Risk and Exposure Assessment to Support the
Review of the SC>2 Primary National Ambient Air
Quality Standards: First Draft
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EPA-452/P-08-003
June 2008
Risk and Exposure Assessment to Support the
Review of the SC>2 Primary National Ambient Air
Quality Standards: First Draft
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina
11
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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
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1 Table of Contents
2
3 LIST OF TABLES VII
4 LIST OF FIGURES IX
5 1.0 INTRODUCTION 1
6 1.1 History 4
7 1.1.1 History of the SO2NAAQS 4
8 1.1.2 Health Evidence from the Previous Review 5
9 1.1.3 Assessment from Previous Review 6
10 1.2 Scope of the Risk and Exposure Assessment for the Current Review 8
11 1.2.1 Overview of Assessment 8
12 1.2.2 Species of Sulfur Oxides Included in Analyses 9
13 1.2.3 Scenarios for the Current Assessment 10
14 2.0 HUMAN EXPOSURE 11
15 2.1 Overview 11
16 2.2 Sources of SO2 11
17 2.3 Ambient levels of SO2 12
18 2.4 Relationship of personal Exposure to Ambient Concentrations 13
19 3.0 AT RISK POPULATIONS 15
20 3.1 Overview 15
21 3.2 Disease and illness 15
22 3.3 Genetic Susceptibility 16
23 3.4 Age 16
24 3.5 Vulnerability 17
25 4.0 HEALTH EFFECTS 18
26 4.1 Introduction 18
27 4.2 Short-Term Peak (
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1 4.3 Short-Term (> 1-hour, Generally 24-Hour) SO2 Exposure and Respiratory Health Effects 22
2 4.3.1 Respiratory Symptoms 22
3 4.3.2 Emergency Department Visits and Hospitalizations for All Respiratory Causes 24
4 4.3.3 Emergency Department Visits and Hospitalizations for Asthma 26
5 5.0 OVERVIEW OF RISK AND EXPOSURE ASSESSMENT 28
6 5.1 Introduction 28
7 5.2 Approach for Assessing Exposure and Risk Associated with 5-Minute Peak So2 Exposures 28
8 5.3 Approach for Assessing Exposure and Risk Associated with Short-Term (>1 hour) So2 Exposures 29
9 6.0 AMBIENT AIR QUALITY AND BENCHMARK HEALTH RISK
10 CHARACTERIZATION FOR 5-MINUTE PEAK SO2 EXPOSURES 31
11 6.1 Introduction 31
12 6.2 Approach 32
13 6.2.1 Monitoring data 32
14 6.2.2 Monitoring Siting 33
15 6.2.3 Statistical Model for Estimating 5-minute Maximum Concentrations 34
16 6.3 Approach for simulating Just Meeting the Current SO2 Standard 47
17 6.3.1 Introduction 47
18 6.3.2 Approach 48
19 6.4 Results 54
20 6.4.1 Measured 5-minute Maximum and 1-Hour Ambient Monitoring SO2 Concentrations 54
21 6.4.2 Measured 1-Hour and Modeled 5-Minute Maximum Ambient Monitoring SO2 Concentrations 64
22 6.4.3 Air Quality Just Meeting the CurrentDaily Standard 81
23 6.5 Uncertainty Analysis 100
24 6.5.1 Air Quality Data 100
25 6.5.2 Measurement Technique for Ambient SO2 101
26 6.5.3 Temporal Representation 101
27 6.5.4 Spatial Representation 102
28 6.5.5 Air Quality Adjustment Procedure 103
29 6.5.6 Ambient Monitor to Exposure Representation 103
30 6.5.7 Statistical Model 104
31 6.5.8 Single vs. Multiple Short-Term Peak Concentrations Ill
32 6.5.9 Health Benchmark 112
33 7.0 EXPOSURE ANALYSIS 113
34 7.1 Overview 113
35 7.2 Overview of Human Exposure Modeling using APEX 113
36 7.3 Characterization of study areas 116
37 7.3.1 Study Area Selection 116
38 7.3.2 Study Area Description 116
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1 7.4 Characterization of Ambient Hourly Air Quality Data Using AERMOD 117
2 7.4.1 Overview 117
3 7.4.2 Introduction 117
4 7.4.3 Meteorological Inputs 119
5 7.4.4 Surface Characteristics and Land Use Analysis 123
6 7.4.5 Meteorological Analysis 124
7 7.4.6 Stationary Sources Emissions Preparation 125
8 7.4.7 Urban and Rural Source Characterization 128
9 7.4.8 Receptor Locations 131
10 7.4.9 Other Modeling Specifications 131
11 7.4.10 Estimate Air Quality Concentrations 131
12 7.5 Population Modeled 134
13 7.5.1 Simulated Individuals 134
14 7.5.2 Employment Probabilities 136
15 7.5.3 Commuting Patterns 137
16 7.5.4 Characterizing Ventilation Rates 138
17 7.6 Construction of Longitudinal Activity Sequences 138
18 7.7 Calculating Microenvironmental Concentrations 140
19 7.7.1 Overview 140
20 7.7.2 Approach for Estimating 5-Minute Peak Concentrations 141
21 7.7.3 Microenvironments Modeled 143
22 7.7.4 Microenvironment Descriptions 143
23 7.8 Exposure and Health Risk Calculations 146
24 7.9 Exposure Modeling and Health Risk Characterization Results 147
25 7.9.1 Introduction 147
26 7.9.2 Number of Exceedances Considering As Is Air Quality 148
27 7.9.2 Number of Exceedances Considering Air Quality Adjusted to Just Meeting the Current Standard 150
28 7.10 Uncertainty Analysis 152
29 7.10.1 Introduction 152
30 7.10.2 Input Data Evaluation 153
31 8.0 HEALTH RISK ASSESSMENT FOR LUNG FUNCTION RESPONSES IN
32 ASTHMATICS ASSOCIATED WITH 5-MINUTE PEAK EXPOSURES 158
33 8.1 Introdution 158
34 8.2 Development of Approach for 5-minute Lung Function Risk Assessment 159
35 8.2.1 General Approach 159
36 8.2.2 Exposure Estimates 161
37 8.2.3 Exposure-Response Functions 161
38 8.2.4 Characterizing Uncertainty and Variability 164
39 9.0 RISK CHACTERIZATION FOR SHORT-TERM (>1 HOUR, GENERALLY 24-
40 HOUR) SO2 EXPOSURES 167
41 9.1 Overview 167
42 9.2 Approach 168
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1 List of Tables
2
3 Table 6-1. Summary of available 5-minute and 1-hour 862 ambient monitoring data 32
4 Table 6-2. Number of duplicate samples within and between max-5 and continuous-5 data sets.
5 33
6 Table 6-3. Comparison of measured and modeled number of 5-minute maximum concentrations
7 above 400 ppb located near a petroleum refinery 46
8 Table 6-4. Estimated population, number of ambient 862 monitors, and concentration adjustment
9 factors for simulating just meeting the current SC>2 NAAQS in selected counties by
10 year 51
11 Table 6-5. Model results from one-way analysis of variance (ANOVA) testing for effect of
12 monitoring year (years 1997-2007) 56
13 Table 6-6. Model results from one-way analysis of variance (ANOVA) testing for effect of
14 monitoring year (years 1997-2007) 66
15 Table 6-7. Descriptive statistics for modeled 5-minute maximum and measured 1-hour SO2
16 concentrations for monitors with 1-hour annual average 862 concentration above 15
17 ppb 67
18 Table 6-8. Ambient monitors containing a daily average 862 concentration greater than 140 ppb
19 and their modeled 5-minute maximum concentrations above selected potential health
20 effect benchmark levels, Years 1997 through 2007 79
21 Table 6-9. Identification of twenty locations for detailed analyses 82
22 Table 6-10. Model results from one-way analysis of variance (ANOVA) testing for effect of
23 monitoring year. Results are from detailed analysis of twenty selected counties, Years
24 2002 through 2006 83
25 Table 6-11. Summary of annual average 862 concentrations and estimated number of 5-minute
26 maximum 862 concentrations above potential health effect benchmark levels per year
27 in 20 counties using 20 model simulations, Years 2002 through 2006, air quality data
28 as is 96
29 Table 6-12. Summary of annual average SO2 concentrations and estimated number of 5-minute
30 maximum 862 concentrations above potential health effect benchmark levels per year
31 in 20 counties using 20 model simulations, Years 2002 through 2006, air quality data
32 adjusted to just meeting the current standards (either one exceedance of 0.14 ppm
33 daily average or no exceedance of 0.03 ppm annual average) 97
34 Table 6-13. Summary of daily average SO2 concentrations and estimated number of 5-minute
35 maximum 862 concentrations above potential health effect benchmark levels per day
36 in 20 counties using 20 model simulations, Years 2002 through 2006, air quality data
37 as is 98
38 Table 6-14. Summary of daily average SO2 concentrations and estimated number of 5-minute
39 maximum SO2 concentrations above potential health effect benchmark levels per day
40 in 20 counties using 20 model simulations, Years 2002 through 2006, air quality data
41 adjusted to just meeting the current standards (either one exceedance of 0.14 ppm
42 daily average or no exceedance of 0.03 ppm annual average) 99
43 Table 6-15. Number of multiple exceedances of potential health effect benchmark levels within
44 an hour Ill
45 Table 7-1. SC>2 dispersion modeling domains for Missouri 118
46 Table 7-2. Surface meteorological stations dictating modeling domains 121
47 Table 7-3. Upper air stations paired to each modeling domain 122
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1 Table 7-4. Seasonal and snow cover specifications by meteorological domain 124
2 Table 7-5. Summary of NEI emission estimates and total emissions used for dispersion
3 modeling in Missouri 128
4 Table 7-6. NLCD92 land use characterization 129
5 Table 7-7. Urban/Rural characterization of each modeling domain 130
6 Table 7-7. Asthma prevalence rates by age for children the Midwestern U.S 135
7 Table 7-8. Asthma prevalence rates by gender for adults the Missouri 136
8 Table 7-9. Population modeled in Missouri modeling domains 136
9 Table 7-10. Studies in CHAD used for the exposure analysis 139
10 Table 7-11. List of microenvironments modeled and calculation methods used 143
11 Table 7-12. Geometric means (GM) and standard deviations (GSD) for air exchange rates by
12 A/C type and temperature range 144
13 Table 7-13. Adjustment factors and potential health effect benchmark levels used by APEX to
14 simulate just meeting the current daily standard in Greene County, Mo 147
15 Table 7-14. Number of all asthmatics at moderate or greater exertion with 5-minute maximum
16 exposures above selected exposure concentrations, all Missouri modeled domains
17 combined, as is air quality 149
18 Table 7-15. Number of asthmatic children at moderate or greater exertion with 5-minute
19 maximum exposures above selected exposure concentrations, all Missouri modeled
20 domains combined, as is air quality 149
21 Table 7-16. Number of all asthmatics at moderate or greater exertion with 5-minute maximum
22 exposures above selected exposure concentrations, Greene County, Mo., air quality
23 adjusted to just meeting the current daily standard 150
24 Table 7-17. Number of asthmatic children at moderate or greater exertion with 5-minute
25 maximum exposures above selected exposure concentrations, Greene County, Mo., air
26 quality adjusted to just meeting the current daily standard 151
27 Table 8-1. Percentage of Asthmatic Individuals in Controlled Human Exposure Studies
28 Experiencing SO2-Induced Decrements in Lung Function 162
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1 List of Figures
2
3 Figure 6-1. The percent of total SC>2 emissions by source types located within 20 km of ambient
4 monitors 35
5 Figure 6-2. Comparison of hourly COV and 5-minute COV at 16 continous-5 monitors, over
6 multiple years of monitoring 38
7 Figure 6-3. Cumulative density functions (CDFs) for hourly COV at 1-hour and 5-minute SC>2
8 monitors 39
9 Figure 6-4. Peak to mean ratio (PMR) distributions for three variability categories and 1-hour
10 concentration groups 41
11 Figure 6-5. Comparison of the mean relative absolute difference in number of predicted and
12 measured peaks above 400 ppb, across progressive model simulations using the
13 monitors that contained measurements for 5-minute maximum SC>2 concentrations.. 44
14 Figure 6-6. Variability in the predicted number of 5-minute maximum concentrations above 400
15 ppb at monitors that measured 5-minute maximum concentrations 45
16 Figure 6-7. Comparison of the mean predicted (from 20 simulations) and the measured number
17 of 5-minute SC>2 concentrations at 98 monitors that measured 5-minute maximum SC>2
18 concentrations. Bars indicate the standard deviation of the mean 47
19 Figure 6-8. Comparison of annual and daily adjustment factors derived from counties containing
20 at least three 1-hour ambient 862 monitors with valid data, years 2002 through 2006.
21 51
22 Figure 6-9. Distribution of the mean SC>2 concentrations, the maximum SC>2 concentrations and
23 the coefficient of variability for each monitor that measured both the 5-minute
24 maximum and 1 -hour concentrations, Years 1997 through 2007 57
25 Figure 6-10. Distribution of the number of measured 5-minute maximum SC>2 concentrations
26 above potential health effect benchmark levels at each monitor, Years 1997 through
27 2007. The top row represents the distribution for all monitors (including those with no
28 exceedances), the bottom row represents the distribution for those monitors with at
29 least one measured exceedance 59
30 Figure 6-11. Number of ambient monitors measuring 5-minute maximum SC>2 concentrations
31 and number of monitors with at least one benchmark exceedance by year, Years 1997
32 through 2007 60
33 Figure 6-12. Frequency of measured 5-minute maximum SC>2 concentrations above potential
34 health effect benchmark levels in each year, normalized to 100,000 measurements,
35 Years 1997 through 2007 61
36 Figure 6-13. Comparison of the number of measured 5-minute maximum SC>2 concentrations
37 above potential health effect benchmark levels at each monitor per year and the
3 8 associated annual average SC>2 concentration, Years 1997 through 2007 62
39 Figure 6-14. Comparison of the number of measured 5-minute maximum SC>2 concentrations
40 above potential health effect benchmark levels at each monitor per day and the
41 associated daily average SC>2 concentration. The 24-hour SC>2 NAAQS of 0.14 ppm is
42 indicated by the dashed line 63
43 Figure 6-15. Distribution of the mean SO2 concentrations, the maximum SO2 concentrations,
44 and the coefficient of variability for each monitor that measured 1-hour
45 concentrations, Years 1997 through 2007. 5-minute maximum SC>2 concentrations
46 were estimated using a statistical model 65
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1 Figure 6-16. Distribution of the modeled 5-minute maximum SC>2 concentrations above potential
2 health effect benchmark levels at each monitor by year, Years 1997 through 2007.
3 The top row represents the distribution for all monitors (including those with no
4 exceedances), the bottom row represents the distribution for those monitors with at
5 least one estimated exceedance 69
6 Figure 6-17. Number of ambient monitors measuring 1-hour average SC>2 concentration
7 concentrations and number of monitors with at least one benchmark exceedance by
8 year, Years 1997 through 2007 70
9 Figure 6-18. Number of modeled 5-minute maximum SC>2 concentrations above potential health
10 effect benchmark levels at each monitor, Years 1997 through 2007. The top row
11 represents the distribution for all monitors excluding Hawaii County and Caribou,
12 Idaho for year 2001, the bottom row represents the distribution for those monitors with
13 at least one estimated exceedance 73
14 Figure 6-19. Frequency of modeled 5-minute maximum SC>2 concentrations above potential
15 health effect benchmark levels in each year, normalized to 100,000 measurements,
16 Years 1997 through 2007, without Hawaii County and Caribou, Id. (2001) 74
17 Figure 6-20. Comparison of the number of modeled 5-minute maximum SC>2 concentrations
18 above potential health effect benchmark levels at each monitor per year and the
19 associated annual average SO2 concentration, Years 1997 through 2006, all 1-hour
20 monitors 75
21 Figure 6-21. Comparison of the number of modeled 5-minute maximum SC>2 concentrations
22 above potential health effect benchmark levels at each monitor per year and the
23 associated annual average SO2 concentration, Years 1997 through 2006, without
24 Hawaii and Caribou Counties (2001 only) 76
25 Figure 6-22. Comparison of the number of modeled 5-minute maximum SC>2 concentrations
26 above potential health effect benchmark levels at each monitor per day and the
27 associated daily average 862 concentration, Years 1997 through 2007 77
28 Figure 6-23. Comparison of the number of modeled 5-minute maximum SC>2 concentrations
29 above potential health effect benchmark levels at each monitor per day and the
30 associated daily average SC>2 concentration, Years 1997 through 2007, all 1-hour SC>2
31 monitors not including Hawaii and Caribou Counties 78
32 Figure 6-24. Mean 862 concentrations for modeled 5-minute maximum and measured 1-hour
33 SC>2 concentrations, Years 2002 through 2006 at 20 selected counties, with air quality
34 as is and air quality adjusted to just meeting the current standards (either one
35 exceedance of 0.14 ppm daily average or no exceedance of 0.03 ppm annual average).
36 84
37 Figure 6-25. Maximum Mean SC>2 concentrations for modeled 5-minute maximum and
38 measured 1-hour SC>2 concentrations, Years 2002 through 2006 at 20 selected
39 counties, with air quality as is and air quality adjusted to just meeting the current
40 standard (either one exceedance of 0.14 ppm daily average or no exceedance of 0.03
41 ppm annual average) 85
42 Figure 26. Coefficient of variability (COV, %) for modeled 5-minute maximum and measured
43 1-hour SC>2 concentrations, Years 2002 through 2006 at 20 selected counties, with air
44 quality as is and air quality adjusted to just meeting the current standards (either one
45 exceedance of 0.14 ppm daily average or no exceedance of 0.03 ppm annual average).
46 86
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1 Figure 6-27. Frequency of modeled 5-minute maximum SC>2 concentrations above potential
2 health effect benchmark levels in each year, normalized to 100,000 measurements,
3 Years 2002 through 2006 at twenty selected counties, with air quality as is and air
4 quality adjusted to just meeting the current standards (either one exceedance of 0.14
5 ppm daily average or no exceedance of 0.03 ppm annual average) 88
6 Figure 6-28. Number of modeled 5-minute maximum SC>2 concentrations above potential health
7 effect benchmark levels at each monitor by year, Years 2002 through 2006 at 20
8 selected counties, with air quality as is and air quality adjusted to just meeting the
9 current standards (either one exceedance of 0.14 ppm daily average or no exceedance
10 of 0.03 ppm annual average) 89
11 Figure 6-29. Comparison of the number of modeled 5-minute maximum SC>2 concentrations
12 above potential health effect benchmark levels at each monitor per year and the
13 associated annual average SC>2 concentration, Years 2002 through 2006 for 20 selected
14 counties, air quality data as is 90
15 Figure 6-30. Comparison of the number of modeled 5-minute maximum SC>2 concentrations
16 above potential health effect benchmark levels at each monitor per year and the
17 associated annual average SC>2 concentration, Years 2002 through 2006 for 20 selected
18 counties, air quality data adjusted to just meet the current standards (either one
19 exceedance of 0.14 ppm daily average or no exceedance of 0.03 ppm annual average).
20 91
21 Figure 6-31. Comparison of the number of modeled 5-minute maximum SC>2 concentrations
22 above potential health effect benchmark levels at each monitor per day and the
23 associated daily average SO2 concentration, Years 2002 through 2006 for 20 selected
24 counties, air quality data as is 92
25 Figure 6-32. Comparison of the number of modeled 5-minute maximum SC>2 concentrations
26 above potential health effect benchmark levels at each monitor per day and the
27 associated daily average 862 concentration, Years 2002 through 2006 for 20 selected
28 counties, air quality data adjusted to just meet the current standards (either one
29 exceedance of 0.14 ppm daily average or no exceedance of 0.03 ppm annual average).
30 93
31 Figure 6-33. Annual average peak to mean ratio (PMR) for each monitor measuring 5-minute
32 maximum and 1-hour SC>2 concentrations, Years 1997 through 2006 105
33 Figure 6-34. 95% prediction intervals for estimated number of 5-minute maximum SC>2
34 concentrations in a year above potential health effect benchmark levels at each
35 monitor, Years 2002 through 2006 for 20 selected counties, air quality data as is. ..108
36 Figure 6-35. 95% prediction intervals for estimated number of 5-minute maximum SC>2
37 concentrations in a year above potential health effect benchmark levels at each
38 monitor, Years 2002 through 2006 for 20 selected counties, air quality data adjusted to
39 just meeting the current standards (either one exceedance of 0.14 ppm daily average or
40 no exceedance of 0.03 ppm annual average) 109
41 Figure 6-36. 95% prediction intervals for estimated number of 5-minute maximum SC>2
42 concentrations per day above potential health effect benchmark levels at each monitor,
43 Years 2002 through 2006 for 20 selected counties, air quality data adjusted to just
44 meeting the current standards (either one exceedance of 0.14 ppm daily average or no
45 exceedance of 0.03 ppm annual average) 110
46 Figure 7-1. Modeling domains for the state of Missouri 119
47 Figure 7-2. Decision tree for selection of meteorological stations 121
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1 Figure 7-3. Distributions of 1-hour SC>2 concentrations in Greene County, Mo., estimated by
2 AERMOD and measured at four ambient monitors 133
3 Figure 7-4. Distributions of 1-hour SO2 concentrations in Greene County, Mo., estimated by
4 AERMOD and measured at one ambient monitor 134
5 Figure 8-1. Major Components of 5-Minute Peak Lung Function Health Risk Assessment Based
6 on Controlled Human Exposure Studies 160
7
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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 Agency has recently decided to make a number of changes to the process for
18 reviewing the NAAQS (described at http://www.epa.gov/ttn/naaqs/). In making these changes,
19 the Agency consulted with CASAC. This new process, which is being applied to the current
20 review of the SO2 NAAQS, contains four major components. Each of these components, as they
21 relate to the review of the SC>2 primary1 NAAQS, is described below.
22 The first component of the review process is the development of an integrated review
23 plan. This plan presents the schedule for the review, the process for conducting the review, and
24 the key policy-relevant science issues that will guide the review. The final integrated review plan
25 is informed by input from CASAC, outside scientists, and the public. The integrated review plan
26 for this review of the SC>2 primary NAAQS is presented in the Integrated Review Plan for the
27 Primary National Ambient Air Quality Standard for Sulfur Dioxide (EPA, 2007a).
:Note that evidence related to environmental effects of SOX will be considered separately as part of the review of the
secondary NAAQS for NO2 and SO2.
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1 The second component of the review process is a science assessment. A concise
2 synthesis of the most policy-relevant science has been compiled into a draft Integrated Science
3 Assessment (draft ISA). The draft ISA is supported by a series of annexes that contain more
4 detailed information about the scientific literature. The current draft of the ISA to support this
5 review of the SC>2 primary NAAQS is presented in the Integrated Science Assessment for Oxides
6 of Sulfur - Health Criteria (Second External Review Draft), henceforth referred to as the draft
7 ISA (EPA, 2008a).
8 The third component of the review process is a risk and exposure assessment (REA), the
9 first draft of which is described in this document. The first draft REA will be informed by the
10 1st and 2nd drafts of the ISA for SOX and will detail the assessment of exposures and risks
11 associated with recent ambient levels of SCh and with levels that just meet the current standards.
12 The second draft REA will be informed by comments from CAS AC, and the public, as well as
13 findings and conclusions contained in the final ISA, and will also include an assessment of the
14 risks and exposures associated with just meeting potential alternative standards. The results of
15 the risk and exposure assessment will be considered alongside the health evidence, as evaluated
16 in the final ISA, to inform the policy assessment and rulemaking process (see below). The plan
17 for conducting the risk and exposure assessment to support the SC>2 primary NAAQS was
18 presented in the Sulfur Dioxide Health Assessment Plan: Scope and Methods for Exposure and
19 Risk Assessment, henceforth referred to as the Health Assessment Plan (EPA, 2007b).
20 The fourth component of the process is the policy assessment and rulemaking. The
21 Agency's views on policy options will be published in the Federal Register as an advance notice
22 of proposed rulemaking (ANPR). This policy assessment will address the adequacy of the
23 current standard and of any potential alternative standards, which will be defined in terms of
24 indicator, averaging time, form, and level. To accomplish this, the policy assessment will
25 consider the results of the final risk and exposure assessment as well as the scientific evidence
26 (including evidence from the epidemiological, controlled human exposure, and animal
27 toxicological literatures) evaluated in the final ISA. Taking into consideration CASAC advice
28 and recommendations as well as public comment on the ANPR, the Agency will publish a
29 proposed rule, to be followed by a public comment period. Taking into account comments
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1 received on the proposed rule, the Agency will issue a final rule to complete the rulemaking
2 process.
3 As mentioned above, an initial step in the review process was the development of an
4 integrated review plan. This plan identified policy relevant questions that would guide the review
5 of the SO2 NAAQS. These questions are particularly important for the REA because they
6 provide a context for both evaluating health effects evidence presented in the draft ISA, as well
7 as for selecting the appropriate analyses for assessing exposure and risks associated with current
8 ambient SC>2 levels, and levels that just meet the current standards. These policy relevant
9 questions are:
10
11 • Has new information altered/substantiated the scientific support for the occurrence of
12 health effects following short- and/or long-term exposure to levels of SOX found in the
13 ambient air?
14 • Does new information impact conclusions from the previous review regarding the effects
15 of SOX on susceptible populations?
16 • At what levels of SOX exposure do health effects of concern occur?
17 • Has new information altered conclusions from previous reviews regarding the plausibility
18 of adverse health effects caused by SOX exposure?
19 • To what extent have important uncertainties identified in the last review been reduced
20 and/or have new uncertainties emerged?
21 • What are the air quality relationships between short-term and longer-term exposures
22 to SOX?
23 Additional questions will become relevant if the evidence suggests that revision of the
24 current standard might be appropriate. These questions are:
25 • Is there evidence for the occurrence of adverse health effects at levels of SOX different
26 than those observed previously? If so, at what levels and what are the important
27 uncertainties associated with that evidence?
28 • Do exposure estimates suggest that levels of concern for SOx-induced health effects will
29 occur with current ambient levels of 862, or with levels that just meet the current, or
30 potential alternative standards? If so, are these exposures of sufficient magnitude such
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1 that the health effects might reasonably be judged to be important from a public health
2 perspective? What are the important uncertainties associated with these exposure
3 estimates?
4 • Do the evidence, the air quality assessment, and risk/exposure assessment, provide
5 support for considering different standard indicators, averaging times, or forms?
6 • What range of levels is supported by the evidence, the air quality assessment, and
7 risk/exposure assessment? What are the uncertainties and limitations in the evidence and
8 assessments?
9 1.1 HISTORY
10 1.1.1 History of the SO2 NAAQS
11 The first SO2 NAAQS was established in 1971. At that time, a 24-hour standard of 0.14
12 ppm, not to be exceeded more than one time per year, and an annual standard of 0.03 ppm were
13 judged to be both adequate and necessary to protect public health. The most recent review of the
14 SO2 NAAQS was completed in 1996 and focused on the question of whether an additional short-
15 term standard (e.g., 5-minute) was necessary to protect against short-term, peak exposures.
16 Based on the scientific evidence, the administrator judged that repeated exposures to 5-minute
17 peak SO2 levels (>0.60 ppm) could pose a risk of significant health effects for asthmatic
18 individuals at elevated ventilation rates. The Administrator also concluded that the likely
19 frequency of such effects should be a consideration in assessing the overall public health risks.
20 Based upon an exposure analysis conducted by EPA, the Administrator concluded that exposure
21 of asthmatics to SO2 levels that could reliably elicit adverse health effects was likely to be a rare
22 event when viewed in the context of the entire population of asthmatics, and therefore did not
23 pose a broad public health problem for which a NAAQS would be appropriate. On May 22,
24 1996, EPA's final decision not to promulgate a 5-minute standard and to retain the existing 24-
25 hour and annual standards was announced in the Federal Register (61 FR 25566).
26 The American Lung Association and the Environmental Defense Fund challenged EPA's
27 decision not to establish a 5-minute standard. On January 30, 1998, the Court of Appeals for the
28 District of Columbia found that EPA had failed to adequately explain its determination that no
29 revision to the SO2 NAAQS was appropriate and remanded the decision back to EPA for further
30 explanation. Specifically, the court required EPA to provide additional rationale to support the
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1 Agency judgment that 5-minute peaks of SC>2 do not pose a public health problem from a
2 national perspective even though those peaks would likely cause adverse health impacts in a
3 subset of asthmatics. In response, EPA has collected and analyzed additional air quality data
4 focused on 5-minute concentrations of 862. These air quality analyses conducted since the last
5 review will help inform the current review, which will address issues raised in the Court's
6 remand of the Agency's last decision. No further Agency action has been taken with respect to
7 responding to the remand.
8 1.1.2 Health Evidence from the Previous Review
9 The 1982 Air Quality Criteria Document (AQCD) for Particulate Matter and Sulfur
10 Oxides (EPA, 1982), and its subsequent addenda and supplement (EPA, 1986a, 1994) presented
11 an evaluation of 862 associated health effects primarily drawn from epidemiological and human
12 clinical studies. In general, these documents identified adverse health effects that were likely
13 associated with both short-(generally hours to days), and long-term (months to years) exposures
14 to SO2 at concentrations present in the ambient mixture of air pollutants. Moreover, these
15 documents presented evidence for bronchoconstriction and respiratory symptoms in exercising
16 asthmatics following controlled exposures to short-term (5-10 minutes) peak concentrations of
17 SO2.
18 Evidence drawn from epidemiological studies supported a likely association between 24-
19 hour average SC>2 exposure and daily mortality, aggravation of bronchitis, and small, reversible
20 declines in children's lung function (EPA 1982, 1994). In addition, a few epidemiological
21 studies found an association between respiratory symptoms and illnesses and annual average 862
22 concentrations (EPA 1982, 1994). However, it was noted that most of these epidemiological
23 studies were conducted in years and cities where particulate matter (PM) counts were also quite
24 high, thus making it difficult to quantitatively determine whether the observed health effects
25 were the result of SC>2, PM, or a combination of exposure to both pollutants.
26 Evidence drawn from clinical studies exposing exercising asthmatics to <1.0 ppm SC>2 for
27 5-10 minutes found that these types of SC>2 exposures evoked health effects that were similar to
28 those asthmatics would experience from other commonly encountered stimuli (e.g. exercise,
29 cold/dry air, psychological stress, etc., EPA, 1994). That is, there was an acute-phase response
30 characterized by bronchoconstriction and/or respiratory symptoms that occurred within 5-10
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1 minutes of exposure but then subsided on its own within 1 to 2 hours. This acute-phase response
2 was followed by a short refractory period where the individual was relatively insensitive to
3 additional 862 challenges. Notably, the SCVinduced acute-phase response was found to be
4 ameliorated by the inhalation of beta-agonist aerosol medications, and to occur without an
5 additional, often more severe, late-phase inflammatory response.
6 The 1994 supplement to the CD noted that of particular concern was the subset of
7 asthmatics in these clinical studies that appeared to be hyperresponsive- those experiencing
8 greater-than-average bronchoconstriction or respiratory symptoms at a given SC>2 concentration.
9 Thus, for a given concentration of SC>2, the number of asthmatics likely to experience
10 bronchoconstriction (and/or symptoms) of a sufficient magnitude to be considered a health
11 concern was estimated. At 0.6 to 1.0 ppm 862, EPA estimated that more than 25% of mild to
12 moderate exercising asthmatics would likely experience decrements in lung function or
13 respiratory symptoms distinctly exceeding typical daily variations in lung function, or the
14 response to commonly encountered stimuli (EPA, 1994). Furthermore, the CD concluded that
15 the severity of effects experienced at 0.6-1.0 ppm was likely to be of sufficient concern to cause
16 a cessation of activity, medication use, and/or the possible seeking of medical attention. In
17 contrast, at 0.2 - 0.5 ppm SC>2, it was estimated that at most 10 - 20% of mild to moderate
18 exercising asthmatics were likely to experience lung function decrements larger than those
19 associated with typical daily activity, or the response to commonly encountered stimuli (EPA,
20 1994).
21 1.1.3 Assessment from Previous Review
22 The risk and exposure assessment from the previous review of the SC>2 NAAQS
23 qualitatively evaluated both the existing 24-hour (0.14 ppm) and annual standards (0.03 ppm),
24 but primarily focused on whether an additional standard was necessary to protect against very
25 short-term (e.g., 5-minute) peak exposures. Based on the human clinical data mentioned above,
26 it was judged that exposures to 5-minute SC>2 levels at or above 0.60 ppm could pose an
27 immediate significant health risk for a substantial proportion of asthmatics at elevated ventilation
28 rates (e.g., while exercising). Thus, EPA analyzed existing ambient monitoring data to estimate
29 the frequency of 5-minute peak concentrations above 0.50, 0.60, and 0.70 ppm, the number of
30 repeated exceedances of these concentrations, and the sequential occurrences of peak
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1 concentrations within given a day (SAI, 1996). The results of this analysis indicated that in the
2 vicinity of local sources, several locations in the U.S. had a substantial number of 5-minute peak
3 concentrations at or above 0.60 ppm.
4 In addition to the ambient air quality analysis, the previous review also included several
5 annual exposure analyses that in general, combined SO2 emission estimates from utility and non-
6 utility sources with exposure modeling to estimate the probability of exposure to short-term peak
7 SC>2 concentrations. The first such analysis conducted by the Agency estimated the number of 5-
8 minute exposures >0.5 ppm associated with four selected coal-fired power utilities (EPA,
9 1986b). An expanded analysis sponsored by the Utility Air Regulatory Group (UARG)
10 considered the frequency of short-term exposure events that might result from the nationwide
11 operation of all power utility boilers (Burton et al., 1987). Additionally, the probability of peak
12 concentrations surrounding non-utility sources was the focus of another study conducted by the
13 Agency (Stoeckenius et al., 1990). The resultant combined exposure estimates considering these
14 early analyses indicated that between 0.7 and 1.8 percent of the total asthmatic population
15 potentially could be exposed one or more times annually, while outdoors at exercise, to 5-minute
16 SC>2 concentrations >0.50 ppm. It also was noted that the frequency of 5-minute exposures
17 above the health effect benchmark of 0.60 ppm, while not part of the analysis, would be
18 anticipated to be lower.
19 In addition to the early analyses mentioned above, two other analyses were considered in
20 the prior review. The first was an exposure assessment sponsored by the UARG (Rosenbaum et
21 al., 1992) that focused on emissions from fossil-fueled power plants. That study accounted for
22 the anticipated reductions in SC>2 emissions after implementation of the acid deposition
23 provisions (Title IV) of the 1990 Clean Air Act Amendments. This UARG-sponsored analysis
24 predicted that these emission reductions would result in a 42% reduction in the number of 5-
25 minute exposures to 0.50 ppm for asthmatic individuals (reducing the number of asthmatics
26 exposed from 68,000 down to 40,000) in comparison with the earlier Burton et al. (1987)
27 analysis. The second was a new exposure analysis submitted by the National Mining
28 Association (Sciences International, Inc. 1995) that reevaluated non-utility sources. In this
29 analysis, revised exposure estimates were provided for four of the seven non-utility source
30 categories by incorporating new emissions data and using less conservative modeling
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1 assumptions in comparison with those used for the earlier Stoeckenius et al. (1990) non-utility
2 analysis. Significantly fewer exposure events (i.e., occurrence of 5-minute 0.50 ppm or greater
3 exposures) were estimated in this industry-sponsored revised analysis, decreasing the range of
4 estimated exposures for these four sources by an order of magnitude (i.e., from 73,000-259,000
5 short-term exposure events in the original analysis to 7,900-23,100 in the revised analysis).
6 1.2 SCOPE OF THE RISK AND EXPOSURE ASSESSMENT FOR THE
7 CURRENT REVIEW
8 1.2.1 Overview of Assessment
9 The overall goal of this document is to describe exposure and risks associated with recent
10 ambient levels of SC>2 and with levels that just meet the current standards. Chapters 2-4 evaluate
11 background information presented in the draft ISA that is relevant for conducting an exposure
12 and risk assessment. This includes information on 1) human exposure to SC>2 2) at-risk
13 populations, and 3) health effects associated with short- and long-term exposures to 862.
14 Considering the information discussed in these chapters, staff found it appropriate to focus its
15 exposure and risks analyses on respiratory morbidity associated with 5-minute peak and short-
16 term (> 1-hour, generally 24-hours) exposures to SO2.
17 With regard to 5-minute peak exposures, staff found sufficient evidence of
18 bronchoconstriction and respiratory symptoms from human exposure studies presented in the
19 draft ISA to conduct a series of analyses to estimate the risks associated with exposure to 0.4-0.6
20 ppm SC>2 in asthmatics at elevated ventilation rates. Chapter 6 presents an air quality
21 characterization for the occurrence of 5-minute peak concentrations above the potential health
22 benchmark values of 0.4, 0.5, and 0.6 ppm under current air quality, and air quality simulated to
23 just meet the current standards. Chapter 7 presents initial results from an exposure analysis case
24 study conducted in the state of Missouri. This analysis provides estimates of the number and
25 percent of asthmatics residing within 20 kilometers (km) of major SC>2 sources experiencing 5-
26 minute exposures to 0.4, 0.5, 0.6 ppm SC>2 while at elevated ventilation rates under the air quality
27 scenarios mentioned above. Chapter 8 of this document describes ongoing work to develop
28 health risk estimates for the number and percent of these exposed asthmatics that would
29 experience moderate or greater lung function decrements under these same air quality scenarios.
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1 Evidence presented in the draft ISA for respiratory morbidity associated with short-term
2 862 exposure was primarily drawn from epidemiological studies. Staff found that the
3 epidemiological evidence presented in the draft ISA was largely mixed, but suggestive of an
4 association between short-term 862 exposure and both respiratory symptoms in children, as well
5 as emergency department (ED) visits and hospitalizations for all respiratory causes and asthma,
6 particularly in children and older adults. Thus, Chapter 9 of this document describes ongoing
7 work that will qualitatively assess the relationship between SC>2 air quality levels at the time key
8 U.S. and Canadian epidemiological studies were conducted and these health endpoints.
9 With respect to long-term exposure, staff concluded that there was insufficient
10 information to conduct a risk assessment based on epidemiological studies examining long-term
11 862 exposure. This was primarily because the draft ISA found the evidence linking long-term
12 SC>2 exposure to morbidity and mortality to be inadequate to infer the presence or absence of a
13 causal relationship (ISA, Table 5-3). The draft ISA noted that a major consideration for this
14 determination was the inability to attribute health effects observed in long-term epidemiological
15 studies to SC>2 alone; the draft ISA found a high correlation among pollutant levels, particularly
16 between long-term average SC>2 and PM concentrations.
17 1.2.2 Species of Sulfur Oxides Included in Analyses
18 The sulfur oxides include multiple gaseous (e.g., SC>2, SOs) and particulate (e.g., sulfate)
19 species. In considering what species of sulfur oxides are relevant to the current review of the
20 862 NAAQS, we note that the health effects associated with particulate species of sulfur oxides
21 have been considered within the context of the Agency's review of the primary NAAQS for
22 particulate matter (PM). In the most recent review of the NAAQS for PM, it was determined that
23 size-fractionated particle mass, rather than particle composition, remains the most appropriate
24 approach for addressing ambient PM. This conclusion will be re-assessed in the parallel review
25 of the PM NAAQS; however, at present it would be redundant to also consider effects of
26 particulate sulfate in this review. Therefore, the current review of the SC>2 NAAQS will focus on
27 gaseous species of sulfur oxides and will not consider health effects directly associated with
28 particulate sulfur oxide species. Additionally, of the gaseous species, EPA has historically
29 determined it appropriate to specify the indicator of the standard in terms of 862 because other
30 gaseous sulfur oxides (e.g. 863) are likely to be found at concentrations many orders of
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1 magnitude lower than SC>2 in the atmosphere, and because the majority of health effects and
2 exposure information is for 862. The draft ISA has again found this to be the case, and
3 therefore this REA will use 862 as a surrogate for all gaseous sulfur oxides.
4 1.2.3 Scenarios for the Current Assessment
5 The first draft REA, described in this document, will be informed by the 1st and 2nd drafts
6 of the ISA for SOX and will detail the assessment of exposures and characterization of health
7 risks associated with recent ambient levels of SC>2 and with levels that just meet the current
8 standards. Moreover, this document will assess exposure and characterize risks associated with
9 SC>2 emissions from anthropogenic sources. In the vast majority of the U.S., most SC>2 emissions
10 originate from industrial point sources, with fossil fuel combustion at electric utilities and other
11 facilities accounting for the majority of total emissions (see section 2.2). The second draft of this
12 document is scheduled to be released in November 2008 and will be informed by comments
13 from CAS AC, and the public, as well as findings and conclusions contained in the final ISA.
14 The second draft REA will include an assessment of the risks and exposures associated with just
15 meeting potential alternative standards. The final REA is scheduled is to be completed in
16 January 2009, and will also be informed by comments from CAS AC, and the public, as well as
17 findings and conclusions contained in the final ISA.
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i 2.0 HUMAN EXPOSURE
2 2.1 OVERVIEW
3 The integrated exposure of a person to a given pollutant is the sum of the exposures over
4 all time intervals for all environments in which the individual spends time. People spend
5 different amounts of time in different microenvironments and each microenvironment is
6 characterized by different pollutant concentrations. There is a large amount of variability in the
7 time that different individuals spend in different microenvironments, but on average people
8 spend the majority of their time (about 87%) indoors. Most of this time spent indoors is spent at
9 home with less time spent in an office/workplace or other indoor locations (draft ISA, figure 2-
10 21). In addition, people spend about 8% of their time outdoors and 6% of their time in vehicles.
11 A potential consequence of multiple sources of exposure or microenvironments is the exposure
12 misclassification that may result when total human exposure is not disaggregated between these
13 various microenvironments. Such misclassification may obscure the true relationship between
14 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 describe the type of exposure experienced. Types of exposure can be characterized
17 as instantaneous, peak, average, or integrated over all the environments a person encounters.
18 These distinctions are important because health effects caused by long-term, low-level exposures
19 may differ from those caused by single or repeated short-term, peak exposures.
20 2.2 SOURCES OF SO2
21 In order to estimate risks associated with SC>2 exposure, principle sources of the pollutant
22 must first be characterized because the majority of human exposures are likely to be in the
23 vicinity of these sources. Anthropogenic SC>2 emissions originate chiefly from point sources,
24 with fossil fuel combustion at electric utilities (-66%) and other industrial facilities (-29%)
25 accounting for the majority of total emissions (draft ISA section 2.1). Other anthropogenic
26 sources of 862 include both the extraction of metal from ore as well as the burning of high sulfur
27 containing fuels by locomotives, large ships, and non-road diesel equipment. Notably, almost
28 the entire sulfur content of fuel is released as SC>2 or SOs during combustion. Thus, based on the
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1 sulfur content in fuel stocks, sulfur emissions can be calculated to a higher degree of accuracy
2 than can emissions for other pollutants such as PM and NC>2 (draft ISA, section 2.1).
3 The largest natural sources of 862 are volcanoes and wildfires. Although 862 constitutes
4 a relatively minor fraction (0.005% by volume) of total volcanic emissions, concentrations in
5 volcanic plumes can be in the range of several to tens of ppm. Volcanic sources of SO2 in the
6 U.S. are limited to the Pacific Northwest, Alaska, and Hawaii. Emissions of SC>2 can also result
7 from burning vegetation. The amount of SC>2 released from burning vegetation is generally in
8 the range of 1 to 2% of the biomass burned and is the result of sulfur from amino acids being
9 released as SC>2 during combustion.
10 2.3 AMBIENT LEVELS OF SO2
11 Since the integrated exposure to a pollutant is the sum of the exposures over all time
12 intervals for all environments in which the individual spends time, understanding the temporal
13 and spatial patterns of 862 levels across the U.S is an important component of conducting an
14 exposure and risk analysis. 862 emissions and ambient concentrations follow a strong west to
15 east gradient due to the large numbers of electric generating units in the Ohio River Valley and
16 upper South regions. In the 12 CMSAs that had at least 4 SO2 regulatory monitors from 2003-
17 2005, 24-hour average concentrations in the continental U.S. ranged from a reported low of
18 -0.001 ppm in Riverside, CA and San Francisco, CA to a high of-0.012 ppm in Pittsburgh, PA
19 and Steubenville, OH (draft ISA section 2.4.4). In addition, inside CMSAs from 2003-2005, the
20 annual average SO2 concentration was 0.004 ppm (draft ISA, Table 2.4). However, spikes in
21 hourly concentrations occurred; the mean 1-hour maximum concentration was 0.013 ppm, with a
22 maximum value of greater than 0.70 ppm (draft ISA, Table 2.4).
23 It should be noted that there is concern about the degree of instrument error associated
24 with the measurement of ambient SO2. The SO2 monitoring network was designed and put into
25 place when SO2 concentrations were considerably higher, and thus, well within the standard
26 monitor's limits of detection. However, SO2 concentrations have fallen considerably over the
27 years (draft ISA, Figure 2-8) and are currently at, or very near these monitors' lower limit of
28 detection (-0.003 ppm). This introduces a degree of uncertainty because as monitors approach
29 their detection limits there can be greater error in their measurements.
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1 EPA has generally conducted NAAQS risk assessments on levels of a pollutant that are in
2 excess of policy relevant background (PRB). Policy relevant background levels are defined as
3 concentrations of a pollutant that would occur in the U. S. in the absence of anthropogenic
4 emissions in continental North America (defined here as the United States, Canada, and Mexico).
5 However, throughout much of the United States, SO2 PRB levels are estimated to be at most 30
6 parts per trillion and contribute less than 1% to present day SC>2 concentrations (draft ISA,
7 section 2.4.6). We note that in the Pacific Northwest and Hawaii, PRB concentrations can be
8 considerably higher due to geothermal activity (e.g. volcanoes); in these areas, PRB can account
9 for 70-80% of total SC>2 concentrations (draft ISA, section 2.4.6). Since we do not plan on
10 conducting 862 risk assessment in areas with high background 862 levels due to natural sources,
11 and the contribution of PRB is negligible in all other areas, EPA is addressing the risks
12 associated with monitored and/or modeled ambient levels without regard to PRB levels.
13 2.4 RELATIONSHIP OF PERSONAL EXPOSURE TO AMBIENT
14 CONCENTRATIONS
15 Of major concern is the ability of SC>2, measured by ambient monitors, to serve as a
16 reliable indicator of personal exposure to SC>2 of ambient origin. The key question is what errors
17 are associated with using SC>2 measured by ambient monitors as a surrogate for personal
18 exposure to ambient 862. There are three aspects to this issue: (1) ambient and personal
19 sampling issues; (2) the spatial variability of ambient 862 concentrations; (3) the associations
20 between ambient concentrations and personal exposures as influenced by exposure factors, e.g.,
21 indoor sources and time spent indoors and outdoors.
22 Determining the relationship between personal exposure and ambient concentrations is
23 often difficult. This is in part because SC>2 levels in general are often below the limits of
24 detection of currently available personal samplers2. In these situations, associations between
25 ambient concentrations and personal exposures are inadequately characterized (ISA, section
26 2.5.3.2). However, the ISA noted that when personal exposure concentrations are above
27 detection limits of personal samplers, a reasonably strong association is observed between
28 personal exposures and ambient concentrations (ISA, section 2.5.3.2).
2 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 2.5.2 of the draft ISA.
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1 There is also uncertainty associated with the spatial and temporal variation of SC>2 across
2 communities. In some U.S. cities, there are low site-to-site correlations of 862 concentrations
3 among monitors (draft ISA, Table 2-3). This suggests that at any given time, 862 concentrations
4 at individual monitoring sites may not highly correlate with the average 862 concentration in the
5 community. This could be the result of local sources (e.g. power plants) causing an uneven
6 spatial distribution of SC>2, monitors being sited to represent concentrations near local sources, or
7 effects related to terrain or weather (draft ISA, section 2.5.4.1.2).
8 Since people spend most of their time indoors there is also uncertainty in the relationship
9 between ambient concentrations measured by local monitors and actual personal exposure
10 related to ambient sources. Indoor, or nonambient, sources of 862 could complicate the
11 interpretation of associations between personal exposure to ambient 862 in exposure studies.
12 Possible sources of indoor 862 are associated with the use of sulfur-containing fuels, with higher
13 levels expected when emissions are poorly vented (draft ISA, section 2.5). In the U.S., the
14 contribution of indoor sources is not thought to be a major contributor to overall SC>2 exposure
15 because the only known indoor source in the U.S. is kerosene heaters and there use is not thought
16 to be widespread (draft ISA, section 2.5).
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i 3.0 AT RISK POPULATIONS
2 3.1 OVERVIEW
3 The risk of an adverse health effect following exposure to a pollutant is dependent on a
4 number of factors, such as the individual's personal attributes (age, gender, preexisting health
5 conditions) and the toxic properties of the pollutant (e.g., as indicated by dose- or concentration-
6 response relationships). The previous review of the SO2 NAAQS identified certain groups
7 within the population that may be more susceptible to the effects of SC>2 exposure, including
8 those with pre-existing respiratory disease and cardiovascular disease (CVD). Individuals in
9 potentially sensitive groups are of concern, as they may experience adverse effects from lower
10 levels of SC>2 compared to the general population or experience a greater impact with the same
11 level of exposure. The draft ISA defined which groups within the population may be more
12 susceptible to adverse health effects associated with 862 exposure. The draft ISA also identified
13 groups considered to be vulnerable to 862 exposure because they are potentially exposed to
14 higher than average SO2 concentrations. Groups considered to be particularly susceptible and/or
15 vulnerable are discussed in more detail below.
16 3.2 DISEASE AND ILLNESS
17 Both recent epidemiological and human clinical studies have strengthened the 1982
18 AQCD conclusion that individuals with pre-existing respiratory disease are likely more
19 susceptible to the effects of SC>2 than the general public (draft ISA, section 4.2.1.1).
20 Epidemiological studies have reported associations between short- and long-term SC>2 ambient
21 concentrations and a range of respiratory symptoms in individuals with respiratory disease.
22 Additionally, numerous controlled human exposure studies have found that asthmatics are more
23 responsive to the respiratory effects of 862 than healthy, non-asthmatic individuals.
24 Specifically, clinical studies have demonstrated that in non-asthmatics, SCVattributible
25 decrements in lung function have generally not been shown at concentrations <1.0 ppm. In
26 contrast, both increases in respiratory symptoms and decrements in lung function have been
27 shown in a significant proportion of exercising mild and moderate asthmatics following 5-10
28 minute exposures to SC>2 concentrations as low as 0.4-0.6 ppm (draft ISA, section 4.2.1.1).
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1 The draft ISA also examined the possible effects of pre-existing CVD on SC>2
2 susceptibility. The draft ISA found that results from a limited number of epidemiological studies
3 provided inconsistent evidence that individuals with pre-existing CVD were more susceptible
4 than the general public to adverse health effects associated with ambient 862 exposure (draft
5 ISA, section 4.2.1.2). Moreover, results from a single human clinical study found no evidence to
6 suggest that patients with stable angina were more susceptible to SO2- related health effects than
7 healthy individuals. Overall, the draft ISA found the limited evidence for an association between
8 pre-existing CVD and increased susceptibility to SC>2 related health effects to be inconclusive
9 (draft ISA, section 4.2.1.2).
10 3.3 GENETIC SUSCEPTIBILITY
11 The draft ISA noted that a consensus now exists among scientists that the potential
12 association between genetic factors and increased susceptibility to ambient air pollution merits
13 serious consideration. Thus, the draft ISA examined the differential effects of air pollution
14 among genetically diverse subpopulations for a number of genes. There was only one study that
15 specifically looked at 862 as the pollutant of interest and it found a significant association
16 between adverse health effects and the homozygous wild-type allele for TNF-a (draft ISA,
17 section 4.2.2). However, the draft ISA concluded that the data were too limited to reach a
18 conclusion regarding the effects of SC>2 exposure on genetically distinct subpopulations at this
19 time.
20 3.4 AGE
21 Although the evidence is limited, the draft ISA identified children (i.e., <18 years of age)
22 and older adults (i.e. >65 years of age) as groups that are potentially more susceptible than the
23 general population to the health effects associated with SC>2 exposure. In children, the
24 developing lung is highly susceptible to damage from environmental toxicants as it continues to
25 develop through adolescence. The basis for increased susceptibility in the elderly is unknown,
26 but one hypothesis is that it may be related to changes in antioxidant defenses in the fluid lining
27 the respiratory tract. However, regardless of the mechanisms involved, the ISA found a number
28 of epidemiological studies that observed increased respiratory symptoms in children associated
29 with increasing SC>2 exposures. In addition, several studies have reported that the excess risk
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1 estimates for ED visits and hospitalizations for all respiratory causes, and to a lesser extent
2 asthma, associated with a 10-ppb increase in 24-hour average 862 concentrations were higher for
3 children and older adults than for all ages together (draft ISA, section 4.2.3).
4 3.5 VULNERABILITY
5 Indoor and personal 862 concentrations are generally much lower than outdoor ambient
6 concentrations. Therefore, people who spend most of their time indoors are generally less
7 vulnerable to 862 related health effects than those who spend a significant amount of time
8 outdoors at increased exertion levels. In addition, the health effects evidence from controlled
9 human exposure studies indicated that some SO2-related health responses (e.g., lung function and
10 respiratory symptoms in asthmatic subjects) occurred at the lowest concentration levels when
11 subjects were engaged in moderate or greater exertion. Thus, children who spend a significant
12 amount of time outdoors at elevated ventilation rates (e.g. while playing) and adult asthmatics
13 who work, exercise, or play outdoors are expected to have increased vulnerability and be at
14 greater risk of experiencing SCVrelated health effects.
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i 4.0 HEALTH EFFECTS
2 4.1 INTRODUCTION
3 The draft 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 SO2 and numerous human health endpoints. For these health effects, the draft ISA
7 characterizes judgments about causality with a hierarchy (for discussion see draft ISA, section
8 1 .x) 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 noted that these judgments about causality were informed by a series of aspects
15 of causality that were based on those set forth by Sir Austin Bradford Hill in 1965 (draft ISA,
16 Table 1-2). These aspects include strength of the observed association, availability of
17 experimental evidence, consistency of the observed association, biological plausibility,
18 coherence of the evidence, temporal relationship of the observed association, and the presence of
19 an exposure-response relationship.
20 For the purpose of characterization of SO2-related health risks, we have focused on health
21 endpoints for which the draft ISA concludes that the available evidence is sufficient to infer a
22 causal relationship. The draft ISA concludes that there is sufficient evidence to infer a causal
23 relationship between respiratory morbidity and short-term exposure to SO2 (draft ISA, section
24 5.2). This conclusion is based on the consistency, coherence, and plausibility of findings
25 observed in controlled human exposure studies examining SO2 exposures of 5-10 minutes,
26 epidemiological studies mostly using 24-hour average exposures, and animal toxicological
27 studies using exposures of minutes to hours (draft ISA, section 5.2). The evidence for causal
28 associations between SO2 exposure and other health endpoints is judged to be less convincing, at
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1 most suggestive but not sufficient to infer a causal relationship, and therefore will not be
2 discussed in this document. Key conclusions reached in the draft ISA are listed below:
3
4 • Sufficient to infer a causal relationship:
5 o Short-Term Respiratory Morbidity
6 • Suggestive but not sufficient to infer a causal relationship:
7 o Short-Term Mortality
8 • Inadequate to infer the presence or absence of a causal relationship
9 o Short-Term Respiratory Morbidity;
10 o Short-Term Cardiovascular Morbidity;
11 o Long-Term Respiratory Morbidity;
12 o Long-Term Mortality;
13 o Long-Term Other Morbidity;
14 A more detailed summary of these conclusions can be found in Table 5-3 of the draft ISA.
15 4.2 SHORT-TERM PEAK (<1-HOUR, GENERALLY 5-10 MINUTES) SO2
16 EXPOSURES AND RESPIRATORY HEALTH EFFECTS
17 4.2.1 Overview
18 The draft ISA concludes that there is sufficient evidence to infer a causal relationship
19 between respiratory morbidity and short-term exposure to 862 (draft ISA, section 5.2). In large
20 part, this determination is based on controlled human exposure studies demonstrating a
21 relationship between short-term peak SO2 exposures and adverse effects on the respiratory
22 system in exercising asthmatics. More specifically, the draft ISA finds consistent evidence from
23 numerous human clinical studies demonstrating increased respiratory symptoms (e.g. cough,
24 chest tightness, wheeze) and decrements in lung function in a substantial proportion of exercising
25 asthmatics (generally classified as mild to moderate asthmatics) following short-term peak
26 exposures to 862 at concentrations > 0.4 ppm. As in previous reviews, the draft ISA also
27 concludes that at concentrations below 1.0 ppm, healthy individuals are relatively insensitive to
28 the respiratory effects of short-term peak 862 exposures (draft ISA, sections 3.1.3.1 and 3.1.3.2).
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1 4.2.2 Respiratory Symptoms
2 The 1994 Supplement to the Second Addendum described multiple studies that evaluated
3 respiratory symptoms (e.g. cough, wheeze, or chest tightness) following controlled exposures of
4 asthmatic subjects to 862. Linn et al. (1983) reported that relative to exposure to clean air,
5 exposure to 862 levels as low 0.4 ppm for 5 minutes in exercising asthmatics resulted in a
6 statistically significant increase in an overall respiratory symptoms score that included wheeze,
7 chest tightness, cough, and substernal irritation. In an additional study, Linn et al. (1987)
8 observed that 43% of exercising asthmatics exhibited respiratory symptoms following a 10-
9 minute exposure to 0.6 ppm SC>2; this study also found that exposure to SC>2 concentrations as
10 low as 0.4 ppm resulted in 15% of study subjects experiencing respiratory symptoms (draft ISA,
11 3.3.3.1). In addition, Balmes et al. (1987) reported that 7 out of 8 asthmatic adults at elevated
12 ventilation rates developed respiratory symptoms following a 3-minute exposure to 0.5 ppm 862
13 (draft ISA section 3.3.3.1).
14 Controlled human exposure studies published since the 1994 Supplement to the Second
15 Addendum have provided additional evidence of short-term peak SC>2 exposures resulting in
16 respiratory symptoms in asthmatics at elevated ventilation rates (draft ISA, section 3.1.3.1). In a
17 study conducted by Gong et al. (1995), unmedicated SO2-sensitive asthmatics were exposed to
18 0-, 0.5-, and 1-ppm SC>2 for 10 minutes while performing different levels of exercise (light,
19 medium, or heavy). The authors found that respiratory symptoms increased with increasing SC>2
20 concentrations. Moreover, they found that exposure to 0.5-ppm 862 during light exercise
21 evoked a more severe symptomatic response than heavy exercise in clean air. In a separate
22 study, Trenga et al. (1999) observed a significant correlation between decreases in FEVi and
23 increases in respiratory symptoms following 10-minute exposures to 0.5 ppm SO2. However, it
24 should be noted that the study conducted by Trenga et al. used a mouthpiece and that these types
25 of studies often produce more exaggerated effects because they deliver SC>2 directly into the
26 mouth, thereby bypassing the natural SC>2 scrubbing effects of the nasal passages and resulting in
27 greater doses reaching the lung.
28 4.2.3 Lung function
29 In the previous review, it was established that subjects with asthma are more sensitive to
30 the respiratory effects of 862 exposure than healthy individuals (draft ISA, section 3.1.3.2).
July 2008 20 Draft - Do Not Quote or Cite
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1 Asthmatics exposed to SC>2 concentrations as low as 0.4-0.6 ppm for 5-10 minutes during
2 exercise have been shown to experience significant bronchoconstriction, measured as an increase
3 in specific airway resistance (sRaw) of >100%, or decrease in forced expiratory volume in the
4 first second (FEVi) of >15% after correction for exercise-induced responses in clean air (Bethel
5 et al., 1983; Linn et al., 1983, 1984, 1987; 1988; 1990; Magnussen et al., 1990; Roger et al.,
6 1985). It was also found that those asthmatics that are the most sensitive to the respiratory effects
7 of SC>2 have been shown to experience significant decrements in lung function following SC>2
8 exposure <0.3 ppm while at exercise (draft ISA, section 3.1.3.2; Horstman et al., 1986; Sheppard
9 et al., 1981). Moreover, the draft ISA finds that among asthmatics, both the magnitude of SC>2-
10 induced lung function decrements and the percent of individuals affected have been shown to
11 increase with increasing 5- to 10-minute SC>2 exposures in the range of 0.2 to 1.0 ppm.
12 The draft ISA also finds supporting evidence in studies published since the previous
13 review. Gong et al. (1995) found that increasing SO2 concentrations resulted in both a decrease
14 in FEVi as well as an increase in sRaw. This same study found that increasing the concentration
15 of SC>2 had a greater effect on sRaw and FEVi than increasing the level of exercise. In a separate
16 study, following a 10-minute exposure to 0.5 ppm SC>2 by mouthpiece (see caveat in section
17 4.2.2), Trenga et al. (1999) observed that 25 out of 47 exercising adult asthmatics experienced a
18 drop in FEVi versus baseline (mean = 17.2%).
19 4.2.4 Decrements in Lung Function in the Presence of Respiratory Symptoms
20 When evaluating health effects associated with short-term peak 862 exposures, the draft
21 ISA recognized recent guidance by the American Thoracic Society (ATS) regarding what
22 constitutes an adverse effect of air pollution (draft ISA, section 3.1.3.2). In its official statement,
23 the ATS recommended that transient loss in lung function associated with clinical respiratory
24 symptoms attributable to air pollution should be considered adverse to individuals (ATS 2000;
25 draft ISA section 3.1.3). Accordingly, in light of this definition the draft ISA re-evaluated
26 experimental data from controlled human exposure studies previously considered in the last SC>2
27 NAAQS review (draft ISA, section 3.1.3.2; Balmes et al., 1987; Linn et al., 1987; 1988; 1990;
28 1983; Roger et al., 1985), along with supporting data from a recent controlled human exposure
29 study (draft ISA section 3.1.3.2; Gong et al., 1995) for evidence of lung function decrements
30 with concurrent respiratory symptoms. This re-evaluation found evidence demonstrating
July 2008 21 Draft - Do Not Quote or Cite
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1 frequent decrements in lung function in the presence of respiratory symptoms in exercising
2 asthmatics exposed to short-term peaks of 862. More specifically, the draft ISA concludes the
3 evidence collectively indicates that at elevated ventilation rates, asthmatic individuals experience
4 moderate or greater decrements in lung function combined with respiratory symptoms following
5 peak exposures to SO2 as low as 0.4-0.6 ppm (draft ISA, section 3.1.3.5; Table 3-1).
6 4.3 SHORT-TERM (> 1 -HOUR, GENERALLY 24-HOUR) SO2 EXPOSURE
7 AND RESPIRATORY HEALTH EFFECTS
8 In addition to the human clinical evidence described above (section 4.2), the draft ISA
9 also bases its causal determination for an association between exposure to short-term (5-minutes
10 to 24-hour) 862 and respiratory morbidity on results from epidemiological studies examining 1)
11 respiratory symptoms, 2) emergency department (ED) visits and hospitalizations for all
12 respiratory causes, asthma, chronic obstruction pulmonary disease (COPD), and other respiratory
13 diseases 3) lung function, 4) respiratory related absences, 5) airway inflammation, and 6) airway
14 hyperresponsiveness and allergy. However, this section will focus on the results presented in the
15 draft ISA concerning respiratory symptoms and hospitalization and ED visit for all respiratory
16 causes and asthma. This is because staff found the results and breadth of the epidemiological
17 evidence for these health endpoints within the broader category of respiratory morbidity to be
18 most robust. Therefore, other respiratory morbidity endpoints (see draft ISA section 3.1.4) will
19 not be discussed in this document, but will be considered qualitatively in the policy assessment
20 that is prepared after completion of the final ISA.
21 4.3.1 Respiratory Symptoms
22 The draft ISA finds that the strongest epidemiological evidence for an association
23 between short-term SC>2 concentrations and respiratory symptoms was in children, and comes
24 from two large U.S. multi-city studies: the National Cooperative Inner-City Asthma Study
25 (NCICAS; Mortimer et al., 2002; ISA section 3.1.4.1.1 and 3.1.4.1.2), and the Childhood
26 Asthma Management Program (CAMP; Schildcrout et al., 2006; ISA section 3.1.4.1.1). Both of
27 these studies found significant associations between level of 862 concentration and the risk of
28 respiratory symptoms in asthmatic children (Mortimer et al., 2002; Schildcrout et al., 2006;).
29 However, it should be noted that the Harvard Six Cities Study (Schwartz et al., 1994) suggested
30 that the association between SO2 and respiratory symptoms in children could be confounded by
July 2008 22 Draft - Do Not Quote or Cite
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1 PMi0; the authors found that the effect of SC>2 was substantially diminished after adjustment for
2 PMio in copollutant models (draft ISA, section 3.1.4.1). These key studies are discussed in more
3 detail below.
4 The National Cooperative Inner-City Asthma Study (NCICAS, Mortimer et al. 2002)
5 included asthmatic children (n = 846) from eight U.S. urban areas and examined the relationship
6 between respiratory symptoms and summertime air pollution levels. The strongest associations
7 were found between morning symptoms and the median 3-hour average SC>2 concentrations
8 during morning hours (8 a.m. to 11 a.m.)- following a 1- to 2-day lag (draft ISA, Figure 3-2). 3 -
9 hour average concentrations ranged from 17 ppb in Detroit to 37 ppb in East Harlem, NY. This
10 relationship remained robust and statistically significant in multi-pollutant models with ozone
11 (63), and nitrogen dioxide (NC^). When PMio was also added to the model, the effect estimate
12 was similar although no longer statistically significant (draft ISA, Figure 3-2), but the ISA notes
13 that this loss of statistical significance could have been the result of reduced statistical power
14 (only three of eight cities were included in this analysis) or collinearity resulting from adjustment
15 of multiple pollutants (draft ISA, section 3.1.4.1).
16 The Childhood Asthma Management Program (CAMP, Schildcrout et al. 2006) examined
17 the association between ambient air pollution and asthma exacerbations in children (n = 990)
18 from eight North American cities. The median 24-hour average SC>2 concentrations (collected in
19 seven of the eight study locations) ranged from 2.2 ppb in San Diego to 7.4 ppb in St. Louis. All
20 lag structures were positively associated with an increased risk of asthma symptoms, but only the
21 3-day moving average was statistically significant (draft ISA, Figure 3-3). In joint-pollutant
22 models with carbon monoxide (CO) and NO2, the 3-day moving average effect estimates
23 remained robust and statistically significant. In a joint-pollutant model with PMio, the 3-day
24 moving average effect estimate remained robust, but was no longer statistically significant (draft
25 ISA figure 3-3).
26 A longitudinal study of 1,844 schoolchildren during the summer months from the
27 Harvard Six Cities Study suggested that the association between 862 and respiratory symptoms
28 could be confounded by PMio (Schwartz et al., 1994). It should be noted that unlike the NCICAS
29 and CAMP studies, this study was not limited to asthmatic children. The median 24-
30 hour average SO2 concentration during this period was 4.1 ppb (10th-90th percentile: 0.8, 17.9;
July 2008 23 Draft - Do Not Quote or Cite
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1 maximum 81.9). SC>2 concentrations were found to be associated with cough incidence and lower
2 respiratory tract symptoms. However, the effect of 862 was substantially reduced after
3 adjustment for PMi0. PMio had the strongest association with respiratory symptoms, and the
4 effect of PMio remained robust in copollutant models. Because PMio concentrations were
5 correlated strongly to SO2-derived sulfate particles (r = 0.80), the reduced SO2 effect estimate
6 may indicate that for PMio dominated by fine sulfate particles, PMio has a slightly stronger
7 association than SC>2 to cough incidence and lower respiratory symptoms (draft ISA, section
8 3.1.4.1.1).
9 In addition to epidemiological studies examining the relationship between ambient SC>2
10 concentrations and respiratory symptoms in children, the draft ISA also describes studies that
11 looked for associations between 862 levels and respiratory symptoms in adults (draft ISA,
12 section 3.1.4.2.1). The draft ISA notes that compared to the number of epidemiological studies
13 examining the association between SO2 exposure and respiratory symptoms in children, fewer
14 studies examined this association in adults. Moreover, results in adults were mixed; some studies
15 demonstrated positive associations while others showed no relationship at current ambient SC>2
16 levels (draft ISA, section 3.1.4.1.2).
17 4.3.2 Emergency Department Visits and Hospitalizations for All Respiratory Causes
18 Respiratory causes for ED and hospitalization visits typically include asthma, pneumonia,
19 bronchitis, emphysema, upper and lower respiratory infections, as well as other minor categories.
20 Overall, the draft ISA concludes that there is suggestive evidence of an association between
21 ambient 862 concentrations and combined ED visits and hospitalizations for all respiratory
22 causes (draft ISA, section 3.1.4.6.1). The ISA also finds that when analyses are restricted by
23 age, the results among children (0-14 years) and older adults (65+ years) are mainly positive, but
24 not always statistically significant (draft ISA, section 3.1.4.6.1). When all age groups are
25 combined, the ISA finds that the results of studies are mainly positive; however, the excess risk
26 estimates are generally smaller compared to children and older adults (see draft ISA, figure 3-6).
27 Results from key epidemiological studies conducted in the U.S. and Canada are described below,
28 and a more detailed discussion of both the U.S. and international epidemiological literature can
29 be found in the draft ISA (draft ISA, section 3.1.4.6.1).
July 2008 24 Draft - Do Not Quote or Cite
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1 Wilson et al. (2005) examined the association between SC>2 levels and ED visits for all
2 respiratory causes in Portland, ME and Manchester, NH. The authors found a negative
3 association in Portland when analyses were limited to children. In Portland, they found a
4 positive and statistically significant 9% (95% CI: 5, 14) excess risk per 10 ppb increase in 24-
5 hour average SO2 in adults. Largest effects were observed among the elderly, with a 16% (95%
6 CI: 7, 26) excess risk per 10 ppb increase in 24-hour average SC>2. When all ages were
7 combined, a positive and statistically significant 7% (95% CI: 3, 12) excess risk per 10 ppb
8 increase in 24-hour average SC>2 was observed in Portland. No relationship was observed
9 between SC>2 concentrations and ED visits for all respiratory causes in Manchester in the
10 analyses of all ages or any age-stratified group.
11 Schwartz (1995) conducted a study in New Haven, CT and Tacoma, WA evaluating the
12 relationship between hospital admissions for all respiratory causes (n ~ 8,800 in New Haven and
13 n ~ 4,600 in Tacoma) and ambient SO2 concentrations in older adults (65+ years). The average
14 24-hour SC>2 concentration was 29.8 ppb in New Haven and 16.8 ppb in Tacoma. Schwartz et al.
15 found positive associations between hospitalizations and SC>2, with a 2% (95% CI: 1, 3) excess
16 risk in New Haven and 3% (95% CI: 1, 6) excess risk in Tacoma per 10 ppb increase in 24-hour
17 average SC>2. Notably, the effect estimate for New Haven remained robust and statistically
18 significant in two-pollutant models with PMi0, but in Tacoma was substantially reduced and no
19 longer statistically significant (draft ISA, Figure 3-8). Additional evidence for an association
20 between 862 exposure and hospital admissions for all respiratory causes in older adults was
21 found in two studies conducted in Vancouver, BC. Fung et al. (2006) and Yang et al. (2003)
22 both found positive associations between hospitalizations and 24-hour average SO2
23 concentrations in older adults.
24 Peel et al. (2005) investigated the relationship between 1-hour maximum SC>2
25 concentrations and respiratory ED visits (n ~ 480,000) for all ages in Atlanta, GA. The mean 1-
26 hour maximum SC>2 concentration was 16.5 ppb. A weak and statistically non-significant
27 relationship was observed for respiratory ED visits. Specifically, Peel et al. found an excess risk
28 of 1.6% (95% CI: -0.6, 3.8) per 40 ppb increase in 1-hour maximum SO2. Tolbert et al (2007)
29 recently reanalyzed the data from this study along with four additional years of data and found
30 similar results.
July 2008 25 Draft - Do Not Quote or Cite
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1 4.3.3 Emergency Department Visits and Hospitalizations for Asthma
2 The draft ISA also finds suggestive evidence of an association between SC>2 levels and
3 ED visits and hospitalizations for asthma. The document notes that most of the effect estimates
4 associated with asthma ED visits are positive (suggesting an association with ambient 862),
5 although few are statistically significant (draft ISA, section 3.1.4.6.1). In an analysis
6 encompassing all ages, Wilson et al. (2005) found a statistically significant positive association
7 between asthma ED visits and SO2, with an 11% (95% CI: 2, 20) excess risk per 10 ppb increase
8 in 24-hour average SC>2 in Portland, ME. In Manchester NH, the authors found a positive,
9 although not statistically significant association with a 6% (95% CI: -4, 17) excess risk per 10
10 ppb increase in 24-hour average SC>2. Ito et al. (2007) also examined the association between
11 SC>2 and asthma ED visits in all ages. This study was conducted in New York City and found a
12 6% (95% CI: 3, 10) excess risk per 10 ppb increase in 24-hour average 862 in all year analyses.
13 Multipollutant analyses were conducted in data limited to the warm season only. While the 862
14 effect estimate was robust and remained statistically significant after adjustment for PM2.s, O3,
15 and CO in two-pollutant models, it was found to diminish to null when adjusting for NC>2. Peel
16 et al. (2005) also examined the association between asthma ED visits and ambient SO2. This
17 study was conducted in Atlanta and found a null association between ED visits for asthma and 1-
18 hour maximum SC>2 levels. In addition to these ED studies, a hospital admissions study
19 conducted by the New York Department of Health (NY DOH, 2006) found a statistically
20 significant 10% (95% CI: 5, 15) excess risk for asthma hospital admissions per 10 ppb increase
21 in 24-hour average SC>2 for residents of the Bronx, but a null association for those living in
22 Manhattan.
23 In three Ohio cities, Jaffe et al. (2003) examined the association between SO2
24 concentrations and asthma ED visits among asthmatics, aged 5-34 years. The mean 24-hour
25 average SO2 concentrations were 14 ppb in Cincinnati, 15 ppb in Cleveland, and 4 ppb in
26 Columbus. A statistically significant association was observed in the multeity analysis. The
27 authors found an excess risk of 6% (95% CI: 1, 11) per 10 ppb increase in 24-hour average SO2.
28 In the city-stratified analyses, statistically significant associations were observed for Cincinnati
29 (17% [95% CI: 5, 31]), but not in Cleveland (3% [95% [CI -4, 11]) or Columbus (13% [95% CI:
30 -14,49]).
July 2008 26 Draft - Do Not Quote or Cite
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1 Lin et al. (2004b) conducted a case-control study of children aged 0-14 years in Bronx
2 County, NY. The authors examined the potential association between daily ambient 862
3 concentrations (categorized into quartiles of both average and maximum levels) and cases
4 admitted into the hospital for asthma, or controls who were admitted for reasons other than
5 asthma. The results of this study demonstrated that cases were exposed to higher daily average
6 concentrations of SC>2 than controls. When the highest exposure quartile (>20 ppb, 24-h average
7 802) was compared with the lowest (2.9-9.4 ppb, 24-h average 802), the odds ratios (ORs) were
8 strongest when a 3-day lag was employed (OR 2.16 [95% CI: 1.77, 2.65]). However, the results
9 were positive and statistically significant for all lag days examined. Lin et al. (2005) observed a
10 weak positive association between hospitalizations for asthma and SC>2 among girls, and a null
11 association for boys (Toronto, ON; mean 24-h average SO2 of 5.36 ppb [SD 5.90]). In addition
12 to these hospitalizations studies, Wilson et al. (2005) found a positive, but not statistically
13 significant 5% (95% CI -12, 25) excess risk per 10 ppb increase in 24-hour average SO2 for
14 asthma ED visits in Portland, ME, and a positive, but not statistically significant 20% (95% CI -
15 3, 49) excess risk in Manchester, NH among children aged 0-14 years.
July 2008 27 Draft - Do Not Quote or Cite
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i 5.0 OVERVIEW OF RISK AND EXPOSURE ASSESSMENT
2 5.1 INTRODUCTION
3 As previously discussed in Chapter 4, the draft ISA concludes that there is a causal
4 relationship between short-term (ranging from 5-minute to 24-hours) 862 exposure and
5 respiratory morbidity, while finding associations between 862 exposure (both short- and long-
6 term) and other health endpoints to be less convincing (draft ISA, Table 5-3). The draft ISA
7 bases these conclusions on the cohesiveness and overall strength of evidence from human
8 clinical, epidemiological, and animal toxicological studies. Thus, based on the scientific
9 evidence presented in the draft ISA, staff concludes that the most appropriate use of time and
10 resources is to conduct an exposure and risk assessment based on select respiratory morbidity
11 endpoints. Moreover, based on the nature of the scientific evidence (predominantly human
12 clinical for peak exposures and epidemiological for short-term exposures), staff judges it most
13 appropriate to perform separate and distinct analyses to evaluate exposure and risks associated
14 with different averaging times of SO2 exposure. A general description of each analysis is
15 described in the following sections.
16 5.2 APPROACH FOR ASSESSING EXPOSURE AND RISK
17 ASSOCIATED WITH 5-MINUTE PEAK SO2 EXPOSURES
18 Three analyses will be performed to assess the risks associated with short-term peak SC>2
19 exposures. The first analysis compares 5-minute potential health effect benchmark values, based
20 on the draft ISA's evaluation of relevant controlled human exposure studies with an air quality
21 analysis to determine the frequency with which these benchmark values are exceeded
22 considering current air quality, and air quality adjusted to simulate just meeting the current
23 standards (Chapter 6). The second analysis combines these same benchmark values derived
24 from controlled human exposure studies with results from exposure modeling to estimate the
25 number of individuals that are likely to experience exposures exceeding these benchmark levels
26 (Chapter 7). Finally, the third analysis is a quantitative risk assessment combining outputs from
27 the exposure analysis with estimated exposure-response functions based on data from controlled
28 human exposure studies to estimate the percentage, and number of asthmatics likely to
29 experience a given decrement in lung function associated with recent air quality and 862 levels
July 2008 28 Draft - Do Not Quote or Cite
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1 adjusted to simulate just meeting the current standards (Chapter 8). This third analysis is not yet
2 complete and, thus, this draft of the REA provides a brief description of the overall approach.
3 To identify potential health benchmarks to be used in combination with the air quality
4 and exposure analyses, staff reviewed the controlled human exposure evidence presented in the
5 draft ISA for evidence of SO2 concentrations that resulted in decrements in lung function in the
6 presence of respiratory symptoms because this combination of lung function decrements and
7 respiratory symptoms is considered to be adverse in ATS guidance, which the staff and CASAC
8 have generally endorsed as appropriate. As discussed above, the draft ISA identified 0.4-0.6
9 ppm SC>2 for 5-10 minutes as an exposure range resulting in a substantial percentage of
10 exercising asthmatics experiencing moderate or greater increases in sRAW, or decreases in FEVi
11 in the presence of respiratory symptoms. Therefore, we judge that 0.4-0.6 ppm 862 is an
12 appropriate range to use in the benchmark analyses associated with 5-minute peak 862
13 concentrations.
14 5.3 APPROACH FOR ASSESSING EXPOSURE AND RISK
15 ASSOCIATED WITH SHORT-TERM (>1 HOUR) SO2 EXPOSURES
16 As discussed in more detail in Chapter 9, staff has concluded that a number of factors
17 make it particularly difficult to quantify with confidence the unique contribution of SO2 to
18 respiratory health effects and therefore, we judge that the results of a quantitative risk assessment
19 based on concentration-response functions from epidemiological studies for these health
20 outcomes would be highly uncertain and of limited utility in the decision-making process.
21 However, even though we do not believe that the body of U.S. and Candian epidemiological
22 literature is robust enough to support a quantitative assessment of risk, we do agree that the
23 results of these studies, as well as supportive evidence from international studies suggest an
24 association between SC>2 exposure and respiratory symptoms in children, and hospital admissions
25 and ED visits for all respiratory causes and asthma, and as a result, warrant a characterization of
26 risk.
27 Staff plans to use epidemiological data from recent U.S. and Canadian studies examining
28 ED visits and hospitalizations for all respiratory causes and asthma, as well as epidemiological
29 studies examining respiratory symptoms, to qualitatively assess the range of SO2 air quality
30 levels that are associated with these health endpoints (see Chapter 9). We requested the authors
July 2008 29 Draft - Do Not Quote or Cite
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1 of key U.S. and Canadian studies identified in the draft ISA to provide more detailed SC>2 air
2 quality distribution data. This data will be used to generate tables and graphs relating specific air
3 quality statistics at the time the studies were conducted to health effect estimates. This data will
4 then be used to compare 862 levels in studies where health effects were observed to those levels
5 that would be estimated to occur in areas just meeting the current 24-hour standard. In addition,
6 the second draft of this document will also compare air quality levels seen in studies that
7 observed respiratory health effects to air quality levels that could occur under any alternative
8 standards that may be under consideration.
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i 6.0 AMBIENT AIR QUALITY AND BENCHMARK HEALTH RISK
2 CHARACTERIZATION FOR 5-MINUTE PEAK SO2 EXPOSURES
3 6.1 INTRODUCTION
4 The first step in evaluating SO2 exposure to 5-minute peaks was to characterize air
5 quality relying largely on ambient SC>2 monitoring data and the information provided in the draft
6 ISA and relevant Annexes. In this analysis, ambient SC>2 concentrations served as a surrogate for
7 total human exposure and were used in developing statistical relationships among various
8 averaging times. This analysis considered information on SC>2 air quality patterns, historic
9 trends, local sources, and 5-minute potential health effect benchmarks in the range of 0.4-0.6
10 ppm; staff identified this range, based on the health effects information presented in the draft ISA
11 (see section 4.2).
12 Staff developed statistical relationships between 5-minute peak concentrations and hourly
13 concentrations using ambient monitoring data. This was done because the averaging times for
14 the current SC>2 NAAQS (daily and annual), much of the ambient monitoring data (1-hour), and
15 outputs from dispersion models (1-hour) were not comparable to the selected health effects
16 averaging time of 5-minutes. Both measured and modeled 5-minute data were then evaluated
17 considering air quality conditions as they existed at the time of measurement (henceforth referred
18 to as "air quality as is "), as well as under simulated conditions that would just meet the current
19 form and level of the primary 24-hour 862 standard of 0.14 ppm (one allowable exceedance) and
20 the annual average 862 standard of 0.03 pm (henceforth referred to as "just meeting the current
21 standards").
22 Overall, the objectives of this analysis are to: (1) evaluate trends in short- and long-term
23 SC>2 concentrations using available 5-minute and 1-hour average ambient SC>2 monitoring data,
24 (2) develop a statistical approach to estimate the 5-minute concentrations associated with 1-hour
25 average ambient monitoring concentrations, (3) estimate the frequency of short-term peak
26 concentrations at ambient monitoring locations above potential health effect benchmark levels
27 using both measured data and statistical model predictions, and (4) identify key uncertainties in
28 the analysis.
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l 6.2 APPROACH
2
3
4
5
6
1
10
11
12
13
14
15
16
17
18
19
20
21
22
23
6.2.1 Monitoring data
SO2 air quality data available since the previous review (1997-2007) was assembled from
EPA's Air Quality System (EPA, 2007c). Monitoring data were collected over 5-minute or 1-
hour averaging times. The 5-minute SC>2 monitoring data exist in either one of two forms; the
single highest 5-minute concentration occurring in a 1-hour period (referred to here as max-5), or
all twelve 5-minute concentrations within a 1-hour period (referred to here as continuous-5). A
summary of all available 5-minute and 1-hour 862 monitoring data is presented in Table 6-1 and
a more detailed description of these data can be found in Appendix A.
Table 6-1. Summary of available 5-minute and 1-hour SO2 ambient monitoring
data.
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
1 DC=District of Columbia, PR=Puerto Rico, VI=Virgin Islands.
The data sets listed in Table 6-1 were screened for locations where monitor IDs contained
multiple parameter occurrence codes (POCs) and identical monitoring times (see Appendix A),
an indication that SC>2 concentrations were measured simultaneously at a given location (i.e., co-
located monitors). As a result, three additional data sets were identified for further analyses
(summarized in Table 6-2.):
1. A data set containing all simultaneous measures collected at the same location and
time for:
A. max-5 duplicates (i.e. simultaneous measurements from co-located max-5
monitors)
B. max-5 and continuous-5 duplicates (i.e. simultaneous measurements from a co-
located max-5 monitor and continuous-5 monitor)
These data were used for quality assurance purposes. Duplicate measures were not used
in the statistical model development.
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32
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1
2
3
4
5
6
7
2. A complete set of 5-minute maximum 862 concentrations without duplicate 5-minute
measures (combined from max-5 and maximums reported in continuous-5 monitoring
data), combined with their corresponding measured 1-hour 862 concentrations. These
data were used for developing the statistical model and for characterizing air quality.
3. All 1-hour SC>2 data that do not have any corresponding 5-minute concentrations.
These data were used for application of the statistical model and characterizing air
quality.
Table 6-2. Number of duplicate samples within and between max-5 and
continuous-5 data sets.
Sample Type
Max-5
Continuous-5
1-hour
Within Set
Duplicates
(n)
300,438
0
0
Available
Data
(n)
3,156,619
283,2021
47,195,533
Combined Set
Duplicates
(n)
29,058
-
Final
Combined
Max-5 Data
(n)
3,410,763
-
Final
Combined
Max-5 Si-
hour
(n)
2,408,420
1 The number of 5-minute maximum samples.
10 6.2.2 Monitoring Siting
11 The siting of the monitors is of particular importance, recognizing that proximity to local
12 sources likely influences measured 862 concentration data. Stationary sources (in particular,
13 power generating utilities using fossil fuels) are the largest contributor to 862 emissions in the
14 U.S. (EPA, 2007b). Analyses were performed here to determine the distances and the types of
15 stationary source emissions to the ambient monitors. Two points are worthy of mention for this
16 analysis; the first being the difference between the number of 5-minute and 1-hour monitors
17 located across the U.S., and the second being the potential for differences in types of sources
18 influencing each of the monitors. While there is overlap in the measurement of 5-minute
19 maximum and its associated 1-hour concentration in some locations (n=98), over 800 1-hour
20 monitors are sited in other locations where 5-minute measurements have not been collected.
21 There is a possibility that sources in close proximity to the 1-hour monitors have a different
July 2008
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1 impact on SC>2 concentrations measured at these monitors compared with those sources
2 influencing concentrations measured at the 5-minute monitors.
3 However, the comparison of the sources located within 20 km of the 5-minute and 1-hour
4 monitors indicates strong similarity in the types of sources potentially influencing the measured
5 concentrations at each type of monitor. Figure 6-1A shows the percent of total SO2 emissions
6 for sources located within 20 km of the 5-minute maximum monitors. Approximately 70% of
7 the stationary source emissions originate from power generation, divided among fossil fuel and
8 hydroelectric utilities. Primary smelters (9%) and petroleum refineries (7%) comprise the next
9 highest sources of emissions, and much of the remaining total emissions (17%) are divided
10 among numerous other sources. Figure 6-IB shows that the emissions sources within 20 km of
11 the available 1-hour SC>2 ambient monitors are similar to the 5-minute maximum monitors in
12 type and percent of total emissions. Seventy-eight percent of total emissions result from power
13 generation, followed next by petroleum refineries (5%) and other lower emitting sources. The
14 largest distinction between the sources surrounding the two groups of monitoring data is the
15 contribution from primary smelters with greater emissions within 20 km of the 5-minute
16 monitors (8.8%) than within 20 km of the 1-hour monitors (1.1%).
17 6.2.3 Statistical Model for Estimating 5-minute Maximum Concentrations
18 6.2.3.1 Background
19 The overwhelming majority of the SC>2 ambient monitoring data is for 1-hour average,
20 while important health effects are associated with 5-minute peak concentrations of SC>2.
21 Therefore, a model needed to be designed to allow for estimation of 5-minute maximum SC>2
22 based on the available 1-hour average monitoring data. Staff reviewed the air quality
23 characterization conducted in the prior 862 NAAQS review and supplementary analyses, much
24 of which focused on evaluating the relationship between the maximum 5-minute SC>2
25 concentration and the 1-hour average 862 concentration, or peak-to-mean ratios (PMRs) (SAI,
26 1995; Thompson, 2000). On average, the PMR was determined to be approximately two;
27 however, the ratio varies. It was shown that there is increased variability in the ratio with
28 decreasing 1-hour average SC>2 concentrations, that is, there is a greater likelihood of values
29 greater than 2 at low hourly average concentrations than expected at high hourly average
30 concentrations. In addition, the occurrence of short-term peak concentrations at ambient
July 2008 34 Draft - Do Not Quote or Cite
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1
2
3
4
5
monitors is likely to be influenced by their distance from local sources and source characteristics
including the magnitude of emissions, temporal operating patterns (e.g., seasonal, time-of-day),
6
7
8
9
10
A)
B)
CerYBnt M anufaeluring
1%
Other PetroleurrfCoal
Manufacturing
1%
Flour Milling
2%
Power Generation,
Transmission &
Distribution
2%
iron and Steel M ills
2%
Petroleum Refineries
Primary Smelting/Refining
9%
Fossil Fuel Po'^
Generation
38%
Hydroelectric Power
Generation
30%
Primary SmeltingfRefining
1%
Electric Power Generation
2%
Iron and Steel M ills
2%
Petroleum Refineries
5%
Power Generation,
Transmission, &
Distribution
Fossil Fuel Povver
Oene ration
45%
Hydroelectric
Generation
25%
Figure 6-1. The percent of total SO2 emissions by source types located within 20
km of ambient monitors. A) 5-minute maximum SO2 monitors, B) 1-hour
SO2 monitors.
July 2008
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1 facility maintenance, and other physical parameters (e.g., stack height, area terrain), as well as by
2 local meteorological conditions. As part of a sensitivity analysis conducted for copper-smelters,
3 the dependence of PMRs on the distance from the source was evaluated for three ranges of
4 normalized 1-hour mean concentrations (Sciences International, 1995).3 Distance was found to
5 be inversely proportional to the PMR in all three of the 1-hour mean stratifications (i.e., < 0.04
6 ppm, 0.04 to < 0.15 ppm, and >0.15 ppm), with the highest 1-hour category containing the
7 lowest range of PMR.
8 6.2.3.2 Current Approach
9 The model used here to generate the relationship between short-term peak and 1-hour
10 concentrations is given in equation 6-1.
11
12 Cmax_5 =PMRxCl_hour equation (6-1)
13
14 where,
15 Cmax-s = estimated maximum 5-minute 862 concentration (ppb)
16 PMR = peak to mean ratio (PMR)
17 C'i-hour = measured 1-hour average SO2 concentration
18
19 The application of this model considers the limited geographic span of the monitoring
20 data and the overall uncertainty regarding the amount of influence of a specific source on any
21 given monitor. This approach is based on hourly concentration levels and relative standard
22 deviations (or coefficient of variation (COV)) observed at the monitors measuring the continuous
23 or maximum 5-minute SC>2 concentrations and simultaneous SC>2 1-hour concentrations. The
24 assumption is that the temporal and spatial pattern in 862 source emissions is influenced by the
25 type of source(s) and its operating conditions and that this emission pattern(s) will be reflected in
26 the ambient 862 concentration distribution measured at the monitor. This approach is discussed
27 in more detail below.
28 6.2.3.3 Relationship Between 1-hour and 5-minute SO2 Concentrations
29 There were multiple analyses performed here using the available 5-minute monitoring
30 data, the first of which involved evaluating the relationship between the variability in 1-hour and
3 In that analysis, normalized 1-hour SO2 concentrations were obtained by dividing by the maximum hourly
concentration.
July 2008 36 Draft - Do Not Quote or Cite
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1 5-minute SC>2 concentrations. As noted above, the variability in these concentrations could serve
2 as a surrogate for source emissions, source types, or distance to sources. The purpose was to
3 develop a categorical variable to use for connecting the statistical model to both the 1-hour
4 monitoring data and 1-hour dispersion model estimates (where no 5-minute 862 data
5 concentration exist).
6 First, all available 5-minute SO2 concentrations from the 16 continuous monitors for all
7 years were averaged for each monitor, that is, all of the continuous-5 data available for each
8 monitor were averaged to generate a single 5-minute mean concentration and its respective
9 standard deviation (a total of 16 monitor- specific 5-minute values). Then, the 5-minute SC>2
10 concentrations were averaged to generate 1-hour average 862 concentrations for each monitor,
11 which were then averaged to generate a single 1-hour mean and its corresponding standard
12 deviation (a total of 16 monitor-specific 1-hour values). The COV for the 1-hour and 5-minute
13 data at each monitor are illustrated in Figure 6-2. As expected, a strong direct linear relationship
14 exists between the variability in 5-minute and 1-hour SC>2 concentrations at each monitor,
15 although the 1-hour monitoring COV is approximately 75% that of the 5-minute monitoring
16 COV. Even with the limited geographic representation (the monitors come from only 6 states
17 plus Washington DC), there is a wide range in the observed concentration variability for both the
18 5-minute and associated hourly measurements (COVs around 75 - 300%). In general, this
19 analysis indicates that variability in 5-minute SO2 concentrations is directly related to the
20 variability in 1-hour SO2 concentrations, and may be used as a categorical parameter to describe
21 the potential variability in emissions and possible source types influencing any ambient SO2
22 monitor.
23 A second comparison was made using the 1-hour concentrations measured at each of the
24 5-minute monitors and the 1-hour monitors. Figure 6-3 illustrates the Cumulative Density
25 Functions (CDFs) for the hourly COV at each of the 98 monitors that measured both 5-minute
26 maximum and 1-hour SO2 concentrations (the final combined max-5 and 1-hour data set) and the
27 927 hourly monitors containing no 5-minute maximum measurements. While the 5-minute
28 monitors exhibit greater variability in hourly concentration at most percentiles of the distribution,
29 the overall shape and span of the distributions are very similar. This could indicate that on the
30 whole, the proximity to sources, their magnitude of emissions, and the types of sources affecting
July 2008 37 Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
either set of ambient monitors (i.e., the 1-hour monitors versus the 5-minute monitors) are
similar. This, combined with the distance and emissions analysis that indicated similar source
type emission proportions in Appendix A, provides further support for using COV as a
categorical parameter to extrapolate PMRs developed from the 5-minute 862 monitors to the 1-
hour monitors.
300
275 -
250 -
225
> 200
O
O
1175^
'S
~ 150 -
o>
u
o 125 -
O
1 100
o
I
75 -
50 -
25 -
0
y = 0.75x + 19
0 25 50 75 100 125 150 175 200 225 250 275 300
5-min Concentration COV (%)
Figure 6-2. Comparison of hourly COV and 5-minute COV at 16 continous-5
monitors, over multiple years of monitoring.
July 2008
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100%
90%
80%
70% -
| 60%
Q.
g 50%
JS
1 40%
O
30%
20%
10% -
0%
Average hourly COV: 1-hr monitors (n=927)
• - - Average hourly COV: 5-min monitors (n=98)
0 50 100 150 200 250 300 350 400 450 500
Ambient Monitor Hourly COV (%)
2 Figure 6-3. Cumulative density functions (CDFs) for hourly COV at 1-hour and 5-
3 minute SO2 monitors.
4
5 6.2.3.4 Development of Peak to Mean Ratio (PMR) Distributions
6 A key parameter in the statistical model to estimate the frequency of maximum 5-minute
7 SC>2 concentrations at locations where only 1-hour average values were measured is the PMR.
8 The method used here builds upon prior analyses conducted by Thompson (2000)4, however the
9 updated approach includes the development of several PMR cumulative density functions
10 (CDFs) based on more recent 5-minute SC>2 monitoring data, and considers a COV categorical
11 parameter describing each monitor and the measured (or modeled) 1-hour SO2 concentration
12 level.
13 First, the PMR data were screened for validity, recognizing that the combined max-5 and
14 1-hour SO2 data set may still contain certain anomalies (e.g., 5-max concentration < 1-hour mean
15 concentration). A value of 1 was selected as the lower bound PMR, accepting that it may be
16 possible that the 5-minute maximum concentrations (and all other 5-minute concentrations
17 within the same hour) may be identical to the 1-hour average concentration. A PMR of 12 was
4 A single semi-empirical distribution of PMRs based on 6 ratio bins was used that assumed independence between
the ratio and the 1-hour concentration.
July 2008
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1 selected as the upper bound since it would be a mathematical impossibility to generate a value
2 above that given there are 12 5-minute measurements within any 1-hour period.5 This screening
3 resulted in a total of nearly 2.4 million valid PMRs.
4 COV has been identified above as an important attribute in characterizing potential
5 sources affecting the ambient monitors. Based on the hourly COV distributions in Figure 6-3,
6 we assigned one of three COV bins to each of the 98 monitors containing both the 5-minute
7 maximum and 1-hour average SO2 concentrations: COV < 100%, 100% < COV < 200%, and
8 COV > 200%. The three COV bins were selected to capture the upper and lower tails of the
9 distribution and a mid-range area. In addition, the level of the 1-hour mean concentration has
10 been identified as an important consideration in defining the appropriate PMR distribution. The
11 PMR CDFs were further stratified by five 1-hour mean concentration ranges: 1-hour mean <
12 33.3 ppb, 33.3 < 1-hour mean < 100 ppb, 100 < 1-hour mean < 200 ppb, 200 < 1-hour mean <
13 300 ppb, and 1-hour mean > 300 ppb. While PMR CDFs were generated for 1-hour
14 concentrations < 33.3 ppb, it should be noted that the corresponding 5-minute concentration
15 would be below that of the lowest potential health effect benchmark level of 400 ppb. The
16 stratification was done by equivalent 100 ppb increments to represent the variability in PMR
17 anticipated across the 1-hour SO2 concentration and COV categories, to allow for a reasonable
18 assignment of PMR to an appropriate 1-hour concentration, while also limiting the total possible
19 number of PMR distributions. Based on the COV and 1-hour mean categories, this resulted in a
20 total of thirteen separate PMR CDFs,6 summarized in Appendix B. Due to the large number of
21 samples available for several of the PMR distributions, the data were summarized into semi-
22 empirical distributions, with the cumulative percentiles ranging from 0 to 100, by increments of
23 1.
24 Figure 6-4 illustrates two trends in the PMRs when comparing the distributions across the
25 stratification categories. First, the monitors with the highest COVs contain the highest PMRs at
26 each of the percentiles of the distribution (Figure 6-4C) when compared with monitors from the
5 As the 5-minute maximum concentration goes to infinity, the other 11 concentrations measured in the hour
comparatively tend to zero, giving PMR = Peak/Mean = Cmax/[(Cmax+ 0* 11)/12] = 12.
6 Although there were a total 15 PMR CDFs possible, the COV < 100% category did not contain any 1-hour
concentrations above 200 ppb. Also note that each of the three lowest concentration category PMRs (<33.3 ppb) are
not illustrated in Figure 4 for improved clarity.
July 2008 40 Draft - Do Not Quote or Cite
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I
Q)
Q_
2
D
o
0)
Q_
D
O
Ci-
-i—•
§
D
O
100
90
80
70 -
60
50 -
40
30
20
10 -
0
100 -
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -
10 -
0
100 -\
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -
10 -
0
1
A) COV < 100%
33.3 <1-hours 100 ppb
1-hour > 100 ppb
B)100 300 ppb
C) COV > 200%
- - - • 33.3 < 1 -hour < 100 ppb
100< 1-hour<200 ppb
200 < 1-hour < 300 ppb
— • 1 -hour > 300 ppb
10
11
12
Peak to Mean Ratio (PMR)
2 Figure 6-4. Peak to mean ratio (PMR) distributions for three variability categories
3 and 1-hour concentration groups. A) COV < 100%, B) 100 < COV < 200%,
4 and C) COV > 200%.
July 2008
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1 other two COV categories (Figures 6-4A and 6-4B), while the mid-range COV category
2 monitors (Figure 6-4B) contained higher PMRs than the lowest COV category (Figure 6-4A).
3 These distinctions in PMR are consistent with the results illustrated in Figure 6-2, that is,
4 variability in the hourly average concentrations is directly related to the variability in the short-
5 term concentrations. Second, differences were observed in the PMR distributions within each
6 PMR category when categorized by 1-hour average concentrations. This is most evident in the
7 highest COV category (Figure 6-4c); the highest 1-hour concentration category (> 300 ppb)
8 contained the lowest PMRs at each of the distribution percentiles compared with the distributions
9 for the lower concentration categories (e.g., 33.3 - 100 ppb). In fact, the maximum PMR for the
10 > 300 ppb category was only 5.4, compared with a maximum PMR of 11.45 for the 33.3 - 100
11 ppb category. The hourly average concentration was used for categorization to prevent use of
12 high PMRs developed from lower hourly concentrations being applied to higher hourly
13 concentrations. This stratification by 1-hour average concentration and COV is designed to
14 control for aberrant assignment of PMRs to 1-hour concentrations.
15 6.2.3.5 Application of Peak to Mean Ratios (PMRs)
16 As described above with respect to the 5-minute monitoring data, each of the 929 1-hour
17 monitors that did not contain 1-hour measurements was characterized by its respective hourly
18 COV value, and placed in one of the three COV bin (COV < 100%, 100% < COV < 200%, and
19 COV > 200%). Based on the monitor COV bin and every 1-hour SO2 concentration, PMRs were
20 randomly sampled7 from the appropriate PMR CDFs for each hour and used to estimate a 5-
21 minute maximum concentration using equation 6-1. After this calculation, each 1-hour ambient
22 monitor contained a simulated 5-minute maximum concentration for each period when the 1-
23 hour SO2 concentration was > 0 (otherwise the 5-minute maximum concentration was estimated
24 as zero). These data were then summarized by calculating the number of times an estimated 5-
25 minute peak concentration above a potential health effect benchmark level occurred.
26 6.2.3.6 Evaluation of Estimation Procedure
27 The procedure for estimating the 5-minute maximum SO2 concentrations was evaluated
28 using the data from the 98 monitors where both 5-minute and 1-hour concentrations were
7 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 CDF percentile value.
July 2008 42 Draft - Do Not Quote or Cite
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1 measured. The statistical model described in sections 6.2.3.2 through 6.2.3.4 was used to
2 generate predicted values at the 5-minute monitors. The precision of the statistical model was
3 then assessed by comparing measured 5-minute maximum 862 concentrations to the predicted
4 values. The objective of this first evaluation was to determine the approximate number of
5 simulations needed to produce stable 5-minute maximum concentrations predictions. Twenty
6 simulations were run for all max-5 monitors (w=98) across all years of data to generate the
7 number of 5-minute maximum SC>2 concentrations above 400 ppb (peak concentrations) for each
8 monitor in each simulation. Predicted versus measured differences in the number of peaks
9 estimated at each monitor were normalized to provide equal weighting for this comparison
10 (equation 6-2). The mean number of predicted peaks (P) at each individual monitor (/) for all
1 1 simulations was first calculated and compared with the measured number of peaks (M) at each
12 individual monitor to estimate an absolute difference between the total simulation average and
13 the measured data. Then predicted differences were calculated for each of the progressive
14 simulations (/' = 1,2,3, ...20} at each monitor and compared to the total simulation difference at
15 each monitor. The calculated value indicates the proportion of the difference, including negative
16 (underestimations) and positive (overestimations) values, and values of zero (where the
17 particular simulation estimate was the same as the measured). There was only one difference in
1 8 predicted versus measured peaks that resulted in a value of zero (P,- = Mj at Monitor ID
19 301 1 10080), therefore results from this monitor were removed from further analysis. The
20 remaining relative differences for each of the 97 monitors were then averaged to generate an
21 average absolute relative difference (Diff) for each progressive simulation as follows:
22
23 Diffi =— - ]- equation (6-2)
n
24
25 Note that at the 20th simulation, Py = Pj and results in an absolute relative difference of
26 1.0 at each of the 98 monitors. Figure 6-5 illustrates the results of this calculation. As expected
27 the estimated number of peaks is most variable over the fewest number of simulations, although
28 though the range of relative difference in these estimates resultant from the fewer simulations is
29 still small (+/- 10%). By approximately 13 simulations, the relative absolute difference appears
July 2008 43 Draft - Do Not Quote or Cite
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1 to straddle 1.0 closely, suggesting that within the range of 13-20 model simulations, much of the
2 variability in the estimation procedure has been represented well by the total number of
3 simulations.
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
1.15
= =
!
I
• -
< -5 UJ
a) s —
> -o S
*i d) o
1.10 -
1.05
1.00 -
0.95
0.90
0.85
10 15
Number of Progressive Simulations
20
Figure 6-5. Comparison of the mean relative absolute difference in number of
predicted and measured peaks above 400 ppb, across progressive
model simulations using the monitors that contained measurements for
5-minute maximum SO2 concentrations.
Variability in the model estimation was also evaluated as a function of the predicted
number of peaks (Figure 6-6) at each monitor. A similar degree of variability, as represented by
a COV of about 25%, was observed for the number of peak estimates ranging from 15 upwards
to 450. Variability increases dramatically when fewer than 15 peak concentrations above 400
ppb are estimated. This is largely the result of estimating a few exceedances in one or a few
simulations, along with zero exceedances in other simulations. This evaluation suggests that
where a monitor has about 15 or more estimated maximum 5-minute SO2 concentrations at or
above the 400 ppb in a year, it is likely that the number of exceedances would be consistently
estimated at that level in each model simulation.
July 2008
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Q_
1
I
Q.
'S
z _ro
™
O «
<
450 w
425
400
375
350
325
300 -
275
250 1
225 ,
200
175
150
£
o
125
100
50
25
L
50 100 150 200 250 300 350 400 450
Predicted Mean Number of 5-Minute SO2 Peaks Above 400 ppb at Each Monitor
500
1
2 Figure 6-6. Variability in the predicted number of 5-minute maximum
3 concentrations above 400 ppb at monitors that measured 5-minute
4 maximum concentrations.
5
6 Accuracy of the procedure was evaluated by comparing the mean monitor estimates from
7 the 20 simulations with the measured values at the ninety-eight 5-minute maximum SC>2
8 monitors (Figure 6-7). Good agreement between predicted and measured was observed when the
9 entire data set was evaluated. A total of 1,808 5-minute maximum SC>2 concentrations at or
10 above 400 ppb were measured, while an average of 1,956 5-minute maximum were predicted by
11 the simulations, an overestimation of only 8%. Larger differences in the estimation were
12 apparent when comparing results for individual monitors, particularly at the monitor that
13 recorded the highest number of concentrations above 400 ppb (monitor ID 290930030). The
14 total estimated mean number of exceedances of 400 ppb was about 450; this was about 375 less
15 than the actual measured number of exceedances (an underestimation of about 45%). This
16 ambient monitor is a source-oriented monitor, located within 1.7 km of a primary smelter
17 containing estimated 862 emissions of 43,340 tpy. This is the only stationary source located
18 within 20 km of this monitor (Appendix A). Another source-oriented monitor in the area
July 2008
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
(monitor ID 290930031), potentially influenced by the same smelter, but located at a greater
distance away (i.e., 4.6 km), exhibited better agreement between the estimated and measured
number of peaks (approximately 13% over-prediction) suggesting the underestimation at the
closer monitor may not simply be a function of the source-type but possibly the proximity of the
monitor to the source emission.
Another notable difference occurred at a different monitoring location (monitor ID
380590002), whereas a total mean of 129 exceedances was predicted by the simulations although
there were no measured values above 400 ppb at this site. This site may be affected by a nearby
petroleum refinery located within 2.6 km with estimated emissions of 4,600 tpy. A comparison
of several monitors located within varying distances (1.5 - 6.6 km) of a petroleum refinery
emitting approximately 720 tpy 862 in a different location exhibits good agreement between
measured and modeled estimates (Table 6-3), suggesting there may be a unique characteristic
about the particular source located at monitor ID 380590002 rather than suggesting there is a
unique pattern of emissions characteristic of the source-type as a whole that is not being captured
by the statistical model. When excluding the two sites with the greatest model over-/under-
estimations, there is improved agreement between the modeled and measured data for the other
ninety-six monitors used (predicted = 1.02 * measured, R2= 0.91).8
Table 6-3. Comparison of measured and modeled number of 5-minute maximum
concentrations above 400 ppb located near a petroleum refinery.
Monitor ID
291831002
301110066
301110079
301110080
301110082
301110083
301110084
301112008
Number of 5-minute
Maximum SO2 > 400 ppb
Measured
0
5
0
3
0
1
0
0
Mean
Modeled
3
13
0
3
0
1
0
0
20
! Using all 98 monitors the regression analysis yields the predicted = 0.61 * measured, R2 = 0.85.
July 2008
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900
2
O
4
5
6
7
10
11
12
13
14
15
16
17
18
100 200 300 400 500 600 700 800
Measured Number of 5-Minute SO2 Peaks Above 400 ppb at each 5-minute Monitor
900
Figure 6-7. Comparison of the mean predicted (from 20 simulations) and the
measured number of 5-minute SO2 concentrations at 98 monitors that
measured 5-minute maximum SO2 concentrations. Bars indicate the
standard deviation of the mean.
6.3 APPROACH FOR SIMULATING JUST MEETING THE CURRENT
SO2 STANDARD
6.3.1 Introduction
A primary goal of this draft of the risk and exposure assessments is to aid in judging
whether or not the current SO2 primary standards of 0.14 ppm, 24-hour average and 0.03 ppm,
annual average adequately protect public health. All areas of the U.S. currently have annual
average levels below the current NAAQS (EPA, 2007c). One site in Northampton County, Pa.,
measured concentrations above the level of the 24-hour standard in 2006. Therefore, in order to
evaluate whether the current standards adequately protect public health, nearly all SC>2
concentrations need to be adjusted upwards for all areas included in our assessment in order to
simulate levels of 862 that would just meet the current standard levels.
July 2008
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1 In developing a simulation approach to adjust air quality to meet a particular standard
2 level, policy-relevant background (PRB) levels in the U.S. were first considered. Policy-relevant
3 background is defined as the distribution of 862 concentrations that would be observed in the
4 U.S. in the absence of anthropogenic emissions of 862 in the U.S., Canada, and Mexico.
5 Estimates of PRB have been reported in the draft ISA and for most of the continental U.S. the
6 PRB is estimated to be less than 10 parts per trillion (ppt) annual average (draft ISA, section
7 2.4.6). In the Ohio River Valley, where present-day SC>2 concentrations are highest (>5 ppb),
8 this amounts to a contribution of less than 1% percent of the total observed ambient SC>2
9 concentration. In the Northwestern U.S. and Hawaii, where there are geothermal sources of SC>2
10 (e.g., volcanic activity) the contribution of PRB to total 862 can be as high as 70 to 80% in the
11 vicinity of volcanic activity. However, since PRB is well below concentrations that might cause
12 potential health effects at most locations, PRB will not be considered separately in any
13 characterization of health risk associated with as is air quality or air quality just meeting the
14 current standards. In monitoring locations where PRB is expected to be of particular importance
15 however (e.g., Hawaii county, HI) data will be noted as under possible influence of natural rather
16 than anthropogenic sources and will not be used in analyses simulating air quality that would just
17 meet the current standards.
18 This procedure for adjusting ambient concentrations was necessary to provide insight
19 into the degree of exposure and risk which would be associated with an increase in ambient SC>2
20 levels such that the levels were just at or near the current standards in the areas analyzed. We
21 recognize that it is extremely unlikely that 862 concentrations in any of the selected areas where
22 concentrations have been adjusted would rise to meet the current NAAQS and that there is
23 considerable uncertainty associated with the simulation of conditions that would just meet the
24 current standards. Nevertheless, this procedure was necessary to assess the ability of the current
25 standards, not current ambient levels, to protect public health.
26 6.3.2 Approach
27 Criteria were identified to select ambient monitoring data that would provide the most
28 support to any conclusions drawn from an analysis of ambient concentrations that are adjusted to
29 simulate just meeting the current standards. The first criteria used was to select locations where
30 monitors had concentrations at or near the current NAAQS and/or where monitors contained a
July 2008 48 Draft - Do Not Quote or Cite
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1 number of 5-minute maximum concentrations at or above the potential health effect benchmark
2 levels. Northampton County, Pa. was selected first based on the exceedance of the 24-hour
3 NAAQS in year 2006. Two counties in Missouri (Iron and Jefferson) contained the most
4 frequently measured 5-minute maximum concentrations above the potential health effect
5 benchmarks (see Appendix C). To expand the number of locations to a total of 20, an additional
6 17 counties were selected using the following criteria. First, the analysis used only the more
7 recent data, specifically years 2002 through years 2006.9 Next, locations of interest were
8 screened for those having at least three 1-hour monitors with valid ambient monitoring
9 concentrations within a county for a given year (based on criteria discussed in Appendix A).
10 Using a county to define the location is consistent with current policies on the designation of
11 appropriate boundaries of non-attainment areas (Meyers, 1983).
12 While annual average concentrations have declined over the time period of analysis, the
13 variability in both the annual average and 1-hour concentrations has remained relatively stable
14 (see results of air quality trends in Appendix C). Therefore, a multiplicative proportional
15 adjustment approach was selected to allow for the simulation of air quality just meeting the
16 current 24-hour and annual SC>2 NAAQS, considering the current deterministic form of each
17 standard. The 24-hour standard of 0.14 ppm is not to be exceeded more than once per year,
18 therefore, the second highest daily mean observed at each monitor was used as the target for
19 adjustment. The rounding convention, which is part of the form of the standard, defines values
20 up to 0.144 ppm as just meeting the 24-hour standard. The form of the current annual standard
21 requires that the standard level of 0.030 ppm is not to be exceeded, therefore, the highest annual
22 average concentration at each monitor served as the target for adjustment. With a rounding
23 convention to the fourth decimal, values of up to 0.0304 ppm would just meet the current
24 standard. For each county (/') and year (/), 24-hour and annual SC>2 concentration adjustment
25 factors (F) were derived by the following equation:
26
27 FV = SIC^ equation (6-3)
28 where,
91-hour concentrations were typically only available through April 2007, therefore most years were incomplete. All
data from 2007 were excluded from this simulation.
July 2008 49 Draft - Do Not Quote or Cite
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1 FJJ = Adjustment factor derived from either the 24-hour or annual average
2 concentrations at monitors in location , for yeary (unitless)
3 S = concentration values allowed that would just meet the current NAAQS
4 (144 ppb for 24-hourr, 30.4 ppb for annual average)
5 Cmax,tj = 2nd highest daily mean SO2 concentration at a monitor in location / and
6 yeary' or the maximum annual average SC>2 concentration at a monitor
7 in location / and yeary (ppb)
8 Further, to conduct a both meaningful and efficient analysis, the potential adjustment
9 factors for the annual and 24-hour average were compared to one another to determine which
10 standard would likely be more protective (i.e., containing the lower adjustment factor). A
11 comparison of the generated adjustment factors using the data screened by year (i.e., 2002
12 through 2006) and number of monitors in a county (> 3) is presented in Figure 6-8. Most
13 locations (64%) contained target concentrations closer to the 24-hour standard than the annual
14 standard. When considering locations containing 2nd highest maximum concentrations within an
15 order of magnitude of the 24-hour standard, an even greater percentage (72 %) of locations
16 contain concentrations closer to the 24-hour standard than the annual standard. For monitors
17 within a factor of five, 85% contained concentrations closer to the 24-hour than the annual
18 standard. Therefore, proximity of the 2nd highest 24-hour concentration to the 24-hour standard
19 was the criterion for selecting locations of particular focus.
20 The mean adjustment factor for each county was calculated using each yearly value and
21 then ranked in ascending order. The remaining 17 counties were selected from the top 17 values,
22 that is, those counties containing the lowest mean daily adjustment factor. The locations
23 selected, the years of monitoring data available for that county, and the adjustment factors used
24 to simulate just meeting the current standards are provided in Table 6-4. Both the annual and
25 daily adjustment factors are given for all counties, however the lower value was selected to
26 adjust concentrations. The variability measure (i.e., COV) indicates the variability associated
27 with each of the calculated factors when considering all of the monitors in a county. Lower
28 COVs indicate similarity in that concentration metric in the county, while higher values indicate
29 less homogeneity in concentrations (whether spatially or temporally).
July 2008 50 Draft - Do Not Quote or Cite
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1
2
3
4
5
70
60
50
§ 40
< 30
re
20
10
0 4-
- •
10
20 30 40
Daily Adjustment Factor
50
60
70
Figure 6-8. Comparison of annual and daily adjustment factors derived from
counties containing at least three 1-hour ambient SO2 monitors with
valid data, years 2002 through 2006.
Table 6-4. Estimated population, number of ambient SO2 monitors, and
concentration adjustment factors for simulating just meeting the current SO2
NAAQS in selected counties by year.
State
DE
FL
IA
County1
(Population)2
New Castle
(500,265 - 525,587)
Hillsborough
(998,948-1,157,738)
Linn
(191,701 -201,853)
Year
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
Daily Adjustment
n
4
5
4
4
4
7
6
6
6
6
3
3
3
3
Factor
2.67
2.75
2.58
2.73
2.68
3.09
3.09
4.95
4.40
4.19
4.70
3.45
2.29
3.41
cov
9
9
13
11
14
16
19
32
25
29
5
5
10
9
Annual Adjustment
n
4
3
3
4
4
6
6
6
6
3
3
3
3
Factor
5.39
3.83
5.23
4.52
4.67
4.66
5.54
7.55
8.19
7.97
8.16
7.83
6.70
COV
8
11
2
7
8
8
14
26
20
5
7
14
12
July 2008
51
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State
IL
IN
Ml
MO
MO
MO
OH
OK
PA
County1
(Population)2
Muscatine
(41,722-72,883)
Madison
(258,941 - 265,303)
Floyd
(70,823 - 72,570)
Wayne
(2,061,162-1,971,853)
Greene
(240,391 - 254,779)
lronj
(10,697-10,279)
Jefferson"
(198,099-216,469)
Cuyahoga
(1,393,978-1,314,241)
Tulsa
(563,299 - 577,795)
Allegheny
Year
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
Daily Adjustment
n
3
3
3
3
3
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
2
2
2
1
1
1
1
1
5
5
4
4
4
3
3
3
3
4
7
Factor
4.10
3.87
4.09
2.78
2.90
2.94
2.88
3.60
3.61
4.19
4.90
4.85
4.14
5.05
4.59
3.64
2.97
3.30
2.99
3.35
2.95
3.47
5.12
5.29
4.87
4.46
2.11
2.44
15.85
3.89
5.65
1.87
2.13
1.93
6.83
3.98
4.54
3.43
4.25
4.51
3.65
4.07
4.92
5.69
2.99
cov
35
11
12
16
17
10
12
6
18
11
16
6
5
16
2
5
15
5
12
7
13
32
26
29
34
19
2
2
6
4
5
11
6
8
2
6
3
4
59
5
Annual Adjustment
n
3
3
3
3
3
4
3
3
3
3
3
3
3
3
1
2
3
3
3
3
3
5
5
5
5
2
1
1
5
4
4
4
4
3
3
3
3
3
6
Factor
7.73
6.05
6.09
4.51
6.54
6.41
5.22
5.84
5.73
6.12
5.52
5.32
5.04
3.98
5.65
6.18
5.85
4.70
4.88
5.41
10.42
8.69
9.45
9.96
9.32
4.38
4.61
2.95
5.10
4.21
4.93
4.11
4.64
5.00
5.11
4.21
4.57
4.95
2.40
COV
44
7
5
9
5
4
6
5
4
7
1
5
16
7
6
3
8
7
11
30
16
23
14
18
1
7
8
8
8
6
6
3
2
2
6
3
July 2008
52
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State
TN
TX
WV
County1
(Population)2
(1,281,666-1,223,411)
Beaver
(181,412-175,736)
Northampton
(267,066 - 291 ,306)
Washington4
(202,897 -206,432)
Shelby
(897,472-911,438)
Jefferson
(252,051 -243,914)
Hancock
(32,667-30,911)
Wayne
(42,903-41,647)
Year
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
2006
2002
2003
2004
2005
Daily Adjustment
n
7
7
7
6
3
3
3
3
3
2
2
2
2
2
3
3
3
3
3
3
3
3
4
3
3
3
3
3
3
9
9
8
7
7
4
4
3
3
Factor
2.23
2.81
2.17
2.97
1.91
1.73
3.02
2.98
2.67
5.95
4.49
3.28
4.24
0.98
3.91
4.41
4.20
3.07
4.89
4.79
3.75
4.46
3.90
4.12
4.82
4.30
4.47
5.67
4.31
2.38
2.30
2.62
2.84
2.97
4.19
3.41
2.87
2.02
cov
5
6
7
8
6
6
6
4
8
3
9
9
8
19
5
2
5
5
4
20
21
20
46
44
4
4
13
7
4
3
3
5
3
2
3
7
9
11
Annual Adjustment
n
7
7
7
6
3
3
3
3
3
2
2
2
2
2
3
3
3
3
3
3
3
3
3
2
3
3
3
3
3
9
9
7
7
7
4
3
3
3
Factor
2.54
2.87
2.35
3.05
2.14
2.82
2.62
2.42
3.28
5.01
3.73
2.28
3.55
2.85
3.11
2.99
3.42
3.18
3.48
6.72
5.24
5.13
6.20
4.78
8.53
8.03
8.99
8.38
8.25
2.45
2.34
2.38
2.22
2.34
3.30
3.47
3.30
3.17
COV
3
3
4
4
5
5
3
4
2
0
11
12
2
10
4
5
1
0
3
3
6
6
3
10
18
7
14
14
16
2
2
2
3
3
1
0
2
3
Notes:
1 Listed counties were selected based on lowest mean concentration adjustment factor, derived from at
least 3 monitors per year for years 2002-2006.
2 Value is from 2000 Census for Year 2000 to that estimated for 2006.
3 Selected based on frequent 5-minute maximum concentrations above potential health effect
benchmark levels.
4 Selected based on exceedance of 24-hour SO2 NAAQS in 2006. Note value for 2006 is a downward
concentration adjustment.
July 2008
53
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1 When simulating a proportional roll-up in ambient SC>2 concentrations using adjustment
2 factors generated by equation (6-3), it was assumed that the current temporal and spatial
3 distribution of air concentrations (as characterized by the current air quality data) was maintained
4 and that increased 862 emissions would contribute to increased 862 concentrations. For the
5 daily averages, the 2nd highest monitor concentration would be adjusted so that it meets the
6 current 0.14 ppm, 24-hour average standard. For the annual average concentration, the
7 maximum monitor concentration would be adjusted so that it meets the current 0.03 ppm, annual
8 average standard. For each county and calendar year, all the hourly concentrations in a location
9 were multiplied by the same constant value F (whichever adjustment value was lower) for that
10 location and year. For example, of the seven monitors measuring 862 in Allegheny County, PA
11 for year 2003, the 2nd highest 24-hour mean concentration was 64.6 ppb, giving an adjustment
12 factor ofF^aiiy = 144/64.6 = 2.23 for that year. This is lower than the adjustment factor
13 considering the maximum annual average concentration for that year (Famuai = 30.4/11.9 = 2.54).
14 All hourly concentrations measured at all monitoring sites in that location would then be
15 multiplied by 2.23, resulting in an upward scaling of all hourly SC>2 concentrations for that year.
16 Therefore, one monitoring site in Allegheny County, Pa. for year 2003 would have the 2nd
17 highest 24-hour average concentration at 0.14 ppm, while all other monitoring sites would have
18 their 2nd highest daily average concentrations below that value, although still proportionally
19 scaled up by 2.23. Then, using the adjusted hourly concentrations to simulate just meeting the
20 current standard (either the daily or annual average standard), 5-minute maximum concentrations
21 were estimated using equation (6-1). Air quality characterization metrics of interest (e.g., annual
22 mean SO2 concentration, daily mean concentrations, the number of potential health effect
23 benchmark exceedances) were estimated for each site and year.
24 6.4 RESULTS
25 6.4.1 Measured 5-minute Maximum and 1-Hour Ambient Monitoring SOi
26 Concentrations
27
28 Ambient monitoring data were evaluated at the 98 locations where both the 1-hour and 5-
29 minute maximum concentrations were measured. Due to the large size of the data sets, mean,
30 maximum, and measures of variability are summarized first in a series of figures, with
July 2008 54 Draft - Do Not Quote or Cite
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1 comprehensive Tables in Appendix C providing more complete descriptive statistics for the 5-
2 minute maximum and 1-hour SC>2 concentrations.
3 Figure 6-9 illustrates the distribution in the mean 5-minute maximum and 1-hour 862
4 concentrations at each monitor by year. In general, annual mean concentrations at these
5 monitors have consistently declined from maximum observed levels in 1998 and currently range
6 from around 2-20 ppb and <1-10 ppb for the 5-minute maximum and 1-hour concentrations,
7 respectively. Results from a one-way analysis of variance (ANOVA) of each of the mean
8 concentrations indicated a statistically significant effect for monitoring year, although the simply
9 constructed models did not account for a large proportion of the variance (Table 6-5).
10 Maximum observed concentrations followed a similar pattern to the mean concentrations.
11 In general, maximum 5-minute maximums and maximum 1-hour SC>2 concentrations have
12 decreased from those measured in 1998. Results from the ANOVA also indicate a statistically
13 significant effect for monitoring year, although a smaller amount of variance is explained for the
14 maximum concentrations compared to the respective mean concentrations (Table 6-6). This is
15 likely due to limited stability in the range of the maximum observed concentrations, most
16 notably in the 5-minute maximum data. Even though fewer monitors contain concentrations at
17 the higher end of the range with increasing monitoring year (thus there is an overall decline in
18 maximum 5-minute max concentrations with increasing monitoring year), the maximum 5-
19 minute maximum SC>2 ranges consistently from around 10 to 1000 ppb across the entire
20 monitoring period.
21 While concentrations have declined with time, the relative variability in those
22 concentrations has remained stable (Figure 6-9). There is no discernable trend over the
23 monitoring period, with a COV range of 50-400% for the 5-minute maximum data and a range of
24 around 50-300% for the 1-hour concentrations. There does appear to be a reduction in the upper
25 level of the COV for years 2005-2007 (i.e, upwards to 250% rather than 300%), however the
26 effect of year on COV from both concentration measures was not significant (Table 6-5).
July 2008 55 Draft - Do Not Quote or Cite
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Table 6-5. Model results from one-way analysis of variance (ANOVA) testing for
effect of monitoring year (years 1997-2007).
SO2 Data
5-minute
maximum
1-hour
Dependent
Variable
Mean
Maximum
COV
Mean
Maximum
COV
R2
0.15
0.06
0.01
0.19
0.07
0.01
F
8.08
2.85
0.69
10.68
3.39
0.66
P
< 0.0001
0.002
0.732
<0.0001
0.0003
0.758
4 Of particular interest is the occurrence of 5-minute 862 concentrations above particular
5 concentrations. As discussed previously, potential health effect benchmark levels of 400, 500,
6 and 600 ppb were selected for comparison with the measured ambient monitoring
7 concentrations. Figure 6-10 shows the distribution of the number of exceedances of each of the
8 benchmarks from those monitors measuring 5-minute maximum concentrations. During the
9 earlier half of the monitoring period (1997-2001), the number of 5-minute maximum SC>2
10 concentrations above 400, 500, 600 ppb was as high as 130, 90, and 60 per year respectively.
11 This frequency was limited to only a few monitors. Only about 15 to 35% of monitors recorded
12 a single peak above the lowest potential health effect benchmark level. Therefore, about 75% of
13 the monitors recording 5-minute maximum SO2 concentrations did not contain a single 5-minute
14 concentration above 400 ppb in a year from 1997-2001.
15 The frequency of concentrations above the benchmark levels declines with increasing
16 monitoring year. When considering more recent air quality (e.g., 2004-2007), the maximum
17 number of concentrations measured above 400 ppb at a monitor was between 25 to 50 times in a
18 year, with most monitors measuring only a few exceedances, if at all. To put additional
19 perspective on the frequency, there are 8,760 possible 5-minute maximum concentration events
20 in a year. Fifty exceedances would account for less than 1% of the total possible events
21 considering the recent as is air quality. Note however that the number of monitors measuring 5-
22 minute maximum SO2 concentrations sharply drops from a peak of 60 in year 2002 to just over
23 20 in year 2007 (Figure 6-11). This could be a contributing factor to the observed downward
24 trend in the number of maximum concentrations above the potential health effect benchmark
25 levels. Although the percent of monitors recording at least one exceedance of 400 ppb over this
July 2008
56
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
5-minute Max
70
-« 60
.a
j| 50
0 40
tn
c 30
re
H 20
10
o
1800
S"
a. 1500
a.
J 1200
CO
E 900
E 600
'5 :
1 300
0
> 500
la
re 400
c
re
^ 300-
o
1 200
*£ 100
0
O
o -
T
n r
i
u
H p h
T T \
r
L
T
~
r T t? ^
" 1
n
D n c
J y I;
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n r
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1 -hour Average
qj
d ^
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iE
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22222222
99900000000 99900000000
99900000000 99900000000
78901234567 78901
Year
234567
Year
Figure 6-9. Distribution of the mean SO2 concentrations, the maximum SO2
concentrations and the coefficient of variability for each monitor that
measured both the 5-minute maximum and 1-hour concentrations, Years
1997 through 2007.
July 2008
57
Draft - Do Not Quote or Cite
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1 time period ranges from about 8 to 17%, there may not be a reduction in concentrations above a
2 given level but a reduction in number of total measurements.
3 To evaluate the impact of a reduction in the number of monitors, the frequency of concentrations
4 above the potential health effect benchmark levels was normalized by the total number of
5 measurements. The results of this analysis for each year of monitoring is summarized in Figure
6 6-12. There is a downward trend in the frequency of concentrations above each of the three
7 potential health effect benchmark concentrations when normalized to the number of samples
8 collected. While the lowest frequency occurs in year 2007, it should be noted that only only four
9 of the 21 monitors contained enough samples to be considered a complete year. In addition, the
10 single monitor in Iron County, Missouri was not in operation beyond year 2003. Previously, that
11 monitor in Iron County frequently measured concentrations above 400 ppb for each year. Thus,
12 while it appears that the normalized frequency of concentrations above selected levels is in
13 decline, possibly due to reduction in episodic peak concentrations, additional reasoning would
14 include the reduced number of monitors in operation and their particular siting.
15 Finally, the occurrence of the short-term peak concentrations was evaluated with regard
16 to the current level of the SC>2 NAAQS. Completeness criteria described in Appendix A for
17 calculating each metric (i.e, 75% complete) were applied to the 1-hour SC>2 monitoring data.
18 Figure 6-13 compares the number of 5-minute maximum SC>2 concentrations above the potential
19 health effect benchmark levels with the annual average SC>2 concentration from each monitor.
20 None of the monitors in this data set contained annual average 862 concentrations above the
21 current NAAQS, however as described above, several of the monitors in several years frequently
22 contained concentrations above the potential health effect benchmark levels. Many of those
23 monitors where frequent exceedances occurred contained annual average SC>2 concentrations
24 between 5 and 15 ppb, with no apparent correlation between the annual average SC>2
25 concentration and number of peaks above any of the selected short-term benchmark levels.
26
July 2008 58 Draft - Do Not Quote or Cite
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1
2
3
4
5
6
150:
*« i
.E o 120 -
? W « :
in c **
? 1 •§ *>':
1 '§ " 60 :
Z 30:
0:
150
i_ :
•i o % 12°
? w j= 90:
w E 5
|| | 60
> 0
-r
^
3 _
> 400 ppb
1
> 500 ppb
-
j
[
laJ
b L
1112
9990
9990
7890
r
][
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Jl
2222
0000
0000
1234
Year
2
-
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00 999
00 999
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T
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22222222
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00000000 999
00000000 999
01234567 789
•
2
0
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E
L E
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2222222
0000000
0000000
1
Year
234567
Year
Figure 6-10. Distribution of the number of measured 5-minute maximum SO2 concentrations above potential health
effect benchmark levels at each monitor, Years 1997 through 2007. The top row represents the distribution
for all monitors (including those with no exceedances), the bottom row represents the distribution for those
monitors with at least one measured exceedance.
July 2008
59
Draft - Do Not Quote or Cite
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1
2
3
4
5
D)
Ig
!
p
O
70
60
50
40 --
Si 30
20
10 --
—•—number of monitors in operation
—-e— number of monitors with at least one exceedance
CD
§
CO
s
Year
Figure 6-11. Number of ambient monitors measuring 5-minute maximum SO2
concentrations and number of monitors with at least one benchmark
exceedance by year, Years 1997 through 2007.
July 2008
60
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Maximum SO2
000 Measures
O N
O C
O O
| o 75
•p m
Number of Measured 5-IV
Above Selected Level p<
M O1
O O1 O
x.
-X. --X--S 400 ppb
'•-.. ..-'x. -+- 'SSOOppb
'•><•'* \ -•*-• >600 ppb
x
-Kv
V * > ,
S I. . -V * .
\ \ / \
^ \ \ * *
___ . )l£ ~.e *~ • "" * \ \ y
''X V— \ \ / /\ \
*
OJOJOJOOOOOOO
OJOJOJOOOOOOO
T-T-T-CMCMCMCMCMCMCM
Year
O
O
CM
Figure 6-12. Frequency of measured 5-minute maximum SO2 concentrations
above potential health effect benchmark levels in each year, normalized
to 100,000 measurements, Years 1997 through 2007.
Figure 6-14 compares the 24-hour average concentrations with the number of 5-minute
SO2 concentrations above potential health effect benchmark levels. Five monitor site-years
contained 24-hour average concentrations above 140 ppb, including 3 in Buchanan County,
Missouri (years 1997, 1998) and one each in Morton County, North Dakota (1998) and
Allegheny County, Pennsylvania (1999). These highest daily average SC>2 concentrations
corresponded to frequent concentrations above the potential health effect benchmark levels at
each of the locations save one, Morton County, which did not have any measured 5-minute
maximum concentrations above 400 ppb. A trend is observed when considering all of the data
above and below a daily mean concentration of 140 ppb; with increasing 24-hour average 862
concentration, there is an increase in the number of 5-minute SO2 concentrations above the
potential health effect benchmark levels. For example, when there were at least 7 5-minute
maximum concentrations above 400 ppb, all 24-hour average concentrations were above 70 ppb.
However there is also a great amount of spread in the relationship, with a wide
range in 24-hour
average concentrations associated with at least 1 exceedance of 400 ppb (5-minute max) in a day
July 2008
61
Draft - Do Not Quote or Cite
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5
'c
15O
125
1OO
75-
50
25
Oi
15O
125
100
75-
5O-
O
150
125 -.
1OO
75
25
A) > 400 ppb
B) > 500 ppb
C) > 600 ppb
2
3
4
5
6
1
8
9
10
10 20 30 40
Annual Average SO2 (ppb)
50
Figure 6-13. Comparison of the number of measured 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each
monitor per year and the associated annual average SO2 concentration,
Years 1997 through 2007. A) number of 5-minute maximums >400
ppb/year, B) number of 5-minute maximums >500 ppb/year, C) number of
5-minute maximums >600 ppb/year. The annual average SO2 NAAQS of
0.03 ppm is indicated by the dashed line.
July 2008
62
Draft - Do Not Quote or Cite
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£
O
JZ
O
m
LU
O
i_
400 ppb
I ODCi Ofr OH. D
B) > 500 ppb
mtf-mi m. ca D !» o o
C) > 600 ppb
o
30
240
1
2
3
4
5
60 80 120 150 180 210
Daily Average SO2 (ppb)
Figure 6-14. Comparison of the number of measured 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each
monitor per day and the associated daily average SO2 concentration.
The 24-hour SO2 NAAQS of 0.14 ppm is indicated by the dashed line.
July 2008
63
Draft - Do Not Quote or Cite
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1 (ranging from 1 to about 90 ppb), along with a similar range in 24-hour average concentrations
2 having no measured exceedances of 400 ppb per day.
3 6.4.2 Measured 1-Hour and Modeled 5-Minute Maximum Ambient Monitoring SOi
4 Concentrations
5 As described in section 6.2.3, a statistical model was developed to estimate 5-minute
6 maximum 862 concentrations using all available 1-hour 862 ambient monitoring concentrations.
7 This was primarily because there were a much greater number of 1-hour ambient monitors sited
8 in the U.S. compared to 5-minute monitors. This expanded monitoring network, and the
9 utilization of modeled 5-minute values derived from 1-hour values (section 6.2.3) allowed for a
10 comprehensive description of the hourly SC>2 ambient monitoring concentrations across the U.S.,
11 and an analysis of potential 5-minute maximum concentration levels where 1-hour, but not 5-
12 minute SC>2 measurements were collected.
13 Twenty separate simulations were performed to estimate the 5-minute maximum SC>2
14 concentration associated with each 1-hour measurement (see section 6.2.3). The individual
15 simulation results were summarized using descriptive statistics and then combined to generate a
16 mean estimate for each of the metrics of interest (e.g., the number of 5-minute concentrations >
17 400 ppb). For example, each 1-hour monitor for every year simulated contains a concentration
18 distribution, defined by parameters such as a mean, a standard deviation and various percentiles.
19 Each of the parameters were averaged from the 20 simulations to give the most representative
20 estimate of the simulations for each of the parameters (i.e., the mean of the mean, the mean of
21 the maximums, etc.). The means were estimated in this manner rather than combining all of the
22 data to generate a single set of parameters from the twenty simulations, since that type of
23 aggregation could allow an individual year to adversely influence particular areas of the
24 distribution. The modeled (5-minute maximum) and measurement (1-hour) data were analyzed
25 in a similar manner as performed on the measured 5-minute maximum and 1-hour SC>2
26 concentrations described in section 6.4.1. Due to the extremely large size of the data sets, the
27 mean, maximum, and measures of variability are summarized primarily in a series figures.
28 Figure 6-15 illustrates temporal trends in the modeled mean 5-minute maximum and
29 measured 1-hour SC>2 concentrations from each monitor. In general, annual mean concentrations
30 have declined from maximum observed levels in 1997 and currently range from around 2-20 ppb
July 2008 64 Draft - Do Not Quote or Cite
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2
3
4
5
6
5-minute Max
50
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p
r
3 E5 E^ B E5 H ^
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99900000000 99900000000
99900000000 99900000000
78901234567 78901
Year
234567
Year
Figure 6-15. Distribution of the mean SO2 concentrations, the maximum SO2
concentrations, and the coefficient of variability for each monitor that measured
1-hour concentrations, Years 1997 through 2007. 5-minute maximum SO2
concentrations were estimated using a statistical model
July 2008
65
Draft - Do Not Quote or Cite
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Table 6-6. Model results from one-way analysis of variance (ANOVA) testing for
effect of monitoring year (years 1997-2007).
SO2 Data
5-minute
maximum
1-hour
Dependent
Variable
Mean
Maximum
COV
Mean
Maximum
COV
R2
0.025
0.026
0.004
0.027
0.019
0.004
F
13.8
14.6
2.4
15.0
10.2
2.27
P
< 0.0001
<0.0001
0.007
<0.0001
<0.0001
0.012
1
2 and <1-10 ppb for the 5-minute maximum and 1-hour concentrations, respectively. This is
3 similar to what was observed in the data set containing the measured 5-minute maximum and
4 associated 1-hour monitoring data. Results from a one-way ANOVA of each of the mean
5 concentrations indicated a statistically significant effect for monitoring year, although the simply
6 constructed models did not account for a large proportion of the variance (Table 6-6).
7 There are a few 1-hour monitors that contained annual average SO2 concentrations within
8 10-20 ppb, along with associated modeled annual average 5-minute maximum SO2
9 concentrations between 20-50 ppb. Many of the highest concentration data were measured at
10 monitors sited in Pennsylvania (PA) and West Virginia (WV), although some of the more recent
11 1-hour average 862 concentrations above 15 ppb were measured in Hawaii (Table 6-7). While
12 the PA and WV monitors are likely influenced by local and regional anthropogenic source
13 emissions, two ambient monitors in Hawaii (ID 150010005 and 150010007) containing these
14 high annual average SO2 concentrations were sited to capture the impact of volcanic activity on
15 ambient SC>2 concentrations in the area.
16 There were similar temporal trends in the distribution of maximum observed
17 concentrations (Figure 6-15) compared to the trends observed using mean concentrations. In
18 general, maximum 5-minute maximum and maximum 1-hour SC>2 concentrations have steadily
19 decreased from those measured in 1997. Results from the ANOVA also indicate a significant
20 effect from monitoring year, although this explains a smaller amount of variance for the
21 maximum concentrations (Table 6-6) than for the mean concentrations (Table 6-5). Again, most
22 of the locations with the highest modeled 5-minute maximum SO2 concentrations, as well as the
July 2008
66
Draft - Do Not Quote or Cite
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Table 6-7. Descriptive statistics for modeled 5-minute maximum and measured 1-hour SO2 concentrations for
monitors with 1-hour annual average SO2 concentration above 15 ppb.
State
WV
NM
WV
IN
PA
HI
IA
NC
PA
WV
WV
NM
PA
PA
OK
WV
PA
IN
WV
NY
NY
AR
AR
PA
PA
ID
HI
WV
WV
MO
HI
WV
HI
HI
HI
HI
TN
HI
HI
County
Marshall
San Juan
Hancock
Vanderburgh
Allegheny
Hawaii
Cerro Gordo
Forsyth
Washington
Marshall
Brooke
San Juan
Beaver
Warren
Tulsa
Hancock
Warren
Warrick
Hancock
Bronx
Kings
Union
Pulaski
Allegheny
Warren
Caribou
Hawaii
Wayne
Wayne
Iron
Hawaii
Brooke
Hawaii
Hawaii
Hawaii
Hawaii
Blount
Hawaii
Hawaii
Monitor ID
540511002
350451 005
540290009
181631002
420030021
150010005
1 9033001 8
370670022
421 250200
540511002
540090007
350451 005
420070005
421230004
401430235
540290016
421230004
181731001
54029001 1
360050080
360470076
051390006
051191002
420033003
421230004
160290031
150010007
540990005
540990003
290930030
150010007
540090007
150010007
150010007
150010005
150010007
470090002
150010005
150010007
Year
1997
1997
1997
1997
1997
1997
1997
1997
1998
1998
1998
1998
1998
1998
1998
1999
1999
1999
1999
2000
2000
2000
2001
2001
2001
2001
2002
2002
2002
2002
2003
2004
2004
2005
2006
2006
2007
2007
2007
n
8615
8398
8681
8639
58
7188
1250
17
14
8712
8436
8481
2087
6388
8661
8542
8575
7630
8584
2881
24
20
5
6992
8686
7501
7662
14
9
15
8346
8672
6447
8177
8358
7892
2062
2746
2578
Modeled 5-minute Maximum SO2
Mean
26
27
29
29
31
33
37
43
24
26
27
28
28
30
32
26
26
27
27
24
31
43
25
26
28
48
28
38
43
45
36
26
35
33
36
41
30
42
48
Std
54
64
40
35
44
131
115
45
22
53
45
67
49
59
42
41
53
58
44
26
23
61
30
39
58
164
113
24
26
111
124
34
112
135
155
142
56
147
152
cov
207
235
138
119
135
392
312
103
87
204
167
242
174
195
131
156
202
217
162
109
69
140
118
152
207
345
406
59
58
238
348
131
323
408
436
347
188
350
315
pO
1
0
1
0
0
0
0
4
6
1
1
0
0
1
0
1
1
0
1
2
12
3
4
0
1
0
0
15
17
1
0
1
0
0
0
0
2
0
0
p50
8
4
17
19
12
4
4
32
17
9
14
4
11
10
18
15
9
11
14
16
25
10
13
14
8
2
0
33
37
5
0
16
0
0
0
2
9
0
0
p97
156
185
118
104
150
286
377
168
87
153
122
188
142
169
123
116
147
160
121
75
115
199
77
116
175
592
272
96
101
420
335
103
297
341
418
399
162
449
446
p98
188
225
140
120
150
414
483
168
87
187
147
232
172
206
142
140
185
203
146
87
115
199
77
137
213
723
366
96
101
420
435
121
389
485
576
532
198
568
586
p99
246
306
186
154
209
634
627
168
87
251
200
326
231
275
179
188
257
282
197
114
115
199
77
175
281
882
584
96
101
420
640
164
585
724
798
735
264
733
799
p100
1161
1076
952
702
209
2738
1240
168
87
1022
982
1115
725
1007
856
927
1032
1092
930
383
115
199
77
842
1030
2330
2284
96
101
420
2047
787
1985
2362
3015
2394
796
2042
1927
Measured 1-hour SO2
Mean
15
16
17
17
18
15
16
24
17
15
16
16
16
17
19
15
15
15
15
17
21
26
15
15
16
22
16
23
27
19
20
15
20
19
16
23
17
19
27
Std
27
32
17
14
19
58
43
19
12
26
20
34
23
29
18
18
25
29
19
12
7
36
18
17
29
75
60
8
8
40
65
14
59
72
70
76
27
63
81
COV
176
205
102
81
110
381
273
80
72
172
131
210
142
166
95
117
168
187
125
73
34
138
121
115
178
335
380
37
29
210
325
92
300
385
436
326
157
341
298
pO
1
0
1
0
0
0
0
3
4
1
1
0
0
1
0
1
1
0
1
2
12
2
3
0
1
0
0
13
14
1
0
1
0
0
0
0
2
0
0
p50
5
3
11
12
8
3
3
26
13
6
9
3
8
6
12
10
6
8
9
13
19
5
9
10
5
1
0
19
29
3
0
10
0
0
0
2
6
0
0
p97
88
105
60
48
56
118
144
70
53
86
65
106
72
94
61
60
80
88
63
46
36
103
47
62
97
271
160
37
37
153
188
50
172
195
162
227
91
182
237
p98
105
123
70
52
56
157
188
70
53
105
76
128
85
109
66
69
101
116
72
52
36
103
47
70
117
366
203
37
37
153
234
59
217
262
232
283
102
226
314
p99
131
159
87
61
72
255
245
70
53
132
97
166
117
145
77
87
138
154
93
59
36
103
47
82
150
450
298
37
37
153
315
73
288
375
369
382
139
308
433
p100
495
500
292
259
72
1024
345
70
53
351
335
345
235
408
230
237
299
476
286
109
36
103
47
192
297
512
967
37
37
153
867
238
987
928
999
963
265
812
857
July 2008
67
Draft - Do Not Quote or Cite
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1 highest measured 1-hour maximum SC>2 concentrations also contained high annual average
2 concentrations (Table 6-7).
3 The coefficient of variability (COV) for the modeled 5-minute maximum concentrations
4 range from 80 to 600%, while the 1-hour measurement COV ranges from about 0 to 400% (re 6-
5 15). These COV ranges are broader than those reported for the 98-monitor measurement data
6 set, however it should be noted that this current data set includes monitors with as few as two 1-
7 hour SO2 measurements and also used reported concentrations that included values of zero10.
8 There appears to be a consistent reduction in the range of COV for recent monitoring years 2004-
9 2007 compared with the earlier years of data, with a significant effect of year on COV from both
10 concentration measures (Table 6-6).
11 As done earlier, the potential health effect benchmark levels of 400, 500, and 600 ppb
12 were selected for comparison with the modeled 5-minute maximum concentrations at monitors
13 that measured 1-hour ambient SO2 concentrations. The number of estimated exceedances for
14 each monitor by year appears in Figure 6-16. For most years, the number of 5-minute maximum
15 concentrations above 400, 500, 600 ppb was estimated to be as high as 150, 100, and 70 per year
16 respectively. Estimated exceedances of the selected concentration levels were observed at a
17 fraction of the total monitors operating during any one year, with between 14-44% (mean of
18 35%) of monitors recording a single peak above the lowest potential health effect benchmark
19 level (Figure 6-17). Therefore, about 65% of the monitors did not contain a single modeled 5-
20 minute concentration above 400 ppb in a year. Even when excluding the monitors where there
21 were no exceedances of the lowest potential benchmark level of 400 ppb, only 262 out of 6,103
22 site years of data (<5%) contained an estimated mean number of exceedances above 10 per year,
23 with less than half of those site years (127) containing greater than 20 exceedances of 400 ppb in
24 a year.
10 Completeness criteria were only used when comparing the ambient monitoring data to the current SO2 NAAQS.
There were also no below detection limit substitutions.
July 2008 68
-------
1
2
3
4
5
6
> 400 ppb
o, 35°
Id1 30°
P CO QCTI -
= »" re 250
10 E tS
•5 1 •§ 200
<5 'x re 150
.a re
|S 100
50
0-
350
d) re 300
•3 E
= o™ -g 250
E co c
„!, £ « 200
o | gj 150
E ^ -2 100
E^— Co
3 c 50
3 0) O"
2 £
> n -
j T
iii i i i i
5 U i — i 1 1
1 1 1
T
-p
;
-
-
•
-
-
-
....I.
>500
ppb
iii i i i i
T
I
-
-
-
-
-
-
-
J
ll
>600
ppb
f
1
22222222 11122222222 1
'
.1.
f
I
1
1 122222222
99900000000 99900000000 99900000000
99900000000 99900000000 99900000000
78901234567 78901234567 78901234567
Year
Year
Year
Figure 6-16. Distribution of the modeled 5-minute maximum SO2 concentrations above potential health effect
benchmark levels at each monitor by year, Years 1997 through 2007. The top row represents the distribution for
all monitors (including those with no exceedances), the bottom row represents the distribution for those
monitors with at least one estimated exceedance.
July 2008
69
Draft - Do Not Quote or Cite
-------
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
/ uu
650
0 600
CO
^ 550
O
JF 500
.I* 45°
400
(o
a)
S 350
0 300
'E
0 250
"S 200
•| 150
Z 100
50
0
jj
T-
— •— number of monitors in operation
: *^^^^^ -«- number of monitors with at least one exceedance
^^^^^^
"^^.
: \
1 ^
o -e~^
^^~^s
\
N
\
\
O
D r*^ oo CD o ^ — CN co ^~ LO CD r*^ c
) C7) C7) C7) O O O O O O O O C
) C7) C7) C7) O O O O O O O O C
\
Year
Figure 6-17. Number of ambient monitors measuring 1-hour average SO2
concentration concentrations and number of monitors with at least one
benchmark exceedance by year, Years 1997 through 2007.
As mentioned earlier, there were a few years where the 1-hour SCh concentrations in
Hawaii County, HI were among some of the highest measured (Table 6-7). The impact of these
measurements on the estimated number of peak concentrations above the selected levels is
indicated in the tails of the distribution Figure 6-16, particularly for years 2005 and 2006. In
addition, an unusual number of concentrations above the benchmark levels were observed in
2001, driven exclusively by results for Caribou, Idaho (monitor ID 16029003 1). This monitor is
a stationary source-oriented monitor sited within close proximity of a chemical manufacturing
facility (0.76 km) with estimated emissions of over 10,000 tpy. In excluding these two locations
from the analysis for clarity regarding all other monitoring sites, additional trends in the
estimated 5-minute maximum concentrations are present (Figure 6-18). There is a decrease in
the number of exceedances with each monitoring year, both for the range and the average
July 2008
70
Draft - Do Not Quote or Cite
-------
1 number of exceedances. When considering more recent air quality (e.g., 2004-2007), the
2 maximum number of concentrations measured above 400 ppb at a monitor was about 20 to 60
3 times in a year (of 8,760 total possible events or less thank 1%, with most monitors measuring a
4 few exceedances, if at all. It should also be noted that this frequency would only apply at
5 locations where exceedances may occur, on average at about one-third of all 1-hour SO2
6 monitors in operation for a given year. As observed with the 5-minute maximum monitoring
7 network, the number of 1-hour monitors steadily drops from a peak of 660 in year 1997 to just
8 under 400 in year 2007 (Figure 6-17). This could be a contributing factor to the observed
9 reduction in the number of estimated concentrations above the potential health effect benchmark
10 levels. While the percent of monitors with at least one estimated exceedance of 400 ppb
11 considering the more recent air quality (i.e., 2004-2007) ranges from about 15 to 32% and
12 appears to be reduced, the effect may be due to a reduction in number of total measurements.
13 To evaluate the impact of a reduction in the number of monitors, the frequency of
14 concentrations above the potential health effect benchmark levels was normalized by the total
15 number of 1-hour measurements. The results of this analysis for each year of monitoring are
16 summarized in Figure 6-19. There is a downward trend in the frequency of concentrations above
17 each of the three potential health effect benchmark concentrations when normalized to the
18 number of samples collected, most dramatic from years 1999 though 2002. A similar frequency
19 in normalized exceedances can be observed for the period from 2002 through 200711, estimated
20 to be around 20, 10 and 5 per 100,000 hourly measurements for the 400, 500, and 600 ppb levels,
21 respectively. Thus, while it appears that the normalized frequency of concentrations above
22 selected levels is in decline possibly due to reduction in episodic peak concentrations when
23 considering the entire monitoring period, the estimated frequency of occurrence may have
24 stabilized since 2002.
25 Finally, the occurrence of the short-term peak concentrations was evaluated with regard
26 to the current SC>2 NAAQS. Completeness criteria described in Appendix A for calculating each
27 metric (i.e, 75% complete) were applied to the 1-hour measurements in the data set. Figures 6-
28 20 (all monitors) and 6-21 (without Hawaii and Caribou County) compare the number of 5-
29 minute maximum SC>2 concentrations above the potential health effect benchmark levels with the
1: It should be noted that the 1 -hour monitoring data foryear2007were incomplete for all locations, it is unclear
whether this would increase or decrease the estimated frequency.
July 2008 71 Draft - Do Not Quote or Cite
-------
1 annual average concentration from each monitor. None of the monitors in this data set contained
2 annual average concentrations near the current NAAQS (0.03 ppm), however as described
3 above, several of the monitors in several years frequently contained concentrations above the
4 potential health effect benchmark levels. Many of those monitors where frequent exceedances
5 occurred contained annual average concentrations between 10 and 20 ppb, with a limited trend
6 indicated between the annual average concentration and the estimated number of peaks above
7 any of the selected short-term concentrations (Figure 6-20). In removing the results for Hawaii
8 and Caribou Counties, the relationship observed between the annual average concentrations and
9 the number of exceedances of the selected benchmark levels is generally weaker, along with
10 containing fewer exceedances at each of the levels (Figure 6-21).
11 Figure 6-22 compares the 24-hour average concentrations with the number of 5-minute
12 SC>2 concentrations above potential health effect benchmark levels. Ninety-two monitor site-
13 days contained 24-hour average SO2 concentrations above 140 ppb, of which 76% were
14 measured in either Hawaii or Caribou County (Table 6-8). Other locations with measured
15 concentrations above 140 ppb were scattered across several years and states, including Illinois,
16 Indiana, Iowa, Louisiana, Oklahoma, Pennsylvania, and Tennessee. These highest daily average
17 SO2 concentrations also corresponded to the most frequent number of concentrations above the
18 potential health effect benchmark levels. There is a clear trend when considering all of the data
19 above and below a daily mean concentration of 140 ppb, that is, with increasing 24-hour average
20 concentration, there is an increase in the number of estimated 5-minute SO2 concentrations above
21 the potential health effect benchmark levels. For example, where there were at least 7 estimated
22 occurrences above 500 ppb in a day, all 24-hour average concentrations were greater than 140
23 ppb. There is also a greater variability in the relationship, with a wide range in 24-hour average
24 concentrations associated with at least 3 estimated exceedances of 500 ppb in a day (ranging
25 from 50 to about 140 ppb), along with a similar range in 24-hour average concentrations (ranging
26 from 0 to about 110 ppb) with no estimated exceedances of 500 ppb per day. Figure 6-23
27 presents the comparison of the 24-hour average concentrations with the number of 5-minute SO2
28 concentrations above potential health effect benchmark levels excluding the results from Hawaii
29 and Caribou Counties.
July 2008 72 Draft - Do Not Quote or Cite
-------
1
2
3
4
5
> 400 ppb
« o 100
3 CM 'E
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2 I I 60
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.iJ
" j_
22222222
99900000000 99900000000 99900000000
99900000000 99900000000 99900000000
78901234567 78901
Year
234567 78901234567
Year
Year
Figure 6-18. Number of modeled 5-minute maximum SO2 concentrations above potential health effect benchmark
levels at each monitor, Years 1997 through 2007. The top row represents the distribution for all monitors
excluding Hawaii County and Caribou, Idaho for year 2001, the bottom row represents the distribution for
those monitors with at least one estimated exceedance.
July 2008
73
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
1
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O
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TO
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500 ppb
600 ppb
.X
f*f
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Year
Figure 6-19. Frequency of modeled 5-minute maximum SO2 concentrations above
potential health effect benchmark levels in each year, normalized to
100,000 measurements, Years 1997 through 2007, without Hawaii County
and Caribou, Id. (2001).
July 2008
74
Draft - Do Not Quote or Cite
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5
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J
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o
£
m
0
Q)
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^
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z
35O
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250
200
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250
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o-
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=
0
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O o
°°
10
20
3O
4O
1
2
3
4
5
6
7
Annual Average SO2 (ppb)
Figure 6-20. Comparison of the number of modeled 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each
monitor per year and the associated annual average SO2 concentration,
Years 1997 through 2006, all 1-hour monitors. A) number of 5-minute
maximums >400 ppb/year, B) number of 5-minute maximums >500
ppb/year, C) number of 5-minute maximums >600 ppb/year. The level of
the annual average SO2 NAAQS of 0.03 ppm is indicated by the dashed
line.
July 2008
75
Draft - Do Not Quote or Cite
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10O
80
6O
4O
20
80
6O
40
2O
u>
3
80
60
4O
20
A) >400ppb
B) >500ppb
C) >600ppb
10
20
3O
4O
1
2
3
4
5
6
1
Annual Average SO2 (ppb)
Figure 6-21. Comparison of the number of modeled 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each
monitor per year and the associated annual average SO2 concentration,
Years 1997 through 2006, without Hawaii and Caribou Counties (2001
only). A) number of 5-minute maximums >400 ppb/year, B) number of 5-
minute maximums >500 ppb/year, C) number of 5-minute maximums
>600 ppb/year. The level of the annual average SO2 NAAQS of 0.03 ppm
is indicated by the dashed line.
July 2008
76
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c
o
u
ns
LU
£
ns
Q
s_
t-
o
1_
400 ppb
B) > 500 ppb
C) > 600 ppb
100
15O
2OO 25O 3OO 35O
Daily Average SO2 (ppb)
2 Figure 6-22. Comparison of the number of modeled 5-minute maximum SO2
3 concentrations above potential health effect benchmark levels at each
4 monitor per day and the associated daily average SO2 concentration,
5 Years 1997 through 2007, all 1-hour SO2 monitors. The level of the 24-
6 hour SO2 NAAQS of 0.14 ppm is indicated by the dashed line.
July 2008
77
Draft - Do Not Quote or Cite
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c
o
O
OS
11J
5
o
Q.
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73
3
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24
21
18
15
12
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O
24:1
18
15
A) > 400 ppb
24
21:
18
15
12
9
6
3
B) > 500 ppb
C) > 600 ppb
rf!i5'
10O
15O
2OO 25O 3OO 35O
2
3
4
5
6
7
Daily Average SO2 (ppb)
Figure 6-23. Comparison of the number of modeled 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each monitor
per day and the associated daily average SO2 concentration, Years 1997 through
2007, all 1-hour SO2 monitors not including Hawaii and Caribou Counties. The
level of the 24-hour SO2 NAAQS of 0.14 ppm is indicated by the dashed line.
July 2008
78
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Table 6-8. Ambient monitors containing a daily average SO2 concentration
greater than 140 ppb and their modeled 5-minute maximum concentrations above
selected potential health effect benchmark levels, Years 1997 through 2007.
Monitor ID
150010005
150010005
150010005
150010005
150010005
150010005
150010005
150010005
150010005
150010005
150010005
191390020
471390007
150010005
150010005
190330018
191390020
150010005
150010005
180630001
150010007
150010007
150010007
160290031
160290031
160290031
160290031
160290031
160290031
160290031
160290031
160290031
160290031
160290031
160290031
150010005
150010005
150010007
150010007
150010007
150010007
State
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
IA
TN
HI
HI
IA
IA
HI
HI
IN
HI
HI
HI
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
HI
HI
HI
HI
HI
HI
County
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Muscatine
Polk
Hawaii
Hawaii
Cerro Gordo
Muscatine
Hawaii
Hawaii
Hendricks
Hawaii
Hawaii
Hawaii
Caribou
Caribou
Caribou
Caribou
Caribou
Caribou
Caribou
Caribou
Caribou
Caribou
Caribou
Caribou
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Year
1997
1997
1997
1997
1997
1998
1998
1998
1998
1998
1998
1998
1998
1999
1999
1999
1999
2000
2000
2000
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2001
2002
2002
2002
2002
2002
2002
Month
8
10
10
11
11
1
1
8
11
11
12
4
1
1
5
10
12
2
3
3
2
3
11
1
1
2
2
3
3
3
7
7
7
8
8
9
10
5
8
9
9
Day
13
10
12
8
16
14
15
11
8
11
8
12
24
16
29
22
1
9
14
24
12
23
22
8
11
10
12
4
5
16
13
16
28
9
21
25
13
12
13
23
25
Daily SO2
(ppb)
Mean
157
164
158
148
181
258
191
157
157
302
153
143
194
197
161
141
148
189
217
144
145
143
150
278
325
185
305
210
292
155
178
169
197
189
299
266
155
194
144
142
224
Std
244
270
234
243
255
232
285
243
294
319
222
121
114
257
256
81
68
206
311
317
119
135
172
186
176
208
180
196
148
186
169
205
216
202
157
274
224
213
273
161
274
Modeled Number of 5-minute
Maximum SO2
>400ppb
7
7
7
7
8
11
9
7
6
12
8
10
13
7
6
8
7
11
10
4
5
5
6
15
19
9
17
12
18
10
13
9
12
12
17
11
8
9
5
6
8
>500ppb
5
4
6
5
7
11
7
6
4
9
6
10
9
7
5
7
6
7
6
4
3
4
4
15
16
9
17
12
17
8
12
9
11
9
15
9
8
8
5
6
7
>600ppb
4
4
4
3
4
9
7
6
3
9
5
6
5
7
5
5
2
5
5
4
2
2
4
14
14
7
15
8
14
8
9
7
11
8
13
6
6
8
5
4
5
July 2008
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Monitor ID
150010007
150010007
150010007
150010005
150010005
150010005
150010007
150010007
150010007
171790004
180510002
150010005
150010007
150010007
150010007
150010005
150010005
150010005
150010005
150010005
150010005
150010005
150010007
150010007
150010007
150010007
150010007
150010005
150010005
150010005
150010005
150010005
150010005
150010007
150010007
150010007
150010007
150010007
150010007
150010007
150010007
150010007
150010007
150010007
220330009
State
HI
HI
HI
HI
HI
HI
HI
HI
HI
IL
IN
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
HI
LA
County
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Tazewell
Gibson
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
Hawaii
East Baton
Rouge
Year
2002
2002
2002
2003
2003
2003
2003
2003
2003
2003
2003
2004
2004
2004
2004
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
Month
10
10
11
3
3
6
6
10
10
5
4
5
11
11
11
1
1
1
3
3
5
12
3
3
5
9
12
2
6
9
9
10
11
2
2
3
6
8
9
10
11
11
11
11
7
Day
13
17
24
5
29
2
2
19
27
11
1
20
6
7
9
25
26
27
17
26
2
17
17
26
2
24
4
11
25
15
25
7
26
17
28
2
25
1
25
7
1
25
26
28
30
Daily SO2
(PPb)
Mean
148
152
152
141
173
162
250
212
160
148
212
143
181
144
140
161
216
151
142
170
185
178
192
299
228
169
156
156
171
153
172
353
161
231
165
163
162
160
166
286
152
143
169
142
313
Std
186
118
146
177
220
166
194
263
121
100
106
203
219
127
239
261
292
319
176
274
234
120
223
316
272
152
203
220
216
216
286
308
339
231
228
129
222
173
211
230
117
142
320
148
242
Modeled Number of 5-minute
Maximum SO2
>400ppb
8
3
4
8
7
8
10
6
8
12
16
5
4
7
5
7
9
4
7
6
8
15
8
10
6
8
5
9
10
7
6
15
5
7
5
5
9
6
5
12
6
4
5
6
12
>500ppb
5
2
3
5
7
7
9
5
3
8
10
4
3
6
4
7
9
4
6
6
5
12
8
10
5
8
5
8
9
7
6
15
5
6
4
3
6
4
5
10
4
3
5
5
12
>600ppb
3
2
3
5
7
4
5
4
2
5
7
3
3
3
3
7
7
4
4
5
4
7
7
9
5
5
3
6
8
6
6
15
5
6
3
2
4
3
4
9
4
1
5
3
12
July 2008
80
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Monitor ID
400219002
420958000
420958000
150010007
150010007
171790004
State
OK
PA
PA
HI
HI
IL
County
Cherokee
Northampton
Northampton
Hawaii
Hawaii
Tazewell
Year
2006
2006
2006
2007
2007
2007
Month
5
11
11
3
3
3
Day
4
12
13
7
15
2
Daily SO2
(PPb)
Mean
322
156
147
157
186
168
Std
84
65
92
161
253
87
Modeled Number of 5-minute
Maximum SO2
>400ppb
22
2
3
6
7
12
>500ppb
17
2
2
6
6
10
>600ppb
13
1
1
4
5
5
2 6.4.3 Air Quality Just Meeting the Current Daily Standard
3 Twenty counties were selected for detailed analyses, including an evaluation of ambient
4 concentration distributions and the estimated numbers of exceedances of the potential health
5 effect benchmark levels using as is air quality and air quality adjusted to just meeting the current
6 standard. The locations were selected based on the number of monitors within the county,
7 containing daily average concentrations closest to the current daily standard, and for a few
8 locations, containing a high frequency of measured concentrations above the potential health
9 effect benchmark levels. The most recent air quality data were used for this analysis, including
10 years 2002 through 2006. Table 6-9 identifies the 20 counties selected for detailed analyses,
11 originating from 13 states and covering various geographic regions. Due to the large size of the
12 data sets, mean, maximum, and measures of variability are summarized mainly in figures, with a
13 few tables containing descriptive statistics for each of the twenty counties at the end of the
14 chapter. Supplemental information for these analyses is provided in section 6.3 (selection
15 criteria and factors used for adjusting air quality), Appendix A (ambient monitor siting and
16 proximity to 862 stationary source emissions), and Appendix C (descriptive statistics for
17 concentrations and estimated exceedances in tables by monitor and monitor year).
18 Twenty simulations were performed to estimate the 5-minute maximum SC>2
19 concentration associated with each 1-hour measurement. These simulation results were
20 combined to generate a mean estimate for each of the metrics of interest (e.g., the number of 5-
21 minute concentrations > 400 ppb) selected here as the most representative estimate from the
22 twenty simulations. The data analysis and aggregation approach for the modeled (5-minute
23 maximum) and measurement (1-hour) data for the 20 selected counties was the same as that
24 performed for all 1-hour monitors (section 6.4.2).
July 2008
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Table 6-9. Identification of twenty locations for detailed analyses.
State
Delaware
Florida
Iowa
Illinois
Indiana
Michigan
Missouri
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
West Virginia
Abbreviation
DE
FL
IA
IL
IN
Ml
MO
OH
OK
PA
TN
TX
WV
County
New Castle
Hillsborough
Linn
Muscatine
Madison
Floyd
Wayne
Greene
Iron
Jefferson
Cuyahoga
Tulsa
Allegheny
Beaver
Northampton
Washington
Shelby
Jefferson
Hancock
Wayne
1
2 Figure 6-24 illustrates temporal trends in the mean 5-minute maximum and 1-hour SC>2
3 concentrations from each monitor in the twenty counties. The illustration includes the air quality
4 data as is (1-hour measured with 5-minute maximum modeled 802) and air quality adjusted to
5 meet the current daily standard (either the 0.14 ppm daily or 0.03 ppm annual average). In
6 general, annual mean concentrations range from around 2-25 ppb and <1-15 ppb for the 5-minute
7 maximum and 1-hour SC>2 concentrations, respectively, and do not appear to be correlated with
8 year of monitoring considering the as is air quality. A similar pattern is noted for the air quality
9 adjusted to just meeting the current standard, although concentrations for both averaging times
10 are about a factor of three greater than as is air quality. Results from a one-way analysis
11 ANOVA of each of the mean concentrations indicate the lack of a statistically significant effect
12 for monitoring year for either air quality scenario (Table 6-10).
13 There were also no temporal trends in the maximum SC>2 concentrations for both the as is
14 air quality and concentrations adjusted to just meeting the current standard (Figure 6-25).
15 Results from a one-way ANOVA of each of the maximum concentrations also indicate the lack
16 of a statistically significant effect for monitoring year for either air quality scenario (Table 6-10).
17 The coefficient of variability (COV) for both concentration measures is presented in Figure 6-26
July 2008
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1
2
3
4
5
6
7
9
10
11
12
13
14
and, by design the values are identical for each air quality scenario. In general, COVs for the
modeled 5-minute maximum concentrations range from 100 to 500%, while the 1-hour
measurement COV ranges from about 50 to 400%. There was not a significant effect of year on
COV for either concentration measure (Table 6-10).
Table 6-10. Model results from one-way analysis of variance (ANOVA) testing for
effect of monitoring year. Results are from detailed analysis of twenty selected
counties, Years 2002 through 2006.
Air Quality
Scenario
As Is
Just
Meeting
the Current
Daily
Standard
SO2 Data
5-minute
maximum
1-hour
5-minute
maximum
1-hour
Dependent
Variable
Mean
Maximum
COV
Mean
Maximum
COV
Mean
Maximum
COV
Mean
Maximum
COV
R2
0.010
0.018
0.007
0.011
0.013
0.008
0.016
0.011
0.006
0.016
0.011
0.008
F
0.93
1.61
0.60
0.99
1.23
0.69
1.44
0.99
0.59
1.44
0.99
0.69
P
0.448
0.171
0.662
0.411
0.297
0.602
0.219
0.414
0.671
0.223
0.412
0.603
The potential health effect benchmark levels of 400, 500, and 600 ppb were selected for
comparison with the modeled 5-minute maximum concentrations at monitors with measured 1-
hour ambient SO2 concentrations. The number of estimated exceedances for each monitor by
year appears in Figure 6-27. The number of 5-minute maximum concentrations above 400, 500,
600 ppb was estimated to be as high as 35, 15 and 8 per year respectively for as is air quality,
although the majority of monitors were estimated to have much less. The estimated number of
exceedances of the selected concentration levels were observed at a fraction of the total monitors
operating during
July 2008
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
5-minute Max
1 -hour Average
—« >
n •+*
Q. —
-S 3
CM Q
O i-
w <
5JS
30
25
20
CO
0
150
•a ra 120
Q. 3
SO
30
0
T T
2
0
0
2
2
0
0
3
2
0
0
4
Year
2
0
0
5
2
0
0
6
2
0
0
2
2
0
0
3
2
0
0
4
Year
2
0
0
5
2
0
0
6
Figure 6-24. Mean SO2 concentrations for modeled 5-minute maximum and
measured 1-hour SO2 concentrations, Years 2002 through 2006 at 20
selected counties, with air quality as is and air quality adjusted to just
meeting the current standards (either one exceedance of 0.14 ppm daily
average or no exceedance of 0.03 ppm annual average).
any one year, with between 26-39% (mean of 33%) of monitors recording a single peak above
the lowest potential health effect benchmark level for the as is air quality. The number of
estimated exceedances however is greater by at least factor of five when considering
concentrations adjusted to just meeting the current standards (Figure 6-27). Nearly all of the
monitors contained at least one exceedance of the lowest potential health effect level when air
quality was adjusted to just meeting the current standard. The mean percentage of monitors
across all years was 98%.
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1
2
3
4
5
6
7
1500
!>
£= 1200
M ™
8? 900
| j« 600
| < 300
0
_ 6000
^1 5000
O1? 4000
W ^
E ^ 3000
3
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
.-= 60°
re -^ 500-
re ^
><§ 4°°
|| 300-
0) (A
o ~ 200
£<
§ 100
o
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o < 300
+; -Q
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r
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t
5-minute Max
I i ' i
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r nr
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r T
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Jot
. T 1
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d b
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22222 22222
00000 00000
00000 00000
23456 23456
Year
Year
Figure 26. Coefficient of variability (COV, %) for modeled 5-minute maximum and
measured 1-hour SO2 concentrations, Years 2002 through 2006 at 20
selected counties, with air quality as is and air quality adjusted to just
meeting the current standards (either one exceedance of 0.14 ppm daily
average or no exceedance of 0.03 ppm annual average).
The number of concentrations above the potential health effect benchmark levels was
normalized by the total number of 1-hour SC>2 measurements to determine temporal trends in the
frequency of exceedances. The results of this analysis for each air quality scenario are
summarized in Figure 6-28. There is a small downward trend in the frequency of concentrations
above each of the three potential health effect benchmark concentrations when normalized to the
number of samples collected, although there is a slight rise in the frequencies for 2006. The
normalized frequency of exceedances was estimated to be around 20, 8, and 4 per 100,000
hourly measurements for the 400, 500, and 600 ppb levels, respectively and appears to have
stabilized in these selected locations since 2002. The frequency of estimated concentrations
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1 above the potential health effect benchmark levels was greater when concentrations were
2 adjusted to just meeting the current standard, with about 575, 350, and 225 exceedances per
3 100,000 measurements per year of the 400, 500, and 600 ppb levels, respectively.
4 Finally, the occurrence of the short-term peak concentrations was evaluated with regard
5 to the current SC>2 NAAQS in the selected counties. Completeness criteria described in
6 Appendix A for calculating each metric (i.e, 75% complete) were applied to the 1-hour SO2
7 measurements in the data set. Figure 6-29 compares the number of 5-minute maximum SC>2
8 concentrations above the potential health effect benchmark levels with the annual average
9 concentration from each monitor using the as is air quality data. None of the monitors in the
10 selected counties contained annual average concentrations near the current NAAQS (0.03 ppm),
11 however as described above, a few of the monitors in some of the years contained modeled
12 concentrations above the potential health effect benchmark levels, with decreasing numbers of
13 exceedances with increasing potential health effect benchmark concentration.
14 Figure 6-30 compares the estimated number of exceeedances of the potential health effect
15 benchmark levels with the annual average SC>2 concentration when the air quality data were
16 adjusted to just meet the standards. Both the number of exceedances and the annual average
17 concentrations have increased dramatically, although there is no clear trend between the two
18 parameters.
19 Figure 6-31 compares the 24-hour average concentrations with the number of 5-minute
20 SO2 concentrations above potential health effect benchmark levels considering as is air quality.
21 The two daily average concentrations above the 140 ppb level were observed in Northampton
22 County, Pa. In general the highest daily average SC>2 concentrations corresponded to the most
23 frequent number of concentrations above the potential health effect benchmark levels, although
24 most locations were estimated to have fewer than 5 exceedances of the lowest potential health
25 effect benchmark level of 400 ppb.
26 Figure 6-32 illustrates a clear trend in 24-hour average concentrations and the estimated
27 number of exceedances when considering air quality adjusted to just meeting the current
28 standard. Increases in 24-hour average concentration correspond to an increase in the number of
29 estimated 5-minute SC>2 concentrations above the potential health effect benchmark levels.
30 Similar to what was noted when using all of the monitors at current air quality conditions, where
31 there were at least seven estimated concentrations above 500 ppb in a day, all 24-hour average
July 2008 87 Draft - DO NOT QUOTE OR CITE
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1
2
3
4
5
6
concentrations were greater than 140 ppb. There is also a broad range in 24-hour average
concentrations associated and any number of exceedances. For example, where there were 3
estimated exceedances of 500 ppb in a day, the daily average concentrations could range from 60
to about 200 ppb. In addition, a similar range in 24-hour average concentrations (ranging from 0
to about 150 ppb) could have no estimated exceedances of the 500 ppb benchmark level per day.
7
8
9
10
1 1
12
13
14
15
5 60 -
o ..-
lute Maximum S
00,000 Measure:
sis
-^ Ol
0 0
^ It
in o.S
HI 30
(U (U 0
° o 20 -
1 CO
C 0
= 0
z 5 10 -
<
$ '" * — — ______^
* — • — — .
_ — z
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I '•-._
i $---....
I „. ""••-*
CM CO •*
888
CM CM CM
--«•-•> 400 ppb As Is
-- A- -2500 ppb As Is
--O--2600 ppb As Is
* > 400 ppb Current Standard -
— • — > 600 ppb Current Standard -
_j :
• —
ID *
in CD
8 8
CM CM
600 •" t:
2! § 8
-^ Ol
8 8
lute Maximum SC
10,000 Measurem
t Meeting the Cur
is
.± " in C
s r = "
300 E 1 -o 55
(ii > 0)
I-I
200 2 S >
fl) d) ~
E^ S
1 S 0
Z O i-
100 55
Year
Figure 6-27. Frequency of modeled 5-minute maximum SO2 concentrations above
potential health effect benchmark levels in each year, normalized to
100,000 measurements, Years 2002 through 2006 at twenty selected
counties, with air quality as is and air quality adjusted to just meeting the
current standards (either one exceedance of 0.14 ppm daily average or
no exceedance of 0.03 ppm annual average). Bars indicate standard
deviation of the mean from the twenty model simulations.
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1
2
3
4
5
6
50
3 >
i 0" £ 40
E W g
«o E 0 30
515
ox tn 20
.Q n —
E S <2 :
3 ^1 1O ~
0;
250-
o £200
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i- .E "0
o x ffl
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^
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3 ^
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I
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0
2
+
5
2
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0
3
2
0
0
4
u
2
0
0
5
t
~i
-
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> 500 ppb
2
0
0
6
j
l_
2
0
0
2
+
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l_
2
0
0
3
Year
.
— i—
2
0
0
4
— >—
2
0
0
5
+
— >—
2
0
0
6
> 600 ppb
I I I I 1
E
3 E
3 E
3 B
22222
00000
00000
23456
Year
Year
Figure 6-28. Number of modeled 5-minute maximum SO2 concentrations above potential health effect benchmark
levels at each monitor by year, Years 2002 through 2006 at 20 selected counties, with air quality as is
and air quality adjusted to just meeting the current standards (either one exceedance of 0.14 ppm daily
average or no exceedance of 0.03 ppm annual average).
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5
c
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o
UJ
£
n
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fl)
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o
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°°
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0
O 1O 2O 3D 4O
Annual Average SO2 (ppb)
Figure 6-29. Comparison of the number of modeled 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each
monitor per year and the associated annual average SO2 concentration,
Years 2002 through 2006 for 20 selected counties, air quality data as is.
A) number of 5-minute maximums >400 ppb/year, B) number of 5-minute
maximums >500 ppb/year, C) number of 5-minute maximums >600
ppb/year. The level of the annual average SO2 NAAQS of 0.03 ppm is
indicated by the dashed line.
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1
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15
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25 3O 35
Annual Average SO2 (ppb)
Figure 6-30. Comparison of the number of modeled 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each
monitor per year and the associated annual average SO2 concentration,
Years 2002 through 2006 for 20 selected counties, air quality data
adjusted to just meet the current standards (either one exceedance of
0.14 ppm daily average or no exceedance of 0.03 ppm annual average).
A) number of 5-minute maximums >400 ppb/year, B) number of 5-minute
maximums >500 ppb/year, C) number of 5-minute maximums >600
ppb/year. The level of the annual average SO2 NAAQS of 0.03 ppm is
indicated by the dashed line.
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1
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1. - 0 " ° "
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o
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25 5O
75 10O 125 15O 175 2OO
Daily Average SO2 (ppb)
Figure 6-31. Comparison of the number of modeled 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each
monitor per day and the associated daily average SO2 concentration,
Years 2002 through 2006 for 20 selected counties, air quality data as is.
The level of the 24-hour SO2 NAAQS of 0.14 ppm is indicated by the
dashed line.
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o
+J
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o
ra
UJ
£
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2OO 25O 30O 35O
Daily Average SO2 (ppb)
Figure 6-32. Comparison of the number of modeled 5-minute maximum SO2
concentrations above potential health effect benchmark levels at each
monitor per day and the associated daily average SO2 concentration,
Years 2002 through 2006 for 20 selected counties, air quality data
adjusted to just meet the current standards (either one exceedance of
0.14 ppm daily average or no exceedance of 0.03 ppm annual average).
The level of the 24-hour SO2 NAAQS of 0.14 ppm is indicated by the
dashed line.
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1 Tables 6-11 through 6-14 summarizes the estimated number of 5-minute maximum SC>2
2 concentrations above the potential health effect benchmark levels in each of the twenty counties
3 across the time period modeled (years 2002 through 2006). Descriptive statistics were estimated
4 from the twenty model simulations for each of the two air quality scenarios and considering two
5 SO2 concentration averaging times (annual and daily means). Each county distribution presents
6 the descriptive statistics for the twenty simulations using from all monitors in operation across
7 the five year period of analysis. There was no additional weighting of the county-level data
8 using monitor-years since nearly all monitors were in operation during 2002 through 2006. The
9 concentration distributions present estimates of the central tendency (means and medians) and
10 associated variability in the daily and annual average 862 concentrations within each county
11 across years 2002-2006, as well as the extremes possible in any one year at a particular
12 monitoring site (98th and 99th percentiles). The distributions for the estimated number of
13 exceedances also represents the county similarly, with measures of central tendency applicable to
14 the county on average and the upper percentiles representing the extreme number of exceedances
15 possible in a year at a particular site within the county.
16 In considering the as is air quality in the selected counties using 1-hour SC>2
17 measurements, all individual monitoring sites contained annual average concentrations under 10
18 ppb, with few exceptions (Table 6-11). The upper percentiles of the distribution in the counties,
19 based on a few to several monitors in operation and the years of monitoring available, indicate
20 little deviation from the mean level at no more than a factor of two for most locations. The mean
21 and median number of estimated exceedances of 400 ppb were similar to one another, each
22 numbering less than five per year in 18 of the 20 counties, with half of the counties estimated to
23 have no exceedances at most monitoring sites and years. As expected, both Jefferson and Iron
24 counties in Missouri contained the highest estimated number of exceedances, averaging around
25 thirty 5-minute maximum SC>2 per year above 400 ppb for each location. Estimated numbers of
26 exceedances at the upper percentiles were less than 10 per year for 75% of the counties, with five
27 counties estimated to contain between 10 and 40 estimated exceedances of 400 ppb per year.
28 Also as expected, the number of exceedances of the higher potential health effect benchmark
29 levels were less than that of the 400 ppb level, with most locations on average containing no
30 exceedances of either the 500 ppb or 600 ppb benchmark level.
July 2008 94 Draft - DO NOT QUOTE OR CITE
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1 In considering SC>2 air quality adjusted to just meeting the current daily standard in the
2 selected counties using 1-hour 862 measurements, annual average concentrations are increased
3 by a factor of about 2 to 4 when compared with the as is air quality concentrations, with most
4 locations containing estimated annual average concentrations between 15 and 30 ppb (Table 6-
5 12). The upper percentiles of the distribution in the counties indicate little deviation from the
6 mean level at no more than a factor of two for most locations. The mean number of estimated
7 exceedances of 400 ppb tended to be greater than the median values, although 75% of the
8 counties contained between 10 and 65 exceedances per year considering either metric. As
9 expected, both Jefferson and Iron counties in Missouri contained the highest estimated mean
10 number of exceedances, averaging around 140 5-minute maximum 862 per year above 400 ppb
11 at either location. All counties contained more than 60 exceedances of 400 ppb in a year when
12 considering the upper percentiles, with over one-half estimated to contain more than 100
13 exceedances, though 90% were below 200 exceedances per year. The number of estimated
14 exceedances per year of the higher potential health effect benchmark levels of 500 ppb and 600
15 ppb were about 30% and 50% less, respectively when compared with the mean or median
16 number of exceedances of the 400 ppb level. Similar percentages were observed when
17 comparing the upper percentile estimates of the number of exceedances of 500 ppb and 600 ppb
18 benchmark levels to the 400 ppb level (25% to 45% less, respectively).
19 The means for the daily average concentrations (Table 6-13) were similar to that reported
20 for the annual averages (Table 6-11) when considering the as is air quality. Most counties had
21 measured daily average concentrations of less than 10 ppb during 2002 through 2006. The upper
22 percentiles for the daily average concentrations were about 3 to 5 times greater than the average
23 concentrations, with 75% of sites within the range of 20 - 40 ppb. There were no estimated
24 exceedances per day at each of the counties, regardless of the benchmark level or percentile,
25 except for Iron and Jefferson counties in Missouri, and Beaver County, Pa. At most there were
26 one to two estimated exceedances per day of 400 ppb concentration at these three counties.
27 Consider however that this is an upper percentile daily estimate that likely occurred at one
28 monitoring site on one day. While it is estimating the upper percentile for a given day, there may
29 be additional days throughout the year at the same monitoring site or other monitoring sites in
July 2008 95 Draft - DO NOT QUOTE OR CITE
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Table 6-11. Summary of annual average SO2 concentrations and estimated number of 5-minute maximum SO2
concentrations above potential health effect benchmark levels per year in 20 counties using 20 model simulations,
Years 2002 through 2006, air quality data as is.
State
DE
FL
IA
IA
IL
IN
Ml
MO
MO
MO
OH
OK
PA
PA
PA
PA
TN
TX
WV
WV
County
New Castle
Hillsborough
Linn
Muscatine
Madison
Floyd
Wayne
Greene
Iron
Jefferson
Cuyahoga
Tulsa
Allegheny
Beaver
Northampton
Washington
Shelby
Jefferson
Hancock
Wayne
Annual
mean
5
3
3
4
4
5
4
2
7
10
5
6
8
9
7
8
4
3
11
8
std
1
1
1
1
1
1
1
1
0
0
1
1
2
2
3
1
1
1
2
1
Average SO2
ppb)
p50
5
3
3
4
4
5
4
2
7
10
5
6
8
9
7
9
4
3
11
8
p98
8
7
5
7
6
8
6
3
7
10
7
7
13
14
13
10
6
4
14
10
p99
8
7
5
7
6
8
6
3
7
10
7
7
13
14
13
10
6
4
14
10
Estimated Number of 5-minute Maximum SO2
> 400 ppb per Year
mean
0
0
1
1
0
1
0
0
29
32
0
0
1
5
2
0
0
1
2
0
std
1
1
2
2
1
2
1
0
5
6
0
0
2
7
5
0
1
1
3
1
p50
0
0
0
0
0
1
0
0
28
32
0
0
0
3
0
0
0
0
2
0
p98
3
3
8
6
3
6
2
1
39
44
1
1
9
30
19
1
2
5
12
3
p99
4
3
9
9
3
7
2
1
43
44
1
1
11
33
20
2
3
6
16
4
> 500 ppb per Year
mean
0
0
0
0
0
1
0
0
14
16
0
0
0
2
1
0
0
1
1
0
std
0
0
1
1
0
1
0
0
3
4
0
0
1
3
3
0
0
1
1
0
p50
0
0
0
0
0
0
0
0
14
17
0
0
0
1
0
0
0
0
0
0
p98
1
1
2
3
1
4
1
0
19
22
0
0
3
13
11
1
1
3
4
1
p99
2
1
3
3
1
4
1
1
19
22
1
1
4
14
12
1
2
3
6
1
> 600 ppb per Year
mean
0
0
0
0
0
0
0
0
8
9
0
0
0
1
0
0
0
0
0
0
std
0
0
0
0
0
1
0
0
2
2
0
0
0
2
1
0
0
1
0
0
p50
0
0
0
0
0
0
0
0
8
9
0
0
0
0
0
0
0
0
0
0
p98
0
1
2
2
0
2
0
0
12
13
0
0
1
7
6
0
1
2
2
0
p99
1
1
2
2
1
3
1
1
12
13
0
0
1
8
6
0
2
3
3
0
July 2008
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Table 6-12. Summary of annual average SO2 concentrations and estimated number of 5-minute maximum SO2
concentrations above potential health effect benchmark levels per year in 20 counties using 20 model simulations,
Years 2002 through 2006, air quality data adjusted to just meeting the current standards (either one exceedance of
0.14 ppm daily average or no exceedance of 0.03 ppm annual average).
State
DE
FL
IA
IA
IL
IN
Ml
MO
MO
MO
OH
OK
PA
PA
PA
PA
TN
TX
WV
WV
County
New Castle
Hillsborough
Linn
Muscatine
Madison
Floyd
Wayne
Greene
Iron
Jefferson
Cuyahoga
Tulsa
Allegheny
Beaver
Northampton
Washington
Shelby
Jefferson
Hancock
Wayne
Annual Average SO2
(ppb)
mean
13
11
11
14
16
22
14
11
15
19
21
24
20
21
22
27
19
13
25
24
std
4
4
4
4
4
5
4
4
1
0
6
4
5
5
10
4
4
4
4
5
p50
13
10
13
14
16
24
14
11
15
19
21
24
19
20
28
28
18
12
25
26
p98
22
20
18
20
24
30
21
18
16
19
30
30
31
30
30
30
26
21
30
30
p99
22
20
18
20
24
30
21
18
16
19
30
30
31
30
30
30
26
21
30
30
Estimated Number of 5-minute Maximum SO2
> 400 ppb per Year
mean
23
33
85
75
47
101
40
48
142
141
38
52
26
47
23
30
28
63
53
31
std
28
37
58
80
38
63
35
56
28
7
21
22
31
36
21
20
21
30
24
22
p50
9
16
97
26
39
86
26
19
142
142
38
48
11
34
15
21
31
59
49
29
p98
96
125
170
212
137
229
116
149
184
153
82
94
108
122
61
72
67
118
113
73
p99
101
130
173
215
139
230
121
155
189
153
84
98
112
126
66
76
69
123
121
78
> 500 ppb per Year
mean
14
22
56
50
32
70
24
31
108
91
23
30
15
30
13
16
18
42
31
18
std
18
27
39
55
28
45
21
37
22
6
13
13
19
26
13
13
13
21
16
14
p50
5
10
64
18
23
59
15
13
109
91
22
28
6
20
8
9
19
41
28
17
p98
62
91
116
150
95
161
72
107
144
101
50
56
63
84
38
44
41
83
74
47
p99
67
95
119
152
98
163
75
111
147
101
53
60
66
88
39
45
43
84
81
50
> 600 ppb per Year
mean
9
15
36
32
21
48
14
20
79
59
15
20
9
20
8
9
11
28
19
11
std
12
19
26
35
20
32
13
23
15
6
9
9
12
18
9
9
9
15
10
9
p50
3
6
40
12
14
41
9
8
80
58
14
18
3
12
4
4
12
27
17
9
p98
40
63
77
99
70
114
42
71
102
69
33
41
41
59
27
28
27
64
51
32
p99
42
65
79
104
73
116
44
75
107
69
36
42
43
61
29
30
30
66
55
33
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Table 6-13. Summary of daily average SO2 concentrations and estimated number of 5-minute maximum SO2
concentrations above potential health effect benchmark levels per day in 20 counties using 20 model simulations,
Years 2002 through 2006, air quality data as is.
State
DE
FL
IA
IA
IL
IN
Ml
MO
MO
MO
OH
OK
PA
PA
PA
PA
TN
TX
WV
WV
County
New Castle
Hillsborough
Linn
Muscatine
Madison
Floyd
Wayne
Greene
Iron
Jefferson
Cuyahoga
Tulsa
Allegheny
Beaver
Northampton
Washington
Shelby
Jefferson
Hancock
Wayne
Daily A
mean
5
3
3
4
4
5
5
2
6
9
5
6
8
9
7
8
4
3
11
8
std
5
3
5
5
5
5
6
3
9
11
4
6
6
8
7
5
3
5
8
6
verage SO2
ppb)
p50
4
2
1
3
3
4
3
2
3
5
4
4
7
7
6
7
3
1
9
7
p98
19
12
19
22
18
21
22
12
36
43
17
21
26
33
22
23
13
17
32
22
p99
25
15
25
29
24
25
27
16
45
56
21
24
33
40
28
28
19
22
37
29
Estimated Number of 5-minute Maximum SO2
> 400 ppb per Day
mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
std
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
0
0
2
2
0
0
0
1
0
0
0
0
0
0
> 500 ppb per Day
mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
std
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
> 600 ppb per Day
mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
std
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
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Table 6-14. Summary of daily average SO2 concentrations and estimated number of 5-minute maximum SO2
concentrations above potential health effect benchmark levels per day in 20 counties using 20 model simulations,
Years 2002 through 2006, air quality data adjusted to just meeting the current standards (either one exceedance of
0.14 ppm daily average or no exceedance of 0.03 ppm annual average).
State
DE
FL
IA
IA
IL
IN
Ml
MO
MO
MO
OH
OK
PA
PA
PA
PA
TN
TX
WV
WV
County
New Castle
Hillsborough
Linn
Muscatine
Madison
Floyd
Wayne
Greene
Iron
Jefferson
Cuyahoga
Tulsa
Allegheny
Beaver
Northampton
Washington
Shelby
Jefferson
Hancock
Wayne
Daily Average SO2
(ppb)
mean
13
11
11
14
16
22
14
11
20
23
21
24
20
21
22
27
18
13
25
24
std
14
12
18
18
17
22
18
14
26
25
19
24
16
19
20
17
15
21
18
18
p50
10
7
5
9
11
16
8
8
8
15
16
15
17
16
16
24
14
5
21
20
p98
52
42
70
71
70
88
69
54
104
102
73
91
65
74
78
74
58
80
74
71
p99
66
53
94
96
94
106
86
78
122
125
88
103
80
91
95
87
77
106
85
94
Estimated Number of 5-minute Maximum SO2
> 400 ppb per Day
mean
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
std
0
1
1
1
1
1
1
1
1
1
0
1
0
1
0
0
0
1
1
0
p50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
1
1
4
3
2
3
2
2
5
5
1
2
1
2
1
1
1
2
2
1
p99
2
2
5
5
3
4
3
4
7
6
2
2
2
2
1
2
2
3
2
2
> 500 ppb per Day
mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
std
0
0
1
1
0
1
0
1
1
1
0
0
0
0
0
0
0
1
0
0
p50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
1
1
2
2
1
3
1
2
4
4
1
1
1
1
1
1
1
2
1
1
p99
1
2
4
4
2
3
2
3
5
5
1
2
1
2
1
1
1
3
2
1
> 600 ppb per Day
mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
std
0
0
1
0
0
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
p50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
1
2
2
1
2
1
1
3
3
1
1
1
1
0
1
0
1
1
1
p99
1
1
3
3
2
3
1
2
4
4
1
1
1
1
1
1
1
2
1
1
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1 the particular county with similar values (i.e., one or two). For example, in comparing this Table
2 6-13 with Table 6-11, there were about 40 estimated exceedances of 400 ppb per year in
3 Jefferson County, Mo., indicating that there were approximately 20 to 30 days where the number
4 of daily exceedances was between 1 and 2.
5 When SC>2 concentrations were adjusted to just meeting the current standards, there were
6 few estimated exceedances of any of the 5-minute benchmark levels per day. Only Iron and
7 Jefferson counties in Missouri contained a mean estimate greater than zero (I/day for the 400
8 ppb and 500 ppb), though all location were estimated to have no exceedances per day when
9 considering the median value. Just under half of the locations contained 1-2 exceedances of
10 400 ppb per day when considering the upper percentiles of the daily estimates, though Iron and
11 Jefferson counties were estimated to have as many as 6 or 7 exceedances per day. The estimated
12 number of 5-minute maximum above 500 ppb or 600 ppb were less frequent, with an increasing
13 number of counties with at most 1-2 estimated exceedances per day with increasing benchmark
14 level.
15 6.5 UNCERTAINTY ANALYSIS
16 This uncertainty analysis identifies the sources of the assessment that do or do not reduce
17 the certainty in the risk and exposure results, and provide a rationale for why this is the case.
18 The analysis is primarily qualitative, however incorporates several of the quantitative elements
19 introduced through the statistical model evaluation performed earlier.
20 6.5.1 Air Quality Data
21 One basic assumption is that the AQS SO2 air quality data used are quality assured
22 already. Reported concentrations contain only valid measures, since values with quality
23 limitations are either removed or flagged. There is likely no selective bias in retention of data
24 that is not of reasonable quality, it is assumed that selection of high concentration poor quality
25 data would be just as likely as low concentration data of poor quality. Given the numbers of
26 measurements used for this analysis, it is likely that even if a few low quality data are present in
27 the data set, they would not have a substantial effect on the results presented here. In addition, a
28 quantitative analysis of available simultaneous measures in Appendix A indicated little to no bias
29 in measured concentrations. Therefore, the air quality data measurements database used likely do
30 not have a negative impact on the generated results.
31 Temporally, some of the ambient monitoring data used in this analysis contained both 5-
32 minute maximum and 1-hour measurements and appropriately accounted for variability in
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1 concentrations that are commonly observed for 862, and by the selection criteria used herein,
2 were representative of either a valid day or year. In addition, having more than one monitor
3 accounted for some of the spatial variability in selected counties. However, the degree of
4 representation of the monitoring data used in this analysis can be evaluated from several
5 perspectives, one of which is how well the temporal and spatial variability are represented. In
6 particular, missing 5-minute maximum or hourly measurements at a monitor may introduce bias
7 (if different periods within a day, month or a year have different numbers of measured values)
8 and reduce certainty in the estimations. Furthermore, the spatial representativeness will be poor
9 if the monitoring network is not dense enough to resolve the spatial variability (reducing
10 certainty) or if the monitors are not evenly distributed (causing a bias). The uncertainty
11 regarding temporal and spatial representation by the monitors is expanded below.
12 6.5.2 Measurement Technique for Ambient SOi
13 The draft ISA notes various positive and negative sources of interference that could
14 reduce certainty in the measurement of SC>2 (draft ISA, sections 2.3.1 and 2.3.2). Many of the
15 identified sources (e.g., polycyclic aromatic hydrocarbons, stray light, collisional quenching)
16 have limited impact to SC>2 measurement due to the presence of instrument controls that prevent
17 the interference. The actual impact on any individual monitor is unknown, i.e., the presence of
18 negative and positive interferences has not been quantitated. Therefore, reported ambient
19 monitoring concentrations could be either over- or under-estimated, but is likely minimally due
20 to instrument controls.
21 6.5.3 Temporal Representation
22 Data are valid 5-minute and 1-hour average measures and are of the same temporal scale
23 as identified health effect benchmarks. There are frequent missing values within a given valid
24 year that may reduce the degree of certainty in concentration distributions and model
25 estimations, however given the level of the benchmark concentrations and the low frequency of
26 exceedances, it is likely of negligible consequence. Bias may be introduced if some seasons,
27 day-types (e.g., weekday/weekend), or times of the day (e.g., nighttime or daytime) are not
28 equally represented. Since 75 percent days/year and hours/day completeness rules were applied
29 for some of the analyses, these potential biases are likely to have been removed. Data were not
30 interpolated in the analysis; missing data were not substituted with estimated values,
31 concentrations reported as zero were used as is. Since the concentrations of interest here are
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1 those orders of magnitude above the detection limits, there is a negligible effect on certainty in
2 the analyses from not estimating these extremely low concentrations.
3 There may be bias and added uncertainty if the years monitored vary significantly
4 between locations and the two monitor averaging times. Monitoring sites across the U.S. have
5 changed over time, with a trend of decreasing number of monitors most evident for those
6 measuring the 5-minute maximum SC>2. The 5-minute monitoring has been performed less
7 frequently than the hourly monitoring, generally only a few years of data exist per 5-minute
8 monitor. Due to the limited number of measurements, all the available 5-minute maximum data
9 were used in developing the statistical relationships and for model evaluation without meeting
10 the completeness criteria. In addition, the use of the older ambient monitoring data in some of
11 the analyses here carries the assumption that the sources present at that time are the same as
12 current sources, potentially reducing certainty if this is not the case. However, the variability in
13 monitoring concentrations (both the 1-hour and 5-minute maximum 802) did not have a
14 significant relationship with monitoring year (i.e., years 1997 though 2007) and contained a
15 comparable range between the two monitor averaging times. Therefore, any negative impact to
16 certainty is expected to be minimal regarding both bias direction and magnitude for analyses
17 performed using each of these data sets across multiple years.
18 6.5.4 Spatial Representation
19 Relative to the physical area, there are only a limited number of monitors in each
20 location, particularly when considering the number of monitors that measured 5-minute
21 maximum SO2. When considering ambient monitoring at the county level, data were assumed to
22 be spatially representative of those particular locations analyzed here. This includes areas
23 between the ambient monitors that may or may not be influenced by similar local sources of SC>2.
24 For these reasons, the potential bias at spatial networks with limited numbers of monitors may be
25 large, although the monitoring network design should have addressed these issues within the
26 available resources and other monitoring constraints. Portions of the air quality characterization
27 used all monitors meeting the 75 percent completeness criteria, without taking into account the
28 monitoring objectives or land use for the monitors. Thus, there may be lack of spatial
29 representation and contribution to uncertainty due to either the inclusion or exclusion of monitors
30 that are near local source emissions of SC>2.
31 In comparing the emission sources in close proximity to the 5-minute maximum or 1-
32 hour monitors, similar distributions in the types of sources impacting both were observed. This
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1 indicates that the relationships derived from the 5-minute measurement data and how they were
2 applied to the 1-hour monitoring likely do not reduce certainty when considering the monitoring
3 data wholly. At any individual monitor there may be very different source types, each at variable
4 proportions influencing the SC>2 concentrations measured at the monitor. This may reduce
5 certainty in estimates at individual monitors, however the method of applying both concentration
6 level and variability measures to each hourly concentration at each monitor should have
7 controlled for some of the variability anticipated by differing source types.
8 6.5.5 Air Quality Adjustment Procedure
9 The empirical method used to estimate exceedances under the current-standard scenario
10 may or may not represent the true relationship between the daily or annual mean concentrations
11 over a calendar year and the number of exceedances. The empirical method assumes that if the
12 daily means change then all the hourly concentrations will change proportionately. Universal
13 application of the proportional simulation approach at each of the selected counties was done for
14 consistency and was designed to preserve the inherent variability in the concentration profile.
15 However, different sources may have different temporal emission profiles, so that applied
16 changes to the daily mean concentrations at monitors may not correspond well to all parts of the
17 concentration distribution equally. Similarly, emissions changes that affect the concentrations at
18 the monitoring site with the 2nd highest daily mean concentration will not necessarily impact
19 lower concentration sites proportionately. This could result in overestimations in the number of
20 exceedances at monitors recording lower 1-hour 862 concentrations within a selected county.
21 6.5.6 Ambient Monitor to Exposure Representation
22 Human exposure is characterized by contact of a pollutant with a person, and as such, the
23 air quality characterization contains the broad assumption that the monitoring concentrations can
24 serve as a surrogate for exposure. The ISA reports that personal exposure measurements are of
25 limited use since ambient concentrations are typically below the detection limit of the personal
26 samplers. There is no method to quantitatively assess the relationship between 5-minute ambient
27 monitoring data and 5-minute personal exposures, particularly since personal exposures are time-
28 averaged over hours or days, and never by 5-minute averages. Therefore the relationship of 5-
29 minute maximum personal exposure concentrations (i.e., attributed to ambient) to 5-minute
30 maximum ambient is unknown and thus may add to uncertainty. An evaluation in the ISA
31 indicates the relationship between longer-term averaged ambient monitoring concentrations and
32 personal exposures is reasonably strong, particularly when ambient concentrations are above
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1 detection limits. The strength of the relationship between personal and ambient concentrations is
2 supported further by the limited presence of indoor sources, much of an individuals' personal
3 exposure is of ambient origin. However, personal exposure concentrations are reportedly a small
4 fraction of ambient concentrations. This is because local outdoor SO2 concentrations are
5 typically /^ of the ambient monitoring concentrations, and indoor concentrations about /^ of the
6 local outdoor concentrations. Therefore, while the relationship between personal exposures and
7 ambient is strong, the use of monitoring data as a surrogate for exposure would likely lead to an
8 overestimate in the number of peak concentrations those individuals might encounter.
9 6.5.7 Statistical Model
10 A criterion was developed to select data from the data sets containing the measured 5-
11 minute maximum and 1-hour 862 concentrations. The generation of peak to mean ratios of <1
12 imply the 5-minute peak is less than the 1-hour average, a physical impossibility, and values >12
13 are a mathematical impossibility. Data were screened for values outside of these bounds,
14 increasing confidence in the PMRs used for development of the statistical model. The use of all
15 screened 5-minute maximum SC>2 data (1997 to 2007) in developing PMR CDFs still carries an
16 assumption that the source emissions present at that time of measurement are similar to recent
17 source emissions, possibly reducing the degree of certainty in results generated in areas where
18 source emissions have changed. However, as noted with the concentration variability, PMRs do
19 not have any apparent trend with monitoring year and have averaged around 1.6 (Figure 6-33).
20 This indicates that the use of older monitoring data may have a negligible impact on model
21 estimates.
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1
2
3
4
5
6
9
10
11
12
13
14
15
16
17
18
19
20
o.u ~
O
us
* 7.0
fi
2 6.0
i
g
"S 5.0
cc
82
2 'E 4.0
A O
O ^~
8 3.0
Q.
nnual Average
-^ N)
b b
n n -
*
•
y=0.02x-38.6
R2 = 0.009
. .
•
• • • •
• . : 5 ! • •
1 1 i i i 1
I i! 1 M I
CQr^CQO)O-<-2. The results indicate that on
average, the statistical model performed well in generating reasonable estimates of short-term
peak concentrations (section 6.2.3.6). However, a few results from this comparison indicate
numbers of 5-minute maximum concentrations above 400 ppb could be either over- or under-
estimated, under certain conditions. The greatest number of maximum concentrations observed
above 400 ppb at one monitor was consistently underestimated by a factor of about two. This
could imply that the number of modeled 5-minute maximum concentrations that are beyond an
apparent linear upper bound (i.e., approximately 300 per monitor) may be underestimated by
approximately a factor of two. In addition, there were a few sites without any measured 5-
minute maximum concentrations above 400 ppb, although the statistical model estimated several
to just over a hundred per monitor. This could imply that some monitors have overestimations in
the number of 5-minute maximum concentrations. Neither situation appeared directly related to
source type, with additional monitors sited in the same area impacted by similar source types
containing reasonable model estimates. Again, when considering individual monitors, there may
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1 be limits to the certainty in the number of estimated exceedances, however in evaluating results
2 for all of the monitors, the uncertainty in the estimation is likely less.
3 Reproducibility in the estimates was determined by performing multiple model
4 simulations. Across the first 10 model runs, the relative absolute difference between the single
5 simulation estimates and those from the total simulation remained within +/- 15 percent, leading
6 to very stable estimates (+/- 1%) at around 15 model simulations (section 6.2.3.6). For the sake
7 of modeling efficiency, a limit of twenty simulations was determined sufficient to generate stable
8 model estimations. Ninety-five percent prediction intervals (PI95) were generated for each
9 monitor in the twenty counties selected for detailed analysis, using the 20 model simulations for
10 each air quality scenario and for each potential health effect benchmark level. The percentile
11 distributions of the twenty simulations were calculated for the number of estimated exceedances
12 at each monitor that were summed by year, with the 2.5th, 50th, and 97.5th percentile values
13 retained. These median peak values were ranked and used to generate a CDF for illustration.
14 The 2.5th and 97.5th percentile provide a 95% interval (i.e., 97.5-2.5=95) about the median
15 estimate. Figure 6-34 presents the results of this analysis for the number of exceedances per year
16 at monitors in the selected counties when using as is air quality data. As noted earlier, nearly
17 70% of the monitor site-years did not have any estimated exceedances of the lowest potential
18 health effect benchmark level of 400 ppb. When a small number of exceedances of 400 ppb
19 were estimated (e.g., 5 or less in a year), the 95% prediction interval tended to include an
20 estimate of zero, suggesting that when a monitor contains this few estimated mean or median
21 number of exceedances, the certainty in the prediction may be limited. At numbers of
22 exceedances of 400 ppb in a year greater than 10, the 95% prediction interval tended to exclude a
23 value of zero, indicating greater certainty about the estimated mean or median number of
24 exceedances. This is best illustrated in Figure 6-35 where the same procedure was applied to the
25 results using the air quality adjusted to just meeting the current standards. The PI95 spans about
26 15 and is consistent across a wide range of estimated number of potential health effect
27 benchmark exceedances and for each level, indicating little bias in the estimation procedure at
28 any individual monitor. The procedure was also applied to each monitor, where numbers of
29 exceedances were summed by day. It was a rare event where the daily number of estimated
30 exceedances were greater than zero, particularly for the as is air quality. Only 212 site-days out
31 of a total of 124,207 contained a median estimated number of exceedances of 400 ppb greater
32 than one, with all of these estimated exceedances at four or less per day. These data were not
33 used to develop PI95 due to the sample size limitations. About 5.5% of the air quality adjusted
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1 to just meeting the current standard contained median number of exceedances of 400 ppb greater
2 than one, all of which were 15 exceedances or less per day (Figure 6-36). With prediction
3 intervals spanning around 10, the estimated number of exceedances of 400 ppb on a given day
4 may be less certain for most site-days, in particular where the number of exceedances is less than
5 five.
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Estimated Number of 5-minute Max SO2/year
Figure 6-34. 95% prediction intervals for estimated number of 5-minute maximum
SO2 concentrations in a year above potential health effect benchmark
levels at each monitor, Years 2002 through 2006 for 20 selected counties,
air quality data as is.
July 2008
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Estimated Number of 5-minute Max SO2/year
Figure 6-35. 95% prediction intervals for estimated number of 5-minute maximum
SO2 concentrations in a year above potential health effect benchmark
levels at each monitor, Years 2002 through 2006 for 20 selected counties,
air quality data adjusted to just meeting the current standards (either one
exceedance of 0.14 ppm daily average or no exceedance of 0.03 ppm
annual average).
July 2008
109
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Figure 6-36. 95% prediction intervals for estimated number of 5-minute maximum
SO2 concentrations per day above potential health effect benchmark
levels at each monitor, Years 2002 through 2006 for 20 selected counties,
air quality data adjusted to just meeting the current standards (either one
exceedance of 0.14 ppm daily average or no exceedance of 0.03 ppm
annual average).
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
6.5.8 Single vs. Multiple Short-Term Peak Concentrations
The model estimates the frequency of a single exceedance of a potential health effect
benchmark in one hour. However, multiple short-term peak concentrations above selected levels
are possible in any hour. Analysis of the 5-minute continuous monitoring data indicates that
multiple occurrences of concentrations above the 400, 500, and 600 ppb within the same hour are
common. Using continuous monitoring data obtained from years 1997-2006, multiple peak
concentrations (i.e., 2 or more) at or above 600 ppb within the same hour occurred with a 35%
frequency (Table 6-15). The frequency in multiple exceedances was greater for the lower 5-
minute 862 concentration levels, where 44% of the time a single exceedance of 500 ppb was
observed, there were two or more exceedances within the same hour. Forty-one of the 66 hourly
periods with a 5-minute concentration at or above 400 ppb had more than one exceedance within
that same hour (or 62% of the time).
These results suggest that a single peak approach for estimating the 5-minute maximum
SC>2 concentrations alone as a surrogate for exposure may lead to an underestimate in the number
of potential exposure events. However, there would be added uncertainty in the extrapolation of
these results since the continuous monitoring data were only from 16 source-oriented monitors,
each with a limited number of monitoring years.
Table 6-15. Number of multiple exceedances of potential health effect benchmark
levels within an hour.
Number of Exceedances of
5-minute SO2 in 1-hour
12
11
10
9
8
7
6
5
4
3
2
1
Total
Number of Hours
> 600 ppb
0
0
0
0
1
0
0
1
0
2
4
15
23
with Multiple 5
> 500 ppb
0
0
0
1
1
0
0
0
2
6
7
22
39
-minute SO2
> 400 ppb
0
0
1
1
1
1
1
4
5
7
20
25
66
19
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1 6.5.9 Health Benchmark
2 The choice of potential health effect benchmarks, and the use of those benchmarks to
3 assess risks, can reduce the level of certainty in the risk assessment results. For example, the
4 potential health effect benchmarks used were from studies where volunteers were exposed to
5 SC>2 for varying lengths of time. Typically, the SC>2 exposure durations were between 5 and 10
6 minutes. This may limit some certainty into the characterization of risk, which compared the
7 potential health effect benchmarks to estimates of exposure over a 5-minute time period. Use of
8 a 5-minute averaging time could over- or under-estimate risks. In addition, the human exposure
9 studies evaluated airways responsiveness in asthmatics. For ethical reasons, more severely
10 affected asthmatics and asthmatic children were not included in these studies. Severe asthmatics
11 and/or asthmatic children may be more susceptible than mildly asthmatic adults to the effects of
12 SO2 exposure. Therefore, the potential health effect benchmarks based on these studies could
13 underestimate risks in populations with greater susceptibility.
14
15
16
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i 7.0 EXPOSURE ANALYSIS
2 7.1 OVERVIEW
3 This section documents the methodology and data used in the inhalation exposure
4 assessment and associated health risk characterization for 862 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. Both air quality and exposure modeling
8 approaches have been used to generate estimates of 5-minute maximum, 24-hour and annual
9 average SO2 exposures within selected areas of the U.S. for year 2002. Exposures were
10 characterized considering recent air quality conditions (as is) and for air quality adjusted to just
11 meet the current 862 standards in selected locations. Briefly, the discussion in this chapter
12 includes the following:
13 • description of the inhalation exposure model and associated input data,
14 • evaluation of estimated 862 exposures,
15 • assessment of the quality and limitations of the input data for supporting the goals of
16 the SC>2 NAAQS exposure and risk characterization.
17 A combined dispersion modeling and exposure modeling approach was used to simulate
18 personal exposures of individuals residing in close proximity to important 862 emission sources.
19 Person-based exposure profiles were generated for a given population under direct impact from
20 these local sources of SC>2, focused on the number of 5-minutes daily peak exposure events in an
21 entire year. This combined dispersion and exposure modeling approach was both time and labor
22 intensive. To date, only the exposure results and the risk characterization comparing exposures
23 against several potential health effect benchmarks for areas within the state of Missouri are
24 complete and are presented in this draft document. As discussed in Chapter 8, the exposure
25 results also will be an input to the health risk assessment for lung function responses related to 5-
26 minute exposures to 862 for the asthmatic population that is currently underway.
27
28 7.2 OVERVIEW OF HUMAN EXPOSURE MODELING USING APEX
29 The purpose of this exposure analysis is to allow comparisons of population exposures to
30 ambient 862 among and within selected locations, and to characterize risks associated with
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1 current air quality levels and with just meeting the current standards. This section provides a
2 brief overview of the model used by EPA to estimate SO2 population exposure.
3 The EPA has developed the Air Pollutants Exposure Model (APEX) model for estimating
4 human population exposure to criteria and air toxic pollutants. APEX serves as the human
5 inhalation exposure model within the Total Risk Integrated Methodology (TRIM) framework
6 (EPA 2006a; 2006b). APEX was recently used to estimate population exposures in 12 urban
7 areas for the O3 NAAQS review (EPA, 2007d; 2007e) and in estimating population NO2
8 exposures in Philadelphia County as part of the NO2 NAAQS review (EPA, 2008).
9 APEX is a probabilistic model designed to account for sources of variability that affect
10 people's exposures. APEX simulates the movement of individuals through time and space and
11 estimates their exposure to a given pollutant in indoor, outdoor, and in-vehicle
12 microenvironments. The model stochastically generates a sample of simulated individuals using
13 census-derived probability distributions for demographic characteristics. The population
14 demographics are drawn from the year 2000 Census at the tract, block-group, or block level, and
15 a national commuting database based on 2000 census data provides home-to-work commuting
16 flows. Any number of simulated individuals can be modeled, and collectively they approximate
17 a random sampling of people residing in a particular study area.
18 Daily activity patterns for individuals in a study area, an input to APEX, are obtained
19 from detailed diaries that are compiled in the Consolidated Human Activity Database (CHAD)
20 (McCurdy et al., 2000; EPA, 2002). The diaries are used to construct a sequence of activity
21 events for simulated individuals consistent with their demographic characteristics, day type, and
22 season of the year, as defined by ambient temperature regimes (Graham and McCurdy, 2004).
23 The time-location-activity diaries input to APEX contain information regarding an individuals'
24 age, gender, race, employment status, occupation, day-of-week, daily maximum hourly average
25 temperature, the location, start time, duration, and type of each activity performed. Much of this
26 information is used to best match the activity diary with the generated personal profile, using
27 age, gender, employment status, day of week, and temperature as first-order characteristics. The
28 approach is designed to capture the important attributes contributing to an individuals' behavior,
29 and of likely importance in this assessment (i.e., time spent outdoors) (Graham and McCurdy,
30 2004). Furthermore, these diary selection criteria give credence to the use of the variable data
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1 that comprise CHAD (e.g., data collected were from different seasons, different states of origin,
2 etc.).
3 APEX has a flexible approach for modeling microenvironmental concentrations, where
4 the user can define the microenvironments to be modeled and their characteristics. Typical
5 indoor microenvironments include residences, schools, and offices. Outdoor microenvironments
6 include for example near roadways, at bus stops, and playgrounds. Inside cars, trucks, and mass
7 transit vehicles are microenvironments which are classified separately from indoors and
8 outdoors. APEX probabilistically calculates the concentration in the microenvironment
9 associated with each event in an individual's activity pattern and sums the event-specific
10 exposures within each hour to obtain a continuous series of hourly exposures spanning the time
11 period of interest. The estimated pollutant concentrations account for the effects of ambient
12 (outdoor) pollutant concentration, penetration factors, air exchange rates, decay/deposit! on rates,
13 proximity to important outdoor sources, and indoor source emissions, each depending on the
14 microenvironment, available data, and estimation method selected by the user. And, since the
15 modeled individuals represent a random sample of the population of interest, the distribution of
16 modeled individual exposures can be extrapolated to the larger population.
17 The model simulation can be summarized in the following five steps:
18 1. Characterize the study area. APEX selects census blocks within a study area -
19 and thus identifies the potentially exposed population - based on user-defined
20 criteria and availability of air quality and meteorological data for the area.
21 2. Generate simulated individuals. APEX stochastically generates a sample of
22 hypothetical individuals based on the census data for the study area and human
23 profile distribution data
24 3. Construct a sequence of activity events. APEX constructs an exposure event
25 sequence spanning the period of the simulation for each of the simulated
26 individuals and based on the activity pattern data.
27 4. Calculate 5-minute and hourly concentrations in microenvironments. APEX
28 users define microenvironments that people in the study area would visit by
29 assigning location codes in the activity pattern to the user-specified
30 microenvironments. The model calculates 5-minute and hourly concentrations of
31 a pollutant in each of these microenvironments for the period of simulation, based
32 on the user-provided microenvironment descriptions, the hourly air quality data,
33 and the PMRs. Microenvironmental concentrations are calculated for each of the
34 simulated individuals.
35 5. Estimate exposures. APEX estimates a concentration for each exposure event
36 based on the microenvironment occupied during the event. These values can be
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1 averaged by clock hour to produce a sequence of hourly average exposures
2 spanning the specified exposure period. The values may be further aggregated to
3 produce daily, monthly, and annual average exposure values.
4 7.3 CHARACTERIZATION OF STUDY AREAS
5 7.3.1 Study Area Selection
6 The selection of areas to include in the exposure analysis takes into consideration the
7 availability of ambient monitoring, the desire to represent a range of geographic areas
8 considering 862 emission sources, population demographics, general climatology, and results of
9 the ambient air quality characterization.
10 The first area of interest was initially identified based on the results of a preliminary
11 screening of the 5-minute ambient SC>2 monitoring data that were available. The state of
12 Missouri was one of only a few states having both 5-minute maximum and continuous 5-minute
13 SC>2 ambient monitoring (approximately 14, including a few collocated monitors), as well as
14 having over 30 1-hour SC>2 monitors in operation at some time during the period from 1997 to
15 2007. In addition, the air quality characterization described in Chapter 6 estimated frequent
16 exceedances above the potential health effect benchmark levels at several of the 1-hour ambient
17 monitors. In a ranking of estimated SC>2 emissions reported in the National Emissions Inventory
18 (NEI), Missouri ranked 7th for the number of stacks with > 1000 tpy emissions out of all US
19 states. These stack emissions were associated with a variety source types such as electrical
20 power generating units, chemical manufacturing, cement processing, and smelters. Two
21 additional states of interest that contained similar ranking for emissions and SC>2 measurement
22 data from several ambient monitors include Pennsylvania (5th) and West Virginia (10th). If it is
23 possible within the time and resource constraints to model additional locations, the primary
24 selection criterion would be based on total number of emission facilities regardless of available
25 ambient 862 monitoring data, which would include in ranked order the following states: Texas,
26 Ohio, Illinois, and Indiana.
27 7.3.2 Study Area Description
28 Although it would be useful to characterize SC>2 exposures nationwide, because the
29 modeling approach is both time and labor intensive, a regional and source-oriented approach was
30 selected to make the study tractable. Based on the criteria in section 7.3.1, several modeling
31 domains were characterized within the selected state of Missouri to test the feasibility of the
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1 modeling methods. These modeling domains were defined as areas within 20 km of a major
2 point source of 862 emission, more completely defined in the next section. Although we report
3 on several of the Missouri modeling domains in this draft risk and exposure assessment,
4 additional analyses are planned for more domains in the state and may expand the study to other
5 U.S. locations.
6
l 7.4 CHARACTERIZATION OF AMBIENT HOURLY AIR QUALITY DATA
8 USING AERMOD
9 7.4.1 Overview
10 Air quality data used for input to APEX were generated using AERMOD, a steady-state,
11 Gaussian plume model (EPA, 2004). For each identified model domain location, the following
12 steps were performed.
13 1 Collect and analyze general input parameters. Meteorological data, processing
14 methodologies used to derive input meteorological fields (e.g., temperature, wind
15 speed, precipitation), and information on surface characteristics and land use are
16 needed to help determine pollutant dispersion characteristics, atmospheric
17 stability and mixing heights.
18 2. Estimate emissions. The emission sources modeled included, major stationary
19 emission sources and non-point source emissions.
20 3. Define receptor locations. Two sets of receptors were identified for the
21 dispersion modeling, including ambient monitoring locations (where available)
22 and census block centroids.
23 4. Estimate concentrations at receptors. Hourly concentrations were estimated for
24 year 2002 by combining concentration contributions from each of the emission
25 sources.
26 Estimated hourly concentrations output from AERMOD were then used as input to the
27 APEX model to estimate population exposure concentrations. Details regarding both modeling
28 approaches and input data used are provided below.
29 7.4.2 Introduction
30 Several regions in the state of Missouri were selected for analysis. AERMOD, a steady-
31 state, Gaussian plume model (EPA, 2004) was used to perform dispersion modeling of SO2
32 emitted from stationary point sources and estimate hourly concentrations at census block
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1
2
3
4
5
6
7
8
9
10
11
12
13
receptors for the 2002 time period. Major facility point sources within the state were included in
the analysis, in a set of modeling subdomains to characterize impacted areas in the state.
Statewide, the majority of SO2 emissions originate from point sources: about 85 percent
in Missouri in 2002 according to the most recent NEI. To capture the impact of these emissions
on populations within the state, point sources at major facilities were identified and paired to a
representative surface meteorological station. For this study major facilities were defined as
those with an SC>2 emission total exceeding 1000 tpy in 2002. Within such facilities, every stack
emitting more than 1 tpy was included in the modeling inventory. Fourteen representative
collections of emission sources were thus created, capturing all major facility point sources in the
state. All block centroids within 20 km of any of these sources were designated as modeling
receptors. The coupled sources, meteorological stations, and block centroid receptors define the
modeling domain for each of the fourteen regions. Table 7-1 lists the fourteen domains and the
corresponding number of sources and receptors and each domain is illustrated in Figure 8-1.
Table 7-1. SO2 dispersion modeling domains for Missouri.
Modeling
Domain
03935
03945
03947
03960
03966
03975
03994
13987
13994
13995
13997
14938
53869
93989
Total
Meteorological
Database
ISH
ISH
ISH
LCD
ISH
LCD
LCD
ISH
ISH
ISH
LCD
LCD
LCD
LCD
Number of
Receptors2
5,323
3,720
29,387
8,131
2,832
3,653
2,945
2,814
29,245
7,469
3,653
1,407
1,262
5,330
107,171
Number of
Stacks
2
9
19
19
4
3
8
1
15
11
2
3
11
8
115
1 As derived from the corresponding surface meteorological
station's WBAN ID.
2 Some receptors are duplicated between some scenarios.
14
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1
2
Sfe Stations
3i§4
With In Met Stn
Randomly Assigned to Only One Domain
Reetptors
ly Associated Sfc Stn
Sfe Stn IDs are
*
03945
»
•
»
•
-
13394
13395
«
14938
*
•-
Emissions Soyrses
o«l«ti By Associated Sfc Mat fitn S Dati Seure«
Sic M«« Stn 10 A 0»tt Sourc«;
A
f
Figure 7-1. Modeling domains for the state of Missouri.
4 7.4.3 Meteorological Inputs
5 All meteorological data used for the AERMOD dispersion model simulations were processed
6 with the AERMET meteorological preprocessor, version 06341. This section describes the input
7 data and processing methodologies used to derive input meteorological fields for each of the
8 fourteen domains modeled within Missouri.
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1 7.4.3.1 Data Selection
2 Raw surface meteorological data for the 2002 period were obtained from both the
3 Integrated Surface Hourly (ISH) Database,12 and the Quality Controlled Local Climatological
4 Database (LCD)13. Both of these databases are maintained by the National Climatic Data Center
5 (NCDC). Two sets of data were required to assure that the most representative meteorological
6 observations were paired to each of the fourteen modeling domains. Both datasets consist of
7 typical hourly surface parameters (including air and dew point temperature, atmospheric
8 pressure, wind speed and direction, precipitation amount, and cloud cover) from hourly
9 Automated Surface Observing System (ASOS) stations. However, the formats of the data differ.
10 ISH data is generally preferable, since the AERMET meteorological preprocessor for the
11 AERMOD model is pre-configured to accept this format. However, there are significantly fewer
12 stations included in this database. The LCD dataset includes more stations, such as minor
13 airports and non-ASOS stations, but must be reformatted before use in the AERMET
14 preprocessor. No on-site observations were used.
15 Grouping of individual stacks to surface meteorological stations was made as follows. To
16 address concerns with use of reprocessed LCD-formatted meteorological data, preference was
17 given to the ISH dataset. That is, when an ISH station was within 50 km of a given stack it was
18 used, even if there was a closer LCD station. The algorithm for pairing meteorological stations
19 and stacks is shown in Figure 7-2. The surface meteorological stations used to define modeling
20 domains for this analysis are detailed in Table 7-2.
21
22
12 http://wwwl .ncdc.noaa.gov/pub/data/techrpts/tr20010 l/tr2001-01 .pdf
13 http://cdo.ncdc.noaa.gov/qclcd/QCLCD
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Yes
2
3
Is there an ISH
station within
50km?
Is theclos
ISH station
closer than the
closest LCD
station?
Figure 7-2. Decision tree for selection of meteorological stations.
Table 7-2. Surface meteorological stations dictating modeling domains.
Modeling
Domain
03935
03945
03947
03960
03966
03975
03994
13987
13994
13995
13997
14938
53869
93989
Call
Sign
CGI
COU
MCI
CPS
SUS
POP
DMO
JLN
STL
SGF
VI H
IRK
HKA
UIN
Name
Cape Girardeau,
MO
Columbia, MO
Kansas City, MO
Cahokia/St.Louis,
IL
St Louis, MO
Poplar Bluff, MO
Sedalia, MO
Joplin, MO
St Louis, MO
Springfield, MO
Rolla/Vichy, MO
Kirksville, MO
Blytheville, AR
Quincy, IL
Location
Cape Girardeau Regional
Airport
Columbia Regional
Airport
Kansas City International
Airport
St Louis Downtown
Airport
Spirit Of St Louis Airport
Poplar Buff Municial
Airport
Sedalia Memorial Airport
Joplin Regional Airport
Lambert-St Louis
International Airport
Spngfld-Branson Regl
Airport
Rolla National Airport
Kirksville Regional Airport
Blytheville Municipal
Airport
Quincy Regional-Baldwin
Field Airport
Latitude
(decimal
degrees)
37.23
38.82
39.30
38.57
38.67
36.77
38.70
37.15
38.75
37.23
38.13
40.10
35.93
39.93
Longitude
(decimal
degrees)
-89.57
-92.22
-94.72
-90.17
-90.67
-90.32
-93.18
-94.50
-90.37
-93.38
-91.77
-92.53
-89.83
-91.18
Station
Height
(m)
107
274
313
126
141
100
276
300
216
387
347
294
78
234
Time
Zone1
(hours)
6
6
6
6
6
6
6
6
6
6
6
6
6
6
1 Time zone is the offset from UTC/GMT to LSI in hours.
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1
2
3
4
5
6
7
8
9
10
The percentages of surface observations per station accepted by AERMET (i.e., those
observations that were both not missing and within the expected ranges of values) were typically
>99%.
Mandatory and significant levels of upper-air data were obtained from the NOAA
Radiosonde Database.14 Upper air observations show less spatial variation than do surface
observations; thus they are both representative of larger areas and measured with less spatial
frequency than are surface observations. Upper-air stations were selected to minimize both the
distance to the emission sources and the number of missing data records. Four upper air stations
were available to characterize the fourteen modeling domains. The selected stations for each
modeling domain are shown in Table 7-3.
Table 7-3. Upper air stations paired to each modeling domain.
Upper
Air
Station
4833
13897
13995
13996
Modeling
Domain
03960
03966
13994
93989
03935
53869
03945
03975
03994
13987
13995
13997
03947
14938
Call
Sign
ILX
BNA
SGF
TOP
Name
Lincoln, IL
Nashville, TN
SpringfielD,
MO
Topeka, KS
Location
Lincoln-Logan
County Ap
Nashville
International
Airport
Springfield-
Branson Regional
Airport
Philip Billard
Municipal Airport
Latitude
40.15
36.25
37.23
39.07
Longitude
89.33
86.57
93.40
95.62
Station
Height
(m)
178
180
394
268
Time
Zone1
6
6
6
6
* Time zone is the offset from UTC/GMT to LSI in hours.
11
12
13
14
15
The percentage of upper-air observations per station per height interval accepted by
AERMET were typically >99% for the pressure, height, and temperature parameters. However,
dewpoint temperature, wind direction, and wind speed parameters had lower acceptance rates
(sometimes <75%), particularly for the greater atmospheric heights.
14
hltp ://raob. fsl .noaa, go v/
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1 7.4.4 Surface Characteristics and Land Use Analysis
2 In addition to the standard meteorological observations of wind, temperature, and cloud
3 cover, AERMET analyzes three principal variables to help determine atmospheric stability and
4 mixing heights: the Bowen ratio15, surface albedo16 as a function of the solar angle, and surface
5 roughness17.
6 AERSUKFACE version 08009 was used to estimate land-use around the meteorological
7 observation site and calculate the Bowen ratio, surface albedo, and surface roughness as part of
8 the AERMET processing. AERSURFACE uses the US Geological Survey (USGS) National
9 Land Cover Data 1992 archives (NLCD92)18. However, to optimize objectivity and efficiency in
10 the analysis of such a large number of stations, AERSURFACE was run in an automated fashion,
11 with the appropriate state land cover data file from USGS and the maximum number of sectors
12 allowed: twelve. These twelve land-use sectors are used to identify the Bowen ratio and surface
13 albedo, which are assumed to represent an area around the station of radius 10 km, and to
14 calculate surface roughness by wind direction.
15 A monthly temporal resolution was used for the Bowen ratio, albedo, and surface
16 roughness for all fourteen meteorological sites defining the modeling domains. Because the
17 fourteen sites were located at airports, a lower surface roughness was calculated for the
18 'Commercial/Industrial/Transportation' land-use type to reflect the dominance of transportation
19 land cover rather than commercial buildings. None of the fourteen regions are arid regions, but
20 the Colombia, Kansas City, Kirksville, and Quincy, IL, stations are each considered to have at
21 least one winter month of continuous snow cover, as they fall within the CLIMAPS19 contours of
22 stations experiencing at least 28.5 days of at least 1 inch (25.4 mm) of ground snow depth. This
23 time period of snow cover was the closest contour interval to 1 month for which data is
15 For any moist surface, the Bowen Ratio is the ratio of heat energy used for sensible heating (conduction and
convection) to the heat energy used for latent heating (evaporation of water or sublimation of snow). The Bowen
ratio ranges from about 0.1 for the ocean surface to more than 2.0 for deserts. Bowen ratio values tend to decrease
with increasing surface moisture for most land-use types.
16 Surface albedo is the ratio of the amount of electromagnetic radiation reflected by the earth's surface to the
amount incident upon it. Values vary with surface composition. For example, snow and ice ranger from 80% to
85% and bare ground from 10% to 20%.
17 Surface roughness refers to the presence of buildings, trees, and other irregular land topography that is associated
with its efficiency as a momentum sink for turbulent air flow, due to the generation of drag forces and increased
vertical wind shear.
18 hltp://scarnlcss.nsgs.gov/
19 NCDC Climate Maps of the United States database (CLIMAPS). See http://cdo.ncdc.noaa.gov/cgi-
bin/climaps/climaps.pl.
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1 available; here we assume these to be contiguous days. This designation increases wintertime
2 albedo and decreases wintertime Bowen ratio and surface roughness for most land-use types
3 compared to snow-free areas.
4 Seasons were assigned for each site on a monthly basis, determined by standard seasonal
5 definitions and modified to local regions based on CLIMAPS data for median date of first freeze,
6 average daily maximum temperature, and median last freeze date. Table 7-4 provides the
7 seasonal and snow cover definitions for each domain.
Table 7-4. Seasonal and snow cover specifications by meteorological domain.
Model
Domain
03935
03945
03947
03960
03966
03975
03994
13987
13994
13995
13997
14938
53869
93989
Snowy
Region
Yes
Yes
Yes
Yes
Winter Months
Dec.,Jan.,Feb.
Dec.,Jan.,Feb.
Dec.,Jan.,Feb.
Dec.,Jan.,Feb.
Dec.,Jan.,Feb.,Mar.
Dec.,Jan.,Feb.,Mar.
Dec.,Jan.,Feb.
Dec.,Jan.,Feb.
Dec.,Jan.,Feb.
Dec.,Jan.,Feb.,Mar.
Dec.,Jan.,Feb.
Dec.,Jan.,Feb.,Mar.
Dec.,Jan.,Feb.,Mar.
Dec.,Jan.,Feb.
Spring Months
Mar., Apr., May
Mar., Apr., May
Mar., Apr., May
Mar., Apr., May
Apr., May
Apr., May
Mar., Apr., May
Mar., Apr., May
Mar., Apr., May
Apr., May
Mar., Apr., May
Apr., May
Apr., May
Mar., Apr., May
Summer Months
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Jun.,Jul.,Aug.
Fall Months
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Sep., Oct., Nov.
Season definitions provided by the AERSURFACE manual:
Winter (continuous snow):
Winter (no snow):
Spring:
Summer:
Fall:
Winter with continuous snow on ground
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
10 7.4.5 Meteorological Analysis
11 The AERMET (version 06341) meteorological preprocessor was run with the surface
12 characteristics and meteorological data discussed above. The application location and elevation
13 were specified as the meteorological monitoring site, which serves as the anchor for each
14 modeling domain. Each site was processed for the 2002 year, creating fourteen complete surface
15 and upper air paired datasets, or one for each modeling domain.
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1 7.4.6 Stationary Sources Emissions Preparation
2 7.4.6.1 Emitting Sources and Locations
3 As discussed above, as a first approximation point sources at major facilities were
4 assumed to represent the 862 emissions throughout Missouri20, where major facilities were
5 defined as those with 862 emissions totals exceeding 1,000 tpy. Nationwide, there are 918 major
6 facilities and 10,651 associated stacks, according to the 2002 NEI. Within Missouri, 281 major
7 facility stacks were identified, but only 115 of these stacks have greater than or equal to 1.0 tpy
8 SC>2 emissions in the 2002 NEI. Each of these stacks was paired to a surface meteorological
9 station, defining its modeling domain. These are the final list of stacks identified in Table 7-1,
10 above.
11 Additionally, the locations of the stacks were corrected based on GIS analysis. This was
12 necessary because many stacks in the NEI are assigned the same location, which often
13 corresponds to a location in the facility - such as the front office - rather than the actual stack
14 locations. To correct for this, stack locations were reassigned manually with the Microsoft®
15 Live Maps® Virtual Earth® tool to visually match stacks from the NEI database to their
16 locations within the facilities using stack heights as a guide to stack identification.
17 7.4.6.2Source Terrain Characterization
18 All corrected locations for the final list of major facility stacks in Missouri were
19 processed with the AERMAP terrain preprocessing tool. Terrain height information was taken
20 from the series of 36 USGS 1 x 1 degree GeoData Digital Elevation Model (DEM)21 data files
21 covering the entire state.
22 7.4.6.3 Emissions Data Sources
23 Data for the parameterization of major facility point sources in Missouri comes primarily
24 from three sources: the 2002 NEI (EPA, 2007f), Clean Air Markets Division (CAMD) Unit
25 Level Emissions Database (EPA, 2007g), and temporal emission profile information contained in
26 the EMS-HAP (version 3.0) emissions model22. The NEI database contains stack locations,
27 emissions release parameters (i.e., height, diameter, exit temperature, exit velocity), and annual
20 After a first round of air dispersion modeling, model-to-monitor comparisons suggested that area sources of SO2
and/or cross-border point sources should be added to some of the modeling domains. The modeling for those
domains was not completed as of the date of this report.
21 http://erg.usgs.gov/isb/pubs/factsheets/fs04000.html
22 http ://www. epa. gov/ttn/chief/emch/proj ection/emshapS 0 .html
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1 SC>2 emissions. The CAMD database has information on hourly SC>2 emission rates for all the
2 electric generating units in the US, where the units are the boilers or equivalent, each of which
3 can have multiple stacks23. These two databases generally contain complimentary information,
4 and were first evaluated for matching facility data. However, CAMD lacks SO2 emissions data
5 for facilities other than electric-generating units. To convert annual total emissions data from the
6 NEI into hourly temporal profiles required for AERMOD, a three tiered approach was used, as
7 follows.
8 1. CAMD hourly concentrations to create relative temporal profiles.
9 2. EMS-HAP seasonal and diurnal temporal profiles for source categorization codes
10 (SCCs).
11 3. Flat profiles.
12 Details of these processes are as follows.
13
14 Tier 1: CAMD to NEI Emissions A lignment and Scaling
15 Of the 115 major facility stacks within MO identified above, 50 were able to be matched
16 directly to sources within the CAMD database. Stack matching was based on the facility name,
17 Office of Regulatory Information Systems (ORIS) identification code (when provided) and
18 facility total SO2 emissions. For these stacks the relative hourly profiles were derived from the
19 hourly values in the CAMD database, and the annual emissions totals were taken from the NEI.
20 That is, hourly emissions in the CAMD database were scaled to match the NEI annual total
21 emissions.
22
23 Tier 2: EMS-HAP to NEI Emissions Profiling
24 Of the 115 major facility stacks within MO, 46 stacks could not be matched to a stack in
25 the in the CAMD database, but had SCC values that corresponded to SCCs that have temporal
26 profiles included in the EMS-HAP emissions model.
27 In these cases, the SCC-specific seasonal and hourly variation (SEASHR) values from
28 the EMS-HAP model were used to characterize the temporal profiles of emissions for each hour
23 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.
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1 of a typical day by season and day type. However, to maintain consistency with the other stacks,
2 these profiles were expanded into a full series of values for each stack for each hour of the year,
3 with each value scaled so that the annual total matched the NEI value.
4
5 Tier 3: Other Emissions Profiling
6 Of the 115 major facility stacks within Misosuri, 18 could not be matched to a stack in
7 CAMD database, or to profiles in the EMS-HAP model by SCC code. In these cases, a flat
8 profile of emissions was assumed. That is, emissions were assumed to be constant for all hours
9 of every day, but with an annual total that equals the values from the NEI.
10
11 A summary of the point source emissions used for modeling domains analyzed in the
12 draft of the assessment is given in Table 7-5. Appendix D, Table D-l contains all 115 stacks in
13 Missouri and the data source used to determine their emissions profiles. As far as the point
14 source emissions that were modeled, most counties were at or near 100%, that is, nearly all of the
15 point sources were accounted for by the dispersion modeling. When considering the total county
16 emissions, several of the locations were also near 100%, with a few containing accounted
17 emissions at around 80%, and one at about 50% of total emissions. The total emissions
18 accounted for most of the modeling domains was at about 80% or greater, indicating reasonable
19 coverage by the approach used here. In counties where a lowered percent of total emissions are
20 accounted for, additional area source modeling may be required. However, in a county such as
21 Cape Girardeau where only 49% total emissions were accounted for, the result of additional area
22 source modeling is likely to be inconsequential due to the overall low total emissions in the
23 county.
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Table 7-5. Summary of NEI emission estimates and total emissions used for
dispersion modeling in Missouri.
Modeling
Domain
3935
3945
3994
53869
13987
13995
14938
93989
County
Cape
Girardeau
Scott
Boone
Osage
Henry
Saline
New Madrid
Jasper
Greene
Randolph
Marion
Pike
NEI Emissions
Point
Source
(tpy)
1,680
6,237
10,621
4,142
15,826
1,450
19,889
4,463
9,218
15,231
1,834
13,496
Total
(tpy)
2,809
6,870
11,795
4,355
16,092
1,830
19,891
5,914
11,819
15,497
2,270
13,799
Emissions Used for Dispersion Modeling
Stacks
(n)
1
1
7
2
6
2
11
1
11
3
4
4
Point
Source
(tpy)
1,362
6,236
9,729
4,142
15,826
1,449
20,570
4,349
9,047
15,221
1,834
13,494
Point
Source
(%)
81%
100%
92%
100%
100%
100%
100%
97%
98%
100%
100%
100%
Total
Emissions
(%)
49%
91%
82%
95%
98%
79%
97%
74%
77%
98%
81%
98%
Total
Domain
Emissions
79%
86%
96%
97%
74%
77%
98%
95%
4 7.4.7 Urban and Rural Source Characterization
5 Additional analysis was made to determine whether the stacks in each domain should be
6 modeled with urban or rural dispersion characteristics. The AERMOD dispersion model defaults
7 to rural dispersion characteristics for all sources unless both the modeling scenario and each
8 individual source is declared urban, in which case additional dispersion effects from increased
9 surface heating within an urban area under stable atmospheric conditions are included. The
10 magnitude of this effect is weakly proportional to the urban area population.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
According to the Environmental Protection Agency (40 CFR Part 51, Appendix W24), the
land use classification procedure to determine appropriate dispersion coefficients involves the
following:
(1) Classify the land use within the total area, A0, circumscribed by a 3 km radius circle
about the source using the meteorological land use typing scheme proposed by Auer;
(2) If land use types II, 12, Cl, R2, and R3 account for 50 percent or more of A0, use
urban dispersion coefficients; otherwise, use appropriate rural dispersion coefficients.
where II, 12, Cl, R2, and R3 are heavy industrial, light/moderate industrial, commercial, and
compact residential (single- and multi-family). Classification of land use in this schema were
not readily available, but land use designation from the NLCD92 database are, from the
AERSURFACE processing for meteorological analysis. Table 7-6 lists these categories.
Table 7-6. NLCD92 land use characterization.
Category
0
11
12
21
22
23
31
32
33
41
42
43
51
61
71
81
82
83
84
85
91
92
99
Land Use Type
Outside Boundary
Open Water
Perennial Ice/Snow
Low Intensity Residential
High Intensity Residential
Commercial/lndustrial/Transp
Bare Rock/Sand/Clay
Quarries/Strip Mines/Gravel
Transitional
Deciduous Forest
Evergreen Forest
Mixed Forest
Shrubland
Orchards/Vineyard/Other
Grasslands/Herbaceous
Pasture/Hay
Row Crops
Small Grains
Fallow
Urban/Recreational Grasses
Woody Wetlands
Emergent Herbaceous Wetlands
Missing Data
24 Part III, Environmental Protection Agency, 40 CFR Part 51, Revision to the Guideline on Air Quality Models:
Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final
Rule, 68218 Federal Register, Vol. 70, No. 216, Wednesday, November 9, 2005, Rules and Regulations.
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1
2 To resolve each scenario as urban or rural, we applied the same 50% threshold criteria
3 within 40 CFR Part 51, Appendix W, but determined the spatial coverage as the sum of the
4 coverage of land use categories 21-23 from the NLCD92. These are the categories considered
5 developed by AERSURFACE.25 However, there was no simple, consistent way to determine the
6 coverage of these land-use types over each of the modeling domains. Thus, the urban or rural
7 designation was made as follows. Within each modeling domain stacks within 10 km of each
8 other were grouped together, resulting in groups of one to thirteen stacks. The AERSURFACE
9 model was then applied to each group to extract the land use within 10 km of any stack. The
10 urban fraction was estimated over the entire modeling domain by averaging the urban fractions
11 around each component stack group. This method is similar to analyzing the land use around
12 each stack in the modeling domain and averaging, but it avoids double counting of the land
13 around multiple stacks in close proximity. It also foregoes a 3 km radius of definition around
14 each stack for a consideration of "whole urban [or rural] complexes", as identified in the
15 modeling guidance.26 Ultimately, no modeling domain in the state was considered urban. Table
16 7-7 shows the overall urban fraction of each modeling domain thus determined, and its resulting
17 urban/rural designation.
Table 7-7. Urban/Rural characterization of each modeling domain
Modeling
Domain
03994
03975
03945
03947
03935
03966
13995
13997
93989
13994
13987
03960
53869
14938
Average
Urban
Fraction
4%
0%
5%
19%
3%
1%
6%
0%
2%
17%
0%
1%
4%
2%
Scenario
Designation
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
25 AERSURFACE User's Guide, U.S. EPA, OAQPS, Research Triangle Park, NC, EPA-454/B-08-001, January
2008.
26 AERMOD Implementation Guide, U.S. EPA, OAQPS, Research Triangle Park, NC, Revised: January 9, 2008.
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1
2 7.4.8 Receptor Locations
3 Receptor locations were selected to represent the locations of census block centroids near
4 major SO2 sources. GIS analysis was used to determine all block centroids in Missouri that lie
5 within 20 km (12 miles) of any of the 115 major facility stacks. Note that although all sources
6 modeled lie within the State of MO, not all receptors do. In total, 107,171 block centroids were
7 selected across all modeling domains, as given by Table 7-1, with some duplication of receptors
8 between domains.27 All receptors were modeled at a breathing height of 5.9 feet (1.8 m).
9 7.4.8.1 Receptor Terrain Characterization
10 All locations for the final list of major facility stacks in Missouri were processed with the
11 AERMAP terrain preprocessing tool. All terrain height information was taken from the series of
12 36 USGS 1 x 1 degree GeoData Digital Elevation Model (DEM)28 data files covering all
13 modeling domains (and extending beyond the state boundaries).
14 7.4.9 Other Modeling Specifications
15 AERMOD was applied to the each of the fourteen modeling domains in Missouri with
16 the emissions and meteorological data and dispersion parameterizations as described above. The
17 AERMOD regulatory default settings were employed in all cases. Because all sources in
18 Missouri are considered rural, SO2 chemistry was not applied by the model.
19 7.4.10 Estimate Air Quality Concentrations
20 The hourly SO2 concentrations estimated from each of the sources within a modeling
21 domain were combined at each receptor. Dispersion modeling runs were completed for several
22 of the modeling domains where there were no ambient monitors available for comparison,
23 therefore based on the total emissions accounted for (Table 7-5) there were no adjustments for
24 sources that may have not been modeled or accounted for. For Greene County, there were five
25 monitors used for comparison with the AERMOD concentration estimates. Rather than compare
26 concentrations estimated at a single modeled receptor point to the ambient monitor
27 concentrations, a distribution of concentrations was developed for the predicted concentrations
28 for all receptors within a 4 km distance of the monitors. Further, instead of a comparison of
27 For receptors located in multiple modeling domains, the concentration contributions from source in each domain
were summed in post-processing and the receptor randomly assigned to one of the domains for input to APEX.
28 http://erg.usgs.gov/isb/pubs/factsheets/fs04000.html
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1 central tendency values (mean or median), the modeled and measurement concentration
2 distributions were used for comparison. At each AERMOD receptor point within 4 km of the
3 monitors, the minimum and maximum modeled concentrations were used to generate two
4 separate concentration distributions (i.e., one distribution for all of the modeled maximum
5 concentrations, and one for the minimum concentrations). Four of the monitors overlapped with
6 the same 4 km AERMOD distributions. Each of the AERMOD concentration distributions are
7 illustrated in Figure 7-3, along with the measured concentration distributions in Greene County,
8 Mo. All of the monitor concentration distributions are completely bounded by the modeled
9 distributions, except for part of one monitor (ID 290770026) exhibiting slightly higher
10 concentrations at the lower percentiles of the distribution. The upper percentiles of the
11 distribution are well represented by the AERMOD predicted concentrations, an important result
12 given that the 1-hour concentrations of most interest here are at or above 33.3 ppb. The
13 concentration distribution from the final monitor in Greene County was also compared with the
14 concentration distribution bounds estimated from AERMOD (Figure 7-4). Over 90% of the
15 measured concentrations are less than 5 ppb, although each is above the upper bound predicted
16 by AERMOD. This indicates that AERMOD is possibly under-predicting at very low
17 concentrations at this location. However, measured concentrations at the upper percentiles of the
18 distribution (i.e, above the 95th %ile ranging from about 6-30 ppb) are completely bounded by
19 the AERMOD distributions, suggesting the modeled are representing these concentration levels
20 well. Based on these comparisons and the high percentage of point source and total emissions
21 modeled in Greene County Table 7-5), none of the AERMOD concentrations were adjusted to
22 any particular monitor concentration.
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100
50
75 100 125 150 175
Hourly Concentration (ppb)
200
225
250
275
-AERMOD Max w/in 4 km •
-Monitor 290770026
-AERMOD Min w/in 4 km -*-Monitor 290770037
- Monitor 290770040 -*- Monitor 290770041
4
5
6
Figure 7-3. Distributions of 1-hour SO2 concentrations in Greene County, Mo.,
estimated by AERMOD and measured at four ambient monitors.
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100
8 85
-------
1 percent data, which is the information compiled from the questions asked of all people and about
2 every housing unit.
3
4 Asthma Prevalence Rates
5 One of the important population subgroups for the exposure assessment is asthmatic
6 children. Evaluation of the exposure of this group with APEX requires the estimation of
7 children's asthma prevalence rates. The proportion of the population of children characterized as
8 being asthmatic was estimated by statistics on asthma prevalence rates recently used in the
9 NAAQS review for 63 (US EPA, 2007g). Specifically, the analysis generated age and gender
10 specific asthma prevalence rates for children ages 0-17 using data provided in the National
11 Health Interview Survey (NHIS) for 2003 (CDC, 2007). These asthma rates were characterized
12 by geographic regions, namely Midwest, Northeast, South, and West. The rates characterized for
13 Midwest children were used for all Missouri modeling domains Table 7-7. Adult asthma
14 prevalence rates were estimated by gender and for each particular modeling domain based on
15 Missouri regional data (Table 7-8, from MO Department of Health, 2002).
Table 7-7. Asthma prevalence rates by age for children the Midwestern U.S.
Region
(Study Area)
Midwest
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Females
Prevalence se L95 U95
0.070
0.071
0.073
0.075
0.081
0.095
0.092
0.090
0.086
0.110
0.162
0.196
0.212
0.170
0.140
0.133
0.140
0.165
0.036
0.020
0.018
0.019
0.022
0.026
0.029
0.026
0.022
0.027
0.035
0.039
0.040
0.034
0.026
0.023
0.022
0.040
0.021
0.037
0.042
0.042
0.044
0.051
0.045
0.047
0.048
0.063
0.098
0.123
0.137
0.107
0.092
0.091
0.098
0.093
0.203
0.130
0.124
0.132
0.144
0.171
0.178
0.166
0.149
0.186
0.255
0.298
0.313
0.258
0.209
0.192
0.198
0.275
Males
Prevalence se L95 U95
0.031
0.063
0.108
0.158
0.216
0.178
0.128
0.121
0.128
0.147
0.177
0.190
0.195
0.169
0.168
0.180
0.201
0.237
0.015
0.018
0.021
0.027
0.037
0.035
0.028
0.026
0.027
0.030
0.030
0.030
0.031
0.028
0.026
0.026
0.030
0.058
0.010
0.033
0.070
0.107
0.145
0.113
0.078
0.074
0.079
0.093
0.120
0.131
0.135
0.115
0.117
0.130
0.142
0.132
0.090
0.115
0.163
0.228
0.308
0.270
0.204
0.193
0.200
0.226
0.254
0.266
0.272
0.242
0.235
0.243
0.277
0.388
se - Standard error of the mean
L95 - Lower 95% interval
U95 - Upper 95% interval
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Table 7-8. Asthma prevalence rates by gender for adults the Missouri.
2
3
4
5
6
MET Station
3935
3945
3947
3960
3966
3975
3994
13987
13994
13995
13997
14938
53869
93989
Region Encompassed
SE
Central
Kansas City/NW
SE/Central/St. Louis
St. Louis
SE/Central
SW/Kansas City /NW/NE
SW
St. Louis
SW
SE/Central
NE
SE
NE/St. Louis
Adult
Females
0.130
0.098
0.149
0.093
0.093
0.130
0.110
0.107
0.093
0.107
0.098
0.108
0.130
0.108
Adult
Males
0.074
0.056
0.085
0.053
0.053
0.074
0.063
0.061
0.053
0.061
0.056
0.061
0.074
0.061
Data Used
SE
Central
Kansas City
St. Louis
SE
State mean
Central
NE
The total population considered in the analysis completed in the draft of the assessment
was approximately % million persons, of which approximately 10% were asthmatics. The model
simulated approximately nearly 200,000 children, of which there were nearly 25,000 asthmatics.
Individual domain populations are provided in Table 7-9.
Table 7-9. Population modeled in Missouri modeling domains.
Modeling
Domain
3935
3945
3994
13987
13995
14938
53869
93989
Total
Population
All Ages
105372
135710
36044
56490
275825
9108
17085
100889
736523
Children (0-1 8)
27504
33393
9177
15775
68675
2538
4339
26046
187447
Asthmatic Population
All Ages
11867
12279
3568
5609
26712
910
1869
9944
72758
Children (0-1 8)
3673
4400
1215
2155
9005
350
595
3594
24987
8 7.5.2 Employment Probabilities
9 Employment data from the 2000 Census provide employment probabilities for each
10 gender and specific age groups for every Census tract. The employment age groupings were: 16-
11 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 years
12 of age. Children under the age of 16 are assigned employment probabilities of zero.
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1 7.5.3 Commuting Patterns
2 To ensure that individuals' daily activities are accurately represented within APEX, it is
3 important to integrate working patterns into the assessment. Commuting data were originally
4 derived from the 2000 Census and were collected as part of the Census Transportation Planning
5 Package (CTPP) (US DOT, 2007). CTPP contains tabulations by place of residence, place of
6 work, and the flows between the residence and work.
7 It is assumed that all persons with home-to-work distances up to 120 km are daily
8 commuters, and that persons who travel further than 120 km do not commute daily. Therefore
9 the list of commuting destinations for each home tract is restricted to only those work tracts that
10 are within 120 km of the home tract.
11 APEX allows the user to specify how to handle individuals who commute to destinations
12 outside the study area. One option is to drop them from the simulation. If they are included, the
13 user specifies values for two additional parameters, called LM and LA (Multiplicative and
14 Additive factors for commuters who Leave the area). While a commuter is at work, if the
15 workplace is outside the study area, then the ambient concentration cannot be determined from
16 any air district (since districts are inside the study area). Instead, it is assumed to be related to
17 the average concentration CAVE ft) over all air districts at the time in question. The ambient
18 concentration outside the study area at time t, Cowft), is estimated as:
19
20 COUT ft) = LM * CAVE ft) + LA
21
22 The microenvironmental concentration (for example, in an office outside the study area)
23 is determined from this ambient concentration by the same model (mass balance or factor) as
24 applied inside the study area. The parameters LM and LA were both set to zero for this modeling
25 analysis; thus, exposures to individuals are set to zero when they are outside of the study area.
26 Although this tends to underestimate exposures, it is a small effect and this was done since we
27 have not estimated ambient concentrations of SO2 in counties outside of the modeled areas.
28 While school age children were simulated as commuting to and from school, they did so
29 to-and-from their home tract. This results in the implicit assumption that children attend a school
30 with ambient SO2 concentrations similar to concentrations near their residence.
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1 7.5.4 Characterizing Ventilation Rates
2 Human activities are variable over time, a wide range of activities are possible even
3 within a single hour of the day. The type of activity an individual performs, such as sleeping or
4 jogging, will influence their breathing rate. The ISA indicates that adverse health effects
5 associated with short-term peak exposures occurs with moderate to heavy exertion levels.
6 Therefore, ventilation rates needed to be defined to further characterize exposures of interest.
7 The target ventilation for adults (both a mix of males and females) experiencing effects from 5-
8 10 minute SC>2 exposures from most of the clinical trials was between 40-50 L/min. Since there
9 were limited clinical data available for asthmatic children, the ventilation targets needed to be
10 adjusted. As done in the 63 NAAQS review (EPA, 2007g), target ventilation rates were
11 normalized to body surface area (BSA) to allow for such an extrapolation from adults to
12 children. The resulting normalization yields an equivalent ventilation rate or EVR. Since BSA
13 was not measured in the clinical trials and the data were reported as grouped, median estimates
14 for males (1.94 m2) and females (1.69 m2) were obtained from EPA (1997) and averaged to
15 normalize the target ventilation rates. Therefore, an EVR = 40/1.81 =22 L/min-m2 was used to
16 characterize the minimum target ventilation rate of interest. Individuals at or above an EVR of
17 22 L/min-m2 (children or adult) would be characterized as performing activities at a moderate
18 ventilation rate.
19 7.6 CONSTRUCTION OF LONGITUDINAL ACTIVITY SEQUENCES
20 Exposure models use human activity pattern data to predict and estimate exposure to
21 pollutants. Different human activities, such as spending time outdoors, indoors, or driving, will
22 result in varying pollutant exposure concentrations. To accurately model individuals and their
23 exposure to pollutants, it is critical to understand their daily activities.
24 The Consolidated Human Activity Database (CHAD) provides data for where people
25 spend time and the activities performed. CHAD was designed to provide a basis for conducting
26 multi-route, multi-media exposure assessments (McCurdy et al., 2000; EPA, 2002). Table 7-10
27 summarizes the studies in CHAD used in this modeling analysis, providing nearly 16,000 diary -
28 days of activity data (3,075 diary-days for ages 5-18) collected between 1982 and 1998.
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Table 7-10. Studies in CHAD used for the exposure analysis.
Study name
Baltimore
California
Adolescents
(GARB)
California Adults
(GARB)
California Children
(GARB)
Cincinnati (EPRI)
Denver (EPA)
Los Angeles:
Elementary School
Los Angeles: High
School
National: NHAPS-
Air
National: NHAPS-
Water
Washington, D.C.
(EPA)
Total diary days
Geographic
coverage
One building in
Baltimore
California
California
California
Cincinnati
metro, area
Denver metro.
area
Los Angeles
Los Angeles
National
National
Wash., D.C.
metro, area
Study time
period
01/1997-02/1997,
07/1998-08/1998
10/1987-09/1988
10/1987-09/1988
04/1989-02/1990
03/1985-04/1985,
08/1 985
11/1982-02/1983
10/1989
09/1990-10/1990
09/1992-10/1994
09/1992-10/1994
11/1982-02/1983
Subject
ages
72-93
12- 17
18-94
<1 -11
<1 -86
18-70
10-12
13-17
<1 -93
<1 -93
18-98
Diary-
days
292
181
1,552
1,200
2,587
791
51
42
4,326
4,332
639
15,993
Diary-days
(ages 5-18)
0
181
36
683
740
7
51
42
634
691
10
3,075
Diary type and
study design
Diary
Recall; Random
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)
Robinson etal. (1989),
Wiley etal. (1991 a)
Wiley etal. (1991b)
Johnson (1989)
Johnson (1984)
Akland etal. (1985)
Spier etal. (1992)
Spier etal. (1992)
Klepeis et al. (1996), Tsang
and Klepeis (1996)
Klepeis et al. (1996), Tsang
and Klepeis (1996)
Hartwell etal. (1984),
Akland etal. (1985)
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1 Typical time-activity pattern data available for inhalation exposure modeling consist of a
2 sequence of location/activity combinations spanning 24-hours, with 1 to 3 diary-days for any
3 single individual. Exposure modeling typically requires information on activity patterns over
4 longer periods of time, e.g., a full year. For example, even for pollutant health effects with short
5 averaging times (e.g., SC>2 5-minute maximum concentration) it may be desirable to know the
6 frequency of exceedances of a concentration over a long period of time (e.g., the annual number
7 of exceedances of a 5-minute SC>2 concentration of 500 ppb for each simulated individual).
8 Long-term multi-day activity patterns can be estimated from single days by combining
9 the daily records in various ways, and the method used for combining them will influence the
10 variability of the long-term activity patterns across the simulated population. This in turn will
11 influence the ability of the model to accurately represent either long-term average high-end
12 exposures, or the number of individuals exposed multiple times to short-term high-end
13 concentrations.
14 An algorithm has been developed and incorporated into APEX to represent the day-to-
15 day correlation of activities for individuals, used most recently in the NC>2 NAAQS Review
16 (EPA, 2008). The algorithms first use cluster analysis to divide the daily activity pattern records
17 into groups that are similar, and then select a single daily record from each group. This limited
18 number of daily patterns is then used to construct a long-term sequence for a simulated
19 individual, based on empirically-derived transition probabilities. This approach is intermediate
20 between the assumption of no day-to-day correlation (i.e., re-selection of diaries for each time
21 period) and perfect correlation (i.e., selection of a single daily record to represent all days).
22 Further details regarding the Cluster-Markov algorithm and supporting evaluations are provided
23 in Appendix F of the draft NO2 TSD (EPA, 2008).
24 7.7 CALCULATING MICROENVIRONMENTAL CONCENTRATIONS
25 7.7.1 Overview
26 Probabilistic algorithms are used to estimate the pollutant concentration associated with
27 each exposure event. The estimated pollutant concentrations account for temporal and spatial
28 variability in ambient (outdoor) pollutant concentration and factors affecting indoor
29 microenvironment, such as a penetration, air exchange rate, and pollutant decay or deposition
30 rate. APEX calculates air concentrations in the various microenvironments visited by the
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1 simulated person by using the ambient air data estimated for the relevant blocks/receptors, the
2 user-specified algorithm, and input parameters specific to each microenvironment. The method
3 used by APEX to estimate the microenvironment depends on the microenvironment, the data
4 available for input to the algorithm, and the estimation method selected by the user. At this time,
5 APEX calculates hourly concentrations in all the microenvironments at each hour of the
6 simulation for each of the simulated individuals using one of two methods: by mass balance or a
7 transfer factors method.
8 The mass balance method simulates an enclosed microenvironment as a well-mixed
9 volume in which the air concentration is spatially uniform at any specific time. The
10 concentration of an air pollutant in such a microenvironment is estimated using the following
11 processes:
12 • Inflow of air into the microenvironment
13 • Outflow of air from the microenvironment
14 • Removal of a pollutant from the microenvironment due to deposition, filtration,
15 and chemical degradation
16 • Emissions from sources of a pollutant inside the microenvironment.
17
18 A transfer factors approach is simpler than the mass balance model, however, most
19 parameters are derived from distributions rather than single values to account for observed
20 variability. It does not calculate concentration in a microenvironment from the concentration in
21 the previous hour as is done by the mass balance method, and it has only two parameters. A
22 proximity factor is used to account for proximity of the microenvironment to sources or sinks of
23 pollution, or other systematic differences between concentrations just outside the
24 microenvironment and the ambient concentrations (at the measurements site or modeled
25 receptor). The second, a penetration factor, quantifies the amount of outdoor pollutant penetrates
26 into the microenvironment.
27 7.7.2 Approach for Estimating 5-Minute Peak Concentrations
28 The 5-minute peak concentrations were estimated probabilistically considering the
29 empirically-derived PMR CDFs developed from recent 5-minute ambient monitoring data
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1 (section 6.2). Thus for every 1-hr concentration estimated at each receptor, an associated 5-
2 minute peak 862 concentration was generated.
3 The approach is designed to generate the maximum 5-minute SO2 concentrations to use
4 in evaluating exceedances of the potential health effects benchmarks. In general, it is not an
5 objective to estimate each of the other eleven 5-minute concentrations within the hour with a
6 high degree of certainty. While the occurrence of multiple peak concentrations is possible
7 (section 6.5), the potential health effect benchmark levels are related to single peak exposures.
8 The APEX model originally used 1-hr ambient 862 concentrations as input prior to the
9 calculation of microenvironmental concentrations. The current APEX model now can use
10 ambient concentrations of most any time step, downward to 5-minutes. The file size was an
1 1 issue with this approach however, since each of the thousands of receptor files generated by
12 AERMOD would be increase by a factor of twelve, creating both disk space and processing
13 difficulties. An algorithm was incorporated into the flexible time-step APEX model to estimate
14 the 5-minute maximum SC>2 concentrations real-time using the 1-hour SO2 concentration, an
15 appropriate PMR (section 6.2), and equation 6-1. The additional eleven 5-minute concentrations
16 within an hour at each receptor approximated using the following:
17 X = - eq(7-l)
n-l
1 8 where,
19 X = 5-minute concentration in each of non-peak concentration periods in the
20 hour at a receptor (ppb)
21 C = 1-hr mean concentration estimated at a receptor (ppb)
22 P = estimated peak concentration at a receptor (ppb) estimated
23 probabilistically using equation 6-1.
24 n = number of time steps within the hour (12)
25
26 In addition to the level of the maximum concentration, the actual time of when the
27 contact occurs with a person is also of importance. There is no reason to expect a temporal
28 relationship of the peak concentrations within the hour, thus clock times for peak values were
29 estimated randomly (i.e., any one of the 12 possible time periods within the hour). The PMR
30 assignment also assumes a standard frequency during any hour of the day.
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1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
7.7.3 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 type of parameters used to calculate the
microenvironment concentrations can be found in Table 7-11.
Table 7-11. 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)
1 AER=air exchange rate, DE=decay-depos
PE=penetration factor
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
tion rate, PR=proximity factor,
7.7.4 Microenvironment Descriptions
7.7.4.1 Microenvironment 1: Indoor-Residence
The Indoor-Residence microenvironment uses several variables that affect NC>2 exposure:
whether or not air conditioning is present, the average outdoor temperature, the NC>2 removal
rate, and an indoor concentration source.
Air conditioning prevalence rates
Since the selection of an air exchange rate distribution is conditioned on the presence or
absence of an air-conditioner, for each modeled area the air conditioning status of the residential
microenvironments is simulated randomly using the probability that a residence has an air
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1 conditioner. A value of 95.5% was calculated to represent location-specific air conditioning
2 prevalence using the data and survey weights for St. Louis, Missouri obtained from the American
3 Housing Survey of 2003 (AHS, 2003a; 2003b).
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Air exchange rates
Air exchange rate data for the indoor residential microenvironment were obtained from
EPA (2007g). Briefly, data were reviewed, compiled and evaluated from the extant literature to
generate location-specific AER distributions categorized by influential factors, namely
temperature and presence of air conditioning. In general, lognormal distributions provided the
best fit, and are defined by a geometric mean (GM) and standard deviation (GSD). To avoid
unusually extreme simulated AER values, bounds of 0.1 and 10 were selected for minimum and
maximum AER, respectively.
There are no AER data available that are specific for Missouri, therefore a distribution
was selected from the study locations thought to have similar characteristics to the city to be
modeled, qualitatively considering 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 AER
distributions used for each of the modeling domains are provided in Table 7-12.
Table 7-12. Geometric means (GM) and standard deviations (GSD) for air
exchange rates by A/C type and temperature range.
Area
Modeled
Missouri (No
A/C)
Derived
Location
Areas
Outside
California
A/C Type
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
19
20
21
22
23
SO2 Removal Rate
According to (Grontoft and Raychaudhuri, 2004), the indoor decay rates depend on
surface materials and relative humidity. Due to differences in morning and afternoon relative
humidity in Missouri we stratified the distributions diurnally. For each time of day we estimated
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1 a lower and upper bound of a uniform distribution based on reasonable variations in the relative
2 composition of surface materials inside homes and offices (e.g., painted wall board, wall paper,
3 wool carpet, synthetic carpet, synthetic floor covering, cloth). Resulting estimates were as
4 follows; morning: 4.9 - 19.8 h"1 and afternoon: 3.4 - 9.8 h"1.
5 7.4.1.2 Microenvironments 2-7: AII Other Indoor Microenvironments
6 The remaining five indoor microenvironments, which represent Bars and Restaurants,
7 Schools, Day Care Centers, Office, Shopping, and Other environments, were all modeled using
8 the same data and functions. As with the Indoor-Residence microenvironment, these
9 microenvironments use both AER and removal rates to calculate exposures within the
10 microenvironment. The air exchange rate distribution (GM = 1.109, GSD = 3.015, Min = 0.07,
11 Max = 13.8) was developed based on an indoor air quality study (Persily et al, 2005; see EPA,
12 2007g for details in derivation). The decay rate is the same as used in the Indoor-Residence
13 microenvironment discussed previously.
14 7.4.1.3 Microenvironments 8-10: Outdoor Microenvironments
15 All outdoor microenvironmental concentrations are well represented by the modeled
16 concentrations. Therefore, both the penetration factor and proximity factor for this
17 microenvironment were set to 1.
18 7.4.1.4 Microenvironments 11 and 12: In Vehicle- Cars and Trucks, and Mass Transit
19 There were no available measurement data for SO2 penetration factors, therefore the
20 penetration factors used were developed from NO2 data provided in Chan and Chung (2003) and
21 used in the recent NOX NAAQS review (EPA, 2008). Inside-vehicle and outdoor NO2
22 concentrations were measured with for three ventilation conditions, air-recirculation, fresh air
23 intake, and with windows. Mean values range from about 0.6 to just over 1.0, with higher values
24 associated with increased ventilation (i.e., window open). A uniform distribution was selected
25 for the penetration factor for Inside-Cars/Trucks (ranging from 0.6 to 1.0) due to the limited data
26 available to describe a more formal distribution and the lack of data available to reasonably
27 assign potentially influential characteristics such as use of vehicle ventilation systems for each
28 location. Mass transit systems, due to the frequent opening and closing of doors, was assigned a
29 point estimate of 1.0 based on the reported mean values for open windows ranging from 0.96 and
30 1.0.
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1
2 7.8 Exposure and Health Risk Calculations
3 APEX calculates exposure as a time-series of exposure concentrations that a simulated
4 individual experiences during the simulation period. APEX calculates exposure by identifying
5 concentrations in the microenvironments visited by the person according to the composite diary.
6 In this manner, a time-series of event exposures are found. Then, the time-step exposure
7 concentration at any clock hour during the simulation period is calculated using the following
8 equation:
N
V c t
Z^, ^ time -step (j) (j)
r< J=l
9 (~-i= ~
T
10 where,
11 Ct = Time-step exposure concentration at clock hour/'of the simulation
12 period (ppm)
13 N = Number of events (i.e., microenvironments visited) in time-step /
14 of the simulation period.
15 Ctime_step(f) = Time-step concentration in microenvironmenty (ppm)
16 t(j) = Time spent in mi croenvironmenty (minutes)
17 T = Length of time-step (or 5 minutes in this analysis)
18
19 From the time-step exposures, APEX calculates time-series of 1-hour, 24-hour, and
20 annual average exposure concentrations that a simulated individual would experience during the
21 simulation period. APEX then statistically summarizes and tabulates the 5-minute time-step (or
22 daily, or annual average) exposures. From this, APEX can calculate two general types of
23 exposure estimates: counts of the estimated number of people exposed to a specified 862
24 concentration level and the number of times per year that they are so exposed; the latter metric is
25 in terms of person-occurrences or person-days. The former highlights the number of individuals
26 exposed at least one or more times per modeling period to the health effect benchmark level of
27 interest. APEX can also report counts of individuals with multiple exposures. This person-
28 occurrences measure estimates the number of times per season that individuals are exposed to the
29 exposure indicator of interest and then accumulates these estimates for the entire population
30 residing in an area.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
APEX tabulates and displays the two measures for exposures above levels ranging from 0
to 800 ppb by 50 ppb increments for all exposures. These results are tabulated for the population
and subpopulations of interest.
To simulate just meeting the current standard, dispersion modeled concentration were not
rolled-up as done in the air quality characterization. A proportional approach was used as
performed in the Air Quality Characterization, but to reduce processing time, the potential health
effect benchmark levels were proportionally reduced by the similar factors described for each
specific location and simulated year. Since it is a proportional adjustment, the end effect of
adjusting concentrations upwards versus adjusting benchmark levels downward within the model
is the same. The difference in the exposure and risk modeling was that the modeled air quality
concentrations were used to generate the adjustment factors. There was only one modeling
domain that contained an ambient monitor for model runs completed in this draft of the exposure
assessment, Greene County, Mo. (modeling domain ID 13995). Table 7-13 provides the
adjustment factors used and the adjusted potential health effect benchmark concentrations to
simulate just meeting the current daily standard (as derived from Table 6-4).
Table 7-13. Adjustment factors and potential health effect benchmark levels used
by APEX to simulate just meeting the current daily standard in Greene County,
Mo.
Simulated
Year (factor)
Greene
County, Mo.
2002
(3.47)
Potential Health Effect
Benchmark Level (ppb)
Actual
400
500
600
Adjusted
115
144
173
7.9 EXPOSURE MODELING AND HEALTH RISK CHARACTERIZATION
RESULTS
7.9.1 Introduction
Exposure results are presented for simulated asthmatic populations residing in several of
the modeling domains in Missouri. Five-minute maximum SC>2 exposures were estimated within
each hour of the day for year 2002. The short-term exposures evaluated for all asthmatics and
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1 asthmatic children corresponded with heightened activity levels. The number of daily maximum
2 5-minute exposures that were at or above any level from 0 through 800 ppb in 50 ppb increments
3 was counted. Therefore, depending on the concentration level, an individual would have at most
4 one exceedance of a particular level per day, or 365 per year, provided that the person was at a
5 moderate (or higher) exertion level.
6 The number of exposures at or above a particular concentration level is presented in a
7 series tables below.
8 7.9.2 Number of Exceedances Considering As Is Air Quality
9 Exposure results are presented for the as is air quality scenario using the modeled
10 concentrations in several modeling domains in Missouri. The number of each of the
11 concentration levels varies as expected, with decreasing numbers of persons estimated to have
12 exposures with increasing concentration level and summarized for all modeling domains
13 completed in this draft (Table 7-14). Considering the % million persons simulated,
14 approximately 10% of which were asthmatic, two were estimated to contain at least one
15 exposure above the lowest potential health effect benchmark concentration of 400 ppb while at a
16 moderate or greater exertion level, while none were estimated to be exposed above 500 ppb.
17 Experiencing more than one 5-minute exposure per year was much less frequent. At most, only
18 3 persons contained at least two exposures above 200 ppb in a year. In general, the exposure
19 results for asthmatic children were similar on a relative scale for each of the concentration levels,
20 with only two persons experiencing exposures above 400 ppb in a year and no others with
21 estimated exposures above 450 ppb (Table 7-15).
22
23
24
25
26
27
28
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Table 7-14. Number of all asthmatics at moderate or greater exertion with 5-minute
maximum exposures above selected exposure concentrations, all Missouri modeled
domains combined, as is air quality.
Exposure
Level
(ppb)
0
50
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
Number of persons with indicated number of exposures
above selected level
1
70579
2311
839
278
87
32
15
2
2
0
0
0
0
0
0
0
0
2
69972
613
145
15
3
0
0
0
0
0
0
0
0
0
0
0
0
3
69479
269
61
5
0
0
0
0
0
0
0
0
0
0
0
0
0
4
68958
155
19
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
68526
111
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
68153
74
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Table 7-15. Number of asthmatic children at moderate or greater exertion with 5-minute
maximum exposures above selected exposure concentrations, all Missouri modeled
domains combined, as is air quality.
Exposure
Level
(ppb)
0
50
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
Number of persons with indicated number of exposures
above selected level
1
24984
1627
585
209
66
25
13
2
2
0
0
0
0
0
0
0
0
2
24984
468
112
15
3
0
0
0
0
0
0
0
0
0
0
0
0
3
24982
218
51
5
0
0
0
0
0
0
0
0
0
0
0
0
0
4
24979
127
17
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
24977
99
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
24974
70
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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1
2
3
4
5
6
7
8
9
10
11
12
7.9.3 Number of Exceedances Considering Air Quality Adjusted to Just Meeting the
Current Standard
Greene County, Missouri was selected for evaluating exposures associated with air
quality the just meets the current daily standard. The number of estimated exceedances of each
of the potential health effect benchmark levels was greater when compared with the as is air
quality. Considering the total asthmatic population (adults and children), nearly 120 were
estimated to contain exposures above the lowest potential health effect benchmark concentration
of 400 ppb while at a moderate or greater exertion level (Table 7-16). This amounts to just under
0.5% of all asthmatics modeled, or about 43 per 100,000 of the total simulated population. In
general, the exposure results for asthmatic children (Table 7-17) were slightly higher on a
relative basis, with 75 individuals experiencing a single 5-minute exposure above 400 ppb in a
year (approximately 0.8%).
Table 7-16. Number of all asthmatics at moderate or greater exertion with 5-
minute maximum exposures above selected exposure concentrations, Greene
County, Mo., air quality adjusted to just meeting the current daily standard.
Exposure
Level
(ppb)
50
100
150
200
250
300
350
400
450
500
550
600
650
700
800
Number of persons with indicated number of exposures
above selected level
1
3683
1274
664
458
306
209
157
119
77
49
36
22
17
11
5
2
1294
268
124
72
52
30
21
11
8
8
5
3
3
3
0
3
635
135
63
36
22
13
8
3
0
0
0
0
0
0
0
4
358
81
38
19
16
11
5
0
0
0
0
0
0
0
0
5
225
69
19
11
11
5
3
0
0
0
0
0
0
0
0
6
159
49
16
11
8
3
0
0
0
0
0
0
0
0
0
13
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Draft Do Not Quote or Cite
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Table 7-17. Number of asthmatic children at moderate or greater exertion with 5-
minute maximum exposures above selected exposure concentrations, Greene
County, Mo., air quality adjusted to just meeting the current daily standard.
Exposure
Level
(ppb)
50
100
150
200
250
300
350
400
450
500
550
600
650
700
800
Number of persons with indicated number of exposures
above selected level
1
2437
880
453
320
209
144
100
75
47
31
19
11
11
8
3
2
956
201
88
50
38
24
21
11
8
8
5
3
3
3
0
3
510
107
55
30
19
13
8
3
0
0
0
0
0
0
0
4
288
66
36
16
13
11
5
0
0
0
0
0
0
0
0
5
190
60
16
11
11
5
3
0
0
0
0
0
0
0
0
6
132
41
16
11
8
3
0
0
0
0
0
0
0
0
0
4
5
6
7
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l 7.10 UNCERTAINTY ANALYSIS
2 7.10.1 Introduction
3 The methods and the model used in this assessment conform to the most contemporary
4 modeling methodologies available. APEX is a powerful and flexible model that allows for the
5 realistic estimation of air pollutant exposure to individuals. Since it is based on human activity
6 diaries and accounts for the most important variables known to affect exposure, it has the ability
7 to effectively approximate actual conditions. In addition, the input data selected were the best
8 available data to generate the exposure results. However, there are constraints and uncertainties
9 with the modeling approach and the input data that limit the realism and accuracy of the model
10 results.
11 All models have limitations that require the use of assumptions. Limitations of APEX lie
12 primarily in the uncertainties associated with data distributions input to the model. Broad
13 uncertainties and assumptions associated with these model inputs, utilization, and application
14 include the following, with more detailed analysis summarized below and presented previously
15 (see EPA, 2007g; Langstaff, 2007). General uncertainties include:
16
17 • The CHAD activity data used in APEX are compiled from a number of studies in
18 different areas, and for different seasons and years. Therefore, the combined data
19 set may not constitute a representative sample for a particular study scenario.
20 • Commuting pattern data were derived from the 2000 U.S. Census. The
21 commuting data address only home-to-work travel. The population not employed
22 outside the home is assumed to always remain in the residential census tract.
23 Furthermore, although several of the APEX microenvironments account for time
24 spent in travel, the travel is assumed to always occur in basically a composite of
25 the home and work block. No other provision is made for the possibility of
26 passing through other blocks during travel.
27 • APEX creates seasonal or annual sequences of daily activities for a simulated
28 individual by sampling human activity data from more than one subject. Each
29 simulated person essentially becomes a composite of several actual people in the
30 underlying activity data.
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1 • The APEX model currently does not capture certain correlations among human
2 activities that can impact microenvironmental concentrations (for example,
3 cigarette smoking leading to an individual opening a window, which in turn
4 affects the amount of outdoor air penetrating the microenvironment).
5 • Certain aspects of the personal profiles are held constant, though in reality they
6 change as individuals age. This is only important for simulations with long
7 timeframes, particularly when simulating young children (e.g., over a year or
8 more).
9 • The estimation of 5-minute SC>2 concentrations from 1-hour SC>2 concentrations
10 considers ambient monitor concentration variability and hourly concentration
11 levels. The air quality characterization indicated that the approach is reasonably
12 accurate and precise when applied to where 5-minute measurements were
13 available. However, the level of uncertainty in the use of the statistical model to
14 estimate 5-minute 862 concentrations at each modeled receptor is dependent on
15 the particular sources affecting each, information that is largely unknown.
16 7.10.2 Input Data Evaluation
17 Modeling results are heavily dependent on the quality of the data that are input to the
18 system. The input data used in this assessment were selected to best simulate actual conditions
19 that affect human exposure. Using well characterized data as inputs to the model lessens the
20 degree of uncertainty in exposure estimates. Still, the limitations and uncertainties of each of the
21 data streams affect the overall quality of the model output. These issues and how they
22 specifically affect each data stream are discussed this section.
23
24 7.10.2.1 Meteorological Data
25 Meteorological data are taken directly from monitoring stations within the modeling
26 domains. One strength of these data is that it is relatively easy to see significant errors if they
27 appear in the data. Because general climactic conditions are known for each area simulation, it
28 would have been apparent upon review if there were outliers in the dataset. Although APEX
29 only uses one temperature value per day and does not represent minute-to-minute variations in
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1 meteorological conditions throughout the day, this likely would not affect 862 exposure
2 estimates within microenvironments.
3 7.10.2.2 Air Quality Data
4 Air quality data used in the exposure modeling was determined through use of EPA's
5 recommended regulatory air dispersion model, AERMOD (version 07026), with meteorological
6 data discussed above and emissions data based on the EPA's National Emissions Inventory for
7 2002 and the CAMD Emissions Database for stationary sources and mobile sources determined
8 from local travel demand modeling and EPA's MOBILE6.2 emission factor model. All of these
9 are high quality data sources. Parameterization of meteorology and emissions in the model were
10 made in as accurate a manner as possible to ensure best representation of air quality for exposure
11 modeling. For some of the domains, minor source emissions were not included in the dispersion
12 modeling. This occurred at several of the modeling domains, some of which contained ambient
13 monitoring data. Where ambient monitoring was available, there was good agreement between
14 the distribution of 1-hour modeled SC>2 concentrations and 1-hour measurement data. This
15 suggests the approach for using only the major point source emissions provides a reasonable
16 approximation of the 1-hour SC>2 concentrations at each receptor.
17 Additional uncertainties associated with the air quality data used for the development of
18 the PMRs used in estimating 5-minute maximum SC>2 concentrations in the exposure modeling
19 are discussed in section 6.5. These include potential effects from changes in source-types over
20 time and for different geographic locations, in addition to the potential for multiple occurrences
21 of peak concentrations within an hour rather than the single occurrence that was modeled here.
22 One additional uncertainty in the 5-minute maximum SC>2 concentration estimation that remains
23 largely unknown is in the application of the PMRs to the 1-hour SC>2 concentrations at each
24 receptor. While SC>2 concentrations were estimated at each receptor considering the contribution
25 from multiple sources (if multiple sources were present), the calculation does not account for a 5-
26 minute 862 concentration profile from each source. Therefore, a calculation using the total 1-
27 hour receptor concentrations would likely overestimate 5-minute maximum SC>2 concentrations
28 where multiple source emissions are present.
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1 7.10.2.3 Population and Commuting Data
2 The population and commuting data are drawn from U.S. Census data from the year
3 2000. This is a high quality data source for nationwide population data in the U.S. However, the
4 data do have limitations. The Census used random sampling techniques instead of attempting to
5 reach all households in the U.S., as it has in the past. While the sampling techniques are well
6 established and trusted, they introduce some uncertainty to the system. The Census has a quality
7 section (http://www.census.gov/quality/) that discusses these and other issues with Census data.
8 In addition to these data quality issues, certain simplifying assumptions were made in
9 order to better match reality or to make the data match APEX input specifications. For example,
10 the APEX dataset does not differentiate people that work at home from those that commute
11 within their home tract, and individuals that commute over 120 km a day were assumed to not
12 commute daily. In addition to emphasizing some of the limitations of the input data, these
13 assumptions introduce uncertainty to the results.
14 Furthermore, the estimation of block-to-block commuter flows relied on the assumption
15 that the frequency of commuting to a workplace block within a tract is proportional to the
16 amount of commercial and industrial land in the block. This assumption introduces additional
17 uncertainty.
18 7.10.2.4 Activity Pattern Data
19 It is probable that the CHAD data used in the system is the most subject to limitations
20 and uncertainty of all the data used in the system. Much of the data used to generate the daily
21 diaries are over 20 years old. Table 7-10 indicates the ages of the CHAD diaries used in this
22 modeling analysis. While the specifics of people's daily activities may not have changed much
23 over the years, it is certainly possible that some differences do exist. In addition, the CHAD data
24 are taken from numerous surveys that were performed for different purposes. Some of these
25 surveys collected only a single diary-day while others went on for several days. Some of the
26 studies were designed to not be representative of the U.S. population, although a most of the data
27 are from National surveys. Furthermore, study collection periods occur at different times of the
28 year, possibly resulting in seasonal differences. A few of these limitations are corrected by the
29 approaches used in the exposure modeling (e.g., weighting by US population demographics for a
30 particular location, adjusting for effects of temperature on human activities).
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1 A sensitivity analysis was performed to evaluate the impact of the activity pattern
2 database on APEX model results for O3 (see Langstaff (2007) and EPA (2007d)). Briefly,
3 exposure results were generated using APEX with all of the CHAD diaries and compared with
4 results generated from running APEX using only the CHAD diaries from the National Human
5 Activity Pattern Study (NHAPS), a nationally representative study in CHAD. There was very
6 good agreement between the APEX results for the 12 cities evaluated, whether all of CHAD or
7 only the NHAPS component of CHAD is used. The absolute difference in percent of persons
8 above a particular concentration level ranged from -1% to about 4%, indicating that the exposure
9 model results are not being overly influenced by any single study in CHAD. It is likely that
10 similar results would be obtained here for 862 exposures, although remains uncertain due to
11 different averaging times (5-minute vs. 8-hour average).
12 7.10.2.5 Air Exchange Rates
13 There are several components of uncertainty in the residential air exchange rate
14 distributions used for this analysis. EPA (2007g) details an analysis of uncertainty due to
15 extrapolation of air exchange rate distributions between-CMSAs and within-CMSA uncertainty
16 due to sampling variation. In addition, the uncertainty associated with estimating daily air
17 exchange rate distributions from air exchange rate measurements with varying averaging times
18 were discussed. The results of those earlier investigations indicate the exposure model results
19 are sensitive to variability in air exchange rates, particularly noting the significant influence of
20 city location (or variability between different cities), while the within-location variability was
21 determined not to be overly influential.
22 7.10.2.6 Air Conditioning Prevalence
23 Because the selection of an air exchange rate distribution is conditioned on the presence
24 or absence of an air-conditioner, for each modeled area, the air conditioning status of the
25 residential microenvironments was simulated randomly using the probability that a residence has
26 an air conditioner, i.e., the residential air conditioner prevalence rate. For this study we used
27 location-specific data from the American Housing Survey of 2003. EPA (2007d) details the
28 specification of uncertainty estimates in the form of confidence intervals for the air conditioner
29 prevalence rate, and compares these with prevalence rates and confidence intervals developed
30 from the Energy Information Administration's Residential Energy Consumption Survey (RECS)
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1 of 2001 for more aggregate geographic subdivision (e.g., states, multi-state Census divisions and
2 regions). Reported standard error on the mean estimate of 95.5% for St. Louis is relatively
3 small, at just under 1.7%. The corresponding upper and lower 95% confidence interval is also
4 small and ranges from approximately 92.3% to 98.8%. The RECS prevalence estimate for
5 Census Divisions was 92% (ranging between 86.4% and 98.4%), while the Census Region
6 prevalence estimate was 83.6% (ranging between 80.0% and 87.2%). This suggests that the air
7 conditioning prevalence used, while likely being representative of a city in Missouri, may be
8 overestimated for non-urban locations. The overall impact on the results generated here is
9 minimal, since the exposure events are most likely to occur outdoors.
10
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i 8.0 HEALTH RISK ASSESSMENT FOR LUNG FUNCTION
2 RESPONSES IN ASTHMATICS ASSOCIATED WITH 5-MINUTE
3 PEAK EXPOSURES
4 8.1 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 (draft ISA, section
7 3.1.3.2). As discussed above in section 4.2, asthmatics exposed to 862 concentrations as low as
8 0.4-0.6 ppm 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 that EPA plans to use to conduct this health risk
18 assessment is presented below. We plan to include a more detailed description of the approach
19 used and results of this risk assessment in the second draft REA document and in a technical
20 support document. The goals of this SC>2 risk assessment are: (1) to develop health risk estimates
21 of the number and percent of the asthmatic population that would experience moderate or greater
22 lung function decrements in response to 5-minute daily maximum peak exposures while engaged
23 in moderate or greater exertion for several air quality scenarios (described below); (2) to develop
24 a better understanding of the influence of various inputs and assumptions on the risk estimates;
25 and (3) to gain insights into the risk levels and patterns of risk reductions associated with
26 meeting alternative SC>2 standards. EPA will estimate health risks 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. As discussed in Chapter 7, the initial geographic scope of the assessment
30 includes selected locations encompassing a variety of 862 emission source types in the state of
July 2008 158 Draft Do Not Quote or Cite
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1 Missouri. The second draft REA document also will evaluate exposures in the remainder of
2 Missouri and we also are currently planning to include areas of Pennsylvania, West Virginia, and
3 other locations with large SO2 emission sources.
4 8.2 DEVELOPMENT OF APPROACH FOR 5-MINUTE LUNG FUNCTION
5 RISK ASSESSMENT
6 The proposed risk assessment is based on the health effects information evaluated in the
7 draft ISA and discussed above in Chapter 4. The basic structure of the risk assessment reflects
8 the fact that we have available controlled human exposure study data from several studies
9 involving volunteer asthmatic subjects who were exposed to 862 concentrations at specified
10 exposure levels while engaged in moderate or greater exertion for 5- or 10-minute exposures. As
11 discussed in the draft ISA (section 3.1.3.5), among asthmatics, both the magnitude of SO2-
12 induced lung function decrements and the percent of individuals affected have been shown to
13 increase with increasing 5- to 10-minute SC>2 exposures in the range of 0.2 to 1.0 ppm.
14 Therefore, for the SC>2 lung function risk assessment we will be developing probabilistic
15 exposure-response relationships based on these data. The analysis will be of the combined data
16 set consisting of all available individual data that describe the relationship between a measure of
17 personal exposure to 862 and measures of lung function recorded in these studies. For the
18 purposes of this risk assessment, all of the individual data, including both 5- and 10-minute
19 exposure duration, will be combined and treated as representing 5-minute responses. These
20 probabilistic exposure-response relationships will be combined with 5-minute daily maximum
21 peak exposure estimates for mild and moderate asthmatics engaged in moderate or greater
22 exertion associated with the various air quality scenarios mentioned above. A more detailed
23 description of the exposure assessment that will be the source of the estimated daily maximum 5-
24 minute peak exposures under moderate or greater exertion is provided above in Chapter 7.
25 8.2.1 General Approach
26 The major components of the lung function health risk assessment are illustrated in
27 Figure 8-1. As shown in Figure 8-1, under the lung function risk assessment, exposure estimates
28 for mild and moderate asthmatics for a number of different air quality scenarios (i.e., recent year
29 of air quality, just meeting the current 24-hour standard, just meeting
July 2008 159 Draft Do Not Quote or Cite
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Air Quality
Ambient Modeling
for Selected 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
2 Figure 8-1. Major Components of 5-Minute Peak Lung Function Health Risk Assessment Based on Controlled
3 Human Exposure Studies
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160
Draft Do Not Quote or Cite
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1 alternative standards) will be combined with probabilistic exposure-response relationships
2 derived from a combined data base consisting of data from several controlled human exposure
3 studies to develop risk estimates. The air quality and exposure analysis components that are
4 integral to this risk assessment are discussed in greater detail in Chapter 7, and only the aspects
5 affecting the scope of the assessment are briefly discussed in section 8.2.2. A brief description
6 of the overall approach to estimating the exposure-response relationship is addressed in section
7 8.2.3 below.
8 8.2.2 Exposure Estimates
9 As noted above, exposure estimates used in the lung function risk assessment will be
10 obtained from running the APEX exposure model asthmatic individuals for selected locations
11 encompassing a variety of 862 emission source types in the state of Missouri. The second draft
12 REA document also will evaluate exposures in the remainder of Missouri and we also are
13 currently planning to include areas of Pennsylvania, West Virginia, and other locations with
14 large SC>2 emission sources. Chapter 7 provides additional details about the inputs and
15 methodology used to estimate 5-minute daily maximum peak exposures for the asthmatic
16 population. Exposure estimates for asthmatic children and adult asthmatics will be combined
17 separately with probabilistic exposure-response relationships for lung function response
18 associated with 5-minute daily maximum peak exposures while engaged in moderate or greater
19 exertion. Only the highest 5-minute peak exposure (with moderate or greater exertion) on each
20 day will be considered in the lung function risk assessment, since the controlled human exposure
21 studies have shown an acute-phase response that was followed by a short refractory period where
22 the individual was relatively insensitive to additional SC>2 challenges.
23 8.2.3 Exposure-Response Functions
24 Similar to the approach used in the ozone lung function risk assessment (Abt Associates,
25 2007), we plan to use a Bayesian Markov Chain Monte Carlo approach to estimate probabilistic
26 exposure-response relationships for lung function decrements associated with 5-minute daily
27 maximum peak exposures while engaged in moderate or greater exertion using the WinBUGS
28 software (Spiegelhalter et al., 1996).29 The combined data set includes all available individual
29 data from controlled human exposure studies of mild-to-moderate asthmatic individuals exposed
29 See Gleman et al. (1995) or Gilks et al. (1996) for an explanation of these methods.
July 2008 161
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Table 8-1. Percentage of Asthmatic Individuals in Controlled Human Exposure Studies Experiencing SO2-lnduced
Decrements in Lung Function.
S02
Level
(PPM)
0.2
0.25
0.3
0.4
0.5
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
S 100% t
> 15% ^
5% (2)
13% (5)
32% (6)
22% (2)
4%(1)
10% (2)
33% (7)
15% (3)
24% (5)
23% (9)
30% (12)
60% (6)
21% (6)
36% (16)
sRaw
S 200% t
FEVi
> 20% ^
0
5% (2)
16% (3)
0
0
5%(1)
10% (2)
0
14% (3)
8% (3)
23% (9)
40% (4)
4%(1)
16% (7)
•2. 300% t
> 30% -I
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
Linn etal. (1987)
Bethel etal. (1985)
Roger etal. (1985)
Linn etal. (1988)3
Linn etal. (1990)3
Linn etal. (1988)
Linn etal. (1990)
Linn etal. (1987)
Linn etal. (1987)
Bethel etal. (1983)
Roger etal. (1985)
Magnussen et al.
(1990)4
Respiratory Symptoms:
Supporting Studies
Some evidence of SO2-induced
increases in respiratory symptoms
in the most sensitive individuals:
Linn etal. (1983; 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. (1983; 1987), Roger
etal. (1985)
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S02
Level
(PPM)
0.6
1.0
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
5 100% t
> 15% 4-
35% (14)
60% (12)
57% (12)
53% (21)
55% (11)
45% (9)
54% (15)
60% (6)
sRaw
5 200% t
FEVi
> 20% 4-
28% (11)
35% (7)
33% (7)
45% (18)
55% (11)
35% (7)
25% (7)
20% (2)
5 300% t
> 30% 4-
18% (7)
10% (2)
14% (3)
20% (8)
5%(1)
19% (4)
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.
2Responses of mild and moderate asthmatics reported in Linn et al. (1987) have been combined.
3Analysis includes data from only mild (1988) and moderate (1990) asthmatics who were not receiving supplemental medication.
Indicates studies in which exposures were conducted using a mouthpiece rather than a chamber.
1 Source: Draft ISA, Table 3-1 (EPA, 2008).
July 2008
163
Draft Do Not Quote or Cite
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1 for 5- or 10-minutes while engaged in moderate or greater exertion. As noted above, for
2 the purposes of this risk assessment, all of the individual data, including both 5- and 10-
3 minute exposure duration, will be combined and treated as representing 5-minute
4 responses. Table 8-1 summarizes the available controlled human exposure data that will
5 be used to develop the probabilistic exposure-response relationships for the lung function
6 risk assessment. Consistent with the way the responses are reported in this table, the risk
7 assessment will be based on responses that have been corrected for the effect of exercise
8 in clean air to remove any bias that might be present in the data attributable to an exercise
9 effect.
10 8.2.4 Characterizing Uncertainty and Variability
11 An important issue associated with any population health risk assessment is the
12 characterization of uncertainty and variability. Uncertainty refers to the lack of
13 knowledge regarding both the actual values of model input variables (parameter
14 uncertainty) and the physical systems or relationships (model uncertainty - e.g., the
15 shapes of exposure-response functions). In any risk assessment, uncertainty is, ideally,
16 reduced to the maximum extent possible, but significant uncertainty often remains. It can
17 be reduced by improved measurement and improved model formulation. In addition, the
18 degree of uncertainty can be characterized, sometimes quantitatively. Variability refers
19 to the heterogeneity in a population or variable of interest that is inherent and cannot be
20 reduced through further research.
21 Our approach to characterizing uncertainty includes both qualitative and
22 quantitative elements. From a quantitative perspective, the statistical uncertainty
23 surrounding the estimated SC>2 exposure-response relationships due to sampling error will
24 be reflected in the credible intervals that will be provided for the risk estimates in the
25 second draft REA document. We also will consider whether sensitivity analyses are
26 appropriate to address possible alternative functional forms to represent the shape of the
27 exposure-response relationships.
28 In addition to uncertainties arising from sampling variability considerations and
29 alternative model forms, there are other uncertainties associated with the use of the
30 exposure-response relationships for lung function responses which will be addressed
31 qualitatively. These additional uncertainties include:
July 2008 164 Draft - Do Not Quote or Cite
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1 • Length of exposure. The 5-minute lung function risk estimates are based on a
2 combined data set from several controlled human exposure studies, most of which
3 evaluated responses associated with 10-minute exposures. However, since some
4 studies which evaluated responses after 5-minute exposures found responses
5 occurring as early as 5-minutes after exposure, we are using all of the 5- and 10-
6 minute exposure data to represent responses associated with 5-minute exposures.
7 We do not believe that this approach would appreciably impact the risk estimates.
8
9 • Exposure-response for mild/moderate asthmatics. The data set that is being
10 used to estimate exposure-response relationships included mild and/or moderate
11 asthmatics. There is uncertainty with regard to how well the population of mild
12 and moderate asthmatics included in the series of controlled human exposure
13 studies represent the distribution of mild and moderate asthmatics in the U.S.
14 population.
15
16 • Extrapolation of exposure-response relationships. It will be necessary to
17 estimate responses at SO2 levels below the lowest exposure levels used in the
18 controlled human exposure studies (i.e., below 0.2 ppm).
19
20 • Reproducibility of SC^-induced response. The risk assessment will assume
21 that the SCVinduced responses for individuals are reproducible.
22
23 • Age and lung function response. Because the vast majority of controlled
24 human exposure studies investigating lung function responses were conducted
25 with adult subjects, the risk assessment will rely on data from adult asthmatic
26 subjects to estimate exposure-response relationships that will be applied to all
27 asthmatic individuals, including children. The draft ISA (section 3.1.3.5)
28 indicates that there is a strong body of evidence that suggests adolescents may
29 experience many of the same respiratory effects at similar 862 levels, but
30 recognizes that these studies administered SO2 via inhalation through a
31 mouthpiece rather than an exposure chamber. This technique bypasses nasal
32 aborption of 862 and can result in an increase in lung 862 uptake. Therefore, the
33 uncertainty will be greater in the risk estimates for asthmatic children.
34
35 • Exposure history. The risk assessment will assume that the SO2-induced
36 response on any given day is independent of previous 862 exposures.
37
38 • Interaction between SO? and other pollutants. Because the controlled human
39 exposure studies that will be used in the risk assessment involved only 862
40 exposures, it will be assumed that estimates of SO2-induced health responses
41 would not be affected by the presence of other pollutants (e.g., PM2.5, 63, NO2).
42
43 With respect to variability, the lung function risk assessment will incorporate
44 some of the variability in key inputs to the analysis by its use of location-specific
45 inputs for the exposure analysis (e.g., location specific population data, air
July 2008 165 Draft - Do Not Quote or Cite
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1 exchange rates, air quality, and temperature data). The extent to which there may
2 be variability in exposure-response relationships for the populations included in
3 the risk assessment residing in different geographic areas is currently unknown.
4 Temporal variability also is more difficult to address, because the risk assessment
5 focuses on some unspecified time in the future. To minimize the degree to which
6 values of inputs to the analysis may be different from the values of those inputs at
7 that unspecified time, we plan to use the most current inputs available.
July 2008 166 Draft - Do Not Quote or Cite
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i 9.0 RISK CHACTERIZATION FOR SHORT-TERM (>1 HOUR,
2 GENERALLY 24-HOUR) SO2 EXPOSURES
3 9.1 OVERVIEW
4 As previously mentioned, the draft ISA concludes that the overall weight of the evidence
5 supports a causal relationship between short-term SC>2 exposure and respiratory morbidity. The
6 ISA bases this conclusion on the consistency, coherence, and plausibility of findings observed in
7 controlled human exposure studies examining SC>2 exposures of 5-10 minutes for mild to
8 moderate asthmatics, epidemiological studies mostly using 24-hour average exposures, and
9 animal toxicological studies using exposures of minutes to hours (draft ISA, section 5.2).
10 Moreover, within the broader category of respiratory morbidity, the draft ISA finds an
11 association between short-term SO2 exposure and respiratory symptoms in children, as well as a
12 suggestive association between SC>2 exposure and hospital admissions and ED visits for all
13 respiratory causes and asthma (draft ISA, section 3.1.4). Supporting evidence for an association
14 between short-term SC>2 exposure and overall respiratory morbidity is found in epidemiological
15 studies examining other respiratory morbidity endpoints (e.g. respiratory illness-related
16 absences), but the overall breadth of the evidence for these endpoints is judged by staff to be too
17 limited to use as a basis for a quantitative risk assessment. However, we do plan to use results
18 from these studies as supporting evidence in the decision making process.
19 It is important to note that the conclusions stated above are based primarily on the
20 strength of both U.S and international epidemiological literature, but for purposes of potentially
21 conducting a quantitative risk assessment for locations in the U.S., staff recommends primarily
22 relying on U.S. studies. Taking this into account, we reviewed the available epidemiological
23 literature and found relatively few studies that focused on the association between short-term
24 SC>2 exposures and respiratory symptoms or ED visits and hospital admissions for all respiratory
25 causes or asthma, were conducted in U.S. cities. In those cities where epidemiological studies
26 had been conducted, many of the 862 effect estimates were positive, but not statistically
27 significant in single pollutant models. Moreover, in the relatively few studies that employed
28 multi-pollutant models, inclusion of PMio in the model often resulted in a loss of statistical
29 significance for the SC>2 effect estimate. Results from the Harvard Six Cities Study (Schwartz et
30 al. 1994) also suggested that the respiratory effects of SC>2 could be confounded by PMio; in this
July 2008 167 Draft - Do Not Quote or Cite
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1 study, there was a significant attenuation of the SO2 effect estimate after including PMio in a
2 two-pollutant model examining respiratory symptoms (draft ISA; section 3.1.4.1.1). Similarly,
3 after inclusion of PMio in a two-pollutant model with SO2, a significant attenuation of the SO2
4 effect estimate was found in a hospital admissions study in Tacoma, WA; although, it should be
5 noted that in the same study, results in New Haven, CT remained positive and statistically
6 significant in a two-pollutant model with PMio (Schwartz et al. 1995; draft ISA, Figure 3-8).
7 Staff also found that very few U.S. studies examined SO2 in a multi-pollutant model with PM2 5,
8 and we believe that this is an important uncertainty given the relationship between SO2 and
9 particulate sulfates. Overall, we conclude that these factors would make it particularly difficult to
10 quantify with confidence the unique contribution of SO2 to respiratory health effects and
11 therefore, we judge that the results of a quantitative risk assessment based on concentration-
12 response functions from epidemiological studies for these health outcomes would be highly
13 uncertain and of limited utility in the decision-making process.
14 However, even though we do not believe that the body of U.S. epidemiological literature
15 is robust enough to support a quantitative assessment of risk, we do agree that the results of these
16 studies suggest an association between SO2 exposure and respiratory symptoms in children, and
17 hospital admissions and ED visits for all respiratory causes and asthma, and as a result, warrant a
18 characterization of risk. Therefore, the overall goal of this chapter will ultimately be to
19 qualitatively assess whether specific SO2 air quality statistics correlate with the observed health
20 effects reported in these epidemiological studies. The results of these analyses will not be
21 available until the 2nd draft of this document; therefore this chapter will focus on the methods
22 that will be employed.
23 9.2 APPROACH
24 Staff sent a request to those authors of U.S. and Canadian epidemiological studies that
25 were identified in Table 5-4 of the draft ISA as providing important information about the
26 association between SO2 exposure and respiratory symptoms in children, and SO2 exposure and
27 ED visits and hospital admissions for all respiratory causes and asthma in all age groups. We
28 specifically requested the 98th and 99th percentile air quality statistics from the monitor recording
29 the highest value for the averaging times (3-hour average, 12-hour average, 24-hour average, or
30 1-hour max) examined in their particular studies. Alternatively, if the authors found it more
31 convenient, we gave them the option of either providing their entire study data set, or the specific
July 2008 168 Draft - Do Not Quote or Cite
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1 study periods and monitor IDs used in their analyses. In these instances, EPA staff would
2 calculate the 98th and 99th percentile statistics from the author's data set directly, or retrieved the
3 relevant data from AQS and performed the necessary calculations.
4 Staff specifically requested information on the 98th and 99th percentile statistics to assess
5 whether the health effects observed in epidemiological studies are being driven by exposure to
6 short-term peaks of SC>2. As described previously in this document (section 4.2), there is strong
7 controlled human exposure evidence demonstrating that exposure to peak SC>2 concentrations
8 can result in adverse effects on the respiratory system (section 4.2). In characterizing this
9 potential risk, we will first assess whether there is a correlation between 98th or 99th percentile
10 SC>2 concentrations and the magnitude of the effect estimates observed in epidemiological
11 studies. Next, we will qualitatively assess whether there is a correlation between these percentile
12 values of SC>2 and the statistical significance of U.S. and Canadian epidemiological results. Staff
13 will also compare these air quality statistics to current air quality, and air quality adjusted to
14 simulate just meeting the current 24-hour standard to estimate the number of times these values
15 are exceeded under these air quality scenarios. Once completed, we will then use the results of
16 these analyses to inform decisions on which potential alternative 862 standards should be
17 analyzed. Finally, these air quality statistics will be compared to air quality levels adjusted to
18 simulate just meeting any potential alternative standards to estimate whether these 98th or 99th
19 percentile values would be exceeded under these alternative standard air quality scenarios.
July 2008 169 Draft - Do Not Quote or Cite
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5 Schwartz, J.; Dockery, D. W.; Neas, L. M.; Wypij, D.; Ware, J. H.; Spengler, J. D.; Koutrakis,
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9 Schwartz, J. (1995) Short Term Fluctuations in Air Pollution and Hospital Admissions of the
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12 Schwartz J. (1996) Air Pollution and Hospital Admissions for Respiratory Disease.
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15 Science International (1995). Estimate of the Nationwide Exercising Asthmatic Exposure
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13 90/129, December 6, 1990.
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24 Antioxidants in Adult Subjects with Asthma. Occup Environ Med 56:544-7.
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27 the National Human Activity Pattern Survey (NHAPS) Data. U.S. Environmental Protection
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15 Respiratory Emergency Room Visits in Two Northern New England Cities: An Ecological Time-
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17
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July 2008 181 Draft - Do Not Quote or Cite
-------
i APPENDIX A: AMBIENT MONITORING SITE
2 CHARACTERIZATION
3 This appendix contains supplementary information on the 862 ambient monitoring data
4 used in the air quality characterization described in Chapter 6 of this document. Included in this
5 appendix are spatial and temporal attributes important for understanding the relationship between
6 the ambient monitor and those sources affecting air quality measurements. In section A-l,
7 important spatial characteristics described include the physical locations of the ambient monitors
8 (e.g., U.S. states, counties, territories, and cities). Temporal attributes of interest include, for
9 example, the number of samples collected, sample averaging times, and years of monitoring data
10 available. Attributes of the monitors that measured both the 5-minute maximum and the 1-hour
11 SC>2 concentrations are provided in Table A-l, while the supplemental characteristics of the 1-
12 hour SC>2 monitors used is given in Table A-2. The method for calculating the proximity of the
13 ambient monitors follows, along with the results summarized in Tables A-3 and A-4. In
14 addition, Table A-5 summarizes the validity criteria used to selecting valid ambient monitoring
15 data for comparison to the NAAQS standards. Section A-2 details the analyses performed on
16 simultaneous measurements at co-located monitors.
17
A-l
-------
l A.1 SPATIAL AND TEMPORAL ATTRIBUTES OF AMBIENT SO2
2 MONITORS
Table A-1. General site attributes of ambient monitors measuring 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
MT
MT
MT
MT
MT
MT
MT
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
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
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
301110066
301110079
301110080
301110082
301110083
301110084
301112008
Latitude
34.756111
34.830556
33.215
39.75119
39.577778
38.897222
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
38.8725
45.788318
45.769439
45.777149
45.783889
45.795278
45.831453
45.786389
Longitude
-92.275833
-92.259444
-92.668889
-104.98762
-75.611111
-76.952778
-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
-90.226389
-108.459536
-108.574292
-108.47436
-108.515
-108.455833
-108.449964
-108.523056
Years
First
2002
1997
1997
1997
1997
2000
2002
2001
2001
2001
2001
2001
2001
2001
2004
2001
1997
1997
2000
1997
1997
1997
1997
2004
1997
1998
2001
1997
2005
1997
1997
1997
1997
1997
2001
1999
2003
1997
Last
2007
2001
2007
2006
1998
2007
2005
2005
2005
2005
2005
2005
2005
2004
2005
2002
2000
2000
2003
2007
2007
2004
2004
2007
2001
2001
2003
2007
2007
1998
2000
2003
2003
2001
2003
2003
2006
1997
n
6
5
11
10
2
6
4
5
5
5
5
5
5
4
2
2
4
4
4
11
11
8
8
4
5
4
3
11
3
2
4
7
4
5
3
5
4
1
A-2
-------
State
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
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
SC
SC
SC
County
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
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Berks
Cambria
Erie
Philadelphia
Philadelphia
Philadelphia
Warren
Warren
Washington
Washington
Washington
Barnwell
Charleston
Charleston
Monitor ID
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
420030067
420030116
420031301
420033003
420033004
420070002
420070005
420110009
420210011
420490003
421010022
421010048
421010136
421230003
421230004
421250005
421250200
421255001
450110001
450190003
450190046
Latitude
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
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
Longitude
-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
-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
Years
First
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
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2000
2000
2000
Last
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
2002
1999
2002
1999
2002
1999
1998
2007
1999
1999
1999
2001
1999
2003
1998
1998
1999
1999
1998
2002
2002
2002
n
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
4
3
4
3
4
3
2
8
3
3
3
5
3
7
2
2
3
3
2
3
3
3
A-3
-------
State
SC
sc
SC
sc
sc
sc
sc
UT
wv
wv
wv
wv
wv
County
Georgetown
Greenville
Lexington
Oconee
Richland
Richland
Richland
Salt Lake
Wayne
Wayne
Wayne
Wayne
Wood
Monitor ID
450430006
450450008
450630008
450730001
450790007
450790021
450791003
490352004
540990002
540990003
540990004
540990005
541071002
Latitude
33.362014
34.838814
34.051017
34.805261
34.093959
33.81468
34.024497
40.736389
38.39186
38.390278
38.380278
38.372222
39.323533
Longitude
-79.294251
-82.402918
-81.15495
-83.2377
-80.962304
-80.781135
-81.036248
-112.210278
-82.583923
-82.585833
-82.583889
-82.588889
-81.552367
Years
First
2000
2000
2001
2000
2000
2000
2001
1997
2002
2002
2002
2002
2001
Last
2002
2002
2002
2002
2002
2002
2002
1998
2002
2005
2005
2005
2005
n
3
3
2
3
3
3
2
2
1
4
4
4
5
A-4
-------
1 Table A-2. General site attributes of ambient monitors measuring 1-hour SO2
2 concentrations.
State
AL
AL
AL
AL
AL
AL
AL
AL
AL
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AR
AR
AR
AR
AR
AR
AR
AR
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
County
Colbert
Colbert
Jackson
Jefferson
Lawrence
Limestone
Mobile
Mobile
Montgomery
Gila
Gila
Maricopa
Maricopa
Maricopa
Maricopa
Maricopa
Pima
Final
Miller
Miller
Miller
Miller
Pulaski
Pulaski
Pulaski
Union
Alameda
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Fresno
Fresno
Fresno
Humboldt
Imperial
Imperial
Kern
Kern
Los Angeles
Los Angeles
Los Angeles
Monitor ID
010330044
010331002
010710020
010731003
010790003
010830004
010970028
010972005
011011002
040070009
040071001
040130019
040133002
040133003
040133010
040139997
040191011
040212001
050910096
050910097
050910098
050910099
051190007
051191002
051191005
051390006
060010010
060130002
060130003
060130006
060130010
060131001
060131002
060131003
060131004
060132001
060133001
060190008
060190243
060190244
060231004
060250005
060250006
060290014
060290232
060370030
060370031
060371002
Latitude
34.690556
34.760556
34.876944
33.485556
34.589571
34.685702
30.958333
30.474674
32.407120
33.399135
33.006179
33.483850
33.457930
33.479680
33.460930
33.503643
32.208333
32.600479
33.187500
33.323055
33.330277
33.205833
34.756111
34.830556
34.676268
33.215000
37.760300
37.936000
37.950000
37.947800
38.031300
38.055556
38.010556
37.964167
37.960280
38.013056
38.029167
36.781389
36.767220
36.803060
40.776944
32.676111
32.677778
35.356111
35.438889
34.035278
33.786111
34.176050
Longitude
-87.821389
-87.650556
-85.720833
-86.915000
-87.109445
-86.880810
-88.028333
-88.141140
-86.256367
-110.858896
-110.785797
-112.142570
-112.046010
-111.917210
-112.117480
-112.095001
-110.872222
-110.633598
-94.023889
-93.997500
-93.998055
-94.003889
-92.275833
-92.259444
-92.337164
-92.668889
-122.192500
-122.026200
-122.356111
-122.365100
-122.131800
-122.219722
-121.641389
-122.339167
-122.356670
-122.133611
-121.902222
-119.772222
-119.827500
-119.769170
-124.177500
-115.483333
-115.389722
-119.040278
-119.015833
-118.216667
-118.246389
-118.317120
Years
First
1997
2002
1997
1997
1997
2003
1997
2000
1997
1999
1999
1997
1997
1997
1997
2005
1997
1997
1998
1998
1998
1998
2004
1997
2002
1997
2001
1997
1997
1997
2001
1997
1997
1997
2002
1997
1997
1997
2003
2003
2007
1997
1997
1997
1997
2001
2001
1997
Last
2006
2003
2006
2007
2000
2004
1999
2006
1998
2007
2007
1998
2007
2007
1999
2007
2007
2007
1999
1999
1999
1999
2004
2001
2002
2005
2003
2007
1997
2007
2003
2007
2007
2002
2007
2007
2007
1997
2003
2003
2007
2007
1998
2001
1997
2002
2002
2007
n
10
2
10
11
4
2
3
7
2
9
9
2
11
11
3
3
11
9
2
2
2
2
1
5
1
8
3
11
1
11
3
11
11
6
6
11
11
1
1
1
1
11
2
3
1
2
2
11
A-5
-------
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
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
County
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Orange
Riverside
Sacramento
Sacramento
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Diego
San Diego
San Diego
San Diego
San Francisco
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
Santa Barbara
Santa Barbara
Santa Cruz
Solano
Solano
Ventura
Adams
Adams
Denver
El Paso
El Paso
Monitor ID
060371103
060374002
060375001
060375005
060591003
060658001
060670002
060670006
060710012
060710014
060710015
060710017
060710306
060711234
060712002
060714001
060730001
060731007
060731010
060732007
060750005
060750006
060791005
060792001
060792004
060794002
060830008
060831007
060831012
060831013
060831015
060831016
060831019
060831020
060831025
060831026
060831027
060832004
060832011
060834003
060835001
060870003
060950001
060950004
061113001
080010007
080013001
080310002
080416001
080416004
Latitude
34.066590
33.823760
33.922880
33.950800
33.674640
33.999580
38.712778
38.614167
34.426111
34.512500
35.775000
34.141944
34.510000
35.763889
34.100020
34.418056
32.631231
32.709172
32.701492
32.552164
37.766000
37.733610
35.043889
35.125000
35.022222
35.028333
34.462222
34.948056
34.451944
34.725556
34.478056
34.477778
34.475278
34.415278
34.489722
34.479444
34.469167
34.637500
34.445278
34.596111
34.780833
37.011944
38.052222
38.102700
34.255000
39.800000
39.838180
39.751190
38.633611
38.921389
Longitude
-118.226880
-118.189210
-118.370260
-118.430430
-117.925680
-117.416010
-121.380000
-121.366944
-117.563056
-117.330000
-117.366667
-116.055000
-117.330556
-117.396111
-117.492010
-117.284722
-117.059075
-117.153975
-117.149653
-116.937772
-122.399100
-122.383330
-120.580278
-120.633333
-120.569444
-120.387222
-120.024444
-120.434444
-120.457778
-120.427778
-120.210833
-120.205556
-120.188889
-119.878611
-120.045833
-120.032500
-120.039444
-120.456389
-119.827778
-120.630278
-120.606389
-122.193333
-122.144722
-122.238200
-119.142500
-104.910833
-104.949840
-104.987620
-104.715556
-104.812500
Years
First
1997
1997
1997
2004
1997
1997
1997
1997
1997
1997
1997
1997
2000
1997
1997
1997
1997
1997
2005
1997
1997
2004
1997
1997
1997
1998
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2001
1997
1998
1997
1997
Last
2007
2007
2004
2007
2007
2007
2007
2007
1998
1999
1997
1997
2007
2007
2007
1998
2007
2005
2007
2007
2007
2005
2002
2004
2007
2006
2007
1998
1998
2007
1998
1998
1998
2007
2007
1999
1999
2007
2007
2007
1997
2007
1997
2007
2004
2004
2007
2007
2001
2001
n
11
11
8
4
11
11
11
11
2
3
1
1
8
11
11
2
11
9
3
11
11
2
6
8
11
9
11
2
2
11
2
2
2
11
11
3
3
11
11
11
1
11
1
11
8
4
11
10
5
5
A-6
-------
State
CO
CO
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
DE
DE
DE
DE
DE
DE
DE
DC
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
County
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
Sussex
District of
Columbia
Brevard
Broward
Duval
Duval
Duval
Duval
Escambia
Escambia
Hamilton
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Manatee
Miami-Dade
Nassau
Nassau
Orange
Palm Beach
Pinellas
Pinellas
Monitor ID
080416011
080416018
090010012
090010017
090011123
090012124
090019003
090031005
090031018
090032006
090090027
090091003
090091123
090092123
090110007
090130003
100031003
100031007
100031008
100031013
100032002
100032004
100051002
110010041
120090011
120110010
120310032
120310080
120310081
120310097
120330004
120330022
120470015
120570021
120570053
120570081
120570095
120570109
120571035
120571065
120574004
120813002
120860019
120890005
120890009
120952002
120993004
121030023
121033002
Latitude
38.846667
38.811389
41.195000
41.003611
41.399167
41.063056
41.118333
42.015833
41.760833
41.742500
41.301111
41.310556
41.310833
41.550556
41.361111
41.730000
39.761111
39.551111
39.577778
39.773889
39.757778
39.739444
38.644444
38.897222
28.469380
26.128611
30.356111
30.308889
30.422222
30.367222
30.525000
30.544722
30.411111
27.947222
27.886389
27.739722
27.922500
27.856389
27.928056
27.892222
27.992500
27.632778
25.897500
30.658333
30.686389
28.599444
26.369722
27.863333
27.871389
Longitude
-104.827222
-104.751389
-73.163333
-73.585000
-73.443056
-73.528889
-73.336667
-72.518056
-72.670833
-72.634444
-72.902778
-72.915556
-72.916944
-73.043611
-72.080000
-72.213611
-75.491944
-75.730833
-75.611111
-75.496389
-75.546389
-75.558056
-75.613056
-76.952778
-80.666830
-80.167222
-81 .635556
-81 .652500
-81.621111
-81.594167
-87.204167
-87.216111
-82.783611
-82.453333
-82.481389
-82.465278
-82.401389
-82.383667
-82.454722
-82.538611
-82.125833
-82.546111
-80.380000
-81 .463333
-81 .447500
-81 .363056
-80.074444
-82.623333
-82.691667
Years
First
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2004
1997
1997
1997
1997
1997
1997
2000
1997
2003
1997
1999
1997
1997
2007
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2001
1998
1998
1997
1997
1997
1997
1997
1997
1997
Last
2001
2001
2006
2006
2006
2005
2006
1999
1998
2006
2006
1998
2004
2006
1999
1999
2003
2007
2007
2007
1998
2007
1997
2007
2007
2007
2007
2007
2007
2007
2007
2006
2007
1999
2006
2007
2007
2007
2007
2002
2006
2007
2007
2007
1998
2007
2007
2007
2007
n
5
5
10
4
10
9
10
3
2
10
3
2
8
10
3
3
7
8
11
5
2
9
1
11
1
11
11
11
11
11
11
10
11
3
10
11
11
11
11
2
9
10
11
10
2
11
11
11
11
A-7
-------
State
FL
FL
FL
FL
FL
FL
FL
FL
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
GA
HI
HI
HI
HI
HI
HI
ID
ID
ID
ID
ID
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
County
Pinellas
Pinellas
Polk
Polk
Putnam
Sarasota
Sarasota
Sarasota
Baldwin
Bartow
Bibb
Chatham
Chatham
Chatham
Dougherty
Fannin
Floyd
Fulton
Fulton
Glynn
Muscogee
Richmond
Hawaii
Hawaii
Honolulu
Honolulu
Honolulu
Honolulu
Bannock
Bannock
Caribou
Caribou
Power
Adams
Champaign
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
DuPage
La Salle
Macon
Macoupin
Monitor ID
121035002
121035003
121050010
121052006
121071008
121151002
121151005
121151006
130090001
130150002
130210012
130510019
130510021
130511002
130950006
131110091
131150003
131210048
131210055
131270006
132150008
132450003
150010005
150010007
150030010
150030011
150031001
150031006
160050004
160050015
160290003
160290031
160770011
170010006
170190004
170310050
170310059
170310063
170310064
170310076
170311018
170311601
170312001
170314002
170314201
170318003
170436001
170990007
171150013
171170002
Latitude
28.090000
28.141667
27.856111
27.896944
29.687500
27.299722
27.306944
27.350278
33.153258
34.103333
32.805244
32.093889
32.069050
32.090278
31.567778
34.985556
34.261113
33.779189
33.720428
31.169530
32.521099
33.393611
19.433611
19.418889
21.329167
21.337222
21.310278
21.347500
42.916389
42.876725
42.661298
42.695278
42.912500
39.933010
40.123796
41.707570
41.687500
41.876969
41.790787
41.751400
41.773889
41.668120
41.662109
41.855243
42.139996
41.631389
41.813049
41.293015
39.866834
39.396075
Longitude
-82.700833
-82.739722
-82.017778
-81 .960278
-81 .656667
-82.524444
-82.570556
-82.480000
-83.235807
-84.915278
-83.543628
-81.151111
-81 .048949
-81.130556
-84.102778
-84.375278
-85.323018
-84.395843
-84.357449
-81 .496046
-84.944695
-82.006389
-155.261111
-155.288056
-158.093333
-158.119167
-157.858056
-158.113333
-112.515833
-112.460347
-111.591443
-111.593889
-112.535556
-91 .404237
-88.229531
-87.568574
-87.536111
-87.634330
-87.601646
-87.713488
-87.815278
-87.990570
-87.696467
-87.752470
-87.799227
-87.568056
-88.072827
-89.049425
-88.925594
-89.809739
Years
First
1997
1998
1997
1997
1997
1997
1997
1999
1998
1997
1997
1997
1997
1998
1998
1997
1997
1997
1997
1999
1999
1997
1997
2001
1997
1997
1997
1997
1997
1997
1997
2001
2004
1997
1997
1997
1997
1997
1997
2004
1997
1997
1997
1997
2004
1997
1997
2006
1997
1997
Last
2007
2007
2005
2003
2007
1999
2001
2007
2006
2005
2007
2002
2007
2007
2001
2007
2007
2007
2007
2007
2005
2004
2007
2007
2007
2007
2007
2007
2006
1999
2002
2006
2005
2007
2000
2007
2000
2007
1997
2007
2004
2007
2003
2007
2007
2002
2000
2007
2007
2007
n
11
10
9
7
11
3
5
9
4
9
6
6
11
7
2
11
11
11
11
4
3
4
11
7
11
11
11
11
10
3
6
6
2
11
4
11
4
11
1
4
8
11
7
11
4
6
4
2
11
11
A-8
-------
State
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
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
County
Madison
Madison
Madison
Madison
Madison
Peoria
Randolph
Rock Island
Saint Clair
Saint Clair
Saint Clair
Sangamon
Tazewell
Wabash
Wabash
Will
Daviess
Dearborn
DeKalb
Floyd
Floyd
Floyd
Fountain
Gibson
Gibson
Hendricks
Hendricks
Hendricks
Jasper
Jasper
Jefferson
Lake
Lake
Lake
LaPorte
LaPorte
Marion
Marion
Marion
Marion
Marion
Morgan
Perry
Perry
Pike
Porter
Porter
Porter
Spencer
Spencer
Monitor ID
171190008
171190017
171191010
171193007
171193009
171430024
171570001
171610003
171630010
171631010
171631011
171670006
171790004
171850001
171851001
171970013
180270002
180290004
180330002
180430004
180430007
180431004
180450001
180510001
180510002
180630001
180630002
180630003
180730002
180730003
180770004
180890022
180891016
180892008
180910005
180910007
180970042
180970054
180970057
180970072
180970073
181091001
181230006
181230007
181250005
181270011
181270017
181270023
181470002
181470010
Latitude
38.890186
38.701944
38.828303
38.860669
38.865984
40.687420
38.176278
41.511944
38.612034
38.592192
38.235000
39.800614
40.556460
38.397222
38.369444
41.459963
38.572778
39.092778
41.364167
38.367778
38.273333
38.308056
39.964167
38.361389
38.392778
39.876944
39.863361
39.880833
41.187778
41.135833
38.776667
41.606667
41.600278
41.639444
41.716944
41.679722
39.646254
39.730278
39.749019
39.768056
39.789167
39.515000
37.994330
37.983773
38.519167
41.633889
41.621944
41.616667
37.982500
37.955360
Longitude
-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
-84.855000
-84.926389
-85.833056
-85.836389
-85.834167
-87.421389
-87.748611
-87.748333
-86.473889
-86.470750
-86.542194
-87.053333
-86.987778
-85.407222
-87.304722
-87.334722
-87.493611
-86.907500
-86.852778
-86.248784
-86.196111
-86.186314
-86.160000
-86.060833
-86.391667
-86.763457
-86.772202
-87.249722
-87.101389
-87.116389
-87.145833
-86.966380
-87.031800
Years
First
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
1998
1998
1998
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2002
Last
2002
2000
2007
2007
2007
2007
2007
2000
2007
2002
2001
2007
2007
2006
2006
2007
2006
2007
1997
2006
2006
2007
2006
2006
2006
2006
2006
2006
2007
2002
2005
2005
1997
2007
2007
2002
2007
1997
2007
2000
2005
2006
2004
2004
2006
2007
2002
2002
2001
2007
n
6
4
11
11
11
11
11
4
11
6
5
11
11
10
10
11
10
11
1
10
10
11
10
10
10
6
6
6
11
6
9
9
1
11
11
6
11
1
11
4
9
4
8
8
10
11
6
6
5
6
A-9
-------
State
IN
IN
IN
IN
IN
IN
IN
IN
IN
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
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
KS
KY
KY
KY
KY
County
Sullivan
Vanderburgh
Vanderburgh
Vigo
Vigo
Warrick
Warrick
Wayne
Wayne
Cerro Gordo
Clinton
Clinton
Clinton
Dubuque
Lee
Lee
Linn
Linn
Linn
Linn
Linn
Linn
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Polk
Scott
Scott
Scott
Van Buren
Van Buren
Van Buren
Wood bury
Linn
Montgomery
Montgomery
Pawnee
Sedgwick
Sumner
Trego
Wyandotte
Wyandotte
Wyandotte
Boyd
Boyd
Boyd
Campbell
Monitor ID
181530004
181630012
181631002
181670018
181671014
181730002
181731001
181770006
181770007
190330018
190450018
190450019
190450020
190610012
191110006
191111007
191130026
191130028
191130029
191130031
191130032
191130034
191130035
191130038
191130039
191390016
191390017
191390020
191530030
191630014
191630015
191630017
191770004
191770005
191770006
191930018
201070002
201250006
201250007
201450001
201730010
201910002
201950001
202090001
202090020
202090021
210190015
210190017
210191003
210370003
Latitude
39.099444
38.021667
37.902500
39.486111
39.514722
37.937500
37.938056
39.812222
39.795833
43.169440
41.824722
41.823283
41.845833
42.525556
40.392222
40.582500
42.008333
41.910556
41.974722
41.983333
41.964722
41.971111
41.943056
41.941111
41.934167
41.419429
41.387969
41.407796
41.603183
41.699174
41.530011
41 .467236
40.711111
40.689167
40.695078
42.399444
38.135833
37.046944
37.062930
38.176250
37.701111
37.476944
38.770278
39.113056
39.151389
39.117500
38.465833
38.459167
38.388611
39.065556
Longitude
-87.470556
-87.569444
-87.671389
-87.401389
-87.407778
-87.314167
-87.345833
-84.890000
-84.880833
-93.202426
-90.212778
-90.211982
-90.216389
-90.641944
-91.400000
-91.427500
-91.678611
-91.651944
-91.666667
-91.662778
-91 .664722
-91 .645278
-91 .622500
-91.633889
-91 .682500
-91.070975
-91.054504
-91.062646
-93.643300
-90.521944
-90.587611
-90.688451
-91.975278
-91.994444
-92.006318
-96.355833
-94.731944
-95.613333
-95.638820
-99.108028
-97.313889
-97.366389
-99.763611
-94.624444
-94.617500
-94.635556
-82.621111
-82.640556
-82.602500
-84.451944
Years
First
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1998
1998
2000
1997
1997
1997
2007
2005
1997
1997
1997
1999
2005
2001
1998
1997
2005
1997
1997
2000
2001
1997
1997
1999
1997
2001
1997
2000
Last
2006
2007
2006
2007
2006
2006
2003
2007
2007
2007
1997
2007
1998
1997
1998
2000
1997
2001
2007
2007
2000
2000
1998
2007
2001
2007
2007
2007
2007
2005
2007
1997
1999
2004
2007
2002
2007
2007
2006
1997
1997
2007
2007
1999
1999
2007
2001
2007
1999
2006
n
10
11
10
11
10
10
7
11
11
11
1
10
2
1
2
4
1
5
11
11
4
4
1
10
2
9
10
11
1
1
11
1
3
5
3
2
10
11
2
1
1
8
7
3
3
9
5
7
3
7
A-10
-------
State
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
KY
LA
LA
LA
LA
LA
LA
LA
LA
LA
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
MD
MD
MD
MD
MD
MA
MA
County
Campbell
Daviess
Fayette
Greenup
Hancock
Henderson
Henderson
Jefferson
Jefferson
Jefferson
Jessamine
Kenton
Livingston
McCracken
McCracken
McCracken
Muhlenberg
Ohio
Pike
Warren
Bossier
Calcasieu
East Baton
Rouge
Jefferson
Ouachita
St. Bernard
St. Bernard
St. Bernard
West Baton
Rouge
Androscoggin
Androscoggin
Aroostook
Aroostook
Aroostook
Aroostook
Aroostook
Aroostook
Cumberland
Cumberland
Hancock
Oxford
Oxford
Allegany
Anne Arundel
Baltimore
Baltimore (City)
Baltimore (City)
Bristol
Bristol
Monitor ID
210371001
210590005
210670012
210890007
210910012
211010013
211010014
211110032
211110051
211111041
211130001
211170007
211390004
211450001
211451024
211451026
211771004
211830032
211950002
212270008
220150008
220190008
220330009
220511001
220730004
220870002
220870007
220870009
221210001
230010011
230013003
230030009
230030012
230031003
230031013
230031018
230031100
230050014
230050027
230090103
230170011
230172007
240010006
240032002
240053001
245100018
245100036
250050010
250051004
Latitude
39.108611
37.780833
38.065000
38.548333
37.938889
37.858889
37.871389
38.182500
38.060833
38.231630
37.893333
39.072500
37.070833
37.131667
37.058056
37.040833
37.227222
37.319725
37.482778
37.036667
32.536260
30.261667
30.461980
30.043333
32.509713
29.981944
29.944750
29.936909
30.501944
44.089406
44.097778
47.351667
47.354444
47.351667
46.123889
46.660899
46.696431
43.659722
43.661944
44.377050
44.550278
44.543056
39.649722
39.159722
39.310833
39.314167
39.265000
41.688056
41.683279
Longitude
-84.476111
-87.075556
-84.500000
-82.731667
-86.896944
-87.575278
-87.463333
-85.861667
-85.896111
-85.826720
-84.589167
-84.525000
-88.334167
-88.813333
-88.572500
-88.541111
-87.158333
-86.956097
-82.535278
-86.250556
-93.748910
-93.284167
-91.179220
-90.275000
-92.046093
-89.998611
-89.976263
-89.955703
-91 .209722
-70.214219
-70.193611
-68.303611
-68.314167
-68.311389
-67.829722
-67.902066
-68.033006
-70.261389
-70.265833
-68.260900
-70.534167
-70.545833
-78.762778
-76.511667
-76.474444
-76.613333
-76.536667
-71.175278
-71.169171
Years
First
1997
1997
1997
1997
1997
1997
2003
1997
1997
1997
2007
2006
1997
1997
2000
1997
2001
2005
2001
2002
1997
1997
1997
2005
1997
1997
2007
2007
1997
1997
1997
1997
1997
1997
1997
2002
2006
1997
1999
2004
1997
1997
1997
1997
2003
1997
1997
1997
1997
Last
1999
2007
2007
2007
2004
2002
2007
2002
2007
2007
2007
2007
2007
1999
2007
1999
2002
2007
2003
2006
2007
2007
2007
2006
2007
2005
2007
2007
2007
2002
1997
1998
1998
1998
1998
2005
2006
1998
2006
2007
1997
2004
1998
2003
2006
1998
1998
1997
2007
n
3
11
11
11
8
6
5
6
11
11
1
2
11
3
8
3
2
3
3
5
11
11
11
2
11
9
1
1
11
6
1
2
2
2
2
4
1
2
8
4
1
8
2
7
4
2
2
1
11
A-ll
-------
State
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
Ml
Ml
Ml
Ml
Ml
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
MN
County
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
Wayne
Wayne
Wayne
Wayne
Wayne
Anoka
Carlton
Dakota
Dakota
Dakota
Dakota
Dakota
Hennepin
Hennepin
Koochiching
Ramsey
Sherburne
Sherburne
Monitor ID
250056001
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
261630025
261630027
261630033
261630062
261630092
270031002
270176316
270370020
270370423
270370439
270370441
270370442
270530954
270530957
270711240
271230864
271410003
271410011
Latitude
41.753889
42.709444
42.515556
42.525000
42.772222
42.108581
42.085556
42.298279
42.474444
42.383611
42.348873
42.316394
42.309417
42.377833
42.340251
42.329400
42.401667
42.267222
42.263877
45.796667
43.047224
43.168336
42.984173
42.513340
44.310555
42.953336
46.288877
42.228620
42.267231
42.302786
42.357808
42.430840
42.423063
42.292231
42.306674
42.340833
42.296111
45.137680
46.733611
44.763230
44.775530
44.748039
44.746800
44.738570
44.980995
45.021111
48.605278
44.991944
45.420278
45.394444
Longitude
-71.197500
-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
-71.038350
-71.082500
-71.031111
-71.798889
-71.794186
-87.089444
-83.670159
-83.461541
-85.671339
-83.005971
-84.891865
-82.456229
-85.950227
-83.208200
-83.132086
-83.106530
-83.096033
-83.000138
-83.426263
-83.106807
-83.148754
-83.062500
-83.116944
-93.207720
-92.418889
-93.032550
-93.062990
-93.043266
-93.026110
-93.004960
-93.273719
-93.281944
-93.402222
-93.183056
-93.871667
-93.897500
Years
First
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2000
1997
1997
2004
1997
1997
2003
1997
1997
2002
1997
2005
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2003
2000
1997
1997
1998
1999
2000
1997
1997
1997
1997
1997
1997
Last
1997
2002
1997
1997
2001
2007
1999
2007
2000
1999
2007
2007
2007
2007
2007
2007
1999
2003
2007
2004
2007
2004
2007
2007
2003
2007
2005
1998
2001
2007
2007
2007
1998
2001
2001
1997
1998
2007
2003
2007
2007
2000
2007
2007
2007
2002
2000
2002
1997
1998
n
1
6
1
1
5
11
3
11
4
3
11
11
11
11
11
8
3
7
4
8
11
2
11
11
2
11
1
2
5
11
11
11
2
5
5
1
2
5
4
11
11
3
9
8
11
6
4
6
1
2
A-12
-------
State
MN
MN
MN
MN
MS
MS
MS
MS
MS
MS
MS
MS
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
MT
MT
MT
MT
MT
MT
County
Sherburne
Sherburne
Washington
Wright
Alcorn
Choctaw
Harrison
Hinds
Jackson
Lee
Marshall
Panola
Buchanan
Buchanan
Clay
Greene
Greene
Greene
Greene
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
Big Horn
Cascade
Cascade
Jefferson
Jefferson
Jefferson
Monitor ID
271410012
271410013
271630436
271710007
280030004
280190001
280470007
280490018
280590006
280810004
280930001
281070001
290210009
290210011
290470025
290770026
290770032
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
300030038
300132000
300132001
300430903
300430908
300430909
Latitude
45.394444
45.369444
44.847370
45.329167
34.909167
33.378889
30.446806
32.296806
30.378425
34.263333
34.955000
34.359944
39.731389
39.731389
39.183889
37.128333
37.205278
37.110000
37.108889
37.108611
37.466389
37.519444
39.104722
38.263300
38.267222
38.252778
38.297694
39.473056
39.372600
39.300000
38.579167
38.872500
38.521667
38.532500
38.613611
38.710900
38.641389
38.766111
38.727222
38.720917
38.542500
38.624167
38.682778
38.672222
45.754462
47.532222
47.530000
46.557679
46.538889
46.554167
Longitude
-93.885000
-93.898056
-92.995400
-93.835833
-88.601667
-89.203889
-89.029139
-90.188306
-88.533985
-88.759722
-89.423000
-89.890889
-94.877500
-94.868333
-94.497500
-93.261667
-93.283333
-93.251944
-93.252778
-93.272222
-90.690000
-90.712500
-94.570556
-90.378500
-90.379444
-90.393333
-90.384333
-91.789167
-90.914400
-94.700000
-90.841111
-90.226389
-90.343611
-90.382778
-90.495833
-90.475900
-90.345833
-90.285833
-90.379444
-90.367028
-90.263611
-90.198611
-90.246667
-90.238889
-107.596336
-111.271111
-111.283611
-111.918098
-111.932500
-111.916944
Years
First
1997
1997
1997
1997
2001
1997
1997
1997
1997
1997
2004
1998
2000
2002
1997
1997
1997
1999
2002
2002
2002
2002
1997
2004
1997
1998
2002
1998
2005
1997
1997
2000
1997
1998
1997
2005
1997
1997
1997
2001
1997
1997
1997
2000
2002
1997
2000
1997
1997
1997
Last
1998
1998
2007
1997
2002
1997
2004
2005
2007
1997
2005
1999
2000
2003
2002
2004
2006
2004
2007
2007
2002
2002
2007
2007
2001
2001
2002
2006
2007
2005
1998
2000
1998
2005
2005
2007
2007
2005
2001
2004
2006
2001
1999
2006
2003
2000
2006
2001
1997
1997
n
2
2
11
1
2
1
8
9
11
1
2
2
1
2
6
5
10
2
6
6
1
1
11
4
5
4
1
3
3
9
2
1
2
8
9
3
11
9
5
4
10
5
3
7
2
4
7
5
1
1
A-13
-------
State
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
MT
NE
NE
NE
NE
NV
NV
NV
NV
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
County
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Lewis and Clark
Lewis and Clark
Lewis and Clark
Musselshell
Rosebud
Rosebud
Rosebud
Rosebud
Rosebud
Rosebud
Rosebud
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Douglas
Douglas
Douglas
Douglas
Clark
Clark
Clark
Clark
Cheshire
Coos
Coos
Coos
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Hillsbo rough
Merrimack
Merrimack
Merrimack
Monitor ID
300430910
300430911
300430912
300430913
300430914
300430915
300430916
300490701
300490702
300490703
300650004
300870700
300870701
300870702
300870760
300870761
300870762
300870763
301110016
301110066
301110079
301110080
301110082
301110083
301110084
301111065
301112005
301112006
301112007
301112008
310550048
310550050
310550053
310550055
320030022
320030078
320030539
320030601
330050007
330070019
330070022
330071007
330110016
330110019
330110020
330111009
330111010
330130007
330131003
330131006
Latitude
46.554444
46.548056
46.542778
46.534722
46.553611
46.550556
46.528889
46.573056
46.583333
46.593889
46.267050
45.886944
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
45.810000
45.832778
45.786389
41.323889
41.332778
41.297778
41.362433
36.390775
35.465050
36.144444
35.978889
42.930556
44.488611
44.458333
44.596667
42.992778
43.000556
43.000556
42.764444
42.701944
43.206944
43.177222
43.132444
Longitude
-111.876111
-111.873333
-111.868611
-111.861389
-111 .862222
-111.860278
-111.858056
-111.910278
-1 1 1 .934444
-111.920000
-108.454808
-106.628056
-106.637778
-106.557778
-106.518889
-106.464167
-106.556667
-106.660556
-108.765833
-108.459536
-108.574292
-108.474360
-108.515000
-108.455833
-108.449964
-108.426111
-108.445556
-108.413056
-108.377778
-108.523056
-95.942778
-95.956389
-95.937500
-95.976112
-114.906810
-114.919615
-115.085556
-114.844167
-72.277778
-71.180278
-71.154167
-71.516667
-71 .459444
-71 .468056
-71 .468056
-71 .467500
-71 .445000
-71.534167
-71 .462500
-71 .458270
Years
First
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2002
1997
1997
1997
1997
1997
1997
1997
1997
2001
2001
1997
2001
2000
2003
1997
1997
1997
1997
1997
1997
1999
1999
2004
1998
2000
1998
2001
1997
1997
1997
1997
1997
1999
2001
1997
1997
1997
1997
2002
Last
1997
2001
1997
2001
1997
1997
1997
1997
2001
2001
2003
2001
2001
2001
2004
2004
2004
1998
2005
2007
2004
2001
2004
2003
2006
2005
2005
2006
2006
1997
1999
2004
2007
2007
2003
2003
2006
2003
2004
2002
1998
2002
1999
2001
2007
2001
2003
2003
2003
2007
n
1
5
1
5
1
1
1
1
5
5
2
5
5
5
8
8
8
2
9
5
4
5
2
4
4
9
9
10
10
1
3
6
9
4
6
4
9
3
8
6
2
6
3
3
7
5
7
7
7
6
A-14
-------
State
NH
NH
NH
NH
NH
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NM
NM
NM
NM
NM
NM
NM
NM
NM
NM
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
County
Merrimack
Rockingham
Rockingham
Rockingham
Sullivan
Atlantic
Bergen
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
San Juan
Albany
Bronx
Bronx
Bronx
Bronx
Bronx
Chautauqua
Chautauqua
Chautauqua
Chemung
Erie
Erie
Erie
Essex
Franklin
Franklin
Hamilton
Herkimer
Kings
Monitor ID
330131007
330150009
330150014
330150015
330190003
340010005
340030001
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
360050133
360130005
360130006
360130011
360150003
360290005
360294002
360298001
360310003
360330004
360337003
360410005
360430005
360470011
Latitude
43.218491
43.078056
43.075278
43.082500
43.364444
39.530240
40.808333
40.882370
40.078060
39.923040
39.684250
39.422270
40.726667
40.722222
39.800340
40.670250
40.731690
40.508880
40.787630
40.662450
40.641440
31.930556
31.795833
32.855556
32.759444
32.691944
31.783333
36.735833
36.742222
36.752778
36.796667
42.680690
40.811389
40.836080
40.865860
40.816160
40.867989
42.290730
42.499450
42.290730
42.111050
42.876840
42.995490
42.818889
44.393090
44.434309
44.980577
43.449570
43.685780
40.732770
Longitude
-71 .458270
-70.762778
-70.748056
-70.761944
-72.338333
-74.460690
-73.992778
-74.042170
-74.857720
-75.097620
-74.861490
-75.025200
-74.144167
-74.146944
-75.212120
-74.126080
-74.066570
-74.268200
-74.676300
-74.214740
-74.208360
-106.630556
-106.557500
-104.411389
-108.131389
-108.124444
-108.497222
-108.238333
-107.976944
-108.716667
-108.472500
-73.756890
-73.910000
-73.920210
-73.880750
-73.902070
-73.878203
-79.589580
-79.318880
-79.586580
-76.802490
-78.809880
-78.901570
-78.840833
-73.858920
-74.246010
-74.695005
-74.516250
-74.985380
-73.947220
Years
First
2004
1997
2003
2001
1997
1997
1997
1997
1997
1997
1997
1997
1997
2001
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2000
1999
2007
1997
1999
1997
1997
1997
1997
1997
1997
2003
2004
1997
1997
1997
Last
2006
2001
2007
2003
2002
2006
1998
2006
2006
2006
2006
2006
1999
2003
2006
2006
2006
2006
2006
2006
2006
2003
2006
2007
2002
2007
2002
2003
2006
1998
2006
2007
1999
2000
2007
2007
2007
2001
2007
2007
2007
2007
2007
1999
2007
2007
2007
2007
2007
1999
n
3
5
5
3
6
10
2
10
10
10
10
10
3
3
10
10
10
10
10
10
10
7
10
11
6
11
6
7
10
2
10
11
3
4
8
9
1
5
9
11
11
11
11
3
11
5
4
11
11
3
A-15
-------
State
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
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
NC
NC
NC
NC
ND
County
Kings
Madison
Monroe
Monroe
Monroe
Nassau
New York
New York
Niagara
Niagara
Onondaga
Onondaga
Putnam
Queens
Queens
Queens
Rensselaer
Rensselaer
Richmond
Schenectady
Steuben
Suffolk
Suffolk
Ulster
Alexander
Beaufort
Beaufort
Beaufort
Chatham
Cumberland
Davie
Duplin
Edgecombe
Forsyth
Johnston
Lincoln
Martin
Martin
Mecklenburg
Mecklenburg
New Hanover
New Hanover
Northampton
Person
Pitt
Rowan
Rowan
Swain
Wake
Billings
Monitor ID
360470076
360530006
360551004
360551007
360556001
360590005
360610010
360610056
360632006
360632008
360670017
360671015
360790005
360810004
360810097
360810124
360830004
360831005
360850067
360930003
361010003
361030002
361030009
361111005
370030003
370130003
370130004
370130006
370370004
370511003
370590002
370610002
370650099
370670022
371010002
371090004
371170001
371170002
371190034
371190041
371290002
371290006
371310002
371450003
371470099
371590021
371590022
371730002
371830014
380070002
Latitude
40.671850
42.730460
43.165450
43.146198
43.161000
40.743160
40.739444
40.759170
43.085833
43.082160
43.042630
43.052380
41.441510
40.735833
40.755270
40.736200
42.781870
42.724440
40.597330
42.799630
42.090710
40.745290
40.827500
42.143800
35.903611
35.357500
35.377241
35.377778
35.757222
34.968889
35.809289
34.954823
35.988333
36.110556
35.590833
35.438556
35.810690
35.830670
35.248611
35.240100
34.364167
34.268403
36.484380
36.306965
35.583333
35.551868
35.534482
35.435509
35.856111
46.894300
Longitude
-73.978240
-75.784430
-77.554790
-77.548130
-77.603570
-73.585490
-73.986111
-73.966510
-78.996389
-79.000990
-76.143310
-76.059200
-73.707620
-73.816944
-73.758610
-73.823170
-73.463610
-73.431660
-74.126190
-73.940190
-77.210250
-73.419190
-73.056940
-74.494140
-81.184167
-76.779722
-76.748997
-76.766944
-79.159722
-78.962500
-80.559115
-77.960781
-77.582778
-80.226667
-78.461944
-81.276750
-76.897820
-76.806310
-80.766389
-80.785683
-77.838611
-77.956529
-77.619980
-79.091970
-77.598889
-80.395039
-80.667560
-83.443697
-78.574167
-103.378530
Years
First
1997
1997
1997
2004
1997
1997
1997
1997
1997
1998
2001
1997
1997
1997
1998
2001
2001
1997
1997
1997
2007
1997
2000
1997
1999
1997
1997
2001
1998
1999
1997
1999
1999
1997
1999
1997
1998
2006
1997
1999
2005
1997
1997
1998
1997
1997
1997
1998
2002
2001
Last
2000
2007
2004
2007
2004
2007
2001
2007
1997
2007
2001
2007
2007
1997
2001
2007
2007
2001
2000
2007
2007
2000
2007
2007
2003
2000
1999
2007
2001
2006
2000
1999
2004
2006
1999
2000
2007
2007
1999
2007
2005
2007
2000
2004
2000
1998
1998
2007
2007
2005
n
4
11
8
4
8
11
5
11
1
10
1
11
11
1
4
7
7
5
4
11
1
4
8
11
2
4
3
7
2
3
2
1
2
8
1
2
4
2
3
9
1
11
2
3
2
2
2
4
6
2
A-16
-------
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
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
County
Billings
Billings
Burke
Burke
Burleigh
Cass
Cass
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
Cuyahoga
Franklin
Franklin
Gallia
Hamilton
Hamilton
Hamilton
Hamilton
Jefferson
Jefferson
Monitor ID
380070003
380070111
380130002
380130004
380150003
380171003
380171004
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
390350026
390350038
390350045
390350060
390350065
390356001
390490004
390490034
390530002
390610010
390610039
390612002
390612003
390810016
390810017
Latitude
46.961900
47.296667
48.990400
48.641930
46.825425
46.910278
46.933754
47.313200
47.581200
47.575278
47.605556
47.606667
47.258853
47.298611
47.325000
47.371667
47.385725
47.400619
46.841750
46.873075
47.185833
47.599703
48.408834
48.392644
38.795000
40.772222
41.959444
39.968056
39.383333
39.530000
39.855556
38.961273
40.634722
40.635000
40.620278
41.445278
41.476944
41.471667
41.493955
41.446389
41.504722
39.992222
40.002500
38.944167
39.214931
39.198056
39.158611
39.228889
40.362778
40.366104
Longitude
-103.356699
-103.095556
-102.781500
-102.401800
-100.768210
-96.795000
-96.855350
-102.527300
-103.299500
-103.968889
-104.017222
-102.036389
-101.783035
-101.766944
-101.765833
-101.780833
-101.862917
-101.928650
-100.870059
-100.905039
-101.428056
-97.899009
-102.907650
-102.910233
-83.535278
-84.051944
-80.572500
-80.747500
-84.544167
-84.392500
-83.997500
-84.094450
-80.546389
-80.546667
-80.580833
-81 .660833
-81.681944
-81 .657222
-81 .678542
-81.661944
-81 .623889
-83.041667
-82.994444
-82.112222
-84.690723
-84.468611
-84.748889
-84.448889
-80.615556
-80.615002
Years
First
1997
1997
2001
2005
2005
1997
2004
1997
1997
1997
1998
1997
1997
2005
1997
1997
1997
1997
1997
2003
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2001
1997
1997
1997
1997
1997
1997
1997
1997
1997
2001
1997
1998
1997
1997
1998
2003
Last
1997
1997
2005
2006
2007
1997
2007
2005
2005
2004
2007
2006
1997
2007
2007
2007
2007
2007
2005
2005
2005
1997
2001
2006
2007
2007
2007
2007
2007
2007
2007
2005
2000
2007
1999
1997
2007
2007
2007
2007
2003
2000
2007
2006
2007
1999
1997
1998
2003
2007
n
1
1
4
2
3
1
4
4
2
3
10
10
1
2
11
11
11
11
2
2
2
1
5
6
11
11
11
10
11
11
11
9
4
7
3
1
11
11
11
11
7
4
11
6
11
2
1
2
6
5
A-17
-------
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
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OK
OR
OR
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Jefferson
Jefferson
Lake
Lake
Lawrence
Lawrence
Lorain
Lorain
Lorain
Lucas
Lucas
Lucas
Mahoning
Mahoning
Meigs
Montgomery
Morgan
Morgan
Scioto
Scioto
Scioto
Stark
Summit
Summit
Tuscarawas
Tuscarawas
Cherokee
Kay
Kay
Kay
Mayes
Muskogee
Oklahoma
Oklahoma
Ottawa
Tulsa
Tulsa
Tulsa
Tulsa
Lincoln
Multnomah
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Monitor ID
390811001
390811012
390850003
390853002
390870006
390871009
390930017
390930026
390931003
390950006
390950008
390950024
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
401431127
410410002
410510080
420030002
420030010
420030021
420030031
420030032
420030064
420030067
420030116
420031301
Latitude
40.321944
40.359444
41.673056
41.722500
38.520278
38.421111
41.368056
41.471667
41.365833
41.648056
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
35.854080
36.705328
36.662778
36.956222
36.228408
35.793134
35.553056
35.614131
36.922222
36.149877
36.126945
36.161270
36.204902
44.612522
45.496667
40.500556
40.445577
40.413611
40.443333
40.414444
40.323611
40.381944
40.473611
40.402500
Longitude
-80.606389
-80.623056
-81 .422500
-81.241944
-82.666667
-82.572222
-82.110556
-82.143611
-82.108333
-83.529167
-83.476667
-83.546667
-80.651944
-80.658611
-82.045556
-84.200000
-81 .673056
-81.670038
-82.917500
-82.822911
-82.834973
-81.378611
-81.468611
-81.516389
-81 .476389
-81.639149
-94.985964
-97.087656
-97.074444
-97.031350
-95.249943
-95.302235
-97.623611
-97.475083
-94.838889
-96.011664
-95.998941
-96.015784
-95.976537
-123.928405
-122.602222
-80.071944
-80.016155
-79.941389
-79.990556
-79.942222
-79.868333
-80.185556
-80.077222
-79.860278
Years
First
1997
1997
1997
1997
1997
1997
2000
1997
1997
1997
1997
1998
1997
2000
1997
1997
1997
2006
1997
2004
2004
1997
1997
1997
1997
2003
1999
1997
1999
2004
2004
1997
1998
2004
2001
1997
1997
1997
2006
2003
2005
1997
1997
1997
1997
1997
1997
1997
1997
1997
Last
2004
1997
2007
2007
2007
1997
2004
2003
2000
1997
2007
2007
1999
2007
2007
2004
2006
2007
2007
2007
2007
2004
2007
2007
2003
2007
2006
2007
2004
2006
2006
2007
2003
2007
2005
2007
2007
2007
2007
2004
2006
2007
2007
2007
2000
1999
2007
2007
2007
2000
n
8
1
11
11
11
1
5
7
4
1
11
10
3
8
11
8
10
2
11
4
4
8
11
11
7
5
8
11
6
3
3
11
6
4
5
11
11
10
2
2
2
11
11
11
4
3
11
11
11
4
A-18
-------
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
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Allegheny
Allegheny
Beaver
Beaver
Beaver
Beaver
Berks
Berks
Blair
Bucks
Cambria
Centre
Dauphin
Delaware
Delaware
Erie
Greene
Indiana
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Lycoming
Lycoming
Mercer
Monroe
Montgomery
Northampton
Northampton
Northampton
Perry
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Schuylkill
Schuylkill
Warren
Warren
Washington
Washington
Washington
Westmoreland
York
Monitor ID
420033003
420033004
420070002
420070004
420070005
420070014
420110009
420110100
420130801
420170012
420210011
420270100
420430401
420450002
420450109
420490003
420590002
420630004
420692006
420710007
420730015
420770004
420791101
420810100
420810403
420850100
420890001
420910013
420950025
420950100
420958000
420990301
421010004
421010022
421010024
421010027
421010029
421010047
421010048
421010055
421010136
421070002
421070003
421230003
421230004
421250005
421250200
421255001
421290008
421330008
Latitude
40.318056
40.305000
40.562520
40.635575
40.684722
40.747796
40.320278
40.335278
40.535278
40.107222
40.309722
40.811389
40.245000
39.835556
39.818715
42.141750
39.816222
40.563330
41.442778
40.046667
40.995848
40.611944
41.265556
41.250800
41.246111
41.215014
40.860004
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
39.927500
40.783889
40.820556
41.857222
41.844722
40.146667
40.170556
40.445278
40.304694
39.965278
Longitude
-79.881111
-79.888889
-80.503948
-80.230605
-80.359722
-80.316442
-75.926667
-75.922778
-78.370833
-74.882222
-78.915000
-77.877028
-76.844722
-75.372500
-75.413973
-80.038611
-80.284917
-78.919972
-75.623056
-76.283333
-80.346442
-75.432500
-75.846389
-76.923800
-76.989722
-80.484779
-75.429614
-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
-76.343611
-76.212222
-79.137500
-79.169722
-79.902222
-80.261389
-80.420833
-79.505667
-76.699444
Years
First
1997
1997
1997
1997
1998
1997
1999
1997
1997
1997
1999
2002
1997
1997
1997
1998
1997
2004
1997
1997
1997
1997
1997
2001
1997
1997
1997
1997
1997
1997
1999
1997
1997
1997
1997
1997
1997
1997
1997
2004
1997
1997
1997
1997
1998
1998
1998
1998
1997
1997
Last
2005
2000
2007
1998
2007
2007
2006
1998
2007
2007
2007
2007
2007
2007
2000
2007
2007
2007
2007
2007
2007
2007
2007
2007
2001
2007
1999
2007
2007
1999
2007
2007
2007
2001
1999
1999
2005
1999
1999
2007
2007
1997
2007
2007
2007
2007
2007
2007
2007
2007
n
9
4
11
2
10
11
8
2
11
11
9
6
11
11
4
10
11
4
11
11
11
11
11
7
5
11
3
11
11
3
9
11
11
5
3
3
9
3
3
4
11
1
11
11
10
10
10
10
11
11
A-19
-------
State
Rl
Rl
Rl
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SD
SD
SD
SD
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
County
Providence
Providence
Providence
Aiken
Anderson
Barnwell
Charleston
Charleston
Georgetown
Greenville
Greenville
Lexington
Oconee
Orangeburg
Richland
Richland
Richland
Richland
Custer
Jackson
Minnehaha
Roberts
Anderson
Blount
Blount
Blount
Bradley
Bradley
Coffee
Davidson
Dickson
Hamblen
Hawkins
Haywood
Haywood
Humphreys
Knox
Loudon
McMinn
Meigs
Montgomery
Montgomery
Montgomery
Obion
Polk
Polk
Polk
Polk
Roane
Roane
Monitor ID
440070012
440071005
440071009
450030003
450070003
450110001
450190003
450190046
450430006
450450008
450450009
450630008
450730001
450750003
450790007
450790021
450791003
450791006
460330132
460710001
460990007
461094003
470010028
470090002
470090006
470090101
470110004
470110102
470310004
470370011
470430009
470630003
470730002
470750002
470750003
470850020
470931030
471050003
471070101
471210104
471250006
471250106
471251010
471310004
471390003
471390007
471390008
471390009
471450009
471451020
Latitude
41.825556
41.878333
41.823611
33.342226
34.776927
33.320344
32.882289
32.941023
33.362014
34.838814
34.899141
34.051017
34.805261
33.299590
34.093959
33.814680
34.024497
33.817902
43.557800
43.745610
43.537626
45.354381
36.027778
35.775000
35.768056
35.631490
35.296111
35.283164
35.582222
36.205000
36.246667
36.307778
36.366944
35.765833
35.468056
36.051944
35.898333
35.790000
35.297330
35.288997
36.520056
36.504529
36.625000
36.345181
35.026111
34.988333
34.995833
34.989722
35.947222
35.885000
Longitude
-71 .405278
-71.378889
-71.411667
-81.788731
-82.490386
-81 .465537
-79.977538
-79.657187
-79.294251
-82.402918
-82.313070
-81.154950
-83.237700
-80.442218
-80.962304
-80.781135
-81 .036248
-80.826596
-103.483900
-101.941218
-96.682001
-96.555279
-84.151389
-83.965833
-83.976667
-83.943512
-84.893611
-84.759371
-86.015556
-86.744722
-87.364444
-83.134472
-82.977778
-89.433889
-89.167778
-87.965000
-83.957222
-84.301944
-84.750760
-84.946044
-87.394167
-87.396675
-87.169167
-89.319208
-84.384722
-84.371667
-84.368333
-84.383889
-84.522222
-84.375278
Years
First
1997
1997
1997
1997
2005
1997
1997
1997
1997
1997
2004
1997
1997
2002
1997
2000
1997
1997
2005
2005
2002
2001
1997
1997
1997
1999
1997
1997
1998
1997
1999
1997
1997
1998
2002
1997
2000
1997
1997
2002
1997
1997
2000
2003
1997
1997
1997
1997
1997
1999
Last
2007
1997
2007
1999
2006
2007
2007
2007
2007
2007
2007
2007
2007
2004
2007
2007
2007
2001
2006
2006
2006
2002
2006
2007
2007
2000
1998
2007
2005
2007
2000
2006
2007
1998
2006
2006
2001
1997
2007
2006
2007
2007
2006
2004
2006
2006
2000
2000
2005
2000
n
11
1
11
3
2
11
10
11
11
10
4
11
11
3
10
8
11
5
2
2
5
2
8
11
11
2
2
11
4
11
2
5
8
1
3
8
2
1
11
4
11
11
4
2
10
10
4
4
7
2
A-20
-------
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
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
UT
UT
UT
UT
UT
UT
UT
County
Sevier
Shelby
Shelby
Shelby
Shelby
Shelby
Stewart
Sullivan
Sullivan
Sumner
Sumner
Bowie
Brewster
Cameron
Cass
Dallas
Ellis
Ellis
Ellis
El Paso
El Paso
El Paso
El Paso
El Paso
Galveston
Galveston
Gregg
Harris
Harris
Harris
Harris
Harris
Harris
Harris
Harris
Jefferson
Jefferson
Jefferson
Kaufman
Nueces
Nueces
Nueces
Travis
Cache
Davis
Davis
Salt Lake
Salt Lake
Salt Lake
Tooele
Monitor ID
471550101
471570034
471570043
471570046
471571034
471572005
471610007
471630007
471630009
471651002
471651005
480370099
480430101
480610006
480670099
481130069
481390015
481390016
481390017
481410033
481410037
481410053
481410057
481410058
481670005
481671002
481830001
482010046
482010051
482010059
482010062
482010070
482010416
482011035
482011050
482450009
482450011
482450020
482570005
483550025
483550026
483550032
484530613
490050004
490110001
490110004
490350012
490351001
490352004
490450002
Latitude
35.696667
35.043400
35.087778
35.272778
35.087222
35.188000
36.389722
36.534804
36.513971
36.341667
36.375000
33.192778
29.302500
25.892509
33.121667
32.819952
32.436944
32.482222
32.473611
31.776944
31.768281
31.758504
31.662189
31.893928
29.385236
29.398611
32.378710
29.827500
29.623611
29.705833
29.625833
29.735129
29.686389
29.733713
29.583032
30.036446
29.894030
30.066070
32.564969
27.765340
27.832409
27.804482
30.418600
41.731111
40.886389
40.902967
40.807500
40.708611
40.736389
40.597778
Longitude
-83.609722
-90.013600
-90.025278
-89.961389
-90.133611
-89.642000
-87.633333
-82.517078
-82.560968
-86.398333
-86.422222
-94.038611
-103.167820
-97.493824
-94.029167
-96.860082
-97.025000
-97.026944
-97.042500
-106.501667
-106.501253
-106.501023
-106.303079
-106.425813
-94.931526
-94.933333
-94.711834
-95.283611
-95.473611
-95.281111
-95.267500
-95.315583
-95.294722
-95.257591
-95.015535
-94.071073
-93.987898
-94.077383
-96.317660
-97.434272
-97.555381
-97.431553
-97.601400
-111.837500
-1 1 1 .882222
-111.884467
-111.921111
-112.094722
-112.210278
-112.466667
Years
First
2006
2000
1997
1997
1997
2005
1997
1997
1997
1997
1998
1998
1999
1997
1998
1997
1997
1997
2004
1997
1997
1997
1999
2000
2004
1997
1999
1997
1997
1997
1997
2000
2006
1997
2001
1997
1997
1997
2000
1997
1997
1997
2003
2002
1997
2003
1997
1997
1997
1997
Last
2007
2005
2000
2007
2007
2006
2005
2007
2007
2007
1999
1999
2000
2000
1999
2007
2007
2007
2006
1999
2007
2007
2000
2007
2007
2004
2007
2007
2007
1998
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2006
2006
2003
2006
2006
2006
2006
1997
n
2
6
4
11
11
2
7
11
11
8
2
2
2
4
2
11
11
11
3
3
11
11
2
8
4
8
9
11
11
2
11
8
2
11
7
11
11
11
8
11
11
11
4
5
7
4
10
10
10
1
A-21
-------
State
VT
VT
VT
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
VA
WA
WA
WA
WA
WA
WA
WA
WA
WA
WA
WA
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
County
Chittenden
Chittenden
Rutland
Charles
Fairfax
Fairfax
Fairfax
Fairfax
Fairfax
Madison
Roanoke
Rockingham
Rockingham
Alexandria City
Hampton City
Norfolk City
Norfolk City
Richmond City
Richmond City
Clallam
Clallam
King
King
Pierce
Pierce
Skagit
Skagit
Skagit
Snohomish
What com
Brooke
Brooke
Cabell
Greenbrier
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Kanawha
Kanawha
Kanawha
Marshall
Monongalia
Monongalia
Monongalia
Monitor ID
500070003
500070014
500210002
510360002
510590005
510590018
510591004
510591005
510595001
511130003
511611004
511650002
511650003
515100009
516500004
517100023
517100024
517600021
517600024
530090010
530090012
530330057
530330080
530530021
530530031
530570012
530570018
530571003
530610016
530730011
540090005
540090007
540110006
540250001
540290005
540290007
540290008
540290009
540290011
540290014
540290015
540290016
540291004
540390004
540390010
540392002
540511002
540610003
540610004
540610005
Latitude
44.478889
44.476200
43.608056
37.343294
38.893889
38.742500
38.868056
38.837517
38.931944
38.521944
37.285556
38.389444
38.477320
38.810833
37.003333
36.850278
36.857778
37.563056
37.562778
48.113333
48.097500
47.563333
47.568333
47.281111
47.265600
48.493611
48.460101
48.486111
47.983333
48.750278
40.341023
40.389655
38.424133
37.819444
40.529021
40.460138
40.615720
40.427372
40.394583
40.435520
40.618353
40.411944
40.421539
38.343889
38.345600
38.416944
39.915961
39.649367
39.633056
39.648333
Longitude
-73.211944
-73.210600
-72.982778
-77.260034
-77.465278
-77.077500
-77.143056
-77.163231
-77.198889
-78.436111
-79.884167
-78.914167
-78.819040
-77.044722
-76.399167
-76.257778
-76.301667
-77.467500
-77.465278
-123.399167
-123.425556
-122.340600
-122.308056
-122.374167
-122.385800
-122.551944
-122.519110
-122.549444
-122.209722
-122.482778
-80.596635
-80.586235
-82.425900
-80.512500
-80.576067
-80.576567
-80.560000
-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
Years
First
1997
2004
1997
1997
1997
1997
1997
2002
1997
1999
1997
1997
2004
1997
1997
1997
2006
1997
1998
1997
1997
1997
2000
1997
1997
1997
2003
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
2000
1997
1997
1997
1997
1997
Last
2000
2004
2006
2007
2007
1998
2001
2007
2007
2007
2007
2004
2007
2007
2007
2005
2007
1997
2007
1998
2004
1999
2006
1999
1999
1999
2006
1999
1999
1999
2007
2007
2007
1998
2007
2007
2007
2007
2007
2003
2007
2004
2007
2000
2007
1999
2007
2007
2001
2006
n
4
1
10
11
11
2
5
6
11
9
11
8
4
11
11
9
2
1
10
2
8
3
7
3
3
3
4
3
3
3
11
11
11
2
11
11
11
11
11
7
11
8
11
4
8
3
11
11
5
10
A-22
-------
State
WV
wv
WV
wv
wv
wv
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
WY
WY
WY
WY
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
VI
VI
VI
VI
VI
County
Ohio
Wayne
Wayne
Wayne
Wayne
Wood
Brown
Dane
Forest
Marathon
Milwaukee
Milwaukee
Milwaukee
Oneida
Sauk
Vilas
Wood
Wood
Campbell
Fremont
Sweetwater
Weston
Barceloneta
Bayamon
Bayamon
Catano
Catano
Catano
Catano
Guayama
Guayanilla
Salinas
San Juan
Yabucoa
St Croix
St Croix
St Croix
St Croix
St Croix
Monitor ID
540690007
540990002
540990003
540990004
540990005
541071002
550090005
550250041
550410007
550730005
550790007
550790026
550790041
550850996
551110007
551250001
551410016
551410017
560050857
560136001
560370200
560450800
720170003
720210004
720210006
720330004
720330007
720330008
720330009
720570009
720590017
721230001
721270009
721510005
780100006
780100011
780100013
780100014
780100015
Latitude
40.120430
38.391860
38.390278
38.380278
38.372222
39.323533
44.516667
43.100833
45.564980
45.028333
43.047222
43.061111
43.075278
45.645278
43.435556
46.048056
44.382500
44.359444
44.277222
42.994444
41.406555
43.845390
18.436111
18.412778
18.416667
18.430556
18.444722
18.440028
18.449964
17.966844
18.025175
17.963002
18.418889
18.052778
17.706944
17.719167
17.722500
17.734444
17.741667
Longitude
-80.699265
-82.583923
-82.585833
-82.583889
-82.588889
-81 .552367
-87.993889
-89.357222
-88.808590
-89.652222
-87.920278
-87.912500
-87.884444
-89.412500
-89.680278
-89.653611
-89.819167
-89.861944
-105.375000
-108.370278
-108.144987
-104.205120
-66.580556
-66.132778
-66.150833
-66.142222
-66.116111
-66.127076
-66.149043
-66.188014
-66.770175
-66.254749
-66.087500
-65.875000
-64.780556
-64.775000
-64.776667
-64.783333
-64.751944
Years
First
1997
1997
1997
1997
1997
1997
1997
1997
2004
1997
1997
2002
1997
1997
2002
2002
1997
2000
2002
2004
2006
2005
1997
1997
1997
1997
2000
2004
2004
2001
2006
2004
2004
1997
1997
1997
1998
1998
1998
Last
2003
2003
2002
2005
2002
2007
2006
1999
2006
1999
2001
2006
2002
2006
2004
2004
2000
2001
2005
2005
2007
2007
2005
2005
2006
2006
2003
2007
2007
2006
2007
2006
2005
1998
2006
2006
2006
2006
2006
n
7
7
6
7
6
8
10
3
3
3
5
5
6
10
3
3
4
2
4
2
2
3
9
9
10
10
4
4
4
6
2
3
2
2
10
10
9
9
9
A-23
-------
1 A.1.1 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(to2) x cos(/o«2 - 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-3 contains the summary of the distance of stationary source emissions to each of
27 the monitors measuring 5-minute SC>2 concentrations. 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-24
-------
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). Similar distributions for the distances to stationary
5 sources and associated emissions were generated for the 1-hour SC>2 monitors (Table A-4), with
6 some of the monitors in close proximity to a single source or few sources of varying emission
7 strength, while others with no significant SC>2 source emissions.
A-25
-------
Table A-3. Distance of 5-minute maximum ambient monitors to stationary sources emitting
within a 20 kilometer distance of monitoring site, and SO2 emissions associated with those
> 5 tons of SO2 per year,
stationary sources.
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
301110066
n
1
1
6
24
24
13
4
4
2
5
4
4
7
0
0
4
28
1
1
4
4
1
1
5
5
5
4
0
2
0
15
4
Distance of monitor to SO2 emission source (km)1
mean
6.3
13.7
7.7
9.2
10.9
11.7
4.5
3.9
1.3
8.7
3.8
4.9
9.5
6.4
5.4
0.7
0.9
8.2
9.2
1.7
4.6
9.7
9.8
10.2
8.3
7.3
12.6
3.1
std
4.2
4.5
6.9
6.5
5.0
3.7
1.0
6.9
3.6
4.4
5.0
4.3
4.7
4.5
6.1
7.4
7.4
7.1
6.6
6.6
3.4
0.5
min
6.3
13.7
1.9
3.9
2.2
0.6
1.1
0.4
0.6
2.4
0.6
0.9
1.1
0.7
2.4
0.7
0.9
2.3
0.6
1.7
4.6
0.2
0.7
1.6
1.4
2.7
4.3
2.6
p2.5
6.3
13.7
1.9
3.9
2.2
0.6
1.1
0.4
0.6
2.4
0.6
0.9
1.1
0.7
2.4
0.7
0.9
2.3
0.6
1.7
4.6
0.2
0.7
1.6
1.4
2.7
4.3
2.6
p50
6.3
13.7
8.8
7.0
13.9
11.5
2.5
3.2
1.3
7.4
3.1
4.0
11.7
7.1
3.4
0.7
0.9
9.3
11.0
1.7
4.6
11.4
11.9
10.6
8.2
7.3
13.5
3.1
p97.5
6.3
13.7
11.7
19.5
19.7
19.8
12.0
8.8
2.0
19.2
8.5
10.4
15.1
10.7
18.1
0.7
0.9
11.8
14.0
1.7
4.6
17.1
17.5
17.3
15.3
12.0
17.3
3.7
max
6.3
13.7
11.7
19.5
19.7
19.8
12.0
8.8
2.0
19.2
8.5
10.4
15.1
10.7
18.1
0.7
0.9
11.8
14.0
1.7
4.6
17.1
17.5
17.3
15.3
12.0
17.3
3.7
SO2 emissions i
mean
20
20
421
1098
1657
1410
1262
2684
4694
6227
7763
7763
1345
9208
1116
3563
3563
2302
2302
43340
43340
11145
11145
11145
8117
6747
4516
1370
std
689
3356
4554
4437
1594
3305
839
6934
6956
6956
1810
10818
3650
2728
2728
10277
10277
10277
8927
934
11970
1322
tpy) from sources within 20 km of monitor1
min
20
20
8
6
5
7
11
20
4101
83
463
463
17
15
6
3563
3563
5
5
43340
43340
243
243
243
243
6087
6
75
p2.5
20
20
8
6
5
7
11
20
4101
83
463
463
17
15
6
3563
3563
5
5
43340
43340
243
243
243
243
6087
6
75
p50
20
20
22
28
60
24
765
1934
4694
3790
7345
7345
336
7845
33
3563
3563
1772
1772
43340
43340
15223
15223
15223
7889
6747
136
1135
p97.5
20
20
1689
15958
19923
16141
3509
6850
5287
15901
15901
15901
4963
21127
18680
3563
3563
5657
5657
43340
43340
23258
23258
23258
16447
7408
45960
3135
p100
20
20
1689
15958
19923
16141
3509
6850
5287
15901
15901
15901
4963
21127
18680
3563
3563
5657
5657
43340
43340
23258
23258
23258
16447
7408
45960
3135
A-26
-------
Monitor ID
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
420030067
420030116
n
4
4
4
4
6
4
9
12
1
1
0
1
1
3
2
1
1
0
2
2
2
1
1
1
0
1
1
19
64
62
64
54
16
19
Distance of monitor to SO2 emission source (km)1
mean
7.8
2.4
3.4
3.4
10.3
4.0
6.3
6.9
11.4
4.0
18.6
9.8
7.7
9.0
13.9
17.3
16.1
2.5
2.7
2.6
5.1
8.5
2.8
1.8
7.4
11.7
13.9
11.7
6.0
15.1
7.4
std
3.0
1.8
2.7
0.7
6.6
2.6
5.7
4.8
6.9
1.1
0.1
2.6
2.0
5.9
3.3
5.1
3.3
5.2
3.5
5.1
min
5.8
0.9
1.7
2.7
3.1
2.3
1.2
0.6
11.4
4.0
18.6
9.8
3.0
8.2
13.9
17.3
16.1
0.7
1.3
2.6
5.1
8.5
2.8
1.8
0.6
3.2
1.3
3.1
2.0
6.1
2.1
p2.5
5.8
0.9
1.7
2.7
3.1
2.3
1.2
0.6
11.4
4.0
18.6
9.8
3.0
8.2
13.9
17.3
16.1
0.7
1.3
2.6
5.1
8.5
2.8
1.8
0.6
4.8
1.4
4.7
2.0
6.1
2.1
p50
6.7
1.9
2.3
3.4
7.4
3.0
3.9
7.1
11.4
4.0
18.6
9.8
4.6
9.0
13.9
17.3
16.1
2.5
2.7
2.6
5.1
8.5
2.8
1.8
8.6
13.1
14.4
13.2
3.1
15.7
7.7
p97.5
12.2
5.0
7.3
4.4
18.6
7.8
17.8
14.5
11.4
4.0
18.6
9.8
15.7
9.7
13.9
17.3
16.2
4.3
4.1
2.6
5.1
8.5
2.8
1.8
18.1
18.0
18.7
18.1
17.9
19.7
17.0
max
12.2
5.0
7.3
4.4
18.6
7.8
17.8
14.5
11.4
4.0
18.6
9.8
15.7
9.7
13.9
17.3
16.2
4.3
4.1
2.6
5.1
8.5
2.8
1.8
18.1
18.7
19.8
18.7
18.2
19.7
17.0
SO2 emissions i
mean
1370
1370
1370
1370
2550
1370
438
2502
283
283
426
4592
257
378
5
210
411
45808
45808
4592
4592
28565
1605
1605
103
819
757
819
213
73
103
std
1322
1322
1322
1322
2627
1322
848
5987
226
119
522
55924
55924
137
5274
5327
5274
741
105
137
tpy) from sources within 20 km of monitor1
min
75
75
75
75
75
75
5
6
283
283
426
4592
15
294
5
210
42
6264
6264
4592
4592
28565
1605
1605
7
5
5
5
5
7
7
p2.5
75
75
75
75
75
75
5
6
283
283
426
4592
15
294
5
210
42
6264
6264
4592
4592
28565
1605
1605
7
7
7
7
6
7
7
p50
1135
1135
1135
1135
1976
1135
46
50
283
283
426
4592
294
378
5
210
411
45808
45808
4592
4592
28565
1605
1605
30
47
46
47
52
29
30
p97.5
3135
3135
3135
3135
7415
3135
2591
20865
283
283
426
4592
462
462
5
210
781
85352
85352
4592
4592
28565
1605
1605
468
5395
468
5395
1164
407
468
p100
3135
3135
3135
3135
7415
3135
2591
20865
283
283
426
4592
462
462
5
210
781
85352
85352
4592
4592
28565
1605
1605
468
42018
42018
42018
5395
407
468
A-27
-------
Monitor ID
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
541071002
n
57
54
55
10
8
13
4
5
66
60
68
2
2
33
1
8
1
16
0
7
12
11
1
10
8
13
3
8
8
8
8
11
Distance of monitor to SO2 emission source (km)1
mean
9.9
5.6
5.9
13.0
9.6
9.8
8.5
3.1
8.0
10.4
8.8
4.0
3.0
15.7
1.1
15.9
13.2
7.2
4.6
11.7
11.5
14.9
14.0
14.7
10.9
9.8
9.7
9.6
9.6
9.5
8.5
std
4.6
5.4
6.0
3.2
5.6
7.1
7.4
1.9
5.6
4.9
5.4
1.2
1.6
4.7
4.1
5.0
4.3
4.5
5.4
4.1
1.2
5.9
8.0
5.5
5.5
6.0
6.4
5.4
min
1.1
1.0
0.6
9.2
2.5
1.3
1.5
1.2
0.9
0.9
1.1
3.2
1.9
1.1
1.1
9.3
13.2
1.1
0.2
2.1
0.5
14.9
6.4
12.3
1.4
2.4
1.7
1.5
1.0
0.9
2.7
p2.5
1.1
1.0
0.7
9.2
2.5
1.3
1.5
1.2
1.0
1.7
1.4
3.2
1.9
1.1
1.1
9.3
13.2
1.1
0.2
2.1
0.5
14.9
6.4
12.3
1.4
2.4
1.7
1.5
1.0
0.9
2.7
p50
11.0
2.3
3.3
11.4
8.8
10.3
8.9
2.6
7.0
10.7
9.3
4.0
3.0
17.5
1.1
17.2
13.2
6.2
3.4
10.7
13.0
14.9
15.9
15.3
10.9
8.9
10.6
10.7
11.3
11.4
8.8
p97.5
17.5
17.8
18.8
18.6
17.1
19.8
14.9
5.4
19.4
18.6
18.7
4.9
4.1
18.7
1.1
19.7
13.2
16.3
13.2
17.4
19.2
14.9
18.7
15.6
18.5
18.3
16.0
15.8
15.8
16.2
17.0
max
17.8
17.8
18.8
18.6
17.1
19.8
14.9
5.4
20.0
19.2
19.8
4.9
4.1
18.7
1.1
19.7
13.2
16.3
13.2
17.4
19.2
14.9
18.7
15.6
18.5
18.3
16.0
15.8
15.8
16.2
17.0
SO2 emissions i
mean
914
213
209
18726
5173
1140
4195
824
285
104
319
2445
2445
257
7
321
65
2183
5834
89
948
5
61
5061
995
1245
1271
1271
1271
1271
4375
std
5587
741
735
19819
10474
3818
5171
1068
1022
318
1042
659
659
945
439
6339
14038
136
2944
103
12720
2730
1415
2194
2194
2194
2194
9095
tpy) from sources within 20 km of monitor1
min
5
5
5
18
9
14
34
10
5
5
5
1979
1979
5
7
7
65
6
6
6
5
5
5
7
5
8
25
25
25
25
7
1 Mean, std , min, p2.5, p50, p97.5, max are the arithmetic average, standard deviation, minimum, 2.5tn, 50
distances and emissions.
p2.5
7
6
6
18
9
14
34
10
5
6
5
1979
1979
5
7
7
65
6
6
6
5
5
5
7
5
8
25
25
25
25
7
p50
47
52
49
15912
157
37
3004
228
26
22
27
2445
2445
47
7
82
65
28
24
20
9
5
18
89
52
939
343
343
343
343
1517
p97.5
5395
1164
1164
59928
30312
13841
10738
2398
4450
560
4450
2911
2911
5395
7
1017
65
25544
37622
411
9820
5
343
36378
9820
2788
6285
6285
6285
6285
31006
p100
42018
5395
5395
59928
30312
13841
10738
2398
6720
2378
6720
2911
2911
5395
7
1017
65
25544
37622
411
9820
5
343
36378
9820
2788
6285
6285
6285
6285
31006
n, 97.5tn percentiles, and maximum
A-28
-------
Table A-4. Distance of 1-hour ambient monitors 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.
Monitor ID2
010330044
010331002
010710020
010731003
010790003
010830004
010970028
010972005
011011002
040070009
040071001
040130019
040133002
040133003
040133010
040139997
040191011
040212001
050910096
050910097
050910098
050910099
051190007
051191002
051191005
051390006
060010010
060130002
060130003
060130006
060130010
060131001
060131002
n
3
4
3
43
5
4
10
9
4
0
2
8
9
9
8
7
1
0
5
5
5
5
1
1
1
6
7
15
9
9
15
13
3
Distance of monitor to SO2 emission source (km)1
mean
6.0
15.6
5.7
11.4
8.4
14.6
7.5
7.7
12.7
1.3
11.0
10.8
12.5
10.0
11.3
6.1
8.4
10.2
10.8
8.0
6.3
13.7
9.7
7.7
8.9
13.5
12.8
13.0
8.3
10.1
11.7
std
0.7
5.3
2.4
5.5
1.7
0.8
5.8
2.1
7.2
0.5
3.6
6.6
4.7
5.3
3.6
0.0
0.0
0.0
0.0
4.2
5.4
2.8
5.8
6.4
5.6
5.9
1.4
min
5.5
7.7
3.1
1.1
5.5
13.8
1.4
4.3
4.5
0.9
5.6
1.9
5.5
5.0
8.1
6.1
8.4
10.2
10.8
8.0
6.3
13.7
9.7
1.9
1.2
9.6
3.3
2.5
1.6
0.2
10.1
p2.5
5.5
7.7
3.1
1.2
5.5
13.8
1.4
4.3
4.5
0.9
5.6
1.9
5.5
5.0
8.1
6.1
8.4
10.2
10.8
8.0
6.3
13.7
9.7
1.9
1.2
9.6
3.3
2.5
1.6
0.2
10.1
p50
5.9
18.0
6.2
13.1
8.6
14.6
6.1
7.2
13.2
1.3
10.2
11.2
12.4
8.5
9.9
6.1
8.4
10.2
10.8
8.0
6.3
13.7
9.7
8.8
9.0
13.3
14.3
15.0
6.4
9.9
12.4
P97.5
6.8
18.6
7.8
16.8
9.8
15.4
19.1
10.1
19.9
1.6
16.9
19.2
18.5
18.9
18.9
6.1
8.5
10.2
10.8
8.1
6.3
13.7
9.7
11.7
16.8
17.8
18.5
19.3
19.7
19.8
12.7
max
6.8
18.6
7.8
19.8
9.8
15.4
19.1
10.1
19.9
1.6
16.9
19.2
18.5
18.9
18.9
6.1
8.5
10.2
10.8
8.1
6.3
13.7
9.7
11.7
16.8
17.8
18.5
19.3
19.7
19.8
12.7
SO2 emissions
mean
16680
12512
15119
151
1787
2229
6613
132
913
9219
23
21
20
23
24
3119
74
74
74
74
20
20
20
421
53
1004
559
559
1189
1507
26
std
28821
24965
25004
227
3416
3776
13057
154
1183
10723
19
19
19
19
21
55
55
55
55
689
66
2007
789
789
1977
2036
21
tpy) from sources within 20 km of monitor1
min
30
8
98
5
6
6
14
5
180
1637
10
10
6
10
10
3119
29
29
29
29
20
20
20
8
5
6
5
5
6
6
6
p2.5
30
8
98
5
6
6
14
5
180
1637
10
10
6
10
10
3119
29
29
29
29
20
20
20
8
5
6
5
5
6
6
6
p50
51
41
1276
38
58
529
214
72
403
9219
19
14
14
18
19
3119
53
53
53
53
20
20
20
22
14
58
38
38
419
793
25
P97.5
49960
49960
43983
786
7852
7852
38917
440
2663
16801
69
69
69
69
69
3119
164
164
164
164
20
20
20
1689
187
7009
1829
1829
7009
7009
48
max
49960
49960
43983
982
7852
7852
38917
440
2663
16801
69
69
69
69
69
3119
164
164
164
164
20
20
20
1689
187
7009
1829
1829
7009
7009
48
A-29
-------
Monitor ID2
060131003
060131004
060132001
060133001
060190008
060190243
060190244
060231004
060250005
060250006
060290014
060290232
060370030
060370031
060371002
060371103
060374002
060375001
060375005
060591003
060658001
060670002
060670006
060710012
060710014
060710015
060710017
060710306
060711234
060712002
060714001
060730001
060731007
060731010
060732007
060750005
n
9
9
15
16
6
4
4
2
1
0
6
7
14
27
3
15
32
31
12
7
4
1
1
1
2
3
0
2
3
2
1
1
3
3
1
6
Distance of monitor to SO2 emission source (km)1
mean
12.6
12.8
8.8
9.8
14.1
10.1
13.2
4.7
18.0
10.8
7.3
10.4
7.2
6.8
13.8
10.4
13.4
9.1
13.9
16.8
14.8
9.7
11.9
8.0
5.7
8.1
4.9
13.2
6.5
4.0
12.9
13.1
16.4
13.3
std
4.8
5.7
5.4
6.1
4.8
3.5
1.9
0.1
2.2
6.8
5.4
5.8
2.1
5.1
5.2
5.9
5.9
5.7
4.7
3.0
6.9
3.3
5.8
2.9
1.3
2.2
6.2
min
5.4
4.1
2.3
0.7
8.5
5.7
10.7
4.6
18.0
7.0
2.1
2.8
1.1
4.7
6.3
4.1
3.7
2.3
5.3
9.8
14.8
9.7
11.9
5.9
1.7
5.7
1.3
11.2
6.5
4.0
11.8
10.8
16.4
1.8
p2.5
5.4
4.1
2.3
0.7
8.5
5.7
10.7
4.6
18.0
7.0
2.1
2.8
1.1
4.7
6.3
4.1
3.7
2.3
5.3
9.8
14.8
9.7
11.9
5.9
1.7
5.7
1.3
11.2
6.5
4.0
11.8
10.8
16.4
1.8
p50
12.2
13.5
6.7
11.4
12.4
10.7
13.5
4.7
18.0
11.5
3.4
8.7
4.3
6.9
12.5
9.3
16.4
6.0
15.6
18.8
14.8
9.7
11.9
8.0
1.8
8.1
1.9
13.2
6.5
4.0
12.5
13.3
16.4
15.2
P97.5
19.0
19.1
19.9
18.6
19.9
13.3
15.2
4.8
18.0
13.1
18.4
17.3
19.0
8.8
19.8
19.5
19.6
19.8
19.7
19.6
14.8
9.7
11.9
10.1
13.7
10.4
11.7
15.3
6.5
4.0
14.4
15.2
16.4
18.3
max
19.0
19.1
19.9
18.6
19.9
13.3
15.2
4.8
18.0
13.1
18.4
17.3
19.0
8.8
19.8
19.5
19.6
19.8
19.7
19.6
14.8
9.7
11.9
10.1
13.7
10.4
11.7
15.3
6.5
4.0
14.4
15.2
16.4
18.3
SO2 emissions
mean
559
559
1189
507
28
30
30
23
7
39
35
39
208
17
37
183
203
192
10
75
5
58
8
126
97
126
97
102
32
21
11
11
21
66
std
789
789
1977
1104
22
28
28
25
52
48
36
336
7
36
313
342
332
5
76
132
85
132
85
112
9
9
83
tpy) from sources within 20 km of monitor1
min
5
5
6
6
9
9
9
5
7
5
5
7
5
10
7
5
5
6
5
17
5
58
8
32
6
32
6
22
32
21
5
5
21
5
p2.5
5
5
6
6
9
9
9
5
7
5
5
7
5
10
7
5
5
6
5
17
5
58
8
32
6
32
6
22
32
21
5
5
21
5
p50
38
38
419
48
24
20
20
23
7
17
11
30
37
17
29
46
61
33
7
50
5
58
8
126
110
126
110
102
32
21
7
7
21
39
P97.5
1829
1829
7009
4337
70
70
70
41
7
138
138
119
1503
24
119
1503
1503
1119
18
181
5
58
8
219
175
219
175
181
32
21
21
21
21
224
max
1829
1829
7009
4337
70
70
70
41
7
138
138
119
1503
24
119
1503
1503
1119
18
181
5
58
8
219
175
219
175
181
32
21
21
21
21
224
A-30
-------
Monitor ID2
060750006
060791005
060792001
060792004
060794002
060830008
060831007
060831012
060831013
060831015
060831016
060831019
060831020
060831025
060831026
060831027
060832004
060832011
060834003
060835001
060870003
060950001
060950004
061113001
080010007
080013001
080310002
080416001
080416004
080416011
080416018
090010012
090010017
090011123
090012124
090019003
n
5
7
7
7
7
3
7
2
2
1
1
1
3
3
3
3
2
3
2
0
1
13
12
2
24
20
24
3
3
3
2
11
3
0
4
10
Distance of monitor to SO2 emission source (km)1
mean
12.2
1.0
10.5
2.5
18.4
9.7
17.3
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
9.8
8.3
9.2
6.2
13.0
9.9
6.8
6.1
9.6
7.4
13.2
std
5.8
0.2
0.2
0.1
0.1
6.2
0.1
0.1
0.1
8.2
8.9
8.0
7.7
0.1
8.4
0.1
3.3
4.9
5.1
6.1
5.8
4.5
8.6
3.9
7.6
0.5
4.9
6.4
6.0
5.1
min
2.5
0.8
10.2
2.3
18.3
2.8
17.1
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
2.4
1.6
3.9
0.8
10.7
2.5
6.5
2.1
5.7
2.3
4.0
p2.5
2.5
0.8
10.2
2.3
18.3
2.8
17.1
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
2.4
1.6
3.9
0.8
10.7
2.5
6.5
2.1
5.7
2.3
4.0
p50
14.4
1.0
10.4
2.6
18.5
11.3
17.3
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
8.2
5.9
7.0
1.8
10.9
9.6
6.8
4.8
6.2
6.5
14.0
P97.5
16.8
1.5
10.9
2.7
18.5
14.9
17.5
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
19.7
19.8
19.5
16.1
17.6
17.7
7.2
19.7
17.0
14.4
19.5
max
16.8
1.5
10.9
2.7
18.5
14.9
17.5
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
19.7
19.8
19.5
16.1
17.6
17.7
7.2
19.7
17.0
14.4
19.5
SO2 emissions
mean
35
536
536
536
536
39
536
554
554
18
18
18
39
39
39
39
554
39
554
722
1371
1480
9
1001
1191
1098
1670
2849
2849
4268
425
252
192
504
std
35
1369
1369
1369
1369
43
1369
357
357
43
43
43
43
357
43
357
2071
2124
3
3352
3657
3356
2857
4920
4920
6026
1198
423
366
1257
tpy) from sources within 20 km of monitor1
min
5
6
6
6
6
10
6
302
302
18
18
18
10
10
10
10
302
10
302
722
6
6
7
8
8
6
7
7
7
7
5
5
5
5
p2.5
5
6
6
6
6
10
6
302
302
18
18
18
10
10
10
10
302
10
302
722
6
6
7
8
8
6
7
7
7
7
5
5
5
5
p50
14
24
24
24
24
18
24
554
554
18
18
18
18
18
18
18
554
18
554
722
790
791
9
25
28
28
34
10
10
4268
21
11
10
10
P97.5
80
3642
3642
3642
3642
89
3642
807
807
18
18
18
89
89
89
89
807
89
807
722
7009
7009
11
15958
15958
15958
4969
8530
8530
8530
4024
741
741
4024
max
80
3642
3642
3642
3642
89
3642
807
807
18
18
18
89
89
89
89
807
89
807
722
7009
7009
11
15958
15958
15958
4969
8530
8530
8530
4024
741
741
4024
A-31
-------
Monitor ID2
090031005
090031018
090032006
090090027
090091003
090091123
090092123
090110007
090130003
100031003
100031007
100031008
100031013
100032002
100032004
100051002
110010041
120090011
120110010
120310032
120310080
120310081
120310097
120330004
120330022
120470015
120570021
120570053
120570081
120570095
120570109
120571035
120571065
120574004
120813002
120860019
n
28
7
6
8
9
9
5
6
0
34
11
24
34
36
39
5
13
5
8
14
15
13
14
6
6
3
18
19
18
17
16
18
20
3
5
7
Distance of monitor to SO2 emission source (km)1
mean
14.2
7.7
4.5
6.3
7.4
7.3
9.0
8.4
9.0
10.5
10.9
9.1
10.2
10.9
8.0
11.7
12.3
11.0
9.0
12.0
7.9
9.2
9.3
7.6
3.0
11.6
10.8
14.4
10.1
9.9
10.6
13.1
14.2
7.2
9.6
std
3.6
6.3
5.1
7.3
8.2
8.2
5.7
3.0
5.2
2.2
6.9
4.8
6.2
6.2
7.6
6.5
2.7
6.1
4.2
4.7
4.6
4.7
4.2
4.4
0.5
6.5
3.5
4.5
6.2
4.0
5.8
4.8
8.5
8.3
5.8
min
3.0
1.9
0.5
1.0
0.7
0.8
0.8
3.3
1.5
9.1
2.2
2.8
1.1
1.3
0.3
0.6
9.7
5.1
1.3
1.1
1.3
3.1
4.9
2.4
2.6
1.4
5.9
8.1
2.5
6.7
1.6
3.3
4.4
0.7
2.6
p2.5
3.0
1.9
0.5
1.0
0.7
0.8
0.8
3.3
1.5
9.1
2.2
2.8
1.1
1.3
0.3
0.6
9.7
5.1
1.3
1.1
1.3
3.1
4.9
2.4
2.6
1.4
5.9
8.1
2.5
6.7
1.6
3.3
4.4
0.7
2.6
p50
14.2
3.7
1.8
3.1
2.6
2.7
8.9
8.7
8.1
9.8
13.9
8.3
9.4
11.1
6.8
11.5
11.6
7.5
9.1
13.3
6.5
7.7
8.9
8.4
2.7
14.3
12.1
15.1
14.2
7.4
12.9
12.5
18.9
2.2
6.9
P97.5
19.9
18.4
11.4
18.6
19.7
19.7
15.2
12.7
19.8
16.2
19.7
18.9
19.7
19.8
18.5
19.8
17.0
19.2
18.5
19.7
15.5
19.5
14.6
12.3
3.6
18.2
17.3
19.6
19.3
19.9
16.3
19.6
19.3
16.5
19.1
max
19.9
18.4
11.4
18.6
19.7
19.7
15.2
12.7
19.8
16.2
19.7
18.9
19.7
19.8
18.5
19.8
17.0
19.2
18.5
19.7
15.5
19.5
14.6
12.3
3.6
18.2
17.3
19.6
19.3
19.9
16.3
19.6
19.3
16.5
19.1
SO2 emissions
mean
45
16
14
595
565
565
86
650
975
3126
1657
975
802
1526
674
1410
3101
2397
2715
2534
2923
2715
7262
7262
755
4986
4728
6781
3845
4084
4986
4502
2872
73
34
std
106
9
7
1388
1302
1302
96
1088
1619
6528
4554
1619
1272
3681
1447
4437
4254
6653
5784
5617
5965
5784
14101
14101
1268
11445
11180
13097
11285
11610
11445
10928
4949
93
45
tpy) from sources within 20 km of monitor1
min
5
5
5
5
5
5
9
7
5
15
5
5
5
5
5
7
10
17
5
5
5
5
6
6
18
6
6
6
6
6
6
6
11
6
5
p2.5
5
5
5
5
5
5
9
7
5
15
5
5
5
5
5
7
10
17
5
5
5
5
6
6
18
6
6
6
6
6
6
6
11
6
5
p50
12
15
15
32
43
43
28
110
112
103
60
112
97
116
47
24
2102
41
287
257
317
287
330
330
27
341
104
1116
61
83
341
156
19
9
12
P97.5
522
30
25
4012
4012
4012
198
2755
6720
19923
19923
6720
5051
19923
3262
16141
10334
18861
20908
20908
20908
20908
35417
35417
2218
47103
47103
47103
47103
47103
47103
47103
8587
208
130
max
522
30
25
4012
4012
4012
198
2755
6720
19923
19923
6720
5051
19923
3262
16141
10334
18861
20908
20908
20908
20908
35417
35417
2218
47103
47103
47103
47103
47103
47103
47103
8587
208
130
A-32
-------
Monitor ID2
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
132450003
150010005
150010007
150030010
150030011
150031001
150031006
160050004
160050015
n
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
15
0
0
7
7
3
7
2
4
Distance of monitor to SO2 emission source (km)1
mean
4.5
4.2
13.8
12.0
7.4
10.3
13.6
9.8
10.4
11.9
5.8
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
8.0
5.0
5.7
10.1
6.1
1.3
13.1
std
5.0
4.6
2.8
3.1
6.4
4.2
0.1
4.0
3.1
6.2
3.4
0.8
5.4
5.2
5.2
4.1
4.4
3.4
5.3
0.4
2.1
3.3
2.8
2.6
1.4
4.6
5.3
8.2
4.8
0.1
7.6
min
1.1
1.1
10.1
7.0
2.3
3.5
13.5
7.0
3.7
2.7
2.6
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
4.7
2.5
2.2
0.7
1.8
1.2
6.5
p2.5
1.1
1.1
10.1
7.0
2.3
3.5
13.5
7.0
3.7
2.7
2.6
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
4.7
2.5
2.2
0.7
1.8
1.2
6.5
p50
2.5
2.4
13.6
12.0
3.7
10.0
13.6
9.8
10.8
13.7
5.6
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
8.2
3.3
4.1
13.8
5.0
1.3
13.1
P97.5
12.0
11.0
17.6
16.7
19.6
15.4
13.7
12.6
14.4
19.9
9.3
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
10.0
15.3
17.5
15.7
16.5
1.4
19.7
max
12.0
11.0
17.6
16.7
19.6
15.4
13.7
12.6
14.4
19.9
9.3
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
10.0
15.3
17.5
15.7
16.5
1.4
19.7
SO2 emissions
mean
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
1335
2231
2231
1043
2231
804
412
std
1594
1594
4
38
7041
7521
21767
21767
2929
2627
12565
90
52282
80047
468
2664
2664
2664
2220
9625
10445
10445
948
3214
2379
2339
2339
1509
2339
606
572
tpy) from sources within 20 km of monitor1
min
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
8
79
79
6
79
376
13
p2.5
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
8
79
79
6
79
376
13
p50
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
545
1566
1566
350
1566
804
201
P97.5
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
8275
6978
6978
2774
6978
1233
1233
max
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
8275
6978
6978
2774
6978
1233
1233
A-33
-------
Monitor ID2
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
171631011
171670006
171790004
171850001
171851001
171970013
n
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
2
5
6
3
3
19
Distance of monitor to SO2 emission source (km)1
mean
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
10.1
9.5
10.5
12.2
12.4
13.2
6.4
11.4
9.3
10.4
4.0
7.3
5.4
2.9
5.9
6.6
std
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
3.7
6.6
6.8
6.9
7.5
5.8
0.4
5.6
4.1
4.3
0.6
3.6
5.2
0.1
0.1
4.8
min
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
3.2
0.5
0.7
2.4
2.9
1.3
6.0
2.3
1.3
1.1
3.6
4.9
0.8
2.8
5.8
1.1
p2.5
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
3.2
0.7
0.7
2.4
2.9
1.3
6.0
2.3
1.3
1.1
3.6
4.9
0.8
2.8
5.8
1.1
p50
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
9.5
11.2
15.6
16.1
14.7
15.5
6.4
12.3
9.6
11.7
4.0
5.6
3.6
2.9
5.8
5.2
P97.5
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
19.7
19.0
18.4
18.9
19.8
18.8
6.7
17.2
18.5
19.4
4.4
13.5
13.8
3.1
6.0
18.6
max
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
19.7
19.6
18.4
18.9
19.8
18.8
6.7
17.2
18.5
19.4
4.4
13.5
13.8
3.1
6.0
18.6
SO2 emissions
mean
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
13148
2170
12212
42452
42452
2439
std
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
18554
3169
13311
25439
25439
6269
tpy) from sources within 20 km of monitor1
min
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
28
9
22
27097
27097
6
p2.5
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
28
9
22
27097
27097
6
p50
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
13148
202
10290
28443
28443
37
P97.5
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
26268
7210
35748
71817
71817
25224
max
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
26268
7210
35748
71817
71817
25224
A-34
-------
Monitor ID2
180270002
180290004
180330002
180430004
180430007
180431004
180450001
180510001
180510002
180630001
180630002
180630003
180730002
180730003
180770004
180890022
180891016
180892008
180910005
180910007
180970042
180970054
180970057
180970072
180970073
181091001
181230006
181230007
181250005
181270011
181270017
181270023
181470002
181470010
181530004
181630012
n
6
7
2
8
10
9
3
3
3
0
1
0
4
4
2
50
52
39
3
2
22
20
20
21
20
3
8
8
6
23
22
21
7
4
3
5
Distance of monitor to SO2 emission source (km)1
mean
6.3
4.2
0.8
13.4
9.2
10.0
9.8
2.0
2.9
19.2
4.3
10.2
4.3
14.1
14.2
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
13.0
12.3
12.1
13.1
std
0.6
4.1
0.0
3.1
6.4
3.5
8.7
0.1
0.0
1.0
1.2
0.1
4.0
4.2
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
3.6
6.6
6.4
7.7
min
5.8
1.2
0.8
8.8
1.1
5.0
4.5
1.8
2.9
19.2
3.5
9.5
4.3
0.8
2.1
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
8.0
3.3
4.8
3.1
p2.5
5.8
1.2
0.8
8.8
1.1
5.0
4.5
1.8
2.9
19.2
3.5
9.5
4.3
1.8
2.1
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
8.0
3.3
4.8
3.1
p50
6.0
3.4
0.8
12.3
7.3
9.8
5.1
2.0
2.9
19.2
4.0
9.7
4.3
14.6
14.8
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
15.0
14.0
14.7
18.0
P97.5
7.3
12.8
0.8
17.7
19.9
14.7
19.8
2.1
3.0
19.2
5.8
12.1
4.4
19.8
19.5
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
16.6
17.9
16.8
19.6
max
7.3
12.8
0.8
17.7
19.9
14.7
19.8
2.1
3.0
19.2
5.8
12.1
4.4
19.9
19.7
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
16.6
17.9
16.8
19.6
SO2 emissions
mean
10869
21579
80
6500
6721
7442
18552
42452
42452
147
6874
6874
19099
1014
1138
938
4166
4599
2358
2554
2554
2433
2547
6006
7033
7033
10869
1703
1363
1427
15627
15099
9270
1806
std
16456
32930
42
10778
10131
10470
32099
25439
25439
1422
1422
1297
1502
1804
1945
4640
6476
6820
7138
7138
6980
7141
9709
17145
17145
16456
2266
1612
1623
24405
25616
8089
2589
tpy) from sources within 20 km of monitor1
min
9
174
50
12
12
12
10
27097
27097
147
6085
6085
18182
5
5
5
20
20
5
5
5
5
5
242
7
7
9
20
20
23
7
20
10
5
p2.5
9
174
50
12
12
12
10
27097
27097
147
6085
6085
18182
6
6
5
20
20
5
5
5
5
5
242
7
7
9
20
20
23
7
20
10
5
p50
2241
1574
80
484
516
798
28
28443
28443
147
6204
6204
19099
188
188
72
3301
4599
36
23
23
19
18
561
38
38
2241
1062
1029
1062
66
3589
12846
382
P97.5
41536
85699
109
23995
23995
23995
55617
71817
71817
147
9002
9002
20016
5951
6318
8443
9178
9178
30896
30896
30896
30896
30896
17216
49028
49028
41536
9178
6318
6318
53196
53196
14955
6004
max
41536
85699
109
23995
23995
23995
55617
71817
71817
147
9002
9002
20016
6318
8443
8443
9178
9178
30896
30896
30896
30896
30896
17216
49028
49028
41536
9178
6318
6318
53196
53196
14955
6004
A-35
-------
Monitor ID2
181631002
181670018
181671014
181730002
181731001
181770006
181770007
190330018
190450018
190450019
190450020
190610012
191110006
191111007
191130026
191130028
191130029
191130031
191130032
191130034
191130035
191130038
191130039
191390016
191390017
191390020
191530030
191630014
191630015
191630017
191770004
191770005
191770006
191930018
201070002
201250006
n
5
6
6
8
8
2
2
4
2
2
2
2
1
2
7
7
7
7
7
7
7
7
7
5
4
4
1
1
7
7
0
0
0
4
0
4
Distance of monitor to SO2 emission source (km)1
mean
8.5
6.8
6.8
2.9
3.0
2.1
3.2
3.9
1.4
1.3
3.4
4.0
3.7
13.3
7.0
5.8
3.8
4.3
3.5
3.6
4.3
3.9
4.6
8.7
3.8
4.9
4.9
18.7
9.5
9.6
6.4
5.8
std
5.3
3.6
5.9
0.4
0.5
1.4
2.5
3.7
0.9
1.0
0.6
2.9
6.3
3.1
2.4
3.1
3.2
2.7
2.5
1.7
1.9
3.0
6.9
3.6
4.4
5.0
4.2
4.3
9.3
min
3.4
5.0
1.9
2.5
2.5
1.1
1.4
0.4
0.7
0.6
3.0
1.9
3.7
8.8
2.8
2.8
0.5
0.5
0.6
0.2
1.3
0.6
1.1
2.4
0.6
0.9
4.9
18.7
1.1
1.1
0.7
0.5
p2.5
3.4
5.0
1.9
2.5
2.5
1.1
1.4
0.4
0.7
0.6
3.0
1.9
3.7
8.8
2.8
2.8
0.5
0.5
0.6
0.2
1.3
0.6
1.1
2.4
0.6
0.9
4.9
18.7
1.1
1.1
0.7
0.5
p50
9.5
5.5
5.5
3.0
2.9
2.1
3.2
3.2
1.4
1.3
3.4
4.0
3.7
13.3
7.7
6.7
4.0
4.7
3.1
2.9
4.8
4.2
4.2
7.4
3.1
4.0
4.9
18.7
11.7
11.2
7.1
1.6
P97.5
16.5
14.1
17.3
3.3
3.7
3.1
5.0
8.8
2.0
2.0
3.8
6.1
3.7
17.7
11.8
8.8
9.2
9.3
8.8
7.4
5.9
6.2
10.3
19.2
8.5
10.4
4.9
18.7
15.1
13.6
10.7
19.7
max
16.5
14.1
17.3
3.3
3.7
3.1
5.0
8.8
2.0
2.0
3.8
6.1
3.7
17.7
11.8
8.8
9.2
9.3
8.8
7.4
5.9
6.2
10.3
19.2
8.5
10.4
4.9
18.7
15.1
13.6
10.7
19.7
SO2 emissions
mean
1806
10842
10842
13636
13636
6446
6446
2684
4694
4694
4694
1886
29
104
2200
2200
2200
2200
2200
2200
2200
2200
2200
6227
7763
7763
20
2329
1345
2120
9208
468
std
2589
25028
25028
16457
16457
9089
9089
3305
839
839
839
52
105
2428
2428
2428
2428
2428
2428
2428
2428
2428
6934
6956
6956
1810
3515
10818
464
tpy) from sources within 20 km of monitor1
min
5
12
12
50
50
19
19
20
4101
4101
4101
1848
29
29
12
12
12
12
12
12
12
12
12
83
463
463
20
2329
17
17
15
11
p2.5
5
12
12
50
50
19
19
20
4101
4101
4101
1848
29
29
12
12
12
12
12
12
12
12
12
83
463
463
20
2329
17
17
15
11
p50
382
417
417
3559
3559
6446
6446
1934
4694
4694
4694
1886
29
104
1954
1954
1954
1954
1954
1954
1954
1954
1954
3790
7345
7345
20
2329
336
303
7845
428
P97.5
6004
61901
61901
41049
41049
12873
12873
6850
5287
5287
5287
1923
29
179
5480
5480
5480
5480
5480
5480
5480
5480
5480
15901
15901
15901
20
2329
4963
8983
21127
1006
max
6004
61901
61901
41049
41049
12873
12873
6850
5287
5287
5287
1923
29
179
5480
5480
5480
5480
5480
5480
5480
5480
5480
15901
15901
15901
20
2329
4963
8983
21127
1006
A-36
-------
Monitor ID2
201250007
201450001
201730010
201910002
201950001
202090001
202090020
202090021
210190015
210190017
210191003
210370003
210371001
210590005
210670012
210890007
210910012
211010013
211010014
211110032
211110051
211111041
211130001
211170007
211390004
211450001
211451024
211451026
211771004
211830032
211950002
212270008
220150008
220190008
220330009
220511001
n
4
0
3
3
0
14
14
13
9
10
8
11
11
4
3
5
9
4
10
14
12
11
2
12
4
7
3
3
3
3
0
1
2
16
28
20
Distance of monitor to SO2 emission source (km)1
mean
7.2
11.4
16.3
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
17.0
11.9
8.0
7.5
18.2
15.3
15.9
13.5
19.1
8.7
7.6
5.8
14.1
std
6.9
3.1
2.4
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
1.4
4.8
6.9
3.4
2.1
2.2
0.3
5.7
0.1
6.1
5.6
2.9
min
3.3
9.0
13.6
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
16.0
4.7
3.1
2.0
15.8
12.7
15.5
7.1
19.1
8.6
1.2
1.5
8.8
p2.5
3.3
9.0
13.6
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
16.0
4.7
3.1
2.0
15.8
12.7
15.5
7.1
19.1
8.6
1.2
1.5
8.8
p50
3.9
10.2
17.3
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
17.0
12.5
5.4
9.4
19.3
16.5
15.9
15.4
19.1
8.7
5.8
3.2
13.1
P97.5
17.5
14.9
18.0
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
18.0
18.6
17.9
11.2
19.5
16.7
16.2
18.0
19.1
8.8
16.7
20.0
18.3
max
17.5
14.9
18.0
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
18.0
18.6
17.9
11.2
19.5
16.7
16.2
18.0
19.1
8.8
16.7
20.0
18.3
SO2 emissions
mean
468
269
269
1388
1388
1494
1323
1193
1271
6817
465
15241
209
961
12162
2256
10948
6208
3259
6268
23589
455
444
8769
587
587
32380
35331
52
77
3352
1406
425
std
464
448
448
2341
2341
2402
2058
1983
2194
20950
664
25506
316
1147
22226
2755
15581
8948
5326
9779
32550
637
869
13010
1005
1005
45236
42262
21
5531
3913
869
tpy) from sources within 20 km of monitor1
min
11
6
6
6
6
6
25
25
25
12
12
26
12
25
7
5
5
38
38
12
573
12
6
174
6
6
38
8893
52
62
6
6
6
p2.5
11
6
6
6
6
6
25
25
25
12
12
26
12
25
7
5
5
38
38
12
573
12
6
174
6
6
38
8893
52
62
6
6
6
p50
428
15
15
34
34
40
401
343
343
268
213
3871
42
401
38
1508
2980
516
168
234
23589
240
11
7435
7
7
13028
13028
52
77
184
45
38
P97.5
1006
785
785
7625
7625
7625
6285
6285
6285
69953
1848
53196
573
2589
53196
6004
41049
23995
14977
23995
46605
1848
1747
37077
1747
1747
84073
84073
52
91
18851
18680
3359
max
1006
785
785
7625
7625
7625
6285
6285
6285
69953
1848
53196
573
2589
53196
6004
41049
23995
14977
23995
46605
1848
1747
37077
1747
1747
84073
84073
52
91
18851
18680
3359
A-37
-------
Monitor ID2
220730004
220870002
220870007
220870009
221210001
230010011
230013003
230030009
230030012
230031003
230031013
230031018
230031100
230050014
230050027
230090103
230170011
230172007
240010006
240032002
240053001
245100018
245100036
250050010
250051004
250056001
250090005
250091004
250091005
250095004
250130016
250131009
250154002
250171701
250174003
250250002
n
1
18
18
18
28
9
9
1
1
1
3
4
5
12
12
1
2
2
2
20
22
21
21
25
24
40
25
23
22
14
34
32
12
55
57
62
Distance of monitor to SO2 emission source (km)1
mean
10.1
8.8
6.4
5.7
5.4
6.6
7.5
1.9
1.0
1.3
4.7
8.5
10.4
6.1
6.0
5.3
0.7
1.0
8.9
11.9
11.9
9.1
6.6
8.1
7.5
13.6
9.6
11.3
11.2
8.6
7.6
8.4
15.8
13.3
12.2
9.6
std
4.2
4.9
5.8
4.7
4.3
4.8
4.5
5.6
7.2
4.8
4.7
0.1
0.1
4.0
4.5
3.3
4.6
3.3
7.2
6.7
5.7
6.6
6.3
6.3
4.2
5.2
4.7
3.4
4.6
4.0
6.1
min
10.1
0.5
0.9
1.3
2.4
1.3
0.8
1.9
1.0
1.3
1.4
0.3
0.7
1.2
0.8
5.3
0.7
1.0
6.0
2.7
4.6
1.4
1.6
0.2
0.1
4.6
0.3
0.8
0.7
0.7
0.5
1.7
9.1
0.4
0.6
0.7
p2.5
10.1
0.5
0.9
1.3
2.4
1.3
0.8
1.9
1.0
1.3
1.4
0.3
0.7
1.2
0.8
5.3
0.7
1.0
6.0
2.7
4.6
1.4
1.6
0.2
0.1
4.7
0.3
0.8
0.7
0.7
0.5
1.7
9.1
2.9
5.6
1.1
p50
10.1
7.8
3.8
2.0
3.4
6.5
8.3
1.9
1.0
1.3
2.8
10.3
10.5
5.0
4.8
5.3
0.7
1.0
8.9
13.3
12.1
7.6
6.8
4.2
3.8
15.5
9.2
12.8
11.9
10.1
7.4
7.4
16.8
15.0
12.4
8.6
P97.5
10.1
19.0
15.9
17.9
18.1
13.3
15.2
1.9
1.0
1.3
9.9
13.0
18.4
16.8
16.6
5.3
0.8
1.1
11.7
19.9
19.2
16.7
16.0
20.0
18.9
19.9
19.9
20.0
18.6
14.7
19.2
18.9
19.7
19.4
19.5
19.5
max
10.1
19.0
15.9
17.9
18.1
13.3
15.2
1.9
1.0
1.3
9.9
13.0
18.4
16.8
16.6
5.3
0.8
1.1
11.7
19.9
19.2
16.7
16.0
20.0
18.9
19.9
19.9
20.0
18.6
14.7
19.2
18.9
19.7
20.0
19.7
19.7
SO2 emissions
mean
2166
419
419
419
1116
31
31
90
90
90
16
193
155
267
267
26
249
249
681
3247
4429
3101
4635
1794
1867
1146
65
878
917
88
216
65
72
139
127
129
std
846
846
846
3650
41
41
17
233
219
628
628
344
344
685
9622
11101
9402
11331
7923
8085
6273
148
3071
3137
197
907
148
113
678
663
639
tpy) from sources within 20 km of monitor1
min
2166
8
8
8
6
5
5
90
90
90
5
7
6
5
5
26
6
6
197
5
5
5
5
6
6
5
6
5
5
8
5
5
6
5
5
5
p2.5
2166
8
8
8
6
5
5
90
90
90
5
7
6
5
5
26
6
6
197
5
5
5
5
6
6
5
6
5
5
8
5
5
6
5
5
5
p50
2166
52
52
52
33
23
23
90
90
90
7
133
15
16
16
26
249
249
681
21
27
22
22
27
31
24
26
16
16
25
14
13
29
15
13
14
P97.5
2166
3009
3009
3009
18680
140
140
90
90
90
36
499
499
2091
2091
26
492
492
1166
39974
39974
39974
39974
39593
39593
21997
762
14132
14132
762
5282
671
363
640
460
640
max
2166
3009
3009
3009
18680
140
140
90
90
90
36
499
499
2091
2091
26
492
492
1166
39974
39974
39974
39974
39593
39593
39593
762
14132
14132
762
5282
671
363
5007
5007
5007
A-38
-------
Monitor ID2
250250019
250250020
250250021
250250040
250250042
250251003
250270020
250270023
260410902
260490021
260492001
260810020
260991003
261130001
261470005
261530001
261630001
261630005
261630015
261630016
261630019
261630025
261630027
261630033
261630062
261630092
270031002
270176316
270370020
270370423
270370439
270370441
270370442
270530954
270530957
270711240
n
50
58
58
59
60
58
28
28
3
4
2
9
3
1
3
0
36
34
32
31
23
6
33
32
31
33
10
5
15
17
14
12
11
24
21
1
Distance of monitor to SO2 emission source (km)1
mean
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
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
std
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
5.9
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
min
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
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
p2.5
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
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
p50
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
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
P97.5
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
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
max
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
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
SO2 emissions
mean
156
138
137
135
133
380
25
25
1407
42
64
60
239
58
524
1780
1894
1070
1104
1358
13
1952
1070
1104
1952
1332
72
610
805
639
720
506
913
878
67
std
710
660
660
654
649
1952
35
35
1264
24
79
96
287
431
5390
5529
2436
2469
2828
14
5605
2436
2469
5605
4067
84
1015
1227
1047
1114
873
2729
2877
tpy) from sources within 20 km of monitor1
min
5
5
5
5
5
5
6
6
671
7
7
9
10
58
31
5
5
5
5
10
5
5
5
5
5
5
5
9
9
9
9
9
5
5
67
p2.5
5
5
5
5
5
5
6
6
671
7
7
9
10
58
31
5
5
5
5
10
5
5
5
5
5
5
5
9
9
9
9
9
5
5
67
p50
14
15
14
14
14
15
12
12
685
48
64
12
148
58
715
109
117
117
121
121
9
121
117
121
121
11
26
104
205
79
79
54
48
12
67
P97.5
640
640
640
640
640
5007
178
178
2867
63
120
280
560
58
826
30171
30171
8913
8913
8913
42
30171
8913
8913
30171
12904
190
3071
3821
3071
3071
2869
12904
12904
67
max
5007
5007
5007
5007
5007
14132
178
178
2867
63
120
280
560
58
826
30171
30171
8913
8913
8913
42
30171
8913
8913
30171
12904
190
3071
3821
3071
3071
2869
12904
12904
67
A-39
-------
Monitor ID2
271230864
271410003
271410011
271410012
271410013
271630436
271710007
280030004
280190001
280470007
280490018
280590006
280810004
280930001
281070001
290210009
290210011
290470025
290770026
290770032
290770037
290770040
290770041
290930030
290930031
290950034
290990004
290990014
290990017
290990018
291370001
291630002
291650023
291830010
291831002
291890001
n
27
1
1
1
1
21
2
2
2
2
5
7
0
1
1
1
1
15
4
4
4
4
4
1
1
14
5
5
5
4
0
2
4
1
15
14
Distance of monitor to SO2 emission source (km)1
mean
12.0
4.9
1.7
1.8
1.1
11.1
11.8
7.1
5.8
6.5
7.3
7.0
19.0
2.3
0.7
0.9
11.9
8.2
7.8
9.2
9.2
8.6
1.7
4.6
8.7
9.7
9.8
10.2
8.3
7.3
17.8
1.7
12.6
14.8
std
4.8
5.6
6.5
0.7
6.2
7.9
5.4
4.9
4.8
4.5
3.9
6.1
6.2
5.5
4.9
7.4
7.4
7.1
6.6
6.6
1.3
3.4
4.4
min
3.9
4.9
1.7
1.8
1.1
0.9
7.2
6.6
1.5
0.9
3.2
3.3
19.0
2.3
0.7
0.9
2.8
2.3
3.0
0.6
0.5
1.2
1.7
4.6
1.4
0.2
0.7
1.6
1.4
2.7
16.0
1.7
4.3
6.4
p2.5
3.9
4.9
1.7
1.8
1.1
0.9
7.2
6.6
1.5
0.9
3.2
3.3
19.0
2.3
0.7
0.9
2.8
2.3
3.0
0.6
0.5
1.2
1.7
4.6
1.4
0.2
0.7
1.6
1.4
2.7
16.0
1.7
4.3
6.4
p50
12.6
4.9
1.7
1.8
1.1
11.4
11.8
7.1
5.8
6.5
6.0
5.4
19.0
2.3
0.7
0.9
10.8
9.3
8.5
11.0
11.0
9.7
1.7
4.6
8.1
11.4
11.9
10.6
8.2
7.3
18.1
1.7
13.5
15.9
P97.5
19.7
4.9
1.7
1.8
1.1
18.4
16.3
7.6
10.2
12.1
16.6
17.3
19.0
2.3
0.7
0.9
18.2
11.8
11.0
14.0
14.1
13.8
1.7
4.6
15.4
17.1
17.5
17.3
15.3
12.0
19.1
1.7
17.3
19.7
max
19.7
4.9
1.7
1.8
1.1
18.4
16.3
7.6
10.2
12.1
16.6
17.3
19.0
2.3
0.7
0.9
18.2
11.8
11.0
14.0
14.1
13.8
1.7
4.6
15.4
17.1
17.5
17.3
15.3
12.0
19.1
1.7
17.3
19.7
SO2 emissions
mean
769
26742
26742
26742
26742
545
13397
19
2376
12535
51
4903
75
5
3563
3563
1682
2302
2302
2302
2302
2302
43340
43340
1388
11145
11145
11145
8117
6747
2757
47610
4516
1748
std
2540
997
18873
19
3351
17718
45
10049
2364
2728
2728
2728
2728
2728
2341
10277
10277
10277
8927
934
3602
11970
4547
tpy) from sources within 20 km of monitor1
min
5
26742
26742
26742
26742
7
52
5
6
6
15
12
75
5
3563
3563
6
5
5
5
5
5
43340
43340
6
243
243
243
243
6087
19
47610
6
8
p2.5
5
26742
26742
26742
26742
7
52
5
6
6
15
12
75
5
3563
3563
6
5
5
5
5
5
43340
43340
6
243
243
243
243
6087
19
47610
6
8
p50
46
26742
26742
26742
26742
104
13397
19
2376
12535
30
96
75
5
3563
3563
105
1772
1772
1772
1772
1772
43340
43340
34
15223
15223
15223
7889
6747
1693
47610
136
35
P97.5
12904
26742
26742
26742
26742
3821
26742
32
4745
25064
128
27207
75
5
3563
3563
7625
5657
5657
5657
5657
5657
43340
43340
7625
23258
23258
23258
16447
7408
7625
47610
45960
16447
max
12904
26742
26742
26742
26742
3821
26742
32
4745
25064
128
27207
75
5
3563
3563
7625
5657
5657
5657
5657
5657
43340
43340
7625
23258
23258
23258
16447
7408
7625
47610
45960
16447
A-40
-------
Monitor ID2
291890004
291890006
291890014
291893001
291895001
291897002
291897003
295100007
295100072
295100080
295100086
300030038
300132000
300132001
300430903
300430908
300430909
300430910
300430911
300430912
300430913
300430914
300430915
300430916
300490701
300490702
300490703
300650004
300870700
300870701
300870702
300870760
300870761
300870762
300870763
301110016
n
9
7
8
29
35
14
18
19
30
34
32
0
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
0
0
0
1
0
Distance of monitor to SO2 emission source (km)1
mean
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
1.3
3.0
4.7
4.5
4.6
4.9
5.6
5.5
5.1
5.1
6.2
7.3
19.8
19.0
19.8
15.2
std
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
min
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
1.3
3.0
4.7
4.5
4.6
4.9
5.6
5.5
5.1
5.1
6.2
7.3
19.8
19.0
19.8
15.2
p2.5
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
1.3
3.0
4.7
4.5
4.6
4.9
5.6
5.5
5.1
5.1
6.2
7.3
19.8
19.0
19.8
15.2
p50
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
1.3
3.0
4.7
4.5
4.6
4.9
5.6
5.5
5.1
5.1
6.2
7.3
19.8
19.0
19.8
15.2
P97.5
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
1.3
3.0
4.7
4.5
4.6
4.9
5.6
5.5
5.1
5.1
6.2
7.3
19.8
19.0
19.8
15.2
max
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
1.3
3.0
4.7
4.5
4.6
4.9
5.6
5.5
5.1
5.1
6.2
7.3
19.8
19.0
19.8
15.2
SO2 emissions
mean
2535
27
33
370
1911
50
403
1312
445
397
421
351
351
234
234
234
234
234
234
234
234
234
234
234
234
234
16735
16735
16735
16735
std
5610
48
47
1164
7823
75
1461
3936
1152
1088
1118
481
481
tpy) from sources within 20 km of monitor1
min
8
6
6
6
6
6
6
8
6
6
6
11
11
234
234
234
234
234
234
234
234
234
234
234
234
234
16735
16735
16735
16735
p2.5
8
6
6
6
6
6
6
8
6
6
6
11
11
234
234
234
234
234
234
234
234
234
234
234
234
234
16735
16735
16735
16735
p50
13
8
10
60
111
16
37
50
68
61
68
351
351
234
234
234
234
234
234
234
234
234
234
234
234
234
16735
16735
16735
16735
P97.5
16447
136
136
6250
45960
277
6250
16447
6250
6250
6250
691
691
234
234
234
234
234
234
234
234
234
234
234
234
234
16735
16735
16735
16735
max
16447
136
136
6250
45960
277
6250
16447
6250
6250
6250
691
691
234
234
234
234
234
234
234
234
234
234
234
234
234
16735
16735
16735
16735
A-41
-------
Monitor ID2
301110066
301110079
301110080
301110082
301110083
301110084
301111065
301112005
301112006
301112007
301112008
310550048
310550050
310550053
310550055
320030022
320030078
320030539
320030601
330050007
330070019
330070022
330071007
330110016
330110019
330110020
330111009
330111010
330130007
330131003
330131006
330131007
330150009
330150014
330150015
330190003
n
4
4
4
4
4
6
4
4
6
6
4
5
5
5
3
4
0
0
0
1
1
1
2
3
3
3
11
16
4
4
4
4
9
9
9
2
Distance of monitor to SO2 emission source (km)1
mean
3.1
7.8
2.4
3.4
3.4
10.3
4.7
4.1
10.1
11.4
4.0
12.7
13.4
11.3
13.0
3.9
0.3
1.7
2.3
0.6
17.3
17.0
17.0
12.7
13.0
7.3
7.7
8.2
9.3
9.0
9.6
8.9
2.5
std
0.5
3.0
1.8
2.7
0.7
6.6
2.7
1.8
7.2
3.9
2.6
7.5
7.5
5.7
7.3
0.0
0.1
1.3
2.3
2.3
6.0
3.0
3.9
5.4
8.8
2.1
6.9
7.0
7.1
1.7
min
2.6
5.8
0.9
1.7
2.7
3.1
0.7
1.5
1.1
4.7
2.3
0.5
1.0
3.3
4.7
3.8
0.3
1.7
2.3
0.6
16.5
15.7
15.7
4.4
7.2
1.4
4.0
1.3
7.5
2.0
1.0
1.9
1.3
p2.5
2.6
5.8
0.9
1.7
2.7
3.1
0.7
1.5
1.1
4.7
2.3
0.5
1.0
3.3
4.7
3.8
0.3
1.7
2.3
0.6
16.5
15.7
15.7
4.4
7.2
1.4
4.0
1.3
7.5
2.0
1.0
1.9
1.3
p50
3.1
6.7
1.9
2.3
3.4
7.4
5.7
4.6
7.6
11.2
3.0
13.6
14.7
10.6
16.1
3.9
0.3
1.7
2.3
0.6
16.6
15.7
15.7
14.7
12.0
9.0
5.6
5.8
8.6
4.4
5.5
4.1
2.5
P97.5
3.7
12.2
5.0
7.3
4.4
18.6
6.7
5.7
18.8
15.3
7.8
19.3
19.6
18.0
18.2
3.9
0.3
1.7
2.3
0.7
18.8
19.6
19.6
19.0
19.0
9.6
15.4
19.8
12.3
19.2
19.9
19.5
3.7
max
3.7
12.2
5.0
7.3
4.4
18.6
6.7
5.7
18.8
15.3
7.8
19.3
19.6
18.0
18.2
3.9
0.3
1.7
2.3
0.7
18.8
19.6
19.6
19.0
19.0
9.6
15.4
19.8
12.3
19.2
19.9
19.5
3.7
SO2 emissions
mean
1370
1370
1370
1370
1370
2550
1370
1370
2550
2550
1370
6370
6370
6370
3845
45
81
638
638
9
10269
10269
10269
41
48
7708
7708
7708
7708
1523
1523
1523
110
std
1322
1322
1322
1322
1322
2627
1322
1322
2627
2627
1322
9218
9218
9218
6637
27
4
10386
10386
10386
42
42
9906
9906
9906
9906
2990
2990
2990
81
tpy) from sources within 20 km of monitor1
min
75
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
41
6
6
6
53
p2.5
75
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
41
6
6
6
53
p50
1135
1135
1135
1135
1135
1976
1135
1135
1976
1976
1135
58
58
58
20
44
81
638
638
9
9754
9754
9754
20
38
4945
4945
4945
4945
52
52
52
110
P97.5
3135
3135
3135
3135
3135
7415
3135
3135
7415
7415
3135
20257
20257
20257
11509
75
81
638
638
12
20902
20902
20902
149
149
20902
20902
20902
20902
8057
8057
8057
168
max
3135
3135
3135
3135
3135
7415
3135
3135
7415
7415
3135
20257
20257
20257
11509
75
81
638
638
12
20902
20902
20902
149
149
20902
20902
20902
20902
8057
8057
8057
168
A-42
-------
Monitor ID2
340010005
340030001
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
360050133
360130005
360130006
360130011
360150003
n
0
74
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
56
0
1
0
2
Distance of monitor to SO2 emission source (km)1
mean
11.1
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
17.2
3.3
6.1
10.8
10.0
10.6
11.2
10.1
11.4
2.0
10.2
std
4.4
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
4.0
8.4
3.5
2.0
3.8
5.2
4.9
5.0
5.6
4.9
5.7
13.6
min
2.9
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
11.9
2.0
3.2
3.5
3.4
1.8
1.6
2.7
1.5
2.0
0.6
p2.5
2.9
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
11.9
2.0
3.2
3.5
3.4
3.0
1.8
2.8
1.7
2.0
0.6
p50
11.1
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
19.2
3.3
3.5
9.0
9.1
9.6
11.3
9.0
11.6
2.0
10.2
P97.5
19.0
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
19.3
4.7
11.9
18.0
19.2
19.5
19.6
19.2
19.9
2.0
19.9
max
19.1
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
19.3
4.7
11.9
18.0
19.7
19.9
19.6
19.7
19.9
2.0
19.9
SO2 emissions
mean
391
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
119
52177
202
std
2221
2442
3104
644
1
198
2471
2431
1281
2471
2267
206
8
3074
3075
92
973
2496
378
10983
46
2309
2344
355
2326
355
270
tpy) from sources within 20 km of monitor1
min
5
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
6
52177
11
p2.5
5
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
6
52177
11
p50
18
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
19
52177
202
P97.5
2302
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
1129
52177
393
max
18958
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
2302
52177
393
A-43
-------
Monitor ID2
360290005
360294002
360298001
360310003
360330004
360337003
360410005
360430005
360470011
360470076
360530006
360551004
360551007
360556001
360590005
360610010
360610056
360632006
360632008
360670017
360671015
360790005
360810004
360810097
360810124
360830004
360831005
360850067
360930003
361010003
361030002
361030009
361111005
370030003
370130003
370130004
n
10
16
9
0
0
2
0
0
77
67
0
4
4
4
12
77
76
12
13
5
4
0
65
60
66
3
2
48
4
2
9
10
0
0
1
1
Distance of monitor to SO2 emission source (km)1
mean
10.2
10.4
13.5
9.8
10.3
11.6
11.0
11.3
10.5
11.8
10.4
9.9
8.5
9.3
7.9
5.9
12.7
14.8
12.5
18.4
17.6
14.0
9.5
15.8
9.3
11.3
2.2
2.7
std
4.7
6.2
5.6
7.7
5.5
4.8
4.2
4.1
6.8
4.8
5.4
5.4
7.0
7.3
5.8
4.3
3.9
4.0
4.0
1.8
1.6
4.0
6.6
1.0
5.8
5.7
min
2.5
1.6
4.6
4.3
0.7
2.3
7.6
6.4
5.2
1.9
0.3
0.3
0.5
0.3
5.0
1.9
1.9
2.9
2.1
16.3
16.5
5.5
2.0
15.1
1.9
2.0
2.2
2.7
p2.5
2.5
1.6
4.6
4.3
1.9
3.1
7.6
6.4
5.2
1.9
1.4
1.4
0.5
0.3
5.0
1.9
2.6
5.0
2.3
16.3
16.5
6.2
2.0
15.1
1.9
2.0
2.2
2.7
p50
11.1
12.3
14.7
9.8
10.8
11.5
10.0
11.9
8.5
11.8
11.1
10.6
9.0
12.2
5.5
5.2
12.8
15.5
12.4
19.3
17.6
14.2
9.7
15.8
7.3
11.9
2.2
2.7
P97.5
15.4
18.3
19.0
15.2
19.2
19.4
16.5
15.0
19.8
19.1
19.4
19.9
19.7
19.8
18.2
11.5
19.6
19.9
19.5
19.6
18.8
19.6
16.5
16.6
18.2
19.3
2.2
2.7
max
15.4
18.3
19.0
15.2
19.7
19.9
16.5
15.0
19.8
19.1
19.6
19.9
19.7
19.8
18.2
11.5
20.0
20.0
20.0
19.6
18.8
19.9
16.5
16.6
18.2
19.3
2.2
2.7
SO2 emissions
mean
4073
2608
4518
1244
377
428
12595
12595
12595
151
375
382
3395
3134
669
820
124
136
122
126
94
515
24
8
156
734
4730
4730
std
12273
9706
12932
1404
2178
2333
14519
14519
14519
301
2178
2192
11213
10777
1428
1602
345
358
342
106
124
2737
26
3
344
2013
tpy) from sources within 20 km of monitor1
min
8
8
8
250
5
5
8
8
8
6
5
5
14
8
8
8
5
5
5
10
6
5
6
6
6
11
4730
4730
p2.5
8
8
8
250
5
5
8
8
8
6
5
5
14
8
8
8
6
6
6
10
6
6
6
6
6
11
4730
4730
p50
182
166
247
1244
18
17
11988
11988
11988
26
17
18
166
118
30
24
21
22
21
153
94
17
14
8
19
42
4730
4730
P97.5
38999
38999
38999
2237
2302
2302
26395
26395
26395
1057
2302
2302
38999
38999
3223
3223
1129
1129
1129
217
182
1845
62
11
1057
6453
4730
4730
max
38999
38999
38999
2237
18958
18958
26395
26395
26395
1057
18958
18958
38999
38999
3223
3223
2302
2302
2302
217
182
18958
62
11
1057
6453
4730
4730
A-44
-------
Monitor ID2
370130006
370370004
370511003
370590002
370610002
370650099
370670022
371010002
371090004
371170001
371170002
371190034
371190041
371290002
371290006
371310002
371450003
371470099
371590021
371590022
371730002
371830014
380070002
380070003
380070111
380130002
380130004
380150003
380171003
380171004
380250003
380530002
380530104
380530111
380550113
380570001
n
1
4
5
4
5
1
9
2
1
2
2
12
12
9
12
3
3
2
6
6
0
4
1
1
0
0
1
1
3
2
1
1
0
2
0
2
Distance of monitor to SO2 emission source (km)1
mean
1.1
17.2
15.8
15.3
12.3
16.1
6.3
10.3
10.7
6.6
5.9
13.3
12.7
14.5
6.9
4.2
18.8
1.3
15.2
13.0
11.5
11.4
4.0
18.6
9.8
7.7
9.0
13.9
17.3
16.1
2.5
std
3.7
2.5
4.3
4.9
5.7
7.5
7.8
2.3
4.7
5.0
4.9
4.8
1.8
0.5
0.0
4.2
4.9
4.9
6.9
1.1
0.1
2.6
min
1.1
11.8
11.5
10.4
4.1
16.1
1.2
5.0
10.7
1.1
4.3
6.3
6.3
2.3
0.6
2.1
18.4
1.3
8.0
5.8
5.2
11.4
4.0
18.6
9.8
3.0
8.2
13.9
17.3
16.1
0.7
p2.5
1.1
11.8
11.5
10.4
4.1
16.1
1.2
5.0
10.7
1.1
4.3
6.3
6.3
2.3
0.6
2.1
18.4
1.3
8.0
5.8
5.2
11.4
4.0
18.6
9.8
3.0
8.2
13.9
17.3
16.1
0.7
p50
1.1
18.6
16.5
15.6
13.1
16.1
3.9
10.3
10.7
6.6
5.9
12.8
12.2
15.4
7.1
5.1
18.7
1.3
15.8
15.4
11.8
11.4
4.0
18.6
9.8
4.6
9.0
13.9
17.3
16.1
2.5
P97.5
1.1
19.9
17.9
19.6
17.0
16.1
17.8
15.6
10.7
12.2
7.6
19.8
19.8
19.0
14.5
5.3
19.3
1.3
19.9
17.3
17.1
11.4
4.0
18.6
9.8
15.7
9.7
13.9
17.3
16.2
4.3
max
1.1
19.9
17.9
19.6
17.0
16.1
17.8
15.6
10.7
12.2
7.6
19.8
19.8
19.0
14.5
5.3
19.3
1.3
19.9
17.3
17.1
11.4
4.0
18.6
9.8
15.7
9.7
13.9
17.3
16.2
4.3
SO2 emissions
mean
4730
119
295
1949
83
325
438
15
10
1713
1713
86
68
3325
2502
805
32251
14
1443
599
17
283
283
426
4592
257
378
5
210
411
45808
std
71
264
3658
132
848
4
2329
2329
121
103
6800
5987
759
54874
3
2950
1184
16
226
119
522
55924
tpy) from sources within 20 km of monitor1
min
4730
12
17
13
6
325
5
12
10
66
66
5
5
6
6
16
5
12
12
12
6
283
283
426
4592
15
294
5
210
42
6264
p2.5
4730
12
17
13
6
325
5
12
10
66
66
5
5
6
6
16
5
12
12
12
6
283
283
426
4592
15
294
5
210
42
6264
p50
4730
148
173
175
36
325
46
15
10
1713
1713
11
11
313
50
871
1136
14
190
139
11
283
283
426
4592
294
378
5
210
411
45808
P97.5
4730
165
675
7432
317
325
2591
17
10
3360
3360
320
320
20865
20865
1529
95610
16
7432
3004
41
283
283
426
4592
462
462
5
210
781
85352
max
4730
165
675
7432
317
325
2591
17
10
3360
3360
320
320
20865
20865
1529
95610
16
7432
3004
41
283
283
426
4592
462
462
5
210
781
85352
A-45
-------
Monitor ID2
380570004
380570102
380570118
380570123
380570124
380590002
380590003
380650002
380910001
381050103
381050105
390010001
390030002
390071001
390133002
390170004
390171004
390230003
390250021
390290016
390290022
390292001
390350026
390350038
390350045
390350060
390350065
390356001
390490004
390490034
390530002
390610010
390610039
390612002
390612003
390810016
n
2
2
2
2
2
1
1
1
0
1
1
1
9
5
5
11
9
4
6
9
9
8
10
10
10
10
10
13
6
6
6
10
11
8
11
17
Distance of monitor to SO2 emission source (km)1
mean
2.7
5.4
10.7
14.3
18.6
2.6
5.1
8.5
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
9.9
9.8
10.1
10.4
9.8
13.8
8.7
9.5
7.0
16.1
7.0
10.3
8.7
9.5
std
2.0
2.3
2.2
1.4
1.0
0.4
0.6
5.1
6.9
6.5
6.1
2.7
3.6
3.6
4.1
4.3
4.9
5.5
5.7
4.3
7.1
3.4
3.0
7.4
3.0
4.9
4.6
5.5
7.1
min
1.3
3.8
9.1
13.3
17.9
2.6
5.1
8.5
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
2.1
1.9
1.2
1.0
2.0
1.7
2.9
3.4
1.0
8.6
2.5
3.0
0.4
1.7
p2.5
1.3
3.8
9.1
13.3
17.9
2.6
5.1
8.5
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
2.1
1.9
1.2
1.0
2.0
1.7
2.9
3.4
1.0
8.6
2.5
3.0
0.4
1.7
p50
2.7
5.4
10.7
14.3
18.6
2.6
5.1
8.5
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
9.8
11.7
10.4
13.3
9.8
16.8
9.2
10.4
3.6
16.8
5.6
10.6
8.0
5.6
P97.5
4.1
7.0
12.2
15.3
19.3
2.6
5.1
8.5
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
14.5
14.3
15.8
15.5
14.5
20.0
12.9
11.5
16.5
19.7
19.5
18.1
19.4
19.0
max
4.1
7.0
12.2
15.3
19.3
2.6
5.1
8.5
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
14.5
14.3
15.8
15.5
14.5
20.0
12.9
11.5
16.5
19.7
19.5
18.1
19.4
19.0
SO2 emissions
mean
45808
45808
45808
45808
45808
4592
4592
28565
1605
1605
19670
442
1731
27781
907
1546
509
15304
20696
20696
22401
740
740
740
740
740
5759
75
75
31718
9265
465
18883
660
13129
std
55924
55924
55924
55924
55924
535
3761
23029
1265
2186
349
28111
19955
19955
20621
916
916
916
916
916
16867
74
74
26583
26865
664
31426
817
20063
tpy) from sources within 20 km of monitor1
min
6264
6264
6264
6264
6264
4592
4592
28565
1605
1605
19670
16
12
795
56
56
105
26
18
18
18
15
15
15
15
15
8
5
5
9
12
12
12
12
10
p2.5
6264
6264
6264
6264
6264
4592
4592
28565
1605
1605
19670
16
12
795
56
56
105
26
18
18
18
15
15
15
15
15
8
5
5
9
12
12
12
12
10
p50
45808
45808
45808
45808
45808
4592
4592
28565
1605
1605
19670
45
34
35454
233
309
492
145
24766
24766
25596
382
382
382
382
382
382
64
64
29551
537
213
1122
268
361
P97.5
85352
85352
85352
85352
85352
4592
4592
28565
1605
1605
19670
1469
8458
56009
3998
6275
946
69953
59928
59928
59928
2453
2453
2453
2453
2453
61629
192
192
74452
85699
1848
85699
2164
59928
max
85352
85352
85352
85352
85352
4592
4592
28565
1605
1605
19670
1469
8458
56009
3998
6275
946
69953
59928
59928
59928
2453
2453
2453
2453
2453
61629
192
192
74452
85699
1848
85699
2164
59928
A-46
-------
Monitor ID2
390810017
390811001
390811012
390850003
390853002
390870006
390871009
390930017
390930026
390931003
390950006
390950008
390950024
390990009
390990013
391051001
391130025
391150003
391150004
391450013
391450020
391450022
391510016
391530017
391530022
391570003
391570006
400219002
400710602
400719003
400719010
400979014
401010167
401090025
401091037
401159004
n
17
13
17
6
3
8
8
3
2
3
10
9
10
10
10
6
6
2
2
0
3
3
7
4
4
7
6
0
2
2
0
6
8
2
2
1
Distance of monitor to SO2 emission source (km)1
mean
9.6
4.9
9.5
9.1
5.3
13.7
10.7
11.4
3.3
11.6
10.8
8.1
11.4
12.4
12.4
13.6
13.4
4.8
5.1
9.6
8.4
6.6
5.0
3.9
12.0
6.4
3.4
1.8
4.7
5.9
8.7
8.8
5.2
std
6.9
5.6
7.3
4.2
6.0
6.0
4.8
2.2
0.5
2.1
6.6
5.5
6.4
7.3
7.5
2.2
5.4
0.2
0.3
6.9
7.5
1.5
2.4
0.7
6.4
6.1
2.3
2.0
1.3
4.2
4.5
7.9
min
2.0
0.3
1.5
5.6
1.1
2.2
5.0
8.9
3.0
9.2
2.8
2.5
3.9
2.0
1.7
11.6
7.3
4.6
4.9
4.6
2.8
4.5
1.4
3.0
0.6
0.4
1.8
0.4
2.7
3.7
5.6
3.2
5.2
p2.5
2.0
0.3
1.5
5.6
1.1
2.2
5.0
8.9
3.0
9.2
2.8
2.5
3.9
2.0
1.7
11.6
7.3
4.6
4.9
4.6
2.8
4.5
1.4
3.0
0.6
0.4
1.8
0.4
2.7
3.7
5.6
3.2
5.2
p50
5.9
2.9
5.3
7.4
2.6
15.5
10.9
12.5
3.3
12.5
8.1
4.5
9.5
15.6
15.8
13.0
13.4
4.8
5.1
6.7
5.4
5.9
6.0
4.1
13.3
5.3
3.4
1.8
5.5
3.7
8.7
8.8
5.2
P97.5
18.6
18.0
19.3
15.2
12.3
19.3
17.8
12.8
3.6
13.1
19.9
14.6
18.6
19.6
19.6
17.8
19.4
4.9
5.3
17.5
16.9
8.7
6.6
4.6
18.6
14.2
5.0
3.2
5.7
15.8
11.9
14.4
5.2
max
18.6
18.0
19.3
15.2
12.3
19.3
17.8
12.8
3.6
13.1
19.9
14.6
18.6
19.6
19.6
17.8
19.4
4.9
5.3
17.5
16.9
8.7
6.6
4.6
18.6
14.2
5.0
3.2
5.7
15.8
11.9
14.4
5.2
SO2 emissions
mean
13129
6005
13129
12044
1600
1425
1271
165
27
165
3745
4149
3745
2107
2107
31718
1609
57763
57763
1450
1450
181
2763
2763
368
426
3502
3502
3180
3751
91
91
62
std
20063
15392
20063
24426
2615
2178
2194
241
29
241
4443
4513
4443
5350
5350
26583
2326
38696
38696
1306
1306
213
2244
2244
741
795
457
457
5200
4529
110
110
tpy) from sources within 20 km of monitor1
min
10
10
10
8
18
25
25
6
6
6
113
204
113
6
6
9
105
30401
30401
25
25
10
863
863
15
15
3178
3178
173
23
13
13
62
p2.5
10
10
10
8
18
25
25
6
6
6
113
204
113
6
6
9
105
30401
30401
25
25
10
863
863
15
15
3178
3178
173
23
13
13
62
p50
361
234
361
2390
163
343
343
47
27
47
2406
3712
2406
353
353
29551
753
57763
57763
1737
1737
43
2091
2091
38
38
3502
3502
713
1130
91
91
62
P97.5
59928
53414
59928
61629
4618
6285
6285
442
47
442
13581
13581
13581
17244
17244
74452
6275
85125
85125
2589
2589
510
6009
6009
2017
2017
3825
3825
13428
9866
169
169
62
max
59928
53414
59928
61629
4618
6285
6285
442
47
442
13581
13581
13581
17244
17244
74452
6275
85125
85125
2589
2589
510
6009
6009
2017
2017
3825
3825
13428
9866
169
169
62
A-47
-------
Monitor ID2
401430175
401430235
401430501
401431127
410410002
410510080
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
420590002
420630004
420692006
420710007
420730015
n
10
10
10
8
1
7
19
55
64
62
64
54
16
19
57
54
55
10
7
8
10
13
12
1
22
4
4
8
57
45
5
1
3
5
5
9
Distance of monitor to SO2 emission source (km)1
mean
11.8
10.7
12.6
12.6
0.3
13.5
7.4
14.2
11.7
13.9
11.7
6.0
15.1
7.4
9.9
5.6
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
11.5
18.4
10.9
3.7
12.5
std
6.9
6.9
6.8
5.0
4.1
5.9
5.6
3.3
5.1
3.3
5.2
3.5
5.1
4.6
5.4
6.0
3.2
5.1
5.6
3.1
7.1
6.3
6.5
7.4
6.2
4.0
5.5
6.4
1.9
1.4
7.4
3.7
5.6
min
1.4
1.5
2.7
5.0
0.3
7.8
0.6
2.5
3.2
1.3
3.1
2.0
6.1
2.1
1.1
1.0
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
11.5
17.0
2.1
0.6
0.6
p2.5
1.4
1.5
2.7
5.0
0.3
7.8
0.6
2.5
4.8
1.4
4.7
2.0
6.1
2.1
1.1
1.0
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
11.5
17.0
2.1
0.6
0.6
p50
13.9
13.4
14.2
12.4
0.3
12.8
8.6
15.5
13.1
14.4
13.2
3.1
15.7
7.7
11.0
2.3
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
11.5
18.4
8.2
2.7
13.2
P97.5
18.3
18.1
19.2
18.7
0.3
18.9
18.1
20.0
18.0
18.7
18.1
17.9
19.7
17.0
17.5
17.8
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
11.5
19.8
19.6
10.1
18.0
max
18.3
18.1
19.2
18.7
0.3
18.9
18.1
20.0
18.7
19.8
18.7
18.2
19.7
17.0
17.8
17.8
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
11.5
19.8
19.6
10.1
18.0
SO2 emissions
mean
938
938
938
1126
307
46
103
85
819
757
819
213
73
103
914
213
209
18726
5881
5173
4400
1140
1231
441
687
4195
1090
107
681
855
824
156
4796
13
75
3206
std
1088
1088
1088
1148
34
137
101
5274
5327
5274
741
105
137
5587
741
735
19819
11104
10474
9400
3818
3973
3033
5171
1267
99
1415
1553
1068
5156
5
109
8423
tpy) from sources within 20 km of monitor1
min
9
9
9
9
307
9
7
5
5
5
5
5
7
7
5
5
5
18
9
9
8
14
14
441
5
34
53
10
5
5
10
156
1497
6
6
6
p2.5
9
9
9
9
307
9
7
7
7
7
7
6
7
7
7
6
6
18
9
9
8
14
14
441
5
34
53
10
5
5
10
156
1497
6
6
6
p50
263
263
263
802
307
47
30
49
47
46
47
52
29
30
47
52
49
15912
118
157
157
37
34
441
27
3004
834
78
47
91
228
156
2154
15
23
28
P97.5
2729
2729
2729
2729
307
109
468
407
5395
468
5395
1164
407
468
5395
1164
1164
59928
30312
30312
30312
13841
13841
441
14266
10738
2638
313
5051
5051
2398
156
10738
18
264
25551
max
2729
2729
2729
2729
307
109
468
468
42018
42018
42018
5395
407
468
42018
5395
5395
59928
30312
30312
30312
13841
13841
441
14266
10738
2638
313
6720
6720
2398
156
10738
18
264
25551
A-48
-------
Monitor ID2
420770004
420791101
420810100
420810403
420850100
420890001
420910013
420950025
420950100
420958000
420990301
421010004
421010022
421010024
421010027
421010029
421010047
421010048
421010055
421010136
421070002
421070003
421230003
421230004
421250005
421250200
421255001
421290008
421330008
440070012
440071005
440071009
450030003
450070003
450110001
450190003
n
13
4
3
3
2
8
28
18
15
16
0
61
66
36
63
67
65
60
66
68
4
6
2
2
33
1
8
3
9
54
55
55
13
8
1
16
Distance of monitor to SO2 emission source (km)1
mean
12.5
12.3
11.3
15.8
10.8
16.4
15.3
13.1
10.4
10.1
10.5
8.0
13.0
9.8
8.3
7.9
10.4
7.9
8.8
12.4
10.4
4.0
3.0
15.7
1.1
15.9
9.8
9.3
8.4
9.1
8.6
15.3
15.4
13.2
7.2
std
5.8
3.4
0.7
1.1
11.8
1.7
4.5
4.3
5.5
5.9
5.2
5.6
3.8
4.6
4.7
4.5
4.9
5.4
5.4
2.8
7.4
1.2
1.6
4.7
4.1
1.4
5.8
5.8
5.5
6.0
1.5
4.1
5.0
min
0.3
7.8
10.6
14.9
2.4
14.1
1.4
4.0
2.5
0.6
1.0
0.9
6.3
0.8
1.1
0.6
0.9
1.3
1.1
8.7
3.3
3.2
1.9
1.1
1.1
9.3
8.7
0.8
0.3
0.9
0.1
11.4
8.5
13.2
1.1
p2.5
0.3
7.8
10.6
14.9
2.4
14.1
1.4
4.0
2.5
0.6
1.3
1.0
6.3
1.7
1.8
0.8
1.7
1.4
1.4
8.7
3.3
3.2
1.9
1.1
1.1
9.3
8.7
0.8
0.4
1.0
0.4
11.4
8.5
13.2
1.1
p50
12.0
12.9
11.2
15.4
10.8
16.4
16.2
14.1
10.7
9.1
10.9
7.0
12.6
11.0
6.8
6.4
10.7
6.8
9.3
13.0
8.8
4.0
3.0
17.5
1.1
17.2
9.3
10.1
5.9
8.4
6.3
15.3
16.0
13.2
6.2
P97.5
19.3
15.8
12.0
16.9
19.1
18.4
20.0
19.7
19.3
18.8
19.2
19.4
19.9
19.7
18.9
17.6
18.6
18.8
18.7
15.0
19.2
4.9
4.1
18.7
1.1
19.7
11.5
17.7
18.9
18.5
19.5
17.5
19.8
13.2
16.3
max
19.3
15.8
12.0
16.9
19.1
18.4
20.0
19.7
19.3
18.8
19.7
20.0
19.9
19.7
19.6
17.9
19.2
20.0
19.8
15.0
19.2
4.9
4.1
18.7
1.1
19.7
11.5
17.7
19.0
19.0
19.9
17.5
19.8
13.2
16.3
SO2 emissions
mean
703
117
28
28
14
1287
171
676
2179
2045
102
285
46
99
262
270
104
286
319
1020
831
2445
2445
257
7
321
24
8943
41
41
41
1654
986
65
2183
std
1041
160
28
28
4
1237
704
1020
5602
5439
316
1022
77
311
1007
1022
318
1022
1042
715
687
659
659
945
439
9
22698
90
89
89
2599
1952
6339
tpy) from sources within 20 km of monitor1
min
7
9
6
6
11
21
5
7
7
7
5
5
5
5
5
5
5
5
5
362
8
1979
1979
5
7
7
16
14
5
5
5
8
6
65
6
p2.5
7
9
6
6
11
21
5
7
7
7
6
5
5
6
5
5
6
5
5
362
8
1979
1979
5
7
7
16
14
5
5
5
8
6
65
6
p50
120
53
18
18
14
1126
15
86
120
86
20
26
13
20
24
26
22
26
27
988
674
2445
2445
47
7
82
22
171
13
13
13
549
40
65
28
P97.5
2888
351
59
59
17
2888
3753
2888
22057
22057
560
4450
407
560
4450
4450
560
4450
4450
1743
1743
2911
2911
5395
7
1017
34
68932
392
392
392
8275
5543
65
25544
max
2888
351
59
59
17
2888
3753
2888
22057
22057
2378
6720
407
2378
6720
6720
2378
6720
6720
1743
1743
2911
2911
5395
7
1017
34
68932
521
521
521
8275
5543
65
25544
A-49
-------
Monitor ID2
450190046
450430006
450450008
450450009
450630008
450730001
450750003
450790007
450790021
450791003
450791006
460330132
460710001
460990007
461094003
470010028
470090002
470090006
470090101
470110004
470110102
470310004
470370011
470430009
470630003
470730002
470750002
470750003
470850020
470931030
471050003
471070101
471210104
471250006
471250106
471251010
n
0
7
12
13
11
1
5
10
8
13
10
0
0
1
1
8
3
3
3
2
2
0
9
0
5
3
1
0
6
7
6
3
2
6
6
3
Distance of monitor to SO2 emission source (km)1
mean
4.6
11.7
10.1
11.5
14.9
8.5
14.0
14.7
10.9
17.5
17.5
6.3
12.2
5.7
5.4
12.1
11.4
2.5
10.4
15.3
2.9
19.7
3.2
11.9
6.9
7.6
16.2
6.2
7.1
12.2
std
4.3
4.5
5.7
5.4
5.1
4.1
1.2
5.9
3.3
6.5
5.7
5.3
6.9
1.6
1.2
3.6
3.5
2.1
1.8
4.7
4.5
10.2
1.6
6.9
7.3
6.4
min
0.2
2.1
4.0
0.5
14.9
3.4
6.4
12.3
1.4
8.2
17.5
6.3
0.9
0.7
1.4
4.2
10.2
1.6
5.6
9.4
1.7
19.7
1.6
6.7
3.3
0.5
15.1
1.0
1.5
8.5
p2.5
0.2
2.1
4.0
0.5
14.9
3.4
6.4
12.3
1.4
8.2
17.5
6.3
0.9
0.7
1.4
4.2
10.2
1.6
5.6
9.4
1.7
19.7
1.6
6.7
3.3
0.5
15.1
1.0
1.5
8.5
p50
3.4
10.7
5.4
13.0
14.9
9.6
15.9
15.3
10.9
18.9
17.5
6.3
12.8
4.5
3.3
15.4
11.4
2.5
10.7
17.1
1.7
19.7
2.6
9.5
6.0
3.0
16.2
2.5
3.5
8.6
P97.5
13.2
17.4
17.3
19.2
14.9
15.8
18.7
15.6
18.5
19.1
17.5
6.3
18.8
11.9
11.3
16.7
12.5
3.4
17.6
18.2
5.2
19.7
6.3
19.6
15.5
19.3
17.3
15.0
16.3
19.6
max
13.2
17.4
17.3
19.2
14.9
15.8
18.7
15.6
18.5
19.1
17.5
6.3
18.8
11.9
11.3
16.7
12.5
3.4
17.6
18.2
5.2
19.7
6.3
19.6
15.5
19.3
17.3
15.0
16.3
19.6
SO2 emissions
mean
5834
89
83
948
5
1433
61
5061
995
4289
496
11756
5595
1421
1421
1421
2719
2719
891
8178
11831
7
18599
762
705
1834
2719
222
222
95
std
14038
136
132
2944
1913
103
12720
2730
11350
14808
2325
2325
2325
3687
3687
2248
9105
10420
44191
1491
1346
3024
3687
401
401
103
tpy) from sources within 20 km of monitor1
min
6
6
6
5
5
5
5
7
5
7
496
11756
7
6
6
6
112
112
9
6
6
7
12
6
7
64
112
8
8
35
p2.5
6
6
6
5
5
5
5
7
5
7
496
11756
7
6
6
6
112
112
9
6
6
7
12
6
7
64
112
8
8
35
p50
24
20
19
9
5
211
18
89
52
89
496
11756
34
153
153
153
2719
2719
60
5377
15822
7
281
191
194
112
2719
35
35
35
P97.5
37622
411
411
9820
5
4088
343
36378
9820
36378
496
11756
42188
4104
4104
4104
5326
5326
6842
19666
19666
7
108788
4104
3437
5326
5326
1025
1025
214
max
37622
411
411
9820
5
4088
343
36378
9820
36378
496
11756
42188
4104
4104
4104
5326
5326
6842
19666
19666
7
108788
4104
3437
5326
5326
1025
1025
214
A-50
-------
Monitor ID2
471310004
471390003
471390007
471390008
471390009
471450009
471451020
471550101
471570034
471570043
471570046
471571034
471572005
471610007
471630007
471630009
471651002
471651005
480370099
480430101
480610006
480670099
481130069
481390015
481390016
481390017
481410033
481410037
481410053
481410057
481410058
481670005
481671002
481830001
482010046
482010051
n
0
1
1
1
1
4
9
1
18
18
2
19
0
3
10
12
4
4
5
0
0
5
9
12
12
12
13
13
13
12
16
43
43
5
29
2
Distance of monitor to SO2 emission source (km)1
mean
3.1
1.6
1.4
1.0
10.9
14.0
18.9
11.4
9.6
6.0
3.5
1.8
3.7
5.7
4.2
4.3
7.3
15.1
12.1
9.5
9.0
9.6
9.8
9.7
9.7
14.2
13.9
2.3
3.6
18.9
12.8
19.1
std
6.7
2.9
2.2
1.7
6.7
5.6
0.2
2.6
6.0
1.8
3.2
0.0
0.0
5.7
5.8
6.3
6.9
2.0
1.8
1.6
0.7
2.3
1.3
1.1
0.5
3.1
0.6
min
3.1
1.6
1.4
1.0
5.3
7.6
18.9
4.8
5.3
1.3
0.5
1.7
1.7
2.0
2.9
0.2
7.2
15.0
2.0
2.3
2.9
1.9
4.0
4.5
5.1
12.7
9.5
1.2
2.5
18.6
6.2
18.7
p2.5
3.1
1.6
1.4
1.0
5.3
7.6
18.9
4.8
5.3
1.3
0.5
1.7
1.7
2.0
2.9
0.2
7.2
15.0
2.0
2.3
2.9
1.9
4.0
4.5
5.1
12.7
9.5
1.3
2.5
18.6
6.2
18.7
p50
3.1
1.6
1.4
1.0
9.5
13.2
18.9
11.8
10.0
6.0
0.7
1.7
2.6
2.7
3.5
5.0
7.3
15.1
12.9
9.4
6.1
7.6
10.1
10.0
9.9
14.4
14.7
2.0
3.3
18.7
13.1
19.1
P97.5
3.1
1.6
1.4
1.0
19.1
17.4
18.9
15.3
11.4
10.8
18.0
1.9
10.7
18.7
6.9
6.9
7.3
15.1
20.0
16.6
17.4
18.6
12.1
12.0
11.9
15.1
16.0
3.3
4.6
19.9
19.6
19.5
max
3.1
1.6
1.4
1.0
19.1
17.4
18.9
15.3
11.4
10.8
18.0
1.9
10.7
18.7
6.9
6.9
7.3
15.1
20.0
16.6
17.4
18.6
12.1
12.0
11.9
15.1
16.0
9.5
9.5
19.9
19.6
19.5
SO2 emissions
mean
1900
1900
1900
1900
19470
9351
66
1204
1204
1973
1150
5561
3010
2513
8593
8593
74
74
34
664
664
664
44
44
44
45
38
185
185
13289
606
13
std
22311
16734
2391
2391
2640
2336
5107
5303
4935
10129
10129
55
55
25
993
993
993
92
92
92
96
83
611
611
12287
1182
8
tpy) from sources within 20 km of monitor1
min
1900
1900
1900
1900
9
7
66
5
5
106
5
21
22
13
88
88
29
29
9
13
13
13
5
5
5
5
5
5
5
6
6
7
p2.5
1900
1900
1900
1900
9
7
66
5
5
106
5
21
22
13
88
88
29
29
9
13
13
13
5
5
5
5
5
6
6
6
6
7
p50
1900
1900
1900
1900
19188
390
66
32
32
1973
35
6580
495
286
7029
7029
53
53
18
57
57
57
11
11
11
11
12
22
22
19024
161
13
P97.5
1900
1900
1900
1900
39495
39495
66
6540
6540
3839
6540
10081
16855
16855
20226
20226
164
164
69
3003
3003
3003
345
345
345
345
345
1937
1937
24837
5097
18
max
1900
1900
1900
1900
39495
39495
66
6540
6540
3839
6540
10081
16855
16855
20226
20226
164
164
69
3003
3003
3003
345
345
345
345
345
3599
3599
24837
5097
18
A-51
-------
Monitor ID2
482010059
482010062
482010070
482010416
482011035
482011050
482450009
482450011
482450020
482570005
483550025
483550026
483550032
484530613
490050004
490110001
490110004
490350012
490351001
490352004
490450002
500070003
500070014
500210002
510360002
510590005
510590018
510591004
510591005
510595001
511130003
511611004
511650002
511650003
515100009
516500004
n
38
37
31
37
39
46
16
27
8
0
17
19
17
3
1
6
6
6
7
3
1
1
1
0
18
5
10
11
13
11
1
8
7
6
11
15
Distance of monitor to SO2 emission source (km)1
mean
10.3
14.8
10.7
11.9
8.6
16.5
14.8
9.0
10.8
6.7
10.0
3.9
12.2
1.8
8.2
9.7
4.9
13.0
9.8
11.6
1.6
1.9
12.1
17.2
13.5
10.9
13.6
14.8
10.8
9.3
12.3
11.4
9.6
11.1
std
5.9
3.8
5.3
5.6
5.4
3.9
6.8
5.3
8.1
3.0
3.3
4.1
0.7
5.8
6.0
3.7
6.5
8.0
7.2
1.6
3.9
3.5
4.3
4.4
5.5
5.1
5.4
5.1
4.9
min
1.8
7.8
2.2
3.3
1.6
5.0
0.4
2.8
1.8
4.2
4.6
0.4
11.8
1.8
1.5
2.3
0.6
2.1
2.4
11.6
1.6
1.9
2.0
15.0
8.4
3.7
4.6
5.1
10.8
2.9
5.1
6.3
1.1
4.0
p2.5
1.8
7.8
2.2
3.3
1.6
5.3
0.4
2.8
1.8
4.2
4.6
0.4
11.8
1.8
1.5
2.3
0.6
2.1
2.4
11.6
1.6
1.9
2.0
15.0
8.4
3.7
4.6
5.1
10.8
2.9
5.1
6.3
1.1
4.0
p50
8.5
15.7
8.7
10.0
7.7
17.9
18.7
7.0
11.3
5.2
11.0
1.7
11.9
1.8
8.1
9.8
4.5
13.0
8.9
11.6
1.6
1.9
13.6
17.3
15.7
11.2
13.8
16.0
10.8
9.7
13.9
10.3
8.6
11.3
P97.5
19.5
20.0
19.5
19.8
17.6
19.1
19.7
18.1
19.9
16.4
13.6
16.0
13.0
1.8
17.7
19.2
8.9
19.6
18.3
11.6
1.6
1.9
19.9
19.4
17.5
16.3
19.0
19.8
10.8
19.1
17.8
17.9
17.9
17.9
max
19.5
20.0
19.5
19.8
17.6
19.9
19.7
18.1
19.9
16.4
13.6
16.0
13.0
1.8
17.7
19.2
8.9
19.6
18.3
11.6
1.6
1.9
19.9
19.4
17.5
16.3
19.0
19.8
10.8
19.1
17.8
17.9
17.9
17.9
SO2 emissions
mean
674
694
790
691
657
243
863
999
170
468
424
468
86
5
468
468
468
833
1245
8
6
6
4818
31
1820
1664
1416
1566
7
85
40
39
1663
285
std
1486
1503
1622
1503
1470
1028
2732
2362
306
1086
1032
1086
90
500
500
500
1006
1415
17274
46
5043
4813
4435
4837
117
36
40
4813
505
tpy) from sources within 20 km of monitor1
min
6
6
6
6
6
6
6
6
6
6
6
6
5
5
8
8
8
8
8
8
6
6
7
8
8
7
7
6
7
5
8
5
7
6
p2.5
6
6
6
6
6
7
6
6
6
6
6
6
5
5
8
8
8
8
8
8
6
6
7
8
8
7
7
6
7
5
8
5
7
6
p50
48
49
161
49
46
36
80
45
64
43
43
43
70
5
366
366
366
712
939
8
6
6
35
11
74
59
59
24
7
34
32
25
59
92
P97.5
6968
6968
6968
6968
6968
829
11064
11064
908
3955
3955
3955
183
5
1332
1332
1332
2788
2788
8
6
6
73839
114
16141
16141
16141
16141
7
341
108
108
16141
1983
max
6968
6968
6968
6968
6968
6968
11064
11064
908
3955
3955
3955
183
5
1332
1332
1332
2788
2788
8
6
6
73839
114
16141
16141
16141
16141
7
341
108
108
16141
1983
A-52
-------
Monitor ID2
517100023
517100024
517600021
517600024
530090010
530090012
530330057
530330080
530530021
530530031
530570012
530570018
530571003
530610016
530730011
540090005
540090007
540110006
540250001
540290005
540290007
540290008
540290009
540290011
540290014
540290015
540290016
540291004
540390004
540390010
540392002
540511002
540610003
540610004
540610005
540690007
n
21
24
14
14
1
1
5
5
3
3
4
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
Distance of monitor to SO2 emission source (km)1
mean
8.3
9.0
9.4
9.4
5.6
5.3
4.0
5.0
3.2
1.8
2.2
3.6
1.7
0.5
16.9
5.3
10.7
13.2
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
std
3.4
5.6
5.8
5.8
6.0
4.2
1.1
0.9
0.8
1.0
0.6
0.1
6.2
5.3
5.3
7.1
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
min
3.6
0.5
1.1
1.2
5.6
5.3
0.6
2.5
2.1
1.2
1.3
2.8
1.1
0.4
0.5
0.9
3.9
0.5
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
p2.5
3.6
0.5
1.1
1.2
5.6
5.3
0.6
2.5
2.1
1.2
1.3
2.8
1.1
0.4
0.5
0.9
3.9
0.5
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
p50
8.3
9.0
10.4
10.3
5.6
5.3
1.3
3.1
3.2
1.3
2.3
3.3
1.7
0.5
19.3
2.7
8.3
16.2
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
P97.5
18.8
18.9
19.8
20.0
5.6
5.3
14.7
12.5
4.3
2.8
3.1
5.1
2.4
0.6
19.7
16.8
18.8
17.2
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
max
18.8
18.9
19.8
20.0
5.6
5.3
14.7
12.5
4.3
2.8
3.1
5.1
2.4
0.6
19.7
16.8
18.8
17.2
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
SO2 emissions
mean
1738
1553
191
191
756
756
241
241
179
179
2238
2238
2238
191
488
6005
13129
1501
22069
9282
20696
9894
13129
9282
20696
10611
10611
1529
1529
22698
27781
45992
24472
32132
37391
std
7026
6571
363
363
301
301
213
213
2630
2630
2630
194
695
15392
20063
2677
20983
17668
19955
18112
20063
17668
19955
17732
17732
1146
1146
47491
23029
63840
44468
51128
22660
tpy) from sources within 20 km of monitor1
min
5
5
6
6
756
756
63
63
11
11
21
21
21
53
8
10
10
124
18
10
18
10
10
10
18
10
10
854
854
750
795
850
850
850
21367
p2.5
5
5
6
6
756
756
63
63
11
11
21
21
21
53
8
10
10
124
18
10
18
10
10
10
18
10
10
854
854
750
795
850
850
850
21367
p50
85
79
16
16
756
756
117
117
109
109
1793
1793
1793
191
349
234
361
401
25596
238
24766
243
361
238
24766
302
302
1008
1008
1009
35454
45992
2952
4412
37391
P97.5
32344
32344
1148
1148
756
756
771
771
419
419
5345
5345
5345
328
2286
53414
59928
6285
59928
59928
59928
59928
59928
59928
59928
59928
59928
3245
3245
107633
56009
91134
91134
91134
53414
max
32344
32344
1148
1148
756
756
771
771
419
419
5345
5345
5345
328
2286
53414
59928
6285
59928
59928
59928
59928
59928
59928
59928
59928
59928
3245
3245
107633
56009
91134
91134
91134
53414
A-53
-------
Monitor ID2
540990002
540990003
540990004
540990005
541071002
550090005
550250041
550410007
550730005
550790007
550790026
550790041
550850996
551110007
551250001
551410016
551410017
560050857
560136001
560370200
560450800
n
8
8
8
8
11
7
7
1
3
9
9
9
2
2
0
6
6
4
1
0
2
Distance of monitor to SO2 emission source (km)1
mean
9.7
9.6
9.6
9.5
8.5
4.2
7.4
8.3
10.7
6.5
7.6
10.1
0.9
14.7
5.3
5.8
4.6
17.0
0.5
std
5.5
5.5
6.0
6.4
5.4
3.4
4.7
9.2
3.4
3.0
3.0
0.1
7.4
2.6
2.6
6.5
0.0
min
1.7
1.5
1.0
0.9
2.7
1.1
2.8
8.3
0.1
1.8
3.5
5.9
0.9
9.5
2.3
2.3
1.1
17.0
0.5
p2.5
1.7
1.5
1.0
0.9
2.7
1.1
2.8
8.3
0.1
1.8
3.5
5.9
0.9
9.5
2.3
2.3
1.1
17.0
0.5
p50
10.6
10.7
11.3
11.4
8.8
3.1
5.2
8.3
15.8
5.9
7.5
10.2
0.9
14.7
4.9
5.6
1.6
17.0
0.5
P97.5
16.0
15.8
15.8
16.2
17.0
9.7
14.7
8.3
16.2
12.9
12.8
14.5
1.0
19.9
9.8
10.3
14.4
17.0
0.5
max
16.0
15.8
15.8
16.2
17.0
9.7
14.7
8.3
16.2
12.9
12.8
14.5
1.0
19.9
9.8
10.3
14.4
17.0
0.5
SO2 emissions
mean
1271
1271
1271
1271
4375
3413
1293
5
4040
1750
1750
1750
1152
31
2374
2374
2527
40
389
std
2194
2194
2194
2194
9095
5045
2743
6715
4858
4858
4858
1617
35
2368
2368
3868
14
tpy) from sources within 20 km of monitor1
min
25
25
25
25
7
9
7
5
24
5
5
5
9
7
6
6
23
40
379
p2.5
25
25
25
25
7
9
7
5
24
5
5
5
9
7
6
6
23
40
379
p50
343
343
343
343
1517
850
71
5
303
28
28
28
1152
31
2032
2032
896
40
389
P97.5
6285
6285
6285
6285
31006
13470
7417
5
11792
14686
14686
14686
2295
56
5782
5782
8291
40
399
max
6285
6285
6285
6285
31006
13470
7417
5
11792
14686
14686
14686
2295
56
5782
5782
8291
40
399
1 Mean, std , min, p2.5, p50, p97.5, max are the arithmetic average, standard deviation, minimum, 2.5tn, 50tn, 97.5tn percentiles, and maximum
distances and emissions.
2 There were no emissions above 5 tpy for located within 20 km of the monitors sited in Puerto Rico and the Virgin Islands.
A-54
-------
Table A-5. Requirements for valid data when comparing ambient SO2 monitoring
concentrations to the current NAAQS.
Standard
Primary
Averaging Time
24-hour
Annual
Level (ppm)
0.14
0.03
Validity Requirements
The day must contain 18 one-hour
measurements.
75% of days in a year (n=274) must
contain valid daily measurements.
A-5 5
-------
A.2 ANALYSIS OF CO-LOCATED MONITOR SO2 MEASUREMENTS
An analysis was performed on the 5-minute maximum SC>2 concentrations where
simultaneous measurements were made. The relative percent difference (RPD) was calculated
for each simultaneous 5-minute maximum concentration, considering measurements within the
5-max data set (n=300,438) and the measurements between the continuous-5 and the max-5 data
sets (n=29,058) separately. We anticipated that small fluctuations in concentration between the
two simultaneous measurements would have a greater influence on the RPD at lower
concentrations than at higher concentrations. Therefore, the two simultaneous measurements
were separated into two concentration groups for analysis; one where the maximum
concentrations were < 10 ppb and the other where concentrations were > 10 ppb. The following
was used to calculate the RPD for each duplicate measurement:
(C -C \
RPD=^ ' 2;x200
(C1+C2)
where,
RPD = Relative percent difference (%)
C] = 5-minute maximum SO2 concentration at the first collocated monitor
€2 = 5-minute maximum SC>2 concentration at the second collocated monitor
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-6 summarizes the distribution of RPDs for where duplicate measurements of
SC>2 concentrations were less than 10 ppb within the max-5 monitoring data set. On average,
there were relatively small differences in the duplicate measures at each of the monitors. Most
duplicate concentrations were within +1-61% of one another, although some are noted at or
above 100% (absolute difference). In considering that these maximum 5-minute 862
concentrations are well below that of potential interest in the exposure and risk analysis, this
degree of agreement between the two monitors at these concentration levels is acceptable.
A-56
-------
Table A-6. Distribution of the relative percent difference (RPD) between
simultaneous measurements by collocated max-5 monitors where SO2
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
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 concentrations > 10 ppb, the RPD was much lower at each of
the monitors (Table A-7). Most of the RPDs are within +/-10%, indicating excellent agreement
among the simultaneous measurements. A small negative bias may exist with selection of the
monitor with the greatest number of samples as the base monitor, but on average the difference
was typically less than 3%.
Table A-7. Distribution of the relative percent difference (RPD) between duplicate
measurements by collocated max-5 monitors where SO2 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
1 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum,
5th, median, 95th, and maximum, respectively.
Analyses were also performed for 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). Since there
were very few numbers of samples with RPDs deviating from zero, the following analysis
included only the samples that were different and at all concentration levels. The distribution for
A-57
-------
the RPD given these monitors and duplicate monitoring events is provided in Table A-8. 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
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.
Table A-8. Distribution of the relative percent difference (RPD) between duplicate
measurements by collocated max-5 and continuous-5 monitors.
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
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.
A-58
-------
APPENDIX B: PEAK-TO-MEAN SUMMARY TABLE
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 aggregated into 15 groups1 based
on the observed variability (3 bins) and concentrations ranges (5 bins) in measured 1-hour
ambient monitor concentrations. Table B-l summarizes the PMR distributions used for
estimating 5-minute maximum concentrations from 1-hour measurements.
Table B-1. Distribution of 5-minute peak to 1-hour mean ratios (PMRs) by
monitors categorized by 1-hour coefficient of variation (COV) and 1-hour mean
concentration.
Monitor
[1-hour]
group1
percentile
pO
P1
P2
p3
P4
P5
p6
P7
p8
p9
p10
p11
P12
p13
p14
p15
p16
P17
p18
p19
p20
p21
P22
p23
p24
p25
p26
P27
COV < 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
1.00
1.03
1.03
1.04
1.05
1.05
1.06
1.06
1.06
1.07
1.08
1.08
1.08
1.09
1.09
1.09
1.10
1.10
1.11
1.11
1.11
1.12
1.12
1.13
1.13
1.13
1.14
1.14
2
1.02
1.02
1.02
1.05
1.05
1.08
1.11
1.11
1.15
1.15
1.15
1.16
1.16
1.24
1.24
1.26
1.28
1.28
1.30
1.30
1.30
1.30
1.30
1.30
1.30
1.31
1.31
1.31
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
1.00
1.14
1.19
1.22
1.25
1.27
1.30
1.32
1.34
1.36
1.38
1.40
1.42
1.43
1.45
1.47
1.48
1.50
1.52
1.53
1.55
1.57
1.58
1.60
1.61
1.63
1.64
1.66
2
1.00
1.15
1.19
1.21
1.22
1.25
1.27
1.29
1.30
1.33
1.35
1.37
1.39
1.41
1.42
1.45
1.46
1.48
1.50
1.52
1.54
1.56
1.57
1.60
1.61
1.63
1.65
1.67
3
1.08
1.19
1.25
1.29
1.32
1.36
1.38
1.42
1.46
1.49
1.50
1.54
1.56
1.58
1.59
1.60
1.62
1.64
1.65
1.68
1.71
1.75
1.76
1.77
1.79
1.80
1.82
1.84
4
1.23
1.25
1.26
1.29
1.30
1.30
1.31
1.32
1.33
1.34
1.36
1.37
1.38
1.43
1.44
1.46
1.47
1.50
1.51
1.53
1.54
1.54
1.57
1.59
1.59
1.61
1.63
1.64
1 The results are for only 13 groups, since there were no values observed for the lowest COV bin (<100%) and the
two highest concentration bins (where the 1-hour mean was between 200-300 ppb and 1-hour mean > 300 ppb).
B-l
-------
Monitor
[1-hour]
group1
percentile
p28
p29
p30
p31
p32
p33
p34
p35
p36
p37
p38
p39
p40
p41
p42
p43
p44
p45
p46
p47
p48
p49
p50
p51
p52
p53
p54
p55
p56
p57
p58
p59
p60
p61
p62
p63
p64
p65
p66
p67
p68
p69
p70
p71
P72
p73
COV<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
1.07
1.09
1.11
1.11
1.13
1.14
1.15
1.17
1.17
1.20
1.20
1.20
1.22
1.25
1.25
1.25
1.25
1.29
1.31
1.33
1.33
1.33
1.33
1.33
1.38
1.40
1.43
1
1.15
1.15
1.16
1.16
1.17
1.17
1.17
1.18
1.18
1.19
1.19
1.20
1.20
1.21
1.21
1.22
1.22
1.23
1.24
1.24
1.24
1.25
1.26
1.26
1.27
1.28
1.28
1.29
1.29
1.30
1.31
1.32
1.32
1.33
1.34
1.34
1.35
1.36
1.37
1.38
1.40
1.41
1.42
1.43
1.44
1.46
2
1.31
1.31
1.32
1.34
1.34
1.35
1.35
1.35
1.36
1.36
1.41
1.41
1.41
1.42
1.42
1.45
1.45
1.45
1.45
1.45
1.47
1.47
1.51
1.55
1.55
1.56
1.56
1.57
1.59
1.59
1.65
1.65
1.65
1.65
1.65
1.65
1.65
1.66
1.66
1.66
1.70
1.70
1.72
1.74
1.74
1.77
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
1.08
1.11
1.14
1.18
1.20
1.24
1.25
1.25
1.30
1.33
1.33
1.33
1.38
1.43
1.48
1.50
1.50
1.50
1.50
1.50
1.53
1.60
1.67
1.71
1
1.68
1.70
1.71
1.73
1.75
1.76
1.78
1.79
1.81
1.83
1.85
1.86
1.88
1.89
1.91
1.93
1.95
1.97
1.98
2.00
2.03
2.05
2.06
2.09
2.11
2.14
2.16
2.18
2.21
2.23
2.26
2.28
2.31
2.34
2.36
2.39
2.42
2.46
2.49
2.52
2.56
2.59
2.63
2.67
2.71
2.76
2
1.70
1.73
1.75
1.77
1.79
1.81
1.83
1.85
1.88
1.91
1.93
1.95
1.98
2.01
2.03
2.06
2.10
2.13
2.16
2.19
2.21
2.23
2.26
2.29
2.31
2.34
2.36
2.39
2.42
2.44
2.48
2.51
2.55
2.59
2.62
2.67
2.71
2.76
2.79
2.83
2.88
2.91
2.95
3.01
3.04
3.08
3
1.86
1.89
1.90
1.91
1.92
1.95
1.97
1.97
1.99
2.02
2.06
2.08
2.11
2.13
2.15
2.16
2.18
2.20
2.21
2.24
2.25
2.27
2.28
2.29
2.31
2.33
2.35
2.36
2.39
2.40
2.43
2.45
2.47
2.50
2.53
2.55
2.59
2.61
2.62
2.63
2.64
2.67
2.67
2.70
2.71
2.75
4
1.64
1.66
1.67
1.69
1.69
1.72
1.73
1.73
1.76
1.77
1.77
1.78
1.78
1.79
1.80
1.80
1.81
1.82
1.82
1.82
1.83
1.84
1.84
1.85
1.87
1.89
1.89
1.91
1.91
1.93
1.94
1.95
1.96
1.97
1.97
1.98
2.00
2.01
2.02
2.04
2.04
2.06
2.07
2.09
2.13
2.14
B-2
-------
Monitor
[1-hour]
group1
percentile
p74
p75
p76
p77
p78
p79
p80
p81
p82
p83
p84
p85
p86
p87
p88
p89
p90
p91
p92
p93
p94
p95
p96
p97
p98
p99
p100
COV<100%
0
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.58
1.67
1.67
1.75
1.93
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.25
2.50
3.00
3.50
12.00
1
1.47
1.48
1.49
1.50
1.51
1.53
1.55
1.57
1.60
1.62
1.64
1.67
1.69
1.72
1.74
1.78
1.82
1.86
1.90
1.96
2.02
2.10
2.22
2.36
2.57
2.95
6.81
2
1.77
1.80
1.82
1.82
1.83
1.83
1.84
1.85
1.85
1.88
1.88
2.09
2.30
2.30
2.50
2.50
2.50
2.50
2.50
2.56
2.56
2.73
2.89
2.89
3.61
3.61
3.61
100 200%
0
1.78
1.85
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.18
2.33
2.50
2.61
2.83
3.00
3.08
3.33
3.75
4.11
5.00
5.67
10.00
10.00
12.00
1
2.80
2.85
2.89
2.94
3.00
3.06
3.13
3.19
3.25
3.32
3.40
3.48
3.57
3.67
3.78
3.91
4.06
4.20
4.37
4.56
4.82
5.08
5.41
5.82
6.49
7.49
11.45
2
3.13
3.17
3.22
3.26
3.35
3.38
3.43
3.48
3.55
3.63
3.71
3.78
3.87
3.94
4.04
4.14
4.23
4.35
4.42
4.53
4.68
4.89
5.18
5.43
5.96
6.63
9.67
3
2.77
2.81
2.84
2.88
2.90
2.92
2.98
2.99
3.01
3.04
3.09
3.11
3.16
3.20
3.25
3.32
3.38
3.41
3.47
3.54
3.62
3.67
3.74
3.80
4.01
4.23
4.60
4
2.15
2.17
2.17
2.19
2.21
2.27
2.31
2.31
2.33
2.36
2.38
2.47
2.49
2.50
2.53
2.54
2.56
2.57
2.61
2.67
2.67
2.70
2.72
2.82
2.97
3.28
5.39
1 1-hour SO2 concentration groups were as follows:
0 = 1-hour mean <33.3 ppb
1 = 33.3 < 1-hour mean < 100 ppb
2 = 100 < 1-hour mean < 200 ppb
3 = 200 < 1-hour mean < 300 ppb
4 = 1-hour mean > 300 ppb.
B-3
-------
APPENDIX C: DETAILED AIR QUALITY CHARACTERIZATION TABLES
C-l
-------
Table C-1. Descriptive statistics for measured 5-minute maximum SO2 concentrations by year and number of
concentrations above potential health effect benchmark levels. Data used were from 98 monitors that measured
both the 5-minute maximum and 1-hour 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
n
7183
7800
7690
6702
8356
2062
8322
6857
6277
7943
8334
8347
7084
6153
8176
8265
6297
7240
4431
4923
8364
2061
7045
4363
1637
2459
5625
6863
6262
4480
Measured 5-minute Maximum SO2 (ppb)1
Mean
4
4
3
3
4
4
2
2
2
3
2
8
10
8
9
5
4
4
4
3
4
4
13
17
14
10
14
10
7
8
Std
3
3
3
2
2
2
2
2
2
3
2
22
19
14
20
8
5
12
8
6
5
2
17
17
15
13
15
13
8
8
cov
77
76
90
78
51
60
79
93
101
109
78
259
198
173
228
176
114
342
235
173
141
63
131
99
105
127
112
127
114
97
PO
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
p50
3
3
2
2
4
3
2
1
1
2
2
3
5
4
3
3
2
2
2
2
3
3
7
11
9
6
8
5
5
5
p97
11
10
11
8
9
10
6
6
6
8
6
54
47
44
59
19
14
15
14
10
9
8
58
58
54
46
52
46
27
28
p98
12
11
12
9
10
12
7
7
7
9
7
69
58
54
77
24
17
17
19
13
13
10
67
65
61
56
61
52
32
31
p99
14
14
15
11
12
13
9
8
8
12
9
105
77
72
111
33
22
23
27
21
20
14
84
79
76
68
73
63
42
36
p100
131
94
47
38
52
28
30
35
80
90
62
361
659
238
313
422
103
511
273
240
306
31
192
216
122
134
199
174
110
86
Number of 5-minute Maximum
>400
ppb
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
1
0
3
0
0
0
0
0
0
0
0
0
0
0
0
>500
PPb
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
>600
PPb
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
0
0
0
0
0
C-2
-------
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
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
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
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
n
4172
6519
7501
4901
3751
8302
8575
4282
2770
6394
8415
8662
6507
4120
518
3718
5179
8676
3713
1346
6773
6193
7472
4153
1962
8597
7698
8167
4255
1603
Measured 5-minute Maximum SC>2 (ppb)1
Mean
7
6
20
18
10
8
9
11
9
6
11
6
6
8
2
2
3
2
1
3
5
4
4
5
4
5
5
5
5
3
Std
7
7
38
34
8
10
9
12
8
7
29
17
15
21
5
5
11
4
3
3
6
7
6
9
6
7
10
8
12
3
cov
106
105
194
190
80
115
100
111
83
115
263
279
275
261
231
242
326
234
191
89
133
160
151
162
162
157
200
178
216
106
PO
1
0.2
1
1
3
1
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
5
4
6
6
8
6
6
8
8
5
2
1
1
1
1
1
1
1
1
2
3
2
2
3
2
3
3
3
3
1
p97
26
24
145
118
29
29
31
31
27
18
88
47
40
67
10
6
21
7
5
9
18
16
17
25
17
18
24
22
30
8
p98
30
27
171
143
33
33
37
34
33
21
110
57
50
84
16
9
29
10
7
11
21
19
20
31
20
23
30
27
40
9
p99
36
33
195
169
42
42
47
45
39
30
152
81
67
103
28
18
52
19
11
13
27
25
26
44
37
37
45
39
65
11
p100
59
104
328
381
108
395
106
482
138
400
467
302
473
297
59
100
166
81
92
25
109
213
129
174
88
151
187
148
166
38
Number of 5-minute Maximum
>400
ppb
0
0
0
0
0
0
0
1
0
1
2
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>500
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
>600
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
C-3
-------
State
IA
IA
IA
IA
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
County
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Scott
Scott
Scott
Scott
Scott
Van Buren
Van Buren
Van Buren
Van Buren
Van Buren
Van Buren
Woodbury
Woodbury
West Baton
Rouge
West Baton
Rouge
West Baton
Rouge
West Baton
Rouge
Buchanan
Buchanan
Buchanan
Buchanan
Buchanan
Monitor ID
191390017
191390017
191390017
191390017
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
Year
2002
2003
2004
2005
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
n
8139
8533
8415
4214
2018
8201
8412
8717
4304
1438
8073
7916
7638
3919
701
6692
7486
5341
1032
3957
1686
4048
4971
7566
7279
7370
8484
8161
7419
5299
1672
Measured 5-minute Maximum SC>2 (ppb)1
Mean
4
5
5
4
7
8
8
11
10
2
3
3
3
4
1
1
1
1
1
1
2
3
13
12
11
14
21
18
5
4
10
Std
7
8
7
9
13
18
21
27
27
3
4
4
4
5
1
1
1
1
1
1
4
5
26
23
21
27
77
61
8
9
19
cov
173
156
151
210
188
219
249
236
272
158
134
128
126
126
75
74
66
109
68
67
174
186
206
188
185
197
362
347
178
211
195
PO
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
1
1
1
1
1
p50
3
4
3
2
2
3
2
3
2
1
1
1
1
2
1
1
1
1
1
1
1
1
5
6
5
6
3
3
3
2
4
p97
13
20
20
17
48
58
66
89
85
9
14
12
12
16
4
4
4
4
4
3
14
17
74
61
58
78
244
184
22
22
67
p98
20
28
25
27
52
73
84
110
110
12
18
14
14
18
5
4
4
5
4
4
18
21
100
86
77
104
315
242
32
31
83
p99
31
43
40
38
68
96
114
142
150
16
23
19
18
24
6
6
5
7
5
5
22
28
139
130
109
143
433
337
44
47
106
p100
204
157
125
185
105
204
256
255
307
46
59
53
41
41
9
31
16
22
7
11
36
59
446
428
401
430
928
728
165
157
156
Number of 5-minute Maximum
>400
ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
106
47
0
0
0
>500
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
61
26
0
0
0
>600
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
24
13
0
0
0
C-4
-------
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
Buchanan
Buchanan
Buchanan
Greene
Greene
Greene
Greene
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
Monitor ID
290210011
290210011
290210011
290770026
290770026
290770026
290770026
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
Year
2001
2002
2003
1997
1998
1999
2000
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
n
6415
6467
5142
4765
5813
7242
8721
8304
7055
7935
6574
8756
8753
6520
6563
8135
8554
5339
6710
6374
8181
6575
8760
8745
6496
8707
8475
6547
4088
5393
7961
6964
1846
6178
Measured 5-minute Maximum SC>2 (ppb)1
Mean
7
8
7
9
12
8
10
9
9
6
6
6
6
6
12
7
6
14
9
9
6
5
6
7
5
22
22
25
41
28
20
22
3
17
Std
13
17
15
19
23
16
21
20
19
13
14
13
15
15
36
18
19
40
27
26
16
13
15
21
15
85
86
91
124
101
79
80
3
59
cov
185
218
208
221
190
203
219
221
213
202
215
227
228
225
307
242
307
277
293
298
253
269
273
295
317
391
394
357
304
356
388
369
107
350
PO
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
1
1
1
1
1
1
1
1
p50
3
3
3
2
2
2
2
2
2
2
1
1
1
1
2
3
2
2
2
2
2
2
2
1
1
3
2
3
3
2
2
3
2
3
p97
49
53
52
63
82
56
74
69
68
44
48
47
53
52
107
57
54
139
84
79
56
36
40
62
46
201
235
267
411
330
225
244
11
120
p98
57
70
67
77
91
65
87
82
78
52
56
55
63
59
145
76
75
178
104
110
69
48
53
82
62
311
334
372
530
437
314
328
12
203
p99
70
95
88
99
107
78
108
101
95
62
66
68
74
73
185
99
115
223
142
143
87
71
82
115
86
492
508
541
675
594
444
453
15
325
p100
133
176
170
230
214
213
211
183
159
173
144
149
123
129
480
265
273
327
329
317
285
192
259
259
185
1001
998
997
1001
945
998
907
22
844
Number of 5-minute Maximum
>400
ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
127
133
117
128
123
102
98
0
41
>500
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
82
87
81
91
80
61
51
0
25
>600
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
52
54
48
61
49
38
30
0
10
C-5
-------
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
Iron
Iron
Iron
Iron
Iron
Iron
Iron
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
Monitor ID
290930031
290930031
290930031
290930031
290930031
290930031
290930031
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
Year
1998
1999
2000
2001
2002
2003
2004
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
n
7991
7919
5172
8426
8665
8230
2172
8034
7144
6525
2125
7543
8130
7828
8259
2730
5721
7289
7162
1045
3495
6306
6009
8280
8426
8714
8617
4347
5358
5951
5125
6519
6170
526
Measured 5-minute Maximum SC>2 (ppb)1
Mean
15
16
18
14
13
13
4
19
23
29
12
16
8
8
5
5
15
20
13
16
13
12
9
3
3
4
3
2
2
2
3
3
2
2
Std
53
59
63
53
46
52
3
49
60
71
31
54
27
24
17
13
54
66
50
43
43
51
39
4
3
3
2
2
2
2
3
2
2
3
cov
351
365
342
383
364
388
76
251
255
244
245
336
349
303
310
271
351
338
376
265
338
407
440
104
104
71
69
83
89
80
95
85
78
115
PO
1
1
1
1
1
1
2
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
1
p50
3
4
3
2
3
3
3
5
5
5
3
5
3
3
2
2
4
5
3
5
3
3
2
2
2
3
3
2
2
2
2
2
2
2
p97
113
109
153
123
93
105
13
118
149
164
72
102
35
41
25
29
86
128
72
111
71
61
38
11
9
11
9
7
7
7
9
8
6
7
p98
179
158
214
175
135
153
15
140
190
215
96
156
45
54
34
38
138
207
127
163
97
104
50
13
10
13
12
8
8
8
11
10
7
8
p99
286
286
318
280
242
256
17
209
306
367
156
247
84
87
57
65
246
332
229
238
183
217
95
17
14
16
14
9
10
10
14
12
9
11
p100
1002
1001
1002
994
950
999
36
957
999
954
467
1645
877
595
575
225
998
960
997
480
968
999
977
98
75
66
26
21
53
26
33
30
38
38
Number of 5-minute Maximum
>400
ppb
36
48
33
42
29
31
0
21
37
57
2
33
9
6
2
0
27
56
30
3
10
29
13
0
0
0
0
0
0
0
0
0
0
0
>500
ppb
16
33
18
19
14
19
0
14
21
32
0
16
5
2
1
0
15
43
19
0
8
20
10
0
0
0
0
0
0
0
0
0
0
0
>600
ppb
10
22
13
10
6
12
0
10
13
23
0
11
3
0
0
0
14
27
12
0
5
12
8
0
0
0
0
0
0
0
0
0
0
0
C-6
-------
State
MO
MO
MO
MO
MO
MO
MO
MO
MO
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
County
Pike
Pike
Pike
Saint
Charles
Saint
Charles
Saint
Charles
Saint
Charles
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
Monitor ID
291630002
291630002
291630002
291830010
291830010
291831002
291831002
291831002
291831002
301110066
301110066
301110066
301110066
301110066
301110066
301110066
301110079
301110079
301110079
301110079
301110080
301110080
301110080
301110080
301110080
301110082
301110082
301110082
301110083
301110083
Year
2005
2006
2007
1997
1998
1997
1998
1999
2000
1997
1998
1999
2000
2001
2002
2003
1997
2001
2002
2003
1997
1998
1999
2000
2001
2001
2002
2003
1999
2000
n
4883
6473
1020
8153
4811
8515
8122
7970
6422
6890
7205
5776
6123
6880
8347
5700
3167
837
8034
5107
5462
5412
5617
6032
2029
2607
8212
5180
2087
3857
Measured 5-minute Maximum SC>2 (ppb)1
Mean
7
6
6
6
6
9
10
8
7
18
15
18
18
17
14
16
7
7
3
5
20
17
17
16
14
7
4
5
15
11
Std
11
9
9
13
9
15
14
13
10
23
20
22
25
23
24
22
7
5
3
4
27
24
23
24
24
7
5
6
16
16
cov
156
160
155
218
153
161
146
156
139
129
131
123
137
137
168
133
109
70
113
79
138
137
135
144
169
110
140
125
104
148
PO
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
1
1
1
1
p50
4
4
3
3
3
5
5
5
4
10
8
11
10
9
6
9
5
6
1
4
11
10
10
8
8
5
2
3
10
6
p97
38
27
25
28
28
42
43
38
31
72
64
72
80
79
68
74
24
19
11
14
82
68
71
75
65
24
15
17
54
46
p98
46
36
36
36
34
52
53
45
38
81
76
84
97
94
81
85
27
22
13
16
98
79
83
88
75
28
19
22
62
51
p99
60
48
48
47
44
76
74
61
53
104
96
103
116
114
102
111.5
34
23
16
19
136
102
106
115
93
38
24
28
82
64
p100
124
209
86
516
190
358
200
275
176
538
344
296
481
215
843
222
106
37
38
40
374
398
478
374
693
110
93
213
172
531
Number of 5-minute Maximum
>400
ppb
0
0
0
2
0
0
0
0
0
2
0
0
1
0
2
0
0
0
0
0
0
0
2
0
1
0
0
0
0
1
>500
ppb
0
0
0
1
0
0
0
0
0
1
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
>600
ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
C-7
-------
State
MT
MT
MT
MT
MT
MT
MT
MT
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
County
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Forsyth
Forsyth
Forsyth
Forsyth
Forsyth
Forsyth
Forsyth
Forsyth
New
Hanover
New
Hanover
New
Hanover
New
Hanover
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Billings
Monitor ID
301110083
301110083
301110083
301110084
301110084
301110084
301110084
301112008
370670022
370670022
370670022
370670022
370670022
370670022
370670022
370670022
371290006
371290006
371290006
371290006
380070002
380070002
380070002
380070002
380070002
380070002
380070002
380070002
380070002
380070002
380070003
Year
2001
2002
2003
2003
2004
2005
2006
1997
1997
1998
1999
2000
2001
2002
2003
2004
1999
2000
2001
2002
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
n
5606
6847
1641
759
2468
2578
1984
2580
8383
7124
6434
5205
7634
7023
8077
4711
8208
7980
8168
8028
1940
3216
2724
2860
3114
342
1256
837
418
221
2657
Measured 5-minute Maximum SC>2 (ppb)1
Mean
9
4
4
5
7
6
5
7
10
10
9
8
7
9
8
8
9
11
15
16
1
1
1
2
2
2
2
2
3
2
3
Std
13
8
6
8
11
11
9
9
11
13
10
9
9
14
10
13
22
25
41
37
1
1
1
2
2
1
2
2
3
2
4
cov
150
181
154
156
171
190
167
123
115
131
117
109
123
150
119
155
263
237
269
239
80
75
77
104
107
66
117
86
91
94
169
PO
1
1
1
1
1
1
1
1
0.2
1
1
0.2
1
1
0.2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
4
2
2
3
3
2
2
4
6
7
6
5
5
5
5
4
1
1
1
2
1
1
1
1
1
2
1
1
2
2
2
p97
41
22
23
26
35
33
29
26
34
36
31
30
28
40
31
36
66
87
136
124
4
4
4
5
6
6
6
7
8
7
9
p98
50
28
25
33
44
40
35
30
40
42
38
34
34
50
37
45
79
101
161
142
5
5
5
7
7
6
7
8
9
8
12
p99
62
38
33
46
60
55
45
37
53
59
52
44
44
68
52
65
101
124
205
178
7
6
7
10
10
7
10
9
10
12
19
p100
253
146
60
92
194
151
119
144
188
494
178
123
163
238
117
219
579
374
652
805
12
12
11
23
53
10
47
17
25
17
97
Number of 5-minute Maximum
>400
ppb
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
3
0
4
2
0
0
0
0
0
0
0
0
0
0
0
>500
ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
3
1
0
0
0
0
0
0
0
0
0
0
0
>600
ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
-------
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
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burke
Burleigh
Burleigh
Burleigh
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Cass
Dunn
Dunn
Dunn
Dunn
Dunn
Dunn
Dunn
Monitor ID
380130002
380130002
380130002
380130002
380130002
380130002
380130002
380130004
380130004
380130004
380130004
380130004
380150003
380150003
380150003
380171003
380171003
380171004
380171004
380171004
380171004
380171004
380171004
380171004
380171004
380171004
380171004
380250003
380250003
380250003
380250003
380250003
380250003
380250003
Year
1999
2000
2001
2002
2003
2004
2005
2003
2004
2005
2006
2007
2005
2006
2007
1997
1998
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
2000
2001
2002
2003
n
3852
5268
5653
5368
6328
5230
3099
882
3198
2238
3152
1228
684
3708
948
2254
2943
2501
3325
1868
1686
2476
1297
3140
928
7863
2258
3313
2688
5099
7455
3576
4485
7289
Measured 5-minute Maximum SC>2 (ppb)1
Mean
6
6
5
5
5
5
6
4
4
4
4
6
6
4
7
2
2
1
1
1
1
1
2
2
2
1
1
2
2
2
2
2
2
2
Std
11
13
10
11
11
11
11
7
6
5
6
8
5
5
8
2
2
0
1
1
1
1
2
1
1
1
1
2
3
3
2
2
2
2
cov
188
227
205
204
216
206
189
158
147
131
164
142
83
122
106
133
97
39
57
61
87
68
92
81
81
130
136
111
142
135
137
110
110
96
PO
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
0.1
0.1
1
1
1
1
1
1
1
p50
1
2
1
1
2
2
2
2
2
2
2
3
4
2
5
1
1
1
1
1
1
1
1
1
1
0.3
0.5
1
1
1
1
1
1
2
p97
33
34
33
31
31
33
35
22
21
17
17
25
18
15
26
7
6
2
3
4
4
3
6
4
6
2.3
3.3
6
10
7
6
8
6
6
p98
37
41
39
39
39
41
42
28
25
20
20
28
19
17
28
10
8
3
3
4
5
4
7
5
7
2.7
3.8
8
12
8
8
9
7
7
p99
50
54
51
50
53
53
53
38
35
25
29
32
22
20
38
13
11
3
4
5
7
5
9
7
8
3.8
5.6
10
19
11
10
12
9
10
p100
172
381
201
182
231
165
151
61
94
77
120
108
29
61
80
26
23
8
9
9
29
17
17
20
11
10.7
20.2
48
52
59
70
30
41
37
Number of 5-minute Maximum
>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
>500
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
>600
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
C-9
-------
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
Dunn
Dunn
Dunn
Dunn
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
Mercer
Monitor ID
380250003
380250003
380250003
380250003
380530002
380530002
380530002
380530002
380530002
380530002
380530002
380530002
380530002
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530111
380530111
380530111
380530111
380530111
380530111
380530111
380530111
380530111
380530111
380570001
Year
2004
2005
2006
2007
1997
1998
2001
2002
2003
2004
2005
2006
2007
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
n
6019
1314
2214
667
2557
1989
754
3361
5345
4614
2525
2897
511
1525
1501
2757
2281
1528
2333
2241
1905
1828
764
2071
2382
2808
3183
2256
2243
2857
2794
2942
724
2826
Measured 5-minute Maximum SC>2 (ppb)1
Mean
2
2
2
3
2
2
2
1
2
2
2
2
3
5
6
4
3
5
5
2
2
2
2
8
5
6
4
5
5
3
2
2
3
6
Std
2
3
2
3
2
2
1
1
2
2
1
1
2
12
16
14
5
17
19
4
6
9
2
20
14
23
5
16
14
14
10
8
10
9
cov
111
125
113
102
111
102
84
83
98
113
81
86
76
232
300
356
145
352
415
204
312
381
84
254
262
380
143
346
315
429
433
328
359
151
PO
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
1
1
1
1
1
1
1
1
p50
1
1
1
2
1
1
1
1
1
1
1
1
2
2
3
2
2
2
1
1
1
1
1
3
2
2
2
2
2
1
1
1
2
3
p97
6
7
8
8
8
7
5
4
6
6
5
5
7
21
19
13
12
19
19
6
5
5
6
33
22
21
16
18
16
8
6
6
8
29
p98
8
9
10
10
9
9
6
5
7
6
6
6
8
27
23
17
14
31
42
7
6
7
6
46
28
27
19
23
24
10
8
8
10
33
p99
10
11
14
13
12
12
7
6
10
8
7
7
8
43
29
28
16
82
103
10
10
11
10
91
46
45
24
48
66
36
18
12
12
44
p100
26
34
26
48
26
25
12
18
40
45
14
18
18
199
387
482
143
284
385
141
138
214
13
288
422
499
91
360
355
319
285
212
245
99
Number of 5-minute Maximum
>400
ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
3
0
0
0
0
0
0
0
0
>500
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
>600
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
C-10
-------
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
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Morton
Oliver
Oliver
Oliver
Oliver
Oliver
Oliver
Monitor ID
380570001
380570001
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380590002
380590002
380590002
380590002
380590002
380590002
380590002
380590002
380590002
380590003
380590003
380590003
380590003
380590003
380590003
380590003
380590003
380650002
380650002
380650002
380650002
380650002
380650002
Year
1998
1999
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
2000
2001
2002
2003
2004
2005
1998
1999
2000
2001
2002
2003
2004
2005
1997
1998
1999
2000
2001
2002
n
4735
320
5584
7348
4648
3701
5555
4678
3046
2756
1133
6552
4699
6838
7964
5952
6261
8034
7534
1452
1924
6529
5988
6351
5248
7991
6341
1014
2360
4178
4860
4766
2404
4483
Measured 5-minute Maximum SC>2 (ppb)1
Mean
6
8
5
4
6
5
4
5
5
5
4
19
19
16
13
16
14
13
14
10
8
11
11
11
10
8
10
9
9
8
6
6
6
5
Std
12
6
10
7
11
10
7
8
7
7
6
40
40
33
28
28
26
29
28
12
17
21
18
18
18
15
17
12
14
15
14
11
12
9
cov
203
72
201
189
203
197
173
157
149
127
146
206
207
203
217
178
189
220
198
123
225
186
172
167
177
206
178
124
167
192
215
199
185
187
PO
1
2
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
1
1
1
1
1
1
p50
3
7
2
1
2
2
2
2
2
3
2
3
3
2
2
3
2
2
2
4
2
3
3
3
3
2
3
5
3
3
2
2
2
2
p97
27
24
26
20
26
22
18
24
21
23
17
146
144
119
101
98
89
106
102
41
50
71
64
63
59
48
59
42
49
46
39
33
39
29
p98
36
26
31
24
35
25
22
29
25
26
21
161
164
132
116
108
104
119
116
44
74
87
75
74
72
61
71
46
57
55
49
41
47
36
p99
51
27
40
32
57
35
34
36
33
33
32
179
189
156
133
125
123
137
132
52
91
106
92
91
93
84
89
57
74
74
67
58
60
47
p100
241
36
260
209
169
274
103
107
95
70
73
348
295
248
297
229
207
366
261
104
197
378
167
222
208
194
183
101
164
203
207
164
173
137
Number of 5-minute Maximum
>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
>500
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
>600
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
C-ll
-------
State
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Oliver
Oliver
Oliver
Oliver
Oliver
Steele
Steele
Steele
Steele
Williams
Williams
Williams
Williams
Williams
Williams
Williams
Williams
Williams
Williams
Williams
Williams
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Monitor ID
380650002
380650002
380650002
380650002
380650002
380910001
380910001
380910001
380910001
381050103
381050103
381050103
381050103
381050103
381050103
381050105
381050105
381050105
381050105
381050105
381050105
420030002
420030002
420030002
420030021
420030021
420030021
420030021
420030031
420030031
420030031
420030032
420030032
420030032
Year
2003
2004
2005
2006
2007
1997
1998
1999
2000
2002
2003
2004
2005
2006
2007
2002
2003
2004
2005
2006
2007
1997
1998
1999
1997
1998
1999
2002
1997
1998
1999
1997
1998
1999
n
6973
6140
2444
3370
781
3134
2804
1845
805
2726
3327
3438
2331
2976
834
2844
3523
4129
4492
2938
263
7825
72
6986
7830
72
8280
7291
8000
68
7445
7951
60
4328
Measured 5-minute Maximum SC>2 (ppb)1
Mean
4
5
8
6
8
1
2
1
1
8
5
5
10
4
7
17
14
14
18
11
10
19
68
16
29
13
12
9
15
14
12
23
55
11
Std
11
11
15
10
14
1
2
1
0
23
10
14
24
8
13
28
23
24
32
19
18
25
55
18
33
10
10
10
13
11
15
32
30
11
cov
266
227
186
172
172
53
94
63
36
290
198
252
240
200
171
163
157
175
184
184
184
132
80
112
112
77
90
101
89
77
123
135
54
96
PO
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
1
1
2
1
1
1
3
1
1
4
1
p50
1
2
3
2
4
1
1
1
1
2
2
2
2
1
3
4
3
3
3
2
3
10
54.5
10
18
11
8
7
11
11
9
13
55.5
8
p97
24
28
48
32
48
3
9
3
2
51
31
31
71
23
45
86
78
69
99
64
58
88
187
61
96
36
37
30
45
40
38
95
113
37
p98
29
37
56
37
56
4
9
4
2
71
37
38
96
31
52
97
86
77
115
72
64
102
245
71
110
41
42
35
50
41
42
110
113
43
p99
47
52
69
49
78
4
11
5
3
120
53
52
120
41
70
124
96
95
165
87
91
125
299
86
138
41
53
44
63
45
49
138
121
53
p100
244
323
257
121
136
7
36
10
5
301
149
398
350
99
98
302
221
485
358
243
124
400
299
290
620
41
158
136
232
45
928
883
121
114
Number of 5-minute Maximum
>400
ppb
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
4
0
0
0
0
0
1
6
0
0
>500
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
3
0
0
0
0
0
1
2
0
0
>600
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
1
0
0
0
0
0
1
2
0
0
C-12
-------
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
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Berks
Berks
Berks
Monitor ID
420030064
420030064
420030064
420030064
420030067
420030067
420030067
420030116
420030116
420030116
420030116
420031301
420031301
420031301
420033003
420033003
420033003
420033003
420033004
420033004
420033004
420070002
420070002
420070005
420070005
420070005
420070005
420070005
420070005
420070005
420070005
420110009
420110009
420110009
Year
1997
1998
1999
2002
1997
1998
1999
1997
1998
1999
2002
1997
1998
1999
1997
1998
1999
2002
1997
1998
1999
1997
1998
1997
1998
2002
2003
2004
2005
2006
2007
1997
1998
1999
n
7527
71
7234
8239
8235
72
5892
7810
70
5687
5403
7665
70
8162
7424
45
6998
7363
7519
66
7411
7889
6207
7450
6388
8491
8706
8656
8578
8457
7556
7805
8643
2790
Measured 5-minute Maximum SC>2 (ppb)1
Mean
16
26
17
15
14
20
13
20
21
19
9
13
17
13
16
16
19
18
13
18
12
19
19
27
26
24
17
18
19
15
15
13
13
13
Std
17
9
22
19
16
14
12
34
13
35
10
16
9
16
19
9
31
27
14
9
13
27
27
49
50
49
29
31
34
34
26
17
15
16
cov
107
34
131
126
111
70
91
167
63
183
116
119
54
122
115
52
161
154
109
49
108
145
142
185
195
206
169
174
178
219
177
130
116
117
PO
1
8
1
1
1
3
1
1
2
1
1
1
6
1
1
2
1
1
1
4
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
11
26
10
9
9
16
9
12
18.5
11
5
9
14
9
10
17
11
9
9
16
9
9
10
12
10
7
6
7
8
4
7
8
9
10
p97
57
41
63
61
51
51
43
78
45
73
33
46
41
47
65
32
78
87
42
41
39
88
93
116
129
124
85
84
83
82
68
53
45
41
p98
66
43
73
72
59
52
50
101
51
96
39
54
46
55
78
33
91
100
49
41
44
101
110
144
160
158
99
98
104
99
82
64
54
54
p99
80
45
90
85
71
54
60
149
55
167
53
70
54
69
96
33
115
123
61
42
55
126
134
210
230
225
131
126
147
151
114
87
77
68
p100
262
45
822
373
463
54
132
806
55
885
157
457
54
439
220
33
938
733
265
42
336
545
356
1099
922
902
494
921
682
771
912
273
279
288
Number of 5-minute Maximum
>400
ppb
0
0
1
0
1
0
0
10
0
8
0
1
0
1
0
0
9
3
0
0
0
2
0
20
17
22
2
4
10
8
1
0
0
0
>500
ppb
0
0
1
0
0
0
0
6
0
4
0
0
0
0
0
0
4
1
0
0
0
1
0
14
10
16
0
4
6
4
1
0
0
0
>600
ppb
0
0
1
0
0
0
0
3
0
2
0
0
0
0
0
0
2
1
0
0
0
0
0
10
8
10
0
3
1
4
1
0
0
0
C-13
-------
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
SC
County
Cambria
Cambria
Cambria
Erie
Erie
Erie
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Warren
Warren
Warren
Warren
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Barnwell
Monitor ID
420210011
420210011
420210011
420490003
420490003
420490003
421010022
421010022
421010022
421010022
421010022
421010048
421010048
421010048
421010136
421010136
421010136
421010136
421010136
421010136
421010136
421230003
421230003
421230004
421230004
421250005
421250005
421250005
421250200
421250200
421250200
421255001
421255001
450110001
Year
1997
1998
1999
1997
1998
1999
1997
1998
1999
2000
2001
1997
1998
1999
1997
1998
1999
2000
2001
2002
2003
1997
1998
1997
1998
1997
1998
1999
1997
1998
1999
1997
1998
2000
n
8129
7908
2835
8173
8418
2779
8297
8065
2670
3631
2094
8456
7286
3941
7532
6492
7147
7045
5149
7275
2585
7158
2126
7022
1966
8374
8540
2822
8369
8658
2830
8425
6559
790
Measured 5-minute Maximum SC>2 (ppb)1
Mean
13
12
12
16
17
18
13
11
12
11
11
16
8
8
6
7
7
8
9
7
9
15
10
31
23
12
11
11
14
13
13
17
18
5
Std
12
13
11
23
27
30
14
11
15
10
10
47
8
9
7
8
9
8
10
8
9
18
10
51
38
11
11
11
16
15
14
22
20
4
cov
94
112
87
146
158
164
110
104
124
93
94
300
97
114
112
110
117
104
109
112
109
116
103
161
163
97
93
98
113
109
105
127
113
76
PO
1
1
2
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
1
1
1
1
2
p50
10
8
10
9
9
10
8
7
7
8
8
7
6
5
4
5
5
5
6
5
6
9
6
11
8
8
8
8
8
8
8
9
11
4
p97
41
42
40
79
90
97
44
40
45
37
38
66
29
31
25
25
27
27
33
25
29
57
37
156
126
39
37
37
54
50
48
78
72
14
p98
47
52
47
96
110
125
51
45
50
41
42
99
32
35
29
29
32
32
39
28
34
67
42
179
142
46
42
43
62
57
54
92
82
16
p99
59
69
56
128
152
171
65
54
62
49
50
208
39
42
36
35
41
38
48
35
44
87
52
217
172
57
53
56
75
70
62
113
104
18
p100
168
211
134
318
304
340
260
181
262
154
98
954
89
215
102
158
224
90
106
180
164
255
96
772
345
150
177
141
181
228
230
357
282
46
Number of 5-minute Maximum
>400
ppb
0
0
0
0
0
0
0
0
0
0
0
35
0
0
0
0
0
0
0
0
0
0
0
14
0
0
0
0
0
0
0
0
0
0
>500
ppb
0
0
0
0
0
0
0
0
0
0
0
26
0
0
0
0
0
0
0
0
0
0
0
12
0
0
0
0
0
0
0
0
0
0
>600
ppb
0
0
0
0
0
0
0
0
0
0
0
14
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
C-14
-------
State
SC
sc
SC
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
UT
UT
wv
wv
wv
wv
wv
County
Barnwell
Barnwell
Charleston
Charleston
Charleston
Charleston
Charleston
Charleston
Georgetown
Georgetown
Georgetown
Greenville
Greenville
Greenville
Lexington
Lexington
Oconee
Oconee
Oconee
Richland
Richland
Richland
Richland
Richland
Richland
Richland
Richland
Salt Lake
Salt Lake
Wayne
Wayne
Wayne
Wayne
Wayne
Monitor ID
450110001
450110001
450190003
450190003
450190003
450190046
450190046
450190046
450430006
450430006
450430006
450450008
450450008
450450008
450630008
450630008
450730001
450730001
450730001
450790007
450790007
450790007
450790021
450790021
450790021
450791003
450791003
490352004
490352004
540990002
540990003
540990003
540990003
540990003
Year
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2001
2002
1997
1998
2002
2002
2003
2004
2005
n
2626
2545
1703
4807
3509
1267
3497
2927
604
2218
1169
1988
6418
4679
3941
4242
1218
4304
3063
1808
6420
4349
912
2706
2507
3347
4324
4529
5797
8711
7417
8060
8659
8142
Measured 5-minute Maximum SC>2 (ppb)1
Mean
4
3
9
6
4
6
3
3
8
8
4
6
5
4
8
9
4
3
2
6
5
4
6
5
4
4
4
5
3
10
13
12
12
13
Std
5
3
9
7
6
6
4
5
9
13
9
6
6
4
18
20
3
2
2
4
6
4
9
8
8
5
5
9
5
10
15
13
12
15
cov
125
94
97
115
136
91
115
143
113
154
196
91
108
109
223
232
76
75
83
70
104
99
151
156
178
111
116
185
132
97
115
109
106
115
PO
1
1
2
1
1
1
1
1
2
1
1
1
1
1
1
1
2
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
3
2
6
4
2
4
2
2
5
4
2
4
4
3
3
3
3
3
2
5
4
3
4
3
2
3
3
3
2
7
8
7
7
8
p97
11
9
30
23
18
19
13
13
35
45
21
20
17
12
54
60
13
10
7
15
18
14
24
27
22
14
14
19
13
36
45
44
46
61
p98
13
10
34
27
21
25
15
16
37
50
32
23
20
14
72
71
14
10
8
17
22
16
36
33
28
18
18
25
15
40
51
49
49
66
p99
17
11
43
35
29
33
22
22
45
68
45
32
27
17
99
101
17
13
9
19
28
20
54
49
42
24
24
35
20
46
59
58
56
75
p100
116
62
104
105
68
50
68
84
71
144
122
60
167
117
273
277
31
23
24
54
99
51
95
93
88
92
79
209
139
112
503
182
226
143
Number of 5-minute Maximum
>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
1
0
0
0
>500
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
1
0
0
0
>600
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
C-15
-------
State
WV
wv
WV
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
County
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wood
Wood
Wood
Wood
Wood
Monitor ID
540990004
540990004
540990004
540990004
540990005
540990005
540990005
540990005
541071002
541071002
541071002
541071002
541071002
Year
2002
2003
2004
2005
2002
2003
2004
2005
2001
2002
2003
2004
2005
n
8560
8571
8673
8587
8283
7930
8681
8454
2152
8648
8641
8581
6219
Measured 5-minute Maximum SC>2 (ppb)1
Mean
14
13
10
11
15
15
9
10
11
15
14
16
13
Std
17
19
10
12
22
27
9
9
20
20
21
23
24
COV
117
141
107
103
148
177
95
97
183
132
151
147
177
PO
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
9
8
6
7
8
8
7
7
5
8
7
9
5
p97
56
58
35
41
78
76
32
32
44
61
58
63
66
p98
65
69
41
49
97
111
37
36
52
73
75
82
80
p99
79
88
53
59
122
150
45
43
82
97
105
116
117
p100
416
750
151
146
215
361
113
213
409
366
374
484
508
Number of 5-minute Maximum
>400
ppb
1
1
0
0
0
0
0
0
1
0
0
1
1
>500
ppb
0
1
0
0
0
0
0
0
0
0
0
0
1
>600
ppb
0
1
0
0
0
0
0
0
0
0
0
0
0
1 Mean, std, COV represent the arithmetic mean, the standard deviation of the mean, and the coefficient of variation (std/mean*100), respectively. Percentiles of the
distribution include pO, p50, p97, p98, p100 representing the minimum, the median, the 97th, 98th, 99th percentiles, and maximum, respectively.
C-16
-------
Table C-2. Descriptive statistics for measured 1-hour SO2 concentrations by year.
Data used were from 98 monitors that measured both the 5-minute maximum and
1-hour 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
CO
CO
DE
DE
DC
DC
DC
DC
DC
DC
FL
FL
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
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
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
080310002
080310002
100031008
100031008
110010041
110010041
110010041
110010041
110010041
110010041
120890005
120890005
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
2005
2006
1997
1998
2000
2001
2002
2003
2004
2007
2002
2003
n
7183
7800
7690
6702
8356
2062
8322
6857
6277
7943
8334
8347
7084
6153
8176
8265
6297
7240
4431
4923
8364
2061
7045
4363
1637
2459
5625
6863
6262
4480
4172
6519
7501
4901
3751
8302
8575
4282
2770
6394
8415
8662
Measured 1-hour SO2 (ppb)1
Mean
3
2
2
2
3
3
2
2
2
2
2
5
6
5
5
3
3
2
2
2
3
3
7
7
7
7
7
5
4
4
4
3
10
9
9
7
7
9
8
5
6
3
Std
1
1
2
1
1
1
1
1
1
1
1
11
7
7
9
4
2
5
3
3
2
1
9
9
8
9
9
7
5
4
4
4
18
15
6
6
6
6
6
4
15
9
cov
53
53
77
61
35
39
61
79
65
68
53
212
115
134
194
124
78
239
124
110
76
43
138
129
122
132
134
136
121
113
104
107
175
169
72
95
83
69
70
74
240
261
PO
1
1
1
1
1
1
1
0
0
0
0
1
1
0
1
0
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
p50
2
2
1
2
3
3
2
1
2
2
2
3
5
3
2
2
2
1
1
2
3
3
3
4
4
4
4
3
2
2
2
2
5
4
7
5
6
7
7
4
2
1
p97
6
6
6
5
6
5
5
5
5
6
5
22
22
19
25
10
8
7
9
6
6
6
31
31
28
28
29
25
15
15
14
13
60
48
24
22
20
23
20
13
43
23
p98
7
6
6
5
6
6
6
6
6
6
5
30
28
24
33
12
10
9
10
7
7
6
37
36
31
35
33
30
18
18
16
15
77
58
27
25
24
25
23
15
59
30
p99
8
7
8
6
7
7
7
8
7
8
6
50
41
33
49
16
12
11
12
10
11
8
46
45
41
46
41
35
23
21
20
18
103
82
31
30
31
31
28
18
82
38
p100
14
16
17
11
13
11
26
22
13
19
17
244
152
108
173
138
42
258
59
110
66
22
135
148
96
87
162
102
60
50
38
53
215
155
82
123
72
79
90
111
322
204
C-17
-------
State
FL
FL
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
IA
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
County
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
Muscatine
Muscatine
Muscatine
Muscatine
Muscatine
Scott
Scott
Scott
Scott
Scott
Van Buren
Van Buren
Van Buren
Van Buren
Van Buren
Van Buren
Woodbury
Woodbury
West Baton
Rouge
West Baton
Rouge
West Baton
Rouge
West Baton
Rouge
Buchanan
Buchanan
Buchanan
Buchanan
Monitor ID
120890005
120890005
190330018
190330018
190330018
190330018
190330018
190450019
190450019
190450019
190450019
190450019
191390016
191390016
191390016
191390016
191390016
191390017
191390017
191390017
191390017
191390017
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
Year
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
2001
2002
2003
2004
2005
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
n
6507
4120
518
3718
5179
8676
3713
1346
6773
6193
7472
4153
1962
8597
7698
8167
4255
1603
8139
8533
8415
4214
2018
8201
8412
8717
4304
1438
8073
7916
7638
3919
701
6692
7486
5341
1032
3957
1686
4048
4971
7566
7279
7370
8484
8161
7419
5299
Measured 1-hour SC>2 (ppb)1
Mean
3
4
1
1
2
1
1
2
3
3
3
4
3
4
4
3
4
2
3
4
3
3
5
5
5
7
5
1
2
2
2
2
1
1
1
1
1
1
1
1
7
8
6
7
8
7
3
2
Std
7
10
3
4
7
3
1
2
3
3
3
4
4
5
7
5
7
2
4
4
4
4
10
10
11
15
13
2
3
3
3
3
1
1
1
1
1
1
2
3
13
11
10
11
32
24
3
3
cov
224
250
275
305
400
330
204
79
104
112
109
112
142
136
185
149
184
84
136
114
113
132
185
199
216
218
252
177
137
127
123
117
89
87
80
155
95
77
168
194
178
142
150
153
381
342
111
128
PO
1
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
1
1
1
1
1
1
1
1
p50
1
1
0
0
0
0
0
2
2
2
2
2
2
3
2
2
2
2
2
3
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
4
5
4
4
2
2
2
2
p97
21
31
9
5
14
5
3
6
12
10
10
14
12
12
14
14
16
6
9
11
12
10
36
31
32
47
37
6
10
8
8
10
3
3
3
3
3
3
8
9
35
27
27
33
58
49
9
7
p98
25
39
11
6
19
7
4
7
13
11
12
16
15
16
18
17
23
7
11
13
14
12
41
40
41
58
49
7
12
9
10
11
4
4
4
4
4
3
9
11
43
35
33
39
96
76
11
9
p99
36
51
19
13
33
12
7
8
15
14
15
20
24
23
27
23
35
8
14
20
18
16
51
53
62
81
70
10
15
13
12
14
5
4
4
6
4
4
13
15
58
54
48
58
158
114
16
14
p100
150
174
36
78
136
65
29
14
40
45
47
53
65
134
166
131
121
17
158
89
80
92
76
123
143
183
200
30
46
35
32
24
8
15
8
21
7
9
23
42
203
185
152
189
626
469
47
73
C-18
-------
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
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
MO
County
Buchanan
Buchanan
Buchanan
Buchanan
Greene
Greene
Greene
Greene
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
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Monitor ID
290210011
290210011
290210011
290210011
290770026
290770026
290770026
290770026
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
290990004
290990004
290990004
290990004
290990014
290990014
290990014
290990014
Year
2000
2001
2002
2003
1997
1998
1999
2000
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
2004
2005
2006
2007
1997
1998
1999
2000
n
1672
6415
6467
5142
4765
5813
7242
8721
8304
7055
7935
6574
8756
8753
6520
6563
8135
8554
5339
6710
6374
8181
6575
8760
8745
6496
8707
8475
6547
4088
5393
7961
6964
1846
6178
7991
7919
5172
8426
8665
8230
2172
8034
7144
6525
2125
7543
8130
7828
8259
Measured 1-hour SC>2 (ppb)1
Mean
5
4
4
4
4
6
4
5
5
4
3
3
3
3
3
5
4
3
6
4
4
3
3
3
3
2
8
8
9
14
9
7
8
2
8
8
8
8
7
6
7
4
10
11
13
6
8
4
5
4
Std
9
5
7
7
10
12
8
10
10
9
6
6
6
7
7
15
7
8
18
11
10
7
5
6
8
6
26
25
28
46
32
24
23
3
25
23
26
25
23
19
21
3
23
25
27
12
19
9
9
6
cov
162
143
183
173
223
204
184
206
213
212
176
200
201
215
221
296
173
246
282
264
242
210
177
199
259
249
319
317
301
323
345
339
306
104
304
303
309
301
354
293
319
72
219
218
207
189
230
212
207
169
PO
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
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
3
2
2
3
1
2
2
2
2
1
2
1
1
1
1
1
3
1
1
1
2
2
2
2
1
1
2
2
3
2
2
2
2
1
2
2
3
2
2
3
2
3
4
4
3
2
4
2
2
2
p97
26
17
19
17
27
40
25
33
30
31
20
22
20
22
22
38
21
17
53
26
27
19
13
15
23
15
49
61
73
126
82
55
64
10
47
45
40
59
42
33
39
11
60
69
78
35
45
19
21
14
p98
36
22
26
25
34
48
30
40
38
38
25
27
25
27
27
52
28
26
72
35
36
27
18
20
31
22
82
88
101
166
115
81
87
11
70
62
58
82
65
50
56
13
70
85
93
43
58
24
27
18
p99
49
31
41
41
44
60
40
51
53
49
35
36
33
38
38
77
40
45
101
54
54
39
26
31
47
33
139
144
157
234
178
130
123
12
125
112
100
125
106
89
88
14
94
120
127
59
90
34
41
27
p100
89
83
92
115
145
154
123
136
122
114
68
68
77
88
107
264
128
125
187
171
144
106
70
122
120
102
548
377
753
798
521
409
497
18
440
746
592
390
466
392
418
22
563
609
415
189
362
255
192
131
C-19
-------
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
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
County
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
Saint
Charles
Saint
Charles
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Monitor ID
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
291831002
291831002
301110066
301110066
301110066
301110066
301110066
301110066
301110066
301110079
301110079
301110079
301110079
301110080
301110080
301110080
301110080
301110080
301110082
301110082
Year
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
1999
2000
1997
1998
1999
2000
2001
2002
2003
1997
2001
2002
2003
1997
1998
1999
2000
2001
2001
2002
n
2730
5721
7289
7162
1045
3495
6306
6009
8280
8426
8714
8617
4347
5358
5951
5125
6519
6170
526
4883
6473
1020
8153
4811
8515
8122
7970
6422
6890
7205
5776
6123
6880
8347
5700
3167
837
8034
5107
5462
5412
5617
6032
2029
2607
8212
Measured 1-hour SC>2 (ppb)1
Mean
3
7
9
6
8
5
6
4
3
2
4
3
2
2
2
2
2
2
2
4
4
3
4
4
6
6
6
5
8
7
8
8
8
7
7
4
5
2
3
8
7
6
6
6
4
2
Std
5
19
22
17
17
12
15
10
3
2
2
2
1
1
1
2
2
1
2
5
5
4
8
6
7
8
7
5
11
9
10
10
10
12
10
4
4
2
3
10
9
8
8
6
5
3
cov
175
256
256
273
214
220
269
236
98
95
66
70
81
82
82
100
89
73
108
124
119
120
183
132
122
125
129
118
134
133
125
133
135
170
135
106
80
101
85
134
134
123
123
114
110
119
PO
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
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
2
3
3
2
3
2
2
2
2
2
3
2
1
1
1
2
1
1
1
3
3
2
2
3
3
4
4
3
4
4
4
4
4
3
4
3
4
1
2
4
4
4
4
4
3
1
p97
13
35
44
27
35
28
26
19
9
7
9
8
6
6
5
7
7
5
5
18
13
12
19
18
22
25
21
17
34
30
32
32
34
32
33
15
13
7
9
31
29
25
25
21
15
9
p98
17
48
61
38
43
34
35
24
11
8
10
10
6
7
6
9
8
6
7
21
16
14
24
22
26
32
25
20
40
35
36
39
41
38
39
16
14
8
10
37
33
30
29
25
17
11
p99
24
76
112
74
68
48
64
36
14
10
13
13
8
8
8
12
10
7
8
28
23
18
33
32
35
41
34
28
52
43
45
51
51
49
50
19
17
10
12
49
42
38
38
30
22
14
p100
97
473
569
507
234
224
328
324
92
39
39
23
18
15
18
28
24
14
27
74
113
43
284
76
122
112
149
89
209
206
148
192
114
502
111
59
26
26
33
194
224
139
104
86
57
56
C-20
-------
State
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
NC
NC
NC
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
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Yellowstone
Forsyth
Forsyth
Forsyth
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
301110082
301110083
301110083
301110083
301110083
301110083
301110084
301110084
301110084
301110084
301112008
370670022
370670022
370670022
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
2003
1999
2000
2001
2002
2003
2003
2004
2005
2006
1997
1997
1998
1999
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
n
5180
2087
3857
5606
6847
1641
759
2468
2578
1984
2580
8383
7124
6434
5205
7634
7023
8077
4711
8208
7980
8168
8028
1940
3216
2724
2860
3114
342
1256
837
418
221
2657
3852
5268
5653
5368
6328
5230
3099
882
3198
2238
3152
1228
684
3708
Measured 1-hour SC>2 (ppb)1
Mean
3
8
5
4
2
2
3
3
3
3
4
7
7
6
6
5
6
6
6
4
5
6
6
1
1
1
1
1
1
1
1
2
1
2
3
3
3
3
3
3
3
3
3
2
2
4
3
2
Std
3
8
5
6
3
3
5
5
5
5
5
7
8
6
6
6
8
6
8
8
9
14
14
1
1
1
1
1
1
1
1
1
1
2
5
6
5
5
5
5
5
4
4
3
3
5
3
3
cov
111
99
115
128
139
135
151
156
168
165
115
99
108
101
101
110
134
105
148
203
191
240
215
80
75
77
82
78
59
69
64
82
84
88
165
195
182
178
183
182
173
138
130
129
140
136
87
111
PO
1
1
1
1
1
1
1
1
1
1
1
0
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
1
1
1
1
1
1
1
1
1
1
p50
2
6
3
2
1
1
2
2
1
1
2
5
5
4
4
3
4
4
3
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
1
p97
10
27
18
18
9
10
12
16
16
14
15
23
22
19
19
18
23
20
23
27
31
43
45
4
4
4
4
4
4
3
4
5
4
5
15
15
16
14
13
14
16
14
12
11
9
17
11
9
p98
12
30
21
22
12
13
15
21
20
16
17
27
26
23
21
21
27
24
28
32
37
54
54
5
5
5
5
4
4
4
4
6
6
5
18
18
20
17
17
17
19
17
14
13
11
18
12
11
p99
16
36
27
28
16
18
20
28
26
24
20
33
36
29
26
27
39
32
41
42
48
76
71
7
6
7
6
6
5
5
5
7
7
8
24
26
26
24
24
26
25
22
19
16
14
22
14
13
p100
71
86
61
97
48
31
68
81
58
90
86
93
181
88
86
101
169
78
149
211
90
162
436
12
12
11
20
26
7
16
8
10
13
27
52
149
88
80
111
83
75
35
40
55
63
81
18
30
C-21
-------
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
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
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
McKenzie
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
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530104
380530111
380530111
380530111
380530111
380530111
380530111
380530111
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
1999
2000
2001
2002
2003
2004
2005
2006
2007
1998
1999
2000
2001
2002
2003
2004
n
948
2254
2943
2501
3325
1868
1686
2476
1297
3140
928
7863
2258
3313
2688
5099
7455
3576
4485
7289
6019
1314
2214
667
2557
1989
754
3361
5345
4614
2525
2897
511
1525
1501
2757
2281
1528
2333
2241
1905
1828
764
2071
2382
2808
3183
2256
2243
2857
Measured 1-hour SC>2 (ppb)1
Mean
4
2
2
1
1
1
1
1
1
1
1
0
1
1
2
2
1
2
1
1
1
1
2
2
1
2
1
1
1
1
1
1
2
2
2
2
2
2
2
1
1
1
1
3
2
3
2
2
2
2
Std
4
2
2
0
1
1
1
0
1
1
1
0
1
1
2
2
1
1
1
1
1
2
2
1
1
2
1
1
1
1
1
1
1
5
4
4
2
4
5
1
2
2
1
7
5
8
2
4
4
6
cov
115
133
97
39
57
61
69
39
65
50
55
107
134
83
116
104
103
93
83
85
84
103
102
91
82
95
64
62
86
85
67
72
82
207
161
207
104
213
267
101
175
135
78
236
229
309
116
188
189
326
PO
1
1
1
1
1
1
1
1
1
1
1
0
0
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
1
1
1
1
1
1
1
1
1
1
1
p50
2
1
1
1
1
1
1
1
1
1
1
0
0
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
1
1
1
1
1
1
1
1
1
1
1
p97
16
7
6
2
3
4
3
2
3
3
3
1
2
4
7
5
4
5
4
4
4
5
5
5
5
5
3
3
4
4
3
3
5
9
7
6
5
7
6
4
3
3
4
12
9
10
7
6
7
5
p98
17
10
8
3
3
4
5
2
4
3
3
2
3
5
8
5
5
6
4
5
5
6
6
6
6
6
4
4
5
5
4
4
6
10
9
8
6
9
10
4
3
4
5
17
11
12
8
8
9
5
p99
22
13
11
3
4
5
6
3
4
4
4
2
4
6
11
7
7
8
6
7
7
7
8
9
7
9
5
4
7
6
5
5
7
14
13
12
8
14
20
5
5
5
6
29
15
18
11
13
17
10
p100
40
26
23
8
9
9
12
6
15
7
8
6
10
17
31
34
50
25
23
23
17
19
18
22
18
23
9
13
27
29
14
21
12
123
66
138
48
100
107
43
80
33
12
141
134
267
47
77
65
166
C-22
-------
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
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
County
McKenzie
McKenzie
McKenzie
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Mercer
Morton
Morton
Morton
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
Monitor ID
380530111
380530111
380530111
380570001
380570001
380570001
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380570004
380590002
380590002
380590002
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
Year
2005
2006
2007
1997
1998
1999
1999
2000
2001
2002
2003
2004
2005
2006
2007
1997
1998
1999
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
n
2794
2942
724
2826
4735
320
5584
7348
4648
3701
5555
4678
3046
2756
1133
6552
4699
6838
7964
5952
6261
8034
7534
1452
1924
6529
5988
6351
5248
7991
6341
1014
2360
4178
4860
4766
2404
4483
6973
6140
2444
3370
781
3134
2804
1845
805
2726
3327
3438
Measured 1-hour SC>2 (ppb)1
Mean
1
1
2
3
3
5
3
2
3
3
2
3
2
3
2
9
9
8
6
7
6
6
7
5
4
5
5
5
4
4
4
4
4
4
3
3
3
3
2
3
4
3
4
1
2
1
1
3
2
3
Std
3
2
2
4
6
3
4
4
5
5
3
4
3
3
3
20
22
17
15
14
12
14
13
6
7
9
8
8
8
6
7
5
7
7
7
6
6
5
6
5
7
4
7
1
2
1
0
8
4
5
cov
235
177
117
146
194
60
152
166
184
173
141
136
134
122
139
218
242
221
225
181
192
219
196
125
201
175
171
165
171
179
158
133
169
184
200
177
171
175
235
187
174
152
170
53
94
63
36
238
150
207
PO
1
1
1
1
1
2
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
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
1
1
1
1
2
4
1
1
1
1
1
1
1
1
1
2
2
1
1
2
2
1
2
2
1
2
2
2
2
1
2
2
2
2
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
p97
3
3
5
13
13
13
13
10
13
10
9
11
10
11
10
72
68
54
51
49
39
48
46
21
18
28
25
26
23
19
22
17
24
21
18
15
20
13
10
14
21
15
18
3
9
3
2
16
12
11
p98
4
4
6
16
16
14
15
12
17
13
11
14
13
14
11
88
85
64
61
56
47
56
54
23
26
36
30
31
29
24
26
21
29
26
24
20
25
16
14
18
26
18
26
4
9
4
2
25
14
15
p99
5
6
9
22
23
15
19
16
26
17
16
19
16
17
15
108
123
87
77
65
60
70
69
29
39
47
44
41
40
32
34
27
36
35
36
26
30
23
22
26
35
23
35
4
11
5
3
40
19
20
p100
102
87
25
53
178
18
66
159
89
131
58
60
43
35
51
159
241
171
161
140
133
157
158
46
113
123
106
115
100
91
88
48
101
121
139
110
85
77
129
87
99
52
81
7
36
10
5
140
55
191
C-23
-------
State
ND
ND
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
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Williams
Williams
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
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Beaver
Monitor ID
381050103
381050103
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
420033003
420033003
420033003
420033003
420033004
420033004
420033004
420070002
420070002
420070005
420070005
420070005
420070005
420070005
Year
2005
2006
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
1997
1998
1999
2002
1997
1998
1999
1997
1998
1997
1998
2002
2003
2004
n
2331
2976
834
2844
3523
4129
4492
2938
263
7825
72
6986
7830
72
8280
7291
8000
68
7445
7951
60
4328
7527
71
7234
8239
8235
72
5892
7810
70
5687
5403
7665
70
8162
7424
45
6998
7363
7519
66
7411
7889
6207
7450
6388
8491
8706
8656
Measured 1-hour SC>2 (ppb)1
Mean
4
2
3
7
6
6
7
4
4
13
43
11
18
10
9
7
11
11
9
15
35
8
12
20
12
11
10
17
10
13
17
12
7
9
13
10
12
11
14
13
9
13
9
12
13
17
16
14
11
12
Std
8
2
5
11
9
11
13
7
6
15
32
11
19
8
8
7
10
9
8
19
21
8
13
8
14
13
11
13
9
18
11
16
8
10
7
10
14
6
20
18
10
6
9
15
16
25
27
27
17
18
cov
228
123
138
161
166
188
191
178
157
120
75
101
104
81
88
100
88
82
87
126
59
95
110
40
118
122
107
74
88
134
65
132
114
105
54
100
117
55
147
144
105
46
106
130
127
152
166
186
158
153
PO
1
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
2
1
1
3
1
1
6
1
1
1
2
1
1
1
1
1
1
5
1
1
1
1
1
1
3
1
1
1
1
1
1
1
1
p50
1
1
2
2
2
2
1
1
1
7
38
8
12
8
7
5
8
9
7
9
35
6
8
20
7
6
7
12
7
8
15
8
4
6
10
7
7
11
8
7
6
12
6
7
8
8
8
5
4
5
p97
20
8
16
35
33
30
42
23
22
52
97
39
57
30
28
25
34
34
30
60
75
28
44
35
47
46
39
42
33
48
37
41
25
33
29
34
48
24
57
63
31
27
29
51
55
70
75
69
54
50
p98
26
10
18
39
38
35
47
27
24
60
168
45
65
34
32
27
39
38
33
69
75
32
51
35
55
52
43
44
38
56
38
48
29
38
34
38
58
26
68
75
36
27
33
57
63
82
94
88
63
59
p99
38
13
24
47
48
43
59
32
30
75
173
53
82
36
40
35
47
39
38
90
92
38
63
38
69
65
52
50
44
79
42
75
40
46
39
46
72
26
85
92
47
28
39
70
82
111
126
127
78
75
p100
190
39
46
118
77
322
193
117
32
193
173
166
421
36
126
89
118
39
73
496
92
84
159
38
420
159
160
50
78
311
42
333
135
160
39
135
135
26
449
350
129
28
256
320
216
474
569
620
302
368
C-24
-------
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
PA
PA
PA
PA
PA
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
SC
County
Beaver
Beaver
Beaver
Berks
Berks
Berks
Cambria
Cambria
Cambria
Erie
Erie
Erie
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
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
Monitor ID
420070005
420070005
420070005
420110009
420110009
420110009
420210011
420210011
420210011
420490003
420490003
420490003
421010022
421010022
421010022
421010022
421010022
421010048
421010048
421010048
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
Year
2005
2006
2007
1997
1998
1999
1997
1998
1999
1997
1998
1999
1997
1998
1999
2000
2001
1997
1998
1999
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
n
8578
8457
7556
7805
8643
2790
8129
7908
2835
8173
8418
2779
8297
8065
2670
3631
2094
8456
7286
3941
7532
6492
7147
7045
5149
7275
2585
7158
2126
7022
1966
8374
8540
2822
8369
8658
2830
8425
6559
790
2626
2545
1703
4807
3509
1267
3497
2927
604
2218
Measured 1-hour SC>2 (ppb)1
Mean
13
9
10
9
9
9
10
9
10
10
11
11
9
7
8
8
8
9
6
6
5
5
6
6
7
5
7
11
8
17
14
9
9
8
11
10
10
13
13
4
3
2
6
4
3
5
3
2
5
5
Std
18
19
14
9
8
8
9
10
8
11
14
15
9
7
8
7
7
18
6
7
6
6
6
6
7
6
7
12
7
28
22
8
8
8
11
10
10
15
13
3
3
2
5
4
3
4
3
3
4
6
cov
145
200
143
103
85
91
94
110
82
115
128
132
102
96
106
90
95
207
96
108
111
105
107
104
110
106
99
110
97
164
156
94
88
92
107
100
97
120
97
72
96
81
86
99
122
91
99
124
89
128
PO
1
1
1
1
1
1
1
1
2
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
1
1
1
1
2
1
1
2
1
1
2
1
1
2
1
p50
7
2
5
6
7
7
7
6
8
7
7
8
6
5
5
6
5
5
4
4
3
3
4
4
5
4
5
7
5
7
6
7
7
6
7
7
7
7
9
3
2
1
4
3
2
3
2
1
4
3
p97
52
46
43
30
26
27
32
31
30
36
40
43
29
25
29
25
26
33
22
22
19
19
20
21
25
19
23
38
26
84
74
29
29
26
40
39
37
54
48
10
8
7
19
15
12
13
10
8
15
22
p98
62
56
50
35
29
31
36
36
34
45
50
56
32
28
33
27
30
41
25
26
22
22
24
24
29
23
26
45
29
100
88
34
32
32
45
44
42
64
56
12
9
7
21
17
13
15
11
10
20
26
p99
87
79
67
45
35
38
43
51
41
60
74
90
40
34
38
31
36
66
28
32
28
27
30
29
37
27
36
58
36
129
110
41
39
41
55
51
49
79
73
15
12
8
26
20
17
23
14
14
23
31
p100
345
423
279
111
155
144
119
165
99
139
182
207
109
71
84
57
58
620
61
106
60
78
93
69
87
108
63
168
68
538
211
115
96
99
130
115
90
244
164
39
57
16
59
59
51
64
38
48
49
89
C-25
-------
State
SC
sc
SC
sc
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
Georgetown
Greenville
Greenville
Greenville
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
450430006
450450008
450450008
450450008
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
2002
2000
2001
2002
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
n
1169
1988
6418
4679
3941
4242
1218
4304
3063
1808
6420
4349
912
2706
2507
3347
4324
4529
5797
8711
7417
8060
8659
8142
8560
8571
8673
8587
8283
7930
8681
8454
2152
8648
8641
8581
6219
Measured 1-hour SC>2 (ppb)1
Mean
3
5
4
3
4
4
4
3
2
4
4
3
4
4
3
3
3
2
2
7
8
9
9
10
9
9
7
8
8
8
7
7
8
10
9
11
8
Std
4
4
4
3
8
9
3
2
2
3
3
3
5
5
5
3
3
3
2
7
9
10
9
12
9
10
7
6
10
11
6
6
13
11
12
13
13
COV
173
77
91
91
186
194
75
72
84
63
89
92
124
131
165
89
97
108
86
95
107
111
103
123
100
115
92
83
116
133
84
83
161
114
129
122
152
PO
1
1
1
1
1
1
2
1
0
2
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
p50
1
4
3
2
2
2
3
2
1
4
3
2
3
2
2
2
2
2
1
5
5
5
6
6
6
6
5
6
5
5
5
5
4
6
5
7
4
p97
9
14
13
10
23
26
11
8
6
11
12
10
14
13
12
9
9
8
6
26
31
39
40
43
32
31
24
23
33
33
22
21
30
36
38
40
38
p98
13
16
15
11
30
33
12
9
6
12
14
11
19
18
16
11
11
9
7
30
35
41
44
60
37
37
27
26
38
40
24
23
36
43
44
47
45
p99
24
20
18
13
39
46
15
11
8
14
17
15
32
27
24
14
14
12
9
34
41
44
48
66
46
48
33
32
50
55
29
26
48
56
60
64
60
p100
83
41
101
59
139
120
30
19
19
31
50
31
74
65
70
38
38
50
32
91
110
100
108
124
96
232
79
92
114
167
62
51
262
136
216
240
197
1 Mean, std, COV represent the arithmetic mean, the standard deviation of the mean, and the coefficient of variation
(std/mean*100), respectively. Percentiles of the distribution include pO, p50, p97, p98, p100 representing the minimum, the
median, the 97th, 98 , 99th percentiles, and maximum, respectively.
C-26
-------
Table C-3. Descriptive statistics for modeled 5-minute maximum and measured 1-hour SO2 concentrations for
monitors in 20 selected counties, Years 2002 through 2006, air quality as is.
State
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
FL
IL
IL
IL
IL
IN
IN
IN
IA
IA
IA
IA
IA
IA
Ml
Ml
Ml
MO
MO
MO
County
New Castle
New Castle
New Castle
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Madison
Madison
Madison
Madison
Floyd
Floyd
Floyd
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Wayne
Wayne
Wayne
Greene
Greene
Greene
Monid
100031003
100031007
100031008
100032004
120570053
120570081
120570095
120570109
120571035
120571065
120574004
171190008
171191010
171193007
171193009
180430004
180430007
180431004
191130029
191130031
191130038
191390016
191390017
191390020
261630015
261630016
261630019
290770026
290770032
290770037
Year
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
n
8573
8614
8631
8546
8663
8708
8477
8623
8634
4323
8696
8656
8676
8673
8700
7497
8142
8559
8607
8663
8659
8597
8141
8202
6452
8707
8024
7055
8656
6374
Modeled 5-minute Maximum SO2
Mean
5
5
10
7
6
5
7
8
11
7
5
6
8
7
8
9
10
11
5
8
6
6
5
11
12
9
6
9
4
8
Std
13
12
27
14
12
15
24
29
21
15
8
12
18
13
20
18
14
23
12
22
20
11
9
27
32
18
11
26
5
25
cov
239
239
277
188
194
276
350
337
188
211
164
196
220
180
245
203
149
211
247
261
329
169
173
245
266
210
184
269
112
299
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
1
p50
0
2
1
4
2
1
1
2
5
2
3
2
3
3
3
3
5
4
1
2
1
4
3
3
4
3
2
2
3
2
p97
34
29
68
36
32
35
51
61
56
39
21
32
45
35
40
47
38
64
31
62
52
26
20
81
80
50
32
73
11
59
p98
41
37
88
45
39
46
72
93
70
49
26
39
57
42
52
59
46
84
40
83
71
33
25
105
101
63
38
96
13
87
p99
56
57
135
63
53
66
123
165
97
66
36
54
83
57
83
85
63
123
58
119
109
48
34
145
139
86
49
138
21
146
p100
287
304
480
333
285
352
500
457
432
264
191
297
365
299
466
485
351
443
226
349
396
279
313
436
1469
341
282
393
95
431
Measured 1-hour SO2
Mean
3
3
6
4
4
3
3
4
7
4
3
4
5
4
5
5
6
5
3
4
3
4
3
5
7
5
4
4
3
4
Std
6
6
14
6
6
7
9
12
10
7
3
6
9
6
10
9
7
9
6
8
8
5
4
10
17
9
5
9
2
10
COV
199
200
248
148
155
237
290
292
159
174
125
156
189
141
214
178
119
179
212
215
283
136
136
199
243
178
143
212
62
242
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
1
p50
0
1
1
2
2
1
1
1
3
1
2
2
2
2
2
2
4
3
1
1
1
3
2
2
3
2
2
1
3
2
p97
19
15
38
18
17
20
20
24
31
22
10
16
25
18
22
26
19
23
17
24
22
12
9
31
46
28
17
31
6
27
p98
22
19
50
21
20
26
28
37
37
26
12
20
31
22
28
31
22
31
22
31
28
16
11
40
56
34
19
38
7
36
p99
28
28
71
30
27
35
47
68
49
34
17
26
43
27
42
42
29
45
31
44
40
23
14
53
72
46
24
49
8
54
p100
105
115
200
103
77
172
169
164
192
90
59
84
129
92
229
274
175
189
88
110
173
134
158
123
832
108
63
114
28
144
C-27
-------
State
MO
MO
MO
MO
MO
OH
OH
OH
OH
OH
OK
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TX
TX
County
Greene
Greene
Iron
Iron
Jefferson
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Jefferson
Jefferson
Monid
290770040
290770041
290930030
290930031
290990018
390350038
390350045
390350060
390350065
390356001
401430175
401430235
401430501
420030002
420030010
420030021
420030064
420030067
420030116
420033003
420070002
420070005
420070014
420950025
420958000
421250005
421250200
421255001
471570034
471570046
471571034
482450009
482450011
Year
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
n
7465
7476
7976
8687
6318
8524
8610
8557
8591
8638
8609
8304
8356
7932
8736
7757
8431
8145
8477
7864
8402
8538
8586
8465
8617
8604
8527
8580
8264
8304
8300
8638
8591
Modeled 5-minute Maximum SO2
Mean
3
1
15
13
12
9
8
10
5
9
11
7
9
15
14
13
19
13
13
22
18
25
12
9
11
9
13
17
5
6
8
6
3
Std
12
3
55
44
34
14
10
14
10
14
18
13
16
22
15
17
28
17
18
36
31
52
21
9
15
13
17
23
5
12
13
18
10
cov
455
264
367
335
292
158
138
134
196
162
170
197
167
143
105
135
147
132
139
163
178
209
170
109
141
138
131
134
95
191
168
291
371
pO
0
0
1
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
p50
0
0
2
4
3
4
4
6
1
4
3
3
4
8
10
8
9
8
7
10
8
7
6
7
6
6
8
9
4
4
4
0
0
p97
14
6
132
82
71
43
31
43
29
39
53
36
48
63
45
50
85
50
51
114
91
131
61
29
43
35
56
69
14
21
37
48
23
p98
20
9
206
126
100
51
36
51
35
47
62
44
56
76
57
59
100
61
61
139
110
170
72
35
51
46
67
83
17
35
47
62
31
p99
35
14
304
227
162
67
49
67
46
67
79
60
71
101
83
78
130
86
80
188
146
256
93
47
67
66
86
111
32
40
67
91
46
p100
394
87
1065
997
843
177
177
179
180
259
254
259
242
354
236
318
392
318
337
619
519
1226
445
201
213
256
388
326
83
334
232
366
229
Measured 1-hour SO2
Mean
2
1
7
6
6
5
4
6
3
5
6
4
5
9
10
7
11
9
7
13
10
14
7
6
6
6
9
10
4
4
5
4
1
Std
6
2
24
19
15
7
5
7
5
7
9
6
8
10
8
7
13
10
8
18
15
26
9
5
7
7
9
11
2
6
6
9
5
COV
408
218
338
293
269
135
111
110
167
141
143
166
140
114
75
100
121
104
108
142
146
186
132
84
116
108
104
109
52
167
141
264
328
pO
0
0
1
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
p50
0
0
2
3
2
3
3
4
1
3
2
2
3
6
8
6
6
7
5
7
5
5
4
5
4
5
6
6
3
3
3
0
0
p97
8
4
55
33
26
23
16
23
16
20
29
19
26
31
28
25
46
31
25
61
49
69
32
17
22
21
31
36
8
7
21
27
14
p98
11
5
83
50
35
27
19
25
18
24
33
24
29
37
31
29
52
35
29
73
59
88
37
18
25
25
36
41
9
11
26
35
18
p99
18
9
131
89
64
33
23
30
23
31
38
32
35
48
37
36
65
42
39
91
73
127
44
22
30
33
45
52
11
23
37
48
26
p100
203
33
409
392
328
80
87
72
84
117
74
112
82
110
124
89
159
142
135
350
185
620
119
92
107
124
204
152
40
155
85
169
113
C-28
-------
State
TX
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
DE
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
IL
IL
IL
IN
IN
IN
IA
IA
County
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Wayne
Wayne
Wayne
Wayne
New Castle
New Castle
New Castle
New Castle
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Madison
Madison
Madison
Floyd
Floyd
Floyd
Linn
Linn
Monid
482450020
540290005
540290007
540290008
540290009
540290011
540290014
540290015
540290016
540291004
540990002
540990003
540990004
540990005
100031003
100031007
100031008
100031013
100032004
120570053
120570081
120570095
120570109
120571035
120574004
171191010
171193007
171193009
180430004
180430007
180431004
191130029
191130031
Year
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
n
8524
8703
8706
8696
8695
8684
8669
8700
8483
8463
8712
7426
8561
8297
731
8549
8609
5947
7703
8693
8604
8697
8688
8718
8672
8699
8700
8653
8124
6602
8703
8627
8646
Modeled 5-minute Maximum SO2
Mean
5
21
18
16
17
17
19
21
14
19
11
15
16
15
11
6
14
13
10
6
4
6
9
9
3
7
6
10
8
7
12
4
8
Std
17
35
26
33
25
28
26
33
20
26
13
20
20
21
15
12
29
17
15
10
13
18
25
15
7
15
13
24
20
11
29
12
25
cov
372
163
147
198
147
158
141
158
140
137
122
133
124
141
132
206
208
136
151
175
285
324
287
162
218
209
210
239
241
157
236
288
299
pO
0
0
1
1
1
1
1
2
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
p50
0
11
10
6
8
9
10
10
9
12
7
8
10
8
7
2
6
8
6
3
1
1
3
5
1
3
3
4
2
4
4
1
1
p97
39
103
72
92
73
81
79
106
59
76
44
60
62
62
45
31
78
51
42
28
29
37
53
44
16
40
30
55
51
28
75
28
62
p98
50
132
89
118
87
97
95
128
71
91
53
70
74
76
54
38
103
61
49
34
37
51
74
53
21
50
37
76
65
35
99
38
85
p99
70
182
127
167
116
129
128
167
95
124
69
90
98
107
71
52
152
81
66
46
54
83
132
70
30
71
54
132
97
48
146
59
131
p100
858
619
446
525
434
598
464
485
432
466
185
282
264
284
180
293
427
310
310
253
312
378
500
318
187
304
403
439
436
218
654
276
454
Measured 1-hour SO2
Mean
3
12
10
9
10
10
11
12
8
11
7
9
9
8
7
3
8
7
6
3
3
3
4
5
2
4
4
6
5
4
6
2
4
Std
9
17
12
16
12
13
12
16
8
12
7
9
9
10
6
5
14
8
6
5
6
7
10
7
3
8
6
12
10
5
12
6
9
COV
338
139
119
173
116
128
112
133
102
106
95
107
100
115
95
165
183
103
112
137
244
248
229
128
173
179
177
213
214
121
205
252
237
pO
0
0
1
1
1
1
1
2
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
p50
0
8
6
4
6
6
7
6
6
8
5
5
6
5
5
1
4
5
4
2
1
1
2
3
1
2
2
3
1
2
3
1
1
p97
22
56
36
50
38
43
41
58
30
38
26
31
32
33
22
16
44
25
21
14
16
15
21
23
8
22
16
29
28
14
27
16
24
p98
27
69
44
67
42
48
47
69
35
43
30
35
37
38
23
19
56
30
23
17
20
20
29
27
10
27
18
41
36
16
36
21
33
p99
39
95
62
88
53
63
62
84
42
59
34
41
46
50
28
25
81
39
29
22
29
31
47
34
14
36
26
69
50
21
56
34
46
p100
457
331
193
255
163
316
193
205
94
225
91
110
96
114
54
90
186
99
68
88
135
131
167
108
60
127
214
171
182
102
266
104
122
C-29
-------
State
IA
IA
IA
IA
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
MO
MO
OH
OH
OH
OH
OH
OK
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Linn
Muscatine
Muscatine
Muscatine
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Iron
Iron
Jefferson
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Monid
191130038
191390016
191390017
191390020
261630015
261630016
261630019
290770026
290770032
290770037
290770040
290770041
290930030
290930031
290990018
390350038
390350045
390350060
390350065
390356001
401430175
401430235
401430501
420030002
420030010
420030021
420030064
420030067
420030116
420033003
420070002
420070005
420070014
Year
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
n
8640
7716
8553
8439
7772
8574
8139
7957
8723
8181
8674
8676
6964
8230
6009
8487
8596
8583
8613
4313
8663
8358
8716
8356
8630
7728
8502
8212
8506
8528
8627
8729
8510
Modeled 5-minute Maximum SO2
Mean
7
6
6
11
9
9
7
7
2
7
4
2
16
14
10
11
7
12
6
10
10
10
8
14
14
12
18
12
13
21
18
19
11
Std
26
13
10
27
23
20
17
17
3
18
9
3
54
48
25
18
13
18
11
16
18
19
15
19
16
17
29
16
17
37
32
35
22
cov
354
213
150
256
250
220
240
234
143
262
228
147
333
351
259
165
174
142
196
163
183
189
193
137
118
136
162
134
131
177
178
190
200
pO
0
0
0
0
0
0
0
0
0
1
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
1
3
4
3
2
3
2
2
1
2
2
2
3
3
3
5
4
7
2
5
2
4
2
8
10
8
9
7
8
9
8
6
4
p97
64
28
25
80
62
55
41
50
8
48
19
8
152
95
53
53
35
53
33
45
54
58
45
55
45
49
83
44
52
112
92
98
62
p98
89
35
31
109
79
69
51
65
10
69
25
10
212
141
69
63
41
61
39
54
63
69
52
64
52
58
100
51
61
140
113
118
73
p99
143
51
46
152
114
96
70
96
12
104
40
14
296
243
101
85
56
80
53
72
78
92
65
84
75
74
135
71
79
187
151
160
96
p100
500
341
222
444
349
352
488
274
72
321
216
88
1031
1128
753
347
257
308
237
299
286
322
273
380
326
354
464
270
378
555
619
644
510
Measured 1-hour SO2
Mean
3
4
4
5
5
5
4
3
1
3
2
2
8
7
4
6
4
7
3
6
6
6
5
8
10
7
10
8
8
12
10
11
6
Std
10
7
4
11
12
10
8
6
1
7
4
2
23
21
10
9
6
8
5
8
9
10
7
8
9
7
14
8
7
18
15
17
10
COV
303
185
114
216
222
189
205
177
98
210
196
104
306
319
236
138
140
113
162
136
154
160
160
102
88
98
133
104
95
151
147
158
158
pO
0
0
0
0
0
0
0
0
0
1
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
1
2
3
2
1
2
1
2
1
2
1
1
2
2
2
4
3
5
2
3
1
2
1
6
8
5
6
6
5
6
6
4
3
p97
27
14
11
32
35
30
23
20
5
19
9
4
64
39
19
28
18
28
18
23
29
32
24
28
29
24
43
29
26
62
50
54
34
p98
35
18
13
41
45
38
27
25
6
27
14
5
87
56
24
33
21
33
22
28
33
39
28
31
32
27
52
32
30
76
60
63
39
p99
52
26
20
62
60
50
34
35
7
39
22
7
123
88
36
42
27
38
26
37
39
47
33
38
38
33
69
39
35
98
78
78
47
p100
177
166
89
143
140
149
223
68
17
106
86
42
497
418
324
165
101
145
71
147
75
153
84
90
163
122
187
135
80
238
209
302
118
C-30
-------
State
PA
PA
PA
PA
PA
TN
TN
TN
TX
TX
TX
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
DE
DE
DE
DE
FL
FL
FL
FL
FL
County
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Wayne
Wayne
Wayne
Wayne
New Castle
New Castle
New Castle
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Monid
420950025
420958000
421250005
421250200
421255001
471570034
471570046
471571034
482450009
482450011
482450020
540290005
540290007
540290008
540290009
540290011
540290014
540290015
540290016
540291004
540990002
540990003
540990004
540990005
100031007
100031008
100031013
100032004
120570053
120570081
120570095
120570109
120571035
Year
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2004
2004
2004
2004
2004
2004
2004
2004
2004
n
8720
8725
8718
8742
8602
8084
8285
8306
8567
8488
8650
8695
8616
8683
8344
8694
8686
8694
8657
8533
2056
8060
8571
7930
6137
8364
8119
8617
8543
8492
8643
8515
8643
Modeled 5-minute Maximum SO2
Mean
6
14
9
14
18
6
6
10
7
4
5
21
18
15
19
20
20
21
15
23
14
15
15
14
5
10
9
9
4
4
2
7
7
Std
9
18
13
16
24
6
12
16
20
23
18
40
30
30
31
33
28
34
20
30
14
21
21
23
10
22
16
13
7
8
6
18
12
cov
157
127
153
117
134
98
182
156
308
516
365
186
173
195
162
162
138
164
130
134
107
137
141
159
182
216
175
155
168
177
293
279
172
pO
0
0
0
1
0
1
3
3
0
0
0
1
1
1
1
1
1
1
1
1
2
1
1
1
0
0
0
0
0
0
0
0
0
p50
3
9
5
9
10
4
4
5
1
0
0
10
9
6
9
10
12
9
10
14
9
8
8
8
3
4
4
5
2
2
0
2
3
p97
24
54
38
54
75
14
21
47
46
31
42
108
81
84
86
96
81
106
59
91
51
68
60
63
26
59
43
37
19
21
14
40
32
p98
28
64
47
63
90
17
34
58
56
42
56
139
101
107
107
118
98
129
72
112
61
78
72
78
32
75
52
45
24
25
20
55
39
p99
39
84
65
82
122
34
38
78
76
59
85
203
151
151
150
161
141
171
98
155
80
97
99
112
43
107
70
60
32
34
29
96
55
p100
176
254
304
265
318
128
318
282
857
1330
345
738
543
508
519
567
472
501
403
506
157
296
446
394
243
414
360
303
172
180
160
400
207
Measured 1-hour SO2
Mean
4
8
6
9
10
4
4
6
4
3
3
12
10
9
11
12
11
12
9
13
9
9
9
8
3
6
5
5
2
3
1
3
4
Std
5
8
7
8
11
2
6
7
10
12
9
20
15
15
15
16
12
17
8
14
7
10
10
11
4
11
7
6
3
4
2
8
6
COV
127
100
122
91
111
49
155
128
273
482
332
163
145
166
135
135
108
138
95
105
78
111
115
133
137
185
138
116
129
140
196
237
143
pO
0
0
0
1
0
1
3
3
0
0
0
1
1
1
1
1
1
1
1
1
2
1
1
1
0
0
0
0
0
0
0
0
0
p50
2
6
4
7
6
3
3
3
1
0
0
6
6
4
6
7
8
6
8
10
7
5
6
5
2
3
3
3
2
2
0
2
2
p97
16
27
22
30
40
9
7
26
26
19
25
60
42
46
45
53
41
58
27
45
29
39
31
33
13
32
22
19
10
10
6
14
17
p98
18
32
26
34
45
10
10
32
32
25
32
74
52
58
54
63
49
71
33
54
35
41
37
40
15
42
26
22
12
12
8
20
20
p99
21
38
33
40
57
11
21
41
41
32
45
103
72
82
78
81
68
88
44
76
40
44
48
55
19
57
35
26
15
16
10
35
27
p100
109
83
131
99
141
71
152
95
409
674
157
290
243
189
252
255
174
173
153
199
58
100
232
167
89
169
96
77
52
76
31
187
79
C-31
-------
State
FL
IL
IL
IL
IN
IN
IN
IA
IA
IA
IA
IA
IA
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
MO
MO
OH
OH
OH
OH
OK
OK
OK
PA
PA
County
Hillsborough
Madison
Madison
Madison
Floyd
Floyd
Floyd
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Iron
Iron
Jefferson
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Monid
120574004
171191010
171193007
171193009
180430004
180430007
180431004
191130029
191130031
191130038
191390016
191390017
191390020
261630015
261630016
261630019
290770026
290770032
290770037
290770040
290770041
290930030
290930031
290990004
390350038
390350045
390350060
390350065
401430175
401430235
401430501
420030002
420030010
Year
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
n
8572
8729
8692
8594
8358
7538
8251
8381
8664
8208
8167
8415
8725
8502
8656
8662
8776
8754
8777
8694
8687
1846
2172
8044
8603
8679
8617
8405
8292
8460
8700
8646
8616
Modeled 5-minute Maximum SO2
Mean
2
8
6
9
10
4
11
3
8
8
6
6
15
11
7
6
7
2
6
6
3
6
9
23
11
5
8
6
13
12
10
13
10
Std
5
16
10
22
20
9
28
11
27
33
11
9
40
27
16
12
17
3
16
8
4
8
9
57
17
10
14
11
22
21
18
18
14
cov
243
206
166
240
192
223
266
309
325
396
187
152
264
237
226
215
244
183
262
144
139
149
104
253
158
178
177
179
176
176
180
141
138
pO
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
p50
0
3
3
3
4
2
2
1
2
1
3
3
3
3
2
2
2
1
2
4
2
2
6
6
5
3
3
3
3
4
3
7
6
p97
11
42
29
49
52
19
73
23
60
64
27
24
118
75
44
34
49
6
38
21
10
28
34
153
51
27
38
32
65
62
52
51
40
p98
13
55
35
67
67
25
96
31
82
96
34
30
158
96
55
41
63
9
53
28
12
33
38
199
61
33
48
37
75
75
60
61
49
p99
19
80
46
106
105
38
134
48
133
176
48
41
216
138
77
55
91
12
86
40
18
41
45
282
82
43
67
50
98
100
77
81
71
p100
126
300
240
435
382
213
611
304
557
613
306
194
606
400
339
291
296
73
294
203
93
84
92
1128
256
194
233
211
299
331
270
342
220
Measured 1-hour SO2
Mean
1
5
4
5
6
2
5
2
4
4
3
3
7
6
4
3
3
1
3
3
2
2
4
10
6
3
4
4
7
7
6
7
7
Std
2
8
4
11
10
4
12
5
10
13
5
4
15
14
8
6
6
1
6
4
2
3
3
23
8
4
7
5
11
11
9
8
8
COV
197
175
127
215
173
193
253
264
245
334
149
113
218
209
191
171
187
139
202
115
97
104
72
219
131
144
147
143
152
151
151
110
108
pO
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
p50
0
2
2
2
3
1
1
1
1
1
2
2
2
2
1
1
1
1
2
3
2
1
3
4
3
2
2
3
2
3
2
5
5
p97
6
23
15
25
28
10
25
12
23
27
14
12
47
43
25
18
22
4
16
9
6
10
11
60
28
14
20
18
35
33
28
26
26
p98
7
29
17
35
35
14
33
17
32
37
17
14
58
53
30
22
26
5
22
13
7
11
13
70
32
16
25
20
39
39
31
30
30
p99
9
43
21
58
52
21
55
26
51
72
23
18
81
73
42
28
35
7
33
19
9
12
14
94
41
20
35
25
45
48
36
39
36
p100
43
109
55
204
163
95
225
102
140
201
131
80
183
156
98
67
68
22
84
92
56
18
22
563
88
61
67
64
130
148
105
173
83
C-32
-------
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TX
TX
TX
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
DE
DE
DE
County
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Wayne
Wayne
Wayne
New Castle
New Castle
New Castle
Monid
420030021
420030064
420030067
420030116
420033003
420070002
420070005
420070014
420950025
420958000
421250005
421250200
421255001
471570034
471570046
471571034
482450009
482450011
482450020
540290005
540290007
540290008
540290009
540290011
540290015
540290016
540291004
540990003
540990004
540990005
100031007
100031008
100031013
Year
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2005
2005
2005
n
8663
8680
8373
8676
8611
8522
8755
8733
8702
8648
8662
8680
8656
8240
8119
8005
8679
8507
8244
8723
8646
8726
8700
8676
8717
4514
8385
8659
8673
8681
7283
8634
7604
Modeled 5-minute Maximum SO2
Mean
10
16
10
11
18
16
20
13
7
23
11
12
15
5
7
10
6
3
5
20
18
15
15
19
19
14
22
16
12
12
6
12
12
Std
15
24
15
15
30
29
35
20
9
21
12
15
23
5
12
16
20
10
16
35
28
28
25
29
31
18
29
21
16
15
12
26
16
cov
139
149
151
138
166
183
174
159
136
93
115
123
146
96
166
151
343
333
337
172
153
187
175
156
166
129
131
132
126
121
183
226
139
pO
0
0
0
0
0
0
0
0
0
0
0
1
0
0
3
2
0
0
0
1
1
1
1
1
1
1
1
1
1
1
0
0
0
p50
6
8
6
6
8
6
8
6
4
18
7
7
8
4
4
6
1
0
0
10
10
6
6
9
8
9
14
9
8
8
3
5
7
p97
42
72
41
44
91
82
95
60
25
68
39
48
69
15
30
46
42
24
38
99
79
81
73
89
95
57
87
69
48
45
32
64
47
p98
50
85
51
51
113
102
115
71
29
82
48
58
82
18
36
57
55
31
50
129
97
100
89
109
114
68
108
79
56
52
38
84
56
p99
67
113
75
68
156
139
162
92
39
120
67
76
112
35
49
82
82
45
75
184
137
137
122
147
154
87
146
100
73
68
52
130
73
p100
283
384
371
323
396
511
738
368
164
340
234
342
313
91
420
258
762
246
281
710
514
461
403
440
435
366
457
341
321
307
295
540
345
Measured 1-hour SO2
Mean
6
9
7
6
11
9
12
7
5
13
7
8
9
4
4
6
3
2
3
12
11
9
8
11
11
8
13
9
7
7
4
7
7
Std
6
11
8
6
15
14
18
9
5
8
7
8
11
2
5
7
11
5
8
17
14
14
12
14
15
8
13
9
7
6
5
13
7
COV
105
119
119
101
141
157
153
129
101
62
87
96
123
49
135
124
317
289
304
149
128
161
146
129
141
96
105
103
92
84
142
197
105
pO
0
0
0
0
0
0
0
0
0
0
0
1
0
0
3
2
0
0
0
1
1
1
1
1
1
1
1
1
1
1
0
0
0
p50
4
6
5
4
6
4
5
4
3
11
6
6
6
3
3
3
1
0
0
6
7
4
4
6
5
6
10
6
5
5
2
3
5
p97
21
37
27
22
49
44
50
32
16
32
23
28
36
9
12
24
24
14
22
53
40
45
39
48
51
28
45
40
24
22
16
35
24
p98
24
42
31
25
60
55
59
37
18
36
26
31
42
10
17
30
30
18
29
67
49
55
47
59
60
36
53
44
27
24
19
46
27
p99
30
55
38
30
80
72
75
45
22
46
31
38
51
12
27
43
43
25
41
96
70
72
62
76
77
43
68
48
33
29
25
67
34
p100
84
135
218
107
163
251
368
195
74
151
103
200
172
30
222
104
399
124
132
354
262
216
152
191
162
98
243
108
79
62
121
238
140
C-33
-------
State
DE
FL
FL
FL
FL
FL
FL
IL
IL
IL
IN
IN
IN
IA
IA
IA
IA
IA
IA
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
OH
OH
OH
OH
OK
County
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Madison
Madison
Madison
Floyd
Floyd
Floyd
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Jefferson
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Monid
100032004
120570053
120570081
120570095
120570109
120571035
120574004
171191010
171193007
171193009
180430004
180430007
180431004
191130029
191130031
191130038
191390016
191390017
191390020
261630015
261630016
261630019
290770026
290770032
290770037
290770040
290770041
290990004
390350038
390350045
390350060
390350065
401430175
Year
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
n
8539
8698
8679
8650
8618
8657
8716
8669
8703
8519
8345
8063
8264
8600
8632
8615
8644
8603
8693
8193
8044
7917
8756
8661
8760
8669
8660
7166
8570
8631
8602
8355
8551
Modeled 5-minute Maximum SO2
Mean
8
4
3
2
6
6
2
8
7
9
13
9
9
4
10
8
8
6
14
11
8
6
6
2
6
5
3
25
12
7
13
7
12
Std
12
6
7
10
19
12
5
16
11
21
20
20
24
11
29
27
21
10
38
26
17
13
17
3
16
13
4
62
20
13
18
13
19
cov
152
165
222
449
337
183
206
197
166
225
150
233
270
274
281
324
255
176
272
238
220
217
267
157
260
269
164
244
171
189
145
201
169
pO
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
p50
5
2
1
0
1
3
1
3
3
3
7
3
2
1
2
1
3
3
3
3
3
2
2
1
2
2
2
5
5
3
7
2
3
p97
36
18
18
13
36
31
12
43
30
50
55
44
61
24
81
69
51
24
114
77
46
35
47
9
38
24
10
183
59
33
55
36
59
p98
43
22
23
20
52
38
15
54
37
67
70
54
83
33
107
99
68
30
150
97
58
43
62
12
52
33
12
231
69
40
63
43
68
p99
56
29
31
35
95
53
21
77
50
107
100
78
126
50
157
154
103
44
206
136
80
57
92
15
84
56
17
311
92
55
85
58
89
p100
284
145
197
275
403
220
123
288
245
364
377
564
476
271
463
426
448
256
574
377
356
282
314
82
389
304
106
1232
342
292
304
316
326
Measured 1-hour SO2
Mean
5
2
2
1
3
4
1
5
4
5
8
5
4
2
5
4
4
3
6
6
4
3
3
1
3
3
2
11
7
4
7
4
7
Std
5
3
4
4
8
6
2
8
5
11
10
10
10
5
10
10
8
4
14
13
8
6
6
2
6
7
2
25
10
6
9
6
10
COV
111
127
182
360
296
151
158
169
130
201
127
207
237
232
230
274
187
140
229
213
189
179
201
119
199
242
121
218
143
151
116
165
145
pO
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
p50
3
1
1
0
1
2
1
2
2
2
5
2
1
1
1
1
2
2
2
2
2
1
1
1
2
2
1
4
3
2
5
1
2
p97
18
9
10
6
14
17
6
23
16
27
28
23
23
13
31
28
22
11
45
43
25
20
20
6
15
12
6
69
33
17
29
19
32
p98
21
11
12
8
18
20
8
29
18
36
35
28
31
18
41
39
30
14
56
55
32
24
25
7
20
18
7
85
35
20
33
23
36
p99
25
13
15
11
36
26
10
41
24
54
49
39
46
27
55
60
41
20
74
71
43
29
33
9
31
32
9
120
45
25
40
29
42
p100
53
40
92
98
151
91
27
121
78
162
168
277
176
100
122
125
121
92
200
145
176
91
77
30
122
138
57
609
150
125
125
174
176
C-34
-------
State
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TN
TX
TX
TX
WV
WV
WV
WV
WV
WV
WV
WV
WV
County
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Wayne
Wayne
Monid
401430235
401430501
420030002
420030010
420030021
420030064
420030067
420030116
420033003
420070002
420070005
420070014
420950025
420958000
421250005
421250200
421255001
471570034
471570046
471571034
471572005
482450009
482450011
482450020
540290005
540290007
540290008
540290009
540290011
540290015
540291004
540990003
540990004
Year
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
n
8442
8515
8639
8731
8650
8658
8689
8699
8490
8682
8626
8660
8512
8652
8603
8720
8606
8121
8282
8160
5864
8360
8071
7797
8684
8702
8701
8687
8541
8705
8651
8142
8622
Modeled 5-minute Maximum SO2
Mean
10
9
11
12
11
16
10
12
23
16
22
12
10
15
14
13
16
6
8
8
2
6
3
3
20
17
12
19
20
16
24
17
13
Std
18
16
17
15
17
26
14
17
41
26
36
20
10
20
15
16
25
6
17
14
2
16
11
13
29
25
23
28
31
26
27
25
17
cov
178
175
151
124
150
161
144
144
181
165
167
167
100
134
109
122
157
106
219
167
120
253
341
378
144
144
187
151
157
165
115
152
125
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
1
0
0
0
1
1
1
1
1
1
1
1
1
p50
4
3
6
8
6
8
6
7
9
7
10
5
8
8
9
8
7
4
4
5
1
1
0
0
11
9
5
9
10
7
16
9
8
p97
49
48
49
42
48
79
40
49
122
76
99
59
33
60
46
48
77
16
33
36
6
47
25
28
88
72
66
83
90
76
85
82
50
p98
60
56
58
50
57
94
47
59
148
92
124
71
40
70
58
59
89
20
36
48
7
59
34
37
109
87
83
100
111
91
102
98
59
p99
79
71
76
71
75
122
61
79
200
124
183
91
59
91
81
81
114
36
57
71
12
82
50
59
151
122
113
134
150
125
138
124
77
p100
411
234
372
257
379
486
260
392
651
449
719
342
165
275
272
260
362
120
628
266
63
251
234
291
424
405
371
584
559
424
394
449
406
Measured 1-hour SO2
Mean
6
5
7
8
7
9
7
7
13
9
13
7
7
9
10
9
9
4
4
5
1
4
2
2
12
10
7
11
11
9
14
10
8
Std
9
8
7
7
7
12
8
7
20
13
18
9
5
9
8
8
12
2
9
7
1
8
6
7
14
11
11
14
15
13
12
12
6
COV
154
148
113
90
112
130
112
104
157
140
145
135
72
108
80
93
131
61
200
140
69
228
307
343
119
116
160
126
134
139
88
123
83
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
1
0
0
0
1
1
1
1
1
1
1
1
1
p50
3
2
4
6
4
5
5
5
6
5
7
4
6
6
7
6
5
3
3
3
1
1
0
0
7
6
3
6
7
5
11
6
6
p97
27
26
25
27
23
42
26
25
69
40
52
32
20
31
27
29
41
9
14
18
3
28
14
16
47
37
36
45
48
41
42
43
23
p98
31
29
29
31
27
50
30
29
80
48
62
37
22
35
32
33
46
10
19
26
4
33
19
20
56
44
45
52
58
49
49
60
26
p99
39
36
35
37
34
62
36
36
99
61
85
45
26
41
37
41
54
13
31
40
5
43
27
31
71
61
60
65
74
65
63
66
32
p100
224
93
115
98
126
138
85
122
295
222
345
98
93
132
116
106
145
53
349
98
13
121
114
151
202
165
155
329
292
183
180
124
92
C-35
-------
State
WV
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
IL
IL
IL
IN
IN
IN
IA
IA
IA
IA
IA
IA
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
OH
County
Wayne
New Castle
New Castle
New Castle
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Madison
Madison
Madison
Floyd
Floyd
Floyd
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Jefferson
Cuyahoga
Monid
540990005
100031007
100031008
100031013
100032004
120570053
120570081
120570095
120570109
120571035
120574004
171191010
171193007
171193009
180430004
180430007
180431004
191130029
191130031
191130038
191390016
191390017
191390020
261630015
261630016
261630019
290770026
290770032
290770037
290770040
290770041
290990004
390350038
Year
2005
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
n
8454
8424
8573
8631
8600
6506
6509
6517
6462
6486
6367
8651
8682
8627
5928
6240
8339
8648
8549
8250
8708
8715
8714
8429
8722
8325
8753
8727
8745
8637
8581
6541
8391
Modeled 5-minute Maximum SO2
Mean
12
5
11
8
8
3
3
1
6
7
3
6
5
9
12
9
11
1
7
9
8
6
10
10
6
5
7
3
7
3
2
30
8
Std
14
9
24
14
11
6
7
5
19
14
5
14
9
21
20
16
30
3
20
24
25
10
32
27
16
11
19
4
21
9
3
69
16
cov
125
172
212
185
144
200
277
419
301
194
178
220
178
238
161
179
264
197
284
281
311
173
308
273
248
233
277
169
303
278
197
232
183
pO
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
p50
7
3
5
4
5
1
0
0
2
3
1
2
2
3
6
5
4
1
1
2
2
3
2
1
2
1
2
2
2
2
1
4
3
p97
43
24
62
37
33
16
16
9
41
36
12
37
25
51
58
37
64
6
54
69
56
26
88
77
44
30
53
12
54
16
6
218
46
p98
50
29
79
46
39
20
20
12
59
46
14
46
32
70
68
48
86
8
72
93
77
33
121
98
55
36
71
13
78
24
8
269
55
p99
67
40
113
64
51
26
27
23
101
64
21
66
42
110
94
70
140
12
105
133
126
45
176
138
75
48
104
20
123
39
13
354
72
p100
308
233
460
351
244
141
265
141
343
257
116
255
194
376
315
358
945
83
350
407
495
263
496
405
347
257
347
121
345
241
112
977
247
Measured 1-hour SO2
Mean
7
3
7
4
5
2
1
1
3
4
1
4
3
5
7
5
5
1
3
4
4
3
5
6
4
3
3
2
3
2
1
13
5
Std
6
4
12
7
5
3
3
2
7
7
2
7
4
11
10
8
12
1
7
9
9
4
12
14
8
5
7
2
8
5
2
27
8
COV
83
128
184
147
102
160
232
302
247
163
134
192
146
215
135
152
232
155
225
234
248
133
257
245
210
190
215
128
259
246
155
207
155
pO
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
p50
5
2
3
3
3
1
0
0
1
2
1
1
2
2
4
4
3
1
1
1
1
2
1
1
1
1
1
1
1
1
1
3
2
p97
21
11
35
19
15
8
8
5
16
19
5
21
14
28
32
19
23
3
21
27
23
12
36
44
25
17
22
7
23
8
4
78
25
p98
23
14
44
24
18
10
10
6
22
23
7
25
16
38
35
24
30
3
27
35
29
15
46
56
31
20
27
9
31
12
4
93
29
p99
26
18
60
32
22
13
14
9
36
33
9
35
21
56
43
32
52
5
37
50
45
22
67
75
41
25
38
11
47
21
6
127
37
p100
51
72
206
163
62
48
143
40
130
137
38
108
75
158
139
169
483
28
105
131
175
83
143
154
86
55
88
44
120
113
62
415
95
C-36
-------
State
OH
OH
OH
OK
OK
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TX
TX
TX
WV
WV
WV
WV
WV
WV
County
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Monid
390350045
390350060
390350065
401430175
401430235
401430501
401431127
420030002
420030010
420030021
420030064
420030067
420030116
420070002
420070005
420070014
420950025
420958000
421250005
421250200
421255001
471570046
471571034
471572005
482450009
482450011
482450020
540290005
540290007
540290008
540290009
540290011
540290015
Year
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
n
8594
8637
8521
7204
7223
7193
431
8690
8612
8711
8665
8568
7567
8682
8673
8627
8712
8512
8693
8609
8695
8189
8156
2867
8553
8417
8647
8340
8550
8636
8690
8605
8678
Modeled 5-minute Maximum SO2
Mean
6
11
8
11
6
8
1
9
10
11
17
8
10
14
16
13
7
18
11
13
11
6
11
2
6
3
6
19
17
13
22
21
16
Std
12
19
18
18
12
13
4
14
12
15
28
12
14
24
36
18
10
36
12
14
18
9
16
2
24
15
21
25
26
23
30
27
26
cov
191
165
225
166
197
167
259
154
123
136
159
148
130
165
228
140
154
195
110
107
170
152
148
127
412
485
324
136
148
177
134
125
155
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
2
1
0
0
0
1
1
1
2
1
1
p50
3
5
3
3
2
3
1
5
7
6
8
5
6
7
4
8
5
11
8
9
4
4
6
1
0
0
0
11
9
6
12
14
8
p97
31
57
42
55
36
39
9
39
33
44
83
34
41
68
86
54
25
74
39
47
52
21
48
7
43
26
44
79
75
68
95
82
78
p98
36
69
53
65
44
46
12
47
39
51
100
40
48
81
108
63
29
96
49
56
65
26
59
9
62
41
59
96
91
84
112
99
93
p99
50
91
77
82
60
59
17
61
58
68
134
58
61
109
158
81
41
146
66
72
90
36
83
12
108
71
92
127
126
115
148
133
123
p100
239
278
372
209
202
193
50
269
223
308
398
197
285
399
966
364
266
921
215
245
267
294
269
57
517
326
790
381
402
396
484
404
410
Measured 1-hour SO2
Mean
4
7
5
6
4
5
1
5
7
6
10
6
6
8
9
7
5
11
8
9
6
4
6
1
3
2
4
11
10
7
13
12
9
Std
5
9
9
9
6
6
2
6
6
6
13
7
6
11
18
8
5
17
6
7
9
4
8
1
13
8
11
12
12
11
14
12
12
COV
156
139
197
144
168
140
209
118
92
103
132
118
95
134
201
103
106
164
81
81
146
122
121
81
386
449
293
109
122
148
110
99
128
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
2
1
0
0
0
1
1
1
2
1
1
p50
3
3
2
2
1
2
0
3
5
4
6
4
4
5
2
5
4
8
6
7
3
3
4
1
0
0
0
7
6
4
8
10
5
p97
15
30
23
30
20
21
6
20
21
22
45
23
20
36
46
28
16
33
21
26
29
6
25
5
25
16
24
42
40
36
50
43
43
p98
20
38
28
34
24
24
7
23
23
25
53
25
23
41
56
32
18
43
25
29
34
8
31
5
35
23
33
49
47
47
58
50
50
p99
25
48
40
40
31
28
8
27
28
31
65
31
27
52
79
39
21
67
33
35
45
15
42
7
57
40
50
61
60
60
73
60
60
p100
103
110
170
75
79
69
20
124
101
83
181
88
114
157
423
80
61
406
108
110
124
141
96
13
230
150
410
148
201
144
242
195
219
C-37
-------
State
WV
County
Hancock
Monid
540291004
Year
2006
n
8678
Modeled 5-minute Maximum SO2
Mean
19
Std
25
cov
133
pO
1
p50
11
p97
75
p98
91
p99
126
p100
380
Measured 1-hour SO2
Mean
11
Std
12
COV
105
pO
1
p50
8
p97
38
p98
45
p99
60
p100
145
C-38
-------
Table C-4. Descriptive statistics for modeled 5-minute maximum and measured
counties, Years 2002 through 2006, air quality adjusted to just meet the current
1-hour SO2 concentrations for monitors in 20 selected
daily standard.
State
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
FL
IA
IA
IA
IA
IA
IA
IL
IL
IL
IL
IN
IN
IN
Ml
Ml
Ml
MO
MO
MO
MO
County
New Castle
New Castle
New Castle
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Madison
Madison
Madison
Madison
Floyd
Floyd
Floyd
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Monitor ID
100031003
100031007
100031008
100032004
120570053
120570081
120570095
120570109
120571035
120571065
120574004
191130029
191130031
191130038
191390016
191390017
191390020
171190008
171191010
171193007
171193009
180430004
180430007
180431004
261630015
261630016
261630019
290770026
290770032
290770037
290770040
Year
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
n
8573
8614
8631
8546
8663
8708
8477
8623
8634
4323
8696
8607
8663
8659
8597
8141
8202
8656
8676
8673
8700
7497
8142
8559
6452
8707
8024
7055
8656
6374
7465
Modeled 5-minute Max SO2
Mean
14
14
26
20
19
17
21
26
35
21
14
23
39
29
25
20
43
18
23
20
24
43
46
54
36
25
18
33
14
29
9
Std
34
33
73
36
37
46
74
88
65
45
24
56
103
96
42
34
104
35
52
36
58
86
68
113
95
53
34
89
16
88
42
cov
240
237
278
184
196
274
349
337
187
210
163
245
262
329
170
171
244
195
221
179
243
200
147
210
264
211
185
270
113
300
446
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
0
0
0
0
3
0
3
0
p50
0
5
3
9
8
4
3
6
15
6
8
7
9
5
15
12
11
7
9
9
9
15
25
19
12
9
7
7
11
9
0
p97
91
78
181
96
101
110
157
191
176
122
66
145
288
246
102
77
311
93
131
101
117
227
185
308
236
148
94
255
39
206
49
p98
110
99
233
121
122
141
224
292
215
149
79
188
388
339
129
95
408
113
165
120
151
286
222
409
295
185
111
337
47
304
69
p99
150
152
359
168
170
202
382
513
297
202
111
271
561
519
186
132
562
156
240
161
239
401
302
596
408
257
145
482
75
504
119
p100
824
800
1374
782
895
1020
1468
1497
1237
844
581
1029
1735
1818
1096
1168
1637
848
997
861
1324
2282
1585
1968
4194
1052
831
1460
354
1503
1276
1-hourSO2
Mean
8
8
15
11
11
10
10
12
20
12
8
13
18
13
14
11
19
10
14
12
14
25
27
25
21
15
11
15
10
14
5
Std
16
16
37
17
17
23
29
36
32
21
11
28
39
37
20
16
39
16
25
16
29
44
32
45
51
26
15
32
6
34
22
COV
199
200
248
148
155
237
290
292
159
174
125
212
215
283
136
136
199
156
189
141
214
178
119
179
243
178
143
212
62
242
408
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
0
0
0
0
3
0
3
0
p50
0
3
3
5
6
3
3
3
9
3
6
5
5
5
10
9
8
6
6
6
6
10
19
15
9
6
6
3
10
7
0
p97
51
40
101
48
53
62
62
74
96
68
31
80
113
103
47
34
120
46
72
52
63
126
92
112
137
83
50
108
21
94
28
p98
59
51
133
56
62
80
86
114
114
80
37
103
146
132
60
41
154
58
89
63
79
150
107
150
166
101
56
132
24
125
38
p99
75
75
189
80
83
108
145
210
151
105
53
146
207
188
88
54
207
75
124
78
121
204
141
218
214
137
71
170
28
188
63
p100
280
307
534
275
238
531
522
507
593
278
182
414
517
813
519
612
478
242
372
265
661
1330
849
917
2470
321
187
396
97
500
705
C-39
-------
State
MO
MO
MO
MO
OH
OH
OH
OH
OH
OK
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TX
TX
TX
County
Greene
Iron
Iron
Jefferson
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Monitor ID
290770041
290930030
290930031
290990018
390350038
390350045
390350060
390350065
390356001
401430175
401430235
401430501
420030002
420030010
420030021
420030064
420030067
420030116
420033003
420070002
420070005
420070014
420950025
420958000
421250005
421250200
421255001
471570034
471570046
471571034
482450009
482450011
482450020
Year
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
n
7476
7976
8687
6318
8524
8610
8557
8591
8638
8609
8304
8356
7932
8736
7757
8431
8145
8477
7864
8402
8538
8586
8465
8617
8604
8527
8580
8264
8304
8300
8638
8591
8524
Modeled 5-minute Max SO2
Mean
4
31
28
45
44
39
53
27
45
47
30
43
36
34
31
45
31
30
53
33
47
24
43
52
29
41
53
26
30
38
30
13
22
Std
11
115
92
132
72
55
73
54
75
80
58
73
52
36
42
68
42
43
89
59
100
39
47
75
40
54
72
25
59
64
87
47
82
cov
268
367
336
292
163
143
138
201
167
169
195
170
145
107
136
151
133
142
168
176
211
168
111
144
139
133
136
96
195
169
292
373
363
pO
0
2
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
p50
0
4
8
12
20
21
29
8
22
14
12
18
20
24
19
22
20
17
23
15
14
11
30
28
19
25
28
19
19
20
0
0
0
p97
21
281
173
275
217
161
221
149
200
239
162
220
153
105
124
203
114
124
272
172
250
114
136
215
106
164
216
68
101
178
230
113
187
p98
30
438
262
390
256
188
258
180
243
278
198
256
184
125
145
241
138
149
329
206
320
135
168
251
133
200
256
82
168
231
300
150
238
p99
48
641
481
627
336
250
336
237
343
352
268
326
240
189
191
312
198
198
443
274
488
176
240
335
204
271
342
151
196
329
435
220
341
p100
310
2247
2101
3280
1071
1064
1095
1029
1335
1135
1109
1165
928
683
803
1124
826
854
1471
1045
2598
862
888
1223
759
1193
1138
425
1557
1125
1715
1117
3643
1-hourSO2
Mean
3
15
13
21
25
22
30
15
26
27
17
25
21
24
18
26
22
18
31
19
27
14
30
30
20
28
30
18
17
22
17
7
13
Std
6
50
39
58
34
25
33
26
37
39
28
35
24
18
18
32
23
19
43
28
51
18
25
35
22
29
33
9
29
31
45
24
44
COV
218
338
293
269
135
111
110
167
141
143
166
140
114
75
100
121
104
108
142
146
186
132
84
116
108
104
109
52
167
141
264
328
338
pO
0
2
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
p50
0
4
6
8
15
15
20
5
15
9
9
14
14
19
13
15
17
12
16
10
9
8
25
20
16
19
19
14
14
14
0
0
0
p97
14
116
70
101
117
82
117
82
102
131
86
117
74
67
61
110
74
60
147
94
131
61
85
110
65
97
112
38
34
101
130
67
106
p98
17
175
105
136
138
97
128
92
122
149
108
131
89
74
69
124
84
70
176
113
169
71
90
125
78
112
128
43
53
125
169
87
130
p99
31
276
188
249
168
117
153
117
158
171
144
158
115
89
86
155
101
94
218
139
242
84
110
150
103
140
162
53
110
177
231
125
188
p100
115
862
827
1277
408
444
367
429
597
333
505
369
264
298
213
382
341
325
840
353
1183
227
461
536
386
635
473
192
743
407
815
545
2203
C-40
-------
State
WV
wv
WV
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
DE
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
IA
IA
IA
IA
IA
IA
IL
IL
IL
County
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Wayne
Wayne
Wayne
Wayne
New Castle
New Castle
New Castle
New Castle
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Madison
Madison
Madison
Monitor ID
540290005
540290007
540290008
540290009
540290011
540290014
540290015
540290016
540291004
540990002
540990003
540990004
540990005
100031003
100031007
100031008
100031013
100032004
120570053
120570081
120570095
120570109
120571035
120574004
191130029
191130031
191130038
191390016
191390017
191390020
171191010
171193007
171193009
Year
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
n
8703
8706
8696
8695
8684
8669
8700
8483
8463
8712
7426
8561
8297
731
8549
8609
5947
7703
8693
8604
8697
8688
8718
8672
8627
8646
8640
7716
8553
8439
8699
8700
8653
Modeled 5-minute Max SO2
Mean
51
42
39
41
41
44
50
34
45
35
48
52
48
31
15
38
35
27
18
14
17
27
29
9
15
28
25
25
26
44
26
22
36
Std
84
62
78
61
65
62
80
47
61
44
66
67
69
41
32
79
48
39
31
39
56
77
48
21
43
84
89
52
39
113
56
46
87
cov
164
148
199
148
156
141
160
139
138
123
137
128
143
130
205
208
138
148
176
284
328
288
162
221
289
299
351
209
150
256
211
209
238
pO
0
2
2
2
2
2
5
2
2
3
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
26
23
14
20
21
25
23
21
28
21
26
30
26
21
5
16
22
15
9
4
5
9
15
4
4
5
3
13
16
11
11
9
14
p97
247
172
220
174
191
186
250
141
178
136
198
209
206
120
84
215
141
114
86
89
116
163
136
49
96
213
217
112
103
327
142
110
197
p98
312
213
282
206
227
225
303
168
215
165
231
245
247
143
102
285
169
135
106
114
158
229
164
64
129
290
307
143
129
448
177
135
275
p99
434
305
398
275
305
303
399
221
293
219
299
325
341
194
143
418
225
179
140
165
258
408
217
91
204
453
486
207
188
619
254
192
465
p100
1513
1079
1343
1043
1339
1097
1163
1011
1147
662
1039
1069
1033
477
754
1225
913
804
770
921
1305
1424
944
617
948
1482
1569
1318
891
1801
1102
1324
1564
1-hourSO2
Mean
29
24
22
24
24
26
29
20
26
25
28
30
28
18
9
22
20
15
10
8
8
13
17
5
9
13
11
14
15
20
15
13
21
Std
41
29
39
27
31
29
39
20
27
24
30
30
32
17
15
40
21
17
14
20
20
30
22
9
22
30
35
27
17
44
27
23
45
COV
139
119
173
116
128
112
133
102
106
95
107
100
115
95
165
183
103
112
137
244
248
229
128
173
252
237
303
185
114
216
179
177
213
pO
0
2
2
2
2
2
5
2
2
3
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
19
14
10
14
14
17
14
14
19
17
17
20
17
14
3
11
14
11
6
3
3
6
9
3
3
3
3
9
11
8
7
7
11
p97
133
86
119
90
102
97
138
71
90
86
102
106
109
60
44
121
69
58
43
49
46
65
71
25
55
83
93
58
43
129
79
58
104
p98
164
105
159
100
114
112
164
83
102
98
116
122
125
63
52
154
82
63
53
62
62
90
83
31
72
114
121
73
54
166
97
65
147
p99
226
147
209
126
150
147
200
100
140
113
135
152
165
77
69
223
107
80
68
90
96
145
105
43
117
159
179
107
83
254
129
94
248
p100
787
459
606
387
751
459
487
223
535
299
363
317
376
148
247
511
272
187
272
417
405
516
334
185
359
421
610
678
363
585
457
770
615
C-41
-------
State
IN
IN
IN
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
MO
MO
OH
OH
OH
OH
OH
OK
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
County
Floyd
Floyd
Floyd
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Iron
Iron
Jefferson
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Monitor ID
180430004
180430007
180431004
261630015
261630016
261630019
290770026
290770032
290770037
290770040
290770041
290930030
290930031
290990018
390350038
390350045
390350060
390350065
390356001
401430175
401430235
401430501
420030002
420030010
420030021
420030064
420030067
420030116
420033003
420070002
420070005
420070014
420950025
Year
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
n
8124
6602
8703
7772
8574
8139
7957
8723
8181
8674
8676
6964
8230
6009
8487
8596
8583
8613
4313
8663
8358
8716
8356
8630
7728
8502
8212
8506
8528
8627
8729
8510
8720
Modeled 5-minute Max SO2
Mean
35
28
51
30
30
24
38
11
36
20
12
39
34
54
44
29
50
23
39
36
38
29
30
31
28
39
26
30
46
31
32
19
21
Std
83
43
119
76
65
56
90
15
94
46
17
131
118
140
73
49
71
47
63
66
71
57
42
36
37
63
35
39
82
56
61
38
32
cov
239
156
235
252
218
239
235
143
262
226
149
332
350
259
167
172
142
200
163
184
188
195
138
118
134
162
135
132
177
180
190
200
157
pO
4
4
0
0
0
0
0
0
5
0
0
2
2
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
8
15
17
7
10
7
10
7
10
10
8
7
8
17
20
14
27
9
20
8
14
7
18
22
17
19
16
18
20
14
10
6
11
p97
209
117
313
206
180
135
258
41
247
100
39
368
229
299
211
139
214
130
179
196
213
164
124
101
109
184
98
117
248
161
171
105
89
p98
269
144
410
265
226
165
338
52
351
129
52
518
344
389
253
165
246
156
218
227
253
191
144
117
128
222
114
136
310
197
205
126
103
p99
400
196
605
383
315
228
490
62
538
201
71
713
596
570
342
222
326
215
291
287
331
239
188
172
163
296
159
176
426
262
275
166
138
p100
1731
935
2583
1249
1108
1479
1559
358
1580
1092
471
2511
2747
4252
1353
1008
1157
1002
1081
1072
1218
1037
873
691
786
1027
627
813
1203
1042
1181
836
659
1-hourSO2
Mean
20
16
24
17
17
14
18
7
17
12
8
18
16
25
25
17
29
13
22
21
22
17
18
22
16
23
18
17
27
18
19
11
15
Std
43
19
49
38
32
28
32
7
36
23
9
57
51
59
35
23
33
22
30
32
35
27
18
19
16
30
19
16
40
26
29
18
18
COV
214
121
205
222
189
205
177
98
210
196
104
306
319
236
138
140
113
162
136
154
160
160
102
88
98
133
104
95
151
147
158
158
127
pO
4
4
0
0
0
0
0
0
5
0
0
2
2
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p50
4
8
12
3
7
3
10
5
10
5
5
5
5
11
16
12
20
8
12
4
7
4
13
18
11
13
13
11
13
10
6
5
7
p97
116
58
112
116
99
76
103
26
97
46
20
156
95
107
111
72
111
72
92
106
117
88
62
65
53
96
65
58
138
86
92
59
60
p98
149
66
149
149
126
89
129
31
136
72
26
212
136
136
131
84
131
88
111
121
142
102
69
71
60
116
71
67
169
104
109
67
67
p99
207
87
232
198
165
112
178
36
200
113
36
300
214
203
167
108
151
104
147
142
172
121
85
85
74
154
87
78
218
135
134
81
78
p100
753
422
1101
463
492
737
348
87
543
441
215
1210
1018
1830
657
402
577
283
585
274
559
307
200
363
272
417
301
178
530
361
520
204
407
C-42
-------
State
PA
PA
PA
PA
TN
TN
TN
TX
TX
TX
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
County
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Wayne
Wayne
Wayne
Wayne
New Castle
New Castle
New Castle
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Monitor ID
420958000
421250005
421250200
421255001
471570034
471570046
471571034
482450009
482450011
482450020
540290005
540290007
540290008
540290009
540290011
540290014
540290015
540290016
540291004
540990002
540990003
540990004
540990005
100031007
100031008
100031013
100032004
120570053
120570081
120570095
120570109
120571035
120574004
Year
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
n
8725
8718
8742
8602
8084
8285
8306
8567
8488
8650
8695
8616
8683
8344
8694
8686
8694
8657
8533
2056
8060
8571
7930
6137
8364
8119
8617
8543
8492
8643
8515
8643
8572
Modeled 5-minute Max SO2
Mean
53
26
40
53
21
24
38
28
19
21
50
40
35
44
47
46
48
35
52
46
52
51
49
14
26
23
22
21
21
10
33
35
9
Std
68
40
48
74
21
45
59
85
99
76
93
69
68
71
75
63
79
45
69
50
71
72
77
26
56
41
34
34
38
31
92
60
23
cov
130
157
120
140
98
186
157
300
524
365
187
170
193
162
161
137
165
130
133
107
137
142
158
181
216
175
155
168
176
296
280
174
247
pO
0
0
3
0
4
11
11
0
0
0
3
2
2
2
2
2
3
2
2
8
3
3
3
0
0
0
0
0
0
0
0
0
0
p50
31
15
24
28
15
15
19
5
0
0
22
20
14
21
23
28
21
23
32
30
27
29
26
7
10
10
12
10
12
0
10
16
0
p97
205
104
146
223
54
79
179
196
137
182
250
186
191
199
222
185
244
135
207
175
231
206
217
67
152
112
96
97
102
71
198
161
56
p98
238
126
180
265
65
127
217
240
181
240
321
233
244
250
270
225
295
163
258
213
265
250
268
83
193
134
115
117
121
99
272
197
66
p99
317
191
253
349
129
140
291
329
259
366
475
343
343
352
371
320
398
223
352
274
333
341
377
112
274
181
154
157
171
145
475
277
98
p100
1057
866
820
1150
479
1293
1032
3428
5728
1456
1677
1191
1142
1133
1227
1088
1126
911
1150
557
976
1515
1338
595
1052
868
794
836
894
787
2022
1078
624
1-hourSO2
Mean
30
18
28
30
15
14
22
16
11
12
28
23
20
25
27
26
28
20
30
32
30
29
28
8
15
13
13
12
12
5
16
20
5
Std
30
22
25
34
7
22
28
44
52
40
46
34
34
34
37
29
38
19
31
25
33
33
38
11
28
19
15
15
17
10
37
28
10
COV
100
122
91
111
49
155
128
273
482
332
163
145
166
135
135
108
138
95
105
78
111
115
133
137
185
138
116
129
140
196
237
143
197
pO
0
0
3
0
4
11
11
0
0
0
2
2
2
2
2
2
2
2
2
7
3
3
3
0
0
0
0
0
0
0
0
0
0
p50
22
12
21
18
11
11
11
4
0
0
14
14
9
14
16
18
14
18
23
24
17
20
17
5
8
8
8
10
10
0
10
10
0
p97
101
66
90
120
34
26
98
112
82
108
138
97
106
104
122
94
133
62
104
99
133
106
113
34
83
57
49
50
50
30
69
84
30
p98
119
78
102
135
38
38
120
138
108
138
170
120
133
124
145
113
163
76
124
120
140
126
137
39
108
67
57
59
59
40
99
99
35
p99
142
99
120
170
41
79
154
176
138
194
237
166
189
179
186
156
202
101
175
137
150
164
188
49
147
90
67
74
79
50
173
134
45
p100
310
392
296
422
266
570
356
1760
2901
676
667
559
435
580
587
400
398
352
458
198
341
792
570
230
437
248
199
257
376
153
926
391
213
C-43
-------
State
IA
IA
IA
IA
IA
IA
IL
IL
IL
IN
IN
IN
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
MO
MO
OH
OH
OH
OH
OK
OK
OK
PA
PA
PA
County
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Madison
Madison
Madison
Floyd
Floyd
Floyd
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Iron
Iron
Jefferson
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Monitor ID
191130029
191130031
191130038
191390016
191390017
191390020
171191010
171193007
171193009
180430004
180430007
180431004
261630015
261630016
261630019
290770026
290770032
290770037
290770040
290770041
290930030
290930031
290990004
390350038
390350045
390350060
390350065
401430175
401430235
401430501
420030002
420030010
420030021
Year
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
n
8381
8664
8208
8167
8415
8725
8729
8692
8594
8358
7538
8251
8502
8656
8662
8776
8754
8777
8694
8687
1846
2172
8044
8603
8679
8617
8405
8292
8460
8700
8646
8616
8663
Modeled 5-minute Max SO2
Mean
8
19
19
17
16
42
28
22
33
53
19
53
34
21
17
36
8
31
29
15
88
142
42
48
24
35
29
51
49
40
35
29
29
Std
25
62
76
31
25
110
58
36
79
103
44
141
79
49
36
88
15
82
44
21
132
149
107
76
43
62
51
89
87
73
50
40
41
cov
317
326
398
187
158
264
205
165
242
195
225
265
236
228
211
243
184
262
149
140
149
104
252
157
178
177
177
176
177
181
142
138
140
pO
0
0
0
0
0
0
0
0
0
5
5
0
0
0
0
5
0
0
0
0
16
16
2
0
0
0
0
0
0
0
0
0
0
p50
2
4
2
9
9
9
11
11
11
22
8
8
9
6
5
11
5
11
20
11
32
94
11
23
14
15
14
12
17
11
20
17
17
p97
53
139
146
76
67
333
150
102
175
267
97
368
225
132
102
257
33
200
114
53
446
540
286
228
120
173
146
263
252
213
144
111
119
p98
71
190
219
94
82
442
193
125
241
346
126
482
288
167
123
334
47
285
148
63
524
611
372
274
152
218
170
306
302
246
172
136
139
p99
110
306
402
133
114
600
288
168
383
533
191
679
411
228
166
477
64
455
212
92
648
729
528
374
200
307
229
399
405
311
227
197
187
p100
770
1311
1508
885
747
1683
1068
806
1638
1988
1067
2693
1187
975
785
1464
409
1548
1102
493
1329
1435
2111
1146
879
1023
953
1216
1402
1102
1077
655
873
1-hourSO2
Mean
5
9
9
10
9
19
16
13
19
30
11
24
19
12
10
17
6
15
17
11
39
61
19
28
14
20
17
29
28
23
20
20
17
Std
12
22
29
14
10
41
29
16
40
52
22
60
40
24
17
33
8
31
20
10
41
43
42
37
20
30
24
45
43
35
22
22
18
COV
264
245
334
149
113
218
175
127
215
173
193
253
209
191
171
187
139
202
115
97
104
72
219
131
144
147
143
152
151
151
110
108
105
pO
0
0
0
0
0
0
0
0
0
5
5
0
0
0
0
5
0
0
0
0
16
16
2
0
0
0
0
0
0
0
0
0
0
p50
2
2
2
6
6
6
7
7
7
15
5
5
6
3
3
5
5
8
16
11
16
48
7
14
9
9
14
8
12
8
14
14
11
p97
28
53
62
39
32
131
83
54
90
141
50
126
129
75
54
114
21
84
48
32
159
174
112
127
64
91
82
142
134
114
73
73
59
p98
39
73
85
47
38
161
105
61
126
176
71
166
158
90
66
139
26
117
69
37
174
206
131
145
73
114
91
159
159
126
84
84
67
p99
60
117
165
65
49
226
155
76
209
262
106
277
218
126
84
184
37
175
101
48
190
222
176
186
91
159
114
183
195
146
110
101
84
p100
234
321
461
364
223
507
393
198
736
821
479
1133
466
293
200
357
116
444
487
296
285
349
1053
400
277
304
291
529
602
427
486
233
236
C-44
-------
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TX
TX
TX
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
DE
DE
DE
DE
County
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Wayne
Wayne
Wayne
New Castle
New Castle
New Castle
New Castle
Monitor ID
420030064
420030067
420030116
420033003
420070002
420070005
420070014
420950025
420958000
421250005
421250200
421255001
471570034
471570046
471571034
482450009
482450011
482450020
540290005
540290007
540290008
540290009
540290011
540290015
540290016
540291004
540990003
540990004
540990005
100031007
100031008
100031013
100032004
Year
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2004
2005
2005
2005
2005
n
8680
8373
8676
8611
8522
8755
8733
8702
8648
8662
8680
8656
8240
8119
8005
8679
8507
8244
8723
8646
8726
8700
8676
8717
4514
8385
8659
8673
8681
7283
8634
7604
8539
Modeled 5-minute Max SO2
Mean
46
29
30
52
41
52
33
15
52
37
42
52
24
31
46
26
13
21
49
44
35
35
45
45
34
53
46
36
35
18
32
32
22
Std
68
43
42
85
76
93
52
22
52
43
52
78
23
50
69
91
43
70
84
68
66
62
69
75
44
70
61
46
42
32
71
45
34
cov
148
151
138
165
185
177
160
142
99
118
124
148
96
161
151
347
331
337
173
154
189
176
156
168
130
132
133
128
120
182
223
142
152
pO
0
0
0
0
0
0
0
0
0
0
3
0
0
13
9
0
0
0
2
2
2
2
2
2
2
2
3
3
3
0
0
0
0
p50
24
17
17
22
17
21
16
9
39
24
24
27
18
19
25
4
0
0
23
24
14
14
22
19
21
33
26
22
22
8
12
19
13
p97
201
114
123
257
215
250
156
58
162
124
157
232
65
133
203
190
107
173
234
190
193
176
214
223
135
208
196
138
127
86
177
130
98
p98
238
142
145
319
265
302
184
68
196
158
191
277
80
160
256
245
139
225
301
233
236
214
259
269
164
255
228
163
147
104
229
154
117
p99
316
210
193
430
365
417
233
91
275
225
264
368
151
219
363
365
200
331
436
329
321
295
348
363
212
351
289
212
191
143
346
201
154
p100
1080
1050
927
1166
1338
1881
1067
414
974
837
1125
1252
432
1733
1092
3665
1033
1311
1544
1233
1108
1018
1112
1130
827
1213
1012
934
905
799
1354
968
758
1-hourSO2
Mean
26
20
18
30
24
30
19
11
30
26
29
30
17
18
26
15
8
12
28
25
20
20
26
26
19
30
26
21
20
10
18
18
13
Std
32
24
18
42
37
46
25
11
19
22
28
37
8
24
33
48
22
36
42
32
33
29
33
36
19
32
27
19
17
14
36
19
14
COV
119
119
101
141
157
153
129
101
62
87
96
123
49
135
124
317
289
304
149
128
161
146
129
141
96
105
103
92
84
142
197
105
111
pO
0
0
0
0
0
0
0
0
0
0
3
0
0
13
9
0
0
0
2
2
2
2
2
2
2
2
3
3
3
0
0
0
0
p50
17
14
11
17
10
14
10
7
25
21
21
21
13
13
13
4
0
0
14
17
10
10
14
12
14
24
17
14
14
5
8
14
8
p97
104
76
62
138
115
131
84
36
73
79
96
123
40
53
107
107
63
98
126
95
107
93
114
121
67
107
115
69
63
44
96
66
49
p98
118
87
70
169
144
155
97
41
82
89
106
144
45
76
134
134
80
129
160
117
131
112
141
143
86
126
126
78
69
52
126
74
57
p99
155
107
84
225
189
196
118
50
105
106
130
175
53
120
192
192
112
183
229
167
171
148
181
183
102
162
138
95
83
68
183
93
68
p100
379
613
301
458
658
966
511
169
344
353
685
589
134
990
464
1782
554
589
843
624
514
362
455
386
233
579
310
227
178
331
650
382
145
C-45
-------
State
FL
FL
FL
FL
FL
FL
IA
IA
IA
IA
IA
IA
IL
IL
IL
IN
IN
IN
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
OH
OH
OH
OH
OK
OK
County
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Madison
Madison
Madison
Floyd
Floyd
Floyd
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Jefferson
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Monitor ID
120570053
120570081
120570095
120570109
120571035
120574004
191130029
191130031
191130038
191390016
191390017
191390020
171191010
171193007
171193009
180430004
180430007
180431004
261630015
261630016
261630019
290770026
290770032
290770037
290770040
290770041
290990004
390350038
390350045
390350060
390350065
401430175
401430235
Year
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
n
8698
8679
8650
8618
8657
8716
8600
8632
8615
8644
8603
8693
8669
8703
8519
8345
8063
8264
8193
8044
7917
8756
8661
8760
8669
8660
7166
8570
8631
8602
8355
8551
8442
Modeled 5-minute Max SO2
Mean
17
15
10
25
28
10
14
35
28
24
16
40
34
29
39
53
34
35
36
26
20
32
10
31
23
12
54
40
23
44
22
53
45
Std
28
33
44
84
52
20
37
98
92
60
28
109
67
47
88
81
80
97
87
57
43
84
16
80
62
20
132
69
43
63
44
90
81
cov
166
222
442
336
184
204
272
281
323
254
177
273
197
164
227
153
233
276
238
220
218
267
158
260
269
162
244
173
186
145
198
170
180
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
4
0
0
0
0
5
0
5
0
0
0
0
0
0
0
0
0
p50
9
6
0
6
13
5
5
7
4
8
9
8
13
14
13
29
12
8
9
9
7
8
6
11
10
7
11
17
10
23
7
16
18
p97
81
81
59
162
138
53
82
274
236
147
68
334
183
127
211
220
174
242
259
152
118
230
43
185
116
48
387
201
114
190
122
268
227
p98
98
101
89
230
169
66
109
370
338
196
86
439
228
154
281
278
214
327
330
194
143
308
56
254
164
58
491
238
135
219
148
312
271
p99
128
139
150
413
233
94
170
535
524
290
125
593
327
212
448
401
304
509
459
272
191
454
71
407
276
82
665
317
186
292
201
403
364
p100
695
905
1113
1802
1010
502
928
1597
1450
1332
790
1628
1117
951
1642
1555
2071
1796
1212
1162
903
1438
427
1888
1509
495
2626
1170
995
1046
1129
1474
1923
1-hourSO2
Mean
10
9
5
12
16
6
8
15
13
12
9
18
19
17
22
30
20
16
21
15
11
14
7
15
13
9
24
23
13
25
13
30
26
Std
13
16
18
36
25
9
18
36
35
23
13
41
33
22
45
39
41
39
44
28
20
29
9
30
32
11
53
33
20
29
21
44
40
COV
127
182
360
296
151
158
232
230
274
187
140
229
169
130
201
127
207
237
213
189
179
201
119
199
242
121
218
143
151
116
165
145
154
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
4
0
0
0
0
5
0
5
0
0
0
0
0
0
0
0
0
p50
4
4
0
4
9
4
3
3
3
6
6
6
8
8
8
20
8
4
7
7
3
5
5
9
10
5
9
10
7
17
3
9
14
p97
40
44
26
62
75
26
44
106
96
65
32
131
96
67
113
112
92
92
144
84
67
96
29
75
58
29
147
113
58
99
65
146
123
p98
48
53
35
79
88
35
61
140
133
86
41
163
122
75
151
139
112
123
184
107
80
122
34
99
88
34
181
120
69
113
79
164
142
p99
57
66
48
158
114
44
92
188
205
120
58
213
172
101
226
195
155
183
238
144
97
163
44
151
156
44
256
154
86
137
99
192
178
p100
176
405
431
665
401
119
341
416
426
352
266
580
507
327
679
669
1103
701
486
589
305
376
146
593
672
278
1298
514
429
429
597
804
1023
C-46
-------
State
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TN
TX
TX
TX
WV
WV
WV
WV
WV
WV
WV
WV
WV
WV
County
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Wayne
Wayne
Wayne
Monitor ID
401430501
420030002
420030010
420030021
420030064
420030067
420030116
420033003
420070002
420070005
420070014
420950025
420958000
421250005
421250200
421255001
471570034
471570046
471571034
471572005
482450009
482450011
482450020
540290005
540290007
540290008
540290009
540290011
540290015
540291004
540990003
540990004
540990005
Year
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
2005
n
8515
8639
8731
8650
8658
8689
8699
8490
8682
8626
8660
8512
8652
8603
8720
8606
8121
8282
8160
5864
8360
8071
7797
8684
8702
8701
8687
8541
8705
8651
8142
8622
8454
Modeled 5-minute Max SO2
Mean
42
25
26
24
36
21
26
49
38
53
29
37
52
42
40
49
22
29
33
7
36
18
19
45
38
27
41
43
35
53
33
27
23
Std
73
38
32
37
57
31
37
88
64
89
49
38
73
46
48
78
23
66
55
8
91
61
72
67
56
52
64
70
58
62
51
33
29
cov
176
153
124
152
162
144
142
180
169
169
169
104
138
109
121
157
103
225
167
114
253
342
377
148
148
191
154
161
169
118
153
125
125
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
12
4
0
0
0
2
2
2
2
2
2
2
2
2
2
p50
12
13
17
13
17
13
15
20
17
24
13
27
28
28
25
21
16
17
18
5
7
0
0
24
20
11
20
22
15
34
18
17
15
p97
219
107
91
103
172
87
108
264
184
239
141
111
212
142
147
235
61
125
140
22
268
139
160
198
160
148
186
202
169
190
167
98
88
p98
254
128
107
122
204
102
130
323
223
296
168
131
247
179
183
272
77
140
185
28
336
189
210
239
195
184
222
243
205
228
199
118
103
p99
327
168
154
164
265
134
171
437
301
431
219
202
331
248
249
346
140
223
276
47
465
281
334
331
265
250
297
335
277
305
250
153
135
p100
1101
853
555
848
1006
533
844
1293
1186
1712
951
711
1134
799
763
1118
443
2680
1025
214
1371
1466
1654
1094
1079
981
1257
1260
1049
1020
934
765
616
1-hourSO2
Mean
24
14
18
14
20
15
15
28
22
30
17
26
30
29
27
29
15
17
19
5
21
10
11
26
22
16
24
25
20
30
19
15
13
Std
36
16
16
16
27
17
16
44
31
44
22
19
33
23
26
38
9
34
27
3
47
32
37
31
25
25
30
34
28
27
24
13
11
COV
148
113
90
112
130
112
104
157
140
145
135
72
108
80
93
131
61
200
140
69
228
307
343
119
116
160
126
134
139
88
123
83
83
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
12
4
0
0
0
2
2
2
2
2
2
2
2
2
2
p50
9
9
13
9
11
11
11
13
12
16
10
21
21
21
18
15
12
12
12
4
6
0
0
16
13
7
13
16
11
24
12
12
10
p97
119
54
59
50
91
56
54
150
97
125
77
71
110
83
89
126
35
55
70
12
159
79
91
104
82
80
100
106
91
93
87
46
42
p98
132
63
67
59
108
65
63
173
116
150
89
78
124
98
101
141
39
74
101
16
187
108
113
124
97
100
115
128
109
109
121
52
46
p99
164
76
80
74
134
78
78
215
148
205
109
92
146
113
126
166
51
121
156
19
244
153
176
157
135
133
144
164
144
140
133
65
52
p100
425
249
212
273
299
184
264
639
537
834
237
331
469
356
325
445
207
1360
382
51
687
647
857
447
365
343
729
647
405
399
250
186
103
C-47
-------
State
DE
DE
DE
DE
FL
FL
FL
FL
FL
FL
IA
IA
IA
IA
IA
IA
IL
IL
IL
IN
IN
IN
Ml
Ml
Ml
MO
MO
MO
MO
MO
MO
OH
OH
County
New Castle
New Castle
New Castle
New Castle
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Hillsborough
Linn
Linn
Linn
Muscatine
Muscatine
Muscatine
Madison
Madison
Madison
Floyd
Floyd
Floyd
Wayne
Wayne
Wayne
Greene
Greene
Greene
Greene
Greene
Jefferson
Cuyahoga
Cuyahoga
Monitor ID
100031007
100031008
100031013
100032004
120570053
120570081
120570095
120570109
120571035
120574004
191130029
191130031
191130038
191390016
191390017
191390020
171191010
171193007
171193009
180430004
180430007
180431004
261630015
261630016
261630019
290770026
290770032
290770037
290770040
290770041
290990004
390350038
390350045
Year
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
n
8424
8573
8631
8600
6506
6509
6517
6462
6486
6367
8648
8549
8250
8708
8715
8714
8651
8682
8627
5928
6240
8339
8429
8722
8325
8753
8727
8745
8637
8581
6541
8391
8594
Modeled 5-minute Max SO2
Mean
14
30
21
21
12
11
5
26
29
11
6
29
35
24
17
30
31
25
42
44
33
41
29
19
13
31
12
31
15
8
58
36
26
Std
23
65
38
30
25
29
23
79
57
19
12
82
99
73
29
93
69
45
100
71
59
107
79
47
32
85
20
95
42
16
134
66
49
cov
170
214
184
142
202
273
427
303
194
173
205
283
281
309
172
307
220
180
237
161
181
264
272
247
237
275
171
305
281
200
233
184
190
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
4
0
0
0
0
4
0
4
0
0
0
0
0
p50
7
13
11
13
5
0
0
8
13
6
4
5
7
6
9
5
10
11
13
22
18
15
4
4
3
7
7
7
7
5
9
13
13
p97
64
169
99
89
65
66
38
173
150
50
26
222
285
166
76
257
183
125
251
208
136
231
228
128
87
235
53
238
74
27
421
192
132
p98
78
214
123
104
82
84
52
242
191
58
33
292
382
230
96
349
229
155
340
247
174
315
294
163
106
317
60
352
106
35
520
229
154
p99
109
309
169
137
107
114
97
421
266
87
49
431
543
371
133
517
329
210
530
337
255
506
413
223
140
470
93
548
165
56
686
303
215
p100
590
1175
943
620
628
1006
624
1522
1114
451
385
1429
1655
1470
731
1471
1141
951
1717
1121
1395
3444
1154
980
782
1300
530
1571
1041
505
1874
1078
981
1-hourSO2
Mean
8
17
12
12
7
6
3
12
17
6
3
13
16
11
10
14
18
15
24
26
19
20
17
11
8
14
8
15
9
6
25
21
15
Std
10
32
18
13
11
14
8
31
28
8
5
30
38
28
13
35
35
21
52
35
29
46
41
23
15
30
10
38
21
9
52
32
23
COV
128
184
147
102
160
232
302
247
163
134
155
225
234
248
133
257
192
146
215
135
152
232
245
210
190
215
128
259
246
155
207
155
156
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
4
0
0
0
0
4
0
4
0
0
0
0
0
p50
5
8
8
8
4
0
0
4
8
4
4
4
4
4
6
3
5
10
10
15
15
11
3
3
3
4
4
4
4
4
6
8
13
p97
29
94
51
40
34
34
21
67
80
21
12
86
111
67
36
106
103
69
137
117
69
84
130
74
50
96
31
102
36
18
150
106
64
p98
38
118
64
48
42
42
25
92
96
29
12
111
143
86
45
134
123
78
186
128
87
109
165
92
59
119
40
139
54
18
179
123
85
p99
48
161
86
59
55
59
38
151
138
38
20
152
205
132
66
196
172
103
275
157
117
189
221
121
74
167
49
209
94
27
245
157
106
p100
193
552
437
166
201
600
168
545
575
159
115
430
537
515
244
421
530
368
775
506
616
1760
455
254
162
394
196
536
504
277
799
403
437
C-48
-------
State
OH
OH
OK
OK
OK
OK
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
TN
TN
TN
TX
TX
TX
WV
WV
WV
WV
WV
WV
WV
County
Cuyahoga
Cuyahoga
Tulsa
Tulsa
Tulsa
Tulsa
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Beaver
Beaver
Northampton
Northampton
Washington
Washington
Washington
Shelby
Shelby
Shelby
Jefferson
Jefferson
Jefferson
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Hancock
Monitor ID
390350060
390350065
401430175
401430235
401430501
401431127
420030002
420030010
420030021
420030064
420030067
420030116
420070002
420070005
420070014
420950025
420958000
421250005
421250200
421255001
471570046
471571034
471572005
482450009
482450011
482450020
540290005
540290007
540290008
540290009
540290011
540290015
540291004
Year
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
n
8637
8521
7204
7223
7193
431
8690
8612
8711
8665
8568
7567
8682
8673
8627
8712
8512
8693
8609
8695
8189
8156
2867
8553
8417
8647
8340
8550
8636
8690
8605
8678
8678
Modeled 5-minute Max SO2
Mean
48
34
53
31
39
7
27
29
32
51
24
31
38
42
34
7
18
38
44
36
25
46
8
25
13
28
44
40
30
53
50
38
44
Std
80
76
89
62
66
16
42
36
45
81
36
41
63
95
47
10
35
43
49
63
37
68
11
105
64
89
62
61
54
73
64
61
60
cov
165
225
169
199
171
227
157
125
139
158
149
132
165
224
137
158
195
113
112
174
150
148
131
415
486
320
140
152
178
139
129
158
134
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
10
4
0
0
0
2
2
2
5
2
3
2
p50
21
13
15
11
15
2
14
20
18
24
14
18
19
10
20
4
11
26
29
15
17
25
5
0
0
0
25
21
13
28
31
18
26
p97
244
178
274
178
193
44
117
99
130
246
101
122
180
228
142
24
71
125
151
185
87
197
29
183
111
188
186
179
157
223
195
183
176
p98
296
224
317
219
226
54
139
116
153
294
119
142
214
287
167
29
93
164
189
225
108
242
39
268
174
257
224
217
195
264
231
216
212
p99
391
320
402
297
291
75
181
177
205
388
172
185
286
422
216
41
141
244
260
301
148
344
49
469
297
401
296
292
265
348
307
282
287
p100
1178
1675
1181
1086
1100
181
862
688
916
1176
574
860
1043
2318
932
287
924
737
903
1102
1131
1125
246
2359
1336
3062
1093
1103
1026
1185
1120
1170
1062
1-hourSO2
Mean
28
20
30
18
22
4
15
21
19
30
17
18
22
24
20
5
10
26
30
21
14
26
6
14
8
16
25
23
17
30
29
22
26
Std
39
39
44
30
31
9
18
19
19
39
20
17
30
49
20
5
17
21
25
31
18
32
5
56
34
47
28
29
26
34
29
28
27
COV
139
197
144
168
140
209
118
92
103
132
118
95
134
201
103
106
164
81
81
146
122
121
81
386
449
293
109
122
148
110
99
128
105
pO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
8
4
0
0
0
2
2
2
5
2
2
2
p50
13
8
10
5
10
1
9
15
12
18
12
12
13
6
13
4
8
21
24
10
12
16
4
0
0
0
16
14
9
19
23
12
19
p97
127
98
149
99
104
28
59
62
65
133
68
59
96
123
75
16
32
73
91
101
25
103
21
108
69
103
98
94
84
117
101
101
89
p98
161
119
168
119
119
33
68
68
74
157
74
68
110
150
86
18
42
87
101
118
33
128
21
151
99
142
115
110
110
136
117
117
105
p99
204
170
198
154
139
39
80
83
92
193
92
80
139
211
104
21
65
115
122
157
62
173
29
246
172
216
143
140
140
171
140
140
140
p100
467
722
372
391
342
99
368
300
246
537
261
338
420
1130
214
60
397
376
383
432
582
396
54
992
647
1768
346
470
337
566
456
512
339
C-49
-------
APPENDIX D: SUPPLEMENTARY FILES FOR AERMOD
MODELING
D-l
-------
Table D-1. Emission parameters by stack for all major facility stacks in Missouri.
Stack
ID
4990
4991
4994
4995
5014
5016
5017
5018
5019
5020
5021
5039
5041
City
CLINTON
ANNAPOLIS
ANNAPOLIS
ANNAPOLIS
COLUMBIA
COLUMBIA
COLUMBIA
COLUMBIA
COLUMBIA
COLUMBIA
COLUMBIA
ST. JOSEPH
ST. JOSEPH
Facility Name
KANSAS CITY POWER
& LIGHT CO-
MONTROSE
GENERATING
STATION
DOE RUN COMPANY-
GLOVER SMELTER
DOE RUN COMPANY-
GLOVER SMELTER
DOE RUN COMPANY-
GLOVER SMELTER
UNIVERSITY OF
MISSOURI -
COLUMBIA-POWER
PLANT
UNIVERSITY OF
MISSOURI -
COLUMBIA-POWER
PLANT
UNIVERSITY OF
MISSOURI -
COLUMBIA-POWER
PLANT
UNIVERSITY OF
MISSOURI -
COLUMBIA-POWER
PLANT
UNIVERSITY OF
MISSOURI -
COLUMBIA-POWER
PLANT
UNIVERSITY OF
MISSOURI -
COLUMBIA-POWER
PLANT
UNIVERSITY OF
MISSOURI -
COLUMBIA-POWER
PLANT
AQUILA INC-LAKE
ROAD PLANT
AQUILA INC-LAKE
NEI Site ID
NEI 7485
NEI 34282
NEI 34282
NEI 34282
NEI
M001 90004
NEI
MO01 90004
NEI
M001 90004
NEI
M001 90004
NEI
MO01 90004
NEI
MO01 90004
NEI
M001 90004
NEI 7487
NEI 7487
UTMX
(m)1
418,276
703,986
704,098
704,182
557,837
557,748
557,750
557,740
557,740
557,732
557,744
339,144
UTMY
(m)1
4,240,693
4,151,076
4,151,018
4,151,029
4,311,095
4,311,019
4,311,008
4,311,005
4,311,015
4,311,009
4,311,009
4,398,873
SO2
Emissions
(tpy)
5,648
3
1,288
42,049
4,842
49
1,242
40
1,056
2,465
36
2,838
724
Stack
Height
(m)
137
129
114
186
99
96
96
96
96
96
96
69
46
Exit
Temp.
(K)
416
376
344
366
450
450
450
450
450
450
450
443
430
Stack
Diam.
(m)
3.1
4.4
2.3
3.7
2.7
3.0
3.0
3.0
3.0
3.0
3.0
3.0
2.1
Exit
Velocity
(m/s)
37
13
16
11
11
17
17
17
17
17
17
21
17
Profile
Method2
TieM
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
TieM
Tier 2
TierS
TieM
Tier 2
D-2
-------
Stack
ID
5043
5045
5046
5049
5050
5051
5054
5063
5064
5066
5068
5069
5070
City
CAPE
GIRARDEAU
MISSOURI
CITY
MISSOURI
CITY
LABADIE
LABADIE
LABADIE
LABADIE
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
Facility Name
ROAD PLANT
LONE STAR
INDUSTRIES INC-
CAPE GIRARDEAU
INDEPENDENCE
POWER AND LIGHT-
MISSOURI CITY
STATION
INDEPENDENCE
POWER AND LIGHT-
MISSOURI CITY
STATION
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
NEI Site ID
NEI 16367
NEI
MO0470096
NEI
M00470096
NEI 7514
NEI 7514
NEI 7514
NEI 7514
NEI 7525
NEI 7525
NEI 7525
NEI 7525
NEI 7525
NEI 7525
UTMX
(m)1
339,251
806,949
387,119
387,100
688,392
688,357
688,461
688,442
476,842
476,853
476,913
476,884
476,890
476,918
UTMY
(m)1
4,398,905
4,130,237
4,343,259
4,343,257
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
S02
Emissions
(tpy)
1,362
25
1,209
10,970
14,753
14,285
7,602
1,137
1,433
757
159
660
567
Stack
Height
(m)
64
91
91
213
213
213
213
107
107
61
61
61
61
Exit
Temp.
(K)
405
401
401
444
444
444
444
422
422
422
422
422
422
Stack
Diam.
(m)
3.4
2.4
2.4
6.2
6.2
8.8
8.8
2.5
2.5
3.7
3.7
3.7
3.7
Exit
Velocity
(mis)
22
17
17
28
28
28
28
15
15
6
6
5
5
Profile
Method2
Tier 2
Tier 2
Tier 2
TieM
TieM
TieM
TieM
Tier 2
TieM
TieM
TieM
TieM
TieM
D-3
-------
Stack
ID
5073
5074
5076
5077
5084
5087
5088
5089
5090
5091
City
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
SPRING-
FIELD
CLINTON
CLINTON
CLINTON
CLINTON
CLINTON
Facility Name
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-
SOUTHWEST POWER
PLANT
KANSAS CITY POWER
& LIGHT CO-
MONTROSE
GENERATING
STATION
KANSAS CITY POWER
& LIGHT CO-
MONTROSE
GENERATING
STATION
KANSAS CITY POWER
& LIGHT CO-
MONTROSE
GENERATING
STATION
KANSAS CITY POWER
& LIGHT CO-
MONTROSE
GENERATING
STATION
KANSAS CITY POWER
& LIGHT CO-
MONTROSE
GENERATING
NEI Site ID
NEI 7525
NEI 7525
NEI 7525
NEI 7525
NEI 12640
NEI 7485
NEI 7485
NEI 7485
NEI 7485
NEI 7485
UTMX
(m)1
476,919
476,952
477,050
476,992
465,416
418,274
418,316
418,295
418,352
418,247
UTMY
(m)1
4,106,930
4,106,940
4,106,880
4,106,881
4,111,816
4,240,761
4,240,766
4,240,722
4,240,716
4,240,717
S02
Emissions
(tpy)
218
255
219
252
3,390
7
4,048
10
6,105
7
Stack
Height
(m)
60
60
60
60
117
137
137
137
137
137
Exit
Temp.
(K)
422
422
422
422
397
416
416
416
416
416
Stack
Diam.
(m)
3.7
3.7
3.7
3.7
3.4
4.6
4.6
4.6
4.6
3.1
Exit
Velocity
(mis)
6
6
6
6
21
37
37
37
37
37
Profile
Method2
TieM
Tierl
TieM
Tierl
Tier 2
Tierl
Tierl
Tierl
Tierl
Tierl
D-4
-------
Stack
ID
5092
5093
5096
5097
5106
5108
5109
5111
5113
5114
5115
5116
5117
5118
5119
City
BOSS
BOSS
KANSAS
CITY
KANSAS
CITY
KANSAS
CITY
SIBLEY
SIBLEY
LOUISIANA
WEST
ALTON
WEST
ALTON
WEST
ALTON
SIBLEY
SIBLEY
SIBLEY
SIBLEY
Facility Name
STATION
DOE RUN COMPANY-
BUICK SMELTER
DOE RUN COMPANY-
BUICK SMELTER
TRIGEN ENERGY
CORPORATION-
GRAND AVENUE
STATION
TRIGEN ENERGY
CORPORATION-
GRAND AVENUE
STATION
KANSAS CITY POWER
SLIGHT CO-
HAWTHORN STATION
AQUILA INC-SIBLEY
GENERATING
STATION
AQUILA INC-SIBLEY
GENERATING
STATION
AQUALONDIVOF
HERCULES INC-
MISSOURI CHEMICAL
WORKS
AMERENUE-SIOUX
PLANT
AMERENUE-SIOUX
PLANT
AMERENUE-SIOUX
PLANT
AQUILA INC-SIBLEY
GENERATING
STATION
AQUILA INC-SIBLEY
GENERATING
STATION
AQUILA INC-SIBLEY
GENERATING
STATION
AQUILA INC-SIBLEY
GENERATING
NEI Site ID
NEI
M00930009
NEI
M00930009
NEI
M00950021
NEI
MO0950021
NEI 7484
NEI 7486
NEI 7486
NEI 34503
NEI 7516
NEI 7516
NEI 7516
NEI 7486
NEI 7486
NEI 7486
NEI 7486
UTMX
(m)1
664,790
664,946
363,375
363,367
372,272
397,709
397,739
669,398
735,034
735,027
734,948
397,722
397,628
397,734
UTMY
(m)1
4,167,123
4,167,101
4,330,430
4,330,423
4,332,280
4,337,274
4,337,279
4,365,781
4,310,876
4,310,819
4,310,864
4,337,273
4,337,247
4,337,235
S02
Emissions
(tpy)
4,144
41
2,714
1,074
3,751
9,160
415
1,765
24,932
21,025
2
415
415
467
467
Stack
Height
(m)
61
3
86
86
92
213
213
39
183
183
65
213
213
213
213
Exit
Temp.
(K)
347
295
430
430
412
423
423
445
427
427
436
423
423
423
423
Stack
Diam.
(m)
5.2
0.0
5.1
5.1
6.2
4.1
4.1
1.5
5.8
5.8
1.4
4.1
4.1
4.1
4.1
Exit
Velocity
(mis)
9
0
19
19
38
32
32
17
29
29
15
32
32
32
32
Profile
Method2
Tier 2
Tier 2
TierS
TierS
TieM
Tierl
TieM
Tier 2
Tierl
Tierl
Tierl
Tierl
Tierl
Tierl
Tierl
D-5
-------
Stack
ID
5120
5125
5127
5129
5130
5131
5141
5145
5147
5148
5149
5150
5151
5153
City
SIBLEY
INDEPEN-
DENCE
INDEPEN-
DENCE
INDEPEN-
DENCE
ASBURY
HERCU-
LANEUM
HERCU-
LANEUM
HERCU-
LANEUM
FESTUS
FESTUS
FESTUS
PALMYRA
PALMYRA
PALMYRA
Facility Name
STATION
AQUILA INC-SIBLEY
GENERATING
STATION
INDEPENDENCE
POWER AND LIGHT-
BLUE VALLEY
STATION
INDEPENDENCE
POWER AND LIGHT-
BLUE VALLEY
STATION
INDEPENDENCE
POWER AND LIGHT-
BLUE VALLEY
STATION
EMPIRE DISTRICT
ELECTRIC CO-
ASBURY 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
BASF AGRI
CHEMICALS-
HANNIBAL PLANT
BASF AGRI
CHEMICALS-
HANNIBAL PLANT
BASF AGRI
CHEMICALS-
HANNIBAL PLANT
NEI Site ID
NEI 7486
NEI 7523
NEI 7523
NEI 7523
NEI 7483
NEI 34412
NEI 34412
NEI 34412
NEI 12618
NEI 12618
NEI 12618
NEI 34442
NEI 34442
NEI 34442
UTMX
(m)1
397,665
397,704
385,328
385,376
385,361
357,877
729,589
729,543
729,537
739,910
739,893
739,931
634,112
634,201
634,153
UTMY
(m)1
4,337,228
4,337,218
4,327,827
4,327,816
4,327,857
4,126,497
4,238,084
4,237,936
4,237,973
4,223,934
4,223,827
4,223,869
4,410,128
4,410,431
4,410,140
S02
Emissions
(tpy)
467
1,360
1,354
1,862
4,349
2
2
15,219
2
10,511
12,744
832
918
34
Stack
Height
(m)
213
76
47
47
123
3
9
168
76
213
213
33
33
38
Exit
Temp.
(K)
423
436
433
431
417
295
287
350
577
405
405
422
422
344
Stack
Diam.
(m)
4.1
2.0
1.7
1.7
4.0
0.0
0.3
6.1
1.5
8.8
8.8
2.7
2.7
1.0
Exit
Velocity
(mis)
32
29
19
19
23
0
6
18
9
25
25
0
0
12
Profile
Method2
TieM
Tierl
Tier 2
Tier 2
Tierl
Tier 2
TierS
Tier 2
Tierl
Tierl
Tierl
Tier 2
Tier 2
Tier 2
D-6
-------
Stack
ID
5156
5159
5160
5181
5182
5183
5189
5190
5191
5192
5193
5194
5196
5197
City
PALMYRA
MARSTON
MARSTON
NEW
MADRID
NEW
MADRID
NEW
MADRID
NEW
MADRID
NEW
MADRID
NEW
MADRID
NEW
MADRID
NEW
MADRID
NEW
MADRID
CHAMOIS
CHAMOIS
Facility Name
BASF AGRI
CHEMICALS-
HANNIBAL PLANT
ASSOCIATED
ELECTRIC
COOPERATIVE INC-
NEW MADRID POWER
PLANT
ASSOCIATED
ELECTRIC
COOPERATIVE INC-
NEW MADRID POWER
PLANT
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
NORANDA ALUMINUM
INC-NORANDA
ALUMINUM INC
CENTRAL ELECTRIC
POWER
COOPERATIVE-
CHAMOIS PLANT
CENTRAL ELECTRIC
POWER
NEI Site ID
NEI 34442
NEI 7526
NEI 7526
NEI 34464
NEI 34464
NEI 34464
NEI 34464
NEI 34464
NEI 34464
NEI 34464
NEI 34464
NEI 34464
NEI 7527
NEI 7527
UTMX
(m)1
634,213
807,900
807,913
807,392
807,843
807,696
807,358
807,674
807,579
807,979
807,382
807,518
608,177
UTMY
(m)1
4,410,449
4,046,536
4,046,552
4,046,098
4,045,978
4,046,215
4,045,789
4,045,798
4,045,878
4,045,995
4,045,903
4,045,798
4,282,519
S02
Emissions
(tpy)
50
8,109
7,689
117
117
117
68
642
642
2,029
180
179
1,226
2,916
Stack
Height
(m)
23
244
244
23
15
17
22
38
38
90
22
22
50
45
Exit
Temp.
(K)
352
430
426
360
344
344
352
359
357
360
352
352
445
431
Stack
Diam.
(m)
1.1
6.1
6.1
2.3
1.7
1.7
1.3
4.4
4.4
7.9
1.3
1.3
2.4
2.1
Exit
Velocity
(mis)
5
24
21
8
13
13
12
12
12
14
12
12
19
11
Profile
Method2
Tier 2
TieM
TieM
Tier 2
TierS
TierS
Tier 2
Tier 2
TierS
Tier 2
Tier 2
Tier 2
TieM
Tier 2
D-7
-------
Stack
ID
5199
5203
5206
5211
5212
5213
5214
5241
5244
5245
5246
City
CLARKS-
VILLE
LOUISIANA
LOUISIANA
WESTON
WESTON
THOMAS
HILL
THOMAS
HILL
THOMAS
HILL
STE. GENE-
VIE VE
STE. GENE-
VIE VE
STE. GENE-
Facility Name
COOPERATIVE-
CHAMOIS PLANT
HOLCIM (US) INC-
CLARKSVILLE
AQUALONDIVOF
HERCULES INC-
MISSOURI CHEMICAL
WORKS
AQUALONDIVOF
HERCULES INC-
MISSOURI CHEMICAL
WORKS
KANSAS CITY POWER
& LIGHT CO-IATAN
GENERATING
STATION
KANSAS CITY POWER
& LIGHT CO-IATAN
GENERATING
STATION
ASSOCIATED
ELECTRIC
COOPERATIVE INC-
THOMAS HILL
ENERGY CENTER-
POWER DIVISION
ASSOCIATED
ELECTRIC
COOPERATIVE INC-
THOMAS HILL
ENERGY CENTER-
POWER DIVISION
ASSOCIATED
ELECTRIC
COOPERATIVE INC-
THOMAS HILL
ENERGY CENTER-
POWER DIVISION
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
COMPANY-
MISSISSIPPI LIME CO
MISSISSIPPI LIME
NEI Site ID
NEI 16369
NEI 34503
NEI 34503
NEI 12573
NEI 12573
NEI 34521
NEI 34521
NEI 34521
NEI
MO1 860001
NEI
M01 860001
NEI
UTMX
(m)1
608,204
676,989
670,124
669,445
329,597
329,574
531,200
531,165
530,982
757,358
757,384
UTMY
(m)1
4,282,496
4,360,616
4,365,823
4,365,767
4,368,256
4,368,270
4,378,118
4,378,157
4,378,218
4,207,065
4,207,015
S02
Emissions
(tpy)
7,408
2,019
2,301
20
14,836
3,287
3,753
8,181
62
89
103
Stack
Height
(m)
76
39
39
215
215
125
122
190
23
23
23
Exit
Temp.
(K)
447
445
445
416
416
451
456
441
519
469
469
Stack
Diam.
(m)
6.4
1.5
1.5
7.3
7.3
5.3
5.3
9.3
3.2
3.4
3.4
Exit
Velocity
(mis)
10
17
17
25
25
10
14
14
4
6
6
Profile
Method2
Tier 2
Tier 2
Tier 2
TieM
TieM
TieM
TieM
TieM
TierS
TierS
TierS
D-8
-------
Stack
ID
5247
5248
5261
5262
5263
5264
5265
5267
5270
5271
5276
5277
5278
5279
5287
City
VI EVE
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
MARSHALL
Facility Name
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
MARSHALL
MUNICIPAL UTILITIES-
MARSHALL
MUNICIPAL UTILITIES
NEI Site ID
MO1 860001
NEI
MO1 860001
NEI
M01 860001
NEI
MO1 860001
NEI
M01 860001
NEI
MO1 860001
NEI
M01 860001
NEI
M01 860001
NEI
MO1 860001
NEI
M01 860001
NEI
MO1 860001
NEI 7515
NEI 7515
NEI 7515
NEI 7515
NEI 7524
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
482,098
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
4,330,328
S02
Emissions
(tpy)
106
105
1,290
1,394
1,505
67
77
2
1
1,199
5,195
6,463
2,359
2,430
1,184
Stack
Height
(m)
23
23
35
35
35
35
35
20
20
35
107
107
76
76
50
Exit
Temp.
(K)
469
469
343
343
344
346
346
367
362
343
463
447
436
436
450
Stack
Diam.
(m)
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
1.5
Exit
Velocity
(mis)
6
6
11
11
13
9
9
15
11
11
33
31
27
27
18
Profile
Method2
TierS
TierS
TierS
TierS
TierS
TierS
TierS
Tier 2
TierS
TierS
Tierl
Tierl
Tierl
Tierl
Tier 2
D-9
-------
Stack
ID
5290
5292
5293
5295
5296
5297
5298
5299
5302
5304
City
MARSHALL
SIKESTON
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
ST. LOUIS
Facility Name
MARSHALL
MUNICIPAL UTILITIES-
MARSHALL
MUNICIPAL UTILITIES
SIKESTON POWER
STATION-SIKESTON
POWER STATION
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
ANHEUSER-BUSCH
INC-ST LOUIS
NEI Site ID
NEI 7524
NEI 12763
NEI 34732
NEI 34732
NEI 34732
NEI 34732
NEI 34732
NEI 34732
NEI 34732
NEI 34732
UTMX
(m)1
482,113
801,228
742,736
742,775
742,750
742,781
742,800
742,759
742,739
742,711
UTMY
(m)1
4,330,323
4,086,762
4,275,786
4,275,743
4,275,704
4,275,753
4,275,764
4,275,714
4,275,677
4,275,740
S02
Emissions
(tpy)
265
6,236
2
176
256
249
158
3,066
2,339
4
Stack
Height
(m)
34
137
30
69
69
69
69
69
69
22
Exit
Temp.
(K)
433
411
371
450
450
450
450
461
439
486
Stack
Diam.
(m)
1.4
4.6
1.2
3.0
3.0
3.0
3.0
3.0
3.0
1.2
Exit
Velocity
(mis)
6
2
3
6
6
6
6
6
6
9
Profile
Method2
Tier 2
TieM
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
nUTM Zone 15 values in all cases.
2Three 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
D-10
-------
United States Office of Air Quality Planning and Standards EPA-452/P-08-003
Environmental Protection Air Quality Strategies and Standards Division July 2008
Agency Research Triangle Park, NC
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