended an approach to
improve the model's pattern selection process to better represent the variability among
individuals. This section summarizes the recommended approach. (For details see
Attachment 3)
Using cluster analysis, first the CHAD daily activity patterns are grouped into either
two or three categories of similar patterns for each of the 30 combinations of day type
(summer weekday, non-summer weekday, and weekend) and demographic group (males or
females; age groups: 0-4, 5-11, 12-17, 18-64, 65+). Next, for each combination of day type
and demographic group, category-to-category transition probabilities are defined by the
relative frequencies of each second-day category associated with each given first-day
category, where the same individual was observed for two consecutive days. (Consecutive
day activity pattern records for a single individual constitute a small subset of the CHAD
data.)
To implement the proposed algorithm, for each day type and demographic group, one
daily activity pattern per category is randomly selected from the corresponding CHAD data
to represent that category. That is, if there are 3 cluster categories for each of 3 day types, 9
unique activity patterns are selected to be averaged together to create an annual average
activity pattern to represent an individual in a given demographic group and census tract.
The weighting for each of the 9 activity patterns used in the averaging process is
determined by the product of two factors. The first is the relative frequency of its day type,
i.e., 0.18 for summer weekdays, 0.54 for non-summer weekdays, and 0.28 for weekends.
The second factor in the weighting for the selected activity pattern is determined by
simulating a sequence of category-types as a one-stage Markov chain process using the
transition probabilities. The category for the first day is selected according to the relative
frequencies of each category. The category for the second day is selected according to the
category-to-category transition probabilities for the category selected for the first day. The
category for the third day is selected according to the transition probabilities for the category
B-104
-------
selected for the second day. This is repeated for all days in the day type (65 for summer
weekdays, 195 for non-summer weekdays, 104 for weekends), producing a sequence of daily
categories. The relative frequency of the category-type in the sequence associated with the
selected activity pattern is the second factor in the weighting.
PROPOSED ALGORITHM STEPS
The proposed algorithm is summarized in Figure 1. Each step is explained in this
section.
Data Preparation
Step 1: Each daily activity pattern in the CHAD data base is summarized by the
total minutes in each of five micro-environments: indoors - residence; indoors - other
building; outdoors - near road; outdoors - away from road; in vehicle. These five
numbers are assumed to represent the most important features of the activity pattern
for their exposure impact.
Step 2: All CHAD activity patterns for a given day-type and demographic
group are subjected to cluster analysis, resulting in 2 or 3 cluster categories. Each
daily activity pattern is tagged with a cluster category.
Step 3: For each day-type and demographic group, the relative frequency of each
day-type in the CHAD data base is determined.
Step 4: All CHAD activity patterns for a given day-type and demographic group
that are consecutive days for a single individual, are analyzed to determine the
category-to-category transition frequencies in the CHAD data base. These transition
frequencies are used to calculate category-to-category transition probabilities.
For example, if there are 2 categories, A and B, then
PAA = the probability that a type A pattern is followed by a type A pattern,
PAB = the probability that a type A pattern is followed by a type B pattern (PAB =
I-PAA),
PBB = the probability that a type B pattern is followed by a type B pattern, and
PBA = the probability that a type B pattern is followed by a type A pattern (PBA =
I-PBB).
Activity Pattern Selection
For each day-type and demographic group in each census tract:
Step 5: One activity pattern is randomly selected from each cluster category group
(i.e., 2 to 3 activity patterns)
Creating Weights for Day-type Averaging
For each day-type and demographic group in each census tract:
B-105
-------
Step 6: A cluster category is selected for the first day of the day-type sequence,
according to the relative frequency of the cluster category days in the CHAD data set.
Step 7: A cluster category is selected for each subsequent day in the day-type
sequence day by day using the category-to-category transition probabilities.
Step 8: The relative frequency of each cluster category in the day-type sequence is
determined.
Step 9: The activity patterns selected for each cluster category (Step 5) are
averaged together using the cluster category frequencies (Step 8) as weights, to create
a day-type average activity pattern.
Creating Annual Average Activity Patterns
For each demographic group in each census tract:
Step 10: The day-type average activity patterns are averaged together using the
relative frequency of day-types as weights, to create an annual average activity
pattern.
Creating Replicates
For each demographic group in each census tract:
Step 11: Steps 5 through 10 are repeated 29 times to create 30 annual average
activity patterns.
EVALUATING THE ALGORITHM
The purpose of this study is to evaluate how well the proposed one-stage Markov
chain algorithm can reproduce observed multi-day activity patterns with respect to
demographic group means and inter-individual variability, while using one-day selection.
In order to accomplish this we propose to apply the algorithm to observed multi-day
activity patterns provided by the WAM, and compare the means and variances of the
predicted multi-day patterns with the observed patterns.
Current APEX Algorithm
Because the algorithm is being considered for incorporation into APEX, we would
like the evaluation to be consistent with the approach taken in APEX for selection of activity
patterns for creating multi-day sequences. The APEX approach for creating multi-day
activity sequences is as follows.
Stepl: A profile for a simulated individual is generated by selection of gender,
age group, and home sector from a given set of distributions consistent with the
population of the study area.
B-106
-------
Step 2: A specific age within the age group is selected from a uniform distribution.
Step 3: The employment status is simulated as a function of the age.
Step 4: For each simulated day, the user defines an initial pool of possible diary
days based on a user-specified function of the day type (e.g., weekday/weekend) and
temperature.
Step 5: The pool is further restricted to match the target gender and employment
status exactly and the age within 2A years for some parameter A. The diary days
within the pool are assigned a weight of 1 if the age is within A years of the target age
and a weight of w (user-defined parameter) if the age difference is between A and 2A
years. For each simulated day, the probability of selecting a given diary day is equal
to the age weight divided by the total of the age weights for all diary days in the pool
for that day.
Approach to Incorporation of Day-to-Day Dependence into APEX Algorithm
If we were going to incorporate day-to-day dependence of activity patterns into the
APEX model, we would propose preparing the data with cluster analysis and transition
probabilities as described in Steps 1-4 for the proposed HAPEM 5 algorithm, with the
following modifications.
For Step 2 the activity patterns would be divided into groups based on day-type
(weekday, weekend), temperature, gender, employment status, and age, with
cluster analysis applied to each group. However, because the day-to-day
transitions in the APEX activity selection algorithm can cross temperature bins,
we would propose to use broad temperature bins for the clustering and transition
probability calculations so that the cluster definitions would be fairly uniform
across temperature bins. Thus we would probably define the bins according to
season (e.g., summer, non-summer).
In contrast to HAPEM, the sequence of activity patterns may be important in
APEX. Therefore, for Step 4 transition probabilities would be specified for
transitions between days with the same day-type and season, as in HAPEM, and
also between days with different day-types and/or seasons. For example,
transition probabilities would be specified for transitions between summer
weekdays of each category and summer weekends of each category.
Another issue for dividing the CHAD activity records for the purposes of clustering
and calculating transition probabilities is that the diary pools specified for the APEX activity
selection algorithm use varying and overlapping age ranges. One way to address this
problem would be to simply not include consideration of age in the clustering process, under
the assumption that cluster categories are similar across age groups, even if the frequency of
each cluster category varies by age group. This assumption could be tested by examination
of the cluster categories stratified by age group that were developed for HAPEM5. If the
assumption is found to be valid, then the cluster categories could be pre-determined for input
B-107
-------
to APEX, while the transition probabilities could be calculated within APEX during the
simulation for each age range specified for dairy pools.
If the assumption is found to be invalid, then an alternative approach could be
implemented that would create overlapping age groups for purposes of clustering as follows.
APEX age group ranges and age window percentages would be constrained to some
maximum values. Then a set of overlapping age ranges that would be at least as large as the
largest possible dairy pool age ranges would be defined for the purposes of cluster analysis
and transition probability calculation. The resulting sets of cluster categories and transition
probabilities would be pre-determined for input into APEX and the appropriate set used by
APEX for each diary pool used during the simulation.
The actual activity pattern sequence selection would be implemented as follows. The
activity pattern for first day in the year would be selected exactly as is currently done in
APEX, as described above. For the selecting the second day's activity pattern, each age
weight would be multiplied by the transition probability PAB where A is the cluster for the
first day's activity pattern and B is the cluster for a given activity pattern in the available pool
of diary days for day 2. (Note that day 2 may be a different day-type and/or season than day
1). The probability of selecting a given diary day on day 2 is equal to the age weight times
PAB divided by the total of the products of age weight and PAB for all diary days in the pool
for day 2. Similarly, for the transitions from day 2 to day 3, day 3 to day 4, etc.
Testing the Approach with the Multi-day Data set
We tested this approach using the available multi-day data set. For purposes of
clustering we characterized the activity pattern records according to time spent in each of 5
microenvironments: indoors-home, indoors-school, indoors-other, outdoors (aggregate of the
3 outdoor microenvironments), and in-transit.
For purposes of defining diary pools and for clustering and calculating transition
probabilities we divided the activity pattern records by day type (i.e., weekday, weekend),
season (i.e., summer or ozone season, non-summer or non-ozone season), age (6-10 and 11-
12), and gender. Since all the subjects are 6-12 years of age and all are presumably
unemployed, we need not account for differences in employment status. For each day type,
season, age, and gender, we found that the activity patterns appeared to group in three
clusters.
In this case, we simulated week-long sequences (Wednesday through Tuesday) for
each of 100 people in each age/gender group for each season, using the transition
probabilities. To evaluate the algorithm we calculated the following statistics for the
predicted multi-day activity patterns for comparison with the actual multi-day diary data.
For each age/gender group for each season, the average time in each
microenvironment
For each age/gender group, season, and microenvironment, the average of the
within-person variance across all simulated persons (We defined the within-
B-108
-------
person variance as the variance of the total time per day spent in the
microenvironment across the week.)
For each age/gender group, season, and microenvironment, the variance across
persons of the mean time spent in that microenvironment
In each case we compared the predicted statistic for the stratum to the statistic for the
corresponding stratum in the actual diary data. 5
We also calculated the mean normalized bias for the statistic, which is a common
performance measure used in dispersion model performance and which is calculated as
follows.
* rr, T , o 100^ (predicted - observed) n /
NBIAS = > %
N i observed
RESULTS
Comparisons of simulated and observed data for time in each of the 5
microenvironments are presented in Tables 1-3 and Figures 2-5.
Average Time in Microenvironment
Table 1 and Figure 2 show the comparisons for the average time spent in each of the
5 microenvironments for each age/gender group and season. Figure 3 shows the comparison
for all the microenvironments except indoor, home in order to highlight the lower values.
Table 1 and the figures show that the predicted time-in-microenvironment averages
match well with the observed values. For combinations of
microenvironment/age/gender/season the normalized bias ranges from -35% to +41%. Sixty
percent of the predicted averages have bias between -9% and +9%, and the mean bias across
any microenvironment ranges from -9% to +4%. Fourteen predictions have positive bias and
23 have negative bias. A Wilcoxon signed rank test that the median bias across the 40
combinations = 0 % was not significant (p-value = 0.40) supporting the conclusion of no
overall bias.
Variance Across Persons
Table 2 and Figure 4 show the comparisons for the variance across persons for the
average time spent in each microenvironment. In this case the bias ranges from -40% to
+120% for any microenvironment/age/gender/season. Sixty-five percent of the predicted
variances have bias between -22% and +24%. The mean normalized bias across any
microenvironment ranges from -10% to +28%. Eighteen predictions have positive bias and
20 have negative bias. Figure 4 suggests a reasonably good match of predicted to observed
5 For the diary data, because the number of days per person varies, the average of the within-person variances
was calculated as a weighted average, where the weight is the degrees of freedom, i.e., one less than the number
of days simulated. Similarly, the variance across persons of the mean time was appropriately adjusted for the
different degrees of freedom using analysis of variance.
B-109
-------
variance in spite of 2 or 3 outliers. A Wilcoxon signed rank test that the median bias across
the 40 combinations = 0 % was not significant (p-value = 0.93) supporting the conclusion of
no overall bias.
Within-Person Variance for Persons
Table 3 and Figure 5 show the comparisons for the within-person variance for time
spent in each microenvironment. In this case the bias ranges from -47% to +150% for any
microenvironment/age/gender/season. Seventy percent of the predicted variances have bias
between -25% and +30%. The mean normalized bias across any microenvironment ranges
from -11% to +47%. Twenty-eight predictions have positive bias and 12 have negative bias,
suggesting some tendency for overprediction of this variance measure. And indeed a
Wilcoxon signed rank test that the median bias across the 40 combinations = 0 % was very
significant (p-value = 0.01) showing that the within-person variance was significantly
overpredicted. Still, Figure 4 suggests a reasonably good match of predicted to observed
variance in most cases, with a few overpredicting outliers at the higher end of the
distribution. So although the positive bias is significant in a statistical sense (i.e., the variance
is more likely to be overpredicted than underpredicted), it is not clear whether the bias is
large enough to be important.
CONCLUSIONS
The proposed algorithm appears to be able to replicate the observed data reasonably
well, although the within-person variance is somewhat overpredicted.
It would be informative to compare this algorithm with the earlier alternative
approaches in order to gain perspective on the degree of improvement, if any, afforded by
this approach.
Two earlier approaches were:
1. Select a single activity pattern for each day-type/season combination from the
appropriate set, and use that pattern for every day in the multi-day sequence that
corresponds to that day-type and season.
2. Re-select an activity pattern for each day in the multi-day sequence from the
appropriate set for the corresponding day-type and season.
Goodness-of-fit statistics could be developed to compare the three approaches and find
which model best fits the data for a given stratum.
B-110
-------
Table 1. Average time spent in each microenvironment: comparison of predicted and observed.
Microenvironment
Indoor, home
Indoor, school
Indoor, other
Demographic
Group
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
Season
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Observed
(hours/day)
15.5
15.8
15.7
15.8
16.2
16.5
16.0
16.2
0.7
2.3
0.8
2.2
0.7
2.1
0.6
2.4
2.9
2.4
2.2
1.9
2.2
Predicted
(hours/day)
16.5
15.5
15.2
16.4
15.3
16.5
15.6
16.1
0.7
2.5
0.5
2.2
0.7
2.4
0.9
2.7
2.4
2.7
2.7
1.8
1.6
Normalized
Bias
6%
-2%
-3%
4%
-5%
0%
-3%
-1%
-1%
-9%
7%
-34%
0%
6%
13%
38%
11%
4%
-14%
13%
21%
-3%
-25%
B-lll
-------
Outdoors
In-vehicle
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
2.2
2.3
1.9
3.7
2.5
4.1
3.1
3.7
2.3
3.9
2.6
1.1
1.0
1.1
1.0
1.2
0.9
1.1
0.9
2.1
2.2
2.0
3.5
2.5
4.3
2.7
5.2
2.1
4.3
2.4
0.9
0.9
1.3
0.9
1.1
0.8
1.0
0.8
-2%
-5%
4%
-2%
-6%
0%
4%
-12%
41%
-5%
9%
-7%
3%
-20%
-13%
13%
-16%
-12%
-15%
-5%
-7%
-9%
B-112
-------
Table 2. Variance across persons for time spent in each microenvironment: comparison of
predicted and observed.
Microenvironment
Indoor, home
Indoor, school
Indoor, other
Demographic
Group
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
Season
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Observed
(hours/day)2
70
67
54
35
56
42
57
39
6.0
9.5
5.6
5.3
4.9
5.4
5.6
9.2
46
44
34
23
21
Predicted
(hours/day)2
42
60
49
30
47
38
63
42
5.2
5.9
3.8
8.2
5.5
5.3
6.0
11
32
46.
33
16
18
Normalized
Bias
-40%
-9%
-9%
-12%
-17%
-10%
12%
8%
-10%
-13%
-38%
-32%
53%
11%
-1%
6%
23%
1%
-30%
6%
-4%
-27%
-15%
B-113
-------
Outdoors
In-vehicle
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
28
33
30
17
9.3
17
8.3
22
9.0
13
10
1.9
1.8
2.5
1.5
3.5
2.8
3.2
1.3
22
31
30
23
6.8
18
7.6
22
9.1
29
11
2.3
1.6
4.7
1.6
4.7
2.0
5.4
1.7
-22%
-6%
0%
-12%
37%
-27%
3%
-8%
0%
1%
120%
8%
17%
24%
-11%
93%
9%
34%
-28%
69%
35%
28%
B-114
-------
Table 3. Average within person variance for time spent in each microenvironment: comparison
of predicted and observed.
Microenvironment
Indoor, home
Indoor, school
Indoor, other
Demographic
Group
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
Season
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Observed
(hours/day)2
20
18
17
15
22
22
21
17
2.3
7.3
2.0
6.7
1.7
7.4
1.4
7.3
14
14
12
10
10
Predicted
(hours/day)2
29
23
30
24
42
25
24
24
2.4
6.4
1.5
5.8
2.1
7.6
2.9
7.8
14
18
17
13
10
Normalized
Bias
49%
25%
75%
64%
93%
13%
16%
38%
47%
5%
-12%
-25%
-14%
29%
3%
101%
6%
12%
-4%
30%
42%
26%
1%
B-115
-------
Outdoors
In-vehicle
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 8-10
Girls, 11-
12
Boys, 11-
12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-
12
Boys, 11-
12
MEAN
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
Summer
Not
Summer
14
11
12
8.4
3.4
6.7
3.4
10
4.0
9.2
4.3
1.0
0.90
1.1
0.81
1.3
1.3
2.4
0.85
15
14
13
9.5
3.2
9.5
4.4
25
4.5
7.4
3.7
0.90
0.48
1.4
0.71
1.3
1.1
1.6
0.85
7%
26%
7%
17%
13%
-3%
42%
28%
150%
11%
-20%
-15%
26%
-13%
-47%
31%
-12%
4%
-16%
-34%
1%
-11%
B-116
-------
Consolidated Human Activity Database - CHAD (CHAD)
Cluster Analysis
ransition
nalysis
Transition
Probabilities
Average Winter
Weekday Pattern
Markov Selection
Weights
Individual Annual Average Activity Pattern
Figure 1. Flow diagram of proposed algorithm for creating annual average activity patterns for HAPEM5.
B-117
-------
on _,
re
2 15
2 15
o
= 10
IS
=5 5
Ģ
0.
0 ]
/^
^Sf
.^
-X'"^
^^^_
.s^
.^
*s^
.x^
^^
X*
Indoor, home
Indoor, school
indoor, other
Outdoor
x In chicle
0 5 10 15 20
Observed (hours/day)
Figure 2. Comparison of predicted and observed average time in each of 5 microenvironments
for age/gender groups and seasons.
I* 4
i s-
c o
oj ^
0
5 -|
i
n
^^
^^
^s^
T^^>
3>r
^s'
3
-------
80
70
Rn
o 'so
o ou
S 40
3 ^U
- ^0
w JU
on
10
0
C
/
-2
/
-r * .
/'.
/*
+Ģ
S"
f
girls, 6-10, summer
girls, 6-10, winter
boys , 6-10, summer
x boys, 6-10, winter
X girls, 11-12, summer
girls, 11-12, winter
+ boys, 11-12, summer
- boys, 11-12, winter
1 1 1 1
) 20 40 60 80
Observed
Figure 4. Comparison of predicted and observed variance across persons for time spent in each
of 5 microenvironments for age/gender groups and seasons.
30 |
OR J
TO -IR
D ID
E
(/) in
/
X S
\/
f
f.
f
girls, 6-10, summer
girls, 6-10, winter
boys , 6-10, summer
boys, 6-10, winter
X girls, 11-12, summer
girls, 11-12, winter
+ boys, 11-12, summer
-boys, 11-12, winter
0 10 20 30
Observed
Figure 5. Comparison of predicted and observed the average within-person variance for time
spent in each of 5 microenvironments by age/gender groups and seasons.
B-119
-------
ATTACHMENT 5: TECHNICAL MEMORANDUM ON
ANALYSIS OF AIR EXCHANGE RATE DATA
B-120
-------
IGF
INTERNATIONAL
DRAFT MEMORANDUM
To: John Langstaff
From: Jonathan Cohen, Hemant Mallya, Arlene Rosenbaum
Date: September 30, 2005
Re: EPA 68D01052, Work Assignment 3-08. Analysis of Air Exchange Rate Data
EPA is planning to use the APEX exposure model to estimate ozone exposure in 12 cities /
metropolitan areas: Atlanta, GA; Boston, MA; Chicago, IL; Cleveland, OH; Detroit, MI;
Houston, TX; Los Angeles, CA; New York, NY; Philadelphia, PA; Sacramento, CA; St. Louis,
MO-IL; Washington, DC. As part of this effort, ICF Consulting has developed distributions of
residential and non-residential air exchange rates (AER) for use as APEX inputs for the cities to
be modeled. This memorandum describes the analysis of the AER data and the proposed APEX
input distributions. Also included in this memorandum are proposed APEX inputs for
penetration and proximity factors for selected microenvironments.
Residential Air Exchange Rates
Studies. Residential air exchange rate (AER) data were obtained from the following seven
studies:
Avol: Avol et al, 1998. In this study, ozone concentrations and AERs were measured at
126 residences in the greater Los Angeles metropolitan area between February and
December, 1994. Measurements were taken in four communities: Lancaster, Lake
Gregory, Riverside, and San Dimas. Data included the daily average outdoor
temperature, the presence or absence of an air conditioner (either central or room), and
the presence or absence of a swamp (evaporative) cooler. Air exchange rates were
computed based on the total house volume and based on the total house volume corrected
for the furniture. These data analyses used the corrected AERs.
RTF Panel: Williams et al, 2003a, 2003b. In this study particulate matter concentrations
and daily average AERs were measured at 37 residences in central North Carolina during
2000 and 2001 (averaging about 23 AER measurements per residence). The residences
belong to two specific cohorts: a mostly Caucasian, non-smoking group aged at least 50
years having cardiac defibrillators living in Chapel Hill; a group of non-smoking, African
Americans aged at least 50 years with controlled hypertension living in a low-to-
moderate SES neighborhood in Raleigh. Data included the daily average outdoor
temperature, and the number of air conditioner units (either central or room). Every
residence had at least one air conditioner unit.
RIOPA: Meng et al, 2004, Weisel et al, 2004. The Relationship of Indoor, Outdoor, and
Personal Air (RIOPA) study was undertaken to estimate the impact of outdoor sources of
air toxics to indoor concentrations and personal exposures. Volatile organic compounds,
B-121
-------
carbonyls, fine particles and AERs were measured once or twice at 310 non-smoking
residences from summer 1999 to spring 2001. Measurements were made at residences in
Elizabeth, NJ, Houston TX, and Los Angeles CA. Residences in California were
randomly selected. Residences in New Jersey and Texas were preferentially selected to
be close (< 0.5 km) to sources of air toxics. The AER measurements (generally over 48
hours) used a PMCH tracer. Data included the daily average outdoor temperature, and the
presence or absence of central air conditioning, room air conditioning, or a swamp
(evaporative) cooler.
TEACH: Chillrud at al, 2004, Kinney et al, 2002, Sax et al, 2004. The Toxic Exposure
Assessment, a Columbia/Harvard (TEACH) study was designed to characterize levels of
and factors influencing exposures to air toxics among high school students living in
inner-city neighborhoods of New York City and Los Angeles, CA. Volatile organic
compounds, aldehydes, fine particles, selected trace elements, and AER were measured at
87 high school student's residences in New York City and Los Angeles in 1999 and
2000. Data included the presence or absence of an air conditioner (central or room) and
hourly outdoor temperatures (which were converted to daily averages for these analyses).
Wilson 1984: Wilson et al, 1986, 1996. In this 1984 study, AER and other data were
collected at about 600 southern California homes with three seven-day tests (in March
and July 1984, and January, 1985) for each home. We obtained the data directly from Mr.
Wilson. The available data consisted of the three seven-day averages, the month, the
residence zip code, the presence or absence of a central air conditioner, and the presence
or absence of a window air conditioner. We matched these data by month and zip code to
the corresponding monthly average temperatures obtained from EPA's SCRAM website
as well as from the archives in www.wunderground.com (personal and airport
meteorological stations). Residences more than 25 miles away from the nearest available
meteorological station were excluded from the analysis. For our analyses, the
city/location was defined by the meteorological station, since grouping the data by zip
code would not have produced sufficient data for most of the zip codes.
Wilson 1991: Wilson et al, 1996. Colome et al, 1993, 1994. In this 1991 study, AER and
other data were collected at about 300 California homes with one two-day test in the
winter for each home. We obtained the data directly from Mr. Wilson. The available data
consisted of the two-day averages, the date, city name, the residence zip code, the
presence or absence of a central air conditioner, the presence or absence of a swamp
(evaporative) cooler, and the presence or absence of a window air conditioner . We
matched these data by date, city, and zip code to the corresponding daily average
temperatures obtained from EPA's SCRAM website as well as from the archives in
www.wunderground.com (personal and airport meteorological stations). Residences
more than 25 miles away from the nearest available meteorological station were excluded
from the analysis. For our analyses, the city/location was defined by the meteorological
station, since grouping the data by zip code would not have produced sufficient data for
most of the zip codes.
Murray and Burmaster: Murray and Burmaster (1995). For this article, Murray and
Burmaster corrected and compiled nationwide residential AER data from several studies
conducted between 1982 and 1987. These data were originally compiled by the Lawrence
Berkeley National Laboratory. We acknowledge Mr. Murray's assistance in obtaining
B-122
-------
these data for us. The available data consisted of AER measurements, dates, cities, and
degree-days. Information on air conditioner presence or absence was not available.
Table A-l summarizes these studies.
For each of the studies, air conditioner usage, window status (open or closed), and fan status (on
or off) was not part of the experimental design, although some of these studies included
information on whether air conditioners or fans were used (and for how long) and whether
windows were closed during the AER measurements (and for how long).
As described above, in the following studies the homes were deliberately sampled from specific
subsets of the population at a given location rather than the entire population: The RTF Panel
study selected two specific cohorts of older subjects with specific diseases. The RIOPA study
was biased towards residences near air toxics sources. The TEACH study focused on inner-city
neighborhoods. Nevertheless, we included all these studies because we determined that any
potential bias would be likely to be small and we preferred to keep as much data as possible.
B-123
-------
Table A-l. Summary of Studies of Residential Air Exchange Rates
Locations
Years
Months/Seasons
Number of
Homes
Total AER
Measurements
Average
Number of
Measurements
per Home
Measurement
Duration
Measurement
Technique
Min AER Value
Max AER Value
Mean AER
Value
Min
Temperature
(C)
Avol
Lancaster, Lake
Gregory,
Riverside, San
Dimas. All in
Southern CA
1994
Feb; Mar; Apr;
May; Jun; Jul;
Aug; Sep; Oct;
Nov
86
161 8
1.87
Not Available
Not Available
0.01
2.702
0.80
-0.04 -
RTF Panel
Research Triangle
Park, NC
2000; 2001
2000 (Jun; Jul;
Aug; Sep; Oct;
Nov), 2001 (Jan;
Feb; Apr; May)
37
54
23.08
24 hour
Perflourocarbon
tracer. PMCH
0.02
1. 44
0.72
2.18
RIOPA
CA; NJ; TX
1999; 2000; 2001
1999 (July to
Dec); 2000 (all
months); 2001
(Jan and Feb)
284
524
1.85
24 to 96 hours
tracer
0.08
87.50
1.41
-6.82
TEACH
Los Angeles, CA;
New York City, NY
1999; 2000
1999 (Feb; Mar; Apr;
Jul; Aug); 2000 (Jan;
Feb; Mar; Sep; Oct)
85 5
151
1.782
Sample time (hours)
reported. Ranges
from about 1 to 7
days.
Perflourocarbon
tracer. Perflourocarbon
0.120
8.87
1.71 1
-1.36
Wilson 1984
Southern CA
1984, 1985
Mar 1984, Jul 1984, Jan
1985
81 288
1,362
34 1
7 days
tracer.
030
11.77
050
11.00
Wilson 1991
Southern CA
1984
Jan, Mar, Jul
316
10
7 days
Perflourocarbon tracer.
01
2.91
57
3.00
Murray
and
Burmaster
AZ, CA, CO,
CT, FL, ID,
MD, MN, MT,
NJ
1982-1987
Various
1,884
2,844
1.51
Not available
Not available
0.01
11.77
0.76
Not available
B-124
-------
Max
Temperature
(C)
Air Conditioner
Categories
Air Conditioner
Measurements
Fan Categories
Fan
Measurements
Window Open/
Closed Data
Comments
Avol
36.25
No A/C; Central
or Room A/C;
Swamp Cooler
only; Swamp +
[Central or Room]
A/C use in
minutes Not
Not available
Time on or off for
various fan types
during sampling
was recorded, but
not included in
database provided.
Duration open
between times
6am- 12 pm; 12pm
- 6 pm; and 6pm -
6am
RTF Panel
30.81
Central or Room
A/C (Y/N)
Available
Fan (Y/N)
Not Available
Windows (open /
closed along with
duration open in
inch-hours units
RIOPA
32.50
Window A/C
(Y/N); Evap
Coolers (Y/N)
Duration
measurements in
Hrs and Mins
Fan (Y/N)
Duration
measurements in
Hrs and Mins
Windows (Open /
Closed) along with
window open
duration
measurements
CA sample was a
random sample of
homes. NJ and TX
homes were
deliberately
chosen to be near
to ambient
sources.
TEACH
32.00 2
Central or Room A/C
(Y/N)
Not Available
Not Available
Not Available
Not Available
Restricted to inner-
city homes with high
school students.
Wilson 1984
8. 002
Central A/C (Y/N);
Room A/C (Y/N);
Not Available Not
Not Available
Not Available
Not Available Not
Contemporaneous
temperature data
obtained for these
analyses from SCRAM
and
www. wunderground. com
meteorological data.
Wilson 1991
5. 00
Central A/C (Y/N);
Room A/C (Y/N);
Swamp Cooler(Y/N)
Available Not
Not Available
Not Available
Available Not
Contemporaneous
temperature data
obtained for these
analyses from SCRAM
and
www.wunderground.com
meteorological data.
Murray
and
Burmaster
Not available
Not available
available
Not available
Not available
available
B-125
-------
We compiled the data from these seven studies to create the following variables, of which some
had missing values:
Study
Date
Time - Time of the day that the AER measurement was made
House_ID - Residence identifier
Measurement_ID - Uniquely identifies each AER measurement for a given study
AER - Air Exchange Rate (per hour)
AER_Duration - Length of AER measurement period
Have_AC - Indicates if the residence has any type of air conditioner (A/C), either a room
A/C or central A/C or swamp cooler or any of them in combination. "Y" = "Yes." "N" =
"No."
Type_of_ACl - Indicates the types of A/C or swamp cooler available in each house
measured. Possible values: "Central A/C" "Central and Room A/C" "Central or Room
A/C" "No A/C" "Swamp + (Central or Room)" "Swamp Cooler only" "Window A/C"
"Window and Evap"
Type_of_AC2 - Indicates if a house measured has either no A/C or some A/C. Possible
values are "No A/C" and "Central or Room A/C."
Have_Fan - Indicates if the house studied has any fans
Mean_Temp - Daily average outside temperature
Min_Temp - Minimum hourly outside temperature
Max_Temp - Maximum hourly outside temperature
State
City
Location - Two character abbreviation
Flag-Data status. Murray and Burmaster study: "Used" or "Not Used." Other studies:
"Used"; "Missing" (missing values for AER, Type_of_AC2, and/or Mean_Temp);
"Outlier".
The main data analysis was based on the first six studies. The Murray and Burmaster data were
excluded because of the absence of information on air conditioner presence. (However, a subset
of these data was used for a supplementary analysis described below.).
Based on our review of the AER data we excluded seven outlying high AER values - above 10
per hour. The main data analysis used all the remaining data that had non-missing values for
AER, Type_of_AC2, and Mean_Temp. We decided to base the A/C type variable on the broad
characterization "No A/C" versus "Central or Room A/C" since this variable could be calculated
from all of the studies (excluding Murray and Burmaster). Information on the presence or
absence of swamp coolers was not available from all the studies, and, also importantly, the
corresponding information on swamp cooler prevalence for the subsequent ozone modeling cities
was not available from the American Housing Survey. It is plausible that AER distributions
B-126
-------
depend upon the presence or absence of a swamp cooler. It is also plausible that AER
distributions also depend upon whether the residence specifically has a central A/C, room or
window A/C, or both. However we determined to use the broader A/C type definition, which in
effect assumes that the exact A/C type and the presence of a swamp cooler are approximately
proportionately represented in the surveyed residences.
Most of the studies had more than one AER measurement for the same house. It is reasonable to
assume that the AER varies with the house as well as other factors such as the temperature. (The
A/C type can be assumed to be the same for each measurement of the same house). We expected
the temperature to be an important factor since the AER will be affected by the use of the
available ventilation (air conditioners, windows, fans), which in turn will depend upon the
outside meteorology. Therefore it is not appropriate to average data for the same house under
different conditions, which might have been one way to account for dependence between
multiple measurements on the same house. To simplify the data analysis, we chose to ignore
possible dependence between measurements on the same house on different days and treat all the
AER values as if they were statistically independent.
Summary Statistics. We computed summary statistics for AER and its natural logarithm
LOG_AER on selected strata defined from the study, city, A/C type, and mean temperature.
Cities were defined as in the original databases, except that for Los Angeles we combined all the
data in the Los Angeles ozone modeling region, i.e. the counties of Los Angeles, Orange,
Ventura, Riverside, and San Bernardino. A/C type was defined from the Type_of_AC2 variable,
which we abbreviated as "NA" = "No A/C" and "AC" = "Central or Room A/C." The mean
temperature was grouped into the following temperature bins: -10 to 0 °C, 0 to 10 °C, 10 to 20
°C, 20 to 25 °C, 25 to 30 °C, 30 to 40 °C.(Values equal to the lower bounds are excluded from
each interval.) Also included were strata defined by study = "All" and/or city = "All," and/or
A/C type = "All" and/or temperature bin = "All." The following summary statistics for AER and
LOG_AER were computed:
Number of values
Arithmetic Mean
Arithmetic Standard Deviation
Arithmetic Variance
Deciles (Min, 10th, 20th ... 90th percentiles, Max)
These calculations exclude all seven outliers and results are not used for strata with 10 or fewer
values, since those summary statistics are extremely unreliable.
Examination of these summary tables clearly demonstrates that the AER distributions vary
greatly across cities and A/C types and temperatures, so that the selected AER distributions for
the modeled cities should also depend upon the city, A/C type and temperature. For example, the
mean AER for residences with A/C ranges from 0.39 for Los Angeles between 30 and 40 °C to
1.73 for New York between 20 and 25 °C. The mean AER for residences without A/C ranges
from 0.46 for San Francisco between 10 and 20 °C to 2.29 for New York between 20 and 25 °C.
The need to account for the city as well as the A/C type and temperature is illustrated by the
B-127
-------
result that for residences with A/C and between 20 and 25 °C, the mean AER ranges from 0.52
for Research Triangle Park to 1.73 for New York. Statistical comparisons are described below.
Statistical Comparisons. Various statistical comparisons were carried out between the different
strata, for the AER and its logarithm. The various strata are defined as in the Summary Statistics
section, excluding the "All" cases. For each analysis, we fixed one or two of the variables Study,
City, A/C type, temperature, and tested for statistically significant differences among other
variables. The comparisons are listed in Table A-2.
Table A-2. Summary of Comparisons of Means
Comparison
Analysis
Number.
1.
2.
O
4.
5. City
6.
Comparison
Variable(s)
"Groups
Compared"
City
Temp. Range
Type of A/C
City
Type of A/C
AND Temp.
Range
Stratification
Variable(s)
(not missing in
worksheet)
Type of A/C AND
Temp. Range
Study AND City
Study AND City
Type of A/C
Temp. Range
Study AND City
Total
Comparisons
128
12
15
2
6
17
Cases with significantly
different means (5 %
level)
AER
5
5
2
5
6
Log AER
8
5
5
2
6
6
For example, the first set of comparisons fix the Type of A/C and the temperature range; there
are twelve such combinations. For each of these twelve combinations, we compare the AER
distributions across different cities. This analysis determines whether the AER distribution is
appropriately defined by the A/C type and temperature range, without specifying the city.
Similarly, for the sixth set of comparisons, the study and city are held fixed (17 combinations)
and in each case we compare AER distributions across groups defined by the combination of the
A/C type and the temperature range.
The F Statistic comparisons compare the mean values between groups using a one way analysis
of variance (ANOVA). This test assumes that the AER or log(AER) values are normally
distributed with a mean that may vary with the comparison variable(s) and a constant variance.
We calculated the F Statistic and its P-value. P-values above 0.05 indicate cases where all the
group means are not statistically significantly different at the 5 percent level. Those results are
summarized in the last two columns of the above table "Summary of Comparisons of Means"
which gives the number of cases where the means are significantly different. Comparison
analyses 2, 3, and 6 show that for a given study and city, slightly less than half of the
comparisons show significant differences in the means across temperature ranges, A/C types, or
both. Comparison analyses 1, 4, and 5 show that for the majority of cases, means vary
significantly across cities, whether you first stratify by temperature range, A/C type, or both.
B-128
-------
The Kruskal-Wallis Statistic comparisons are non-parametric tests that are extensions of the
more familiar Wilcoxon tests to two or more groups. The analysis is valid if the AER minus the
group median has the same distribution for each group, and tests whether the group medians are
equal. (The test is also consistent under weaker assumptions against more general alternatives)
The P-values show similar patterns to the parametric F test comparisons of the means. Since the
logarithm is a strictly increasing function and the test is non-parametric, the Kruskal-Wallis tests
give identical results for AER and Log (AER).
The Mood Statistic comparisons are non-parametric tests that compare the scale statistics for two
or more groups. The scale statistic measures variation about the central value, which is a non-
parametric generalization of the standard deviation. Specifically, suppose there is a total of N
AER or log(AER) values, summing across all the groups. These N values are ranked from 1 to
N, and the j'th highest value is given a score of (j - (N+l)/2}2. The Mood statistic uses a one
way ANOVA statistic to compare the total scores for each group. Generally, the Mood statistics
show that in most cases the scale statistics are not statistically significantly different. Since the
logarithm is a strictly increasing function and the test is non-parametric, the Mood tests give
identical results for AER and Log (AER).
Fitting Distributions. Based on the summary statistics and the statistical comparisons, the need
to fit different AER distributions to each combination of A/C type, city, and temperature is
apparent. For each combination with a minimum of 11 AER values, we fitted and compared
exponential, log-normal, normal, and Weibull distributions to the AER values.
The first analysis used the same stratifications as in the above "Summary Statistics" and
"Statistical Comparisons" sections. Results are not reported for all strata because of the
minimum data requirement of 11 values. Results for each combination of A/C type, city, and
temperature (i.e., A, C, and T) were analyzed. Each combination has four rows, one for each
fitted distribution. For each distribution we report the fitted parameters (mean, standard
deviation, scale, shape) and the p-value for three standard goodness-of-fit tests: Kolmogorov-
Smirnov (K-S), Cramer-Von-Mises (C-M), Anderson-Darling (A-D). Each goodness-of-fit test
compares the empirical distribution of the AER values to the fitted distribution. The K-S and C-
M tests are different tests examining the overall fit, while the Anderson-Darling test gives more
weight to the fit in the tails of the distribution. For each combination, the best-fitting of the four
distributions has the highest p-value and is marked by an x in the final three columns. The mean
and standard deviation (Std_Dev) are the values for the fitted distribution. The scale and shape
parameters are defined by:
Exponential: density = a"1 exp(-x/a), where shape = mean = a
Log-normal: density = {axV(2:r)}"1 exp{ -(log x - Q2 / (2a2)}, where shape = a and
scale = C,. Thus the geometric mean and geometric standard deviation are given by
exp(Q and exp(a), respectively.
Normal: density = {aV(2:r)}"1 exp{ -(x - (j,)2 / (2a2)}, where mean = (j, and standard
deviation = a
Weibull: density = (c/o) (x/a)0"1 exp{-(x/a)c}, where shape = c and scale = a
B-129
-------
Generally, the log-normal distribution was the best-fitting of the four distributions, and so, for
consistency, we recommend using the fitted log-normal distributions for all the cases.
One limitation of the initial analysis was that distributions were available only for selected cities,
and yet the summary statistics and comparisons demonstrate that the AER distributions depend
upon the city as well as the temperature range and A/C type. As one option to address this issue,
we considered modeling cities for which distributions were not available by using the AER
distributions across all cities and dates for a given temperature range and A/C type.
Another important limitation of the initial analysis was that distributions were not fitted to all of
the temperature ranges due to inadequate data. There are missing values between temperature
ranges, and the temperature ranges are all bounded. To address this issue, the temperature ranges
were regrouped to cover the entire range of temperatures from minus to plus infinity, although
obviously the available data to fit these ranges have finite temperatures. Stratifying by A/C type,
city, and the new temperature ranges produces results for four cities: Houston (AC and NA); Los
Angeles (AC and NA); New York (AC and NA); Research Triangle Park (AC). For each of the
fitted distributions we created histograms to compare the fitted distributions with the empirical
distributions.
AER Distributions for The First Nine Cities. Based upon the results for the above four cities
and the corresponding graphs, we propose using those fitted distributions for the three cities
Houston, Los Angeles, and New York. For another 6 of the cities to be modeled, we propose
using the distribution for one of the four cities thought to have similar characteristics to the city
to be modeled with respect to factors that might influence AERs. These factors include the age
composition of housing stock, construction methods, and other meteorological variables not
explicitly treated in the analysis, such as humidity and wind speed patterns. The distributions
proposed for these cities are as follows:
Atlanta, GA, A/C: Use log-normal distributions for Research Triangle Park. Residences
with A/C only.
Boston, MA: Use log-normal distributions for New York
Chicago, IL: Use log-normal distributions for New York
Cleveland, OH: Use log-normal distributions for New York
Detroit, MI: Use log-normal distributions for New York
Houston, TX: Use log-normal distributions for Houston
Los Angeles, CA: Use log-normal distributions for Los Angeles
New York, NY: Use log-normal distributions for New York
Philadelphia, PA: Use log-normal distributions for New York
Since the AER data for Research Triangle Park was only available for residences with air
conditioning, AER distributions for Atlanta residences without air conditioning are discussed
below.
To avoid unusually extreme simulated AER values, we propose to set a minimum AER value of
0.01 and a maximum AER value of 10.
B-130
-------
Obviously, we would be prefer to model each city using data from the same city, but this
approach was chosen as a reasonable alternative, given the available AER data.
AER Distributions for Sacramento and St. Louis. For these two cities, a direct mapping to one
of the four cities Houston, Los Angeles, New York, and Research Triangle Park is not
recommended because the cities are likely to be too dissimilar. Instead, we decided to use the
distribution for the inland parts of Los Angeles to represent Sacramento and to use the aggregate
distributions for all cities outside of California to represent St. Louis. The results for the city
Sacramento were obtained by combining all the available AER data for Sacramento, Riverside,
and San Bernardino counties. The results for the city St. Louis were obtained by combining all
non-California AER data.
AER Distributions for Washington DC. Washington DC was judged likely to have similar
characteristics both to Research Triangle Park and to New York City. To choose between these
two cities, we compared the Murray and Burmaster AER data for Maryland with AER data from
each of those cities. The Murray and Burmaster study included AER data for Baltimore and for
Gaithersburg and Rockville, primarily collected in March. April, and May 1987, although there
is no information on mean daily temperatures or A/C type. We collected all the March, April,
and May AER data for Research Triangle Park and for New York City, and compared those
distributions with the Murray and Burmaster Maryland data for the same three months.
The results for the means and central values show significant differences at the 5 percent level
between the New York and Maryland distributions. Between Research Triangle Park and
Maryland, the central values and the mean AER values are not statistically significantly
different, and the differences in the mean log (AER) values are much less statistically significant
than between New York and Maryland. The scale statistic comparisons are not statistically
significantly different between New York and Maryland, but were statistically significantly
different between Research Triangle Park and Maryland. Since matching central and mean
values is generally more important than matching the scales, we propose to model Washington
DC residences with air conditioning using the Research Triangle Park distributions, stratified by
temperature:
Washington DC, A/C: Use log-normal distributions for Research Triangle Park.
Residences with A/C only.
Since the AER data for Research Triangle Park was only available for residences with air
conditioning, the estimated AER distributions for Washington DC residences without air
conditioning are discussed below.
AER Distributions for Washington DC and Atlanta GA Residences With No A/C. For
Atlanta and Washington DC we have proposed to use the AER distributions for Research
Triangle Park. However, all the Research Triangle Park data (from the RTF Panel study) were
from houses with air conditioning, so there are no available distributions for the "No A/C" cases.
For these two cities, one option is to use AER distributions fitted to all the study data for
residences without A/C, stratified by temperature. We propose applying the "No A/C"
B-131
-------
distributions for modeling these two cities for residences without A/C. However, since Atlanta
and Washington DC residences are expected to be better represented by residences outside of
California, we instead propose to use the "No A/C" AER distributions aggregated across cities
outside of California, which is the same as the recommended choice for the St. Louis "No A/C"
AER distributions.
A/C Type and Temperature Distributions. Since the proposed AER distribution is conditional
on the A/C type and temperature range, these values also need to be simulated using APEX in
order to select the appropriate AER distribution. Mean daily temperatures are one of the
available APEX inputs for each modeled city, so that the temperature range can be determined
for each modeled day according to the mean daily temperature. To simulate the A/C type, we
obtained estimates of A/C prevalence from the American Housing Survey. Thus for each
city/metropolitan area, we obtained the estimated fraction of residences with Central or Room
A/C (see Table A-3), which gives the probability p for selecting the A/C type "Central or Room
A/C." Obviously, 1-p is the probability for "No A/C." For comparison with Washington DC and
Atlanta, we have included the A/C type percentage for Charlotte, NC (representing Research
Triangle Park, NC). As discussed above, we propose modeling the 96-97 % of Washington DC
and Atlanta residences with A/C using the Research Triangle Park AER distributions, and
modeling the 3-4 % of Washington DC and Atlanta residences without A/C using the combined
study No A/C AER distributions.
Table A-3. Fraction of residences with central or room A/C (from American Housing
Survey)
CITY
Atlanta Atlanta,
Boston Boston,
Chicago Chicago,
Cleveland Cleveland,
Detroit Detroit,
Houston Houston,
Los Angeles
New York
Philadelphia Philadelphia,
Sacramento Sacram
St. Louis
Washington DC
Research Triangle Park
SURVEY AREA & YEAR
2003
2003
2003
2003
2003
2003
Los Angeles, 2003
New York, 2003
2003
ento, 2003
St. Louis, 2003
Washington DC, 2003
Charlotte, 2002
PERCENTAGE
97.01
85.23
87.09
74.64
81.41
98.70
55.05
81.57
90.61
94.63
95.53
96.47
96.56
Other AER Studies
We recently became aware of some additional residential and non-residential AER studies that
might provide additional information or data. Indoor / outdoor ozone and PAN distributions were
studied by Jakobi and Fabian (1997). Liu et al (1995) studied residential ozone and AER
distributions in Toronto, Canada. Weschler and Shields (2000) describes a modeling study of
B-132
-------
ventilation and air exchange rates. Weschler (2000) includes a useful overview of residential and
non-residential AER studies.
AER Distributions for Other Indoor Environments
To estimate AER distributions for non-residential, indoor environments (e.g., offices and
schools), we obtained and analyzed two AER data sets: "Turk" (Turk et al, 1989); and "Persily"
(Persily and Gorfain 2004; Persily et al. 2005).
The earlier "Turk" data set (Turk et al, 1989) includes 40 AER measurements from offices (25
values), schools (7 values), libraries (3 values), and multi-purpose (5 values), each measured
using an SF6 tracer over two- or four-hours in different seasons of the year.
The more recent "Persily" data (Persily and Gorfain 2004; Persily et al. 2005) were derived
from the U.S. EPA Building Assessment Survey and Evaluation (BASE) study, which was
conducted to assess indoor air quality, including ventilation, in a large number of randomly
selected office buildings throughout the U.S. The data base consists of a total of 390 AER
measurements in 96 large, mechanically ventilated offices; each office was measured up to four
times over two days, Wednesday and Thursday AM and PM. The office spaces were relatively
large, with at least 25 occupants, and preferably 50 to 60 occupants. AERs were measured both
by a volumetric method and by a CO2 ratio method, and included their uncertainty estimates. For
these analyses, we used the recommended "Best Estimates" defined by the values with the lower
estimated uncertainty; in the vast majority of cases the best estimate was from the volumetric
method.
Another study of non-residential AERs was performed by Lagus Applied Technology (1995)
using a tracer gas method. That study was a survey of AERs in 16 small office buildings, 6 large
office buildings, 13 retail establishments, and 14 schools. We plan to obtain and analyze these
data and compare those results with the Turk and Persily studies.
Due to the small sample size of the Turk data, the data were analyzed without stratification by
building type and/or season. For the Persily data, the AER values for each office space were
averaged, rather using the individual measurements, to account for the strong dependence of the
AER measurements for the same office space over a relatively short period.
Summary statistics of AER and log (AER) for the two studies are presented in Table A-4.
Table A-4. AER summary statistics for offices and other non-residential buildings
Study
Persily 2
Turk
Persily
Turk Lo
Variable
LER
AER
'Og(AER)
?(AER)
N
96
40
96
40
Mean
1.9616
1.5400
0.1038
0.2544
Std Dev
2.3252
0.8808
1.1036
0.6390
Min
0.0712
0.3000
-2.6417
-1.2040
25th %ile
0.5009
0.8500 1.
-0.6936
-0.1643
Median
1.0795
50002.050
0.0765
0.4055
75th %ile
2.7557
D 4. 1000
1.0121
0.7152
Max
13.8237
2.6264
1.4110
B-133
-------
The mean values are similar for the two studies, but the standard deviations are about twice as
high for the Persily data. The proposed AER distributions were derived from the more recent
Persily data only.
Similarly to the analyses of the residential AER distributions, we fitted exponential, log-normal,
normal, and Weibull distributions to the 96 office space average AER values. The results are
shown in Table A-5.
Table A-5. Best fitting office AER distributions from the Persily et al. (2004, 2005)
Scale
1.9616
0.1038
1.9197 (
Shape
.10362.C
).9579 1.9
Mean
1.9616
397
1.9616
568
Std_Dev
1.9616
3.1469
2.3252
2.0433
Distribution
Exponential
Lognormal
Normal
Weibull
P-Value
Kolmogorov-
Smirnov
0.13
0.15
0.01
P-Value
Cramer-
von
Mises
0.04
0.46
0.01
0.01
P-Value
Anderson-
Darling
0.05
0.47
0.01
0.01
(For an explanation of the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling P-
values see the discussion residential AER distributions above.) According to all three goodness-
of-fit measures the best-fitting distribution is the log-normal. Reasonable choices for the lower
and upper bounds are the observed minimum and maximum AER values.
We therefore propose the following indoor, non-residential AER distributions.
AER distribution for indoor, non-residential microenvironments: Lognormal, with scale
and shape parameters 0.1038 and 1.1036, i.e., geometric mean = 1.1094, geometric
standard deviation = 3.0150. Lower Bound = 0.07. Upper bound = 13.8.
Proximity and Penetration Factors For Outdoors, In-vehicle, and Mass Transit
For the APEX modeling of the outdoor, in-vehicle, and mass transit micro-environments, an
approach using proximity and penetration factors is proposed, as follows.
Outdoors Near Road
Penetration factor = 1.
For the Proximity factor, we propose using ratio distributions developed from the Cincinnati
Ozone Study (American Petroleum Institute, 1997, Appendix B; Johnson et al. 1995). The field
study was conducted in the greater Cincinnati metropolitan area in August and September, 1994.
Vehicle tests were conducted according to an experimental design specifying the vehicle type,
road type, vehicle speed, and ventilation mode. Vehicle types were defined by the three study
vehicles: a minivan, a full-size car, and a compact car. Road types were interstate highways
(interstate), principal urban arterial roads (urban), and local roads (local). Nominal vehicle
B-134
-------
speeds (typically met over one minute intervals within 5 mph) were at 35 mph, 45 mph, or 55
mph. Ventilation modes were as follows:
Vent Open: Air conditioner off. Ventilation fan at medium. Driver's window half open.
Other windows closed.
Normal A/C. Air conditioner at normal. All windows closed.
Max A/C: Air conditioner at maximum. All windows closed.
Ozone concentrations were measured inside the vehicle, outside the vehicle, and at six fixed site
monitors in the Cincinnati area.
The proximity factor can be estimated from the distributions of the ratios of the outside-vehicle
ozone concentrations to the fixed-site ozone concentrations, reported in Table 8 of Johnson et al.
(1995). Ratio distributions were computed by road type (local, urban, interstate, all) and by the
fixed-site monitor (each of the six sites, as well as the nearest monitor to the test location). For
this analysis we propose to use the ratios of outside-vehicle concentrations to the concentrations
at the nearest fixed site monitor, as shown in Table A-6.
Table A-6. Ratio of outside-vehicle ozone to ozone at nearest fixed site1
Road
Type1
Local
Urban
Interstate
All
Number
of cases1
191
299
241
731
Mean1
0.755
0.754
0.364
0.626
Standard
Deviation1
0.203
0.243
0.165
0.278
25th
Percentile1
0.645 0.7
0.5850.7
0.2320.3
0.4170.6
50th
Percentile1
420.911 0.4:
220.8960.3:
69 0.484 0.0<
230.8080.1'
75th
Percentile1
>2
55
>3
'0
Estimated
5th
Percentile2
1. From Table 8 of Johnson et al. (1995). Data excluded if fixed-site concentration < 40
ppb.
2. Estimated using a normal approximation as Mean - 1.64 x Standard Deviation
For the outdoors-near- road microenvironment, we recommend using the distribution for local
roads, since most of the outdoors-near-road ozone exposure will occur on local roads. The
summary data from the Cincinnati Ozone Study are too limited to allow fitting of distributions,
but the 25th and 75th percentiles appear to be approximately equidistant from the median (50th
percentile). Therefore we propose using a normal distribution with the observed mean and
standard deviation. A plausible upper bound for the proximity factor equals 1. Although the
normal distribution allows small positive values and can even produce impossible, negative
values (with a very low probability), the titration of ozone concentrations near a road is limited.
Therefore, as an empirical approach, we recommend a lower bound of the estimated 5th
percentile, as shown in the final column of the above table. Therefore in summary we propose:
Penetration factor for outdoors, near road: 1.
B-135
-------
Proximity factor for outdoors, near road: Normal distribution. Mean = 0.755. Standard
Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
Outdoors, Public Garage / Parking Lot
This micro-environment is similar to the outdoors-near-road microenvironment. We therefore
recommend the same distributions as for outdoors-near-road:
Penetration factor for outdoors, public garage / parking lot: 1.
Proximity factor for outdoors, public garage / parking lot: Normal distribution. Mean =
0.755. Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
Outdoors, Other
The outdoors, other ozone concentrations should be well represented by the ambient monitors.
Therefore we propose:
Penetration factor for outdoors, other: 1.
Proximity factor for outdoors, other: 1.
In-Vehicle
For the proximity factor for in-vehicle, we also recommend using the results of the Cincinnati
Ozone Study presented in Table A-6. For this microenvironment, the ratios depend upon the road
type, and the relative prevalences of the road types can be estimated by the proportions of
vehicle miles traveled in each city. The proximity factors are assumed, as before, to be normally
distributed, the upper bound to be 1, and the lower bound to be the estimated 5th percentile.
Proximity factor for in-vehicle, local roads: Normal distribution. Mean = 0.755. Standard
Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
Proximity factor for in-vehicle, urban roads: Normal distribution. Mean = 0.754.
Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.
Proximity factor for in-vehicle, interstates: Normal distribution. Mean = 0.364. Standard
Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.
To complete the specification, the distribution of road type needs to be estimated for each city to
be modeled. Vehicle miles traveled (VMT) in 2003 by city (defined by the Federal-Aid
urbanized area) and road type were obtained from the Federal Highway Administration.
(http://www.fhwa.dot.gov/policy/ohim/hs03/htm/hm71.htm). For local and interstate road types,
the VMT for the same DOT categories were used. For urban roads, the VMT for all other road
types was summed (Other freeways/expressways, Other principal arterial, Minor arterial,
Collector). The computed VMT ratios for each city are shown in Table A-7.
B-136
-------
Table A-7. Vehicle Miles Traveled by City and Road Type in 2003 (FHWA, October 2004)
FEDERAL-AID URBANIZED
AREA
Atlanta 0.3 8
Boston 0.31
Chicago 0.30
Cleveland 0.39
Detroit 0.26
Houston 0.24
Los Angeles
New York
Philadelphia 0.23
Sacramento 0.21
St. Louis
Washington 0.31
FRACTION VMT BY ROAD TYPE
INTERSTATE
0.29
0.18
0.36
URBAN
0.45
0.55
0.59
0.45
0.63
0.72
0.65
0.67
0.65
0.69
0.45
0.61
LOCAL
0.18
0.14
0.12
0.16
0.11
0.04
0.06
0.15
0.11
0.09
0.19
0.08
Note that a "Federal-Aid Urbanized Area" is an area with 50,000 or more persons that at a
minimum encompasses the land area delineated as the urbanized area by the Bureau of the
Census. Urbanized areas that have been combined with others for reporting purposes are not
shown separately. The Illinois portion of Round Lake Beach-McHenry-Grayslake has been
reported with Chicago.
Thus to simulate the proximity factor in APEX, we propose to first select the road type according
to the above probability table of road types, then select the AER distribution (normal) for that
road type as defined in the last set of bullets.
For the penetration factor for in-vehicle, we recommend using the inside-vehicle to outside-
vehicle ratios from the Cincinnati Ozone Study. The ratio distributions were summarized for all
the data and for stratifications by vehicle type, vehicle speed, road type, traffic (light, moderate,
or heavy), and ventilation. The overall results and results by ventilation type are shown in Table
A-8.
Table A-8. Ratio of inside-vehicle ozone to outside-vehicle ozone1
Ventilation1
Vent Open
Normal
A/C
Maximum
A/C
All
Number
of
cases1
226
332
254
812
Mean1
0.361
0.417
0.093
0.300
Standard
Deviation1
0.217
0.211
0.088
0.232
25th
Percentile1
0.1990.3
0.2360.4
0.0160.0
0.1170.2
50th
Percentile1
07 0.519 0.0(
080.5850.0'
71 0.149
51 0.463
75th
Percentile1
)5
'1
Estimated
5th
Percentile2
O.OOO3
O.OOO3
B-137
-------
1. From Table 7 of Johnson et al.(1995). Data excluded if outside-vehicle concentration <
20 ppb.
2. Estimated using a normal approximation as Mean - 1.64 x Standard Deviation
3. Negative estimate (impossible value) replaced by zero.
Although the data in Table A-8 indicate that the inside-to-outside ozone ratios strongly depend
upon the ventilation type, it would be very difficult to find suitable data to estimate the
ventilation type distributions for each modeled city. Furthermore, since the Cincinnati Ozone
Study was scripted, the ventilation conditions may not represent real-world vehicle ventilation
scenarios. Therefore, we propose to use the overall average distributions.
Penetration factor for in-vehicle: Normal distribution. Mean = 0.300. Standard Deviation
= 0.232. Lower Bound = 0.000. Upper Bound = 1.
Mass Transit
The mass transit microenvironment is expected to be similar to the in-vehicle microenvironment.
Therefore we recommend using the same APEX modeling approach:
Proximity factor for mass transit, local roads: Normal distribution. Mean = 0.755.
Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
Proximity factor for mass transit, urban roads: Normal distribution. Mean = 0.754.
Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.
Proximity factor for mass transit, interstates: Normal distribution. Mean = 0.364.
Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.
Road type distributions for mass transit: See Table A-6
Penetration factor for mass transit: Normal distribution. Mean = 0.300. Standard
Deviation = 0.232. Lower Bound = 0.000. Upper Bound = 1.
References
American Petroleum Institute (1997). Sensitivity testing ofpNEM/O3 exposure to changes in the
model algorithms. Health and Environmental Sciences Department.
Avol, E. L., W. C. Navidi, and S. D. Colome (1998) Modeling ozone levels in and around
southern California homes. Environ. Sci. Technol. 32, 463-468.
Chilrud, S. N., D. Epstein, J. M. Ross, S. N. Sax, D. Pederson, J. D. Spengler, P. L. Kinney
(2004). Elevated airborne exposures of teenagers to manganese, chromium, and iron from steel
dust and New York City's subway system. Environ. Sci. Technol. 38, 732-737.
Colome, S.D., A. L. Wilson, Y. Tian (1993). California Residential Indoor Air Quality Study,
Volume 1, Methodology and Descriptive Statistics. Report prepared for the Gas Research
Institute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Southern California Gas Co.
B-138
-------
Colome, S.D., A. L. Wilson, Y. Tian (1994). California Residential Indoor Air Quality Study,
Volume 2, Carbon Monoxide and Air Exchange Rate: An Univariate andMultivariate Analysis.
Chicago, IL. Report prepared for the Gas Research Institute, Pacific Gas & Electric Co., San
Diego Gas & Electric Co., Southern California Gas Co. GRI-93/0224.3
Jakobi, G and Fabian, P. (1997). Indoor/outdoor concentrations of ozone and peroxyacetyl nitrate
(PAN). Int. J. Biometeorol. 40: 162-165..
Johnson, T., A. Pakrasi, A. Wisbeth, G. Meiners, W. M. Ollison (1995). Ozone exposures within
motor vehicles - results of a field study in Cincinnati, Ohio. Proceedings 88th annual meeting
and exposition of the Air & Waste Management Association, June 18-23, 1995. San Antonio,
TX. Preprint paper 95-WA84A.02.
Kinney, P. L., S. N. Chillrud, S. Ramstrom, J. Ross, J. D. Spengler (2002). Exposures to multiple
air toxics in New York City. Environ Health Perspect 110, 539-546.
Lagus Applied Technology, Inc. (1995) Air change rates in non-residential buildings in
California. Sacramento CA, California Energy Commission, contract 400-91-034.
Liu, L.-J. S, P. Koutrakis, J. Leech, I. Broder, (1995) Assessment of ozone exposures in the
greater metropolitan Toronto area. J. Air Waste Manage. Assoc. 45: 223-234.
Meng, Q. Y., B. J. Turpin, L. Korn, C. P. Weisel, M. Morandi, S. Colome, J. J. Zhang, T. Stock,
D. Spektor, A. Winer, L. Zhang, J. H. Lee, R. Giovanetti, W. Cui, J. Kwon, S. Alimokhtari, D.
Shendell, J. Jones, C. Farrar, S. Maberti (2004). Influence of ambient (outdoor) sources on
residential indoor and personal PM2 5 concentrations: Analyses of RIOPA data. Journal of
Exposure Analysis and Environ Epidemiology. Preprint.
Murray, D. M. and D. E. Burmaster (1995). Residential Air Exchange Rates in the United
States: Empirical and Estimated Parametric Distributions by Season and Climatic Region. Risk
Analysis, Vol. 15, No. 4, 459-465.
Persily, A. and J. Gorfain.(2004). Analysis of ventilation data from the U.S. Environmental
Protection Agency Building Assessment Survey and Evaluation (BASE) Study. National Institute
of Standards and Technology, NISTIR 7145, December 2004.
Persily, A., J. Gorfain, G. Brunner.(2005). Ventilation design and performance in U.S. office
buildings. ASHRAE Journal. April 2005, 30-35.
Sax, S. N., D. H. Bennett, S. N. Chillrud, P. L. Kinney, J. D. Spengler (2004) Differences in
source emission rates of volatile organic compounds in inner-city residences of New York City
and Los Angeles. Journal of Exposure Analysis and Environ Epidemiology. Preprint.
Turk, B. H., D. T. Grimsrud, J. T. Brown, K. L. Geisling-Sobotka, J. Harrison, R. J. Prill (1989).
Commercial building ventilation rates and particle concentrations. ASHRAE, No. 3248.
B-139
-------
Weschler, C. J. (2000) Ozone in indoor environments: concentration and chemistry. Indoor Air
10: 269-288.
Weschler, C. J. and Shields, H. C. (2000) The influence of ventilation on reactions among indoor
pollutants: modeling and experimental observations. Indoor Air. 10: 92-100.
Weisel, C. P., J. J. Zhang, B. J. Turpin, M. T. Morandi, S. Colome, T. H. Stock, D. M. Spektor,
L. Korn, A. Winer, S. Alimokhtari, J. Kwon, K. Mohan, R. Harrington, R. Giovanetti, W. Cui,
M. Afshar, S. Maberti, D. Shendell (2004). Relationship of Indoor, Outdoor and Personal Air
(RIOPA) study; study design, methods and quality assurance / control results. Journal of
Exposure Analysis and Environ Epidemiology. Preprint.
Williams, R., J. Suggs, A. Rea, K. Leovic, A. Vette, C. Croghan, L. Sheldon, C. Rodes, J.
Thornburg, A. Ejire, M. Herbst, W. Sanders Jr. (2003a). The Research Triangle Park particulate
matter panel study: PM mass concentration relationships. Atmos Env 37, 5349-5363.
Williams, R., J. Suggs, A. Rea, L. Sheldon, C. Rodes, J. Thornburg (2003b). The Research
Triangle Park particulate patter panel study: modeling ambient source contribution to personal
and residential PM mass concentrations. Atmos Env 37, 5365-5378.
Wilson, A. L., S. D. Colome, P. E. Baker, E. W. Becker (1986). Residential Indoor Air Quality
Characterization Study of Nitrogen Dioxide, Phase I, Final Report. Prepared for Southern
California Gas Company, Los Angeles.
Wilson, A. L., S. D. Colome, Y. Tian, P. E. Baker, E. W. Becker, D. W. Behrens, I. H. Billick,
C. A. Garrison (1996). California residential air exchange rates and residence volumes. Journal
of Exposure Analysis and Environ Epidemiology. Vol. 6, No. 3.
B-140
-------
ATTACHMENT 6. TECHNICAL MEMORANDUM ON THE
UNCERTAINTY ANALYSIS OF RESIDENTIAL AIR
EXCHANGE RATE DISTRIBUTIONS
B-141
-------
INTERNATIONAL
MEMORANDUM
To: John Langstaff, EPA OAQPS
From: Jonathan Cohen, Arlene Rosenbaum, ICF International
Date: June 5, 2006
Re: Uncertainty analysis of residential air exchange rate distributions
This memorandum describes our assessment of some of the sources of the uncertainty of city-
specific distributions of residential air exchange rates that were fitted to the available study data.
City-specific distributions for use with the APEX ozone model were developed for 12 modeling
cities, as detailed in the memorandum by Cohen, Mallya and Rosenbaum, 20056 (Appendix A of
this report). In the first part of the memorandum, we analyze the between-city uncertainty by
examining the variation of the geometric means and standard deviations across cities and studies.
In the second part of the memorandum, we assess the within-city uncertainty by using a
bootstrap distribution to estimate the effects of sampling variation on the fitted geometric means
and standard deviations for each city. The bootstrap distributions assess the uncertainty due to
random sampling variation but do not address uncertainties due to the lack of representativeness
of the available study data, the matching of the study locations to the modeled cities, and the
variation in the lengths of the AER monitoring periods.
Variation of geometric means and standard deviations across cities and studies
The memorandum by Cohen, Mallya and Rosenbaum, 20057 (Attachment 5 of this report)
describes the analysis of residential air exchange rate (AER) data that were obtained from seven
studies. The AER data were subset by location, outside temperature range, and the A/C type, as
defined by the presence or absence of an air conditioner (central or window). In each case we
chose to fit a log-normal distribution to the AER data, so that the logarithm of the AER for a
given city, temperature range, and A/C type is assumed to be normally distributed. If the AER
data has geometric mean GM and geometric standard deviation GSD, then the logarithm of the
AER is assumed to have a normal distribution with mean log(GM) and standard deviation
log(GSD).
Table D-l shows the assignment of the AER data to the 12 modeled cities. Note that for Atlanta,
GA and Washington DC, the Research Triangle Park, NC data for houses with A/C was used to
represent the AER distributions for houses with A/C, and the non-California data for houses
without A/C was used to represent the AER distributions for houses without A/C. Sacramento,
CA AER distributions were estimated using the AER data from the inland California counties of
6 Cohen, I, H. Mallya, and A. Rosenbaum. 2005. Memorandum to John Langstaff. EPA 68D01052, Work
Assignment 3-08. Analysis of Air Exchange Rate Data. September 30, 2005.
7 Op. Cit.
B-142
-------
Sacramento, Riverside, and San Bernardino; these combined data are referred to by the City
Name "Inland California." St Louis, MO AER distributions were estimated using the AER data
from all states except for California and so are referred to be the City Name "Outside
California."
Table D-1. Assignment of Residential AER distributions to modeled cities
Modeled city
Atlanta, GA, A/C
Atlanta, GA, no A/C
Boston, MA
Chicago, IL
Cleveland, OH
Detroit, MI
Houston, TX
Los Angeles, CA
New York, NY
Philadelphia, PA
Sacramento
St. Louis
Washington, DC, A/C
Washington, DC, no
A/C
AER distribution
Research Triangle Park, A/C only
All non-California, no A/C ("Outside
California")
New York
New York
New York
New York
Houston
Los Angeles
New York
New York
Inland parts of Los Angeles ("Inland
California")
All non-California ("Outside California")
Research Triangle Park, A/C only
All non-California, no A/C ("Outside
California")
It is evident from Table D-l that for some of the modeled cities, some potentially large
uncertainty was introduced because we modeled their AER distributions using available data
from another city or group of cities thought to be representative of the first city on the basis of
geography and other characteristics. This was necessary for cities where we did not have any or
sufficient AER data measured in the same city that also included the necessary temperature and
A/C type information. One way to assess the impact of these assignments on the uncertainty of
the AER distributions is to evaluate the variation of the fitted log-normal distributions across the
cities with AER data. In this manner we can examine the effect on the AER distribution if a
different allocation of study data to the modeled cities had been used.
Even for the cities where we have study AER data, there is uncertainty about the fitted AER
distributions. First, the studies used different measurement and residence selection methods. In
some cases the residences were selected by a random sampling method designed to represent the
entire population. In other cases the residences were selected to represent sub-populations. For
example, for the RTF study, the residences belong to two specific cohorts: a mostly Caucasian,
B-143
-------
non-smoking group aged at least 50 years having cardiac defibrillators living in Chapel Hill; a
group of non-smoking, African Americans aged at least 50 years with controlled hypertension
living in a low-to-moderate SES neighborhood in Raleigh. The TEACH study was restricted to
residences of inner-city high school students. The RIOPA study was a random sample for Los
Angeles, but was designed to preferentially sample locations near major air toxics sources for
Elizabeth, NJ and Houston TX. Furthermore, some of the studies focused on different towns or
cities within the larger metropolitan areas, so that, for example, the Los Angeles data from the
Avol study was only measured in Lancaster, Lake Gregory, Riverside, and San Dimas but the
Los Angeles data from the Wilson studies were measured in multiple cities in Southern
California. One way to assess the uncertainty of the AER distributions due to variations of study
methodologies and study sampling locations is to evaluate the variation of the fitted log-normal
distributions within each modeled city across the different studies.
We evaluated the variation between cities, and the variation within cities and between studies, by
tabulating and plotting the AER distributions for all the study/city combinations. Since the
original analyses by Cohen, Mallya and Rosenbaum, 2005 clearly showed that the AER
distribution depends strongly on the outside temperature and the A/C type (whether or not the
residence has air conditioning), this analysis was stratified by the outside temperature range and
the A/C type. Otherwise, study or city differences would have been confounded by the
temperature and A/C type differences and you would not be able to tell how much of the AER
difference was due to the variation of temperature and A/C type across cities or studies. In order
to be able to compare cities and studies we could not use different temperature ranges for the
different modeled cities as we did for the original AER distribution modeling. For these analyses
we stratified the temperature into the ranges <= 10, 10-20, 20-25, and >25 °C and categorized the
A/C type as "Central or Window A/C" versus 'No A/C," giving 8 temperature and A/C type
strata.
Table D-2 shows the geometric means and standard deviations by city and study. These
geometric mean and standard deviation pairs are plotted in Figure D-l through D-8. Each figure
shows the variation across cities and studies for a given temperature range and A/C type. The
results for a city with only one available study are shown with a blank study name. For cities
with multiple studies, results are shown for the individual studies and the city overall distribution
is designated by a blank value for the study name.
Table D-2. Geometric means and standard deviations by city and study.
A/C Type
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Temperature
<=10
<= 10
<=10
<=10
<= 10
<=10
<= 10
<=10
<=10
<= 10
10-20
City
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
Research Triangle Park
Sacramento
San Francisco
Stockton
Arcata
Study*
Avol
RIOPA
Wilson 1991
N
2
5
2
1
2
20
157
3
2
7
1
Geo Mean
0.32
0.62
0.72
0.31
0.77
0.71
0.96
0.38
0.43
0.48
0.17
Geo Std Dev**
1.80
1.51
1.22
1.12
2.02
1.81
1.82
1.00
1.64
B-144
-------
Table D-2. Geometric means and standard deviations by city and study.
A/C Type
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
Temperature
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
>25
>25
>25
>25
>25
>25
>25
>25
>25
<= 10
<=10
<= 10
<= 10
<=10
<= 10
<=10
<= 10
City
Bakersfield
Fresno
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Redding
Research Triangle Park
Sacramento
San Diego
San Francisco
Santa Maria
Stockton
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Red Bluff
Research Triangle Park
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Research Triangle Park
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Study*
Avol
RIOPA
TEACH
Wilson 1984
Wilson 1991
RIOPA
TEACH
Avol
RIOPA
Wilson 1984
RIOPA
TEACH
Avol
RIOPA
Wilson 1984
RIOPA
TEACH
Avol
RIOPA
Wilson 1991
RIOPA
TEACH
N
2
8
13
716
33
11
1
634
37
5
4
1
1
320
7
23
5
1
4
20
273
32
26
215
37
20
17
2
196
79
114
25
10
79
19
14
5
145
13
18
14
2
2
48
44
4
Geo Mean
0.36
0.30
0.42
0.59
0.48
0.60
0.68
0.59
0.64
1.36
1.20
2.26
0.31
0.56
0.26
0.41
0.42
0.23
0.73
0.47
1.10
0.61
0.90
1.23
1.11
0.93
1.37
0.61
0.40
0.43
0.72
0.37
0.94
0.86
1.24
1.23
1.29
0.38
0.66
0.54
0.51
0.72
0.60
1.02
1.04
0.79
Geo Std Dev**
1.34
1.62
2.19
1.90
1.87
1.87
1.89
2.11
2.34
2.53
1.91
1.67
1.55
1.25
1.42
1.94
2.36
1.95
2.42
2.33
2.74
2.91
2.52
3.20
1.89
2.17
2.60
3.10
1.71
2.33
2.18
2.28
2.04
1.71
1.68
3.09
3.60
1.11
1.00
2.14
2.20
1.28
B-145
-------
Table D-2. Geometric means and standard deviations by city and study.
A/C Type
NoA/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
Temperature
<= 10
<=10
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
>25
>25
>25
>25
>25
>25
>25
>25
>25
City
Sacramento
San Francisco
Bakersfield
Fresno
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
Sacramento
San Diego
San Francisco
Santa Maria
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Red Bluff
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Study*
Avol
RIOPA
TEACH
Wilson 1984
Wilson 1991
Avol
RIOPA
Wilson 1984
RIOPA
TEACH
Avol
RIOPA
TEACH
Wilson 1984
RIOPA
TEACH
N
3
9
1
4
28
390
23
87
9
241
30
59
1
49
15
2
10
148
19
38
91
26
19
7
1
2
25
6
4
3
12
6
3
3
Geo Mean
0.58
0.39
0.85
0.90
0.63
0.75
0.78
0.78
2.32
0.70
0.75
0.79
1.09
0.47
0.34
0.27
0.92
1.37
0.95
.30
.52
.62
.50
.99
0.55
0.92
0.99
1.56
1.33
0.86
0.74
1.54
1.73
1.37
Geo Std Dev**
1.30
1.42
2.42
2.92
2.09
2.55
1.96
2.05
2.06
1.82
2.04
1.95
3.05
1.23
2.41
2.28
1.87
2.11
2.40
2.24
2.30
2.11
3.96
1.97
1.36
1.37
1.02
2.29
1.65
2.00
1.38
* For a given city, if AER data were available from only one study, then the study name is missing.
for two or more studies, then the overall city distribution is shown in the row where the study name
distributions by study and city are shown in the rows with a specific study name.
** The geometric standard deviation is undefined if the sample size equals 1.
If AER data were available
is missing, and the
In general, there is a relatively wide variation across different cities. This implies that the AER
modeling results would be very different if the matching of modeled cities to study cities was
changed, although a sensitivity study using the APEX model would be needed to assess the
impact on the ozone exposure estimates. In particular the ozone exposure estimates may be
sensitive to the assumption that the St. Louis AER distributions can be represented by the
combined non-California AER data. One way to address this is to perform a Monte Carlo
analysis where the first stage is to randomly select a city outside of California, the second stage
picks the A/C type, and the third stage picks the AER value from the assigned distribution for the
B-146
-------
city, A/C type and temperature range. Note that this will result in a very different distribution to
the current approach that fits a single log-normal distribution to all the non-California data for a
given temperature range and A/C type. The current approach weights each data point equally, so
that cities like New York with most of the data values get the greatest statistical weight. The
Monte Carlo approach gives the same total statistical weight for each city and fits a mixture of
log-normal distributions rather than a single distribution.
In general, there is also some variation within studies for the same city, but this is much smaller
than the variation across cities. This finding tends to support the approach of combining different
studies. Note that the graphs can be deceptive in this regard because some of the data points are
based on very small sample sizes (N); those data points are less precise and the differences
would not be statistically significant. For example, for the No A/C data in the range 10-20 °C,
the Los Angeles TEACH study had a geometric mean of 2.32 based on only nine AER values,
but the overall geometric mean, based on 390 values, was 0.75 and the geometric means for the
Los Angeles Avol, RIOPA, Wilson 1984, and Wilson 1991 studies were each close to 0.75. One
noticeable case where the studies show big differences for the same city is for the A/C houses in
Los Angeles in the range 20-25 °C where the study geometric means are 0.61 (Avol, N=32), 0.90
(RIOPA, N=26) and 1.23 (Wilson 1984, N=215).
Bootstrap analyses
The 39 AER subsets defined in the Cohen, Mallya, and Rosenbaum, 2005 memorandum
(Appendix A of this report) and their allocation to the 12 modeled cities are shown in Table D-3.
To make the distributions sufficiently precise in each AER subset and still capture the variation
across temperature and A/C type, different modeled cities were assigned different temperature
range and A/C type groupings. Therefore these temperature range groupings are sometimes
different to those used to develop Table D-2 and Figure D-l through D-8.
Table D-3. AER subsets by city, A/C type, and temperature range.
Subset City
Name
Houston Houston
Houston Houston
Houston Houston
Houston Houston
Houston
Houston
Inland California
Inland California
Inland California
Inland California
Study Cities
Houston
Houston
Houston
Sacramento, Riverside,
and San Bernardino
counties, CA
Sacramento, Riverside,
and San Bernardino
counties, CA
Sacramento, Riverside,
and San Bernardino
counties, CA
Sacramento, Riverside,
and San Bernardino
counties, CA
Represents
Modeled Cities:
Houston, TX
Houston, TX
Houston, TX
Houston, TX
Houston, TX
Houston, TX
Houston, TX
Sacramento, CA
Sacramento, CA
Sacramento, CA
Sacramento, CA
A/C Type
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
No A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
Temperature
Range (°C)
<=20
20-25
25-30
>30
<=10
10-20
>20
<=25
>25
<=10
10-20
B-147
-------
Table D-3. AER subsets by city, A/C type, and temperature range.
Subset City
Name
Inland California
Inland California
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
New York City
Study Cities
Sacramento, Riverside,
and San Bernardino
counties, CA
Sacramento, Riverside,
and San Bernardino
counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
New York, NY
New York, NY
New York, NY
New York, NY
Represents
Modeled Cities:
Sacramento, CA
Sacramento, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
A/C Type
NoA/C
No A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
No A/C
No A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
Temperature
Range (°C)
20-25
>25
<=20
20-25
25-30
>30
<=10
10-20
20-25
>25
<=10
10-25
>25
<=10
B-148
-------
Table D-3. AER subsets by city, A/C type, and temperature range.
Subset City
Name
New York City
New York City
Outside California
Outside California
Outside California
Outside California
Outside California
Outside California
Outside California
Outside California
Research Triangle Park
Research Triangle Park
Research Triangle Park
Research Triangle Park
Study Cities
New York, NY
New York, NY
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Research Triangle
Park, NC
Research Triangle
Park, NC
Research Triangle
Park, NC
Research Triangle
Park, NC
Represents
Modeled Cities:
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
St. Louis, MO
St. Louis, MO
St. Louis, MO
St. Louis, MO
St. Louis, MO
St. Louis, MO
Atlanta, GA
Washington DC
St. Louis, MO
Atlanta, GA
Washington DC
St. Louis, MO
Atlanta, GA
Washington DC
Atlanta, GA
Washington DC
Atlanta, GA
Washington DC
Atlanta, GA
Washington DC
Atlanta, GA
Washington DC
A/C Type
NoA/C
No A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
No A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Temperature
Range (°C)
10-20
>20
<=10
10-20
20-25
25-30
>30
<=10
10-20
>20
<=10
10-20
20-25
>25
The GM and GSD values that define the fitted log-normal distributions for these 39 AER subsets
are shown in Table D-4. Examples of these pairs are also plotted in Figures D-9 through D-19, to
be further described below. Each of the example figures D-9 through D-19 corresponds to a
single GM/GSD "Original Data" pair. The GM and GSD values for the "Original Data" are at
the intersection of the horizontal and vertical lines that are parallel to the x- and y-axes in the
figures.
B-149
-------
Table D-4. Geometric means and standard deviations for AER subsets by city, A/C type,
and temperature range.
Subset City
Name
Houston
Houston
Houston
Houston
Houston No
Houston No
No
Inland California
Inland California
Inland California
Inland California
Inland California
Inland California
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
New York City
New York City
New York City
Outside California
Outside California
Outside California
Outside California
A/C Type
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
A/C
A/C
A/C
Central or Room
A/C
Central or Room
A/C
No A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
No A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Temperature
Range (°C)
<=20
20-25
25-30
>30
<=10
10-20
>20
<=25
>25
<=10
10-20
20-25
>25
<=20
20-25
25-30
>30
<=10
10-20
20-25
>25
<=10
10-25
>25
<=10
10-20
>20
<=10
10-20
20-25
25-30
N
150.
200.
650.
140.
130.
280.
120.
2260.
830.
170.
520.
13 1.
140.
7210.
273 1.
1020.
120.
180.
3900.
148 1.
250.
200.
42 1.
19 1.
48 1.
590.
32 1.
1790.
3380.
2530.
2190.
Geometric
Mean
4075
4675
4221
4989
6557
6254
9161
5033
8299
5256
6649
0536
8271
5894
1003
8128
2664
5427
7470
3718
9884
7108
1392
2435
0165
7909
6062
9185
5636
4676
4235
Geometric
Standard
Deviation
2.1135
1.9381
2.2579
1.7174
1.6794
2.9162
2.4512
1.9210
2.3534
3.1920
2.1743
1.7110
2.2646
1.8948
2.3648
2.4151
2.7899
3.0872
2.0852
2.2828
1.9666
2.0184
2.6773
2.1768
2.1382
2.0417
2.1189
1.8589
1.9396
2.2011
2.0373
B-150
-------
Table D-4. Geometric means and standard deviations for AER subsets by city, A/C type,
and temperature range.
Subset City
Name
Outside California
Outside California
Outside California
Outside California
Research Triangle
Park
Research Triangle
Park
Research Triangle
Park
Research Triangle
Park
A/C Type
Central or Room
A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Temperature
Range (°C)
>30
<=10
10-20
>20
<=10
10-20
20-25
>25
N
240.
610.
870.
44 1.
1570.
3200.
1960.
1450.
Geometric
Mean
5667
9258
7333
3782
9617
5624
3970
3803
Geometric
Standard
Deviation
1.9447
2.0836
2.3299
2.2757
1.8094
1.9058
1.8887
1.7092
To evaluate the uncertainty of the GM and GSD values, a bootstrap simulation was performed,
as follows. Suppose that a given AER subset has N values. A bootstrap sample is obtained by
sampling N times at random with replacement from the N AER values. The first AER value in
the bootstrap sample is selected randomly from the N values, so that each of the N values is
equally likely. The second, third, ..., N'th values in the bootstrap sample are also selected
randomly from the N values, so that for each selection, each of the N values is equally likely.
The same value can be selected more than once. Using this bootstrap sample, the geometric
mean and geometric standard deviation of the N values in the bootstrap sample was calculated.
This pair of values is plotted as one of the points in a figure for that AER subset. 1,000 bootstrap
samples were randomly generated for each AER subset, producing a set of 1,000 geometric mean
and geometric standard deviation pairs, which were plotted in example Figures D-9 through D-
19.
The bootstrap distributions display the part of the uncertainty of the GM and GSD that is entirely
due to random sampling variation. The analysis is based on the assumption that the study AER
data are a random sample from the population distribution of AER values for the given city,
temperature range, and A/C type. On that basis, the 1,000 bootstrap GM and GSD pairs estimate
the variation of the GM and GSD across all possible samples of N values from the population.
Since each GM, GSD pair uniquely defines a fitted log-normal distribution, the pairs also
estimate the uncertainty of the fitted log-normal distribution. The choice of 1,000 was made as a
compromise between having enough pairs to accurately estimate the GM, GSD distribution and
not having too many pairs so that the graph appears as a smudge of overlapped points. Note that
even if there were infinitely many bootstrap pairs, the uncertainty distribution would still be an
estimate of the true uncertainty because the N is finite, so that the empirical distribution of the N
measured AER values does not equal the unknown population distribution.
In most cases the uncertainty distribution appears to be a roughly circular or elliptical geometric
mean and standard deviation region. The size of the region depends upon the sample size and on
the variability of the AER values; the region will be smallest when the sample size N is large
B-151
-------
and/or the variability is small, so that there are a large number of values that are all close
together.
The bootstrap analyses show that the geometric standard deviation uncertainty for a given
CMSA/air-conditioning-status/temperature-range combination tends to have a range of at most
from "fitted GSD-1.0 hr"1" to "fitted GSD+1.0 hr"1", but the intervals based on larger AER
sample sizes are frequently much narrower. The ranges for the geometric means tend to be
approximately from "fitted GM-0.5 hr"1" to "fitted GM+0.5 hr"1", but in some cases were much
smaller.
The bootstrap analysis only evaluates the uncertainty due to the random sampling. It does not
account for the uncertainty due to the lack of representativeness, which in turn is due to the fact
that the samples were not always random samples from the entire population of residences in a
city, and were sometimes used to represent different cities. Since only the GM and GSD were
used, the bootstrap analyses does not account for uncertainties about the true distributional
shape, which may not necessarily be log-normal. Furthermore, the bootstrap uncertainty does not
account for the effect of the calendar year (possible trends in AER values) or of the uncertainty
due to the AER measurement period; the distributions were intended to represent distributions of
24 hour average AER values although the study AER data were measured over a variety of
measurement periods.
To use the bootstrap distributions to estimate the impact of sample size on the fitted distributions,
a Monte Carlo approach could be used with the APEX model. Instead of using the Original Data
distributions, a bootstrap GM, GSD pair could be selected at random and the AER value could be
selected randomly from the log-normal distribution with the bootstrap GM and GSD.
B-152
-------
4.0
3.5
Figure D-1
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: Central or Room A/C
Temperature Range: <= 10 Degrees Celsius
Q
o
,0
C
"5
O
3.0
2.5
2.0
1.5
1.0
E
AG F
T
B
cn
H
III 1
0.5
1.0 1.5 2.0
Geometric Mean
2.5
3.0
AAAHouston
E E ENewYorkCity
I I I Stockton
B B BLosAngeles C C CLosAngeles-Avol
F F FResearchTnanglePark GGGSacramento
D D DLosAngeles-Wilson 1991
H H Ffs anFranci sco
B-153
-------
Figure D-2
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: Central or Room A/C
Temperature Range: 10-20 Degrees Celsius
4.0-
3.5-
-------
Figure D-3
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
Q
T3
-tŧ
(Si
c
"5
o
-------
Q
T3
-tŧ
(Si
c
"5
o
25 Degrees Celsius
A
D
H
0.0
0.5
1.0 1.5
Geometric Mean
2.0
2.5
3.0
AAAHouston B B BLosAngeles
E E ELosAngeles-Wilson 1984 F F FNewYorkCity
I I I ResearchTnanglePark
C C CLosAngeles-Avol D D DLosAngeles-RIOPA
GGGNewYorkCity-RIOPA HHHNewYorkCity-TEACH
B-156
-------
Q
T3
-t ŧ
(Si
c
"5
o
-------
Figure D-6
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: No A/C
Temperature Range: 10-20 Degrees Celsius
Q
T3
-tŧ
(Si
c
"5
o
-------
Figure D-7
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: No A/C
Temperature Range: 20-25 Degrees Celsius
4.0
3.5
CD
Q 3.0H
.g 2.5
"CD
o 2 0
CD ^V I
O
1.5
1.0
A
D
H
0.0
0.5
1.0 1.5
Geometric Mean
2.0
2.5
3.0
AAAHouston B B BLosAngeles
E E ELosAngeles-Wilson 1984 F F FNewYorkCity
C C CLosAngeles-Avol
G G GNewYorkCity-RIOP A
D D DLosAngeles-RIOPA
H H HNewYorkCity-TEACH
B-159
-------
Q
T3
-t ŧ
(Si
c
"5
o
(U
O
4.0
3.5
3.0H
1.5
1.0
Figure D-8
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: No A/C
Temperature Range: > 25 Degrees Celsius
B
H
G
DI C
E
1 1 1
0.0 0.5 1.0 1.5 2.0 2
5 3.0
Geometric Mean
AAAHouston B B BLosAngeles C C CLosAngeles-Avol DDDLosAngeles-RIOPA
E E ELosAngeles-TEACH F F FLosAngeles-Wilsonl984 GGGNewYorkCity HHHNewYorkCity-RIOPA
I I iNewYorkCity-TEACH
B-160
-------
Figure D-9
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Houston
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
Q
"2
GO
Geometric
4.0
3.5
3.0
2.5
2.0
1.5
1.0
ii
d3
'ijl
. *. / *
ifc
1^?i-
i i i i i i i
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-161
-------
Figure D-10
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Houston
Air Conditioner Type: No A/C
Temperature Range: >20 Degrees Celsius
4.0
3.5
Q 3.0-
17-5
+2
.2 2.5
S '
s
g 2.0
O
1.5
1.0
. \
''%&&
"':"? \ ;. ; v
*
1 1
0.0 0.5
!§&%''
' ' ""."'" '.'' !"*
i i i i i
1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-162
-------
Figure D-11
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Inland California
Air Conditioner Type: Central or Room A/C
Temperature Range: <=25 Degrees Celsius
4.0
3.5
Q 3.0-
3
w
g 2.5H
1
g 2.01
O
1.5
1.0
0.0
0.5
\ \
1.0 1.5
Geometric Mean
2.0
2.5
nr
3.0
Bootstrapped Data +++Original Data
B-163
-------
Figure D-12
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Inland California
Air Conditioner Type: No A/C
Temperature Range: 20-25 Degrees Celsius
4.0
3.5
Q 3.0-
1
g 2.5
1
g 2.0
O
1.5
1.0
;,,^g^
".t-V5Ģj
1
lĢĢ'
i i i i i i i
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-164
-------
Figure D-13
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Los Angeles
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
4.0
3.5
Q 3.0-
1
g 2.5
1
g 2.0
O
1.5
1.0
iBk^..
" ^IF
_
i i i i i i i
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-165
-------
Figure D-14
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Los Angeles
Air Conditioner Type: No A/C
Temperature Range: 20-25 Degrees Celsius
4.0
3.5
Q 3.0-
1
g 2.5
1
g 2.0
O
1.5
1.0
m
"^^ffitt^" "
-TTS^S
t&F"?-~
1 1 1 1 1 1 1
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-166
-------
Figure D-15
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: New York City
Air Conditioner Type: Central or Room A/C
Temperature Range: 10-25 Degrees Celsius
4.0
3.5
Q 3.0-
Geometric Std
K> K>
o Lft
1 1
1.5
1.0
f
-V'^
"" ;.?>'^MS
**
',^:. ...
^S*Vj*7 1.*."
te^M- .
l>hi'!J?'Ŧ.' .ŧ' '
"..*". . .
1 1 1 1 1 1 1
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-167
-------
Figure D-16
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: New York City
Air Conditioner Type: No A/C
Temperature Range: >20 Degrees Celsius
4.0
3.5
Q 3.0-
1
g 2.5
1
g 2.0
O
1.5
1.0
1- ^'-Ow
v/--^i^d!
.
^|p^ :'
^^r""''**'.*'"
I I I I I I I
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-168
-------
Figure D-17
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Outside California
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
Ģ
Q
3
V)
Geometric
4.0
3.5
3.0
2.5
2.0
1.5
1.0
M
1
r
i i i i i i i
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-169
-------
Figure D-18
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Outside California
Air Conditioner Type: No A/C
Temperature Range: >20 Degrees Celsius
4.0
3.5
Q 3.0-
3
M
o 7 ^
1
g 2.0
O
1.5
1.0
'!'*
^ f ! r * * '
1 * -JJ&J*
':^rf
;_'.-" : .
'*fi''y~ i-'i*" '
pii^.-;v.-
1 1 1 1 1 1 1
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-170
-------
Figure D-19
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Research Triangle Park
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
4.0
3.5
>
Q 3.0
1
c 2-5~
S
g 2.0
O
1.5
1.0
1 1 1 1 1 1 1
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
Bootstrapped Data +++Original Data
B-171
-------
ATTACHMENT 7. TECHNICAL MEMORANDUM ON THE
DISTRIBUTIONS OF AIR EXCHANGE RATE AVERAGES
OVER MULTIPLE DAYS
B-172
-------
INTERNATIONAL
MEMORANDUM
To: John Langstaff, EPA OAQPS
From: Jonathan Cohen, Arlene Rosenbaum, ICF International
Date: June 8, 2006
Re: Distributions of air exchange rate averages over multiple days
As detailed in the memorandum by Cohen, Mallya and Rosenbaum, 20058 (Appendix A of this
report) we have proposed to use the APEX model to simulate the residential air exchange rate
(AER) using different log-normal distributions for each combination of outside temperature
range and the air conditioner type, defined as the presence or absence of an air conditioner
(central or room).
Although the averaging periods for the air exchange rates in the study databases varied from one
day to seven days, our analyses did not take the measurement duration into account and treated
the data as if they were a set of statistically independent daily averages. In this memorandum we
present some analyses of the Research Triangle Park Panel Study that show extremely strong
correlations between consecutive 24-hour air exchange rates measured at the same house. This
provides support for the simplified approach of treating all averaging periods as if they were 24-
hour averages.
In the current version of the APEX model, there are several options for stratification of time
periods with respect to AER distributions, and for when to re-sample from a distribution for a
given stratum. The options selected for this current set of simulations resulted in a uniform AER
for each 24-hour period and re-sampling of the 24-hour AER for each simulated day. This re-
sampling for each simulated day implies that the simulated AERs on consecutive days in the
same microenvironment are statistically independent. Although we have not identified sufficient
data to test the assumption of uniform AERs throughout a 24-hour period, the analyses described
in this memorandum suggest that AERs on consecutive days are highly correlated. Therefore, we
performed sensitivity simulations to assess the impact of the assumption of temporally
independent air exchange rates, but found little difference between APEX predictions for the two
scenarios (i.e., temporally independent and autocorrelated air exchange rates).
8 Cohen, I, H. Mallya, and A. Rosenbaum. 2005. Memorandum to John Langstaff. EPA 68D01052, Work
Assignment 3-08. Analysis of Air Exchange Rate Data. September 30, 2005.
B-173
-------
Distributions of multi-day averages from the RTF Panel Study
The RTF Panel study included measurements of 24-hour averages at 38 residences for up to four
periods of at least seven days. These periods were in different seasons and/or calendar years.
Daily outside temperatures were also provided. All the residences had either window or room air
conditioners or both. We used these data to compare the distributions of daily averages taken
over 1, 2, 3, .. 7 days.
The analysis is made more complicated because the previous analyses showed the dependence of
the air exchange rate on the outside temperature, and the daily temperatures often varied
considerably. Two alternative approaches were employed to group consecutive days. For the first
approach, A, we sorted the data by the HOUSE_ID number and date and began a new group of
days for each new HOUSE_ID and whenever the sorted measurement days on the same
HOUSE_ID were 30 days or more apart. In most cases, a home was measured over four different
seasons for seven days, potentially giving 38 x 4 = 152 groups; the actual number of groups was
124. For the second approach, B, we again sorted the data by the HOUSE_ID number and date,
but this time we began a new group of days for each new HOUSE_ID and whenever the sorted
measurement days on the same HOUSE_ID were 30 days or more apart or were for different
temperature ranges. We used the same four temperature ranges chosen for the analysis in the
Cohen, Mallya, and Rosenbaum, 2005, memorandum (Appendix A): <= 10, 10-20, 20-25, and >
25 °C. For example, if the first week of measurements on a given HOUSE_ID had the first three
days in the <= 10 °C range, the next day in the 10-20 °C range, and the last three days in the <=
10 °C range, then the first approach would treat this as a single group of days. The second
approach would treat this as three groups of days, i.e., the first three days, the fourth day, and the
last three days. Using the first approach, the days in each group can be in different temperature
ranges. Using the second approach, every day in a group is in the same temperature range. Using
the first approach we treat groups of days as being independent following a transition to a
different house or season. Using the second approach we treat groups of days as being
independent following a transition to a different house or season or temperature range.
To evaluate the distributions of multi-day air exchange rate (AER) averages, we averaged the
AERs over consecutive days in each group. To obtain a set of one-day averages, we took the
AERs for the first day of each group. To obtain a set of two-day averages, we took the average
AER over the first two days from each group. We continued this process to obtain three-, four-,
five-, six-, and seven-day averages. There were insufficiently representative data for averaging
periods longer then seven days. Averages over non-consecutive days were excluded. Each
averaging period was assigned the temperature range using the average of the daily temperatures
for the averaging period. Using Approach A, some or all of the days in the averaging period
might be in different temperature ranges than the overall average. . Using Approach B, every day
is in the same temperature range as the overall average. For each averaging period and
temperature range, we calculated the mean, standard deviation, and variance of the period
average AER and of its natural logarithm. Note than the geometric mean equals e raised to the
power Mean log (AER) and the geometric standard deviation equals e raised to the power Std
Dev log (AER). The results are shown in Tables E-l (Approach A) and E-2 (Approach B).
B-174
-------
Table E-l. Distribution of AER averaged over K days and its logarithm. Groups defined by Approach A.
Temperature
(°C)
<= 10
<=10
<= 10
<=10
<=10
<= 10
<=10
10-20 1
10-20 2
10-20 3
10-20 4
10-20 5
10-20 6
10-20 7
20-25 1
20-25 2
20-25 3
20-25 4
20-25 5
20-25 6
20-25 7
>25
>25
>25
>25
>25
>25
>25
K
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Groups
35
30
28
28
24
24
29
48
55
51
50
53
49
34
32
28
27
17
17
17
14
9
11
12
23
23
23
17
Mean
AER
1.109
1.149
1.065
1.081
1.103
1.098
1.054
0.652
0.654
0.641
0.683
0.686
0.677
0.638
0.500
0.484
0.495
0.536
0.543
0.529
0.571
0.470
0.412
0.411
0.385
0.390
0.399
0.438
Mean
log(AER)
-0.066
-0.009
-0.088
-0.090
-0.082
-0.083
-0.109
-0.659
-0.598
-0.622
-0.564
-0.546
-0.533
-0.593
-1.005
-0.972
-0.933
-0.905
-0.905
-0.899
-0.889
-1.058
-1.123
-1.036
-1.044
-1.028
-1.010
-0.950
Std Dev
AER
0.741
0.689
0.663
0.690
0.754
0.753
0.704
0.417
0.411
0.416
0.440
0.419
0.379
0.343
0.528
0.509
0.491
0.623
0.672
0.608
0.745
0.423
0.314
0.243
0.176
0.175
0.193
0.248
Std Dev
log(AER)
0.568
0.542
0.546
0.584
0.598
0.589
0.556
0.791
0.607
0.603
0.619
0.596
0.544
0.555
0.760
0.623
0.604
0.652
0.649
0.617
0.683
0.857
0.742
0.582
0.429
0.425
0.435
0.506
Variance
AER
0.549
0.474
0.440
0.476
0.568
0.567
0.496
0.174
0.169
0.173
0.194
0.175
0.144
0.118
0.279
0.259
0.241
0.389
0.452
0.370
0.555
0.179
0.098
0.059
0.031
0.031
0.037
0.061
Variance
log(AER)
0.322
0.294
0.298
0.341
0.358
0.347
0.309
0.625
0.368
0.363
0.384
0.355
0.296
0.308
0.577
0.388
0.365
0.425
0.421
0.381
0.466
0.734
0.551
0.339
0.184
0.181
0.189
0.256
Using both approaches, Tables E-l and E-2 show that the mean values for the AER and its
logarithm are approximately constant for the same temperature range but different averaging
periods. This is expected if the daily AER values all have the same statistical distribution,
regardless of whether or not they are independent. More interesting is the observation that the
standard deviations and variances are also approximately constant for the same temperature
range but different averaging periods, except for the data at > 25 °C where the standard
deviations and variances tend to decrease as the length of the averaging period increases. If the
daily AER values were statistically independent, then the variance of an average over K days is
given by Var / K, where Var is the variance of a single daily AER value. Clearly this formula
does not apply. Since the variance is approximately constant for different values of K in the same
temperature range (except for the relatively limited data at > 25 °C), this shows that the daily
AER values are strongly correlated. Of course the correlation is not perfect, since otherwise the
AER for a given day would be identical to the AER for the next day, if the temperature range
were the same, which did not occur.
B-175
-------
Table E-2. Distribution of AER averaged over K days and its logarithm. Groups defined by Approach B.
Temperature
(°C)
<=10
<= 10
<= 10
<=10
<= 10
10-20
10-20
10-20
10-20
10-20
10-20
10-20
20-25
20-25
20-25
20-25
>25
>25
>25
>25
>25
>25
>25
K
162
241
332
4 17
55
1 10
281
363
427
522
6 12
76
1 10
263
323
43
1 54
232
323
4 12
5 12
66
76
Groups
9
7
Mean
AER
1.125
1.059
1.104
1.292
1.534
0.778
0.702
0.684
0.650
0.629
0.614
0.720
0.514
0.511
0.577
1.308
0.488
0.486
0.427
0.401
0.410
0.341
0.346
Mean
log(AER)
-0.081
-0.063
-0.040
0.115
0.264
-0.482
-0.532
-0.540
-0.626
-0.660
-0.679
-0.587
-0.915
-0.930
-0.837
-0.484
-0.949
-0.900
-0.970
-1.029
-1.003
-1.164
-1.144
Std Dev
AER
0.832
0.595
0.643
0.768
1.087
0.579
0.451
0.409
0.414
0.417
0.418
0.529
0.518
0.584
0.641
1.810
0.448
0.351
0.218
0.207
0.207
0.129
0.125
Std Dev
log(AER)
0.610
0.481
0.530
0.531
0.608
0.721
0.603
0.580
0.663
0.654
0.638
0.816
0.639
0.603
0.659
1.479
0.626
0.595
0.506
0.509
0.507
0.510
0.494
Variance
AER
0.692
0.355
0.413
0.590
1.182
0.336
0.204
0.167
0.171
0.174
0.175
0.280
0.269
0.341
0.411
3.277
0.201
0.123
0.048
0.043
0.043
0.017
0.016
Variance
log(AER)
0.372
0.231
0.281
0.282
0.370
0.520
0.363
0.336
0.440
0.428
0.407
0.667
0.409
0.364
0.434
2.187
0.392
0.354
0.256
0.259
0.257
0.261
0.244
These arguments suggest that, based on the RTF Panel study data, to a reasonable
approximation, the distribution of an AER measurement does not depend upon the length of the
averaging period for the measurement, although it does depend upon the average temperature.
This supports the methodology used in the Cohen, Mallya, and Rosenbaum, 2005, analyses that
did not take into account the length of the averaging period.
The above argument suggests that the assumption that daily AER values are statistically
independent is not justified. Statistical modeling of the correlation structure between consecutive
daily AER values is not easy because of the problem of accounting for temperature effects, since
temperatures vary from day to day. In the next section we present some statistical models of the
daily AER values from the RTF Panel Study.
Statistical models of AER auto-correlations from the RTF Panel Study
We used the MIXED procedure from SAS to fit several mixed models with fixed effects and
random effects to the daily values of AER and log(AER). The fixed effects are the population
average values of AER or log(AER), and are assumed to depend upon the temperature range.
The random effects have expected values of zero and define the correlations between pairs of
B-176
-------
measurements from the same Group, where the Groups are defined either using Approach A or
Approach B above. As described above, a Group is a period of up to 14 consecutive days of
measurements at the same house. For these mixed model analyses we included periods with one
or more missing days. For all the statistical models, we assume that AER values in different
Groups are statistically independent, which implies that data from different houses or in different
seasons are independent.
The main statistical model for AER was defined as follows:
AER = Mean(Temp Range) + A(Group, Temp Range)
+ B(Group, Day Number) + Error(Group, Day Number)
Mean(Temp Range) is the fixed effects term. There is a different overall mean value for each of
the four temperature ranges.
A(Group, Temp Range) is the random effect of temperature. For each Group, four error terms are
independently drawn from four different normal distributions, one for each temperature range.
These normal distributions all have mean zero, but may have different variances. Because of this
term, there is a correlation between AER values measured in the same Group of days for a pair
of days in the same temperature range.
B(Group, Day Number) is the repeated effects term. The day number is defined so that the first
day of a Group has day number 1, the next calendar day has day number 2, and so on. In some
cases AER's were missing for some of the day numbers. B(Group, Day Number) is a normally
distributed error term for each AER measurement. The expected value (i.e., the mean) is zero.
The variance is V. The covariance between B(Group g, day i) and B(Group h, day j) is zero for
days in different Groups g and h, and equals V x exp(d x i-j|) for days in the same Group. V and
d are fitted parameters. This is a first order auto-regressive model. Because of this term, there is a
correlation between AER values measured in the same Group of days, and the correlation
decreases if the days are further apart.
Finally, Error(Group, Day Number) is the Residual Error term. There is one such error term for
every AER measurement, and all these terms are independently drawn from the same normal
distribution, with mean 0 and variance W.
We can summarize this rather complicated model as follows. The AER measurements are
uncorrelated if they are from different Groups. If they are in the same Group, they have a
correlation that decreases with the day difference, and they have an additional correlation if they
are in the same temperature range.
Probably the most interesting parameter for these models is the parameter d, which defines the
strength of the auto-correlation between pairs of days. This parameter d lies between -1 (perfect
negative correlation) and +1 (perfect positive correlation) although values exactly equal to +1 or
-1 are impossible for a stationary model. Negative values of d would be unusual since they
would imply a tendency for a high AER day to be followed by a low AER day, and vice versa.
The case d=0 is for no auto-correlation.
B-177
-------
Table E-3 gives the fitted values of d for various versions of the model. The variants considered
were:
model AER or log(AER)
include or exclude the term A(Group, Temp Range) (the "random" statement in the SAS
code)
use Approach A or Approach B to define the Groups
Since Approach B forces the temperature ranges to be the same for very day in a Group, the
random temperature effect term is difficult to distinguish from the other terms. Therefore this
term was not fitted using Approach B.
Table E-3. Autoregressive parameter d for various statistical models for the RTF Panel
Study AERs.
Dependent variable
AER Yes
AER No
AER No
Log(AER) Yes
Log(AER) No
Log(AER) No
Include A(Group,
Temp Range)?
Approach
A
A
B
A
A
B
d
0.80
0.82
0.80
0.87
0.87
0.85
In all cases, the parameter d is 0.8 or above, showing the very strong correlations between AER
measurements on consecutive or almost consecutive days.
Impact of accounting for daily average AER auto-correlation
In the current version of the APEX model, there are several options for stratification of time
periods with respect to AER distributions, and for when to re-sample from a distribution for a
given stratum. The options selected for this current set of simulations resulted in a uniform AER
for each 24-hour period and re-sampling of the 24-hour AER for each simulated day. This re-
sampling for each simulated day implies that the simulated AERs on consecutive days in the
same microenvironment are statistically independent. Although we have not identified sufficient
data to test the assumption of uniform AERs throughout a 24-hour period, the analyses described
in this memorandum suggest that AERs on consecutive days are highly correlated.
Therefore, in order to determine if bias was introduced into the APEX estimates with respect to
either the magnitudes or variability of exposure concentrations by implicitly assuming
uncorrelated air exchange rates, we re-ran the 2002 base case simulations using the option to not
re-sample the AERs. For this option APEX selects a single AER for each
microenvironment/stratum combination and uses it throughout the simulation.
The comparison of the two scenarios indicates little difference in APEX predictions, probably
because the AERs pertain only to indoor microenvironments and for the base cases most
exposure to elevated concentrations occurs in the "other outdoors" microenvironment. Figures E-
B-178
-------
1 and E-2 below present the comparison for exceedances of 8-hour average concentration during
moderate exertion for active person in Boston and Houston, respectively.
200
Figure E-l
Air Exchange Rate Resampling Sensitivity:
Days/Person with Exceedances of
8-Hour Average Exposure Concentration During Moderate Exertion
-Active Persons, Boston, 2002-
20 40 60
Cumulative Percentile
80
100
-base-. 01
-rsoff- .01
base -.02
rsoff- .02
-base -.03
-rsoff- .03
-base - .04
-rsoff- .04
-base - .05
rsoff- .05
Figure E-2
Air Exchange Rate Resampling Sensitivity:
Days/Person with Exceedances of
8-Hour Average Exposure Concentration During Moderate Exertion
Active Persons, Houston, 2002-
re
20 40 60
Cumulative Percentile
80
100
-base-. 01
-rsoff- .01
base - .02
rsoff- .02
-base -.03
-rsoff- .03
-base - .04
-rsoff- .04
-base - .05
rsoff- .05
B-179
-------
B-180
-------
Appendix C
Sulfur Dioxide Health Risk Assessment
Draft Report
March 2009
Prepared for
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
Prepared by
Ellen Post
Jin Huang
Andreas Maier
Hardee Mahoney
Abt Associates Inc.
Work funded through
Contract No. EP-W-05-022
Work Assignments 2-63 and 3-63, and
Contract No. EP-D-08-100
Work Assignment 0-04
Harvey Richmond, Work Assignment Manager
Catherine Turner, Project Officer, Contract No. EP-W-05-022
Nancy Riley, Project Officer, Contract No. EP-D-08-100
-------
DISCLAIMER
This report is being furnished to the U.S. Environmental Protection Agency
(EPA) by Abt Associates Inc. in partial fulfillment of Contract No. EP-W-05-022, Work
Assignments 2-63 and 3-63, and Contract No. EP-D-08-100, Work Assignment 0-4. Any
opinions, findings, conclusions, or recommendations are those of the authors and do not
necessarily reflect the views of the EPA or Abt Associates. Any questions concerning
this document should be addressed to Harvey Richmond, U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, C504-06, Research Triangle Park,
North Carolina 27711 (email: richmond.harvey@epa.gov).
Abt Associates Inc. i March 2009
-------
Table of Contents
1 INTRODUCTION 1-1
2 PRELIMINARY CONSIDERATIONS 2-1
2.1 The Broad Empirical Basis for a Relationship Between SCh and Adverse
Health Effects 2-1
2.2 Basic Structure of the Risk Assessment 2-2
2.3 Air Quality Considerations 2-2
3 METHODS 3-1
3.1 Selection of health endpoints and target population 3-1
3.2 Development of exposure-response functions 3-3
3.2.1 Calculation of risk estimates 3-9
3.2.2 Selection of urban areas 3-11
3.2.3 Addressing variability and uncertainty 3-12
4 Results 4-15
5 REFERENCES 5-1
Abt Associates Inc. ii March 2009
-------
List of Tables
Table 3-1. Study-Specific SOi Exposure-Response Data for Lung Function
Decrements 3-4
Table 3-2. Example: Calculation of Number of Occurrences of Lung Function
Response, Defined as an Increase in sRaw > 100%, Among Asthmatics in St.
Louis Engaged in Moderate or Greater Exertion Associated with Exposure to
SOi Concentrations that Just Meet an Alternative 1-Hour 99th Percentile 100
ppb Standard 3-10
Table 3-3. Example: Calculation of the Number of Asthmatics in St. Louis Engaged
in Moderate or Greater Exertion Estimated to Experience at Least One Lung
Function Response, Defined as an Increase in sRaw > 100%, Associated with
Exposure to SOi Concentrations that Just Meet an Alternative 1-Hour 99th
Percentile 100 ppb Standard 3-11
Table 4-1. Number of Occurrences (in Hundreds) of Lung Function Response
(Defined in Terms of sRaw) Among Asthmatics Engaged in Moderate or
Greater Exertion Associated with Exposure to "As Is" SOi Concentrations,
SOi Concentrations that Just Meet the Current Standards, and SOi
Concentrations that Just Meet Alternative Standards 4-16
Table 4-2. Number of Occurrences (in Hundreds) of Lung Function Response
(Defined in Terms of sRaw) Among Asthmatic Children Engaged in
Moderate or Greater Exertion Associated with Exposure to "As Is" SOi
Concentrations, SOi Concentrations that Just Meet the Current Standards,
and SOi Concentrations that Just Meet Alternative Standards 4-17
Table 4-3. Number of Occurrences (in Hundreds) of Lung Function Response
(Defined in Terms of FEVi) Among Asthmatics Engaged in Moderate or
Greater Exertion Associated with Exposure to "As Is" SOi Concentrations,
SOi Concentrations that Just Meet the Current Standards, and SOi
Concentrations that Just Meet Alternative Standards 4-18
Table 4-4. Number of Occurrences (in Hundreds) of Lung Function Response
(Defined in Terms of FEVi) Among Asthmatic Children Engaged in
Moderate or Greater Exertion Associated with Exposure to "As Is" SOi
Concentrations, SOi Concentrations that Just Meet the Current Standards,
and SOi Concentrations that Just Meet Alternative Standards 4-19
Table 4-5. Number of Asthmatics Engaged in Moderate or Greater Exertion
Estimated to Experience At Least One Lung Function Response (Defined in
Terms of sRaw) Associated with Exposure to "As Is" SOi Concentrations,
SOi Concentrations that Just Meet the Current Standards, and SOi
Concentrations that Just Meet Alternative Standards 4-20
Table 4-6. Number of Asthmatic Children Engaged in Moderate or Greater Exertion
Estimated to Experience At Least One Lung Function Response (Defined in
Terms of sRaw) Associated with Exposure to "As Is" SOi Concentrations,
SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards 4-21
Abt Associates Inc. iii March 2009
-------
Table 4-7. Number of Asthmatics Engaged in Moderate or Greater Exertion
Estimated to Experience At Least One Lung Function Response (Defined in
Terms of FEVi) Associated with Exposure to "As Is" SO2 Concentrations,
SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards 4-22
Table 4-8. Number of Asthmatic Children Engaged in Moderate or Greater Exertion
Estimated to Experience At Least One Lung Function Response (Defined in
Terms of FEVi) Associated with Exposure to "As Is" SO2 Concentrations,
SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards 4-23
Table 4-9. Percent of Asthmatics Engaged in Moderate or Greater Exertion
Estimated to Experience At Least One Lung Function Response (Defined in
Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations,
SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards 4-24
Table 4-10. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion
Estimated to Experience At Least One Lung Function Response (Defined in
Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations,
SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards 4-25
Table 4-11. Percent of Asthmatics Engaged in Moderate or Greater Exertion
Estimated to Experience At Least One Lung Function Response (Defined in
Terms of FEVi) Associated with Exposure to "As Is" SO2 Concentrations,
SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards 4-26
Table 4-12. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion
Estimated to Experience At Least One Lung Function Response (Defined in
Terms of FEVi) Associated with Exposure to "As Is" SO2 Concentrations,
SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards 4-27
Abt Associates Inc. iv March 2009
-------
List of Figures
Figure 3-1. Components of SC>2 Health Risk Assessment Based on Controlled
Human Exposure Studies 3-2
Figure 3-2. Bayesian-Estimated Median Exposure-Response Functions: Increase in
sRaw > 100% for 5-Minute Exposures of Asthmatics Engaged in Moderate or
Greater Exertion 3-7
Figure 3-3. Bayesian-Estimated Median Exposure-Response Functions: Increase in
sRaw > 200% for 5-Minute Exposures of Asthmatics Engaged in Moderate or
Greater Exertion 3-7
Figure 3-4. Bayesian-Estimated Median Exposure-Response Functions: Decrease in
FEVi > 15% for 5-Minute Exposures of Asthmatics Engaged in Moderate or
Greater Exertion 3-8
Figure 3-5. Bayesian-Estimated Median Exposure-Response Functions: Decrease in
FEVi > 20% for 5-Minute Exposures of Asthmatics Engaged in Moderate or
Greater Exertion 3-8
Figure 4-1. Percent of Asthmatics Engaged in Moderate or Greater Exertion
Exhibiting Lung Function Response (Defined as an Increase in sRaw >
100%) Attributable to SO2 Within Given Ranges Under Different Air Quality
Scenarios 4-28
Figure 4-2. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion
Exhibiting Lung Function Response (Defined as an Increase in sRaw >
100%) Attributable to SO2 Within Given Ranges Under Different Air Quality
Scenarios 4-29
Figure 4-3. Percent of Asthmatics Engaged in Moderate or Greater Exertion
Exhibiting Lung Function Response (Defined as a Decrease in FEVi > 15%)
Attributable to SOi Within Given Ranges Under Different Air Quality
Scenarios 4-30
Figure 4-4. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion
Exhibiting Lung Function Response (Defined as a Decrease in FEVi > 15%)
Attributable to SOi Within Given Ranges Under Different Air Quality
Scenarios 4-31
Figure 4-5. Number of Occurrences of Lung Function Response (Defined as an
Increase in sRaw > 100%) Among Asthmatics Engaged in Moderate or
Greater Exertion Attributable to SOi Within Given Ranges Under Different
Air Quality Scenarios 4-32
Figure 4-6. Number of Occurrences of Lung Function Response (Defined as an
Increase in sRaw > 100%) Among Asthmatic Children Engaged in Moderate
or Greater Exertion Attributable to SOi Within Given Ranges Under
Different Air Quality Scenarios 4-33
Figure 4-7. Number of Occurrences of Lung Function Response (Defined as a
Decrease in FEVi > 15%) Among Asthmatics Engaged in Moderate or
Greater Exertion Attributable to SOi Within Given Ranges Under Different
Air Quality Scenarios 4-34
Figure 4-8. Number of Occurrences of Lung Function Response (Defined as a
Decrease in FEVi > 15%) Among Asthmatic Children Engaged in Moderate
Abt Associates Inc. v March 2009
-------
or Greater Exertion Attributable to SOi Within Given Ranges Under
Different Air Quality Scenarios 4-35
Figure 4-9. Legend for Figures 4-1 - 4-8 4-36
Abt Associates Inc. vi March 2009
-------
Sulfur Dioxide Health Risk Assessment
1 INTRODUCTION
The U.S. Environmental Protection Agency (EPA) is presently conducting a
review of the national ambient air quality standards (NAAQS) for sulfur dioxide (802).
Sections 108 and 109 of the Clean Air Act (Act) govern the establishment and periodic
review of the NAAQS. These standards are established for pollutants that may
reasonably be anticipated to endanger public health and welfare, and whose presence in
the ambient air results from numerous or diverse mobile or stationary sources. The
NAAQS are to be based on air quality criteria, which are to accurately reflect the latest
scientific knowledge useful in indicating the kind and extent of identifiable effects on
public health or welfare that may be expected from the presence of the pollutant in
ambient air. The EPA Administrator is to promulgate and periodically review, at five-
year intervals, "primary" (health-based) and "secondary" (welfare-based) NAAQS for
such pollutants.1 Based on periodic reviews of the air quality criteria and standards, the
Administrator is to make revisions in the criteria and standards, and promulgate any new
standards, as may be appropriate. The Act also requires that an independent scientific
review committee advise the Administrator as part of this NAAQS review process, a
function performed by the Clean Air Scientific Advisory Committee (CASAC).
EPA's plan and schedule for this SC>2 NAAQS review is presented in the
"Integrated Plan for Review of the Primary National Ambient Air Quality Standards for
Sulfur Oxides" (U.S. EPA, 2007a). The plan discusses the preparation of two key
components in the NAAQS review process: an Integrated Science Assessment (ISA) and
risk/exposure assessments. The ISA critically evaluates and integrates scientific
information on the health effects associated with exposure to oxides of sulfur (SOX) in the
ambient air. The risk/exposure assessments develop, as appropriate, quantitative
estimates of human exposure and health risk and related variability and uncertainties,
drawing upon the information summarized in the ISA.
In May 2008 EPA's National Center for Environmental Assessment (NCEA)
released a draft version of the "Integrated Science Assessment for Oxides of Sulfur -
Health Criteria, henceforth referred to as the draft ISA (U.S. EPA, 2008a). In June 2008,
EPA's Office of Air Quality Planning and Standards (OAQPS) released a first draft of its
"Risk and Exposure Assessment to Support the Review of the SO2 Primary National
Ambient Air Quality Standard," henceforth referred to as the 1st draft REA (U.S. EPA,
2008b). Both of these documents were reviewed by the CASAC SO2 Panel on July 30-
31, 2008. Based on its review of the draft ISA, OAQPS decided to expand the health risk
assessment to include a quantitative assessment of lung function responses indicative of
1 Section 109(b)(l) [42 U.S.C. 7409] of the Act defines a primary standard as one "the attainment and
maintenance of which in the judgment of the Administrator, based on such criteria and allowing an
adequate margin of safety, are requisite to protect the public health."
Abt Associates Inc. 1-1 March 2009
-------
bronchoconstriction experienced by asthmatic subjects associated with 5 to 10 minute
exposures to SC>2 while engaged in moderate or greater exertion. In September 2008,
NCEA released the final version of the ISA, "Integrated Science Assessment for Oxides
of Sulfur - Health Criteria, henceforth referred to as the ISA (U.S. EPA, 2008c).
SC>2 is one of a group of compounds known as sulfur oxides (SOX), which include
multiple chemicals (e.g., SO2, SO, SOs). However only SO2 is present at concentrations
significant for human exposures and the ISA indicates there is limited adverse health
effect data for the other gaseous compounds. Therefore, as in past NAAQS reviews, SO2
is considered as a surrogate for gaseous SOX species in this assessment, with the
secondarily formed particulate species (i.e., sulfate or 804) addressed as part of the
particulate matter (PM) NAAQS review.
In the previous review, concluded in 1996, it was clearly established that subjects
with asthma are more sensitive to the respiratory effects of SO2 exposure than healthy
individuals (ISA, section 3.1.3.2). Asthmatics exposed to SO2 concentrations as low as
0.2-0.3 ppm for 5-10 minutes during exercise have been shown to experience significant
bronchoconstriction, measured as an increase in specific airway resistance (sRaw)
(>100%) or a decrease in forced expiratory volume in one second (FEVi) (>15%) after
correction for exercise-induced responses in clean air.
The basic structure of the SO2 health risk assessment described in this document
reflects the fact that we have available controlled human exposure study data from
several studies involving volunteer asthmatic subjects who were exposed to SO2
concentrations at specified exposure levels while engaged in moderate or greater exertion
for 5- or 10-minute exposures.2 The risk assessment estimates lung function risks for (1)
recent ambient levels of SO2, (2) air quality adjusted to simulate just meeting the current
primary 24-hour and annual standards,3 and (3) air quality adjusted to simulate just
meeting selected alternative 1-hour standards in selected locations encompassing a
variety of SO2 emission source types in the Greene County and the St. Louis area within
Missouri.
The SO2 health risk assessment builds upon the methodology, analyses, and
lessons learned from the assessments conducted for the last SO2 NAAQS review in 1996,
as well as the methodology and lessons learned from the health risk assessment work
conducted for the recently concluded Os NAAQS review (Abt Associates, 2007a) - in
particular, the assessment of risk based on controlled human exposure studies described
in Chapter 3 of that document. The SO2 risk assessment is based on our current
understanding of the SO2 scientific literature as reflected in the evaluation provided in the
final ISA.
2 An additional characterization of risk may involve use of concentration-response functions, if sufficient
and relevant epidemiological data are identified in the ISA to support development of functions that are
related to ambient SO2 concentrations.
3 There is a 3-hr secondary standard as well. However, this risk assessment is taking into account only the
primary standards. The current primary SO2 standards include a 24-hour standard set at 0.14 parts per
million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc. 1-2 March 2009
-------
The goals of this SC>2 health risk assessment are: (1) to develop health risk
estimates of the number and percent of the asthmatic population in the selected study area
locations that would experience moderate or greater lung function decrements in response
to daily 5-minute maximum peak exposures while engaged in moderate or greater
exertion for a recent year of air quality and under a scenario in which the SC>2
concentrations are adjusted to simulate just meeting the current 24-hour standard; (2) to
develop a better understanding of the influence of various inputs and assumptions on the
risk estimates; and (3) to gain insights into the risk levels and patterns of risk reductions
associated with air quality simulating just meeting alternative 1-hour SC>2 standards. The
risk assessment is intended as a tool that, together with other information on lung
function and other health effects evaluated in the SO2 ISA, can aid the Administrator in
judging whether the current primary standards protect public health with an adequate
margin of safety, or whether revisions to the standards are appropriate.
Preliminary considerations and the basic structure of the risk assessment are
described in section 2. Section 3 describes the methods used, and section 4 presents the
results of the risk assessment.
Abt Associates Inc. 1-3 March 2009
-------
2 PRELIMINARY CONSIDERATIONS
The health risk assessment described in this document estimated lung function
decrements (measured as increases in sRaw or decreases in FEVi) associated with SC>2
exposures under several scenarios: (1) recent ambient levels of SO2, (2) air quality
adjusted to simulate just meeting the current 24-hour and annual standards, and (3) air
quality adjusted to simulate just meeting several alternative 1-hour standards. In this
section we address preliminary considerations. Section 2.1 briefly discusses the broad
empirical basis for a relationship between SO2 exposures and adverse health effects.
Section 2.2 describes the basic structure of the risk assessment. Finally, section 2.3
addresses air quality considerations.
2.1 The Broad Empirical Basis for a Relationship Between SOi and Adverse
Health Effects
The ISA concludes that the health evidence "is sufficient to infer a causal
relationship between respiratory morbidity and short-term exposure to 802" (ISA, p. 3-
33). In support of this conclusion, the ISA notes the following:
The strongest evidence for this causal relationship comes from
human clinical studies reporting respiratory symptoms and decreased lung
function following peak exposures of 5-10 min duration to SO2. These
effects have been observed consistently across studies involving
exercising mild to moderate asthmatics. Statistically significant
decrements in lung function accompanied by respiratory symptoms
including wheeze and chest tightness have been clearly demonstrated
following exposure to 0.4-0.6 ppm 862. Although studies have not
reported statistically significant respiratory effects following exposure to
0.2-0.3 ppm SC>2, some asthmatic subjects (5-30%) have been shown to
experience moderate to large decrements in lung function at these
exposure concentrations.
A larger body of evidence supporting this determination of
causality comes from numerous epidemiological studies reporting
associations with respiratory symptoms, ED visits, and hospital
admissions with short-term 862 exposures, generally of 24-h avg.
Important new multicity studies and several other studies have found an
association between 24-h avg ambient SC>2 concentrations and respiratory
symptoms in children, particularly those with asthma....
... Collectively, the findings from both human clinical and
epidemiological studies provide a strong basis for concluding a causal
relationship between respiratory morbidity and short-term exposure to
SO2.
Abt Associates Inc. 2-1 March 2009
-------
2.2 Basic Structure of the Risk Assessment
As noted above, this SC>2 health risk assessment is based on controlled human
exposure studies involving volunteer subjects who were exposed while engaged in
different exercise regimens to specified levels of SC>2 under controlled conditions for 5 or
10 minute periods. The responses measured in these studies were measures of lung
function decrements, including increases in SRaw and decreases in FEVi. We used
probabilistic exposure-response relationships, based on analysis of individual data, that
describe the relationships between a measure of personal exposure to SO2 and the
measure(s) of lung function recorded in these studies. These probabilistic exposure-
response relationships were combined with daily 5-minute maximum peak exposure
estimates associated with the air quality scenarios mentioned above for mild and
moderate asthmatics engaged in moderate or greater exertion. Estimates of personal
exposures to varying ambient concentrations associated with several air quality scenaros
including recent air quality levels, and air quality levels simulating just meeting the
current SO2 primary standard and several alternative primary 1-hour standards were
derived through exposure modeling. The details of the exposure modeling are described
in Chapter 8 and Appendix B of the second draft Risk and Exposure Assessment
document (EPA, 2009).
The characteristics that are relevant to carrying out a risk assessment based on
controlled human exposure studies can be summarized as follows:
A risk assessment based on controlled human exposure studies uses exposure-
response functions, and therefore requires as input (modeled) personal
exposures to SC>2.
Controlled human exposure studies, carried out in laboratory settings, are
generally not specific to any particular real world location. A controlled
human exposure studies-based risk assessment can therefore appropriately be
carried out for any location for which there are adequate air quality data on
which to base the modeling of personal exposures.
The methods for the SC>2 risk assessment are discussed in section 3 below. The risk
assessment was implemented within a new probabilistic version of TRIM.Risk, the
component of EPA's Total Risk Integrated Methodology (TRIM) model that estimates
human health risks.4
2.3 Air Quality Considerations
The SC>2 health risk assessment estimates lung function risks associated with (1)
"as is" ambient levels of SC>2, (2) air quality simulating just meeting the current 24-hour
4 TRIM.Risk was most recently applied to EPA's O3 health risk assessment. A User's Guide for the
Application of TRIM.Risk to the O3 health risk assessment (Abt Associates, 2007b) is available online at:
http://epa.gov/ttn/fera/data/trim/trimrisk ozone ra userguide 8-6-07.pdf.
Abt Associates Inc. 2-2 March 2009
-------
and annual standards, and (3) air quality simulating just meeting several alternative 1-
hour standards in a recent year (2002) in two selected locations encompassing a variety of
SC>2 emission source types in Greene County, Missouri and St. Louis, Missouri.
In order to estimate health risks associated with just meeting the current 24-hour
and annual standards and alternative 1-hour SC>2 standards, it is necessary to estimate the
distribution of short-term (5-minute) SC>2 concentrations that would occur under any
given standard. Since compliance with the current SO2 standards is based on a single
year, air quality data from 2002 were used to determine the change in 862 concentrations
required to meet the current standards. Estimated design values were used to determine
the adjustment necessary to just meet the current 24-hour and annual standards. The
approach to simulating just meeting the current standards and alternative 1-hour
standards is described in section 8.8.1 of the second draft Risk and Exposure Assessment
document (EPA, 2009).
The risk estimates developed for the recently concluded PM and O3 NAAQS
reviews represented risks associated with PM and 63 levels in excess of estimated policy-
relevant background (PRB) levels in the U.S. PRB levels have been historically defined
by EPA as concentrations of a pollutant that would occur in the U.S. in the absence of
anthropogenic emissions in continental North America (defined as the United States,
Canada, and Mexico). The ISA notes that PRB 862 concentrations are below 10 parts
per trillion (ppt) over much of the United States and are generally less than 30 ppt. With
the exception of a few locations on the West Coast and locations in Hawaii, where
volcanic SO2 emissions cause high PRB concentrations, PRB contributes less than 1% to
present-day 862 concentrations in surface air. Since PRB is well below concentrations
that might cause potential health effects, there was no adjustment made for risks
associated with PRB concentrations in the current SO2 health risk assessment.
Abt Associates Inc. 2-3 March 2009
-------
3 METHODS
The major components of the SC>2 lung function risk assessment are illustrated in
Figure 3-1. The air quality and exposure analysis components that are integral to the risk
assessment are discussed in Chapters 6 and 7, respectively, of the 2nd draft REA. As
described in the ISA and the 2nd draft REA, there are numerous controlled human
exposure studies reporting lung function decrements (as measured by increases in SRaw
and/or decreases in FEVi) among mild and/or moderate asthmatic adults associated with
short-term (5 or 10 minute) peak exposures to various levels of SO2 while engaged in
moderate or greater exercise. The 862 lung function risk assessment focuses on these
lung function responses among asthmatic children and adults.
3.1 Selection of health endpoints and target population
The ISA concluded that there is sufficient evidence to infer a causal relationship
between respiratory morbidity and short-term exposure to SC>2 (ISA, section 5.2). This
determination was based in large part on controlled human exposure studies
demonstrating a relationship between short-term (5- or 10-minute) peak SO2 exposures
and adverse effects on the respiratory system in exercising asthmatics. More specifically,
the ISA found consistent evidence from numerous controlled human exposure studies
demonstrating increased respiratory symptoms (e.g. cough, chest tightness, wheeze) and
decrements in lung function in a substantial proportion of exercising asthmatics
(generally classified as mild to moderate asthmatics) following short-term peak exposures
to SC>2 at concentrations > 0.4 ppm. As in previous reviews, the ISA also concluded that
at concentrations below 1.0 ppm, healthy individuals are relatively insensitive to the
respiratory effects of short-term peak SO2 exposures (ISA, sections 3.1.3.2). Therefore,
the SO2 lung function risk assessment focuses on asthmatics. Exposure estimates for
asthmatic children and adult asthmatics were combined separately with probabilistic
exposure-response relationships (described below) for lung function response associated
with daily 5-minute maximum peak exposures while engaged in moderate or greater
exertion.5
5 Only the highest 5-minute peak exposure (with moderate or greater exertion) on each day will be
considered in the lung function risk assessment, since the controlled human exposure studies have shown
an acute-phase response that was followed by a short refractory period where the individual was relatively
insensitive to additional SO2 challenges.
Abt Associates Inc. 3-1 March 2009
-------
Figure 3-1. Components of SO2 Health Risk Assessment Based on Controlled Human Exposure Studies
Air Quality
Ambient
Monitoring for
Selected Urban
Areas
Air Quality
Adjustment
Procedures
Current and
Alternative
Proposed
Standards
Recent
("As Is")
Ambient
S02
Levels
Exposure
Exposure
Model
Exposure Estimates
Associated with:
Recent Air Quality
Current Standard
Alternative
Standards
Exposure-Response
Controlled Human
Exposure Studies
Probabilistic
Exposure -
Response
Relationships
Health
Risk
Model
Risk Estimates:
Recent Air
Quality
Current
Standard
Alternative
Standards
Abt Associates Inc.
3-2
March 2009
-------
Two measures of lung function response - specific airway resistance (sRaw) and forced
expiratory volume in one second (FEVi) - have been used in the controlled human exposure
studies that have focused on the effects of exposure to SO2 on exercising asthmatics. Negative
effects are measured as the percent increase in sRaw or the percent decrease in FEVi. As
explained below, we estimated exposure-response relationships for four different definitions of
response:
An increase in sRaw > 100%
An increase in sRaw > 200%
A decrease in FEVi > 15%
A decrease in FEVi > 20%.
3.2 Development of exposure-response functions
We used a Bayesian Markov Chain Monte Carlo approach to estimate probabilistic
exposure-response relationships for lung function decrements associated with 5- or 10-minute
exposures at moderate or greater exertion, using the WinBUGS software (Spiegelhalter et al.
(1996)). For an explanation of these methods, see Gelman et al. (1995) or Gilks et al. (1996).
We treated both 5- and 10-minute exposures as if they were all 5-minute exposures.
The combined data set from Linn et al. (1987, 1988, 1990), Bethel et al. (1983, 1985),
Roger et al. (1985), and Kehrl et al. (1987) provide data with which to estimate exposure-
response relationships between responses defined in terms of sRaw and 5- or 10-minute
exposures to SC>2 at levels of 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, and 1.0 ppm.6 As noted above, two
definitions of response were used: (1) an increase in sRaw > 100% and (2) an increase in sRaw
> 200%.
The combined data set from Linn et al. (1987, 1988, 1990) provide data with which to
estimate exposure-response relationships between responses defined in terms of FEVi and 5- or
10-minute exposures to 862 at levels of 0.2, 0.3, 0.4, and 0.6 ppm. As noted above, two
definitions of response were used: a decrease in FEVi > 15% and a decrease in FEVi > 20%.
Before being used to estimate exposure-response relationships for 5-minute exposures,
the data from these controlled human exposure studies were corrected for the effect of exercising
in clean air to remove any systematic bias that might be present in the data attributable to an
exercise effect.7 Generally, this correction for exercise in clean air is small relative to the total
effects measures in the SO2-exposed cases. The resulting study-specific results, based on the
corrected data, are shown in Table 3-1.
6 Data from Magnussen et al. (1990) were not used in the estimation of sRaw exposure-response functions because
exposures in this study were conducted using a mouthpiece rather than a chamber.
7 Corrections were subject-specific. A correction was made by subtracting the subject's percent change (in FEVi or
sRaw) under the no-SO2 protocol from his or her percent change (in FEVi or sRaw) under the given SO2 protocol,
and rounding the result to the nearest integer. For example, if a subject's percent change in sRaw under the no-SO2
protocol was 110.12% and his percent change in sRaw under the 0.6 ppm SO2 protocol was 185.92%, then his
percent change in sRaw due to SO2 is 185.92% -110.12% = 75.8%, which rounds to 76%.
Abt Associates Inc. 3-3 March 2009
-------
Table 3-1. Study-Specific SO2 Exposure-Response Data for Lung Function Decrements
Study and SO2 Level
Increase in sRaw > 100%
Number
Exposed
Number
Responding
Increase in sRaw > 200%
Number
Exposed
Number
Responding
Decrease in FEV!>15%
Number
Exposed
Number
Responding
Decrease in FEVi>20%
Number
Exposed
Number
Responding
0.2ppmSO2
Linn etal. (1987)
40
2
40
0
40
5
40
2
0.25ppmSO2
Bethel etal. (1985)
Roger etal. (1985)
19
9
28
6
2
1
19
9
28
3
0
0
0.3ppmSO2
Linn etal. (1988)
Linn etal. (1990)
20
21
2
7
20
21
1
2
20
21
3
5
20
21
0
3
0.4ppmSO2
Linn etal. (1987)
40
9
40
3
40
12
40
9
0.5ppmSO2
Bethel etal. (1983)
Roger etal. (1985)
Magnussen et al. (1990)*
10
28
45
6
5
16
10
28
45
4
1
7
0.6ppmSO2
Linn etal. (1987)
Linn etal. (1988)
Linn etal. (1990)
40
20
21
14
12
13
40
20
21
11
7
6
40
20
21
21
11
9
40
20
21
19
11
7
1.0ppmSO2
Roger etal. (1985)
Kehrl etal. (1987)
28
10
14
6
28
10
7
2
*Data from Magnussen et al. (1990) were not used in the estimation of sRaw exposure-response functions because exposures in this study were conducted using a
mouthpiece rather than a chamber.
Abt Associates Inc.
3-4
March 2009
-------
We considered two different functional forms for the exposure-response functions: a
2-parameter logistic model and a probit model. In particular, we used the data in Table 3-1 to
estimate the logistic function,
and the probit function,
' -<2/2dt (3-2)
for each of the four lung function responses defined above, where x denotes the SC>2
concentration (in ppm) to which the individual is exposed, ln(x) is the natural logarithm of x, y
denotes the corresponding probability of response (increase in sRaw > 100% or > 200% or
decrease in FEVi > 1 5% or > 20%), and ft and y are the two parameters whose values are
estimated. 8
We assumed that the number of responses, st, out of TV, subjects exposed to a given
SC>2 concentration, xf, has a binomial distribution with response probability given by equation
(3-1) when we assume the logistic model and equation (3-2) when we assume the probit
model. The likelihood function is therefore
In some of the controlled human exposure studies, subjects were exposed to a given
SO2 concentration more than once. However, because there were insufficient data to estimate
subject-specific response probabilities, we assumed a single response probability (for a given
definition of response) for all individuals and treated the repeated exposures for a single
subject as independent exposures in the binomial distribution.
For each model, we derived a Bayesian posterior distribution using this binomial
likelihood function in combination with uniform prior distributions for each of the unknown
parameters.9 We used 4000 iterations as the "burn-in" period followed by a sufficient number
of iterations to ensure convergence of the resulting posterior density. Each iteration
corresponds to a set of values for the parameters of the logistic or probit exposure-response
function.
For ease of exposition, we use the same two Greek letters to indicate two unknown parameters in the logistic
and probit models; this does not imply, however, that the values of these two parameters are the same in the two
models.
9 We used the following uniform prior distributions for the 2-parameter logistic model: (3 ~ U(-10, 0); and y ~
U(-10,0); we used the following normal prior distributions for the probit model: (3 ~ N(0, 1000); and y ~
N(0,1000).
Abt Associates Inc. 3-5 March 2009
-------
For any SC>2 concentration, x, we could then derive the nih percentile response value,
for any n, by evaluating the exposure-response function at x using each of the 18,000 sets of
parameter values. The resulting median (50th percentile) logistic and probit exposure-
response functions are shown together, along with the data used to estimate these functions,
for increases in sRaw > 100% and > 200% and decreases in FEVi > 15% and > 20% in
Figures 3-2, 3-3, 3-4, and 3-5, respectively.
As can be seen in Figures 3-2 through 3-5, there were only limited data with which to
estimate the logistic and probit exposure-response functions, and in all cases it wasn't clear
that one function fit the data better than the other. In fact, for each of the four lung function
response definitions there was little difference between the estimated logistic and probit
models. Because the estimated exposure-response functions based on these two models were
so similar to each other, for each of the four lung function definitions, and because the risk
results from the two models for the same lung function definition would thus be almost the
same, we used only one of the models, the logistic, to estimate the risks associated with
exposure to 862 under the different air quality scenarios considered. The 2.5th percentile,
median, and 97.5th percentile logistic exposure-response curves, along with the response data
to which they were fit, are shown separately for each of the four response definitions in
Appendix A.
Abt Associates Inc. 3-6 March 2009
-------
Figure 3-2. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw > 100% for 5-
Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
The data
probit
2-parameter logistic
0.2 0.4 0.6
SO2 Concentration (ppm)
0.8
Figure 3-3. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw > 200% for 5-
Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion
100%
90% -
80% -
70% -
| 60%
a>
tn
m
-------
Figure 3-4. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEVi > 15% for 5-
Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion
100%
90% -
80% -
70% -
A The data
probit
2-parameter logistic
0.2 0.4 0.6
SO2 Concentration (ppm)
0.8
Figure 3-5. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEVt > 20% for 5-
Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion
100% -i
90% -
80% -
70% -
60% -
50% -
40% -
30% -
20% -
10% -
0%
0
A The data
probit
2-parameter logistic
0.2 0.4 0.6
SO2 Concentration (ppm)
0.8
Abt Associates Inc.
March 2009
-------
3.2.1 Calculation of risk estimates
We generated two measures of risk for each of the lung function response definitions.
The first measure of risk is simply the number of occurrences of the lung function response in
the designated population (e.g., asthmatics) in a year associated with SO2 concentrations
under a given air quality scenario. To calculate this measure of risk we started with the
number of exposures among the population that are at or above each benchmark level (i.e., 0
ppb, 50 ppb, 100 ppb, etc.), estimated from the exposure modeling. From this we calculated
the number of exposures within each 50 ppb exposure "bin" (e.g., < 50 ppb, 50 - 100 ppb,
etc.). 10 We then calculated the number of occurrences of lung function response by
multiplying the number of exposures in an exposure bin by the response probability (given by
our logistic exposure-response function for the specified definition of lung function response)
associated with the midpoint of that bin and summing the results across the bins.
Because response probabilities are calculated for each of several percentiles of a
probabilistic exposure-response distribution, estimated numbers of occurrences are similarly
percentile-specific. The kth percentile number of occurrences, Ok, associated with SO2
concentrations under a given air quality scenario is:
]X(Rk\ej) (3-3)
;=i
where:
6j = (the midpoint of) the jth category of personal exposure to SC>2;
Nj = the number of exposures to Cj ppb 862, given ambient 862 concentrations under
the specified air quality scenario;
Rk ej = the kth percentile response probability at SO2 concentration ef, and
n = the number of intervals (categories) of SC>2 personal exposure concentration.
An example calculation is given in Table 3-2.
10 The final exposure bin was from 750 to 800 ppb SO2. In at least one of the alternative standard scenarios,
there were exposures greater than 800 ppb. For any exposures that exceeded 800 ppb, we assumed a final bin
from 800 to 850 ppb, and assigned them the midpoint value of that bin, 825 ppb. This will result in a slight
downward bias in the estimate of risk.
Abt Associates Inc. 3-9 March 2009
-------
Table 3-2. Example: Calculation of Number of Occurrences of Lung Function Response, Defined as an
Increase in sRaw > 100%, Among Asthmatics in St. Louis Engaged in Moderate or Greater Exertion
Associated with Exposure to SO2 Concentrations that Just Meet an Alternative 1-Hour 99th Percentile 100
ppb Standard
SO2 Exposure Bin
Lower
Bound
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
Upper
Bound
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
Midpoint
(D
0.025
0.075
0.125
0.175
0.225
0.275
0.325
0.375
0.425
0.475
0.525
0.575
0.625
0.675
0.725
0.775
Number of
Exposures
(2)
16519000
136621
15760
3826
1051
413
175
83
31
24
8
0
0
8
0
0
Probability of
Response at Midpoint
SO2 Level
(3)
0.00406
0.02334
0.05162
0.08563
0.12300
0.16220
0.20210
0.24190
0.28060
0.31830
0.35430
0.38850
0.42090
0.45150
0.46600
0.49380
Expected Number of
Occurrences of Lung
Function Response
=(2) x (3)
67067
3189
814
328
129
67
35
20
9
8
3
0
0
4
0
0
Expected Number
Total Number of Exposures: 16677000 of Occurrences: 71672
The second measure of risk generated for each lung function response definition is the
number of individuals in the designated population to experience at least one lung function
response in a year associated with SO2 concentrations under a specified air quality scenario.
The calculation of this measure of risk is similar to the calculation of the first measure of risk
- however, here we started with estimates, from the exposure modeling, of the number of
individuals exposed at least once to x ppb SO2 or higher, for x = 0, 50, 100, etc. From this we
calculated the number of individuals exposed at least once to SC>2 concentrations within each
SC>2 exposure bin defined above. We then multiplied the numbers of individuals in an
exposure bin by the response probability (given by our logistic exposure-response function for
the specified definition of lung function response) corresponding to the midpoint of the
exposure bin, and summed the results across the bins.
Because response probabilities are calculated for each of several percentiles of a
probabilistic exposure-response distribution, estimated numbers of individuals with at least
one lung function response are similarly percentile-specific. The kth percentile number of
individuals, Yk, associated with 862 concentrations under a given air quality scenario is:
e,)
(3-4)
Where ejt Rk ej, and n are as defined above, and NIj is the number of individuals whose
highest exposure is to ej ppb 862, given ambient 862 concentrations under the specified air
quality scenario. An example calculation is given in Table 3-3.
Abt Associates Inc.
3-10
March 2009
-------
Table 3-3. Example: Calculation of the Number of Asthmatics in St. Louis Engaged in Moderate or
Greater Exertion Estimated to Experience at Least One Lung Function Response, Defined as an
Increase in sRaw > 100%, Associated with Exposure to SO2 Concentrations that Just Meet an
Alternative 1-Hour 99th Percentile 100 ppb Standard
SO2 Exposure Bin
Lower
Bound
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
Upper
Bound
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
Midpoint
(D
0.025
0.075
0.125
0.175
0.225
0.275
0.325
0.375
0.425
0.475
0.525
0.575
0.625
0.675
0.725
0.775
Number of
Asthmatics with
At Least One
Exposure in Bin
(2)
53711
34236
9835
3059
929
368
145
84
31
22
8
0
0
8
0
0
Probability of
Response at Midpoint
SO2 Level
(3)
0.00406
0.02334
0.05162
0.08563
0.12300
0.16220
0.20210
0.24190
0.28060
0.31830
0.35430
0.38850
0.42090
0.45150
0.46600
0.49380
Estimated Number of
Asthmatics Experiencing
at Least One Lung
Function Response
=(2) x (3)
218
799
508
262
114
60
29
20
9
7
3
0
0
4
0
0
Total: 102436 Total: 2032
Note that this calculation assumes that individuals who do not respond at the highest
SC>2 concentration to which they are exposed will not respond to any lower SC>2
concentrations to which they are exposed.
Note also that, in contrast to the risk estimates calculated for the O?, health risk
assessment, the risk estimates calculated for the SC>2 health risk assessment do not subtract out
risk given the personal exposures associated with estimated policy relevant background (PRB)
ambient 862 concentrations, because PRB 862 concentrations are so low (see section 2.3).
3.2.2 Selection of urban areas
Although it would be useful to characterize SCVrelated lung function risks associated
with "as is" SC>2 ambient concentrations and SC>2 concentrations that just meet the current and
alternative SC>2 standards nationwide, because the modeling of personal exposures is both
time and labor intensive, a regional and source-oriented approach was selected instead. The
selection of areas to include in the exposure analysis, and therefore the risk assessment, took
into consideration the availability of ambient monitoring, the desire to represent a range of
geographic areas considering SC>2 emission sources, population demographics, general
climatology, and results of the ambient air quality characterization.
Abt Associates Inc.
3-11
March 2009
-------
The first area of interest was initially identified based on the results of a preliminary
screening of the 5-minute ambient SC>2 monitoring data that were available. The state of
Missouri was one of only a few states having both 5-minute maximum and continuous 5-
minute SC>2 ambient monitoring, as well as having over 30 1-hour SC>2 monitors in operation
at some time during the period from 1997 to 2007. In addition, the air quality characterization,
described in Chapter 6 of the 1st draft REA, estimated frequent exceedances above the
potential health effect benchmark levels at several of the 1-hour ambient monitors. In a
ranking of estimated SC>2 emissions reported in the National Emissions Inventory (NEI),
Missouri ranked 7th for the number of stacks with > 1000 tpy SOX emissions out of all U.S.
states. These stack emissions were associated with a variety of source types such as electrical
power generating units, chemical manufacturing, cement processing, and smelters. For all
these reasons, the current SO2 lung function risk assessment focuses on Missouri and, within
Missouri, on those areas within 20 km of a major point source of SC>2 emissions in Greene
County and the St. Louis area.
3.2.3 Addressing variability and uncertainty
Any estimation of risks associated with "as is" 862 concentrations or with 862
concentrations that just meet the current or alternative SO2 standards should address both the
variability and uncertainty that generally underlie such an analysis. Uncertainty refers to the
lack of knowledge regarding the actual values of model input variables (parameter
uncertainty) and of physical systems or relationships (model uncertainty - e.g., the shapes of
exposure-response and concentration-response functions). The goal of the analyst is to reduce
uncertainty to the maximum extent possible. Uncertainty can be reduced by improved
measurement and improved model formulation. In a health risk assessment, however,
significant uncertainty often remains.
The degree of uncertainty can be characterized, sometimes quantitatively. For
example, the statistical uncertainty surrounding the estimated 862 coefficients in the
exposure-response functions is reflected in confidence or credible intervals provided for the
risk estimates.
As described in section 3.2 above, we used a Bayesian Markov Chain Monte Carlo
approach to estimate exposure-response functions as well as to characterize uncertainty
attributable to sampling error based on sample size considerations. Using this approach, we
could derive the nih percentile response value, for any n, for any SC^concentration, x, as
described above (see section 3.2). Because our exposure estimates were generated at the
midpoints of 0.05 ppm intervals (i.e., for 0.025 ppm, 0.075 ppm, etc.), we derived 2.5th
percentile, 50th percentile (median), and 97.5th percentile response estimates for SO2
concentrations at these midpoint values. The 2.5th percentile and 97.5th percentile response
estimates comprise the lower and upper bounds of the credible interval around each point
estimate (median estimate) of response.
In addition to uncertainties arising from sampling variability, other uncertainties
associated with the use of the exposure-response relationships for lung function responses are
briefly summarized below. Additional uncertainties with respect to the exposure inputs to the
Abt Associates Inc. 3-12 March 2009
-------
risk assessment are described in section 7.1 of the 2nd draft REA. The main additional
uncertainties with respect to the approach used to estimate exposure-response relationships
include:
Length of exposure. The 5-minute lung function risk estimates are based on a combined
data set from several controlled human exposure studies, most of which evaluated
responses associated with 10-minute exposures. However, since some studies which
evaluated responses after 5-minute exposures found responses occurring as early as 5-
minutes after exposure, we used all of the 5- and 10- minute exposure data to represent
responses associated with 5-minute exposures. We do not believe that this approach would
appreciably impact the risk estimates.
Exposure-response for mild/moderate asthmatics. The data set that was used to estimate
exposure-response relationships included mild and/or moderate asthmatics. There is
uncertainty with regard to how well the population of mild and moderate asthmatics
included in the series of controlled human exposure studies represent the distribution of
mild and moderate asthmatics in the U.S. population. As indicated in the ISA (p. 3-9), the
subjects studied represent the responses "among groups of relatively healthy asthmatics
and cannot necessarily be extrapolated to the most sensitive asthmatics in the population
who are likely more susceptible to the respiratory effects of exposure to SO2."
Extrapolation of exposure-response relationships. It was necessary to estimate responses
at SC>2 levels below the lowest exposure levels used in free-breathing controlled human
studies (i.e., 0.2 ppm). We did not include alternative models that incorporate
hypothetical population thresholds, given the lack of evidence supporting the choice of
potential hypothetical threshold levels. As discussed later in this document, we have
presented information on the contribution of different exposure intervals to the total
estimated lung function risk. This information provides insights on how much of the
estimated risk is attributed to 862 exposures at the lower exposure levels (i.e., 0 to 50 ppb,
50 to 100 ppb, 100 to 150 ppb, etc.). One can use this information to get a rough sense of
the SC>2-related risk that would exist under alternative threshold assumptions.
Reproducibility of SO^-induced responses. The risk assessment assumed that the SC>2-
induced responses for individuals are reproducible. We note that this assumption has
some support in that one study (Linn et al., 1987) exposed the same subjects on two
occasions to 0.6 ppm and the authors reported a high degree of correlation (r > 0.7 for
mild asthmatics and r > 0.8 for moderate asthmatics, p < 0.001), while observing much
lower and nonsignificant correlations (r = 0.0 - 0.4) for the lung function response
observed in the clean air with exercise exposures.
Age and lung function response. Because the vast majority of controlled human exposure
studies investigating lung function responses were conducted with adult subjects, the risk
assessment relies on data from adult asthmatic subjects to estimate exposure-response
relationships that were applied to all asthmatic individuals, including children. The ISA
(section 3.1.3.5) indicates that there is a strong body of evidence that suggests adolescents
may experience many of the same respiratory effects at similar SO2 levels, but recognizes
that these studies administered 862 via inhalation through a mouthpiece rather than an
Abt Associates Inc. 3-13 March 2009
-------
exposure chamber. This technique bypasses nasal absorption of 862 and can result in an
increase in lung SC>2 uptake. Therefore, the uncertainty will be greater in the risk estimates
for asthmatic children.
Exposure history. The risk assessment assumed that the SCVinduced response on any
given day is independent of previous SC>2 exposures.
Interaction between SO? and other pollutants. Because the controlled human exposure
studies used in the risk assessment involved only SC>2 exposures, it was assumed that
estimates of SC>2-induced health responses would not be affected by the presence of other
pollutants (e.g., PM2.5, O3, NO2).
Variability refers to the heterogeneity in a population or parameter. Even if there is no
uncertainty surrounding inputs to the analysis, there may still be variability. For example,
there may be variability among exposure-response functions describing the relationship
between SC>2 and lung function in different locations. This variability does not imply
uncertainty about the exposure-response function in any location, but only that these functions
are different in the different locations, reflecting differences in the populations and/or other
factors that may affect the relationship between 862 and the associated health endpoint. In
general, it is possible to have uncertainty but no variability (if, for instance, there is a single
parameter whose value is uncertain) or variability but little or no uncertainty (for example,
people's heights vary considerably but can be accurately measured with little uncertainty).
The SC>2 lung function risk assessment addresses variability-related concerns by using
location-specific inputs for the exposure analysis (e.g., location-specific population data, air
exchange rates, air quality and temperature data). The extent to which there may be
variability in exposure-response relationships for the populations included in the risk
assessment residing in different geographic areas is currently unknown.
Temporal variability is more difficult to address, because the risk assessment focuses
on some unspecified time in the future. To minimize the degree to which values of inputs to
the analysis may be different from the values of those inputs at that unspecified time, we are
using the most current inputs available.
Abt Associates Inc. 3-14 March 2009
-------
4 Results
The results of the SO2 risk assessment are presented in Tables 4-1 through 4-12.
Each table includes results for both of the locations included in the risk assessment and
for all of the air quality scenarios considered. Tables 4-1 and 4-2 show the numbers of
occurrences of lung function response in a year, defined in terms of sRaw, for asthmatics
and for asthmatic children, respectively, engaged in moderate or greater exertion
associated with SO2 concentrations under each of the different air quality scenarios
considered in each of the two locations. Tables 4-3 and 4-4 show the corresponding
results when lung function response is defined in terms of FEVi. Tables 4-5 and 4-6
show the numbers of asthmatics and asthmatic children, respectively, engaged in
moderate or greater exertion estimated to experience at least one lung function response
in a year, defined in terms of sRaw, under each of the different air quality scenarios in
each of the two locations. Tables 4-7 and 4-8 show the corresponding results when lung
function response is defined in terms of FEVi. Finally, Tables 4-9 through 4-12 show
results analogous to those shown in Tables 4-5 through 4-8, only as percentages of all
asthmatics (asthmatic children) engaged in moderate or greater exertion.
In addition, responses attributable to exposure to SO2 within different
concentration ranges are shown in Figures 4-1 through 4-8. The exposure ranges are in
50 ppb increments - i.e., SO2 < 50 ppb, 50 ppb < SO2 < 100 ppb, 100 ppb < SO2 < 150
ppb, ... , SO2 > 500 ppb. Figures 4-la and b show the percent of asthmatics engaged in
moderate or greater exertion in Greene Co., MO and St. Louis, MO, respectively,
estimated to experience at least one lung function response in a year, defined as an
increase in sRaw > 100%, attributable to exposure to SO2 in each exposure "bin."
Figures 4-2a and b show the corresponding percents for asthmatic children engaged in
moderate or greater exertion in each location, respectively. Figures 4-3a and b, and 4-4a
and b, show the corresponding percents for asthmatics and asthmatic children,
respectively, when lung function response is defined as a decrease in FEV1> 15%.
Figures 4-5a and b show the number of occurrences of lung function response, defined as
an increase in sRaw > 100%, among asthmatics engaged in moderate or greater exertion
attributable to exposure to SO2 in each exposure "bin." Figures 4-6a and b show the
corresponding numbers of occurrences among asthmatic children. Finally, Figures 4-7a
and b and 4-8a and b show the corresponding numbers of occurrences of lung function
response for asthmatics and asthmatic children, respectively, when lung function
response is defined as a decrease in FEV1> 15%. Figure 4-9 shows the legend that is
used in Figures 4-1 through 4-8.
Abt Associates Inc. 4-15 March 2009
-------
Table 4-1. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of sRaw) Among Asthmatics Engaged in Moderate or
Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
SO2
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene County, MO
St. Louis, MO
125
(24 - 572)
657
(128-2985)
127
(25 - 577)
1672
(663 - 4740)
125
(24 - 572)
652
(125-2975)
125
(24 - 572)
686
(141 -3041)
125
(24 - 573)
762
(176-3184)
126
(24 - 573)
880
(234 - 3398)
126
(24 - 575)
1036
(315-3673)
126
(24 - 574)
997
(295 - 3604)
Response = Increase in sRaw >= 200%
Greene County, MO
St. Louis, MO
38
(4-310)
201
(21 -1614)
39
(4-312)
560
(165-2407)
38
(4-310)
199
(20-1609)
38
(4-310)
211
(24-1639)
38
(4-310)
237
(32-1703)
38
(4-310)
278
(47-1799)
39
(4-311)
332
(68-1923)
39
(4-311)
319
(63-1892)
'Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2
coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest whole number.
"The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03
ppm, calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-16
March 2009
-------
Table 4-2. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of sRaw) Among Asthmatic Children Engaged in
Moderate or Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards,
and SO2 Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene County, MO
St. Louis, MO
71
(13-324)
417
(81 -1893)
72
(14-327)
1179
(484 - 3209)
71
(13-324)
413
(80-1885)
71
(14-324)
439
(91 -1935)
71
(14-324)
497
(118-2043)
71
(14-325)
586
(162-2206)
71
(14-325)
704
(222-2413)
71
(14-325)
674
(207-2361)
Response = Increase in sRaw >= 200%
Greene County, MO
St. Louis, MO
22
(2-175)
128
(13-1023)
22
(2-177)
397
(122-1618)
22
(2-175)
126
(13-1019)
22
(2-175)
135
(15-1042)
22
(2-175)
155
(22-1091)
22
(2-176)
186
(33-1164)
22
(2-176)
227
(49-1257)
22
(2-176)
217
(45-1234)
'Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2
coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest whole number.
"The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
"The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-17
March 2009
-------
Table 4-3. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of FEVi) Among Asthmatics Engaged in Moderate or
Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Decrease in FEV1 >= 15%
Greene County, MO
St. Louis, MO
69
(6 - 675)
366
(33 - 3520)
71
(7 - 680)
1341
(454 - 5632)
69
(6 - 675)
361
(32 - 3507)
69
(6 - 675)
391
(41 - 3587)
69
(6 - 675)
461
(66 - 3759)
69
(6 - 676)
570
(108-4016)
70
(6 - 677)
718
(169-4346)
70
(6 - 677)
681
(154-4264)
Response = Decrease in FEV1 >= 20%
Greene County, MO
St. Louis, MO
3
(0 - 53)
15
(1 -279)
3
(0 - 54)
310
(133-1045)
3
(0 - 53)
14
(0 - 276)
3
(0 - 53)
20
(2 - 299)
3
(0 - 53)
35
(7-351)
3
(0 - 53)
62
(17-435)
3
(0 - 53)
104
(34 - 550)
3
(0 - 53)
93
(30-521)
'Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2
coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest whole number.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-18
March 2009
-------
Table 4-4. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of FEVi) Among Asthmatic Children Engaged in
Moderate or Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards,
and SO2 Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Decrease in FEV1 >= 15%
Greene County, MO
St. Louis, MO
39
(3 - 382)
232
(21 -2231)
40
(4 - 386)
965
(338-3816)
39
(3 - 382)
229
(20 - 2222)
39
(3 - 382)
252
(27 - 2282)
39
(3 - 382)
304
(46-2412)
39
(3 - 383)
387
(77 - 2608)
40
(4 - 384)
499
(123-2857)
40
(4 - 383)
471
(112-2795)
Response = Decrease in FEV1 >= 20%
Greene County, MO
St. Louis, MO
1
(0 - 30)
10
(0-178)
2
(0-31)
231
(99 - 753)
1
(0 - 30)
9
(0-175)
1
(0 - 30)
13
(1 -192)
1
(0 - 30)
24
(5 - 232)
2
(0 - 30)
45
(13-295)
2
(0 - 30)
76
(26 - 382)
2
(0 - 30)
68
(22 - 360)
'Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2
coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest whole number.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-19
March 2009
-------
Table 4-5. Number of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Function Response (Defined
in Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene County, MO
St. Louis, MO
90
(20 - 390)
1010
(340-3010)
210
(80 - 620)
13460
(9740-18510)
80
(20 - 380)
730
(220 - 2490)
90
(20 - 390)
1990
(860 - 4690)
100
(20 - 420)
3650
(1900-7100)
120
(30 - 460)
5520
(3230 - 9490)
160
(50 - 520)
7500
(4770-11850)
140
(40 - 500)
7050
(4410- 11320)
Response = Increase in sRaw >= 200%
Greene County, MO
St. Louis, MO
30
(0-210)
330
(70-1520)
70
(20-310)
5520
(3400 - 8960)
30
(0-210)
230
(40-1290)
30
(0-210)
670
(210-2270)
30
(0 - 220)
1280
(510-3360)
40
(10-240)
2010
(940 - 4470)
50
(10-270)
2830
(1470-5590)
50
(10-260)
2640
(1340-5330)
'Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2
coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest ten.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-20
March 2009
-------
Table 4-6. Number of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Function Response
(Defined in Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and
SO2 Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene County, MO
St. Louis, MO
30
(10- 130)
590
(220-1570)
110
(40 - 270)
8020
(6080-10370)
30
(10-130)
400
(130- 1210)
30
(10-140)
1220
(560 - 2620)
40
(10- 150)
2240
(1240-4010)
50
(20- 180)
3370
(2090 - 5350)
70
(30-210)
4560
(3060 - 6680)
60
(20 - 200)
4290
(2840 - 6390)
Response = Increase in sRaw >= 200%
Greene County, MO
St. Louis, MO
10
(0 - 70)
190
(50 - 780)
40
(10-130)
3380
(2190-5070)
10
(0 - 70)
130
(30-610)
10
(0 - 70)
410
(140-1240)
10
(0 - 80)
800
(340-1870)
20
(0 - 90)
1250
(620 - 2500)
20
(10-110)
1750
(970-3140)
20
(10-100)
1640
(890 - 3000)
'Numbers are median (50th percentile) numbers of asthmatic children. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding
the SO2 coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest ten.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-21
March 2009
-------
Table 4-7. Number of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Function Response (Defined
in Terms of FEVi) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Decrease in FE\A, >= 15%
Greene County, MO
St. Louis, MO
50
(10-460)
750
(180-3580)
170
(50 - 730)
15220
(10280-22530)
50
(0 - 450)
510
(100-2950)
50
(10-460)
1700
(580 - 5590)
60
(10-490)
3460
(1520-8500)
80
(20 - 540)
5570
(2880-11400)
110
(30-610)
7910
(4550-14280)
100
(20 - 590)
7370
(4160- 13640)
Response = Decrease in FE\A, >= 20%
Greene County, MO
St. Louis, MO
0
(0 - 40)
100
(20 - 570)
30
(10-130)
9240
(6110-13840)
0
(0 - 40)
50
(10-380)
0
(0 - 40)
350
(110-1290)
0
(0 - 50)
1020
(430 - 2680)
10
(0 - 60)
2100
(1060-4450)
20
(0 - 80)
3540
(1990-6540)
10
(0 - 80)
3190
(1760-6050)
'Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2
coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest ten.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-22
March 2009
-------
Table 4-8. Number of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Function Response
(Defined in Terms of FEVi) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and
SO2 Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Decrease in FE\A, >= 15%
Greene County, MO
St. Louis, MO
20
(0-160)
460
(120-1870)
90
(30 - 320)
9310
(6620-12680)
20
(0-150)
290
(60- 1440)
20
(0- 160)
1080
(390-3130)
30
(0- 180)
2200
(1030-4810)
40
(10-210)
3510
(1930-6440)
50
(10-250)
4950
(3030 - 8070)
50
(10-240)
4630
(2780 - 7720)
Response = Decrease in FE\A, >= 20%
Greene County, MO
St. Louis, MO
0
(0-10)
70
(10-350)
20
(10-70)
6150
(4190-8700)
0
(0-10)
30
(10-220)
0
(0-10)
240
(80 - 820)
0
(0 - 20)
700
(300-1710)
0
(0 - 30)
1430
(740 - 2830)
10
(0 - 40)
2410
(1400-4160)
10
(0 - 40)
2170
(1240-3850)
'Numbers are median (50th percentile) numbers of asthmatic children. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding
the SO2 coefficient in the 2-parameter logistic exposure-response function. Numbers are rounded to the nearest hundred.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-23
March 2009
-------
Table 4-9. Percent of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Function Response (Defined
in Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene County, MO
St. Louis, MO
0.4%
(0.1% -1.8%)
1%
(0.3% - 2.9%)
1%
(0.4% - 2.9%)
13.1%
(9. 5% -18.1%)
0.4%
(0.1% -1.8%)
0.7%
(0.2% - 2.4%)
0.4%
(0.1%- 1.8%)
1.9%
(0.8% -4. 6%)
0.5%
(0.1% -2%)
3.6%
(1.9% -6. 9%)
0.6%
(0.2% -2.1%)
5.4%
(3.2% - 9.3%)
0.7%
(0.2% - 2.4%)
7.3%
(4.7% - 1 1 .6%)
0.7%
(0.2% - 2.3%)
6.9%
(4.3%- 11.1%)
Response = Increase in sRaw >= 200%
Greene County, MO
St. Louis, MO
0.1%
(0%-1%)
0.3%
(0.1% -1.5%)
0.3%
(0.1% -1.5%)
5.4%
(3.3% - 8.7%)
0.1%
(0%-1%)
0.2%
(0%-1.3%)
0.1%
(0%-1%)
0.7%
(0.2% - 2.2%)
0.2%
(0%-1%)
1.3%
(0.5% - 3.3%)
0.2%
(0%-1.1%)
2%
(0.9% - 4.4%)
0.2%
(0%-1.3%)
2.8%
(1.4% -5.5%)
0.2%
(0% - 1 .2%)
2.6%
(1.3% -5.2%)
*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding
the SO2 coefficient in the 2-parameter logistic exposure-response function. Percents are rounded to the nearest tenth.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-24
March 2009
-------
Table 4-10. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Function Response
(Defined in Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and
SO2 Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Increase in sRaw >= 100%
Greene County, MO
St. Louis, MO
0.4%
(0.1%- 1.8%)
1 .4%
(0.5% - 3.8%)
1 .4%
(0.6% - 3.7%)
19.2%
(14. 6% -24. 9%)
0.4%
(0.1% -1.8%)
0.9%
(0.3% -2. 9%)
0.4%
(0.1% -1.9%)
2.9%
(1.3% -6. 3%)
0.5%
(0.1% -2.1%)
5.4%
(3% - 9.6%)
0.7%
(0.2% - 2.4%)
8.1%
(5% -12. 8%)
1%
(0.3% - 2.9%)
10.9%
(7. 3% -16%)
0.9%
(0.3% - 2.7%)
10.3%
(6.8%- 15.3%)
Response = Increase in sRaw >= 200%
Greene County, MO
St. Louis, MO
0.1%
(0%-1%)
0.5%
(0.1% -1.9%)
0.5%
(0.1% -1.8%)
8.1%
(5.3% -12.2%)
0.1%
(0%-1%)
0.3%
(0.1% -1.5%)
0.1%
(0%-1%)
1%
(0.3% - 3%)
0.2%
(0%-1.1%)
1.9%
(0.8% - 4.5%)
0.2%
(0%-1.3%)
3%
(1.5% -6%)
0.3%
(0.1% -1.5%)
4.2%
(2.3% - 7.5%)
0.3%
(0.1% -1.4%)
3.9%
(2.1% -7.2%)
*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding
the SO2 coefficient in the 2-parameter logistic exposure-response function. Percents are rounded to the nearest tenth.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-25
March 2009
-------
Table 4-11. Percent of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Function Response (Defined
in Terms of FEVi) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and SO2
Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Decrease in FEV1 >= 15%
Greene County, MO
St. Louis, MO
0.2%
(0%-2.1%)
0.7%
(0.2% - 3.5%)
0.8%
(0.2% - 3.4%)
14.9%
(10% -22%)
0.2%
(0%-2.1%)
0.5%
(0.1% -2. 9%)
0.2%
(0%-2.1%)
1.7%
(0.6% - 5.5%)
0.3%
(0% - 2.3%)
3.4%
(1.5% -8. 3%)
0.4%
(0.1% -2. 5%)
5.4%
(2. 8% -11.1%)
0.5%
(0.1% -2. 9%)
7.7%
(4. 4% -13. 9%)
0.5%
(0.1% -2. 8%)
7.2%
(4.1%- 13.3%)
Response = Decrease in FEV1 >= 20%
Greene County, MO
St. Louis, MO
0%
(0% - 0.2%)
0.1%
(0% - 0.6%)
0.1%
(0% - 0.6%)
9%
(6% -13.5%)
0%
(0% - 0.2%)
0.1%
(0% - 0.4%)
0%
(0% - 0.2%)
0.3%
(0.1% -1.3%)
0%
(0% - 0.2%)
1%
(0.4% - 2.6%)
0%
(0% - 0.3%)
2.1%
(1%-4.3%)
0.1%
(0% - 0.4%)
3.5%
(1.9% -6.4%)
0.1%
(0% - 0.4%)
3.1%
(1.7% -5.9%)
*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding
the SO2 coefficient in the 2-parameter logistic exposure-response function. Percents are rounded to the nearest tenth.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-26
March 2009
-------
Table 4-12. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Function Response
(Defined in Terms of FEVi) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Standards, and
SO2 Concentrations that Just Meet Alternative Standards*
Location
"As is" SO2
Concentrations**
S02
Concentrations
that Just Meet the
Current
Standards***
SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (in
ppb) of m (Standard Denoted n/m):
99/50
99/100
99/150
99/200
99/250
98/200
Response = Decrease in FEV1 >= 15%
Greene County, MO
St. Louis, MO
0.2%
(0% - 2.2%)
1.1%
(0.3% - 4.5%)
1 .2%
(0.4% - 4.4%)
22.3%
(15. 9% -30. 4%)
0.2%
(0%-2.1%)
0.7%
(0.2% -3. 5%)
0.2%
(0% - 2.2%)
2.6%
(0.9% - 7.5%)
0.3%
(0.1% -2. 4%)
5.3%
(2. 5% -11. 5%)
0.5%
(0.1% -2. 9%)
8.4%
(4. 6% -15. 4%)
0.7%
(0.2% - 3.5%)
11.9%
(7. 3% -19. 3%)
0.6%
(0.2% - 3.2%)
11.1%
(6.7%- 18.5%)
Response = Decrease in FEV1 >= 20%
Greene County, MO
St. Louis, MO
0%
(0% - 0.2%)
0.2%
(0% - 0.8%)
0.2%
(0.1% -0.9%)
14.7%
(10.1% -20.8%)
0%
(0% - 0.2%)
0.1%
(0% - 0.5%)
0%
(0% - 0.2%)
0.6%
(0.2% - 2%)
0%
(0% - 0.3%)
1 .7%
(0.7% -4.1%)
0.1%
(0% - 0.4%)
3.4%
(1.8% -6.8%)
0.1%
(0% - 0.5%)
5.8%
(3.4% -10%)
0.1%
(0% - 0.5%)
5.2%
(3% - 9.2%)
*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding
the SO2 coefficient in the 2-parameter logistic exposure-response function. Percents are rounded to the nearest tenth.
""The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
"The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm,
calculated as the arithmetic mean of hourly averages.
Abt Associates Inc.
4-27
March 2009
-------
Figure 4-1. Percent of Asthmatics Engaged in Moderate or Greater Exertion Exhibiting Lung
Function Response (Defined as an Increase in sRaw > 100%) Attributable to SO2 Within Given
Ranges Under Different Air Quality Scenarios*
a) Greene Co.
30% -
= 25%
o
in
o>
20% -
O)
c
15% -
10% -
o>
I
o
o>
o>
o>
o>
C!
o>
o>
in
C!
o>
o>
*For the legend for these figures see Figure 4-9.
Abt Associates Inc.
4-28
March 2009
-------
Figure 4-2. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion Exhibiting
Lung Function Response (Defined as an Increase in sRaw > 100%) Attributable to SO2 Within Given
Ranges Under Different Air Quality Scenarios*
a) Greene Co.
30% -
= 25%
o
1
Q.
U)
20% -
15% -
I 10%
J2 re
2 5
I
I
o
10
o>
o>
C!
o>
o>
in
C!
o>
o>
C!
CO
o>
b) St. Louis
30% -
B
v>
o
in
C!
o>
o>
C!
CO
en
*For the legend for these figures see Figure 4-9.
Abt Associates Inc.
4-29
March 2009
-------
Figure 4-3. Percent of Asthmatics Engaged in Moderate or Greater Exertion Exhibiting Lung
Function Response (Defined as a Decrease in FEVi > 15%) Attributable to SO2 Within Given Ranges
Under Different Air Quality Scenarios*
a) Greene Co.
30% -
o
^ 25% -
&)
O>
a
TO 20% -
O)
_c
T3
c 15% -
O
Q.
(A
0)
^ 10% -
c
Q)
U
0)
Q- 5% -
0% -
_L_1_L_L_L-L-L_L
. _; o o o o o o
, 2 in o in o in o
~ w 0> ^ ^ E^E^E;!
(/) (Q *J 0> 0> 0> 0> 0> 00
" 3 C 0) 0) 0) 0) 0)
S. a Ģ
: 3
O
b) St. Louis
30% -
O
,0 25% -
IO
O>
T3
TO 20% -
O)
c
T3
c 15% -
O
0.
(A
0)
m% -
c
Q)
u- o% -
0% -I
T
|
ąi
(/) (Q
~ 3
J*
-
~_
_
~_
z
Ĩ
J
;>
;
w
i
)
>
5
)
L e
I , I_L_I ! tZ
O C
in c
S Ģ
0> C
o
3
9
9
5
>
m
-y,
c
U
T
C
a
-K
9
9
9
}
af
>
cs
c
c
E
c
a
s
9
9
i|
>
I>
^
Ŧl
V
'#
C
U
E
c
c
m
ft
X
s
a
>i
>
r>
^
y
~>
^
c
c
E
c
a
m
^
fl
>~
5=S
9
9
4
3
)
*For the legend for these figures see Figure 4-9.
Abt Associates Inc.
4-30
Marc/z 2009
-------
Figure 4-4. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion Exhibiting
Lung Function Response (Defined as a Decrease in FEVi > 15%) Attributable to SO2 Within Given
Ranges Under Different Air Quality Scenarios*
a) Greene Co.
30% -
25% -
20% -
O)
c 15% -
O
Q.
(A
Q)
2: 10%
0)
0)
Q- 5%
0%
<
"Ŧ
o
to
s
|
O
o
o
o>
o>
b) St. Louis
30% -
" 25% -
in
T3
_TO 20% -
0)
c 1 *=;% -
o
Q.
(A
(1)
(V*
+^ 10% -
c
0)
0)
Q- 5% -
0% -I
-!
s
Is
A
?
S
_^
: - U
Ŧ 15 *
^ ^ !
V) Q 1
t
:-
5
33
+'
+'
^ rrn
2 1
) S
; O>
i
5
)
F
c
c
T
0
a
CT
9
9
5
r>
\
=
y
c
u
T
a
a
a
^
9
)
5
ŧ
\
3E
5
^
C
E
a
a
1=1
R
&
9
9
1
5
ŧ
\
~
IĢE
V
a
c
I
0
a
~
U
r*
'i
^
3
^
Ŧl
s
r>
i
. _
TT
!!!!!
Ŧ
C
C
o
a
_
TT?
:!!:!!
$
^
9
9
1
5
}
:
*For the legend for these figures see Figure 4-9.
Abt Associates Inc.
4-31
March 2009
-------
Figure 4-5. Number of Occurrences of Lung Function Response (Defined as an Increase in sRaw >
100%) Among Asthmatics Engaged in Moderate or Greater Exertion Attributable to SO2 Within
Given Ranges Under Different Air Quality Scenarios*
a) Greene Co.
600 -,
o
500 -
400 -
300 -
u
u
O
200 -
Ŧ 100
b) St. Louis
600 -,
o>
o>
o>
o>
o>
C!
o>
o>
in
C!
o>
o>
*For the legend for these figures see Figure 4-9.
Abt Associates Inc.
4-32
March 2009
-------
Figure 4-6. Number of Occurrences of Lung Function Response (Defined as an Increase in sRaw >
100%) Among Asthmatic Children Engaged in Moderate or Greater Exertion Attributable to SO2
Within Given Ranges Under Different Air Quality Scenarios*
a) Greene Co.
600 -i
o
1 500-
o
c
3.
in
c 400 -
re
U)
0
c 300 -
u
c
V
3 200 -
u
u
O
'o
jj 100 -
E
3
Z
0 -I
-L-L-L-L-L-L-L-L
. _; o o o o o o
< > Ģ in o in o in o
: ąĢ CO o ^ ^ C! C! C!
(/) (Q +^ O> O> 0) 0) 0) 00
^ ^ C 0> 0> 0> 0) 0)
4 5 e
o
b) St. Louis
600 i
J2 "re
CO
I
o
0> ^
0> 0>
0>
o>
o>
C!
o>
o>
*For the legend for these figures see Figure 4-9.
Abt Associates Inc.
4-33
March 2009
-------
Figure 4-7. Number of Occurrences of Lung Function Response (Defined as a Decrease in FEVi >
15%) Among Asthmatics Engaged in Moderate or Greater Exertion Attributable to SO2 Within
Given Ranges Under Different Air Quality Scenarios*
a) Greene Co.
o
§ 500 -
O
C
3.
in
c 400 -
re
U)
o
.c
c 300 -
in
u
0)
5 200 -
u
u
O
'o
Ŧ 100 -
z
0 -
IIIIIIII
; o o o o o o
< > S jo o to o to o
(/) (5 *^ O) O) O) O) O) 00
~~ ^ C O) O) O) O) O)
4 a g
o
b) St. Louis
600 -,
o>
in
C!
o>
o>
C!
CO
o>
*For the legend for these figures see Figure 4-9.
Abt Associates Inc.
4-34
March 2009
-------
Figure 4-8. Number of Occurrences of Lung Function Response (Defined as a Decrease in FEVi >
15%) Among Asthmatic Children Engaged in Moderate or Greater Exertion Attributable to SO2
Within Given Ranges Under Different Air Quality Scenarios*
a) Greene Co.
o
§ 500 -I
D
400 -
300 -
200 -
100 -
<
"Ŧ
o
to
s
I
o
o
o
o>
o>
o>
o>
o
o
o>
o>
o
to
o>
o>
o
o
CO
o>
b) St. Louis
600
o
^
§ 500 H
D
400 -
300 -
200 -
2 "re
0
CO
o>
o>
o>
o>
C!
o>
o>
in
C!
o>
o>
*For the legend for these figures see Figure 4-9.
Abt Associates Inc.
4-35
March 2009
-------
Figure 4-9. Legend for Figures 4-1 - 4-8.
Q Attributable to 500 ppb<=S02
E Attributable to 450 ppb<=S02<500 ppb
D Attributable to 400 ppb<=S02<450 ppb
C Attributable to 350 ppb<=S02<400 ppb
Attribuable to 300 ppb<=S02<350 ppb
H Attributable to 250 ppb<=S02<300 ppb
D Attributable to 200 ppb<=S02<250 ppb
0 Attributable to 150 ppb<=S02<200 ppb
Q Attributable to 100 ppb<=S02<150 ppb
D Attributable to 50 ppb<=S02<100 ppb
Attributable to S02<50 ppb
The current primary SC>2 standards include a 24-hour standard set at 0.14 parts per
million (ppm), not to be exceeded more than once per year, and an annual standard set at
0.03 ppm, calculated as the arithmetic mean of hourly averages. In St. Louis, 862
concentrations that are predicted to occur if the current standards were just met are
substantially higher than "as is" air quality (based on 2002 monitoring and modeling
data) and also substantially higher than they would be under any of the alternative 1-hr
standards considered in this analysis. Consequently, the levels of response that would be
seen if the current standard were just met are well above the levels that would be seen
under the "as is" air quality scenario or under any of the alternative 1-hr standards - for
asthmatics and for asthmatic children, and for all four definitions of lung function
response.
For example, of the estimated approximately 102,400 asthmatics engaged in
moderate or greater exertion in St. Louis, about 13,500 (or 13.1%) are estimated to have
at least one lung function response, defined as an increase in sRaw > 100%, if the current
standards were just met. Under "as is" air quality conditions, the corresponding number
is about 1,000 (1%). Only the most stringent alternative 99th percentile 1-hr standard, set
at 50 ppb (denoted "99/50" in the above tables of results), is predicted to lower the
numbers of responders below the levels estimated under the "as is" scenario. As the
Abt Associates Inc.
4-36
March 2009
-------
alternative 1-hr standards become less stringent (i.e., as the level is raised from 50 ppb to
100 ppb, to 150 ppb, etc.), the numbers responding correspondingly rise.
The pattern seen in St. Louis for lung function response, defined as an increase in
sRaw > 100%, is also seen for the other definitions of lung function response. For
example, of the estimated roughly 102,400 asthmatics engaged in moderate or greater
exertion, 750 are estimated to have at least one lung function response, defined as a
decrease in FEVi > 15%, under the "as is" air quality scenario; the corresponding number
(percent) if the current standards were just met is about 15,200 (14.9%); the
corresponding numbers for the alternative 1-hr standards denoted 99/50, 99/100, 99/150,
99/200, 99/250, and 98/200 are about 500 (0.5%), 1700 (1.7%), 3500 (3.4%), 5600
(5.4%), 7900 (7.7%), and 7400 (7.2%), respectively.
Although the basic pattern across air quality scenarios seen in St. Louis is
repeated in Greene County, the impact of changing from one air quality scenario to
another is substantially dampened in Greene County. This is because of the different
patterns of exposures in the two locations. In St. Louis there is a wide range of 862
concentrations to which asthmatics are exposed under the current standards scenario -
i.e., substantial percentages of asthmatics are exposed to relatively higher concentrations
of SC>2 under this scenario. There is thus much room for improvement. Under the most
stringent alternative 1-hr standard (99/50), much of that exposure is pushed down to the
lowest SC>2 concentration "bins." Under the current standards scenario, for example, only
about 22 percent of asthmatics in St. Louis have exposures no greater than 100 ppb;
under the most stringent alternative 1-hr standard (99/50), that increases to 98 percent.
In Greene County, in contrast, about 95 percent of asthmatics have exposures no
greater than 100 ppb under the current standards scenario. There is therefore little room
for improvement. Under the most stringent alternative 1-hr standard (99/50), that 95
percent becomes 100 percent. The situation is even more extreme for person days of
exposure. Under the current standards scenario, 99.9 percent of person days of exposure
are to < 100 ppb SC>2 in Greene County; the corresponding figure for St. Louis is 95.2
percent.
The generally lower levels of SC>2 to which asthmatics in Greene County are
exposed, relative to asthmatics in St. Louis, and the corresponding greater preponderance
of responses associated with the lowest 862 concentration "bins" in Greene County, can
be readily seen in Figures 4-1 through 4-8.u
Although the numbers are smaller for asthmatic children (because the underlying
populations are smaller), the patterns seen in St. Louis and in Greene County across the
different air quality scenarios, and the comparisons between the two locations, are fairly
similar for asthmatic children as for asthmatics for all lung function response definitions.
11 In several cases, responses associated with exposures in SO2 bins cannot be seen in the figures, because
the percent responding, or numbers of occurrences of lung function response are so small. We chose to
scale the y-axis the same on all comparable figures to facilitate comparisons between figures. This meant,
however, that some "response bars" essentially became visually undetectable.
Abt Associates Inc. 4-37 March 2009
-------
In general, however, the percentages of asthmatic children engaged in moderate or
greater exertion who experience at least one lung function response, for each of the
different lung function response definitions, tend to be greater than the corresponding
percentages of asthmatics. This presumably is a reflection of the greater amount of time
spent outdoors by asthmatic children relative to adults.
Finally, we note that, while in several air quality scenarios the great majority of
occurrences of lung function response are in the lowest exposure bin, the numbers of
individuals with at least one lung function response attributable to exposures in that
lowest bin are typically quite small. This is because the calculation of numbers of
individuals with at least one lung function response uses individuals' highest exposure
only. While individuals may be exposed mostly to low SO2 concentrations, many are
exposed at least occasionally to higher levels. Thus, the percentage of individuals in a
designated population with at least one lung function response associated with SC>2
concentrations in the lowest bin is likely to be very small, since most individuals are
exposed at least once to higher SO2 levels. For example, defining lung function response
as an increase in sRaw > 100%, under a scenario in which 862 concentrations just meet
an alternative 1-hour 99th percentile 100 ppb standard, about 93 percent of occurrences of
lung function response among asthmatics in St. Louis are associated with SC>2 exposures
in the lowest exposure bin (0 - 50 ppb). However, the lowest SO2 exposure bin accounts
for only about 0.2 percent of asthmatics estimated to experience at least 1 SCVrelated
lung function response. For this very small percent of the population, the lowest
exposure bin represents their highest SC>2 exposures under moderate exertion in a year.
Thus Figure 4-5b shows virtually all of the occurrences among asthmatics in St. Louis
associated with the lowest 862 exposure bin; however, Figure 4-lb shows a relatively
small proportion of asthmatics in St. Louis experiencing at least one response to be
experiencing those responses because of exposures in that lowest exposure bin.
Abt Associates Inc. 4-38 March 2009
-------
5 REFERENCES
Abt Associates Inc. 2007a. Ozone Health Risk Assessment for Selected Urban Areas.
Prepared for Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, Research Triangle Park, NC., July 2007, Under Contract No. 68-D-
03-002, Work Assignment 3-39 and 4-56. Available electronically on the internet at:
http://www.epa.gOv/ttn/naaqs/standards/ozone/s o3crtd.html.
Abt Associates Inc. 2007b. TRIM: Total Risk Integrated Methodology. Users Guide for
TRIM.RiskHuman Health-Probabilistic Application for the Ozone NAAQS Risk
Assessment. Available online at:
http://epa.gov/ttn/fera/data/tri m/trimrisk_ozone_ra_userguide_8-6-07.pdf
Bethel RA, Epstein J, Sheppard D, Nadel JA, Boushey HA. 1983. Sulfur dioxide-induced
bronchoconstriction in freely breathing, exercising, asthmatic subjects. Am Rev Respir
Dis 128:987-90.
Bethel RA, Sheppard D, Geffroy B, Tarn E, Nadel JA, Boushey HA. 1985. Effect of 0.25
ppm sulfur dioxide on airway resistance in freely breathing, heavily exercising, asthmatic
subjects. Am Rev Respir Dis 131:659-61.
Gelman, A, Carlin, J. C., Stern, H., and Rubin, D. B. 1995. Bayesian Data Analysis.
Chapman and Hall, New York.
Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (Eds.) 1996. Markov Chain Monte
Carlo in Practice. Chapman and Hall, London, UK.
Kehrl HR, Roger LJ, Hazucha MJ, Horstman DH. 1987. Differing response of asthmatics
to sulfur dioxide exposure with continuous and intermittent exercise. Am Rev Respir Dis
135:350-5.
Linn WS, Avol EL, Peng RC, Shamoo DA, Hackney JD. 1987. Replicated dose-response
study of sulfur dioxide effects in normal, atopic, and asthmatic volunteers. Am Rev Respir
Dis 136:1127-34.
Linn WS, Avol EL, Shamoo DA, Peng RC, Spier CE, Smith MN, Hackney JD. 1988.
Effect of metaproterenol sulfate on mild asthmatics' response to sulfur dioxide exposure
and exercise. Arch Environ Health 43:399-406.
Linn WS, Shamoo DA, Peng RC, Clark KW, Avol EL, Hackney JD. 1990. Responses to
sulfur dioxide and exercise by medication-dependent asthmatics: effect of varying
medication levels. Arch Environ Health 45:24-30.
Abt Associates Inc. 5-1 March 2009
-------
Magnussen H, Torres R, Wagner HM, von Nieding G. 1990. Relationship between the
airway response to inhaled sulfur dioxide, isocapnic hyperventilation, and histamine in
asthmatic subjects. IntArch Occup Environ Health 62:485-91.
Roger LJ, Kehrl HR, Hazucha M, Horstman DH. 1985. Bronchoconstriction in
asthmatics exposed to sulfur dioxide during repeated exercise. J ApplPhysiol 59:784-
91.Spiegelhalter, D., Thomas, A., Best, N. and Gilks, W. 1996. Bugs .5 Bayesian
inference using Gibbs sampling. Manual, version ii. MRC Biostatistics Unit, Institute of
Public Health. Cambridge, U.K.
U.S. EPA. 2007a. Integrated Plan for Review of the Primary National Ambient Air
Quality Standards for Sulfur Oxides. October 2007. Available online at:
http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html.
U.S. EPA, National Center for Environmental Assessment. 2008a. Integrated Science
Assessment for Oxides of Sulfur - Health Criteria (Second External Review Draft).
EPA/600/R-08/047. May 2008. Available online at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=l 90346.
U.S. EPA, National Center for Environmental Assessment. 2008c. Integrated Science
Assessment (ISA) for Sulfur Oxides - Health Criteria (Final Report). EPA/600/R-
08/047F. September 2008. Available online at:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=l 98843.
U.S. EPA, Office of Air Quality Planning and Standards. 2008b. Risk and Exposure
Assessment to Support the Review of the SO2 Primary National Ambient Air Quality
Standard: First Draft. EPA-452/P-08-003. June 2008. Available online at:
http://www.epa.gov/ttn/naaqs/standards/so2/sso2cr rea.html.
U.S. EPA, Office of Air Quality Planning and Standards. 2009. Risk and Exposure
Assessment to Support the Review of the SO2 Primary National Ambient Air Quality
Standards: Second Draft. EPA-452/P-09-003. March 2009. Available online at:
http://www.epa.gOv/ttn/naaqs/standards/so2/s so2 cr rea.html.
Abt Associates Inc. 5-2 March 2009
-------
Appendix: Bayesian-Estimated Logistic Exposure-Response Functions: Median,
2.5th Percentile, and 97.5th Percentile Curves
Abt Associates Inc. A-l March 2009
-------
Figure A-l. Bayesian-Estimated Logistic Exposure-Response Function: Increase in sRaw > 100%
for 5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion
100%
90% -
80% -
70% -
TO 60% -
o
Q.
50% -
40% -
30% -
20% -
10% -
0%
A The data
median curve
2.5th percentile curve
97.5th percentile curve
0
0.2 0.4 0.6
SO2 Concentration (ppm)
0.8
Figure A-2. Bayesian-Estimated Logistic Exposure-Response Function: Increase in sRaw > 200%
for 5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion
100%
90%
80%
70%
ra 60% -
A The data
median curve
2.5th percentile curve
97.5th percentile curve
0.2
0.4 0.6
SO2 Concentration (ppm)
0.8
Abt Associates Inc.
A-2
March 2009
-------
Figure A-3. Bayesian-Estimated Logistic Exposure-Response Function: Decrease in FEVi > 15% for
5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion
100%
90%
80%
70%
ra 60% -
A The data
median curve
2.5th percentile curve
97.5th percentile curve
0.2
0.4 0.6
SO2 Concentration (ppm)
0.8
Figure A-4. Bayesian-Estimated Logistic Exposure-Response Function: Decrease in FEVi >; 20% for
5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion
100%
90% -
80% -
70% -
ra 60% -
A The data
median curve
2.5th percentile curve
97.5th percentile curve
0.2 0.4 0.6 0.8
SO2 Concentration (ppm)
Abt Associates Inc.
A-3
March 2009
-------
i APPENDIX D: SUPPLEMENTAL INFORMATION FOR POLICY
2 ASSESSMENT (CHAPTER 10)
D-l
-------
ith
Table D-1. 99 percentile 24-hour average SO2 concentrations for 2005 given just
meeting the alternative 1-hour daily maximum standards analyzed in the risk
assessment (concentrations in ppb).
State Count
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
Ml
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
99th percentile
50
7
14
10
11
10
8
5
14
9
17
16
13
14
8
12
19
18
25
9
12
17
19
12
15
14
10
11
16
19
19
17
7
9
21
13
14
11
43
10015
14
27
20
22
20
15
9
27
17
35
32
26
28
17
25
38
36
49
18
25
33
37
24
30
29
20
22
32
38
38
35
13
18
43
25
27
21
87
020
20
41
31
33
29
23
14
41
26
52
48
39
43
25
37
57
55
74
28
37
50
56
35
44
43
29
33
48
57
56
52
20
26
64
38
41
32
130
025
27
55
41
44
39
31
19
54
34
70
64
52
57
34
50
76
73
98
37
50
66
74
47
59
58
39
45
65
76
75
70
27
35
86
51
54
42
173
0
34
69
51
55
49
38
24
68
43
87
79
65
71
42
62
96
91
123
46
62
83
93
59
74
72
49
56
81
95
94
87
33
44
107
64
68
53
217
98th percentile
200
36
66
56
51
56
40
21
71
43
80
72
58
73
48
59
97
90
113
44
56
78
89
53
67
73
47
71
81
87
87
83
38
42
96
64
64
54
194
D-2
-------
ith
Table D-2. 99 percentile 24-hour average SO2 concentrations for 2006 given just
meeting the alternative 1-hour daily maximum standards analyzed in the risk
assessment (concentrations in ppb).
State Count
DE
IL
IN
IN
IN
IA
IA
Ml
MO
MO
NH
NY
NY
NY
OH
OH
OH
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
New Castle
Madison
Floyd
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
99th percentile
50
11
9
8
5
5
11
15
17
17
7
14
23
7
7
14
11
12
12
9
16
15
11
16
16
8
11
17
12
14
10
10015
23
18
15
10
11
23
31
34
33
13
28
46
13
15
28
23
24
23
19
32
29
22
32
31
17
23
35
24
28
21
020
34
28
23
14
16
34
46
51
50
20
41
69
20
22
43
34
35
35
28
48
44
33
48
47
25
34
52
36
42
31
025
46
37
30
19
22
45
62
68
66
26
55
92
27
29
57
46
47
46
38
63
59
45
65
62
34
45
70
49
56
42
0
57
46
38
24
27
56
77
85
83
33
69
115
33
36
71
57
59
58
47
79
73
56
81
78
42
56
87
61
70
52
98th percentile
200
55
43
40
25
27
52
70
76
86
37
66
106
32
33
67
55
53
59
46
101
74
51
75
74
49
53
78
61
66
53
D-3
-------
>nd
Table D-3. 2 highest 24-hour average SO2 concentrations (i.e. the current 24-
hour standard) for 2005 given just meeting the alternative 1-hour daily maximum
standards analyzed in the risk assessment (concentrations in ppb)
State Count
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
Ml
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
99th percentile levels
50
7
18
13
12
8
9
5
19
12
19
18
17
18
10
18
22
18
29
12
14
26
22
12
16
18
11
11
17
23
23
22
9
10
22
14
16
12
48
10015
15
36
26
24
20
18
11
38
23
38
37
34
37
20
35
45
45
57
19
27
53
44
24
31
36
21
23
33
46
46
43
19
20
49
28
32
23
95
020
22
54
38
36
30
28
16
56
35
57
55
50
55
29
53
67
68
86
37
41
79
66
36
47
55
32
35
50
69
69
65
28
30
74
42
48
35
143
025
29
72
51
48
41
37
22
75
46
76
73
67
73
39
71
89
90
115
49
54
105
88
49
63
73
42
47
66
92
92
87
37
39
98
56
64
47
190
0
37
90
64
60
51
46
27
94
58
95
92
84
92
49
88
111
113
144
62
68
132
110
61
79
91
53
58
83
115
115
108
46
49
123
70
80
58
238
98th percentile
level
200
39
86
71
56
58
49
25
98
57
88
83
75
95
55
84
113
112
132
59
61
125
106
55
72
93
52
74
84
106
107
103
54
46
110
71
76
59
213
D-4
-------
>nd
Table D-4. 2 highest 24-hour average SO2 concentrations (i.e. the current 24-
hour standard) for 2006 given just meeting the alternative 1-hour daily maximum
standards analyzed in the risk assessment (concentrations in ppb)
State Count
DE
IL
IN
IN
IN
IA
IA
Ml
MO
MO
NH
NY
NY
NY
OH
OH
OH
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
New Castle
Madison
Floyd
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
99th percentile levels
50
18
10
12
6
7
16
18
24
20
7
19
25
8
12
21
16
13
13
12
50
19
14
21
20
10
13
20
14
15
11
10015
37
20
23
11
14
32
36
48
40
14
38
49
15
24
43
33
26
27
24
101
38
29
41
41
21
26
41
28
31
22
020
55
31
35
17
21
48
54
72
60
32
57
74
23
36
64
49
39
40
35
151
57
43
62
61
31
39
61
42
46
34
025
73
41
47
27
28
63
72
96
80
43
76
99
30
47
85
65
52
53
47
202
76
58
83
82
42
52
82
56
61
45
0
92
51
58
34
35
79
90
120
100
54
95
124
38
59
106
82
65
66
59
252
95
72
104
102
52
65
102
70
76
56
98th percentile
level
200
88
48
61
36
35
73
82
107
103
61
90
114
36
54
101
79
58
68
57
321
96
66
96
97
60
61
91
71
72
57
D-5
-------
Table D-5. Annual average SO2 concentrations for 2005 given just meeting the
alternative 1-hour daily maximum standards analyzed in the risk assessment
(concentrations in ppb).
State Count
AZ
DE
FL
IL
IL
IN
IN
IN
IN
IA
IA
Ml
MO
MO
NH
NJ
NJ
NY
NY
NY
OH
OH
OH
OK
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
WV
Gila
New Castle
Hillsborough
Madison
Wabash
Floyd
Gibson
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Hudson
Union
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Tulsa
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
Wayne
99th percentile levels
50
1.5
2.3
1.5
1.8
1.5
2.3
0.8
1.8
1.9
2.0
2.3
2.4
1.9
1.4
2.4
6.4
6.2
6.9
2.1
2.3
4.6
2.8
2.7
3.6
3.6
2.8
2.9
3.2
4.7
2.9
2.9
1.5
1.4
7.8
4.5
4.3
2.6
6.0
10015
2.9
4.6
2.9
3.7
3.0
4.5
1.7
3.6
3.7
4.1
4.6
4.9
3.8
2.8
4.8
12.9
12.3
13.7
4.3
4.5
9.3
5.7
5.4
7.2
7.1
5.5
5.9
6.5
9.3
5.8
5.7
3.0
2.8
15.5
8.9
8.6
5.2
12.0
020
4.3
6.9
4.4
5.5
4.5
6.8
2.5
5.4
5.5
6.1
6.9
7.3
5.7
4.2
7.2
19.3
18.4
20.6
6.4
6.8
13.9
8.5
8.1
10.7
10.7
8.3
8.8
9.7
14.0
8.7
8.6
4.5
4.2
23.2
13.4
13.0
7.8
18.0
025
5.8
9.2
5.8
7.4
6.0
9.0
3.4
7.1
7.4
8.2
9.1
9.7
7.6
5.6
9.5
25.7
24.6
27.4
8.6
9.1
18.6
11.3
10.8
14.3
14.2
11.0
11.7
13.0
18.7
11.7
11.5
6.1
5.6
31.0
17.9
17.3
10.3
24.0
0
7.2
11.5
7.3
9.2
7.5
11.3
4.2
8.9
9.2
10.2
11.4
12.1
9.5
7.1
11.9
32.1
30.7
34.3
10.7
11.3
23.2
14.1
13.5
17.9
17.8
13.8
14.6
16.2
23.3
14.6
14.4
7.6
7.1
38.7
22.4
21.6
12.9
30.0
98th percentile
level
200
7.7
11.0
8.0
8.6
8.6
11.9
3.8
9.3
9.1
9.4
10.3
10.8
9.8
8.0
11.4
32.5
30.4
31.6
10.3
10.2
22.1
13.6
12.1
16.3
18.1
13.4
18.7
16.3
21.5
13.5
13.6
8.7
6.6
34.7
22.6
20.5
13.2
26.8
D-6
-------
Table D-6. Annual average SO2 concentrations for 2006 given just meeting the
alternative 1-hour daily maximum standards analyzed in the risk assessment
(concentrations in ppb).
State Count
DE
IL
IN
IN
IN
IA
IA
Ml
MO
MO
NH
NY
NY
NY
OH
OH
OH
PA
PA
PA
PA
PA
TN
TN
TN
TX
VA
WV
WV
WV
New Castle
Madison
Floyd
Lake
Vigo
Linn
Muscatine
Wayne
Greene
Jefferson
Merrimack
Bronx
Chautauqua
Erie
Cuyahoga
Lake
Summit
Allegheny
Beaver
Northampton
Warren
Washington
Blount
Shelby
Sullivan
Jefferson
Fairfax
Brooke
Hancock
Monongalia
99th percentile levels
50
2.2
1.7
1.6
1.7
1.4
1.8
1.7
2.2
2.0
1.5
2.1
6.5
1.6
1.5
4.1
2.4
2.2
2.7
2.0
3.7
2.5
4.3
3.0
3.7
1.8
1.4
6.9
3.9
4.1
2.0
10015
4.4
3.5
3.2
3.3
2.8
3.6
3.4
4.4
4.0
3.0
4.3
13.0
3.1
3.1
8.2
4.8
4.3
5.5
4.0
7.3
4.9
8.5
6.0
7.5
3.6
2.9
13.9
7.7
8.2
3.9
020
6.7
5.2
4.8
5.0
4.2
5.4
5.2
6.6
6.1
4.5
6.4
19.5
4.6
4.6
12.4
7.2
6.5
8.2
6.0
11.0
7.4
12.8
8.9
11.2
5.3
4.3
20.8
11.6
12.3
5.8
025
8.9
6.9
6.3
6.6
5.6
7.2
6.9
8.8
8.1
5.9
8.5
26.0
6.2
6.1
16.5
9.6
8.7
10.9
8.0
14.6
9.9
17.1
11.9
14.9
7.1
5.7
27.7
15.5
16.3
7.8
0
11.1
8.6
7.9
8.3
7.0
9.1
8.6
10.9
10.1
7.4
10.7
32.5
7.7
7.6
20.6
12.0
10.9
13.7
10.0
18.3
12.3
21.3
14.9
18.6
8.9
7.2
34.6
19.3
20.4
9.7
98th percentile
level
200
10.6
8.1
8.4
8.7
6.9
8.3
7.8
9.8
10.4
8.4
10.1
29.9
7.4
6.9
19.6
11.6
9.8
13.9
9.7
23.3
12.4
19.6
13.8
17.7
10.3
6.7
31.0
19.5
19.4
9.9
D-7
-------
United States Office of Air Quality Planning and Standards EPA-452/P-09-003
Environmental Protection Health and Environmental Impacts Division March 2009
Agency Research Triangle Park, NC
------- |