Risk and Exposure Assessment to Support the 

Review of the SO2 Primary National Ambient Air 

Quality Standards: Final Report 
 ii 

 EPA-452/R-09-007 

July 2009 

Risk and Exposure Assessment to Support the 

Review of the SO2 Primary National Ambient Air 

Quality Standards 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

 Research Triangle Park, North Carolina 
 iii 

Disclaimer 

This 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. For questions concerning this document, please contact Dr. Stephen 

Graham (919-541-4344; graham.stephen@epa.gov), Mr. Harvey Richmond (919-541-5271; 

richmond.harvey@epa.gov), or Dr. Michael Stewart (919-541-7524; stewart.michael@epa.gov)
July 2009 iv

Table of Contents 

LIST OF TABLES ..........................................................................................................IX

LIST OF FIGURES.......................................................................................................XIII

LIST OF ACRONYMS/ABBREVIATIONS................................................................... XX

1. INTRODUCTION......................................................................................................... 1

1.1 History ....................................................................................................................................................................5

1.1.1 History of the SO2 NAAQS .............................................................................................................................5

1.1.2 Health Evidence from the Previous Review ....................................................................................................6

1.1.3 Assessment from Previous Review..................................................................................................................8

1.2 Scope of the Risk and Exposure Assessment for the Current Review ..............................................................9

1.2.1 Overview of the Risk and Exposure Assessment.............................................................................................9

1.2.2 Species of Sulfur Oxides Included in Analyses .............................................................................................11

2. OVERVIEW OF HUMAN EXPOSURE...................................................................... 12

2.1 Background ..........................................................................................................................................................13

2.2 Sources of SO2 ......................................................................................................................................................13

2.3 Background on the SO2 Monitoring Network ...................................................................................................14

2.4 Ambient levels of SO2 ..........................................................................................................................................18

2.5 Relationship of Personal Exposure to Ambient Concentrations .....................................................................20

2.6 Key Observations.................................................................................................................................................22

3. AT RISK POPULATIONS ......................................................................................... 23

3.1 Overview...............................................................................................................................................................23

3.2 Pre-existing Respiratory Disease........................................................................................................................24

3.3 Genetics ................................................................................................................................................................24

3.4 Age ........................................................................................................................................................................25

3.5 Time Spent Outdoors ..........................................................................................................................................26

3.6 Ventillation Rate..................................................................................................................................................26

3.7 Socioeconomic Status ..........................................................................................................................................26
July 2009 v

3.8 Number of At Risk Individuals...........................................................................................................................26

3.9 Key Observations.................................................................................................................................................27

4. INTEGRATION OF HEALTH EVIDENCE................................................................. 28

4.1 Introduction .........................................................................................................................................................28

4.2 Respiratory Morbidity Following Short-term SO2 Exposure..........................................................................31

4.2.1 Overview........................................................................................................................................................31

4.2.2 Integration of Respiratory Morbidity Health Evidence..................................................................................31

4.2.3 Medication as an Effect Modifier ..................................................................................................................34

4.3 What Constitutes an Adverse Health Impact from SO2 Exposure?................................................................35

4.4 Key Observations.................................................................................................................................................38

5. SELECTION OF POTENTIAL ALTERNATIVE STANDARDS FOR ANALYSIS...... 39

5.1 Introduction .........................................................................................................................................................39

5.2 Indicator ...............................................................................................................................................................39

5.3 Averaging Time....................................................................................................................................................39

5.4 Form......................................................................................................................................................................42

5.5 Level......................................................................................................................................................................43

5.6 Key Observations.................................................................................................................................................52

6. OVERVIEW OF RISK CHARACTERIZATION AND EXPOSURE ASSESSMENT .. 53

6.1 Introduction .........................................................................................................................................................53

6.2 Potential Health Effect Benchmark Levels........................................................................................................54

6.3 Approaches for Assessing Exposure and Risk Associated with 5-Minute Peak SO2 Exposures...................56

6.4 Approach for Estimating 5-Minute Peak SO2 Concentrations........................................................................59

6.5 Approach for Simulating the Current and Alternative Air Quality Standard Scenarios .............................62

6.6 Approaches for characterizing Variability and Uncertainty ...........................................................................64

6.6.1 Characterization of Variability.......................................................................................................................65

6.6.2 Characterization of Uncertainty.....................................................................................................................65

6.7 Key Observations.................................................................................................................................................68

7. AMBIENT AIR QUALITY AND BENCHMARK HEALTH RISK 

CHARACTERIZATION FOR 5-MINUTE PEAK SO2 EXPOSURES.............................. 69
July 2009 vi

7.1 Overview...............................................................................................................................................................69

7.2 Approach..............................................................................................................................................................73

7.2.1 Screening of Air Quality Data .......................................................................................................................73

7.2.2 Site Characteristics of Ambient SO2 Monitors ..............................................................................................78

7.2.3 Statistical model to estimate 5-minute maximum SO2 concentrations...........................................................90

7.2.4 Adjustment of Ambient Concentrations to Evaluate the Current and Potential Alternative Air Quality 

Scenarios...............................................................................................................................................................104

7.2.5 Air Quality Concentration Metrics ..............................................................................................................114

7.3 Results.................................................................................................................................................................120

7.3.1 Measured 5-minute Maximum and Measured 1-Hour SO2 Concentrations at Ambient Monitors – As Is Air 

Quality ..................................................................................................................................................................120

7.3.2 Measured 1-Hour and Modeled 5-minute Maximum SO2 Concentrations at All Ambient Monitors – As Is

Air Quality............................................................................................................................................................127

7.3.3 Modeled 1-Hour and Modeled 5-minute Maximum SO2 Concentrations at Ambient Monitors in 40 Counties 

– Air Quality Adjusted to Just Meet the Current and Potential Alternative Standards .........................................133

7.4 Variability Analysis and Uncertainty Characterization.................................................................................151

7.4.1 Variability Analysis .....................................................................................................................................151

7.4.2 Uncertainty Characterization .......................................................................................................................151

7.5 Key Observations...............................................................................................................................................181

8. EXPOSURE ANALYSIS ......................................................................................... 184

8.1 Overview.............................................................................................................................................................184

8.2 Overview of Human Exposure Modeling using APEX...................................................................................185

8.3 Characterization of study areas........................................................................................................................188

8.3.1 Study Area Selection....................................................................................................................................188

8.3.2 Study Area Descriptions ..............................................................................................................................190

8.3.3 Time Period of Analysis ..............................................................................................................................193

8.3.4 Populations Analyzed ..................................................................................................................................193

8.4 Characterization of Ambient Hourly Air Quality Data Using AERMOD....................................................193

8.4.1 Overview......................................................................................................................................................193

8.4.2 General Model Inputs ..................................................................................................................................194

8.4.3 Stationary Sources Emissions Preparation...................................................................................................196

8.4.4 Receptor Locations ......................................................................................................................................206

8.4.5 Modeled Air Quality Evaluation..................................................................................................................207

8.5 Simulated Population ........................................................................................................................................221

8.5.1 Population Counts and Employment Probabilities.......................................................................................221

8.5.2 Asthma Prevalence.......................................................................................................................................222

8.5.3 Commuting Database...................................................................................................................................223

8.5.4 Body Surface Area.......................................................................................................................................224

8.5.5 Activity-Specific Ventilation Rates .............................................................................................................224

8.6 Construction of Longitudinal Activity Sequences...........................................................................................227

8.7 Calculating Microenvironmental Concentrations ..........................................................................................228

8.7.1 Approach for Estimating 5-Minute Maximum SO2 Concentrations ............................................................229

8.7.2 Microenvironments Modeled.......................................................................................................................231
July 2009 vii

8.7.3 Microenvironment Descriptions ..................................................................................................................231

8.8 Exposure Measures and Health Risk Characterization .................................................................................235

8.8.1 Estimation of Exposure................................................................................................................................235

8.8.2 Estimation of Target Ventilation Rates........................................................................................................236

8.8.3 Adjustment for Just Meeting the Current and Alternative Standards...........................................................237

8.9 Exposure Modeling and Health Risk Characterization Results....................................................................242

8.9.1 Asthmatic Exposures to 5-minute SO2 Concentrations in Greene County ..................................................242

8.9.2 Asthmatic Exposures to 5-minute SO2 in St. Louis .....................................................................................248

8.10 Representativeness of Exposure Results ........................................................................................................255

8.10.1 Introduction................................................................................................................................................255

8.10.2 Time spent outdoors...................................................................................................................................256

8.10.3 SO2 Emissions and Ambient Concentrations.............................................................................................258

8.10.4 American Housing Survey (AHS) Data.....................................................................................................265

8.10.5 Asthma Prevalence.....................................................................................................................................266

8.11 Variability Analysis and Uncertainty Characterization...............................................................................268

8.11.1 Variability Analysis ...................................................................................................................................268

8.11.2 Uncertainty Characterization .....................................................................................................................269

8.12 Key Observations.............................................................................................................................................311

9. HEALTH RISK ASSESSMENT FOR LUNG FUNCTION RESPONSES IN 

ASTHMATICS ASSOCIATED WITH 5-MINUTE PEAK EXPOSURES ...................... 314

9.1 Introduction .......................................................................................................................................................314

9.2 Development of Approach for 5-minute Lung Function Risk Assessment..................................................315

9.2.1 General Approach...................................................................................................................................316

9.2.2 Exposure Estimates ................................................................................................................................321

9.2.3 Exposure-Response Functions................................................................................................................322

9.3 Lung Function Risk Estimates .........................................................................................................................332

9.4 Characterizing Uncertainty and Variability ...................................................................................................345

9.5 Key Observations...............................................................................................................................................350

10. EVIDENCE- AND EXPOSURE/RISK-BASED CONSIDERATIONS RELATED TO 

THE PRIMARY SO2 NAAQS ...................................................................................... 353

10.1 Introduction .....................................................................................................................................................353

10.2 General Approach ...........................................................................................................................................354

10.3 Adequacy of the Current 24-hour Standard .................................................................................................356

10.3.1 Introduction................................................................................................................................................356

10.3.2 Evidence-based considerations ..................................................................................................................358

10.3.3 Air Quality, exposure and risk-based considerations.................................................................................359

10.4 Adequacy of the Current Annual Standard ..................................................................................................367

10.4.1 Introduction................................................................................................................................................367
July 2009 viii

10.4.2 Evidence-based considerations ..................................................................................................................368

10.4.3 Risk-based considerations..........................................................................................................................369

10.4.4 Conclusions regarding the adequacy of the current annual standard .........................................................370

10.5 Potential Alternative Standards .....................................................................................................................370

10.5.1 Indicator.....................................................................................................................................................370

10.5.2 Averaging Time .........................................................................................................................................371

10.5.3 Form...........................................................................................................................................................381

10.5.4 Level ..........................................................................................................................................................387

10.6 Key Observations.............................................................................................................................................397

REFERENCES............................................................................................................ 398

APPENDICES 

Appendix A- Supplement to the SO2 Air Quality Characterization 

Appendix B- Supplement to the SO2 Exposure Assessment 

Appendix C- Sulfur Dioxide Health Risk Assessment 

Appendix D- Supplement to the Policy Assessment 
July 2009 ix

LIST OF TABLES

Number Page

Table 2-1. SO2 network monitoring objective distribution........................................................... 17 

Table 2-2. SO2 network distribution across measurement scales. ................................................ 18 

Table 4-1. Weight of evidence for causal determination.............................................................. 29 

Table 7-1. Summary of all available 5-minute and 1-hour SO2 ambient monitoring data, years 

1997-2007, pre-screened.............................................................................................. 74 

Table 7-2. Analytical data sets generated using the continuous-5, max-5, and 1-hour ambient 

SO2 monitoring data, following screening................................................................... 75 

Table 7-3. Counts of complete and incomplete site-years of 1-hour SO2 ambient monitoring data 

for 1997-2006............................................................................................................... 79 

Table 7-4. Descriptive statistics of the population residing within a 5 km radius of ambient 

monitors by monitoring objective: monitors reporting 5-minute maximum SO2

concentrations and the broader SO2 monitoring network. ........................................... 89 

Table 7-5. Comparison of prediction errors and model variance parameters for the four models 

evaluated. ................................................................................................................... 103 

Table 7-6. Prediction errors of the statistical model used in estimating 5-minute maximum SO2

concentrations above benchmark levels..................................................................... 104 

Table 7-7. Counties selected for evaluation of air quality adjusted to just meeting the current and 

potential alternative SO2 standards and the number of monitors in each COV bin... 112 

Table 7-8. The co-occurrence of daily 5-minute maximum and 1-hour daily maximum SO2

concentrations using measured ambient monitoring data. ......................................... 115 

Table 7-9. Example of how the probability of exceeding a 400 ppb 5-minute benchmark would 

be calculated given 1-hour daily maximum SO2 concentration bins. ........................ 118 

Table 7-10. Percent of days having a modeled daily 5-minute maximum SO2 concentration 

above the potential health effect benchmark levels given air quality as is and air 

quality adjusted to just meeting the current and each of the potential alternative 

standards..................................................................................................................... 138 

Table 7-11. Modeled mean number of days per year with 5-minute maximum concentrations 

above 100 ppb in 40 selected counties given 2001-2006 air quality as is and air quality 

adjusted to just meet the current and alternative standards........................................ 147 

Table 7-12. Mean number of modeled days per year with 5-minute maximum concentrations 

above 200 ppb in 40 selected counties given 2001-2006 air quality as is and that 

adjusted to just meet the current and alternative standards........................................ 148 

Table 7-13. Mean number of modeled days per year with 5-minute maximum concentrations 

above 300 ppb in 40 selected counties given 2001-2006 air quality as is and that 

adjusted to just meet the current and alternative standards........................................ 149 

Table 7-14. Mean number of modeled days per year with 5-minute maximum concentrations 

above 400 ppb in 40 selected counties given 2001-2006 air quality as is and that 

adjusted to just meet the current and alternative standards........................................ 150 

Table 7-15. Summary of how variability was incorporated into the air quality characterization.

.................................................................................................................................... 151 

Table 7-16. Summary of qualitative uncertainty analysis for the air quality and health risk 

characterization. ......................................................................................................... 153 
July 2009 x

Table 7-17. Summary of descriptive statistics for the data removed using peak-to-mean ratio 

criterion and the final 1-hour and 5-minute maximum SO2 data set used to develop 

PMRs.......................................................................................................................... 169 

Table 7-18. The number and percent of days having multiple benchmark exceedances occurring 

in the same day, using monitors reporting the 5-minute maximum SO2 concentrations.

.................................................................................................................................... 181 

Table 8-1. Surface stations for the SO2 study areas................................................................... 195 

Table 8-2. Upper air stations for the SO2 study areas................................................................ 195 

Table 8-3. Seasonal monthly assignments................................................................................. 196 

Table 8-4. NLCD2001 land use characterization. ..................................................................... 199 

Table 8-5. Summary of NEI emission estimates and total emissions used for dispersion 

modeling in Greene County and St. Louis modeling domains. ................................. 205 

Table 8-6. Measured and modeled number of days in year 2002 with at least one 5-minute SO2

benchmark exceedance at ambient monitors in Greene County. ............................... 211 

Table 8-7. Asthma prevalence rates by age and gender used in Greene County and St. Louis 

modeling domains. ..................................................................................................... 223 

Table 8-8. Population modeled in Greene County and St. Louis modeling domains................ 223 

Table 8-9. Ventilation coefficient parameter estimates (bi) and residuals distributions (ei) from 

Graham and McCurdy (2005). ................................................................................... 227 

Table 8-10. List of microenvironments modeled and calculation methods used. ..................... 231 

Table 8-11. Geometric means (GM) and standard deviations (GSD) for air exchange rates by 

A/C type and temperature range. ............................................................................... 232 

Table 8-12. Final parameter estimates of SO2 deposition distributions in several indoor 

microenvironments modeled in APEX. ..................................................................... 233 

Table 8-13. Comparison of benchmark levels, adjusted benchmark levels to just meet the current 

standard, the benchmark level distribution percentiles, and the number of 5-minute 

SO2 benchmark exceedances at monitor 390350045 in Cuyahoga County for year 

2002............................................................................................................................ 240 

Table 8-14. Exposure concentrations and adjusted potential health effect benchmark levels used 

by APEX to simulate just meeting the current and potential alternative standards in the 

Greene County and St Louis modeling domains. ...................................................... 241 

Table 8-15. Absolute difference in APEX exposure estimates for Greene County using either a 

98th or 99th percentile form potential alternative standard at a 1-hour daily maximum 

level of 200 ppb.......................................................................................................... 244 

Table 8-16. Absolute difference in APEX exposure estimates for St. Louis using either a 98th or 

99th percentile form potential alternative standard at a 1-hour daily maximum level of 

200 ppb....................................................................................................................... 255 

Table 8-17. States used to define five regions of the U.S. and characterize CHAD data diaries.

.................................................................................................................................... 256 

Table 8-18. Time spent outdoors by geographic region for children ages 5-17 based on CHAD 

time-location-activity diaries. .................................................................................... 257 

Table 8-19. Ranking of selected exposure locations using the modeled number of days with 5-

minute benchmark exceedances and the total emissions within 20 km of ambient 

monitors. .................................................................................................................... 259 

Table 8-20. Total SO2 emissions and total port SO2 emissions in the St. Louis and the 40 

Counties used in the air quality characterization. ...................................................... 261 
July 2009 xi

Table 8-21. The top 40 counties with the greatest total port SO2 emissions, including SO2

emissions from ports in the St. Louis modeling domain............................................ 262 

Table 8-22. SO2 emission density the two exposure modeling domains and several counties 

within selected U.S. Cities. ........................................................................................ 263 

Table 8-23. Residential A/C prevalence for housing units in several metropolitan locations in the 

U.S. (AHS, 2008)....................................................................................................... 266 

Table 8-24. Asthma prevalence rates for children in four regions of the U.S. .......................... 267 

Table 8-25. Asthma prevalence rates for adults in five regions of the U.S. .............................. 267 

Table 8-26. Summary of how variability was incorporated into the exposure assessment. ...... 268 

Table 8-27. Summary of qualitative uncertainty analysis for the exposure assessment............ 270 

Table 8-28. Distribution of APEX estimated annual average SO2 exposures for simulated 

individuals in the Greene County and St. Louis modeling domains.......................... 282 

Table 8-29. Personal SO2 exposure measurement data from the extant literature..................... 283 

Table 8-30. Percent of waking hours spent outdoors at an elevated activity level.................... 292 

Table 8-31. Number of multiple exceedances of potential health effect benchmark levels within 

an hour........................................................................................................................ 303 

Table 9-1. Example calculation of the number of asthmatics in st. louis engaged in moderate or 

greater exertion estimated to experience at least one lung function response (defined as 

an increase in sRaw ≥ 100%) associated with exposure to SO2 concentrations just 

meeting a 99th percentile, 1-hour 100 ppb standard................................................... 320 

Table 9-2. Example calculation of number of occurrences of lung function response (defined as 

an increase in sRaw ≥ 100%), among asthmatics in St. Louis engaged in moderate or 

greater exertion associated with exposure to SO2 concentrations that just meet a 99th

percentile 1-hour, 100 ppb standard........................................................................... 321 

Table 9-3. Percentage of asthmatic individuals in controlled human exposure studies 

experiencing SO2-induced decrements in lung function............................................ 324 

Table 9-4. Number of asthmatics engaged in moderate or greater exertion estimated to 

experience at least one lung function response associated with exposure to SO2

concentrations under alternative air quality scenarios in a year. ............................... 334 

Table 9-5. Percent of asthmatics engaged in moderate or greater exertion estimated to 

experience at least one lung function response associated with exposure to SO2

concentrations under alternative air quality scenarios in a year. ............................... 335 

Table 9-6. Number of occurrences (in hundreds) of a lung function response among asthmatics 

engaged in moderate or greater exertion associated with exposure to SO2

concentrations under alternative air quality scenarios in a year. ............................... 336 

Table 9-7. number of asthmatic children engaged in moderate or greater exertion estimated to 

experience at least one lung function response associated with exposure to SO2

concentrations under alternative air quality scenarios in a year. ............................... 337 

Table 9-8. Percent of asthmatic children engaged in moderate or greater exertion estimated to 

experience at least one lung function response associated with exposure to SO2

concentrations under alternative air quality scenarios in a year. ............................... 338 

Table 9-9. number of occurrences (in hundreds) of a lung function response among asthmatic 

children engaged in moderate or greater exertion associated with exposure to SO2

concentrations under alternative air quality scenarios in a year. ............................... 339 

Table 9-10. Explanation of labels on the x-axis of Figures 9-7 and 9-8.................................... 341 

Table 9-11. Characterization of key uncertainties in the lung function response health risk 

assessment for St. Louis and Greene County, Missouri............................................. 347 
July 2009 xii

Table 10-1 Ratios of 99th percentile 5-minute daily maximums to 99th percentile 24-hour average 

and 1-hour daily maximum SO2 concentrations for monitors reporting measured 5-

minute data from years 2004-2006 ............................................................................ 374 

Table 10-2. 99th percentile 24-hour average SO2 concentrations for 2004 given just meeting the 

alternative 1-hour daily maximum 99th and 98th percentile standards analyzed in the air 

quality assessment (note: concentrations in ppb)....................................................... 376 

Table 10-3. 2nd highest 24-hour average SO2 concentrations (i.e., the current 24-hour standard) 

for 2004 given just meeting the alternative 1-hour daily maximum 99th and 98th

percentile standards analyzed in the air quality assessment (note: concentrations in 

ppb). ........................................................................................................................... 378 

Table 10-4. Annual average SO2 concentrations for 2004 given just meeting the alternative 99th

and 98th percentile 1-hour daily maximum standards analyzed in the air quality 

assessment (note: concentrations in ppb)................................................................... 379 

Table 10-5. SO2 concentrations (ppb) corresponding to the 2nd-9th daily maximum and 98th/99th

percentile forms for alternative 1-hour daily maximum standards (2004-2006). ...... 383 

Table 10-6. Percent of counties that may be above the level of alternative standards (based on 

years 2004-2006)........................................................................................................ 388 
July 2009 xiii

LIST OF FIGURES 

Number Page

Figure 1-1. Overview of the analyses described in this document and their interconnections...... 4 

Figure 5-1. Effect estimates for U.S. all respiratory ED visit studies and associated 98th and 99th

percentile 1-hour daily maximum SO2 levels. ............................................................. 45 

Figure 5-2. 24-hour effect estimates for U.S. asthma ED visit studies and associated 98th and 

99th percentile 1-hour daily maximum SO2 levels. ...................................................... 46 

Figure 5-3. 1-hour effect estimates for U.S. asthma ED visit studies and associated 98th and 99th

percentile 1-hour daily maximum SO2 levels. ............................................................. 47 

Figure 5-4. 24-hour effect estimates for U.S. hospitalization studies and associated 98th and 99th

percentile 1-hour daily maximum SO2 levels............................................................... 48 

Figure 5-5. Effect estimates for Canadian ED visits and hospitalization studies and associated 

98th and 99th percentile 1-hour daily maximum SO2 levels. ........................................ 49 

Figure 6-1. Overview of analyses addressing exposures and risks associated with 5-minute peak 

SO2 exposures. All three outputs are calculated considering current air quality, air 

quality just meeting the current standards, and air quality just meeting potential 

alternative standards. Note: this schematic was modified from Figure 1-1................ 54 

Figure 6-2. Example of an hourly time-series of measured 1-hour and measured 5-minute 

maximum SO2 concentrations...................................................................................... 60 

Figure 7-1. Location of the 98 monitors that reported 5-minute maximum SO2 concentrations 

and comprising the first data analysis group................................................................ 77 

Figure 7-2. Location of the 809 monitors comprising the broader SO2 ambient monitoring 

network (i.e., the second data analysis group). ............................................................ 80 

Figure 7-3. Distribution of site-years of data considering monitoring objectives and scale: 

monitors that reported 5-minute maximum SO2 concentrations (top) and the broader 

SO2 monitoring network (bottom). .............................................................................. 84 

Figure 7-4. Distribution of site-years of data considering land-use and setting: monitors that 

reported 5-minute maximum SO2 concentrations (top) and the broader SO2 monitoring 

network (bottom).......................................................................................................... 85 

Figure 7-5. The percent of total SO2 emissions of sources located within 20 km of ambient 

monitors: monitors reporting 5-minute maximum SO2 concentrations (top) and the 

broader SO2 monitoring network (bottom). ................................................................. 87 

Figure 7-6. Distribution of the population residing within a 5 km radius of ambient monitors: 

monitors reporting 5-minute maximum SO2 concentrations and the broader SO2

monitoring network...................................................................................................... 89 

Figure 7-7. Comparison of hourly and 5-minute concentration COVs and GSDs at sixteen 

monitors reporting all twelve 5-minute SO2 concentrations over multiple years of 

monitoring.................................................................................................................... 92 

Figure 7-8. Cumulative density functions (CDFs) of hourly COVs (top) and GSDs (bottom) at 

ambient monitors: monitors reporting 5-minute maximum SO2 concentrations and the 

broader SO2 monitoring network. ................................................................................ 94 

Figure 7-9. Peak-to-mean ratio (PMR) distributions for three COV and GSD variability bins and 

seven 1-hour SO2 concentration stratifications............................................................ 97 
July 2009 xiv

Figure 7-10. Distribution of total SO2 emissions (tpy) within 20 km of monitors by COV (left) 

and GSD (right) concentration variability bins: monitors reporting 5-minute maximum 

SO2 concentrations (top) and the broader SO2 monitoring network (bottom)............. 98 

Figure 7-11. Percent of monitors within each concentration variability bin where the monitoring 

objective was source-oriented: monitors reporting 5-minute maximum SO2

concentrations (solid) and the broader SO2 monitoring network (slotted). ................. 99 

Figure 7-12. Distribution of the measured number of daily 5-minute maximum SO2

concentrations above 200 ppb (left) and 400 ppb (right) in a year by hourly 

concentration COV (top) and GSD (bottom) variability bins. Data were from the 98 

ambient monitors reporting 5-minute maximum concentrations (471 site-years)..... 100 

Figure 7-13. Comparison of measured daily maximum SO2 concentration percentiles in Beaver 

County, PA for a high concentration year (1992) versus a low concentration year 

(2007) at two ambient monitors (from Rizzo, 2009). ................................................ 106 

Figure 7-14. Distributions of hourly SO2 concentrations at five ambient monitors in Cuyahoga 

County, as is (top) and air quality adjusted to just meet the current 24-hour SO2

standard (bottom), Year 2001. ................................................................................... 109 

Figure 7-15. Locations of the 128 ambient monitors comprising the 40 County data set (i.e., the 

third data analysis group)........................................................................................... 113 

Figure 7-16. Percent of monitors in each COV bin for the three data analysis groups: monitors 

reporting 5-minute maximum SO2 concentrations, the broader SO2 monitoring 

network, and SO2 monitors selected for detailed analysis in 40 counties.................. 113 

Figure 7-17. Example of empirically-based probability curves. The probability of a 5-minute 

SO2 benchmark exceedance (P) was estimated by dividing the number of days with an 

exceedance by the total number of days within each 1-hour daily maximum SO2

concentration bin........................................................................................................ 116 

Figure 7-18. Example of logistic-modeled probability curves. The data used to generate these 

modeled curves were the same used in generating the empirically-based curves in 

Figure 7-17................................................................................................................. 119

Figure 7-19. The number of days per year with measured 5-minute maximum SO2

concentrations above potential health effect benchmark levels at 98 monitors given the 

annual average SO2 concentration, 1997-2007 air quality as is. The level of the annual 

average SO2 NAAQS of 30 ppb is indicated by the dashed line. .............................. 121 

Figure 7-20. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 24-hour average SO2 concentration, using empirical 

data (left) and a fitted log-probit model (right), 1997-2007 air quality as is. Both the 

5-minute maximum and 24-hour SO2 concentrations were from measurements 

collected at 98 ambient monitors and separated by population density..................... 124 

Figure 7-21. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 1-hour daily maximum SO2 concentration, using 

empirical data (left) and a fitted log-probit model (right), 1997-2007 air quality as is. 

Both the 5-minute maximum and 1-hour SO2 concentrations were from measurements 

collected at 98 ambient monitors and separated by population density..................... 126 

Figure 7-22. The number of days per year with modeled daily 5-minute maximum SO2

concentrations above potential health effect benchmark levels at 809 ambient monitors 

given the annual average SO2 concentration, 1997-2006 air quality as is. The level of 

the annual average SO2 NAAQS of 30 ppb is indicated by the dashed line.............. 129 
July 2009 xv

Figure 7-23. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 24-hour average SO2 concentrations, using empirical 

data (left) and a fitted log-probit model (right), 1997-2006 air quality as is. The 5-

minute maximum SO2 concentrations were modeled from 1-hour measurements 

collected at 809 ambient monitors and then separated by population density........... 131 

Figure 7-24. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 1-hour daily maximum SO2 concentrations, using 

empirical data (left) and a fitted log-probit model (right), 1997-2006 air quality as is. 

The 5-minute maximum SO2 concentrations were modeled from 1-hour measurements 

collected at 809 ambient monitors and then separated by population density........... 132 

Figure 7-25. The number of days per year with modeled 5-minute maximum SO2 concentrations 

above potential health effect benchmark levels per year at 128 ambient monitors in 40 

selected counties given the annual average SO2 concentration, 2001-2006 air quality 

adjusted to just meet the current NAAQS. The level of the annual average SO2

NAAQS of 30 ppb is indicated by the dashed line. ................................................... 135 

Figure 7-26. The number of modeled daily 5-minute maximum SO2 concentrations above 200 

ppb per year at 128 ambient monitors in 40 selected counties given the annual average 

SO2 concentration, 2001-2006 air quality as is and that adjusted to just the current and 

four potential alternative standards (text in graph indicate standard evaluated). The 

level of the annual average SO2 NAAQS of 30 ppb is indicated by the dashed line. 137 

Figure 7-27. The number of days per year with modeled 5-minute maximum SO2 concentrations 

above benchmark levels given the 99th and 98th percentile forms, using the 40-county 

air quality data set adjusted to just meet a 1-hour daily maximum level of 200 ppb. 139 

Figure 7-28. The number of days per year with modeled 5-minute maximum SO2 concentrations 

above benchmark levels given the 99th and 98th percentile forms, using the 40-county 

air quality data set adjusted to just meet a 1-hour daily maximum level of 100 ppb. 140 

Figure 7-29. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 1-hour daily maximum SO2 concentrations, 2001-2006 

air quality as is and that adjusted to just meet the current NAAQS. The 5-minute 

maximum concentrations were modeled from 1-hour measurements collected at 128 

ambient monitors from 40 selected counties and then separated by population density 

within 5 km of monitors............................................................................................. 143 

Figure 7-30. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 1-hour daily maximum SO2 concentrations, 2001-2006 

air quality adjusted to just meet the current and each of the potential alternative 

standards (99th percentile form). The 5-minute maximum concentrations were 

modeled from 1-hour measurements collected at 128 ambient monitors from 40 

selected counties, high-population density monitors. ................................................ 144 

Figure 7-31. Temporal trends in the number of ambient monitors in operation per year for 

monitors reporting both 5-minute and 1-hour SO2 concentrations. ........................... 161 

Figure 7-32. Temporal trends in the coefficient of variability (COV) for 5-minute maximum and 

1-hour concentrations at the monitors that reported both 5-minute and 1-hour SO2

concentrations. The number of monitors operating in each year is depicted in Figure 

7-31. ........................................................................................................................... 162 

Figure 7-33. Comparison of measured daily maximum SO2 concentration percentiles in 

Allegheny County PA for one high concentration year (1998) versus a low 

concentration years (2007) at five ambient monitors................................................. 167 
July 2009 xvi

Figure 7-34. Distributions of annual average peak-to-mean ratios (PMRs) derived from the 98 

monitors reporting both 5-minute maximum and 1-hour SO2 concentrations, Years 

1997 through 2007. .................................................................................................... 170 

Figure 7-35. Example histogram of peak-to-mean ratios (PMRs) compared with four fitted 

distributions derived from monitors reporting the 5-minute maximum and 1-hour SO2

concentrations (left) and the same PMRs compared with expected lognormal 

percentiles (right). PMRs were derived from monitors with medium level variability 

(COVbin = b) and 1-hour concentrations between 75 and 150 ppb (COVconcbin = 4).

.................................................................................................................................... 171 

Figure 7-36. Example of a measured peak-to-mean ratio (PMRs) distribution with the 

percentiles of a fitted lognormal distribution. PMRs were derived from monitors with 

high COV (COVbin = c) and 1-hour concentrations between 5 and 10 ppb 

(COVconcbin = 2)...................................................................................................... 172 

Figure 7-37. Comparison of observed and predicted number of daily benchmark exceedances in 

a year at the 98 monitors reporting 5-minute maximum SO2 concentrations............ 175 

Figure 7-38. Distributions of the maximum difference in the estimated mean number of 

exceedances per site-year given 10 independent model runs (with 20 simulations per 

run). Data used are from 40 county as is air quality (610 site-years). Box represents 

the inner quartile range (IQR, or the 25th to 75th percentile), + indicates the mean, 

whiskers are 1.5 times the IQR. ................................................................................. 177 

Figure 8-1. General process flow used for SO2 exposure assessment. ...................................... 186 

Figure 8-2. Modeling domain for Greene County Mo., along with identified emissions sources, 

air quality receptors, ambient monitors, and meteorological station. ........................ 191 

Figure 8-3. Three county modeling domain for St. Louis, Mo., along with identified emissions 

sources, air quality receptors, ambient monitors, and meteorological station. .......... 192 

Figure 8-4. Derived best-fit non-point area source diurnal emission profile for the St. Louis 

domain, compared to other possible profiles. ............................................................ 205 

Figure 8-5. Derived best-fit non-point area source diurnal emission profile for the Greene 

County domain, compared to other possible profiles................................................. 206 

Figure 8-6. Comparison of measured ambient monitor SO2 concentration distribution and 

diurnal profile with the modeled monitor receptor and receptors within 4 km of 

monitors 290770026 and 29077032 in Greene County, Mo. Maximum 1-hour 

concentration.............................................................................................................. 212 

Figure 8-7. Comparison of measured ambient monitor SO2 concentration distribution and 

diurnal profile with the modeled monitor receptor and receptors within 4 km of 

monitors 290770040 and 29077041 in Greene County, Mo. Maximum 1-hour 

concentration percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.

.................................................................................................................................... 213 

Figure 8-8. Comparison of measured ambient monitor SO2 concentration distribution and 

diurnal profile with the modeled monitor receptor and receptors within 4 km of 

monitor 290770037 in Greene County, Mo. Maximum 1-hour concentration 

percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.................. 214 

Figure 8-9. Comparison of measured ambient monitor SO2 concentration distribution and 

diurnal profile with the modeled monitor receptor and receptors within 4 km of 

monitors 291890004 and 291890006 in St Louis, Mo. Maximum 1-hour 

concentration percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.

.................................................................................................................................... 217 
July 2009 xvii

Figure 8-10. Comparison of measured ambient monitor SO2 concentration distribution and 

diurnal profile with the modeled monitor receptor and receptors within 4 km of 

monitors 291893001 and 291895001 in St Louis, Mo. Maximum 1-hour 

concentration percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.

.................................................................................................................................... 218 

Figure 8-11. Comparison of measured ambient monitor SO2 concentration distribution and 

diurnal profile with the modeled monitor receptor an d receptors within 4 km of 

monitors 291897003 and 295100007 in St Louis, Mo. Maximum 1-hour 

concentration percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.

.................................................................................................................................... 219 

Figure 8-12. Comparison of measured ambient monitor SO2 concentration distribution and 

diurnal profile with the modeled monitor receptor and receptors within 4 km of 

monitor 295100086 in St Louis, Mo. Maximum 1-hour concentration percentile is 

defined as 0.01 (or 100-99.99) because log(0) is undefined...................................... 220 

Figure 8-13. Comparison of adjusted ambient monitoring concentrations or adjusted benchmark 

level (dashed line) to simulate just meeting the current annual average standard at one 

ambient monitor in Cuyahoga County for year 2002................................................. 239 

Figure 8-14. Comparison of the upper percentile modeled daily 5-minute maximum SO2

concentrations using either adjusted 1-hour ambient SO2 concentrations or an adjusted 

benchmark level (with as is air quality) to simulate just meeting the current annual 

standard at monitor 390350045 in Cuyahoga County for year 2002. Complete 

distributions are provided in Figure 8-13................................................................... 239 

Figure 8-15. Number of all asthmatics (top) and asthmatic children (bottom) experiencing at 

least one day with a 5-minute SO2 exposure above selected benchmark levels in 

Greene County, year 2002 air quality as is and adjusted to just meeting the current and 

potential alternative standards.................................................................................... 245 

Figure 8-16. Percent of all asthmatics (top) and asthmatic children (bottom) experiencing at least 

one day with a 5-minute SO2 exposure above selected benchmark levels in Greene 

County, year 2002 air quality as is and adjusted to just meeting the current and 

potential alternative standards.................................................................................... 246 

Figure 8-17. Number person days all asthmatics (top) and asthmatic children (bottom) 

experience a 5-minute SO2 exposure above selected benchmark levels in Greene 

County, year 2002 air quality as is and adjusted to just meeting the current and 

potential alternative standards.................................................................................... 247 

Figure 8-18. Number of all asthmatics (top) and asthmatic children (bottom) experiencing at 

least one day with a 5-minute SO2 exposure above selected benchmark levels in St. 

Louis, year 2002 air quality as is and adjusted to just meeting the current and potential 

alternative standards................................................................................................... 251 

Figure 8-19. Percent of all asthmatics (top) and asthmatic children (bottom) experiencing at least 

one day with a 5-minute SO2 exposure above selected benchmark levels in St. Louis, 

year 2002 air quality as is and adjusted to just meeting the current and potential 

alternative standards................................................................................................... 252 

Figure 8-20. Number person days all asthmatics (top) and asthmatic children (bottom) 

experience a 5-minute SO2 exposure above selected benchmark levels in St. Louis, 

year 2002 air quality as is and adjusted to just meeting the current and potential 

alternative standards................................................................................................... 253 
July 2009 xviii

Figure 8-21. The frequency of estimated exposure level exceedances in indoor, outdoor, and 

vehicle microenvironments given as is air quality (top), air quality adjusted to just 

meeting the current standard (middle) and that adjusted to just meeting a 99th

percentile 1-hour daily maximum standard level of 150 ppb (bottom) in St. Louis.. 254 

Figure 8-22. Means of weekly average personal O3 exposures, measured and modeled (APEX), 

Upland Ca. Figure obtained from EPA (2007d). ...................................................... 284 

Figure 8-23. Daily average personal NO2 exposures, measured and modeled (APEX), Atlanta 

Ga. Figure obtained from EPA (2008d).................................................................... 285 

Figure 8-24. Example comparison of estimated geometric mean and geometric standard 

deviations of AER (h-1) for homes with air conditioning in several cities................. 298 

Figure 8-25. Example of boot strap simulation results used in evaluating random sampling 

variation of AER (h-1) distributions (data from cities outside California). Parameters 

of the original distribution are given by the intersection of the two inner grid lines. 299 

 Figure 8-26. Example of boot strap simulation results used in evaluating random sampling 

variation of AER (h-1) distributions (data from cities outside California). Parameters 

of the original distribution are given by the intersection of the two inner grid lines. 300 

Figure 8-27. Duration of time spent outdoors (in minutes) using all CHAD events................. 306 

Figure 8-28. Percent of asthmatic children above given exposure level for two APEX 

simulations: one using multiple peak concentrations in an hour, the other assuming a 

single peak concentration. Continuous 5-minute monitoring data (ID 42007005, year 

2002) were used as the air quality input..................................................................... 309 

Figure 8-29. Percent of asthmatic children above given exposure level for two APEX 

simulations: one using multiple peak concentrations in an hour, the other assuming a 

single peak concentration. Continuous 5-minute monitoring data (ID 42007005, year 

2005) were used as the air quality input..................................................................... 309 

Figure 8-30. Frequency of exposure exceedances indoors for two APEX simulations: one using 

multiple peak concentrations in an hour, the other assuming a single peak 

concentration. Continuous 5-minute monitoring data (ID 42007005, year 2002) were 

used as the air quality input........................................................................................ 310 

Figure 9-1. Major components of 5-minute peak lung function health risk assessment based on 

controlled human exposure studies. ........................................................................... 318 

Figure 9-2. Bayesian-estimated median exposure-response functions: increase in sRaw ≥ 100% 

for 5-Minute exposures of asthmatics under moderate or greater exertion. .............. 330 

Figure 9-3. Bayesian-estimated median exposure-response functions: increase in sRaw ≥ 200% 

for 5-minute exposures of asthmatics under moderate or greater exertion................ 330 

Figure 9-4. Bayesian-estimated median exposure-response functions: decrease in FEV1 ....... 331 

Figure 9-5. Bayesian-estimated median exposure-response functions: decrease in FEV1 ≥ 20% 

for 5-minute exposures of asthmatics under moderate or greater exertion................ 331 

Figure 9-6. Legend for Figures 9-7 and 9-8 showing total and contribution of risk attributable to 

SO2 exposure ranges. ................................................................................................. 341 

Figure 9-7. Estimated percent of asthmatics experiencing one or more lung function responses 

(defined as ≥ 100% increase in sRaw) per year associated with short-term (5-minute) 

exposures to SO2 concentrations associated with alternative air quality scenarios – 

total and contribution of 5-minute SO2 exposure ranges (see Figure 9-6 for legend and 

Table 9-10 for description of air quality scenarios included on x-axis). ................... 342 

Figure 9-8. Estimated percent of asthmatic children experiencing one or more lung function 

responses (defined as ≥ 100% increase in sRaw) per year associated with short-term 
July 2009 xix

(5-minute) exposures to SO2 concentrations associated with alternative air quality 

scenarios – total and contribution of 5-minute SO2 exposure ranges (see Figure 9-6 for 

legend and Table 9-10 for description of air quality scenarios included on x-axis).. 343 

Figure 10-1. Design value trends from 4 of the 54 sites analyzed in Thompson 2009. ............ 385 

Figure 10-2. Boxplots of the distributions of standard deviations for alternative air quality 

standard forms............................................................................................................ 386 
July 2009 xx

LIST OF ACRONYMS/ABBREVIATIONS 

A/C Air conditioning 

AER Air exchange rate 

AERMOD American Meteorological Society (AMS)/EPA Regulatory Model 

AHS American Housing Survey 

APEX EPA’s Air Pollutants Exposure model, version 4 

ANOVA One-way analysis of variance 

AQI Air Quality Index 

ATL Atlanta Hartsfield airport 

AQS EPA’s Air Quality System 

AQCD Air Quality Criteria Document 

BRFSS Behavioral Risk Factor Surveillance System 

CAA Clean Air Act 

CAMD EPA’s Clean Air Markets Division 

CASAC Clean Air Scientific Advisory Committee 

CDC Centers for Disease Control 

CDF Cumulative density function 

CFR Code of Federal Regulations 

CHAD EPA’s Consolidated Human Activity Database 

Clev/Cinn Cleveland and Cincinnati, Ohio 

CMSA Consolidated metropolitan statistical area 

CO Carbon monoxide 

COPD Chronic Obstructive Pulmonary Disease 

COV Coefficient of Variation 

C-R Concentration-Response 

CTPP Census Transportation Planning Package 

ED Emergency Department 

EPA Environmental Protection Agency 

EMS-HAP Emissions Modeling System for Hazardous Pollutants model 

ER Emergency room 

EOC Exposure of Concern 

FEM Federal Equivalent Method 

FEV1 Forced expiratory volume in the first second 

GM Geometric mean 

GSD Geometric standard deviation 

GST Glutathione S-transferase 

ISCST Industrial Source Complex - Short Term dispersion model 

ID Identification 

ISA Integrated Science Assessment 

ISH Integrated Surface Hourly Database 

JST Jefferson Street SEARCH monitor near Georgia Tech 

km Kilometer 

L95 Lower limit of the 95th confidence interval 
July 2009 xxi

LOEL Lowest Observed Effect Level 

m Meter 

max Maximum 

ME Microenvironment 

med Median 

min Minimum 

MSA Metropolitan statistical area 

NAAQS National Ambient Air Quality Standards 

NAICS North American Industrial Classification System 

NAMS National Ambient Monitoring Stations 

NCEA National Center for Environmental Assessment 

NEI National Emissions Inventory 

NEM NAAQS Exposure Model 

NCDC National Climatic Data Center 

NHAPS National Human Activity Pattern Study 

NHIS National Health Interview Survey 

NO2 Nitrogen dioxide 

NOx Oxides of nitrogen 

NWS National Weather Service 

NYC New York City 

NYDOH New York Department of Health 

O3 Ozone 

OAQPS Office of Air Quality Planning and Standards 

OR Odds ratio 

ORD Office of Research and Development 

ORIS Office of Regulatory Information Systems identification code 

PAMS Photochemical Assessment Monitoring Stations 

POC Parameter occurrence code 

ppb Parts per billion 

PEM Personal exposure measurements 

PEN Penetration factor 

PM Particulate matter 

PMR Peak-to-mean ratio 

ppm Parts per million 

PRB Policy-Relevant Background 

PROX Proximity factor 

R2

 R-square or the coefficient of determination 

REA Risk and Exposure Assessment 

RECS Residential Energy Consumption Survey 

RIU Rescue inhaler use 

RR Relative risk 

SAF Spatial allocation factors 

SAS Statistical Analysis Software 

SB Shortness of breath 

SEARCH Southeast Aerosol Research and Characterization study (SEARCH) monitoring 

SES Social-economic status 

SIC Standard Industrial Code 
July 2009 xxii

SD Standard deviation 

Se Standard error 

SLAMS State and Local Ambient Monitoring Stations 

SO2 Sulfur dioxide 

SO3 Sulfur trioxide 

SO4

- Sulfate 

SOx Oxides of Sulfur 

sRaw Specific Airway Resistance 

tpy Tons per year 

TRIM EPA’s Total Risk Integrated Methodology 

U95 Upper limit of the 95th confidence interval 

UA Urbanized area 

UC Urban cluster 

UARG Utility Air Regulatory Group 

USGS United States Geological Survey 

Vs Ventilation rate 
July 2009 1

1. INTRODUCTION 

 The U.S. Environmental Protection Agency (EPA) is presently conducting a review of 

the primary, health-based national ambient air quality standards (NAAQS) for sulfur dioxide 

(SO2). Sections 108 and 109 of the Clean Air Act (The Act) govern the establishment and 

periodic review of the NAAQS. These standards are established for pollutants that may 

reasonably be anticipated to endanger public health and welfare, and whose presence in the 

ambient air results from numerous or diverse mobile or stationary sources. The NAAQS are to 

be based on air quality criteria, which are to accurately reflect the latest scientific knowledge 

useful in indicating the kind and extent of identifiable effects on public health or welfare that 

may be expected from the presence of the pollutant in ambient air. The EPA Administrator is to 

promulgate and periodically review, at five-year intervals, primary (health-based) and secondary 

(welfare-based) NAAQS for such pollutants. Based on periodic reviews of the air quality criteria 

and standards, the Administrator is to make revisions in the criteria and standards and 

promulgate any new standards as may be appropriate. The Act also requires that an independent 

scientific review committee advise the Administrator as part of this NAAQS review process, a 

function performed by the Clean Air Scientific Advisory Committee (CASAC). 

The first step in the SO2 NAAQS review was the development of an integrated review 

plan. This plan presented the schedule for the review, the process for conducting the review, and 

the key policy-relevant science issues that would guide the review. The final integrated review 

plan was informed by input from CASAC, outside scientists, and the public. This plan was 

presented in the Integrated Review Plan for the Primary National Ambient Air Quality Standards 

for Sulfur Oxides (EPA, 2007a). It was made available to the public in October 2007 and can be 

found at: http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_pd.html. 

The second step in this review was a science assessment. A concise synthesis of the most 

policy-relevant science was compiled into an Integrated Science Assessment (ISA). The ISA 

was supported by a series of annexes that contained more detailed information about the 

scientific literature. The final ISA to support this review of the SO2 primary NAAQS was 

presented in the Integrated Science Assessment for Oxides of Sulfur - Health Criteria, henceforth 

referred to as the ISA (EPA, 2008a). This document was made available to the public in 
July 2009 2

September 2008 and can be found at: 

http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_pd.html. 

 The third step in the primary SO2 NAAQS review is a risk and exposure assessment 

(REA) that describes exposures and characterizes risks associated with SO2 emissions from 

anthropogenic sources. The plan for conducting the risk and exposure assessment to support the 

SO2 primary NAAQS review was presented in the Sulfur Dioxide Health Assessment Plan: 

Scope and Methods for Exposure and Risk Assessment, henceforth referred to as the Health 

Assessment Plan (EPA, 2008b). This document was made available to the public in November 

2007 and can be found at: http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_pd.html. The 

first draft SO2 REA was informed by comments from the public and CASAC on the Health 

Assessment Plan, as well as the first and second drafts of the ISA for SOx. The first draft SO2 

REA developed estimates of human exposures and risks associated with recent ambient levels of 

SO2 and levels that just met the current SO2 standards. The first draft REA was presented in the 

Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air 

Quality Standards: First Draft. It was made available to the public in July 2008 and can be 

found at: http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_rea.html 

The second draft SO2 REA was informed by comments from CASAC and the public on 

the first draft REA, as well as findings and conclusions contained in the final ISA. This 

document developed estimates of human exposures and risks associated with: (1) recent ambient 

levels of SO2, (2) levels that just met the current SO2 standards, and (3) levels that just met 

potential alternative standards: defined in terms of indicator, averaging time, form, and level. 

This document also contained a draft policy assessment that addressed the adequacy of the 

current SO2 NAAQS and potential alternative standards. More specifically, the policy 

assessment considered epidemiologic, human exposure, and animal toxicological evidence 

presented in the ISA (EPA, 2008a), as well as the air quality, exposure, and risk characterization 

results presented in the first draft REA, as they related to the adequacy of the current SO2

NAAQS and potential alternative primary SO2 standards (see Figure 1-1). The second draft REA 

was presented in the Risk and Exposure Assessment to Support the Review of the SO2 Primary 

National Ambient Air Quality Standards: Second Draft. It was made available to the public in 

March 2009 and can be found at: http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_rea.html. 
July 2009 3

 The final REA is this document, and has been informed by comments from CASAC and 

the public on the second draft REA, as well as findings and conclusions contained in the final 

ISA. The final REA further develops estimates of human exposures and risks associated with: 

(1) recent ambient levels of SO2, (2) levels that just meet the current SO2 standards, and (3) 

levels that just meet potential alternative standards. This document also contains a final policy 

assessment (see Chapter 10). The final policy assessment will consider epidemiologic, 

controlled human exposure, and animal toxicological evidence presented in the final ISA (EPA, 

2008a), as well as the air quality, exposure, and risk characterization results presented in this 

document, as they related to the adequacy of the current SO2 NAAQS and potential alternative 

primary SO2 standards (Figure 1-1). 

The final step in the review of the SO2 NAAQS will be the rulemaking process. This 

process will be informed by the risk and exposure information contained in the final REA, as 

well the scientific evidence described in the final ISA. The rulemaking process will also take 

into account CASAC advice and recommendations, as well as public comment on any policy 

options under consideration. Notably, EPA is now under a consent decree to complete its review 

of the SO2 primary NAAQS by issuing a proposed rule no later than November 16, 2009 and a 

final rule by June 2, 2010. 
July 2009 4

Evaluate Health Evidence in ISA

Characterization of human 

clinical results

Characterization of 

epidemiology

5-10 minute exposures

Identify potential 

alternative standards

Identification of 

potential 5-minute 

health benchmark 

values

Air Quality 

analysis

Exposure 

analysis

≥ 1-hour 

concentrations

Quantitative risk analysis

Estimation of 

exposure-response 

function

Risk-based considerations to 

inform standard setting

Evidence based 

considerations to inform 

standard setting

Output 1: Number of days 

per year 5-minute daily 

maximum SO2

concentrations at 

ambient monitors 

exceed 5-minute potential 

health benchmark values

Output 2: The probability 

of a daily 5-minute 

exceedance of 

benchmark values given 

a particular 24-hour 

average, or 1-hour daily 

maximum SO2 

concentration.

Output: Estimates the 

number and percent of 

asthmatics at elevated 

ventilation rates exposed 

to 5-minute daily maximum 

SO2 concentrations that 

exceed 5-minute potential 

health benchmark values

Output: Number and 

percentage of exposed 

asthmatics that would 

experience moderate or greater 

lung function responses

Characterization 

of toxicology

exposures of 

minutes to hours

# of exposed

asthmatics

Air Quality 

characterization 

based on 

epidemiology

Evaluate Health Evidence in ISA

Characterization of human 

clinical results

Characterization of 

epidemiology

5-10 minute exposures

Identify potential 

alternative standards

Identification of 

potential 5-minute 

health benchmark 

values

Air Quality 

analysis

Exposure 

analysis

≥ 1-hour 

concentrations

Quantitative risk analysis

Estimation of 

exposure-response 

function

Risk-based considerations to 

inform standard setting

Evidence based 

considerations to inform 

standard setting

Output 1: Number of days 

per year 5-minute daily 

maximum SO2

concentrations at 

ambient monitors 

exceed 5-minute potential 

health benchmark values

Output 2: The probability 

of a daily 5-minute 

exceedance of 

benchmark values given 

a particular 24-hour 

average, or 1-hour daily 

maximum SO2 

concentration.

Output: Estimates the 

number and percent of 

asthmatics at elevated 

ventilation rates exposed 

to 5-minute daily maximum 

SO2 concentrations that 

exceed 5-minute potential 

health benchmark values

Output: Number and 

percentage of exposed 

asthmatics that would 

experience moderate or greater 

lung function responses

Characterization 

of toxicology

exposures of 

minutes to hours

# of exposed

asthmatics

Air Quality 

characterization 

based on 

epidemiology

Figure 1-1. Overview of the analyses described in this document and their interconnections 

As mentioned above, an initial step in the review process was the development of an 

integrated review plan. This plan identified policy relevant questions that would guide the 

review of the SO2 NAAQS. These questions are particularly important for the REA because they 

provide a context for both evaluating health effects evidence presented in the ISA, as well as for 

selecting the appropriate analyses for assessing exposure and risks associated with current 

ambient SO2 levels, SO2 levels that just meet the current standards, and SO2 levels that just meet 

potential alternative standards. These policy relevant questions are: 

 Has new information altered/substantiated the scientific support for the occurrence of 

health effects following short- and/or long-term exposure to levels of SOx found in the 

ambient air? 
July 2009 5

 Does new information impact conclusions from the previous review regarding the effects 

of SOx on susceptible populations? 

 At what levels of SOx exposure do health effects of concern occur? 

 Has new information altered conclusions from previous reviews regarding the plausibility 

of adverse health effects caused by SOx exposure? 

 To what extent have important uncertainties identified in the last review been reduced 

and/or have new uncertainties emerged? 

 What are the air quality relationships between short-term and longer-term exposures 

to SOx? 

Additional questions will become relevant if the evidence suggests that revision of the 

current standard might be appropriate. These questions are: 

 Is there evidence for the occurrence of adverse health effects at levels of SOx different 

than those observed previously? If so, at what levels and what are the important 

uncertainties associated with that evidence? 

 Do exposure estimates suggest that levels of concern for SOx-induced health effects will 

occur with current ambient levels of SO2, or with levels that just meet the current, or 

potential alternative standards? If so, are these exposures of sufficient magnitude such 

that the health effects might reasonably be judged to be important from a public health 

perspective? What are the important uncertainties associated with these exposure 

estimates? 

 Do the evidence, the air quality assessment, and the risk/exposure assessment provide 

support for considering different standard indicators, averaging times, or forms? 

 What range of levels is supported by the evidence, the air quality assessment, and 

risk/exposure assessment? What are the uncertainties and limitations in the evidence and 

assessments? 

1.1 HISTORY 

1.1.1 History of the SO2 NAAQS 

The first SO2 NAAQS was established in 1971. At that time, a 24-hour standard of 0.14 

ppm, not to be exceeded more than one time per year, and an annual standard of 0.03 ppm were 

judged to be both adequate and necessary to protect public health. The most recent review of the 

SO2 NAAQS was completed in 1996 and focused on the question of whether an additional shortterm standard (e.g., 5-minute) was necessary to protect against short-term, peak exposures. 

Based on the scientific evidence, the Administrator judged that repeated exposures to 5-minute 

peak SO2 levels (≥ 600 ppb) could pose a risk of significant health effects for asthmatic 
July 2009 6

individuals at elevated ventilation rates. The Administrator also concluded that the likely 

frequency of such effects should be a consideration in assessing the overall public health risks. 

Based upon an exposure analysis conducted by EPA, the Administrator concluded that exposure 

of asthmatics to SO2 levels that could reliably elicit adverse health effects was likely to be a rare 

event when viewed in the context of the entire population of asthmatics and therefore, did not 

pose a broad public health problem for which a NAAQS would be appropriate. On May 22, 

1996, EPA’s final decision not to promulgate a 5-minute standard and to retain the existing 24-

hour and annual standards was announced in the Federal Register (61 FR 25566). 

The American Lung Association and the Environmental Defense Fund challenged EPA’s 

decision not to establish a 5-minute standard. On January 30, 1998, the Court of Appeals for the 

District of Columbia found that EPA had failed to adequately explain its determination that no 

revision to the SO2 NAAQS was appropriate and remanded the decision back to EPA for further 

explanation. Specifically, the court gave EPA the opportunity to provide additional rationale to 

support the Agency judgment that 5-minute peaks of SO2 do not pose a public health problem 

from a national perspective even though those peaks would likely cause adverse health impacts 

in a subset of asthmatics. In response, EPA has collected and analyzed additional air quality data 

focused on 5-minute concentrations of SO2. These air quality analyses conducted since the last 

review will help inform the current review, which will answer the issues raised in the Court’s 

remand of the Agency’s last decision. 

1.1.2 Health Evidence from the Previous Review 

The 1982 Air Quality Criteria Document (AQCD) for Particulate Matter and Sulfur 

Oxides (EPA, 1982), and its subsequent addenda and supplement (EPA, 1986b, 1994a) presented 

an evaluation of SO2 associated health effects primarily drawn from epidemiologic and human 

clinical studies. In general, these documents identified adverse health effects that were likely 

associated with both short- (generally hours to days), and long-term (months to years) exposures 

to SO2 at concentrations present in the ambient mixture of air pollutants. Moreover, these 

documents presented evidence for bronchoconstriction and respiratory symptoms in exercising 

asthmatics following controlled exposures to 5-10 minute peak concentrations of SO2. 

Evidence drawn from epidemiologic studies supported a likely association between 24-

hour average SO2 concentrations and daily mortality, aggravation of bronchitis, and small, 
July 2009 7

reversible declines in children’s lung function (EPA 1982, 1994a). In addition, a few 

epidemiologic studies found an association between respiratory symptoms and illnesses and 

annual average SO2 concentrations (EPA 1982, 1994a). However, it was noted that most of these 

epidemiologic studies were conducted in years and cities where particulate matter (PM) counts 

were also quite high, thus making it difficult to quantitatively determine whether the observed 

associations were the result of SO2, PM, or a combination of both pollutants. 

 Evidence drawn from clinical studies exposing exercising asthmatics to <1000 ppb SO2

for 5-10 minutes found that these types of SO2 exposures evoked health effects that were similar 

to those asthmatics would experience from other commonly encountered stimuli (e.g., exercise, 

cold/dry air, psychological stress, etc. (EPA, 1994a). That is, there was an acute-phase response 

characterized by bronchoconstriction and/or respiratory symptoms that occurred within 5-10 

minutes of exposure but then subsided on its own within 1 to 2 hours. This acute-phase response 

was followed by a short refractory period where the individual was relatively insensitive to 

additional SO2 challenges. Notably, the SO2-induced acute-phase response was found to be 

ameliorated by the inhalation of beta-agonist aerosol medications, and to occur without an 

additional, often more severe, late-phase inflammatory response. 

The 1994 supplement to the AQCD noted that of particular concern was the subset of 

asthmatics in these clinical studies that appeared to be hyperresponsive (i.e., those experiencing 

greater-than-average bronchoconstriction or respiratory symptoms at a given SO2 concentration). 

Thus, for a given concentration of SO2, EPA estimated the number of asthmatics likely to 

experience bronchoconstriction (and/or symptoms) of a sufficient magnitude to be considered a 

health concern. At 600 to 1000 ppb SO2, EPA estimated that more than 25% of mild to moderate 

exercising asthmatics would likely experience decrements in lung function distinctly exceeding 

typical daily variations in lung function, or the response to commonly encountered stimuli (EPA, 

1994a). Furthermore, the AQCD concluded that the severity of effects experienced at 600-1000 

ppb was likely to be of sufficient concern to cause a cessation of activity, medication use, and/or 

the possible seeking of medical attention. In contrast, at 200 – 500 ppb SO2, it was estimated 

that at most 10 – 20% of mild to moderate exercising asthmatics were likely to experience lung 

function decrements larger than those associated with typical daily activity, or the response to 

commonly encountered stimuli (EPA, 1994a). 
July 2009 8

1.1.3 Assessment from Previous Review 

The risk and exposure assessment from the previous review of the SO2 NAAQS 

qualitatively evaluated both the existing 24-hour (0.14 ppm) and annual standards (0.03 ppm), 

but primarily focused on whether an additional standard was necessary to protect against shortterm (e.g., 5-minute) peak exposures. Based on the human clinical data mentioned above, it was 

judged that exposures to 5-minute SO2 levels at or above 600 ppb could pose an immediate 

significant health risk for a substantial proportion of asthmatics at elevated ventilation rates (e.g., 

while exercising). Thus, EPA analyzed existing ambient monitoring data to estimate the 

frequency of 5-minute peak concentrations above 500, 600, and 700 ppb, the number of repeated 

exceedances of these concentrations, and the sequential occurrences of peak concentrations 

within a given day (SAI, 1996). The results of this analysis indicated that in the vicinity of local 

sources, several locations in the U.S. had a substantial number of 5-minute peak concentrations 

at or above 600 ppb. 

In addition to the ambient air quality analysis, the previous review also included several 

annual exposure analyses that in general, combined SO2 emission estimates from utility and nonutility sources with exposure modeling to estimate the probability of exposure to short-term peak 

SO2 concentrations. The first such analysis conducted by the Agency estimated the number of 5-

minute exposures ≥ 500 ppb associated with four selected coal-fired power utilities (EPA, 

1986a). An expanded analysis sponsored by the Utility Air Regulatory Group (UARG) 

considered the frequency of short-term exposure events that might result from the nationwide 

operation of all power utility boilers (Burton et al., 1987). Additionally, the probability of peak 

concentrations surrounding non-utility sources was the focus of another study conducted by the 

Agency (Stoeckenius et al., 1990). The resultant combined exposure estimates based on these 

early analyses indicated that between 0.7 and 1.8% of the total asthmatic population potentially 

could be exposed one or more times annually, while outdoors at exercise, to 5-minute SO2

concentrations ≥ 500 ppb. It also was noted that the frequency of 5-minute exposures above the 

health effect benchmark of 600 ppb, while not part of the analysis, would be anticipated to be 

lower. 

In addition to the early analyses mentioned above, two other analyses were considered in 

the prior review. The first was an exposure assessment sponsored by the UARG (Rosenbaum et 

al., 1992) that focused on emissions from fossil-fueled power plants. That study accounted for 
July 2009 9

the anticipated reductions in SO2 emissions after implementation of the acid deposition 

provisions (Title IV) of the 1990 Clean Air Act Amendments. This UARG-sponsored analysis 

predicted that these emission reductions would result in a 42% reduction in the number of 5-

minute exposures to 500 ppb for asthmatic individuals (reducing the number of asthmatics 

exposed from 68,000 down to 40,000) in comparison with the earlier Burton et al. (1987) 

analysis. The second was a new exposure analysis submitted by the National Mining 

Association (Sciences International, Inc. 1995) that reevaluated non-utility sources. In this 

analysis, revised exposure estimates were provided for four of the seven non-utility source 

categories by incorporating new emissions data and using less conservative modeling 

assumptions in comparison to those used for the earlier Stoeckenius et al. (1990) non-utility 

analysis. Significantly fewer exposure events (i.e., occurrence of 5-minute 500 ppb or greater 

exposures) were estimated in this industry-sponsored revised analysis, decreasing the range of 

estimated exposures for these four sources by an order of magnitude (i.e., from 73,000-259,000 

short-term exposure events in the original analysis to 7,900-23,100 in the revised analysis). 

1.2 SCOPE OF THE RISK AND EXPOSURE ASSESSMENT FOR THE 

CURRENT REVIEW 

1.2.1 Overview of the Risk and Exposure Assessment 

The REA describes exposure and risks associated with recent ambient levels of SO2,

levels that just meet the current SO2 standards, and levels that just meet potential alternative 

standards. This REA also contains a policy discussion regarding the adequacy of the current SO2

NAAQS, and potential alternative primary standards. A concise overview of the information, 

analyses, and policy discussion contained in this document is presented below. 

Chapters 2-4 evaluate information presented in the ISA that is relevant for conducting an 

exposure and risk assessment. This includes information on 1) human exposure to SO2; 2) at-risk 

populations; and 3) health effects associated with short- and long-term exposures to SO2. 

Chapter 5 presents the rationale for the selection of the indicator, averaging time, forms, and 

levels for the potential alternative standards that were assessed in the exposure and risk chapters 

of the document. Specifically, these potential alternative standards are 99th percentile 1-hour 

daily maximum SO2 levels of 50, 100, 150, 200, and 250 ppb, and 98th percentile 1-hour daily 

maximum SO2 levels of 200 ppb, and in some instances in the air quality analysis, 100 ppb. In 
July 2009 10

brief, the rationale takes into consideration both human exposure and epidemiologic evidence 

from the ISA, as well as a qualitative analysis conducted by staff characterizing 98th and 99th

percentile 1-hour daily maximum SO2 levels in cities and time periods corresponding to key U.S. 

and Canadian hospitalization and ED visit studies for all respiratory causes and asthma (key 

studies are identified in Table 5-5 of the ISA). Chapter 6 is an overview of the technical 

analyses that are presented in the subsequent chapters of this document. This chapter also 

presents the rationale for the selection of specific potential health benchmark values1 derived 

from the human exposure literature. 

Chapters 7-9 present the analytical portion of the document. Staff considered both 

evidence of bronchoconstriction and respiratory symptoms from human exposure studies, as well 

as CASAC advice on the first and second draft REA, and judged it appropriate to conduct a 

series of three analyses to estimate risks associated with 5-minute SO2 exposures ranging from 

100-400 ppb in exercising asthmatics (see Figure 1-1 and Chapter 6). Chapter 7 presents an air 

quality characterization that uses monitored and statistically estimated 5-minute ambient SO2

concentrations as a surrogate for exposure. This analysis estimates the number of days per year 

measured or statistically estimated 5-minute daily maximum SO2 concentrations meet or exceed 

the potential health benchmark values of 100, 200, 300 and 400 ppb This air quality analysis is 

done under scenarios reflecting current air quality, air quality simulated to just meet the current 

standards, and air quality simulated to just meet the potential alternative standards (i.e., 99th

percentile 1-hour daily maximum SO2 levels of 50, 100, 150, 200 and 250 ppb and an 98th

percentile 1-hour daily maximum SO2 level of 200 ppb). Chapter 8 presents results from 

exposure analysis case studies conducted in the St. Louis modeling domain (henceforth referred 

to as St. Louis) and Greene County Missouri (MO). These analyses provide estimates of the 

number and percent of asthmatics residing within 20 kilometers (km) of major SO2 sources 

experiencing 5-minute exposures to 100, 200, 300, and 400 ppb SO2, while at elevated 

ventilation rates under the air quality scenarios mentioned above (i.e., recent air quality, and air 

quality adjusted to just meet the current and potential alternative standards). Chapter 9 is a 

quantitative risk assessment that produces health risk estimates for the number and percent of 

 

1

 In general, potential health benchmark values are pollutant exposure levels that have consistently been shown to 

induce adverse health effects in individuals participating in free-breathing human chamber studies. 
July 2009 11

exposed asthmatics (as determined by the exposure analysis; see Figure 1-1) that would 

experience moderate or greater lung function responses under the air quality scenarios previously 

described. 

In addition to the technical analyses presented in Chapters 7-9, Chapter 10 integrates the 

scientific evidence and the air quality, exposure, and risk information as they pertain to 

informing decisions about the primary SO2 NAAQS. More specifically, Chapter 10 considers 

the epidemiologic, controlled human exposure, and animal toxicological evidence presented in 

the ISA (EPA, 2008a), as well as the air quality, exposure, and risk characterization results 

presented in this document, as they relate to the adequacy of the current SO2 NAAQS and 

potential alternative primary SO2 standards. 

1.2.2 Species of Sulfur Oxides Included in Analyses 

The sulfur oxides include multiple gaseous (e.g., SO2, SO3) and particulate (e.g., sulfate) 

species. In considering what species of sulfur oxides are relevant to the current review of the 

SO2 NAAQS, we note that the health effects associated with particulate species of sulfur oxides 

have been considered within the context of the Agency’s review of the primary NAAQS for 

particulate matter (PM). In the most recent review of the NAAQS for PM, it was determined 

that size-fractionated particle mass, rather than particle composition, remains the most 

appropriate approach for addressing ambient PM. This conclusion will be re-assessed in the 

parallel review of the PM NAAQS; however, at present it would be redundant to also consider 

effects of particulate sulfate in this review. Therefore, the current review of the SO2 NAAQS 

will focus on gaseous species of sulfur oxides and will not consider health effects directly 

associated with particulate sulfur oxide species. Additionally, of the gaseous species, EPA has 

historically determined it appropriate to specify the indicator of the standard in terms of SO2

because other gaseous sulfur oxides (e.g., SO3) are likely to be found at concentrations many 

orders of magnitude lower than SO2 in the atmosphere, and because most all of the health effects 

and exposure information is for SO2. The ISA has again found this to be the case, and therefore 

this REA will use SO2 as a surrogate for all gaseous sulfur oxides. 
July 2009 12

2. OVERVIEW OF HUMAN EXPOSURE 

 In order to help inform the air quality, exposure, and risk analyses presented in Chapters 

7-9, staff has briefly summarized relevant human exposure information from the ISA. After 

defining the concept of “integrated exposure,” this chapter discusses major sources of SO2 

emissions. Characterizing these SO2 sources helps identify the most relevant locations for 

conducting air quality, exposure, and health risk analyses. This chapter then presents a 

description of the SO2 monitoring network, and discusses ambient levels of SO2 associated with 

1-hour, 24-hour, and annual averaging times. SO2 concentrations associated with these 

averaging times are relevant to the air quality, exposure, and risk analyses because the current 

SO2 standards have 24-hour and annual averaging times, and EPA is considering potential 

alternative 1-hour averaging time standards (see section 5.3). Next, this chapter describes the 

small subset of SO2 monitors that report 5-minute SO2 concentrations, as well as a broad 

characterization of ambient 5-minute SO2 levels (a more thorough discussion of these topics can 

be found in Chapters 6 and 7). This discussion is particularly relevant to the analyses described 

in this document because the potential health effect benchmarks and the outputs of the air 

quality, exposure, and risk assessments are presented with respect to a 5-minute averaging time 

(see section 6.2). More specifically, as previously described in section 1.2.1, an output of the air 

quality analysis presented in Chapter 7 is the number of days per year measured, or statistically 

estimated (see Chapter 6) 5-minute daily maximum SO2 concentrations exceed 5-minute 

potential health effect benchmark levels. Similarly, the output of the exposure analysis in 

Chapter 8 is the number of exercising asthmatics exposed to 5-minute SO2 concentrations above 

benchmark levels. Outputs of the exposure analysis (i.e., the number of exercising asthmatics 

exposed to 5-minute SO2 concentrations above benchmark levels) are then used as inputs into the 

quantitative risk assessment in Chapter 9 to estimate the number and percent of exposed 

exercising asthmatics expected to experience a moderate or greater lung function response (see 

Figure 6-1). 

 In addition to providing information relevant to the air quality, exposure, and risk 

analyses, this Chapter also provides information relevant to the Chapter 4 health discussion and 

the Chapter 10 policy assessment. That is, the current chapter highlights uncertainties involved 
July 2009 13

with using ambient SO2 concentrations as a surrogate for personal exposure in epidemiologic 

studies, as well as the ISA’s conclusions on this topic. 

2.1 BACKGROUND 

The integrated exposure of a person to a given pollutant is the sum of the exposures over 

all time intervals for all environments in which the individual spends time. People spend 

different amounts of time in different microenvironments and each microenvironment is 

characterized by different pollutant concentrations. There is a large amount of variability in the 

time that individuals spend in different microenvironments, but on average people spend the 

majority of their time (about 87%) indoors. Most of this time is spent at home with less time 

spent in an office/workplace or other indoor locations (ISA, Figure 2-36). In addition, people 

spend on average about 8% of their time outdoors and 6% of their time in vehicles. A potential 

consequence of multiple sources of exposure or microenvironments is the exposure 

misclassification that may result when total human exposure is not disaggregated between these 

various microenvironments. In epidemiologic studies that rely on ambient pollutant levels as a 

surrogate for exposure to ambient SO2, such misclassification may obscure the true relationship 

between ambient air pollutant exposures and health outcomes. 

 In addition to accounting for the times spent in different microenvironments, it is also 

important to note the duration of exposure experienced. This is important because health effects 

caused by long-term, low-level exposures may differ from those caused by relatively higher 

shorter-term exposures. 

2.2 SOURCES OF SO2

 In order to estimate risks associated with SO2 exposure, principle sources of the pollutant 

must first be characterized because the majority of human exposures are likely to result from the 

release of emissions from these sources. Anthropogenic SO2 emissions originate chiefly from 

point sources, with fossil fuel combustion at electric utilities (~66%) and other industrial 

facilities (~29%) accounting for the majority of total emissions (ISA, section 2.1). Other 

anthropogenic sources of SO2 include both the extraction of metal from ore as well as the 

burning of high sulfur containing fuels by locomotives, large ships, and non-road diesel 

equipment. Notably, almost the entire sulfur content of fuel is released as SO2 or SO3 during 
July 2009 14

combustion. Thus, based on the sulfur content in fuel stocks, oxides of sulfur emissions can be 

calculated to a higher degree of accuracy than can emissions for other pollutants such as PM and 

NO2 (ISA, section 2.1). 

The largest natural sources of SO2 are volcanoes and wildfires. Although SO2 constitutes 

a relatively minor fraction (0.005% by volume) of total volcanic emissions, concentrations in 

volcanic plumes can be in the range of several to tens of ppm (thousands of ppb). Volcanic 

sources of SO2 in the U.S. are limited to the Pacific Northwest, Alaska, and Hawaii. Emissions 

of SO2 can also result from burning vegetation. The amount of SO2 released from burning 

vegetation is generally in the range of 1 to 2% of the biomass burned and is the result of sulfur 

from amino acids being released as SO2 during combustion. 

2.3 BACKGROUND ON THE SO2 MONITORING NETWORK 

The following section provides general background on the SO2 monitoring network. A 

more detailed description of this network can be found in Watkins (2009). The SO2 monitoring 

network was originally deployed to support implementation of the SO2 NAAQS established in 

1971. Despite the establishment of an SO2 standard, uniform minimum monitoring requirements 

for SO2 monitoring did not appear until May 1979. From the time of the implementation of the 

1979 monitoring rule through 2008, the SO2 network has steadily decreased in size from 

approximately 1496 sites in 1980 to the approximately 488 sites operating in 2008. 

The 1979 monitoring rule established two categories of SO2 monitoring sites: State and 

Local Ambient Monitoring Stations (SLAMS) and the smaller set of National Ambient 

Monitoring Stations (NAMS). No minimum requirements were established for SLAMS. 

Minimum requirements (described below) were established for NAMS. The 1979 rule also 

required that SO2 only be monitored using Federal Reference Methods (FRMs) or Federal 

Equivalent Methods (FEMs). The 1979 monitoring rule called for a range of number of sites in a 

metropolitan statistical area (MSA) based both on population size and known concentrations 

relative to the NAAQS (at that point in time; see Watkins, 2009). 

In October 2006, EPA revised the monitoring requirements for SO2 in light of the fact 

that there was not an SO2 non-attainment problem (Watkins, 2009). The 2006 rule eliminated 

the minimum requirements for the number of SO2 monitoring sites. The current SO2 monitoring 

rule, 40 CFR Part 58, Appendix D, section 4.4 states: 
July 2009 15

Sulfur Dioxide (SO2) Design Criteria. 

(a) There are no minimum requirements for the number of SO2 monitoring sites. 

Continued operation of existing SLAMS SO2 sites using FRM or FEM is required until 

discontinuation is approved by the EPA Regional Administrator. Where SLAMS SO2

monitoring is ongoing, at least one of the SLAMS SO2 sites must be a maximum 

concentration site for that specific area. 

(b) The appropriate spatial scales for SO2 SLAMS monitoring are the microscale, middle, 

and possibly neighborhood scales. The multi-pollutant NCore sites can provide for 

metropolitan area trends analyses and general control strategy progress tracking. Other 

SLAMS sties are expected to provide data that are useful in specific compliance actions, 

for maintenance plan agreements, or for measuring near specific stationary sources of 

SO2. 

 (1) Micro and middle scale – Some data uses associated with microscale and 

middle scale measurements for SO2 include assessing the effects of control strategies to 

reduce concentrations (especially for the 3-hour and 24-hour averaging times) and 

monitoring air pollution episodes. 

 (2) Neighborhood scale – This scale applies where there is a need to collect air 

quality data as part of an ongoing SO2 stationary source impact investigation. Typical 

locations might include suburban areas adjacent to SO2 stationary sources for example, or 

for determining background concentrations as part of these studies of population 

responses to exposure to SO2. 

(c) Technical guidance in reference 1 of this appendix should be used to evaluate the 

adequacy of each existing SO2 site, to relocate an existing site, or to locate new sites. 

To ascertain what the current SO2 network is addressing or characterizing, and in light of 

the relatively recent removal of a specific SO2 monitoring requirement, EPA reviewed some of 

the SO2 network meta-data (Watkins, 2009). The data reviewed are those available from AQS 

for calendar year 2008, for any monitors reporting data at any point during the year. The metadata fields are usually created by state and locals whenever a monitor or site is opened, moved, 

or has a certain characteristic re-characterized. Often, EPA Regions consult with states and 

locals on some of these metadata characteristics, but it is the responsibility of the state or local to 

classify their own sites. With that, it should be noted that EPA must caveat such a review due to 

the fact the AQS meta-data may have missing or ‘old’ meta-data field entries, as states and locals 

do not have a routine or enforced process by which they must update or correct meta-data fields 

(Watkins, 2009). 

Monitoring Objective:

The monitoring objective meta-data field describes what the data from the monitor are 

intended to characterize. The focus of the data presented is to show the nature of the network in 
July 2009 16

terms of its attempt to generally characterize health effects, source impacts, transport, or welfare 

effects. In 2008, there were 488 SO2 monitors reporting data to AQS at some point during the 

year. Any particular monitor can have multiple monitor objectives, however for this analysis 

(see Watkins, 2009) we have selected one reported objective based on a hierarchy to represent an 

individual monitor. The hierarchy used was to select, in order of priority: 1) source oriented, 2) 

high concentration, 3) population exposure, or 4) general background, if they existed at a site 

with multiple monitoring objectives. Table 2-1 presents the monitor objective distribution across 

all SO2 sites from the available AQS data. There are 12 categories of monitor objective for any 

pollutant monitor within AQS. The “other” category is for sites likely addressing a state or local 

need outside of the routine objectives, and the “unknown” category represents missing metadata. The six primary categories appropriate for use with SO2 monitoring efforts stem directly 

from categorizations of site types within the CFR. In 40 CFR Part 58 Appendix D, they are 

defined as: 

1. Sites located to determine the highest concentration expected to occur in the area 

covered by the network (Highest Concentration). 

2. Sites located to measure typical concentrations in areas of high population 

(Population Exposure). 

3. Sites located to determine the impact of significant sources or source categories 

on air quality (Source Oriented). 

4. Sites located to determine general background concentration levels (General 

Background). 

5. Sites located to determine the extent of regional pollutant transport among 

populated areas; and in support of secondary standards (Regional Transport). 

6. Sites located to measure air pollution impacts on visibility, vegetation damage, or 

other welfare-based impacts (Welfare Related Impacts). 

The remaining four categories available are a result of updating the AQS database. In the 

more recent upgrade to AQS, the data handlers inserted the available site types for 

Photochemical Assessment Monitoring Stations (PAMS) network as options for monitoring site 

objectives. In our metadata review, three SO2 monitors have a listed monitoring objective that 

EPA intended to be applied only to NOX or O3 sites. As a result these three sites are presumably 

co-located with a NOX or O3 monitor with the same objective. 
July 2009 17

Measurement Scales 

The spatial (measurement) scales are laid out in 40 CFR Part 58, Appendix D, Section 1 

“Monitoring Objectives and Spatial Scales.” This part of the regulation spells out what data 

from a monitor can represent in terms of air volumes associated with area dimensions: 

 Microscale - 0 to 100 meters 

 Middle Scale - 100 to 500 meters 

 Neighborhood Scale - 500 meters to 4 kilometers 

 Urban Scale - 4 to 50 kilometers 

 Regional Scale - 50 kilometers up to 1000km 

There are meta-data records for the SO2 network to indicate what the measurement scale 

of a particular monitor represents. In addition to the scales presented above, “industrial” scale 

sites are an available option for characterizing SO2 monitor sites in AQS. These “industrial” 

scale sites are typically operated by industry, and are likely representative of the same scales that 

are associated with sites having source oriented and high concentration monitoring objectives, 

but we are unable to determine what spatial scale these monitors actually represent through AQS. 

It is also noted that a monitor can only have one measurement scale, as opposed to the possibility 

of a single monitor having multiple monitor objectives. Table 2-2 shows the measurement scale 

distribution across all SO2 sites from the available data in AQS of monitors reporting data in 

2008. 

Table 2-1. SO2 network monitoring objective distribution. 

SO2 Monitoring 

Objective 

Number of Monitoring 

Objective Records 

Percent Distribution 

Population Exposure 208 42.6 % 

Source Oriented 88 18.0 % 

Highest Concentration 83 17.0 % 

General Background 55 11.3 % 

Regional Transport 12 2.5 % 

Other 5 1.0 % 

Max Precursor Impact (PAMS 

Type 2 Site) 

3 0.6 % 

Welfare Related Impacts 1 0.2 % 

Unknown 33 6.8 % 

Totals: 488 100 %
July 2009 18

Table 2-2. SO2 network distribution across measurement scales. 

Measurement Scale Number of Measurement 

Scale Records 

Percent Distribution 

Microscale 1 0.2 % 

Middle Scale 35 7.2 % 

Neighborhood 309 63.3 % 

Urban Scale 61 12.5 % 

Regional Scale 41 8.4 % 

Industrial Scale 6 1.2 % 

Unknown 35 7.2 % 

Totals: 488 100% 

Urban/Rural Location Analysis

The US Census Bureau (http://www.census.gov/geo/www/ua/ua_2k.html) defines the 

term “urban” as all territory, population, and housing units located within an urbanized area (UA) or an 

urban cluster (UC). The Census bureau uses UA and UC boundaries to encompass densely settled 

territory, which consists of: 

 core census block groups or blocks that have a population density of at least 1,000 people 

per square mile and 

 surrounding census blocks that have an overall density of at least 500 people per square 

mile 

 Conversely, the Census Bureau's classification of "rural" consists of all territory, 

population, and housing units located outside of UAs and UCs. Counties, metropolitan 

areas, and the territory outside metropolitan areas, often are "split" between “urban” and 

“rural” territory. A spatial analysis of the SO2 monitors against the Census Bureau’s 

defined UAs and UCs shows that 63% of SO2 monitors are in an “urban” setting and 37% 

are in a “rural” setting. 

2.4 AMBIENT LEVELS OF SO2

Since the integrated exposure to a pollutant is the sum of the exposures over all time 

intervals for all environments in which the individual spends time, understanding the temporal 

and spatial patterns of SO2 levels across the U.S is an important component of conducting air 

quality, exposure, and risk analyses. SO2 emissions and ambient concentrations follow a strong 

east to west gradient due to the large numbers of coal-fired electric generating units in the Ohio 

River Valley and upper Southeast regions. In the 12 CMSAs that had at least 4 SO2 regulatory 

monitors from 2003-2005, 24-hour average concentrations in the continental U.S. ranged from a 

reported low of ~1 ppb in Riverside, CA and San Francisco, CA to a high of ~12 ppb in 
July 2009 19

Pittsburgh, PA and Steubenville, OH (ISA, section 2.4.4). In addition, inside CMSAs from 

2003-2005, the annual average SO2 concentration was 4 ppb (ISA, Table 2-8). However, spikes 

in hourly concentrations occurred; the mean 1-hour maximum concentration was 130 ppb, with a 

maximum value of greater than 700 ppb (ISA, Table 2-8). 

In addition to considering 1-hour, 24-hour, and annual SO2 levels in this document, 

examining the temporal and spatial patterns of 5-minute peaks of SO2 is also important given 

that human clinical studies have demonstrated exposure to these peaks can result in adverse 

respiratory effects in exercising asthmatics (see Chapter 4). Although the total number of SO2

monitors across the continuous U.S. can vary from year to year, in 2006 there were 

approximately 500 SO2 monitors in the NAAQS monitoring network (ISA, section 2.5.2). State 

and local agencies responsible for these monitors are required to report 1-hour average SO2

concentrations to the EPA Air Quality System (AQS). However, a small number of sites, only 

98 total from 1997 to 2007, and not the same sites in all years, voluntarily reported 5-minute 

block average data to AQS (ISA, section 2.5.2). Of these, 16 reported all twelve 5-minute 

averages in each hour for at least part of the time between 1997 and 2007. The remainder 

reported only the maximum 5-minute average in each hour. When maximum 5-minute 

concentrations were reported, the absolute highest concentration over the ten-year period 

exceeded 4000 ppb, but for all individual monitors, the 99th percentile was below 200 ppb (ISA, 

section 2.5.2). Medians from these monitors reporting data ranged from 1 ppb to 8 ppb, and the 

average for each maximum 5-minute level ranged from 3 ppb to 17 ppb. Delaware, 

Pennsylvania, Louisiana, and West Virginia had mean values for maximum 5-minute data 

exceeding 10 ppb (ISA, section 2.5.2). Among aggregated within-state data for the 16 monitors 

from which all 5-minute average intervals were reported, the median values ranged from 1 ppb to 

5 ppb, and the means ranged from 3 ppb to 11 ppb (ISA, section 2.5.2). The highest reported 

concentration was 921 ppb, but the 99th percentile values for aggregated within-state data were 

all below 90 ppb (ISA, section 2.5.2). 

EPA has generally conducted NAAQS risk assessments that focus on the risks associated 

with levels of a pollutant that are in excess of policy relevant background (PRB). Policy relevant 

background levels are defined as concentrations of a pollutant that would occur in the U.S. in the 

absence of anthropogenic emissions in continental North America (defined here as the United 
July 2009 20

States, Canada, and Mexico). However, throughout much of the United States, SO2 PRB levels 

are estimated to be at most 30 parts per trillion and contribute less than 1% to present day SO2

concentrations (ISA, section 2.5.3). We note that in the Pacific Northwest and Hawaii, PRB 

concentrations can be considerably higher due to geogenic activity (e.g., volcanoes); in these 

areas, PRB can account for 70-80% of total SO2 concentrations (ISA, section 2.5.3). Since we 

do not plan on conducting SO2 risk assessments in areas with high background SO2 levels due to 

natural sources, and the contribution of PRB is negligible in all other areas, EPA is addressing 

the risks associated with monitored and/or modeled ambient SO2 levels without regard to PRB 

levels. 

2.5 RELATIONSHIP OF PERSONAL EXPOSURE TO AMBIENT 

CONCENTRATIONS 

To help inform the evaluation of the epidemiologic evidence in Chapter 4 and the 

evidence-based considerations presented in Chapter 10, this section discusses the relationship of 

personal SO2 exposure to ambient SO2 concentrations. Many epidemiologic studies rely on 

measures of ambient SO2 concentrations as surrogates for personal exposure to ambient SO2. 

Thus, it is important to consider the potential sources of error that are associated with using SO2

measured by ambient monitors as a surrogate for personal exposure to ambient SO2. Key aspects 

related to this issue include: (1) ambient and personal sampling issues, (2) the spatial variability 

of ambient SO2 concentrations, and (3) the relationship between ambient concentrations and 

personal exposures as influenced by exposure factors (e.g., indoor sources). 

Only a limited number of studies have focused on the relationship between personal 

exposure and ambient concentrations of SO2, in part because ambient SO2 levels have declined 

markedly over the past few decades. Indoor and outdoor SO2 concentrations are often below 

detection limits for personal samplers2 and in these situations, the ISA notes that associations 

between ambient concentrations and personal exposures are inadequately characterized (ISA, 

section 2.6.3.2). However, in studies with personal measurements above detection limits, the 

ISA states that a reasonably strong association was observed between personal SO2 exposure and 

ambient concentrations (Brauer et al., 1989; Sarnat et al., 2006; described in ISA section 2.6.3.2). 

 

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 2.6.2 of the ISA. 
July 2009 21

In addition, the ISA notes that no study has examined the relationship between concentrations 

measured at ambient monitors and the community average exposure: a relationship that is more 

relevant than that of ambient concentration to personal exposure for community time-series 

studies (ISA, section 5.3). 

Because epidemiologic studies rely on ambient SO2 measurements at fixed site monitors, 

there is concern about the extent to which instrument error could influence the results of these 

studies. That is, the SO2 monitoring network was designed and put into place when SO2

concentrations were considerably higher, and thus, well within the standard monitor’s limits of 

detection. However, SO2 concentrations have fallen considerably over the years and are 

currently at, or very near these monitors’ lower limit of detection (~3 ppb). As a result, greater 

relative error is most often observed at lower ambient concentrations compared to less frequent 

higher concentrations. Notably, the ISA states that it is unclear how instrument error will 

influence the effect estimates of epidemiologic studies relying on these measurements (ISA, 

section 2.6.4.1). As an additional matter, staff notes that the lower detection limit of these 

monitors is not considered problematic with respect to determining attainment of SO2 NAAQS 

because the current 24-hour and annual standards, as well as the potential alternative 1-hour 

daily maximum standards, are all well within the detection limits of the SO2 monitoring network. 

Uncertainty in epidemiologic studies is also associated with the spatial and temporal 

variation of SO2 across communities. The ISA finds that site-to-site correlations of SO2

concentrations among monitors in U.S. cities ranges from very low to very high (ISA, section 

2.6.4.1; ISA, Table 2-9). This suggests that at any given time, SO2 concentrations at individual 

monitoring sites may not highly correlate with the average SO2 concentration in the community. 

This could be the result of local sources (e.g., power plants) causing an uneven spatial 

distribution of SO2, monitors being sited to represent concentrations near local sources, or effects 

related to terrain or weather (ISA, section 2.6.4.1). However, this type of error is not thought to 

bias community time-series results in a positive direction because it generally tends to reduce, 

rather than increase, effect estimates. 

In epidemiologic studies, since people spend most of their time indoors, there is also 

uncertainty in the relationship between ambient concentrations measured by local monitors and 

actual personal exposure related to ambient sources. That is, the presence of indoor or 
July 2009 22

nonambient sources of SO2 could complicate the interpretation of associations between personal 

exposure and ambient SO2 in exposure studies. Sources of indoor SO2 are associated with the 

use of sulfur-containing fuels, with higher levels expected when emissions are poorly vented. In 

the U.S., the contribution of indoor sources is not thought to be a major contributor to overall 

SO2 exposure because the only known indoor source is kerosene heaters and their use is not 

thought to be widespread (ISA, section 2.6.4.1). 

 The ISA concludes that exposure error caused by using ambient concentrations of SO2 as 

a surrogate for exposure to ambient SO2 is a source of uncertainty for epidemiologic studies. 

However, in community time-series and short-term panel epidemiologic studies, exposure error 

would tend to bias the effect estimate towards the null (ISA, section 2.6.4.4. and 5.3). 

2.6 KEY OBSERVATIONS 

 SO2 emissions and ambient concentrations follow a strong east to west gradient due to the 

large numbers of coal-fired electric generating units in the Ohio River Valley and upper 

Southeast regions. 

 In the 12 CMSAs that had at least 4 SO2 regulatory monitors from 2003-2005, 24-hour 

average concentrations in the continental U.S. ranged from a reported low of ~1 ppb in 

Riverside, CA and San Francisco, CA to a high of ~12 ppb in Pittsburgh, PA and 

Steubenville, OH. 

 Inside CMSAs from 2003-2005, the annual average SO2 concentration was 4 ppb. 

 Inside CMSAs from 2003-2005, the mean 1-hour maximum concentration was 130 ppb, 

with a maximum value of greater than 700 ppb. 

 A small number of sites, only 98 total from 1997 to 2007, and not the same sites in all 

years—voluntarily reported 5-minute block average data to AQS. Of these, 16 reported 

all twelve 5-minute averages in each hour, while the remainder reported only the 

maximum 5-minute average in each hour. 

 Throughout much of the United States, SO2 PRB levels are estimated to be at most 30 

parts per trillion and contribute less than 1% to present day SO2 concentrations. 

 The ISA concludes that exposure error caused by using ambient concentrations of SO2 as 

a surrogate for exposure to ambient SO2 is a source of uncertainty for epidemiologic 

studies. However, in community time-series and short-term panel epidemiologic studies, 

exposure error would tend to bias the effect estimate towards the null. Thus, results of 

these studies can be used, in part, to evaluate the adequacy of the current and potential 

alternative SO2 standards (see Chapter 10) 
July 2009 23

3. AT RISK POPULATIONS 

3.1 OVERVIEW 

Interindividual variation in human responses to air pollutants indicates that some 

subpopulations are at increased risk for the detrimental effects of ambient exposure to SO2. The 

NAAQS are intended to provide an adequate margin of safety for both general populations and 

sensitive subpopulations, or those subgroups potentially at increased risk for health effects in 

response to ambient air pollution. To facilitate the identification of subpopulations at the 

greatest risk for SO2-related health effects, studies have identified factors that contribute to the 

susceptibility and/or vulnerability of an individual to SO2. Susceptible individuals are broadly 

defined as those with a greater likelihood of an adverse outcome given a specific exposure in 

comparison with the general population (American Lung Association, 2001). The susceptibility 

of an individual to SO2 can encompass a multitude of factors which represent normal 

developmental phases (e.g., age) or biologic attributes (e.g., gender); however, other factors (e.g., 

socioeconomic status (SES)) may influence the manifestation of disease and also increase an 

individual’s susceptibility (American Lung Association, 2001). In addition, subpopulations may 

be vulnerable to SO2 in response to an increase in their exposure during certain windows of life 

(e.g., childhood or old age) or as a result of external factors (e.g., SES) that contribute to an 

individual being disproportionately exposed to higher concentrations than the general population. 

It should be noted that in some cases specific factors may affect both the susceptibility and 

vulnerability of a subpopulation to SO2. For example, a subpopulation that is characterized as 

having low SES may have less access to healthcare resulting in the manifestation of a disease, 

which increases their susceptibility to SO2, but they may also reside in a location that results in 

exposure to higher concentrations of SO2, increasing their vulnerability to SO2. 

 To examine whether SO2 differentially affects certain subpopulations, stratified analyses 

are often conducted in epidemiologic investigations to identify the presence or absence of effect 

modification. A thorough evaluation of potential effect modifiers may help identify 

subpopulations that are more susceptible and/or vulnerable to SO2. These analyses require the 

proper identification of confounders and their subsequent adjustment in statistical models, which 

helps separate a spurious, from a true causal association. Although the design of toxicological 

and human clinical studies does not allow for an extensive examination of effect modifiers, the 
July 2009 24

use of animal models of disease and the study of individuals with underlying disease or genetic 

polymorphisms do allow for comparisons between subgroups. Therefore, the results from these 

studies, combined with those results obtained through stratified analyses in epidemiologic 

studies, contribute to the overall weight of evidence for the increased susceptibility and 

vulnerability of specific subpopulations to SO2. Those groups identified in the ISA to be 

potentially at greater risk of experiencing an adverse health effect from SO2 exposure are 

described in more detail below. 

3.2 PRE-EXISTING RESPIRATORY DISEASE 

 In human clinical studies, asthmatics have been shown to be more responsive to the 

respiratory effects of SO2 exposure than healthy non-asthmatics. While SO2-attributable 

decrements in lung function have generally not been demonstrated at concentrations ≤ 1000 ppb 

in non-asthmatics, statistically significant increases in respiratory symptoms and decreases in 

lung function have consistently been observed in exercising asthmatics following 5 to 10 minute 

SO2 exposures at concentrations ranging from 400-600 ppb (ISA, section 4.2.1.1). Moderate or 

greater SO2-induced decrements in lung function have also consistently been observed at SO2

concentrations ranging from 200-300 ppb in some asthmatics. The ISA also notes that a number 

of epidemiologic studies have reported respiratory morbidity in asthmatics associated with SO2

exposure (ISA 4.2.1.1). For example, numerous epidemiologic studies have observed positive 

associations between ambient SO2 concentrations and ED visits and hospitalizations for asthma 

(ISA section 4.2.1.1). Overall, the ISA concludes that epidemiologic and controlled human 

exposure studies indicate that individuals with pre-existing respiratory diseases, particularly 

asthma, are at greater risk than the general population of experiencing SO2-associated health 

effects (ISA, section 4.2.1.1). 

3.3 GENETICS 

The ISA notes that a consensus now exists among scientists that the potential for genetic 

factors to increase the risk of experiencing adverse health effects due to ambient air pollution 

merits serious consideration. Several criteria must be satisfied in selecting and establishing 

useful links between polymorphisms in candidate genes and adverse respiratory effects. First, 

the product of the candidate gene must be significantly involved in the pathogenesis of the effect 
July 2009 25

of interest, which is often a complex trait with many determinants. Second, polymorphisms in 

the gene must produce a functional change in either the protein product or in the level of 

expression of the protein. Third, in epidemiologic studies, the issue of effect modification by 

other genes or environmental exposures must be carefully considered (ISA section 4.2.2). 

 While many studies have examined the association between genetic polymorphisms and 

susceptibility to air pollution in general, only one study has specifically examined the effects of 

SO2 exposure on genetically distinct subpopulations. Winterton et al. (2001) found a significant 

association between SO2-induced decrements in Forced Expiratory Volume in the first second 

(FEV1) and the homozygous wild-type allele in the promoter region of Tumor Necrosis Factor-α

(TNF- α; AA, position -308). However, the ISA concluded that the overall body of evidence was 

too limited to reach a conclusion regarding the effects of SO2 exposure on genetically distinct 

subpopulations at this time. 

3.4 AGE 

The ISA identifies children (i.e., <18 years of age) and older adults (i.e., >65 years of 

age) as groups that are potentially at greater risk of experiencing SO2-associated adverse health 

effects. In children, the developing lung is prone to damage from environmental toxicants as it 

continues to develop through adolescence. The biological basis for increased risk in the elderly 

is unknown, but one hypothesis is that it may be related to changes in antioxidant defenses in the 

fluid lining the respiratory tract. The ISA found a number of epidemiologic studies that observed 

increased respiratory symptoms in children associated with increasing SO2 concentrations. In 

addition, several studies have reported that the excess risk estimates for ED visits and 

hospitalizations for all respiratory causes, and to a lesser extent asthma, associated with a 10-ppb 

increase in 24-hour average SO2 concentrations were higher for children and older adults than for 

all ages together (ISA, section 4.2.3). However, the ISA also notes that the evidence from 

controlled human exposure studies does not suggest that adolescents are either more or less at 

risk than adults to the respiratory effects of SO2, but rather adolescents may experience similar 

respiratory effects at a given exposure concentration (ISA, sections 3.1.3.5 and 4.2.3). Overall, 

the ISA finds that compared to the general population, there is limited evidence to suggest that 

children and older adults are at greater risk of experiencing SO2-associated health effects (ISA, 

section 4.2.3). 
July 2009 26

3.5 TIME SPENT OUTDOORS 

Outdoor SO2 concentrations are generally much higher than indoor concentrations. Thus, 

the ISA notes that individuals who spend a significant amount of time outdoors are likely at 

greater risk of experiencing SO2-associated health effects than those who spend most of their 

time indoors (ISA section 4.2.5). 

3.6 VENTILLATION RATE 

 Controlled human exposure studies have demonstrated that decrements in lung function 

and respiratory symptoms occur at significantly lower SO2 exposure levels in exercising subjects 

compared to resting subjects. As ventilation rate increases, breathing shifts from nasal to 

oronasal, thus resulting in greater uptake of SO2 in the tracheobronchial airways due to the 

diminished absorption of SO2 in the nasal passages. Therefore, individuals who spend a 

significant amount of time at elevated ventilation rates (e.g. while playing, exercising, or 

working) are expected to be at greater risk of experiencing SO2-associated health effects (ISA 

section 4.2.5). 

 3.7 SOCIOECONOMIC STATUS 

There is limited evidence that increased risk to SO2 exposure is associated with lower 

SES (ISA section 4.2.5). Finkelstein et al. (2003) found that among people with below-median 

income, the relative risk for above-median exposure to SO2 was 1.18 (95% CI: 1.11, 1.26); the 

corresponding relative risk among subjects with above-median income was 1.03 (95% CI: 0.83, 

1.28). However, the ISA concludes that there is insufficient evidence to reach a conclusion 

regarding SES and exposure to SO2 at this time (ISA section 4.2.5). 

3.8 NUMBER OF AT RISK INDIVIDUALS 

Considering the size of the groups mentioned above, large proportions of the U.S. 

population are likely to have a relatively high risk of experiencing SO2-related health effects. In 

the United States, approximately 10% of adults and 13% of children have been diagnosed with 

asthma. Notably, the prevalence and severity of asthma is higher among certain ethnic or racial 

groups such as Puerto Ricans, American Indians, Alaskan Natives, and African Americans (ISA 

for NOx, section 4.4). Furthermore, a higher prevalence of asthma among persons of lower SES 

and an excess burden of asthma hospitalizations and mortality in minority and inner-city 
July 2009 27

communities have been observed. In addition, population groups based on age comprise 

substantial segments of individuals that may be potentially at risk for SO2-related health impacts. 

Based on U.S. census data from 2000, about 72.3 million (26%) of the U.S. population are under 

18 years of age, 18.3 million (7.4%) are under 5 years of age, and 35 million (12%) are 65 years 

of age or older. There is also concern for the large segment of the population that is potentially 

at risk to SO2-related health effects because of increased time spent outdoors at elevated 

ventilation rates (those who work or play outdoors). Overall, the considerable size of the 

population groups at risk indicates that exposure to ambient SO2 could have a significant impact 

on public health in the United States. 

3.9 KEY OBSERVATIONS 

 The susceptibility of an individual to SO2 can encompass a multitude of factors which 

represent normal developmental phases (e.g., age) or biologic attributes (e.g., gender); 

however, other factors (e.g., SES) may influence the manifestation of disease and also 

increase an individual’s susceptibility. 

 Subpopulations may be vulnerable to SO2 in response to an increase in their exposure 

during certain windows of life (e.g., childhood or old age) or as a result of external 

factors (e.g., SES) that contribute to an individual being disproportionately exposed to 

higher concentrations than the general population. 

 In some cases specific factors may affect both the susceptibility and vulnerability of a 

subpopulation to SO2. 

 The ISA concludes that individuals with pre-existing respiratory disease are likely at 

greater risk than the general population of experiencing SO2-associated health effects. 

 Epidemiologic studies suggest that children and older adults may be at greater risk of 

experiencing SO2-associated health effects. However, the evidence from controlled 

human exposure studies suggests that adolescents are neither more nor less at risk than 

adults. 

 People who spend extended periods of time outdoors and/or at elevated ventilation rates 

are likely at increased risk of experiencing adverse health effects from SO2 exposure. 

 Large proportions of the U.S. population are likely to be at increased risk of experiencing 

SO2-related health effects. Thus, exposure to ambient SO2 could have a significant 

impact on public health in the United States 
July 2009 28

4. INTEGRATION OF HEALTH EVIDENCE 

4.1 INTRODUCTION 

The ISA, along with its annexes, integrates newly available epidemiologic, human 

clinical, and animal toxicological evidence with consideration of key findings and conclusions 

from prior reviews to draw conclusions about the relationship between short- and long-term 

exposure to SO2 and numerous human health categories. For these health effects, the ISA 

characterizes judgments about causality with a hierarchy (for discussion see ISA section 1.3.7) 

that contains the following five levels: 

 Sufficient to infer a causal relationship 

 Sufficient to infer a likely causal relationship (i.e., more likely than not) 

 Suggestive but not sufficient to infer a causal relationship 

 Inadequate to infer the presence or absence of a causal relationship 

 Suggestive of no causal relationship 

The ISA notes that these judgments about causality are informed by a series of aspects of 

causality that are based on those set forth by Sir Austin Bradford Hill in 1965 (ISA section 

1.3.6). These aspects include strength of the observed association, availability of experimental 

evidence, consistency of the observed association, biological plausibility, coherence of the 

evidence, temporal relationship of the observed association, and the presence of an exposureresponse relationship. A summary of each of the five levels of the hierarchy is provided in Table 

1-2 of the ISA, which has also been included below (Table 4-1). 
July 2009 29

Table 4-1. Weight of evidence for causal determination. 

 Considering the framework presented in Table 4-1, the ISA concludes that there is 

sufficient evidence to infer a causal relationship between respiratory morbidity and short-term 

exposure to SO2 (ISA, section 5.2). The ISA bases this conclusion on the consistency, 

RELATIONSHIP DESCRIPTION 

Causal relationship Evidence is sufficient to conclude that there is a causal relationship 

between relevant pollutant exposures and the health outcome. That is, a 

positive association has been observed between the pollutant and the 

outcome in studies in which chance, bias, and confounding could be ruled 

out with reasonable confidence. Evidence includes, for example, controlled 

human exposure studies; or observational studies that cannot be explain 

by plausible alternatives or are supported by other lines of evidence (e.g. 

animal studies or mechanism of action information). Evidence includes 

replicated and consistent high-quality studies by multiple investigators. 

Likely to be a causal 

relationship 

Evidence is sufficient to conclude that a causal relationship is likely to exist 

between relevant pollutant exposures and the health outcome but 

important uncertainties remain. That is, a positive association has been 

observed between the pollutant and the outcome in studies in which 

chance and bias can be ruled out with reasonable confidence but potential 

issues remain. For example: a) observational studies show positive 

associations but copollutant exposures are difficult to address and/or other 

lines of evidence (controlled human exposure, animal, or mechanism of 

action information) are limited or inconsistent; or b) animal evidence from 

multiple studies, sex, or species is positive but limited or no human data 

are available. Evidence generally includes replicated and high-quality 

studies by multiple investigators. 

Suggestive of a causal 

relationship 

Evidence is suggestive of a causal relationship between relevant pollutant 

exposures and the health outcome, but is limited because chance, bias 

and confounding cannot be ruled out. For example, at least one highquality study shows a positive association but the results of other studies 

are inconsistent. 

Inadequate to infer a causal 

relationship 

Evidence is inadequate to determine that a causal relationship exists 

between relevant pollutant exposures and the health outcome. The 

available studies are of insufficient quantity, quality, consistency or 

statistical power to permit a conclusion regarding the presence or absence 

of an association between relevant pollutant exposure and the outcome. 

Suggestive of no causal 

relationship 

Evidence is suggestive of no causal relationship between relevant 

pollutant exposures and the health outcome Several adequate studies, 

covering the full range of levels of exposure that human beings are known 

to encounter and considering sensitive subpopulations, are mutually 

consistent in not showing a positive association between exposure and the 

outcome at any level of exposure. The possibility of a very small elevation 

in risk at the levels of exposure studied can never be excluded. 
July 2009 30

coherence, and plausibility of findings observed in controlled human exposure studies of 5-10 

minutes, epidemiologic studies mostly using 24-hour average concentrations, and animal 

toxicological studies using exposures of minutes to hours (ISA, section 5.2). The evidence of an 

association between SO2 exposure and other health categories is judged to be less convincing, at 

most suggestive but not sufficient to infer a causal relationship. Key conclusions from the ISA 

are summarized below and are described in greater detail in Table 5-3 of the ISA. 

 Sufficient to infer a causal relationship:

o Short-Term Exposure to SO2 and Respiratory Morbidity 

 Suggestive but not sufficient to infer a causal relationship: 

o Short-Term Exposure to SO2 and Mortality 

 Inadequate to infer the presence or absence of a causal relationship 

o Short-Term Exposure to SO2 and Cardiovascular Morbidity; 

o Long-Term Exposure to SO2 and Respiratory Morbidity; 

o Long-Term Exposure to SO2 and Other Morbidity; 

o Long-Term Exposure to SO2 and Mortality 

 The integrated health discussion in this chapter will focus on health effect categories for 

which the ISA finds a causal or likely causal relationship, as these effect categories are the basis 

for the potential health effect benchmarks and quantitative health risk assessment included in 

Chapters 7 through 9 of this document. As a result, this chapter will present an integrated 

discussion of the health evidence related to respiratory morbidity following short-term exposure 

to SO2. This is because respiratory morbidity is the only health effect category found by the ISA 

to have either a causal or likely causal association with SO2. The focus on health effect 

categories with the strongest evidence for purposes of the quantitative evaluation is consistent 

with prior NAAQS reviews, including the recent NO2 REA. However, we note that other health 

endpoints will be considered as part of the policy discussion in Chapter 10 and during the 

rulemaking process. 

In addition to an integrated discussion of the respiratory morbidity health evidence, 

section 4.3 of this chapter will discuss whether SO2-associated health effects can reasonably be 
July 2009 31

considered adverse. Briefly, this discussion will integrate: 1) respiratory morbidity health 

evidence; 2) conclusions from previous NAAQS reviews regarding adversity of effect; 3) ATS 

guidelines on what constitutes an adverse health effect of air pollution; and 4) CASAC views 

regarding the impact of moderate decrements in lung function or respiratory symptoms on 

individuals with pre-existing lung disease. 

4.2 RESPIRATORY MORBIDITY FOLLOWING SHORT-TERM SO2

EXPOSURE 

4.2.1 Overview 

The ISA concludes that there is sufficient evidence to infer a causal relationship between 

respiratory morbidity and short-term (5-minutes to 24-hours) exposure to SO2 (ISA, section 5.2). 

In large part, this determination is based on the results of controlled human exposure studies in 

exercising asthmatics demonstrating a relationship between 5-10 minute peak SO2 exposures and 

decrements in lung function that are frequently accompanied by respiratory symptoms. In fact, 

the ISA describes the controlled human exposure studies as being the “definitive evidence” for 

its causal determination between short-term SO2 exposure and respiratory morbidity (ISA, 

section 5.2). In addition to the controlled human exposure evidence, the ISA finds supporting 

evidence for its causal determination from a large body of epidemiologic studies observing 

positive associations between ambient SO2 levels and respiratory symptoms, as well as ED visits 

and hospital admissions for all respiratory causes and asthma (ISA, section 5.2). An integrated 

discussion of the controlled human exposure and epidemiologic evidence from the ISA is 

presented below. In addition, section 4.2.3 discusses the effect of medication on SO2-induced 

respiratory morbidity. 

4.2.2 Integration of Respiratory Morbidity Health Evidence 

As previously mentioned, the ISA’s finding of a causal relationship between respiratory 

morbidity and short-term SO2 exposure is based in large part on results from controlled human 

exposure studies involving exercising asthmatics. In general, these studies demonstrate that 

asthmatic individuals exposed to SO2 concentrations as low as 200-300 ppb for 5-10 minutes 

during exercise experience moderate or greater bronchoconstriction, measured as a decrease in 

FEV1 of ≥ 15% or an increase in specific airway resistance (sRaw) of ≥ 100% after correction for 

exercise-induced responses in clean air (Bethel et al., 1983; Linn et al., 1983, 1984, 1987; 1988; 
July 2009 32

1990; Magnussen et al., 1990; Roger et al., 1985; Gong et al., 1995; Trenga et al., 1999). In 

addition, the ISA finds that among asthmatics, both the percentage of individuals affected, and 

the severity of the response increases with increasing SO2 concentrations. That is, at 

concentrations ranging from 200-300 ppb, the lowest levels tested in free breathing chamber 

studies3 , 5-30% percent of exercising asthmatics experience moderate or greater decrements in 

lung function (ISA, Table 3-1). At concentrations ≥ 400 ppb, moderate or greater decrements in 

lung function occur in 20-60% of exercising asthmatics, and compared to exposures at 200-300 

ppb, a larger percentage of asthmatics experience severe decrements in lung function (i.e., ≥

200% increase in sRaw, and/or a ≥ 20% decrease in FEV1) (ISA, Table 3-1). Moreover, at SO2

concentrations ≥ 400 ppb, moderate or greater decrements in lung function are frequently 

accompanied by respiratory symptoms (e.g., cough, wheeze, chest tightness, shortness of breath) 

(Balmes et al., 1987; Gong et al., 1995; Linn et al., 1983; 1987; 1988; 1990; ISA, Table 3-1). 

Further analysis and discussion of the individual studies leading to the conclusions presented 

above can be found in Sections 3.1.1 to 3.1.3.5 of the ISA. 

Supporting the human clinical evidence is a relatively larger body of epidemiologic 

studies published since the last review. In general, these studies observed positive associations 

between ambient SO2 concentrations and respiratory symptoms, as well as ED visits and 

hospitalizations for all respiratory causes (particularly among children and older adults) and 

asthma. Moreover, although copollutant adjustment had varying degrees of influence on the SO2

effect estimate in ED visit and hospitalization studies, the effect of SO2 appeared to be generally 

robust and independent of gaseous copollutants, including NO2 (Anderson et al., 1998; Lin et al., 

2004a; Sunyer et al., 1997) and O3 (Anderson et al., 1998; Hajat et al., 1999; Tsai et al., 2006; 

Yang et al., 2003; 2005). With respect to potential confounding by PM10, the evidence of an 

independent SO2 effect on respiratory health was less consistent, with some positive associations 

with ED visit and hospitalization results becoming negative (although the negative results were 

not statistically significant) after inclusion of PM10 in regression models (Galan et al., 2003; 

Schwartz, 1995 [in New Haven, CT]; Tsai et al., 2006). However, several other ED visit and 

hospitalization studies found the SO2 effect estimate to be generally robust after inclusion of 

 

3

 The ISA cites one chamber study with intermittent exercise where healthy and asthmatic children were exposed to 

100 ppb SO2 in a mixture with ozone and sulfuric acid. The ISA notes that compared to exposure to filtered air, 

exposure to the pollutant mix did not result in statistically significant changes in lung function or respiratory 

symptoms (ISA section 3.1.3.4) 
July 2009 33

PM10 in regression models (Burnett et al., 1997; Hagen et al., 2000; Hajat et al., 1999; Schwartz, 

1995 [in Tacoma, WA]). Furthermore, in most (Van der Zee et al., 1999; Mortimer et al., 2002 

and Schildcrout et al., 2006), but not all (Schwartz et al., 1994) studies of respiratory symptoms, 

the SO2 effect estimate remained robust and relatively unchanged after inclusion of PM10 in 

multipolutant models (although the effect estimate may have lost statistical significance). In 

addition, SO2-effect estimates generally remained robust in the limited number of studies that 

included PM2.5 and/or PM10-2.5 in multipolutant models (Burnett et al., 1997; Ito et al., 2007; Lin 

et al., 2003; NY DOH, 2006). Taken together, the ISA ultimately concludes that studies 

employing multipollutant models suggest that SO2 has an independent effect on respiratory 

morbidity outcomes (ISA, section 5.2). 

The ISA further characterizes the epidemiologic results of increases in respiratory 

symptoms as well as increases in hospital admissions and ED visits as being consistent and 

coherent. The evidence is consistent in that associations are reported in studies conducted in 

numerous locations and with a variety of methodological approaches (ISA, section 5.2). 

Epidemiologic results are coherent in that respiratory symptoms results from epidemiologic 

studies with short-term (≥ 1-hour) exposures are generally in agreement with respiratory 

symptom results from controlled human exposure studies of 5-10 minutes. However, the ISA 

notes the differences in averaging times associated with respiratory effects in human exposure 

and epidemiologic studies. That is, while adverse respiratory effects are observed following 5-

10 minute exposures in human clinical studies, the majority of positive respiratory results from 

epidemiologic studies are associated with a 24-hour averaging time- the only averaging time 

evaluated in the vast majority of these studies. As a potential explanation for the difference in 

averaging times employed across study designs, the ISA suggests that it is possible that results 

from epidemiologic studies are being driven, at least in part, by shorter-term peak SO2

concentrations (ISA section 5.2). More specifically, with respect to epidemiologic studies of 

respiratory symptoms, the ISA states “that it is possible that these associations are determined in 

large part by peak exposures within a 24-hour period” (ISA, section 5.2). Similarly, the ISA 

states that the respiratory effects following peak SO2 exposures in controlled human exposure 

studies provides a basis for a progression of respiratory morbidity that could result in increased 

ED visits and hospital admissions (ISA, section 5.2). Also, it should be noted there is 
July 2009 34

epidemiologic evidence to suggest that shorter-term peak SO2 concentrations can result in 

adverse respiratory effects. That is, there are a relatively small number of epidemiologic studies 

demonstrating positive associations between 1-hour daily maximum SO2 concentrations and 

respiratory symptoms, as well ED visits and hospitalizations (ISA, Tables 5-4 and 5-5). While 

these studies are not limiting the exposure to a defined 1-hour period, they provide additional 

evidence that the shorter term peaks result in adverse respiratory effects. 

The ISA also finds that the respiratory effects of SO2 are consistent with the mode of 

action as it is currently understood from animal toxicological and human exposure studies (ISA, 

section 5.2). The immediate effect of SO2 on the respiratory system is bronchoconstriction. This 

response is mediated by chemosensitive receptors in the tracheobronchial tree. Activation of 

these receptors triggers central nervous system reflexes that result in bronchoconstriction and 

respiratory symptoms that are often followed by rapid shallow breathing (ISA, section 5.2). The 

ISA notes that asthmatics are likely more sensitive to the respiratory effects of SO2 due to 

preexisting inflammation associated with the disease. For example, pre-existing inflammation 

may lead to enhanced release of inflammatory mediators, and/or enhanced sensitization of the 

chemosensitive receptors (ISA, section 5.2). 

Taken together, the ISA concludes that the controlled human exposure, epidemiologic, 

and toxicological evidence support its determination of a causal relationship between respiratory 

morbidity and short-term (5-minutes to 24-hours) exposure to SO2. Results from controlled 

human exposure studies provide the definitive evidence for this conclusion, while supporting 

evidence is found in numerous epidemiologic studies of respiratory symptoms and ED visits and 

hospitalizations (ISA, section 5.2). The ISA further notes that both lines of evidence are 

consistent with the SO2 mode of action as it is currently understood (ISA, section 5.2). 

4.2.3 Medication as an Effect Modifier 

As mentioned above, the immediate effect of SO2 on the respiratory system is 

bronchoconstriction. Thus, we note that quick-relief and long-term-control asthma medications 

have been shown to provide varying degrees of protection against SO2-induced 

bronchoconstriction in mild and moderate asthmatics (ISA section 3.1.3.2 and Annex Table D1). More specifically, while no therapy has been shown to completely eliminate SO2-induced 

respiratory effects in exercising asthmatics, some short- and long-acting asthma medications are 
July 2009 35

capable of significantly reducing SO2-induced bronchoconstriction (Gong et al., 1996; 2001; 

Koenig et al., 1987; Linn et al., 1990). However, the ISA notes that asthma is often poorly 

controlled even among severe asthmatics due to inadequate drug therapy or poor compliance 

among those who are on regular medication (Rabe et al., 2004). Moreover, the ISA also notes 

that mild asthmatics, who constitute the majority of asthmatic individuals, are much less likely to 

use asthma medication than asthmatics with more severe disease (O’Byrne, 2007; Rabe et al., 

2004). Therefore, the ISA finds that it is reasonable to conclude that all asthmatics (i.e., mild, 

moderate, and severe), are at high risk of experiencing adverse respiratory effects from SO2

exposure (ISA section 3.1.3.2). 

4.3 WHAT CONSTITUTES AN ADVERSE HEALTH IMPACT FROM SO2

EXPOSURE? 

In making judgments as to when various SO2 -related health effects become regarded as 

adverse to the health of individuals, staff has relied upon the guidelines published by the 

American Thoracic Society (ATS), conclusions from previous NAAQS reviews, and the advice 

of CASAC. Taken together, staff concludes that for asthmatics, SO2-induced respiratory effects 

are adverse. The rationale for this conclusion is presented below. 

 The ATS has previously defined adverse respiratory health effects as “medically 

significant physiologic changes generally evidenced by one or more of the following: (1) 

interference with the normal activity of the affected person or persons, (2) episodic respiratory 

illness, (3) incapacitating illness, (4) permanent respiratory injury, and/or (5) progressive 

respiratory dysfunction” (ATS 1985). The ATS has also recommended that transient loss in lung 

function with accompanying respiratory symptoms, or detectable effects of air pollution on 

clinical measures (e.g., medication use) be considered adverse (ATS 1985). We also note that 

during the last O3 NAAQS review, the CD and Staff Paper indicated that for many people with 

lung disease (e.g., asthma), even moderate decrements in lung function (e.g., FEV1 decrements > 

10% but < 20% and/or ≥100% increases in sRaw) or respiratory symptoms would likely interfere 

with normal activities and result in additional and more frequent use of medication (EPA 2006, 

EPA 2007e). In addition, CASAC has previously indicated that in the context of standard 

setting, a focus on the lower end of the range of moderate functional responses is most 

appropriate for estimating potentially adverse lung function decrements in people with lung 
July 2009 36

disease (73 FR16463). Finally, we note that in the current SO2 NAAQS review, clinicians on the 

CASAC Panel again advised that moderate or greater decrements in lung function can be 

clinically significant in some individuals with respiratory disease (CASAC transcripts, July 30-

31 2008, pages 211-213) 

 Considering the advice and recommendations described above, as well as key 

conclusions in the ISA, staff finds that for asthmatics, SO2-induced respiratory effects are 

adverse. Human exposure studies are described in the ISA as being the “definitive evidence” for 

a causal association between short-term SO2 exposure and respiratory morbidity (ISA, section 

5.2). These studies have consistently demonstrated that exposure to SO2 concentrations as low 

as 200-300 ppb for 5-10 minutes can result in moderate or greater decrements in lung function, 

evidenced by a ≥15% decline in FEV1 and/or ≥ 100% increase in sRaw in a significant 

percentage of exercising asthmatics (see section 4.2.2). It is highly likely that these decrements 

in lung function will result in increased medication use and a disruption of normal activities for a 

significant percentage of these asthmatics. This expectation is supported by a number of human 

exposure studies reporting that some exercising asthmatics required the use of medication to treat 

the respiratory effects that followed a 5-10 minute SO2 exposure (EPA 1994a). It is also 

supported by CASAC views during the previous O3 review that moderate declines in FEV1can be 

clinically significant in some individuals (Henderson 2006). As an additional matter, we note 

that human exposure studies have also reported that at SO2 concentrations ≥ 400 ppb, lung 

function decrements (i.e., ≥ 15% decline in FEV1 and/or ≥ 100% increase in sRaw) are 

frequently accompanied by respiratory symptoms. Taken together, staff concludes that human 

exposure studies demonstrate that adverse respiratory effects occur in exercising asthmatics 

following 5-10 minute SO2 exposures as low as 200 ppb. However, we also note that the 

subjects participating in these exposure studies do not represent the most sensitive asthmatics 

(i.e., severe asthmatics), and therefore, it is possible that adverse respiratory effects could occur 

at lower SO2 concentrations in these individuals. 

 Epidemiologic studies also indicate that adverse respiratory morbidity effects are 

associated with SO2. In reaching the conclusion of a causal relationship between respiratory 

morbidity and short-term SO2 exposure, the ISA generally found positive associations between 

ambient SO2 concentrations and ED visits and hospitalizations for all respiratory causes and 
July 2009 37

asthma (see section 4.2.2). Notably, ED visits and hospitalizations attributable to air pollution 

are considered adverse effects under ATS guidelines. These studies also indicate that SO2 is 

associated with episodic respiratory illness and aggravation of respiratory diseases, which under 

ATS guidance, would also be considered adverse effects of air pollution. 

 In 2000, the ATS published updated guidelines on what constitutes an adverse health 

effect of air pollution (ATS, 2000). These guidelines expanded those released in 1985 (ATS 

1985). Among other considerations, the 2000 guidelines stated that measurable negative effects 

of air pollution on quality of life should be considered adverse (ATS 2000). These updated 

guidelines also indicated that exposure to air pollution that increases the risk of an adverse effect 

to the entire population is adverse, even though it may not increase the risk of any individual to 

an unacceptable level (ATS 2000). For example, a population of asthmatics could have a 

distribution of lung function such that no individual has a level associated with significant 

impairment. Exposure to air pollution could shift the distribution to lower levels that still do not 

bring any individual to a level that is associated with clinically relevant effects. However, this 

would be considered adverse because individuals within the population would have diminished 

reserve function, and therefore would be at increased risk if affected by another agent (ATS 

2000). 

 The 2000 ATS guidelines further strengthen the conclusion that SO2-induced respiratory 

effects are adverse. As previously mentioned, human clinical studies have consistently 

demonstrated that SO2 exposure can result in moderate or greater decrements in FEV1 and sRaw 

at levels as low as 200-300 ppb in a significant percentage of exercising asthmatics. Staff finds 

that these results could reasonably indicate an SO2-induced shift in these lung function 

measurements for this population. As a result, a significant percentage of exercising asthmatics 

exposed to SO2 concentrations as low as 200 ppb would have diminished reserve lung function 

and would be at greater risk if affected by another respiratory agent (e.g., viral infection). 

Importantly, diminished reserve lung function in a population that is attributable to air pollution 

is an adverse effect under ATS guidance. 

 Staff finds multiple lines of evidence indicating that exposure to SO2 concentrations at 

least as low as 200 ppb can result in adverse respiratory effects. We note that this is in 

agreement with CASAC comments offered on the first draft SO2 REA. The CASAC letter to the 
July 2009 38

Administrator states: “CASAC believes strongly that the weight of clinical and epidemiology 

evidence indicates there are detectable clinically relevant health effects in sensitive 

subpopulations down to a level at least as low as 0.2 ppm SO2 (Henderson 2008).” Thus, when 

examining the adequacy of the current and potential alternative standards (see Chapter 10), staff 

finds it appropriate to consider the degree of protection these standards provide, or would 

provide, against moderate or greater decrements in lung function and/or respiratory symptoms in 

asthmatics at elevated breathing ventilation rates. 

4.4 KEY OBSERVATIONS 

 The ISA concludes that there is sufficient evidence from human exposure, epidemiologic, 

and toxicological studies to infer a causal relationship between respiratory morbidity and 

short-term exposure to SO2

 The ISA characterizes no other health endpoints as having a causal or likely causal 

association with short or long-term exposure to SO2. 

 Human exposure studies demonstrate that at SO2 concentrations ranging from 200-300 

ppb, the lowest levels tested in free breathing chamber studies, 5-30% percent of 

exercising asthmatics experience moderate or greater decrements in lung function (i.e., ≥

100% increase in sRaw, and/or a ≥ 15% decrease in FEV1). At concentrations ≥ 400 ppb, 

moderate or greater decrements in lung function occur in 20-60% of exercising 

asthmatics, and compared to exposures at 200-300 ppb, a larger percentage of asthmatics 

experience severe decrements in lung function (i.e., ≥ 200% increase in sRaw, and/or a ≥

20% decrease in FEV1). 

 At SO2 concentrations ≥ 400 ppb, moderate or greater decrements in lung function are 

frequently accompanied by respiratory symptoms. 

 In general, epidemiologic studies observed positive associations between ambient SO2

concentrations and respiratory symptoms, as well as ED visits and hospitalizations for all 

respiratory causes and asthma. In studies using multipollutant models, the effects of SO2

were generally independent of effects of other ambient air pollutants 

 No medication regimen has been shown to completely eliminate SO2-induced respiratory 

effects in exercising asthmatics. 

 Staff finds multiple lines of evidence indicating that SO2 exposure can result in 

respiratory effects that can reasonably be considered adverse to the health of asthmatics. 
July 2009 39

5. SELECTION OF POTENTIAL ALTERNATIVE STANDARDS 

FOR ANALYSIS 

5.1 INTRODUCTION 

 The primary goals of the SO2 risk and exposure assessment described in this document 

are to estimate short-term exposures and potential human health risks associated with 1) recent 

levels of ambient SO2; 2) SO2 levels associated with just meeting the current standards; and 3) 

SO2 levels associated with just meeting potential alternative standards. This section presents the 

rationale for the selection of the potential alternative standards that are assessed in the 

quantitative analyses discussed in Chapters 7 through 9. These potential alternative standards are 

defined in terms of indicator, averaging time, form, and level. 

5.2 INDICATOR 

The SOx include multiple gaseous (e.g., SO2, SO3) and particulate (e.g., sulfate) species. 

In considering the appropriateness of different indicators, we note that the health effects 

associated with particulate species of SOx have been considered within the context of the health 

effects of ambient particles in the Agency’s review of the PM NAAQS. Thus, as discussed in 

the Integrated Review Plan (2007a), the current review of the SO2 NAAQS is focused on the 

gaseous species of SOx and will not consider health effects directly associated with particulate 

species of SOx. Of the gaseous species, EPA has historically determined it appropriate to specify 

the indicator of the standard in terms of SO2 because other gaseous sulfur oxides (e.g., SO3) are 

likely to be found at concentrations many orders of magnitude lower than SO2 in the atmosphere, 

and because most all of the health effects evidence and exposure information is related to SO2. 

The final ISA has again found this to be the case. Therefore, staff concluded that SO2 remains 

the most appropriate indicator for the alternative standards that are analyzed in this document. 

 5.3 AVERAGING TIME 

Staff concluded that the most robust evidence for SO2-induced respiratory morbidity 

exists for exposure durations ≤ 1-hour. The strongest evidence for this conclusion comes from 

controlled human exposure studies that have consistently demonstrated that exposure to SO2 for 

5-10 minutes can result in significant bronchoconstriction and/or respiratory symptoms in 

exercising asthmatics (see section 4.2). In fact, the ISA describes the controlled human exposure 
July 2009 40

studies as being the “definitive evidence” for its causal determination between SO2 exposure and 

short-term respiratory morbidity (ISA, section 5.2). In addition to these controlled human 

exposure studies, there is a relatively small body of epidemiologic evidence describing positive 

associations between 1-hour maximum SO2 levels and respiratory symptoms as well as hospital 

admissions and ED visits for all respiratory causes and asthma (ISA, Tables 5.4 and 5.5). In 

addition to the epidemiologic evidence for effects related to the 1-hour maximum concentration 

in a 24-hour period, there is a considerably larger body of epidemiologic studies reporting 

associations between 24-hour average SO2 levels and respiratory symptoms, as well as 

hospitalizations and ED visits; however, the ISA notes that it is possible that associations 

observed in these 24-hour studies are being driven, at least in part, by short-term SO2 peaks of 

duration < 24-hours. More specifically, when describing epidemiologic studies observing 

associations between ambient SO2 and respiratory symptoms, the ISA states “that it is possible 

that these associations are determined in large part by peak exposures within a 24-hour period” 

(ISA, section 5.2). The ISA also states that the respiratory effects following peak SO2 exposures 

in controlled human exposure studies provides a basis for a progression of respiratory morbidity 

that could result in increased ED visits and hospital admissions (ISA, section 5.2). It should also 

be noted that epidemiologic studies conducted in Paris, France (Dab et al., 1996) and in 

Manhattan and Bronx, NY (NY DOH, 2006) used both 24-hour average and 1-hour daily 

maximum air quality levels and found similar effect estimates with regard to hospital admissions 

for all respiratory causes (Dab et al., 1996) and asthma ED visits (NY DOH, 2006). Finally, in 

addition to the controlled human exposure and epidemiologic evidence, the ISA describes key 

toxicological studies with exposures ranging from minutes to hours resulting in decrements in 

lung function, airway inflammation, and/or hyperresponsiveness in laboratory animals (ISA, 

Table 5-2). 

The scientific evidence described above suggests that at a minimum, averaging time(s) 

selected for further risk and exposure analyses should address respiratory effects associated with 

SO2 exposures of ≤ 1-hour. We note that analyses conducted in the ISA demonstrate that at 

monitors measuring all twelve 5-minute SO2 levels in an hour (n=16), there is a high Pearson 

correlation between the 5-minute maximum level and the corresponding 1-hour average SO2

concentration, with only one monitor observing a correlation ≤ 0.9 (ISA, section 2.5.2; ISA, 
July 2009 41

Table 2-12). Thus, for the purpose of conducting quantitative exposure and risk analyses, staff 

concluded that the focus should be on potential alternative SO2 standards with an averaging time 

of 1-hour. Staff believes that alternative standards with an averaging time of 1-hour will limit 

both 5-minute peak concentrations within an hour, as well as other peak SO2 concentrations (≥ 1-

hour) that are likely in part, driving the respiratory outcomes described in epidemiologic studies. 

Staff also considered examining alternative 5-minute standards in the risk and exposure 

assessment, but concluded for several reasons that such an analysis would be of questionable 

utility in the decision-making process. We note that EPA historically conducts air quality, 

exposure, and risk analyses of alternative standards by adjusting measured, not modeled air 

quality data. This is an issue in evaluating alternative 5-minute standards for SO2 because there 

were, and continue to be relatively few locations reporting 5-minute SO2 concentrations. As 

described in Appendix A, from 1997-2007, there were a total of 98 monitors in 13 states and the 

District of Columbia measuring maximum 5-minute SO2 concentrations in an hour. In 

comparison, there were 933 monitors in 49 states, the District of Columbia, Puerto Rico and the 

Virgin Islands measuring 1-hour SO2 concentrations. Moreover, it is important to consider that 

those monitors reporting 5-minute concentrations do not represent data from a dedicated 5-

minute monitoring network, but rather a voluntary submission of 5-minute values from monitors 

placed for the purpose of evaluating attainment of 24-hour and annual average SO2 NAAQS. 

Thus, staff has little confidence that this limited set of data, from monitors sited for a different 

purpose, can provide the input required for a comprehensive air quality, exposure, and risk 

analysis of a much shorter averaging time standard. In fact, given the spatial heterogeneity of 5-

minute peaks, and the aforementioned issues with monitor siting, staff is not confident (based on 

5-minute monitoring data alone) that even in the 13 locations reporting 5-minute concentrations, 

that those reported values adequately reflect the extent to which 5-minute peaks are occurring in 

those areas. 

While we have chosen to evaluate alternative 1-hour averaging time standards in the air 

quality, exposure, and risk chapters of this document, this choice did not preclude the possibility 

of considering 5-minute standards as part of the policy assessment discussion in Chapter 10, or 

during the rulemaking process. Consideration of potential alternative 5-minute standards could 
July 2009 42

be based on evidence-based considerations, drawn from the discussion of the scientific evidence 

related to 5-10 minute exposures from the ISA, and presented below in Chapter 10. 

 5.4 FORM 

Staff recognizes that the adequacy of the public health protection provided by a 1-hour 

daily maximum potential alternative standard will be dependent on the combination of form and 

level (see section 5.5). It is therefore important that the particular form selected for a 1-hour 

daily maximum potential alternative standard reflect the nature of the health risks posed by 

increasing SO2 concentrations. That is, the form of the standard should reflect results from 

human exposure studies demonstrating that the percentage of asthmatics affected, and the 

severity of the respiratory response (i.e., decrements in lung function, respiratory symptoms) 

increases as SO2 concentrations increase (see section 4.2.2). Taking this into consideration, staff 

concluded that a concentration-based form is more appropriate than an exceedance-based form. 

This is because a concentration-based form averaged over three years (see below) would give 

proportionally greater weight to 1-hour daily maximum SO2 concentrations that are well above 

the level of the standard, than to 1-hour daily maximum SO2 concentrations that are just above 

the level of the standard. In contrast, an expected exceedance form would give the same weight 

to 1-hour daily maximum SO2 concentrations that are just above the level of the standard, as to 

1-hour daily maximum SO2 concentrations that are well above the level of the standard. 

Therefore, a concentration-based form better reflects the continuum of health risks posed by 

increasing SO2 concentrations (i.e., the percentage of asthmatics affected and the severity of the 

response increases with increasing SO2 concentrations). Concentration-based forms also provide 

greater regulatory stability than a form based on allowing only a single expected exceedance. 

Staff also recognizes that it is important to have a form that achieves a balance between 

limiting the occurrence of peak concentrations and providing a stable and robust regulatory 

target. The most recent review of the PM NAAQS (completed in 2006) judged that using a 98th

percentile form averaged over 3 years provides an appropriate balance between limiting the 

occurrence of peak concentrations and providing a stable regulatory target (71 FR 61144). In 

that review, staff also considered other forms within the range of the 95th to the 99th percentiles. 

In making recommendations regarding the form, staff considered the impact on risk of different 

forms, the year-to-year stability in the air quality statistic, and the extent to which different forms 
July 2009 43

of the standard would allow different numbers of days per year to be above the level of the 

standard in areas that achieve the standard. Based on these considerations, staff recommended 

either a 98th percentile form or a 99th percentile form. We have made similar judgments in 

selecting appropriate forms for the potential alternative 1-hour daily maximum SO2 standards 

assessed in this REA. As a result of these judgments, we decided to consider both 98th and 99th

percentile SO2 concentrations, averaged over 3 years. We have judged that the 98th and 99th

percentile, when combined with the selected range of alternative levels of a 1-hour daily 

maximum standard (see below), will likely offer a sufficient range of options to balance the 

objective of providing a stable regulatory target against the objective of limiting the occurrence 

of peak 5-minute concentrations. 

 Notably, for a given 1-hour standard level, staff’s initial judgment is that a 99th percentile 

form will be appreciably more protective against 5-minute peaks than a 98th percentile form. 

Staff finds this is likely the case because compared to a standard with a 98th percentile form, a 

standard with a 99th percentile form (at the same level) will limit a greater number of peak 1-hour 

concentrations, and thus, a greater number of peak 5-minute concentrations. Therefore, all 

potential alternative standard levels (see section 5.5) were assessed with a 99th percentile form in 

the air quality, exposure and risk analyses. However, as a comparison between forms, one 

alternative standard level was examined with a 98th percentile form in the exposure and risk 

analyses, and two alternative standard levels were examined with a 98th percentile form in the air 

quality analysis. 

5.5 LEVEL 

When considering the appropriate range of levels for alternative 1-hour daily maximum 

standards to analyze in the exposure and risk analyses, staff examined both the controlled human 

exposure and epidemiologic evidence evaluated in the ISA. Controlled human exposure 

evidence demonstrates that there is a continuum of SO2-related health effects following 5-10 

minute peak SO2 exposures in exercising asthmatics. That is, the ISA finds that the percentage 

of asthmatics affected and the severity of the response increases with increasing SO2

concentrations. At concentrations ranging from 200-300 ppb, approximately 5-30% percent of 

exercising asthmatics are likely to experience moderate or greater bronchoconstriction (ISA, 

Table 3-1). At concentrations ≥ 400 ppb, moderate or greater bronchoconstriction occurs in 
July 2009 44

approximately 20-60% of exercising asthmatics, and compared to exposures at 200-300 ppb, a 

larger percentage of subjects experience severe bronchoconstriction (ISA, Table 3-1). Moreover, 

at concentrations ≥ 400 ppb, moderate or greater bronchoconstriction was frequently 

accompanied with respiratory symptoms (ISA, Table3-1). 

 In addition to the controlled human exposure evidence, we also considered the 

epidemiologic evidence, as well as an air quality analysis conducted by staff characterizing 1-

hour daily maximum SO2 air quality levels in cities and time periods corresponding to key U.S. 

and Canadian ED visit and hospital admission studies for all respiratory causes and asthma4 (key 

studies are identified in Table 5-5 of the ISA). Figures 5-1 to 5-5 show standardized effect 

estimates and the 98th and 99th percentile 1-hour daily maximum SO2 levels for locations and 

time periods corresponding to these key U.S. (Figures 5-1 to 5-4) and Canadian5

 (Figure 5-5) 

studies. In general, staff concluded that the results presented in these figures demonstrate that 

most of these epidemiologic studies show positive, although frequently not statistically 

significant associations with SO2. Furthermore, we concluded that Figures 5-1 to 5-5 

demonstrate that positive effect estimates, including some that are statistically significant, are 

found in locations that span a broad range of 98th and 99th percentile 1-hour daily maximum SO2

concentrations (98th percentile range: 19- 401 ppb; 99th percentile range: 21-457 ppb). Thus, 

staff decided to utilize the 1-hour daily maximum air quality data presented in these figures to 

help inform both the upper and lower ranges of alternative SO2 standards for analysis in this 

REA (see Chapters 7-9). 

 

4

 Authors of relevant U.S. and Canadian studies were contacted and air quality statistics from the study monitor that 

recorded the highest SO2 levels were requested. In some cases, U.S. authors provided the AQS monitor IDs used in 

their studies and the statistics from the highest reporting monitor were calculated by EPA. In cases where U.S. 

authors were unable to provide the requested data (Schwartz 1995, Schwartz 1996, and Jaffe 2003), EPA identified 

the maximum reporting monitor from all monitors located in the study area and calculated the 98th and 99th

percentile statistics (see Thompson and Stewart 2009). 

5

 The Canadian statistics presented in Figure 5-5 were calculated from a data set provided by Dr. Richard Burnett 

and were used for all relevant single city studies on which he was an author. Note that air quality statistics presented 

for Canadian studies are likely not directly comparable to those presented for U.S. studies. This is because SO2

concentrations presented for Canadian studies represent the 98th and 99th percentile 1-hour daily maximum SO2

concentrations across a given city, rather than concentrations from the single monitor that recorded the highest 98th

and 99th percentile SO2 levels in a given city (see Thompson and Stewart, 2009). 
July 2009 45

-15

-10

-5

0

5

10

15

20

25

30

Percent Excess Risk

Wilson 2005

Manchester

1-hr 99: 69

1-hr 98: 59

W ilson 2005

Portland

1-hr 99: 47

1-hr 98: 36

Peel 2005

Atlanta

1-hr 99: 81

1-hr 98: 70

Tolbert 2007

Atlanta

1-hr 99: 76

1-hr 98: 62

EDRA

EDRA

EDRA

EDRA

EDRC

EDRC

EDRW

EDRW

EDRE

EDRE

24-hour effect estimates 1-hour effect estimates

Legend:

EDRA: ED visits for all respiratory causes- all ages 

EDRC: ED visits for all respiratory causes- children

EDRW : ED visits for all respiratory causes- ages 15-64

EDRE: ED visits for all respiratory causes- ages 65+

-15

-10

-5

0

5

10

15

20

25

30

Percent Excess Risk

Wilson 2005

Manchester

1-hr 99: 69

1-hr 98: 59

W ilson 2005

Portland

1-hr 99: 47

1-hr 98: 36

Peel 2005

Atlanta

1-hr 99: 81

1-hr 98: 70

Tolbert 2007

Atlanta

1-hr 99: 76

1-hr 98: 62

EDRA

EDRA

EDRA

EDRA

EDRC

EDRC

EDRW

EDRW

EDRE

EDRE

24-hour effect estimates 1-hour effect estimates

Legend:

EDRA: ED visits for all respiratory causes- all ages 

EDRC: ED visits for all respiratory causes- children

EDRW : ED visits for all respiratory causes- ages 15-64

EDRE: ED visits for all respiratory causes- ages 65+

Figure 5-1. Effect estimates for U.S. all respiratory ED visit studies and associated 98th and 99th

percentile 1-hour daily maximum SO2 levels. 
July 2009 46

Legend:

EDAA: ED visits for asthma- all ages 

EDAC: ED visits for asthma- children

EDAW: ED visits for asthma- ages 15-64 

EDAE: ED visits for asthma- ages 65+

EDAJ: ED visits for asthma- ages 5-34

-40

-20

0

20

40

60

80

100

Percent Excess Risk

Manchester

1-hr 99: 69

1-hr 98: 59

Portland

1-hr 99: 47

1-hr 98: 36

NYDOH 2006

Manhattan

1-hr 99: 80

1-hr 98: 62

Bronx

1-hr 99: 78 

1-hr 98: 65

Jaffe 2003

New York

1-hr 99: 82

1-hr 98: 71

Columbus

1-hr 99: 51

1-hr 98: 42

Cleveland

1-hr 99: 211

1-hr 98: 175

Cincinnati

1-hr 99: 457

1-hr 98: 401

EDAA

EDAA

EDAA

EDAA

EDAC

EDAC

EDAA

EDAW

EDAW

EDAE

EDAE EDAJ

EDAJ

EDAJ

Wilson 2005 Wilson 2005 Ito 2007

24-hour effect estimates

Figure 5-2. 24-hour effect estimates for U.S. asthma ED visit studies and associated 98th and 99th

percentile 1-hour daily maximum SO2 levels. 
July 2009 47

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

Percent Excess Risk

NYDOH 2006

Manhattan

1-hr 99:80

1-hr 98: 62

Bronx

1-hr 99: 78 

1-hr 98: 65

Peel 2005

Atlanta

1-hr 99: 81

1-hr 98: 70

Legend:

EDAA: ED visits for asthma- all ages 

EDAA

EDAA

EDAA

1-hour effect estimates

Figure 5-3. 1-hour effect estimates for U.S. asthma ED visit studies and associated 98th and 99th

percentile 1-hour daily maximum SO2 levels. 
July 2009 48

-10

-5

0

5

10

15

20

25

30

35

Percent Excess Risk

Schwartz 1995 Schwartz 1996 Sheppard 2003 Lin 2004

Tacoma

1-hr 99: 100

1-hr 98: 89

New Haven

1-hr 99: 150

1-hr 98: 126

Cleveland

1-hr 99: 170

1-hr 98: 150

Seattle

1-hr 99: 84

1-hr 98: 70

Bronx

1-hr 99:119

1-hr 98: 93

Legend:

HARE: Hospital admissions for all respiratory causes- ages 65+ 

HAAS: Hospital admissions for asthma- ages <65

HAAL: Hospital admissions for asthma- ages 0-14

HAAS

HARE

HARE

HARE

HAAL

24-hour effect estimates

-10

-5

0

5

10

15

20

25

30

35

Percent Excess Risk

Schwartz 1995 Schwartz 1996 Sheppard 2003 Lin 2004

Tacoma

1-hr 99: 100

1-hr 98: 89

New Haven

1-hr 99: 150

1-hr 98: 126

Cleveland

1-hr 99: 170

1-hr 98: 150

Seattle

1-hr 99: 84

1-hr 98: 70

Bronx

1-hr 99:119

1-hr 98: 93

Legend:

HARE: Hospital admissions for all respiratory causes- ages 65+ 

HAAS: Hospital admissions for asthma- ages <65

HAAL: Hospital admissions for asthma- ages 0-14

HAAS

HARE

HARE

HARE

HAAL

24-hour effect estimates

Figure 5-4. 24-hour effect estimates for U.S. hospitalization studies and associated 98th and 99th

percentile 1-hour daily maximum SO2 levels.6

 

6

 There were no key U.S. hospitalization studies with 1-hour effect estimates identified in Table 5-5 of the ISA 
July 2009 49

-40

-20

0

20

40

60

80

Yang 2003 Burnett 1997 Lin 2003

Vancouver

1-hr 99: 41

1-hr 98: 35

Toronto

1-hr 99: 21

1-hr 98: 19

Toronto

1-hr 99: 58

1-hr 98: 50

Legend:

HARY: Hospital admissions for all respiratory causes- ages <3 

HARE: Hospital admissions for all respiratory causes- ages 65+

HARA: Hospital admissions for all respiratory causes- all ages

HAAB: Hospital admissions for asthma- boys ages 6-12

HAAG: Hospital admissions for asthma- girls ages 6-12

HARA

HARE

HARY

HAAG

HAAB

24-hour effect estimates 1-hour effect estimate 24-hour effect estimates

Percent Excess Risk

-40

-20

0

20

40

60

80

Yang 2003 Burnett 1997 Lin 2003

Vancouver

1-hr 99: 41

1-hr 98: 35

Toronto

1-hr 99: 21

1-hr 98: 19

Toronto

1-hr 99: 58

1-hr 98: 50

Legend:

HARY: Hospital admissions for all respiratory causes- ages <3 

HARE: Hospital admissions for all respiratory causes- ages 65+

HARA: Hospital admissions for all respiratory causes- all ages

HAAB: Hospital admissions for asthma- boys ages 6-12

HAAG: Hospital admissions for asthma- girls ages 6-12

HARA

HARE

HARY

HAAG

HAAB

24-hour effect estimates 1-hour effect estimate 24-hour effect estimates

-40

-20

0

20

40

60

80

Yang 2003 Burnett 1997 Lin 2003

Vancouver

1-hr 99: 41

1-hr 98: 35

Toronto

1-hr 99: 21

1-hr 98: 19

Toronto

1-hr 99: 58

1-hr 98: 50

Legend:

HARY: Hospital admissions for all respiratory causes- ages <3 

HARE: Hospital admissions for all respiratory causes- ages 65+

HARA: Hospital admissions for all respiratory causes- all ages

HAAB: Hospital admissions for asthma- boys ages 6-12

HAAG: Hospital admissions for asthma- girls ages 6-12

HARA

HARE

HARY

HAAG

HAAB

24-hour effect estimates 1-hour effect estimate 24-hour effect estimates

Percent Excess Risk

Figure 5-5. Effect estimates for Canadian ED visits and hospitalization studies and associated 98th

and 99th percentile 1-hour daily maximum SO2 levels. 

The highest 98th and 99th percentile 1-hour daily maximum air quality levels were found 

in analyses conducted in the cities of Cincinnati (Figure 5-2), Cleveland (Figures 5-2 and 5-4) 

and New Haven (Figure 5-4). These studies showed positive associations7

 with respiratoryrelated hospital admissions or ED visits during time periods when 98th and 99th percentile 1-hour 

daily maximum SO2 concentrations ranged from 126 ppb to 457 ppb. Notably, this range of 1-

hour daily maximum SO2 levels overlaps considerably with 5-10 minute SO2 concentrations (≥

200 ppb) that have consistently been shown in controlled human exposure studies to result in 

lung function responses in exercising asthmatics. Of particular concern are the air quality levels 

that were found in Cincinnati (Jaffe et al., 2003). The 98th and 99th percentile 1-hour daily 

maximum SO2 concentrations were in excess of 400 ppb. Levels ≥ 400 ppb have consistently 

been shown in human exposure studies to result in moderate or greater bronchoconstriction in the 

 

7

 Results in Cincinnati (Jaffe et al., 2003) and New Haven (Schwartz et al., 1996) were statistically significant. 
July 2009 50

presence of respiratory symptoms in a considerable percentage of exercising asthmatics. As a 

result, staff decided to analyze alternative standard levels up to 250 ppb. We concluded that a 

98th or 99th percentile 1-hour daily maximum standard at this level had the potential to 

substantially limit the number of days when the 1-hour daily maximum SO2 concentration is ≥

200 ppb, while also potentially limiting the number of 5-10 minute SO2 peaks ≥ 400 ppb. 

 In selecting the lower end of the range of alternative standards to be analyzed, staff again 

considered controlled human exposure and epidemiologic evidence. However, with regard to the 

controlled human exposure evidence, several additional factors were considered. First, we 

considered that the subjects in human exposure studies do not represent the most SO2 sensitive 

asthmatics; that is, these studies included mild and moderate, but not severe asthmatics. Also, 

while human clinical studies have been conducted in adolescents, younger children have not 

been included in these exposure studies, and thus, it is possible asthmatic children represent a 

population that is more sensitive to the respiratory effects of SO2 than the individuals who have 

been examined to date. Moreover, we considered that approximately 5-30% of asthmatics who 

engaged in moderate or greater exertion experienced bronchoconstriction following exposure to 

200-300 ppb SO2, which are the lowest levels tested in free breathing chamber studies (ISA, 

Table 3-1). Thus, we concluded that it was highly likely that a subset of the asthmatic 

population would also experience bronchoconstriction following exposure to levels lower than 

200 ppb. 

 As an additional consideration, we noted that Figure 5-5 contains two epidemiologic 

analyses observing positive associations between ambient SO2 concentrations and hospital 

admissions in Canadian cities when 99th percentile 1-hour daily maximum SO2 levels were < 47 

ppb. More specifically, positive associations between SO2 and hospital admissions were found 

in Toronto, (Burnett al., 1997) and Vancouver (Yang et. al., 2003) when 99th percentile 1-hour 

daily maximum SO2 levels were approximately 21 ppb and 41 ppb, respectively. However, as 

previously noted, the 99th percentile 1-hour daily maximum SO2 concentrations reported for 

Canadian studies are not directly comparable to those reported for U.S. studies. That is, the 

concentrations reported for Canadian studies represent the average 98th or 99th percentile 1-hour 

daily maximum levels across multiple monitors in a given city (Figure 5-5), rather than 98th or 

99th percentile concentrations from the single monitor that recorded the highest SO2 levels 
July 2009 51

(Figures 5-1 to 5-4; see Thompson and Stewart, 2009). As a result, the SO2 concentrations 

presented in Figure 5-5 for Canadian studies would be relatively lower (potentially significantly 

lower) than those levels presented in Figures 5-1 to 5-4 for U.S. epidemiologic studies. In 

addition to these Canadian studies, we also noted that a U.S. study, Delfino et al. (2003), 

observed a statistically significant association between ambient SO2 and respiratory symptoms in 

Hispanic children when the 1-hour daily maximum SO2 concentration in Los Angeles was 26 

ppb (ISA Table 5-4). However, this epidemiologic study was very small (n=22), and did not 

examine potential confounding by co-pollutants. Thus, staff concluded that these three studies 

alone do not provide sufficient evidence for considering alternative 1-hour daily maximum SO2

standards below 50 ppb. 

 Staff noted that numerous studies reported positive associations between ambient SO2

and hospital admissions and ED visits in cities and time frames when 98th and/or 99th percentile 

1-hour daily maximum SO2 concentrations ranged from approximately 50 to 100 ppb (Figures 5-

1 to 5-5). Moreover, although most of these positive effect estimates were not statistically 

significant, there were some statistically significant results in single pollutant models (Portland, 

Wilson, 1995; Bronx, NYDOH, 2006; NYC, Ito, 2006; and Schwartz, 1995), as well as some 

evidence of statistically significant associations in multi-pollutant models with PM8 (Bronx, 

NYDOH, 2006 and NYC, Ito, 2007). Given these epidemiologic and air quality results, as well 

as the considerations mentioned above regarding the controlled human exposure evidence, staff 

concluded it was appropriate to examine a range of alternative standards in the air quality, 

exposure, and risk analyses that include a level of 50 ppb as the lower bound. We judged that a 

98th or 99th percentile 1-hour daily maximum standard at this level would both limit the number 

of days when 1-hour daily maximum SO2 levels are ≥ 50 ppb, while also limiting 5-10 minute 

peaks of SO2 ≥ 100 ppb. Moreover, we noted that a level of 50 ppb is substantially below the 

98th and 99th percentile 1-hour daily maximum SO2 levels observed in the Bronx during the 

NYDOH analysis and in NYC during the period analyzed by Ito et al., (2006): two studies where 

 

8

 In the NYDOH study (2006), the Bronx positive effect estimate remained statistically significant in the presence of 

PM2.5. In Ito et al., (2007), the NYC positive effect estimate was statistically significant in the presence of PM2.5 

during the warm season. We also note that in Schwartz et al., (1995), the positive effect estimate in New Haven, but 

not Tacoma remained statistically significant in the presence of PM10 when the 99th percentile 1-hour daily 

maximum SO2 concentration in New Haven was 150 ppb. 
July 2009 52

the SO2 effect estimate remained robust and statistically significant in multi-pollutant models 

with PM2.5 (ISA, Table 5-5). 

5.6 KEY OBSERVATIONS 

 Staff concluded that SO2 remains the most appropriate indicator for the potential 

alternative standards to be analyzed in the air quality, exposure, and risk analyses 

described in this document. 

 For the purpose of conducting quantitative air quality, exposure, and risk analyses, staff 

concluded that the focus should be on potential alternative SO2 standards with an 

averaging time of 1-hour. 

 Staff also considered examining alternative 5-minute standards in the risk and exposure 

assessment, but concluded that there was insufficient data to do so. However, this did not 

preclude the possibility of considering 5-minute standards as part of the policy 

assessment discussion in Chapter 10, or during the rulemaking process. 

 With regard to the form of the potential alternative standards to be analyzed in the air 

quality, exposure, and risk analyses, staff concluded that it was appropriate to consider 

the annual 98th and 99th percentile SO2 concentrations averaged over a 3 year period. 

Staff found that a concentration-based form better reflected the continuum of health risks 

posed by increasing SO2 concentrations, and provided greater regulatory stability than a 

form based on allowing only a single expected exceedance. 

 Based on findings from controlled human exposure and epidemiologic studies, and 

evaluation of air quality information from key U.S. and Canadian studies of ED visits and 

hospitalizations, staff concluded that it was appropriate to examine alternative 1-hour 

daily maximum standards in the air quality, exposure, and risk analyses in the range of 

50-250 ppb. 
July 2009 53

6. OVERVIEW OF RISK CHARACTERIZATION AND 

EXPOSURE ASSESSMENT

6.1 INTRODUCTION 

The assessments presented in the subsequent chapters of this document characterize 

short-term exposures (i.e., 5-minutes) and potential health risks associated with: (1) recent 

ambient levels of SO2, (2) levels associated with just meeting the current SO2 NAAQS, and (3) 

levels associated with just meeting several potential alternative standards (see Chapter 5 of this 

document for the discussion of potential alternative standards). To characterize health risks, we 

employed three approaches (Figure 6-1). With each approach, we characterize health risks 

associated with the air quality scenarios mentioned above (i.e., recent air quality unadjusted, air 

quality adjusted to simulate just meeting the current standards, and air quality adjusted to 

simulate just meeting several potential alternative standards). In the first approach, SO2 air 

quality levels are compared to potential health effect benchmark values (see section 6.2) derived 

from the controlled human exposure literature (Chapter 7). In the second approach, modeled 

estimates of human exposure are compared to the same potential health effect benchmark values 

derived from the human exposure literature (Chapter 8). In the third approach, outputs from the 

exposure analysis are combined with exposure-response functions derived from the human 

clinical literature to estimate the number and percent of exposed asthmatics that would 

experience moderate or greater lung function responses under the different air quality scenarios 

(Chapter 9). A more detailed overview of each of these approaches to characterizing health risks 

is provided below (section 6.3), and each approach is described in more detail in their respective 

chapters and associated appendices. In addition, this chapter also describes important 

methodologies used throughout these analyses. This includes the approach used to estimate 5-

minute SO2 concentrations from 1-hour data (section 6.4), how recent air quality was adjusted to 

simulate alternative air quality standards scenarios (section 6.5), and an overview of how 

uncertainty was characterized in each of the analyses performed (section 6.6). 
July 2009 54

Evaluation of Human Clinical 

Evidence in the ISA

5-10 minute exposures in 

exercising asthmatics

Air Quality Analysis in 

locations across U.S.

Exposure Analysis: modeling exercise that 

assesses the likelihood an asthmatic will be 

at moderate or greater exertion while 

experiencing a 5-minute daily maximum SO2

concentration above a benchmark level

Output1: Number of days per year 5-

minute daily maximum SO2

concentrations exceed 5-minute 

potential health benchmark values

Output 2: The probability of a daily 5-

minute exceedance of benchmark 

values given a particular 24-hour 

average, or 1-hour daily maximum SO2

concentration

Identification of 

potential 5-minute 

health benchmark 

values: 100- 400 ppb

Informs location 

selection

Output: Estimates the number and 

percent of asthmatics at elevated 

ventilation rates exposed to 5-minute 

daily maximum SO2 concentrations that 

exceed 5-minute potential health 

benchmark values

Estimation of 

exposure-response 

function

Quantitative Risk Analysis:

combines outputs of the exposure 

analysis with estimated exposureresponse function

Output: Estimates the number and 

percentage of exposed asthmatics that 

would experience moderate or greater 

lung function responses

# of exposed asthmatics

Evaluation of Human Clinical 

Evidence in the ISA

5-10 minute exposures in 

exercising asthmatics

Air Quality Analysis in 

locations across U.S.

Exposure Analysis: modeling exercise that 

assesses the likelihood an asthmatic will be 

at moderate or greater exertion while 

experiencing a 5-minute daily maximum SO2

concentration above a benchmark level

Output1: Number of days per year 5-

minute daily maximum SO2

concentrations exceed 5-minute 

potential health benchmark values

Output 2: The probability of a daily 5-

minute exceedance of benchmark 

values given a particular 24-hour 

average, or 1-hour daily maximum SO2

concentration

Identification of 

potential 5-minute 

health benchmark 

values: 100- 400 ppb

Informs location 

selection

Output: Estimates the number and 

percent of asthmatics at elevated 

ventilation rates exposed to 5-minute 

daily maximum SO2 concentrations that 

exceed 5-minute potential health 

benchmark values

Estimation of 

exposure-response 

function

Quantitative Risk Analysis:

combines outputs of the exposure 

analysis with estimated exposureresponse function

Output: Estimates the number and 

percentage of exposed asthmatics that 

would experience moderate or greater 

lung function responses

# of exposed asthmatics

Figure 6-1. Overview of analyses addressing exposures and risks associated with 5-minute peak 

SO2 exposures. All three outputs are calculated considering current air quality, air 

quality just meeting the current standards, and air quality just meeting potential 

alternative standards. Note: this schematic was modified from Figure 1-1. 

6.2 POTENTIAL HEALTH EFFECT BENCHMARK LEVELS 

Potential health benchmark values used in the air quality, exposure, and risk analyses 

were derived solely from the human exposure literature. This is primarily because 

concentrations used in human clinical studies represent actual personal exposures rather than 

concentrations measured at fixed site ambient monitors. In addition, human exposure studies can 

examine the health effects of SO2 in the absence of co-pollutants that can confound results in 

epidemiological analyses; thus, health effects observed in clinical studies can confidently be 

attributed to a defined exposure level of SO2. 

 The ISA presents human exposure evidence demonstrating decrements in lung function 

in approximately 5-30% of exercising asthmatics exposed to 200-300 ppb SO2 for 5-10 minutes. 
July 2009 55

However, it is important to note: (1) subjects in human exposure studies do not include 

individuals who may be most susceptible to the respiratory effects of SO2, (e.g., severe 

asthmatics and children) and (2) given that 5-30% of exercising asthmatics experienced 

bronchoconstriction following exposure to 200-300 ppb SO2 (the lowest levels tested in freebreathing chamber studies), it is likely that a percentage of asthmatics would also experience 

bronchoconstriction following exposure to levels lower than 200 ppb. That is, there is no 

evidence to suggest that 200 ppb represents a threshold level below which no adverse respiratory 

effects occur. We also noted that small SO2-induced lung function decrements have been 

observed in asthmatics at concentrations as low as 100 ppb when SO2 is administered via 

mouthpiece9

 (ISA, section 3.1.3). Considering this information, staff concluded it was 

appropriate to examine potential 5-minute benchmark values in the range of 100-400 ppb. The 

lower end of the range considers the factors mentioned above, while the upper end of the range 

recognizes that 400 ppb represents the lowest concentration at which statistically significant 

decrements in lung function are seen in conjunction with statistically significant respiratory 

symptoms. Moreover, we note that this range of benchmark values is in general agreement with 

consensus CASAC comments on earlier drafts of this document. 

As an additional matter, we note that in the outputs of the air quality and exposure 

analyses (see section 6.3), staff considered the number of days with a 5-minute maximum SO2

concentration above benchmark levels rather than all 5-minute exceedances of benchmark levels 

in a given day. This is because human exposure studies have suggested that after an initial SO2

exposure, there is approximately a 5-hour period of time when asthmatics are less sensitive to 

subsequent SO2 challenges (ISA, section 3.1.3.2). As a result, there is uncertainty as to whether 

an additional SO2 exposure(s) on a given day would be associated with an additional adverse 

respiratory outcome(s) (i.e., moderate decrements in lung function and/or respiratory symptoms). 

On the other hand, we recognize that not counting multiple exceedances in a day could possibly 

 

9

 Studies utilizing a mouthpiece exposure system cannot be directly compared to studies involving freely breathing 

subjects, as nasal absorption of SO2 is bypassed during oral breathing, thus allowing a greater fraction of inhaled 

SO2 to reach the tracheobronchial airways. As a result, individuals exposed to SO2 through a mouthpiece are likely 

to experience greater respiratory effects from a given SO2 exposure. In addition, the two mouthpiece studies cited in 

the ISA as exposing exercising asthmatics to 100 ppb SO2 (Koenig et al., 1990 and Sheppard et al., 1981) had a 

small number of exposures at this concentration (e.g., Sheppard et al., exposed two subjects to 100 ppb SO2) and 

observed very small changes in FEV1 or sRaw. Nonetheless, these studies do provide very limited evidence for 

SO2-induced respiratory effects at 100 ppb. 
July 2009 56

lead to an underestimate in the number of asthmatics experiencing an SO2 concentration above a 

benchmark level, and thus, an adverse respiratory outcome. Therefore, there is further 

discussion and/or analysis of this topic and its relevance to uncertainty in each of the air quality, 

exposure, and risk analysis outputs (see sections 7.4, 8.11 and 9.3). 

6.3 APPROACHES FOR ASSESSING EXPOSURE AND RISK 

ASSOCIATED WITH 5-MINUTE PEAK SO2 EXPOSURES 

In the first approach (i.e., the air quality characterization), we have compared SO2 air 

quality with the potential health effect benchmark levels for SO2. Scenario-driven air quality 

analyses were performed using ambient SO2 concentrations for the years 1997 though 2006. All 

U.S. monitoring sites where 1-hour SO2 data have been collected are represented by this analysis 

and, as such, the results generated are considered a broad characterization of national air quality 

and potential human exposures that might be associated with these concentrations.10 The output 

of the air quality characterization is an estimate of the number of exceedances of the potential 

health effect benchmark levels for several air quality scenarios. An advantage of this approach is 

its relative simplicity; however, there is uncertainty associated with the assumption that SO2 air 

quality can adequately serve as an indicator of exposure to ambient SO2. Actual exposures will 

be influenced by factors not considered by this approach, such as the spatial and temporal 

variability in human activities. 

In the second approach (i.e., the exposure assessment), we have used an inhalation 

exposure model to generate estimates of personal SO2 exposures. The estimates of personal 

exposure have also been compared to the potential health benchmark levels as was done in the 

air quality characterization. This results in estimates of the number of individuals that are likely 

to experience exposures exceeding these benchmark levels. For this exposure analysis, a 

probabilistic approach was used to model individual exposures considering the time people 

spend in different microenvironments and the variable SO2 concentrations that occur within these 

microenvironments across time, space, and microenvironment type. The exposure model also 

accounts for activities that individuals perform within the microenvironments, allowing for 

estimation of exposures that coincide with varying activity levels. As such, this approach to 

 

10 Two additional subsets of the broader SO2 monitoring network were also used in detailed analyses, thus by 

definition are not representative of the full set of monitors in the U.S. 
July 2009 57

assessing exposures was more resource intensive than evaluating ambient air quality; therefore, 

staff has included the analysis of two specific locations in the U.S. (Greene County, MO. and St. 

Louis, MO.)11 Although the geographic scope of this analysis is restricted, the approach 

provides realistic estimates of SO2 exposures, particularly those exposures associated with 

important emission sources of SO2 and serves to complement the broad air quality 

characterization. 

Staff used a range of short-term potential health effect benchmarks to characterize risk in 

both the air quality and the exposure modeling analyses described above. The levels of potential 

benchmarks are based on SO2 exposure levels that have been associated with respiratory 

symptoms and decrements in lung function in exercising asthmatics during controlled human 

exposure studies (ISA, section 5.2; see above section 6.2 for discussion). Benchmark values of 

100, 200, 300, and 400 ppb have been compared to both SO2 air quality (measured and modeled 

5-minute SO2 concentrations) and to estimates of SO2 exposures. In characterizing the SO2 air 

quality using ambient monitors, the output of the analysis is an estimate of the number of days 

per year specific locations experience 5-minute daily maximum levels of SO2 above a particular 

benchmark. When personal exposures are simulated, the output of the analysis is an estimate of 

the number of individuals at risk for experiencing daily maximum 5-minute levels of SO2 of 

ambient origin that exceed a particular benchmark. 

In the third approach (i.e., the quantitative risk assessment), we combine outputs from the 

exposure analysis with exposure-response functions derived from controlled human exposure 

studies. This analysis estimates the percentage and number of asthmatics likely to experience a 

given decrement in lung function associated with recent air quality and SO2 levels adjusted to 

simulate just meeting the current and potential alternative standards. Staff concluded that it was 

appropriate to limit the scope of the quantitative risk assessment to lung function responses based 

on findings from the controlled human exposure studies and the basis for this decision is 

described below. 

 

11 In the 1st draft REA, staff presented the results of an exposure analysis for Greene County (or Springfield, MO.) 

and several other source-based modeling domains in Missouri. Based on CASAC comments received on that 

exposure analysis, staff refined the modeling approach and applied those refinements to the Greene County analysis 

presented in the 2nd draft REA and completed the exposure assessment in St. Louis which had been started at the 

time of the 1st draft REA. 
July 2009 58

As discussed above in Chapter 4, the ISA concludes that the overall weight of the 

evidence supports a causal relationship between short-term SO2 exposure and respiratory 

morbidity. The ISA states that the “definitive evidence” for its causal determination is from 

controlled human exposure studies demonstrating decrements in lung function and/or respiratory 

symptoms in exercising asthmatics exposed to ≥ 200 ppb SO2 (ISA, section 5.2). The ISA 

further notes that supporting this causal determination is a larger body of U.S and international 

epidemiological studies examining respiratory symptoms and ED visits and hospitalizations for 

all respiratory causes and asthma (ISA, section 5.2). 

As previously described, staff is utilizing both the epidemiological evidence in the ISA, 

and an air quality analysis based on U.S. and Canadian ED visit and hospitalization studies for 

all respiratory causes and asthma (Figures 5-1 to 5-5), to qualitatively inform: (1) the selection of 

potential 1-hour daily maximum alternative standards to be analyzed in the air quality, exposure, 

and risk chapters of this document (see Chapter 5), and (2) the adequacy of the current standard 

and consideration of potential alternative standards (Chapter 10). However, staff did not find the 

overall breadth of the epidemiological evidence was robust enough to support a quantitative 

assessment of risk. 

We first note that for purposes of conducting a quantitative risk assessment for locations 

in the U.S., staff concludes that only U.S. studies should be considered given differences in 

monitoring networks, levels of co-pollutants, and other factors across different locations that may 

well alter SO2-concentration-response relationships. Taking this into account, we reviewed the 

available epidemiological literature and found relatively few studies that focused on these 

endpoints were conducted in U.S. cities. In those U.S. cities where epidemiological studies had 

been conducted, many of the SO2 effect estimates were positive, but not statistically significant 

in single pollutant models. Moreover, in the relatively few studies that employed multi-pollutant 

models, inclusion of PM10 in the model resulted in a loss of statistical significance for the SO2

effect estimate in about half of these studies (although the effect estimate may have remained 

positive). Overall, we conclude that these factors would make it particularly difficult to quantify 

with confidence the magnitude of respiratory health effects related to SO2 exposures and 

therefore, we judge that the results of a quantitative risk assessment based on concentrationresponse functions from epidemiological studies for these health outcomes would be of limited 
July 2009 59

utility in the decision-making process given the nature of the uncertainties associated with these 

studies. 

6.4 APPROACH FOR ESTIMATING 5-MINUTE PEAK SO2

CONCENTRATIONS 

Health effects evaluated in this REA include those associated with 5-10 minute peak 

concentrations of SO2. While there are 98 ambient monitors that have reported 5-minute SO2

concentrations some time during 1997-2007, the spatial and temporal representation is limited to 

a few states and often only a few years of monitoring. Most of these monitors report the 5-

minute maximum SO2 concentration occurring within an hour, though there were some that 

reported all twelve continuous 5-minute SO2 concentrations measured within the hour. The 

ambient monitors reporting continuous SO2 values are limited to fewer locations and number of 

monitoring years, with sixteen monitors deployed within six US states and Washington DC, ten 

of which operated only during one year. The overwhelming majority of the SO2 ambient 

monitoring data are for 1-hour average concentrations (upwards to 935 monitors), comprising a 

broad monitoring network that includes most U.S. states and territories. Because the health 

effects of greatest interest were associated with short-term exposures (5-10 minutes) and a 

greater number of monitors and monitor-years were available for the 5-minute maximum SO2

concentrations than 10-minute maximum concentrations, a model was developed to estimate 5-

minute maximum SO2 concentrations from the comprehensive 1-hour SO2 ambient monitoring 

data. 

Staff first reviewed the air quality characterization conducted in the prior SO2 NAAQS 

review and supplementary analyses. In these prior analyses, relationships between maximum 5-

minute SO2 concentrations and the 1-hour average SO2 concentrations, or peak-to-mean ratios 

(PMRs) were evaluated and used to approximate 5-minute maximum SO2 concentrations from 1-

hour values (EPA, 1986a; EPA, 1994b; SAI, 1996; Thompson, 2000). While the relationship 

between the two metrics is not expected to be linear, the temporal patterns in the two averaging 

times are consistent. Five-minute maximum SO2 concentrations are often much greater than that 

of the corresponding 1-hour SO2 concentrations, and observed increases in a given 1-hour SO2

concentration often coincide with increases in the 5-minute maximum SO2 concentration. As an 

example of this pattern, the time-series of 1-hour average and 5-minute maximum SO2
July 2009 60

concentrations measured at an ambient monitor across a 3-day period in 2005 is illustrated in 

Figure 6-2. 

March 10-12, 2005 - ID 290770037

0

50

100

150

200

250

300

3/10/2005 0:003/10/2005 4:003/10/2005 8:00

3/10/2005 12:003/10/2005 16:003/10/2005 20:00

3/11/2005 0:003/11/2005 4:003/11/2005 8:00

3/11/2005 12:003/11/2005 16:003/11/2005 20:00

3/12/2005 0:003/12/2005 4:003/12/2005 8:00

3/12/2005 12:003/12/2005 16:003/12/2005 20:00

Hour of Day

SO2 Concentration (ppb)

5-minute maximum

1-hour

Figure 6-2. Example of an hourly time-series of measured 1-hour and measured 5-minute 

maximum SO2 concentrations. 

In general, PMRs were determined to be approximately two in some of the earlier studies 

when used in estimating 5-minute peak SO2 concentrations; though for the exposure analyses 

conducted for the last NAAQS review, a distribution of PMRs was used with values of up to 

eleven (EPA, 1994b). In each of the analyses conducted previously, estimates of PMRs were 

derived using ambient monitoring data (i.e., where both 5-minute maximum and 1-hour average 

SO2 were measured) and then used to estimate the occurrence of peak 5-minute SO2

concentrations given a 1-hour ambient SO2 concentration. The approach was generally as 

follows: 

C 5max  CPMR 1hour equation (6-1) 
July 2009 61

where, 

Cmax-5 = estimated 5-minute maximum SO2 concentration (ppb) 

PMR = peak-to-mean ratio (PMR) 

C1-hour = measured 1-hour average SO2 concentration 

 At the time of the last NAAQS review, there were very few monitors reporting 5-minute 

SO2 data. In fact, distributions of PMRs from ambient monitors surrounding a single coal-fired 

power utility served as the primary source used in estimating 5-minute peak concentrations used 

in the exposure analyses (EPA, 1994b). As mentioned above, the PMRs were determined to be 

approximately two in these earlier studies; however, the ratio can vary depending on a several 

factors. It has been shown that there can be increased variability in the ratio with decreasing 1-

hour average SO2 concentrations, that is, there is a greater likelihood of values greater than two 

at low hourly average concentrations than expected at high hourly average concentrations (EPA, 

1986a). It has also been argued that the occurrence of short-term peak concentrations at ambient 

monitors may be influenced by particular SO2 emission sources (EPA, 1994b). Different sources 

have variable emission amounts, temporal operating patterns (e.g., seasonal, time-of-day), 

facility maintenance, and other physical parameters (e.g., stack height, area terrain) that likely 

contribute to variability in 5-minute maximum SO2 concentrations. In addition, a sensitivity 

analysis conducted for copper-smelters determined that distance from the source was inversely 

proportional to the PMR in all three of the 1-hour mean stratifications evaluated (i.e., ≤ 0.04 

ppm, 0.04 to ≤ 0.15 ppm, and >0.15 ppm), with the highest 1-hour category having the lowest 

range of PMR (Sciences International, 1995).12 

There are some data available for the current SO2 monitoring network regarding the type 

of sources that may be near the ambient monitors, the magnitude of emissions, the temporal 

variation in emissions, and distance from specific sources; however, staff determined that there 

was no practical way to define every ambient monitor as being exclusively influenced by a single 

source or a defined mix of sources. Given other conditions that may vary within a specific 

source category (monitor-to-source distances, local meteorology, operating conditions, etc.), staff 

also determined that there was no practical way to use such data quantitatively in the 

 

12 In that analysis, normalized 1-hour SO2 concentrations were obtained by dividing by the maximum hourly 

concentration. 
July 2009 62

construction of the PMR statistical model and apply such a model to the 1-hour SO2 ambient 

monitor data. 

In recognizing the limited geographic span of the monitors reporting the 5-minute 

maximum SO2 concentrations and the overall uncertainty regarding the amount of influence of a 

specific source on any given monitor, staff developed an approach based on hourly SO2

concentration levels and the variability observed at the monitors reporting both the 5-minute 

maximum and 1-hour average SO2 concentrations. The main assumption in the approach is that 

the temporal and spatial pattern in SO2 source emissions is influenced by the type of source(s) 

present, its operating conditions, and that the emission pattern(s) is reflected in the ambient SO2

concentration distribution measured at the monitor. Thus, measures of concentration level and 

associated variability at each monitor were used as a surrogate for the variability in the source 

characteristics that may impact concentrations at a particular monitor. Each monitor reporting 5-

minute maximum SO2 concentrations was categorized based on the coefficient of variation 

(COV) of 1-hour average SO2 concentrations and then used to estimate distribution of PMRs for 

range of 1-hour SO2 concentrations. This approach, that fully utilizes all of the available 5-

minute maximum SO2 data, is detailed in section 7.2.3. 

6.5 APPROACH FOR SIMULATING THE CURRENT AND 

ALTERNATIVE AIR QUALITY STANDARD SCENARIOS 

A primary goal of the risk and exposure assessments described in this document is to 

evaluate the ability of the current SO2 primary standards (30 ppb annual average, 140 ppb 24-

hour average)13 and potential alternative standards (99th percentile 1-hour daily maximum SO2

levels of 50, 100, 150, 200, and 250 ppb, and 98th percentile 1-hour daily maximum SO2 levels: 

200 ppb; see Chapter 5 of this document) to protect public health. To evaluate the ability of a 

specific standard to protect public health, ambient SO2 concentrations need to be adjusted such 

that they simulate levels of SO2 that just meet that standard. Such adjustments allow for 

comparison of the level of public health protection that could be associated with just meeting the 

current and potential alternative standards. 

 

13 For consistency, the concentration units in this chapter are reported as ppb, even though the SO2 NAAQS have 

units of ppm. 
July 2009 63

All areas of the United States currently have ambient SO2 levels below the current annual 

standard (EPA, 2007c). One site in Northampton County, Pa., measured concentrations above 

the level of the 24-hour standard in 2006. Therefore, to evaluate whether the current standards 

adequately protect public health, nearly all SO2 concentrations need to be adjusted upwards in all 

areas included in our assessment to simulate levels of SO2 that would just meet the current 

standard levels. Similarly, to simulate a potential air quality standard that is below current air 

quality standards, those current levels must be adjusted downward. 

Ambient SO2 concentrations and exposures were characterized by considering as is air 

quality (unadjusted concentrations) and several hypothetical air quality scenarios. Each of the 

hypothetical air quality scenarios had an ambient concentration target, derived from the form and 

level of the current NAAQS or from potential alternative standards. Staff chose a proportional 

approach to adjust the SO2 concentrations to simulate each of the current and alternative air 

quality standard scenarios.14 A proportional approach was selected based on the mostly linear 

relationship between older high concentration years of air quality when compared with recent 

low concentration years at several locations (Rizzo, 2009). Briefly, for each location of interest 

(i) and year (j), SO2 concentration adjustment factors (F) were derived by the following 

equation: 

ij CSF max,ij  / equation (6-2) 

where, 

Fij = Adjustment factor derived from the air quality standard target 

concentration in location i and year j(unitless) 

S = concentration values allowed that would just meet the air quality 

standard level (ppb) 

Cmax,ij = maximum measured SO2 concentration given particular form of 

standard at a monitor in location i and year j (ppb) 

 

14 The particular equation used to derive each of the adjustment factors is dependent on the form and level of the 

standard considered, however the equations all share proportionality between the target level and ambient 

concentration. To evaluate the current and alternative air quality scenarios in the exposure assessment (Chapter 8), 

a mathematically equivalent proportional approach was used to adjust the benchmark levels rather than adjusting the 

ambient concentrations as done for the air quality characterization (Chapter 7). 
July 2009 64

In these cases where staff simulated a proportional adjustment in ambient SO2

concentrations using equation (6-2), it was assumed that the current temporal and spatial 

distribution of air concentrations (as characterized by the current air quality data) is maintained 

and increased SO2 emissions contribute to increased SO2 concentrations. All the hourly SO2

concentrations in a location were multiplied by the same constant value F, whereas the highest 

monitor (in terms of concentration) is adjusted such that it just meets the standard target level. 

This procedure for adjusting either the ambient concentrations (i.e., in the air quality 

characterization) or health effect benchmark levels (i.e., in the exposure assessment) was 

necessary to provide insight into the degree of exposure and risk which would be associated with 

an increase in ambient SO2 levels such that the levels were just at the current standards in the 

areas analyzed. Staff recognizes that it is extremely unlikely that SO2 concentrations in any of 

the selected areas where concentrations have been adjusted would rise to meet the current 

NAAQS and that there is considerable uncertainty associated with the simulation of conditions 

that would just meet the current standards. Nevertheless, this procedure was necessary to assess 

the ability of the current standards, not current ambient SO2 concentrations, to protect public 

health. This process of adjusting SO2 concentrations to simulate just meeting a specific standard 

is described in more detail in sections 7.2.4 and 8.8.1. 

6.6 APPROACHES FOR CHARACTERIZING VARIABILITY AND 

UNCERTAINTY 

An important issue associated with any population exposure or risk assessment is the 

characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in 

a population or variable of interest (e.g., residential air exchange rates) and cannot be reduced 

through further research, only better characterized with additional measurement. Uncertainty

categorically refers to the lack of knowledge regarding the values of model input variables (i.e., 

parameter uncertainty), the physical systems or relationships used (i.e., use of input variables to 

estimate exposure or risk or model uncertainty), and in specifying the scenario that is consistent 

with purpose of the assessment (i.e., scenario uncertainty). Uncertainty is, ideally, reduced to 

the maximum extent possible through improved measurement of key parameters and iterative 

model refinement. The approaches used to assess variability and characterize uncertainty in this 

REA are discussed in the following two sections. 
July 2009 65

6.6.1 Characterization of Variability 

The purpose for addressing variability in this REA is to ensure that the characterization of 

air quality and the estimates of exposure and risk reflect the variability of ambient SO2

concentrations and associated SO2 exposure and health risk across the study locations and 

population. In this REA, there are several algorithms that account for variability of input data 

when generating the number of estimated benchmark exceedances or health risk outputs. For 

example, variability may result from the number of monitors operating in an area and their 

associated temporal and spatial heterogeneity in ambient SO2 concentrations. Variability may 

also arise from differences in the population residing within a census block (e.g., age 

distribution) and the activities that may affect SO2 population exposure (e.g., time spent 

outdoors), and/or the influential risk factors (e.g., the fraction of the population responding to an 

SO2 exposure). A complete range of potential exposure levels and associated risk estimates can 

be generated when appropriately addressing variability in exposure and risk assessments; note 

however that the range of values obtained would be within the constraints of the algorithm or 

modeling system used, not the complete range of the true exposure or risk values. 

Where possible, staff identified and incorporated any observed variability in input data 

sets and estimated parameters within each of the analyses performed in Chapters 7-9 rather than 

employing standard default assumptions and/or using point estimates to describe model inputs. 

The details regarding variability distributions used in data inputs are described in the methods 

sections of each assessment and summarized in sections 7.4, 8.11, and 9.3 for the air quality 

characterization, the exposure assessment, and the risk characterization, respectively. 

6.6.2 Characterization of Uncertainty 

While it may be possible to capture a full range of exposure or risk values by accounting 

for variability inherent to influential factors, the true exposure or risk for any given individual is 

largely unknown. To characterize health risks, exposure and risk assessors commonly use an 

iterative process of gathering data, developing models, and estimating exposures and risks, given 

the goals of the assessment, scale of the assessment performed, and the limitations of the input 

data available. However, significant uncertainty often remains and emphasis is then placed on 

characterizing the nature of that uncertainty and its impact on exposure and risk estimates. 
July 2009 66

The characterization of uncertainty can include either qualitative or quantitative 

evaluations, or a combination of both. The approach can also be tiered, that is, the analysis can 

begin with a simple qualitative uncertainty characterization then progress to a complex 

probabilistic analysis. This could follow when a lower tier analysis indicates a high degree of 

uncertainty for certain identified sources, the sources are highly influential to exposure and risk 

estimates, and sufficient information and resources are available to conduct a quantitative 

uncertainty assessment. This is not to suggest that quantitative uncertainty analyses should 

always be performed in all exposure and risk assessments. The decision regarding the type of 

uncertainty characterization performed is also be informed by the intended scope and purpose of 

the assessment, whether the selected analysis will provide additional information to the overall 

decision regarding health protection, whether sufficient data are available to conduct a complex 

quantitative analysis, and if time and resources are available for higher tier characterizations 

(EPA, 2004b; WHO, 2008). 

The primary purpose of the uncertainty characterization approach selected in this REA is 

to identify and compare the relative impact important sources of uncertainty may have on the 

potential health effect benchmarks and/or respiratory effects endpoints estimated in Chapters 7-9. 

The approach used to evaluate uncertainty was adapted from guidelines outlining how to conduct 

a qualitative uncertainty characterization (WHO, 2008), though staff also performed several 

quantitative sensitivity analyses to iteratively inform both model development and the qualitative 

uncertainty characterization, where possible. While it may be considered ideal to follow a tiered 

approach in the REA to quantitatively characterize all identified uncertainties, staff selected the 

mainly qualitative approach given the limited data available to inform probabilistic analyses, and 

time and resource constraints. 

The qualitative approach used in this REA varies from that of WHO (2008) in that a 

greater focus of the characterization performed was placed on evaluating the direction and the 

magnitude15 of the uncertainty; that is, qualitatively rating how the source of uncertainty, in the 

presence of alternative information, may affect the estimated air quality, exposure, and health 

risk assessment results. In addition and consistent with the WHO (2008) guidance, staff discuss 

the uncertainty in the knowledge-base (e.g., the accuracy of the data used, acknowledgement of 

 

15 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2. 
July 2009 67

data gaps) and decisions made (e.g., selection of particular model forms), though qualitative 

ratings were assigned only to uncertainty regarding the knowledge-base. 

First, staff identified the key sources of the assessment that may contribute to uncertainty 

in the air quality, exposure, and risk estimates and provide the rationale for their inclusion. 

Then, staff characterized the magnitude and direction each identified source of uncertainty 

influences the assessment results. Consistent with the WHO (2008) guidance, staff subjectively 

scaled the overall impact of the uncertainty by considering the degree of severity of the 

uncertainty as implied by the relationship between the source of the uncertainty and the output of 

the air quality characterization. Where the magnitude of uncertainty was rated low, it was judged 

that large changes within the source of uncertainty would have only a small effect on the 

assessment results. A designation of medium implies that a change within the source of 

uncertainty would likely have a moderate (or proportional) effect on the results. A 

characterization of high implies that a small change in the source would have a large effect on 

results. Staff also included the direction of influence, indicating how the source of uncertainty 

was judged to affect estimated benchmark exceedances or risk estimates; either the estimated 

values were likely over- or under-estimated. In the instance where the component of uncertainty 

can affect the assessment endpoint in either direction, the influence was judged as both. Staff 

characterized the direction of influence as unknown when there was no evidence available to 

judge the directional nature of uncertainty associated with the particular source. Staff also 

subjectively scaled the knowledge-base uncertainty associated with each identified source using 

a three level scale: low indicated significant confidence in the data used and its applicability to 

the assessment endpoints, medium implied that there were some limitations regarding 

consistency and completeness of the data used or scientific evidence presented, and high

indicated the knowledge-base was extremely limited. 

The output of the uncertainty characterization was a summary describing, for each 

identified source of uncertainty, the magnitude of the impact and the direction of influence the 

uncertainty may have on the air quality, exposure, and risk characterization results. And finally, 

an evaluation of the uncertainties presented in Chapters 7-9 is discussed in Chapter 10, providing 

the overall implications in informing staff’s evaluation of exposures and risks associated with 
July 2009 68

level, form, and averaging time related to judging the adequancy of the current standard and 

consideration of potential alternative primary SO2 standards. 

6.7 KEY OBSERVATIONS 

 Potential health effect benchmark values were derived from the controlled human 

exposure literature. 

 Staff concluded that there is no evidence from human exposure studies to suggest that 

200 ppb represents a threshold level below which no adverse respiratory effects occur. 

 Staff concluded that it was appropriate to consider 5-minute benchmark levels in the 

range of 100 to 400 ppb in the air quality and exposure analyses.
July 2009 69

7. AMBIENT AIR QUALITY AND BENCHMARK HEALTH RISK 

CHARACTERIZATION FOR 5-MINUTE PEAK SO2 EXPOSURES 

7.1 OVERVIEW 

Ambient monitoring data for each of the years 1997 through 2007 were used in this 

chapter to characterize SO2 air quality across the U.S. The measured air quality, as well as 

additional SO2 concentrations derived from the measured air quality data, were used as an 

indicator of potential human exposure. While an ambient monitor measures SO2 concentrations 

at a stationary location, the monitor may well represent the concentrations to which persons 

residing nearby are exposed. The quality of the extrapolation of ambient monitor concentration 

to personal exposure depends upon the spatial representativeness of the monitoring network, the 

corresponding spatial distribution of important emission sources, local meteorological conditions 

and geographical features, and a consideration of places that persons visit. Staff considers the 

analyses presented in this chapter to be a broad characterization of national air quality and 

potential human exposures that might be associated with a variety of scenario-driven 

concentrations. This is because many of the SO2 ambient monitoring sites used in this analysis 

target public health monitoring objectives and some of the analysis results were separated by the 

population density surrounding the ambient monitors. 

As previously discussed in Chapter 4, the ISA finds the evidence for an association 

between respiratory morbidity and SO2 exposure to be “sufficient to infer a causal relationship” 

(ISA, section 5.2). The ISA states that the “definitive evidence” for this conclusion comes from 

the results of human exposure studies demonstrating decrements in lung function and/or 

respiratory symptoms in exercising asthmatics following exposure to SO2 levels as low as 200 to 

300 ppb for 5-10 minutes (ISA, section 5.2). Accordingly, 5-minute potential health effect 

benchmark levels ranging from 100-400 ppb were derived from the human exposure literature 

(see section 6.2 for benchmark level rationale) and compared to measured and statistically 

modeled 5-minute ambient concentrations. A broad analysis is first presented that evaluates the 

potential health risk at all ambient monitors, and then for more detailed analyses, at monitors 

located within selected U.S. counties (see section 7.2.4). Staff estimated the number of days in a 

year with 5-minute benchmark exceedances and the probability of benchmark exceedances given 
July 2009 70

the occurrence of 1-hour daily maximum or 24-hour average SO2 concentrations at ambient 

monitors. 

All ambient SO2 monitors report hourly concentrations; a subset of those report 5-minute 

maximum SO2 concentrations as well, with a subset of these reporting continuous 5-minute SO2

concentrations. Because there were two distinct sample averaging times reported for the 

available ambient monitoring data (i.e., ambient monitors reporting 1-hour SO2 concentration 

measurements alone and monitors reporting both 5-minute and 1-hour average SO2

concentrations), the data used in the analyses were separated by staff as follows. 

The first set of ambient air quality data was from monitors reporting both 5-minute and 1-

hour SO2 concentrations. Staff 1) analyzed the ambient monitoring data for trends in 1-hour and 

5-minute SO2 concentrations, 2) counted the number of measured daily 5-minute maximum SO2

concentrations above the potential health effect benchmark levels given the annual average SO2

concentrations, 3) estimated the probability of benchmark exceedances given the 24-hour 

average and 1-hour daily maximum SO2 concentrations, 4) developed a statistical model to 

estimate 5-minute maximum SO2 concentrations from 1-hour SO2 concentrations, and 5) 

evaluated the performance of the statistical model by comparing the model’s predicted versus 

measured numbers of exceedances (see section 7.2.3). 

The second set of ambient data was comprised of 1-hour SO2 concentrations from the 

broader SO2 monitoring network; therefore this set also included 1-hour SO2 concentrations from 

those monitors where 5-minute SO2 data were reported, though the vast majority of the 1-hour 

data were from monitors that did not report 5-minute concentration measurements. Staff applied 

the statistical model that related 5-minute to 1-hour SO2 measurements to this second set of 

ambient monitoring data to estimate 5-minute maximum SO2 concentrations. As was done with 

the 5-minute SO2 ambient measurement data, staff 1) evaluated trends in SO2 concentrations, 2) 

counted the number of statistically modeled potential health effect benchmark exceedances in a 

day using the same longer-term averaging times, and 3) estimated the probability of peak 

concentrations associated with 1-hour daily maximum and 24-hour average SO2 concentrations. 

Staff considered three data analysis groups to characterize the ambient SO2 air quality. In 

the first group, we evaluated the combined 5-minute and 1-hour SO2 measurement data as they 

were reported, representing the conditions at the time of monitoring (termed in this assessment 
July 2009 71

“as is”). The second group also considered the as is air quality; however staff analyzed the 

statistically modeled 5-minute SO2 concentrations that were generated from as is 1-hour SO2

measurements. This second data analysis group expanded the geographic scope of the 5-minute 

air quality characterization by using the broader SO2 monitoring network. The third data 

analysis group considered 1-hour SO2 concentrations adjusted to just meeting the current 

NAAQS16 and each of the potential alternative 1-hour daily maximum standard levels of 50, 100, 

150, 200 and 250 ppb (see Chapter 5 for details). The data used to simulate the current and 

alternative standard scenarios were limited to the most recent and comprehensive ambient 

monitoring data available (i.e., 2001-2006) in forty selected U.S. counties.17 Due to the form of 

the potential alternative standards considered here (98th and 99th percentiles of the 1-hour daily 

maximum concentrations averaged over 3 years), the recent ambient monitoring data set was 

evaluated using two three-year periods, 2001-2003 and 2004-2006.18 Whereas the first analysis 

group used entirely 1-hour and 5-minute SO2 measurement data, the second and third analysis 

groups used statistically modeled 5-minute SO2 concentrations that were generated from 1-hour 

SO2 concentrations. The third data analysis group also included an adjustment of the 1-hour SO2

concentrations to evaluate several air quality standard scenarios in 40 selected counties. 

Staff expected that there would be variability in the number of persons living within close 

proximity of each monitor (both the 5-minute and 1-hour SO2 monitors) given the particular 

siting characteristics of the ambient monitors (e.g., either source- or population-oriented 

monitoring objectives). Therefore, we separated some of the air quality results within each 

scenario by using the population density surrounding each ambient monitor. First, each monitor 

was characterized by having one of three population densities (i.e., low, medium, and high), 

groupings defined by the three characteristic regions of the population distribution generated 

from the broader SO2 monitoring network (section 7.2.2). Then, staff counted the number days 

 

16 Just meeting the current NAAQS levels could either be meeting a 30 ppb annual average or the 140 ppb 24-hour 

average concentration (one allowed exceedance), whichever is the controlling standard at that ambient monitor (see 

section 7.2.4). 

17 At the time of the initial data download from the AQS data mart, many of the monitors did not have complete 

years of data available for 2007, therefore the most recent data for most monitors was from 2006. These complete 

site-year data are a subset of the broader ambient monitoring data set available. 

18 A number of 3-year groups are within 2001-2006 (e.g., 2001-2003, 2002-2004, etc.) and a number of years of 

monitoring data are outside the 2001-2006 time frame that could have been used in an extended 3-year grouping of 

2001-2006 air quality (e.g., 2000-2002). For convenience, the upper and lower groupings were chosen by staff to 

represent 3-year air quality within the 6-year period when considering just meeting the potential alternative 

standards. 
July 2009 72

with 5-minute benchmark exceedances per year at each monitor, either measured or estimated 

depending on the data analysis group considered, and aggregated the results by the population 

density group. Rather than count the total number of 5-minute SO2 concentrations above a 

particular benchmark, staff calculated the number of days in a year with a 5-minute SO2

concentration above a potential health effect benchmark.19

One output of this air quality characterization is an estimate of the number of days per 

year a monitor experienced 5-minute SO2 concentration above those that may cause adverse 

health effects in susceptible individuals (i.e., benchmark level exceedances). These counts are a 

useful metric in comparing one ambient monitor or monitoring location to another and in 

identifying where and when frequent benchmark exceedances could occur. However, earlier 

analyses indicated that the relationship between the annual average SO2 concentration and the 

number of 5-minute benchmark exceedances was generally weak (1st draft SO2 REA). 

Therefore, a comparison of the number of days/year with benchmark exceedances to the annual 

average SO2 concentration is of limited use. This absence of a strong relationship highlights the 

ineffectiveness of long-term averaged concentrations in controlling short-term peak 

concentrations. Furthermore, while there was an improved relationship between the number of 

5-minute maximum SO2 concentrations and 24-hour average concentrations, it was also shown 

that the number of benchmark exceedances in a day was variable given a specific 24-hour 

average concentration.20 For example, there could be as many as five 5-minute maximum SO2

concentrations above a selected benchmark levels at a particular 24-hour average SO2

concentration, while in other instances there may be no benchmark exceedances at the same 24-

hour concentration. 

Given that there is variability in the number of 5-minute peak SO2 concentrations 

associated with concentrations of longer-term averaging times, that a daily maximum 5-minute 

SO2 concentration was the metric of interest, and that the potential alternative standards 

 

19 In the 1st draft SO2 REA, as well as the early draft NO2 REAs, all benchmark exceedances for any hour of the day 

were reported. The use of the daily maximum exceedance was selected in the final NO2 REA as well in the 2nd draft 

and final SO2 REA to improve the temporal perspective for the metric in the air quality analysis (i.e., the number of 

daily maximum exceedances also gives the number of days in a year with an exceedance of a selected benchmark), 

and to be consistent with the exposure and risk analyses. The implication of not counting multiple exceedances is 

discussed further in sections 7.4, 8.11, and 10.3.3.1. 

20 In the 1st draft SO2 REA, multiple exceedances within a day (if any) were counted. In the 2nd draft and final SO2

REA, there is only one counted maximum exceedance per day. Additional analysis of multiple exceedances within 

the day is given in section 8.11.211. 
July 2009 73

investigated use 1-hour daily maximum SO2 concentrations, staff decided that an appropriate 

comparison would be between the frequency of peak 5-minute SO2 concentrations given 1-hour 

daily maximum SO2 concentrations. Thus, the second output of this air quality characterization 

is presented as the probability of a benchmark exceedance given a daily maximum 1-hour SO2

concentration. In addition, the probability of a 5-minute benchmark exceedance given a 24-hour 

average concentration is also provided to offer additional perspective on this averaging time. 

7.2 APPROACH 

There were five broad steps to characterize the SO2 air quality. The first step involved 

compiling and screening the ambient air quality data collected since 1997 to ensure consistency 

with the SO2 NAAQS requirements and for usefulness in this air quality characterization. Next, 

due to potential variable influence of SO2 emission sources on ambient monitor concentrations, 

the monitors from each of the two data sets (i.e., combined 5-minute and 1-hour, broader 1-hour 

only) were categorized and evaluated according to their monitoring site attributes, including land 

use characteristics, location type, monitoring objective, distance to emissions sources, and 

population density. In addition, the variability in 5-minute and 1-hour SO2 concentrations was 

evaluated and used to categorize each ambient monitor. Staff used concentration variability in 

the development and application of a statistical model used to estimate 5-minute maximum SO2

concentrations. Then, a concentration adjustment approach was developed and applied in 

selected locations to evaluate several air quality scenarios. And finally, air quality metrics of 

interest (i.e., the number and probability of potential health effect benchmark exceedances) were 

calculated using the air quality data from each scenario. 

The following provides an overview of the five steps used to characterize air quality and 

summarizes key portions of the analysis. Briefly, the five steps include: 1) screening of air 

quality data; 2) evaluation of site characteristics of ambient SO2 monitors; 3) development of a 

statistical model to estimate 5-minute maximum SO2 concentrations; 4) adjustment of air quality; 

and 5) generation of air quality metrics. Details regarding the ambient monitors used for 

characterizing air quality and associated descriptive meta-data are provided in Appendix A.1.

7.2.1 Screening of Air Quality Data 

SO2 air quality data and associated documentation from the years 1997 through 2007 

were downloaded from EPA’s Air Quality System for this analysis (EPA, 2007c, h). Data 
July 2009 74

obtained were used as reported by these sources; there were no substitutions performed for any 

missing or zero concentration data. The total available SO2 ambient monitoring data, reported 

for either 5-minute or 1-hour averaging times, are summarized in Table 7-1. The 5-minute SO2

monitoring data existed in either one of two forms; the single highest 5-minute concentration 

occurring in a 1-hour period (referred to here as max-5 data set), or all twelve 5-minute 

concentrations within a 1-hour period (referred to here as continuous-5 data set). 

Table 7-1. Summary of all available 5-minute and 1-hour SO2 ambient monitoring data, years 1997-

2007, pre-screened. 

Sample Type 

Number of 

Monitors 

Number of 

States1

Years in 

Operation 

Number of 

Measurements2

Max-5 104 13 + DC 1997-2007 3,457,057 

Continuous-5 16 6 + DC 1999-2007 3,328,725 

1-hour 935 49 + DC, PR, VI 1997-2007 47,206,918 

Notes: 

1

 DC=District of Columbia, PR=Puerto Rico, VI=Virgin Islands. 

2

 For the max-5 and 1-hour data sets, this number represents the number of hours a sample was 

collected/reported. The number for the continuous-5 data set is the number of 5-minute samples. 

The total number of hours where measurements for the continuous-5 set were collected is 

283,202 (see Table 7-2). 

Staff evaluated the data for inconsistencies and duplication. The reported measurement 

units varied within each of the data sets, therefore the staff converted all concentrations to parts 

per billion (ppb). Next staff screened each of the three data sets listed in Table 7-1 for where 

monitor IDs had multiple parameter occurrence codes (POCs) and identical monitoring times. 

These duplicate measures could either result from co-location of ambient monitors (i.e., more 

than one measurement instrument) or from duplicate reporting of ambient concentrations (i.e., 

the 5-minute maximum concentration in the max-5 data set is the same as the maximum 5-

minute concentration reported from the continuous-5 data set). As a result of this evaluation and 

additional concentration level screening (see below), staff constructed several data sets for 

analysis in this REA. These data sets are summarized in Table 7-2 and are described in detail 

below. 
July 2009 75

Table 7-2. Analytical data sets generated using the continuous-5, max-5, and 1-hour ambient SO2

monitoring data, following screening. 

Sample Type 

Within Set 

Duplicates 

(n) 

Available 

Data 

(n) 

Combined Set 

Duplicates 

(n) 

Final 

Combined 

Max-5 Data 

(n) 

Final 

Combined 

1-hour 

(n) 

Final 

Combined 

Max-5 & 1-

hour 

(n) 

Max-5 300,438 3,156,619 

Continuous-5 

with 1-hour1 0 283,2022 29,058 3,410,763 

1-hour 0 47,188,640 

258,457 47,213,3853 2,367,6864

Notes: 

1

 1-hour concentrations from continuous-5 data were calculated from all 5-minute values within the hour. 

2

 The number of 5-minute maximum SO2 samples. 3

 There were a total of 24,745 unique 1-hour values added from the continuous-5 monitors. 

4

 There were a total of 2,408,351 values where the 5-minute maximum and 1-hour measurements were 

reported at the same time at the same monitor. Of these, a total of 40,665 were screened out for not 

meeting the peak-to-mean (PMR) criterion. 

Boxes spanning two rows are comprised of data from the two sample types. For example, there were 

29,058 duplicate values when considering the max-5 and continuous-5 data sets. Therefore, in creating 

the “Final Combined Max-5 Data” (n= 3,410,763), this was the sum of the max-5 (n=3,156,619) and 

continuous-5 (n=283,202) minus the duplicates (n=29,058). 

1. Simultaneously reported/measured ambient SO2 data 

Two separate data sets were constructed that had multiple 5-minute SO2 measurements 

collected at the same monitoring location and time for: 

 max-5 duplicates (i.e., simultaneous measurements of 5-minute maximum SO2

concentrations from co-located max-5 monitors; n=300,438) 

 max-5 and continuous-5 duplicates (i.e., simultaneous 5-minute maximum SO2

concentrations reported in max-5 and continuous-5 datasets; n=29,058) 

A third data set was constructed that had simultaneous 1-hour SO2 measurements 

collected at the same monitoring location and time for: 

 1-hour duplicates (i.e., from 1-hour SO2 monitors and from averaging the continuous-5 

monitors; n=258,457) 

Each of these duplicate data sets were used for quality assurance purposes only, the 

evaluation of which is presented in Appendix A.2. The duplicate values were not used in the 

statistical model development or for any other 5-minute or 1-hour SO2 concentration analysis. 
July 2009 76

2. Combined 5-minute and 1-hour ambient SO2 data 

A complete set of 5-minute maximum SO2 concentrations,21 generated from the max-5 

data set and from the maximum 5-minute concentrations reported by the continuous-5 monitors, 

was then combined with their corresponding measured 1-hour SO2 concentrations (see below). 

Then, the combined data were screened for validity, recognizing that the combined max-5 and 1-

hour SO2 data set may have certain anomalies (e.g., 5-minute maximum SO2 concentrations < 1-

hour mean SO2 concentration). A value of 1 was selected as the lower bound peak-to-mean ratio 

(PMR),22 accepting the possibility that the 5-minute maximum concentrations (and all other 5-

minute concentrations within the same hour) may be identical to the 1-hour average 

concentration. A PMR of <12 was selected as the upper bound since it would be a mathematical 

impossibility to generate a value at or above 12 given there are twelve 5-minute measurements 

within any 1-hour period.23 This screening resulted in a total of nearly 2.4 million values 

comprising the combined 5-minute maximum and 1-hour SO2 concentration dataset. The 

locations of these 98 monitoring sites comprising this dataset are illustrated in Figure 7-1. Staff 

used this data set to develop a statistical model (section 7.2.3) and to characterize the measured 

5-minute maximum ambient air quality. Details on the monitors used and site attributes (e.g., 

latitude, longitude, operating years, monitoring objective) are provided in Appendix A.1. 

 

21 A single 5-minute and 1-hour SO2 concentration was used in each of data set 2 and 3. The criteria for selection of 

a particular value was first based on whether the 1-hour concentration was calculated from the continuous-5 data 

(where present) followed by the monitor ID POC that had the greatest overall number of samples. 22 The peak-to-mean ratio is the maximum 5-minute SO2 concentration within an hour divided by the 1-hour average 

SO2 concentration. 23 As the 5-minute maximum concentration approaches infinity, the other 11 concentrations measured in the hour 

comparatively tend towards zero, giving a maximum PMR = Peak/Mean = Cmax/[(Cmax + (Cothers 0) × 11)/12] < 

12. 
July 2009 77

Figure 7-1. Location of the 98 monitors that reported 5-minute maximum SO2 concentrations and 

comprising the first data analysis group. 

3. Broader 1-hour ambient SO2 data 

This data set was comprised of all 1-hour SO2 data, whether obtained from the 1-hour 

ambient monitoring data set or from averaging 5-minute concentrations from the continuous-5 

data set. The raw 1-hour data from a total of 935 ambient monitors were first screened for 

negative concentrations (n=3,555) and for where concentrations were less than 0.1 ppb 

(n=14,723). The screened data were not used in any analyses. The refined 1-hour data 

(n=47,188,640) were then combined with the 1-hour average concentrations obtained from the 

continuous 5-monitors. Staff retained the 1-hour average concentrations from the continuous-5 

monitors where duplicate values existed. This was done to better maintain the relationship 

between the 5-minute maximum and 1-hour SO2 concentrations. As described above for data set 

1, staff removed duplicate 1-hour values identified at each monitoring location originating from 

the 1-hour and continuous-5 monitors for separate analysis (Appendix A-2). The remaining 1-

hour SO2 data set (with duplicate 1-hour values removed) was then combined with the complete 

5-minute maximum data set described above for data set 2 (with duplicate 5-minute maximum 

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July 2009 78

SO2 values removed). Staff used data set 2 in developing the statistical model to estimate 5-

minute maximum SO2 concentrations (section 7.2.3). 

Additional screening of the 1-hour SO2 data set was performed using a 75% 

completeness criterion. This monitoring data requirement is used in demonstrating attainment of 

the SO2 NAAQS (61 FR 25579).24 For an ambient monitor to have a valid year of data, first, 

valid days were selected as those with at least 18 hours of data. Then, each monitor was required 

to have 75% of each calendar quarter with complete days (either 68 or 69 days per quartile). 

This 75% completeness criterion was applied to the available monitoring data to generate a total 

of 4,692 valid site-years of data obtained from 809 ambient monitors. The number of valid 

monitoring site-years available as a result of this screening is presented in Table 7-3, effectively 

encompassing ambient SO2 monitoring in 48 US States, Washington DC, Puerto Rico and the 

US Virgin Islands over years 1997 through 2006.25 The locations of the 809 monitors 

comprising the broader SO2 monitoring network are illustrated in Figure 7-2. This data set was 

used in the second data air quality characterization scenario that considered the measured as is 1-

hour SO2 concentrations with statistically modeled 5-minute maximum concentrations. Details 

on the monitors used and site attributes (e.g., latitude, longitude, operating years, monitoring 

objective) are provided in Appendix A.1. 

7.2.2 Site Characteristics of Ambient SO2 Monitors 

The siting of the monitors is of particular importance, recognizing that proximity to local 

sources could have an influence on the measured SO2 concentration data and subsequent 

interpretation of the air quality characterization. Staff evaluated the attributes of monitors within 

each of the two data sets; the first data set was comprised of monitors that reported 5-minute 

maximum SO2 concentrations, and the second was generated from monitors within the broader 

SO2 monitoring network and having valid 1-hour SO2 concentrations. Two points are worth 

mentioning for this analysis; the first being the number of monitors and the second being the 

potential for differences in types of sources influencing each monitor. While there is overlap in 

 

24 See http://www.epa.gov/air/oaqps/greenbook/40cfr50_2001.pdf 

25 Based on the version date of the files downloaded from EPA’s AQS data mart (6/20/2007), all 1-hour SO2 data 

from 2007 were less than complete. In addition, two monitors located in Hawaii County, HI were identified in the 

1st draft REA as having concentrations influenced by natural sources. Therefore, monitor IDs 150010005 and 

150010007, while meeting the completeness criteria, were removed from the valid 1-hour SO2 data set due to the 

influence of volcanic activity on measured SO2 concentrations at these locations. Alaska had no SO2 monitors 

during the period of analysis. 
July 2009 79

Table 7-3. Counts of complete and incomplete site-years of 1-hour SO2 ambient monitoring data 

for 1997-2006. 

State Number of Site-Years 

Number of Valid 

Monitors per year 

Abbr. Code Complete Incomplete

Percent

Valid Minimum Maximum 

AL 01 36 15 71 1 5 

AZ 04 44 24 65 1 6 

AR 05 17 14 55 1 2 

CA 06 308 136 69 7 41 

CO 08 33 13 72 1 6 

CT 09 69 18 79 6 12 

DE 10 27 16 63 2 4 

DC 11 10 1 91 1 1 

FL 12 223 76 75 3 28 

GA 13 65 34 66 5 9 

HI 15 31 19 62 2 4 

ID 16 17 10 63 1 3 

IL 17 235 30 89 18 30 

IN 18 276 80 78 13 34 

IA 19 110 33 77 8 14 

KS 20 28 27 51 2 4 

KY 21 104 42 71 2 13 

LA 22 57 11 84 5 6 

ME 23 25 18 58 1 7 

MD 24 10 7 59 1 3 

MA 25 102 33 76 6 15 

MI 26 84 28 75 5 15 

MN 27 74 23 76 5 12 

MS 28 25 11 69 1 4 

MO 29 166 40 81 11 21 

MT 30 121 50 71 2 18 

NE 31 9 13 41 1 2 

NV 32 16 6 73 1 4 

NH 33 63 26 71 3 11 

NJ 34 117 21 85 12 14 

NM 35 56 24 70 3 9 

NY 36 229 72 76 21 24 

NC 37 61 29 68 4 9 

ND 38 155 45 78 10 18 

OH 39 309 74 81 28 35 

OK 40 59 32 65 3 9 

OR 41 0 4 0 0 0 

PA 42 398 97 80 33 51 

RI 44 21 2 91 2 3 

SC 45 90 34 73 5 11 

SD 46 7 4 64 1 3 

TN 47 175 70 71 12 23 
July 2009 80

State Number of Site-Years 

Number of Valid 

Monitors per year 

Abbr. Code Complete Incomplete

Percent

Valid Minimum Maximum 

TX 48 172 71 71 10 21 

UT 49 33 14 70 3 4 

VT 50 11 4 73 1 2 

VA 51 94 28 77 8 11 

WA 53 18 24 43 1 7 

WV 54 203 28 88 14 25 

WI 55 39 18 68 2 7 

WY 56 3 8 27 1 1 

PR 72 33 32 51 1 6 

VI 78 24 23 51 1 5 

Total or 

Average1 4692 1612 68 6 12 

Notes: 

1

 Columns of complete and incomplete site years were summed. The percent 

valid site-years and the monitors in operation per year with valid data were 

averaged. 

Figure 7-2. Location of the 809 monitors comprising the broader SO2 ambient monitoring network 

(i.e., the second data analysis group). 

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July 2009 81

the measurement of 5-minute maximum and its associated 1-hour SO2 concentration at some 

locations (n=98), the remainder of SO2 monitors with valid data (n=711) are sited in other 

locations where 5-minute SO2 measurements have not been reported. Staff evaluated the 

ambient monitor attributes within each data set because there may be influential attributes in the 

subset of data used to develop the statistical model (i.e., monitors reporting 5-minute maximum 

SO2 concentrations) that are not applicable to the broader SO2 monitoring network. Staff 

acknowledges that the information available and the monitoring site characteristics considered 

can limit how well the monitoring data serve as an indicator of human exposure. 

First, staff evaluated the specific monitoring site characteristics provided in AQS, 

including the monitoring objective, measurement scale, and predominant land-use. Additional 

features such as proximity to SO2 emission sources and the population residing within various 

distances of each monitor were estimated using monitoring site and emission source geographic 

coordinates and U.S Census data. Each of these attributes is summarized here to provide 

perspective on the attributes of where 5-minute maximum SO2 concentrations were reported 

versus the attributes of the broader SO2 monitoring network. A more thorough discussion of the 

purpose of the existing ambient SO2 monitoring network is provided in Chapter 2. Individual 

monitor site characteristics are given in Appendix A.1. 

The monitoring objective meta-data field describes the nature of the monitor in terms of 

its attempt to generally characterize health effects, the presence of point sources, regional 

transport, or welfare effects. In recognizing that there were variable numbers of ambient 

monitors in operation and variation in the number of valid site-years available for each data set, 

staff weighted the monitoring objectives by the number of site-years. This was done to provide 

perspective on the air quality characterization results that are based on the total site-years of data 

available, not just the number of ambient monitors. In addition, the monitors can have more than 

one objective. Where multiple objectives were designated, staff selected a single objective to 

characterize each monitor using the following order: population exposure, source-oriented, high 

concentration, general/background, unknown.26 All other objectives (whether known or 

indicated as “none”) were grouped by staff into an “Other” category. Figure 7-3 summarizes the 

 

26 This order was selected to characterize the monitors with a specific objective. Most of the time where there were 

multiple objectives at a monitor, there was a specific objective (e.g., population exposure) and a non-specific 

objective (e.g., unknown). 
July 2009 82

objectives for the monitors comprising each data set. Each of the data sets had a large proportion 

of site-years that would target public health objectives through the population exposure and 

highest concentration categories, though the monitors in the broader SO2 monitoring network 

had a greater percentage than the monitors reporting both 5-minute maximum and 1-hour SO2

concentrations. The monitors reporting 5-minute concentrations had approximately twice the 

percentage of site-years from source-oriented monitors when compared with the broader SO2

monitoring network. 

Similarly, the overall measurement scale of the monitors used for the air quality 

characterization in each location was evaluated based on the weighting of valid site-years of 

data. The measurement scale represents the air volumes associated with the monitoring area 

dimensions. While a monitor can have multiple objectives, each monitor typically has only one 

measurement scale. Figure 7-3 also summarizes the measurement scales for the monitoring siteyears comprising each data set. Both data sets had their greatest proportion of monitoring siteyears associated with neighborhood measurement scales (500 m to 4 km), though monitors 

recording 1-hour concentrations had about 22 percentage points greater than the monitors 

reporting 5-minute maximum concentrations. Furthermore, monitors reporting 5-minute values 

had a larger proportion of site-years of data characterized at an urban (4 to 50 km) and regional 

scale (50 km to 1,000 km) compared with the broader SO2 monitoring network. 

The land-use meta-data indicate the prevalent land-use within 1⁄4 mile of the monitoring 

site. Figure 7-4 summarizes the land-use surrounding monitors that reported 5-minute 

maximum concentrations and the monitors in the broader 1-hour SO2 monitoring network. Over 

half of the site-years are from residential and industrial areas and are of similar proportions for 

both data sets considered. The greatest difference in the surrounding land-use was for the 

percent of site-years associated with monitors sited in agricultural and commercial areas. The 

monitors reporting 5-minute maximum SO2 concentrations had about 10 percentage points more 

site-years from monitors within agricultural areas and 10 percentage points less in commercial 

areas when compared to the respective land use of the broader SO2 monitoring network. 

The setting is a general description of the environment within which the site is located. 

Figure 7-4 also summarizes the setting of the monitors comprising each data set. For monitors 

reporting 5-minute concentrations, the greatest proportion of site-years is from ambient monitors 
July 2009 83

with a rural setting (49%). Most of the site-years in the broader SO2 monitoring network were 

from monitors within a suburban setting (40%).
July 2009 84

Figure 7-3. Distribution of site-years of data considering monitoring objectives and scale: monitors that reported 

SO2 concentrations (top) and the broader SO2 monitoring network (bottom). 

Objective Scale 

OTHER

3%

POPULATION 

EXPOSURE

39%

SOURCE 

ORIENTED

9%

UNKNOWN

19%

GENERAL/

BACKGROUND

4%

HIGHEST 

CONCENTRATION

26%

HIGHEST 

CONCENTRATION

15%

OTHER

4%

POPULATION 

EXPOSURE

31%

SOURCE 

ORIENTED

21%

UNKNOWN

18%

GENERAL/

BACKGROUND

11%

MIDDLE SCALE

1%

NONE

28%

REGIONAL SCALE

12%

URBAN SCALE

19%

NONE

21%

REGIONAL SCALE

4%

URBAN SCALE

9%

MICROSCALE

0% MIDDLE S

4%

All Monitors 5-Minute Monitors
July 2009 85

Figure 7-4. Distribution of site-years of data considering land-use and setting: monitors that reported 5-minute ma

concentrations (top) and the broader SO2 monitoring network (bottom). 

SUBURBAN

30%

URBAN AND 

CENTER CITY

21% AGRICULTURAL 27%

COMMERCIAL

13%

FOREST

5% INDUSTRIAL 20%

RESIDENTIAL

33%

UNKNOWN

2%

AGRICULTURAL

13%

COMMERCIAL

23%

FOREST

3%

INDUSTRIAL

23%

RESIDENTIAL

36%

UNKNOWN

1%

DESERT

0%

MOBILE

1%

SUBURBAN

40%

URBAN AND 

CENTER CITY

28%

UNKNOWN

1%

All Monitors 5-Minute Monitors

Land-Use Setting 
July 2009 86

Stationary sources (in particular, power generating utilities using fossil fuels) are the 

largest contributor to SO2 emissions in the U.S. (ISA, section 2.1). First, staff determined the 

distances, amounts of, and types of stationary source emissions associated with each of the 

ambient SO2 monitors. Then, staff selected the sources in close proximity of each monitor to 

identify whether there are differences in the distribution of emission sources that could affect the 

monitored concentrations. Stationary sources emitting > 5 tons per year (tpy) SO2 and within 20 

km of each monitor were identified using data from the 2002 National Emissions Inventory 

(NEI).27 Details on the number of sources, the distribution of emissions, and the method for 

determining the distances to each individual ambient monitor are provided in Appendix A.1. 

The total SO2 source emissions within 20 km of every monitor were summed by their 

source descriptions; the top eight source types were selected for evaluation followed by a 

summing of all other remaining source types in a final source description group (“other”).28 

These emission results are presented in Figure 7-5 for the monitors reporting 5-minute maximum 

SO2 concentrations and for the broader SO2 monitoring network. A comparison of the sources 

located within 20 km of monitors comprising both data sets indicates strong similarity in the 

types of sources present. Approximately 70% of the stationary source emissions local to 

monitors comprising either data set originate from fossil fuel power generation.29 Similarity in 

emission contributions from several other source categories is also evident (i.e., petroleum 

refineries, iron and steel mills, cement manufacturing). One of the largest distinctions between 

the sources surrounding the two data sets is the emission contribution from primary smelters. 

There were greater source emissions from smelters located within 20 km of the monitors 

reporting 5-minute maximum SO2 concentrations (8.8%) than within 20 km of the broader SO2

monitoring network (1.1%). A second difference between the two sets of data existed in the 

emission contribution from a combined power generation, transmission and distribution 

description; this source category contributes approximately 11% to emissions proximal monitors 

 

27 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. 28 Details for the number of sources and emissions surrounding each monitor are given in Appendix A.1.1 and A.1.2 

29 This emission category was summed from fossil fuel power generation (NEI code 221112) and hydroelectric 

utilities (NEI code 221111). Hydroelectric utility SO2 emissions arise from power generating facility operations that 

require fossil fuel combustion (e.g., diesel-fueled backup generators). 
July 2009 87

in the broader SO2 monitoring network compared with only 2% at monitors measuring 5-minute 

SO2 concentrations. 

Figure 7-5. The percent of total SO2 emissions of sources located within 20 km of ambient 

monitors: monitors reporting 5-minute maximum SO2 concentrations (top) and the 

broader SO2 monitoring network (bottom). 

 

Fossil Fuel Power 

Generation

69%

Cement 

Manufacturing

1%

Flour Milling

1%

Power Generation, 

Transmission and 

Distribution

11%

Other 

Petroleum/Coal 

Products 

Manufacturing

1%

Iron and Steel Mills

2%

Petroleum 

Refineries

5%

Primary 

Smelting/Refining

1%

Others

9%

Fossil Fuel Power 

Generation

68%

Cement 

Manufacturing

1%

Other 

Petroleum/Coal 

Products 

Manufacturing

1% Flour Milling

2%

Power Generation, 

Transmission and 

Distribution

2%

Others

8%

Iron and Steel Mills

2%

Petroleum 

Refineries

7%

Primary 

Smelting/Refining

9%

5-minute SO2 Monitors 

All SO2 Monitors 
July 2009 88

The population residing within four buffer distances of each ambient monitor was 

estimated using ArcView. First, staff obtained block group population data from the US Census 

and converted the location of each block group polygon to single central point. Then buffers 

were created around each monitor location at progressive 5 km distances to a final buffer 

distance of 20 km. The total population was estimated by summing the population of all block 

group centroids that fell within the monitor buffers. We then created population distribution 

functions (across monitors) for the monitors reporting 5-minuute maximum SO2 concentrations 

and for the broader SO2 monitoring network. An example of the population distribution 

represented by the monitors comprising each data set is given by Figure 7-6, with the population 

within each of the buffer distances given in Appendix A.1.30 In general, the shape of the 

population distribution was similar for each data set, though as a whole, the monitors reporting 

5-minute SO2 concentrations tended to be sited in locations with lower population density when 

considering any of the population buffers. Staff created population density groups of low, mid, 

and high to categorize all ambient monitors using the population distribution within 5 km, by 

apportioning each data set into three sample size groupings. The low-population density group 

included those monitors with populations under 10,000 persons. Mid-population density 

included those monitors with between 10,000 and 50,000 persons, while the high-population 

density group was assigned to monitors with greater than 50,000 persons within a 5 km buffer. 

These population density groups of low, mid, and high were used in separating some of the air 

quality characterization results. 

The population density surrounding each monitor was compared with its monitoring 

objective. The descriptive statistics for each monitoring objective, separately considering those 

monitors that reported 5-minute maximum SO2 concentrations and the broader SO2 monitoring 

network, are provided in Table 7-4. The calculated population statistics generally support 

expectations given the designated monitoring objectives. There are similarities in the population 

density around monitors characterized as having highest concentration and population exposure

monitor objectives, both of which having the greatest number of persons residing within 5 km of 

the monitors. Source-oriented monitors had consistently lower population densities, though 

monitors assigned the general/background objective had the lowest population densities. 

 

30 If the estimated population was zero, then the monitor value was not plotted in the figure. 
July 2009 89

0

10

20

30

40

50

60

70

80

90

100

1E+0 1E+1 1E+2 1E+3 1E+4 1E+5 1E+6 1E+7

Population Residing Within 5 km of Ambient Monitors

Cumulative Percentile (%)

All SO2 Monitors (5 km)

5-minute SO2 Monitors (5 km)

Figure 7-6. Distribution of the population residing within a 5 km radius of ambient monitors: 

monitors reporting 5-minute maximum SO2 concentrations and the broader SO2

monitoring network. 

Table 7-4. Descriptive statistics of the population residing within a 5 km radius of ambient 

monitors by monitoring objective: monitors reporting 5-minute maximum SO2 concentrations and 

the broader SO2 monitoring network. 

Population residing within 5 km of Ambient Monitor2 Data 

Source Objective1

 n mean max p95 p75 p50 p25 p5 min 

GEN 10 8537 28224 28224 17957 1330 0 0 0 

OTH 6 8881 35872 35872 11967 2396 655 0 0 

SRC 15 9216 42208 42208 17925 1103 0 0 0 

UNK 18 40177 262592 262592 33774 20360 4587 0 0 

HIC 19 59958 316944 316944 90863 17963 13314 0 0 

5-

minute 

monitors 

POP 30 67886 382995 216129 70221 49283 21784 3280 2118 

GEN 45 18096 378415 78376 7883 1947 492 0 0 

SRC 68 20594 136288 76896 30070 9844 1112 0 0 

UNK 179 58477 1215989 200253 59772 16676 3403 0 0 

OTH 30 61878 1205886 320320 11205 4270 787 0 0 

HIC 202 86485 1301071 222716 94449 48179 14142 905 0 

All SO2

Monitors 

POP 285 87406 1173879 276378 105796 54986 21336 1865 0 

Notes: 

1

 Objectives are POP=Population Exposure; HIC=Highest Concentration; SRC=Source Oriented; 

GEN=General/Background; OTH=Other; UNK=Unknown. 

2

 p5, p25, p50, p75, and p95 are the 5th, 25th, 50th, 75th, and 95th percentiles, respectively. The minimum (min), 

maximum (max), and arithmetic average (mean) are also provided. 
July 2009 90

7.2.3 Statistical model to estimate 5-minute maximum SO2 concentrations 

As described earlier, staff noted there were a limited number of ambient monitors that 

reported 5-minute maximum SO2 concentrations. The majority of the SO2 monitoring network 

reports 1-hour average SO2 concentrations. Staff developed a statistical model to extend the 5-

minute SO2 air quality characterization to locations where 5-minute concentrations were not 

reported. This statistical model was briefly introduced in section 6.4; this section details the 

development of the statistical model designed to estimate 5-minute maximum SO2

concentrations from 1-hour SO2 concentrations, using the combined 5-minute maximum and 1-

hour SO2 measurement data set (see section 7.2.1). 

Fundamental to the statistical model are the peak-to-mean ratios or PMRs. Peak-to-mean 

ratios are derived by dividing the 5-minute maximum SO2 concentration by the 1-hour average 

SO2 concentration. These derived PMRs can be useful in estimating 5-minute maximum SO2

concentrations when only the 1-hour SO2 concentration is known. The values of PMRs derived 

from the monitoring data can be variable and are likely dependent on local source emissions, site 

meteorology, and other influential factors. Each of these factors will have variable influence on 

the measured 1-hour and 5-minute SO2 concentrations at the ambient monitors. Therefore, to 

develop a useful tool for extrapolating from the measurement data, at a minimum, the approach 

needed to account for variability in ambient concentrations. It is within this context that the 

statistical model was developed. 

Staff selected the variability in SO2 concentrations at each individual ambient monitor as 

a surrogate for source emissions, source types, and/or distance to sources to allow for a 

purposeful application of the statistical model to the broader 1-hour SO2 measurement data. 

Many of the meta-data described earlier in section 7.2.2, while useful for qualitatively describing 

characteristics of monitors in the SO2 monitoring network, were not considered robust in 

quantifying how sources might influence monitored concentrations. The utility of the meta-data 

is also diminished when the monitor attributes were reported as unknown, missing entries, or 

possibly mischaracterized. In addition, while individual source types, emissions, and distances 

to the monitors are presented as quantitative measures, the use of this data can be problematic. 

This is because 1) source characteristics can change over time, 2) it is largely unknown what 

source(s) influence many of the ambient monitors and by how much, 3) there is uncertainty in 

source emission estimates, and 4) even similar source types will not have the same emission 
July 2009 91

characteristics. Staff considered several ways to link the statistical model developed from 

monitors reporting 5-minute maximum concentrations to the broader SO2 ambient monitoring 

network, including the use of the ambient monitoring site characteristics. Staff decided that the 

measured concentrations had the most to offer in efficiently designing such a linkage given the 

strong relationships between averaging times, concentration variability, and the frequency of 

peak concentrations. Where possible, staff compared the relevant monitor attributes described in 

section 7.2.2 with selected variability metrics used in developing and applying the statistical 

model. 

The purpose of the first analysis that follows is to determine an appropriate variable to 

reasonably connect the statistical model derived from 5-minute and 1-hour concentrations to any 

1-hour SO2 concentration data set where there are no 5-minute SO2 measurements. Staff first 

evaluated variability metrics associated with 5-minute and 1-hour SO2 ambient monitoring 

concentrations as a basis for linking the statistical model to 1-hour concentrations. Next, staff 

generated distributions of PMRs for use in estimating 5-minute concentrations. Then the 

statistical model was applied to where 5-minute measurements were reported and evaluated 

using cross-validation. 

7.2.3.1 Relationship Between 5-minute and 1-hour SO2 Concentrations 

Because the statistical model employs 5-minute and 1-hour SO2 concentrations, staff 

evaluated the relationship between the concentrations for the two averaging times. The monitors 

reporting all twelve 5-minute concentrations within the hour were used for this analysis (n=16). 

First, all of the continuous-5 minute data available for each monitor were averaged to generate a 

single 5-minute mean concentration (both in an arithmetic and geometric mean form) and their 

respective standard deviations, yielding a total of 16 monitor-specific 5-minute SO2 values.31 

Staff performed a second calculation to generate similar statistics using the continuous 5-minute 

data, though a 1-hour averaging time was of interest. To obtain the 1-hour statistics, the 5-

minute SO2 concentrations within an hour were averaged to generate 1-hour mean SO2

concentrations for each monitor, which were then averaged to generate a single 1-hour mean SO2

 

31 Each of the 16 continuous-5 monitors was characterized by four statistics, arithmetic and geometric means and 

their respective standard deviations. 
July 2009 92

concentration (both in an arithmetic and geometric mean form) and their corresponding standard 

deviations, yielding a total of 16 monitor-specific 1-hour SO2 values. 

y = 0.95x + 0.07

R2

 = 0.99

y = 0.75x + 20

R2

 = 0.98

1

1.5

2

2.5

3

3.5

4

01234

Ambient Monitor 5-minute GSD 

Ambient Monitor Hourly GSD

0

50

100

150

200

250

300

0 50 100 150 200 250 300 350

Ambient Monitor 5-minute COV (%)

Ambient Monitor Hourly COV 

(%)

GSD

COV

Figure 7-7. Comparison of hourly and 5-minute concentration COVs and GSDs at sixteen 

monitors reporting all twelve 5-minute SO2 concentrations over multiple years of 

monitoring. 

Staff selected the coefficient of variation (COV)32 and geometric standard deviation 

(GSD) as metrics to compare concentration variability in both 1-hour and 5-minute averaging 

times, each of which are illustrated in Figure 7-7. As expected, a strong direct linear relationship 

exists between the variability in 5-minute and 1-hour SO2 concentrations at each monitor. Even 

with the limited geographic representation (these monitors are from only six U.S. States and 

Washington DC), there is a wide range in the observed concentration variability for both the 5-

minute and associated hourly measurements (i.e., COVs range from about 75 – 300%, GSDs 

range from about 1.7 – 3.7). In general, this analysis demonstrates that variability in 5-minute 

 

32 The COV used here is calculated by dividing the standard deviation by the arithmetic mean, then multiplying by 

100. The statistic gives a relative measure of variation, to better facilitate the comparison of data having different 

mean concentrations or units of measure. 
July 2009 93

SO2 concentrations is directly related to the variability in 1-hour SO2 concentrations, and these 

measures of variability may be used to describe the potential variability in concentrations 

measured at any ambient SO2 monitor, similarly for either the 1-hour or 5-minute measured 

concentrations. Note that there is a difference in the slope of the two lines, indicating that there 

is not a constant relationship between the COV and GSD. This means that in characterizing the 

variability at any ambient monitor, an identified COV (e.g., either low or high COV) does not 

necessarily correspond to the same GSD characterization. 

Next, staff compared the variability in 1-hour SO2 concentrations using data from the 

monitors reporting 5-minute maximum SO2 concentrations (n=98) to variability observed for the 

broader SO2 monitoring network (n=809). The objective of this evaluation was to determine if 

the distribution of the observed hourly concentration variability was similar for the two sets of 

data. As done above for the monitors reporting 5-minute maximum SO2 concentrations, four 

statistics were generated for each ambient monitor within the broader SO2 monitoring network 

using the 1-hour concentrations, with the variability at each monitor represented by its COV and 

GSD. Figure 7-8 illustrates the cumulative density functions (CDFs) for the hourly COVs and 

GSDs at each of the 98 monitors that reported 5-minute maximum SO2 concentrations (i.e., the 

data set used for developing the statistical model) and the 809 monitors from the broader SO2

monitoring network (i.e., the final 1-hour SO2 data set having valid site-years). While the subset 

of monitors reporting the 5-minute maximum SO2 concentrations exhibit greater variability in 

hourly concentration at most percentiles of the distribution, the overall shape and span of the 

distribution is very similar to that of the monitors within the broader SO2 monitoring network 

using either variability metric. The similarity in variability distributions could indicate that the 

monitor proximity to sources, the magnitude and temporal profile of source emissions, and the 

types of sources affecting concentrations at either set of data (i.e., the monitors reporting 5-

minute SO2 concentrations versus the broader SO2 monitoring network) are similar. This, 

combined with the meta-data evaluation and the source type, distance, and emissions analysis 

that indicated similar source type emission proportions between the two sets of ambient 

monitoring data (7.2.2), provides support for using concentration variability as a variable to 

extrapolate information from the 5-minute SO2 monitors to the 1-hour SO2 monitors. 
July 2009 94

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 50 100 150 200 250 300 350 400 450 500

Ambient Monitor Hourly COV

Cumulative Percentile

All SO2 Monitors (n=809)

5-Minute Monitors (n=98)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5

Ambient Monitor Hourly GSD

Cumulative Percentile

All SO2 Monitors (n=809)

5-minute SO2 Monitors (n=98)

Figure 7-8. Cumulative density functions (CDFs) of hourly COVs (top) and GSDs (bottom) at 

ambient monitors: monitors reporting 5-minute maximum SO2 concentrations and the 

broader SO2 monitoring network. 

7.2.3.2 Development of Peak-to-Mean Ratio (PMR) Distributions 

A key variable in the statistical model to estimate the 5-minute maximum SO2

concentrations where only 1-hour average SO2 concentrations were measured is the peak-tomean ratio (PMR). Peak-to-mean ratios are obtained by dividing the 5-minute maximum SO2

concentration occurring within an hour by the 1-hour SO2 concentration. The use of a PMR or 

distributions of PMRs in estimating 5-minute maximum SO2 concentrations is not new to the 

current NAAQS review. Both individual PMRs and distributions of PMRs were used in the 
July 2009 95

previous NAAQS review in characterizing 5-minute SO2 air quality (Thrall et al, 1982; EPA, 

1986a; 1994b; Thompson 2000) and in estimating human exposures to 5-minute SO2

concentrations (Burton et al. 1987; EPA, 1986a, 1994b; Stoeckenius et al. 1990; Rosenbaum et 

al., 1992; Science International, 1995). In this review, staff generated distributions of PMRs to 

estimate 5-minute maximum SO2 concentrations at ambient monitors (this chapter) and at air 

quality modeled census block centroid receptors (chapter 8). The distributions of PMRs used 

here build upon recent PMR analyses conducted by Thompson (2000).33 In the current PMR 

analysis, staff developed several distributions of PMRs using more recent 5-minute SO2

monitoring data (through 2007) and used concentration level and variability as categorical 

variables in defining the distributions of PMRs. 

Concentration variability has been identified as a potential attribute in characterizing 

sources affecting concentrations measured at the ambient monitors (section 7.2.3.1). Instead of 

designing a continuous function from the variability distribution, staff chose to use categorical 

variables to describe the monitors comprising each data set. The approach involved the creation 

of variability bins, such that PMR data from several monitors would comprise each bin. Staff 

decided this approach would better balance the potential number of PMRs available in 

generating the distributions of PMR given the variable number of samples collected and years of 

monitoring at monitors that reported the 5-minute maximum SO2 concentrations (Appendix A2). Using the hourly COV or GSD distributions in illustrated Figure 7-8, staff assigned one of 

three COV or GSD bins to each of the 98 monitors reporting the 5-minute maximum SO2

concentrations: for COV, the bins were defined as low (COV ≤ 100%), mid (100% < COV ≤

200%), and high (COV > 200%). These three COV bins were selected to capture the upper and 

lower tails of the variability distribution and a mid-range area.34 Similarly and based on the 

same percentile ranges selected for binning the COV, three GSD bins were selected as follows: 

low (GSD ≤ 2.17), mid (2.17 < GSD ≤ 2.94), and high (GSD > 2.94). 

In addition, the level of the 1-hour mean SO2 concentration has been identified as an 

important consideration in defining an appropriate PMR distribution to use in estimating 5-

minute maximum SO2 concentrations (EPA, 1986a). Therefore, staff further stratified the PMRs 

 

33 In the Thompson (2000) analysis, a single distribution of PMRs was employed based on 6 ratio bins and assumed 

independence between the ratio and the 1-hour SO2 concentration. 34 For monitors reporting the 5-minute maximum SO2 concentrations, these groupings corresponded to 

approximately the 25th and the 84th percentile of the variability distribution. 
July 2009 96

by seven 1-hour mean concentration ranges: 1-hour mean < 5 ppb, 5 ≤ 1-hour mean < 10 ppb, 10 

≤ 1-hour mean < 25 ppb, 25 ≤ 1-hour mean < 75 ppb, 75 ≤ 1-hour mean < 150 ppb, 150 ≤ 1-

hour mean < 250 ppb, and 1-hour mean > 250 ppb.35 Staff selected these 1-hour concentration 

stratifications to maximize any observed differences in the PMR distributions within a given 

variability and concentration bin and to limit the total possible number of PMR distributions for 

computational manageability. 

Based on the concentration variability and 1-hour concentration bins, staff generated a 

total of 19 separate PMR distributions.36 Due to the large number of PMRs available for several 

of the variability and concentration bins (the number of samples ranged from 100 to 800,000), all 

of the empirical data were summarized into distributions using the cumulative percentiles 

ranging from 0 to 100, by increments of 1. Figure 7-9 illustrates two patterns in the PMR 

distributions when comparing the different stratification bins. First, the monitors with the 

highest COVs or GSDs contain the highest PMRs at each of the percentiles of the distribution 

(bottom graph of each variability bin in Figure 7-9) when compared with monitors from the other 

two variability bins (top and middle graphs), while the mid-range variability bins (middle graph) 

had a greater proportion of high PMRs than the low variability bin (top graph). These 

distinctions in the PMR distributions are consistent with the results illustrated in Figure 7-7, that 

is, the variability in the hourly average concentrations is directly related to the variability in the 

5-minute concentrations as summarized across monitors. 

Second, differences were observed in the PMR distributions within each variability bin 

when stratified by 1-hour SO2 concentration. This is most evident in the highest variability bin 

(bottom graph of Figure 7-9); the highest 1-hour concentration category (> 250 ppb) had lower 

PMRs at each of the distribution percentiles compared with the PMR distributions derived for the 

lower concentration categories, most prevalent at the upper percentiles of the distribution. In 

fact, the maximum PMRs for the > 250 ppb concentration bin were only 5.4 and 3.6 for the COV 

and GSD high variability bin, respectively, compared with maximum PMRs of about 11.5 at 

 

35 While PMR distributions were generated for 1-hour SO2 concentrations < 5 ppb, it should be noted that any 

estimated 5-minute maximum SO2 concentration would be below that of the lowest potential health effect 

benchmark level of 100 ppb. 

36 Although there were a total of 21 PMR distributions possible (i.e., 3 × 7), the COV < 100% and GSD <2.17 

categories had only three 1-hour concentrations above 150 ppb. Therefore, the two highest concentration bins do 

not have a distribution, and concentrations > 75 ppb constituted the highest concentration bin in the low COV or low 

GSD bins. All PMR distributions are provided in Appendix A-3. 
July 2009 97

many of the other concentration bins. Again, this inverse relationship between the PMR and 

concentration level has been shown by other researchers (EPA, 1986a). The stratification of 

PMRs by the 1-hour concentration was done to avoid applying high PMRs calculated from low 

hourly concentrations to high hourly concentrations. The observed patterns in the PMR 

distributions support the staff selection of variability bins and 1-hour concentration stratifications 

in controlling for the aberrant assignment of PMRs to particular 1-hour concentrations. 

Figure 7-9. Peak-to-mean ratio (PMR) distributions for three COV and GSD variability bins and 

seven 1-hour SO2 concentration stratifications.

0

10

20

30

40

50

60

70

80

90

100

Cumulative Percentile (%)

1-hour < 5 ppb

5 ≤ 1-hour < 10 ppb

10 ≤ 1-hour < 25 ppb

25 ≤ 1-hour < 75 ppb

1-hour ≥ 75 ppb

1-hour < 5 ppb

5 ≤ 1-hour < 10 ppb

10 ≤ 1-hour < 25 ppb

25 ≤ 1-hour < 75 ppb

1-hour ≥ 75 ppb

0

10

20

30

40

50

60

70

80

90

100

Cumulative Percentile (%)

1-hour < 5 ppb

5 ≤ 1-hour < 10 ppb

10 ≤ 1-hour < 25 ppb

25 ≤ 1-hour < 75 ppb

75 ≤ 1-hour < 150 ppb

150 ≤ 1-hour < 250 ppb

1-hour ≥ 250 ppb

1-hour < 5 ppb

5 ≤ 1-hour < 10 ppb

10 ≤ 1-hour < 25 ppb

25 ≤ 1-hour < 75 ppb

75 ≤ 1-hour < 150 ppb

150 ≤ 1-hour < 250 ppb

1-hour ≥ 250 ppb

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12

Peak to Mean Ratio (PMR)

Cumulative Percentile (%)

1-hour < 5 ppb

5 ≤ 1-hour < 10 ppb

10 ≤ 1-hour < 25 ppb

25 ≤ 1-hour < 75 ppb

75 ≤ 1-hour < 150 ppb

150 ≤ 1-hour < 250 ppb

1-hour ≥ 250 ppb

1 2 3 4 5 6 7 8 9 10 11 12

Peak to Mean Ratio (PMR)

1-hour < 5 ppb

5 ≤ 1-hour < 10 ppb

10 ≤ 1-hour < 25 ppb

25 ≤ 1-hour < 75 ppb

75 ≤ 1-hour < 150 ppb

150 ≤ 1-hour < 250 ppb

1-hour ≥ 250 ppb

100 < COV ≤ 200%

COV ≤ 100%

COV > 200%

GSD ≤ 2.17

2.17 < GSD ≤ 2.94

GSD > 2.94%

COV Bins GSD Bins 
July 2009 98

Staff then evaluated the assigned concentration variability bin using two ambient 

monitoring site characteristics described in section 7.2.2 and using the observed number of 

benchmark exceedances at each monitor. The purpose of this analysis was to determine to what 

extent the selected variability bins were representing variability local source characteristics and 

the likelihood of benchmark exceedances. First, staff compared the total emissions within 20 km 

of each monitor with the assigned concentration variability bin using the monitors reporting 5-

minute maximum SO2 concentrations and the broader SO2 monitoring network (Figure 7-10). 

The purpose of this comparison was to determine whether increased emissions were associated 

with greater variability in monitoring concentrations. In general, a pattern of increased 

emissions was associated with an increase in the concentration variability bin, though the pattern 

was more prominent when considering the COV bins. This indicates the variability bins may be 

useful as a surrogate for local source emission characteristics. 

Figure 7-10. Distribution of total SO2 emissions (tpy) within 20 km of monitors by COV (left) and 

GSD (right) concentration variability bins: monitors reporting 5-minute maximum SO2

concentrations (top) and the broader SO2 monitoring network (bottom). 

COV Bins GSD Bins

Total Emissions (tpy) within 

20 km of All SO2 Monitors

Total Emissions (tpy) within 

20 km of 5-min. SO2 Monitors

Low Mid High Low Mid High 

 n = 21 n = 54 n = 16 n = 21 n = 55 n = 15

 n = 196 n = 462 n = 80 n = 228 n = 435 n = 75
July 2009 99

The second ambient monitoring site characteristic evaluated using the selected 

concentration variability bins was the monitoring objective, principally when it was noted as 

source-oriented. The purpose of this analysis was to determine whether high variability in SO2

concentration was related to source-oriented monitor siting. Staff calculated the percent of 

source-oriented monitors in each variability bin for the two sets of data; the set comprised of 

monitors that reported 5-minute maximum SO2 concentrations and those within the broader SO2

monitoring network. In general, there is an increasing percent of source-oriented monitors in the 

higher concentration variability bins when using either the COV or GSD metrics (Figure 7-11), 

though the pattern is more consistent with the COV metric than with the GSD metric. This 

comparison also indicates that the concentration variability metric may be useful as a surrogate 

for local source emission characteristics. 

0

5

10

15

20

25

30

Low Mid High

Variability Bin

Percent of Monitors with Source-Oriented 

Objective

COV (5-Minute SO2 Monitors)

GSD (5-Minute SO2 Monitors

COV (All SO2 Monitors)

GSD (All SO2 Monitors)

Figure 7-11. Percent of monitors within each concentration variability bin where the monitoring 

objective was source-oriented: monitors reporting 5-minute maximum SO2

concentrations (solid) and the broader SO2 monitoring network (slotted). 
July 2009 100

Staff evaluated the number of measured benchmark exceedances in a site-year given the 

variability bins used to characterize the ambient monitors. The purpose of this analysis was to 

determine whether monitors exhibiting greater variability in SO2 concentration also have a 

greater number of benchmark exceedances. Figure 7-12 summarizes the distribution of 

exceedances of the 200 and 400 ppb benchmark level by each of the COV and GSD variability 

bins (patterns for the 100 ppb and 300 ppb benchmarks were similar). Clearly, monitors having 

the greatest variability in 1-hour SO2 concentration are the monitors most likely to have 5-minute 

SO2 benchmark exceedances and a greater number of exceedances per year. This analysis 

provides further support to the binning of monitors by concentration variability to appropriately 

extrapolate the relationships derived from monitors reporting 5-minute maximum concentrations 

to monitors reporting only 1-hour SO2 concentrations (and at the dispersion model receptors). 

Figure 7-12. Distribution of the measured number of daily 5-minute maximum SO2 concentrations 

above 200 ppb (left) and 400 ppb (right) in a year by hourly concentration COV (top) and 

GSD (bottom) variability bins. Data were from the 98 ambient monitors reporting 5-

minute maximum concentrations (471 site-years). 

Number of Daily 5-minute Maximum SO2

Benchmark Exceedances PerYear

Concentration Variability Bin
July 2009 101

7.2.3.3 Application of Peak to Mean Ratios (PMRs) 

 As described above in section 7.2.3.2 regarding the monitors reporting 5-minute 

maximum SO2 concentrations, staff characterized the monitors within the broader SO2

monitoring network (n=809) by their respective hourly concentration variability and assigned to 

one of the three COV bins (COV ≤ 100%, 100% < COV ≤ 200%, and COV > 200%) and GSD 

bins (GSD ≤ 2.17, 2.17 < GSD ≤ 2.94, and GSD > 2.94). Based on the monitor’s assigned 

concentration variability bin (either from the COV or GSD, not mixed) and the 1-hour SO2

concentration, PMRs can be randomly sampled37 from the appropriate PMR distribution to 

estimate a 5-minute maximum SO2 concentration using the following equation: 

C 5max  CPMR 1, houriij equation (7-1) 

where, 

Cmax-5 = estimated 5-minute maximum SO2 concentration (ppb) for each hour 

PMRij = peak-to-mean ratio (PMR) randomly sampled from the i concentration 

variability and j 1-hour mean SO2 concentration distribution 

Ci,1-hour= measured 1-hour average SO2 concentration at an i concentration 

variability monitor 

 As a result of this calculation, every 1-hour ambient SO2 concentration has an estimated 

5-minute maximum SO2 concentration.38 These statistically modeled 5-minute maximum SO2

concentrations were then summarized using the output metrics described in section 7.2.5. 

7.2.3.4 Evaluation of Statistical Model Performance 

Staff evaluated the performance of the statistical model using cross-validation (Stone, 

1974). Details of the evaluation are provided by Langstaff (2009). Briefly, PMR distributions 

were estimated using 97 of the 98 monitors that reported both the 1-hour and 5-minute maximum 

SO2 concentrations. All ambient monitors were characterized using the same variability bins 

described in section 7.2.3.2. The 1-hour concentrations were also characterized using the same 

 

37 The random sampling was based selection of a value from a uniform distribution {0,100}, whereas that value was 

used to select the PMR from the corresponding distribution percentile value. 

38 When the 1-hour SO2 concentration was > 0, otherwise the 5-minute maximum SO2 concentration was estimated 

as zero). 
July 2009 102

stratifications discussed earlier. Then staff used the newly constructed PMR distributions from 

the 97 monitors and equation 7-1 to predict the 5-minute maximum SO2 concentrations at the 

single monitor not included in developing the PMR distributions. This modeling was performed 

98 times, i.e., removing every single monitor (one monitor at a time), generating new PMR 

distributions, and predicting 5-minute maximum SO2 concentrations at the removed monitor. 

Staff then compared the predicted and measured daily 5-minute maximum SO2 concentrations to 

generate a distribution of model prediction errors (e.g., median errors, median absolute errors) 

and general model statistics (i.e., the root mean square error or RMSEs, and R2

, a measure of the 

amount of variance explained by the model). 

Four statistical models were evaluated: two models constructed from the variability bins 

(either COV or GSD) using all percentiles of the PMR distributions, and two similar models 

constructed without the minimum and maximum percentiles of the PMR distributions. The 

models were evaluated at the benchmark concentration levels as well as at selected percentiles in 

the 5-minute SO2 concentration distribution. In comparing the model predictions, the model 

using variability bins defined by the COV and excluding the minimum and maximum percentiles 

had the lowest prediction errors (e.g., see Table 7-5).39 Based on these results, staff used this 

COV model (excluding the 0th and 100th percentiles of the PMR distribution) to estimate 5-

minute maximum SO2 concentrations from 1-hour SO2 concentrations. 

 

39 Table 7-5 presents a few of the prediction error statistics used to compare each of the models, though several other 

prediction errors were evaluated (e.g., the 75th and 99th). Results for the other percentiles were consistent with 

median results discussed in the text, that is the alt. COV model had the lowest error when compared with the other 

models evaluated. See Langstaff (2009) for the additional percentile comparisons for each of the models. 
July 2009 103

Table 7-5. Comparison of prediction errors and model variance parameters for the four models 

evaluated. 

Benchmark Level 

(ppb) Model1

Median 

Prediction Error2 RMSE R2

COV 2.6 18.9 0.72 

alt. COV 0.4 14.1 0.81 

GSD 2.5 24.8 0.48 

100 

alt. GSD 0.3 19.8 0.63 

COV 1 10.7 0.66 

alt. COV 0.1 8.6 0.74 

GSD 1.3 12.8 0.49 

200 

alt. GSD 0.4 10.2 0.64 

COV 0.6 6.5 0.73 

alt. COV 0 5.6 0.78 

GSD 0.6 8.2 0.55 

300 

alt. GSD 0.1 7.1 0.64 

COV 0.3 4.5 0.76 

alt. COV 0 3.9 0.8 

GSD 0.3 6 0.55 

400 

alt. GSD 0 5.5 0.61 

Notes: 

1

 The “alt.” abbreviation denotes the alternative model was used: the minimum and 

maximum percentiles of the PMR distributions were not used. 

2

 The absolute value of the prediction differences is calculated (predicted minus the 

observed number of exceedances in a year), generating a distribution of prediction 

errors. The value reported here is the (50th percentile) of that distribution. 

 Staff performed supplementary evaluations using the prediction errors associated with the 

selected statistical model. Additional percentiles of the prediction error distribution were 

calculated to estimate the magnitude and direction of the statistical model bias. Table 7-6 

summarizes the prediction errors for each benchmark level. When considering paired percentiles 

(e.g., the 25th and the 75th or prediction intervals) and the 50th percentile as a pivot point there 

appears to be an over-estimation bias at each of the benchmark levels. For example, there is a 

greater overestimation of the 400 ppb benchmark level at the 95th percentile (i.e., 5 exceedances), 

than compared with the under estimation at the 5th percentile (i.e., one exceedance). However, 

there is good agreement in the predicted versus observed number of exceedances, whereas 90% 

of the predicted exceedances of 400 ppb were within -1 to 5 exceedances per year. There is a 

wider range in the prediction intervals at the lower benchmark levels, partly a function of the 
July 2009 104

greater number of exceedances at the lower benchmark levels rather than the degree of 

agreement (Table 7-6). At the extreme ends of the distribution for each of the benchmarks, the 

agreement between the predicted and observed exceedances widens, indicating that for some 

site-years (approximately 2%), the number of days with a benchmark exceedance can be over- or 

under-estimated by 20 to 50 in a year. 

Table 7-6. Prediction errors of the statistical model used in estimating 5-minute maximum SO2

concentrations above benchmark levels. 

Prediction Error at Benchmark Level1

Percentile 100 200 300 400 

1 -31 -17 -18 -19 

5 -15 -7 -3 -1 

25 -1 0 0 0 

50 0 0 0 0 

75 7 1 1 0 

95 32 20 10 5 

99 48 43 26 14 

Mean Number of Benchmark Exceedances2

Benchmark 100 200 300 400 

Observed 148 81 69 56 

Predicted 150 100 67 45 

Notes: 

1

 The percentiles are based on the distribution of predicted minus the 

observed values for each benchmark. Units are the number of 

exceedances per year. 

2

 This is the average of all site-years. Units are the number of 

exceedances per year. 

7.2.4 Adjustment of Ambient Concentrations to Evaluate the Current and Potential 

Alternative Air Quality Scenarios 

Staff evaluated multiple hypothetical air quality scenarios in this assessment, each 

defined by the form and level of a selected standard. Collectively, the purpose of these air 

quality scenarios was to estimate the relative level of public health protection associated with just 

meeting the current and potential alternative standards. The measured ambient SO2

concentrations needed adjustment to reflect concentrations that might be observed given the 

hypothetical air quality scenarios. To maintain a computationally manageable data set given the 

number of air quality scenarios (i.e., eight) and potential health effect benchmark levels 

investigated (i.e., four), staff used the recent ambient monitoring data from 40 counties, 
July 2009 105

specifically years 2001 through 2006.40 The following two sections discuss the concentration 

adjustment approach and the selection criteria used for selecting counties for analysis. 

7.2.4.1 Approach 

There are two important considerations in developing an approach to adjust air quality 

concentrations. One is the relative contribution of policy-relevant background (PRB) to ambient 

concentrations and the other is in understanding how the distribution of ambient concentrations 

measured at a particular monitor has changed over time. 

In developing a simulation approach to adjust air quality to meet a particular standard 

level, PRB levels in the U.S. were first considered. As described in section 2.3, PRB is well 

below concentrations that might cause potential health effects and constitutes a small percent 

(<1%) of the total ambient SO2 concentrations at most locations. Based on the small 

contribution, PRB will not be considered separately in any characterization of health risk 

associated with as is air quality or air quality just meeting the current or potential alternative 

standards. In monitoring locations where PRB is expected to be of particular importance 

however (e.g., Hawaii County, HI), data were noted by staff as influenced by significant natural 

sources rather than anthropogenic sources and were not used in any of the air quality analyses. 

 While annual average concentrations have declined significantly over time, the 

variability in the SO2 concentrations (both the 5-minute and 1-hour concentrations) has remained 

relatively constant. This trend is present when considering ambient concentration data 

collectively (section 7.4.2.3) and when considering monitors individually (Rizzo, 2009). For 

example, Figure 7-13 compares the distribution of daily maximum SO2 1-hour concentration 

percentiles at the two ambient monitors in Beaver County, Pa. that were in operation as far back 

as 1978 and are currently part of the broader SO2 monitoring network. Staff selected a recent 

year of data (2007) to constitute a low concentration year along with an historical year of data 

(1992) constituting a high concentration year, with each year of ambient monitoring common to 

both monitors. As shown in Figure 7-13, the relationships between the low and high 

concentration years at each of the daily maximum concentration percentiles are mostly linear, 

with regression coefficients of determination (R2

 values) greater than 0.98. Where deviation 

 

40 As described in the section 7.2.1, at the time the 1-hour concentrations were downloaded, none of the monitors 

had a complete year of data for 2007. All data from 2007 were excluded from the 1-hour monitor simulations. 
July 2009 106

from linearity did occur (as was observed in many of the other low-to-high concentration 

comparisons performed), it occurred primarily at the extreme upper or lower portions of the 

distribution, often times at the maximum daily maximum or the minimum daily maximum 1-

hour SO2 concentration (Rizzo, 2009). In addition, the absolute values for the simple linear 

regression intercepts were typically 1-3 ppb (Rizzo, 2009). This indicates that the rate of 

decrease in ambient air quality concentrations at the mean value for the monitors evaluated is 

consistent with the rate of change at the lower and upper daily maximum 1-hour concentration 

percentiles. This evaluation provides support for the use of a proportional approach to adjust 

current ambient concentrations to represent air quality under both the current and alternative 

standard scenarios. 

Figure 7-13. Comparison of measured daily maximum SO2 concentration percentiles in Beaver 

County, PA for a high concentration year (1992) versus a low concentration year (2007) 

at two ambient monitors (from Rizzo, 2009). 

The current deterministic form of each standard was used to approximate concentration 

adjustment factors to simulate just meeting the current 24-hour and annual SO2 NAAQS. The 
July 2009 107

24-hour standard of 140 ppb is not to be exceeded more than once per year, therefore, the 2nd

highest 24-hour average observed at each monitor was used as the target for adjustment. The 

rounding convention, which is part of the form of the standard, defines values up to 144 ppb as 

just meeting the 24-hour standard. The form of the current annual standard requires that a level 

of 30 ppb is not to be exceeded; therefore, with a rounding convention to the fourth decimal, 

annual average concentrations of up to 30.4 ppb would just meet the current annual standard. 

Staff limited the analysis of alternative air quality scenarios to particular locations using 

designated geographic boundaries (not just the monitors individually). Counties were used to 

define the locations of interest in the alternative air quality standard scenarios. Use of a county is 

consistent with current policies on the designation of appropriate boundaries of non-attainment 

areas (Meyers, 1983). 

For each location (i) and year (j), 24-hour and annual SO2 concentration adjustment 

factors (F) were derived by the following equation: 

ij CSF max,ij  / equation (7-2) 

where, 

Fij = Adjustment factor derived from either the 24-hour or the annual 

average concentrations at monitors in location i for year j(unitless) 

S = concentration values allowed that would just meet the current NAAQS 

(either 144 ppb for 24-hour or 30.4 ppb for annual average) 

Cmax,ij = the maximum 2nd highest 24-hour average SO2 concentration at a 

monitor in location i and year j or the maximum annual average SO2

concentration at a monitor in location i and year j (ppb) 

In adjusting concentrations to just meet the current standard, the highest monitor (in 

terms of concentration) within a county was adjusted so that it just meets either a 30.4 ppb 

annual average or a 144 ppb 24-hour average (2nd highest), whichever was the controlling 

standard.41 For monitors in each county and calendar year, all hourly SO2 concentrations were 

 

41 The controlling standard by definition would be the standard that allows air quality to just meet either the annual 

concentration level of 30.4 ppb (i.e., the annual standard is the controlling standard) or the 2nd highest 24-hour 

concentration level of 144 ppb (i.e., the 24-hour standard is the controlling standard). The factor selected is derived 
July 2009 108

multiplied by the same constant value F, though only one monitor would have an annual mean 

equal to 30.4 ppb or the 2nd highest 24-hour average equal to 144 ppb for that county and year. 

For example, of five monitors measuring hourly SO2 in Cuyahoga County for year 2001 

(Figure 7-14, top), the maximum annual average concentration was 7.5 ppb (ID 390350060), 

giving an adjustment factor of F = 30.4/7.5 = 4.06 for that year. The 2nd highest 24-hour SO2

concentration at a monitor in a year was 35.5 (ID 390350038), giving an adjustment factor of F = 

144/35.5 = 4.05 for year 2001. Because the adjustment factor derived from the 24-hour average 

concentration was lower, the 24-hour average concentration was the controlling standard. All 1-

hour concentrations measured at all five monitoring sites in Cuyahoga County were multiplied 

by 4.05, resulting in an upward scaling of hourly SO2 concentrations to simulate air quality just 

meeting the current standard for that year. Therefore, one monitoring site in Cuyahoga County 

for year 2001 would have a 2nd highest 24-hour average concentration of 144 ppb, while all other 

monitoring sites would have a 2nd highest 24-hour average concentration below that value, 

although still proportionally scaled up by 4.05 (Figure 7-14, bottom). 

Proportional adjustment factors were also derived considering the form, averaging time, 

and levels of the potential alternative standards under consideration. Discussion regarding the 

staff selection of each of these components of the potential alternative standards is provided in 

Chapter 5 of this document. The 98th and 99th percentile 1-hour daily maximum SO2

concentrations averaged across three years of monitoring were used in calculating the adjustment 

factors at each of five standard levels as follows: 

i

j

ijk

likl

C

SF

max,

3

1

3

/































 equation (7-3) 

where, 

Fikl = SO2 concentration adjustment factor in location i given alternative standard 

percentile form k and standard level l across a 3-year period (unitless) 

 

from a single monitor within each county (even if there is more than one monitor in the county) for a given year. A 

different (or the same) monitor in each county could be used to derive the factor for other years; the only 

requirement for selection is that it be the lowest factor, whether derived from the annual or 24-hour standard level. 
July 2009 109

Sl = Standard level l (i.e., 50, 100, 150, 200, and 250 ppb 1-hour SO2 concentration) 

(ppb) 

Cijk = Selected percentile k (i.e., 98th or 99th) 1-hour daily maximum SO2

concentration at a monitor in location i for each year j (ppb) 

Figure 7-14. Distributions of hourly SO2 concentrations at five ambient monitors in Cuyahoga 

County, as is (top) and air quality adjusted to just meet the current 24-hour SO2

standard (bottom), Year 2001. 
July 2009 110

As described above for adjustments made in simulating just meeting the current 

standards, the highest monitor (in terms of the 3-year average at the 98th or 99th percentile) was 

adjusted so that it just meets the level of the particular 1-hour alternative standard. All other 

monitor concentrations in that location were adjusted using the same factor, only resulting in 

concentrations at those monitors below the level of the selected 1-hour alternative standard. 

Since the alternative standard levels range from 50 ppb through 250 ppb, both proportional 

upward and downward adjustments were made to the 1-hour ambient SO2 concentrations. Due 

to the form of the alternative standards, the expected utility of such an analysis, and the limited 

time available to conduct the analysis, only the more recent air quality data were used (i.e., years 

2001-2006). The 1-hour ambient SO2 concentrations were adjusted in a similar manner 

described above for just meeting the current standard, however, due to the form of these 

standards, only one factor was derived for two 3-year periods (i.e., 2001-2003, 2004-2006), 

rather than one factor for each calendar year. 

7.2.4.2 Selection of Locations 

The first criterion used to select locations for the alternative air quality analyses was 

whether monitors had a high number of daily 5-minute maximum SO2 concentrations at or above 

the potential health effect benchmark levels. Ambient monitors located in two counties in 

Missouri (Iron and Jefferson) had the most frequently measured daily 5-minute maximum SO2

concentrations above the potential health effect benchmarks (see Appendix A-5). While there 

were limited data available from these ambient monitors (4 and 2 years out of 8 total site-years 

did not met the completeness criteria for each of Jefferson and Iron counties, respectively), it was 

decided by staff that lack of a complete year should not preclude their use in this focused 

analysis given the high number of measured daily 5-minute maximum SO2 concentrations at 

these monitors. All other monitoring data used in this focused analysis were selected from where 

1-hour ambient monitoring met the completeness criteria described in section 7.2.1. 

Staff selected an additional 38 counties based on the relationship of the ambient SO2

concentrations within the county to the current annual and 24-hour NAAQS to expand the 

number of counties investigated to a total of 40.42 An additional criterion to be met for county 

selection included having at least two monitors operating in the county for at least five of the six 

 

42 In the 1st draft SO2 REA, a total of 20 counties were selected to evaluate the current standard scenario only. 
July 2009 111

possible years of monitoring.43 First, the 24-hour and annual concentration adjustment factors 

were derived by equation 7-2 for each county and year. Then the mean 24-hour and mean annual 

factor for each county was calculated by averaging the site-years available at each monitor, with 

the selection of the lowest mean factor retained to characterize the county. Each county was then 

ranked in ascending order based on this selected mean factor. The 38 counties were selected 

from the top 38 values, that is, those counties having the lowest mean adjustment factors and 

having at least two monitors. 

The complete list of the 40 counties selected and the mean factors used to select each 

location given the above selection criteria are provided in Table 7-7. In addition, Table 7-7 gives 

the number of monitors in each COV bin that were used to characterize the air quality in the 40 

counties. The locations of ambient monitors comprising the 40 county dataset (i.e., the third data 

analysis group) are illustrated in Figure 7-15. Compared with the two other data analysis groups, 

the 40 county data set has a greater number of mid and high COV bin monitors and notably 

fewer low COV bin monitors (Figure 7-16). This is not unexpected given the concentrationbased selection criteria used in identifying the 40 counties. 

 Following the selection of the 40 counties, staff retained the adjustment factors 

calculated for each monitoring site-year (not simply the mean factor that was used for the county 

selection) to simulate air quality just meeting the current standard (either the daily or annual 

factor, whichever was lower). These adjustment factors are given in Appendix A, Table A.4-1. 

Then using equation 7-3, staff calculated the adjustment factors needed for evaluating the 

potential alternative standards. Each of these alternative air quality scenarios were used as an 

input to the statistical model to estimate 5-minute maximum SO2 concentrations (equation 7-1). 

Then, air quality characterization metrics of interest were estimated for each site and year as 

described in section 7.2.5. 

 

43 In the 1st draft SO2 REA, having at least three monitors for all six years of the monitoring period was required. 

These earlier criteria were relaxed in the 2nd draft and in this final REA to allow for additional locations that may 

have ambient concentrations close to the current annual and daily standard levels. 
July 2009 112

Table 7-7. Counties selected for evaluation of air quality adjusted to just meeting the current and 

potential alternative SO2 standards and the number of monitors in each COV bin. 

# of Monitors in COV bin4

State County1 Mean Factor 

Closest 

Standard2 Low Mid High 

Arizona Gila 3.44 A 1 1 

Delaware New Castle 2.80 D 5 

Florida Hillsborough 3.81 D 4 2 

Linn 3.58 D 3 2 Iowa Muscatine 3.46 D 1 2 

Madison 3.78 D 4 Illinois Wabash 3.39 D 2 

Floyd 4.38 D 2 1 

Gibson 2.60 D 2 

Lake 4.41 D 2 Indiana 

Vigo 4.80 D 2 

Michigan Wayne 3.13 D 3 

Greene 4.47 D 2 1 2 

Iron3 Missouri 5.49 A 2 

Jefferson3

 3.53 D 4 

New Hampshire Merrimack 2.98 D 3 1 

Hudson 3.90 A 2 New Jersey Union 3.81 A 2 

Bronx 3.09 A 2 

New York Chautauqua 4.19 D 1 1 

Erie 3.17 D 1 1 

Cuyahoga 4.51 A 5 

Ohio Lake 2.99 D 2 

Summit 3.13 D 2 

Oklahoma Tulsa 4.61 A 3 

Allegheny 2.65 D 2 5 

Beaver 2.39 D 3 

Northampton 3.26 A 1 1 

Warren 1.74 D 2 

Pennsylvania 

Washington 3.19 A 2 1 

Blount 1.86 D 2 

Tennessee Shelby 4.08 D 1 2 

Sullivan 3.45 D 2 

Texas Jefferson 4.38 D 3 

Virginia Fairfax 4.80 A 3 

US Virgin Islands St Croix 4.60 D 2 3 

Brooke 2.32 A 2 

Hancock 2.32 A 9 

Monongalia 2.93 D 2 West Virginia 

Wayne 3.07 D 1 3 

Notes: 

1 Listed counties were selected based on lowest mean concentration adjustment factor, derived from 

at least 2 monitors per year for years 2001-2006 and ≥5 years of data. 2

 Ambient concentrations were closest to either the annual (A) or daily (D) NAAQS level. 3

 County selected based on frequent 5-minute benchmark level exceedances. 

4

 COV bins were low (COV≤100%); mid (100%<COV≤200%); high (COV>200%). 
July 2009 113

Figure 7-15. Locations of the 128 ambient monitors comprising the 40 County data set (i.e., the 

third data analysis group). 

 

Figure 7-16. Percent of monitors in each COV bin for the three data analysis groups: monitors 

reporting 5-minute maximum SO2 concentrations, the broader SO2 monitoring network, 

and SO2 monitors selected for detailed analysis in 40 counties. 

5-Minute Max SO2 Monitors (n=98)

Low COV

24%

Mid COV

60%

High COV

16%

Broader SO2 Network (n=809)

Low COV

27%

Mid COV

62%

High COV

11%

40 County SO2 Monitors (n=128)

Low COV

14%

Mid COV

66%

High COV

20%





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

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

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















 

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



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

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







 



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





 
July 2009 114

7.2.5 Air Quality Concentration Metrics 

For each of the data analysis groups and air quality scenarios considered, several 

concentration metrics were calculated; these included the annual average, 24-hour, and 1-hour 

daily maximum SO2 concentrations for each site-year of data and the number of exceedances of 

the potential health effect benchmark levels. The numbers of daily maximum 5-minute 

concentration exceedances in a year were counted (i.e., either 1 or none per day) rather than total 

number of exceedances (i.e., which confounds numbers of exceedances and days with 

exceedances). To characterize the relationship between the number of days with a 5-minute 

benchmark exceedance and the ambient concentration levels, staff generated two additional 

outputs given the different concentration averaging times. 

The first output was a comparison of the annual average SO2 concentration and the 

number of daily 5-minute maximum SO2 concentrations above the benchmark levels in a year. 

The output of this is the number of days per year a monitor had a measured or modeled 

exceedance, given an annual average SO2 concentration. In general, these results are graphically 

depicted in this REA, though most of the individual results displayed in the figures are provided 

in Appendix A-5. When considering the 40 counties used for detailed analysis, the results are 

presented at the county-level, some of which had multiple ambient monitors. Therefore, the 

results for the monitors within counties were aggregated to generate mean values representing 

the central tendency of the county’s annual average concentrations and the numbers of days in a 

year with benchmark exceedances. 

The second output was the probability of potential health effect benchmark exceedances 

given concentrations of short-term averaging times. It was proposed in Chapter 5 that the 1-hour 

daily maximum SO2 concentration would be of an appropriate averaging time in controlling the 

number of daily 5-minute maximum SO2 concentrations. Staff evaluated such a relationship 

using the measured 5-minute and 1-hour ambient SO2 concentrations to determine if this indeed 

was the case. A tally was made every time a daily 5-minute maximum SO2 concentration 

occurred during the same hour of the day as the 1-hour daily maximum SO2 concentration. The 

results of this analysis, separated by benchmark exceedance level, are given in Table 7-8. The 

co-occurrence of the daily 5-minute maximum and the 1-hour daily maximum SO2
July 2009 115

concentrations is greater than 70% at each of the benchmark levels indicating a strong 

relationship between the two concentration averaging times. 

Table 7-8. The co-occurrence of daily 5-minute maximum and 1-hour daily maximum SO2

concentrations using measured ambient monitoring data. 

Concentration/Level 

Co-occurring 5-

minute and 1-hour 

daily maximums1

(n) 

Total Paired 

Samples2

(n) 

Percent 

Co-occurring 

(%) 

All concentrations 106,115 130,296 81.4 

> 100 ppb 6,192 8,817 70.2 

> 200 ppb 2,030 2,793 72.7 

> 300 ppb 1,067 1,476 72.3 

> 400 ppb 700 961 72.8 

Notes: 

1

 the number of events the 5-minute maximum occurred in the same hour as the 1-hour 

daily maximum. 

2

 total events with both a 5-minute maximum and 1-hour SO2 concentration 

measurement. 

Given the form of the current 24-hour standard, the form of the potential alternative 

standards (1-hour daily maximum), and the frequency of 5-minute SO2 benchmark exceedances 

(i.e., either one or none per concentration), staff generated probability functions to estimate the 

likelihood of a 5-minute benchmark exceedance. These functions are useful in estimating the 

probability of a 5-minute benchmark exceedance given a range of SO2 concentrations at 

alternative averaging times (i.e., either a 24-hour average or 1-hour daily maximum 

concentration). Two approaches were used to generate the probability functions: the first was 

empirically-based while the second employed a logistic regression model. 

To generate the empirically-based probability functions, concentration data were first 

stratified into bins using concentration midpoints, with each bin separated by 10 ppb. For 

example a concentration of 53 ppb would be included in the 50 ppb bin, while a concentration of 

55 ppb would fall within the 60 ppb bin. Then, the presence or absence of a daily 5-minute 

benchmark exceedance given the number of values in each concentration bin (that originate from 

all monitored concentrations within the bin range) was used to estimate the probability of an 

exceedance. For example, if there were 105 exceedances of the 200 ppb benchmark level out of 
July 2009 116

239 instances of a 1-hour daily maximum binned concentration of 110 ppb44, the probability of a 

200 ppb benchmark exceedance would be 105/239 = 0.44 or 44 % given a 1-hour daily 

maximum concentration of around 110 ppb. An example of an output from this empiricallybased probability function is illustrated in Figure 7-17 for each of the four benchmark levels. 

P=105/239

P=27/37

P=16/37

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350 400

Daily Maximum 1-hour SO2 Concentration (ppb)

Probability of Daily 5-minute Maximum 

Exceedance (%)

> 100 ppb

> 200 ppb

> 300 ppb

> 400 ppb

Figure 7-17. Example of empirically-based probability curves. The probability of a 5-minute SO2

benchmark exceedance (P) was estimated by dividing the number of days with an 

exceedance by the total number of days within each 1-hour daily maximum SO2

concentration bin. 

In constructing the empirical probability curves, staff noted there were fewer samples 

with increasing concentrations (either 1-hour daily maximum or 24-hour average). Having too 

few samples generated instability in the empirically-based probability curves at the highest 1-

hour daily maximum or 24-hour average concentrations. For example, there were very few 

measured 1-hour daily maximum SO2 concentrations above the 130 ppb bin considering the high 

 

44 Therefore, there were 134 instances whereby the 1-hour daily maximum of 110 ppb did not correspond to a 5-

minute maximum concentration above 200 ppb. 
July 2009 117

population density group (Table 7-9). A total of 116 1-hour daily maximum SO2 concentrations 

out of 26,983 were scattered across the bins of 140 through 620 ppb, concentrations associated 

with the presence or absence of a 300 ppb 5-minute benchmark exceedance. There were 

increasing probabilities of 5-minute benchmark exceedances with increasing 1-hour daily 

maximum SO2 concentration starting at 100 ppb; however, at 170, 210, and 230 ppb there were 

lower estimated probabilities of exceedances than the preceding lower 1-hour daily maximum 

SO2 concentration. If using the probability data alone in Table 7-9, this would imply that at 1-

hour daily maximum concentrations of about 210–230 ppb, the likelihood of an exceedance is 

less than that when considering 1-hour daily maximum concentrations between 190–200 ppb. 

This is likely not the case, and in this instance, the wide range in estimated probabilities are more 

a function of the small sample sizes (no more than 3 samples per bin in this case) rather than the 

1-hour daily maximum SO2 concentrations. Therefore, in viewing the occurrence of this issue at 

small sample sizes, staff selected concentration bins having at least thirty 1-hour daily maximum 

(or 24-hour average) concentrations (whether it was all, none, or a mixture of exceedances) for 

inclusion in the empirically-based probability curves. As a result, the sample size limits 

compressed the range of predictability offered by the empirically-based probability curves. As 

an example, Figure 7-17 indicates that there were fewer than 30 samples available for 

concentration bins above a 1-hour daily maximum SO2 concentration of 200 ppb (note the 200 

ppb bin contained 37 samples). 
July 2009 118

Table 7-9. Example of how the probability of exceeding a 400 ppb 5-minute benchmark would be 

calculated given 1-hour daily maximum SO2 concentration bins. 

Number of times: 

Daily Maximum 

1-hour bin 

With no 

exceedances 

With one 

exceedance 

Probability of 

Exceedance 

(%)

100 71 0 0 

110 45 2 4 

120 43 1 2 

130 34 1 3 

140 17 1 6 

150 15 2 12 

160 11 4 27 

170 10 2  17 

180 8 3 27 

190 1 4 80 

200 1 3 75 

210 1 0  0 

220 1 2 67 

230 2 0  0 

240 0 2 100 

250 0 2 100 

Notes: 

 %  notes sharp decrease in probability from prior concentration 

bin. 

Data used in this table is from the high population density monitors 

reporting 5-minute concentrations. 

 

In the second approach, we generated probability curves for each of the four benchmark 

levels and the time-averaged SO2 concentrations (i.e., 1-hour daily maximum or 24-hour average 

concentration) using proc logistic and a probit link function (SAS, 2004). The probit link 

function used can be described with the following: 







 

)(

2/

2/1

2

)2(

1 ),;(

x

t xy dte





 equation (7-4) 

where x denotes the time averaged SO2 concentration (either 1-hour daily maximum or 

the 24-hour average in ppb), y denotes the corresponding probability of a 5-minute exceedance, 

and β and γ are two model estimated parameters used to generate predicted values. The logisticmodeled predictions were then used to generate probability curves using all available 

measurements, thereby extending the range of predictability beyond that of the empirically-based 

curves. Figure 7-18 illustrates an example of logistic-modeled probability curves using the same 
July 2009 119

data used in generating the probability curves shown in Figure 7-17. Note that predictions for 

the modeled curves extend beyond the 1-hour daily maximum limits of 200 ppb when using the 

empirical curves. 

Prior to estimating either the empirically-based or logistic-modeled probability curves, 

staff separated the monitors within each data analysis group by the population density groups; 

either low (≤ 10,000 persons within 5 km), mid (10,001 to ≤ 50,000 persons within 5 km), or 

high (> 50,000 persons within 5 km). Staff hypothesized that there may be different exceedance 

probabilities in dense population areas compared with locations having fewer residents given the 

siting characteristics of the monitors with regard to the presence of emission sources. This 

separation of the monitoring results by the surrounding population should be useful in 

appropriately characterizing the air quality because the monitoring data are used as indicators of 

potential human exposure; the results from monitors sited within greater population densities 

should be more representative of potential population exposure. 

Figure 7-18. Example of logistic-modeled probability curves. The data used to generate these 

modeled curves were the same used in generating the empirically-based curves in 

Figure 7-17. 
July 2009 120

7.3 RESULTS 

7.3.1 Measured 5-minute Maximum and Measured 1-Hour SO2 Concentrations at 

Ambient Monitors – As Is Air Quality 

In this first data analysis group, staff analyzed the as is air quality data solely based on 

the SO2 ambient monitor measurements. Ambient monitoring data were evaluated at the 98 

locations where both the 1-hour and 5-minute maximum SO2 concentrations were reported for 

years 1997 through 2007. Due to the large size of the data set (i.e., 471 site-years), staff 

summarized the number of potential health effect benchmark exceedances in a series of figures. 

This analysis centered on the relationship between various concentration averaging times and the 

daily 5-minute maximum SO2 concentration exceedances. Descriptive statistics for the measured 

daily 5-minute maximum and the 1-hour SO2 concentrations are provided in Appendix A-5 and 

in the SOx ISA (ISA, section 2.5.2), the latter of which includes additional discussion of the 

spatial and temporal variability of the 5-minute maximum and continuous 5-minute SO2

concentrations. Staff performed two broad analyses using this data analysis group; first staff 

evaluated the relationship between annual average concentrations and number of days per year 

with at least one 5-minute concentration above benchmark levels and then estimated the 

probability of having at least one 5-minute concentration above benchmark levels given shortterm averaging times (i.e., 1-hour daily maximum and 24-hour average). 

First, staff evaluated the occurrence of the daily 5-minute maximum SO2 concentration 

exceedances in a year. Figure 7-19 compares the number of days with 5-minute maximum SO2

concentrations above the potential health effect benchmark levels along with the corresponding 

annual average SO2 concentration from each max-5 monitor. Overall, there are few days in a 

year with 5-minute maximum SO2 concentrations above each of the potential health effect 

benchmark levels. Given the data in Table 7-8, no more than 7% of the total days with 

measurements had 5-minute maximum SO2 concentrations above the 100 ppb benchmark, while 

approximately 2%, 1%, and 0.7% of days had daily 5-minute maximum SO2 concentrations 

above the 200, 300, and 400 ppb levels, respectively. None of the monitors in this data set had 

annual average SO2 concentrations above the current annual NAAQS of 30 ppb. However, 

several of the monitors in several years frequently had daily 5-minute maximum SO2

concentrations above the potential health effect benchmark levels. Many of those monitors 

where frequent 5-minute benchmark exceedances occurred had annual average SO2
July 2009 121

concentrations between 5 and 15 ppb, with little to no correlation between the annual average 

SO2 concentration and the number of daily 5-minute maximum SO2 concentrations above the 

potential health effect benchmark levels. These data are useful in determining the number of 

days in a year a particular monitor had a daily maximum exceedance of a selected benchmark 

level, however from a practical perspective, the annual average concentration would be 

ineffective at controlling daily 5-minute maximum SO2 concentrations given the observed weak 

relationships. 

Figure 7-19. The number of days per year with measured 5-minute maximum SO2 concentrations 

above potential health effect benchmark levels at 98 monitors given the annual average 

SO2 concentration, 1997-2007 air quality as is. The level of the annual average SO2

NAAQS of 30 ppb is indicated by the dashed line. 

> 100 ppb > 200 ppb 

> 300 ppb > 400 ppb 

Number of Daily 5-minute Maximum SO2 Above Selected Level Per Site-Year
July 2009 122

Second, the probability of potential health effect benchmark exceedances was estimated 

given the 24-hour average and 1-hour daily maximum SO2 concentrations. Figure 7-20 presents 

the empirically-based and logistic-modeled probability curves given the 24-hour average SO2

concentrations and separated by the three population densities. There is an increasing probability 

of a daily 5-minute maximum SO2 concentration exceedance with increasing 24-hour average 

concentrations at each of the potential health effect benchmark levels and for each of the 

population density groups. Some deviation from increasing probability occurs near the end of 

the empirically-based curves derived from the mid-population density monitors. As discussed 

earlier, this observed behavior is likely a function of the small sample size rather than variability 

in 24-hour SO2 concentrations. The logistic-modeled curves are consistent with the empiricallybased curves; however, the modeled curves illustrate an extended concentration range and a 

consistent pattern of increasing probability of 5-minute benchmark exceedances with increasing 

24-hour concentration. 

Probability curves generated from monitors sited in low-population density areas exhibit 

a steeper slope when compared with the other population density groups, indicating a greater 

probability of a 5-minute SO2 benchmark exceedance given the same 24-hour SO2 concentration. 

For example, the probability of exceeding a daily 5-minute maximum concentration of 200 ppb 

using the empirically-based curves is 30% at the low-population density monitors given a 24-

hour average concentration of about 20 ppb. In comparison, empirically-based curves generated 

from the mid- and high-population density monitors indicate that the probability of a 5-minute 

benchmark exceedance at the same 24-hour concentration of 20 ppb is only about 14% and 3%, 

respectively. There is a small probability (about 10%) of exceeding the 300 and 400 ppb in the 

high-population density areas given a 24-hour average concentration of about 40 ppb (using 

either the empirical or modeled curves), though at monitors sited in the low-population areas this 

probability is greater than 50%. 

The empirically-based curves are limited to estimating exceedance probabilities at or 

below 24-hour concentrations of 60 ppb, with mostly unknown probabilities associated with 

many of the benchmark levels and at concentrations approaching the current 24-hour standard. 

For example, while the estimated probability of a daily 5-minute maximum SO2 concentration 

above 100 ppb is at or near 100% considering any of the population density groups, little can be 
July 2009 123

construed from the other empirically-based curves at 24-hour concentrations above 60 ppb, 

particularly at monitors sited in mid- to high-population density areas. The logistic-modeled 

curves however provide the probability of benchmark exceedances at higher 24-hour 

concentrations. For example, according to Figure 7-20 there would be a 100% probability of 

exceeding all benchmark levels at about a 24-hour concentration of 100-120 ppb, when 

considering monitors in either the mid- or high-population density areas. 
July 2009 124

Figure 7-20. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 24-hour average SO2 concentration, using empirical data 

(left) and a fitted log-probit model (right), 1997-2007 air quality as is. Both the 5-minute 

maximum and 24-hour SO2 concentrations were from measurements collected at 98 

ambient monitors and separated by population density. 

Probability (%) of a Daily 5-Minute Maximum SO

2 Benchamark Exceedance

Mid 

Population

Density

Mid 

Population

Density

Low 

Population

Density

Low 

Population

Density

High 

Population

Density

24-hour Average SO2 Concentration (ppb)

High 

Population

Density
July 2009 125

Figure 7-21 presents similar probability curves generated from the 5-minute and 1-hour 

ambient measurement data, but the probabilities of benchmark exceedances are associated with 

the 1-hour daily maximum SO2 concentrations instead of 24-hour average concentrations. At 

each of the benchmark levels and population densities, Figure 7-21 shows increasing 

probabilities of exceedances with increasing 1-hour daily maximum SO2 concentrations. 

Further, the probability curves have steeper slopes associated with the low-population density 

group compared to the slopes of the higher population density groups. Note that while there is 

uncertainty regarding the extrapolation beyond the limits imposed on the empirically-based 

curves (i.e., 30 or greater samples per bin), one can be assured that the probability of an 

exceedance of a daily 5-minute maximum SO2 concentration of 400 ppb is 100% given a 1-hour 

daily maximum SO2 concentration of 400 ppb (and so on for the other 5-minute benchmark/1-

hour daily maximum SO2 concentration combinations).45 As observed using the 24-hour average 

concentrations, the shape of the curves beyond the imposed limits of the empirical data can be 

informed by the logistic regression modeling (right column, Figure 7-21). In using the logisticmodeled benchmark curves, a 100% probability of an exceedance is estimated to occur at about a 

1-hour daily maximum concentration 50-100 ppb less that of the respective 5-minute benchmark 

level. 

It also should be noted that when comparing any of the 24-hour average probability 

curves with corresponding 1-hour daily maximum probability curves (e.g., Figure 7-20 and 

Figure 7-21) the relative slopes of the 24-hour curves are steeper. Therefore, changes in 24-hour 

average SO2 concentration (either higher or lower) will effectively result in greater changes in 

the probability of exceedances when compared to a similar 1-hour daily maximum concentration 

shift. For example, to reduce the likelihood of a 200 ppb benchmark exceedance from about 

90% to 10%, 24-hour average concentrations would need to go from a level of about 50 to 20 

ppb using the logistic-modeled mid-population curves. This same reduction in probability would 

correspond to a 1-hour daily maximum concentration reduction of about 150 ppb to 70 ppb. 

 

45 Technically, if all 5-minute concentrations were exactly 400 ppb, the 1-hour average concentration would be 400 

ppb and the 5-minute maximum would not actually exceed 400 ppb. However, note that probability of exceeding 

the 100 or 200 ppb benchmarks approaches 100% at less than a 1-hour daily maximum of 100 an 200 ppb, 

respectively (Figure 7-18). 
July 2009 126

Figure 7-21. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 1-hour daily maximum SO2 concentration, using 

empirical data (left) and a fitted log-probit model (right), 1997-2007 air quality as is. 

Both the 5-minute maximum and 1-hour SO2 concentrations were from measurements 

collected at 98 ambient monitors and separated by population density. 

Low 

Population

Density

Low 

Population

Density

Mid 

Population

Density

Mid 

Population

Density

High 

Population

Density

High 

Population

Density

Probability (%) of a Daily 5-Minute Maximum SO

2 Benchamark Exceedance

1-Hour Daily Maximum SO2 Concentration (ppb)
July 2009 127

7.3.2 Measured 1-Hour and Modeled 5-minute Maximum SO2 Concentrations at All 

Ambient Monitors – As Is Air Quality 

In the second data analysis group, staff analyzed the as is air quality using a combination 

of measurement and modeled data. As described in section 7.2.3, a statistical model was applied 

to 1-hour ambient SO2 measurements to estimate 5-minute maximum SO2 concentrations. This 

was done because there are a greater number of monitors in the broader SO2 monitoring network 

compared to subset of monitors reporting the 5-minute maximum SO2 concentrations (section 

7.3.1). This larger monitoring data set included 809 ambient monitors in operation at some time 

during the years 1997 through 2006 that met the completeness criteria described in section 7.2.1. 

This data set included 4,692 site-years of data, and combined with the estimated 5-minute SO2

concentrations using the measured 1-hour values, allowed for a comprehensive characterization 

of the hourly and 5-minute SO2 air quality at ambient monitors located across the U.S. 

Descriptive statistics for the measured 1-hour SO2 concentrations are provided in the SOx ISA 

(ISA, section 2.5.1) including additional discussion of the spatial and temporal variability in 1-

hour SO2 concentrations. 

Staff performed twenty separate model simulations to estimate the 5-minute maximum 

SO2 concentration associated with each 1-hour measurement. The individual simulation results 

at each monitor were averaged to generate a mean number of days per year with a 5-minute 

benchmark exceedance. The modeled (5-minute maximum) and measurement (1-hour) data 

were analyzed in a similar manner as performed on the measured 5-minute maximum and 1-hour 

SO2 concentrations described in section 7.3.1. The results provided in this section were 

generated using the modeled daily 5-minute maximums and the measured hourly SO2

concentrations considering 1-hour, 24-hour, and annual averaging times. Staff performed two 

broad analyses; first staff evaluated the relationship between annual average concentrations and 

number of days per year with at least one 5-minute concentration above benchmark levels and 

then estimated the probability of having at least one 5-minute concentration above benchmark 

levels given short-term averaging times (1-hour daily maximum and 24-hour average). 

First, Figure 7-22 shows the number of days per year with a 5-minute SO2 concentration 

above benchmark levels versus the annual average SO2 concentration. Fewer than 5% of total 

days per year had a 5-minute SO2 concentration above the 100 ppb benchmark, while 

approximately 1%, 0.5%, and 0.2% of days had at least one 5-minute concentration above the 
July 2009 128

200, 300, and 400 ppb benchmark levels, respectively. None of the site-years of data had annual 

average SO2 concentrations at or above the level of the current annual NAAQS (30 ppb). 

However as described above, several site-years had predicted 5-minute SO2 concentrations above 

the potential health effect benchmark levels. Many of the monitors with frequent 5-minute 

benchmark exceedances had annual average SO2 concentrations between 10 and 20 ppb, with a 

pattern of increasing number of days per year with at least one 5-minute concentration above the 

benchmark levels with increasing annual average concentrations. This pattern was most 

prominent at the 100 ppb benchmark level, with progressively weaker relationships between the 

number of 5-minute benchmark exceedances and annual average concentrations at each of the 

higher benchmark levels. 
July 2009 129

Figure 7-22. The number of days per year with modeled daily 5-minute maximum SO2

concentrations above potential health effect benchmark levels at 809 ambient monitors 

given the annual average SO2 concentration, 1997-2006 air quality as is. The level of 

the annual average SO2 NAAQS of 30 ppb is indicated by the dashed line. 

 

Next, empirical and logistic-modeled probability curves were generated for this second 

data analysis group. Figure 7-23 illustrates the probability of benchmark exceedances using the 

modeled daily 5-minute maximum SO2 concentrations and 24-hour average concentrations. 

These probability curves exhibit patterns similar to that described using the pure measurement 

data (Figure 7-20). For example, the probability curves generated from low-population density 

area monitors are steeper than those generated using the higher population density monitors at 

> 100 ppb > 200 ppb 

> 300 ppb > 400 ppb 

Number of Daily 5-minute Maximum SO2 Above Selected Level Per Site-Year
July 2009 130

each of the benchmark levels considered. In addition, the slopes of the probability curves are 

generally consistent between the measured and modeled 5-minute maximum data, where 

comparable 24-hour average concentrations exist. 

The broader SO2 monitoring network to estimate daily 5-minute maximum SO2

concentrations provides insight as to the potential shape of each empirically-based probability 

curve at greater 24-hour average concentrations. The upper range of 24-hour concentrations 

extends to around 70-100 ppb (Figure 7-23), while at the monitors reporting 5-minute maximum 

SO2 concentrations the maximum 24-hour average concentrations extends to at most between 50 

and 60 ppb (Figure 7-20). The extended range of 24-hour concentrations in the empiricallybased curves provides additional support to what was stated earlier using the pure measurement 

data, that is, there is a strong likelihood of 5-minute peak concentrations above the benchmark 

levels at 24-hour average concentrations well below the level of the current standard. This is 

further confirmed by the logistic-modeled probability curves that estimate all benchmark levels 

would be exceeded at about a 24-hour concentration of 60-100 ppb, the level of which dependent 

on where the monitor is sited. 

The probability curves generated using the modeled 5-minute maximum and 1-hour daily 

maximum SO2 concentrations (Figure 7-24) also exhibit patterns consistent with those patterns 

observed using the pure measurement data (Figure 7-21). Again, a wider range of 1-hour daily 

maximum concentrations is observed in using the broader monitoring network when compared 

with the results using the monitors reporting the 5-minute maximum SO2 concentrations, giving 

greater ability to discern the probability of benchmark exceedances at higher 1-hour daily 

maximum SO2 concentrations. When using either the empirically-based or logistic modeled 

curves, a 100% probability of exceeding the 100, 200, 300, and 400 ppb benchmarks is estimated 

to occur at 1-hour daily maximum concentrations of about 80, 150, 225, and 300 ppb, 

respectively. 
July 2009 131

Figure 7-23. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 24-hour average SO2 concentrations, using empirical 

data (left) and a fitted log-probit model (right), 1997-2006 air quality as is. The 5-minute 

maximum SO2 concentrations were modeled from 1-hour measurements collected at 

809 ambient monitors and then separated by population density. 

Probability (%) of a Daily 5-Minute Maximum SO

2 Benchamark Exceedance

24-hour Average SO2 Concentration (ppb)

Low 

Population 

Density

Low 

Population 

Density

Mid 

Population 

Density

High 

Population 

Density

High 

Population 

Density

Mid 

Population 

Density
July 2009 132

 Figure 7-24. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 1-hour daily maximum SO2 concentrations, using 

empirical data (left) and a fitted log-probit model (right), 1997-2006 air quality as is. The 

5-minute maximum SO2 concentrations were modeled from 1-hour measurements 

collected at 809 ambient monitors and then separated by population density. 

1-Hour Daily Maximum SO2 Concentration (ppb)

Probability (%) of a Daily 5-Minute Maximum SO

2 Benchamark Exceedance

Low 

Population 

Density 

Mid 

Population 

Density 

High 

Population 

Density 

Low 

Population 

Density 

Mid 

Population 

Density 

High 

Population 

Density 
July 2009 133 

7.3.3 Modeled 1-Hour and Modeled 5-minute Maximum SO2 Concentrations at Ambient 

Monitors in 40 Counties – Air Quality Adjusted to Just Meet the Current and Potential 

Alternative Standards 

Staff selected forty counties to analyze 5-minute benchmark exceedances under several 

air quality scenarios: as is air quality and air quality adjusted to just meeting the current and 

alternative standards. The forty counties were selected using criteria discussed in section 7.2.4. 

Specifically, we chose the 38 counties with 1-hour ambient monitor SO2 concentrations nearest 

the current NAAQS levels and two counties with a high frequency of measured daily 5-minute 

maximum SO2 concentrations above the potential health effect benchmark levels. The 1-hour 

SO2 measurement data were from 128 ambient monitors and totaled 610 site-years of 

monitoring, a subset of data from the broader SO2 monitoring network (see section 7.3.2). Staff 

evaluated multiple alternative air quality scenarios by first adjusting the 1-hour ambient 

monitoring concentrations to just meet a particular standard level (section 7.4). Then, as was 

done in section 7.3.2, staff performed twenty simulations to estimate the 5-minute maximum SO2

concentration associated with each 1-hour adjusted concentration using the statistical model 

described in section 7.2.3. These simulation results were combined to generate a mean estimate 

for each of the metrics of interest (e.g., the number of days in a year with 5-minute maximum 

SO2 concentrations > 200 ppb) selected here as the best estimate from the twenty simulations. 

Staff 1) evaluated the relationship between annual average concentrations and number of days 

per year with at least one 5-minute concentration above benchmark levels, 2) summarized the 

number of days per year with at least one 5-minute concentration above benchmark levels for 

each air quality scenario, 3) compared number of days per year with at least one 5-minute 

concentration above benchmark levels using two percentile forms of the potential alternative 1-

hour daily maximum standards (i.e., 98th and 99th percentile), and 4) estimated the probability of 

having at least one 5-minute concentration above benchmark levels given short-term averaging 

times (1-hour daily maximum and 24-hour average). 

First, staff evaluated the relationship between the short-term peak concentrations and the 

level of the current annual SO2 NAAQS in the selected counties. Figure 7-25 illustrates the 

number of days per year with 5-minute daily maximum SO2 concentrations above the potential 

health effect benchmark levels along with the corresponding annual average concentrations. 

Each data point represents a monitor site-year generated from the modeled 5-minute peaks and 
July 2009 134 

air quality adjusted to just meeting the current SO2 standards. None of the site-years in the 

selected counties had annual average concentrations above the level of the current NAAQS (30 

ppb) by design46, however there are many more site-years with a greater number of modeled 

daily 5-minute maximum SO2 concentrations above the potential health effect benchmark levels 

than compared with that of the as is air quality. There are a decreasing number of exceedances 

with increasing benchmark concentrations, though there is a greater proportion of monitors with 

exceedances when considering concentrations adjusted to just meeting the current standard than 

when using the as is air quality (e.g., see Figure 7-19). When considering concentrations 

adjusted to just meeting the current standard, there is a stronger relationship between the annual 

average concentrations and the number of benchmark exceedances than observed previously 

with the as is air quality however, the strength of that relationship weakens with increasing 

benchmark levels. 

 

46 The current annual SO2 NAAQS is 30 ppb. Concentrations of up to 30.4 ppb are possible due to a rounding 

convention. This is why there are several data points just to the right of the dashed line in Figure 7-22. 
July 2009 135 

Figure 7-25. The number of days per year with modeled 5-minute maximum SO2 concentrations 

above potential health effect benchmark levels per year at 128 ambient monitors in 40 

selected counties given the annual average SO2 concentration, 2001-2006 air quality 

adjusted to just meet the current NAAQS. The level of the annual average SO2 NAAQS 

of 30 ppb is indicated by the dashed line. 

> 100 ppb > 200 ppb 

> 300 ppb > 400 ppb 

Number of Daily 5-minute Maximum SO2 Above Selected Level Per Site-Year
July 2009 136 

Similar relationships are present between the annual average SO2 concentrations and the 

number of benchmark exceedances when considering the potential alternative standards. As a 

reminder, to just meet the current and potential alternative standards staff estimated a unique 

adjustment factor to simulate the alternative air quality. The direction of the adjustment factor 

(either upwards or >1; downwards or <1) and magnitude of the adjustment factor used has a 

direct impact on the estimated number of 5-minute benchmark exceedances. In general, the air 

quality distributions that just meet the potential alternative standards were enveloped by the as is

air quality (i.e., a distribution with low concentrations) and the air quality adjusted to just 

meeting the current standard (i.e., a distribution with generally high concentrations). Therefore, 

the estimated number of days with exceedances also fell within the range of exceedances 

generated using the as is air quality or the air quality adjusted to just meet the current standard. 

For example, a comparison of the annual average SO2 concentrations and number of daily 5-

minute maximum exceedances of 200 ppb is presented in Figure 7-26 for six air quality 

scenarios: four of the 99th percentile 1-hour daily maximum potential alternative standards (i.e., 

the 100, 150, 200, and 250 ppb); the air quality adjusted to just meet the current standards; and 

as is air quality. 

Clearly, in using the air quality adjustment procedure combined with the statistical model 

to estimate 5-minute maximum SO2 concentrations, the current standard air quality scenario 

allows for the greatest estimated number of days per year with potential health effect benchmark 

exceedances (Figure 7-26). However, at a minimum the annual standard does provide protection 

against annual average concentrations above the level of the current standard. While there were 

fewer 5-minute benchmark exceedances using the 1-hour daily maximum forms of a potential 

alternative standard, two of the levels (1-hour daily maximums of 200 and 250 ppb) did not 

prevent annual average concentrations from exceeding the current annual standard (Figure 7-26). 

High annual average concentrations become less of an issue when considering the lower levels of 

the 1-hour daily maximum potential alternative standards. Even though the 99th percentile 1-

hour daily maximum standards of 100 or 150 ppb allow for greater annual average 

concentrations than when considering as is air quality, all but one site-year are below the level of 

the current annual standard and there are fewer estimated days per year with benchmark 

exceedances. These results further demonstrate the stronger relationship 5-minute peak 

concentrations have with 1-hour SO2 concentrations than with annual average concentrations. 
July 2009 137 

Figure 7-26. The number of modeled daily 5-minute maximum SO2 concentrations above 200 ppb per year at 128 a

selected counties given the annual average SO2 concentration, 2001-2006 air quality as is and that adjus

current and four potential alternative standards (text in graph indicate standard evaluated). The level of SO2 NAAQS of 30 ppb is indicated by the dashed line. 

99th percentile 

Daily 1-hour maximum of 100 ppb 

99th percentile Daily 1-hour maximum of 150 ppb 

99th percentile 

Daily 1-hour maximum of 200 ppb 

99th percentile 

Daily 1-hour maximum of 250 ppb 

Number of Daily 5-minute Maximum Above 200 ppb per Site-Year Current annu

daily standaAs is air qua
July 2009 138 

Table 7-10. Percent of days having a modeled daily 5-minute maximum SO2 concentration above 

the potential health effect benchmark levels given air quality as is and air quality adjusted to just 

meeting the current and each of the potential alternative standards. 

Percent of Days With Daily 5-minute Maximum SO2

Air Quality Concentrations Above Benchmark Levels 

Scenario1 > 100 ppb > 200 ppb > 300 ppb > 400 ppb 

as is 9.1 2.4 0.9 0.5 

CS 41.0 17.2 9.1 5.3 

99-50 0.7 0.0 0.0 0.0 

99-100 4.5 0.7 0.2 0.0 

98-100 6.9 1.2 0.3 0.1 

99-150 10.6 2.2 0.7 0.3 

99-200 17.2 4.5 1.6 0.7 

99-250 23.6 7.4 2.9 1.3 

98-200 22.5 6.9 2.6 1.2 

Notes: 

1 as is air quality is unadjusted; CS is air quality adjusted to just meet the 

current standard; x-y are the xth percentile form of a 1-hour daily maximum 

level of y. 

Second, staff summarized the number of days per year with 5-minute maximum SO2

concentrations above benchmark levels within the 40-county data set for additional comparisons 

of the air quality scenarios. Table 7-10 provides the percent of all days above each of the 

benchmark levels considering each of the air quality scenarios. Again, the scenario where air 

quality just meets the current standard has the greatest percent of days with benchmark 

exceedances. With each progressive decrease in the 1-hour daily maximum SO2 concentration 

levels of the potential alternative standards, there are fewer days with benchmark exceedances. 

The percent of all days with benchmark exceedances using as is air quality was between a 

potential 1-hour daily maximum alternative standard level of 100 and 150 ppb (99th percentile 

form), or similar to that of the 98th percentile form at a level of 100 ppb. 

Third, staff evaluated two forms of the potential alternative standards: the 99th and 98th

percentile forms, each having a 1-hour daily maximum level of either 100 or 200 ppb. For 

example, Figure 7-27 indicates that nearly all site-years have a greater estimated number of days 

per year with benchmark exceedances given the 98th percentile form when compared with a 99th

percentile form at the same level. This is expected given the number of allowable 1-hour SO2

concentrations above the 200 ppb level for each of the percentile forms. The two air quality 

scenarios were compared on a monitor-to-monitor basis and on average, the 98th percentile form 

allowed for approximately 46, 68, 84, and 86% more benchmark exceedances considering the 
July 2009 139 

100, 200, 300, and 400 ppb benchmark levels, respectively when compared with the 99th

percentile form. 

Figure 7-27. The number of days per year with modeled 5-minute maximum SO2 concentrations 

above benchmark levels given the 99th and 98th percentile forms, using the 40-county 

air quality data set adjusted to just meet a 1-hour daily maximum level of 200 ppb. 

When a 1-hour daily maximum level of 100 ppb was considered, on average the 98th

percentile form of the potential alternative standard allowed for approximately 68, 90, 84, and 

> 100 ppb > 200 ppb

> 300 ppb > 400 ppb

Number of Days per Year with a 5-Minute Benchmark Exceedance

- 98th Percentile 1-hour Daily Maximum 200 ppb Standard

Number of Days per Year with a 5-Minute Benchmark Exceedance

- 99th Percentile 1-hour Daily Maximum 200 ppb Standard
July 2009 140 

74% more benchmark exceedances at each monitor considering the 100, 200, 300, and 400 ppb 

benchmark levels, respectively when compared with the 99th percentile form. While generally 

there were greater differences in the percent of exceedances for the two forms when considering 

the 100 ppb level compared with the 200 ppb level, there were far fewer site-years with 

benchmark exceedances (Figure 7-28). 

Figure 7-28. The number of days per year with modeled 5-minute maximum SO2 concentrations 

above benchmark levels given the 99th and 98th percentile forms, using the 40-county 

air quality data set adjusted to just meet a 1-hour daily maximum level of 100 ppb. 

Number of Days per Year with a 5-Minute Benchmark Exceedance

- 99th Percentile 1-hour Daily Maximum 100 ppb Standard

Number of Days per Year with a 5-Minute Benchmark Exceedance

- 98th Percentile 1-hour Daily Maximum 100 ppb Standard

> 100 ppb > 200 ppb 

> 300 ppb > 400 ppb 
July 2009 141 

Fourth, staff estimated the probability of potential health effect benchmark exceedances 

given the adjusted air quality scenarios and short-term averaging times. Again, patterns in the 

curves were consistent with what was observed and described previously; monitors within lowpopulation density areas had steeper probability curves compared with those in higher population 

density areas. Further, there were similarities in the shape and the steepness of the curves when 

comparing the adjusted air quality probability curves with the curves developed from the 

corresponding as is air quality. Therefore, for the sake of brevity, all of the probability curves 

for each of the alternative standards are not presented. However, there were some differences in 

the probability curves worthy of presentation and discussion, using the empirically-based curves 

for the demonstration. 

Figure 7-29 presents the probability of a 5-minute benchmark exceedance using as is air 

quality and air quality adjusted to just meet the current standard, given 1-hour daily maximum 

SO2 concentrations. In general, all of the corresponding probability curves for all of the air 

quality scenarios overlap when considering the 100 and 200 ppb benchmark levels. However, 

the probability curves associated with exceeding the 300 and 400 ppb benchmark levels were of 

similar slope, but shifted to the left when considering the as is air quality compared with the 

current standard scenario. This is likely a function of the non-linear form of the statistical model 

used to estimate the 5-minute maximum SO2 concentrations, the proportional adjustment 

procedure to simulate alternative standards, and the form of the air quality characterization 

metric used. 

When adjusting the 1-hour SO2 concentrations upwards using a proportional factor, a 

corresponding proportional increase in the number of days per year with benchmark exceedances 

does not necessarily follow. The statistical model uses multiple distributions of PMRs, not 

linearly related to 1-hour SO2 concentrations. Certainly, the total number of days in a year with 

benchmark exceedances will increase with an upward adjustment of air quality, and does so as 

observed in Figure 7-26. However, the greatest proportion of monitoring days within any of the 

air quality scenarios is comprised of days without an exceedance (see Table 7-10). The 

frequency of exceedances of the higher benchmarks is very low using the as is air quality; the 

few added days with estimated exceedances of 300 or 400 ppb using the simulated air quality is 

not proportional to the universal increase in hourly concentrations applied to all 1-hour 

concentrations. Therefore the probability curves tend to be less steep with the upward 1-hour 
July 2009 142 

concentration adjustments when considering the higher benchmark levels. Furthermore, days 

already having an exceedance are only counted once, that is, if there were an exceedance on a 

given day using the as is air quality, it is likely that the same day would also have an exceedance 

using the adjusted air quality, only it is associated with a greater 1-hour (or 24-hour average) 

concentration. Again, the 1-hour concentrations are increased without corresponding 

proportional increase in the number of exceedances when comparing the two air quality 

scenarios. Conversely, it could also be argued that there may be an increased probability of daily 

5-minute exceedances of 300 and 400 ppb when using air quality with a relatively low 

concentration distribution (such as with the as is air quality) compared with a distribution of 

higher concentrations (such as with the current standard scenario). However, it should be noted 

that the total number of benchmark level exceedances in a year (and the absence of exceedances 

at the same high 1-hour daily maximum concentration) under either of these scenarios would be 

very few, with far fewer numbers of exceedances associated with the relatively low 

concentration air quality. 

This discussion of probability curves can be extended to each of the potential alternative 

standards. For example, Figure 7-30 illustrates a range in each of the probability curves given 

each of the alternative air quality scenarios and using monitors sited within high-population 

density areas. The 100 and 200 ppb benchmark level probability curves exhibit a narrow range 

across each of the adjusted air quality scenarios. While the estimated 300 and 400 ppb 

probability curves are wider than the 100 and 200 ppb curves, there is still agreement in the 

estimated probabilities at many of the 1-hour daily maximum SO2 values. The range in 

probability curves tended to be widest at the lowest probabilities/1-hour daily maximum SO2

concentrations within a given benchmark, likely indicating a greater uncertainty in the 

relationship between exceedance of the daily 5-minute maximum SO2 concentrations of 300 and 

400 ppb and 1-hour daily maximum SO2 concentrations less than 130 ppb and 180 ppb, 

respectively.
July 2009 143 

Figure 7-29. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 1-hour daily maximum SO2 concentrations, 2001-2006 air 

quality as is and that adjusted to just meet the current NAAQS. The 5-minute maximum 

concentrations were modeled from 1-hour measurements collected at 128 ambient 

monitors from 40 selected counties and then separated by population density within 5 

km of monitors.

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350 400

Daily Maximum 1-hour SO2 Concentration (ppb)

Probability of Daily 5-minute Maximum 

Exceedance

> 100 ppb CS

> 200 ppb CS

> 300 ppb CS

> 400 ppb CS

> 100 ppb AS IS

> 200 ppb AS IS

> 300 ppb AS IS

> 400 ppb AS IS

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350 400

Probability of Daily 5-minute Maximum 

Exceedance

> 100 ppb CS

> 200 ppb CS

> 300 ppb CS

> 400 ppb CS

> 100 ppb AS IS

> 200 ppb AS IS

> 300 ppb AS IS

> 400 ppb AS IS

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350 400

Probability of Daily 5-minute Maximum 

Exceedance

> 100 ppb CS

> 200 ppb CS

> 300 ppb CS

> 400 ppb CS

> 100 ppb AS IS

> 200 ppb AS IS

> 300 ppb AS IS

> 400 ppb AS IS

Low 

Population 

Density 

Mid 

Population 

Density 

High 

Population 

Density 
July 2009 144 

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350 400

Daily Maximum 1-hour SO2 Concentration (ppb)

Probability of Daily 5-minute Maximum 

Exceedance

> 100 ppb

> 200 ppb

> 300 ppb

> 400 ppb

Figure 7-30. Probability of daily 5-minute maximum SO2 concentrations above potential health 

effect benchmark levels given 1-hour daily maximum SO2 concentrations, 2001-2006 air 

quality adjusted to just meet the current and each of the potential alternative standards 

(99th percentile form). The 5-minute maximum concentrations were modeled from 1-

hour measurements collected at 128 ambient monitors from 40 selected counties, highpopulation density monitors. 

While there are similarities in the probability of daily 5-minute maximum benchmark 

exceedances for each of the potential alternative standard scenarios given either the 1-hour daily 

maximum or 24-hour average SO2 concentrations, there are large differences in the total number 

of exceedances given a particular county and air quality scenario. Table 7-11 presents the mean 

number of days in a year where the daily 5-minute maximum SO2 concentration was above 100 

ppb in each of the 40 selected counties and for all air quality scenarios. In considering air quality 

adjusted to just meeting the current standard and the level of the highest potential alternative 

standards (200 and 250 ppb 1-hour daily maximum), counties such as Hudson NJ, Tulsa OK, and 

Wayne WV were estimated to have the greatest number of benchmark exceedances. On average 

there would be between 100 and 200 days of the year with 5-minute maximum SO2

concentrations above 100 ppb in these counties. Most of the other locations though had fewer 

than 100 benchmark exceedances in a year, particularly when considering the two potential 

alternative 1-hour daily maximum standards. Air quality simulating just meeting the current 

standard was associated with the greatest number of estimated exceedances at most locations. 

This consistent pattern was observed with each of the benchmark levels (see below) indicating 

the limited influence the current standard has on the estimated number of 5-minute benchmark 
July 2009 145 

exceedances. Decreases in the potential alternative standard level corresponded with decreases 

in the number of days per year with benchmark exceedances. Most counties have fewer mean 

estimated 5-minute benchmark exceedances of 100 ppb using air quality adjusted to just meeting 

the 99th percentile daily 1-hour maximum concentration of 100 ppb, than that estimated using the 

as is air quality. The were 11 counties that only achieve reduction in the number of benchmark 

level exceedances from as is air quality when considering the 99th percentile daily 1-hour 

maximum concentration of 50 ppb. This means that to improve current air quality in most 

locations, a level below 100 ppb would need to be selected when using a 99th percentile 1-hour 

daily maximum standard form. 

In addition, the two percentile forms of the alternative standards (98th and 99th) were 

evaluated each at two 1-hour daily maximum standard levels (100 and 200 ppb) (Table 7-11). 

The estimated number of exceedances using a 98th percentile 1-hour daily maximum alternative 

standard level of 100 ppb fell within those estimated using 99th percentile levels of 100 and 150 

ppb. The estimated number of exceedances using a 98th percentile 1-hour daily maximum 

alternative standard level of 200 ppb was similar to the 99th percentile using a 250 ppb 1-hour 

concentration level. Both of these patterns were consistent when comparing the different 

standard forms for each the 5-minute benchmarks (see Tables 7-12 through 7-14). 

There were fewer estimated exceedances of 200 ppb given the potential alternative 

standards than compared with the current standard scenario (Table 7-12). Most counties had 

fewer than forty days per year with 5-minute SO2 concentrations above 200 ppb considering the 

1-hour daily maximum standards, while the number of exceedances was approximately double 

that when using air quality adjusted to just meet the current standard. With progressive 

decreases in the 1-hour daily maximum standard level, the number of days per year with 5-

minute maximum SO2 concentrations also decreases. In 75% of counties, the estimated number 

of benchmark exceedances using as is air quality was above that estimated using 1-hour daily 

maximum standard level of 100 ppb. The 99th percentile 1-hour daily maximum concentration 

level of 50 ppb was associated with the fewest days with 5-minute maximum SO2 concentrations 

above 200 ppb. On average most locations had zero exceedances of the 200 ppb benchmark 

level. 

Similar results are presented for each the 300 ppb (Table 7-13) and the 400 ppb (Table 7-

14) 5-minute benchmark levels, though the difference in the number of exceedances between the 
July 2009 146 

current standard and the other air quality scenarios is much greater than was observed for the 

lower benchmark levels. Most counties had a 5-fold (or greater) number of days with daily 5-

minute maximum SO2 concentrations above 300 or 400 ppb when considering air quality just 

meeting the current standard compared with air quality adjusted to just meet the 99th percentile 1-

hour daily maximum level of 250 ppb. The number of exceedances given as is air quality was 

still within the range of values estimated using the potential standard levels of 100 and 200 ppb; 

in most counties it was fewer than 10 days per year. Most counties did not have any estimated 

days per year with 5-minute maximum SO2 concentrations above 400 ppb given a 99th percentile 

1-hour daily maximum of 100 ppb, while 75% of the counties had 1 or fewer exceedances of 300 

ppb considering this same potential alternative standard. 
July 2009 147 

Table 7-11. Modeled mean number of days per year with 5-minute maximum concentrations 

above 100 ppb in 40 selected counties given 2001-2006 air quality as is and air quality adjusted to 

just meet the current and alternative standards. 

99th percentile1

 98th percentile1

State County as is1 CS1 50 100 150 200 250 100 200 

AZ Gila 119 234 9 36 63 89 111 47 107 

DE New Castle 21 123 1 8 19 34 50 12 46 

FL Hillsborough 22 127 3 12 23 37 50 18 53 

IL Madison 24 166 1 11 25 42 60 18 61 

IL Wabash 42 139 6 17 30 43 54 29 64 

IN Floyd 47 211 8 24 43 62 81 34 83 

IN Gibson 58 122 8 23 37 50 63 29 61 

IN Lake 17 186 3 20 41 64 91 31 93 

IN Vigo 27 184 2 12 27 44 63 21 68 

IA Linn 29 103 8 25 42 56 68 32 66 

IA Muscatine 34 123 9 26 41 54 68 32 65 

MI Wayne 29 134 2 18 40 62 80 25 76 

MO Greene 20 92 8 24 37 47 59 30 57 

MO Iron 65 108 9 30 40 48 55 34 54 

MO Jefferson 70 150 6 22 37 50 61 31 61 

NH Merrimack 46 118 7 31 52 68 81 37 76 

NJ Hudson 3 145 1 20 62 111 161 35 150 

NJ Union 2 117 1 16 51 98 141 25 122 

NY Bronx 8 124 2 28 71 115 155 39 137 

NY Chautauqua 38 172 6 18 33 50 70 23 65 

NY Erie 60 163 13 34 52 68 83 39 75 

OH Cuyahoga 16 203 2 23 55 93 122 39 129 

OH Lake 44 164 3 20 41 61 80 27 73 

OH Summit 51 198 3 23 51 81 110 30 96 

OK Tulsa 26 202 4 43 93 133 162 62 154 

PA Allegheny 30 159 1 8 22 41 65 12 58 

PA Beaver 76 194 2 11 30 55 83 18 79 

PA Northampton 14 130 2 25 56 87 114 41 127 

PA Warren 63 110 3 17 33 48 62 25 62 

PA Washington 25 185 2 21 53 88 125 29 110 

TN Blount 62 116 3 19 42 63 83 26 75 

TN Shelby 11 144 3 13 26 39 53 21 57 

TN Sullivan 75 201 2 20 49 74 94 40 100 

TX Jefferson 24 132 3 19 40 58 75 24 68 

VA Fairfax 0 109 1 17 54 98 143 29 129 

WV Brooke 76 220 3 25 62 101 140 40 135 

WV Hancock 78 207 2 21 52 86 118 32 110 

WV Monongalia 39 172 3 15 26 38 50 22 54 

WV Wayne 30 201 4 33 83 138 180 47 166 

VI St Croix 8 67 1 4 11 20 30 10 37 

Notes: 

1

 These are the air quality scenarios evaluated: as is is unadjusted air quality; CS is air quality adjusted to just 

meet the current standard; the levels of the two percentile forms (99th and 98th) of a 1-hour daily maximum 

potential alternative standard are given. 
July 2009 148 

Table 7-12. Mean number of modeled days per year with 5-minute maximum concentrations 

above 200 ppb in 40 selected counties given 2001-2006 air quality as is and that adjusted to just 

meet the current and alternative standards. 

99th percentile1

 98th percentile1

State County as is1 CS1 50 100 150 200 250 100 200 

AZ Gila 55 171 0 9 22 36 49 15 47 

DE New Castle 4 38 0 1 4 8 13 2 12 

FL Hillsborough 6 50 1 3 7 12 17 5 18 

IL Madison 5 66 0 1 5 11 17 3 18 

IL Wabash 17 75 1 6 11 17 23 11 29 

IN Floyd 17 117 1 7 16 24 33 12 34 

IN Gibson 28 70 1 8 16 22 30 11 29 

IN Lake 2 80 0 3 10 20 31 6 31 

IN Vigo 6 90 0 2 6 12 19 4 21 

IA Linn 10 53 2 8 17 25 34 12 33 

IA Muscatine 14 57 1 9 18 26 34 12 32 

MI Wayne 5 61 0 2 9 18 29 4 25 

MO Greene 6 47 1 8 16 24 31 12 30 

MO Iron 44 77 0 9 21 29 36 13 34 

MO Jefferson 38 99 0 6 14 22 29 11 31 

NH Merrimack 14 68 1 7 18 30 42 10 37 

NJ Hudson 0 31 0 1 7 20 39 3 34 

NJ Union 0 22 0 1 6 15 31 2 24 

NY Bronx 0 32 0 2 11 27 48 3 38 

NY Chautauqua 15 88 1 6 11 18 25 8 24 

NY Erie 29 86 2 13 24 34 43 15 38 

OH Cuyahoga 1 85 0 2 10 23 38 5 38 

OH Lake 11 71 0 3 10 20 30 4 26 

OH Summit 11 96 0 3 12 24 37 4 31 

OK Tulsa 2 112 0 5 19 42 69 9 62 

PA Allegheny 5 52 0 1 3 8 14 1 12 

PA Beaver 17 88 0 2 5 11 20 3 18 

PA Northampton 2 40 0 3 10 25 40 5 41 

PA Warren 25 52 0 3 9 17 25 6 25 

PA Washington 3 66 0 2 10 21 36 4 29 

TN Blount 19 54 0 3 10 20 31 4 26 

TN Shelby 2 35 0 3 7 13 20 5 21 

TN Sullivan 21 121 0 2 9 21 35 6 39 

TX Jefferson 5 71 0 3 10 19 29 5 25 

VA Fairfax 0 21 0 1 6 17 34 2 28 

WV Brooke 16 96 0 3 12 26 43 6 40 

WV Hancock 17 96 0 2 9 21 36 4 32 

WV Monongalia 15 63 0 3 9 15 21 6 22 

WV Wayne 3 71 0 4 16 33 58 6 48 

VI St Croix 2 24 0 1 3 4 7 2 10 

Notes: 

1

 These are the air quality scenarios evaluated: as is is unadjusted air quality; CS is air quality adjusted to just 

meet the current standard; the levels of the two percentile forms (99th and 98th) of a 1-hour daily maximum 

potential alternative standard are given. 
July 2009 149 

Table 7-13. Mean number of modeled days per year with 5-minute maximum concentrations 

above 300 ppb in 40 selected counties given 2001-2006 air quality as is and that adjusted to just 

meet the current and alternative standards. 

99th percentile1

 98th percentile1

State County as is1 CS1 50 100 150 200 250 100 200 

AZ Gila 31 130 0 2 9 18 27 4 25 

DE New Castle 1 17 0 0 1 3 5 1 5 

FL Hillsborough 3 27 0 1 3 6 8 2 9 

IL Madison 1 35 0 0 1 4 7 1 7 

IL Wabash 9 50 0 2 6 9 13 6 17 

IN Floyd 8 75 0 3 8 13 18 5 19 

IN Gibson 16 47 0 3 7 13 18 4 17 

IN Lake 0 42 0 1 3 8 13 2 13 

IN Vigo 2 49 0 1 2 5 8 1 9 

IA Linn 5 35 0 4 8 14 19 6 19 

IA Muscatine 6 39 0 4 9 15 20 6 19 

MI Wayne 1 32 0 0 2 6 12 1 10 

MO Greene 2 32 0 3 7 13 19 5 18 

MO Iron 33 61 0 1 9 17 24 4 23 

MO Jefferson 24 72 0 1 6 11 17 4 18 

NH Merrimack 5 46 0 3 7 14 22 4 19 

NJ Hudson 0 7 0 0 1 4 10 0 9 

NJ Union 0 5 0 0 1 3 8 0 6 

NY Bronx 0 9 0 0 2 7 16 0 11 

NY Chautauqua 9 52 0 2 6 10 13 3 12 

NY Erie 17 59 0 5 13 20 27 6 24 

OH Cuyahoga 0 39 0 0 2 7 13 1 13 

OH Lake 3 41 0 0 2 7 13 1 10 

OH Summit 2 51 0 1 3 8 15 1 12 

OK Tulsa 0 60 0 1 4 12 26 2 22 

PA Allegheny 1 21 0 0 1 2 4 0 4 

PA Beaver 6 42 0 0 2 4 7 1 6 

PA Northampton 1 16 0 1 3 7 15 1 14 

PA Warren 11 31 0 1 3 7 11 1 11 

PA Washington 1 28 0 0 2 7 13 1 10 

TN Blount 7 28 0 0 3 7 13 1 10 

TN Shelby 0 19 0 1 3 6 9 2 10 

TN Sullivan 7 83 0 0 2 6 12 2 15 

TX Jefferson 1 43 0 1 3 7 13 1 10 

VA Fairfax 0 5 0 0 1 4 9 0 7 

WV Brooke 5 45 0 1 4 8 16 2 15 

WV Hancock 4 48 0 0 2 6 12 1 10 

WV Monongalia 7 36 0 1 3 6 11 2 12 

WV Wayne 1 31 0 1 4 10 21 1 16 

VI St Croix 0 11 0 0 1 2 3 1 4 

Notes: 

1

 These are the air quality scenarios evaluated: as is is unadjusted air quality; CS is air quality adjusted to just 

meet the current standard; the levels of the two percentile forms (99th and 98th) of a 1-hour daily maximum 

potential alternative standard are given. 
July 2009 150 

Table 7-14. Mean number of modeled days per year with 5-minute maximum concentrations 

above 400 ppb in 40 selected counties given 2001-2006 air quality as is and that adjusted to just 

meet the current and alternative standards. 

99th percentile1

 98th percentile1

State County as is1 CS1 50 100 150 200 250 100 200 

AZ Gila 18 102 0 0 3 9 15 1 14 

DE New Castle 0 9 0 0 0 1 2 0 2 

FL Hillsborough 2 17 0 1 2 3 5 1 5 

IL Madison 0 21 0 0 0 1 3 0 3 

IL Wabash 6 36 0 1 4 6 8 3 10 

IN Floyd 5 52 0 1 4 8 11 3 12 

IN Gibson 10 34 0 1 4 8 11 2 12 

IN Lake 0 23 0 0 1 3 6 1 6 

IN Vigo 1 30 0 0 1 2 3 1 4 

IA Linn 2 24 0 2 5 9 12 3 12 

IA Muscatine 3 28 0 2 5 9 13 2 12 

MI Wayne 0 18 0 0 1 2 5 0 4 

MO Greene 1 23 0 1 4 8 12 2 11 

MO Iron 25 50 0 0 3 9 15 1 13 

MO Jefferson 16 54 0 0 2 6 10 1 11 

NH Merrimack 2 31 0 1 3 7 12 1 10 

NJ Hudson 0 2 0 0 0 1 3 0 3 

NJ Union 0 1 0 0 0 1 2 0 2 

NY Bronx 0 2 0 0 0 2 5 0 3 

NY Chautauqua 6 34 0 1 3 6 9 2 8 

NY Erie 10 44 0 2 7 13 18 3 15 

OH Cuyahoga 0 19 0 0 1 2 5 0 5 

OH Lake 1 25 0 0 1 3 6 0 4 

OH Summit 1 30 0 0 1 3 6 0 5 

OK Tulsa 0 30 0 0 1 4 10 0 8 

PA Allegheny 0 10 0 0 0 1 2 0 1 

PA Beaver 3 22 0 0 1 2 3 0 3 

PA Northampton 0 7 0 0 1 3 6 1 5 

PA Warren 5 19 0 0 1 3 6 0 6 

PA Washington 0 13 0 0 1 2 5 0 4 

TN Blount 3 15 0 0 1 3 5 0 4 

TN Shelby 0 12 0 0 1 3 5 1 5 

TN Sullivan 3 58 0 0 1 2 5 0 6 

TX Jefferson 1 27 0 0 1 3 6 1 5 

VA Fairfax 0 1 0 0 0 1 3 0 2 

WV Brooke 2 24 0 0 1 3 7 0 7 

WV Hancock 1 25 0 0 0 2 5 0 4 

WV Monongalia 3 25 0 0 1 3 5 1 6 

WV Wayne 0 14 0 0 1 4 8 0 6 

VI St Croix 0 6 0 0 0 1 2 0 2 

Notes: 

1

 These are the air quality scenarios evaluated: as is is unadjusted air quality; CS is air quality adjusted to just 

meet the current standard; the levels of the two percentile forms (99th and 98th) of a 1-hour daily maximum 

potential alternative standard are given. 
July 2009 151 

7.4 VARIABILITY ANALYSIS AND UNCERTAINTY 

CHARACTERIZATION 

As discussed in section 6.6, there can be variability and uncertainty in risk and exposure 

assessments. This section presents a summary of and associated discussions regarding the 

degree to which variability was incorporated in the air quality analyses and how the uncertainty 

was characterized for the estimated air quality benchmark exceedances. 

7.4.1 Variability Analysis 

To the maximum extent possible given the data, time, and resources available for the 

assessment, staff accounted for variability within the two main components of the air quality 

characterization: the ambient monitoring concentrations and the statistical model used to 

estimate 5-minute maximum SO2 concentrations. The variability accounted for in this analysis is 

summarized in Table 7-15. 

Table 7-15. Summary of how variability was incorporated into the air quality characterization. 

Component Variability Comment 

Temporal: 10 to 11 years of 1-

hour and 5-minute monitoring 

data 

Broader SO2 monitoring network and monitors 

reporting 5-minute maximum concentrations. 

Subset of 40 counties for detailed analyses 

comprised two 3-year periods (2001-2003; 2004-

2006) 

Spatial: 48 states plus 3 US 

territories totaling 407 

counties. 

Broader SO2 monitoring network. Other analyses 

considered monitor results separated by 

population density. Subset of 40 counties for 

detailed analyses comprised 18 states and 1 US 

territory. 

Ambient SO2 

Monitoring Data 

9 air quality scenarios 

40 county analysis included air quality as is, just 

meeting the current standard and 5 levels (50, 

100, 150, 200, 250 ppb) of two percentile forms 

(98th and 99th); effectively creating a varying 

decision surface. 

5-Minute Peak 

Statistical Model 

19 peak-to-mean (PMR) 

distributions 

PMR distributions used non-parametric form 

derived from measurement data (complete range 

of values from 1 to <12). Three monitor 

concentration variability bins used as a surrogate 

for variability in local source emissions, along with 

seven concentration bins. Twenty simulations 

using random sampling generated a best estimate 

of exceedances per site-year of data. 

7.4.2 Uncertainty Characterization 

As discussed in section 6.6, the approach for evaluating uncertainty was adapted from 

guidelines outlining how to conduct a qualitative uncertainty characterization (WHO, 2008). 

Staff selected the mainly qualitative approach given the limited data available to inform a 
July 2009 152 

probabilistic uncertainty characterization, and time and resource constraints. This qualitative 

approach used here varies from that of WHO (2008) in that the primary focus is placed on 

evaluating the impact of the uncertainty; that is, staff qualitatively rate how the source of 

uncertainty, in the presence of alternative and possibly improved data or information, may affect 

the estimated number of days with benchmark exceedances. In addition, and consistent with the 

WHO (2008) guidance, staff discuss the uncertainty in the knowledge-base (e.g., the accuracy of 

the data used, acknowledgement of data gaps) and decisions made (e.g., selection of particular 

model forms), though qualitative ratings were assigned only to uncertainty regarding the 

knowledge-base. 

After identifying the key sources of the assessment that may contribute to uncertainty, staff 

subjectively scaled the magnitude47 of each identified source of uncertainty and the associated 

direction of potential influence to the number of benchmark exceedances. We used a three level 

scale to rate the magnitude: low indicated that large changes within the source of uncertainty 

would have only a small effect on the estimated number of exceedances, medium implied that a 

change within the source of uncertainty may have a proportional effect on the results, and high

indicated that a small change in the source would have a large effect on results. The direction of 

influence on number of exceedances was subjectively assigned as over-estimated, underestimated, both (uncertainty affects assessment endpoint in either direction), or unknown (no 

evidence to judge the uncertainty). Staff also subjectively scaled the knowledge-base uncertainty 

associated with each identified source using a three level scale: low indicated significant 

confidence in the data used and its applicability to the assessment endpoints, medium implied 

that there were some limitations regarding consistency and completeness of the data used or 

scientific evidence presented, and high indicated the knowledge-base was extremely limited. 

Table 7-16 provides a summary of the sources of uncertainty identified in the air quality 

characterization, the level of uncertainty, and the overall judged bias of each. Further discussion 

regarding each of these sources of uncertainty and how conclusions were drawn is given in the 

sections that follow.

 

47 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2. 
July 2009 153 

Table 7-16. Summary of qualitative uncertainty analysis for the air quality and health risk characterization. 

Influence of Uncertainty on 

Air Quality Benchmark 

Exceedances 

Source Type Direction Magnitude 

KnowledgeBase 

Uncertainty Comme

Air Quality Data Database Quality Over Low Low 

INF: There may be a limited numconcentration data within the anpotentially influencing the numbexceedances. KB: Data used in the analyses ais no other source of monitoring Data are being used in a mannethe defined purposes of ambien

Ambient Measurement Technique 

Interference Both Low – Medium Medium 

INF: Potential interferences can influence may be of greater magupward concentration adjustmeKB: Limited knowledge on conchigh concentrations. Limited kncontrols applied at individual mo

Scale Unknown Low – Medium Medium 

INF: Temporal scale is appropriMost data used are screened fohowever where 5-minute concedata were not screened for comKB: Limited knowledge on direchowever 60% of data used woucompleteness criteria. 

Temporal Representation of Monitoring Data 

Missing Data Under Low Low 

INF: Staff assumed there was amissing low and high concentrameasurements; there could be a concentration data that would lebenchmark exceedances. No inperformed. KB: All available data are quality data used were temporally com
July 2009 154 

Influence of Uncertainty on 

Air Quality Benchmark 

Exceedances 

Source Type Direction Magnitude 

KnowledgeBase 

Uncertainty Comme

Years Evaluated Over Low Low 

INF & KB: Little variation in COV analysis. Estimates of the problikely not affected. Estimated ncould be influenced by historica

Broader SO2

Network and 40 County Data Set 

Under Medium High 

INF: It is possible that the curreadequately capturing 1-hour SO

sources. However, given the pupurpose of the assessment, stamost a medium level of influencspatial representation. KB: Many site-years available frhour concentrations; However, tto evaluate the spatial represennetwork. 

Spatial Representation of Monitoring Network 

5-minute Maximum SO2 Under Medium High 

INF: Distribution of sources potemonitors is similar to that of the 

with limited geographic span. KB: Very few site-years availab5-minute measurements. 

Air Quality Adjustment Procedure 

Proportional Approach Used Both Low – Medium Medium 

INF: Depends on the degree of quality distribution and the magconcentration adjustment. KB: Proportional approach judgrepresenting the alternative air qHowever, evaluation only conduwas dependent on historic air qalternative scenarios, and there deviation from proportionality. Amethod was investigated. 
July 2009 155 

Influence of Uncertainty on 

Air Quality Benchmark 

Exceedances 

Source Type Direction Magnitude 

KnowledgeBase 

Uncertainty Comme

Spatial Scale Both Medium High 

INF: The rate of change in concmoderately different at monitors KB: Analysis is dependent on hrepresentative of alternative air is lack of knowledge regarding hwould affect multiple monitors in 

Data Screening Over Low Low 

INF & KB: Less than 2% of data Physically realistic PMR bounds were mostly of low 1-hour concegenerate a benchmark exceeda

Temporal Variation in PMRs 

None Low Low 

INF: Consistency in PMRs acroKB: Consistency in PMRs when and early 1990s ambient monito

Distribution Form of PMRs None Low Low INF & KB: Non-parametric distri

the most appropriate for the ana

Accuracy Both Low – Medium Medium 

INF: Accuracy assessment indicthough at upper and lower tails the number of exceedances weestimated, respectively. KB: Though cross-validation resthere may be additional influentimportant in the model construcavailable in extrapolating to the 

Statistical Model Used for Estimating 5-minute SO2

Concentrations 

Reproducibility None Low Low 

INF & KB: Limited variation obsmean number of benchmark exrandom sampling error analysis

Potential Health Risk Endpoints Used2

Ambient SO2 as 

an Indicator of SO2 Exposure 

Over Medium High 

INF: Long-term time averaging strong proportional relationship concentration and personal expKB: The relationship between 5and ambient concentration is no
July 2009 156 

Influence of Uncertainty on 

Air Quality Benchmark 

Exceedances 

Source Type Direction Magnitude 

KnowledgeBase 

Uncertainty Comme

Consideration of Susceptible Populations 

Unknown Low Medium 

INF & KB: Severe asthmatics arin clinical studies due to expectaadverse response. Potential he

under-estimated depending on tbenchmark selected to represenKB: There is no clear quantitativlowest benchmark would either level a susceptible individual wo

Averaging Time None Low Low INF & KB: consistently no differ

responses from either 5- or 10-m

Single Counts of Exceedances versus Multiple Exceedances per day 

Under Low Medium 

INF: Potential health risk may bbecause approximately 50% of exceedance correspond with anexceedance(s) in that same dayquality analysis, time of exposulimiting the relevance of multiple KB: Frequency of multiple exceexisting measurement data is knmonitoring sites. 

Notes: 1 INF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base 2 In these cases the influence of the uncertainty to the potential health risk is discussed, not the influence to the estimated 
July 2009 157

7.4.2.1 Air Quality Data 

The purpose of this section is to discuss staff assumptions and potential uncertainties 

associated with the data used to construct the various analytical data sets. While the data are 

being used in a manner consistent with one of the defined purposes of ambient monitoring (i.e., 

assessing population exposure), both the source of data and its associated quality are discussed. 

The uncertainty regarding temporal and spatial components of the ambient monitoring data sets 

is discussed in sections 7.4.2.3 and 7.4.2.4, respectively. 

The Air Quality System (AQS) contains ambient SO2 concentrations collected by EPA, 

state, local, and tribal air pollution control agencies from hundreds of monitoring stations across 

the U.S. There are no alternative ambient monitoring data sets available that are as 

comprehensive as those within AQS. There might be ambient monitoring data available that are 

not included in the AQS however, staff assumed that given similar collection techniques and 

quality assurance methods that they would be complementary to AQS monitoring data. 

One basic assumption is that the AQS SO2 air quality data used are quality assured 

already. Methods exist for ensuring the precision and accuracy of the ambient monitoring data 

(e.g., EPA, 1983). Reported concentrations contain only valid measures, since values with 

quality limitations are not entered into the system or are removed following determination of 

being of lower quality or flagged. There is likely no selection bias in retaining data that are not 

of reasonable quality if the data are in error; it was assumed that selection of high concentration 

poor quality data would be just as likely as low concentration data of poor quality. However, the 

retention of poor quality high concentration data would have greater impact on estimated 

numbers of exceedances than poor quality low concentration data. Given the numbers of 

measurements used for the analyses though, it is likely that even if a few poor quality high 

concentration data are present in the analytical data sets, they would not have a large impact on 

the results presented here. In addition, a quantitative analysis of available duplicate measures 

(i.e., originating from co-location of ambient monitors or by duplicate reporting of ambient 

concentrations, see Appendix A-3) indicated little to no difference in the duplicate values or in 

the selection of one particular reported (or measured) value over another. 

Based on this evaluation, the source and the quality of the ambient monitoring data used 

likely contribute minimally to uncertainty in the estimated number of benchmark exceedances. 
July 2009 158

Thus, there is both a low level of uncertainty in the knowledge-base and in the subjectivity of 

choices made by staff. 

7.4.2.2 Ambient Measurement Technique 

One potential source of uncertainty within the SO2 air quality measurements is from 

interference with other compounds. The ISA notes several sources of positive and negative 

interference that could increase the uncertainty in the measurement of ambient SO2 

concentrations (ISA, sections 2.3.1 and 2.3.2). Many of the identified sources (e.g., polycyclic 

aromatic hydrocarbons, stray light, collisional quenching) were described as having limited 

impact on SO2 measurement due to the presence of instrument controls that prevent the 

interference. 

The actual impact on any individual monitor though is unknown; the presence of either 

negative or positive interference, and the degree of interference contributed by one or the other, 

has not been quantified for any ambient monitor. In addition, it is not known whether there is a 

concentration dependence on the amount of interference. This may be an important uncertainty 

in considering the air quality concentrations adjusted to just meet the current and potential 

alternative standards. 

Reported ambient monitoring concentrations could be either over- or under-estimated 

depending on the type of interference present. Staff judges the magnitude of influence as low to 

medium, given the potential range of instrument controls present (low magnitude) and possibility 

for concentration dependence (medium magnitude). The uncertainty in the knowledge-base is 

judged as medium given the limited quantitative evidence available to assess the potential 

direction and magnitude of interference at individual monitors, as well as limited evidence 

regarding the presence of concentration dependence. 

7.4.2.3 Temporal Representation of Monitoring Data 

Three components of uncertainty were evaluated regarding the temporal representation of 

the monitoring data. These include uncertainty in the temporal scale (i.e., averaging time of 

measurements and completeness criteria), how missing data were treated in the analysis, and 

long term trends in ambient monitoring and concentration variability. 

The air quality analysis relied on quality assured 5-minute and 1-hour average SO2

measurement data (see section 7.4.2.1) and are of the same temporal scale as identified potential 
July 2009 159

health effect benchmarks, where 5-minute measurements were reported. There are frequent 

missing values within a given valid year that may increase the level of uncertainty in temporal 

concentration distributions and model estimations (see below); however, given the level of the 

benchmark concentrations and the low frequency of benchmark exceedances and overall 

completeness of the monitoring data, it is likely of limited consequence. The magnitude of 

impact on estimated benchmark exceedances could be significant if some seasons, day-types 

(e.g., weekday/weekend), or times of the day (e.g., nighttime or daytime) were not equally 

represented in the data analysis group. For the analyses performed using the broader SO2

monitoring network and the 40-county data set, a valid year of ambient monitoring was based on 

75 percent complete hours/day and days/quarter, and having all four complete quarters/year. The 

process of assuring temporal completeness prevented potentially influential monitoring data from 

adversely affecting the air quality characterization using these data sets. 

However, there is greater uncertainty in the temporal representation of the combined 5-

minute and 1-hour measurement data set because all of the available data were used without 

considering the standard 75% completeness criteria. Staff elected to use all of the 5-minute SO2

measurement data rather than further reducing the already limited number of samples and 

locations represented. The 5-minute measurement data set did however undergo a limited 

screening that improved the quality of the data set. This included removal of duplicate 

reporting/measurements, exclusion of concentrations < 0.1 ppb, and screening for technically 

impossible PMRs (see section 7.4.2.6). These screenings and use of the 5-minute data without 

the same completeness criteria as the other data analysis groups though would tend to decrease 

the temporal representation, potentially influencing the observed probability and the estimated 

number of benchmark exceedances. 

Therefore, staff judges the magnitude of influence from this source of uncertainty as low 

to medium, with a greater magnitude of influence assigned to observations reported for the 5-

minute data set and its application in the statistical model. While staff has not performed 

analyses to determine direction and magnitude of impact in applying the completeness criteria to 
July 2009 160

the 5-minute data set, the uncertainty in the knowledge-base is judged as medium given the 

overall temporal representation of most of the site-years of data.48 

Data were not interpolated in the analysis; missing data were not substituted with 

estimated values and concentrations reported as zero were used as is. For the missing data, it is 

assumed here that missing values are not systematic, i.e., both high and low concentration data 

would be absent in equal proportions. There are methods available that can account for time-ofday, day-of-week, and seasonal variation in ambient monitoring concentrations. However, if a 

method were selected, it would have to not simply interpolate the data but also accurately 

estimate the probability of peak 1-hour SO2 concentrations that could occur outside the 

predictive range of the method. It was judged that if such a method was available or one was 

developed to substitute data, it would likely add to a similar level of uncertainty as not choosing 

to substitute the missing values. Again, this can be viewed as having a limited impact on the 

estimated number of exceedances because using the validity criteria selected for the most 

temporally representative and complete ambient monitoring data sets possible. In addition, when 

using the concentrations reported as zero, there is likely limited impact on the estimated number 

of exceedances and associated probability of exceedances. It is possible that some missing data 

could have been at a high enough concentration to either exceed a benchmark or result in an 

estimated benchmark exceedance, implying the direction of influence is towards underestimating benchmark exceedances. However, given the temporal completeness of much of the 

data used characterizing air quality, staff judges both the magnitude of influence of missing data 

and the uncertainty associated with the knowledge-base to be low. 

There is uncertainty associated with the selection of monitoring years, particularly if 

concentrations vary significantly between monitors and across the two averaging times. When 

using historical monitoring data, staff assumed that the sources present at that time have similar 

emissions and emission profiles as the current sources. It is clear that the number of SO2

monitoring sites in the U.S. has changed over time, with a trend of decreasing number of 

monitors most evident for those reporting the 5-minute maximum SO2 concentrations (Figure 7-

31). Five-minute SO2 concentrations have been reported in fewer monitors than the 1-hour SO2

concentrations; generally only a few site-years of data exist for 5-minute SO2 concentrations 

 

48 Screening for completeness using the 75% hours/day and days/year criteria would have resulted in only 85 siteyears of data. However, this screened data set would include 1,431,470 hours or 60% of the data set used in the 

current analyses. 
July 2009 161

(Appendix A, Table A.1-1). This is the reason why, given the limited number of measurements, 

all of the 5-minute maximum SO2 data were used in developing the statistical relationships and 

for the model evaluation without requiring the 75% completeness criteria to be met. 

Figure 7-31. Temporal trends in the number of ambient monitors in operation per year for 

monitors reporting both 5-minute and 1-hour SO2 concentrations. 

However, the variability in monitoring concentrations (both the 1-hour and 5-minute 

maximum SO2) does not change significantly across most monitoring years (i.e., years 1997 

though 2004) and there is a comparable range between the two averaging times (Figure 7-32). 

There is some compression in the range of COVs considering some of the more recent years of 

data, most notable for year 2007. This is possibly due to the reduction in the number of ambient 

monitors in operation (Figure 7-31) rather than a reduction in the temporal variability in 5-

minute or 1-hour concentrations at particular monitors. There may be an over-estimate in the 

number of benchmark exceedances where there is a broad range of years used in the 

characterization. However, the estimated probability of exceedances is likely not influenced by 

year given that the analysis controls for concentration levels and variability changes that may 

have occurred over time. Furthermore, the selection of a subset of the recent air quality data 

(2001-2006) used for detailed analyses may reduce the potential impact from changes in 

national- or location-specific source influences (if one is present). Therefore, due to the limited 

variation in temporal trends in COV for both 5-minute and 1-hour SO2 and analysis design (i.e., Number of Monitor Reporting 5-minute Maximum SO2
July 2009 162

controlling for concentration level changes, limiting the span of years analyzed) the overall 

magnitude of influence is expected to be low. 

Figure 7-32. Temporal trends in the coefficient of variability (COV) for 5-minute maximum and 1-

hour concentrations at the monitors that reported both 5-minute and 1-hour SO2

concentrations. The number of monitors operating in each year is depicted in Figure 7-

31. 

7.4.2.4 Spatial Representation of Monitoring Network 

The spatial representativeness of the monitoring network can be a source of uncertainty, 

particularly if the monitoring network is not dense enough to resolve the spatial variability in 

ambient SO2 concentrations and if the monitors are not effectively distributed to reflect 

population exposure. Relative to the physical area, staff acknowledges there are only a few 

monitors, particularly when considering the set of monitors that reported 5-minute maximum 

SO2. The magnitude and direction of influence on the modeled or measured benchmark 

exceedances will depend on ambient monitoring objectives, monitoring scale, the distribution of 

SO2 emission sources, and whether there is large variability in monitoring surface, i.e., areas of 

differing terrain that are not adequately represented by the current distribution of monitors. 

These elements will be broadly discussed for each of the data sets used in the air quality 

characterization and how they could potentially affect the number and probability of benchmark 

exceedances. The three data sets of interest include monitors from the broader SO2 network 

5-Minute Maximum SO2 1-hour SO2
July 2009 163

(including monitors within the 40 selected counties) and the monitors reporting 5-minute SO2

concentrations. 

The broader 1-hour monitoring network, by definition, is the most comprehensive data 

set of the three when considering the number of monitors (n=809) and geographic representation 

(48 U.S. States, Washington DC, Puerto Rico, and U.S. Virgin Islands). The air quality 

characterization is improved with the inclusion of modeled 5-minute benchmark exceedances in 

these areas where 5-minute measurements were not reported. In addition, the use of the broader 

SO2 monitoring network in this assessment could assist in identifying and prioritizing locations 

to begin reporting 5-minute SO2 measurements. However, the broader geographic span of 

ambient monitoring does not necessarily confer spatial representativeness. The spatial 

representativeness of the broader SO2 monitoring network would remain dependent on the siting 

of the monitors with respect to important emission sources and potentially exposed populations. 

Staff assumes that the network design, to a large degree, provides adequate spatial representation 

of the ambient SO2 air quality. This may apply to a greater degree to the 40-County data set that 

used a minimum number of monitors (i.e., >2) to represent a set geographical area (i.e., a 

county). 

Staff acknowledges that in using the broader SO2 monitoring network and 40-County 

data set as an indicator of exposure, there could be local areas that are spatially underrepresented. Furthermore, portions of the air quality characterization used monitors meeting a 75 

percent completeness criterion, without taking into account the monitoring objectives, scale, or 

land use. Thus, there may be a reduction in spatial representation due to either the inclusion or 

exclusion of monitors sited near local SO2 source emissions as a result of the completeness 

screening. Staff estimates that the magnitude of influence to the number of benchmark 

exceedances may be at most a medium level in the presence of supplemental spatial monitoring, 

given the purposes of both the current monitoring network and the air quality characterization. 

We also judge there would be limited influence on the probability of exceedances with improved 

spatial representation, given that the probability estimate is driven by ambient concentration 

level and concentration variability, two variables that have been well characterized by the current 

ambient monitoring network. In the absence of additional measurements or modeling of the 

spatial heterogeneity of 1-hour ambient SO2 concentrations though, staff assigns a high level of 

uncertainty to the knowledge-base. 
July 2009 164

The overall SO2 monitoring network design is also responsible for siting monitors that 

reported 5-minute concentrations. As a result, staff expects that monitor siting is appropriate and 

spatially representative for the same reasons discussed above. However, because the monitors 

reporting 5-minute concentrations are not part of a designed 5-minute SO2 monitoring network 

but are entirely voluntary, the direction and magnitude of influence on observed or estimated 

benchmark exceedances is largely unknown. Note that there were far fewer monitors reporting 

5-minute concentrations used in certain analyses (n=98), representing a limited geographic scope 

in comparison with the broader SO2 monitoring network. In addition, a greater percentage of 

monitors reporting 5-minute concentrations had a source-oriented objective (Figure 7-3). 

However, an analysis of the monitoring attributes indicated similar distributions in the types of 

sources and the total emissions potentially impacting both sets of data (Figure 7-5). This 

suggests that the spatial representation of the monitors reporting 5-minute concentrations may be 

similar to that of the broader SO2 monitoring network regarding proximity to similar SO2

sources. In the absence of additional measurements or modeling of the spatial heterogeneity of 

5-minute ambient SO2 concentrations, staff assigns a high level of uncertainty to the knowledgebase. 

7.4.2.5 Air Quality Adjustment Procedure 

There is uncertainty in the air quality adjustment procedure due to the uncertainty of the 

true relationship between the adjusted concentrations that are simulating a hypothetical scenario 

and the as is air quality. The adjustment factors used for the current and the potential alternative 

standards each assumed that all hourly concentrations will change proportionately at each 

ambient monitoring site. Two elements of this source of uncertainty are discussed, namely 

uncertainty regarding the proportional approach used and the universal application of the 

approach to all ambient monitors within each location. 

Different sources have different temporal emission profiles, so that equally applied 

changes to the concentrations at the ambient monitors to simulate hypothetical changes in 

emissions may not correspond well within all portions of the concentration distribution. When 

adjusting concentrations upward to just meeting the current standard, the proportional adjustment 

used an equivalent multiplicative factor derived from the annual mean or daily mean 

concentration and equally applied that factor to all portions of the concentration distribution, that 

is, the upper tails were treated the same as the area of central tendency. This may not necessarily 
July 2009 165

reflect changes in an overall emissions profile that may result from, for example, an increase in 

the number of sources in a location. It is possible that while the mean concentration measured at 

an ambient monitor may increase with an increase in the source emissions affecting 

concentrations measured at the monitor, the tails of the hourly concentration distribution might 

not have the same proportional increase. The increase in concentration at the tails of the 

distribution could be greater or it could be less than that observed at the mean and is dependent 

largely on the type of sources influencing the monitor and the source operating conditions. 

Adjusting the ambient concentrations upwards to simulate the potential alternative standards also 

carries a similar level of uncertainty although the multiplicative factors were derived from the 

upper percentiles of the 1-hour daily maximum SO2 concentrations, rather than the mean, and 

then applied to the 1-hour SO2 concentrations equally. If there are deviations from 

proportionality, the magnitude of influence is likely related to the magnitude of the concentration 

adjustment factor used. Therefore, there is likely greater uncertainty in the estimated benchmark 

levels when evaluating the current and the 250 ppb 99th percentile alternative standards (which 

have the highest adjustment factors), than when considering the 50 ppb and 100 ppb 99th

percentile alternative standards (which have the lowest adjustment factors). 

In each of these instances of adjusting the concentrations upwards, one could argue that 

there may be an associated over-estimation in the concentrations at the upper tails of the 

distributions, possibly leading to over-estimation in the numbers of exceedances of benchmark 

levels. An analysis was performed using monitors from seven counties evaluated in the air 

quality characterization to investigate how distributions of hourly SO2 concentrations have 

changed over time (Rizzo, 2009). The analysis indicates that a proportional approach is a 

reasonable model for simulating higher concentrations at most monitoring sites, since 

historically, SO2 concentrations have decreased linearly across the entire concentration 

distribution at each of the monitoring sites and counties evaluated. 

At some of monitoring sites analyzed however, there were features not consistent with a 

completely proportional relationship. This included deviation from linearity primarily at the 

maximum or minimum percentile concentrations, some indication of curvilinear relationships, 

and the presence of either a positive or negative regression intercept (Rizzo, 2009). Where 

multiple monitors were present in a location, there tended to be a mixture of each of these 

conditions including proportionality (e.g., see Figure 7-33). Not all of the counties analyzed as 
July 2009 166

part of the air quality characterization were included in the evaluation, thus staff assumed that the 

findings of the Rizzo (2009) analysis were applicable of the 40-County data set. Given the 

observed range of deviations from proportionality and the level of the concentration adjustment, 

we judge the magnitude of influence to the estimated benchmark exceedances as between low to 

medium. The estimated number of benchmark exceedances could be either over- or underestimated, dependent largely on an individual monitor’s air quality distribution and its 

relationship with proportionality. While staff judged the proportional approach as appropriate, it 

was based on analyses using historical monitoring data. The uncertainty about future source 

emission control scenarios is largely unknown. In addition, only one approach was investigated, 

suggesting that the level of the knowledge-base uncertainty is medium. 

Staff applied the proportional adjustment approach universally to all monitors in each 

county for consistency. The purpose was to preserve the inherent variability in the concentration 

distribution which has been shown to be relatively consistent with large changes in concentration 

level. There is however uncertainty associated with emission changes that would affect the 

concentrations at the monitor having the highest concentration (e.g., the highest annual mean, 

98th or 99th percentile 1-hour concentration) that may not necessarily be reflected in the same 

proportion at other lower concentration sites. This could result in either over- or underestimations in the number of exceedances at lower concentration sites within a county where the 

current or alternative standard scenarios were evaluated. For example, Figure 7-33 shows the 

daily maximum 1-hour SO2 concentration percentiles for five ambient monitors in Allegheny 

County PA, where each of the ambient monitors were in operation for years 1998 and 2007. 

While all five of the monitors generally demonstrate features of proportionality, the differences 

in regression slope indicate that the rate of change in the concentration distribution was not equal 

when comparing these monitors for these two monitoring years. These results suggest that even 

if all monitors within a county demonstrate proportionality, there may be either over- or underestimations in SO2 concentrations following the 1-hour concentration adjustment. Staff had 

limited time and resources to investigate the potential impact of this on the number of benchmark 

exceedances, though we estimate the magnitude of influence as medium based on the range of 

observed slopes in the seven counties investigated. The level of uncertainty in the knowledgebase is judged high. This rating is based on the uncertainty regarding how the historical and 

recent ambient data comparisons relate to the simulated air quality scenario and the lack of 
July 2009 167

knowledge regarding how source emission changes would affect multiple monitors within a 

county. 

Figure 7-33. Comparison of measured daily maximum SO2 concentration percentiles in Allegheny 

County PA for one high concentration year (1998) versus a low concentration years 

(2007) at five ambient monitors. 

7.4.2.6 Statistical Model Used for Estimating 5-minute SO2 Concentrations 

Five components of uncertainty were identified regarding the statistical model and its 

impact on the estimated number of benchmark exceedances. These include 1) the impact from 

how the PMR data were screened, 2) the temporal representation of data used in the statistical 

model development, 3) the form of the distribution used to represent the PMRs, 4) the accuracy 

of the model in predicting daily 5-minute maximum concentrations, and (5) the reproducibility of 

the model predictions. 

Staff identified data for removal from the final combined 5-minute and 1-hour ambient 

measurement data set using the PMR as a screening criterion. The calculation of PMRs less than 

1 implies the 5-minute peak is less than the 1-hour average, a physical impossibility, and values 
July 2009 168

>12 are a mathematical impossibility. The 5-minute ambient monitoring data were screened for 

values outside of these bounds, 49 increasing confidence in the relevance of PMRs used for 

development of the statistical model. While a total of 40,665 data points were excluded from the 

data set using the PMR criterion, this comprised less than 2% of the data available to develop the 

PMR relationship. It was assumed that the criterion used for the data removal would not 

adversely influence the estimated number of benchmark exceedances in the modeling performed 

since it was only directed towards identifying unrealistic 5-minute and 1-hour concentration 

combinations. 

Analysis of the data screened by staff revealed that nearly all of the data are for where the 

calculated PMR was less than one (98% of screened samples) and most of the 1-hour 

concentrations (approximately 95%) were less than or equal to 5 ppb (Table 7-17). An 

alternative approach to developing the PMR distributions could have been to include the 

screened data with an assigned PMR value of one (for where the original PMR was less than 

one) or twelve (for where the original PMR was greater than twelve) based the 5-minute and 1-

hour concentration distributions. If included, these data would have virtually no influence on the 

estimated number of benchmark exceedances. This is because 1-hour concentrations < 8.3 ppb 

combined with the PMR distribution principally affected by inclusion of newly assigned ratios 

(i.e., the < 5 ppb concentration bin) would never generate a benchmark exceedance. Given the 

limited number of samples removed from further analysis and recognizing there would be less 

uncertainty when using a data set comprised of PMRs with realistic bounds rather than one using 

all possible PMR values, staff judges the magnitude of the influence associated with the 

screening of the 5-minute data as low. In excluding the mostly lower concentration data (as 

compared to the final data set used) there may be an over-estimation in the percent and 

probability of exceedances. 

 

49 It is possible to have a PMR equal to 12. This value is achieved with one 5-minute concentration above zero and 

the other eleven 5-minute values reporting concentrations of zero. Data used in developing the statistical 

relationship were screened for values with a PMR equal to 12 however, because it could not be used in the 

AERMOD/APEX modeling. It is of little consequence because the distributions chosen in estimating the 5-minute 

concentrations included the 1st through the 99th percentiles, not the minimum and maximum values. 
July 2009 169

Table 7-17. Summary of descriptive statistics for the data removed using peak-to-mean ratio 

criterion and the final 1-hour and 5-minute maximum SO2 data set used to develop PMRs. 

Data removed Final data set 

PMR < 1 

(n = 39,861) 

PMR ≥ 12 

(n = 804) (n = 2,367,686) 

Statistic1

5-min max 

(ppb) 

1-hour 

(ppb) 

5-min max 

(ppb) 

1-hour 

(ppb) 

5-min max 

(ppb) 

1-hour 

(ppb) 

mean 1 2 29 2 10 6 

p99 6 10 174 10 100 50 

p95 3 5 82 4 37 21 

p50 1 1.6 15.5 1 3 2 

p5 1 1.1 12 0.9 1 1 

p1 0.2 0.45 4 0.1 1 0.2 

Notes: 

1

 mean is the arithmetic average; p99, p95, p50, p5, p1 are the 99th, 95th, 50th, 5th and 1st

percentiles of the concentration distribution. 

The use of all screened 5-minute maximum SO2 data (1997 to 2007) in developing the 

PMR distributions assumes that the source emissions present at that time of measurement are 

similar to other year source emissions. It could be possible that there is greater uncertainty in the 

estimated number of exceedances in areas where year-to-year source emissions deviate from a 

consistent pattern. However, as noted with the concentration variability, the PMRs derived from 

the 5-minute maximum measurement data do not have a clear trend with monitoring year. Over 

the 11-year period, the mean of each monitor’s annual average PMR is about 1.6 (medians of 

1.5; 25th percentiles of 1.4; 75th percentiles of 1.7) (Figure 7-34). This general trend in mean 

PMRs is consistent with the population-based value used by Stoeckenius et al. (1990) for 

exposure analyses (mean of 1.6; median of 1.5) and ambient monitor concentration analyses 

conducted by SAI (1996) (mean 1.7; median 1.5). 50 While there is some indication of greater 

variability in the PMRs for years 2004-2005 compared with some of the other years used, overall 

the consistent pattern over time indicates that the use of the older ambient monitoring data in 

developing the statistical model would have a negligible impact on the predicted concentrations 

and subsequently the estimated number of benchmark exceedances (i.e., low influence with no 

apparent direction). Given the consistency of the PMRs derived using recent air quality with that 

of the earlier analyses, the uncertainty regarding the knowledge-base is judged as low. 

 

50 Data from Table 2-18 of Stoeckenius (1990) for the Scottish Rites monitoring site and Table 5-2 of SAI (1996). 
July 2009 170

Figure 7-34. Distributions of annual average peak-to-mean ratios (PMRs) derived from the 98 

monitors reporting both 5-minute maximum and 1-hour SO2 concentrations, Years 1997 

through 2007. 

The PMRs distributions for each COV and concentration bin were represented by a nonparametric form condensed to single percentiles, with each value from the distribution having an 

equal probability of selection. While there may be other distribution forms that could be 

alternatively selected, staff judged that use of a fitted distribution would not improve the 

representation of the true population of PMRs compared with a non-parametric form, and that 

there would likely be no reduction in the uncertainty of estimated number of exceedances if 

using a parameterized distribution. While some of the PMR distributions were similar to a 

lognormal distribution (for example see Figure 7-35), 93 of 95 possible statistical tests performed 

indicated the distributions were statistically distinct (p<0.01) from any of the tested forms (i.e., 

normal, lognormal, Weibull, gamma, and exponential) (see Figure 7-35 as an example). The 

PMRs derived from monitors having the greatest COV (all concentration bins) and those derived 

from the lowest concentration bins (all COV bins) were most common in exhibiting atypical 

distribution forms. Even when considering practical judgments regarding a potential parametric 

form (i.e., beyond simply using statistically significant differences as a criterion), most of the 

observed PMR distributions had large deviations from parametric distributions such as that 

illustrated by Figure 7-36. 
July 2009 171

Figure 7-35. Example histogram of peak-to-mean ratios (PMRs) compared with four fitted 

distributions derived from monitors reporting the 5-minute maximum and 1-hour SO2

concentrations (left) and the same PMRs compared with expected lognormal 

percentiles (right). PMRs were derived from monitors with medium level variability 

(COVbin = b) and 1-hour concentrations between 75 and 150 ppb (COVconcbin = 4). 

In addition, while there is uncertainty associated with the use of the empirically-derived 

data in representing the true population of PMRs, assuming a fitted distribution would not be 

without its own uncertainties. For example, using a lognormal distribution may underestimate 

the observed frequency of certain values of PMRs while overestimating others. For PMR 

distributions that are of similar form with the lognormal distribution, it is likely that the small 

variation in PMRs selected from a fitted lognormal distribution would have only limited impact 

on the estimated 5-minute maximum SO2 concentrations. For distributions exhibiting no 

similarities to any parametric distribution, experimental justification criteria would need to be 

developed in selecting the most appropriate form of the distribution, likely requiring multiple test 

iterations, potentially yielding distributions with greater uncertainty than those of a nonparametric form (e.g., WHO, 2008 page 28). Each of these additional evaluations and iterations 

would require time and resources not available to staff. Furthermore, the sample sizes for many 

of the PMR distributions used are well above 1,000 (only 5 of the 19 distributions had fewer than 
July 2009 172

1,000, with all distributions having greater than 100 samples), providing support that the true 

distribution may be well-represented by the non-parametric form. Each of these factors 

mentioned (uncertainty in the form of the distribution, limits on time and resources available, and 

numbers of samples available) were considered and it was decided by staff that the nonparametric distribution derived from the measurement data would be most appropriate. 

Therefore, it is judged that the magnitude of influence on the estimated benchmark exceedances 

is low along with no apparent direction of influence. Since staff employed both statistical and 

practical comparisons in selection of the distribution form to the maximum extent allowable, the 

uncertainty regarding the knowledge-base is judged as low. 

Figure 7-36. Example of a measured peak-to-mean ratio (PMRs) distribution with the percentiles 

of a fitted lognormal distribution. PMRs were derived from monitors with high COV 

(COVbin = c) and 1-hour concentrations between 5 and 10 ppb (COVconcbin = 2). 

The accuracy in the predicted daily 5-minute maximum SO2 concentrations above each of 

the benchmark levels was evaluated using measured concentrations. The results indicated that 

on average, the statistical model performed well in estimating of these short-term peak 

concentrations (section 7.2.3.4). There was reasonable agreement in observed versus predicted 
July 2009 173

numbers of benchmark exceedances for most of the monitoring site-years (i.e., about 90% of the 

data set) and for all of the benchmark levels. Based on this overall assessment of model 

accuracy, the magnitude of influence the selected model has on contributing uncertainty to the 

estimated number of exceedances is judged by staff to be low. There was no particular direction 

of influence; model predictions were equally over- or under-estimated (Figure 7-37, Table 7-6). 

The accuracy assessment indicated the estimated number of days with benchmark 

exceedances could be either over- or under-estimated by as many as 20 to 50 days in a year, 

primarily at the tails of the prediction distribution. These model prediction errors were limited to 

several site-years from a few monitors. Figure 7-37 illustrates the model predicted versus the 

observed number of benchmark exceedances at each of the benchmark levels. While there is 

generally uniform agreement between the predicted and observed values at the 100 ppb 

benchmark, there is deviation in the agreement at the greatest and lowest number of days with 

exceedances for the 200, 300, and 400 ppb benchmark levels. For example, there were a few 

site-years without any observed benchmark exceedances of 400 ppb, although the statistical 

model predicted between 2-15 days in a year. This could indicate that a few of the site-years 

may have moderate over-estimations in the number of days with 5-minute maximum SO2

concentration exceedances, where the estimated number of exceedances is 15 or less. In 

addition, site-years with the greatest number of observed exceedances of 400 ppb (about 50 per 

year) were consistently under-estimated by the model by about 30%. This could imply that when 

the estimated number of days with 5-minute maximum SO2 concentrations above 400 ppb is 40 

per year, the under-estimate may be as large as 15 days per year. 

Neither of these model errors appeared systematically related to an individual source 

type. Additional monitors sited in the same areas impacted by similar source types had good 

agreement between the observed and predicted concentrations. For example, at the monitor with 

the greatest number of measured benchmark exceedances (ID 290930030) and largest underprediction error, one could argue that variable terrain may be an influential factor. This monitor 

is about 1.7 km from a primary smelter and located proximal to a ravine running between the 

source and the monitoring site. The nearby monitor (ID 290930030) sited in elevated terrain 

(Hogan Mountain) at about 4.6 km from the same source had small prediction errors. These 

differences in agreement suggest that when considering any individual monitor, there may be 

factors not accounted for by the statistical model that are important in estimating benchmark 
July 2009 174

exceedances (e.g., terrain). Based on this model accuracy assessment, the magnitude of 

influence the selected model has on contributing uncertainty to the estimated number of 

exceedances for individual monitors is likely medium at the lower and upper tails of the 

prediction distribution. The direction of the influence is likely over-estimation at the lower 

number of exceedances and under-estimation at the greatest number of exceedances. 

Though the cross-validation results are encouraging, there may be additional influential 

variables not included in the construction of the statistical model that may be important and have 

the potential to improve the agreement between the observed and predicted values. There is also 

the possibility of influential variables that are not within the data set used for statistical model 

development, but exist in the broader 1-hour SO2 monitoring data set. Staff judged the 

concentration variability and level as appropriate variables for linking the statistical model with 

the 1-hour measurement data. In addition, the comparison of ambient monitoring attributes (e.g., 

objectives, local source emissions) also indicated consistency between the monitors reporting 5-

minute maximum concentrations and those reporting only 1-hour average concentrations. 

However, in the absence of additional 5-minute measurements in areas where there may be 

unique conditions (e.g., terrain or climatologic influences), staff judges there remains a medium 

level of uncertainty in the knowledge-base regarding the accuracy in the extrapolation using the 

statistical model. 
July 2009 175

Figure 7-37. Comparison of observed and predicted number of daily benchmark exceedances in a 

year at the 98 monitors reporting 5-minute maximum SO2 concentrations. 

Staff needed to evaluate the reproducibility of the statistical model because random 

sampling was employed in generating the PMRs used to estimate 5-minute SO2 concentrations. 

The purpose of this analysis was to determine the effect of random sampling error on the 

estimated number of benchmark exceedances. First, to define terminology used in this analysis: 

a model simulation is where each monitor had all of its years of 1-hour data SO2 used in 

estimating 5-minute maximum concentrations and as a result, the number benchmark 

exceedances was calculated; a model run is comprised of twenty such independent simulations 

(i.e., differing by random number seed) and used to generate a mean number of daily 5-minute 

0

10

20

30

40

50

60

70

80

90

100

110

0 10 20 30 40 50 60 70 80 90 100 110

0

20

40

60

80

100

120

140

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0 20 40 60 80 100 120 140 160 180

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10

20

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0 10 20 30 40 50 60 70 80

0

10

20

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40

50

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0 10 20 30 40 50 60 70

100 ppb 200 ppb 

300 ppb 400 ppb 

Observed number of daily benchmark exceedances in a year 

Predicted number of daily benchmark exceedances in a year
July 2009 176

maximum SO2 concentration exceedances for each site-year. This is the same process (i.e., a 

model run) that was used in generating the air quality characterization. 

The reproducibility of the estimated number of benchmark exceedances was evaluated by 

performing ten independent modeling runs (with twenty simulations per model run) using the 40-

county as is air quality data set (i.e., having 610 site-years per model simulation). The output 

from each model run was the mean number of days per site-year an exceedance occurred; 

therefore, ten mean numbers of exceedances were generated for each of the four benchmarks 

using the 610 site-years of data. The maximum difference in those ten means was calculated (the 

minimum mean value subtracted from the maximum mean value) giving the range of the ten 

means for each benchmark and site-year. For example, in one site-year there were 51, 52, 52, 

53, 52, 52, 52, 51, 52, and 52 estimated mean numbers of exceedances of 100 ppb from the 10 

model runs. Therefore the range (or maximum difference) is equal to two. 

The distributions of the range in mean exceedances by benchmark level are illustrated in 

Figure 7-38. The range in the mean number of exceedances based on the ten model runs is less 

than five for all benchmark levels and consistently decreases with increasing benchmark level. 

On average, maximum difference in the estimated mean numbers of exceedances of 100 ppb was 

2 exceedances, while at greater benchmark levels the range was 1 or less. This indicates that the 

random sampling error has a low impact to the estimated mean number of exceedances per siteyear. 
July 2009 177

Figure 7-38. Distributions of the maximum difference in the estimated mean number of 

exceedances per site-year given 10 independent model runs (with 20 simulations per 

run). Data used are from 40 county as is air quality (610 site-years). Box represents the 

inner quartile range (IQR, or the 25th to 75th percentile), + indicates the mean, whiskers 

are 1.5 times the IQR. 
July 2009 178

7.4.2.7 Potential Health Risk Endpoints Used 

The choice of potential health effect benchmarks levels and the use of those benchmarks 

to characterize risks are important uncertainties in the air quality characterization results. Human 

exposure is characterized by contact of a pollutant with a person, and as such, the air quality 

characterization assumes that the ambient monitoring concentrations can serve as an indicator of 

exposure. The ISA reports that personal exposure measurements (PEM) are of limited use since 

ambient SO2 concentrations are typically below the detection limit of the personal samplers. 

There is no method to quantitatively assess the relationship between 5-minute ambient 

monitoring data and 5-minute personal exposures, particularly since personal exposures are timeaveraged over days to weeks, and never by 5-minute averages. Therefore the fraction of actual 

5-minute maximum personal exposure concentrations attributed to 5-minute maximum ambient 

SO2 is unknown and thus contributes to uncertainty when using ambient air quality data as an 

indicator of human exposure. 

An evaluation in the ISA indicates the relationship between longer-term averaged 

ambient monitoring concentrations and personal exposures is strong, particularly when ambient 

concentrations are above the limit of detection. The strength of the relationship between 

personal and ambient SO2 concentrations is supported further by the limited presence of indoor 

sources of SO2; much of an individuals’ personal exposure is of ambient origin. However, SO2

personal exposure concentrations are reportedly a small fraction of ambient concentrations. This 

is because local outdoor SO2 concentrations are typically half that of the ambient monitoring SO2

concentrations, and indoor concentrations about half that of the local outdoor SO2 concentrations 

(ISA). Therefore, while the relationship between personal exposures and ambient SO2

concentrations is strong, the use of monitoring data as an indicator of SO2 exposure may lead to 

an overestimate in the number of peak concentrations those individuals might encounter. While 

the magnitude of the uncertainty about the true relationship between actual human exposure and 

any given ambient monitor short-term concentration exceedance is unknown, it is judged by staff 

to be of a medium magnitude given what is known regarding the relationship between longerterm PEM and ambient SO2 concentrations. 

There is uncertainty regarding how susceptible populations were considered in 

developing the potential health benchmark levels. The human clinical exposure studies 
July 2009 179

evaluated airways responsiveness in mild to moderate asthmatics. Health effect symptoms and 

responses were observed in these test subjects exposed to concentrations as low as 200 ppb in the 

free-breathing chamber studies. As such, a concentration of 200 ppb could well represent a 

lower range of the benchmark level for mild to moderate asthmatics. However, for ethical 

reasons, adults with severe asthma and younger asthmatics are not commonly challenged in air 

pollutant studies. This is because severe asthmatics and/or asthmatic children may be more 

susceptible than mild asthmatic adults to the effects of SO2 exposure. Therefore, exposure levels 

(and hence selected benchmark levels) lower than those used in free-breathing chamber studies 

may be important in representing populations with greater susceptibility. Staff selected 100 ppb 

as the lowest benchmark level based on effects observed in mild to moderate asthmatics using 

facemasks at that level and to consider potential effects in susceptible populations at lower 5-

minute concentrations. In the absence of strong quantitative evidence it is difficult to determine 

if 100 ppb would be health protective for asthmatics (mild, moderate, or severe) or if 100 ppb is 

a concentration that would elicit an adverse effect. Based on this, staff acknowledges there is 

medium uncertainty in the knowledge-base regarding representativeness of the lowest 

benchmark level selected, but judge that the magnitude of influence to the estimated health risk 

is low given the inclusion of the 100 ppb level. 

Staff also acknowledges that there may be uncertainty in the selected potential health 

effect benchmark averaging time. For example, the used in this assessment were from studies 

where volunteers were exposed to SO2 for varying lengths of time. Typically, the SO2 exposure 

durations in the controlled human studies were between 5 and 10 minutes. This could be an 

important uncertainty because the potential health effect benchmark levels were compared to 

concentration exceedances occurring over 5-minutes. That is, if there were a difference in the 

response rate for a given concentration level and averaging time, the use of a 5-minute averaging 

time could either lead to over- or under-estimation in the health risk characterization. The true 

exposure-response relationship may be dependent on both the combined concentration level and 

the exposure duration, that is, it is possible that a particular response rate observed at a 10-minute 

exposure level of concentration x may be similar to that of a 5-minute exposure level equal to or 

greater than concentration x. In this hypothetical scenario, if benchmarks were derived from 10 

minute exposures and applied in the evaluation of 5-minute ambient concentrations, the risk 

characterization may well be over-estimated. However, the ISA did not distinguish between 
July 2009 180

health effects observed following either 5- or 10-minute exposures. Therefore the direction of 

influence to the potential health risk is judged as none, and given a general consistency in the 

observed responses involving either 5- or 10-minute exposures, staff judges the uncertainty in the 

knowledge-base as low. 

The health effect endpoint used in the air quality characterization was the observed or 

estimated number days the maximum 5-minute SO2 concentration exceeded a particular 

benchmark level. Staff acknowledges that this choice could result in the risk characterization 

under-estimating the health risk because there can be multiple exceedances of the benchmark 

levels in a day (Table 7-18). Using the monitors reporting 5-minute SO2 maximum 

concentrations, approximately half of the time there was a single benchmark exceedance in a 

day. For most days having an exceedance (about 80-90%), there were no more than three that 

occurred in a day. There were several days having many benchmark exceedances within a day 

(e.g., > 5), particularly when considering the lowest benchmark levels. However in this air 

quality analysis, none of the elements of exposure are considered (e.g., whether or not time of 

exposure occurs coincident with elevated activity level), thus limiting the relevance of multiple 

exceedances within a day. While the risk characterization could be considered under-estimated, 

the magnitude of influence by this source of uncertainty is judged by staff as low given the 

defined limits of the air quality characterization. Furthermore, staff acknowledges that multiple 

benchmark exceedances of 5-minutes can occur within an hour. This issue and its implications 

for characterizing health risk are more relevant to human exposure than the air quality analysis 

and are discussed in greater detail in section 8.11. 
July 2009 181

Table 7-18. The number and percent of days having multiple benchmark exceedances occurring 

in the same day, using monitors reporting the 5-minute maximum SO2 concentrations. 

5-minute SO2 Benchmark Level 

> 100 ppb > 200 ppb > 300 ppb > 400 ppb 

Number of 

Exceedances 

per Day1 days2

 percent2 days percent days percent days percent

1 3806 43 1390 50 740 50 512 53

2 1923 22 613 22 349 24 248 26

3 1093 12 327 12 183 12 111 12

4 640 7 152 5 87 6 46 5

5 424 5 114 4 48 3 19 2

6 286 3 60 2 25 2 15 2

7 185 2 52 2 22 1 8 1

8 127 1 27 1 8 1 0 0

9 100 1 21 1 4 0 0 0

10 68 1 14 1 5 0 0 0

11 45 1 7 0 2 0 0 0

12 38 0 7 0 0 0 0 0

13 18 0 4 0 1 0 0 0

14 27 0 1 0 0 0 1 0

15 7 0 1 0 1 0 1 0

16 11 0 2 0 0 0 0 0

17 3 0 0 0 0 0 0 0

18 6 0 1 0 1 0 0 0

19 5 0 0 0 0 0 0 0

20 2 0 0 0 0 0 0 0

21 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0

23 3 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 0

Sum 8817 2793 1476 961 

Notes: 

1

 The number of 5-minute maximum benchmark exceedances within a day could range from 1 to 24 

given the number of hours in a day. 

2

 The total number of days having the given number of multiple exceedances within the day. 3

 The percent of days having an exceedance with the given number of multiple exceedances per 

day. 

7.5 KEY OBSERVATIONS 

Presented below are key observations resulting from the SO2 air quality characterization: 

 For unadjusted as is air quality at ambient monitors measuring 5-minute maximum 

concentrations, nearly 70% of the 471 site-years analyzed had at least one daily 5-minute 

maximum concentration above 100 ppb and over 100 site-years (more than 21%) had ≥

25 days with a daily 5-minute maximum concentration above 100 ppb. Less than half 

(44%) of the site-years had at least one daily 5-minute maximum concentration above 

200 ppb and only 36 site-years had ≥ 25 days with a daily 5-minute maximum 
July 2009 182

concentration above 200 ppb. Approximately 25% and 17% of the 471 site-years 

analyzed had at least one daily 5-minute maximum concentration above 300 and 400 ppb, 

respectively, with 23 and 12 site-years having ≥ 25 days with a daily 5-minute maximum 

concentration above 300 and 400 ppb, respectively (Appendix A, Table A.5-1). 

 For any of the air quality scenarios considered, the probability of exceeding the 5-minute 

maximum benchmark levels was consistently greater at monitors sited in low-population 

density areas compared with high-population density areas. In addition, an increased 

probability of any 5-minute benchmark exceedance was consistently related to either 

increased 24-hour average or 1-hour daily maximum concentrations. 

 For unadjusted air quality in the 40 counties selected for detailed analysis, most counties 

are estimated to have, on average, fewer than 50 days per year where the daily 5-minute 

maximum ambient SO2 concentrations are > 100 ppb. Most counties are estimated to 

have, on average, 25 days per year with daily 5-minute maximum ambient SO2

concentrations > 200 ppb. Very few counties are estimated to have more than ten days 

with 5-minute maximum SO2 concentrations > 300 ppb, while nearly half did not have 

any days with 5-minute maximum SO2 concentrations > 400 ppb (Tables 7-11 to 7-14). 

 When air quality is adjusted to simulate just meeting the current annual standard in the 40 

counties selected for detailed analysis, a hypothetical scenario requiring air quality to be 

adjusted upward, all locations evaluated are estimated to have multiple days per year 

where 5-minute maximum ambient SO2 concentrations are > 100 ppb. Most counties are 

estimated to have, on average, 100 days or more per year with 5-minute maximum 

ambient SO2 concentrations > 100 ppb, while eight of the forty counties are estimated to 

have 200 days or more per year with 5-minute maximum ambient SO2 concentrations > 

100 ppb. Fewer benchmark exceedances are estimated to occur with higher benchmark 

levels. For example, only five counties are estimated to have 60 or more days per year 

with 5-minute maximum ambient SO2 concentrations that exceed 300 ppb (Table 7-13) 

and only four counties are estimated to 50 or more days per year with 5-minute maximum 

ambient SO2 concentrations that exceed 400 ppb (Table 7-14). 

 In all 40 counties, potential alternative standard levels of 100 and 150 ppb are estimated 

to result in fewer days per year with 5-minute maximum SO2 concentrations > 300 and > 

400 ppb than with the current standards and the potential alternative standard levels of 

200 and 250 ppb (Tables 7-13 and 7-14). 

 When considering the potential 1-hour daily maximum potential alternative standard 

levels of 100 and 200 ppb in all 40 counties, corresponding annual average SO2

concentrations were typically between 3 and 15 ppb, similar to a range of concentrations 

using unadjusted air quality (Appendix A). When considering the potential alternative 

standard levels of 200 and 250 ppb, corresponding annual average SO2 concentrations 

were typically between 10 and 30 ppb, similar to the range of concentrations observed 

when using adjusted air quality that just meets the current annual standard. 

 Of the fifteen uncertainties qualitatively judged to influence the estimated number of days 

with air quality benchmark exceedances, three may be associated with over-estimation, 

three may be associated with under-estimation, while the remaining uncertainties could 

affect results in both directions (four sources), no direction (four sources), or unknown 
July 2009 183

direction (one source) (see Table 7-16). The magnitude of influence for four of the six 

uncertainties associated with either over- or under-estimation was estimated as low (or 

negligible magnitude of influence). Staff judged the two remaining uncertainties as 

having a medium magnitude of influence in under-estimating the number of days with 

benchmark exceedances, both of which were associated with the spatial representation of 

the monitoring network. Based on this overall characterization regarding the direction 

and magnitude of influence identified sources of uncertainty, there may be a medium 

level under-estimate in the number of days with air quality benchmark exceedances. 

 For the most part, the knowledge-base uncertainty for sources with unknown or 

bidirectional influence ranged from low (four sources) to medium (four sources), though 

uncertainty regarding the spatial scale of the air quality adjustment procedure (direction 

of influence was both, medium magnitude) was judged as high. The knowledge-base 

uncertainty was low for four of the six sources associated with either an under- or overestimation direction of influence. A high degree of uncertainty in the knowledge-base 

was assigned to the spatial representation of the monitoring network. Based on this 

overall characterization regarding the knowledge-base, there is a high level of uncertainty 

associated with the most influential source. 

 Staff identified four other sources of uncertainty in the air quality characterization as 

having influence on the characterization of health risk. The most influential and most 

uncertain source of the four is associated with the direct use of air quality benchmark 

exceedances as an indicator of exposure. The number of days with 5-minute exposures 

above benchmark levels would likely be lower than the number of days where there were 

ambient SO2 concentrations above benchmark levels. Thus, the air quality 

characterization may over-estimate the health risk due to this factor 
July 2009 184

8. EXPOSURE ANALYSIS 

8.1 OVERVIEW 

This section documents the methodology and data staff used in the inhalation exposure 

assessment and associated health risk characterization for SO2 conducted in support of the 

current review of the SO2 primary NAAQS. Two important components of the analysis include 

the approach for estimating temporally and spatially variable SO2 concentrations and simulating 

human contact with these pollutant concentrations. The approach was designed to better reflect 

exposures that may occur near SO2 emission sources, not necessarily reflected by the existing 

ambient monitoring data alone. 

Staff used a combined air quality and exposure modeling approach to generate estimates 

of 5-minute maximum, 24-hour, and annual average SO2 exposures within Greene County, MO. 

and three Counties within the St. Louis Metropolitan Statistical Area (MSA) for the year 2002. 

AERMOD, an EPA recommended dispersion model, was used to estimate 1-hour ambient SO2

concentrations using emissions estimates from stationary, non-point, and port sources. The Air 

Pollutants Exposure (APEX) model, an EPA human exposure model, was used to estimate 5-

minute population exposures using the census block level hourly SO2 concentrations estimated 

by AERMOD and the statistical model described in section 7.2.3. Staff used the person-based 

exposure profiles to calculate the number of days per year an individual had at least one 5-minute 

exposure above the potential health effect benchmark levels of 100, 200, 300, and 400 ppb. 

 Exposure and potential health risk were characterized considering recent air quality 

conditions (as is), for air quality adjusted to just meet the current SO2 primary standards (0.030 

ppm, annual average; 0.14 ppm, 24-hour average), and for just meeting potential alternative 

standards (see Chapter 5 for selection justification). Specifically, APEX reported the number of 

times an individual experienced a day with a 5-minute exposure in excess of 100 ppb through 

800 ppb.51 The exposures for each individual were estimated over an entire year therefore, 

multiple occurrences of exposures above the benchmark levels are also available. 

 

51 The complete output from APEX includes 5-minute exposure concentrations at 50 ppb increments through 800 

ppb which served as an input to the risk assessment performed in Chapter 9. The health effect benchmarks 

evaluated in the exposure assessment were defined as 100 to 400 ppb by increments of 100 ppb. 
July 2009 185

The approaches used for assessing exposures in Greene County and St. Louis are 

described below. Additional model input data and supporting discussion of APEX modeling are 

provided in Appendix B. Briefly, the discussion in this Chapter includes the following. 

 Description of the inhalation exposure model and associated input data used for Green 

County and St. Louis; 

 Evaluation of estimated SO2 air quality concentrations and exposures; and 

 Assessment of the quality and limitations of the input data for supporting the goals of the 

SO2 NAAQS exposure and risk characterization. 

The overall flow of the exposure modeling process performed for this SO2 NAAQS 

review is illustrated in Figure 8-1. Several models were used in addition to APEX and 

AERMOD including emission factors and meteorological processing models, as well as a 

number of databases and literature sources to populate the model input parameters. Each of 

these is described within this Chapter, supplemented with additional details in Appendix B. 

8.2 OVERVIEW OF HUMAN EXPOSURE MODELING USING APEX 

The EPA has developed the APEX model for estimating human population exposure to 

criteria and air toxic pollutants. APEX serves as the human inhalation exposure model within 

the Total Risk Integrated Methodology (TRIM) framework (EPA 2009a; 2009b). APEX was 

recently used to estimate population exposures in 12 urban areas for the O3 NAAQS review 

(EPA, 2007d; 2007e) and in estimating population NO2 exposures in Atlanta as part of the NO2

NAAQS review (EPA, 2008d). 

APEX is a probabilistic model designed to account for sources of variability that affect 

people’s exposures. APEX simulates the movement of individuals through time and space and 

estimates their exposure to a given pollutant in indoor, outdoor, and in-vehicle 

microenvironments. The model stochastically generates a sample of simulated individuals using 

census-derived probability distributions for demographic characteristics. The population 

demographics are drawn from the year 2000 Census at the tract, block-group, or block-level, and 

a national commuting database based on 2000 census data provides home-to-work commuting 

flows. Any number of simulated individuals can be modeled, and collectively they approximate 

a random sampling of people residing in a particular study area. 
July 2009 186

Figure 8-1. General process flow used for SO2 exposure assessment. 
July 2009 187

Daily activity patterns for individuals in a study area, an input to APEX, are obtained 

from detailed diaries that are compiled in the Consolidated Human Activity Database (CHAD) 

(McCurdy et al., 2000; EPA, 2002). The diaries are used to construct a sequence of activity 

events for simulated individuals consistent with their demographic characteristics, day type, and 

season of the year, as defined by ambient temperature regimes (Graham and McCurdy, 2004). 

The time-location-activity diaries input to APEX contain information regarding an individuals’ 

age, gender, race, employment status, occupation, day-of-week, daily maximum hourly average 

temperature, the location, start time, duration, and type of each activity performed. Much of this 

information is used to best match the activity diary with the generated personal profile, using 

age, gender, employment status, day of week, and temperature as first-order characteristics. The 

approach is designed to capture the important attributes contributing to an individuals’ behavior, 

and of likely importance in this assessment (i.e., time spent outdoors) (Graham and McCurdy, 

2004). Furthermore, these diary selection criteria give credence to the use of the variable data 

that comprise CHAD (e.g., data collected were from different seasons, different states of origin, 

etc.). 

APEX has a flexible approach for modeling microenvironmental concentrations, where 

the user can define the microenvironments to be modeled and their characteristics. Typical 

indoor microenvironments include residences, schools, and offices. Outdoor microenvironments 

include for example near roadways, at bus stops, and playgrounds. Inside cars, trucks, and mass 

transit vehicles are microenvironments which are classified separately from indoors and 

outdoors. APEX probabilistically calculates the concentration in the microenvironment 

associated with each event in an individual’s activity pattern and sums the event-specific 

exposures within each hour to obtain a continuous series of hourly exposures spanning the time 

period of interest. The estimated microenvironmental concentrations account for the 

contribution of ambient (outdoor) pollutant concentration and influential factors such as the 

penetration rate into indoor microenvironments, air exchange rates, decay/deposition rates, 

proximity to important outdoor sources, and indoor source emissions. Each of these influential 

factors are dependent on the microenvironment modeled, available data to define model inputs, 

and estimation method selected by the model user. And, because the modeled individuals 

represent a random sample of the population of interest, the distribution of modeled individual 

exposures can be extrapolated to the larger population within the modeling domain. 
July 2009 188

The exposure modeling simulations can be summarized by five steps, each of which is 

detailed in the subsequent sections of this document. Briefly, the five steps are as follows: 

1. Characterize the study area. APEX selects the census blocks within a study 

area – and thus identifies the potentially exposed population – based on userdefined criteria and availability of air quality and meteorological data for the area. 

2. Generate simulated individuals. APEX stochastically generates a sample of 

hypothetical individuals based on the demographic data for the study area and 

estimates anthropometric and physiological parameters for the simulated 

individuals. 

3. Construct a sequence of activity events. APEX constructs an exposure event 

sequence spanning the period of the simulation for each of the simulated 

individuals using time-location-activity pattern data. 

4. Calculate 5-minute and hourly concentrations in microenvironments. APEX 

users define microenvironments that people in the study area would visit by 

assigning location codes in the activity pattern to the user-specified 

microenvironments. The model calculates all 5-minute concentrations occurring 

within the hour (one maximum along with eleven other 5-minute values 

normalized to the hourly mean) in each microenvironment for the period of 

simulation, based on the user-provided microenvironment descriptions, the hourly 

air quality data, and peak-to-mean ratios (PMRs; see section 7.2.3). 

Microenvironmental concentrations are calculated independently for each of the 

simulated individuals. 

5. Estimate exposures. APEX estimates a concentration for each exposure event52

based on the microenvironment occupied during the event. In this assessment, 

APEX estimated 5-minute exposures. These exposures can also be averaged by 

clock hour to produce a sequence of hourly average exposures spanning the 

specified exposure period. The values may be further aggregated to produce 

daily, monthly, and annual average exposure values. 

8.3 CHARACTERIZATION OF STUDY AREAS 

8.3.1 Study Area Selection 

The selection of areas to include in the exposure analysis takes into consideration the 

availability of ambient monitoring, the presence of significant and diverse SO2 emission sources, 

population demographics, and results of the ambient air quality characterization. Although it 

could be useful to characterize SO2 exposures nationwide, because the exposure modeling 

approach is both time and labor intensive, a regional and source-oriented approach was selected 

 

52 An exposure event is a continuous period of time during which the factors that affect exposure (microenvironment 

inhabited, activity performed, ventilation rate, and pollutant concentration) can be considered constant. 
July 2009 189

to make the analysis tractable and with the goal of focusing on areas most likely to have elevated 

SO2 peak concentrations and with sufficient data to conduct the analysis. 

A broad study area was first identified based on the results of a preliminary screening of 

the 5-minute ambient SO2 monitoring data that were available. The state of Missouri was one of 

only a few states reporting both 5-minute maximum and continuous 5-minute SO2 ambient 

monitoring data (14 total monitors), as well as having over thirty monitors in operation at some 

time during the period from 1997 to 2007 that measured 1-hour SO2 concentrations. In addition, 

the air quality characterization described in Chapter 7 estimated frequent exceedances above the 

potential health effect benchmark levels at several of the 1-hour ambient monitors within 

Missouri. In a ranking of estimated SO2 emissions reported in the National Emissions Inventory 

(NEI), Missouri ranked 7th out of all U.S. states for the number of stacks with annual emissions 

greater than 1,000 tons. These stack emissions were associated with a variety source types such 

as electrical power generating units, chemical manufacturing, cement processing, smelters, and 

emissions associated with port operations. 

In the 1st draft SO2 REA, several modeling domains were characterized within the 

selected state of Missouri to assess the feasibility of the modeling methods. These modeling 

domains were defined as areas within 20 km of a major point source of SO2 emissions. While 

modeled air quality and exposure results were generated for several of these domains in the 1st

draft REA, changes in the methodology used in this 2nd draft REA precluded additional analysis 

for most of the domains originally selected. Staff judged the availability of relevant ambient 

monitoring data within the model domain as essential in evaluating the dispersion model 

performance, increasing confidence in the predicted air quality and exposure modeling results. 

For example, when comparing the modeled air quality to ambient monitoring data in Greene 

County in the 1st draft REA, it was judged by staff that non-point source emissions may 

contribute to a large proportion of measured ambient concentrations. Addressing non-point 

source emissions then added a layer to the already complex modeling performed, further limiting 

the potential number of locations analyzed. Second, to assess the impact of potential alternative 

standards, baseline conditions (as is air quality) need to be known, again requiring ambient 

monitoring data. Because Greene County had a number of ambient monitors and most of the 

model input data were already well-defined, it was selected for further modeling in the 2nd draft 

REA. Additionally, staff decided that modeling a large urban area would be advantageous in 
July 2009 190

combining both large emission sources and large potentially exposed populations. Modeling for 

St. Louis, Mo. was already underway at the time the 1st draft REA was completed, therefore it 

was decided that exposure modeling in this domain should be continued and expanded for other 

sources for the 2nd draft and the final REAs. 

8.3.2 Study Area Descriptions 

8.3.2.1 Greene County, Mo. 

The greater Springfield, Mo., Metropolitan Statistical Area (MSA) consists of five 

counties in southwestern Missouri including Christian, Dallas, Greene, Polk, and Webster 

counties. The only city in the region with a population greater than 150,000 is Springfield, in 

Greene County. Greene County has a total area of approximately 678 mi2

 (1,756 km2). Due to 

the complexity of the air quality and exposure modeling performed in this exposure assessment 

and the focus on receptors within 20 km of stationary sources, the modeling domain was limited 

to Greene County (see Figure 8-2). The Springfield-Branson Regional Airport (WBAN 13995) 

served as the source of meteorological data used in the Greene County modeling domain. 

8.3.2.2 St. Louis, Mo. Area 

The greater St. Louis Metropolitan Statistical Area (MSA) is the 18th largest MSA in the 

United States and includes the independent City of St. Louis; the Missouri counties of St. Louis, 

St. Charles, Jefferson, Franklin, Lincoln, Warren, and Washington; as well as the Illinois 

counties of Madison, St. Clair, Macoupin, Clinton, Monroe, Jersey, Bond, and Calhoun. The 

total MSA has an area of approximately 8,846 mi2

 (22,911 km2). Due to the complexity of the 

air quality and exposure modeling performed in this exposure assessment and the focus on 

receptors within 20 km of stationary sources, staff limited the modeling domain to three counties 

directly surrounding the city of St. Louis: St. Louis City, St. Louis County, and St. Charles 

County (see Figure 8-3). These three counties comprise much of the urban center of the St. 

Louis MSA, with a combined population of about 1.15 million (2000 Census), which is 

approximately 45 percent of the Greater St. Louis MSA population. 
July 2009 191

Figure 8-2. Modeling domain for Greene County Mo., along with identified emissions sources, air 

quality receptors, ambient monitors, and meteorological station. 

The St. Louis modeling domain defined in this REA was assembled from three separate 

modeling domains described in the 1st draft SO2 REA, aggregated to utilize the most reliable 

hourly meteorological data available (St. Louis International-Lambert Field; WBAN 13994). It 

was then reduced to just the three counties of the urban core described above. Figure 8-3 shows 

the modeling domain for the greater St. Louis, MO area.
July 2009 192

Figure 8-3. Three county modeling domain for St. Louis, Mo., along with identified emissions sources, air quality r

monitors, and meteorological station. 
July 2009 193

8.3.3 Time Period of Analysis 

Calendar year 2002 was simulated for both modeling domains to characterize the most 

recent year of emissions data available for the study locations. Year 2002 temperature and 

precipitation used in the dispersion modeling was compared with 30-year climate normal period 

data from 1978 through 2007. For Greene County, 2002 temperatures were similar to the 30-

year normal (56.2 °F compared to 56.3 °F) though drier than the 30-year normal (37.8 in. 

compared to 40.2 in.). For St. Louis, 2002 temperatures were warmer on average than the 30-

year normal (57.9 °F compared to 56.8 °F) and received an annual rainfall total that was similar 

with the 30-year normal (40.9 in. compared to 39.1 in.). See Appendix B, Attachment 1 for 

further details. 

8.3.4 Populations Analyzed 

The exposure assessment included the total population residing in each modeled area and 

population subgroups that were considered more susceptible as identified in the ISA. These 

population subgroups include: 

 Asthmatic children (5-18 years in age) 

 All Asthmatics (all ages) 

In addition, based on the observed responses in the human clinical trials, all asthmatic 

exposures were characterized only when the individual was at moderate or greater exertion levels 

during the exposure events (see sections 8.5.5 and 8.8.2). 

8.4 CHARACTERIZATION OF AMBIENT HOURLY AIR QUALITY DATA 

USING AERMOD 

8.4.1 Overview 

Air quality data used for input to APEX were generated using AERMOD, a steady-state, 

Gaussian plume model (EPA, 2004a). For both modeling domains, the following steps were 

performed. 

1. Collect and analyze general input parameters. Meteorological data, processing 

methodologies used to derive input meteorological fields (e.g., temperature, wind 

speed, precipitation), and information on surface characteristics and land use are 

needed to help determine pollutant dispersion characteristics, atmospheric 

stability and mixing heights. 
July 2009 194

2. Define sources and estimate emissions. The emission sources modeled included: 

a. Major stationary emission sources within the domain, 

b. Major stationary emission sources outside the domain (cross-border 

stacks) 

c. Non-point source area emissions, 

d. Emissions from ports, and 

e. Background sources not otherwise captured. 

However, note that not all source categories were present in both modeling 

domains. 

3. Define air quality receptor locations. Two sets of receptors were identified for 

the dispersion modeling, including ambient monitoring locations (where 

available) and census block centroids. 

4. Estimate concentrations at receptors. Full annual time series of hourly 

concentration were estimated for 2002 by summing concentration contributions 

from each of the emission sources at each of the defined air quality receptors. 

Estimated hourly concentrations output from AERMOD were then used as input to the 

APEX model to estimate population exposure concentrations. Details regarding both modeling 

approaches and input data used are provided below. Supplemental information regarding model 

inputs and methodology is provided in Appendix B. 

8.4.2 General Model Inputs 

8.4.2.1 Meteorological Inputs 

All meteorological data used for the AERMOD dispersion model simulations were processed 

with the AERMET meteorological preprocessor, version 06341. The National Weather Service 

(NWS) served as the source of input meteorological data for AERMOD. Tables 8-1 and 8-2 list 

the surface and upper air NWS stations chosen for the two areas. A potential concern related to 

the use of NWS meteorological data is the often high incidence of calms and variable wind 

conditions reported for the Automated Surface Observing Stations (ASOS) in use at most NWS 

stations. A variable wind observation may include wind speeds up to 6 knots, but the wind 

direction is reported as missing. The AERMOD model currently cannot simulate dispersion 

under these conditions. To reduce the number of calms and missing winds in the surface data for 

each of the four stations, archived one-minute winds for the ASOS stations were used to 

calculate hourly average wind speed and directions, which were used to supplement the standard 

archive of winds reported for each station in the Integrated Surface Hourly (ISH) database. 

Details regarding this procedure are described in Appendix B, Attachment 1. 
July 2009 195

Table 8-1. Surface stations for the SO2 study areas. 

Area Station Identifier WMO 

(WBAN) 

Latitude1 Longitude1

 Elevation 

(m) 

Time 

Zone2

Greene 

County 

SpringfieldBranson 

Regional 

AP 

SGF 724400 

(13995) 

37.23528 -93.40028 387 6 

St. Louis Lambert-St. 

Louis 

International 

AP 

STL 724340 

(13994) 

38.7525 -90.37361 161 6 

Notes: 

1

 Latitude and longitude are the best approximation coordinates of the meteorological towers. 2

 Time zone is the offset from UTC/GMT to LST in hours. 

Table 8-2. Upper air stations for the SO2 study areas. 

Area Station Identifier WMO 

(WBAN) 

Latitude Longitude Elevation 

(m) 

Time 

Zone1

Greene 

County 

SpringfieldBranson 

Regional 

AP 

SGF 724400 

(13995) 

37.23 -93.40 394 6 

St. Louis LincolnLogan 

County AP, 

IL 

ILX 724340 (4833) 40.15 -89.33 178 6 

Notes: 

1

 Time zone is the offset from UTC/GMT to LST in hours. 

8.4.2.2 Surface Characteristics and Land Use Analysis 

The AERSURFACE tool (US EPA, 2008e) was used to determine surface characteristics 

(albedo, Bowen ratio, and surface roughness) for input to AERMET. Surface characteristics 

were calculated for the location of the ASOS meteorological towers, approximated by using 

aerial photos and the station history from the National Climatic Data Center (NCDC). A draft 

version of AERSURFACE (08256) that utilizes 2001 National Land Cover Data (NLCD) was 

used to determine the surface characteristics for this application since the 2001 land cover data 

will be more representative of the meteorological data period than the 1992 NLCD data 

supported by the current version of AERSURFACE available on EPA’s SCRAM website. All 

stations considered were located at an airport. Monthly seasonal assignments were defined as 

shown in Table 8-3 and because the AERSURFACE default seasonal assignments were not used, 

the surface characteristics were output by month. Note, the winter options can be winter (no 
July 2009 196

snow) or winter (continuous snow on ground).53 The exposure modeling domains experienced 

less than 28.5 days per year of at least one inch (25.4 mm) of ground snow depth according to 

CLIMAP contours,54 so no month was expected to have continuous snow on ground and hence 

the designation of winter (no snow) only. 

Table 8-3. Seasonal monthly assignments. 

Station Winter (no snow) Spring Summer Autumn 

SGF December, January, 

February, March 

April, May June, July, August September, 

October, 

November 

STL December, January, 

February 

March, April, May June, July, August September, 

October, 

November 

Seasonal definitions 

Winter (no snow) Late autumn after frost and harvest, or winter with no snow 

Spring Transitional spring with partial green coverage or short annuals 

Summer Midsummer with lush vegetation 

Autumn Autumn with unharvested cropland 

8.4.3 Stationary Sources Emissions Preparation 

8.4.3.1 Emission Sources and Locations 

Point Sources 

Point sources at major facilities were identified and paired to a representative surface 

meteorological station. Any stacks listed as in the same location with identical release 

parameters within a certain resolution (typically to the nearest integer value) were aggregated 

into a single stack to simplify modeling but retain all emissions. For this analysis, major 

facilities were defined as those with an SO2 emission total exceeding 1,000 tpy in 2002. Within 

such facilities, every stack emitting more than one tpy was included in the modeling inventory. 

This process resulted in the identification of 11 (combined) stacks in Greene County and 38 

(combined) stacks in St. Louis. Additionally, 45 (combined) stacks were identified across the 

state border that could influence concentrations in St. Louis. These cross-border stacks were 

modeled the same as the within-state stacks. The locations of all emitting stacks were corrected 

based on GIS analysis. This was necessary because many stacks in the NEI are assigned the 

 

53 The designation of winter (continuous snow) would tend to increase wintertime albedo and decrease wintertime 

Bowen ratio and surface roughness for most land-use types compared to snow-free areas. 54 NCDC Climate Maps of the United States database (CLIMAPS). See http://cdo.ncdc.noaa.gov/cgibin/climaps/climaps.pl. 
July 2009 197

same location, which often corresponds to a location in the facility – such as the front office – 

rather than the actual stack locations. To correct for this, stack locations were reassigned 

manually with the Microsoft® Live Maps® Virtual Earth® tool to visually match stacks from 

the NEI database to their locations within the facilities using stack heights as a guide to stack 

identification. All release heights and other stack parameters were taken from the values listed in 

the NEI. Table B.3-1 (in Appendix B) lists all stacks in both domains. 

Port-Related Sources 

Only the St. Louis modeling domain has relevant port emissions. The Port of St. Louis is 

one of the nation’s largest inland river ports. Activity from this port was modeled as fourteen 

area sources along the waterfront. All port-related emission sources were considered as nonpoint area emissions with boundaries based on GIS analysis of aerial photographic images. A 

release height of 5.0 m with a plume initial vertical standard deviation (zi) of 2.33 m was used 

in all cases to represent emissions from Category 1 and 2 commercial marine vessels. Port 

emission strength was taken from the NEI for appropriate activity within St. Louis City and 

allocated uniformly by emission density for all harbor areas. That is, all ports were modeled 

with the same emission density. The emission profile was taken as the seasonal hourly value 

from the Emissions Modeling System for Hazardous Pollutants (EMS-HAP) model. 

Non-Point Sources 

Non-point sources constitute industrial, commercial and institutional facilities as 

identified in the NEI. Emissions from non-point sources in Greene County are identified for 

each tract in the County. In Greene County, spatial allocation factors (SAFs) from EPA’s EMSHAP database55 were used to disaggregate the county-wide emissions from the NEI to census 

tracts. Tracts with total non-point emission densities greater than 12 tons per year/square mile 

were digitized and characterized as non-point source area polygons. These tracts accounted for 

about 87% of the total non-point source emissions in Greene County. 

The release heights for non-point area sources are 10.0 m for rural tracts and 20.0 m for 

urban tracts. Initial vertical dispersion coefficients (zi) were 4.67 m for rural tracts and 9.34 m 

for urban tracts. Because these sources are not well-defined, the release parameters were derived 

 

55 The SAFs were derived from land use data. 
July 2009 198

though a series of sensitivity runs to characterize model performance at the ambient monitor 

locations. 

For the St. Louis domain, staff chose a slightly different approach to characterize nonpoint emissions sources. During model-to-monitor comparisons, it became clear that the spatial 

allocation of county-wide non-point emissions to tracts, based on SAFs, resulted in an inaccurate 

spatial pattern of emissions. Therefore, the spatial resolution of non-point sources in this domain 

was retained at the county level. However, to improve the numerical representation of these 

emissions in the model, the two counties with the highest non-point source emissions – St. Louis 

City and St. Louis County – were subdivided into regular grid cells. St. Louis County grid cells 

were 5 km by 5 km; St. Louis City grid cells were 1 km by 1 km, more closely approximating the 

smaller and denser census tracts in that region. All county-wide non-point source emissions 

were spatially allocated uniformly to the grid cells. St. Charles County was modeled as a single 

area source, with edges approximating the full county boundaries. 

The release parameters for the St. Louis domain varied according to the urban and rural 

designation of individual grid cells. Rural grid cells have a release height of 10 m and initial 

dispersion length of 4.67 m. Urban grid cells have a release height of 20 m and initial dispersion 

length of 9.34 m. 

Background Sources 

For the Greene County modeling domain, background sources were assembled to account 

for any emissions not otherwise included. These were comprised of any point sources in 

facilities not meeting the 1,000 tpy selection criteria and any residual non-point sources, as well 

as on-road and non-road mobile sources. In addition, all emission sources in neighboring 

Christian County were modeled as a rural, county-wide non-point area source with uniform 

density. Both background sources were characterized as county-wide polygon rural area sources 

with release heights of 10.0 m and initial dispersion length of 4.67 m. 

For the St. Louis modeling domain, emissions from residual point sources, on-road 

mobile sources, and non-road mobile sources were combined with the county-wide non-point 

sources as described above. Thus, no separate background sources were simulated. 
July 2009 199

8.4.3.2 Urban vs. Rural Designations 

This section describes how urban and rural designations were determined for each 

emission source type. AERMOD has somewhat different treatment for urban and rural sources. 

For example, when regulatory default settings are employed as they were in this application, no 

chemical decay is assumed for rural sources, while a 4-hour half-life is assumed for urban 

sources. Another difference in AERMOD’s treatment of urban and rural sources is that for urban 

sources, additional dispersion is simulated at night to account for increased surface heating 

within an urban area under stable atmospheric conditions. The magnitude of this effect is weakly 

proportional to the urban area population. 

Point Sources 

Urban or rural designations for point sources were made according to EPA guidance 

based on the land use within 3 km of the source. The 2001 NLCD database was used to make 

this determination. Table 8-4 lists the land use categories in the 2001 NLCD. 

Table 8-4. NLCD2001 land use characterization. 

Category Land Use Type Category Land Use Type 

11 Open Water 73 Lichens 

12 Perennial Ice/Snow 74 Moss 

21 Developed, Open Space 81 Pasture/Hay 

22 Developed, Low Intensity 82 Cultivated Crops 

23 Developed, Medium Intensity 90 Woody Wetlands 

24 Developed, High Intensity 91 Palustrine Forested Wetland1 

31 Barren Land (Rock/Sand/Clay) 92 Palustrine Scrub/Shrub Wetland1

32 Unconsolidated Shore1 93 Estuarine Forested Wetland1 

41 Deciduous Forest 94 Estuarine Scrub/Shrub Wetland1

42 Evergreen Forest 95 Emergent Herbaceous Wetlands 

43 Mixed Forest 96 Palustrine Emergent Wetland (Persistent) 1

51 Dwarf Scrub 97 Estuarine Emergent Wetland1 

52 Shrub/Scrub 98 Palustrine Aquatic Bed1 

71 Grassland/Herbaceous 99 Estuarine Aquatic Bed1 

72 Sedge/Herbaceous 

Notes: 

1

 Coastal NLCD class only. 
July 2009 200

Each stack where more than half the land use within 3 km fell into categories 21-24 were 

designated as urban. These categories are consistent with those considered developed by 

AERSURFACE.56 

Non-Point Sources 

Non-point area sources were defined as rural or urban using a similar methodology as 

that for the point sources. As noted in the 2008 AERMOD Implementation Guide,57 in some 

cases, a population density is more appropriate than a land use characterization. Therefore, nonpoint area sources were evaluated from both a land use and population density perspective. 

In Greene County, area sources were defined as corresponding to the census tract 

boundaries. Each tract was then considered urban or rural by considering both the population 

density and land use fraction from NLCD2001. If the population density was greater than 750 

persons/km2

 or the developed land use categories 22-24 throughout the tract was greater than 50 

percent, the tract was designated as urban. In addition, if a tract was surrounded by urban tracts 

it was designated as urban, since the emissions from such a tract would likely be subject to urban 

dispersion conditions. 

As explained above, for the St. Louis modeling domain, the counties with the greatest 

non-point emissions – St. Louis City and St. Louis County – were subdivided into regular grid 

cells, while St. Charles County was represented as a polygon area source with its political 

boundaries. The urban or rural designation was then assigned to each based on population 

density. St. Charles County and all but eleven of the 5 km grid cells in St. Louis County were 

designated rural; the remaining cells in St. Louis County and all of St. Louis City were 

designated urban. 

Port-Related Sources 

Only the St. Louis modeling domain has relevant port emissions. The fourteen portrelated non-point area sources described above were designated urban, given their location in the 

urban core along the waterfront and their associated industrial activities. 

 

56 AERSURFACE User’s Guide, U.S. EPA, OAQPS, Research Triangle Park, NC, EPA-454/B-08-001, January 

2008. 

57 AERMOD IMPLEMENTATION GUIDE, AERMOD Implementation Workgroup, US EPA, OAQPS, Air Quality 

Assessment Division, Research Triangle Park, NC, Revised January 9, 2008, 
July 2009 201

Background Sources 

Background area sources for Greene County were classified with the same procedures as 

for non-point area sources. Both Greene and Christian counties were designated rural. 

8.4.3.3 Source Terrain Characterization 

All corrected locations for the final list of major facility stacks in St. Louis and Greene 

County domains were processed with a pre-release version of the AERMAP terrain 

preprocessing tool. This version is functionally equivalent to the current release version of the 

tool (version 08280). In particular, this updated version allows use of 1 arc-second terrain data 

from the USGS Seamless Server58 which allows for more highly resolved values of the source 

and receptor heights as well as the hill height scales. 

Terrain height information for point sources was processed through AERMAP with input 

data taken from the USGS server. For all area sources (non-point and background source types), 

the outputs from AERMAP were modified. In these cases, rather than using a single point to 

represent these large areas, the terrain height for each vertex of the area was estimated with 

AERMAP. The terrain height for the entire source polygon was then characterized as the 

average terrain height from all vertices. 

8.4.3.4 Emissions Data Sources 

Point Sources 

Data for the parameterization of major facility point sources in the two modeling domains 

comes primarily from three sources: the 2002 NEI (EPA, 2007f), Clean Air Markets Division 

(CAMD) Unit Level Emissions Database (EPA, 2007g), and temporal emission profile 

information contained in the EMS-HAP (version 3.0) emissions model.59 The NEI database 

contains stack locations, emissions release parameters (i.e., height, diameter, exit temperature, 

exit velocity), and annual SO2 emissions. The CAMD database has information on hourly SO2

emission rates for all the electric generating units in the US, where the units are the boilers or 

equivalent, each of which can have multiple stacks. 60 These two databases generally contain 

 

58 http://seamless.usgs.gov/index.php 

59 http://www.epa.gov/ttn/chief/emch/projection/emshap30.html 

60 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 
July 2009 202

complimentary information, and were first evaluated for matching facility data. However, 

CAMD lacks SO2 emissions data for facilities other than electric-generating units. To convert 

annual total emissions data from the NEI into hourly temporal profiles required for AERMOD, a 

three tiered prioritization was used, as follows. 

1. CAMD hourly concentrations to create relative temporal profiles. 

2. EMS-HAP seasonal and diurnal temporal profiles for source categorization codes 

(SCCs). 

3. Flat profiles, that is, a uniform emission rate throughout the day. 

Details of these processes were as follows: 

Tier 1: CAMD to NEI Emissions Alignment and Scaling 

Of the 94 major facility stacks within the model domains identified above (11 in Greene 

County and 45 cross-border and 38 within-state in the St. Louis domain), 35 (11 in Greene 

County and 7 cross-border and 17 in-state in the St. Louis domain) were able to be matched 

directly to sources within the CAMD database. Stack matching was based on the facility name, 

Office of Regulatory Information Systems (ORIS) identification code (when provided) and 

facility total SO2 emissions. For these stacks the relative hourly profiles were derived from the 

hourly values in the CAMD database, and the annual emissions totals were taken from the NEI. 

Hourly emissions in the CAMD database were scaled to match the NEI annual total emissions by 

proportionally scaling each hour. Although the CAMD emissions may be more accurate than the 

corresponding values in the NEI because they are based on direct emissions monitoring, because 

CAMD emissions estimates were available for only a subset of sources, the NEI emission totals 

were used so that the emission estimates would be consistent across all sources. 

Tier 2: EMS-HAP to NEI Emissions Profiling 

Of the 94 major facility stacks within the two MO domains, 38 stacks (all of which are 

cross-border stacks in the St. Louis domain) could not be matched to a stack in the in the CAMD 

database, but had SCC values that corresponded to SCCs that have temporal profiles included in 

the EMS-HAP emissions model. In these cases, the SCC-specific seasonal and hourly variation 

(SEASHR) values from the EMS-HAP model were used to characterize the temporal profiles of 

emissions for each hour of a typical day by season and day type. 

 

temporal profiles could be approximated by NO2 temporal profiles. However, there were no such cases for MO 

facilities. 
July 2009 203

Tier 3: Other Emissions Profiling 

Of the 94 major facility stacks within the two MO model domains, 21 (all from the St. 

Louis in-state domain) could not be matched to a stack in CAMD database, or to profiles in the 

EMS-HAP model by SCC code. In these cases, a flat profile of emissions was assumed. That is, 

emissions were assumed to be constant for all hours of every day, but with an annual total that 

equals the values from the NEI. A summary of the point source emissions used for the two 

modeling domains is given in Table 8-5. Appendix B, Table B.3-1 contains all 94 stacks within 

the modeling domains and the data source used to determine their emissions profiles. 

Nearly all of the point sources in both domains were accounted for directly in the 

dispersion modeling. Table 8-5 shows the point source contribution captured directly within each 

modeling domain. 

Port-Related Sources 

Ports were the only non-road sector explicitly simulated in either modeling domain. Only 

the St. Louis domain had port emissions. All relevant port emissions were directly captured, 

comprising 51 percent of the total non-road emissions for the domain. Emission profiles for 

port-related activity were taken from the EMS-HAP model for sectors matching the modeled 

activity. Table 8-5 shows the port source contribution modeled directly within each modeling 

domain and compares it to the total non-road emissions. 

Non-Point and Background Sources 

Non-point polygon area sources were developed to capture non-point 

commercial/institutional and industrial emissions within the domains, as specified in the NEI. 

For the St. Louis modeling domain, all non-point emissions were included either in gridded area 

sources over St. Louis City and St. Louis County or a polygon area source over St. Charles 

County, as described above. For the Greene County modeling domain, commercial/institutional 

and industrial non-point area source polygons were created to represent the individual census 

tracts within the county that captured approximately 87 percent of the relevant emissions 

countywide from the NEI. Other non-point sources, as well as on-road mobile and non-road 

mobile sources were included in the background source 

Because non-point area source and background area source temporal profiles are 

unknown, staff derived profiles that provided a best-fit match between the model predictions and 

monitor data. To determine the most representative average non-point area source emission 
July 2009 204

profile across each modeling domain, we first selected monitors where ambient concentrations 

were expected to be primarily influenced by area sources. Due to their locations relative to 

sources, all but one monitor (ID 290770032) in Greene County indicated ambient concentrations 

were primarily influenced by point source emissions. In St. Louis, all seven ambient monitors 

(IDs 291890004, 291890006, 291893001, 291895001, 291897003, 295100007, and 295100086) 

indicated significant influence from area source emissions. Next, simulations were conducted 

with all sources modeled in detail – except area sources, which were modeled with uniform 

emission profiles. A weighting function was then determined based on the modeled error for 

each hour of the day at the one Greene County monitor and as an average of the errors at the 

seven individual St. Louis area monitors. In both cases, the error function was defined as the 

ratio of the total observed concentration, minus the total concentration due to all non-point 

sources, to the concentration predicted by the non-point sources alone. This diurnal error 

function was then normalized such that its average value is unity. Finally, a corrected non-point 

emission profile was determined by combining this normalized weighting function with the 

uniform emission profile. 

Figures 8-4 and 8-5 show the diurnal emissions profiles derived for both the St. Louis and 

Greene County domains compared to other profiles for industrial and commercial/institutional 

area sources derived from commonly used emissions models, such as SMOKE and EMS-HAP. 

The shape of the derived temporal profiles imply that the emission sources are active almost 

exclusively during the daytime from approximately 8 am to 8pm, in contrast to those derived 

from SMOKE and EMS-HAP, which show less extreme daytime-dominated patterns. Given the 

large uncertainties about the actual emission sources represented by the industrial and 

commercial/institutional non-point category and given that such sources are likely to be small 

facilities, it is reasonable to assume that their cumulative emissions occur almost exclusively 

during daytime hours. Table 8-5 shows the non-point source contribution modeled directly 

within each modeling domain and compares it to the total non-point emissions.61

 

61 Table 8-5 does not have the relevant background contribution for each domain. This is because the total 

background in each domain includes not only the counties in the modeling domain (three in the St. Louis domain 

and one in the Greene County domain), but also adjacent counties that could influence concentrations within the 

modeling domain. In those cases, the total countywide emissions are included in the background. Thus, directly 

expressing those values would be confusing and are thus omitted. 
July 2009 205

Table 8-5. Summary of NEI emission estimates and total emissions used for dispersion modeling 

in Greene County and St. Louis modeling domains. 

Point Sources Area Sources Non-road Sources 

Modeling 

Domain 

NEI 

Emissions 

(tpy) 

Modeled 

Emissions 

(tpy) (%)

NEI 

Emissions 

(tpy) 

Modeled 

Emissions

(tpy) (%)

NEI 

Emissions 

(tpy) 

Modeled 

Emissions

(tpy) (%)

Greene Co. 9,255 9,047 98% 2,055 1,781 87% N/A N/A N/A

St. Louis 70,016 68,656 98% 15,137 15,137 

100

% 3,058 1,559 51%

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 6 12 18 24

Hour of the Day

Normalized Emissions (unitless) 

EMS-HAP: Industrial

SMOKE

EMS-HAP: Commercial/Institutional

Best-fit to Monitors

Figure 8-4. Derived best-fit non-point area source diurnal emission profile for the St. Louis 

domain, compared to other possible profiles. 
July 2009 206

0

0.02

0.04

0.06

0.08

0.1

0.12

0 6 12 18 24

Hour of the Day

Normalized Emissions (unitless) 

EMS-HAP: Industrial

SMOKE

EMS-HAP: Commercial/Institutional

Best-fit to Monitors

Figure 8-5. Derived best-fit non-point area source diurnal emission profile for the Greene County 

domain, compared to other possible profiles.

8.4.4 Receptor Locations 

Two sets of receptors were chosen to represent the locations of interest within each of the 

modeling domains. The first set was selected to represent the locations of the residential 

population of the modeling domain. These receptors were US Census block centroids in the 

Greene County and St. Louis modeling domains, (Figures 8-2 and 8-3, respectively), that lie 

within 20 km (12 miles) of any of the major facility stacks.62 Each of these receptors was 

modeled at ground level. A total of 17,703 receptors were selected in the St. Louis modeling 

domain and a total of 5,359 receptors were selected in the Greene County modeling domain. 

The second set of receptors included the locations of the available ambient SO2 monitors. 

These receptors were used in evaluating the dispersion model performance. In Greene County, 

there were five ambient monitors with valid ambient monitoring concentrations (Figure 8-2). 

Within the three St. Louis counties, there were seven monitors (Figure 8-3). 

 

62 The block centroids used for this analysis are actually population-weighted locations reported in the ESRI 

database. They were derived from geocoded addresses within the block taken from the Acxiom Corporation 

InfoBase household database (Skuta and Wombold, 2008; ESRI, 2008). These centroids differ from the “internal 

points” reported by the US Census, which are often referred to as centroids because they are designed to represent 

the approximate geographic center of the block. 
July 2009 207

8.4.5 Modeled Air Quality Evaluation 

The hourly SO2 concentrations estimated from each of the sources within a modeling 

domain were combined at each receptor. These concentration predictions were then compared 

with the measured concentrations at ambient SO2 monitors. Rather than compare concentrations 

estimated at a single modeled receptor point to the ambient monitor concentrations, a distribution 

of concentrations was developed for the predicted concentrations for all receptors within a 4 km 

distance of the monitors. Further, instead of a comparison of central tendency values (mean or 

median), the full modeled and measurement concentration distributions were used for 

comparison. 

As an initial comparison of modeled versus measured air quality, all modeled receptors 

within 4 km of each ambient monitor location were used to generate a prediction envelope.63 

This envelope was constructed based on selected percentiles from the modeled concentration 

distribution at each receptor for comparison to the ambient monitor concentration distribution. 

The 2.5th and 97.5th percentiles from all monitor distribution percentiles64 were selected to create 

the lower and upper bounds of the envelope. The full 1-hour distributions for the ambient 

measurement data, the modeled monitor receptor,65 and the prediction envelope were compared 

using their respective cumulative density functions (CDFs). When illustrating these 

distributions, the percentiles were plotted on a log-scale as the difference between 100 and the 

CDF value to allow for visual expansion of the extreme upper percentiles of the distribution. For 

illustrative purposes, the maximum concentration was defined as 100-99.99 (or 0.01) because the 

logarithm of zero is undefined. 

A second comparison between the modeled and monitored data was performed to 

evaluate the diurnal variation in SO2 concentrations. AERMOD receptor concentrations during 

each hour-of-the-day were averaged (i.e., 365 values for hour 1, 365 values for hour 2, and so 

on) to generate an annual average SO2 concentration for each hour at each modeled receptor. 

Prediction envelopes were constructed similar to that described above from modeled receptors 

 

63 500 m to 4 km is the area of representation of a neighborhood-scale monitor, according to EPA guidance. 

64 As an example, suppose there are 1,000 receptors surrounding a monitor, each receptor containing 8,760 hourly 

values used to create a concentration distribution. Then say the 73rd percentile concentration prediction is to be 

estimated for each receptor. The lower bound of the 73rd percentile of the modeled receptors would represented by 

the 2.5th percentile of all the calculated 73rd percentile concentration predictions, i.e., the 25th highest 73rd percentile 

concentration prediction across the 1,000 73rd percentile values generated from all of the receptors. Note that at any 

given percentile along either of the envelope bounds as well as at the central tendency distribution (the receptor 50th

percentile), the concentration from a different receptor may be used. 

65 The modeled monitor is the modeled air quality at the ambient monitoring location. 
July 2009 208

located within 4 km of each ambient monitor. The measured ambient monitoring data was also 

averaged to generate the diurnal profile. Then, annual averaged concentrations for the ambient 

measurement data, the modeled monitor receptor, and the prediction envelope were plotted by 

hour-of-the-day for comparison. 

Staff also evaluated potential impact of the differences between the predicted and 

measured 1-hour SO2 concentrations by comparing the modeled and measured number of 5-

minute air quality benchmark exceedances that would result from using each 1-hour 

concentration distribution. The full year of 1-hour ambient monitored and AERMOD modeled 

SO2 concentrations (at the monitor receptor location) were used as input to the 5-minute 

statistical model and processed as described in section 7.2.5. Measured 5-minute maximum SO2

concentrations were only available for two of the monitors in Greene County (290770026 and 

290770040). These monitoring locations were used to generate the number of days per year with 

at least one benchmark exceedance. Further, the concentration distributions given by the 

AERMOD prediction envelopes (i.e., the 2.5th and 97.5th) were used to approximate lower and 

upper prediction bounds for the number of days per year with 5-minute benchmark exceedances. 

To do this, first the total numbers of benchmark exceedances in a year66 were estimated for each 

monitor using the 1-hour concentration percentiles representing each AERMOD distribution 

(i.e., the AERMOD monitor receptor, the AERMOD 2.5th, and the AERMOD 97.5th). Then, 

scaling factors were calculated by dividing each the AERMOD 2.5th and AERMOD 97.5th

benchmark exceedance results by that of the exceedances estimated using the AERMOD monitor 

receptor. These scaling factors were then applied to the full AERMOD monitor receptor 

predictions that estimated the number of days per year with exceedances to estimate the lower 

and upper bounds. 

8.4.5.1 Greene County Modeled Air Quality Evaluation 

For Greene County, there were five monitors used for comparison with the AERMOD 1-

hour concentration estimates. For each monitor, staff plotted the model-predicted versus 

ambient measured concentrations using two methods; the first used a CDF, the second used the 

 

66 Because the AERMOD p2.5 and p97.5 prediction envelopes are not representing a particular time but are a 

temporal and spatial mixture of low and high concentrations surrounding each monitor, specific counts of days per 

year could not be calculated. Staff assumed a proportional relationship existed between the total number of 

exceedances in a year and the number of days per year with exceedances. Thus, scaling factors can be calculated 

using the AERMOD monitor receptor data, which had both the percentile form and 8,760 concentrations at specific 

hours of the day and days of the year. 
July 2009 209

diurnal profile. In each plot, four concentration distributions were used; the distribution of the 

modeled 1-hour SO2 concentrations estimated for the monitor receptor, the upper and lower 

bounds of the receptor envelope (i.e., generated from all receptors within 4 km of monitor 

receptor), and the hourly concentration distribution measured at each ambient monitor. The 

results for Greene County are provided in Figures 8-6 to 8-8. The data used to generate the 

figures are provided in Appendix B. 

When considering the total hourly distribution or CDFs, monitor concentration 

distributions are generally bounded by the modeled distributions. At some of the upper 

percentiles of the distributions, the deviations were of varying direction (over- or underprediction) and magnitude (a few ppb to tens of ppb). For example, monitor ID 290770026 

(Figure 8-6) exhibits higher measured concentrations at the upper percentiles of the distribution 

that extend beyond the AERMOD prediction envelope, however the deviation occurred beyond 

the 99.5th percentile (maximum observed =114 ppb, AERMOD 97.5th = 101 ppb). At monitor ID 

290770032 (Figure 8-6), the measured concentrations fall below the prediction envelope, 

beginning just beyond the 95th percentile 1-hour concentration. 

Even though ambient monitors 290770040 and 290770041 (Figure 8-2) are located 

approximately 150 m from one another, they exhibited very different measured concentrations at 

the extreme upper percentiles (Figure 8-7). The greatest difference is in comparing the 

maximum observed concentrations; 203 ppb versus 33 ppb. The AERMOD predictions followed 

a similar pattern at the upper percentiles, i.e., the modeled concentrations for the monitor 

location were greater (50 to 100%) at monitor ID 290770040 when compared with 290770041, 

but not nearly as great a difference noted at the maximum measured concentrations. The 

AERMOD prediction envelope was similar for both of these monitors, encompassing the 

ambient measured concentrations from the 80th through the 99.5th percentiles for both, while 

completely enveloping all 1-hour concentrations at monitor ID 290770041. 

The pattern in the AERMOD modeled concentrations at the monitor location and the 

ambient measurement concentration distribution for monitor ID 290770037 is nearly identical. 

The only difference observed is that the measured concentrations are 1-3 ppb greater than the 

modeled concentrations within the 99th percentile of the distribution. Much of the measured 

distribution falls within the AERMOD prediction envelope, with deviation occurring just beyond 

the 99.5th percentile. 
July 2009 210

The diurnal pattern observed at each of the ambient monitors is represented well by the 

modeled concentrations; in general concentrations are elevated during the midday hours and 

lowest during the late-night and early-morning hours. In addition, most of the measured 

concentrations fall within the AERMOD prediction envelopes at all hours of the day, with a few 

exceptions. For example, all observed concentrations for monitor ID 290770032 are below that 

of the upper AERMOD prediction envelope, though at monitor ID 290770026, measured 

concentrations are above those modeled during the early-morning and late-night hours (Figure 8-

6). Much of the deviation during these hours-of-the-day is likely a result of the concentrations at 

or below the 80th percentile, where measured concentrations were always greater than any of the 

predicted concentrations at corresponding percentiles of the distribution. While the prediction 

envelopes encompassed the diurnal pattern observed at monitor IDs 290770040 and 290770041 

(Figure 8-7), the results for the modeled concentrations at the monitor locations were not equally 

representative. The diurnal pattern and magnitude of concentrations was well reproduced at 

monitor ID 290770041, while modeled concentrations at the monitor location during the midday 

and evening hours were greater than the measured concentrations at monitor ID 290770040. 

Staff evaluated the potential impact the predicted 1-hour concentrations would have on 5-

minute air quality benchmark exceedances (Table 8-6). In general, the results for the estimated 

numbers of days per year with 5-minute concentrations above benchmark levels followed similar 

patterns to those observed above when considering comparisons of the 1-hour SO2 concentration 

distributions. The numbers of benchmark exceedances at monitor ID 290770026 were underpredicted by AERMOD just as was the 1-hour SO2 concentrations at that monitoring location. 

However, the number of days with 5-minute concentrations above the benchmark levels for both 

the measured and modeled ambient concentrations fell within the range of the AERMOD 

prediction envelopes. There was good agreement in the number of days per year with air quality 

benchmark exceedances at each of the four other monitors, whether there were none, a few, or 

several days with expected benchmark exceedances. These results indicate that the magnitude of 

observed differences in predicted versus measured 1-hour SO2 concentration does not result in 

unexpected differences in the number of days per year having 5-minute SO2 concentrations 

above the benchmark levels. 
July 2009 211

Table 8-6. Measured and modeled number of days in year 2002 with at least one 5-minute SO2

benchmark exceedance at ambient monitors in Greene County. 

Number of Days per Year with a 5-minute SO2

Concentration Above Air Quality Benchmark Level 

Ambient Monitor1 AERMOD2

Monitor ID 

5-minute SO2

Benchmark 

(ppb) Modeled Measured p2.5 Monitor p97.5 

100 57 27 2 19 103 

200 18 0 0 2 9 

300 6 0 0 0 2 

290770026 

400 2 0 0 0 0 

100 0 - 0 0 0 

200 0 - 0 0 0 

300 0 - 0 0 0 

290770032 

400 0 - 0 0 0 

100 33 44 1 40 81 

200 14 12 0 13 22 

300 7 1 0 5 6 

290770037 

400 4 0 0 2 3 

100 7 - 0 25 42 

200 3 - 0 3 5 

300 1 - 0 0 0 

290770040 

400 0 - 0 0 0 

100 0 - 0 2 17 

200 0 - 0 0 0 

300 0 - 0 0 0 

290770041 

400 0 - 0 0 0 

Notes: 

1

 The modeled numbers of 5-minute benchmark exceedances were generated from 1-hour 

SO2 ambient monitor measurements input to the 5-minute statistical model. The measured 

numbers of 5-minute benchmark exceedances were calculated from ambient monitors 

reporting 5-minute SO2 concentrations. Both of these values were normalized to a full year 

(n=365 days) for comparison with the AERMOD predictions. 

2

 AERMOD monitor 5-minute benchmark exceedances were generated from 1-hour SO2

ambient predictions (at monitor receptor location) input to the 5-minute statistical model. 

AERMOD p2.5 and p97.5 benchmark exceedances were generated from the 

corresponding hourly prediction envelope distribution and input to the 5-minute statistical 

model. 

 

 
July 2009 212

Figure 8-6. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the 

receptor and receptors within 4 km of monitors 290770026 and 29077032 in Greene County, Mo. Maximum 1-hour percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.

Monitor ID 290770026

012345678

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb)AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor

Monitor ID 290770032

012345

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb)AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor

Monitor ID 290770026

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100 110 120

1-hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

Monitor ID 290770032

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70

1-hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor
July 2009 213

Figure 8-7. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the m

receptor and receptors within 4 km of monitors 290770040 and 29077041 in Greene County, Mo. Maximconcentration percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined. 

Monitor ID 290770040

012345678

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb)AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor

Monitor ID 290770041

012345678

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb)AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor

Monitor ID 290770041

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100 110

1-hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

Monitor ID 290770040

0.01

0.1

1

10

100

0 20 40 60 80 100 120 140 160 180 200

1-hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor
July 2009 214

Figure 8-8. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the m

receptor and receptors within 4 km of monitor 290770037 in Greene County, Mo. Maximum 1-hour concdefined as 0.01 (or 100-99.99) because log(0) is undefined. 

Monitor ID 290770037

012345678

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb)AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor

Monitor ID 290770037

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

1-hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor
July 2009 215

8.4.5.2 St. Louis Modeled Air Quality Evaluation 

For St. Louis, there were seven monitors used for comparison with the AERMOD 

concentration estimates. The distribution of the modeled 1-hour SO2 concentrations estimated 

for the monitor receptor, the receptor envelope (i.e., all receptors within 4 km of monitor 

receptor), and the hourly concentration distribution measured at each ambient monitor are 

provided in Figures 8-9 to 8-12. Data used to generate the figures is provided in Appendix B. 

There are distinct differences in the comparison of modeled versus measured 

concentration distributions at ambient monitoring locations in St. Louis when compared with 

Greene County. Most noticeable is the width of the prediction envelopes; St. Louis prediction 

envelopes were not as wide as those generated for Greene County. This indicates that, in 

comparison with the Greene County modeling domain, there is less spatial variability in the 

concentrations modeled at receptors surrounding the ambient monitoring locations in St. Louis. 

This is likely a result of the emission source contributions; four of five ambient monitors in 

Greene County were primarily influenced by point sources, while most of the concentration 

contribution for St. Louis monitors was from area source emissions. 

The modeled concentrations at the monitor locations and ambient measured concentration 

distributions showed better overall agreement at the St. Louis monitors, though many of the 

measured concentrations are outside of the prediction envelopes. For example, at monitor ID 

291890006 all measured concentrations up to the 99th percentile fell below the prediction 

envelope (Figure 8-9) (the maximum was within). Note however that the difference in the 

measured concentrations was only about 1 ppb when compared with concentrations at any of the 

envelope percentiles and at most 2 ppb when compared with the modeled concentrations at the 

monitor receptor. In addition, because most of these under-predictions occur at concentrations 

well below levels of interest, it is not of great consequence. At the upper percentiles, many of 

the ambient concentrations fell within the prediction envelopes; 6 of 7 monitors at the maximum 

percentile were within, 3 of 7 monitors at the 99th percentile were within, and 4 of 7 monitors at 

the 95th percentile were within the prediction envelopes. Where measured upper percentile 

concentrations were outside of the prediction envelopes, it was consistently beneath the 2.5th

prediction, possibly indicating AERMOD over-prediction at these monitors at certain percentiles 

of the distribution. When comparing the AERMOD monitor concentrations with the measured 

ambient concentrations between the 80th and 99th percentile of the distribution, most of the 
July 2009 216

predicted values were greater than the measured concentrations. The magnitude of this overprediction ranged from about 1 to 2 ppb, although one monitor had a 7 ppb difference at the 99th

percentile. Predictions at the maximum concentrations were more balanced; 4 of the 7 monitors 

had over-predictions, while all predictions (under or over) were approximately within 10 to 35 

ppb of the measured concentrations. 

The diurnal pattern was reproduced at the St. Louis monitoring locations, with some of 

the prediction envelopes encompassing much of the measured ambient concentrations (e.g., 

Figure 8-9, monitor ID 291890004; Figure 8-11 monitor ID 291897003). Again where deviation 

did occur at a few of the monitors, the contribution of the lower concentrations (i.e., mostly those 

beneath the 90th percentile) likely played a role in the magnitude of the disagreement. This can 

be seen at monitor ID 291890006 (Figure 8-10) where most (99%) of the predicted 

concentrations are consistently above the measure concentrations by 1 to 2 ppb. It is not 

surprising to see that the difference in comparing the measured versus modeled diurnal profile at 

every hour-of-the-day is also between 1 to 2 ppb. 
July 2009 217

Figure 8-9. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the m

receptor and receptors within 4 km of monitors 291890004 and 291890006 in St Louis, Mo. Maximum 1-percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined. 

Monitor ID 291890004

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100 110

1- hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

Monitor ID 291890006

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100

1- hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

Monitor ID 291890004

0123456

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb) AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor

Monitor ID 291890006

0123456

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb) AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor
July 2009 218

Figure 8-10. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the 

receptor and receptors within 4 km of monitors 291893001 and 291895001 in St Louis, Mo. Maximum 1-percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined. 

Monitor ID 291895001

0.01

0.1

1

10

100

0 20 40 60 80 100 120 140 160 180 200

1- hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

AERMOD P97.5

Maximum = 545 ppb

Monitor ID 291893001

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100

1- hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

Monitor ID 291893001

0123456789

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb) AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor

Monitor ID 291895001

01234567

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb) AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor
July 2009 219

Figure 8-11. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the 

receptor and receptors within 4 km of monitors 291897003 and 295100007 in St Louis, Mo. Maximum 1-percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined. 

Monitor ID 295100007

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140

1- hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

Monitor ID 291897003

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140

1- hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

Monitor ID 291897003

01234567

0 6 12 1

Hour of the Day

Annual Mean SO2 Concentration (ppb) AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor

Monitor ID 295100007

0123456789

10

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb) AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor
July 2009 220

Figure 8-12. Comparison of measured ambient monitor SO2 concentration distribution and diurnal profile with the 

receptor and receptors within 4 km of monitor 295100086 in St Louis, Mo. Maximum 1-hour concentratidefined as 0.01 (or 100-99.99) because log(0) is undefined. 

Monitor ID 295100086

0.01

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100 110 120 130

1- hour SO2 Concentration (ppb)

100-Cumulative Percentile

Ambient MonitorAERMOD P2.5AERMOD P97.5AERMOD Monitor

Monitor ID 295100086

0123456789

10

0 6 12 18 

Hour of the Day

Annual Mean SO2 Concentration (ppb) AERMOD P2.5 AERMOD P97.5 Ambient Monitor AERMOD Monitor
July 2009 221

8.4.4.3. Using unadjusted AERMOD predicted SO2 concentrations

The SO2 concentrations estimated using AERMOD do not have a particular directional 

influence in over- or under-estimating concentrations, save for small over-estimation primarily 

observed at the lowest concentrations and some difficulty in reproducing some of the maximum 

measured concentrations. Most ambient monitoring concentrations fell within the modeled 

prediction envelopes constructed of modeled receptors surrounding the monitor. In generating 

the modeled air quality, staff made judgments in appropriately modifying model inputs including 

an adjustment of the area source temporal emission profile to improve the comparison of the 

model predictions with the measurement data. Staff went through several iterations of evaluating 

the model performance in each modeling domain following model input adjustments to obtain 

the current modeled air quality results. Given the time and resources to perform this assessment, 

the good agreement in the model-to-monitor comparisons, the degree of confidence in the 

dispersion modeling system, the spatial representation of the monitors compared with receptors 

modeled, and the number of comparisons available, staff did not perform any further adjustments 

to the modeled concentrations to improve the relationship between modeled versus measured 

concentration at each monitor. Additional details on the staff's reasoning are provided in section 

8.11. 

8.5 SIMULATED POPULATION 

APEX takes population characteristics into account to develop accurate representations of 

study area demographics. Specifically, age- and gender-specific population counts and 

employment probability estimates, asthma prevalence rates, and home-to-work commuting 

locations and probabilities were used to develop representative profiles of hypothetical 

individuals used in the exposure modeling simulation. In addition, body surface area (BSA) and 

activity-specific ventilation rates are two important attributes used by APEX to characterize 

when simulated individuals were at moderate or greater activity levels. Each of these is 

discussed in the following sections. 

8.5.1 Population Counts and Employment Probabilities 

Block-level population counts were obtained from the 2000 Census of Population and 

Housing Summary File 1 (SF-1). Estimates of employment were also developed form census 

information (US Census Bureau, 2007) and separated into gender and age groups. Children 
July 2009 222

under 16 years of age were assumed to be not employed. Staff also assumed that employment 

probabilities for a census tract apply uniformly to the constituent census blocks. Further details 

are provided in Appendix B.2.2.2. 

8.5.2 Asthma Prevalence 

The population subgroups included in this exposure assessment are asthmatics and 

asthmatic children. Evaluating exposures of these subgroups with APEX requires the estimation 

of children’s asthma prevalence rates. The proportion of the population of children characterized 

as being asthmatic was estimated by statistics on asthma prevalence rates recently used in the 

NAAQS review for O3 (US EPA, 2007d). See Appendix B, Attachment 2 for details on the 

derivation. Specifically, an analysis of data provided in the National Health Interview Survey 

(NHIS) for 2003 (CDC, 2007) generated age and gender specific asthma prevalence rates for 

children ages 0-17. Staff used these data rather than the aggregate data available at the county 

level, to retain the variability in asthma prevalence observed with children of different ages. 

Adult asthma prevalence rates were estimated by gender and for each particular modeling 

domain based on Missouri regional data (MO DOH, 2002). Table 8-7 provides a summary of the 

asthma prevalence used in the exposure analysis, stratified by age and gender. 

The total population simulated within the two modeling domains was approximately 1.4 

million persons, of which there was a total simulated population of about 130,000 asthmatics. 

The model simulated over 360,000 children ages 5 through 17, of which there were nearly 

50,000 asthmatics. The individual populations for each modeling domain and subpopulation of 

interest are provided in Table 8-8. For comparison, staff weighted the asthma prevalence by 

population in the three counties reported by the MO Department of Health (2003) for all ages 

(i.e., St. Charles-8.8%, St. Louis-5.8%, and St. Louis City-16.4%) to generate an asthma 

prevalence of 8.8%. This asthma prevalence is similar to the 9.2% modeled here using APEX. 

In Greene County, the reported asthma prevalence was 10.2% (MO Department of Health, 

2003), while 9.8% of the simulated population was asthmatic. 
July 2009 223

Table 8-7. Asthma prevalence rates by age and gender used in Greene County and St. Louis 

modeling domains. 

Modeling Asthma Prevalence (%) 

Domain

(Region)

 

Age1 Females Males 

0 7.0 3.1 

1 7.1 6.3 

2 7.3 10.8 

3 7.5 15.8 

4 8.1 21.6 

5 9.5 17.8 

6 9.2 12.8 

7 9.0 12.1 

8 8.6 12.8 

9 11.0 14.7 

10 16.2 17.7 

11 19.6 19.0 

12 21.2 19.5 

13 17.0 16.9 

14 14.0 16.8 

15 13.3 18.0 

16 14.0 20.1 

Greene Co. 

and St. Louis 

(Midwest) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

17 16.5 23.7 

Greene Co. >17 10.7 6.1 

St. Louis >17 9.3 5.3 

Notes: 

1

 Ages 0-17 from the National Health Interview Survey 

(NHIS) for 2003 (CDC, 2007); ages >17 from (MO DOH, 

2002). 

Table 8-8. Population modeled in Greene County and St. Louis modeling domains. 

Modeling Population Asthmatic Population 

Domain All Ages Children (5 – 18) All Ages Children (5 – 18) 

Green Co. 224,145 54,373 21,948 7,285 

St. Louis 1,151,094 308,939 105,456 41,714 

8.5.3 Commuting Database 

Commuting data were originally derived from the 2000 Census, collected as part of the 

Census Transportation Planning Package (CTPP) (US DOT, 2007). The data used here contain 

counts of individuals commuting from home-to-work locations at a number of geographic scales. 

These data were processed to calculate fractions for tract-to-tract flow on a national level (all 50 

U.S. states and Washington, D.C.). A software pre-processor was then developed to generate 
July 2009 224

block-level commuting files for APEX using the tract-level commuting data and finely-resolved 

land use data, assuming the frequency of commuting to a workplace block within a tract is 

proportional to the amount of commercial and industrial land in the block. Further details are 

provided in Appendix B.2.2.2. 

Note that while travel on roads was accounted for by APEX for other individuals (e.g., 

unemployed, children, persons who work at home) it was assumed that the vehicle travel (e.g., 

car, bus, train) occurred within the block the individual resides. 

8.5.4 Body Surface Area 

Age- and gender-specific BSA is estimated for each simulated individual. Briefly, the 

BSA calculation is based on logarithmic relationships developed by Burmaster (1998) that use 

body mass (BM) as an independent variable as follows: 

2781.2 6821.0 BMeBSA   equation (8-1) 

where, 

BSA = body surface area (m2

) 

BM = body mass (kg) 

Each simulated individual’s body mass was randomly sampled from age- and genderspecific body mass distributions generated from National Health and Nutrition Examination 

Survey (NHANES) data for the years 1999-2004.67 Details in their development and the 

parameter values are provided in Appendix B, Attachment 3. 

8.5.5 Activity-Specific Ventilation Rates 

Ventilation is a general term describing the movement of air into and out of the lungs. 

The rate of ventilation is determined by the type of activity an individual performs which in turn 

is related to the amount of oxygen required to perform the activity. Minute or total ventilation 

rate is used to describe the volume of air moved in or out of the lungs per minute. 

 

67 Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES studies were obtained 

from http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm. 
July 2009 225

Quantitatively, the volume of air breathed in per minute ( 

VI ) is slightly greater than the volume 

expired per minute (V E



). Clinically, however, this difference is not important, and by 

convention, the ventilation rate is always measured on an expired sample or V E



. 

The rate of oxygen consumption (V O2



) is related to the rate of energy usage in 

performing activities as follows: 

O  ECFEEV2 equation (8-2) 

where, 

V O2



 = Oxygen consumption rate (liters O2/minute) 

EE = Energy expenditure (kcal/minute) 

ECF = Energy conversion factor (liters O2/kcal). 

The ECF shows little variation and typically, a value between 0.20 and 0.21 is used to 

represent the conversion from energy units to oxygen consumption units. In this REA, APEX 

randomly sampled from a uniform distribution defined by these lower and upper bounds to 

estimate an ECF once for each simulated individual. The activity-specific energy expenditure is 

highly variable and can be estimated using metabolic equivalents (METs). The METs are ratios 

of the rate of energy consumption for non-rest activities to the resting rate of energy 

consumption. Thus energy expenditure can be represented by the following: 

 RMRMETEE equation (8-3) 

 where, 

EE = Energy expenditure (kcal/minute) 

MET = Metabolic equivalent of work (unitless) 

RMR = Resting metabolic rate (kcal/minute) 

The CHAD database (EPA, 2002) contains distributions of METs for all activities that 

might be performed by simulated individuals. APEX randomly samples from the various METs 

distributions to obtain values for every activity performed by each individual. Age- and gender-
July 2009 226

specific RMR are estimated once for each simulated individual using a linear regression model 

(see Johnson et al., 2000)68 as follows: 

 

 10   ])([ FBMbbRMR equation (8-4) 

where, 

RMR = Resting metabolic rate (kcal/min) 

bo = Regression intercept (MJ/day) 

b1 = Regression slope (MJ/day/kg) 

BM = body mass (kg) 

ε = randomly sampled error term, N{0, se)69 (MJ/day) 

F = Factor for converting MJ/day to kcal/min (0.166) 

Finally, Graham and McCurdy (2005) describe an approach to estimate V E



 using V O2



. 

In that report, a series of age- and gender-specific multiple linear regression equations were 

derived from data generated in 32 clinical exercise studies. The algorithm accounts for 

variability in ventilation rate due to variation in oxygen consumption, the variability within age 

groups, and both inter- and intra-personal and variability. The basic algorithm follows: 

O wb E  bBMVbbBMV   eegenderbage  

2 3 2 10 )/ln( )1(ln)/ln( equation (8-5) 

where, 

ln = natural logarithm of variable 

E / BMV

 = activity-specific ventilation rate, body mass normalized (liter air/kg) 

bi = see below 

O2 / BMV

 = activity-specific oxygen consumption rate, body mass normalized 

(liter/O2/kg) 

age = the age of the individual (years) 

gender = gender value (-1 for males and +1 for females) 

eb = randomly sampled error term for between persons N{0, se), (liter air/kg) 

ew = randomly sampled error term for within persons N{0, se), (liter air/kg) 

 

68 The regression equations were adapted by Johnson et al. (2000) using data reported by Schofield (1985). The 

regression coefficients and error terms used by APEX are provided in Appendix B Attachment 3. 

69 The value used for each individual is sampled from a normal distribution (N) having a mean of zero (0) and 

variability described by the standard error (se). 
July 2009 227

As indicated above, the random error (ε) is allocated to two variance components and 

used to estimate the between-person (inter-individual variability) residuals distribution (eb) and 

within-person (intra-individual variability) residuals distribution (ew). The regression parameters 

b0, b1, b2, and b3 are assumed to be constant over time for all simulated persons, eb is sampled 

once per person, while whereas ew is sampled from event to event. Point estimates of the 

regression coefficients and standard errors of the residuals distributions are given in Table 8-9. 

Table 8-9. Ventilation coefficient parameter estimates (bi) and residuals distributions (ei) from 

Graham and McCurdy (2005). 

Regression Coefficients1

 Random Error1,2 Age 

group b0 b1 b2 b3 eb ew

<20 4.3675 1.0751 -0.2714 0.0479 0.0955 0.1117 

20-<34 3.7603 1.2491 0.1416 0.0533 0.1217 0.1296 

34-<61 3.2440 1.1464 0.1856 0.0380 0.1260 0.1152 

61+ 2.5826 1.0840 0.2766 -0.0208 0.1064 0.0676 

Notes: 

1

 These are the values of the coefficients and residuals distributions described by 

equation 8-5. 

2

 The unique value used for each individual is sampled from a normal distribution (N) 

having a mean of zero (0) and variability described by the standard error (se). 

8.6 CONSTRUCTION OF LONGITUDINAL ACTIVITY SEQUENCES 

Exposure models use human activity pattern data to predict and estimate exposure to 

pollutants. Different human activities, such as spending time outdoors, indoors, or driving, will 

result in varying pollutant exposure concentrations. To accurately model individuals and their 

exposure to pollutants, it is critical to understand their daily activities. EPA’s CHAD provides 

data for where people spend time and the activities they perform. Typical time-activity pattern 

data available for inhalation exposure modeling consist of a sequence of location/activity 

combinations spanning 24-hours, with 1 to 3 diary-days for any single study individual. 

The exposure assessment performed here requires information on activity patterns over a 

full year. Long-term multi-day activity patterns were estimated from single days by combining 

the daily records using an algorithm that represents the day-to-day correlation of activities for 

individuals. The algorithm first uses cluster analysis to divide the daily activity pattern records 

into groups that are similar, and then select a single daily record from each group. This limited 
July 2009 228

number of daily patterns is then used to construct a long-term sequence for a simulated 

individual, based on empirically-derived transition probabilities. This approach is intermediate 

between an assumption of no day-to-day correlation (i.e., re-selection of diaries for each time 

period) and perfect correlation (i.e., selection of a single daily record to represent all days). 

Details regarding the algorithm and supporting evaluations are provided in Appendix B, 

Attachments 4 and 5. 

8.7 CALCULATING MICROENVIRONMENTAL CONCENTRATIONS 

Probabilistic algorithms are used to estimate the pollutant concentration associated with 

each exposure event. The estimated pollutant concentrations account for temporal and spatial 

variability in ambient (outdoor) pollutant concentration and factors affecting indoor 

microenvironments, such as a penetration, air exchange rate, and pollutant decay or deposition 

rate. APEX calculates air concentrations in the various microenvironments visited by the 

simulated person by using the ambient air data estimated for the relevant blocks/receptors, the 

user-specified algorithm, and input parameters specific to each microenvironment. The method 

used by APEX to estimate the microenvironmental concentration depends on the 

microenvironment, the data available for input to the algorithm, and the estimation method 

selected by the user. The current version of APEX calculates hourly concentrations in all the 

microenvironments at each hour of the simulation for each of the simulated individuals using one 

of two methods: a mass balance model or a transfer factors method. Details regarding the 

algorithms used for estimating specific microenvironments and associated input data derivations 

are provided in Appendix B. 

Briefly, the mass balance method simulates an enclosed microenvironment as a wellmixed volume in which the air concentration is spatially uniform at any specific time. The 

concentration of an air pollutant in such a microenvironment is estimated using the following 

processes: 

 Inflow of air into the microenvironment 

 Outflow of air from the microenvironment 

 Removal of a pollutant from the microenvironment due to deposition, filtration, and 

chemical degradation 

 Emissions from sources of a pollutant inside the microenvironment. 
July 2009 229

A transfer factors approach is simpler than the mass balance model; however, most 

parameters are derived from distributions rather than single values to account for observed 

variability. The transfer factors approach does not calculate concentration in a 

microenvironment from the concentration in the previous hour as is done by the mass balance 

method and contains only two parameters. A proximity factor is used to account for proximity 

of the microenvironment to sources or sinks of pollution, or other systematic differences between 

concentrations just outside the microenvironment and the ambient concentrations (at the 

measurements site or modeled receptor). The second parameter, a penetration factor, quantifies 

the amount of outdoor pollutant that penetrates into the microenvironment. 

8.7.1 Approach for Estimating 5-Minute Maximum SO2 Concentrations 

Five-minute maximum SO2 concentrations in each exposure modeling domain were 

estimated using the empirically-derived PMRs (developed from recent 5-minute SO2 ambient 

monitoring data, see section 7.2) and the AERMOD predicted 1-hour SO2 concentrations. Thus, 

for every 1-hour SO2 concentration estimated at every receptor, an associated 5-minute 

maximum SO2 concentration was generated (i.e., twenty-four 5-minute maximum SO2 

concentrations per day). These statistically modeled 5-minute maximum SO2 concentrations 

were then used to estimate the eleven other 5-minute concentrations that occur within every hour 

(see below). This spatially complete (at the block level) and consecutive time-series of 5-minute 

SO2 concentrations then served as the ambient concentrations input to algorithms within APEX 

that estimate the microenvironmental concentrations. 

The current version of APEX can use ambient concentrations of almost any time step, 

including an averaging time of 5-minutes. However, if all of the individual block-level receptor 

files were generated as an input to APEX in this assessment, the size and number of files would 

become an issue. In this exposure assessment, each of the thousands of receptor files generated 

by AERMOD would increase by a factor of twelve, creating disk space, pre-processing, and 

exposure modeling difficulties. In addition, the APEX default exposure output for modeled 

individuals is the single greatest exposure within a day, thus requiring model changes to obtain 

output of a different form. Staff believed that to reasonably estimate multiple peak 

concentrations that might occur within an hour by addressing these issues would further 

encumber the limited time and resources already available to staff to conduct the assessment. 
July 2009 230

Staff elected to use a simplified approach to generate all other 5-minute SO2

concentrations that occur within the hour. The objective of the approach used was not to 

estimate each of the other eleven 5-minute concentrations with a high degree of certainty; each 

of these concentrations, by definition, would be lower than the maximum for that hour. While 

the occurrence of multiple peak concentrations above benchmark levels within an hour is 

possible, staff assumed that use of the twenty-four 5-minute maximum SO2 concentrations could 

provide an accurate estimate of the maximum exposure an individual might experience in a 

day.70 Further discussion regarding multiple peak exposures within an hour is given in section 

8.11. 

The technical approach to estimating SO2 concentrations real-time within the APEX 

model rather than modeled externally is as follows. An algorithm was incorporated into the 

flexible time-step APEX model to estimate the 5-minute maximum SO2 concentrations using the 

1-hour SO2 concentration, an appropriate PMR (section 7.2), and equation 7-1. The additional 

eleven 5-minute concentrations within an hour at each receptor were approximated using the 

following: 

1

__



  nPCn X equation (8-6) 

where, 

X = 5-minute SO2 concentration in each of non-peak concentration periods in 

the hour at a receptor (ppb) __

C = 1-hour SO2 concentration estimated at a receptor (ppb) 

P = estimated 5-minute maximum SO2 concentration at a receptor (ppb) using 

equation 7-1. 

n = number of time steps within the hour (or 12) 

In addition to the level of the 5-minute maximum SO2 concentration, the actual time of 

when the contact occurs with a person is also of importance. There is no reason to expect a 

temporal relationship of the peak concentrations within the hour, thus clock times for peak 

values were estimated randomly (i.e., any one of the 12 possible time periods within the hour). 

The PMR assignment also assumes a standard frequency during any hour of the day. 

 

70 Note that the model still uses all of the statistically-modeled twenty-four 5-minute maximum SO2 concentrations 

(one for every hour in the day) in estimating microenvironmental concentrations and personal exposures. 
July 2009 231

8.7.2 Microenvironments Modeled 

In APEX, microenvironments represent the exposure locations for simulated individuals. 

For exposures to be estimated accurately, it is important to have realistic microenvironments that 

match closely to the locations where actual people spend time on a daily basis. As discussed 

above, the two methods available in APEX for calculating pollutant levels within 

microenvironments are mass balance or a transfer factors approach. Table 8-10 lists the 

microenvironments used in this study, the calculation method used, and the type of parameters 

used to calculate the microenvironment concentrations. 

Table 8-10. List of microenvironments modeled and calculation methods used. 

Microenvironment 

Calculation 

Method 

Parameter Types 

used 1

Indoors – Residence Mass balance AER and DE 

Indoors – Bars and restaurants Mass balance AER and DE 

Indoors – Schools Mass balance AER and DE 

Indoors – Day-care centers Mass balance AER and DE 

Indoors – Office Mass balance AER and DE 

Indoors – Shopping Mass balance AER and DE 

Indoors – Other Mass balance AER and DE 

Outdoors – Near road Factors PR 

Outdoors – Public garage - parking lot Factors PR 

Outdoors – Other Factors None 

In-vehicle – Cars and Trucks Factors PE and PR 

In-vehicle - Mass Transit (bus, subway, 

train) Factors PE and PR 

1 AER=air exchange rate, DE=decay-deposition rate, PR=proximity factor, 

PE=penetration factor 

8.7.3 Microenvironment Descriptions 

8.7.3.1 Microenvironment 1: Indoor-Residence 

The Indoor-Residence microenvironment uses several variables that affect SO2 exposure: 

whether or not air conditioning is present, the average outdoor temperature, the SO2 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 
July 2009 232

conditioner. A value of 96% was used to represent the air conditioning prevalence rate in both 

Greene County and St. Louis, using the data obtained from the St. Louis American Housing 

Survey of 2004 (AHS, 2005). Air conditioning prevalence is noted as distinct from usage rate, 

the latter being represented by the air exchange rate distribution and dependent on temperature 

(see next section). 

Air exchange rates 

Air exchange rate data for the indoor residential microenvironment were the same used in 

APEX for the most recent O3 NAAQS review (EPA, 2007d; see Appendix B, Attachment 6). 

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. Table 8-11 summarizes the AER distributions used in modeling indoor residential 

exposures, separated by A/C prevalence and temperature categories. See Appendix B, 

Attachment 6 for additional details. 

Table 8-11. Geometric means (GM) and standard deviations (GSD) for air exchange rates by A/C 

type and temperature range. 

A/C Type1

Temp 

(oC) N GM GSD 

<=10 179 0.9185 1.8589 

10-20 338 0.5636 1.9396 

20-25 253 0.4676 2.2011 

25-30 219 0.4235 2.0373 

Central or 

Room A/C 

>30 24 0.5667 1.9447 

<=10 61 0.9258 2.0836 

10-20 87 0.7333 2.3299 

No A/C 

>20 44 1.3782 2.2757 

Notes: 

1

 All distributions derived from data reported in non-California cities. See 

Appendix B, Attachment 6 for details in the data used and distribution 

derivation. 

The AER data obtained was limited in the number of samples, particularly when 

considering these influential factors. When categorizing by temperature, a range of temperatures 

was used to maintain a reasonable number of samples within each category to allow for some 

variability within the category, while still allowing for differences across categories. Several 
July 2009 233

distribution forms were investigated (i.e., exponential, log-normal, normal, and Weibull) and in 

general, lognormal distributions provided the best fit. Fitted lognormal distributions were 

defined by a geometric mean (GM) and standard deviation (GSD). Because no fitted distribution 

was available specifically for St. Louis or Greene County, distributions were selected from other 

locations thought to have similar characteristics, qualitatively considering factors that might 

influence AERs including the age composition of housing stock, construction methods, and other 

meteorological variables not explicitly treated in the analysis, such as humidity and wind speed 

patterns. 

SO2 Removal Rate 

Staff estimated distributions of indoor SO2 deposition rates by applying a Monte Carlo 

sampling approach to configurations of indoor microenvironments of interest. The relative 

composition of particular surface materials (e.g., painted wall board, wall paper, wool carpet, 

synthetic carpet, synthetic floor covering, cloth) within various sized buildings were 

probabilistically modeled to estimate 1,000 SO2 deposition rates that in turn were used to 

parameterize lognormal distributions (Table 8-12). The modeling was fundamentally based on a 

review of SO2 deposition conducted by Grontoft and Raychaudhuri (2004) for a variety of 

building material surfaces under differing conditions of relative humidity. Details on the data 

used and derivation of removal rates are provided in Appendix B, section 4.

Table 8-12. Final parameter estimates of SO2 deposition distributions in several indoor 

microenvironments modeled in APEX. 

Heating or Air Conditioning in Use 

or Low Ambient Humidity1

Air Conditioning Not in Use 

(Summertime Ambient Morning 

Relative Humidity of 90%) Microenvironment Geom. 

Mean 

(hr-1) 

Geom. 

Stand. 

Dev. 

(hr-1) 

Lower 

Limit 

(hr-1) 

Upper 

Limit 

(hr-1) 

Geom. 

Mean 

(hr-1) 

Geom. 

Stand.

Dev. 

(hr-1) 

Lower 

Limit 

(hr-1) 

Upper 

Limit 

(hr-1) 

Residence 3.14 1.11 2.20 5.34 13.41 1.11 10.31 26.96 

Office 3.99 1.04 3.63 4.37 N/A N/A N/A N/A

School/ 

Day Care 

Center 

4.02 1.02 3.90 4.21 N/A N/A N/A N/A

Restaurant 2.36 1.28 1.64 4.17 N/A N/A N/A N/A

Other 

Indoors 2.82 1.21 1.71 4.12 N/A N/A N/A N/A

Notes: 

1 Summertime ambient afternoon relative humidity of 50%. 

N/A not applicable, assumed by staff to always have A/C in operation. 
July 2009 234

8.7.3.2 Microenvironments 2-7: All Other Indoor Microenvironments 

The remaining six indoor microenvironments, which represent Bars and Restaurants, 

Schools, Day Care Centers, Office, Shopping, and the broadly defined Other Indoor 

microenvironments, were all modeled using the same data and functions. An air exchange rate 

distribution (GM = 1.109, GSD = 3.015, Min = 0.07, Max = 13.8) was based on an indoor air 

quality study (Persily et al., 2005). This is the same distribution in APEX used for the most 

recent O3 NAAQS review (EPA, 2007d) and NO2 REA (EPA, 2008d). See Appendix B, 

Attachment 6 for details in the data used and derivation. The SO2 removal rates in these six 

indoor microenvironments were estimated as explained in section 8.7.3.1, and described in more 

detail in Appendix B, section 4. The resulting lognormal distributions for removal rates are 

presented in Table 8-12. These microenvironments are all assumed to have air-conditioning. 

8.7.3.3 Microenvironments 8-10: Outdoor Microenvironments 

All outdoor microenvironmental concentrations are well represented by the modeled 

concentrations. Therefore, both the penetration factor and proximity factor for this 

microenvironment were set to 1. 

8.7.3.4 Microenvironments 11 and 12: In Vehicle- Cars and Trucks, and Mass Transit 

There were no available measurement data for SO2 penetration factors, therefore the 

penetration factors used were developed from NO2 data provided in Chan and Chung (2003) and 

used in the recent NO2 NAAQS review (EPA, 2008d). NO2 and SO2 are expected to have 

similar penetration rates inside vehicles since both are gases. Although the in-vehicle NO2

measurements used in the in-vehicle-to-outdoor-ratios might include a small amount of invehicle emissions, resulting in some discrepancy between effective penetration factors for NO2

and SO2, the additional uncertainty is expected to be small compared to the overall uncertainty 

implied by the broad uniform distributions. 

Inside-vehicle and outdoor NO2 concentrations were measured for three ventilation 

conditions: air-recirculation, fresh air intake, and with windows open. Mean in-vehicle-tooutdoor ratio values ranged from about 0.6 to just over 1.0, with higher values associated with 

increased ventilation (i.e., window open). A uniform distribution U{0.6, 1.0} was selected for 

the penetration factor for Inside-Cars/Trucks due to the limited data available to describe a more 
July 2009 235

formal distribution and the lack of data available to reasonably assign potentially influential 

characteristics such as use of vehicle ventilation systems for each location. Mass transit systems, 

due to the frequent opening and closing of doors, was assigned a uniform distribution U{0.8, 

1.0} based on the reported mean values for fresh-air intake (0.796) and open windows (1.032) on 

urban streets. 

8.8 EXPOSURE MEASURES AND HEALTH RISK CHARACTERIZATION 

8.8.1 Estimation of Exposure 

APEX calculates exposure as a time-series of exposure concentrations that a simulated 

individual experiences during the simulation period. APEX calculates exposure by identifying 

concentrations in the microenvironments visited by the person according to the composite diary. 

In this manner, a time-series of event exposures are found. Then, the time-step exposure 

concentration at any clock hour during the simulation period is calculated using the following 

equation: 

T

C t

C

N

j

jjsteptime

i





 1

)()(

 equation (8-7) 

 

where, 

Ci = Time-step exposure concentration at clock hour i of the simulation 

period (ppm) 

N = Number of events (i.e., microenvironments visited) in time-step i

of the simulation period. 

C  jsteptime )( = Time-step concentration in microenvironment j (ppm) 

t(j) = Time spent in microenvironment j (minutes) 

T = Length of time-step (or 5 minutes in this analysis) 

From the time-step exposures, APEX calculates time-series of 5-minute, 1-hour, 24-hour, 

and annual average exposure concentrations that a simulated individual would experience during 

the simulation period. APEX then statistically summarizes and tabulates the 5-minute time-step 

(or daily, or annual average) exposures. From this, APEX can calculate two general types of 

exposure estimates: counts of the estimated number of people whose exposure exceeded a 

specified SO2 concentration level 1 or more times in a year and the number of times per year that 

they are so exposed; the latter metric is in terms of person-occurrences or person-days. The 
July 2009 236

former highlights the number of individuals whose exposure exceeded at least one or more times 

per modeling period the health effect benchmark level of interest. APEX can also report counts 

of individuals with multiple exposures. This person-occurrences measure estimates the number 

of times per season that individuals are exposed to the exposure indicator of interest and then 

accumulates these estimates for the entire population residing in an area. 

In this exposure assessment, 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 total population and subpopulations (i.e., asthmatics, asthmatic 

children) of interest. 

8.8.2 Estimation of Target Ventilation Rates 

Human activities are variable over time, a wide range of activities are possible even 

within a single hour of the day. The type of activity an individual performs, such as sleeping or 

jogging, will influence their breathing rate. As discussed above in section 8.5.5, APEX estimates 

minute-by-minute ventilation rates that account for the expected variability in the activities 

performed by simulated individuals. The ISA indicates that the adverse lung function responses 

associated with short-term peak exposures at levels below 1,000 ppb coincide with moderate to 

heavy exertion levels. Therefore, staff needed to identify a target ventilation rate in the 

simulated individuals to further characterize the estimated exposures of interest. 

The target ventilation for adults (both a mix of males and females) experiencing effects 

from 5-10 minute SO2 exposures in many of the controlled human exposure studies was 

approximately between 40-50 L/min (Table 3-1, ISA).71 Since there were limited controlled 

human exposure study data available for asthmatic children, the ventilation targets needed to be 

normalized. Normalized ventilation rates allow for extrapolation of the adult target ventilation 

rate and, hence the health effect response associated with that ventilation rate to asthmatic 

children. One method used to normalize ventilation rate is to generate an equivalent ventilation 

rate (EVR) based on normalizing the simulated individuals activity-specific ventilation rate ( .

VE ) 

to their body surface area (BSA). Staff has used EVR in previous O3 NAAQS reviews to also 

 

71 Note that study subjects were free-breathing; thus it is expected that there was a mixture of nasal, oral, and 

oronasal breathing that occurred across the study subjects. Without information regarding the breathing method 

used by any subject and their corresponding health response, staff assumed that the mixture in breathing method is 

representative for the simulated population. 
July 2009 237

identify comparable activity-specific ventilation rates for children and adults (EPA, 2007d; 

Whitfield et al., 1996). In these reviews, an EVR ranging from 16-30 L/min-m2

 was associated 

with moderate exertion over a 1-hour exposure event, while an EVR ranging from 13-27 L/minm2

 was associated with moderate exertion over an 8-hour exposure event. 

As was done in the O3 NAAQS reviews, target ventilation rates were identified in this 

exposure assessment by normalizing ventilation rates reported in the clinical studies on adults 

(i.e., 40-50 L/min, also see Table 9-3) to body surface area (BSA) to allow for such an 

extrapolation from adults to children. Body surface area was not measured in the controlled 

human exposure studies and the relevant ventilation data were not separated by gender. Staff 

obtained median estimates of BSA for males (1.94 m2

) and females (1.69 m2) (EPA, 1997) and 

calculated a mean value of 1.81 m2

. Based on this data, an EVR = 40/1.81 = 22 L/min-m2 was 

used to characterize the minimum target ventilation rate of interest. Individuals at or above an 

EVR of 22 L/min-m2

 (children or adult) for a 5-minute exposure event were characterized as 

performing activities at or above a moderate ventilation rate. 

8.8.3 Adjustment for Just Meeting the Current and Alternative Standards 

We used a different approach to simulate just meeting the current and alternative 

standards than was used in the Air Quality Characterization (see section 7.2.4). In this case, 

instead of proportionally adjusting the ambient concentrations, we proportionally adjusted the 

health effect benchmark levels used in each exposure modeling domain. The benchmark levels 

were adjusted rather than the air quality to reduce the processing time associated with the 

modeling of several thousands of receptors in each of the large exposure modeling domains. A 

proportional adjustment of the selected benchmark level (i.e., division by the adjustment factor) 

is mathematically equivalent to a proportional adjustment of the air quality concentrations (i.e., 

multiplication by the adjustment factor).72 Therefore, the end effect of adjusting exposure model 

input concentrations upward versus adjusting exposure model benchmark levels downward is 

identical. 

 For example, an adjustment factor of 5.10 was determined for year 2002 in Cuyahoga 

County to simulate ambient concentrations just meeting the current standard. This value was 

 

72 To evaluate the current and most of the proposed alternative standards, 1-hour ambient concentrations were 

typically adjusted upwards to just meet the standards. This would correspond to downward adjustments to the 

benchmark levels. 
July 2009 238

based on an annual average SO2 concentration of 5.96 ppb observed at an ambient monitor (ID 

390350060) for that year (see Appendix A, section A.3). Therefore in the exposure analysis, the 

5-minute potential health effect benchmark levels of 100, 200, 300, and 400 ppb were 

proportionally adjusted downward to 19.6, 39.2, 58.8, and 78.4 ppb, respectively for year 2002. 

APEX reported the number of days an individual was exposed above each of the adjusted 

benchmark levels using the as is air quality as the ambient concentration input. To illustrate the 

relationship between the two procedures (air quality adjustment versus benchmark adjustment), a 

comparison of the distributions and benchmark exceedances is presented in Figure 8-13. This 

example used the distribution of hourly SO2 concentrations measured at one ambient monitor (ID 

390350045) within the Cuyahoga County modeling domain for year 2002. Staff used the 

statistical model (section 7.2.3) to estimate 5-minute maximum SO2 concentrations from both the 

adjusted and unadjusted 1-hour SO2 concentrations. If one were interested in the number of days 

per year with 5-minute SO2 benchmark exceedances of 400 ppb under the current standard 

scenario for example, this would be equivalent to counting the number of days with 5-minute 

maximum SO2 concentrations above 78.4 ppb using the as is air quality. 

For additional clarity, the same ambient air quality data are presented in Figure 8-14, only 

with expansion of the highest percentiles on the graph to allow for improved visualization of the 

number of exceedances. When using the air quality adjusted to just meet the current standard, 

there were 14 days where the maximum 5-minute concentration was greater than 400 ppb.73 

When considering the as is air quality without adjustment but with a downward adjustment of 

the benchmark by the same factor of 5.10, there are the same number of days with exceedances 

(i.e., 14 exceedances). Due to the relationship between the two procedures, the estimated 

number of exceedances at each of the other benchmark levels is identical (Table 8-13). 

The values for each adjusted benchmark level considering each of the air quality standard 

scenarios are given in Table 8-14. Staff applied the benchmark adjustment in each of the 

exposure modeling domains to simulate exposures associated with just meeting the current and 

alternative standards. 

 

73 Only 12 points are observed in Figure 8-13 however, three peak concentrations were identical within each of the 

simulations. 
July 2009 239

MODELED CUYAHOGA COUNTY Daily 5-Minute Max (2002)

As Is and Adjusted to Just Meet the Current Standard (CS)

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350 400 450 500 550 600 650 700

5-minute Daily Maximum SO2 (ppb)

Cumulative Percentile

390350045 - As Is Air Quality

390350045 - Adjusted Air Quality

Adjusted 400 ppb 

Benchmark Level 

to Simulate Just 

Meeting the 

Current Standard

(78.4 ppb)

400 ppb 

Benchmark 

Level

 

Figure 8-13. Comparison of adjusted ambient monitoring concentrations or adjusted benchmark 

level (dashed line) to simulate just meeting the current annual average standard at one 

ambient monitor in Cuyahoga County for year 2002. 

95

96

97

98

99

100

50 100 150 200 250 300 350 400 450 500 550 600 650 700

Daily 5-minute Maximum SO2 (ppb)

Cumulative Percentile

390350045 - As Is Air Quality

390350045 - Adjusted Air Quality

Adjusted 400 ppb 

Benchmark Level 

to Simulate Just 

Meeting the 

Current Standard

(78.4 ppb)

400 ppb 

Benchmark 

Level

Figure 8-14. Comparison of the upper percentile modeled daily 5-minute maximum SO2

concentrations using either adjusted 1-hour ambient SO2 concentrations or an adjusted 

benchmark level (with as is air quality) to simulate just meeting the current annual 

standard at monitor 390350045 in Cuyahoga County for year 2002. Complete 

distributions are provided in Figure 8-13. 
July 2009 240

Table 8-13. Comparison of benchmark levels, adjusted benchmark levels to just meet the current 

standard, the benchmark level distribution percentiles, and the number of 5-minute SO2

benchmark exceedances at monitor 390350045 in Cuyahoga County for year 2002.

Benchmark 

Level 

(ppb) 

Adjusted 

Benchmark Level1 

(ppb) 

Concentration 

Distribution 

Percentile2

Number of Days 

with a Benchmark 

Exceedance3

100 19.6 37.3 230 

200 39.2 76.7 86 

300 58.8 92.0 30 

400 78.4 97.0 14 

Notes: 

1

 The adjustment factor to simulate just meeting the current standard was 5.10. 

2

 The percentile of the distribution for each benchmark and adjusted benchmark 

level was the same. 

3

 The number of days with a benchmark exceedance when using either air quality 

adjusted to just meet the current standard or applying adjusted benchmarks to as 

is air quality was the same. 
July 2009 241

Table 8-14. Exposure concentrations and adjusted potential health effect benchmark levels used by APEX to simu

current and potential alternative standards in the Greene County and St Louis modeling domains. 

Exposure Concentrations and Adjusted Potential Health Effect Benchmar

Modeling 

Domain Form1 Level2 50 100 150 200 250 300 350 400 450 500 550 600 

98 200 20.3 40.5 60.8 81 101.3 121.5 141.8 162 182.3 202.5 222.8 243 99 50 94.3 188.7 283 377.3 471.7 566 660.3 754.7 849 943.3 1037.7 1132 99 100 47.2 94.3 141.5 188.7 235.8 283 330.2 377.3 424.5 471.7 518.8 566 99 150 31.4 62.9 94.3 125.8 157.2 188.7 220.1 251.6 283 314.4 345.9 377.3 99 200 23.6 47.2 70.8 94.3 117.9 141.5 165.1 188.7 212.3 235.8 259.4 283 99 250 18.9 37.7 56.6 75.5 94.3 113.2 132.1 150.9 169.8 188.7 207.5 226.4 

CS 14.4 28.8 43.2 57.6 72 86.4 100.8 115.2 129.6 144 158.3 172.7 

Greene 

County

as is 50 100 150 200 250 300 350 400 450 500 550 600 

98 200 13.3 26.5 39.8 53 66.3 79.5 92.8 106 119.3 132.5 145.8 159 99 50 63.3 126.7 190 253.3 316.7 380 443.3 506.7 570 633.3 696.7 760 99 100 31.7 63.3 95 126.7 158.3 190 221.7 253.3 285 316.7 348.3 380 99 150 21.1 42.2 63.3 84.4 105.6 126.7 147.8 168.9 190 211.1 232.2 253.3 99 200 15.8 31.7 47.5 63.3 79.2 95 110.8 126.7 142.5 158.3 174.2 190 99 250 12.7 25.3 38 50.7 63.3 76 88.7 101.3 114 126.7 139.3 152 

CS 8 16 24 32 40 48 56 64 72 80 88 96 

St. Louis 

as is 50 100 150 200 250 300 350 400 450 500 550 600 

Notes: 1 The form of the standard used to adjust the air quality. 98 is the 98th percentile 1-hour daily maximum alternative standar

daily maximum alternative standard, CS is either the current annual average or 24-hour SO2 NAAQS (whichever had the lo

air quality. 

2 The level of the potential alternative standards, i.e., the 1-hour daily maximum at the noted percentile of the distribution. 3 Exposure levels were defined in 50 ppb increments from 0 through 800 ppb even though the selected potential health eff

to 400 ppb in 100 ppb increments. 
July 2009 242

8.9 EXPOSURE MODELING AND HEALTH RISK CHARACTERIZATION 

RESULTS 

Exposure results are presented for simulated asthmatic populations residing in the two 

modeling domains in Missouri. For each individual, APEX estimates the number of days with a 

5-minute SO2 exposure above the potential health effect benchmark levels year 2002. These 

short-term exposures were evaluated for all asthmatics and asthmatic children when the exposure 

corresponded with moderate or greater activity levels (i.e., the simulated individuals EVR during 

a 5-minute exposure event was >22 L/minute-m2

). The number of persons and days with at least 

one 5-minute SO2 exposure at or above any level from 0 through 800 ppb in 50 ppb increments 

was reported by APEX. Therefore, for each concentration level, an individual at a moderate (or 

higher) exertion level while exposed would have at most one exceedance of a particular level per 

day, or 365 per year. 

Multiple air quality scenarios were evaluated, including unadjusted air quality (termed as 

is), air quality adjusted to just meet the current NAAQS, and air quality adjusted to just meet 

several potential alternative 1-hr daily maximum standards. Exposure results are presented in a 

series of figures that allow for simultaneous comparison of exposures associated with each air 

quality scenario. Four types of results are provided for each exposure modeling domain: (1) the 

number of persons in the simulated subpopulation exposed at or above selected levels 1 or more 

times in a year, (2) the percent of the simulated subpopulation exposed at or above selected 

levels 1 or more times in a year, (3) the total number of days in a year the simulated 

subpopulation is exposed (or person days) at or above selected levels, and (4) the percent of time 

associated with the exposures at or above the selected levels. Tables summarizing all of the 

exposure results for each modeling domain, air quality scenario, exposure level, and 

subpopulation are provided in Appendix B.4. 

8.9.1 Asthmatic Exposures to 5-minute SO2 Concentrations in Greene County 

When considering the lowest 5-minute benchmark level of 100 ppb, approximately one 

thousand asthmatics are estimated to be exposed at least once in the year 2002 while at moderate 

or greater exertion and when considering the current standard air quality scenario (top of Figure 

8-15). Each of the potential alternative 1-hr standard air quality scenarios as well as the as is air 

quality scenario result in fewer asthmatics exposed when compared with the current standard 

scenario, and progressively fewer persons were exposed with decreases in the 1-hour daily 
July 2009 243

maximum concentration levels of the potential alternative standards. The 99th percentile 1-hour 

daily maximum standard levels of 50 and 100 ppb produced the same number of persons with at 

least one 5-minute exposure at or above 100 ppb as the as is air quality (i.e., 13). With 

progressive increases in benchmark level, there were corresponding decreases in the number of 

individuals exposed. None of the asthmatics had a day where 5-minute exposures were above 

100 ppb when considering the as is air quality scenario. Asthmatic children exhibited similar 

patterns in the estimated number of exposures at each of the exposure levels, thus comprising a 

large proportion of the total asthmatics exposed (bottom of Figure 8-15). 

The difference between all asthmatics and asthmatic children is best demonstrated by 

comparing the percent of the subpopulation exposed. Asthmatic children have nearly double the 

percentage of the subpopulation exposed at any of the benchmark levels considered when 

compared with that of all asthmatics (Figure 8-16). For example, approximately 1% of asthmatic 

children experience at least one day with a 5-minute SO2 exposure at or above 200 ppb in a year 

in considering the current standard scenario, while approximately 0.6% of all asthmatics 

experienced a similar exposure. As observed with the numbers of persons exposed, a lower 

estimated percent of persons was exposed at the higher benchmark levels, though again, the 

current standard scenario contains the greatest percent of asthmatics exposed when compared 

with all of the other 1-hour air quality standard scenarios analyzed. 

The number of person days or occurrences of exposures is greater than the number of 

persons exposed, indicating that some of the simulated asthmatics had more than one day with 5-

minute exposures above selected benchmark levels (Figure 8-17). For example, when 

considering all asthmatics and the current standard scenario, there were approximately 22 person 

days with exposures at or above 300 ppb. This corresponds with the 18 asthmatics estimated to 

experience at least one day with a 5-minute SO2 concentration above this level, indicating that a 

number of persons may have experienced at least 2 benchmark exceedances in the year. For 

both subpopulations considered, there were no estimated exposures above 300 ppb when 

considering the 99th percentile 1-hour daily maximum alternative standard level of 200 ppb. 

Staff evaluated the microenvironments where the peak exposures frequently occurred. 

There were very few persons exposed to benchmark levels of 100 ppb or higher considering the 

as is air quality, though 99% or greater experienced their 5-minute maximum SO2 exposure in an 

outdoor microenvironment (i.e., outdoors or outdoors near-roads) when considering any of the 
July 2009 244

benchmark levels. For the current standard air quality scenario, approximately 7% of persons 

were exposed to the 100 ppb benchmark level indoors (i.e., primarily in the persons residence), 

though with increasing benchmark level (e.g., 300 ppb) the percent of persons with any 

benchmark exceedances indoors approached zero (i.e., > 99% occurred outdoors). The inside 

vehicle microenvironment also comprised a small percent of the cases where the exposures 

above selected levels occurred; at most 2% of benchmark exceedances occurred inside vehicles 

when considering the lowest benchmark levels. 

Two forms of the potential alternative standard were evaluated in Greene County, i.e., the 

99th and 98th forms of a 1-hour daily maximum level of 200 ppb. The difference in the exposure 

results generated for each of these air quality scenarios is provided in Table 8-15. The 99th

percentile form of the potential alternative standard results in fewer persons, person-days, and 

percent of asthmatic persons exposed when compared with estimated exposures using air quality 

adjusted to just meet a 200 ppb 1-hour daily maximum 98th form. The values listed in the table 

are small, but from a relative perspective, the percent difference can be large. For example, there 

is approximately a 40% reduction in the percent of persons exposed when considering the 99th

percentile form and the 100 ppb benchmark level. Where there were other higher benchmark 

levels that were exceeded, the reduction was greater (66% to 100%). For additional relative 

comparisons for these two standard forms, see the corresponding Figures 8-15 to 8-17. 

Table 8-15. Absolute difference in APEX exposure estimates for Greene County using either a 98th

or 99th percentile form potential alternative standard at a 1-hour daily maximum level of 200 ppb. 

Absolute Difference in Estimated Exposures 

using 98th and 99th form1

Population 

Benchmark 

Level (ppb) 

Number of 

Person-days 

Number of 

Persons 

Percentage 

Points2

100 274 157 0.7 

200 27 27 0.1 

300 13 13 0.1 

All Asthmatics 

(21,948) 

400 0 0 0 

100 161 81 0.4 

200 18 18 0 

300 4 4 0 

Asthmatic 

Children 

(7,285) 400 0 0 0 

Notes: 

1

 Both the 98th and 99th 1-hour daily maximum air quality scenarios were simulated by 

APEX, using a level of 200 ppb. The value reported is the difference between the 98th

and the 99th. 2

 Difference between the percent of persons exposed (98th-200 minus the 99th-200) at 

each benchmark level. 
July 2009 245

1

10

100

1000

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Number of All Asthmatics with at Least 

One Exposure at or Above Level While at 

Moderate or Greater Exertion

99 50

99 100

99 150

99 200

99 250

98 200

CS

AS IS

1

10

100

1,000

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Number of Asthmatic Children with at 

Least One Exposure at or Above Level 

While at Moderate or Greater Exertion

99 50

99 100

99 150

99 200

99 250

98 200

CS

AS IS

Figure 8-15. Number of all asthmatics (top) and asthmatic children (bottom) experiencing at least 

one day with a 5-minute SO2 exposure above selected benchmark levels in Greene 

County, year 2002 air quality as is and adjusted to just meeting the current and 

potential alternative standards.
July 2009 246

0%

1%

2%

3%

4%

5%

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum Exposure SO2 Level (ppb)

Percent of All Asthmatics with at Least One 

Exposure at or Above Level While at 

Moderate or Greater Exertion

99 50

99 100

99 150

99 200

99 250

98 200

CS

AS IS

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Percent of Asthmatic Children with at 

Least One Exposure at or Above Level 

While at Moderate or Greater Exertion

99 50

99 100

99 150

99 200

99 250

98 200

CS

AS IS

Figure 8-16. Percent of all asthmatics (top) and asthmatic children (bottom) experiencing at least 

one day with a 5-minute SO2 exposure above selected benchmark levels in Greene 

County, year 2002 air quality as is and adjusted to just meeting the current and 

potential alternative standards. 
July 2009 247

1

10

100

1,000

10,000

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Number of Person Days All Asthmatics 

Experience at Least One Exposure at or 

Above Level While at Moderate or Greater 

Exertion

99 50

99 100

99 150

99 200

99 250

98 200

CS

AS IS

1

10

100

1,000

10,000

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Number of Person Days Asthmatic 

Children Experience at Least One 

Exposure at or Above Level While at 

Moderate or Greater Exertion

99 50

99 100

99 150

99 200

99 250

98 200

CS

AS IS

Figure 8-17. Number person days all asthmatics (top) and asthmatic children (bottom) experience 

a 5-minute SO2 exposure above selected benchmark levels in Greene County, year 2002 

air quality as is and adjusted to just meeting the current and potential alternative 

standards. 
July 2009 248

8.9.2 Asthmatic Exposures to 5-minute SO2 in St. Louis 

The patterns in the number of persons (either asthmatics or asthmatic children) exposed 

in St. Louis were different from those observed in Greene County; a greater number of persons 

were estimated to be exposed in St. Louis at each of the corresponding benchmark levels and air 

quality scenarios (Figure 8-18). For example, nearly 80,000 asthmatics were estimated to 

experience at least one day with a 5-minute SO2 exposure at or above 100 ppb when considering 

the current standard scenario compared to the one thousand asthmatics estimated in Greene 

County (section 8.9.1). In addition, there were more persons exposed to the higher benchmark 

levels in St. Louis compared with Greene County. For example, none of the asthmatics 

experienced a 5-minute SO2 concentration exposure above 450 ppb in Greene County 

considering any of the air quality scenarios. In St. Louis many of the air quality scenarios had 

persons with exceedances of 450 ppb; the estimated number of persons experiencing at least one 

day with a 5-minute SO2 exposure above 450 ppb ranged from a low of 16 (the 99th percentile 1-

hour daily maximum standard level of 100 ppb) to over 10,000 (the current standard air quality 

scenario). We note though, in considering the as is air quality scenario, none of the asthmatics in 

St. Louis had 5-minute SO2 exposures above a 450 ppb exposure level. 

There were also differences in the estimated percent of asthmatics and asthmatic children 

exposed to concentrations above the benchmark levels in St. Louis when compared with Greene 

County. For example, over 40% of asthmatic children were estimated to experience at least one 

day with a 5-minute exposure above 300 ppb in St. Louis considering the current standard air 

quality scenario, while less than 1% of asthmatic children in Greene County experienced a 

similar exposure (Figure 8-19). Just as observed with the Greene County estimates though, there 

were decreases in the percent of persons exposed with decreases in the 1-hour daily maximum 

level of the potential alternative standards. For example, less than 3% of asthmatic children 

were estimated to have at least one day with a 5-minute SO2 exposure above 300 ppb when 

considering a 99th percentile 1-hour daily maximum standard level of 150 ppb. 

The discussion regarding the patterns observed in the number of persons exposed in St. 

Louis can be extended to the number of person days (i.e., both a greater number and at higher 

benchmark levels when compared with Greene County). In addition, St. Louis had a greater 

number of persons with multiple exceedances when compared with Greene County (Figure 8-

20). For example, given the 22 person days at or above 300 ppb in Greene County experienced 
July 2009 249

by the 18 asthmatics considering air quality just meeting the current standard, on average this 

amounts to approximately 1.2 exposures per person per year. In contrast, approximately 26,000 

asthmatics had nearly 50,000 person days at the same benchmark level and air quality scenario in 

St. Louis; on average each person is estimated to experience 1.9 exposures exceeding this 

benchmark level in a year. 

Staff also evaluated the microenvironments where the peak exposures occurred in St. 

Louis, and again, there were differences when compared with the exposures in Greene County. 

In St. Louis, there were a greater percentage of benchmark exceedances within indoor and inside 

vehicle microenvironments, although overall still comprising a small percentage of where the 

exceedances were occurring. At the 100 ppb benchmark level, approximately 10% of the 

exposures occur within indoor microenvironments (i.e., principally inside residences) and about 

5% occur inside vehicles considering as is air quality (Figure 8-21). The percentage increases 

when considering air quality adjusted to just meeting the current standard, with approximately 

30% of benchmark exceedances of 100 ppb occurring indoors and 20% occurring inside 

vehicles. Just beyond the benchmark level of 400 ppb, nearly all of the exceedances occur 

outdoors when considering the as is air quality, while indoor microenvironments still contribute 

to around 10% of exceedances, up to a 5-minute exposure level of 800 ppb. For comparison, air 

quality adjusted to just meet a 99th percentile 1-hour daily maximum standard level of 150 ppb is 

also shown, and falls within the range of values provided by the as is and current standard 

scenarios. 

Two forms of potential alternative standards were also evaluated in St. Louis, using the 

99th and 98th percentile forms of a 1-hour daily maximum level of 200 ppb. The difference in the 

exposure results generated for each of these air quality scenarios is provided in Table 8-16. The 

99th percentile form of the potential alternative standard results in fewer persons, person-days, 

and percent of asthmatic persons exposed when compared with estimated exposures using air 

quality adjusted to just meet a 200 ppb 1-hour daily maximum 98th percentile form. The impact 

of the different scenario is greater than that observed in Greene County from a pure numbers 

perspective given so few persons exposed to concentrations above the benchmark levels in 

Greene County. From a relative perspective, the percent difference between the two scenarios 

can also be large. The reduction in the percent of persons exposed when considering the 99th
July 2009 250

percentile form ranges from approximately 10% to 50%. For additional relative comparisons 

between these two standard forms, see the corresponding Figures 8-18 to 8-20. 
July 2009 251

1

10

100

1,000

10,000

100,000

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Number of All Asthmatics with at Least 

One Exposure at or Above Level While at 

Moderate or Greater Exertion

99 50 99 100

99 150 99 200

99 250 98 200

CS AS IS

1

10

100

1,000

10,000

100,000

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Number of Asthmatic Children with at 

Least One Exposure at or Above Level 

While at Moderate or Greater Exertion

99 50 99 100

99 150 99 200

99 250 98 200

CS AS IS

Figure 8-18. Number of all asthmatics (top) and asthmatic children (bottom) experiencing at least 

one day with a 5-minute SO2 exposure above selected benchmark levels in St. Louis, 

year 2002 air quality as is and adjusted to just meeting the current and potential 

alternative standards. 
July 2009 252

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Percent of All Asthmatics with at Least One 

Exposure at or Above Level While at 

Moderate or Greater Exertion

99 50

99 100

99 150

99 200

99 250

98 200

CS

AS IS

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Percent of Asthmatic Children with at 

Least One Exposure at or Above Level 

While at Moderate or Greater Exertion

99 50

99 100

99 150

99 200

99 250

98 200

CS

AS IS

Figure 8-19. Percent of all asthmatics (top) and asthmatic children (bottom) experiencing at least 

one day with a 5-minute SO2 exposure above selected benchmark levels in St. Louis, 

year 2002 air quality as is and adjusted to just meeting the current and potential 

alternative standards. 
July 2009 253

 

1

10

100

1,000

10,000

100,000

1,000,000

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Number of Person Days Asthmatics 

Experience at Least One Exposure at or 

Above Level While at Moderate or Greater 

Exertion

99 50 99 100

99 150 99 200

99 250 98 200

CS AS IS

1

10

100

1,000

10,000

100,000

1,000,000

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5- Minute Daily Maximum SO2 Exposure Level (ppb)

Number of Person Days Asthmatic 

Children Experience at Least One 

Exposure at or Above Level While at 

Moderate or Greater Exertion

99 50 99 100

99 150 99 200

99 250 98 200

CS AS IS

Figure 8-20. Number person days all asthmatics (top) and asthmatic children (bottom) experience 

a 5-minute SO2 exposure above selected benchmark levels in St. Louis, year 2002 air 

quality as is and adjusted to just meeting the current and potential alternative 

standards. 
July 2009 254

Air Quality As Is

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5-Minute Daily Maximum SO2 Exposure (ppb)

Frequency of Exposure in 

Microenvironment (%)

Indoors

Outdoors

Vehicles

Air Quality Adjusted to Just Meet the Current Standard

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5-Minute Daily Maximum SO2 Exposure (ppb)

Frequency of Exposure in 

Microenvironment (%)

Indoors

Outdoors

Vehicles

Air Quality Adjusted to Just Meet the 99th %ile 150 ppb Standard

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

5-Minute Daily Maximum SO2 Exposure (ppb)

Frequency of Exposure in 

Microenvironment (%)

Indoors

Outdoors

Vehicles

Figure 8-21. The frequency of estimated exposure level exceedances in indoor, outdoor, and 

vehicle microenvironments given as is air quality (top), air quality adjusted to just 

meeting the current standard (middle) and that adjusted to just meeting a 99th

percentile 1-hour daily maximum standard level of 150 ppb (bottom) in St. Louis.
July 2009 255 

Table 8-16. Absolute difference in APEX exposure estimates for St. Louis using either a 98th or 

99th percentile form potential alternative standard at a 1-hour daily maximum level of 200 ppb. 

Absolute Difference in Estimated 

Exposures using 98th and 99th form1

Population 

Benchmark 

Level (ppb) 

Number of 

Persondays 

Number 

of 

Persons

Percentage 

Points2

100 91490 9142 8.7 

200 64531 22194 6.7 

300 31441 16922 3.2 

All Asthmatics 

(105,456) 

400 16705 11330 1.5 

100 69420 3826 9.2 

200 11682 4856 11.6 

300 3496 2425 5.8 

Asthmatic 

Children 

(41,714) 400 1449 1150 2.8 

Notes: 

1

 Both the 98th and 99th 1-hour daily maximum air quality scenarios were 

simulated by APEX, using a level of 200 ppb. The value reported is the 

difference between the 98th and the 99th. 2

 Difference between the percent of persons exposed (98th-200 minus the 

99th-200) at each benchmark level. 

8.10 REPRESENTATIVENESS OF EXPOSURE RESULTS 

8.10.1 Introduction 

Due to time and resource constraints the exposure assessment evaluating the current and 

alternative standards was only applied to the two locations in Missouri. A natural question is 

how might the estimates from this assessment of exposures in Greene County and St. Louis 

compare with other areas in the United States that may have elevated short-term SO2

concentrations. To address this question, additional data were compiled and analyzed to provide 

context to the exposure modeling results. Because most estimated exceedances were associated 

with the outdoor microenvironments, this analysis and discussion is centered on time spent 

outdoors to allow for comparison of the two modeling domains with several other broad regions. 

In addition, further context is given regarding the SO2 emissions and air quality in these locations 

with respect the 39 other counties evaluated in the air quality characterization. The distribution 

of air conditioning and asthma prevalence rates in the U.S. U.S. and how that distribution 

compares with those estimated for the two modeling domains is also discussed. 
July 2009 256 

8.10.2 Time spent outdoors 

The time spent outdoors by children age 5-17 was calculated from CHAD-Master74 for 

five regions of the country. The U.S. states used in the air quality characterization (Chapter 7) 

were of interest, which already includes Missouri (representing the two exposure modeling 

domains). Staff analyzed the outdoor time by broad geographic regions because it was thought 

that the regional climate would have influence on each population. In addition, most of the 

location descriptors are already broadly defined to protect the identity of persons in CHAD; finer 

spatial scale such as at a city-level is uncommon. Table 8-17 has the States used to identify 

CHAD diaries available to populate a data set for each of the five regions. Staff further 

separated the diaries by time-of-year (school year versus summer)75 and the day-of-week 

(weekdays versus weekends), both important factors influencing time spent outdoors (Graham 

and McCurdy, 2004). Summer days were not separated by day of week; staff assumed that the 

variation in outdoor time during the summer would not be greatly influenced by this factor for 

children. The results for time spent outdoors in each region are given in Table 8-18. 

Table 8-17. States used to define five regions of the U.S. and characterize CHAD data diaries. 

Region States 

Mid-Atlantic (MA) New York, New Jersey, Delaware, Maryland, District of Columbia, Virginia, West 

Virginia, Pennsylvania 

Midwest (MW) Ohio, Iowa, Missouri, Illinois, Indiana, Kentucky 

Northeast (NE) Maine, New Hampshire, Vermont, Massachusetts, Connecticut, Rhode Island 

Southeast (SE) North Carolina, South Carolina, Georgia, Florida, Tennessee, Alabama, 

Mississippi, Arkansas, Louisiana 

Southwest (SW) Nevada, Utah, Colorado, Arizona, New Mexico, Texas, Oklahoma 

 Participation rates for the selected time of year and day of week groupings were similar 

for each of the regions. In general, a smaller percent of children spend time outdoors during the 

school year (about 45-50%) compared to the summer (about 70-77%). There was no apparent 

pattern in the day-of week participation rates considering the school year days. However, 

children did spend more time outdoors on weekend days compared to weekdays at all percentiles 

of the distribution and within all regions. In addition, children consistently spent more time 

 

74Currently available through EPA at mccurdy.tom@epa.gov. 75A traditional school year was considered (months of September-May); summer months included June-August. 
July 2009 257 

outdoors during summer days within all regions. There were few differences in outdoor time 

when comparing each of the regions. Children in Northeastern States had the widest range in the 

distributions for time spent outdoors. In this region of the U.S., children spent the least amount 

of time outdoors during the school-year days-of-the-week and the greatest amount of time 

outdoors on average during the summer. Based on this analysis, it is not expected that the results 

generated for the two Missouri modeling domains would be largely different from results 

generated in most areas of the U.S. when considering time spent outdoors, though there may be 

differences in exposures estimated in Northeastern states. 76 Depending on when the peak 

exposure events occur in the year, the exposures estimated in these states may be lower or 

higher. 

Table 8-18. Time spent outdoors by geographic region for children ages 5-17 based on CHAD 

time-location-activity diaries. 

Doers1 Time Spent Outdoors (minutes) 

Region 

Time of 

Year 

Day of 

Week (n) (%) Mean SD Min Med P95 Max GM GSD 

weekdays 400 45 113 97 1 90 301 700 73 3.0 school MA weekends 317 43 158 159 2 120 365 1440 105 2.7 

summer all 474 71 193 140 5 165 462 1210 146 2.3 

weekdays 336 42 109 92 2 88 300 550 73 2.7 school MW weekends 258 41 152 131 1 116 422 870 102 2.7 

summer all 154 71 193 180 5 143 565 1250 131 2.6 

weekdays 70 48 106 89 2 75 290 335 66 3.1 school NE weekends 54 43 148 128 15 115 480 574 105 2.4 

summer all 23 77 217 148 30 175 465 635 172 2.1 

weekdays 641 49 120 98 2 95 325 555 84 2.6 school SE weekends 593 52 157 126 1 123 404 810 112 2.5 

summer all 244 70 185 147 5 150 480 935 135 2.4 

weekdays 253 46 119 106 1 90 315 650 80 2.8 school SW weekends 232 50 162 142 7 120 405 1390 116 2.4 

summer all 273 76 187 137 2 150 450 840 136 2.5 

Notes: 

1

 Doers are those engaged in the particular activity, in this case those children that had at least 1 minute of 

outdoor time recorded in their CHAD time-location-activity diary. The participation rate (%) was estimated by 

the total number of persons in each subgroup (not included). The n indicates the person-days of diaries used 

to calculate the outdoor time statistics. 

 

76 Note however that all of the Northeastern data have the fewest number of person days available, in particular the 

summer days (n=23). 
July 2009 258 

8.10.3 SO2 Emissions and Ambient Concentrations 

St. Louis was not one of the 40 selected counties for the Air Quality Characterization due 

to its not meeting the selection criteria (see section 7.2.4.2). To provide additional perspective 

on the exposure results for both the Greene County and St. Louis modeling domains, staff 

compared the air quality in each of these locations with the other 39 counties, beginning with the 

estimated number of benchmark exceedances using the available ambient monitoring data.77 

Five-minute maximum SO2 concentrations were estimated in St. Louis as was done with the 

other 40 Counties (including Greene County) using the hourly ambient monitoring data (2001-

2006). Staff simulated all air quality scenarios (as is, current standard, potential alternative 

standards) and estimated 5-minute maximum SO2 concentrations using the statistical model. 

Then, the mean number of days with a 5-minute maximum concentration above a benchmark 

level in a year for St. Louis were combined with the exceedance results for the 40-counties and 

ranked in descending order. In addition, two other rank statistics were generated; the average 

total SO2 emissions within 20 km of ambient monitors and the average population within 5km of 

the ambient monitors, both statistics considering the 40 counties and St. Louis area. Each of the 

two additional variables was also ranked in descending order. 

Greene county estimated air quality exceedances rank within the upper quartile (i.e., 

having some of the highest estimated number of days with 5-minute benchmark exceedances) for 

many alternative standard scenarios (Table 8-19). Most scenarios have exceedances ranked 

within upper 50th percentile (including the as is scenario), while having the 37th highest ranked 

emissions. The population ranking was moderate (19th of 41 locations). St. Louis air quality 

exceedances rank within the 50th-75th percentile for most of the alternative standard scenarios, 

with a few of the scenarios (e.g., the current standard, and the higher alternative standard) ranked 

in the upper quartile, while having moderately ranked emissions (26th highest). The number of 

days with benchmark exceedances for the as is scenario in St. Louis was ranked low in 

comparison with the other 39 counties (approximately the 90th-95th percentile). The mean 

estimated population surrounding the monitors is ranked in the upper quartile (9th of 41). 

 

77 The exposure modeling domain was comprised of three counties (St. Charles, St. Louis, and St. Louis City), while 

the available ambient monitoring data was only available for the latter two counties for years 2001-2006. 
July 2009 259 

Table 8-19. Ranking of selected exposure locations using the modeled number of days with 5-

minute benchmark exceedances and the total emissions within 20 km of ambient monitors. 

Benchmark Exceedance Rank (out of 41)1 Exposure Modeling 

Domain 

Air Quality 

Scenario 100 ppb 200 ppb 300 ppb 400 ppb 

as is 31 23 22 21 

Current Standard 40 33 27 23 

99-50 8 4 4 22.5 

99-100 13 6 5 4 

99-150 27 9 7 5 

99-200 32 14 8 8 

99-250 34 22 9 7 

Greene County, MO 

Population – 19th

Emissions – 37th

98-200 36 21 9 8 

as is 38 37 39 38.5 

Current Standard 2 3 8 14 

99-50 30 22.5 27 22.5 

99-100 20 30 25 24 

99-150 13 27 30 28.5 

99-200 9 21 29 30 

99-250 8 15 27 28 

St. Louis, MO 

Population – 9th

Emissions – 26th

98-200 8 16 24 26 

Notes: 

1

 Benchmark exceedances for the exposure modeling domains were compared with the 40 

counties selected for the air quality characterization. 

Given these ranked statistics and the results of the exposure assessment (i.e., St. Louis 

had a much higher percent of asthmatics exposed above benchmark levels than Greene County), 

the number and percent of persons exposed above benchmark levels are likely more a function of 

the population density and where the persons reside, rather than just total SO2 emission levels or 

the number of air quality benchmark exceedances. In addition, total SO2 emissions are not 

necessarily a good indicator of estimated air quality exceedances. Greene County has a high 

ranking for most of the air quality scenarios but only a moderate ranking for total emissions. 

Ambient monitors with a high COV (>200%) account for the greatest number of days/year with 

air quality benchmark exceedances. For example, in Gila County AZ, one of the two monitors in 

the county had a high COV and was located within 2 km of primary smelter emissions. This 

county ranked 1st in days/year with exceedances using as is air quality, though ranked only 36th

for SO2 emissions (18,000 tpy). Figure 7-10 provided support for the variability bins selected 

and their relationship with the number of measured air quality benchmark exceedances. Clearly, 

ambient monitors with the greatest variability in 1-hour SO2 concentration are the monitors most 

likely to have 5-minute SO2 benchmark exceedances. 
July 2009 260 

Greene County was retained in the final exposure assessment based analyses in the 1st

draft SO2 REA. At the time of the analysis, it was noted by staff that the county had a number of 

ambient monitors available for use in calibrating the dispersion model (two of which were rated 

as having high COVs), there were some measured benchmark exceedances using as is air 

quality, and there was a moderate population density surrounding the monitors/source emissions. 

However, based on the air quality characterization and exposure modeling performed here that 

includes St. Louis, it appears that a less dense population surrounding the potentially important 

SO2 emission sources in Greene County primarily contributed to the resultant small percent of 

asthmatics exposed. This is a common attribute noted at the high COV monitors; most of these 

monitors are located in areas having low population density. Eighty-nine of the 809 monitors in 

the broader SO2 monitoring network were rated as having a high COV; 52 of these monitors 

(58%) were associated with low population density (<10,000 persons within 5km), 28 moderate 

population density (31%, 10,000-50,000 persons within 5km), and 9 high population density 

(10%, >50,000 persons within 5km). It is possible that, in areas having several days/year with 

air quality benchmark exceedances and a low to moderate population density, the exposure 

results would be similar to that estimated for Greene County. For example, if an exposure 

assessment was performed in Gila County AZ (ranked 1st in as is air quality benchmark 

exceedances), it is possible that the percent of persons exposed would be low (ranked 38th in 

population). 

Staff also calculated the total SO2 emissions from marine vessels, generally referred to as 

port emissions in this document. Using the data in the 2002 NEI, the total port emissions were 

calculated for each of the 40 counties used in the air quality characterization and ranked (Table 

8-20). The St. Louis modeling domain had the 5th highest total port SO2 emissions when 

considering the 40 counties, though these emissions only comprise 2% of the total SO2 emissions 

in St. Louis. Thirteen of the 40 counties did not have port emissions, one of which was Greene 

County. The amount of port emissions in St. Louis was also compared with the top 40 counties 

in the U.S that had the highest port emissions (Table 8-21). The total SO2 emissions from ports 

in St. Louis were ranked 28th, while seven counties had greater port emissions than Jefferson 

County TX (one of the 40 counties included in the air quality characterization). Note that most 

of the counties with the greatest port emissions were not evaluated in the air quality 

characterization because they did not meet the high SO2 concentration-based selection criterion. 
July 2009 261 

Table 8-20. Total SO2 emissions and total port SO2 emissions in the St. Louis and the 40 Counties 

used in the air quality characterization. 

SO2 Emissions1

Total Ports 

State County (tpy) (tpy) Rank 

% of 

Total 

Rank of 

% 

TX Jefferson County 33,608 4,489 1 13.4% 3 

PA Allegheny County 56,411 2,666 2 4.7% 9 

FL Hillsborough County 70,231 2,168 3 3.1% 12 

NJ Hudson County 22,300 2,044 4 9.2% 4 

MO St. Louis (3-County Area) 90,135 1,860 5 2.1% 13 

DE New Castle County 53,626 1,693 6 3.2% 11 

NJ Union County 3,840 1,657 7 43.2% 1 

TN Shelby County 31,023 1,243 8 4.0% 10 

OH Cuyahoga County 12,681 631 9 5.0% 8 

NY Bronx County 3,747 295 10 7.9% 7 

OH Lake County 73,316 294 11 0.4% 18 

IN Lake County 40,063 209 12 0.5% 16 

WV Hancock County 2,055 177 13 8.6% 6 

MI Wayne County 74,832 177 14 0.2% 23 

WV Wayne County 1,071 150 15 14.0% 2 

MO Jefferson County 40,481 132 16 0.3% 19 

PA Beaver County 42,685 130 17 0.3% 20 

WV Brooke County 1,355 119 18 8.7% 5 

NY Erie County 50,858 108 19 0.2% 24 

OK Tulsa County 8,181 90 20 1.1% 14 

IL Madison County 27,396 81 21 0.3% 21 

IA Muscatine County 24,890 71 22 0.3% 22 

TN Blount County 5,164 43 23 0.8% 15 

PA Washington County 8,189 41 24 0.5% 17 

WV Monongalia County 92,677 20 25 0.0% 27 

IN Floyd County 48,653 20 26 0.0% 25 

NY Chautauqua County 57,835 9 27 0.0% 28 

VA Fairfax County 3,741 1 28 0.0% 26 

IN Gibson County 127,934 - 29 0.0% 29 

IN Vigo County 66,170 - 29 0.0% 29 

PA Northampton County 58,598 - 29 0.0% 29 

MO Iron County 47,562 - 29 0.0% 29 

NH Merrimack County 31,812 - 29 0.0% 29 

TN Sullivan County 30,999 - 29 0.0% 29 

AZ Gila County 18,594 - 29 0.0% 29 

IA Linn County 17,324 - 29 0.0% 29 

OH Summit County 12,868 - 29 0.0% 29 

MO Greene County 11,819 - 29 0.0% 29 

PA Warren County 5,222 - 29 0.0% 29 

VI St Croix 122 - 29 0.0% 29 

IL Wabash County 55 - 29 0.0% 29 

Notes: 

1

 SO2 emissions were calculated from the 2002 NEI. Emissions originating from ports were 

calculated using SCC for marine vessels: 2280002100, 2280002200, 2280003100, 

2280003200, 2282020005. 

 
July 2009 262 

Table 8-21. The top 40 counties with the greatest total port SO2 emissions, including SO2

emissions from ports in the St. Louis modeling domain. 

State County Name 

Port 

Emissions 

(tpy) Rank 

CA Los Angeles 13,817 1 

LA St. John the Baptist Parish 10,605 2 

CA Santa Barbara County 8,831 3 

TX Harris County 8,142 4 

CA San Diego County 5,408 5 

MD Baltimore City 4,582 6 

LA Orleans Parish 4,579 7 

TX Jefferson, Co 4,489 8 

TX Nueces County 3,545 9 

LA East Baton Rouge Parish 3,435 10 

LA Iberville Parish 3,179 11 

TX Galveston County 3,123 12 

OR Multnomah County 3,004 13 

LA Calcasieu Parish 2,728 14 

PA Allegheny County 2,666 15 

AL Mobile County 2,582 16 

WV Cabell County 2,575 17 

CA Ventura County 2,406 18 

AK Valdez-Cordova 2,243 19 

NH Cheshire County 2,231 20 

FL Hillsborough County 2,168 21 

NY Kings County 2,112 22 

PA Philadelphia County 2,069 23 

NH Strafford County 2,044 24 

NH Hillsborough County 1,998 25 

MN St. Louis County 1,987 26 

VA Norfolk City 1,980 27 

MO St. Louis 3-County Area 1,860 28 

NY Richmond County 1,818 29 

CA Orange County 1,770 30 

MI Presque Isle County 1,748 31 

CA Contra Costa County 1,716 32 

DE New Castle County 1,693 33 

NH Union County 1,657 34 

CA Orange County 1,615 35 

CA San Francisco County 1,530 36 

TX Brazoria County 1,367 37 

WA Clallam County 1,356 38 

NY Queens County 1,341 39 

MI Alger County 1,284 40 

TN Shelby County 1,243 41 

Notes: 

1

 SO2 emissions were calculated from the 2002 NEI. Emissions originating from ports were calculated 

using SCC for marine vessels: 2280002100, 2280002200, 2280003100, 2280003200, 2282020005. 
July 2009 263 

Table 8-22. SO2 emission density the two exposure modeling domains and several counties 

within selected U.S. Cities. 

State City FIPS1

 County 

Total SO2

Emissions2

(tpy) 

Land 

Area3

(miles2

) 

Emission 

Density 

(tons/miles2

)

NY New York 

36005 

36047 

36061 

36081 

36085 

Bronx 

Kings 

New York 

Queens 

Richmond 38,036 303 125 

OH Cleveland 

39035 

39085 

Cuyahoga 

Lake 85,997 686 125 

MI Detroit 26163 Wayne 74,832 614 122 

PA Philadelphia 42101 Philadelphia 11,614 135 86 

IN Gary 18089 Lake County 40,063 497 81 

MO St. Louis 

29183, 

29189, 

29510 

St. Charles 

St. Louis 

St. Louis (city) 90,135 1,130 80 

PA Pittsburgh 42003 Allegheny 56,411 730 77 

FL Tampa 12057 Hillsborough 70,231 1,051 67 

NY Buffalo 36029 Erie County 50,858 1,044 49 

IL Chicago 17031 Cook 35,191 946 37 

TX Beaumont-Port Arthur 48245 Jefferson 33,608 904 37 

TX Houston 48201 Harris 60,924 1,729 35 

GA Atlanta 

13067 

13089 

13121 

13135 

Cobb 

DeKalb 

Fulton 

Gwinett 48,606 1,570 31 

MA Boston 

25017 

25019 

25021 

Middlesex 

Norfolk 

Suffolk 23,712 1,282 19 

MO Springfield 29077 Greene County 11,819 675 18 

CA Los Angeles 06037 Los Angeles 17,175 4,061 4 

Notes: 

1

 Federal Information Processing Standard Code 

2

 The emissions totals come from tier 1 data in the 2002 NEI (02nei_v3tier_summary_oct_15_2007.zip). 3

 The county land area statistics come from the Census 2000 STF1. Available at : 

http://factfinder.census.gov/servlet/ 

Staff evaluated the emission density within the two exposure modeling domains and for 

counties within several highly populated U.S. Cities. The emission density was calculated by 

dividing the total emissions (tpy) by the physical area (mile2

) of the location. These data are 

presented in Table 8-22. Greene County (or Springfield, Mo.) has one of the lowest emission 

densities, another attribute of the county that could have led to the few estimated number of 

persons exposed above benchmark levels. On the other hand, St. Louis has a medium-to-high 
July 2009 264 

emission density, likely one of the factors contributing to the much greater estimated numbers of 

persons exposed above benchmark levels. The emission density in St. Louis is similar in 

magnitude with counties in Philadelphia PA, Gary IN, and Pittsburgh PA, though much higher 

than several counties within large U.S cities such as Atlanta, Boston, Chicago, Houston, and Los 

Angeles. Three cities had a distinctly higher emission density than St. Louis: New York, 

Cleveland, and Detroit. We note that four counties within these cities with the greatest emission 

density were evaluated in the air quality characterization: the Bronx, Cuyahoga, Lake, and 

Wayne. 

In considering the air quality benchmark exceedance rankings of other counties combined 

with their emissions and population density rankings, one could possibly argue for other 

locations to conduct an exposure analysis that may provide different results for the as is air 

quality scenario.78 Staff began assessing two additional locations for detailed exposure 

modeling, i.e., Allegheny and Cuyahoga counties.79 Unresolved technical issues remained 

regarding the agreement between dispersion-modeled and ambient measured concentrations, 

preventing their inclusion in this final REA. The numbers of estimated air quality benchmark 

exceedances in these two counties were ranked similarly to St. Louis (both counties were within 

the 50th-75th percentiles). In addition, all of the monitors in Allegheny and Cuyahoga County 

had at most moderately rated COVs (between 100-200%), suggesting that exposure results 

estimated in those locations would be similar to that estimated in St. Louis. However, the high 

emission density for Cuyahoga and Lake Counties (Cleveland) could indicate that a greater 

number of persons might be exposed above benchmark levels when using the as is air quality. 

While locations such as Los Angeles have greater estimated emissions originating from ports, the 

SO2 concentration levels measured at ambient monitoring data in these locations did not 

approach the levels used for selection in the air quality characterization. In addition, the 

emission density in Los Angeles County was the lowest of all of the cities selected for that 

evaluation. Given each of the above rankings and available monitoring data, staff judges St. 

Louis and Greene County as reasonable choices for the detailed exposure assessment, 

particularly considering the range of air quality scenarios investigated. 

 

78 For example, Hillsborough Fl. has a few bin C monitors, ranks 7th in population, 21st in emissions within 20 km of 

monitors, 21st in countywide port emissions, and medium emission density. 79 Allegheny county ranked 10th in population, 31st in SO2 emissions within 20 km of monitors, and 23rd in 

countywide port emissions. Cuyahoga county ranked 5th in population, 25th in SO2 emissions within 20 km of 

monitors, though not ranked within the top 40 counties using port emissions. 
July 2009 265 

8.10.4 American Housing Survey (AHS) Data 

The American Housing Survey (AHS), conducted by the Bureau of the Census for the 

Department of Housing and Urban Development (HUD), collects data on the nation's housing. 

Relevant housing characteristic data, including residential prevalence of air conditioning are 

summarized for 13 locations using the available metropolitan areas surveyed by the AHS (Table 

8-23). Because survey years differ for each location and some locations contained more than one 

survey, the most recent data or data closest to 2002 were selected (the year for the exposure 

modeling). The A/C prevalence can vary greatly across urban areas, based largely on climate 

differences. The air conditioning prevalence can influence the air exchange rate in a residence, 

potentially affecting the infiltration rate of outdoor air concentrations into the indoors residential 

microenvironment. St. Louis was estimated to have one of the highest air conditioning 

prevalence rates, though similar rates could be found in Miami, Phoenix, Atlanta, and 

Washington D.C. A few of the urban areas listed have much lower A/C prevalence rates, 

including Los Angeles with 57.4% and Boston with 63.1%. For locations having a low A/C 

prevalence, it is expected that the number of indoor residential exposures to daily maximum NO2

concentrations above selected benchmarks would be greater compared to those estimated in St. 

Louis. However, given the limited contribution of the indoor microenvironment to the number 

of exceedances even considering much lower A/C prevalence rates (section 8.11.2.2.9; also EPA 

2008d), modeled increases in the numbers of persons exposed in these other locations would 

likely be small. 
July 2009 266 

Table 8-23. Residential A/C prevalence for housing units in several metropolitan locations in the 

U.S. (AHS, 2008). 

8.10.5 Asthma Prevalence 

 Staff compared regional asthma prevalence statistics for children <18 years in age and all 

persons. For children, the estimated age-adjusted percents of ever having asthma are presented 

in Table 8-24 using data from Dey et al. (2004). There are similar prevalence rates for asthmatic 

children in three of the four regions of the U.S. (Midwest, South, and West), suggesting that 

exposure analyses conducted in these broader regions may result in similar distributions in the 

percent of asthmatics exposed to the two Missouri modeling domains used in this assessment. 

The Northeastern U.S. has a higher percentage of asthmatic children. This suggests that there 

may be a greater percentage of peak exposures to asthmatic children in the Northeast than 

compared with the percent modeled in St. Louis or Greene County, holding all other influential 

variables are constant (e.g., time spent outdoors, a similar air quality distribution). 

Staff weighted the BRFSS 2002 state-level adult asthma prevalence rates (self-reported) 

to generate prevalence rates for five U.S regions (Table 8-25).80 Similar rates (between 7.6-

 

80 http://www.cdc.gov/asthma/brfss/02/current/tableC1.htm. Regions were mapped using Table 8-12. 

Location

AHS 

Survey 

Year 

A/C 

Prevalence1

(%)

Atlanta 2004 97.2 

Boston 1998 63.1 

Chicago 2003 89.6 

Cleveland 2004 75.8 

Denver 2004 66.9 

Detroit 2003 82.4 

Los Angeles 2003 57.4 

Miami 2002 98.1 

New York 2003 83.3 

Philadelphia 2003 91.4 

Phoenix 2002 94.4 

St. Louis2 2004 96.7 

Washington DC 1998 96.0 

Notes: 

1

 Represents the percent of total year-round housing 

units having central or room unit air conditioners 

(AHS, 2008). 

2

 Note, a truncated value of 96% was used as input 

to APEX. The effect of this to estimated exposures is 

negligible. See section 8.11.2.2.9. 
July 2009 267 

7.9%) were estimated for three of the five regions (Mid-Atlantic, Midwest, and the Southwest), 

suggesting that exposure analyses conducted in these broader regions may result in similar 

distributions in the percent of asthmatics exposed to the two Missouri modeling domains used in 

this assessment. Consistent with that observed for asthmatic children, the Northeastern U.S. has 

the greatest percent of asthmatic adults. The Southeastern states on average were estimated to 

have the lowest adult asthma prevalence. This suggests that there may be a greater percentage of 

peak exposures to asthmatic adults in the Northeast and a lower percentage of peak exposures in 

the Southeast when compared with the percent modeled in St. Louis or Greene County, holding 

all other influential variables are constant (e.g., time spent outdoors, a similar air quality 

distribution). 

Table 8-24. Asthma prevalence rates for children in four regions of the U.S. 

Region 

Asthma Prevalence1 

(%) 

Northeast 15.2 

Midwest 11.6 

South 11.9 

West 11.1 

Notes: 

1

 prevalence is based on the question, ‘‘Has 

a doctor or other health professional ever 

told you that [child’s name] had asthma?’” 

(Dey et al., 2004)

Table 8-25. Asthma prevalence rates for adults in five regions of the U.S. 

Region1

Asthma Prevalence2 

(%) 

Mid-Atlantic 7.9 

Midwest 7.7 

Northeast 8.9 

Southeast 6.9 

Southwest 7.6 

Notes: 

1

 Table 8-17 was used in mapping the states to regions. 

2

 state level data obtained from 

http://www.cdc.gov/asthma/brfss/02/current/tableC1.htm. 
July 2009 268 

8.11 VARIABILITY ANALYSIS AND UNCERTAINTY 

CHARACTERIZATION 

As discussed in section 6.6, there can be variability and uncertainty in risk and exposure 

assessments. This section presents a summary and discussion of the degree to which variability 

was incorporated in the exposure analyses and how the uncertainty was characterized for the 

estimated number of persons and person days with exposure benchmark exceedances. 

8.11.1 Variability Analysis 

To the maximum extent possible given the data, time, and resources available for the 

assessment, staff accounted for variability within the exposure modeling. APEX has been 

designed to account for variability in nearly all of the input data, including the physiological 

variables that are important inputs to determining exertion level. As a result, APEX addresses 

much of the variability in exposure estimates given variability in factors that affect human 

exposure. The variability accounted for in this analysis is summarized in Table 8-26. 

Table 8-26. Summary of how variability was incorporated into the exposure assessment. 

Component Variability Source Comment 

Population data Individuals are randomly sampled from U.S. census 

blocks used in model domains, by age and gender. 

Activity patterns 

Data diaries are stratified from CHAD based on 30 daytype (summer weekday, non-summer weekday, 

weekend) and demographic group (males/females, ages 

0-4, 5-11, 12-17, 18-64, 65+). 

Block-level commuting 

An individuals’ commuting location is randomly sampled, 

using adjusted U.S. census tract data that account for 

fine-scale land use at the block level. 

Simulated 

Individuals 

Employment Work status is randomly generated from U.S. census 

data at the tract-level by age and gender. 

Modeled ambient SO2

concentrations 

Spatial: modeled ambient SO2 to block-level receptors. 

Temporal: 1-hour SO2 for an entire year predicted using 

AERMOD; 5-minute SO2 within each hour estimated 

Ambient Input using APEX. 

Meteorological data 

Spatial: Local surface and upper air NWS stations used. 

Temporal: 1-hour NWS wind data for 2002 

(supplemented by1-minute ASOS data). 

Resting metabolic rate 

Six age-group and two gender-specific regression 

equations using body mass as an independent variable 

(Johnson et al., 2000). 

Metabolic equivalents 

by activity (METS) 

Values randomly sampled from distributions developed 

for specific activities (some age-specific) (EPA, 2002). 

Oxygen uptake per unit 

of energy expended 

Values randomly sampled from a uniform distribution 

(Johnson et al., 2000). 

Physiological 

Factors Relevant to 

Ventilation Rate 

Body mass Values randomly sampled from lognormal distributions 

by gender and age (Isaacs and Smith, 2005). 
July 2009 269 

Component Variability Source Comment 

Body surface area Gender specific exponential equations using body mass 

as independent variable (Burmaster, 1998). 

Height 

Separate regression equation for children and adults, 

both gender and age-specific (4-groups); children use 

age as an independent variable; adults use body weight 

(Johnson et al., 2000). 

Air exchange rates 

Residential values randomly selected from lognormal 

distributions, stratified by 4 temperature groups and 

presence/absence of air conditioning. Other indoor 

values randomly sampled from a separate lognormal 

distribution. 

Air conditioning 

prevalence rates 

Values randomly sampled AHS survey data for St. 

Louis. 

Removal rates Values randomly selected for 5 microenvironmentspecific distributions, stratified by air conditioning usage. 

Physical Factors 

Relevant to 

Microenvironmental 

Concentrations 

Penetration factors 

Indoor/outdoor ratios randomly sampled from two 

uniform distributions for inside-vehicle 

microenvironments. 

8.11.2 Uncertainty Characterization 

The methods and the models used in this exposure assessment conform to the most 

contemporary modeling methodologies available. A similar combined dispersion and exposure 

modeling approach has been used recently in estimating human exposures for the NO2 NAAQS 

REA (EPA, 2008d). This increased level of complexity in the type and number of models used, 

the overall exposure modeling approaches, and its application in exposure assessments does not 

necessarily confer decreased levels of uncertainty. Staff believes however, that these types of 

complex assessments serve as an important step towards raising the degree of confidence in 

estimating exposures, particularly when the sources of uncertainty are systematically evaluated. 

Following the same general approach described in sections 6.6 and 7.8 and adapted from 

WHO (2008), staff performed a qualitative characterization of the components contributing to 

uncertainty in the exposure results. First, staff identified the important uncertainties. Then, we 

qualitatively characterized the magnitude (low, medium, and high) and direction of influence 

(over, under, both, and unknown) the source of uncertainty may have on the estimated number of 

persons and person days above benchmark levels. Finally, staff also qualitatively rated the 

uncertainty in the knowledge-base regarding each source using low, medium, and high

categories. Even though uncertainties in AERMOD concentrations predictions are an APEX 

input uncertainty, the uncertainties associated with each of the models are addressed separately 

here for clarity. Table 8-27 summarizes the results of the qualitative uncertainty analysis 

conducted by staff for the SO2 exposure assessment. 
July 2009 270 

Table 8-27. Summary of qualitative uncertainty analysis for the exposure assessment. 

Influence of Uncertainty 

on Exposure Benchmark 

Exceedances 

Source Type Direction Magnitude 

KnowledgeBase 

Uncertainty Comments1

Algorithms Unknown Low Low 

INF & KB: Multiple historical model evaluationunbiased ambient concentrations under varietpotential dispersion scenarios may not be adeunknown as to how they apply in this applicatimonitor comparisons in this application indicat

Meteorological Data Unknown Low – Medium Low 

INF: A limited number of missing hours of wind leading to under-estimation. Model predictionsensitivity to surface roughness characteristicsappropriate for the site of the meteorological dKB: Data are from a well-known and quality-asASOS wind data used to supplement 1-hour dcompleteness, reducing the number of calms 

Point Source Emissions and Profiles 

Both Low Low 

INF: Temporal emission characteristics are wemodeled point sources. KB: Most temporal data are from a well-known direct measurements. 

AERMOD Inputs and Algorithms2

Area Source Emissions and Profiles 

Both Low – Medium High 

INF: Temporal concentration characteristics wusing a generalized area source emission procovering a variety of emission source types. Hselected can be very influential to 1-hour concare a significant contributor to emissions. KB: While there were two alternative profiles aevaluated, a local generalized temporal emisson yielding the best model-to-monitor agreemewhether the generalized profile is an appropriatemporal profiles that exist for modeled area s

APEX Inputs and Algorithms 

AERMOD Modeled 1-hour Concentrations 

Both Low – Medium Medium 

INF: Model-to-monitor comparisons indicated the overestimations in concentration occurred concentrations (Figures 8-8 and 8-9), limiting testimated 5-minute concentrations. The spatconcentrations using modeling is likely an impconcentrations from the limited number of ambKB: While model-to-monitor agreement was vewell all other modeled receptors are represent
July 2009 271 

Accuracy of 5-minute Exposure Estimation 

Both Low – Medium High 

INF: The accuracy of the statistical model use

ambient concentrations was rated as having ainfluence (see section 7.4.2.6 and Table 7-16)KB: APEX annual average SO2 exposures are 

personal exposures of daily to multi-day averano 5-minute SO2 personal exposure data that 

output. 

Population Database Both Low Low 

INF & KB: Data are from a reliable, quality assthe limited uncertainty in the database would hexposure results. 

Commuting Database and Algorithm 

Both Low Medium 

INF: Most exposures above benchmark levels vehicles. Also note there is limited modeled s

concentrations in St. Louis. KB: Data are from a reliable, quality assured swas used as a surrogate for distributing the trablock-level. 

Activity Pattern Database Over Low – Medium Medium 

INF: Most of the potentially influential factors a

assumed) bounds or are controlled for by the Though most components are rated as potentinfluence in either direction, not accounting for 

asthmatics could result in a medium level of oKB: Data are from a reliable, quality assured sliterature was used for many of the compariso

in direct correspondence and applicability. 

Longitudinal Profile Algorithm 

Both Low – Medium Medium 

INF: The magnitude of potential influence wouestimates of multiday exposures. KB: Method compared reasonably well with aand two other methods, however long-term (i.eprofiles do not exist for a population. 

Meteorological Data Both Low Low 

INF: Daily maximum temperatures are only usdiaries to simulate individuals and in selecting distributions. KB: Data are from a well-known and quality-asASOS wind data used to supplement 1-hour dcompleteness, reducing the number of calms 

Air Exchange Rates Under Low – Medium Medium 

INF: Most peak exposures occur outdoors, tho

underestimated when not using all 5-minute co

(section 8.11.2.2.11). KB: Data used are not specific to St. Louis or 
July 2009 272 

A/C Prevalence Under Low Low 

INF: Most peak exposures occur outdoors, thounderestimated when not using all 5-minute co

(section 8.11.2.2.11). However a previous se

indicates extremely low A/C prevalence has litpercent of persons exposed. KB: Data used are specific for St. Louis, there estimate, and compares reasonably with data 

Indoor Removal Rate Unknown Low Medium 

INF: Most peak exposures occur outdoors, thounderestimated when not using all 5-minute co

(section 8.11.2.2.11). KB: Data used were obtained from comprehe

rates, however many assumptions were neederate distributions. 

Occurrence of Multiple Exceedances Within an Hour 

Under Low – Medium Medium 

INF: Analyses indicate that ignoring multiple pestimates exposure and hence the number of 35%. KB: While the frequency of multiple exceedanestimated, there are limited continuous 5-minurepresentativeness of the available data to mo

Asthma Prevalence Rate 

Both Low – Medium Low 

INF: The percent of asthmatics for Greene couwas similar to that of another independent estdistributions were not used in St. Louis, there estimate in the number of persons exposed. KB: Data for asthma prevalence are from reliasources. 

Notes: 1 INF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rat2 The magnitude/direction of influence and the uncertainty associated with the knowledge-base for each source identified for A

the predicted 1-hour concentrations, not the 5-minute benchmark exceedances. 
July 2009 273 

8.11.2.1 Dispersion Modeling Uncertainties 

Air quality data used in the exposure modeling was determined through use of EPA’s 

recommended regulatory air dispersion model, AERMOD (version 07026 (EPA, 2004a)), with 

meteorological data and emissions data discussed above. Parameterization of meteorology and 

emissions in the model were made in as accurate a manner as possible to ensure best 

representation of air quality for exposure modeling. Thus, the resulting air quality values are 

likely free of systematic errors to the best approximation available through application of 

modeled data. 

The characterization of uncertainty associated with this application of AERMOD is 

separated into two main sources: 1) model algorithms, and 2) model inputs. While it is 

convenient to discuss uncertainties in this context, it is also important to recognize that there is 

some interdependence between the two in the sense that an increase in the complexity of model 

algorithms may entail an increase in the potential uncertainty associated with model inputs. In 

the characterization that follows, AERMOD uncertainties are discussed regarding the impact to 

predicted 1-hour SO2 concentrations. 

8.11.2.1.1 Algorithms 

The AERMOD model was promulgated by EPA in 2006 as a “refined” dispersion model 

for near-field applications (with plume transport distances nominally up to 50 kilometers), based 

on a demonstration that the model produces largely unbiased estimates of ambient concentrations 

across a range of source characteristics, as well as a wide range of meteorological conditions and 

topographic settings (Perry, et al., 2005; EPA, 2003). While a majority of the 17 field study 

databases used in evaluating the performance of AERMOD are associated with elevated plumes 

from stationary sources (i.e., typically electrical generating units), a number of evaluations 

included low-level releases. Moreover, the range of dispersion conditions represented by these 

evaluation studies provides some confidence that the fundamental dispersion formulations within 

the model will provide robust performance in other settings. 

AERMOD is a steady-state, straight-line plume model, which implies limitations on the 

model’s ability to simulate certain aspects of plume dispersion. For example, AERMOD treats 

each hour of simulation as independent, with no memory of plume impacts from one hour to the 

next. As a result, AERMOD may not adequately treat dispersion under conditions of 
July 2009 274 

atmospheric stagnation or recirculation when emissions may build up within a region over 

several hours. This could lead to ambient concentration under-predictions by AERMOD during 

such periods. On the other hand, AERMOD assumes that each plume may impact the entire 

domain for each hour, regardless of whether the actual transport time for a particular sourcereceptor combination exceeds an hour. This could lead to ambient concentration overpredictions by AERMOD. While these assumptions imply some degree of physically unrealistic 

behavior when considering the impacts of an individual plume simulation, their importance in 

terms of overall uncertainty will vary depending upon the application. The degree of uncertainty 

attributable to these basic model assumptions is likely to be more significant for individual 

plume simulations than for a cumulative analysis based on a large inventory. This question 

deserves further investigation to better define the limits and capabilities of a modeling system 

such as AERMOD for large scale exposure assessments such as this. The evidence provided by 

the model-to-monitor comparisons presented in section 8.4.5 is encouraging as to the viability of 

the approach in this application when adequate meteorological and other inputs are available. 

However, each modeling domain and inventory will present its own challenges and will require a 

separate assessment based on the specifics of the application. 

One of the improvements in the AERMOD model formulations relative to the Industrial 

Source Complex - Short Term (ISCST) model which it replaced is a more refined treatment of 

enhanced turbulence and other boundary layer processes associated with the nighttime heat 

island influence in urban areas. The magnitude of the urban influence in AERMOD is scaled 

based on the urban population specified by the user. Since the sensitivity of AERMOD model 

concentrations to the user-specified population is roughly proportional to population to the 1/4th

power, this is not a significant source of uncertainty. The population areas of interest for this 

application are also well-defined, thus reducing any uncertainty associated with specification of 

the population or with defining the extent of the modeling domain treated as urban. 

Therefore, based on the evidence in historical and recent model evaluations and the 

improved AERMOD model formulations, staff judges that algorithm uncertainty has a low 

magnitude of influence on the estimated 1-hour concentrations. The direction of influence is 

largely unknown, given the limitations in determining how the basic model assumptions apply to 

a large-scale analysis. While the AERMOD model algorithms are not considered to be a 

significant source of uncertainty for this assessment, the representativeness of modeled 
July 2009 275 

concentrations for any application are strongly dependent on the quality and representativeness 

of the model inputs. The main categories of model inputs that may contribute to uncertainty are 

the meteorological input data and emissions estimates. These issues are addressed in the 

following sections. 

8.11.2.1.2 Meteorological Data 

Details regarding the representativeness of the meteorological data inputs for AERMOD 

are addressed separately in section 8.4.2 and in Attachment 1 in Appendix B. The data are from 

a well-known, reliable source (NWS) and assumed vetted for extraordinary values by the 

database architects and data users. Calm and missing 1-hour wind data have been supplemented 

with 1-minute ASOS data averaged to the hour, decreasing the number of each within the input 

data sets used. A limited number of missing values remained (1.1 – 1.5%), however staff 

expects these to have a negligible effect on the overall 1-hour concentration profile. 

An important issue associated with representativeness is the sensitivity of the AERMOD 

model to surface roughness, because the roughness at the location of the meteorological tower 

site used to process the meteorological data for use in AERMOD may be very different from the 

surface roughness across the full domain of sources. This issue has been shown to be more 

significant for low-level sources due to the importance of mechanical shear-stress induced 

turbulence on dispersion for such sources. A previous application of the AERMOD model to 

support the REA for the NO2 NAAQS review (EPA, 2008d) provided an opportunity for a direct 

assessment of this issue by comparing AERMOD modeled concentrations based on processed 

meteorological data from the Atlanta Hartsfield airport (ATL) with concentrations based on 

processed meteorological data from a Southeast Aerosol Research and Characterization study 

(SEARCH) monitoring station located on Jefferson Street (JST) near Georgia Tech. The ATL 

data were representative of an open exposure, low roughness, site typical for an airport 

meteorological station. The JST data were representative of a higher roughness exposure more 

typical of many locations within an urban area. Surface roughness lengths were generally about 

an order of magnitude higher at the JST site relative to the ATL site. A comparison of 

AERMOD modeled concentrations for the mobile source NOx inventory, representing near 

ground-level emissions, showed relatively good agreement in modeled concentrations based on 

the two sets of meteorological inputs, at least for the peak of the concentration distribution at 

four monitor locations across the modeling domain. This suggests that the sensitivity of 
July 2009 276 

AERMOD model results to variations in surface roughness may be less significant than 

commonly believed, provided that meteorological data inputs are processed with surface 

characteristics appropriate for the meteorological site. 

Therefore, based on the improved completeness of the wind data used and the low 

sensitivity of peak model predictions to surface roughness characteristics, as long as they are 

appropriate for the site of the meteorological data inputs, staff judges the potential magnitude of 

influence from the meteorological data as low to medium. While it is possible that 1-hour 

concentrations may be under-estimated based on missing wind data, it is largely unknown what 

the overall direction of influence might be when considering the potential influence of other 

meteorological parameters such as surface roughness. 

8.11.2.1.3 Point Source Emissions and Profiles 

As explained in section 8.4.3, point source emission levels were derived from the NEI 

with source locations independently verified with GIS analysis of aerial photography. Temporal 

profiles were derived from a variety of databases. Temporal profiles for all the modeled point 

sources in Greene County and almost half of those in the St. Louis modeling domain were 

derived from the CAMD database, which provides hourly emission profiles. For the remaining 

modeled stacks inside the St. Louis domain, a uniform temporal profile was used. For most of 

point sources located outside of the St. Louis domain but close enough to influence its air 

quality, the temporal profiles were from the EMS-HAP emission model. 

Therefore, given that the emissions data are from well-known quality-assured sources, 

the emission source locations were independently verified, and that the temporal profiles for 

most of the emission sources were known, staff judges the magnitude of influence from this 

potential source of uncertainty as low and assumes there is an equal tendency to over- or underestimate 1-hour SO2 concentrations. Further, staff also characterizes the knowledge-base for this 

source as having a low level of uncertainty. 

8.11.2.1.4 Area Source Emissions and Profiles 

Details regarding the modeling of non-point and background area sources in AERMOD 

were addressed in Section 8.4.3. In the case of SO2, the area source emissions category for 

AERMOD represents a cumulative approximation of several lesser point sources, such as small 

commercial/industrial boilers, which are not represented as individual sources within the existing 

emissions inventories due to their limited emissions. There is a lack of detailed information 
July 2009 277 

regarding the location and release characteristics of these small emission sources, thus estimated 

emissions are typically aggregated at a county level within the emission inventories. Given these 

limitations in terms the emission inventory, two of the main uncertainties associated with 

modeling these sources are the temporal and spatial profiles used in simulating their releases. 

Lacking detailed location information, the emissions are assumed to be uniformly distributed 

across a specified area, typically at a county or census tract level since the emissions are 

aggregated at the county level, and spatially allocated using population as one of the surrogates. 

An additional uncertainty associated with the area source category for SO2 emissions is the 

likelihood that the actual emissions may be associated with some plume buoyancy that cannot be 

explicitly treated using the area source algorithm within the dispersion model. At best, the 

anticipated aggregate effect of plume buoyancy can be reflected through the release height 

assigned to the area source. 

As discussed in Section 8.4.3, all emissions in the regions of interest were simulated, 

either through their representative group (point sources, port-related sources, or other non-point 

area sources) or through cumulative background sources. Staff obtained emission estimates from 

the 2002 National Emissions Inventory (NEI) however, only annual total emissions at the county 

level are provided. To better parameterize these emissions for the hourly, census block-level 

dispersion modeling conducted here, we relied on additional data and an algorithm to optimize 

model performance based on available model-to-monitor comparisons. 

Additional data related to the spatial distribution of non-point emissions was used to 

spatially allocate county-wide emissions to census tracts in the Greene County domain. Staff 

used the spatial allocation factors (SAFs), based on land use patterns, from EPA’s EMS-HAP 

database to allocate 87% of the non-point emissions to the subset of specific tracts expected to 

contain the most emissions. Emissions within each modeled tract were simulated as uniform 

over the tract, while emissions outside the modeled tracts and other residual emissions were 

characterized as uniform over an entire county. The performance obtained by using tract-level 

emission sources in Greene County was verified by model-to-monitor comparisons. In the St. 

Louis area, model performance evaluations using factors from the EMS-HAP database made it 

apparent that the spatial allocations were mischaracterized for this area. Thus, in the St. Louis 

area, spatial bias was avoided by modeling non-point emissions with a uniform density 

throughout each of the counties of interest instead of allocating emissions to specific census 
July 2009 278 

tracts. In both cases, using spatially uniform emissions resolved to the tract or county level 

improves spatial representation and reduces the overall level of uncertainty. 

Unlike point sources, where the temporal profile was based largely on direct observations 

via the CAMD database, these non-point emission profiles are based on generalized emissions 

surrogates and may not well represent a specific source or local group of sources. Model 

performance evaluations of diurnal profiles suggested that temporal factors derived from the 

EMS-HAP emission model inadequately represented the true, aggregate, temporal release 

profile.81 Unlike the spatial allocations, however, uniformly distributing the emissions in time 

resulted in significantly worse model-to-monitor agreement than using these sample profiles. In 

order to account for these uncertainties in the temporal profiles of area source emissions, an 

algorithm was developed to determine the optimal temporal emission release profile in each area. 

Examination of the diurnal profiles of modeled and monitored concentrations with uniform and 

with EMS-HAP emission profiles for monitors in locations dominated by area sources showed 

that, while monitored concentrations increased during the daytime, modeled concentrations 

actually decreased. An examination of the dispersion characteristics showed that increased 

dilution during the daytime overcame the small increase in emission strength predicted using the 

EMS-HAP profile, which lacks local emission information. Thus, it is reasonable to conclude 

that industrial and commercial/institutional area source emissions in the St. Louis and Greene 

County areas would have a more pronounced diurnal cycle than is reflected in the EMS-HAP 

temporal profile. 

This method of determining an appropriate, local, non-point source emission profile has 

the advantage of preserving total emissions reflected in the emission inventory while deducing 

what the actual temporal emission profile from these local sources should be, based on the 

observed trends in each region. Essentially, it derives an emission profile that best agrees with 

observations when coupled with local meteorology and pollutant dispersion. This is justified 

given the lack of detail regarding emission characteristics of local area sources. This derived 

profile implies that the emission sources are active almost exclusively during the daytime from 

approximately 8 am to 8pm. Given that the emission sources represented by the industrial and 

commercial/institutional non-point category are small, the possibility that their cumulative 

 

81 Figures 8-4 and 8-5 also show the corresponding temporal profile from the SMOKE emission model, which is 

very similar to the temporal profile obtained from the EMS-HAP model. 
July 2009 279 

emissions occur almost exclusively during daytime hours is plausible. However, in knowing that 

there are large variations in the assumed local emission characteristics versus limited and broadly 

defined emission characteristics for potential area sources, there is high level of uncertainty in 

the knowledge-base. The selected approach though effectively mitigates the magnitude of 

influence the uncertainty has on the modeling results by the application of a systematic approach 

to minimize discrepancies between predicted and observed values. Based on the discussion 

regarding the use of spatial allocation factors and the adjustments made to the area source 

temporal profile, staff judges the magnitude of influence to range from low to medium. 

8.11.2.2 Exposure Modeling Uncertainties 

APEX is a powerful and flexible model that allows for the reasonable estimation of air 

pollutant exposure to individuals. Since it is based on actual human time-location-activity 

diaries and accounts for the most important variables known to affect exposure (i.e., where 

people are located and what they are doing), it has the ability to effectively approximate actual 

human exposure conditions. In addition, staff selected to the best available input data to 

temporally and spatially represent the ambient concentrations and exposures given the time and 

resources allocated for the assessment. However, there are constraints and uncertainties 

associated with the input data and modeling approaches that may correspond to uncertainties in 

the modeling results. 

In the characterization that follows, exposure modeling uncertainties are discussed 

regarding their influence to the estimated number of persons and person-days above benchmark 

exceedances. Staff primarily focused on the uncertainties and assumptions associated with SO2

specific exposure model inputs, their utilization, and application in this exposure assessment. 

Note also that some sensitivity analyses for certain components of APEX (see EPA, 2007d; 

Langstaff, 2007) or input variables (EPA, 2008d) have been performed previously in other 

NAAQS reviews. Those previous analyses that are relevant to the current SO2 NAAQS review 

are also included, though only summarized below. 

8.11.2.2.1 AERMOD Modeled 1-hour Concentrations 

The AERMOD model-to-monitor comparisons (section 8.4.5) indicated very good 

agreement. Most over-estimations in 1-hour SO2 concentrations occurred at the lowest 1-hour 

concentrations, effectively limiting the potential magnitude of influence on estimated 5-minute 
July 2009 280 

air quality and exposure concentrations. At the upper tails of the distribution (> 80th percentile), 

there was a mixture of over- and under-estimation in 1-hour SO2 concentrations, most of which 

were on the order of 1-2 ppb. Staff performed an additional evaluation in Greene County to 

compare estimated benchmark exceedances resultant from the variable concentration 

distributions given by the ambient monitoring data and AERMOD predictions (rather than 

simply comparing the 1-hour concentrations). The results indicated there was not a significant 

influence to the estimated air quality benchmark exceedances from the limited differences 

observed in the upper percentiles of the 1-hour concentration distributions. 

Further, AERMOD was used in this exposure assessment to improve the spatial 

representation of ambient concentrations given the limited number of ambient monitors in each 

modeling domain. The dispersion modeling of SO2 concentrations to census block receptors is 

judged by staff as improvement over using monitored concentrations alone as an input to APEX. 

This may be of greater importance in Greene County where there was greater variability in the 

modeled concentrations at the receptors surrounding each ambient monitor (see section 8.4.5). 

In addition, the use of concentrations estimated at the census block centroids is judged by staff a 

reasonable. This is because the centroids are not expected to be at systematically farther 

distances from emission source than specific percentages of the population residing within the 

census block. 

Therefore, based on the above discussion, staff judges the potential magnitude of 

influence from this source of uncertainty as low to medium, recognizing there could be some 

conditions that would lead to over- or under-estimation of 5-minute SO2 concentrations. While 

there are limited differences in the modeled versus measured data, it is unknown how the modelto-monitor agreement represents all other modeled receptors in the absence of additional ambient 

monitoring data. Based on the discussion above regarding the current and historical AERMOD 

performance evaluations (section 8.11.2.1.1), staff judges the knowledge-base as having a 

medium level of uncertainty. 

8.11.2.2.2 Accuracy of 5-minute Exposure Estimation 

Uncertainties in the accuracy of the statistical model used in calculating 5-minute SO2

ambient concentrations was rated as having between low and medium levels of influence 

(section 7.4.2.6 and Table 7-16). Staff assumes, because of the strong relationship between 

ambient concentrations and personal exposures (in the absence of indoor sources), the same 
July 2009 281 

influence rating would apply here with mainly limited opportunities for both over- and underestimation of 5-minute benchmark exceedances. This strong relationship between ambient 

concentration and personal exposure though is noted as based solely on longer term averaging 

times (single day to weeks in duration) and was discussed earlier in section 7.4.2.7. 

Staff performed an additional qualitative analysis using the personal exposure 

measurements reported in the ISA. As a default output from the APEX model, annual average 

exposures were generated for each simulated individual (i.e., the full population rather than just 

the identified subpopulation). Exposure results for the entire population (e.g., annual average 

exposure concentrations) are assumed by staff as representative of exposures the asthmatic 

population would receive because the asthmatic population should not have its 

microenvironmental concentrations estimated any differently from those of the total 

population.82 

Selected percentiles of the distribution of annual average exposures for the APEX 

simulated individuals is given in Table 8-28. Annual average AERMOD predicted ambient SO2

concentrations were calculated for every receptor in the two modeling domains. The selected 

percentiles of the distribution of annual average concentrations for the AERMOD predicted 

ambient SO2 is also given in Table 8-28. As expected, the APEX exposure concentrations are 

consistently lower than the AERMOD predicted ambient concentrations. The relationship 

between exposure and ambient, as determined by the ratio of the medians, are approximately 

0.18 and 0.23 for St. Louis and Greene Counties, respectively. For general comparison, the 

range of values developed from personal/ambient concentration linear regression slopes reported 

by the ISA (ISA, section 2.3.6.2) is generally from 0.07 to 0.13. These measurement values 

describing the relationship between personal exposure and ambient concentrations may be lower 

than expected due to presence of personal exposure measurements below the limit of detection. 

Note, the upper range (i.e., 0.13) was reported from a study containing the greatest percent of 

samples above the limit of detection (ISA, section 2.3.6.2). We also lack information regarding 

the value of the regression intercepts in these studies (i.e., if any were non-zero) to approximate 

ratios that would be more comparable to the modeled values presented here. 

For additional comparison, personal exposure measurements conducted in Baltimore, 

Boston, and Steubenville are presented in Table 8-29 (see ISA Tables 2-14 and 2-15). While 

 

82 Assumptions regarding activity patterns of asthmatics and non-asthmatics is discussed further in section 8.11.2.5 
July 2009 282 

there are large differences in averaging time, sample size, study year, and city selected, the 

personal exposure measurement concentrations compare well with selected percentiles of the 

APEX exposure concentration distribution for the total simulated population in Greene County 

and St. Louis. 

Table 8-28. Distribution of APEX estimated annual average SO2 exposures for simulated 

individuals in the Greene County and St. Louis modeling domains. 

Greene County 

(n=50,000)2

St. Louis 

(n=150,000)2

Annual Average 

SO2 (ppb)1 APEX - Exposure 

AERMOD - 

Ambient 

APEX - 

Exposure 

AERMOD - 

Ambient 

mean 0.4 2.0 1.4 8.2 

std 0.2 1.5 0.3 2.4 

p0 0.1 0.1 0.4 1.2 

p1 0.1 0.2 0.8 2.3 

p5 0.2 0.2 1.0 4.5 

p10 0.2 0.3 1.1 5.7 

p25 0.3 0.6 1.2 6.8 

p50 0.4 1.6 1.4 7.9 

p75 0.5 3.1 1.6 10.0 

p90 0.6 4.2 1.8 11.2 

p95 0.6 4.7 2.0 11.6 

p99 0.8 5.5 2.4 13.2 

p100 1.1 6.0 8.6 45.2 

Notes: 

1

 mean is the arithmetic mean; std is the arithmetic standard deviation; percentile of the 

distribution is given by number following “p” (e.g., p25 is the 25th percentile). 2

 number of simulated individuals. 
July 2009 283 

Table 8-29. Personal SO2 exposure measurement data from the extant literature. 

Study1

Sarnat 

(2000) 

Sarnat 

(2001)2

Sarnat 

(2005) 

Sarnat 

(2005) 

Brauer 

(1989) 

Sarnat 

(2006) 

Sarnat 

(2006) 

City Baltimore Baltimore Boston Boston Boston Steubenville Steubenville 

Season Winter Winter Summer Winter Summer Summer Winter 

Averaging 

Time 12 days 1 day 1 day 1 day 1 day 11 Weeks 12 Weeks 

n3 14 45 28 29 48 10 10 

SO2 Personal Exposures (ppb)4

mean - - 0.3 - 0.5 ND - 1.9 - 1.5 0.7 

std - - - - - 3.3 1.9 

p0 ND - - - - - - 

p5 - ND - - - - - 

p10 - - - - 0.4 - - 

p90 - - - - 1.8 - - 

p95 - 3 - - - - - 

p100 1.2 - - - - - - 

Notes: 

1

 See ISA Tables 2-14 and 2-15 for further details regarding study conditions. Reference is provided here using 

primary author and year of publication. 

2

 The cohort for Sarnat (2001) consisted of 15 seniors, 15 children, and 15 COPD patients. Seniors and COPD 

patients had similar exposures, with children having somewhat higher exposure. 3

 number persons in study. 

4

 mean is the arithmetic mean; std is the arithmetic standard deviation; percentile of the distribution is given by 

number following “p” (e.g., p10 is the 10th percentile); ND is not detected. 

APEX modeled exposures have previously been compared with personal exposure 

measurements for O3 (EPA, 2007d). Briefly, APEX O3 simulation results were compared with 

weekly personal O3 concentration measurements for children ages 7-12 (Xue et al., 2005; Geyh 

et al., 2000). Two separate areas of San Bernardino County were surveyed: urban Upland CA, 

and the combined small mountain towns of Lake Arrowhead, Crestline, and Running Springs, 

CA. Available ambient monitoring data for these locations were used as the air quality input to 

APEX. APEX predicted personal exposures for both locations reasonably well for much of the 

concentration distribution, but tended to underestimate exposures at the upper percentiles of the 

distribution. The average difference between the weekly means was less than 1 ppb, with a 

range of -11 ppb to 8 ppb, though predicted upper bounds for a few weeks with higher exposure 

concentrations were under-predicted by up to 24 ppb (e.g., Figure 8-22). In addition, modeled 

exposure concentration variability was less than that observed in the personal exposure 

measurements. These differences appear to be driven by under-estimation of the spatial 

variability of the outdoor concentrations (EPA, 2007d). 
July 2009 284 

0

20

40

60

80

1

Concentration (ppb)

Measured APEX range

measured APEX 

Figure 8-22. Means of weekly average personal O3 exposures, measured and modeled (APEX), 

Upland Ca. Figure obtained from EPA (2007d). 

In addition, APEX modeled exposures have previously been compared with personal 

NO2 exposure measurements in Atlanta (EPA, 2008d). Daily personal NO2 exposure 

measurements were obtained from Suh (2008) for 30 participants of a 1999-2000 Atlanta 

epidemiological study conducted by Wheeler et al. (2006) across two seasons.83 An exposure 

distribution was constructed for each individual, simply using the individual’s minimum, 

median, and maximum daily mean exposures (e.g., Figure 8-23, top). Daily mean NO2

exposures estimated using APEX were also evaluated in a similar manner, by stratifying the 

results based on the same two seasons. The specific period from 1999-2000 was not modeled by 

APEX; simulation results for year 2002 were used in the comparison. A distribution of each 

person’s estimated daily exposure was also constructed, using the median daily exposure to 

represent the central tendency and a 95 % prediction interval to represent the lower and upper 

bounds of exposure (e.g., Figure 8-23, bottom). The distributions of median daily exposures 

compared better for the spring season, along with the range of estimated daily mean exposures 

given by the 95% prediction interval. However, APEX estimated exposures were greater during 

the fall. Median estimated daily exposures were consistently about 2 ppb higher than the 

personal exposure measurements across most of the percentiles of the distribution, and the APEX 

 

83 The minimum number of exposure measurements per subject was three days, the maximum was seven days. Fall 

was designated for sample collection dates reported in the months of September, October, and November 1999; 

Spring was designated where sample collection dates were reported in the months of April and May 2000. Only 

personal NO2 from ambient sources are discussed here. 
July 2009 285 

upper prediction intervals ranged consistently higher (between 10 and 40 ppb) compared with the 

maximum personal exposure measurement day (between 10 and 20 ppb).84 

Figure 8-23. Daily average personal NO2 exposures, measured and modeled (APEX), Atlanta Ga. 

Figure obtained from EPA (2008d). 

It is encouraging that the APEX longer-term exposure estimates are comparable to 

personal exposure measurements. When also noting that there is a strong relationship between 

ambient SO2 concentration and exposure, staff believes that the estimated numbers of days with 

5-minute exposures above benchmark levels are also likely reasonable. However, without the 

 

84 While a direct comparison of APEX estimated maximum daily exposure concentrations with the maximum 

observed daily personal exposure concentrations is considered qualitative given the large discrepancy in sample 

sizes and the difference in years compared, it should be noted that considering both seasons, approximately 99.1% 

of APEX simulated persons had their estimated maximum daily exposure concentrations within the maximum 

observed daily personal exposure measurement of 78.2 ppb. 

APEX Personal NO2 Exposures – Spring 2002 

0

10

20

30

40

50

60

70

80

90

100

0.1 1 10 100

Daily Mean NO2 Exposure (ppb)

Cumulative Percent (%)

p50

p2.5

p97.5

0

10

20

30

40

50

60

70

80

90

100

Cumulative Percent (%)

p50

min

max

Personal NO2 Exposure Measurements- Spring 2000 
July 2009 286 

availability of 5-minute personal exposure measurements that more closely represent the 

modeled population, the level of uncertainty in the knowledge-base is judged as high. 

 8.11.2.2.3 Population Database 

The population data are drawn from U.S. Census data from the year 2000. This is a high 

quality data source for nationwide population data in the U.S., there is none considered as 

complete and as appropriate for its application in our exposure assessment. As such, uncertainty 

regarding the knowledge-base is considered low. The data do have some limitations. The 

Census used random sampling techniques instead of attempting to reach all households in the 

U.S., as it has in the past. While the sampling techniques are well established and trusted, they 

may serve as a limited source of uncertainty in exposure results. The Census has a quality 

section (http://www.census.gov/quality/) that discusses these and other issues with Census data. 

It is likely the uncertainty in population representation within this data would not affect the 

APEX exposure results in any particular direction, and given the use of randomly sampled 

demographics to represent the simulated population, it is expected that the magnitude of 

influence this source of uncertainty has on the exposure results is low. 

8.11.2.2.4 Commuting Database and Algorithm 

Commuting pattern data were also derived from the 2000 U.S. Census, again a welldocumented, quality-assured source. The data are used in addressing home-to-work travel, 

certainly within the bounds of the objectives associated with the original data collection. Staff 

had to make a few simplifying assumptions to allow for practical use of this database to reflect a 

simulated individual’s commute. First, there were a few commuter identifications that 

necessitated a restriction of their movement from a home-block to a work-block. This is not to 

suggest that they never travelled on roads, only that their home and work blocks were the same 

and served as the only source of ambient concentration data for those individuals. Persons 

restricted to a single block for ambient concentrations include the population not employed 

outside the home, individuals indicated as commuting within their home-block, and individuals 

that commute over 120 km a day. This could lead to either over- or under-estimations in 

exposures if they were in fact to visit a block with either higher or lower SO2 concentrations. 

Given that the number of individuals who meet these conditions is likely a small fraction of the 

total population, staff considers the magnitude of influence as low and associated with either 

small over- or under-estimation of exposure benchmark exceedances. 
July 2009 287 

Second, although several of the APEX microenvironments account for time spent in 

travel, the travel is assumed to always occur in basically a composite of the home- and workblocks. No other provision is made for the possibility of passing through other census blocks 

during travel. This could also contribute to either over- or under-estimating exposure 

concentrations, dependent on the number of blocks the simulated individual would actually 

traverse and the spatial variability of the concentration across different blocks. This could 

potentially affect a large portion of the population, since we expect that at the block-level, many 

persons would have a commute transect that included more than two blocks, although the actual 

number of persons and the number of blocks per commute and the spatial variability across 

blocks has not been directly quantified. In addition, the commuting route (i.e., which roads 

individuals are traveling on during the commute) is not accounted for. From a practical 

perspective though, if staff was to consider multi-block commuting in an exposure modeling 

exercise, further complexity would need to be added to the modeling while also requiring 

additional input data that is not readily available (e.g., commuting route data for simulated 

individuals). These model adjustments would come with a number of additional uncertainties 

and require additional time and resources not available for the assessment. Therefore, staff 

elected to not account for multi-block commuting. Note however that the modeled spatial 

variability within 4 km of ambient monitors in St. Louis was much less than that of the modeled 

spatial variability Greene County, suggesting that ignoring multi-block commuting transects may 

be of lesser importance in St. Louis. 

Furthermore, the estimation of block-to-block commuter flows relied on the assumption 

that the frequency of commuting to a workplace block within a tract is proportional to the 

amount of commercial and industrial land in the block. This assumption could result in overestimating exposures if 1) the blocks with greater commercial/industrial land density also have 

greater concentrations when compared with lower density commercial/industrial density blocks, 

and 2) most persons commute to lower commercial/industrial density blocks. It should also be 

noted that recent surveys, notably the National Household Transportation Survey (NHTS), have 

found that most trips taken and most VMT accrued by households are non-work trips, 

particularly social/recreational and shopping-related travel (Hu and Reuscher, 2004). In 

addition, geographic differences in infrastructure could lead to differences in commuting method 

that is not weighted by either the CHAD diaries or the Census commuter dataset. These 
July 2009 288 

constitute non-quantified sources of uncertainty that are not addressed by the Census commuter 

dataset. 

Overall, in assessing the influence the commuting database and algorithm have on 

estimated exposures above benchmark levels, staff judges the magnitude to be low even in 

Greene County particularly since most benchmark exceedances occur outdoors and not inside 

vehicles or indoor microenvironments. Even though staff judged the use of land-use is a 

reasonable surrogate for identifying where people might work, staff believes that, in the absence 

of block-to-block commuting information to further support this relationship, the uncertainty 

regarding the knowledge-base is medium. 

8.11.2.2.5 Activity Pattern Database 

The CHAD time-location activity diaries used are the most comprehensive source of such 

data and realistically represent where individuals are located and what they are doing. The 

diaries are sequential records of each persons activities performed and microenvironments 

visited. There are, however, uncertainties in the exposure results as a result of the CHAD diaries 

used for simulating individuals. Specific elements of uncertainty include an evaluation of 1) the 

representativeness of CHAD in reflecting recent human activity patterns, 2) the approach used to 

allow for geographical representation of influential characteristics, 3) the similarities of 

asthmatic and non-asthmatic activity patterns, and 4) response of asthmatics to air quality 

notifications. Discussion regarding the use of individual CHAD diary days in developing 

longitudinal profiles is presented in section 8.11.2.2.6. 

First, a large percentage of the data used to generate the daily diaries were gathered from 

survey studies conducted between 20 to 30 years ago. While the trends in people’s daily 

activities may not have changed much over the years, it is certainly possible that some 

differences do exist such as the amount of time spent outdoors, time spent performing activities 

at a particular level of exertion, and the microenvironments where moderate or greater exertion is 

likely to occur. It would be extremely difficult to determine real differences in the distribution of 

these factors that may influence SO2 exposure. For example, much of the data that is available to 

test such differences is survey-based. The survey methods used to collect data are not entirely 

consistent with one another and most of the studies collecting time-location-activity data did not 

have exposure modeling objectives in their design (Graham and McCurdy, 2004). If one were to 

test the hypothesis of no observed differences in time spent outdoors using historical and recent 
July 2009 289 

data, it is likely significant effects would result from differences in survey methods or overall 

study design rather than measurable changes in population activities. Staff assumed that if there 

were a difference between the time spent outdoors (the most important microenvironment for 

SO2 exposures) for the simulated population and historical data diaries used to represent them, 

the difference would be negligible. Therefore, staff judges the magnitude of influence on the 

number of days with exposures above benchmark levels as low. 

Second, CHAD is a collection of data from numerous activity pattern surveys, many 

having differing data collection objectives. Some of the studies were single city surveys, 

although a large portion of the data is from National surveys designed to be representative of the 

U.S. population. In addition, study collection periods occur at different times of the year, 

possibly resulting in seasonal variation not representative of the modeled locations. 

Furthermore, the CHAD diaries selected by APEX to represent the Greene County and St. Louis 

population are not necessarily from individuals residing in these cities, the State of Missouri, or 

from the Midwest, albeit some of the diaries may be. Each of these factors could contribute to 

uncertainty in the exposure results if there are location-specific characteristics of the CHAD 

surveyed population that are distinct from those of the simulated population. However, a few of 

the limitations associated with the use of diaries from different locations or seasons are corrected 

by the sampling approaches used in the exposure modeling. For example, diaries used are 

weighted by population demographics (i.e., U.S. census based age and gender distributions at the 

modeled census block) and temperature is used as a classification variable to account for 

expected differences in a location’s climate and its effect on human activities. 

A sensitivity analysis was recently performed to evaluate the effect that using different 

CHAD studies has on APEX results for the recent O3 NAAQS review (see Langstaff (2007) and 

EPA (2007d)). Briefly, O3 exposure results were generated using APEX with all of the CHAD 

diaries and compared with results generated from running APEX using only the CHAD diaries 

from the National Human Activity Pattern Study (NHAPS), a nationally representative study in 

CHAD. There was good agreement between the APEX exposure results for the 12 metropolitan 

areas evaluated (one of which was St. Louis), whether all of CHAD or only the NHAPS 

component of CHAD is used. The absolute difference in percent of persons above a particular 

concentration level ranged from -1% to about 4%, indicating that the exposure model results are 

not being overly influenced by any single study in CHAD. It is likely that similar results would 
July 2009 290 

be obtained here for SO2 exposures. Therefore, staff judges the magnitude of influence from 

using appropriately sampled CHAD diaries in representing the simulated population as low. 

Third, due to limited number of CHAD diaries with health-specific information, all 

diaries are assumed as appropriate for any simulated individual, provided they concur with age, 

gender, temperature, and microenvironmental time selection criteria. In addition, data 

summaries85 output from the current version of APEX could only be output for the entire 

simulated population rather than the particular subpopulation. This is a reasonable modeling 

assumption when considering the calculation of the microenvironmental concentrations, because 

it is not expected that the asthmatic population would have microenvironmental concentrations 

different from those of the total population. However, there is uncertainty in the use of all 

CHAD diaries in simulating any individual without considering the health status of both the 

surveyed population and the simulated population if in fact health status affects the activity 

pattern of the simulated individual. In this exposure assessment it was shown that the most 

important location for contacting the 5-minute peak concentration were outdoor 

microenvironments. Therefore, if there is a difference in the time spent outdoors (e.g., total time, 

time-of-day) and activities performed outdoors between asthmatics and healthy individuals, there 

may be a greater impact to the estimated number of asthmatics exposed (and number of person 

days) than if there were no difference. 

Briefly, the assumption of modeling asthmatics similarly to healthy individuals (i.e., 

using the same time-location-activity profiles) is supported by the findings of van Gent et al. 

(2007), at least when considering children 7-10 years in age. These researchers used three 

different activity-level measurement techniques; an accelerometer recording 1-minute time 

intervals, a written diary considering 15-minute time blocks, and a categorical scale of activity 

level. Based on analysis of 5-days of monitoring, van Gent et al. (2007) showed no difference in 

the activity data collection methods used as well as no difference between asthmatic children and 

healthy children when comparing their respective activity levels. Contrary to this, an analysis of 

2000 BRFSS data by Ford et al. (2003) indicated a statistically significant difference between the 

percent of current asthmatics (30.9%) and non asthmatics (27.8%) characterized as inactive. In 

addition, these researchers found significant differences in the percent of asthmatic (26.6%) and 

 

85 For example, the time spent in microenvironments at or above a potential health effect benchmark level. 
July 2009 291 

non-asthmatic (28.1%) adults achieving recommended levels of physical activity (i.e., either 

moderate or greater activity levels). 

Note though, the issue is not just outdoor time and activity levels, but the intersection of 

the two that are of importance as well as recognizing the performance capabilities of persons 

with asthma. A person’s overall physical activity level is strongly linked with their time spent 

outdoors and is considered an important correlate in encouraging increased physical activity 

among children and adults alike (e.g., Sallis et al., 1998). In addition, introducing regular 

exercise has been shown to improve physical fitness in asthmatic children, with statistically 

significant increases in ventilation measures such as maximum minute ventilation rate (VEmax) 

maximum oxygen uptake (VO2max) (e.g., van Vledhoven et al., 2001). Further, in other related 

research, Santuz et al. (1997) indicated no statistically significant difference between asthmatic 

and non asthmatic children when comparing maximum exercise performance levels, provided the 

individuals were conditioned through habitual exercise. Thus it appears that asthmatics are 

likely to perform activities at elevated levels and do so in outdoor microenvironments. 

To support the assumption that there is no difference in CHAD activity patterns used to 

represent the asthmatic population, staff compared the amount of time spent outdoors at elevated 

activity levels obtained from three individual asthma studies with estimates of the same metric 

using the CHAD database. In addition, some of the studies incorporated in CHAD reported 

whether the individual was asthmatic, non-asthmatic, or not classified. Therefore, staff 

categorized the data and results as such in this analysis. Table 8-30 summarizes data reported 

from the three studies and results generated using CHAD data and the known health status. 

When considering the three asthma studies, the amount of time spent outdoors at 

moderate activity level ranges from a low of approximately 2% to a high of about 11% of 

waking hours. The estimates of outdoor time associated with moderate activity level using 

CHAD diaries fall within that range (i.e., between 6.5 and 7.5%) with small differences observed 

between the CHAD asthmatic and CHAD non-asthmatic population. This limited comparison 

indicates that the CHAD diaries may reasonably approximate the amount of time spent outdoors 

at moderate activity levels. In addition, comparison of the CHAD asthmatic and non asthmatic 

population supports the assumption that all CHAD diaries are appropriate in representing 

asthmatic individuals, regardless of health status. However, the percent of outdoor time 

associated with strenuous activities using the CHAD database was lower when compared with 
July 2009 292 

the three asthma studies. It is difficult to judge whether the time spent outdoors at strenuous 

activity levels is under-represented by CHAD or it is over-represented by the three asthma 

studies. 

Staff recognizes that there are a number of differences that exist among the three 

asthmatic studies used along with the CHAD diary data that could contribute to variation in the 

time spent outdoors at elevated activity levels. This would include: the diary/survey collection 

methods used, the classification of activities performed and associated activity levels, the number 

of study subjects, and sample selection methods. The particulars regarding how each of these 

were addressed across the various studies is wide ranging and could potentially influence the 

results. However, based on the comparable results observed in time spent outdoors at moderate 

activity levels, staff judges the magnitude of influence as low with no apparent direction in overor under-estimation. 

Table 8-30. Percent of waking hours spent outdoors at an elevated activity level. 

 EPRI (1988)1 EPRI (1992)2

 Shamoo (1994)3 CHAD4

Location Los Angeles Cincinnati Los Angeles All 

Time of Year April August Summer Winter Any 

Population Asthmatic Asthmatic Asthmatic Asthmatic Asthmatic Not 

Asthmatic

Unknown

n 52 136 48 45 1,475 15,848 4,821 

Mean age 

(min-max) 

- 26 

(1– 78) 

33 

(18 – 50) 

23 

(<0 – 99) 

27 

(<1 – 93) 

31 

(<1 – 94) 

Activity Level Percent of Asthmatic Waking Hours Spent Outdoors at Given Activity Level 

Moderate 7 11 1.9 1.7 7.5 6.5 6.7 

Strenuous 2.4 3.3 0.2 0.2 0.04 0.01 0.2 

Notes: 

1

 Hour diary questionnaire form used for up to three activities per hour. Non-random sample of 26 

mild/moderate, 26 moderate/severe asthmatics selected from voluntary clinical studies. 2

 Hour diary questionnaire form used for up to three activities per hour. Random digit dialing and 

multiplicity sampling used. 

3 Number of minutes performing three self-rated activity levels for three locations per hour. Non-random 

sample selected from voluntary clinical studies.

4

 Combination of random and non random selection studies, national and city-specific, as well as varying 

diary protocol (see Graham and McCurdy, 2004). Original CHAD database (n=22,968; EPA, 2002) was 

screened for persons with no age (n=223) and no sleep (n=601) reported. Median METS values from 

each activity-specific distribution were assigned to each person’s activities. Moderate and vigorous 

activity levels were selected based on activities having a METS value of 3 to <6 and ≥6, respectively. 

Finally, there is also a possibility that information regarding bad air quality may affect 

the activities performed by the asthmatic population. There has been research regarding averting 

behavior, that is, there is a reduction in time spent outdoors when the individual is informed of 

the potential for bad air quality days (e.g., Bresnahan, et al. 1997; Mansfield, 2005; KDEH, 
July 2009 293 

2006; Wen et al., 2009). One study reviewed by staff reported no effect on outdoor time (e.g., 

Yen et. al. 2004). Of the limited studies reviewed by staff, most were focused on the population 

response to ozone (or smog) air pollution alerts, EPAs Air Quality Index (AQI), or simply selfperceived bad air quality. 

In the most recent U.S. study conducted in six states,86 it was reported that approximately 

25-30% of asthmatic adults altered their outdoor activity due to either perceived bad air quality 

or media alerts, compared with about half as many (12%-16%) non asthmatics altering their 

outdoor activities (Wen et al., 2009). The media alert response rate was requisite on awareness 

of the bad air quality media alert for both children (Mansfield et al., 2005) and adults (KDEH, 

2006; Wen, 2009). Parents of asthmatic children checked air quality alerts more frequently than 

parents of non-asthmatic children and, though reported as statistically significant, only about 

25% of parents of asthmatic children checked the air quality on a daily basis (Mansfield et. al., 

2005). Approximately half of asthmatic and non asthmatic adults were aware of the media alerts 

(Wen et al., 2009), though among all adults living in the Kansas City MSA,87 the percent aware 

is much greater (70%; KDEH, 2006). Of the persons that reported altering their outdoor 

activities, approximately 60% did so three or fewer times per year. 

If there is averting behavior by asthmatics in response to air pollution events, the degree 

to which an asthmatic’s SO2 exposure would be altered is highly uncertain. Staff acknowledges 

that there may be fewer asthmatics exposed using APEX if accounting for averting behavior. 

However, information missing from the published studies that are of importance include 1) the 

amount outdoor time was reduced, 2) the time-of-day the outdoor time reduction occurred, 3) the 

distinction between all outdoor activities or moderate or greater activities, 4) influence of asthma 

severity on aversion rate, 4) the relationship between ozone air quality and the occurrence of 

short-term SO2 pollution events modeled here. Given the above averting behavior statistics, 

there could be at most a 30% over-estimation in the number of persons exposed (i.e., a medium 

level), though the over-estimation is likely to be less given how the unknown conditions noted 

above affect averting behavior. 

 

86 The six states were Colorado, Florida, Indiana, Kansas, Massachusetts, Wisconsin. 87 Note that Kansas City is in close geographic proximity to both of the Mo. exposure modeling domains. 
July 2009 294 

8.11.2.2.6 Longitudinal Profile Algorithm 

Some of the surveys comprising CHAD collected only a single diary-day while others 

collected several diary days per individual. In this exposure assessment, individuals are 

simulated for an entire year. APEX creates the annual sequences of daily activities for a 

simulated individual by sampling human activity data from more than one subject. Therefore, 

each simulated person essentially becomes a composite of several actual people from within the 

underlying activity data. Certain aspects of the personal profiles are held constant, though in 

reality they may change as an individual ages (e.g., body mass). This is likely more important 

for simulations with long timeframes (e.g., over a year or more), particularly when simulating 

young children. The method used to link the individual activity diaries together could influence 

the estimated number of persons exposed, although there would be greater uncertainty in 

estimating multiple exposures per individual per year rather than single exposures per year. Note 

however, estimating multiple exposures per individual was not a focus of the exposure 

assessment. 

In a prior analysis, staff evaluated the cluster algorithm used in constructing longitudinal 

profiles against a sequence of available multiday diaries sets collected as part of the Harvard 

Southern California Chronic Ozone Exposure Study (Xue et al., 2005; Geyh et al., 2000). Diary 

data were collected from children between the ages 7 and 12 for six consecutive days/month for 

an entire year. See Appendix B, Attachment 4 and 5 for details of the comparison. Briefly, the 

activity pattern records were characterized according to time spent in each of five aggregate 

microenvironments: indoors-home, indoors-school, indoors-other, outdoors, and in-transit. The 

predicted value for each stratum was compared to the value for the corresponding stratum in the 

actual diary data using a mean normalized bias statistic. The evaluation indicated the cluster 

algorithm can replicate the observed sequential diary data, with some exceptions. The predicted 

time-in-microenvironment averages matched well with the observed values. For combinations of 

microenvironment/age/gender/season, the normalized bias ranges from –35% to +41%. Sixty 

percent of the predicted averages have bias between –9% and +9%, and the mean bias across any 

microenvironment ranges from -9% to +4%. Although on occasion there were large differences 

in replicating variance across persons and within-person variance subsets, about two-thirds of the 

predictions for each case were within 30% of the observed time spent in each microenvironment. 
July 2009 295 

The longitudinal approach used in the exposure assessment was an intermediate between 

random selection of diaries (a new diary used for every day for each person in the year) and 

perfect correlation (same diary used for every day for each person in the year). The cluster 

algorithm used here was also compared with two other algorithms; one that used random 

sampling and the other employing diversity (D) and autocorrelation (A) statistics (see EPA, 

2007g for details on this latter algorithm). The number of persons with at least one or more 

exposure to a given O3 concentration was about 30% less when using the cluster algorithm than 

when using random sampling, while the number of multiple exposures for those persons exposed 

was greater using the cluster algorithm (by about 50%). The algorithm employing the D and A

statistics exhibited similar patterns, although were lower in magnitude when compared with 

random sampling (about 5% fewer persons with one or more exposures, about 15% greater 

multiple exposures). These exposure results using the cluster algorithm in APEX appeared to be 

the result of a greater correlation of diaries selected in comparison with the other two algorithms. 

This outcome conforms to an expectation of correlation between the daily activities of 

individuals. While the evaluation was performed using 8-hour O3 as the exposure output, it is 

expected that similar results would be obtained for 5-minute SO2 exposures. That is, the 

characteristics of the diaries that contribute greatly to any pollutant exposure above a given 

threshold (e.g., time spent outdoors, vehicle driving time, time spent indoors) are likely a strong 

component in developing each longitudinal profile. Given these results and that the REA is not 

necessarily focused on health effects resulting from multiday exposures, staff judges the 

longitudinal approach may have a low to medium magnitude of influence on estimated number 

of persons exposed. When comparing the modeled profiles with the measurement data, there 

was a balanced mix of over- and under-estimation of microenvironmental time. Therefore, the 

direction of influence on the estimated number of persons exposed could be in either direction. 

Uncertainty in the knowledge-base is rated as medium given the limited longitudinal 

measurement data available for comparison. 

8.11.2.2.7 Meteorological Data 

Details regarding the representativeness of the meteorological data inputs for APEX are 

addressed separately in section 8.4.2 and in Attachment 1 in Appendix B. In addition, 

uncertainties associated with the data are discussed in section 8.11.2.1.2. Briefly, meteorological 

data are taken directly from monitoring stations in the assessment areas. Staff assumed that 
July 2009 296 

most of the data used are error free and have undergone required quality assurance review. One 

strength of these data is that it is relatively easy to see significant errors if they appear in the data. 

Because general climatic conditions are known for the simulated area, it would have been 

apparent upon review if there were outliers in the dataset, and at this time none were identified. 

If there were errors remaining in the data, it would be expected to be limited in extent and occur 

randomly. In addition, to reduce the number of calms and missing winds in the 1-hour MET 

data, archived one-minute winds for the ASOS stations in each model domain were used to 

calculate hourly average wind speed and directions. This approach reduces the number of 

estimated zero concentrations that would be output by AERMOD if not supplemented by the 

additional wind data, thus preventing a downward bias in the predicted 1-hour SO2

concentrations. Therefore, staff judges the MET data as having a low level of influence and 

equally applied to either under- or over-estimation in the number of persons exposed. 

There are some limitations in the use of the meteorological data in APEX. APEX only 

uses the 1-hour daily maximum temperature in selecting an appropriate CHAD diary and indoor 

microenvironment air exchange rate. Because the model does not represent hour-to-hour 

variations in meteorological conditions throughout the day, there could be uncertainty in some of 

the exposure estimates associated with indoor microenvironments (see the next section). 

8.11.2.2.8 Air Exchange Rates (AER) 

The residential air exchange rate (AER) distributions used to estimate indoor exposures 

may contribute to uncertainty in the exposure results. Three components of the AER analyzed 

previously by EPA (2007d) include 1) the extrapolation of air exchange rate distributions 

between-CMSAs, 2) analysis of within-CMSA uncertainty due to sampling variation, and 3) the 

uncertainty associated with estimating daily AER distributions from AER measurements with 

different averaging times. The results of those previous investigations are briefly summarized 

here. See Appendix B, Attachments 7 and 8 for details in the data used to generate the AER and 

the sensitivity analyses performed. It should be recognized that in this assessment, the indoor 

microenvironments have been shown to be largely unimportant in estimating exposure 

exceedances. Note however, that in ignoring all twelve 5-minute concentrations, the influence of 

the indoor-residential microenvironment may be under-estimated (section 8.11.2.2.11). 

Extrapolation of AER among locations 
July 2009 297 

Air exchange rate (AER) distributions were assigned in the APEX model, as described in 

the indoors-residential microenvironment. Because location-specific AER data for St. Louis and 

Greene County were not available and that there were no AER data from cities thought to have 

similar influential characteristics affecting AER,88 staff constructed an aggregate distribution of 

the available AER data from cities outside California to represent the distribution of AERs in St. 

Louis and Greene County (see Appendix B, Attachment 7). 

In the absence of location-specific data for the microenvironments modeled by APEX 

within each model domain, only limited evaluations were performed. To assess the uncertainty 

associated with deriving AERs from one city and applying those to another city, betweenlocation uncertainty was evaluated by examining the variation of the geometric means and 

standard deviations across several cities and originating from several different studies. The 

evaluation showed a relatively wide variation across different cities in their AER geometric 

means and standard deviations, stratified by air-conditioning status, and temperature range. For 

example, Figure 8-24 illustrates the GM and GSD of AERs estimated for several cities in the 

U.S. where A/C was present and within the temperature range of 20-25 °C. The wide range in 

GM and GSD pairs implies that the modeling results may be very different if the matching of 

modeled location to a particular study location was changed. For example, the SO2 exposure 

estimates may be sensitive to use of an alternative distribution, say those in New York City, 

compared with results generated using the aggregate non-California AER distributions. It is 

possible though that the true distribution could be more similar to the selected distribution from 

all non-California cities than that of the specific locations given the population of available AER 

data. It is unclear as to the direction of influence given the limited number of data available for 

comparison. It is likely that the impact to the number of exceedances is low, given that most of 

the exceedances occurred outdoors for most of the air quality scenarios evaluated. 

 

88 Such potential influential factors would include age, composition of housing stock, construction methods used, 

and other meteorological variables not explicitly treated in the analysis, such as humidity and wind speed patterns. 
July 2009 298 

Figure 8-24. Example comparison of estimated geometric mean and geometric standard 

deviations of AER (h-1) for homes with air conditioning in several cities. 

Within location uncertainty 

There is also variation in AERs within studies for the same location (e.g., Outside 

California data), but this is much smaller than the observed variation across different CMSAs. 

This finding tends to support the approach of combining different studies for a CMSA, where 

data were available. The within-city uncertainty was assessed by using a bootstrap distribution 

to estimate the effects of sampling variation on the fitted geometric means and standard 

deviations for the non-California data used to represent the St. Louis and Greene County AERs. 

These bootstrap distributions assess the uncertainty due to random sampling variation. They do 

not address other uncertainties such as the lack of representativeness of the available study data 

or the variation in the lengths of the AER monitoring periods. Because only the GM and GSD 

were used, the bootstrap analyses does not account for uncertainties about the true distributional 

shape, which may not necessarily be lognormal. 

One-thousand bootstrap samples were randomly generated for each AER subset (of size 

N), producing a set of 1,000 pairs of geometric mean (GM) and geometric standard deviation 
July 2009 299 

(GSD). The analysis of the non-California city data used to represent Greene County and St. 

Louis indicated that the GSD uncertainty for a given AER temperature group tended to have a 

range within ±0.3 fitted GSD (hr-1), with smaller intervals surrounding the GM (i.e., about ± 0.10 

fitted GM (hr-1) (Figure 8-25). Broader ranges were generated from the bootstrap simulation for 

AER distributions used for Greene County and St. Louis homes without A/C (Figure 8-26), 

although both still within ±0.5 of the fitted GM and GSD values. Given the limited range in 

GMs and GSDs, staff judges the magnitude of influence as low and mainly associated with both 

under- and over estimation of indoor exposure concentrations. See Appendix B, Attachment 7 

for further details. 

Figure 8-25. Example of boot strap simulation results used in evaluating random sampling 

variation of AER (h-1) distributions (data from cities outside California). Parameters of 

the original distribution are given by the intersection of the two inner grid lines 
July 2009 300 

Figure 8-26. Example of boot strap simulation results used in evaluating random 

sampling variation of AER (h-1) distributions (data from cities outside California). 

Parameters of the original distribution are given by the intersection of the two inner 

grid lines 

Variation in AER measurement averaging times 

Although the averaging periods for the air exchange rates in the study data varied from 

one day to seven days, the analyses did not take the measurement duration into account and 

treated the data as if they were a set of statistically independent daily averages. To investigate 

the uncertainty of this assumption, correlations between consecutive 24-hour air exchange rates 

measured at the same house were investigated using data from the Research Triangle Park Panel 

Study (Appendix B, Attachment 8). The results showed extremely strong correlations, providing 

support for the simplified approach of treating multi-day averaging periods as if they were 24-

hour averages. Therefore, staff judges the magnitude of influence as low with unknown 

direction on the number of persons exposed. 

8.11.2.2.9 Air Conditioning Prevalence 

Because the selection of an air exchange rate distribution is conditioned on the presence 

or absence of an air-conditioner, the air conditioning status of the residential microenvironment 

was simulated randomly using the probability that a residence has an air conditioner, i.e., the 

residential air conditioner prevalence rate. For this study we used location-specific data for St. 

Louis (AHS, 2005) and applied that data to Greene County as well. EPA (2007d) details the 
July 2009 301 

specification of uncertainty estimates in the form of confidence intervals for the air conditioner 

prevalence rate, and compares these with prevalence rates and confidence intervals developed 

from the Residential Energy Consumption Survey (RECS) of 2001 for several aggregate 

geographic subdivision (e.g., states, multi-state Census divisions and regions) (EIA, 2001). 

Briefly, the A/C prevalence rates used for St. Louis were 96%, with reported standard 

errors of 1.7% (AHS, 2003). Estimated 95% confidence intervals were also small and span 

approximately 6.5 percentage points (AHS, 2003). The RECS prevalence estimate for Census 

Divisions was 92% (ranging between 86.4% and 98.4%), while the Census Region prevalence 

estimate was 83.6% (ranging between 80.0% and 87.2%). This suggests that the A/C prevalence 

used, while likely being representative of a city in Missouri, may be over-estimated for nonurban locations (such as Greene County). 

Furthermore, a sensitivity analysis was performed using a low (55%) and high (97%) A/C 

prevalence rates as input to APEX in an Atlanta, Ga. exposure assessment used for the recent 

NO2 NAAQS review (EPA, 2008d). Upper percentile benchmark exceedances were also of 

interest in that exposure assessment, only the averaging time was 1-hour instead of 5-minutes 

used here. Indoor microenvironments were also found in the NO2 exposure assessment to be 

unimportant in estimating exposure exceedances. Results from the sensitivity analysis indicated 

that there was no difference in the percent of the asthmatic population with NO2 exposure 

benchmark exceedances with a decreased A/C prevalence. Only a few additional persons (about 

100 out of a simulated population of 200,000) experienced exposures above exceedances when 

using the lower A/C prevalence. Based on the above discussion, staff judges the magnitude of 

influence to estimated exposures as low, particularly given that indoor exposures to 

concentrations above the benchmark levels rarely occurs. 

8.11.2.2.10 Indoor Removal Rate 

There may be uncertainty in the exposure results when considering the estimated 

parameters, the form (i.e., lognormal) and limits (limited by the bounds of the measurement data) 

of the distribution used to represent indoor decay. The data used to develop the distribution were 

obtained from a review of several studies that analyzed SO2 removal for a variety of building 

material surfaces (Grontoft and Raychaudhuri, 2004). Potential influential factors such as 

humidity and air exchange rate were accounted for in developing and applying the removal 

distributions within the indoor microenvironments. In addition, the distributions were based on a 
July 2009 302 

large empirical database and likely well represent expected SO2 removal within indoor 

microenvironments. 

However, several assumptions were made to characterize the materials used within a 

simulated indoor microenvironment, some of which were data-based, others in the absence of 

supporting data, were based solely on professional judgment (see Appendix B.4.1). Staff 

performed a Monte Carlo simulation using the removal data and 1,000 simulated interior rooms 

of buildings to generate a distribution of SO2 removal rates, weighted by the approximated room 

configurations and proportion of materials present. There are many assumptions staff made that 

could be modified with newly available data, particularly where inputs were based on 

professional judgment. It is largely unknown what the direction of influence is in the absence of 

new or refined input data. While some of the assumptions used may be largely uncertain, the 

magnitude of the influence is judged by staff as low given the relative contribution of the indoor 

microenvironments to exposure concentrations above the potential health effect benchmark 

levels. 

8.11.2.2.11 Occurrence of Multiple Exceedances within an Hour 

The statistical model described in section 7.2 was used within APEX to estimate a single 

5-minute maximum SO2 concentration for every hour. However, multiple short-term peak 

concentrations above selected levels are possible within any hour. Analysis of the 5-minute 

continuous monitoring data indicates that multiple occurrences of 5-minute concentrations above 

the 100, 200, 300, and 400 ppb within the same hour can be common. Using the continuous 

monitoring data obtained from years 1997-2007, multiple peak concentrations (i.e., 2 or more) at 

or above 400 ppb within the same hour occurred with a 61% frequency (Table 8-31). The 

frequency of multiple exceedances was similar for the lower 5-minute SO2 concentration levels, 

where 63, 56, and 53% of the time there were two or more exceedances within the same hour at 

the 100, 200, and 300 ppb benchmark levels, respectively. These results may suggest that a 

single peak approach (i.e., 24 peak concentrations per day) for estimating the number of persons 

and days with 5-minute SO2 exposures as a surrogate for all possible peak exposure events may 

lead to an under-estimate in the number of potential exposures. 
July 2009 303 

Table 8-31. Number of multiple exceedances of potential health effect benchmark levels within an 

hour. 

Number of Exceedances of Number of Hours with Multiple 5-minute SO2

5-minute SO2 in 1-hour1 > 100 ppb > 200 ppb > 300 ppb > 400 ppb 

1 1248 267 76 26 

2 658 122 31 20 

3 411 78 21 7 

4 257 35 10 5 

5 242 28 6 4 

6 153 25 4 1 

7 125 14 5 1 

8 89 11 2 1 

9 64 6 3 1 

10 49 6 1 1 

11 50 3 0 0 

12 73 5 1 0 

Total 3419 600 160 67 

Notes: 

1

 The analysis is based on the 16 monitors reporting all 5-minute SO2 concentrations in an 

hour (n=3,328,725). 

 

In using the data in Table 8-31 alone, the magnitude of the under-estimation may be 

somewhat overstated however, particularly when considering the benchmark levels of 200, 300, 

and 400 ppb. A detailed analysis of the multiple exceedances by each monitor indicated that one 

of the monitors (ID 420070005) was highly influential in generating the values in Table 8-31, 

contributing greatly to the multiple peak occurrences at the higher benchmark levels. This 

Beaver Pa. urban-scale monitor is identified as population-based, within a rural setting, and 

having agricultural land use (Appendix A). Five out of eight of the sources located within 20 km 

of this monitor had SO2 emissions <250 tpy, one smelter emitting about 7,000 tpy was within 2.5 

km, and two power generating facilities located approximately 3.4 and 7.5 km from the monitor 

had SO2 emissions of 3,000 and 30,000 tpy, respectively. Of the number of hours having 

multiple exceedances, monitor 420070005 contributed to 61, 73, and 80% of the hours with 

multiple peaks >200, >300, and >400 ppb, respectively. Following removal of this monitor from 

the full data set, the occurrence of multiple exceedances of each the 200, 300, and 400 ppb 

benchmark lowered to approximately 40% of all hours having co-occurring peaks. 

This suggests there would be increased uncertainty in the exposure results if the 

continuous monitoring data were used to design an approach for estimating multiple exceedances 

within an hour. These continuous monitoring data were available only from 16 ambient 
July 2009 304 

monitors, each having a limited number of monitoring years. The analyses above indicated that 

one of the monitors contributed to most of the hours with multiple peak concentrations. How 

this one monitor (as well as any other monitor having multiple exceedances) reflects what may 

occur at the APEX modeled receptors in St. Louis and Greene County (or other different 

locations) is unknown. There is no simple extrapolation possible using the continuous 

monitoring data because the time of the peak (and hence multiple peak) concentrations modeled 

are not known with respect to the simulated individuals’ time spent outdoors. 

The PMR statistical model is based on both concentration and variability measures, 

implemented by APEX in estimating a single maximum 5-minute SO2 concentration for every 

hour at every receptor. This is based on known concentration and variability relationships 

described in section 7.2. While APEX can model all twelve 5-minute concentrations, staff chose 

to normalize the eleven remaining 5-minute SO2 concentrations within an hour to the 1-hour 

mean concentration. This decision was based on the already large size of the air quality files 

used (thousands of receptors across a year) that also required a time consuming post-processing 

step prior to input in APEX and ultimately, the run time associated with the exposure model 

simulations. Estimating the 5-minute maximum SO2 concentrations and the other 11 

concentrations within APEX was more efficient than pre-processing all twelve 5-minute SO2

concentrations. 

Having all eleven other 5-minute SO2 concentrations normalized to the mean could result 

in under-estimating the number of persons exposed. The exposure simulation could miss a 

persons’ exposure that might have occurred if in fact there are multiple peak concentrations 

within the same hour (a likely event given the continuous monitoring data, roughly between 40-

60%). The CHAD time-location-activity diaries used in APEX are fixed, that is, the modeled 

time spent outdoors is based on the actual time of day and amount of time recorded by the 

surveyed individual. APEX models exposure on a minute-by-minute basis; if most persons 

spend time outdoors for a short time (e.g., 5-minutes), then it is possible that persons are not 

realistically encountering peak concentrations given the normalization of the eleven 5-minute 

SO2 concentrations. Therefore, staff analyzed outdoor activities in the CHAD diaries used by 

APEX to determine the duration of time spent outdoors for each outdoor event. 

Figure 8-27 illustrates the distribution of time spent outdoors, given activity outdoor 

events defined by clock-hour increments (already part of the CHAD design). Thirty-five percent 
July 2009 305 

of all outdoor events are for the entire hour; if the event corresponds with the same hour as a 

simulated peak concentration, there would be no under-estimation in exposure occurring during 

these events. Therefore, occurrence of multiple peaks within an hour is potentially not an issue 

for 35% of all exposure events that occur outdoors. However, at each of the other outdoor 

events, there is a probability of under-estimating the exposure, given by the duration of the event 

divided by 60 minutes. For example, approximately 15% of outdoor events were 30 minutes. If 

these outdoor events occurred at the time where there was a second estimated peak concentration 

in the same hour, there is a 50% chance that the exposure is missed. The probability of missing a 

potential exposure increases with decreasing duration of the outdoor event and, given the data in 

Figure 8-27, this could be a frequent occurrence (i.e., about 65% of outdoor events may have 

some probability of missing an exposure). This analysis does not account for multiple outdoor 

events that may increase an individual’s chance of an exposure above a benchmark level, 

regardless of the event duration. It also assumes the each of the outdoor events evaluated have 

an equal probability of occurring at the time of the peak concentration, which may or may not be 

the case. In addition, the outdoor time distribution is based on all of the CHAD diary days, 

potentially not the same distribution of diaries that were used in the APEX exposure simulations. 

A better method to determine the potential number of missing exposures is to model the 

exposures using two input data sets: air quality with all continuous 5-minute measurements, and 

air quality having the measured 5-minute maximum and the eleven other 5-minute 

concentrations within the hour normalized to the 1-hour mean. Staff constructed a data set using 

measurements from the continuous-5 ambient monitoring. While there were two monitors 

reporting continuous 5-minute measurements in Greene County (monitor IDs 290770037 and 

290770026), there were only two years with exceedances of the 200 ppb benchmark level, and 

no exceedances of the 300 or 400 ppb benchmarks. To explore the maximum effect of multiple 

peak concentrations within an hour, staff used two years of data from monitor ID 420070005, 

noted above as having the greatest number of air quality benchmark exceedances in a year (years 

2002 and 2005 were selected). 
July 2009 306 

0 5 10 15 20 25 30 35 40 45 50 55 60

0

5

10

15

20

25

30

35

P

e

r

c

e

n

t

out doort i me

Figure 8-27. Duration of time spent outdoors (in minutes) using all CHAD events 

First, staff replaced missing concentrations (approximately 5% of each year) using the 

time-of-day monthly averaged SO2 concentration. This data set served as the multiple peak air 

quality data set to be tested; all measured 5-minute concentrations were used as is. Next, staff 

constructed a similar data set, only this second data set had the maximum measured 5-minute 

concentration retained and all other eleven 5-minute concentrations within the hour were 

normalized using the 1-hour mean. This single peak data set reflects what was being modeled by 

APEX. Each of the data sets were used as the air quality input to an APEX simulation, 

controlling for all model sampling, the algorithms used, microenvironments modeled, and 

persons simulated. The only difference in the two runs was the air quality input. Fifty thousand 

persons were simulated using APEX, 13% of which were asthmatic children. Figure 8-28 

illustrates the percent of asthmatic children exposed to selected 5-minute maximum 

concentrations for each of the two scenarios; a multiple peak scenario and a single maximum 

peak concentration, using two site-years of continuous monitoring data with the greatest number 

of benchmark exceedances. As expected, there are more asthmatic children exposed when 

considering the occurrence of multiple peaks in an hour. The difference in the percent of 

asthmatic children exposed at each of the benchmark levels is small, about 2-5 percentage points 
July 2009 307 

differ between the two simulations. However, considering the percent difference in the numbers 

of persons exposed at most of the benchmarks levels, the simulations using the single peak air 

quality method had between 20-35% fewer persons exposed than the multiple peak simulation. 

Similar results were generated in simulations using the site-year with the 2nd highest number of 

exceedances only the under-estimation using the single peak method was about 15-30% (Figure 

8-29). Based on these analyses, at most the estimated number of persons exposed in St. Louis 

and Greene County may be under-estimated by 35% when using a single peak method. The 

actual amount of under-estimation is likely smaller given that these results were generated using 

site-years of monitoring data having the greatest numbers of exceedances and contributing 

significantly to the high frequency of multiple peak exceedances. 

The location where exposures occur may also be influenced by the presence or absence of 

multiple peak concentrations. In particular, the modeled indoor 5-minute maximum 

concentrations may be markedly diluted if the indoor air exchange rate is low and all eleven 

other 5-minute values within the same hour are normalized to the 1-hour mean concentration. 

APEX estimates all microenvironmental concentrations using a mass balance method for 5-

minute time-steps (equation 8-7) that accounts for estimated microenvironmental concentrations 

from the previous time-step (EPA, 2009b). While dilution of the indoor air is not an unusual 

circumstance considering the physical process modeled, it is possible that the number of 

exposure events from indoor sources is under-estimated when the prior time-step concentration is 

artificially reduced. 

Staff evaluated the microenvironments where peak exposures occurred, by aggregating 

the time 5-minute exposures occurred into three broad microenvironmental groups: indoors, 

outdoors, and in-vehicles. A comparison of the APEX simulations using the two air quality 

input simulations (i.e., multiple peak versus single peak, monitor 420070005 – year 2002) and 

considering how often peak exposures occur indoors is presented in Figure 8-30. The 

differences in the percent of indoor exposure exceedances are consistent with the design of the 

model and the particular input data used. For exposures less than the 400 ppb level, a greater 

percent of the overall exposures occur indoors using the single peak method than compared with 

the multiple peak data set. For exposures at or above the 400 ppb level, a smaller percent of the 

overall exposures occur indoors using the single peak method than compared with the multiple 

peak data set. In fact, the multiple peak simulation had indoor peak exposures at levels not 
July 2009 308 

observed using the single peak method. This is likely a function of the normalized 

concentrations, that when used in the mass balance equation as the prior time-step 

microenvironmental concentration, the microenvironmental concentration at time t is less than 

what would be expected. 

While this analysis and its findings are encouraging, context is needed to assign relevance 

to the current exposure analyses in St. Louis and Greene County. As stated earlier, the data set 

used had the greatest number of benchmark exceedances, designed by staff to observe the effect 

that multiple peaks within the hour has on estimated exposures. The observed differences in the 

contribution from the indoor microenvironment may be more appropriately applied in 

discussions regarding air quality scenarios with high concentrations distributions (e.g., air quality 

adjusted to just meeting the current standard, Figure 8-21). While the differences in the highest 

benchmark exceedances are likely of greatest interest when investigating the possibility of 

missing exposure events, it should be noted that the greatest proportion of all exposure events 

still occur outdoors (in this simulation, >70% of exposures above 400 ppb occurred outdoors). 

In addition, the differences observed at the lower benchmarks indicated the role of indoor 

exposures was fairly similar. At most the difference was four percentage points, with the 

multiple peak simulation having a consistently lower contribution of exceedances from indoor 

exposures. Therefore, based on the above discussion, staff judges the magnitude of the potential 

under-estimation as low to medium. 
July 2009 309 

0%

10%

20%

30%

40%

50%

60%

70%

80%

100150200250300350400450500550600650700750800

5-minute SO2 Exposure Level (ppb)

% of Asthmatic Children at or Above

Exposure Level - 420070005 - 2002

-40%

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

% Difference in Persons Exposed

(Multi-Peak minus Single-Peak Run)

5-minute Max with Normalized 11

Multiple Peak Data Set

Percent Difference in Exposures

Figure 8-28. Percent of asthmatic children above given exposure level for two APEX simulations: 

one using multiple peak concentrations in an hour, the other assuming a single peak 

concentration. Continuous 5-minute monitoring data (ID 42007005, year 2002) were 

used as the air quality input. 

0%

10%

20%

30%

40%

50%

60%

70%

100150200250300350400450500550600650700750800

5-minute SO2 Exposure Level (ppb)

% of Asthmatic Children at or Above

Exposure Level - 420070005 - 2005

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

% Difference in Persons Exposed

(Multi-Peak minus Single-Peak Run)

5-minute Max with Normalized 11

5-minute Max with Measured 11

Percent Difference in Exposures

Figure 8-29. Percent of asthmatic children above given exposure level for two APEX simulations: 

one using multiple peak concentrations in an hour, the other assuming a single peak 

concentration. Continuous 5-minute monitoring data (ID 42007005, year 2005) were 

used as the air quality input.
July 2009 310 

0

5

10

15

20

25

30

100150200250300350400450500550600650700750800

SO2 Exposure Level (ppb)

Frequency of Exposure Exceedances Indoors

5-minute Max with Normalized 11

Multiple Peak Data Set

Figure 8-30. Frequency of exposure exceedances indoors for two APEX simulations: one using 

multiple peak concentrations in an hour, the other assuming a single peak 

concentration. Continuous 5-minute monitoring data (ID 42007005, year 2002) were 

used as the air quality input. 

8.11.2.2.12 Asthma Prevalence Rate 

The best estimate of asthma prevalence used in this analysis was generated using a 

comprehensive and widely used data set (CDC, 2007). Staff judged that variability in the asthma 

prevalence based on age was an important attribute to represent in simulating SO2 exposures, one 

of the principal reasons for selection of the particular data set. There are however limitations in 

using the data that may add to uncertainty in the generated exposure results. The percent of 

asthmatics simulated by APEX using a combined regional (children by age) and local (adults all 

ages) prevalence was comparable with an independent estimate of the percent of asthmatics 

within the four counties modeled (9.3% versus 8.8% of the population, respectively). Therefore, 

the uncertainty in the overall total percent of asthmatics exposed is likely low, particularly in 

Greene County. In Greene County, 9.8% of the simulated population was asthmatic and 

compares well with the 10.2% asthma prevalence reported by MO DOH (2003). However, the 

asthma prevalence across the three-county domain in St. Louis was variable, with St. Louis City 

County having a high estimated prevalence rate (16.4%) and St Louis County having a much 

lower prevalence rate (5.8%). This variable distribution was not represented in the exposure 
July 2009 311 

modeling simulation; all children and adults in each of the counties used the data summarized in 

Table 8-7. Therefore in St. Louis City County, the asthma prevalence may have been underestimated, while in St. Louis County the asthma prevalence may have been over-estimated. This 

may add to medium level of influence to the total number of asthmatics exposed in St. Louis (not 

the percent of asthmatics exposed), though the direction of influence is largely unknown because 

individual county level exposures are not output by the model. 

8.12 KEY OBSERVATIONS 

Presented below are key observations resulting from the exposure assessment: 

 5-minute exposures to SO2 were estimated for two areas in Missouri (i.e., Greene County 

and St. Louis), with both locations having significant SO2 emission sources. Air quality 

scenarios investigated by staff included as is air quality, air quality adjusted to simulate 

just meeting the current annual and 24-hour SO2 standards, and just meeting several 

alternative 1-hour daily maximum SO2 standards. 

 A number of factors would be expected to contribute to differences in SO2 exposures 

across different locations. These include differences such as population density, SO2

emission density, location and types of SO2 sources, prevalence of air conditioning, time 

spent outdoors, and asthma prevalence (section 8.10). As discussed in section 8.10, St. 

Louis County has a medium-to-high SO2 emissions density and a medium-to-high 

population density relative to other urban areas. Relative to the St. Louis study area, 

Greene County is a more rural county having much lower population density and much 

lower SO2 emissions density. Taken together, the estimated exposures for these two 

locations provide useful insights about urban and rural counties with SO2 emission 

sources. 

 St. Louis had both a greater number and percent of asthmatic children and adults exposed 

above the benchmark levels than did Greene County for all air quality scenarios. This is 

not unexpected given the greater population density and the much greater SO2 emissions 

density in St. Louis. Staff believes that the St. Louis exposure estimates provide a useful 

perspective on the likely overall magnitude and pattern of exposures associated with 

various SO2 air quality scenarios in urban areas within the U.S. that have similar 

population densities, SO2 emissions densities, and asthma prevalence. Similarly, staff 

believes that the results for Greene County provide perspective on exposures in more 

rural areas within the U.S. that have similar emission and population attributes to Greene 

County. 

 Modeled concentrations are reasonable given comparisons to available measurement data 

- AERMOD 1-hour SO2 concentrations at ambient monitoring receptors and their 

associated prediction envelops generally replicate and encompass those measured at the 

ambient monitor. Model-to-monitor agreement was better in St. Louis than in Greene 

County. 
July 2009 312 

- The degree of under- or over-estimation of 1-hour SO2 concentrations by AERMOD at 

ambient monitoring locations in Greene County did not appreciably affect the estimated 

number of days per year with 5-minute concentrations above benchmark levels. 

- APEX-modeled annual mean SO2 exposures in St. Louis and Green County (arithmetic 

means, 0.5-1.4 ppb) are comparable to daily and weekly personal exposure 

measurements in other locations (arithmetic means, 0.3-1.9 ppb). 

 Estimated exposures above 5-minute potential health effect benchmark levels at moderate 

or greater exertion using APEX occurred most frequently outdoors (around 50 to >90%, 

depending on the air quality scenario and modeling domain). 

 Simulating air quality that just meets the current annual standard resulted in the greatest 

number and percent of asthmatic persons exposed at all benchmark levels. The value 

depended on both the benchmark level and modeling domain. For example, the percent 

of asthmatic children exposed at least one day above a benchmark concentration ranged 

from 0% (400 ppb benchmark) to 8% (100 ppb benchmark) in Greene County, while in 

St. Louis the corresponding range was 24% to 97 %. 

 The exposure results using as is air quality were similar to that estimated using air quality 

adjusted to a 99th percentile 1-hour daily maximum of 50 or 100 ppb in Greene County, 

though in each of these scenarios, there were only a few persons exposed. In St. Louis, 

the estimated exposure associated with as is air quality was also between that estimated 

by simulating the 50 and 100 ppb 99th percentile 1-hour daily maximum air quality 

scenario. 

 Staff compared exposure results using the 50 ppb 99th percentile air quality scenario 

relative to as is air quality in St. Louis to estimate the reduction in the number and 

percent of asthmatic children exposed above each 5-minute health effect benchmark 

level. No asthmatic children were exposed above the 400 ppb 5-minute benchmark for 

either the as is or 50 ppb 99th percentile alternative standard scenario. There were 121 

fewer asthmatic children exposed above the 200 ppb 5-minute benchmark, corresponding 

to a 76% reduction in exposures, when considering the 50 ppb standard level. Similarly, 

reductions also were observed at the 100 ppb 5-minute benchmark when considering the 

50 ppb standard compared with as is air quality: 1,641 (59%) fewer asthmatic children 

were exposed. (Appendix B.4). 

 In both St. Louis and Greene County, there were no reductions in the numbers or percent 

of persons exposed at any of the 5-minute benchmark levels when comparing exposure 

results using the 100 ppb 99th percentile air quality standard scenario relative to as is air 

quality. 

 Using a 99th versus a 98th percentile form at the same standard level (i.e., 200 ppb) 

resulted in fewer persons being exposed above benchmark levels when using the 99th

percentile. Approximately 1,000 to 5,000 fewer asthmatic children, 1,000 to 90,000 

fewer person days, and 2 to 12 fewer percent of persons were exposed above benchmark 

levels in St. Louis. 

 Of the fifteen uncertainties qualitatively judged to influence the estimated number of 

persons with at least one exposure above the 5-minute SO2 benchmark levels, one may be 

associated with over-estimation, three could result in under-estimations, while the 
July 2009 313 

remaining uncertainties could affect exposure results in both (nine sources) or unknown 

direction (two sources) (see Table 8-27). Nine of these eleven sources with bidirectional 

influence were rated by staff as being low-medium magnitude of influence. The 

magnitude of influence for three of the four uncertainties associated with either over- or 

under-estimation was estimated as being low to medium influence, while the remaining 

source (i.e., A/C prevalence) was ranked as being low or a negligible magnitude of 

influence. Two of these four sources of uncertainty (i.e., A/C prevalence and indoor 

AERs) were parameters used to estimate indoor exposures, which staff believes do not 

contribute significantly to exposures above benchmark levels. The remaining two 

sources (i.e., uncertainty in the activity pattern database used and the occurrence of 

multiple exceedances within an hour) could have an offsetting influence in estimating the 

number of persons exposed. This is because both of these sources were rated by staff as 

being low to medium in magnitude, though in opposing direction. Based on this overall 

characterization related to the direction and magnitude of influence for identified sources 

of uncertainty, we are unable to characterize the likelihood of the estimates being either 

over- or under-estimated with respect to the number of persons exposed above 

benchmark levels. 

 The knowledge-base uncertainty for sources with unknown or bidirectional influence 

ranged from low (five sources) to medium (four sources). Note that most of these 

sources were rated above as being of low-medium magnitude of influence. A high 

degree of uncertainty in the knowledge-base was assigned to two sources: the area source 

emission profile (direction of influence characterized as both, with low-medium rated 

magnitude) and the accuracy of 5-minute exposures estimated by APEX (direction of 

influence characterized as both, with low-medium rated magnitude). The knowledgebase uncertainty was medium for three of the four sources identified above that were 

associated with either under- or over-estimating 5-minute exposures (the remaining 

source was rated as low). Based on this overall characterization, there is a low-medium 

level of uncertainty in the knowledge-base for most sources. While two sources were 

rated as having high knowledge-base uncertainty, they were noted as having similar 

magnitude of influence on the estimated 1-hour or 5-minute concentrations.
July 2009 314 

9. HEALTH RISK ASSESSMENT FOR LUNG FUNCTION 

RESPONSES IN ASTHMATICS ASSOCIATED WITH 5-MINUTE 

PEAK EXPOSURES 

9.1 INTRODUCTION 

In the previous review, it was clearly established that subjects with asthma are more 

sensitive to the respiratory effects of SO2 exposure than healthy individuals (ISA, section 

3.1.3.2). As discussed above in section 4.2, asthmatics exposed to SO2 concentrations as low as 

200-300 ppb for 5-10 minutes during exercise have been shown to experience moderate or 

greater bronchoconstriction, measured as an increase in sRaw (≥100%) or decrease in FEV1

(≥15%) after correction for exercise-induced responses in clean air. These studies exposed 

asthmatic volunteers to SO2 in the absence of other pollutants that often confound associations in 

the epidemiological literature. Therefore, these controlled human exposure studies provide 

direct evidence of a causal relationship between exposure to SO2 and respiratory health effects. 

Staff judges the controlled human exposure evidence presented in the ISA with respect to lung 

function effects in exercising asthmatic subjects as providing an appropriate basis for conducting 

a quantitative risk assessment for this health endpoint and exposure scenario. 

As described in Chapters 5 and 6, staff is utilizing both the epidemiological evidence in 

the ISA, and an air quality analysis based on U.S. and Canadian ED visit and hospitalization 

studies for all respiratory causes and asthma to qualitatively inform: (1) the selection of potential 

1-hour daily maximum alternative standards to be analyzed in the air quality, exposure, and risk 

chapters of this document (see Chapter 5), and (2) the adequacy of the current, and potential 

alternative standards (Chapter 10). However, for the reasons discussed in more detail in section 

6.1, staff did not find the overall breadth of the epidemiological evidence to be robust enough to 

support a quantitative assessment of risk. 

A brief description of the approach used to conduct this health risk assessment is 

presented below. More detailed discussion of the approach can be found in the risk assessment 

technical support document, prepared by Abt Associates, which is included as Appendix C to 

this document. The goals of this SO2 risk assessment are: (1) to develop health risk estimates of 

the number and percent of the asthmatic population that would experience moderate or greater 

lung function decrements in response to 5-minute daily maximum peak exposures while engaged 

in moderate or greater exertion for several air quality scenarios (described below); (2) to develop 
July 2009 315 

a better understanding of the influence of various inputs and assumptions on these risk estimates; 

and (3) to gain insights into the risk levels and patterns of risk reductions associated with 

meeting several alternative 1-hour daily maximum SO2 standards. Health risks for lung function 

effects in exercising asthmatics have been estimated for the following three scenarios: (1) ”as is”

ambient levels of SO2, (2) air quality adjusted to simulate just meeting the current 24-hour 

standard, and (3) air quality adjusted to simulate just meeting several alternative 1-hour 

standards. 

As discussed in Chapter 8, the geographic scope of the assessment includes selected 

locations encompassing a variety of SO2 emission source types in two areas within the state of 

Missouri (i.e., Greene County and St. Louis). These areas were identified based on the results of 

a preliminary screening of the 5-minute ambient SO2 monitoring data that were available. The 

state of Missouri was one of only a few states having both 5-minute maximum and continuous 5-

minute SO2 ambient monitoring, as well as having over 30 1-hour SO2 monitors in operation at 

some time during the period from 1997 to 2007. In addition, the air quality characterization, 

described in Chapter 7, estimated frequent exceedances above the potential health effect 

benchmark levels at several of the 1-hour ambient monitors in Missouri. In a ranking of 

estimated SO2 emissions reported in the National Emissions Inventory (NEI), Missouri ranked 

7th for the number of stacks with > 1000 tpy SOx emissions out of all U.S. states. These stack 

emissions were associated with a variety of source types such as electrical power generating 

units, chemical manufacturing, cement processing, and smelters. For all these reasons, the 

current SO2 lung function risk assessment focuses on Missouri and, within Missouri, on those 

areas within 20 km of a major point source of SO2 emissions in Greene County and the St. Louis 

area. 

9.2 DEVELOPMENT OF APPROACH FOR 5-MINUTE LUNG FUNCTION 

RISK ASSESSMENT 

The lung function risk assessment is based on the health effects information evaluated in 

the ISA and discussed above in Chapter 4. The basic structure of the risk assessment reflects the 

fact that we have available controlled human exposure study data from several studies involving 

volunteer asthmatic subjects who were exposed to SO2 concentrations at specified exposure 

levels while engaged in moderate or greater exertion for 5- or 10-minute exposures. As 

discussed in the ISA (section 3.1.3.5), among asthmatics, both the magnitude of SO2-induced 
July 2009 316 

lung function decrements observed in responding individuals and the percent of individuals 

affected in the group exposed have been shown to increase with increasing 5- to 10-minute SO2

exposure levels in the range of 200 to 1,000 ppb. Therefore, for the SO2 lung function risk 

assessment we have developed probabilistic exposure-response relationships based on these data. 

The analysis was based on the combined data set consisting of all available individual data that 

describe the relationship between a measure of personal exposure to SO2 and measures of lung 

function recorded in these studies. For the purposes of this risk assessment, all of the individual 

data, including both 5- and 10-minute exposure duration, were combined and treated as 

representing 5-minute responses. These probabilistic exposure-response relationships were then 

combined with 5-minute daily maximum peak exposure estimates for mild and moderate 

asthmatics engaged in moderate or greater exertion associated with the various air quality 

scenarios mentioned above. A more detailed description of the exposure assessment that was the 

source of the estimated daily maximum 5-minute peak exposures under moderate or greater 

exertion is provided above in Chapter 8. 

9.2.1 General Approach 

 The major components of the lung function health risk assessment are illustrated in 

Figure 9-1. As shown in Figure 9-1, under the lung function risk assessment, exposure estimates 

for mild and moderate asthmatics for a number of different air quality scenarios (i.e., “as is” air 

quality (representing 2002), just meeting the current 24-hour standard, just meeting alternative 

standards) are combined with probabilistic exposure-response relationships derived using a 

combined data base consisting of data from several controlled human exposure studies to 

develop risk estimates. The air quality and exposure analysis components that are integral to this 

risk assessment are discussed in greater detail in Chapters 7 and 8 of this document and in the 

Exposure Assessment TSD (included as Appendix B to this document). Only the air quality and 

exposure aspects affecting the scope of the lung function risk assessment are briefly discussed in 

section 9.2.2. A description of the overall approach to estimating the exposure-response 

relationship is included in section 9.2.3 below. 

 Two types of risk measures were generated for the lung function risk assessment. The 

first type included estimates of the number and percentage of all asthmatics (or asthmatic 

children) experiencing one or more occurrences of a defined lung function response associated 

with 5-minute exposures to SO2 while engaged in moderate or greater exertion under a given air 
July 2009 317 

quality scenario. The second type of risk measure generated for each defined lung function 

response is the number of occurrences of the lung function response in asthmatics (or asthmatic 

children) in a year associated with 5-minute exposures at moderate or greater exertion under a 

given air quality scenario. Since asthmatic school age children are a subset of all asthmatics, the 

risk estimates presented for these two groups should not be combined. 

To obtain risk estimates associated with SO2 concentrations under different scenarios, we 

estimated expected risk given the personal exposures associated with SO2 concentrations under 

each scenario – i.e., associated with 

 “as is” ambient SO2 concentrations representing 2002 air quality, 

 SO2 air quality levels simulating just meeting the current 24-hour and annual standards, 

and 

 SO2 air quality levels simulating just meeting specified alternative 1-hour standards. 

 Note that, in contrast to the headcount risk estimates calculated for the O3 health risk 

assessment, the headcount risk estimates calculated for the SO2 health risk assessment reflect 

risks associated with all ambient SO2 concentrations, not just risks in excess of estimated policyrelevant background ambient SO2 concentrations. This is because policy-relevant background 

SO2 concentrations are estimated to be at most 30 parts per trillion and they contribute less than 

1% to present day SO2 ambient concentrations (ISA, section 2.4.6) and thus would have little 

impact on the risk estimates. 

The first measure of risk (i.e., the number or percent of individuals in the designated 

population to experience at least one lung function response in a year) is calculated as follows: 

 1) From the exposure modeling described in Chapter 8, we obtain the number of 

 individuals exposed at least once to x ppb SO2 or higher, for x = 0, 50, 100, ... to 800; 

 2) We then calculate the number of individuals exposed at least once to SO2

 concentrations within each SO2 exposure bin defined above (item 2 in the illustrative 

 example in Table 9-1 below); 

3) We then multiply the number of individuals in each exposure bin (item 2 in Table 9-1 

 below) by the response probability (item 3 in Table 9-1 below) corresponding to the 

 midpoint of the exposure bin (item 1 in Table 9-1 below); and 

 4) We sum the results across all of the bins. 

 
July 2009 318

Figure 9-1. Major components of 5-minute peak lung function health risk assessment based on controlled human 
July 2009 319

 Because response probabilities are calculated for each of several percentiles of a 

probabilistic exposure-response distribution, estimated numbers of individuals with at least one 

SO2-related lung function response are similarly percentile-specific. For example, the kth

percentile number of individuals, Yk associated with SO2 concentrations under a given air quality 

scenario is: 

 )|(

1

jk

n

j

k j  eRxNIY 

 (equation 9-1) 

where: 

ej = (the midpoint of) the jth category of personal exposure to SO2, given “as is” ambient 

SO2 concentrations; 

NIj = the number of individuals whose highest exposure is to ej ppb SO2, given ambient 

SO2 concentrations under the specified air quality scenario; 

jk | = the k eRR th percentile response rate at SO2 concentration ej; and 

n = the number of intervals (categories) of SO2 personal exposure concentration. 

The kth percentile estimate of the total number responding is then calculated by multiplying the 

kth percentile risk by the number of people in the relevant population. An example is given in 

Table 9-1, for the median (i.e., 50th percentile) risk estimate using personal exposures associated 

with a 99th percentile 100 ppb 1-hour daily maximum SO2 standard for asthmatics in the St. 

Louis modeling domain. We note that this calculation assumes that individuals who do not 

respond at the highest SO2 concentration to which they are exposed will not respond to any lower 

SO2 concentrations to which they are exposed. 

The second type of risk measure, the number of occurrences of a defined lung function 

response in the designated population (i.e., asthmatics or asthmatic children) in a year associated 

with SO2 concentrations under a given air quality scenario is calculated as follows: 

 1) From the exposure modeling described in Chapter 8, we obtain the number of 

 exposure occurrences among the population at and above each benchmark level (i.e., 

 0 ppb, 50 ppb, 100 ppb, ... 800 ppb); 

 2) We then calculate the number of exposure occurrences within each 50 ppb exposure 

 "bin" (e.g., < 50 ppb, 50-100 ppb, etc.) 89(item 2 in the illustrative example in Table 9-2 

 below); 

 

89 The final exposure bin was from 750 to 800 ppb SO2. In at least one of the alternative standard scenarios, there 

were a few individuals whose exposure was greater than 800 ppb. For anyone whose exposure exceeded 800 ppb, 
July 2009 320

Table 9-1. Example calculation of the number of asthmatics in st. louis engaged in moderate or 

greater exertion estimated to experience at least one lung function response (defined as an 

increase in sRaw ≥ 100%) associated with exposure to SO2 concentrations just meeting a 99th

percentile, 1-hour 100 ppb standard. 

Number of Probability of Estimated Number of 

Lower Upper Midpoint Asthmatics with Response at Midpoint Asthmatics Experiencing

Bound Bound At Least One SO2 Level at Least One Lung 

Exposure in Bin Function Response

(1) (2) (3) =(2) x (3)

0 50 25 53711 0.00406 218

50 100 75 34236 0.02334 799

100 150 125 9835 0.05162 508

150 200 175 3059 0.08563 262

200 250 225 929 0.12300 114

250 300 275 368 0.16220 60

300 350 325 145 0.20210 29

350 400 375 84 0.24190 20

400 450 425 31 0.28060 9

450 500 475 22 0.31830 7

500 550 525 8 0.35430 3

550 600 575 0 0.38850 0

600 650 625 0 0.42090 0

650 700 675 8 0.45150 4

700 750 725 0 0.46600 0

750 800 775 0 0.49380 0

Total : 102436 Total: 2032

SO2 Exposure Bin (ppb)

 3) We then multiply the number of occurrences in each exposure bin (item 2 in Table 9-2 

 below) by the response probability (item 3 in Table 9-2 below) corresponding to the 

 midpoint (item 1 in Table 9-2 below) of the exposure bin; and 

 4) We sum the results across all of the bins. 

 Similar to the first type of risk measure discussed above, because response probabilities 

are calculated for each of several percentiles of a probabilistic exposure-response distribution, 

estimated numbers of occurrences are similarly percentile-specific. The kth percentile number of 

occurrences, Ok, associated with SO2 concentrations under a given air quality scenario is: 

 )|(

1

jk

n

j

k j  eRxNO 

 (equation 9-2) 

where: 

ej = (the midpoint of) the jth category of personal exposure to SO2; 

 

we assumed a final bin from 800 to 850 ppb, and assigned them the midpoint value of that bin,825 ppb. This will 

result in a slight downward bias in the estimate of risk. 
July 2009 321

Nj = the number of exposures to ej ppb SO2, given ambient SO2 concentrations under the 

specified air quality scenario; 

jk | = the k eR th percentile response probability at SO2 concentration ej; and 

n = the number of intervals (categories) of SO2 personal exposure concentration. 

An example calculation is given in Table 9-2. 

Table 9-2. Example calculation of number of occurrences of lung function response (defined as 

an increase in sRaw ≥ 100%), among asthmatics in St. Louis engaged in moderate or greater 

exertion associated with exposure to SO2 concentrations that just meet a 99th percentile 1-hour, 

100 ppb standard. 

Number of Probability of Expected Number of 

Lower Upper Midpoint Exposures Response at Midpoint Occurrences of Lung 

Bound Bound SO2 Level Function Response

(1) (2) (3) =(2) x (3)

0 50 25 16519000 0.00406 67067

50 100 75 136621 0.02334 3189

100 150 125 15760 0.05162 814

150 200 175 3826 0.08563 328

200 250 225 1051 0.12300 129

250 300 275 413 0.16220 67

300 350 325 175 0.20210 35

350 400 375 83 0.24190 20

400 450 425 31 0.28060 9

450 500 475 24 0.31830 8

500 550 525 8 0.35430 3

550 600 575 0 0.38850 0

600 650 625 0 0.42090 0

650 700 675 8 0.45150 4

700 750 725 0 0.46600 0

750 800 775 0 0.49380 0

Expected Number 

Total Number of Exposures: 16677000 of Occurrences: 71672

SO2 Exposure Bin (ppb)

9.2.2 Exposure Estimates 

 As noted above, exposure estimates used in the lung function risk assessment were 

obtained from running the APEX exposure model for the population of individuals with asthma 

for selected locations encompassing a variety of SO2 emission source types within two areas in 

the state of Missouri (i.e., St. Louis and Greene County). Chapter 8 provides additional details 

about the inputs and methodology used to estimate 5-minute daily maximum peak SO2 exposures 

while engaged in moderate or greater exertion for the asthmatic population in these two areas. 
July 2009 322

These 5-minute exposure estimates for asthmatic children and adult asthmatics have been 

combined separately with probabilistic exposure-response relationships for lung function 

response associated with 5-minute SO2 exposures. Only the highest 5-minute peak exposure 

(with moderate or greater exertion) on each day has been considered in the lung function risk 

assessment, since the controlled human exposure studies have shown an acute-phase response 

that was followed by a short period where the individual was relatively insensitive to additional 

SO2 challenges. Staff recognizes that consideration of only the highest 5-minute exposure (with 

moderate or greater exertion) on each day likely leads to some underestimation of health risks 

since we are not including the health impact of other 5-minute exposures (with moderate or 

greater exertion) occurring on the same day. 

 As described in section 8.8.1, instead of adjusting upward90 the air quality concentrations 

to simulate just meeting the current SO2 standards and potential alternative 1-hr daily maximum 

standards, to reduce computer processing time, the exposure assessment simulated exposures 

associated with just meeting various standards by adjusting the health effect benchmark levels by 

the same factors described for each specific modeling domain and simulated year (see Table 8-

11). 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 same follows 

for where as is concentrations were in excess of an alternative standard level (e.g., 50 ppb for the 

99th percentile averaged over three years), only the associated benchmarks are adjusted upwards 

(i.e., a higher threshold concentration that would simulate lower exposures). 

 9.2.3 Exposure-Response Functions 

 Similar to the approach used in the ozone lung function risk assessment (Abt Associates, 

2007), we have used a Bayesian Markov Chain Monte Carlo approach to estimate probabilistic 

exposure-response relationships for lung function decrements associated with 5-minute daily 

maximum peak exposures while engaged in moderate or greater exertion using the WinBUGS 

software (Spiegelhalter et al., 1996).91 The combined data set includes all available individual 

data from controlled human exposure studies of mild-to-moderate asthmatic individuals exposed 

for 5- or 10-minutes while engaged in moderate or greater exertion that was summarized in the 

 

90 To evaluate the current and most of the alternative 1-hr standards analyzed, “as is” ambient concentrations were 

lower than air quality that would just meet the standards. 

91 See Gleman et al. (1995) or Gilks et al. (1996) for an explanation of these methods. 
July 2009 323

final ISA. As noted above, for the purposes of this risk assessment, all of the individual response 

data, including both 5- and 10-minute exposure durations, have been combined and treated as 

representing 5-minute responses. Table 9-3 summarizes the available controlled human 

exposure data that have been used to develop the probabilistic exposure-response relationships 

for the lung function risk assessment. 

 
July 2009 324

Table 9-3. Percentage of asthmatic individuals in controlled human exposure studies experiencing SO2-induced d

Cumulative Percentage of 

Responders 

(Number of Subjects)1

sRaw ≥ 100% 

 ≥ 200% 

 ≥ 300% 



FEV1

SO2 

Level 

(ppb) 

Exposure 

Duration 

No. of 

Subjects 

Ventilation 

(L/min) 

Lung 

Funct. 

≥ 15% 

 ≥ 20% 

 ≥ 30% 



Reference Resp

Supp

10 min 40 ~40 sRaw 5% (2) 0 0 Linn et al. (1987)2 200 10 min 40 ~40 FEV1 13% (5) 5% (2) 3% (1) Linn et al. (1987) 

5 min 19 ~50-60 sRaw 32% (6) 16% (3) 0 5 min 9 ~80-90 sRaw 22% (2) 0 0 

250 Bethel et al. (1985) 

10 min 28 ~40 sRaw 4% (1) 0 0 Roger et al. (1985) 10 min 20 ~50 sRaw 10% (2) 5% (1) 5% (1) Linn et al. (1988)310 min 21 ~50 sRaw 33% (7) 10% (2) 0 Linn et al. (1990)310 min 20 ~50 FEV1 15% (3) 0 0 Linn et al. (1988) 

300 

10 min 21 ~50 FEV1 24% (5) 14% (3) 10% (2) Linn et al. (1990) 

Limited 

inducesymptoLinn et 1988; (1984) 

10 min 40 ~40 sRaw 23% (9) 8% (3) 3% (1) 400 Linn et al. (1987) 10 min 40 ~40 FEV1 30% (12) 23% (9) 13% (5) Linn et al. (1987) 

5 min 10 ~50-60 sRaw 60% (6) 40% (4) 20% (2) Bethel et al. (1983) 

10 min 28 ~40 sRaw 18% (5) 4% (1) 4% (1) Roger et al. (1985) 

500 

10 min 45 ~30 sRaw 36% (16) 16% (7) 13% (6) Magnussen et al. (1990)4

Strongstatistiin respBalmeet al. (1987), 

600 10 min 40 ~40 sRaw 35% (14) 28% (11) 18% (7) Linn et al. (1987) Clear a
July 2009 325

Cumulative Percentage of 

Responders 

(Number of Subjects)1

sRaw ≥ 100% 

 ≥ 200% 

 ≥ 300% 



FEV1

SO2 

Level 

(ppb) 

Exposure 

Duration 

No. of 

Subjects 

Ventilation 

(L/min) 

Lung 

Funct. 

≥ 15% 

 ≥ 20% 

 ≥ 30% 



Reference Resp

Supp

10 min 20 ~50 sRaw 60% (12) 35% (7) 10% (2) Linn et al. (1988) 10 min 21 ~50 sRaw 62% (13) 29% (6) 14% (3) Linn et al. (1990) 10 min 40 ~40 FEV1 53% (21) 48% (19) 20% (8) Linn et al. (1987) 10 min 20 ~50 FEV1 55% (11) 55% (11) 5% (1) Linn et al. (1988) 10 min 21 ~50 FEV1 43% (9) 33% (7) 14% (3) Linn et al. (1990) 10 min 28 ~40 sRaw 50% (14) 25% (7) 14% (4) 1,000 Roger et al. (1985) 10 min 10 ~40 sRaw 60% (6) 20% (2) 0 Kehrl et al. (1987) 

in SO2

sympto1987; (1995)

Notes: 1Data presented from all references from which individual data were available. Percentage of individuals who experienced greater than o

300% increase in specific airway resistance (sRaw), or a 15, 20, or 30% decrease in FEV1. Lung function decrements are adjusted for e

clean air (calculated as the difference between the percent change relative to baseline with excersise/SO2 and the percent change relativ

exercise/clean air). Quality control of data was performed by two EPA staff scientists. 2Responses of mild and moderate asthmatics reported in Linn et al. (1987) have been combined. Data reported only for the first 10 min 

first round of exposures. 

3Analysis includes data from only mild (1988) and moderate (1990) asthmatics who were not receiving supplemental medication. 4One subject was not exposed to 1,000 ppb due to excessive wheezing and chest tightness experienced at 500 ppb. For this subject, th

ppb were also used for 1,000 ppb under the assumptions that the response at 1,000 ppb would be equal to or greater than the response 

5Indicates studies in which exposures were conducted using a mouthpiece rather than a chamber.Source: ISA, Table 3-1 (EPA, 2008c, p.3-10).
July 2009 326

 The combined data set from Linn et al. (1987, 1988, 1990), Bethel et al. (1983, 

1985), Roger et al. (1985), and Kehrl et al. (1987), summarized in Table 9-3, provide data 

with which to estimate exposure-response relationships between responses defined in 

terms of sRaw and 5-minute exposures to SO2 at levels of 200, 250, 300, 400, 500, 600, 

and 1,000 ppb (the exposure levels included in these studies).92 Two definitions of 

response have been used: (1) an increase in sRaw ≥ 100% representing moderate or 

greater responses and (2) an increase in sRaw ≥ 200% reflecting severe decrements in 

lung function. 

 Likewise, the combined data set from Linn et al. (1987, 1988, 1990), summarized 

in Table 9-3, provide data with which to estimate exposure-response relationships 

between responses defined in terms of FEV1 and 5-minute exposures to SO2 at levels of 

200, 300, 400, and 600 ppb (the exposure levels included in these studies). Again, two 

definitions of response have been used in the health risk assessment: (1) a decrease in 

FEV1 ≥ 15% representing moderate or greater responses and (2) a decrease in FEV1 ≥

20% representing severe decrements in lung function. 

 Before estimating exposure-response relationships for 5-minute exposures, we 

corrected the data from these controlled human exposure studies for the effect of exercise 

in clean air to remove any systematic bias that might be present in the data attributable to 

an exercise effect. This correction is reflected in the summary of the response data 

provided in Table 9-3.93 Generally, this correction for exercise in clean air is small 

relative to the total effects measures in the SO2-exposed cases. 

 Public comments on the 2nd draft REA stated that there were errors in the data 

used to create Table 9-3 (UARG, 2009). Johns (2009) describes EPA’s evaluation of 

these data, building upon an initial EPA analysis conducted in the previous NAAQS 

review (Smith, 1994). The vast majority of the alleged errors were described as rounding 

errors of the second decimal place introduced by the original study authors. Of the 640 

 

92 Data from Magnussen et al. (1990) were not used in the estimation of sRaw exposure-response functions 

because exposures in this study were conducted using a mouthpiece rather than a chamber. 93 Corrections were subject-specific. A correction was made by subtracting the subject’s percent change 

(in FEV1 or sRaw) under the no-SO2 protocol from his or her percent change (in FEV1 or sRaw) under the 

given SO2 protocol, and rounding the result to the nearest integer. For example, if a subject’s percent 

change in sRaw under the no-SO2 protocol was 110.12% and his percent change in sRaw under the 0.6 ppm 

SO2 protocol was 185.92%, then his percent change in sRaw due to SO2 is 185.92% - 110.12% = 75.8%, 

which rounds to 76%. 
July 2009 327

values of sRaw and FEV1 from Linn et al. (1987), commenters identified 11 

discrepancies between the original EPA analysis (Smith, 1994) and what was included in 

the analysis conducted more recently by EPA (Johns, 2009). EPA has reviewed these 

comments, and recognizes that some discrepancies were clearly due to transcription 

errors, while others were due to difficulties reading the last decimal place of the raw data. 

Commenters also identified 9 cases where the calculated average of individual lung 

function measurements did not equal the average values presented in Smith (1994). 

While staff placed more confidence in the average values presented rather than the 

calculated average of the individual measurements, EPA nonetheless conducted a 

preliminary re-analysis using the 20 apparent “corrected” values provided by 

commenters. This resulted in relatively minor and variable changes in SO2-induced 

changes in lung function, which did not substantively change the percent responders as 

presented in Table 9-3. Further, incorporating these 20 changes resulted in an increase in 

the percent of responders in three table entries, while no decreases in the percent of 

responders were observed. Although the data presented in Table 9-3 were subjected to 

quality control procedures (see Johns, 2009), EPA is currently in the process of 

conducting a full quality assurance review of the data in response to these public 

comments and expects to present the quantitative results of its evaluation as part of the 

record for the November proposal. The risk assessment results presented in this 

document are based on the Johns (2009) summary. 

 We considered two different functional forms for the exposure-response 

functions: a 2-parameter logistic model and a probit model. In particular, we used the 

data in Table 9-3 to estimate the logistic function, 

 )1(

1 ),;( x)*ln( e

xy      (equation 9-3) 

and the probit function, 

 





 

)ln(

2/

2/1

2

)2(

1 ),;(

x

t xy dte





 (equation 9-4) 
July 2009 328

for each of the four lung function responses defined above, where x denotes the SO2

concentration (in ppm) to which the individual is exposed, ln(x) is the natural logarithm 

of x, y denotes the corresponding probability of response (increase in sRaw > 100% or >

200% or decrease in FEV1 > 15% or > 20%), and β and γ are the two parameters whose 

values are estimated. 94 

 We assumed that the number of responses, si, out of Ni subjects exposed to a 

given SO2 concentration, xi, has a binomial distribution with response probability given 

by equation (9-3) when we assume the logistic model and equation (9-4) when we 

assume the probit model. The likelihood function is therefore 

 i ii sN

i

s

i

i i

i

xy xy

s

N

dataL   













  );,(     )],;(1[),;( . (equation 9-5) 

 Some subjects in the controlled human exposure studies participated in more than 

one study and were exposed to a given SO2 concentration more than once. However, 

because there were insufficient data to estimate subject-specific response probabilities, 

we assumed a single response probability (for a given definition of response) for all 

individuals and treated the repeated exposures for a single subject as independent 

exposures in the binomial distribution. 

 For each model, we derived a Bayesian posterior distribution using this binomial 

likelihood function in combination with uniform prior distributions for each of the 

unknown parameters.95 We used 4,000 iterations as the “burn-in” period followed by 

10,000 iterations, a number sufficient to ensure convergence of the resulting posterior 

distribution. Each iteration corresponds to a set of values for the parameters of the 

logistic or probit exposure-response function. 

 For any SO2 concentration, x, we could then derive the n

th percentile response 

value, for any n, by evaluating the exposure-response function at x using each of the 

18,000 sets of parameter values. The resulting median (50th percentile) exposure-

 

94 For ease of exposition, the same two Greek letters are used to indicate two unknown parameters in the 

logistic and probit models; this does not imply, however, that the values of these two parameters are the 

same in the two models. 

95 We used the following uniform prior distributions for the 2-parameter logistic model: β ~ U(-10, 0); and 

γ ~ U(-10,0); we used the following normal prior distributions for the probit model: β ~ N(0, 1000); and γ ~ 

N(0,1000). 
July 2009 329

response functions based on the 2-parameter logistic and probit models are shown 

together, along with the data used to estimate these functions, for increases in sRaw >

100% and > 200% and decreases in FEV1 > 15% and > 20% in Figures 9-2, 9-3, 9-4, and 

9-5, respectively. The 2.5th percentile, median, and 97.5th percentile curves, along with 

the response data to which they were fit, are shown separately for each of the eight 

combinations of (four) response definitions and (two) exposure-response models in 

Appendix C. 

 We note that there were only limited data with which to estimate the logistic and 

probit exposure-response functions, and that the logistic and probit models both appear to 

fit the data equally well. We also note that since the data being fit has already been 

corrected to account for the lung function response due to exercise in clean air, then the 

response must by definition be zero associated with 0 ppm SO2 exposure. While the 

CASAC panel in its comments on the 2nd draft REA suggested a possible a priori reason 

to prefer the probit model (based on a hypothesized lognormal distribution of individual 

thresholds for response), in staff’s judgment there is not sufficient evidence to select one 

model over the other. Therefore, we have chosen to include both the 2-parameter logistic 

and probit models to develop the risk estimates associated with exposure to SO2 under the 

different air quality scenarios considered. While the estimated exposure-response 

relationships using the two alternative models do not appear to be that different based on 

visual inspection of Figures 9-2 through 9-5, the differences do translate into substantial 

differences in the estimated aggregate number of sRaw and FEV1 responses for St. Louis 

as discussed later in this chapter. 
July 2009 330

Figure 9-2. Bayesian-estimated median exposure-response functions: increase in sRaw ≥

100% for 5-Minute exposures of asthmatics under moderate or greater 

exertion.* 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

probit

2-parameter logistic

Figure 9-3. Bayesian-estimated median exposure-response functions: increase in sRaw ≥

200% for 5-minute exposures of asthmatics under moderate or greater 

exertion.* 

*

Derived using method described in text based on all of the individual response data from Linn et al. 

(1987), Linn et al. (1988), Linn et al. (1990), Bethel et al. (1983), Bethel et al. (1985), Roger et al. (1985), 

and Kehrl et al. (1987).

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

The data

probit

2-parameter logistic

Response Rate
July 2009 331

Figure 9-4. Bayesian-estimated median exposure-response functions: decrease in FEV1 ≥

15% for 5-minute exposures of asthmatics under moderate or greater 

exertion*. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

probit

2-parameter logistic

Figure 9-5. Bayesian-estimated median exposure-response functions: decrease in FEV1 ≥

20% for 5-minute exposures of asthmatics under moderate or greater exertion.* *Derived using method described in text based on all of the individual response data from Linn et al. 

(1987), Linn et al. (1988), and Linn et al. (1990). 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

The data

probit

2-parameter logistic

Response Rate
July 2009 332

9.3 LUNG FUNCTION RISK ESTIMATES 

 In this section, we present and discuss risk estimates associated with several air 

quality scenarios, including “as is” air quality represented by 2002 monitoring data. In 

addition, risk estimates are presented for several hypothetical scenarios, equivalent to 

adjusting air quality upward to simulate just meeting the current annual SO2 24-hour 

standard and to adjusting air quality (either up or down) to simulate just meeting potential 

alternative 98th and 99th percentile daily maximum 1-h standards. As discussed 

previously in Chapter 5, potential alternative 1-h standards with levels set at 50, 100, 150, 

200, and 250 ppb have been included in the risk assessment. Only selected risk estimates 

are presented in this section and additional risk estimates are presented in Appendix C. 

Throughout this section and Appendix C the uncertainty surrounding risk estimates 

resulting from the statistical uncertainty in the SO2 exposure-response relationships due 

to sampling error is characterized by ninety-five percent credible intervals around 

estimates of occurrences, number of asthmatics experiencing one or more lung function 

response, and percent of total incidence that is SO2-related. 

 Risk estimates for selected lung function responses for all asthmatics and 

asthmatic children associated with 5-minute exposures to ambient SO2 concentrations 

while engaged in moderate or greater exertion are presented in Tables 9-4 through 9-9. 

Tables 9-4 through 9-6 are for all asthmatics and Tables 9-7 through 9-9 are for asthmatic 

children. Each table includes risk estimates for both Greene County and St. Louis, 

Missouri. Each table also includes risk estimates based on use of both the 2-parameter 

logistic and probit exposure-response models. As discussed in section 9.2.3, the risk 

assessment included two types of lung function responses (i.e., sRaw and FEV1) and two 

levels of response for each type of lung function response (≥ 100 and 200% increase for 

sRaw and ≥ 15 and 20% decrease for FEV1). Risk estimates using sRaw as the measure 

of lung function response are included in this section because the exposure-response 

relationships were developed based on a larger set of data from individual subjects, which 

gives us more confidence in the exposure-response relationship. Additional risk 

estimates using FEV1 as the indicator of lung function response are included in Tables 4-

3, 4-4, 4-7, and 4-8 in Appendix C and show similar patterns across the current and 

alternative standards for the two study areas. 
July 2009 333

 Tables 9-4 and 9-5 summarize the estimated number and percent of asthmatics 

that would experience 1 or more lung function responses in a year, where lung function 

response was defined as ≥ 100% and ≥ 200% increase in sRaw, in all asthmatics 

associated with ambient 5-minute SO2 exposures estimated to occur under “as is” air 

quality (i.e., air quality based on 2002 monitored and modeled SO2 air quality data) and 

under air quality representing just meeting the current SO2 standards and several 

alternative 1-hour daily maximum SO2 standards. Tables 9-7 and 9-8 present the same 

types of estimates for asthmatic children. The median estimates are presented in each 

cell of the table with the 95% credible intervals based on statistical uncertainty 

surrounding the SO2 coefficient in the exposure-response relationship shown in 

parentheses below the median estimates. 

 Tables 9-6 and 9-9 summarize the estimated number of occurrences of two 

defined levels of lung function response (≥ 100% and ≥ 200% increase in sRaw) in all 

asthmatics and in asthmatic children, respectively, associated with ambient 5-minute SO2

exposures estimated to occur under “as is” air quality (i.e., air quality based on 2002 

monitored and modeled SO2 air quality data) and under air quality representing just 

meeting the current SO2 standards and several alternative 1-hour daily maximum SO2

standards. 

 The current primary SO2 standards include a 24-hour standard set at 0.14 parts per 

million (ppm), not to be exceeded more than once per year, and an annual standard set at 

0.03 ppm, calculated as the arithmetic mean of hourly averages. In St. Louis, SO2

concentrations that are predicted to occur if the current standards were just met are 

substantially higher than “as is” air quality (based on 2002 monitoring and modeling 

data) and also substantially higher than they would be under any of the alternative 1-hr 

standards considered in this analysis. Consequently, the levels of response that would be 

seen if the current standard were just met are well above the levels that would be seen 

under the “as is” air quality scenario or under any of the alternative 1-hr standards – for 

asthmatics and for asthmatic children, and for all four definitions of lung function 

response. We also note that the only standard resulting in decreases in lung function 

responses relative to the “as is” scenario is the 50 ppb, 99th percentile 1-hr daily 

maximum standard (corresponding to the 99/50 column in Tables 9-6 through 9-9). 
July 2009 334

Table 9-4. Number of asthmatics engaged in moderate or greater exertion estimated to experience at least one lunassociated with exposure to SO2 concentrations under alternative air quality scenarios in a year.* 

99/50 99/100 99/150 99/200 99

90 210 80 90 100 120 1

(20 - 390) (80 - 620) (20 - 380) (20 - 390) (20 - 420) (30 - 460) (50 

10 110 10 10 20 40 

(0 - 180) (40 - 410) (0 - 170) (0 - 180) (0 - 210) (10 - 250) (20 

1010 13460 730 1990 3650 5520 7

(340 - 3010) (9740 - 18510) (220 - 2490) (860 - 4690) (1900 - 7100) (3230 - 9490) (4770 

500 13050 290 1340 2930 4810 6

(140 - 1990) (9430 - 18100) (70 - 1470) (520 - 3690) (1450 - 6200) (2760 - 8710) (4310 

30 70 30 30 30 40 

(0 - 210) (20 - 310) (0 - 210) (0 - 210) (0 - 220) (10 - 240) (10 

0 300 010 10 

(0 - 80) (10 - 180) (0 - 80) (0 - 90) (0 - 100) (0 - 110) (0 

330 5520 230 670 1280 2010 2

(70 - 1520) (3400 - 8960) (40 - 1290) (210 - 2270) (510 - 3360) (940 - 4470) (1470 

120 5180 60 350 870 1560 2

(20 - 880) (3150 - 8570) (10 - 660) (90 - 1590) (310 - 2680) (690 - 3820) (1200 

Response = Increase in sRaw >= 100%

 Greene County, MO

St. Louis, MO

"As is" SO2 

Concentrations**

Exposure-Response 

Model

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m):

2-Parameter Logistic

Probit

2-Parameter Logistic

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncoefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.

2-Parameter Logistic

Response = Increase in sRaw >= 200%

Probit

St. Louis, MO

Probit

Greene County, MO

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

Probit

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
July 2009 335

Table 9-5. Percent of asthmatics engaged in moderate or greater exertion estimated to experience at least one lunassociated with exposure to SO2 concentrations under alternative air quality scenarios in a year.* 

99/50 99/100 99/150 99/200 99

0.4% 1% 0.4% 0.4% 0.5% 0.6% 0

(0.1% - 1.8%) (0.4% - 2.9%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 2%) (0.2% - 2.1%) (0.2% 

0.1% 0.5% 0.1% 0.1% 0.1% 0.2% 0

(0% - 0.8%) (0.2% - 1.9%) (0% - 0.8%) (0% - 0.8%) (0% - 1%) (0% - 1.2%) (0.1% 

1% 13.1% 0.7% 1.9% 3.6% 5.4% 7

(0.3% - 2.9%) (9.5% - 18.1%) (0.2% - 2.4%) (0.8% - 4.6%) (1.9% - 6.9%) (3.2% - 9.3%) (4.7% 

0.5% 12.7% 0.3% 1.3% 2.9% 4.7% 6

(0.1% - 1.9%) (9.2% - 17.7%) (0.1% - 1.4%) (0.5% - 3.6%) (1.4% - 6.1%) (2.7% - 8.5%) (4.2% 

0.1% 0.3% 0.1% 0.1% 0.2% 0.2% 0

(0% - 1%) (0.1% - 1.5%) (0% - 1%) (0% - 1%) (0% - 1%) (0% - 1.1%) (0% 

0% 0.1% 0% 0% 0% 0% 0

(0% - 0.4%) (0% - 0.8%) (0% - 0.4%) (0% - 0.4%) (0% - 0.5%) (0% - 0.5%) (0% 

0.3% 5.4% 0.2% 0.7% 1.3% 2% 2

(0.1% - 1.5%) (3.3% - 8.7%) (0% - 1.3%) (0.2% - 2.2%) (0.5% - 3.3%) (0.9% - 4.4%) (1.4% 

0.1% 5.1% 0.1% 0.3% 0.8% 1.5% 2

(0% - 0.9%) (3.1% - 8.4%) (0% - 0.6%) (0.1% - 1.6%) (0.3% - 2.6%) (0.7% - 3.7%) (1.2% 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statisthe SO2 coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth. 

Probit

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
July 2009 336

Table 9-6. Number of occurrences (in hundreds) of a lung function response among asthmatics engaged in modeexertion associated with exposure to SO2 concentrations under alternative air quality scenarios in a year.* 

99/50 99/100 99/150 99/200 99

125 127 125 125 125 126 1

(24 - 572) (25 - 577) (24 - 572) (24 - 572) (24 - 573) (24 - 573) (24 

16 18 16 16 16 16 

(0 - 256) (1 - 261) (0 - 256) (0 - 256) (1 - 257) (1 - 257) (1 

657 1672 652 686 762 880 1

(128 - 2985) (663 - 4740) (125 - 2975) (141 - 3041) (176 - 3184) (234 - 3398) (315 

90 933 86 111 170 264 3

(4 - 1346) (393 - 3107) (3 - 1336) (11 - 1402) (33 - 1543) (72 - 1756) (128 

38 39 38 38 38 38 

(4 - 310) (4 - 312) (4 - 310) (4 - 310) (4 - 310) (4 - 310) (4 

232222

(0 - 123) (0 - 124) (0 - 122) (0 - 123) (0 - 123) (0 - 123) (0 

201 560 199 211 237 278 3

(21 - 1614) (165 - 2407) (20 - 1609) (24 - 1639) (32 - 1703) (47 - 1799) (68 

13 258 12 18 33 59 

(0 - 643) (86 - 1388) (0 - 639) (1 - 666) (5 - 725) (12 - 814) (24 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an a

ppm, calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncoefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number. 

Probit

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
July 2009 337

Table 9-7. number of asthmatic children engaged in moderate or greater exertion estimated to experience at least 

response associated with exposure to SO2 concentrations under alternative air quality scenarios in a year.* 

99/50 99/100 99/150 99/200 99

30 110 30 30 40 50 

(10 - 130) (40 - 270) (10 - 130) (10 - 140) (10 - 150) (20 - 180) (30 

10 60 0 10 10 20 

(0 - 60) (20 - 200) (0 - 60) (0 - 60) (0 - 80) (10 - 100) (10 

590 8020 400 1220 2240 3370 4

(220 - 1570) (6080 - 10370) (130 - 1210) (560 - 2620) (1240 - 4010) (2090 - 5350) (3060 

340 7950 190 890 1910 3080 4

(100 - 1150) (6020 - 10320) (50 - 790) (360 - 2220) (1000 - 3690) (1860 - 5110) (2870 

10 40 10 10 10 20 

(0 - 70) (10 - 130) (0 - 70) (0 - 70) (0 - 80) (0 - 90) (10 

0 20 0 0 0 10 

(0 - 30) (0 - 90) (0 - 30) (0 - 30) (0 - 40) (0 - 50) (0 

190 3380 130 410 800 1250 1

(50 - 780) (2190 - 5070) (30 - 610) (140 - 1240) (340 - 1870) (620 - 2500) (970 

80 3290 40 240 580 1030 1

(10 - 500) (2110 - 5000) (10 - 350) (60 - 950) (220 - 1590) (480 - 2250) (830 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Numbers are median (50th percentile) numbers of asthmatic children. Numbers in parentheses below the median are 95% credible intervals based on statithe SO2 coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.

Probit

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
July 2009 338

Table 9-8. Percent of asthmatic children engaged in moderate or greater exertion estimated to experience at least 

response associated with exposure to SO2 concentrations under alternative air quality scenarios in a year.*

99/50 99/100 99/150 99/200 99

0.4% 1.4% 0.4% 0.4% 0.5% 0.7% 

(0.1% - 1.8%) (0.6% - 3.7%) (0.1% - 1.8%) (0.1% - 1.9%) (0.1% - 2.1%) (0.2% - 2.4%) (0.3% 

0.1% 0.9% 0.1% 0.1% 0.2% 0.3% 0

(0% - 0.9%) (0.3% - 2.7%) (0% - 0.8%) (0% - 0.9%) (0% - 1.1%) (0.1% - 1.4%) (0.2% 

1.4% 19.2% 0.9% 2.9% 5.4% 8.1% 10

(0.5% - 3.8%) (14.6% - 24.9%) (0.3% - 2.9%) (1.3% - 6.3%) (3% - 9.6%) (5% - 12.8%) (7.3% 

0.8% 19.1% 0.4% 2.1% 4.6% 7.4% 10

(0.2% - 2.8%) (14.4% - 24.7%) (0.1% - 1.9%) (0.9% - 5.3%) (2.4% - 8.8%) (4.5% - 12.3%) (6.9% 

0.1% 0.5% 0.1% 0.1% 0.2% 0.2% 0

(0% - 1%) (0.1% - 1.8%) (0% - 1%) (0% - 1%) (0% - 1.1%) (0% - 1.3%) (0.1% 

0% 0.2% 0% 0% 0% 0.1% 0

(0% - 0.4%) (0.1% - 1.2%) (0% - 0.4%) (0% - 0.4%) (0% - 0.5%) (0% - 0.6%) (0% 

0.5% 8.1% 0.3% 1% 1.9% 3% 4

(0.1% - 1.9%) (5.3% - 12.2%) (0.1% - 1.5%) (0.3% - 3%) (0.8% - 4.5%) (1.5% - 6%) (2.3% 

0.2% 7.9% 0.1% 0.6% 1.4% 2.5% 3

(0% - 1.2%) (5% - 12%) (0% - 0.8%) (0.2% - 2.3%) (0.5% - 3.8%) (1.2% - 5.4%) (2% 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statisthe SO2 coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth. 

Exposure-Response 

Model

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

Probit

2-Parameter Logistic

Probit

2-Parameter Logistic

Probit

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m):

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

Probit

 Greene County, MO

St. Louis, MO

Greene County, MO

"As is" SO2 

Concentrations**
July 2009 339

Table 9-9. number of occurrences (in hundreds) of a lung function response among asthmatic children engaged iexertion associated with exposure to SO2 concentrations under alternative air quality scenarios in a year.*

99/50 99/100 99/150 99/200 99

71 72 71 71 71 71 

(13 - 324) (14 - 327) (13 - 324) (14 - 324) (14 - 324) (14 - 325) (14 

9 109 9 9 9

(0 - 145) (1 - 148) (0 - 145) (0 - 145) (0 - 145) (0 - 146) (0 

417 1179 413 439 497 586 7

(81 - 1893) (484 - 3209) (80 - 1885) (91 - 1935) (118 - 2043) (162 - 2206) (222 

58 692 55 74 118 189 2

(3 - 855) (296 - 2176) (2 - 847) (8 - 896) (25 - 1004) (53 - 1166) (96 

22 22 22 22 22 22 

(2 - 175) (2 - 177) (2 - 175) (2 - 175) (2 - 175) (2 - 176) (2 

121111

(0 - 69) (0 - 71) (0 - 69) (0 - 69) (0 - 69) (0 - 70) (0 

128 397 126 135 155 186 2

(13 - 1023) (122 - 1618) (13 - 1019) (15 - 1042) (22 - 1091) (33 - 1164) (49 

8 1928 12 24 43 

(0 - 408) (65 - 967) (0 - 405) (1 - 425) (4 - 470) (9 - 538) (18 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncoefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number. 

Exposure-Response 

Model

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

Probit

2-Parameter Logistic

Probit

2-Parameter Logistic

Probit

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m):

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

Probit

 Greene County, MO

St. Louis, MO

Greene County, MO

"As is" SO2 

Concentrations**
July 2009 340

 As an illustration of the changes in the number of occurrences of sRaw increases ≥ 100% 

in all asthmatics across the range of standards analyzed in the St. Louis modeling domain, under 

the current SO2 standards the median estimate is 117,900. These estimated occurrences decrease 

for increasingly more stringent alternative 1-hour standards with the 50 ppb, 99th percentile daily 

maximium 1-hour standard, the most stringent alternative standard analyzed, reducing the 

median estimated number of occurrences of this lung function response to 41,300. The pattern 

of reductions observed for all asthmatics is similar to that observed in asthmatic children. 

 The estimated occurrences of sRaw responses are much lower in Greene County both due 

to a smaller population as well as fewer exposure occurrences of elevated 5-minute SO2

concentrations. We also note that the differences in estimated occurrences of lung function 

responses associated with all of the air quality scenarios analyzed are much smaller for Greene 

County than in St. Louis. The minimal differences observed in Greene County among the air 

quality scenarios analyzed is due to the relatively small differences in the distribution of 

exposures while engaged in moderate or greater exertion among the air quality scenarios 

analyzed. 

 Figures 9-7 (a) and (b) show the percent of asthmatics based on use of the logistic and 

probit exposure-response models, respectively, engaged in moderate or greater exertion in St. 

Louis, MO estimated to experience at least one lung function response in a year, defined as an 

increase in sRaw ≥ 100%, attributable to exposure to SO2 in each exposure “bin” or interval. 

Figures 9-8(a) and (b) show these same estimates for the percent of asthmatic children. Figure 9-

6 displays the legend for Figures 9-7 and 9-8 indicating the exposure bins used in these figures 

and Table 9-10 provides definitions of the figures’ x-axis labels, which represent alternative air 

quality scenarios. Similar figures are included in Appendix C for lung function responses 

defined in terms of ≥ 15% and ≥ 20% decrements in FEV1 for both asthmatics and asthmatic 

children. Appendix C also includes similar figures for the Greene County study area. As 

apparent in Figures 9-7 (a) and (b) and in Figures 9-8(a) and (b), the pattern of the contribution 

of exposures from different concentration intervals on lung function response is very similar for 

this risk metric using the two alternative exposure-response models. In comparing the risk 

estimates for all asthmatics (Figure 9-7) with the risk estimates for asthmatic children (Figure 9-

8) the total percent responding is higher for asthmatic children. This is due to the greater 

percentage of 5-minute exposures while engaged in moderate or greater exertion for asthmatic 
July 2009 341

 

0%

5%

10%

15%

20%

25%

30%

35%

40%

"As Is" Air Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Attributable to 500 ppb<=SO2

Attributable to 450 ppb<=SO2<500 ppb

Attributable to 400 ppb<=SO2<450 ppb

Attributable to 350 ppb<=SO2<400 ppb

Attribuable to 300 ppb<=SO2<350 ppb

Attributable to 250 ppb<=SO2<300 ppb

Attributable to 200 ppb<=SO2<250 ppb

Attributable to 150 ppb<=SO2<200 ppb

Attributable to 100 ppb<=SO2<150 ppb

Attributable to 50 ppb<=SO2<100 ppb

Attributable to SO2<50 ppb

Figure 9-6. Legend for Figures 9-7 and 9-8 showing total and contribution of risk attributable to 

SO2 exposure ranges. 

Table 9-10. Explanation of labels on the x-axis of Figures 9-7 and 9-8. 

Label Explanation 

"As Is" 

Air 

Quality 

Reflects air quality in 2002 

Current 

Standard 

Refers to the current suite of standards, which includes a 24-hr standard of 

0.14 ppm which is not to be exceeded more than once per year and an annual 

standard set at 0.03 ppm 

99/50 Refers to an alternative standard in which the 99th percentile of the 1-hr daily 

maximum concentrations must be ≤ 50 ppb. 

99/100 Refers to an alternative standard in which the 99th percentile of the 1-hr daily 

maximum concentrations must be ≤ 100 ppb. 

99/150 Refers to an alternative standard in which the 99th percentile of the 1-hr daily 

maximum concentrations must be ≤ 150 ppb. 

99/200 Refers to an alternative standard in which the 99th percentile of the 1-hr daily 

maximum concentrations must be ≤ 200 ppb. 

99/250 Refers to an alternative standard in which the 99th percentile of the 1-hr daily 

maximum concentrations must be ≤ 250 ppb. 

98/200 Refers to an alternative standard in which the 98th percentile of the 1-hr daily 

maximum concentrations must be ≤ 200 ppb. 
July 2009 342

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

 a) Based on Logistic Exposure-Response Model 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Based on Probit Exposure-Response Model 

Figure 9-7. Estimated percent of asthmatics experiencing one or more lung function responses 

(defined as ≥ 100% increase in sRaw) per year associated with short-term (5-minute) 

exposures to SO2 concentrations associated with alternative air quality scenarios – 

total and contribution of 5-minute SO2 exposure ranges (see Figure 9-6 for legend and 

Table 9-10 for description of air quality scenarios included on x-axis).
July 2009 343

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

a) Based on Logistic Exposure-Response Model 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Based on Probit Exposure-Response Model 

Figure 9-8. Estimated percent of asthmatic children experiencing one or more lung function 

responses (defined as ≥ 100% increase in sRaw) per year associated with short-term (5-

minute) exposures to SO2 concentrations associated with alternative air quality 

scenarios – total and contribution of 5-minute SO2 exposure ranges (see Figure 9-6 for 

legend and Table 9-10 for description of air quality scenarios included on x-axis).
July 2009 344

children compared to all asthmatics due to the higher frequency of exercise in children compared 

to adults. Of course the actual number of persons affected is smaller for asthmatic children since 

they are a subset of all asthmatics. 

 The numbers of individuals with at least one lung function response attributable to 

exposures in the lowest exposure concentration bin (i.e., 0 to 50 ppb) are typically quite small. 

This is because the calculation of numbers of individuals with at least one lung function response 

uses individuals’ highest exposure only. While individuals may be exposed mostly to low SO2

concentrations, many are exposed at least occasionally to higher levels. Thus, the percentage of 

individuals in a designated population with at least one lung function response associated with 

SO2 concentrations in the lowest bin is likely to be very small, since most individuals are 

exposed at least once to higher SO2 levels. For example, the lowest SO2 exposure bin accounts 

for only about 0.2 percent of asthmatics estimated to experience at least 1 SO2-related lung 

function response. For this very small percent of the population, the lowest exposure bin 

represents their highest SO2 exposures under moderate exertion in a year. Figure 9-7 (a) shows a 

relatively small proportion of asthmatics in St. Louis experiencing at least one response to be 

experiencing those responses because of exposures in that lowest exposure bin. 

 While exposures in the lowest bin are not responsible for the greatest portion of the 

estimated risk for the risk metric expressed as incidence or percent incidence of a defined lung 

function response 1 or more times per year, exposures in the lowest bin (i.e., 0 to 50 ppb) are 

responsible for the bulk of the risks expressed as total occurrences of a defined lung function 

response. As noted in public comments on the 2nd draft SO2 REA, the assignment of response 

probability to the midpoint of the exposure bin combined with the lack of more finely divided 

intervals in this range can lead to significant overestimation of risks based on total occurrences 

of a defined lung function response. This is because the distribution of population exposures for 

occurrences is not evenly distributed across the bin, but rather is more heavily weighted toward 

the lower range of the bin. Thus, combining all exposures estimated to occur in the lowest bin 

with a response probability assigned to the midpoint of the bin results in a significant 

overestimate of the risk. Therefore, staff places less weight on the estimated number of 

occurrences of lung function responses. This overestimation of total occurrences does not 

impact the risk metric expressed as incidence or percent incidence of a defined lung function 
July 2009 345

response 1 or more times per year because the bulk of the exposures contributing to these risk 

metrics are not skewed toward the lower range of the reported exposure bins. 

9.4 CHARACTERIZING UNCERTAINTY AND VARIABILITY 

An important issue associated with any population health risk assessment is the 

characterization of uncertainty and variability (see section 6.6 for definitions of uncertainty and 

variability). This section presents a summary and discussion regarding the degree to which 

variability was incorporated in the health risk assessment for lung function responses and how 

the uncertainty was characterized for the risk estimates of number and percent of asthmatics and 

asthmatic children experiencing defined lung function responses associated with 5-minute SO2

exposures under moderate or greater exertion associated with alternative air quality scenarios. 

 With respect to variability, the lung function risk assessment incorporates some of the 

variability in key inputs to the analysis by its use of location-specific inputs for the exposure 

analysis (e.g., location specific population data, air exchange rates, air quality, and temperature 

data). The extent to which there may be variability in exposure-response relationships for the 

populations included in the risk assessment residing in different geographic areas is currently 

unknown. Temporal variability also is more difficult to address, because the risk assessment 

focuses on some unspecified time in the future. To minimize the degree to which values of 

inputs to the analysis may be different from the values of those inputs at that unspecified time, 

we have used the most current inputs available. 

Our approach to characterizing uncertainty includes both qualitative and quantitative 

elements. From a quantitative perspective, the statistical uncertainty surrounding the estimated 

SO2 exposure-response relationships due to sampling error is reflected in the credible intervals 

that have been provided for the risk estimates in this document. Staff selected a mainly 

qualitative approach to address other uncertainties in the assessment given the limited data 

available to inform a probabilistic uncertainty characterization, and time and resource 

constraints. Following the same general approach described in sections 6.6, 7.4, and 8.11.2 and 

adapted from WHO (2008), staff performed a qualitative characterization of the components 

contributing to uncertainty in the lung function risks for all asthmatics and asthmatic children 

attributable to 5-minute SO2 exposures under moderate or greater exertion. First, staff identified 

the important uncertainties. Then, we qualitatively characterized the magnitude (low, medium, 

and high) and direction of influence (over, under, both, and unknown) the source of uncertainty 
July 2009 346

may have on the estimated number or percent of persons experiencing a defined lung function 

response.96 Finally, staff also qualitatively rated the uncertainty in the knowledge-base regarding 

each source using low, medium, and high categories. Staff’s ratings were based on professional 

judgment in the context of the knowledge-base for the criteria air pollutants. 

Table 9-11 provides a summary of the sources of uncertainty identified in the health risk 

assessment, the level of uncertainty, and the overall judged bias of each. A brief summary 

discussion regarding those sources of uncertainty not already examined in Chapters 7 and 8 is 

included in the comments section of Table 9-11. 

The 5-minute daily maximum exposure estimates for asthmatics and asthmatic children 

while engaged in moderate or greater exertion is an important input to the lung function response 

risk assessment. A qualitative characterization of uncertainties associated with the exposure 

model and the inputs to the exposure model are summarized in Table 8-27 and discussed in 

section 8.11.2. 

 

96 Definitions of the rating scales can be found in section 6.6. 
July 2009 347

Table 9-11. Characterization of key uncertainties in the lung function response health risk assessment for St. LouMissouri. 

Influence of Uncertainty on Lung 

Function Risk Estimates Source of 

Uncertainty Direction Magnitude 

KnowledgeBase 

Uncertainty 

Comments1 

Exposure Model (APEX) Inputs and Algorithms 

Unknown Unknown Medium to High 

See Table 8-27 and section 8.11.2 

Spatial representation Both Medium High 

See Table 7-16 and discussion in section 7.4.2.4 

Air quality adjustment Both Low-Medium Medium See Table 7-16 and discussion in section 7.4.2.5 Causality Over Low-Medium 

Low – for levels 

above 100 ppb 

Medium – for 

levels below 

100 ppb 

INF: While there is very strong support for SO2 bei

responses within the range of tested exposure leve

down to the100 ppb level (where SO2 was admini

et al. 1981; Koenig et al., 1990)), there is increasin

is causally related to lung-function effects at lower Since this assessment assumes there is a causal ppb, the influence of this source of uncertainty wouKB: The SO2-related lung function responses have 

human exposure studies and, thus there is little un

responsible for the lung function responses observ

range of levels tested. Given the lack of chamber KB uncertainty is rated as medium. 

Use of 2-parameter logistic and probit models to estimate probabilistic exposure-response relationships 

Unknown 

Low - for levels at 

and above 100 

ppb 

Medium – for 

levels below 100 

ppb 

Low - for levels 

above100 ppb 

Medium – for 

levels below 

100 ppb 

KB: It was necessary to estimate responses at SO

exposure levels tested (i.e., 200 to 1,000 ppb) as wlevels used in free-breathing controlled human expppb). We have developed probabilistic exposure-rdifferent functional forms (i.e., probit and 2-parameforms provide reasonable fits to the data in the avathe risks attributable to exposure levels below 200 free-breathing chamber studies, and particularly betested in face mask chamber studies, there is grea

Use of 5- and 10-minute lung function response data to estimate 5-

Over Low Low 

INF: It is reasonable to hypothesize that 10-minute lung function responses, so inclusion of 10-minute 

used to estimate 5-minute responses would be moestimating risks. However, there is some evidence 
July 2009 348

Influence of Uncertainty on Lung 

Function Risk Estimates Source of 

Uncertainty Direction Magnitude 

KnowledgeBase 

Uncertainty 

Comments1 

minute lung function risk estimates 

in the first few minutes of exposure (see ISA, sectioverestimation is likely to be very modest in terms KB: The 5-minute lung function risk estimates are from several controlled human exposure studies, mresponses associated with 10-minute exposures. which evaluated responses after 5-minute exposuearly as 5-minutes after exposure, we are using alexposure data to represent responses associated not believe that this factor appreciably impacts the 

Use of exposureresponse data from studies of mild/moderate asthmatics to represent all asthmatics 

Under Medium Medium 

INF & KB: The data set that was used to estimate included mild and/or moderate asthmatics. There well the population of mild and moderate asthmatic

controlled human exposure studies represent the dasthmatics in the U.S. population. As indicated in studied represent the responses “among groups ocannot necessarily be extrapolated to the most sepopulation who are likely more susceptible to the r

SO2." Thus, the influence of this uncertainty is like

risks and we judge the magnitude of the influence function risk estimates to be medium.

Reproducibility of SO2-induced lung 

function response 

Unknown Unknown Low 

INF & KB: The risk assessment assumes that the 

individuals are reproducible. We note that this assone study (Linn et al., 1987) exposed the same suppm and the authors reported a high degree of coasthmatics and r > 0.8 for moderate asthmatics, p lower and nonsignificant correlations (r = 0.0 – 0.4observed in the clean air with exercise exposures. 

Use of adult asthmatic lung function response data to estimate exposure-response relationships for asthmatic children 

Unknown Unknown Low to Medium 

INF & KB: Because the vast majority of controlled investigating lung function responses were conducassessment relies on data from adult asthmatic suresponse relationships that have been applied to achildren. The ISA (section 3.1.3.5) indicates that t

that suggests adolescents may experience many osimilar SO2 levels, but recognizes that these studie

through a mouthpiece rather than an exposure chanasal absorption of SO2 and can result in an increa
July 2009 349

Influence of Uncertainty on Lung 

Function Risk Estimates Source of 

Uncertainty Direction Magnitude 

KnowledgeBase 

Uncertainty 

Comments1 

Therefore, the uncertainty is greater in the risk estdirection and magnitude of this uncertainty on the unknown. 

Exposure history Both Low Medium 

INF & KB: The risk assessment assumes that the 

given day is independent of previous SO2 exposur

ozone) prior exposure history can lead to both enhfunction responses depending on the pattern of exonly included the highest daily 5-minute exposure exertion, and the influence of prior exposures mighdiminished response based on what we know abostaff rated the magnitude of the influence of this unlack of available information to directly assess this 

chamber studies staff rated the KB uncertainty to b

Assumed no interaction effect of other co-pollutants on SO2-related 

lung function responses 

Under Medium Medium 

INF: Staff judges that it is more likely that exposurincrease the magnitude of lung function response estimates. Thus, assuming no interaction is more 

risks. KB: Because the controlled human exposure studiinvolved only SO2 exposures, there is little informa

estimates of SO2-induced health responses are aff

pollutants (e.g., PM2.5, O3, NO2). 

Notes: 1INF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rating. 

1 
July 2009 350

9.5 KEY OBSERVATIONS 

 Presented below are key observations related to the risk assessment for lung function 

responses in asthmatics and asthmatic children associated with 5-minute exposures to SO2 while 

engaged in moderate or greater exertion: 

 Lung function responses estimated to result from 5-minute exposures to SO2 were 

estimated for two areas in Missouri (i.e., Greene County and St. Louis) which have 

significant emission sources of SO2 for 2002 air quality and for air quality adjusted to 

simulate just meeting the current suite of annual and 24-hour SO2 standards and just 

meeting several alternative 1-hour daily maximum SO2 standards. 

 A number of factors would be expected to contribute to differences in estimated SO2-

related lung function responses across different locations. These include exposurerelated differences, such as population density, SO2 emission density, location and types 

of SO2 sources, prevalence of air conditioning, and time spent outdoors, which are 

discussed in section 8.10, as well as other factors such as differences in population 

sensitivity to SO2 and asthma prevalence rates. As discussed in section 8.10, St. Louis 

County has a medium to high SO2 emission density and a medium to high population 

density relative to other medium to high population density urban areas in the U.S. 

Relative to the St. Louis study area, Greene County is a more rural county with much 

lower SO2 emission density and much lower population density. Taken together, the risk 

estimates for these two locations provide useful insights about urban and rural counties 

with significant SO2 emission sources. 

 The lung function risk estimates for the St. Louis study area are much higher than for 

Greene County, which is not unexpected given the greater population density and the 

much greater SO2 emission density. Staff believes that the St. Louis risk estimates 

provide a useful perspective on the likely overall magnitude and pattern of lung function 

responses associated with various SO2 air quality scenarios in urban areas within the U.S. 

that have similar population densities and SO2 emission densities. 

 Risk estimates for Greene County are considerably lower than for the St. Louis study area 

both with respect to estimated number of asthmatics and the percentage of asthmatics 

estimated to experience one or more moderate or severe lung function responses. As 

discussed above, this is not unexpected given the rural nature of Greene County and the 

fact that it has much lower SO2 emission density and lower population density than the 

St. Louis study area. 

 Of the alternative regulatory scenarios analyzed, only the 50 ppb/99th percentile daily 

maximum 1-hr standard is estimated to reduce risks in one of the two modeling study 

areas (i.e., St. Louis) relative to the "as is" air quality scenario. This reduction is 

observed for both number and percent of asthmatics and asthmatic children estimated to 

experience 1 or more lung function responses per year. 

 For the St. Louis study area median risk estimates for 1 or more occurrences of moderate 

lung function responses (i.e., based on sRaw ≥ 100%) per year range from about 11% 

down to 0.9% of asthmatic children using the 2-parameter logistic exposure-response 
July 2009 351

model compared to 10.4% down to 0.4% of asthmatic children using the probit exposureresponse model for alternative 99th percentile daily maximum 1-hour standards ranging 

from 250 ppb down to 50 ppb. In general, the risk estimates associated with the use of 

the probit exposure-response model are lower than those based on the logistic model. 

 For the St. Louis study area median risk estimates for 1 or more occurrences of severe 

lung function responses (i.e., based on sRaw ≥ 200%) per year range from 4.2% down to 

0.3% of asthmatic children using the 2-parameter logistic exposure-response model 

compared to 3.7% down to 0.1% of asthmatic children using the probit exposureresponse model for alternative 99th percentile daily maximum 1-hour standars ranging 

from 250 ppb down to 50 ppb. 

 In terms of estimated percentage of asthmatics or asthmatic children experiencing 1 or 

more lung function responses, risks are greater for asthmatic children, likely because they 

spend more time at higher exertion levels than adults. 

 A broad range of SO2 exposure concentration intervals, as high as 500 ppb, contributes to 

the estimated risks of experiencing 1 or more lung function responses per year for some 

of the standards considered in the assessment. For standards in the range of 100 to 150 

ppb SO2 exposure concentration intervals below 200 ppb contribute most of the estimated 

risks of experiencing 1 or more lung function response per year. 

 Important uncertainties and limitations associated with the risk assessment which were 

discussed above in section 9.3 and which should be kept in mind as one considers the 

quantitative risk estimates include: 

- uncertainties related to the exposure estimates which are an important input to the 

risk assessment which staff rated as medium to high with respect to the knowledge 

base and which staff rated the overall influence of these uncertainties on the 

magnitude of the lung function risk estimates as unknown; 

- uncertainties associated with the air quality adjustment procedure that was used to 

simulate just meeting the current annual and several alternative 1-h daily maximum 

standards which staff rated as medium with respect to the knowledge base uncertainty 

and low-medium in terms of the influence of this uncertainty on the magnitude of the 

lung function risk estimates; 

- statistical uncertainty due to sampling error which is characterized in the 

assessment through presentation of 95% credible intervals; 

- uncertainty about the shape of the exposure-response relationship for lung function 

responses at levels well below 200 ppb, the lowest level examined in free-breathing 

single pollutant controlled human exposure studies which staff rated as low for levels 

at and above 100 ppb and medium for levels below 100 ppb with respect to 

knowledge base uncertainty and the influence of this uncertainty on the lung function 

risk estimates; 

- uncertainty with respect to how well the estimated exposure-response relationships 

reflect asthmatics with more severe disease than those tested in chamber studies 

which staff rated as medium with respect to knowledge base uncertainty and the 

influence of this uncertainty on the magnitude of the lung function risk estimates; 
July 2009 352

- uncertainty about whether the presence of other pollutants in the ambient air would 

enhance the SO2-related responses observed in the controlled human exposure studies 

which staff rated as medium with respect to knowledge base uncertainty and the 

influence of this uncertainty on the magnitude of the lung function risk estimates; 

- uncertainty about the extent to which the risk estimates presented for the two 

modeled areas in Missouri are representative of other locations in the U.S. with 

significant SO2 point and area sources which staff rated as high with respect to 

knowledge base uncertainty and medium for the influence of this uncertainty on the 

magnitude of the lung function risk estimates; 

- other uncertainties such as the assumption about causality, use of both 5- and 10-

minute data to estimate 5-minute effects, the assumption of reproducible responses, 

use of adult data to estimate exposure-response for children, and influence of 

exposure history were generally rated as low to medium with respect to knowledge 

base uncertainty and low or unknown impact on the magnitude of these uncertainties 

on the lung function risk estimates. 
July 2009 353

10. EVIDENCE- AND EXPOSURE/RISK-BASED 

CONSIDERATIONS RELATED TO THE PRIMARY SO2 NAAQS 

10.1 INTRODUCTION 

This chapter considers the scientific evidence in the ISA (EPA, 2008a) and the air 

quality, exposure and risk characterization results presented in this document as they relate to the 

adequacy of the current SO2 primary NAAQS and potential alternative primary SO2 standards. 

The available scientific evidence includes epidemiologic, controlled human exposure, and animal 

toxicological studies. The SO2 air quality, exposure, and risk analyses described in Chapters 7-9 

of this document include characterization of air quality, exposure, and health risks associated 

with recent SO2 concentrations and with SO2 concentrations adjusted to simulate scenarios just 

meeting the current suite of standards and potential alternative 1-hour standards. In considering 

the scientific evidence and the exposure- and risk-based information, we have also considered 

relevant uncertainties. Section 10.2 of this chapter presents our general approach to considering 

the adequacy of the current standards and the need for potential alternative standards. Sections 

10.3 and 10.4 focus on evidence- and exposure-/risk-based considerations related to the 

adequacy of the current 24-hour and annual standards respectively, while section 10.5 focuses on 

such considerations related to the need for potential alternative standards (in terms of the 

indicator, averaging time, form, and level). 

These considerations are intended to inform the Agency’s policy assessment of a range of 

options with regard to the SO2 NAAQS. A final decision will draw upon scientific information 

and analyses about health effects, population exposure and risks, and policy judgments about the 

appropriate response to the range of uncertainties that are inherent in the scientific evidence and 

air quality, exposure, and risk analyses. Our approach to informing these judgments, discussed 

more fully below, is based on a recognition that the available health effects evidence reflects a 

continuum consisting of ambient levels at which scientists generally agree that health effects are 

likely to occur through lower levels at which the likelihood and magnitude of the response 

become increasingly uncertain. This approach is consistent with the requirements of the 

NAAQS provisions of the Act and with how EPA and the courts have historically interpreted the 

Act. These provisions require the Administrator to establish primary standards that, in the 

Administrator's judgment, are requisite to protect public health with an adequate margin of 

safety. In so doing, the Administrator seeks to establish standards that are neither more nor less 
July 2009 354

stringent than necessary for this purpose. The Act does not require that primary standards be set 

at a zero-risk level but rather at a level of protection that avoids unacceptable risks to public 

health, including the health of at risk populations. 

10.2 GENERAL APPROACH 

This section describes the general approach that staff is taking to inform decisions 

regarding the need to retain or revise the current SO2 NAAQS. The current standards, a 24-hour 

average of 0.14 ppm (equivalent to 144 ppb), not to be exceeded more than one time per year, 

and an annual average of 0.03 ppm (equivalent to 30.4 ppb) were retained by the Administrator 

in the most recent review completed in 1996 (61 FR 25566). The decision to retain the 24-hour 

standard was largely based on an assessment of epidemiologic studies that supported a likely 

association between 24-hour average SO2 exposure and daily mortality, aggravation of 

bronchitis, and small, reversible declines in children’s lung function (EPA 1982, 1994a). 

Similarly, the decision to retain the annual standard (see section 10.4) was largely based on an 

assessment of epidemiologic studies finding an association between respiratory 

symptoms/illnesses and annual average SO2 concentrations (EPA 1982, 1994a). 

The previous review of the SO2 NAAQS also addressed the question of whether an 

additional short-term standard (e.g., 5-minute) was necessary to protect against short-term peak 

SO2 exposures. Based on the scientific evidence, the Administrator judged that repeated 

exposures to 5-minute peak levels ≥ 600 ppb could pose a risk of significant health effects for 

asthmatic individuals at elevated ventilation rates (61 FR 25566). The Administrator also 

concluded that the likely frequency of such effects should be a consideration in assessing the 

overall public health risks. Based upon an exposure analysis conducted by EPA (see section 

1.1.3), the Administrator concluded that exposure of asthmatics to SO2 levels that could reliably 

elicit adverse health effects was likely to be a rare event when viewed in the context of the entire 

population of asthmatics, and therefore did not pose a broad public health problem for which a 

NAAQS would be appropriate (61 FR 25566). On May 22, 1996, EPA published its final 

decision to retain the existing 24-hour and annual standards and not to promulgate a 5-minute 

standard (61 FR 25566). The decision not to set a 5-minute standard was ultimately challenged 

by the American Lung Association and remanded back to EPA for further explanation on 

January 30, 1998 by the D.C. Circuit Court of Appeals (see section 1.1.1). Specifically, the court 

gave EPA the opportunity to provide additional rationale to support the Agency judgment that 5-
July 2009 355

minute peaks of SO2 do not pose a public health problem when viewed from a national 

perspective. 

To inform the range of options that the Agency will consider in the current review of the 

primary SO2 NAAQS, the general approach we have adopted builds upon the approaches used in 

reviews of other criteria pollutants, including the most recent reviews of the Pb, O3, PM, and 

NO2 NAAQS (EPA, 2007i; EPA, 2007e; EPA, 2005, EPA 2008d). As in these other reviews, we 

consider the implications of placing more or less weight or emphasis on different aspects of the 

scientific evidence and the exposure/risk-based information, recognizing that the weight to be 

given to various elements of the evidence and exposure/risk information is part of the public 

health policy judgments that the Administrator will make in reaching decisions on the standards. 

A series of general questions frames our approach to considering the scientific evidence 

and exposure/risk-based information. First, our consideration of the scientific evidence and 

exposure/risk-based information with regard to the adequacy of the current standards is framed 

by the following questions: 

 To what extent does evidence and exposure/risk-based information that has become 

available since the last review reinforce or call into question evidence for SO2-associated 

effects that were identified in the last review? 

 To what extent has evidence for different health effects and/or sensitive populations 

become available since the last review? 

 To what extent have uncertainties identified in the last review been reduced and/or have 

new uncertainties emerged? 

 To what extent does evidence and exposure/risk-based information that has become 

available since the last review reinforce or call into question any of the basic elements of 

the current standards? 

To the extent that the available evidence and exposure/risk-based information suggests it 

may be appropriate to consider revision of the current standards, we consider that evidence and 

information with regard to its support for consideration of standards that are either more or less 

protective than the current standards. This evaluation is framed by the following questions: 

• Is there evidence that associations, especially causal or likely causal associations, 

extend to ambient SO2 concentrations as low as, or lower than, the concentrations that 

have previously been associated with health effects? If so, what are the important 

uncertainties associated with that evidence? 
July 2009 356

• Are exposures above benchmark levels and/or health risks estimated to occur in areas 

that meet the current standards? If so, are the estimated exposures and health risks 

important from a public health perspective? What are the important uncertainties 

associated with the estimated risks? 

To the extent that there is support for consideration of a revised standard, we then 

consider the specific elements of the standard (indicator for gaseous SOx, averaging time, form, 

and level) within the context of the currently available information. In so doing, we address the 

following questions: 

 Does the evidence provide support for considering a different indicator for gaseous SOx? 

 Does the evidence provide support for considering different averaging times? 

 What ranges of levels and forms of alternative standards are supported by the evidence, 

and what are the associated uncertainties and limitations? 

 To what extent do specific averaging times, levels, and forms of alternative standards 

reduce the estimated exposures above benchmark levels and estimated risks attributable 

to SO2, and what are the uncertainties associated with the estimated exposure and risk 

reductions? 

The following discussion addresses the questions outlined above and presents staff’s 

conclusions regarding the scientific evidence and the exposure-/risk-based information 

specifically as they relate to the current and potential alternative standards. This discussion is 

intended to inform the Agency’s consideration of policy options that will be presented during the 

rulemaking process, together with the scientific support for such options. Sections 10.3 and 10.4 

consider the adequacy of the current standards while section 10.5 considers potential alternative 

standards in terms of indicator, averaging time, form, and level. Each of these sections considers 

key conclusions as well as the uncertainties associated with the evidence and exposure/risk 

analyses. 

10.3 ADEQUACY OF THE CURRENT 24-HOUR STANDARD 

10.3.1 Introduction 

In the last review of the SO2 NAAQS, retention of the 24-hour standard was based 

largely on epidemiologic studies conducted in London in the 1950’s and 1960’s. The results of 

those studies suggested an association between 24-hour average levels of SO2 and increased daily 

mortality and aggravation of bronchitis when in the presence of elevated levels of PM (53 FR 

14927). Additional epidemiologic evidence suggested that elevated SO2 levels were associated 
July 2009 357

with the possibility of small, reversible declines in children’s lung function (53 FR 14927). 

However, it was noted that in the locations where these epidemiologic studies were conducted, 

high SO2 levels were usually accompanied by high levels of PM, thus making it difficult to 

disentangle the individual contribution each pollutant had on these health outcomes. It was also 

noted that rather than 24-hour average SO2 levels, the health effects observed in these studies 

may have been related, at least in part, to the occurrence of shorter-term peaks of SO2 within a 

24-hour period (53 FR 14927). 

In this review, as described in Chapter 4, the ISA concludes that there is sufficient 

evidence to infer “a causal relationship between respiratory morbidity and short-term exposure to 

SO2" (ISA, section 5.2). The ISA states that the strongest evidence for this judgment is from 

human exposure studies demonstrating decreased lung function and/or increased respiratory 

symptoms in exercising asthmatics exposed for 5-10 minutes to ≥ 200 ppb SO2 (ISA, section 

5.2). Supporting this conclusion is a larger body of epidemiologic studies published since the 

last review observing positive associations between 1-hour daily maximum or 24-hour average 

SO2 concentrations and respiratory symptoms, ED visits, and hospital admissions (ISA, section 

5.2). Thus, the ISA bases its causal determination between short-term SO2 exposure and 

respiratory morbidity on respiratory effects associated with averaging times from 5-minutes to 

24-hours. 

Here, we will examine the health information first presented in Chapter 4 as it relates to 

the adequacy of the current 24-hour standard (as well as the annual standard, see section 10.4). 

Section 10.3.2 will discuss the epidemiologic results. The epidemiologic literature is particularly 

relevant for evaluating the adequacy of the current 24-hour standard given that the majority of 

these studies examined possible associations between 24-hour average SO2 concentrations and 

respiratory morbidity endpoints (e.g. ED visits or hospitalizations for all respiratory causes). 

Section 10.3.3 will then discuss the air quality, exposure, and risk based information as it relates 

to the adequacy of the current 24-hour standard. These analyses are first presented in Chapters 

7-9 and describe exposures and their associated health risks given air quality just meeting the 

current standards. More specifically, these analyses simulate air quality to just meet the current 

24-hour or annual standard, whichever is controlling in a given area, and then describe exposure 

and health risks associated with 5-minute SO2 benchmark concentrations. As described in 

section 6.2, these benchmark concentrations are SO2 exposure levels found in controlled human 
July 2009 358

exposure studies to result in decrements in lung function and/or respiratory symptoms in 

exercising asthmatics. Finally, considering the evidence presented in section 10.3.2 and the air 

quality, exposure, and risk information presented in section 10.3.3, staff presents conclusions 

with regard to the overall adequacy of the current 24-hour standard in section 10.3.4. 

 10.3.2 Evidence-based considerations 

As mentioned above, the ISA found supporting evidence for its conclusion that there is a 

causal relationship between short-term SO2 exposures and respiratory morbidity from the 

reported associations observed in epidemiologic studies of respiratory symptoms and ED visits 

and hospitalizations. In considering the adequacy of the current 24-hour standard, we note that 

many epidemiologic studies demonstrating positive associations between ambient SO2 and 

respiratory symptoms, ED visits, and hospitalizations were conducted in areas where SO2

concentrations were less than the level of the current 24-hour (as well as the annual; see section 

10.4) NAAQS. With regard to these epidemiologic studies, we note that the ISA characterizes 

the evidence for respiratory effects as consistent and coherent. The evidence is consistent in that 

positive associations are reported in studies conducted in numerous locations and with a variety 

of methodological approaches (ISA, section 5.2). It is coherent in the sense that respiratory 

symptom results from epidemiologic studies predominantly using 1-hour daily maximum or 24-

hour average SO2 concentrations are generally in agreement with the respiratory symptom results 

from controlled human exposure studies of 5-10 minutes. These results are also coherent in that 

the respiratory effects observed in controlled human exposure studies of 5-10 minutes provide a 

basis for a progression of respiratory morbidity that could lead to the ED visits and 

hospitalizations observed in epidemiologic studies (ISA, section 5.2). 

However, it should be noted that interpretation of the epidemiologic literature is 

complicated by the fact that SO2 is but one component of a complex mixture of pollutants 

present in the ambient air. The matter is further complicated by the fact that SO2 is a precursor 

to sulfate, which can be a principal component of PM. Ultimately, this uncertainty calls into 

question the extent to which effect estimates from epidemiologic studies reflect the independent 

contribution of SO2 to the adverse respiratory outcomes assessed in these studies. In order to 

provide some perspective on this uncertainty, the ISA evaluates epidemiologic studies that 

employ multi-pollutant models. The ISA concludes that these analyses indicate that although 

copollutant adjustment has varying degrees of influence on SO2 effect estimates, the effect of 
July 2009 359

SO2 on respiratory health outcomes appears to be generally independent of the effects of gaseous 

copollutants, including NO2 and O3 (ISA, section 5.2). With respect to PM10, evidence of an 

independent SO2 effect on respiratory health is less consistent, with some of the positive ED visit 

and hospitalization results becoming negative (although results were not statistically significantly 

negative) after inclusion of PM10 in regression models (ISA, section 3.1.4.6). In epidemiologic 

studies of respiratory symptoms, the SO2 effect estimate often remained relatively unchanged 

after inclusion of PM10 in multipolutant models (although the effect estimate may have lost 

statistical significance; ISA, section 3.1.4.1). The ISA also finds that SO2-effect estimates 

generally remained relatively unchanged in the limited number of studies that included PM2.5 

and/or PM10-2.5 in multipolutant models (ISA, section 3.1.4.6). Taken together, the ISA 

concludes studies employing multi-pollutant models do suggest that SO2 has an independent 

effect on respiratory morbidity outcomes (see Chapter 4; ISA, section 5.2). Thus, the results of 

experimental and epidemiologic studies form a plausible and coherent data set that supports a 

relationship between SO2 exposures and respiratory morbidity endpoints, and calls into question 

the adequacy of the 24-hour standard to protect public health. 

10.3.3 Air Quality, exposure and risk-based considerations 

In addition to the evidence-based considerations described above, staff has considered the 

extent to which exposure- and risk-based information can inform decisions regarding the 

adequacy of the current 24-hour SO2 standard, taking into account key uncertainties associated 

with the estimated exposures and risks. For this review, we have employed three approaches. 

In the first approach, SO2 air quality levels were used as a surrogate for exposure. In the second 

approach, modeled estimates of human exposure were developed for all asthmatics and asthmatic 

children living in Greene County and St. Louis MO. Notably, this second approach considers 

time spent in different microenvironments, as well as time spent at elevated ventilation rates. In 

each of the first two approaches, health risks have been characterized by comparing estimates of 

air quality or exposure to 5-minute potential health effect benchmarks. These benchmarks are 

based on controlled human exposure studies involving known 5-10 minute SO2 exposure levels 

and corresponding decrements in lung function, and/or increases in respiratory symptoms in 

asthmatics at elevated ventilation rates (e.g., while exercising; see section 6.2 for further 

discussion of benchmark levels). In addition to these analyses, staff conducted a quantitative risk 

assessment for lung function responses associated with 5-minute exposures to characterize SO2-
July 2009 360

related health risks. This assessment combined outputs from the exposure analysis with 

estimated exposure-response functions derived from the combined individual data from 

controlled human exposure studies to estimate the number and percent of exposed asthmatics 

that would experience moderate or greater lung function responses (in terms of FEV1 and sRaw) 

at least once per year and to estimate the total number of occurrences of these lung function 

responses per year (see Chapter 9). 

 The respiratory effects (i.e., decrements in FEV1, increases in sRaw, and/or respiratory 

symptoms) considered in the air quality, exposure, and risk analyses mentioned above are 

considered by staff to be adverse to the health of asthmatics. As described in section 4.3, staff 

bases this conclusion on: 1) guidelines published by the ATS; 2) conclusions from the ISA and 

previous NAAQS reviews; and 3) advice from CASAC. Being mindful of this conclusion, we 

note the following key points from the ISA: 

 Approximately 5-30% of exercising asthmatics are expected to experience moderate or 

greater lung function decrements (i.e., ≥ 100% increase in sRaw and/or a ≥ 15% decrease 

in FEV1) following exposure to 200- 300 ppb SO2 for 5-10 minutes (ISA, section 3.1). 

 Approximately 20-60% of exercising asthmatics are expected to experience moderate or 

greater lung function decrements (i.e ≥ 100% increase in sRaw and/or a ≥ 15% decrease 

in FEV1) following exposure to 400-1000 ppb SO2 for 5-10 minutes (ISA, Table 5-3). 

 At concentrations ≥ 400 ppb, moderate or greater statistically significant decrements in 

lung function are frequently associated with respiratory symptoms (ISA, section 3.1). 

 There is no evidence to indicate that exposure to 200-300 ppb SO2 for 5- 10 minutes 

represents a threshold below which no respiratory effects occur. 

 Given the discussion in section 4.3 and the key points presented above, staff concludes 

that exposure to 5-10 minute SO2 concentrations at least as low as 200 ppb can result in adverse 

respiratory effects in some asthmatics. We note that this conclusion is in agreement with 

CASAC comments offered on the first draft SO2 REA. The CASAC letter to the Administrator 

states: “CASAC believes strongly that the weight of clinical and epidemiology evidence 

indicates there are detectable clinically relevant health effects in sensitive subpopulations down 

to a level at least as low as 0.2 ppm SO2 (Henderson 2008).” This CASAC letter also states: 

“these sensitive subpopulations represent a substantial segment of the at-risk population 

(Henderson 2008).” As an additional matter, we note that over 20 million people in the U.S. 

have asthma (EPA 2008d), and therefore, exposure to SO2 likely represents a significant public 

health issue. 
July 2009 361

 Thus, staff finds it is appropriate to consider the air quality, exposure and risk results as 

they relate to the adequacy of the current 24-hour standard (as well as the current annual (see 

section 10.4) and potential alternative (see section 10.5) standards). This is because these 

analyses provide useful information with respect to the current 24-hour standard’s ability to 

limit: 1) 5-10 minute SO2 concentrations associated with decrements in lung function and/or 

respiratory symptoms in exercising asthmatics; and 2) the estimated number of exercising 

asthmatics expected to experience a moderate or greater lung function response. 

10.3.3.1 Key Uncertainties 

 The way in which air quality, exposure, and risk results will inform ultimate decisions 

regarding the SO2 standard will depend upon the weight placed on each of the analyses when 

uncertainties associated with those analyses are taken into consideration. Sources of uncertainty 

associated with each of the analyses (air quality, exposure, and quantitative risk) are briefly 

presented below and are described in more detail in Chapters 7-9 of this document. Although 

we are discussing these uncertainties within the context of the adequacy of the 24-hour standard, 

they apply equally to consideration of the annual, as well as alternative 1-hour standards. 

Air Quality Analysis 

 A number of key uncertainties should be considered when interpreting air quality results 

with regard to decisions on the standards. A general description of such uncertainties is 

highlighted below, and these, as well as other sources of uncertainty are discussed in greater 

depth in section 7.4 of this document. 

 Staff used the broader SO2 ambient monitoring network, in addition to subsets of data 

from this network, to characterize air quality in the U.S. There was general agreement in 

the monitor site attributes and emissions sources potentially influencing ambient 

monitoring concentrations for each set of data analyzed. However, staff noted that the 

greatest uncertainty, compared to several other sources of uncertainty, was in the spatial 

representativeness of both the overall monitoring network and the subsets chosen for 

detailed analyses. 

 Staff developed a statistical model to estimate 5-minute maximum SO2 concentrations at 

monitors that reported only 1-hour SO2 concentrations. Cross-validation of the statistical 

model for where 5-minute SO2 measurements existed indicated reasonable model 

performance. The greatest difference in the predicted versus observed numbers of 

benchmark exceedances occurred at the lower and upper tails of the distribution, 

indicating greater uncertainty in the predictions at similarly representative monitors. 
July 2009 362

 The air quality characterization assumes that the ambient monitoring data and the 

estimated days per year with benchmark exceedances can serve as an indicator of 

exposure. Longer-term personal SO2 exposure (i.e., days to weeks) concentrations are 

correlated with and are a fraction of ambient SO2 concentrations. However, uncertainty 

remains in this relationship when considering short-term (i.e., 5-minute) averaging times 

because of the lack of comparable measurement data. 

St Louis and Greene Counties Exposure Analysis 

 A number of key uncertainties should be considered when interpreting the St. Louis and 

Greene County exposure results with regard to decisions on the standards. Such uncertainties are 

highlighted below, and these, as well as other sources of uncertainty, are also discussed in greater 

depth in section 8.11 of this document. 

 It was necessary for staff to derive an area source emission profile rather than use a 

default profile to improve the agreement between ambient measurements and predicted 1-

hour SO2 concentrations. The improved model performance reduces uncertainty in the 1-

hour SO2 concentrations predictions, but nonetheless remains as an important uncertainty 

in the absence of actual local source emission profiles. 

 Staff performed the exposure assessment to better reflect both the temporal and spatial 

representation of ambient concentrations and to estimate the rate of contact of individuals 

with 5-minute SO2 concentrations while engaged in moderate or greater exertion. 

Estimated annual average SO2 exposures in the two exposure modeling domains are 

consistent with long-term personal exposures (i.e., days to weeks) measured in other U.S. 

locations. However, uncertainty remains in the estimated number of persons with 5-

minute SO2 concentrations above benchmark levels because of the lack of comparable 

measurement data, particularly considering both the short-term averaging time and 

geographic location. 

 While all 5-minute ambient SO2 concentrations were estimated by the exposure model, 

each hour was comprised of the maximum 5-minute SO2 concentration and eleven other 

5-minute SO2 concentrations normalized to the 1-hour mean concentration. Staff 

assumed that this approach would reasonably estimate the number of individuals exposed 

to peak concentrations. Sensitivity analyses revealed that both the number of persons 

exposed and where peak exposures occur can vary when considering an actual 5-minute 

temporal profile.
July 2009 363

St Louis and Greene Counties Quantitative Risk Analysis 

 A number of key uncertainties should be considered when interpreting the St. Louis and 

Greene County quantitative risk estimated for lung function responses with regard to decisions 

on the standards. Such uncertainties are highlighted below, and these, as well as other sources of 

uncertainty, are also discussed in greater depth in section 9.3 of this document. 

 It was necessary to estimate responses at SO2 levels below the lowest exposure levels 

used in the free-breathing controlled human exposure studies (i.e., below 200 ppb). We 

have developed probabilistic exposure-response relationships using two different 

functional forms (i.e., probit and 2-parameter logistic), but nonetheless there remains 

greater uncertainty in responses below 200 ppb because of the lack of comparable 

experimental data. 

 The risk assessment assumes that the SO2-induced responses for individuals are 

reproducible. We note that this assumption has some support in that one study (Linn et 

al., 1987) exposed the same subjects on two occasions to 600 ppb and the authors 

reported a high degree of correlation while observing a much lower correlation for the 

lung function response observed in the clean air with exercise exposure. 

 Because the vast majority of controlled human exposure studies investigating lung 

function responses were conducted with adult subjects, the risk assessment relies on data 

from adult asthmatic subjects to estimate exposure-response relationships that have been 

applied to all asthmatic individuals, including children. The ISA (section 3.1.3.5) 

indicates that there is a strong body of evidence that suggests adolescents may experience 

many of the same respiratory effects at similar SO2 levels, but recognizes that these 

studies administered SO2 via inhalation through a mouthpiece (which can result in an 

increase in lung SO2 uptake) rather than an exposure chamber. Therefore, the uncertainty 

is greater in the risk estimates for asthmatic children. 

 Because the controlled human exposure studies used in the risk assessment involved only 

SO2 exposures, it is assumed that estimates of SO2-induced health responses are not 

affected by the presence of other pollutants (e.g., PM2.5, O3, NO2). 

10.3.3.2 Assessment Results 

 As previously mentioned, the ISA finds the evidence for an association between 

respiratory morbidity and SO2 exposure to be “sufficient to infer a causal relationship” (ISA, 

section 5.2) and that the “definitive evidence” for this conclusion comes from the results of 

controlled human exposure studies demonstrating decrements in lung function and/or respiratory 

symptoms in exercising asthmatics (ISA, section 5.2). Accordingly, the exposure and risk 

analyses presented in this document focused on exposures and risks associated with 5-minute 

peaks of SO2 in excess of the potential health effect benchmark values of 100, 200, 300, and 400 

ppb SO2 (see section 6.2). In considering the results presented in these analyses, we particularly 
July 2009 364

note exceedances or exposures with respect to the 200 and 400 ppb 5-minute benchmark levels. 

We highlight these benchmark levels because (1) 400 ppb represents the lowest concentration in 

human exposure studies where statistically significant moderate or greater lung function 

decrements are frequently accompanied by respiratory symptoms; (2) 200 ppb is the lowest level 

at which effects have been observed (and the lowest level tested) for moderate or greater 

decrements in lung function in free-breathing human exposure studies. Notably, we also 

recognize that there is very limited evidence demonstrating small decrements in lung function at 

100 ppb from two mouthpiece exposure studies (see section 6.2). However, as previously noted 

(see section 6.2), the results of these studies are not directly comparable to free-breathing 

chamber studies, and thus, staff is primarily considering exceedances of the 200 ppb and 400 ppb 

benchmark levels in its evaluation of the adequacy of the current standards. 

 Exposures and risks have been estimated for two study areas in Missouri (i.e., Greene 

County and several counties representing the St. Louis urban area) which have significant 

emission sources of SO2. As noted in section 8.10, there were differences in the number of 

exposures above benchmark values when the results of the Greene County and St. Louis 

exposure assessments were compared. Moreover, given that the results of the exposure 

assessment were used as inputs into the quantitative risk assessment, it was not surprising that 

there were also far fewer asthmatics at elevated ventilation rates estimated to have a moderate or 

greater lung function response in Greene county when compared to St. Louis. The difference in 

the St. Louis and Greene County exposure and quantitative risk results are likely indicative of the 

different types of locations they represent (see section 8.10). Greene County is a rural county 

with much lower population and emission densities, compared to the St. Louis study area which 

has population and emissions density similar to other urban areas in the U.S. It therefore follows 

that there would be greater exposures, and hence greater numbers and percentages of asthmatics 

at elevated ventilation rates experiencing moderate or greater lung function responses in the St. 

Louis study area. Thus, when considering the risk and exposure results as they relate to the 

adequacy of the current standards (as well as the need for considering potential alternative 

standards), the St. Louis results are more informative in that they suggest that the current 

standards may not adequately protect public health. Moreover, staff judges that the exposure and 

risk estimates for the St. Louis study area provide useful insights into exposures and risks for 

other urban areas in the U.S. with similar population and SO2 emissions densities. 
July 2009 365

Air Quality Assessment 

 The results of our air quality assessment provide additional perspective on the public 

health impacts of exposure to ambient levels of SO2. In considering these results, we first note 

that the benchmark values derived from the controlled human exposure literature are associated 

with a 5-minute averaging time, but very few state and local agencies in the U.S. report 

measured 5-minute concentrations since such monitoring is not required. As a result, staff 

developed a statistical relationship to estimate the highest 5-minute level in an hour, given a 

reported 1-hour average SO2 concentration (see section 7.2.3). Thus, many of the outputs of the 

air quality analysis are presented with respect to statistically estimated 5-minute concentrations 

in excess of potential health effect benchmark values. Results of these analyses, as they relate to 

the adequacy of the current standards, are discussed below. 

 A key output of the air quality analysis is the predicted number of statistically estimated 

5-minute daily maximum SO2 concentrations above benchmark levels given air quality simulated 

to just meet the level of the current 24-hour or annual SO2 standards, whichever is controlling for 

a given county. Under this scenario, in 40 counties selected for detailed analysis, we note that 

the predicted yearly mean number of statistically estimated 5-minute daily maximum 

concentrations > 400 ppb ranges from 1-102 days per year97, with most counties in this analysis 

experiencing a mean of at least 20 days per year when statistically estimated 5-minute daily SO2

concentrations exceed 400 ppb (Table 7-14). In addition, the predicted yearly mean number of 

statistically estimated 5-minute daily maximum concentrations >200 ppb ranges from 21-171 

days per year, with about half of the counties in this analysis experiencing ≥ 70 days per year 

when 5-minute daily maximum SO2 concentrations exceed 200 ppb (Table 7-12). 

Exposure Assessment 

 When considering the St. Louis exposure results as they relate to the adequacy of the 

current standard, we focus on the number of asthmatics at elevated ventilation rates estimated to 

experience at least one benchmark exceedance given air quality that is adjusted upward to 

simulate just meeting the current 24-hour standard (i.e., the controlling standard in St. Louis). 

We note that in these analyses, if SO2 concentrations are such that the St Louis area just meets 

the current standard, approximately 13% of asthmatics would be estimated to experience at least 

 

97 Air quality estimates presented in this section represent the mean number of days per year when 5-minute daily 

maximum SO2 concentrations exceed a particular benchmark level given 2001-2006 air quality adjusted to just meet 

the current standards (see Tables 7-11 to 7-14). 
July 2009 366

one SO2 exposure concentration greater than or equal to a 400 ppb benchmark level while at 

elevated ventilation rates (Figure 8-19). Similarly, approximately 46% of asthmatics would be 

expected to experience at least one SO2 exposure concentration greater than or equal to a 200 

ppb benchmark level while at elevated ventilation rates. When the St. Louis results are restricted 

to asthmatic children at elevated ventilation rates, approximately 25% and 73% of these children 

would be estimated to experience at least one SO2 exposure concentration greater than or equal 

to the 400 ppb and 200 ppb benchmark levels, respectively (Figure 8-19). 

Risk results 

 When considering the St. Louis risk results as they relate to the adequacy of the current 

standard, we note the percent of asthmatics at elevated ventilation rates likely to experience at 

least one lung function response given air quality that is adjusted upward to simulate just 

meeting the current standards. Under this scenario, 12.7% to 13.1% of exposed asthmatics at 

elevated ventilation rates are estimated to experience at least one moderate lung function 

response (defined as an increase in sRaw ≥ 100% (Table 9-5))98. Furthermore, 5.1% to 5.4% of 

exposed asthmatics at elevated ventilation rates are estimated to experience at least one large 

lung function response (defined as an increase in sRaw ≥ 200% (Table 9-5)). We also note that 

estimates from this analysis indicate that the percentage of exposed asthmatic children in St. 

Louis estimated to experience at least one moderate or large lung function response is somewhat 

greater than the percentage for the asthmatic population as a whole (Table 9-8). In addition, we 

note that comparable results were observed when moderate or greater lung function responses 

were defined in terms of FEV1. 

10.3.4 Conclusions regarding the adequacy of the 24-hour standard 

As noted above, several lines of scientific evidence are relevant to consider in evaluating 

the adequacy of the current 24-hour standard to protect the public health. These include 

causality judgments made in the ISA, as well as the human exposure and epidemiologic evidence 

supporting those judgments. In particular, we note that numerous epidemiologic studies 

reporting positive associations between ambient SO2 and respiratory morbidity endpoints were 

conducted in locations that met the current 24-hour standard. To the extent that these 

 

98 The risk results presented represent the median estimate of exposed asthmatics expected to experience moderate 

or greater lung function decrements. Results are presented for both the probit and 2-parameter logistic functional 

forms. The full range of estimates can be found in Chapter 9, and in all instances the smaller estimate is a result of 

using the probit function to estimate the exposure-response relationship. 
July 2009 367

considerations are emphasized, the adequacy of the current standard to protect the public health 

would clearly be called into question. This suggests consideration of a revised 24-hour standard 

and/or that an additional shorter-averaging time standard may be needed to provide additional 

health protection for sensitive groups, including asthmatics and individuals who spend time 

outdoors at elevated ventilation rates. Moreover, this also suggests that an alternative SO2

standard(s) should protect against health effects ranging from lung function responses and 

increased respiratory symptoms following 5-10 minute peak SO2 exposures, to increased 

respiratory symptoms and respiratory-related ED visits and hospital admissions associated with 

1-hour daily maximum or 24-hour average SO2 concentrations. 

In examining the exposure- and risk-based information with regard to the adequacy of the 

current 24-hour SO2 standard to protect the public health, we note that the results described 

above (and in more detail in Chapters 7-9) indicate that 5-minute exposures that can reasonably 

be judged important from a public health perspective are associated with air quality adjusted 

upward to simulate just meeting the current 24-hour standard. Therefore, exposure- and riskbased considerations reinforce the scientific evidence in supporting the conclusion that 

consideration should be given to revising the current 24-hour standard and/or setting a new 

shorter averaging time standard (e.g., 1-hour or less) to provide increased public health 

protection, especially for sensitive groups (e.g., asthmatics), from SO2-related adverse health 

effects. 

10.4 ADEQUACY OF THE CURRENT ANNUAL STANDARD 

10.4.1 Introduction 

In the last review of the SO2 NAAQS, retention of the annual standard was largely based 

on an assessment of qualitative evidence gathered from a limited number of epidemiologic 

studies. The strongest evidence for an association between annual SO2 concentrations and 

adverse health effects in the 1982 AQCD was from a study conducted by Lunn et al (1967). The 

authors found that among children a likely association existed between chronic upper and lower 

respiratory tract illnesses and annual SO2 levels of 70 -100 ppb in the presence of 230-301 ug/m3

black smoke. Three additional studies described in the 1986 Second Addendum also suggested 

that long-term exposure to SO2 was associated with adverse respiratory effects. Notably, studies 

conducted by Chapman et al. (1985) and Dodge et al. (1985) found associations between long-
July 2009 368

term SO2 concentrations (with or without high particle concentrations) and cough in children and 

young adults. However, it was noted that there was considerable uncertainty associated with 

these studies because they were conducted in locations subject to high, short-term peak SO2

concentrations (i.e., locations near point sources); therefore it was difficult to discern whether 

this increase in cough was the result of long-term, low level SO2 exposure, or repeated shortterm peak SO2 exposures. 

It was concluded in the last review that there was no quantitative rationale to support a 

specific range for an annual standard (EPA, 1994b). However, it was also found that while no 

single epidemiologic study provided clear quantitative conclusions, there appeared to be some 

consistency across studies indicating the possibility of respiratory effects associated with longterm exposure to SO2 just above the level of the existing annual standard (EPA, 1994b). In 

addition, air quality analyses conducted during the last review indicated that the short-term 

standards being considered (1-hour and/or 24-hour) could not by themselves prevent long-term 

concentrations of SO2 from exceeding the level of the existing annual standard in several large 

urban areas. Ultimately, both the scientific evidence and the air quality analyses were used by 

the Administrator to conclude that retaining the existing annual standard was requisite to protect 

human health. 

10.4.2 Evidence-based considerations 

The ISA presents numerous studies published since the last review examining possible 

associations between long-term SO2 exposure and mortality and morbidity outcomes. This 

includes discussion of additional epidemiologic studies examining possible associations between 

long-term SO2 exposure and respiratory effects in children (in part, the basis for retaining the 

annual standard in the last review; see section 10.4.1). In addition, the ISA presents results from 

epidemiologic and animal toxicological studies published since the last review examining 

possible associations between long-term ambient SO2 concentrations and adverse respiratory, 

cardiovascular, and birth outcomes, as well as carcinogenesis. The current ISA also discusses 

the possible association between long-term SO2 exposure and mortality. 

As an initial consideration with regard to the adequacy of the current annual standard, 

staff notes that the evidence relating long-term (weeks to years) SO2 exposure to adverse health 

effects (respiratory morbidity, carcinogenesis, adverse prenatal and neonatal outcomes, and 

mortality) is judged to be “inadequate to infer the presence or absence of a causal relationship” 
July 2009 369

(ISA, Table 5-3). That is, the ISA finds this health evidence to be of insufficient quantity, 

quality, consistency, or statistical power to make a determination as to whether SO2 is truly 

associated with these health endpoints (ISA, Table 1-2). With respect specifically to respiratory 

morbidity in children, the ISA presents recent epidemiologic evidence of an association with 

long-term exposure to SO2 (ISA, section 3.4.2). However, the ISA finds the strength of these 

epidemiologic studies to be limited because of 1) variability in results across studies with respect 

to specific respiratory morbidity endpoints, 2) high correlations between long-term average SO2

and co-pollutant concentrations, particularly PM, and 3) a lack of evaluation of potential 

confounding (ISA, section 3.4.2.1). 

We also note that many epidemiologic studies demonstrating positive associations 

between 1-hour daily maximum or 24-hour average SO2 concentrations and respiratory 

symptoms, ED visits, and hospitalizations were conducted in areas where ambient SO2

concentrations were well below the current annual NAAQS. This evidence suggests that the 

current annual standard is not providing adequate protection against health effects associated 

with shorter-term SO2 concentrations. 

10.4.3 Risk-based considerations 

Results of the risk characterization based on the air quality assessment provide additional 

insight into the adequacy of the current annual standard. Analyses in this document describe the 

extent to which the current annual standard provides protection against 5-minute peaks of SO2 in 

excess of potential health effect benchmark levels. Figure 7-16 counts the number of measured

5-minute daily maximum SO2 concentrations above the 100 -400 ppb benchmark levels for a 

given annual average SO2 concentration. None of the monitors in this data set reported annual 

average SO2 concentrations above the current NAAQS, but several of the monitors in several of 

the years frequently reported 5-minute daily maximum concentrations above the potential health 

effect benchmark levels. Many of these monitors where frequent exceedances were reported had 

annual average SO2 concentrations between 5 and 15 ppb, with little to no correlation between 

the annual average SO2 concentration and the number of 5-minute daily maximum 

concentrations above potential health effect benchmark levels. This suggests that the annual 

standard adds little in the way of protection against 5-minute peaks of SO2 (see section 7.3.1). 
July 2009 370

10.4.4 Conclusions regarding the adequacy of the current annual standard 

As noted above, the ISA concludes that the evidence relating long-term (weeks to years) 

SO2 exposure to adverse health effects (respiratory morbidity, carcinogenesis, adverse prenatal 

and neonatal outcomes, and mortality) is “inadequate to infer the presence or absence of a causal 

relationship” (ISA, Table 5-3). The ISA also reports that many epidemiologic studies 

demonstrating positive associations between short-term (i.e. 1-hour daily maximum, 24-hour 

average) SO2 concentrations and respiratory symptoms, as well as ED visits and hospitalizations, 

were conducted in areas where annual ambient SO2 concentrations were well below the level of 

the current annual NAAQS. In addition, analyses conducted in this REA suggest that the 

current annual standard is not providing protection against 5-10 minute peaks of SO2. Thus, the 

scientific evidence and the risk and exposure information suggest that the current annual SO2

standard: 1) is likely not needed to protect against health risks associated with long term 

exposure to SO2; and 2) does not provide adequate protection from the health effects associated 

with shorter-term (i.e. ≤ 24-hours). This suggests that consideration should be given to either 

revoking the annual standard or retaining it without revision, in conjunction with setting an 

appropriate short-term standard(s). 

10.5 POTENTIAL ALTERNATIVE STANDARDS 

10.5.1 Indicator 

In the last review, EPA focused on SO2 as the most appropriate indicator for ambient 

SOx. This was in large part because other gaseous sulfur oxides (e.g., SO3) are likely to be found 

at concentrations many orders of magnitude lower than SO2 in the atmosphere, and because most 

all of the health effects and exposure information was for SO2. The current ISA has again found 

this to the case, and although the presence of gaseous SOx species other than SO2 has been 

recognized, no alternative to SO2 has been advanced as being a more appropriate surrogate for 

ambient gaseous SOx. Importantly, controlled human exposure studies and animal toxicology 

studies provide specific evidence for health effects following exposure to SO2. Epidemiologic 

studies also typically report levels of SO2, as opposed to other gaseous SOx. Because emissions 

that lead to the formation of SO2 generally also lead to the formation of other SOx oxidation 

products, measures leading to reductions in population exposures to SO2 can generally be 

expected to lead to reductions in population exposures to other gaseous SOx. Therefore, meeting 

an SO2 standard that protects the public health can also be expected to provide some degree of 
July 2009 371

protection against potential health effects that may be independently associated with other 

gaseous SOx even though such effects are not discernable from currently available studies 

indexed by SO2 alone. Given these key points, staff judges that the available evidence supports 

the retention of SO2 as the indicator in the current review. We also note that this would be in 

agreement with CASAC comments offered on the second draft REA. The consensus CASAC 

response to Agency charge questions from the second draft REA states: “For indicator, SO2 is 

clearly the preferred choice (Samet 2009).” 

10.5.2 Averaging Time 

EPA established the current 24-hour and annual averaging times for the primary SO2

NAAQS in 1971. As previously described, (see section 10.3.1) the 24-hour NAAQS was based 

on epidemiologic studies that observed associations between 24-hour average SO2 levels and 

adverse respiratory effects and daily mortality (EPA 1982, 1994b). The annual standard was 

supported by a few epidemiologic studies that found an association between adverse respiratory 

effects and annual average SO2 concentrations (EPA 1982, 1994b). Based on currently available 

evidence, staff concludes that different averaging time(s) be established for the primary 

standard(s) as part of the current review. In reaching this conclusion, staff has considered 

causality judgments from the ISA, results from controlled human exposure and epidemiologic 

studies, and SO2 air quality correlations. These considerations are described in more detail 

below. 

10.5.2.1 Evidence-based considerations 

As an initial consideration regarding the most appropriate averaging time (e.g., shortterm, long-term, or a combination of both) for alternative SO2 standard(s), we note (as in 10.4.1 

above) that the ISA finds evidence relating long-term (weeks to years) SO2 exposures to adverse 

health effects to be “inadequate to infer the presence or absence of a causal relationship” (ISA, 

Table 5-3). In contrast, the ISA judges evidence relating short-term (5-minutes to 24-hours) SO2

exposure to respiratory morbidity to be “sufficient to infer a causal relationship” and short-term 

exposure to SO2 and mortality to be “suggestive of a causal relationship” (ISA, Table 5-3). 

Taken together, these judgments most directly support standard averaging time(s) that focus 

protection on SO2 exposures from 5-minutes to 24-hours. 
July 2009 372

In considering the level of support available for specific short-term averaging times, we 

first note the strength of evidence from human exposure and epidemiologic studies. Controlled 

human exposure studies exposed exercising asthmatics to 5-10 minute peak concentrations of 

SO2 and consistently found decrements in lung function and/or respiratory symptoms. 

Importantly, the ISA describes the controlled human exposure studies as being the “definitive 

evidence” for its conclusion that there is a causal association between short-term (5-minutes to 

24-hours) SO2 exposure and respiratory morbidity (ISA, section 5.2). Supporting the controlled 

human exposure evidence is a relatively small body of epidemiologic studies describing positive 

associations between 1-hour daily maximum SO2 levels and respiratory symptoms as well as 

hospital admissions and ED visits for all respiratory causes and asthma (ISA Tables 5.4 and 5.5). 

In addition to the 1-hour daily maximum epidemiologic evidence, there is a considerably larger 

body of epidemiologic studies reporting positive associations between 24-hour average SO2 

levels and respiratory symptoms, as well as hospitalizations and ED visits for all respiratory 

causes and asthma. However, as in the last review, there remains considerable uncertainty as to 

whether these positive associations are due to 24-hour average SO2 exposures, or exposure (or 

multiple exposures) to short-term peaks of SO2 within a 24-hour period. More specifically, when 

describing epidemiologic studies observing positive associations between ambient SO2 and 

respiratory symptoms, the ISA states “that it is possible that these associations are determined in 

large part by peak exposures within a 24-hour period” (ISA, section 5.2). The ISA also states 

that the respiratory effects following 5-10 minute SO2 exposures in controlled human exposure 

studies provides a basis for a progression of respiratory morbidity that could result in increased 

ED visits and hospital admissions (ISA, section 5.2). 

The controlled human exposure evidence described above provides support for an 

averaging time that protects against 5-10 minute peak exposures. Results from the 

epidemiologic evidence provides support for both 1-hour and 24-hour averaging times. 

However, it is worth noting again that the effects observed in epidemiologic studies also may be 

due, at least in part and especially in 24-hour epidemiologic studies, to shorter-term peaks of 

SO2. Overall, the evidence mentioned above suggests that a primary concern with regard to 

averaging time is the level of protection provided against 5-10 minute peak SO2 exposures. The 

evidence described above also suggests it would be appropriate to consider the degree of 
July 2009 373

protection averaging times under consideration provide against both 1-hour daily maximum and 

24-hour average SO2 concentrations. 

10.5.2.2 Air Quality considerations 

The shortest averaging time for the current primary SO2 standard is 24-hours. We 

therefore evaluate the potential for a standard based on 24-hour average SO2 concentrations to 

limit 5-minute peak SO2 exposures. Table 10-1 reports the ratio between 99th percentile 5-

minute daily maximum and 99th percentile 24-hour average SO2 concentrations for 42 monitors 

reporting measured 5-minute data for any year between 2004-2006. Across this set of monitors 

in 2004, ratios of 99th percentile 5-minute daily maximum to 99th percentile 24-hour average SO2

concentrations spanned a range of 2.0 to 14.1, with an average ratio of 6.7 (Table 10-1). These 

results suggest that a standard based on 24-hour average SO2 concentrations would not likely be 

an effective or efficient approach for addressing 5-minute peak SO2 concentrations. That is, 

using a 24-hour average standard to address 5-minute peaks would likely result in overcontrolling in some areas, while under-controlling in others. This analysis also suggests that a 5-

minute standard would not likely be an effective or efficient means for controlling 24-hour 

average SO2 concentrations. 

Table 10-1 also reports the ratios between 99th percentile 5-minute daily maximum and 

99th percentile1-hour daily maximum SO2 levels from this set of monitors. Compared to the 

ratios discussed above (5-minute daily maximum to 24-hour average), there is far less variability 

between 5-minute daily maximum and 1-hour daily maximum ratios. More specifically, 39 of 

the 42 monitors had 99th percentile 5-minute daily maximum to 99th percentile 1-hour daily 

maximum ratios in the range of 1.2 to 2.5 (Table 10-1). The remaining 3 monitors had ratios of 

3.6, 4.2 and 4.6 respectively. Overall, this relatively narrow range of ratios suggests that a 

standard with a 1-hour averaging time would be more efficient and effective at limiting 5-minute 

peaks of SO2 than a standard with a 24-hour averaging time. These results also suggest that a 5-

minute standard could be a relatively effective means of controlling 1-hour daily maximum SO2

concentrations. 
July 2009 374

Table 10-1 Ratios of 99th percentile 5-minute daily maximums to 99th percentile 24-hour average 

and 1-hour daily maximum SO2 concentrations for monitors reporting measured 5-minute data 

from years 2004-200699

Monitor ID # of years 

5-minute daily max: 

24-hour average 

5-minute daily max:1-

hour daily maximum 

110010041 1 3.8 1.4 

191770005 1 4.1 1.7 

290930030 1 2.9 1.2 

290930031 1 3.4 1.6 

370670022 1 5.5 1.6 

120890005 2 9.4 2.2 

190330018 2 8.2 2 

190450019 2 11.2 3.6 

191390016 2 6.9 1.5 

191390017 2 9.8 2.2 

191390020 2 6.2 1.8 

191630015 2 4.5 1.5 

191770006 2 3.1 1.3 

291630002 2 7 1.8 

380130002 2 8.4 1.9 

380150003 2 4.8 1.6 

380590002 2 5.6 1.9 

380590003 2 8.4 1.9 

540990003 2 2 1.4 

540990004 2 5.9 2 

540990005 2 5.3 2 

541071002 2 8.1 1.6 

051190007 3 4.7 2.2 

051390006 3 12 2.3 

080310002 3 5.5 1.7 

290770026 3 6.6 1.7 

290770037 3 8.1 2.2 

290990004 3 14.1 2.5 

291370001 3 2.4 1.3 

301110084 3 5.8 1.6 

380070002 3 6.3 2.1 

380130004 3 6.1 1.8 

380171004 3 4.3 1.6 

380250003 3 5.1 1.6 

380530002 3 4 1.4 

380530104 3 7.9 4.2 

380530111 3 11.6 4.6 

380570004 3 7.5 2.3 

380650002 3 7.3 1.9 

381050103 3 9.7 2.5 

381050105 3 6.4 2.4 

420070005 3 10.5 2 

 

99 99th percentile 5-minute daily maximum, 1-hour daily maximum, and 24-hour average values were identified for 

each year a given monitor was in operation from 2004-2006. If a monitor was in operation for multiple years over 

that span, 99th percentile values were identified for each year, averaged, and then the appropriate ratio was 

determined. 
July 2009 375

Staff further evaluated the potential of the 1-hour daily maximum standards analyzed in 

this REA to provide protection against 24-hour average SO2 exposures. The 99th percentile 24-

hour average SO2 concentrations in cities where key U.S. ED visit and hospitalization studies 

(for all respiratory causes and asthma) were conducted ranged from 16 ppb to 115 ppb 

(Thompson and Stewart, 2009). Moreover, effect estimates that remained statistically significant 

in multipollutant models with PM were found in cities with 99th percentile 24-hour average SO2

concentrations ranging from approximately 36 ppb to 64 ppb. Table 10-2 uses 2004 air quality 

data and suggests that a 99th percentile 1-hour daily maximum standard set at a level of 50- 100 

ppb would limit 99th percentile 24-hour average SO2 concentrations observed in epidemiologic 

studies where statistically significant results were observed in multi-pollutant models with PM. 

That is, given a 50 ppb 99th percentile 1-hour daily maximum standard, none of the 39 counties 

analyzed would be expected to have 24-hour average SO2 concentrations ≥ 36 ppb; and, given a 

100 ppb 99th percentile 1-hour daily maximum standard, only 6 of the 39 counties (Linn, Union, 

Bronx, Fairfax, Hudson, and Wayne) included in this analysis would be estimated to have 99th

percentile 24-hour average SO2 concentrations ≥ 36 ppb. This analysis was also done for the 

years 2005 and 2006 and similar results were found (Appendix D). 
July 2009 376

Table 10-2. 99th percentile 24-hour average SO2 concentrations for 2004 given just meeting the 

alternative 1-hour daily maximum 99th and 98th percentile standards analyzed in the air quality 

assessment (note: concentrations in ppb)100. 

99th percentile 98th percentile 

State County 50 100 150 200 250 100 200 

AZ Gila 6 12 18 25 31 16 32 

DE New Castle 12 23 35 47 59 28 56 

FL Hillsborough 10 20 30 40 50 28 55 

IL Madison 12 24 36 48 60 28 56 

IL Wabash 7 13 20 27 33 19 38 

IN Floyd 8 15 23 31 39 20 41 

IN Gibson 9 18 27 36 45 20 41 

IN Lake 12 24 36 48 60 31 62 

IN Vigo 10 19 29 39 48 24 48 

IA Linn 21 42 64 85 106 49 98 

IA Muscatine 17 34 51 68 85 38 76 

MI Wayne 17 33 50 66 83 37 74 

MO Greene 12 24 36 48 60 31 62 

MO Jefferson 9 18 27 36 45 25 51 

NH Merrimack 17 33 50 66 83 39 79 

NJ Hudson 19 38 57 76 95 48 96 

NJ Union 18 36 54 72 90 44 89 

NY Bronx 23 47 70 93 117 54 107 

NY Chautauqua 13 27 40 54 67 32 65 

NY Erie 14 27 41 54 68 30 61 

OH Cuyahoga 17 34 51 67 84 40 80 

OH Lake 10 19 29 39 48 23 47 

OH Summit 12 24 36 48 61 27 55 

OK Tulsa 16 32 47 63 79 36 72 

PA Allegheny 12 23 35 47 59 30 60 

PA Beaver 10 20 30 40 51 25 49 

PA Northampton 11 23 34 45 56 36 72 

PA Warren 11 22 33 44 56 28 56 

PA Washington 15 31 46 62 77 36 71 

TN Blount 15 31 46 61 77 35 71 

TN Shelby 17 34 51 68 85 41 81 

TN Sullivan 8 16 24 32 39 23 46 

TX Jefferson 9 17 26 35 44 21 41 

VA Fairfax 23 46 69 92 116 52 103 

WV Brooke 12 24 37 49 61 31 62 

WV Hancock 15 29 44 58 73 35 69 

WV Monongalia 10 20 30 40 50 25 51 

WV Wayne 30 59 89 119 149 67 133 

VI St Croix 14 27 41 54 68 51 101 

 

100 99th or 98th percentile 1-hour daily maximum concentrations were determined for each monitor in a given county 

for the years completed data were available from 2004-2006. These concentrations were averaged, and the monitor 

with the highest average in a given county was determined. Based on this highest average, all monitors in a given 

county were adjusted to just meet the potential alternative standards defined above, and for each of the years, the 

99th percentile 24-hour average SO2 concentration was identified. Iron County did not meet completeness criteria 

for any of these years and is therefore not part of this analysis. Results for the years 2005 and 2006 are presented in 

Appendix D. 
July 2009 377

As an additional matter, we note that a 99th percentile 1-hour daily maximum standard at 

a level of 50-150 ppb could have the effect of maintaining SO2 concentrations below the level of 

the current 24-hour and annual standards. That is, under these alternative standard scenarios 

(using 2004 air quality data), there would be no counties in this analysis with a 2nd highest 24-

hour average greater than 144 ppb (Table 10-3). Similarly, under these alternative standard 

scenarios (using 2004 air quality data), there would be no counties in this analysis with an annual 

average SO2 concentration in excess of the current annual standard (30.4 ppb; Table 10-4). 

These analyses were also done with air quality from the years 2005 and 2006 and similar results 

were found (Appendix D). 
July 2009 378

Table 10-3. 2nd highest 24-hour average SO2 concentrations (i.e., the current 24-hour standard) for 

2004 given just meeting the alternative 1-hour daily maximum 99th and 98th percentile standards 

analyzed in the air quality assessment (note: concentrations in ppb).101

99th percentile 98th percentile 

State County 50 100 150 200 250 100 200 

AZ Gila 7 14 21 27 34 18 36 

DE New Castle 12 38 57 76 95 45 91 

FL Hillsborough 11 23 34 45 57 31 63 

IL Madison 14 28 42 55 69 32 65 

IL Wabash 10 19 29 39 48 28 55 

IN Floyd 8 17 25 34 42 22 44 

IN Gibson 11 21 32 43 53 24 48 

IN Lake 15 29 44 58 73 38 76 

IN Vigo 10 20 30 40 50 25 50 

IA Linn 28 57 85 113 142 65 130 

IA Muscatine 17 38 57 75 94 43 86 

MI Wayne 19 38 56 75 94 42 84 

MO Greene 17 34 51 67 84 44 87 

MO Jefferson 11 22 33 45 56 31 63 

NH Merrimack 18 37 55 74 92 44 88 

NJ Hudson 21 43 64 86 107 54 109 

NJ Union 19 38 57 77 96 47 95 

NY Bronx 25 51 76 102 127 59 117 

NY Chautauqua 21 42 63 83 104 50 100 

NY Erie 15 31 46 61 77 35 69 

OH Cuyahoga 19 38 58 77 96 47 91 

OH Lake 13 27 40 54 67 32 65 

OH Summit 17 35 52 70 87 39 79 

OK Tulsa 19 38 57 76 95 43 87 

PA Allegheny 13 28 42 56 70 32 71 

PA Beaver 10 21 31 42 52 25 51 

PA Northampton 15 30 45 60 75 48 96 

PA Warren 13 27 40 54 67 34 68 

PA Washington 16 31 50 67 84 36 77 

TN Blount 17 34 50 67 84 39 78 

TN Shelby 19 38 57 76 95 45 90 

TN Sullivan 10 21 31 42 52 30 60 

TX Jefferson 13 25 38 50 63 29 59 

VA Fairfax 26 52 78 104 130 58 117 

WV Brooke 18 36 54 72 90 46 91 

WV Hancock 17 35 52 69 86 41 82 

WV Monongalia 12 24 35 47 59 30 60 

WV Wayne 33 67 100 134 167 75 150 

VI St Croix 17 34 51 68 85 63 126 

 

101 99th or 98th percentile 1-hour daily maximum concentrations were determined for each monitor in a given county 

for the years completed data were available from 2004-2006. These concentrations were averaged, and the monitor 

with the highest average in a given county was determined. Based on this highest average, all monitors in a given 

county were adjusted to just meet the potential alternative standards defined above, and for each of the years, the 2nd

highest 24-hour maximum concentration was identified. Iron County did not meet completeness criteria for any of 

these years and is therefore not part of this analysis. Results for years 2005 and 2006 are presented in Appendix D. 
July 2009 379

Table 10-4. Annual average SO2 concentrations for 2004 given just meeting the alternative 99th

and 98th percentile 1-hour daily maximum standards analyzed in the air quality assessment (note: 

concentrations in ppb).102

99th percentile 98th percentile 

State County 50 100 150 200 250 100 200 

AZ Gila 1.7 3.4 5.1 6.8 8.5 4.5 9.0 

DE New Castle 2.0 4.0 6.0 7.9 9.9 4.7 9.5 

FL Hillsborough 1.6 3.2 4.7 6.3 7.9 4.4 8.7 

IL Madison 1.8 3.6 5.4 7.2 9.0 4.2 8.5 

IL Wabash 0.8 1.6 2.3 3.1 3.9 2.2 4.4 

IN Floyd 1.8 3.6 5.3 7.1 8.9 4.7 9.4 

IN Gibson 1.5 2.9 4.4 5.9 7.3 3.3 6.7 

IN Lake 2.0 4.1 6.1 8.2 10.2 5.3 10.7 

IN Vigo 1.5 3.1 4.6 6.1 7.7 3.8 7.6 

IA Linn 1.8 3.5 5.3 7.0 8.8 4.0 8.1 

IA Muscatine 2.5 5.0 7.5 10.0 12.5 5.6 11.2 

MI Wayne 2.5 5.1 7.6 10.2 12.7 5.7 11.3 

MO Greene 2.0 4.1 6.1 8.2 10.2 5.3 10.6 

MO Jefferson 1.5 2.9 4.4 5.8 7.3 4.1 8.3 

NH Merrimack 2.2 4.4 6.5 8.7 10.9 5.2 10.4 

NJ Hudson 6.4 12.8 19.3 25.7 32.1 16.2 32.5 

NJ Union 6.4 12.7 19.1 25.4 31.8 15.7 31.4 

NY Bronx 7.6 15.1 22.7 30.2 37.8 17.4 34.8 

NY Chautauqua 2.6 5.3 7.9 10.5 13.2 6.3 12.7 

NY Erie 3.1 6.1 9.2 12.2 15.3 6.9 13.8 

OH Cuyahoga 3.9 7.7 11.6 15.5 19.3 9.2 18.4 

OH Lake 2.3 4.7 7.0 9.3 11.6 5.6 11.2 

OH Summit 2.6 5.1 7.7 10.2 12.8 5.8 11.5 

OK Tulsa 3.9 7.8 11.7 15.5 19.4 8.9 17.7 

PA Allegheny 2.9 5.8 8.7 11.6 14.5 7.4 14.8 

PA Beaver 2.5 5.1 7.6 10.1 12.7 6.1 12.3 

PA Northampton 4.6 9.1 13.7 18.3 22.8 14.6 29.1 

PA Warren 2.3 4.5 6.7 9.0 11.2 5.7 11.3 

PA Washington 4.3 8.7 13.0 17.4 21.7 10.0 20.0 

TN Blount 3.0 6.1 9.1 12.1 15.2 7.0 14.0 

TN Shelby 3.5 7.0 10.4 13.9 17.4 8.2 16.5 

TN Sullivan 2.1 4.2 6.3 8.4 10.4 6.0 12.0 

TX Jefferson 1.3 2.6 3.9 5.3 6.6 3.1 6.2 

VA Fairfax 7.7 15.5 23.2 30.9 38.6 17.3 34.6 

WV Brooke 4.8 9.6 14.3 19.1 23.9 12.1 24.2 

WV Hancock 4.0 8.0 12.0 16.1 20.1 9.5 19.1 

WV Monongalia 2.2 4.3 6.5 8.7 10.9 5.5 11.1 

WV Wayne 6.1 12.2 18.3 24.4 30.6 13.7 27.4 

VI St Croix 1.2 2.4 3.7 4.9 6.1 4.5 9.1 

 

102 99th or 98th percentile 1-hour daily maximum concentrations were determined for each monitor in a given county 

for the years completed data were available from 2004-2006. These concentrations were averaged, and the monitor 

with the highest average in a given county was determined. Based on this highest average, all monitors in a given 

county were adjusted to just meet the potential alternative standards defined above, and for each of the years, the 

annual concentration was calculated. Iron County did not meet completeness criteria for any of these years and is 

therefore not part of this analysis. Results for the years 2005 and 2006 are presented in Appendix D 
July 2009 380

10.5.2.3 Conclusions regarding averaging time 

 The air quality analyses presented above strongly support that it is likely an alternative 

99th percentile (see form discussion in 10.5.3) 1-hour daily maximum standard set at an 

appropriate level (see level discussion in 10.5.4) can substantially reduce: (1) 5-10 minute peaks 

of SO2 shown in human exposure studies to result in respiratory symptoms and/or decrements in 

lung function in exercising asthmatics, (2) 99th percentile 1-hour daily maximum air quality 

concentrations in cities observing positive effect estimates in epidemiologic studies of hospital 

admissions and ED visits for all respiratory causes and asthma, and (3) 99th percentile 24-hour 

average air quality concentrations found in U.S. cities where ED visit and hospitalization studies 

(for all respiratory causes and asthma) observed statistically significant associations in multipollutant models with PM (i.e., 99th percentile 24-hour average SO2 concentration ≥ 36 ppb). 

Thus, staff concludes that a 1-hour daily maximum standard, with an appropriate form and level, 

can provide adequate protection against the range of health outcomes associated with averaging 

times from 5-minutes to 24-hours. As an additional matter, we note that this conclusion is in 

agreement with CASAC comments offered on the second draft SO2 REA. The CASAC letter to 

the Administrator states: “CASAC is in agreement with having a short-term standard and finds 

that the REA supports a one-hour standard as protective of public health (Samet 2009).” 

We note that based solely on the controlled human exposure evidence, staff also 

considered a 5-minute averaging time. However, staff does not favor such an approach. As in 

past NAAQS reviews, we have considered the stability of the design of pollution control 

programs in considering the elements of a NAAQS, since more stable programs are more 

effective, and hence result in enhanced public safety. In this review, staff has concerns about the 

stability of a 5-minute averaging time standard. Specific concerns relate to the number of 

monitors needed and the placement of such monitors given the temporal and spatial 

heterogeneity of 5-minute SO2 concentrations. Moreover, staff is concerned that compared to 

longer averaging times (e.g., 1-hour, 24-hour), year-to-year variation in 5-minute SO2

concentrations is likely to be substantially more temporally and spatially diverse. Consequently, 

staff judges that a 5-minute averaging time would not provide a stable regulatory target and 

therefore, is not the preferred approach to provide adequate public health protection. However, 

as noted above, staff ‘s view is that a 1-hour averaging time, given an appropriate form (see 
July 2009 381

10.5.3) and level (see 10.5.4), can adequately limit 5-minute SO2 exposures and provide a more 

stable regulatory target than setting a 5-minute standard. 

10.5.3 Form 

When evaluating alternative forms in conjunction with specific levels, staff considers the 

adequacy of the public health protection provided by the combination of level and form to be the 

foremost consideration. In addition, we recognize that it is important that the standard have a 

form that is reasonably stable. As just explained in the context of a five-minute averaging time, 

a standard set with a high degree of instability could have the effect of reducing public health 

protection because shifting in and out of attainment could disrupt an area’s ongoing 

implementation plans and associated control programs. 

10.5.3.1 Evidence-based considerations 

As previously mentioned, staff recognizes that the adequacy of the public health 

protection provided by a 1-hour daily maximum potential alternative standard will be dependent 

on the combination of form and level. It is therefore important that the particular form selected 

for a 1-hour daily maximum potential alternative standard reflect the nature of the health risks 

posed by increasing SO2 concentrations. That is, the form of the standard should reflect results 

from human exposure studies demonstrating that the percentage of asthmatics affected, and the 

severity of the respiratory response (i.e. decrements in lung function, respiratory symptoms) 

increases as SO2 concentrations increase (see section 4.2.2). Taking this into consideration, staff 

finds that a concentration-based form is more appropriate than an exceedance-based form. This 

is because a concentration-based form averaged over three years (see below) would give 

proportionally greater weight to 1-hour daily maximum SO2 concentrations that are well above 

the level of the standard, than to 1-hour daily maximum SO2 concentrations that are just above 

the level of the standard. In contrast, an expected exceedance form would give the same weight 

to 1-hour daily maximum SO2 concentrations that are just above the level of the standard, as to 

1-hour daily maximum SO2 concentrations that are well above the level of the standard. 

Therefore, a concentration-based form better reflects the continuum of health risks posed by 

increasing SO2 concentrations (i.e. the percentage of asthmatics affected and the severity of the 

response increases with increasing SO2 concentrations). 
July 2009 382

10.5.3.2 Risk-based considerations 

 In considering specific concentration-based forms, we recognize the importance of: 1) 

minimizing the number of days per year that an area could exceed the level of the standard and 

still attain the standard; 2) limiting the prevalence of 5-minute peaks of SO2; and 3) providing a 

stable regulatory target to prevent areas from frequently shifting in and out of attainment. Given 

this, we have focused on 98th and 99th percentile forms averaged over 3 years. We first note that 

in most locations analyzed, the 99th percentile form of a 1-hour daily maximum standard would 

correspond to the 4th highest daily maximum concentration in a year, while a 98th percentile form 

would correspond approximately to the 7th to 8th highest daily maximum concentration in a year 

(Table 10-5; see Thompson, 2009). In addition, results from the air quality analysis suggest that 

at a given SO2 standard level, a 99th percentile form is appreciably more effective at limiting 5-

minute peak SO2 concentrations than a 98th percentile form (Figures 7-27 and 7-28103). 

Compared to the same standard with a 99th percentile form, a 98th percentile 1-hour daily 

maximum standard set at 200 ppb allows for on average, an estimated 68 and 86% more days per 

year when 5-minute SO2 concentrations are greater than 200 and 400 ppb respectively (Figure 7-

27). Similarly, compared to the same standard with a 99th percentile form, a 98th percentile 1-

hour daily maximum standard at 100 ppb allows for on average, an estimated 90 and 74% more 

days per year when SO2 concentrations are greater than 200 and 400 ppb respectively104 (Figure 

7-28). We also note that in the 40 counties selected for detailed air quality analysis, the 

estimated number of benchmark exceedances using a 98th percentile 1-hour daily maximum 

standard level of 200 ppb was similar to the corresponding 99th percentile standard at 250 ppb 

(Tables 7-11 through 7-14). Similarly, the estimated number of benchmark exceedances 

considering a 98th percentile standard at 100 ppb fell within the range of benchmark exceedances 

estimated for 99th percentile standards at 100 and 150 ppb (Tables 7-11 through 7-14). 

 

103 In these figures, the two air quality scenarios were compared on a monitor-to-monitor basis (see section 7.3) 104 Compared to a 200 ppb standard, a standard at 100 ppb results in far fewer site-years experiencing benchmark 

exceedances (see Figures 7-27 and 7-28). 
July 2009 383

Table 10-5. SO2 concentrations (ppb) corresponding to the 2nd-9th daily maximum and 98th/99th

percentile forms for alternative 1-hour daily maximum standards (2004-2006).105

SO2 Daily Maximums Percentiles 

County State 2nd 3rd 4th 5th 6th 7th 8th 9th 99th 98th 

Gila AZ 36 33 28 26 25 23 22 21 28 22 

New Castle DE 17 15 15 13 13 12 12 12 15 12 

Hillsborough FL 13 12 12 11 11 10 9 8 12 9 

Madison IL 16 15 14 14 13 13 12 12 14 12 

Wabash IL 21 19 17 17 15 13 13 12 15 13 

Floyd IN 21 19 17 16 14 14 13 12 17 13 

Lake IN 15 12 11 11 10 9 9 8 11 9 

Vigo IN 15 13 13 12 11 11 10 10 13 10 

Linn IA 11 10 10 9 10 9 10 9 10 8 

Muscatine IA 15 15 14 13 13 12 12 12 14 12 

Wayne MI 14 13 13 12 12 12 11 11 13 12 

Greene MO 10 9 8 7 7 6 6 6 8 6 

Jefferson MO 50 43 41 34 31 29 28 27 35 25 

Merrimack NH 16 16 15 14 14 13 13 13 15 13 

Hudson NJ 6 6 6 6 5 5 5 5 6 5 

Union NJ 7 7 6 5 5 5 5 4 6 5 

Bronx NY 8 7 7 7 6 6 6 6 6 6 

Chautauqua NY 11 11 10 10 9 9 8 8 10 8 

Erie NY 17 15 13 12 12 12 11 11 13 11 

Cuyahoga OH 9 9 8 7 7 7 7 7 8 7 

Lake OH 19 19 18 17 16 15 15 14 18 15 

Summit OH 17 16 15 14 14 14 13 13 15 13 

Tulsa OK 11 9 8 8 7 7 7 7 8 7 

Allegheny PA 13 12 11 10 10 9 9 9 11 9 

Beaver PA 30 25 23 22 20 19 19 18 13 11 

Northampton PA 19 17 15 14 12 10 9 9 15 9 

Warren PA 26 24 23 22 19 18 18 18 23 18 

Washington PA 12 11 10 10 9 9 9 9 10 9 

Blount TN 21 20 19 19 18 17 17 16 19 17 

Shelby TN 12 10 9 8 8 8 7 7 9 7 

Sullivan TN 24 22 21 19 16 15 14 14 21 14 

Jefferson TX 15 14 13 12 12 11 11 10 13 11 

Fairfax VA 5 5 4 4 4 4 4 4 4 4 

Brooke WV 21 18 16 14 14 13 12 12 16 12 

Hancock WV 18 17 16 15 15 14 13 13 16 13 

Monongalia WV 23 22 18 17 16 15 14 14 17 14 

St Croix VI 16 13 9 8 5 5 5 4 5 4 

As an additional matter, staff compared trends in 98th and 99th percentile design values, as 

well as design values based on the 4th highest daily maximum from 54 sites located in the 40 

counties selected for detailed analysis (see Thompson, 2009). These results suggest that at the 

 

105 Table 10-5 displays the 2nd through 9th highest, and the 99th and 98th percentiles of the daily maximums for 

each of the counties. For the alternative daily metrics (the nth maximum and percentiles), the statistics were 

computed for each year and then averaged over 2004-2006 (see Thompson 2009). 
July 2009 384

vast majority of sites, there would have been similar changes in 98th and 99th percentile design 

values over the last ten years (i.e. based evaluating overlapping three year intervals over the last 

ten years; see Thompson, 2009). These results also demonstrate that design values based on the 

4th highest daily maximum are virtually indistinguishable from design values based on the 99th

percentile. For illustrative purposes, design value trends for four of these sites are presented in 

Figure 10-1. As part of this analysis, all of the design values over this ten year period for all 54 

sites were aggregated and the standard deviation calculated (see Thompson, 2009). Results 

demonstrate similar standard deviations – i.e. similar stability -- based on aggregated 98th or 

aggregated 99th percentile design values over the ten year period (Figure 10-2; see Thompson 

2009). 
July 2009 385 3-year Design Value 3-year Design Value 3-year Design Value 3-year Design Value

Figure 10-1. Design value trends from 4 of the 54 sites analyzed in Thompson 2009.106

 

106 There were 8 possible 3-year design values from 1997 to 2007 (e.g. 1997-1999, 1998- 2000, etc.). Thus, the 

design values presented in Figure 10-1 represent the 3-year average of the annual 98th percentile or 99th percentile 1-

hour daily maximum, or the 3-year average of 4th highest of the 1-hour daily maximum. (Thompson 2009). 
July 2009 386

0.00

0.01

0.02

0.03

0.04

0.05

0.06

p98 p99 4th Max

Standard Deviation

0.00

0.01

0.02

0.03

0.04

0.05

0.06

p98 p99 4th Max

Standard Deviation

Figure 10-2. Boxplots of the distributions of standard deviations for alternative air quality 

standard forms. 

10.5.3.3 Conclusions regarding form 

 Staff concludes that a concentration-based form provides the best protection against the 

health risks posed by increasing SO2 concentrations (see 10.5.3.1). We also find that at a given 

standard level, a 99th percentile or 4th highest daily maximum form provides appreciably more 

public health protection against 5-minute peaks than a 98th percentile or 7th - 8th highest daily 

maximum form (see 10.5.3.2). In addition, over the last 10 years and for the vast majority of the 

sites examined, there appears to be little difference in 98th and 99th percentile design value 

stability (see 10.5.3.2). Thus, staff concludes that consideration be given primarily to a 1-hour 

daily maximum standard with a 99th percentile or 4th highest daily maximum form.
July 2009 387

 10.5.4 Level 

In sections 10.3.3.3 and 10.4.4 staff concluded that the health evidence presented above 

in Chapter 4 and the air quality, exposure, and risk information presented in Chapters 7-9 clearly 

call into question the adequacy of the current SO2 standards to protect public health with an 

adequate margin of safety from the respiratory effects of SO2. In considering potential 

alternative standards that would provide increased public health protection against these 

respiratory effects, staff concluded in section 10.5.1 that the most appropriate indicator remains 

SO2. In section 10.5.2, staff concluded that an alternative standard with a 1-hour averaging time, 

set at an appropriate level, can provide adequate protection against the range of respiratory 

effects observed in both controlled human exposure studies of 5-10 minutes, as well as 

epidemiologic studies using longer averaging times. In addition, section 10.5.3 concluded that a 

99th percentile or 4th highest daily maximum form averaged over three years was most 

appropriate for potential standards using a 1-hour averaging time. Here, we consider 99th

percentile 1-hour daily maximum alternative standard levels that would provide greater public 

health protection against SO2-related adverse respiratory effects than that afforded by the current 

standards. As an initial consideration, we note that Table 10-6 demonstrates that although all 

counties in the U.S. meet the current 24-hour and annual standards, all of the potential alternative 

1-hour daily maximum standard levels (50-250 ppb) analyzed in the air quality, exposure, and 

risk analyses would be estimated to result in counties in the U.S. with air quality above the level 

of the given alternative standard. Thus, to varying extents, meeting any of the potential 

alternative 1-hour daily maximum standards analyzed in this document would represent 

reductions in ambient SO2 levels based on air quality from 2004-2006, as well as reductions from 

SO2 concentrations that would be allowed under the current standards. All of these potential 

standards would consequently result in some increased public health protection. 
July 2009 388

Table 10-6. Percent of counties that may be above the level of alternative standards (based on years 2004-2006) 

Percent of counties, total and by region not likely to meet a given standard 

Alternative Standards and 

Levels (ppb) 

Total Counties 

(population in 

millions) Northeast Southeast

Industrial 

Midwest 

Upper 

Midwest Southwest Northw

Number of counties with 

monitors 211 (96.5) 52 40 75 19 7 9 

3 year 99th percentile daily 1-hour max: 250 1 (0.4) 0 0 1 0 14 0 200 3 (0.8) 0 3 4 0 14 0 150 10 (2.4) 2 5 20 5 14 0 100 22 (13.5) 8 13 47 5 14 0 50 54 (43.5) 38 55 81 37 14 22 3 year 98th percentile daily 1-hour max: 200 1 (0.4) 0 0 1 0 14 0 
July 2009 389

10.5.4.1 Evidence, Air Quality, Exposure and Risk-based considerations 

 Chapter 4 discussed the controlled human exposure and epidemiologic evidence with 

respect to the judgments of causality presented in the ISA. In Chapter 5, our evaluation of the 

health evidence informed the selection of potential alternative SO2 standards that would be 

analyzed in the air quality, exposure, and risk analyses. In Chapter 6, potential health effect 

benchmark values for use in the air quality and exposure analyses were derived from SO2

concentrations found in controlled human exposure studies to result in decrements in lung 

function and/or respiratory symptoms in exercising asthmatics. In this chapter, staff also used 

the controlled human exposure and the epidemiologic evidence to inform judgments about the 

adequacy of the current SO2 standards, and to inform staff conclusions about the indicator, 

averaging time, and form for potential alternative SO2 standards. 

 Staff now considers the health evidence as it relates to evaluating 99th percentile 1-hour 

daily maximum alternative standard levels.107 In doing so, we have considered the extent to 

which a variety of alternative standard levels would limit the magnitude and frequency of 1-hour 

SO2 concentrations to provide sufficient protection for at-risk populations against experiencing 

various respiratory health effects including moderate or greater decrements in lung function, 

respiratory symptoms, and respiratory-related ED visits and hospitalizations. We note that these 

health endpoints are logically linked together in that the controlled human exposure evidence 

demonstrating moderate or greater decrements in lung function and/or respiratory symptoms in 

exercising asthmatics is recognized by the ISA as supporting the plausibility of associations 

between ambient SO2 and the respiratory morbidity endpoints (i.e., respiratory symptoms, 

emergency department visits, and hospital admissions) reported in epidemiologic studies. 

 In assessing the extent to which potential alternative standard levels with a 1-hour 

averaging time and a 99th percentile form limit the array of health outcomes reported in both 

controlled human exposure and epidemiologic studies, we first note the air quality information 

provided by authors of key U.S. ED visit and hospitalization epidemiologic studies. This 

information was presented earlier in Figures 5-1 to 5-4 and is described in detail in Thompson 

and Stewart (2009). This information characterizes 99th percentile 1-hour daily maximum SO2

air quality levels in cities and time periods corresponding to key U.S. studies of ED visits and 

 

107 We note that these considerations are also relevant for consideration of alternative standard levels in conjunction 

with a 4th highest daily maximum form. 
July 2009 390

hospitalizations for all respiratory causes and asthma. This information provides the most direct 

evidence for effects in cities with particular 99th percentile 1-hour SO2 levels, and hence, is of 

particular relevance here. This information suggests that the strongest epidemiologic evidence of 

an association between ambient SO2 and ED visits and hospitalizations is in cities where 99th

percentile 1-hour daily maximum SO2 concentrations ranged from about 75 to150 ppb. In this 

range, there are numerous studies that reported positive associations between ambient SO2 and 

respiratory related ED visits and hospitalizations (although all results were not statistically 

significant). In addition, this range of SO2 levels importantly contains a cluster of epidemiologic 

studies demonstrating statistically significant results in multi-pollutant models with PM. More 

specifically, in epidemiologic studies conducted in the Bronx, NY (78 ppb; NYDOH 2006,) and 

in NYC, NY (82 ppb; Ito et al., 2007), the SO2 effect estimate remained positive and statistically 

significant in multi-pollutant models with PM2.5 (ISA, Table 5-5). Moreover, in an 

epidemiologic study conducted in New Haven, CT (150 ppb; Schwartz et al., 1995), the SO2

effect estimate remained positive and statistically significant in a multi-pollutant model with 

PM10. Staff notes that while statistical significance in co-pollutant models is an important 

consideration, it is not necessary for appropriate consideration of and reliance on such 

epidemiologic evidence.108 However, the existence of these studies particularly supports 

consideration of standards levels at and below the range observed in these studies. Given this 

body of epidemiologic evidence, staff concludes that alternative standard levels at and below 75 

ppb should be considered to provide protection against the effects observed in these studies. 

 With regard to the epidemiologic studies mentioned above, we also note that most of the 

ED visit and hospitalization effect estimates reported in these studies are with respect to 24-hour 

average SO2 concentrations. Thus, staff investigated whether a 99th percentile 1-hour daily 

maximum standard at approximately 75 ppb would also limit the 99th percentile 24-hour average 

SO2 concentrations observed in the cluster of studies finding statistically significant results in 

multipollutant models with PM. Considering these studies, we note that the lowest 99th

percentile 24-hour average SO2 concentration reported in a study location finding statistically 

significant associations in a multipollutant model with PM was 36 ppb in Bronx, NY (NYDOH 

 

108 For example, evidence of a pattern of results from a group of studies that find effect estimates similar in direction 

and magnitude would warrant consideration of and reliance on such studies even if the studies did not all report 

statistically significant associations in single- or multi-pollutant models 
July 2009 391

2006). A standard of approximately 75 ppb was not analyzed in the air quality analysis, but 

given a 50 ppb 99th percentile 1-hour daily maximum standard, none of the counties analyzed in 

our analysis would be expected to have 99th percentile 24-hour average SO2 concentrations ≥ 36 

ppb (Table 10-2). However, given a 100 ppb 99th percentile 1-hour daily maximum standard, 

six of the counties included in the 40-county air quality analysis would be estimated to have 99th 

percentile 24-hour average SO2 concentrations ≥ 36 ppb109. Thus, although not directly 

analyzed, a 1-hour standard set at 75 ppb would be expected to limit 24-hour average 

concentrations from exceeding 36 ppb in most, if not all, these counties. This analysis further 

indicates that a 99th percentile 1-hour daily maximum standard level should be considered at or 

below 75 ppb to provide protection against the effects observed in this cluster of epidemiologic 

studies. 

 Staff also considered findings from controlled human exposure studies when evaluating 

potential alternative standard levels. In doing so, we again note that the ISA finds that the most 

consistent evidence of decrements in lung function and/or respiratory symptoms is from 

controlled human exposure studies exposing exercising asthmatics to SO2 concentrations ≥ 400 

ppb (ISA, section 3.1.3.5). At SO2 concentrations ≥ 400 ppb, moderate or greater 

bronchoconstriction occurs in 20-60% of exercising asthmatics, and compared to exposures at 

200- 300 ppb, a larger percentage of subjects experience severe bronchoconstriction. Moreover, 

at concentrations ≥ 400 ppb, statistically significant moderate or greater bronchoconstriction is 

frequently accompanied by respiratory symptoms (ISA, Table 5-1). Controlled human exposure 

evidence has also demonstrated decrements in lung function in exercising asthmatics following 

5-10 minute SO2 exposures starting as low as 200-300 ppb in free-breathing chamber studies. At 

concentrations ranging from 200 - 300 ppb, the lowest levels tested in free breathing chamber 

studies, 5-30% percent of exercising asthmatics are likely to experience moderate or greater 

bronchoconstriction. However, at these lower levels, moderate or greater bronchoconstriction 

has not been shown to be statistically significant, nor is it frequently accompanied by respiratory 

symptoms. On the other hand, for understandable ethical reasons, it must also be noted that the 

subjects participating in these controlled human exposure studies do not necessarily represent the 

most SO2 sensitive individuals (e.g. severe asthmatics). Thus, it is reasonable to anticipate that 

 

109 Given a 99th percentile 1-hour daily maximum standard at 100 ppb, 99th percentile 24-hour average SO2

concentrations are estimated to be greater than 36 ppb in Linn, Union, Bronx, Hudson, Fairfax, and Wayne counties 

(Table 10-2) 
July 2009 392

individuals who are more SO2 sensitive would have a greater response at 200-300 ppb SO2, 

and/or would respond to SO2 concentrations even lower than 200 ppb. Similarly, there is no 

evidence to suggest that 200 ppb represents a threshold below which no adverse respiratory 

effects occur. In fact, very limited evidence from two mouthpiece exposure studies suggests that 

exposure to 100 ppb SO2 can result in small decrements in lung function110. Moreover, while not 

directly comparable to free-breathing chamber studies, findings from these mouthpiece studies 

may be particularly relevant to those asthmatics who breathe oronasally even at rest (EPA, 

1994b). Taken together, staff concludes that the level of a 99th percentile 1-hour daily maximum 

SO2 standard should be set so as to substantially limit the number of estimated 5-minute peaks ≥

400 ppb, while also appreciably limiting SO2 concentrations ≥ 200 ppb. 

 In evaluating the extent to which alternative standard levels provide substantial protection 

against 5-minute SO2 concentrations ≥ 400 ppb, we first note the results of our 40 county air 

quality analysis. As described above, epidemiologic studies support consideration of levels of a 

99th percentile 1-hour daily maximum standard at or below 75 ppb. Thus, it would be instructive 

to determine if a standard set at approximately 75 ppb would also substantially limit 5-minute 

SO2 concentrations > 400 ppb. Results of the air quality analysis indicate that just meeting a 99th

percentile 1-hour daily maximum standard at 50 ppb would result in 0 days per year when 

statistically estimated 5-minute daily maximum SO2 concentrations are > 400 ppb, whereas a 

standard at 100 ppb would result in at most 2 days per year when statistically estimated 5-minute 

daily maximum SO2 concentrations are > 400 ppb (Table 7-14)111. Given the results associated 

with 99th percentile 1-hour daily maximum standards at 50 and 100 ppb, it is reasonable to 

conclude that a 99th percentile 1-hour daily maximum standard at 75 ppb would also 

substantially limit ambient 5-minute SO2 concentrations ≥ 400 ppb. 

 In further evaluating the extent to which potential alternative standard levels limit 5-

minute SO2 exposures >400 ppb, we consider the results of the St. Louis exposure analysis.112 

 

110 As first noted in Chapter 6, studies utilizing a mouthpiece exposure system cannot be directly compared to 

studies involving freely breathing subjects, as nasal absorption of SO2 is bypassed during oral breathing, thus 

allowing a greater fraction of inhaled SO2 to reach the tracheobronchial airways. As a result, individuals exposed to 

SO2 through a mouthpiece are likely to experience greater respiratory effects from a given SO2 exposure. 

Nonetheless, these studies do provide very limited evidence for SO2-induced respiratory effects at 100 ppb. 111 Air quality estimates presented in this section represent the mean number of days per year when 5-minute daily 

maximum SO2 concentrations exceed a particular benchmark level given 2001-2006 air quality adjusted to just meet 

alternative 99th percentile 1-hour daily maximum standards at 50, 100, or 150 ppb (see Tables 7-11 to 7-14). 112 As described in section 10.3.3.2, staff is primarily considering the St. Louis exposure and risk results when 

evaluating the adequacy of the current and potential alternative 99th percentile 1-hour daily maximum SO2 standards. 
July 2009 393

Results indicate air quality just meeting a 99th percentile 1-hour daily maximum standard at 50 or 

100 ppb would result in an estimated < 1% of asthmatics at elevated ventilation rates 

experiencing at least one 5-minute daily maximum SO2 exposure ≥ 400 ppb (Figure 8-19). 

Similarly, this analysis also indicates that air quality just meeting a 50 or 100 ppb standard would 

result in an estimated < 1% of asthmatic children at elevated ventilation rates experiencing at 

least one 5-minute daily maximum SO2 exposure ≥ 400 ppb. These results necessarily suggest 

that a standard at approximately 75 ppb would also substantially limit exposures of all asthmatics 

and asthmatic children to SO2 concentrations ≥ 400 ppb. 

 We next evaluated the extent to which 99th percentile 1-hour daily maximum standard 

levels provide appreciable protection against 5-minute SO2 concentrations ≥ 200 ppb. Results of 

the 40 county air quality analysis indicate that a standard level of 50 ppb would result in at most 

2 days per year when statistically estimated 5-minute daily maximum SO2 concentrations would 

be > 200 ppb, whereas a standard level of 100 ppb would result in at most 13 days per year when 

statistically estimated 5-minute daily maximum SO2 concentrations would be > 200 ppb (Table 

7-12). Thus, a standard set at 75 ppb would result in somewhere between 2 and 13 days per year 

when statistically estimated 5-minute daily maximum SO2 concentrations would be > 200 ppb.

 Results from the St. Louis exposure analysis estimate that air quality just meeting a 50 

ppb, or 100 ppb 1-hour daily maximum standard would result in a corresponding < 1% or 1.5% 

of asthmatics at elevated ventilation rates experiencing at least one 5-minute daily maximum SO2

exposure ≥ 200 ppb (Figure 8-19). Moreover, just meeting a 50 ppb, or 100 ppb 99th percentile 

1-hour daily maximum standard would be estimated to result in a corresponding <1% or 2.7% of 

asthmatic children at elevated ventilation rates experiencing at least one 5-minute daily 

maximum SO2 exposure ≥ 200 ppb (Figure 8-19). Thus, a standard set at 75 ppb would be 

estimated to result in somewhere between <1 and 1.5% of asthmatics, or <1 and 2.7% of 

asthmatic children, at elevated ventilation rates experiencing at least one 5-minute daily 

maximum SO2 exposure ≥ 200 ppb. 

 As an additional consideration, we note the results of the St. Louis risk assessment 

indicate that a 99th percentile 1-hour daily maximum standard at 75 ppb would likely provide 

appreciable protection against moderate or greater lung function responses. More specifically, 

given a 99th percentile 1-hour daily maximum standard at 50 ppb, the median percentage of 

asthmatics at elevated ventilation rates estimated to experience at least one ≥ 100% increase in 
July 2009 394

sRaw ranges from 0.3% to 0.7% (and 0.4% to 0.9% for asthmatic children)113. In addition, given 

air quality just meeting a 100 ppb standard, the estimated median percentage of asthmatics at 

elevated ventilation rates experiencing at least one ≥ 100% increase in sRaw ranges from 1.3 to 

1.9% (and 2.1 to 2.9% for asthmatic children) (Table 9-5). Thus, we can expect that a standard 

at 75 ppb would limit risk estimates to somewhere between the risks associated with the 50 and 

100 ppb, 99th percentile 1-hour daily maximum standards. 

 Being mindful that the most severe effects associated with SO2 exposure are those 

observed in epidemiologic studies (i.e. respiratory-related ED visits and hospitalizations), staff 

concludes that consideration also should be given to a standard level of 50 ppb. A 99th percentile 

1-hour daily maximum standard at 50 ppb would provide an increased margin safety against the 

air quality levels observed in the cluster of epidemiologic studies observing statistically 

significant positive associations between SO2 and respiratory-related ED visits and 

hospitalizations in studies with multipollutant models with PM (i.e. 99th percentile 1-hour daily 

maximum SO2 concentrations ≥ 78 ppb). Moreover, as demonstrated in Table10-2, a 99th

percentile 1-hour daily maximum standard set at 50 ppb would also be expected to limit 99th

percentile 24-hour average SO2 concentrations significantly. That is, given a 1-hour daily 

maximum standard set at 50 ppb, Table 10-2 demonstrates that most counties included in the 40-

county air quality analysis would have 99th percentile 24-hour average SO2 concentrations

below 15 ppb, ranging from 6-30 ppb. 

 Recognizing that there are important uncertainties associated with the controlled human 

exposure evidence, we note that a 99th percentile 1-hour daily maximum standard set at 50 ppb 

could also be considered if emphasis is placed on the: 1) uncertainty that the participants in 

controlled human exposure studies do not represent the most SO2 sensitive individuals; and/or 2) 

very limited evidence suggesting decrements in lung function down to 100 ppb when SO2 is 

administered via mouthpiece (see section 6.2). Under this scenario, we note that a standard set at 

50 ppb would provide increased protection against 5-minute SO2 concentrations ≥ 100 ppb. 

Results from the 40 county air quality analysis indicate that a 99th percentile 1-hour daily 

maximum standard set at 50 ppb would be estimated to result in at most 13 days per year when 

statistically estimated 5-minute daily maximum SO2 concentrations are > 100 ppb (Table 7-11). 

 

113 As first noted in section 10.3.3.2, results are presented for both the probit and 2-parameter logistic functional 

forms. The full range of estimates can be found in Chapter 9, and in all instances the smaller estimate is a result of 

using the probit function to estimate the exposure-response relationship. 
July 2009 395

In addition, the St. Louis exposure analysis estimates that a 50 ppb 99th percentile 1-hour daily 

maximum standard would likely result in 1.5% of asthmatics, and 2.7% of asthmatic children at 

elevated ventilation rates experiencing at least one SO2 concentration ≥ 100 ppb per year (Figure 

8-19). 

 In considering alternative standard levels > 100 ppb, we first note that as mentioned in 

section 10.3.3, staff concluded that exposure to 5-10 minute SO2 concentrations at least as low as 

200 ppb can result in adverse respiratory effects in some asthmatics. Thus, in order to limit 5-10 

minute SO2 concentrations from exceeding 200 ppb, the level of a 99th percentile 1-hour daily 

maximum standard would have to be < 200 ppb. We note that this conclusion is in accord with 

consensus CASAC comments following their review of the second draft REA. The CASAC 

letter to the Administrator states: “the draft REA appropriately implies that levels greater than 

150 ppb are not adequately supported.” 

 This letter also stated that “an upper limit of 150 ppb posited in Chapter 10 could be 

justified under some interpretations of weight of evidence, uncertainties, and policy choices 

regarding margin of safety” (Samet 2009). A 99th percentile 1-hour daily maximum SO2

standard set in this range would have to place considerable weight on the uncertainties in the 

epidemiologic health evidence presented in the ISA. That is, the emphasis on the uncertainties 

would have to lead to a judgment that effects reported in epidemiologic studies are due in large 

part to co-occurring pollutants, rather than to SO2. Under this scenario, results of the 40 county 

air quality analysis indicate that just meeting a 99th percentile 1-hour daily maximum standard 

set at a level of 150 ppb would result in at most 7 days per year when statistically estimated 5-

minute daily maximum SO2 concentrations would be > 400 ppb (Table 7-14). In addition, the St. 

Louis exposure analysis indicates that a 99th percentile 1-hour daily maximum standard at 150 

ppb would be estimated to result in ≤ 1% of asthmatics, or asthmatic children at elevated 

ventilation rates experiencing at least one SO2 exposure ≥ 400 ppb (Figure 8-19). Taken 

together, it can reasonable be concluded that a 99th percentile 1-hour daily maximum standard up 

to 150 ppb could similarly limit SO2 exposures ≥ 400 ppb when compared to standards in the 

range of 50-100 ppb114. 

 

114 Given a 50 or 100 ppb standard, the 40 county air quality analysis estimated at most 0 to 2 days per year when 

statistically estimated 5-minute daily maximum SO2 concentrations would be ≥ 400 ppb. In addition, the St. Louis 

exposure analysis indicated that ≤ 1% of asthmatics, or asthmatic children at elevated ventilation rates would be 

expected to experience at least one SO2 exposure ≥ 400 ppb. 
July 2009 396

 However, it is important to note that a 99th percentile 1-hour daily maximum standard up 

to 150 ppb would provide considerably less protection against 5-minute SO2 concentrations ≥

200 ppb than standards in the range of 50 -100 ppb. Results of the 40 county air quality analysis 

indicate that a 99th percentile 1-hour daily maximum standard at 150 ppb would result in at most 

24 days per year when statistically estimated 5-minute daily maximum SO2 concentrations would 

be > 200 ppb. Moreover, the St. Louis exposure analysis indicates that a 150 ppb standard 

would be estimated to result in 6.4% of all asthmatics, and 11.6% of asthmatic children 

experiencing an SO2 exposure ≥ 200 ppb (Figure 8-19). Finally, we consider the results of the 

St. Louis risk assessment. This assessment indicates that given a 150 ppb standard, the estimated 

median percentage of exposed asthmatics at elevated ventilation rates estimated to experience at 

least one ≥ 100% increase in sRaw per year ranges from 2.9% to 3.6% (and 4.6% to 5.4% for 

asthmatic children). Several aspects of these assessment results raise questions as to the 

sufficiency of the protection that would be provided by a standard set at this level, when 

compared to similar standards at or below 75 ppb. 

10.5.4.1 Conclusions regarding level 

Staff concludes that the health evidence and the air quality, exposure, and risk 

information presented above most strongly support consideration of 99th percentile 1-hour daily 

maximum standards in the range of 50- 75 ppb. However, if significant weight is placed on the 

uncertainties in the epidemiologic and controlled human exposure evidence, levels up to 150 ppb 

could be considered, recognizing the questions that would be raised by levels at the higher end of 

this range. Staff recognizes that selecting an appropriate level that will protect public health with 

an adequate margin of safety will be based on the relative weight given to different types of 

information from the air quality, exposure, and risk assessment, as well as to the evidence, and 

the uncertainties associated with the evidence and assessments. 

10.5.4.2 Implications for the Current SO2 Standards 

 Finally, staff recognizes that the particular level selected for a new 1-hour daily 

maximum standard will have implications for reaching decisions on whether to retain or revoke 

the current 24-hour and annual standards. That is, with respect to SO2-induced respiratory 

morbidity, the lower the level selected for a 99th percentile 1-hour daily maximum standard, the 

less additional public health protection the current standards would be expected to provide. As 
July 2009 397

an initial consideration, we note that all 99th percentile 1-hour daily maximum SO2 standard 

levels being considered (i.e. 50 – 150 ppb) are expected to prevent ambient SO2 concentrations 

in the 40 counties analyzed in the air quality analysis from exceeding the levels of the current 24-

hour and annual standards (Tables 10-3 and 10-4). Moreover, Table 10-6 demonstrates that given 

any of the potential alternative 1-hour daily maximum standards in this range, there would be 

counties in the U.S. expected to have air quality above the level of that standard. However, this 

does not rule out the possibility that the current standards could still offer some degree of 

additional protection in some parts of the country not currently monitoring for SO2. 

 Based on these considerations, staff finds it reasonable to conclude that if a new 99th

percentile 1-hour daily maximum standard is selected with a level from the upper end of the 

range that staff has identified for consideration, then in addition to setting a 99th percentile 1-

hour daily maximum standard, consideration should also be given to retaining the existing 24-

hour and/or annual standards. However, if the selected level of a 99th percentile 1-hour daily 

maximum standard is in the lower end of the range, it could reasonably be concluded that 

consideration should be given to revoking the current 24-hour and/or annual NAAQS. 

10.6 KEY OBSERVATIONS 

 The following observations reflect staff’s views and conclusions: 

 The scientific evidence and the risk and exposure information call into question the 

adequacy of the current standards to protect public health with an adequate margin of 

safety. 

 In considering potential alternative standards, SO2 remains the most appropriate indicator 

ambient SOx. 

 A 1-hour daily maximum standard, set at an appropriate level, can provide adequate 

protection against the range of health outcomes associated with averaging times from 5-

minutes to 24-hours. 

 Consideration should be given primarily to establishing a new 1-hour daily maximum 

standard with a 99th percentile or 4th highest daily maximum form. 

 The health evidence and the air quality, exposure, and risk information presented above 

most strongly support consideration of 99th percentile (or 4th highest) 1-hour daily 

maximum standards in the range of 50- 75 ppb. Consideration should also be given to 

standard levels above this range, up to 150 ppb, to the extent that significant weight is 

placed on the uncertainties in the epidemiologic and controlled human exposure 

evidence, recognizing the questions that would be raised by levels at the higher end of 

this range. 
July 2009 398

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A-1

Appendix A: Supplement to the SO2 Air Quality 

Characterization 
A-2

Overview 

This appendix contains supplementary information on the SO2 ambient monitoring data 

used in the air quality characterization described in Chapter 7 of the SO2 REA. Included in this 

appendix are spatial and temporal attributes important for understanding the relationship between 

the ambient monitor and those sources affecting air quality measurements. 

In section A.1, important spatial characteristics described include the physical locations 

of the ambient monitors (e.g., U.S. states, counties, territories, and cities). Temporal attributes of 

interest include, for example, the number of samples collected, sample averaging times, and 

years of monitoring data available. Attributes of the monitors that reported both the 5-minute 

maximum and the 1-hour SO2 concentrations are given in Tables A.1-1 and A.1-2, while the 

supplemental characteristics of the broader ambient monitoring network are given in Table A.1-3 

and A.1-4. The method for calculating the proximity of the ambient monitors follows, along 

with the distance and emission results summarized in Table A.1-5. 

Section A.2 details the analyses performed on simultaneous concentrations, some of 

which are the result of co-located monitoring instruments, others the result of duplicate 

reporting. Simultaneous measurements were identified by staff using monitor IDs and multiple 

concentrations present given the hour-of-day on each available date. Staff estimated a relative 

percent difference between the simultaneous measurements at each monitor. 

Section A-3 has the tables summarizing the COV and GSD peak-to-mean ratio (PMRs). 

Section A-4 has tables summarizing the individual factors used in adjusting ambient air quality 

to just meet the current and potential alternative SO2 air quality standards. Section A-5 

summarizes measured 1-hour concentrations and number of days per year with air quality 

benchmark exceedances occurring at the 98 monitors reporting 5-minute maximum SO2

concentrations. 
A-3

A.1 Spatial and Temporal Attributes of Ambient SO2 Monitors 
A-4

Table A.1-1. Meta-data for 98 ambient monitors reporting 5-minute maximum and corresponding 1concentrations. 

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 He

(

AR Pulaski 051190007 34.756111 -92.275833 POP URB COM NEI AR Pulaski 051191002 34.830556 -92.259444 HIC RUR FOR NEI AR Union 051390006 33.215 -92.668889 UNK URB COM CO Denver 080310002 39.75119 -104.98762 HIC URB COM NEI DC District of Columbia 110010041 38.897222 -76.952778 POP URB RES NEI DE New Castle 100031008 39.577778 -75.611111 UNK RUR AGR FL Nassau 120890005 30.658333 -81.463333 HIC SUB IND NEI IA Cerro Gordo 190330018 43.16944 -93.202426 UNK SUB RES IA Clinton 190450019 41.823283 -90.211982 UNK URB IND MID IA Muscatine 191390016 41.419429 -91.070975 UNK URB RES IA Muscatine 191390017 41.387969 -91.054504 UNK SUB IND IA Muscatine 191390020 41.407796 -91.062646 UNK SUB IND IA Scott 191630015 41.530011 -90.587611 HIC URB RES NEI IA Van Buren 191770005 40.689167 -91.994444 UNK RUR FOR IA Van Buren 191770006 40.695078 -92.006318 GEN RUR FOR IA Woodbury 191930018 42.399444 -96.355833 POP URB RES LA West Baton Rouge 221210001 30.501944 -91.209722 HIC SUB COM MO Buchanan 290210009 39.731389 -94.8775 GEN URB IND NEI MO Buchanan 290210011 39.731389 -94.868333 GEN URB IND NEI MO Greene 290770026 37.128333 -93.261667 POP SUB RES MO Greene 290770037 37.11 -93.251944 POP RUR RES MO Iron 290930030 37.466389 -90.69 SRC RUR RES NEI MO Iron 290930031 37.519444 -90.7125 UNK RUR AGR MO Jefferson 290990004 38.2633 -90.3785 POP RUR IND MO Jefferson 290990014 38.267222 -90.379444 OTH RUR RES NEI MO Jefferson 290990017 38.252778 -90.393333 UNK SUB RES MO Jefferson 290990018 38.297694 -90.384333 HIC SUB RES NEI MO Monroe 291370001 39.473056 -91.789167 UNK RUR UNK MO Pike 291630002 39.3726 -90.9144 HIC RUR RES NEI MO Saint Charles 291830010 38.579167 -90.841111 UNK RUR AGR 
A-5

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 He

(

MO Saint Charles 291831002 38.8725 -90.226389 UNK RUR AGR MT Yellowstone 301110066 45.788318 -108.459536 SRC RUR RES NEI 3MT Yellowstone 301110079 45.769439 -108.574292 POP SUB COM 4MT Yellowstone 301110080 45.777149 -108.47436 UNK RUR AGR MT Yellowstone 301110082 45.783889 -108.515 POP URB COM NEI MT Yellowstone 301110083 45.795278 -108.455833 SRC SUB AGR MT Yellowstone 301110084 45.831453 -108.449964 POP SUB RES NEI 4MT Yellowstone 301112008 45.786389 -108.523056 UNK URB RES NC Forsyth 370670022 36.110556 -80.226667 POP URB RES NEI NC New Hanover 371290006 34.268403 -77.956529 GEN RUR IND URB ND Billings 380070002 46.8943 -103.37853 GEN RUR AGR REG 1ND Billings 380070003 46.9619 -103.356699 HIC RUR IND URB ND Burke 380130002 48.9904 -102.7815 SRC RUR AGR REG ND Burke 380130004 48.64193 -102.4018 REG RUR AGR REG ND Burleigh 380150003 46.825425 -100.76821 POP SUB RES URB ND Cass 380171003 46.910278 -96.795 POP SUB RES URB ND Cass 380171004 46.933754 -96.85535 POP SUB AGR URB ND Dunn 380250003 47.3132 -102.5273 GEN RUR AGR REG ND McKenzie 380530002 47.5812 -103.2995 GEN RUR AGR REG ND McKenzie 380530104 47.575278 -103.968889 SRC RUR AGR URB ND McKenzie 380530111 47.605556 -104.017222 SRC RUR IND URB ND Mercer 380570001 47.258853 -101.783035 POP SUB RES NEI ND Mercer 380570004 47.298611 -101.766944 POP RUR AGR URB ND Morton 380590002 46.84175 -100.870059 SRC SUB IND NEI ND Morton 380590003 46.873075 -100.905039 SRC SUB IND NEI ND Oliver 380650002 47.185833 -101.428056 SRC RUR AGR URB ND Steele 380910001 47.599703 -97.899009 GEN RUR AGR REG ND Williams 381050103 48.408834 -102.90765 SRC RUR IND URB ND Williams 381050105 48.392644 -102.910233 SRC RUR IND URB PA Allegheny 420030002 40.500556 -80.071944 POP SUB RES NEI PA Allegheny 420030021 40.413611 -79.941389 POP SUB RES NEI PA Allegheny 420030031 40.443333 -79.990556 POP URB COM NEI PA Allegheny 420030032 40.414444 -79.942222 UNK SUB RES 
A-6

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 He

(

PA Allegheny 420030064 40.323611 -79.868333 POP SUB RES NEI PA Allegheny 420030067 40.381944 -80.185556 GEN RUR RES NEI PA Allegheny 420030116 40.473611 -80.077222 POP SUB RES NEI PA Allegheny 420031301 40.4025 -79.860278 HIC SUB RES NEI PA Allegheny 420033003 40.318056 -79.881111 POP SUB IND PA Allegheny 420033004 40.305 -79.888889 UNK SUB RES PA Beaver 420070002 40.56252 -80.503948 REG RUR AGR REG PA Beaver 420070005 40.684722 -80.359722 POP RUR AGR URB PA Berks 420110009 40.320278 -75.926667 HIC SUB RES NEI PA Cambria 420210011 40.309722 -78.915 HIC URB COM NEI PA Erie 420490003 42.14175 -80.038611 HIC SUB COM NEI PA Philadelphia 421010022 39.916667 -75.188889 HIC URB IND NEI PA Philadelphia 421010048 39.991389 -75.080833 UNK RUR RES PA Philadelphia 421010136 39.9275 -75.222778 POP URB RES NEI PA Warren 421230003 41.857222 -79.1375 HIC SUB RES NEI PA Warren 421230004 41.844722 -79.169722 HIC RUR FOR NEI PA Washington 421250005 40.146667 -79.902222 POP SUB COM NEI PA Washington 421250200 40.170556 -80.261389 POP SUB RES NEI PA Washington 421255001 40.445278 -80.420833 REG RUR AGR REG SC Barnwell 450110001 33.320344 -81.465537 SRC RUR FOR URB 3SC Charleston 450190003 32.882289 -79.977538 POP URB COM NEI 4SC Charleston 450190046 32.941023 -79.657187 SRC RUR FOR REG SC Georgetown 450430006 33.362014 -79.294251 SRC URB IND NEI 2SC Greenville 450450008 34.838814 -82.402918 POP URB COM NEI SC Lexington 450630008 34.051017 -81.15495 SRC SUB COM NEI 3SC Oconee 450730001 34.805261 -83.2377 REG RUR FOR REG 4SC Richland 450790007 34.093959 -80.962304 OTH SUB COM NEI SC Richland 450790021 33.81468 -80.781135 GEN RUR FOR URB 4SC Richland 450791003 34.024497 -81.036248 POP URB COM MID UT Salt Lake 490352004 40.736389 -112.210278 HIC RUR IND WV Wayne 540990002 38.39186 -82.583923 POP RUR IND NEI WV Wayne 540990003 38.390278 -82.585833 HIC RUR RES NEI WV Wayne 540990004 38.380278 -82.583889 HIC RUR RES NEI 
A-7

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 He

(

WV Wayne 540990005 38.372222 -82.588889 HIC RUR RES NEI WV Wood 541071002 39.323533 -81.552367 POP SUB IND URB 

Notes: 

1 Objectives are POP=Population Exposure; HIC=Highest Concentration; SRC=Source Oriented; GEN=General/Backgro

Transport; OTH=Other; UNK=Unknown 2 Settings are R=Rural; U=Urban and Center City; S=Suburban 3 Land Uses are AGR=Agricultural; COM=Commercial; IND=Industrial; FOR=Forest; RES=Residential; UNK=U

4 Scales are NEI=Neighborhood; MID=Middle; URB=URBAN; REG=Regional 

1 2 
A-8

Table A.1-2. Population density, concentration variability, and total SO2 emissions associated with 

monitors reporting 5-minute maximum and corresponding 1-hour SO2 concentrations. 

Population Residing Within: Analysis Bins 

State County Monitor ID 5 km 10 km 15 km 20 km Population1 COV2 GSD3AR Pulaski 051190007 67784 178348 270266 334649 hi a a AR Pulaski 051191002 45800 109372 230200 310362 mid a a AR Union 051390006 21877 29073 32652 36340 mid b a CO Denver 080310002 189782 574752 1158644 1608099 hi b b DE New Castle 100031008 5386 80025 192989 391157 low b b DC District of Columbia 110010041 216129 813665 1461563 2029936 hi a a FL Nassau 120890005 17963 21386 38521 48316 mid c b IA Cerro Gordo 190330018 21247 30341 39284 45105 mid c c IA Clinton 190450019 24561 37638 42404 45947 mid b c IA Muscatine 191390016 20360 27101 31886 40248 mid b b IA Muscatine 191390017 11109 27101 31696 36604 mid b b IA Muscatine 191390020 20360 27101 31886 40290 mid c c IA Scott 191630015 90863 201277 268535 293627 hi b c IA Van Buren 191770005 994 2252 3764 6984 low b b IA Van Buren 191770006 994 2252 3764 6984 low a b IA Woodbury 191930018 4449 44815 92956 112802 low b c LA West Baton Rouge 221210001 21249 137455 239718 366741 mid b b MO Buchanan 290210009 23253 72613 87121 93365 mid c b MO Buchanan 290210011 28224 75073 86317 93365 mid b b MO Greene 290770026 41036 146752 224445 256158 mid c b MO Greene 290770037 21784 110681 210953 254437 mid c b MO Iron 290930030 1121 1121 4507 8447 low c c MO Iron 290930031 0 3799 6585 8436 low c b MO Jefferson 290990004 15049 33379 64516 124301 mid c c MO Jefferson 290990014 11967 35082 61963 125932 mid c b MO Jefferson 290990017 19711 36471 60199 116882 mid c b MO Jefferson 290990018 12258 41709 79196 170110 mid c b MO Monroe 291370001 0 1439 2093 5612 low a a MO Pike 291630002 645 2077 6916 11249 low b b MO Saint Charles 291830010 2637 6349 34541 90953 low b b MO Saint Charles 291831002 4587 95765 273147 431484 low b b 
A-9

Population Residing Within: Analysis Bins 

State County Monitor ID 5 km 10 km 15 km 20 km Population1 COV2 GSD3MT Yellowstone 301110066 27389 79644 98733 107178 mid b c MT Yellowstone 301110079 61645 89282 102887 114640 hi b a MT Yellowstone 301110080 33774 86065 104825 108399 mid b b MT Yellowstone 301110082 58256 94753 103200 106046 hi b a MT Yellowstone 301110083 27620 76641 98733 109475 mid b b MT Yellowstone 301110084 22577 59919 97912 110980 mid b b MT Yellowstone 301112008 61335 95574 103200 106046 hi b b NC Forsyth 370670022 61669 170320 258102 325974 hi b b NC New Hanover 371290006 17957 83529 145330 170260 mid c c ND Billings 380070002 0 0 1887 1887 low a a ND Billings 380070003 0 888 1887 1887 low a a ND Burke 380130002 0 0 0 625 low b b ND Burke 380130004 655 655 655 655 low b b ND Burleigh 380150003 49591 67377 83082 84415 mid b a ND Cass 380171003 48975 134561 144878 154455 mid b a ND Cass 380171004 2118 91149 145789 148002 low a b ND Dunn 380250003 0 0 0 537 low a a ND McKenzie 380530002 0 596 596 596 low a a ND McKenzie 380530104 0 521 521 2283 low b a ND McKenzie 380530111 0 0 2283 5771 low c a ND Mercer 380570001 3280 3280 5902 6465 low b b ND Mercer 380570004 3280 4428 5902 7455 low b a ND Morton 380590002 17925 67959 75685 84415 mid c c ND Morton 380590003 10305 31348 75685 82584 mid b b ND Oliver 380650002 0 0 2057 2670 low b b ND Steele 380910001 0 934 934 934 low a a ND Williams 381050103 0 1259 1259 1827 low c b ND Williams 381050105 0 1259 1259 1827 low b c PA Allegheny 420030002 83332 277442 651551 961378 hi b b PA Allegheny 420030021 170777 560187 921490 1142754 hi b b PA Allegheny 420030031 183843 580429 877668 1145039 hi a b PA Allegheny 420030032 174072 558904 922097 1144558 hi b b PA Allegheny 420030064 64846 201143 520438 943781 hi b c 
A-10

Population Residing Within: Analysis Bins 

State County Monitor ID 5 km 10 km 15 km 20 km Population1 COV2 GSD3PA Allegheny 420030067 13277 86792 324154 610975 mid a b PA Allegheny 420030116 96820 331624 704601 996267 hi b b PA Allegheny 420031301 115432 411867 766188 1088115 hi b b PA Allegheny 420033003 55221 202092 509708 944188 hi b c PA Allegheny 420033004 38588 170065 461433 904760 mid b b PA Beaver 420070002 3434 28961 68617 120780 low b b PA Beaver 420070005 17292 77240 143738 224631 mid b c PA Berks 420110009 121330 203799 250610 309553 hi a b PA Cambria 420210011 50440 79710 102905 124592 hi a b PA Erie 420490003 81199 150626 190212 209983 hi b b PA Philadelphia 421010022 316944 985213 1726387 2446142 hi a b PA Philadelphia 421010048 262592 1102727 1938877 2607877 hi b b PA Philadelphia 421010136 382995 985957 1718068 2381173 hi b b PA Warren 421230003 14142 19940 25715 32490 mid b b PA Warren 421230004 13965 18884 28805 33523 mid b c PA Washington 421250005 31276 68512 111222 183285 mid a b PA Washington 421250200 32125 52910 83324 118188 mid b b PA Washington 421255001 1359 15854 43364 126091 low b b SC Barnwell 450110001 0 4022 13647 21554 low a a SC Charleston 450190003 40872 132716 273298 364953 mid b b SC Charleston 450190046 1103 1103 9529 22255 low b a SC Georgetown 450430006 10567 18215 22467 34357 mid b b SC Greenville 450450008 70221 173012 284047 379022 hi a a SC Lexington 450630008 42208 131361 257820 355854 mid b b SC Oconee 450730001 0 2260 11136 26182 low a a SC Richland 450790007 35872 121006 255135 353072 mid a a SC Richland 450790021 1666 4643 13324 33098 low b a SC Richland 450791003 87097 213836 300874 396116 hi a a UT Salt Lake 490352004 0 4074 35159 124394 low a a WV Wayne 540990002 17320 62645 124477 178576 mid a b WV Wayne 540990003 17320 59989 123349 177744 mid b b WV Wayne 540990004 16553 54251 122072 179815 mid b b WV Wayne 540990005 13314 48330 114824 173807 mid b b 
A-11

Population Residing Within: Analysis Bins 

State County Monitor ID 5 km 10 km 15 km 20 km Population1 COV2 GSD3WV Wood 541071002 24917 70324 104458 128127 mid b b Notes: 1 Population bins: low (≤10,000); mid (10,001 to 50,000); hi (>50,000) using population within 5 km of ambient monitor. 2 COV bins: a (≤100%); b (>100 to ≤200); c (>200). 3 GSD bins: a (≤2.17); b (>2.17 to ≤2.94); c (>2.94). 4 Sum of emissions within 20 km radius of ambient monitor based on 2002 NEI. 
A-12

Table A.1-3. Meta-data for 809 ambient monitors in the broader SO2 monitoring network. 

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeAL Colbert 010330044 34.690556 -87.821389 UNK RUR AGR AL Jackson 010710020 34.876944 -85.720833 UNK RUR AGR AL Jefferson 010731003 33.485556 -86.915 HIC SUB RES NEI AL Lawrence 010790003 34.589571 -87.109445 UNK RUR AGR URB AL MOB 010970028 30.958333 -88.028333 HIC SUB IND NEI AL MOB 010972005 30.474674 -88.14114 POP RUR AGR NEI AL Montgomery 011011002 32.40712 -86.256367 HIC SUB COM NEI AZ Gila 040070009 33.399135 -110.858896 SRC URB RES AZ Gila 040071001 33.006179 -110.785797 SRC URB IND AZ Maricopa 040130019 33.48385 -112.14257 UNK SUB RES AZ Maricopa 040133002 33.45793 -112.04601 HIC URB RES NEI 1AZ Maricopa 040133003 33.47968 -111.91721 POP SUB RES NEI 5AZ Pima 040191011 32.208333 -110.872222 POP SUB RES NEI AZ Pinal 040212001 32.600479 -110.633598 POP SUB RES AR Pulaski 051190007 34.756111 -92.275833 POP URB COM NEI AR Pulaski 051191002 34.830556 -92.259444 HIC RUR FOR NEI AR Union 051390006 33.215 -92.668889 UNK URB COM CA Alameda 060010010 37.7603 -122.1925 POP SUB RES NEI CA Contra Costa 060130002 37.936 -122.0262 SRC SUB RES NEI 8CA Contra Costa 060130006 37.9478 -122.3651 UNK URB IND NEI 8CA Contra Costa 060130010 38.0313 -122.1318 POP URB COM NEI CA Contra Costa 060131001 38.055556 -122.219722 SRC SUB IND CA Contra Costa 060131002 38.010556 -121.641389 UNK RUR AGR CA Contra Costa 060131003 37.964167 -122.339167 UNK URB COM CA Contra Costa 060131004 37.96028 -122.35667 POP URB COM 2CA Contra Costa 060132001 38.013056 -122.133611 UNK URB RES CA Contra Costa 060133001 38.029167 -121.902222 HIC URB RES NEI CA Imperial 060250005 32.676111 -115.483333 UNK SUB RES CA Los Angeles 060371002 34.17605 -118.31712 UNK URB COM CA Los Angeles 060371103 34.06659 -118.22688 UNK URB RES 1CA Los Angeles 060374002 33.82376 -118.18921 POP SUB RES NEI 
A-13

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeCA Los Angeles 060375001 33.92288 -118.37026 POP URB COM NEI CA Los Angeles 060375005 33.9508 -118.43043 UPW SUB RES NEI CA Orange 060591003 33.67464 -117.92568 UNK SUB RES MID CA Riverside 060658001 33.99958 -117.41601 POP SUB RES NEI CA Sacramento 060670002 38.712778 -121.38 UNK SUB RES CA Sacramento 060670006 38.614167 -121.366944 HIC SUB RES NEI CA San Bernardino 060710012 34.426111 -117.563056 UNK RUR COM CA San Bernardino 060710014 34.5125 -117.33 UNK SUB RES CA San Bernardino 060710306 34.51 -117.330556 UNK SUB RES CA San Bernardino 060711234 35.763889 -117.396111 OTH RUR DES CA San Bernardino 060712002 34.10002 -117.49201 POP SUB IND NEI CA San Bernardino 060714001 34.418056 -117.284722 UNK SUB RES CA San Diego 060730001 32.631231 -117.059075 POP SUB RES NEI CA San Diego 060731007 32.709172 -117.153975 POP URB COM NEI CA San Diego 060732007 32.552164 -116.937772 POP RUR MOB NEI CA San Francisco 060750005 37.766 -122.3991 UNK URB IND CA San Luis Obispo 060791005 35.043889 -120.580278 UNK RUR COM CA San Luis Obispo 060792001 35.125 -120.633333 UNK SUB RES NEI CA San Luis Obispo 060792004 35.022222 -120.569444 UNK RUR IND CA San Luis Obispo 060794002 35.028333 -120.387222 POP RUR RES REG CA Santa Barbara 060830008 34.462222 -120.024444 POP RUR UNK REG CA Santa Barbara 060831012 34.451944 -120.457778 UNK RUR AGR REG CA Santa Barbara 060831013 34.725556 -120.427778 UNK RUR AGR NEI CA Santa Barbara 060831015 34.478056 -120.210833 UNK RUR AGR NEI CA Santa Barbara 060831016 34.477778 -120.205556 UNK RUR AGR NEI CA Santa Barbara 060831019 34.475278 -120.188889 UNK RUR AGR NEI CA Santa Barbara 060831020 34.415278 -119.878611 UNK RUR AGR NEI CA Santa Barbara 060831025 34.489722 -120.045833 UNK RUR AGR NEI CA Santa Barbara 060831026 34.479444 -120.0325 UNK RUR AGR NEI CA Santa Barbara 060831027 34.469167 -120.039444 UNK RUR AGR NEI CA Santa Barbara 060832004 34.6375 -120.456389 POP URB COM NEI CA Santa Barbara 060832011 34.445278 -119.827778 POP SUB RES NEI CA Santa Barbara 060834003 34.596111 -120.630278 UNK RUR AGR NEI 
A-14

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeCA Santa Cruz 060870003 37.011944 -122.193333 UNK RUR RES CA Solano 060950001 38.052222 -122.144722 UNK URB COM CA Solano 060950004 38.1027 -122.2382 UNK URB COM CA Ventura 061113001 34.255 -119.1425 HIC RUR RES NEI CO Adams 080010007 39.8 -104.910833 POP URB RES NEI CO Adams 080013001 39.83818 -104.94984 POP RUR AGR NEI CO Denver 080310002 39.75119 -104.98762 HIC URB COM NEI CO El Paso 080416001 38.633611 -104.715556 UNK RUR IND CO El Paso 080416004 38.921389 -104.8125 UNK URB RES CO El Paso 080416011 38.846667 -104.827222 UNK URB RES CO El Paso 080416018 38.811389 -104.751389 UNK URB COM CT Fairfield 090010012 41.195 -73.163333 HIC URB RES NEI CT Fairfield 090010017 41.003611 -73.585 UNK SUB RES CT Fairfield 090011123 41.399167 -73.443056 UNK SUB RES CT Fairfield 090012124 41.063056 -73.528889 HIC URB RES NEI CT Fairfield 090019003 41.118333 -73.336667 POP RUR FOR NEI CT Hartford 090031005 42.015833 -72.518056 POP RUR AGR REG CT Hartford 090031018 41.760833 -72.670833 POP URB COM NEI CT Hartford 090032006 41.7425 -72.634444 HIC SUB IND NEI CT New Haven 090090027 41.301111 -72.902778 POP URB COM NEI 3.CT New Haven 090091003 41.310556 -72.915556 UNK SUB IND CT New Haven 090091123 41.310833 -72.916944 HIC URB RES NEI CT New Haven 090092123 41.550556 -73.043611 POP URB MOB NEI CT New London 090110007 41.361111 -72.08 UNK SUB RES CT Tolland 090130003 41.73 -72.213611 UNK SUB COM NEI DE New Castle 100031003 39.761111 -75.491944 HIC SUB RES NEI DE New Castle 100031007 39.551111 -75.730833 UNK RUR AGR DE New Castle 100031008 39.577778 -75.611111 UNK RUR AGR DE New Castle 100031013 39.773889 -75.496389 POP SUB RES DE New Castle 100032002 39.757778 -75.546389 POP URB COM NEI DE New Castle 100032004 39.739444 -75.558056 UNK URB COM DC 

District of Columbia 110010041 38.897222 -76.952778 POP URB RES NEI 
A-15

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeFL Broward 120110010 26.128611 -80.167222 HIC SUB RES NEI FL Duval 120310032 30.356111 -81.635556 HIC SUB COM NEI FL Duval 120310080 30.308889 -81.6525 HIC SUB COM MID FL Duval 120310081 30.422222 -81.621111 HIC SUB RES MID FL Duval 120310097 30.367222 -81.594167 POP SUB COM NEI FL Escambia 120330004 30.525 -87.204167 POP SUB IND NEI FL Escambia 120330022 30.544722 -87.216111 HIC SUB COM NEI FL Hamilton 120470015 30.411111 -82.783611 UNK RUR IND FL Hillsborough 120570021 27.947222 -82.453333 HIC RUR RES NEI FL Hillsborough 120570053 27.886389 -82.481389 POP SUB RES NEI FL Hillsborough 120570081 27.739722 -82.465278 UNK UNK UNK FL Hillsborough 120570095 27.9225 -82.401389 HIC SUB COM NEI FL Hillsborough 120570109 27.856389 -82.383667 POP SUB COM NEI FL Hillsborough 120571035 27.928056 -82.454722 POP SUB RES NEI FL Hillsborough 120574004 27.9925 -82.125833 HIC SUB RES NEI FL Manatee 120813002 27.632778 -82.546111 POP RUR IND NEI FL Miami-Dade 120860019 25.8975 -80.38 POP UNK UNK NEI FL Nassau 120890005 30.658333 -81.463333 HIC SUB IND NEI FL Nassau 120890009 30.686389 -81.4475 HIC SUB RES NEI FL Orange 120952002 28.599444 -81.363056 HIC URB COM NEI FL Palm Beach 120993004 26.369722 -80.074444 HIC SUB COM NEI 1FL Pinellas 121030023 27.863333 -82.623333 POP RUR IND NEI FL Pinellas 121033002 27.871389 -82.691667 HIC SUB COM NEI FL Pinellas 121035002 28.09 -82.700833 HIC RUR RES NEI FL Pinellas 121035003 28.141667 -82.739722 HIC SUB RES NEI FL Polk 121050010 27.856111 -82.017778 HIC RUR IND NEI FL Polk 121052006 27.896944 -81.960278 HIC SUB IND NEI FL Putnam 121071008 29.6875 -81.656667 HIC RUR IND NEI FL Sarasota 121151002 27.299722 -82.524444 HIC SUB RES NEI FL Sarasota 121151005 27.306944 -82.570556 POP SUB RES URB FL Sarasota 121151006 27.350278 -82.48 POP SUB RES NEI GA Baldwin 130090001 33.153258 -83.235807 SRC RUR RES NEI GA Bartow 130150002 34.103333 -84.915278 POP SUB AGR REG 
A-16

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeGA Bibb 130210012 32.805244 -83.543628 POP RUR IND URB GA Chatham 130510019 32.093889 -81.151111 HIC SUB IND URB GA Chatham 130510021 32.06905 -81.048949 SRC SUB COM NEI 1GA Chatham 130511002 32.090278 -81.130556 POP URB IND NEI GA Dougherty 130950006 31.567778 -84.102778 HIC SUB RES MID GA Fannin 131110091 34.985556 -84.375278 POP URB IND NEI GA Floyd 131150003 34.261113 -85.323018 POP RUR RES NEI GA Fulton 131210048 33.779189 -84.395843 HIC URB COM NEI GA Fulton 131210055 33.720428 -84.357449 POP SUB COM NEI GA Glynn 131270006 31.16953 -81.496046 POP SUB RES NEI GA Muscogee 132150008 32.521099 -84.944695 POP SUB RES NEI GA Richmond 132450003 33.393611 -82.006389 POP SUB IND NEI HI Honolulu 150030010 21.329167 -158.093333 SRC RUR IND HI Honolulu 150030011 21.337222 -158.119167 SRC RUR COM NEI HI Honolulu 150031001 21.310278 -157.858056 POP URB COM NEI 1HI Honolulu 150031006 21.3475 -158.113333 UNK RUR IND ID Bannock 160050004 42.916389 -112.515833 HIC RUR IND NEI ID Caribou 160290003 42.661298 -111.591443 POP URB RES NEI ID Caribou 160290031 42.695278 -111.593889 SRC RUR DES MIC ID Power 160770011 42.9125 -112.535556 SRC RUR IND IL Adams 170010006 39.93301 -91.404237 POP URB COM NEI IL Champaign 170190004 40.123796 -88.229531 POP SUB RES NEI IL Cook 170310050 41.70757 -87.568574 POP SUB IND NEI IL Cook 170310059 41.6875 -87.536111 HIC SUB IND NEI 1IL Cook 170310063 41.876969 -87.63433 POP URB MOB NEI IL Cook 170310064 41.790787 -87.601646 POP SUB RES NEI 1IL Cook 170310076 41.7514 -87.713488 POP SUB RES URB IL Cook 170311018 41.773889 -87.815278 HIC SUB IND NEI IL Cook 170311601 41.66812 -87.99057 POP SUB RES NEI IL Cook 170312001 41.662109 -87.696467 HIC SUB IND NEI IL Cook 170314002 41.855243 -87.75247 POP SUB RES NEI IL Cook 170314201 42.139996 -87.799227 POP SUB RES URB IL Cook 170318003 41.631389 -87.568056 POP SUB RES NEI 
A-17

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeIL DuPage 170436001 41.813049 -88.072827 POP SUB AGR NEI 1IL La Salle 170990007 41.293015 -89.049425 SRC SUB IND NEI IL Macon 171150013 39.866834 -88.925594 POP SUB IND NEI IL Macoupin 171170002 39.396075 -89.809739 POP RUR AGR REG IL Madison 171190008 38.890186 -90.148031 SRC SUB IND NEI 1IL Madison 171190017 38.701944 -90.149167 HIC URB MOB NEI IL Madison 171191010 38.828303 -90.058433 SRC SUB IND NEI IL Madison 171193007 38.860669 -90.105851 POP SUB IND NEI 1IL Madison 171193009 38.865984 -90.070571 SRC SUB COM NEI IL Peoria 171430024 40.68742 -89.606943 POP SUB COM NEI IL Randolph 171570001 38.176278 -89.788459 GEN RUR IND NEI IL Rock Island 171610003 41.511944 -90.514167 HIC URB COM NEI IL Saint Clair 171630010 38.612034 -90.160477 POP SUB IND NEI IL Saint Clair 171631010 38.592192 -90.165081 HIC SUB IND NEI IL Saint Clair 171631011 38.235 -89.841944 SRC RUR IND NEI IL Sangamon 171670006 39.800614 -89.591225 SRC SUB IND NEI IL Tazewell 171790004 40.55646 -89.654028 SRC SUB IND NEI IL Wabash 171850001 38.397222 -87.773611 HIC URB MOB NEI IL Wabash 171851001 38.369444 -87.834444 HIC RUR AGR NEI IL Will 171970013 41.459963 -88.182019 SRC RUR IND NEI 1IN Daviess 180270002 38.572778 -87.214722 HIC RUR AGR NEI IN Dearborn 180290004 39.092778 -84.855 HIC SUB COM NEI IN Floyd 180430004 38.367778 -85.833056 HIC RUR AGR NEI IN Floyd 180430007 38.273333 -85.836389 SRC RUR RES NEI IN Floyd 180431004 38.308056 -85.834167 POP SUB RES NEI IN Fountain 180450001 39.964167 -87.421389 HIC RUR AGR NEI IN Gibson 180510001 38.361389 -87.748611 HIC RUR AGR NEI IN Gibson 180510002 38.392778 -87.748333 HIC RUR AGR NEI IN Hendricks 180630001 39.876944 -86.473889 HIC RUR IND IN Hendricks 180630002 39.863361 -86.47075 HIC SUB COM IN Hendricks 180630003 39.880833 -86.542194 HIC SUB COM IN Jasper 180730002 41.187778 -87.053333 HIC RUR AGR NEI IN Jasper 180730003 41.135833 -86.987778 HIC RUR AGR URB 
A-18

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeIN Jefferson 180770004 38.776667 -85.407222 HIC SUB COM NEI IN Lake 180890022 41.606667 -87.304722 UNK URB IND IN Lake 180892008 41.639444 -87.493611 HIC SUB COM NEI IN LaPorte 180910005 41.716944 -86.9075 HIC URB IND NEI IN LaPorte 180910007 41.679722 -86.852778 HIC RUR RES NEI IN Marion 180970042 39.646254 -86.248784 POP RUR AGR URB IN Marion 180970054 39.730278 -86.196111 HIC URB IND NEI IN Marion 180970057 39.749019 -86.186314 HIC URB RES NEI IN Marion 180970072 39.768056 -86.16 POP URB COM MID IN Marion 180970073 39.789167 -86.060833 POP URB RES NEI IN Morgan 181091001 39.515 -86.391667 HIC SUB RES NEI IN Perry 181230006 37.99433 -86.763457 UNK RUR IND IN Perry 181230007 37.983773 -86.772202 UNK RUR IND IN Pike 181250005 38.519167 -87.249722 HIC RUR AGR NEI IN Porter 181270011 41.633889 -87.101389 HIC RUR IND NEI IN Porter 181270017 41.621944 -87.116389 HIC RUR IND NEI IN Porter 181270023 41.616667 -87.145833 HIC SUB IND NEI IN Spencer 181470002 37.9825 -86.96638 HIC RUR AGR NEI IN Spencer 181470010 37.95536 -87.0318 HIC RUR AGR NEI IN Sullivan 181530004 39.099444 -87.470556 HIC RUR AGR NEI IN Vanderburgh 181630012 38.021667 -87.569444 POP URB COM NEI IN Vanderburgh 181631002 37.9025 -87.671389 UNK RUR AGR IN Vigo 181670018 39.486111 -87.401389 POP URB RES NEI IN Vigo 181671014 39.514722 -87.407778 HIC RUR COM NEI IN Warrick 181730002 37.9375 -87.314167 HIC RUR IND NEI IN Warrick 181731001 37.938056 -87.345833 HIC RUR IND NEI IN Wayne 181770006 39.812222 -84.89 HIC SUB IND NEI IN Wayne 181770007 39.795833 -84.880833 HIC RUR IND NEI IA Cerro Gordo 190330018 43.16944 -93.202426 UNK SUB RES IA Clinton 190450018 41.824722 -90.212778 UNK SUB RES IA Clinton 190450019 41.823283 -90.211982 UNK URB IND MID IA Clinton 190450020 41.845833 -90.216389 HIC SUB COM URB IA Lee 191110006 40.392222 -91.4 UNK URB IND 
A-19

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeIA Lee 191111007 40.5825 -91.4275 UNK RUR IND 1IA Linn 191130028 41.910556 -91.651944 HIC SUB COM NEI IA Linn 191130029 41.974722 -91.666667 HIC URB COM NEI 1IA Linn 191130031 41.983333 -91.662778 SRC URB RES MID IA Linn 191130032 41.964722 -91.664722 UNK URB RES IA Linn 191130034 41.971111 -91.645278 UNK URB RES IA Linn 191130038 41.941111 -91.633889 SRC SUB IND MID 4IA Linn 191130039 41.934167 -91.6825 SRC URB IND IA Muscatine 191390016 41.419429 -91.070975 UNK URB RES IA Muscatine 191390017 41.387969 -91.054504 UNK SUB IND IA Muscatine 191390020 41.407796 -91.062646 UNK SUB IND IA Scott 191630015 41.530011 -90.587611 HIC URB RES NEI IA Scott 191630017 41.467236 -90.688451 UNK RUR IND NEI IA Van Buren 191770004 40.711111 -91.975278 HIC RUR FOR IA Van Buren 191770005 40.689167 -91.994444 UNK RUR FOR IA Van Buren 191770006 40.695078 -92.006318 GEN RUR FOR IA Woodbury 191930018 42.399444 -96.355833 POP URB RES KS Linn 201070002 38.135833 -94.731944 REG RUR AGR REG KS Montgomery 201250006 37.046944 -95.613333 POP URB RES NEI KS Pawnee 201450001 38.17625 -99.108028 POP SUB RES NEI KS Sedgwick 201730010 37.701111 -97.313889 POP URB RES NEI KS Sumner 201910002 37.476944 -97.366389 REG RUR RES REG KS Trego 201950001 38.770278 -99.763611 GEN RUR AGR REG KS Wyandotte 202090001 39.113056 -94.624444 HIC URB COM NEI 1KS Wyandotte 202090020 39.151389 -94.6175 POP URB IND NEI KS Wyandotte 202090021 39.1175 -94.635556 POP URB RES NEI KY Boyd 210190015 38.465833 -82.621111 POP URB RES NEI KY Boyd 210190017 38.459167 -82.640556 POP SUB RES NEI KY Boyd 210191003 38.388611 -82.6025 POP SUB IND NEI KY Campbell 210370003 39.065556 -84.451944 POP SUB RES NEI KY Campbell 210371001 39.108611 -84.476111 POP URB RES NEI KY Daviess 210590005 37.780833 -87.075556 POP SUB COM NEI KY Fayette 210670012 38.065 -84.5 POP SUB RES NEI 
A-20

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeKY Greenup 210890007 38.548333 -82.731667 POP SUB RES NEI KY Hancock 210910012 37.938889 -86.896944 POP RUR RES NEI KY Henderson 211010013 37.858889 -87.575278 POP SUB RES NEI KY Henderson 211010014 37.871389 -87.463333 POP RUR COM NEI KY Jefferson 211110032 38.1825 -85.861667 HIC SUB RES NEI KY Jefferson 211110051 38.060833 -85.896111 POP SUB RES NEI KY Jefferson 211111041 38.23163 -85.82672 POP SUB IND NEI KY Livingston 211390004 37.070833 -88.334167 HIC RUR AGR NEI KY McCracken 211450001 37.131667 -88.813333 HIC RUR IND NEI KY McCracken 211451024 37.058056 -88.5725 POP SUB COM NEI KY McCracken 211451026 37.040833 -88.541111 POP SUB RES NEI KY Warren 212270008 37.036667 -86.250556 POP RUR RES URB LA Bossier 220150008 32.53626 -93.74891 POP URB COM LA Calcasieu 220190008 30.261667 -93.284167 POP RUR IND NEI LA 

East Baton Rouge 220330009 30.46198 -91.17922 HIC URB COM NEI 

LA Ouachita 220730004 32.509713 -92.046093 GEN URB IND LA St. Bernard 220870002 29.981944 -89.998611 SRC SUB RES LA 

West Baton Rouge 221210001 30.501944 -91.209722 HIC SUB COM 

ME Androscoggin 230010011 44.089406 -70.214219 HIC URB COM NEI ME Aroostook 230030009 47.351667 -68.303611 UNK SUB RES ME Aroostook 230030012 47.354444 -68.314167 UNK URB IND ME Aroostook 230031003 47.351667 -68.311389 UNK SUB RES ME Aroostook 230031013 46.123889 -67.829722 UNK URB COM ME Aroostook 230031018 46.660899 -67.902066 SRC RUR IND NEI ME Cumberland 230050014 43.659722 -70.261389 HIC URB COM NEI ME Cumberland 230050027 43.661944 -70.265833 HIC URB IND NEI ME Oxford 230172007 44.543056 -70.545833 UNK SUB IND MD Allegany 240010006 39.649722 -78.762778 POP URB COM NEI MD Anne Arundel 240032002 39.159722 -76.511667 POP SUB RES NEI MD Baltimore 240053001 39.310833 -76.474444 POP SUB RES NEI MD Baltimore (City) 245100018 39.314167 -76.613333 POP URB RES NEI 
A-21

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeMD Baltimore (City) 245100036 39.265 -76.536667 HIC URB RES NEI MA Bristol 250051004 41.683279 -71.169171 HIC SUB COM NEI MA Essex 250090005 42.709444 -71.146389 HIC URB RES NEI MA Essex 250091004 42.515556 -70.931389 UNK SUB RES MA Essex 250091005 42.525 -70.934167 UNK SUB RES MA Essex 250095004 42.772222 -71.061111 OTH SUB RES MA Hampden 250130016 42.108581 -72.590614 POP URB COM NEI MA Hampden 250131009 42.085556 -72.579722 HIC SUB RES NEI MA Hampshire 250154002 42.298279 -72.333904 OTH RUR FOR URB MA Middlesex 250171701 42.474444 -71.111111 UNK SUB RES MA Middlesex 250174003 42.383611 -71.213889 POP RUR AGR NEI MA Suffolk 250250002 42.348873 -71.097163 HIC URB COM NEI MA Suffolk 250250019 42.316394 -70.967773 OTH RUR RES MA Suffolk 250250020 42.309417 -71.055573 OTH URB COM MA Suffolk 250250021 42.377833 -71.027138 HIC URB RES NEI MA Suffolk 250250040 42.340251 -71.03835 POP URB IND NEI MA Suffolk 250250042 42.3294 -71.0825 POP URB COM NEI MA Suffolk 250251003 42.401667 -71.031111 POP SUB RES NEI MA Worcester 250270020 42.267222 -71.798889 HIC URB COM NEI MA Worcester 250270023 42.263877 -71.794186 POP URB COM URB MI Delta 260410902 45.796667 -87.089444 UNK RUR IND MI Genesee 260490021 43.047224 -83.670159 POP URB RES NEI MI Genesee 260492001 43.168336 -83.461541 GEN RUR AGR MI Kent 260810020 42.984173 -85.671339 POP URB IND NEI MI Macomb 260991003 42.51334 -83.005971 POP SUB RES NEI MI Missaukee 261130001 44.310555 -84.891865 GEN RUR FOR MI St. Clair 261470005 42.953336 -82.456229 HIC SUB RES NEI MI Schoolcraft 261530001 46.288877 -85.950227 GEN RUR FOR MI Wayne 261630001 42.22862 -83.2082 POP SUB COM NEI MI Wayne 261630005 42.267231 -83.132086 HIC SUB IND NEI MI Wayne 261630015 42.302786 -83.10653 HIC URB COM NEI MI Wayne 261630016 42.357808 -83.096033 POP URB RES NEI MI Wayne 261630019 42.43084 -83.000138 POP SUB RES NEI 
A-22

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeMI Wayne 261630025 42.423063 -83.426263 POP SUB COM NEI MI Wayne 261630027 42.292231 -83.106807 HIC URB IND MID MI Wayne 261630033 42.306674 -83.148754 HIC SUB IND MID MI Wayne 261630062 42.340833 -83.0625 POP URB RES NEI MI Wayne 261630092 42.296111 -83.116944 HIC URB RES MID MN Anoka 270031002 45.13768 -93.20772 POP SUB RES URB 4.MN Carlton 270176316 46.733611 -92.418889 SRC RUR AGR MN Dakota 270370020 44.76323 -93.03255 UNK RUR IND NEI MN Dakota 270370423 44.77553 -93.06299 UNK RUR IND NEI 3.MN Dakota 270370439 44.748039 -93.043266 UNK RUR IND MN Dakota 270370441 44.7468 -93.02611 UNK RUR IND MN Dakota 270370442 44.73857 -93.00496 UNK RUR AGR NEI 3MN Hennepin 270530954 44.980995 -93.273719 HIC URB COM NEI MN Hennepin 270530957 45.021111 -93.281944 HIC URB IND MID 1MN Koochiching 270711240 48.605278 -93.402222 UNK URB IND 1MN Ramsey 271230864 44.991944 -93.183056 POP SUB RES NEI MN Sherburne 271410003 45.420278 -93.871667 UNK RUR AGR MN Sherburne 271410011 45.394444 -93.8975 UNK RUR IND NEI MN Sherburne 271410012 45.394444 -93.885 UNK URB MOB NEI MN Sherburne 271410013 45.369444 -93.898056 UNK RUR IND NEI MN Washington 271630436 44.84737 -92.9954 UNK SUB IND MID 4.MN Wright 271710007 45.329167 -93.835833 UNK RUR AGR MS Harrison 280470007 30.446806 -89.029139 HIC SUB RES NEI MS Hinds 280490018 32.296806 -90.188306 POP URB COM NEI MS Jackson 280590006 30.378425 -88.533985 POP URB COM NEI MS Lee 280810004 34.263333 -88.759722 UNK SUB COM MO Buchanan 290210009 39.731389 -94.8775 GEN URB IND NEI MO Buchanan 290210011 39.731389 -94.868333 GEN URB IND NEI MO Clay 290470025 39.183889 -94.4975 POP SUB RES NEI MO Greene 290770026 37.128333 -93.261667 POP SUB RES MO Greene 290770032 37.205278 -93.283333 UNK URB RES MO Greene 290770037 37.11 -93.251944 POP RUR RES MO Greene 290770040 37.108889 -93.252778 SRC SUB RES 
A-23

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeMO Greene 290770041 37.108611 -93.272222 SRC SUB RES MO Iron 290930030 37.466389 -90.69 SRC RUR RES NEI MO Iron 290930031 37.519444 -90.7125 UNK RUR AGR MO Jackson 290950034 39.104722 -94.570556 UNK URB COM MO Jefferson 290990004 38.2633 -90.3785 POP RUR IND MO Jefferson 290990014 38.267222 -90.379444 OTH RUR RES NEI MO Jefferson 290990017 38.252778 -90.393333 UNK SUB RES MO Jefferson 290990018 38.297694 -90.384333 HIC SUB RES NEI MO Monroe 291370001 39.473056 -91.789167 UNK RUR UNK MO Pike 291630002 39.3726 -90.9144 HIC RUR RES NEI MO Platte 291650023 39.3 -94.7 UNK SUB MOB MO Saint Charles 291830010 38.579167 -90.841111 UNK RUR AGR MO Saint Charles 291831002 38.8725 -90.226389 UNK RUR AGR MO Saint Louis 291890001 38.521667 -90.343611 POP SUB RES NEI MO Saint Louis 291890004 38.5325 -90.382778 POP SUB RES NEI MO Saint Louis 291890006 38.613611 -90.495833 UNK RUR RES MO Saint Louis 291890014 38.7109 -90.4759 HIC RUR RES NEI MO Saint Louis 291893001 38.641389 -90.345833 UNK SUB COM MO Saint Louis 291895001 38.766111 -90.285833 UNK SUB COM MO Saint Louis 291897002 38.727222 -90.379444 POP SUB RES NEI MO Saint Louis 291897003 38.720917 -90.367028 POP SUB RES NEI MO St. Louis City 295100007 38.5425 -90.263611 HIC URB RES NEI MO St. Louis City 295100072 38.624167 -90.198611 POP URB COM NEI 1MO St. Louis City 295100080 38.682778 -90.246667 UNK URB RES MO St. Louis City 295100086 38.672222 -90.238889 POP URB RES NEI MT Cascade 300132000 47.532222 -111.271111 SRC SUB AGR MT Cascade 300132001 47.53 -111.283611 SRC SUB IND NEI 3MT Jefferson 300430903 46.557679 -111.918098 UNK RUR AGR MT Jefferson 300430911 46.548056 -111.873333 UNK RUR AGR MT Jefferson 300430913 46.534722 -111.861389 UNK RUR AGR MT Lewis and Clark 300490702 46.583333 -111.934444 UNK RUR AGR MT Lewis and Clark 300490703 46.593889 -111.92 UNK RUR RES MT Rosebud 300870700 45.886944 -106.628056 UNK SUB RES 
A-24

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeMT Rosebud 300870701 45.901944 -106.637778 UNK RUR AGR MT Rosebud 300870702 45.863889 -106.557778 UNK RUR AGR MT Rosebud 300870760 45.668056 -106.518889 SRC RUR FOR MT Rosebud 300870761 45.603056 -106.464167 SRC RUR FOR MT Rosebud 300870762 45.648333 -106.556667 OTH RUR FOR MT Rosebud 300870763 45.976667 -106.660556 UNK RUR IND MT Yellowstone 301110016 45.656389 -108.765833 UNK RUR AGR MT Yellowstone 301110066 45.788318 -108.459536 SRC RUR RES NEI 3MT Yellowstone 301110079 45.769439 -108.574292 POP SUB COM 4MT Yellowstone 301110080 45.777149 -108.47436 UNK RUR AGR MT Yellowstone 301110082 45.783889 -108.515 POP URB COM NEI MT Yellowstone 301110083 45.795278 -108.455833 SRC SUB AGR MT Yellowstone 301110084 45.831453 -108.449964 POP SUB RES NEI 4MT Yellowstone 301111065 45.801944 -108.426111 UNK SUB RES MT Yellowstone 301112005 45.803889 -108.445556 UNK SUB IND MT Yellowstone 301112006 45.81 -108.413056 OTH SUB AGR MT Yellowstone 301112007 45.832778 -108.377778 OTH RUR RES NE Douglas 310550048 41.323889 -95.942778 HIC URB RES NEI NE Douglas 310550050 41.332778 -95.956389 HIC URB RES NEI NE Douglas 310550053 41.297778 -95.9375 POP URB IND NEI NE Douglas 310550055 41.362433 -95.976112 HIC SUB RES NEI NV Clark 320030022 36.390775 -114.90681 REG RUR IND NEI 3NV Clark 320030078 35.46505 -114.919615 REG RUR DES REG NV Clark 320030539 36.144444 -115.085556 POP SUB MOB URB 3NV Clark 320030601 35.978889 -114.844167 POP SUB COM NEI NH Cheshire 330050007 42.930556 -72.277778 UNK URB COM NEI NH Coos 330070019 44.488611 -71.180278 POP UNK UNK NEI NH Coos 330070022 44.458333 -71.154167 UNK RUR IND NH Coos 330071007 44.596667 -71.516667 POP URB IND NEI NH Hillsborough 330110016 42.992778 -71.459444 HIC URB COM NEI NH Hillsborough 330110019 43.000556 -71.468056 UNK URB COM NH Hillsborough 330110020 43.000556 -71.468056 UNK URB COM NEI NH Hillsborough 330111009 42.764444 -71.4675 HIC URB COM NEI 
A-25

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeNH Hillsborough 330111010 42.701944 -71.445 UNK SUB IND MIC NH Merrimack 330130007 43.206944 -71.534167 UNK URB COM NEI NH Merrimack 330131003 43.177222 -71.4625 UNK RUR RES NEI NH Merrimack 330131006 43.132444 -71.45827 OTH SUB RES NEI NH Merrimack 330131007 43.218491 -71.45827 OTH URB COM URB NH Rockingham 330150009 43.078056 -70.762778 UNK SUB COM NH Rockingham 330150014 43.075278 -70.748056 POP URB RES NEI NH Rockingham 330150015 43.0825 -70.761944 POP SUB COM NEI NH Sullivan 330190003 43.364444 -72.338333 UNK URB RES NEI NJ Atlantic 340010005 39.53024 -74.46069 UNK RUR RES NJ Bergen 340035001 40.88237 -74.04217 POP URB COM NEI NJ Burlington 340051001 40.07806 -74.85772 HIC URB COM NEI NJ Camden 340070003 39.92304 -75.09762 POP SUB RES NEI NJ Camden 340071001 39.68425 -74.86149 GEN RUR COM URB NJ Cumberland 340110007 39.42227 -75.0252 UNK RUR IND NJ Essex 340130011 40.726667 -74.144167 UNK URB IND NJ Essex 340130016 40.722222 -74.146944 POP URB IND NEI NJ Gloucester 340150002 39.80034 -75.21212 UNK RUR AGR NJ Hudson 340170006 40.67025 -74.12608 POP URB COM NEI NJ Hudson 340171002 40.73169 -74.06657 HIC URB COM NEI NJ Middlesex 340232003 40.50888 -74.2682 HIC URB COM NEI NJ Morris 340273001 40.78763 -74.6763 UNK RUR AGR NJ Union 340390003 40.66245 -74.21474 POP URB COM MID NJ Union 340390004 40.64144 -74.20836 HIC SUB IND NEI NM Dona Ana 350130008 31.930556 -106.630556 UNK RUR AGR NM Dona Ana 350130017 31.795833 -106.5575 SRC SUB COM URB NM Eddy 350151004 32.855556 -104.411389 SRC URB COM NEI NM Grant 350170001 32.759444 -108.131389 UNK SUB IND NM Grant 350171003 32.691944 -108.124444 SRC RUR IND NEI NM Hidalgo 350230005 31.783333 -108.497222 UNK RUR UNK NM San Juan 350450008 36.735833 -108.238333 UNK RUR DES NM San Juan 350450009 36.742222 -107.976944 SRC RUR IND NEI NM San Juan 350450017 36.752778 -108.716667 UNK RUR UNK 
A-26

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeNM San Juan 350451005 36.796667 -108.4725 UNK UNK UNK NY Albany 360010012 42.68069 -73.75689 HIC RUR AGR NEI NY Bronx 360050073 40.811389 -73.91 UNK URB RES 1NY Bronx 360050080 40.83608 -73.92021 HIC URB RES MID 1NY Bronx 360050083 40.86586 -73.88075 UNK URB COM NY Bronx 360050110 40.81616 -73.90207 OTH URB RES NY Chautauqua 360130005 42.29073 -79.58958 POP URB IND NY Chautauqua 360130006 42.49945 -79.31888 HIC URB IND NEI NY Chautauqua 360130011 42.29073 -79.58658 POP RUR AGR REG NY Chemung 360150003 42.11105 -76.80249 UNK URB COM NY Erie 360290005 42.87684 -78.80988 POP URB RES NEI NY Erie 360294002 42.99549 -78.90157 HIC SUB IND NEI NY Erie 360298001 42.818889 -78.840833 HIC URB IND NEI NY Essex 360310003 44.39309 -73.85892 GEN RUR FOR NEI NY Franklin 360330004 44.434309 -74.24601 GEN RUR COM NY Hamilton 360410005 43.44957 -74.51625 POP RUR COM URB NY Herkimer 360430005 43.68578 -74.98538 POP RUR FOR REG NY Kings 360470011 40.73277 -73.94722 HIC URB IND NEI 1NY Kings 360470076 40.67185 -73.97824 POP URB RES 1NY Madison 360530006 42.73046 -75.78443 POP RUR AGR REG NY Monroe 360551004 43.16545 -77.55479 POP SUB RES NEI NY Monroe 360551007 43.146198 -77.54813 POP URB RES NY Monroe 360556001 43.161 -77.60357 HIC URB COM NEI 1NY Nassau 360590005 40.74316 -73.58549 UNK SUB COM NEI NY New York 360610010 40.739444 -73.986111 HIC URB RES NEI 3NY New York 360610056 40.75917 -73.96651 HIC URB COM MID 1NY Niagara 360632008 43.08216 -79.00099 POP SUB IND NEI NY Onondaga 360671015 43.05238 -76.0592 POP SUB COM NEI NY Putnam 360790005 41.44151 -73.70762 UNK RUR FOR NY Queens 360810097 40.75527 -73.75861 GEN URB RES 1NY Queens 360810124 40.7362 -73.82317 POP SUB RES NY Rensselaer 360830004 42.78187 -73.46361 OTH RUR FOR NY Rensselaer 360831005 42.72444 -73.43166 GEN RUR FOR 
A-27

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeNY Richmond 360850067 40.59733 -74.12619 POP SUB RES NEI 2NY Schenectady 360930003 42.79963 -73.94019 POP SUB RES NEI NY Suffolk 361030002 40.74529 -73.41919 HIC SUB IND NEI NY Suffolk 361030009 40.8275 -73.05694 UNK SUB RES NY Ulster 361111005 42.1438 -74.49414 POP RUR COM URB NC Alexander 370030003 35.903611 -81.184167 GEN SUB COM URB NC Beaufort 370130003 35.3575 -76.779722 SRC RUR IND NEI NC Beaufort 370130004 35.377241 -76.748997 HIC RUR FOR NEI NC Beaufort 370130006 35.377778 -76.766944 SRC RUR IND NEI NC Chatham 370370004 35.757222 -79.159722 GEN RUR AGR MIC NC Cumberland 370511003 34.968889 -78.9625 POP SUB COM NEI NC Davie 370590002 35.809289 -80.559115 GEN SUB IND NC Duplin 370610002 34.954823 -77.960781 GEN URB RES NEI NC Edgecombe 370650099 35.988333 -77.582778 GEN RUR AGR REG NC Forsyth 370670022 36.110556 -80.226667 POP URB RES NEI NC Johnston 371010002 35.590833 -78.461944 GEN RUR AGR URB NC Lincoln 371090004 35.438556 -81.27675 GEN RUR RES NEI NC Martin 371170001 35.81069 -76.89782 GEN RUR AGR URB NC Mecklenburg 371190034 35.248611 -80.766389 POP SUB RES NEI NC Mecklenburg 371190041 35.2401 -80.785683 POP URB RES NEI NC New Hanover 371290002 34.364167 -77.838611 POP RUR AGR URB NC New Hanover 371290006 34.268403 -77.956529 GEN RUR IND URB NC Northampton 371310002 36.48438 -77.61998 SRC RUR COM URB NC Person 371450003 36.306965 -79.09197 GEN RUR AGR URB NC Pitt 371470099 35.583333 -77.598889 GEN RUR COM REG NC Swain 371730002 35.435509 -83.443697 GEN SUB RES NEI ND Billings 380070002 46.8943 -103.37853 GEN RUR AGR REG 12ND Billings 380070111 47.296667 -103.095556 HIC RUR IND NEI ND Burke 380130002 48.9904 -102.7815 SRC RUR AGR REG ND Burke 380130004 48.64193 -102.4018 REG RUR AGR REG ND Burleigh 380150003 46.825425 -100.76821 POP SUB RES URB ND Cass 380171003 46.910278 -96.795 POP SUB RES URB ND Cass 380171004 46.933754 -96.85535 POP SUB AGR URB 
A-28

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeND Dunn 380250003 47.3132 -102.5273 GEN RUR AGR REG ND McKenzie 380530002 47.5812 -103.2995 GEN RUR AGR REG ND McKenzie 380530104 47.575278 -103.968889 SRC RUR AGR URB ND McKenzie 380530111 47.605556 -104.017222 SRC RUR IND URB ND McLean 380550113 47.606667 -102.036389 POP RUR AGR URB ND Mercer 380570001 47.258853 -101.783035 POP SUB RES NEI ND Mercer 380570004 47.298611 -101.766944 POP RUR AGR URB ND Mercer 380570102 47.325 -101.765833 SRC RUR IND URB ND Mercer 380570118 47.371667 -101.780833 SRC RUR IND URB ND Mercer 380570123 47.385725 -101.862917 SRC RUR IND URB ND Mercer 380570124 47.400619 -101.92865 SRC RUR IND URB ND Morton 380590002 46.84175 -100.870059 SRC SUB IND NEI ND Morton 380590003 46.873075 -100.905039 SRC SUB IND NEI ND Oliver 380650002 47.185833 -101.428056 SRC RUR AGR URB ND Steele 380910001 47.599703 -97.899009 GEN RUR AGR REG ND Williams 381050103 48.408834 -102.90765 SRC RUR IND URB ND Williams 381050105 48.392644 -102.910233 SRC RUR IND URB OH Adams 390010001 38.795 -83.535278 POP SUB RES NEI OH Allen 390030002 40.772222 -84.051944 POP UNK AGR URB OH Ashtabula 390071001 41.959444 -80.5725 POP SUB RES URB OH Belmont 390133002 39.968056 -80.7475 POP SUB IND NEI OH Butler 390170004 39.383333 -84.544167 POP SUB COM NEI OH Butler 390171004 39.53 -84.3925 POP SUB COM NEI OH Clark 390230003 39.855556 -83.9975 POP RUR AGR NEI OH Clermont 390250021 38.961273 -84.09445 HIC URB RES URB OH Columbiana 390290016 40.634722 -80.546389 POP SUB RES NEI OH Columbiana 390290022 40.635 -80.546667 POP SUB COM MIC OH Columbiana 390292001 40.620278 -80.580833 POP URB COM NEI 2OH Cuyahoga 390350038 41.476944 -81.681944 HIC URB IND NEI OH Cuyahoga 390350045 41.471667 -81.657222 POP URB IND NEI OH Cuyahoga 390350060 41.493955 -81.678542 POP URB COM NEI OH Cuyahoga 390350065 41.446389 -81.661944 HIC URB RES NEI OH Cuyahoga 390356001 41.504722 -81.623889 POP SUB COM NEI 
A-29

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeOH Franklin 390490004 39.992222 -83.041667 HIC SUB COM NEI OH Franklin 390490034 40.0025 -82.994444 POP URB COM NEI OH Gallia 390530002 38.944167 -82.112222 POP SUB RES NEI 1OH Hamilton 390610010 39.214931 -84.690723 POP RUR IND NEI OH Hamilton 390612003 39.228889 -84.448889 HIC SUB IND NEI OH Jefferson 390810016 40.362778 -80.615556 POP URB COM NEI 1OH Jefferson 390810017 40.366104 -80.615002 HIC URB COM NEI OH Jefferson 390811001 40.321944 -80.606389 HIC URB IND MID OH Lake 390850003 41.673056 -81.4225 UNK SUB RES NEI OH Lake 390853002 41.7225 -81.241944 HIC SUB COM MID 1OH Lawrence 390870006 38.520278 -82.666667 POP SUB RES NEI OH Lorain 390930017 41.368056 -82.110556 POP URB COM NEI OH Lorain 390930026 41.471667 -82.143611 POP SUB IND NEI OH Lorain 390931003 41.365833 -82.108333 HIC URB COM NEI OH Lucas 390950008 41.663333 -83.476667 HIC URB IND NEI OH Lucas 390950024 41.644167 -83.546667 POP URB IND NEI OH Mahoning 390990009 41.098333 -80.651944 HIC URB COM NEI OH Mahoning 390990013 41.096111 -80.658611 GEN URB RES NEI OH Meigs 391051001 39.037778 -82.045556 POP SUB RES URB OH Montgomery 391130025 39.758333 -84.2 HIC URB COM NEI OH Morgan 391150003 39.631667 -81.673056 HIC RUR AGR URB OH Morgan 391150004 39.634221 -81.670038 SRC RUR AGR URB OH Scioto 391450013 38.754167 -82.9175 HIC SUB IND MID 1OH Scioto 391450020 38.609048 -82.822911 HIC RUR FOR NEI OH Scioto 391450022 38.588034 -82.834973 UPW RUR IND NEI OH Stark 391510016 40.827778 -81.378611 HIC SUB RES NEI OH Summit 391530017 41.063333 -81.468611 HIC SUB IND NEI OH Summit 391530022 41.080278 -81.516389 POP URB COM NEI OH Tuscarawas 391570003 40.516389 -81.476389 POP URB IND URB OH Tuscarawas 391570006 40.511416 -81.639149 POP RUR RES NEI 1OK Cherokee 400219002 35.85408 -94.985964 REG RUR RES NEI OK Kay 400710602 36.705328 -97.087656 UNK URB RES OK Kay 400719003 36.662778 -97.074444 POP RUR RES NEI 
A-30

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeOK Kay 400719010 36.956222 -97.03135 GEN RUR AGR NEI OK Mayes 400979014 36.228408 -95.249943 GEN RUR AGR NEI OK Muskogee 401010167 35.793134 -95.302235 SRC RUR COM NEI OK Oklahoma 401090025 35.553056 -97.623611 POP SUB RES URB OK Oklahoma 401091037 35.614131 -97.475083 POP SUB RES URB OK Ottawa 401159004 36.922222 -94.838889 UNK RUR RES NEI OK Tulsa 401430175 36.149877 -96.011664 UNK SUB IND NEI OK Tulsa 401430235 36.126945 -95.998941 SRC URB IND MID OK Tulsa 401430501 36.16127 -96.015784 UNK URB COM PA Allegheny 420030002 40.500556 -80.071944 POP SUB RES NEI PA Allegheny 420030010 40.445577 -80.016155 POP URB COM URB PA Allegheny 420030021 40.413611 -79.941389 POP SUB RES NEI PA Allegheny 420030031 40.443333 -79.990556 POP URB COM NEI 1PA Allegheny 420030032 40.414444 -79.942222 UNK SUB RES PA Allegheny 420030064 40.323611 -79.868333 POP SUB RES NEI PA Allegheny 420030067 40.381944 -80.185556 GEN RUR RES NEI PA Allegheny 420030116 40.473611 -80.077222 POP SUB RES NEI PA Allegheny 420031301 40.4025 -79.860278 HIC SUB RES NEI PA Allegheny 420033003 40.318056 -79.881111 POP SUB IND PA Allegheny 420033004 40.305 -79.888889 UNK SUB RES PA Beaver 420070002 40.56252 -80.503948 REG RUR AGR REG PA Beaver 420070004 40.635575 -80.230605 HIC URB IND NEI PA Beaver 420070005 40.684722 -80.359722 POP RUR AGR URB PA Beaver 420070014 40.747796 -80.316442 POP URB RES URB PA Berks 420110009 40.320278 -75.926667 HIC SUB RES NEI PA Berks 420110100 40.335278 -75.922778 UNK URB COM PA Blair 420130801 40.535278 -78.370833 POP SUB IND NEI PA Bucks 420170012 40.107222 -74.882222 POP SUB RES NEI PA Cambria 420210011 40.309722 -78.915 HIC URB COM NEI 1PA Centre 420270100 40.811389 -77.877028 POP RUR AGR NEI PA Dauphin 420430401 40.245 -76.844722 HIC RUR COM NEI PA Delaware 420450002 39.835556 -75.3725 HIC URB IND NEI PA Delaware 420450109 39.818715 -75.413973 UNK URB IND 
A-31

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HePA Erie 420490003 42.14175 -80.038611 HIC SUB COM NEI PA Indiana 420630004 40.56333 -78.919972 POP RUR COM NEI PA Lackawanna 420692006 41.442778 -75.623056 HIC SUB RES NEI PA Lancaster 420710007 40.046667 -76.283333 HIC SUB IND NEI PA Lawrence 420730015 40.995848 -80.346442 POP SUB IND NEI PA Lehigh 420770004 40.611944 -75.4325 POP SUB COM NEI PA Luzerne 420791101 41.265556 -75.846389 POP SUB RES NEI PA Lycoming 420810100 41.2508 -76.9238 POP URB RES URB 3PA Lycoming 420810403 41.246111 -76.989722 POP URB COM NEI PA Mercer 420850100 41.215014 -80.484779 POP URB COM NEI PA Montgomery 420910013 40.112222 -75.309167 POP SUB RES NEI PA Northampton 420950025 40.628056 -75.341111 POP SUB COM NEI PA Northampton 420950100 40.676667 -75.216667 UNK SUB IND PA Northampton 420958000 40.692224 -75.237156 POP SUB RES NEI PA Perry 420990301 40.456944 -77.165556 GEN RUR UNK REG PA Philadelphia 421010004 40.008889 -75.097778 POP URB RES NEI PA Philadelphia 421010022 39.916667 -75.188889 HIC URB IND NEI PA Philadelphia 421010024 40.076389 -75.011944 UNK SUB IND PA Philadelphia 421010027 40.010556 -75.151944 UNK URB MOB PA Philadelphia 421010029 39.957222 -75.173056 POP URB COM NEI 1PA Philadelphia 421010047 39.944722 -75.166111 POP URB RES NEI PA Philadelphia 421010048 39.991389 -75.080833 UNK RUR RES PA Philadelphia 421010055 39.922517 -75.186783 POP URB RES NEI PA Philadelphia 421010136 39.9275 -75.222778 POP URB RES NEI PA Schuylkill 421070003 40.820556 -76.212222 POP RUR RES NEI PA Warren 421230003 41.857222 -79.1375 HIC SUB RES NEI PA Warren 421230004 41.844722 -79.169722 HIC RUR FOR NEI PA Washington 421250005 40.146667 -79.902222 POP SUB COM NEI PA Washington 421250200 40.170556 -80.261389 POP SUB RES NEI PA Washington 421255001 40.445278 -80.420833 REG RUR AGR REG PA Westmoreland 421290008 40.304694 -79.505667 POP SUB COM URB PA York 421330008 39.965278 -76.699444 HIC SUB RES NEI RI Providence 440070012 41.825556 -71.405278 POP URB COM NEI 2
A-32

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeRI Providence 440071005 41.878333 -71.378889 HIC URB RES NEI RI Providence 440071009 41.823611 -71.411667 HIC URB COM NEI SC Aiken 450030003 33.342226 -81.788731 HIC SUB RES URB 4.SC Barnwell 450110001 33.320344 -81.465537 SRC RUR FOR URB 3SC Charleston 450190003 32.882289 -79.977538 POP URB COM NEI 4SC Charleston 450190046 32.941023 -79.657187 SRC RUR FOR REG SC Georgetown 450430006 33.362014 -79.294251 SRC URB IND NEI 2.SC Greenville 450450008 34.838814 -82.402918 POP URB COM NEI SC Greenville 450450009 34.899141 -82.31307 WEL SUB RES NEI SC Lexington 450630008 34.051017 -81.15495 SRC SUB COM NEI 3.SC Oconee 450730001 34.805261 -83.2377 REG RUR FOR REG 4SC Orangeburg 450750003 33.29959 -80.442218 SRC RUR FOR NEI 3SC Richland 450790007 34.093959 -80.962304 OTH SUB COM NEI SC Richland 450790021 33.81468 -80.781135 GEN RUR FOR URB 4.SC Richland 450791003 34.024497 -81.036248 POP URB COM MID SC Richland 450791006 33.817902 -80.826596 GEN RUR FOR MIC SD Custer 460330132 43.5578 -103.4839 REG RUR FOR REG 3.SD Jackson 460710001 43.74561 -101.941218 GEN RUR AGR REG SD Minnehaha 460990007 43.537626 -96.682001 POP URB RES NEI TN Anderson 470010028 36.027778 -84.151389 UNK SUB RES TN Blount 470090002 35.775 -83.965833 HIC RUR COM MID TN Blount 470090006 35.768056 -83.976667 HIC SUB RES MID TN Blount 470090101 35.63149 -83.943512 GEN RUR FOR REG 1TN Bradley 470110102 35.283164 -84.759371 UNK URB RES TN Coffee 470310004 35.582222 -86.015556 UNK RUR AGR TN Davidson 470370011 36.205 -86.744722 POP URB RES NEI 1TN Hawkins 470730002 36.366944 -82.977778 UNK RUR AGR TN Humphreys 470850020 36.051944 -87.965 UNK RUR AGR TN McMinn 471070101 35.29733 -84.75076 HIC SUB AGR NEI TN Montgomery 471250006 36.520056 -87.394167 UNK RUR IND NEI TN Montgomery 471250106 36.504529 -87.396675 HIC RUR RES MID TN Polk 471390003 35.026111 -84.384722 POP SUB COM NEI TN Polk 471390007 34.988333 -84.371667 POP URB COM NEI 
A-33

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeTN Polk 471390008 34.995833 -84.368333 UNK RUR RES TN Polk 471390009 34.989722 -84.383889 UNK RUR IND TN Roane 471450009 35.947222 -84.522222 UNK SUB RES TN Shelby 471570034 35.0434 -90.0136 HIC SUB RES NEI TN Shelby 471570043 35.087778 -90.025278 HIC SUB COM NEI TN Shelby 471570046 35.272778 -89.961389 POP SUB IND URB TN Shelby 471571034 35.087222 -90.133611 UNK RUR AGR MID TN Stewart 471610007 36.389722 -87.633333 OTH RUR AGR TN Sullivan 471630007 36.534804 -82.517078 HIC SUB RES NEI TN Sullivan 471630009 36.513971 -82.560968 HIC RUR RES NEI TN Sumner 471651002 36.341667 -86.398333 OTH RUR AGR TX Cameron 480610006 25.892509 -97.493824 HIC URB COM NEI TX Dallas 481130069 32.819952 -96.860082 POP URB COM NEI TX Ellis 481390015 32.436944 -97.025 HIC SUB AGR NEI TX Ellis 481390016 32.482222 -97.026944 GEN SUB AGR NEI TX Ellis 481390017 32.473611 -97.0425 OTH RUR RES TX El Paso 481410037 31.768281 -106.501253 POP URB COM NEI TX El Paso 481410053 31.758504 -106.501023 HIC URB COM NEI TX El Paso 481410058 31.893928 -106.425813 POP URB RES NEI TX Galveston 481670005 29.385236 -94.931526 HIC URB RES NEI TX Galveston 481671002 29.398611 -94.933333 HIC SUB RES NEI TX Gregg 481830001 32.37871 -94.711834 GEN RUR RES NEI TX Harris 482010046 29.8275 -95.283611 POP SUB RES NEI TX Harris 482010051 29.623611 -95.473611 SRC SUB RES NEI TX Harris 482010059 29.705833 -95.281111 HIC SUB RES NEI TX Harris 482010062 29.625833 -95.2675 POP SUB RES NEI TX Harris 482010070 29.735129 -95.315583 GEN SUB RES NEI 1TX Harris 482011035 29.733713 -95.257591 POP SUB IND NEI TX Harris 482011050 29.583032 -95.015535 HIC SUB RES MID 1TX Jefferson 482450009 30.036446 -94.071073 HIC SUB RES NEI 6.TX Jefferson 482450011 29.89403 -93.987898 SRC URB IND NEI TX Jefferson 482450020 30.06607 -94.077383 SRC URB IND NEI TX Kaufman 482570005 32.564969 -96.31766 HIC SUB COM NEI 
A-34

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeTX Nueces 483550025 27.76534 -97.434272 POP URB RES NEI TX Nueces 483550026 27.832409 -97.555381 HIC URB RES NEI TX Nueces 483550032 27.804482 -97.431553 POP SUB RES UT Cache 490050004 41.731111 -111.8375 POP URB COM UT Davis 490110001 40.886389 -111.882222 POP SUB COM UT Davis 490110004 40.902967 -111.884467 POP SUB RES NEI UT Salt Lake 490350012 40.8075 -111.921111 UNK SUB IND UT Salt Lake 490351001 40.708611 -112.094722 HIC SUB RES NEI UT Salt Lake 490352004 40.736389 -112.210278 HIC RUR IND VT Chittenden 500070003 44.478889 -73.211944 HIC URB COM NEI VT Chittenden 500070014 44.4762 -73.2106 POP URB COM MID VT Rutland 500210002 43.608056 -72.982778 POP URB COM NEI VA Charles 510360002 37.343294 -77.260034 HIC SUB RES NEI VA Fairfax 510590005 38.893889 -77.465278 POP RUR AGR NEI VA Fairfax 510590018 38.7425 -77.0775 UNK SUB RES VA Fairfax 510591004 38.868056 -77.143056 UNK SUB COM 1VA Fairfax 510591005 38.837517 -77.163231 POP SUB RES VA Fairfax 510595001 38.931944 -77.198889 UNK SUB RES VA Madison 511130003 38.521944 -78.436111 UNK RUR FOR VA Roanoke 511611004 37.285556 -79.884167 HIC SUB RES NEI VA Rockingham 511650002 38.389444 -78.914167 POP RUR AGR NEI VA Rockingham 511650003 38.47732 -78.81904 POP SUB COM NEI VA Alexandria City 515100009 38.810833 -77.044722 POP URB RES NEI 1VA Hampton City 516500004 37.003333 -76.399167 HIC SUB RES NEI VA Norfolk City 517100023 36.850278 -76.257778 POP URB COM NEI VA Richmond City 517600024 37.562778 -77.465278 HIC URB COM NEI WA Clallam 530090010 48.113333 -123.399167 UNK SUB RES WA Clallam 530090012 48.0975 -123.425556 UNK SUB RES NEI WA King 530330057 47.563333 -122.3406 HIC SUB IND NEI 1WA King 530330080 47.568333 -122.308056 POP URB RES URB WA Pierce 530530021 47.281111 -122.374167 HIC SUB RES NEI WA Pierce 530530031 47.2656 -122.3858 POP SUB IND NEI WA Skagit 530570012 48.493611 -122.551944 UNK SUB RES 
A-35

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeWA Skagit 530571003 48.486111 -122.549444 UNK RUR IND WA Snohomish 530610016 47.983333 -122.209722 UNK URB COM WA Whatcom 530730011 48.750278 -122.482778 UNK URB IND 1WV Brooke 540090005 40.341023 -80.596635 POP SUB IND NEI WV Brooke 540090007 40.389655 -80.586235 POP RUR RES NEI WV Cabell 540110006 38.424133 -82.4259 POP SUB COM NEI 13WV Greenbrier 540250001 37.819444 -80.5125 UNK RUR AGR WV Hancock 540290005 40.529021 -80.576067 POP SUB RES URB WV Hancock 540290007 40.460138 -80.576567 POP RUR RES URB WV Hancock 540290008 40.61572 -80.56 POP SUB RES NEI WV Hancock 540290009 40.427372 -80.592318 POP SUB RES NEI WV Hancock 540290011 40.394583 -80.612017 POP SUB RES NEI WV Hancock 540290014 40.43552 -80.600579 POP SUB RES MID WV Hancock 540290015 40.618353 -80.540616 POP URB RES URB WV Hancock 540290016 40.411944 -80.601667 HIC SUB RES WV Hancock 540291004 40.421539 -80.580717 HIC SUB RES NEI WV Kanawha 540390004 38.343889 -81.619444 POP SUB COM NEI WV Kanawha 540390010 38.3456 -81.628317 POP URB COM URB 1WV Kanawha 540392002 38.416944 -81.846389 HIC SUB IND NEI WV Marshall 540511002 39.915961 -80.733858 POP SUB RES URB WV Monongalia 540610003 39.649367 -79.920867 POP SUB COM URB 4WV Monongalia 540610004 39.633056 -79.957222 UNK SUB RES WV Monongalia 540610005 39.648333 -79.957778 UNK SUB RES URB 10WV Ohio 540690007 40.12043 -80.699265 HIC SUB RES NEI WV Wayne 540990002 38.39186 -82.583923 POP RUR IND NEI WV Wayne 540990003 38.390278 -82.585833 HIC RUR RES NEI WV Wayne 540990004 38.380278 -82.583889 HIC RUR RES NEI WV Wayne 540990005 38.372222 -82.588889 HIC RUR RES NEI WV Wood 541071002 39.323533 -81.552367 POP SUB IND URB WI Brown 550090005 44.516667 -87.993889 POP URB RES NEI 1WI Dane 550250041 43.100833 -89.357222 POP URB RES NEI WI FOR 550410007 45.56498 -88.80859 GEN RUR FOR REG WI Marathon 550730005 45.028333 -89.652222 HIC RUR FOR MID 
A-36

State County Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 HeWI Milwaukee 550790007 43.047222 -87.920278 POP URB COM NEI WI Milwaukee 550790026 43.061111 -87.9125 POP URB COM NEI WI Milwaukee 550790041 43.075278 -87.884444 HIC URB RES NEI WI Oneida 550850996 45.645278 -89.4125 UNK URB IND WI Sauk 551110007 43.435556 -89.680278 GEN RUR FOR REG WI Vilas 551250001 46.048056 -89.653611 GEN RUR FOR REG 1WI Wood 551410016 44.3825 -89.819167 POP URB RES NEI WY Campbell 560050857 44.277222 -105.375 SRC RUR IND NEI PR Barceloneta 720170003 18.436111 -66.580556 UNK RUR RES PR Bayamon 720210004 18.412778 -66.132778 HIC SUB IND NEI PR Bayamon 720210006 18.416667 -66.150833 POP SUB IND NEI PR Catano 720330004 18.430556 -66.142222 UNK SUB RES PR Catano 720330007 18.444722 -66.116111 POP URB RES NEI PR Catano 720330008 18.440028 -66.127076 POP URB COM PR Catano 720330009 18.449964 -66.149043 POP URB RES PR Guayama 720570009 17.966844 -66.188014 SRC RUR COM NEI PR Salinas 721230001 17.963002 -66.254749 SRC RUR AGR VI St Croix 780100006 17.706944 -64.780556 HIC RUR IND NEI VI St Croix 780100011 17.719167 -64.775 HIC RUR IND NEI VI St Croix 780100013 17.7225 -64.776667 POP SUB RES NEI VI St Croix 780100014 17.734444 -64.783333 POP RUR AGR NEI VI St Croix 780100015 17.741667 -64.751944 SRC RUR AGR NEI Notes: 1 Objectives are POP=Population Exposure; HIC=Highest Concentration; SRC=Source Oriented; GEN=General/Background; 

Transport; OTH=Other; UNK=Unknown; UPW=Upwind Background; WEL=Welfare Related Impacts 2 Settings are R=Rural; U=Urban and Center City; S=Suburban 3 Land Uses are AGR=Agricultural; COM=Commercial; IND=Industrial; FOR=Forest; RES=Residential; UNK=Unknown; DES=

4 Scales are NEI=Neighborhood; MID=Middle; URB=URBAN; REG=Regional; MIC=Micro 
A-37

Table A.1-4. Population density, concentration variability, and total SO2 emissions associated with monitors in the broader SO2 monitoring network. 

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CAL Colbert 010330044 2195 7954 25394 62838 low AL Jackson 010710020 1902 8137 19317 29686 low AL Jefferson 010731003 76802 196682 344386 489181 hi AL Lawrence 010790003 3952 28674 73092 91057 low AL Mobile 010970028 5966 7758 17087 39111 low AL Mobile 010972005 3017 18106 52682 111608 low AL Montgomery 011011002 45389 156786 213606 259730 mod AR Pulaski 051190007 67784 178348 270266 334649 hi AR Pulaski 051191002 45800 109372 230200 310362 mod AR Union 051390006 21877 29073 32652 36340 mod AZ Gila 040070009 7801 14076 17280 17633 low AZ Gila 040071001 1359 1359 3098 5401 low AZ Maricopa 040130019 197458 613618 1036233 1447648 hi AZ Maricopa 040133002 144581 490123 980730 1612687 hi AZ Maricopa 040133003 91955 340325 829051 1518806 hi AZ Pima 040191011 111215 354473 561487 639921 hi AZ Pinal 040212001 4375 7679 9577 10125 low CA Alameda 060010010 236320 532827 841443 1342267 hi CA Contra Costa 060130002 136288 303088 445297 598861 hi CA Contra Costa 060130006 119088 231479 471471 968983 hi CA Contra Costa 060130010 29809 123220 403137 685185 mod CA Contra Costa 060131001 53051 181259 321500 610171 hi CA Contra Costa 060131002 4033 39708 117118 173196 low CA Contra Costa 060131003 146336 256417 420619 856435 hi CA Contra Costa 060131004 125350 233220 433669 876585 hi CA Contra Costa 060132001 34743 155226 433934 807706 mod CA Contra Costa 060133001 64019 152758 303597 478310 hi CA Imperial 060250005 27033 31895 56234 84405 mod CA Los Angeles 060371002 167653 827729 2001363 3286038 hi CA Los Angeles 060371103 378843 1618324 3027507 4530714 hi 
A-38

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CCA Los Angeles 060374002 240505 913176 1850549 3218392 hi CA Los Angeles 060375001 276378 890302 2071144 3561110 hi CA Los Angeles 060375005 94836 652173 1628468 2848126 hi CA Orange 060591003 200253 744882 1303743 1829713 hi CA Riverside 060658001 78757 360234 734267 1141466 hi CA Sacramento 060670002 92433 328190 645533 916197 hi CA Sacramento 060670006 132584 472019 866437 1180898 hi CA San Bernardino 060710012 6720 17620 29756 69717 low CA San Bernardino 060710014 58937 114149 193928 224008 hi CA San Bernardino 060710306 59772 114149 193046 224008 hi CA San Bernardino 060711234 0 0 1911 1911 low CA San Bernardino 060712002 89732 314392 650533 1142460 hi CA San Bernardino 060714001 40799 114888 174610 219525 mod CA San Diego 060730001 168237 528890 866015 1177835 hi CA San Diego 060731007 169117 616102 1097387 1449106 hi CA San Diego 060732007 9376 15849 218480 452120 low CA San Francisco 060750005 433367 827164 1227784 1729715 hi CA San Luis Obispo 060791005 4725 56677 85064 152491 low CA San Luis Obispo 060792001 39236 55657 61709 121393 mod CA San Luis Obispo 060792004 2135 34056 113260 162669 low CA San Luis Obispo 060794002 0 51508 95245 141786 low CA Santa Barbara 060830008 655 1678 17486 67965 low CA Santa Barbara 060831012 0 0 960 3201 low CA Santa Barbara 060831013 6617 41576 59590 89777 low CA Santa Barbara 060831015 0 0 2391 17826 low CA Santa Barbara 060831016 0 0 4034 17826 low CA Santa Barbara 060831019 0 0 4689 17826 low CA Santa Barbara 060831020 39222 71015 117832 170206 mod CA Santa Barbara 060831025 655 1678 11216 56132 low CA Santa Barbara 060831026 655 1678 15659 63963 low CA Santa Barbara 060831027 655 1678 13618 62298 low CA Santa Barbara 060832004 38688 49356 58271 59279 mod CA Santa Barbara 060832011 55496 105491 170865 181894 hi 
A-39

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CCA Santa Barbara 060834003 0 0 8430 51692 low CA Santa Cruz 060870003 0 6016 51831 124792 low CA Solano 060950001 27872 130319 359105 620107 mod CA Solano 060950004 102003 166693 247861 374613 hi CA Ventura 061113001 47248 227525 401656 427503 mod CO Adams 080010007 45071 360261 903964 1344766 mod CO Adams 080013001 81896 334611 784343 1205604 hi CO Denver 080310002 189782 574752 1158644 1608099 hi CO El Paso 080416001 0 24520 54194 111518 low CO El Paso 080416004 84979 242841 368203 430076 hi CO El Paso 080416011 97849 288563 407401 448545 hi CO El Paso 080416018 93065 266008 388801 438812 hi CT Fairfield 090010012 164887 291072 393358 528453 hi CT Fairfield 090010017 30184 188214 330125 672435 mod CT Fairfield 090011123 72689 126452 191805 277225 hi CT Fairfield 090012124 121109 209567 343909 476656 hi CT Fairfield 090019003 28181 151905 313449 546288 mod CT Hartford 090031005 33414 147625 319902 484462 mod CT Hartford 090031018 152497 329646 523045 693079 hi CT Hartford 090032006 91965 333744 510929 671515 hi CT New Haven 090090027 140329 290735 389117 529118 hi CT New Haven 090091003 156879 293853 414381 552021 hi CT New Haven 090091123 154781 292598 417546 557442 hi CT New Haven 090092123 104191 189838 276310 447334 hi CT New London 090110007 58457 97870 141173 182476 hi CT Tolland 090130003 23441 47285 78649 115317 mod DC District of Columbia 110010041 216129 813665 1461563 2029936 hi DE New Castle 100031003 68790 223079 369450 603736 hi DE New Castle 100031007 14297 67478 178295 274942 mod DE New Castle 100031008 5386 80025 192989 391157 low DE New Castle 100031013 79498 221315 386624 618604 hi DE New Castle 100032002 111236 245832 400217 624587 hi DE New Castle 100032004 111609 245173 411000 600168 hi 
A-40

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CFL Broward 120110010 173204 475485 953527 1459284 hi FL Duval 120310032 81831 270954 439516 620929 hi FL Duval 120310080 70468 288474 506828 704506 hi FL Duval 120310081 23862 152805 305323 463770 mod FL Duval 120310097 59980 225163 418997 600591 hi FL Escambia 120330004 43464 133022 233520 303319 mod FL Escambia 120330022 32534 122295 223566 291695 mod FL Hamilton 120470015 582 1733 6459 12479 low FL Hillsborough 120570021 90125 287073 539627 762352 hi FL Hillsborough 120570053 54303 140247 307460 668911 hi FL Hillsborough 120570081 5101 24672 48751 228142 low FL Hillsborough 120570095 28554 192630 493886 719140 mod FL Hillsborough 120570109 11493 81649 287436 509661 mod FL Hillsborough 120571035 63839 244436 463185 764479 hi FL Hillsborough 120574004 32134 66598 149341 346648 mod FL Manatee 120813002 2043 18810 82190 281383 low FL Miami-Dade 120860019 54755 283528 685044 1386189 hi FL Nassau 120890005 17963 21386 38521 48316 mod FL Nassau 120890009 8627 18803 27645 59574 low FL Orange 120952002 85060 389159 808816 1031221 hi FL Palm Beach 120993004 54596 222249 446441 718156 hi FL Pinellas 121030023 40222 180398 488170 901428 mod FL Pinellas 121033002 74280 310490 633807 907997 hi FL Pinellas 121035002 58164 184586 401002 655181 hi FL Pinellas 121035003 48341 174960 304905 492683 mod FL Polk 121050010 1499 21899 60024 142707 low FL Polk 121052006 8128 49090 125120 198136 low FL Putnam 121071008 10853 21601 35511 44711 mod FL Sarasota 121151002 78620 180672 237782 332704 hi FL Sarasota 121151005 28895 140026 244918 356779 mod FL Sarasota 121151006 65360 188269 295631 386824 hi GA Baldwin 130090001 7410 22059 44230 50761 low GA Bartow 130150002 1628 15879 50084 91503 low 
A-41

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CGA Bibb 130210012 5430 38736 102539 153254 low GA Chatham 130510019 24119 107149 188444 220328 mod GA Chatham 130510021 47852 121273 183343 220814 mod GA Chatham 130511002 40337 113925 186077 222588 mod GA Dougherty 130950006 28572 73138 101552 117779 mod GA Fannin 131110091 3943 9432 19045 24026 low GA Floyd 131150003 2671 22348 46960 74655 low GA Fulton 131210048 139962 429736 806001 1253530 hi GA Fulton 131210055 103612 409533 779857 1209013 hi GA Glynn 131270006 22992 38643 61789 67649 mod GA Muscogee 132150008 63822 167389 234866 254253 hi GA Richmond 132450003 30694 124609 206847 298992 mod HI Honolulu 150030010 24951 89592 181585 344307 mod HI Honolulu 150030011 16119 58440 160177 277456 mod HI Honolulu 150031001 197479 344436 483321 672198 hi HI Honolulu 150031006 16676 66976 180191 300444 mod IA Cerro Gordo 190330018 21247 30341 39284 45105 mod IA Clinton 190450018 24561 37638 42404 45947 mod IA Clinton 190450019 24561 37638 42404 45947 mod IA Clinton 190450020 25544 36227 41370 48214 mod IA Lee 191110006 11675 18308 24246 25010 mod IA Lee 191111007 1202 11474 20995 34036 low IA Linn 191130028 9112 77687 143283 189856 low IA Linn 191130029 72325 146914 168250 179312 hi IA Linn 191130031 76896 148919 170320 179312 hi IA Linn 191130032 66674 131315 169310 183904 hi IA Linn 191130034 63548 146044 170320 185547 hi IA Linn 191130038 30007 108042 163636 180807 mod IA Linn 191130039 30134 106631 160903 180968 mod IA Muscatine 191390016 20360 27101 31886 40248 mod IA Muscatine 191390017 11109 27101 31696 36604 mod IA Muscatine 191390020 20360 27101 31886 40290 mod IA Scott 191630015 90863 201277 268535 293627 hi 
A-42

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CIA Scott 191630017 3486 43003 159186 245960 low IA Van Buren 191770004 0 2252 3764 7809 low IA Van Buren 191770005 994 2252 3764 6984 low IA Van Buren 191770006 994 2252 3764 6984 low IA Woodbury 191930018 4449 44815 92956 112802 low ID Bannock 160050004 16523 57823 64147 69313 mod ID Caribou 160290003 0 1351 3211 4218 low ID Caribou 160290031 0 604 3211 3211 low ID Power 160770011 7702 50773 64147 69313 low IL Adams 170010006 40173 49711 54168 64300 mod IL Champaign 170190004 91239 126127 134689 152309 hi IL Cook 170310050 162765 649556 1310508 1997666 hi IL Cook 170310059 67237 496359 1055079 1759830 hi IL Cook 170310063 307232 1205813 2476802 3318024 hi IL Cook 170310064 299183 965573 1758392 2786664 hi IL Cook 170310076 289574 1034471 2000564 2971446 hi IL Cook 170311018 113572 617444 1657665 3102521 hi IL Cook 170311601 23495 167647 466741 1000711 mod IL Cook 170312001 138992 604707 1380464 2117578 hi IL Cook 170314002 406933 1482581 2777797 3752141 hi IL Cook 170314201 63731 232428 627873 1254146 hi IL Cook 170318003 111959 456791 1004517 1682955 hi IL DuPage 170436001 83416 401929 787802 1266818 hi IL La Salle 170990007 4862 26956 37974 63052 low IL Macon 171150013 54806 92426 103292 112667 hi IL Macoupin 171170002 0 5005 16518 19043 low IL Madison 171190008 36580 84254 152472 330907 mod IL Madison 171190017 37113 201161 536687 950679 mod IL Madison 171191010 9382 70816 176153 323143 low IL Madison 171193007 32393 71861 172196 353090 mod IL Madison 171193009 27788 69631 136629 273179 mod IL Peoria 171430024 76341 167513 232727 269180 hi IL Randolph 171570001 5095 10038 16360 29336 low 
A-43

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CIL Rock Island 171610003 87160 228445 275180 296786 hi IL Saint Clair 171630010 48405 274406 621019 999843 mod IL Saint Clair 171631010 49630 269778 593969 973751 mod IL Saint Clair 171631011 1148 9915 18231 27769 low IL Sangamon 171670006 41165 123641 154447 171401 mod IL Tazewell 171790004 32800 50160 99136 194767 mod IL Wabash 171850001 8738 9493 13312 27993 low IL Wabash 171851001 1069 10899 11617 21643 low IL Will 171970013 12237 66320 171777 249868 mod IN Daviess 180270002 905 9377 21937 32380 low IN Dearborn 180290004 11932 21347 69595 151228 mod IN Floyd 180430004 17205 86512 201325 363262 mod IN Floyd 180430007 65510 228353 408246 607160 hi IN Floyd 180431004 45432 169258 351938 532952 mod IN Fountain 180450001 788 2536 9505 19361 low IN Gibson 180510001 792 10900 18174 30700 low IN Gibson 180510002 6276 9493 16779 29981 low IN Hendricks 180630001 4657 29661 66108 183728 low IN Hendricks 180630002 7481 31567 79685 205437 low IN Hendricks 180630003 1776 11450 41400 79693 low IN Jasper 180730002 991 8080 16959 28865 low IN Jasper 180730003 1688 4551 12127 20725 low IN Jefferson 180770004 11228 22061 32050 36387 mod IN Lake 180890022 40318 152401 292371 500754 mod IN Lake 180892008 97669 293157 745205 1339901 hi IN LaPorte 180910005 28928 42982 60818 97304 mod IN LaPorte 180910007 29106 54698 82651 112181 mod IN Marion 180970042 19283 109791 306701 564512 mod IN Marion 180970054 53595 301941 612446 863127 hi IN Marion 180970057 79478 349455 640054 909257 hi IN Marion 180970072 115856 380088 684608 922620 hi IN Marion 180970073 100599 357454 585925 880596 hi IN Morgan 181091001 4178 26279 53331 105208 low 
A-44

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CIN Perry 181230006 6348 13158 20298 30372 low IN Perry 181230007 6153 15700 19228 29270 low IN Pike 181250005 3991 7372 12598 29314 low IN Porter 181270011 12202 44110 101993 210946 mod IN Porter 181270017 14162 59080 118122 223900 mod IN Porter 181270023 13645 79678 136098 256849 mod IN Spencer 181470002 1935 4701 13255 32146 low IN Spencer 181470010 2483 5934 14936 32405 low IN Sullivan 181530004 1735 8313 15494 25746 low IN Vanderburgh 181630012 45373 141869 184521 225094 mod IN Vanderburgh 181631002 1289 30177 123286 201383 low IN Vigo 181670018 50963 82314 98561 115726 hi IN Vigo 181671014 25046 72089 100022 118986 mod IN Warrick 181730002 2200 27584 60538 123354 low IN Warrick 181731001 11943 28798 80348 155370 mod IN Wayne 181770006 34483 51601 59606 71062 mod IN Wayne 181770007 31811 48948 59606 72278 mod KS Linn 201070002 1728 3741 4705 6412 low KS Montgomery 201250006 9331 14142 17807 21677 low KS Pawnee 201450001 5329 6038 6038 6038 low KS Sedgwick 201730010 102842 276624 380868 426333 hi KS Sumner 201910002 1476 13125 56924 120034 low KS Trego 201950001 0 0 578 578 low KS Wyandotte 202090001 63756 288005 588511 868652 hi KS Wyandotte 202090020 41751 237368 491118 742170 mod KS Wyandotte 202090021 61336 271585 571758 840225 hi KY Boyd 210190015 31077 78140 124766 179511 mod KY Boyd 210190017 34804 79205 119732 161810 mod KY Boyd 210191003 14960 58723 117154 181371 mod KY Campbell 210370003 67933 285451 616440 910551 hi KY Campbell 210371001 153388 421973 754366 1016145 hi KY Daviess 210590005 25889 70609 81162 92902 mod KY Fayette 210670012 92980 195446 267016 309266 hi 
A-45

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CKY Greenup 210890007 19411 45899 85066 109294 mod KY Hancock 210910012 3345 4280 20931 39607 low KY Henderson 211010013 21591 35051 126144 202537 mod KY Henderson 211010014 2594 30452 135741 194289 low KY Jefferson 211110032 49825 208276 375535 586335 mod KY Jefferson 211110051 13446 52332 121743 257453 mod KY Jefferson 211111041 81560 281755 485759 676730 hi KY Livingston 211390004 1695 8508 15337 31298 low KY McCracken 211450001 1336 15733 28279 64951 low KY McCracken 211451024 17904 48907 63098 83436 mod KY McCracken 211451026 9706 42285 62036 82624 low KY Warren 212270008 1865 8137 23083 68407 low LA Bossier 220150008 43077 149478 247738 295731 mod LA Calcasieu 220190008 12932 68406 137949 154942 mod LA East Baton Rouge 220330009 76518 193981 321486 408305 hi LA Ouachita 220730004 24260 87999 116037 131643 mod LA St. Bernard 220870002 97021 407863 672107 856519 hi LA West Baton Rouge 221210001 21249 137455 239718 366741 mod MA Bristol 250051004 89767 169077 221707 372963 hi MA Essex 250090005 125952 225058 376322 598605 hi MA Essex 250091004 123377 309194 545716 906225 hi MA Essex 250091005 109921 314258 523212 870238 hi MA Essex 250095004 57974 128881 316108 422519 hi MA Hampden 250130016 136483 296109 450050 532663 hi MA Hampden 250131009 127283 278577 447646 541476 hi MA Hampshire 250154002 5182 23547 50329 123102 low MA Middlesex 250171701 109401 512228 1210094 1773702 hi MA Middlesex 250174003 164954 629764 1334022 1860034 hi MA Suffolk 250250002 486825 1141656 1582622 1955479 hi MA Suffolk 250250019 6913 437626 1118549 1681211 low MA Suffolk 250250020 320320 899106 1461574 1895175 hi MA Suffolk 250250021 243006 887256 1488386 1966520 hi MA Suffolk 250250040 261273 962956 1475999 1921168 hi 
A-46

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CMA Suffolk 250250042 441455 1048879 1536036 1941989 hi MA Suffolk 250251003 260061 829040 1436251 1951612 hi MA Worcester 250270020 155688 248143 316330 404489 hi MA Worcester 250270023 151851 252264 318317 403312 hi MD Allegany 240010006 28416 49750 66814 79171 mod MD Anne Arundel 240032002 40618 134276 372761 829885 mod MD Baltimore 240053001 99648 383980 785111 1155009 hi MD Baltimore (City) 245100018 360916 823207 1195508 1472306 hi MD Baltimore (City) 245100036 105632 490543 1004531 1351499 hi ME Androscoggin 230010011 46561 61938 83767 101615 mod ME Aroostook 230030009 3403 4534 6561 9030 low ME Aroostook 230030012 3403 4534 6561 9030 low ME Aroostook 230031003 3403 4534 6561 9030 low ME Aroostook 230031013 6476 6476 10298 11213 low ME Aroostook 230031018 2387 8245 15656 21187 low ME Cumberland 230050014 65123 122951 151066 187005 hi ME Cumberland 230050027 67865 124508 153138 190157 hi ME Oxford 230172007 5903 10118 12717 17231 low MI Delta 260410902 7503 26225 28725 31746 low MI Genesee 260490021 94710 227235 323367 388490 hi MI Genesee 260492001 4058 17555 47495 126929 low MI Kent 260810020 122533 294283 453477 553989 hi MI Macomb 260991003 116002 549258 1171414 1769656 hi MI Missaukee 261130001 0 2308 7840 14456 low MI St. Clair 261470005 32599 64545 82832 98014 mod MI Schoolcraft 261530001 0 0 0 1389 low MI Wayne 261630001 151437 338726 682793 1135095 hi MI Wayne 261630005 86804 350207 804947 1386398 hi MI Wayne 261630015 98193 423093 975303 1647773 hi MI Wayne 261630016 203577 654802 1283000 1934280 hi MI Wayne 261630019 210099 695836 1189529 1756001 hi MI Wayne 261630025 81534 280589 668415 1150319 hi MI Wayne 261630027 79205 384693 915619 1574294 hi 
A-47

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CMI Wayne 261630033 150194 544634 1115397 1730610 hi MI Wayne 261630062 123532 491879 1104610 1743263 hi MI Wayne 261630092 96517 429048 973432 1618949 hi MN Anoka 270031002 57502 226660 496686 903982 hi MN Carlton 270176316 9236 17582 28511 56009 low MN Dakota 270370020 3854 64533 221239 432974 low MN Dakota 270370423 8572 101147 265053 574966 low MN Dakota 270370439 1487 55052 218201 411081 low MN Dakota 270370441 1487 36683 191183 384938 low MN Dakota 270370442 2705 24656 153752 332905 low MN Hennepin 270530954 224357 608888 1082178 1517123 hi MN Hennepin 270530957 157024 542309 1022041 1489863 hi MN Koochiching 270711240 6444 8075 8923 10210 low MN Ramsey 271230864 112909 599029 1052764 1510602 hi MN Sherburne 271410003 5629 7667 35016 50427 low MN Sherburne 271410011 5629 9806 29985 51661 low MN Sherburne 271410012 5629 9806 29774 50884 low MN Sherburne 271410013 0 10957 33889 58410 low MN Washington 271630436 46665 149177 354337 679510 mod MN Wright 271710007 5377 28368 39511 77671 low MO Buchanan 290210009 23253 72613 87121 93365 mod MO Buchanan 290210011 28224 75073 86317 93365 mod MO Clay 290470025 40627 163217 366686 617013 mod MO Greene 290770026 41036 146752 224445 256158 mod MO Greene 290770032 96594 180831 208384 244406 hi MO Greene 290770037 21784 110681 210953 254437 mod MO Greene 290770040 18988 109888 210953 254437 mod MO Greene 290770041 24455 120781 213312 256766 mod MO Iron 290930030 1121 1121 4507 8447 low MO Iron 290930031 0 3799 6585 8436 low MO Jackson 290950034 84236 310816 605775 921037 hi MO Jefferson 290990004 15049 33379 64516 124301 mod MO Jefferson 290990014 11967 35082 61963 125932 mod 
A-48

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CMO Jefferson 290990017 19711 36471 60199 116882 mod MO Jefferson 290990018 12258 41709 79196 170110 mod MO Monroe 291370001 0 1439 2093 5612 low MO Pike 291630002 645 2077 6916 11249 low MO Platte 291650023 2159 36438 113990 238276 low MO Saint Charles 291830010 2637 6349 34541 90953 low MO Saint Charles 291831002 4587 95765 273147 431484 low MO Saint Louis 291890001 95190 327257 630767 966432 hi MO Saint Louis 291890004 61422 315539 647834 1020228 hi MO Saint Louis 291890006 68741 235858 488837 927852 hi MO Saint Louis 291890014 48016 223506 550275 1005593 mod MO Saint Louis 291893001 117492 487564 929037 1305061 hi MO Saint Louis 291895001 108578 358731 617042 941386 hi MO Saint Louis 291897002 82790 336688 729925 1170973 hi MO Saint Louis 291897003 88786 383007 764342 1192267 hi MO St. Louis City 295100007 107568 375790 678820 979578 hi MO St. Louis City 295100072 101305 393971 726063 1097105 hi MO St. Louis City 295100080 154740 463092 861774 1168442 hi MO St. Louis City 295100086 145966 473923 857733 1177204 hi MS Harrison 280470007 18607 88520 139495 181694 mod MS Hinds 280490018 54986 171385 273630 332464 hi MS Jackson 280590006 39463 49647 65034 75787 mod MS Lee 280810004 24421 44442 61390 74867 mod MT Cascade 300132000 40281 64778 68296 70181 mod MT Cascade 300132001 42971 64778 70181 70181 mod MT Jefferson 300430903 1767 25076 47509 49340 low MT Jefferson 300430911 0 11616 36425 49340 low MT Jefferson 300430913 0 6845 27041 47509 low MT Lewis and Clark 300490702 10126 38881 49340 49340 mod MT Lewis and Clark 300490703 7706 31421 48723 49340 low MT Rosebud 300870700 2353 2353 2353 3131 low MT Rosebud 300870701 2353 2353 3131 3131 low MT Rosebud 300870702 0 2353 2353 3131 low 
A-49

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CMT Rosebud 300870760 0 0 643 2928 low MT Rosebud 300870761 0 643 3524 3524 low MT Rosebud 300870762 0 0 2928 2928 low MT Rosebud 300870763 0 1536 3131 3131 low MT Yellowstone 301110016 8526 9747 14953 39121 low MT Yellowstone 301110066 27389 79644 98733 107178 mod MT Yellowstone 301110079 61645 89282 102887 114640 hi MT Yellowstone 301110080 33774 86065 104825 108399 mod MT Yellowstone 301110082 58256 94753 103200 106046 hi MT Yellowstone 301110083 27620 76641 98733 109475 mod MT Yellowstone 301110084 22577 59919 97912 110980 mod MT Yellowstone 301111065 13350 59574 97912 110980 mod MT Yellowstone 301112005 24420 68288 97912 109475 mod MT Yellowstone 301112006 11205 46767 86788 110980 mod MT Yellowstone 301112007 5391 26316 69446 104067 low NC Alexander 370030003 7574 16738 40689 80547 low NC Beaufort 370130003 1085 1762 5519 8488 low NC Beaufort 370130004 0 1762 6616 8488 low NC Beaufort 370130006 0 1762 6616 8488 low NC Chatham 370370004 4146 12138 23134 72477 low NC Cumberland 370511003 32970 108671 203822 280713 mod NC Davie 370590002 4799 16224 44277 93569 low NC Duplin 370610002 850 6058 12866 29813 low NC Edgecombe 370650099 0 11321 25673 51492 low NC Forsyth 370670022 61669 170320 258102 325974 hi NC Johnston 371010002 9854 32163 67759 129979 low NC Lincoln 371090004 10568 32515 62768 125735 mod NC Martin 371170001 573 5282 14427 26518 low NC Mecklenburg 371190034 90874 276915 474624 629520 hi NC Mecklenburg 371190041 105796 295729 494494 647110 hi NC New Hanover 371290002 2584 20636 67021 127088 low NC New Hanover 371290006 17957 83529 145330 170260 mod NC Northampton 371310002 12284 29917 38134 46966 mod 
A-50

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CNC Person 371450003 2620 8081 24203 41995 low NC Pitt 371470099 5860 10688 23742 72588 low NC Swain 371730002 3268 8992 15036 18230 low ND Billings 380070002 0 0 1887 1887 low ND Billings 380070111 0 0 0 0 low ND Burke 380130002 0 0 0 625 low ND Burke 380130004 655 655 655 655 low ND Burleigh 380150003 49591 67377 83082 84415 mod ND Cass 380171003 48975 134561 144878 154455 mod ND Cass 380171004 2118 91149 145789 148002 low ND Dunn 380250003 0 0 0 537 low ND McKenzie 380530002 0 596 596 596 low ND McKenzie 380530104 0 521 521 2283 low ND McKenzie 380530111 0 0 2283 5771 low ND McLean 380550113 0 632 698 698 low ND Mercer 380570001 3280 3280 5902 6465 low ND Mercer 380570004 3280 4428 5902 7455 low ND Mercer 380570102 1574 4428 5902 7455 low ND Mercer 380570118 0 1574 6898 7455 low ND Mercer 380570123 0 557 3837 5981 low ND Mercer 380570124 557 557 557 3903 low ND Morton 380590002 17925 67959 75685 84415 mod ND Morton 380590003 10305 31348 75685 82584 mod ND Oliver 380650002 0 0 2057 2670 low ND Steele 380910001 0 934 934 934 low ND Williams 381050103 0 1259 1259 1827 low ND Williams 381050105 0 1259 1259 1827 low NE Douglas 310550048 50168 209209 371395 532173 hi NE Douglas 310550050 45166 187855 367828 525602 mod NE Douglas 310550053 82663 264396 424100 578351 hi NE Douglas 310550055 13902 109385 299381 473231 mod NH Cheshire 330050007 16719 30003 39998 53389 mod NH Coos 330070019 9280 13603 14203 14928 low 
A-51

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CNH Coos 330070022 8360 12552 14928 14928 low NH Coos 330071007 2438 2438 6025 8364 low NH Hillsborough 330110016 107911 145660 196209 270491 hi NH Hillsborough 330110019 104650 140235 189502 266391 hi NH Hillsborough 330110020 104650 140235 189502 266391 hi NH Hillsborough 330111009 72131 130360 219169 438168 hi NH Hillsborough 330111010 37423 145620 333540 467236 mod NH Merrimack 330130007 27595 54309 75576 101847 mod NH Merrimack 330131003 8787 45710 74945 138179 low NH Merrimack 330131006 8066 35862 104656 218207 low NH Merrimack 330131007 9351 43118 73240 98696 low NH Rockingham 330150009 25227 48762 88743 157669 mod NH Rockingham 330150014 25984 48762 78775 148875 mod NH Rockingham 330150015 25227 48762 92738 152363 mod NH Sullivan 330190003 11339 17306 34644 48414 mod NJ Atlantic 340010005 6123 33910 71617 160179 low NJ Bergen 340035001 209619 973093 3404473 5751193 hi NJ Burlington 340051001 71953 261206 561157 1133142 hi NJ Camden 340070003 193686 806251 1761045 2534030 hi NJ Camden 340071001 8015 46392 121996 262931 low NJ Cumberland 340110007 26454 77939 109030 160091 mod NJ Essex 340130011 209592 1133321 2811759 5933785 hi NJ Essex 340130016 200779 1136145 2763272 5837087 hi NJ Gloucester 340150002 32432 107924 537340 1392192 mod NJ Hudson 340170006 158136 930071 3370494 5894707 hi NJ Hudson 340171002 343775 1754575 5021807 8159098 hi NJ Middlesex 340232003 95281 371119 839280 1615249 hi NJ Morris 340273001 13515 60394 181888 361716 mod NJ Union 340390003 221266 868022 1790660 3314852 hi NJ Union 340390004 194256 750485 1727936 3277263 hi NM Dona Ana 350130008 10195 49347 114220 181522 mod NM Dona Ana 350130017 40832 158545 258940 387481 mod NM Eddy 350151004 12050 12050 14785 16465 mod 
A-52

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CNM Grant 350170001 4292 6951 21790 23982 low NM Grant 350171003 1429 5721 9904 24316 low NM Hidalgo 350230005 0 0 0 0 low NM San Juan 350450008 22921 41258 51483 68906 mod NM San Juan 350450009 2930 18431 32213 58595 low NM San Juan 350450017 0 6492 10898 10936 low NM San Juan 350451005 491 2247 11772 16909 low NV Clark 320030022 0 0 0 10778 low NV Clark 320030078 0 0 2836 2836 low NV Clark 320030539 226197 557934 933583 1236711 hi NV Clark 320030601 13570 22316 71616 97845 mod NY Albany 360010012 108841 255221 371301 484970 hi NY Bronx 360050073 1215989 3522226 5762144 8036800 hi NY Bronx 360050080 1278526 3040232 5159927 7489995 hi NY Bronx 360050083 1162835 2294809 4245952 6315293 hi NY Bronx 360050110 1205886 3444711 5621679 7878863 hi NY Chautauqua 360130005 3605 6928 15645 22519 low NY Chautauqua 360130006 14144 29535 39906 47684 mod NY Chautauqua 360130011 3605 6928 15645 22519 low NY Chemung 360150003 41915 68619 82014 101244 mod NY Erie 360290005 150194 458758 680793 839570 hi NY Erie 360294002 80118 328976 575596 768392 hi NY Erie 360298001 66153 237799 503575 729503 hi NY Essex 360310003 492 2054 7005 10934 low NY Franklin 360330004 0 2880 5697 11358 low NY Hamilton 360410005 0 0 454 2054 low NY Herkimer 360430005 2043 2043 2043 2043 low NY Kings 360470011 1301071 3958499 6872002 8807020 hi NY Kings 360470076 1173879 3316779 5595972 7596057 hi NY Madison 360530006 806 4985 7448 17313 low NY Monroe 360551004 149439 384621 579436 665760 hi NY Monroe 360551007 129608 381741 570995 669909 hi NY Monroe 360556001 222716 407438 582031 678777 hi 
A-53

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CNY Nassau 360590005 172837 677944 1424915 2365352 hi NY New York 360610010 1062324 3421130 6487922 8988411 hi NY New York 360610056 1289280 3673609 6607580 8980807 hi NY Niagara 360632008 60505 96530 176040 348603 hi NY Onondaga 360671015 56156 207136 329787 395331 hi NY Putnam 360790005 15437 57790 111398 223357 mod NY Queens 360810097 378415 1589364 3438261 7138176 hi NY Queens 360810124 823992 2441512 5839274 8419326 hi NY Rensselaer 360830004 1987 5975 22806 118285 low NY Rensselaer 360831005 1222 6357 19071 69278 low NY Richmond 360850067 282277 653196 2026407 4371801 hi NY Schenectady 360930003 100404 157970 233426 383092 hi NY Suffolk 361030002 80740 526254 950326 1417428 hi NY Suffolk 361030009 101641 341308 551178 802861 hi NY Ulster 361111005 755 1541 7851 10684 low OH Adams 390010001 4630 6792 15822 22444 low OH Allen 390030002 15401 67353 90874 114512 mod OH Ashtabula 390071001 11409 17288 23848 42433 mod OH Belmont 390133002 17529 41346 95392 120821 mod OH Butler 390170004 68823 163124 276076 487924 hi OH Butler 390171004 47209 96458 152032 287701 mod OH Clark 390230003 19786 66337 175311 410155 mod OH Clermont 390250021 7297 20144 53435 96496 low OH Columbiana 390290016 21336 46769 67377 101068 mod OH Columbiana 390290022 21336 46769 67377 101068 mod OH Columbiana 390292001 25779 43920 64319 92597 mod OH Cuyahoga 390350038 136697 547523 932680 1214114 hi OH Cuyahoga 390350045 151001 564795 962245 1221356 hi OH Cuyahoga 390350060 116933 512974 907112 1201852 hi OH Cuyahoga 390350065 132176 562942 968826 1244026 hi OH Cuyahoga 390356001 191842 529243 883601 1165619 hi OH Franklin 390490004 133697 467572 806703 1042146 hi OH Franklin 390490034 157233 482749 868013 1090438 hi 
A-54

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 COH Gallia 390530002 1087 13134 30170 49474 low OH Hamilton 390610010 15310 124569 345879 632705 mod OH Hamilton 390612003 71390 325799 683493 1079723 hi OH Jefferson 390810016 28019 70995 96550 122094 mod OH Jefferson 390810017 30069 71838 96408 122094 mod OH Jefferson 390811001 21833 53684 91514 119322 mod OH Lake 390850003 48791 145694 238216 407417 mod OH Lake 390853002 40430 92415 141902 209471 mod OH Lawrence 390870006 26563 71376 94538 131453 mod OH Lorain 390930017 58361 129195 249878 362235 hi OH Lorain 390930026 54867 114602 202571 298148 hi OH Lorain 390931003 58580 124277 251182 365323 hi OH Lucas 390950008 62606 205665 356815 487567 hi OH Lucas 390950024 134960 319708 466184 528531 hi OH Mahoning 390990009 79207 210961 293714 378289 hi OH Mahoning 390990013 78376 214611 294367 375287 hi OH Meigs 391051001 5440 15029 21812 31834 low OH Montgomery 391130025 123978 304826 511565 645130 hi OH Morgan 391150003 1122 3168 9162 22426 low OH Morgan 391150004 1122 3168 9871 24252 low OH Scioto 391450013 15699 47369 61292 77940 mod OH Scioto 391450020 4530 11216 45697 87756 low OH Scioto 391450022 3469 12081 40548 82103 low OH Stark 391510016 99075 208779 291216 350367 hi OH Summit 391530017 104817 292059 470747 574282 hi OH Summit 391530022 140332 329963 454363 570258 hi OH Tuscarawas 391570003 26914 40238 61526 85938 mod OH Tuscarawas 391570006 2710 15439 38518 72765 low OK Cherokee 400219002 993 22584 28182 36130 low OK Kay 400710602 25029 31461 31461 36740 mod OK Kay 400719003 6614 29697 32746 35459 low OK Kay 400719010 1123 3516 16273 20121 low OK Mayes 400979014 1947 14224 26265 29243 low 
A-55

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 COK Muskogee 401010167 5633 39252 56271 64455 low OK Oklahoma 401090025 78654 254952 384825 552894 hi OK Oklahoma 401091037 46197 141934 258441 459371 mod OK Ottawa 401159004 6272 22614 29716 37508 low OK Tulsa 401430175 53094 207546 357175 485641 hi OK Tulsa 401430235 65020 235972 405434 515780 hi OK Tulsa 401430501 46840 187023 333482 468989 mod PA Allegheny 420030002 83332 277442 651551 961378 hi PA Allegheny 420030010 168140 536314 842237 1114184 hi PA Allegheny 420030021 170777 560187 921490 1142754 hi PA Allegheny 420030031 183843 580429 877668 1145039 hi PA Allegheny 420030032 174072 558904 922097 1144558 hi PA Allegheny 420030064 64846 201143 520438 943781 hi PA Allegheny 420030067 13277 86792 324154 610975 mod PA Allegheny 420030116 96820 331624 704601 996267 hi PA Allegheny 420031301 115432 411867 766188 1088115 hi PA Allegheny 420033003 55221 202092 509708 944188 hi PA Allegheny 420033004 38588 170065 461433 904760 mod PA Beaver 420070002 3434 28961 68617 120780 low PA Beaver 420070004 35152 104660 203430 317823 mod PA Beaver 420070005 17292 77240 143738 224631 mod PA Beaver 420070014 36335 82468 134467 220614 mod PA Berks 420110009 121330 203799 250610 309553 hi PA Berks 420110100 118553 202746 254794 310286 hi PA Blair 420130801 44392 72996 94779 124536 mod PA Bucks 420170012 85719 324327 638218 1212911 hi PA Cambria 420210011 50440 79710 102905 124592 hi PA Centre 420270100 60659 76595 96267 107078 hi PA Dauphin 420430401 86638 219394 324647 384070 hi PA Delaware 420450002 74840 237232 510590 1091830 hi PA Delaware 420450109 59762 209503 446058 812243 hi PA Erie 420490003 81199 150626 190212 209983 hi PA Indiana 420630004 1110 8662 23057 57759 low 
A-56

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CPA Lackawanna 420692006 68522 144913 189515 246604 hi PA Lancaster 420710007 97205 174296 254789 344292 hi PA Lawrence 420730015 40803 57962 81815 118770 mod PA Lehigh 420770004 133092 298181 395772 501878 hi PA Luzerne 420791101 68639 157363 215050 265123 hi PA Lycoming 420810100 15088 60400 83910 108961 mod PA Lycoming 420810403 41897 69102 80935 103969 mod PA Mercer 420850100 40443 69465 96468 184589 mod PA Montgomery 420910013 91275 239337 706445 1623890 hi PA Northampton 420950025 79756 173911 398867 513651 hi PA Northampton 420950100 71422 118395 209567 317220 hi PA Northampton 420958000 71626 133639 228524 330629 hi PA Perry 420990301 6450 13169 26326 49400 low PA Philadelphia 421010004 400078 1147634 1971579 2631448 hi PA Philadelphia 421010022 316944 985213 1726387 2446142 hi PA Philadelphia 421010024 197076 588104 1351349 2063868 hi PA Philadelphia 421010027 472813 1348135 2026206 2632847 hi PA Philadelphia 421010029 484661 1229942 1999611 2574304 hi PA Philadelphia 421010047 410380 1153434 1989848 2573573 hi PA Philadelphia 421010048 262592 1102727 1938877 2607877 hi PA Philadelphia 421010055 341893 1020004 1774411 2476647 hi PA Philadelphia 421010136 382995 985957 1718068 2381173 hi PA Schuylkill 421070003 19152 30388 59370 100508 mod PA Warren 421230003 14142 19940 25715 32490 mod PA Warren 421230004 13965 18884 28805 33523 mod PA Washington 421250005 31276 68512 111222 183285 mod PA Washington 421250200 32125 52910 83324 118188 mod PA Washington 421255001 1359 15854 43364 126091 low PA Westmoreland 421290008 35656 82661 148990 213978 mod PA York 421330008 85574 156166 216656 284208 hi PR Barceloneta 720170003 29823 83433 134176 243828 mod PR Bayamon 720210004 192976 679576 1002864 1292141 hi PR Bayamon 720210006 208167 587003 956783 1256603 hi 
A-57

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CPR Catano 720330004 154575 583552 958456 1233122 hi PR Catano 720330007 95500 576841 983702 1219701 hi PR Catano 720330008 99778 594607 972270 1238188 hi PR Catano 720330009 110439 457427 883511 1164315 hi PR Guayama 720570009 12086 49373 90444 174005 mod PR Salinas 721230001 20645 31312 68199 174332 mod RI Providence 440070012 223521 487990 638092 816597 hi RI Providence 440071005 148802 390751 615465 809993 hi RI Providence 440071009 226940 493584 646894 821476 hi SC Aiken 450030003 752 6505 18533 55485 low SC Barnwell 450110001 0 4022 13647 21554 low SC Charleston 450190003 40872 132716 273298 364953 mod SC Charleston 450190046 1103 1103 9529 22255 low SC Georgetown 450430006 10567 18215 22467 34357 mod SC Greenville 450450008 70221 173012 284047 379022 hi SC Greenville 450450009 56686 151862 279293 356410 hi SC Lexington 450630008 42208 131361 257820 355854 mod SC Oconee 450730001 0 2260 11136 26182 low SC Orangeburg 450750003 2904 7856 14446 24656 low SC Richland 450790007 35872 121006 255135 353072 mod SC Richland 450790021 1666 4643 13324 33098 low SC Richland 450791003 87097 213836 300874 396116 hi SC Richland 450791006 1666 5435 15920 47548 low SD Custer 460330132 0 0 3940 4686 low SD Jackson 460710001 0 0 0 0 low SD Minnehaha 460990007 65647 119287 138918 147218 hi TN Anderson 470010028 11872 59225 153931 292415 mod TN Blount 470090002 28887 70731 105939 198408 mod TN Blount 470090006 36020 72290 104178 189214 mod TN Blount 470090101 0 12650 44702 81010 low TN Bradley 470110102 2540 11940 46188 84762 low TN Coffee 470310004 1286 9718 23113 35158 low TN Davidson 470370011 77459 228349 410925 583532 hi 
A-58

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CTN Hawkins 470730002 6748 14441 22457 39857 low TN Humphreys 470850020 2474 6672 13621 23460 low TN McMinn 471070101 2540 11940 37322 84929 low TN Montgomery 471250006 21032 79399 112883 139621 mod TN Montgomery 471250106 16569 74449 109087 138438 mod TN Polk 471390003 1613 9042 14124 24537 low TN Polk 471390007 2491 9432 19726 24026 low TN Polk 471390008 2491 9432 17401 25902 low TN Polk 471390009 2491 10239 17235 24026 low TN Roane 471450009 8848 21677 37175 57683 low TN Shelby 471570034 74216 277713 497847 695164 hi TN Shelby 471570043 94449 325228 534950 751299 hi TN Shelby 471570046 18782 113964 273306 473443 mod TN Shelby 471571034 886 97506 277857 484234 low TN Stewart 471610007 787 4566 8854 20362 low TN Sullivan 471630007 28689 78826 112565 153445 mod TN Sullivan 471630009 28254 77403 117095 151856 mod TN Sumner 471651002 5070 38555 53602 119241 low TX Cameron 480610006 70071 151247 160048 167993 hi TX Dallas 481130069 93552 455917 991123 1609774 hi TX Ellis 481390015 6089 13876 35210 113413 low TX Ellis 481390016 7883 18193 68740 191352 low TX Ellis 481390017 5723 17592 50332 152699 low TX El Paso 481410037 56009 182473 337222 522824 hi TX El Paso 481410053 49083 163206 325118 519008 mod TX El Paso 481410058 78658 126481 299419 524259 hi TX Galveston 481670005 37427 62491 98724 182464 mod TX Galveston 481671002 38619 65658 98768 196215 mod TX Gregg 481830001 1349 17138 52116 105781 low TX Harris 482010046 65125 350122 756166 1283440 hi TX Harris 482010051 123431 372470 896497 1380154 hi TX Harris 482010059 151412 475338 902121 1392348 hi TX Harris 482010062 73770 352818 695432 1108749 hi 
A-59

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CTX Harris 482010070 153479 511407 991134 1610993 hi TX Harris 482011035 99581 451485 891195 1287766 hi TX Harris 482011050 23794 83705 224120 405297 mod TX Jefferson 482450009 33143 87386 182005 237033 mod TX Jefferson 482450011 13164 93985 121116 140687 mod TX Jefferson 482450020 35739 101563 177284 223336 mod TX Kaufman 482570005 6583 9190 28396 43226 low TX Nueces 483550025 99888 186846 231717 280479 hi TX Nueces 483550026 16215 28033 92841 177008 mod TX Nueces 483550032 48320 128230 228351 272861 mod UT Cache 490050004 49600 64094 80592 86020 mod UT Davis 490110001 56718 82741 178141 311810 hi UT Davis 490110004 52464 83909 154925 295333 hi UT Salt Lake 490350012 57910 183684 370433 630857 hi UT Salt Lake 490351001 31709 107346 260423 522228 mod UT Salt Lake 490352004 0 4074 35159 124394 low VA Charles 510360002 3370 32169 76679 176978 low VA Fairfax 510590005 34561 183637 408647 687195 mod VA Fairfax 510590018 87725 293189 730360 1388941 hi VA Fairfax 510591004 215952 660586 1410007 2092422 hi VA Fairfax 510591005 203219 670880 1238334 1844099 hi VA Fairfax 510595001 80603 358173 1098236 2041931 hi VA Madison 511130003 1316 4823 13930 28417 low VA Roanoke 511611004 33161 123148 197615 235072 mod VA Rockingham 511650002 17897 58020 76316 85276 mod VA Rockingham 511650003 13821 47577 71219 92912 mod VA Alexandria City 515100009 137533 622283 1320784 1894197 hi VA Hampton City 516500004 73011 182507 356676 601943 hi VA Norfolk City 517100023 124263 379455 703082 871632 hi VA Richmond City 517600024 109306 309672 524083 656099 hi VI St Croix 780100006 0 0 0 0 low VI St Croix 780100011 0 0 0 0 low VI St Croix 780100013 0 0 0 0 low 
A-60

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CVI St Croix 780100014 0 0 0 0 low VI St Croix 780100015 0 0 0 0 low VT Chittenden 500070003 50990 89229 110853 133530 hi VT Chittenden 500070014 54166 87471 110853 133749 hi VT Rutland 500210002 21330 30052 35316 46525 mod WA Clallam 530090010 17871 26073 30255 37672 mod WA Clallam 530090012 20830 27014 30036 36843 mod WA King 530330057 131605 394412 730218 1093083 hi WA King 530330080 116769 423064 811856 1157199 hi WA Pierce 530530021 68072 250876 548806 839357 hi WA Pierce 530530031 55628 275358 555755 820805 hi WA Skagit 530570012 3580 22573 32120 70660 low WA Skagit 530571003 1733 21622 32120 75069 low WA Snohomish 530610016 46071 152230 303720 432356 mod WA Whatcom 530730011 60525 83632 111425 126291 hi WI Brown 550090005 79060 158940 201226 215144 hi WI Dane 550250041 79610 189421 306132 353861 hi WI Forest 550410007 1330 3913 5514 6669 low WI Marathon 550730005 5095 42173 61417 93151 low WI Milwaukee 550790007 248317 606921 865925 1037293 hi WI Milwaukee 550790026 214859 572784 834939 1014161 hi WI Milwaukee 550790041 137816 455868 765734 964876 hi WI Oneida 550850996 8351 17018 17018 23821 low WI Sauk 551110007 2743 15039 24240 43368 low WI Vilas 551250001 934 934 8639 10755 low WI Wood 551410016 19525 33790 43315 50360 mod WV Brooke 540090005 25010 64711 92813 118070 mod WV Brooke 540090007 30794 70187 95823 120385 mod WV Cabell 540110006 50835 88879 125923 164495 hi WV Greenbrier 540250001 2158 9273 18280 23902 low WV Hancock 540290005 6006 23418 77160 125873 low WV Hancock 540290007 14924 44311 83167 128126 mod WV Hancock 540290008 24095 49351 63727 91485 mod 
A-61

Population Residing within: Analysis 

State County Monitor ID 5km 10km 15km 20km Population1 CWV Hancock 540290009 20946 61117 90717 115283 mod WV Hancock 540290011 31890 76198 95992 115162 mod WV Hancock 540290014 22857 58620 89998 120724 mod WV Hancock 540290015 20793 45848 65851 102031 mod WV Hancock 540290016 19278 70483 96151 114992 mod WV Hancock 540291004 24761 63677 91977 115615 mod WV Kanawha 540390004 46977 80511 120631 164476 mod WV Kanawha 540390010 48231 83340 123101 172217 mod WV Kanawha 540392002 21694 61059 111812 164912 mod WV Marshall 540511002 13403 32048 55054 95735 mod WV Monongalia 540610003 43902 65672 80405 98315 mod WV Monongalia 540610004 44079 63708 80385 98966 mod WV Monongalia 540610005 46591 61019 77800 99544 mod WV Ohio 540690007 20818 60048 91967 126981 mod WV Wayne 540990002 17320 62645 124477 178576 mod WV Wayne 540990003 17320 59989 123349 177744 mod WV Wayne 540990004 16553 54251 122072 179815 mod WV Wayne 540990005 13314 48330 114824 173807 mod WV Wood 541071002 24917 70324 104458 128127 mod WY Campbell 560050857 3288 11413 23902 25752 low 

Notes: 

1 Population bins: low (≤10,000); mid (10,001 to 50,000); hi (>50,000) using population within 5 km of ambie

2 COV bins: a (≤100%); b (>100 to ≤200); c (>200). 3 GSD bins: a (≤2.17); b (>2.17 to ≤2.94); c (>2.94). 4 Sum of emissions within 20 km radius of ambient monitor based on 2002 NEI. 
A-62

A.1.2 Analysis of SO2 Emission Sources Surrounding Ambient Monitors 

Distances of the 5-minute and 1-hour ambient monitoring sites to stationary sources 

emitting SO2 were estimated using data from the 2002 National Emissions Inventory1

 (NEI). 

The NEI database reports emissions of SO2 in tons per year (tpy) for 98,667 unique emission 

sources at various points of release. The release locations were all taken from the latitude 

longitude values within the NEI. First, all SO2 emissions were summed for identical latitude and 

longitude entries while retaining source codes for the emissions (e.g., Standard Industrial Code 

(SIC), or North American Industrial Classification System (NAICS)). Therefore, any facility 

containing similar emission processes were summed at the stack location, resulting in 32,521 

observations. These data were then screened for sources with emissions greater than 5 tpy, 

yielding 6,104 unique SO2 emission sources. Locations of these stationary source emissions 

were compared with ambient monitoring locations using the following formula: 

d arccos(sin  lat  lat  lat  lat cos()cos()cos()sin(  ))rlonlon 1 2 1 2 12

where 

d = distance (kilometers) 

lat1 = latitude of a monitor (radians) 

lat2 = latitude of source emission (radians) 

lon1 = longitude of monitor (radians) 

lon2 = longitude of source emission (radians) 

r = approximate radius of the earth (or 6,371 km) 

Location data for monitors and sources provided in the AQS and NEI data bases were 

given in units of degrees therefore, these were first converted to radians by dividing by 180/π. 

For each monitor, source emissions within 20 km of the monitor were retained. 

Table A.1-5 contains the summary of the distance of stationary source emissions to each 

of the monitors in the broader SO2 monitoring network. There were varying numbers of sources 

emitting >5 tpy of SO2 and located within a 20 km radius for many of the monitors. Some of the 

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-63

influenced by a specific single sources (e.g., Missouri monitor IDs 290210009, 290210011, 

290930030), or by several sources (e.g., Pennsylvania monitor IDs 420030021, 420030031) of 

varying emission strength. A few of the monitors contained no source emissions >5 tpy (e.g., 

Iowa monitor IDs 191770005, 191770006).
A-64

Table A.1-5. Distance of ambient SO2 monitors (all used in analysis) to stationary sources emitting 

per year, within a 20 kilometer distance of monitoring site, and SO2 emissions associated with thos

sources. 

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 010330044 3 16680 28821 30 30 51 49960 49960 6.0 0.7 5.5 5.5 010710020 3 15119 25004 98 98 1276 43983 43983 5.7 2.4 3.1 3.1 010731003 43 151 227 5 5 38 786 982 11.4 5.5 1.1 1.2 010790003 5 1787 3416 6 6 58 7852 7852 8.4 1.7 5.5 5.5 010970028 10 6613 13057 14 14 214 38917 38917 7.5 5.8 1.4 1.4 010972005 9 132 154 5 5 72 440 440 7.7 2.1 4.3 4.3 011011002 4 913 1183 180 180 403 2663 2663 12.7 7.2 4.5 4.5 040070009 0 0.0 0.0 0.0 0.0 040071001 2 9219 10723 1637 1637 9219 16801 16801 1.3 0.5 0.9 0.9 040130019 8 23 19 10 10 19 69 69 11.0 3.6 5.6 5.6 040133002 9 21 19 10 10 14 69 69 10.8 6.6 1.9 1.9 040133003 9 20 19 6 6 14 69 69 12.5 4.7 5.5 5.5 040191011 1 3119 3119 3119 3119 3119 3119 6.1 0.0 6.1 6.1 040212001 0 0.0 0.0 0.0 0.0 051190007 1 20 20 20 20 20 20 6.3 0.0 6.3 6.3 051191002 1 20 20 20 20 20 20 13.7 0.0 13.7 13.7 051390006 6 421 689 8 8 22 1689 1689 7.7 4.2 1.9 1.9 060010010 7 53 66 5 5 14 187 187 8.9 5.4 1.2 1.2 060130002 15 1004 2007 6 6 58 7009 7009 13.5 2.8 9.6 9.6 060130006 9 559 789 5 5 38 1829 1829 13.0 6.4 2.5 2.5 060130010 15 1189 1977 6 6 419 7009 7009 8.3 5.6 1.6 1.6 060131001 13 1507 2036 6 6 793 7009 7009 10.1 5.9 0.2 0.2 060131002 3 26 21 6 6 25 48 48 11.7 1.4 10.1 10.1 060131003 9 559 789 5 5 38 1829 1829 12.6 4.8 5.4 5.4 060131004 9 559 789 5 5 38 1829 1829 12.8 5.7 4.1 4.1 060132001 15 1189 1977 6 6 419 7009 7009 8.8 5.4 2.3 2.3 060133001 16 507 1104 6 6 48 4337 4337 9.8 6.1 0.7 0.7 060250005 1 7 7 7 7 7 7 18.0 0.0 18.0 18.0 060371002 3 17 7 10 10 17 24 24 6.8 2.1 4.7 4.7 060371103 15 37 36 7 7 29 119 119 13.8 5.1 6.3 6.3 060374002 32 183 313 5 5 46 1503 1503 10.4 5.2 4.1 4.1 060375001 31 203 342 5 5 61 1503 1503 13.4 5.9 3.7 3.7 
A-65

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 060375005 12 192 332 6 6 33 1119 1119 9.1 5.9 2.3 2.3 060591003 7 10 5 5 5 7 18 18 13.9 5.7 5.3 5.3 060658001 4 75 76 17 17 50 181 181 16.8 4.7 9.8 9.8 060670002 1 5 5 5 5 5 5 14.8 0.0 14.8 14.8 060670006 1 58 58 58 58 58 58 9.7 0.0 9.7 9.7 060710012 1 8 8 8 8 8 8 11.9 0.0 11.9 11.9 060710014 2 126 132 32 32 126 219 219 8.0 3.0 5.9 5.9 060710306 2 126 132 32 32 126 219 219 8.1 3.3 5.7 5.7 060711234 3 97 85 6 6 110 175 175 4.9 5.8 1.3 1.3 060712002 2 102 112 22 22 102 181 181 13.2 2.9 11.2 11.2 060714001 1 32 32 32 32 32 32 6.5 0.0 6.5 6.5 060730001 1 21 21 21 21 21 21 4.0 0.0 4.0 4.0 060731007 3 11 9 5 5 7 21 21 12.9 1.3 11.8 11.8 060732007 1 21 21 21 21 21 21 16.4 0.0 16.4 16.4 060750005 6 66 83 5 5 39 224 224 13.3 6.2 1.8 1.8 060791005 7 536 1369 6 6 24 3642 3642 1.0 0.2 0.8 0.8 060792001 7 536 1369 6 6 24 3642 3642 10.5 0.2 10.2 10.2 060792004 7 536 1369 6 6 24 3642 3642 2.5 0.1 2.3 2.3 060794002 7 536 1369 6 6 24 3642 3642 18.4 0.1 18.3 18.3 060830008 3 39 43 10 10 18 89 89 9.7 6.2 2.8 2.8 060831012 2 554 357 302 302 554 807 807 16.5 0.1 16.4 16.4 060831013 2 554 357 302 302 554 807 807 14.1 0.1 14.1 14.1 060831015 1 18 18 18 18 18 18 15.6 0.0 15.6 15.6 060831016 1 18 18 18 18 18 18 15.2 0.0 15.2 15.2 060831019 1 18 18 18 18 18 18 13.7 0.0 13.7 13.7 060831020 3 39 43 10 10 18 89 89 7.3 8.2 2.0 2.0 060831025 3 39 43 10 10 18 89 89 10.9 8.9 0.8 0.8 060831026 3 39 43 10 10 18 89 89 9.9 8.0 0.9 0.9 060831027 3 39 43 10 10 18 89 89 10.3 7.7 1.7 1.7 060832004 2 554 357 302 302 554 807 807 4.2 0.1 4.2 4.2 060832011 3 39 43 10 10 18 89 89 10.4 8.4 3.9 3.9 060834003 2 554 357 302 302 554 807 807 16.4 0.1 16.4 16.4 060870003 1 722 722 722 722 722 722 0.8 0.0 0.8 0.8 060950001 13 1371 2071 6 6 790 7009 7009 7.1 3.3 2.1 2.1 060950004 12 1480 2124 6 6 791 7009 7009 13.0 4.9 5.5 5.5 061113001 2 9 3 7 7 9 11 11 10.4 5.1 6.8 6.8 
A-66

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 080010007 24 1001 3352 8 8 25 15958 15958 9.8 6.1 2.4 2.4 080013001 20 1191 3657 8 8 28 15958 15958 8.3 5.8 1.6 1.6 080310002 24 1098 3356 6 6 28 15958 15958 9.2 4.5 3.9 3.9 080416001 3 1670 2857 7 7 34 4969 4969 6.2 8.6 0.8 0.8 080416004 3 2849 4920 7 7 10 8530 8530 13.0 3.9 10.7 10.7 080416011 3 2849 4920 7 7 10 8530 8530 9.9 7.6 2.5 2.5 080416018 2 4268 6026 7 7 4268 8530 8530 6.8 0.5 6.5 6.5 090010012 11 425 1198 5 5 21 4024 4024 6.1 4.9 2.1 2.1 090010017 3 252 423 5 5 11 741 741 9.6 6.4 5.7 5.7 090011123 0 0.0 0.0 0.0 0.0 090012124 4 192 366 5 5 10 741 741 7.4 6.0 2.3 2.3 090019003 10 504 1257 5 5 10 4024 4024 13.2 5.1 4.0 4.0 090031005 28 45 106 5 5 12 522 522 14.2 3.6 3.0 3.0 090031018 7 16 9 5 5 15 30 30 7.7 6.3 1.9 1.9 090032006 6 14 7 5 5 15 25 25 4.5 5.1 0.5 0.5 090090027 8 595 1388 5 5 32 4012 4012 6.3 7.3 1.0 1.0 090091003 9 565 1302 5 5 43 4012 4012 7.4 8.2 0.7 0.7 090091123 9 565 1302 5 5 43 4012 4012 7.3 8.2 0.8 0.8 090092123 5 86 96 9 9 28 198 198 9.0 5.7 0.8 0.8 090110007 6 650 1088 7 7 110 2755 2755 8.4 3.0 3.3 3.3 090130003 0 0.0 0.0 0.0 0.0 100031003 34 975 1619 5 5 112 6720 6720 9.0 5.2 1.5 1.5 100031007 11 3126 6528 15 15 103 19923 19923 10.5 2.2 9.1 9.1 100031008 24 1657 4554 5 5 60 19923 19923 10.9 6.9 2.2 2.2 100031013 34 975 1619 5 5 112 6720 6720 9.1 4.8 2.8 2.8 100032002 36 802 1272 5 5 97 5051 5051 10.2 6.2 1.1 1.1 100032004 39 1526 3681 5 5 116 19923 19923 10.9 6.2 1.3 1.3 110010041 13 1410 4437 7 7 24 16141 16141 11.7 6.5 0.6 0.6 120110010 8 2397 6653 17 17 41 18861 18861 11.0 6.1 5.1 5.1 120310032 14 2715 5784 5 5 287 20908 20908 9.0 4.2 1.3 1.3 120310080 15 2534 5617 5 5 257 20908 20908 12.0 4.7 1.1 1.1 120310081 13 2923 5965 5 5 317 20908 20908 7.9 4.6 1.3 1.3 120310097 14 2715 5784 5 5 287 20908 20908 9.2 4.7 3.1 3.1 120330004 6 7262 14101 6 6 330 35417 35417 9.3 4.2 4.9 4.9 120330022 6 7262 14101 6 6 330 35417 35417 7.6 4.4 2.4 2.4 120470015 3 755 1268 18 18 27 2218 2218 3.0 0.5 2.6 2.6 
A-67

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 120570021 18 4986 11445 6 6 341 47103 47103 11.6 6.5 1.4 1.4 120570053 19 4728 11180 6 6 104 47103 47103 10.8 3.5 5.9 5.9 120570081 18 6781 13097 6 6 1116 47103 47103 14.4 4.5 8.1 8.1 120570095 17 3845 11285 6 6 61 47103 47103 10.1 6.2 2.5 2.5 120570109 16 4084 11610 6 6 83 47103 47103 9.9 4.0 6.7 6.7 120571035 18 4986 11445 6 6 341 47103 47103 10.6 5.8 1.6 1.6 120574004 3 2872 4949 11 11 19 8587 8587 14.2 8.5 4.4 4.4 120813002 5 73 93 6 6 9 208 208 7.2 8.3 0.7 0.7 120860019 7 34 45 5 5 12 130 130 9.6 5.8 2.6 2.6 120890005 4 1262 1594 11 11 765 3509 3509 4.5 5.0 1.1 1.1 120890009 4 1262 1594 11 11 765 3509 3509 4.2 4.6 1.1 1.1 120952002 5 9 4 5 5 10 14 14 13.8 2.8 10.1 10.1 120993004 6 39 38 5 5 32 103 103 12.0 3.1 7.0 7.0 121030023 7 3546 7041 6 6 104 18822 18822 7.4 6.4 2.3 2.3 121033002 6 4136 7521 23 23 156 18822 18822 10.3 4.2 3.5 3.5 121035002 2 15398 21767 7 7 15398 30790 30790 13.6 0.1 13.5 13.5 121035003 2 15398 21767 7 7 15398 30790 30790 9.8 4.0 7.0 7.0 121050010 9 2386 2929 6 6 1210 8587 8587 10.4 3.1 3.7 3.7 121052006 13 1691 2627 6 6 230 8587 8587 11.9 6.2 2.7 2.7 121071008 3 9965 12565 12 12 5799 24083 24083 5.8 3.4 2.6 2.6 121151002 0 0.0 0.0 0.0 0.0 121151005 0 0.0 0.0 0.0 0.0 121151006 2 71 90 7 7 71 135 135 15.8 0.8 15.2 15.2 130090001 2 36975 52282 6 6 36975 73943 73943 11.3 5.4 7.5 7.5 130150002 4 40604 80047 21 21 862 160673 160673 10.4 5.2 2.5 2.5 130210012 11 245 468 6 6 17 1576 1576 10.1 5.2 1.5 1.5 130510019 14 1362 2664 8 8 235 7969 7969 7.1 4.1 0.4 0.4 130510021 14 1362 2664 8 8 235 7969 7969 6.8 4.4 1.4 1.4 130511002 14 1362 2664 8 8 235 7969 7969 6.2 3.4 1.6 1.6 130950006 4 1693 2220 5 5 932 4905 4905 6.3 5.3 2.2 2.2 131110091 1 1900 1900 1900 1900 1900 1900 1.6 0.0 1.6 1.6 131150003 8 4057 9625 5 5 101 27594 27594 1.4 0.4 1.1 1.1 131210048 7 4339 10445 68 68 169 27993 27993 10.3 2.1 8.4 8.4 131210055 7 4339 10445 68 68 169 27993 27993 15.1 3.3 8.0 8.0 131270006 3 821 948 14 14 586 1865 1865 3.6 2.8 1.8 1.8 132150008 4 1740 3214 8 8 197 6559 6559 12.5 2.6 10.1 10.1 
A-68

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 132450003 15 1335 2379 8 8 545 8275 8275 8.0 1.4 4.7 4.7 150030010 7 2231 2339 79 79 1566 6978 6978 5.0 4.6 2.5 2.5 150030011 7 2231 2339 79 79 1566 6978 6978 5.7 5.3 2.2 2.2 150031001 3 1043 1509 6 6 350 2774 2774 10.1 8.2 0.7 0.7 150031006 7 2231 2339 79 79 1566 6978 6978 6.1 4.8 1.8 1.8 160050004 2 804 606 376 376 804 1233 1233 1.3 0.1 1.2 1.2 160290003 13 967 2904 7 7 33 10544 10544 2.9 0.4 2.7 2.7 160290031 13 967 2904 7 7 33 10544 10544 1.4 1.1 0.8 0.8 160770011 2 804 606 376 376 804 1233 1233 0.8 0.1 0.7 0.7 170010006 4 965 614 392 392 817 1834 1834 4.8 4.5 1.9 1.9 170190004 3 121 182 10 10 21 331 331 1.8 0.9 0.8 0.8 170310050 47 900 1775 5 5 65 5951 8443 11.0 5.0 2.1 3.4 170310059 40 910 1928 5 5 65 7381 8443 7.5 5.2 1.5 1.5 170310063 23 1041 1800 5 5 17 6229 6229 11.0 6.8 0.9 0.9 170310064 50 1015 1902 5 5 51 6229 8443 14.6 4.1 3.9 6.4 170310076 36 930 1976 5 5 26 8443 8443 13.2 4.0 4.9 4.9 170311018 26 924 1721 5 5 16 6229 6229 10.7 6.8 0.5 0.5 170311601 12 3807 5540 7 7 1090 15934 15934 14.1 6.4 4.0 4.0 170312001 43 920 1807 5 5 64 6229 8443 16.5 3.2 3.4 8.4 170314002 25 982 1738 5 5 17 6229 6229 9.0 3.1 3.9 3.9 170314201 4 165 230 7 7 77 498 498 18.0 3.0 13.4 13.4 170318003 36 835 1797 5 5 70 8443 8443 8.8 2.4 2.9 2.9 170436001 12 2986 5690 6 6 17 15934 15934 16.5 5.1 1.5 1.5 170990007 4 890 1527 6 6 189 3178 3178 7.2 6.1 0.5 0.5 171150013 11 1251 2596 22 22 164 8032 8032 3.3 2.3 1.8 1.8 171170002 0 0.0 0.0 0.0 0.0 171190008 15 4510 11972 6 6 111 45960 45960 10.1 3.7 3.2 3.2 171190017 40 877 2339 6 6 117 9663 12063 9.5 6.6 0.5 0.7 171191010 28 954 2564 6 6 183 12063 12063 10.5 6.8 0.7 0.7 171193007 28 2595 8875 6 6 214 45960 45960 12.2 6.9 2.4 2.4 171193009 26 2789 9193 6 6 247 45960 45960 12.4 7.5 2.9 2.9 171430024 10 7333 11752 5 5 67 35748 35748 13.2 5.8 1.3 1.3 171570001 2 13148 18554 28 28 13148 26268 26268 6.4 0.4 6.0 6.0 171610003 10 945 1612 7 7 169 4963 4963 11.4 5.6 2.3 2.3 171630010 30 445 1152 6 6 68 6250 6250 9.3 4.1 1.3 1.3 171631010 30 445 1152 6 6 68 6250 6250 10.4 4.3 1.1 1.1 
A-69

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 171631011 2 13148 18554 28 28 13148 26268 26268 4.0 0.6 3.6 3.6 171670006 5 2170 3169 9 9 202 7210 7210 7.3 3.6 4.9 4.9 171790004 6 12212 13311 22 22 10290 35748 35748 5.4 5.2 0.8 0.8 171850001 3 42452 25439 27097 27097 28443 71817 71817 2.9 0.1 2.8 2.8 171851001 3 42452 25439 27097 27097 28443 71817 71817 5.9 0.1 5.8 5.8 171970013 19 2439 6269 6 6 37 25224 25224 6.6 4.8 1.1 1.1 180270002 6 10869 16456 9 9 2241 41536 41536 6.3 0.6 5.8 5.8 180290004 7 21579 32930 174 174 1574 85699 85699 4.2 4.1 1.2 1.2 180430004 8 6500 10778 12 12 484 23995 23995 13.4 3.1 8.8 8.8 180430007 10 6721 10131 12 12 516 23995 23995 9.2 6.4 1.1 1.1 180431004 9 7442 10470 12 12 798 23995 23995 10.0 3.5 5.0 5.0 180450001 3 18552 32099 10 10 28 55617 55617 9.8 8.7 4.5 4.5 180510001 3 42452 25439 27097 27097 28443 71817 71817 2.0 0.1 1.8 1.8 180510002 3 42452 25439 27097 27097 28443 71817 71817 2.9 0.0 2.9 2.9 180630001 0 0.0 0.0 0.0 0.0 180630002 1 147 147 147 147 147 147 19.2 0.0 19.2 19.2 180630003 0 0.0 0.0 0.0 0.0 180730002 4 6874 1422 6085 6085 6204 9002 9002 4.3 1.0 3.5 3.5 180730003 4 6874 1422 6085 6085 6204 9002 9002 10.2 1.2 9.5 9.5 180770004 2 19099 1297 18182 18182 19099 20016 20016 4.3 0.1 4.3 4.3 180890022 50 1014 1502 5 6 188 5951 6318 14.1 4.0 0.8 1.8 180892008 39 938 1945 5 5 72 8443 8443 6.4 4.1 1.6 1.6 180910005 3 4166 4640 20 20 3301 9178 9178 9.1 9.7 0.4 0.4 180910007 2 4599 6476 20 20 4599 9178 9178 6.0 0.8 5.4 5.4 180970042 22 2358 6820 5 5 36 30896 30896 11.2 2.9 7.8 7.8 180970054 20 2554 7138 5 5 23 30896 30896 3.3 2.3 0.9 0.9 180970057 20 2554 7138 5 5 23 30896 30896 4.2 2.0 0.9 0.9 180970072 21 2433 6980 5 5 19 30896 30896 6.9 3.5 0.8 0.8 180970073 20 2547 7141 5 5 18 30896 30896 13.7 2.3 6.2 6.2 181091001 3 6006 9709 242 242 561 17216 17216 4.3 2.4 2.1 2.1 181230006 8 7033 17145 7 7 38 49028 49028 7.7 4.3 2.8 2.8 181230007 8 7033 17145 7 7 38 49028 49028 6.8 4.2 2.1 2.1 181250005 6 10869 16456 9 9 2241 41536 41536 3.0 4.7 0.9 0.9 181270011 23 1703 2266 20 20 1062 9178 9178 6.7 6.2 2.2 2.2 181270017 22 1363 1612 20 20 1029 6318 6318 5.4 5.7 2.0 2.0 181270023 21 1427 1623 23 23 1062 6318 6318 4.1 4.4 1.1 1.1 
A-70

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 181470002 7 15627 24405 7 7 66 53196 53196 13.0 3.6 8.0 8.0 181470010 4 15099 25616 20 20 3589 53196 53196 12.3 6.6 3.3 3.3 181530004 3 9270 8089 10 10 12846 14955 14955 12.1 6.4 4.8 4.8 181630012 5 1806 2589 5 5 382 6004 6004 13.1 7.7 3.1 3.1 181631002 5 1806 2589 5 5 382 6004 6004 8.5 5.3 3.4 3.4 181670018 6 10842 25028 12 12 417 61901 61901 6.8 3.6 5.0 5.0 181671014 6 10842 25028 12 12 417 61901 61901 6.8 5.9 1.9 1.9 181730002 8 13636 16457 50 50 3559 41049 41049 2.9 0.4 2.5 2.5 181731001 8 13636 16457 50 50 3559 41049 41049 3.0 0.5 2.5 2.5 181770006 2 6446 9089 19 19 6446 12873 12873 2.1 1.4 1.1 1.1 181770007 2 6446 9089 19 19 6446 12873 12873 3.2 2.5 1.4 1.4 190330018 4 2684 3305 20 20 1934 6850 6850 3.9 3.7 0.4 0.4 190450018 2 4694 839 4101 4101 4694 5287 5287 1.4 0.9 0.7 0.7 190450019 2 4694 839 4101 4101 4694 5287 5287 1.3 1.0 0.6 0.6 190450020 2 4694 839 4101 4101 4694 5287 5287 3.4 0.6 3.0 3.0 191110006 1 29 29 29 29 29 29 3.7 0.0 3.7 3.7 191111007 2 104 105 29 29 104 179 179 13.3 6.3 8.8 8.8 191130028 7 2200 2428 12 12 1954 5480 5480 5.8 2.4 2.8 2.8 191130029 7 2200 2428 12 12 1954 5480 5480 3.8 3.1 0.5 0.5 191130031 7 2200 2428 12 12 1954 5480 5480 4.3 3.2 0.5 0.5 191130032 7 2200 2428 12 12 1954 5480 5480 3.5 2.7 0.6 0.6 191130034 7 2200 2428 12 12 1954 5480 5480 3.6 2.5 0.2 0.2 191130038 7 2200 2428 12 12 1954 5480 5480 3.9 1.9 0.6 0.6 191130039 7 2200 2428 12 12 1954 5480 5480 4.6 3.0 1.1 1.1 191390016 5 6227 6934 83 83 3790 15901 15901 8.7 6.9 2.4 2.4 191390017 4 7763 6956 463 463 7345 15901 15901 3.8 3.6 0.6 0.6 191390020 4 7763 6956 463 463 7345 15901 15901 4.9 4.4 0.9 0.9 191630015 7 1345 1810 17 17 336 4963 4963 9.5 5.0 1.1 1.1 191630017 7 2120 3515 17 17 303 8983 8983 9.6 4.2 1.1 1.1 191770004 0 0.0 0.0 0.0 0.0 191770005 0 0.0 0.0 0.0 0.0 191770006 0 0.0 0.0 0.0 0.0 191930018 4 9208 10818 15 15 7845 21127 21127 6.4 4.3 0.7 0.7 201070002 0 0.0 0.0 0.0 0.0 201250006 4 468 464 11 11 428 1006 1006 5.8 9.3 0.5 0.5 201450001 0 0.0 0.0 0.0 0.0 
A-71

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 201730010 3 269 448 6 6 15 785 785 11.4 3.1 9.0 9.0 201910002 3 269 448 6 6 15 785 785 16.3 2.4 13.6 13.6 201950001 0 0.0 0.0 0.0 0.0 202090001 14 1388 2341 6 6 34 7625 7625 9.2 5.9 3.5 3.5 202090020 14 1388 2341 6 6 34 7625 7625 9.0 6.1 0.6 0.6 202090021 13 1494 2402 6 6 40 7625 7625 8.6 5.5 3.4 3.4 210190015 9 1323 2058 25 25 401 6285 6285 12.3 5.5 1.6 1.6 210190017 10 1193 1983 25 25 343 6285 6285 12.8 5.4 2.9 2.9 210191003 8 1271 2194 25 25 343 6285 6285 9.3 5.4 1.3 1.3 210370003 11 6817 20950 12 12 268 69953 69953 12.0 3.0 8.1 8.1 210371001 11 465 664 12 12 213 1848 1848 8.5 3.4 4.2 4.2 210590005 4 15241 25506 26 26 3871 53196 53196 7.4 6.8 2.2 2.2 210670012 3 209 316 12 12 42 573 573 3.2 2.2 1.2 1.2 210890007 5 961 1147 25 25 401 2589 2589 10.9 6.2 5.1 5.1 210910012 9 12162 22226 7 7 38 53196 53196 10.4 5.1 1.2 1.2 211010013 4 2256 2755 5 5 1508 6004 6004 10.2 5.5 2.0 2.0 211010014 10 10948 15581 5 5 2980 41049 41049 12.9 1.4 11.5 11.5 211110032 14 6208 8948 38 38 516 23995 23995 11.4 5.6 2.4 2.4 211110051 12 3259 5326 38 38 168 14977 14977 10.6 7.5 1.6 1.6 211111041 11 6268 9779 12 12 234 23995 23995 9.1 7.3 1.3 1.3 211390004 4 444 869 6 6 11 1747 1747 8.0 6.9 3.1 3.1 211450001 7 8769 13010 174 174 7435 37077 37077 7.5 3.4 2.0 2.0 211451024 3 587 1005 6 6 7 1747 1747 18.2 2.1 15.8 15.8 211451026 3 587 1005 6 6 7 1747 1747 15.3 2.2 12.7 12.7 212270008 1 52 52 52 52 52 52 19.1 0.0 19.1 19.1 220150008 2 77 21 62 62 77 91 91 8.7 0.1 8.6 8.6 220190008 16 3352 5531 6 6 184 18851 18851 7.6 6.1 1.2 1.2 220330009 28 1406 3913 6 6 45 18680 18680 5.8 5.6 1.5 1.5 220730004 1 2166 2166 2166 2166 2166 2166 10.1 0.0 10.1 10.1 220870002 18 419 846 8 8 52 3009 3009 8.8 4.2 0.5 0.5 221210001 28 1116 3650 6 6 33 18680 18680 5.4 4.7 2.4 2.4 230010011 9 31 41 5 5 23 140 140 6.6 4.3 1.3 1.3 230030009 1 90 90 90 90 90 90 1.9 0.0 1.9 1.9 230030012 1 90 90 90 90 90 90 1.0 0.0 1.0 1.0 230031003 1 90 90 90 90 90 90 1.3 0.0 1.3 1.3 230031013 3 16 17 5 5 7 36 36 4.7 4.5 1.4 1.4 
A-72

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 230031018 4 193 233 7 7 133 499 499 8.5 5.6 0.3 0.3 230050014 12 267 628 5 5 16 2091 2091 6.1 4.8 1.2 1.2 230050027 12 267 628 5 5 16 2091 2091 6.0 4.7 0.8 0.8 230172007 2 249 344 6 6 249 492 492 1.0 0.1 1.0 1.0 240010006 2 681 685 197 197 681 1166 1166 8.9 4.0 6.0 6.0 240032002 20 3247 9622 5 5 21 39974 39974 11.9 4.5 2.7 2.7 240053001 22 4429 11101 5 5 27 39974 39974 11.9 3.3 4.6 4.6 245100018 21 3101 9402 5 5 22 39974 39974 9.1 4.6 1.4 1.4 245100036 21 4635 11331 5 5 22 39974 39974 6.6 3.3 1.6 1.6 250051004 24 1867 8085 6 6 31 39593 39593 7.5 6.7 0.1 0.1 250090005 25 65 148 6 6 26 762 762 9.6 6.6 0.3 0.3 250091004 23 878 3071 5 5 16 14132 14132 11.3 6.3 0.8 0.8 250091005 22 917 3137 5 5 16 14132 14132 11.2 6.3 0.7 0.7 250095004 14 88 197 8 8 25 762 762 8.6 4.2 0.7 0.7 250130016 34 216 907 5 5 14 5282 5282 7.6 5.2 0.5 0.5 250131009 32 65 148 5 5 13 671 671 8.4 4.7 1.7 1.7 250154002 12 72 113 6 6 29 363 363 15.8 3.4 9.1 9.1 250171701 55 139 678 5 5 15 640 5007 13.3 4.6 0.4 2.9 250174003 57 127 663 5 5 13 460 5007 12.2 4.0 0.6 5.6 250250002 62 129 639 5 5 14 640 5007 9.6 6.1 0.7 1.1 250250019 50 156 710 5 5 14 640 5007 12.0 3.8 0.7 4.2 250250020 58 138 660 5 5 15 640 5007 10.0 5.0 1.1 3.0 250250021 58 137 660 5 5 14 640 5007 10.6 4.7 1.8 3.4 250250040 59 135 654 5 5 14 640 5007 10.2 5.3 1.0 1.4 250250042 60 133 649 5 5 14 640 5007 9.4 5.8 0.5 0.7 250251003 58 380 1952 5 5 15 5007 14132 11.0 4.6 1.0 2.1 250270020 28 25 35 6 6 12 178 178 5.0 5.9 0.1 0.1 250270023 28 25 35 6 6 12 178 178 5.1 5.8 0.6 0.6 260410902 3 1407 1264 671 671 685 2867 2867 2.5 1.5 0.8 0.8 260490021 4 42 24 7 7 48 63 63 10.9 4.5 4.2 4.2 260492001 2 64 79 7 7 64 120 120 19.0 0.4 18.8 18.8 260810020 9 60 96 9 9 12 280 280 10.5 5.6 4.3 4.3 260991003 3 239 287 10 10 148 560 560 14.0 3.4 10.2 10.2 261130001 1 58 58 58 58 58 58 10.3 0.0 10.3 10.3 261470005 3 524 431 31 31 715 826 826 8.7 5.9 3.8 3.8 261530001 0 0.0 0.0 0.0 0.0 
A-73

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 261630001 36 1780 5390 5 5 109 30171 30171 10.9 4.0 5.4 5.4 261630005 34 1894 5529 5 5 117 30171 30171 6.1 5.4 1.2 1.2 261630015 32 1070 2436 5 5 117 8913 8913 5.5 4.2 1.5 1.5 261630016 31 1104 2469 5 5 121 8913 8913 9.0 2.7 3.6 3.6 261630019 23 1358 2828 10 10 121 8913 8913 17.3 4.5 3.7 3.7 261630025 6 13 14 5 5 9 42 42 14.8 2.4 11.2 11.2 261630027 33 1952 5605 5 5 121 30171 30171 5.5 5.2 0.4 0.4 261630033 32 1070 2436 5 5 117 8913 8913 5.0 4.5 0.4 0.4 261630062 31 1104 2469 5 5 121 8913 8913 9.0 2.9 3.1 3.1 261630092 33 1952 5605 5 5 121 30171 30171 5.4 5.1 0.9 0.9 270031002 10 1332 4067 5 5 11 12904 12904 14.3 4.4 4.7 4.7 270176316 5 72 84 5 5 26 190 190 13.7 6.8 2.2 2.2 270370020 15 610 1015 9 9 104 3071 3071 11.9 6.1 0.9 0.9 270370423 17 805 1227 9 9 205 3821 3821 11.6 5.5 0.4 0.4 270370439 14 639 1047 9 9 79 3071 3071 12.5 5.8 2.6 2.6 270370441 12 720 1114 9 9 79 3071 3071 11.6 5.7 1.6 1.6 270370442 11 506 873 9 9 54 2869 2869 12.2 5.5 2.3 2.3 270530954 24 913 2729 5 5 48 12904 12904 10.9 5.8 0.6 0.6 270530957 21 878 2877 5 5 12 12904 12904 10.7 5.3 0.9 0.9 270711240 1 67 67 67 67 67 67 0.3 0.0 0.3 0.3 271230864 27 769 2540 5 5 46 12904 12904 12.0 4.8 3.9 3.9 271410003 1 26742 26742 26742 26742 26742 26742 4.9 0.0 4.9 4.9 271410011 1 26742 26742 26742 26742 26742 26742 1.7 0.0 1.7 1.7 271410012 1 26742 26742 26742 26742 26742 26742 1.8 0.0 1.8 1.8 271410013 1 26742 26742 26742 26742 26742 26742 1.1 0.0 1.1 1.1 271630436 21 545 997 7 7 104 3821 3821 11.1 5.6 0.9 0.9 271710007 2 13397 18873 52 52 13397 26742 26742 11.8 6.5 7.2 7.2 280470007 2 12535 17718 6 6 12535 25064 25064 6.5 7.9 0.9 0.9 280490018 5 51 45 15 15 30 128 128 7.3 5.4 3.2 3.2 280590006 7 4903 10049 12 12 96 27207 27207 7.0 4.9 3.3 3.3 280810004 0 0.0 0.0 0.0 0.0 290210009 1 3563 3563 3563 3563 3563 3563 0.7 0.0 0.7 0.7 290210011 1 3563 3563 3563 3563 3563 3563 0.9 0.0 0.9 0.9 290470025 15 1682 2364 6 6 105 7625 7625 11.9 4.8 2.8 2.8 290770026 4 2302 2728 5 5 1772 5657 5657 8.2 4.5 2.3 2.3 290770032 4 2302 2728 5 5 1772 5657 5657 7.8 3.9 3.0 3.0 
A-74

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 290770037 4 2302 2728 5 5 1772 5657 5657 9.2 6.1 0.6 0.6 290770040 4 2302 2728 5 5 1772 5657 5657 9.2 6.2 0.5 0.5 290770041 4 2302 2728 5 5 1772 5657 5657 8.6 5.5 1.2 1.2 290930030 1 43340 43340 43340 43340 43340 43340 1.7 0.0 1.7 1.7 290930031 1 43340 43340 43340 43340 43340 43340 4.6 0.0 4.6 4.6 290950034 14 1388 2341 6 6 34 7625 7625 8.7 4.9 1.4 1.4 290990004 5 11145 10277 243 243 15223 23258 23258 9.7 7.4 0.2 0.2 290990014 5 11145 10277 243 243 15223 23258 23258 9.8 7.4 0.7 0.7 290990017 5 11145 10277 243 243 15223 23258 23258 10.2 7.1 1.6 1.6 290990018 4 8117 8927 243 243 7889 16447 16447 8.3 6.6 1.4 1.4 291370001 0 0.0 0.0 0.0 0.0 291630002 2 6747 934 6087 6087 6747 7408 7408 7.3 6.6 2.7 2.7 291650023 4 2757 3602 19 19 1693 7625 7625 17.8 1.3 16.0 16.0 291830010 1 47610 47610 47610 47610 47610 47610 1.7 0.0 1.7 1.7 291831002 15 4516 11970 6 6 136 45960 45960 12.6 3.4 4.3 4.3 291890001 14 1748 4547 8 8 35 16447 16447 14.8 4.4 6.4 6.4 291890004 9 2535 5610 8 8 13 16447 16447 14.0 3.1 9.8 9.8 291890006 7 27 48 6 6 8 136 136 14.7 4.6 8.4 8.4 291890014 8 33 47 6 6 10 136 136 11.9 6.2 3.2 3.2 291893001 29 370 1164 6 6 60 6250 6250 15.2 4.2 5.1 5.1 291895001 35 1911 7823 6 6 111 45960 45960 15.1 3.1 6.7 6.7 291897002 14 50 75 6 6 16 277 277 13.2 5.7 3.9 3.9 291897003 18 403 1461 6 6 37 6250 6250 14.1 5.4 3.5 3.5 295100007 19 1312 3936 8 8 50 16447 16447 12.5 6.0 0.5 0.5 295100072 30 445 1152 6 6 68 6250 6250 8.8 3.8 2.0 2.0 295100080 34 397 1088 6 6 61 6250 6250 10.7 4.3 0.4 0.4 295100086 32 421 1118 6 6 68 6250 6250 9.8 3.9 1.7 1.7 300132000 2 351 481 11 11 351 691 691 4.1 3.6 1.5 1.5 300132001 2 351 481 11 11 351 691 691 4.1 4.9 0.7 0.7 300430903 1 234 234 234 234 234 234 3.3 0.0 3.3 3.3 300430911 1 234 234 234 234 234 234 4.5 0.0 4.5 4.5 300430913 1 234 234 234 234 234 234 4.9 0.0 4.9 4.9 300490702 1 234 234 234 234 234 234 6.2 0.0 6.2 6.2 300490703 1 234 234 234 234 234 234 7.3 0.0 7.3 7.3 300870700 1 16735 16735 16735 16735 16735 16735 19.8 0.0 19.8 19.8 300870701 1 16735 16735 16735 16735 16735 16735 19.0 0.0 19.0 19.0 
A-75

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 300870702 1 16735 16735 16735 16735 16735 16735 19.8 0.0 19.8 19.8 300870760 0 0.0 0.0 0.0 0.0 300870761 0 0.0 0.0 0.0 0.0 300870762 0 0.0 0.0 0.0 0.0 300870763 1 16735 16735 16735 16735 16735 16735 15.2 0.0 15.2 15.2 301110016 0 0.0 0.0 0.0 0.0 301110066 4 1370 1322 75 75 1135 3135 3135 3.1 0.5 2.6 2.6 301110079 4 1370 1322 75 75 1135 3135 3135 7.8 3.0 5.8 5.8 301110080 4 1370 1322 75 75 1135 3135 3135 2.4 1.8 0.9 0.9 301110082 4 1370 1322 75 75 1135 3135 3135 3.4 2.7 1.7 1.7 301110083 4 1370 1322 75 75 1135 3135 3135 3.4 0.7 2.7 2.7 301110084 6 2550 2627 75 75 1976 7415 7415 10.3 6.6 3.1 3.1 301111065 4 1370 1322 75 75 1135 3135 3135 4.7 2.7 0.7 0.7 301112005 4 1370 1322 75 75 1135 3135 3135 4.1 1.8 1.5 1.5 301112006 6 2550 2627 75 75 1976 7415 7415 10.1 7.2 1.1 1.1 301112007 6 2550 2627 75 75 1976 7415 7415 11.4 3.9 4.7 4.7 310550048 5 6370 9218 6 6 58 20257 20257 12.7 7.5 0.5 0.5 310550050 5 6370 9218 6 6 58 20257 20257 13.4 7.5 1.0 1.0 310550053 5 6370 9218 6 6 58 20257 20257 11.3 5.7 3.3 3.3 310550055 3 3845 6637 6 6 20 11509 11509 13.0 7.3 4.7 4.7 320030022 4 45 27 16 16 44 75 75 3.9 0.0 3.8 3.8 320030078 0 0.0 0.0 0.0 0.0 320030539 0 0.0 0.0 0.0 0.0 320030601 0 0.0 0.0 0.0 0.0 330050007 1 81 81 81 81 81 81 0.3 0.0 0.3 0.3 330070019 1 638 638 638 638 638 638 1.7 0.0 1.7 1.7 330070022 1 638 638 638 638 638 638 2.3 0.0 2.3 2.3 330071007 2 9 4 6 6 9 12 12 0.6 0.1 0.6 0.6 330110016 3 10269 10386 149 149 9754 20902 20902 17.3 1.3 16.5 16.5 330110019 3 10269 10386 149 149 9754 20902 20902 17.0 2.3 15.7 15.7 330110020 3 10269 10386 149 149 9754 20902 20902 17.0 2.3 15.7 15.7 330111009 11 41 42 6 6 20 149 149 12.7 6.0 4.4 4.4 330111010 16 48 42 6 6 38 149 149 13.0 3.0 7.2 7.2 330130007 4 7708 9906 41 41 4945 20902 20902 7.3 3.9 1.4 1.4 330131003 4 7708 9906 41 41 4945 20902 20902 7.7 5.4 4.0 4.0 330131006 4 7708 9906 41 41 4945 20902 20902 8.2 8.8 1.3 1.3 
A-76

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 330131007 4 7708 9906 41 41 4945 20902 20902 9.3 2.1 7.5 7.5 330150009 9 1523 2990 6 6 52 8057 8057 9.0 6.9 2.0 2.0 330150014 9 1523 2990 6 6 52 8057 8057 9.6 7.0 1.0 1.0 330150015 9 1523 2990 6 6 52 8057 8057 8.9 7.1 1.9 1.9 330190003 2 110 81 53 53 110 168 168 2.5 1.7 1.3 1.3 340010005 0 0.0 0.0 0.0 0.0 340035001 61 457 2442 6 6 22 2302 18958 14.8 3.7 2.2 5.2 340051001 21 719 3104 5 5 35 14266 14266 10.7 6.7 1.5 1.5 340070003 60 179 644 5 5 25 2378 4450 9.7 3.4 2.0 2.8 340071001 2 8 1 8 8 8 9 9 10.2 0.5 9.9 9.9 340110007 4 161 198 28 28 81 456 456 7.5 6.6 1.8 1.8 340130011 59 465 2471 5 6 25 1845 18958 13.1 4.9 1.6 2.2 340130016 61 453 2431 5 6 25 1845 18958 13.4 5.0 1.8 2.7 340150002 50 529 1281 5 6 44 4450 6720 13.2 3.7 2.1 4.6 340170006 59 467 2471 5 5 25 1845 18958 13.0 4.6 2.0 3.2 340171002 71 421 2267 5 5 18 2302 18958 11.9 5.0 0.8 0.8 340232003 21 80 206 6 6 16 958 958 8.6 4.6 1.8 1.8 340273001 2 19 8 13 13 19 25 25 17.7 3.1 15.5 15.5 340390003 38 610 3074 5 5 19 18958 18958 11.5 5.3 2.3 2.3 340390004 38 609 3075 5 5 19 18958 18958 11.2 5.6 0.7 0.7 350130008 1 37 37 37 37 37 37 17.9 0.0 17.9 17.9 350130017 13 44 92 5 5 11 345 345 14.8 4.0 1.7 1.7 350151004 4 1058 973 168 168 983 2099 2099 8.6 8.4 0.9 0.9 350170001 1 263 263 263 263 263 263 6.1 0.0 6.1 6.1 350171003 1 263 263 263 263 263 263 1.5 0.0 1.5 1.5 350230005 0 0.0 0.0 0.0 0.0 350450008 7 2478 2496 11 11 2554 5919 5919 17.2 3.5 11.9 11.9 350450009 2 293 378 25 25 293 560 560 3.3 2.0 2.0 2.0 350450017 0 0.0 0.0 0.0 0.0 350451005 8 6274 10983 11 11 2630 32847 32847 6.1 3.8 3.2 3.2 360010012 9 40 46 7 7 20 153 153 10.8 5.2 3.5 3.5 360050073 68 399 2309 5 6 22 2302 18958 10.0 4.9 3.4 3.4 360050080 66 406 2344 5 6 18 2302 18958 10.6 5.0 1.8 3.0 360050083 56 119 355 6 6 19 1129 2302 11.2 5.6 1.6 1.8 360050110 67 402 2326 5 6 21 2302 18958 10.1 4.9 2.7 2.8 360130005 0 0.0 0.0 0.0 0.0 
A-77

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 360130006 1 52177 52177 52177 52177 52177 52177 2.0 0.0 2.0 2.0 360130011 0 0.0 0.0 0.0 0.0 360150003 2 202 270 11 11 202 393 393 10.2 13.6 0.6 0.6 360290005 10 4073 12273 8 8 182 38999 38999 10.2 4.7 2.5 2.5 360294002 16 2608 9706 8 8 166 38999 38999 10.4 6.2 1.6 1.6 360298001 9 4518 12932 8 8 247 38999 38999 13.5 5.6 4.6 4.6 360310003 0 0.0 0.0 0.0 0.0 360330004 0 0.0 0.0 0.0 0.0 360410005 0 0.0 0.0 0.0 0.0 360430005 0 0.0 0.0 0.0 0.0 360470011 77 377 2178 5 5 18 2302 18958 10.3 5.5 0.7 1.9 360470076 67 428 2333 5 5 17 2302 18958 11.6 4.8 2.3 3.1 360530006 0 0.0 0.0 0.0 0.0 360551004 4 12595 14519 8 8 11988 26395 26395 11.0 4.2 7.6 7.6 360551007 4 12595 14519 8 8 11988 26395 26395 11.3 4.1 6.4 6.4 360556001 4 12595 14519 8 8 11988 26395 26395 10.5 6.8 5.2 5.2 360590005 12 151 301 6 6 26 1057 1057 11.8 4.8 1.9 1.9 360610010 77 375 2178 5 5 17 2302 18958 10.4 5.4 0.3 1.4 360610056 76 382 2192 5 5 18 2302 18958 9.9 5.4 0.3 1.4 360632008 13 3134 10777 8 8 118 38999 38999 9.3 7.3 0.3 0.3 360671015 4 820 1602 8 8 24 3223 3223 5.9 4.3 1.9 1.9 360790005 0 0.0 0.0 0.0 0.0 360810097 60 136 358 5 6 22 1129 2302 14.8 4.0 2.9 5.0 360810124 66 122 342 5 6 21 1129 2302 12.5 4.0 2.1 2.3 360830004 3 126 106 10 10 153 217 217 18.4 1.8 16.3 16.3 360831005 2 94 124 6 6 94 182 182 17.6 1.6 16.5 16.5 360850067 48 515 2737 5 6 17 1845 18958 14.0 4.0 5.5 6.2 360930003 4 24 26 6 6 14 62 62 9.5 6.6 2.0 2.0 361030002 9 156 344 6 6 19 1057 1057 9.3 5.8 1.9 1.9 361030009 10 734 2013 11 11 42 6453 6453 11.3 5.7 2.0 2.0 361111005 0 0.0 0.0 0.0 0.0 370030003 0 0.0 0.0 0.0 0.0 370130003 1 4730 4730 4730 4730 4730 4730 2.2 0.0 2.2 2.2 370130004 1 4730 4730 4730 4730 4730 4730 2.7 0.0 2.7 2.7 370130006 1 4730 4730 4730 4730 4730 4730 1.1 0.0 1.1 1.1 370370004 4 119 71 12 12 148 165 165 17.2 3.7 11.8 11.8 
A-78

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 370511003 5 295 264 17 17 173 675 675 15.8 2.5 11.5 11.5 370590002 4 1949 3658 13 13 175 7432 7432 15.3 4.3 10.4 10.4 370610002 5 83 132 6 6 36 317 317 12.3 4.9 4.1 4.1 370650099 1 325 325 325 325 325 325 16.1 0.0 16.1 16.1 370670022 9 438 848 5 5 46 2591 2591 6.3 5.7 1.2 1.2 371010002 2 15 4 12 12 15 17 17 10.3 7.5 5.0 5.0 371090004 1 10 10 10 10 10 10 10.7 0.0 10.7 10.7 371170001 2 1713 2329 66 66 1713 3360 3360 6.6 7.8 1.1 1.1 371190034 12 86 121 5 5 11 320 320 13.3 4.7 6.3 6.3 371190041 12 68 103 5 5 11 320 320 12.7 5.0 6.3 6.3 371290002 9 3325 6800 6 6 313 20865 20865 14.5 4.9 2.3 2.3 371290006 12 2502 5987 6 6 50 20865 20865 6.9 4.8 0.6 0.6 371310002 3 805 759 16 16 871 1529 1529 4.2 1.8 2.1 2.1 371450003 3 32251 54874 5 5 1136 95610 95610 18.8 0.5 18.4 18.4 371470099 2 14 3 12 12 14 16 16 1.3 0.0 1.3 1.3 371730002 0 0.0 0.0 0.0 0.0 380070002 1 283 283 283 283 283 283 11.4 0.0 11.4 11.4 380070111 0 0.0 0.0 0.0 0.0 380130002 0 0.0 0.0 0.0 0.0 380130004 1 426 426 426 426 426 426 18.6 0.0 18.6 18.6 380150003 1 4592 4592 4592 4592 4592 4592 9.8 0.0 9.8 9.8 380171003 3 257 226 15 15 294 462 462 7.7 6.9 3.0 3.0 380171004 2 378 119 294 294 378 462 462 9.0 1.1 8.2 8.2 380250003 1 5 5 5 5 5 5 13.9 0.0 13.9 13.9 380530002 1 210 210 210 210 210 210 17.3 0.0 17.3 17.3 380530104 0 0.0 0.0 0.0 0.0 380530111 2 411 522 42 42 411 781 781 16.1 0.1 16.1 16.1 380550113 0 0.0 0.0 0.0 0.0 380570001 2 45808 55924 6264 6264 45808 85352 85352 2.5 2.6 0.7 0.7 380570004 2 45808 55924 6264 6264 45808 85352 85352 2.7 2.0 1.3 1.3 380570102 2 45808 55924 6264 6264 45808 85352 85352 5.4 2.3 3.8 3.8 380570118 2 45808 55924 6264 6264 45808 85352 85352 10.7 2.2 9.1 9.1 380570123 2 45808 55924 6264 6264 45808 85352 85352 14.3 1.4 13.3 13.3 380570124 2 45808 55924 6264 6264 45808 85352 85352 18.6 1.0 17.9 17.9 380590002 1 4592 4592 4592 4592 4592 4592 2.6 0.0 2.6 2.6 380590003 1 4592 4592 4592 4592 4592 4592 5.1 0.0 5.1 5.1 
A-79

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 380650002 1 28565 28565 28565 28565 28565 28565 8.5 0.0 8.5 8.5 380910001 0 0.0 0.0 0.0 0.0 381050103 1 1605 1605 1605 1605 1605 1605 2.8 0.0 2.8 2.8 381050105 1 1605 1605 1605 1605 1605 1605 1.8 0.0 1.8 1.8 390010001 1 19670 19670 19670 19670 19670 19670 11.4 0.0 11.4 11.4 390030002 9 442 535 16 16 45 1469 1469 8.5 0.4 7.9 7.9 390071001 5 1731 3761 12 12 34 8458 8458 17.3 0.6 16.6 16.6 390133002 5 27781 23029 795 795 35454 56009 56009 14.5 5.1 6.0 6.0 390170004 11 907 1265 56 56 233 3998 3998 14.7 6.9 0.9 0.9 390171004 9 1546 2186 56 56 309 6275 6275 6.5 6.5 1.7 1.7 390230003 4 509 349 105 105 492 946 946 12.2 6.1 5.8 5.8 390250021 6 15304 28111 26 26 145 69953 69953 15.0 2.7 12.7 12.7 390290016 9 20696 19955 18 18 24766 59928 59928 12.7 3.6 7.2 7.2 390290022 9 20696 19955 18 18 24766 59928 59928 12.7 3.6 7.2 7.2 390292001 8 22401 20621 18 18 25596 59928 59928 11.4 4.1 4.6 4.6 390350038 10 740 916 15 15 382 2453 2453 9.8 4.9 1.9 1.9 390350045 10 740 916 15 15 382 2453 2453 10.1 5.5 1.2 1.2 390350060 10 740 916 15 15 382 2453 2453 10.4 5.7 1.0 1.0 390350065 10 740 916 15 15 382 2453 2453 9.8 4.3 2.0 2.0 390356001 13 5759 16867 8 8 382 61629 61629 13.8 7.1 1.7 1.7 390490004 6 75 74 5 5 64 192 192 8.7 3.4 2.9 2.9 390490034 6 75 74 5 5 64 192 192 9.5 3.0 3.4 3.4 390530002 6 31718 26583 9 9 29551 74452 74452 7.0 7.4 1.0 1.0 390610010 10 9265 26865 12 12 537 85699 85699 16.1 3.0 8.6 8.6 390612003 11 660 817 12 12 268 2164 2164 8.7 5.5 0.4 0.4 390810016 17 13129 20063 10 10 361 59928 59928 9.5 7.1 1.7 1.7 390810017 17 13129 20063 10 10 361 59928 59928 9.6 6.9 2.0 2.0 390811001 13 6005 15392 10 10 234 53414 53414 4.9 5.6 0.3 0.3 390850003 6 12044 24426 8 8 2390 61629 61629 9.1 4.2 5.6 5.6 390853002 3 1600 2615 18 18 163 4618 4618 5.3 6.0 1.1 1.1 390870006 8 1425 2178 25 25 343 6285 6285 13.7 6.0 2.2 2.2 390930017 3 165 241 6 6 47 442 442 11.4 2.2 8.9 8.9 390930026 2 27 29 6 6 27 47 47 3.3 0.5 3.0 3.0 390931003 3 165 241 6 6 47 442 442 11.6 2.1 9.2 9.2 390950008 9 4149 4513 204 204 3712 13581 13581 8.1 5.5 2.5 2.5 390950024 10 3745 4443 113 113 2406 13581 13581 11.4 6.4 3.9 3.9 
A-80

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 390990009 10 2107 5350 6 6 353 17244 17244 12.4 7.3 2.0 2.0 390990013 10 2107 5350 6 6 353 17244 17244 12.4 7.5 1.7 1.7 391051001 6 31718 26583 9 9 29551 74452 74452 13.6 2.2 11.6 11.6 391130025 6 1609 2326 105 105 753 6275 6275 13.4 5.4 7.3 7.3 391150003 2 57763 38696 30401 30401 57763 85125 85125 4.8 0.2 4.6 4.6 391150004 2 57763 38696 30401 30401 57763 85125 85125 5.1 0.3 4.9 4.9 391450013 0 0.0 0.0 0.0 0.0 391450020 3 1450 1306 25 25 1737 2589 2589 9.6 6.9 4.6 4.6 391450022 3 1450 1306 25 25 1737 2589 2589 8.4 7.5 2.8 2.8 391510016 7 181 213 10 10 43 510 510 6.6 1.5 4.5 4.5 391530017 4 2763 2244 863 863 2091 6009 6009 5.0 2.4 1.4 1.4 391530022 4 2763 2244 863 863 2091 6009 6009 3.9 0.7 3.0 3.0 391570003 7 368 741 15 15 38 2017 2017 12.0 6.4 0.6 0.6 391570006 6 426 795 15 15 38 2017 2017 6.4 6.1 0.4 0.4 400219002 0 0.0 0.0 0.0 0.0 400710602 2 3502 457 3178 3178 3502 3825 3825 3.4 2.3 1.8 1.8 400719003 2 3502 457 3178 3178 3502 3825 3825 1.8 2.0 0.4 0.4 400719010 0 0.0 0.0 0.0 0.0 400979014 6 3180 5200 173 173 713 13428 13428 4.7 1.3 2.7 2.7 401010167 8 3751 4529 23 23 1130 9866 9866 5.9 4.2 3.7 3.7 401090025 2 91 110 13 13 91 169 169 8.7 4.5 5.6 5.6 401091037 2 91 110 13 13 91 169 169 8.8 7.9 3.2 3.2 401159004 1 62 62 62 62 62 62 5.2 0.0 5.2 5.2 401430175 10 938 1088 9 9 263 2729 2729 11.8 6.9 1.4 1.4 401430235 10 938 1088 9 9 263 2729 2729 10.7 6.9 1.5 1.5 401430501 10 938 1088 9 9 263 2729 2729 12.6 6.8 2.7 2.7 420030002 19 103 137 7 7 30 468 468 7.4 5.9 0.6 0.6 420030010 55 85 101 5 7 49 407 468 14.2 5.6 2.5 2.5 420030021 64 819 5274 5 7 47 5395 42018 11.7 3.3 3.2 4.8 420030031 62 757 5327 5 7 46 468 42018 13.9 5.1 1.3 1.4 420030032 64 819 5274 5 7 47 5395 42018 11.7 3.3 3.1 4.7 420030064 54 213 741 5 6 52 1164 5395 6.0 5.2 2.0 2.0 420030067 16 73 105 7 7 29 407 407 15.1 3.5 6.1 6.1 420030116 19 103 137 7 7 30 468 468 7.4 5.1 2.1 2.1 420031301 57 914 5587 5 7 47 5395 42018 9.9 4.6 1.1 1.1 420033003 54 213 741 5 6 52 1164 5395 5.6 5.4 1.0 1.0 
A-81

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 420033004 55 209 735 5 6 49 1164 5395 5.9 6.0 0.6 0.7 420070002 10 18726 19819 18 18 15912 59928 59928 13.0 3.2 9.2 9.2 420070004 7 5881 11104 9 9 118 30312 30312 14.5 5.1 7.4 7.4 420070005 8 5173 10474 9 9 157 30312 30312 9.6 5.6 2.5 2.5 420070014 10 4400 9400 8 8 157 30312 30312 12.0 3.1 7.1 7.1 420110009 13 1140 3818 14 14 37 13841 13841 9.8 7.1 1.3 1.3 420110100 12 1231 3973 14 14 34 13841 13841 8.7 6.3 1.5 1.5 420130801 1 441 441 441 441 441 441 1.3 0.0 1.3 1.3 420170012 22 687 3033 5 5 27 14266 14266 11.1 6.5 1.2 1.2 420210011 4 4195 5171 34 34 3004 10738 10738 8.5 7.4 1.5 1.5 420270100 4 1090 1267 53 53 834 2638 2638 10.4 6.2 2.3 2.3 420430401 8 107 99 10 10 78 313 313 5.4 4.0 0.8 0.8 420450002 57 681 1415 5 5 47 5051 6720 13.6 5.5 1.3 1.9 420450109 45 855 1553 5 5 91 5051 6720 12.4 6.4 0.5 1.6 420490003 5 824 1068 10 10 228 2398 2398 3.1 1.9 1.2 1.2 420630004 3 4796 5156 1497 1497 2154 10738 10738 18.4 1.4 17.0 17.0 420692006 5 13 5 6 6 15 18 18 10.9 7.4 2.1 2.1 420710007 5 75 109 6 6 23 264 264 3.7 3.7 0.6 0.6 420730015 9 3206 8423 6 6 28 25551 25551 12.5 5.6 0.6 0.6 420770004 13 703 1041 7 7 120 2888 2888 12.5 5.8 0.3 0.3 420791101 4 117 160 9 9 53 351 351 12.3 3.4 7.8 7.8 420810100 3 28 28 6 6 18 59 59 11.3 0.7 10.6 10.6 420810403 3 28 28 6 6 18 59 59 15.8 1.1 14.9 14.9 420850100 2 14 4 11 11 14 17 17 10.8 11.8 2.4 2.4 420910013 28 171 704 5 5 15 3753 3753 15.3 4.5 1.4 1.4 420950025 18 676 1020 7 7 86 2888 2888 13.1 4.3 4.0 4.0 420950100 15 2179 5602 7 7 120 22057 22057 10.4 5.5 2.5 2.5 420958000 16 2045 5439 7 7 86 22057 22057 10.1 5.9 0.6 0.6 420990301 0 0.0 0.0 0.0 0.0 421010004 61 102 316 5 6 20 560 2378 10.5 5.2 1.0 1.3 421010022 66 285 1022 5 5 26 4450 6720 8.0 5.6 0.9 1.0 421010024 36 46 77 5 5 13 407 407 13.0 3.8 6.3 6.3 421010027 63 99 311 5 6 20 560 2378 9.8 4.6 0.8 1.7 421010029 67 262 1007 5 5 24 4450 6720 8.3 4.7 1.1 1.8 421010047 65 270 1022 5 5 26 4450 6720 7.9 4.5 0.6 0.8 421010048 60 104 318 5 6 22 560 2378 10.4 4.9 0.9 1.7 
A-82

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 421010055 66 286 1022 5 5 26 4450 6720 7.9 5.4 1.3 1.4 421010136 68 319 1042 5 5 27 4450 6720 8.8 5.4 1.1 1.4 421070003 6 831 687 8 8 674 1743 1743 10.4 7.4 3.3 3.3 421230003 2 2445 659 1979 1979 2445 2911 2911 4.0 1.2 3.2 3.2 421230004 2 2445 659 1979 1979 2445 2911 2911 3.0 1.6 1.9 1.9 421250005 33 257 945 5 5 47 5395 5395 15.7 4.7 1.1 1.1 421250200 1 7 7 7 7 7 7 1.1 0.0 1.1 1.1 421255001 8 321 439 7 7 82 1017 1017 15.9 4.1 9.3 9.3 421290008 3 24 9 16 16 22 34 34 9.8 1.4 8.7 8.7 421330008 9 8943 22698 14 14 171 68932 68932 9.3 5.8 0.8 0.8 440070012 54 41 90 5 5 13 392 521 8.4 5.8 0.3 0.4 440071005 55 41 89 5 5 13 392 521 9.1 5.5 0.9 1.0 440071009 55 41 89 5 5 13 392 521 8.6 6.0 0.1 0.4 450030003 13 1654 2599 8 8 549 8275 8275 15.3 1.5 11.4 11.4 450110001 1 65 65 65 65 65 65 13.2 0.0 13.2 13.2 450190003 16 2183 6339 6 6 28 25544 25544 7.2 5.0 1.1 1.1 450190046 0 0.0 0.0 0.0 0.0 450430006 7 5834 14038 6 6 24 37622 37622 4.6 4.3 0.2 0.2 450450008 12 89 136 6 6 20 411 411 11.7 4.5 2.1 2.1 450450009 13 83 132 6 6 19 411 411 10.1 5.7 4.0 4.0 450630008 11 948 2944 5 5 9 9820 9820 11.5 5.4 0.5 0.5 450730001 1 5 5 5 5 5 5 14.9 0.0 14.9 14.9 450750003 5 1433 1913 5 5 211 4088 4088 8.5 5.1 3.4 3.4 450790007 10 61 103 5 5 18 343 343 14.0 4.1 6.4 6.4 450790021 8 5061 12720 7 7 89 36378 36378 14.7 1.2 12.3 12.3 450791003 13 995 2730 5 5 52 9820 9820 10.9 5.9 1.4 1.4 450791006 10 4289 11350 7 7 89 36378 36378 17.5 3.3 8.2 8.2 460330132 0 0.0 0.0 0.0 0.0 460710001 0 0.0 0.0 0.0 0.0 460990007 1 496 496 496 496 496 496 17.5 0.0 17.5 17.5 470010028 8 5595 14808 7 7 34 42188 42188 12.2 6.5 0.9 0.9 470090002 3 1421 2325 6 6 153 4104 4104 5.7 5.7 0.7 0.7 470090006 3 1421 2325 6 6 153 4104 4104 5.4 5.3 1.4 1.4 470090101 3 1421 2325 6 6 153 4104 4104 12.1 6.9 4.2 4.2 470110102 2 2719 3687 112 112 2719 5326 5326 2.5 1.2 1.6 1.6 470310004 0 0.0 0.0 0.0 0.0 
A-83

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 470370011 9 891 2248 9 9 60 6842 6842 10.4 3.6 5.6 5.6 470730002 3 11831 10420 6 6 15822 19666 19666 2.9 2.1 1.7 1.7 470850020 6 18599 44191 12 12 281 108788 108788 3.2 1.8 1.6 1.6 471070101 3 1834 3024 64 64 112 5326 5326 7.6 10.2 0.5 0.5 471250006 6 222 401 8 8 35 1025 1025 6.2 6.9 1.0 1.0 471250106 6 222 401 8 8 35 1025 1025 7.1 7.3 1.5 1.5 471390003 1 1900 1900 1900 1900 1900 1900 3.1 0.0 3.1 3.1 471390007 1 1900 1900 1900 1900 1900 1900 1.6 0.0 1.6 1.6 471390008 1 1900 1900 1900 1900 1900 1900 1.4 0.0 1.4 1.4 471390009 1 1900 1900 1900 1900 1900 1900 1.0 0.0 1.0 1.0 471450009 4 19470 22311 9 9 19188 39495 39495 10.9 6.7 5.3 5.3 471570034 18 1204 2391 5 5 32 6540 6540 11.4 2.2 4.8 4.8 471570043 18 1204 2391 5 5 32 6540 6540 9.6 1.7 5.3 5.3 471570046 2 1973 2640 106 106 1973 3839 3839 6.0 6.7 1.3 1.3 471571034 19 1150 2336 5 5 35 6540 6540 3.5 5.6 0.5 0.5 471610007 3 5561 5107 21 21 6580 10081 10081 1.8 0.2 1.7 1.7 471630007 10 3010 5303 22 22 495 16855 16855 3.7 2.6 1.7 1.7 471630009 12 2513 4935 13 13 286 16855 16855 5.7 6.0 2.0 2.0 471651002 4 8593 10129 88 88 7029 20226 20226 4.2 1.8 2.9 2.9 480610006 0 0.0 0.0 0.0 0.0 481130069 9 34 25 9 9 18 69 69 12.1 5.7 2.0 2.0 481390015 12 664 993 13 13 57 3003 3003 9.5 5.8 2.3 2.3 481390016 12 664 993 13 13 57 3003 3003 9.0 6.3 2.9 2.9 481390017 12 664 993 13 13 57 3003 3003 9.6 6.9 1.9 1.9 481410037 13 44 92 5 5 11 345 345 9.7 1.8 4.5 4.5 481410053 13 44 92 5 5 11 345 345 9.7 1.6 5.1 5.1 481410058 16 38 83 5 5 12 345 345 13.9 2.3 9.5 9.5 481670005 43 185 611 5 6 22 1937 3599 2.3 1.3 1.2 1.3 481671002 43 185 611 5 6 22 1937 3599 3.6 1.1 2.5 2.5 481830001 5 13289 12287 6 6 19024 24837 24837 18.9 0.5 18.6 18.6 482010046 29 606 1182 6 6 161 5097 5097 12.8 3.1 6.2 6.2 482010051 2 13 8 7 7 13 18 18 19.1 0.6 18.7 18.7 482010059 38 674 1486 6 6 48 6968 6968 10.3 5.9 1.8 1.8 482010062 37 694 1503 6 6 49 6968 6968 14.8 3.8 7.8 7.8 482010070 31 790 1622 6 6 161 6968 6968 10.7 5.3 2.2 2.2 482011035 39 657 1470 6 6 46 6968 6968 8.6 5.4 1.6 1.6 
A-84

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 482011050 46 243 1028 6 7 36 829 6968 16.5 3.9 5.0 5.3 482450009 16 863 2732 6 6 80 11064 11064 14.8 6.8 0.4 0.4 482450011 27 999 2362 6 6 45 11064 11064 9.0 5.3 2.8 2.8 482450020 8 170 306 6 6 64 908 908 10.8 8.1 1.8 1.8 482570005 0 0.0 0.0 0.0 0.0 483550025 17 468 1086 6 6 43 3955 3955 6.7 3.0 4.2 4.2 483550026 19 424 1032 6 6 43 3955 3955 10.0 3.3 4.6 4.6 483550032 17 468 1086 6 6 43 3955 3955 3.9 4.1 0.4 0.4 490050004 1 5 5 5 5 5 5 1.8 0.0 1.8 1.8 490110001 6 468 500 8 8 366 1332 1332 8.2 5.8 1.5 1.5 490110004 6 468 500 8 8 366 1332 1332 9.7 6.0 2.3 2.3 490350012 6 468 500 8 8 366 1332 1332 4.9 3.7 0.6 0.6 490351001 7 833 1006 8 8 712 2788 2788 13.0 6.5 2.1 2.1 490352004 3 1245 1415 8 8 939 2788 2788 9.8 8.0 2.4 2.4 500070003 1 6 6 6 6 6 6 1.6 0.0 1.6 1.6 500070014 1 6 6 6 6 6 6 1.9 0.0 1.9 1.9 500210002 0 0.0 0.0 0.0 0.0 510360002 18 4818 17274 7 7 35 73839 73839 12.1 7.2 2.0 2.0 510590005 5 31 46 8 8 11 114 114 17.2 1.6 15.0 15.0 510590018 10 1820 5043 8 8 74 16141 16141 13.5 3.9 8.4 8.4 510591004 11 1664 4813 7 7 59 16141 16141 10.9 3.5 3.7 3.7 510591005 13 1416 4435 7 7 59 16141 16141 13.6 4.3 4.6 4.6 510595001 11 1566 4837 6 6 24 16141 16141 14.8 4.4 5.1 5.1 511130003 1 7 7 7 7 7 7 10.8 0.0 10.8 10.8 511611004 8 85 117 5 5 34 341 341 9.3 5.5 2.9 2.9 511650002 7 40 36 8 8 32 108 108 12.3 5.1 5.1 5.1 511650003 6 39 40 5 5 25 108 108 11.4 5.4 6.3 6.3 515100009 11 1663 4813 7 7 59 16141 16141 9.6 5.1 1.1 1.1 516500004 15 285 505 6 6 92 1983 1983 11.1 4.9 4.0 4.0 517100023 21 1738 7026 5 5 85 32344 32344 8.3 3.4 3.6 3.6 517600024 14 191 363 6 6 16 1148 1148 9.4 5.8 1.2 1.2 530090010 1 756 756 756 756 756 756 5.6 0.0 5.6 5.6 530090012 1 756 756 756 756 756 756 5.3 0.0 5.3 5.3 530330057 5 241 301 63 63 117 771 771 4.0 6.0 0.6 0.6 530330080 5 241 301 63 63 117 771 771 5.0 4.2 2.5 2.5 530530021 3 179 213 11 11 109 419 419 3.2 1.1 2.1 2.1 
A-85

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 530530031 3 179 213 11 11 109 419 419 1.8 0.9 1.2 1.2 530570012 4 2238 2630 21 21 1793 5345 5345 2.2 0.8 1.3 1.3 530571003 4 2238 2630 21 21 1793 5345 5345 1.7 0.6 1.1 1.1 530610016 2 191 194 53 53 191 328 328 0.5 0.1 0.4 0.4 530730011 9 488 695 8 8 349 2286 2286 16.9 6.2 0.5 0.5 540090005 13 6005 15392 10 10 234 53414 53414 5.3 5.3 0.9 0.9 540090007 17 13129 20063 10 10 361 59928 59928 10.7 5.3 3.9 3.9 540110006 5 1501 2677 124 124 401 6285 6285 13.2 7.1 0.5 0.5 540250001 0 0.0 0.0 0.0 0.0 540290005 8 22069 20983 18 18 25596 59928 59928 9.3 5.3 4.7 4.7 540290007 16 9282 17668 10 10 238 59928 59928 13.1 3.8 4.8 4.8 540290008 9 20696 19955 18 18 24766 59928 59928 12.1 4.2 6.3 6.3 540290009 15 9894 18112 10 10 243 59928 59928 11.0 3.5 1.0 1.0 540290011 17 13129 20063 10 10 361 59928 59928 10.7 5.2 3.2 3.2 540290014 16 9282 17668 10 10 238 59928 59928 11.8 4.0 1.5 1.5 540290015 9 20696 19955 18 18 24766 59928 59928 12.1 3.5 7.1 7.1 540290016 16 10611 17732 10 10 302 59928 59928 10.8 4.3 1.1 1.1 540291004 16 10611 17732 10 10 302 59928 59928 11.5 3.9 1.8 1.8 540390004 4 1529 1146 854 854 1008 3245 3245 10.2 4.3 6.0 6.0 540390010 4 1529 1146 854 854 1008 3245 3245 9.7 4.6 5.2 5.2 540392002 5 22698 47491 750 750 1009 107633 107633 9.1 5.6 2.3 2.3 540511002 5 27781 23029 795 795 35454 56009 56009 10.1 4.7 2.2 2.2 540610003 2 45992 63840 850 850 45992 91134 91134 4.6 1.4 3.6 3.6 540610004 4 24472 44468 850 850 2952 91134 91134 11.8 8.9 0.8 0.8 540610005 3 32132 51128 850 850 4412 91134 91134 9.2 9.7 1.0 1.0 540690007 2 37391 22660 21367 21367 37391 53414 53414 13.9 1.8 12.7 12.7 540990002 8 1271 2194 25 25 343 6285 6285 9.7 5.5 1.7 1.7 540990003 8 1271 2194 25 25 343 6285 6285 9.6 5.5 1.5 1.5 540990004 8 1271 2194 25 25 343 6285 6285 9.6 6.0 1.0 1.0 540990005 8 1271 2194 25 25 343 6285 6285 9.5 6.4 0.9 0.9 541071002 11 4375 9095 7 7 1517 31006 31006 8.5 5.4 2.7 2.7 550090005 7 3413 5045 9 9 850 13470 13470 4.2 3.4 1.1 1.1 550250041 7 1293 2743 7 7 71 7417 7417 7.4 4.7 2.8 2.8 550410007 1 5 5 5 5 5 5 8.3 0.0 8.3 8.3 550730005 3 4040 6715 24 24 303 11792 11792 10.7 9.2 0.1 0.1 550790007 9 1750 4858 5 5 28 14686 14686 6.5 3.4 1.8 1.8 
A-86

SO2 emissions (tpy) from sources within 20 km of monitor1 Distance of monitor to SO2 

Monitor ID n mean std min p2.5 p50 p97.5 p100 mean std min p2.5 550790026 9 1750 4858 5 5 28 14686 14686 7.6 3.0 3.5 3.5 550790041 9 1750 4858 5 5 28 14686 14686 10.1 3.0 5.9 5.9 550850996 2 1152 1617 9 9 1152 2295 2295 0.9 0.1 0.9 0.9 551110007 2 31 35 7 7 31 56 56 14.7 7.4 9.5 9.5 551250001 0 0.0 0.0 0.0 0.0 551410016 6 2374 2368 6 6 2032 5782 5782 5.3 2.6 2.3 2.3 560050857 4 2527 3868 23 23 896 8291 8291 4.6 6.5 1.1 1.1 Notes: 1 Mean, std , min, p2.5, p50, p97.5, max are the arithmetic average, standard deviation, minimum, 2.5th, 50th, 97.5th percentiles, 

and emissions. 

2 There were no emissions above 5 tpy for sources located within 20 km of the monitors sited in Puerto Rico and the Virgin Islan
A-87

A.2 Analysis of Duplicate SO2 Values at Ambient Monitor Locations 

During the screening of each of the ambient monitoring data sets, it became evident that 

simultaneous measurements were present. Staff analyzed the duplicate SO2 measurements to 

discern if there were any differences in the reported/measured values because ultimately only 

one value would be selected for use in each of the final screened data sets. Staff was not 

interested in whether multiple monitors were present at a particular monitoring site or if there 

were duplicate reporting of SO2 concentrations, only to determine that the selection of a 

particular value used in the final data sets were not biased. 

In selecting which of the duplicate concentrations to use for final REA data sets, staff 

made the following judgements. First, the ambient monitor POC containing the greatest number 

of samples was used to populate the max-5 data set. Second, where continous-5 measurements 

were available and coincided with max-5 measurements, staff selected the 5-minute maximum 

SO2 concentration from the continuous-5 data set. And finally, where continuous-5 data were 

available and used to estimate a 1-hour average SO2 concentration that coincided with a reported 

1-hour ambient monitor concentration, the continuous-5 1-hour average concentration was used. 

Staff designed the following analyses to explore the effect the selection of one concentration 

over another may have on the final data set used. 

Staff calculated the relative percent difference (RPD) for each duplicate concentration, 

considering measurements within the 5-max data set (n=300,438), duplicate reporting between 

the continuous-5 and the max-5 data sets (n=29,058), and duplicate values between the 1-hour 

and the continuous-5 data sets (n=258,457), separately. We anticipated that small fluctuations in 

concentration between the duplicate data would have a greater influence on the RPD at lower 

concentrations than at higher concentrations. Therefore, staff separated the duplicate values into 

concentration groups for this analysis. Two groups were constructed; one with concentrations ≤

10 ppb and the other conrtaining concentrations > 10 ppb. The following formula calculates the 

RPD for each duplicate value: 

200

)(

)(

21

21 



  CCCC RPD equation A.2-1 

where, 

RPD = Relative percent difference (%) 
A-88

C1 = First SO2 concentration value 

C2 = Second SO2 concentration value 

Depending on the difference in concentration, the value for the calculated RPD could be 

as low as -200 or as high +200, indicating the maximum difference between any two values, 

while an RPD of zero indicates no difference. The sign of the value can also indicate the 

direction of bias when comparing the first concentration to the second. 

In the first comparison (i.e., the within max-5 duplicates), C1 was selected as the ambient 

monitor containing the overall greater sample size/duration. Table A.2-1 summarizes the 

distribution of RPDs for where duplicate values of SO2 concentrations were less than 10 ppb 

within the max-5 monitoring data set. On average, there were relatively small differences in the 

duplicate values reported at each of the monitoring locations. Most duplicate values were within 

+/-67% of one another, although some were noted at or above 100% (absolute difference). In 

considering that these maximum 5-minute SO2 concentrations are well below that of potential 

interest in the exposure and risk analysis, this degree of agreement between the two values at 

these concentration levels is acceptable. 

Table A.2-1. Distribution of the relative percent difference (RPD) between 

duplicate 5-minute maximum SO2 values at max-5 monitors, where 

concentrations were ≤ 10 ppb. 

Relative Percent Difference (%)1

Monitor ID n mean std min p5 p50 p95 max 

290210009 25868 0 34 -196 -50 0 67 100 

290210011 22247 -7 22 -143 -40 0 18 67 

290930030 54904 8 34 -181 -40 0 67 100 

290930031 48417 -14 29 -122 -67 0 67 67 

290990004 22788 -8 27 -120 -50 0 67 100 

290990014 33245 -12 29 -133 -67 0 29 67 

290990017 21460 2 30 -120 -50 0 67 120 

290990018 17025 2 25 -156 -40 0 67 100 

291630002 11528 -3 34 -164 -40 0 67 67 

Notes: 

1

 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum, 

5th, median, 95th, and maximum, respectively. 

When considering duplicate values > 10 ppb, the RPD was much lower at each of the 

monitors (Table A.2-2). Most of the RPDs are within +/-10%, indicating excellent agreement 

among the duplicate values. A small negative bias may exist with selection of the monitor with 
A-89

the greatest number of samples as the base monitor, but on average the difference was typically 

less than 3%. 

Table A.2-2. Distribution of the relative percent difference (RPD) between 

duplicate 5-minute maximum SO2 values at max-5 monitors, where 

concentrations were > 10 ppb. 

Relative Percent Difference (%)1

Monitor ID n mean std min p5 p50 p95 max 

290210009 2333 -2 6 -133 -10 0 6 18 

290210011 2344 0 3 -66 -6 0 5 18 

290930030 8068 -1 6 -120 -9 0 4 24 

290930031 7652 -3 6 -134 -13 -2 0 10 

290990004 8627 -1 4 -100 -7 0 5 20 

290990014 4973 2 16 -17 -8 0 9 184 

290990017 5138 -1 7 -137 -11 0 10 32 

290990018 2626 0 6 -81 -7 0 10 32 

291630002 1195 -6 32 -137 -133 0 11 29 

Notes: 

1

 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum, 

5th, median, 95th, and maximum, respectively. 

Staff also analyzed data where the max-5 sampling times corresponded with the 

continuous-5 monitoring at the same location. Of the 29,058 duplicate measurement values, only 

312 contained different values among the two sample types (i.e, a non-zero RPD). This indicates 

that the majority of the data are duplicate values reported in each of the two data sets. Since 

there were very few samples with RPDs deviating from zero (i.e., 1.1%), the following analysis 

included only the samples that had a non-zero difference and at any concentration levels. The 

distribution for the RPD given these monitors and duplicate monitoring events is provided in 

Table A.2-3. On average there may be a small positive bias in selecting the continuous-5 

monitoring concentrations where differences existed, however given that there were only 1% of 

samples that differed among the two data sets, the overall impact to the below estimation 

procedure is determined as negligible. In addition, selection of the continuous-5 measurement 

preserves the relationship between the actual 5-minute maximum and the calculated 1-hour 

concentration derived from the multiple 5-minute measurements that occurred within the hour, 

not adding to uncertainty regarding the true relationship between the 1-hour and 5-minute 

maximum concentrations. 
A-90

Table A.2-3. Distribution of the relative percent difference (RPD) between 

simultaneous 5-minute SO2 maximum values in the max-5 and continuous-5 data 

sets, where concentrations > 0 ppb. 

Relative Percent Difference (%) 2

Monitor ID n 1 mean std min p5 p50 p95 max 

301110066 76 26 57 -143 -117 16 133 160 

301110079 149 27 48 -178 -67 29 67 164 

301110082 47 25 52 -67 -67 29 67 186 

301110083 40 78 64 -120 -53 67 160 160 

Notes: 

1 This distribution is for the number of samples where the RPD was non-zero. The majority of the 

duplicate measures (n=28,746) were identical. 

2

 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum, 5th, 

median, 95th, and maximum, respectively. 

 In the last comparison (i.e., the 1-hour concentration duplicates), the 1-hour 

concentration from the continous-5 ambient monitors was selected as C1 in equation A.2-1. 

Table A.2-4 summarizes the distribution of RPDs for where duplicate measurements of SO2

concentrations were less than 10 ppb within the max-5 monitoring data set. While nearly 20% 

had no difference between the duplicate values, on average, there were greater differences in the 

duplicate 1-hour values at most of the monitors than was observed for the 5-minute duplicates. 

Nearly 20% of the concentrations were noted at or above 100% one another (absolute 

difference), however all of these were due to where reported values were zero at the 1-hour 

monitor and concentrations of 1 ppb were reported for the continuous-5 monitor. This factor 

contributes to the observed positive bias at most of the monitors, however in considering that 

these 1-hour SO2 concentrations are below that of potential interest in the exposure and risk 

analysis, this degree of limited agreement between the two data sets at these concentration levels 

should be acceptable. 

Table A.2-4. Distribution of the relative percent difference (RPD) between 

duplicate 1-hour SO2 values in the continuous-5 and 1-hour data sets, where 

concentrations were ≤ 10 ppb. 

Relative Percent Difference (%)1

Monitor ID n mean std min p5 p50 p95 max 

110010041 2049 0 7 -34 -12 0 12 45 

120890005 25163 88 99 -175 -11 15 200 200 

290770026 24286 91 99 -105 -9 15 200 200 

290770037 24822 41 80 -46 -13 0 200 200 

301110066 6640 24 62 -100 -13 0 200 200 

301110079 7906 119 95 -133 -9 200 200 200 

301110082 7930 69 92 -165 -13 12 200 200 

301110083 4757 82 96 -105 -9 15 200 200 

371290006 27954 -45 83 -193 -133 -59 200 200 
A-91

Relative Percent Difference (%)1

Monitor ID n mean std min p5 p50 p95 max 

420030021 4594 34 81 -175 -18 2 200 200 

420030064 5174 20 71 -172 -29 -4 200 200 

420030116 4231 3 25 -61 -18 0 19 200 

420033003 4640 23 69 -67 -23 -1 200 200 

420070005 30386 63 91 -133 -10 6 200 200 

540990002 6592 1 10 -40 -13 0 19 90 

541071002 23864 1 11 -156 -13 0 17 200 

Notes: 

1

 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum, 

5th, median, 95th, and maximum, respectively. 

When considering duplicate 1-hour concentrations > 10 ppb, the RPD was much lower at 

each of these same monitors (Table A.2-5). Most RPD distributions were within +/-5%, 

indicating excellent agreement among the duplicate 1-hour values at concentrations above 10 

ppb. A very small positive bias may exist with selection of the continuous-5 monitor data for use 

in the air quality characterization when compared with the reported 1-hour concentrations, but on 

average, the difference was typically less than 1% when considering concentrations above 10 

ppb. 

Table A.2-5. Distribution of the relative percent difference (RPD) between 

duplicate 1-hour SO2 values in the continuous-5 and 1-hour data sets, where 

concentrations were > 10 ppb. 

Relative Percent Difference (%)1

Monitor ID n mean std min p5 p50 p95 max 

110010041 202 0 2 -5 -4 0 4 5 

120890005 2400 0 4 -90 -3 0 3 34 

290770026 1906 0 2 -10 -3 0 3 7 

290770037 1373 0 2 -5 -3 0 3 7 

301110066 1616 0 5 -50 -3 0 4 173 

301110079 71 0 3 -6 -4 -1 4 6 

301110082 176 0 2 -5 -3 0 4 6 

301110083 85 1 3 -4 -3 1 5 20 

371290006 3747 1 25 -108 -15 -2 12 186 

420030021 1852 1 14 -59 -4 0 4 200 

420030064 2892 -2 2 -10 -6 -2 0 11 

420030116 1145 0 9 -34 -4 0 4 200 

420033003 2625 -1 5 -36 -5 -1 2 187 

420070005 15034 0 2 -23 -3 0 3 73 

540990002 2062 0 2 -5 -3 0 4 10 

541071002 10283 0 2 -87 -3 0 3 65 

Notes: 

1

 the mean, std, min, p5, p50, p95, max are the arithmetic average, standard deviation, minimum, 

5th, median, 95th, and maximum, respectively. 
A-92

A.3 Peak-To-Mean Ratio Distributions 

Peak-to-mean ratios (PMR) were calculated using the measured values for each the 5-

minute maximum and 1-hour SO2 concentrations. PMRs were seperated into 19 groups2 based 

on the observed variability (3 bins) and concentrations ranges (7 bins) in measured 1-hour 

ambient monitor concentrations (n=2,367,686). Table A.3-1 summarizes the PMR distributions 

used for estimating 5-minute maximum concentrations from 1-hour measurements where 

ambient monitors were characterized by the 1-hour coefficient of variation (COV). These are the 

PMR distributions used in the statistical modeling of 5-minute maximum SO2 concentrations in 

the air quality characterization and in the exposure modeling.3 Table A.3-2 summarizes the 

PMR distributions used for estimating 5-minute maximum SO2 concentrations from 1-hour 

measurements where ambient monitors were characterized by the 1-hour coefficient of variation 

(GSD). Peak-to-mean ratios estimated by categorizing the ambient monitors by GSD were used 

only in evaluating an alternative method of estimating 5-minute SO2 concentrations. 

 

2

 Although there are 21 PMR distributions possible (i.e., 3 × 7), the COV < 100% and GSD <2.17 categories had 

only three 1-hour concentrations above 150 ppb. Therefore, the two highest concentration bins do not have a 

distribution, and concentrations > 75 ppb constituted the highest concentration bin in the low COV or low GSD bins 

3

 Note that the minimum and maximum values of each distribution were not used in the final statistical model to 

estimate 5-minute maximum concentrations. This was determined in the model evaluations described in section 

7.2.5 of the SO2 REA. 
A-93

Table A.3-1. Distribution of 5-minute maximum peak to 1-hour mean SO2 concentration ratios (PMmonitors categorized by 1-hour coefficient of variation (COV) and 1-hour mean concentration. 

 COV ≤ 100% 100% < COV ≤ 200% COConcbin1 0 1 2 3 4 0 1 2 3 4 5 6 0 1 2 

Pct2 - 0 1.00 1.00 1.00 1.00 1.01 1.00 1.00 1.00 1.00 1.00 1.05 1.02 1.00 1.00 1.0

- 1 1.00 1.00 1.00 1.03 1.02 1.00 1.00 1.00 1.00 1.05 1.08 1.04 1.00 1.00 1.0- 2 1.00 1.00 1.00 1.03 1.02 1.00 1.00 1.00 1.03 1.07 1.12 1.14 1.00 1.00 1.0- 3 1.00 1.00 1.00 1.04 1.04 1.00 1.00 1.00 1.04 1.08 1.14 1.14 1.00 1.00 1.0- 4 1.00 1.00 1.00 1.04 1.04 1.00 1.00 1.05 1.06 1.10 1.16 1.15 1.00 1.00 1.0- 5 1.00 1.00 1.05 1.05 1.05 1.00 1.00 1.06 1.06 1.11 1.20 1.16 1.00 1.00 1.0- 6 1.00 1.00 1.06 1.06 1.06 1.00 1.00 1.06 1.07 1.12 1.21 1.18 1.00 1.00 1.1- 7 1.00 1.00 1.06 1.06 1.07 1.00 1.00 1.07 1.08 1.13 1.21 1.18 1.00 1.00 1.1- 8 1.00 1.00 1.06 1.07 1.08 1.00 1.00 1.08 1.09 1.14 1.22 1.22 1.00 1.00 1.1- 9 1.00 1.00 1.07 1.07 1.09 1.00 1.00 1.08 1.10 1.15 1.23 1.24 1.00 1.00 1.1

- 10 1.00 1.00 1.07 1.07 1.09 1.00 1.00 1.08 1.10 1.16 1.23 1.24 1.00 1.00 1.1- 11 1.00 1.00 1.08 1.07 1.10 1.00 1.05 1.09 1.11 1.17 1.25 1.24 1.00 1.00 1.1- 12 1.00 1.00 1.08 1.08 1.10 1.00 1.08 1.09 1.12 1.18 1.27 1.30 1.00 1.00 1.2- 13 1.00 1.00 1.08 1.08 1.11 1.00 1.11 1.10 1.12 1.19 1.27 1.30 1.00 1.06 1.2- 14 1.00 1.00 1.08 1.09 1.11 1.00 1.11 1.10 1.13 1.19 1.28 1.32 1.00 1.11 1.2- 15 1.00 1.00 1.08 1.09 1.11 1.00 1.11 1.10 1.14 1.20 1.28 1.33 1.00 1.11 1.2- 16 1.00 1.00 1.08 1.10 1.12 1.00 1.13 1.10 1.15 1.21 1.29 1.34 1.00 1.13 1.2- 17 1.00 1.00 1.09 1.10 1.13 1.00 1.13 1.11 1.15 1.22 1.30 1.36 1.00 1.13 1.2- 18 1.00 1.04 1.09 1.11 1.14 1.00 1.13 1.13 1.16 1.23 1.31 1.36 1.00 1.14 1.3- 19 1.00 1.11 1.09 1.11 1.15 1.00 1.13 1.13 1.17 1.24 1.32 1.37 1.00 1.14 1.3- 20 1.00 1.11 1.09 1.11 1.15 1.00 1.14 1.14 1.17 1.24 1.32 1.38 1.00 1.14 1.3- 21 1.00 1.11 1.10 1.12 1.16 1.00 1.14 1.14 1.18 1.25 1.34 1.39 1.00 1.14 1.3- 22 1.00 1.11 1.10 1.12 1.17 1.00 1.14 1.15 1.19 1.26 1.34 1.43 1.00 1.17 1.3- 23 1.00 1.13 1.10 1.12 1.17 1.00 1.14 1.16 1.20 1.27 1.35 1.45 1.00 1.17 1.3- 24 1.00 1.13 1.10 1.13 1.18 1.00 1.15 1.17 1.20 1.28 1.36 1.45 1.00 1.17 1.4- 25 1.00 1.13 1.10 1.13 1.18 1.00 1.17 1.17 1.21 1.29 1.37 1.46 1.00 1.17 1.4- 26 1.00 1.13 1.11 1.13 1.19 1.00 1.17 1.18 1.22 1.30 1.38 1.46 1.00 1.17 1.4- 27 1.00 1.13 1.12 1.14 1.19 1.00 1.17 1.18 1.23 1.30 1.38 1.46 1.00 1.18 1.4- 28 1.00 1.13 1.13 1.14 1.20 1.00 1.17 1.19 1.23 1.31 1.38 1.47 1.00 1.20 1.5- 29 1.00 1.14 1.13 1.15 1.20 1.00 1.17 1.20 1.24 1.32 1.39 1.47 1.00 1.20 1.5
A-94

 COV ≤ 100% 100% < COV ≤ 200% COConcbin1 0 1 2 3 4 0 1 2 3 4 5 6 0 1 2 

- 30 1.00 1.14 1.13 1.15 1.23 1.00 1.17 1.20 1.25 1.33 1.40 1.48 1.00 1.20 1.5- 31 1.00 1.14 1.14 1.16 1.23 1.00 1.18 1.20 1.26 1.34 1.42 1.49 1.00 1.20 1.5- 32 1.00 1.14 1.14 1.16 1.23 1.00 1.20 1.20 1.27 1.35 1.42 1.51 1.00 1.20 1.5- 33 1.00 1.14 1.15 1.16 1.24 1.00 1.20 1.21 1.28 1.36 1.43 1.51 1.00 1.20 1.6- 34 1.00 1.14 1.15 1.17 1.24 1.00 1.20 1.22 1.28 1.36 1.44 1.51 1.00 1.20 1.6- 35 1.00 1.15 1.16 1.17 1.24 1.00 1.20 1.23 1.29 1.38 1.44 1.54 1.00 1.20 1.6- 36 1.00 1.17 1.17 1.18 1.24 1.00 1.20 1.24 1.30 1.38 1.46 1.54 1.00 1.22 1.6- 37 1.00 1.17 1.17 1.18 1.25 1.00 1.20 1.25 1.31 1.39 1.46 1.55 1.00 1.24 1.6- 38 1.00 1.17 1.17 1.19 1.26 1.04 1.20 1.26 1.32 1.40 1.48 1.55 1.00 1.25 1.7- 39 1.00 1.17 1.18 1.19 1.29 1.08 1.20 1.27 1.33 1.41 1.49 1.56 1.00 1.27 1.7- 40 1.00 1.17 1.18 1.19 1.29 1.11 1.22 1.27 1.34 1.42 1.50 1.57 1.00 1.29 1.7- 41 1.00 1.17 1.18 1.20 1.29 1.13 1.22 1.29 1.35 1.43 1.51 1.57 1.00 1.29 1.8- 42 1.00 1.17 1.18 1.21 1.30 1.18 1.24 1.29 1.36 1.44 1.52 1.58 1.00 1.33 1.8- 43 1.00 1.17 1.19 1.21 1.30 1.22 1.25 1.30 1.37 1.45 1.53 1.60 1.00 1.33 1.8- 44 1.00 1.17 1.20 1.22 1.31 1.25 1.25 1.30 1.38 1.46 1.55 1.64 1.00 1.33 1.8- 45 1.00 1.20 1.20 1.22 1.31 1.25 1.27 1.31 1.39 1.47 1.57 1.64 1.00 1.34 1.9- 46 1.00 1.20 1.20 1.23 1.32 1.25 1.29 1.33 1.40 1.48 1.57 1.65 1.00 1.38 1.9- 47 1.00 1.20 1.20 1.23 1.32 1.25 1.29 1.33 1.41 1.49 1.58 1.67 1.00 1.40 1.9- 48 1.00 1.20 1.20 1.24 1.34 1.25 1.29 1.35 1.42 1.50 1.59 1.68 1.00 1.40 2.0- 49 1.00 1.20 1.21 1.24 1.35 1.29 1.33 1.36 1.43 1.51 1.61 1.68 1.00 1.40 2.0- 50 1.00 1.20 1.21 1.25 1.35 1.33 1.33 1.36 1.44 1.52 1.62 1.69 1.00 1.40 2.0- 51 1.00 1.20 1.22 1.25 1.36 1.33 1.33 1.38 1.46 1.54 1.62 1.72 1.00 1.43 2.0- 52 1.00 1.20 1.23 1.26 1.37 1.33 1.33 1.40 1.47 1.55 1.63 1.72 1.00 1.44 2.0- 53 1.00 1.20 1.24 1.27 1.39 1.33 1.33 1.40 1.48 1.56 1.64 1.74 1.00 1.50 2.1- 54 1.00 1.20 1.25 1.27 1.40 1.33 1.37 1.41 1.50 1.57 1.65 1.74 1.00 1.50 2.1- 55 1.00 1.20 1.25 1.28 1.41 1.33 1.38 1.42 1.51 1.58 1.67 1.76 1.00 1.50 2.1- 56 1.00 1.20 1.25 1.28 1.42 1.42 1.40 1.44 1.52 1.60 1.68 1.78 1.00 1.56 2.2- 57 1.00 1.22 1.27 1.29 1.42 1.43 1.40 1.45 1.54 1.61 1.70 1.81 1.00 1.57 2.2- 58 1.05 1.22 1.27 1.30 1.45 1.50 1.40 1.47 1.55 1.62 1.71 1.82 1.04 1.60 2.2- 59 1.11 1.24 1.27 1.31 1.45 1.50 1.40 1.49 1.57 1.63 1.73 1.82 1.11 1.60 2.3- 60 1.20 1.25 1.29 1.31 1.46 1.50 1.40 1.50 1.58 1.65 1.74 1.83 1.17 1.63 2.3- 61 1.25 1.25 1.29 1.32 1.46 1.50 1.43 1.50 1.60 1.66 1.75 1.83 1.25 1.67 2.3- 62 1.25 1.25 1.30 1.32 1.47 1.50 1.43 1.53 1.61 1.67 1.75 1.86 1.25 1.67 2.4
A-95

 COV ≤ 100% 100% < COV ≤ 200% COConcbin1 0 1 2 3 4 0 1 2 3 4 5 6 0 1 2 

- 63 1.25 1.29 1.30 1.33 1.52 1.50 1.45 1.55 1.63 1.69 1.77 1.90 1.25 1.74 2.4- 64 1.25 1.29 1.31 1.34 1.54 1.50 1.50 1.56 1.65 1.70 1.79 1.93 1.33 1.78 2.5- 65 1.30 1.29 1.31 1.35 1.55 1.50 1.50 1.58 1.67 1.71 1.81 1.93 1.33 1.80 2.5- 66 1.33 1.33 1.33 1.36 1.57 1.50 1.50 1.60 1.68 1.72 1.82 1.93 1.33 1.83 2.5- 67 1.33 1.33 1.33 1.37 1.58 1.58 1.55 1.62 1.70 1.74 1.83 1.96 1.33 1.86 2.6- 68 1.33 1.33 1.35 1.38 1.58 1.67 1.57 1.64 1.72 1.76 1.86 1.99 1.42 1.89 2.6- 69 1.33 1.33 1.36 1.39 1.60 1.67 1.58 1.67 1.74 1.78 1.88 2.02 1.50 2.00 2.6- 70 1.33 1.33 1.36 1.40 1.64 1.67 1.60 1.68 1.76 1.79 1.90 2.02 1.50 2.00 2.7- 71 1.43 1.33 1.38 1.42 1.64 1.75 1.60 1.70 1.78 1.81 1.92 2.04 1.50 2.00 2.7- 72 1.50 1.38 1.39 1.42 1.65 1.85 1.63 1.73 1.80 1.83 1.93 2.06 1.50 2.10 2.8- 73 1.50 1.38 1.40 1.44 1.65 2.00 1.67 1.75 1.82 1.84 1.96 2.07 1.50 2.14 2.8- 74 1.50 1.40 1.40 1.44 1.65 2.00 1.67 1.78 1.85 1.86 1.98 2.07 1.50 2.18 2.9- 75 1.50 1.40 1.42 1.46 1.66 2.00 1.71 1.80 1.87 1.88 2.00 2.08 1.50 2.22 2.9- 76 1.50 1.40 1.43 1.47 1.67 2.00 1.75 1.83 1.90 1.90 2.02 2.09 1.60 2.29 3.0- 77 1.50 1.40 1.45 1.48 1.68 2.00 1.78 1.86 1.92 1.93 2.05 2.11 1.67 2.34 3.0- 78 1.50 1.40 1.46 1.50 1.69 2.00 1.80 1.90 1.96 1.95 2.06 2.13 1.71 2.40 3.1- 79 1.50 1.43 1.48 1.52 1.70 2.00 1.83 1.92 1.98 1.97 2.08 2.16 1.85 2.46 3.1- 80 1.50 1.44 1.50 1.52 1.71 2.00 1.86 1.96 2.01 2.00 2.14 2.20 2.00 2.56 3.2- 81 1.58 1.50 1.50 1.54 1.74 2.00 1.89 2.00 2.05 2.02 2.15 2.23 2.00 2.60 3.3- 82 1.67 1.50 1.53 1.57 1.75 2.00 2.00 2.05 2.08 2.05 2.17 2.25 2.00 2.67 3.3- 83 1.75 1.50 1.55 1.59 1.77 2.00 2.00 2.09 2.12 2.08 2.22 2.29 2.00 2.78 3.4- 84 2.00 1.55 1.58 1.61 1.77 2.00 2.00 2.14 2.15 2.11 2.25 2.29 2.00 2.83 3.5- 85 2.00 1.57 1.60 1.64 1.82 2.00 2.11 2.19 2.20 2.14 2.27 2.31 2.00 3.00 3.6- 86 2.00 1.60 1.63 1.67 1.85 2.11 2.17 2.24 2.24 2.17 2.39 2.32 2.00 3.00 3.7- 87 2.00 1.60 1.65 1.70 1.86 2.33 2.20 2.30 2.29 2.20 2.47 2.39 2.00 3.17 3.8- 88 2.00 1.63 1.69 1.72 1.88 2.50 2.29 2.36 2.35 2.23 2.50 2.39 2.00 3.29 3.9- 89 2.00 1.67 1.71 1.76 1.91 2.50 2.35 2.43 2.40 2.27 2.53 2.39 2.00 3.40 4.0- 90 2.00 1.71 1.75 1.80 1.98 2.94 2.43 2.50 2.46 2.31 2.58 2.50 2.00 3.56 4.1- 91 2.00 1.78 1.80 1.85 2.10 3.00 2.56 2.60 2.54 2.37 2.66 2.51 2.44 3.68 4.2- 92 2.00 1.80 1.83 1.89 2.25 3.00 2.65 2.70 2.62 2.43 2.73 2.57 2.67 3.86 4.3- 93 2.00 1.86 1.90 1.96 2.26 3.00 2.80 2.81 2.71 2.48 2.77 2.59 3.00 4.00 4.5- 94 2.00 2.00 1.95 2.02 2.30 3.33 3.00 2.94 2.81 2.56 2.81 2.65 3.00 4.29 4.7- 95 2.00 2.00 2.05 2.10 2.50 4.00 3.14 3.10 2.93 2.66 2.91 2.65 3.75 4.57 5.0
A-96

 COV ≤ 100% 100% < COV ≤ 200% COConcbin1 0 1 2 3 4 0 1 2 3 4 5 6 0 1 2 

- 96 2.33 2.14 2.14 2.22 2.53 4.00 3.38 3.30 3.08 2.82 3.11 2.66 4.75 4.88 5.2- 97 2.67 2.29 2.27 2.37 2.56 5.00 3.67 3.56 3.28 3.01 3.25 2.71 6.00 5.29 5.6- 98 3.00 2.50 2.50 2.63 3.12 6.00 4.17 3.93 3.56 3.33 3.30 3.16 10.00 5.86 6.3- 99 4.00 2.89 2.91 3.02 3.61 10.00 5.00 4.64 4.07 3.77 3.82 3.27 10.00 6.86 7.2

- 100 11.67 10.60 10.08 6.81 6.10 11.75 11.67 11.94 11.41 8.51 6.63 3.51 11.75 11.50 11.9

n3 352735 74053 42876 6895 147 802624 259701 179452 53053 3807 398 104 475572 55341 355

Notes: 1 1-hour SO2 concentration bins are: 0 = 1-hour mean < 5 ppb; 2 = 5 ≤ 1-hour mean < 10 ppb; 2 = 10 ≤ 1-hour mean < 25 ppb

ppb; 4 = 75 ≤ 1-hour mean < 150 ppb ; 5 = 150 ≤ 1-hour mean 250 ppb; 6 = 1-hour mean > 250 ppb. 2 pct – x indicates the percentile of the distribution. 3 n is the number of 5-minute maximum and 1-hour SO2 measurements used to develop distribution. 
A-97

Table A.3-2. Distribution of 5-minute maximum peak to 1-hour mean SO2 concentration ratios (PMmonitors categorized by 1-hour geometric standard deviation (GSD) and 1-hour mean concentratio

 GSD ≤ 2.17 2.17 < GSD ≤ 2.94 GConcbin1 0 1 2 3 4 0 1 2 3 4 5 6 0 1 2 

Pct2 - 0 1.00 1.00 1.00 1.00 1.01 1.00 1.00 1.00 1.00 1.00 1.05 1.13 1.00 1.00 1.0

- 1 1.00 1.00 1.00 1.03 1.07 1.00 1.00 1.00 1.00 1.05 1.13 1.19 1.00 1.00 1.0- 2 1.00 1.00 1.00 1.04 1.17 1.00 1.00 1.00 1.03 1.08 1.21 1.26 1.00 1.00 1.0- 3 1.00 1.00 1.00 1.04 1.21 1.00 1.00 1.00 1.04 1.10 1.23 1.28 1.00 1.00 1.0- 4 1.00 1.00 1.00 1.06 1.22 1.00 1.00 1.05 1.05 1.12 1.26 1.29 1.00 1.00 1.0- 5 1.00 1.00 1.00 1.07 1.24 1.00 1.00 1.06 1.06 1.14 1.28 1.30 1.00 1.00 1.0- 6 1.00 1.00 1.00 1.08 1.25 1.00 1.00 1.06 1.07 1.16 1.29 1.33 1.00 1.00 1.0- 7 1.00 1.00 1.04 1.10 1.27 1.00 1.00 1.07 1.08 1.17 1.31 1.34 1.00 1.03 1.0- 8 1.00 1.00 1.06 1.11 1.29 1.00 1.00 1.07 1.08 1.18 1.32 1.35 1.00 1.05 1.- 9 1.00 1.00 1.06 1.12 1.30 1.00 1.00 1.08 1.09 1.20 1.34 1.37 1.00 1.07 1.

- 10 1.00 1.00 1.07 1.13 1.30 1.00 1.00 1.08 1.10 1.21 1.35 1.38 1.00 1.09 1.- 11 1.00 1.00 1.07 1.14 1.31 1.00 1.00 1.08 1.11 1.23 1.38 1.43 1.00 1.10 1.- 12 1.00 1.00 1.07 1.14 1.32 1.00 1.05 1.09 1.12 1.24 1.39 1.45 1.00 1.11 1.- 13 1.00 1.00 1.08 1.15 1.32 1.00 1.10 1.09 1.12 1.25 1.40 1.46 1.00 1.11 1.- 14 1.00 1.00 1.08 1.16 1.33 1.00 1.11 1.10 1.13 1.26 1.42 1.47 1.00 1.13 1.- 15 1.00 1.00 1.08 1.17 1.34 1.00 1.11 1.10 1.14 1.28 1.43 1.48 1.00 1.13 1.- 16 1.00 1.00 1.08 1.19 1.35 1.00 1.11 1.10 1.15 1.29 1.44 1.50 1.00 1.13 1.- 17 1.00 1.00 1.09 1.19 1.36 1.00 1.13 1.10 1.15 1.30 1.46 1.51 1.00 1.14 1.- 18 1.00 1.00 1.09 1.20 1.36 1.00 1.13 1.11 1.16 1.31 1.47 1.52 1.00 1.14 1.- 19 1.00 1.00 1.09 1.21 1.37 1.00 1.13 1.12 1.17 1.32 1.49 1.53 1.00 1.14 1.- 20 1.00 1.00 1.09 1.22 1.40 1.00 1.13 1.13 1.17 1.34 1.50 1.54 1.00 1.15 1.2- 21 1.00 1.00 1.10 1.23 1.43 1.00 1.14 1.13 1.18 1.35 1.52 1.54 1.00 1.16 1.2- 22 1.00 1.07 1.10 1.24 1.44 1.00 1.14 1.14 1.19 1.36 1.53 1.56 1.00 1.17 1.2- 23 1.00 1.11 1.10 1.25 1.44 1.00 1.14 1.15 1.20 1.38 1.55 1.58 1.00 1.17 1.2- 24 1.00 1.11 1.10 1.26 1.46 1.00 1.14 1.15 1.21 1.39 1.57 1.58 1.00 1.17 1.2- 25 1.00 1.11 1.10 1.27 1.47 1.00 1.14 1.16 1.21 1.40 1.57 1.58 1.00 1.18 1.2- 26 1.00 1.13 1.11 1.28 1.48 1.00 1.17 1.17 1.22 1.41 1.59 1.59 1.00 1.19 1.2- 27 1.00 1.13 1.12 1.29 1.49 1.00 1.17 1.17 1.23 1.42 1.60 1.59 1.00 1.20 1.2- 28 1.00 1.13 1.13 1.30 1.50 1.00 1.17 1.18 1.24 1.43 1.61 1.61 1.00 1.20 1.2- 29 1.00 1.14 1.14 1.30 1.50 1.00 1.17 1.18 1.25 1.44 1.64 1.63 1.00 1.20 1.2
A-98

 GSD ≤ 2.17 2.17 < GSD ≤ 2.94 GConcbin1 0 1 2 3 4 0 1 2 3 4 5 6 0 1 2 

- 30 1.00 1.14 1.14 1.31 1.52 1.00 1.17 1.18 1.26 1.46 1.65 1.64 1.00 1.20 1.3- 31 1.00 1.14 1.15 1.32 1.53 1.00 1.17 1.19 1.27 1.47 1.68 1.65 1.00 1.20 1.3- 32 1.00 1.14 1.16 1.33 1.55 1.00 1.17 1.20 1.28 1.48 1.70 1.66 1.00 1.22 1.3- 33 1.00 1.14 1.17 1.34 1.57 1.00 1.18 1.20 1.29 1.50 1.72 1.68 1.00 1.23 1.3- 34 1.00 1.15 1.17 1.35 1.58 1.00 1.20 1.20 1.30 1.51 1.74 1.69 1.00 1.25 1.3- 35 1.00 1.17 1.18 1.36 1.59 1.00 1.20 1.21 1.31 1.52 1.75 1.69 1.00 1.25 1.3- 36 1.00 1.17 1.18 1.37 1.60 1.00 1.20 1.21 1.32 1.54 1.76 1.70 1.00 1.26 1.3- 37 1.00 1.17 1.18 1.39 1.60 1.00 1.20 1.23 1.32 1.55 1.78 1.72 1.00 1.28 1.3- 38 1.00 1.17 1.20 1.40 1.62 1.00 1.20 1.23 1.33 1.57 1.80 1.73 1.03 1.29 1.3- 39 1.00 1.17 1.20 1.40 1.63 1.00 1.20 1.25 1.35 1.58 1.82 1.73 1.05 1.29 1.4- 40 1.00 1.17 1.20 1.42 1.63 1.00 1.20 1.25 1.36 1.60 1.83 1.74 1.11 1.32 1.4- 41 1.00 1.17 1.20 1.43 1.64 1.00 1.20 1.26 1.37 1.61 1.85 1.76 1.11 1.33 1.4- 42 1.00 1.18 1.21 1.44 1.65 1.00 1.20 1.27 1.38 1.63 1.86 1.77 1.11 1.33 1.4- 43 1.00 1.20 1.22 1.44 1.66 1.00 1.22 1.27 1.39 1.64 1.89 1.78 1.15 1.33 1.4- 44 1.00 1.20 1.23 1.46 1.67 1.00 1.22 1.29 1.40 1.66 1.90 1.78 1.18 1.35 1.4- 45 1.00 1.20 1.25 1.47 1.70 1.00 1.25 1.29 1.42 1.67 1.91 1.79 1.21 1.37 1.5- 46 1.00 1.20 1.25 1.48 1.70 1.04 1.25 1.30 1.43 1.69 1.93 1.80 1.25 1.38 1.5- 47 1.00 1.20 1.27 1.50 1.71 1.11 1.25 1.30 1.44 1.70 1.95 1.80 1.25 1.40 1.5- 48 1.00 1.20 1.27 1.51 1.72 1.13 1.29 1.31 1.45 1.72 1.97 1.81 1.25 1.40 1.5- 49 1.00 1.20 1.27 1.52 1.74 1.20 1.29 1.33 1.47 1.74 1.99 1.82 1.25 1.40 1.5- 50 1.00 1.20 1.29 1.54 1.74 1.25 1.29 1.33 1.48 1.75 2.00 1.82 1.29 1.41 1.5- 51 1.00 1.20 1.30 1.56 1.75 1.25 1.29 1.35 1.49 1.77 2.02 1.82 1.33 1.43 1.5- 52 1.00 1.20 1.30 1.57 1.76 1.25 1.33 1.36 1.51 1.79 2.05 1.83 1.33 1.44 1.6- 53 1.00 1.20 1.31 1.59 1.76 1.25 1.33 1.37 1.52 1.81 2.06 1.84 1.33 1.47 1.6- 54 1.00 1.20 1.32 1.61 1.77 1.25 1.33 1.38 1.54 1.83 2.09 1.84 1.33 1.50 1.6- 55 1.00 1.22 1.33 1.63 1.78 1.29 1.33 1.40 1.56 1.84 2.11 1.87 1.38 1.50 1.6- 56 1.00 1.25 1.35 1.65 1.80 1.33 1.33 1.40 1.57 1.86 2.14 1.88 1.43 1.50 1.6- 57 1.00 1.25 1.36 1.67 1.81 1.33 1.38 1.42 1.59 1.88 2.15 1.89 1.43 1.54 1.7- 58 1.00 1.27 1.37 1.69 1.81 1.33 1.38 1.43 1.61 1.90 2.17 1.91 1.48 1.57 1.7- 59 1.07 1.29 1.38 1.71 1.83 1.33 1.40 1.45 1.62 1.92 2.19 1.91 1.50 1.58 1.7- 60 1.14 1.29 1.40 1.72 1.83 1.33 1.40 1.46 1.64 1.94 2.21 1.93 1.50 1.60 1.7- 61 1.22 1.32 1.40 1.74 1.84 1.33 1.40 1.47 1.66 1.96 2.22 1.94 1.50 1.60 1.8- 62 1.25 1.33 1.42 1.75 1.91 1.43 1.40 1.50 1.68 1.98 2.25 1.95 1.50 1.63 1.8
A-99

 GSD ≤ 2.17 2.17 < GSD ≤ 2.94 GConcbin1 0 1 2 3 4 0 1 2 3 4 5 6 0 1 2 

- 63 1.25 1.33 1.44 1.78 1.93 1.50 1.40 1.50 1.70 1.99 2.27 1.96 1.50 1.67 1.8- 64 1.29 1.33 1.45 1.80 1.93 1.50 1.43 1.52 1.72 2.02 2.29 2.00 1.54 1.67 1.8- 65 1.33 1.33 1.47 1.81 1.99 1.50 1.43 1.54 1.74 2.04 2.31 2.02 1.62 1.70 1.9- 66 1.33 1.38 1.50 1.84 2.00 1.50 1.44 1.56 1.76 2.07 2.32 2.04 1.67 1.72 1.9- 67 1.33 1.40 1.50 1.87 2.05 1.50 1.50 1.58 1.79 2.10 2.35 2.06 1.67 1.76 1.9- 68 1.33 1.40 1.53 1.89 2.08 1.50 1.50 1.60 1.81 2.12 2.37 2.07 1.71 1.80 2.0- 69 1.36 1.40 1.55 1.91 2.09 1.50 1.50 1.62 1.83 2.15 2.41 2.08 1.80 1.80 2.0- 70 1.50 1.40 1.58 1.94 2.11 1.50 1.56 1.64 1.85 2.17 2.43 2.11 1.86 1.83 2.0- 71 1.50 1.40 1.60 1.97 2.15 1.50 1.57 1.67 1.88 2.20 2.44 2.13 2.00 1.86 2.- 72 1.50 1.43 1.62 2.00 2.18 1.50 1.60 1.69 1.91 2.23 2.49 2.14 2.00 1.89 2.- 73 1.50 1.43 1.64 2.02 2.19 1.67 1.60 1.71 1.94 2.26 2.51 2.15 2.00 1.98 2.- 74 1.50 1.49 1.67 2.04 2.23 1.67 1.60 1.73 1.97 2.30 2.55 2.17 2.00 2.00 2.2- 75 1.50 1.50 1.69 2.07 2.26 1.68 1.67 1.77 2.00 2.33 2.61 2.17 2.00 2.00 2.2- 76 1.50 1.50 1.71 2.10 2.27 1.75 1.67 1.80 2.03 2.36 2.63 2.21 2.00 2.03 2.2- 77 1.50 1.56 1.73 2.12 2.28 2.00 1.71 1.82 2.06 2.41 2.69 2.23 2.00 2.13 2.3- 78 1.50 1.57 1.77 2.15 2.31 2.00 1.75 1.86 2.10 2.46 2.73 2.24 2.00 2.17 2.3- 79 1.67 1.60 1.80 2.19 2.40 2.00 1.80 1.90 2.13 2.49 2.75 2.27 2.00 2.20 2.4- 80 1.75 1.60 1.83 2.22 2.46 2.00 1.80 1.93 2.17 2.54 2.79 2.27 2.00 2.24 2.4- 81 2.00 1.63 1.87 2.27 2.47 2.00 1.83 2.00 2.22 2.60 2.84 2.30 2.00 2.31 2.5- 82 2.00 1.67 1.91 2.30 2.47 2.00 1.86 2.00 2.26 2.67 2.87 2.31 2.29 2.37 2.6- 83 2.00 1.71 1.94 2.36 2.49 2.00 1.96 2.08 2.31 2.72 2.89 2.33 2.50 2.40 2.6- 84 2.00 1.77 2.00 2.43 2.53 2.00 2.00 2.11 2.36 2.78 2.92 2.37 2.50 2.50 2.7- 85 2.00 1.80 2.00 2.48 2.68 2.00 2.00 2.18 2.41 2.85 2.97 2.44 2.75 2.57 2.7- 86 2.00 1.83 2.09 2.56 2.74 2.00 2.11 2.23 2.47 2.90 3.00 2.48 3.00 2.63 2.8- 87 2.00 1.86 2.13 2.63 2.78 2.00 2.17 2.30 2.54 2.97 3.11 2.56 3.00 2.73 2.9- 88 2.00 1.97 2.20 2.69 2.81 2.00 2.20 2.38 2.60 3.06 3.19 2.59 3.33 2.83 3.0- 89 2.00 2.00 2.27 2.79 2.85 2.00 2.30 2.45 2.68 3.14 3.25 2.62 3.33 2.94 3.- 90 2.00 2.00 2.33 2.88 2.96 2.25 2.40 2.54 2.76 3.26 3.31 2.66 4.00 3.00 3.2- 91 2.00 2.14 2.42 2.97 3.06 2.50 2.50 2.64 2.86 3.36 3.41 2.67 4.67 3.18 3.3- 92 2.00 2.20 2.54 3.08 3.24 2.50 2.60 2.75 2.96 3.44 3.55 2.68 5.00 3.33 3.4- 93 2.00 2.29 2.64 3.24 3.39 3.00 2.78 2.89 3.08 3.59 3.67 2.70 5.50 3.50 3.6- 94 2.25 2.40 2.79 3.50 3.55 3.00 2.92 3.02 3.22 3.74 3.84 2.71 10.00 3.71 3.7- 95 2.50 2.56 2.93 3.61 3.68 3.00 3.14 3.21 3.40 3.92 3.92 3.01 10.00 4.00 4.0
A-100

 GSD ≤ 2.17 2.17 < GSD ≤ 2.94 GConcbin1 0 1 2 3 4 0 1 2 3 4 5 6 0 1 2 

- 96 3.00 2.71 3.13 3.91 3.93 3.50 3.40 3.44 3.62 4.12 4.22 3.11 10.00 4.25 4.2- 97 3.00 3.00 3.40 4.23 4.13 4.00 3.75 3.73 3.93 4.35 4.38 3.41 10.00 4.67 4.5- 98 3.50 3.29 3.85 4.71 4.49 5.00 4.20 4.17 4.37 4.84 4.52 3.44 10.00 5.22 5.0- 99 4.00 4.00 4.68 5.58 5.09 6.00 5.14 5.00 5.20 5.50 5.13 3.79 10.00 6.20 5.9

- 100 11.75 11.57 11.94 10.14 6.10 11.75 11.50 11.93 11.41 9.67 6.48 5.39 11.67 11.67 11

n3 456580 54454 16117 1925 150 876986 271059 186098 49555 3888 613 219 297365 63582 556

Notes: 1 1-hour SO2 concentration bins are: 0 = 1-hour mean < 5 ppb; 2 = 5 ≤ 1-hour mean < 10 ppb; 2 = 10 ≤ 1-hour mean < 25 ppb

ppb; 4 = 75 ≤ 1-hour mean < 150 ppb ; 5 = 150 ≤ 1-hour mean 250 ppb; 6 = 1-hour mean > 250 ppb. 2 pct – x indicates the percentile of the distribution. 3 n is the number of 5-minute maximum and 1-hour SO2 measurements used to develop distribution. 
A-101

A.4 Factors Used in Adjusting Air Quality to Just Meet the Current and Potential 

Alternative SO2 Air Quality Standards 

The adjustment factors used for simulating just meeting particular forms and levels of 

SO2 standards are described here in two sections. This was done given the difference in how the 

adjustment factors were derived and applied to each of the air quality scenarios and given the 

number of factors generated for the potential alternative standards. The first section includes the 

factors used for adjusting air quality to just meet the current standards (either the 24-hour or 

annual average), while the second section note the concentrations used in deriving the factors 

applied to simulate just meeting potential alternative standards. 

 A.4.1 Adjustment factors for just meeting the current standard 

Both annual and daily adjustment factors were calculated for all selected counties in 

evaluating the current annual and daily standards however, the lowest value of the two was 

selected for use in adjusting concentrations (see REA section 7.2.4). The adjustment factors for 

each county, year, and the standard from which the factors were derived is given in Table A.4-1. 

In addion, the coefficient of variation (i.e., COV) was used as a measure to indicate the 

variability associated with each of the calculated factors when considering all of the monitors in 

a county. Within a given year, the COV generally indicates the extent of spatial variability in 

ambient concentrations, considering the number of monitors in operation. Variation in the COV 

across different years can indicate the temporal variability in a county however, year-to-year 

differences in the number and location of ambient monitors may confound this comparison. 

Lower COVs indicate similarity in that concentration metric in the county, while higher values 

indicate less homogeneity in concentrations (whether spatially or temporally). 

Table A.4-1. Adjustment factors used in simulating air quality just meeting the 

current SO2 NAAQS in selected counties by year. 

State 

Abbreviation County Year 

Monitors 

(n) 

Adjustment 

Factor COV 

Closest 

Standard1

AZ Gila 2001 2 3.12 4 D 

AZ Gila 2002 2 3.53 5 A 

AZ Gila 2003 2 3.82 12 A 

AZ Gila 2004 2 3.04 21 A 

AZ Gila 2005 2 3.33 5 D 

AZ Gila 2006 2 4.40 1 D 

DE New Castle 2001 4 3.38 16 D 

DE New Castle 2002 4 2.67 9 D 
A-102

State 

Abbreviation County Year 

Monitors 

(n) 

Adjustment 

Factor COV 

Closest 

Standard1

DE New Castle 2003 5 2.75 9 D 

DE New Castle 2004 4 2.58 13 D 

DE New Castle 2005 4 2.73 11 D 

DE New Castle 2006 4 2.68 14 D 

FL Hillsborough 2001 7 3.14 13 D 

FL Hillsborough 2002 7 3.09 16 D 

FL Hillsborough 2003 6 3.09 19 D 

FL Hillsborough 2004 6 4.95 32 D 

FL Hillsborough 2005 6 4.40 25 D 

FL Hillsborough 2006 6 4.19 29 D 

IL Madison 2001 4 3.51 7 D 

IL Madison 2002 4 2.88 12 D 

IL Madison 2003 3 3.60 6 D 

IL Madison 2004 3 3.61 18 D 

IL Madison 2005 3 4.19 11 D 

IL Madison 2006 3 4.90 16 D 

IL Wabash 2001 2 3.25 1 D 

IL Wabash 2002 2 3.33 3 D 

IL Wabash 2003 2 2.95 5 D 

IL Wabash 2004 2 3.98 1 D 

IL Wabash 2005 2 3.80 7 D 

IL Wabash 2006 2 3.01 5 D 

IN Floyd 2001 3 3.98 2 D 

IN Floyd 2002 3 4.85 6 D 

IN Floyd 2003 3 4.14 5 D 

IN Floyd 2004 2 5.04 6 A 

IN Floyd 2005 3 3.98 11 A 

IN Floyd 2006 3 3.64 5 D 

IN Gibson 2001 2 2.34 6 D 

IN Gibson 2002 2 2.68 19 D 

IN Gibson 2003 2 1.17 13 D 

IN Gibson 2004 2 2.99 10 D 

IN Gibson 2005 2 4.78 3 D 

IN Gibson 2006 2 1.67 16 D 

IN Lake 2001 2 4.87 0 D 

IN Lake 2002 2 4.43 17 D 

IN Lake 2003 2 4.94 7 D 

IN Lake 2004 2 4.39 14 D 

IN Lake 2005 2 3.39 16 D 

IN Lake 2006 1 8.12 0 A 

IN Vigo 2001 2 2.47 16 D 

IN Vigo 2002 2 4.65 18 A 

IN Vigo 2003 2 4.06 13 A 

IN Vigo 2004 2 5.28 1 D 

IN Vigo 2005 2 4.57 5 D 

IN Vigo 2006 2 6.97 5 D 

IA Linn 2001 5 3.53 18 D 

IA Linn 2002 3 4.70 5 D 
A-103

State 

Abbreviation County Year 

Monitors 

(n) 

Adjustment 

Factor COV 

Closest 

Standard1

IA Linn 2003 3 3.45 5 D 

IA Linn 2004 3 2.29 10 D 

IA Linn 2005 3 3.41 9 D 

IA Linn 2006 3 4.10 35 D 

IA Muscatine 2001 3 4.20 12 D 

IA Muscatine 2002 3 3.87 11 D 

IA Muscatine 2003 3 4.09 11 D 

IA Muscatine 2004 3 2.78 16 D 

IA Muscatine 2005 3 2.90 17 D 

IA Muscatine 2006 3 2.94 10 D 

MI Wayne 2001 6 3.21 9 D 

MI Wayne 2002 3 2.97 15 D 

MI Wayne 2003 3 3.30 5 D 

MI Wayne 2004 3 2.99 12 D 

MI Wayne 2005 3 3.35 7 D 

MI Wayne 2006 3 2.95 13 D 

MO Greene 2001 3 3.57 17 D 

MO Greene 2002 5 3.47 32 D 

MO Greene 2003 5 5.12 26 D 

MO Greene 2004 5 5.29 29 D 

MO Greene 2005 5 4.87 34 D 

MO Greene 2006 5 4.46 19 D 

MO Iron 2001 2 2.26 0 D 

MO Iron 2002 2 2.11 2 D 

MO Iron 2003 2 2.44 2 D 

MO Iron 2004 2 7.96 22 A 

MO Jefferson 2001 3 5.74 10 D 

MO Jefferson 2002 1 3.89 0 D 

MO Jefferson 2003 1 5.65 0 D 

MO Jefferson 2004 1 1.87 0 D 

MO Jefferson 2005 1 2.13 0 D 

MO Jefferson 2006 1 1.93 0 D 

NH Merrimack 2001 2 3.07 21 D 

NH Merrimack 2002 3 3.71 18 D 

NH Merrimack 2003 3 3.31 10 D 

NH Merrimack 2004 2 2.59 17 D 

NH Merrimack 2005 2 2.70 18 D 

NH Merrimack 2006 2 2.51 28 D 

NJ Hudson 2001 2 3.39 6 A 

NJ Hudson 2002 1 5.26 0 A 

NJ Hudson 2003 2 3.52 6 A 

NJ Hudson 2004 2 3.67 4 A 

NJ Hudson 2005 2 3.67 1 A 

NJ Hudson 2006 2 6.25 5 D 

NJ Union 2001 2 3.71 7 A 

NJ Union 2002 2 3.52 11 A 

NJ Union 2003 2 3.70 8 A 

NJ Union 2004 2 3.99 8 A 
A-104

State 

Abbreviation County Year 

Monitors 

(n) 

Adjustment 

Factor COV 

Closest 

Standard1

NJ Union 2005 2 4.12 7 A 

NJ Union 2006 2 7.98 4 D 

NY Bronx 2001 1 2.95 0 A 

NY Bronx 2002 2 3.04 3 A 

NY Bronx 2003 2 2.82 1 D 

NY Bronx 2004 2 2.96 3 A 

NY Bronx 2005 1 3.26 0 A 

NY Bronx 2006 2 3.44 6 A 

NY Chautauqua 2001 3 1.85 12 D 

NY Chautauqua 2002 2 2.34 18 D 

NY Chautauqua 2003 2 2.30 13 D 

NY Chautauqua 2004 2 3.42 16 D 

NY Chautauqua 2005 2 5.78 11 D 

NY Chautauqua 2006 2 9.47 2 D 

NY Erie 2001 2 2.66 13 D 

NY Erie 2002 2 2.01 16 D 

NY Erie 2003 2 1.85 16 D 

NY Erie 2004 2 3.65 20 D 

NY Erie 2005 2 4.14 14 D 

NY Erie 2006 2 4.72 17 D 

OH Cuyahoga 2001 5 4.05 6 D 

OH Cuyahoga 2002 5 5.10 11 A 

OH Cuyahoga 2003 5 3.98 5 D 

OH Cuyahoga 2004 4 4.54 11 D 

OH Cuyahoga 2005 4 3.43 6 D 

OH Cuyahoga 2006 4 4.25 8 D 

OH Lake 2001 2 3.78 8 A 

OH Lake 2002 2 3.34 15 A 

OH Lake 2003 2 2.79 10 D 

OH Lake 2004 2 3.05 13 D 

OH Lake 2005 2 1.87 13 D 

OH Lake 2006 2 2.51 16 D 

OH Summit 2001 2 3.25 3 D 

OH Summit 2002 2 2.39 8 D 

OH Summit 2003 2 2.65 2 D 

OH Summit 2004 2 2.75 11 D 

OH Summit 2005 2 3.76 14 A 

OH Summit 2006 2 3.79 9 D 

OK Tulsa 2001 3 4.16 10 A 

OK Tulsa 2002 3 4.51 2 D 

OK Tulsa 2003 3 3.65 6 D 

OK Tulsa 2004 3 4.07 3 D 

OK Tulsa 2005 3 4.57 4 A 

OK Tulsa 2006 4 5.69 59 D 

PA Allegheny 2001 7 2.72 5 D 

PA Allegheny 2002 5 2.80 4 A 

PA Allegheny 2003 7 2.23 5 D 

PA Allegheny 2004 7 2.81 6 D 
A-105

State 

Abbreviation County Year 

Monitors 

(n) 

Adjustment 

Factor COV 

Closest 

Standard1

PA Allegheny 2005 7 2.17 7 D 

PA Allegheny 2006 6 2.97 8 D 

PA Beaver 2001 3 2.01 5 D 

PA Beaver 2002 3 1.91 6 D 

PA Beaver 2003 3 1.73 6 D 

PA Beaver 2004 3 2.64 6 A 

PA Beaver 2005 3 2.42 7 A 

PA Beaver 2006 3 2.67 8 D 

PA Northampton 2001 2 2.15 28 A 

PA Northampton 2002 2 5.01 0 A 

PA Northampton 2003 2 3.73 18 A 

PA Northampton 2004 2 2.28 21 A 

PA Northampton 2005 2 3.55 3 A 

PA Northampton 2006 2 0.98 19 D 

PA Warren 2001 2 1.66 11 D 

PA Warren 2002 2 1.45 15 D 

PA Warren 2003 2 1.40 11 D 

PA Warren 2004 2 2.37 15 D 

PA Warren 2005 2 1.91 17 D 

PA Warren 2006 2 1.68 19 D 

PA Washington 2001 3 2.95 6 A 

PA Washington 2002 3 3.11 6 A 

PA Washington 2003 3 2.99 8 A 

PA Washington 2004 3 3.42 2 A 

PA Washington 2005 3 3.07 5 D 

PA Washington 2006 3 3.48 6 A 

TN Blount 2001 2 1.62 18 D 

TN Blount 2002 2 2.05 10 D 

TN Blount 2003 2 1.88 12 D 

TN Blount 2004 2 2.22 1 D 

TN Blount 2005 2 1.61 7 D 

TN Blount 2006 2 1.79 10 D 

TN Shelby 2001 3 3.47 19 D 

TN Shelby 2002 3 4.79 20 D 

TN Shelby 2003 3 3.75 21 D 

TN Shelby 2004 3 4.46 20 D 

TN Shelby 2005 4 3.90 46 D 

TN Shelby 2006 3 4.12 44 D 

TN Sullivan 2001 2 2.95 8 A 

TN Sullivan 2002 2 3.26 10 D 

TN Sullivan 2003 2 3.28 4 D 

TN Sullivan 2004 2 3.33 3 D 

TN Sullivan 2005 2 3.72 4 D 

TN Sullivan 2006 2 3.33 3 D 

TX Jefferson 2001 3 2.68 8 D 

TX Jefferson 2002 3 4.82 4 D 

TX Jefferson 2003 3 4.30 4 D 

TX Jefferson 2004 3 4.47 13 D 
A-106

State 

Abbreviation County Year 

Monitors 

(n) 

Adjustment 

Factor COV 

Closest 

Standard1

TX Jefferson 2005 3 5.67 7 D 

TX Jefferson 2006 3 4.31 4 D 

VA Fairfax 2001 2 4.50 18 A 

VA Fairfax 2002 2 4.49 14 A 

VA Fairfax 2003 3 4.89 15 A 

VA Fairfax 2004 3 4.80 19 A 

VA Fairfax 2005 3 4.79 19 A 

VA Fairfax 2006 3 5.35 18 A 

WV Brooke 2001 2 2.13 5 A 

WV Brooke 2002 2 2.49 4 A 

WV Brooke 2003 2 2.63 3 A 

WV Brooke 2004 2 2.02 6 A 

WV Brooke 2005 2 2.16 5 A 

WV Brooke 2006 2 2.50 8 A 

WV Hancock 2001 9 2.20 3 A 

WV Hancock 2002 9 2.38 3 D 

WV Hancock 2003 9 2.30 3 D 

WV Hancock 2004 7 2.38 4 A 

WV Hancock 2005 7 2.22 5 A 

WV Hancock 2006 7 2.34 4 A 

WV Monongalia 2001 3 2.37 3 D 

WV Monongalia 2002 2 2.22 2 D 

WV Monongalia 2003 2 3.26 1 D 

WV Monongalia 2004 2 3.25 1 D 

WV Monongalia 2005 2 3.13 3 A 

WV Monongalia 2006 2 3.20 1 D 

WV Wayne 2001 4 2.85 4 D 

WV Wayne 2002 4 3.31 3 A 

WV Wayne 2003 4 3.41 7 D 

WV Wayne 2004 3 2.87 9 D 

WV Wayne 2005 3 2.02 11 D 

VI St Croix 2001 5 3.41 83 D 

VI St Croix 2002 5 3.46 64 D 

VI St Croix 2003 5 3.66 66 D 

VI St Croix 2004 5 3.26 56 D 

VI St Croix 2005 5 9.25 15 D 

VI St Croix 2006 5 4.59 25 D 

Notes: 

1

 Ambient SO2 concentrations were closest to either the annual (A) or daily (D) NAAQS level. 
A-107

A.4.2 Adjustment factors for just meeting the potential alternative standards 

Five potential alternative standards (i.e., 50, 100, 150, 200, and 250 ppb daily maximum 

1-hour) given a 99th percentile form and one alternative standard (200 ppb daily maximum 1-

hour) given a 98th percentile form were selected for evaluation (for details, see REA chapter 5). 

Adjustment factors were derived for each of two 3-year groups of recent air quality (i.e., 2001-

2003 and 2004-2006). For the sake of brevity, only the maximum 3-year averaged 

concentrations for each of the percentile forms are provided in Table A.4-2, rather than all of the 

adjustment factors. The actual adjustment factors used in simulating air quality can be derived 

for each of the concentration levels by dividing by the county concentration for each year goup. 

For example, the adjustement factor applied to the 2002 hourly mean concentrations in New 

Castle DE to simulate just meeting a 99th percentile daily maximum 1-hour of 100 ppb is 

100/164 = 0.61. That is to say, to meet this particular standard, the hourly concentrations need to 

be adjusted downward by a factor of 0.61. The COV is also used to represent the temporal 

variability over the three years of monitoring (where such data exist). 

Table A.4-2. Concentrations used in developing adjustment factors when 

simulating air quality just meeting potential alternative SO2 NAAQS in selected 

counties by year. 

98th Percentile 99th Percentile 

State 

Abbreviation County Year Group 

Years 

(n) 

Conc 

(ppb) 

COV 

(%) 

Years 

(n) 

Conc 

(ppb) 

COV 

(%) 

AZ Gila 2001-2003 3 226 10 3 260 10 

AZ Gila 2004-2006 2 222 6 2 294 1 

DE New Castle 2001-2003 2 138 5 2 164 0 

DE New Castle 2004-2006 3 123 20 3 147 31 

FL Hillsborough 2001-2003 3 117 12 3 146 2 

FL Hillsborough 2004-2006 2 93 8 2 128 8 

IA Linn 2001-2003 3 82 21 3 105 12 

IA Linn 2004-2006 3 96 17 3 111 27 

IA Muscatine 2001-2003 3 92 13 3 113 9 

IA Muscatine 2004-2006 3 120 10 3 135 8 

IL Madison 2001-2003 3 110 22 3 144 24 

IL Madison 2004-2006 3 123 5 3 144 7 

IL Wabash 2001-2003 1 139 1 216 

IL Wabash 2004-2006 1 131 1 187 

IN Floyd 2001-2003 3 124 17 1 151 

IN Floyd 2004-2006 3 129 14 3 170 6 

IN Gibson 2001-2003 2 185 12 2 235 19 
A-108

98th Percentile 99th Percentile 

State 

Abbreviation County Year Group 

Years 

(n) 

Conc 

(ppb) 

COV 

(%) 

Years 

(n) 

Conc 

(ppb) 

COV 

(%) 

IN Gibson 2004-2006 1 199 1 226 

IN Lake 2001-2003 3 68 5 2 84 52 

IN Lake 2004-2006 2 87 1 2 113 3 

IN Vigo 2001-2003 3 114 7 3 159 25 

IN Vigo 2004-2006 2 110 8 2 136 2 

MI Wayne 2001-2003 2 102 3 2 126 4 

MI Wayne 2004-2006 3 115 2 3 128 2 

MO Greene 2001-2003 3 81 13 3 94 13 

MO Greene 2004-2006 3 63 29 3 81 25 

MO Iron 2001-2003 3 289 20 3 341 9 

MO Iron 2004-2006 1 20 1 22 

MO Jefferson 2001-2003 1 230 1 234 

MO Jefferson 2004-2006 3 244 10 3 346 16 

NH Merrimack 2001-2003 3 110 30 3 125 34 

NH Merrimack 2004-2006 3 127 2 3 151 9 

NJ Hudson 2001-2003 2 54 9 2 61 1 

NJ Hudson 2004-2006 2 51 3 2 65 1 

NJ Union 2001-2003 3 52 13 3 57 7 

NJ Union 2004-2006 2 49 10 2 60 9 

NY Bronx 2001-2003 2 64 1 2 71 7 

NY Bronx 2004-2006 2 59 7 2 68 2 

NY Chautauqua 2001-2003 3 238 2 3 285 12 

NY Chautauqua 2004-2006 3 84 47 3 101 54 

NY Erie 2001-2003 3 206 10 3 225 8 

NY Erie 2004-2006 3 114 33 3 129 24 

OH Cuyahoga 2001-2003 2 76 1 2 101 1 

OH Cuyahoga1 2004-2006 3 67 8 3 80 9 

OH Cuyahoga1 2004-2006 3 67 18 

OH Lake 2001-2003 3 129 10 3 145 4 

OH Lake 2004-2006 3 146 5 3 175 9 

OH Summit 2001-2003 3 131 12 3 148 12 

OH Summit 2004-2006 3 133 9 3 150 13 

OK Tulsa 2001-2003 3 63 22 3 76 7 

OK Tulsa 2004-2006 2 82 32 2 93 33 

PA Allegheny 2001-2003 1 149 1 164 

PA Allegheny 2004-2006 2 144 16 2 183 36 

PA Beaver 2001-2003 3 200 28 3 245 31 

PA Beaver 2004-2006 3 188 6 3 228 8 

PA Northampton 2001-2003 3 55 9 3 65 3 

PA Northampton 2004-2006 3 92 41 3 146 65 

PA Warren 2001-2003 3 218 6 3 270 12 

PA Warren 2004-2006 3 180 22 3 226 15 

PA Washington 2001-2003 3 99 10 3 111 11 

PA Washington 2004-2006 3 89 10 3 102 11 

TN Blount 2001-2003 1 189 1 204 

TN Blount 2004-2006 3 168 5 3 194 6 

TN Shelby 2001-2003 3 70 29 3 101 35 
A-109

98th Percentile 99th Percentile 

State 

Abbreviation County Year Group 

Years 

(n) 

Conc 

(ppb) 

COV 

(%) 

Years 

(n) 

Conc 

(ppb) 

COV 

(%) 

TN Shelby1 2004-2006 3 72 35 3 85 33 

TN Shelby1 2004-2006 3 72 2 

TN Sullivan 2001-2003 3 157 13 3 195 19 

TN Sullivan 2004-2006 3 145 7 3 208 17 

TX Jefferson 2001-2003 3 92 20 3 103 16 

TX Jefferson 2004-2006 3 109 49 3 129 46 

VA Fairfax 2001-2003 3 38 15 3 48 24 

VA Fairfax 2004-2006 3 37 8 3 41 11 

VI St Croix 2001-2003 2 103 6 2 126 18 

VI St Croix 2004-2006 1 70 1 130 

WV Brooke 2001-2003 3 154 20 3 180 17 

WV Brooke 2004-2006 3 125 8 3 158 19 

WV Hancock 2001-2003 3 182 17 3 217 23 

WV Hancock 2004-2006 3 134 24 3 159 19 

WV Monongalia 2001-2003 3 163 22 3 218 26 

WV Monongalia 2004-2006 2 148 3 2 188 16 

WV Wayne 2001-2003 3 93 7 3 109 14 

WV Wayne 2004-2006 2 67 11 2 75 0 

Notes: 

1

 Two monitors in the county had the same average 98th percentile daily 1-hour maximum 

concentrations. Concentrations, monitoring years, and COVs for both monitors are indicated. 
A-110

A.5 Supplementary Results Tables for 5-minute Measurement Data 
A-111

Table A.5-1. Annual average SO2 concentrations and number of measured 5-minute daily maximu

concentrations above potential health effect benchmark levels. Data used were from 98 monitors 

the 5-minute maximum and 1-hour SO2 concentrations for years 1997 through 2007. 

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

AR Pulaski 051190007 2002 339 7138 2.76 1.43 2.44 1.65 1 0 0 AR Pulaski 051190007 2003 365 7799 2.47 1.3 2.18 1.64 0 0 0 AR Pulaski 051190007 2004 359 7687 2.08 1.61 1.69 1.84 0 0 0 AR Pulaski 051190007 2005 350 6702 1.91 1.17 1.65 1.69 0 0 0 AR Pulaski 051190007 2006 365 8356 3.2 1.13 3.03 1.39 0 0 0 AR Pulaski 051190007 2007 90 2062 2.88 1.12 2.71 1.39 0 0 0 AR Pulaski 051191002 1997 365 6607 2.33 1.5 1.99 1.74 0 0 0 AR Pulaski 051191002 1998 329 5997 1.62 1.3 1.35 1.74 0 0 0 AR Pulaski 051191002 1999 275 3833 2.31 1.51 1.85 2.04 0 0 0 AR Pulaski 051191002 2000 352 5596 2.38 1.63 1.77 2.44 0 0 0 AR Pulaski 051191002 2001 364 6529 2.28 1.18 2.02 1.63 0 0 0 AR Union 051390006 1997 365 7624 5.27 11.3 3.28 2.15 30 11 5 AR Union 051390006 1998 313 6766 6.4 7.45 5.14 1.73 17 3 1 AR Union 051390006 1999 275 5101 5.39 6.94 3.66 2.44 12 1 0 AR Union 051390006 2000 357 5792 6.21 10.95 3.76 2.29 44 7 2 AR Union 051390006 2001 364 7474 3.09 3.86 2.28 2.06 5 1 1 AR Union 051390006 2002 275 6296 2.92 2.27 2.5 1.65 1 0 0 AR Union 051390006 2003 364 7239 2.14 5.13 1.59 1.88 2 2 2 AR Union 051390006 2004 334 4267 2.15 2.74 1.63 1.89 3 2 0 AR Union 051390006 2005 249 4922 2.36 2.58 1.94 1.76 2 1 0 AR Union 051390006 2006 365 8364 2.89 2.19 2.61 1.49 1 1 1 AR Union 051390006 2007 90 2061 2.99 1.3 2.81 1.39 0 0 0 CO Denver 080310002 1997 365 7014 6.77 9.36 3.75 2.86 23 0 0 CO Denver 080310002 1998 360 4311 7.37 9.45 4.29 2.79 18 2 0 CO Denver 080310002 1999 156 1626 6.77 8.21 4.01 2.76 3 0 0 CO Denver 080310002 2000 137 2434 6.53 8.62 3.84 2.69 4 0 0 CO Denver 080310002 2001 360 5575 6.63 8.85 3.84 2.75 8 0 0 CO Denver 080310002 2002 365 6830 5.36 7.27 3.11 2.67 6 0 0 CO Denver 080310002 2003 362 6250 3.83 4.62 2.54 2.34 1 0 0 CO Denver 080310002 2004 337 4412 3.68 4.09 2.48 2.31 0 0 0 
A-112

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

CO Denver 080310002 2005 337 3599 3.92 4.2 2.57 2.42 0 0 0 CO Denver 080310002 2006 349 6199 3.38 3.62 2.33 2.26 1 0 0 DE New Castle 100031008 1997 330 7490 10.29 17.99 5.23 2.86 103 33 1 DE New Castle 100031008 1998 257 4898 8.86 14.99 4.35 3.03 64 16 2 DC District of Columbia 110010041 2000 160 3731 8.64 6.17 7.25 1.77 1 0 0 DC District of Columbia 110010041 2001 358 7774 7 6.51 4.83 2.45 3 1 1 DC District of Columbia 110010041 2002 365 8365 6.89 5.62 5.29 2.11 1 0 0 DC District of Columbia 110010041 2003 181 4267 8.63 5.92 7.28 1.75 5 1 1 DC District of Columbia 110010041 2004 119 2765 7.88 5.51 6.3 2.06 1 0 0 DC District of Columbia 110010041 2007 268 6394 5.05 3.74 4.24 1.76 1 1 1 FL Nassau 120890005 2002 357 8415 6.39 15.33 2.65 2.95 69 23 6 FL Nassau 120890005 2003 365 8662 3.44 8.95 1.6 2.5 26 5 1 FL Nassau 120890005 2004 275 6507 3.2 7.18 1.68 2.37 11 5 1 FL Nassau 120890005 2005 175 4120 4.06 10.16 1.65 2.71 26 4 0 IA Cerro Gordo 190330018 2001 38 513 1.22 3.38 0.44 3.16 0 0 0 IA Cerro Gordo 190330018 2002 254 3325 1.16 3.83 0.33 4.07 0 0 0 IA Cerro Gordo 190330018 2003 296 5032 1.88 7.57 0.27 4.83 4 0 0 IA Cerro Gordo 190330018 2004 366 8141 0.8 2.84 0.23 3.4 0 0 0 IA Cerro Gordo 190330018 2005 173 3528 0.69 1.49 0.31 3.16 0 0 0 IA Clinton 190450019 2001 70 1276 2.14 1.69 1.52 2.54 0 0 0 IA Clinton 190450019 2002 345 6516 3.29 3.37 1.96 3.02 3 0 0 IA Clinton 190450019 2003 333 5939 2.89 3.2 1.68 3.14 4 1 0 IA Clinton 190450019 2004 353 7093 2.83 3.06 1.67 3.12 3 0 0 IA Clinton 190450019 2005 177 3323 3.99 4.31 2.35 3.11 2 0 0 IA Muscatine 191390016 2001 91 1733 3.27 4.61 1.89 2.93 0 0 0 IA Muscatine 191390016 2002 365 7391 4.07 5.36 2.78 2.39 4 0 0 IA Muscatine 191390016 2003 353 6570 3.87 7.01 2.21 2.86 4 0 0 IA Muscatine 191390016 2004 365 6664 3.92 5.67 2.43 2.7 5 0 0 IA Muscatine 191390016 2005 181 3629 4.22 7.55 2.34 2.79 9 0 0 IA Muscatine 191390017 2001 83 1373 2.14 1.86 1.42 2.66 0 0 0 IA Muscatine 191390017 2002 364 7242 3.12 3.82 2.05 2.62 3 1 0 IA Muscatine 191390017 2003 365 7586 3.93 4.26 2.69 2.51 4 0 0 IA Muscatine 191390017 2004 363 7322 3.56 3.92 2.24 2.79 2 0 0 IA Muscatine 191390017 2005 181 3441 3.16 4.14 2.03 2.59 4 0 0 
A-113

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

IA Muscatine 191390020 2001 92 1909 5.36 9.76 2.04 3.95 1 0 0 IA Muscatine 191390020 2002 363 7682 5.27 10.27 2.22 3.61 31 1 0 IA Muscatine 191390020 2003 365 7695 5.31 11.2 2.11 3.71 42 5 0 IA Muscatine 191390020 2004 366 7757 7.36 15.39 3.02 3.2 60 14 0 IA Muscatine 191390020 2005 181 3931 5.55 13.61 2.02 3.64 27 12 1 IA Scott 191630015 2001 85 1345 1.15 2.13 0.45 4 0 0 0 IA Scott 191630015 2002 364 7505 2.28 3.17 0.87 4.7 0 0 0 IA Scott 191630015 2003 364 7451 2.09 2.68 1.02 3.79 0 0 0 IA Scott 191630015 2004 336 6696 2.11 2.65 1.06 3.68 0 0 0 IA Scott 191630015 2005 177 3436 2.56 3.05 1.17 4.17 0 0 0 IA Van Buren 191770005 2001 65 597 0.9 0.92 0.64 2.33 0 0 0 IA Van Buren 191770005 2002 353 6350 1.03 0.92 0.72 2.48 0 0 0 IA Van Buren 191770005 2003 358 7118 1.1 0.91 0.78 2.48 0 0 0 IA Van Buren 191770005 2004 305 5011 0.88 1.45 0.5 2.87 0 0 0 IA Van Buren 191770006 2004 53 877 0.85 0.94 0.55 2.53 0 0 0 IA Van Buren 191770006 2005 181 3349 0.9 0.79 0.69 2.09 0 0 0 IA Woodbury 191930018 2001 85 1578 1.32 2.28 0.77 2.45 0 0 0 IA Woodbury 191930018 2002 280 3875 1.5 2.94 0.7 3.14 0 0 0 LA West Baton Rouge 221210001 1997 277 4966 7.04 12.51 3.94 2.65 42 13 4 LA West Baton Rouge 221210001 1998 353 7566 7.52 10.67 5.03 2.29 50 18 2 LA West Baton Rouge 221210001 1999 354 7272 6.4 9.59 4.01 2.44 55 12 1 LA West Baton Rouge 221210001 2000 361 7360 7.3 11.13 4.51 2.46 76 26 7 MO Buchanan 290210009 1997 361 8484 8.3 31.64 2.77 2.8 94 79 57 MO Buchanan 290210009 1998 364 8161 7.06 24.17 2.8 2.64 92 67 44 MO Buchanan 290210009 1999 362 7415 2.77 3.07 2.08 2 3 0 0 MO Buchanan 290210009 2000 264 5297 2.37 3.04 1.81 1.88 7 0 0 MO Buchanan 290210011 2000 72 1672 5.27 8.53 3.45 2.15 8 0 0 MO Buchanan 290210011 2001 329 6412 3.7 5.3 2.52 2.15 6 0 0 MO Buchanan 290210011 2002 331 6457 4.01 7.33 2.52 2.23 21 0 0 MO Buchanan 290210011 2003 253 5141 4.06 7.04 2.59 2.25 13 0 0 MO Greene 290770026 1997 339 4763 4.32 9.65 2.02 2.69 20 2 0 MO Greene 290770026 1998 350 5810 5.73 11.66 2.35 3.07 39 1 0 MO Greene 290770026 1999 362 7242 4.09 7.53 2.22 2.5 13 1 0 MO Greene 290770026 2000 366 8721 4.97 10.21 2.41 2.67 52 1 0 
A-114

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

MO Greene 290770026 2001 365 8304 4.52 9.62 2.17 2.63 36 0 0 MO Greene 290770026 2002 360 7054 4.28 9.08 1.94 2.72 27 0 0 MO Greene 290770026 2003 362 7935 3.5 6.16 2.02 2.36 5 0 0 MO Greene 290770026 2004 274 6574 3.21 6.41 1.64 2.45 3 0 0 MO Greene 290770026 2005 365 8756 2.95 5.94 1.58 2.35 5 0 0 MO Greene 290770026 2006 365 8753 3.15 6.77 1.58 2.42 8 0 0 MO Greene 290770026 2007 272 6520 3.2 7.07 1.59 2.43 9 0 0 MO Greene 290770037 1997 356 6559 4.98 14.73 1.89 2.78 52 21 8 MO Greene 290770037 1998 361 8134 4.27 7.37 2.76 2.18 30 2 0 MO Greene 290770037 1999 363 8554 3.13 7.72 1.72 2.23 31 3 0 MO Greene 290770037 2000 341 5318 6.36 17.9 2.13 3.04 46 23 3 MO Greene 290770037 2001 355 6707 4.04 10.65 1.91 2.49 37 9 2 MO Greene 290770037 2002 335 6373 4 9.68 2.15 2.27 40 11 1 MO Greene 290770037 2003 363 8179 3.32 6.96 1.93 2.21 19 1 0 MO Greene 290770037 2004 274 6575 2.71 4.79 1.79 2.05 13 0 0 MO Greene 290770037 2005 365 8760 3.05 6.06 1.93 2.11 20 1 0 MO Greene 290770037 2006 365 8745 3.26 8.44 1.57 2.38 37 4 0 MO Greene 290770037 2007 272 6496 2.42 6.03 1.37 2.08 16 0 0 MO Iron 290930030 1997 365 8575 8.24 26.43 3.12 2.89 93 78 63 MO Iron 290930030 1998 365 8475 7.9 25.09 2.73 2.99 85 70 62 MO Iron 290930030 1999 356 6546 9.33 28.07 3.29 3.09 83 74 63 MO Iron 290930030 2000 324 4071 14.3 46.11 3.2 3.95 95 77 69 MO Iron 290930030 2001 356 5388 9.32 32.18 2.37 3.41 88 74 64 MO Iron 290930030 2002 354 7960 6.95 23.55 2.2 2.98 99 73 58 MO Iron 290930030 2003 363 6963 7.58 23.2 2.69 2.94 99 81 64 MO Iron 290930030 2004 90 1846 2.47 2.56 1.76 2.11 0 0 0 MO Iron 290930031 1997 352 6177 8.09 24.57 2.92 3.17 77 55 37 MO Iron 290930031 1998 363 7991 7.56 22.94 3.03 2.94 88 57 37 MO Iron 290930031 1999 341 7918 8.41 25.99 3.93 2.63 92 54 37 MO Iron 290930031 2000 332 5170 8.27 24.93 2.81 3.21 86 53 35 MO Iron 290930031 2001 365 8426 6.62 23.42 2.47 2.79 95 60 40 MO Iron 290930031 2002 365 8665 6.32 18.53 3.19 2.35 88 54 28 MO Iron 290930031 2003 350 8230 6.6 21.05 2.89 2.64 88 54 39 MO Iron 290930031 2004 91 2172 3.82 2.74 3.2 1.74 0 0 0 
A-115

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

MO Jefferson 290990004 2004 346 8033 10.32 22.63 4.78 2.96 106 41 26 MO Jefferson 290990004 2005 351 7144 11.41 24.87 4.62 3.34 118 68 47 MO Jefferson 290990004 2006 343 6524 13.12 27.2 4.3 4.02 134 78 53 MO Jefferson 290990004 2007 90 2125 6.31 11.92 3.08 2.88 21 8 4 MO Jefferson 290990014 1997 359 7174 8.38 19 4.14 2.79 87 54 31 MO Jefferson 290990014 1998 365 7770 4.57 9.67 2.62 2.48 37 23 13 MO Jefferson 290990014 1999 363 7591 4.6 9.49 2.48 2.57 32 19 11 MO Jefferson 290990014 2000 361 6588 3.87 7.06 2.36 2.35 28 7 4 MO Jefferson 290990014 2001 132 2433 3.15 5.64 1.95 2.25 7 1 0 MO Jefferson 290990017 1998 289 5721 7.37 18.87 3.47 2.87 59 33 22 MO Jefferson 290990017 1999 360 7289 8.65 22.19 3.8 3.01 90 57 42 MO Jefferson 290990017 2000 355 7153 6.06 16.54 2.87 2.77 59 40 26 MO Jefferson 290990017 2001 74 1044 7.72 16.53 3.69 3.02 13 9 5 MO Jefferson 290990018 2001 219 3492 5.33 11.74 2.53 2.84 34 18 9 MO Jefferson 290990018 2002 352 6305 5.51 14.84 2.59 2.75 56 36 24 MO Jefferson 290990018 2003 272 6009 4.41 10.38 2.4 2.54 27 18 10 MO Monroe 291370001 1997 364 8280 2.92 2.86 2.38 1.79 0 0 0 MO Monroe 291370001 1998 364 8411 2.35 2.25 1.86 1.87 0 0 0 MO Monroe 291370001 1999 365 8714 3.58 2.36 3.13 1.63 0 0 0 MO Monroe 291370001 2000 366 8617 2.93 2.06 2.54 1.65 0 0 0 MO Monroe 291370001 2001 309 4346 1.78 1.44 1.47 1.74 0 0 0 MO Monroe 291370001 2002 321 5358 1.81 1.48 1.48 1.75 0 0 0 MO Monroe 291370001 2003 336 5948 1.82 1.48 1.51 1.73 0 0 0 MO Monroe 291370001 2004 316 5123 2.29 2.31 1.77 1.91 0 0 0 MO Monroe 291370001 2005 348 6518 2.03 1.81 1.63 1.81 0 0 0 MO Monroe 291370001 2006 338 6169 1.73 1.26 1.47 1.68 0 0 0 MO Monroe 291370001 2007 51 526 1.86 2 1.48 1.8 0 0 0 MO Pike 291630002 2005 311 4879 4.37 5.43 2.89 2.33 5 0 0 MO Pike 291630002 2006 348 6469 3.94 4.67 2.78 2.2 3 0 0 MO Pike 291630002 2007 68 1019 3.08 3.69 2.09 2.24 0 0 0 MO Saint Charles 291830010 1997 365 8152 4.35 7.95 2.6 2.45 5 1 1 MO Saint Charles 291830010 1998 230 4810 4.32 5.69 2.77 2.38 1 0 0 MO Saint Charles 291831002 1997 365 8514 5.72 6.95 3.65 2.5 23 2 1 MO Saint Charles 291831002 1998 362 8122 6.31 7.9 4.02 2.5 25 0 0 
A-116

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

MO Saint Charles 291831002 1999 363 7969 5.61 7.24 3.58 2.5 17 2 0 MO Saint Charles 291831002 2000 331 6421 4.6 5.45 3.01 2.42 5 0 0 MT Yellowstone 301110066 1997 362 6873 8.06 10.76 4.4 3 45 7 1 MT Yellowstone 301110066 1998 357 7198 7.14 9.49 4 2.9 42 5 1 MT Yellowstone 301110066 1999 352 5767 7.75 9.65 4.31 2.99 34 5 0 MT Yellowstone 301110066 2000 355 6099 7.72 10.26 4.25 2.97 66 7 2 MT Yellowstone 301110066 2001 365 6872 7.77 10.46 4.13 3.06 56 4 0 MT Yellowstone 301110066 2002 364 8347 6.81 11.61 3.46 3.04 52 4 2 MT Yellowstone 301110066 2003 347 5691 7.37 9.92 4.06 2.93 39 2 0 MT Yellowstone 301110079 1997 180 3166 3.84 4.06 2.65 2.28 1 0 0 MT Yellowstone 301110079 2001 55 837 4.64 3.71 3.43 2.23 0 0 0 MT Yellowstone 301110079 2002 353 8034 1.9 1.91 1.48 1.83 0 0 0 MT Yellowstone 301110079 2003 350 5107 3.02 2.55 2.3 2.06 0 0 0 MT Yellowstone 301110080 1997 363 5433 7.54 10.11 4.29 2.86 59 11 3 MT Yellowstone 301110080 1998 358 5371 6.85 9.12 3.98 2.79 38 14 6 MT Yellowstone 301110080 1999 350 5588 6.36 7.81 3.79 2.75 47 7 4 MT Yellowstone 301110080 2000 360 5999 6.22 7.65 3.68 2.74 59 10 1 MT Yellowstone 301110080 2001 150 2015 5.55 6.3 3.54 2.56 12 2 1 MT Yellowstone 301110082 2001 169 2605 4.19 4.62 2.87 2.32 1 0 0 MT Yellowstone 301110082 2002 365 8212 2.32 2.77 1.7 1.99 0 0 0 MT Yellowstone 301110082 2003 361 5173 2.93 3.25 2.11 2.11 1 1 0 MT Yellowstone 301110083 1999 112 2087 8.07 8.01 5.01 2.81 4 0 0 MT Yellowstone 301110083 2000 341 3845 4.68 5.36 3 2.49 10 1 1 MT Yellowstone 301110083 2001 357 5604 4.36 5.59 2.71 2.51 11 1 0 MT Yellowstone 301110083 2002 360 6847 2.31 3.21 1.65 1.98 1 0 0 MT Yellowstone 301110083 2003 166 1641 2.29 3.08 1.62 1.99 0 0 0 MT Yellowstone 301110084 2003 99 759 2.99 4.51 1.99 2.19 0 0 0 MT Yellowstone 301110084 2004 294 2465 3.48 5.45 2.14 2.37 2 0 0 MT Yellowstone 301110084 2005 291 2577 2.96 4.98 1.79 2.28 2 0 0 MT Yellowstone 301110084 2006 273 1983 2.75 4.56 1.71 2.23 1 0 0 MT Yellowstone 301112008 1997 177 2579 3.96 4.57 2.65 2.35 2 0 0 NC Forsyth 370670022 1997 362 7822 7.06 6.91 5.13 2.2 10 0 0 NC Forsyth 370670022 1998 364 7122 6.98 7.54 4.72 2.48 13 1 1 NC Forsyth 370670022 1999 352 6428 5.85 5.92 4.13 2.29 3 0 0 
A-117

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

NC Forsyth 370670022 2000 266 5203 5.52 5.58 3.77 2.39 1 0 0 NC Forsyth 370670022 2001 361 7634 5.12 5.64 3.46 2.38 5 0 0 NC Forsyth 370670022 2002 362 7022 6.12 8.19 3.87 2.51 15 3 0 NC Forsyth 370670022 2003 363 8075 5.87 6.19 4.17 2.24 11 0 0 NC Forsyth 370670022 2004 259 4710 5.56 8.21 3.37 2.55 6 1 0 NC New Hanover 371290006 1999 360 8208 4.1 8.34 1.92 2.73 54 8 4 NC New Hanover 371290006 2000 335 7980 4.67 8.92 2.13 2.87 76 6 3 NC New Hanover 371290006 2001 358 8168 5.71 13.73 2.08 3.09 109 54 10 NC New Hanover 371290006 2002 352 8028 6.44 13.85 2.61 3.12 127 39 7 ND Billings 380070002 1998 143 1940 1.31 1.04 1.16 1.48 0 0 0 ND Billings 380070002 1999 276 3216 1.38 1.04 1.21 1.53 0 0 0 ND Billings 380070002 2000 248 2724 1.42 1.1 1.24 1.56 0 0 0 ND Billings 380070002 2001 283 2860 1.37 1.12 1.2 1.51 0 0 0 ND Billings 380070002 2002 275 3113 1.43 1.11 1.26 1.53 0 0 0 ND Billings 380070002 2003 26 341 1.48 0.87 1.32 1.54 0 0 0 ND Billings 380070002 2004 164 1256 1.24 0.85 1.13 1.41 0 0 0 ND Billings 380070002 2005 128 835 1.44 0.92 1.27 1.55 0 0 0 ND Billings 380070002 2006 106 418 1.53 1.25 1.29 1.64 0 0 0 ND Billings 380070002 2007 43 221 1.5 1.26 1.29 1.6 0 0 0 ND Billings 380070003 1997 167 2657 1.72 1.52 1.43 1.7 0 0 0 ND Burke 380130002 1999 297 3852 2.79 4.61 1.65 2.31 3 0 0 ND Burke 380130002 2000 347 5268 2.96 5.77 1.77 2.27 7 1 1 ND Burke 380130002 2001 338 5653 2.72 4.97 1.62 2.25 3 1 0 ND Burke 380130002 2002 346 5367 2.64 4.72 1.58 2.24 4 0 0 ND Burke 380130002 2003 353 6328 2.6 4.77 1.62 2.16 7 1 0 ND Burke 380130002 2004 340 5229 2.77 5.03 1.65 2.26 6 0 0 ND Burke 380130002 2005 263 3098 2.88 4.99 1.67 2.33 4 0 0 ND Burke 380130004 2003 63 882 2.89 3.99 1.84 2.26 0 0 0 ND Burke 380130004 2004 315 3198 2.76 3.59 1.83 2.21 0 0 0 ND Burke 380130004 2005 244 2238 2.47 3.18 1.72 2.09 0 0 0 ND Burke 380130004 2006 302 3152 2.27 3.16 1.59 2.02 1 0 0 ND Burke 380130004 2007 99 1227 3.8 5.18 2.27 2.53 1 0 0 ND Burleigh 380150003 2005 60 683 3.4 2.97 2.47 2.2 0 0 0 ND Burleigh 380150003 2006 294 3686 2.33 2.6 1.68 2.04 0 0 0 
A-118

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

ND Burleigh 380150003 2007 97 947 3.77 4.32 2.49 2.36 0 0 0 ND Cass 380171003 1997 206 2254 1.74 2.31 1.32 1.79 0 0 0 ND Cass 380171003 1998 132 2943 1.88 1.83 1.5 1.8 0 0 0 ND Cass 380171004 1998 162 2501 1.11 0.43 1.07 1.27 0 0 0 ND Cass 380171004 1999 246 3325 1.32 0.75 1.2 1.46 0 0 0 ND Cass 380171004 2000 213 1868 1.37 0.84 1.23 1.5 0 0 0 ND Cass 380171004 2001 203 1686 1.34 0.93 1.2 1.49 0 0 0 ND Cass 380171004 2002 274 2476 1.12 0.43 1.08 1.27 0 0 0 ND Cass 380171004 2003 200 1297 1.25 0.82 1.15 1.41 0 0 0 ND Cass 380171004 2004 256 3140 1.21 0.6 1.13 1.37 0 0 0 ND Cass 380171004 2005 146 928 1.24 0.68 1.15 1.41 0 0 0 ND Cass 380171004 2006 358 7385 0.39 0.42 0.28 2.19 0 0 0 ND Cass 380171004 2007 116 2256 0.55 0.74 0.33 2.6 0 0 0 ND Dunn 380250003 1997 224 3313 1.38 1.14 1.2 1.54 0 0 0 ND Dunn 380250003 1998 242 2688 1.78 2.07 1.39 1.79 0 0 0 ND Dunn 380250003 1999 323 5099 1.5 1.56 1.26 1.62 0 0 0 ND Dunn 380250003 2000 353 7455 1.4 1.44 1.2 1.55 0 0 0 ND Dunn 380250003 2001 276 3575 1.6 1.48 1.34 1.66 0 0 0 ND Dunn 380250003 2002 334 4484 1.31 1.09 1.16 1.48 0 0 0 ND Dunn 380250003 2003 355 7289 1.5 1.28 1.29 1.58 0 0 0 ND Dunn 380250003 2004 347 6019 1.34 1.13 1.17 1.51 0 0 0 ND Dunn 380250003 2005 183 1314 1.48 1.53 1.23 1.62 0 0 0 ND Dunn 380250003 2006 262 2213 1.53 1.57 1.26 1.65 0 0 0 ND Dunn 380250003 2007 79 667 1.65 1.5 1.37 1.69 0 0 0 ND McKenzie 380530002 1997 238 2552 1.5 1.23 1.28 1.61 0 0 0 ND McKenzie 380530002 1998 144 1989 1.66 1.57 1.36 1.7 0 0 0 ND McKenzie 380530002 2001 108 754 1.31 0.84 1.18 1.47 0 0 0 ND McKenzie 380530002 2002 262 3361 1.23 0.77 1.13 1.4 0 0 0 ND McKenzie 380530002 2003 305 5345 1.5 1.29 1.28 1.6 0 0 0 ND McKenzie 380530002 2004 303 4614 1.4 1.19 1.22 1.55 0 0 0 ND McKenzie 380530002 2005 225 2515 1.29 0.82 1.17 1.46 0 0 0 ND McKenzie 380530002 2006 276 2896 1.28 0.85 1.16 1.45 0 0 0 ND McKenzie 380530002 2007 73 511 1.64 1.34 1.38 1.67 0 0 0 ND McKenzie 380530104 1998 224 1525 2.38 4.92 1.59 2.04 4 0 0 
A-119

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

ND McKenzie 380530104 1999 240 1500 2.3 3.7 1.66 1.97 3 3 1 ND McKenzie 380530104 2000 294 2755 1.96 4.07 1.44 1.85 5 2 1 ND McKenzie 380530104 2001 283 2281 1.68 1.75 1.38 1.72 1 0 0 ND McKenzie 380530104 2002 236 1526 1.9 4.04 1.34 1.83 9 2 0 ND McKenzie 380530104 2003 293 2333 1.98 5.29 1.3 1.84 15 3 1 ND McKenzie 380530104 2004 271 2231 1.34 1.34 1.19 1.49 1 0 0 ND McKenzie 380530104 2005 245 1900 1.32 2.32 1.14 1.46 2 0 0 ND McKenzie 380530104 2006 234 1827 1.32 1.78 1.14 1.46 4 1 0 ND McKenzie 380530104 2007 71 764 1.44 1.13 1.26 1.56 0 0 0 ND McKenzie 380530111 1998 258 2063 3.11 7.34 1.8 2.29 7 2 0 ND McKenzie 380530111 1999 294 2379 2.36 5.4 1.56 2.02 7 2 1 ND McKenzie 380530111 2000 329 2805 2.68 8.27 1.65 2.1 7 5 4 ND McKenzie 380530111 2001 336 3183 1.81 2.09 1.4 1.81 0 0 0 ND McKenzie 380530111 2002 297 2255 1.87 3.52 1.38 1.8 8 3 1 ND McKenzie 380530111 2003 288 2243 2.03 3.84 1.44 1.87 7 2 1 ND McKenzie 380530111 2004 308 2857 1.82 5.94 1.27 1.72 3 1 1 ND McKenzie 380530111 2005 296 2790 1.39 3.28 1.14 1.5 5 2 0 ND McKenzie 380530111 2006 304 2896 1.35 2.43 1.16 1.48 4 1 0 ND McKenzie 380530111 2007 78 722 1.61 1.89 1.3 1.69 1 1 0 ND Mercer 380570001 1997 243 2824 2.93 4.29 1.87 2.26 0 0 0 ND Mercer 380570001 1998 319 4735 3.33 6.47 2.09 2.28 5 2 0 ND Mercer 380570001 1999 14 320 5.18 3.12 4.43 1.73 0 0 0 ND Mercer 380570004 1999 334 5584 2.6 3.94 1.66 2.2 3 1 0 ND Mercer 380570004 2000 362 7348 2.29 3.8 1.55 2.06 3 1 0 ND Mercer 380570004 2001 338 4647 2.9 5.34 1.76 2.26 8 0 0 ND Mercer 380570004 2002 336 3701 2.65 4.59 1.73 2.17 2 1 0 ND Mercer 380570004 2003 351 5555 2.21 3.11 1.55 2.01 1 0 0 ND Mercer 380570004 2004 344 4678 2.62 3.57 1.73 2.19 1 0 0 ND Mercer 380570004 2005 273 3037 2.43 3.25 1.68 2.08 0 0 0 ND Mercer 380570004 2006 301 2755 2.77 3.37 1.86 2.21 0 0 0 ND Mercer 380570004 2007 107 1133 2.48 3.44 1.7 2.1 0 0 0 ND Morton 380590002 1997 346 6547 9.31 20.26 2.93 3.67 102 19 1 ND Morton 380590002 1998 290 4696 9.3 22.47 2.78 3.75 75 8 0 ND Morton 380590002 1999 359 6837 7.7 16.99 2.53 3.55 90 4 0 
A-120

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

ND Morton 380590002 2000 363 7964 6.47 14.58 2.22 3.31 73 3 0 ND Morton 380590002 2001 346 5947 7.48 13.57 2.81 3.5 66 2 0 ND Morton 380590002 2002 355 6258 6.26 12.03 2.49 3.25 59 1 0 ND Morton 380590002 2003 365 8033 6.25 13.66 2.33 3.18 82 3 1 ND Morton 380590002 2004 363 7532 6.74 13.2 2.62 3.29 76 2 0 ND Morton 380590002 2005 111 1450 4.85 6.08 2.7 2.82 1 0 0 ND Morton 380590003 1998 95 1924 3.71 7.47 2.01 2.48 8 0 0 ND Morton 380590003 1999 353 6522 5.06 8.84 2.48 2.88 41 2 1 ND Morton 380590003 2000 351 5984 4.71 8.04 2.44 2.74 24 0 0 ND Morton 380590003 2001 357 6345 4.94 8.17 2.54 2.81 27 1 0 ND Morton 380590003 2002 342 5245 4.41 7.53 2.35 2.68 26 1 0 ND Morton 380590003 2003 364 7991 3.55 6.34 1.96 2.49 27 0 0 ND Morton 380590003 2004 344 6338 4.44 7.03 2.5 2.59 24 0 0 ND Morton 380590003 2005 106 1012 3.84 5.1 2.42 2.39 1 0 0 ND Oliver 380650002 1997 244 2356 4.28 7.23 2.3 2.63 7 0 0 ND Oliver 380650002 1998 319 4175 3.92 7.23 2.1 2.58 12 1 0 ND Oliver 380650002 1999 349 4856 3.47 6.94 1.93 2.42 15 1 0 ND Oliver 380650002 2000 351 4765 3.14 5.54 1.89 2.32 8 0 0 ND Oliver 380650002 2001 214 2404 3.42 5.86 1.96 2.42 1 0 0 ND Oliver 380650002 2002 350 4482 2.71 4.75 1.69 2.21 4 0 0 ND Oliver 380650002 2003 357 6953 2.37 5.58 1.47 2.05 10 1 0 ND Oliver 380650002 2004 354 6138 2.76 5.16 1.65 2.24 7 1 1 ND Oliver 380650002 2005 275 2443 3.86 6.7 2.05 2.62 6 2 0 ND Oliver 380650002 2006 325 3369 2.85 4.32 1.77 2.28 1 0 0 ND Oliver 380650002 2007 101 780 4.12 6.99 2.35 2.53 2 0 0 ND Steele 380910001 1997 216 3134 1.41 0.74 1.28 1.5 0 0 0 ND Steele 380910001 1998 202 2804 2.22 2.1 1.72 1.91 0 0 0 ND Steele 380910001 1999 152 1845 1.25 0.79 1.14 1.42 0 0 0 ND Steele 380910001 2000 83 805 1.11 0.4 1.07 1.26 0 0 0 ND Williams 381050103 2002 319 2724 3.18 7.56 1.68 2.36 8 3 1 ND Williams 381050103 2003 339 3323 2.48 3.71 1.64 2.13 3 0 0 ND Williams 381050103 2004 348 3438 2.52 5.21 1.62 2.12 5 3 1 ND Williams 381050103 2005 301 2331 3.51 8 1.85 2.45 20 3 1 ND Williams 381050103 2006 322 2976 1.88 2.32 1.4 1.87 0 0 0 
A-121

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

ND Williams 381050103 2007 86 834 3.35 4.62 2.07 2.4 0 0 0 ND Williams 381050105 2002 302 2843 6.77 10.88 2.93 3.34 35 4 1 ND Williams 381050105 2003 342 3523 5.67 9.39 2.55 3.12 13 1 0 ND Williams 381050105 2004 346 4129 5.64 10.64 2.55 3.1 19 2 2 ND Williams 381050105 2005 349 4492 6.79 13 2.49 3.46 52 12 1 ND Williams 381050105 2006 262 2938 3.74 6.66 1.91 2.62 14 1 0 ND Williams 381050105 2007 24 263 3.59 5.63 1.99 2.53 1 0 0 PA Allegheny 420030002 1997 357 7821 12.57 15.05 7.69 2.68 70 8 2 PA Allegheny 420030002 1998 3 72 43.18 32.27 31.63 2.43 3 1 0 PA Allegheny 420030002 1999 325 6986 11.04 11.16 7.36 2.53 31 2 0 PA Allegheny 420030021 1997 355 7830 18.11 18.87 11.07 2.93 87 19 5 PA Allegheny 420030021 1998 3 72 10.22 8.23 7.48 2.27 0 0 0 PA Allegheny 420030021 1999 362 8279 9 7.94 6.64 2.2 3 0 0 PA Allegheny 420030021 2002 313 7291 7.32 7.33 4.49 2.85 3 0 0 PA Allegheny 420030031 1997 362 8000 10.98 9.63 8.05 2.24 12 1 0 PA Allegheny 420030031 1998 3 68 11.38 9.36 8.2 2.3 0 0 0 PA Allegheny 420030031 1999 360 7443 8.98 7.84 6.43 2.33 1 0 0 PA Allegheny 420030032 1997 364 7951 15.4 19.34 9.39 2.73 84 15 6 PA Allegheny 420030032 1998 3 60 35.2 20.65 27.51 2.26 2 0 0 PA Allegheny 420030032 1999 210 4326 8.18 7.8 5.66 2.41 2 0 0 PA Allegheny 420030064 1997 361 7526 11.9 13.08 7.16 2.86 17 2 0 PA Allegheny 420030064 1998 3 71 20.11 7.99 18.41 1.56 0 0 0 PA Allegheny 420030064 1999 355 7232 12.11 14.34 7.35 2.78 18 3 2 PA Allegheny 420030064 2002 350 8239 10.9 13.26 5.91 3.15 18 5 1 PA Allegheny 420030067 1997 364 8231 10.43 11.13 6.69 2.62 12 2 1 PA Allegheny 420030067 1998 3 72 17.01 12.54 12.63 2.25 0 0 0 PA Allegheny 420030067 1999 257 5891 10.05 8.81 7.35 2.22 1 0 0 PA Allegheny 420030116 1997 361 7767 13.26 17.76 8.33 2.6 60 19 12 PA Allegheny 420030116 1998 3 70 17 11.04 12.59 2.46 0 0 0 PA Allegheny 420030116 1999 299 5684 12.12 16.01 7.82 2.54 50 26 13 PA Allegheny 420030116 2002 232 5403 7 7.96 4.56 2.5 3 0 0 PA Allegheny 420031301 1997 363 7663 9.37 9.8 6.25 2.48 21 4 1 PA Allegheny 420031301 1998 3 70 12.66 6.88 11.29 1.58 0 0 0 PA Allegheny 420031301 1999 363 8161 9.64 9.62 6.57 2.44 21 3 1 
A-122

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

PA Allegheny 420033003 1997 356 7422 11.8 13.86 7.01 2.85 27 1 0 PA Allegheny 420033003 1998 2 45 11.47 6.31 9.35 2.09 0 0 0 PA Allegheny 420033003 1999 350 6998 13.59 19.91 7.86 2.86 37 2 2 PA Allegheny 420033003 2002 316 7363 12.66 18.25 6.32 3.29 53 8 5 PA Allegheny 420033004 1997 362 7461 9.18 9.66 6.17 2.47 12 2 0 PA Allegheny 420033004 1998 3 66 13.12 6.01 11.71 1.65 0 0 0 PA Allegheny 420033004 1999 361 7408 8.55 9.09 5.79 2.47 6 3 2 PA Beaver 420070002 1997 351 7889 11.83 15.38 6.83 2.84 91 11 1 PA Beaver 420070002 1998 270 6205 12.96 16.48 7.8 2.71 74 6 2 PA Beaver 420070005 1997 359 7447 16.57 25.11 8.65 3.14 98 39 17 PA Beaver 420070005 1998 277 6388 16.14 26.85 8.36 3.01 92 39 21 PA Beaver 420070005 2002 361 8491 14.24 26.51 5.28 4.12 113 49 23 PA Beaver 420070005 2003 365 8706 10.79 17.07 4.38 3.83 75 16 3 PA Beaver 420070005 2004 364 8656 11.59 17.68 5.55 3.39 74 22 10 PA Beaver 420070005 2005 362 8578 12.57 18.18 6.82 3.04 75 26 12 PA Beaver 420070005 2006 361 8457 9.26 18.5 3.49 3.78 71 30 11 PA Beaver 420070005 2007 324 7556 9.79 13.98 4.94 3.26 45 12 4 PA Berks 420110009 1997 350 7805 8.66 8.87 5.87 2.44 35 4 0 PA Berks 420110009 1998 365 8641 8.93 7.56 7.11 1.92 33 3 0 PA Berks 420110009 1999 119 2790 9.22 8.38 6.86 2.17 9 1 0 PA Cambria 420210011 1997 361 8129 9.76 9.15 6.72 2.47 8 0 0 PA Cambria 420210011 1998 356 7908 8.78 9.69 5.65 2.62 16 1 0 PA Cambria 420210011 1999 120 2835 9.74 7.99 7.61 1.99 1 0 0 PA Erie 420490003 1997 363 8169 9.76 11.22 6.68 2.33 60 9 1 PA Erie 420490003 1998 363 8416 10.57 13.5 7.09 2.35 60 12 1 PA Erie 420490003 1999 120 2778 11.48 15.12 7.46 2.48 26 7 3 PA Philadelphia 421010022 1997 364 8297 8.56 8.74 5.63 2.57 7 1 0 PA Philadelphia 421010022 1998 363 8065 7.3 7.04 4.82 2.56 2 0 0 PA Philadelphia 421010022 1999 137 2665 7.79 8.26 4.76 2.79 4 1 0 PA Philadelphia 421010022 2000 179 3630 7.63 6.88 5.05 2.62 1 0 0 PA Philadelphia 421010022 2001 98 2094 7.53 7.17 5.16 2.44 0 0 0 PA Philadelphia 421010048 1997 365 8456 8.88 18.38 4.96 2.74 59 40 27 PA Philadelphia 421010048 1998 356 7285 6.27 6.03 4.21 2.48 0 0 0 PA Philadelphia 421010048 1999 178 3939 6.08 6.57 3.95 2.53 1 1 0 
A-123

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

PA Philadelphia 421010136 1997 360 7532 4.99 5.52 3.29 2.43 1 0 0 PA Philadelphia 421010136 1998 339 6491 5.25 5.52 3.5 2.44 2 0 0 PA Philadelphia 421010136 1999 337 7144 5.63 6.04 3.71 2.48 2 1 0 PA Philadelphia 421010136 2000 351 7044 5.76 5.97 3.74 2.54 0 0 0 PA Philadelphia 421010136 2001 266 5149 6.77 7.43 4.38 2.55 2 0 0 PA Philadelphia 421010136 2002 359 7271 5.38 5.7 3.57 2.47 3 0 0 PA Philadelphia 421010136 2003 119 2585 6.74 6.71 4.62 2.42 2 0 0 PA Warren 421230003 1997 346 7157 10.53 11.59 6.64 2.68 26 3 0 PA Warren 421230003 1998 89 2126 7.62 7.38 5.41 2.26 0 0 0 PA Warren 421230004 1997 355 7022 17.14 28.18 7.47 3.66 148 44 14 PA Warren 421230004 1998 89 1966 13.97 21.76 6.8 3.18 30 6 2 PA Washington 421250005 1997 364 8374 8.95 8.41 6.45 2.25 7 0 0 PA Washington 421250005 1998 362 8540 8.88 7.78 6.68 2.14 4 0 0 PA Washington 421250005 1999 120 2821 8.32 7.68 6.36 2.02 1 0 0 PA Washington 421250200 1997 364 8369 10.52 11.23 6.99 2.45 17 0 0 PA Washington 421250200 1998 365 8656 10.46 10.49 7.18 2.37 15 1 0 PA Washington 421250200 1999 120 2829 10.15 9.81 7 2.4 3 1 0 PA Washington 421255001 1997 365 8425 12.71 15.24 8.39 2.36 57 5 1 PA Washington 421255001 1998 277 6559 13.46 13.09 10.28 1.97 42 3 0 SC Barnwell 450110001 2000 100 789 3.95 2.83 3.39 1.66 0 0 0 SC Barnwell 450110001 2001 267 2625 2.72 2.61 2.13 1.93 1 0 0 SC Barnwell 450110001 2002 202 2544 2.11 1.72 1.67 1.88 0 0 0 SC Charleston 450190003 2000 114 1703 6.24 5.36 4.77 2.02 1 0 0 SC Charleston 450190003 2001 344 4806 4.16 4.12 2.95 2.22 1 0 0 SC Charleston 450190003 2002 201 3509 2.85 3.49 1.97 2.16 0 0 0 SC Charleston 450190046 2000 100 1252 4.61 3.9 3.71 1.84 0 0 0 SC Charleston 450190046 2001 269 3497 2.64 2.6 1.99 2 0 0 0 SC Charleston 450190046 2002 189 2927 2.34 2.89 1.68 2.02 0 0 0 SC Georgetown 450430006 2000 71 604 4.92 4.35 3.97 1.82 0 0 0 SC Georgetown 450430006 2001 241 2218 4.76 6.11 3.13 2.33 3 0 0 SC Georgetown 450430006 2002 140 1169 2.5 4.33 1.67 2.08 1 0 0 SC Greenville 450450008 2000 113 1987 4.84 3.75 3.95 1.82 0 0 0 SC Greenville 450450008 2001 356 6418 4.24 3.86 3.18 2.1 3 0 0 SC Greenville 450450008 2002 212 4679 3.06 2.8 2.29 2.09 1 0 0 
A-124

Annual Hourly (ppb) Number of 5-minute Daily 

State County Monitor ID Year

Days 

(n) 

Hours 

(n) Mean std GM GSD > 100 ppb > 200 ppb > 300 p

SC Lexington 450630008 2001 263 3941 4.2 7.8 2.37 2.44 26 3 0 SC Lexington 450630008 2002 211 4242 4.5 8.74 2.33 2.61 22 3 0 SC Oconee 450730001 2000 89 1218 3.85 2.87 3.26 1.7 0 0 0 SC Oconee 450730001 2001 288 4304 2.9 2.1 2.35 1.89 0 0 0 SC Oconee 450730001 2002 188 3063 1.82 1.52 1.43 1.95 0 0 0 SC Richland 450790007 2000 110 1808 4.48 2.81 3.86 1.69 0 0 0 SC Richland 450790007 2001 365 6419 3.88 3.47 2.99 2.02 0 0 0 SC Richland 450790007 2002 210 4335 2.95 2.71 2.23 2.04 0 0 0 SC Richland 450790021 2000 109 911 4.43 5.47 3.4 1.85 0 0 0 SC Richland 450790021 2001 283 2700 3.73 4.89 2.64 2.1 0 0 0 SC Richland 450790021 2002 202 2505 2.94 4.85 1.92 2.16 0 0 0 SC Richland 450791003 2001 193 3346 3.14 2.8 2.46 1.96 0 0 0 SC Richland 450791003 2002 212 4323 2.87 2.8 2.16 2.04 0 0 0 UT Salt Lake 490352004 1997 335 4524 2.31 2.5 1.76 1.94 6 1 0 UT Salt Lake 490352004 1998 354 5792 1.94 1.66 1.58 1.78 0 0 0 WV Wayne 540990002 2002 365 8711 7.49 7.14 5.13 2.42 1 0 0 WV Wayne 540990003 2002 361 7417 8.48 9.1 5.21 2.75 7 2 1 WV Wayne 540990003 2003 362 8057 8.76 9.73 5.56 2.58 8 0 0 WV Wayne 540990003 2004 366 8659 9.21 9.46 6.38 2.31 5 1 0 WV Wayne 540990003 2005 365 8141 9.58 11.8 5.96 2.61 6 0 0 WV Wayne 540990004 2002 362 8560 9.21 9.18 6.37 2.37 22 1 1 WV Wayne 540990004 2003 365 8570 8.53 9.77 5.84 2.35 26 4 3 WV Wayne 540990004 2004 366 8673 7.22 6.66 5.36 2.12 6 0 0 WV Wayne 540990004 2005 363 8586 7.67 6.39 5.97 2 7 0 0 WV Wayne 540990005 2002 365 8283 8.44 9.75 5.38 2.58 67 3 0 WV Wayne 540990005 2003 365 7927 8.31 11.03 5.02 2.7 52 20 5 WV Wayne 540990005 2004 366 8681 7.03 5.92 5.25 2.16 2 0 0 WV Wayne 540990005 2005 365 8453 6.68 5.52 4.89 2.26 4 1 0 WV Wood 541071002 2001 92 2152 7.76 12.51 4.04 3.04 9 3 2 WV Wood 541071002 2002 365 8648 9.9 11.29 6.21 2.63 42 7 1 WV Wood 541071002 2003 365 8641 9.48 12.26 5.8 2.61 53 9 2 WV Wood 541071002 2004 366 8581 10.88 13.25 7 2.55 57 13 3 WV Wood 541071002 2005 266 6219 8.34 12.71 4.07 3.23 42 12 1 
B-1

Appendix B: Supplement to the SO2 Exposure Assessment 
B-2

B.1 OVERVIEW 

This appendix contains supplemental descriptions of the methods and data used in the 

SO2 exposure assessment, as well as detailed results from the exposure analyses performed. 

First, a broad description of the exposure modeling approach is described (section B.2), 

applicable to the two exposure modeling domains conducted: Greene County, Mo. and St. Louis, 

MO. Supplementary input data used in AERMOD are provided in section B.3, as well as the 

model predictions and ambient monitor measurements in each modeling domain. Section B.4 

has additional input and output data for APEX. 

A series of Attachments also follow, further documenting some of the data sources and 

modeling approaches used, as well as previously conducted uncertainty analyses on selected 

input parameters in APEX: 

Attachment 1. Technical Memorandum on Meteorological Data Preparation for AERMOD for 

SO2 REA for Greene County And St. Louis Modeling Domains, Year 2002. 

Attachment 2. Technical Memorandum on the Analysis of NHIS Asthma Prevalence Data. 

Attachment 3. Technical Memorandum on Estimating Physiological Parameters for the Exposure 

Model 

Attachment 4. Technical Memorandum on Longitudinal Diary Construction Approach 

Attachment 5. Technical Memorandum on the Evaluation Cluster-Markov Algorithm 

Attachment 6. Technical Memorandum on Analysis of Air Exchange Rate Data 

Attachment 7. Technical Memorandum on the Uncertainty Analysis of Residential Air Exchange 

Rate Distributions 

Attachment 8. Technical Memorandum on the Distributions of Air Exchange Rate Averages 

Over Multiple Days 
B-3

B.2 HUMAN EXPOSURE MODELING USING APEX 

The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program 

designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and 

consolidated metropolitan levels. APEX, also known as TRIM.Expo, is the human inhalation 

exposure module of EPA’s Total Risk Integrated Methodology (TRIM) model framework (US 

EPA, 1999), a modeling system with multimedia capabilities for assessing human health and 

ecological risks from hazardous and criteria air pollutants. It is developed to support evaluations 

with a scientifically sound, flexible, and user-friendly methodology. Additional information on 

the TRIM modeling system, as well as downloads of the APEX Model, user’s guide, and other 

supporting documentation, are on EPA’s Technology Transfer Network (TTN) at 

http://www.epa.gov/ttn/fera. 

B.2.1 History 

APEX was derived from the National Ambient Air Quality Standards (NAAQS) 

Exposure Model (NEM) series of models, developed to estimate exposure to the criteria 

pollutants (e.g., carbon monoxide (CO), ozone O3). In 1979, EPA began by assembling a 

database of human activity patterns that could be used to estimate exposures to indoor and 

outdoor pollutants (Roddin et al., 1979). These data were then combined with measured outdoor 

concentrations in NEM to estimate exposures to CO (Biller et al., 1981; Johnson and Paul, 

1983). In 1988, OAQPS began to incorporate probabilistic elements into the NEM methodology 

and use activity pattern data based on various human activity diary studies to create an early 

version of probabilistic NEM for O3 (i.e., pNEM/O3). In 1991, a probabilistic version of NEM 

was extended to CO (pNEM/CO) that included a one-compartment mass-balance model to 

estimate CO concentrations in indoor microenvironments. The application of this model to 

Denver, Colorado has been documented in Johnson et al. (1992). Additional enhancements to 

pNEM/O3 in the early- to mid-1990’s allowed for probabilistic exposure assessments in nine 

urban areas for the general population, outdoor children, and outdoor workers (Johnson et al., 

1996a; 1996b; 1996c). Between 1999 and 2001, updated versions of pNEM/CO (versions 2.0 

and 2.1) were developed that relied on activity diary data from EPA’s Consolidated Human 

Activities Database (CHAD) and enhanced algorithms for simulating gas stove usage, estimating 

alveolar ventilation rate (a measure of human respiration), and modeling home-to-work 

commuting patterns. 
B-4

The first version of APEX was essentially identical to pNEM/CO (version 2.0) except 

that it was capable of running on a PC instead of a mainframe. The next version, APEX2, was 

substantially different, particularly in the use of a personal profile approach (i.e., simulation of 

individuals) rather than a cohort simulation (i.e., groups of similar persons). APEX3 introduced 

a number of new features including automatic site selection from national databases, a series of 

new output tables providing summary exposure and dose statistics, and a thoroughly reorganized 

method of describing microenvironments and their parameters. Most of the spatial and temporal 

constraints of pNEM and APEX1 were removed or relaxed by version 3. 

The version of APEX used in this exposure assessment is APEX4.3, described in the 

APEX User’s Guide and the APEX Technical Support Document (US EPA, 2009a; 2009b) and 

referred to here as the APEX User’s Guide and TSD. This latest version has the added flexibility 

of addressing user defined exposure timesteps within an hour. 

B.2.2 APEX Model Overview 

APEX estimates human exposure to criteria and toxic 

air pollutants at the local, urban, or consolidated metropolitan 

area levels using a stochastic, microenvironmental approach. 

The model randomly selects data for a sample of hypothetical 

individuals from an actual population database and simulates 

each hypothetical individual’s movements through time and 

space (e.g., at home, in vehicles) to estimate their exposure to a pollutant. APEX simulates 

commuting, and thus exposures that occur at home and work locations, for individuals who work 

in different areas than they live. 

APEX can be conceptualized as a simulated field study that would involve selecting an 

actual sample of specific individuals who live in (or work and live in) a geographic area and then 

continuously monitoring their activities and subsequent inhalation exposure to a specific air 

pollutant during a specific period of time. 

The main differences between APEX and an actual field study are that in APEX: 

 The sample of individuals is a virtual sample, not actual persons. However, the 

population of individuals appropriately balanced according to various demographic 

A microenvironment is a threedimensional space in which human 

contact with an environmental 

pollutant takes place and which can 

be treated as a well-characterized, 

relatively homogeneous location 

with respect to pollutant 

concentrations for a specified time 

period. 
B-5

variables and census data using their relative frequencies, in order to obtain a 

representative sample (to the extent possible) of the actual people in the study area 

 The activity patterns of the sampled individuals (e.g., the specification of indoor and 

other microenvironments visited and the time spent in each) are assumed by the 

model to be comparable to individuals with similar demographic characteristics, 

according to activity data such as diaries compiled in EPA’s Consolidated Human 

Activity Database (or CHAD; US EPA, 2002; McCurdy et al., 2000) 

 The pollutant exposure concentrations are estimated by the model using a set of userinput ambient outdoor concentrations (either modeled or measured) and information 

on the behavior of the pollutant in various microenvironments; 

 Variation in ambient air quality levels can be simulated by either adjusting air quality 

concentrations to just meet alternative ambient standards, or by reducing source 

emissions and obtaining resulting air quality modeling outputs that reflect these 

potential emission reductions, and 

 The model accounts for the most significant factors contributing to inhalation 

exposure – the temporal and spatial distribution of people and pollutant 

concentrations throughout the study area and among microenvironments – while also 

allowing the flexibility to adjust some of these factors for alternative scenarios and 

sensitivity analyses. 

APEX is designed to simulate human population exposure to criteria and air toxic 

pollutants at local, urban, and regional scales. The user specifies the geographic area to be 

modeled and the number of individuals to be simulated to represent this population. APEX then 

generates a personal profile for each simulated person that specifies various parameter values 

required by the model. The model next uses diary-derived time/activity data matched to each 

personal profile to generate an exposure event sequence (also referred to as activity pattern or 

diary) for the modeled individual that spans a specified time period, such as one year. Each 

event in the sequence specifies a start time, exposure duration, geographic location, 

microenvironment, and activity performed. Probabilistic algorithms are used to estimate the 

pollutant concentration associated with each exposure event. The estimated pollutant 

concentrations account for the effects of ambient (outdoor) pollutant concentration, penetration 

factors, air exchange rates, decay/deposition rates, and proximity to emission sources, depending 
B-6

on the microenvironment, available data, and estimation method selected by the user. Because 

the modeled individuals represent a random sample of the population of interest, the distribution 

of modeled individual exposures can be extrapolated to the larger population. Additional 

discussion regarding the five basic exposure modeling steps noted in the SO2 REA are described 

in sections that follow. 

B.2.2.1 Study Area Characterization 

The APEX study area has traditionally been on the scale of a city or slightly larger 

metropolitan area, although it is now possible to model larger areas such as combined statistical 

areas (CSAs). In the exposure analyses performed as part of this NAAQS review, the study area 

is defined by either a single or a few counties. The demographic data used by the model to 

create personal profiles is provided at the census block level. For each block the model requires 

demographic information representing the distribution of age, gender, race, and work status 

within the study population. Each block has a location specified by latitude and longitude for 

some representative point (e.g., geographic center). The current release of APEX includes input 

files that already contain this demographic and location data for all census tracts, block groups, 

and blocks in the 50 United States, based on the 2000 Census. In this assessment, exposures 

were evaluated at the block level. 

Air Quality Data 

Air quality data can be input to the model as measured data from an ambient monitor or 

that generated by air quality modeling. This exposure analysis used modeled air quality data, 

whereas the principal emission sources included both mobile and stationary sources as well as 

fugitive emissions. Air quality data used for input to APEX were generated using AERMOD, a 

steady-state, Gaussian plume model (US EPA, 2004). The following steps were performed using 

AERMOD. 

In APEX, the ambient air quality data are assigned to geographic areas called districts. 

The districts are used to assign pollutant concentrations to the blocks/tracts and 

microenvironments being modeled. The ambient air quality data are provided by the user as 

hourly time series for each district. As with blocks/tracts, each district has a representative 

location (latitude and longitude). APEX calculates the distance from each block/tract to each 

district center, and assigns the block/tract to the nearest district, provided the block/tract 

representative location point (e.g., geographic center) is in the district. Each block/tract can be 
B-7

assigned to only one district. In this assessment the district was synonymous with the receptor 

modeled in the dispersion modeling. 

Meteorological Data 

Ambient temperatures are input to APEX for different sites (locations). As with districts, 

APEX calculates the distance from each block to each temperature site and assigns each block to 

the nearest site. Hourly temperature data are from the National Climatic Data Center Surface 

Airways Hourly TD-3280 dataset (NCDC Surface Weather Observations). Daily average and 1-

hour maxima are computed from these hourly data. 

There are two files that are used to provide meteorological data to APEX. One file, the 

meteorological station location file, contains the locations of meteorological data recordings 

expressed in latitude and longitude coordinates. This file also contains start and end dates for the 

data recording periods. The temperature data file contains the data from the locations in the 

temperature zone location file. This file contains hourly temperature readings for the period 

being modeled for the meteorological stations in and around the study area. 

B.2.2.2 Generate Simulated Individuals 

APEX stochastically generates a user-specified number of simulated persons to represent 

the population in the study area. Each simulated person is represented by a personal profile, a 

summary of personal attributes that define the individual. APEX generates the simulated person 

or profile by probabilistically selecting values for a set of profile variables (Table B.2-1). The 

profile variables could include: 

 Demographic variables, generated based on the census data; 

 Physical variables, generated based on sets of distribution data; 

 Other daily varying variables, generated based on literature-derived distribution data 

that change daily during the simulation period. 

APEX first selects demographic and physical attributes for each specified individual, and 

then follows the individual over time and calculates his or her time series of exposure. 
B-8

Table B.2-1. Examples of profile variables in APEX. 

Variable 

Type Profile Variables Description

Age Age (years) 

Gender Male or Female 

Home block Block in which a simulated person lives 

Work tract Tract in which a simulated person works 

Demographic 

Employment status Indicates employment outside home 

Physical Air conditioner Indicates presence of air conditioning at home 

Gas Stove Indicates presence of gas stove at home 

Population Demographics 

APEX takes population characteristics into account to develop accurate representations of 

study area demographics. Specifically, population counts by area and employment probability 

estimates are used to develop representative profiles of hypothetical individuals for the 

simulation. 

APEX is flexible in the resolution of population data provided. As long as the data are 

available, any resolution can be used (e.g., county, census tract, census block). For this 

application of the model, census block level data were used. Block-level population counts come 

from the 2000 Census of Population and Housing Summary File 1 (SF-1). This file contains the 

100-percent data, which is the information compiled from the questions asked of all people and 

about every housing unit. 

As part of the population demographics inputs, it is important to integrate working 

patterns into the assessment. In the 2000 U.S. Census, estimates of employment were developed 

by census information (US Census Bureau, 2007). The employment statistics are broken down 

by gender and age group, so that each gender/age group combination is given an employment 

probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are: 

16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75. 

Children under 16 years of age were assumed to be not employed. 

Since this analysis was conducted at the census block level, block level employment 

probabilities were required. It was assumed that the employment probabilities for a census tract 

apply uniformly to the constituent census blocks. 
B-9

Commuting 

In addition to using estimates of employment by tract, APEX also incorporates home-towork commuting data. Commuting data were originally derived from the 2000 Census and were 

collected as part of the Census Transportation Planning Package (CTPP) (US DOT, 2007). The 

data used contain counts of individuals commuting from home to work locations at a number of 

geographic scales. These data were processed to calculate fractions for each tract-to-tract flow to 

create the national commuting data distributed with APEX. This database contains commuting 

data for each of the 50 states and Washington, D.C. 

Commuting within the Home Tract 

The APEX data set does not differentiate people that work at home from those that 

commute within their home tract. 

Commuting Distance Cutoff 

A preliminary data analysis of the home-work counts showed that a graph of log(flows) 

versus log(distance) had a near-constant slope out to a distance of around 120 kilometers. 

Beyond that distance, the relationship also had a fairly constant slope but it was flatter, meaning 

that flows were not as sensitive to distance. A simple interpretation of this result is that up to 

120 km, the majority of the flow was due to persons traveling back and forth daily, and the 

numbers of such persons decrease rapidly with increasing distance. Beyond 120 km, the 

majority of the flow is comprised of persons who stay at the workplace for extended times, in 

which case the separation distance is not as crucial in determining the flow. 

To apply the home-work data to commuting patterns in APEX, a simple rule was chosen. 

It was assumed that all persons in home-work flows up to 120 km are daily commuters, and no 

persons in more widely separated flows commute daily. This meant that the list of destinations 

for each home tract was restricted to only those work tracts that are within 120 km of the home 

tract. When the same cutoff was performed on the 1990 census data, it resulted in 4.75% of the 

home-work pairs in the nationwide database being eliminated, representing 1.3% of the workers. 

The assumption is that this 1.3% of workers do not commute from home to work on a daily 

basis. It is expected that the cutoff reduced the 2000 data by similar amounts. 

Eliminated Records 

A number of tract-to-tract pairs were eliminated from the database for various reasons. A 

fair number of tract-to-tract pairs represented workers who either worked outside of the U.S. 
B-10

(9,631 tract pairs with 107,595 workers) or worked in an unknown location (120,830 tract pairs 

with 8,940,163 workers). An additional 515 workers in the commuting database whose data 

were missing from the original files, possibly due to privacy concerns or errors, were also 

deleted. 

Commuting outside the study area 

APEX allows for some flexibility in the treatment of persons in the modeled population 

who commute to destinations outside the study area. By specifying “KeepLeavers = No” in the 

simulation control parameters file, people who work inside the study area but live outside of it 

are not modeled, nor are people who live in the study area but work outside of it. By specifying 

“KeepLeavers = Yes,” these commuters are modeled. This triggers the use of two additional 

parameters, called LeaverMult and LeaverAdd. While a commuter is at work, if the workplace is 

outside the study area, then the ambient concentration is assumed to be related to the average 

concentration over all air districts at the same point in time, and is calculated as: 

Ambient Concentrat ion  LeaverMult  tavg )(  LeaverAdd equation (B-1) 

where: 

 Ambient Concentration = Calculated ambient air concentrations for locations outside 

of the study area (ppm or ppm) 

 LeaverMult = Multiplicative factor for city-wide average concentration, 

applied when working outside study area 

 avg(t) = Average ambient air concentration over all air districts in 

study area, for time t (ppm or ppm)

 LeaverAdd = Additive term applied when working outside study area 

All microenvironmental concentrations for locations outside of the study area are 

determined from this ambient concentration by the same function as applies inside the study 

area. 

Block-level commuting 

For census block simulations, APEX requires block-level commuting file. A special 

software preprocesser was created to generate these files for APEX on the basis of the tract-level 
B-11

commuting data and finely-resolved land use data. The software calculates commuting flows 

between census blocks for the employed population according equation (B-2). 

Flowblock tract

pop

FFFlow land equation (B-2) 

where: 

Flow block = flow of working population between a home block and a work block. 

Flow tract = flow of working population between a home tract and a work tract. 

F pop = fraction of home tract’s working population residing in the home block. 

F land = fraction of work tract’s commercial/industrial land area in the work 

block 

Thus, it is assumed that the frequency of commuting to a workplace block within a tract 

is proportional to the amount of commercial and industrial land in the block. 

Profile Functions 

A Profile Functions file contains settings used to generate results for variables related to 

simulated individuals. While certain settings for individuals are generated automatically by 

APEX based on other input files, including demographic characteristics, others can be specified 

using this file. For example, the file may contain settings for determining whether the profiled 

individual’s residence has an air conditioner, a gas stove, etc. As an example, the Profile 

Functions file contains fractions indicating the prevalence of air conditioning in the cities 

modeled in this assessment (Figure B.2-1). APEX uses these fractions to stochastically generate 

air conditioning status for each individual. The derivation of particular data used in specific 

microenvironments is provided below. 

 

AC_Home 

! Has air conditioning at home 

TABLE 

INPUT1 PROBABILITY 2 “A/C probabilities” 

0.85 0.15 

RESULT INTEGER 2 “Yes/No” 

1 2 

# 

Figure B.2-1. Example of a profile function file for A/C prevalence. 
B-12

B.2.2.3 Longitudinal Activity Pattern Sequences 

Exposure models use human activity pattern data to predict and estimate exposure to 

pollutants. Different human activities, such as spending time outdoors, indoors, or driving, will 

have varying pollutant exposure concentrations. To accurately model individuals and their 

exposure to pollutants, it is critical to understand their daily activities. 

The Consolidated Human Activity Database (CHAD) provides data for where people 

spend time and the activities performed. CHAD was designed to provide a basis for conducting 

multi-route, multi-media exposure assessments (McCurdy et al., 2000). The data contained 

within CHAD come from multiple activity pattern surveys with varied structures (Table B.2-2), 

however the surveys have commonality in containing daily diaries of human activities and 

personal attributes (e.g., age and gender). 

There are four CHAD-related input files used in APEX. Two of these files can be 

downloaded directly from the CHADNet (http://www.epa.gov/chadnet1), and adjusted to fit into 

the APEX framework. These are the human activity diaries file and the personal data file, and 

are discussed below. A third input file contains metabolic information for different activities 

listed in the diary file, these are not used in this exposure analysis. The fourth input file maps 

five-digit location codes used in the diary file to APEX microenvironments; this file is discussed 

in the section describing microenvironmental calculations (Section B.2.2.4.4). 

Personal Information file 

 Personal attribute data are contained in the CHAD questionnaire file that is distributed 

with APEX. This file also has information for each day individuals have diaries. The different 

variables in this file are: 

 The study, person, and diary day identifiers 

 Day of week 

 Gender 

 Employment status 

 Age in years 

 Maximum temperature in degrees Celsius for this diary day 

 Mean temperature in degrees Celsius for this diary day 

 Occupation code 
B-13

 Time, in minutes, during this diary day for which no data are included in the database 

Diary Events file 

The human activity diary data are contained in the events file that is distributed with 

APEX. This file contains the activities for the nearly 23,000 people with intervals ranging from 

one minute to one hour. An individuals’ diary varies in length from one to 15 days. This file 

contains the following variables: 

 The study, person, and diary day identifiers 

 Start time of this activity 

 Number of minutes for this activity 

 Activity code (a record of what the individual was doing) 

 Location code (a record of where the individual was) 
B-14

Table B.2-2. Summary of activity pattern studies used in CHAD. 

Construction of Longitudinal Activity Sequences 

Typical time-activity pattern data available for inhalation exposure modeling consist of a 

sequence of location/activity combinations spanning a 24-hour duration, with 1 to 3 diary-days 

for any single individual. Exposure modeling requires information on activity patterns over 

longer periods of time, e.g., a full year. For example, even for pollutant health effects with short 

averaging times (e.g., SO2 5-minute average concentration) it may be desirable to know the 

frequency of exceedances of a concentration over a long period of time (e.g., the annual number 

of exceedances of a 24-hour average SO2 concentration of 100 ppb for each simulated 

individual). 

Study Name Location 

Study 

time 

period Ages Persons 

Person

-days 

Diary type 

/study 

design Reference 

Baltimore A single 

building in 

Baltimore 

01/1997-

02/1997, 

07/1998-

08/1998 

72-93 26 292 Diary Williams et al. (2000) 

California 

Adolescents 

and Adults 

(CARB) 

California 10/1987-

09/1988 

12-17 

18-94 

181 

1,552 

181 

1,552 

Recall 

/Random 

Robinson et al. 

(1989); 

Wiley et al. (1991a) 

California 

Children 

(CARB) 

California 04/1989- 

02/1990 

0-11 1,200 1,200 Recall 

/Random 

Wiley et al. (1991b) 

Cincinnati 

(EPRI) 

Cincinnati 

MSA 

03/1985-

04/1985, 

08/1985 

0-86 888 2,587 Diary 

/Random 

Johnson (1989) 

Denver 

(EPA) 

Denver 

MSA 

11/1982- 

02/1983 

18-70 432 791 Diary 

/Random 

Johnson (1984); 

Akland et al. (1985) 

Los Angeles: 

Elementary 

School 

Children 

Los 

Angeles 

10/1989 10-12 17 51 Diary Spier et al. (1992) 

Los Angeles: 

High School 

Adolescents 

Los 

Angeles 

09/1990-

10/1990 

13-17 19 42 Diary Spier et al. (1992) 

National: 

NHAPS-Air 

National 09/1992-

10/1994 

0-93 4,326 4,326 Recall 

/Random 

Klepeis et al. (1996); 

Tsang and Klepeis 

(1996) 

National: 

NHAPSWater 

National 09/1992-

10/1994 

0-93 4,332 4,332 Recall 

/Random 

Klepeis et al. (1996); 

Tsang and Klepeis 

(1996) 

Washington, 

D.C. (EPA) 

Wash. DC 

MSA 

11/1982-

02/1983 

18-98 639 639 Diary 

/Random 

Hartwell et al. (1984); 

Akland et al. (1985) 
B-15

Long-term multi-day activity patterns can be estimated from single days by combining 

the daily records in various ways, and the method used for combining them will influence the 

variability of the long-term activity patterns across the simulated population. This in turn will 

influence the ability of the model to accurately represent either long-term average high-end 

exposures, or the number of individuals exposed multiple times to short-term high-end 

concentrations. 

A common approach for constructing long-term activity patterns from short-term records 

is to re-select a daily activity pattern from the pool of data for each day, with the implicit 

assumption that there is no correlation between activities from day to day for the simulated 

individual. This approach tends to result in long-term activity patterns that are very similar 

across the simulated population. Thus, the resulting exposure estimates are likely to 

underestimate the variability across the population, and therefore, underestimate the high-end 

exposure concentrations or the frequency of exceedances. 

A contrasting approach is to select a single activity pattern (or a single pattern for each 

season and/or weekday-weekend) to represent a simulated individual’s activities over the 

duration of the exposure assessment. This approach has the implicit assumption that an 

individual’s day-to-day activities are perfectly correlated. This approach tends to result in longterm activity patterns that are very different across the simulated population, and therefore may 

over-estimate the variability across the population. 

Cluster-Markov Algorithm 

A new algorithm has been developed and incorporated into APEX to represent the dayto-day correlation of activities for individuals. The algorithms first use cluster analysis to divide 

the daily activity pattern records into groups that are similar, and then select a single daily record 

from each group. This limited number of daily patterns is then used to construct a long-term 

sequence for a simulated individual, based on empirically-derived transition probabilities. This 

approach is intermediate between the assumption of no day-to-day correlation (i.e., re-selection 

for each time period) and perfect correlation (i.e., selection of a single daily record to represent 

all days). 

The steps in the algorithm are as follows. 

1. For each demographic group (age, gender, employment status), temperature range, and 

day-of-week combination, the associated time-activity records are partitioned into 3 
B-16

groups using cluster analysis. The clustering criterion is a vector of 5 values: the time 

spent in each of 5 microenvironment categories (indoors – residence; indoors – other 

building; outdoors – near road; outdoors – away from road; in vehicle). 

2. For each simulated individual, a single time-activity record is randomly selected from 

each cluster. 

3. A Markov process determines the probability of a given time-activity pattern occurring 

on a given day based on the time-activity pattern of the previous day and cluster-tocluster transition probabilities. The cluster-to-cluster transition probabilities are 

estimated from the available multi-day time-activity records. If insufficient multi-day 

time-activity records are available for a demographic group, season, day-of-week 

combination, then the cluster-to-cluster transition probabilities are estimated from the 

frequency of time-activity records in each cluster in the CHAD data base. 

Details regarding the Cluster-Markov algorithm and supporting evaluations are provided in 

Attachments 4 and 5. 

B.2.2.4 Calculating Microenvironmental Concentrations 

Probabilistic algorithms estimate the pollutant concentration associated with each 

exposure event. The estimated pollutant concentrations account for the effects of ambient 

(outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate, 

and proximity to microenvironments can use the transfer factors method while the others use the 

mass balance emission sources, depending on the microenvironment, available data, and the 

estimation method selected by the user. 

APEX calculates air concentrations in the various microenvironments visited by the 

simulated person by using the ambient air data for the relevant blocks, the user-specified 

estimation method, and input parameters specific to each microenvironment. APEX calculates 

hourly concentrations in all the microenvironments at each hour of the simulation for each of the 

simulated individuals using one of two methods: by mass balance or a transfer factors method. 

Mass Balance Model 

The mass balance method simulates an enclosed microenvironment as a well-mixed 

volume in which the air concentration is spatially uniform at any specific time. The following 

processes are used estimate the concentration of an air pollutant in such a microenvironment: 

 Inflow of air into the microenvironment 
B-17

 Outflow of air from the microenvironment 

 Removal of a pollutant from the microenvironment due to deposition, filtration, and 

chemical degradation 

 Pollutant emissions inside the microenvironment. 

Table B.2-3 lists the parameters required by the mass balance method to calculate 

concentrations in a microenvironment. A proximity factor (fproximity) is used to account for 

differences in ambient concentrations between the geographic location represented by the 

ambient air quality data (e.g., a regional fixed-site monitor or modeled concentration) and the 

geographic location of the microenvironment (e.g., near a roadway). This factor could take a 

value either greater than or less than 1. Emission source (ES) represents the emission rate for the 

emission source and concentration source (CS) is the mean air concentration resulting from the 

source. Rremoval is defined as the removal rate of a pollutant from a microenvironment due to 

deposition, filtration, and chemical reaction. The air exchange rate (Rair exchange) is expressed in 

air changes per hour. 

Table B.2-3. Mass balance model parameters. 

Variable Definition Units Value Range

f proximity Proximity factor unitless f proximity ≥ 0 

CS Concentration source ppb CS ≥ 0 

R removal Removal rate due to deposition, 

filtration, and chemical reaction 

1/hr Rremoval ≥ 0 

R air exchange Air exchange rate 1/hr Rair exchange ≥ 0 

V Volume of microenvironment m3 V > 0 

The mass balance equation for a pollutant in a microenvironment is described by: 

in out CCC removal  Csource dt

ME (t)dC equation (B-3) 

where: 

 dCME(t) = Change in concentration in a microenvironment at time t (ppb), 

Cin = Rate of change in microenvironmental concentration due to influx 

of air (ppb/hour), 

Cout = Rate of change in microenvironmental concentration due to outflux 

of air (ppb/hour), 
B-18

Cremoval = Rate of change in microenvironmental concentration due to 

removal processes (ppb/hour), and 

Csource = Rate of change in microenvironmental concentration due to an 

emission source inside the microenvironment (ppb/hour). 

Within the timestep selected, each of the rates of change, Cin, Cout, Cremoval, and 

Csource, is assumed to be constant. At each timestep of the simulation period, APEX estimates 

the equilibrium, ending, and mean concentrations using a series of equations that account for 

concentration changes expected to occur due to these physical processes. Details regarding these 

equations are provided in the APEX TSD (US EPA, 2009b). The calculation continues to the 

next timestep by using the end concentration for the previous timestep as the initial 

microenvironmental concentration. A brief description of the input parameters estimates used 

for microenvironments using the mass balance approach is provided below. 

Factors Model 

The factors method is simpler than the mass balance method. It does not calculate 

concentration in a microenvironment from the concentration in the previous hour and it has 

fewer parameters. Table B.2-4 lists the parameters required by the factors method to calculate 

concentrations in a microenvironment without emissions sources. 

Table B.2-4. Factors model parameters. 

Variable Definition Units Value Range

f proximity Proximity factor unitless f proximity ≥ 0 

f penetration Penetration factor unitless 0 ≤ f penetration ≤ 1 

The factors method uses the following equation to calculate the timestep concentration in 

a microenvironment from the user-provided hourly air quality data: 

 ambient proximity npenetratio

timestep

ME  fxfxCC equation (B-4) 

where: 

timestep CME = Timestep concentration in a microenvironment (ppb) 

Cambient = Timestep concentration in ambient environment (ppb) 

fproximity = Proximity factor (unitless) 

fpenetration = Penetration factor (unitless) 
B-19

The ambient NO2 concentrations are from the air quality data input file. The proximity 

factor is a unitless parameter that represents the proximity of the microenvironment to a 

monitoring station. The penetration factor is a unitless parameter that represents the fraction of 

pollutant entering a microenvironment from outside the microenvironment via air exchange. The 

development of the specific proximity and penetration factors used in this analysis are discussed 

below for each microenvironment using this approach. 

Microenvironments Modeled 

In APEX, microenvironments represent the exposure locations for simulated individuals. 

For exposures to be estimated accurately, it is important to have realistic microenvironments that 

match closely to the locations where actual people spend time on a daily basis. As discussed 

above, the two methods available in APEX for calculating pollutant levels within 

microenvironments are: 1) factors and 2) mass balance. A list of microenvironments used in this 

study, the calculation method used, and the parameters used to calculate the microenvironment 

concentrations can be found in Table B.2-5. 

Each of the microenvironments is designed to simulate an environment in which people 

spend time during the day. CHAD locations are linked to the different microenvironments in the 

Microenvironment Mapping File (see below). There are many more CHAD locations than 

microenvironment locations (there are 113 CHAD codes versus 12 microenvironments in this 

assessment), therefore most of the microenvironments have multiple CHAD locations mapped to 

them. 
B-20

Table B.2-5. List of microenvironments and calculation methods used. 

Microenvironment 

No. Name 

Calculation 

Method 

Parameter 

Types used 1

1 Indoors – Residence Mass balance AER and DE 

2 Indoors – Bars and restaurants Mass balance AER and DE 

3 Indoors – Schools Mass balance AER and DE 

4 Indoors – Day-care centers Mass balance AER and DE 

5 Indoors – Office Mass balance AER and DE 

6 Indoors – Shopping Mass balance AER and DE 

7 Indoors – Other Mass balance AER and DE 

8 Outdoors – Near road Factors PR 

9 Outdoors – Public garage - parking lot Factors PR 

10 Outdoors – Other Factors None 

11 In-vehicle – Cars and Trucks Factors PE and PR 

12 In-vehicle - Mass Transit (bus, subway, train) Factors PE and PR 

0 Not modeled 

1 AER=air exchange rate, DE=decay-deposition rate, PR=proximity factor, PE=penetration 

factor 

Mapping of APEX Microenvironments to CHAD Diaries 

The Microenvironment Mapping file matches the APEX Microenvironments to CHAD 

Location codes. Table B.2-6 gives the mapping used for the APEX simulations. 

Table B.2-6. Mapping of CHAD activity locations to APEX microenvironments. 

CHAD Loc. Description APEX micro 

--------- ------------------------------------------------- 

U Uncertain of correct code = -1 Unknown 

X No data = -1 Unknown 

30000 Residence, general = 1 Indoors-Residence 

30010 Your residence = 1 Indoors-Residence 

30020 Other residence = 1 Indoors-Residence 

30100 Residence, indoor = 1 Indoors-Residence 

30120 Your residence, indoor = 1 Indoors-Residence 

30121 ..., kitchen = 1 Indoors-Residence 

30122 ..., living room or family room = 1 Indoors-Residence 

30123 ..., dining room = 1 Indoors-Residence 

30124 ..., bathroom = 1 Indoors-Residence 

30125 ..., bedroom = 1 Indoors-Residence 

30126 ..., study or office = 1 Indoors-Residence 

30127 ..., basement = 1 Indoors-Residence 

30128 ..., utility or laundry room = 1 Indoors-Residence 

30129 ..., other indoor = 1 Indoors-Residence 

30130 Other residence, indoor = 1 Indoors-Residence 

30131 ..., kitchen = 1 Indoors-Residence 

30132 ..., living room or family room = 1 Indoors-Residence 

30133 ..., dining room = 1 Indoors-Residence 

30134 ..., bathroom = 1 Indoors-Residence 

30135 ..., bedroom = 1 Indoors-Residence 
B-21

30136 ..., study or office = 1 Indoors-Residence 

30137 ..., basement = 1 Indoors-Residence 

30138 ..., utility or laundry room = 1 Indoors-Residence 

30139 ..., other indoor = 1 Indoors-Residence 

30200 Residence, outdoor = 10 Outdoors-Other 

30210 Your residence, outdoor = 10 Outdoors-Other 

30211 ..., pool or spa = 10 Outdoors-Other 

30219 ..., other outdoor = 10 Outdoors-Other 

30220 Other residence, outdoor = 10 Outdoors-Other 

30221 ..., pool or spa = 10 Outdoors-Other 

30229 ..., other outdoor = 10 Outdoors-Other 

30300 Residential garage or carport = 7 Indoors-Other 

30310 ..., indoor = 7 Indoors-Other 

30320 ..., outdoor = 10 Outdoors-Other 

30330 Your garage or carport = 1 Indoors-Residence 

30331 ..., indoor = 1 Indoors-Residence 

30332 ..., outdoor = 10 Outdoors-Other 

30340 Other residential garage or carport = 1 Indoors-Residence 

30341 ..., indoor = 1 Indoors-Residence 

30342 ..., outdoor = 10 Outdoors-Other 

30400 Residence, none of the above = 1 Indoors-Residence 

31000 Travel, general = 11 In Vehicle-Cars_and_Trucks 

31100 Motorized travel = 11 In Vehicle-Cars_and_Trucks 

31110 Car = 11 In Vehicle-Cars_and_Trucks 

31120 Truck = 11 In Vehicle-Cars_and_Trucks 

31121 Truck (pickup or van) = 11 In Vehicle-Cars_and_Trucks 

31122 Truck (not pickup or van) = 11 In Vehicle-Cars_and_Trucks 

31130 Motorcycle or moped = 8 Outdoors-Near_Road 

31140 Bus = 12 In Vehicle-Mass_Transit 

31150 Train or subway = 12 In Vehicle-Mass_Transit 

31160 Airplane = 0 Zero_concentration 

31170 Boat = 10 Outdoors-Other 

31171 Boat, motorized = 10 Outdoors-Other 

31172 Boat, other = 10 Outdoors-Other 

31200 Non-motorized travel = 10 Outdoors-Other 

31210 Walk = 10 Outdoors-Other 

31220 Bicycle or inline skates/skateboard = 10 Outdoors-Other 

31230 In stroller or carried by adult = 10 Outdoors-Other 

31300 Waiting for travel = 10 Outdoors-Other 

31310 ..., bus or train stop = 8 Outdoors-Near_Road 

31320 ..., indoors = 7 Indoors-Other 

31900 Travel, other = 11 In Vehicle-Cars_and_Trucks 

31910 ..., other vehicle = 11 In Vehicle-Cars_and_Trucks 

32000 Non-residence indoor, general = 7 Indoors-Other 

32100 Office building/ bank/ post office = 5 Indoors-Office 

32200 Industrial/ factory/ warehouse = 5 Indoors-Office 

32300 Grocery store/ convenience store = 6 Indoors-Shopping 

32400 Shopping mall/ non-grocery store = 6 Indoors-Shopping 

32500 Bar/ night club/ bowling alley = 2 Indoors-Bars_and_Restaurants 

32510 Bar or night club = 2 Indoors-Bars_and_Restaurants 

32520 Bowling alley = 2 Indoors-Bars_and_Restaurants 

32600 Repair shop = 7 Indoors-Other 

32610 Auto repair shop/ gas station = 7 Indoors-Other 

32620 Other repair shop = 7 Indoors-Other 

32700 Indoor gym /health club = 7 Indoors-Other 

32800 Childcare facility = 4 Indoors-Day_Care_Centers 

32810 ..., house = 1 Indoors-Residence 

32820 ..., commercial = 4 Indoors-Day_Care_Centers 

32900 Large public building = 7 Indoors-Other 

32910 Auditorium/ arena/ concert hall = 7 Indoors-Other 

32920 Library/ courtroom/ museum/ theater = 7 Indoors-Other 

33100 Laundromat = 7 Indoors-Other 

33200 Hospital/ medical care facility = 7 Indoors-Other 

33300 Barber/ hair dresser/ beauty parlor = 7 Indoors-Other 

33400 Indoors, moving among locations = 7 Indoors-Other 
B-22

33500 School = 3 Indoors-Schools 

33600 Restaurant = 2 Indoors-Bars_and_Restaurants 

33700 Church = 7 Indoors-Other 

33800 Hotel/ motel = 7 Indoors-Other 

33900 Dry cleaners = 7 Indoors-Other 

34100 Indoor parking garage = 7 Indoors-Other 

34200 Laboratory = 7 Indoors-Other 

34300 Indoor, none of the above = 7 Indoors-Other 

35000 Non-residence outdoor, general = 10 Outdoors-Other 

35100 Sidewalk, street = 8 Outdoors-Near_Road 

35110 Within 10 yards of street = 8 Outdoors-Near_Road 

35200 Outdoor public parking lot /garage = 9 Outdoors-Public_Garage-Parking 

35210 ..., public garage = 9 Outdoors-Public_Garage-Parking 

35220 ..., parking lot = 9 Outdoors-Public_Garage-Parking 

35300 Service station/ gas station = 10 Outdoors-Other 

35400 Construction site = 10 Outdoors-Other 

35500 Amusement park = 10 Outdoors-Other 

35600 Playground = 10 Outdoors-Other 

35610 ..., school grounds = 10 Outdoors-Other 

35620 ..., public or park = 10 Outdoors-Other 

35700 Stadium or amphitheater = 10 Outdoors-Other 

35800 Park/ golf course = 10 Outdoors-Other 

35810 Park = 10 Outdoors-Other 

35820 Golf course = 10 Outdoors-Other 

35900 Pool/ river/ lake = 10 Outdoors-Other 

36100 Outdoor restaurant/ picnic = 10 Outdoors-Other 

36200 Farm = 10 Outdoors-Other 

36300 Outdoor, none of the above = 10 Outdoors-Other 

B.2.2.5 Exposure Calculations 

APEX calculates exposure as a time series of exposure concentrations that a simulated 

individual experiences during the simulation period. APEX determines the exposure using 

hourly ambient air concentrations, calculated concentrations in each microenvironment based on 

these ambient air concentrations (and indoor sources if present), and the minutes spent in a 

sequence of microenvironments visited according to the composite diary. The hourly exposure 

concentration at any clock hour during the simulation period is determined using the following 

equation: 

T

tC

C

N

j

j

timestep

jME

i

  1

)()(

 equation (B-5) 

where: 

 Ci = Hourly exposure concentration at clock hour i of the simulation period 

(ppb) 

N = Number of events (i.e., microenvironments visited) in clock hour i of the 

simulation period. 

timestep C jME )( = Timestep concentration in microenvironment j (ppm) 

 t(j) = Time spent in microenvironment j (minutes) 
B-23

 T = Length of timestep (minutes) 

From the timestep exposures, APEX calculates time series of 1-hour, 8-hour and daily average 

exposures that a simulated individual would experience during the simulation period. APEX then 

statistically summarizes and tabulates the timestep, hourly, 8-hour, and daily exposures. Note 

that if the APEX timestep is greater than an hour, the 1-hour and 8-hour exposures are not 

calculated and the corresponding tables are not produced. Exposures are calculated 

independently for all pollutants in the simulation. 

From the timestep exposures, APEX can calculate the time-series of 1-hour, 8-hour, and 

daily average exposures that a simulated individual would experience during the simulation 

period. APEX then statistically summarizes and tabulates the timestep (or hourly, daily, annual 

average) exposures. In this analysis, the exposure indicator is 5-minute exposures above 

potential health effect benchmark levels. From this, APEX can calculate two general types of 

exposure estimates: counts of the estimated number of people exposed to a specified SO2 

concentration level and the number of times per year that they are so exposed; the latter metric is 

typically expressed in terms of person-occurrences or person-days. The former highlights the 

number of individuals exposed at least one or more times per modeling period to the health 

effect benchmark level of interest. APEX can also report counts of individuals with multiple 

exposures. This person-occurrences measure estimates the number of times per season that 

individuals are exposed to the exposure indicator of interest and then accumulates these 

estimates for the entire population residing in an area. 

APEX tabulates and displays the two measures for exposures above levels ranging from 

any number of benchmark levels, by any increment (e.g., 0 to 800 ppb by 50 ppb increments for 

5-minute exposures). These exposure results are tabulated for the population and subpopulations 

of interest. 

Exposure Model Output 

All of the output files written by APEX are ASCII text files. Table B.2-7 lists each of the 

output data files written for these simulations and provides descriptions of their content. 

Additional output files that can produced by APEX are given in Table 5-1 of the APEX User’s 

Guide, and include hourly exposure, ventilation, and energy expenditures, and even detailed 

event-level information, if desired. The names and locations, as well as the output table levels 
B-24

(e.g., output percentiles, cut-points), for these output files are specified by the user in the 

simulation control parameters file. 

Table B.2-7. Example of APEX output files. 

Output File Type Description

Log The Log file contains the record of the APEX model simulation as it progresses. 

If the simulation completes successfully, the log file indicates the input files and 

parameter settings used for the simulation and reports on a number of different 

factors. If the simulation ends prematurely, the log file contains error messages 

describing the critical errors that caused the simulation to end. 

Profile Summary The Profile Summary file provides a summary of each individual modeled in the 

simulation. 

Microenvironment 

Summary

The Microenvironment Summary file provides a summary of the time and 

exposure by microenvironment for each individual modeled in the simulation. 

Sites The Sites file lists the tracts, districts, and zones in the study area, and identifies 

the mapping between them. 

Output Tables The Output Tables file contains a series of tables summarizing the results of the 

simulation. The percentiles and cut-off points used in these tables are defined 

in the simulation control parameters file. 
B-25

B.3 Supplemental AERMOD Dispersion Modeling Data 

 
B-26

B.3-1 AERMOD Input data Table B.3-1. Emission parameters by stack for all major facility stacks in Missouri. 

Stack ID City Facility Name NEI Site ID 

UTM X 

(m)1

UTM Y 

(m)1

SO2

Emissions 

(tpy) 

Stack 

Height 

(m) 

Exi

Tem

(K) 

5049 LABADIE 

AMERENUE-LABADIE PLANT NEI 7514 688,392 4,270,394 10,970 213 4

5050 LABADIE 

AMERENUE-LABADIE PLANT NEI 7514 688,357 4,270,439 14,753 213 4

5051 LABADIE 

AMERENUE-LABADIE PLANT NEI 7514 688,461 4,270,338 14,285 213 4

5054 LABADIE 

AMERENUE-LABADIE PLANT NEI 7514 688,442 4,270,322 7,602 213 4

5063 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 476,842 4,106,944 1,137 107 4

5064 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 476,853 4,106,922 1,433 107 4

5066 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 476,913 4,106,929 757 61 4

5068 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 476,884 4,106,932 159 61 4

5069 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 476,890 4,106,922 660 61 4

5070 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 476,918 4,106,919 567 61 4

5073 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 476,919 4,106,930 218 60 4

5074 SPRINGCITY UTILITIES OF SPRINGFIELD NEI 7525 255 60 4
B-27

Stack ID City Facility Name NEI Site ID 

UTM X 

(m)1

UTM Y 

(m)1

SO2

Emissions 

(tpy) 

Stack 

Height 

(m) 

Exi

Tem

(K) 

FIELD MISSOURI-JAMES 

RIVER POWER PLANT 

476,952 4,106,940 

5076 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 477,050 4,106,880 219 60 4

5077 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURI-JAMES RIVER POWER PLANT NEI 7525 476,992 4,106,881 252 60 4

5084 

SPRINGFIELD 

CITY UTILITIES OF SPRINGFIELD MISSOURISOUTHWEST POWER PLANT NEI 12640 465,416 4,111,816 3,390 117 3

5113 

WEST ALTON 

AMERENUE-SIOUX PLANT NEI 7516 735,034 4,310,876 24,932 183 4

5114 

WEST ALTON 

AMERENUE-SIOUX PLANT NEI 7516 735,027 4,310,819 21,025 183 4

5115 

WEST ALTON 

AMERENUE-SIOUX PLANT NEI 7516 734,948 4,310,864 2 65 4

5131 

HERCULANEUM DOE RUN COMPANY- HERCULANEUM SMELTER NEI 34412 729,589 4,238,084 2 3 2

5141 

HERCULANEUM DOE RUN COMPANY- HERCULANEUM SMELTER NEI 34412 729,543 4,237,936 2 9 2

5145 

HERCULANEUM DOE RUN COMPANY- HERCULANEUM SMELTER NEI 34412 729,537 4,237,973 15,219 168 3

5147 FESTUS 

AMERENUE-RUSH ISLAND PLANT NEI 12618 739,910 4,223,934 2 76 5

5148 FESTUS 

AMERENUE-RUSH ISLAND PLANT NEI 12618 739,893 4,223,827 10,511 213 4

5149 FESTUS 

AMERENUE-RUSH ISLAND PLANT NEI 12618 739,931 4,223,869 12,744 213 4

5244 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,358 4,207,065 62 23 5

5245 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,384 4,207,015 89 23 4
B-28

Stack ID City Facility Name NEI Site ID 

UTM X 

(m)1

UTM Y 

(m)1

SO2

Emissions 

(tpy) 

Stack 

Height 

(m) 

Exi

Tem

(K) 

5246 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,697 4,206,939 103 23 4

5247 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,666 4,206,950 106 23 4

5248 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,697 4,206,981 105 23 4

5261 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,561 4,206,988 1,290 35 3

5262 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,735 4,206,971 1,394 35 3

5263 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,727 4,206,997 1,505 35 3

5264 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,550 4,206,964 67 35 3

5265 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,524 4,206,924 77 35 3

5267 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,633 4,206,999 2 20 3

5270 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,627 4,206,989 1 20 3

5271 

STE. GENEVIEVE MISSISSIPPI LIME COMPANY- MISSISSIPPI LIME CO NEI MO1860001 757,540 4,206,931 1,199 35 3

5276 ST. LOUIS 

AMERENUEMERAMEC PLANT NEI 7515 732,584 4,253,799 5,195 107 4

5277 ST. LOUIS 

AMERENUEMERAMEC PLANT NEI 7515 732,631 4,253,790 6,463 107 4

5278 ST. LOUIS 

AMERENUEMERAMEC PLANT NEI 7515 732,677 4,253,784 2,359 76 4

5279 ST. LOUIS 

AMERENUEMERAMEC PLANT NEI 7515 732,714 4,253,779 2,430 76 4

5293 ST. LOUIS 

ANHEUSER-BUSCH INC-ST LOUIS NEI 34732 742,736 4,275,786 2 30 3
B-29

Stack ID City Facility Name NEI Site ID 

UTM X 

(m)1

UTM Y 

(m)1

SO2

Emissions 

(tpy) 

Stack 

Height 

(m) 

Exi

Tem

(K) 

5295 ST. LOUIS 

ANHEUSER-BUSCH INC-ST LOUIS NEI 34732 742,775 4,275,743 176 69 4

5296 ST. LOUIS 

ANHEUSER-BUSCH INC-ST LOUIS NEI 34732 742,750 4,275,704 256 69 4

5297 ST. LOUIS 

ANHEUSER-BUSCH INC-ST LOUIS NEI 34732 742,781 4,275,753 249 69 4

5298 ST. LOUIS 

ANHEUSER-BUSCH INC-ST LOUIS NEI 34732 742,800 4,275,764 158 69 4

5299 ST. LOUIS 

ANHEUSER-BUSCH INC-ST LOUIS NEI 34732 742,759 4,275,714 3,066 69 4

5302 ST. LOUIS 

ANHEUSER-BUSCH INC-ST LOUIS NEI 34732 742,739 4,275,677 2,339 69 4

5304 ST. LOUIS 

ANHEUSER-BUSCH INC-ST LOUIS NEI 34732 742,711 4,275,740 4 22 4

Notes: 1 UTM Zone 15 values in all cases. 2 Three methods were possible to convert annual total emissions data from the NEI into hourly temporal profiles required for Adata:  Tier 1: CAMD hourly concentrations to create relative temporal profiles.  Tier 2: EMS-HAP seasonal and diurnal temporal profiles for source categorization codes (SCCs).  Tier 3: Flat profiles 
B-30

Table B.3-2. Emission parameters by stack for all major cross-border facility stacks in the St. Lou

Stack ID City Facility Name NEI Site ID UTM X (m)1 UTM Y (m)1 SO2 Emissions (tpy) Stack Height (m) Exit Temp. (K) Stac

Diam

(m) 

1 East Alton 

DYNEGY MIDWEST GENERATION INC 

NEI52119 748,654 4,305,518 1536.2 76.2 427.6 5.

2 East Alton 

DYNEGY MIDWEST GENERATION INC 

NEI52119 748,654 4,305,518 5725.8 106.7 416.5 4.

3 Baldwin 

DYNEGY MIDWEST GENERATION INC 

NEI52781 775,316 4,233,202 9931.4 184.4 425.4 5.

4 Baldwin 

DYNEGY MIDWEST GENERATION INC 

NEI52781 775,316 4,233,202 9053 184.4 428.7 5.

5 Baldwin 

DYNEGY MIDWEST GENERATION INC 

NEI52781 775,316 4,233,202 7283 184.4 424.8 5.

9 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 753,003 4,302,381 131.95786 33.5 533.2 1.

10 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 752,783 4,302,408 907.24 19.9 502.0 1.

11 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 752,886 4,302,285 132.9 24.1 519.3 2.
B-31

Stack ID City Facility Name NEI Site ID UTM X (m)1 UTM Y (m)1 SO2 Emissions (tpy) Stack Height (m) Exit Temp. (K) Stac

Diam

(m) 

12 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 752,883 4,302,377 106.67 24.4 533.2 1.

13 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 753,003 4,302,381 79 36.0 533.2 1.

14 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 752,886 4,302,285 66.43 16.8 677.6 1.

15 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 752,783 4,302,408 4.90219 35.4 570.4 1.

16 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 753,003 4,302,381 171.36006 30.5 533.2 1.

17 Hartford 

Premcor Refining Group (prev. Clark Oil and Refining Corp.) 

NEI52159 752,783 4,302,408 7.42 30.7 513.2 1.

18 Sauget BIG RIVER ZINC CORP NEI53013 746,429 4,276,339 1.34 21.3 317.6 0.19 Sauget BIG RIVER ZINC CORP NEI53013 746,429 4,276,339 1377.28 25.9 422.0 0.20 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 755,000 4,302,599 15.38 106.7 472.0 4.
B-32

Stack ID City Facility Name NEI Site ID UTM X (m)1 UTM Y (m)1 SO2 Emissions (tpy) Stack Height (m) Exit Temp. (K) Stac

Diam

(m) 

21 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 755,000 4,302,599 7.27 106.7 463.7 4.

22 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 755,188 4,302,550 1.2 45.7 628.2 2.

23 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 754,405 4,303,105 1.25 56.4 432.6 2.

24 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 754,997 4,302,691 1.45 61.0 672.0 3.

25 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 754,505 4,302,984 1.53 95.1 483.7 4.

26 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 754,994 4,302,783 3.39 40.2 491.5 2.

27 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 755,084 4,303,003 1.15 45.7 699.8 2.

28 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 754,658 4,302,515 385.25 36.9 754.8 3.

29 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 754,994 4,302,783 3.24 45.7 431.5 3.

30 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 755,000 4,302,599 16.73 106.7 483.7 4.

31 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 754,658 4,302,515 11677.82 10.1 293.7 0.

32 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 755,231 4,302,561 212.41 45.7 699.8 2.
B-33

Stack ID City Facility Name NEI Site ID UTM X (m)1 UTM Y (m)1 SO2 Emissions (tpy) Stack Height (m) Exit Temp. (K) Stac

Diam

(m) 

33 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 755,231 4,302,561 206.96 45.7 672.0 2.

34 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 753,801 4,303,085 110.6 38.1 792.0 2.

35 Roxana 

ConocoPhillips Co. (prev. Phillips 66 Co.) 

NEI55835 755,231 4,302,561 108.6 45.7 672.0 2.

36 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 747,795 4,286,723 61.88 30.5 616.5 2.

37 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 750,041 4,286,824 506.7 46.3 441.5 2.

38 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 749,778 4,286,692 228.47 24.5 372.0 1.

39 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 749,897 4,286,788 421.58883 68.6 460.9 4.

40 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 749,970 4,286,761 375.19 15.4 453.7 0.

41 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 750,041 4,286,824 351.93 46.3 441.5 2.

42 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 749,847 4,286,849 264.95442 61.0 460.9 3.
B-34

Stack ID City Facility Name NEI Site ID UTM X (m)1 UTM Y (m)1 SO2 Emissions (tpy) Stack Height (m) Exit Temp. (K) Stac

Diam

(m) 

43 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 750,280 4,286,925 923.52 43.5 538.7 2.

44 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 749,828 4,286,663 501.19 43.5 538.7 2.

46 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 747,842 4,286,755 85.86 30.5 616.5 2.

47 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 750,180 4,286,983 20.99 24.9 335.9 1.

50 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 748,053 4,287,055 8 19.2 323.7 2.

51 Granite City 

NATIONAL STEEL CORP - GRANITE CITY DIV 

NEI55848 750,255 4,286,924 959.82 43.5 538.7 2.

Notes: 1 UTM Zone 15 values in all cases. 2 Three methods were possible to convert annual total emissions data from the NEI into hourly temporal profiles required for Aavailability of data:  Tier 1: CAMD hourly concentrations to create relative temporal profiles.  Tier 2: EMS-HAP seasonal and diurnal temporal profiles for source categorization codes (SCCs).  Tier 3: Flat profiles 
B-35

B.3.2 AERMOD Air Quality Evaluation Data 

Table B.3-2. Measured ambient monitor SO2 concentration distributions and the 

modeled monitor receptor and receptors within 4 km of the ambient monitors in 

Greene County for year 2002. 

Ambient Percentile Concentration (ppb) 

Monitor ID Receptor(s)1 100 99 95 90 80 70 60 50 25 0

AERMOD P2.5 29 6 2 1 0 0 0 0 0 0

AERMOD P50 48 12 4 2 1 1 0 0 0 0

AERMOD P97.5 101 46 18 8 2 1 1 1 0 0

Ambient Monitor 114 46 16 7 3 2 1 1 1 0

290770026 

AERMOD Monitor 48 22 11 6 2 1 1 1 0 0

AERMOD P2.5 30 10 5 3 2 1 1 1 0 0

AERMOD P50 41 12 6 4 3 2 2 2 1 0

AERMOD P97.5 62 14 8 6 5 4 3 3 2 0

Ambient Monitor 28 8 6 5 4 4 3 3 2 0

290770032 

AERMOD Monitor 42 14 8 6 5 4 3 3 1 0

AERMOD P2.5 35 5 2 1 0 0 0 0 0 0

AERMOD P50 53 13 3 2 1 0 0 0 0 0

AERMOD P97.5 106 55 21 8 2 1 1 0 0 0

Ambient Monitor 144 49 8 4 2 2 2 2 0 0

290770037 

AERMOD Monitor 115 42 5 3 1 1 1 0 0 0

AERMOD P2.5 34 5 2 1 0 0 0 0 0 0

AERMOD P50 53 13 3 2 1 0 0 0 0 0

AERMOD P97.5 106 55 21 8 2 1 1 0 0 0

Ambient Monitor 203 18 6 3 2 1 1 0 0 0

290770040 

AERMOD Monitor 116 45 6 3 1 1 1 0 0 0

AERMOD P2.5 31 5 2 1 0 0 0 0 0 0

AERMOD P50 52 14 3 2 1 0 0 0 0 0

AERMOD P97.5 108 56 22 8 2 1 1 0 0 0

Ambient Monitor 33 9 3 2 1 1 1 0 0 0

290770041 

AERMOD Monitor 73 23 4 2 1 1 1 0 0 0

Notes: 

1

 AERMOD concentrations are for the given percentile (p2.5 = 2.5th; p50 = 50th; p97.5 = 97.5th) of the 

modeled distribution of all modeled air quality receptors within 4 km of ambient monitor. AERMOD 

monitor is the concentration prediction at the ambient monitor location using AERMOD. 
B-36

Table B.3-3. Measured ambient monitor SO2 concentration diurnal profile and the 

modeled monitor receptor and receptors within 4 km of the ambient monitors in 

Greene County for year 2002. 

Annual Average SO2 Concentration (ppb) at Given Receptor 

Monitor ID 

Hour 

of 

Day 

AERMOD 

P2.5 

AERMOD 

P50 

AERMOD 

P97.5 

Ambient 

Monitor 

AERMOD 

Monitor 

1 0.2 0.4 1.6 2.7 1.2 

2 0.2 0.4 1.8 2.4 1.0 

3 0.2 0.4 2.0 2.5 0.9 

4 0.1 0.3 1.6 2.5 0.9 

5 0.1 0.3 1.5 2.9 0.8 

6 0.1 0.3 1.7 2.9 0.9 

7 0.2 0.7 2.3 3.6 1.4 

8 0.5 1.5 4.2 4.2 2.8 

9 0.7 1.5 5.6 4.6 3.6 

10 0.8 1.6 5.6 5.3 3.4 

11 0.7 1.4 6.0 5.0 3.7 

12 0.5 1.2 6.3 4.7 3.2 

13 0.6 1.1 6.1 4.5 2.8 

14 0.6 1.0 6.0 4.1 2.6 

15 0.5 1.0 5.8 3.7 2.6 

16 0.6 1.0 5.1 3.8 2.5 

17 0.6 1.3 4.6 3.7 2.9 

18 0.5 1.2 3.7 2.9 2.9 

19 0.3 0.9 2.3 2.6 2.4 

20 0.2 0.5 1.7 3.0 1.4 

21 0.2 0.5 1.7 2.8 1.4 

22 0.2 0.5 1.9 3.0 1.4 

23 0.2 0.4 1.9 2.9 1.1 

290770026 

24 0.2 0.4 1.9 2.9 1.1 

1 1.5 2.3 3.2 2.8 3.0 

2 1.4 2.1 3.0 2.6 2.9 

3 1.4 2.2 3.4 2.5 2.8 

4 1.3 2.0 2.8 2.5 2.7 

5 1.1 1.7 2.4 2.3 2.5 

6 1.1 1.8 2.5 2.2 2.4 

7 1.4 2.0 2.5 2.3 2.5 

8 1.9 2.2 2.7 2.7 2.9 

9 1.7 2.3 3.4 3.0 3.2 

10 1.5 2.3 3.9 3.3 3.6 

11 1.3 2.2 4.0 3.2 3.5 

12 1.2 2.2 4.1 3.2 3.6 

13 1.1 2.1 4.3 3.3 3.6 

14 1.1 2.0 4.1 3.2 3.6 

15 1.0 2.0 4.0 3.2 3.5 

16 1.1 2.1 4.1 3.1 3.4 

17 1.4 2.4 3.6 3.2 3.5 

18 1.6 2.4 3.3 3.1 3.5 

290770032 

19 1.8 2.4 3.2 3.1 3.4 
B-37

Annual Average SO2 Concentration (ppb) at Given Receptor 

Monitor ID 

Hour 

of 

Day 

AERMOD 

P2.5 

AERMOD 

P50 

AERMOD 

P97.5 

Ambient 

Monitor 

AERMOD 

Monitor 

20 1.7 2.5 3.2 3.1 3.4 

21 1.8 2.4 3.3 3.1 3.3 

22 1.7 2.6 3.6 3.1 3.4 

23 1.6 2.5 3.4 3.1 3.4 

24 1.4 2.4 3.2 2.9 3.1 

1 0.2 0.2 1.4 1.6 0.5 

2 0.2 0.2 1.5 1.5 0.5 

3 0.2 0.2 1.7 1.5 0.5 

4 0.1 0.2 1.5 1.9 0.4 

5 0.1 0.2 1.4 1.9 0.3 

6 0.1 0.2 1.6 1.8 0.3 

7 0.1 0.4 2.4 1.9 0.3 

8 0.4 1.0 4.5 2.3 1.0 

9 0.6 1.2 6.2 3.1 1.9 

10 0.8 1.5 6.4 3.8 3.7 

11 0.7 1.4 7.0 4.1 4.6 

12 0.6 1.3 6.9 4.8 5.4 

13 0.6 1.3 7.0 5.0 5.0 

14 0.6 1.2 7.2 5.2 4.6 

15 0.5 1.2 6.5 5.3 4.3 

16 0.5 1.2 5.7 4.9 3.1 

17 0.5 1.2 5.2 4.2 2.4 

18 0.4 1.0 4.2 3.0 1.9 

19 0.3 0.5 2.5 2.2 0.7 

20 0.2 0.3 1.4 2.2 0.5 

21 0.2 0.3 1.5 1.9 0.5 

22 0.2 0.3 1.7 1.8 0.6 

23 0.2 0.2 1.8 1.9 0.5 

290770037 

24 0.2 0.2 1.6 2.1 0.5 

1 0.2 0.2 1.4 1.0 0.5 

2 0.2 0.2 1.5 0.8 0.5 

3 0.2 0.2 1.7 1.0 0.5 

4 0.1 0.2 1.5 1.0 0.4 

5 0.1 0.2 1.4 1.0 0.3 

6 0.1 0.2 1.6 1.0 0.3 

7 0.1 0.4 2.4 1.0 0.3 

8 0.4 1.0 4.5 1.2 0.8 

9 0.6 1.2 6.2 1.6 1.8 

10 0.8 1.5 6.4 2.2 3.7 

11 0.7 1.4 7.0 2.4 4.8 

12 0.5 1.3 6.9 2.9 6.2 

13 0.6 1.3 7.0 2.4 5.9 

14 0.6 1.2 7.2 3.0 4.8 

15 0.5 1.2 6.5 2.8 4.6 

16 0.5 1.2 5.7 2.2 3.2 

17 0.5 1.1 5.2 1.8 2.6 

290770040 

18 0.4 1.0 4.2 1.8 1.8 
B-38

Annual Average SO2 Concentration (ppb) at Given Receptor 

Monitor ID 

Hour 

of 

Day 

AERMOD 

P2.5 

AERMOD 

P50 

AERMOD 

P97.5 

Ambient 

Monitor 

AERMOD 

Monitor 

19 0.3 0.5 2.5 1.2 0.8 

20 0.2 0.3 1.4 1.0 0.5 

21 0.2 0.3 1.5 1.0 0.5 

22 0.2 0.3 1.7 0.9 0.6 

23 0.2 0.2 1.8 0.9 0.5 

24 0.2 0.2 1.6 1.0 0.5 

1 0.2 0.3 1.5 0.6 0.6 

2 0.2 0.3 1.7 0.6 0.5 

3 0.2 0.3 1.9 0.6 0.6 

4 0.1 0.2 1.5 0.5 0.5 

5 0.1 0.2 1.4 0.4 0.4 

6 0.1 0.2 1.7 0.6 0.4 

7 0.1 0.6 2.4 0.6 0.6 

8 0.5 1.2 4.9 0.8 1.5 

9 0.6 1.4 6.2 1.1 1.7 

10 0.7 1.7 6.5 1.4 1.8 

11 0.6 1.5 7.2 1.5 2.4 

12 0.4 1.4 7.0 1.5 2.1 

13 0.5 1.4 7.4 1.6 2.4 

14 0.4 1.3 7.9 1.3 2.0 

15 0.4 1.2 6.6 1.1 2.1 

16 0.4 1.3 6.2 1.0 2.3 

17 0.4 1.2 5.4 0.9 2.1 

18 0.4 1.1 4.2 0.7 1.8 

19 0.3 0.5 2.6 0.6 0.8 

20 0.2 0.3 1.5 0.6 0.6 

21 0.2 0.3 1.6 0.7 0.6 

22 0.2 0.4 1.8 0.7 0.7 

23 0.2 0.3 1.9 0.6 0.6 

290770041 

24 0.2 0.3 1.8 0.7 0.6 
B-39

Table B.3-4. Measured ambient monitor SO2 concentration distributions and the 

modeled monitor receptor and receptors within 4 km of the ambient monitors in 

St. Louis for year 2002. 

Ambient Percentile Concentration (ppb) 

Monitor ID Receptor(s)1 100 99 95 90 80 70 60 50 25 0

AERMOD P2.5 60 20 11 7 4 3 2 2 1 0

AERMOD P50 69 22 12 8 5 3 2 2 1 0

AERMOD P97.5 103 25 14 9 5 4 3 2 1 0

Ambient Monitor 99 24 13 8 5 3 2 1 0 0

291890004 

AERMOD Monitor 67 22 11 7 4 3 2 2 1 0

AERMOD P2.5 48 19 10 7 4 3 2 2 1 0

AERMOD P50 55 20 11 7 5 3 2 2 1 0

AERMOD P97.5 94 20 12 8 5 4 3 2 1 0

Ambient Monitor 85 18 9 6 3 2 1 1 0 0

291890006 

AERMOD Monitor 73 20 11 8 5 3 3 2 1 0

AERMOD P2.5 58 24 13 9 5 4 3 2 1 0

AERMOD P50 75 26 14 10 6 4 3 2 1 0

AERMOD P97.5 91 30 17 12 8 6 5 3 2 0

Ambient Monitor 80 24 12 8 5 4 3 2 1 0

291893001 

AERMOD Monitor 71 25 14 10 6 4 3 2 1 0

AERMOD P2.5 97 32 13 8 5 4 3 2 1 0

AERMOD P50 168 38 14 9 6 4 3 2 1 0

AERMOD P97.5 545 51 15 10 6 4 3 2 1 0

Ambient Monitor 158 23 12 8 5 4 3 2 1 0

291895001 

AERMOD Monitor 191 40 14 9 6 4 3 2 1 0

AERMOD P2.5 67 25 11 7 4 3 2 2 1 0

AERMOD P50 89 28 12 8 5 3 3 2 1 0

AERMOD P97.5 138 32 13 9 5 4 3 2 1 0

Ambient Monitor 91 24 12 8 5 4 3 2 1 0

291897003 

AERMOD Monitor 99 27 12 8 5 4 3 2 1 0

AERMOD P2.5 71 26 13 8 4 3 2 2 1 0

AERMOD P50 93 31 18 11 7 5 4 3 1 0

AERMOD P97.5 137 43 23 16 10 8 6 5 2 0

Ambient Monitor 64 25 14 10 6 5 3 2 1 0

295100007 

AERMOD Monitor 100 32 17 11 7 6 5 4 2 0

AERMOD P2.5 71 29 15 11 7 5 4 3 1 0

AERMOD P50 91 32 18 13 9 6 5 4 2 0

AERMOD P97.5 124 36 22 16 11 8 6 5 3 0

Ambient Monitor 86 30 16 11 7 5 4 3 1 0

295100086 

AERMOD Monitor 111 31 17 13 8 6 5 4 2 0

Notes: 

1

 AERMOD concentrations are for the given percentile (p2.5 = 2.5th; p50 = 50th; p97.5 = 97.5th) of the 

modeled distribution of all modeled air quality receptors within 4 km of ambient monitor. AERMOD 

monitor is the concentration prediction at the ambient monitor location using AERMOD. 
B-40

Table B.3-5. Measured ambient monitor SO2 concentration diurnal profile and the 

modeled monitor receptor and receptors within 4 km of the ambient monitors in 

St. Louis for year 2002. 

Annual Average SO2 Concentration (ppb) at Given Receptor 

Ambient 

Monitor ID 

Hour 

of 

Day 

AERMOD 

P2.5 

AERMOD 

P50 

AERMOD 

P97.5 

Ambient 

Monitor 

AERMOD 

Monitor 

1 2.0 2.6 3.2 2.4 2.2

2 1.7 2.2 2.7 2.1 1.8

3 1.5 1.9 2.3 1.8 1.7

4 1.5 1.9 2.3 1.8 1.6

5 1.4 1.8 2.1 1.8 1.7

6 1.4 1.8 2.6 2.0 1.8

7 1.8 2.2 2.6 2.4 2.0

8 2.7 3.2 4.0 3.3 3.0

9 4.0 4.5 4.8 4.1 4.2

10 4.6 5.0 5.4 4.6 4.7

11 4.8 5.0 5.3 4.6 4.8

12 4.6 5.2 5.6 4.5 4.8

13 4.4 4.9 5.2 4.1 4.5

14 4.1 4.7 5.0 4.3 4.2

15 3.9 4.3 4.6 4.0 4.0

16 3.7 4.2 4.4 3.6 3.7

17 3.8 4.4 4.7 3.8 3.9

18 3.5 4.2 4.6 3.8 3.5

19 3.1 3.6 4.0 3.4 3.1

20 3.0 3.5 3.7 2.9 3.1

21 2.7 3.4 3.7 2.8 2.9

22 2.4 2.9 3.3 2.9 2.6

23 2.1 2.7 3.3 2.9 2.4

291890004 

24 2.1 2.6 3.0 2.5 2.2

1 2.4 2.6 2.9 1.6 2.6

2 2.1 2.3 2.6 1.6 2.5

3 1.7 1.9 2.1 1.5 2.0

4 1.8 2.0 2.3 1.2 2.1

5 1.6 1.8 1.9 1.2 1.8

6 1.5 1.6 1.7 1.2 1.5

7 2.0 2.0 2.1 1.4 2.0

8 2.5 2.7 2.7 1.9 2.5

9 4.2 4.3 4.4 2.7 4.3

10 4.4 4.6 4.8 3.4 4.7

11 4.8 4.9 5.3 3.5 5.1

12 4.3 4.5 4.8 3.8 4.6

13 4.2 4.3 4.7 3.5 4.6

14 4.1 4.2 4.6 2.9 4.6

15 3.9 4.0 4.4 2.6 4.3

16 3.7 3.8 4.2 2.9 4.2

17 3.8 4.0 4.6 2.7 4.3

18 3.4 3.7 4.5 2.6 4.1

291890006 

19 3.0 3.3 3.8 2.4 3.4
B-41

20 2.9 3.1 3.4 2.4 3.0

21 2.8 3.0 3.4 2.0 3.1

22 2.5 2.7 3.0 2.0 2.8

23 2.3 2.8 3.2 1.9 2.8

24 2.2 2.4 2.8 1.8 2.7

1 3.0 3.6 4.2 2.2 3.5

2 2.8 3.2 3.9 2.2 3.2

3 2.3 2.7 3.3 2.0 2.7

4 2.5 2.8 3.4 1.7 2.8

5 2.1 2.5 2.8 1.8 2.5

6 2.2 2.6 3.1 2.3 2.7

7 2.6 3.0 3.7 2.8 3.1

8 3.2 3.7 4.6 3.7 3.6

9 4.5 5.1 6.5 4.5 5.0

10 5.2 5.6 7.8 4.9 5.7

11 5.3 5.8 8.1 4.8 5.8

12 5.3 5.8 8.2 4.7 5.8

13 5.0 5.4 7.7 4.4 5.3

14 4.6 5.1 7.8 4.5 5.0

15 4.5 4.8 7.3 4.1 4.8

16 4.3 4.7 7.1 3.8 4.7

17 4.5 4.8 6.4 3.8 4.7

18 4.3 4.8 6.0 4.1 4.8

19 3.9 4.3 5.3 3.7 4.2

20 3.6 3.9 4.7 3.5 3.9

21 3.6 4.0 4.6 3.4 4.0

22 3.3 3.8 4.7 3.2 3.7

23 3.2 3.8 4.5 3.0 3.9

291893001 

24 3.0 3.7 4.4 2.7 3.6

1 3.2 3.7 4.9 3.1 3.8

2 3.1 3.7 5.1 2.9 3.5

3 2.8 3.2 3.8 2.7 3.2

4 2.8 3.3 4.3 2.7 3.2

5 2.4 3.0 3.9 2.7 3.3

6 2.5 3.6 4.9 2.8 3.8

7 2.6 3.2 3.9 3.2 3.0

8 3.2 4.3 5.3 4.0 4.7

9 4.5 5.5 6.1 4.6 5.4

10 5.3 6.0 6.6 4.7 6.0

11 5.5 5.9 6.1 5.2 6.0

12 5.6 5.7 6.0 5.1 6.0

13 4.6 5.3 5.7 4.6 5.3

14 4.6 5.1 5.3 4.1 5.2

15 4.4 4.8 5.0 4.1 4.9

16 4.1 4.6 4.9 3.9 4.6

17 4.1 4.5 4.8 4.1 4.7

18 4.5 4.8 5.3 3.9 4.9

19 3.2 4.2 5.3 3.7 3.7

20 3.3 4.1 5.5 3.2 4.0

291895001 

21 3.1 4.3 5.6 3.5 3.9
B-42

22 3.5 4.4 5.6 3.3 4.7

23 3.7 4.4 6.3 3.2 5.6

24 3.8 4.3 5.1 2.9 4.3

1 2.3 2.8 3.3 2.6 2.7

2 2.6 3.0 3.2 2.4 3.2

3 2.1 2.6 2.9 2.5 2.7

4 2.2 2.5 3.0 2.4 2.4

5 1.7 2.2 2.7 2.3 2.2

6 2.1 2.5 3.2 2.5 2.5

7 1.8 2.1 2.6 3.2 2.1

8 2.9 3.4 3.9 4.1 3.2

9 4.1 4.4 5.0 4.7 4.5

10 4.9 5.3 5.8 4.8 5.4

11 4.8 5.2 5.6 5.1 5.4

12 4.5 5.0 5.6 4.7 5.1

13 4.3 4.7 5.1 4.5 4.9

14 4.0 4.4 4.8 4.4 4.6

15 3.9 4.4 4.7 4.2 4.6

16 3.6 4.1 4.4 3.8 4.3

17 3.7 4.3 4.6 3.7 4.5

18 3.2 4.2 4.7 3.9 4.4

19 3.1 3.8 4.5 3.6 3.8

20 2.8 3.2 4.0 3.5 3.2

21 2.8 3.4 3.9 3.3 3.2

22 3.4 3.7 4.0 3.1 3.9

23 2.7 3.3 4.2 3.1 3.6

291897003 

24 3.0 3.4 3.8 2.9 3.6

1 2.2 3.7 5.8 3.4 4.0

2 2.1 3.4 5.6 3.2 3.5

3 1.6 3.0 5.3 3.2 3.3

4 1.7 3.1 5.2 3.0 3.3

5 1.4 3.0 5.1 2.9 3.1

6 1.6 3.1 5.2 3.1 3.2

7 2.9 4.5 7.6 3.7 4.6

8 3.6 5.2 7.8 4.1 5.2

9 5.0 6.6 8.4 5.2 6.6

10 5.1 6.8 8.2 5.7 7.0

11 5.1 7.1 8.5 5.5 7.5

12 4.9 7.0 8.2 4.9 7.2

13 4.6 6.9 8.1 4.7 7.1

14 4.5 6.8 8.1 4.6 7.1

15 4.0 6.3 7.6 4.4 6.6

16 4.0 6.2 7.8 4.2 6.6

17 4.4 6.2 8.2 4.2 6.6

18 4.2 6.0 8.7 3.9 6.3

19 3.6 5.7 10.0 3.8 6.5

20 3.5 5.1 7.4 4.2 5.2

21 3.2 4.8 7.1 4.1 4.9

22 2.7 4.4 6.6 4.0 4.5

295100007 

23 2.6 4.0 5.9 4.1 4.0
B-43

24 2.3 3.9 5.8 3.7 3.9

1 3.8 4.9 6.5 4.3 4.8

2 3.3 4.7 6.7 4.2 4.4

3 3.1 4.3 5.5 3.7 4.1

4 3.0 4.0 5.6 3.9 4.0

5 3.0 3.8 5.8 3.9 3.6

6 2.3 3.9 5.9 4.3 4.0

7 3.7 4.4 7.0 4.4 4.3

8 4.2 5.5 7.0 5.4 5.3

9 5.8 7.0 8.0 6.3 6.9

10 6.7 8.1 8.4 6.0 8.1

11 6.6 8.0 8.4 6.0 8.0

12 6.5 7.8 8.3 5.4 7.8

13 6.2 7.7 8.1 5.1 7.6

14 6.1 7.5 7.9 4.9 7.4

15 5.7 7.1 7.4 4.6 7.1

16 5.6 7.2 7.5 4.7 7.2

17 5.3 7.2 7.9 4.9 6.9

18 5.2 7.2 8.5 4.4 7.0

19 5.0 6.5 9.2 4.6 6.2

20 4.7 6.2 8.0 4.7 6.0

21 4.6 6.3 7.7 4.9 6.1

22 4.3 5.6 7.9 4.8 5.1

23 4.1 5.4 7.3 4.3 5.1

295100086 

24 3.8 5.1 6.9 4.2 4.8
B-44

B.4 SUPPLEMENTAL APEX EXPOSURE MODELING 

DATA 

B.4.1 APEX Input Data Distributions for SO2 deposition 

In recognizing the relationship between SO2 deposition rate and various surface 

types within indoor microenvironments and that the presence of these surfaces would 

vary in proportions dependent on the microenvironment, staff estimated the APEX input 

SO2 deposition rate distributions using a Monte Carlo sampling approach. First, 1,000 

different hypothetical indoor microenvironments were simulated, each with a different 

ratio of wall area to floor area and furniture area to floor area. Based on these ratios, 

surface area to volume ratios were estimated in each sample indoor microenvironment. 

Then, surface area to volume ratios were used to convert the deposition velocities to 

deposition rates in hr-1 by dividing the velocities by the surface area to volume ratio and 

then making an appropriate unit conversion. And finally, the deposition rate for each 

surface type was combined using a weighted average to estimate an effective deposition 

rate, as follows: 

floor

furniture

floor

ceiling

floor

furntiture

furniture

floor

ceiling

floor ceiling

eff

A

A

A

A

A

A

D

A

A

DD

D



 



1

* *

 equation (B-6) 

where D denotes deposition rate, A denotes area of the indoor microenvironment, 

and Deff is the effective deposition rate. If more than one surface type is present in the 

sample indoor microenvironment (e.g. both carpet and non-carpeted floors), these values 

were first averaged using the fraction of the room that contains each. Details regarding 

the data used for estimating the SO2 deposition rate within simulated indoor 

microenvironments are provided in the following sections. 

B.4.1.1 Surface deposition data and surface type mapping 

Staff obtained SO2 deposition velocities from a literature review conducted by 

GrØntoft and Raychaudhuri (2004). These authors categorized the data by several 

relative humidities and considering several different surface types. Staff mapped the 
B-45

surface classes reported in GrØntoft and Raychaudhuri (2004) to surface types typically 

found within indoor microenvironments (Table B.4-1). 

Table B.4-1. Classification of SO2 deposition data for several 

microenvironmental surfaces. 

Deposition in cm/s1

Surface 

Category Surface Type Surface Class1 50% 

Relative 

Humidity

70% 

Relative 

Humidity 

90% 

Relative 

Humidity

Carpet 

Average of the wool 

and synthetic carpet 

values 

0.0625 0.075 0.117 

Floor 

Floor 

Synthetic Floor 

Covering – medium 

worn 

0.007 0.015 0.032 

Ceiling Tile Coarse composite 

panels 0.14 0.15 0.18 

Ceiling 

Ceiling Wallboard Treated gypsum 

wallboard 0.048 0.16 0.27 

Wallpaper Wall paper 0.036 0.043 0.068 

Wall Wallboard Treated gypsum 

wallboard 

Wall 0.048 0.16 0.27 

Wood paneling Surface treated wood 

work and wall boards 0.014 0.047 0.078 

Furniture Furniture Cloth 0.019 0.023 0.036 

Notes: 

1

 Obtained from Table 6 of GrØntoft and Raychaudhuri (2004). 

B.4.1.2 Indoor Microenvironment Configurations 

Because the configuration of rooms within a building will affect the wall area to 

floor area ratio, staff first estimated the areas of several indoor microenvironments. Staff 

had to make several assumptions due to the limited availability of data. The first broad 

assumption was that a single room within the indoor microenvironment could represent 

all potential rooms within the particular building type. Secondly, staff assumed all rooms 

were square to calculate the area distributions. Additional assumptions specific to the 

type of indoor microenvironment are provided below, along with the estimated indoor 

microenvironment area distributions. 
B-46

Residential area distributions 

In residences, the American Housing Survey (AHS, 2008) provides a matrix that 

gives the number of survey homes within a given total square footage and a given 

number of rooms category. Staff converted the data to probabilities using the total 

number of homes in each category (Table B.4-2). In calculating the room area using 

these distributions, a series of two independent random numbers were used to select a 

square footage category and then to find the number of rooms within that square footage 

category, accounting for the inherent correlation of the number of rooms in a given 

building with the total square footage. Staff derived a representative room area by 

dividing the square footage by the number of rooms. 

Table B.4-2. Distributions used to calculate a representative room size in an 

indoor residential microenvironment. 

Square Footage 

250 750 1250 1750 2250 2750 

Cumulative 

probability 

for each 

square 

footage 

class 0.01 0.10 0.35 0.60 0.77 1.00 

Rooms 

1 0.07 0.00 0.00 0.00 0.00 0.00 

2 0.13 0.00 0.00 0.00 0.00 0.00 

3 0.40 0.07 0.01 0.00 0.00 0.00 

4 0.64 0.47 0.13 0.04 0.02 0.01 

5 0.81 0.80 0.54 0.28 0.15 0.08 

6 0.90 0.94 0.86 0.65 0.41 0.23 

7 0.97 0.98 0.96 0.90 0.71 0.46 

8 0.99 0.99 0.99 0.98 0.92 0.71 

9 0.99 1.00 1.00 0.99 0.98 0.87 

Cumulative 

probability 

for number 

of rooms 

within 

each 

square 

footage 

class 

10 1.00 1.00 1.00 1.00 

Non-residential area distributions 

An office can contain many different rooms, each with either one or two 

occupants (usually a smaller office) or a collection of cubicles (usually a larger office). 
B-47

Staff used the Building Assessment Survey and Evaluation study (BASE; US EPA, 

2008a) to generate representative office areas for simulated buildings. The BASE data 

provided the mean, standard deviation, minimum, and maximum of the total square 

footage and the number of people per square meter of occupied space (Table B.4-3).1

 

Based on this, staff represented the data as a normal distribution and set the lower and 

upper limits using the minimum and maximum observations. BASE (US EPA, 2008a) 

also provides the average number of occupants in private or semi-private work areas 

(40%) compared to shared space (60%).2

 Staff assumed that the private and semi-private 

offices have an average of two people in each and the shared spaces have an average of 

six people in each. In calculating the area, two independent random numbers were used 

to select the total floor area and the number of occupants in that floor area. The total 

square footage of the office was then divided by the number of rooms to obtain the 

representative office area. 

For schools, the distribution of the total building square footage is available from 

the Commercial Building Energy Consumption Survey (CBECS; US DOE, 2003); 

however, information on the number of rooms in each square footage class is not 

available. As an alternate data source, information was available on the range of the 

square footage of a typical school classroom (600 to 1,400 square feet) to generate a 

uniform distribution bounded by these extremes (NCBG, 2008; US Army Corps of 

Engineers, 2002). For restaurants and other buildings, staff assumed that the entire 

building was one room; therefore, the CBECS (US DOE, 2003) provided data for this 

building category to estimate square footage distributions (Table B.4-3). 

Table B.4-3. Distributions used to calculate representative room size for 

non-residential microenvironments. 

 Microenvironment Parameter 

1a

Parameter 

2b 

Parameter 

3c

 

Parameter 

4d

Distribution 

Type 

Office, Building size 

(ft2

) 16,632 8,035 4,612 69,530 Normal 

Office, number of 

people per m2

. 4.0 1.5 1.5 8.5 Normal 

 

1

 http://www.epa.gov/iaq/base/pdfs/test_space_characteristics/tc-0.pdf 2

 http://www.epa.gov/iaq/base/pdfs/test_space_characteristics/tc-1.pdf 
B-48

School (ft2) 600 1,400 N/A N/A Uniform 

Restaurant (ft2) 5,340 31 668 42,699 Lognormal 

Other Buildings (ft2) 3,750 24 750 18,796 Lognormal 

Notes: 

a

 Mean for normal, geometric mean for lognormal, lower limit for uniform distribution. 

b

 Standard deviation for normal, geometric standard deviation for lognormal, upper limit for 

uniform. 

c

 Minimum value for normal and lognormal. 

d

 Maximum value for normal and lognormal.

Additional specifications 

Two additional specifications were required to calculate the room volumes and 

surface areas: the ceiling heights and surface area of furniture within the rooms. Table 

B.4-4 provides the data values and sources used to estimate each of these variables. 

Table B.4-4. Ceiling heights and furniture surface area to floor ratios for 

simulated indoor microenvironments. 

Indoor 

Microenvironment Ceiling Heighta

Furniture Surface Area 

to Floor Ratio 

Residence 8 ft 2b

Office 10 ft 4c

School 10 ft 4c

Restaurant 10 ft 4c

Other Buildings 10 ft 4c

Notes: 

a

 Assumed by staff. 

b

 Thatcher et al. (2002) and Singer et al. (2002). 

c

 The surface area to volume ratio was assumed higher in the commercial 

microenvironments than in residences. A value of 4 was selected since it 

kept the range of total surface area to volume ratio within a typical range of 2 

to 4 (Lawrence Berkeley National Laboratory., 2003). 

B.4.1.3 Surface type probabilities 

Following the calculation of the basic dimensions of the simulated room, staff 

performed additional probabilistic sampling to specify the surface types present. In some 

microenvironments, it is possible that only a single surface type be present (e.g., a public 

access building likely contains only hard floors and no carpet). However, in other cases, 

a typical building may have multiple surface types (e.g., a residence may have a mixture 
B-49

of both hard floor and carpet). Thus, in each microenvironment, staff estimated a 

probability of occurrence for each surface type. If more than one surface type is possible 

at the same time, then staff also approximated the fraction of each. Table B.4-5 

summarizes both the probabilities and fractions assumed by staff for each 

microenvironment. 

B.4.1.4 Final SO2 deposition distributions 

Following the estimation of the room dimensions and surface types within each 

simulated indoor microenvironment, an effective deposition rate was estimated for all 

1,000 sample buildings. The geometric mean and geometric standard deviation were 

calculated across all 1,000 samples and used to parameterize a lognormal distribution 

(Table B.4-6). In applying these to the relative humidity conditions in the study areas, 

staff assumed that the relative humidity is below 50% when the air conditioning or 

heating unit is on. If the building has no air conditioner, the ambient summer humidity 

was used (90 % in the morning, 50% in the afternoon). Staff also assumed that all nonresidential buildings had air-conditioning. 

As far as mapping to the APEX microenvironments, residences, offices, and 

restaurants are explicitely modeled microenvironments. The daycare microenvironment 

used the school deposition distribution, while other indoor microenvironments (i.e., 

shopping or other) used the other building deposition distribution.
B-50

Table B.4-5. Probability of occurrence and fractional quantity for surface types in indoor micronIndoor Floor Ceiling Microenvironment Carpet Hard floor Wallboard Ceiling Tile Wallboard WaResidence 

P = 1 

F = 

N{0.52, 0.23}a

P = 1 

F = 

1 - fraction 

carpeteda

P = 1c P = 0c

P = 1 

F= 5/6 

if wallpaper is 

presentc

P 

F

wa

p

Office 

P = 1 

F = 5/6 if hard 

floor presentc

P = 0.34 

F= 1/6 if hard 

floor is presentb

P = 0c P = 1c

P = 1 

F is adjusted if 

wallpaper and/or 

wood paneling is 

presentc

P 

F

wa

p

School P = 0c P = 1c P = 0c P = 1c P = 1c Restaurant P = 0.1d P = 0.9d P = 0.55d P = 0.45d

P = 1 

F is adjusted if 

wallpaper and/or 

wood paneling is 

presentc

P 

F 

wa

p

Other Buildings P = 0.1d P = 0.9d P = 0.19d P = 0.81d

P = 1 

F is adjusted if 

wallpaper and/or 

wood paneling is 

presentc

P 

F 

wa

p

Notes: a US EPA, 2008b. b BASE study, Table 4 (US EPA, 2008a); the fraction of 1/6 is based on professional judgment. c Assumed by staff. d Source Ranking Database (SRD, US EPA, 2004b). The fraction of buildings value in the database was used to specify a 

occurs in the microenvironment. SRD names were matched to the APEX environments. Most categories in the SRD have 

values. To map to the necessary surface types: Carpet – Networx represented carpet; Ceiling tile represented ceiling tile; v

represented wallpaper; and Hardwood plywood paneling represented wood paneling. Fractions were assumed by staff. Tremaining surface types were calculated assuming either only one type could be present or multiple types could be presen
B-51

Table B.4-6. Final parameter estimates of SO2 deposition distributions in several 

indoor microenvironments modeled in APEX. 

Heating or Air Conditioning in Use 

Air Conditioning Not in Use 

(Summertime Ambient Morning 

Relative Humidity of 90%) 

Microenvironment Geom. 

Mean 

(hr-1) 

Geom. 

Stand. 

Dev. 

(hr-1) 

Lower 

Limit 

(hr-1) 

Upper 

Limit 

(hr-1) 

Geom. 

Mean 

(hr-1) 

Geom

. 

Stand.

Dev. 

(hr-1) 

Lower 

Limit 

(hr-1) 

Upper 

Limit 

(hr-1) 

Residence 3.14 1.11 2.20 5.34 13.41 1.11 10.31 26.96 

Office 3.99 1.04 3.63 4.37 N/A N/A N/A N/A

School 4.02 1.02 3.90 4.21 N/A N/A N/A N/A

Restaurant 2.36 1.28 1.64 4.17 N/A N/A N/A N/A

Other 

Buildings 2.82 1.21 1.71 4.12 N/A N/A N/A N/A

Notes: 

N/A not applicable, assumed by staff to always have A/C in operation. 
B-52

B.4.2 APEX Exposure Output 

Table B.4-7. APEX estimated SO2 exposures in Greene County (as is air quality 

scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) 

Number 

of 

Person 

Days 

Number 

of 

Persons Subpopulation 

Fraction of 

Total 

Population 

0 3218000 21262 DMTS,ASTHMA,MOD 0.97

50 309 193 DMTS,ASTHMA,MOD 0.01

100 18 13 DMTS,ASTHMA,MOD 0.00

150 0 0 DMTS,ASTHMA,MOD 0.00

200 0 0 DMTS,ASTHMA,MOD 0.00

250 0 0 DMTS,ASTHMA,MOD 0.00

300 0 0 DMTS,ASTHMA,MOD 0.00

350 0 0 DMTS,ASTHMA,MOD 0.00

400 0 0 DMTS,ASTHMA,MOD 0.00

450 0 0 DMTS,ASTHMA,MOD 0.00

500 0 0 DMTS,ASTHMA,MOD 0.00

550 0 0 DMTS,ASTHMA,MOD 0.00

600 0 0 DMTS,ASTHMA,MOD 0.00

650 0 0 DMTS,ASTHMA,MOD 0.00

700 0 0 DMTS,ASTHMA,MOD 0.00

750 0 0 DMTS,ASTHMA,MOD 0.00

800 0 0 DMTS,ASTHMA,MOD 0.00

0 1821000 7280 DMTS,ASTHMACHILD,MOD 1.00

50 193 108 DMTS,ASTHMACHILD,MOD 0.01

100 9 4 DMTS,ASTHMACHILD,MOD 0.00

150 0 0 DMTS,ASTHMACHILD,MOD 0.00

200 0 0 DMTS,ASTHMACHILD,MOD 0.00

250 0 0 DMTS,ASTHMACHILD,MOD 0.00

300 0 0 DMTS,ASTHMACHILD,MOD 0.00

350 0 0 DMTS,ASTHMACHILD,MOD 0.00

400 0 0 DMTS,ASTHMACHILD,MOD 0.00

450 0 0 DMTS,ASTHMACHILD,MOD 0.00

500 0 0 DMTS,ASTHMACHILD,MOD 0.00

550 0 0 DMTS,ASTHMACHILD,MOD 0.00

600 0 0 DMTS,ASTHMACHILD,MOD 0.00

650 0 0 DMTS,ASTHMACHILD,MOD 0.00

700 0 0 DMTS,ASTHMACHILD,MOD 0.00

750 0 0 DMTS,ASTHMACHILD,MOD 0.00

800 0 0 DMTS,ASTHMACHILD,MOD 0.00
B-53

Table B.4-8. APEX estimated SO2 exposures in Greene County (current standard 

air quality scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) 

Number 

of 

Person 

Days 

Number 

of 

Persons Subpopulation 

Fraction of 

Total 

Population 

0 3218000 21262 DMTS,ASTHMA,MOD 0.97

50 9598 4322 DMTS,ASTHMA,MOD 0.20

100 1659 982 DMTS,ASTHMA,MOD 0.04

150 511 323 DMTS,ASTHMA,MOD 0.01

200 197 139 DMTS,ASTHMA,MOD 0.01

250 90 67 DMTS,ASTHMA,MOD 0.00

300 22 18 DMTS,ASTHMA,MOD 0.00

350 18 13 DMTS,ASTHMA,MOD 0.00

400 13 13 DMTS,ASTHMA,MOD 0.00

450 4 4 DMTS,ASTHMA,MOD 0.00

500 0 0 DMTS,ASTHMA,MOD 0.00

550 0 0 DMTS,ASTHMA,MOD 0.00

600 0 0 DMTS,ASTHMA,MOD 0.00

650 0 0 DMTS,ASTHMA,MOD 0.00

700 0 0 DMTS,ASTHMA,MOD 0.00

750 0 0 DMTS,ASTHMA,MOD 0.00

800 0 0 DMTS,ASTHMA,MOD 0.00

0 1821000 7280 DMTS,ASTHMACHILD,MOD 1.00

50 6393 2609 DMTS,ASTHMACHILD,MOD 0.36

100 1036 569 DMTS,ASTHMACHILD,MOD 0.08

150 323 188 DMTS,ASTHMACHILD,MOD 0.03

200 112 72 DMTS,ASTHMACHILD,MOD 0.01

250 49 40 DMTS,ASTHMACHILD,MOD 0.01

300 13 9 DMTS,ASTHMACHILD,MOD 0.00

350 9 4 DMTS,ASTHMACHILD,MOD 0.00

400 4 4 DMTS,ASTHMACHILD,MOD 0.00

450 4 4 DMTS,ASTHMACHILD,MOD 0.00

500 0 0 DMTS,ASTHMACHILD,MOD 0.00

550 0 0 DMTS,ASTHMACHILD,MOD 0.00

600 0 0 DMTS,ASTHMACHILD,MOD 0.00

650 0 0 DMTS,ASTHMACHILD,MOD 0.00

700 0 0 DMTS,ASTHMACHILD,MOD 0.00

750 0 0 DMTS,ASTHMACHILD,MOD 0.00

800 0 0 DMTS,ASTHMACHILD,MOD 0.00
B-54

Table B.4-9. APEX estimated SO2 exposures in Greene County (99th %ile, 50 ppb 

alternative standard scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) 

Number 

of 

Person 

Days 

Number 

of 

Persons Subpopulation 

Fraction of 

Total 

Population 

0 3218000 21262 DMTS,ASTHMA,MOD 0.97 

50 18 13 DMTS,ASTHMA,MOD 0.00 

100 0 0 DMTS,ASTHMA,MOD 0.00 

150 0 0 DMTS,ASTHMA,MOD 0.00 

200 0 0 DMTS,ASTHMA,MOD 0.00 

250 0 0 DMTS,ASTHMA,MOD 0.00 

300 0 0 DMTS,ASTHMA,MOD 0.00 

350 0 0 DMTS,ASTHMA,MOD 0.00 

400 0 0 DMTS,ASTHMA,MOD 0.00 

450 0 0 DMTS,ASTHMA,MOD 0.00 

500 0 0 DMTS,ASTHMA,MOD 0.00 

550 0 0 DMTS,ASTHMA,MOD 0.00 

600 0 0 DMTS,ASTHMA,MOD 0.00 

650 0 0 DMTS,ASTHMA,MOD 0.00 

700 0 0 DMTS,ASTHMA,MOD 0.00 

750 0 0 DMTS,ASTHMA,MOD 0.00 

800 0 0 DMTS,ASTHMA,MOD 0.00 

0 1821000 7280 DMTS,ASTHMACHILD,MOD 1.00 

50 9 4 DMTS,ASTHMACHILD,MOD 0.00 

100 0 0 DMTS,ASTHMACHILD,MOD 0.00 

150 0 0 DMTS,ASTHMACHILD,MOD 0.00 

200 0 0 DMTS,ASTHMACHILD,MOD 0.00 

250 0 0 DMTS,ASTHMACHILD,MOD 0.00 

300 0 0 DMTS,ASTHMACHILD,MOD 0.00 

350 0 0 DMTS,ASTHMACHILD,MOD 0.00 

400 0 0 DMTS,ASTHMACHILD,MOD 0.00 

450 0 0 DMTS,ASTHMACHILD,MOD 0.00 

500 0 0 DMTS,ASTHMACHILD,MOD 0.00 

550 0 0 DMTS,ASTHMACHILD,MOD 0.00 

600 0 0 DMTS,ASTHMACHILD,MOD 0.00 

650 0 0 DMTS,ASTHMACHILD,MOD 0.00 

700 0 0 DMTS,ASTHMACHILD,MOD 0.00 

750 0 0 DMTS,ASTHMACHILD,MOD 0.00 

800 0 0 DMTS,ASTHMACHILD,MOD 0.00 
B-55

Table B.4-10. APEX estimated SO2 exposures in Greene County (99th %iIe, 100 

ppb alternative standard scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) 

Number 

of 

Person 

Days 

Number 

of 

Persons Subpopulation 

Fraction of 

Total 

Population 

0 3218000 21262 DMTS,ASTHMA,MOD 0.97 

50 359 229 DMTS,ASTHMA,MOD 0.01 

100 18 13 DMTS,ASTHMA,MOD 0.00 

150 0 0 DMTS,ASTHMA,MOD 0.00 

200 0 0 DMTS,ASTHMA,MOD 0.00 

250 0 0 DMTS,ASTHMA,MOD 0.00 

300 0 0 DMTS,ASTHMA,MOD 0.00 

350 0 0 DMTS,ASTHMA,MOD 0.00 

400 0 0 DMTS,ASTHMA,MOD 0.00 

450 0 0 DMTS,ASTHMA,MOD 0.00 

500 0 0 DMTS,ASTHMA,MOD 0.00 

550 0 0 DMTS,ASTHMA,MOD 0.00 

600 0 0 DMTS,ASTHMA,MOD 0.00 

650 0 0 DMTS,ASTHMA,MOD 0.00 

700 0 0 DMTS,ASTHMA,MOD 0.00 

750 0 0 DMTS,ASTHMA,MOD 0.00 

800 0 0 DMTS,ASTHMA,MOD 0.00 

0 1821000 7280 DMTS,ASTHMACHILD,MOD 1.00 

50 229 139 DMTS,ASTHMACHILD,MOD 0.02 

100 9 4 DMTS,ASTHMACHILD,MOD 0.00 

150 0 0 DMTS,ASTHMACHILD,MOD 0.00 

200 0 0 DMTS,ASTHMACHILD,MOD 0.00 

250 0 0 DMTS,ASTHMACHILD,MOD 0.00 

300 0 0 DMTS,ASTHMACHILD,MOD 0.00 

350 0 0 DMTS,ASTHMACHILD,MOD 0.00 

400 0 0 DMTS,ASTHMACHILD,MOD 0.00 

450 0 0 DMTS,ASTHMACHILD,MOD 0.00 

500 0 0 DMTS,ASTHMACHILD,MOD 0.00 

550 0 0 DMTS,ASTHMACHILD,MOD 0.00 

600 0 0 DMTS,ASTHMACHILD,MOD 0.00 

650 0 0 DMTS,ASTHMACHILD,MOD 0.00 

700 0 0 DMTS,ASTHMACHILD,MOD 0.00 

750 0 0 DMTS,ASTHMACHILD,MOD 0.00 

800 0 0 DMTS,ASTHMACHILD,MOD 0.00 
B-56

Table B.4-11. APEX estimated SO2 exposures in Greene County (99th %iIe, 150 

ppb alternative standard scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) 

Number 

of 

Person 

Days 

Number 

of 

Persons Subpopulation 

Fraction of 

Total 

Population 

0 3218000 21262 DMTS,ASTHMA,MOD 0.97

50 1327 811 DMTS,ASTHMA,MOD 0.04

100 139 103 DMTS,ASTHMA,MOD 0.00

150 18 13 DMTS,ASTHMA,MOD 0.00

200 9 9 DMTS,ASTHMA,MOD 0.00

250 0 0 DMTS,ASTHMA,MOD 0.00

300 0 0 DMTS,ASTHMA,MOD 0.00

350 0 0 DMTS,ASTHMA,MOD 0.00

400 0 0 DMTS,ASTHMA,MOD 0.00

450 0 0 DMTS,ASTHMA,MOD 0.00

500 0 0 DMTS,ASTHMA,MOD 0.00

550 0 0 DMTS,ASTHMA,MOD 0.00

600 0 0 DMTS,ASTHMA,MOD 0.00

650 0 0 DMTS,ASTHMA,MOD 0.00

700 0 0 DMTS,ASTHMA,MOD 0.00

750 0 0 DMTS,ASTHMA,MOD 0.00

800 0 0 DMTS,ASTHMA,MOD 0.00

0 1821000 7280 DMTS,ASTHMACHILD,MOD 1.00

50 798 466 DMTS,ASTHMACHILD,MOD 0.06

100 67 49 DMTS,ASTHMACHILD,MOD 0.01

150 9 4 DMTS,ASTHMACHILD,MOD 0.00

200 4 4 DMTS,ASTHMACHILD,MOD 0.00

250 0 0 DMTS,ASTHMACHILD,MOD 0.00

300 0 0 DMTS,ASTHMACHILD,MOD 0.00

350 0 0 DMTS,ASTHMACHILD,MOD 0.00

400 0 0 DMTS,ASTHMACHILD,MOD 0.00

450 0 0 DMTS,ASTHMACHILD,MOD 0.00

500 0 0 DMTS,ASTHMACHILD,MOD 0.00

550 0 0 DMTS,ASTHMACHILD,MOD 0.00

600 0 0 DMTS,ASTHMACHILD,MOD 0.00

650 0 0 DMTS,ASTHMACHILD,MOD 0.00

700 0 0 DMTS,ASTHMACHILD,MOD 0.00

750 0 0 DMTS,ASTHMACHILD,MOD 0.00

800 0 0 DMTS,ASTHMACHILD,MOD 0.00
B-57

Table B.4-12. APEX estimated SO2 exposures in Greene County (99th %iIe, 200 

ppb alternative standard scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) 

Number 

of 

Person 

Days 

Number 

of 

Persons Subpopulation 

Fraction of 

Total 

Population 

0 3218000 21262 DMTS,ASTHMA,MOD 0.97 

50 2779 1600 DMTS,ASTHMA,MOD 0.07 

100 359 229 DMTS,ASTHMA,MOD 0.01 

150 94 72 DMTS,ASTHMA,MOD 0.00 

200 18 13 DMTS,ASTHMA,MOD 0.00 

250 13 13 DMTS,ASTHMA,MOD 0.00 

300 0 0 DMTS,ASTHMA,MOD 0.00 

350 0 0 DMTS,ASTHMA,MOD 0.00 

400 0 0 DMTS,ASTHMA,MOD 0.00 

450 0 0 DMTS,ASTHMA,MOD 0.00 

500 0 0 DMTS,ASTHMA,MOD 0.00 

550 0 0 DMTS,ASTHMA,MOD 0.00 

600 0 0 DMTS,ASTHMA,MOD 0.00 

650 0 0 DMTS,ASTHMA,MOD 0.00 

700 0 0 DMTS,ASTHMA,MOD 0.00 

750 0 0 DMTS,ASTHMA,MOD 0.00 

800 0 0 DMTS,ASTHMA,MOD 0.00 

0 1821000 7280 DMTS,ASTHMACHILD,MOD 1.00 

50 1757 955 DMTS,ASTHMACHILD,MOD 0.13 

100 229 139 DMTS,ASTHMACHILD,MOD 0.02 

150 54 45 DMTS,ASTHMACHILD,MOD 0.01 

200 9 4 DMTS,ASTHMACHILD,MOD 0.00 

250 4 4 DMTS,ASTHMACHILD,MOD 0.00 

300 0 0 DMTS,ASTHMACHILD,MOD 0.00 

350 0 0 DMTS,ASTHMACHILD,MOD 0.00 

400 0 0 DMTS,ASTHMACHILD,MOD 0.00 

450 0 0 DMTS,ASTHMACHILD,MOD 0.00 

500 0 0 DMTS,ASTHMACHILD,MOD 0.00 

550 0 0 DMTS,ASTHMACHILD,MOD 0.00 

600 0 0 DMTS,ASTHMACHILD,MOD 0.00 

650 0 0 DMTS,ASTHMACHILD,MOD 0.00 

700 0 0 DMTS,ASTHMACHILD,MOD 0.00 

750 0 0 DMTS,ASTHMACHILD,MOD 0.00 

800 0 0 DMTS,ASTHMACHILD,MOD 0.00 
B-58

Table B.4-13. APEX estimated SO2 exposures in Greene County (99th %iIe, 250 

ppb alternative standard scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) 

Number 

of 

Person 

Days 

Number 

of 

Persons Subpopulation 

Fraction of 

Total 

Population 

0 3218000 21262 DMTS,ASTHMA,MOD 0.97 

50 4918 2726 DMTS,ASTHMA,MOD 0.12 

100 780 484 DMTS,ASTHMA,MOD 0.02 

150 202 143 DMTS,ASTHMA,MOD 0.01 

200 63 54 DMTS,ASTHMA,MOD 0.00 

250 18 13 DMTS,ASTHMA,MOD 0.00 

300 13 13 DMTS,ASTHMA,MOD 0.00 

350 4 4 DMTS,ASTHMA,MOD 0.00 

400 0 0 DMTS,ASTHMA,MOD 0.00 

450 0 0 DMTS,ASTHMA,MOD 0.00 

500 0 0 DMTS,ASTHMA,MOD 0.00 

550 0 0 DMTS,ASTHMA,MOD 0.00 

600 0 0 DMTS,ASTHMA,MOD 0.00 

650 0 0 DMTS,ASTHMA,MOD 0.00 

700 0 0 DMTS,ASTHMA,MOD 0.00 

750 0 0 DMTS,ASTHMA,MOD 0.00 

800 0 0 DMTS,ASTHMA,MOD 0.00 

0 1821000 7280 DMTS,ASTHMACHILD,MOD 1.00 

50 3201 1659 DMTS,ASTHMACHILD,MOD 0.23 

100 457 256 DMTS,ASTHMACHILD,MOD 0.04 

150 117 76 DMTS,ASTHMACHILD,MOD 0.01 

200 40 31 DMTS,ASTHMACHILD,MOD 0.00 

250 9 4 DMTS,ASTHMACHILD,MOD 0.00 

300 4 4 DMTS,ASTHMACHILD,MOD 0.00 

350 4 4 DMTS,ASTHMACHILD,MOD 0.00 

400 0 0 DMTS,ASTHMACHILD,MOD 0.00 

450 0 0 DMTS,ASTHMACHILD,MOD 0.00 

500 0 0 DMTS,ASTHMACHILD,MOD 0.00 

550 0 0 DMTS,ASTHMACHILD,MOD 0.00 

600 0 0 DMTS,ASTHMACHILD,MOD 0.00 

650 0 0 DMTS,ASTHMACHILD,MOD 0.00 

700 0 0 DMTS,ASTHMACHILD,MOD 0.00 

750 0 0 DMTS,ASTHMACHILD,MOD 0.00 

800 0 0 DMTS,ASTHMACHILD,MOD 0.00 
B-59

Table B.4-14. APEX estimated SO2 exposures in Greene County (98th %iIe, 200 

ppb alternative standard scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) 

Number 

of 

Person 

Days 

Number 

of 

Persons Subpopulation 

Fraction of 

Total 

Population 

0 3218000 21262 DMTS,ASTHMA,MOD 0.97

50 4138 2304 DMTS,ASTHMA,MOD 0.10

100 632 386 DMTS,ASTHMA,MOD 0.02

150 161 117 DMTS,ASTHMA,MOD 0.01

200 45 40 DMTS,ASTHMA,MOD 0.00

250 18 13 DMTS,ASTHMA,MOD 0.00

300 13 13 DMTS,ASTHMA,MOD 0.00

350 0 0 DMTS,ASTHMA,MOD 0.00

400 0 0 DMTS,ASTHMA,MOD 0.00

450 0 0 DMTS,ASTHMA,MOD 0.00

500 0 0 DMTS,ASTHMA,MOD 0.00

550 0 0 DMTS,ASTHMA,MOD 0.00

600 0 0 DMTS,ASTHMA,MOD 0.00

650 0 0 DMTS,ASTHMA,MOD 0.00

700 0 0 DMTS,ASTHMA,MOD 0.00

750 0 0 DMTS,ASTHMA,MOD 0.00

800 0 0 DMTS,ASTHMA,MOD 0.00

0 1821000 7280 DMTS,ASTHMACHILD,MOD 1.00

50 2654 1390 DMTS,ASTHMACHILD,MOD 0.19

100 390 220 DMTS,ASTHMACHILD,MOD 0.03

150 85 58 DMTS,ASTHMACHILD,MOD 0.01

200 27 22 DMTS,ASTHMACHILD,MOD 0.00

250 9 4 DMTS,ASTHMACHILD,MOD 0.00

300 4 4 DMTS,ASTHMACHILD,MOD 0.00

350 0 0 DMTS,ASTHMACHILD,MOD 0.00

400 0 0 DMTS,ASTHMACHILD,MOD 0.00

450 0 0 DMTS,ASTHMACHILD,MOD 0.00

500 0 0 DMTS,ASTHMACHILD,MOD 0.00

550 0 0 DMTS,ASTHMACHILD,MOD 0.00

600 0 0 DMTS,ASTHMACHILD,MOD 0.00

650 0 0 DMTS,ASTHMACHILD,MOD 0.00

700 0 0 DMTS,ASTHMACHILD,MOD 0.00

750 0 0 DMTS,ASTHMACHILD,MOD 0.00

800 0 0 DMTS,ASTHMACHILD,MOD 0.00
B-60

Table B.4-15. APEX estimated SO2 exposures in St. Louis (as is air quality 

scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) Subpopulation 

Number 

of 

Persons 

Number 

of Person 

Days 

Fraction of 

Total 

Population 

0 DMTS,ASTHMA,MOD 102436 16677000 0.97 

50 DMTS,ASTHMA,MOD 24405 44100 0.23 

100 DMTS,ASTHMA,MOD 3866 4631 0.04 

150 DMTS,ASTHMA,MOD 789 896 0.01 

200 DMTS,ASTHMA,MOD 229 244 0.00 

250 DMTS,ASTHMA,MOD 69 69 0.00 

300 DMTS,ASTHMA,MOD 23 23 0.00 

350 DMTS,ASTHMA,MOD 8 8 0.00 

400 DMTS,ASTHMA,MOD 8 8 0.00 

450 DMTS,ASTHMA,MOD 0 0 0.00 

500 DMTS,ASTHMA,MOD 0 0 0.00 

550 DMTS,ASTHMA,MOD 0 0 0.00 

600 DMTS,ASTHMA,MOD 0 0 0.00 

650 DMTS,ASTHMA,MOD 0 0 0.00 

700 DMTS,ASTHMA,MOD 0 0 0.00 

750 DMTS,ASTHMA,MOD 0 0 0.00 

800 DMTS,ASTHMA,MOD 0 0 0.00 

0 DMTS,ASTHMACHILD,MOD 41714 10560000 1.00 

50 DMTS,ASTHMACHILD,MOD 16938 32800 0.41 

100 DMTS,ASTHMACHILD,MOD 2776 3357 0.07 

150 DMTS,ASTHMACHILD,MOD 575 651 0.01 

200 DMTS,ASTHMACHILD,MOD 160 176 0.00 

250 DMTS,ASTHMACHILD,MOD 39 38 0.00 

300 DMTS,ASTHMACHILD,MOD 16 15 0.00 

350 DMTS,ASTHMACHILD,MOD 0 0 0.00 

400 DMTS,ASTHMACHILD,MOD 0 0 0.00 

450 DMTS,ASTHMACHILD,MOD 0 0 0.00 

500 DMTS,ASTHMACHILD,MOD 0 0 0.00 

550 DMTS,ASTHMACHILD,MOD 0 0 0.00 

600 DMTS,ASTHMACHILD,MOD 0 0 0.00 

650 DMTS,ASTHMACHILD,MOD 0 0 0.00 

700 DMTS,ASTHMACHILD,MOD 0 0 0.00 

750 DMTS,ASTHMACHILD,MOD 0 0 0.00 

800 DMTS,ASTHMACHILD,MOD 0 0 0.00 
B-61

Table B.4-16. APEX estimated SO2 exposures in St. Louis (current standard air 

quality scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) Subpopulation 

Number 

of 

Persons 

Number of 

Person Days 

Fraction of 

Total 

Population 

0 DMTS,ASTHMA,MOD 102436 16677000 0.97 

50 DMTS,ASTHMA,MOD 93692 2889400 0.89 

100 DMTS,ASTHMA,MOD 79422 793000 0.75 

150 DMTS,ASTHMA,MOD 63016 316400 0.60 

200 DMTS,ASTHMA,MOD 48211 153990 0.46 

250 DMTS,ASTHMA,MOD 36315 84540 0.34 

300 DMTS,ASTHMA,MOD 26363 49440 0.25 

350 DMTS,ASTHMA,MOD 19278 31700 0.18 

400 DMTS,ASTHMA,MOD 14181 20719 0.13 

450 DMTS,ASTHMA,MOD 10448 14242 0.10 

500 DMTS,ASTHMA,MOD 7853 10060 0.07 

550 DMTS,ASTHMA,MOD 5880 7229 0.06 

600 DMTS,ASTHMA,MOD 4431 5343 0.04 

650 DMTS,ASTHMA,MOD 3336 3972 0.03 

700 DMTS,ASTHMA,MOD 2631 3099 0.02 

750 DMTS,ASTHMA,MOD 1985 2253 0.02 

800 DMTS,ASTHMA,MOD 1556 1747 0.01 

0 DMTS,ASTHMACHILD,MOD 41714 10560000 1.00 

50 DMTS,ASTHMACHILD,MOD 41607 2158300 1.00 

100 DMTS,ASTHMACHILD,MOD 40319 602800 0.97 

150 DMTS,ASTHMACHILD,MOD 36287 239310 0.87 

200 DMTS,ASTHMACHILD,MOD 30504 116260 0.73 

250 DMTS,ASTHMACHILD,MOD 24386 63570 0.58 

300 DMTS,ASTHMACHILD,MOD 18254 36830 0.44 

350 DMTS,ASTHMACHILD,MOD 13539 23507 0.32 

400 DMTS,ASTHMACHILD,MOD 9991 15304 0.24 

450 DMTS,ASTHMACHILD,MOD 7547 10636 0.18 

500 DMTS,ASTHMACHILD,MOD 5658 7420 0.14 

550 DMTS,ASTHMACHILD,MOD 4237 5295 0.10 

600 DMTS,ASTHMACHILD,MOD 3204 3901 0.08 

650 DMTS,ASTHMACHILD,MOD 2376 2851 0.06 

700 DMTS,ASTHMACHILD,MOD 1909 2231 0.05 

750 DMTS,ASTHMACHILD,MOD 1426 1609 0.03 

800 DMTS,ASTHMACHILD,MOD 1111 1240 0.03 
B-62

Table B.4-17. APEX estimated SO2 exposures in St. Louis (99th %ile, 50 ppb air 

quality scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) Subpopulation 

Number 

of 

Persons 

Number 

of Person 

Days 

Fraction of 

Total 

Population

0 DMTS,ASTHMA,MOD 102436 16677000 0.97

50 DMTS,ASTHMA,MOD 14488 21379 0.14

100 DMTS,ASTHMA,MOD 1595 1794 0.02

150 DMTS,ASTHMA,MOD 298 328 0.00

200 DMTS,ASTHMA,MOD 69 69 0.00

250 DMTS,ASTHMA,MOD 16 15 0.00

300 DMTS,ASTHMA,MOD 8 8 0.00

350 DMTS,ASTHMA,MOD 0 0 0.00

400 DMTS,ASTHMA,MOD 0 0 0.00

450 DMTS,ASTHMA,MOD 0 0 0.00

500 DMTS,ASTHMA,MOD 0 0 0.00

550 DMTS,ASTHMA,MOD 0 0 0.00

600 DMTS,ASTHMA,MOD 0 0 0.00

650 DMTS,ASTHMA,MOD 0 0 0.00

700 DMTS,ASTHMA,MOD 0 0 0.00

750 DMTS,ASTHMA,MOD 0 0 0.00

800 DMTS,ASTHMA,MOD 0 0 0.00

0 DMTS,ASTHMACHILD,MOD 41714 10560000 1.00

50 DMTS,ASTHMACHILD,MOD 10229 15835 0.25

100 DMTS,ASTHMACHILD,MOD 1135 1272 0.03

150 DMTS,ASTHMACHILD,MOD 214 237 0.01

200 DMTS,ASTHMACHILD,MOD 39 38 0.00

250 DMTS,ASTHMACHILD,MOD 8 8 0.00

300 DMTS,ASTHMACHILD,MOD 0 0 0.00

350 DMTS,ASTHMACHILD,MOD 0 0 0.00

400 DMTS,ASTHMACHILD,MOD 0 0 0.00

450 DMTS,ASTHMACHILD,MOD 0 0 0.00

500 DMTS,ASTHMACHILD,MOD 0 0 0.00

550 DMTS,ASTHMACHILD,MOD 0 0 0.00

600 DMTS,ASTHMACHILD,MOD 0 0 0.00

650 DMTS,ASTHMACHILD,MOD 0 0 0.00

700 DMTS,ASTHMACHILD,MOD 0 0 0.00

750 DMTS,ASTHMACHILD,MOD 0 0 0.00

800 DMTS,ASTHMACHILD,MOD 0 0 0.00
B-63

Table B.4-18. APEX estimated SO2 exposures in St. Louis (99th %ile, 100 ppb air 

quality scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) Subpopulation 

Number 

of 

Persons 

Number 

of Person 

Days 

Fraction of 

Total 

Population 

0 DMTS,ASTHMA,MOD 102436 16677000 0.97 

50 DMTS,ASTHMA,MOD 48725 158000 0.46 

100 DMTS,ASTHMA,MOD 14488 21379 0.14 

150 DMTS,ASTHMA,MOD 4654 5619 0.04 

200 DMTS,ASTHMA,MOD 1595 1794 0.02 

250 DMTS,ASTHMA,MOD 666 742 0.01 

300 DMTS,ASTHMA,MOD 298 328 0.00 

350 DMTS,ASTHMA,MOD 153 152 0.00 

400 DMTS,ASTHMA,MOD 69 69 0.00 

450 DMTS,ASTHMA,MOD 38 38 0.00 

500 DMTS,ASTHMA,MOD 16 15 0.00 

550 DMTS,ASTHMA,MOD 8 8 0.00 

600 DMTS,ASTHMA,MOD 8 8 0.00 

650 DMTS,ASTHMA,MOD 8 8 0.00 

700 DMTS,ASTHMA,MOD 0 0 0.00 

750 DMTS,ASTHMA,MOD 0 0 0.00 

800 DMTS,ASTHMA,MOD 0 0 0.00 

0 DMTS,ASTHMACHILD,MOD 41714 10560000 1.00 

50 DMTS,ASTHMACHILD,MOD 30703 119350 0.74 

100 DMTS,ASTHMACHILD,MOD 10229 15835 0.25 

150 DMTS,ASTHMACHILD,MOD 3349 4100 0.08 

200 DMTS,ASTHMACHILD,MOD 1135 1272 0.03 

250 DMTS,ASTHMACHILD,MOD 491 551 0.01 

300 DMTS,ASTHMACHILD,MOD 214 237 0.01 

350 DMTS,ASTHMACHILD,MOD 99 99 0.00 

400 DMTS,ASTHMACHILD,MOD 39 38 0.00 

450 DMTS,ASTHMACHILD,MOD 31 31 0.00 

500 DMTS,ASTHMACHILD,MOD 8 8 0.00 

550 DMTS,ASTHMACHILD,MOD 0 0 0.00 

600 DMTS,ASTHMACHILD,MOD 0 0 0.00 

650 DMTS,ASTHMACHILD,MOD 0 0 0.00 

700 DMTS,ASTHMACHILD,MOD 0 0 0.00 

750 DMTS,ASTHMACHILD,MOD 0 0 0.00 

800 DMTS,ASTHMACHILD,MOD 0 0 0.00 
B-64

Table B.4-19. APEX estimated SO2 exposures in St. Louis (99th %ile 150 ppb air 

quality scenario) while at moderate or greater exertion level. 

5-minute 

Exposure 

Level 

(ppb) Subpopulation 

Number 

of 

Persons 

Number 

of Person 

Days 

Fraction of 

Total 

Population

0 DMTS,ASTHMA,MOD 102436 16677000 0.97

50 DMTS,ASTHMA,MOD 68830 429400 0.65

100 DMTS,ASTHMA,MOD 33447 73000 0.32

150 DMTS,ASTHMA,MOD 14488 21379 0.14

200 DMTS,ASTHMA,MOD 6702 8403 0.06

250 DMTS,ASTHMA,MOD 3212 3817 0.03

300 DMTS,ASTHMA,MOD 1595 1794 0.02

350 DMTS,ASTHMA,MOD 844 958 0.01

400 DMTS,ASTHMA,MOD 521 582 0.00

450 DMTS,ASTHMA,MOD 298 328 0.00

500 DMTS,ASTHMA,MOD 198 198 0.00

550 DMTS,ASTHMA,MOD 130 130 0.00

600 DMTS,ASTHMA,MOD 69 69 0.00

650 DMTS,ASTHMA,MOD 38 38 0.00

700 DMTS,ASTHMA,MOD 23 23 0.00

750 DMTS,ASTHMA,MOD 16 15 0.00

800 DMTS,ASTHMA,MOD 8 8 0.00

0 DMTS,ASTHMACHILD,MOD 41714 10560000 1.00

50 DMTS,ASTHMACHILD,MOD 38024 325900 0.91

100 DMTS,ASTHMACHILD,MOD 22721 54890 0.54

150 DMTS,ASTHMACHILD,MOD 10229 15835 0.25

200 DMTS,ASTHMACHILD,MOD 4843 6177 0.12

250 DMTS,ASTHMACHILD,MOD 2323 2767 0.06

300 DMTS,ASTHMACHILD,MOD 1135 1272 0.03

350 DMTS,ASTHMACHILD,MOD 621 705 0.01

400 DMTS,ASTHMACHILD,MOD 376 422 0.01

450 DMTS,ASTHMACHILD,MOD 214 237 0.01

500 DMTS,ASTHMACHILD,MOD 138 137 0.00

550 DMTS,ASTHMACHILD,MOD 76 76 0.00

600 DMTS,ASTHMACHILD,MOD 39 38 0.00

650 DMTS,ASTHMACHILD,MOD 31 31 0.00

700 DMTS,ASTHMACHILD,MOD 16 15 0.00

750 DMTS,ASTHMACHILD,MOD 8 8 0.00

800 DMTS,ASTHMACHILD,MOD 0 0 0.00
B-65

Table B.4-20. APEX estimated SO2 exposures in St. Louis (99th %ile, 200 ppb air 

quality scenario) while at moderate or greater exertion level.

5-minute 

Exposure 

Level 

(ppb) Subpopulation 

Number 

of 

Persons 

Number 

of Person 

Days 

Fraction of 

Total 

Population

0 DMTS,ASTHMA,MOD 102436 16677000 0.97

50 DMTS,ASTHMA,MOD 79775 813700 0.76

100 DMTS,ASTHMA,MOD 48725 158000 0.46

150 DMTS,ASTHMA,MOD 27030 51270 0.26

200 DMTS,ASTHMA,MOD 14488 21379 0.14

250 DMTS,ASTHMA,MOD 8097 10427 0.08

300 DMTS,ASTHMA,MOD 4654 5619 0.04

350 DMTS,ASTHMA,MOD 2707 3198 0.03

400 DMTS,ASTHMA,MOD 1595 1794 0.02

450 DMTS,ASTHMA,MOD 1050 1180 0.01

500 DMTS,ASTHMA,MOD 666 742 0.01

550 DMTS,ASTHMA,MOD 428 458 0.00

600 DMTS,ASTHMA,MOD 298 328 0.00

650 DMTS,ASTHMA,MOD 214 229 0.00

700 DMTS,ASTHMA,MOD 153 152 0.00

750 DMTS,ASTHMA,MOD 107 107 0.00

800 DMTS,ASTHMA,MOD 69 69 0.00

0 DMTS,ASTHMACHILD,MOD 41714 10560000 1.00

50 DMTS,ASTHMACHILD,MOD 40388 618700 0.97

100 DMTS,ASTHMACHILD,MOD 30703 119350 0.74

150 DMTS,ASTHMACHILD,MOD 18690 38210 0.45

200 DMTS,ASTHMACHILD,MOD 10229 15835 0.25

250 DMTS,ASTHMACHILD,MOD 5856 7718 0.14

300 DMTS,ASTHMACHILD,MOD 3349 4100 0.08

350 DMTS,ASTHMACHILD,MOD 1947 2292 0.05

400 DMTS,ASTHMACHILD,MOD 1135 1272 0.03

450 DMTS,ASTHMACHILD,MOD 773 857 0.02

500 DMTS,ASTHMACHILD,MOD 491 551 0.01

550 DMTS,ASTHMACHILD,MOD 314 336 0.01

600 DMTS,ASTHMACHILD,MOD 214 237 0.01

650 DMTS,ASTHMACHILD,MOD 145 160 0.00

700 DMTS,ASTHMACHILD,MOD 99 99 0.00

750 DMTS,ASTHMACHILD,MOD 61 61 0.00

800 DMTS,ASTHMACHILD,MOD 39 38 0.00
B-66

Table B.4-21. APEX estimated SO2 exposures in St. Louis (99th %ile, 250 ppb air 

quality scenario) while at moderate or greater exertion level.

5-minute 

Exposure 

Level 

(ppb) Subpopulation 

Number 

of 

Persons 

Number 

of Person 

Days 

Fraction of 

Total 

Population

0 DMTS,ASTHMA,MOD 102436 16677000 0.97

50 DMTS,ASTHMA,MOD 85784 1276000 0.81

100 DMTS,ASTHMA,MOD 60235 278550 0.57

150 DMTS,ASTHMA,MOD 39121 97390 0.37

200 DMTS,ASTHMA,MOD 23681 42330 0.22

250 DMTS,ASTHMA,MOD 14488 21379 0.14

300 DMTS,ASTHMA,MOD 9180 12037 0.09

350 DMTS,ASTHMA,MOD 5750 7061 0.05

400 DMTS,ASTHMA,MOD 3696 4416 0.04

450 DMTS,ASTHMA,MOD 2452 2843 0.02

500 DMTS,ASTHMA,MOD 1595 1794 0.02

550 DMTS,ASTHMA,MOD 1150 1287 0.01

600 DMTS,ASTHMA,MOD 751 858 0.01

650 DMTS,ASTHMA,MOD 574 643 0.01

700 DMTS,ASTHMA,MOD 405 435 0.00

750 DMTS,ASTHMA,MOD 298 328 0.00

800 DMTS,ASTHMA,MOD 229 244 0.00

0 DMTS,ASTHMACHILD,MOD 41714 10560000 1.00

50 DMTS,ASTHMACHILD,MOD 41147 967000 0.99

100 DMTS,ASTHMACHILD,MOD 35351 210680 0.85

150 DMTS,ASTHMACHILD,MOD 25834 73310 0.62

200 DMTS,ASTHMACHILD,MOD 16477 31530 0.39

250 DMTS,ASTHMACHILD,MOD 10229 15835 0.25

300 DMTS,ASTHMACHILD,MOD 6686 8975 0.16

350 DMTS,ASTHMACHILD,MOD 4138 5166 0.10

400 DMTS,ASTHMACHILD,MOD 2637 3173 0.06

450 DMTS,ASTHMACHILD,MOD 1786 2070 0.04

500 DMTS,ASTHMACHILD,MOD 1135 1272 0.03

550 DMTS,ASTHMACHILD,MOD 849 941 0.02

600 DMTS,ASTHMACHILD,MOD 536 613 0.01

650 DMTS,ASTHMACHILD,MOD 422 475 0.01

700 DMTS,ASTHMACHILD,MOD 298 321 0.01

750 DMTS,ASTHMACHILD,MOD 214 237 0.01

800 DMTS,ASTHMACHILD,MOD 160 176 0.00
B-67

Table B.4-21. APEX estimated SO2 exposures in St. Louis (98th %ile 200 ppb air 

quality scenario) while at moderate or greater exertion level.

5-minute 

Exposure 

Level 

(ppb) Subpopulation 

Number 

of 

Persons 

Number 

of Person 

Days 

Fraction of 

Total 

Population 

0 DMTS,ASTHMA,MOD 102436 16677000 0.97 

50 DMTS,ASTHMA,MOD 84633 1159900 0.80 

100 DMTS,ASTHMA,MOD 57867 249490 0.55 

150 DMTS,ASTHMA,MOD 36682 85910 0.35 

200 DMTS,ASTHMA,MOD 21576 37060 0.20 

250 DMTS,ASTHMA,MOD 12925 18498 0.12 

300 DMTS,ASTHMA,MOD 8014 10304 0.08 

350 DMTS,ASTHMA,MOD 5022 6041 0.05 

400 DMTS,ASTHMA,MOD 3174 3772 0.03 

450 DMTS,ASTHMA,MOD 2023 2299 0.02 

500 DMTS,ASTHMA,MOD 1387 1539 0.01 

550 DMTS,ASTHMA,MOD 913 1035 0.01 

600 DMTS,ASTHMA,MOD 666 742 0.01 

650 DMTS,ASTHMA,MOD 474 512 0.00 

700 DMTS,ASTHMA,MOD 314 344 0.00 

750 DMTS,ASTHMA,MOD 229 252 0.00 

800 DMTS,ASTHMA,MOD 198 198 0.00 

0 DMTS,ASTHMACHILD,MOD 41714 10560000 1.00 

50 DMTS,ASTHMACHILD,MOD 41070 880000 0.98 

100 DMTS,ASTHMACHILD,MOD 34529 188770 0.83 

150 DMTS,ASTHMACHILD,MOD 24576 64600 0.59 

200 DMTS,ASTHMACHILD,MOD 15085 27517 0.36 

250 DMTS,ASTHMACHILD,MOD 9168 13677 0.22 

300 DMTS,ASTHMACHILD,MOD 5774 7596 0.14 

350 DMTS,ASTHMACHILD,MOD 3648 4446 0.09 

400 DMTS,ASTHMACHILD,MOD 2285 2721 0.05 

450 DMTS,ASTHMACHILD,MOD 1464 1655 0.04 

500 DMTS,ASTHMACHILD,MOD 1011 1110 0.02 

550 DMTS,ASTHMACHILD,MOD 675 759 0.02 

600 DMTS,ASTHMACHILD,MOD 491 551 0.01 

650 DMTS,ASTHMACHILD,MOD 352 375 0.01 

700 DMTS,ASTHMACHILD,MOD 222 245 0.01 

750 DMTS,ASTHMACHILD,MOD 160 183 0.00 

800 DMTS,ASTHMACHILD,MOD 138 137 0.00 
B-68

B.5 REFERENCES 

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ATTACHMENT 1. TECHNICAL MEMORANDUM ON 

METEOROLOGICAL DATA PREPARATION FOR AERMOD 

FOR SO2 REA FOR GREENE COUNTY AND ST. LOUIS 

MODELING DOMAINS, YEAR 2002 
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Meteorological data preparation for AERMOD for SO2 REA for Greene County, MO and 

St. Louis, MO 

James Thurman and Roger Brode 

U.S. EPA, OAQPS, AQAD 

Air Quality Modeling Group 

1. Introduction 

National Weather Service (NWS) meteorological data are often used as the source of input 

meteorological data for AERMOD (U. S. EPA, 2004a). For the SO2 Risk and Exposure 

Assessment, two study areas were chosen: Greene County, Missouri, which includes the city of 

Springfield, and St. Louis, Missouri. Tables 1 and 2 list the surface and upper air NWS stations 

chosen for the two areas. Figure 1 shows the relationship between each surface station and its 

paired upper air station. 

For the St. Louis domain, two other stations were also considered: Spirit of St. Louis Airport 

(SUS) and St. Louis Downtown Airport (CPS). SUS and CPS were used in the 1st draft REA 

(U.S. EPA 2008a). The spatial relationship between the St. Louis area stations is shown in 

Figure 2. Preliminary analysis of the three stations for the St. Louis domain revealed that CPS 

and SUS contained significantly more calms and missing hours than STL. It was therefore 

determined that STL would be more representative for the majority of emission sources for the 

St. Louis modeling domain, and would be used for all of the St. Louis modeling. Given the 

distances shown in Figures 2 and 3 between the stations, the choice was not unreasonable. 

Table 1. Surface stations for the SO2 study areas. Latitude and longitude are the best 

approximation coordinates of the meteorological towers. 

Area Station Identifier WMO 

(WBAN) 

Latitude Longi tude Elevation 

(m) 

GMT 

offset 

Greene 

County 

SpringfieldBranson 

Regional 

AP 

SGF 724400 (13995) 37.23528 -93.40028 387 6 

St. Louis Lambert-St. 

Louis 

International 

AP 

STL 724340 (13994) 38.7525 -90.37361 161 6 

Table 2. Upper air stations for the SO2 study areas. 

Area Station Identifier WMO 

(WBAN) 

Latitude Longi tude Elevation 

(m) 

GMT 

offset 

Greene 

County 

SpringfieldBranson 

Regional 

AP 

SGF 724400 (13995) 37.23 -93.40 394 6 

St. Louis LincolnLogan 

County AP, 

IL 

ILX 724340 (4833) 40.15 -89.33 178 6 
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Figure 1. Location of surface stations (red dots) relative to upper air stations (crosses) 

for Greene County and St. Louis, MO. 

A potential concern related to the use of NWS meteorological data for dispersion modeling is the 

often high incidence of calms and variable wind conditions reported for the Automated Surface 

Observing Stations (ASOS) in use at most NWS stations since the mid-1990’s. A variable wind 

observation may include wind speeds up to 6 knots, but the wind direction is reported as missing. 

The AERMOD model currently cannot simulate dispersion under these conditions. To reduce 

the number of calms and missing winds in the surface data for each of the four stations, archived 

one-minute winds for the ASOS stations were used to calculate hourly average wind speed and 

directions, which were used to supplement the standard archive of winds reported for each 

station in the Integrated Surface Hourly (ISH) database. Details regarding this procedure are 

described below. 
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Section 2 describes preparation of the surface and upper data from the ISH database and FSL 

website including the preparation of data and calculation of hourly winds from one-minute 

ASOS data, Section 3 describes AERSURFACE processing for surface characteristics, and 

Section 4 describes the AERMET processing. Section 5 provides a brief analysis of the 

AERMET output for the stations. References are listed in Section 6. 

Figure 2. Distance between STL and SUS and CPS. 
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2. Surface and upper air data preparation 

2.1 Surface data and hourly averaged wind calculations 

One year of surface data for 2002 for each of the stations listed in Table 1 were downloaded 

from the ISH archive at NCDC. Surface data from NWS locations often contain a large number 

of calms and variable winds. This is due to the implementation of the ASOS program to replace 

observer-based data beginning in the mid-1990’s, and the adoption of the METAR standard for 

reporting NWS observations in July 1996. Currently, the wind speed and direction used to 

represent the hour in AERMOD is based on a single two-minute average, usually reported about 

10 minutes before the hour. The METAR system reports winds of less than three knots as calm 

(coded as 0 knots), and winds up to six knots will be reported as variable when the variation in 

the 2-minute wind direction is more than 60 degrees. This variable wind is reported as a nonzero wind speed with a missing wind direction. The number of calms and variable winds can 

influence concentration calculations in AERMOD because concentrations are not calculated for 

calms or variable wind hours. Significant numbers of calm and variable hours may compromise 

the representativeness of NWS surface data for AERMOD applications. This is especially of 

concern for applications involving low-level releases since the worst-case dispersion conditions 

for such sources are associated with low wind speeds, and the hours being discarded as calm or 

variable are biased toward this condition. 

Recently, NCDC began archiving the two-minute average wind speeds for each minute of the 

hour for most ASOS stations for public access. These values have not been subjected to the 

METAR coding for calm and variable winds. Recent work in AQMG has focused on utilizing 

these 1 minute winds to calculate hourly average winds to reduce the number of calms and 

variable winds for a given station and year. For data input into AERMOD, one minute winds for 

SGF and STL were used to calculate hourly average winds for 2002 (the 1-minute ASOS wind 

data were not available for SUS or CPS for 2002). These hourly average winds are input to 

AERMET and replace the winds reported for the hour from the ISH dataset. Following is the 

methodology used to calculate the hourly average winds for this application: 

One minute data files are monthly, so each month for 2002 was downloaded. 

1. Each line of the data file was read and QA performed on the format of the line to check if 

the line is valid data line. Currently, the one minute data files loosely follow a fixed 

format, but there are numerous exceptions. The program performed several checks on 

the line to ensure that wind direction and wind speed were in the correct general location. 

If a minute was listed twice, the second line for that minute was assumed to be the correct 

line. In the files, wind directions were recorded at the nearest whole degree and wind 

speed to the nearest whole knot. 

2. If the reported wind speed was less than 2 knots, the wind speed was reset to 1 knot. This 

was done because anything less than 2 knots was considered below the instrument 

threshold (if the anemometer is not a sonic anemometer, which was the case for SGF and 

STL for 2002). This generally conforms to the meteorological monitoring guidance 

recommendation of applying a wind speed of one half the threshold value to each wind 
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sample below threshold when processing samples to obtain hourly averages. At the same 

time, the x- and y-components of the wind direction were calculated using equations 1 

and 2 below, which are the functions inside the summation of equations 6.2.17 and 6.2.18 

of the meteorological guidance document (U.S. EPA, 2000). The components were only 

calculated for minutes that did not require resetting. 

 

vx  sin (1) 

vy  cos (2) 

where vx and vy are the x- and y-components of the one minute wind direction θ. 

3. For all minutes that passed the QA check in step 1, the wind speeds were converted from 

knots to m/s. 

4. Before calculating hourly averages, the number of valid minutes (those with wind 

directions) was checked for each hour. An hourly average would be calculated if the 

there were at least two valid 2-minute averages reported for the hour. This could be even 

minutes, odd minutes, or a mixture of non-overlapping even and odd minutes. Even 

minutes were given priority over odd. If at least two valid minutes were found, then all 

available (non-overlapping) minutes would be used to calculate hourly averages. The 

most observations that could be used were 30 2-minute values (30 even or 30 odd). 

5. For wind speed averages, all available non-overlapping minutes’ speeds were used, even 

those subject to resets as described in step 2. The hourly wind speed was an arithmetic 

average of the wind speeds used. 

6. For wind directions, the x- and y-components were summed according to equations 

6.2.17 and 6.2.18 of the meteorological monitoring guidance (U.S. EPA, 2000), 

summarized in equations 3 and 4 below with vxi and vyi calculated in equations 1 and 2. 

The hourly wind direction was calculated based on a unit-vector approach, using equation 

6.2.19 of the meteorological monitoring guidance (U.S.EPA, 2000), summarized in 

equation 5. The one minute average wind directions do not use the flow correction as 

shown in equation 6.2.19, since the calculated direction is the direction from which the 

wind was blowing, not the direction in which it is blowing, as shown by the flow 

correction in 6.2.19. Instead, the one minute program corrected for the direction from 

which the wind was blowing. 





N

i

X xi v

N

V

1

1 (3) 





N

i

Y yi v

N

V

1

1 (4) 

CORR V

V Arc

Y

X  



 



   tan (5) 

Where Vx and Vy are the hourly averaged x- and y-components of the wind, θ is the 

hourly averaged wind direction, N is the number of observations used for the hour, and 
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= 180 for Vx > 0 and Vy > 0 or Vx < 0 and Vy > 0 

CORR = 0 for Vx < 0 and Vy < 0 

= 360 for Vx ≥ 0 and Vy < 0 

2.2 Upper air data 

For AERMET processing, an upper air station must be paired with the surface station, as shown 

in Table 2. Upper air data in the Forecast System Laboratory (FSL) format was downloaded 

from the FSL, (currently named Global Systems Division) website, http://www.fsl.noaa.gov/. 

The data period chosen was January 1, 2002 through December 31, 2002 for all times and all 

levels. The selected wind speed units were chosen as tenths of a meter per second. Each station 

was downloaded as a separate file. 
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3. AERSURFACE 

The AERSURFACE tool (U.S. EPA, 2008b) was used to determine surface characteristics 

(albedo, Bowen ratio, and surface roughness) for input to AERMET. Surface characteristics 

were calculated for the location of the ASOS meteorological towers. As noted in the 

AERSURFACE User’s Guide (U.S. EPA, 2008), AERSURFACE should be run for the location 

of the actual meteorological tower to ensure accurate representation of the conditions around the 

site. The approximate locations of the meteorological towers were determined using aerial 

photos and the station history from NCDC. The coordinates used are listed in Table 1. 

A draft version of AERSURFACE (08256) that utilizes 2001 NLCD was used to determine the 

surface characteristics for this application since the 2001 land cover data will be more 

representative of the meteorological data period than the 1992 NLCD data supported by the 

current version of AERSURFACE available on EPA’s SCRAM website. All stations were 

considered “at an airport” for the low, medium, and high intensity developed categories. SGF 

and STL did not have continuous snow cover as outlined in the 1st draft SO2 REA (U.S. EPA, 

2008a). Monthly seasonal assignments did not follow the defaults as outlined in the 

AERSURFACE User’s Guide (U.S. EPA, 2008a) and the monthly seasonal assignments were 

defined as shown in Table 3. Since the default seasonal assignments were not used, the surface 

characteristics were output by month. 

Table 3. Seasonal monthly assignments. 

Station Winter (no snow) Spring Summer Autumn 

SGF December, January, February, 

March 

April, May June, July, August September, October, 

November 

STL December, January, February March, April, May June, July, August September, October, 

November 

Seasonal definitions 

Winter (no snow) Late autumn after frost and harvest, or winter with no snow 

Spring Transitional spring with partial green coverage or short annuals 

Summer Midsummer with lush vegetation 

Autumn Autumn with unharvested cropland 

Moisture conditions (average, dry or wet) for Bowen ratio were based on annual precipitation 

using the methodology outlined in the AERSURFACE User’s Guide (U.S. EPA, 2008b): Years 

in the top 30% of the 30-year precipitation distribution are considered wet. Those in the bottom 

30% of the distribution are dry. Otherwise, a given year is considered average. For the two 

surface stations, the 2007 local climatological database was used to look at 30 years (1978-2007) 

annual precipitation. For SGF, 2002 was considered dry while STL was considered average. 

The ranked 30 year distributions are shown in Table 4 with time series of the annual precipitation 

in Figure 3. 
B-79

Table 4. Annual precipitation (inches) for Springfield and St. Louis. Years in green are 

top 30% of distribution (wettest), years in brown are the bottom 30% of the distribution 

(driest) and years in white are the middle 40%. 2002 is denoted in bold. 30 year 

averages are denoted by yellow rows. 

Springfield St. Louis 

Year 

Precipitation 

(inches) Year 

Precipitation 

(inches) 

1990 63.19 1982 54.97 

1985 56.50 1993 54.76 

1993 55.78 1984 51.65 

1987 55.49 1985 50.73 

1994 49.02 2003 46.06 

1979 48.94 1981 45.52 

1998 48.47 1990 45.09 

1988 48.46 1983 44.80 

1992 48.04 1996 43.67 

1982 47.67 1998 43.62 

1984 45.78 2004 42.27 

2001 45.29 1995 41.68 

1983 45.05 2002 40 .95 

1996 44.86 1987 38.38 

2007 44.27 2005 37.85 

1978 43.95 1978 37.71 

1981 43.72 2000 37.37 

2004 43.23 2001 35.29 

2003 42.61 1986 34.88 

1995 41.86 1994 34.70 

1999 41.53 1999 34.06 

1986 40.19 1988 33.93 

2006 38.87 1992 33.49 

1997 38.48 1991 33.48 

2002 37.82 1997 31.23 

1991 37.59 2007 30.57 

2000 35.36 2006 29.93 

2005 35.32 1979 29.48 

1989 31.50 1989 28.60 

1980 27.36 1980 27.48 

30-year 

average 40.21 

30-year 

average 39.14 
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Figure 3. 30 year time series of annual precipitation (inches) for a) Springfield, and b) 

St. Louis. Annual averages are in red, 30-year averages in blue, and 2002 denoted by 

asterisk. 

AERSURFACE also allows for the surface roughness to be defined by up to 12 sectors. The 

landuse around SGF and STL were analyzed using the NLCD data and aerial photographs. The 

resulting sectors are shown in Figures 4 and 5. 

After determining the moisture conditions and surface roughness sectors, AERSURFACE was 

run for each station with output by month and sector. The resulting surface characteristics were 

input into AERMET stage 3. The surface characteristics are shown in Appendix A. 
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Figure 4. Surface roughness sectors for SGF with a) 2001 NLCD landuse and b) 2003 

aerial photograph. 
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Figure 5. Surface roughness sectors for STL with a) 2001 NLCD landuse and b) 2002 

aerial photograph. 
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4. AERMET 

The meteorological data files for each station (upper air, ISH data, one minute data) were 

processed in AERMET (U.S. EPA, 2004), which includes three “Stages” for processing of 

meteorological data. Stage 1 was used to read in all the data files and perform initial QA. The 

upper air data was processed via the UPPERAIR pathway. The ISH data was processed via the 

SURFACE pathway, and the one minute hourly average winds were processed via the ONSITE 

pathway. Hourly averaged winds were read into AERMET for the one minute hourly average 

winds. For the hourly averaged one minute winds, the threshold was set to 0.01 m/s. The lowest 

wind speeds for SGF and STL, including one minute data, was around 0.54 m/s. 

In Stage 2, the upper air, ISH surface data, and hourly averaged winds were merged together for 

each station. After Stage 2, Stage 3 was run to create the input files for AERMOD. When 

hourly averaged winds were available, those winds would be used for the hour and all other data 

would come from the ISH data (temperature, cloud cover, precipitation, etc.) If no hourly 

averaged winds were available for the hour, all surface data came from the ISH data via the 

SUBNWS keyword in the Stage 3 input file. As noted in Section 3, surface characteristics from 

AERSURFACE are input into Stage 3. The resulting output from Stage 3 were the .SFC and 

.PFL files input into AERMOD. 

An AERMOD run, using a single source and receptor, was used to determine the number of 

calms and missing hours for each station. Missing hours can be due to missing winds, 

temperatures or soundings. Missing hours can also result from variable winds. The number of 

calms and missing hours for each station are shown in Table 5. Also shown in Table 5 are the 

number of calms and missing when using only the ISH winds for surface winds, no hourly 

averaged one minute winds included. Note that including the hourly averaged winds 

dramatically reduces the number of calms and missing hours. 

Table 5. Number of calms and missing hours for each station. Totals reflect the use of 

hourly averaged one minute winds. 

 With hourly 

averaged winds 

Without hourly 

averaged winds 

Station Calms Missing Calms Missing 

SGF 116 135 830 448

STL 67 98 648 401
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5. Analysis 

Wind roses for 2002 for the two stations are shown in Figure 6. For SGF, the wind was 

predominantly from the south and south-southeast. For STL, winds were predominantly from 

the south but strong components of the wind were from the westerly direction (northwest, west, 

and southwest). 

Figure 6. 2002 wind roses after AERMET processing for a) SGF and b) STL. 

For SGF and STL, 2002 was compared against 30-year climatology for precipitation and 

temperature. Precipitation has been discussed in Section 3 (Table 4 and Figure 3). A 

distribution of the annual mean temperatures from 1978 to 2007 is shown in Table 6 with time 

series of mean temperatures shown in Figure 7. For Springfield, 2002 was drier than the 30-year 

average (Table 4 and Figure 3) and about average for the mean temperature (Table 6 and Figure 

7). For St. Louis, the precipitation was slightly above the 30-year average (Table 4 and Figure 3) 

with the mean temperature about one degree above the 30-year average (Table 6 and Figure 7). 
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Table 6. 30-year distribution of mean annual temperatures (Fahrenheit) for Springfield 

and St. Louis. 2002 is denoted in bold. 

Springfield St. Louis 

Year Temperature Year Temperature 

2006 58.9 1991 59.2 

2007 58.1 1990 59.0 

1998 57.9 1998 58.7 

2005 57.9 2006 58.5 

1990 57.8 2007 58.3 

1999 57.6 1987 58.2 

1991 57.5 1999 58.0 

1987 57.3 2005 58.0 

1986 57.2 2002 5 7.9 

1980 57.1 1994 57.7 

1981 57.1 2001 57.7 

1994 57.1 1986 57.6 

1984 56.7 2004 57.6 

2004 56.7 1992 57.2 

2001 56.6 1988 57.0 

1982 56.3 1995 57.0 

1992 56.3 2003 56.5 

1995 56.2 1980 56.4 

2002 5 6.2 1984 56.4 

1983 56.0 2000 56.2 

2000 55.9 1981 56.1 

2003 55.8 1983 55.9 

1988 55.3 1989 55.7 

1985 55.1 1993 55.6 

1993 54.9 1985 55.2 

1997 54.5 1997 55.1 

1996 54.4 1996 54.9 

1989 54.0 1982 54.8 

1978 53.9 1979 54.1 

1979 53.5 1978 53.2 

30-year 

average 56.3 

30-year 

average 56.8 
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Figure 7. 30 year time series of mean annual temperatures (Fahrenheit) for a) 

Springfield, and b) St. Louis. Annual averages are in red, 30-year averages in blue, and 

2002 denoted by asterisk. 
B-87

6. References 

U.S. EPA, 2000: Meteorological Monitoring Guidance for Regulatory Modeling Applications. 

EPA-454/R-99-005. U.S. Environmental Protection Agency, Research Triangle Park, NC 

27711. 

U.S. EPA, 2004: User’s Guide for the AERMOD Meteorological Preprocessor (AERMET). 

EPA-454/B-03-002. U.S. Environmental Protection Agency, Research Triangle Park, 

NC 27711. 

U.S. EPA, 2008a: Risk and Exposure Assessment to Support the Review of the SO2 Primary 

National Ambient Air Quality Standard: First Draft. EPA-452/P-08-003. U.S. 

Environmental Protection Agency, Research Triangle Park, NC 27711. 

U.S. EPA, 2008b. AERSURFACE User’s Guide. EPA-454/B-08-001. U.S. Environmental 

Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/7thconf/aermod/aersurface_userguide.pdf 
B-88

Appendix A. Surface characteristics. 

Tables A1 and A2 show the surface characteristics for Springfield and St. Louis for 2002 based 

on 2001 landuse. 

Table A1. Springfield monthly surface characteristics by sector. 

Month Sector Albedo 

Bowen 

ratio 

Surface 

roughness Month Sector Albedo 

Bowen 

ratio 

Surface 

roughness 

1 0.18 2.06 0.022 1 0.18 1.36 0.102 

2 0.18 2.06 0.021 2 0.18 1.36 0.146 

3 0.18 2.06 0.037 3 0.18 1.36 0.206 January 

4 0.18 2.06 0.022 

July 

4 0.18 1.36 0.147 

1 0.18 2.06 0.022 1 0.18 1.36 0.102 

2 0.18 2.06 0.021 2 0.18 1.36 0.146 

3 0.18 2.06 0.037 3 0.18 1.36 0.206 February 

4 0.18 2.06 0.022 

August 

4 0.18 1.36 0.147 

1 0.18 2.06 0.022 1 0.18 2.06 0.095 

2 0.18 2.06 0.021 2 0.18 2.06 0.145 

3 0.18 2.06 0.037 3 0.18 2.06 0.205 

March 

4 0.18 2.06 0.022 

September 

4 0.18 2.06 0.145 

1 0.15 1.2 0.033 1 0.18 2.06 0.095 

2 0.15 1.2 0.032 2 0.18 2.06 0.145 

3 0.15 1.2 0.055 3 0.18 2.06 0.205 April 

4 0.15 1.2 0.034 

October 

4 0.18 2.06 0.145 

1 0.15 1.2 0.033 1 0.18 2.06 0.095 

2 0.15 1.2 0.032 2 0.18 2.06 0.145 

3 0.15 1.2 0.055 3 0.18 2.06 0.205 May 

4 0.15 1.2 0.034 

November 

4 0.18 2.06 0.145 

1 0.18 1.36 0.102 1 0.18 2.06 0.022 

2 0.18 1.36 0.146 2 0.18 2.06 0.021 

3 0.18 1.36 0.206 3 0.18 2.06 0.037 

June 

4 0.18 1.36 0.147 

December 

4 0.18 2.06 0.022 
B-89

Table A2. St. Louis monthly surface characteristics by sector. 

Month Sector Albedo 

Bowen 

ratio 

Surface 

roughness Month Sector Albedo 

Bowen 

ratio 

Surface 

roughness 

1 0.18 1.02 0.036 1 0.17 0.81 0.048 

2 0.18 1.02 0.077 2 0.17 0.81 0.081 

3 0.18 1.02 0.059 3 0.17 0.81 0.065 

4 0.18 1.02 0.036 4 0.17 0.81 0.046 

January 

5 0.18 1.02 0.041 

July 

5 0.17 0.81 0.051 

1 0.18 1.02 0.036 1 0.17 0.81 0.048 

2 0.18 1.02 0.077 2 0.17 0.81 0.081 

3 0.18 1.02 0.059 3 0.17 0.81 0.065 

4 0.18 1.02 0.036 4 0.17 0.81 0.046 

February 

5 0.18 1.02 0.041 

August 

5 0.17 0.81 0.051 

1 0.16 0.76 0.043 1 0.17 1.02 0.043 

2 0.16 0.76 0.079 2 0.17 1.02 0.079 

3 0.16 0.76 0.063 3 0.17 1.02 0.063 

4 0.16 0.76 0.041 4 0.17 1.02 0.041 

March 

5 0.16 0.76 0.047 

September 

5 0.17 1.02 0.047 

1 0.16 0.76 0.043 1 0.17 1.02 0.043 

2 0.16 0.76 0.079 2 0.17 1.02 0.079 

3 0.16 0.76 0.063 3 0.17 1.02 0.063 

4 0.16 0.76 0.041 4 0.17 1.02 0.041 

April 

5 0.16 0.76 0.047 

October 

5 0.17 1.02 0.047 

1 0.16 0.76 0.043 1 0.17 1.02 0.043 

2 0.16 0.76 0.079 2 0.17 1.02 0.079 

3 0.16 0.76 0.063 3 0.17 1.02 0.063 

4 0.16 0.76 0.041 4 0.17 1.02 0.041 

May 

5 0.16 0.76 0.047 

November 

5 0.17 1.02 0.047 

1 0.17 0.81 0.048 1 0.18 1.02 0.036 

2 0.17 0.81 0.081 2 0.18 1.02 0.077 

3 0.17 0.81 0.065 3 0.18 1.02 0.059 

4 0.17 0.81 0.046 4 0.18 1.02 0.036 

June 

5 0.17 0.81 0.051 

December 

5 0.18 1.02 0.041 
B-90

ATTACHMENT 2. TECHNICAL MEMORANDUM ON THE 

ANALYSIS OF NHIS ASTHMA PREVALENCE DATA 
B-91

DRAFT MEMORANDUM 

To: John Langstaff 

From: Jonathan Cohen, Arlene Rosenbaum 

Date: September 30, 2005 

Re: EPA 68D01052, Work Assignment 3-08. Analysis of NHIS Asthma Prevalence 

Data 

This memorandum describes our analysis of children’s asthma prevalence data from the National 

Health Interview Survey (NHIS) for 2003. Asthma prevalence rates for children aged 0 to 17 

years were calculated for each age, gender, and region. The regions defined by NHIS are 

“Midwest,” “Northeast,” “South,” and “West.” For this project, asthma prevalence was defined 

as the probability of a Yes response to the question CASHMEV: “Ever been told that ... had 

asthma?” among those that responded Yes or No to this question. The responses were weighted 

to take into account the complex survey design of the NHIS survey. Standard errors and 

confidence intervals for the prevalence were calculated using a logistic model, taking into 

account the survey design. Prevalence curves showing the variation of asthma prevalence 

against age for a given gender and region were plotted. A scatterplot smoothing technique using 

the LOESS smoother was applied to smooth the prevalence curves and compute the standard 

errors and confidence intervals for the smoothed prevalence estimates. Logistic analysis of the 

prevalence curves shows statistically significant differences in prevalence by gender and by 

region. Therefore we did not combine the prevalence rates for different genders or regions. 

Logistic Models

NHIS survey data for 2003 were provided by EPA. One obvious approach to calculate 

prevalence rates and their uncertainties for a given gender, region, and age is to calculate the 

proportion of Yes responses among the Yes and No responses for that demographic group, 

weighting each response by the survey weight. Although that approach was initially used, two 

problems are that the distributions of the estimated prevalence rates are not well approximated by 

normal distributions, and that the estimated confidence intervals based on the normal 

approximation often extend outside the [0, 1] interval. A better approach is to use a logistic 

transformation and fit a model of the form: 

Prob (asthma) = exp(beta) / (1 + exp(beta) ), 
B-92

where beta may depend on the explanatory variables for age, gender, or region. This is 

equivalent to the model: 

 Beta = logit {prob (asthma) } = log { prob (asthma) / [1 – prob (asthma)] }. 

The distribution of the estimated values of beta is more closely approximated by a normal 

distribution than the distribution of the corresponding estimates of prob (asthma). By applying a 

logit transformation to the confidence intervals for beta, the corresponding confidence intervals 

for prob (asthma) will always be inside [0, 1]. Another advantage of the logistic modeling is that 

it can be used to compare alternative statistical models, such as models where the prevalence 

probability depends upon age, region, and gender, or on age and region but not gender. 

A variety of logistic models for asthma prevalence were fit and compared, where the transformed 

probability variable beta is a given function of age, gender, and region. SAS’s 

SURVEYLOGISTIC procedure was used to fit the logistic models, taking into account the NHIS 

survey weights and survey design (stratification and clustering). 

The following Table G-1 lists the models fitted and their log-likelihood goodness-of-fit 

measures. 16 models were fitted. The Strata column lists the four possible stratifications: no 

stratification, by gender, by region, by region and gender. For example, “4. region, gender” 

means that separate prevalence estimates were made for each combination of region and gender. 

As another example, “2. gender” means that separate prevalence estimates were made for each 

gender, so that for each gender, the prevalence is assumed to be the same for each region. The 

prevalence estimates are independently calculated for each stratum. 

Table G-1. Alternative logistic models for asthma prevalence. 

Model Description Strata - 2 Log Likelihood DF

1 1. logit(prob) = linear in age 1. none 54168194.62 2

2 1. logit(prob) = linear in age 2. gender 53974657.17 4

3 1. logit(prob) = linear in age 3. region 54048602.57 8

4 1. logit(prob) = linear in age 4. region, gender 53837594.97 16

5 2. logit(prob) = quadratic in age 1. none 53958021.20 3

6 2. logit(prob) = quadratic in age 2. gender 53758240.99 6

7 2. logit(prob) = quadratic in age 3. region 53818198.13 12

8 2. logit(prob) = quadratic in age 4. region, gender 53593569.84 24

9 3. logit(prob) = cubic in age 1. none 53849072.76 4

10 3. logit(prob) = cubic in age 2. gender 53639181.24 8

11 3. logit(prob) = cubic in age 3. region 53694710.66 16

12 3. logit(prob) = cubic in age 4. region, gender 53441122.98 32

13 4. logit(prob) = f(age) 1. none 53610093.48 18
B-93

Model Description Strata - 2 Log Likelihood DF

14 4. logit(prob) = f(age) 2. gender 53226610.02 36

15 4. logit(prob) = f(age) 3. region 53099749.33 72

16 4. logit(prob) = f(age) 4. region, gender 52380000.19 144

The Description column describes how beta depends upon the age: 

 Linear in age: Beta =  +  × age, where  and  vary with the strata. 

Quadratic in age: Beta =  +  × age +  × age2

 where   and  vary with the 

strata. 

Cubic in age: Beta =  +  × age +  × age2

 +  × age3 where  , , and  vary with the 

strata. 

f(age) Beta = arbitrary function of age, with different functions for different strata 

The category f(age) is equivalent to making age one of the stratification variables, and is also 

equivalent to making beta a polynomial of degree 16 in age (since the maximum age for children 

is 17), with coefficients that may vary with the strata. 

The fitted models are listed in order of complexity, where the simplest model (1) is an 

unstratified linear model in age and the most complex model (16) has a prevalence that is an 

arbitrary function of age, gender, and region. Model 16 is equivalent to calculating independent 

prevalence estimates for each of the 144 combinations of age, gender, and region. 

Table G-1 also includes the -2 Log Likelihood, a goodness-of-fit measure, and the degrees of 

freedom, DF, which is the total number of estimated parameters. Two models can be compared 

using their -2 Log Likelihood values; lower values are preferred. If the first model is a special 

case of the second model, then the approximate statistical significance of the first model is 

estimated by comparing the difference in the -2 Log Likelihood values with a chi-squared 

random variable with r degrees of freedom, where r is the difference in the DF. This is a 

likelihood ratio test. For all pairs of models from Table G-1, all the differences are at least 

70,000 and the likelihood ratios are all extremely statistically significant at levels well below 5 

percent. Therefore the model 16 is clearly preferred and was used to model the prevalences. 

The SURVEYLOGISTIC model predictions are tabulated in Table G-2 below and plotted in 

Figures 1 and 3 below. Also shown in Table G-2 and in Figures 2 and 4 are results for smoothed 

curves calculated using a LOESS scatterplot smoother, as discussed below. 

The SURVEYLOGISTIC procedure produces estimates of the beta values and their 95 % 

confidence intervals for each combination of age, region, and gender. Applying the inverse logit 

transformation, 

Prob (asthma) = exp( beta) / (1 + exp(beta) ), 

converted the beta values and 95 % confidence intervals into predictions and 95 % confidence 

intervals for the prevalence, as shown in Table G-2 and Figures 1 and 3. The standard error for 

the prevalence was estimated as 
B-94

Std Error {Prob (asthma)} = Std Error (beta) × exp(- beta) / (1 + exp(beta) )2

, 

which follows from the delta method (a first order Taylor series approximation). 

Loess Smoother

The estimated prevalence curves shows that the prevalence is not a smooth function of age. The 

linear, quadratic, and cubic functions of age modeled by SURVEYLOGISTIC were one strategy 

for smoothing the curves, but they did not provide a good fit to the data. One reason for this 

might be due to the attempt to fit a global regression curve to all the age groups, which means 

that the predictions for age A are affected by data for very different ages. We instead chose to 

use a local regression approach that separately fits a regression curve to each age A and its 

neighboring ages, giving a regression weight of 1 to the age A, and lower weights to the 

neighboring ages using a tri-weight function: 

 Weight = {1 – [ |age – A| / q ] 3

}, where | age – A| <= q. 

The parameter q defines the number of points in the neighborhood of the age a. Instead of calling 

q the smoothing parameter, SAS defines the smoothing parameter as the proportion of points in 

each neighborhood. We fitted a quadratic function of age to each age neighborhood, separately 

for each gender and region combination. We fitted these local regression curves to the beta 

values, the logits of the asthma prevalence estimates, and then converted them back to estimated 

prevalence rates by applying the inverse logit function exp(beta) / (1 + exp(beta) ). In addition to 

the tri-weight variable, each beta value was assigned a weight of 

1 / [std error (beta)]2

, to account for their uncertainties. 

The SAS LOESS procedure was applied to estimate smoothed curves for beta, the logit of the 

prevalence, as a function of age, separately for each region and gender. We fitted curves using 

the choices 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 for the smoothing parameter in an effort to 

determine the optimum choice based on various regression diagnostics.3,4 

 

3 Two outlier cases were adjusted to avoid wild variations in the “smoothed” curves: For the West region, males, 

age 0, there were 97 children surveyed that all gave No answers to the asthma question, leading to an estimated 

value of -15.2029 for beta with a standard error of 0.14. For the Northeast region, females, age 0, there were 29 

children surveyed that all gave No answers to the asthma question, leading to an estimated value of -15.2029 for 

beta with a standard error of 0.19. In both cases the raw probability of asthma equals zero, so the corresponding 

estimated beta would be negative infinity, but SAS’s software gives -15.2029 instead. To reduce the impact of these 

outlier cases, we replaced their estimated standard errors by 4, which is approximately four times the maximum 

standard error for all other region, gender, and age combinations. 

4 With only 18 points, a smoothing parameter of 0.2 cannot be used because the weight function assigns zero 

weights to all ages except age A, and a quadratic model cannot be uniquely fitted to a single value. A smoothing 

parameter of 0.3 also cannot be used because that choice assigns a neighborhood of 5 points only (0.3 × 18 = 5, 

rounded down), of which the two outside ages have assigned weight zero, making the local quadratic model fit 

exactly at every point except for the end points (ages 0, 1, 16 and 17). Usually one uses a smoothing parameter 

below one so that not all the data are used for the local regression at a given x value. 
B-95

Quantities predicted in these smoothing parameter tests were the predicted value, standard error, 

confidence interval lower bound and confidence interval upper bound for the betas, and the 

corresponding values for the prevalence rates. 

The polygonal curves joining values for different ages show the predicted values with vertical 

lines indicating the confidence intervals in Figures 3 and 4 for smoothing parameters 0 (i.e., no 

smoothing) and 0.5, respectively. Note that the confidence intervals are not symmetric about the 

predicted values because of the inverse logit transformation. 

Note that in our application of LOESS, we used weights of 1 / [std error (beta)] 2

, so that 

2

 = 1 

for this application. The LOESS procedure estimates 

2

 from the weighted sum of squares. Since 

in our application we assume 

2

 = 1, we multiplied the estimated standard errors by 1 / 

estimated , and adjusted the widths of the confidence intervals by the same factor. 

Additionally, because the true value of  equals 1, the best choices of smoothing parameter 

should give residual standard errors close to one. Using this criterion the best choice varies with 

gender and region between smoothing parameters 0.4 (3 cases), 0.5 (2 cases), 0.6 (1 case), and 

0.7 (1 case). 

 As a further regression diagnostic the residual errors from the LOESS model were divided by 

std error (beta) to make their variances approximately constant. These approximately studentized 

residuals, ‘student,’ should be approximately normally distributed with a mean of zero and a 

variance of 

2

 = 1. To test this assumption, normal probability plots of the residuals were 

created for each smoothing parameter, combining all the studentized residuals across genders, 

regions, and ages. The plots for smoothing parameters seem to be equally straight for each 

smoothing parameter. 

The final regression diagnostic is a plot of the studentized residuals against the smoothed beta 

values. Ideally there should be no obvious pattern and an average studentized residual close to 

zero. The plots indeed showed no unusual patterns, and the results for smoothing parameters 0.5 

and 0.6 seem to showed a fitted LOESS close to the studentized residual equals zero line. 

The regression diagnostics suggested the choice of smoothing parameter as 0.4 or 0.5. Normal 

probability plots did not suggest any preferred choices. The plots of residuals against smoothed 

predictions suggest the choices of 0.5 or 0.6. We therefore chose the final value of 0.5. These 

predictions, standard errors, and confidence intervals are presented in tabular form below as 

Table G-2. 
B-96

region=Midwest

gender Female Male

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B-98

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B-104

Table G-2. Raw and smoothed prevalence rates, with confidence intervals, by region, 

gender, and age. 

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

1 Midwest Female 0 No 0.04161 0.02965 0.01001 0.15717

2 Midwest Female 0 Yes 0.06956 0.03574 0.02143 0.20330

3 Midwest Female 1 No 0.10790 0.04254 0.04840 0.22336

4 Midwest Female 1 Yes 0.07078 0.01995 0.03736 0.13008

5 Midwest Female 2 No 0.05469 0.02578 0.02131 0.13325

6 Midwest Female 2 Yes 0.07324 0.01778 0.04228 0.12395

7 Midwest Female 3 No 0.06094 0.03474 0.01936 0.17579

8 Midwest Female 3 Yes 0.07542 0.01944 0.04205 0.13163

9 Midwest Female 4 No 0.09049 0.03407 0.04233 0.18298

10 Midwest Female 4 Yes 0.08100 0.02163 0.04417 0.14393

11 Midwest Female 5 No 0.08463 0.03917 0.03317 0.19942

12 Midwest Female 5 Yes 0.09540 0.02613 0.05106 0.17131

13 Midwest Female 6 No 0.14869 0.08250 0.04643 0.38520

14 Midwest Female 6 Yes 0.09210 0.02854 0.04534 0.17808

15 Midwest Female 7 No 0.04757 0.02927 0.01389 0.15051

16 Midwest Female 7 Yes 0.09032 0.02563 0.04728 0.16571

17 Midwest Female 8 No 0.10444 0.03638 0.05160 0.19997

18 Midwest Female 8 Yes 0.08612 0.02181 0.04842 0.14857

19 Midwest Female 9 No 0.09836 0.04283 0.04062 0.21943

20 Midwest Female 9 Yes 0.11040 0.02709 0.06298 0.18643

21 Midwest Female 10 No 0.10916 0.04859 0.04400 0.24600

22 Midwest Female 10 Yes 0.16190 0.03486 0.09838 0.25484

23 Midwest Female 11 No 0.27341 0.06817 0.16112 0.42437

24 Midwest Female 11 Yes 0.19597 0.03920 0.12296 0.29763

25 Midwest Female 12 No 0.10055 0.04780 0.03816 0.23952

26 Midwest Female 12 Yes 0.21214 0.03957 0.13724 0.31309

27 Midwest Female 13 No 0.22388 0.05905 0.12907 0.35959
B-105

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

28 Midwest Female 13 Yes 0.16966 0.03371 0.10716 0.25807

29 Midwest Female 14 No 0.10511 0.04233 0.04637 0.22104

30 Midwest Female 14 Yes 0.14020 0.02603 0.09164 0.20857

31 Midwest Female 15 No 0.12026 0.03805 0.06327 0.21670

32 Midwest Female 15 Yes 0.13341 0.02266 0.09056 0.19226

33 Midwest Female 16 No 0.13299 0.03933 0.07288 0.23037

34 Midwest Female 16 Yes 0.14040 0.02235 0.09764 0.19777

35 Midwest Female 17 No 0.17497 0.04786 0.09970 0.28884

36 Midwest Female 17 Yes 0.16478 0.04037 0.09320 0.27468

37 Midwest Male 0 No 0.06419 0.03612 0.02068 0.18227

38 Midwest Male 0 Yes 0.03134 0.01537 0.01042 0.09046

39 Midwest Male 1 No 0.02824 0.01694 0.00859 0.08879

40 Midwest Male 1 Yes 0.06250 0.01751 0.03321 0.11457

41 Midwest Male 2 No 0.05102 0.02343 0.02040 0.12189

42 Midwest Male 2 Yes 0.10780 0.02078 0.06960 0.16328

43 Midwest Male 3 No 0.18650 0.04864 0.10898 0.30057

44 Midwest Male 3 Yes 0.15821 0.02705 0.10696 0.22775

45 Midwest Male 4 No 0.24649 0.05823 0.15035 0.37686

46 Midwest Male 4 Yes 0.21572 0.03661 0.14543 0.30774

47 Midwest Male 5 No 0.11609 0.04818 0.04973 0.24793

48 Midwest Male 5 Yes 0.17822 0.03525 0.11280 0.27003

49 Midwest Male 6 No 0.14158 0.05280 0.06576 0.27873

50 Midwest Male 6 Yes 0.12788 0.02799 0.07751 0.20375

51 Midwest Male 7 No 0.09726 0.03614 0.04588 0.19448

52 Midwest Male 7 Yes 0.12145 0.02642 0.07391 0.19317

53 Midwest Male 8 No 0.16718 0.05814 0.08134 0.31276

54 Midwest Male 8 Yes 0.12757 0.02700 0.07864 0.20031

55 Midwest Male 9 No 0.13406 0.04783 0.06458 0.25769

56 Midwest Male 9 Yes 0.14718 0.02976 0.09254 0.22603

57 Midwest Male 10 No 0.13986 0.04422 0.07331 0.25050
B-106

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

58 Midwest Male 10 Yes 0.17728 0.02996 0.12020 0.25366

59 Midwest Male 11 No 0.23907 0.05031 0.15449 0.35075

60 Midwest Male 11 Yes 0.18961 0.03044 0.13100 0.26639

61 Midwest Male 12 No 0.13660 0.04784 0.06668 0.25946

62 Midwest Male 12 Yes 0.19487 0.03078 0.13541 0.27221

63 Midwest Male 13 No 0.18501 0.04498 0.11230 0.28945

64 Midwest Male 13 Yes 0.16939 0.02841 0.11528 0.24195

65 Midwest Male 14 No 0.16673 0.05094 0.08886 0.29104

66 Midwest Male 14 Yes 0.16795 0.02631 0.11734 0.23459

67 Midwest Male 15 No 0.14583 0.04241 0.08054 0.24967

68 Midwest Male 15 Yes 0.17953 0.02561 0.12951 0.24347

69 Midwest Male 16 No 0.24965 0.06037 0.15033 0.38489

70 Midwest Male 16 Yes 0.20116 0.03048 0.14187 0.27721

71 Midwest Male 17 No 0.21152 0.06481 0.11131 0.36490

72 Midwest Male 17 Yes 0.23741 0.05816 0.13243 0.38835

73 Northeast Female 0 No 0.00000 0.00000 0.00000 0.00000

74 Northeast Female 0 Yes 0.06807 0.06565 0.00670 0.44174

75 Northeast Female 1 No 0.12262 0.07443 0.03476 0.35164

76 Northeast Female 1 Yes 0.07219 0.03765 0.02088 0.22109

77 Northeast Female 2 No 0.07217 0.03707 0.02561 0.18713

78 Northeast Female 2 Yes 0.07522 0.02212 0.03764 0.14468

79 Northeast Female 3 No 0.08550 0.03991 0.03324 0.20269

80 Northeast Female 3 Yes 0.07709 0.02021 0.04162 0.13840

81 Northeast Female 4 No 0.08704 0.03804 0.03596 0.19592

82 Northeast Female 4 Yes 0.08171 0.02252 0.04269 0.15080

83 Northeast Female 5 No 0.07597 0.03754 0.02801 0.18998

84 Northeast Female 5 Yes 0.11603 0.03012 0.06258 0.20515

85 Northeast Female 6 No 0.19149 0.06960 0.08937 0.36372

86 Northeast Female 6 Yes 0.16106 0.03737 0.09219 0.26629

87 Northeast Female 7 No 0.22034 0.07076 0.11195 0.38783
B-107

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

88 Northeast Female 7 Yes 0.18503 0.04087 0.10844 0.29764

89 Northeast Female 8 No 0.11002 0.05128 0.04241 0.25654

90 Northeast Female 8 Yes 0.17054 0.04039 0.09628 0.28407

91 Northeast Female 9 No 0.17541 0.07488 0.07159 0.36981

92 Northeast Female 9 Yes 0.14457 0.03538 0.08042 0.24618

93 Northeast Female 10 No 0.12980 0.04964 0.05930 0.26087

94 Northeast Female 10 Yes 0.13487 0.03098 0.07799 0.22319

95 Northeast Female 11 No 0.15128 0.05287 0.07366 0.28547

96 Northeast Female 11 Yes 0.14072 0.03068 0.08367 0.22704

97 Northeast Female 12 No 0.11890 0.04426 0.05568 0.23597

98 Northeast Female 12 Yes 0.16615 0.03375 0.10211 0.25877

99 Northeast Female 13 No 0.22638 0.06285 0.12650 0.37158

100 Northeast Female 13 Yes 0.17374 0.03402 0.10861 0.26626

101 Northeast Female 14 No 0.15807 0.05513 0.07694 0.29719

102 Northeast Female 14 Yes 0.15137 0.02946 0.09519 0.23220

103 Northeast Female 15 No 0.07460 0.03409 0.02971 0.17506

104 Northeast Female 15 Yes 0.14564 0.02761 0.09279 0.22127

105 Northeast Female 16 No 0.13603 0.05328 0.06081 0.27686

106 Northeast Female 16 Yes 0.14601 0.03095 0.08805 0.23241

107 Northeast Female 17 No 0.19074 0.07382 0.08451 0.37568

108 Northeast Female 17 Yes 0.15662 0.05374 0.06784 0.32151

109 Northeast Male 0 No 0.03904 0.03829 0.00547 0.23095

110 Northeast Male 0 Yes 0.04768 0.03299 0.00991 0.20023

111 Northeast Male 1 No 0.05533 0.03425 0.01596 0.17461

112 Northeast Male 1 Yes 0.04564 0.01831 0.01850 0.10821

113 Northeast Male 2 No 0.05525 0.03119 0.01781 0.15872

114 Northeast Male 2 Yes 0.05161 0.01505 0.02680 0.09709

115 Northeast Male 3 No 0.03842 0.02923 0.00840 0.15853

116 Northeast Male 3 Yes 0.06766 0.01784 0.03734 0.11955

117 Northeast Male 4 No 0.07436 0.02906 0.03393 0.15522
B-108

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

118 Northeast Male 4 Yes 0.09964 0.02330 0.05859 0.16441

119 Northeast Male 5 No 0.17601 0.04519 0.10393 0.28234

120 Northeast Male 5 Yes 0.14854 0.02948 0.09428 0.22623

121 Northeast Male 6 No 0.23271 0.09319 0.09832 0.45756

122 Northeast Male 6 Yes 0.20731 0.04235 0.12875 0.31640

123 Northeast Male 7 No 0.13074 0.05195 0.05785 0.26922

124 Northeast Male 7 Yes 0.22820 0.04524 0.14338 0.34311

125 Northeast Male 8 No 0.33970 0.08456 0.19726 0.51855

126 Northeast Male 8 Yes 0.22240 0.04298 0.14157 0.33157

127 Northeast Male 9 No 0.13761 0.05024 0.06507 0.26785

128 Northeast Male 9 Yes 0.21238 0.04071 0.13589 0.31617

129 Northeast Male 10 No 0.21785 0.06659 0.11464 0.37465

130 Northeast Male 10 Yes 0.17652 0.03731 0.10824 0.27460

131 Northeast Male 11 No 0.11448 0.05849 0.04005 0.28601

132 Northeast Male 11 Yes 0.16617 0.03516 0.10200 0.25907

133 Northeast Male 12 No 0.17736 0.05489 0.09349 0.31067

134 Northeast Male 12 Yes 0.18279 0.03589 0.11611 0.27581

135 Northeast Male 13 No 0.19837 0.05450 0.11222 0.32635

136 Northeast Male 13 Yes 0.17078 0.03078 0.11288 0.25000

137 Northeast Male 14 No 0.16201 0.04973 0.08618 0.28386

138 Northeast Male 14 Yes 0.17033 0.02889 0.11547 0.24408

139 Northeast Male 15 No 0.11894 0.04584 0.05417 0.24139

140 Northeast Male 15 Yes 0.18246 0.02858 0.12740 0.25438

141 Northeast Male 16 No 0.24306 0.05798 0.14759 0.37326

142 Northeast Male 16 Yes 0.20406 0.03216 0.14187 0.28447

143 Northeast Male 17 No 0.22559 0.06980 0.11748 0.38930

144 Northeast Male 17 Yes 0.24185 0.06066 0.13291 0.39898

145 South Female 0 No 0.02459 0.01116 0.01002 0.05906

146 South Female 0 Yes 0.03407 0.01282 0.01465 0.07723

147 South Female 1 No 0.08869 0.03373 0.04118 0.18067
B-109

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

148 South Female 1 Yes 0.05182 0.01167 0.03127 0.08472

149 South Female 2 No 0.05097 0.02373 0.02012 0.12319

150 South Female 2 Yes 0.07110 0.01386 0.04584 0.10869

151 South Female 3 No 0.08717 0.03240 0.04122 0.17500

152 South Female 3 Yes 0.08759 0.01718 0.05624 0.13394

153 South Female 4 No 0.11010 0.03209 0.06113 0.19035

154 South Female 4 Yes 0.09897 0.01914 0.06387 0.15025

155 South Female 5 No 0.09409 0.02943 0.05015 0.16968

156 South Female 5 Yes 0.11870 0.02157 0.07855 0.17548

157 South Female 6 No 0.15318 0.04317 0.08611 0.25777

158 South Female 6 Yes 0.12150 0.02282 0.07925 0.18182

159 South Female 7 No 0.09608 0.03538 0.04565 0.19105

160 South Female 7 Yes 0.11192 0.02171 0.07204 0.16985

161 South Female 8 No 0.09955 0.03288 0.05111 0.18493

162 South Female 8 Yes 0.09287 0.01897 0.05850 0.14436

163 South Female 9 No 0.07477 0.02719 0.03606 0.14864

164 South Female 9 Yes 0.09117 0.01786 0.05855 0.13929

165 South Female 10 No 0.10602 0.03214 0.05750 0.18732

166 South Female 10 Yes 0.10821 0.02026 0.07077 0.16201

167 South Female 11 No 0.14411 0.04267 0.07875 0.24907

168 South Female 11 Yes 0.13237 0.02251 0.08989 0.19071

169 South Female 12 No 0.12646 0.02981 0.07860 0.19723

170 South Female 12 Yes 0.12346 0.02004 0.08543 0.17519

171 South Female 13 No 0.11376 0.03270 0.06365 0.19510

172 South Female 13 Yes 0.09653 0.01717 0.06458 0.14190

173 South Female 14 No 0.02915 0.01339 0.01174 0.07054

174 South Female 14 Yes 0.09469 0.01619 0.06436 0.13721

175 South Female 15 No 0.11985 0.03357 0.06801 0.20259

176 South Female 15 Yes 0.09988 0.01586 0.06978 0.14099

177 South Female 16 No 0.14183 0.03685 0.08366 0.23028
B-110

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

178 South Female 16 Yes 0.11501 0.01620 0.08365 0.15612

179 South Female 17 No 0.13141 0.03007 0.08280 0.20226

180 South Female 17 Yes 0.14466 0.02946 0.09067 0.22291

181 South Male 0 No 0.01164 0.00852 0.00275 0.04790

182 South Male 0 Yes 0.04132 0.01867 0.01487 0.10956

183 South Male 1 No 0.10465 0.03216 0.05629 0.18635

184 South Male 1 Yes 0.06981 0.01623 0.04125 0.11576

185 South Male 2 No 0.11644 0.03486 0.06353 0.20382

186 South Male 2 Yes 0.10189 0.01672 0.07024 0.14557

187 South Male 3 No 0.10794 0.03253 0.05874 0.19005

188 South Male 3 Yes 0.12852 0.02139 0.08793 0.18405

189 South Male 4 No 0.08480 0.02973 0.04190 0.16410

190 South Male 4 Yes 0.14393 0.02379 0.09861 0.20534

191 South Male 5 No 0.22243 0.04227 0.15052 0.31592

192 South Male 5 Yes 0.16450 0.02373 0.11821 0.22430

193 South Male 6 No 0.13908 0.03392 0.08485 0.21964

194 South Male 6 Yes 0.16386 0.02460 0.11613 0.22617

195 South Male 7 No 0.10695 0.04272 0.04747 0.22347

196 South Male 7 Yes 0.13329 0.02322 0.08951 0.19392

197 South Male 8 No 0.13660 0.03841 0.07712 0.23049

198 South Male 8 Yes 0.13818 0.02276 0.09484 0.19702

199 South Male 9 No 0.15978 0.03742 0.09920 0.24720

200 South Male 9 Yes 0.16839 0.02450 0.12062 0.23012

201 South Male 10 No 0.21482 0.04702 0.13676 0.32086

202 South Male 10 Yes 0.17848 0.02453 0.13021 0.23972

203 South Male 11 No 0.15078 0.03440 0.09492 0.23112

204 South Male 11 Yes 0.16247 0.02224 0.11881 0.21820

205 South Male 12 No 0.13727 0.03260 0.08489 0.21438

206 South Male 12 Yes 0.14480 0.01976 0.10610 0.19453

207 South Male 13 No 0.14136 0.03119 0.09049 0.21409
B-111

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

208 South Male 13 Yes 0.14318 0.01928 0.10537 0.19165

209 South Male 14 No 0.16110 0.03444 0.10438 0.24037

210 South Male 14 Yes 0.15339 0.01875 0.11612 0.19992

211 South Male 15 No 0.16172 0.03519 0.10394 0.24291

212 South Male 15 Yes 0.15088 0.01746 0.11598 0.19398

213 South Male 16 No 0.15836 0.03879 0.09614 0.24974

214 South Male 16 Yes 0.14038 0.01773 0.10533 0.18467

215 South Male 17 No 0.11156 0.02737 0.06810 0.17746

216 South Male 17 Yes 0.12247 0.02596 0.07537 0.19286

217 West Female 0 No 0.00983 0.00990 0.00135 0.06802

218 West Female 0 Yes 0.01318 0.00987 0.00248 0.06700

219 West Female 1 No 0.02367 0.01862 0.00497 0.10522

220 West Female 1 Yes 0.03105 0.01312 0.01204 0.07769

221 West Female 2 No 0.08097 0.03759 0.03170 0.19166

222 West Female 2 Yes 0.05440 0.01482 0.02948 0.09825

223 West Female 3 No 0.07528 0.03851 0.02679 0.19404

224 West Female 3 Yes 0.07444 0.01842 0.04257 0.12701

225 West Female 4 No 0.09263 0.03196 0.04621 0.17703

226 West Female 4 Yes 0.07696 0.02064 0.04194 0.13701

227 West Female 5 No 0.01976 0.01347 0.00513 0.07302

228 West Female 5 Yes 0.07737 0.02123 0.04157 0.13949

229 West Female 6 No 0.15792 0.07301 0.06009 0.35487

230 West Female 6 Yes 0.07298 0.01985 0.03947 0.13107

231 West Female 7 No 0.06955 0.02567 0.03321 0.13989

232 West Female 7 Yes 0.08146 0.01987 0.04691 0.13776

233 West Female 8 No 0.07753 0.02825 0.03731 0.15417

234 West Female 8 Yes 0.09062 0.01994 0.05507 0.14558

235 West Female 9 No 0.13440 0.04481 0.06802 0.24832

236 West Female 9 Yes 0.10215 0.02347 0.06061 0.16709

237 West Female 10 No 0.06573 0.03719 0.02102 0.18736
B-112

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

238 West Female 10 Yes 0.12152 0.02660 0.07376 0.19374

239 West Female 11 No 0.15354 0.04584 0.08329 0.26584

240 West Female 11 Yes 0.12719 0.02688 0.07852 0.19950

241 West Female 12 No 0.10120 0.03594 0.04934 0.19631

242 West Female 12 Yes 0.13054 0.02498 0.08440 0.19650

243 West Female 13 No 0.14759 0.04125 0.08346 0.24769

244 West Female 13 Yes 0.11968 0.02369 0.07629 0.18284

245 West Female 14 No 0.08748 0.03284 0.04105 0.17675

246 West Female 14 Yes 0.11063 0.02132 0.07145 0.16744

247 West Female 15 No 0.10099 0.03841 0.04674 0.20471

248 West Female 15 Yes 0.11236 0.02051 0.07428 0.16645

249 West Female 16 No 0.12538 0.04343 0.06188 0.23755

250 West Female 16 Yes 0.12224 0.02210 0.08108 0.18021

251 West Female 17 No 0.14672 0.04582 0.07743 0.26052

252 West Female 17 Yes 0.14371 0.03992 0.07558 0.25621

253 West Male 0 No 0.00000 0.00000 0.00000 0.00000

254 West Male 0 Yes 0.03075 0.02534 0.00437 0.18642

255 West Male 1 No 0.05457 0.02662 0.02056 0.13695

256 West Male 1 Yes 0.04584 0.01889 0.01729 0.11595

257 West Male 2 No 0.07833 0.02789 0.03833 0.15342

258 West Male 2 Yes 0.06254 0.01442 0.03627 0.10573

259 West Male 3 No 0.05897 0.02530 0.02500 0.13281

260 West Male 3 Yes 0.07844 0.01913 0.04398 0.13607

261 West Male 4 No 0.07267 0.03354 0.02870 0.17208

262 West Male 4 Yes 0.09122 0.02482 0.04765 0.16763

263 West Male 5 No 0.19732 0.10033 0.06632 0.45969

264 West Male 5 Yes 0.11262 0.02937 0.06021 0.20092

265 West Male 6 No 0.13335 0.04859 0.06322 0.25970

266 West Male 6 Yes 0.12119 0.02916 0.06799 0.20680

267 West Male 7 No 0.08881 0.03493 0.04015 0.18508
B-113

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

268 West Male 7 Yes 0.12691 0.02806 0.07464 0.20758

269 West Male 8 No 0.15183 0.05484 0.07210 0.29200

270 West Male 8 Yes 0.13161 0.02705 0.08037 0.20811

271 West Male 9 No 0.17199 0.05164 0.09260 0.29715

272 West Male 9 Yes 0.15079 0.02837 0.09590 0.22915

273 West Male 10 No 0.12897 0.03747 0.07151 0.22159

274 West Male 10 Yes 0.16356 0.02584 0.11192 0.23279

275 West Male 11 No 0.19469 0.04002 0.12785 0.28505

276 West Male 11 Yes 0.16965 0.02623 0.11699 0.23956

277 West Male 12 No 0.13214 0.04542 0.06547 0.24865

278 West Male 12 Yes 0.17494 0.02738 0.12002 0.24792

279 West Male 13 No 0.19947 0.04814 0.12127 0.31029

280 West Male 13 Yes 0.16217 0.02773 0.10747 0.23732

281 West Male 14 No 0.10759 0.03838 0.05220 0.20880

282 West Male 14 Yes 0.16487 0.02644 0.11214 0.23582

283 West Male 15 No 0.18459 0.05348 0.10138 0.31235

284 West Male 15 Yes 0.17018 0.02480 0.11996 0.23578

285 West Male 16 No 0.19757 0.04862 0.11892 0.30993

286 West Male 16 Yes 0.17888 0.02540 0.12718 0.24569

287 West Male 17 No 0.18078 0.04735 0.10548 0.29227

288 West Male 17 Yes 0.19218 0.04291 0.11118 0.31153

 
 

B-114

ATTACHMENT 3. TECHNICAL MEMORANDUM ON 

ESTIMATING PHYSIOLOGICAL PARAMETERS FOR THE 

EXPOSURE MODEL 
 

B-115

TECHNICAL MEMORANDUM 

TO: Tom McCurdy, WA-COR, NERL WA 10

FROM: Kristin Isaacs and Luther Smith, Alion Science and Technology

DATE: December 20, 2005 

SUBJECT: New Values for Physiological Parameters for the Exposure Model 

Input File Physiology.txt. 

Table of Contents 

List of Figures................................................................................................................ 116 

1. Introduction.............................................................................................................. 117 

2. Evaluation of the Current Physiology File Data ................................................... 117 

2.1 Normalized Maximal Oxygen Uptake (nvo2max)........................................... 117 

2.2 Body Mass. ...................................................................................................... 118 

2.3 Resting Metabolic Rate..................................................................................... 118 

2.4 Hemoglobin Content and Blood Volume Factor. ............................................ 118 

2.5 Summary of Findings........................................................................................ 118 

3. Derivation of New Distributions for Body Mass ................................................... 119 

3.1 The NHANES Body Mass Dataset. ................................................................. 119 

3.2 Calculation of the New Sampling Weights for the Combined NHANES Dataset.

................................................................................................................... 120 

3.3 Fitting the Body Mass Data. ............................................................................. 120 

4. Derivation of New Distributions for Normalized Vo2max.................................... 126 

4.1 The Nvo2max Data ........................................................................................... 126 

4.2 Determining the NVo2max Distributions......................................................... 130 

5. Derivation of New Distributions for Hemoglobin Content (Hemoglobin Density)

 138 

6. Blood Volume as a Function of Height and Weight.............................................. 141 

References...................................................................................................................... 142 

Appendix A. SAS Code for Estimating the Body Mass Distributions ..................... 156 

Appendix B. SAS Code for Estimating the Normalized Vo2Max Distributions..... 156 

Appendix C. SAS Code for Estimating the Hemoglobin Content Data................... 157 

Appendix D. The New Physiology.txt file .................................................................. 159 

Appendix E. All Derived Physiological Parameters ................................................. 170 
 

B-116

LIST OF FIGURES 

Figure 1. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a 

Function of Age, Derived from NHANES 1999-2004 Study Data. ............................... 122 

Figure 2. Geometric Standard Deviations for the Best-fit Lognormal Distributions for 

Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data...... 123 

Figure 3. Minimums (1st Percentile) for Body Mass as a Function of Age, Derived from 

NHANES 1999-2004 Study Data. .................................................................................. 124 

Figure 4. Maximums (99th Percentile) for Body Mass as a Function of Age, Derived 

from NHANES 1999-2004 Study Data. ......................................................................... 125 

Figure 5. Individual Nvo2max Measurements for Males and Females, Derived from 

Literature Studies and Experimental Measurements. ..................................................... 127 

Figure 6. Grouped Mean Nvo2max Measurements for Males and Females, Derived from 

Literature Studies............................................................................................................ 128 

Figure 7. Nvo2max Standard Deviations for Males and Females, Derived from Literature 

Studies............................................................................................................................. 129 

Figure 8. Combined Nvo2max Group Means for Males and Females .......................... 132 

Figure 9. Combined Nvo2max Group Standard Deviations........................................... 133 

Figure 10. Nvo2max Normal Distribution Fits: Raw Fit Means and Smoothed Fits. ... 134 

Figure 11. Nvo2max Normal Distribution Fits: Raw Fit Standard Deviations and 

Smoothed Fits. ................................................................................................................ 135 

Figure 12. Nvo2max Minimums. 1st Percentile of the Best-fit Normal Distribution. .. 136 

Figure 13. Nvo2max Maximums. 99th Percentile of the Best-fit Normal Distribution. 137 

Figure 14. Mean Values of Hemoglobin Content as Derived from the 1999-2002 

NHANES Dataset, with Comparison to Current Physiology.txt Values........................ 139 

Figure 15. Values of Hemoglobin Content Standard Deviation as Derived from the 1999-

2002 NHANES Dataset, with Comparison to Current Physiology.txt Values............... 140 
 

B-117

1. INTRODUCTION 

The purpose of this memo is to present an updated version of the physiological 

parameters input file (Physiology.txt) for the APEX model. Portions of this file are also 

used as input for SHEDS-PM and SHEDS-AirToxics. 

The physiology file contains age- and gender-based information for several physiological 

parameters used in human exposure modeling. This information includes distributional 

shapes and parameters for all age and gender cohorts from age 0 to 100 years for 

normalized maximal oxygen uptake (nvo2max), body mass, resting metabolic rate 

(RMR), and blood hemoglobin content. In addition, a parameter called blood volume 

factor (BVF), which is a cohort-dependent parameter in the equation for blood volume as 

a function of body mass, is present in the file as well. 

New age- and gender-dependent distributions were developed based the best available 

physiological data from the literature. In this report, a summary of the current state of the 

physiology file is presented, followed by the derivation of new physiological data for 

body mass, normalized vo2max, and hemoglobin content. Portions of the SAS code 

used for analysis are included (Appendices A-C), as is the new Physiology.txt file 

(Appendix D). The final appendix (Appendix E) contains tables of all the derived 

physiological parameters. 

2. EVALUATION OF THE CURRENT PHYSIOLOGY FILE 

DATA 

The physiology.txt file was originally generated for the PNEM model by T. Johnson. It 

was last updated 6/11/1998, as documented in the report User’s Guide: Software for 

Estimating Ventilation (Respiration) Rates for Use in Dosimetry Models, (T. Johnson and 

J. Capel). In that report, the original references for the data in the file were provided. An 

evaluation of the data in the file was included in a previous memo to the WA-COR under 

this work assignment. A summary of those findings is repeated here. 

2.1 Normalized Maximal Oxygen Uptake (nvo2max). 

The nvo2max data were derived from a number of sources. The data for males, 

especially, were pieced together from a variety of studies (a total of 6), leading to 

discontinuities in the distributional parameters. However, in each age and gender cohort, 

the distributions parameters were derived from a single published study. Additionally, 

much of the nvo2max data is quite old. The data for males at age 20 and at 28-69 came 

from a study from 1960 [1]. Data for males aged 0-8 and 16-19, and females 0-19 came 

from a figure in a textbook from 1977 [2], which in turn was based on limited earlier 

data. An additional issue with the 1977 data is (according to the report mentioned above) 

that values for certain ages (very young or elderly) were acquired by simple tangential 

extrapolation of the data in the figure. 
 

B-118

In addition, in some cases it was not clear how the parameters were derived from the 

referenced studies. For example, Heil et al. [3] was referenced as the source of the values 

for females aged 66-100. However, an examination of that study provided no clues as to 

how the values were actually determined. As far as can be determined, in no place did 

the authors break down the means and SDs of their data into groups separated by both 

gender and age simultaneously. 

2.2 Body Mass. 

The current body mass data were derived from an in-depth analysis [4, 5] of the second 

CDC National Health and Nutrition Examination Survey (NHANES II) body mass data 

[6]. The data were relatively comprehensive, and the methods used to generate the 

lognormal distributions were sound. However, the NHANES II data were compiled for 

the years 1976-1980, so an analysis of more recent data is necessary to accurately 

account for changes in human activity patterns in adults and especially children. 

2.3 Resting Metabolic Rate. 

Not included for evaluation, per discussion with WA-COR. 

2.4 Hemoglobin Content and Blood Volume Factor. 

The original references for the hemoglobin content or blood volume factor values given 

in the current physiology.txt file could not be identified. Therefore, their validity could 

not be evaluated and it was desirable that new statistics be calculated. 

2.5 Summary of Findings 

 In some cases, especially for nvo2max, the data are unnecessarily and confusingly 

disjointed across ages. 

 It is also unclear how some of the nvo2max values were derived from the 

referenced studies. 

 With the exception of the Schofield equations for the BM/RMR regression, 

parameter distributions at each age and gender cohort were derived from data from a 

single study. 

 Many of the studies used are very old (ex. 1960, 1977). 

 Some the data is of questionable validity (for example, the extrapolation of a 

textbook figure is used), although it may have been the best available at the time of 

the compilation of the file. 

 The original source of the hemoglobin content and blood volume factor data could 

not be identified. 
 

B-119

 Given these conclusions, we recommended a full review and update of the current 

physiology.txt file data. Specifically, we recommended that where possible, new 

distributions or equations should be developed based on thorough, compiled data 

from appropriate studies. 

3. DERIVATION OF NEW DISTRIBUTIONS FOR BODY 

MASS 

3.1 The NHANES Body Mass Dataset. 

New body mass distributions were generated from data from the National Health and 

Nutrition Examination Survey (NHANES). This survey is an ongoing study carried out 

by the National Center for Health Statistics of the Centers for Disease Control. EPA 

recognizes the utility of this dataset in characterizing the American population for risk 

assessment and policy support purposes [7]. 

Older NHANES data (for the years 1976-1980) have been used previously to develop 

population estimates of body mass distributions [4,5]. The current Physiology.txt file 

body mass distributions are based on this work. However, the analysis presented here is 

based on the most recent NHANES data, for the years 1999-2004 [8]. 

Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES 

studies were downloaded from the NHANES website. The files were downloaded as 

SAS xpt datasets. The downloaded files were as follows: 

1999-2000 

BMX.xpt 

Demo.xpt 

2001-2002

BMX_b_r.xpt 

Demo_b.xpt 

2003-2004 

BMX_c.xpt 

Demo_c.xpt 

The Demographic datasets contained the age and gender values for each survey 

participant, while the Body Measurement datasets contained the body weights for each 

subject. The combined dataset comprised 31,126 individuals. This resulted in 

approximately 400-500 persons in each age 0-18 year cohort, and approximately 80-150 

persons in each age 19-85 year cohort (the NHANES studies more heavily sampled 

children). 
 

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3.2 Calculation of the New Sampling Weights for the Combined 

NHANES Dataset. 

In the analysis of the NHANES data, sampling weights must be used to ensure that the 

data are weighted to appropriately represent the national population. Sampling weights 

for the combined NHANES body mass dataset were derived as recommended by the 

documentation provided with the most recent NHANES release [9]. Specifically, the 

sampling weight for each subject was calculated as: 

20042003 3

1

wcombined  w  (1) 

20021999 3

2

wcombined  w  (2) 

where wcombined is the sampling weight for the combined dataset, w2003-2004 is the weight 

for the subjects in the most recent study, and w1999-2002 is the weight for subjects in 

combined 4-year (1999-2000 and 2001-2002) NHANES dataset. (Both weights are 

provided with the appropriate NHANES release. The combined 1999-2002 weight, 

which is not a simply half of that for the corresponding 2-year periods, was explicitly 

calculated for researcher use by CDC since the two 2-year periods use different census 

data.) 

By using the sampling weights, once can consider any 2-year NHANES dataset or any 

combination of datasets as a nationally representative sample. 

3.3 Fitting the Body Mass Data. 

In the current physiology file, body mass is modeled as a two-parameter lognormal 

distribution. The NHANES body mass data were fit to several types of distributions 

(including normal, beta, and three-parameter lognormal distributions). It was determined 

that overall, the distribution that provided the best combination of good behavior over 

ages and good fit to the data was a two-parameter lognormal distribution. 

The data were fit to the lognormal distributions using the SAS PROC UNIVARIATE 

procedure. The FREQ option of the procedure was used to apply the sampling weights. 

The SAS code used to generate the body mass distributions is provided in Appendix A. 

As the NHANES 1999-2003 studies only covered persons up to age 85, linear forecasts 

were made for ages 86-100, as based on the data for ages 60 and greater. 

3.4 Body Mass Results. 
 

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Geometric means and standard deviations (SD) for the best-fit lognormal distributions for 

body mass are given in Figures 1 and 2. The means behaved fairly smoothly across ages. 

Note that for children age 0-18, the values of the new fits are similar, but slightly higher 

than those in the current Physiology.txt file, which were derived from earlier NHANES 

studies. The new means also capture the trend towards decreasing body weight in older 

persons that was previously neglected in the Physiology.txt file. 

The maximum and minimum values for the distributions are presented in Figures 3 and 4. 

The minimums and maximums were calculated as the 1st and 99th percentile of the raw 

body mass data for the cohort. (Note that these values differ from the 1st and 99th

percentiles of the fitted lognormals.) While the minimum value is consistent with the 

current Physiology.txt (which was based on earlier NHANES studies), the new cohort 

maximums are generally higher than before. 

The behavior of several of the body mass parameters (especially the SD) is fairly noisy, 

especially for adults. This is most likely due to the smaller number of samples for adults 

as compared to children. Therefore, it may desirable to use age-grouped data or running 

averages over years in these age ranges. While the attached prepared Physiology.txt file 

uses the “raw” parameters, smoothed results using 5-year running averages are provided 

in the attached data tables (Appendix E, plots not shown). These could be used at the 

direction of EPA; changing the “official” release Physiology.txt file would be trivial. 
 

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MALES: Body Mass Geometric Mean

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

80.000

90.000

100.000

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

FEMALES: Body Mass Geometric Mean

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

80.000

90.000

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

Figure 8. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a 

Function of Age, Derived from NHANES 1999-2004 Study Data.
 

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MALES: Body Mass GSD

1.000

1.050

1.100

1.150

1.200

1.250

1.300

1.350

1.400

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

FEMALES: Body Mass GSD

1.000

1.050

1.100

1.150

1.200

1.250

1.300

1.350

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

Figure 9. Geometric Standard Deviations for the Best-fit Lognormal Distributions for Body 

Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.
 

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FEMALES: Body Mass Minimum

0

10

20

30

40

50

60

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology File

MALES: Body Mass Minimum

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

Figure 10. Minimums (1st Percentile) for Body Mass as a Function of Age, Derived from 

NHANES 1999-2004 Study Data.
 

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MALES: Body Mass Maximum

0

50

100

150

200

250

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

FEMALES: Body Mass Maximum

0

20

40

60

80

100

120

140

160

180

200

0 20 40 60 80 100

Age (years)

Mass (kg)

New Values

Current Physiology

File

Figure 11. Maximums (99th Percentile) for Body Mass as a Function of Age, Derived from 

NHANES 1999-2004 Study Data. 
 

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4. DERIVATION OF NEW DISTRIBUTIONS FOR 

NORMALIZED VO2MAX 

4.1 The Nvo2max Data 

The NHANES studies do report data for vo2max in individuals. However, the NHANES 

vo2max values are estimated values, i.e. they are not measured directly. Such estimated 

values are not appropriate for use in this context (as per discussion with the WA-COR). 

Therefore, nvo2max distributional shapes were determined from a large database of 

experimental and literature vo2max measurements for different age/gender cohorts. 

A PubMed-based literature search located a number of studies in which vo2max was 

directly measured. In addition, a large number of scientific papers (~350) reporting 

vo2max were also provided to Alion by the WA-COR. All the studies were evaluated for 

use by determining if: 1) any normalized vo2max data for individuals were reported or 2) 

any group means for narrow age-gender cohorts were reported. Studies in which the 

studied age group was very broad or contained both males and females were discarded. 

Also discarded were any studies in which vo2max was not normalized by body mass, or 

for which no age data were reported. Data for ill or highly-trained individuals were not 

used; however, studies in which subjects underwent mild or moderate exercise training 

were included. Two large databases, one of individual vo2max data and one of grouped 

means and SDs, were constructed from the valid studies. 

The database of individual data comprised age versus nvo2max data for 1949 men and 

1558 women. The data were pulled from either tables or graphs in 20 published studies 

[11-30]. Additional raw experimental data were provided by the WA-COR [31]. In the 

case of the graphical data, the original source was digitized and the data points were 

pulled from the digital figure using graphics software. (This was accomplished by 

calibrating the pixels of the digitized image with the range of age and nvo2max values.) 

The individual nvo2max data for males and females are shown in Figure 5. 

The grouped mean and SD data were derived from 136 studies [32-167]. These data 

comprised approximately 550 means and SDs for different age/gender cohorts. Single 

age/gender cohort means and SD values for the Adams data [31] were also included in 

this dataset. Only data for subject groups having an age SD of less than approximately 2-

3 years were considered. The grouped mean values for men and women are shown in 

Figure 6, while the group SD values are shown in Figure 7. 
 

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MALES: Nvo2max

0

20

40

60

80

100

0 20 40 60 80 100

Age (years)

nvo2max (ml/kg) nn

n

FEMALES: Nvo2max

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100

age (years)

nvo2max (ml/kg

Figure 12. Individual Nvo2max Measurements for Males and Females, Derived from 

Literature Studies and Experimental Measurements. 

N=1558 

N=1949 
 

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MALES: Study Means, Nvo2max

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

0.0 20.0 40.0 60.0 80.0

Age (Years)

nvo2max (ml/kg

FEMALES: Study Means, Nvo2max

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

Age (Years)

nvo2max (ml/kg

Figure 13. Grouped Mean Nvo2max Measurements for Males and Females, Derived from 

Literature Studies. 
 

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MALES: Study Standard Deviations: Nvo2max

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

0.0 20.0 40.0 60.0 80.0

Age (Years)

nvo2max (ml/kg

FEMALES: Study Standard Deviations: Nvo2max

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

Age (Years)

nvo2max (ml/kg

Figure 14. Nvo2max Standard Deviations for Males and Females, Derived from Literature 

Studies. 
 

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4.2 Determining the NVo2max Distributions

Both the grouped mean and the individual datasets were evaluated for use in deriving the 

nvo2max parameters. 

The group means and SD were combined into single age/gender cohort values. The 

combined means were calculated as mean of the group means, weighted by the number of 

subjects. The group SD were calculated by transforming each group SD to a group 

variance, calculating the mean variance (weighted by the number of subjects in each 

study) and retransforming the variances to SDs. The combined group means and SDs are 

given in Figures 8 and 9. 

The combined group means were fairly well-behaved across age and gender cohorts (see 

Figure 8), while the SD data (Figure 9) were noisier. These data may be appropriate for 

use in the Physiology.txt file; however, it was noted that the group mean data, while 

plentiful for children, were not very well represented in the adult (30+ years) age range 

(especially for women). This is mainly due to the fact that very few investigators use 

narrow age cohorts when studying adults, rather, it was far more common for broader age 

groups to be used. These data were not included in the grouped mean analysis, as the 

mean nvo2max for a broad age group cannot be assumed valid for the cohort represented 

by the study age mean. Therefore, we opted to use the database of individual nvo2max 

measurements to develop new distributions for the Physiology.txt file. 

The individual nvo2max data were fit to several types of distributions (including normal, 

beta, and lognormal distributions). It was determined that the normal distribution fit the 

data best. The parameters (means and standard deviations) of the best-fit distributions 

were obtained using the SAS PROC UNIVARIATE procedure. The SAS code used to fit 

the data is given in Appendix B. 

Both raw and smoothed nvo2max fits were calculated. Calculating 5-year running 

averages did not smooth the data considerably. Therefore, the smoothed fits were 

determined by choosing a best-fit functional form for the nv02max data. The data were 

fit to functions as follows: 

Mean (Age 0-20): Linear function 

Mean (Age 21-100): Parabolic function 

SD (Age 0-26): Linear function 

SD (Age 27-100): Parabolic function 

Fitting the data in this manner also allowed for all age/gender cohorts to be represented. 

Since only cohorts having N>10 were fit to distributions, there were some cohorts for 

which no parameters were calculated. The raw and smoothed fits for means are given in 

Figure 10; analogous data for SD is given in Figure 11. The raw nvo2max parameters 

were not as clean across ages as the body mass data (probably due to the much smaller 

sample size), and thus the smoothed fits were selected for use in the attached 

Physiology.txt file. As with body mass, the raw fits may be used at the direction of EPA. 
 

B-131

The results for the nvo2max means were in fact quite close to those in the current file. 

However, the values exhibited much more consistent behavior across ages, and the values 

for elderly persons were lower than previously. The SD values were also in the same 

range as the current values, yet they no longer demonstrate nonsensical discontinuities 

across ages. 

The minimum and maximum nvo2max values were assumed to be the 1st and 99th

percentile of the best-fit lognormal distribution. (Note: this is different from the method 

used for estimating the body mass limits. In that case, the samples were large enough 

that the percentiles of the raw data were appropriate for use as minimum and maximum. 

As the nvo2max data cohorts had much smaller N than the NHANES studies, the raw 

percentiles were less appropriate.) The maximum and minimum values are shown in 

Figures 12 and 13. 
 

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MALES: Nvo2max, Combined Group Means

0

10

20

30

40

50

60

0 20 40 60 80

Age (Years)

nv02max (ml/k

g

FEMALES: Nvo2max, Combined Group Means

0

10

20

30

40

50

0 10 20 30 40 50 60 70

Age (Years)

nv02max (ml/k

g

Figure 15. Combined Nvo2max Group Means for Males and Females 
 

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MALES: Nvo2max, Combined Group SD

0

2

4

6

8

10

12

14

16

0 20 40 60 80

Age (Years)

nv02max (ml/k

g

FEMALES: Nvo2max, Combined Group SD

0

2

4

6

8

10

0 10 20 30 40 50 60 70

Age (Years)

nv02max (ml/k

g

Figure 16. Combined Nvo2max Group Standard Deviations. 
 

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MALES: MEAN Nvo2max

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Fit Values

Smoothed Fit Values

Current Physiology.txt

FEMALES: MEAN Nvo2max

0

10

20

30

40

50

60

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Fit Values

Smoothed Fit Values

Current Physiology.txt

Figure 17. Nvo2max Normal Distribution Fits: Raw Fit Means and Smoothed Fits. 
 

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MALES: Nvo2max Standard Deviation

0

2

4

6

8

10

12

14

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Values

Fit Values

Current Physiology.txt

FEMALES: Nvo2max Standard Deviation

0

2

4

6

8

10

12

0 20 40 60 80 100

Age (Years)

Nvo2max (ml/kg

Raw Values

Fit Values

Current Physiology.txt

Figure 18. Nvo2max Normal Distribution Fits: Raw Fit Standard Deviations and Smoothed 

Fits. 
 

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MALES: Nvo2max Minimum

0

10

20

30

40

50

60

0 20 40 60 80 100

Age (Years)

nvo2max (ml/k

g

New Values

Current Physiology.txt

FEMALES: Nvo2max Minimum

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10

15

20

25

30

35

40

45

0 20 40 60 80 100

Age (Years)

Nvo2max (ml/k

g

New Values

Current Physiology.txt

Figure 19. Nvo2max Minimums. 1st Percentile of the Best-fit Normal Distribution. 
 

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MALES: Nvo2max Maximum

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100

Age (Years)

nvo2max (ml/k

g

New Values

Current Physiology.txt

FEMALES: Nvo2max Maximum

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10

20

30

40

50

60

70

0 20 40 60 80 100

Age (Years)

Nvo2max (ml/k

g

New Values

Current Physiology.txt

Figure 20. Nvo2max Maximums. 99th Percentile of the Best-fit Normal Distribution. 
 

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5. Derivation of New Distributions for Hemoglobin Content 

(Hemoglobin Density)

The new hemoglobin content values were derived from the combined NHANES 1999-

2000 and 2001-2002 datasets. As of December 2005, hemoglobin data had not yet been 

released for the 2003-2004 study. The age data was provided in the Demographic 

datasets (Demo.xpt and Demo_b.xpt, previously downloaded for the body mass analysis) 

for the two survey periods, while hemoglobin content (in g/dL) was provided in the 

Laboratory #25 (Complete Blood Count) datasets (lab25.xpt and l25_b.xpt, which were 

downloaded for this analysis). The dataset comprised 20,321 individuals; appropriate 

sample weights were used for the combined 4-year (1999-2002) dataset as provided with 

the NHANES 2001-2002 data release. Similarly to the body mass data, the hemoglobin 

content values were analyzed in SAS. The age and hemoglobin datasets were merged 

and fit to normal distributions using the SAS PROC UNIVARIATE procedure. The 

FREQ option of the procedure was used to apply the sampling weights. The SAS code in 

provided in the Appendix C. 

Hemoglobin content statistics were estimated for single-year age and gender cohorts for 

ages 1-19, as the behavior of the means were smooth in this age range. For persons 20 

and over, the data were grouped in 5-year cohorts (20-24, 25-29, etc.) No blood count 

data were available for subjects under 1 year of age or greater than 90. The age 0 mean 

values were obtained by a linear regression of ages 1-20 (males) or 1 to 11 (females) back 

to age 0. These were the ages for which the hemoglobin content demonstrated an 

increase with age. The 91-95 and 96-100 mean values were obtained by a linear 

regression of the 61-65 and older age groups. As the standard deviations did not appear 

to behave as smoothly with age as did the mean values, the age 0 value was assumed 

equal to the age 1 value, and the age 91-95 and 96-100 value was assumed equal to the 

age 90-94 value. 

The resulting means and standard deviations for the best-for normal distributions for 

hemoglobin content are given in Figures 14 and 15. The current hemoglobin content 

values are shown for comparison. 

The main conclusions that can be made is that the current Physiology.txt input file 

overestimates mean hemoglobin content in children and in older persons. The standard 

deviation values in the current physiology.txt file are fairly close to those found in this 

analysis. The new values are not very smooth over ages; EPA may elect to continue to 

use the current values. It should be noted that the original reference for the current 

hemoglobin statistics is unknown.

Note: In the current implementation of APEX, the hemoglobin content statistics affect 

only the CO dose algorithm calculations. 
 

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MALES: Mean Hemoglobin Content

10

11

12

13

14

15

16

17

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

New Values

Current Physiology.txt

FEMALES: Mean Hemoglobin Content

10

11

12

13

14

15

16

17

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

Figure 21. Mean Values of Hemoglobin Content as Derived from the 1999-2002 NHANES 

Dataset, with Comparison to Current Physiology.txt Values 
 

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MALES: Hemoglobin Content Standard Deviation

0

0.5

1

1.5

2

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

New Values

Current Physiology.txt

FEMALES: Hemoglobin Content Standard Deviation

0

0.5

1

1.5

2

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

Figure 22. Values of Hemoglobin Content Standard Deviation as Derived from the 1999-

2002 NHANES Dataset, with Comparison to Current Physiology.txt Values 
 

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6. BLOOD VOLUME AS A FUNCTION OF HEIGHT AND 

WEIGHT 

In APEX, blood volume is estimated as a function of height and weight by the following 

equation: 

30 3 V BVF*Weight+ K*Height - blood 

where Vblood is the blood volume (ml), Weight is in pounds, and height is in inches. BVF 

is the blood volume factor that is read in from the physiology file, and K is a genderdependent constant (0.00683 for males, 0.00678 for females). This is a modification of 

Allen’s equation [168] to include the age/gender dependent BVF and adjusted for the 

given units. 

As previously mentioned, the data upon which the BVF values in the physiology file 

were based could not be identified. The available documentation for pNEM documents a 

non-age-dependent use of these equations. 

In addition, no appropriate data were found for deriving new estimates for the BVF 

variable as a function of age and gender for use with the Allen equations. It should be 

noted however, that these equations were modified by Nadler [169]. These equations 

seem to be used somewhat more often than the originals in the literature. 

In addition, other (more recent) equations exist for estimation of blood volume from 

height and weight specifically in children [170,171] or body surface area [172]. In 

particular, Linderkamp et al. [170] derived prediction equations for blood volume as a 

function of a number of physiological parameters for children in three different age 

groups. It is recommended that further analysis of this study and others be undertaken. 

However, inclusion of new blood volume equations in APEX would require changes 

beyond the current physiology file (i.e. other, more intensive, code changes would be 

needed). Thus, at the present time, no specific improvements to the current BVF values 

in the physiology file can be made. 
 

B-142

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166. Yoshizawa S, Honda H, Urushibara M, Nakamura N. Effects of endurance run on 

circulorespiratory system in young children. J Hum Ergol (Tokyo). 1990 Jun;19(1):41-

52. 

167. Yoshizawa S, Ishizaki T, Honda H. Physical fitness of children aged 5 and 6 

years. J Hum Ergol (Tokyo). 1977 Sep;6(1):41-51. 

168. Allen TH, Peng MT, et al. Prediction of blood volume and adiposity in man from 

body weight and cube of height. Metabolism. 1956; 5: 328. 

169. Nadler SB, Hidalgo JU, Bloch T. Prediction of blood volume in normal human 

adults. Surgery. 1962;51:224-232. 

170. Linderkamp O, Versmold HT, et al. Estimation and prediction of blood volume in 

infants and children. Europ J Pediat. 1977; 125: 227-234. 

171. Cropp GJA. Changes in blood and plasma volumes during growth. J Pediatrics. 

1971; 78: 220-229. 
 

B-155

172. Smetannikov Y, Hopkins D. Intraoperative bleeding: A mathematical model for 

minimizing hemoglobin loss. Transfusion. 1996; 36: 832-835. 
 

B-156

Appendix A. SAS Code for Estimating the Body Mass Distributions

/* This program calculates lognormal distributions for BM from the NHANES 1999-2004 Data 

K K Isaacs 10/2005 

Alion Science and Technology 

Distributions are derived from raw body mass and age data downloaded from the CDC site at 

http://www.cdc.gov/nchs/about/major/nhanes/ 

The data are stored in the downloaded datasets: 

1999-2000 (SAS export files) 

BMX.xpt (NHANES Body Measurement Data, contains body wt in kg) 

Demo.xpt (NHAMES Demographic Data, contains age in years or months) 

2001-2002 (SAS export files) 

BMX_b_r.xpt (NHANES Body Measurement Data, contains body wt in kg) 

Demo_b.xpt (NHAMES Demographic Data, contains age in years or months) 

2003-2004 (SAS export files) 

BMX_c.xpt (NHANES Body Measurement Data, contains body wt in kg) 

Demo_c.xpt (NHAMES Demographic Data, contains age in years or months) 

*/

* Merge the Body Measurement and Demographics datasets;

Data weight; 

 merge Demo Demo_b Demo_c Bmx Bmx_b_r Bmx_c; 

 by SEQN; 

 mass=BMXWT; 

 gen=RIAGENDR; 

 ageyrs=RIDAGEYR; 

 agemonths=RIDAGEEX; 

 wt = (2/3)*WTMEC4YR; 

 if (SEQN>21004) THEN wt=(1/3)*WTMEC2YR; 

 if agemonths<12 and agemonths>0 THEN ageyrs=0; 

 keep SEQN mass gen ageyrs agemonths wt; 

run; 

proc sort data=weight; 

 by gen ageyrs; 

run; 

Proc univariate data=weight; 

by gen ageyrs; 

var mass; 

freq wt; 

histogram mass / lognormal; 

run;

APPENDIX B. SAS CODE FOR ESTIMATING THE 

NORMALIZED VO2MAX DISTRIBUTIONS 

/***********************************************************************/

/* This is a program to fit the V02Max (Adams and others) data to different 

distributional shapes. 

Adams experimental data provided in Excel form by Stephen Graham and Tom McCurdy, EPA 

Other data collected by Alion Science and Tech. 

This work was performed for WA 10, APEX/SHEDS Physiology File Update 
 

B-157

K. K. Isaacs October 2005 

Alion Science and Technology 

/***********************************************************************/; 

*load datasets;

Data alldata ; 

 infile 'H:\kki-05-PHYSIOLOGY_10\NVO2MAX\vo2max.csv' DLM="," END=eof; 

 input age nvo2max gender; 

 output alldata; 

 

proc sort data=alldata; 

by gender age; 

run; 

Proc univariate data=alldata; 

by gender age; 

var nvo2max; 

histogram nvo2max / normal; 

output out=outputdata1 N=samplesize mean=Mean 

 std=StdDeviation ProbN=NormalFit; 

run; 

Proc export data=outputdata1 outfile="H:\kki-05-PHYSIOLOGY_10\Alldata_vo2max.csv"

replace; 

run; 

APPENDIX C. SAS CODE FOR ESTIMATING THE 

HEMOGLOBIN CONTENT DATA 

/* This program calculates best fit normal distributions for hemoglobin content 

from the NHANES 1999-2000 and 2001-2002 datasets. 

Alion Science and Technology 

K K Isaacs 12/2005 

Distributions are derived from hemoglobin content and age data downloaded from the CDC 

site at 

http://www.cdc.gov/nchs/about/major/nhanes/nhanes99-00.htm 

and 

http://www.cdc.gov/nchs/about/major/nhanes/nhanes01-02.htm 

The data are stored in the downloaded datasets: 

1999-2000 

lab25.xpt (NHANES Lab dataset #25) 

Demo.xpt (NHANES Demographic Data, contains age in years or months) 

2001-2002 

l25_b.xpt (NHANES Lab dataset #25) 

Demo_b.xpt (NHANES Demographic Data, contains age in years or months) 

*/

*Data are read into SAS by loading the xpt files. 

* Merge the Laboratory and Demographics datasets;

Data Hb; 

 merge Demo Lab25 Demo_b L25_b; 

 by SEQN; * Sample number;

 Hb=LBXHGB; * Hb content g/dL;

 gen=RIAGENDR; * Gender;

 ageyrs=RIDAGEYR; * Age in years; 

 agemonths=RIDAGEEX; * Age in months;

 wt = WTMEC4YR; * 4-year sample weights;

 if agemonths<12 and agemonths>0 THEN ageyrs=0; * Age 0;

 if ageyrs>20 then ageyrs=(floor(ageyrs/5)+1)*5; * Bin in 5-year incs;
 

B-158

 keep SEQN Hb gen ageyrs agemonths wt; 

run; 

proc sort data=Hb; 

 by gen ageyrs; 

run; 

Proc univariate data=Hb; 

by gen ageyrs; 

var Hb; 

req wt; * Apply sample weights;

histogram Hb / normal; * Fit to Normal;

output out=outputs N=samplesize mean=Mean 

 std=StdDeviation ProbN=NormalFit; 

run; 

Proc export data=outputs outfile="H:\kki-05-PHYSIOLOGY_10\Hemoglobin\HbFitswt.csv"

replace; 

run;
 

B-159

APPENDIX D. THE NEW PHYSIOLOGY.TXT FILE 

Note: The values contained in the file conform to the current APEX read formats. That 

is, the number of decimal places for each parameter is dictated by the APEX code. It is 

likely that this will change in the future, at which point more significant digits could be 

added to the Physiology.txt file. 

Males age 0-100, then females age 0-100 (last revised 12-20-05) 

 NVO2max distribution 

 Age Source Distr Mean SD Lower Upper Assumptions 

 0 NA Normal 48.3 1.7 44.3 52.2 

 1 NA Normal 48.6 2.0 43.8 53.3 

 2 NA Normal 48.9 2.4 43.4 54.4 

 3 NA Normal 49.2 2.7 43.0 55.4 

 4 NA Normal 49.5 3.0 42.5 56.5 

 5 NA Normal 49.8 3.3 42.1 57.6 

 6 NA Normal 50.1 3.7 41.6 58.6 

 7 NA Normal 50.4 4.0 41.2 59.7 

 8 NA Normal 50.8 4.3 40.8 60.8 

 9 NA Normal 51.1 4.6 40.3 61.8 

 10 NA Normal 51.4 5.0 39.9 62.9 

 11 NA Normal 51.7 5.3 39.4 64.0 

 12 NA Normal 52.0 5.6 39.0 65.0 

 13 NA Normal 52.3 5.9 38.6 66.1 

 14 NA Normal 52.6 6.2 38.1 67.2 

 15 NA Normal 53.0 6.6 37.7 68.2 

 16 NA Normal 53.3 6.9 37.3 69.3 

 17 NA Normal 53.6 7.2 36.8 70.4 

 18 NA Normal 53.9 7.5 36.4 71.4 

 19 NA Normal 54.2 7.9 35.9 72.5 

 20 NA Normal 54.5 8.2 35.5 73.6 

 21 NA Normal 54.2 8.5 34.5 74.0 

 22 NA Normal 53.4 8.8 32.9 74.0 

 23 NA Normal 52.6 9.2 31.4 73.9 

 24 NA Normal 51.8 9.5 29.8 73.9 

 25 NA Normal 51.1 9.8 28.3 73.9 

 26 NA Normal 50.3 10.7 25.5 75.2 

 27 NA Normal 49.6 10.5 25.2 74.0 

 28 NA Normal 48.8 10.3 24.9 72.8 

 29 NA Normal 48.1 10.1 24.6 71.6 

 30 NA Normal 47.4 9.9 24.3 70.4 

 31 NA Normal 46.7 9.7 24.0 69.3 

 32 NA Normal 46.0 9.6 23.8 68.2 

 33 NA Normal 45.3 9.4 23.5 67.1 

 34 NA Normal 44.6 9.2 23.2 66.0 

 35 NA Normal 44.0 9.0 23.0 65.0 

 36 NA Normal 43.3 8.9 22.7 64.0 

 37 NA Normal 42.7 8.7 22.4 62.9 

 38 NA Normal 42.1 8.6 22.2 61.9 

 39 NA Normal 41.4 7.3 25.5 54.1 

 40 NA Normal 40.8 5.5 28.4 50.0 

 41 NA Normal 40.2 5.5 28.4 50.0 

 42 NA Normal 39.7 5.5 28.4 50.0 

 43 NA Normal 39.1 5.5 28.4 50.0 

 44 NA Normal 38.5 5.5 28.4 50.0 

 45 NA Normal 38.0 5.5 28.4 50.0 

 46 NA Normal 37.4 5.5 28.4 50.0 

 47 NA Normal 36.9 5.5 28.4 50.0 

 48 NA Normal 36.4 5.5 28.4 50.0 

 49 NA Normal 35.9 5.5 28.4 50.0 

 50 NA Normal 35.4 4.9 23.5 42.7 

 51 NA Normal 34.9 4.9 23.5 42.7 

 52 NA Normal 34.5 4.9 23.5 42.7 

 53 NA Normal 34.0 4.9 23.5 42.7 

 54 NA Normal 33.6 4.9 23.5 42.7 

 55 NA Normal 33.1 4.9 23.5 42.7 

 56 NA Normal 32.7 4.9 23.5 42.7 

 57 NA Normal 32.3 4.9 23.5 42.7 

 58 NA Normal 31.9 4.9 23.5 42.7 

 59 NA Normal 31.5 4.9 23.5 42.7 

 60 NA Normal 31.1 5.3 21.0 41.8 

 61 NA Normal 30.7 5.3 21.0 41.8 

 62 NA Normal 30.4 5.3 21.0 41.8 

 63 NA Normal 30.0 5.3 21.0 41.8 
 

B-160

 64 NA Normal 29.7 5.3 21.0 41.8 

 65 NA Normal 29.4 5.3 21.0 41.8 

 66 NA Normal 29.1 5.3 21.0 41.8 

 67 NA Normal 28.8 5.3 21.0 41.8 

 68 NA Normal 28.5 5.3 21.0 41.8 

 69 NA Normal 28.2 5.3 21.0 41.8 

 70 NA Normal 27.9 5.7 16.1 38.3 

 71 NA Normal 27.7 5.7 16.1 38.3 

 72 NA Normal 27.4 5.7 16.1 38.3 

 73 NA Normal 27.2 5.7 16.1 38.3 

 74 NA Normal 27.0 5.7 16.1 38.3 

 75 NA Normal 26.7 5.7 16.1 38.3 

 76 NA Normal 26.5 5.7 16.1 38.3 

 77 NA Normal 26.4 5.7 16.1 38.3 

 78 NA Normal 26.2 5.7 16.1 38.3 

 79 NA Normal 26.0 5.7 16.1 38.3 

 80 NA Normal 25.8 5.7 16.1 38.3 

 81 NA Normal 25.7 5.7 16.1 38.3 

 82 NA Normal 25.6 5.7 16.1 38.3 

 83 NA Normal 25.4 5.7 16.1 38.3 

 84 NA Normal 25.3 5.7 16.1 38.3 

 85 NA Normal 25.2 5.7 16.1 38.3 

 86 NA Normal 25.1 5.7 16.1 38.3 

 87 NA Normal 25.1 5.7 16.1 38.3 

 88 NA Normal 25.0 5.7 16.1 38.3 

 89 NA Normal 24.9 5.7 16.1 38.3 

 90 NA Normal 24.9 5.7 16.1 38.3 

 91 NA Normal 24.9 5.7 16.1 38.3 

 92 NA Normal 24.8 5.7 16.1 38.3 

 93 NA Normal 24.8 5.7 16.1 38.3 

 94 NA Normal 24.8 5.7 16.1 38.3 

 95 NA Normal 24.8 5.7 16.1 38.3 

 96 NA Normal 24.9 5.7 16.1 38.3 

 97 NA Normal 24.9 5.7 16.1 38.3 

 98 NA Normal 25.0 5.7 16.1 38.3 

 99 NA Normal 25.0 5.7 16.1 38.3 

 100 NA Normal 25.1 5.7 16.1 38.3 

 0 NA Normal 35.9 5.9 22.2 49.6 

 1 NA Normal 36.2 6.0 22.3 50.2 

 2 NA Normal 36.5 6.1 22.4 50.7 

 3 NA Normal 36.9 6.2 22.5 51.3 

 4 NA Normal 37.2 6.3 22.6 51.8 

 5 NA Normal 37.5 6.4 22.7 52.4 

 6 NA Normal 37.9 6.5 22.8 52.9 

 7 NA Normal 38.2 6.6 22.9 53.5 

 8 NA Normal 38.5 6.7 23.0 54.0 

 9 NA Normal 38.9 6.8 23.1 54.6 

 10 NA Normal 39.2 6.9 23.3 55.1 

 11 NA Normal 39.5 7.0 23.4 55.7 

 12 NA Normal 39.9 7.0 23.5 56.2 

 13 NA Normal 40.2 7.1 23.6 56.8 

 14 NA Normal 40.5 7.2 23.7 57.3 

 15 NA Normal 40.9 7.3 23.8 57.9 

 16 NA Normal 41.2 7.4 23.9 58.5 

 17 NA Normal 41.5 7.5 24.0 59.0 

 18 NA Normal 41.8 7.6 24.1 59.6 

 19 NA Normal 42.2 7.7 24.2 60.1 

 20 NA Normal 42.5 7.8 24.4 60.7 

 21 NA Normal 42.1 7.9 23.7 60.5 

 22 NA Normal 41.5 8.0 22.9 60.1 

 23 NA Normal 40.8 8.1 22.0 59.6 

 24 NA Normal 40.2 8.2 21.1 59.2 

 25 NA Normal 39.6 8.3 20.3 58.8 

 26 NA Normal 39.0 8.4 19.5 58.4 

 27 NA Normal 38.4 8.4 18.9 57.8 

 28 NA Normal 37.8 8.1 18.8 56.7 

 29 NA Normal 37.2 7.9 18.7 55.6 

 30 NA Normal 36.6 7.7 18.6 54.6 

 31 NA Normal 36.0 7.6 18.5 53.6 

 32 NA Normal 35.5 7.4 18.4 52.6 

 33 NA Normal 34.9 7.2 18.2 51.7 

 34 NA Normal 34.4 7.0 18.1 50.7 

 35 NA Normal 33.9 6.8 18.0 49.8 

 36 NA Normal 33.4 6.7 17.8 48.9 

 37 NA Normal 32.9 6.5 17.7 48.0 

 38 NA Normal 32.4 6.4 17.6 47.2 

 39 NA Normal 31.9 6.2 17.4 46.4 

 40 NA Normal 31.4 6.1 17.3 45.6 

 41 NA Normal 31.0 6.0 17.1 44.8 

 42 NA Normal 30.5 5.8 17.0 44.0 

 43 NA Normal 30.1 5.7 16.8 43.3 
 

B-161

 44 NA Normal 29.6 5.6 16.6 42.6 

 45 NA Normal 29.2 5.5 16.5 41.9 

 46 NA Normal 28.8 5.4 16.3 41.2 

 47 NA Normal 28.4 5.3 16.1 40.6 

 48 NA Normal 28.0 5.2 16.0 40.0 

 49 NA Normal 27.6 5.1 15.8 39.4 

 50 NA Normal 27.2 5.0 15.6 38.8 

 51 NA Normal 26.8 4.9 15.4 38.3 

 52 NA Normal 26.5 4.8 15.2 37.7 

 53 NA Normal 26.1 4.8 15.1 37.2 

 54 NA Normal 25.8 4.7 14.9 36.7 

 55 NA Normal 25.5 4.7 14.7 36.3 

 56 NA Normal 25.2 4.6 14.5 35.9 

 57 NA Normal 24.9 4.6 14.3 35.4 

 58 NA Normal 24.6 4.5 14.1 35.1 

 59 NA Normal 24.3 4.5 13.9 34.7 

 60 NA Normal 24.0 4.5 13.6 34.3 

 61 NA Normal 23.7 4.4 13.4 34.0 

 62 NA Normal 23.5 4.4 13.2 33.7 

 63 NA Normal 23.2 4.4 13.0 33.4 

 64 NA Normal 23.0 4.4 12.8 33.2 

 65 NA Normal 22.7 4.4 12.5 33.0 

 66 NA Normal 22.5 4.4 12.3 32.7 

 67 NA Normal 22.3 4.4 12.1 32.5 

 68 NA Normal 22.1 4.4 11.9 32.3 

 69 NA Normal 21.9 4.4 11.7 32.1 

 70 NA Normal 21.7 4.4 11.5 32.0 

 71 NA Normal 21.6 4.4 11.4 31.8 

 72 NA Normal 21.4 4.4 11.2 31.6 

 73 NA Normal 21.3 4.4 11.1 31.5 

 74 NA Normal 21.1 4.4 10.9 31.3 

 75 NA Normal 21.0 4.4 10.8 31.2 

 76 NA Normal 20.9 4.4 10.7 31.1 

 77 NA Normal 20.8 4.4 10.6 31.0 

 78 NA Normal 20.7 4.4 10.4 30.9 

 79 NA Normal 20.6 4.4 10.4 30.8 

 80 NA Normal 20.5 4.4 10.3 30.7 

 81 NA Normal 20.4 4.4 10.2 30.6 

 82 NA Normal 20.3 4.4 10.1 30.6 

 83 NA Normal 20.3 4.4 10.1 30.5 

 84 NA Normal 20.3 4.4 10.0 30.5 

 85 NA Normal 20.2 4.4 10.0 30.4 

 86 NA Normal 20.2 4.4 10.0 30.4 

 87 NA Normal 20.2 4.4 10.0 30.4 

 88 NA Normal 20.2 4.4 10.0 30.4 

 89 NA Normal 20.2 4.4 10.0 30.4 

 90 NA Normal 20.2 4.4 10.0 30.4 

 91 NA Normal 20.2 4.4 10.0 30.4 

 92 NA Normal 20.3 4.4 10.1 30.5 

 93 NA Normal 20.3 4.4 10.1 30.5 

 94 NA Normal 20.4 4.4 10.2 30.6 

 95 NA Normal 20.4 4.4 10.2 30.6 

 96 NA Normal 20.5 4.4 10.3 30.7 

 97 NA Normal 20.6 4.4 10.4 30.8 

 98 NA Normal 20.7 4.4 10.5 30.9 

 99 NA Normal 20.8 4.4 10.6 31.0 

 100 NA Normal 20.9 4.4 10.7 31.1 

Males age 0-100, then females age 0-100 (last revised 12-20-05) 

 Body mass distribution, kg 

 Age Source Distr GM GSD Lower Upper Assumptions 

 0 CDC LN 7.8 1.301 3.6 11.8 

 1 CDC LN 11.4 1.143 8.2 16.1 

 2 CDC LN 13.9 1.146 9.8 20.9 

 3 CDC LN 16.0 1.154 11.7 23.7 

 4 CDC LN 18.5 1.165 11.1 28.1 

 5 CDC LN 21.6 1.234 13.7 42.4 

 6 CDC LN 23.1 1.213 16.1 41.1 

 7 CDC LN 27.1 1.216 19.3 46.8 

 8 CDC LN 31.7 1.302 19.1 66.2 

 9 CDC LN 34.7 1.265 24.0 69.9 

 10 CDC LN 38.3 1.280 24.3 72.9 

 11 CDC LN 44.1 1.308 26.2 83.8 

 12 CDC LN 48.0 1.315 27.7 94.8 

 13 CDC LN 55.4 1.340 27.7 106.6 

 14 CDC LN 62.8 1.293 35.7 121.0 

 15 CDC LN 67.7 1.255 41.5 117.9 

 16 CDC LN 72.5 1.267 45.8 139.1 

 17 CDC LN 73.1 1.248 49.9 136.6 

 18 CDC LN 75.1 1.243 51.2 144.2 

 19 CDC LN 77.2 1.245 52.6 134.5 

 20 CDC LN 78.0 1.250 50.5 130.0 
 

B-162

 21 CDC LN 78.2 1.297 46.8 199.2 

 22 CDC LN 83.8 1.292 53.3 155.4 

 23 CDC LN 80.6 1.222 50.5 137.6 

 24 CDC LN 81.7 1.251 50.6 132.6 

 25 CDC LN 84.8 1.206 50.2 136.1 

 26 CDC LN 81.8 1.273 48.9 164.5 

 27 CDC LN 85.2 1.249 50.0 153.9 

 28 CDC LN 84.3 1.272 51.0 167.2 

 29 CDC LN 82.1 1.236 50.6 147.2 

 30 CDC LN 81.6 1.262 52.5 139.0 

 31 CDC LN 81.3 1.249 48.8 170.6 

 32 CDC LN 84.7 1.235 49.7 135.8 

 33 CDC LN 88.2 1.231 64.8 146.3 

 34 CDC LN 81.2 1.221 53.1 136.9 

 35 CDC LN 87.2 1.251 61.0 193.3 

 36 CDC LN 83.4 1.228 45.8 140.5 

 37 CDC LN 85.8 1.241 59.3 150.9 

 38 CDC LN 84.1 1.260 52.8 149.7 

 39 CDC LN 84.6 1.196 61.2 140.6 

 40 CDC LN 90.1 1.246 58.5 154.0 

 41 CDC LN 87.4 1.173 61.3 117.7 

 42 CDC LN 88.3 1.205 62.2 144.0 

 43 CDC LN 88.4 1.233 54.0 145.3 

 44 CDC LN 88.5 1.200 56.6 128.9 

 45 CDC LN 87.1 1.205 60.6 160.2 

 46 CDC LN 88.2 1.243 54.2 154.3 

 47 CDC LN 86.5 1.229 49.9 188.3 

 48 CDC LN 84.8 1.186 56.3 128.3 

 49 CDC LN 86.2 1.240 47.0 171.3 

 50 CDC LN 84.7 1.179 53.4 124.4 

 51 CDC LN 88.0 1.208 57.9 143.6 

 52 CDC LN 89.9 1.216 55.2 144.9 

 53 CDC LN 89.0 1.228 58.2 143.3 

 54 CDC LN 90.1 1.216 64.1 155.2 

 55 CDC LN 88.3 1.222 55.1 138.6 

 56 CDC LN 84.8 1.195 45.0 110.3 

 57 CDC LN 87.5 1.253 58.3 160.0 

 58 CDC LN 85.1 1.266 51.6 179.0 

 59 CDC LN 84.2 1.182 58.7 112.4 

 60 CDC LN 87.0 1.232 57.3 141.7 

 61 CDC LN 89.0 1.207 49.9 162.8 

 62 CDC LN 84.8 1.228 56.0 152.1 

 63 CDC LN 89.1 1.262 56.3 171.6 

 64 CDC LN 90.0 1.193 59.1 119.0 

 65 CDC LN 89.9 1.215 58.1 126.3 

 66 CDC LN 86.8 1.228 54.0 150.1 

 67 CDC LN 86.2 1.207 43.1 127.5 

 68 CDC LN 85.2 1.191 61.2 163.2 

 69 CDC LN 87.1 1.222 50.7 127.2 

 70 CDC LN 82.8 1.210 46.5 125.5 

 71 CDC LN 79.6 1.240 51.0 122.8 

 72 CDC LN 82.0 1.204 51.9 132.7 

 73 CDC LN 85.6 1.196 56.2 128.3 

 74 CDC LN 83.0 1.217 53.3 120.0 

 75 CDC LN 84.5 1.185 56.5 133.5 

 76 CDC LN 78.7 1.207 55.9 121.1 

 77 CDC LN 79.4 1.170 58.7 109.3 

 78 CDC LN 79.9 1.195 41.1 115.1 

 79 CDC LN 77.6 1.155 56.4 107.8 

 80 CDC LN 79.9 1.174 56.0 111.9 

 81 CDC LN 75.4 1.157 55.8 111.9 

 82 CDC LN 76.8 1.180 54.4 111.8 

 83 CDC LN 74.6 1.158 53.2 107.0 

 84 CDC LN 75.3 1.205 41.5 109.5 

 85 CDC LN 71.8 1.191 46.9 105.8 

 86 CDC LN 74.0 1.170 50.6 101.1 

 87 CDC LN 73.4 1.170 50.4 99.1 

 88 CDC LN 72.7 1.160 50.2 97.2 

 89 CDC LN 72.1 1.160 50.0 95.2 

 90 CDC LN 71.5 1.160 49.8 93.2 

 91 CDC LN 70.9 1.160 49.6 91.3 

 92 CDC LN 70.3 1.160 49.4 89.3 

 93 CDC LN 69.6 1.150 49.3 87.4 

 94 CDC LN 69.0 1.150 49.1 85.4 

 95 CDC LN 68.4 1.150 48.9 83.4 

 96 CDC LN 67.8 1.150 48.7 81.5 

 97 CDC LN 67.1 1.140 48.5 79.5 

 98 CDC LN 66.5 1.140 48.3 77.6 

 99 CDC LN 65.9 1.140 48.1 75.6 

 100 CDC LN 65.3 1.140 47.9 73.6 

 0 CDC LN 7.4 1.304 3.7 12.1 
 

B-163

 1 CDC LN 11.1 1.163 7.4 15.3 

 2 CDC LN 13.3 1.158 10.1 20.4 

 3 CDC LN 15.6 1.160 11.0 27.9 

 4 CDC LN 18.0 1.171 12.8 29.1 

 5 CDC LN 20.4 1.229 12.6 40.4 

 6 CDC LN 22.5 1.194 15.9 36.7 

 7 CDC LN 26.5 1.239 16.9 51.0 

 8 CDC LN 30.5 1.315 19.8 60.8 

 9 CDC LN 35.2 1.271 20.3 58.6 

 10 CDC LN 40.6 1.304 22.7 71.2 

 11 CDC LN 46.6 1.302 27.7 84.6 

 12 CDC LN 50.7 1.274 27.8 93.3 

 13 CDC LN 56.6 1.275 33.4 99.5 

 14 CDC LN 57.2 1.248 37.7 110.0 

 15 CDC LN 60.1 1.249 34.9 108.4 

 16 CDC LN 61.6 1.255 40.9 113.8 

 17 CDC LN 61.2 1.248 41.5 133.1 

 18 CDC LN 64.6 1.281 42.4 123.6 

 19 CDC LN 66.2 1.274 41.6 118.5 

 20 CDC LN 67.0 1.262 41.5 122.6 

 21 CDC LN 67.2 1.262 39.7 123.7 

 22 CDC LN 66.8 1.273 42.0 123.5 

 23 CDC LN 69.7 1.304 40.3 143.0 

 24 CDC LN 70.3 1.289 47.5 144.5 

 25 CDC LN 66.3 1.283 44.8 131.8 

 26 CDC LN 73.0 1.281 45.3 128.9 

 27 CDC LN 70.6 1.281 41.4 140.9 

 28 CDC LN 74.4 1.312 44.3 142.1 

 29 CDC LN 69.1 1.250 39.3 116.3 

 30 CDC LN 70.6 1.305 42.1 151.5 

 31 CDC LN 73.0 1.278 43.7 125.9 

 32 CDC LN 72.9 1.281 41.5 139.7 

 33 CDC LN 72.7 1.307 44.9 135.2 

 34 CDC LN 69.8 1.230 46.6 115.3 

 35 CDC LN 73.0 1.306 44.2 138.4 

 36 CDC LN 73.5 1.289 44.6 150.1 

 37 CDC LN 70.0 1.284 48.1 152.1 

 38 CDC LN 75.6 1.295 43.7 151.7 

 39 CDC LN 72.3 1.251 41.6 123.1 

 40 CDC LN 72.9 1.289 45.5 137.4 

 41 CDC LN 73.4 1.268 50.5 156.9 

 42 CDC LN 73.7 1.270 47.1 146.1 

 43 CDC LN 73.4 1.314 45.6 159.5 

 44 CDC LN 75.7 1.266 49.5 153.0 

 45 CDC LN 76.8 1.308 41.6 141.5 

 46 CDC LN 77.5 1.304 46.6 145.8 

 47 CDC LN 72.8 1.298 47.8 130.6 

 48 CDC LN 74.6 1.303 44.2 166.0 

 49 CDC LN 72.8 1.261 45.1 125.5 

 50 CDC LN 75.2 1.292 48.4 175.7 

 51 CDC LN 72.9 1.240 42.5 120.2 

 52 CDC LN 74.5 1.283 45.7 146.6 

 53 CDC LN 74.7 1.259 46.2 176.6 

 54 CDC LN 72.4 1.281 44.3 123.1 

 55 CDC LN 76.0 1.231 53.6 125.6 

 56 CDC LN 77.3 1.315 45.6 134.9 

 57 CDC LN 72.4 1.252 48.6 122.6 

 58 CDC LN 74.5 1.267 45.0 117.7 

 59 CDC LN 80.6 1.277 50.9 133.0 

 60 CDC LN 75.8 1.260 51.3 128.3 

 61 CDC LN 77.1 1.240 50.7 125.6 

 62 CDC LN 73.3 1.198 49.7 121.1 

 63 CDC LN 72.3 1.238 46.9 119.9 

 64 CDC LN 75.4 1.281 41.1 132.5 

 65 CDC LN 72.9 1.254 35.9 113.7 

 66 CDC LN 73.1 1.242 48.4 113.3 

 67 CDC LN 75.8 1.266 47.2 123.8 

 68 CDC LN 73.2 1.250 39.3 120.7 

 69 CDC LN 74.4 1.225 48.0 118.0 

 70 CDC LN 69.0 1.188 45.9 102.8 

 71 CDC LN 69.1 1.232 45.5 108.1 

 72 CDC LN 69.9 1.240 40.7 103.8 

 73 CDC LN 71.4 1.240 47.4 127.6 

 74 CDC LN 70.4 1.277 37.4 106.4 

 75 CDC LN 70.5 1.216 46.8 117.4 

 76 CDC LN 69.5 1.199 48.8 101.7 

 77 CDC LN 70.1 1.240 40.3 119.8 

 78 CDC LN 66.4 1.211 44.1 109.8 

 79 CDC LN 67.8 1.200 46.2 98.4 

 80 CDC LN 62.2 1.255 41.2 121.4 

 81 CDC LN 65.4 1.184 42.7 91.4 
 

B-164

 82 CDC LN 64.8 1.260 40.6 120.0 

 83 CDC LN 62.9 1.196 44.7 101.2 

 84 CDC LN 62.2 1.216 43.5 108.4 

 85 CDC LN 61.5 1.209 42.3 93.2 

 86 CDC LN 62.4 1.210 41.9 101.2 

 87 CDC LN 61.8 1.210 41.7 100.3 

 88 CDC LN 61.3 1.210 41.5 99.4 

 89 CDC LN 60.7 1.210 41.3 98.4 

 90 CDC LN 60.2 1.210 41.1 97.5 

 91 CDC LN 59.6 1.200 40.9 96.6 

 92 CDC LN 59.1 1.200 40.7 95.7 

 93 CDC LN 58.5 1.200 40.5 94.8 

 94 CDC LN 58.0 1.200 40.3 93.9 

 95 CDC LN 57.4 1.200 40.1 93.0 

 96 CDC LN 56.9 1.200 39.9 92.1 

 97 CDC LN 56.3 1.200 39.7 91.2 

 98 CDC LN 55.8 1.190 39.5 90.3 

 99 CDC LN 55.2 1.190 39.3 89.4 

 100 CDC LN 54.7 1.190 39.1 88.5 

Males age 0-100 then females age 0-100 (last revised 6-11-98) 

 Regression equation Estimate for RMR 

 Age Source DV IV Slope Interc SE Units med. wgt 

 0 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.1 

 1 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.7 

 2 R47g BMR BM 0.244 -0.127 0.280 MJ/day 3.2 

 3 R47h BMR BM 0.095 2.110 0.280 MJ/day 3.6 

 4 R47h BMR BM 0.095 2.110 0.280 MJ/day 3.8 

 5 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.0 

 6 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.3 

 7 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.5 

 8 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.8 

 9 R47h BMR BM 0.095 2.110 0.280 MJ/day 5.0 

 10 R47i BMR BM 0.074 2.754 0.440 MJ/day 5.4 

 11 R47i BMR BM 0.074 2.754 0.440 MJ/day 5.7 

 12 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.0 

 13 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.3 

 14 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.9 

 15 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.2 

 16 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.7 

 17 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.6 

 18 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.3 

 19 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.4 

 20 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 21 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 22 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 23 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 24 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 25 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 26 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 27 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 28 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 29 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

 30 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 31 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 32 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 33 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 34 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 35 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 36 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 37 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 38 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 39 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 40 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 41 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 42 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 43 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 44 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 45 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 46 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 47 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 48 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 49 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 50 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 51 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 52 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 53 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 54 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 55 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 56 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 57 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 58 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 
 

B-165

 59 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 60 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 61 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 62 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 63 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 64 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 65 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 66 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 67 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 68 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 69 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 70 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

 71 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 72 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 73 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 74 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 75 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 76 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 77 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 78 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 79 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 80 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 81 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 82 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 83 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 84 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 85 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 86 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 87 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 88 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 89 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 90 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 91 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 92 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 93 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 94 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 95 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 96 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 97 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 98 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 99 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 100 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

 0 R47a BMR BM 0.244 -0.130 0.250 MJ/day 2.0 

 1 R47a BMR BM 0.244 -0.130 0.250 MJ/day 2.5 

 2 R47a BMR BM 0.244 -0.130 0.250 MJ/day 3.0 

 3 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.3 

 4 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.5 

 5 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.7 

 6 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.9 

 7 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.1 

 8 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.4 

 9 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.7 

 10 R47c BMR BM 0.056 2.898 0.470 MJ/day 4.9 

 11 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.2 

 12 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.5 

 13 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.7 

 14 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.9 

 15 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.0 

 16 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.1 

 17 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.2 

 18 R47d BMR BM 0.062 2.036 0.500 MJ/day 5.7 

 19 R47d BMR BM 0.062 2.036 0.500 MJ/day 5.8 

 20 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 21 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 22 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 23 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 24 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 25 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 26 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 27 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 28 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 29 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

 30 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 31 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 32 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 33 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 34 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 35 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 36 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 37 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 38 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 
 

B-166

 39 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 40 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 41 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 42 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 43 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 44 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 45 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 46 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 47 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 48 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 49 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 50 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 51 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 52 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 53 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 54 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 55 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 56 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 57 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 58 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 59 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

 60 R47e BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 61 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 62 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 63 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 64 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 65 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 66 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 67 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 68 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 69 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 70 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 71 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 72 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 73 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 74 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 75 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 76 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 77 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 78 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 79 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 80 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 81 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 82 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 83 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 84 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 85 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 86 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 87 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 88 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 89 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 90 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 91 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 92 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 93 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 94 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 95 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 96 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 97 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 98 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 99 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

 100 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

Males age 0-100 then females age 0-100 (HG last revised 12-20-05) 

Blood Volume factor and Hemoglobin content 

 Age BLDFAC HGMN HGSTD 

 0 17.0 11.9 1.0 

 1 17.0 12.2 1.0 

 2 17.0 12.4 0.8 

 3 17.0 12.7 0.8 

 4 17.0 12.8 0.8 

 5 17.0 13.0 0.9 

 6 17.0 13.2 0.9 

 7 17.0 13.5 0.8 

 8 17.0 13.4 0.8 

 9 17.0 13.6 1.0 

 10 17.0 13.6 0.9 

 11 17.0 13.7 0.7 

 12 17.0 14.0 1.0 

 13 17.0 14.3 1.0 

 14 17.0 14.7 1.0 

 15 17.0 15.1 1.0 
 

B-167

 16 17.0 15.4 1.0 

 17 17.0 15.5 1.0 

 18 17.0 15.7 1.0 

 19 20.4 15.8 0.8 

 20 20.4 15.8 0.9 

 21 20.4 15.7 0.9 

 22 20.4 15.7 0.9 

 23 20.4 15.7 0.9 

 24 20.4 15.7 0.9 

 25 20.4 15.7 0.9 

 26 20.4 15.7 1.0 

 27 20.4 15.7 1.0 

 28 20.4 15.7 1.0 

 29 20.4 15.7 1.0 

 30 20.4 15.7 1.0 

 31 20.4 15.6 1.0 

 32 20.4 15.6 1.0 

 33 20.4 15.6 1.0 

 34 20.4 15.6 1.0 

 35 20.4 15.6 1.0 

 36 20.4 15.4 1.0 

 37 20.4 15.4 1.0 

 38 20.4 15.4 1.0 

 39 20.4 15.4 1.0 

 40 20.4 15.4 1.0 

 41 20.4 15.4 1.1 

 42 20.4 15.4 1.1 

 43 20.4 15.4 1.1 

 44 20.4 15.4 1.1 

 45 20.4 15.4 1.1 

 46 20.4 15.4 1.1 

 47 20.4 15.4 1.1 

 48 20.4 15.4 1.1 

 49 20.4 15.4 1.1 

 50 20.4 15.4 1.1 

 51 20.4 15.3 1.1 

 52 20.4 15.3 1.1 

 53 20.4 15.3 1.1 

 54 20.4 15.3 1.1 

 55 20.4 15.3 1.1 

 56 20.4 15.3 1.0 

 57 20.4 15.3 1.0 

 58 20.4 15.3 1.0 

 59 20.4 15.3 1.0 

 60 20.4 15.3 1.0 

 61 20.4 15.1 1.2 

 62 20.4 15.1 1.2 

 63 20.4 15.1 1.2 

 64 20.4 15.1 1.2 

 65 20.4 15.1 1.2 

 66 20.4 15.0 1.2 

 67 20.4 15.0 1.2 

 68 20.4 15.0 1.2 

 69 20.4 15.0 1.2 

 70 20.4 15.0 1.2 

 71 20.4 14.7 1.4 

 72 20.4 14.7 1.4 

 73 20.4 14.7 1.4 

 74 20.4 14.7 1.4 

 75 20.4 14.7 1.4 

 76 20.4 14.5 1.5 

 77 20.4 14.5 1.5 

 78 20.4 14.5 1.5 

 79 20.4 14.5 1.5 

 80 20.4 14.5 1.5 

 81 20.4 14.5 1.4 

 82 20.4 14.5 1.4 

 83 20.4 14.5 1.4 

 84 20.4 14.5 1.4 

 85 20.4 14.5 1.4 

 86 20.4 14.0 1.8 

 87 20.4 14.0 1.8 

 88 20.4 14.0 1.8 

 89 20.4 14.0 1.8 

 90 20.4 14.0 1.8 

 91 20.4 13.8 1.8 

 92 20.4 13.8 1.8 

 93 20.4 13.8 1.8 

 94 20.4 13.8 1.8 

 95 20.4 13.8 1.8 

 96 20.4 13.5 1.8 
 

B-168

 97 20.4 13.5 1.8 

 98 20.4 13.5 1.8 

 99 20.4 13.5 1.8 

 100 20.4 13.5 1.8 

 0 17.0 12.2 0.7 

 1 17.0 12.3 0.7 

 2 17.0 12.6 0.8 

 3 17.0 12.5 1.0 

 4 17.0 12.8 0.8 

 5 17.0 12.9 1.0 

 6 17.0 13.0 0.8 

 7 17.0 13.1 0.8 

 8 17.0 13.3 0.8 

 9 17.0 13.4 0.8 

 10 17.0 13.6 1.0 

 11 17.0 13.5 0.9 

 12 17.0 13.6 0.9 

 13 17.0 13.5 1.0 

 14 17.0 13.6 1.0 

 15 17.0 13.5 0.9 

 16 17.0 13.5 1.1 

 17 17.0 13.5 1.1 

 18 17.0 13.5 1.2 

 19 14.6 13.4 1.1 

 20 14.6 13.5 1.1 

 21 14.6 13.5 1.2 

 22 14.6 13.5 1.2 

 23 14.6 13.5 1.2 

 24 14.6 13.5 1.2 

 25 14.6 13.5 1.2 

 26 14.6 13.3 1.1 

 27 14.6 13.3 1.1 

 28 14.6 13.3 1.1 

 29 14.6 13.3 1.1 

 30 14.6 13.3 1.1 

 31 14.6 13.3 1.1 

 32 14.6 13.3 1.1 

 33 14.6 13.3 1.1 

 34 14.6 13.3 1.1 

 35 14.6 13.3 1.1 

 36 14.6 13.5 1.2 

 37 14.6 13.5 1.2 

 38 14.6 13.5 1.2 

 39 14.6 13.5 1.2 

 40 14.6 13.5 1.2 

 41 14.6 13.5 1.3 

 42 14.6 13.5 1.3 

 43 14.6 13.5 1.3 

 44 14.6 13.5 1.3 

 45 14.6 13.5 1.3 

 46 14.6 13.6 1.2 

 47 14.6 13.6 1.2 

 48 14.6 13.6 1.2 

 49 14.6 13.6 1.2 

 50 14.6 13.6 1.2 

 51 14.6 13.7 1.1 

 52 14.6 13.7 1.1 

 53 14.6 13.7 1.1 

 54 14.6 13.7 1.1 

 55 14.6 13.7 1.1 

 56 14.6 13.8 1.2 

 57 14.6 13.8 1.2 

 58 14.6 13.8 1.2 

 59 14.6 13.8 1.2 

 60 14.6 13.8 1.2 

 61 14.6 13.8 1.1 

 62 14.6 13.8 1.1 

 63 14.6 13.8 1.1 

 64 14.6 13.8 1.1 

 65 14.6 13.8 1.1 

 66 14.6 13.8 1.1 

 67 14.6 13.8 1.1 

 68 14.6 13.8 1.1 

 69 14.6 13.8 1.1 

 70 14.6 13.8 1.1 

 71 14.6 13.8 1.1 

 72 14.6 13.8 1.1 

 73 14.6 13.8 1.1 

 74 14.6 13.8 1.1 

 75 14.6 13.8 1.1 

 76 14.6 13.8 1.3 
 

B-169

 77 14.6 13.8 1.3 

 78 14.6 13.8 1.3 

 79 14.6 13.8 1.3 

 80 14.6 13.8 1.3 

 81 14.6 13.6 1.2 

 82 14.6 13.6 1.2 

 83 14.6 13.6 1.2 

 84 14.6 13.6 1.2 

 85 14.6 13.6 1.2 

 86 14.6 13.4 1.6 

 87 14.6 13.4 1.6 

 88 14.6 13.4 1.6 

 89 14.6 13.4 1.6 

 90 14.6 13.4 1.6 

 91 14.6 13.2 1.6 

 92 14.6 13.2 1.6 

 93 14.6 13.2 1.6 

 94 14.6 13.2 1.6 

 95 14.6 13.2 1.6 

 96 14.6 13.0 1.6 

 97 14.6 13.0 1.6 

 98 14.6 13.0 1.6 

 99 14.6 13.0 1.6 

 100 14.6 13.0 1.6
 

B-170

Appendix E. All Derived Physiological Parameters

Table 7. Nv02max Values for Males: Raw and Smoothed Fits. 

 MALES 

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit Values

Raw 

Fit Values

Smoothed

 Fit Values (1st Pctl) (99th Pctl)

0.00 48.25 1.71 44.26 52.24

1.00 48.56 2.04 43.82 53.30

2.00 48.88 2.36 43.39 54.37

3.00 49.19 2.68 42.95 55.43

4.00 49.50 3.01 42.51 56.50

5.00 49.82 3.33 42.07 57.56

6.00 50.13 3.65 41.63 58.63

7.00 51.37 50.44 2.86 3.98 41.19 59.70

8.00 53.46 50.76 2.86 4.30 40.76 60.76

9.00 51.10 51.07 6.26 4.62 40.32 61.83

10.00 51.28 51.39 5.87 4.95 39.88 62.89

11.00 50.13 51.70 6.04 5.27 39.44 63.96

12.00 50.70 52.01 7.13 5.59 39.00 65.02

13.00 52.74 52.33 5.13 5.92 38.56 66.09

14.00 52.93 52.64 4.72 6.24 38.13 67.16

15.00 53.18 52.95 5.57 6.56 37.69 68.22

16.00 49.46 53.27 6.06 6.89 37.25 69.29

17.00 49.77 53.58 6.93 7.21 36.81 70.35

18.00 51.98 53.90 7.48 7.53 36.37 71.42

19.00 59.88 54.21 9.65 7.86 35.93 72.48

20.00 56.80 54.52 9.31 8.18 35.50 73.55

21.00 54.60 54.23 8.17 8.50 34.45 74.01

22.00 54.61 53.42 8.40 8.83 32.89 73.95

23.00 53.76 52.63 9.60 9.15 31.35 73.91

24.00 57.23 51.84 10.44 9.47 29.81 73.88

25.00 50.90 51.07 10.63 9.80 28.29 73.86

26.00 50.06 50.31 9.66 10.69 25.45 75.17

27.00 46.38 49.56 8.95 10.49 25.16 73.96

28.00 48.32 48.82 10.47 10.29 24.88 72.77

29.00 51.02 48.10 12.31 10.10 24.60 71.59

30.00 45.59 47.38 9.91 9.92 24.32 70.44

31.00 45.86 46.67 10.14 9.73 24.04 69.31

32.00 46.90 45.98 11.03 9.55 23.76 68.20

33.00 42.08 45.30 9.08 9.38 23.49 67.10

34.00 44.48 44.63 8.95 9.20 23.22 66.03

35.00 38.63 43.97 10.10 9.03 22.95 64.98

36.00 42.63 43.32 7.11 8.87 22.69 63.95

37.00 40.41 42.68 8.81 8.71 22.42 62.94

38.00 39.70 42.05 6.22 8.55 22.16 61.94
 

B-171

39.00 40.62 41.44 8.01 8.40 21.90 60.97

40.00 39.02 40.83 8.28 8.25 21.64 60.02

41.00 39.72 40.24 9.96 8.10 21.39 59.09

42.00 35.58 39.66 9.85 7.96 21.14 58.18

43.00 39.98 39.09 6.46 7.82 20.89 57.28

44.00 38.65 38.53 7.60 7.69 20.64 56.41

45.00 40.15 37.98 6.59 7.56 20.40 55.56

46.00 40.67 37.44 7.89 7.43 20.16 54.73

47.00 41.51 36.92 9.68 7.31 19.91 53.92

48.00 38.92 36.40 10.52 7.19 19.68 53.12

49.00 34.65 35.90 7.68 7.07 19.44 52.35

50.00 33.85 35.41 6.49 6.96 19.21 51.60

51.00 32.52 34.92 4.51 6.86 18.98 50.87

52.00 36.31 34.45 7.08 6.75 18.75 50.16

53.00 36.23 34.00 7.31 6.65 18.52 49.47

54.00 33.91 33.55 5.29 6.56 18.30 48.79

55.00 33.40 33.11 5.08 6.46 18.08 48.14

56.00 31.68 32.69 6.52 6.37 17.86 47.51

57.00 32.47 32.27 6.33 6.29 17.64 46.90

58.00 33.24 31.87 6.32 6.21 17.43 46.31

59.00 33.05 31.48 6.45 6.13 17.22 45.74

60.00 29.02 31.10 3.59 6.06 17.01 45.19

61.00 31.68 30.73 6.95 5.99 16.80 44.66

62.00 29.72 30.37 5.09 5.92 16.60 44.14

63.00 30.90 30.02 8.06 5.86 16.40 43.65

64.00 30.65 29.69 5.32 5.80 16.20 43.18

65.00 29.86 29.36 6.90 5.75 16.00 42.73

66.00 28.60 29.05 5.51 5.70 15.80 42.30

67.00 29.47 28.75 5.25 5.65 15.61 41.89

68.00 28.95 28.46 5.63 5.61 15.42 41.50

69.00 31.13 28.18 6.43 5.57 15.23 41.13

70.00 27.12 27.91 3.44 5.53 15.05 40.78

71.00 27.65 5.50 14.86 40.45

72.00 28.56 27.41 5.71 5.47 14.68 40.13

73.00 27.62 27.17 5.03 5.45 14.50 39.84

74.00 27.84 26.95 6.27 5.43 14.33 39.57

75.00 26.74 5.41 14.15 39.32

76.00 25.05 26.54 6.68 5.40 13.98 39.09

77.00 23.74 26.35 4.99 5.39 13.81 38.88

78.00 26.17 5.38 13.65 38.69

79.00 26.00 5.38 13.48 38.52

80.00 25.84 5.39 13.32 38.37

81.00 23.68 25.70 5.88 5.39 13.17 38.22

82.00 25.57 5.39 13.04 38.09

83.00 25.44 5.39 12.92 37.97

84.00 25.33 5.39 12.81 37.86

85.00 25.23 5.39 12.70 37.76

86.00 25.14 5.39 12.62 37.67

87.00 25.06 5.39 12.54 37.59

88.00 25.00 5.39 12.47 37.52
 

B-172

89.00 24.94 5.39 12.42 37.47

90.00 24.90 5.39 12.37 37.42

91.00 24.86 5.39 12.34 37.39

92.00 24.84 5.39 12.32 37.37

93.00 24.83 5.39 12.31 37.36

94.00 24.83 5.39 12.31 37.36

95.00 24.84 5.39 12.32 37.37

96.00 24.87 5.39 12.34 37.39

97.00 24.90 5.39 12.37 37.43

98.00 24.95 5.39 12.42 37.47

99.00 25.00 5.39 12.48 37.53

100.00 25.07 5.39 12.54 37.60

Table 8. Nv02max Values for Females: Raw and Smoothed Fits 

 FEMALES 

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit 

Values 

Raw 

Fit 

Values 

Smoothed

 Fit 

Values 

(1st 

Pctl) (99th Pctl) 

0.00 35.88 5.90 22.15 49.61

1.00 36.21 6.00 22.26 50.17

2.00 36.54 6.09 22.37 50.72

3.00 36.87 6.19 22.48 51.27

4.00 37.20 6.28 22.59 51.82

5.00 37.54 6.38 22.70 52.37

6.00 37.87 6.47 22.81 52.93

7.00 38.20 6.57 22.92 53.48

8.00 38.53 6.66 23.03 54.03

9.00 30.56 38.86 9.90 6.76 23.14 54.58

10.00 45.53 39.19 6.27 6.85 23.25 55.13

11.00 43.88 39.52 5.26 6.95 23.36 55.69

12.00 43.03 39.85 6.88 7.04 23.47 56.24

13.00 42.00 40.18 7.48 7.14 23.58 56.79

14.00 37.57 40.51 6.79 7.23 23.69 57.34

15.00 39.57 40.85 5.43 7.33 23.80 57.89

16.00 35.51 41.18 5.36 7.42 23.91 58.45

17.00 38.22 41.51 8.86 7.52 24.02 59.00

18.00 45.67 41.84 8.53 7.61 24.13 59.55

19.00 43.87 42.17 7.83 7.71 24.24 60.10

20.00 42.52 42.50 7.69 7.80 24.35 60.65

21.00 43.45 42.10 8.51 7.90 23.73 60.48

22.00 43.22 41.45 7.59 7.99 22.86 60.05

23.00 43.87 40.81 10.13 8.09 21.99 59.63

24.00 41.14 40.18 8.22 8.18 21.14 59.22

25.00 38.20 39.56 7.09 8.28 20.30 58.82

26.00 38.98 38.95 11.12 8.37 19.47 58.43

27.00 34.94 38.35 8.02 8.35 18.93 57.76
 

B-173

28.00 38.08 37.75 9.80 8.14 18.82 56.69

29.00 35.13 37.17 6.30 7.94 18.71 55.64

30.00 35.79 36.60 9.10 7.74 18.59 54.61

31.00 35.22 36.04 7.89 7.55 18.47 53.60

32.00 36.06 35.48 6.93 7.37 18.35 52.62

33.00 34.95 34.94 9.51 7.19 18.23 51.66

34.00 38.13 34.41 7.08 7.01 18.10 50.72

35.00 32.63 33.88 4.88 6.84 17.97 49.80

36.00 33.59 33.37 6.17 6.68 17.83 48.91

37.00 31.11 32.87 5.13 6.52 17.70 48.04

38.00 33.12 32.37 3.76 6.37 17.55 47.19

39.00 28.80 31.89 5.14 6.22 17.41 46.37

40.00 29.06 31.42 5.74 6.08 17.26 45.57

41.00 29.54 30.95 8.00 5.95 17.11 44.79

42.00 30.90 30.50 6.82 5.82 16.96 44.03

43.00 27.60 30.05 4.32 5.70 16.80 43.30

44.00 29.33 29.62 4.17 5.58 16.64 42.59

45.00 28.53 29.19 4.90 5.47 16.48 41.90

46.00 29.41 28.78 6.00 5.36 16.31 41.24

47.00 30.49 28.37 7.15 5.26 16.14 40.60

48.00 27.92 27.97 6.05 5.16 15.97 39.98

49.00 26.48 27.59 5.36 5.07 15.79 39.38

50.00 29.80 27.21 5.13 4.99 15.61 38.81

51.00 27.49 26.84 3.66 4.91 15.43 38.26

52.00 28.95 26.49 5.83 4.83 15.24 37.73

53.00 23.77 26.14 3.56 4.77 15.06 37.23

54.00 25.34 25.80 4.61 4.70 14.86 36.74

55.00 26.05 25.48 4.29 4.65 14.67 36.29

56.00 26.30 25.16 4.91 4.60 14.47 35.85

57.00 26.06 24.85 4.07 4.55 14.27 35.44

58.00 24.55 4.51 14.06 35.05

59.00 24.27 4.48 13.85 34.68

60.00 23.67 23.99 4.81 4.45 13.64 34.33

61.00 24.70 23.72 4.65 4.43 13.43 34.01

62.00 21.63 23.46 4.99 4.41 13.21 33.71

63.00 26.64 23.21 7.38 4.40 12.99 33.44

64.00 23.84 22.97 3.77 4.39 12.76 33.18

65.00 20.26 22.74 3.83 4.39 12.53 32.95

66.00 20.38 22.52 4.39 12.31 32.73

67.00 20.49 22.31 4.39 12.10 32.52

68.00 22.05 22.11 3.90 4.39 11.90 32.32

69.00 21.92 21.92 4.56 4.39 11.71 32.13

70.00 20.38 21.74 4.15 4.39 11.53 31.95

71.00 25.30 21.57 4.39 11.36 31.78

72.00 21.21 21.41 4.39 11.20 31.62

73.00 20.46 21.26 4.59 4.39 11.05 31.47

74.00 20.63 21.12 4.39 10.91 31.33

75.00 20.60 20.99 3.80 4.39 10.78 31.20

76.00 20.91 20.87 4.39 10.66 31.08

77.00 22.27 20.76 4.39 10.55 30.97
 

B-174

78.00 19.93 20.65 4.39 10.44 30.86

79.00 22.80 20.56 4.39 10.35 30.77

80.00 23.19 20.48 4.39 10.27 30.69

81.00 19.29 20.41 4.39 10.20 30.62

82.00 13.44 20.34 4.39 10.13 30.55

83.00 28.03 20.29 4.39 10.08 30.50

84.00 17.00 20.25 4.39 10.04 30.46

85.00 18.69 20.21 4.39 10.00 30.42

86.00 18.18 20.19 4.39 9.98 30.40

87.00 20.18 4.39 9.97 30.39

88.00 27.15 20.17 4.39 9.96 30.38

89.00 20.18 4.39 9.97 30.39

90.00 18.18 20.20 4.39 9.98 30.41

91.00 20.22 4.39 10.01 30.43

92.00 20.26 4.39 10.05 30.47

93.00 20.30 4.39 10.09 30.51

94.00 20.36 4.39 10.15 30.57

95.00 20.42 4.39 10.21 30.63

96.00 20.50 4.39 10.28 30.71

97.00 20.58 4.39 10.37 30.79

98.00 20.67 4.39 10.46 30.88

99.00 20.78 4.39 10.57 30.99

100.00 20.89 4.39 10.68 31.10

Table 3. Body Mass Raw Fits. 

 

 MALES FEMALES 

Age 

Geometric 

Mean GSD Min Max 

Geometric

 Mean GSD Min Max 

0.00 7.767 1.301 3.6 11.8 7.429 1.304 3.7 12.1

1.00 11.440 1.143 8.2 16.1 11.119 1.163 7.4 15.3

2.00 13.932 1.146 9.8 20.9 13.258 1.158 10.1 20.4

3.00 15.967 1.154 11.7 23.7 15.587 1.160 11 27.9

4.00 18.475 1.165 11.1 28.1 18.005 1.171 12.8 29.1

5.00 21.618 1.234 13.7 42.4 20.353 1.229 12.6 40.4

6.00 23.142 1.213 16.1 41.1 22.454 1.194 15.9 36.7

7.00 27.072 1.216 19.3 46.8 26.483 1.239 16.9 51

8.00 31.651 1.302 19.1 66.2 30.534 1.315 19.8 60.8

9.00 34.656 1.265 24 69.9 35.235 1.271 20.3 58.6

10.00 38.329 1.280 24.3 72.9 40.550 1.304 22.7 71.2

11.00 44.149 1.308 26.2 83.8 46.579 1.302 27.7 84.6

12.00 47.988 1.315 27.7 94.8 50.673 1.274 27.8 93.3

13.00 55.364 1.340 27.7 106.6 56.649 1.275 33.4 99.5

14.00 62.832 1.293 35.7 121 57.214 1.248 37.7 110

15.00 67.650 1.255 41.5 117.9 60.091 1.249 34.9 108.4

16.00 72.460 1.267 45.8 139.1 61.582 1.255 40.9 113.8

17.00 73.081 1.248 49.9 136.6 61.229 1.248 41.5 133.1
 

B-175

18.00 75.060 1.243 51.2 144.2 64.591 1.281 42.4 123.6

19.00 77.182 1.245 52.6 134.5 66.156 1.274 41.6 118.5

20.00 77.952 1.250 50.5 130 66.981 1.262 41.5 122.6

21.00 78.239 1.297 46.8 199.2 67.218 1.262 39.7 123.7

22.00 83.845 1.292 53.3 155.4 66.823 1.273 42 123.5

23.00 80.607 1.222 50.5 137.6 69.721 1.304 40.3 143

24.00 81.706 1.251 50.6 132.6 70.284 1.289 47.5 144.5

25.00 84.818 1.206 50.2 136.1 66.300 1.283 44.8 131.8

26.00 81.812 1.273 48.9 164.5 72.973 1.281 45.3 128.9

27.00 85.166 1.249 50 153.9 70.604 1.281 41.4 140.9

28.00 84.321 1.272 51 167.2 74.363 1.312 44.3 142.1

29.00 82.144 1.236 50.6 147.2 69.110 1.250 39.3 116.3

30.00 81.581 1.262 52.5 139 70.616 1.305 42.1 151.5

31.00 81.275 1.249 48.8 170.6 73.039 1.278 43.7 125.9

32.00 84.715 1.235 49.7 135.8 72.938 1.281 41.5 139.7

33.00 88.188 1.231 64.8 146.3 72.710 1.307 44.9 135.2

34.00 81.163 1.221 53.1 136.9 69.773 1.230 46.6 115.3

35.00 87.192 1.251 61 193.3 73.044 1.306 44.2 138.4

36.00 83.404 1.228 45.8 140.5 73.547 1.289 44.6 150.1

37.00 85.759 1.241 59.3 150.9 70.019 1.284 48.1 152.1

38.00 84.132 1.260 52.8 149.7 75.587 1.295 43.7 151.7

39.00 84.611 1.196 61.2 140.6 72.295 1.251 41.6 123.1

40.00 90.071 1.246 58.5 154 72.888 1.289 45.5 137.4

41.00 87.425 1.173 61.3 117.7 73.363 1.268 50.5 156.9

42.00 88.290 1.205 62.2 144 73.697 1.270 47.1 146.1

43.00 88.423 1.233 54 145.3 73.438 1.314 45.6 159.5

44.00 88.528 1.200 56.6 128.9 75.742 1.266 49.5 153

45.00 87.102 1.205 60.6 160.2 76.795 1.308 41.6 141.5

46.00 88.157 1.243 54.2 154.3 77.544 1.304 46.6 145.8

47.00 86.547 1.229 49.9 188.3 72.849 1.298 47.8 130.6

48.00 84.793 1.186 56.3 128.3 74.646 1.303 44.2 166

49.00 86.235 1.240 47 171.3 72.844 1.261 45.1 125.54

50.00 84.659 1.179 53.4 124.4 75.217 1.292 48.4 175.7

51.00 87.975 1.208 57.9 143.6 72.941 1.240 42.5 120.2

52.00 89.886 1.216 55.2 144.9 74.472 1.283 45.7 146.6

53.00 89.012 1.228 58.2 143.3 74.733 1.259 46.2 176.6

54.00 90.098 1.216 64.1 155.2 72.413 1.281 44.3 123.1

55.00 88.268 1.222 55.1 138.6 75.951 1.231 53.6 125.6

56.00 84.796 1.195 45 110.3 77.322 1.315 45.6 134.9

57.00 87.501 1.253 58.3 160 72.378 1.252 48.6 122.6

58.00 85.116 1.266 51.6 179 74.548 1.267 45 117.7

59.00 84.190 1.182 58.7 112.4 80.638 1.277 50.9 133

60.00 87.044 1.232 57.3 141.7 75.777 1.260 51.3 128.3

61.00 89.007 1.207 49.9 162.8 77.121 1.240 50.7 125.6

62.00 84.788 1.228 56.04 152.1 73.347 1.198 49.7 121.1

63.00 89.137 1.262 56.3 171.6 72.308 1.238 46.9 119.9

64.00 89.974 1.193 59.1 119 75.440 1.281 41.1 132.5

65.00 89.891 1.215 58.1 126.3 72.910 1.254 35.9 113.7

66.00 86.814 1.228 54 150.1 73.101 1.242 48.4 113.3

67.00 86.207 1.207 43.1 127.5 75.835 1.266 47.2 123.8
 

B-176

68.00 85.172 1.191 61.2 163.2 73.207 1.250 39.3 120.7

69.00 87.116 1.222 50.7 127.2 74.368 1.225 48 118

70.00 82.775 1.210 46.5 125.5 68.977 1.188 45.9 102.8

71.00 79.630 1.240 51 122.8 69.083 1.232 45.5 108.1

72.00 82.011 1.204 51.9 132.7 69.898 1.240 40.7 103.8

73.00 85.590 1.196 56.2 128.3 71.360 1.240 47.4 127.6

74.00 83.001 1.217 53.3 120 70.410 1.277 37.4 106.4

75.00 84.465 1.185 56.5 133.5 70.526 1.216 46.8 117.4

76.00 78.733 1.207 55.9 121.1 69.549 1.199 48.8 101.7

77.00 79.376 1.170 58.7 109.3 70.128 1.240 40.3 119.8

78.00 79.909 1.195 41.1 115.1 66.375 1.211 44.1 109.8

79.00 77.629 1.155 56.4 107.8 67.780 1.200 46.2 98.4

80.00 79.866 1.174 56 111.9 62.214 1.255 41.2 121.4

81.00 75.405 1.157 55.8 111.9 65.397 1.184 42.7 91.4

82.00 76.798 1.180 54.4 111.8 64.755 1.260 40.6 120

83.00 74.611 1.158 53.2 107 62.886 1.196 44.7 101.2

84.00 75.325 1.205 41.5 109.5 62.215 1.216 43.5 108.4

85.00 71.776 1.191 46.9 105.8 61.453 1.209 42.3 93.2

86.00 73.986494 1.17 50.57 101.07 62.400356 1.21 41.85 101.16

87.00 73.364276 1.17 50.38 99.113 61.847614 1.21 41.66 100.26

88.00 72.742058 1.16 50.19 97.154 61.294872 1.21 41.47 99.351

89.00 72.11984 1.16 50 95.194 60.74213 1.21 41.27 98.445

90.00 71.497622 1.16 49.81 93.235 60.189388 1.21 41.08 97.538

91.00 70.875404 1.16 49.62 91.276 59.636646 1.2 40.88 96.632

92.00 70.253186 1.16 49.44 89.317 59.083904 1.2 40.69 95.726

93.00 69.630968 1.15 49.25 87.358 58.531162 1.2 40.49 94.82

94.00 69.00875 1.15 49.06 85.399 57.97842 1.2 40.3 93.914

95.00 68.386532 1.15 48.87 83.44 57.425678 1.2 40.1 93.008

96.00 67.764314 1.15 48.68 81.481 56.872936 1.2 39.91 92.102

97.00 67.142096 1.14 48.49 79.522 56.320194 1.2 39.71 91.195

98.00 66.519878 1.14 48.3 77.563 55.767452 1.19 39.52 90.289

99.00 65.89766 1.14 48.11 75.604 55.21471 1.19 39.32 89.383

100.00 65.275442 1.14 47.92 73.645 54.661968 1.19 39.13 88.477

 **Dark shading (age 86+) designates linear forecast. 

Table 4. Body Mass Smoothed Fits (5-Year Running Averages). 

 

 MALES FEMALES 

Age 

Geometric 

 Mean GSD Min Max 

Geometric 

 Mean GSD Min Max 

0.00 7.767209794 1.300901 3.6 11.8 7.428916349 1.304229 3.7 12.1

1.00 11.44008024 1.143324 8.2 16.1 11.11947416 1.162608 7.4 15.3

2.00 13.93227373 1.145566 9.8 20.9 13.25797158 1.158434 10.1 20.4

3.00 15.96664726 1.153689 11.7 23.7 15.58684049 1.159883 11 27.9

4.00 18.47458493 1.164972 11.1 28.1 18.00506307 1.17108 12.8 29.1

5.00 21.61756114 1.233822 13.7 42.4 20.35285099 1.229237 12.6 40.4
 

B-177

6.00 23.14243627 1.213499 16.1 41.1 22.45431948 1.194119 15.9 36.7

7.00 27.07246068 1.215834 19.3 46.8 26.48323788 1.23892 16.9 51

8.00 31.6505017 1.301873 19.1 66.2 30.53391399 1.315137 19.8 60.8

9.00 34.65600448 1.265317 24 69.9 35.23472141 1.271364 20.3 58.6

10.00 38.32939135 1.279707 24.3 72.9 40.54996835 1.303997 22.7 71.2

11.00 44.14863459 1.30753 26.2 83.8 46.57910267 1.302182 27.7 84.6

12.00 47.98795299 1.314848 27.7 94.8 50.67329267 1.273946 27.8 93.3

13.00 55.36374737 1.33952 27.7 106.6 56.64881107 1.275455 33.4 99.5

14.00 62.83159173 1.292533 35.7 121 57.21362103 1.24795 37.7 110

15.00 67.65031426 1.254999 41.5 117.9 60.09135575 1.24897 34.9 108.4

16.00 72.45980541 1.267468 45.8 139.1 61.58214656 1.255162 40.9 113.8

17.00 73.08089659 1.248405 49.9 136.6 61.22931022 1.248057 41.5 133.1

18.00 75.06031573 1.243204 51.2 144.2 64.59054256 1.281298 42.4 123.6

19.00 77.18236513 1.244928 52.6 134.5 66.15556407 1.274083 41.6 118.5

20.00 77.95205826 1.250326 50.5 130 66.98146906 1.261822 41.5 122.6

21.00 78.45564692 1.265585 50.88 152.66 66.35375002 1.270386 41.44 122.38

22.00 79.56489519 1.261251 50.74 151.34 67.37976393 1.274844 41.02 126.26

23.00 80.46958232 1.262527 50.34 150.96 68.20537834 1.277813 42.2 131.46

24.00 81.84267254 1.253588 50.28 152.18 68.06901959 1.282127 42.86 133.3

25.00 82.55729313 1.248802 50.7 145.24 69.21992781 1.285979 43.98 134.34

26.00 82.82151847 1.240222 50.04 144.94 69.97607936 1.287735 43.86 137.82

27.00 83.56439112 1.250399 50.14 150.86 70.90453453 1.289413 44.66 137.64

28.00 83.65195203 1.247428 50.14 153.78 70.66975978 1.28161 43.02 132

29.00 83.00459482 1.258753 50.6 154.36 71.53295767 1.285847 42.48 135.94

30.00 82.89721864 1.253937 50.58 155.58 71.54621552 1.285108 42.16 135.34

31.00 82.80701235 1.251132 50.52 151.96 72.01313142 1.28495 42.18 135.1

32.00 83.58034187 1.242848 53.28 147.78 71.6826276 1.283915 42.3 133.72

33.00 83.38418057 1.239735 53.78 145.72 71.81523165 1.280002 43.76 133.52

34.00 84.50647805 1.237533 55.48 156.58 72.30094254 1.280205 44.18 130.9

35.00 84.9321819 1.233184 54.88 150.56 72.40264379 1.282492 44.36 135.74

36.00 85.14102649 1.234298 56.8 153.58 71.81884258 1.283151 45.68 138.22

37.00 84.32994666 1.240177 54.4 154.26 72.3941641 1.280709 45.44 141.52

38.00 85.01958212 1.235131 56.02 155 72.89859355 1.284821 44.44 143.08

39.00 85.59524544 1.233983 55.52 147.14 72.86733489 1.281407 44.7 142.88

40.00 86.39949423 1.223065 58.62 142.58 72.830387 1.277165 45.88 144.24

41.00 86.90564401 1.215924 59.2 141.2 73.56585153 1.274483 45.68 143.04

42.00 87.76379051 1.210495 59.44 140.32 73.13604869 1.278433 46.06 144.6

43.00 88.54719729 1.211458 58.52 137.98 73.82543503 1.281514 47.64 150.58

44.00 87.95342484 1.203416 58.94 139.22 74.60684165 1.285327 46.86 151.4

45.00 88.09985934 1.217379 57.52 146.54 75.44302619 1.292445 46.08 149.18

46.00 87.751282 1.222211 55.06 155.4 75.27348935 1.29795 46.22 146.08

47.00 87.02523405 1.212835 55.52 152 75.51517243 1.295658 45.94 147.38

48.00 86.56661258 1.220669 53.6 160.48 74.93569966 1.29459 45.06 141.888

49.00 86.07815707 1.215489 52.16 153.32 74.62001355 1.291492 46.42 148.728

50.00 86.04175058 1.208607 52.9 151.18 73.69947055 1.278764 45.6 143.608

51.00 86.70964624 1.206031 53.96 142.5 74.02400492 1.27574 45.18 146.808

52.00 87.55345712 1.2144 54.34 145.5 74.04127315 1.266893 45.58 148.928

53.00 88.32616726 1.209663 57.76 142.28 73.95491798 1.270898 45.42 148.44

54.00 89.04784314 1.218268 58.1 145.12 74.10188224 1.25873 46.46 138.42

55.00 88.4120991 1.215526 55.52 138.46 74.97813364 1.273724 47.08 141.36
 

B-178

56.00 87.93495739 1.222906 56.14 141.48 74.55937637 1.267547 47.66 136.56

57.00 87.15584772 1.230391 54.82 148.62 74.52242942 1.269258 47.42 124.78

58.00 85.97418819 1.223617 53.74 140.06 76.16748501 1.268509 48.74 126.76

59.00 85.72952642 1.22558 54.18 140.68 76.13252691 1.274205 48.28 127.3

60.00 86.57173577 1.228074 55.16 151.18 76.09221736 1.259138 49.3 125.44

61.00 86.0292098 1.222944 54.708 149.6 76.28599922 1.248419 49.52 125.14

62.00 86.83331368 1.22222 55.648 148.12 75.83796229 1.242552 49.9 125.58

63.00 87.99005122 1.224363 55.728 149.44 74.79845832 1.243365 47.94 125.48

64.00 88.55927286 1.220869 55.888 146.36 74.22522224 1.242246 44.86 122.56

65.00 88.12051692 1.225034 56.708 143.82 73.42130739 1.242692 44.4 120.1

66.00 88.40439667 1.220898 54.12 138.9 73.91902253 1.256261 43.9 120.64

67.00 87.61146369 1.206714 55.1 137.22 74.09892054 1.258602 42.38 120.8

68.00 87.03986775 1.212565 53.42 138.86 73.88448789 1.247351 43.76 117.9

69.00 85.61667034 1.211601 51.1 138.7 73.09783811 1.234088 45.76 115.72

70.00 84.17987726 1.213949 50.5 133.24 72.29417333 1.23216 45.18 114.68

71.00 83.34052655 1.213444 52.26 134.28 71.10679374 1.227009 43.88 110.68

72.00 83.42413149 1.214423 51.26 127.3 70.73734313 1.225132 45.5 112.06

73.00 82.60108145 1.213508 51.78 125.86 69.94568967 1.235452 43.38 109.74

74.00 82.93914453 1.208433 53.78 127.46 70.25540111 1.241 43.56 112.66

75.00 82.75981666 1.201809 54.76 127.12 70.34868617 1.23444 44.22 111.38

76.00 82.23282472 1.19492 56.12 122.44 70.39465694 1.234265 44.14 114.58

77.00 81.09670348 1.194698 53.1 119.8 69.39757689 1.228511 43.48 111.02

78.00 80.02242628 1.182282 53.72 117.36 68.87151054 1.213229 45.24 109.42

79.00 79.10265965 1.180128 53.62 113.04 67.20924709 1.221048 44.12 110.22

80.00 78.43698141 1.170309 53.6 111.2 66.37891246 1.217987 42.9 108.16

81.00 77.92142176 1.17229 52.74 111.7 65.30424541 1.222093 42.96 108.2

82.00 76.86173441 1.164819 55.16 110.08 64.60647334 1.219074 43.08 106.48

83.00 76.40090269 1.174796 52.18 110.42 63.49351577 1.22226 42.54 108.48

84.00 74.7828307 1.17822 50.36 109.2 63.34131978 1.213219 42.76 102.84

85.00 74.4992137 1.180574 49.31362 107.0343 62.74189138 1.218822 42.59095 104.7926

86.00 73.81250877 1.177977 48.50949 104.4969 62.16041309 1.208932 42.80295 100.844

87.00 73.43871959 1.179377 47.90759 102.5276 61.84223228 1.211505 42.15598 100.4742

88.00 72.79767378 1.170756 49.60794 99.66646 61.5476723 1.209819 41.71005 98.48308

89.00 72.74205786 1.164535 50.19052 97.15354 61.29487188 1.209189 41.46516 99.35077

90.00 72.11983988 1.162177 50.00172 95.19446 60.74212987 1.207753 41.27036 98.44462

91.00 71.4976219 1.159818 49.81292 93.23538 60.18938785 1.206317 41.07556 97.53846

92.00 70.87540393 1.157459 49.62412 91.27631 59.63664584 1.20488 40.88075 96.63231

93.00 70.25318595 1.1551 49.43532 89.31723 59.08390383 1.203444 40.68595 95.72615

94.00 69.63096798 1.152742 49.24652 87.35815 58.53116181 1.202008 40.49115 94.82

95.00 69.00875 1.150383 49.05772 85.39908 57.9784198 1.200571 40.29634 93.91385

96.00 68.38653203 1.148024 48.86892 83.44 57.42567778 1.199135 40.10154 93.00769

97.00 67.76431405 1.145665 48.68012 81.48092 56.87293577 1.197699 39.90674 92.10154

98.00 67.14209607 1.143307 48.49132 79.52185 56.32019375 1.196263 39.71193 91.19538

99.00 66.5198781 1.140948 48.30252 77.56277 55.76745174 1.194826 39.51713 90.28923

100.00 66.20876911 1.139769 48.20812 76.58323 55.49108073 1.194108 39.41973 89.83615
 

B-179

Table 5. Hemoglobin Content. 

 

 MALES FEMALES 

Age MEAN STD 

0 11.927 0.993545 12.209 0.729499905 

1 12.20959 1.013091 12.27307 0.719158646 

2 12.42075 0.823171 12.55018 0.843436666 

3 12.69015 0.83159 12.4519 0.965868504 

4 12.8006 0.80152 12.83442 0.773409545 

5 12.95822 0.878515 12.87154 0.969254536 

6 13.19574 0.893008 13.01866 0.828912341 

7 13.46198 0.836639 13.09899 0.754370806 

8 13.35161 0.833121 13.25291 0.826349227 

9 13.59742 0.971019 13.36671 0.808377267 

10 13.63062 0.906785 13.58919 1.034306588 

11 13.66 0.726155 13.52681 0.90041802 

12 13.9727 0.955869 13.6273 0.884271668 

13 14.28293 1.036749 13.46986 0.97623121 

14 14.70654 1.020254 13.58878 1.034527514 

15 15.13583 1.04546 13.47154 0.856131982 

16 15.36442 1.021623 13.50562 1.088863466 

17 15.45945 0.979296 13.49842 1.117860417 

18 15.7487 1.02514 13.46091 1.18250671 

19 15.76812 0.831813 13.35445 1.090493585 

20 15.79371 0.880956 13.5016 1.072791517 

25 15.71703 0.91072 13.47168 1.170602542 

30 15.70837 1.045808 13.2967 1.145254677 

35 15.55635 0.959964 13.34583 1.134192006 

40 15.43525 1.021741 13.4881 1.163867696 

45 15.44038 1.105939 13.48617 1.348669176 

50 15.41492 1.096952 13.61113 1.193756618 

55 15.31983 1.123792 13.67737 1.106237392 

60 15.27653 0.97796 13.83717 1.237714453 

65 15.07274 1.192645 13.76529 1.093354796 

70 14.96193 1.24457 13.81911 1.093565513 

75 14.72786 1.418355 13.79013 1.056812752 

80 14.51 1.476879 13.84426 1.30818261 

85 14.52915 1.352814 13.57546 1.238910845 

90 13.97647 1.757686 13.43767 1.552685662 

95 13.801 1.757686 13.2085 1.552685662 

100 13.534 1.757686 13.005 1.552685662 
 

B-180

ATTACHMENT 4. TECHNICAL MEMORANDUM ON 

LONGITUDINAL DIARY CONSTRUCTION APPROACH 
B-181 

TECHNICAL MEMORANDUM 

TO: Stephen Graham and John Langstaff, US EPA 

FROM: Arlene Rosenbaum 

DATE: February 29, 2008 

SUBJECT: The Cluster-Markov algorithm in APEX

Background 

The goals of population exposure assessment generally include an accurate estimate of 

both the average exposure concentration and the high end of the exposure distribution. One of 

the factors influencing the number of exposures at the high end of the concentration distribution 

is time-activity patterns that differ from the average, e.g., a disproportionate amount of time 

spent near roadways. Whether a model represents these exposure scenarios well depends on 

whether the treatment of activity pattern data accurately characterizes differences among 

individuals. 

Human time-activity data for population exposure models are generally derived from 

demographic surveys of individuals’ daily activities, the amount of time spent engaged in those 

activities, and the ME locations where the activities occur. Typical time-activity pattern data 

available for inhalation exposure modeling consist of a sequence of location/activity 

combinations spanning a 24-hour duration, with 1 to 3 records for any single individual. But 

modeling assessments of exposure to air pollutants typically require information on activity 

patterns over long periods of time, e.g., a full year. For example, even for pollutant health 

effects with short averaging times (e.g., ozone 8-hour average) it may be desirable to know the 

frequency of exceedances of a threshold concentration over a long period of time (e.g., the 

annual number of exceedances of an 8-hour average ozone concentration of 0.07 ppm for each 

simulated individual). 

Long-term activity patterns can be estimated from daily ones by combining the daily 

records in various ways, and the method used for combining them will influence the variability 

of the long-term activity patterns across the simulated population. This in turn will influence the 

ability of the model to accurately represent either long-term average high-end exposures, or the 

number of individuals exposed multiple times to short-term high-end concentrations. 

A common approach for constructing long-term activity patterns from short-term records 

is to re-select a daily activity pattern from the pool of data for each day, with the implicit 

assumption that there is no correlation between activities from day to day for the simulated 

individual. This approach tends to result in long-term activity patterns that are very similar 

across the simulated population. Thus, the resulting exposure estimates are likely to 
 

B-182

underestimate the variability across the population, and therefore, underestimate the high-end 

concentrations. 

A contrasting approach is to select a single activity pattern (or a single pattern for each 

season and/or weekday-weekend) to represent a simulated individual’s activities over the 

modeling period. This approach has the implicit assumption that an individual’s day to day 

activities are perfectly correlated. This approach tends to result in long-term activity patterns 

that are very different across the simulated population, and therefore may over-estimate the 

variability across the population. 

The Cluster-Markov Algorithm 

Recently, a new algorithm has been developed and incorporated into APEX that attempts 

to more realistically represent the day-to-day correlation of activities for individuals. The 

algorithms first use cluster analysis to divide the daily activity pattern records into groups that 

are similar, and then select a single daily record from each group. This limited number of daily 

patterns is then used to construct a long-term sequence for a simulated individual, based on 

empirically-derived transition probabilities. This approach is intermediate between the 

assumption of no day-to-day correlation (i.e., re-selection for each time period) and perfect 

correlation (i.e., selection of a single daily record to represent all days). 

The steps in the algorithm are as follows. 

 For each demographic group (age, gender, employment status), temperature range, 

and day-of-week combination, the associated time-activity records are partitioned into 

3 groups using cluster analysis. The clustering criterion is a vector of 5 values: the 

time spent in each of 5 microenvironment categories (indoors – residence; indoors – 

other building; outdoors – near road; outdoors – away from road; in vehicle). 

 For each simulated individual, a single time-activity record is randomly selected from 

each cluster. 

 Next the Markov process determines the probability of a given time-activity pattern 

occurring on a given day based on the time-activity pattern of the previous day and 

cluster-to-cluster transition probabilities. The cluster-to-cluster transition 

probabilities are estimated from the available multi-day time-activity records. (If 

insufficient multi-day time-activity records are available for a demographic group, 

season, day-of-week combination, then the cluster-to-cluster transition probabilities 

are estimated from the frequency of time-activity records in each cluster in the CHAD 

data base.). 

Figure 1 illustrates the Cluster-Markov algorithm in flow chart format. 
B-183 

Figure 1. Flow chart of Cluster-Markov algorithm used for constructing longitudinal time-activity diaries. 
B-184 

Evaluation of modeled diary profiles versus observed diary profiles 

The Cluster-Markov algorithm is also incorporated into the Hazardous Air 

Pollutant Exposure Model (HAPEM). Rosebaum and Cohen (2004) incorporated the 

algorithm in HAPEM and tested modeled longitudinal profiles with multi-day diary data 

sets collected as part of the Harvard Southern California Chronic Ozone Exposure Study 

(Xue et al. 2005, Geyh et al. 2000). In this study, 224 children in ages between 7 and 12 

yr were followed for 1 year from June 1995 to May 1996, for 6 consecutive days each 

month. The subjects resided in two separate areas of San Bernardino County: urban 

Upland CA, and the small mountain towns of Lake Arrowhead, Crestline, and Running 

Springs, CA. 

For purposes of clustering the activity pattern records were characterized 

according to time spent in each of 5 aggregate microenvironments: indoors-home, 

indoors-school, indoors-other, outdoors, and in-transit. For purposes of defining diary 

pools and for clustering and calculating transition probabilities the activity pattern 

records were divided by day type (i.e., weekday, weekend), season (i.e., summer or ozone 

season, non-summer or non-ozone season), age (7-10 and 11-12), and gender. 

Week-long sequences (Wednesday through Tuesday) for each of 100 people in 

each age/gender group for each season were simulated. To evaluate the algorithm the 

following statistics were calculated for the predicted multi-day activity patterns and 

compared them with the actual multi-day diary data. 

 For each age/gender group for each season, the average time in each 

microenvironment 

 For each simulated person-week and microenvironment, the average of the 

within-person variance across all simulated persons. (The within-person 

variance was defined as the variance of the total time per day spent in the 

microenvironment across the week.) 

 For each simulated person-week the variance across persons of the mean time 

spent in each microenvironment. 

In each case the predicted statistic for the stratum was compared to the statistic for 

the corresponding stratum in the actual diary data. The mean normalized bias for the 

statistic, which is a common performance measure used in dispersion model performance 

and was also calculated as follows. 

  

N

observed

predicted observed

N

NBIAS

1

(100 )

The predicted time-in-microenvironment averages matched well with the 

observed values. For combinations of microenvironment/age/gender/season the 

normalized bias ranges from –35% to +41%. Sixty percent of the predicted averages 

have bias between –9% and +9%, and the mean bias across any microenvironment ranges 

from -9% to +4%. Fourteen predictions have positive bias and 23 have negative bias. 
 

B-185

For the variance across persons for the average time spent in each 

microenvironment, the bias ranged from –40% to +120% for any 

microenvironment/age/gender/season. Sixty-five percent of the predicted variances had 

bias between –22% and +24%. The mean normalized bias across any microenvironment 

ranged from –10% to +28%. Eighteen predictions had positive bias and 20 had negative 

bias. 

For the within-person variance for time spent in each microenvironment, the bias 

ranged from –47% to +150% for any microenvironment/age/gender/season. Seventy 

percent of the predicted variances had bias between –25% and +30%. The mean 

normalized bias across any microenvironment ranged from –11% to +47%. Twenty-eight 

predictions had positive bias and 12 had negative bias, suggesting some tendency for 

overprediction of this variance measure. 

The overall conclusion was that the proposed algorithm appeared to be able to 

replicate the observed data reasonably well. Although some discrepancies were rather 

large for some of the “variance across persons” and “within-person variance” subsets, 

about two-thirds of the predictions for each case were within 30% of the observed value. 

A detailed description of the evaluation using HAPEM is presented in Attachment 5. 

Comparison of Cluster-Markov approach with other algorithms 

As part of the application of APEX in support of US EPA’s recent review of the 

ozone NAAQS several sensitivity analyses were conducted (US EPA, 2007). One of 

these was to make parallel simulations using each of the three algorithms for constructing 

multi-day time-activity sequences that are incorporated into APEX. 

Table 1 presents the results for the number of persons in Atlanta population 

groups with moderate exertion exposed to 8-hour average concentrations exceeding 0.07 

ppm. The results show that the predictions made with alternative algorithm ClusterMarkov algorithm are substantially different from those made with simple re-sampling or 

with the Diversity-Autocorrelation algorithm (“base case”). Note that for the cluster 

algorithm approximately 30% of the individuals with 1 or more exposure have 3 or more 

exposures. The corresponding values for the other algorithms range from about 13% to 

21%. 

Table 2 presents the results for the mean and standard deviation of number of 

days/person with 8-hour average exposures exceeding 0.07 ppm with moderate or greater 

exertion. The results show that although the mean for the Cluster-Markov algorithm is 

similar to the other approaches, the standard deviation is substantially higher, i.e., the 

Cluster-Markov algorithm results in substantially higher inter-individual variability. 
 

B-186

Table 1. Sensitivity to longitudinal diary algorithm: 2002 simulated counts of Atlanta 

general population and children (ages 5-18) with any or three or more 8-hour ozone 

exposures above 0.07 ppm concomitant with moderate or greater exertion (after US EPA 

2007). 

One or more exposures Three or more exposures 

Population 

Group Simple 

re-sampling 

DiversityAutocorrelationClusterMarkov Simple 

re-sampling 

DiversityAutocorrelationClusterMarkov 

General 

Population 

979,533 939,663 

(-4%) 

668,004 

(-32%) 

124,687 144,470 

(+16%) 

188,509 

(+51%) 

Children (5-18) 411,429 389,372 

(-5%) 

295,004 

(-28%) 

71,174 83,377 

(+17%) 

94,216 

(+32%) 

Table 2. Sensitivity to longitudinal diary algorithm: 2002 days per person with 8-hour 

ozone exposures above 0.07 ppm concomitant with moderate or greater exertion for 

Atlanta general population and children (ages 5-18) (after US EPA 2007). 

Mean Days/Person Standard Deviation 

Population 

Group Simple 

re-sampling Base case 

ClusterMarkov Simple resampling Base case ClusterMarkov 

General 

Population 

0.332 0.335 

(+1%) 

0.342 

(+3%) 

0.757 0.802 

(+6%) 

1.197 

(+58%) 

Children (5-18) 0.746 0.755 

(+1%) 

0.758 

(+2%) 

1.077 1.171 

(+9%) 

1.652 

(+53%) 

References 

Geyh AS, Xue J, Ozkaynak H, Spengler JD. (2000). The Harvard Southern California 

chronic ozone exposure study: Assessing ozone exposure of grade-school-age 

children in two Southern California communities. Environ Health Persp. 108:265-

270. 

Rosenbaum AS and Cohen JP. (2004). Evaluation of a multi-day activity pattern 

algorithm for creating longitudinal activity patterns. Memorandum prepared for Ted 

Palma, US EPA OAQPS, by ICF International. 

US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. EPA452/R-07-010. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007-01_o3_exposure_tsd.pdf. 

Xue J, Liu SV, Ozkaynak H, Spengler J. (2005). Parameter evaluation and model 

validation of ozone exposure assessment using Harvard Southern California Chronic 

Ozone Exposure Study Data. J. Air & Waste Manage Assoc. 55:1508–1515.
B-187 

ATTACHMENT 5. TECHNICAL MEMORANDUM ON THE 

EVALUATION CLUSTER-MARKOV ALGORITHM 
B-188 

TECHNICAL MEMORANDUM 

TO: Ted Palma, US EPA 

FROM: Arlene Rosenbaum and Jonathan Cohen, ICF Consulting 

DATE: November 4, 2004 

SUBJECT: Evaluation of a multi-day activity pattern algorithm for creating longitudinal 

activity patterns.

BACKGROUND 

In previous work ICF reviewed the HAPEM4 modeling approach for developing 

annual average activity patterns from the CHAD database and recommended an approach to 

improve the model’s pattern selection process to better represent the variability among 

individuals. This section summarizes the recommended approach. (For details see 

Attachment 4) 

Using cluster analysis, first the CHAD daily activity patterns are grouped into either 

two or three categories of similar patterns for each of the 30 combinations of day type 

(summer weekday, non-summer weekday, and weekend) and demographic group (males or 

females; age groups: 0-4, 5-11, 12-17, 18-64, 65+). Next, for each combination of day type 

and demographic group, category-to-category transition probabilities are defined by the 

relative frequencies of each second-day category associated with each given first-day 

category, where the same individual was observed for two consecutive days. (Consecutive 

day activity pattern records for a single individual constitute a small subset of the CHAD 

data.) 

To implement the proposed algorithm, for each day type and demographic group, one 

daily activity pattern per category is randomly selected from the corresponding CHAD data 

to represent that category. That is, if there are 3 cluster categories for each of 3 day types, 9 

unique activity patterns are selected to be averaged together to create an annual average 

activity pattern to represent an individual in a given demographic group and census tract. 

The weighting for each of the 9 activity patterns used in the averaging process is 

determined by the product of two factors. The first is the relative frequency of its day type, 

i.e., 0.18 for summer weekdays, 0.54 for non-summer weekdays, and 0.28 for weekends. 

The second factor in the weighting for the selected activity pattern is determined by 

simulating a sequence of category-types as a one-stage Markov chain process using the 

transition probabilities. The category for the first day is selected according to the relative 

frequencies of each category. The category for the second day is selected according to the 

category-to-category transition probabilities for the category selected for the first day. The 

category for the third day is selected according to the transition probabilities for the category 
 B-189

selected for the second day. This is repeated for all days in the day type (65 for summer 

weekdays, 195 for non-summer weekdays, 104 for weekends), producing a sequence of daily 

categories. The relative frequency of the category-type in the sequence associated with the 

selected activity pattern is the second factor in the weighting. 

PROPOSED ALGORITHM STEPS 

The proposed algorithm is summarized in Figure 1. Each step is explained in this 

section. 

Data Preparation 

Step 1: Each daily activity pattern in the CHAD data base is summarized by the 

total minutes in each of five micro-environments: indoors – residence; indoors – other 

building; outdoors – near road; outdoors – away from road; in vehicle. These five 

numbers are assumed to represent the most important features of the activity pattern 

for their exposure impact. 

Step 2: All CHAD activity patterns for a given day-type and demographic 

group are subjected to cluster analysis, resulting in 2 or 3 cluster categories. Each 

daily activity pattern is tagged with a cluster category. 

Step 3: For each day-type and demographic group, the relative frequency of each 

day-type in the CHAD data base is determined. 

Step 4: All CHAD activity patterns for a given day-type and demographic group 

that are consecutive days for a single individual, are analyzed to determine the 

category-to-category transition frequencies in the CHAD data base. These transition 

frequencies are used to calculate category-to-category transition probabilities. 

For example, if there are 2 categories, A and B, then 

PAA = the probability that a type A pattern is followed by a type A pattern, 

PAB = the probability that a type A pattern is followed by a type B pattern (PAB = 

1 – PAA), 

PBB = the probability that a type B pattern is followed by a type B pattern, and 

PBA = the probability that a type B pattern is followed by a type A pattern (PBA = 

1 – PBB). 

Activity Pattern Selection 

For each day-type and demographic group in each census tract: 

Step 5: One activity pattern is randomly selected from each cluster category group 

(i.e., 2 to 3 activity patterns) 

Creating Weights for Day-type Averaging 

For each day-type and demographic group in each census tract: 
 B-190

Step 6: A cluster category is selected for the first day of the day-type sequence, 

according to the relative frequency of the cluster category days in the CHAD data set. 

Step 7: A cluster category is selected for each subsequent day in the day-type 

sequence day by day using the category-to-category transition probabilities. 

Step 8: The relative frequency of each cluster category in the day-type sequence is 

determined. 

Step 9: The activity patterns selected for each cluster category (Step 5) are 

averaged together using the cluster category frequencies (Step 8) as weights, to create 

a day-type average activity pattern. 

Creating Annual Average Activity Patterns 

For each demographic group in each census tract: 

Step 10: The day-type average activity patterns are averaged together using the 

relative frequency of day-types as weights, to create an annual average activity 

pattern. 

Creating Replicates 

For each demographic group in each census tract: 

Step 11: Steps 5 through 10 are repeated 29 times to create 30 annual average 

activity patterns. 

EVALUATING THE ALGORITHM 

The purpose of this study is to evaluate how well the proposed one-stage Markov 

chain algorithm can reproduce observed multi-day activity patterns with respect to 

demographic group means and inter-individual variability, while using one-day selection. 

In order to accomplish this we propose to apply the algorithm to observed multi-day 

activity patterns provided by the WAM, and compare the means and variances of the 

predicted multi-day patterns with the observed patterns. 

 

Current APEX Algorithm 

Because the algorithm is being considered for incorporation into APEX, we would 

like the evaluation to be consistent with the approach taken in APEX for selection of activity 

patterns for creating multi-day sequences. The APEX approach for creating multi-day 

activity sequences is as follows. 

Step1: A profile for a simulated individual is generated by selection of gender, 

age group, and home sector from a given set of distributions consistent with the 

population of the study area. 
 B-191

Step 2: A specific age within the age group is selected from a uniform distribution. 

Step 3: The employment status is simulated as a function of the age. 

Step 4: For each simulated day, the user defines an initial pool of possible diary 

days based on a user-specified function of the day type (e.g., weekday/weekend) and 

temperature. 

Step 5: The pool is further restricted to match the target gender and employment 

status exactly and the age within 2A years for some parameter A. The diary days 

within the pool are assigned a weight of 1 if the age is within A years of the target age 

and a weight of w (user-defined parameter) if the age difference is between A and 2A 

years. For each simulated day, the probability of selecting a given diary day is equal 

to the age weight divided by the total of the age weights for all diary days in the pool 

for that day. 

Approach to Incorporation of Day-to-Day Dependence into APEX Algorithm 

If we were going to incorporate day-to-day dependence of activity patterns into the 

APEX model, we would propose preparing the data with cluster analysis and transition 

probabilities as described in Steps 1-4 for the proposed HAPEM 5 algorithm, with the 

following modifications. 

 For Step 2 the activity patterns would be divided into groups based on day-type 

(weekday, weekend), temperature, gender, employment status, and age, with 

cluster analysis applied to each group. However, because the day-to-day 

transitions in the APEX activity selection algorithm can cross temperature bins, 

we would propose to use broad temperature bins for the clustering and transition 

probability calculations so that the cluster definitions would be fairly uniform 

across temperature bins. Thus we would probably define the bins according to 

season (e.g., summer, non-summer). 

 In contrast to HAPEM, the sequence of activity patterns may be important in 

APEX. Therefore, for Step 4 transition probabilities would be specified for 

transitions between days with the same day-type and season, as in HAPEM, and 

also between days with different day-types and/or seasons. For example, 

transition probabilities would be specified for transitions between summer 

weekdays of each category and summer weekends of each category. 

Another issue for dividing the CHAD activity records for the purposes of clustering 

and calculating transition probabilities is that the diary pools specified for the APEX activity 

selection algorithm use varying and overlapping age ranges. One way to address this 

problem would be to simply not include consideration of age in the clustering process, under 

the assumption that cluster categories are similar across age groups, even if the frequency of 

each cluster category varies by age group. This assumption could be tested by examination 

of the cluster categories stratified by age group that were developed for HAPEM5. If the 

assumption is found to be valid, then the cluster categories could be pre-determined for input 
 B-192

to APEX, while the transition probabilities could be calculated within APEX during the 

simulation for each age range specified for dairy pools. 

If the assumption is found to be invalid, then an alternative approach could be 

implemented that would create overlapping age groups for purposes of clustering as follows. 

APEX age group ranges and age window percentages would be constrained to some 

maximum values. Then a set of overlapping age ranges that would be at least as large as the 

largest possible dairy pool age ranges would be defined for the purposes of cluster analysis 

and transition probability calculation. The resulting sets of cluster categories and transition 

probabilities would be pre-determined for input into APEX and the appropriate set used by 

APEX for each diary pool used during the simulation. 

The actual activity pattern sequence selection would be implemented as follows. The 

activity pattern for first day in the year would be selected exactly as is currently done in 

APEX, as described above. For the selecting the second day’s activity pattern, each age 

weight would be multiplied by the transition probability PAB where A is the cluster for the 

first day’s activity pattern and B is the cluster for a given activity pattern in the available pool 

of diary days for day 2. (Note that day 2 may be a different day-type and/or season than day 

1). The probability of selecting a given diary day on day 2 is equal to the age weight times 

PAB divided by the total of the products of age weight and PAB for all diary days in the pool 

for day 2. Similarly, for the transitions from day 2 to day 3, day 3 to day 4, etc. 

 

Testing the Approach with the Multi-day Data set 

We tested this approach using the available multi-day data set. For purposes of 

clustering we characterized the activity pattern records according to time spent in each of 5 

microenvironments: indoors-home, indoors-school, indoors-other, outdoors (aggregate of the 

3 outdoor microenvironments), and in-transit. 

For purposes of defining diary pools and for clustering and calculating transition 

probabilities we divided the activity pattern records by day type (i.e., weekday, weekend), 

season (i.e., summer or ozone season, non-summer or non-ozone season), age (6-10 and 11-

12), and gender. Since all the subjects are 6-12 years of age and all are presumably 

unemployed, we need not account for differences in employment status. For each day type, 

season, age, and gender, we found that the activity patterns appeared to group in three 

clusters. 

In this case, we simulated week-long sequences (Wednesday through Tuesday) for 

each of 100 people in each age/gender group for each season, using the transition 

probabilities. To evaluate the algorithm we calculated the following statistics for the 

predicted multi-day activity patterns for comparison with the actual multi-day diary data. 

 For each age/gender group for each season, the average time in each 

microenvironment 

 For each age/gender group, season, and microenvironment, the average of the 

within-person variance across all simulated persons (We defined the within-
 B-193

person variance as the variance of the total time per day spent in the 

microenvironment across the week.) 

 For each age/gender group, season, and microenvironment, the variance across 

persons of the mean time spent in that microenvironment 

In each case we compared the predicted statistic for the stratum to the statistic for the 

corresponding stratum in the actual diary data.5 

We also calculated the mean normalized bias for the statistic, which is a common 

performance measure used in dispersion model performance and which is calculated as 

follows.

  

N

observed

predicted observed

N

NBIAS

1

(100 )

 % 

RESULTS 

Comparisons of simulated and observed data for time in each of the 5 

microenvironments are presented in Tables 1 – 3 and Figures 2-5. 

Average Time in Microenvironment 

Table 1 and Figure 2 show the comparisons for the average time spent in each of the 

5 microenvironments for each age/gender group and season. Figure 3 shows the comparison 

for all the microenvironments except indoor, home in order to highlight the lower values. 

Table 1 and the figures show that the predicted time-in-microenvironment averages 

match well with the observed values. For combinations of 

microenvironment/age/gender/season the normalized bias ranges from –35% to +41%. Sixty 

percent of the predicted averages have bias between –9% and +9%, and the mean bias across 

any microenvironment ranges from -9% to +4%. Fourteen predictions have positive bias and 

23 have negative bias. A Wilcoxon signed rank test that the median bias across the 40 

combinations = 0 % was not significant (p-value = 0.40) supporting the conclusion of no 

overall bias. 

Variance Across Persons 

Table 2 and Figure 4 show the comparisons for the variance across persons for the 

average time spent in each microenvironment. In this case the bias ranges from –40% to 

+120% for any microenvironment/age/gender/season. Sixty-five percent of the predicted 

variances have bias between –22% and +24%. The mean normalized bias across any 

microenvironment ranges from –10% to +28%. Eighteen predictions have positive bias and 

20 have negative bias. Figure 4 suggests a reasonably good match of predicted to observed 

 

5 For the diary data, because the number of days per person varies, the average of the within-person variances 

was calculated as a weighted average, where the weight is the degrees of freedom, i.e., one less than the number 

of days simulated. Similarly, the variance across persons of the mean time was appropriately adjusted for the 

different degrees of freedom using analysis of variance. 
 B-194

variance in spite of 2 or 3 outliers. A Wilcoxon signed rank test that the median bias across 

the 40 combinations = 0 % was not significant (p-value = 0.93) supporting the conclusion of 

no overall bias. 

Within-Person Variance for Persons 

Table 3 and Figure 5 show the comparisons for the within-person variance for time 

spent in each microenvironment. In this case the bias ranges from –47% to +150% for any 

microenvironment/age/gender/season. Seventy percent of the predicted variances have bias 

between –25% and +30%. The mean normalized bias across any microenvironment ranges 

from –11% to +47%. Twenty-eight predictions have positive bias and 12 have negative bias, 

suggesting some tendency for overprediction of this variance measure. And indeed a 

Wilcoxon signed rank test that the median bias across the 40 combinations = 0 % was very 

significant (p-value = 0.01) showing that the within-person variance was significantly 

overpredicted. Still, Figure 4 suggests a reasonably good match of predicted to observed 

variance in most cases, with a few overpredicting outliers at the higher end of the 

distribution. So although the positive bias is significant in a statistical sense (i.e., the variance 

is more likely to be overpredicted than underpredicted), it is not clear whether the bias is 

large enough to be important. 

CONCLUSIONS 

The proposed algorithm appears to be able to replicate the observed data reasonably 

well, although the within-person variance is somewhat overpredicted. 

It would be informative to compare this algorithm with the earlier alternative 

approaches in order to gain perspective on the degree of improvement, if any, afforded by 

this approach. 

Two earlier approaches were: 

1. Select a single activity pattern for each day-type/season combination from the 

appropriate set, and use that pattern for every day in the multi-day sequence that 

corresponds to that day-type and season. 

2. Re-select an activity pattern for each day in the multi-day sequence from the 

appropriate set for the corresponding day-type and season. 

Goodness-of-fit statistics could be developed to compare the three approaches and find 

which model best fits the data for a given stratum.
 B-195

Table 1. Average time spent in each microenvironment: comparison of predicted and observed. 

Microenvironment 

Demographic 

Group Season 

Observed 

(hours/day) 

Predicted 

(hours/day) 

Normalized 

Bias 

Indoor, home Girls, 6-10 Summer 15.5 16.5 6% 

 

Not 

Summer 15.8 15.5 -2% 

 Boys, 6-10 Summer 15.7 15.2 -3% 

 

Not 

Summer 15.8 16.4 4% 

Girls, 11-

12 Summer 16.2 15.3 -5% 

 

Not 

Summer 16.5 16.5 0% 

Boys, 11-

12 Summer 16.0 15.6 -3% 

 

Not 

Summer 16.2 16.1 -1% 

 MEAN -1% 

Indoor, school Girls, 6-10 Summer 0.7 0.7 -9% 

 

Not 

Summer 2.3 2.5 7% 

 Boys, 6-10 Summer 0.8 0.5 -34% 

 

Not 

Summer 2.2 2.2 0% 

Girls, 11-

12 Summer 0.7 0.7 6% 

 

Not 

Summer 2.1 2.4 13% 

Boys, 11-

12 Summer 0.6 0.9 38% 

 

Not 

Summer 2.4 2.7 11% 

 MEAN 4% 

Indoor, other Girls, 6-10 Summer 2.9 2.4 -14% 

 

Not 

Summer 2.4 2.7 13% 

 Boys, 6-10 Summer 2.2 2.7 21% 

 

Not 

Summer 1.9 1.8 -3% 

Girls, 11- Summer 2.2 1.6 -25% 
 B-196

12 

 

Not 

Summer 2.2 2.1 -2% 

Boys, 11-

12 Summer 2.3 2.2 -5% 

 

Not 

Summer 1.9 2.0 4% 

 MEAN -2% 

Outdoors Girls, 6-10 Summer 3.7 3.5 -6% 

 

Not 

Summer 2.5 2.5 0% 

 Boys, 6-10 Summer 4.1 4.3 4% 

 

Not 

Summer 3.1 2.7 -12% 

Girls, 11-

12 Summer 3.7 5.2 41% 

 

Not 

Summer 2.3 2.1 -5% 

Boys, 11-

12 Summer 3.9 4.3 9% 

 

Not 

Summer 2.6 2.4 -7% 

 MEAN 3% 

In-vehicle Girls, 6-10 Summer 1.1 0.9 -20% 

 

Not 

Summer 1.0 0.9 -13% 

 Boys, 6-10 Summer 1.1 1.3 13% 

 

Not 

Summer 1.0 0.9 -16% 

Girls, 11-

12 Summer 1.2 1.1 -12% 

 

Not 

Summer 0.9 0.8 -15% 

Boys, 11-

12 Summer 1.1 1.0 -5% 

 

Not 

Summer 0.9 0.8 -7% 

 MEAN -9% 
 B-197

Table 2. Variance across persons for time spent in each microenvironment: comparison of 

predicted and observed. 

Microenvironment 

Demographic 

Group Season 

Observed 

(hours/day)2 

Predicted 

(hours/day)2

Normalized 

Bias 

Indoor, home Girls, 6-10 Summer 70 42 -40% 

 

Not 

Summer 67 60 -9% 

 Boys, 6-10 Summer 54 49 -9% 

 

Not 

Summer 35 30 -12% 

Girls, 11-

12 Summer 56 47 -17% 

 

Not 

Summer 42 38 -10% 

Boys, 11-

12 Summer 57 63 12% 

 

Not 

Summer 39 42 8% 

 MEAN -10% 

Indoor, school Girls, 6-10 Summer 6.0 5.2 -13% 

 

Not 

Summer 9.5 5.9 -38% 

 Boys, 6-10 Summer 5.6 3.8 -32% 

 

Not 

Summer 5.3 8.2 53% 

Girls, 11-

12 Summer 4.9 5.5 11% 

 

Not 

Summer 5.4 5.3 -1% 

Boys, 11-

12 Summer 5.6 6.0 6% 

 

Not 

Summer 9.2 11 23% 

 MEAN 1% 

Indoor, other Girls, 6-10 Summer 46 32 -30% 

 

Not 

Summer 44 46. 6% 

 Boys, 6-10 Summer 34 33 -4% 

 

Not 

Summer 23 16 -27% 

Girls, 11- Summer 21 18 -15% 
 B-198

12 

 

Not 

Summer 28 22 -22% 

Boys, 11-

12 Summer 33 31 -6% 

 

Not 

Summer 30 30 0% 

 MEAN -12% 

Outdoors Girls, 6-10 Summer 17 23 37% 

 

Not 

Summer 9.3 6.8 -27% 

 Boys, 6-10 Summer 17 18 3% 

 

Not 

Summer 8.3 7.6 -8% 

Girls, 11-

12 Summer 22 22 0% 

 

Not 

Summer 9.0 9.1 1% 

Boys, 11-

12 Summer 13 29 120% 

 

Not 

Summer 10 11 8% 

 MEAN 17% 

In-vehicle Girls, 6-10 Summer 1.9 2.3 24% 

 

Not 

Summer 1.8 1.6 -11% 

 Boys, 6-10 Summer 2.5 4.7 93% 

 

Not 

Summer 1.5 1.6 9% 

Girls, 11-

12 Summer 3.5 4.7 34% 

 

Not 

Summer 2.8 2.0 -28% 

Boys, 11-

12 Summer 3.2 5.4 69% 

 

Not 

Summer 1.3 1.7 35% 

 MEAN 28% 
 B-199

Table 3. Average within person variance for time spent in each microenvironment: comparison 

of predicted and observed. 

Microenvironment 

Demographic 

Group Season 

Observed 

(hours/day)2

Predicted 

(hours/day)2

Normalized 

Bias 

Indoor, home Girls, 6-10 Summer 20 29 49% 

 

Not 

Summer 18 23 25% 

 Boys, 6-10 Summer 17 30 75% 

 

Not 

Summer 15 24 64% 

Girls, 11-

12 Summer 22 42 93% 

 

Not 

Summer 22 25 13% 

Boys, 11-

12 Summer 21 24 16% 

 

Not 

Summer 17 24 38% 

 MEAN 47% 

Indoor, school Girls, 6-10 Summer 2.3 2.4 5% 

 

Not 

Summer 7.3 6.4 -12% 

 Boys, 6-10 Summer 2.0 1.5 -25% 

 

Not 

Summer 6.7 5.8 -14% 

Girls, 11-

12 Summer 1.7 2.1 29% 

 

Not 

Summer 7.4 7.6 3% 

Boys, 11-

12 Summer 1.4 2.9 101% 

 

Not 

Summer 7.3 7.8 6% 

 MEAN 12% 

Indoor, other Girls, 6-10 Summer 14 14 -4% 

 

Not 

Summer 14 18 30% 

 Boys, 6-10 Summer 12 17 42% 

 

Not 

Summer 10 13 26% 

Girls, 11- Summer 10 10 1% 
 B-200

12 

 

Not 

Summer 14 15 7% 

Boys, 11-

12 Summer 11 14 26% 

 

Not 

Summer 12 13 7% 

 MEAN 17% 

Outdoors Girls, 6-10 Summer 8.4 9.5 13% 

 

Not 

Summer 3.4 3.2 -3% 

 Boys, 8-10 Summer 6.7 9.5 42% 

 

Not 

Summer 3.4 4.4 28% 

Girls, 11-

12 Summer 10 25 150% 

 

Not 

Summer 4.0 4.5 11% 

Boys, 11-

12 Summer 9.2 7.4 -20% 

 

Not 

Summer 4.3 3.7 -15% 

 MEAN 26% 

In-vehicle Girls, 6-10 Summer 1.0 0.90 -13% 

 

Not 

Summer 0.90 0.48 -47% 

 Boys, 6-10 Summer 1.1 1.4 31% 

 

Not 

Summer 0.81 0.71 -12% 

Girls, 11-

12 Summer 1.3 1.3 4% 

 

Not 

Summer 1.3 1.1 -16% 

Boys, 11-

12 Summer 2.4 1.6 -34% 

 

Not 

Summer 0.85 0.85 1% 

 MEAN -11% 
B-201 

Figure 1. Flow diagram of proposed algorithm for creating annual average activity patterns for HAPEM5. 

Consolidated Human Activity Database - CHAD (CHAD) 

Cluster Analysis 

Cluster 1 

Pattern 

Group 

Cluster 2 

Pattern 

Group

Cluster 3 

Pattern 

Group

Transition Analysis 

Transition 

Probabilities

Markov Selection

Individual Annual Average Activity Pattern

Winter Weekday 

Pattern Group

Summer Weekday 

Pattern Group

Weekend 

Pattern Group

Cluster 1 

Pattern 

Cluster 2 

Pattern 

Cluster 3 

Pattern

Weights Average Winter Weekday Pattern 

0.54 

Cluster Analysis Cluster Analysi
 B-202

0

5

10

15

20

0 5 10 15 20

Observed (hours/day)

Predicted (hours/day)

Indoor, home

Indoor, school

indoor, other

Outdoor

In vehicle

Figure 2. Comparison of predicted and observed average time in each of 5 microenvironments 

for age/gender groups and seasons. 

0

1

2

3

4

5

012345

Observed (hours/day)

Predicted (hours/day)

Indoor, school

indoor, other

Outdoor

In vehicle

Figure 3. Comparison of predicted and observed average time in each of 4 microenvironments 

for age/gender groups and seasons. 
 B-203

0

10

20

30

40

50

60

70

80

0 20 40 60 80

Observed

Simulated

girls, 6-10, summer

girls, 6-10, w inter

boys , 6-10, summer

boys, 6-10, w inter

girls, 11-12, summer

girls, 11-12, w inter

boys, 11-12, summer

boys, 11-12, w inter

Figure 4. Comparison of predicted and observed variance across persons for time spent in each 

of 5 microenvironments for age/gender groups and seasons. 

0

5

10

15

20

25

30

0 10 20 30

Observed

Simulated

girls, 6-10, summer

girls, 6-10, w inter

boys , 6-10, summer

boys, 6-10, w inter

girls, 11-12, summer

girls, 11-12, w inter

boys, 11-12, summer

boys, 11-12, w inter

Figure 5. Comparison of predicted and observed the average within-person variance for time 

spent in each of 5 microenvironments by age/gender groups and seasons. 
 B-204

ATTACHMENT 6. TECHNICAL MEMORANDUM ON 

ANALYSIS OF AIR EXCHANGE RATE DATA 
 B-205

DRAFT MEMORANDUM 

To: John Langstaff 

From: Jonathan Cohen, Hemant Mallya, Arlene Rosenbaum 

Date: September 30, 2005 

Re: EPA 68D01052, Work Assignment 3-08. Analysis of Air Exchange Rate Data 

 

EPA is planning to use the APEX exposure model to estimate ozone exposure in 12 cities / 

metropolitan areas: Atlanta, GA; Boston, MA; Chicago, IL; Cleveland, OH; Detroit, MI; 

Houston, TX; Los Angeles, CA; New York, NY; Philadelphia, PA; Sacramento, CA; St. Louis, 

MO-IL; Washington, DC. As part of this effort, ICF Consulting has developed distributions of 

residential and non-residential air exchange rates (AER) for use as APEX inputs for the cities to 

be modeled. This memorandum describes the analysis of the AER data and the proposed APEX 

input distributions. Also included in this memorandum are proposed APEX inputs for 

penetration and proximity factors for selected microenvironments. 

Residential Air Exchange Rates

Studies. Residential air exchange rate (AER) data were obtained from the following seven 

studies: 

Avol: Avol et al, 1998. In this study, ozone concentrations and AERs were measured at 

126 residences in the greater Los Angeles metropolitan area between February and 

December, 1994. Measurements were taken in four communities: Lancaster, Lake 

Gregory, Riverside, and San Dimas. Data included the daily average outdoor 

temperature, the presence or absence of an air conditioner (either central or room), and 

the presence or absence of a swamp (evaporative) cooler. Air exchange rates were 

computed based on the total house volume and based on the total house volume corrected 

for the furniture. These data analyses used the corrected AERs. 

RTP Panel: Williams et al, 2003a, 2003b. In this study particulate matter concentrations 

and daily average AERs were measured at 37 residences in central North Carolina during 

2000 and 2001 (averaging about 23 AER measurements per residence). The residences 

belong to two specific cohorts: a mostly Caucasian, non-smoking group aged at least 50 

years having cardiac defibrillators living in Chapel Hill; a group of non-smoking, African 

Americans aged at least 50 years with controlled hypertension living in a low-tomoderate SES neighborhood in Raleigh. Data included the daily average outdoor 

temperature, and the number of air conditioner units (either central or room). Every 

residence had at least one air conditioner unit. 

RIOPA: Meng et al, 2004, Weisel et al, 2004. The Relationship of Indoor, Outdoor, and 

Personal Air (RIOPA) study was undertaken to estimate the impact of outdoor sources of 

air toxics to indoor concentrations and personal exposures. Volatile organic compounds, 
 B-206

carbonyls, fine particles and AERs were measured once or twice at 310 non-smoking 

residences from summer 1999 to spring 2001. Measurements were made at residences in 

Elizabeth, NJ, Houston TX, and Los Angeles CA. Residences in California were 

randomly selected. Residences in New Jersey and Texas were preferentially selected to 

be close (< 0.5 km) to sources of air toxics. The AER measurements (generally over 48 

hours) used a PMCH tracer. Data included the daily average outdoor temperature, and the 

presence or absence of central air conditioning, room air conditioning, or a swamp 

(evaporative) cooler. 

TEACH: Chillrud at al, 2004, Kinney et al, 2002, Sax et al, 2004. The Toxic Exposure 

Assessment, a Columbia/Harvard (TEACH) study was designed to characterize levels of 

and factors influencing exposures to air toxics among high school students living in 

inner-city neighborhoods of New York City and Los Angeles, CA. Volatile organic 

compounds, aldehydes, fine particles, selected trace elements, and AER were measured at 

87 high school student’s residences in New York City and Los Angeles in 1999 and 

2000. Data included the presence or absence of an air conditioner (central or room) and 

hourly outdoor temperatures (which were converted to daily averages for these analyses). 

Wilson 1984: Wilson et al, 1986, 1996. In this 1984 study, AER and other data were 

collected at about 600 southern California homes with three seven-day tests (in March 

and July 1984, and January, 1985) for each home. We obtained the data directly from Mr. 

Wilson. The available data consisted of the three seven-day averages, the month, the 

residence zip code, the presence or absence of a central air conditioner, and the presence 

or absence of a window air conditioner. We matched these data by month and zip code to 

the corresponding monthly average temperatures obtained from EPA’s SCRAM website 

as well as from the archives in www.wunderground.com (personal and airport 

meteorological stations). Residences more than 25 miles away from the nearest available 

meteorological station were excluded from the analysis. For our analyses, the 

city/location was defined by the meteorological station, since grouping the data by zip 

code would not have produced sufficient data for most of the zip codes. 

Wilson 1991: Wilson et al, 1996. Colome et al, 1993, 1994. In this 1991 study, AER and 

other data were collected at about 300 California homes with one two-day test in the 

winter for each home. We obtained the data directly from Mr. Wilson. The available data 

consisted of the two-day averages, the date, city name, the residence zip code, the 

presence or absence of a central air conditioner, the presence or absence of a swamp 

(evaporative) cooler, and the presence or absence of a window air conditioner . We 

matched these data by date, city, and zip code to the corresponding daily average 

temperatures obtained from EPA’s SCRAM website as well as from the archives in 

www.wunderground.com (personal and airport meteorological stations). Residences 

more than 25 miles away from the nearest available meteorological station were excluded 

from the analysis. For our analyses, the city/location was defined by the meteorological 

station, since grouping the data by zip code would not have produced sufficient data for 

most of the zip codes. 

 

Murray and Burmaster: Murray and Burmaster (1995). For this article, Murray and 

Burmaster corrected and compiled nationwide residential AER data from several studies 

conducted between 1982 and 1987. These data were originally compiled by the Lawrence 

Berkeley National Laboratory. We acknowledge Mr. Murray’s assistance in obtaining 
 B-207

these data for us. The available data consisted of AER measurements, dates, cities, and 

degree-days. Information on air conditioner presence or absence was not available. 

Table A-1 summarizes these studies. 

For each of the studies, air conditioner usage, window status (open or closed), and fan status (on 

or off) was not part of the experimental design, although some of these studies included 

information on whether air conditioners or fans were used (and for how long) and whether 

windows were closed during the AER measurements (and for how long). 

As described above, in the following studies the homes were deliberately sampled from specific 

subsets of the population at a given location rather than the entire population: The RTP Panel 

study selected two specific cohorts of older subjects with specific diseases. The RIOPA study 

was biased towards residences near air toxics sources. The TEACH study focused on inner-city 

neighborhoods. Nevertheless, we included all these studies because we determined that any 

potential bias would be likely to be small and we preferred to keep as much data as possible. 
B-208 

Table A-1. Summary of Studies of Residential Air Exchange Rates 

Avol RTP Panel RIOPA TEACH Wilson 1984 Wilson 1991 

Locations 

Lancaster, Lake Gregory, Riverside, San Dimas. All in Southern CA 

Research Triangle Park, NC CA; NJ; TX 

Los Angeles, CA; New York City, NY Southern CA Southern CA 

Years 1994 2000; 2001 1999; 2000; 2001 1999; 2000 1984, 1985 1984 Months/Seasons 

Feb; Mar; Apr; May; Jun; Jul; Aug; Sep; Oct; Nov 

2000 (Jun; Jul; Aug; Sep; Oct; Nov), 2001 (Jan; Feb; Apr; May) 

1999 (July to Dec); 2000 (all months); 2001 (Jan and Feb) 

1999 (Feb; Mar; Apr; Jul; Aug); 2000 (Jan; Feb; Mar; Sep; Oct) 

Mar 1984, Jul 1984, Jan 1985 Jan, Mar, Jul 

Number of Homes 86 37 284 85 581 288 Total AER Measurements 161 854 524 151 1,362 316 Average Number of Measurements per Home 1.87 23.08 1.85 1.78 2.34 1.10 Measurement Duration 

Not Available 24 hour 24 to 96 hours 

Sample time (hours) reported. Ranges from about 1 to 7 days. 7 days 7 days 

Measurement Technique Not Available 

Perflourocarbon tracer. PMCH tracer 

Perflourocarbon tracer. Perflourocarbon tracer. Perflourocarbon tracer. 

Min AER Value 0.01 0.02 0.08 0.12 0.03 0.01 Max AER Value 2.70 21.44 87.50 8.87 11.77 2.91 Mean AER Value 0.80 0.72 1.41 1.71 1.05 0.57 Min Temperature (C) -0.04 -2.18 -6.82 -1.36 11.00 3.00 
B-209

Avol RTP Panel RIOPA TEACH Wilson 1984 Wilson 1991 

Max Temperature (C) 36.25 30.81 32.50 32.00 28.00 25.00 Air Conditioner Categories 

No A/C; Central or Room A/C; Swamp Cooler only; Swamp + [Central or Room] 

Central or Room A/C (Y/N) 

Window A/C (Y/N); Evap Coolers (Y/N) 

Central or Room A/C (Y/N) 

Central A/C (Y/N); Room A/C (Y/N); 

Central A/C (Y/N); Room A/C (Y/N); Swamp Cooler(Y/N) 

Air Conditioner Measurements A/C use in minutes Not Available 

Duration measurements in Hrs and Mins Not Available Not Available Not Available 

Fan Categories Not available Fan (Y/N) Fan (Y/N) Not Available Not Available Not Available Fan Measurements 

Time on or off for various fan types during sampling was recorded, but not included in database provided. Not Available 

Duration measurements in Hrs and Mins Not Available Not Available Not Available 

Window Open/ Closed Data 

Duration open between times 6am-12 pm; 12pm - 6 pm; and 6pm - 6am 

Windows (open / closed along with duration open in inch-hours units 

Windows (Open / Closed) along with window open duration measurements Not Available Not Available Not Available 

Comments 

CA sample was a random sample of homes. NJ and TX homes were deliberately chosen to be near to ambient sources. 

Restricted to innercity homes with high school students. 

Contemporaneous temperature data obtained for these analyses from SCRAM and www.wunderground.com meteorological data. 

Contemporaneous temperature data obtained for these analyses from SCRAM and www.wunderground.com meteorological data. 
 B-210

We compiled the data from these seven studies to create the following variables, of which some 

had missing values: 

 Study 

 Date 

 Time – Time of the day that the AER measurement was made 

 House_ID – Residence identifier 

 Measurement_ID – Uniquely identifies each AER measurement for a given study 

 AER – Air Exchange Rate (per hour) 

 AER_Duration – Length of AER measurement period 

 Have_AC – Indicates if the residence has any type of air conditioner (A/C), either a room 

A/C or central A/C or swamp cooler or any of them in combination. “Y” = “Yes.” “N” = 

“No.” 

 Type_of_AC1 – Indicates the types of A/C or swamp cooler available in each house 

measured. Possible values: “Central A/C” “Central and Room A/C” “Central or Room 

A/C” “No A/C” “Swamp + (Central or Room)” “Swamp Cooler only” “Window A/C” 

“Window and Evap” 

 Type_of_AC2 – Indicates if a house measured has either no A/C or some A/C. Possible 

values are “No A/C” and “Central or Room A/C.” 

 Have_Fan – Indicates if the house studied has any fans 

 Mean_Temp – Daily average outside temperature 

 Min_Temp – Minimum hourly outside temperature 

 Max_Temp – Maximum hourly outside temperature 

 State 

 City 

 Location – Two character abbreviation 

 Flag – Data status. Murray and Burmaster study: “Used” or “Not Used.” Other studies: 

“Used”; “Missing” (missing values for AER, Type_of_AC2, and/or Mean_Temp); 

“Outlier”. 

The main data analysis was based on the first six studies. The Murray and Burmaster data were 

excluded because of the absence of information on air conditioner presence. (However, a subset 

of these data was used for a supplementary analysis described below.) . 

Based on our review of the AER data we excluded seven outlying high AER values – above 10 

per hour. The main data analysis used all the remaining data that had non-missing values for 

AER, Type_of_AC2, and Mean_Temp. We decided to base the A/C type variable on the broad 

characterization “No A/C” versus “Central or Room A/C” since this variable could be calculated 

from all of the studies (excluding Murray and Burmaster). Information on the presence or 

absence of swamp coolers was not available from all the studies, and, also importantly, the 

corresponding information on swamp cooler prevalence for the subsequent ozone modeling cities 

was not available from the American Housing Survey. It is plausible that AER distributions 
 B-211

depend upon the presence or absence of a swamp cooler. It is also plausible that AER 

distributions also depend upon whether the residence specifically has a central A/C, room or 

window A/C, or both. However we determined to use the broader A/C type definition, which in 

effect assumes that the exact A/C type and the presence of a swamp cooler are approximately 

proportionately represented in the surveyed residences. 

Most of the studies had more than one AER measurement for the same house. It is reasonable to 

assume that the AER varies with the house as well as other factors such as the temperature. (The 

A/C type can be assumed to be the same for each measurement of the same house). We expected 

the temperature to be an important factor since the AER will be affected by the use of the 

available ventilation (air conditioners, windows, fans), which in turn will depend upon the 

outside meteorology. Therefore it is not appropriate to average data for the same house under 

different conditions, which might have been one way to account for dependence between 

multiple measurements on the same house. To simplify the data analysis, we chose to ignore 

possible dependence between measurements on the same house on different days and treat all the 

AER values as if they were statistically independent. 

Summary Statistics. We computed summary statistics for AER and its natural logarithm 

LOG_AER on selected strata defined from the study, city, A/C type, and mean temperature. 

Cities were defined as in the original databases, except that for Los Angeles we combined all the 

data in the Los Angeles ozone modeling region, i.e. the counties of Los Angeles, Orange, 

Ventura, Riverside, and San Bernardino. A/C type was defined from the Type_of_AC2 variable, 

which we abbreviated as “NA” = “No A/C” and “AC” = “Central or Room A/C.” The mean 

temperature was grouped into the following temperature bins: -10 to 0 oC, 0 to 10 oC, 10 to 20 

oC, 20 to 25 oC, 25 to 30 oC, 30 to 40 oC.(Values equal to the lower bounds are excluded from 

each interval.) Also included were strata defined by study = “All” and/or city = “All,” and/or 

A/C type = “All” and/or temperature bin = “All.” The following summary statistics for AER and 

LOG_AER were computed: 

 Number of values 

 Arithmetic Mean 

 Arithmetic Standard Deviation 

 Arithmetic Variance 

 Deciles (Min, 10th, 20th ... 90th percentiles, Max) 

These calculations exclude all seven outliers and results are not used for strata with 10 or fewer 

values, since those summary statistics are extremely unreliable. 

Examination of these summary tables clearly demonstrates that the AER distributions vary 

greatly across cities and A/C types and temperatures, so that the selected AER distributions for 

the modeled cities should also depend upon the city, A/C type and temperature. For example, the 

mean AER for residences with A/C ranges from 0.39 for Los Angeles between 30 and 40 oC to 

1.73 for New York between 20 and 25 oC. The mean AER for residences without A/C ranges 

from 0.46 for San Francisco between 10 and 20 oC to 2.29 for New York between 20 and 25 oC. 

The need to account for the city as well as the A/C type and temperature is illustrated by the 
 B-212

result that for residences with A/C and between 20 and 25 oC, the mean AER ranges from 0.52 

for Research Triangle Park to 1.73 for New York. Statistical comparisons are described below. 

Statistical Comparisons. Various statistical comparisons were carried out between the different 

strata, for the AER and its logarithm. The various strata are defined as in the Summary Statistics 

section, excluding the “All” cases. For each analysis, we fixed one or two of the variables Study, 

City, A/C type, temperature, and tested for statistically significant differences among other 

variables. The comparisons are listed in Table A-2. 

Table A-2. Summary of Comparisons of Means 

Cases with significantly 

different means (5 % 

level) 

Comparison 

Analysis 

Number. 

Comparison 

Variable(s) 

“Groups 

Compared” 

Stratification 

Variable(s) 

(not missing in 

worksheet) 

Total 

Comparisons

AER Log AER 

1. City Type of A/C AND 

Temp. Range 

12 8 8 

2. Temp. Range Study AND City 12 5 5 

3. Type of A/C Study AND City 15 5 5 

4. City Type of A/C 2 2 2 

5. City Temp. Range 6 5 6 

6. Type of A/C 

AND Temp. 

Range 

Study AND City 17 6 6 

For example, the first set of comparisons fix the Type of A/C and the temperature range; there 

are twelve such combinations. For each of these twelve combinations, we compare the AER 

distributions across different cities. This analysis determines whether the AER distribution is 

appropriately defined by the A/C type and temperature range, without specifying the city. 

Similarly, for the sixth set of comparisons, the study and city are held fixed (17 combinations) 

and in each case we compare AER distributions across groups defined by the combination of the 

A/C type and the temperature range. 

The F Statistic comparisons compare the mean values between groups using a one way analysis 

of variance (ANOVA). This test assumes that the AER or log(AER) values are normally 

distributed with a mean that may vary with the comparison variable(s) and a constant variance. 

We calculated the F Statistic and its P-value. P-values above 0.05 indicate cases where all the 

group means are not statistically significantly different at the 5 percent level. Those results are 

summarized in the last two columns of the above table “Summary of Comparisons of Means” 

which gives the number of cases where the means are significantly different. Comparison 

analyses 2, 3, and 6 show that for a given study and city, slightly less than half of the 

comparisons show significant differences in the means across temperature ranges, A/C types, or 

both. Comparison analyses 1, 4, and 5 show that for the majority of cases, means vary 

significantly across cities, whether you first stratify by temperature range, A/C type, or both. 
 B-213

The Kruskal-Wallis Statistic comparisons are non-parametric tests that are extensions of the 

more familiar Wilcoxon tests to two or more groups. The analysis is valid if the AER minus the 

group median has the same distribution for each group, and tests whether the group medians are 

equal. (The test is also consistent under weaker assumptions against more general alternatives) 

The P-values show similar patterns to the parametric F test comparisons of the means. Since the 

logarithm is a strictly increasing function and the test is non-parametric, the Kruskal-Wallis tests 

give identical results for AER and Log (AER). 

The Mood Statistic comparisons are non-parametric tests that compare the scale statistics for two 

or more groups. The scale statistic measures variation about the central value, which is a nonparametric generalization of the standard deviation. Specifically, suppose there is a total of N 

AER or log(AER) values, summing across all the groups. These N values are ranked from 1 to 

N, and the j’th highest value is given a score of {j - (N+1)/2}2

. The Mood statistic uses a one 

way ANOVA statistic to compare the total scores for each group. Generally, the Mood statistics 

show that in most cases the scale statistics are not statistically significantly different. Since the 

logarithm is a strictly increasing function and the test is non-parametric, the Mood tests give 

identical results for AER and Log (AER). 

Fitting Distributions. Based on the summary statistics and the statistical comparisons, the need 

to fit different AER distributions to each combination of A/C type, city, and temperature is 

apparent. For each combination with a minimum of 11 AER values, we fitted and compared 

exponential, log-normal, normal, and Weibull distributions to the AER values. 

The first analysis used the same stratifications as in the above “Summary Statistics” and 

“Statistical Comparisons” sections. Results are not reported for all strata because of the 

minimum data requirement of 11 values. Results for each combination of A/C type, city, and 

temperature (i.e., A, C, and T) were analyzed. Each combination has four rows, one for each 

fitted distribution. For each distribution we report the fitted parameters (mean, standard 

deviation, scale, shape) and the p-value for three standard goodness-of-fit tests: KolmogorovSmirnov (K-S), Cramer-Von-Mises (C-M), Anderson-Darling (A-D). Each goodness-of-fit test 

compares the empirical distribution of the AER values to the fitted distribution. The K-S and CM tests are different tests examining the overall fit, while the Anderson-Darling test gives more 

weight to the fit in the tails of the distribution. For each combination, the best-fitting of the four 

distributions has the highest p-value and is marked by an x in the final three columns. The mean 

and standard deviation (Std_Dev) are the values for the fitted distribution. The scale and shape 

parameters are defined by: 

 

 Exponential: density = 

-1 exp(-x/), where shape = mean = 

 Log-normal: density = {x(2)}-1 exp{ -(log x - )

2

 / (2

2

)}, where shape =  and 

scale = . Thus the geometric mean and geometric standard deviation are given by 

exp() and exp(), respectively. 

 Normal: density = {(2)}-1 exp{ -(x - )

2

 / (2

2

)}, where mean =  and standard 

deviation = 

 Weibull: density = (c/) (x/)

c-1 exp{-(x/)c

}, where shape = c and scale = 
 B-214

Generally, the log-normal distribution was the best-fitting of the four distributions, and so, for 

consistency, we recommend using the fitted log-normal distributions for all the cases. 

One limitation of the initial analysis was that distributions were available only for selected cities, 

and yet the summary statistics and comparisons demonstrate that the AER distributions depend 

upon the city as well as the temperature range and A/C type. As one option to address this issue, 

we considered modeling cities for which distributions were not available by using the AER 

distributions across all cities and dates for a given temperature range and A/C type. 

Another important limitation of the initial analysis was that distributions were not fitted to all of 

the temperature ranges due to inadequate data. There are missing values between temperature 

ranges, and the temperature ranges are all bounded. To address this issue, the temperature ranges 

were regrouped to cover the entire range of temperatures from minus to plus infinity, although 

obviously the available data to fit these ranges have finite temperatures. Stratifying by A/C type, 

city, and the new temperature ranges produces results for four cities: Houston (AC and NA); Los 

Angeles (AC and NA); New York (AC and NA); Research Triangle Park (AC). For each of the 

fitted distributions we created histograms to compare the fitted distributions with the empirical 

distributions. 

AER Distributions for The First Nine Cities. Based upon the results for the above four cities 

and the corresponding graphs, we propose using those fitted distributions for the three cities 

Houston, Los Angeles, and New York. For another 6 of the cities to be modeled, we propose 

using the distribution for one of the four cities thought to have similar characteristics to the city 

to be modeled with respect to factors that might influence AERs. These factors include the age 

composition of housing stock, construction methods, and other meteorological variables not 

explicitly treated in the analysis, such as humidity and wind speed patterns. The distributions 

proposed for these cities are as follows: 

 Atlanta, GA, A/C: Use log-normal distributions for Research Triangle Park. Residences 

with A/C only. 

 Boston, MA: Use log-normal distributions for New York 

 Chicago, IL: Use log-normal distributions for New York 

 Cleveland, OH: Use log-normal distributions for New York 

 Detroit, MI: Use log-normal distributions for New York 

 Houston, TX: Use log-normal distributions for Houston 

 Los Angeles, CA: Use log-normal distributions for Los Angeles 

 New York, NY: Use log-normal distributions for New York 

 Philadelphia, PA: Use log-normal distributions for New York 

Since the AER data for Research Triangle Park was only available for residences with air 

conditioning, AER distributions for Atlanta residences without air conditioning are discussed 

below. 

To avoid unusually extreme simulated AER values, we propose to set a minimum AER value of 

0.01 and a maximum AER value of 10. 
 B-215

Obviously, we would be prefer to model each city using data from the same city, but this 

approach was chosen as a reasonable alternative, given the available AER data. 

AER Distributions for Sacramento and St. Louis. For these two cities, a direct mapping to one 

of the four cities Houston, Los Angeles, New York, and Research Triangle Park is not 

recommended because the cities are likely to be too dissimilar. Instead, we decided to use the 

distribution for the inland parts of Los Angeles to represent Sacramento and to use the aggregate 

distributions for all cities outside of California to represent St. Louis. The results for the city 

Sacramento were obtained by combining all the available AER data for Sacramento, Riverside, 

and San Bernardino counties. The results for the city St. Louis were obtained by combining all 

non-California AER data. 

AER Distributions for Washington DC. Washington DC was judged likely to have similar 

characteristics both to Research Triangle Park and to New York City. To choose between these 

two cities, we compared the Murray and Burmaster AER data for Maryland with AER data from 

each of those cities. The Murray and Burmaster study included AER data for Baltimore and for 

Gaithersburg and Rockville, primarily collected in March. April, and May 1987, although there 

is no information on mean daily temperatures or A/C type. We collected all the March, April, 

and May AER data for Research Triangle Park and for New York City, and compared those 

distributions with the Murray and Burmaster Maryland data for the same three months. 

 

The results for the means and central values show significant differences at the 5 percent level 

between the New York and Maryland distributions. Between Research Triangle Park and 

Maryland, the central values and the mean AER values are not statistically significantly 

different, and the differences in the mean log (AER) values are much less statistically significant 

than between New York and Maryland. The scale statistic comparisons are not statistically 

significantly different between New York and Maryland, but were statistically significantly 

different between Research Triangle Park and Maryland. Since matching central and mean 

values is generally more important than matching the scales, we propose to model Washington 

DC residences with air conditioning using the Research Triangle Park distributions, stratified by 

temperature: 

 Washington DC, A/C: Use log-normal distributions for Research Triangle Park. 

Residences with A/C only. 

Since the AER data for Research Triangle Park was only available for residences with air 

conditioning, the estimated AER distributions for Washington DC residences without air 

conditioning are discussed below. 

AER Distributions for Washington DC and Atlanta GA Residences With No A/C. For 

Atlanta and Washington DC we have proposed to use the AER distributions for Research 

Triangle Park. However, all the Research Triangle Park data (from the RTP Panel study) were 

from houses with air conditioning, so there are no available distributions for the “No A/C” cases. 

For these two cities, one option is to use AER distributions fitted to all the study data for 

residences without A/C, stratified by temperature. We propose applying the “No A/C” 
 B-216

distributions for modeling these two cities for residences without A/C. However, since Atlanta 

and Washington DC residences are expected to be better represented by residences outside of 

California, we instead propose to use the “No A/C” AER distributions aggregated across cities 

outside of California, which is the same as the recommended choice for the St. Louis “No A/C” 

AER distributions.

A/C Type and Temperature Distributions. Since the proposed AER distribution is conditional 

on the A/C type and temperature range, these values also need to be simulated using APEX in 

order to select the appropriate AER distribution. Mean daily temperatures are one of the 

available APEX inputs for each modeled city, so that the temperature range can be determined 

for each modeled day according to the mean daily temperature. To simulate the A/C type, we 

obtained estimates of A/C prevalence from the American Housing Survey. Thus for each 

city/metropolitan area, we obtained the estimated fraction of residences with Central or Room 

A/C (see Table A-3), which gives the probability p for selecting the A/C type “Central or Room 

A/C.” Obviously, 1-p is the probability for “No A/C.” For comparison with Washington DC and 

Atlanta, we have included the A/C type percentage for Charlotte, NC (representing Research 

Triangle Park, NC). As discussed above, we propose modeling the 96-97 % of Washington DC 

and Atlanta residences with A/C using the Research Triangle Park AER distributions, and 

modeling the 3-4 % of Washington DC and Atlanta residences without A/C using the combined 

study No A/C AER distributions. 

Table A-3. Fraction of residences with central or room A/C (from American Housing 

Survey) 

 

CITY SURVEY AREA & YEAR PERCENTAGE 

Atlanta Atlanta, 2003 97.01 

Boston Boston, 2003 85.23 

Chicago Chicago, 2003 87.09 

Cleveland Cleveland, 2003 74.64 

Detroit Detroit, 2003 81.41 

Houston Houston, 2003 98.70 

Los Angeles Los Angeles, 2003 55.05 

New York New York, 2003 81.57 

Philadelphia Philadelphia, 2003 90.61 

Sacramento Sacramento, 2003 94.63 

St. Louis St. Louis, 2003 95.53 

Washington DC Washington DC, 2003 96.47 

Research Triangle Park Charlotte, 2002 96.56 

Other AER Studies 

We recently became aware of some additional residential and non-residential AER studies that 

might provide additional information or data. Indoor / outdoor ozone and PAN distributions were 

studied by Jakobi and Fabian (1997). Liu et al (1995) studied residential ozone and AER 

distributions in Toronto, Canada. Weschler and Shields (2000) describes a modeling study of 
 B-217

ventilation and air exchange rates. Weschler (2000) includes a useful overview of residential and 

non-residential AER studies. 

AER Distributions for Other Indoor Environments

To estimate AER distributions for non-residential, indoor environments (e.g., offices and 

schools), we obtained and analyzed two AER data sets: “Turk” (Turk et al, 1989); and “Persily” 

(Persily and Gorfain 2004; Persily et al. 2005). 

The earlier “Turk” data set (Turk et al, 1989) includes 40 AER measurements from offices (25 

values), schools (7 values), libraries (3 values), and multi-purpose (5 values), each measured 

using an SF6 tracer over two- or four-hours in different seasons of the year. 

The more recent “Persily” data (Persily and Gorfain 2004; Persily et al. 2005) were derived 

from the U.S. EPA Building Assessment Survey and Evaluation (BASE) study, which was 

conducted to assess indoor air quality, including ventilation, in a large number of randomly 

selected office buildings throughout the U.S. The data base consists of a total of 390 AER 

measurements in 96 large, mechanically ventilated offices; each office was measured up to four 

times over two days, Wednesday and Thursday AM and PM. The office spaces were relatively 

large, with at least 25 occupants, and preferably 50 to 60 occupants. AERs were measured both 

by a volumetric method and by a CO2 ratio method, and included their uncertainty estimates. For 

these analyses, we used the recommended “Best Estimates” defined by the values with the lower 

estimated uncertainty; in the vast majority of cases the best estimate was from the volumetric 

method. 

Another study of non-residential AERs was performed by Lagus Applied Technology (1995) 

using a tracer gas method. That study was a survey of AERs in 16 small office buildings, 6 large 

office buildings, 13 retail establishments, and 14 schools. We plan to obtain and analyze these 

data and compare those results with the Turk and Persily studies. 

Due to the small sample size of the Turk data, the data were analyzed without stratification by 

building type and/or season. For the Persily data, the AER values for each office space were 

averaged, rather using the individual measurements, to account for the strong dependence of the 

AER measurements for the same office space over a relatively short period. 

Summary statistics of AER and log (AER) for the two studies are presented in Table A-4. 

Table A-4. AER summary statistics for offices and other non-residential buildings 

Study Variable N Mean Std Dev Min 25th %ile Median 75th %ile Max 

Persily AER 96 1.9616 2.3252 0.0712 0.5009 1.0795 2.7557 13.8237 

Turk AER 40 1.5400 0.8808 0.3000 0.8500 1.5000 2.0500 4.1000 

Persily Log(AER) 96 0.1038 1.1036 -2.6417 -0.6936 0.0765 1.0121 2.6264 

Turk Log(AER) 40 0.2544 0.6390 -1.2040 -0.1643 0.4055 0.7152 1.4110 

 
 B-218

The mean values are similar for the two studies, but the standard deviations are about twice as 

high for the Persily data. The proposed AER distributions were derived from the more recent 

Persily data only. 

Similarly to the analyses of the residential AER distributions, we fitted exponential, log-normal, 

normal, and Weibull distributions to the 96 office space average AER values. The results are 

shown in Table A-5. 

Table A-5. Best fitting office AER distributions from the Persily et al. (2004, 2005) 

Scale Shape Mean Std_Dev Distribution

P-Value 

KolmogorovSmirnov 

P-Value 

Cramervon 

Mises 

P-Value 

AndersonDarling 

1.9616 1.9616 1.9616 Exponential 0.13 0.04 0.05 

0.1038 1.1036 2.0397 3.1469 Lognormal 0.15 0.46 0.47 

 1.9616 2.3252 Normal 0.01 0.01 0.01 

1.9197 0.9579 1.9568 2.0433 Weibull 0.01 0.01 

(For an explanation of the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling Pvalues see the discussion residential AER distributions above.) According to all three goodnessof-fit measures the best-fitting distribution is the log-normal. Reasonable choices for the lower 

and upper bounds are the observed minimum and maximum AER values. 

We therefore propose the following indoor, non-residential AER distributions. 

 AER distribution for indoor, non-residential microenvironments: Lognormal, with scale 

and shape parameters 0.1038 and 1.1036, i.e., geometric mean = 1.1094, geometric 

standard deviation = 3.0150. Lower Bound = 0.07. Upper bound = 13.8. 

Proximity and Penetration Factors For Outdoors, In-vehicle, and Mass Transit

For the APEX modeling of the outdoor, in-vehicle, and mass transit micro-environments, an 

approach using proximity and penetration factors is proposed, as follows. 

Outdoors Near Road

Penetration factor = 1. 

For the Proximity factor, we propose using ratio distributions developed from the Cincinnati 

Ozone Study (American Petroleum Institute, 1997, Appendix B; Johnson et al. 1995). The field 

study was conducted in the greater Cincinnati metropolitan area in August and September, 1994. 

Vehicle tests were conducted according to an experimental design specifying the vehicle type, 

road type, vehicle speed, and ventilation mode. Vehicle types were defined by the three study 

vehicles: a minivan, a full-size car, and a compact car. Road types were interstate highways 

(interstate), principal urban arterial roads (urban), and local roads (local). Nominal vehicle 
 B-219

speeds (typically met over one minute intervals within 5 mph) were at 35 mph, 45 mph, or 55 

mph. Ventilation modes were as follows: 

 Vent Open: Air conditioner off. Ventilation fan at medium. Driver’s window half open. 

Other windows closed. 

 Normal A/C. Air conditioner at normal. All windows closed. 

 Max A/C: Air conditioner at maximum. All windows closed. 

Ozone concentrations were measured inside the vehicle, outside the vehicle, and at six fixed site 

monitors in the Cincinnati area. 

The proximity factor can be estimated from the distributions of the ratios of the outside-vehicle 

ozone concentrations to the fixed-site ozone concentrations, reported in Table 8 of Johnson et al. 

(1995). Ratio distributions were computed by road type (local, urban, interstate, all) and by the 

fixed-site monitor (each of the six sites, as well as the nearest monitor to the test location). For 

this analysis we propose to use the ratios of outside-vehicle concentrations to the concentrations 

at the nearest fixed site monitor, as shown in Table A-6. 

Table A-6. Ratio of outside-vehicle ozone to ozone at nearest fixed site1

Road 

Type1

Number 

of cases1

Mean1 Standard 

Deviation1

25th

Percentile1

50th

Percentile1

75th

Percentile1

Estimated 

5th

Percentile2

Local 191 0.755 0.203 0.645 0.742 0.911 0.422 

Urban 299 0.754 0.243 0.585 0.722 0.896 0.355 

Interstate 241 0.364 0.165 0.232 0.369 0.484 0.093 

All 731 0.626 0.278 0.417 0.623 0.808 0.170 

 

1. From Table 8 of Johnson et al. (1995). Data excluded if fixed-site concentration < 40 

ppb. 

2. Estimated using a normal approximation as Mean – 1.64 × Standard Deviation 

For the outdoors-near- road microenvironment, we recommend using the distribution for local 

roads, since most of the outdoors-near-road ozone exposure will occur on local roads. The 

summary data from the Cincinnati Ozone Study are too limited to allow fitting of distributions, 

but the 25th and 75th percentiles appear to be approximately equidistant from the median (50th

percentile). Therefore we propose using a normal distribution with the observed mean and 

standard deviation. A plausible upper bound for the proximity factor equals 1. Although the 

normal distribution allows small positive values and can even produce impossible, negative 

values (with a very low probability), the titration of ozone concentrations near a road is limited. 

Therefore, as an empirical approach, we recommend a lower bound of the estimated 5th

percentile, as shown in the final column of the above table. Therefore in summary we propose: 

 Penetration factor for outdoors, near road: 1.
 B-220

 Proximity factor for outdoors, near road: Normal distribution. Mean = 0.755. Standard 

Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

Outdoors, Public Garage / Parking Lot

This micro-environment is similar to the outdoors-near-road microenvironment. We therefore 

recommend the same distributions as for outdoors-near-road: 

 Penetration factor for outdoors, public garage / parking lot: 1.

 Proximity factor for outdoors, public garage / parking lot: Normal distribution. Mean = 

0.755. Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

Outdoors, Other

The outdoors, other ozone concentrations should be well represented by the ambient monitors. 

Therefore we propose: 

 Penetration factor for outdoors, other: 1.

 Proximity factor for outdoors, other: 1.

In-Vehicle

For the proximity factor for in-vehicle, we also recommend using the results of the Cincinnati 

Ozone Study presented in Table A-6. For this microenvironment, the ratios depend upon the road 

type, and the relative prevalences of the road types can be estimated by the proportions of 

vehicle miles traveled in each city. The proximity factors are assumed, as before, to be normally 

distributed, the upper bound to be 1, and the lower bound to be the estimated 5th percentile. 

 Proximity factor for in-vehicle, local roads: Normal distribution. Mean = 0.755. Standard 

Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

 Proximity factor for in-vehicle, urban roads: Normal distribution. Mean = 0.754. 

Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.

 Proximity factor for in-vehicle, interstates: Normal distribution. Mean = 0.364. Standard 

Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.

To complete the specification, the distribution of road type needs to be estimated for each city to 

be modeled. Vehicle miles traveled (VMT) in 2003 by city (defined by the Federal-Aid 

urbanized area) and road type were obtained from the Federal Highway Administration. 

(http://www.fhwa.dot.gov/policy/ohim/hs03/htm/hm71.htm). For local and interstate road types, 

the VMT for the same DOT categories were used. For urban roads, the VMT for all other road 

types was summed (Other freeways/expressways, Other principal arterial, Minor arterial, 

Collector). The computed VMT ratios for each city are shown in Table A-7. 
 B-221

Table A-7. Vehicle Miles Traveled by City and Road Type in 2003 (FHWA, October 2004) 

FRACTION VMT BY ROAD TYPE 

FEDERAL-AID URBANIZED 

AREA INTERSTATE URBAN LOCAL 

Atlanta 0.38 0.45 0.18 

Boston 0.31 0.55 0.14 

Chicago 0.30 0.59 0.12 

Cleveland 0.39 0.45 0.16 

Detroit 0.26 0.63 0.11 

Houston 0.24 0.72 0.04 

Los Angeles 0.29 0.65 0.06 

New York 0.18 0.67 0.15 

Philadelphia 0.23 0.65 0.11 

Sacramento 0.21 0.69 0.09 

St. Louis 0.36 0.45 0.19 

Washington 0.31 0.61 0.08 

Note that a “Federal-Aid Urbanized Area" is an area with 50,000 or more persons that at a 

minimum encompasses the land area delineated as the urbanized area by the Bureau of the 

Census. Urbanized areas that have been combined with others for reporting purposes are not 

shown separately. The Illinois portion of Round Lake Beach-McHenry-Grayslake has been 

reported with Chicago. 

 

Thus to simulate the proximity factor in APEX, we propose to first select the road type according 

to the above probability table of road types, then select the AER distribution (normal) for that 

road type as defined in the last set of bullets. 

For the penetration factor for in-vehicle, we recommend using the inside-vehicle to outsidevehicle ratios from the Cincinnati Ozone Study. The ratio distributions were summarized for all 

the data and for stratifications by vehicle type, vehicle speed, road type, traffic (light, moderate, 

or heavy), and ventilation. The overall results and results by ventilation type are shown in Table 

A-8.

Table A-8. Ratio of inside-vehicle ozone to outside-vehicle ozone1

Ventilation1

Number 

of 

cases1

Mean1 Standard 

Deviation1

25th

Percentile1

50th

Percentile1

75th

Percentile1

Estimated 

5th

Percentile2

Vent Open 226 0.361 0.217 0.199 0.307 0.519 0.005 

Normal 

A/C 332 0.417 0.211 0.236 0.408 0.585 0.071 

Maximum 

A/C 254 0.093 0.088 0.016 0.071 0.149 0.0003

All 812 0.300 0.232 0.117 0.251 0.463 0.0003
 B-222

 

1. From Table 7 of Johnson et al.(1995). Data excluded if outside-vehicle concentration < 

20 ppb. 

2. Estimated using a normal approximation as Mean – 1.64 × Standard Deviation 

3. Negative estimate (impossible value) replaced by zero. 

Although the data in Table A-8 indicate that the inside-to-outside ozone ratios strongly depend 

upon the ventilation type, it would be very difficult to find suitable data to estimate the 

ventilation type distributions for each modeled city. Furthermore, since the Cincinnati Ozone 

Study was scripted, the ventilation conditions may not represent real-world vehicle ventilation 

scenarios. Therefore, we propose to use the overall average distributions. 

 Penetration factor for in-vehicle: Normal distribution. Mean = 0.300. Standard Deviation 

= 0.232. Lower Bound = 0.000. Upper Bound = 1. 

Mass Transit

The mass transit microenvironment is expected to be similar to the in-vehicle microenvironment. 

Therefore we recommend using the same APEX modeling approach: 

 Proximity factor for mass transit, local roads: Normal distribution. Mean = 0.755. 

Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

 Proximity factor for mass transit, urban roads: Normal distribution. Mean = 0.754. 

Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.

 Proximity factor for mass transit, interstates: Normal distribution. Mean = 0.364. 

Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.

 Road type distributions for mass transit: See Table A-6 

 Penetration factor for mass transit: Normal distribution. Mean = 0.300. Standard 

Deviation = 0.232. Lower Bound = 0.000. Upper Bound = 1. 

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ATTACHMENT 7. TECHNICAL MEMORANDUM ON THE 

UNCERTAINTY ANALYSIS OF RESIDENTIAL AIR EXCHANGE 

RATE DISTRIBUTIONS 
 B-226

MEMORANDUM 

To: John Langstaff, EPA OAQPS 

From: Jonathan Cohen, Arlene Rosenbaum, ICF International 

Date: June 5, 2006 

Re: Uncertainty analysis of residential air exchange rate distributions 

This memorandum describes our assessment of some of the sources of the uncertainty of cityspecific distributions of residential air exchange rates that were fitted to the available study data. 

City-specific distributions for use with the APEX ozone model were developed for 12 modeling 

cities, as detailed in the memorandum by Cohen, Mallya and Rosenbaum, 20056 (Appendix A of 

this report). In the first part of the memorandum, we analyze the between-city uncertainty by 

examining the variation of the geometric means and standard deviations across cities and studies. 

In the second part of the memorandum, we assess the within-city uncertainty by using a 

bootstrap distribution to estimate the effects of sampling variation on the fitted geometric means 

and standard deviations for each city. The bootstrap distributions assess the uncertainty due to 

random sampling variation but do not address uncertainties due to the lack of representativeness 

of the available study data, the matching of the study locations to the modeled cities, and the 

variation in the lengths of the AER monitoring periods. 

Variation of geometric means and standard deviations across cities and studies 

The memorandum by Cohen, Mallya and Rosenbaum, 2005 (Attachment 6 of this report) 

describes the analysis of residential air exchange rate (AER) data that were obtained from seven 

studies. The AER data were subset by location, outside temperature range, and the A/C type, as 

defined by the presence or absence of an air conditioner (central or window). In each case we 

chose to fit a log-normal distribution to the AER data, so that the logarithm of the AER for a 

given city, temperature range, and A/C type is assumed to be normally distributed. If the AER 

data has geometric mean GM and geometric standard deviation GSD, then the logarithm of the 

AER is assumed to have a normal distribution with mean log(GM) and standard deviation 

log(GSD). 

Table D-1 shows the assignment of the AER data to the 12 modeled cities. Note that for Atlanta, 

GA and Washington DC, the Research Triangle Park, NC data for houses with A/C was used to 

represent the AER distributions for houses with A/C, and the non-California data for houses 

without A/C was used to represent the AER distributions for houses without A/C. Sacramento, 

CA AER distributions were estimated using the AER data from the inland California counties of 

Sacramento, Riverside, and San Bernardino; these combined data are referred to by the City 

 

6 Cohen, J., H. Mallya, and A. Rosenbaum. 2005. Memorandum to John Langstaff. EPA 68D01052, Work 

Assignment 3-08. Analysis of Air Exchange Rate Data. September 30, 2005.
 B-227

Name “Inland California.” St Louis, MO AER distributions were estimated using the AER data 

from all states except for California and so are referred to be the City Name “Outside 

California.” 

Table D-1. Assignment of Residential AER distributions to modeled cities 

Modeled city AER distribution 

Atlanta, GA, A/C Research Triangle Park, A/C only 

Atlanta, GA, no A/C All non-California, no A/C (“Outside California”) 

Boston, MA New York 

Chicago, IL New York 

Cleveland, OH New York 

Detroit, MI New York 

Houston, TX Houston 

Los Angeles, CA Los Angeles 

New York, NY New York 

Philadelphia, PA New York 

Sacramento Inland parts of Los Angeles (“Inland California”) 

St. Louis All non-California (“Outside California”) 

Washington, DC, A/C Research Triangle Park, A/C only 

Washington, DC, no 

A/C All non-California, no A/C (“Outside California”) 

It is evident from Table D-1 that for some of the modeled cities, some potentially large 

uncertainty was introduced because we modeled their AER distributions using available data 

from another city or group of cities thought to be representative of the first city on the basis of 

geography and other characteristics. This was necessary for cities where we did not have any or 

sufficient AER data measured in the same city that also included the necessary temperature and 

A/C type information. One way to assess the impact of these assignments on the uncertainty of 

the AER distributions is to evaluate the variation of the fitted log-normal distributions across the 

cities with AER data. In this manner we can examine the effect on the AER distribution if a 

different allocation of study data to the modeled cities had been used. 

Even for the cities where we have study AER data, there is uncertainty about the fitted AER 

distributions. First, the studies used different measurement and residence selection methods. In 

some cases the residences were selected by a random sampling method designed to represent the 

entire population. In other cases the residences were selected to represent sub-populations. For 

example, for the RTP study, the residences belong to two specific cohorts: a mostly Caucasian, 

non-smoking group aged at least 50 years having cardiac defibrillators living in Chapel Hill; a 
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group of non-smoking, African Americans aged at least 50 years with controlled hypertension 

living in a low-to-moderate SES neighborhood in Raleigh. The TEACH study was restricted to 

residences of inner-city high school students. The RIOPA study was a random sample for Los 

Angeles, but was designed to preferentially sample locations near major air toxics sources for 

Elizabeth, NJ and Houston TX. Furthermore, some of the studies focused on different towns or 

cities within the larger metropolitan areas, so that, for example, the Los Angeles data from the 

Avol study was only measured in Lancaster, Lake Gregory, Riverside, and San Dimas but the 

Los Angeles data from the Wilson studies were measured in multiple cities in Southern 

California. One way to assess the uncertainty of the AER distributions due to variations of study 

methodologies and study sampling locations is to evaluate the variation of the fitted log-normal 

distributions within each modeled city across the different studies. 

We evaluated the variation between cities, and the variation within cities and between studies, by 

tabulating and plotting the AER distributions for all the study/city combinations. Since the 

original analyses by Cohen, Mallya and Rosenbaum, 2005 clearly showed that the AER 

distribution depends strongly on the outside temperature and the A/C type (whether or not the 

residence has air conditioning), this analysis was stratified by the outside temperature range and 

the A/C type. Otherwise, study or city differences would have been confounded by the 

temperature and A/C type differences and you would not be able to tell how much of the AER 

difference was due to the variation of temperature and A/C type across cities or studies. In order 

to be able to compare cities and studies we could not use different temperature ranges for the 

different modeled cities as we did for the original AER distribution modeling. For these analyses 

we stratified the temperature into the ranges <= 10, 10-20, 20-25, and >25 oC and categorized the 

A/C type as “Central or Window A/C” versus ‘No A/C,” giving 8 temperature and A/C type 

strata. 

Table D-2 shows the geometric means and standard deviations by city and study. These 

geometric mean and standard deviation pairs are plotted in Figure D-1 through D-8. Each figure 

shows the variation across cities and studies for a given temperature range and A/C type. The 

results for a city with only one available study are shown with a blank study name. For cities 

with multiple studies, results are shown for the individual studies and the city overall distribution 

is designated by a blank value for the study name. 

Table D-2. Geometric means and standard deviations by city and study. 

A/C Type Temperature City Study* N Geo Mean Geo Std Dev** 

Central or Room A/C <= 10 Houston 2 0.32 1.80 

Central or Room A/C <= 10 Los Angeles 5 0.62 1.51 

Central or Room A/C <= 10 Los Angeles Avol 2 0.72 1.22 

Central or Room A/C <= 10 Los Angeles RIOPA 1 0.31 

Central or Room A/C <= 10 Los Angeles Wilson 1991 2 0.77 1.12 

Central or Room A/C <= 10 New York City 20 0.71 2.02 

Central or Room A/C <= 10 Research Triangle Park 157 0.96 1.81 

Central or Room A/C <= 10 Sacramento 3 0.38 1.82 

Central or Room A/C <= 10 San Francisco 2 0.43 1.00 

Central or Room A/C <= 10 Stockton 7 0.48 1.64 

Central or Room A/C 10-20 Arcata 1 0.17 

Central or Room A/C 10-20 Bakersfield 2 0.36 1.34 
 B-229

Table D-2. Geometric means and standard deviations by city and study. 

A/C Type Temperature City Study* N Geo Mean Geo Std Dev** 

Central or Room A/C 10-20 Fresno 8 0.30 1.62 

Central or Room A/C 10-20 Houston 13 0.42 2.19 

Central or Room A/C 10-20 Los Angeles 716 0.59 1.90 

Central or Room A/C 10-20 Los Angeles Avol 33 0.48 1.87 

Central or Room A/C 10-20 Los Angeles RIOPA 11 0.60 1.87 

Central or Room A/C 10-20 Los Angeles TEACH 1 0.68 

Central or Room A/C 10-20 Los Angeles Wilson 1984 634 0.59 1.89 

Central or Room A/C 10-20 Los Angeles Wilson 1991 37 0.64 2.11 

Central or Room A/C 10-20 New York City 5 1.36 2.34 

Central or Room A/C 10-20 New York City RIOPA 4 1.20 2.53 

Central or Room A/C 10-20 New York City TEACH 1 2.26 

Central or Room A/C 10-20 Redding 1 0.31 

Central or Room A/C 10-20 Research Triangle Park 320 0.56 1.91 

Central or Room A/C 10-20 Sacramento 7 0.26 1.67 

Central or Room A/C 10-20 San Diego 23 0.41 1.55 

Central or Room A/C 10-20 San Francisco 5 0.42 1.25 

Central or Room A/C 10-20 Santa Maria 1 0.23 

Central or Room A/C 10-20 Stockton 4 0.73 1.42 

Central or Room A/C 20-25 Houston 20 0.47 1.94 

Central or Room A/C 20-25 Los Angeles 273 1.10 2.36 

Central or Room A/C 20-25 Los Angeles Avol 32 0.61 1.95 

Central or Room A/C 20-25 Los Angeles RIOPA 26 0.90 2.42 

Central or Room A/C 20-25 Los Angeles Wilson 1984 215 1.23 2.33 

Central or Room A/C 20-25 New York City 37 1.11 2.74 

Central or Room A/C 20-25 New York City RIOPA 20 0.93 2.91 

Central or Room A/C 20-25 New York City TEACH 17 1.37 2.52 

Central or Room A/C 20-25 Red Bluff 2 0.61 3.20 

Central or Room A/C 20-25 Research Triangle Park 196 0.40 1.89 

Central or Room A/C > 25 Houston 79 0.43 2.17 

Central or Room A/C > 25 Los Angeles 114 0.72 2.60 

Central or Room A/C > 25 Los Angeles Avol 25 0.37 3.10 

Central or Room A/C > 25 Los Angeles RIOPA 10 0.94 1.71 

Central or Room A/C > 25 Los Angeles Wilson 1984 79 0.86 2.33 

Central or Room A/C > 25 New York City 19 1.24 2.18 

Central or Room A/C > 25 New York City RIOPA 14 1.23 2.28 

Central or Room A/C > 25 New York City TEACH 5 1.29 2.04 

Central or Room A/C > 25 Research Triangle Park 145 0.38 1.71 

No A/C <= 10 Houston 13 0.66 1.68 

No A/C <= 10 Los Angeles 18 0.54 3.09 

No A/C <= 10 Los Angeles Avol 14 0.51 3.60 

No A/C <= 10 Los Angeles RIOPA 2 0.72 1.11 

No A/C <= 10 Los Angeles Wilson 1991 2 0.60 1.00 

No A/C <= 10 New York City 48 1.02 2.14 

No A/C <= 10 New York City RIOPA 44 1.04 2.20 

No A/C <= 10 New York City TEACH 4 0.79 1.28 

No A/C <= 10 Sacramento 3 0.58 1.30 
 B-230

Table D-2. Geometric means and standard deviations by city and study. 

A/C Type Temperature City Study* N Geo Mean Geo Std Dev** 

No A/C <= 10 San Francisco 9 0.39 1.42 

No A/C 10-20 Bakersfield 1 0.85 

No A/C 10-20 Fresno 4 0.90 2.42 

No A/C 10-20 Houston 28 0.63 2.92 

No A/C 10-20 Los Angeles 390 0.75 2.09 

No A/C 10-20 Los Angeles Avol 23 0.78 2.55 

No A/C 10-20 Los Angeles RIOPA 87 0.78 1.96 

No A/C 10-20 Los Angeles TEACH 9 2.32 2.05 

No A/C 10-20 Los Angeles Wilson 1984 241 0.70 2.06 

No A/C 10-20 Los Angeles Wilson 1991 30 0.75 1.82 

No A/C 10-20 New York City 59 0.79 2.04 

No A/C 10-20 Sacramento 1 1.09 

No A/C 10-20 San Diego 49 0.47 1.95 

No A/C 10-20 San Francisco 15 0.34 3.05 

No A/C 10-20 Santa Maria 2 0.27 1.23 

No A/C 20-25 Houston 10 0.92 2.41 

No A/C 20-25 Los Angeles 148 1.37 2.28 

No A/C 20-25 Los Angeles Avol 19 0.95 1.87 

No A/C 20-25 Los Angeles RIOPA 38 1.30 2.11 

No A/C 20-25 Los Angeles Wilson 1984 91 1.52 2.40 

No A/C 20-25 New York City 26 1.62 2.24 

No A/C 20-25 New York City RIOPA 19 1.50 2.30 

No A/C 20-25 New York City TEACH 7 1.99 2.11 

No A/C 20-25 Red Bluff 1 0.55 

No A/C > 25 Houston 2 0.92 3.96 

No A/C > 25 Los Angeles 25 0.99 1.97 

No A/C > 25 Los Angeles Avol 6 1.56 1.36 

No A/C > 25 Los Angeles RIOPA 4 1.33 1.37 

No A/C > 25 Los Angeles TEACH 3 0.86 1.02 

No A/C > 25 Los Angeles Wilson 1984 12 0.74 2.29 

No A/C > 25 New York City 6 1.54 1.65 

No A/C > 25 New York City RIOPA 3 1.73 2.00 

No A/C > 25 New York City TEACH 3 1.37 1.38 

* For a given city, if AER data were available from only one study, then the study name is missing. If AER data were available 

for two or more studies, then the overall city distribution is shown in the row where the study name is missing, and the 

distributions by study and city are shown in the rows with a specific study name. 

** The geometric standard deviation is undefined if the sample size equals 1.

In general, there is a relatively wide variation across different cities. This implies that the AER 

modeling results would be very different if the matching of modeled cities to study cities was 

changed, although a sensitivity study using the APEX model would be needed to assess the 

impact on the ozone exposure estimates. In particular the ozone exposure estimates may be 

sensitive to the assumption that the St. Louis AER distributions can be represented by the 

combined non-California AER data. One way to address this is to perform a Monte Carlo 

analysis where the first stage is to randomly select a city outside of California, the second stage 

picks the A/C type, and the third stage picks the AER value from the assigned distribution for the 

city, A/C type and temperature range. Note that this will result in a very different distribution to 
 B-231

the current approach that fits a single log-normal distribution to all the non-California data for a 

given temperature range and A/C type. The current approach weights each data point equally, so 

that cities like New York with most of the data values get the greatest statistical weight. The 

Monte Carlo approach gives the same total statistical weight for each city and fits a mixture of 

log-normal distributions rather than a single distribution. 

In general, there is also some variation within studies for the same city, but this is much smaller 

than the variation across cities. This finding tends to support the approach of combining different 

studies. Note that the graphs can be deceptive in this regard because some of the data points are 

based on very small sample sizes (N) ; those data points are less precise and the differences 

would not be statistically significant. For example, for the No A/C data in the range 10-20 oC, 

the Los Angeles TEACH study had a geometric mean of 2.32 based on only nine AER values, 

but the overall geometric mean, based on 390 values, was 0.75 and the geometric means for the 

Los Angeles Avol, RIOPA, Wilson 1984, and Wilson 1991 studies were each close to 0.75. One 

noticeable case where the studies show big differences for the same city is for the A/C houses in 

Los Angeles in the range 20-25 oC where the study geometric means are 0.61 (Avol, N=32), 0.90 

(RIOPA, N=26) and 1.23 (Wilson 1984, N=215). 

Bootstrap analyses

The 39 AER subsets defined in the Cohen, Mallya, and Rosenbaum, 2005 memorandum 

(Appendix A of this report) and their allocation to the 12 modeled cities are shown in Table D-3. 

To make the distributions sufficiently precise in each AER subset and still capture the variation 

across temperature and A/C type, different modeled cities were assigned different temperature 

range and A/C type groupings. Therefore these temperature range groupings are sometimes 

different to those used to develop Table D-2 and Figure D-1 through D-8. 

Table D-3. AER subsets by city, A/C type, and temperature range. 

Subset City 

Name 

Study Cities Represents 

Modeled Cities: 

A/C Type Temperature 

Range (oC) 

Houston Houston Houston, TX Central or Room A/C <=20 

Houston Houston Houston, TX Central or Room A/C 20-25 

Houston Houston Houston, TX Central or Room A/C 25-30 

Houston Houston Houston, TX Central or Room A/C >30 

Houston Houston Houston, TX No A/C <=10 

Houston Houston Houston, TX No A/C 10-20 

Houston Houston, TX No A/C >20 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA Central or Room A/C <=25 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA Central or Room A/C >25 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA No A/C <=10 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA No A/C 10-20 

Inland California Sacramento, Riverside, Sacramento, CA No A/C 20-25 
 B-232

Table D-3. AER subsets by city, A/C type, and temperature range. 

Subset City 

Name 

Study Cities Represents 

Modeled Cities: 

A/C Type Temperature 

Range (oC) 

and San Bernardino 

counties, CA 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA No A/C >25 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA Central or Room A/C <=20 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA Central or Room A/C 20-25 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA Central or Room A/C 25-30 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA Central or Room A/C >30 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA No A/C <=10 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA No A/C 10-20 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA No A/C 20-25 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA No A/C >25 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

Central or Room A/C <=10 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

Central or Room A/C 10-25 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

Central or Room A/C >25 

New York City New York, NY Boston, MA, 

Chicago, IL, 

No A/C <=10 
 B-233

Table D-3. AER subsets by city, A/C type, and temperature range. 

Subset City 

Name 

Study Cities Represents 

Modeled Cities: 

A/C Type Temperature 

Range (oC) 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

No A/C 10-20 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

No A/C >20 

Outside California Cities outside CA St. Louis, MO Central or Room A/C <=10 

Outside California Cities outside CA St. Louis, MO Central or Room A/C 10-20 

Outside California Cities outside CA St. Louis, MO Central or Room A/C 20-25 

Outside California Cities outside CA St. Louis, MO Central or Room A/C 25-30 

Outside California Cities outside CA St. Louis, MO Central or Room A/C >30 

Outside California Cities outside CA St. Louis, MO 

Atlanta, GA 

Washington DC 

No A/C <=10 

Outside California Cities outside CA St. Louis, MO 

Atlanta, GA 

Washington DC 

No A/C 10-20 

Outside California Cities outside CA St. Louis, MO 

Atlanta, GA 

Washington DC 

No A/C >20 

Research Triangle Park Research Triangle 

Park, NC 

Atlanta, GA 

Washington DC 

Central or Room A/C <=10 

Research Triangle Park Research Triangle 

Park, NC 

Atlanta, GA 

Washington DC 

Central or Room A/C 10-20 

Research Triangle Park Research Triangle 

Park, NC 

Atlanta, GA 

Washington DC 

Central or Room A/C 20-25 

Research Triangle Park Research Triangle 

Park, NC 

Atlanta, GA 

Washington DC 

Central or Room A/C >25 

The GM and GSD values that define the fitted log-normal distributions for these 39 AER subsets 

are shown in Table D-4. Examples of these pairs are also plotted in Figures D-9 through D-19, to 

be further described below. Each of the example figures D-9 through D-19 corresponds to a 

single GM/GSD “Original Data” pair. The GM and GSD values for the “Original Data” are at 

the intersection of the horizontal and vertical lines that are parallel to the x- and y-axes in the 

figures. 
 B-234

Table D-4. Geometric means and standard deviations for AER subsets by city, A/C type, 

and temperature range. 

Subset City 

Name A/C Type Temperature Range (oC) N Geometric Mean 

Geometric 

Standard 

Deviation 

Houston Central or Room 

A/C <=20 15 0.4075 2.1135 

Houston Central or Room 

A/C 20-25 20 0.4675 1.9381 

Houston Central or Room 

A/C 25-30 65 0.4221 2.2579 

Houston Central or Room 

A/C >30 14 0.4989 1.7174 

Houston No A/C <=10 13 0.6557 1.6794 

Houston No A/C 10-20 28 0.6254 2.9162 

 No A/C >20 12 0.9161 2.4512 

Inland California Central or Room 

A/C <=25 226 0.5033 1.9210 

Inland California Central or Room 

A/C >25 83 0.8299 2.3534 

Inland California No A/C <=10 17 0.5256 3.1920 

Inland California No A/C 10-20 52 0.6649 2.1743 

Inland California No A/C 20-25 13 1.0536 1.7110 

Inland California No A/C >25 14 0.8271 2.2646 

Los Angeles Central or Room 

A/C <=20 721 0.5894 1.8948 

Los Angeles Central or Room 

A/C 20-25 273 1.1003 2.3648 

Los Angeles Central or Room 

A/C 25-30 102 0.8128 2.4151 

Los Angeles Central or Room 

A/C >30 12 0.2664 2.7899 

Los Angeles No A/C <=10 18 0.5427 3.0872 

Los Angeles No A/C 10-20 390 0.7470 2.0852 

Los Angeles No A/C 20-25 148 1.3718 2.2828 

Los Angeles No A/C >25 25 0.9884 1.9666 

New York City Central or Room 

A/C <=10 20 0.7108 2.0184 

New York City Central or Room 

A/C 10-25 42 1.1392 2.6773 

New York City Central or Room 

A/C >25 19 1.2435 2.1768 

New York City No A/C <=10 48 1.0165 2.1382 

New York City No A/C 10-20 59 0.7909 2.0417 

New York City No A/C >20 32 1.6062 2.1189 

Outside California Central or Room 

A/C <=10 179 0.9185 1.8589 

Outside California Central or Room 

A/C 10-20 338 0.5636 1.9396 

Outside California Central or Room 

A/C 20-25 253 0.4676 2.2011 

Outside California Central or Room 

A/C 25-30 219 0.4235 2.0373 
 B-235

Table D-4. Geometric means and standard deviations for AER subsets by city, A/C type, 

and temperature range. 

Subset City 

Name A/C Type Temperature Range (oC) N Geometric Mean 

Geometric 

Standard 

Deviation 

Outside California Central or Room 

A/C >30 24 0.5667 1.9447 

Outside California No A/C <=10 61 0.9258 2.0836 

Outside California No A/C 10-20 87 0.7333 2.3299 

Outside California No A/C >20 44 1.3782 2.2757 

Research Triangle 

Park 

Central or Room 

A/C <=10 157 0.9617 1.8094 

Research Triangle 

Park 

Central or Room 

A/C 10-20 320 0.5624 1.9058 

Research Triangle 

Park 

Central or Room 

A/C 20-25 196 0.3970 1.8887 

Research Triangle 

Park 

Central or Room 

A/C >25 145 0.3803 1.7092 

To evaluate the uncertainty of the GM and GSD values, a bootstrap simulation was performed, 

as follows. Suppose that a given AER subset has N values. A bootstrap sample is obtained by 

sampling N times at random with replacement from the N AER values. The first AER value in 

the bootstrap sample is selected randomly from the N values, so that each of the N values is 

equally likely. The second, third, ..., N’th values in the bootstrap sample are also selected 

randomly from the N values, so that for each selection, each of the N values is equally likely. 

The same value can be selected more than once. Using this bootstrap sample, the geometric 

mean and geometric standard deviation of the N values in the bootstrap sample was calculated. 

This pair of values is plotted as one of the points in a figure for that AER subset. 1,000 bootstrap 

samples were randomly generated for each AER subset, producing a set of 1,000 geometric mean 

and geometric standard deviation pairs, which were plotted in example Figures D-9 through D19. 

The bootstrap distributions display the part of the uncertainty of the GM and GSD that is entirely 

due to random sampling variation. The analysis is based on the assumption that the study AER 

data are a random sample from the population distribution of AER values for the given city, 

temperature range, and A/C type. On that basis, the 1,000 bootstrap GM and GSD pairs estimate 

the variation of the GM and GSD across all possible samples of N values from the population. 

Since each GM, GSD pair uniquely defines a fitted log-normal distribution, the pairs also 

estimate the uncertainty of the fitted log-normal distribution. The choice of 1,000 was made as a 

compromise between having enough pairs to accurately estimate the GM, GSD distribution and 

not having too many pairs so that the graph appears as a smudge of overlapped points. Note that 

even if there were infinitely many bootstrap pairs, the uncertainty distribution would still be an 

estimate of the true uncertainty because the N is finite, so that the empirical distribution of the N 

measured AER values does not equal the unknown population distribution. 

In most cases the uncertainty distribution appears to be a roughly circular or elliptical geometric 

mean and standard deviation region. The size of the region depends upon the sample size and on 

the variability of the AER values; the region will be smallest when the sample size N is large 
 B-236

and/or the variability is small, so that there are a large number of values that are all close 

together. 

The bootstrap analyses show that the geometric standard deviation uncertainty for a given 

CMSA/air-conditioning-status/temperature-range combination tends to have a range of at most 

from “fitted GSD-1.0 hr-1” to “fitted GSD+1.0 hr-1”, but the intervals based on larger AER 

sample sizes are frequently much narrower. The ranges for the geometric means tend to be 

approximately from “fitted GM-0.5 hr-1” to “fitted GM+0.5 hr-1”, but in some cases were much 

smaller. 

The bootstrap analysis only evaluates the uncertainty due to the random sampling. It does not 

account for the uncertainty due to the lack of representativeness, which in turn is due to the fact 

that the samples were not always random samples from the entire population of residences in a 

city, and were sometimes used to represent different cities. Since only the GM and GSD were 

used, the bootstrap analyses does not account for uncertainties about the true distributional 

shape, which may not necessarily be log-normal. Furthermore, the bootstrap uncertainty does not 

account for the effect of the calendar year (possible trends in AER values) or of the uncertainty 

due to the AER measurement period; the distributions were intended to represent distributions of 

24 hour average AER values although the study AER data were measured over a variety of 

measurement periods. 

To use the bootstrap distributions to estimate the impact of sample size on the fitted distributions, 

a Monte Carlo approach could be used with the APEX model. Instead of using the Original Data 

distributions, a bootstrap GM, GSD pair could be selected at random and the AER value could be 

selected randomly from the log-normal distribution with the bootstrap GM and GSD. 
B-237 

Figure D-1 
B-238

Figure D-2 
B-239

Figure D-3 
B-240

Figure D-4 
B-241

Figure D-5 
B-242

Figure D-6 
B-243

Figure D-7 
B-244

Figure D-8 
B-245

Figure D-9 
B-246

Figure D-10 
B-247

Figure D-11 
B-248

Figure D-12 
B-249

Figure D-13 
B-250

Figure D-14 
B-251

Figure D-15 
B-252

Figure D-16 
B-253

Figure D-17 
B-254

Figure D-18 
B-255

Figure D-19 
B-256

ATTACHMENT 8. TECHNICAL MEMORANDUM ON THE 

DISTRIBUTIONS OF AIR EXCHANGE RATE AVERAGES 

OVER MULTIPLE DAYS 
B-257

MEMORANDUM 

To: John Langstaff, EPA OAQPS 

From: Jonathan Cohen, Arlene Rosenbaum, ICF International 

Date: June 8, 2006 

Re: Distributions of air exchange rate averages over multiple days 

As detailed in the memorandum by Cohen, Mallya and Rosenbaum, 20057 (Appendix A of this 

report) we have proposed to use the APEX model to simulate the residential air exchange rate 

(AER) using different log-normal distributions for each combination of outside temperature 

range and the air conditioner type, defined as the presence or absence of an air conditioner 

(central or room). 

Although the averaging periods for the air exchange rates in the study databases varied from one 

day to seven days, our analyses did not take the measurement duration into account and treated 

the data as if they were a set of statistically independent daily averages. In this memorandum we 

present some analyses of the Research Triangle Park Panel Study that show extremely strong 

correlations between consecutive 24-hour air exchange rates measured at the same house. This 

provides support for the simplified approach of treating all averaging periods as if they were 24-

hour averages. 

In the current version of the APEX model, there are several options for stratification of time 

periods with respect to AER distributions, and for when to re-sample from a distribution for a 

given stratum. The options selected for this current set of simulations resulted in a uniform AER 

for each 24-hour period and re-sampling of the 24-hour AER for each simulated day. This resampling for each simulated day implies that the simulated AERs on consecutive days in the 

same microenvironment are statistically independent. Although we have not identified sufficient 

data to test the assumption of uniform AERs throughout a 24-hour period, the analyses described 

in this memorandum suggest that AERs on consecutive days are highly correlated. Therefore, we 

performed sensitivity simulations to assess the impact of the assumption of temporally 

independent air exchange rates, but found little difference between APEX predictions for the two 

scenarios (i.e., temporally independent and autocorrelated air exchange rates). 

 

7 Cohen, J., H. Mallya, and A. Rosenbaum. 2005. Memorandum to John Langstaff. EPA 68D01052, Work 

Assignment 3-08. Analysis of Air Exchange Rate Data. September 30, 2005. 
B-258

Distributions of multi-day averages from the RTP Panel Study

The RTP Panel study included measurements of 24-hour averages at 38 residences for up to four 

periods of at least seven days. These periods were in different seasons and/or calendar years. 

Daily outside temperatures were also provided. All the residences had either window or room air 

conditioners or both. We used these data to compare the distributions of daily averages taken 

over 1, 2, 3, .. 7 days. 

The analysis is made more complicated because the previous analyses showed the dependence of 

the air exchange rate on the outside temperature, and the daily temperatures often varied 

considerably. Two alternative approaches were employed to group consecutive days. For the first 

approach, A, we sorted the data by the HOUSE_ID number and date and began a new group of 

days for each new HOUSE_ID and whenever the sorted measurement days on the same 

HOUSE_ID were 30 days or more apart. In most cases, a home was measured over four different 

seasons for seven days, potentially giving 38  4 = 152 groups; the actual number of groups was 

124. For the second approach, B, we again sorted the data by the HOUSE_ID number and date, 

but this time we began a new group of days for each new HOUSE_ID and whenever the sorted 

measurement days on the same HOUSE_ID were 30 days or more apart or were for different 

temperature ranges. We used the same four temperature ranges chosen for the analysis in the 

Cohen, Mallya, and Rosenbaum, 2005, memorandum (Appendix A): <= 10, 10-20, 20-25, and > 

25 oC. For example, if the first week of measurements on a given HOUSE_ID had the first three 

days in the <= 10 oC range, the next day in the 10-20 oC range, and the last three days in the <= 

10 oC range, then the first approach would treat this as a single group of days. The second 

approach would treat this as three groups of days, i.e., the first three days, the fourth day, and the 

last three days. Using the first approach, the days in each group can be in different temperature 

ranges. Using the second approach, every day in a group is in the same temperature range. Using 

the first approach we treat groups of days as being independent following a transition to a 

different house or season. Using the second approach we treat groups of days as being 

independent following a transition to a different house or season or temperature range. 

To evaluate the distributions of multi-day air exchange rate (AER) averages, we averaged the 

AERs over consecutive days in each group. To obtain a set of one-day averages, we took the 

AERs for the first day of each group. To obtain a set of two-day averages, we took the average 

AER over the first two days from each group. We continued this process to obtain three-, four-, 

five-, six-, and seven-day averages. There were insufficiently representative data for averaging 

periods longer then seven days. Averages over non-consecutive days were excluded. Each 

averaging period was assigned the temperature range using the average of the daily temperatures 

for the averaging period. Using Approach A, some or all of the days in the averaging period 

might be in different temperature ranges than the overall average. . Using Approach B, every day 

is in the same temperature range as the overall average. For each averaging period and 

temperature range, we calculated the mean, standard deviation, and variance of the period 

average AER and of its natural logarithm. Note than the geometric mean equals e raised to the 

power Mean log (AER) and the geometric standard deviation equals e raised to the power Std 

Dev log (AER). The results are shown in Tables E-1 (Approach A) and E-2 (Approach B). 
B-259

Table E-1. Distribution of AER averaged over K days and its logarithm. Groups defined by Approach A. 

Temperature 

(oC) K Groups 

Mean 

AER 

Mean 

log(AER) 

Std Dev 

AER 

Std Dev 

log(AER) 

Variance 

AER 

Variance 

log(AER) 

<= 10 1 35 1.109 -0.066 0.741 0.568 0.549 0.322 

<= 10 2 30 1.149 -0.009 0.689 0.542 0.474 0.294 

<= 10 3 28 1.065 -0.088 0.663 0.546 0.440 0.298 

<= 10 4 28 1.081 -0.090 0.690 0.584 0.476 0.341 

<= 10 5 24 1.103 -0.082 0.754 0.598 0.568 0.358 

<= 10 6 24 1.098 -0.083 0.753 0.589 0.567 0.347 

<= 10 7 29 1.054 -0.109 0.704 0.556 0.496 0.309 

10-20 1 48 0.652 -0.659 0.417 0.791 0.174 0.625 

10-20 2 55 0.654 -0.598 0.411 0.607 0.169 0.368 

10-20 3 51 0.641 -0.622 0.416 0.603 0.173 0.363 

10-20 4 50 0.683 -0.564 0.440 0.619 0.194 0.384 

10-20 5 53 0.686 -0.546 0.419 0.596 0.175 0.355 

10-20 6 49 0.677 -0.533 0.379 0.544 0.144 0.296 

10-20 7 34 0.638 -0.593 0.343 0.555 0.118 0.308 

20-25 1 32 0.500 -1.005 0.528 0.760 0.279 0.577 

20-25 2 28 0.484 -0.972 0.509 0.623 0.259 0.388 

20-25 3 27 0.495 -0.933 0.491 0.604 0.241 0.365 

20-25 4 17 0.536 -0.905 0.623 0.652 0.389 0.425 

20-25 5 17 0.543 -0.905 0.672 0.649 0.452 0.421 

20-25 6 17 0.529 -0.899 0.608 0.617 0.370 0.381 

20-25 7 14 0.571 -0.889 0.745 0.683 0.555 0.466 

> 25 1 9 0.470 -1.058 0.423 0.857 0.179 0.734 

> 25 2 11 0.412 -1.123 0.314 0.742 0.098 0.551 

> 25 3 12 0.411 -1.036 0.243 0.582 0.059 0.339 

> 25 4 23 0.385 -1.044 0.176 0.429 0.031 0.184 

> 25 5 23 0.390 -1.028 0.175 0.425 0.031 0.181 

> 25 6 23 0.399 -1.010 0.193 0.435 0.037 0.189 

> 25 7 17 0.438 -0.950 0.248 0.506 0.061 0.256 

Using both approaches, Tables E-1 and E-2 show that the mean values for the AER and its 

logarithm are approximately constant for the same temperature range but different averaging 

periods. This is expected if the daily AER values all have the same statistical distribution, 

regardless of whether or not they are independent. More interesting is the observation that the 

standard deviations and variances are also approximately constant for the same temperature 

range but different averaging periods, except for the data at > 25 oC where the standard 

deviations and variances tend to decrease as the length of the averaging period increases. If the 

daily AER values were statistically independent, then the variance of an average over K days is 

given by Var / K, where Var is the variance of a single daily AER value. Clearly this formula 

does not apply. Since the variance is approximately constant for different values of K in the same 

temperature range (except for the relatively limited data at > 25 oC), this shows that the daily 

AER values are strongly correlated. Of course the correlation is not perfect, since otherwise the 

AER for a given day would be identical to the AER for the next day, if the temperature range 

were the same, which did not occur. 
B-260

Table E-2. Distribution of AER averaged over K days and its logarithm. Groups defined by Approach B. 

Temperature 

(oC) K Groups 

Mean 

AER 

Mean 

log(AER) 

Std Dev 

AER 

Std Dev 

log(AER) 

Variance 

AER 

Variance 

log(AER) 

<= 10 1 62 1.125 -0.081 0.832 0.610 0.692 0.372 

<= 10 2 41 1.059 -0.063 0.595 0.481 0.355 0.231 

<= 10 3 32 1.104 -0.040 0.643 0.530 0.413 0.281 

<= 10 4 17 1.292 0.115 0.768 0.531 0.590 0.282 

<= 10 5 5 1.534 0.264 1.087 0.608 1.182 0.370 

10-20 1 109 0.778 -0.482 0.579 0.721 0.336 0.520 

10-20 2 81 0.702 -0.532 0.451 0.603 0.204 0.363 

10-20 3 63 0.684 -0.540 0.409 0.580 0.167 0.336 

10-20 4 27 0.650 -0.626 0.414 0.663 0.171 0.440 

10-20 5 22 0.629 -0.660 0.417 0.654 0.174 0.428 

10-20 6 12 0.614 -0.679 0.418 0.638 0.175 0.407 

10-20 7 6 0.720 -0.587 0.529 0.816 0.280 0.667 

20-25 1 107 0.514 -0.915 0.518 0.639 0.269 0.409 

20-25 2 63 0.511 -0.930 0.584 0.603 0.341 0.364 

20-25 3 23 0.577 -0.837 0.641 0.659 0.411 0.434 

20-25 4 3 1.308 -0.484 1.810 1.479 3.277 2.187 

> 25 1 54 0.488 -0.949 0.448 0.626 0.201 0.392 

> 25 2 32 0.486 -0.900 0.351 0.595 0.123 0.354 

> 25 3 23 0.427 -0.970 0.218 0.506 0.048 0.256 

> 25 4 12 0.401 -1.029 0.207 0.509 0.043 0.259 

> 25 5 12 0.410 -1.003 0.207 0.507 0.043 0.257 

> 25 6 6 0.341 -1.164 0.129 0.510 0.017 0.261 

> 25 7 6 0.346 -1.144 0.125 0.494 0.016 0.244 

These arguments suggest that, based on the RTP Panel study data, to a reasonable 

approximation, the distribution of an AER measurement does not depend upon the length of the 

averaging period for the measurement, although it does depend upon the average temperature. 

This supports the methodology used in the Cohen, Mallya, and Rosenbaum, 2005, analyses that 

did not take into account the length of the averaging period. 

The above argument suggests that the assumption that daily AER values are statistically 

independent is not justified. Statistical modeling of the correlation structure between consecutive 

daily AER values is not easy because of the problem of accounting for temperature effects, since 

temperatures vary from day to day. In the next section we present some statistical models of the 

daily AER values from the RTP Panel Study. 

Statistical models of AER auto-correlations from the RTP Panel Study

 

We used the MIXED procedure from SAS to fit several mixed models with fixed effects and 

random effects to the daily values of AER and log(AER). The fixed effects are the population 

average values of AER or log(AER), and are assumed to depend upon the temperature range. 

The random effects have expected values of zero and define the correlations between pairs of 
B-261

measurements from the same Group, where the Groups are defined either using Approach A or 

Approach B above. As described above, a Group is a period of up to 14 consecutive days of 

measurements at the same house. For these mixed model analyses we included periods with one 

or more missing days. For all the statistical models, we assume that AER values in different 

Groups are statistically independent, which implies that data from different houses or in different 

seasons are independent. 

The main statistical model for AER was defined as follows: 

AER = Mean(Temp Range) + A(Group, Temp Range) 

 + B(Group, Day Number) + Error(Group, Day Number) 

Mean(Temp Range) is the fixed effects term. There is a different overall mean value for each of 

the four temperature ranges. 

A(Group, Temp Range) is the random effect of temperature. For each Group, four error terms are 

independently drawn from four different normal distributions, one for each temperature range. 

These normal distributions all have mean zero, but may have different variances. Because of this 

term, there is a correlation between AER values measured in the same Group of days for a pair 

of days in the same temperature range. 

B(Group, Day Number) is the repeated effects term. The day number is defined so that the first 

day of a Group has day number 1, the next calendar day has day number 2, and so on. In some 

cases AER’s were missing for some of the day numbers. B(Group, Day Number) is a normally 

distributed error term for each AER measurement. The expected value (i.e., the mean) is zero. 

The variance is V. The covariance between B(Group g, day i) and B(Group h, day j) is zero for 

days in different Groups g and h, and equals V  exp(d  |i-j|) for days in the same Group. V and 

d are fitted parameters. This is a first order auto-regressive model. Because of this term, there is a 

correlation between AER values measured in the same Group of days, and the correlation 

decreases if the days are further apart. 

Finally, Error(Group, Day Number) is the Residual Error term. There is one such error term for 

every AER measurement, and all these terms are independently drawn from the same normal 

distribution, with mean 0 and variance W. 

We can summarize this rather complicated model as follows. The AER measurements are 

uncorrelated if they are from different Groups. If they are in the same Group, they have a 

correlation that decreases with the day difference, and they have an additional correlation if they 

are in the same temperature range. 

Probably the most interesting parameter for these models is the parameter d, which defines the 

strength of the auto-correlation between pairs of days. This parameter d lies between -1 (perfect 

negative correlation) and +1 (perfect positive correlation) although values exactly equal to +1 or 

-1 are impossible for a stationary model. Negative values of d would be unusual since they 

would imply a tendency for a high AER day to be followed by a low AER day, and vice versa. 

The case d=0 is for no auto-correlation. 
B-262

Table E-3 gives the fitted values of d for various versions of the model. The variants considered 

were: 

 model AER or log(AER) 

 include or exclude the term A(Group, Temp Range) (the “random” statement in the SAS 

code) 

 use Approach A or Approach B to define the Groups 

Since Approach B forces the temperature ranges to be the same for very day in a Group, the 

random temperature effect term is difficult to distinguish from the other terms. Therefore this 

term was not fitted using Approach B. 

Table E-3. Autoregressive parameter d for various statistical models for the RTP Panel 

Study AERs. 

Dependent variable Include A(Group, 

Temp Range)? Approach d 

AER Yes A 0.80 

AER No A 0.82 

AER No B 0.80 

Log(AER) Yes A 0.87 

Log(AER) No A 0.87 

Log(AER) No B 0.85 

 

In all cases, the parameter d is 0.8 or above, showing the very strong correlations between AER 

measurements on consecutive or almost consecutive days. 

Impact of accounting for daily average AER auto-correlation 

In the current version of the APEX model, there are several options for stratification of time 

periods with respect to AER distributions, and for when to re-sample from a distribution for a 

given stratum. The options selected for this current set of simulations resulted in a uniform AER 

for each 24-hour period and re-sampling of the 24-hour AER for each simulated day. This resampling for each simulated day implies that the simulated AERs on consecutive days in the 

same microenvironment are statistically independent. Although we have not identified sufficient 

data to test the assumption of uniform AERs throughout a 24-hour period, the analyses described 

in this memorandum suggest that AERs on consecutive days are highly correlated. 

Therefore, in order to determine if bias was introduced into the APEX estimates with respect to 

either the magnitudes or variability of exposure concentrations by implicitly assuming 

uncorrelated air exchange rates, we re-ran the 2002 base case simulations using the option to not 

re-sample the AERs. For this option APEX selects a single AER for each 

microenvironment/stratum combination and uses it throughout the simulation. 

The comparison of the two scenarios indicates little difference in APEX predictions, probably 

because the AERs pertain only to indoor microenvironments and for the base cases most 

exposure to elevated concentrations occurs in the “other outdoors” microenvironment. Figures E-
B-263

1 and E-2 below present the comparison for exceedances of 8-hour average concentration during 

moderate exertion for active person in Boston and Houston, respectively. 

Figure E-1 

Air Exchange Rate Resampling Sensitivity:

Days/Person with Exceedances of 

 8-Hour Average Exposure Concentration During Moderate Exertion

--Active Persons, Boston, 2002--

0

50

100

150

200

0 20 40 60 80 100

Cumulative Percentile

Days/Person

base-.01

rsoff - .01

base - .02

rsoff - .02

base -.03

rsoff - .03

base - .04

rsoff - .04

base - .05

rsoff - .05

Figure E-2 

Air Exchange Rate Resampling Sensitivity:

Days/Person with Exceedances of 

 8-Hour Average Exposure Concentration During Moderate Exertion

--Active Persons, Houston, 2002--

0

50

100

150

200

250

300

350

0 20 40 60 80 100

Cumulative Percentile

Days/Person

base-.01

rsoff - .01

base - .02

rsoff - .02

base -.03

rsoff - .03

base - .04

rsoff - .04

base - .05

rsoff - .05
Appendix C 

Sulfur Dioxide Health Risk Assessment 

Prepared for 

Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 

Prepared by 

Ellen Post 

Jin Huang 

Andreas Maier 

Hardee Mahoney 

Abt Associates Inc. 

Work funded through 

Contract No. EP-W-05-022 

Work Assignments 2-63 and 3-63, and 

Contract No. EP-D-08-100 

Work Assignment 0-04 

Harvey Richmond, Work Assignment Manager 

Catherine Turner, Project Officer, Contract No. EP-W-05-022 

Nancy Riley, Project Officer, Contract No. EP-D-08-100 

June 2009 
Abt Associates Inc. i June 2009 

DISCLAIMER

 This report is being furnished to the U.S. Environmental Protection Agency 

(EPA) by Abt Associates Inc. in partial fulfillment of Contract No. EP-W-05-022, Work 

Assignments 2-63 and 3-63, and Contract No. EP-D-08-100, Work Assignment 0-4. Any 

opinions, findings, conclusions, or recommendations are those of the authors and do not 

necessarily reflect the views of the EPA or Abt Associates. Any questions concerning 

this document should be addressed to Harvey Richmond, U.S. Environmental Protection 

Agency, Office of Air Quality Planning and Standards, C504-06, Research Triangle Park, 

North Carolina 27711 (email: richmond.harvey@epa.gov). 

 
Abt Associates Inc. ii June 2009 

Table of Contents 

1 INTRODUCTION .................................................................................................. 1-1

2 PRELIMINARY CONSIDERATIONS................................................................ 2-1

2.1 The Broad Empirical Basis for a Relationship Between SO2 and Adverse 

Health Effects ............................................................................................... 2-1 

2.2 Basic Structure of the Risk Assessment ....................................................... 2-2 

2.3 Air Quality Considerations........................................................................... 2-2 

3 METHODS.............................................................................................................. 3-1

3.1 Selection of health endpoints and target population.....................................3-1 

3.2 Development of exposure-response functions.............................................. 3-3 

3.2.1 Calculation of risk estimates ...............................................................3-9 

3.2.2 Selection of urban areas ....................................................................3-11 

3.2.3 Addressing variability and uncertainty ............................................. 3-12 

4 RESULTS .............................................................................................................. 4-15

5 REFERENCES ....................................................................................................... 5-1
Abt Associates Inc. iii June 2009 

List of Tables 

Table 3-1. Study-Specific SO2 Exposure-Response Data for Lung Function 

Decrements .......................................................................................................... 3-4 

Table 3-2. Example: Calculation of Number of Occurrences of Lung Function 

Response, Defined as an Increase in sRaw ≥ 100%, Among Asthmatics in St. 

Louis Engaged in Moderate or Greater Exertion Associated with Exposure to 

SO2 Concentrations that Just Meet an Alternative 1-Hour 99th Percentile 100 

ppb Standard ...................................................................................................... 3-10 

Table 3-3. Example: Calculation of the Number of Asthmatics in St. Louis Engaged 

in Moderate or Greater Exertion Estimated to Experience at Least One Lung 

Function Response, Defined as an Increase in sRaw ≥ 100%, Associated with 

Exposure to SO2 Concentrations that Just Meet an Alternative 1-Hour 99th

Percentile 100 ppb Standard ..............................................................................3-11 

Table 4-1. Number of Occurrences (in Hundreds) of Lung Function Response 

(Defined in Terms of sRaw) Among Asthmatics Engaged in Moderate or 

Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-16 

Table 4-2. Number of Occurrences (in Hundreds) of Lung Function Response 

(Defined in Terms of sRaw) Among Asthmatic Children Engaged in 

Moderate or Greater Exertion Associated with Exposure to "As Is" SO2

Concentrations, SO2 Concentrations that Just Meet the Current Standards, 

and SO2 Concentrations that Just Meet Alternative Standards.......................... 4-17 

Table 4-3. Number of Occurrences (in Hundreds) of Lung Function Response 

(Defined in Terms of FEV1) Among Asthmatics Engaged in Moderate or 

Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-18 

Table 4-4. Number of Occurrences (in Hundreds) of Lung Function Response 

(Defined in Terms of FEV1) Among Asthmatic Children Engaged in 

Moderate or Greater Exertion Associated with Exposure to "As Is" SO2

Concentrations, SO2 Concentrations that Just Meet the Current Standards, 

and SO2 Concentrations that Just Meet Alternative Standards.......................... 4-19 

Table 4-5. Number of Asthmatics Engaged in Moderate or Greater Exertion 

Estimated to Experience At Least One Lung Function Response (Defined in 

Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-20 

Table 4-6. Number of Asthmatic Children Engaged in Moderate or Greater Exertion 

Estimated to Experience At Least One Lung Function Response (Defined in 

Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-21 
Abt Associates Inc. iv June 2009 

Table 4-7. Number of Asthmatics Engaged in Moderate or Greater Exertion 

Estimated to Experience At Least One Lung Function Response (Defined in 

Terms of FEV1) Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-22 

Table 4-8. Number of Asthmatic Children Engaged in Moderate or Greater Exertion 

Estimated to Experience At Least One Lung Function Response (Defined in 

Terms of FEV1) Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-23 

Table 4-9. Percent of Asthmatics Engaged in Moderate or Greater Exertion 

Estimated to Experience At Least One Lung Function Response (Defined in 

Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-24 

Table 4-10. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion 

Estimated to Experience At Least One Lung Function Response (Defined in 

Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-25 

Table 4-11. Percent of Asthmatics Engaged in Moderate or Greater Exertion 

Estimated to Experience At Least One Lung Function Response (Defined in 

Terms of FEV1) Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-26 

Table 4-12. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion 

Estimated to Experience At Least One Lung Function Response (Defined in 

Terms of FEV1) Associated with Exposure to "As Is" SO2 Concentrations, 

SO2 Concentrations that Just Meet the Current Standards, and SO2

Concentrations that Just Meet Alternative Standards ........................................ 4-27 
Abt Associates Inc. v June 2009 

List of Figures 

Figure 3-1. Components of SO2 Health Risk Assessment Based on Controlled 

Human Exposure Studies..................................................................................... 3-2 

Figure 3-2. Bayesian-Estimated Median Exposure-Response Functions: Increase in 

sRaw > 100% for 5-Minute Exposures of Asthmatics Engaged in Moderate or 

Greater Exertion...................................................................................................3-7 

Figure 3-3. Bayesian-Estimated Median Exposure-Response Functions: Increase in 

sRaw > 200% for 5-Minute Exposures of Asthmatics Engaged in Moderate or 

Greater Exertion...................................................................................................3-7 

Figure 3-4. Bayesian-Estimated Median Exposure-Response Functions: Decrease in 

FEV1 > 15% for 5-Minute Exposures of Asthmatics Engaged in Moderate or 

Greater Exertion...................................................................................................3-8 

Figure 3-5. Bayesian-Estimated Median Exposure-Response Functions: Decrease in 

FEV1 > 20% for 5-Minute Exposures of Asthmatics Engaged in Moderate or 

Greater Exertion...................................................................................................3-8 

Figure 4-1. Percent of Asthmatics Engaged in Moderate or Greater Exertion in St. 

Louis Exhibiting Lung Function Response (Defined as an Increase in sRaw ≥

100%) Attributable to SO2 Within Given Ranges Under Different Air Quality 

Scenarios............................................................................................................4-28 

Figure 4-2. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion 

in St. Louis Exhibiting Lung Function Response (Defined as an Increase in 

sRaw ≥ 100%) Attributable to SO2 Within Given Ranges Under Different Air 

Quality Scenarios...............................................................................................4-29 

Figure 4-3. Percent of Asthmatics Engaged in Moderate or Greater Exertion in St. 

Louis Exhibiting Lung Function Response (Defined as a Decrease in FEV1 ≥

15%) Attributable to SO2 Within Given Ranges Under Different Air Quality 

Scenarios............................................................................................................4-30 

Figure 4-4. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion 

in St. Louis Exhibiting Lung Function Response (Defined as a Decrease in 

FEV1 ≥ 15%) Attributable to SO2 Within Given Ranges Under Different Air 

Quality Scenarios...............................................................................................4-31 

Figure 4-5. Number of Occurrences of Lung Function Response (Defined as an 

Increase in sRaw ≥ 100%) Among Asthmatics Engaged in Moderate or 

Greater Exertion in St. Louis Attributable to SO2 Within Given Ranges 

Under Different Air Quality Scenarios.............................................................. 4-32 

Figure 4-6. Number of Occurrences of Lung Function Response (Defined as an 

Increase in sRaw ≥ 100%) Among Asthmatic Children Engaged in Moderate 

or Greater Exertion in St. Louis Attributable to SO2 Within Given Ranges 

Under Different Air Quality Scenarios.............................................................. 4-33 

Figure 4-7. Number of Occurrences of Lung Function Response (Defined as a 

Decrease in FEV1 ≥ 15%) Among Asthmatics Engaged in Moderate or 

Greater Exertion in St. Louis Attributable to SO2 Within Given Ranges 

Under Different Air Quality Scenarios.............................................................. 4-34 

Figure 4-8. Number of Occurrences of Lung Function Response (Defined as a 

Decrease in FEV1 ≥ 15%) Among Asthmatic Children Engaged in Moderate 
Abt Associates Inc. vi June 2009 

or Greater Exertion in St. Louis Attributable to SO2 Within Given Ranges 

Under Different Air Quality Scenarios.............................................................. 4-35 

Figure 4-9. Percent of Asthmatics Engaged in Moderate or Greater Exertion in 

Greene Co. Exhibiting Lung Function Response (Defined as an Increase in 

sRaw ≥ 100%) Attributable to SO2 Within Given Ranges Under Different Air 

Quality Scenarios...............................................................................................4-36 

Figure 4-10. Percent of Asthmatic Children Engaged in Moderate or Greater 

Exertion in Greene Co. Exhibiting Lung Function Response (Defined as an 

Increase in sRaw ≥ 100%) Attributable to SO2 Within Given Ranges Under 

Different Air Quality Scenarios.........................................................................4-37 

Figure 4-11. Percent of Asthmatics Engaged in Moderate or Greater Exertion in 

Greene Co. Exhibiting Lung Function Response (Defined as a Decrease in 

FEV1 ≥ 15%) Attributable to SO2 Within Given Ranges Under Different Air 

Quality Scenarios...............................................................................................4-38 

Figure 4-12. Percent of Asthmatic Children Engaged in Moderate or Greater 

Exertion in Greene Co. Exhibiting Lung Function Response (Defined as a 

Decrease in FEV1 ≥ 15%) Attributable to SO2 Within Given Ranges Under 

Different Air Quality Scenarios.........................................................................4-39 

Figure 4-13. Number of Occurrences of Lung Function Response (Defined as an 

Increase in sRaw ≥ 100%) Among Asthmatics Engaged in Moderate or 

Greater Exertion in Greene Co. Attributable to SO2 Within Given Ranges 

Under Different Air Quality Scenarios.............................................................. 4-40 

Figure 4-14. Number of Occurrences of Lung Function Response (Defined as an 

Increase in sRaw ≥ 100%) Among Asthmatic Children Engaged in Moderate 

or Greater Exertion in Greene Co. Attributable to SO2 Within Given Ranges 

Under Different Air Quality Scenarios.............................................................. 4-41 

Figure 4-15. Number of Occurrences of Lung Function Response (Defined as a 

Decrease in FEV1 ≥ 15%) Among Asthmatics Engaged in Moderate or 

Greater Exertion in Greene Co. Attributable to SO2 Within Given Ranges 

Under Different Air Quality Scenarios.............................................................. 4-42 

Figure 4-16. Number of Occurrences of Lung Function Response (Defined as a 

Decrease in FEV1 ≥ 15%) Among Asthmatic Children Engaged in Moderate 

or Greater Exertion in Greene Co. Attributable to SO2 Within Given Ranges 

Under Different Air Quality Scenarios.............................................................. 4-43 

Figure 4-17. Legend for Figures 4-1 - 4-16. .................................................................4-44 
Abt Associates Inc. 1-1 June 2009 

Sulfur Dioxide Health Risk Assessment 

1 INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) is presently conducting a 

review of the national ambient air quality standards (NAAQS) for sulfur dioxide (SO2). 

Sections 108 and 109 of the Clean Air Act (Act) govern the establishment and periodic 

review of the NAAQS. These standards are established for pollutants that may 

reasonably be anticipated to endanger public health and welfare, and whose presence in 

the ambient air results from numerous or diverse mobile or stationary sources. The 

NAAQS are to be based on air quality criteria, which are to accurately reflect the latest 

scientific knowledge useful in indicating the kind and extent of identifiable effects on 

public health or welfare that may be expected from the presence of the pollutant in 

ambient air. The EPA Administrator is to promulgate and periodically review, at fiveyear intervals, “primary” (health-based) and “secondary” (welfare-based) NAAQS for 

such pollutants.1

 Based on periodic reviews of the air quality criteria and standards, the 

Administrator is to make revisions in the criteria and standards, and promulgate any new 

standards, as may be appropriate. The Act also requires that an independent scientific 

review committee advise the Administrator as part of this NAAQS review process, a 

function performed by the Clean Air Scientific Advisory Committee (CASAC). 

 EPA’s plan and schedule for this SO2 NAAQS review is presented in the 

“Integrated Plan for Review of the Primary National Ambient Air Quality Standards for 

Sulfur Oxides” (U.S. EPA, 2007a). The plan discusses the preparation of two key 

components in the NAAQS review process: an Integrated Science Assessment (ISA) and 

risk/exposure assessments. The ISA critically evaluates and integrates scientific 

information on the health effects associated with exposure to oxides of sulfur (SOx) in the 

ambient air. The risk/exposure assessments develop, as appropriate, quantitative 

estimates of human exposure and health risk and related variability and uncertainties, 

drawing upon the information summarized in the ISA. 

 In May 2008 EPA’s National Center for Environmental Assessment (NCEA) 

released a draft version of the “Integrated Science Assessment for Oxides of Sulfur – 

Health Criteria, henceforth referred to as the draft ISA (U.S. EPA, 2008a). In June 2008, 

EPA’s Office of Air Quality Planning and Standards (OAQPS) released a first draft of its 

“Risk and Exposure Assessment to Support the Review of the SO2 Primary National 

Ambient Air Quality Standard,” henceforth referred to as the 1st draft REA (U.S. EPA, 

2008b). Both of these documents were reviewed by the CASAC SO2 Panel on July 30-

31, 2008. Based on its review of the draft ISA, OAQPS decided to expand the health risk 

assessment to include a quantitative assessment of lung function responses indicative of 

 

1 Section 109(b)(1) [42 U.S.C. 7409] of the Act defines a primary standard as one “the attainment and 

maintenance of which in the judgment of the Administrator, based on such criteria and allowing an 

adequate margin of safety, are requisite to protect the public health.” 
Abt Associates Inc. 1-2 June 2009 

bronchoconstriction experienced by asthmatic subjects associated with 5 to 10 minute 

exposures to SO2 while engaged in moderate or greater exertion. In September 2008, 

NCEA released the final version of the ISA, “Integrated Science Assessment for Oxides 

of Sulfur – Health Criteria, henceforth referred to as the ISA (U.S. EPA, 2008c). A 

second draft REA (EPA, 2009a) was made available to the CASAC and public in March 

2009. The second draft REA was reviewed by the CASAC SO2 Panel on April 16-17, 

2009. This final report has been informed by comments from CASAC and the public on 

the second draft REA, as well as findings and conclusions contained in the final ISA. 

 

 SO2 is one of a group of compounds known as sulfur oxides (SOx), which include 

multiple chemicals (e.g., SO2, SO, SO3). However only SO2 is present at concentrations 

significant for human exposures and the ISA indicates there is limited adverse health 

effect data for the other gaseous compounds. Therefore, as in past NAAQS reviews, SO2

is considered as a surrogate for gaseous SOx species in this assessment, with the 

secondarily formed particulate species (i.e., sulfate or SO4) addressed as part of the 

particulate matter (PM) NAAQS review. 

In the previous review, concluded in 1996, it was clearly established that subjects 

with asthma are more sensitive to the respiratory effects of SO2 exposure than healthy 

individuals (ISA, section 3.1.3.2). Asthmatics exposed to SO2 concentrations as low as 

0.2-0.3 ppm for 5-10 minutes during exercise have been shown to experience significant 

bronchoconstriction, measured as an increase in specific airway resistance (sRaw) 

(≥100%) or a decrease in forced expiratory volume in one second (FEV1) (≥15%) after 

correction for exercise-induced responses in clean air. 

 

The basic structure of the SO2 health risk assessment described in this document 

reflects the fact that we have available controlled human exposure study data from 

several studies involving volunteer asthmatic subjects who were exposed to SO2

concentrations at specified exposure levels while engaged in moderate or greater exertion 

for 5- or 10-minute exposures.2 The risk assessment estimates lung function risks for (1) 

recent ambient levels of SO2, (2) air quality adjusted to simulate just meeting the current 

primary 24-hour and annual standards,3 and (3) air quality adjusted to simulate just 

meeting selected alternative 1-hour standards in selected locations encompassing a 

variety of SO2 emission source types in the Greene County and the St. Louis area within 

Missouri. 

The SO2 health risk assessment builds upon the methodology, analyses, and 

lessons learned from the assessments conducted for the last SO2 NAAQS review in 1996, 

as well as the methodology and lessons learned from the health risk assessment work 

conducted for the recently concluded O3 NAAQS review (Abt Associates, 2007a) – in 

 

2

 An additional characterization of risk may involve use of concentration-response functions, if sufficient 

and relevant epidemiological data are identified in the ISA to support development of functions that are 

related to ambient SO2 concentrations. 3

 There is a 3-hr secondary standard as well. However, this risk assessment is taking into account only the 

primary standards. The current primary SO2 standards include a 24-hour standard set at 0.14 parts per 

million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm, 

calculated as the arithmetic mean of hourly averages. 
Abt Associates Inc. 1-3 June 2009 

particular, the assessment of risk based on controlled human exposure studies described 

in Chapter 3 of that document. The SO2 risk assessment is based on our current 

understanding of the SO2 scientific literature as reflected in the evaluation provided in the 

final ISA. 

The goals of this SO2 health risk assessment are: (1) to develop health risk 

estimates of the number and percent of the asthmatic population in the selected study area 

locations that would experience moderate or greater lung function decrements in response 

to daily 5-minute maximum peak exposures while engaged in moderate or greater 

exertion for a recent year of air quality and under a scenario in which the SO2 

concentrations are adjusted to simulate just meeting the current 24-hour standard; (2) to 

develop a better understanding of the influence of various inputs and assumptions on the 

risk estimates; and (3) to gain insights into the risk levels and patterns of risk reductions 

associated with air quality simulating just meeting alternative 1-hour SO2 standards. The 

risk assessment is intended as a tool that, together with other information on lung 

function and other health effects evaluated in the SO2 ISA, can aid the Administrator in 

judging whether the current primary standards protect public health with an adequate 

margin of safety, or whether revisions to the standards are appropriate. 

 Preliminary considerations and the basic structure of the risk assessment are 

described in section 2. Section 3 describes the methods used, and section 4 presents the 

results of the risk assessment. 

 
Abt Associates Inc. 2-1 June 2009 

2 PRELIMINARY CONSIDERATIONS 

 The health risk assessment described in this document estimated lung function 

decrements (measured as increases in sRaw or decreases in FEV1) associated with SO2

exposures under several scenarios: (1) recent ambient levels of SO2, (2) air quality 

adjusted to simulate just meeting the current 24-hour and annual standards, and (3) air 

quality adjusted to simulate just meeting several alternative 1-hour standards. In this 

section we address preliminary considerations. Section 2.1 briefly discusses the broad 

empirical basis for a relationship between SO2 exposures and adverse health effects. 

Section 2.2 describes the basic structure of the risk assessment. Finally, section 2.3 

addresses air quality considerations. 

2.1 The Broad Empirical Basis for a Relationship Between SO2 and Adverse 

Health Effects 

 The ISA concludes that the health evidence “is sufficient to infer a causal 

relationship between respiratory morbidity and short-term exposure to SO2” (ISA, p. 3-

33). In support of this conclusion, the ISA notes the following: 

 The strongest evidence for this causal relationship comes from 

human clinical studies reporting respiratory symptoms and decreased lung 

function following peak exposures of 5-10 min duration to SO2. These 

effects have been observed consistently across studies involving 

exercising mild to moderate asthmatics. Statistically significant 

decrements in lung function accompanied by respiratory symptoms 

including wheeze and chest tightness have been clearly demonstrated 

following exposure to 0.4-0.6 ppm SO2. Although studies have not 

reported statistically significant respiratory effects following exposure to 

0.2-0.3 ppm SO2, some asthmatic subjects (5-30%) have been shown to 

experience moderate to large decrements in lung function at these 

exposure concentrations. 

 A larger body of evidence supporting this determination of 

causality comes from numerous epidemiological studies reporting 

associations with respiratory symptoms, ED visits, and hospital 

admissions with short-term SO2 exposures, generally of 24-h avg. 

Important new multicity studies and several other studies have found an 

association between 24-h avg ambient SO2 concentrations and respiratory 

symptoms in children, particularly those with asthma.... 

 ... Collectively, the findings from both human clinical and 

epidemiological studies provide a strong basis for concluding a causal 

relationship between respiratory morbidity and short-term exposure to 

SO2. 
Abt Associates Inc. 2-2 June 2009 

2.2 Basic Structure of the Risk Assessment 

 As noted above, this SO2 health risk assessment is based on controlled human 

exposure studies involving volunteer subjects who were exposed while engaged in 

different exercise regimens to specified levels of SO2 under controlled conditions for 5 or 

10 minute periods. The responses measured in these studies were measures of lung 

function decrements, including increases in sRaw and decreases in FEV1. We used 

probabilistic exposure-response relationships, based on analysis of individual data, that 

describe the relationships between a measure of personal exposure to SO2 and the 

measure(s) of lung function recorded in these studies. These probabilistic exposureresponse relationships were combined with daily 5-minute maximum peak exposure 

estimates associated with the air quality scenarios mentioned above for mild and 

moderate asthmatics engaged in moderate or greater exertion. Estimates of personal 

exposures to varying ambient concentrations associated with several air quality scenarios 

including recent air quality levels, and air quality levels simulating just meeting the 

current SO2 primary standard and several alternative primary 1-hour standards were 

derived through exposure modeling. The details of the exposure modeling are described 

in Chapter 8 and Appendix B of the final REA (EPA, 2009b). 

 The characteristics that are relevant to carrying out a risk assessment based on 

controlled human exposure studies can be summarized as follows: 

 A risk assessment based on controlled human exposure studies uses exposureresponse functions, and therefore requires as input (modeled) personal 

exposures to SO2. 

 

 Controlled human exposure studies, carried out in laboratory settings, are 

generally not specific to any particular real world location. A controlled 

human exposure studies-based risk assessment can therefore appropriately be 

carried out for any location for which there are adequate air quality data on 

which to base the modeling of personal exposures. 

The methods for the SO2 risk assessment are discussed in section 3 below. The risk 

assessment was implemented within a new probabilistic version of TRIM.Risk, the 

component of EPA’s Total Risk Integrated Methodology (TRIM) model that estimates 

human health risks.4

2.3 Air Quality Considerations 

The SO2 health risk assessment estimates lung function risks associated with (1) 

“as is” ambient levels of SO2, (2) air quality simulating just meeting the current 24-hour 

and annual standards, and (3) air quality simulating just meeting several alternative 1-

 

4

 TRIM.Risk was most recently applied to EPA’s O3 health risk assessment. A User’s Guide for the 

Application of TRIM.Risk to the O3 health risk assessment (Abt Associates, 2007b) is available online at: 

http://epa.gov/ttn/fera/data/trim/trimrisk_ozone_ra_userguide_8-6-07.pdf. 
Abt Associates Inc. 2-3 June 2009 

hour standards in a recent year (2002) in two selected locations encompassing a variety of 

SO2 emission source types in Greene County, Missouri and St. Louis, Missouri. 

In order to estimate health risks associated with just meeting the current 24-hour 

and annual standards and alternative 1-hour SO2 standards, it is necessary to estimate the 

distribution of short-term (5-minute) SO2 concentrations that would occur under any 

given standard. Since compliance with the current SO2 standards is based on a single 

year, air quality data from 2002 were used to determine the change in SO2 concentrations 

required to meet the current standards. Estimated design values were used to determine 

the adjustment necessary to just meet the current 24-hour and annual standards. The 

approach to simulating just meeting the current standards and alternative 1-hour 

standards is described in section 8.8.1 of the final REA (EPA, 2009b). 

The risk estimates developed for the recently concluded PM and O3 NAAQS 

reviews represented risks associated with PM and O3 levels in excess of estimated policyrelevant background (PRB) levels in the U.S. PRB levels have been historically defined 

by EPA as concentrations of a pollutant that would occur in the U.S. in the absence of 

anthropogenic emissions in continental North America (defined as the United States, 

Canada, and Mexico). The ISA notes that PRB SO2 concentrations are below 10 parts 

per trillion (ppt) over much of the United States and are generally less than 30 ppt. With 

the exception of a few locations on the West Coast and locations in Hawaii, where 

volcanic SO2 emissions cause high PRB concentrations, PRB contributes less than 1% to 

present-day SO2 concentrations in surface air. Since PRB is well below concentrations 

that might cause potential health effects, there was no adjustment made for risks 

associated with PRB concentrations in the current SO2 health risk assessment. 
Abt Associates Inc. 3-1 June 2009 

3 METHODS 

 

 The major components of the SO2 lung function risk assessment are illustrated in 

Figure 3-1. The air quality and exposure analysis components that are integral to the risk 

assessment are discussed in Chapters 6 and 7, respectively, of the 2nd draft REA. As 

described in the ISA and the 2nd draft REA, there are numerous controlled human 

exposure studies reporting lung function decrements (as measured by increases in SRaw 

and/or decreases in FEV1) among mild and/or moderate asthmatic adults associated with 

short-term (5 or 10 minute) peak exposures to various levels of SO2 while engaged in 

moderate or greater exercise. The SO2 lung function risk assessment focuses on these 

lung function responses among asthmatic children and adults. 

3.1 Selection of health endpoints and target population 

 The ISA concluded that there is sufficient evidence to infer a causal relationship 

between respiratory morbidity and short-term exposure to SO2 (ISA, section 5.2). This 

determination was based in large part on controlled human exposure studies 

demonstrating a relationship between short-term (5- or 10-minute) peak SO2 exposures 

and adverse effects on the respiratory system in exercising asthmatics. More specifically, 

the ISA found consistent evidence from numerous controlled human exposure studies 

demonstrating increased respiratory symptoms (e.g. cough, chest tightness, wheeze) and 

decrements in lung function in a substantial proportion of exercising asthmatics 

(generally classified as mild to moderate asthmatics) following short-term peak exposures 

to SO2 at concentrations ≥ 0.4 ppm (400 ppb). As in previous reviews, the ISA also 

concluded that at concentrations below 1.0 ppm (1,000 ppb), healthy individuals are 

relatively insensitive to the respiratory effects of short-term peak SO2 exposures (ISA, 

sections 3.1.3.2). Therefore, the SO2 lung function risk assessment focuses on 

asthmatics. Exposure estimates for asthmatic children and adult asthmatics were 

combined separately with probabilistic exposure-response relationships (described 

below) for lung function response associated with daily 5-minute maximum peak 

exposures while engaged in moderate or greater exertion.5

 

 

5

 Only the highest 5-minute peak exposure (with moderate or greater exertion) on each day will be 

considered in the lung function risk assessment, since the controlled human exposure studies have shown 

an acute-phase response that was followed by a short refractory period where the individual was relatively 

insensitive to additional SO2 challenges. 
Abt Associates Inc. 3-2 

Figure 3-1. Components of SO2 Health Risk Assessment Based on Controlled Human Exposure Studies 

Ambient 

Monitoring for 

Selected Urban 

Areas

Air Quality 

Adjustment 

Procedures

Current and 

Alternative 

Proposed 

Standards

Health 

Risk 

Model

Controlled Human 

Exposure Studies

Probabilistic 

Exposure -

Response 

Relationships

Exposure-Response

Air Quality

Recent 

(“As Is”) 

Ambient 

SO2

Levels

Exposure 

Model

Exposure Estimates Associated with:• Recent Air Quality• Current Standard• Alternative 

Standards

Exposure

Ambient 

Monitoring for 

Selected Urban 

Areas

Air Quality 

Adjustment 

Procedures

Current and 

Alternative 

Proposed 

Standards

Health 

Risk 

Model

Controlled Human 

Exposure Studies

Probabilistic 

Exposure -

Response 

Relationships

Exposure-Response

Air Quality

Recent 

(“As Is”) 

Ambient 

SO2

Levels

Exposure 

Model

Exposure Estimates Associated with:• Recent Air Quality• Current Standard• Alternative 

Standards

Exposure
Abt Associates Inc. 3-3 June 2009 

 Two measures of lung function response – specific airway resistance (sRaw) and forced 

expiratory volume in one second (FEV1) – have been used in the controlled human exposure 

studies that have focused on the effects of exposure to SO2 on exercising asthmatics. Negative 

effects are measured as the percent increase in sRaw or the percent decrease in FEV1. As 

explained below, we estimated exposure-response relationships for four different definitions of 

response: 

 An increase in sRaw ≥ 100% 

 An increase in sRaw ≥ 200% 

 A decrease in FEV1 ≥ 15% 

 A decrease in FEV1 ≥ 20%. 

3.2 Development of exposure-response functions 

 We used a Bayesian Markov Chain Monte Carlo approach to estimate probabilistic 

exposure-response relationships for lung function decrements associated with 5- or 10-minute 

exposures at moderate or greater exertion, using the WinBUGS software (Spiegelhalter et al. 

(1996)). For an explanation of these methods, see Gelman et al. (1995) or Gilks et al. (1996). 

We treated both 5- and 10-minute exposures as if they were all 5-minute exposures. 

 The combined data set from Linn et al. (1987, 1988, 1990), Bethel et al. (1983, 1985), 

Roger et al. (1985), and Kehrl et al. (1987) provide data with which to estimate exposureresponse relationships between responses defined in terms of sRaw and 5- or 10-minute 

exposures to SO2 at levels of 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, and 1.0 ppm.6

 As noted above, two 

definitions of response were used: (1) an increase in sRaw ≥ 100% and (2) an increase in sRaw 

≥ 200%. 

 The combined data set from Linn et al. (1987, 1988, 1990) provide data with which to 

estimate exposure-response relationships between responses defined in terms of FEV1 and 5- or 

10-minute exposures to SO2 at levels of 0.2, 0.3, 0.4, and 0.6 ppm. As noted above, two 

definitions of response were used: a decrease in FEV1 ≥ 15% and a decrease in FEV1 ≥ 20%. 

 Before being used to estimate exposure-response relationships for 5-minute exposures, 

the data from these controlled human exposure studies were corrected for the effect of exercising 

in clean air to remove any systematic bias that might be present in the data attributable to an 

exercise effect.7

 Generally, this correction for exercise in clean air is small relative to the total 

effects measures in the SO2-exposed cases. The resulting study-specific results, based on the 

corrected data, are shown in Table 3-1. 

 

6

 Data from Magnussen et al. (1990) were not used in the estimation of sRaw exposure-response functions because 

exposures in this study were conducted using a mouthpiece rather than a chamber. 7

 Corrections were subject-specific. A correction was made by subtracting the subject’s percent change (in FEV1 or 

sRaw) under the no-SO2 protocol from his or her percent change (in FEV1 or sRaw) under the given SO2 protocol, 

and rounding the result to the nearest integer. For example, if a subject’s percent change in sRaw under the no-SO2

protocol was 110.12% and his percent change in sRaw under the 0.6 ppm (600 ppb) SO2 protocol was 185.92%, 

then his percent change in sRaw due to SO2 is 185.92% - 110.12% = 75.8%, which rounds to 76%. 
Abt Associates Inc. 

3-4

Table 3-1. Study-Specific SO2 Exposure-Response Data for Lung Function Decrements 

Increase in sRaw > 100% Increase in sRaw > 200% Decrease in FEV1>15% 

Study and SO2 Level Number 

Exposed

Number Responding

Number 

Exposed 

Number 

Responding 

Number 

Exposed 

Number 

Responding 

0.2 ppm SO2Linn et al. (1987) 40 2 40 0 40 5 0.25 ppm SO2Bethel et al. (1985) 19 6 19 3 

9 2 9 0 

Roger et al. (1985) 28 1 28 0 0.3 ppm SO2Linn et al. (1988) 20 2 20 1 20 3 Linn et al. (1990) 21 7 21 2 21 5 0.4 ppm SO2Linn et al. (1987) 40 9 40 3 40 12 0.5 ppm SO2Bethel et al. (1983) 10 6 10 4 Roger et al. (1985) 28 5 28 1 Magnussen et al. (1990)* 45 16 45 7 0.6 ppm SO2Linn et al. (1987) 40 14 40 11 40 21 Linn et al. (1988) 20 12 20 7 20 11 Linn et al. (1990) 21 13 21 6 21 9 1.0 ppm SO2Roger et al. (1985) 28 14 28 7 Kehrl et al. (1987) 10 6 10 2 

*Data from Magnussen et al. (1990) were not used in the estimation of sRaw exposure-response functions because exposures in this stumouthpiece rather than a chamber. 
Abt Associates Inc. June 2009

 

3-5

 

 We considered two different functional forms for the exposure-response functions: a 

2-parameter logistic model and a probit model. In particular, we used the data in Table 3-1 to 

estimate the logistic function, 

 )1(

1 ),;( x)*ln( e

xy      (3-1) 

and the probit function, 

 





 

)ln(

2/

2/1

2

)2(

1 ),;(

x

t xy dte





 (3-2) 

for each of the four lung function responses defined above, where x denotes the SO2

concentration (in ppb) to which the individual is exposed, ln(x) is the natural logarithm of x, y

denotes the corresponding probability of response (increase in sRaw > 100% or > 200% or 

decrease in FEV1 > 15% or > 20%), and β and γ are the two parameters whose values are 

estimated. 8 

 We assumed that the number of responses, si, out of Ni subjects exposed to a given 

SO2 concentration, xi, has a binomial distribution with response probability given by equation 

(3-1) when we assume the logistic model and equation (3-2) when we assume the probit 

model. The likelihood function is therefore 

 i ii sN

i

s

i

i i

i

xy xy

s

N

dataL   













  );,(     )],;(1[),;( . 

 

 In some of the controlled human exposure studies, subjects were exposed to a given 

SO2 concentration more than once. However, because there were insufficient data to estimate 

subject-specific response probabilities, we assumed a single response probability (for a given 

definition of response) for all individuals and treated the repeated exposures for a single 

subject as independent exposures in the binomial distribution. 

 For each model, we derived a Bayesian posterior distribution using this binomial 

likelihood function in combination with uniform prior distributions for each of the unknown 

parameters.9

 We used 4000 iterations as the “burn-in” period followed by a sufficient number 

of iterations to ensure convergence of the resulting posterior density. Each iteration 

corresponds to a set of values for the parameters of the logistic or probit exposure-response 

function. 

 

8

 For ease of exposition, we use the same two Greek letters to indicate two unknown parameters in the logistic 

and probit models; this does not imply, however, that the values of these two parameters are the same in the two 

models. 

9

 We used the following uniform prior distributions for the 2-parameter logistic model: β ~ U(-10, 0); and γ ~ 

U(-10,0); we used the following normal prior distributions for the probit model: β ~ N(0, 1000); and γ ~ 

N(0,1000). 
Abt Associates Inc. June 2009

 

3-6

 For any SO2 concentration, x, we could then derive the n

th percentile response value, 

for any n, by evaluating the exposure-response function at x using each of the 18,000 sets of 

parameter values. The resulting median (50th percentile) logistic and probit exposureresponse functions are shown together, along with the data used to estimate these functions, 

for increases in sRaw > 100% and > 200% and decreases in FEV1 > 15% and > 20% in 

Figures 3-2, 3-3, 3-4, and 3-5, respectively. 

 As can be seen in Figures 3-2 through 3-5, there were only limited data with which to 

estimate the logistic and probit exposure-response functions, and in all cases it wasn’t clear 

that one function fit the data better than the other. In fact, for each of the four lung function 

response definitions there was little difference between the estimated logistic and probit 

models in the range of the data used to estimate the functions. However, most of the 

exposures occur below the range of the data, where there are differences between the two 

functions.10 We therefore estimated the risks associated with exposure to SO2 under the 

different air quality scenarios considered using both the logistic and the probit exposureresponse functions. The 2.5th percentile, median, and 97.5th percentile logistic and probit 

exposure-response curves, along with the response data to which they were fit, are shown 

separately for each of the four response definitions in Appendix A. 

 

 

10 The differences are relatively small, as can be seen in Figures 3-2 through 3-2; however, even these relatively 

small differences result in substantial differences in estimates of risk. 
Abt Associates Inc. June 2009

 

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Figure 3-2. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw > 100% for 5-

Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

probit

2-parameter logistic

Figure 3-3. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw > 200% for 5-

Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

probit

2-parameter logistic
Abt Associates Inc. June 2009

 

3-8

Figure 3-4. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEV1 > 15% for 5-

Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

probit

2-parameter logistic

Figure 3-5. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEV1 > 20% for 5-

Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

probit

2-parameter logistic
Abt Associates Inc. June 2009

 

3-9

3.2.1 Calculation of risk estimates 

 We generated two measures of risk for each of the lung function response definitions. 

The first measure of risk is simply the number of occurrences of the lung function response in 

the designated population (e.g., asthmatics) in a year associated with SO2 concentrations 

under a given air quality scenario. To calculate this measure of risk we started with the 

number of exposures among the population that are at or above each benchmark level (i.e., 0 

ppb, 50 ppb, 100 ppb, etc.), estimated from the exposure modeling. From this we calculated 

the number of exposures within each 50 ppb exposure “bin” (e.g., < 50 ppb, 50 – 100 ppb, 

etc.). 11 We then calculated the number of occurrences of lung function response by 

multiplying the number of exposures in an exposure bin by the response probability (given by 

our logistic or probit exposure-response function for the specified definition of lung function 

response) associated with the midpoint of that bin and summing the results across the bins. 

 Because response probabilities are calculated for each of several percentiles of a 

probabilistic exposure-response distribution, estimated numbers of occurrences are similarly 

percentile-specific. The kth percentile number of occurrences, Ok, associated with SO2

concentrations under a given air quality scenario is: 

 )|(

1

jk

n

j

k j  eRxNO 

 (3-3) 

where: 

ej = (the midpoint of) the jth category of personal exposure to SO2; 

Nj = the number of exposures to ej ppb SO2, given ambient SO2 concentrations under 

the specified air quality scenario; 

jk | = the k eR th percentile response probability at SO2 concentration ej; and 

n = the number of intervals (categories) of SO2 personal exposure concentration. 

An example calculation, using the logistic exposure-response function, is given in Table 3-2. 

 

11 The final exposure bin was from 750 to 800 ppb SO2. In at least one of the alternative standard scenarios, 

there were exposures greater than 800 ppb. For any exposures that exceeded 800 ppb, we assumed a final bin 

from 800 to 850 ppb, and assigned them the midpoint value of that bin, 825 ppb. This will result in a slight 

downward bias in the estimate of risk. 
Abt Associates Inc. June 2009

 

3-10

Table 3-2. Example: Calculation of Number of Occurrences of Lung Function Response, Defined as an 

Increase in sRaw ≥ 100%, Among Asthmatics in St. Louis Engaged in Moderate or Greater Exertion 

Associated with Exposure to SO2 Concentrations that Just Meet an Alternative 1-Hour 99th Percentile 100 

ppb Standard* 

Number of Probability of Expected Number of 

Lower Upper Midpoint Exposures Response at Midpoint Occurrences of Lung 

Bound Bound SO2 Level Function Response

(1) (2) (3) =(2) x (3)

0 50 25 16519000 0.00406 67067

50 100 75 136621 0.02334 3189

100 150 125 15760 0.05162 814

150 200 175 3826 0.08563 328

200 250 225 1051 0.12300 129

250 300 275 413 0.16220 67

300 350 325 175 0.20210 35

350 400 375 83 0.24190 20

400 450 425 31 0.28060 9

450 500 475 24 0.31830 8

500 550 525 8 0.35430 3

550 600 575 0 0.38850 0

600 650 625 0 0.42090 0

650 700 675 8 0.45150 4

700 750 725 0 0.46600 0

750 800 775 0 0.49380 0

Expected Number 

Total Number of Exposures: 16677000 of Occurrences: 71672

SO2 Exposure Bin (ppb)

 *Calculations were made using the logistic exposure-response function. 

 The second measure of risk generated for each lung function response definition is the 

number of individuals in the designated population to experience at least one lung function 

response in a year associated with SO2 concentrations under a specified air quality scenario. 

The calculation of this measure of risk is similar to the calculation of the first measure of risk 

– however, here we started with estimates, from the exposure modeling, of the number of 

individuals exposed at least once to x ppb SO2 or higher, for x = 0, 50, 100, etc. From this we 

calculated the number of individuals exposed at least once to SO2 concentrations within each 

SO2 exposure bin defined above. We then multiplied the numbers of individuals in an 

exposure bin by the response probability (given by our logistic or probit exposure-response 

function for the specified definition of lung function response) corresponding to the midpoint 

of the exposure bin, and summed the results across the bins. 

 Because response probabilities are calculated for each of several percentiles of a 

probabilistic exposure-response distribution, estimated numbers of individuals with at least 

one lung function response are similarly percentile-specific. The kth percentile number of 

individuals, Yk, associated with SO2 concentrations under a given air quality scenario is: 

 )|(

1

jk

n

j

k j  eRxNIY 

 (3-4) 

Where ej, jk | , and eR n are as defined above, and NIj is the number of individuals whose 

highest exposure is to ej ppb SO2, given ambient SO2 concentrations under the specified air 
Abt Associates Inc. June 2009

 

3-11

quality scenario. An example calculation, using the logistic exposure-response function, is 

given in Table 3-3. 

Table 3-3. Example: Calculation of the Number of Asthmatics in St. Louis Engaged in Moderate or 

Greater Exertion Estimated to Experience at Least One Lung Function Response, Defined as an 

Increase in sRaw ≥ 100%, Associated with Exposure to SO2 Concentrations that Just Meet an 

Alternative 1-Hour 99th Percentile 100 ppb Standard* 

Number of Probability of Estimated Number of 

Lower Upper Midpoint Asthmatics with Response at Midpoint Asthmatics Experiencing

Bound Bound At Least One SO2 Level at Least One Lung 

Exposure in Bin Function Response

(1) (2) (3) =(2) x (3)

0 50 25 53711 0.00406 218

50 100 75 34236 0.02334 799

100 150 125 9835 0.05162 508

150 200 175 3059 0.08563 262

200 250 225 929 0.12300 114

250 300 275 368 0.16220 60

300 350 325 145 0.20210 29

350 400 375 84 0.24190 20

400 450 425 31 0.28060 9

450 500 475 22 0.31830 7

500 550 525 8 0.35430 3

550 600 575 0 0.38850 0

600 650 625 0 0.42090 0

650 700 675 8 0.45150 4

700 750 725 0 0.46600 0

750 800 775 0 0.49380 0

Total : 102436 Total: 2032

SO2 Exposure Bin (ppb)

*Calculations were made using the logistic exposure-response function.

 Note that this calculation assumes that individuals who do not respond at the highest 

SO2 concentration to which they are exposed will not respond to any lower SO2

concentrations to which they are exposed. 

 Note also that, in contrast to the risk estimates calculated for the O3 health risk 

assessment, the risk estimates calculated for the SO2 health risk assessment do not subtract out 

risk given the personal exposures associated with estimated policy relevant background (PRB) 

ambient SO2 concentrations, because PRB SO2 concentrations are so low (see section 2.3). 

 

3.2.2 Selection of urban areas 

Although it would be useful to characterize SO2-related lung function risks associated 

with “as is” SO2 ambient concentrations and SO2 concentrations that just meet the current and 

alternative SO2 standards nationwide, because the modeling of personal exposures is both 

time and labor intensive, a regional and source-oriented approach was selected instead. The 

selection of areas to include in the exposure analysis, and therefore the risk assessment, took 

into consideration the availability of ambient monitoring, the desire to represent a range of 
Abt Associates Inc. June 2009

 

3-12

geographic areas considering SO2 emission sources, population demographics, general 

climatology, and results of the ambient air quality characterization. 

The first area of interest was initially identified based on the results of a preliminary 

screening of the 5-minute ambient SO2 monitoring data that were available. The state of 

Missouri was one of only a few states having both 5-minute maximum and continuous 5-

minute SO2 ambient monitoring, as well as having over 30 1-hour SO2 monitors in operation 

at some time during the period from 1997 to 2007. In addition, the air quality characterization, 

described in Chapter 6 of the 1st draft REA (EPA, 2008b), estimated frequent exceedances 

above the potential health effect benchmark levels at several of the 1-hour ambient monitors. 

In a ranking of estimated SO2 emissions reported in the National Emissions Inventory (NEI), 

Missouri ranked 7th for the number of stacks with > 1000 tpy SOx emissions out of all U.S. 

states. These stack emissions were associated with a variety of source types such as electrical 

power generating units, chemical manufacturing, cement processing, and smelters. For all 

these reasons, the current SO2 lung function risk assessment focuses on Missouri and, within 

Missouri, on those areas within 20 km of a major point source of SO2 emissions in Greene 

County and the St. Louis area. 

3.2.3 Addressing variability and uncertainty 

Any estimation of risks associated with “as is” SO2 concentrations or with SO2

concentrations that just meet the current or alternative SO2 standards should address both the 

variability and uncertainty that generally underlie such an analysis. Uncertainty refers to the 

lack of knowledge regarding the actual values of model input variables (parameter 

uncertainty) and of physical systems or relationships (model uncertainty – e.g., the shapes of 

exposure-response and concentration-response functions). The goal of the analyst is to reduce 

uncertainty to the maximum extent possible. Uncertainty can be reduced by improved 

measurement and improved model formulation. In a health risk assessment, however, 

significant uncertainty often remains. 

 

The degree of uncertainty can be characterized, sometimes quantitatively. For 

example, the statistical uncertainty surrounding the estimated SO2 coefficients in the 

exposure-response functions is reflected in confidence or credible intervals provided for the 

risk estimates. 

 As described in section 3.2 above, we used a Bayesian Markov Chain Monte Carlo 

approach to estimate exposure-response functions as well as to characterize uncertainty 

attributable to sampling error based on sample size considerations. Using this approach, we 

could derive the n

th percentile response value, for any n, for any SO2concentration, x, as 

described above (see section 3.2). Because our exposure estimates were generated at the 

midpoints of 0.05 ppm intervals (i.e., for 0.025 ppm, 0.075 ppm, etc.), we derived 2.5th

percentile, 50th percentile (median), and 97.5th percentile response estimates for SO2

concentrations at these midpoint values. The 2.5th percentile and 97.5th percentile response 

estimates comprise the lower and upper bounds of the credible interval around each point 

estimate (median estimate) of response. 
Abt Associates Inc. June 2009

 

3-13

 In addition to uncertainties arising from sampling variability, other uncertainties 

associated with the use of the exposure-response relationships for lung function responses are 

briefly summarized below. Additional uncertainties with respect to the exposure inputs to the 

risk assessment are described in section 8.11 of the final REA (EPA, 2009b). The main 

additional uncertainties with respect to the approach used to estimate exposure-response 

relationships include: 

 Length of exposure. The 5-minute lung function risk estimates are based on a combined 

data set from several controlled human exposure studies, most of which evaluated 

responses associated with 10-minute exposures. However, since some studies which 

evaluated responses after 5-minute exposures found responses occurring as early as 5-

minutes after exposure, we used all of the 5- and 10- minute exposure data to represent 

responses associated with 5-minute exposures. We do not believe that this approach would 

appreciably impact the risk estimates. 

 Exposure-response for mild/moderate asthmatics. The data set that was used to estimate 

exposure-response relationships included mild and/or moderate asthmatics. There is 

uncertainty with regard to how well the population of mild and moderate asthmatics 

included in the series of controlled human exposure studies represent the distribution of 

mild and moderate asthmatics in the U.S. population. As indicated in the ISA (p. 3-9), the 

subjects studied represent the responses “among groups of relatively healthy asthmatics 

and cannot necessarily be extrapolated to the most sensitive asthmatics in the population 

who are likely more susceptible to the respiratory effects of exposure to SO2." 

 Extrapolation of exposure-response relationships. It was necessary to estimate responses 

at SO2 levels below the lowest exposure levels used in free-breathing controlled human 

studies (i.e., 0.2 ppm or 200 ppb). We did not include alternative models that incorporate 

hypothetical population thresholds, given the lack of evidence supporting the choice of 

potential hypothetical threshold levels. As discussed later in this document, we have 

presented information on the contribution of different exposure intervals to the total 

estimated lung function risk. This information provides insights on how much of the 

estimated risk is attributed to SO2 exposures at the lower exposure levels (i.e., 0 to 50 ppb, 

50 to 100 ppb, 100 to 150 ppb, etc.). One can use this information to get a rough sense of 

the SO2-related risk that would exist under alternative threshold assumptions. 

 Reproducibility of SO2-induced responses. The risk assessment assumed that the SO2-

induced responses for individuals are reproducible. We note that this assumption has 

some support in that one study (Linn et al., 1987) exposed the same subjects on two 

occasions to 0.6 ppm (600 ppb) and the authors reported a high degree of correlation (r > 

0.7 for mild asthmatics and r > 0.8 for moderate asthmatics, p < 0.001), while observing 

much lower and nonsignificant correlations (r = 0.0 – 0.4) for the lung function response 

observed in the clean air with exercise exposures. 

 Age and lung function response. Because the vast majority of controlled human exposure 

studies investigating lung function responses were conducted with adult subjects, the risk 

assessment relies on data from adult asthmatic subjects to estimate exposure-response 

relationships that were applied to all asthmatic individuals, including children. The ISA 
Abt Associates Inc. June 2009

 

3-14

(section 3.1.3.5) indicates that there is a strong body of evidence that suggests adolescents 

may experience many of the same respiratory effects at similar SO2 levels, but recognizes 

that these studies administered SO2 via inhalation through a mouthpiece rather than an 

exposure chamber. This technique bypasses nasal absorption of SO2 and can result in an 

increase in lung SO2 uptake. Therefore, the uncertainty will be greater in the risk estimates 

for asthmatic children. 

 Exposure history. The risk assessment assumed that the SO2-induced response on any 

given day is independent of previous SO2 exposures. 

 Interaction between SO2 and other pollutants. Because the controlled human exposure 

studies used in the risk assessment involved only SO2 exposures, it was assumed that 

estimates of SO2-induced health responses would not be affected by the presence of other 

pollutants (e.g., PM2.5, O3, NO2). 

Variability refers to the heterogeneity in a population or parameter. Even if there is no 

uncertainty surrounding inputs to the analysis, there may still be variability. For example, 

there may be variability among exposure-response functions describing the relationship 

between SO2 and lung function in different locations. This variability does not imply 

uncertainty about the exposure-response function in any location, but only that these functions 

are different in the different locations, reflecting differences in the populations and/or other 

factors that may affect the relationship between SO2 and the associated health endpoint. In 

general, it is possible to have uncertainty but no variability (if, for instance, there is a single 

parameter whose value is uncertain) or variability but little or no uncertainty (for example, 

people’s heights vary considerably but can be accurately measured with little uncertainty). 

The SO2 lung function risk assessment addresses variability-related concerns by using 

location-specific inputs for the exposure analysis (e.g., location-specific population data, air 

exchange rates, air quality and temperature data). The extent to which there may be 

variability in exposure-response relationships for the populations included in the risk 

assessment residing in different geographic areas is currently unknown. 

 

Temporal variability is more difficult to address, because the risk assessment focuses 

on some unspecified time in the future. To minimize the degree to which values of inputs to 

the analysis may be different from the values of those inputs at that unspecified time, we are 

using the most current inputs available. 
Abt Associates Inc. June 2009 4-15 

4 RESULTS 

 The results of the SO2 risk assessment are presented in Tables 4-1 through 4-12. 

Each table includes results for both of the locations included in the risk assessment and 

for all of the air quality scenarios considered, using both 2-parameter logistic and probit 

exposure-response functions. Tables 4-1 and 4-2 show the numbers of occurrences of 

lung function response in a year, defined in terms of sRaw, for asthmatics and for 

asthmatic children, respectively, engaged in moderate or greater exertion associated with 

SO2 concentrations under each of the different air quality scenarios considered in each of 

the two locations. Tables 4-3 and 4-4 show the corresponding results when lung function 

response is defined in terms of FEV1. Tables 4-5 and 4-6 show the numbers of 

asthmatics and asthmatic children, respectively, engaged in moderate or greater exertion 

estimated to experience at least one lung function response in a year, defined in terms of 

sRaw, under each of the different air quality scenarios in each of the two locations. 

Tables 4-7 and 4-8 show the corresponding results when lung function response is 

defined in terms of FEV1. Finally, Tables 4-9 through 4-12 show results analogous to 

those shown in Tables 4-5 through 4-8, only as percentages of all asthmatics (asthmatic 

children) engaged in moderate or greater exertion. 

 In addition, responses attributable to exposure to SO2 within different 

concentration ranges are shown in Figures 4-1 through 4-16. The exposure ranges are in 

50 ppb increments – i.e., SO2 < 50 ppb, 50 ppb ≤ SO2 < 100 ppb, 100 ppb ≤ SO2 < 150 

ppb, ... , SO2 ≥ 500 ppb. Figures 4-1a and b show the percent of asthmatics engaged in 

moderate or greater exertion in St. Louis, MO, using the logistic and probit exposureresponse functions, respectively, estimated to experience at least one lung function 

response in a year, defined as an increase in sRaw ≥ 100%, attributable to exposure to 

SO2 in each exposure “bin.” Figures 4-2a and b show the corresponding percents for 

asthmatic children engaged in moderate or greater exertion in St. Louis, MO, using the 

logistic and probit exposure-response functions, respectively. Figures 4-3a and b, and 4-

4a and b, show the corresponding percents for asthmatics and asthmatic children, 

respectively, in St. Louis, MO, when lung function response is defined as a decrease in 

FEV1≥ 15%. 

Figures 4-5a and b show the number of occurrences of lung function response, 

defined as an increase in sRaw ≥ 100%, among asthmatics engaged in moderate or 

greater exertion in St. Louis, MO, using the logistic and probit exposure-response 

functions, respectively, attributable to exposure to SO2 in each exposure “bin.” Figures 

4-6a and b show the corresponding numbers of occurrences among asthmatic children in 

St. Louis, MO. Figures 4-7a and b and 4-8a and b show the corresponding numbers of 

occurrences of lung function response for asthmatics and asthmatic children, respectively, 

when lung function response is defined as a decrease in FEV1≥ 15%. Figures 4-9a and b 

through 4-16a and b are the corresponding figures for Greene Co., MO. Figure 4-17 

shows the legend that is used in Figures 4-1 through 4-16. 
Abt Associates Inc. 

4-16

Table 4-1. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of sRaw) Among Asthmatics E

Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Sta

Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

125 127 125 125 125 126 1

(24 - 572) (25 - 577) (24 - 572) (24 - 572) (24 - 573) (24 - 573) (24 

16 18 16 16 16 16 

(0 - 256) (1 - 261) (0 - 256) (0 - 256) (1 - 257) (1 - 257) (1 

657 1672 652 686 762 880 1

(128 - 2985) (663 - 4740) (125 - 2975) (141 - 3041) (176 - 3184) (234 - 3398) (315 

90 933 86 111 170 264 3

(4 - 1346) (393 - 3107) (3 - 1336) (11 - 1402) (33 - 1543) (72 - 1756) (128 

38 39 38 38 38 38 

(4 - 310) (4 - 312) (4 - 310) (4 - 310) (4 - 310) (4 - 310) (4 

232222

(0 - 123) (0 - 124) (0 - 122) (0 - 123) (0 - 123) (0 - 123) (0 

201 560 199 211 237 278 3

(21 - 1614) (165 - 2407) (20 - 1609) (24 - 1639) (32 - 1703) (47 - 1799) (68 

13 258 12 18 33 59 

(0 - 643) (86 - 1388) (0 - 639) (1 - 666) (5 - 725) (12 - 814) (24 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an a

ppm, calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncoefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number. 

Probit

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
Abt Associates Inc. 

4-17

Table 4-2. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of sRaw) Among Asthmatic CModerate or Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet th

and SO2 Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

71 72 71 71 71 71 

(13 - 324) (14 - 327) (13 - 324) (14 - 324) (14 - 324) (14 - 325) (14 

9 109 9 9 9

(0 - 145) (1 - 148) (0 - 145) (0 - 145) (0 - 145) (0 - 146) (0 

417 1179 413 439 497 586 7

(81 - 1893) (484 - 3209) (80 - 1885) (91 - 1935) (118 - 2043) (162 - 2206) (222 

58 692 55 74 118 189 2

(3 - 855) (296 - 2176) (2 - 847) (8 - 896) (25 - 1004) (53 - 1166) (96 

22 22 22 22 22 22 

(2 - 175) (2 - 177) (2 - 175) (2 - 175) (2 - 175) (2 - 176) (2 

121111

(0 - 69) (0 - 71) (0 - 69) (0 - 69) (0 - 69) (0 - 70) (0 

128 397 126 135 155 186 2

(13 - 1023) (122 - 1618) (13 - 1019) (15 - 1042) (22 - 1091) (33 - 1164) (49 

8 1928 12 24 43 

(0 - 408) (65 - 967) (0 - 405) (1 - 425) (4 - 470) (9 - 538) (18 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncoefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number. 

Exposure-Response 

Model

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

Probit

2-Parameter Logistic

Probit

2-Parameter Logistic

Probit

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m):

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

Probit

 Greene County, MO

St. Louis, MO

Greene County, MO

"As is" SO2 

Concentrations**
Abt Associates Inc. 

4-18

Table 4-3. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of FEV1) Among Asthmatics 

Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current Sta

Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

69 71 69 69 69 69 

(6 - 675) (7 - 680) (6 - 675) (6 - 675) (6 - 675) (6 - 676) (6 

686666

(0 - 418) (0 - 424) (0 - 417) (0 - 418) (0 - 418) (0 - 419) (0 

366 1341 361 391 461 570 7

(33 - 3520) (454 - 5632) (32 - 3507) (41 - 3587) (66 - 3759) (108 - 4016) (169 

36 866 33 55 109 198 3

(1 - 2189) (322 - 4471) (0 - 2175) (5 - 2262) (20 - 2448) (49 - 2727) (94 

333333

(0 - 53) (0 - 54) (0 - 53) (0 - 53) (0 - 53) (0 - 53) (0 

000000

(0 - 5) (0 - 7) (0 - 5) (0 - 5) (0 - 5) (0 - 6) (0 

15 310 14 20 35 62 1

(1 - 279) (133 - 1045) (0 - 276) (2 - 299) (7 - 351) (17 - 435) (34 

1 240 0 3 13 33 

(0 - 32) (120 - 697) (0 - 30) (1 - 47) (5 - 89) (14 - 158) (29 

St. Louis, MO

Probit

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

2-Parameter Logistic

Probit

2-Parameter Logistic

Probit

 Greene County, MO

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

Greene County, MO

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

Exposure-Response 

Model

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical un

coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number. 

Probit

2-Parameter Logistic

Response = Decrease in FEV1 >= 15%Response = Decrease in FEV1 >= 20%
Abt Associates Inc. 

4-19

Table 4-4. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of FEV1) Among Asthmatic C

Moderate or Greater Exertion Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet th

and SO2 Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

39 40 39 39 39 39 

(3 - 382) (4 - 386) (3 - 382) (3 - 382) (3 - 382) (3 - 383) (4 

343334

(0 - 236) (0 - 240) (0 - 236) (0 - 236) (0 - 237) (0 - 237) (0 

232 965 229 252 304 387 4

(21 - 2231) (338 - 3816) (20 - 2222) (27 - 2282) (46 - 2412) (77 - 2608) (123 

23 648 21 38 79 146 2

(1 - 1389) (242 - 3101) (0 - 1379) (4 - 1444) (15 - 1585) (37 - 1797) (70 

121112

(0 - 30) (0 - 31) (0 - 30) (0 - 30) (0 - 30) (0 - 30) (0 

000000

(0 - 3) (0 - 4) (0 - 3) (0 - 3) (0 - 3) (0 - 3) (0 

10 231 9 13 24 45 

(0 - 178) (99 - 753) (0 - 175) (1 - 192) (5 - 232) (13 - 295) (26 

0 180 0 2 10 25 

(0 - 21) (90 - 521) (0 - 19) (1 - 32) (3 - 63) (10 - 116) (21 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncoefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number. 

Probit

2-Parameter Logistic

Response = Decrease in FEV1 >= 15%Response = Decrease in FEV1 >= 20%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
Abt Associates Inc. 

4-20

Table 4-5. Number of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Funcin Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current St

Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

90 210 80 90 100 120 1

(20 - 390) (80 - 620) (20 - 380) (20 - 390) (20 - 420) (30 - 460) (50 

10 110 10 10 20 40 

(0 - 180) (40 - 410) (0 - 170) (0 - 180) (0 - 210) (10 - 250) (20 

1010 13460 730 1990 3650 5520 7

(340 - 3010) (9740 - 18510) (220 - 2490) (860 - 4690) (1900 - 7100) (3230 - 9490) (4770 

500 13050 290 1340 2930 4810 6

(140 - 1990) (9430 - 18100) (70 - 1470) (520 - 3690) (1450 - 6200) (2760 - 8710) (4310 

30 70 30 30 30 40 

(0 - 210) (20 - 310) (0 - 210) (0 - 210) (0 - 220) (10 - 240) (10 

0 300 010 10 

(0 - 80) (10 - 180) (0 - 80) (0 - 90) (0 - 100) (0 - 110) (0 

330 5520 230 670 1280 2010 2

(70 - 1520) (3400 - 8960) (40 - 1290) (210 - 2270) (510 - 3360) (940 - 4470) (1470 

120 5180 60 350 870 1560 2

(20 - 880) (3150 - 8570) (10 - 660) (90 - 1590) (310 - 2680) (690 - 3820) (1200 

Response = Increase in sRaw >= 100%

 Greene County, MO

St. Louis, MO

"As is" SO2 

Concentrations**

Exposure-Response 

Model

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m):

2-Parameter Logistic

Probit

2-Parameter Logistic

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncoefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.

2-Parameter Logistic

Response = Increase in sRaw >= 200%

Probit

St. Louis, MO

Probit

Greene County, MO

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

Probit

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.
Abt Associates Inc. 

4-21

Table 4-6. Number of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lu

(Defined in Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the C

SO2 Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

30 110 30 30 40 50 

(10 - 130) (40 - 270) (10 - 130) (10 - 140) (10 - 150) (20 - 180) (30 

10 60 0 10 10 20 

(0 - 60) (20 - 200) (0 - 60) (0 - 60) (0 - 80) (10 - 100) (10 

590 8020 400 1220 2240 3370 4

(220 - 1570) (6080 - 10370) (130 - 1210) (560 - 2620) (1240 - 4010) (2090 - 5350) (3060 

340 7950 190 890 1910 3080 4

(100 - 1150) (6020 - 10320) (50 - 790) (360 - 2220) (1000 - 3690) (1860 - 5110) (2870 

10 40 10 10 10 20 

(0 - 70) (10 - 130) (0 - 70) (0 - 70) (0 - 80) (0 - 90) (10 

0 20 0 0 0 10 

(0 - 30) (0 - 90) (0 - 30) (0 - 30) (0 - 40) (0 - 50) (0 

190 3380 130 410 800 1250 1

(50 - 780) (2190 - 5070) (30 - 610) (140 - 1240) (340 - 1870) (620 - 2500) (970 

80 3290 40 240 580 1030 1

(10 - 500) (2110 - 5000) (10 - 350) (60 - 950) (220 - 1590) (480 - 2250) (830 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Numbers are median (50th percentile) numbers of asthmatic children. Numbers in parentheses below the median are 95% credible intervals based on statithe SO2 coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.

Probit

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
Abt Associates Inc. 

4-22

Table 4-7. Number of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Funcin Terms of FEV1) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current St

Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

50 170 50 50 60 80 1

(10 - 460) (50 - 730) (0 - 450) (10 - 460) (10 - 490) (20 - 540) (30 

10 100 0 10 20 30 

(0 - 290) (30 - 590) (0 - 280) (0 - 290) (0 - 330) (10 - 380) (10 

750 15220 510 1700 3460 5570 7

(180 - 3580) (10280 - 22530) (100 - 2950) (580 - 5590) (1520 - 8500) (2880 - 11400) (4550 

410 15040 220 1250 2970 5130 7

(80 - 2880) (10140 - 22670) (30 - 2200) (370 - 5070) (1230 - 8210) (2580 - 11280) (4280 

0 30 0 0 0 10 

(0 - 40) (10 - 130) (0 - 40) (0 - 40) (0 - 50) (0 - 60) (0 

0 200 0 0 0

(0 - 10) (10 - 80) (0 - 0) (0 - 10) (0 - 10) (0 - 20) (0 

100 9240 50 350 1020 2100 3

(20 - 570) (6110 - 13840) (10 - 380) (110 - 1290) (430 - 2680) (1060 - 4450) (1990 

40 9260 20 240 870 1950 3

(10 - 320) (6200 - 13820) (0 - 170) (80 - 960) (390 - 2340) (1020 - 4170) (1980 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncoefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.

Probit

2-Parameter Logistic

Response = Decrease in FEV1 >= 15%

Response = Decrease in FEV1 >= 20%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
Abt Associates Inc. 

4-23

Table 4-8. Number of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lu

(Defined in Terms of FEV1) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the C

SO2 Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

20 90 20 20 30 40 

(0 - 160) (30 - 320) (0 - 150) (0 - 160) (0 - 180) (10 - 210) (10 

0 600 010 20 

(0 - 100) (20 - 280) (0 - 100) (0 - 100) (0 - 120) (0 - 160) (10 

460 9310 290 1080 2200 3510 4

(120 - 1870) (6620 - 12680) (60 - 1440) (390 - 3130) (1030 - 4810) (1930 - 6440) (3030 

280 9320 150 840 1970 3350 4

(50 - 1630) (6630 - 12800) (20 - 1160) (260 - 2990) (860 - 4800) (1790 - 6510) (2930 

0 200 0 0 0

(0 - 10) (10 - 70) (0 - 10) (0 - 10) (0 - 20) (0 - 30) (0 

0 100 0 0 0 

(0 - 0) (0 - 40) (0 - 0) (0 - 0) (0 - 10) (0 - 10) (0 

70 6150 30 240 700 1430 2

(10 - 350) (4190 - 8700) (10 - 220) (80 - 820) (300 - 1710) (740 - 2830) (1400 

30 6210 10 170 610 1370 2

(10 - 220) (4280 - 8780) (0 - 110) (60 - 650) (280 - 1560) (730 - 2750) (1410 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

*Numbers are median (50th percentile) numbers of asthmatic children. Numbers in parentheses below the median are 95% credible intervals based on statithe SO2 coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest hundred.

Exposure-Response 

Model

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

Probit

2-Parameter Logistic

Probit

2-Parameter Logistic

Probit

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m):

2-Parameter Logistic

Response = Decrease in FEV1 >= 15%

Response = Decrease in FEV1 >= 20%

Probit

 Greene County, MO

St. Louis, MO

Greene County, MO

"As is" SO2 

Concentrations**
Abt Associates Inc. 

4-24

Table 4-9. Percent of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Func

in Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current St

Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

0.4% 1% 0.4% 0.4% 0.5% 0.6% 0

(0.1% - 1.8%) (0.4% - 2.9%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 2%) (0.2% - 2.1%) (0.2% 

0.1% 0.5% 0.1% 0.1% 0.1% 0.2% 0

(0% - 0.8%) (0.2% - 1.9%) (0% - 0.8%) (0% - 0.8%) (0% - 1%) (0% - 1.2%) (0.1% 

1% 13.1% 0.7% 1.9% 3.6% 5.4% 7

(0.3% - 2.9%) (9.5% - 18.1%) (0.2% - 2.4%) (0.8% - 4.6%) (1.9% - 6.9%) (3.2% - 9.3%) (4.7% 

0.5% 12.7% 0.3% 1.3% 2.9% 4.7% 6

(0.1% - 1.9%) (9.2% - 17.7%) (0.1% - 1.4%) (0.5% - 3.6%) (1.4% - 6.1%) (2.7% - 8.5%) (4.2% 

0.1% 0.3% 0.1% 0.1% 0.2% 0.2% 0

(0% - 1%) (0.1% - 1.5%) (0% - 1%) (0% - 1%) (0% - 1%) (0% - 1.1%) (0% 

0% 0.1% 0% 0% 0% 0% 0

(0% - 0.4%) (0% - 0.8%) (0% - 0.4%) (0% - 0.4%) (0% - 0.5%) (0% - 0.5%) (0% 

0.3% 5.4% 0.2% 0.7% 1.3% 2% 2

(0.1% - 1.5%) (3.3% - 8.7%) (0% - 1.3%) (0.2% - 2.2%) (0.5% - 3.3%) (0.9% - 4.4%) (1.4% 

0.1% 5.1% 0.1% 0.3% 0.8% 1.5% 2

(0% - 0.9%) (3.1% - 8.4%) (0% - 0.6%) (0.1% - 1.6%) (0.3% - 2.6%) (0.7% - 3.7%) (1.2% 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statisthe SO2 coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth. 

Probit

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
Abt Associates Inc. 

4-25

Table 4-10. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One L

(Defined in Terms of sRaw) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the C

SO2 Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

0.4% 1.4% 0.4% 0.4% 0.5% 0.7% 

(0.1% - 1.8%) (0.6% - 3.7%) (0.1% - 1.8%) (0.1% - 1.9%) (0.1% - 2.1%) (0.2% - 2.4%) (0.3% 

0.1% 0.9% 0.1% 0.1% 0.2% 0.3% 0

(0% - 0.9%) (0.3% - 2.7%) (0% - 0.8%) (0% - 0.9%) (0% - 1.1%) (0.1% - 1.4%) (0.2% 

1.4% 19.2% 0.9% 2.9% 5.4% 8.1% 10

(0.5% - 3.8%) (14.6% - 24.9%) (0.3% - 2.9%) (1.3% - 6.3%) (3% - 9.6%) (5% - 12.8%) (7.3% 

0.8% 19.1% 0.4% 2.1% 4.6% 7.4% 10

(0.2% - 2.8%) (14.4% - 24.7%) (0.1% - 1.9%) (0.9% - 5.3%) (2.4% - 8.8%) (4.5% - 12.3%) (6.9% 

0.1% 0.5% 0.1% 0.1% 0.2% 0.2% 0

(0% - 1%) (0.1% - 1.8%) (0% - 1%) (0% - 1%) (0% - 1.1%) (0% - 1.3%) (0.1% 

0% 0.2% 0% 0% 0% 0.1% 0

(0% - 0.4%) (0.1% - 1.2%) (0% - 0.4%) (0% - 0.4%) (0% - 0.5%) (0% - 0.6%) (0% 

0.5% 8.1% 0.3% 1% 1.9% 3% 4

(0.1% - 1.9%) (5.3% - 12.2%) (0.1% - 1.5%) (0.3% - 3%) (0.8% - 4.5%) (1.5% - 6%) (2.3% 

0.2% 7.9% 0.1% 0.6% 1.4% 2.5% 3

(0% - 1.2%) (5% - 12%) (0% - 0.8%) (0.2% - 2.3%) (0.5% - 3.8%) (1.2% - 5.4%) (2% 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statisthe SO2 coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth. 

Exposure-Response 

Model

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

Probit

2-Parameter Logistic

Probit

2-Parameter Logistic

Probit

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m):

2-Parameter Logistic

Response = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%

Probit

 Greene County, MO

St. Louis, MO

Greene County, MO

"As is" SO2 

Concentrations**
Abt Associates Inc. 

4-26

Table 4-11. Percent of Asthmatics Engaged in Moderate or Greater Exertion Estimated to Experience At Least One Lung Fun

in Terms of FEV1) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the Current St

Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

0.2% 0.8% 0.2% 0.2% 0.3% 0.4% 0

(0% - 2.1%) (0.2% - 3.4%) (0% - 2.1%) (0% - 2.1%) (0% - 2.3%) (0.1% - 2.5%) (0.1% 

0% 0.5% 0% 0% 0.1% 0.1% 0

(0% - 1.3%) (0.1% - 2.8%) (0% - 1.3%) (0% - 1.4%) (0% - 1.5%) (0% - 1.8%) (0.1% 

0.7% 14.9% 0.5% 1.7% 3.4% 5.4% 7

(0.2% - 3.5%) (10% - 22%) (0.1% - 2.9%) (0.6% - 5.5%) (1.5% - 8.3%) (2.8% - 11.1%) (4.4% 

0.4% 14.7% 0.2% 1.2% 2.9% 5% 7

(0.1% - 2.8%) (9.9% - 22.1%) (0% - 2.1%) (0.4% - 4.9%) (1.2% - 8%) (2.5% - 11%) (4.2% 

0% 0.1% 0% 0% 0% 0% 0

(0% - 0.2%) (0% - 0.6%) (0% - 0.2%) (0% - 0.2%) (0% - 0.2%) (0% - 0.3%) (0% 

0% 0.1% 0% 0% 0% 0% 

(0% - 0%) (0% - 0.4%) (0% - 0%) (0% - 0%) (0% - 0.1%) (0% - 0.1%) (0% 

0.1% 9% 0.1% 0.3% 1% 2.1% 3

(0% - 0.6%) (6% - 13.5%) (0% - 0.4%) (0.1% - 1.3%) (0.4% - 2.6%) (1% - 4.3%) (1.9% 

0% 9% 0% 0.2% 0.8% 1.9% 3

(0% - 0.3%) (6% - 13.5%) (0% - 0.2%) (0.1% - 0.9%) (0.4% - 2.3%) (1% - 4.1%) (1.9% 

***The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an an

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statisthe SO2 coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth. 

Exposure-Response 

Model

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

Probit

2-Parameter Logistic

Probit

2-Parameter Logistic

Probit

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m):

2-Parameter Logistic

Response = Decrease in FEV1 >= 15%Response = Decrease in FEV1 >= 20%

Probit

 Greene County, MO

St. Louis, MO

Greene County, MO

"As is" SO2 

Concentrations**
Abt Associates Inc. 

4-27

Table 4-12. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion Estimated to Experience At Least One L

(Defined in Terms of FEV1) Associated with Exposure to "As Is" SO2 Concentrations, SO2 Concentrations that Just Meet the C

SO2 Concentrations that Just Meet Alternative Standards* 

99/50 99/100 99/150 99/200 99

0.2% 1.2% 0.2% 0.2% 0.3% 0.5% 0

(0% - 2.2%) (0.4% - 4.4%) (0% - 2.1%) (0% - 2.2%) (0.1% - 2.4%) (0.1% - 2.9%) (0.2% 

0% 0.8% 0% 0% 0.1% 0.2% 0

(0% - 1.4%) (0.2% - 3.8%) (0% - 1.3%) (0% - 1.4%) (0% - 1.7%) (0% - 2.1%) (0.1% 

1.1% 22.3% 0.7% 2.6% 5.3% 8.4% 11

(0.3% - 4.5%) (15.9% - 30.4%) (0.2% - 3.5%) (0.9% - 7.5%) (2.5% - 11.5%) (4.6% - 15.4%) (7.3% 

0.7% 22.3% 0.4% 2% 4.7% 8% 11

(0.1% - 3.9%) (15.9% - 30.7%) (0.1% - 2.8%) (0.6% - 7.2%) (2.1% - 11.5%) (4.3% - 15.6%) (7% - 

0% 0.2% 0% 0% 0% 0.1% 0

(0% - 0.2%) (0.1% - 0.9%) (0% - 0.2%) (0% - 0.2%) (0% - 0.3%) (0% - 0.4%) (0% 

0% 0.1% 0% 0% 0% 0% 0

(0% - 0%) (0.1% - 0.6%) (0% - 0%) (0% - 0%) (0% - 0.1%) (0% - 0.2%) (0% 

0.2% 14.7% 0.1% 0.6% 1.7% 3.4% 5

(0% - 0.8%) (10.1% - 20.8%) (0% - 0.5%) (0.2% - 2%) (0.7% - 4.1%) (1.8% - 6.8%) (3.4% 

0.1% 14.9% 0% 0.4% 1.5% 3.3% 5

(0% - 0.5%) (10.3% - 21%) (0% - 0.3%) (0.1% - 1.6%) (0.7% - 3.7%) (1.7% - 6.6%) (3.4% 

**The current primary SO2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an ann

calculated as the arithmetic mean of hourly averages.

2-Parameter Logistic

SO2 

Concentrations 

that Just Meet the 

Current 

Standards***

SO2 Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum 

ppb) of m (Standard Denoted n/m): "As is" SO2 

Concentrations**

**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.

St. Louis, MO

*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statisthe SO2 coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth. 

Probit

2-Parameter Logistic

Response = Decrease in FEV1 >= 15%Response = Decrease in FEV1 >= 20%

ProbitProbit

2-Parameter Logistic

Probit

Exposure-Response 

Model

 Greene County, MO

St. Louis, MO

Greene County, MO

2-Parameter Logistic
Abt Associates Inc. June 2009 4-28 

Figure 4-1. Percent of Asthmatics Engaged in Moderate or Greater Exertion in St. Louis Exhibiting 

Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Attributable to SO2 Within Given 

Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Probit Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

*For the legend for these figures see Figure 4-17.
Abt Associates Inc. June 2009 4-29 

Figure 4-2. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion in St. Louis 

Exhibiting Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Attributable to SO2

Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Probit Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

*For the legend for these figures see Figure 4-17. 
Abt Associates Inc. June 2009 4-30 

Figure 4-3. Percent of Asthmatics Engaged in Moderate or Greater Exertion in St. Louis Exhibiting 

Lung Function Response (Defined as a Decrease in FEV1 ≥ 15%) Attributable to SO2 Within Given 

Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Probit Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

*For the legend for these figures see Figure 4-17. 
Abt Associates Inc. June 2009 4-31 

Figure 4-4. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion in St. Louis 

Exhibiting Lung Function Response (Defined as a Decrease in FEV1 ≥ 15%) Attributable to SO2

Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Probit Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

*For the legend for these figures see Figure 4-17. 
Abt Associates Inc. June 2009 4-32 

Figure 4-5. Number of Occurrences of Lung Function Response (Defined as an Increase in sRaw ≥

100%) Among Asthmatics Engaged in Moderate or Greater Exertion in St. Louis Attributable to SO2

Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

*For the legend for these figures see Figure 4-17.
Abt Associates Inc. June 2009 4-33 

Figure 4-6. Number of Occurrences of Lung Function Response (Defined as an Increase in sRaw ≥

100%) Among Asthmatic Children Engaged in Moderate or Greater Exertion in St. Louis 

Attributable to SO2 Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

*For the legend for these figures see Figure 4-17.
Abt Associates Inc. June 2009 4-34 

 Figure 4-7. Number of Occurrences of Lung Function Response (Defined as a Decrease in FEV1 ≥

15%) Among Asthmatics Engaged in Moderate or Greater Exertion in St. Louis Attributable to SO2

Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

*For the legend for these figures see Figure 4-17.
Abt Associates Inc. June 2009 4-35 

Figure 4-8. Number of Occurrences of Lung Function Response (Defined as a Decrease in FEV1 ≥

15%) Among Asthmatic Children Engaged in Moderate or Greater Exertion in St. Louis 

Attributable to SO2 Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

*For the legend for these figures see Figure 4-17. 
Abt Associates Inc. June 2009 4-36 

Figure 4-9. Percent of Asthmatics Engaged in Moderate or Greater Exertion in Greene Co. 

Exhibiting Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Attributable to SO2

Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Probit Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

*For the legend for these figures see Figure 4-17.
Abt Associates Inc. June 2009 4-37 

Figure 4-10. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion in Greene Co. 

Exhibiting Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Attributable to SO2

Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Probit Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

*For the legend for these figures see Figure 4-17. 
Abt Associates Inc. June 2009 4-38 

Figure 4-11. Percent of Asthmatics Engaged in Moderate or Greater Exertion in Greene Co. 

Exhibiting Lung Function Response (Defined as a Decrease in FEV1 ≥ 15%) Attributable to SO2

Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Probit Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

*For the legend for these figures see Figure 4-17. 
Abt Associates Inc. June 2009 4-39 

Figure 4-12. Percent of Asthmatic Children Engaged in Moderate or Greater Exertion in Greene Co. 

Exhibiting Lung Function Response (Defined as a Decrease in FEV1 ≥ 15%) Attributable to SO2

Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

b) Probit Exposure-Response Function 

0%

5%

10%

15%

20%

25%

30%

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Percent Responding (and 95% CI)

*For the legend for these figures see Figure 4-17. 
Abt Associates Inc. June 2009 4-40 

Figure 4-13. Number of Occurrences of Lung Function Response (Defined as an Increase in sRaw ≥

100%) Among Asthmatics Engaged in Moderate or Greater Exertion in Greene Co. Attributable to 

SO2 Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

*For the legend for these figures see Figure 4-17.
Abt Associates Inc. June 2009 4-41 

Figure 4-14. Number of Occurrences of Lung Function Response (Defined as an Increase in sRaw ≥

100%) Among Asthmatic Children Engaged in Moderate or Greater Exertion in Greene Co. 

Attributable to SO2 Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

*For the legend for these figures see Figure 4-17.
Abt Associates Inc. June 2009 4-42 

 Figure 4-15. Number of Occurrences of Lung Function Response (Defined as a Decrease in FEV1 ≥

15%) Among Asthmatics Engaged in Moderate or Greater Exertion in Greene Co. Attributable to 

SO2 Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

*For the legend for these figures see Figure 4-17.
Abt Associates Inc. June 2009 4-43 

Figure 4-16. Number of Occurrences of Lung Function Response (Defined as a Decrease in FEV1 ≥

15%) Among Asthmatic Children Engaged in Moderate or Greater Exertion in Greene Co. 

Attributable to SO2 Within Given Ranges Under Different Air Quality Scenarios* 

a) Logistic Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function 

0

100

200

300

400

500

600

"As Is" Air

Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Number of Occurences, in Thousands (and 95% CI)

*For the legend for these figures see Figure 4-17. 
Abt Associates Inc. June 2009 4-44 

Figure 4-17. Legend for Figures 4-1 - 4-16. 

0%

5%

10%

15%

20%

25%

30%

35%

40%

"As Is" Air Quality

Current Std.

99/50

99/10099/15099/20099/25098/200

Attributable to 500 ppb<=SO2

Attributable to 450 ppb<=SO2<500 ppb

Attributable to 400 ppb<=SO2<450 ppb

Attributable to 350 ppb<=SO2<400 ppb

Attribuable to 300 ppb<=SO2<350 ppb

Attributable to 250 ppb<=SO2<300 ppb

Attributable to 200 ppb<=SO2<250 ppb

Attributable to 150 ppb<=SO2<200 ppb

Attributable to 100 ppb<=SO2<150 ppb

Attributable to 50 ppb<=SO2<100 ppb

Attributable to SO2<50 ppb

The current primary SO2 standards include a 24-hour standard set at 0.14 parts per 

million (ppm), not to be exceeded more than once per year, and an annual standard set at 

0.03 ppm, calculated as the arithmetic mean of hourly averages. In St. Louis, SO2

concentrations that are predicted to occur if the current standards were just met are 

substantially higher than “as is” air quality (based on 2002 monitoring and modeling 

data) and also substantially higher than they would be under any of the alternative 1-hr 

standards considered in this analysis. Consequently, the levels of response that would be 

seen if the current standard were just met are well above the levels that would be seen 

under the “as is” air quality scenario or under any of the alternative 1-hr standards – for 

asthmatics and for asthmatic children, and for all four definitions of lung function 

response. 

For example, of the estimated approximately 102,400 asthmatics engaged in 

moderate or greater exertion in St. Louis, about 13,500 (or 13.1%) are estimated to have 

at least one lung function response, defined as an increase in sRaw ≥ 100%, if the current 

standards were just met. Under “as is” air quality conditions, the corresponding number 

is about 1,000 (1%). Only the most stringent alternative 99th percentile 1-hr standard, set 

at 50 ppb (denoted “99/50” in the above tables of results), is predicted to lower the 

numbers of responders below the levels estimated under the “as is” scenario. As the 
Abt Associates Inc. June 2009 4-45 

alternative 1-hr standards become less stringent (i.e., as the level is raised from 50 ppb to 

100 ppb, to 150 ppb, etc.), the numbers responding correspondingly rise. 

The pattern seen in St. Louis for lung function response, defined as an increase in 

sRaw ≥ 100%, is also seen for the other definitions of lung function response. For 

example, of the estimated roughly 102,400 asthmatics engaged in moderate or greater 

exertion, 750 are estimated to have at least one lung function response, defined as a 

decrease in FEV1 ≥ 15%, under the “as is” air quality scenario; the corresponding number 

(percent) if the current standards were just met is about 15,200 (14.9%); the 

corresponding numbers for the alternative 1-hr standards denoted 99/50, 99/100, 99/150, 

99/200, 99/250, and 98/200 are about 500 (0.5%), 1700 (1.7%), 3500 (3.4%), 5600 

(5.4%), 7900 (7.7%), and 7400 (7.2%), respectively. 

Although the basic pattern across air quality scenarios seen in St. Louis is 

repeated in Greene County, the impact of changing from one air quality scenario to 

another is substantially dampened in Greene County. This is because of the different 

patterns of exposures in the two locations. In St. Louis there is a wide range of SO2

concentrations to which asthmatics are exposed under the current standards scenario – 

i.e., substantial percentages of asthmatics are exposed to relatively higher concentrations 

of SO2 under this scenario. There is thus much room for improvement. Under the most 

stringent alternative 1-hr standard (99/50), much of that exposure is pushed down to the 

lowest SO2 concentration “bins.” Under the current standards scenario, for example, only 

about 22 percent of asthmatics in St. Louis have exposures no greater than 100 ppb; 

under the most stringent alternative 1-hr standard (99/50), that increases to 98 percent. 

In Greene County, in contrast, about 95 percent of asthmatics have exposures no 

greater than 100 ppb under the current standards scenario. There is therefore little room 

for improvement. Under the most stringent alternative 1-hr standard (99/50), that 95 

percent becomes 100 percent. The situation is even more extreme for person days of 

exposure. Under the current standards scenario, 99.9 percent of person days of exposure 

are to ≤ 100 ppb SO2 in Greene County; the corresponding figure for St. Louis is 95.2 

percent. 

The generally lower levels of SO2 to which asthmatics in Greene County are 

exposed, relative to asthmatics in St. Louis, and the corresponding greater preponderance 

of responses associated with the lowest SO2 concentration “bins” in Greene County, can 

be readily seen in Figures 4-1 through 4-8.12 

Although the numbers are smaller for asthmatic children (because the underlying 

populations are smaller), the patterns seen in St. Louis and in Greene County across the 

different air quality scenarios, and the comparisons between the two locations, are fairly 

similar for asthmatic children as for asthmatics for all lung function response definitions. 

 

12 In several cases, responses associated with exposures in SO2 bins cannot be seen in the figures, because 

the percent responding, or numbers of occurrences of lung function response are so small. We chose to 

scale the y-axis the same on all comparable figures to facilitate comparisons between figures. This meant, 

however, that some “response bars” essentially became visually undetectable. 
Abt Associates Inc. June 2009 4-46 

In general, however, the percentages of asthmatic children engaged in moderate or 

greater exertion who experience at least one lung function response, for each of the 

different lung function response definitions, tend to be greater than the corresponding 

percentages of asthmatics. This presumably is a reflection of the greater amount of time 

spent outdoors by asthmatic children relative to adults. 

 Finally, we note that, while in several air quality scenarios the great majority of 

occurrences of lung function response are in the lowest exposure bin, the numbers of 

individuals with at least one lung function response attributable to exposures in that 

lowest bin are typically quite small. This is because the calculation of numbers of 

individuals with at least one lung function response uses individuals’ highest exposure 

only. While individuals may be exposed mostly to low SO2 concentrations, many are 

exposed at least occasionally to higher levels. Thus, the percentage of individuals in a 

designated population with at least one lung function response associated with SO2

concentrations in the lowest bin is likely to be very small, since most individuals are 

exposed at least once to higher SO2 levels. For example, defining lung function response 

as an increase in sRaw ≥ 100%, under a scenario in which SO2 concentrations just meet 

an alternative 1-hour 99th percentile 100 ppb standard, about 93 percent of occurrences of 

lung function response among asthmatics in St. Louis are associated with SO2 exposures 

in the lowest exposure bin (0 – 50 ppb). However, the lowest SO2 exposure bin accounts 

for only about 0.2 percent of asthmatics estimated to experience at least 1 SO2-related 

lung function response. For this very small percent of the population, the lowest 

exposure bin represents their highest SO2 exposures under moderate exertion in a year. 

Thus Figure 4-5b shows virtually all of the occurrences among asthmatics in St. Louis 

associated with the lowest SO2 exposure bin; however, Figure 4-1b shows a relatively 

small proportion of asthmatics in St. Louis experiencing at least one response to be 

experiencing those responses because of exposures in that lowest exposure bin. 
Abt Associates Inc. June 2009 5-1 

5 REFERENCES 

Abt Associates Inc. 2007a. Ozone Health Risk Assessment for Selected Urban Areas. 

Prepared for Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC., July 2007, Under Contract No. 68-D03-002, Work Assignment 3-39 and 4-56. Available electronically on the internet at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html. 

Abt Associates Inc. 2007b. TRIM: Total Risk Integrated Methodology. Users Guide for 

TRIM.RiskHuman Health-Probabilistic Application for the Ozone NAAQS Risk 

Assessment. Available online at: 

http://epa.gov/ttn/fera/data/trim/trimrisk_ozone_ra_userguide_8-6-07.pdf. 

Bethel RA, Epstein J, Sheppard D, Nadel JA, Boushey HA. 1983. Sulfur dioxide-induced 

bronchoconstriction in freely breathing, exercising, asthmatic subjects. Am Rev Respir 

Dis 128:987-90. 

Bethel RA, Sheppard D, Geffroy B, Tam E, Nadel JA, Boushey HA. 1985. Effect of 0.25 

ppm sulfur dioxide on airway resistance in freely breathing, heavily exercising, asthmatic 

subjects. Am Rev Respir Dis 131:659-61. 

Gelman, A, Carlin, J. C., Stern, H., and Rubin, D. B. 1995. Bayesian Data Analysis. 

Chapman and Hall, New York. 

Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (Eds.) 1996. Markov Chain Monte 

Carlo in Practice. Chapman and Hall, London, UK. 

Kehrl HR, Roger LJ, Hazucha MJ, Horstman DH. 1987. Differing response of asthmatics 

to sulfur dioxide exposure with continuous and intermittent exercise. Am Rev Respir Dis

135:350-5. 

Linn WS, Avol EL, Peng RC, Shamoo DA, Hackney JD. 1987. Replicated dose-response 

study of sulfur dioxide effects in normal, atopic, and asthmatic volunteers. Am Rev Respir 

Dis 136:1127-34. 

Linn WS, Avol EL, Shamoo DA, Peng RC, Spier CE, Smith MN, Hackney JD. 1988. 

Effect of metaproterenol sulfate on mild asthmatics’ response to sulfur dioxide exposure 

and exercise. Arch Environ Health 43:399-406. 

Linn WS, Shamoo DA, Peng RC, Clark KW, Avol EL, Hackney JD. 1990. Responses to 

sulfur dioxide and exercise by medication-dependent asthmatics: effect of varying 

medication levels. Arch Environ Health 45:24-30. 
Abt Associates Inc. June 2009 5-2 

Magnussen H, Jorres R, Wagner HM, von Nieding G. 1990. Relationship between the 

airway response to inhaled sulfur dioxide, isocapnic hyperventilation, and histamine in 

asthmatic subjects. Int Arch Occup Environ Health 62:485-91. 

Roger LJ, Kehrl HR, Hazucha M, Horstman DH. 1985. Bronchoconstriction in 

asthmatics exposed to sulfur dioxide during repeated exercise. J Appl Physiol 59:784-

91.Spiegelhalter, D., Thomas, A., Best, N. and Gilks, W. 1996. Bugs .5 Bayesian 

inference using Gibbs sampling. Manual, version ii. MRC Biostatistics Unit, Institute of 

Public Health. Cambridge, U.K. 

U.S. EPA. 2007a. Integrated Plan for Review of the Primary National Ambient Air 

Quality Standards for Sulfur Oxides. October 2007. Available online at: 

http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html. 

U.S. EPA, National Center for Environmental Assessment. 2008a. Integrated Science 

Assessment for Oxides of Sulfur – Health Criteria (Second External Review Draft). 

EPA/600/R-08/047. May 2008. Available online at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=190346. 

U.S. EPA, National Center for Environmental Assessment. 2008c. Integrated Science 

Assessment (ISA) for Sulfur Oxides – Health Criteria (Final Report). EPA/600/R08/047F. September 2008. Available online at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843. 

U.S. EPA, Office of Air Quality Planning and Standards. 2008b. Risk and Exposure 

Assessment to Support the Review of the SO2 Primary National Ambient Air Quality 

Standard: First Draft. EPA-452/P-08-003. June 2008. Available online at: 

http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_rea.html. 

U.S. EPA, Office of Air Quality Planning and Standards. 2009a. Risk and Exposure 

Assessment to Support the Review of the SO2 Primary National Ambient Air Quality 

Standards: Second Draft. EPA-452/P-09-003. March 2009. Available online at: 

http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_rea.html. 

U.S. EPA, Office of Air Quality Planning and Standards. 2009b. Risk and Exposure 

Assessment to Support the Review of the SO2 Primary National Ambient Air Quality 

Standards. EPA-452/R-09-007. July 2009. Available online at: 

http://www.epa.gov/ttn/naaqs/standards/so2/s_so2_cr_rea.html. 
Abt Associates Inc. June 2009 A-1 

Appendix A: Bayesian-Estimated Logistic and Probit Exposure-Response 

Functions: Median, 2.5th Percentile, and 97.5th Percentile Curves 
Abt Associates Inc. June 2009 A-2 

Figure A-1a. Bayesian-Estimated Logistic Exposure-Response Function: Increase in sRaw > 100% 

for 5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

median curve

2.5th percentile curve

97.5th percentile curve

Figure A-1b. Bayesian-Estimated Probit Exposure-Response Function: Increase in sRaw > 100% for 

5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

median curve

2.5th percentile curve

97.5th percentile curve
Abt Associates Inc. June 2009 A-3 

Figure A-2a. Bayesian-Estimated Logistic Exposure-Response Function: Increase in sRaw > 200% 

for 5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

median curve

2.5th percentile curve

97.5th percentile curve

Figure A-2b. Bayesian-Estimated Probit Exposure-Response Function: Increase in sRaw > 200% for 

5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

median curve

2.5th percentile curve

97.5th percentile curve
Abt Associates Inc. June 2009 A-4 

Figure A-3a. Bayesian-Estimated Logistic Exposure-Response Function: Decrease in FEV1 > 15% 

for 5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

median curve

2.5th percentile curve

97.5th percentile curve

Figure A-3b. Bayesian-Estimated Probit Exposure-Response Function: Decrease in FEV1 > 15% for 

5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

median curve

2.5th percentile curve

97.5th percentile curve
Abt Associates Inc. June 2009 A-5 

Figure A-4a. Bayesian-Estimated Logistic Exposure-Response Function: Decrease in FEV1 > 20% 

for 5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

median curve

2.5th percentile curve

97.5th percentile curve

Figure A-4b. Bayesian-Estimated Probit Exposure-Response Function: Decrease in FEV1 > 20% for 

5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

SO2 Concentration (ppm)

Response Rate

The data

median curve

2.5th percentile curve

97.5th percentile curve
D-1

APPENDIX D: SUPPLEMENT TO THE POLICY ASSESSMENT 
D-2

Table D-1. 99th percentile 24-hour average SO2 concentrations for 2005 given just 

meeting the alternative 1-hour daily maximum standards analyzed in the risk 

assessment (concentrations in ppb). 

99th percentile 98th percentile 

State County 50 10 0 150 200 250 100 200 

AZ Gi la 7 14 20 27 34 18 36 

DE New Castle 14 27 41 55 69 33 66 

FL Hillsb orough 10 20 31 41 51 28 56 

IL M adison 11 22 33 44 55 26 51 

IL Waba sh 10 20 29 39 49 28 56 

IN Fl oyd 8 15 23 31 38 20 40 

IN Gi bson 5 9 14 19 24 11 21 

IN Lake 14 27 41 54 68 35 71 

IN Vi go 9 17 26 34 43 21 43 

IA Li nn 17 35 52 70 87 40 80 

IA M uscatine 16 32 48 64 79 36 72 

MI Way ne 13 26 39 52 65 29 58 

MO Gree ne 14 28 43 57 71 37 73 

MO Jeffe rson 8 17 25 34 42 24 48 

NH M errimack 12 25 37 50 62 29 59 

NJ Hu dson 19 38 57 76 96 48 97 

NJ Uni on 18 36 55 73 91 45 90 

NY B ronx 25 49 74 98 123 57 113 

NY C hautauqua 9 18 28 37 46 22 44 

NY Eri e 12 25 37 50 62 28 56 

OH C uyahoga 17 33 50 66 83 39 78 

OH Lake 19 37 56 74 93 45 89 

OH Sum mit 12 24 35 47 59 27 53 

OK Tul sa 15 30 44 59 74 34 67 

PA Al legheny 14 29 43 58 72 37 73 

PA B eaver 10 20 29 39 49 24 47 

PA No rthampton 11 22 33 45 56 36 71 

PA War ren 16 32 48 65 81 41 81 

PA Was hington 19 38 57 76 95 44 87 

TN B lount 19 38 56 75 94 43 87 

TN Shel by 17 35 52 70 87 41 83 

TN Su llivan 7 13 20 27 33 19 38 

TX Jeffe rson 9 18 26 35 44 21 42 

VA Fai rfax 21 43 64 86 107 48 96 

WV B rooke 13 25 38 51 64 32 64 

WV Hanc ock 14 27 41 54 68 32 64 

WV M onongalia 11 21 32 42 53 27 54 

WV Wayne 43 87 13 0 17 3 217 97 194 
D-3

Table D-2. 99th percentile 24-hour average SO2 concentrations for 2006 given just 

meeting the alternative 1-hour daily maximum standards analyzed in the risk 

assessment (concentrations in ppb). 

99th percentile 98th percentile 

State County 50 10 0 150 200 250 100 200 

DE New Castle 11 23 34 46 57 27 55 

IL M adison 9 18 28 37 46 22 43 

IN Fl oyd 8 15 23 30 38 20 40 

IN Lake 5 10 14 19 24 12 25 

IN Vi go 5 11 16 22 27 13 27 

IA Li nn 11 23 34 45 56 26 52 

IA M uscatine 15 31 46 62 77 35 70 

MI Way ne 17 34 51 68 85 38 76 

MO Gree ne 17 33 50 66 83 43 86 

MO Jeffe rson 7 13 20 26 33 19 37 

NH M errimack 14 28 41 55 69 33 66 

NY B ronx 23 46 69 92 115 53 106 

NY C hautauqua 7 13 20 27 33 16 32 

NY Eri e 7 15 22 29 36 16 33 

OH C uyahoga 14 28 43 57 71 34 67 

OH Lake 11 23 34 46 57 28 55 

OH Sum mit 12 24 35 47 59 27 53 

PA Al legheny 12 23 35 46 58 30 59 

PA B eaver 9 19 28 38 47 23 46 

PA No rthampton 16 32 48 63 79 50 101 

PA War ren 15 29 44 59 73 37 74 

PA Was hington 11 22 33 45 56 26 51 

TN B lount 16 32 48 65 81 37 75 

TN Shel by 16 31 47 62 78 37 74 

TN Su llivan 8 17 25 34 42 24 49 

TX Jeffe rson 11 23 34 45 56 26 53 

VA Fai rfax 17 35 52 70 87 39 78 

WV B rooke 12 24 36 49 61 31 61 

WV Hanc ock 14 28 42 56 70 33 66 

WV M onongalia 10 21 31 42 52 27 53 
D-4

Table D-3. 2nd highest 24-hour average SO2 concentrations (i.e. the current 24-

hour standard) for 2005 given just meeting the alternative 1-hour daily maximum 

standards analyzed in the risk assessment (concentrations in ppb) 

99th percentile 98th percentile 

State County 50 10 0 150 200 250 100 200 

AZ Gi la 7 15 22 29 37 19 39 

DE New Castle 18 36 54 72 90 43 86 

FL Hillsb orough 13 26 38 51 64 35 71 

IL M adison 12 24 36 48 60 28 56 

IL Waba sh 8 20 30 41 51 29 58 

IN Fl oyd 9 18 28 37 46 24 49 

IN Gi bson 5 11 16 22 27 12 25 

IN Lake 19 38 56 75 94 49 98 

IN Vi go 12 23 35 46 58 29 57 

IA Li nn 19 38 57 76 95 44 88 

IA M uscatine 18 37 55 73 92 41 83 

MI Way ne 17 34 50 67 84 37 75 

MO Gree ne 18 37 55 73 92 47 95 

MO Jefferson 10 20 29 39 49 28 55 

NH M errimack 18 35 53 71 88 42 84 

NJ Hu dson 22 45 67 89 111 56 113 

NJ Uni on 18 45 68 90 113 56 112 

NY Bronx 29 57 86 11 5 144 66 132 

NY C hautauqua 12 19 37 49 62 23 59 

NY Eri e 14 27 41 54 68 30 61 

OH Cuyahoga 26 53 79 10 5 132 63 125 

OH Lake 22 44 66 88 110 53 106 

OH Sum mit 12 24 36 49 61 27 55 

OK Tul sa 16 31 47 63 79 36 72 

PA Al legheny 18 36 55 73 91 46 93 

PA B eaver 11 21 32 42 53 26 52 

PA No rthampton 11 23 35 47 58 37 74 

PA War ren 17 33 50 66 83 42 84 

PA Was hington 23 46 69 92 115 53 106 

TN B lount 23 46 69 92 115 53 107 

TN Shel by 22 43 65 87 108 51 103 

TN Su llivan 9 19 28 37 46 27 54 

TX Jefferson 10 20 30 39 49 23 46 

VA Fai rfax 22 49 74 98 123 55 110 

WV B rooke 14 28 42 56 70 35 71 

WV Hanc ock 16 32 48 64 80 38 76 

WV M onongalia 12 23 35 47 58 30 59 

WV Wayne 48 95 14 3 190 23 8 106 213 
D-5

Table D-4. 2nd highest 24-hour average SO2 concentrations (i.e. the current 24-

hour standard) for 2006 given just meeting the alternative 1-hour daily maximum 

standards analyzed in the risk assessment (concentrations in ppb) 

99th percentile 98th percentile 

State County 50 10 0 150 200 250 100 200 

DE New Castle 18 37 55 73 92 44 88 

IL M adison 10 20 31 41 51 24 48 

IN Fl oyd 12 23 35 47 58 31 61 

IN Lake 6 11 17 27 34 18 36 

IN Vi go 7 14 21 28 35 17 35 

IA Li nn 16 32 48 63 79 36 73 

IA M uscatine 18 36 54 72 90 41 82 

MI Way ne 24 48 72 96 120 54 107 

MO Gree ne 20 40 60 80 100 52 103 

MO Jefferson 7 14 32 43 54 20 61 

NH M errimack 19 38 57 76 95 45 90 

NY B ronx 25 49 74 99 124 57 114 

NY C hautauqua 8 15 23 30 38 18 36 

NY Eri e 12 24 36 47 59 27 54 

OH C uyahoga 21 43 64 85 106 51 101 

OH Lake 16 33 49 65 82 39 79 

OH Sum mit 13 26 39 52 65 29 58 

PA Al legheny 13 27 40 53 66 34 68 

PA B eaver 12 24 35 47 59 29 57 

PA No rthampton 50 101 151 202 252 161 321 

PA War ren 19 38 57 76 95 48 96 

PA Was hington 14 29 43 58 72 33 66 

TN B lount 21 41 62 83 104 48 96 

TN Shel by 20 41 61 82 102 48 97 

TN Su llivan 10 21 31 42 52 30 60 

TX Jefferson 13 26 39 52 65 31 61 

VA Fai rfax 20 41 61 82 102 46 91 

WV B rooke 14 28 42 56 70 35 71 

WV Hanc ock 15 31 46 61 76 36 72 

WV M onongalia 11 22 34 45 56 29 57 
D-6

Table D-5. Annual average SO2 concentrations for 2005 given just meeting the 

alternative 1-hour daily maximum standards analyzed in the risk assessment 

(concentrations in ppb). 

99th percentile 98th percentile 

State County 50 100 15 0 20 0 250 100 200 

AZ Gila 1.5 2.9 4. 3 5. 8 7.2 3.8 7.7 

DE New Castle 2.3 4.6 6. 9 9. 2 11.5 5.5 11.0 

FL Hillsborough 1.5 2.9 4 .4 5 .8 7.3 4.0 8.0 

IL Madison 1.8 3.7 5. 5 7. 4 9.2 4.3 8.6 

IL Wabash 1.5 3.0 4. 5 6. 0 7.5 4.3 8.6 

IN Floyd 2.3 4.5 6. 8 9. 0 11.3 5.9 11.9 

IN Gibson 0.8 1.7 2. 5 3. 4 4.2 1.9 3.8 

IN Lake 1.8 3.6 5. 4 7. 1 8.9 4.7 9.3 

IN Vigo 1.9 3.7 5. 5 7. 4 9.2 4.6 9.1 

IA Linn 2.0 4.1 6. 1 8. 2 10.2 4.7 9.4 

IA Muscatine 2.3 4.6 6. 9 9. 1 11.4 5.2 10.3 

MI Wayne 2.4 4.9 7. 3 9. 7 12.1 5.4 10.8 

MO Greene 1.9 3.8 5. 7 7. 6 9.5 4.9 9.8 

MO Jefferson 1.4 2.8 4. 2 5. 6 7.1 4.0 8.0 

NH Merrimack 2. 4 4. 8 7.2 9.5 11.9 5.7 11.4 

NJ Hudson 6.4 12.9 19 .3 25 .7 32.1 16.3 32.5 

NJ Union 6.2 12.3 18 .4 24 .6 30.7 15.2 30.4 

NY Bronx 6.9 13.7 20 .6 27 .4 34.3 15.8 31.6 

NY Chautauqua 2.1 4.3 6. 4 8. 6 10.7 5.1 10.3 

NY Erie 2.3 4.5 6. 8 9. 1 11.3 5.1 10.2 

OH C uyahoga 4.6 9.3 13.9 18.6 23.2 11.0 22.1 

OH Lake 2.8 5.7 8.5 11.3 14.1 6.8 13.6 

OH Sum mit 2.7 5.4 8.1 10.8 13.5 6.1 12.1 

OK Tul sa 3.6 7.2 10.7 14.3 17.9 8.2 16.3 

PA Al legheny 3.6 7.1 10.7 14.2 17.8 9.0 18.1 

PA B eaver 2.8 5.5 8.3 11.0 13.8 6.7 13.4 

PA No rthampton 2.9 5.9 8.8 11.7 14.6 9.3 18.7 

PA War ren 3.2 6.5 9.7 13.0 16.2 8.2 16.3 

PA Was hington 4.7 9.3 14.0 18.7 23.3 10.7 21.5 

TN B lount 2.9 5.8 8.7 11.7 14.6 6.7 13.5 

TN Shel by 2.9 5.7 8.6 11.5 14.4 6.8 13.6 

TN Sullivan 1.5 3.0 4 .5 6 .1 7.6 4.4 8.7 

TX Jefferson 1.4 2.8 4. 2 5. 6 7.1 3.3 6.6 

VA Fairfax 7.8 15.5 23 .2 31 .0 38.7 17.3 34.7 

WV B rooke 4.5 8.9 13.4 17.9 22.4 11.3 22.6 

WV Hanc ock 4.3 8.6 13.0 17.3 21.6 10.2 20.5 

WV M onongalia 2.6 5.2 7.8 10.3 12.9 6.6 13.2 

WV Wayne 6.0 12.0 18 .0 24 .0 30.0 13.4 26.8 
D-7

Table D-6. Annual average SO2 concentrations for 2006 given just meeting the 

alternative 1-hour daily maximum standards analyzed in the risk assessment 

(concentrations in ppb). 

99th percentile 98th percentile 

State County 50 100 15 0 20 0 250 100 200 

DE New Castle 2.2 4.4 6. 7 8. 9 11.1 5.3 10.6 

IL Madison 1.7 3.5 5. 2 6. 9 8.6 4.0 8.1 

IN Floyd 1.6 3.2 4. 8 6. 3 7.9 4.2 8.4 

IN Lake 1.7 3.3 5. 0 6. 6 8.3 4.3 8.7 

IN Vigo 1.4 2.8 4. 2 5. 6 7.0 3.4 6.9 

IA Linn 1.8 3.6 5. 4 7. 2 9.1 4.2 8.3 

IA Muscatine 1.7 3.4 5. 2 6. 9 8.6 3.9 7.8 

MI Wayne 2.2 4.4 6. 6 8. 8 10.9 4.9 9.8 

MO Greene 2.0 4.0 6. 1 8. 1 10.1 5.2 10.4 

MO Jefferson 1.5 3.0 4. 5 5. 9 7.4 4.2 8.4 

NH Merrimack 2. 1 4. 3 6.4 8.5 10.7 5.1 10.1 

NY Bronx 6.5 13.0 19 .5 26 .0 32.5 15.0 29.9 

NY Chautauqua 1.6 3.1 4. 6 6. 2 7.7 3.7 7.4 

NY Erie 1.5 3.1 4. 6 6. 1 7.6 3.4 6.9 

OH C uyahoga 4.1 8.2 12.4 16.5 20.6 9.8 19.6 

OH Lake 2.4 4.8 7. 2 9. 6 12.0 5.8 11.6 

OH Summit 2.2 4.3 6. 5 8. 7 10.9 4.9 9.8 

PA Al legheny 2.7 5.5 8.2 10.9 13.7 7.0 13.9 

PA Beaver 2.0 4.0 6. 0 8. 0 10.0 4.9 9.7 

PA No rthampton 3.7 7.3 11.0 14.6 18.3 11.6 23.3 

PA Warren 2.5 4.9 7. 4 9. 9 12.3 6.2 12.4 

PA Was hington 4.3 8.5 12.8 17.1 21.3 9.8 19.6 

TN B lount 3.0 6.0 8.9 11.9 14.9 6.9 13.8 

TN Shel by 3.7 7.5 11.2 14.9 18.6 8.8 17.7 

TN Sullivan 1.8 3.6 5 .3 7 .1 8.9 5.1 10.3 

TX Jefferson 1.4 2.9 4. 3 5. 7 7.2 3.4 6.7 

VA Fairfax 6.9 13.9 20 .8 27 .7 34.6 15.5 31.0 

WV B rooke 3.9 7.7 11.6 15.5 19.3 9.8 19.5 

WV Hanc ock 4.1 8.2 12.3 16.3 20.4 9.7 19.4 

WV Monongalia 2.0 3.9 5. 8 7. 8 9.7 5.0 9.9 
United States Office of Air Quality Planning and Standards EPA-452/R-09

Environmental Protection Health and Environmental Impacts Division July 2009 

Agency Research Triangle Park, NC