# Risk and Exposure Assessment to Support the Review of the SO 2 

# Primary National Ambient Air Quality Standards: Final Reportii EPA-452/R-09-007 July 2009



# Risk and Exposure Assessment to Support the Review of the SO 2 

# 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 Carolinaiii 

### Disclaimer

This document has been prepared by staff fr om 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 concerni ng 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 St ewart (919-541-7524; stewart.michael@epa.gov)July 2009 iv 

## Table of Contents

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

LIST OF FI GURES .......................................................................................................XIII

LIST OF ACRONYMS /ABBREVIAT IONS................................................................... XX

1. INTRO DUCTION......................................................................................................... 1

1.1 History ....................................................................................................................................................................5 1.1.1 History of the SO 2 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 Exposu re Assessment for th e Current Review ........................................................... ...9 1.2.1 Overview of the Risk and Exposure Assessment............................................................................. ................9 1.2.2 Species of Sulfur Oxid es Included in Analyses ............................................................................ .................11

2. OVERVIEW OF HUMAN EXP OSURE ...................................................................... 12

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

2.2 Sources of SO 2 ............................................................................................................................... .......................13

2.3 Background on the SO 2 Monitoring Network ...................................................................................................14

2.4 Ambient levels of SO 2 ............................................................................................................................... ...........18

2.5 Relationship of Personal Ex posure to Ambient Concentrations ................................................................ .....20

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

3. AT RISK PO PULATION S ......................................................................................... 23

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

3.2 Pre-existing Re spiratory Disease........................................................................................... .............................24

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

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

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

3.6 Ventillati on Rate.......................................................................................................... ........................................26

3.7 Socioeconomic Status ....................................................................................................... ...................................26July 2009 v 3.8 Number of At Risk Individuals.............................................................................................. .............................26

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

4. INTEGRATION OF HEALTH EV IDENCE................................................................. 28

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

4.2 Respiratory Morbidit y Following Short-term SO 2 Exposure..........................................................................31 4.2.1 Overview................................................................................................................. .......................................31 4.2.2 Integration of Respirator y Morbidity Hea lth Evidence..................................................................... .............31 4.2.3 Medication as an Effect Modifier ......................................................................................... .........................34

4.3 What Constitutes an Adverse Health Impact from SO 2 Exposure?................................................................35

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

5. SELECTION OF POTENTIAL ALTERN ATIVE 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 Ef fect Benchmark Levels................................................................................... .....................54

6.3 Approaches for Assessing Exposure and Risk Associated with 5-Minute Peak SO 2 Exposures...................56

6.4 Approach for Estimating 5-Minute Peak SO 2 Concentrations........................................................................59

6.5 Approach for Simulating the Current and Al ternative Air Quality St andard Scenarios .............................62

6.6 Approaches for ch aracterizing Variabilit y and Uncertainty .................................................................. .........64 6.6.1 Characteriza tion of Va riability.......................................................................................... .............................65 6.6.2 Characterizati on of Uncertainty.......................................................................................... ...........................65

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

7. AMBIENT AIR QUALITY AND BENCHMARK HEALTH RISK CHARACTERIZATION FOR 5-MINUTE PEAK SO 2 EXPOSURE S.............................. 69July 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 SO 2 Monitors ..............................................................................................78 7.2.3 Statistical model to estimate 5-minute maximum SO 2 concentrations...........................................................90 7.2.4 Adjustment of Ambient Concentrations to Evalua te the Current and Potential Alternative Air Quality Scenarios...................................................................................................................... .........................................104 7.2.5 Air Quality Conc entration Metrics ..............................................................................................................114

7.3 Results.................................................................................................................... .............................................120 7.3.1 Measured 5-minute Maximum and Measured 1-Hour SO 2 Concentrations at Ambient Monitors – As Is Air Quality ..................................................................................................................................................................120 7.3.2 Measured 1-Hour and Modeled 5-minute Maximum SO 2 Concentrations at All Ambient Monitors – As Is Air Qu ality............................................................................................................................................................127 7.3.3 Modeled 1-Hour and Modeled 5-minute Maximum SO 2 Concentrations at Ambient Monitors in 40 Counties – Air Quality Adjusted to Just Meet the Current and Potential Alternative St andards .........................................133

7.4 Variability Analys is and Uncertainty Characterization.................................................................................151 7.4.1 Variabilit y Anal ysis .....................................................................................................................................151 7.4.2 Uncertainty Characteri zation .......................................................................................................................151

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

8. EXPOSURE AN ALYSIS ......................................................................................... 184

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

8.2 Overview of Human Expo sure 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 I nputs ..................................................................................................................................194 8.4.3 Stationary Sources Emissions Preparation................................................................................. ..................196 8.4.4 Receptor Locations ....................................................................................................... ...............................206 8.4.5 Modeled Air Qu ality Eval uation..................................................................................................................207

8.5 Simulated Population ....................................................................................................... .................................221 8.5.1 Population Counts an d Employment Probab ilities

........................................................................... ............221 8.5.2 Asthma Prevalence........................................................................................................ ...............................222 8.5.3 Commuting Database....................................................................................................... ............................223 8.5.4 Body Surface Area........................................................................................................ ...............................224 8.5.5 Activity-Specifi c Ventilati on Rates ...................................................................................... .......................224

8.6 Construction of Longit udinal Activi ty Sequences............................................................................ ...............227

8.7 Calculating Microenviro nmental Conc entrations ..........................................................................................228 8.7.1 Approach for Estimating 5-Minute Maximum SO 2 Concentrations ............................................................229 8.7.2 Microenvironm ents Modeled................................................................................................ .......................231July 2009 vii 8.7.3 Microenvironm ent Descriptions ............................................................................................ ......................231

8.8 Exposure Measures and Health Risk Characterization ......................................................................... ........235 8.8.1 Estimation of Exposure................................................................................................... .............................235 8.8.2 Estimation of Targ et Ventilati on Rates................................................................................... .....................236 8.8.3 Adjustment for Just Meeting th e Current and Altern ative Standards........................................................ ...237

8.9 Exposure Modeling and Health Ri sk Characterization Results................................................................. ...242 8.9.1 Asthmatic Exposures to 5-minute SO 2 Concentrations in Greene County ..................................................242 8.9.2 Asthmatic Exposures to 5-minute SO 2 in St. Louis .....................................................................................248

8.10 Representativeness of Exposure Results .................................................................................... ....................255 8.10.1 Introduction............................................................................................................ ....................................255 8.10.2 Time sp ent out doors...................................................................................................................................256 8.10.3 SO 2 Emissions and Ambi ent Concentrations.......................................................................................... ...258 8.10.4 American Housin g Survey (AHS) Data...................................................................................... ...............265 8.10.5 Asthma Prevalence....................................................................................................... ..............................266

8.11 Variability Analysis and Uncertainty Char acterization...............................................................................268 8.11.1 Variabil ity Anal ysis ...................................................................................................................................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-minu te Lung Function Risk Assessment..................................................315 9.2.1 General Ap proach...................................................................................................................................316 9.2.2 Exposure Es timates ................................................................................................................................321 9.2.3 Exposure-Response Functions.................................................................................................... ............322

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

9.4 Characterizing Uncert ainty and Va riabilit y ................................................................................. ..................345

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

10. EVIDENCE- AND EXPOSURE/RISK-BA SED CONSIDERATIONS RELATED TO THE PRIMARY SO 2 NAAQS ...................................................................................... 353

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

10.2 Genera l Approach .......................................................................................................... .................................354

10.3 Adequacy of the Current 24-hour Standard .................................................................................. ...............356 10.3.1 Intr oduction................................................................................................................................................356 10.3.2 Evidence-bas ed considerations ........................................................................................... .......................358 10.3.3 Air Quality, e xposure and risk-bas ed considerations..................................................................... ............359

10.4 Adequacy of the Current Annual Standard ................................................................................... ...............367 10.4.1 Intr oduction................................................................................................................................................367July 2009 viii 10.4.2 Evidence-bas ed considerations ........................................................................................... .......................368 10.4.3 Risk-based considerations............................................................................................... ...........................369 10.4.4 Conclusions regarding the adeq uacy of the current annual standard ....................................................... ..370

10.5 Potential Alt ernative Standards ........................................................................................... ..........................370 10.5.1 In dicat or.....................................................................................................................................................370 10.5.2 Aver aging Time .......................................................................................................... ...............................371 10.5.3 Form.................................................................................................................... .......................................381 10.5.4 Level ................................................................................................................... .......................................387

10.6 Key Ob servations.............................................................................................................................................397

REFERENC ES............................................................................................................ 398

APPENDICES Appendix A- Supplement to the SO 2 Air Quality Characterization Appendix B- Supplement to the SO 2 Exposure Assessment Appendix C- Sulfur Dioxide Health Risk Assessment Appendix D- Supplement to the Policy AssessmentJuly 2009 ix 

## LIST OF TABLES

Number Page

Table 2-1. SO 2 network monitoring obj ective distribution........................................................... 17 Table 2-2. SO 2 network distribution acro ss measurement scales. ................................................ 18 Table 4-1. Weight of evidence for causal determination.............................................................. 29 Table 7-1. Summary of all av ailable 5-minute and 1-hour SO 2 ambient monitoring data, years 1997-2007, pre-screened.............................................................................................. 74 Table 7-2. Analytical data sets generated us ing the continuous-5, max-5, and 1-hour ambient SO 2 monitoring data, following screening................................................................... 75 Table 7-3. Counts of complete and incomplete site-years of 1-hour SO 2 ambient monitoring data for 1997-2006............................................................................................................... 79 Table 7-4. Descriptive statisti cs of the population residing with in a 5 km radius of ambient monitors by monitoring objective: mon itors reporting 5-minute maximum SO 2 concentrations and the broader SO 2 monitoring network. ........................................... 89 Table 7-5. Comparison of predicti on 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 SO 2 concentrations above benchmark levels..................................................................... 104 Table 7-7. Counties selected for evaluation of air quality adjusted to just meeting the current and potential alternative SO 2 standards and the number of m onitors in each COV bin... 112 Table 7-8. The co-occurrence of daily 5- minute maximum and 1-hour daily maximum SO 2 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 SO 2 concentration bins. ........................ 118 Table 7-10. Percent of days having a modeled daily 5-minute maximum SO 2 concentration above the potential health effect benchmark levels given air quality as is and air quality adjusted to just meeting the curre nt 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 curre nt 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 curre nt 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 curre nt 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 curre nt and alternative standards........................................ 150 Table 7-15. Summary of how vari ability was incorporated into the air quality ch aracterization.

............................................................................................................................... ..... 151 Table 7-16. Summary of qualitati ve uncertainty analysis for th e air quality and health risk characterization. ......................................................................................................... 153July 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 SO 2 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 SO 2 concentrations.

............................................................................................................................... ..... 181 Table 8-1. Surface stations for the SO 2 study areas................................................................... 195 Table 8-2. Upper air stations for the SO 2 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 modele d number of days in year 2 002 with at least one 5-minute SO 2 benchmark exceedance at ambient monitors in Greene County. ............................... 211 Table 8-7. Asthma prevalence rates by age an d gender used in Greene County and St. Louis modeling domains. ..................................................................................................... 223 Table 8-8. Population modele d in Greene County and St. Louis modeling domains................ 223 Table 8-9. Ventilation coefficient parameter estimates ( b i ) and residuals distributions ( e i ) 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 SO 2 deposition distributions in several indoor microenvironments mo deled in APEX. ..................................................................... 233 Table 8-13. Comparison of benchmark levels, adjust ed benchmark levels to just meet the current standard, the benchmark level distribution percentiles, and the number of 5-minute SO 2 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 curren t and potential alternat ive standards in the Greene County and St Louis modeling domains. ...................................................... 241 Table 8-15. Absolute difference in APEX expos ure estimates for Greene County using either a 98 th or 99 th percentile form potential alternative standard at a 1-hour daily maximum level of 200 ppb.......................................................................................................... 244 Table 8-16. Absolute difference in APEX expos ure estimates for St. Louis using either a 98 th or 99 th percentile form potential alternative sta ndard at a 1-hour daily maximum level of 200 ppb....................................................................................................................... 255 Table 8-17. States used to defi ne five regions of the U.S. a nd 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 m odeled number of days with 5- minute benchmark exceedances and the tota l emissions within 20 km of ambient monitors. .................................................................................................................... 259 Table 8-20. Total SO 2 emissions and total port SO 2 emissions in the St. Louis and the 40 Counties used in the air qua lity characterization. ...................................................... 261July 2009 xi Table 8-21. The top 40 counties wi th the greatest total port SO 2 emissions, including SO 2 emissions from ports in the St. Louis modeling domain............................................ 262 Table 8-22. SO 2 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 severa l metropolitan locations in the U.S. (AHS, 2008)....................................................................................................... 266 Table 8-24. Asthma prevalence rates for ch ildren in four regions of the U.S. .......................... 267 Table 8-25. Asthma prevalence rates for a dults in five regions of the U.S. .............................. 267 Table 8-26. Summary of how va riability was incorporated into the exposure assessment. ...... 268 Table 8-27. Summary of qualita tive uncertainty analysis for the exposure assessment............ 270 Table 8-28. Distribu tion of APEX estimated annual average SO 2 exposures for simulated individuals in the Greene County a nd St. Louis modeling domains.......................... 282 Table 8-29. Personal SO 2 exposure measurement data from the extant literature..................... 283 Table 8-30. Percent of waking hours spent out doors at an elevated activity level.................... 292 Table 8-31. Number of multiple ex ceedances of potential health e ffect benchmark levels within an hour........................................................................................................................ 303 Table 9-1. Example calculation of the number of as thmatics in st. louis engaged in moderate or greater exertion estimated to experience at l east one lung function re sponse (defined as an increase in sRaw ≥ 100%) associated with exposure to SO 2 concentrations just meeting a 99 th percentile, 1-hour 100 ppb standard................................................... 320 Table 9-2. Example calculation of number of occurrences of l ung function response (defined as an increase in sRaw ≥ 100%), among asthmatics in St. Louis engaged in moderate or greater exertion associated with exposure to SO 2 concentrations that just meet a 99 th percentile 1-hour, 100 ppb standard........................................................................... 321 Table 9-3. Percentage of as thmatic individuals in contro lled human exposure studies experiencing SO 2 -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 re sponse associated with exposure to SO 2 concentrations under alte rnative 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 re sponse associated with exposure to SO 2 concentrations under alte rnative air quality scenarios in a year. ............................... 335 Table 9-6. Number of occurr ences (in hundreds) of a lung fu nction response among asthmatics engaged in moderate or greater exer tion associated with exposure to SO 2 concentrations under alte rnative air quality scenarios in a year. ............................... 336 Table 9-7. number of asthmatic children engage d in moderate or greater exertion estimated to experience at least one lung function re sponse associated with exposure to SO 2 concentrations under alte rnative air quality scenarios in a year. ............................... 337 Table 9-8. Percent of asthmatic children engage d in moderate or greater exertion estimated to experience at least one lung function re sponse associated with exposure to SO 2 concentrations under alte rnative 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 SO 2 concentrations under alte rnative air quality scenarios in a year. ............................... 339 Table 9-10. Explanation of labels on th e 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 a nd Greene County, Missouri............................................. 347July 2009 xii Table 10-1 Ratios of 99 th percentile 5-minute daily maximums to 99 th percentile 24-hour average and 1-hour daily maximum SO 2 concentrations for monitors reporting measured 5- minute data from years 2004-2006 ............................................................................ 374 Table 10-2. 99 th percentile 24-hour average SO 2 concentrations for 2004 given just meeting the alternative 1-hour daily maximum 99 th and 98 th percentile standards analyzed in the air quality assessment (note: concentrations in ppb)....................................................... 376 Table 10-3. 2 nd highest 24-hour average SO 2 concentrations (i.e., th e current 24-hour standard) for 2004 given just meeting the al ternative 1-hour daily maximum 99 th and 98 th percentile standards analyzed in the air qua lity assessment (note: concentrations in ppb). ........................................................................................................................... 378 Table 10-4. Annual average SO 2 concentrations for 2004 given ju st meeting the alternative 99 th and 98 th percentile 1-hour daily maximum st andards analyzed in the air quality assessment (note: concen trations in ppb)................................................................... 379 Table 10-5. SO 2 concentrations (ppb) corresponding to the 2 nd -9 th daily maximum and 98 th /99 th percentile forms for altern ative 1-hour daily maximum standards (2004-2006). ...... 383 Table 10-6. Percent of counties th at may be above the level of alternative standards (based on years 2004-2006)........................................................................................................ 388July 2009 xiii 

## LIST OF FIGURES

Number Page

Figure 1-1. Overview of the an alyses described in this docum ent and their interconnections...... 4 Figure 5-1. Effect estimates for U.S. all re spiratory ED visit studi es and associated 98 th and 99 th percentile 1-hour daily maximum SO 2 levels. ............................................................. 45 Figure 5-2. 24-hour effect estimates for U.S. asthma ED visit studies and associated 98 th and 99 th percentile 1-hour daily maximum SO 2 levels. ...................................................... 46 Figure 5-3. 1-hour effect estimates for U.S. asthma ED visit studi es and associated 98 th and 99 th percentile 1-hour daily maximum SO 2 levels. ............................................................. 47 Figure 5-4. 24-hour effect estimates for U.S. hospitalization studies and associated 98 th and 99 th percentile 1-hour daily maximum SO 2 levels............................................................... 48 Figure 5-5. Effect estimates for Canadian ED vi sits and hospitalization studies and associated 98 th and 99 th percentile 1-hour daily maximum SO 2 levels. ........................................ 49 Figure 6-1. Overview of analyses addressing exposures and risks associated with 5-minute peak SO 2 exposures. All three output s are calculated consideri ng current air quality, air quality just meeting the current standard s, and air quality ju st meeting potential alternative standards. Note: this sche matic was modified from Figure 1-1................ 54 Figure 6-2. Example of an hourly time-serie s of measured 1-hour and measured 5-minute maximum SO 2 concentrations...................................................................................... 60 Figure 7-1. Location of the 98 monitors that reported 5-minute maximum SO 2 concentrations and comprising the first data analysis group................................................................ 77 Figure 7-2. Location of the 809 mo nitors comprising the broader SO 2 ambient monitoring network (i.e., the second data analysis group). ............................................................ 80 Figure 7-3. Distribution of site -years of data considering mo nitoring objectives and scale: monitors that reported 5-minute maximum SO 2 concentrations (t op) and the broader SO 2 monitoring network (bottom). .............................................................................. 84 Figure 7-4. Distribution of site-y ears of data considering land-u se and setting: monitors that reported 5-minute maximum SO 2 concentrations (top) and the broader SO 2 monitoring network (bottom).......................................................................................................... 85 Figure 7-5. The percent of total SO 2 emissions of sources locate d within 20 km of ambient monitors: monitors reporting 5-minute maximum SO 2 concentrations (top) and the broader SO 2 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 SO 2 concentrations and the broader SO 2 monitoring network...................................................................................................... 89 Figure 7-7. Comparison of hourly and 5-minute concentration COVs and GSDs at sixteen monitors reporting all twelve 5-minute SO 2 concentrations over multiple years of monitoring.................................................................................................................... 92 Figure 7-8. Cumulative density f unctions (CDFs) of hourly COVs (top) and GSDs (bottom) at ambient monitors: monitors reporting 5-minute maximum SO 2 concentrations and the broader SO 2 monitoring network. ................................................................................ 94 Figure 7-9. Peak-to-mean ratio (PMR) distributions for three C OV and GSD variability bins and seven 1-hour SO 2 concentration stratifications............................................................ 97July 2009 xiv Figure 7-10. Distribution of total SO 2 emissions (tpy) within 20 km of monitors by COV (left) and GSD (right) concentration variability bi ns: monitors reporting 5-minute maximum SO 2 concentrations (top) and the broader SO 2 monitoring network (bottom)............. 98 Figure 7-11. Percent of monitors within each co ncentration variability bin where the monitoring objective was source-oriented: monitors reporting 5-minute maximum SO 2 concentrations (solid ) and the broader SO 2 monitoring network (slotted). ................. 99 Figure 7-12. Distribution of the measur ed number of daily 5-minute maximum SO 2 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 maximu m concentrations (471 site-years)..... 100 Figure 7-13. Comparison of measured daily maximum SO 2 concentration percentiles in Beaver County, PA for a high concentr ation year (1992) versus a low concentration year (2007) at two ambient mon itors (from Rizzo, 2009). ................................................ 106 Figure 7-14. Distributions of hourly SO 2 concentrations at five ambient monitors in Cuyahoga County, as is (top) and air quality adjusted to just meet the current 24-hour SO 2 standard (bottom), Year 2001. ................................................................................... 109 Figure 7-15. Locations of the 128 ambient monito rs comprising the 40 County data set (i.e., the third data analysis group)........................................................................................... 113 Figure 7-16. Percent of monitors in each COV bi n for the three data an alysis groups: monitors reporting 5-minute maximum SO 2 concentrations, the broader SO 2 monitoring network, and SO 2 monitors selected for detailed analysis in 40 counties.................. 113 Figure 7-17. Example of empirically-based prob ability curves. The pr obability of a 5-minute SO 2 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 SO 2 concentration bin........................................................................................................ 116 Figure 7-18. Example of logistic-modeled probabil ity 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 y ear with measured 5-minute maximum SO 2 concentrations above potential health effect benchmark levels at 98 monitors given the annual average SO 2 concentration, 1997-2007 air quality as is . The level of the annual average SO 2 NAAQS of 30 ppb is indicated by the dashed line. .............................. 121 Figure 7-20. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels given 24-hour average SO 2 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 SO 2 concentrations were from measurements collected at 98 ambient monitors an d separated by population density..................... 124 Figure 7-21. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels gi ven 1-hour daily maximum SO 2 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 SO 2 concentrations were from measurements collected at 98 ambient monitors an d separated by population density..................... 126 Figure 7-22. The number of days per year with modeled daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels at 809 ambient monitors given the annual average SO 2 concentration, 1997-2006 air quality as is . The level of the annual average SO 2 NAAQS of 30 ppb is indicated by the dashed line.............. 129July 2009 xv Figure 7-23. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels given 24-hour average SO 2 concentrations, using empirical data (left) and a fitted log-probit model (right), 1997-2006 air quality as is . The 5- minute maximum SO 2 concentrations were modeled from 1-hour measurements collected at 809 ambient monitors and th en separated by population density........... 131 Figure 7-24. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels gi ven 1-hour daily maximum SO 2 concentrations, using empirical data (left) and a fitted log- probit model (right), 1997-2006 air quality as is

. The 5-minute maximum SO 2 concentrations were modeled from 1-hour measurements collected at 809 ambient monitors and th en separated by population density........... 132 Figure 7-25. The number of days per year with modeled 5-minute maximum SO 2 concentrations above potential health effect benchmark levels per year at 128 ambient monitors in 40 selected counties given the annual average SO 2 concentration, 2001-2006 air quality adjusted to just meet the current NAAQS. The level of the annual average SO 2 NAAQS of 30 ppb is indicated by the dashed line. ................................................... 135 Figure 7-26. The number of modeled daily 5-minute maximum SO 2 concentrations above 200 ppb per year at 128 ambient monitors in 40 selected counties given the annual average SO 2 concentration, 2001-2006 air quality as is and that adjusted to just the current and four potential alternative sta ndards (text in graph indicate standard evaluated). The level of the annual average SO 2 NAAQS of 30 ppb is indi cated by the dashed line. 137 Figure 7-27. The number of days per year with modeled 5-minute maximum SO 2 concentrations above benchmark levels given the 99 th and 98 th percentile forms, using the 40-county air quality data set adjusted to just meet a 1-hour da ily maximum level of 200 ppb. 139 Figure 7-28. The number of days per year with modeled 5-minute maximum SO 2 concentrations above benchmark levels given the 99 th and 98 th percentile forms, using the 40-county air quality data set adjusted to just meet a 1-hour da ily maximum level of 100 ppb. 140 Figure 7-29. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels gi ven 1-hour daily maximum SO 2 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 countie s and then separated by population density within 5 km of monitors............................................................................................. 143 Figure 7-30. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels gi ven 1-hour daily maximum SO 2 concentrations, 2001-2006 air quality adjusted to just meet the curre nt and each of the potential alternative standards (99 th 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 monito rs in operation per year for monitors reporting both 5-minute and 1-hour SO 2 concentrations. ........................... 161 Figure 7-32. Temporal trends in the coefficien t of variability (COV) for 5-minute maximum and 1-hour concentrations at the monitors that reported both 5-minute and 1-hour SO 2 concentrations. The number of monitors operating in each y ear is depicted in Figure 7-31. ........................................................................................................................... 162 Figure 7-33. Comparison of measured daily maximum SO 2 concentration percentiles in Allegheny County PA for one high concen tration year (1998) versus a low concentration years (2007) at five ambient monitors................................................. 167July 2009 xvi Figure 7-34. Distributions of annual average pe ak-to-mean ratios (PMRs) derived from the 98 monitors reporting both 5- minute maximum and 1-hour SO 2 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 re porting the 5-minute maximum and 1-hour SO 2 concentrations (left) and the same PM Rs compared with expected lognormal percentiles (right). PMRs were derived fr om 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-t o-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 predic ted number of daily benchmark exceedances in a year at the 98 monitors reporting 5-minute maximum SO 2 concentrations............ 175 Figure 7-38. Distributions of the maximum difference in the estimated mean number of exceedances per site-year given 10 independe nt model runs (with 20 simulations per run). Data used are from 40 county as is air quality (610 site-y ears). Box represents the inner quartile ra nge (IQR, or the 25 th to 75 th percentile), + indicates the mean, whiskers are 1.5 times the IQR. ................................................................................. 177 Figure 8-1. General process flow used for SO 2 exposure assessment. ...................................... 186 Figure 8-2. Modeling domain for Greene County M o., along with identifie d emissions sources, air quality receptors, am bient monitors, and meteorological station. ........................ 191 Figure 8-3. Three county modeling domain for St. Louis, Mo., al ong with identified emissions sources, air quality receptors, ambient m onitors, and meteorological station. .......... 192 Figure 8-4. Derived best-fit non-po int area source diurnal emission profile for the St. Louis domain, compared to ot her 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 m easured ambient monitor SO 2 concentration distribution and diurnal profile with the m odeled monitor receptor and re ceptors within 4 km of monitors 290770026 and 29077032 in Greene County, Mo. Maximum 1-hour concentration.............................................................................................................. 212 Figure 8-7. Comparison of m easured ambient monitor SO 2 concentration distribution and diurnal profile with the m odeled monitor receptor and re ceptors 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 m easured ambient monitor SO 2 concentration distribution and diurnal profile with the m odeled monitor receptor and re ceptors within 4 km of monitor 290770037 in Greene County, Mo. Maximum 1-hour concentration percentile is defined as 0.01 (or 100-9 9.99) because log(0) is undefined.................. 214 Figure 8-9. Comparison of m easured ambient monitor SO 2 concentration distribution and diurnal profile with the m odeled monitor receptor and re ceptors 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.

............................................................................................................................... ..... 217July 2009 xvii Figure 8-10. Comparison of measured ambient monitor SO 2 concentration distribution and diurnal profile with the m odeled monitor receptor and re ceptors 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 SO 2 concentration distribution and diurnal profile with the m odeled 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 SO 2 concentration distribution and diurnal profile with the m odeled monitor receptor and re ceptors within 4 km of monitor 295100086 in St Louis, Mo. Maximu m 1-hour concentration percentile is defined as 0.01 (or 100-99.99) becau se log(0) is undefined...................................... 220 Figure 8-13. Comparison of adjusted ambient m onitoring 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 per centile modeled daily 5-minute maximum SO 2 concentrations using either adjusted 1-hour ambient SO 2 concentrations or an adjusted benchmark level (with as is air quality) to simulate just meeting the current annual standard at monitor 390350045 in Cuya hoga County for year 2002. Complete distributions are provid ed 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 SO 2 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 asth matics (top) and asthmatic children (bottom) experiencing at least one day with a 5-minute SO 2 exposure above selected be nchmark 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 SO 2 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 asth matics (top) and asthmatic children (bottom) experiencing at least one day with a 5-minute SO 2 exposure above selected benc hmark levels in St. Louis, year 2002 air quality as is and adjusted to just mee ting the current and potential alternative standards................................................................................................... 252 Figure 8-20. Number person days all asthmatics (top) and asthmatic children (bottom) experience a 5-minute SO 2 exposure above selected benc hmark levels in St. Louis, year 2002 air quality as is and adjusted to just mee ting the current and potential alternative standards................................................................................................... 253July 2009 xviii Figure 8-21. The frequency of estimated expos ure level exceedances in indoor, outdoor, and vehicle microenvironments given as is air quality (top), air quality adjusted to just meeting the current standard (middle) a nd that adjusted to just meeting a 99 th percentile 1-hour daily maximum standard le vel of 150 ppb (bottom) in St. Louis.. 254 Figure 8-22. Means of w eekly average personal O 3 exposures, measured and modeled (APEX), Upland Ca. Figure obtained from EPA (2007d). ...................................................... 284 Figure 8-23. Daily average personal NO 2 exposures, measured and modeled (APEX), Atlanta Ga. Figure obtained fr om EPA (2008d).................................................................... 285 Figure 8-24. Example comparison of estimated geometric mean and geometric standard deviations of AER (h -1 ) for homes with air conditi oning in several cities................. 298 Figure 8-25. Example of boot strap simulati on 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 simulati on 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 outdoor s (in minutes) using all CHAD events................. 306 Figure 8-28. Percent of asthmatic childre n 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 childre n 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 exceedance s indoors for two APEX simulations: one using multiple peak concentrations in an hour, the other assuming a single peak concentration. Continuous 5-minute m onitoring 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 e xposure-response functions : increase in sRaw ≥ 100% for 5-Minute exposures of asthmatics unde r moderate or greater exertion. .............. 330 Figure 9-3. Bayesian-estimated median e xposure-response functions : increase in sRaw ≥ 200% for 5-minute exposures of asthmatics under moderate or greater exertion................ 330 Figure 9-4. Bayesian-estimated median expos ure-response functions: decrease in FEV1 ....... 331 Figure 9-5. Bayesian-estimated median e xposure-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 SO 2 exposure ranges. ................................................................................................. 341 Figure 9-7. Estimated percent of asthmatics ex periencing one or more lung function responses (defined as ≥ 100% increase in sRaw) per year asso ciated with short-term (5-minute) exposures to SO 2 concentrations associated with alternative air quality scenarios – total and contribution of 5-minute SO 2 exposure ranges (see Figure 9-6 for legend and Table 9-10 for description of air qual ity scenarios included on x-axis). ................... 342 Figure 9-8. Estimated percent of asthmatic ch ildren experiencing one or more lung function responses (defined as ≥ 100% increase in sRaw) per year associated with short-termJuly 2009 xix (5-minute) exposures to SO 2 concentrations associated with alternative air quality scenarios – total and contribution of 5-minute SO 2 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 dist ributions of standard deviati ons for alternative air quality standard forms............................................................................................................ 386July 2009 xx 

## LIST OF ACRONYMS/ABBREVIATIONS

A/C Air conditioning AER Air exchange rate AERMOD American Meteorological So ciety (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 Fact or 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 me tropolitan 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 FEV 1 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 95 th confidence intervalJuly 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 Industr ial 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 NO 2 Nitrogen dioxide NO x Oxides of nitrogen NWS National Weather Service NYC New York City NYDOH New York Department of Health O 3 Ozone OAQPS Office of Air Qualit y Planning and Standards OR Odds ratio ORD Office of Research and Development ORIS Office of Regulatory Inform ation 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 R 2 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 CodeJuly 2009 xxii SD Standard deviation Se Standard error SLAMS State and Local Ambient Monitoring Stations SO 2 Sulfur dioxide SO 3 Sulfur trioxide SO 4 - Sulfate SO x Oxides of Sulfur sRaw Specific Airway Resistance tpy Tons per year TRIM EPA’s Total Risk Integrated Methodology U95 Upper limit of the 95 th confidence interval UA Urbanized area UC Urban cluster UARG Utility Air Regulatory Group USGS United States Geological Survey V

s Ventilation rateJuly 2009 1 

## 1. INTRODUCTION The U.S. Environmental Protection Agency (EPA) is presently conducting a review of the primary, health-based nationa l ambient air quality standard s (NAAQS) for sulfur dioxide (SO 2 ). Sections 108 and 109 of the Clean Air Ac t (The Act) govern the establishment and periodic review of the NAAQS. These sta ndards are established fo r pollutants that may reasonably be anticipated to endanger public he alth and welfare, and whose presence in the

ambient air results from numerous or diverse mob ile 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 iden tifiable 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 revi ew, at five-year intervals, prim ary (health-based) and secondary (welfare-based) NAAQS for such pollutants. Based on periodic reviews of th e air quality criteria and standards, the Administrator is to make revisions in the criteria and standards and promulgate any new standards as may be appropriat e. The Act also requir es that an independent

scientific review committee advi se the Administrator as part of this NAAQS review process, a function performed by the Clean Air Scient ific Advisory Committee (CASAC). The first step in the SO 2 NAAQS review was the developm ent of an integrated review plan. This plan presented the schedule for th e review, the process for conducting the review, and the key policy-relevant science i ssues 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 Primar y 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/naaq s/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 In tegrated Science Assessment (ISA). The ISA was supported by a series of annexes that co ntained more detailed information about the

scientific literature. The final IS A to support this review of the SO 2 primary NAAQS was presented in the Integrated Science Asse ssment for Oxides of Sulfur - Health Criteria , henceforth referred to as the ISA (EPA, 2008a). This doc ument was made available to the public inJuly 2009 2 September 2008 and can be found at: http://www.epa.gov/ttn/naaqs/st andards/so2/s_so2_cr_pd.html. The third step in the primary SO 2 NAAQS review is a risk and exposure assessment (REA) that describes exposures and ch aracterizes risks associated with SO 2 emissions from anthropogenic sources. The plan for conducting the risk and exposure assessment to support the

SO 2 primary NAAQS review was presented in the Sulfur Dioxide Health Assessment Plan: Scope and Methods for Expos ure 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 SO 2 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 SO x . The first draft SO 2 REA developed estimates of human exposures and risks associated with recent ambient levels of

SO 2 and levels that just met the current SO 2 standards. The first draft REA was presented in the Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standards: First Draft . It was made available to th e 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 SO 2 REA was informed by comments from CASAC and the public on the first draft REA, as well as findings and c onclusions contained in the final ISA. This document developed estimates of human exposures and risks associated with: (1) recent ambient

levels of SO 2 , (2) levels that just met the current SO 2 standards, and (3) levels that just met potential alternative standards: defined in terms of indicator, averaging ti me, form, and level. This document also contained a draft policy asse ssment that addressed the adequacy of the

current SO 2 NAAQS and potential alternative standa rds. More specifically, the policy assessment considered epidemiologic, human ex posure, and animal toxicological evidence presented in the ISA (EPA, 2008a), as well as the air quality, exposure, an d risk characterization results presented in the first draft REA, as th ey related to the ade quacy of the current SO 2 NAAQS and potential alternative primary SO 2 standards (see Figure 1-1). The second draft REA was presented in the Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Qualit y 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 SO 2 , (2) levels that just meet the current SO 2 standards, and (3) levels that just meet potential alternative standa rds. This document also contains a final policy assessment (see Chapter 10). The final policy assessment will consider epidemiologic, controlled human exposure, and animal toxicologi cal evidence presented in the final ISA (EPA, 2008a), as well as the air quality, exposure, and ri sk characterization resu lts presented in this document, as they related to the adequacy of the current SO 2 NAAQS and potential alternative primary SO 2 standards (Figure 1-1). The final step in the review of the SO 2 NAAQS will be the rulemaking process. This process will be informed by the risk and exposur e information contained in the final REA, as

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

into account CASAC advice a nd 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 SO 2 primary NAAQS by issuing a proposed ru le 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 SO 2 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 SO 2 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

#

o f e x p o s e d

a s t h m a t i c s 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 SO 2 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 SO 2 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

#

o f e x p o s e d

a s t h m a t i c s 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 SO 2 NAAQS. These questions are particular ly important for the REA because they provide a context for both evaluatin g health effects evidence presente d in the ISA, as well as for selecting the appropriate analys es for assessing exposure and ri sks associated with current

ambient SO 2 levels, SO 2 levels that just meet the current standards, and SO 2 levels that just meet potential alternative standards. Thes e 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 SO x found in the ambient air?July 2009 5  Does new information impact conclusions from the previous review regarding the effects of SO x on susceptible populations?

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

 Has new information altered conclusions from previous reviews regard ing the plausibility of adverse health effects caused by SO x exposure?

 To what extent have important uncertainties identified in th e last review been reduced and/or have new uncertainties emerged?

 What are the air quality relationships be tween short-term and longer-term exposures to SO x ? Additional questions will become relevant if the evidence suggests that revision of the current standard might be appropr iate. These questions are:

 Is there evidence for the occurrence of a dverse health effects at levels of SO x different than those observed previously? If so, at what levels and what are the important uncertainties associated with that evidence?

 Do exposure estimates suggest th at levels of concern for SO x -induced health effects will occur with current ambient levels of SO 2 , 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 uncer tainties 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 uncerta inties and limitations in the evidence and assessments?



### 1.1 HISTORY

1.1.1 History of the SO 2 NAAQS The first SO 2 NAAQS was established in 1971. At th at time, a 24-hour standard of 0.14 ppm, not to be exceeded more than one time pe r year, and an annual standard of 0.03 ppm were judged to be both adequate and necessary to protec t public health. The most recent review of the

SO 2 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 SO 2 levels ( ≥ 600 ppb) could pose a risk of signi ficant health effects for asthmaticJuly 2009 6 individuals at elevated ventilat ion rates. The Administrator al so concluded that the likely frequency of such effects should be a consideratio n in assessing the overall public health risks. Based upon an exposure analysis conducted by EPA, the Administrator concluded that exposure

of asthmatics to SO 2 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 appr opriate. On May 22, 1996, EPA’s final decision not to promulgate a 5-mi nute standard and to re tain 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-mi nute 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 SO 2 NAAQS was appropriate and remanded th e decision back to EPA for further explanation. Specifically, the c ourt gave EPA the opportunity to provide additional rationale to support the Agency judgment that 5-minute peaks of SO 2 do not pose a public health problem from a national perspective even though those pe aks would likely cause ad verse health impacts in a subset of asthmatics. In response, EPA has collected and analyzed additional air quality data

focused on 5-minute concentrations of SO 2 . These air quality analys es conducted since the last review will help inform the current review, whic h 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 adde nda and supplement (EPA, 1986b, 1994a) presented

an evaluation of SO 2 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- (gen erally hours to days), and long-t erm (months to years) exposures

to SO 2 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 c oncentrations of SO 2 . Evidence drawn from epidemiologic studies supported a likely asso ciation between 24- hour average SO 2 concentrations and daily mortality, a ggravation of bronchitis, and small,July 2009 7 reversible declines in children’s lung f unction (EPA 1982, 1994a). In addition, a few epidemiologic studies found an association betw een respiratory symptoms and illnesses and annual average SO 2 concentrations (EPA 1982, 1994a). Howeve r, it was noted that most of these epidemiologic studies were conduc ted in years and cities where particulate matter (PM) counts were also quite high, thus making it difficult to quantitatively determin e whether the observed

associations were the result of SO 2 , PM, or a combination of both pollutants.

Evidence drawn from clinical studies exposing exercising asthmatics to <1000 ppb SO 2 for 5-10 minutes found that these types of SO 2 exposures evoked health effects that were similar to those asthmatics would experi ence from other commonly enc ountered stimuli (e.g., exercise, cold/dry air, psychological stress, etc. (EPA, 1994a). That is, th ere was an acute-phase response characterized by bronchoconstricti on 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 th e individual was relatively insensitive to

additional SO 2 challenges. Notably, the SO 2 -induced acute-phase response was found to be ameliorated by the inhalation of beta-agonist ae rosol 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 appear ed to be hyperresponsive (i.e., those experiencing greater-than-average bronchoconstriction or respiratory sympto ms at a given SO 2 concentration). Thus, for a given concentration of SO 2 , 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 SO 2 , EPA estimated that more than 25% of mild to moderate exercising asthmatics would likely experience decr ements in lung function distinctly exceeding typical daily variations in l ung function, or the response to co mmonly encountered stimuli (EPA, 1994a). Furthermore, the AQCD concluded that the severity of effect s 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 atten tion. In contrast, at 200 – 500 ppb SO 2 , it was estimated

that at most 10 – 20% of mild to moderate ex ercising asthmatics were likely to experience lung function decrements larger than those associated with typical daily activ ity, 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 SO 2 NAAQS qualitatively evaluated both the existing 24- hour (0.14 ppm) and annual standards (0.03 ppm), but primarily focused on whether an additional st andard was necessary to protect against shortterm (e.g., 5-minute) peak exposures. Based on th e human clinical data mentioned above, it was judged that exposur es to 5-minute SO 2 levels at or above 600 ppb could pose an immediate significant health risk for a subs tantial proportion of as thmatics at elevated ventilation rates (e.g., while exercising). Thus, EPA analyzed exis ting 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 se quential 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 subs tantial 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 SO 2 emission estimates from utility and nonutility sources with exposure modeling to estimate the probability of exposure to short-term peak

SO 2 concentrations. The first such analysis cond ucted 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 exposur e 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 resultan t 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 annuall y, while outdoors at exercise, to 5-minute SO 2 concentrations ≥ 500 ppb. It also was noted that the fr equency 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 a bove, two other analyses were considered in the prior review. The first was an exposure as sessment sponsored by the UARG (Rosenbaum et al., 1992) that focused on emissions from fossil- fueled power plants. That study accounted forJuly 2009 9 the anticipated reductions in SO 2 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 re ductions would result in a 42 % reduction in the number of 5- minute exposures to 500 ppb for asthmatic indi viduals (reducing the number of asthmatics exposed from 68,000 down to 40,000) in comparis on with the earlier Bu rton et al. (1987) analysis. The second was a new exposure analysis submitted by the National Mining Association (Sciences Internationa l, Inc. 1995) that reevaluated non-utility sources. In this analysis, revised exposure estimates were provi ded for four of the seven non-utility source categories by incorporating new emissions da ta and using less conservative modeling assumptions in comparison to those used for th e 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 th is industry-sponsored revised an alysis, 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 an alysis to 7,900-23,100 in th e revised analysis).



### 1.2 SCOPE OF THE RISK AND EX POSURE ASSESSMENT FOR THE CURRENT REVIEW

1.2.1 Overview of the Risk and Exposure Assessment The REA describes exposure and risks associ ated with recent ambient levels of SO 2, levels that just meet the current SO 2 standards, and levels that just meet potential alternative

standards. This REA also cont ains a policy discussion regarding the adequacy of the current SO 2 NAAQS, and potential alternative primary standa rds. A concise overview of the information, analyses, and policy discussion contained in th is document is presented below. Chapters 2-4 evaluate informa tion presented in the ISA that is relevant for conducting an exposure and risk assessment. This include s information on 1) human exposure to SO 2; 2) at-risk populations; and 3) health effects associated with short- and long-term exposures to SO 2 . Chapter 5 presents the rationale for the selecti on of the indicator, averaging time, forms, and levels for the potential alternativ e standards that were assessed in the exposure and risk chapters of the document. Specifically, these potential alternative standards are 99 th percentile 1-hour daily maximum SO 2 levels of 50, 100, 150, 200, and 250 ppb, and 98 th percentile 1-hour daily

maximum SO 2 levels of 200 ppb, and in some instances in the air quality an alysis, 100 ppb. InJuly 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 98 th and 99 th percentile 1-hour daily maximum SO 2 levels in cities and time pe riods corresponding to key U.S. and Canadian hospitalization and ED visit studies for all respir atory 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 ch apters of this document. This chapter also presents the rationale for the selection of specific potential health benchmark values 1 derived from the human exposure literature. Chapters 7-9 present the analytical porti on of the document. Staff considered both

evidence of bronchoconstriction and respiratory symptoms from huma n 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 SO 2 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 SO 2 concentrations as a surrogate for exposure. This analysis estimates the number of days per year measured or statistically estim ated 5-minute daily maximum SO 2 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 curren t air quality, air quali ty simulated to just meet the current standards, and air quality simulated to just meet the potential altern ative standards (i.e., 99 th percentile 1-hour daily maximum SO 2 levels of 50, 100, 150, 200 and 250 ppb and an 98 th percentile 1-hour daily maximum SO 2 level of 200 ppb). Chapter 8 presents results from exposure analysis case studies c onducted 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 SO 2 sources experiencing 5-minute exposures to 100, 200, 300, and 400 ppb SO 2, while at elevated ventilation rates under the air qual ity scenarios mentioned above (i .e., recent air quality, and air quality adjusted to just meet the current and po tential 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 expos ure analysis; see Figure 1-1) that would experience moderate or greater lung function responses under the ai r quality scenarios previously

described. In addition to the technical analyses presente d in Chapters 7-9, Chapter 10 integrates the

scientific evidence and the air quality, exposure, and risk info rmation as they pertain to informing decisions about the primary SO 2 NAAQS. More specifically, Chapter 10 considers the epidemiologic, controlled human exposure, a nd animal toxicological evidence presented in the ISA (EPA, 2008a), as well as the air qualit y, exposure, and risk characterization results presented in this document, as they re late to the adequacy of the current SO 2 NAAQS and potential alternative primary SO 2 standards.

1.2.2 Species of Sulfur Oxides Included in Analyses The sulfur oxides include multiple gaseous (e.g., SO 2 , SO 3 ) and particulate (e.g., sulfate) species. In considering what speci es of sulfur oxides ar e relevant to the current review of the

SO 2 NAAQS, we note that the health effects associ ated 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 addres sing 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 SO 2 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 specif y the indicator of the standard in terms of SO 2 because other gaseous sulfur oxides (e.g., SO 3 ) are likely to be found at concentrations many orders of magnitude lower than SO 2 in the atmosphere, and because most all of the health effects and exposure information is for SO 2 . The ISA has again found this to be the case, and therefore

this REA will use SO 2 as a surrogate for all gaseous sulfur oxides.July 2009 12 

## 2. OVERVIEW OF HUMAN EXPOSURE In order to help inform the air quality, e xposure, and risk analyses presented in Chapters 7-9, staff has briefly summarized relevant human exposure information from the ISA. After defining the concept of “integ rated exposure,” this chapter discusses major sources of SO 2 emissions. Characterizing these SO 2 sources helps identify the most relevant locations for conducting air quality, exposure, and health risk analyses. This chap ter then presents a description of the SO 2 monitoring network, and disc usses ambient levels of SO 2 associated with 1-hour, 24-hour, and annual averaging times. SO 2 concentrations associated with these averaging times are relevant to the air quality, exposure, and risk analyses because the current

SO 2 standards have 24-hour and annual averag ing times, and EPA is considering potential alternative 1-hour averaging time standards (see section 5.3). Next , this chapter describes the

small subset of SO 2 monitors that report 5-minute SO 2 concentrations, as well as a broad

characterization of ambient 5-minute SO 2 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 presen ted with respect to a 5-minute averaging time (see section 6.2). More specificall y, as previously described in section 1.2.1, an output of the air quality analysis presented in Chapter 7 is the numb er of days per year measured, or statistically estimated (see Chapter 6) 5-minute daily maximum SO 2 concentrations exceed 5-minute potential health effect benchmark levels. Sim ilarly, the output of the exposure analysis in Chapter 8 is the number of exercisi ng asthmatics exposed to 5-minute SO 2 concentrations above benchmark levels. Outputs of the exposure anal ysis (i.e., the number of exercising asthmatics exposed to 5-minute SO 2 concentrations above benchmark levels ) are then used as inputs into the quantitative risk assessment in Chapter 9 to estimate the number a nd percent of exposed exercising asthmatics expected to experience a moderate or great er lung function response (see Figure 6-1). In addition to providing information releva nt to the air qualit y, 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 involvedJuly 2009 13 with using ambient SO 2 concentrations as a surrogate fo r personal exposure in epidemiologic studies, as well as the ISA’s c onclusions 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 wh ich 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 larg e amount of variability in the time that individuals spend in different microenvironments, but on av erage 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 lo cations (ISA, Figure 2-36). In addition, people spend on average about 8% of thei r time outdoors and 6% of their time in vehicles. A potential consequence of multiple sources of exposur e or microenvironments is the exposure misclassification that may result when total hum an exposure is not disaggregated between these various microenvironments. In epidemiologic studies that rely on ambient pollutant levels as a surrogate for exposure to ambient SO 2 , such misclassification may obscure the true relationship between ambient air pollutant e xposures and health outcomes. In addition to accounting for the times spent in different microenvironments, it is also important to note the duration of exposure experien ced. 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 SO 2 In order to estimate risks associated with SO 2 exposure, principle s ources of the pollutant

must first be characterized because the majority of human exposure s are likely to result from the release of emissions from these sources. Anthropogenic SO 2 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 SO 2 include both the extraction of metal from ore as well as the burning of high sulfur containing fuels by loco motives, large ships, and non-road diesel equipment. Notably, almost the entire su lfur content of fuel is released as SO 2 or SO 3 duringJuly 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

NO 2 (ISA, section 2.1). The largest natural sources of SO 2 are volcanoes and wildfires. Although SO 2 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 (thousa nds of ppb). Volcanic

sources of SO 2 in the U.S. are limited to the Pacific Northwest, Alaska, and Hawaii. Emissions

of SO 2 can also result from burning vegetation. The amount of SO 2 released from burning vegetation is generally in the rang e of 1 to 2% of the biomass burned and is the result of sulfur from amino acids being released as SO 2 during combustion.



### 2.3 BACKGROUND ON THE SO 2 

### MONITORING NETWORK The following section provides general background on the SO 2 monitoring network. A more detailed description of this networ k can be found in Watkins (2009). The SO 2 monitoring network was originally deployed to support implementation of the SO 2 NAAQS established in 1971. Despite the establishment of an SO 2 standard, uniform minimum monitoring requirements

for SO 2 monitoring did not appear until May 1979. From the time of the implementation of the 1979 monitoring rule through 2008, the SO 2 network has steadily decreased in size from approximately 1496 sites in 1980 to the a pproximately 488 sites operating in 2008. The 1979 monitoring rule estab lished two categories of SO 2 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 SO 2 only be monitored using Federal Re ference Methods (FRMs) or Federal Equivalent Methods (FEMs). The 1979 monitoring rule called for a ra nge 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 SO 2 in light of the fact

that there was not an SO 2 non-attainment problem (Watkins, 2009). The 2006 rule eliminated the minimum requirements for the number of SO 2 monitoring sites. The current SO 2 monitoring rule, 40 CFR Part 58, Appendix D, section 4.4 states:July 2009 15 Sulfur Dioxide (SO 2 ) Design Criteria. (a) There are no minimum requirements for the number of SO 2 monitoring sites. Continued operation of existing SLAMS SO 2 sites using FRM or FEM is required until discontinuation is approved by the EPA Re gional Administrator. Where SLAMS SO 2 monitoring is ongoing, at least one of the SLAMS SO 2 sites must be a maximum concentration site for th at specific area. (b) The appropriate spatial scales for SO 2 SLAMS monitoring are the microscale, middle, and possibly neighborhood scales. The multi-pollutant NCore sites can provide for metropolitan area trends analyses and genera l 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 meas uring near specific stationary sources of SO 2 . (1) Micro and middle scale – Some data uses associated with microscale and middle scale measurements for SO 2 include assessing the effect s of control strategies to reduce concentrations (especially for th e 3-hour and 24-hour averaging times) and monitoring air pollution episodes. (2) Neighborhood scale – This scale applie s where there is a n eed to collect air quality data as part of an ongoing SO 2 stationary source impact investigation. Typical locations might include suburban areas adjacent to SO 2 stationary sources for example, or for determining background concentrations as part of these studies of population responses to exposure to SO 2 . (c) Technical guidance in reference 1 of this appendix should be used to evaluate the adequacy of each existing SO 2 site, to relocate an existing si te, or to locate new sites.

To ascertain what the current SO 2 network is addressing or char acterizing, and in light of the relatively recent removal of a specific SO 2 monitoring requirement, EPA reviewed some of

the SO 2 network meta-data (Watkins, 2009). The data reviewed are those available from AQS for calendar year 2008, for any mon itors reporting data at any poi nt 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 ch aracteristics, but it is the responsibil ity 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 whic h they must update or correct meta-data fields (Watkins, 2009). Monitoring Objective: The monitoring objective meta-data field descri bes what the data from the monitor are intended to characterize. The focus of the data pr esented is to show the nature of the network inJuly 2009 16 terms of its attempt to generally characterize health effects, source impacts, transport, or welfare effects. In 2008, there were 488 SO 2 monitors reporting data to AQS at some point during the year. Any particular monitor can have multiple monitor objectives, howev er for this analysis (see Watkins, 2009) we have selected one reporte d objective based on a hier archy to represent an individual monitor. The hierarchy used was to se lect, in order of priority : 1) source oriented, 2) high concentration, 3) population exposure, or 4) general backgr ound, if they existed at a site with multiple monitoring objectives. Table 2-1 pr esents the monitor objective distribution across

all SO 2 sites from the available AQS data. There are 12 categories of mon itor objective for any pollutant monitor within AQS. The “other” categor y is for sites likely addressing a state or local need outside of the routine objectives, and th e “unknown” category repr esents missing metadata. The six primary categories appropriate for use with SO 2 monitoring efforts stem directly from categorizations of site types within th e CFR. In 40 CFR Part 58 Appendix D, they are

defined as:

1. Sites located to determine the highest con centration 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 de termine general background con centration levels (General Background). 5. Sites located to de termine the extent of regi onal pollutant transport among populated areas; and in suppor t of secondary standard s (Regional Transport). 6. Sites located to measure air pollution im pacts on visibility, vegetation damage, or other welfare-based impacts (Welfare Related Impacts).

The remaining four categories available are a resu lt of updating the AQS database. In the more recent upgrade to AQS, the data handler s inserted the available site types for Photochemical Assessment Monitoring Stations (P AMS) network as options for monitoring site objectives. In our metadata review, three SO 2 monitors have a listed monitoring objective that EPA intended to be applied only to NO X or O 3 sites. As a result these three sites are presumably

co-located with a NO X or O 3 monitor with the same objective.July 2009 17 Measurement Scales The spatial (measurement) scal es 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 ai r 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 SO 2 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 op tion for characterizing SO 2 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 havi ng source oriented and high concen tration monitoring objectives, but we are unable to determine what spatial sc ale these monitors actuall y 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 SO 2 sites from the available data in AQS of monitors reporting data in

2008.

Table 2-1. SO 2 network monitoring objective distribution

. SO 2 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. SO 2 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 bl ocks 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 classificati on of "rural" consists of all territory, population, and housing units locat ed 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 SO 2 monitors against the Census Bureau’s defined UAs and UCs shows that 63% of SO 2 monitors are in an “urban” setting and 37% are in a “rural” setting.



### 2.4 AMBIENT LEVELS OF SO 2 Since the integrated exposure to a pollutant is the sum of the exposures over all time

intervals for all environments in which the in dividual spends time, understanding the temporal and spatial patterns of SO 2 levels across the U.S is an important component of conducting air quality, exposure, and risk analyses. SO 2 emissions and ambient concentrations follow a strong east to west gradient due to the large numbers of coal-fired electr ic generating units in the Ohio River Valley and upper Southeast regions. In the 12 CMSAs that had at least 4 SO 2 regulatory monitors from 2003-2005, 24-hour average concentrati ons in the continental U.S. ranged from a reported low of ~1 ppb in Riverside, CA a nd San Francisco, CA to a high of ~12 ppb inJuly 2009 19 Pittsburgh, PA and Steubenville, OH (ISA, sect ion 2.4.4). In addition, inside CMSAs from 2003-2005, the annual average SO 2 concentration was 4 ppb (ISA, Table 2-8). However, spikes in hourly concentrations occurr ed; 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 SO 2 levels in this document, examining the temporal and spatia l patterns of 5-minute peaks of SO 2 is also important given

that human clinical studies ha ve demonstrated exposure to th ese peaks can re sult in adverse respiratory effects in exercising asthmatics (s ee Chapter 4). Although the total number of SO 2 monitors across the continuous U.S. can vary from year to year, in 2006 there were approximately 500 SO 2 monitors in the NAAQS monitoring ne twork (ISA, section 2.5.2). State and local agencies responsibl e for these monitors are requi red to report 1-hour average SO 2 concentrations to the EPA Air Quality System (A QS). 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 ove r the ten-year period exceeded 4000 ppb, but for all individual monitors, the 99 th percentile was below 200 ppb (ISA, section 2.5.2). Medians from th ese monitors reporting data ra nged 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 aggregat ed 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 va lues for aggregated within-state data were all below 90 ppb (ISA, section 2.5.2). EPA has generally conducted NAAQS risk assessm ents 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 con centrations of a pollutant that would occur in the U.S. in the absence of anthropogenic emissions in continenta l North America (defined here as the UnitedJuly 2009 20 States, Canada, and Mexico). However, throughout much of the United States, SO 2 PRB levels are estimated to be at most 30 parts per trilli on and contribute less than 1% to present day SO 2 concentrations (ISA, section 2.5.3). We note th at 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 SO 2 concentrations (ISA, section 2.5.3). Since we do not plan on conducting SO 2 risk assessments in areas with high background SO 2 levels due to natural sources, and the contribution of PRB is ne gligible in all other areas, EPA is addressing

the risks associated with mon itored and/or modeled ambient SO 2 levels without regard to PRB

levels.



### 2.5 RELATIONSHIP OF PERSON AL EXPOSURE TO AMBIENT CONCENTRATIONS To help inform the evaluation of the ep idemiologic evidence in Chapter 4 and the evidence-based considerations presented in Chap ter 10, this section discu sses the relationship of personal SO 2 exposure to ambient SO 2 concentrations. Many epidemiologic studies rely on

measures of ambient SO 2 concentrations as surrogates for personal exposure to ambient SO 2 . Thus, it is important to consider the potential sources of error that are associated with using SO 2 measured by ambient monitors as a surroga te for personal exposure to ambient SO 2 . Key aspects related to this issue include: (1) ambient and personal sampling issues, (2) the spatial variability

of ambient SO 2 concentrations, and (3) the relationshi p between ambient concentrations and personal exposures as influenced by e xposure factors (e.g., indoor sources). Only a limited number of studies have focu sed on the relationship between personal exposure and ambient concentrations of SO 2 , in part because ambient SO 2 levels have declined markedly over the past few decades. Indoor and outdoor SO 2 concentrations are often below detection limits for personal samplers 2 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 pers onal measurements above detection limits, the

ISA states that a reasona bly strong association was observed between personal SO 2 exposure and ambient concentrations (Brauer et al., 1989; Sarn at et al., 2006; describe d 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 exam ined the relationship between concentrations measured at ambient monitors and the community average exposure: a relationship that is more

relevant than that of ambien t concentration to personal exposure for community time-series studies (ISA, section 5.3). Because epidemiologic studies rely on ambient SO 2 measurements at fixed site monitors,

there is concern about the extent to which instrument error coul d influence the results of these studies. That is, the SO 2 monitoring network was designed and put into place when SO 2 concentrations were considerably higher, and th us, well within the standard monitor’s limits of detection. However, SO 2 concentrations have fallen considerably over the years and are currently at, or very near these monitors’ lower limit of detecti on (~3 ppb). As a result, greater

relative error is most often obser ved at lower ambient concentrati ons compared to less frequent higher concentrations. Notably, the ISA states that it is unclear how instrument error will influence the effect estimates of epidemiologi c studies relying on thes e 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 SO 2 NAAQS

because the current 24-hour and annual standard s, as well as the poten tial alternative 1-hour daily maximum standards, are all well within the detection limits of the SO 2 monitoring network. Uncertainty in epidemiologic studies is also associated with the spatial and temporal

variation of SO 2 across communities. The ISA finds that site-to-site correlations of SO 2 concentrations among monitors in U.S. cities ra nges from very low to very high (ISA, section 2.6.4.1; ISA, Table 2-9). This sugge sts that at any given time, SO 2 concentrations at individual monitoring sites may not highly correlate with the average SO 2 concentration in the community.

This could be the result of local sources (e.g., power plants) causi ng an uneven spatial

distribution of SO 2 , monitors being sited to represent concen trations 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, sin ce people spend most of thei r time indoors, there is also uncertainty in the relationship between ambient concentrations measured by local monitors and actual personal exposure related to ambient s ources. That is, the presence of indoor orJuly 2009 22 nonambient sources of SO 2 could complicate the interpretation of associations between personal exposure and ambient SO 2 in exposure studies. Sources of indoor SO 2 are associated with the use of sulfur-containing fuels, w ith 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

SO 2 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 cause d by using ambient concentrations of SO 2 as a surrogate for exposure to ambient SO 2 is a source of uncertainty for epidemiologic studies. However, in community time-series and short-te rm panel epidemiologic studies, exposure error

would tend to bias the effect estimate to wards the null (ISA, section 2.6.4.4. and 5.3).



### 2.6 KEY OBSERVATIONS

 SO 2 emissions and ambient concentrations follow a strong east to west gradient due to the large numbers of coal-fired electric genera ting units in the Ohio River Valley and upper Southeast regions.

 In the 12 CMSAs that had at least 4 SO 2 regulatory monitors from 2003-2005, 24-hour average concentrations in th e 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 SO 2 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 aver age data to AQS. Of these, 16 reported all twelve 5-minute averages in each hour, while the re mainder reported only the maximum 5-minute average in each hour.

 Throughout much of the United States, SO 2 PRB levels are estimated to be at most 30 parts per trillion and contribute less than 1% to present day SO 2 concentrations.

 The ISA concludes that exposure error cause d by using ambient concentrations of SO 2 as a surrogate for exposure to ambient SO 2 is a source of uncertainty for epidemiologic studies. However, in community time-series and short-term panel epidemiologic studies, exposure error would tend to bi as the effect estimate towards the null. Thus, results of these studies can be used, in part, to evalua te the adequacy of the current and potential alternative SO 2 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 de trimental effects of ambient exposure to SO 2 . 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 facilita te the identification of subpopulations at the greatest risk for SO 2 -related health effects, stud ies have identified factor s that contribute to the susceptibility and/or vulnerabil ity of an individual to SO 2 . 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 (America n Lung Association, 2001) . The susceptibility

of an individual to SO 2 can encompass a multitude of factors which represent normal developmental phases (e.g., age) or biologic attri butes (e.g., gender); howeve r, other factors (e.g., socioeconomic status (SES)) may influence the ma nifestation of disease and also increase an individual’s susceptibility (A merican Lung Association, 2001). In addition, subpopulations may

be vulnerable to SO 2 in response to an increase in thei r exposure during certain windows of life (e.g., childhood or old age) or as a result of exte rnal 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 SO 2 . For example, a subpopulati on 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 SO 2 , but they may also reside in a location that results in exposure to higher concentrations of SO 2 , increasing their vulnerability to SO 2 . To examine whether SO 2 differentially affects certain s ubpopulations, stratified analyses are often conducted in epidemiologi c investigations to identify the presence or absence of effect modification. A thorough evaluation of poten tial effect modifiers may help identify subpopulations that are more sus ceptible and/or vulnerable to SO 2 . 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. Al though the design of toxicological

and human clinical studies does not allow for an extensive examination of effect modifiers, theJuly 2009 24 use of animal models of disease and the study of individuals with underl ying disease or genetic polymorphisms do allow for comparisons between s ubgroups. Therefore, the results from these studies, combined with those results obtained through stratified analyses in epidemiologic studies, contribute to the overa ll weight of evidence for the increased susceptibility and vulnerability of speci fic subpopulations to SO 2 . Those groups identified in the ISA to be potentially at greater risk of experiencing an advers e health effect from SO 2 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 SO 2 exposure than healthy non-asthmatics. While SO 2 -attributable decrements in lung function have generally not been demonstrated at concentrations ≤ 1000 ppb in non-asthmatics, statistically significant increases in respirat ory symptoms and decreases in lung function have consistently been observed in exercising asthmatics following 5 to 10 minute

SO 2 exposures at concentrations ranging from 400-600 ppb (ISA, section 4.2.1.1). Moderate or greater SO 2 -induced decrements in lung function have also consistently been observed at SO 2 concentrations ranging from 200-300 ppb in some asth matics. The ISA also notes that a number of epidemiologic studies have re ported respiratory morbidity in asthmatics associated with SO 2 exposure (ISA 4.2.1.1). For example, numerous epidemiologic studies have observed positive

associations between ambient SO 2 concentrations and ED visits and hospitalizations for asthma (ISA section 4.2.1.1). Overall, the ISA conclude s that epidemiologic and controlled human exposure studies indicate that i ndividuals with pre-existing resp iratory diseases, particularly asthma, are at greater risk than th e general population of experiencing SO 2 -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 eff ects due to ambient air pollution

merits serious consideration. Se veral 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 mu st be significantly involved in the pathogenesis of the effectJuly 2009 25 of interest, which is often a complex trait w ith many determinants. Second, polymorphisms in the gene must produce a functional change in ei ther the protein product or in the level of expression of the protein. Third, in epidemiol ogic 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 a ssociation between genetic polymorphisms and susceptibility to air pollution in general, only one study has specifically examined the effects of

SO 2 exposure on genetically distin ct subpopulations. Winterton et al. (2001) found a significant

association between SO 2 -induced decrements in Forced Expi ratory Volume in the first second (FEV 1 ) and the homozygous wild-type allele in the promoter region of Tumor Necrosis Factor- α (TNF- α ; AA, position -308). However, the ISA concl uded that the overall body of evidence was

too limited to reach a conclusi on regarding the effects of SO 2 exposure on genetically distinct subpopulations at this time.



### 3.4 AGE The ISA identifies children (i.e ., <18 years of age) and olde r adults (i.e., >65 years of age) as groups that are potentiall y at greater risk of experiencing SO 2 -associated adverse health effects. In children, the developing lung is prone to damage from environmental toxicants as it continues to develop through adolescence. The bi ological basis for increase d 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 epid emiologic studies that observed increased respiratory symptoms in children associated with increasing SO 2 concentrations. In addition, several studies have re ported 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 SO 2 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 e ither more or less at risk than adults to the respiratory effects of SO 2 , but rather adolescents may experience similar respiratory effects at a given exposure concentr ation (ISA, sections 3.1.3.5 and 4.2.3). Overall, the ISA finds that compared to the general popu lation, there is limited ev idence to suggest that

children and older adults are at greater risk of experiencing SO 2 -associated health effects (ISA, section 4.2.3).July 2009 26 

### 3.5 TIME SPENT OUTDOORS Outdoor SO 2 concentrations are generally much hi gher than indoor conc entrations. Thus, the ISA notes that individuals who spend a sign ificant amount of time outdoors are likely at greater risk of experiencing SO 2 -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 dem onstrated that decrements in lung function and respiratory symptoms occur at significantly lower SO 2 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 SO 2 in the tracheobronchial airways due to the diminished absorption of SO 2 in the nasal passages. Therefore, individuals who spend a significant amount of time at elevated ventilat ion rates (e.g. while pl aying, exercising, or working) are expected to be at greater risk of experiencing SO 2 -associated health effects (ISA section 4.2.5).



### 3.7 SOCIOECONOMIC STATUS There is limited evidence that increased risk to SO 2 exposure is associated with lower SES (ISA section 4.2.5). Finkels tein et al. (2003) found that among people with below-median income, the relative risk fo r above-median exposure to SO 2 was 1.18 (95% CI: 1.11, 1.26); the corresponding relative risk am ong 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 SO 2 at this time (ISA section 4.2.5).



### 3.8 NUMBER OF AT RISK INDIVIDUALS Considering the size of the groups menti oned above, large proportions of the U.S. population are likely to have a rela tively high risk of experiencing SO 2 -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 Indian s, Alaskan Natives, and African Americans (ISA

for NO x , section 4.4). Furthermore, a higher preval ence of asthma among persons of lower SES and an excess burden of asthma hospitalizations and mortality in mi nority and inner-cityJuly 2009 27 communities have been observed. In additi on, population groups based on age comprise substantial segments of individuals that may be potentially at risk for SO 2 -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, a nd 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 SO 2 -related health effects because of incr eased time spent outdoors at elevated ventilation rates (those who work or play outdoors). Overall, the considerable size of the population groups at risk indicate s that exposure to ambient SO 2 could have a significant impact on public health in the United States.



### 3.9 KEY OBSERVATIONS

 The susceptibility of an individual to SO 2 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 influenc e the manifestation of disease and also increase an individual’s susceptibility.

 Subpopulations may be vulnerable to SO 2 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 contri bute to an individual being disproportionately exposed to higher concentrations th an the general population.

 In some cases specific factors may affect both the susceptibility and vulnerability of a subpopulation to SO 2 .

 The ISA concludes that indivi duals with pre-existing respir atory disease are likely at greater risk than the general population of experiencing SO 2 -associated health effects.

 Epidemiologic studies suggest that children a nd older adults may be at greater risk of experiencing SO 2 -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 outd oors and/or at elevated ventilation rates are likely at increased ri sk of experiencing adverse health effects from SO 2 exposure.

 Large proportions of the U.S. population are likely to be at in creased risk of experiencing SO 2 -related health effects. Thus, exposure to ambient SO 2 could have a significant impact on public health in the United StatesJuly 2009 28 

## 4. INTEGRATION OF HEALTH EVIDENCE

4.1 INTRODUCTION The ISA, along with its annexes, integrat es newly available epidemiologic, human clinical, and animal toxicological evidence with consideration of key fi ndings and conclusions from prior reviews to draw conclusions about the relationship between short- and long-term exposure to SO 2 and numerous human health categories. For these health effects, the ISA characterizes judgments about causality with a hierarchy (for discussi on see ISA section 1.3.7) that contains the following five levels:

 Sufficient to infer a causal relationship

 Sufficient to infer a likely causal rela tionship (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 causal ity 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 obser ved association, availab ility of experimental evidence, consistency of the observed associati on, biological plausibil ity, coherence of the evidence, temporal relationship of the observed associ ation, 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 Ta ble 4-1, the ISA concludes that there is sufficient evidence to infer a causal relationship between respiratory morbidity and short-term exposure to SO 2 (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 observ ed between the pollutant and the outcome in studies in which chance, bias, and confounding could be ruled out with reasonable confidence. Evidenc e includes, for example, controlled human exposure studies; or observational studies that cannot be explain by plausible alternatives or are suppor ted by other lines of evidence (e.g. animal studies or mechanism of action information). Evidence includes replicated and consistent high-quality st udies 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 incons istent; 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 re lationship between relevant pollutant exposures and the health outcome, but is limited because chance, biasand confounding cannot be ruled out. For example, at least one highquality study shows a positive associat ion but the results of other studies are inconsistent. Inadequate to infer a causal relationship Evidence is inadequate to determine th at 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 positiv e 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 observe d in controlled human e xposure studies of 5-10 minutes, epidemiologic studies mostly using 24-hour average concentrations, and animal toxicological studies using exposur es of minutes to hours (ISA, sec tion 5.2). The evidence of an

association between SO 2 exposure and other health categories is judged to be less convincing, at most suggestive but not sufficient to infer a cau sal relationship. Key conclusions from the ISA are summarized below and are described in gr eater detail in Table 5-3 of the ISA.

 Sufficient to infer a causal relationship:

o Short-Term Exposure to SO 2 and Respiratory Morbidity

 Suggestive but not sufficient to infer a causal relationship:

o Short-Term Exposure to SO 2 and Mortality

 Inadequate to infer the presence or absence of a causal relationship

o Short-Term Exposure to SO 2 and Cardiovascular Morbidity;

o Long-Term Exposure to SO 2 and Respiratory Morbidity;

o Long-Term Exposure to SO 2 and Other Morbidity;

o Long-Term Exposure to SO 2 and Mortality

The integrated health discussion in this chap ter will focus on health effect categories for which the ISA finds a causal or likely causal relati onship, as these effect categories are the basis for the potential health effect be nchmarks and quantitative health risk assessment included in Chapters 7 through 9 of this document. As a re sult, this chapter will present an integrated

discussion of the health evidence related to re spiratory morbidity following short-term exposure

to SO 2 . 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 SO 2 . 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 NO 2 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 SO 2 -associated health effects can reasonably beJuly 2009 31 considered adverse. Briefly, this discussion wi ll 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 polluti on; and 4) CASAC views regarding the impact of moderate decrements in lung function or respiratory symptoms on individuals with pre-exis ting lung disease.



### 4.2 RESPIRATORY MORBIDITY FOLLOWING SHORT-TERM SO 2 

### EXPOSURE

4.2.1 Overview The ISA concludes that there is sufficient ev idence to infer a causal relationship between respiratory morbidity and short-term (5-minutes to 24-hours) exposure to SO 2 (ISA, section 5.2). In large part, this determinati on is based on the results of cont rolled human exposure studies in exercising asthmatics demonstrating a re lationship between 5-10 minute peak SO 2 exposures and decrements in lung function that are frequently accompanied by respiratory symptoms. In fact, the ISA describes the controlled human exposur e studies as being the “definitive evidence” for

its causal determination between short-term SO 2 exposure and respiratory morbidity (ISA, section 5.2). In addition to the controlled hum an exposure evidence, the ISA finds supporting evidence for its causal determination from a large body of epidemiol ogic studies observing positive associations between ambient SO 2 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 SO 2 -induced respiratory morbidity.

4.2.2 Integration of Respirator y Morbidity Health Evidence As previously mentioned, the ISA’s finding of a causal relationship between respiratory morbidity and short-term SO 2 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 SO 2 concentrations as low as 200-300 ppb for 5-10 minutes during exercise experience moderate or greate r bronchoconstriction, measured as a decrease in

FEV 1 of ≥ 15% or an increase in specific airway resistance (sRaw) of ≥ 100% after correction for exercise-induced responses in clean air (B ethel et al., 1983; Linn et al., 1983, 1984, 1987; 1988;July 2009 32 1990; Magnussen et al., 1990; Roge r et al., 1985; Gong et al., 1995; Trenga et al., 1999). In addition, the ISA finds that among asthmatics, both the percentage of in dividuals affected, and the severity of the response increases with increasing SO 2 concentrations. That is, at concentrations ranging from 200-300 ppb, the lowest levels tested in free breathing chamber

studies 3 , 5-30% percent of exercising asthmatics e xperience moderate or gr eater 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 FEV 1 ) (ISA, Table 3-1). Moreover, at SO 2 concentrations ≥ 400 ppb, moderate or greater decremen ts in lung function are frequently accompanied by respiratory symptoms (e.g., cough, wh eeze, chest tightness, s hortness 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 leadin g 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 SO 2 concentrations and respiratory sy mptoms, as well as ED visits and hospitalizations for all respiratory causes (par ticularly among children and older adults) and asthma. Moreover, although copo llutant adjustment had varying degrees of influence on the SO 2 effect estimate in ED visit and hospi talization studies, the effect of SO 2 appeared to be generally robust and independent of gase ous copollutants, including NO 2 (Anderson et al., 1998; Lin et al., 2004a; Sunyer et al., 1997) and O 3 (Anderson et al., 1998; Hajat et al., 1999; Tsai et al., 2006; Yang et al., 2003; 2005). With respect to potential confounding by PM 10, the evidence of an independent SO 2 effect on respiratory health was less consistent, with some positive associations with ED visit and hospitalizati on results becoming negative (a lthough the negative results were not statistically significant) after inclusion of PM 10 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 SO 2 effect estimate to be generally robust after inclusion of

3 The ISA cites one chamber study with intermittent exerci se where healthy and asthmati c children were exposed to 100 ppb SO 2 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 PM 10 in regression models (Burnett et al., 1997; Ha gen et al., 2000; Hajat et al., 1999; Schwartz, 1995 [in Tacoma, WA]). Furthermor e, in most (Van der Zee et al., 1999; Mortimer et al., 2002 and Schildcrout et al., 2006), but not all (Schwartz et al., 1994) st udies of respiratory symptoms,

the SO 2 effect estimate remained robust and re latively unchanged after inclusion of PM 10 in multipolutant models (although the effect estimate may have lost statisti cal significance). In addition, SO 2 -effect estimates generally remained robus t in the limited number of studies that

included PM 2.5 and/or PM 10-2.5 in multipolutant models (Burnett et al., 1997; Ito et al., 2007; Lin et al., 2003; NY DOH, 2006). Ta ken together, the ISA ultimately concludes that studies employing multipollutant models suggest that SO 2 has an independent effect on respiratory morbidity outcomes (ISA, section 5.2). The ISA further characterizes the epidemiol ogic results of increases in respiratory symptoms as well as increases in hospital admi ssions 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 approa ches (ISA, section 5.2). Epidemiologic results are coherent in that re spiratory symptoms results from epidemiologic studies with short-term ( ≥ 1-hour) exposures are generally in agreement with respiratory symptom results from controlled human exposur e studies of 5-10 minutes. However, the ISA notes the differences in averaging times associat ed with respiratory effects in human exposure and epidemiologic studies. That is, while adve rse respiratory effects are observed following 5- 10 minute exposures in human clin ical 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 th at it is possible that results from epidemiologic studies are being drive n, at least in part, by shorter-term peak SO 2 concentrations (ISA section 5.2). More specifica lly, with respect to epidemiologic studies of respiratory symptoms, the ISA states “that it is po ssible that these associations are determined in large part by peak exposures within a 24-hour period” (ISA, s ection 5.2). Similarly, the ISA states that the respiratory effects following peak SO 2 exposures in controlled human exposure studies provides a basis for a progression of resp iratory morbidity that could result in increased ED visits and hospital admissions (ISA, secti on 5.2). Also, it should be noted there isJuly 2009 34 epidemiologic evidence to suggest that shorter-term peak SO 2 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 SO 2 concentrations and respiratory symptoms, as well ED visits and hos pitalizations (ISA, Tabl es 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 resu lt in adverse respirat ory effects.

The ISA also finds that th e respiratory effects of SO 2 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 SO 2 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 SO 2 due to preexisting inflammation associated with the dise ase. For example, pre-existing inflammation may lead to enhanced release of inflammatory me diators, 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 re lationship between respiratory morbidity and short-term (5-minutes to 24-hours) exposure to SO 2 . Results from controlled human exposure studies provide the definitive ev idence for this conclusion, while supporting evidence is found in numerous epidemiologic studie s of respiratory sympto ms and ED visits and hospitalizations (ISA, section 5.2 ). The ISA further notes th at both lines of evidence are

consistent with the SO 2 mode of action as it is current ly understood (ISA, section 5.2).

4.2.3 Medication as an Effect Modifier As mentioned above, the immediate effect of SO 2 on the respiratory system is bronchoconstriction. Thus, we note that quick-reli ef and long-term-control asthma medications have been shown to provide varyin g degrees of protection against SO 2 -induced

bronchoconstriction in mild and moderate as thmatics (ISA section 3.1.3.2 and Annex Table D1). More specifically, while no therapy has been shown to completely eliminate SO 2 -induced respiratory effects in exercising asthmatics, some short- and long-acting asthma medications areJuly 2009 35 capable of significantly reducing SO 2 -induced bronchoconstriction (Gong et al., 1996; 2001; Koenig et al., 1987; Linn et al., 1990). Howeve r, 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 mo re severe disease (O’Byrne, 2007; Rabe et al., 2004). Therefore, the ISA finds that it is reasona ble to conclude that all asthmatics (i.e., mild, moderate, and severe), are at high risk of experiencing adverse respir atory effects from SO 2 exposure (ISA section 3.1.3.2).



### 4.3 WHAT CONSTITUTES AN ADVE RSE HEALTH IMPACT FROM SO 2 

### EXPOSURE? In making judgments as to when various SO 2 -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 fr om previous NAAQS reviews, and the advice of CASAC. Taken together, staff concludes that for asthmatics, SO 2 -induced respiratory effects

are adverse. The rationale for th is conclusion is presented below. The ATS has previously defined adverse re spiratory health effects as “medically significant physiologic changes generally evid enced by one or more of the following: (1) interference with the normal activity of the aff ected person or persons, (2) episodic respiratory illness, (3) incapacitating illness, (4) permanen t respiratory injury, and/or (5) progressive respiratory dysfunction” (ATS 1985). The ATS ha s 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 cons idered adverse (ATS 1985). We also note that during the last O 3 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., FEV 1 decrements >

10% but < 20% and/or ≥ 100% increases in sRaw) or respirat ory 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 functiona l responses is most appropriate for estimating potentially adverse lung function decrements in people with lungJuly 2009 36 disease (73 FR16463). Finally, we note that in the current SO 2 NAAQS review, clinicians on the CASAC Panel again advised that moderate or greater decrements in lung function can be clinically significant in some i ndividuals with respiratory diseas e (CASAC transcripts, July 30- 31 2008, pages 211-213) Considering the advice and recommendati ons described above, as well as key conclusions in the ISA, staff finds that for asthmatics, SO 2 -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 SO 2 exposure and respiratory morbidity (ISA, section 5.2). These studies have consistently demonstrated that exposure to SO 2 concentrations as low as 200-300 ppb for 5-10 minutes can result in mode rate or greater decrements in lung function, evidenced by a ≥ 15% decline in FEV 1 and/or ≥ 100% increase in sR aw in a significant percentage of exercising asthmatic s (see section 4.2.2). It is highl y likely that these decrements in lung function will result in in creased medication use and a disruption of normal activities for a significant percentage of these as thmatics. This expectation is supported by a number of human exposure studies reporting that some exercising as thmatics required the use of medication to treat the respiratory effects that followed a 5-10 minute SO 2 exposure (EPA 1994a). It is also supported by CASAC views during the previous O 3 review that moderate declines in FEV 1 can be clinically significant in some individuals (Henderson 2006). As an additional matter, we note that human exposure studies ha ve also reported that at SO 2 concentrations ≥ 400 ppb, lung function decrements (i.e., ≥ 15% decline in FEV 1 and/or ≥ 100% increase in sRaw) are frequently accompanied by respiratory symptoms. Taken together, staff concludes that human exposure studies demonstrate that adverse resp iratory effects occur in exercising asthmatics following 5-10 minute SO 2 exposures as low as 200 ppb. However, we also note that the subjects participating in these exposure studies do not represen t the most sensitive asthmatics (i.e., severe asthmatics), and therefore, it is possi ble that adverse respiratory effects could occur

at lower SO 2 concentrations in these individuals. Epidemiologic studies also indicate that adverse respiratory morbidity effects are

associated with SO 2 . In reaching the conclusion of a cau sal relationship between respiratory morbidity and short-term SO 2 exposure, the ISA generally f ound positive associations between

ambient SO 2 concentrations and ED visits and hosp italizations for all respiratory causes andJuly 2009 37 asthma (see section 4.2.2). Notably, ED visits an d hospitalizations attributable to air pollution

are considered adverse effect s under ATS guidelines. These studies also indicate that SO 2 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 guide lines expanded those re leased in 1985 (ATS 1985). Among other considerations, the 2000 guidelin es stated that measurable negative effects of air pollution on quality of life should be c onsidered adverse (ATS 2000). These updated guidelines also indicated that expo sure to air pollution that increases the risk of an adverse effect to the entire population is advers e, even though it may not increase the risk of any individual to an unacceptable level (ATS 2000). For exampl e, a population of asthmatics could have a distribution of lung function such that no individual has a leve l 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 be cause individuals with in the population would have diminished reserve function, and therefore w ould be at increased risk if affected by another agent (ATS 2000). The 2000 ATS guidelines further st rengthen the conclusion that SO 2 -induced respiratory effects are adverse. As prev iously mentioned, human clinical studies have consistently

demonstrated that SO 2 exposure can result in moderate or greater decrements in FEV 1 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 SO 2 -induced shift in these lung function measurements for this population. As a result, a significant percentage of exercising asthmatics exposed to SO 2 concentrations as low as 200 ppb would have diminished reserve lung function and would be at greater risk if affected by a nother respiratory agent (e.g., viral infection). Importantly, diminished reserve lung function in a population that is attrib utable to air pollution is an adverse effect under ATS guidance. Staff finds multiple lines of evidence indicating that exposure to SO 2 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 SO 2 REA. The CASAC letter to theJuly 2009 38 Administrator states: “CASAC believes strongly that the weight of clinical and epidemiology

evidence indicates there are det ectable clinically relevant health effects in sensitive subpopulations down to a level at least as low as 0.2 ppm SO 2 (Henderson 2008).” Thus, when examining the adequacy of the current and potentia l 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 hu man exposure, epidemiologic, and toxicological studies to infer a causal relationship between respiratory morbidity and short-term exposure to SO 2

 The ISA characterizes no othe r health endpoints as having a causal or likely causal association with short or long-term exposure to SO 2 .

 Human exposure studies demonstrate that at SO 2 concentrations ranging from 200-300 ppb, the lowest levels tested in free br eathing 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 FEV 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 per centage of asthmatics experience severe decrements in lung function (i.e., ≥ 200% increase in sRaw, and/or a ≥ 20% decrease in FEV 1 ).

 At SO 2 concentrations ≥ 400 ppb, moderate or greater d ecrements in lung function are frequently accompanied by respiratory symptoms.

 In general, epidemiologic studies observe d positive associations between ambient SO 2 concentrations and respiratory symptoms, as we ll as ED visits and hospitalizations for all respiratory causes and asthma. In studies using multipollutant models, the effects of SO 2 were generally independent of eff ects of other ambient air pollutants

 No medication regimen has been shown to completely eliminate SO 2 -induced respiratory effects in exercising asthmatics.

 Staff finds multiple lines of evidence indicating that SO 2 exposure can result in respiratory effects that can r easonably 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 SO 2 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 SO 2 ; 2) SO 2 levels associated with just mee ting the current standards; and 3)

SO 2 levels associated with just meeting potential a lternative standards. This section presents the rationale for the selection of the potential al ternative standards that are assessed in the quantitative analyses discussed in Chapters 7 th rough 9. These potential a lternative standards are defined in terms of indicator, av eraging time, form, and level.



### 5.2 INDICATOR The SO x include multiple gaseous (e.g., SO 2 , SO 3 ) and particulate (e.g., sulfate) species. In considering the appropriateness of different indicators, we note th at the health effects associated with particulate species of SO x have been considered within the context of the health effects of ambient particles in the Agency’s re view of the PM NAAQS. Thus, as discussed in the Integrated Review Plan (2007a ), the current review of the SO 2 NAAQS is focused on the gaseous species of SO x and will not consider health effects di rectly associated with particulate species of SO x . Of the gaseous species, EPA has historic ally determined it appropriate to specify

the indicator of the standard in terms of SO 2 because other gaseous sulfur oxides (e.g., SO 3 ) are likely to be found at concentrations many orders of magnitude lower than SO 2 in the atmosphere,

and because most all of the health effects evid ence and exposure information is related to SO 2 . The final ISA has again found this to be the case. Therefore, st aff concluded that SO 2 remains the most appropriate indicator for the alternative standards that are analy zed in this document.



### 5.3 AVERAGING TIME Staff concluded that the most robust evidence for SO 2 -induced respiratory morbidity exists for exposure durations ≤ 1-hour. The strongest evidence for this conclusion comes from controlled human exposure studies that have co nsistently demonstrated that exposure to SO 2 for 5-10 minutes can result in significant bronchocon striction and/or respiratory symptoms in exercising asthmatics (see section 4.2). In fact, the ISA describes the controlled human exposureJuly 2009 40 studies as being the “definitive evidence” for its causal determination between SO 2 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 SO 2 levels and respiratory symptoms as well as hospital admissions and ED visits for all respiratory cau ses and asthma (ISA, Tables 5.4 and 5.5). In addition to the epidemiologic evidence for effect s 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 SO 2 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 SO 2 peaks of duration < 24-hours. More specifically, when describing epidemiologic studies observing

associations between ambient SO 2 and respiratory symptoms, the IS A states “that it is possible

that these associations are dete rmined in large part by peak e xposures within a 24-hour period” (ISA, section 5.2). The ISA also states th at the respiratory effects following peak SO 2 exposures in controlled human exposure st udies provides a basis for a progr ession of respiratory morbidity that could result in increased ED visits and hosp ital admissions (ISA, section 5.2). It should also be noted that epidemiologic st udies 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) a nd asthma ED visits (NY DOH, 2006). Finally, in addition to the controlled human exposure and ep idemiologic evidence, the ISA describes key toxicological studies with expos ures ranging from minutes to hours resulting in decrements in lung function, airway inflammation, and/or hype rresponsiveness in laboratory animals (ISA, Table 5-2). The scientific evidence described above sugge sts that at a minimu m, averaging time(s)

selected for further risk and e xposure analyses should address resp iratory effects associated with

SO 2 exposures of ≤ 1-hour. We note that analyses conduc ted in the ISA demonstrate that at monitors measuring all twelve 5-minute SO 2 levels in an hour (n=16), there is a high Pearson

correlation between the 5-minute maximum le vel and the corresponding 1-hour average SO 2 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 SO 2 standards with an averaging time

of 1-hour. Staff believes that alternative sta ndards with an averagi ng time of 1-hour will limit both 5-minute peak concentrations with in an hour, as well as other peak SO 2 concentrations ( ≥ 1- hour) that are likely in part, driving the respirator y 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 not e that EPA historically conducts air quality, exposure, and risk analyses of alternative st andards by adjusting measured, not modeled air quality data. This is an issue in eval uating alternative 5-mi nute standards for SO 2 because there were, and continue to be relativel y few locations reporting 5-minute SO 2 concentrations. As described in Appendix A, from 1997-2007, there were a total of 98 m onitors in 13 states and the District of Columbia measuring maximum 5-minute SO 2 concentrations in an hour. In comparison, there were 933 monitors in 49 states, th e District of Columbia, Puerto Rico and the Virgin Islands measuring 1-hour SO 2 concentrations. Moreover, it is important to consider that those monitors reporting 5-minute concentratio ns do not represent data from a dedicated 5- minute monitoring network, but rath er a voluntary submission of 5- minute values from monitors placed for the purpose of evaluating atta inment of 24-hour and annual average SO 2 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 standar d. In fact, given the sp atial 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 adequa tely reflect the extent to whic h 5-minute peaks are occurring in

those areas.

While we have chosen to evaluate alternativ e 1-hour averaging time standards in the air quality, exposure, and risk chapters of this docum ent, this choice did not preclude the possibility of considering 5-minute standards as part of the policy assessme nt discussion in Chapter 10, or during the rulemaking process. Consideration of potential alternative 5-minute standards couldJuly 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 protecti on 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 th erefore important that the partic ular form selected for a 1-hour daily maximum potential alternative standard refl ect the nature of the health risks posed by increasing SO 2 concentrations. That is, the form of the standard should reflect results from human exposure studies demonstr ating that the percentage of asthmatics affected, and the severity of the respiratory response (i.e., decr ements in lung function, respiratory symptoms)

increases as SO 2 concentrations increase (see section 4.2.2) . Taking this into consideration, staff

concluded that a concentration-based form is mo re appropriate than an exceedance-based form.

This is because a concentrati on-based form averaged over three years (see below) would give proportionally greater weight to 1-hour daily maximum SO 2 concentrations that are well above the level of the standard, than to 1-hour daily maximum SO 2 concentrations that are just above the level of the standard. In contrast, an exp ected exceedance form would give the same weight to 1-hour daily maximum SO 2 concentrations that are just above the level of the standard, as to 1-hour daily maximum SO 2 concentrations that are well a bove the level of the standard. Therefore, a concentration-based form better re flects the continuum of health risks posed by increasing SO 2 concentrations (i.e., the percentage of as thmatics affected and the severity of the response increases with increasing SO 2 concentrations). Concentra tion-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 c oncentrations and providing a stable and robust regulatory target. The most recent review of the PM NAA QS (completed in 2006) judged that using a 98 th percentile form averaged over 3 years provide s an appropriate balance between limiting the occurrence of peak concentrati ons and providing a stable regul atory target (71 FR 61144). In that review, staff also considered ot her forms within the range of the 95 th to the 99 th percentiles. In making recommendations regarding the form, sta ff considered the impact on risk of different forms, the year-to-year stability in the air quality statistic, and the extent to which different formsJuly 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 98 th percentile form or a 99 th percentile form. We have made similar judgments in selecting appropriate forms for the poten tial alternative 1-hour daily maximum SO 2 standards

assessed in this REA. As a result of thes e judgments, we decided to consider both 98 th and 99 th percentile SO 2 concentrations, averaged over 3 y ears. We have judged that the 98 th and 99 th percentile, when combined with the selected range of alternative levels of a 1-hour daily maximum standard (see below), wi ll likely offer a sufficient range of options to balance the objective of providing a stable regu latory target against the objective of limiting the occurrence of peak 5-minute concentrations. Notably, for a given 1-hour standard leve l, staff’s initial judgment is that a 99 th percentile form will be appreciably more protective against 5-minute peaks than a 98 th percentile form. Staff finds this is likely the case because compared to a standard with a 98 th percentile form, a

standard with a 99 th percentile form (at the same level) wi ll 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 (s ee section 5.5) were assessed with a 99 th percentile form in the air quality, exposure and risk analyses. However, as a comparison between forms, one

alternative standard level was examined with a 98 th percentile form in the exposure and risk analyses, and two alternative standard levels were examined with a 98 th 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 expos ure and risk analyses, staff examined both the controlled human exposure and epidemiologic evidence evaluate d in the ISA. Controlled human exposure

evidence demonstrates that there is a continuum of SO 2 -related health effects following 5-10 minute peak SO 2 exposures in exercising asthmatics. That is, the ISA finds that the percentage of asthmatics affected and the severity of the response increases with increasing SO 2 concentrations. At concentrations ranging from 200-300 ppb, appr oximately 5-30% percent of exercising asthmatics are likely to experience m oderate or greater bronchoconstriction (ISA, Table 3-1). At concentrations ≥ 400 ppb, moderate or greater bronchoconstriction occurs inJuly 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 br onchoconstriction was frequently accompanied with respiratory symptoms (ISA, Table3-1).

In addition to the controlled human e xposure evidence, we also considered the epidemiologic evidence, as well as an air quali ty analysis conducted by staff characterizing 1- hour daily maximum SO 2 air quality levels in cities and time periods corresponding to key U.S. and Canadian ED visit and hospital admission st udies for all respiratory causes and asthma 4 (key

studies are identified in Table 5- 5 of the ISA). Figures 5-1 to 5-5 show standardized effect

estimates and the 98 th and 99 th percentile 1-hour daily maximum SO 2 levels for locations and time periods corresponding to these key U. S. (Figures 5-1 to 5-4) and Canadian 5 (Figure 5-5)

studies

. In general, staff concluded th at the results presented in these figures demonstrate that most of these epidemiologic studies show positive, although frequently not statistically significant associations with SO 2 . Furthermore, we conclude d that Figures 5-1 to 5-5 demonstrate that positive effect estimates, including some that are statistically significant, are found in locations that sp an a broad range of 98 th and 99 th percentile 1-hour daily maximum SO 2 concentrations (98 th percentile range: 19- 401 ppb; 99 th 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 SO 2 standards for analysis in this REA (see Chapters 7-9).

4 Authors of relevant U.S. and Canadian studies were c ontacted and air quality statistics from the study monitor that recorded the highest SO 2 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 EP A. 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 98 th and 99 th 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 SO 2 concentrations presented for Canadian studies represent the 98 th and 99 th percentile 1-hour daily maximum SO 2 concentrations across a given city, rather than concentratio ns from the single monitor that recorded the highest 98 th and 99 th percentile SO 2 levels in a given city (see Thompson and Stewart, 2009).July 2009 45 -15 -10 -5 0 5 10 15 20 25 30Percent 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 30Percent 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 98 th and 99 th percentile 1-hour daily maximum SO 2 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 100Percent 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 98 th and 99 th percentile 1-hour daily maximum SO 2 levels.July 2009 47 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25Percent 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 98 th and 99 th percentile 1-hour daily maximum SO 2 levels.July 2009 48 -10 -5 0 5 10 15 20 25 30 35Percent 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 35Percent 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 98 th and 99 th percentile 1-hour daily maximum SO 2 levels. 6

6 There were no key U.S. hospitalization studies with 1-hour effect estimates identified in Table 5-5 of the ISAJuly 2009 49 -40 -20

0 20 40 60 80 Lin 2003 Burnett 1997 Yang 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 24-hour effect estimates 1-hour effect estimatePercent Excess Risk

-40 -20

0 20 40 60 80 Lin 2003 Burnett 1997 Yang 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 24-hour effect estimates 1-hour effect estimate

-40 -20

0 20 40 60 80 Lin 2003 Burnett 1997 Yang 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 24-hour effect estimates 1-hour effect estimatePercent Excess Risk

Figure 5-5. Effect estimates for Canadian ED visits and hospitalization studies and associated 98 th and 99 th percentile 1-hour daily maximum SO 2 levels.

The highest 98 th and 99 th 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 associations 7 with respiratoryrelated hospital admissions or ED visits during time periods when 98 th and 99 th percentile 1-hour daily maximum SO 2 concentrations ranged from 126 ppb to 457 ppb. Notably, this range of 1- hour daily maximum SO 2 levels overlaps considerably with 5-10 minute SO 2 concentrations ( ≥ 200 ppb) that have consistently been shown in c ontrolled human exposure studies to result in lung function responses in exercising asthmatics. Of particular concern ar e the air quality levels

that were found in Cincinnati (Jaffe et al., 2003). The 98 th and 99 th percentile 1-hour daily

maximum SO 2 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 considerab le percentage of exercising asthmatics. As a result, staff decided to analyze alternative standard levels up to 250 ppb. We concluded that a

98 th or 99 th percentile 1-hour daily maximum standard at this level had the potential to substantially limit the nu mber of days when the 1-hour daily maximum SO 2 concentration is ≥ 200 ppb, while also potentially limiting the number of 5-10 minute SO 2 peaks ≥ 400 ppb. In selecting the lower end of the range of alternative sta ndards to be analyzed, staff again considered controlled human exposure and epidemio logic evidence. However, with regard to the controlled human exposure evidence, several additional factors were considered. First, we considered that the subjects in human e xposure studies do not re present the most SO 2 sensitive asthmatics; that is, these studies included mild and moderate, but not severe asthmatics. Also,

while human clinical studies have been conduc ted in adolescents, younger children have not been included in these exposure studies, and t hus, it is possible asthmatic children represent a population that is more sensitive to the respiratory effects of SO 2 than the individuals who have been examined to date. Moreover, we consider ed that approximately 5-30% of asthmatics who engaged in moderate or greater exertion experienced bronchoc onstriction following exposure to 200-300 ppb SO 2 , which are the lowest levels tested in free breathing chamber studies (ISA, Table 3-1). Thus, we concluded that it was hi ghly 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 th at Figure 5-5 contains two epidemiologic analyses observing positive a ssociations between ambient SO 2 concentrations and hospital

admissions in Canadian cities when 99 th percentile 1-hour daily maximum SO 2 levels were < 47 ppb. More specifically, positive associations between SO 2 and hospital admissions were found in Toronto, (Burnett al., 1997) and Va ncouver (Yang et. al., 2003) when 99 th percentile 1-hour daily maximum SO 2 levels were approximately 21 ppb and 41 ppb, respectively. However, as previously noted, the 99 th percentile 1-hour daily maximum SO 2 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 98 th or 99 th percentile 1-hour daily maximum levels across multiple monitors in a given city (Figure 5-5), rather than 98 th or 99 th percentile concentrations from the si ngle monitor that recorded the highest SO 2 levelsJuly 2009 51 (Figures 5-1 to 5-4; see Thompson a nd Stewart, 2009). As a result, the SO 2 concentrations presented in Figure 5-5 for Canadi an studies would be relatively lo wer (potentially significantly lower) than those levels presente d in Figures 5-1 to 5-4 for U.S. epidemiologic studies. In addition to these Canadian studies, we also not ed that a U.S. study, De lfino et al. (2003), observed a statistically significant association between ambient SO 2 and respiratory symptoms in Hispanic children when the 1-hour daily maximum SO 2 concentration in Los Angeles was 26 ppb (ISA Table 5-4). However, this epidemiol ogic study was very small (n=22), and did not examine potential confounding by co-po llutants. Thus, staff concl uded that these three studies alone do not provide sufficient evidence for c onsidering alternative 1-hour daily maximum SO 2 standards below 50 ppb. Staff noted that numerous studies reported positive associations between ambient SO 2 and hospital admissions and ED visits in cities and time frames when 98 th and/or 99 th percentile 1-hour daily maximum SO 2 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 pollu tant models (Portland, Wilson, 1995; Bronx, NYDOH, 2006; NYC, Ito, 2006; and Schwartz, 1995), as well as some evidence of statistically significant associ ations in multi-pollutant models with PM 8 (Bronx, NYDOH, 2006 and NYC, Ito, 2007). Given these epidemiologic a nd air quality results, as well

as the considerations mentione d 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 leve l of 50 ppb as the lower bound. We judged that a

98 th or 99 th percentile 1-hour daily maximum standard at this level would both limit the number of days when 1-hour daily maximum SO 2 levels are ≥ 50 ppb, while also limiting 5-10 minute peaks of SO 2 ≥ 100 ppb. Moreover, we noted that a le vel of 50 ppb is substantially below the

98 th and 99 th percentile 1-hour daily maximum SO 2 levels observed in the Bronx during the NYDOH analysis and in NYC duri ng the period analyzed by Ito et al., (2006): two studies where

8 In the NYDOH study (2006), the Bronx positive effect estima te remained statistically significant in the presence of PM 2.5. In Ito et al., (2007), the NYC positive effect estimat e was statistically significant in the presence of PM 2.5 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 PM 10 when the 99 th percentile 1-hour daily maximum SO 2 concentration in New Haven was 150 ppb.July 2009 52 the SO 2 effect estimate remained robust and statistic ally significant in multi-pollutant models

with PM 2.5 (ISA, Table 5-5).



### 5.6 KEY OBSERVATIONS

 Staff concluded that SO 2 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 qu ality, exposure, and risk analyses, staff concluded that the focus should be on potential alternative SO 2 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 insuffi cient 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 alte rnative standards to be analyzed in the air quality, exposure, and risk analyses, staff c oncluded that it was appropriate to consider the annual 98 th and 99 th percentile SO 2 concentrations averaged over a 3 year period. Staff found that a concentration-based form be tter reflected the continuum of health risks posed by increasing SO 2 concentrations, and provided gr eater 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 stud ies of ED visits and hospitalizations, staff concluded that it was appropriate to examine alternative 1-hour daily maximum standards in the air quality, e xposure, and risk analys es 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 potentia l health risks associated with: (1) recent

ambient levels of SO 2 , (2) levels associated with just meeting the current SO 2 NAAQS, and (3) levels associated with just meeting several pote ntial alternative standard s (see Chapter 5 of this document for the discussion of potenti al 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 altern ative standards). In the first approach, SO 2 air quality levels are compared to pot ential 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 estima te the number and percent of exposed asthmatics that would experience moderate or greater lung function responses under the di fferent 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 deta il in their respective chapters and associated appendices. In addi tion, this chapter also describes important methodologies used throughout these analyses. This includes the approach used to estimate 5-

minute SO 2 concentrations from 1-hour data (section 6.4), how recent air quality was adjusted to simulate alternative air quality standards scen arios (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 SO 2

concentration above a benchmark level

Output1: Number of days per year 5- minute daily maximum SO 2 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 SO 2 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 SO 2 concentrations that exceed 5-minute potential health benchmark values Estimation of exposure-response function

Quantitative Risk Analysis: combines outputs of the exposureanalysis with estimated exposureresponse function

Output: Estimates the number and percentage of exposed asthmatics that would experience moderate or greater lung function responses #

o f ex

po

s e d a

s th

m

a t i cs 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 SO 2

concentration above a benchmark level

Output1: Number of days per year 5- minute daily maximum SO 2 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 SO 2 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 SO 2 concentrations that exceed 5-minute potential health benchmark values Estimation of exposure-response function

Quantitative Risk Analysis: combines outputs of the exposureanalysis with estimated exposureresponse function

Output: Estimates the number and percentage of exposed asthmatics that would experience moderate or greater lung function responses #

o f ex

po

s e d a

s th

m

a t i cs

Figure 6-1. Overview of analyses addressing ex posures and risks associated with 5-minute peak SO 2 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 EF FECT BENCHMARK LEVELS Potential health benchmark values used in the air quality, exposure, and risk analyses were derived solely from the human exposur e 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 SO 2 in the absence of co-polluta nts that can confound results in epidemiological analyses; thus, health effects obs erved in clinical studi es can confidently be

attributed to a define d exposure level of SO 2 . The ISA presents human exposure evidence demonstrating decremen ts in lung function in approximately 5-30% of exercising asthmatics exposed to 200-300 ppb SO 2 for 5-10 minutes.July 2009 55 However, it is important to note: (1) subject s in human exposure studies do not include individuals who may be most susceptib le to the respiratory effects of SO 2, (e.g., severe asthmatics and children) and (2) given that 5-30% of exercising asthmatics experienced

bronchoconstriction followi ng exposure to 200-300 ppb SO 2 (the lowest levels tested in freebreathing chamber studies), it is likely that a percentage of asthmatic s would also experience bronchoconstriction following expos ure to levels lower than 200 ppb. That is, there is no evidence to suggest that 200 ppb represents a thre shold level below which no adverse respiratory

effects occur. We also noted that small SO 2 -induced lung function decrements have been observed in asthmatics at concentrations as low as 100 ppb when SO 2 is administered via mouthpiece 9 (ISA, section 3.1.3). Considering this information, staff concluded it was appropriate to examine potenti al 5-minute benchmark values in the range of 100-400 ppb. The lower end of the range consider s the factors mentioned above, wh ile the upper end of the range recognizes that 400 ppb represents the lowest con centration at which sta tistically significant decrements in lung function are seen in conjuncti on 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 ai r quality and exposure analyses (see section 6.3), staff considered the number of days with a 5-minute maximum SO 2 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 SO 2 exposure, there is approximately a 5-hour period of time when asthmatics are less sensitive to subsequent SO 2 challenges (ISA, section 3.1.3.2). As a re sult, there is uncertainty as to whether

an additional SO 2 exposure(s) on a given day would be a ssociated with an additional adverse respiratory outcome(s) (i.e., moderate decrements in lung function and/or re spiratory symptoms). On the other hand, we recognize that not countin g 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 nasa l absorption of SO 2 is bypassed during oral breathing, thus allowing a greater fraction of inhaled SO 2 to reach the tracheobronchial airways. As a result, individuals exposed to SO 2 through a mouthpiece are likely to experience greater respirat ory effects from a given SO 2 exposure. In addition, the two mouthpiece studies cited in the ISA as exposing exercising asthmatics to 100 ppb SO 2 (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 SO 2 ) and observed very small changes in FEV 1 or sRaw. Nonetheless, these studies do provide very limited evidence for SO 2 -induced respiratory effects at 100 ppb.July 2009 56 lead to an underestimate in the num ber of asthmatics experiencing an SO 2 concentration above a benchmark level, and thus, an adverse respir atory 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 out puts (see sections 7.4, 8.11 and 9.3).



### 6.3 APPROACHES FOR ASSESSING EXPOSURE AND RISK ASSOCIATED WITH 5-MINUTE PEAK SO 2 

### EXPOSURES In the first approach (i.e., the air qual ity characterization), we have compared SO 2 air quality with the potential health effect benchmark levels for SO 2 . Scenario-driven air quality analyses were performed using ambient SO 2 concentrations for th e years 1997 though 2006. All U.S. monitoring sites where 1-hour SO 2 data have been collected ar e represented by this analysis and, as such, the results generate d are considered a broad characte rization of national air quality and potential human exposures that might be associated with these concentrations. 10 The output of the air quality characterizati on 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 SO 2 air quality can adequately serve as an indicator of exposure to ambient SO 2 . 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 SO 2 exposures. The estimates of personal exposure have also been compared to the potential health benchm ark 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 benchm ark levels. For this exposure analysis, a probabilistic approach was used to model indi vidual exposures considering the time people spend in different microenvironments and the variable SO 2 concentrations that occur within these microenvironments across time, space, and micr oenvironment type. The exposure model also

accounts for activities that indi viduals perform within the microenvironments, allowing for estimation of exposures that coin cide with varying activity levels . As such, this approach to

10 Two additional subsets of the broader SO 2 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 lo cations in the U.S. (Greene County, MO. and St. Louis, MO.) 11 Although the geographic scope of this analysis is restri cted, the approach provides realistic estimates of SO 2 exposures, particularly thos e exposures associated with important emission sources of SO 2 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 expos ure modeling analyses described above. The levels of potential

benchmarks are based on SO 2 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 sec tion 6.2 for discussion). Benchmark values of 100, 200, 300, and 400 ppb have been compared to both SO 2 air quality (measured and modeled

5-minute SO 2 concentrations) and to estimates of SO 2 exposures. In characterizing the SO 2 air quality using ambient monitors, th e output of the analysis is an estimate of the number of days per year specific locations experience 5-minute daily maximum levels of SO 2 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 experi encing daily maximum 5-minute levels of SO 2 of ambient origin that exceed a particular benchmark. In the third approach (i.e., the quantitative ri sk assessment), we combine outputs from the exposure analysis with exposur e-response functions derived fr om 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 SO 2 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 1 st draft REA, staff presented the resu lts of an exposure analysis for Greene County (or Springfield, MO.) and several other source-based modeling domains in Misso uri. 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 2 nd draft REA and completed the exposure assessment in St. Louis which had been started at the time of the 1 st draft REA.July 2009 58 As discussed above in Chapter 4, the ISA c oncludes that the overall weight of the evidence supports a causal rela tionship between short-term SO 2 exposure and respiratory morbidity. The ISA states that the “definitive evidence” for its causal determination is from controlled human exposure studies demonstrating d ecrements in lung function and/or respiratory symptoms in exercising asthmatics exposed to ≥ 200 ppb SO 2 (ISA, section 5.2). The ISA further notes that supporting this causal determination is a larg er body of U.S and international epidemiological studies examining respiratory sy mptoms and ED visits and hospitalizations for all respiratory causes and asthma (ISA, section 5.2). As previously described, staff is utilizing both the epidemiol ogical evidence in the ISA, and an air quality analysis based on U.S. and Ca nadian ED visit and hos pitalization 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 a lternative standards to be analy zed 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 alte rnative standards (Chapter 10). However, staff did not find the overall breadth of the epidemiological evid ence 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. studi es should be considered given differences in monitoring networks, levels of co -pollutants, and other factors acr oss different locations that may

well alter SO 2 -concentration-response relationships. Ta king 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 thos e U.S. cities where epidemiological studies had been conducted, many of the SO 2 effect estimates were positive, but not statistically significant in single pollutant models. Moreover, in the re latively few studies that employed multi-pollutant models, inclusion of PM 10 in the model resulted in a loss of statistical significance for the SO 2 effect estimate in about half of these studies (although the eff ect estimate may have remained positive). Overall, we conclude that these factor s would make it particularly difficult to quantify with confidence the magnitude of respiratory health effects related to SO 2 exposures and therefore, we judge that the results of a quan titative risk assessment based on concentrationresponse functions from epidemiological studies for these health outcomes would be of limitedJuly 2009 59 utility in the decision-making proc ess given the nature of the uncertainties associated with these

studies.



### 6.4 APPROACH FOR ESTIMA TING 5-MINUTE PEAK SO 2 

### CONCENTRATIONS Health effects evaluated in this REA incl ude those associated with 5-10 minute peak

concentrations of SO 2 . While there are 98 ambient monito rs that have reported 5-minute SO 2 concentrations some time during 1997-2007, the spat ial and temporal representation is limited to a few states and often only a few years of mon itoring. Most of these monitors report the 5-

minute maximum SO 2 concentration occurring within an hour, though there were some that reported all twelve continuous 5-minute SO 2 concentrations measured within the hour. The ambient monitors reporting continuous SO 2 values are limited to fewe r 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 SO 2 ambient monitoring data are for 1-hour average concentra tions (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-year s were available for the 5-minute maximum SO 2 concentrations than 10-minute maximum concentr ations, a model was developed to estimate 5-

minute maximum SO 2 concentrations from the comprehensive 1-hour SO 2 ambient monitoring

data. Staff first reviewed the air quality ch aracterization conducted in the prior SO 2 NAAQS review and supplementary analyses. In these prior analyses, relations hips between maximum 5-

minute SO 2 concentrations and the 1-hour average SO 2 concentrations, or peak-to-mean ratios (PMRs) were evaluated and used to approximate 5-minute maximum SO 2 concentrations from 1- hour values (EPA, 1986a; EPA, 1994b; SAI, 1996; Thompson, 2000). While the relationship

between the two metrics is not ex pected to be linear, the tempor al patterns in the two averaging

times are consistent. Five-minute maximum SO 2 concentrations are often much greater than that of the corresponding 1-hour SO 2 concentrations, and observed increases in a given 1-hour SO 2 concentration often coincide with increases in the 5-minute maximum SO 2 concentration. As an example of this pattern, the time-series of 1-hour average and 5-minute maximum SO 2July 2009 60 concentrations measured at an ambient monitor across a 3-day period in 2005 is illustrated in Figure 6-2.



### March 10-12, 200 5 - ID 290770037

0 50 100 150 200 250 3003/10/2005 0:003/10/2005 4:003/10/2005 8:003/10/2005 12:003/10/2005 16:003/10/2005 20:003/11/2005 0:003/11/2005 4:003/11/2005 8:003/11/2005 12:003/11/2005 16:003/11/2005 20:003/12/2005 0:003/12/2005 4:003/12/2005 8:003/12/2005 12:003/12/2005 16:003/12/2005 20:00



### Hour of DaySO2Concentration (ppb)

5-minute maximum 1-hour

Figure 6-2. Example of an hourly time-s eries of measured 1-hour and measured 5-minute maximum SO 2 concentrations.

In general, PMRs were determined to be a pproximately two in some of the earlier studies when used in estimating 5-minute peak SO 2 concentrations; though for the exposure analyses conducted for the last NAAQS review, a distributi on of PMRs was used with values of up to eleven (EPA, 1994b). In each of the analyses conducted previous ly, estimates of PMRs were derived using ambient monitori ng data (i.e., where both 5-minute maximum and 1-hour average

SO 2 were measured) and then used to es timate the occurrence of peak 5-minute SO 2 concentrations given a 1-hour ambient SO 2 concentration. The approach was generally as

follows:

hour CPMR C

   1 5max equation (6-1)July 2009 61 where,

C max-5 = estimated 5-minute maximum SO 2 concentration (ppb)

PMR = peak-to-mean ratio (PMR)

C 1-hour = measured 1-hour average SO 2 concentration

At the time of the last NAAQS review, ther e were very few monitors reporting 5-minute

SO 2 data. In fact, distributions of PMRs from ambient monitors surroundi ng 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 ment ioned above, the PMRs were determined to be approximately two in these earlier studies; how ever, the ratio can vary depending on a several

factors. It has been shown that there can be in creased variability in th e ratio with decreasing 1- hour average SO 2 concentrations, that is, th ere is a greater lik elihood of values greater than two at low hourly average concentrations than expect ed at high hourly average concentrations (EPA, 1986a). It has also been argued that the occurren ce of short-term peak concentrations at ambient monitors may be influenced by particular SO 2 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 hei ght, area terrain) that likely contribute to variability in 5-minute maximum SO 2 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 SO 2 monitoring network regarding the type of sources that may be near the ambient mon itors, the magnitude of emissions, the temporal variation in emissions, and distan ce from specific sources; however, staff determined that there was no practical way to define ev ery ambient monitor as being ex clusively influenced by a single

source or a defined mix of sour ces. Given other conditions that may vary within a specific source category (monitor-to-source distances, loca l meteorology, operating c onditions, etc.), staff also determined that there was no practical way to use such data quantitatively in the

12 In that analysis, normalized 1-hour SO 2 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 SO 2 ambient

monitor data. In recognizing the limited geographic span of the monitors reporting the 5-minute

maximum SO 2 concentrations and the ove rall uncertainty regarding th e amount of influence of a specific source on any given monitor, staff developed an approach based on hourly SO 2 concentration levels and the variability observe d at the monitors reporting both the 5-minute maximum and 1-hour average SO 2 concentrations. The main assumption in the approach is that the temporal and spatial pattern in SO 2 source emissions is influenced by the type of source(s) present, its operating conditions, and that the em ission pattern(s) is reflected in the ambient SO 2 concentration distribution measured at the monitor. Thus, measur es 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 SO 2 concentrations was categorized based on the coefficient of variation (COV) of 1-hour average SO 2 concentrations and then used to estimate distribution of PMRs for range of 1-hour SO 2 concentrations. This approach, that fully utilizes all of the available 5-

minute maximum SO 2 data, is detailed in section 7.2.3.



### 6.5 APPROACH FOR SIMULA TING THE CURRENT AND ALTERNATIVE AIR QUALIT Y STANDARD SCENARIOS A primary goal of the risk and exposure asse ssments described in this document is to evaluate the ability of the current SO 2 primary standards (30 p pb annual average, 140 ppb 24- hour average) 13 and potential alternative standards (99 th percentile 1-hour daily maximum SO 2 levels of 50, 100, 150, 200, and 250 ppb, and 98 th percentile 1-hour daily maximum SO 2 levels: 200 ppb; see Chapter 5 of this document) to protec t public health. To eval uate the ability of a specific standard to protec t public health, ambient SO 2 concentrations need to be adjusted such that they simulate levels of SO 2 that just meet that standard. Such adjustments allow for comparison of the level of public he alth protection that could be a ssociated 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 SO 2 NAAQS have units of ppm.July 2009 63 All areas of the United States currently have ambient SO 2 levels below the current annual standard (EPA, 2007c). One site in Northamp ton County, Pa., measured concentrations above

the level of the 24-hour standard in 2006. Theref ore, to evaluate whether the current standards adequately protect public health, nearly all SO 2 concentrations need to be adjusted upwards in all

areas included in our assessment to simulate levels of SO 2 that would just meet the current standard levels. Similarly, to simulate a potentia l air quality standard that is below current air quality standards, those current leve ls must be adjusted downward.

Ambient SO 2 concentrations and exposures we re characterized by considering as is air quality (unadjusted concentrations ) and several hypotheti cal air quality scenar ios. 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 alte rnative standards. Staff chose a proportional approach to adjust the SO 2 concentrations to simulate each of the current and alternative air quality standard scenarios. 14 A proportional approach was sel ected based on the mostly linear relationship between older high co ncentration years of air quality when compared with recent low concentration years at severa l locations (Rizzo, 2009). Briefl y, for each location of interest ( i ) and year ( j ), SO 2 concentration adjustment factors ( F

) were derived by the following equation:

ij ij CSF max, /  equation (6-2)

where,

F

ij

= 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)

C

max,ij = maximum measured SO 2 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 shar e 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 SO 2 concentrations using equation (6-2), it was a ssumed that the current temporal and spatial

distribution of air concentrations (as characterized by the current air quality data) is maintained

and increased SO 2 emissions contribute to increased SO 2 concentrations. All the hourly SO 2 concentrations in a location were multiplied by the same constant value F , whereas the highest monitor (in terms of concentration) is adjusted su ch that it just meets the standard target level. This procedure for adjusting either the ambi ent concentrations (i.e., in the air quality characterization) or health e ffect 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 SO 2 levels such that the levels were just at the current standards in the areas analyzed. Staff recognizes th at it is extremely unlikely that SO 2 concentrations in any of

the selected areas where concentr ations 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. Ne vertheless, this procedur e was necessary to assess the ability of the current sta ndards, not current ambient SO 2 concentrations, to protect public health. This process of adjusting SO 2 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 CHARAC TERIZING VARIABILITY AND UNCERTAINTY An important issue associated with any population exposure or risk assessment is the

characterization of variab ility and uncertainty. Variability refers to the inherent heterogeneity in a population or variable of interest (e.g., reside ntial air exchange rates) and cannot be reduced through further research, only better charac terized with additional measurement. Uncertainty categorically refers to the lack of knowledge re garding 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 m easurement 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 RE A is to ensure that the characterization of air quality and the estimates of exposure and risk reflect the variability of ambient SO 2 concentrations and associated SO 2 exposure and health risk ac ross the study locations and population. In this REA, there are several algorithms that account for variab ility of input data when generating the number of estimated benchmar k exceedances or health risk outputs. For example, variability may result from the numbe r of monitors operating in an area and their associated temporal and spatia l heterogeneity in ambient SO 2 concentrations. Variability may also arise from differences in the populati on residing within a census block (e.g., age distribution) and the activities that may affect SO 2 population exposure (e.g., time spent outdoors), and/or the influential ri sk factors (e.g., the fraction of the population responding to an

SO 2 exposure). A complete range of potential expos ure levels and associated risk estimates can be generated when appropriately addressing vari ability 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 incorporat ed any observed variab ility 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 usi ng 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 s ections 7.4, 8.11, and 9.3 for the air quality characterization, the exposure assessment, a nd the risk characterization, respectively.

6.6.2 Characterization of Uncertainty While it may be possible to capture a full ra nge of exposure or risk values by accounting for variability inherent to influe ntial factors, the true exposure or risk for any given individual is largely unknown. To characteri ze health risks, exposure and ri sk assessors commonly use an iterative process of gathering da ta, developing models, and estima ting exposures and risks, given the goals of the assessment, scale of the assessm ent performed, and the limitations of the input data available. However, significant uncertain ty 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 in clude 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 i ndicates a high degree of uncertainty for certain identified sources, the sour ces are highly influential to exposure and risk estimates, and sufficient information and resour ces 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 perfor med is also be informed by th e intended scope and purpose of the assessment, whether the selected analysis will provide additional information to the overall decision regarding health protection, whether su fficient data are available to conduct a complex quantitative analysis, and if tim e and resources are available fo r higher tier characterizations (EPA, 2004b; WHO, 2008). The primary purpose of the uncertainty character ization approach selected in this REA is to identify and compare the rela tive impact important sources of uncertainty may have on the potential health effect benchmarks and/or respiratory effects endpoint s estimated in Chapters 7-9. The approach used to evaluate uncertainty wa s adapted from guidelines outlining how to conduct a qualitative uncertainty char acterization (WHO, 2008), though st aff 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 characteri ze all identified uncertainties, staff selected the mainly qualitative approach give n the limited data available to inform probabilistic analyses, and

time and resource constraints. The qualitative approach used in this REA va ries from that of WHO (2008) in that a greater focus of the characteriz ation performed was placed on ev aluating the direction and the magnitude 15 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 a ddition and consistent with the W HO (2008) guidance, staff discuss the uncertainty in the knowledge-base (e.g., the accuracy of th e 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 uncer tainty 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 dire ction each identified source of uncertainty

influences the assessment results. Consistent with the WHO (2008) guida nce, staff subjectively scaled the overall impact of the uncertainty by considering th e degree of severity of the uncertainty as implied by the relationship between the source of the uncerta inty 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 uncer tainty 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 (o r proportional) effect on the results. A

characterization of high implies that a small change in th e 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 exceedan ces or risk estimates; either the estimated values were likely over - or under-estimated . In the instance where th e 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 associ ated 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 th e data used and its applicability to the assessment endpoints, medium implied that there were some limitations regarding consistency and completeness of the data us ed or scientific evidence presented, and high indicated the knowledge-bas e was extremely limited. The output of the uncertainty characteri zation 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 di scussed in Chapter 10, providing the overall implications in informing staff’s eval uation of exposures and risks associated withJuly 2009 68 level, form, and averaging time related to judg ing the adequancy of the current standard and consideration of potential alternative primary SO 2 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 fr om human exposure stud ies to suggest that 200 ppb represents a threshold level below wh ich 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 SO 2 

## EXPOSURES

7.1 OVERVIEW Ambient monitoring data for each of the years 1997 through 2007 were used in this chapter to characterize SO 2 air quality across the U.S. The measured air quality, as well as

additional SO 2 concentrations derived from the measured air quality data, were used as an indicator of potential human exposure. While an ambient monitor measures SO 2 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 ambien t monitor concentration to personal exposure depends upon th e 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 br oad characterization of national air quality and potential human exposures that might be asso ciated with a variet y of scenario-driven concentrations. This is because many of the SO 2 ambient monitoring sites used in this analysis target public health monitoring ob jectives and some of the analys is 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 SO 2 exposure to be “sufficient to infer a causal relationship” (ISA, section 5.2). The ISA states that the “d efinitive evidence” for this conclusion comes from the results of human exposure studies demonstrating decremen ts in lung function and/or respiratory symptoms in exercising asthmatics following exposure to SO 2 levels as low as 200 to 300 ppb for 5-10 minutes (ISA, section 5.2). A ccordingly, 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 le vel 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, a nd then for more detailed analyses, at monitors

located within selected U.S. coun ties (see section 7.2.4). Staff es timated the number of days in a year with 5-minute benchmark exceedances and th e probability of benchmark exceedances givenJuly 2009 70 the occurrence of 1-hour daily maximum or 24-hour average SO 2 concentrations at ambient

monitors.

All ambient SO 2 monitors report hourly c oncentrations; a subset of those report 5-minute

maximum SO 2 concentrations as well, with a subset of these reporting continuous 5-minute SO 2 concentrations. Because there were two dis tinct sample averaging times reported for the available ambient monitoring data (i.e ., ambient monitors reporting 1-hour SO 2 concentration measurements alone and monitors repor ting both 5-minute and 1-hour average SO 2 concentrations), the data used in the an alyses were separated by staff as follows. The first set of ambient air quality data was from monitors reporting both 5-minute and 1-

hour SO 2 concentrations. Staff 1) analyzed the ambi ent monitoring data for trends in 1-hour and

5-minute SO 2 concentrations, 2) counted the number of measured dail y 5-minute maximum SO 2 concentrations above the potential health eff ect benchmark levels given the annual average SO 2 concentrations, 3) estimated the probability of benchmark exceedances given the 24-hour average and 1-hour daily maximum SO 2 concentrations, 4) devel oped a statistical model to

estimate 5-minute maximum SO 2 concentrations from 1-hour SO 2 concentrations, and 5) evaluated the performance of the statistical m odel 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 SO 2 concentrations from the

broader SO 2 monitoring network; therefore th is set also included 1-hour SO 2 concentrations from

those monitors where 5-minute SO 2 data were reported, though th e 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 SO 2 measurements to this second set of ambient monitoring data to estimate 5-minute maximum SO 2 concentrations. As was done with

the 5-minute SO 2 ambient measurement data, staf f 1) evaluated trends in SO 2 concentrations, 2) counted the number of statistically modeled pot ential health effect be nchmark exceedances in a day using the same longer-term averaging times, and 3) estimated the probability of peak

concentrations associated with 1-hou r daily maximum and 24-hour average SO 2 concentrations. Staff considered three data analysis groups to characte rize the ambient SO 2 air quality. In the first group, we evaluated th e combined 5-minute and 1-hour SO 2 measurement data as they were reported, representing the conditions at the time of monitoring (ter med in this assessmentJuly 2009 71 “ as is ”). The second group also considered the as is air quality; however staff analyzed the statistically modeled 5-minute SO 2 concentrations that were generated from as is 1-hour SO 2 measurements. This second data analysis gr oup expanded the geographic scope of the 5-minute air quality characterizati on by using the broader SO 2 monitoring network. The third data analysis group considered 1-hour SO 2 concentrations adjusted to just meeting the current NAAQS 16 and each of the potential a lternative 1-hour daily maximum standard levels of 50, 100, 150, 200 and 250 ppb (see Chapter 5 for details). Th e data used to simu late 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 sta ndards considered here (98 th and 99 th percentiles of the 1-hour daily maximum concentrations averaged over 3 years), the recent ambient monitoring data set was evaluated using two three-ye ar periods, 2001-2003 and 2004-2006. 18 Whereas the first analysis group used entirely 1-hour and 5-minute SO 2 measurement data, the second and third analysis groups used statistically modeled 5-minute SO 2 concentrations that we re generated from 1-hour

SO 2 concentrations. The third data analysis grou p also included an adju stment of the 1-hour SO 2 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 SO 2 monitors) given the particular siting characteristics of the am bient monitors (e.g., either so urce- or population-oriented monitoring objectives). Therefor e, 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 re gions of the population distribution generated

from the broader SO 2 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, ther efore the most recent data for most m onitors 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 th e 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 re sults by the population density group. Rather than c ount the total number of 5-minute SO 2 concentrations above a particular benchmark, staff calculated the num ber of days in a year with a 5-minute SO 2 concentration above a potentia l health effect benchmark. 19 One output of this air quality characterization is an estimat e of the number of days per year a monitor experienced 5-minute SO 2 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 monito r or monitoring location to another and in identifying where and when frequent benchmar k exceedances could occur. However, earlier analyses indicated that the relatio nship between the annual average SO 2 concentration and the number of 5-minute benchmark exceedances was generally weak (1 st draft SO 2 REA). Therefore, a comparison of the number of days /year with benchmark exceedances to the annual average SO 2 concentration is of limited use. This ab sence 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 SO 2 concentrations and 24-hour averag e 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 SO 2 concentrations above a selected benchmark levels at a particular 24-hour average SO 2 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 SO 2 concentrations associated with concentrations of longer-ter m averaging times, that a daily maximum 5-minute

SO 2 concentration was the metric of interest, and that the potential alternative standards

19 In the 1 st draft SO 2 REA, as well as the early draft NO 2 REAs, all benchmark exceedances for any hour of the day were reported. The use of the daily maxi mum exceedance was selected in the final NO 2 REA as well in the 2 nd draft and final SO 2 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 1 st draft SO 2 REA, multiple exceedances within a da y (if any) were counted. In the 2 nd draft and final SO 2 REA, there is only one counted maximum exceedance per day. Additional analysis of mu ltiple exceedances within the day is given in section 8.11.211.July 2009 73 investigated use 1-hour daily maximum SO 2 concentrations, staff decided that an appropriate comparison would be between the frequency of peak 5-minute SO 2 concentrations given 1-hour daily maximum SO 2 concentrations. Thus, the second output of this air qual ity characterization is presented as the probabili ty of a benchmark exceedance given a daily maximum 1-hour SO 2 concentration. In addition, the probability of a 5-minute be nchmark exceedance given a 24-hour average concentration is also provided to offe r additional perspective on this averaging time.



### 7.2 APPROACH There were five broad steps to characterize the SO 2 air quality. The first step involved compiling and screening the ambient air quality da ta collected since 1997 to ensure consistency

with the SO 2 NAAQS requirements and for usefulness in th is air quality characterization. Next, due to potential variable influence of SO 2 emission sources on ambi ent 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 obj ective, distance to emissions sources, and population density. In addition, the variability in 5-minute and 1-hour SO 2 concentrations was evaluated and used to categorize each ambient mon itor. Staff used concentration variability in the development and application of a statistic al model used to estimate 5-minute maximum SO 2 concentrations. Then, a concentration adjust ment 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 pot ential 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 charac terize air quality and summarizes key portions of the anal ysis. Briefly, the five steps include: 1) screening of air quality data; 2) evaluation of site characteristics of ambient SO 2 monitors; 3) development of a

statistical model to estimate 5-minute maximum SO 2 concentrations; 4) adju stment of air quality; and 5) generation of air quality metrics. De tails regarding the ambient monitors used for characterizing air quality and associated desc riptive meta-data are provided in Appendix A.1.

7.2.1 Screening of Air Quality Data SO 2 air quality data and associated do cumentation from the years 1997 through 2007 were downloaded from EPA’s Air Quality System for this analysis (EPA, 2007c, h). DataJuly 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 SO 2 ambient monitoring data, reported for either 5-minute or 1-hour averaging times, ar e summarized in Table 7-1. The 5-minute SO 2 monitoring data existed in eith er one of two forms; the singl e 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 (refe rred to here as continuous-5 data set).

Table 7-1. Summary of all available 5-minute and 1-hour SO 2 ambient monitoring data, years 1997- 2007, pre-screened.

Sample Type Number of Monitors Number of States 1 Years in Operation Number of Measurements 2

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=P uerto 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 conve rted all concentrations to parts per billion (ppb). Next staff scr eened each of the three data sets listed in Table 7-1 for where monitor IDs had multiple parameter occurrence co des (POCs) and identical monitoring times. These duplicate measures could e ither result from co-l ocation of ambient monitors (i.e., more than one measurement instrument) or from dupli cate 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 summ arized in Table 7-2 and are described in detail

below.July 2009 75 Table 7-2. Analytical data sets generated usi ng the continuous-5, max-5, and 1-hour ambient SO 2 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-hour 1 0 283,202 2 29,058 3,410,763

1-hour 0 47,188,640 258,457 47,213,385 3 2,367,686 4

Notes: 1 1-hour concentrations from continuous-5 data were ca lculated from all 5-minute values within the hour. 2 The number of 5-minute maximum SO 2 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-m ean (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), th is 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 SO 2 data Two separate data sets were constructed that had multiple 5-minute SO 2 measurements collected at the same monitoring location and time for:

 max-5 duplicates (i.e., simultaneous m easurements of 5-minute maximum SO 2 concentrations from co-located max-5 monitors; n=300,438)

 max-5 and continuous-5 duplicates (i .e., simultaneous 5-minute maximum SO 2 concentrations reported in max-5 and continuous-5 datasets; n=29,058) A third data set was constructe d that had simultaneous 1-hour SO 2 measurements collected at the same monitoring location and time for:

 1-hour duplicates (i.e., from 1-hour SO 2 monitors and from averaging the continuous-5 monitors; n=258,457)

Each of these duplicate data sets were us ed 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 develo pment or for any other 5-minute or 1-hour SO 2 concentration analysis.July 2009 76 2. Combined 5-minute and 1-hour ambient SO 2 data A complete set of 5-minute maximum SO 2 concentrations, 21 generated from the max-5

data set and from the maximum 5-minute concentr ations reported by the continuous-5 monitors, was then combined with their corresponding measured 1-hour SO 2 concentrations (see below). Then, the combined data were screened for valid ity, recognizing that the combined max-5 and 1-

hour SO 2 data set may have certain anomalies (e.g., 5-minute maximum SO 2 concentrations < 1-

hour mean SO 2 concentration). A value of 1 was select ed as the lower bound peak-to-mean ratio (PMR), 22 accepting the possibility that the 5-minut e 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 wo uld 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 SO 2 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 mode l (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 ye ars, monitoring objective) ar e provided in Appendix A.1.

21 A single 5-minute and 1-hour SO 2 concentration was used in each of data se t 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 th at had the greatest overall number of samples. 22 The peak-to-mean ratio is the maximum 5-minute SO 2 concentration within an hour divided by the 1-hour average SO 2 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 = C max /[(C max + (C others  0) × 11)/12] < 12.July 2009 77 Figure 7-1. Location of the 98 monito rs that reported 5-minute maximum SO 2 concentrations and comprising the first data analysis group.

3. Broader 1-hour ambient SO 2 data This data set was comprised of all 1-hour SO 2 data, whether obtained from the 1-hour ambient monitoring data set or from averagi ng 5-minute concentrations from the continuous-5

data set. The raw 1-hour data from a total of 935 ambient mon itors were first screened for negative concentrations (n=3,555) and for wher e 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 concentrat ions obtained from the

continuous 5-monitors. Staff retained the 1-hou r 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 SO 2 concentrations. As described above for data set 1, staff removed duplicate 1-hour values identifie d at each monitoring location originating from the 1-hour and continuous-5 monitors for separa te analysis (Appendix A-2). The remaining 1-

hour SO 2 data set (with duplicate 1-hour values remove d) was then combined with the complete

5-minute maximum data set described above fo r data set 2 (with duplicate 5-minute maximum 

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  July 2009 78 SO 2 values removed). Staff used data set 2 in developing the statistical model to estimate 5-

minute maximum SO 2 concentrations (section 7.2.3). Additional screening of the 1-hour SO 2 data set was performed using a 75% completeness criterion. This monitoring data requirement is used in demonstrating attainment of

the SO 2 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 leas t 18 hours of data. Then, each monitor was required to have 75% of each calendar quart er with complete days (either 68 or 69 days per quartile). This 75% completeness criterion wa s applied to the available monitoring data to generate a total of 4,692 valid site-years of da ta obtained from 809 ambient m onitors. The number of valid monitoring site-years available as a result of this screening is presented in Table 7-3, effectively encompassing ambient SO 2 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 i llustrated in Figure 7-2. This data set was used in the second data air quality characteriza tion scenario that considered the measured as is 1-

hour SO 2 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 SO 2 Monitors The siting of the monitors is of particular importance, recognizing that proximity to local

sources could have an influence on the measured SO 2 concentration data and subsequent interpretation of the air quality ch aracterization. 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 SO 2 concentrations, and the second was genera ted from monitors within the broader

SO 2 monitoring network and having valid 1-hour SO 2 concentrations. Two points are worth mentioning for this analysis; the first being th e number of monitors and the second being the potential for differences in types of sources influencing each monito r. 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 SO 2 data from 2007 were less than complete. In addition, two mon itors located in Hawaii County, HI were identified in the 1 st

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 SO 2 data set due to the influence of volcanic ac tivity on measured SO 2 concentrations at these locations. Alaska had no SO 2 monitors during the period of analysis.July 2009 79 Table 7-3. Counts of complete and incomplete site-years of 1-hour SO 2 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 23July 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 Average 1 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.

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Puerto Vir g in IslandsJuly 2009 81 the measurement of 5-minute maximum and its associated 1-hour SO 2 concentration at some locations (n=98), th e remainder of SO 2 monitors with valid data (n=711) are sited in other

locations where 5-minute SO 2 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 statistica l model (i.e., monitors reporting 5-minute maximum

SO 2 concentrations) that are not applicable to the broader SO 2 monitoring network. Staff acknowledges that the information available and the monitoring site characteristics considered

can limit how well the monitori ng data serve as an indicator of human exposure. First, staff evaluated the specific monito ring site characteris tics provided in AQS, including the monitoring objective, measurement scale, and predominant land-use. Additional features such as proximity to SO 2 emission sources and the populat ion residing within various distances of each monitor were estimated using monitoring site and emission source geographic

coordinates and U.S Census data. Each of thes e attributes is summarized here to provide perspective on the attributes of where 5-minute maximum SO 2 concentrations were reported

versus the attributes of the broader SO 2 monitoring network. A mo re thorough discussion of the purpose of the existing ambient SO 2 monitoring network is provide d in Chapter 2. Individual

monitor site characteristics are given in Appendix A.1. The monitoring objective meta-data field describe s the nature of the monitor in terms of its attempt to generally characterize health ef fects, the presence of point sources, regional transport, or welfare effects. In recognizing that there were variable numbers of ambient monitors in operation and variati on in the number of valid site-yea rs available for each data set, staff weighted the monitoring objectives by the numb er of site-years. This was done to provide perspective on the air quality charact erization 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 de signated, staff selected a single objective to characterize each monitor using the following or der: population exposure, source-oriented, high concentration, ge neral/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 characteri ze the monitors with a specific objective. Most of the time where there were multiple objectives at a monitor, th ere was a specific objective (e.g., po pulation 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 targ et public health obje ctives through the population exposure and highest concentration categories, th ough the monitors in the broader SO 2 monitoring network had a greater percentage than the monitors reporting both 5-minute maximum and 1-hour SO 2 concentrations. The monitors reporting 5-minut e concentrations had approximately twice the percentage of site-years from source-oriented monitors when compared with the broader SO 2 monitoring network. Similarly, the overall measurement scale of the monitors used for the air quality

characterization in each locati on was evaluated based on the wei ghting 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 se ts had their greatest proportion of monitoring siteyears associated with neighbor hood 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. Furt hermore, monitors reporting 5-minute values had a larger proportion of site-yea rs of data characterized at an urban (4 to 50 km) and regional scale (50 km to 1,000 km) compared with the broader SO 2 monitoring network. The land-use meta-data indicate the prevalen t 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 m onitors in the broader 1-hour SO 2 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 diff erence 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 SO 2 concentrations had about 10 percentage points more site-years from monitors within agricultural ar eas and 10 percentage po ints less in commercial areas when compared to the respective land use of the broader SO 2 monitoring network. The setting is a general description of the e nvironment within which the site is located. Figure 7-4 also summarizes the setting of the mon itors comprising each data set. For monitors reporting 5-minute concentrations, th e greatest proportion of site-years is from ambient monitorsJuly 2009 83 with a rural setting (49%). Most of the site-years in the broader SO 2 monitoring network were from monitors within a suburban setting (40%).July 200984Figure 7-3. Distribution of site-years of data considering monitoring objectives and scale: monitors that reported 5-minute maximumSO2concentrations (top) and the broader SO2monitoring network (bottom).Objective ScaleOTHER3%POPULATIONEXPOSURE39%SOURCEORIENTED9%UNKNOWN19%GENERAL/BACKGROUND4%HIGHESTCONCENTRATION26%HIGHESTCONCENTRATION15%OTHER4%POPULATIONEXPOSURE31%SOURCEORIENTED21%UNKNOWN18%GENERAL/BACKGROUND11%MIDDLE SCALE1%NEIGHBORHOOD40%NONE28%REGIONAL SCALE12%URBAN SCALE19%NEIGHBORHOOD62%NONE21%REGIONAL SCALE4%URBAN SCALE9%MICROSCALE0%MIDDLE SCALE4%

All Monitors 5-Minute MonitorsJuly 200985Figure 7-4. Distribution of site-years of data considering land-use and setting: monitors that reported 5-minute maximum SO2concentrations (top) and the broader SO2monitoring network (bottom).RURAL49%SUBURBAN30%URBAN ANDCENTER CITY21%AGRICULTURAL27%COMMERCIAL13%FOREST5%INDUSTRIAL20%RESIDENTIAL33%UNKNOWN2%AGRICULTURAL13%COMMERCIAL23%FOREST3%INDUSTRIAL23%RESIDENTIAL36%UNKNOWN1%DESERT0%MOBILE1%RURAL31%SUBURBAN40%URBAN ANDCENTER CITY28%UNKNOWN1%

All Monitors 5-Minute MonitorsLand-Use SettingJuly 2009 86 Stationary sources (in particular, power gene rating utilities using fossil fuels) are the largest contributor to SO 2 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 SO 2 monitors. Then, staff selected the sour ces in close proximity of each monitor to identify whether there are differences in the dist ribution of emission sources that could affect the monitored concentrations. Stationary sources emitting > 5 tons per year (tpy) SO 2 and within 20 km of each monitor were identified using da ta from the 2002 National Emissions Inventory (NEI). 27 Details on the number of sources, the di stribution of emissions, and the method for determining the distances to each individual ambient monitor ar e provided in Appendix A.1.

The total SO 2 source emissions within 20 km of every monitor were summed by their source descriptions; the top eight source type s were selected for evaluation followed by a summing of all other remaining source types in a final sour ce description group (“other”). 28 These emission results are presented in Figure 7-5 for the monitors reporting 5-minute maximum

SO 2 concentrations and for the broader SO 2 monitoring network. A comparison of the sources

located within 20 km of monitors comprising both data sets indi cates strong similarity in the types of sources present. A pproximately 70% of the stationa ry source emissions local to monitors comprising either data set orig inate 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 manufacturi ng). 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 sme lters located within 20 km of the monitors reporting 5-minute maximum SO 2 concentrations (8.8%) than within 20 km of the broader SO 2 monitoring network (1.1%). A s econd difference between the two sets of data existed in the emission contribution from a combined power generation, transmission and distribution description; this source categor y 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 SO 2 emissions arise from power generating facility operations that require fossil fuel combustion (e.g., diesel-fueled backup generators).July 2009 87 in the broader SO 2 monitoring network compared with only 2% at monitors measuring 5-minute

SO 2 concentrations.

Figure 7-5. The percent of total SO 2 emissions of sources located within 20 km of ambient monitors: monitors reporting 5-minute maximum SO 2 concentrations (top) and the broader SO 2 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 SO 2 Monitors

All SO 2 MonitorsJuly 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 po lygon to single central point. Then buffers were created around each monitor location at pr ogressive 5 km distances to a final buffer distance of 20 km. The total population was es timated by summing the population of all block group centroids that fell within the monitor buffe rs. We then created population distribution functions (across monitors) for the m onitors reporting 5-minuute maximum SO 2 concentrations

and for the broader SO 2 monitoring network. An exam ple 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 di stances 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 SO 2 concentrations tended to be sited in lo cations with lower p opulation density when considering any of the population buffers. Staff created population density groups of low , mid , and high to categorize all ambient monitors usin g 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 unde r 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 great er 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 monito r was compared with its monitoring objective. The descriptive statistics for each mo nitoring objective, separately considering those monitors that reporte d 5-minute maximum SO 2 concentrations and the broader SO 2 monitoring network, are provided in Table 7-4. The calculated population statistics generally support expectations given the designate d 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 greate st 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 MonitorsCumulative 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 SO 2 concentrations and the broader SO 2 monitoring network.

Table 7-4. Descriptive statistics of the populat ion residing within a 5 km radius of ambient monitors by monitoring objective: monitors reporting 5-minute maximum SO 2 concentrations and the broader SO 2 monitoring network. Population residing within 5 km of Ambient Monitor 2 Data Source Objective 1 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 SO 2 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; OT H=Other; UNK=Unknown. 2 p5, p25, p50, p75, and p95 are the 5 th , 25 th , 50 th , 75 th , and 95 th percentiles, respectively. The minimum (min), maximum (max), and arithmetic aver age (mean) are also provided.July 2009 90 7.2.3 Statistical model to es timate 5-minute maximum SO 2 concentrations As described earlier, staff not ed there were a limited numbe r of ambient monitors that reported 5-minute maximum SO 2 concentrations. The majority of the SO 2 monitoring network reports 1-hour average SO 2 concentrations. Staff developed a statistical model to extend the 5-

minute SO 2 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 SO 2 concentrations from 1-hour SO 2 concentrations, using the co mbined 5-minute maximum and 1-

hour SO 2 measurement data set (s ee section 7.2.1).

Fundamental to the statistical model are the peak-to-mean ratios or PMRs. Peak-to-mean ratios are derived by divi ding the 5-minute maximum SO 2 concentration by the 1-hour average

SO 2 concentration. These derived PMRs can be useful in estimating 5-minute maximum SO 2 concentrations when only the 1-hour SO 2 concentration is known. The values of PMRs derived from the monitoring data can be variable and ar e 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 SO 2 concentrations at the ambien t monitors. Therefore, to develop a useful tool for extrap olating from the measurement data, at a minimum, the approach needed to account for variability in ambient concentrations. It is within this c ontext that the statistical model was developed. Staff selected the variability in SO 2 concentrations at each in dividual ambient monitor as a surrogate for source emissions, source types, and/or distance to sources to allow for a purposeful application of the statis tical model to the broader 1-hour SO 2 measurement data. Many of the meta-data described earlier in section 7.2.2, while useful for qualitatively describing

characteristics of monitors in the SO 2 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 attribut es were reported as unknown, missing entries, or possibly mischaracterized. In addition, while in dividual 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 char acteristics can change over time, 2) it is largely unknown what source(s) influence many of the ambient monitors and by how much, 3) th ere is uncertainty in source emission estimates, and 4) even simila r source types will not have the same emissionJuly 2009 91 characteristics. Staff considered several ways to link the statistical model developed from monitors reporting 5-minute maximum concentrations to the broader SO 2 ambient monitoring network, including the use of the ambient monitori ng site characteristics. Staff decided that the

measured concentrations had the most to offer in efficiently designing su ch a linkage given the strong relationships between averaging times, c oncentration variability, and the frequency of peak concentrations. Where possible, staff compar ed the relevant monitor attributes described in

section 7.2.2 with selected vari ability metrics used in develo ping and applying the statistical

model. The purpose of the first analysis that follows is to determine an appropriate variable to reasonably connect the st atistical model derived from 5-minute and 1-hour concentrations to any

1-hour SO 2 concentration data set where there are no 5-minute SO 2 measurements. Staff first evaluated variability metrics associ ated with 5-minute and 1-hour SO 2 ambient monitoring

concentrations as a basis for li nking the statistical model to 1-hour concentrations. Next, staff generated distributions of PMRs for use in es timating 5-minute concentrations. Then the statistical model was applied to where 5-minut e measurements were reported and evaluated using cross-validation.

7.2.3.1 Relationship Between 5-minute and 1-hour SO 2 Concentrations Because the statistical model employs 5-minute and 1-hour SO 2 concentrations, staff evaluated the relationship between the concentrati ons for the two averaging times. The monitors reporting all twelve 5-minute concen trations within the hour were us ed for this analysis (n=16). First, all of the continuous-5 minute data availa ble 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 SO 2 values. 31 Staff performed a second calculation to generate similar statistics using the continuous 5-minute data, though a 1-hour averaging tim e was of interest. To obtain the 1-hour statistics, the 5-

minute SO 2 concentrations within an hour were averaged to generate 1-hour mean SO 2 concentrations for each monitor, which were then averaged to generate a single 1-hour mean SO 2

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 arithm etic and geometric mean form) and their corresponding standard deviations, yielding a total of 16 monitor-specific 1-hour SO 2 values.

y = 0.95x + 0.07 R 2 = 0.99 y = 0.75x + 20

R 2 = 0.98

1 1.5

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01234 Ambient Monitor 5-minute GSDAmbient 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 SO 2 concentrations over multiple years of monitoring. Staff selected the coefficient of variation (COV) 32 and geometric standard deviation (GSD) as metrics to compare c oncentration variability in both 1-hour and 5-minute averaging times, each of which are illustrated in Figure 7-7. As expected, a strong dire ct linear relationship exists between the variability in 5-minute and 1-hour SO 2 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 ob served 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 an alysis 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 SO 2 concentrations is directly related to the variability in 1-hour SO 2 concentrations, and these measures of variability may be used to descri be the potential variability in concentrations measured at any ambient SO 2 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 be tween the COV and GSD. This means that in characterizing the variability at any ambient monitor, an identifi ed COV (e.g., either lo w or high COV) does not necessarily correspond to the same GSD characterization. Next, staff compared the variability in 1-hour SO 2 concentrations using data from the monitors reporting 5-minute maximum SO 2 concentrations (n=98) to variability observed for the

broader SO 2 monitoring network (n=809). The objective of this evaluation was to determine if the distribution of the observed hourly concentrati on variability was similar for the two sets of

data. As done above for the m onitors reporting 5-minute maximum SO 2 concentrations, four statistics were generated for each am bient monitor within the broader SO 2 monitoring network using the 1-hour concentrations, with the variabi lity at each monitor re presented by its COV and GSD. Figure 7-8 illustrates the cumulative de nsity functions (CDFs) for the hourly COVs and GSDs at each of the 98 monitors that reported 5-minute maximum SO 2 concentrations (i.e., the data set used for developing the statistical m odel) and the 809 monitors from the broader SO 2 monitoring network (i.e., the final 1-hour SO 2 data set having valid site-years). While the subset of monitors reporting the 5-minute maximum SO 2 concentrations exhibit greater variability in hourly concentration at most per centiles of the distribution, th e overall shape and span of the distribution is very similar to that of the monitors within the broader SO 2 monitoring network using either variability metric. The similarity in variability dist ributions could indicate that the monitor proximity to sources, the magnitude and temporal profile of source emissions, and the types of sources affecting concentrations at e ither set of data (i.e., the monitors reporting 5-

minute SO 2 concentrations ve rsus the broader SO 2 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 SO 2 monitors to the 1-hour SO 2 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 COVCumulative 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 GSDCumulative 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 SO 2 concentrations and the broader SO 2 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 SO 2 concentrations where only 1-hour average SO 2 concentrations were measured is the peak-tomean ratio (PMR). Peak-to-mean ratios ar e obtained by dividing the 5-minute maximum SO 2 concentration occurring with in an hour by the 1-hour SO 2 concentration. The use of a PMR or

distributions of PMRs in estimating 5-minute maximum SO 2 concentrations is not new to the current NAAQS review. Both indi vidual PMRs and distributions of PMRs were used in theJuly 2009 95 previous NAAQS review in characterizing 5-minute SO 2 air quality (Thrall et al, 1982; EPA, 1986a; 1994b; Thompson 2000) and in esti mating human exposures to 5-minute SO 2 concentrations (Burton et al. 1987; EPA, 1986a , 1994b; Stoeckenius et al. 1990; Rosenbaum et al., 1992; Science Internati onal, 1995). In this review, staff generated distributions of PMRs to

estimate 5-minute maximum SO 2 concentrations at ambient monitors (this chapter) and at air quality modeled census block centr oid receptors (chapter 8). Th e distributions of PMRs used here build upon recent PMR analyses conducted by Thompson (2000). 33 In the current PMR analysis, staff developed several distributi ons of PMRs using more recent 5-minute SO 2 monitoring data (through 2007) a nd used concentration level a nd variability as categorical variables in defining the di stributions 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 monito rs 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 PM R given the variable number of samples collected and years of monitoring at monitors that re ported the 5-minute maximum SO 2 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 SO 2 concentrations: for COV, the bi ns were defined as low (COV ≤ 100%), mid (100% < COV ≤ 200%), and high (COV > 200%). These three COV bi ns were selected to capture the upper and lower tails of the variability di stribution 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 SO 2 concentration has been identified as an important consideration in defining an appropr iate PMR distribution to use in estimating 5-

minute maximum SO 2 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 SO 2 concentration. 34 For monitors reporting the 5-minute maximum SO 2 concentrations, these groupings corresponded to approximately the 25 th and the 84 th percentile of the variability distribution.July 2009 96 by seven 1-hour mean concentrati on 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 differen ces in the PMR distribu tions within a given variability and concentration bin and to limit th e 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. Fi gure 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 th e 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 middl e graphs), while the mid-range variability bins (middle graph) had a greater proportion of high PMRs than th e low variability bin (top graph). These

distinctions in the PMR distributi ons are consistent with the results illustrated in Figure 7-7, that is, the variability in the hourly av erage 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 SO 2 concentration. This is most evident in the highest variability bin (bottom graph of Figure 7-9); the highest 1-hou r concentration categor y (> 250 ppb) had lower PMRs at each of the distribution percentiles comp ared with the PMR distri butions 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, co mpared with maximum PMRs of about 11.5 at

35 While PMR distributions were generated for 1-hour SO 2 concentrations < 5 ppb, it should be noted that any estimated 5-minute maximum SO 2 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 re searchers (EPA, 1986a). The stratification of PMRs by the 1-hour concentration was done to a void applying high PMRs calculated from low hourly concentrations to high hourly concentrations. The observed patterns in the PMR distributions support the staff sel ection of variability bins and 1- hour concentration stratifications in controlling for the aberrant assignment of PMRs to part icular 1-hour concentrations.

Figure 7-9. Peak-to-mea n ratio (PMR) distributions for three COV and GSD variability bins and seven 1-hour SO 2 concentration stratifications. 0 10 20 30 40 50 60 70 80 90 100Cumulative 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 100Cumulative 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 BinsJuly 2009 98 Staff then evaluated the assigned concentr ation 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 repres enting variability local so urce 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 SO 2 concentrations and the broader SO 2 monitoring network (Figure 7-10). The purpose of this comparison was to determine whether increased emissions were associated with greater variability in monitoring concentr ations. In general, a pattern of increased

emissions was associated with an increase in the concentration va riability bin, though the pattern was more prominent when considering the COV bins . This indicates the va riability bins may be useful as a surrogate for local source emission characteristics.

Figure 7-10. Distribution of total SO 2 emissions (tpy) within 20 km of monitors by COV (left) and GSD (right) concentration variability bins: monitors reporting 5-minute maximum SO 2 concentrations (top) and the broader SO 2 monitoring network (bottom). COV Bins GSD BinsTotal Emissions (tpy) within20 km of All SO2MonitorsTotal Emissions (tpy) within20 km of 5-min. SO2Monitors

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 = 75July 2009 99 The second ambient monitoring site charac teristic evaluated using the selected concentration variability bins was the monitoring objective, prin cipally when it was noted as source-oriented. The purpose of this analysis was to determine whether high variability in SO 2 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 reporte d 5-minute maximum SO 2 concentrations and those within the broader SO 2 monitoring network. In general, there is an increasing percent of source-oriented monitors in the higher concentration variability bins when usi ng 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 th e 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 BinPercent of Monitors with Source-OrientedObjective 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 SO 2 concentrations (solid) and the broader SO 2 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 SO 2 concentration also have a greater number of benchmark exceedances. Fi gure 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 benchmar ks were similar). Clearly, monitors having the greatest variability in 1-hour SO 2 concentration are the monitors most likely to have 5-minute

SO 2 benchmark exceedances and a greater number of exceedances per year. This analysis provides further support to the binning of monito rs by concentration variability to appropriately extrapolate the relationships derived from mon itors reporting 5-minute maximum concentrations to monitors reporting only 1-hour SO 2 concentrations (and at the disper sion model receptors).

Figure 7-12. Distribution of the measu red number of daily 5-minute maximum SO 2 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 SO2Benchmark Exceedances PerYear



### Concentration Variability BinJuly 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 SO 2 concentrations, staff characterized the monitors within the broader SO 2 monitoring network (n=809) by their respective hour ly concentration variab ility 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 SO 2 concentration, PMRs can be randomly sampled 37 from the appropriate PMR distribution to

estimate a 5-minute maximum SO 2 concentration using the following equation:

houriij CPMR C

   1, 5max equation (7-1)

where,

C max-5 = estimated 5-minute maximum SO 2 concentration (ppb) for each hour

PMR ij = peak-to-mean ratio (PMR) randomly sampled from the i concentration variability and j 1-hour mean SO 2 concentration distribution

C

i,1-hour = measured 1-hour average SO 2 concentration at an i concentration variability monitor

As a result of this calculation, every 1-hour ambient SO 2 concentration has an estimated

5-minute maximum SO 2 concentration. 38 These statistically modeled 5-minute maximum SO 2 concentrations were then summarized using th e 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 evaluati on 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

SO 2 concentrations. All ambient monitors were characterized using the same variability bins

described in section 7.2.3.2. The 1-hour concen trations were also char acterized 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 SO 2 concentration was > 0, otherwise the 5-minute maximum SO 2 concentration was estimated as zero).July 2009 102 stratifications discussed earlier. Then staff us ed the newly constructed PMR distributions from the 97 monitors and equation 7-1 to predict the 5-minute maximum SO 2 concentrations at the single monitor not included in developing the PMR dist ributions. This modeling was performed 98 times, i.e., removing every single monitor (one monitor at a time), generating new PMR distributions, and predic ting 5-minute maximum SO 2 concentrations at the removed monitor. Staff then compared the predicted and measured daily 5-minute maximum SO 2 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 R 2 , a measure of the amount of variance expl ained 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 minimu m and maximum percentiles of the PMR distributions. The

models were evaluated at the benc hmark concentration levels as well as at selected percentiles in

the 5-minute SO 2 concentration distribution. In comp aring the model predictions, the model using variability bins define d by the COV and excluding the mi nimum and maximum percentiles had the lowest prediction e rrors (e.g., see Table 7-5). 39 Based on these results, staff used this COV model (excluding the 0 th and 100 th percentiles of the PMR distribution) to estimate 5-

minute maximum SO 2 concentrations from 1-hour SO 2 concentrations.

39 Table 7-5 presents a few of the predic tion error statistics used to compare each of the models, though several other prediction errors were evaluated (e.g., the 75 th and 99 th ). Results for the other percentiles were consistent with median results discussed in the text, that is the alt. CO V model had the lowest error wh en 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) Model 1 Median Prediction Error 2 RMSE R 2

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 al ternative model was used: the minimum and maximum percentiles of the PMR distributions were not used. 2 The absolute value of the prediction diffe rences is calculated (predicted minus the observed number of exceedances in a year), generating a distribution of prediction errors. The value reported here is the (50 th percentile) of that distribution.

Staff performed supplementary evaluations usin g the prediction errors associated with the selected statistical model. Additional percen tiles of the prediction error distribution were calculated to estimate the magnitude and directio n of the statistical model bias. Table 7-6 summarizes the prediction errors for each benchmar k level. When considering paired percentiles (e.g., the 25 th and the 75 th or prediction intervals) and the 50 th 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 95 th percentile (i.e., 5 exceedances), than compared with the under estimation at the 5 th percentile (i.e., one exceedance). However, there is good agreement in the predicted vers us observed number of exceedances, whereas 90% of the predicted exceedances of 400 ppb were with in -1 to 5 exceedances per year. There is a wider range in the prediction intervals at the lo wer benchmark levels, partly a function of theJuly 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 statisti cal model used in estimating 5-minute maximum SO 2 concentrations above benchmark levels.

Prediction Error at Benchmark Level 1 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 Exceedances 2 Benchmark 100 200 300 400

Observed 148 81 69 56 Predicted 150 100 67 45 Notes: 1 The percentiles are based on the di stribution 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 quali ty scenarios in this assessment, each defined by the form and level of a selected sta ndard. Collectively, th e purpose of these air quality scenarios was to estimate the relative level of public health protection associated with just meeting the current and potential alternat ive standards. The measured ambient SO 2 concentrations needed adjustment to reflect c oncentrations that might be observed given the hypothetical air quality scenarios. To maintain a computationally manageable data set given the number of air quality scenario s (i.e., eight) and potential heal th effect benchmark levels investigated (i.e., four), staff used the r ecent 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 sele cting counties for analysis.

7.2.4.1 Approach There are two important considerations in de veloping an approach to adjust air quality

concentrations. One is the re lative contribution of policy-relevant background (PRB) to ambient

concentrations and the other is in understanding how the distribu tion of ambient concentrations measured at a particular m onitor 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 SO 2 concentrations at most lo cations. Based on the small contribution, PRB will not be considered separa tely 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 note d by staff as influenced by significant natural sources rather than anthropogenic sources and were not used in a ny of the air quality analyses. While annual average concentrations ha ve declined significantly over time, the variability in the SO 2 concentrations (both th e 5-minute and 1-hour concentrations) has remained relatively constant. This trend is present when considering ambien t 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 SO 2 1-hour concentration percentiles at the two am bient monitors in Beaver County, Pa. that were in operation as far back as 1978 and are currently part of the broader SO 2 monitoring network. Staff selected a recent year of data (2007) to constitute a low concentrati on year along with an historical year of data (1992) constituting a high concentration year, w ith 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 maximu m concentration percentiles are mostly linear, with regression coefficients of determination (R 2 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 ma ny 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 SO 2 concentration (Rizzo, 2009). In addition, the absolute va lues for the simple linear regression intercepts were typi cally 1-3 ppb (Rizzo, 2009). This indicates that the rate of decrease in ambient air quality concentrations at the mean valu e for the monitors evaluated is consistent with the rate of change at the lower and upper daily maxi mum 1-hour concentration percentiles. This evaluation pr ovides support for the use of a proportional approach to adjust current ambient concentrations to represent ai r quality under both the current and alternative

standard scenarios.

Figure 7-13. Comparison of measured daily maximum SO 2 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 sta ndard was used to approximate concentration adjustment factors to simulate just meeting the current 24-hour and annual SO 2 NAAQS. TheJuly 2009 107 24-hour standard of 140 ppb is not to be exceeded more than once per year, therefore, the 2 nd 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 conven tion to the fourth decimal, annual average concentrations of up to 30.4 ppb woul d just meet the current annual standard. Staff limited the analysis of alternative air qu ality scenarios to particular locations using designated geographic boundaries (n ot just the monitors individua lly). Counties were used to

define the locations of interest in the alternative air qua lity standard scenarios. Use of a county is consistent with current policies on the designa tion of appropriate boundar ies of non-attainment areas (Meyers, 1983). For each location ( i ) and year ( j ), 24-hour and annual SO 2 concentration adjustment factors ( F ) were derived by the following equation:

ij ij CSF max, /  equation (7-2)

where,

F

ij

= 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)

C

max,ij = the maximum 2 nd highest 24-hour average SO 2 concentration at a

monitor in location i and year j or the maximum annual average SO 2 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 ad justed so that it just meets either a 30.4 ppb annual average or a 144 ppb 24-hour average (2 nd highest), whichever was the controlling

standard. 41 For monitors in each county and calendar year, all hourly SO 2 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 2 nd highest 24-hour concentration level of 144 ppb (i.e., the 24-hour standard is the controlling standard). The factor selected is derivedJuly 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 2 nd highest 24-hour average equal to 144 ppb for that county and year. For example, of five monitors measuring hourly SO 2 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 2 nd highest 24-hour SO 2 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 adjustme nt factor derived from the 24-hour average concentration was lower, the 24-hour average con centration 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 SO 2 concentrations to simulate air quality just meeting the current standard for that year. Th erefore, one monitoring site in Cuyahoga County for year 2001 would have a 2 nd highest 24-hour average concentr ation of 144 ppb, while all other monitoring sites would have a 2 nd 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 de rived 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 98 th and 99 th percentile 1-hour daily maximum SO 2 concentrations averaged across th ree 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,

F

ikl

= SO 2 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 mo re 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, wh ether derived from the annual or 24-hour standard level.July 2009 109 S

l = Standard level l (i.e., 50, 100, 150, 200, and 250 ppb 1-hour SO 2 concentration) (ppb)

C

ijk = Selected percentile k (i.e., 98 th or 99 th ) 1-hour daily maximum SO 2 concentration at a monitor in location i for each year j (ppb)

Figure 7-14. Distributions of hourly SO 2 concentrations at five ambient monitors in Cuyahoga County, as is (top) and air quality adjusted to just meet the current 24-hour SO 2 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 98 th or 99 th 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 ad justed using the same factor, only resulting in

concentrations at those monitors below the level of the sele cted 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 SO 2 concentrations. Due to the form of the alternative standards, the expe cted 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 SO 2 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 fo r 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 SO 2 concentrations at or above the potential health effect benchmark levels. Ambient monitors located in two counties in Missouri (Iron and Jefferson) had the most fr equently measured daily 5-minute maximum SO 2 concentrations above the potential health eff ect benchmarks (see Appendix A-5). While there

were limited data available from these ambient mon itors (4 and 2 years out of 8 total site-years did not met the completeness criteria for each of Je fferson and Iron counties, respectively), it was decided by staff that lack of a complete year should not preclude thei r use in this focused analysis given the high number of measured daily 5-minute maximum SO 2 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 ba sed on the relationsh ip of the ambient SO 2 concentrations within the c ounty 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 count y for at least five of the six

42 In the 1 st draft SO 2 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 co ncentration 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 calcula ted by averaging the si te-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 provid ed 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 ambi ent monitors comprising the 40 count y dataset (i.e., the third data analysis group) are illust rated 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 (Fi gure 7-16). This is not unexp ected 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 (eith er 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 al ternative air quality scenarios were used as an input to the statistical model to estimate 5-minute maximum SO 2 concentrations (equation 7-1). Then, air quality characterization metrics of inte rest were estimated for each site and year as

described in section 7.2.5.

43 In the 1 st draft SO 2 REA, having at least three monitors for all si x years of the monitoring period was required. These earlier criteria were relaxed in the 2 nd 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 ai r quality adjusted to just meeting the current and potential alternative SO 2 standards and the number of monitors in each COV bin.

# of Monitors in COV bin 4 State County 1 Mean Factor Closest Standard 2 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 Iron 3 5.49 A 2 Missouri Jefferson 3 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 Chautauqua 4.19 D 1 1 New York Erie 3.17 D 1 1 Cuyahoga 4.51 A 5 Lake 2.99 D 2 Ohio 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 Shelby 4.08 D 1 2 Tennessee 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 me an 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 SO 2 concentrations, the broader SO 2 monitoring network, and SO 2 monitors selected for detailed analysis in 40 counties. 5-Minute Max SO 2 Monitors (n=98)

Low COV

24%

Mid COV

60% High COV 16% Broader SO 2 Network (n=809)

Low COV

27%

Mid COV

62% High COV 11% 40 County SO 2 Monitors (n=128)

Low COV 14%

Mid COV

66% High COV 20%    

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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 we re calculated; these included the annual average, 24-hour, and 1-hour daily maximum SO 2 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 concentr ation levels, staff generated two additional outputs given the different c oncentration averaging times. The first output was a comparison of the annual average SO 2 concentration and the number of daily 5-minute maximum SO 2 concentrations above the be nchmark levels in a year. The output of this is the number of days pe r year a monitor had a measured or modeled exceedance, given an annual average SO 2 concentration. In general, these results are graphically depicted in this REA, though most of the individu al results displayed in the figures are provided in Appendix A-5. When considering the 40 countie s 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 ag gregated 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 pot ential health effect benchmark exceedances given concentrations of short-term averaging times . It was proposed in Chapter 5 that the 1-hour daily maximum SO 2 concentration would be of an appropr iate averaging time in controlling the number of daily 5-minute maximum SO 2 concentrations. Staff evaluated such a relationship using the measured 5-minute and 1-hour ambient SO 2 concentrations to determine if this indeed was the case. A tally was made every time a daily 5-minute maximum SO 2 concentration occurred during the same hour of the day as the 1-hour daily maximum SO 2 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 ma ximum and the 1-hour daily maximum SO 2July 2009 115 concentrations is greater than 70% at each of the benchmark levels indicating a strong relationship between the two c oncentration averaging times.

Table 7-8. The co-occurrence of daily 5- minute maximum and 1-hour daily maximum SO 2 concentrations using measured ambient monitoring data.

Concentration/Level Co-occurring 5- minute and 1-hour daily maximums 1 (n) Total Paired Samples 2 (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 SO 2 concentration measurement.

Given the form of the current 24-hour standa rd, the form of the potential alternative standards (1-hour daily maximum), and the frequency of 5-minute SO 2 benchmark exceedances (i.e., either one or none per concentration), sta ff generated probability functions to estimate the

likelihood of a 5-minute benchmark exceedance. Th ese functions are useful in estimating the probability of a 5-minute benchmark exceedance given a range of SO 2 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 employe d a logistic regression model. To generate the empirically-b ased probability functions, co ncentration data were first stratified into bins using c oncentration midpoints, with each bin separated by 10 ppb. For example a concentration of 53 ppb would be include d in the 50 ppb bin, while a concentration of 55 ppb would fall within the 60 ppb bin. Then, the presence or absenc e 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 rang e) was used to estimate the probability of an exceedance. For example, if there were 105 exceedances of the 200 ppb benchmark level out ofJuly 2009 116 239 instances of a 1-hour daily maximum binned concentration of 110 ppb 44 , the probability of a 200 ppb benchmark exceedance would be 105/23 9 = 0.44 or 44 % given a 1-hour daily maximum concentration of around 110 ppb. An ex ample of an output from this empiricallybased probability function is illu strated 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 SO 2 Concentration (ppb)Probability of Daily 5-minute MaximumExceedance (%)

> 100 ppb > 200 ppb > 300 ppb > 400 ppb

Figure 7-17. Example of empiri cally-based probability curves. The probability of a 5-minute SO 2 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 SO 2 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 empirica lly-based probability cu rves at the highest 1- hour daily maximum or 24-hour average concentr ations. For example, there were very few measured 1-hour daily maximum SO 2 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 SO 2 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 SO 2 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

SO 2 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 da ily 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 SO 2 concentrations. Therefore, in view ing 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-bas ed 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 indica tes that there were fewer than 30 samples available for concentration bins above a 1-hour daily maximum SO 2 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 SO 2 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 probabi lity curves for each of the four benchmark levels and the time-averaged SO 2 concentrations (i.e., 1-hour da ily 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 dte xy 

  equation (7-4)

where x denotes the time averaged SO 2 concentration (either 1-hour daily maximum or the 24-hour average in ppb), y denotes the corresponding probab ility 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 gene rate probability curves using all available measurements, thereby extending th e range of predictability beyond that of the empirically-based curves. Figure 7-18 illustrates an example of logistic-modeled probability curves using the sameJuly 2009 119 data used in generating the probability curves sh own in Figure 7-17. Note that predictions for the modeled curves extend beyond the 1-hour da ily 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 da ta 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 hypothe sized that there may be different exceedance probabilities in dense population areas compared with locations having fe wer residents given the siting characteristics of the monitors with rega rd to the presence of emission sources. This separation of the monitoring results by the surrounding population should be useful in appropriately characterizing the ai r quality because the monitoring da ta are used as indicators of potential human exposure; the re sults from monitors sited with in 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 SO 2 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 SO 2 ambient monitor measurements. Ambient monitoring data were evaluated at the 98

locations where both the 1- hour and 5-minute maximum SO 2 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 SO 2 concentration exceedances. Descri ptive statistics for the measured daily 5-minute maximum and the 1-hour SO 2 concentrations are provided in Appendix A-5 and

in the SO x 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 SO 2 concentrations. Staff performed two broad analys es using this data anal ysis 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 con centration above benchmark levels given shortterm averaging times (i.e., 1-hour daily maximum and 24-hour average). First, staff evaluated the occurren ce of the daily 5-minute maximum SO 2 concentration exceedances in a year. Figure 7-19 compares th e number of days with 5-minute maximum SO 2 concentrations above the potential health eff ect benchmark levels along with the corresponding annual average SO 2 concentration from each max-5 monitor. Overall, there are few days in a year with 5-minute maximum SO 2 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 SO 2 concentrations above the 100 ppb benchmark, while approximately 2%, 1%, and 0.7% of days had daily 5-minute maximum SO 2 concentrations above the 200, 300, and 400 ppb levels, respectively. None of the monitors in this data set had annual average SO 2 concentrations above the current annual NAAQS of 30 ppb. However,

several of the monitors in several years frequently had daily 5-minute maximum SO 2 concentrations above the potential health eff ect benchmark levels. Many of those monitors where frequent 5-minute benchmark exceed ances occurred had annual average SO 2July 2009 121 concentrations between 5 and 15 ppb, with little to no correla tion between the annual average

SO 2 concentration and the number of daily 5-minute maximum SO 2 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 da ily maximum exceedance of a selected benchmark level, however from a practical perspective, the annual av erage concentration would be ineffective at controlling daily 5-minute maximum SO 2 concentrations given the observed weak relationships.

Figure 7-19. The number of days per year with measured 5-minute maximum SO 2 concentrations above potential health effect benchmark levels at 98 monitors given the annual average SO 2 concentration, 1997-2007 air quality as is . The level of the annual average SO 2 NAAQS of 30 ppb is indicated by the dashed line. > 100 ppb > 200 ppb

> 300 ppb > 400 ppbNumber of Daily 5-minute Maximum SO2Above Selected Level Per Site-YearJuly 2009 122 Second, the probability of pot ential health effect benchmark exceedances was estimated given the 24-hour average and 1-hour daily maximum SO 2 concentrations. Figure 7-20 presents the empirically-based and logi stic-modeled probabil ity curves given the 24-hour average SO 2 concentrations and separated by the three population densities. There is an increasing probability of a daily 5-minute maximum SO 2 concentration exceedance w ith increasing 24-hour average

concentrations at each of th e potential health effect benchm ark 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 SO 2 concentrations. The logistic-modeled cu rves are consistent with the empiricallybased curves; however, the modele d curves illustrate an extend ed 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 SO 2 benchmark exceedance given the same 24-hour SO 2 concentration. For example, the probability of exceeding a daily 5-minute maximum concentration of 200 ppb using the empirically-bas ed curves is 30% at the low-popul ation 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 prob ability of a 5-minute

benchmark exceedance at the same 24-hour concen tration 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 gi ven a 24-hour average concentr ation of about 40 ppb (using either the empirical or modeled curves), though at monitors site d 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 concentrat ions approaching the current 24-hour standard. For example, while the estimated probab ility of a daily 5-minute maximum SO 2 concentration above 100 ppb is at or near 100% considering an y of the population density groups, little can beJuly 2009 123 construed from the other empirically-based cu rves at 24-hour concen trations above 60 ppb, particularly at monitors sited in mid- to hi gh-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 mi d- or high-populati on density areas.July 2009 124 Figure 7-20. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels given 24-hour average SO 2 concentration, using empirical data (left) and a fitted log-probit mode l (right), 1997-2007 air quality as is . Both the 5-minute maximum and 24-hour SO 2 concentrations were from measurements collected at 98 ambient monitors and separated by population density.Probability (%) of a Daily 5-Minute Maximum SO2Benchamark Exceedance

Mid Population

Densit y Mid Population

Densit y Low Population

Densit y Low Population

Densit y

High Population

Densit y



## 24-hour Average SO 2 

## Concentration (ppb) High Population

Densit yJuly 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 SO 2 concentrations instead of 24-hour average concentrations. At each of the benchmark levels and populati on densities, Figure 7-21 shows increasing probabilities of exceedances with in creasing 1-hour daily maximum SO 2 concentrations. Further, the probability curves have steeper sl opes associated with th e low-population density group compared to the slopes of the higher populati on 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 SO 2 concentration of 400 ppb is 100% given a 1-hour daily maximum SO 2 concentration of 400 ppb (and so on for the other 5-minute benchmark/1- hour daily maximum SO 2 concentration combinations). 45 As observed using the 24-hour average concentrations, the shape of th e curves beyond the imposed limits of the empirical data can be informed by the logistic regres sion 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 comp aring any of the 24-hour average probability curves with corresponding 1-hour daily maximu m 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 SO 2 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 re duce the likelihood of a 200 ppb be nchmark exceedance from about 90% to 10%, 24-hour average concentrations woul d 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 concentra tion 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 SO 2 concentrations above potential health effect benchmark levels given 1-hour daily maximum SO 2 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 SO 2 concentrations were from measurements collected at 98 ambient monitors and separated by population density. Low Population

Densit y Low Population

Densit y

Mid Population

Densit y Mid Population

Densit y

High Population

Densit y High Population

Densit yProbability (%) of a Daily 5-Minute Maximum SO2Benchamark Exceedance



## 1-Hour Daily Maximum SO 2 

## Concentration (ppb)July 2009 127 7.3.2 Measured 1-Hour and Modeled 5-minute Maximum SO 2 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 SO 2 measurements to estimate 5-minute maximum SO 2 concentrations. This was done because there are a greater nu mber of monitors in the broader SO 2 monitoring network compared to subset of monitors reporting the 5-minute maximum SO 2 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 complete ness criteria described in section 7.2.1. This data set included 4,692 site -years of data, and combined with the estimated 5-minute SO 2 concentrations using the measured 1-hour values , allowed for a comprehensive characterization of the hourly and 5-minute SO 2 air quality at ambient monito rs located across the U.S. Descriptive statistics for the measured 1-hour SO 2 concentrations are provided in the SO x ISA (ISA, section 2.5.1) including additional discussion of the spatial and temporal variability in 1-

hour SO 2 concentrations. Staff performed twenty separate model simu lations to estimate the 5-minute maximum

SO 2 concentration associated with each 1-hour m easurement. The individual simulation results at each monitor were averaged to generate a m ean 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 perfor med on the measured 5-minute maximum and 1-hour

SO 2 concentrations described in section 7.3.1. The results provided in this section were generated using the modeled daily 5-minut e maximums and the measured hourly SO 2 concentrations considering 1-hour, 24-hour, and annual averaging times. Staff performed two broad analyses; first staff eval uated the relationship between a nnual average concentrations and number of days per year with at least one 5-minute concentrat ion 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 SO 2 concentration above benchmark levels versus the annual average SO 2 concentration. Fewer than 5% of total days per year had a 5-minute SO 2 concentration above the 100 ppb benchmark, while approximately 1%, 0.5%, and 0.2% of days had at least one 5-minute concentration above theJuly 2009 128 200, 300, and 400 ppb benchmark levels, respectively. None of the site-year s of data had annual average SO 2 concentrations at or above the level of the current annual NAAQS (30 ppb). However as described above, several site-years had pr edicted 5-minute SO 2 concentrations above the potential health effect benchmark levels. Many of the monitors with frequent 5-minute benchmark exceedances had annual average SO 2 concentrations between 10 and 20 ppb, with a pattern of increasing numb er of days per year with at least one 5-minute concentration above the benchmark levels with increasing annual aver age 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 a nnual 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 SO 2 concentrations above potential health effect benchmark levels at 809 ambient monitors given the annual average SO 2 concentration, 1997-2006 air quality as is . The level of the annual average SO 2 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 th e probability of benchmark exceedances using the modeled daily 5-minute maximum SO 2 concentrations and 24-hour average concentrations. These probability curves exhibi t 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 ppbNumber of Daily 5-minute Maximum SO2Above Selected Level Per Site-YearJuly 2009 130 each of the benchmark levels considered. In add ition, 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 SO 2 monitoring network to estima te daily 5-minute maximum SO 2 concentrations provides insight as to the potential shape of eac h empirically-based probability curve at greater 24-hour averag e concentrations. The upper ra nge of 24-hour concentrations extends to around 70-100 ppb (Figure 7-23), while at the monitors reporting 5-minute maximum

SO 2 concentrations the maximum 24-hour average c oncentrations 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 con centrations above the benchmark levels at 24-hour average concentrations well belo w the level of the current standard. This is further confirmed by the logistic-modeled probabi lity curves that estimate all benchmark levels

would be exceeded at about a 24-hour concentrat ion 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 SO 2 concentrations (Figure 7-24) also exhibit patterns consis tent with those patterns observed using the pure measuremen t 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 monito rs reporting the 5-minute maximum SO 2 concentrations, giving greater ability to discern the probability of benchmark exceedances at higher 1-hour daily

maximum SO 2 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 concentr ations of about 80, 150, 225, and 300 ppb, respectively.July 2009 131 Figure 7-23. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels given 24-hour average SO 2 concentrations, using empirical data (left) and a fitted log-probit mo del (right), 1997-2006 air quality as is . The 5-minute maximum SO 2 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 SO2Benchamark Exceedance



## 24-hour Average SO 2 

## Concentration (ppb) Low Population

Densit y Low Population

Densit y

Mid Population

Densit y

High Population

Densit y High Population

Densit y

Mid Population

Densit yJuly 2009 132 Figure 7-24. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels given 1-hour daily maximum SO 2 concentrations, using empirical data (left) and a fitted log- probit model (right), 1997-2006 air quality as is . The 5-minute maximum SO 2 concentrations were modeled from 1-hour measurements collected at 809 ambient monitors and then separated by population density. 

## 1-Hour Daily Maximum SO 2 

## Concentration (ppb)Probability (%) of a Daily 5-Minute Maximum SO2Benchamark Exceedance

Low Population

Density

Mid Population

Density

High Population

Density Low Population

Density

Mid Population

Density

High Population

DensityJuly 2009 133 7.3.3 Modeled 1-Hour and Mo deled 5-minute Maximum SO 2 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 sele cted using criteria discussed in section 7.2.4. Specifically, we chose the 38 countie s with 1-hour ambient monitor SO 2 concentrations nearest the current NAAQS levels and two counties with a high frequency of measured daily 5-minute

maximum SO 2 concentrations above the potential health effect benchmark levels. The 1-hour

SO 2 measurement data were from 128 ambient monitors and totaled 610 site-years of monitoring, a subset of data from the broader SO 2 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 SO 2 concentration associated with each 1-hour adjust ed 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 num ber of days in a year with 5-minute maximum

SO 2 concentrations > 200 ppb) selected here as th e best estimate from the twenty simulations. Staff 1) evaluated the relationship between annua l average concentrations and number of days per year with at least one 5-minute concentra tion above benchmark levels, 2) summarized the number of days per year with at least one 5-minute concentrat ion 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., 98 th and 99 th percentile), and 4) esti mated the probability of having at least one 5-minute concentration above benchmark levels given short-term averaging times (1-hour daily maximu m and 24-hour average). First, staff evaluated the rela tionship between the short-term peak concentrations and the

level of the current annual SO 2 NAAQS in the selected countie s. Figure 7-25 illustrates the number of days per year w ith 5-minute daily maximum SO 2 concentrations above the potential health effect benchmark levels along with th e corresponding annual average concentrations. Each data point represents a monitor site-year generated from the modeled 5-minute peaks andJuly 2009 134 air quality adjusted to just meeting the current SO 2 standards. None of the site-years in the selected counties had annual average concentrati ons above the level of the current NAAQS (30 ppb) by design 46 , however there are many more site-years with a greater nu mber of modeled daily 5-minute maximum SO 2 concentrations above the potential health eff ect benchmark levels than compared with that of the as is air quality. There are a decreasing number of exceedances with increasing benchmark concentr ations, though there is a greater proportion of monitors with exceedances when considering concentrations adjust ed 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, th ere is a stronger relationship between the annual average concentrations and the number of benc hmark exceedances than observed previously

with the as is air quality however, the st rength of that relationship weakens with increasing

benchmark levels.

46 The current annual SO 2 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 SO 2 concentrations above potential health effect benchmark levels per year at 128 ambient monitors in 40 selected counties given the annual average SO 2 concentration, 2001-2006 air quality adjusted to just meet the current NAAQS. The level of the annual average SO 2 NAAQS of 30 ppb is indicated by the dashed line. > 100 ppb > 200 ppb

> 300 ppb > 400 ppbNumber of Daily 5-minute Maximum SO2Above Selected Level Per Site-YearJuly 2009 136 Similar relationships are present between the annual average SO 2 concentrations and the

number of benchmark exceedances when consideri ng the potential al ternative standards. As a reminder, to just meet the current and potential alternative standards staff estimated a unique adjustment factor to simulate th e alternative air qualit y. The direction of the adjustment factor (either upwards or >1; downwards or <1) and ma gnitude of the adjustment factor used has a direct impact on the estimated number of 5-minu te benchmark exceedances. In general, the air quality distributions that just meet the potential alternative st andards were enveloped by the as is air quality (i.e., a distribution with low concentr ations) 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 exceedance s 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 SO 2 concentrations and number of daily 5- minute maximum exceedances of 200 ppb is presen ted in Figure 7-26 for six air quality

scenarios: four of the 99 th percentile 1-hour daily maximum po tential alternative standards (i.e., the 100, 150, 200, and 250 ppb); the ai r quality adjusted to just m eet the current standards; and

as is air quality. Clearly, in using the air quality adjustment pr ocedure combined with the statistical model

to estimate 5-minute maximum SO 2 concentrations, the current standard air quality scenario allows for the greatest estimated number of days per year with pot ential health effect benchmark exceedances (Figure 7-26). However, at a mini mum the annual standard does provide protection against annual average concentra tions above the level of the current standard. While there were fewer 5-minute benchmark exceedances using th e 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 exceed ing the current annual standard (Figure 7-26). High annual average concentrations become less of an issue when consideri ng the lower levels of the 1-hour daily maximum potential alternative standards. Even though the 99 th 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 fewe r estimated days per year with benchmark

exceedances. These results further demonstr ate the stronger relationship 5-minute peak

concentrations have with 1-hour SO 2 concentrations than with annual average concentrations.July 2009 137Figure 7-26. The number of modeled daily 5-minute maximum SO2concentrations above 200 ppb per year at 128 ambient monitors in 40selected counties given the annual average SO2concentration, 2001-2006 air quality as isand that adjusted to just thecurrent and four potential alternative standards (text in graphindicate standard evaluated). The level of the annual averageSO2NAAQS of 30 ppb is indicated by the dashed line.99thpercentileDaily 1-hourmaximum of 100 ppb99thpercentileDaily 1-hourmaximum of 150 ppb99thpercentileDaily 1-hourmaximum of 200 ppb99thpercentileDaily 1-hourmaximum of 250 ppb

Number of Daily 5-minute Maxi mum Above 200 ppb per Site-YearCurrent annual ordaily standardAs is air qualityJuly 2009 138 Table 7-10. Percent of days having a modeled daily 5-minute maximum SO 2 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 SO 2 Concentrations Above Benchmark Levels Air Quality Scenario 1 > 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 x th percentile form of a 1-hour daily maximum level of y.

Second, staff summarized the number of days per year with 5-minute maximum SO 2 concentrations above benchmark levels within the 40-county data set for additional comparisons of the air quality scenarios. Table 7-10 provi des 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 th e greatest percent of days with benchmark exceedances. With each progressive d ecrease in the 1-hour daily maximum SO 2 concentration levels of the potential alternative standards, th ere 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 alterna tive standard level of 100 and 150 ppb (99 th percentile form), or similar to that of the 98 th percentile form at a level of 100 ppb. Third, staff evaluated two forms of the potential alternative standards: the 99 th and 98 th percentile forms, each having a 1-hour daily ma ximum level of either 100 or 200 ppb. For example, Figure 7-27 indicates that nearly all site-years have a gr eater estimated number of days per year with benchmark exceedances given the 98 th percentile form when compared with a 99 th percentile form at the same level. This is expected given the number of allowable 1-hour SO 2 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 98 th percentile form allowed for approximately 46, 68, 84, and 86% more benchmark exceedances considering theJuly 2009 139 100, 200, 300, and 400 ppb benchmark levels, respectively when compared with the 99 th percentile form.

Figure 7-27. The number of days per year with modeled 5-minute maximum SO 2 concentrations above benchmark levels given the 99 th and 98 th 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 98 th percentile form of the potential alternative sta ndard allowed for approximately 68, 90, 84, and > 100 pp b > 200 pp b

> 400 pp b > 300 pp bNumber of Days per Year with a 5-Minute Benchmark Exceedance- 98thPercentile 1-hour Daily Maximum 200 ppb Standard

Number of Days per Year with a 5-Minute Benchmark Exceedance - 99 th Percentile 1-hour Dail y Maximum 200 ppb StandardJuly 2009 140 74% more benchmark exceedances at each mon itor considering the 100, 200, 300, and 400 ppb benchmark levels, respectively when compared with the 99 th 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 leve l, 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 SO 2 concentrations above benchmark levels given the 99 th and 98 th 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 - 99 th Percentile 1-hour Dail y Maximum 100 ppb StandardNumber of Days per Year with a 5-Minute Benchmark Exceedance- 98thPercentile 1-hour Daily Maximum 100 ppb Standard > 100 ppb > 200 ppb

> 400 ppb > 300 ppbJuly 2009 141 Fourth, staff estimated the probability of potential health effect benchmark exceedances given the adjusted air quality s cenarios 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 curv es 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 presen ted. 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 stan dard, given 1-hour daily maximum

SO 2 concentrations. In general, all of the corresponding probability curv es for all of the air quality scenarios overlap when considering th e 100 and 200 ppb benchmark levels. However, the probability curves associated with exceedi ng 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-lin ear form of the statistical model

used to estimate the 5-minute maximum SO 2 concentrations, the proportional adjustment procedure to simulate alternative standards, a nd the form of the air quality characterization

metric used. When adjusting the 1-hour SO 2 concentrations upwards us ing a proportional factor, a corresponding proportional increase in the number of days per year with benchmark exceedances does not necessarily follow. The statistical mo del uses multiple distributions of PMRs, not linearly related to 1-hour SO 2 concentrations. Certainly, the to tal number of days in a year with benchmark exceedances will increase with an upwa rd 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 compri sed 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 universa l increase in hourly concentrat ions applied to all 1-hour

concentrations. Therefore the probability curves tend to be less steep with the upward 1-hourJuly 2009 142 concentration adjustments when considering the higher benchmark levels. Furthermore, days already having an exceedance are only counted on ce, 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 associ ated 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 exceed ances when comparing the two air quality scenarios. Conversely, it could al so be argued that there may be an increased probability of daily 5-minute exceedances of 300 and 400 ppb when us ing air quality with a relatively low

concentration distributio n (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 exceed ances in a year (and the absence of exceedances at the same high 1-hour daily ma ximum concentration) under either of these scenarios would be very few, with far fewer numbers of exceed ances 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 site d within high-population density areas. The 100 and 200 ppb benchmark leve l probability curves e xhibit a narrow range across each of the adjusted air quality scenarios. While the estimated 300 and 400 ppb probability curves are wider th an the 100 and 200 ppb curves, ther e is still agreement in the estimated probabilities at many of the 1-hour daily maximum SO 2 values. The range in probability curves tended to be widest at th e lowest probabilities/1-hour daily maximum SO 2 concentrations within a given benchmark, lik ely indicating a greater uncertainty in the relationship between exceedance of the daily 5-minute maximum SO 2 concentrations of 300 and 400 ppb and 1-hour daily maximum SO 2 concentrations less than 130 ppb and 180 ppb, respectively.July 2009 143 Figure 7-29. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels given 1-hour daily maximum SO 2 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 SO 2 Concentration (ppb)Probability of Daily 5-minute MaximumExceedance > 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 400Probability of Daily 5-minute MaximumExceedance > 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 400Probability of Daily 5-minute MaximumExceedance > 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

DensityJuly 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 SO 2 Concentration (ppb)Probability of Daily 5-minute MaximumExceedance

> 100 ppb > 200 ppb > 300 ppb > 400 ppb

Figure 7-30. Probability of daily 5-minute maximum SO 2 concentrations above potential health effect benchmark levels given 1-hour daily maximum SO 2 concentrations, 2001-2006 air quality adjusted to just meet the current and each of the potential alternative standards (99 th percentile form). The 5-minute maximu m 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 st andard scenarios given either the 1-hour daily maximum or 24-hour average SO 2 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 SO 2 concentration was above 100 ppb in each of the 40 selected counti es and for all air quality scenario s. In considering air quality adjusted to just meeting the current standard a nd the level of the highest potential alternative standards (200 and 250 ppb 1-hour da ily 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 SO 2 concentrations above 100 ppb in these counties. Most of the other lo cations though had fewer than 100 benchmark exceedances in a year, partic ularly when considering the two potential alternative 1-hour daily maximum standards. Ai r quality simulating just meeting the current standard was associated with the greatest numbe r 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 benchmarkJuly 2009 145 exceedances. Decreases in the po tential alternative st andard 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 99 th percentile daily 1-hour maxi mum concentration of 100 ppb, th an 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 99 th percentile daily 1-hour maximum concentration of 50 ppb. This means th at to improve current air quality in most locations, a level below 100 ppb would need to be selected when using a 99 th percentile 1-hour daily maximum standard form. In addition, the two percentile form s of the alternative standards (98 th and 99 th ) were evaluated each at two 1-hour daily maximum sta ndard levels (100 and 200 ppb) (Table 7-11). The estimated number of exceedances using a 98 th percentile 1-hour dail y maximum alternative standard level of 100 ppb fell w ithin those estimated using 99 th percentile levels of 100 and 150 ppb. The estimated number of exceedances using a 98 th percentile 1-hour daily maximum

alternative standard level of 200 ppb was similar to the 99 th 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 benchm arks (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 standa rd scenario (Table 7-12). Most counties had fewer than forty days per year with 5-minute SO 2 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 SO 2 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 99 th percentile 1-hour daily maximum concentration level of 50 ppb was associated with th e fewest days with 5-minute maximum SO 2 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 differe nce in the number of exceedances between theJuly 2009 146 current standard and the other air quality scenar ios 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 SO 2 concentrations above 300 or 400 ppb when considering air quality just meeting the current standard compared with air quality adjusted to just meet the 99 th 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 st andard 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 SO 2 concentrations above 400 ppb given a 99 th percentile 1-hour daily maximum of 100 ppb, while 75% of th e 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 percentile 1 98th percentile 1

State County as is 1 CS 1 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 (99 th and 98 th ) 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 percentile 1 98th percentile 1

State County as is 1 CS 1 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 (99 th and 98 th ) 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 percentile 1 98th percentile 1

State County as is 1 CS 1 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 (99 th and 98 th ) 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 percentile 1 98th percentile 1

State County as is 1 CS 1 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 (99 th and 98 th ) of a 1-hour daily maximum potential alternative standard are given.July 2009 151 

### 7.4 VARIABILITY ANALY SIS AND UNCERTAINTY CHARACTERIZATION As discussed in section 6.6, there can be variability and un certainty in risk and exposure assessments. This section presents a summary of and associated discussions regarding the degree to which variability was in corporated in the air quality an alyses 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 da ta, time, and resources available for the assessment, staff accounted for variability within the two main component s of the air quality characterization: the ambient monitoring concen trations and the statistical model used to

estimate 5-minute maximum SO 2 concentrations. The variability accounted for in this analysis is

summarized in Table 7-15.

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

Component Variability Comment

Temporal: 10 to 11 years of 1- hour and 5-minute monitoring data Broader SO 2 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 SO 2 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 SO 2 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 (98 th and 99 th ); 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 aJuly 2009 152 probabilistic uncertainty characte rization, 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 exceed ances. In addition, a nd 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 assessm ent that may contribute to uncertainty, staff subjectively scaled the magnitude 47 of each identified source of uncertainty and the associated direction of potential influence to the number of benchmark exceed ances. We used a three level scale to rate the magnitude: low indicated that large changes w ithin 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 un certainty 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 exceedan ces was subjectively assigned as over

- estimated , underestimated , both (uncertainty affects assessment e ndpoint in either direction), or unknown (no evidence to judge the uncer tainty). Staff also subjectively scaled the knowledge-base uncertainty

associated with each identified so urce using a three level scale: low indicated significant confidence in the data used and its ap plicability 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 wa s extremely limited. Table 7-16 provides a summary of the sources of uncertainty identified in the air quality characterization, the leve l 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 153Table 7-16. Summary of qualitative uncertainty analysis for the air quality and health risk characterization.Influence of Uncertainty onAir Quality BenchmarkExceedancesSource TypeDirection MagnitudeKnowledgeBaseUncertaintyComments1Air Quality DataDatabaseQualityOver Low LowINF: There may be a limited number of poor quality highconcentration data within the analytical data sets,potentially influencing the number of benchmarkexceedances.KB: Data used in the analyses are of high quality. Thereis no other source of monitoring data as comprehensive.Data are being used in a manner consistent with one ofthe defined purposes of ambient monitoring.AmbientMeasurementTechniqueInterference BothLow –MediumMediumINF: Potential interferences can be controlled; theinfluence may be of greater magnitude when consideringupward concentration adjustment procedure.KB: Limited knowledge on concentration dependencies athigh concentrations. Limited knowledge of interferencecontrols applied at individual monitors.Scale UnknownLow –MediumMediumINF: Temporal scale is appropriate for analysis performed.Most data used are screened for temporal completeness;however where 5-minute concentrations were reported,data were not screened for completeness.KB: Limited knowledge on direction or magnitude;however 60% of data used would have passedcompleteness criteria.TemporalRepresentationof MonitoringDataMissing Data Under Low LowINF: Staff assumed there was an equal probability ofmissing low and high concentration 5-minutemeasurements; there could be a few missing highconcentration data that would lead to underestimation inbenchmark exceedances. No interpolation wasperformed.KB: All available data are quality assured; most of thedata used were temporally complete.July 2009 154Influence of Uncertainty onAir Quality BenchmarkExceedancesSource TypeDirection MagnitudeKnowledgeBaseUncertaintyComments1YearsEvaluatedOver Low LowINF & KB: Little variation in COV and PMRs over years ofanalysis. Estimates of the probability of exceedances arelikely not affected. Estimated number of exceedancescould be influenced by historically high concentrations.Broader SO2Network and 40County DataSetUnder Medium HighINF: It is possible that the current network is notadequately capturing 1-hour SO2from a few localizedsources. However, given the purpose of the network andpurpose of the assessment, staff judges there may be atmost a medium level of influence on results with improvedspatial representation.KB: Many site-years available from monitors reporting 1-hour concentrations; However, there are no data availableto evaluate the spatial representativeness of existingnetwork.SpatialRepresentationof MonitoringNetwork5-minuteMaximum SO2Under Medium HighINF: Distribution of sources potentially influencingmonitors is similar to that of the broader SO2network evenwith limited geographic span.KB: Very few site-years available from monitors reporting5-minute measurements.Air QualityAdjustmentProcedureProportionalApproach UsedBothLow –MediumMediumINF: Depends on the degree of proportionality in the airquality distribution and the magnitude of the ambientconcentration adjustment.KB: Proportional approach judged adequate inrepresenting the alternative air quality scenarios.However, evaluation only conducted in 7 of 40 counties,was dependent on historic air quality as representative ofalternative scenarios, and there was some evidence ofdeviation from proportionality. Also only one adjustmentmethod was investigated.July 2009 155Influence of Uncertainty onAir Quality BenchmarkExceedancesSource TypeDirection MagnitudeKnowledgeBaseUncertaintyComments1Spatial Scale Both Medium HighINF: The rate of change in concentrations over time wasmoderately different at monitors within a county.KB: Analysis is dependent on historic air quality asrepresentative of alternative air quality scenarios. Thereis lack of knowledge regarding how changes in emissionswould affect multiple monitors in a county.Data Screening OverLowLowINF & KB: Less than 2% of data were removed.Physically realistic PMR bounds were set. Screened datawere mostly of low 1-hour concentrations that would nevergenerate a benchmark exceedance.TemporalVariation inPMRsNone Low LowINF: Consistency in PMRsacross period of analysis.KB: Consistency in PMRs whencompared with late 1980sand early 1990s ambient monitoring data.DistributionForm of PMRsNone Low LowINF & KB: Non-parametric distributions were determinedthe most appropriate for the analysis.Accuracy BothLow –MediumMediumINF: Accuracy assessment indicated good agreement,though at upper and lower tails of prediction distribution,the number of exceedanceswere under- and overestimated, respectively.KB: Though cross-validation results were reasonable,there may be additional influential variables that may beimportant in the model construction and possibly notavailable in extrapolating to the broader data set.Statistical ModelUsed forEstimating 5-minute SO2ConcentrationsReproducibility None Low LowINF & KB: Limited variation observed in the estimatedmean number of benchmark exceedances followingrandom sampling error analysis.Potential HealthRisk EndpointsUsed2Ambient SO2asan Indicator ofSO2ExposureOver Medium HighINF: Long-term time averaging comparisons indicate astrong proportional relationship between ambientconcentration and personal exposure.KB: The relationship between 5-minute personal exposureand ambient concentration is not known.July 2009 156Influence of Uncertainty onAir Quality BenchmarkExceedancesSource TypeDirection MagnitudeKnowledgeBaseUncertaintyComments1Considerationof SusceptiblePopulationsUnknown Low MediumINF & KB: Severe asthmatics are typically not challengedin clinical studies due to expectations of a significantadverse response. Potentialhealth risk could be over- orunder-estimated depending on the level of the lowestbenchmark selected to represent susceptible individuals.KB: There is no clear quantitative evidence indicatinglowest benchmark would either be health protective or at alevel a susceptible individual would respond.Averaging Time NoneLowLowINF & KB: consistently no difference reported in observedresponses from either 5- or 10-minute clinical studies.Single CountsofExceedancesversus MultipleExceedancesper dayUnder Low MediumINF: Potential health risk may be under-estimatedbecause approximately 50%of days with a singleexceedance correspond with another (or more)exceedance(s) in that same day. However, in this airquality analysis, time of exposure is not considered, thuslimiting the relevance ofmultiple exceedances.KB: Frequency of multiple exceedances per day usingexisting measurement data is known for limited number ofmonitoring sites.Notes:1INF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rating.2In these cases the influence of the uncertainty to the potential health risk is discussed, not the influence to the estimatednumber of exceedances.July 2009 157 7.4.2.1 Air Quality Data The purpose of this section is to discuss st aff assumptions and potential uncertainties

associated with the data used to construct the va rious 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 asso ciated quality are discussed. The uncertainty regarding temporal and spatial components of the ambien t monitoring data sets is discussed in sections 7.4.2.3 and 7.4.2.4, respectively. The Air Quality System (AQS) contains ambient SO 2 concentrations collected by EPA, state, local, and tribal air po llution control agencies from hundr eds of monitoring stations across

the U.S. There are no alternative ambient m onitoring data sets av ailable 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 colle ction techniques and quality assurance methods that they would be complementary to AQS monitoring data. One basic assumption is that the AQS SO 2 air quality data used are quality assured already. Methods exist for ensuring the precis ion and accuracy of the ambient monitoring data (e.g., EPA, 1983). Reported concentrations cont ain only valid measures, since values with quality limitations are not entered into the syst em or are removed following determination of being of lower quality or flagged. There is likel y no selection bias in reta ining 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 quant itative analysis of available duplicate measures (i.e., originating from co-loca tion 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 partic ular 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 th e estimated number of benchmark exceedances.July 2009 158 Thus, there is both a low level of uncertainty in th e 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 SO 2 air quality measurements is from interference with other compounds . The ISA notes several sources of positive and negative

interference that could increase the uncerta inty in the measurement of ambient SO 2 concentrations (ISA, sections 2.3.1 and 2.3.2). Many of the identified sources (e.g., polycyclic aromatic hydrocarbons, stray light, collisional qu enching) were described as having limited impact on SO 2 measurement due to the presence of instrument controls that prevent the

interference. The actual impact on any indivi dual 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 kno wn whether there is a concentration dependence on the amount of interfere nce. This may be an important uncertainty in considering the air quality c oncentrations 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 quantitativ e 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 evaluate d 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 mi ssing data were treated in the analysis, and long term trends in ambient monito ring and concentration variability. The air quality analysis relied on quality assured 5-minute and 1-hour average SO 2 measurement data (see section 7.4. 2.1) and are of the same tempor al scale as identified potentialJuly 2009 159 health effect benchmarks, where 5-minute measur ements 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, gi ven the level of the benchmark concentrations and the low frequency of benchmark exceedances and overall completeness of the monitoring data, it is likel y 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. Fo r the analyses performed using the broader SO 2 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 comp leteness 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 a ll of the available data were used without considering the standard 75% completeness criteria . Staff elected to use all of the 5-minute SO 2 measurement data rather than further reduc ing the already limited number of samples and locations represented. The 5-minute measur ement 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 concentrat ions < 0.1 ppb, and screening for technically impossible PMRs (see section 7.4.2.6). These sc reenings 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, pot entially influencing the observed probability and the estimated

number of benchmark exceedances. Therefore, staff judges the magnitude of influe nce from this source of uncertainty as low to medium, with a greater magnitude of influe nce assigned to observations reported for the 5- minute data set and its application in the stat istical model. While staff has not performed analyses to determine direction and magnitude of impact in applying the completeness criteria toJuly 2009 160 the 5-minute data set, the uncertainty in th e 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 analys is; 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 system atic, i.e., both high and low concentration data would be absent in equal proportions. There ar e methods available that can account for time-ofday, day-of-week, and seasonal variation in ambi ent monitoring concentrations. However, if a method were selected, it would have to not simp ly interpolate the data but also accurately estimate the probability of peak 1-hour SO 2 concentrations that could occur outside the predictive range of the method. It was judged th at if such a method was available or one was developed to substitute data, it would likely add to a similar leve l 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 th e validity criteria selected for the most temporally representative and complete ambient m onitoring data sets possibl e. In addition, when using the concentrations reported as zero, there is likely limited impact on the estimated number of exceedances and associated probability of exc eedances. 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, give n the temporal completeness of much of the data used characterizing air quali ty, staff judges both the magnitude of influence of missing data and the uncertainty associated wi th the knowledge-base to be low. There is uncertainty associated with the sele ction of monitoring y ears, particularly if concentrations vary significantly between mon itors 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 SO 2 monitoring sites in the U.S. has changed over time, with a trend of decreasing number of

monitors most evident for thos e reporting the 5-minute maximum SO 2 concentrations (Figure 7- 31). Five-minute SO 2 concentrations have been reported in fewer monitors than the 1-hour SO 2 concentrations; generally only a few site -years of data exist for 5-minute SO 2 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 in clude 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 SO 2 data were used in developing the statistical relationships and for the model evaluation without requiring the 75% completeness cr iteria 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 SO 2 concentrations.

However, the variability in monitoring c oncentrations (both th e 1-hour and 5-minute

maximum SO 2 ) does not change significantly across most monitoring years (i.e., years 1997 though 2004) and there is a comparable range betw een 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 possi bly due to the reduction in the number of ambient monitors in operation (Figure 7-31) rather than a reduction in the tem poral 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 probab ility of exceedances is likely not influenced by year given that the analysis controls for concen tration levels and variab ility changes that may have occurred over time. Furthermore, the selecti on of a subset of the recent air quality data (2001-2006) used for detailed analyses may redu ce the potential impact from changes in national- or location-specific sour ce influences (if one is present) . Therefore, due to the limited variation in temporal trends in COV for both 5-minute and 1-hour SO 2 and analysis design (i.e.,Number of Monitor Reporting5-minute Maximum SO2July 2009 162 controlling for concentration leve l changes, limiting the span of years analyzed) the overall magnitude of influence is expected to be low.

Figure 7-32. Temporal trends in the coefficien t of variability (COV) for 5-minute maximum and 1- hour concentrations at the monitors that reported both 5-minute and 1-hour SO 2 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 monitori ng network can be a s ource of uncertainty, particularly if the monitoring network is not de nse enough to resolve the spatial variability in

ambient SO 2 concentrations and if the monitors ar e not effectively dist ributed to reflect population exposure. Relative to the physical area, staff ac knowledges there are only a few monitors, particularly when considering the se t of monitors that re ported 5-minute maximum

SO 2 . The magnitude and direction of influe nce on the modeled or measured benchmark exceedances will depend on ambient monitoring object ives, monitoring scale, the distribution of

SO 2 emission sources, and whether there is large va riability in monitoring surface, i.e., areas of differing terrain that are not ad equately represented by the current distribution of monitors. These elements will be broadly discussed for eac h 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 inte rest include monitors from the broader SO 2 network 5-Minute Maximum SO 2 1-hour SO 2July 2009 163 (including monitors within the 40 selected counties) and the monitors reporting 5-minute SO 2 concentrations. The broader 1-hour monitoring network, by defi nition, is the most comprehensive data

set of the three when consideri ng 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

SO 2 monitoring network in this assessment could assist in identifying a nd prioritizing locations to begin reporting 5-minute SO 2 measurements. However, the broader geographic span of ambient monitoring does not necessarily confer spatial representativeness. The spatial representativeness of the broader SO 2 monitoring network would re main dependent on the siting of the monitors with respect to important emi ssion sources and potentially exposed populations. Staff assumes that the network design, to a large degree, provides adequate spatial representation

of the ambient SO 2 air quality. This may apply to a grea ter 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 SO 2 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 qua lity characterization used monitors meeting a 75 percent completeness criterion, without taking in to account the monitoring objectives, scale, or land use. Thus, there may be a reduction in spat ial representation due to either the inclusion or

exclusion of monitors sited near local SO 2 source emissions as a result of the completeness screening. Staff estimates that the magnit ude 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 SO 2 concentrations though, staff assigns a high level of uncertainty to the knowledge-base.July 2009 164 The overall SO 2 monitoring network design is also re sponsible 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 di scussed above. However, because the monitors reporting 5-minute concentrations are not part of a designed 5-minute SO 2 monitoring network but are entirely voluntary, the direction and ma gnitude 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 analys es (n=98), representing a limited geographic scope in comparison with the broader SO 2 monitoring network. In addi tion, a greater percentage of monitors reporting 5-minute concentrations ha d a source-oriented obj ective (Figure 7-3). However, an analysis of the monitoring attributes indicated similar distri butions in the types of sources and the total emissions potentially impact ing both sets of data (Figure 7-5). This suggests that the spatial represen tation of the monitors reporting 5-minute concentrations may be

similar to that of the broader SO 2 monitoring network regarding proximity to similar SO 2 sources. In the absence of additional measuremen ts or modeling of the spatial heterogeneity of

5-minute ambient SO 2 concentrations, staff assigns a high le vel of uncertainty to the knowledgebase.

7.4.2.5 Air Quality Adjustment Procedure There is uncertainty in the air quality adjust ment procedure due to the uncertainty of the true relationship between the adjusted concentrations that are simulati ng 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 concen trations will change pr oportionately at each ambient monitoring site. Two elements of this source of uncertainty are discussed, namely uncertainty regarding the propor tional approach used and the universal application of the approach to all ambient mon itors within each location.

Different sources have differe nt 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 porti ons of the concentratio n 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 th at factor to all portions of the concentration distribution, that is, the upper tails were treated th e same as the area of central tende ncy. This may not necessarilyJuly 2009 165 reflect changes in an overall emissions profile th at 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 ta ils of the hourly concentr ation distribution might not have the same proportional increase. The increase in concentrati on at the tails of the distribution could be greate r or it could be less than that observed at the mean and is dependent largely on the type of sources influencing th e monitor and the source operating conditions. Adjusting the ambient concentrations upwards to si mulate the potential alternative standards also

carries a similar level of uncertainty although the multiplicati ve factors were derived from the upper percentiles of the 1-hour daily maximum SO 2 concentrations, rather than the mean, and then applied to the 1-hour SO 2 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 lik ely greater uncertainty in the estimated benchmark levels when evaluating the current and the 250 ppb 99 th percentile alterna tive standards (which have the highest adjustment factors), th an when considering the 50 ppb and 100 ppb 99 th percentile alternative standa rds (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 mon itors from seven counties evaluated in the air quality characterization to investigate how distributions of hourly SO 2 concentrations have changed over time (Rizzo, 2009). The analysis indicates that a proportional approach is a reasonable model for simulating higher concentr ations at most monitoring sites, since historically, SO 2 concentrations have decreased lin early across the entire concentration

distribution at each of the monito ring sites and counties evaluated. At some of monitoring sites an alyzed however, there were features not consistent with a completely proportional relationship. This includ ed deviation from linea rity primarily at the maximum or minimum percentile concentrations, some indication of curvilinear relationships, and the presence of either a pos itive or negative regression intercept (Rizzo, 2009). Where multiple monitors were present in a location, th ere tended to be a mixture of each of these conditions including proportionality (e.g., see Figure 7-33). Not a ll of the counties analyzed asJuly 2009 166 part of the air quality characterization were includ ed in the evaluation, thus staff assumed that the findings of the Rizzo (2009) analysis were appl icable 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 es timated 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 j udged the proportional approach as appropriate, it was based on analyses using historical monito ring 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 know ledge-base uncertainty is medium. Staff applied the proportional adjustment approach universally to a ll 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,

98 th or 99 th percentile 1-hour concentration) that may not necessarily be reflected in the same proportion at other lower concentration sites. This could result in e ither over- or underestimations in the number of exceedances at lowe r 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 SO 2 concentration percentiles for fi ve 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 demons trate features of proportio nality, the differences in regression slope indicate that the rate of ch ange in the concentration distribution was not equal when comparing these monitors for these two mon itoring years. These results suggest that even if all monitors within a county demonstrate pr oportionality, there may be either over- or underestimations in SO 2 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 uncerta inty 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 si mulated air quality scenario and the lack ofJuly 2009 167 knowledge regarding how source emission changes would affect multiple monitors within a county.

Figure 7-33. Comparison of measured daily maximum SO 2 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 SO 2 Concentrations Five components of uncertainty were identified regarding the statistical model and its impact on the estimated number of benchmark ex ceedances. 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 distribu tion 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 screenin g criterion. The calculation of PMRs less than 1 implies the 5-minute peak is le ss than the 1-hour aver age, a physical impossibility, and valuesJuly 2009 168 >12 are a mathematical impossibility. The 5-minut e 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 to tal 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 cr iterion used for the data removal would not adversely influence the estimated number of benchmark exceedances in the modeling performed since it was only directed towa rds identifying unrealistic 5-mi nute 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 th an or equal to 5 ppb (Table 7-17). An alternative approach to devel oping the PMR distributions coul d 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 wa s greater than twelve) based the 5-minute and 1- hour concentration distributions. If included, th ese data would have virtually no influence on the

estimated number of benchmark exceedances. Th is is because 1-hour c oncentrations < 8.3 ppb

combined with the PMR distributi on principally affected by inclus ion of newly assigned ratios (i.e., the < 5 ppb concentration bi n) 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 PM Rs 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 beca use the distributions chosen in estimating the 5-minute concentrations included the 1 st through the 99 th 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 SO 2 data set used to develop PMRs.

Data removed Final data set PMR < 1 (n = 39,861) PMR ≥ 12 (n = 804) (n = 2,367,686)

Statistic 1 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 99 th , 95 th , 50 th , 5 th and 1 st percentiles of the concentration distribution.

The use of all screened 5-minute maximum SO 2 data (1997 to 2007) in developing the

PMR distributions assumes that the source emissi ons 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; 25 th percentiles of 1.4; 75 th percentiles of 1.7) (Figure 7-34) . This general trend in mean PMRs is consistent with the population-based value used by Stoecken ius 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 ot her 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 neg ligible impact on the predicted concentrations and subsequently the estimated number of be nchmark 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 knowle dge-base is judged as low.

50 Data from Table 2-18 of Stoeckenius (1990) for the Sc ottish 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 SO 2 concentrations, Years 1997 through 2007.

The PMRs distributions for each COV and c oncentration 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 co mpared with a non-parametric form, and that there would likely be no reduction in the uncerta inty 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 statis tical tests performed indicated the distributions were statistically distinct (p<0.01) from any of the tested forms (i.e., normal, lognormal, Weibull, gamma, and exponen tial) (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 prac tical judgments regarding a potential parametric form (i.e., beyond simply using statistically signi ficant differences as a criterion), most of the

observed PMR distributions had la rge 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 re porting the 5-minute maximum and 1-hour SO 2 concentrations (left) and the same PMRs compared with expected lognormal percentiles (right). PMRs were derived fr om monitors with medium level variability (COVbin = b) and 1-hour concentrations between 75 and 150 ppb (COVconcbin = 4).

In addition, while there is uncertainty associ ated with the use of the empirically-derived data in representing the true population of PM Rs, 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 SO 2 concentrations. For di stributions exhibiting no similarities to any parametric di stribution, experimental justificat ion criteria would need to be developed in selecting the most appropriate form of the distribution, likely requiring multiple test iterations, potentially yielding distributions wi th 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 1,0 00 (only 5 of the 19 distributions had fewer thanJuly 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 distri bution, limits on time and resources available, and numbers of samples available) were consider ed and it was decided by staff that the nonparametric distribution derived from the meas urement 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 infl uence. Since staff empl oyed both statistical and practical comparisons in selection of the distribu tion form to the maximum extent allowable, the uncertainty regarding the knowle dge-base is judged as low.

Figure 7-36. Example of a measure d 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 SO 2 concentrations above each of the benchmark levels was evaluated using measured concentrations. The results indicated that on average, the statistical model performed we ll in estimating of these short-term peak concentrations (section 7.2.3.4). There was reas onable agreement in observed versus predictedJuly 2009 173 numbers of benchmark exceedances for most of th e 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 ov er- or under-estimated (Fig ure 7-37, Table 7-6). The accuracy assessment indicated the esti mated number of days with benchmark

exceedances could be either over- or under-estim ated 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 mode l predicted versus the

observed number of benchmark exceedances at each of the benchmark levels. While there is generally uniform agreement between the pr edicted 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 observ ed benchmark exceedances of 400 ppb, although the statistical model predicted between 2-15 days in a year. Th is could indicate that a few of the site-years may have moderate over-estimations in th e number of days with 5-minute maximum SO 2 concentration exceedances, where the estimated number of exceedances is 15 or less. In addition, site-years with the gr eatest number of observed ex ceedances of 400 ppb (about 50 per year) were consistently under-estimated by the m odel by about 30%. This could imply that when the estimated number of days with 5-minute maximum SO 2 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 con centrations. For example, at the monitor with the greatest number of measured benc hmark exceedances (ID 290930030) and largest underprediction error, one could argue th at variable terrain may be an in fluential factor. This monitor is about 1.7 km from a primary smelter and lo cated proximal to a ravine running between the source and the monitoring site. The nearby mo nitor (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 c onsidering any individual monitor, there may be factors not accounted for by the statistical model that are impo rtant in estimating benchmarkJuly 2009 174 exceedances (e.g., terrain). Based on this model accuracy assessment, the magnitude of

influence the selected model has on contribu ting uncertainty to the estimated number of exceedances for individual monitors is likely medium at the lower and upper tails of the prediction distribution. The di rection 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 cons truction of the statistical model that may be important and have the potential to improve the agreement between th e observed and predicted values. There is also the possibility of influential variab les that are not within the data set used for statistical model development, but exist in the broader 1-hour SO 2 monitoring data set. Staff judged the concentration variability and leve l as appropriate variables for li nking the statistical model with the 1-hour measurement data. In addition, the co mparison of ambient monitoring attributes (e.g., objectives, local source emissions) also indicated consistency be tween the monitors reporting 5- minute maximum concentrations and those repo rting only 1-hour aver age 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 regardi ng 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 SO 2 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 SO 2 concentrations. The purpose of this analysis was to determin e the effect of rando m 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 SO 2 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 se ed) and used to generate a m ean 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 80 100 120 140 160 180

0 20 40 60 80 100 120 140 160 180

0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70

0 10 20 30 40 50 60 70 100 ppb 200 ppb

300 ppb 400 ppb

Observed number of daily benchmark exceedances in a yearPredicted number of daily benchmark exceedances in a yearJuly 2009 176 maximum SO 2 concentration exceedances for each site-yea r. 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 tw enty simulations per model run) using the 40- county as is air quality data set (i.e ., having 610 site-years per mode l 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 fo r 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 maximu m mean value) giving the range of the ten means for each benchmark and site-year. For exam ple, 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 ma ximum difference) is equal to two. The distributions of the range in mean exceed ances 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 consistent ly decreases with increasing benchmark level. On average, maximum difference in the estima ted mean numbers of exceedances of 100 ppb was 2 exceedances, while at greater benchmark levels th e range was 1 or less. This indicates that the random sampling error has a low impact to the es timated 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 25 th to 75 th 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 e ffect 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 contac t of a pollutant with a person, and as such, the air quality

characterization assumes that the ambient monitori ng concentrations can serv e as an indicator of exposure. The ISA reports that personal exposur e measurements (PEM) are of limited use since

ambient SO 2 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, pa rticularly since personal exposures are timeaveraged over days to weeks, and never by 5-mi nute averages. Therefore the fraction of actual 5-minute maximum personal exposure concentrati ons attributed to 5-minute maximum ambient

SO 2 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 re lationship between longer-term averaged ambient monitoring concentrations and personal exposures is str ong, particularly when ambient

concentrations are above the limit of detecti on. The strength of the relationship between personal and ambient SO 2 concentrations is supported furthe r by the limited presence of indoor

sources of SO 2 ; much of an individuals’ personal expos ure is of ambient origin. However, SO 2 personal exposure concentrations are reportedly a small fraction of ambient concentrations. This

is because local outdoor SO 2 concentrations are typically half that of the ambient monitoring SO 2 concentrations, and indoor concentrations abou t half that of the local outdoor SO 2 concentrations (ISA). Therefore, while the relationship between personal exposures and ambient SO 2 concentrations is strong, the use of monitoring data as an indicator of SO 2 exposure may lead to an overestimate in the number of peak concentr ations 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 concentra tion 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 SO 2 concentrations. There is uncertainty regarding how suscep tible populations were considered in developing the potential health benchmark levels. The human clinical exposure studiesJuly 2009 179 evaluated airways responsiveness in mild to moderate asthmatics. Health effect symptoms and responses were observed in these test subjects ex posed 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 common ly 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 SO 2 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 potentia l 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 asthmatic s (mild, moderate, or severe) or if 100 ppb is

a concentration that would elicit an adverse e ffect. Based on this, st aff acknowledges there is medium uncertainty in the knowledge-base regarding repres entativeness of the lowest benchmark level selected, but judge that the magnit ude of influence to the estimated health risk is low given the inclusion of the 100 ppb level. Staff also acknowledges that there may be un certainty in the sele cted potential health effect benchmark averaging time. For example, the used in this assessment were from studies where volunteers were exposed to SO 2 for varying lengths of time. Typically, the SO 2 exposure durations in the controlled hu man 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 concentr ation level and averaging time, the use of a 5-minute averaging

time could either lead to over- or under-estimation in the health risk char acterization. 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 re sponse 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 betweenJuly 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 give n a general consistency in the observed responses involving either 5- or 10-minut e exposures, staff judges the uncertainty in the knowledge-base as low. The health effect endpoint us ed in the air quality charac terization was the observed or estimated number days the maximum 5-minute SO 2 concentration exceeded a particular benchmark level. Staff acknowledges that this choice could result in th e 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 SO 2 maximum concentrations, approximately half of the tim e 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 lo west 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 activit y level), thus limiting the relevance of multiple exceedances within a day. While the risk char acterization could be considered under-estimated, the magnitude of influence by this source of unc ertainty 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 th e air quality analysis and are discussed in greate r 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 SO 2 concentrations.

5-minute SO 2 Benchmark Level > 100 ppb > 200 ppb > 300 ppb > 400 ppb Number of Exceedances per Day 1 days 2 percent 2 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 exceeda nces 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 numbe r 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 obser vations resulting from the SO 2 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 con centration 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 maximumJuly 2009 182 concentration above 200 ppb. Approximate ly 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 consider ed, 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 exceedan ce was consistently related to either increased 24-hour average or 1-hour daily maximum concentrations.

 For unadjusted air quality in the 40 counties se lected for detailed an alysis, most counties are estimated to have, on average, fewer than 50 days per year where the daily 5-minute maximum ambient SO 2 concentrations are > 100 ppb. Most counties are estimated to have, on average, 25 days per year w ith daily 5-minute maximum ambient SO 2 concentrations > 200 ppb. Very few counties ar e estimated to have more than ten days with 5-minute maximum SO 2 concentrations > 300 ppb, while nearly half did not have any days with 5-minute maximum SO 2 concentrations > 400 ppb (Tables 7-11 to 7-14).

 When air quality is adjusted to simulate just meeting the current a nnual 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 SO 2 concentrations are > 100 ppb. Most counties are estimated to have, on average, 100 days or more per year with 5-minute maximum ambient SO 2 concentrations > 100 ppb, while eight of the forty counties are estimated to have 200 days or more per year with 5-minute maximum ambient SO 2 concentrations > 100 ppb. Fewer benchmark exceedances are es timated to occur with higher benchmark levels. For example, only five counties are es timated to have 60 or more days per year with 5-minute maximum ambient SO 2 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 SO 2 concentrations that exceed 400 ppb (Table 7-14).

 In all 40 counties, potential alternative sta ndard levels of 100 and 150 ppb are estimated to result in fewer days per year with 5-minute maximum SO 2 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 c ounties, corresponding annual average SO 2 concentrations were typically between 3 and 15 ppb, similar to a range of concentrations using unadjusted air quality (Appendix A). Wh en considering the potential alternative standard levels of 200 and 250 ppb, corresponding annual average SO 2 concentrations were typically between 10 and 30 ppb, simila r to the range of c oncentrations observed when using adjusted air quality that ju st 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-estimati on, while the remaini ng uncertainties could affect results in both directions (four sour ces), no direction (f our sources), or unknownJuly 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). Sta ff 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 spatia l representation of the monitoring network. Based on this overa ll characterization rega rding 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 un certainty for source s with unknown or bidirectional influence ranged from low (four sources) to medium (four sources), though uncertainty regarding the spatial scale of th e 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 source s 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 regard ing the knowledge-base, there is a high le vel of uncertainty associated with the most influential source.

 Staff identified four other sources of uncerta inty in the air quality characterization as having influence on the characterization of hea lth risk. The most influential and most uncertain source of the four is associated w ith the direct use of air quality benchmark exceedances as an indicator of exposure. Th e number of days with 5-minute exposures above benchmark levels would likely be lower than the number of days where there were ambient SO 2 concentrations above benchmark levels. Thus, the air quality characterization may over-estimate the health risk due to this factorJuly 2009 184 

## 8. EXPOSURE ANALYSIS

8.1 OVERVIEW This section documents the methodology and da ta staff used in the inhalation exposure

assessment and associated health risk characterization for SO 2 conducted in support of the

current review of the SO 2 primary NAAQS. Two important components of the analysis include the approach for estimating temporally and spatially variable SO 2 concentrations and simulating human contact with these pollutant concentrations . The approach was designed to better reflect exposures that may occur near SO 2 emission sources, not necessar ily reflected by the existing ambient monitoring data alone. Staff used a combined air quality and exposur e modeling approach to generate estimates of 5-minute maximum, 24-hour, and annual average SO 2 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 SO 2 concentrations using emissions estimates from st ationary, non-point, and port sources. The Air Pollutants Exposure (APEX) model, an EPA huma n exposure model, was used to estimate 5- minute population exposures using the census block level hourly SO 2 concentrations estimated by AERMOD and the statistical mo del described in section 7.2.3. Staff used the person-based exposure profiles to calculate the nu mber of days per year an indi vidual had at least one 5-minute exposure above the potential heal th effect benchmark levels of 100, 200, 300, and 400 ppb. Exposure and potential health risk were characterized consider ing recent air quality conditions ( as is ), for air quality adjusted to just meet the current SO 2 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 justificatio n). 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 es timated 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 expos ures in Greene County and St. Louis are described below. Additional model input data and supporting discussion of APEX modeling are provided in Appendix B. Brie fly, 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 SO 2 air quality concentra tions and exposures; and

 Assessment of the quality and limitations of th e input data for supporting the goals of the SO 2 NAAQS exposure and risk characterization. The overall flow of the exposure mo deling process performed for this SO 2 NAAQS review is illustrated in Figure 8-1. Severa l models were used in addition to APEX and AERMOD including emission factors and meteor ological 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, suppl emented with additional details in Appendix B.



### 8.2 OVERVIEW OF HUMAN EXPOSURE MODELING USING APEX The EPA has developed the APEX model fo r 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 e xposures in 12 urban areas for the O 3 NAAQS review (EPA, 2007d; 2007e) and in estimating population NO 2 exposures in Atlanta as part of the NO 2 NAAQS review (EPA, 2008d). APEX is a probabilistic model designed to acco unt for sources of variability that affect people’s exposures. APEX simulates the move ment of individuals through time and space and estimates their exposure to a given pollu tant in indoor, outdoo r, and in-vehicle microenvironments. The model stochastically gene rates 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 cen sus data provides home-to-work commuting flows. Any number of simulate d individuals can be modeled, a nd 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 SO 2 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 th e Consolidated Human Ac tivity Database (CHAD) (McCurdy et al., 2000; EPA, 2002). The diaries ar e used to construct a sequence of activity

events for simulated individuals consistent with their demographic characteristics, day type, and season of the year, as define d by ambient temperature regime s (Graham and McCurdy, 2004). The time-location-activity diaries input to APEX contain information rega rding an individuals’ age, gender, race, employment status, occupa tion, day-of-week, daily maximum hourly average temperature, the location, start time, duration, and type of each activity perf ormed. 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 th e important attributes contributi ng 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 fro m different seasons, different states of origin, etc.). APEX has a flexible approach for modeli ng microenvironmental concentrations, where

the user can define the microenv ironments to be modeled and th eir characteristics. Typical indoor microenvironments include residences, sc hools, 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 microenvi ronmental concentrati ons account for the contribution of ambient (outdoor) pollutant concen tration 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 mode led, available data to define model inputs, and estimation method selected by the model user. And, because the modeled individuals represent a random sample of th e population of interest, the di stribution of modeled individual exposures can be extrapolated to the la rger population within the modeling domain.July 2009 188 The exposure modeling simulations can be summa rized by five steps, each of which is detailed in the subsequent sections of this doc ument. 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 potenti ally exposed population – based on userdefined criteria and availabi lity of air quality and mete orological data for the area.

2. Generate simulated individuals . APEX stochastically generates a sample of hypothetical individuals based on the de mographic data for the study area and estimates anthropometric and physiologi cal 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 con centrations in microenvironments . APEX users define microenvironments that people in the study area would visit by assigning location codes in the activit y pattern to the user-specified microenvironments. The model calculates all 5-minute concentr ations 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 mi croenvironment descriptions, the hourly air quality data, and peak-to-mean ratios (PMRs; see section 7.2.3). Microenvironmental concentrations are ca lculated independently for each of the simulated individuals.

5. Estimate exposures . APEX estimates a concentr ation for each exposure event 52 based on the microenvironment occupied dur ing the event. In this assessment, APEX estimated 5-minute exposures. Thes e exposures can also be averaged by clock hour to produce a sequence of hour ly 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 incl ude in the exposure analysis takes into consideration the availability of ambient monitoring, the presence of significant and diverse SO 2 emission sources, population demographics, and results of the ambi ent air quality characte rization. Although it

could be useful to characterize SO 2 exposures nationwide, because the exposure modeling approach is both time and labor intensive, a regi onal 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 po llutant 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

SO 2 peak concentrations and with suffi cient 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 SO 2 monitoring data that were availabl e. The state of Missouri was one of only a few states reporting both 5-mi nute maximum and continuous 5-minute SO 2 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 SO 2 concentrations. In addition, the air quality characterization described in Ch apter 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 SO 2 emissions reported in the Na tional Emissions Inventory (NEI), Missouri ranked 7 th out of all U.S. states for the num ber of stacks with annual emissions greater than 1,000 tons. These st ack emissions were associated w ith a variety source types such as electrical power generating units, chemical manufacturing, cement processing, smelters, and emissions associated with port operations.

In the 1 st draft SO 2 REA, several modeling domains were characterized within the

selected state of Missouri to assess the feasib ility of the modeling methods. These modeling

domains were defined as areas within 20 km of a major point source of SO 2 emissions. While modeled air quality and exposure results were ge nerated for several of these domains in the 1 st draft REA, changes in the methodology used in this 2 nd draft REA precluded a dditional analysis for most of the domains origina lly selected. Staff judged the av ailability of relevant ambient monitoring data within the model domain as essential in evaluating the dispersion model performance, increasing confidence in the predic ted air quality and exposure modeling results. For example, when comparing the modeled air quality to ambient monitoring data in Greene County in the 1 st draft REA, it was judged by staff that non-point source emissions may contribute to a large proportion of measured ambient concentr ations. Addressing non-point source emissions then added a layer to the already complex modeling performed, further limiting the potential number of locations analyzed. Seco nd, to assess the impact of potential alternative standards, baseline conditions ( as is air quality) need to be k nown, again requiring ambient monitoring data. Because Greene County had a nu mber of ambient monitors and most of the model input data were already well-defined, it was selected for further modeling in the 2 nd draft REA. Additionally, staff decided that modeli ng a large urban area woul d be advantageous inJuly 2009 190 combining both large emission sources and large potentially exposed populations. Modeling for St. Louis, Mo. was already underway at the time the 1 st draft REA was completed, therefore it was decided that exposure modeling in this dom ain should be continued and expanded for other

sources for the 2 nd 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 Miss ouri including Christian, Dallas, Greene, Polk, and Webster counties. The only city in the region with a populati on greater than 150,000 is Springfield, in Greene County. Greene County has a total area of approximately 678 mi 2 (1,756 km 2 ). 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 Spri ngfield-Branson Regiona l Airport (WBAN 13995)

served as the source of mete orological 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 18 th largest MSA in the

United States and includes the i ndependent City of St. Louis; th e Missouri counties of St. Louis, St. Charles, Jefferson, Franklin, Lincoln, Warre n, and Washington; as well as the Illinois counties of Madison, St. Clair, Macoupin, Clin ton, Monroe, Jersey, Bond, and Calhoun. The total MSA has an area of approximately 8,846 mi 2 (22,911 km 2 ). Due to the complexity of the air quality and exposure modeli ng performed in this exposure assessment and the focus on receptors within 20 km of stationary sources, st aff limited the modeling domain to three counties directly surrounding the c ity of St. Louis: St. Louis City, St. Louis County, and St. Charles County (see Figure 8-3). These three counties co mprise 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 th is REA was assembled from three separate modeling domains described in the 1 st draft SO 2 REA, aggregated to utilize the most reliable hourly meteorological data available (St. Louis International-Lambert Field; WBAN 13994). It was then reduced to just the three counties of the urban core described above. Figure 8-3 shows the modeling domain for the greater St. Louis, MO area.July 2009192Figure 8-3. Three county modeling domain for St. Louis, Mo., along with identified emissions sources, air quality receptors, ambientmonitors, and meteorological station.July 2009 193 8.3.3 Time Period of Analysis Calendar year 2002 was simulated for both m odeling domains to characterize the most recent year of emissions data available for the study locations. Year 2002 temperature and precipitation used in the disper sion modeling was compared with 30-year climate normal period data from 1978 through 2007. For Greene Count y, 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 temp eratures were warmer on average than the 30- year normal (57.9 °F compared to 56.8 °F) and r eceived 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 popul ation residing in each modeled area and population subgroups that were considered more su sceptible 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 indivi dual was at moderate or greater exertion levels during the exposure events (see sections 8.5.5 and 8.8.2).



### 8.4 CHARACTERIZATION OF AMBI ENT HOURLY AIR QUALITY DATA USING AERMOD

8.4.1 Overview Air quality data used for input to APEX we re 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 meteorologi cal fields (e.g., temperature, wind speed, precipitation), and information on su rface characteristic s 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 cate gories 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 us ed as input to the APEX model to estimate populati on exposure concentrations. De tails regarding both modeling approaches and input data used are provided below. Supplemen tal information regarding model inputs and methodology is provi ded 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 i nput meteorological data for AE RMOD. 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 of ten high incidence of cal ms and variable wind conditions reported for the Automated Surface Obse rving Stations (ASOS) in use at most NWS

stations. A variable wind obs ervation may include wind speeds up to 6 knots, but the wind direction is reported as missi ng. The AERMOD model curren tly 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 us ed to supplement the standard archive of winds reported for each station in the Integrated Surface Hourly (ISH) database. Details regarding this pro cedure are described in A ppendix B, Attachment 1.July 2009 195 Table 8-1. Surface stations for the SO 2 study areas.

Area Station Identifier WMO (WBAN) Latitude 1 Longitude 1 Elevation (m) Time Zone 2 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 approximatio n 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 SO 2 study areas.

Area Station Identifier WMO (WBAN) Latitude Longitude Elevation (m) Time Zone 1 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) fo r 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 Na tional Climatic Data Center (NCDC). A draft version of AERSURFACE (08256) that utili zes 2001 National Land Cover Data (NLCD) was

used to determine the surface ch aracteristics for this applicat ion 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 AERSURFA CE available on EPA’s SCRAM website. All

stations considered were located at an airport. Monthly season al 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 wint er options can be winter (noJuly 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 st acks listed as in the same lo cation with identical release parameters within a certain reso lution (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 SO 2 emission total exceeding 1, 000 tpy in 2002. Within such facilities, every stack emitting more than on e 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. Th e locations of all emitti ng stacks were corrected based on GIS analysis. This was necessary b ecause 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 co mpared 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 locati on in the facility – such as the front office –

rather than the actual stack locations. To corre ct 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 st ack 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. Ac tivity from this port was modeled as fourteen area sources along the waterfront. All port-rela ted emission sources were considered as nonpoint area emissions with boundari es based on GIS analysis of aerial photographic images. A release height of 5.0 m with a plume initial ve rtical standard deviation (  zi ) of 2.33 m was used in all cases to represent emissions from Cate gory 1 and 2 commercial marine vessels. Port emission strength was taken from the NEI for appr opriate activity within St. Louis City and allocated uniformly by emission density for all ha rbor 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, co mmercial and institu tional facilities as identified in the NEI. Emissions from non-poi nt sources in Greene C ounty are identified for each tract in the County. In Greene County, spat ial allocation factors (SAFs) from EPA’s EMSHAP database 55 were used to disaggrega te the county-wide emissions from the NEI to census tracts. Tracts with total non-poi nt 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-poi nt area sources are 10.0 m fo r 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 we ll-defined, the release parameters were derived

55 The SAFs were derived from land use data.July 2009 198 though a series of sensitivity r uns to characterize model perfor mance 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 tr acts, based on SAFs, resulted in an inaccurate spatial pattern of emissions. Th erefore, the spatial re solution of non-point s ources in this domain was retained at the county level. However, to improve the nu merical representation of these emissions in the model, the two counties with th e 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 we re 1 km by 1 km, more closely approximating the

smaller and denser census tracts in that re gion. All county-wide non-point source emissions were spatially allocated uniformly to the grid ce lls. St. Charles County was modeled as a single area source, with edges approxi mating the full county boundaries. The release parameters for the St. Louis dom ain varied according to the urban and rural designation of individual gr id 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. Th ese were comprised of any point sources in facilities not meeting the 1,000 t py selection criteria and any re sidual 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 characte rized as county-wide po lygon 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 we re combined with the county-wide non-point sources as described above. Thus, no sepa rate background sources were simulated.July 2009 199 8.4.3.2 Urban vs. Rural Designations This section describes how urban and rura l designations were determined for each emission source type. AERMOD has somewhat di fferent 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 in creased surface heating within an urban area under stable atmospheric cond itions. The magnitude of this effect is weakly proportional to the urban area population.

Point Sources Urban or rural designations for point sour ces 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 categorie s 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 Wetland 1 31 Barren Land (Rock/Sand/Clay) 92 Palustrine Scrub/Shrub Wetland 1 32 Unconsolidated Shore 1 93 Estuarine Forested Wetland 1 41 Deciduous Forest 94 Estuarine Scrub/Shrub Wetland 1 42 Evergreen Forest 95 Emergent Herbaceous Wetlands 43 Mixed Forest 96 Palustrine Emergent Wetland (Persistent) 1 51 Dwarf Scrub 97 Estuarine Emergent Wetland 1 52 Shrub/Scrub 98 Palustrine Aquatic Bed 1 71 Grassland/Herbaceous 99 Estuarine Aquatic Bed 1 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 ca tegories are consistent with those considered developed by

AERSURFACE. 56

Non-Point Sources Non-point area sources were defined as ru ral 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 ch aracterization. Therefore, nonpoint area sources were evaluate d from both a land use and population density perspective. In Greene County, area sources were defi ned as corresponding to the census tract

boundaries. Each tract was then considered urba n or rural by considering both the population density and land use fraction fr om NLCD2001. If the population density was greater than 750 persons/km 2 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 fr om such a tract would likely be subject to urban dispersion conditions. As explained above, for the St. Louis modeli ng domain, the counties with the greatest non-point emissions – St. Louis C ity and St. Louis County – were subdivided into regular grid cells, while St. Charles County was represente d 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 SourcesOnly 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 thei r associated industr ial 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 cl assified with the same procedures as for non-point area sources. Both Greene and Ch ristian 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 th e current release version of the tool (version 08280). In particul ar, this updated version allows us e of 1 arc-second terrain data

from the USGS Seamless Server 58 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 th ese 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 facil ity 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 (E PA, 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 parame ters (i.e., height, diameter, exit temperature, exit velocity), and annual SO 2 emissions. The CAMD database has information on hourly SO 2 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 NO 2 emission data for both electric generating units and other types of industrial facilities. In the case of facilities for which CAMD has hourly NO 2 data but not SO 2 data, SO 2 relativeJuly 2009 202 complimentary information, and were first eval uated for matching facility data. However,

CAMD lacks SO 2 emissions data for facilities other than electric-generating units. To convert

annual total emissions data from the NEI into ho urly temporal profiles required for AERMOD, a three tiered prioritization was used, as follows.

1. CAMD hourly concentrations to cr eate relative tem poral profiles.

2. EMS-HAP seasonal and diurnal temporal pr ofiles 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 m odel domains identified above (11 in Greene County and 45 cross-border and 38 within-state in the St. Lo uis domain), 35 (11 in Greene County and 7 cross-border and 17 in-state in th e 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 SO 2 emissions. For these stacks the relativ e hourly profiles were derived from the hourly values in the CAMD database, and the annua l emissions totals were taken from the NEI. Hourly emissions in the CAMD database were sc aled 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 ar e 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 estimate s would be consistent across all sources.

Tier 2: EMS-HAP to NE I Emissions Profiling Of the 94 major facility stacks within the tw o MO domains, 38 stacks (all of which are

cross-border stacks in the St. L ouis 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 season al 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 NO 2 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 ho urs 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. Appendi x 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 do mains were accounted for directly in the dispersion modeling. Table 8-5 shows the point s ource 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 re levant 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 EM S-HAP model for sectors matching the modeled activity. Table 8-5 shows the port source contri bution modeled directly within each modeling domain and compares it to the total non-road emissions.

Non-Point and Background Sources Non-point polygon area s ources were developed to capture non-point

commercial/institutional and industrial emissions with in the domains, as specified in the NEI. For the St. Louis modeling domain, all non-point em issions were included either in gridded area sources over St. Louis City and St. Louis C ounty or a polygon area so urce 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 count y that captured approximately 87 pe rcent of the relevant emissions countywide from the NEI. Other non-point sour ces, as well as on-road mobile and non-road

mobile sources were incl uded in the background source Because non-point area source and backgr ound area source temporal profiles are unknown, staff derived profiles that provided a best-fit match betw een the model predictions and monitor data. To determine the most represen tative average non-point area source emissionJuly 2009 204 profile across each modeling domain, we first selected monitors where ambient concentrations were expected to be primarily influenced by ar ea sources. Due to their locations relative to sources, all but one monitor (ID 290770032) in Greene County indicat ed 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. Ne xt, 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 mon itor and as an average of the errors at the

seven individual St. Louis area monitors. In bo th cases, the error function was defined as the ratio of the total observed concentration, minus the total concentrati on due to all non-point sources, to the concentration pr edicted by the non-point sources alone. This diurnal error function was then normalized such that its averag e 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 emi ssions models, such as SMOKE and EMS-HAP. The shape of the derived temporal profiles impl y 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 extr eme daytime-dominated patterns. Given the large uncertainties about the actual emission sources represented by the industrial and commercial/institutional non-point ca tegory and given that such sources are likely to be small facilities, it is reasonable to assume that thei r cumulative emissions occur almost exclusively during daytime hours. Table 8-5 shows the non- point source contribu tion modeled directly within each modeling domain and compar es it to the total non-point emissions. 61

61 Table 8-5 does not have the relevant background c ontribution for each domain. This is because the total background in each domain includes not only the counties in the modeling dom ain (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 a nd 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 DayNormalized Emissions (unitless) EMS-HAP: Industrial SMOKE EMS-HAP: Commercial/Institutional Best-fit to Monitors

Figure 8-4. Derived best-fit non-point area sour ce 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 DayNormalized 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 represen t 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 recept ors were US Census block centroids in the Greene County and St. Louis modeling domains, (Fi gures 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 tota l of 17,703 receptors were select ed in the St. Louis modeling domain and a total of 5,359 receptors were se lected in the Greene County modeling domain. The second set of receptors included the locations of the available ambient SO 2 monitors. These receptors were used in evaluating the di spersion model performance. In Greene County,

there were five ambient monitors with valid am bient monitoring concentrations (Figure 8-2). Within the three St. Louis counties, ther e were seven monitors (Figure 8-3).

62 The block centroids used for this an alysis 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 SO 2 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 SO 2 monitors. Rather than compare concentrations estimated at a single modeled receptor point to th e ambient monitor concen trations, 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 we re 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 monito r concentration distribution.

The 2.5 th and 97.5 th percentiles from all mon itor distribution percentiles 64 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 f unctions (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 ex treme 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 SO 2 concentrations. AERMOD r eceptor concentrations during each hour-of-the-day were averaged (i.e., 365 valu es for hour 1, 365 values for hour 2, and so on) to generate an annual average SO 2 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 concentra tion distribution. Then say the 73 rd percentile concentration prediction is to be estimated for each receptor. The lower bound of the 73 rd percentile of the modeled receptors would represented by the 2.5 th percentile of all the calculated 73 rd percentile concentration predictions, i.e., the 25 th highest 73 rd percentile concentration prediction across the 1,000 73 rd percentile values generated from all of the receptors. Note that at any given percentile along either of the en velope bounds as well as at the centra l tendency distribution (the receptor 50 th 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 diurna l 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 SO 2 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 monitore d and AERMOD modeled

SO 2 concentrations (at the mon itor receptor location) were us ed as input to the 5-minute statistical model and processed as described in section 7.2.5. Measured 5-minute maximum SO 2 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 2.5 th and 97.5 th ) 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 year 66 were estimated for each monitor using the 1-hour concentration percentiles representing each AERMOD distribution (i.e., the AERMOD monitor receptor, the AERMOD 2.5 th , and the AERMOD 97.5 th ). Then, scaling factors were calculated by dividing each the AERMOD 2.5 th and AERMOD 97.5 th benchmark exceedance results by that of the ex ceedances estimated using the AERMOD monitor receptor. These scaling factors were then applied to the full AERM OD 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 monito r, 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 re presenting a particular time but are a temporal and spatial mixture of low and high concentratio ns surrounding each monitor, sp ecific counts of days per year could not be calculated. Sta ff 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 th e 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 dist ributions were used; th e distribution of the

modeled 1-hour SO 2 concentrations estimated for the monitor receptor, the upper and lower bounds of the receptor envelope (i.e., generated from all receptors with in 4 km of monitor receptor), and the hourly concentration distribut ion 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 tota l hourly distribution or CD Fs, monitor concentration distributions are generally bounded by the mode led distributions. At some of the upper percentiles of the distributions , the deviations were of vary ing direction (over- or underprediction) and magnitude (a few ppb to tens of ppb). For example, monitor ID 290770026 (Figure 8-6) exhibits higher meas ured concentrations at the uppe r percentiles of the distribution that extend beyond the AERMOD prediction envelope, however the deviation occurred beyond

the 99.5 th percentile (maximum obser ved =114 ppb, AERMOD 97.5 th = 101 ppb). At monitor ID 290770032 (Figure 8-6), the measured concentrat ions fall below the prediction envelope, beginning just beyond the 95 th 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 m onitor 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 fo r both of these monitors, encompassing the

ambient measured concentrations from the 80 th through the 99.5 th percentiles for both, while completely enveloping all 1-hour con centrations at monitor ID 290770041. The pattern in the AERMOD modeled concentr ations at the monitor location and the

ambient measurement concentration distributi on for monitor ID 290770037 is nearly identical. The only difference observed is th at the measured concentrations are 1-3 ppb greater than the

modeled concentrations within the 99 th percentile of the distributi on. Much of the measured distribution falls within the AERMOD prediction envelope, with deviation occurring just beyond

the 99.5 th percentile.July 2009 210 The diurnal pattern observed at each of the ambient monitors is represented well by the modeled concentrations; in general concentrat ions are elevated duri ng 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 concen trations for monitor ID 290770032 are below that of the upper AERMOD predic tion 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 re sult of the concentrations at

or below the 80 th percentile, where measured concentrations were always greater than any of the predicted concentrations at corr esponding percentiles of the dist ribution. While the prediction envelopes encompassed the diurnal patte rn observed at monitor IDs 290770040 and 290770041 (Figure 8-7), the results for the modeled concentra tions at the monitor locations were not equally representative. The diurnal pa ttern and magnitude of concentr ations was well reproduced at monitor ID 290770041, while modeled concentrations at the monitor location during the midday and evening hours were greater than the meas ured concentrations at monitor ID 290770040. Staff evaluated the potential impact the predic ted 1-hour concentrations would have on 5- minute air quality benchmark exceedances (Table 8-6) . In general, the re sults for the estimated numbers of days per year with 5-minute concentr ations above benchmark levels followed similar patterns to those observed above when c onsidering comparisons of the 1-hour SO 2 concentration

distributions. The numbers of benchmar k exceedances at monitor ID 290770026 were underpredicted by AERMOD just as was the 1-hour SO 2 concentrations at that monitoring location. However, the number of days with 5-minute con centrations above the benchmark levels for both

the measured and modeled ambient concentra tions fell within the range of the AERMOD prediction envelopes. There was good agreement in the number of days pe r 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 SO 2 concentration does not result in unexpected differences in the number of days per year having 5-minute SO 2 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 SO 2 benchmark exceedance at ambient monitors in Greene County. Number of Days per Year with a 5-minute SO 2 Concentration Above Air Quality Benchmark Level Ambient Monitor 1 AERMOD 2 Monitor ID 5-minute SO 2 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 SO 2 ambient monitor measurements input to t he 5-minute statistical model. The measured numbers of 5-minute benchmark exceedances were calculated from ambient monitors reporting 5-minute SO 2 concentrations. Both of these va lues 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 SO 2 ambient predictions (at monitor receptor locati on) input to the 5-minute statistical model. AERMOD p2.5 and p97.5 benchmark exceedances were generated from the corresponding hourly prediction envelope distri bution and input to the 5-minute statistical model.July 2009212Figure 8-6. Comparison of measured ambient monitor SO2concentration distribution and diurnal profile with the modeled monitorreceptor and receptors within 4 km of monitors 290770026 and 29077032 in Greene County, Mo. Maximum 1-hour concentrationpercentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.Monitor ID 29077002601234567806121824Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorMonitor ID 29077003201234506121824Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorMonitor ID 2907700260.010.11101000 10 20 30 40 50 60 70 80 90 100 110 1201-hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorMonitor ID 2907700320.010.11101000 10 20 30 40 50 60 701-hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorJuly 2009213Figure 8-7. Comparison of measured ambient monitor SO2concentration distribution and diurnal profile with the modeled monitorreceptor and receptors within 4 km of monitors 290770040 and 29077041 in Greene County, Mo. Maximum 1-hourconcentration percentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.Monitor ID 29077004001234567806121824Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorMonitor ID 29077004101234567806121824Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorMonitor ID 2907700410.010.11101000 10 20 30 40 50 60 70 80 90 100 1101-hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorMonitor ID 2907700400.010.11101000 20 40 60 80 100 120 140 160 180 2001-hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorJuly 2009214Figure 8-8. Comparison of measured ambient monitor SO2concentration distribution and diurnal profile with the modeled monitorreceptor and receptors within 4 km of monitor 290770037 in Greene County, Mo. Maximum 1-hour concentration percentile isdefined as 0.01 (or 100-99.99) because log(0) is undefined.Monitor ID 29077003701234567806121824Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorMonitor ID 2907700370.010.11101000 10 20 30 40 50 60 70 80 90 100 110 120 130 140 1501-hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorJuly 2009 215 8.4.5.2 St. Louis Modeled Air Quality Evaluation For St. Louis, there were seven monito rs used for comparison with the AERMOD

concentration estimates. The dist ribution of the m odeled 1-hour SO 2 concentrations estimated for the monitor receptor, the receptor envelope (i.e., all receptors with in 4 km of monitor receptor), and the hourly concentration distri bution 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 generate d for Greene County. This indicates that, in comparison with the Greene County modeling doma in, there is less spatial variability in the concentrations modeled at recep tors surrounding the ambient monito ring 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 lo cations and ambient measured concentration

distributions showed better overall agreement at the St. L ouis monitors, though many of the measured concentrations are outside of the pred iction envelopes. For example, at monitor ID 291890006 all measured concentrations up to the 99 th percentile fell below the prediction envelope (Figure 8-9) (the ma ximum was within). Note however that the difference in the measured concentrations was only about 1 ppb when compared with concentr ations at any of the envelope percentiles and at most 2 ppb when comp ared with the modeled concentrations at the monitor receptor. In addition, because most of these under-predictions oc cur 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 predic tion envelopes; 6 of 7 monitors at the maximum percentile were within, 3 of 7 monitors at the 99 th percentile were within, and 4 of 7 monitors at

the 95 th percentile were within th e prediction envelopes. Where measured upper percentile concentrations were outside of the prediction envelopes, it was consis tently beneath the 2.5 th prediction, possibly indicating AE RMOD over-prediction at these mon itors at certain percentiles of the distribution. When comparing the AERM OD monitor concentrations with the measured

ambient concentrations between the 80 th and 99 th percentile of the dist ribution, most of theJuly 2009 216 predicted values were greater than the measuredconcentrations. The magnitude of this overprediction ranged from about 1 to 2 ppb, alt hough one monitor had a 7 ppb difference at the 99 th percentile. Predictions at the maximum concentra tions were more balanced; 4 of the 7 monitors had over-predictions, while all pr edictions (under or over) were approximately within 10 to 35 ppb of the measured concentrations. The diurnal pattern was reproduc ed 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 90 th 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 concentrati ons 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 2009217Figure 8-9. Comparison of measured ambient monitor SO2concentration distribution and diurnal profile with the modeled monitorreceptor and receptors within 4 km of monitors 291890004 and 291890006 in St Louis, Mo. Maximum 1-hour concentrationpercentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.Monitor ID 2918900040.010.11101000 10 20 30 40 50 60 70 80 90 100 1101- hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorMonitor ID 2918900060.010.11101000 10 20 30 40 50 60 70 80 90 1001- hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorMonitor ID 29189000401234560 6 12 18 24Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorMonitor ID 291890006012345606121824Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorJuly 2009218Figure 8-10. Comparison of measured ambient monitor SO2concentration distribution and diurnal profile with the modeled monitorreceptor and receptors within 4 km of monitors 291893001 and 291895001 in St Louis, Mo. Maximum 1-hour concentrationpercentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.Monitor ID 2918950010.010.11101000 20 40 60 80 100 120 140 160 180 2001- hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorAERMOD P97.5Maximum = 545 ppbMonitor ID 2918930010.010.11101000 10 20 30 40 50 60 70 80 90 1001- hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorMonitor ID 291893001012345678906121824Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorMonitor ID 291895001012345670 6 12 18 24Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorJuly 2009219Figure 8-11. Comparison of measured ambient monitor SO2concentration distribution and diurnal profile with the modeled monitorreceptor and receptors within 4 km of monitors 291897003 and 295100007 in St Louis, Mo. Maximum 1-hour concentrationpercentile is defined as 0.01 (or 100-99.99) because log(0) is undefined.Monitor ID 2951000070.010.11101000 10 20 30 40 50 60 70 80 90 100 110 120 130 1401- hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorMonitor ID 2918970030.010.11101000 10 20 30 40 50 60 70 80 90 100 110 120 130 1401- hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorMonitor ID 291897003012345670 6 12 18 24Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorMonitor ID 2951000070123456789100 6 12 18 24Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorJuly 2009220Figure 8-12. Comparison of measured ambient monitor SO2concentration distribution and diurnal profile with the modeled monitorreceptor and receptors within 4 km of monitor 295100086 in StLouis, Mo. Maximum 1-hour concentration percentile isdefined as 0.01 (or 100-99.99) because log(0) is undefined.Monitor ID 2951000860.010.11101000 10 20 30 40 50 60 70 80 90 100 110 120 1301- hour SO2Concentration (ppb)

100-Cumulative PercentileAmbient MonitorAERMOD P2.5AERMOD P97.5AERMOD MonitorMonitor ID 2951000860123456789100 6 12 18 24Hour of the Day

Annual Mean SO 2 Concentration (ppb)AERMOD P2.5AERMOD P97.5Ambient MonitorAERMOD MonitorJuly 2009 221 8.4.4.3. Using unadjusted AERMOD predicted SO 2 concentrations The SO 2 concentrations estimated using AERMOD do not have a particular directional

influence in over- or under-estim ating 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 monito ring 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 modifyi ng model inputs including an adjustment of the area source temporal em ission profile to improve the comparison of the model predictions with the measurement data. St aff went through several iterations of evaluating the model performance in each modeling domain following model input adjustments to obtain the current modeled air quality re sults. Given the time and resour ces to perform this assessment, the good agreement in the model-to-monitor co mparisons, the degree of confidence in the dispersion modeling system, the spatial representa tion 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 detail s on the staff's reasoning are provided in section

8.11.



### 8.5 SIMULATED POPULATION APEX takes population characteristics into acco unt to develop accurate representations of study area demographics. Specifically, ag e- and gender-specific population counts and employment probability estimates, asthma prev alence rates, and home-to-work commuting locations and probabilities were used to de velop representative profiles of hypothetical individuals used in the exposure modeling simu lation. In addition, body surface area (BSA) and activity-specific ventilation rates are two importa nt 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. ChildrenJuly 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 th ese subgroups with APEX requires the estimation of children’s asthma prevalence rates. The propor tion of the population of children characterized as being asthmatic was estimated by statistics on asthma prevalence rates recently used in the NAAQS review for O 3 (US EPA, 2007d). See Appendix B, Attachment 2 for details on the derivation. Specifically, an anal ysis of data provided in the Na tional 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 prev alence observed with children of different ages. Adult asthma prevalence rates were estimat ed 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 si mulated 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 we ighted the asthma prevalence by population in the three counties re ported 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% mode led 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. Asthma Prevalence (%)

Modeling Domain (Region) Age 1 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 Natio nal Health Interview Survey (NHIS) for 2003 (CDC, 2007); ages >17 from (MO DOH, 2002)

.

Table 8-8. Population modeled in Green e County and St. Louis modeling domains. Population Asthmatic Population Modeling 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 fr om 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 generateJuly 2009 224 block-level commuting files for APEX using the tract-level commuting data and finely-resolved land use data, assuming the frequency of commut ing to a workplace block within a tract is proportional to the amount of commercial and indust rial land in the block. Further details are provided in Appendix B.2.2.2.

Note that while travel on roads was account ed 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 fo r each simulated individual. Briefly, the BSA calculation is based on logarithmic relations hips developed by Burmaster (1998) that use body mass (BM) as an independent variable as follows:

6821.0 2781.2 BMeBSA   equation (8-1)

where,

BSA = body surface area (m 2 )

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 perfo rm the activity. Minute or total ventilation

rate is used to describe th e 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/nhane s/nhanes_questionnaires.htm.July 2009 225 Quantitatively, the volume of air breathed in per minute ( 

I V ) is slightly greater than the volume

expired per minute ( E V  ). Clinically, however, this difference is not important, and by

convention, the ventilation rate is alwa ys measured on an expired sample or E V 

. The rate of oxygen consumption ( 2 O V  ) is related to the rate of energy usage in performing activities as follows:

ECFEEV

O   2 equation (8-2)

where,

2 O V  = Oxygen consumption rate (liters O 2 /minute)

EE = Energy expenditure (kcal/minute)

ECF = Energy conversion factor (liters O 2 /kcal).

The ECF shows little variation and typically, a value between 0.20 and 0.21 is used to represent the conversion from en ergy units to oxygen consumption units. In this REA, APEX randomly sampled from a uniform distributi on 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 meta bolic 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 dist ributions 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 simulate d individual using a linear regression model (see Johnson et al., 2000) 68 as follows:

FBMbbRMR ])([

10    equation (8-4) where,

RMR = Resting metabolic rate (kcal/min)

b o = Regression intercept (MJ/day)

b 1 = 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) de scribe an approach to estimate E V  using 2 O V 

. 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 consump tion, the variability within age groups, and both inter- and intra-personal and variability. The basic algorithm follows:

wb O E eegenderbage bBMVbbBMV     

3 2 2 10 )1(ln)/ln( )/ln( equation (8-5) where,

ln = natural logarithm of variable

BMV

E /  = activity-specific ventilation rate, body mass normalized (liter air/kg) b i = see below

BMV

O /

2  = activity-specific oxygen consum ption rate, body mass normalized (liter/O 2 /kg) age = the age of the individual (years) gender = gender value (-1 for males and +1 for females)

e b = randomly sampled error term for betw een persons N{0, se), (liter air/kg)

e w = randomly sampled error term for with in 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-ind ividual variability) residuals distribution ( e b ) and within-person (intra-i ndividual variability) re siduals distribution ( e w ). The regression parameters

b 0 , b 1 , b 2 , and b 3 are assumed to be constant over time for all simulated persons, e b is sampled once per person, while whereas e w is sampled from event to event. Point estimates of the regression coefficients and standard errors of th e residuals distributions are given in Table 8-9.

Table 8-9. Ventilation coefficient parameter estimates ( b i ) and residuals distributions ( e i ) from Graham and McCurdy (2005). Regression Coefficients 1 Random Error 1,2 Age group b 0 b 1 b 2 b 3 e b e w

<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 coefficient s 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 LONGIT UDINAL ACTIVITY SEQUENCES Exposure models use human activity pattern data to predict and estimate exposure to pollutants. Different human activities, such as spending time outdoors, i ndoors, or driving, will result in varying pollutant exposure concentratio ns. To accurately model individuals and their exposure to pollutants, it is cr itical 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 pattern s were estimated from single days by combining the daily records using an algorithm that represen ts 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 limitedJuly 2009 228 number of daily patterns is th en 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 correlat ion (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 supportin g evaluations are provi ded in Appendix B,

Attachments 4 and 5.



### 8.7 CALCULATING MICROENVIRON MENTAL 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 (outd oor) pollutant concen tration 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 AP EX calculates hourly concentrations in all the

microenvironments at each hour of the simulatio n for each of the simulated individuals using one

of two methods: a mass balance model or a tran sfer factors method. Details regarding the algorithms used for estimating specific microenvir onments 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 mi croenvironment is estimated using the following

processes:

 Inflow of air into the microenvironment

 Outflow of air from the microenvironment

 Removal of a pollutant from the microenvi ronment 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 cal culate 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 microenviron ment and the ambient concentrations (at the measurements site or modeled receptor). The s econd parameter, a penetr ation factor, quantifies the amount of outdoor pollutant that pe netrates into the microenvironment.

8.7.1 Approach for Estimating 5-Minute Maximum SO 2 Concentrations Five-minute maximum SO 2 concentrations in each ex posure modeling domain were estimated using the empirically-derived PMRs (developed from recent 5-minute SO 2 ambient monitoring data, see section 7.2) and the AERMOD predicted 1-hour SO 2 concentrations. Thus, for every 1-hour SO 2 concentration estimated at every receptor, an associated 5-minute

maximum SO 2 concentration was generated (i.e ., twenty-four 5-minute maximum SO 2 concentrations per day). These statistically modeled 5-minute maximum SO 2 concentrations

were then used to estimate the eleven other 5-mi nute concentrations that occur within every hour (see below). This spatially complete (at the bloc k level) and consecutive time-series of 5-minute

SO 2 concentrations then served as the ambient c oncentrations input to algorithms within APEX

that estimate the microenvironmental concentrations.

The current version of APEX can use ambien t 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 th is 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 twel ve, 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 h our by addressing thes e 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 SO 2 concentrations that occur within the hour. The objective of the approach used was not to

estimate each of the other eleven 5-minute concen trations 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 SO 2 concentrations could provide an accurate estimate of the maximum exposure an individual might experience in a day. 70 Further discussion regarding multiple peak e xposures within an hour is given in section

8.11. The technical approach to estimating SO 2 concentrations real-t ime 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 SO 2 concentrations using the

1-hour SO 2 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 __

 
 n

PCn X equation (8-6)

where, X = 5-minute SO 2 concentration in each of non-p eak concentration periods in the hour at a receptor (ppb)

__ C = 1-hour SO 2 concentration estimated at a receptor (ppb) P = estimated 5-minute maximum SO 2 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 SO 2 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 SO 2 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 expo sure locations for simulated individuals. For exposures to be estimated accurately, it is impor tant to have realistic microenvironments that match closely to the locations wh ere 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 transf er 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 micr oenvironment 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-depos ition rate, PR=proximity factor, PE=penetration factor

8.7.3 Microenvironment Descriptions

8.7.3.1 Microenvironment 1: Indoor-Residence The Indoor-Residence microenvironment us es several variables that affect SO 2 exposure: whether or not air conditioning is present, the average outdoor temperature, the SO 2 removal rate, and an indoor concentration source.

Air conditioning prevalence rates Since the selection of an air exchange rate distribution is conditioned on the presence or absence of an air-conditioner, for each modeled area the air conditioning status of the residential microenvironments is simulated randomly using the probability that a residence has an airJuly 2009 232 conditioner. A value of 96% was used to repres ent 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 pr evalence 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 resident ial microenvironment were the same used in

APEX for the most recent O 3 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, logno rmal distributions provide d 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 summa rizes the AER distributions used in modeling indoor residential exposures, separated by A/C prevalence and te mperature 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 Type 1 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 re ported 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 fact ors. When categorizing by temper ature, a range of temperatures

was used to maintain a reasonable number of sa mples within each category to allow for some variability within the category, while still allo wing for differences across categories. SeveralJuly 2009 233 distribution forms were investig ated (i.e., exponential, log-normal , normal, and Weibull) and in general, lognormal distributions provided the best fit. Fitted lognor mal 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 consider ing 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.

SO 2 Removal Rate Staff estimated distributions of indoor SO 2 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 SO 2 deposition rates that in turn were used to parameterize lognormal distributions (Table 8- 12). The modeling was fundamentally based on a

review of SO 2 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 ar e provided in Appendix B, section 4.

Table 8-12. Final parameter estimates of SO 2 deposition distributions in several indoor microenvironments modeled in APEX.

Heating or Air Conditioning in Use or Low Ambient Humidity 1 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, Shoppi ng, and the broadly defined Other Indoor microenvironments, were all modeled using the sa me 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 th e same distribution in APEX used for the most

recent O 3 NAAQS review (EPA, 2007d) and NO 2 REA (EPA, 2008d). See Appendix B,

Attachment 6 for details in the da ta used and derivation. The SO 2 removal rates in these six indoor microenvironments were estimated as explai ned in section 8.7.3.1, and described in more detail in Appendix B, section 4. The resul ting lognormal distributions for removal rates are presented in Table 8-12. These microenvironme nts are all assumed to have air-conditioning.

8.7.3.3 Microenvironments 8-10: Outdoor Microenvironments All outdoor microenvironmenta l concentrations are well represented by the modeled concentrations. Therefore, both the penetr ation factor and proximity factor for this

microenvironment were set to 1.

8.7.3.4 Microenvironments 11 and 12: In Vehi cle- Cars and Trucks, and Mass Transit There were no available measurement data for SO 2 penetration factors, therefore the penetration factors used were developed from NO 2 data provided in Chan and Chung (2003) and

used in the recent NO 2 NAAQS review (EPA, 2008d). NO 2 and SO 2 are expected to have similar penetration rates inside vehicles sin ce both are gases. A lthough the in-vehicle NO 2 measurements used in the in-vehicle-to-outdoo r-ratios might include a small amount of invehicle emissions, resulting in some discrepa ncy between effective pe netration factors for NO 2 and SO 2 , the additional uncertainty is expected to be small compared to the overall uncertainty implied by the broad uniform distributions.

Inside-vehicle and outdoor NO 2 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 unif orm 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 moreJuly 2009 235 formal distribution and the lack of data availa ble to reasonably assign potentially influential

characteristics such as use of vehicle ventilatio n systems for each location. Mass transit systems, due to the frequent opening and closing of door s, was assigned a uniform distribution U{0.8, 1.0} based on the reported mean values for fres h-air intake (0.796) a nd open windows (1.032) on

urban streets.



### 8.8 EXPOSURE MEASUR ES 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 calculate s exposure by identifying

concentrations in the microenvironments visited by the person according to the composite diary. In this manner, a time-series of event expos ures are found. Then, the time-step exposure concentration at any clock hour during the simula tion period is calculat ed using the following equation:



### T t C C N

j jjsteptime

i 

# 

 



###  1 )()( equation (8-7)

where,

C

i = Time-step exposure concentration at clock hour i of the simulation period (ppm) N

= Number of events (i.e., micr oenvironments visited) in time-step i of the simulation period.

)( jsteptime C

 = 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 calcula tes time-series of 5-minute, 1-hour, 24-hour, and annual average exposure concentrations th at a simulated individua l would experience during the simulation period. APEX th en 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 peopl e whose exposure exceeded a specified SO 2 concentration level 1 or more times in a ye ar and the number of times per year that they are so exposed; the latter metric is in terms of person-occurrences or person-days. TheJuly 2009 236 former highlights the number of indivi duals whose exposure exceeded at least one or more times per modeling period the health effect benchmark le vel 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 enti re population residing in an area. In this exposure assessment, APEX tabu lates 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 to tal population and subpopulation s (i.e., asthmatics, asthmatic children) of interest.

8.8.2 Estimation of Target Ventilation Rates Human activities are variable over time, a wi de range of activities are possible even within a single hour of the day. The type of activity an individual perfor ms, such as sleeping or jogging, will influence their breathing rate. As di scussed above in section 8.5.5, APEX estimates minute-by-minute ventilation rates that account fo r the expected variabil ity in the activities performed by simulated individuals. The ISA i ndicates that the adverse lung function responses associated with short-term peak exposures at le vels 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 character ize the estimated exposures of interest. The target ventilation for adults (both a mix of males and females) experiencing effects

from 5-10 minute SO 2 exposures in many of the contro lled 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 asso ciated with that ventil ation 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 ( . E V ) to their body surface area (BSA). Staff has used EVR in previous O 3 NAAQS reviews to also

71 Note that study subjects were free-breathing; thus it is expected that there was a mi xture 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 ventilatio n rates for children and adults (EPA, 2007d; Whitfield et al., 1996). In these revi ews, an EVR ranging from 16-30 L/min-m 2 was associated with moderate exertion over a 1-hour exposure event, while an EVR ranging from 13-27 L/minm 2 was associated with moderate exer tion over an 8-hour exposure event.

As was done in the O 3 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 re levant ventilation data were not separated by gender. Staff obtained median estimates of BSA for males (1.94 m 2 ) and females (1.69 m 2 ) (EPA, 1997) and

calculated a mean value of 1.81 m 2 . Based on this data, an EVR = 40/1.81 = 22 L/min-m 2 was used to characterize the minimum ta rget ventilation rate of interest . Individuals at or above an

EVR of 22 L/min-m 2 (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 Char acterization (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 ex posure 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 benchmar k level (i.e., division by the adjustment factor) is mathematically equivalent to a proportional ad justment 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 adjusti ng 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 m eet the standards. This would correspond to downward adjustments to the benchmark levels.July 2009 238 based on an annual average SO 2 concentration of 5.96 ppb observ ed 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 concentr ation input. To illustrate the relationship between the two procedures (air qual ity 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 SO 2 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 SO 2 concentrations from both the adjusted and unadjusted 1-hour SO 2 concentrations. If one were in terested in the number of days per year with 5-minute SO 2 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 SO 2 concentrations above 78.4 ppb using the as is air quality. For additional clarity, the same ambient air qu ality 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 qualit y adjusted to just meet the current standard, there were 14 days where the maximum 5-mi nute 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, ther e are the same number of days with exceedances (i.e., 14 exceedances). Due to the relationsh ip between the two procedures, the estimated

number of exceedances at each of the other benc hmark levels is identical (Table 8-13). The values for each adjusted benchmark level co nsidering 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 SO 2 (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 mo nitoring 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 SO 2 (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 pe rcentile modeled daily 5-minute maximum SO 2 concentrations using either adjusted 1-hour ambient SO 2 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 SO 2 benchmark exceedances at monitor 390350045 in Cuyahoga County for year 2002.

Benchmark Level (ppb) Adjusted Benchmark Level 1 (ppb) Concentration Distribution Percentile 2 Number of Days with a Benchmark Exceedance 3 100 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 standa rd or applying adjusted benchmarks to as is air quality was the same.July 2009241Table 8-14. Exposure concentrations and adjusted potential health effect benchmark levels used by APEX to simulate just meeting thecurrent and potential alternative standards in the Greene County and St Louis modeling domains.Exposure Concentrations and Adjusted Potential Health Effect Benchmark Levels (ppb)3ModelingDomain Form1Level250 100 150 200 250 300 350 400 450 500 550 600 650 700 750 80098 200 20.3 40.5 60.8 81 101.3 121.5 141.8162 182.3 202.5 222.8 243 263.3 283.5 303.8 32499 50 94.3 188.7 283 377.3 471.7 566 660.3 754.7 849 943.3 1037.7 1132 1226.3 1320.7 1415 1509.399 100 47.2 94.3 141.5 188.7 235.8 283 330.2 377.3 424.5 471.7 518.8 566 613.2 660.3 707.5 754.799 150 31.4 62.9 94.3 125.8 157.2 188.7 220.1 251.6 283 314.4 345.9 377.3 408.8 440.2 471.7 503.199 200 23.6 47.2 70.8 94.3 117.9 141.5 165.1 188.7 212.3 235.8 259.4 283 306.6 330.2 353.8 377.399 250 18.9 37.7 56.6 75.5 94.3 113.2 132.1 150.9 169.8 188.7 207.5 226.4 245.3 264.1 283 301.9CS 14.4 28.8 43.2 57.6 72 86.4 100.8 115.2 129.6 144 158.3 172.7 187.1 201.5 215.9 230.3GreeneCountyas is50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 80098 200 13.3 26.5 39.8 53 66.3 79.5 92.8106 119.3 132.5 145.8 159 172.3 185.5 198.8 21299 50 63.3 126.7 190 253.3 316.7 380 443.3 506.7 570 633.3 696.7 760 823.3 886.7 950 1013.399 100 31.7 63.3 95 126.7 158.3 190 221.7 253.3 285 316.7 348.3 380 411.7 443.3 475 506.799 150 21.1 42.2 63.3 84.4 105.6 126.7 147.8 168.9 190 211.1 232.2 253.3 274.4 295.6 316.7 337.899 200 15.8 31.7 47.5 63.3 79.2 95 110.8 126.7 142.5 158.3 174.2 190 205.8 221.7 237.5 253.399 250 12.7 25.3 38 50.7 63.3 76 88.7 101.3 114 126.7 139.3 152 164.7 177.3 190 202.7CS 8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128St. Louisas is50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800Notes:1The form of the standard used to adjust the air quality. 98 is the 98thpercentile 1-hour daily maximumalternative standard, 99 is the 99thpercentile 1-hourdaily maximum alternative standard, CS is either the current annual average or 24-hour SO2NAAQS (whichever had the lowest factor),as isis unadjustedair quality.2The level of the potential alternativestandards, i.e., the 1-hour daily maximumat the noted percentile of the distribution.3Exposure levels were defined in 50 ppb increments from 0 through 800 ppb even though the selected potential health effect benchmark levels were 100to 400 ppb in 100 ppb increments.July 2009 242 

### 8.9 EXPOSURE MODELING AND HE ALTH RISK CHARACTERIZATION RESULTS Exposure results are presented for simulate d asthmatic populations residing in the two modeling domains in Missouri. For each individual , APEX estimates the number of days with a

5-minute SO 2 exposure above the potential health effect benchmark levels year 2002. These short-term exposures were evalua ted for all asthmatics and asthma tic children when the exposure corresponded with moderate or greater activity le vels (i.e., the simulated individuals EVR during a 5-minute exposure event was >22 L/minute-m 2 ). The number of persons and days with at least

one 5-minute SO 2 exposure at or above any level from 0 through 800 ppb in 50 ppb increments was reported by APEX. Therefore, for each concen tration level, an indivi dual 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 curren t NAAQS, and air quality adjusted to just meet several potential al ternative 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 pr ovided 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 percen t 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 abov e selected levels, and (4) the percent of time associated with the exposures at or above the se lected levels. Tables summarizing all of the exposure results for each modeling domain, ai r quality scenario, exposure level, and subpopulation are provided in Appendix B.4.

8.9.1 Asthmatic Exposures to 5-minute SO 2 Concentrations in Greene County When considering the lowest 5-minute benc hmark level of 100 ppb, approximately one

thousand asthmatics are estimated to be exposed at least once in th e year 2002 while at moderate or greater exertion and when considering the curre nt standard air quality scenario (top of Figure 8-15). Each of the potential alternative 1-hr standard air quality sc enarios as well as the as is air quality scenario result in fewer asthmatics expo sed when compared with the current standard scenario, and progressively fewer persons were exposed with decreases in the 1-hour dailyJuly 2009 243 maximum concentration levels of the po tential alternative standards. The 99 th 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 benchmar k level, there were correspondi ng 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 asthmatic s 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 SO 2 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 si mulated asthmatics had more than one day with 5- minute exposures above selected benchmark le vels (Figure 8-17). For example, when considering all asthmatics and the current standa rd 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 SO 2 concentration above this level, indicating that a number of persons may have experienced at l east 2 benchmark exceedances in the year. For both subpopulations considered, there were no estimated exposures above 300 ppb when considering the 99 th percentile 1-hour daily maximum alte rnative standard level of 200 ppb.

Staff evaluated the microenvironments where the peak exposures frequently occurred. There were very few persons exposed to benchm ark levels of 100 ppb or higher considering the

as is air quality, though 99% or greater experienced their 5-minute maximum SO 2 exposure in an outdoor microenvironment (i.e., outdoors or outd oors near-roads) when considering any of theJuly 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 sm all 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

99 th and 98 th 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 99 th percentile form of the potential alternative standard results in fewer persons, person-days, and percent of asthmatic persons exposed when compar ed with estimated expos ures using air quality adjusted to just meet a 200 ppb 1-hour daily maximum 98 th form. The values listed in the table are small, but from a relative perspective, the per cent difference can be large. For example, there is approximately a 40% reductio n in the percent of persons ex posed when considering the 99 th percentile form and the 100 ppb benchmark level. Where there were other higher benchmark levels that were exceeded, the reduction was gr eater (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 exposur e estimates for Greene County using either a 98 th or 99 th percentile form potential alternative standard at a 1-hour daily maximum level of 200 ppb. Absolute Difference in Estimated Exposures using 98 th and 99 th form 1

Population Benchmark Level (ppb) Number of Person-days Number of Persons Percentage Points 2 100 274 157 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 98 th and 99 th 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 98 th and the 99 th

. 2 Difference between the perc ent of persons exposed (98 th -200 minus the 99 th -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 SO 2 Exposure Level (ppb)Number of All Asthmatics with at LeastOne Exposure at or Above Level While atModerate 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 SO 2 Exposure Level (ppb)Number of Asthmatic Children with atLeast One Exposure at or Above LevelWhile 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 SO 2 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 SO 2 Level (ppb)Percent of All Asthmatics with at Least OneExposure at or Above Level While atModerate 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 SO 2 Exposure Level (ppb)Percent of Asthmatic Children with atLeast One Exposure at or Above LevelWhile 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 SO 2 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 SO 2 Exposure Level (ppb)Number of Person Days All AsthmaticsExperience at Least One Exposure at orAbove Level While atModerate or GreaterExertion 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 SO 2 Exposure Level (ppb)Number of Person Days AsthmaticChildren Experience at Least OneExposure at or Above Level While atModerate 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 SO 2 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 SO 2 in St. Louis The patterns in the number of persons (eithe r asthmatics or asthmatic children) exposed

in St. Louis were different fr om those observed in Greene Count y; 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 exampl e, nearly 80,000 asthmatics were estimated to experience at least one day with a 5-minute SO 2 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 we re more persons exposed to the higher benchmark levels in St. Louis compared with Greene C ounty. For example, none of the asthmatics experienced a 5-minute SO 2 concentration exposure ab ove 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 SO 2 exposure above 450 ppb ranged from a low of 16 (the 99 th percentile 1- hour daily maximum standard level of 100 ppb) to over 10,000 (the current standard air quality scenario). We note th ough, in considering the as is air quality scenario, none of the asthmatics in

St. Louis had 5-minute SO 2 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 le vels in St. Louis when compared with Greene County. For example, over 40% of asthmatic ch ildren 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 asth matic children in Greene County experienced a similar exposure (Figure 8-19). Just as obser ved with the Greene Count y estimates though, there were decreases in the percent of persons exposed with decrea ses 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 SO 2 exposure above 300 ppb when considering a 99 th percentile 1-hour daily maximu m standard level of 150 ppb. The discussion regarding the patterns observ ed in the number of persons exposed in St. Louis can be extended to the number of person days (i.e., both a greate r number and at higher benchmark levels when compared with Greene C ounty). In addition, St. Louis had a greater number of persons with multiple exceedances wh en compared with Greene County (Figure 8- 20). For example, given the 22 person days at or above 300 ppb in Greene County experiencedJuly 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 sa me benchmark level and air quality scenario in St. Louis; on average each person is estimat ed 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 co mpared with the exposures in Greene County. In St. Louis, there were a grea ter percentage of benchmark exceed ances within indoor and inside vehicle microenvironments, although overall stil l comprising a small pe rcentage 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 micr oenvironments still contribute to around 10% of exceedances, up to a 5-minute e xposure level of 800 ppb. For comparison, air quality adjusted to just meet a 99 th 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 we re also evaluated in St. Louis, using the

99 th and 98 th percentile forms of a 1-hour daily maximu m 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

99 th percentile form of the potent ial alternative standard result s 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 98 th percentile form. The impact of the different scenario is gr eater than that observed in Gr eene County from a pure numbers perspective given so few persons exposed to c oncentrations above the benchmark levels in Greene County. From a relative perspective, th e percent difference between the two scenarios can also be large. The reduction in the per cent of persons exposed when considering the 99 thJuly 2009 250 percentile form ranges from approximately 10% to 50%. For additional relative comparisons between these two standard forms, see th e 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 SO 2 Exposure Level (ppb)Number of All Asthmatics with at LeastOne Exposure at or Above Level While atModerate 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 SO 2 Exposure Level (ppb)Number of Asthmatic Children with atLeast One Exposure at or Above LevelWhile 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 SO 2 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 SO 2 Exposure Level (ppb)Percent of All Asthmatics with at Least OneExposure at or Above Level While atModerate 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 SO 2 Exposure Level (ppb)Percent of Asthmatic Children with atLeast One Exposure at or Above LevelWhile 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 SO 2 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 SO 2 Exposure Level (ppb)Number of Person Days AsthmaticsExperience at Least One Exposure at orAbove Level While at Moderate or GreaterExertion 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 SO 2 Exposure Level (ppb)Number of Person Days AsthmaticChildren Experience at Least OneExposure at or Above Level While atModerate 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 SO 2 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 SO 2 Exposure (ppb)Frequency of Exposure inMicroenvironment (%) 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 SO 2 Exposure (ppb)Frequency of Exposure inMicroenvironment (%) Indoors

Outdoors

Vehicles

Air Quality Adjusted to Just Meet the 99 th %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 SO 2 Exposure (ppb)Frequency of Exposure inMicroenvironment (%) 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) a nd that adjusted to just meeting a 99 th percentile 1-hour daily maximum standard level of 150 ppb (bottom) in St. Louis.July 2009 255 Table 8-16. Absolute difference in APEX expos ure estimates for St. Louis using either a 98 th or 99 th percentile form potential alternative standard at a 1-hour daily maximum level of 200 ppb.

Absolute Difference in Estimated Exposures using 98 th and 99 th form 1

Population Benchmark Level (ppb) Number ofPersondays Number of Persons Percentage Points 2 100 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 98 th and 99 th 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 98 th and the 99 th

. 2 Difference between the perc ent of persons exposed (98 th -200 minus the 99 th -200) at each benchmark level.



### 8.10 REPRESENTATIVENESS OF EXPOSURE RESULTS

8.10.1 Introduction Due to time and resource constraints the ex posure assessment evaluating the current and alternative standards was only applied to the tw o 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 SO 2 concentrations. To address this question, additional data were compiled and analyzed to provide context to the exposure modeling results. Beca use most estimated exceedances were associated with the outdoor microenvironments, this analys is and discussion is centered on time spent outdoors to allow for comparison of the two mode ling domains with several other broad regions. In addition, further contex t is given regarding the SO 2 emissions and air quality in these locations with respect the 39 other counties evaluated in the air quality char acterization. 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-Master 74 for five regions of the country. The U.S. states us ed in the air quality char acterization (Chapter 7) were of interest, which already includes Missouri (representing th e two exposure modeling domains). Staff analyzed the outdoor time by br oad 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 br oadly defined to protect the iden tity 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 popul ate a data set for each of the five regions. Staff further separated the diaries by time-of-y ear (school year versus summer) 75 and the day-of-week (weekdays versus weekends), both important fa ctors influencing time spent outdoors (Graham and McCurdy, 2004). Summer days were not se parated 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 Caroli na, 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 smalle r percent of children spend time outdoors during the school year (about 45-50%) compared to the su mmer (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 outdoor s on weekend days compared to weekdays at all percentiles of the distribution and within all regions. In addition, children consis tently spent more time

74 Currently available through EPA at mccurdy.tom@epa.gov. 75 A traditional school year was considered (months of September-May); summer months included June-August.July 2009 257 outdoors during summer days within all region s. There were few differences in outdoor time when comparing each of the region s. Children in Northeastern Stat es had the widest range in the distributions for time spent outdoors. In this re gion of the U.S., childre n spent the least amount of time outdoors during the school-year days-o f-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 domain s would be largely different from results generated in most areas of th e U.S. when considering time sp ent outdoors, though there may be differences in exposures estimated in Northeastern states. 76 Depending on when the peak exposure events occur in the ye ar, 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.

Doers 1 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 weekends 317 43 158 159 2 120 365 1440 105 2.7 MA

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 weekends 258 41 152 131 1 116 422 870 102 2.7 MW

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 weekends 54 43 148 128 15 115 480 574 105 2.4 NE

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 weekends 593 52 157 126 1 123 404 810 112 2.5 SE

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 weekends 232 50 162 142 7 120 405 1390 116 2.4 SW

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 SO 2 Emissions and Ambient Concentrations St. Louis was not one of the 40 selected coun ties for the Air Quality Characterization due to its not meeting the selection criteria (see sect ion 7.2.4.2). To provide additional perspective on the exposure results for both the Greene County and St. Louis mo deling domains, staff compared the air quality in each of these locations with the other 39 counties, beginning with the

estimated number of benchmark exceedances us ing the available ambient monitoring data. 77

Five-minute maximum SO 2 concentrations were estimated in St. Louis as was done with the other 40 Counties (including Green e 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 SO 2 concentrations using th e 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 w ith the exceedance results for the 40-counties and ranked in descending order. In addition, two other rank statistics were generated; the average

total SO 2 emissions within 20 km of ambient monito rs 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 exceedance s 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 50 th percentile (including the as is scenario), while having the 37 th highest ranked emissions. The population ranking was moderate (19 th of 41 locations). St. Louis air quality

exceedances rank within the 50 th -75 th percentile for most of the a lternative standard scenarios, with a few of the scenarios (e.g., the current st andard, and the higher alte rnative standard) ranked in the upper quartile, while having moderately ranked emissions (26 th 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 90 th -95 th percentile). The mean estimated population surrounding the monito rs is ranked in the upper quartile (9 th of 41).

77 The exposure modeling domain was comprised of three coun ties (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 emi ssions 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 – 19 th Emissions – 37 th

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 – 9 th Emissions – 26 th

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 pers ons reside, rather than just total SO 2 emission levels or the number of air quality benchmark exceedances. In addition, total SO 2 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%) acc ount for the greatest numb er 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 with in 2 km of primary smelter emissions. This county ranked 1 st in days/year with exceedances using as is air quality, though ranked only 36 th

for SO 2 emissions (18,000 tpy). Figure 7-10 provid ed support for the variability bins selected and their relationship with the nu mber of measured air quality benchmark exceedances. Clearly, ambient monitors with the greatest variability in 1-hour SO 2 concentration are the monitors most likely to have 5-minute SO 2 benchmark exceedances.July 2009 260 Greene County was retained in the final exposure assessment based analyses in the 1 st

draft SO 2 REA. At the time of the analysis, it was no ted 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 characterizat ion and exposure modeling performed here that includes St. Louis, it appears th at a less dense population surro unding the potentially important

SO 2 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 populati on density. Eighty-nine of the 809 monitors in

the broader SO 2 monitoring network were rated as having a high COV; 52 of these monitors (58%) were associated with low population dens ity (<10,000 persons within 5km), 28 moderate population density (31%, 10,000-50,000 persons w ithin 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 1 st in as is air quality benchmark exceedances), it is possible that the percen t of persons exposed would be low (ranked 38 th in population).

Staff also calculated the total SO 2 emissions from marine vessel s, 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 th e air quality characterization and ranked (Table 8-20). The St. Louis modeling domain had the 5 th highest total port SO 2 emissions when considering the 40 counties, though these em issions only comprise 2% of the total SO 2 emissions

in St. Louis. Thirteen of the 40 counties did no t have port emissions, one of which was Greene County. The amount of port emissions in St. Lo uis was also compared with the top 40 counties in the U.S that had the highest port emissions (Table 8-21). The total SO 2 emissions from ports

in St. Louis were ranked 28 th , while seven counties had greate r port emissions than Jefferson County TX (one of the 40 counties included in the air quality characterizati on). Note that most of the counties with the great est port emissions were not evaluated in the air quality

characterization because th ey did not meet the high SO 2 concentration-based selection criterion.July 2009 261 Table 8-20. Total SO 2 emissions and total port SO 2 emissions in the St. Louis and the 40 Counties used in the air quality characterization.

SO 2 Emissions 1 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 SO 2 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 SO 2 emissions, including SO 2 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 SO 2 emissions were calculated from the 2002 NEI. Emi ssions originating from ports were calculated using SCC for marine vessels: 2280002100, 2280002200, 2280003100, 2280003200, 2282020005.July 2009 263 Table 8-22. SO 2 emission density the two exposure modeling domains and several counties within selected U.S. Cities.

State City FIPS 1 County Total SO 2 Emissions 2 (tpy) Land Area 3 (miles 2 ) Emission Density (tons/miles 2 )

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.cen sus.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 (t py) by the physical area (mile 2 ) of the location. These data are presented in Table 8-22. Greene County (or Spri ngfield, 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-highJuly 2009 264 emission density, likely one of the factors contri buting 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 high er emission density than St. Louis: New York, Cleveland, and Detroit. We note that four coun ties within these cities with the greatest emission density were evaluated in the air quality ch aracterization: the Bronx, Cuyahoga, Lake, and Wayne. In considering the air quality benchmark ex ceedance 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 additi onal 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 50 th -75 th 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 th at estimated in St. Louis. However, the high emission density for Cuyahoga and Lake Count ies (Cleveland) could i ndicate that a greater number of persons might be exposed a bove benchmark levels when using the as is air quality. While locations such as Los Angeles have greate r estimated emissions originating from ports, the

SO 2 concentration levels measured at ambient monitoring data in these locations did not approach the levels used for selection in the air quality characterizati on. 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 mon itoring data, staff judges St. Louis and Greene County as reasonable ch oices 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 7 th in population, 21 st in emissions within 20 km of monitors, 21 st in countywide port emissions, and medium emission density. 79 Allegheny county ranked 10 th in population, 31 st in SO 2 emissions within 20 km of monitors, and 23 rd in countywide port emissions. Cuyahoga county ranked 5 th in population, 25 th in SO 2 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, includin g residential prevalence of air conditioning are summarized for 13 locations using the availabl e metropolitan areas surveyed by the AHS (Table 8-23). Because survey years differ for each locatio n 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 in fluence 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 th e 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 residentia l exposures to daily maximum NO 2 concentrations above selected benchmarks would be greater compared to those estimated in St. Louis. However, given the limited contributio n of the indoor microenvi ronment 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 st atistics 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 prev alence rates for asthmatic

children in three of the four re gions of the U.S. (Midwest, Sout h, and West), suggesting that exposure analyses conducted in th ese 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 out doors, a similar air qu ality distribution). Staff weighted the BRFSS 2002 state-level ad ult 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 Prevalence 1 (%) 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. Louis 2 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 regi ons (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 So utheastern states on av erage were estimated to have the lowest adult asthma prevalence. This su ggests that there may be a greater percentage of peak exposures to asthmatic adults in the North east and a lower percentage of peak exposures in the Southeast when compared with the percent modeled in St. Louis orGreene 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 Prevalence 1 (%) 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.

Region 1 Asthma Prevalence 2 (%) 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/br fss/02/current/tableC1.htm

.July 2009 268 

### 8.11 VARIABILITY ANALY SIS AND UNCERTAINTY CHARACTERIZATION As discussed in section 6.6, th ere can be variability and uncer tainty 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 da ys 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 with in 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 determinin g exertion level. As a result, APEX addresses much of the variability in exposure estimates gi ven variability in factors that affect human exposure. The variability accounted for in th is 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 SO 2 concentrations Spatial: modeled ambient SO 2 to block-level receptors. Temporal: 1-hour SO 2 for an entire year predicted using AERMOD; 5-minute SO 2 within each hour estimated using APEX. Ambient Input

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 (s ome 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 NO 2 NAAQS REA (EPA, 2008d). This increased level of comple xity in the type and nu mber of models used, the overall exposure modeling approaches, and it s application in exposure assessments does not necessarily confer decreased levels of uncertain ty. 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 describe d 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 AE RMOD concentrations predictions are an APEX input uncertainty, the uncertainti es associated with each of the models are addressed separately here for clarity. Table 8-27 summarizes the re sults of the qualitative uncertainty analysis conducted by staff for the SO 2 exposure assessment.July 2009 270Table 8-27. Summary of qualitative uncertainty analysis for the exposure assessment.Influence of Uncertaintyon Exposure BenchmarkExceedancesSource TypeDirection MagnitudeKnowledgeBaseUncertainty Comments1Algorithms Unknown Low LowINF & KB: Multiple historical model evaluations consistently demonstrateunbiased ambient concentrations under variety of conditions. Somepotential dispersion scenarios may not be adequately represented and areunknown as to how they apply in this application. However, model-tomonitor comparisons in this application indicate very good agreement.MeteorologicalDataUnknownLow –MediumLowINF: A limited number of missing hours of wind data remain, potentiallyleading to under-estimation. Model predictions have low to mediumsensitivity to surface roughness characteristics, as long as they areappropriate for the site of the meteorological data inputs.KB: Data are from a well-known and quality-assured source. One minuteASOS wind data used to supplement 1-hour data for improvedcompleteness, reducing the number of calms and missing data.Point SourceEmissions andProfilesBoth Low LowINF: Temporal emission characteristics are well represented for mostmodeled point sources.KB: Most temporal data are from a well-known quality-assured source ofdirect measurements.AERMODInputs andAlgorithms2Area SourceEmissions andProfilesBothLow –MediumHighINF: Temporal concentration characteristics were well represented whenusing a generalized area source emission profile, i.e., an aggregate profilecovering a variety of emission source types. However, the temporal profileselected can be very influential to 1-hour concentrations where area sourcesare a significant contributor to emissions.KB: While there were two alternativeprofiles available, one of which wasevaluated, a local generalized temporalemission profile was selected basedon yielding the best model-to-monitor agreement. It is largely unknownwhether the generalized profile is an appropriate representation of the truetemporal profiles that exist for modeled area sources.APEXInputs andAlgorithmsAERMODModeled 1-hourConcentrationsBothLow –MediumMediumINF: Model-to-monitor comparisons indicated very good agreement. Most ofthe overestimations in concentration occurred at the lowest 1-hourconcentrations (Figures 8-8 and 8-9), limiting the magnitude of influence onestimated 5-minute concentrations. The spatial representation of ambientconcentrations using modeling is likely an improvement over usingconcentrations from the limited number of ambient monitors.KB: While model-to-monitor agreement was very good, it is unknown howwell all other modeled receptors are represented.July 2009 271Accuracy of 5-minuteExposureEstimationBothLow –MediumHighINF: The accuracy of the statistical model used in calculating 5-minute SO2ambient concentrations was rated as having at most a medium level ofinfluence (see section 7.4.2.6 and Table 7-16).KB: APEX annual average SO2exposures are comparable reportedpersonal exposures of daily to multi-dayaveraging time. However, there areno 5-minute SO2personal exposure data that can be used to evaluate APEXoutput.PopulationDatabaseBoth Low LowINF & KB: Data are from a reliable, quality assured source. Staff assumedthe limited uncertainty in the database would have negligible influence onexposure results.CommutingDatabase andAlgorithmBoth Low MediumINF: Most exposures above benchmark levels occur outdoors, not insidevehicles. Also note there is limited modeled spatial heterogeneity in SO2concentrations in St. Louis.KB: Data are from a reliable, quality assured source. However land-use datawas used as a surrogate for distributing the tract-level commuting data to theblock-level.Activity PatternDatabaseOverLow –MediumMediumINF: Most of the potentially influential factors are within the expected (orassumed) bounds or are controlled for by the exposure modeling approach.Though most components are rated aspotentially having a low magnitude ofinfluence in either direction, notaccounting for averting behavior byasthmatics could result in a medium level of over-estimation.KB: Data are from a reliable, quality assured source. Available publishedliterature was used for many of the comparisons, though some were limitedin direct correspondence and applicability.LongitudinalProfileAlgorithmBothLow –MediumMediumINF: The magnitude of potential influence would be mostly directed towardestimates of multiday exposures.KB: Method compared reasonably well with available measurement dataand two other methods, however long-term (i.e., monthly, annual) diaryprofiles do not exist for a population.MeteorologicalDataBoth Low LowINF: Daily maximum temperatures are only used when selecting appropriatediaries to simulate individuals and in selecting air exchange ratedistributions.KB: Data are from a well-known and quality-assured source. One minuteASOS wind data used to supplement 1-hour data for improvedcompleteness, reducing the number of calms and missing data.Air ExchangeRatesUnderLow –MediumMediumINF: Most peak exposures occur outdoors, though indoor exposures may beunderestimated when not using all 5-minute concentrations within the hour(section 8.11.2.2.11).KB: Data used are not specificto St. Louis or Greene County Mo.July 2009 272A/C Prevalence Under Low LowINF: Most peak exposures occur outdoors, though indoor exposures may beunderestimated when not using all 5-minute concentrations within the hour(section 8.11.2.2.11). However a previous sensitivity analysis (EPA, 2008d)indicates extremely low A/C prevalence has little influence on number andpercent of persons exposed.KB: Data used are specific for St. Louis, there is limited variability in theestimate, and compares reasonably with data from a different source.IndoorRemoval RateUnknown Low MediumINF: Most peak exposures occur outdoors, though indoor exposures may beunderestimated when not using all 5-minute concentrations within the hour(section 8.11.2.2.11).KB: Data used were obtained from comprehensive review of SO2removalrates, however many assumptions were needed in developing the removalrate distributions.Occurrence ofMultipleExceedancesWithin an HourUnderLow –MediumMediumINF: Analyses indicate that ignoring multiple peaks within the hour underestimates exposure and hence the numberof persons exposed upwards to35%.KB: While the frequency of multiple exceedances within an hour can beestimated, there are limited continuous 5-minute data available. Therepresentativeness of the available data to modeled receptors is unknown.AsthmaPrevalenceRateBothLow –MediumLowINF: The percent of asthmatics for Greene county’s simulated populationwas similar to that of another independent estimate. County specific asthmadistributions were not used in St. Louis, there may be an over or underestimate in the number of persons exposed.KB: Data for asthma prevalence are from reliable and quality assuredsources.Notes:1INF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rating.2The magnitude/direction of influence and the uncertainty associated with the knowledge-base for each source identified for AERMOD is characterized forthe 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 m odeling was determined through use of EPA’s recommended regulatory air dispersion model, AERMOD (version 07026 (EPA, 2004a)), with meteorological data and emissions data discu ssed 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 modeli ng. 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 associat ed with this application of AERMOD is separated into two main sources: 1) model al gorithms, and 2) model inputs. While it is

convenient to discuss uncertainties in this context, it is also impo rtant 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 uncerta inty associated with model inputs. In the characterization that follows, AERMOD uncertai nties are discussed regarding the impact to predicted 1-hour SO 2 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 largel y unbiased estimates of ambient concentrations across a range of source characteri stics, 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 electr ical 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 plum e 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 adequa tely treat dispersi on under conditions ofJuly 2009 274 atmospheric stagnation or recirculation when em issions may build up within a region over

several hours. This could l ead to ambient concentration und er-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 fo r 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 in dividual plume simulation, their importance in terms of overall uncertainty will vary depending upo n 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 secti on 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 us er. Since the sensitivity of AERMOD model concentrations to the user-specified population is roughly proportional to population to the 1/4 th power, this is not a significant source of uncerta inty. 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 histor ical and recent model evaluations and the improved AERMOD model formulations, staff judg es 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 determ ining how the basic model assumptions apply to a large-scale analysis. While the AERMOD mode l algorithms are not considered to be a significant source of uncertainty for this assessment, the representativeness of modeledJuly 2009 275 concentrations for any application are strongly dependent on the quality and representativeness of the model inputs. The main categories of mode l inputs that may contri bute to uncertainty are the meteorological input data and emissions es timates. 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 A ttachment 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 mi ssing 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 represen tativeness is the sensitivity of the AERMOD model to surface roughness, because the roughne ss at the location of the meteorological tower site used to process the meteorological data fo r 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 im portance of mechanical shear-stress induced turbulence on dispersion for such sources. A previous application of the AERMOD model to support the REA for the NO 2 NAAQS review (EPA, 2008d) provi ded an opportunity for a direct assessment of this issue by comparing AERM OD modeled concentratio ns based on processed meteorological data from the Atlanta Hartsfield airport (ATL) with c oncentrations based on processed meteorological data from a South east Aerosol Research and Characterization study (SEARCH) monitoring station located on Jefferson Street (JST) near Georgia Tech. The ATL data were representative of an open exposur e, low roughness, site typical for an airport meteorological station. The JST data were re presentative 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 NO x inventory, representing near ground-level emissions, showed relatively good agreement in m odeled concentrations based on the two sets of meteorological in puts, at least for the peak of the concentration distribution at four monitor locations across the modeling domain. This suggests that the sensitivity ofJuly 2009 276 AERMOD model results to variations in su rface roughness may be less significant than commonly believed, provided th at meteorological da ta inputs are processed with surface characteristics appropriate fo r the meteorological site. Therefore, based on the improved complete ness 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 aeria l photography. Temporal profiles were derived from a vari ety of databases. Temporal pr ofiles 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 un iform 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 quali ty-assured sources, the emission source locations were independently verified, and that the temporal profiles for most of the emission sources were known, sta ff judges the magnitude of influence from this potential source of uncertainty as low and assume s there is an equal tend ency to over- or underestimate 1-hour SO 2 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-poin t and background area sources in AERMOD

were addressed in Section 8.4.3. In the case of SO 2 , 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 represen ted as individual sources within the existing

emissions inventories due to their limited emissions. There is a lack of detailed informationJuly 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 emi ssion inventories. Given these limitations in terms the emission inventory, two of the main uncertainties associated with modeling these sources are the temporal and spatia l 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 SO 2 emissions is the likelihood that the actual emissions may be associ ated with some plume buoyancy that cannot be explicitly treated using the area source algorith m 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 sour ces. Staff obtained emission estimates from the 2002 National Emissions Inventory (NEI) howeve r, only annual total emissions at the county level are provided. To better parameterize thes e 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 distri bution of non-point emissions was used to spatially allocate county-wide emissions to censu s tracts in the Greene County domain. Staff used the spatial allocation factors (SAFs), ba sed 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 mode led 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 usin g factors from the EMS-HAP database made it apparent that the spatial allocations were mischar acterized 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 in stead of allocating emissi ons to specific censusJuly 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 prof ile was based largely on direct observations via the CAMD database, these non-point emissi on profiles are based on generalized emissions surrogates and may not well represent a specif ic source or local gr oup of sources. Model performance evaluations of diur nal profiles suggested that tem poral factors derived from the EMS-HAP emission model inadequately represen ted 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 ag reement 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 te mporal emission release profile in each area. Examination of the diurnal profiles of modeled and monitored concentrat ions with uniform and with EMS-HAP emission profiles for monitors in locations dominated by area sources showed that, while monitored concentrations increase d during the daytime, modeled concentrations actually decreased. An examination of the disp ersion 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 inform ation. 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 diur nal cycle than is reflected in the EMS-HAP temporal profile. This method of determining an appropriate, local, non-point sour ce emission profile has the advantage of preserving total emissions refl ected in the emission i nventory while deducing what the actual temporal emission profile from these local sources should be, based on the observed trends in each region. Essentially, it de rives 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 char acteristics of local area sources. This derived profile implies that the emission sources are act ive almost exclusively during the daytime from approximately 8 am to 8pm. Given that the em ission 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 ho urs is plausible. However, in knowing that there are large variations in the assumed local emission characteristics versus limited and broadly

defined emission characteristics fo r potential area sources, there is high level of uncertainty in the knowledge-base. The selected approach th ough 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 an d observed values. Based on the discussion regarding the use of spatial a llocation 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 va riables known to affect exposure (i.e., where people are located and what they are doing), it ha s 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 expos ures given the time and

resources allocated for the assessment. Howe ver, there are constraints and uncertainties associated with the input data and modeling appr oaches that may correspond to uncertainties in the modeling results. In the characterization that follows, expo sure modeling uncertainties are discussed regarding their influence to the estimated numbe r of persons and person-days above benchmark exceedances. Staff primarily focused on the unce rtainties and assumptions associated with SO 2 specific exposure model inputs, their utilization, and application in this exposure assessment. Note also that some sensitivity analyses fo r 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 SO 2 NAAQS review are also included, though only summarized below.

8.11.2.2.1 AERMOD Modeled 1-hour Concentrations The AERMOD model-to-monitor comparison s (section 8.4.5) indicated very good agreement. Most over-estimations in 1-hour SO 2 concentrations occurred at the lowest 1-hour concentrations, effectively limiting the potential magnitude of influence on estimated 5-minuteJuly 2009 280 air quality and exposure concentrations. At the upper tails of the distribution (> 80 th percentile),

there was a mixture of over- an d under-estimation in 1-hour SO 2 concentrations, most of which were on the order of 1-2 ppb. Staff performe d an additional evaluation in Greene County to compare estimated benchmark exceedances resultant from the variable concentration distributions given by the ambient monitori ng data and AERMOD predictions (rather than simply comparing the 1-hour con centrations). The re sults indicated there was not a significant influence to the estimated air quality benchm ark exceedances from the limited differences observed in the upper percentiles of th e 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 SO 2 concentrations to census block receptors is judged by staff as improvement over using monitore d concentrations alone as an input to APEX. This may be of greater importance in Greene C ounty where there was greater variability in the modeled concentrations at the receptors surroun ding each ambient monitor (see section 8.4.5). In addition, the use of concentra tions 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 th an specific percentage s 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 lo w to medium, recognizing there could be some

conditions that would lead to over- or under-estimation of 5-minute SO 2 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 abov e 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 SO 2 ambient concentrations was rated as having between low and medium levels of influence (section 7.4.2.6 and Table 7-16). Staff assume s, because of the strong relationship between ambient concentrations and personal exposures (i n the absence of indoor sources), the sameJuly 2009 281 influence rating would apply here with mainlylimited opportunities for both over- and underestimation of 5-minute benchmark exceedances. This strong relationship between ambient concentration and personal exposure though is no ted as based solely on longer term averaging times (single day to weeks in duration) and wa s 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 defa ult output from the APEX model, annual average exposures were generated for each simulated indi vidual (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 e xposures the asthmatic population would receive because the asth matic 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 averag e AERMOD predicted ambient SO 2 concentrations were calculated for every recep tor in the two modeling domains. The selected percentiles of the distribution of annual average concentrations for the AERMOD predicted

ambient SO 2 is also given in Table 8-28. As exp ected, the APEX exposure concentrations are consistently lower than the AERMOD predicte d 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 re gression 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 ambien t 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 th e 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 Tabl e 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 8.11.2.5July 2009 282 there are large differences in averaging time, sa mple size, study year, and city selected, the personal exposure measurement concentrations co mpare well with selected percentiles of the APEX exposure concentration distribution for the total simulated popul ation in Greene County

and St. Louis.

Table 8-28. Distribution of A PEX estimated annual average SO 2 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 SO 2 (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 ar ithmetic standard deviation; percentile of the distribution is given by number following “p” (e.g., p25 is the 25 th percentile). 2 number of simulated individuals.July 2009 283 Table 8-29. Personal SO 2 exposure measurement data from the extant literature.

Study 1 Sarnat (2000) Sarnat (2001) 2 Sarnat (2005) Sarnat (2005) Brauer (1989) Sarnat (2006) Sarnat (2006) City Baltimore Baltimore Bost on 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

n 3 14 45 28 29 48 10 10 SO 2 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 regardin g study conditions. Reference is provided here using primary author and year of publication. 2 The cohort for Sarnat (2001) consisted of 15 senior s, 15 children, and 15 COPD patients. Seniors and COPD patients had similar exposures, with ch ildren having somewhat higher exposure. 3 number persons in study. 4 mean is the arithmetic mean; std is the arithmetic standard dev iation; percentile of the distribution is given by number following “p” (e.g., p10 is the 10 th percentile); ND is not detected.

APEX modeled exposures have previously been compared with personal exposure

measurements for O 3 (EPA, 2007d). Briefly, APEX O 3 simulation results were compared with weekly personal O 3 concentration measurements for child ren ages 7-12 (Xue et al., 2005; Geyh et al., 2000). Two separate areas of San Bern ardino County were surveyed: urban Upland CA,

and the combined small mountain towns of La ke 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 bo th locations reasonably well for much of the concentration distribution, but tended to underes timate exposures at the upper percentiles of the distribution. The average difference between th e weekly means was less than 1 ppb, with a range of -11 ppb to 8 ppb, though predicted up per 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 variabil ity 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 c oncentrations (EPA, 2007d).July 2009 284 0 20 40 60 80

1Concentration (ppb)

Measured APEX range measured APE X Figure 8-22. Means of weekly average personal O 3 exposures, measured and modeled (APEX), Upland Ca. Figure obtained from EPA (2007d).

In addition, APEX modeled exposures have previously been compared with personal

NO 2 exposure measurements in Atlanta (EPA, 2008d). Daily personal NO 2 exposure measurements were obtained from Suh (2008) for 30 participants of a 1999-2000 Atlanta epidemiological study conducted by Wh eeler et al. (2006) across two seasons. 83 An exposure

distribution was constructed fo r each individual, simply usin g the individual’s minimum, median, and maximum daily mean exposures (e .g., Figure 8-23, top). Daily mean NO 2 exposures estimated using APEX were also evaluated in a sim ilar 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 us ed in the comparison. A distribution of each person’s estimated daily exposure was also cons tructed, using the median daily exposure to represent the central tendency and a 95 % predic tion 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 we re consistently about 2 ppb higher than the personal exposure measurements across most of th e percentiles of the distribution, and the APEX

83 The minimum number of exposure measurements per su bject 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 NO 2 from ambient sources are discussed here.July 2009 285 upper prediction interv als 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 NO 2 exposures, measured and modeled (APEX), Atlanta Ga. Figure obtained from EPA (2008d).

It is encouraging that the APEX longer-t erm exposure estimates are comparable to personal exposure measurements. When also no ting that there is a st rong relationship between

ambient SO 2 concentration and exposure, staff believes that the estimated numbers of days with 5-minute exposures above benchmark levels are al so likely reasonable. However, without the

84 While a direct comparison of APEX estimated maxi mum 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 NO 2 Exposures – Spring 2002

0 10 20 30 40 50 60 70 80 90 100

0.1 1 10 100 Daily Mean NO 2 Exposure (ppb)Cumulative Percent (%) p50 p2.5 p97.5 0 10 20 30 40 50 60 70 80 90 100Cumulative Percent (%) p50 min

max Personal NO 2 Exposure Measurements- Spring 2000July 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 uncertain ty in population representation within this data would not affect the APEX exposure results in any particular dire ction, and given the use of randomly sampled demographics to represent the simulated popula tion, 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 asso ciated 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 thei r 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 concen tration data for those individuals. Persons restricted to a single block for ambient con centrations include the population not employed outside the home, individuals indicated as comm uting 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 vis it a block with either higher or lower SO 2 concentrations.

Given that the number of individuals who meet th ese 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 pos sibility of passing throug h other census blocks during travel. This could also contribute to either over- or under-estimating exposure concentrations, dependent on the number of bl ocks the simulated individual would actually traverse and the spatial variability of the con centration 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 av ailable (e.g., commuting ro ute data for simulated individuals). These model adjustments would co me with a number of additional uncertainties and require additional time and resources not av ailable 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 grea ter 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/i ndustrial 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/recreationa l 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 diar ies or the Census commuter dataset. TheseJuly 2009 288 constitute non-quantified sources of uncertainty that are not ad dressed 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 ar e the most comprehensive source of such data and realistically represen t where individuals are located and what they are doing. The diaries are sequential records of each person s activities performed and microenvironments visited. There are, however, uncertainties in th e exposure results as a re sult of the CHAD diaries used for simulating individuals. Specific elements of uncertainty include an evaluation of 1) the representativeness of CHAD in refl ecting recent human activ ity patterns, 2) the approach used to allow for geographical representation of influe ntial 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 year s 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 microe nvironments 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 SO 2 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 usin g historical and recentJuly 2009 289 data, it is likely significant effects would result from differences in survey methods or overall study design rather than measurable changes in p opulation activities. Staff assumed that if there were a difference between the time spent outdoors (the most important microenvironment for

SO 2 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 Nati onal surveys designed to be representative of the U.S. population. In addition, study collection periods occur at differe nt 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 resi ding in these citie s, 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 ar e location-specific charac teristics 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 demogra phics (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 c limate 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 O 3 NAAQS review (see Langstaff (2007) and EPA (2007d)). Briefly, O 3 exposure results were generated using APEX with all of the CHAD diaries and compared with results generated fr om running APEX using only the CHAD diaries from the National Human Activity Pattern Study (NHAPS), a nationally repr esentative study in CHAD. There was good agreement between the APEX exposure results for the 12 metropolitan areas evaluated (one of which was St. Loui s), whether all of CHAD or only the NHAPS component of CHAD is used. The absolute differe nce in percent of pers ons above a particular concentration level ranged from -1% to about 4% , indicating that the expo sure model results are not being overly influenced by a ny single study in CHAD. It is likely that similar results wouldJuly 2009 290 be obtained here for SO 2 exposures. Therefore, staff judges the magnitude of influence from using appropriately sampled C HAD diaries in representing the simulated population as low. Third, due to limited number of CHAD diarie s with health-specific information, all diaries are assumed as appropriate for any simulated individual, provided they concur with age, gender, temperature, and microenvironmental ti me selection criteria. In addition, data

summaries 85 output from the current version of AP EX 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 withou t 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 contact ing the 5-minute peak concentration were outdoor microenvironments. Therefore, if there is a diffe rence in the time spent outdoors (e.g., total time, time-of-day) and activities performed outdoors be tween 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 considerin g children 7-10 years in age. These researchers used three different activity-level measurement technique s; 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 monitori ng, 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 activ ity levels. Contrary to this, an analysis of 2000 BRFSS data by Ford et al. (2003) indicated a statistically significan t difference between the percent of current asthmatics (30. 9%) and non asthmatics (27.8%) ch aracterized as inactive. In addition, these researchers found significant diff erences in the percent of asthmatic (26.6%) and

85 For example, the time spent in micr oenvironments at or above a potential health effect benchmark level.July 2009 291 non-asthmatic (28.1%) adults achieving recommen ded levels of physical activity (i.e., either moderate or greater activity levels). Note though, the issue is not just outdoor tim e and activity levels, but the intersection of the two that are of importance as well as re cognizing the performanc e capabilities of persons with asthma. A person’s overal l physical activity level is strong ly linked with their time spent outdoors and is considered an important correl ate 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 f itness in asthmatic children, with statistically significant increases in ventilation measures such as maximum minute ventilation rate (VE max ) maximum oxygen uptake (VO 2max ) (e.g., van Vledhoven et al., 2001) . Further, in other related research, Santuz et al. (1997) indicated no statistically signifi cant difference between asthmatic and non asthmatic children when comparing maxi mum exercise performan ce levels, provided the

individuals were conditioned th rough habitual exercise. Thus it appears that asthmatics are likely to perform activities at elevated leve ls 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 as thma studies with estimates of the same metric using the CHAD database. In addition, some of the studies incorpor ated in CHAD reported

whether the individual was as thmatic, 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 us ing CHAD data and the known health status. When considering the three asthma studie s, 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 ra nge (i.e., between 6.5 and 7.5%) w ith small differences observed

between the CHAD asthmatic and CHAD non-asth matic population. This limited comparison

indicates that the CHAD diaries may reasonably approximate the amount of time spent outdoors at moderate activity levels. In addition, comp arison of the CHAD asthmatic and non asthmatic population supports the assumption that all C HAD diaries are appropri ate in representing asthmatic individuals, regardless of health status. However, the percent of outdoor time

associated with strenuous activ ities using the CHAD database was lower when compared withJuly 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 da ta 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 wi de ranging and could pot entially influence the results. However, based on the comparable resu lts observed in time spent outdoors at moderate activity levels, staff judges the magnitude of infl uence 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 CHAD 4 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 thr ee 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 studi es, national and city-specific, as well as varying diary protocol (see Graham and Mc Curdy, 2004). Original CHAD da tabase (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 info rmation regarding bad air quality may affect the activities performed by the asthmatic popula tion. There has been research regarding averting

behavior , that is, there is a reduction in time spent ou tdoors 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 Qualit y 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 aler t for both children (Mansfield et al., 2005) and adults (KDEH, 2006; Wen, 2009). Parents of asthmatic children ch ecked air quality alerts more frequently than parents of non-asthmatic children and, though reported as statistic ally 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 as thmatic adults were awar e 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 pe rsons 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 SO 2 exposure would be altered is highly uncertain. Staff acknowledges that there may be fewer asthmatics exposed usin g 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-d ay 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 SO 2 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 collect ed only a single diary-day while others collected several diary days per individual. In this exposure assessm ent, individuals are simulated for an entire year. APEX creates the annual sequences of daily activities for a simulated individual by sampling human activity da ta from more than one subject. Therefore, each simulated person essentially becomes a compos ite 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 indi vidual activity diaries to gether could influence the estimated number of persons exposed, al though 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 ag es 7 and 12 for six consecutive days/month for an entire year. See Appendix B, Attachment 4 an d 5 for details of the comparison. Briefly, the activity pattern records were ch aracterized according to time sp ent 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 evaluati on indicated the cluster algorithm can replicate the observed sequential di ary 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 be tween –9% and +9%, and the mean bias across any microenvironment ranges from -9% to +4%. Al though on occasion there were large differences in replicating variance across persons and within-p erson variance subsets, about two-thirds of the predictions for each case were within 30% of th e observed time spent in each microenvironment.July 2009 295 The longitudinal approach used in the expos ure 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 pers ons with at least one or more exposure to a given O 3 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 cluste r 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 usi ng the cluster algorithm in APEX appeared to be the result of a greater correlation of diaries select ed in comparison with the other two algorithms. This outcome conforms to an expectation of correlation between the daily activities of

individuals. While the evaluati on was performed using 8-hour O 3 as the exposure output, it is expected that similar results wo uld be obtained for 5-minute SO 2 exposures. That is, the

characteristics of the diaries th at contribute greatly to any pollutant exposure above a given threshold (e.g., time spent outdoors, vehicle driv ing time, time spent indoo rs) are likely a strong component in developing each longitudinal profile. Given these results and that the REA is not necessarily focused on health effects resul ting from multiday exposures, staff judges the longitudinal approach may have a low to medi um magnitude of influence on estimated number of persons exposed. When comparing the mode led profiles with the measurement data, there

was a balanced mix of over- an d 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 discusse d in section 8.11.2.1.2. Briefly, meteorological data are taken directly from monitoring stations in the assessment areas. Staff assumed thatJuly 2009 296 most of the data used are error free and have un dergone 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 know n 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 direc tions. 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 SO 2 concentrations. Therefore, staff judges the M ET data as having a low level of influence and equally applied to either unde r- or over-estimation in the number of persons exposed.

There are some limitations in the use of th e 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) dist ributions used to estimate indoor exposures may contribute to uncertainty in the exposure re sults. Three components of the AER analyzed previously by EPA (2007d) include 1) the extr apolation of air exchange rate distributions between-CMSAs, 2) analysis of within-CMSA uncer tainty due to sampling variation, and 3) the uncertainty associated with estimating daily AE R distributions from AE R measurements with different averaging times. The results of those previous investigations are briefly summarized here. See Appendix B, Attachments 7 and 8 for de tails 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 locationsJuly 2009 297 Air exchange rate (AER) distributions were as signed in the APEX model, as described in

the indoors-residential microenvironment. Becau se 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 Californi a to represent the distribution of AERs in St. Louis and Greene County (see Appendix B, Attachment 7). In the absence of location-specific data fo r the microenvironments modeled by APEX within each model domain, only limited evaluati ons were performed. To assess the uncertainty associated with deriving AERs from one city and applying those to another city, betweenlocation uncertainty was evaluated by examinin g the variation of the geometric means and

standard deviations across seve ral cities and originating from several different studies. The evaluation showed a relatively wide variation ac ross different cities in their AER geometric means and standard deviations, stratified by ai r-conditioning status, and temperature range. For example, Figure 8-24 illustrates the GM and GS D of AERs estimated for several cities in the U.S. where A/C was present and within the temp erature range of 20-25 °C. The wide range in GM and GSD pairs implies that the modeling resu lts may be very different if the matching of modeled location to a particular study lo cation was changed. For example, the SO 2 exposure estimates may be sensitive to use of an altern ative distribution, say those in New York City, compared with results generated using the aggr egate 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 speci fic locations given the population of available AER

data. It is unclear as to the direction of influe nce 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, constr uction methods used, and other meteorological variables not explicitly treated in th e 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 uncertain ty was assessed by usi ng 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 representative ness 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 accoun t for uncertainties about the true distributional shape, which may not necessarily be lognormal. One-thousand bootstrap samples were randoml y generated for each AER subset (of size N), producing a set of 1,000 pairs of geometri c mean (GM) and geometric standard deviationJuly 2009 299 (GSD). The analysis of the non-California city data used to represen t 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 surro unding the GM (i.e., about ± 0.10 fitted GM (hr -1 ) (Figure 8-25). Broader ranges were ge nerated 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 con centrations. See Appendix B, Attachment 7

for further details.

Figure 8-25. Example of boot strap simulati on 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 linesJuly 2009 300 Figure 8-26. Example of boot strap simu lation 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 ta ke 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 result s showed extremely strong correlations, providing support for the simplified approach of treating mu lti-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 ai r 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 Gr eene County as well. EPA (2007d) details theJuly 2009 301 specification of uncertainty estimates in the form of confidence intervals for the air conditioner prevalence rate, and compares these with preval ence rates and confidence intervals developed from the Residential Energy Consumption Su rvey (RECS) of 2001 fo r several aggregate geographic subdivision (e.g., states, multi-stat e 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% co nfidence 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 pe rformed using a low (55%) and high (97%) A/C prevalence rates as input to APEX in an Atlant a, Ga. exposure assessment used for the recent

NO 2 NAAQS review (EPA, 2008d). Upper percenti le benchmark exceedances were also of interest in that exposure assessment, only the averaging time was 1-hour instead of 5-minutes

used here. Indoor microenvironm ents were also found in the NO 2 exposure assessment to be unimportant in estimating exposure exceedances. Results from the sensitivity analysis indicated that there was no difference in the per cent of the asthmatic population with NO 2 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 a bove discussion, staff judges the magnitude of influence to estimated exposures as low, pa rticularly 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 repres ent indoor decay. The data used to develop the distribution were obtained from a review of several studies that analyzed SO 2 removal for a variety of building material surfaces (Grontoft and Raychaudhuri, 2004). Potential influential factors such as humidity and air exchange rate were accounte d for in developing a nd applying the removal

distributions within the indoor microenvironments. In addition, the distributions were based on aJuly 2009 302 large empirical database and lik ely well represent expected SO 2 removal within indoor

microenvironments. However, several assumptions were made to characterize the materials used within a simulated indoor microenvironment, some of whic h were data-based, others in the absence of supporting data, were based solely on professi onal judgment (see Appendix B.4.1). Staff performed a Monte Carlo simulation using the re moval data and 1,000 simu lated interior rooms of buildings to generate a distribution of SO 2 removal rates, weighted by the approximated room configurations and proportion of materials present. There are ma ny 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 influenc e 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 co ntribution of the indoor microenvironments to exposure concentrations above the potential hea lth 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 SO 2 concentration for every hour. However, multiple short-term peak concentrations above selected levels are possib le within any hour. An alysis 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, mu ltiple 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 SO 2 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 SO 2 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 Hours with Multiple 5-minute SO 2

Number of Exceedances of

5-minute SO 2 in 1-hour 1 > 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 SO 2 concentrations in an hour (n=3,328,725).

In using the data in Table 8-31 alone, th e 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 multip le exceedances by each monitor indicated that one of the monitors (ID 420070005) was highly influe ntial in generating the values in Table 8-31, contributing greatly to the multiple peak occurr ences at the higher benchmark levels. This

Beaver Pa. urban-scale monitor is identified as populat ion-based, within a rural setting, and having agricultural land use (Appen dix A). Five out of eight of the sources located within 20 km

of this monitor had SO 2 emissions <250 tpy, one smelter emitting about 7,000 tpy was within 2.5 km, and two power generating facilities located approximately 3.4 and 7.5 km from the monitor

had SO 2 emissions of 3,000 and 30,000 tpy, respectively. Of the number of hours having multiple exceedances, monitor 420070005 contribut ed 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 occurre nce 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 un certainty 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 ambientJuly 2009 304 monitors, each having a limited nu mber of monitoring years. Th e analyses above indicated that

one of the monitors contributed to most of th e hours with multiple peak concentrations. How this one monitor (as well as any other monito r 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 va riability measures, implemented by APEX in estimating a single maximum 5-minute SO 2 concentration for every hour at every receptor. This is based on kn own concentration and variability relationships

described in section 7.2. While APEX can mode l all twelve 5-minute concentrations, staff chose to normalize the eleven remaining 5-minute SO 2 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, th e run time associated with the exposure model simulations. Estimating the 5-minute maximum SO 2 concentrations and the other 11

concentrations within APEX was more effici ent than pre-processing all twelve 5-minute SO 2 concentrations. Having all eleven other 5-minute SO 2 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 likel y event given the continuous mo nitoring data, roughly between 40- 60%). The CHAD time-location-act ivity diaries used in APEX ar e fixed, that is, the modeled time spent outdoors is based on the actual tim e 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-minute s), then it is possible that persons are not realistically encountering peak concentrations given the normali zation of the eleven 5-minute

SO 2 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 percentJuly 2009 305 of all outdoor events are for th e entire hour; if the event corr esponds with the same hour as a simulated peak concentration, there would be no under-estimation in exposure occurring during these events. Therefore, occurr ence 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-estimatin g the exposure, given by the duration of the event divided by 60 minutes. For example, approximatel y 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 durati on of the outdoor event and, given the data in Figure 8-27, this could be a frequent occurren ce (i.e., about 65% of outdoor events may have some probability of missing an exposure). Th is analysis does not account for multiple outdoor events that may increase an individual’s chan ce of an exposure above a benchmark level, regardless of the event duration. It also assu mes 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 nu mber of missing exposures is to model the exposures using two input data se ts: 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 mo nitoring. While there were two monitors reporting continuous 5-minute measurements in Greene County (monitor IDs 290770037 and 290770026), there were only two ye ars 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 us ed two years of data from monitor ID 420070005, noted above as having the greatest number of ai r quality benchmark exceeda nces 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 m e 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 SO 2 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 sec ond data set had the maximum measured 5-minute

concentration retained and all other eleven 5-minute concentr ations 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 tw o runs was the air quality input. Fifty thousand persons were simulated using APEX, 13% of wh ich were asthmatic chil dren. 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 conti nuous 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 benchmar k levels is small, about 2-5 percentage pointsJuly 2009 307 differ between the two simulations. However, c onsidering the percent difference in the numbers of persons exposed at most of the benchmarks le vels, 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 2 nd 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 es timated 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 monitori ng data having the greatest numb ers 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 in door 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 fo r 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-estim ated 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 th ree broad microenvironmental groups: indoors, outdoors, and in-vehicles. A comparison of th e APEX simulations usi ng the two air quality input simulations (i.e., multiple peak versus si ngle peak, monitor 420070005 – year 2002) and considering how often peak exposures occur in doors is presented in Figure 8-30. The differences in the percent of indoor exposure exc eedances are consistent w ith 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 us ing 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 simu lation had indoor peak exposures at levels notJuly 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 mi croenvironmental concentration at time t is less than what would be expected. While this analysis and its findings are encour aging, context is needed to assign relevance to the current exposure analyses in St. Louis an d 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 microenvironme nt may be more appropriately applied in discussions regarding air quality s cenarios with high concentrations distributions (e.g., air quality adjusted to just meeting the current standard, Figu re 8-21). While the differences in the highest benchmark exceedances are likely of greatest inte rest when investigating the possibility of missing exposure events, it should be noted that the greatest pr oportion of all exposure events still occur outdoors (in this simulation, >70% of exposures above 400 ppb occurred outdoors). In addition, the differences obse rved at the lower benchmarks indicated the role of indoor exposures was fairly similar. At most the di fference was four percen tage points, with the multiple peak simulation having a consistently lower contribution of exceedances from indoor exposures. Therefore, based on the above discussion, staff judge s 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 SO 2 Exposure Level (ppb)% of Asthmatic Children at or AboveExposure 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 abov e 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 moni toring data (ID 42007005, year 2002) were used as the air quality input.

0% 10% 20% 30% 40% 50% 60% 70%100150200250300350400450500550600650700750800

5-minute SO 2 Exposure Level (ppb)% of Asthmatic Children at or AboveExposure 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 abov e 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 moni toring data (ID 42007005, year 2005) were used as the air quality input.July 2009 310 0 5 10 15 20 25 30100150200250300350400450500550600650700750800

SO 2 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 moni toring 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 SO 2 exposures, one of the principal reasons for selection of the partic ular data set. There are however limitations in using the data that may add to uncertainty in the generated exposure resu lts. The percent of asthmatics simulated by APEX using a combined re gional (children by age) and local (adults all ages) prevalence was comparable with an inde pendent 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 as thmatics 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 repr esented in the exposureJuly 2009 311 modeling simulation; all children and adults in e ach 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 influe nce to the total number of asth matics exposed in St. Louis (not the percent of asthmatics exposed), though the di rection of influence is largely unknown because individual county level exposu res are not output by the model.



### 8.12 KEY OBSERVATIONS

Presented below are key observations re sulting from the exposure assessment:

 5-minute exposures to SO 2 were estimated for two areas in Missouri (i.e., Greene County and St. Louis), with both locations having significant SO 2 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 SO 2 standards, and just meeting several alternative 1-hour daily maximum SO 2 standards.

 A number of factors would be expected to contribute to differences in SO 2 exposures across different locations. These include differences such as population density, SO 2 emission density, location and types of SO 2 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 SO 2 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 SO 2 emissions density. Taken together, the estimated exposures for these two locations provide useful insights ab out urban and rural counties with SO 2 emission

sources.

 St. Louis had both a greater number and percent of asthmatic children and adults exposed above the benchmark levels than did Greene Coun ty for all air quality scenarios. This is not unexpected given the gr eater population density and the much greater SO 2 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 SO 2 air quality scenarios in urban areas within the U.S. that have similar population densities, SO 2 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 SO 2 concentrations at ambient monitoring receptors and their associated prediction envelops generally repl icate 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 SO 2 concentrations by AERMOD at ambient monitoring locations in Greene Count y did not appreciably affect the estimated number of days per year with 5-minute co ncentrations above benchmark levels.

- APEX-modeled annual mean SO 2 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 h ealth 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 curr ent 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 m odeling 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 99 th 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 99 th percentile 1-hour daily maximum air quality scenario.

 Staff compared exposure results using the 50 ppb 99 th 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 ab ove each 5-minute health effect benchmark level. No asthmatic children were expo sed above the 400 ppb 5-minute benchmark for either the as is or 50 ppb 99 th percentile alternative standa rd scenario. There were 121 fewer asthmatic children exposed above the 200 ppb 5-minute benchmark, corresponding to a 76% reduction in exposures, when consid ering 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 we re 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 99 th percentile air quality standard scenario relative to as is air quality.

 Using a 99 th versus a 98 th percentile form at the same standard level (i.e., 200 ppb) resulted in fewer persons being exposed above benchmark levels when using the 99 th percentile. Approximately 1,000 to 5,000 fe wer asthmatic children, 1,000 to 90,000 fewer person days, and 2 to 12 fewer percen t of persons were exposed above benchmark levels in St. Louis.

 Of the fifteen uncertainties qualitatively judg ed to influence the estimated number of persons with at least one exposure above the 5-minute SO 2 benchmark levels, one may be associated with over-estimation, three could result in under-estimations, while theJuly 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 lo w-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 a bove benchmark levels. The remaining two sources (i.e., uncertainty in the activity pa ttern database used a nd the occurrence of multiple exceedances within an hour) could ha ve 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 th e 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 (f our 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 s ources: the area source emission profile (direction of influence char acterized as both, with low-medium rated magnitude) and the accuracy of 5-minute ex posures estimated by APEX (direction of influence characterized as both, with low- medium rated magnitude). The knowledgebase uncertainty was medium for three of th e 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 ove rall characterization, there is a low-medium level of uncertainty in the knowledge-base for most sour ces. While two sources were rated as having high knowledge-base uncertain ty, 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 estab lished that subjects with asthma are more sensitive to the respiratory effects of SO 2 exposure than healthy i ndividuals (ISA, section 3.1.3.2). As discussed above in section 4.2, asthmatics exposed to SO 2 concentrations as low as 200-300 ppb for 5-10 minutes during exercise have been shown to experience moderate or greater bronchoconstriction, measur ed as an increase in sRaw ( ≥ 100%) or decrease in FEV 1 ( ≥ 15%) after correction for exercise-induced responses in cl ean air. These studies exposed

asthmatic volunteers to SO 2 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 rela tionship between exposure to SO 2 and respiratory health effects. Staff judges the controlled human exposure evid ence presented in the IS A with respect to lung function effects in exercising as thmatic 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 qua litatively inform: (1) the selection of potential 1-hour daily maximum alternative standards to be analyzed in the air qua lity, 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 as sessment 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 Asso ciates, which is included as Appendix C to this document. The goals of this SO 2 risk assessment are: (1) to de velop health risk estimates of the number and percent of the asthmatic populati on that would experience moderate or greater lung function decrements in response to 5-minu te daily maximum peak exposures while engaged in moderate or greater exertion for several air qu ality scenarios (described below); (2) to developJuly 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 SO 2 standards. Health risks for lung function effects in exercising asthmatics have been es timated for the following three scenarios: (1) ”as is”

ambient levels of SO 2 , (2) air quality adjusted to simulate just m eeting the current 24-hour standard, and (3) air quality adjusted to simu late 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 SO 2 emission source types in tw o areas within the state of Missouri (i.e., Greene County and St. Louis). Thes e areas were identified based on the results of a preliminary screening of the 5-minute ambient SO 2 monitoring data that were available. The state of Missouri was one of only a few states having both 5-minute ma ximum and continuous 5-

minute SO 2 ambient monitoring, as we ll as having over 30 1-hour SO 2 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 ex ceedances above the potential health effect

benchmark levels at several of the 1-hour am bient monitors in Missouri. In a ranking of

estimated SO 2 emissions reported in the National Emi ssions Inventory (NEI), Missouri ranked 7th for the number of stacks with > 1000 tpy SO x emissions out of all U.S. states. These stack emissions were associated with a variety of so urce types such as electrical power generating units, chemical manufacturing, cement processi ng, and smelters. For all these reasons, the

current SO 2 lung function risk assessment focuses on Missouri and, within Missouri, on those areas within 20 km of a major point source of SO 2 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 ba sic structure of the risk assessment reflects the

fact that we have available c ontrolled human exposure study data from several studies involving volunteer asthmatic subjects who were exposed to SO 2 concentrations at specified exposure levels while engaged in modera te or greater exertion for 5- or 10-minute exposures. As discussed in the ISA (section 3.1.3.5), am ong asthmatics, both the magnitude of SO 2 -inducedJuly 2009 316 lung function decrements observed in respondin g individuals and the percent of individuals affected in the group exposed have been shown to increase with increasing 5- to 10-minute SO 2 exposure levels in the range of 200 to 1,000 ppb. Therefore, for the SO 2 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 SO 2 and measures of lung function recorded in these studies. For the purpose s of this risk assessment, all of the individual data, including both 5- and 10-minute exposur e 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 exer tion associated with the various air quality

scenarios mentioned above. A more detailed description of the ex posure assessment that was the source of the estimated daily maximum 5-minut e 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 th e lung function risk assessm ent, exposure estimates

for mild and moderate asthmatics for a numbe r of different air quality scenarios (i.e., “as is” air quality (representing 2002), just meeting the cu rrent 24-hour standard, ju st 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 ar e briefly discussed in section 9.2.2. A description of the overal l approach to estimati ng 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 mo re occurrences of a defined lung function response associated with 5-minute exposures to SO 2 while engaged in moderate or greater exertion under a given airJuly 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 modera te 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 SO 2 concentrations under di fferent scenarios, we estimated expected risk given the personal exposures associated with SO 2 concentrations under each scenario – i.e., associated with

 “as is” ambient SO 2 concentrations representing 2002 air quality,

 SO 2 air quality levels simulating just meetin g the current 24-hour and annual standards, and

 SO 2 air quality levels simulating just meetin g specified alternative 1-hour standards. Note that, in contrast to the headco unt risk estimates calculated for the O 3 health risk assessment, the headcount risk estimates calculated for the SO 2 health risk assessment reflect

risks associated w ith all ambient SO 2 concentrations, not just risk s in excess of estimated policyrelevant background ambient SO 2 concentrations. This is because policy-relevant background

SO 2 concentrations are estimated to be at most 30 parts per trillion and th ey contribute less than 1% to present day SO 2 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 individua ls in the designated population to experience at least one lung function response in a y ear) 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 SO 2 or higher, for x = 0, 50, 100, ... to 800;

2) We then calculate the number of individuals exposed at least once to SO 2 concentrations within each SO 2 exposure bin defined above (item 2 in the illustrative example in Table 9-1 below);

3) We then multiply the number of individu als 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 2009318Figure 9-1. Major components of 5-minute peak lung functionhealth risk assessment based on controlled human exposure studies.July 2009 319 Because response probabilitie s are calculated for each of several percentiles of a probabilistic exposure-response dist ribution, estimated numbers of individuals with at least one

SO 2 -related lung function response are similarly percentile-specific. For example, the k th percentile number of individuals, Y k associated with SO 2 concentrations under a given air quality

scenario is:

)|(

1 jk n

j j k eRxNIY 

## 

  (equation 9-1)

where:

e j = (the midpoint of) the j th category of personal exposure to SO 2 , given “as is” ambient

SO 2 concentrations;

NI j = the number of individuals wh ose highest exposure is to e j ppb SO 2 , given ambient

SO 2 concentrations under the specif ied air quality scenario;

jk eRR | = the k th percentile response rate at SO 2 concentration e j ; and

n = the number of interv als (categories) of SO 2 personal exposure concentration.

The k th percentile estimate of the total number responding is then calculated by multiplying the

k

th percentile risk by the number of people in the relevant populat ion. An example is given in Table 9-1, for the median (i.e., 50 th percentile) risk estimate using personal exposures associated

with a 99 th percentile 100 ppb 1-hour daily maximum SO 2 standard for asthmatics in the St. Louis modeling domain. We note that this cal culation assumes that individuals who do not respond at the highest SO 2 concentration to which they are exposed will not respond to any lower

SO 2 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 SO 2 concentrations under a given air quality scenario is calcu lated 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 e xposure 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 SO 2 . In at least one of the altern ative 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 SO 2 concentrations just meeting a 99 th 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 SO 2 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 SO 2 Exposure Bin (ppb)

3) We then multiply the numb er of occurrences in each expo sure 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 be low) of the exposure bin; and

4) We sum the results across all of the bins.

Similar to the first type of risk measur e discussed above, becaus e response probabilities are calculated for each of several percentiles of a probabilistic exposure-response distribution, estimated numbers of occurrences are similarly percentile-specific. The k th percentile number of

occurrences, O k , associated with SO 2 concentrations under a give n air quality scenario is:

)|(

1 jk n

j j k eRxNO 

## 

  (equation 9-2)

where:

e j = (the midpoint of) the j th category of personal exposure to SO 2 ;

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 N

j = the number of exposures to e j ppb SO 2 , given ambient SO 2 concentrations under the specified air quality scenario;

jk eR | = the k th percentile response probability at SO 2 concentration e j ; and

n = the number of interv als (categories) of SO 2 personal exposure concentration.

An example calculation is given in Table 9-2.

Table 9-2. Example calculation of number of oc currences 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 SO 2 concentrations that just meet a 99 th 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 SO 2 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 SO 2 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 ex posure model for the population of individuals with asthma for selected locations encompassing a variety of SO 2 emission source types within two areas in the state of Missouri (i.e., St. Louis and Greene County). Chapte r 8 provides additional details about the inputs and methodology used to estimate 5-minute daily maximum peak SO 2 exposures while engaged in moderate or greater exertion fo r 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 expos ure-response relationships for lung function response associated with 5-minute SO 2 exposures. Only the highe st 5-minute peak exposure (with moderate or greater exertio n) 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 in dividual was relatively in sensitive to additional

SO 2 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-minut e exposures (with moderate or greater exertion) occurring on the same day.

As described in section 8.8. 1, instead of adjusting upward 90 the air quality concentrations to simulate just meeting the current SO 2 standards and potential alte rnative 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 mo deling domain and simulated year (see Table 8- 11). Since it is a proportional adjustment, the end effect of ad justing concentrations upwards versus adjusting benchmark levels downward within the model is the same. The same follows

for where as is concentrations we re in excess of an alternative standard level (e.g., 50 ppb for the

99 th percentile averaged over three years), only th e 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 assessm ent (Abt Associates, 2007), we have used a Bayesian Markov Chain M onte Carlo approach to estimate probabilistic exposure-response relationships for lung function decrements associated with 5-minute daily maximum peak exposures while engaged in mode rate or greater exer tion 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 dura tions, 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 devel op the probabilistic exposur e-response relationships for the lung function risk assessment.July 2009324Table 9-3. Percentage of asthmatic individualsin controlled human exposure studies experiencing SO2-induced decrements in lung function.Cumulative Percentage ofResponders(Number of Subjects)1sRaw≥100%≥200%≥300%FEV1SO2Level(ppb)ExposureDurationNo. ofSubjectsVentilation(L/min)LungFunct.≥15%≥20%≥30%Reference Respiratory Symptoms:Supporting Studies10 min 40 ~40 sRaw 5% (2) 0 0Linn et al. (1987)220010 min 40 ~40 FEV113% (5) 5% (2) 3% (1)Linn et al. (1987)5 min 19 ~50-60 sRaw 32% (6) 16% (3) 05 min 9 ~80-90 sRaw 22% (2) 0 0Bethel et al. (1985)25010 min 28 ~40 sRaw 4% (1) 0 0Roger 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) 0Linn et al. (1990)310 min 20 ~50 FEV115% (3) 0 0Linn et al. (1988)30010 min 21 ~50 FEV124% (5) 14% (3) 10% (2)Linn et al. (1990)Limited evidence of SO2-induced increases in respiratorysymptoms in some asthmatics:Linn et al. (1983; 1984; 1987;1988; 1990), Schacter et al.(1984)10 min 40 ~40 sRaw 23% (9) 8% (3) 3% (1)Linn et al. (1987)40010 min 40 ~40 FEV130% (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)50010 min 45 ~30 sRaw 36% (16) 16% (7) 13% (6)Magnussen et al.(1990)4Stronger evidence with somestatistically significant increasesin respiratory symptoms:Balmes et al. (1987)4, Gonget al. (1995), Linn et al. (1983;1987), Roger et al. (1985)60010 min 40 ~40 sRaw 35% (14) 28% (11) 18% (7)Linn et al. (1987)Clear and consistent increasesJuly 2009325Cumulative Percentage ofResponders(Number of Subjects)1sRaw≥100%≥200%≥300%FEV1SO2Level(ppb)ExposureDurationNo. ofSubjectsVentilation(L/min)LungFunct.≥15%≥20%≥30%Reference Respiratory Symptoms:Supporting Studies10 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 FEV153% (21) 48% (19) 20% (8)Linn et al. (1987)10 min 20 ~50 FEV155% (11) 55% (11) 5% (1)Linn et al. (1988)10 min 21 ~50 FEV143% (9) 33% (7) 14% (3)Linn et al. (1990)10 min 28 ~40 sRaw 50% (14) 25% (7) 14% (4)Roger et al. (1985)1,00010 min 10 ~40 sRaw 60% (6) 20% (2) 0Kehrl et al. (1987)in SO2-induced respiratorysymptoms: Linn et al.(1984;1987; 1988; 1990), Gong et al.(1995), Horstman et al. (1988)Notes:1Data presented from all references from which individual data were available. Percentage of individuals who experienced greaterthan or equal to a 100, 200, or300% increase in specific airway resistance (sRaw), or a 15, 20, or 30% decrease in FEV1. Lung function decrements are adjusted for effects of exercise inclean air (calculated as the difference between the percent change relative to baseline with excersise/SO2and the percent change relative to baseline withexercise/clean air). Quality control ofdata 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 first10 min period of exercise in thefirst round of exposures.3Analysis includes data from only mild (1988) and moderate (1990)asthmatics who were not receiving supplemental medication.4One subject was not exposed to 1,000 ppb due to excessive wheezing and chest tightness experienced at 500 ppb. For this subject, the values used for 500ppb were also used for 1,000 ppb under the assumptions that the response at 1,000 ppb would be equalto or greater than the response at 500 ppb.5Indicates studies in which exposures were conducted using a mouthpiecerather 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 re lationships between responses defined in terms of sRaw and 5-minute exposures to SO 2 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 defi ned in terms of FEV 1 and 5-minute exposures to SO 2 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

FEV 1 ≥ 15% representing moderate or greate r responses and (2) a decrease in FEV 1 ≥ 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 studie s 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 re flected 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 SO 2 -exposed cases.

Public comments on the 2 nd draft REA stated that ther e 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 an alysis 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 conduc ted using a mouthpiece rather than a chamber. 93 Corrections were subject-specific. A correction wa s made by subtracting the subject’s percent change (in FEV 1 or sRaw) under the no-SO 2 protocol from his or her percent change (in FEV 1 or sRaw) under the given SO 2 protocol, and rounding the result to the nearest integer. For example, if a subject’s percent change in sRaw under the no-SO 2 protocol was 110.12% and his percent change in sRaw under the 0.6 ppm SO 2 protocol was 185.92%, then his percent change in sRaw due to SO 2 is 185.92% - 110.12% = 75.8%, which rounds to 76%.July 2009 327 values of sRaw and FEV 1 from Linn et al. (1987), commenters identified 11 discrepancies between the orig inal EPA analysis (Smith, 1994) and what was included in the analysis conducted more recently by EP A (Johns, 2009). EPA has reviewed these comments, and recognizes that some discrepa ncies 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 av erage values presented in Smith (1994). While staff placed more confidence in the av erage values presented rather than the calculated average of the individual measurements, EP A nonetheless conducted a preliminary re-analysis using the 20 a pparent “corrected” values provided by commenters. This resulted in relatively minor and variable changes in SO 2 -induced changes in lung function, which did not subs tantively change the percent responders as presented in Table 9-3. Furthe r, incorporating these 20 change s 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 assura nce review of the data in response to these public comments and expects to present the quantitative results of it s 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 functi onal 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 ),;( )*ln( x e xy  

   (equation 9-3)

and the probit function, 

##  

   )ln( 2/ 2/1 2 )2( 1 ),;( x t dte xy 

  (equation 9-4)July 2009 328 for each of the four lung function responses defined above, where x denotes the SO 2 concentration (in ppm) to which the individual is exposed, ln( x ) is the natural logarithm

of x , y denotes the corresponding probability of response (increase in sRaw > 100% or >

200% or decrease in FEV 1 > 15% or > 20%), and β and γ are the two parameters whose

values are estimated. 94 We assumed that the number of responses, s i , out of N i subjects exposed to a given SO 2 concentration, x i , has a binomial distribution w ith response probability given by equation (9-3) when we assume the logi stic model and equa tion (9-4) when we assume the probit model. Th e likelihood function is therefore

ii i sN i s i i i i xy xy s N dataL 

        

 

##  )],;(1[),;( );,(    . (equation 9-5)

Some subjects in the controlled human e xposure studies participated in more than one study and were exposed to a given SO 2 concentration more than once. However,

because there were insufficient data to es timate subject-specific response probabilities, we assumed a single response pr obability (for a given definition of response) for all

individuals and treated the re peated exposures for a sing le subject as independent exposures in the binomial distribution. For each model, we derived a Bayesian pos terior distribution us ing this binomial

likelihood function in combination with unifo rm 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 SO 2 concentration, x , we could then derive the n th percentile response value, for any n , by evaluating the exposur e-response function at x using each of the 18,000 sets of parameter values. The resulting median (50 th 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, howeve r, 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-paramete r logistic and probit models are shown together, along with the data used to estim ate these functions, for increases in sRaw >

100% and > 200% and decreases in FEV 1 > 15% and > 20% in Figures 9-2, 9-3, 9-4, and 9-5, respectively. The 2.5 th percentile, median, and 97.5 th 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 respons e due to exercise in clean air, then the response must by definition be zero associated with 0 ppm SO 2 exposure. While the CASAC panel in its comments on the 2 nd draft REA suggested a possible a priori reason to prefer the probit model (based on a hypot hesized lognormal distri bution of individual thresholds for response), in staff’s judgment there is not sufficient evidence to select one model over the other. Therefore, we have chos en to include both the 2-parameter logistic and probit models to develop the risk es timates associated with exposure to SO 2 under the different air quality scenar ios considered. While the estimated exposure-response relationships using the two alte rnative 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 aggr egate number of sRaw and FEV 1 responses for St. Louis as discussed later in this chapter.July 2009 330 Figure 9-2. Bayesian-estimated median ex posure-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 SO 2 Concentration (ppm)Response Rate The data probit 2-parameter logistic

Figure 9-3. Bayesian-estimated median ex posure-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 SO 2 Concentration (ppm) The data probit 2-parameter logistic

Response RateJuly 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 SO 2 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 SO 2 Concentration (ppm) The data probit 2-parameter logistic

Response RateJuly 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 qua lity represented by 2002 monitoring data. In addition, risk estimates are presented for se veral hypothetical scenar ios, equivalent to adjusting air quality upward to simula te just meeting the current annual SO 2 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 ar e presented in Appendix C. Throughout this section and Appendix C th e uncertainty surroundi ng risk estimates resulting from the statistical uncertainty in the SO 2 exposure-response relationships due to sampling error is characterized by nine ty-five percent cred ible intervals around estimates of occurrences, number of asthma tics experiencing one or more lung function response, and percent of to tal incidence that is SO 2 -related. Risk estimates for selected lung f unction responses for all asthmatics and

asthmatic children associated with 5-minute exposures to ambient SO 2 concentrations while engaged in moderate or greater exerti on 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 esti mates for both Greene County and St. Louis,

Missouri. Each table also includes risk es timates 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 FEV 1 ) and two levels of response for each t ype of lung function response ( ≥ 100 and 200% increase for

sRaw and ≥ 15 and 20% decrease for FEV 1 ). Risk estimates using sRaw as the measure of lung function response are included in th is 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 exposu re-response relationship. Additional risk estimates using FEV 1 as the indicator of lung function re sponse are included in Tables 4- 3, 4-4, 4-7, and 4-8 in Appendix C and s how similar patterns across the current and alternative standards for the two study areas.July 2009 333 Tables 9-4 and 9-5 summarize the estim ated number and per cent of asthmatics that would experience 1 or mo re 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 SO 2 exposures estimated to occur under “as is” air quality (i.e., air quality based on 2002 monitored and modeled SO 2 air quality data) and under air quality representing just meeting the current SO 2 standards and several alternative 1-hour daily maximum SO 2 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 SO 2 coefficient in the exposure- response relationship shown in parentheses below the median estimates.

Tables 9-6 and 9-9 summarize the es timated number of occurrences of two defined levels of lung function response ( ≥ 100% and ≥ 200% increase in sRaw) in all asthmatics and in asthmatic children, respec tively, associated with ambient 5-minute SO 2 exposures estimated to occur under “as is” air quality (i.e., air quality based on 2002

monitored and modeled SO 2 air quality data) and under air quality representing just meeting the current SO 2 standards and several altern ative 1-hour daily maximum SO 2 standards. The current primary SO 2 standards include a 24-hour standard se t at 0.14 parts per million (ppm), not to be exceeded more than on ce per year, and an annual standard set at 0.03 ppm, calculated as the arithmetic mean of hourly averages. In St. Louis, SO 2 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. Conse quently, 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 qual ity 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 stan dard resulting in decr eases 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 2009334Table 9-4. Number of asthmatics engaged in moderate or greater exertion estimated to experience at least one lung function responseassociated with exposure to SO2concentrations under alternative air quality scenarios in a year.*99/5099/10099/15099/20099/25098/200902108090100120160140(20 - 390) (80 - 620) (20 - 380) (20 - 390) (20 - 420) (30 - 460) (50 - 520) (40 - 500)10 110 10 10 20 40 70 60(0 - 180) (40 - 410) (0 - 170) (0 - 180) (0 - 210) (10 - 250) (20 - 310) (20 - 280)10101346073019903650552075007050(340 - 3010) (9740 - 18510) (220 - 2490) (860 - 4690) (1900 - 7100) (3230 - 9490) (4770 - 11850) (4410 - 11320)5001305029013402930481068606400(140 - 1990) (9430 - 18100) (70 - 1470) (520 - 3690) (1450 - 6200) (2760 - 8710) (4310 - 11190) (3950 - 10640)3070303030405050(0 - 210) (20 - 310) (0 - 210) (0 - 210) (0 - 220) (10 - 240) (10 - 270) (10 - 260)0 300 010 10 20 10(0 - 80) (10 - 180) (0 - 80) (0 - 90) (0 - 100) (0 - 110) (0 - 140) (0 - 130)33055202306701280201028302640(70 - 1520) (3400 - 8960) (40 - 1290) (210 - 2270) (510 - 3360) (940 - 4470) (1470 - 5590) (1340 - 5330)120518060350870156023802190(20 - 880) (3150 - 8570) (10 - 660) (90 - 1590) (310 - 2680) (690 - 3820) (1200 - 5000) (1070 - 4730)Response = Increase in sRaw >= 100%Greene County, MOSt. Louis, MO"As is" SO2Concentrations**Exposure-ResponseModelSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):2-Parameter LogisticProbit2-Parameter Logistic*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.2-Parameter LogisticResponse = Increase in sRaw >= 200%ProbitSt. Louis, MOProbitGreene County, MO***The current primary SO2standards 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.2-Parameter LogisticProbit**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.July 2009335Table 9-5. Percent of asthmatics engaged in moderate or greater exertion estimated to experience at least one lung function responseassociated with exposure to SO2concentrations under alternative air quality scenarios in a year.*99/5099/10099/15099/20099/25098/2000.4%1%0.4%0.4%0.5%0.6%0.7%0.7%(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% - 2.4%) (0.2% - 2.3%)0.1%0.5%0.1%0.1%0.1%0.2%0.3%0.3%(0% - 0.8%) (0.2% - 1.9%) (0% - 0.8%) (0% - 0.8%) (0% - 1%) (0% - 1.2%) (0.1% - 1.4%) (0.1% - 1.3%)1%13.1%0.7%1.9%3.6%5.4%7.3%6.9%(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% - 11.6%) (4.3% - 11.1%)0.5%12.7%0.3%1.3%2.9%4.7%6.7%6.2%(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% - 10.9%) (3.9% - 10.4%)0.1%0.3%0.1%0.1%0.2%0.2%0.2%0.2%(0% - 1%) (0.1% - 1.5%) (0% - 1%) (0% - 1%) (0%- 1%) (0% - 1.1%) (0% - 1.3%) (0% - 1.2%)0%0.1%0%0%0%0%0.1%0.1%(0% - 0.4%) (0% - 0.8%) (0% - 0.4%) (0% - 0.4%) (0% - 0.5%) (0% - 0.5%) (0% - 0.6%) (0% - 0.6%)0.3%5.4%0.2%0.7%1.3%2%2.8%2.6%(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% - 5.5%) (1.3% - 5.2%)0.1%5.1%0.1%0.3%0.8%1.5%2.3%2.1%(0% - 0.9%) (3.1% - 8.4%) (0% - 0.6%) (0.1% - 1.6%) (0.3% - 2.6%) (0.7%- 3.7%) (1.2% - 4.9%) (1% - 4.6%)***The current primary SO2standards 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.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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 statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth.Probit2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticJuly 2009336Table 9-6. Number of occurrences (in hundreds) of a lung function response among asthmatics engaged in moderate or greaterexertion associated with exposure to SO2concentrations under alternative air quality scenarios in a year.*99/5099/10099/15099/20099/25098/200125127125125125126126126(24 - 572) (25 - 577) (24 - 572) (24 - 572) (24 - 573) (24 - 573) (24 - 575) (24 - 574)1618161616161717(0 - 256) (1 - 261) (0 - 256) (0 - 256) (1 - 257) (1 - 257) (1 - 258) (1 - 258)65716726526867628801036997(128 - 2985) (663 - 4740) (125 - 2975) (141 - 3041) (176 - 3184) (234 - 3398) (315 - 3673) (295 - 3604)9093386111170264392360(4 - 1346) (393 - 3107) (3 - 1336) (11 - 1402) (33 - 1543) (72 - 1756) (128 - 2031) (114 - 1963)3839383838383939(4 - 310) (4 - 312) (4 - 310) (4 - 310) (4 - 310) (4 - 310) (4 - 311) (4 - 311)23222222(0 - 123) (0 - 124) (0 - 122) (0 - 123) (0 - 123) (0 - 123) (0 - 123) (0 - 123)201560199211237278332319(21 - 1614) (165 - 2407) (20 - 1609) (24 - 1639) (32 - 1703) (47 - 1799) (68 - 1923) (63 - 1892)13 258 12 18 33 59 95 86(0 - 643) (86 - 1388) (0 - 639) (1 - 666) (5 - 725) (12 - 814) (24 - 930) (21 - 901)***The current primary SO2standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and anannual standard set at 0.03ppm, calculated as the arithmetic mean of hourly averages.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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 uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number.Probit2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticJuly 2009337Table 9-7. number of asthmatic children engaged in moderate orgreater exertion estimated to experience at least one lung functionresponse associated with exposure to SO2concentrations under alternative air quality scenarios in a year.*99/5099/10099/15099/20099/25098/20030 110 30 30 40 50 70 60(10 - 130) (40 - 270) (10 - 130) (10 - 140) (10 - 150) (20 - 180) (30 - 210) (20 - 200)10 60 0 10 10 20 40 30(0 - 60) (20 - 200) (0 - 60) (0 - 60) (0 - 80) (10 - 100) (10 - 140) (10 - 130)590802040012202240337045604290(220 - 1570) (6080 - 10370) (130 - 1210) (560 - 2620) (1240 - 4010) (2090 - 5350) (3060 - 6680) (2840 - 6390)34079501908901910308043304060(100 - 1150) (6020 - 10320) (50 - 790) (360 - 2220) (1000 - 3690) (1860 - 5110) (2870 - 6510) (2640 - 6210)1040101010202020(0 - 70) (10 - 130) (0 - 70) (0 - 70) (0 - 80) (0 - 90) (10 - 110) (10 - 100)020000101010(0 - 30) (0 - 90) (0 - 30) (0 - 30) (0 - 40) (0 - 50) (0 - 60) (0 - 60)1903380130410800125017501640(50 - 780) (2190 - 5070) (30 - 610) (140 - 1240) (340 - 1870) (620 - 2500) (970 - 3140) (890 - 3000)80329040240580103015601440(10 - 500) (2110 - 5000) (10 - 350) (60 - 950) (220 - 1590) (480 - 2250) (830 - 2940) (740 - 2790)***The current primary SO2standards 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.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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% credibleintervals based on statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.Probit2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticJuly 2009338Table 9-8. Percent of asthmatic children engaged in moderate orgreater exertion estimated to experience at least one lung functionresponse associated with exposure to SO2concentrations under alternative air quality scenarios in a year.*99/5099/10099/15099/20099/25098/2000.4%1.4%0.4%0.4%0.5%0.7%1%0.9%(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% - 2.9%) (0.3% - 2.7%)0.1%0.9%0.1%0.1%0.2%0.3%0.5%0.4%(0% - 0.9%) (0.3% - 2.7%) (0% - 0.8%) (0% - 0.9%) (0% - 1.1%) (0.1% -1.4%) (0.2% - 1.9%) (0.1% - 1.7%)1.4%19.2%0.9%2.9%5.4%8.1%10.9%10.3%(0.5% - 3.8%) (14.6% - 24.9%) (0.3% - 2.9%) (1.3% - 6.3%) (3% - 9.6%) (5% - 12.8%) (7.3% - 16%) (6.8% - 15.3%)0.8%19.1%0.4%2.1%4.6%7.4%10.4%9.7%(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% - 15.6%) (6.3% - 14.9%)0.1%0.5%0.1%0.1%0.2%0.2%0.3%0.3%(0% - 1%) (0.1% - 1.8%) (0% - 1%) (0% - 1%) (0%- 1.1%) (0% - 1.3%) (0.1%- 1.5%) (0.1% - 1.4%)0%0.2%0%0%0%0.1%0.1%0.1%(0% - 0.4%) (0.1% - 1.2%) (0% - 0.4%) (0% - 0.4%) (0% - 0.5%) (0% - 0.6%) (0% - 0.8%) (0% - 0.8%)0.5%8.1%0.3%1%1.9%3%4.2%3.9%(0.1% - 1.9%) (5.3% - 12.2%) (0.1% - 1.5%) (0.3% - 3%) (0.8% - 4.5%) (1.5% - 6%) (2.3% - 7.5%) (2.1% - 7.2%)0.2%7.9%0.1%0.6%1.4%2.5%3.7%3.4%(0% - 1.2%) (5% - 12%) (0% - 0.8%) (0.2% - 2.3%) (0.5% - 3.8%) (1.2% - 5.4%) (2% - 7%) (1.8% - 6.7%)***The current primary SO2standards 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.2-Parameter Logistic*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth.Exposure-ResponseModel**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.St. Louis, MOProbit2-Parameter LogisticProbit2-Parameter LogisticProbitSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitGreene County, MOSt. Louis, MOGreene County, MO"As is" SO2Concentrations**July 2009339Table 9-9. number of occurrences (in hundreds) of a lung function response among asthmatic children engaged in moderate or greaterexertion associated with exposure to SO2concentrations under alternative air quality scenarios in a year.*99/5099/10099/15099/20099/25098/2007172717171717171(13 - 324) (14 - 327) (13 - 324) (14 - 324) (14 - 324) (14 - 325) (14 - 325) (14 - 325)9 109 9 9 910 10(0 - 145) (1 - 148) (0 - 145) (0 - 145) (0 - 145) (0 - 146) (0 - 146) (0 - 146)4171179413439497586704674(81 - 1893) (484 - 3209) (80 - 1885) (91 - 1935) (118 - 2043) (162 - 2206) (222 - 2413) (207 - 2361)586925574118189286262(3 - 855) (296 - 2176) (2 - 847) (8 - 896) (25 - 1004) (53 - 1166) (96 - 1373) (85 - 1321)2222222222222222(2 - 175) (2 - 177) (2 - 175) (2 - 175) (2 - 175) (2 - 176) (2 - 176) (2 - 176)12111111(0 - 69) (0 - 71) (0 - 69) (0 - 69) (0 - 69) (0 - 70) (0 - 70) (0 - 70)128397126135155186227217(13 - 1023) (122 - 1618) (13 - 1019) (15 - 1042) (22 - 1091) (33 - 1164) (49 - 1257) (45 - 1234)8 1928 12 24 43 70 63(0 - 408) (65 - 967) (0 - 405) (1 - 425) (4 - 470) (9 - 538) (18 - 625) (16 - 603)***The current primary SO2standards 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.2-Parameter Logistic*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number.Exposure-ResponseModel**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.St. Louis, MOProbit2-Parameter LogisticProbit2-Parameter LogisticProbitSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitGreene County, MOSt. Louis, MOGreene County, MO"As is" SO2Concentrations**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 th e St. Louis modeling domain, under

the current SO 2 standards the median estimate is 117,900. These estimated occurrences decrease for increasingly more stringent alterna tive 1-hour standards with the 50 ppb, 99 th percentile daily maximium 1-hour standard, the most stringent alternative standard analyzed, reducing the

median estimated number of occurrences of th is lung function response to 41,300. The pattern

of reductions observed for all asthmatics is sim ilar to that observed in asthmatic children. The estimated occurrences of sRaw respons es are much lower in Greene County both due to a smaller population as well as fewer e xposure occurrences of elevated 5-minute SO 2 concentrations. We also note that the differe nces 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 diff erences observed in Greene County among the air quality scenarios analyzed is due to the rela tively small differences in the distribution of exposures while engaged in moderate or grea ter exertion among the air quality scenarios analyzed. Figures 9-7 (a) and (b) show the percent of asthmatics base d on use of the logistic and probit exposure-response models, re spectively, engaged in moderate or greater exertion in St. Louis, MO estimated to experien ce at least one lung function res ponse in a year, defined as an

increase in sRaw ≥ 100%, attributable to exposure to SO 2 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 exposur e 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 incl uded in Appendix C for lung function responses

defined in terms of ≥ 15% and ≥ 20% decrements in FEV 1 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 con centration interval s on lung function response is very similar for this risk metric using the two alternative e xposure-response models. In comparing the risk estimates for all asthmatics (Figure 9-7) with th e 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 asthmaticJuly 2009 341 0% 5% 10% 15% 20% 25% 30% 35% 40%"As Is" Air QualityCurrent Std.99/5099/10099/15099/20099/25098/200 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 SO 2 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 standard s, 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 percentil e of the 1-hr daily maximum concentrations must be ≤ 50 ppb. 99/100 Refers to an alternative standard in which the 99th percentil e of the 1-hr daily maximum concentrations must be ≤ 100 ppb. 99/150 Refers to an alternative standard in which the 99th percentil e of the 1-hr daily maximum concentrations must be ≤ 150 ppb. 99/200 Refers to an alternative standard in which the 99th percentil e of the 1-hr daily maximum concentrations must be ≤ 200 ppb. 99/250 Refers to an alternative standard in which the 99th percentil e of the 1-hr daily maximum concentrations must be ≤ 250 ppb. 98/200 Refers to an alternative standard in which the 98th percentil e of the 1-hr daily maximum concentrations must be ≤ 200 ppb.July 2009 342 0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

a) Based on Logistic Exposure-Response Model

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 SO 2 concentrations associated with alternative air quality scenarios – total and contribution of 5-minute SO 2 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" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

a) Based on Logistic Exposure-Response Model

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 SO 2 concentrations associated with alternative air quality scenarios – total and contribution of 5-minute SO 2 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 highe r 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 leas t one lung function res ponse 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 SO 2 concentrations, many are exposed at least occasionally to higher levels. Thus, the percentage of individuals in a designated populat ion with at least one lung func tion response associated with

SO 2 concentrations in the lowest bin is likely to be very small, since most individuals are exposed at least once to higher SO 2 levels. For example, the lowest SO 2 exposure bin accounts for only about 0.2 percent of asthmatics estimated to experience at least 1 SO 2 -related lung function response. For this very small per cent of the population, th e lowest exposure bin represents their highest SO 2 exposures under moderate exertion in a year. Figure 9-7 (a) shows a relatively small proportion of asthmatics in St. L ouis 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 gr eatest 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, expos ures in the lowest bi n (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 2 nd draft SO 2 REA, the assignment of response probability to the midpoint of the exposure bin co mbined 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 be cause the distribution of population exposures for occurrences is not evenly distribut ed 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 pl aces less weight on the estimated number of occurrences of lung function responses. This overestimation of tota l occurrences does not impact the risk metric expressed as incidence or percent incidence of a defined lung functionJuly 2009 345 response 1 or more times per year because the bu lk of the exposures contributing to these risk

metrics are not skewed toward the lower ra nge of the reported exposure bins.



### 9.4 CHARACTERIZING UNCERT AINTY 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 summar y and discussion regardi ng the degree to which variability was incorporated in the health risk assessment fo r lung function responses and how the uncertainty was characterized for the risk es timates of number and pe rcent of asthmatics and asthmatic children experiencing defined lung function responses associated with 5-minute SO 2 exposures under moderate or great er exertion associated with alte rnative air quali ty scenarios. With respect to variability, the lung functi on 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 popul ation data, air exchange rates, air quality, and temperature data). The extent to which there may be vari ability in exposure-response relationships for the populations included in the risk assessment resi ding in different geograp hic areas is currently unknown. Temporal variability also is more diff icult 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 diffe rent from the values of those inputs at that unspecified time,

we have used the most cu rrent inputs available. Our approach to characterizing uncertainty includes both qualitative and quantitative elements. From a quantitative perspective, th e statistical uncertainty surrounding the estimated

SO 2 exposure-response relationships due to sampling error is reflect ed 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 asse ssment given the limited data available to inform a probabilistic uncertain ty characterization, and time and resource constraints. Following the same general approach described in s ections 6.6, 7.4, and 8.11.2 and adapted from WHO (2008), sta ff performed a qualitative char acterization of the components contributing to uncertainty in the lung function risks for all asthmatics and asthmatic children

attributable to 5-minute SO 2 exposures under moderate or greater exertion. First, staff identified the important uncertainties. Then, we qua litatively characterized the magnitude ( low , medium , and high ) and direction of influence ( over, under, both, and unknown ) the source of uncertaintyJuly 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-b ase for the criteria air pollutants. Table 9-11 provides a summary of the sources of uncertainty id entified 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 2009347Table 9-11. Characterization of key uncertainties in the lung function response health risk assessment for St. Louis and Greene County,Missouri.Influence of Uncertainty on LungFunction Risk EstimatesSource ofUncertaintyDirection MagnitudeKnowledgeBaseUncertaintyComments1Exposure Model(APEX) Inputs andAlgorithmsUnknown Unknown Medium to HighSee Table 8-27 and section 8.11.2SpatialrepresentationBoth Medium HighSee Table 7-16 and discussion in section 7.4.2.4Air qualityadjustmentBoth Low-Medium MediumSee Table 7-16 and discussion in section 7.4.2.5Causality Over Low-MediumLow – for levelsabove 100 ppbMedium – forlevels below100 ppbINF: While there is very strong support for SO2being causally linked to lung functionresponses within the range of tested exposure levels (i.e.,≥200 ppb) and evendown to the100 ppb level (where SO2was administered by mouthpiece (Sheppardet al. 1981; Koenig et al., 1990)), there isincreasing uncertainty about whether SO2is causally related to lung-function effects at lower exposure levels below 100 ppb.Since this assessment assumes there is a causal relationship at levels below 100ppb, the influence of this source of uncertainty would be to over-estimate risk.KB: The SO2-related lung function responses have been observed in controlledhuman exposure studies and, thus there is little uncertainty that SO2exposures areresponsible for the lung function responses observed for SO2exposures in therange of levels tested. Given the lack ofchamber data at levels below 100 ppb, theKB uncertainty is rated as medium.Use of 2-parameterlogistic and probitmodels to estimateprobabilisticexposure-responserelationshipsUnknownLow - for levels atand above 100ppbMedium – forlevels below 100ppbLow - for levelsabove100 ppbMedium – forlevels below100 ppbKB: It was necessary to estimate responses at SO2levels both within the range ofexposure levels tested (i.e., 200 to 1,000 ppb) as well as below the lowest exposurelevels used in free-breathing controlled human exposure studies (i.e., below 200ppb). We have developed probabilistic exposure-response relationships using twodifferent functional forms (i.e., probit and 2-parameter logistic). Both functionalforms provide reasonable fits to the data in the available range of levels tested. Forthe risks attributable to exposure levels below 200 ppb, the lowest level tested infree-breathing chamber studies, and particularly below 100 ppb, the lowest leveltested in face mask chamber studies, there is greater uncertainty.Use of 5- and 10-minute lungfunction responsedata to estimate 5-Over Low LowINF: It is reasonable to hypothesize that10-minute exposures might lead to largerlung function responses, so inclusion of10-minute response data in the data baseused to estimate 5-minute responses would be more likely to result in overestimating risks. However, there is some evidence that responses generally occurJuly 2009348Influence of Uncertainty on LungFunction Risk EstimatesSource ofUncertaintyDirection MagnitudeKnowledgeBaseUncertaintyComments1minute lungfunction riskestimatesin the first few minutes of exposure (seeISA, section 3.1.3.2), suggesting that anyoverestimation is likely to be very modest in terms of magnitude.KB: The 5-minute lung function risk estimates are based on a combined data setfrom several controlled human exposurestudies, most of which evaluatedresponses associated with 10-minute exposures. However, since some studieswhich evaluated responses after 5-minute exposures found responses occurring asearly as 5-minutes after exposure, weare using all of the 5- and 10-minuteexposure data to represent responses associated with 5-minute exposures. We donot believe that this factor appreciably impacts the risk estimates.Use of exposureresponse data fromstudies ofmild/moderateasthmatics torepresent allasthmaticsUnder Medium MediumINF & KB: The data set that was used to estimate exposure-response relationshipsincluded mild and/or moderate asthmatics.There is uncertainty with regard to howwell the population of mild and moderate asthmatics included in the series of SO2controlled human exposure studies represent the distribution of mild and moderateasthmatics in the U.S. population. As indicated in the ISA (p. 3-9), the subjectsstudied represent the responses “among groups of relatively healthy asthmatics andcannot necessarily be extrapolated tothe most sensitive asthmatics in thepopulation who are likely more susceptible tothe respiratory effects of exposure toSO2."Thus, the influence of this uncertaintyis likely to lead to under-estimatingrisks and we judge the magnitude of the influence of this uncertainty on the lungfunction risk estimates to be medium.Reproducibility ofSO2-induced lungfunction responseUnknown Unknown LowINF & KB: The risk assessment assumes that the SO2-induced responses forindividuals are reproducible. We note thatthis assumption has some support in thatone study (Linn et al., 1987) exposed the same subjects on two occasions to 0.6ppm and the authors reported a high degreeof correlation (r > 0.7 for mildasthmatics and r > 0.8 for moderate asthmatics, p < 0.001), while observing muchlower and nonsignificant correlations (r = 0.0 – 0.4) for the lung function responseobserved in the clean air with exercise exposures.Use of adultasthmatic lungfunction responsedata to estimateexposure-responserelationships forasthmatic childrenUnknown Unknown Low to MediumINF & KB: Because the vast majority of controlled human exposure studiesinvestigating lung function responses were conducted with adult subjects, the riskassessment relies on data from adult asthmatic subjects to estimate exposureresponse relationships that have been applied to all asthmatic individuals, includingchildren. The ISA (section 3.1.3.5) indicates that there is a strong body of evidencethat suggests adolescents may experience many of the same respiratory effects atsimilar SO2levels, but recognizes that these studies administered SO2via inhalationthrough a mouthpiece rather than an exposure chamber. This technique bypassesnasal absorption of SO2and can result in an increase in lung SO2uptake.July 2009349Influence of Uncertainty on LungFunction Risk EstimatesSource ofUncertaintyDirection MagnitudeKnowledgeBaseUncertaintyComments1Therefore, the uncertainty is greater in the risk estimates for asthmatic children. Thedirection and magnitude of this uncertainty on the lung function risk estimates isunknown.Exposure historyBothLowMediumINF & KB: The risk assessment assumes that the SO2-induced response on anygiven day is independent of previous SO2exposures. For some pollutants (e.g.,ozone) prior exposure history can lead to both enhanced and diminished lungfunction responses depending on the pattern of exposure. Since the assessment isonly included the highest daily 5-minuteexposure under moderate or greaterexertion, and the influence of prior exposures might lead to either enhanced ordiminished response based on what we know about other pollutants (i.e., ozone),staff rated the magnitude of the influence of this uncertainty to be low. Given thelack of available information to directly assess this uncertainty for SO2exposures inchamber studies staff rated the KB uncertainty to be medium.Assumed nointeraction effect ofother co-pollutantson SO2-relatedlung functionresponsesUnder Medium MediumINF: Staff judges that it is more likely that exposure to other pollutants mightincrease the magnitude of lung function response and thus increase the riskestimates. Thus, assuming no interaction ismore likely to result in under-estimatingrisks.KB: Because the controlled human exposurestudies used in the risk assessmentinvolved only SO2exposures, there is little information to judge whether or notestimates of SO2-induced health responses are affected by the presence of otherpollutants (e.g., PM2.5, O3, NO2).Notes:1INF refers to comments associated with the influence rating; KBrefers to comments associated with the knowledge-base rating.1July 2009 350 

### 9.5 KEY OBSERVATIONS Presented below are key observations related to the risk assessment for lung function responses in asthmatics and as thmatic children associated with 5-minute exposures to SO 2 while engaged in moderate or greater exertion:

 Lung function responses estimated to re sult from 5-minute exposures to SO 2 were estimated for two areas in Missouri (i.e., Greene County and St. Louis) which have significant emission sources of SO 2 for 2002 air quality and for air quality adjusted to simulate just meeting the curre nt suite of annual and 24-hour SO 2 standards and just meeting several alternative 1-hour daily maximum SO 2 standards.

 A number of factors would be expected to contribute to differences in estimated SO 2 - related lung function responses across diffe rent locations. These include exposurerelated differences, such as population density, SO 2 emission density, location and types of SO 2 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 SO 2 and asthma prevalence rates. As di scussed in section 8.10, St. Louis County has a medium to high SO 2 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 SO 2 emission density and much lower populat ion density. Taken together, the risk estimates for these two locations provide us eful insights about ur ban and rural counties with significant SO 2 emission sources.

 The lung function risk estimates for the St. Louis study area are much higher than for Greene County, which is not unexpected give n the greater populat ion density and the much greater SO 2 emission density. Staff believes th at the St. Louis risk estimates provide a useful perspective on the likely ove rall magnitude and patte rn of lung function responses associated with various SO 2 air quality scenarios in ur ban areas within the U.S. that have similar population densities and SO 2 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 func tion responses. As discussed above, this is not unexpected give n the rural nature of Greene County and the fact that it has much lower SO 2 emission density and lower population dens ity than the St. Louis study area.

 Of the alternative regulator y scenarios analyzed, only th e 50 ppb/99th per centile daily maximum 1-hr standard is estimated to re duce 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 estimat es 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-paramete r logistic exposure-responseJuly 2009 351 model compared to 10.4% down to 0.4% of asthmatic children usi ng the probit exposureresponse model for alternative 99 th 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 m odel are lower than those based on the logistic model.

 For the St. Louis study area median risk estimat es 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-pa rameter logistic exposure-response model compared to 3.7% down to 0.1% of asth matic children using the probit exposureresponse model for alternative 99 th percentile daily maximum 1-hour standars ranging from 250 ppb down to 50 ppb.

 In terms of estimated percentage of asthma tics or asthmatic children experiencing 1 or more lung function responses, risks are greater for asthmatic children, likely because they spend more time at higher ex ertion levels than adults.

 A broad range of SO 2 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 standa rds in the range of 100 to 150 ppb SO 2 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 associ ated with the risk assessment which were discussed above in section 9.3 and which s hould be kept in mind as one considers the quantitative risk estimates include: - uncertainties related to the exposure estimates which ar e 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 qual ity 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 knowle dge 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 sampli ng error which is characterized in the assessment through presentation of 95% credible intervals; - uncertainty about the sh ape 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 st udies which staff rated as low for levels at and above 100 ppb and medium for le vels below 100 ppb with respect to knowledge base uncertainty and the influen ce 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 diseas e 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 magnit ude of the lung function risk estimates;July 2009 352 - uncertainty about whether the presence of other pollutants in the ambient air would enhance the SO 2 -related responses observed in th e controlled human exposure studies which staff rated as medium with respect to knowledge base uncertainty and the influence of this uncertainty on the magnit ude 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 SO 2 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 assumpti on 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 lo w 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 SO 2 

## NAAQS

10.1 INTRODUCTION This chapter considers the scientific ev idence in the ISA (EPA, 2008a) and the air quality, exposure and risk characte rization results presented in this document as they relate to the adequacy of the current SO 2 primary NAAQS and potential alternative primary SO 2 standards.

The available scientific evidence includes epidemiologic, controlled human exposure, and animal toxicological studies. The SO 2 air quality, exposure, and risk an alyses described in Chapters 7-9

of this document include characterization of air quality, exposure, and health risks associated

with recent SO 2 concentrations and with SO 2 concentrations adjusted to simulate scenarios just meeting the current suite of standards and potential alternative 1-hour standa rds. In considering

the scientific evidence and the exposure- and risk-based informa tion, we have also considered relevant uncertainties. Section 10.2 of this chap ter presents our general approach to considering the adequacy of the current standards and the need for potential alternativ e standards. Sections 10.3 and 10.4 focus on evidence- and exposure-/r isk-based considerations related to the adequacy of the current 24-hour and annual sta ndards respectively, while section 10.5 focuses on

such considerations related to the need for potential alterna tive 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 SO 2 NAAQS. A final decision will draw upon scientific information and analyses about health effect s, population exposure and risks, and policy judgments about the appropriate response to the range of uncertainties that are inherent in the scientif ic evidence and air quality, exposure, and risk analyses. Our a pproach 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 sc ientists generally agree that health effects are likely to occur through lower levels at whic h the likelihood and magn itude of the response become increasingly uncertain. This approach is consistent with the requirements of the NAAQS provisions of the Act and with how EPA a nd 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 hea lth with an adequate margin of safety. In so doing, the Administrator seeks to establish standards that are neither more nor lessJuly 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 pr otection that avoids unacc eptable risks to public health, including the health of at risk populations.



### 10.2 GENERAL APPROACH This section describes the gene ral approach that staff is taking to inform decisions regarding the need to retain or revise the current SO 2 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 SO 2 exposure and daily mort ality, aggravation of bronchitis, and small, reversible declines in children’s lung func tion (EPA 1982, 1994a). Similarly, the decision to retain the annual standard (see secti on 10.4) was largely based on an assessment of epidemiologic studies findi ng an association between respiratory symptoms/illnesses and annual average SO 2 concentrations (EPA 1982, 1994a). The previous review of the SO 2 NAAQS also addressed th e question of whether an additional short-term standard (e.g., 5-minute) was necessary to protect against short-term peak

SO 2 exposures. Based on the scientific evid ence, the Administrator judged that repeated exposures to 5-minute peak levels ≥ 600 ppb could pose a risk of si gnificant health effects for

asthmatic individuals at elevat ed ventilation rates (61 FR 25566). The Administrator also concluded that the likely frequency of such e ffects 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 SO 2 levels that could reliably elicit adverse health effects was likely to be a rare event when viewed in th e context of the entire population of asthmatics, and therefore did not po se 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 annua l standards and not to promulgate a 5-minute standard (61 FR 25566). The decision not to se t 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 (s ee section 1.1.1). Specifically, the court gave EPA the opportunity to provi de additional rationale to suppor t the Agency judgment that 5-July 2009 355 minute peaks of SO 2 do not pose a public health probl em when viewed from a national perspective. To inform the range of options that the Agency will consider in the current review of the primary SO 2 NAAQS, the general approach we have a dopted builds upon the approaches used in reviews of other criteria pollutants, incl uding the most recent reviews of the Pb, O 3 , PM, and

NO 2 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 exposur e/risk-based information, rec ognizing that the weight to be given to various elements of the evidence and ex posure/risk information is part of the public health policy judgments that the Administrator w ill make in reaching decisions on the standards. A series of general questions frames our appr oach to considering the scientific evidence and exposure/risk-based information. First, ou r consideration of the scientific evidence and exposure/risk-based information with regard to th e 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 reinfo rce or call into question evidence for SO 2 -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 consider ation of standards that are either more or less protective than the current standards. This ev aluation is framed by the following questions:

• Is there evidence that associations, especi ally causal or likely causal associations, extend to ambient SO 2 concentrations as low as, or lower than, the concentrations that have previously been associated with health effects? If so, wh at 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 cons ideration of a revised standard, we then consider the specific elements of th e standard (indicator for gaseous SO x , averaging time, form, and level) within the context of the currently avai lable information. In so doing, we address the following questions:

 Does the evidence provide support for consid ering a different indicator for gaseous SO x ?

 Does the evidence provide support for c onsidering different averaging times?

 What ranges of levels and forms of alternat ive 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 SO 2 , and what are the uncertainties associat ed with the estimated exposure and risk reductions? The following discussion addresses the questions outlined above and presents staff’s conclusions regarding the scie ntific evidence and the expos ure-/risk-based information specifically as they relate to th e current and potential al ternative 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 wh ile section 10.5 consider s potential alternative standards in terms of indicator, averaging time, form , and level. Each of these sections considers key conclusions as well as the uncertainties as sociated 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 SO 2 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 associati on between 24-hour average levels of SO 2 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 SO 2 levels were associatedJuly 2009 357 with the possibility of small, reversible dec lines in children’s l ung function (53 FR 14927). However, it was noted that in the locations wh ere these epidemiologic studies were conducted, high SO 2 levels were usually accompanied by high levels of PM, thus making it difficult to disentangle the individual contribut ion each pollutant had on these health outcomes. It was also noted that rather than 24-hour average SO 2 levels, the health effect s observed in these studies may have been related, at least in part, to the occurrence of shorter-term peaks of SO 2 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 re spiratory morbidity and short-term exposure to

SO 2 " (ISA, section 5.2). The ISA states that th e strongest evidence for this judgment is from human exposure studies demonstrating decrease d lung function and/or increased respiratory symptoms in exercising asthmatics exposed for 5-10 minutes to ≥ 200 ppb SO 2 (ISA, section 5.2). Supporting this conclusion is a larger body of epidemiol ogic studies published since the last review observing positive associations be tween 1-hour daily maximum or 24-hour average

SO 2 concentrations and respiratory symptoms, ED visits, and hospital admissions (ISA, section 5.2). Thus, the ISA bases its causal determination between short-term SO 2 exposure and respiratory morbidity on respiratory effects asso ciated with averaging times from 5-minutes to

24-hours. Here, we will examine the health information fi rst presented in Chapter 4 as it relates to the adequacy of the current 24-hour standard (as well as the annual standa rd, 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 curre nt 24-hour standard given that the majority of these studies examined possible asso ciations between 24-hour average SO 2 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, expos ure, 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 th eir associated health risks give n 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 controlli ng in a given area, and then describe exposure

and health risks associated with 5-minute SO 2 benchmark concentrations. As described in section 6.2, these benchmark concentrations are SO 2 exposure levels found in controlled humanJuly 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 th e current 24-hour standard in section 10.3.4.

10.3.2 Evidence-based considerations As mentioned above, the ISA f ound supporting evidence for its c onclusion that there is a causal relationship between short-term SO 2 exposures and respiratory morbidity from the reported associations observed in epidemiologic studies of respiratory symptoms and ED visits and hospitalizations. In consider ing the adequacy of the curren t 24-hour standard, we note that many epidemiologic studies demonstrating positive associations between ambient SO 2 and respiratory symptoms, ED visits, and hospita lizations were conducted in areas where SO 2 concentrations were less than the level of the current 24-hour (a s well as the annual; see section 10.4) NAAQS. With regard to these epidemiologi c 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 conduc ted 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 pr edominantly using 1-hour daily maximum or 24- hour average SO 2 concentrations are generally in agreement with the respiratory symptom results from controlled human exposure studies of 5-10 minut es. These results are al so coherent in that the respiratory effects observed in controlled human exposure st udies of 5-10 minutes provide a basis for a progression of respiratory morbidity that could lead to the ED visits and hospitalizations observed in epidemiol ogic studies (ISA, section 5.2). However, it should be noted that interpre tation of the epidemio logic literature is complicated by the fact that SO 2 is but one component of a complex mixture of pollutants present in the ambient air. The matter is further complicated by the fact that SO 2 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 fr om epidemiologic studies reflect the independent

contribution of SO 2 to the adverse respiratory outcomes a ssessed in these studies. In order to provide some perspective on this uncertainty, the ISA evaluates epidemiologic studies that employ multi-pollutant models. The ISA concl udes that these analyses indicate that although copollutant adjustment has vary ing degrees of influence on SO 2 effect estimates, the effect ofJuly 2009 359 SO 2 on respiratory health outcomes appears to be ge nerally independent of the effects of gaseous copollutants, including NO 2 and O 3 (ISA, section 5.2). With respect to PM 10, evidence of an independent SO 2 effect on respiratory health is less consis tent, with some of the positive ED visit and hospitalization results becoming negative (alt hough results were not statistically significantly negative) after inclusion of PM 10 in regression models (ISA, section 3.1.4.6). In epidemiologic studies of respiratory symptoms, the SO 2 effect estimate often remained relatively unchanged

after inclusion of PM 10 in multipolutant models (although the effect estimate may have lost statistical significance; ISA, section 3. 1.4.1). The ISA also finds that SO 2 -effect estimates generally remained relatively unchanged in th e limited number of st udies that included PM 2.5 and/or PM 10-2.5 in multipolutant models (ISA, secti on 3.1.4.6). Taken together, the ISA concludes studies employing multi-pollutant models do suggest that SO 2 has an independent effect on respiratory morbidity outcomes (see Chapte r 4; ISA, section 5.2). Thus, the results of experimental and epidemiologic studies form a pl ausible and coherent data set that supports a relationship between SO 2 exposures and respiratory morbidit y 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 consideratio ns described above, sta ff has considered the extent to which exposure- and risk-based in formation can inform decisions regarding the adequacy of the current 24-hour SO 2 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, SO 2 air quality levels were used as a surrogate for exposure. In the second approach, modeled estimates of human exposure we re developed for all asthmatics and asthmatic children living in Greene County an d 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 ha ve been characterized by comparing estimates of air quality or exposure to 5-minut e potential health effect benchmarks. These benchmarks are based on controlled human exposure studies involving kno wn 5-10 minute SO 2 exposure levels and corresponding decrements in lung function, a nd/or increases in resp iratory 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 SO 2 -July 2009 360 related health risks. This assessment combin ed outputs from the exposure analysis with estimated exposure-response f unctions 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 FEV 1 and sRaw) at least once per year and to estimate the tota l number of occurrences of these lung function responses per year (see Chapter 9). The respiratory effects (i.e., decrements in FEV 1 , increases in sRaw, and/or respiratory symptoms) considered in the air quality, exposur e, 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) gui delines published by the ATS; 2) conclusions from the ISA and previous NAAQS reviews; and 3) advice from CASAC. Being mi ndful 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 FEV 1 ) following exposure to 200- 300 ppb SO 2 for 5-10 minutes (ISA, section 3.1).

 Approximately 20-60% of exercising asthmatic s are expected to experience moderate or greater lung function decrements (i.e ≥ 100% increase in sRaw and/or a ≥ 15% decrease in FEV 1 ) following exposure to 400-1000 ppb SO 2 for 5-10 minutes (ISA, Table 5-3).

 At concentrations ≥ 400 ppb, moderate or greater sta tistically significant decrements in lung function are frequently a ssociated with respiratory symptoms (ISA, section 3.1).

 There is no evidence to indicat e that exposure to 200-300 ppb SO 2 for 5- 10 minutes represents a threshold below which no respiratory effects occur. Given the discussion in section 4.3 and th e key points presented above, staff concludes that exposure to 5-10 minute SO 2 concentrations at least as lo w as 200 ppb can result in adverse respiratory effects in some asthmatics. We not e that this conclusion is in agreement with

CASAC comments offered on the first draft SO 2 REA. The CASAC letter to the Administrator states: “CASAC believes strong ly that the weight of clin ical and epidemiology evidence

indicates there are detectable c linically relevant h ealth effects in sens itive subpopulations down to a level at least as low as 0.2 ppm SO 2 (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 SO 2 likely represents a significant public

health issue.July 2009 361 Thus, staff finds it is appropriate to consid er the air quality, exposure and risk results as they relate to the adequacy of the current 24-hou r standard (as well as the current annual (see section 10.4) and potential alternative (see sect ion 10.5) standards). This is because these analyses provide useful information with respec t to the current 24-hour standard’s ability to limit: 1) 5-10 minute SO 2 concentrations associated with decrements in lung function and/or respiratory symptoms in exercising asthmatics; and 2) the estimated number of exercising asthmatics expected to expe rience a moderate or great er 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 SO 2 standard will depend upon the weight pl aced on each of the analyses when

uncertainties associated with thos e analyses are taken into consider ation. Sources of uncertainty associated with each of the analyses (air qu ality, exposure, and quantit ative 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 considera tion of the annual, as well as alternative 1-hour standards. Air Quality Analysis A number of key uncertainties should be cons idered when interpreti ng 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 SO 2 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 mode l to estimate 5-minute maximum SO 2 concentrations at monitors that reported only 1-hour SO 2 concentrations. Cross-vali dation of the statistical model for where 5-minute SO 2 measurements existed indicated reasonable model performance. The greatest difference in the predicted versus observed numbers of benchmark exceedances occurred at the lo wer and upper tails of the distribution, indicating greater uncertainty in the predicti ons at similarly representative monitors.July 2009 362  The air quality characteriza tion assumes that the ambien t monitoring data and the estimated days per year with benchmark exceedances can serve as an indicator of exposure. Longer-term personal SO 2 exposure (i.e., days to w eeks) concentrations are correlated with and are a fraction of ambient SO 2 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 Coun ties Exposure Analysis A number of key uncertainties should be c onsidered when interpre ting the St. Louis and Greene County exposure results with regard to deci sions 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 ar ea source emission profile rather than use a default profile to improve the agreement be tween ambient measurements and predicted 1- hour SO 2 concentrations. The improved model pe rformance reduces uncertainty in the 1- hour SO 2 concentrations predictions, but nonethele ss remains as an important uncertainty in the absence of actual lo cal source emission profiles.

 Staff performed the exposure assessment to be tter reflect both the temporal and spatial representation of ambient concen trations and to estimate the ra te of contact of individuals with 5-minute SO 2 concentrations while engaged in moderate or greater exertion. Estimated annual average SO 2 exposures in the two e xposure 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 SO 2 concentrations above benchmark levels because of the lack of comparable measurement data, particularly consideri ng both the short-term averaging time and geographic location.

 While all 5-minute ambient SO 2 concentrations were estimated by the exposure model, each hour was comprised of the maximum 5-minute SO 2 concentration and eleven other 5-minute SO 2 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 c onsidered when interpre ting 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 SO 2 levels below the lowest exposure levels used in the free-breathing controlled human exposure stud ies (i.e., below 200 ppb). We have developed probabilistic exposure-res ponse 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 SO 2 -induced responses for individuals are reproducible. We note that th is assumption has some support in that one study (Linn et al., 1987) exposed the same subjects on tw o occasions to 600 ppb and the authors reported a high degree of correlation while obs erving 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 conducte d with adult subject s, the risk assessm ent relies on data from adult asthmatic subjects to estimate e xposure-response relationshi ps that have been applied to all asthmatic individuals, incl uding children. The ISA (section 3.1.3.5) indicates that there is a strong body of evid ence that suggests adolescents may experience many of the same respiratory effects at similar SO 2 levels, but recognizes that these studies administered SO 2 via inhalation through a mouthpiece (which can result in an increase in lung SO 2 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 SO 2 exposures, it is assume d that estimates of SO 2 -induced health re sponses are not affected by the presence of other pollutants (e.g., PM 2.5 , O 3 , NO 2 ).

10.3.3.2 Assessment Results As previously mentioned, the ISA finds the evidence for an association between respiratory morbidity and SO 2 exposure to be “sufficient to infer a causal relationship” (ISA, section 5.2) and that the “def initive evidence” for this conclusion comes from the results of controlled human exposure studies demonstrating d ecrements in lung functi on and/or respiratory symptoms in exercising asthmatics (ISA, sectio n 5.2). Accordingly, the exposure and risk analyses presented in this document focused on exposures and risks associated with 5-minute peaks of SO 2 in excess of the potential health effect benchmark values of 100, 200, 300, and 400 ppb SO 2 (see section 6.2). In consid ering the results presented in th ese analyses, we particularlyJuly 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 lo west concentration in human exposure studies where st atistically signifi cant moderate or greater lung function decrements are frequently accompanied by respirat ory symptoms; (2) 200 ppb is the lowest level at which effects have been observed (and the lo west 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 de crements in lung function at 100 ppb from two mouthpiece exposure studies (see se ction 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 repr esenting the St. Louis urban ar ea) which have significant

emission sources of SO 2 . 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 quantita tive risk assessment, it was not surprising that

there were also far fewer asthmatics at elevated ve ntilation rates estimated to have a moderate or greater lung function response in Gr eene county when compared to St. Louis. The difference in the St. Louis and Greene County exposure and quantita tive 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 densitie s, 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 e xposures, and hence greater numbers and percentages of asthmatics at elevated ventilation rates experiencing modera te or greater lung function responses in the St. Louis study area. Thus, when cons idering the risk and exposure re sults 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 inform ative 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 SO 2 emissions densities.July 2009 365 Air Quality Assessment The results of our air qual ity assessment provide additi onal perspective on the public health impacts of exposure to ambient levels of SO 2 . In considering these results, we first note

that the benchmark values derived from the cont rolled 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 mo nitoring is not required. As a result, staff developed a statistical relations hip to estimate the highest 5-mi nute level in an hour, given a reported 1-hour average SO 2 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 standa rds, are discussed below. A key output of the air quality analysis is the predicted number of statistically estimated 5-minute daily maximum SO 2 concentrations above benchmark levels given air quality simulated to just meet the level of the current 24-hour or annual SO 2 standards, whichever is controlling for a given county. Under this scenario, in 40 countie s selected for detailed analysis, we note that the predicted yearly mean number of sta tistically estimated 5-minute daily maximum concentrations > 400 ppb ranges from 1-102 days per year 97 , with most counties in this analysis experiencing a mean of at least 20 days per ye ar when statistically estimated 5-minute daily SO 2 concentrations exceed 400 ppb (Table 7-14). In a ddition, the predicted yearly mean number of statistically estimated 5-mi nute daily maximum concentra tions >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 SO 2 concentrations exceed 200 ppb (Table 7-12). Exposure Assessment When considering the St. Louis exposure resu lts as they relate to the adequacy of the current standard, we focus on the number of asthma tics 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 SO 2 concentrations are such that the St Louis area just meets the current standard, approximately 13% of asthma tics 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 SO 2 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 SO 2 exposure concentration greater than or e qual to a 400 ppb benchmark level while at elevated ventilation rates (Figure 8-19). Sim ilarly, approximately 46% of asthmatics would be expected to experience at least one SO 2 exposure concentration greater than or equal to a 200 ppb benchmark level while at elevated ventilation ra tes. When the St. Loui s results are restricted

to asthmatic children at elevated ventilation rate s, approximately 25% and 73% of these children would be estimated to experience at least one SO 2 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 elev ated ventilation rates likely to experience at least one lung function response given air quality that is adju sted 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 l ung 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 wh en moderate or greater lung function responses

were defined in terms of FEV 1 .

10.3.4 Conclusions regarding the ad equacy of the 24-hour standard As noted above, several lines of scientific ev idence 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 SO 2 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 sugge sts consideration of a re vised 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 SO 2 standard(s) should prot ect against health effects ranging from lung function responses and increased respiratory symptoms following 5-10 minute peak SO 2 exposures, to increased respiratory symptoms and respiratory-related ED visits and hospital admi ssions associated with 1-hour daily maximum or 24-hour average SO 2 concentrations. In examining the exposure- and risk-based info rmation with regard to the adequacy of the

current 24-hour SO 2 standard to protect the public health, we note that the results described above (and in more detail in Chapters 7-9) indi cate that 5-minute exposures that can reasonably be judged important from a public health perspec tive are associated with air quality adjusted upward to simulate just meeting the current 24-h our standard. Therefor e, exposure- and riskbased considerations reinforce the scientific evidence in supporti ng the conclusion that consideration should be given to revising the current 24-hour st andard 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 SO 2 -related adverse health

effects.



### 10.4 ADEQUACY OF THE CUR RENT ANNUAL STANDARD

10.4.1 Introduction In the last review of the SO 2 NAAQS, retention of the annua l standard was largely based on an assessment of qualitative evidence gath ered from a limited number of epidemiologic studies. The strongest evidence fo r an association between annual SO 2 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 associ ation existed between chronic upper and lower respiratory tract illnesses and annual SO 2 levels of 70 -100 ppb in the presence of 230-301 ug/m 3 black smoke. Three additional studies described in the 1986 Second Addendum also suggested that long-term exposure to SO 2 was associated with adverse respir atory effects. Notably, studies conducted by Chapman et al. ( 1985) and Dodge et al. (1985) f ound associations between long-July 2009 368 term SO 2 concentrations (with or without high particle concentrations) and cough in children and young adults. However, it was noted that there wa s considerable uncertainty associated with these studies because they were conducted in locations subject to high, short-term peak SO 2 concentrations (i.e., locations ne ar point sources); therefore it was difficult to discern whether this increase in cough was the result of long-term, low level SO 2 exposure, or repeated shortterm peak SO 2 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 provide d clear quantitative c onclusions, there appeared to be some consistency across studies indicat ing the possibility of respirator y effects associated with longterm exposure to SO 2 just above the level of the existi ng 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 SO 2 from exceeding the level of the existing annual standard in several large urban areas. Ultimately, both the scientific evid ence 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 SO 2 exposure and mortality and morbidity outcomes. This includes discussion of additional epidemiologic st udies examining possible associations between long-term SO 2 exposure and respiratory effects in child ren (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 studie s published since the last review examining possible associations between long-term ambient SO 2 concentrations and adverse respiratory, cardiovascular, and birth outcomes, as well as ca rcinogenesis. The current ISA also discusses the possible association between long-term SO 2 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) SO 2 exposure to adverse health effects (respiratory morbidity, carcinogenesis, adverse prenatal and neonatal outcomes, and mortality) is judged to be “inade quate 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 SO 2 is truly associated with these health endpoints (ISA, Table 1-2). With resp ect specifically to respiratory morbidity in children, the ISA presents recent epidemiologic evidence of an association with long-term exposure to SO 2 (ISA, section 3.4.2). However, th e ISA finds the strength of these epidemiologic studies to be limited because of 1) variability in results ac ross studies with respect to specific respiratory morbidity endpoints, 2) high correlations between long-term average SO 2 and co-pollutant concentrations , particularly PM, and 3) a la ck 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 SO 2 concentrations and respiratory symptoms, ED visits, and hospitalizations were conducted in areas where ambient SO 2 concentrations were well below the current annual NAAQS. This evidence suggests that the

current annual standard is not providing adequate protection agai nst health effects associated

with shorter-term SO 2 concentrations.

10.4.3 Risk-based considerations Results of the risk character ization 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 SO 2 in excess of potential health effect benchmar k levels. Figure 7-16 counts the number of measured 5-minute daily maximum SO 2 concentrations above the 100 -400 ppb benchmark levels for a given annual average SO 2 concentration. None of the monito rs in this data set reported annual average SO 2 concentrations above the current NAAQS, but several of the mon itors in several of the years frequently reported 5-minute daily ma ximum concentrations above the potential health effect benchmark levels. Many of these monitors where frequent exceedances were reported had annual average SO 2 concentrations between 5 and 15 ppb, w ith little to no correlation between the annual average SO 2 concentration and the number of 5-minute daily maximum concentrations above potential hea lth effect benchmark levels. This suggests that the annual standard adds little in the way of protection against 5-minute peaks of SO 2 (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)

SO 2 exposure to adverse health e ffects (respiratory morbidity, car cinogenesis, adverse prenatal and neonatal outcomes, and mortalit y) is “inadequate to infer the presence or absence of a causal relationship” (ISA, Table 5-3). The ISA al so reports that many epidemiologic studies demonstrating positive associati ons between short-term (i.e. 1-hour daily maximum, 24-hour average) SO 2 concentrations and respiratory symptoms, as well as ED visits and hospitalizations,

were conducted in areas where annual ambient SO 2 concentrations were well below the level of the current annual NAAQS. In addition, anal yses conducted in this REA suggest that the

current annual standard is not providing protection agai nst 5-10 minute peaks of SO 2 . Thus, the

scientific evidence and the risk and exposure information suggest that the current annual SO 2 standard: 1) is likely no t needed to protect against health risks associated with long term exposure to SO 2 ; and 2) does not provide adequate protec tion from the health effects associated with shorter-term (i.e. ≤ 24-hours). This suggests that consid eration should be given to either revoking the annual standard or retaining it withou t 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 SO 2 as the most appropriate indicator for ambient

SO x . This was in large part because other gaseous sulfur oxides (e.g., SO 3 ) are likely to be found at concentrations many orders of magnitude lower than SO 2 in the atmosphere, and because most

all of the health effects and exposure information was for SO 2 . The current ISA has again found this to the case, and altho ugh the presence of gaseous SO x species other than SO 2 has been recognized, no alternative to SO 2 has been advanced as being a more appropriate surrogate for ambient gaseous SO x . Importantly, controlled human expos ure studies and animal toxicology studies provide specific evidence for health effects following exposure to SO 2 . Epidemiologic studies also typically report levels of SO 2 , as opposed to other gaseous SO x . Because emissions

that lead to the formation of SO 2 generally also lead to the formation of other SO x oxidation products, measures leading to reduc tions in population exposures to SO 2 can generally be expected to lead to reductions in po pulation exposures to other gaseous SO x . Therefore, meeting

an SO 2 standard that protects the pub lic health can also be expect ed to provide some degree ofJuly 2009 371 protection against potential health effects that may be independently associated with other gaseous SO x even though such effects are not discer nable from currently available studies indexed by SO 2 alone. Given these key poi nts, staff judges that the available evidence supports

the retention of SO 2 as the indicator in the current review. We also note that this would be in agreement with CASAC comments offered on th e second draft REA. The consensus CASAC response to Agency charge questions from th e second draft REA states: “For indicator, SO 2 is clearly the preferred choice (Samet 2009).”

10.5.2 Averaging Time EPA established the current 24-hour and a nnual averaging times for the primary SO 2 NAAQS in 1971. As previously described, (s ee section 10.3.1) the 24-hour NAAQS was based on epidemiologic studies that observed associations between 24-hour average SO 2 levels and adverse respiratory effects a nd daily mortality (EPA 1982, 19 94b). The annual standard was supported by a few epidemiologic studies that f ound an association between adverse respiratory effects and annual average SO 2 concentrations (EPA 1982, 1994b). Based on currently available evidence, staff concludes that different aver aging 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 SO 2 air quality correlations. These consider ations are described in more detail

below.

10.5.2.1 Evidence-based considerationsAs an initial consideration regarding the most appropriate averaging time (e.g., shortterm, long-term, or a combination of both) for alternative SO 2 standard(s), we note (as in 10.4.1 above) that the ISA finds evidence re lating long-term (weeks to years) SO 2 exposures to adverse health effects to be “inadequate to infer the pr esence or absence of a causal relationship” (ISA, Table 5-3). In contrast, the IS A judges evidence relating short-te rm (5-minutes to 24-hours) SO 2 exposure to respiratory morbidity to be “sufficient to infer a cau sal relationship” and short-term exposure to SO 2 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 SO 2 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 mi nute peak concentrations of

SO 2 and consistently found decrements in lu ng 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 causa l association between short-term (5-minutes to 24-hours) SO 2 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 SO 2 levels and respiratory symptoms as well as hospital admissions and ED visits for all respiratory causes and asth ma (ISA Tables 5.4 and 5.5). In addition to the 1-hour daily maximum epidemiol ogic evidence, there is a considerably larger body of epidemiologic studies reporting positiv e associations betw een 24-hour average SO 2 levels and respiratory symptoms, as well as hosp italizations and ED visits for all respiratory causes and asthma. However, as in the last revi ew, there remains considerable uncertainty as to whether these positive associations are due to 24-hour average SO 2 exposures, or exposure (or multiple exposures) to short-term peaks of SO 2 within a 24-hour period. More specifically, when describing epidemiologic studies observing positive associations between ambient SO 2 and respiratory symptoms, the ISA states “that it is po ssible that these associations are determined in large part by peak exposures w ithin a 24-hour period” (ISA, secti on 5.2). The ISA also states that the respiratory eff ects following 5-10 minute SO 2 exposures in controlled human exposure studies provides a basis for a progression of resp iratory morbidity that could result in increased ED visits and hospital admissions (ISA, section 5.2). The controlled human exposure evidence de scribed above provides support for an averaging time that protect s against 5-10 minute peak e xposures. Results from the epidemiologic evidence provides support fo r both 1-hour and 24-hour averaging times. However, it is worth noting again that the effect s observed in epidemiologi c studies also may be due, at least in part and especi ally in 24-hour epidemiologic studi es, to shorter-term peaks of

SO 2 . Overall, the evidence mentioned above sugges ts that a primary con cern with regard to averaging time is the level of protec tion provided against 5-10 minute peak SO 2 exposures. The

evidence described above also s uggests it would be appropriate to consider the degree ofJuly 2009 373 protection averaging times under consideration provide against both 1- hour daily maximum and 24-hour average SO 2 concentrations.

10.5.2.2 Air Quality considerations The shortest averaging time for the current primary SO 2 standard is 24-hours. We therefore evaluate the potential for a standard based on 24-hour average SO 2 concentrations to limit 5-minute peak SO 2 exposures. Table 10-1 re ports the ratio between 99 th percentile 5- minute daily maximum and 99 th percentile 24-hour average SO 2 concentrations for 42 monitors reporting measured 5-minute data for any year between 2004-2006. Across this set of monitors in 2004, ratios of 99 th percentile 5-minute daily maximum to 99 th percentile 24-hour average SO 2 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 standa rd based on 24-hour average SO 2 concentrations would not likely be an effective or efficient appro ach for addressing 5-minute peak SO 2 concentrations. That is, using a 24-hour average standa rd 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 SO 2 concentrations. Table 10-1 also reports the ratios between 99 th percentile 5-minute daily maximum and

99 th percentile1-hour daily maximum SO 2 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 99 th percentile 5-minute daily maximum to 99 th 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 SO 2 than a standard with a 24-hour averaging tim e. These results also suggest that a 5- minute standard could be a relatively effectiv e means of controlling 1-hour daily maximum SO 2 concentrations.July 2009 374 Table 10-1 Ratios of 99 th percentile 5-minute daily maximums to 99 th percentile 24-hour average and 1-hour daily maximum SO 2 concentrations for monitors reporting measured 5-minute data from years 2004-2006 99

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 99 th percentile 5-minute daily maximum, 1-hour daily maxi mum, and 24-hour average values were identified for each year a given monitor was in opera tion from 2004-2006. If a monitor was in operation for multiple years over that span, 99 th 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 st andards analyzed in this REA to provide protec tion against 24-hour average SO 2 exposures. The 99 th percentile 24- hour average SO 2 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 es timates that remained statistically significant in multipollutant models with PM were found in cities with 99 th percentile 24-hour average SO 2 concentrations ranging from approximately 36 ppb to 64 ppb. Table 10-2 uses 2004 air quality data and suggests that a 99 th percentile 1-hour daily maximum st andard set at a level of 50- 100 ppb would limit 99 th percentile 24-hour average SO 2 concentrations observed in epidemiologic studies where statistically significant results were observed in multi-pollutant models with PM. That is, given a 50 ppb 99 th percentile 1-hour daily maximum standard, none of the 39 counties analyzed would be expected to have 24-hour average SO 2 concentrations ≥ 36 ppb; and, given a 100 ppb 99 th percentile 1-hour daily maximum standard, only 6 of the 39 counties (Linn, Union, Bronx, Fairfax, Hudson, and Wayne) included in th is analysis would be estimated to have 99 th percentile 24-hour average SO 2 concentrations ≥ 36 ppb. This analysis was also done for the years 2005 and 2006 and similar resu lts were found (Appendix D).July 2009 376 Table 10-2. 99 th percentile 24-hour average SO 2 concentrations for 2004 given just meeting the alternative 1-hour daily maximum 99 th and 98 th percentile standards analyzed in the air quality assessment (note: concentrations in ppb) 100

. 99 th percentile 98 th 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 99 th or 98 th percentile 1-hour daily maximum concentrations we re 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 th e potential alternative standards define d above, and for each of the years, the 99 th percentile 24-hour average SO 2 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 99 th percentile 1-hour daily maximum standard at a level of 50-150 ppb could have the effect of maintaining SO 2 concentrations below the level of

the current 24-hour and annual standards. That is, under these alternat ive standard scenarios (using 2004 air quality data), there would be no counties in this analysis with a 2 nd 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 SO 2 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. 2 nd highest 24-hour average SO 2 concentrations (i.e., the current 24-hour standard) for 2004 given just meeting the alternative 1-hour daily maximum 99 th and 98 th percentile standards analyzed in the air quality assessment (note: concentrations in ppb). 101

99 th percentile 98 th 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 99 th or 98 th percentile 1-hour daily maximum concentrations we re 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 th e potential alternative standards defined above, and for each of the years, the 2 nd 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 SO 2 concentrations for 2004 given just meeting the alternative 99 th and 98 th percentile 1-hour daily maximum standards analyzed in the air quality assessment (note: concentrations in ppb). 102

99 th percentile 98 th 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 99 th or 98 th percentile 1-hour daily maximum concentrations we re 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 th e potential alternative standards define d 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 DJuly 2009 380 10.5.2.3 Conclusions regarding averaging time The air quality analyses presented above str ongly support that it is likely an alternative

99 th 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 SO 2 shown in human exposure studies to result in respiratory symptoms and/or decrements in lung function in exerci sing asthmatics, (2) 99 th 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) 99 th 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., 99 th percentile 24-hour average SO 2 concentration ≥ 36 ppb). Thus, staff concludes that a 1-hour daily maximum standard, with an appropriate form and level, can provide adequate protection ag ainst the range of health outcomes associated with averaging

times from 5-minutes to 24-hours. As an additio nal matter, we note that this conclusion is in agreement with CASAC comments offered on the second draft SO 2 REA. The CASAC letter to the Administrator states: “CASAC is in agreemen t 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 NA AQS, 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 SO 2 concentrations. Moreover, staff is concerned that compared to longer averaging times (e.g., 1-hour, 24-hour), year-to-year vari ation in 5-minute SO 2 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 ap proach to provide adequate public health protection. However, as noted above, staff ‘s view is that a 1-hour averaging time, given an appropriate form (seeJuly 2009 381 10.5.3) and level (see 10.5.4), can adequately limit 5-minute SO 2 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 addi tion, we recognize that it is impor tant that the standard have a form that is reasonably stable. As just explaine d 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 asso ciated control programs.

10.5.3.1 Evidence-based considerations As previously mentioned, st aff recognizes that the ade quacy 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 sta ndard reflect the nature of the health risks posed by increasing SO 2 concentrations. That is, the form of the standard s hould reflect results from human exposure studies demonstrating that the percentage of asth matics affected, and the severity of the respiratory response (i.e. decr ements in lung function, respiratory symptoms)

increases as SO 2 concentrations increase (see section 4.2. 2). Taking this into consideration, staff

finds that a concentration-based form is more a ppropriate than an exceedance-based form. This is because a concentration-based form averag ed over three years (see below) would give proportionally greater weight to 1-hour daily maximum SO 2 concentrations that are well above the level of the standard, than to 1-hour daily maximum SO 2 concentrations that are just above the level of the standard. In contrast, an exp ected exceedance form would give the same weight to 1-hour daily maximum SO 2 concentrations that are just above the level of the standard, as to 1-hour daily maximum SO 2 concentrations that are well a bove the level of the standard. Therefore, a concentration-based form better re flects the continuum of health risks posed by increasing SO 2 concentrations (i.e. the percentage of as thmatics affected and the severity of the response increases with increasing SO 2 concentrations).July 2009 382 10.5.3.2 Risk-based considerations In considering specific c oncentration-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 SO 2 ; and 3) providing a stable regulatory target to prevent areas from fr equently shifting in and out of attainment. Given this, we have focused on 98 th and 99 th percentile forms averaged over 3 years. We first note that in most locations analyzed, the 99 th percentile form of a 1-hour daily maximum standard would correspond to the 4 th highest daily maximum concentration in a year, while a 98 th percentile form would correspond approximately to the 7 th to 8 th highest daily maximum c oncentration in a year (Table 10-5; see Thompson, 2009). In addition, resu lts from the air quality analysis suggest that at a given SO 2 standard level, a 99 th percentile form is appreciably more effective at limiting 5- minute peak SO 2 concentrations than a 98 th percentile form (Figures 7-27 and 7-28 103 ). Compared to the same standard with a 99 th percentile form, a 98 th 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 SO 2 concentrations are greater than 200 and 400 ppb respectively (Figure 7- 27). Similarly, compared to the same standard with a 99 th percentile form, a 98 th percentile 1- hour daily maximum standard at 100 ppb allows for on average, an estimated 90 and 74% more days per year when SO 2 concentrations are greater than 200 and 400 ppb respectively 104 (Figure 7-28). We also note that in the 40 counties se lected for detailed air quality analysis, the estimated number of benchmark exceedances using a 98 th percentile 1-hour daily maximum standard level of 200 ppb was similar to the corresponding 99 th percentile standard at 250 ppb (Tables 7-11 through 7-14). Similarly, the estimated number of benchmark exceedances considering a 98 th percentile standard at 100 ppb fell with in the range of benchmark exceedances

estimated for 99 th percentile standards at 100 a nd 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 pp b results in far fewer site-years experiencing benchmark exceedances (see Figures 7-27 and 7-28).July 2009 383 Table 10-5. SO 2 concentrations (ppb) corresponding to the 2 nd -9 th daily maximum and 98 th /99 th percentile forms for alternative 1-hour daily maximum standards (2004-2006). 105

SO 2 Daily Maximums Percentiles County State 2nd 3 rd 4th 5 th 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, st aff compared trends in 98 th and 99 th percentile design values, as well as design values based on the 4 th highest daily maximum from 54 sites located in the 40

counties selected for detailed an alysis (see Thompson, 2009). Thes e 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 th e alternative daily metrics (the nth maximum and percentiles), the statistics were computed for each year and then averag ed over 2004-2006 (see Thompson 2009).July 2009 384 vast majority of sites, there would have been similar changes in 98 th and 99 th percentile design values over the last ten years (i.e. based evaluatin g overlapping three year intervals over the last ten years; see Thompson, 2009). These results al so demonstrate that design values based on the

4 th highest daily maximum are vi rtually indistinguishable from design values based on the 99 th percentile. For illustrative purpos es, design value trends for four of these sites are presented in Figure 10-1. As part of this analysis, all of th e design values over this ten year period for all 54 sites were aggregated and the standard devi ation calculated (see Thompson, 2009). Results

demonstrate similar standard deviations – i.e. similar stability -- based on aggregated 98 th or aggregated 99 th percentile design values over the ten year period (Figure 10-2; see Thompson 2009).July 2009 3853-year Design Value3-year Design Value3-year Design Value3-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 re present the 3-year average of the annual 98 th percentile or 99 th percentile 1- hour daily maximum, or the 3-year average of 4 th 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 MaxStandard Deviation

0.00 0.01 0.02 0.03 0.04 0.05 0.06

p98 p99 4th MaxStandard 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 SO 2 concentrations (see 10.5.3.1). We also find that at a given standard level, a 99 th percentile or 4 th highest daily maximum form provides appreciably more public health protection agai nst 5-minute peaks than a 98 th percentile or 7 th - 8 th highest daily maximum form (see 10.5.3.2). In addition, over th e last 10 years and for the vast majority of the sites examined, there appears to be little difference in 98 th and 99 th percentile design value stability (see 10.5.3.2). Thus, st aff concludes that consideration be given primarily to a 1-hour daily maximum standard with a 99 th percentile or 4 th highest daily maximum form.July 2009 387 10.5.4 Level In sections 10.3.3.3 and 10.4.4 staff concl uded 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 SO 2 standards to protect public health with an adequate margin of safety from the respiratory effects of SO 2 . In considering potential alternative standards that would provide incr eased public health protection against these respiratory effects, staff concl uded in section 10.5.1 that the mo st appropriate indicator remains

SO 2 . In section 10.5.2, staff concluded that an al ternative standard with a 1-hour averaging time, set at an appropriate level, can provide adequa te protection against th e range of respiratory

effects observed in both controlled human e xposure studies of 5-10 minutes, as well as epidemiologic studies using longer averaging times. In addition, s ection 10.5.3 concluded that a 99th percentile or 4 th highest daily maximum form averaged over three years was most appropriate for potential standards using a 1- hour averaging time. Here, we consider 99 th percentile 1-hour daily maximum a lternative standard levels that would provide greater public health protection against SO 2 -related adverse respiratory effects than that afforded by the current standards. As an initial consideration, we not e that Table 10-6 demonstrates that although all

counties in the U.S. meet the current 24-hour and a nnual standards, all of the potential alternative 1-hour daily maximum standard le vels (50-250 ppb) analyzed in the air quality, exposure, and risk analyses would be estimated to result in coun ties in the U.S. with air quality above the level of the given alternative standa rd. Thus, to varying extents, meeting any of the potential alternative 1-hour daily maximum standards an alyzed in this document would represent

reductions in ambient SO 2 levels based on air quality from 2004-2006, as well as reductions from

SO 2 concentrations that would be allowed under th e current standards. All of these potential standards would consequently result in some increased public health protection.July 2009388Table 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 standardAlternative Standards andLevels (ppb)Total Counties(population inmillions)Northeast SoutheastIndustrialMidwestUpperMidwestSouthwest NorthwestSouthernCAOutsideRegionsNumber of counties withmonitors 211 (96.5) 52 40 75 19 7 9 6 33 year 99thpercentile daily 1-hour max:2501 (0.4) 0 0 1 0 14 0 0 332003 (0.8) 0 3 4 0 14 0 0 33150 10(2.4)25205140033100 22(13.5)81347514003350 54(43.5)3855813714220333 year 98thpercentile daily 1-hour max:2001 (0.4) 0 0 1 0 14 0 0 33July 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 causali ty presented in the ISA. In Chapter 5, our evaluation of the

health evidence informed the sel ection of potential alternative SO 2 standards that would be analyzed in the air quality, exposure, and risk an alyses. In Chapter 6, potential health effect benchmark values for use in the air quality and exposure analyses were derived from SO 2 concentrations found in controlled human exposur e 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 SO 2 standards, and to inform staff conclusions about the indicator, averaging time, and form for potential alternative SO 2 standards.

Staff now considers the health evid ence as it relates to evaluating 99 th percentile 1-hour daily maximum alternative standard levels. 107 In doing so, we have considered the extent to which a variety of altern ative standard levels would limit th e magnitude and frequency of 1-hour

SO 2 concentrations to provide sufficient protect ion for at-risk populations against experiencing various respiratory heal th effects including moderate or gr eater decrements in lung function, respiratory symptoms, and respirat ory-related ED visits and hospita lizations. We note that these health endpoints are logically linked together in that the c ontrolled human exposure evidence demonstrating moderate or greate r decrements in lung function a nd/or respiratory symptoms in exercising asthmatics is recognized by the ISA as supporting the plausibility of associations

between ambient SO 2 and the respiratory morbidity e ndpoints (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 99 th percentile form limit the array of health outcomes reported in both controlled human exposure and epidemiologic studi es, 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 99 th percentile 1-hour daily maximum SO 2 air quality levels in ci ties and time periods corresponding to ke y 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 4 th highest daily maximum form.July 2009 390 hospitalizations for all respiratory causes and asth ma. This information pr ovides the most direct evidence for effects in cities with particular 99 th percentile 1-hour SO 2 levels, and hence, is of particular relevance here. This information sugge sts that the strongest ep idemiologic evidence of

an association between ambient SO 2 and ED visits and hospitalizations is in cities where 99 th percentile 1-hour daily maximum SO 2 concentrations ranged from about 75 to150 ppb. In this range, there are numerous studies that reported positive associations between ambient SO 2 and respiratory related ED visits and hospitalizati ons (although all results were not statistically significant). In additi on, this range of SO 2 levels importantly contains a cluster of epidemiologic studies demonstrating statistically significant resu lts in multi-pollutant models with PM. More specifically, in epidemiologi c studies conducted in the Br onx, NY (78 ppb; NYDOH 2006,) and in NYC, NY (82 ppb; Ito et al., 2007), the SO 2 effect estimate remained positive and statistically significant in multi-pollutant models with PM 2.5 (ISA, Table 5-5). Moreover, in an epidemiologic study conducted in New Haven, CT (150 ppb; Schwartz et al., 1995), the SO 2 effect estimate remained positive and statistically significant in a multi-pollutant model with

PM 10. Staff notes that while statistical significance in co-pollutant models is an important consideration, it is not nece ssary for appropriate consider ation 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 provi de protection against the effect s observed in these studies. With regard to the epidemiologic studies me ntioned above, we also note that most of the ED visit and hospitalization eff ect estimates reported in thes e studies are with respect to 24-hour average SO 2 concentrations . Thus, staff investigated whether a 99 th percentile 1-hour daily maximum standard at approximate ly 75 ppb would also limit the 99 th percentile 24-hour average

SO 2 concentrations observed in the cluster of studies fi nding statistically significant results in multipollutant models with PM. Considering these studies, we note that the lowest 99 th percentile 24-hour average SO 2 concentration reported in a study locati on 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 modelsJuly 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 maximu m standard, none of the counties analyzed in our analysis would be expected to have 99 th percentile 24-hour average SO 2 concentrations ≥ 36 ppb (Table 10-2). However, given a 100 ppb 99t h percentile 1-hour dail y maximum standard, six of the counties included in the 40-county air quality analysis would be estimated to have 99th percentile 24-hour average SO 2 concentrations ≥ 36 ppb 109 . Thus, although not directly analyzed, a 1-hour standard set at 75 ppb w ould 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 99 th 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 cont rolled human exposure studies when evaluating potential alternative standard leve ls. In doing so, we again note th at the ISA finds that the most consistent evidence of decrements in lung f unction and/or respiratory symptoms is from controlled human exposure studies e xposing exercising asthmatics to SO 2 concentrations ≥ 400 ppb (ISA, section 3.1.3.5). At SO 2 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 ex perience severe bronchoconstriction. Moreover,

at concentrations ≥ 400 ppb, statistically significant mode rate 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 SO 2 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 statisti cally significant, nor is it freq uently accompanied by respiratory symptoms. On the other hand, for understandable ethical reasons, it must al so be noted that the subjects participating in these controlled human exposure studies do not ne cessarily represent the

most SO 2 sensitive individuals (e.g. severe asthmatics). Thus, it is reasonable to anticipate that

109 Given a 99 th percentile 1-hour daily maximum standard at 100 ppb, 99 th percentile 24-hour average SO 2 concentrations are estimated to be great er than 36 ppb in Linn, Union, Bronx, Hudson, Fairfax, and Wayne counties (Table 10-2)July 2009 392 individuals who are more SO 2 sensitive would have a gr eater response at 200-300 ppb SO 2 , and/or would respond to SO 2 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 fr om two mouthpiece exposure studies suggests that exposure to 100 ppb SO 2 can result in small decr ements in lung function 110 . Moreover, while not directly comparable to free-breathing chamber studies, findings from these mouthpiece studies may be particularly relevant to those asthma tics who breathe oronasall y even at rest (EPA, 1994b). Taken together, staff conc ludes that the level of a 99 th percentile 1-hour daily maximum

SO 2 standard should be set so as to substantia lly limit the number of estimated 5-minute peaks ≥ 400 ppb, while also appreciably limiting SO 2 concentrations ≥ 200 ppb. In evaluating the extent to which alternative standard levels provide substantial protection against 5-minute SO 2 concentrations ≥ 400 ppb, we first note the results of our 40 county air quality analysis. As described above, epidemiol ogic studies support consider ation of levels of a

99 th percentile 1-hour daily maximu m 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

SO 2 concentrations > 400 ppb. Results of the air qua lity analysis indicate that just meeting a 99 th percentile 1-hour daily maximum standard at 50 ppb would result in 0 days per year when statistically estimated 5-minute daily maximum SO 2 concentrations are > 400 ppb, whereas a standard at 100 ppb would result in at most 2 da ys per year when statistically estimated 5-minute daily maximum SO 2 concentrations are > 400 ppb (Table 7-14) 111 . Given the results associated

with 99 th percentile 1-hour daily maximum standa rds at 50 and 100 ppb, it is reasonable to

conclude that a 99 th percentile 1-hour daily maximu m standard at 75 ppb would also substantially limit ambient 5-minute SO 2 concentrations ≥ 400 ppb. In further evaluating the extent to which potential alternative standard levels limit 5-

minute SO 2 exposures > 400 ppb, we consider the results of the St. Louis exposure analysis. 112

110 As first noted in Chapter 6, studies utilizing a mout hpiece exposure system cannot be directly compared to studies involving freely breathing subjects, as nasal absorption of SO 2 is bypassed during oral breathing, thus allowing a greater fraction of inhaled SO 2 to reach the tracheobronchial airways. As a result, individuals exposed to SO 2 through a mouthpiece are likely to experience greater respiratory effects from a given SO 2 exposure. Nonetheless, these studies do prov ide very limited evidence for SO 2 - induced respiratory e ffects at 100 ppb. 111 Air quality estimates presented in this section represent the mean number of days per year when 5-minute daily maximum SO 2 concentrations exceed a particular benchmark level given 2001-2006 air quality adjusted to just meet alternative 99 th 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 c onsidering the St. Louis exposure and risk results when evaluating the adequacy of the cu rrent and potential alternative 99 th percentile 1-hour daily maximum SO 2 standards.July 2009 393 Results indicate air quality just meeting a 99 th 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 SO 2 exposure ≥ 400 ppb (Figure 8-19). Similarly, this analysis also i ndicates that air quality just meet ing a 50 or 100 ppb standard would

result in an estimated < 1% of asthmatic childre n at elevated ventilation rates experiencing at least one 5-minute daily maximum SO 2 exposure ≥ 400 ppb. These results necessarily suggest that a standard at approximately 75 ppb would also substantially limit expo sures of all asthmatics

and asthmatic children to SO 2 concentrations ≥ 400 ppb.

We next evaluated the extent to which 99 th percentile 1-hour daily maximum standard levels provide appreciable protection against 5-minute SO 2 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 SO 2 concentrations would be > 200 ppb, whereas a standard level of 100 ppb w ould result in at most 13 days per year when statistically estimated 5-minute daily maximum SO 2 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 estimate d 5-minute daily maximum SO 2 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 maximu m standard would result in a corresponding < 1% or 1.5%

of asthmatics at elevated ventilation rates ex periencing at least one 5-minute daily maximum SO 2
exposure ≥ 200 ppb (Figure 8-19). Moreover, just meeting a 50 ppb, or 100 ppb 99 th percentile 1-hour daily maximum standa rd would be estimated to result in a corresponding <1% or 2.7% of

asthmatic children at elevated ventilation ra tes experiencing at least one 5-minute daily

maximum SO 2 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 le ast one 5-minute daily

maximum SO 2 exposure ≥ 200 ppb. As an additional consideration, we note th e results of the St. Louis risk assessment

indicate that a 99 th percentile 1-hour daily maximum sta ndard at 75 ppb would likely provide appreciable protection against m oderate or greater lung function responses. More specifically, given a 99 th 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 inJuly 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) (Tab le 9-5). Thus, we can expect that a standard at 75 ppb would limit risk estimates to somewhere between the risks associ ated with the 50 and 100 ppb, 99th percentile 1-hour dail y maximum standards. Being mindful that the most severe effects associated with SO 2 exposure are those observed in epidemiologic studies (i.e. respiratory-related ED vis its and hospitalizations), staff

concludes that consideration also should be given to a standard level of 50 ppb. A 99 th percentile 1-hour daily maximum standard at 50 ppb would provide an increas ed margin safety against the air quality levels observed in the cluster of epidemiologic studies observing statistically significant positive associations between SO 2 and respiratory-related ED visits and hospitalizations in studies with multipollutant models with PM (i.e. 99 th percentile 1-hour daily

maximum SO 2 concentrations ≥ 78 ppb). Moreover, as demons trated in Table10-2, a 99 th percentile 1-hour daily maximum standard set at 50 ppb would also be expected to limit 99 th percentile 24-hour average SO 2 concentrations significantly. That is , given a 1-hour daily maximum standard set at 50 ppb, Table 10-2 demons trates that most counties included in the 40- county air quality analysis would have 99 th percentile 24-hour average SO 2 concentrations below 15 ppb, ranging from 6-30 ppb. Recognizing that there are important uncertain ties associated with the controlled human exposure evidence, we note that a 99 th percentile 1-hour daily ma ximum 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 SO 2 sensitive individuals; and/or 2) very limited evidence suggesting decrements in lung function down to 100 ppb when SO 2 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 SO 2 concentrations ≥ 100 ppb. Results from the 40 county air qual ity analysis indicate that a 99 th 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 SO 2 concentrations are > 100 ppb (Table 7-11).

113 As first noted in section 10.3.3.2, results are presente d 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 99 th 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 SO 2 concentration ≥ 100 ppb per year (Figure 8-19). In considering alternative st andard levels > 100 ppb, we fi rst note that as mentioned in section 10.3.3, staff concluded that exposure to 5-10 minute SO 2 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 SO 2 concentrations from exceeding 200 ppb, the level of a 99 th 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 re view 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 li mit 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 99 th percentile 1-hour daily maximum SO 2 standard set in this range woul d have to place considerable weight on the uncertainties in the epidemiologic health evidence presented in the IS A. That is, the emphasis on the uncertainties would have to lead to a judgment that effects re ported in epidemiologic studies are due in large part to co-occurring pollutants, rather than to SO 2 . Under this scenario, results of the 40 county air quality analysis indicate that just meeting a 99 th percentile 1-hour da ily 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 SO 2 concentrations would be > 400 ppb (T able 7-14). In addition, the St. Louis exposure analysis indicates that a 99 th 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 experi encing at least one SO 2 exposure ≥ 400 ppb (Figure 8-19). Taken together, it can reasonable be concluded that a 99 th percentile 1-hour dail y maximum standard up to 150 ppb could similarly limit SO 2 exposures ≥ 400 ppb when compared to standards in the range of 50-100 ppb 114

.

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 SO 2 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 SO 2 exposure ≥ 400 ppb.July 2009 396 However, it is important to note that a 99 th percentile 1-hour daily maximum standard up to 150 ppb would provide considerably less protection against 5-minute SO 2 concentrations ≥ 200 ppb than standards in the range of 50 -100 ppb. Results of the 40 county air quality analysis

indicate that a 99 th percentile 1-hour daily maximum standa rd at 150 ppb would result in at most 24 days per year when statistically estimated 5-minute daily maximum SO 2 concentrations would be > 200 ppb. Moreover, the St. Louis exposur e 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 SO 2 exposure ≥ 200 ppb (Figure 8-19). Finally, we consider the results of the

St. Louis risk assessment. This assessment i ndicates that given a 150 ppb standard, the estimated median percentage of exposed asthmatics at elevat ed ventilation rates estimated to experience at

least one ≥ 100% increase in sRaw per year ranges fr om 2.9% to 3.6% (and 4.6% to 5.4% for asthmatic children). Several aspects of these a ssessment results raise questions as to the sufficiency of the protection that would be provided by a standa rd 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. Ho wever, if significant weight is placed on the uncertainties in the epidemiologic and contro lled 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 SO 2 Standards Finally, staff recognizes that the particular level se lected for a new 1-hour daily maximum standard will have implications for re aching decisions on whether to retain or revoke

the current 24-hour and annual standard s. That is, with respect to SO 2 -induced respiratory morbidity, the lower the level selected for a 99 th percentile 1-hour daily maximum standard, the less additional public health prot ection the current standards would be expected to provide. AsJuly 2009 397 an initial consideration, we note that all 99 th percentile 1-hour daily maximum SO 2 standard levels being considered (i.e. 50 – 150 ppb) are expected to prevent ambient SO 2 concentrations in the 40 counties analyzed in the air quality anal ysis 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 ma ximum 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 co uld still offer some degree of additional protection in some parts of th e country not currently monitoring for SO 2 . Based on these considerations, staff finds it reasonable to concl ude that if a new 99 th percentile 1-hour daily maximum standard is sele cted with a level from the upper end of the range that staff has identified for consid eration, then in addition to setting a 99 th percentile 1- hour daily maximum standard, consideration should also be given to retaining the existing 24-

hour and/or annual standards. Howeve r, if the select ed level of a 99 th percentile 1-hour daily

maximum standard is in the lower end of the range, it could reasonabl y 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 standa rds to protect public health with an adequate margin of safety.

 In considering potential alternative standards, SO 2 remains the most appropriate indicator ambient SO x .

 A 1-hour daily maximum standard, set at an appropriate level, can provide adequate protection against the range of health outcome s associated with averaging times from 5- minutes to 24-hours.

 Consideration should be give n primarily to establishing a new 1-hour daily maximum standard with a 99 th percentile or 4 th highest daily maximum form.

 The health evidence and the air quality, expos ure, and risk information presented above most strongly support considera tion of 99th percentile (or 4 th 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 si gnificant weight is placed on the uncertainties in the epidem iologic and controlled human exposure evidence, recognizing the questi ons that would be raised by levels at the higher end of this range.July 2009 398 

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. 108:877-685.A-1 

## Appendix A: Supplement to the SO 2 

## Air Quality CharacterizationA-2 

## Overview

This appendix contains supplementary information on the SO 2 ambient monitoring data used in the air quality characterization described in Chapter 7 of the SO 2 REA. Included in this appendix are spatial and temporal attributes im portant for understanding the relationship between the ambient monitor and those sources affecting air quality measurements. In section A.1, important spatial characteris tics described include the physical locations of the ambient monitors (e.g., U.S. states, counties, te rritories, and cities). Temporal attributes of interest include, for example, the number of samples collecte d, sample averaging times, and years of monitoring data available. Attributes of the monitors that reported both the 5-minute

maximum and the 1-hour SO 2 concentrations are given in Ta bles A.1-1 and A.1-2, while the supplemental characteristics of the broader ambien t monitoring network are given in Table A.1-3 and A.1-4. The method for calculating the prox imity of the ambient monitors follows, along

with the distance and emission results summarized in Table A.1-5.

Section A.2 details the anal yses performed on simultaneous concentrations, some of

which are the result of co-located monitori ng instruments, others the result of duplicate reporting. Simultaneous measurements were iden tified by staff using monitor IDs and multiple concentrations present given the hour-of-day on e ach available date. St aff 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 summarizi ng the individual factors used in adjusting ambient air quality to just meet the current and potential alternative SO 2 air quality standa rds. Section A-5

summarizes measured 1-hour concentrations and number of days per ye ar with air quality benchmark exceedances occurring at the 98 monitors reporting 5-minute maximum SO 2 concentrations.A-3 

### A.1 Spatial and Temporal Attributes of Ambient SO 2 

### MonitorsA-4Table A.1-1. Meta-data for 98 ambient monitorsreporting 5-minute maximum and corresponding 1-hour SO2concentrations.YearsState CountyMonitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Height(m)n First LastAR Pulaski 051190007 34.756111 -92.275833 POP URB COM NEI 4 6 2002 2007AR Pulaski 051191002 34.830556 -92.259444 HIC RUR FOR NEI 4 5 1997 2001AR Union051390006 33.215 -92.668889 UNK URB COM 4 11 1997 2007CO Denver 080310002 39.75119 -104.98762 HIC URB COM NEI 5 10 1997 2006DC District of Columbia 110010041 38.897222 -76.952778 POP URB RES NEI 6 2000 2007DE New Castle 100031008 39.577778 -75.611111 UNK RUR AGR 2 1997 1998FL Nassau 120890005 30.658333 -81.463333 HIC SUB IND NEI 2 4 2002 2005IA Cerro Gordo 190330018 43.16944 -93.202426 UNK SUB RES 4 5 2001 2005IA Clinton 190450019 41.823283 -90.211982 UNK URB IND MID 5 2001 2005IA Muscatine 191390016 41.419429 -91.070975 UNK URB RES 3 5 2001 2005IA Muscatine 191390017 41.387969 -91.054504 UNK SUB IND 4 5 2001 2005IA Muscatine 191390020 41.407796 -91.062646 UNK SUB IND 4 5 2001 2005IA Scott191630015 41.530011 -90.587611 HIC URB RES NEI 4 5 2001 2005IA Van Buren 191770005 40.689167 -91.994444 UNK RUR FOR 3 4 2001 2004IA Van Buren 191770006 40.695078 -92.006318 GEN RUR FOR 3 2 2004 2005IA Woodbury 191930018 42.399444 -96.355833 POP URB RES 3 2 2001 2002LA West Baton Rouge 221210001 30.501944 -91.209722 HIC SUB COM 2 4 1997 2000MO Buchanan 290210009 39.731389 -94.8775 GEN URB IND NEI 3 4 1997 2000MO Buchanan 290210011 39.731389 -94.868333 GEN URB IND NEI 3 4 2000 2003MO Greene 290770026 37.128333 -93.261667 POP SUB RES 3 11 1997 2007MO Greene 290770037 37.11 -93.251944 POP RUR RES 4 11 1997 2007MO Iron290930030 37.466389 -90.69 SRC RUR RES NEI 4 8 1997 2004MO Iron290930031 37.519444 -90.7125 UNK RUR AGR 2 8 1997 2004MO Jefferson 290990004 38.2633 -90.3785 POP RUR IND 3 4 2004 2007MO Jefferson 290990014 38.267222 -90.379444 OTH RUR RES NEI 4 5 1997 2001MO Jefferson 290990017 38.252778 -90.393333 UNK SUB RES 5 4 1998 2001MO Jefferson 290990018 38.297694 -90.384333 HIC SUB RES NEI 5 3 2001 2003MO Monroe 291370001 39.473056 -91.789167 UNK RUR UNK 11 1997 2007MO Pike291630002 39.3726 -90.9144 HIC RUR RES NEI 3 3 2005 2007MO Saint Charles 291830010 38.579167 -90.841111 UNK RUR AGR 3 2 1997 1998A-5YearsState CountyMonitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Height(m)n First LastMO Saint Charles 291831002 38.8725 -90.226389 UNK RUR AGR 2 4 1997 2000MT Yellowstone 301110066 45.788318 -108.459536 SRC RUR RES NEI 3.5 7 1997 2003MT Yellowstone 301110079 45.769439 -108.574292 POP SUB COM 4.5 4 1997 2003MT Yellowstone 301110080 45.777149 -108.47436 UNK RUR AGR 4 5 1997 2001MT Yellowstone 301110082 45.783889 -108.515 POP URB COM NEI 3 3 2001 2003MT Yellowstone 301110083 45.795278 -108.455833 SRC SUB AGR 4 5 1999 2003MT Yellowstone 301110084 45.831453 -108.449964 POP SUB RES NEI 4.5 4 2003 2006MT Yellowstone 301112008 45.786389 -108.523056 UNK URB RES 3 1 1997 1997NC Forsyth 370670022 36.110556 -80.226667 POP URB RES NEI 3 8 1997 2004NC New Hanover 371290006 34.268403 -77.956529 GEN RUR IND URB 3 4 1999 2002ND Billings 380070002 46.8943 -103.37853 GEN RUR AGR REG 12.2 10 1998 2007ND Billings 380070003 46.9619 -103.356699 HIC RUR IND URB 4 1 1997 1997ND Burke380130002 48.9904 -102.7815 SRC RUR AGR REG 4 7 1999 2005ND Burke380130004 48.64193 -102.4018 REG RUR AGR REG 4 5 2003 2007ND Burleigh 380150003 46.825425 -100.76821 POP SUB RES URB 4 3 2005 2007ND Cass380171003 46.910278 -96.795 POP SUB RES URB 4 2 1997 1998ND Cass380171004 46.933754 -96.85535 POP SUB AGR URB 3 10 1998 2007ND Dunn380250003 47.3132 -102.5273 GEN RUR AGR REG 4 11 1997 2007ND McKenzie 380530002 47.5812 -103.2995 GEN RUR AGR REG 4 9 1997 2007ND McKenzie 380530104 47.575278 -103.968889 SRC RUR AGR URB 3 10 1998 2007ND McKenzie 380530111 47.605556 -104.017222 SRC RUR IND URB 3 10 1998 2007ND Mercer 380570001 47.258853 -101.783035 POP SUB RES NEI 5 3 1997 1999ND Mercer 380570004 47.298611 -101.766944 POP RUR AGR URB 4 9 1999 2007ND Morton 380590002 46.84175 -100.870059 SRC SUB IND NEI 4 9 1997 2005ND Morton 380590003 46.873075 -100.905039 SRC SUB IND NEI 4 8 1998 2005ND Oliver380650002 47.185833 -101.428056 SRC RUR AGR URB 3 11 1997 2007ND Steele380910001 47.599703 -97.899009 GEN RUR AGR REG 3 4 1997 2000ND Williams 381050103 48.408834 -102.90765 SRC RUR IND URB 4 6 2002 2007ND Williams 381050105 48.392644 -102.910233 SRC RUR IND URB 4 6 2002 2007PA Allegheny 420030002 40.500556 -80.071944 POP SUB RES NEI 6 3 1997 1999PA Allegheny 420030021 40.413611 -79.941389 POP SUB RES NEI 6 4 1997 2002PA Allegheny 420030031 40.443333 -79.990556 POP URB COM NEI 13 3 1997 1999PA Allegheny 420030032 40.414444 -79.942222 UNK SUB RES 5 3 1997 1999A-6YearsState CountyMonitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Height(m)n First LastPA Allegheny 420030064 40.323611 -79.868333 POP SUB RES NEI 8 4 1997 2002PA Allegheny 420030067 40.381944 -80.185556 GEN RUR RES NEI 9 3 1997 1999PA Allegheny 420030116 40.473611 -80.077222 POP SUB RES NEI 5 4 1997 2002PA Allegheny 420031301 40.4025 -79.860278 HIC SUB RES NEI 9 3 1997 1999PA Allegheny 420033003 40.318056 -79.881111 POP SUB IND 5 4 1997 2002PA Allegheny 420033004 40.305 -79.888889 UNK SUB RES 8 3 1997 1999PA Beaver 420070002 40.56252 -80.503948 REG RUR AGR REG 3 2 1997 1998PA Beaver 420070005 40.684722 -80.359722 POP RUR AGR URB 3 8 1997 2007PA Berks420110009 40.320278 -75.926667 HIC SUB RES NEI 4 3 1997 1999PA Cambria 420210011 40.309722 -78.915 HIC URB COM NEI 12 3 1997 1999PA Erie420490003 42.14175 -80.038611 HIC SUB COM NEI 4 3 1997 1999PA Philadelphia 421010022 39.916667 -75.188889 HIC URB IND NEI 7 5 1997 2001PA Philadelphia 421010048 39.991389 -75.080833 UNK RUR RES 5 3 1997 1999PA Philadelphia 421010136 39.9275 -75.222778 POP URB RES NEI 4 7 1997 2003PA Warren 421230003 41.857222 -79.1375 HIC SUB RES NEI 4 2 1997 1998PA Warren 421230004 41.844722 -79.169722 HIC RUR FOR NEI 4 2 1997 1998PA Washington 421250005 40.146667 -79.902222 POP SUB COM NEI 2 3 1997 1999PA Washington 421250200 40.170556 -80.261389 POP SUB RES NEI 4 3 1997 1999PA Washington 421255001 40.445278 -80.420833 REG RUR AGR REG 4 2 1997 1998SC Barnwell 450110001 33.320344 -81.465537 SRC RUR FOR URB 3.1 3 2000 2002SC Charleston 450190003 32.882289 -79.977538 POP URB COM NEI 4.3 3 2000 2002SC Charleston 450190046 32.941023 -79.657187 SRC RUR FOR REG 4 3 2000 2002SC Georgetown 450430006 33.362014 -79.294251 SRC URB IND NEI 2.13 3 2000 2002SC Greenville 450450008 34.838814 -82.402918 POP URB COM NEI 4 3 2000 2002SC Lexington 450630008 34.051017 -81.15495 SRC SUB COM NEI 3.35 2 2001 2002SC Oconee 450730001 34.805261 -83.2377 REG RUR FOR REG 4.3 3 2000 2002SC Richland 450790007 34.093959 -80.962304 OTH SUB COM NEI 3 3 2000 2002SC Richland 450790021 33.81468 -80.781135 GEN RUR FOR URB 4.42 3 2000 2002SC Richland 450791003 34.024497 -81.036248 POP URB COM MID 4 2 2001 2002UT Salt Lake 490352004 40.736389 -112.210278 HIC RUR IND 2 1997 1998WV Wayne540990002 38.39186 -82.583923 POP RUR IND NEI 4 1 2002 2002WV Wayne540990003 38.390278 -82.585833 HIC RUR RES NEI 3 4 2002 2005WV Wayne540990004 38.380278 -82.583889 HIC RUR RES NEI 3 4 2002 2005A-7YearsState CountyMonitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Height(m)n First LastWV Wayne540990005 38.372222 -82.588889 HIC RUR RES NEI 3 4 2002 2005WV Wood541071002 39.323533 -81.552367 POP SUB IND URB 4 5 2001 2005Notes:1Objectives are POP=Population Exposure; HIC=Highest Concentration; SRC=Source Oriented; GEN=General/Background; REG=RegionalTransport; OTH=Other; UNK=Unknown2Settings are R=Rural; U=Urban and Center City; S=Suburban3Land Uses are AGR=Agricultural; COM=Commercial; IND=Industrial; FOR=Forest; RES=Residential; UNK=Unknown4Scales are NEI=Neighborhood; MID=Middle; URB=URBAN; REG=Regional12A-8Table A.1-2. Population density, concentration variability, and total SO2emissions associated with 98 ambientmonitors reporting 5-minute maximum and corresponding 1-hour SO2concentrations.Population Residing Within:Analysis BinsState CountyMonitor ID5 km 10 km 15 km 20 km Population1COV2GSD3Emissions(tpy)4AR Pulaski051190007 67784 178348 270266 334649 hi a a 20AR Pulaski051191002 45800 109372 230200 310362 mid a a 20AR Union051390006 21877 29073 32652 36340 mid b a 2527CO Denver080310002 189782 574752 1158644 1608099 hi b b 26354DE New Castle 100031008 5386 80025 192989 391157 low b b 39757DC District of Columbia 110010041 216129 813665 1461563 2029936 hi a a 18325FL Nassau 120890005 17963 21386 38521 48316 mid c b 5050IA Cerro Gordo 190330018 21247 30341 39284 45105 mid c c 10737IA Clinton 190450019 24561 37638 42404 45947 mid b c 9388IA Muscatine 191390016 20360 27101 31886 40248 mid b b 31137IA Muscatine 191390017 11109 27101 31696 36604 mid b b 31054IA Muscatine 191390020 20360 27101 31886 40290 mid c c 31054IA Scott191630015 90863 201277 268535 293627 hi b c 9415IA Van Buren 191770005 994 2252 3764 6984 low b bIA Van Buren 191770006 994 2252 3764 6984 low a bIA Woodbury 191930018 4449 44815 92956 112802 low b c 36833LA West Baton Rouge 221210001 21249 137455 239718 366741 mid b b 31242MO Buchanan 290210009 23253 72613 87121 93365 mid c b 3563MO Buchanan 290210011 28224 75073 86317 93365 mid b b 3563MO Greene290770026 41036 146752 224445 256158 mid c b 9206MO Greene290770037 21784 110681 210953 254437 mid c b 9206MO Iron290930030 1121 1121 4507 8447 low c c 43340MO Iron290930031 0 3799 6585 8436 low c b 43340MO Jefferson 290990004 15049 33379 64516 124301 mid c c 55725MO Jefferson 290990014 11967 35082 61963 125932 mid c b 55725MO Jefferson 290990017 19711 36471 60199 116882 mid c b 55725MO Jefferson 290990018 12258 41709 79196 170110 mid c b 32468MO Monroe291370001 0 1439 2093 5612 low a aMO Pike291630002 645 2077 6916 11249 low b b 13495MO Saint Charles 291830010 2637 6349 34541 90953 low b b 47610MO Saint Charles 291831002 4587 95765 273147 431484 low b b 67735A-9Population Residing Within:Analysis BinsState CountyMonitor ID5 km 10 km 15 km 20 km Population1COV2GSD3Emissions(tpy)4MT Yellowstone 301110066 27389 79644 98733 107178 mid b c 5480MT Yellowstone 301110079 61645 89282 102887 114640 hi b a 5480MT Yellowstone 301110080 33774 86065 104825 108399 mid b b 5480MT Yellowstone 301110082 58256 94753 103200 106046 hi b a 5480MT Yellowstone 301110083 27620 76641 98733 109475 mid b b 5480MT Yellowstone 301110084 22577 59919 97912 110980 mid b b 15298MT Yellowstone 301112008 61335 95574 103200 106046 hi b b 5480NC Forsyth370670022 61669 170320 258102 325974 hi b b 3945NC New Hanover 371290006 17957 83529 145330 170260 mid c c 30020ND Billings380070002 0 0 1887 1887 low a a 283ND Billings380070003 0 888 1887 1887 low a a 283ND Burke380130002 0 0 0 625 low b bND Burke380130004 655 655 655 655 low b b 426ND Burleigh 380150003 49591 67377 83082 84415 mid b a 4592ND Cass380171003 48975 134561 144878 154455 mid b a 771ND Cass380171004 2118 91149 145789 148002 low a b 756ND Dunn380250003 0 0 0 537 low a a 5ND McKenzie 380530002 0 596 596 596 low a a 210ND McKenzie 380530104 0 521 521 2283 low b aND McKenzie 380530111 0 0 2283 5771 low c a 823ND Mercer380570001 3280 3280 5902 6465 low b b 91617ND Mercer380570004 3280 4428 5902 7455 low b a 91617ND Morton380590002 17925 67959 75685 84415 mid c c 4592ND Morton380590003 10305 31348 75685 82584 mid b b 4592ND Oliver380650002 0 0 2057 2670 low b b 28565ND Steele380910001 0 934 934 934 low a aND Williams 381050103 0 1259 1259 1827 low c b 1605ND Williams 381050105 0 1259 1259 1827 low b c 1605PA Allegheny 420030002 83332 277442 651551 961378 hi b b 1964PA Allegheny 420030021 170777 560187 921490 1142754 hi b b 52447PA Allegheny 420030031 183843 580429 877668 1145039 hi a b 46957PA Allegheny 420030032 174072 558904 922097 1144558 hi b b 52447PA Allegheny 420030064 64846 201143 520438 943781 hi b c 11490A-10Population Residing Within:Analysis BinsState CountyMonitor ID5 km 10 km 15 km 20 km Population1COV2GSD3Emissions(tpy)4PA Allegheny 420030067 13277 86792 324154 610975 mid a b 1167PA Allegheny 420030116 96820 331624 704601 996267 hi b b 1964PA Allegheny 420031301 115432 411867 766188 1088115 hi b b 52100PA Allegheny 420033003 55221 202092 509708 944188 hi b c 11490PA Allegheny 420033004 38588 170065 461433 904760 mid b b 11501PA Beaver420070002 3434 28961 68617 120780 low b b 187257PA Beaver420070005 17292 77240 143738 224631 mid b c 41385PA Berks420110009 121330 203799 250610 309553 hi a b 14817PA Cambria 420210011 50440 79710 102905 124592 hi a b 16779PA Erie420490003 81199 150626 190212 209983 hi b b 4122PA Philadelphia 421010022 316944 985213 1726387 2446142 hi a b 18834PA Philadelphia 421010048 262592 1102727 1938877 2607877 hi b b 6214PA Philadelphia 421010136 382995 985957 1718068 2381173 hi b b 21700PA Warren421230003 14142 19940 25715 32490 mid b b 4890PA Warren421230004 13965 18884 28805 33523 mid b c 4890PA Washington 421250005 31276 68512 111222 183285 mid a b 8484PA Washington 421250200 32125 52910 83324 118188 mid b b 7PA Washington 421255001 1359 15854 43364 126091 low b b 2566SC Barnwell 450110001 0 4022 13647 21554 low a a 65SC Charleston 450190003 40872 132716 273298 364953 mid b b 34934SC Charleston 450190046 1103 1103 9529 22255 low b aSC Georgetown 450430006 10567 18215 22467 34357 mid b b 40841SC Greenville 450450008 70221 173012 284047 379022 hi a a 1067SC Lexington 450630008 42208 131361 257820 355854 mid b b 10433SC Oconee 450730001 0 2260 11136 26182 low a a 5SC Richland 450790007 35872 121006 255135 353072 mid a a 613SC Richland 450790021 1666 4643 13324 33098 low b a 40492SC Richland 450791003 87097 213836 300874 396116 hi a a 12935UT Salt Lake 490352004 0 4074 35159 124394 low a a 3735WV Wayne540990002 17320 62645 124477 178576 mid a b 10172WV Wayne540990003 17320 59989 123349 177744 mid b b 10172WV Wayne540990004 16553 54251 122072 179815 mid b b 10172WV Wayne540990005 13314 48330 114824 173807 mid b b 10172A-11Population Residing Within:Analysis BinsState CountyMonitor ID5 km 10 km 15 km 20 km Population1COV2GSD3Emissions(tpy)4WV Wood541071002 24917 70324 104458 128127 mid b b 48124Notes:1Population bins: low (≤10,000); mid (10,001 to 50,000); hi (>50,000) using population within 5 km of ambient monitor.2COV bins: a (≤100%); b (>100 to≤200); c (>200).3GSD bins: a (≤2.17); b (>2.17 to≤2.94); c (>2.94).4Sum of emissions within 20 km radius of ambient monitor based on 2002 NEI.A-12Table A.1-3. Meta-data for 809 ambient monitors in the broader SO2monitoring network.YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastAL Colbert 010330044 34.690556 -87.821389 UNK RUR AGR 9 1997 2005AL Jackson 010710020 34.876944 -85.720833 UNK RUR AGR 4 9 1997 2005AL Jefferson 010731003 33.485556 -86.915 HIC SUB RES NEI 4 9 1997 2006AL Lawrence 010790003 34.589571 -87.109445 UNK RUR AGR URB 2 1998 1999AL MOB 010970028 30.958333 -88.028333 HIC SUB IND NEI 4 3 1997 1999AL MOB 010972005 30.474674 -88.14114 POP RUR AGR NEI 1 3 2002 2004AL Montgomery 011011002 32.40712 -86.256367 HIC SUB COM NEI 6 1 1997 1997AZ Gila 040070009 33.399135 -110.858896 SRC URB RES 7 1999 2005AZ Gila 040071001 33.006179 -110.785797 SRC URB IND 4 7 1999 2005AZ Maricopa 040130019 33.48385 -112.14257 UNK SUB RES 1 1998 1998AZ Maricopa 040133002 33.45793 -112.04601 HIC URB RES NEI 11.3 9 1997 2006AZ Maricopa 040133003 33.47968 -111.91721 POP SUB RES NEI 5.8 7 1998 2006AZ Pima 040191011 32.208333 -110.872222 POP SUB RES NEI 5 9 1998 2006AZ Pinal 040212001 32.600479 -110.633598 POP SUB RES 4 4 1998 2005AR Pulaski 051190007 34.756111 -92.275833 POP URB COM NEI 4 5 2002 2006AR Pulaski 051191002 34.830556 -92.259444 HIC RUR FOR NEI 4 5 1997 2001AR Union 051390006 33.215 -92.668889 UNK URB COM 4 7 1997 2006CA Alameda 060010010 37.7603 -122.1925 POP SUB RES NEI 1 2002 2002CA Contra Costa 060130002 37.936 -122.0262 SRC SUB RES NEI 8.3 9 1997 2005CA Contra Costa 060130006 37.9478 -122.3651 UNK URB IND NEI 8.5 9 1997 2005CA Contra Costa 060130010 38.0313 -122.1318 POP URB COM NEI 1 2002 2002CA Contra Costa 060131001 38.055556 -122.219722 SRC SUB IND 7 8 1997 2004CA Contra Costa 060131002 38.010556 -121.641389 UNK RUR AGR 7 9 1997 2005CA Contra Costa 060131003 37.964167 -122.339167 UNK URB COM 6 4 1998 2001CA Contra Costa 060131004 37.96028 -122.35667 POP URB COM 20 3 2003 2005CA Contra Costa 060132001 38.013056 -122.133611 UNK URB RES 9 9 1997 2005CA Contra Costa 060133001 38.029167 -121.902222 HIC URB RES NEI 7 9 1997 2005CA Imperial 060250005 32.676111 -115.483333 UNK SUB RES 6 1999 2005CA Los Angeles 060371002 34.17605 -118.31712 UNK URB COM 5 7 1998 2005CA Los Angeles 060371103 34.06659 -118.22688 UNK URB RES 11 6 1997 2005CA Los Angeles 060374002 33.82376 -118.18921 POP SUB RES NEI 7 9 1997 2005A-13YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastCA Los Angeles 060375001 33.92288 -118.37026 POP URB COM NEI 2 7 1997 2003CA Los Angeles 060375005 33.9508 -118.43043 UPW SUB RES NEI 4 1 2005 2005CA Orange 060591003 33.67464 -117.92568 UNK SUB RES MID 6 9 1997 2005CA Riverside 060658001 33.99958 -117.41601 POP SUB RES NEI 7 7 1997 2005CA Sacramento 060670002 38.712778 -121.38 UNK SUB RES 5 7 1997 2006CA Sacramento 060670006 38.614167 -121.366944 HIC SUB RES NEI 5 9 1997 2006CA San Bernardino 060710012 34.426111 -117.563056 UNK RUR COM 1 1997 1997CA San Bernardino 060710014 34.5125 -117.33 UNK SUB RES 4 3 1997 1999CA San Bernardino 060710306 34.51 -117.330556 UNK SUB RES 4 7 2000 2006CA San Bernardino 060711234 35.763889 -117.396111 OTH RUR DES 1 8 1998 2006CA San Bernardino 060712002 34.10002 -117.49201 POP SUB IND NEI 5 5 1997 2005CA San Bernardino 060714001 34.418056 -117.284722 UNK SUB RES 1 1997 1997CA San Diego 060730001 32.631231 -117.059075 POP SUB RES NEI 7 9 1997 2005CA San Diego 060731007 32.709172 -117.153975 POP URB COM NEI 5 8 1997 2004CA San Diego 060732007 32.552164 -116.937772 POP RUR MOB NEI 5 8 1997 2004CA San Francisco 060750005 37.766 -122.3991 UNK URB IND 9 1997 2005CA San Luis Obispo 060791005 35.043889 -120.580278 UNK RUR COM 4 5 1997 2001CA San Luis Obispo 060792001 35.125 -120.633333 UNK SUB RES NEI 5 5 1997 2002CA San Luis Obispo 060792004 35.022222 -120.569444 UNK RUR IND 4 9 1997 2006CA San Luis Obispo 060794002 35.028333 -120.387222 POP RUR RES REG 4 7 2000 2006CA Santa Barbara 060830008 34.462222 -120.024444 POP RUR UNK REG 4 9 1997 2005CA Santa Barbara 060831012 34.451944 -120.457778 UNK RUR AGR REG 1 1997 1997CA Santa Barbara 060831013 34.725556 -120.427778 UNK RUR AGR NEI 9 1997 2005CA Santa Barbara 060831015 34.478056 -120.210833 UNK RUR AGR NEI 1 1997 1997CA Santa Barbara 060831016 34.477778 -120.205556 UNK RUR AGR NEI 1 1997 1997CA Santa Barbara 060831019 34.475278 -120.188889 UNK RUR AGR NEI 1 1997 1997CA Santa Barbara 060831020 34.415278 -119.878611 UNK RUR AGR NEI 6 1997 2005CA Santa Barbara 060831025 34.489722 -120.045833 UNK RUR AGR NEI 9 1997 2005CA Santa Barbara 060831026 34.479444 -120.0325 UNK RUR AGR NEI 2 1997 1998CA Santa Barbara 060831027 34.469167 -120.039444 UNK RUR AGR NEI 2 1997 1998CA Santa Barbara 060832004 34.6375 -120.456389 POP URB COM NEI 9 1997 2005CA Santa Barbara 060832011 34.445278 -119.827778 POP SUB RES NEI 9 1997 2005CA Santa Barbara 060834003 34.596111 -120.630278 UNK RUR AGR NEI 8 1997 2005A-14YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastCA Santa Cruz 060870003 37.011944 -122.193333 UNK RUR RES 9 1997 2006CA Solano 060950001 38.052222 -122.144722 UNK URB COM 6 1 1997 1997CA Solano 060950004 38.1027 -122.2382 UNK URB COM 8 9 1997 2005CA Ventura 061113001 34.255 -119.1425 HIC RUR RES NEI 4 7 1997 2003CO Adams 080010007 39.8 -104.910833 POP URB RES NEI 4 2 2002 2003CO Adams 080013001 39.83818 -104.94984 POP RUR AGR NEI 4 9 1997 2005CO Denver 080310002 39.75119 -104.98762 HIC URB COM NEI 5 8 1997 2006CO El Paso 080416001 38.633611 -104.715556 UNK RUR IND 4 4 1997 2000CO El Paso 080416004 38.921389 -104.8125 UNK URB RES 4 3 1997 1999CO El Paso 080416011 38.846667 -104.827222 UNK URB RES 3 4 1997 2000CO El Paso 080416018 38.811389 -104.751389 UNK URB COM 3 3 1998 2000CT Fairfield 090010012 41.195 -73.163333 HIC URB RES NEI 3 9 1997 2005CT Fairfield 090010017 41.003611 -73.585 UNK SUB RES 3 1 1997 1997CT Fairfield 090011123 41.399167 -73.443056 UNK SUB RES 3 9 1997 2005CT Fairfield 090012124 41.063056 -73.528889 HIC URB RES NEI 8 1997 2004CT Fairfield 090019003 41.118333 -73.336667 POP RUR FOR NEI 8 1998 2005CT Hartford 090031005 42.015833 -72.518056 POP RUR AGR REG 3 2 1997 1998CT Hartford 090031018 41.760833 -72.670833 POP URB COM NEI 3 1 1997 1997CT Hartford 090032006 41.7425 -72.634444 HIC SUB IND NEI 9 9 1997 2005CT New Haven 090090027 41.301111 -72.902778 POP URB COM NEI 3.67 1 2005 2005CT New Haven 090091003 41.310556 -72.915556 UNK SUB IND 5 1 1997 1997CT New Haven 090091123 41.310833 -72.916944 HIC URB RES NEI 5 7 1997 2003CT New Haven 090092123 41.550556 -73.043611 POP URB MOB NEI 5 9 1997 2005CT New London 090110007 41.361111 -72.08 UNK SUB RES 3 2 1997 1998CT Tolland 090130003 41.73 -72.213611 UNK SUB COM NEI 3 2 1997 1998DE New Castle 100031003 39.761111 -75.491944 HIC SUB RES NEI 4 6 1997 2002DE New Castle 100031007 39.551111 -75.730833 UNK RUR AGR 3 2002 2006DE New Castle 100031008 39.577778 -75.611111 UNK RUR AGR 8 1997 2006DE New Castle 100031013 39.773889 -75.496389 POP SUB RES 2 2004 2006DE New Castle 100032002 39.757778 -75.546389 POP URB COM NEI 6 2 1997 1998DE New Castle 100032004 39.739444 -75.558056 UNK URB COM 6 2000 2006DCDistrict ofColumbia110010041 38.897222 -76.952778 POP URB RES NEI 10 1997 2006A-15YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastFL Broward 120110010 26.128611 -80.167222 HIC SUB RES NEI 4 8 1997 2005FL Duval 120310032 30.356111 -81.635556 HIC SUB COM NEI 3 8 1997 2004FL Duval 120310080 30.308889 -81.6525 HIC SUB COM MID 3 8 1997 2005FL Duval 120310081 30.422222 -81.621111 HIC SUB RES MID 4 8 1997 2005FL Duval 120310097 30.367222 -81.594167 POP SUB COM NEI 5 8 1997 2005FL Escambia 120330004 30.525 -87.204167 POP SUB IND NEI 4 9 1997 2005FL Escambia 120330022 30.544722 -87.216111 HIC SUB COM NEI 6 8 1997 2005FL Hamilton 120470015 30.411111 -82.783611 UNK RUR IND 3 10 1997 2006FL Hillsborough 120570021 27.947222 -82.453333 HIC RUR RES NEI 2 3 1997 1999FL Hillsborough 120570053 27.886389 -82.481389 POP SUB RES NEI 4 9 1997 2005FL Hillsborough 120570081 27.739722 -82.465278 UNK UNK UNK 4 8 1997 2005FL Hillsborough 120570095 27.9225 -82.401389 HIC SUB COM NEI 4 9 1997 2005FL Hillsborough 120570109 27.856389 -82.383667 POP SUB COM NEI 3 9 1997 2005FL Hillsborough 120571035 27.928056 -82.454722 POP SUB RES NEI 4 9 1997 2005FL Hillsborough 120574004 27.9925 -82.125833 HIC SUB RES NEI 4 6 2000 2005FL Manatee 120813002 27.632778 -82.546111 POP RUR IND NEI 4 5 1999 2004FL Miami-Dade 120860019 25.8975 -80.38 POP UNK UNK NEI 4 7 1997 2003FL Nassau 120890005 30.658333 -81.463333 HIC SUB IND NEI 2 8 1997 2006FL Nassau 120890009 30.686389 -81.4475 HIC SUB RES NEI 4 1 1997 1997FL Orange 120952002 28.599444 -81.363056 HIC URB COM NEI 4 9 1997 2005FL Palm Beach 120993004 26.369722 -80.074444 HIC SUB COM NEI 10 6 1997 2002FL Pinellas 121030023 27.863333 -82.623333 POP RUR IND NEI 4 9 1997 2005FL Pinellas 121033002 27.871389 -82.691667 HIC SUB COM NEI 3 9 1997 2005FL Pinellas 121035002 28.09 -82.700833 HIC RUR RES NEI 4 9 1997 2005FL Pinellas 121035003 28.141667 -82.739722 HIC SUB RES NEI 4 7 1999 2005FL Polk 121050010 27.856111 -82.017778 HIC RUR IND NEI 2 8 1997 2004FL Polk 121052006 27.896944 -81.960278 HIC SUB IND NEI 4 6 1997 2002FL Putnam 121071008 29.6875 -81.656667 HIC RUR IND NEI 10 1997 2006FL Sarasota 121151002 27.299722 -82.524444 HIC SUB RES NEI 5 1 1997 1997FL Sarasota 121151005 27.306944 -82.570556 POP SUB RES URB 4 4 1997 2000FL Sarasota 121151006 27.350278 -82.48 POP SUB RES NEI 5 4 2000 2003GA Baldwin 130090001 33.153258 -83.235807 SRC RUR RES NEI 5 3 1998 2006GA Bartow 130150002 34.103333 -84.915278 POP SUB AGR REG 5 5 1997 2004A-16YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastGA Bibb 130210012 32.805244 -83.543628 POP RUR IND URB 4 3 1998 2003GA Chatham 130510019 32.093889 -81.151111 HIC SUB IND URB 4 1 2000 2000GA Chatham 130510021 32.06905 -81.048949 SRC SUB COM NEI 10 6 1998 2006GA Chatham 130511002 32.090278 -81.130556 POP URB IND NEI 5 3 2004 2006GA Dougherty 130950006 31.567778 -84.102778 HIC SUB RES MID 4 1 1998 1998GA Fannin 131110091 34.985556 -84.375278 POP URB IND NEI 3 9 1997 2006GA Floyd 131150003 34.261113 -85.323018 POP RUR RES NEI 4 10 1997 2006GA Fulton 131210048 33.779189 -84.395843 HIC URB COM NEI 5 8 1999 2006GA Fulton 131210055 33.720428 -84.357449 POP SUB COM NEI 5 10 1997 2006GA Glynn 131270006 31.16953 -81.496046 POP SUB RES NEI 8 1 1999 1999GA Muscogee 132150008 32.521099 -84.944695 POP SUB RES NEI 4 2 1999 2005GA Richmond 132450003 33.393611 -82.006389 POP SUB IND NEI 4 3 1997 2001HI Honolulu 150030010 21.329167 -158.093333 SRC RUR IND 9 1997 2005HI Honolulu 150030011 21.337222 -158.119167 SRC RUR COM NEI 4 6 2000 2005HI Honolulu 150031001 21.310278 -157.858056 POP URB COM NEI 10 7 1998 2004HI Honolulu 150031006 21.3475 -158.113333 UNK RUR IND 9 1997 2005ID Bannock 160050004 42.916389 -112.515833 HIC RUR IND NEI 3 9 1997 2005ID Caribou 160290003 42.661298 -111.591443 POP URB RES NEI 3 3 1999 2001ID Caribou 160290031 42.695278 -111.593889 SRC RUR DES MIC 4 4 2002 2005ID Power 160770011 42.9125 -112.535556 SRC RUR IND 1 2004 2004IL Adams 170010006 39.93301 -91.404237 POP URB COM NEI 9 10 1997 2006IL Champaign 170190004 40.123796 -88.229531 POP SUB RES NEI 5 4 1997 2000IL Cook 170310050 41.70757 -87.568574 POP SUB IND NEI 8 10 1997 2006IL Cook 170310059 41.6875 -87.536111 HIC SUB IND NEI 10 4 1997 2000IL Cook 170310063 41.876969 -87.63433 POP URB MOB NEI 3 10 1997 2006IL Cook 170310064 41.790787 -87.601646 POP SUB RES NEI 15 1 1997 1997IL Cook 170310076 41.7514 -87.713488 POP SUB RES URB 4 3 2004 2006IL Cook 170311018 41.773889 -87.815278 HIC SUB IND NEI 4 8 1997 2004IL Cook 170311601 41.66812 -87.99057 POP SUB RES NEI 4 10 1997 2006IL Cook 170312001 41.662109 -87.696467 HIC SUB IND NEI 9 7 1997 2003IL Cook 170314002 41.855243 -87.75247 POP SUB RES NEI 4 10 1997 2006IL Cook 170314201 42.139996 -87.799227 POP SUB RES URB 8 2 2004 2005IL Cook 170318003 41.631389 -87.568056 POP SUB RES NEI 4 6 1997 2002A-17YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastIL DuPage 170436001 41.813049 -88.072827 POP SUB AGR NEI 14 4 1997 2000IL La Salle 170990007 41.293015 -89.049425 SRC SUB IND NEI 5 1 2006 2006IL Macon 171150013 39.866834 -88.925594 POP SUB IND NEI 5 10 1997 2006IL Macoupin 171170002 39.396075 -89.809739 POP RUR AGR REG 5 10 1997 2006IL Madison 171190008 38.890186 -90.148031 SRC SUB IND NEI 15 6 1997 2002IL Madison 171190017 38.701944 -90.149167 HIC URB MOB NEI 3 4 1997 2000IL Madison 171191010 38.828303 -90.058433 SRC SUB IND NEI 5 10 1997 2006IL Madison 171193007 38.860669 -90.105851 POP SUB IND NEI 10 10 1997 2006IL Madison 171193009 38.865984 -90.070571 SRC SUB COM NEI 7 9 1997 2006IL Peoria 171430024 40.68742 -89.606943 POP SUB COM NEI 5 10 1997 2006IL Randolph 171570001 38.176278 -89.788459 GEN RUR IND NEI 5 10 1997 2006IL Rock Island 171610003 41.511944 -90.514167 HIC URB COM NEI 8 4 1997 2000IL Saint Clair 171630010 38.612034 -90.160477 POP SUB IND NEI 5 10 1997 2006IL Saint Clair 171631010 38.592192 -90.165081 HIC SUB IND NEI 7 6 1997 2002IL Saint Clair 171631011 38.235 -89.841944 SRC RUR IND NEI 5 5 1997 2001IL Sangamon 171670006 39.800614 -89.591225 SRC SUB IND NEI 8 10 1997 2006IL Tazewell 171790004 40.55646 -89.654028 SRC SUB IND NEI 6 10 1997 2006IL Wabash 171850001 38.397222 -87.773611 HIC URB MOB NEI 2 5 1997 2005IL Wabash 171851001 38.369444 -87.834444 HIC RUR AGR NEI 2 6 1997 2005IL Will 171970013 41.459963 -88.182019 SRC RUR IND NEI 13 10 1997 2006IN Daviess 180270002 38.572778 -87.214722 HIC RUR AGR NEI 2 9 1997 2005IN Dearborn 180290004 39.092778 -84.855 HIC SUB COM NEI 5 9 1997 2005IN Floyd 180430004 38.367778 -85.833056 HIC RUR AGR NEI 5 5 1997 2005IN Floyd 180430007 38.273333 -85.836389 SRC RUR RES NEI 4 5 1997 2005IN Floyd 180431004 38.308056 -85.834167 POP SUB RES NEI 5 10 1997 2006IN Fountain 180450001 39.964167 -87.421389 HIC RUR AGR NEI 2 6 1997 2005IN Gibson 180510001 38.361389 -87.748611 HIC RUR AGR NEI 5 5 1997 2005IN Gibson 180510002 38.392778 -87.748333 HIC RUR AGR NEI 9 5 1997 2004IN Hendricks 180630001 39.876944 -86.473889 HIC RUR IND 2 2004 2005IN Hendricks 180630002 39.863361 -86.47075 HIC SUB COM 2 2004 2005IN Hendricks 180630003 39.880833 -86.542194 HIC SUB COM 2 2004 2005IN Jasper 180730002 41.187778 -87.053333 HIC RUR AGR NEI 3 10 1997 2006IN Jasper 180730003 41.135833 -86.987778 HIC RUR AGR URB 3 6 1997 2002A-18YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastIN Jefferson 180770004 38.776667 -85.407222 HIC SUB COM NEI 5 8 1997 2004IN Lake 180890022 41.606667 -87.304722 UNK URB IND 8 1998 2005IN Lake 180892008 41.639444 -87.493611 HIC SUB COM NEI 5 9 1997 2006IN LaPorte 180910005 41.716944 -86.9075 HIC URB IND NEI 4 10 1997 2006IN LaPorte 180910007 41.679722 -86.852778 HIC RUR RES NEI 3 6 1997 2002IN Marion 180970042 39.646254 -86.248784 POP RUR AGR URB 4 10 1997 2006IN Marion 180970054 39.730278 -86.196111 HIC URB IND NEI 9 1 1997 1997IN Marion 180970057 39.749019 -86.186314 HIC URB RES NEI 4 10 1997 2006IN Marion 180970072 39.768056 -86.16 POP URB COM MID 3 4 1997 2000IN Marion 180970073 39.789167 -86.060833 POP URB RES NEI 5 9 1997 2005IN Morgan 181091001 39.515 -86.391667 HIC SUB RES NEI 2 2 1997 2005IN Perry 181230006 37.99433 -86.763457 UNK RUR IND 5 1998 2003IN Perry 181230007 37.983773 -86.772202 UNK RUR IND 5 1998 2003IN Pike 181250005 38.519167 -87.249722 HIC RUR AGR NEI 4 8 1997 2005IN Porter 181270011 41.633889 -87.101389 HIC RUR IND NEI 4 10 1997 2006IN Porter 181270017 41.621944 -87.116389 HIC RUR IND NEI 6 1997 2002IN Porter 181270023 41.616667 -87.145833 HIC SUB IND NEI 6 1997 2002IN Spencer 181470002 37.9825 -86.96638 HIC RUR AGR NEI 5 5 1997 2001IN Spencer 181470010 37.95536 -87.0318 HIC RUR AGR NEI 5 4 2002 2005IN Sullivan 181530004 39.099444 -87.470556 HIC RUR AGR NEI 2 7 1997 2005IN Vanderburgh 181630012 38.021667 -87.569444 POP URB COM NEI 5 10 1997 2006IN Vanderburgh 181631002 37.9025 -87.671389 UNK RUR AGR 9 10 1997 2006IN Vigo 181670018 39.486111 -87.401389 POP URB RES NEI 5 10 1997 2006IN Vigo 181671014 39.514722 -87.407778 HIC RUR COM NEI 5 8 1997 2005IN Warrick 181730002 37.9375 -87.314167 HIC RUR IND NEI 4 5 1997 2006IN Warrick 181731001 37.938056 -87.345833 HIC RUR IND NEI 4 4 1997 2002IN Wayne 181770006 39.812222 -84.89 HIC SUB IND NEI 5 10 1997 2006IN Wayne 181770007 39.795833 -84.880833 HIC RUR IND NEI 9 10 1997 2006IA Cerro Gordo 190330018 43.16944 -93.202426 UNK SUB RES 4 9 1998 2006IA Clinton 190450018 41.824722 -90.212778 UNK SUB RES 4 1 1997 1997IA Clinton 190450019 41.823283 -90.211982 UNK URB IND MID 10 1997 2006IA Clinton 190450020 41.845833 -90.216389 HIC SUB COM URB 7 1 1997 1997IA Lee 191110006 40.392222 -91.4 UNK URB IND 5 2 1997 1998A-19YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastIA Lee 191111007 40.5825 -91.4275 UNK RUR IND 15 2 1998 2000IA Linn 191130028 41.910556 -91.651944 HIC SUB COM NEI 8 5 1997 2001IA Linn 191130029 41.974722 -91.666667 HIC URB COM NEI 16 9 1997 2006IA Linn 191130031 41.983333 -91.662778 SRC URB RES MID 4 10 1997 2006IA Linn 191130032 41.964722 -91.664722 UNK URB RES 2 1998 1999IA Linn 191130034 41.971111 -91.645278 UNK URB RES 2 1998 1999IA Linn 191130038 41.941111 -91.633889 SRC SUB IND MID 4.5 8 1999 2006IA Linn 191130039 41.934167 -91.6825 SRC URB IND 1 2001 2001IA Muscatine 191390016 41.419429 -91.070975 UNK URB RES 3 10 1997 2006IA Muscatine 191390017 41.387969 -91.054504 UNK SUB IND 4 10 1997 2006IA Muscatine 191390020 41.407796 -91.062646 UNK SUB IND 4 10 1997 2006IA Scott 191630015 41.530011 -90.587611 HIC URB RES NEI 4 8 1997 2005IA Scott 191630017 41.467236 -90.688451 UNK RUR IND NEI 4 1 1997 1997IA Van Buren 191770004 40.711111 -91.975278 HIC RUR FOR 3 2 1997 1998IA Van Buren 191770005 40.689167 -91.994444 UNK RUR FOR 3 4 2000 2003IA Van Buren 191770006 40.695078 -92.006318 GEN RUR FOR 3 2 2005 2006IA Woodbury 191930018 42.399444 -96.355833 POP URB RES 3 1 2002 2002KS Linn 201070002 38.135833 -94.731944 REG RUR AGR REG 4 6 1999 2004KS Montgomery 201250006 37.046944 -95.613333 POP URB RES NEI 4 7 1998 2005KS Pawnee 201450001 38.17625 -99.108028 POP SUB RES NEI 3 1 1997 1997KS Sedgwick 201730010 37.701111 -97.313889 POP URB RES NEI 4 1 1997 1997KS Sumner 201910002 37.476944 -97.366389 REG RUR RES REG 4 3 2001 2005KS Trego 201950001 38.770278 -99.763611 GEN RUR AGR REG 4 3 2002 2005KS Wyandotte 202090001 39.113056 -94.624444 HIC URB COM NEI 15 2 1997 1998KS Wyandotte 202090020 39.151389 -94.6175 POP URB IND NEI 9 1 1997 1997KS Wyandotte 202090021 39.1175 -94.635556 POP URB RES NEI 4 4 2000 2005KY Boyd 210190015 38.465833 -82.621111 POP URB RES NEI 4 3 1997 2000KY Boyd 210190017 38.459167 -82.640556 POP SUB RES NEI 3 4 2002 2005KY Boyd 210191003 38.388611 -82.6025 POP SUB IND NEI 5 3 1997 1999KY Campbell 210370003 39.065556 -84.451944 POP SUB RES NEI 4 6 2000 2005KY Campbell 210371001 39.108611 -84.476111 POP URB RES NEI 4 3 1997 1999KY Daviess 210590005 37.780833 -87.075556 POP SUB COM NEI 4 9 1997 2005KY Fayette 210670012 38.065 -84.5 POP SUB RES NEI 4 9 1997 2005A-20YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastKY Greenup 210890007 38.548333 -82.731667 POP SUB RES NEI 4 9 1997 2005KY Hancock 210910012 37.938889 -86.896944 POP RUR RES NEI 4 7 1998 2004KY Henderson 211010013 37.858889 -87.575278 POP SUB RES NEI 4 5 1997 2001KY Henderson 211010014 37.871389 -87.463333 POP RUR COM NEI 4 2 2004 2005KY Jefferson 211110032 38.1825 -85.861667 HIC SUB RES NEI 4 3 1997 2001KY Jefferson 211110051 38.060833 -85.896111 POP SUB RES NEI 4 10 1997 2006KY Jefferson 211111041 38.23163 -85.82672 POP SUB IND NEI 5 8 1997 2006KY Livingston 211390004 37.070833 -88.334167 HIC RUR AGR NEI 4 9 1997 2005KY McCracken 211450001 37.131667 -88.813333 HIC RUR IND NEI 5 3 1997 1999KY McCracken 211451024 37.058056 -88.5725 POP SUB COM NEI 4 6 2000 2005KY McCracken 211451026 37.040833 -88.541111 POP SUB RES NEI 6 2 1997 1998KY Warren 212270008 37.036667 -86.250556 POP RUR RES URB 4 3 2003 2005LA Bossier 220150008 32.53626 -93.74891 POP URB COM 3 10 1997 2006LA Calcasieu 220190008 30.261667 -93.284167 POP RUR IND NEI 5 10 1997 2006LAEast BatonRouge 220330009 30.46198 -91.17922 HIC URB COM NEI 5 10 1997 2006LA Ouachita 220730004 32.509713 -92.046093 GEN URB IND 4 10 1997 2006LA St. Bernard 220870002 29.981944 -89.998611 SRC SUB RES 2 7 1998 2004LAWest BatonRouge221210001 30.501944 -91.209722 HIC SUB COM 2 10 1997 2006ME Androscoggin 230010011 44.089406 -70.214219 HIC URB COM NEI 4 4 1997 2002ME Aroostook 230030009 47.351667 -68.303611 UNK SUB RES 1 1 1997 1997ME Aroostook 230030012 47.354444 -68.314167 UNK URB IND 9 1 1997 1997ME Aroostook 230031003 47.351667 -68.311389 UNK SUB RES 3 1 1997 1997ME Aroostook 230031013 46.123889 -67.829722 UNK URB COM 4 1 1997 1997ME Aroostook 230031018 46.660899 -67.902066 SRC RUR IND NEI 1 2004 2004ME Cumberland 230050014 43.659722 -70.261389 HIC URB COM NEI 4 1 1997 1997ME Cumberland 230050027 43.661944 -70.265833 HIC URB IND NEI 4 7 2000 2006ME Oxford 230172007 44.543056 -70.545833 UNK SUB IND 4 8 1997 2004MD Allegany 240010006 39.649722 -78.762778 POP URB COM NEI 5 1 1997 1997MD Anne Arundel 240032002 39.159722 -76.511667 POP SUB RES NEI 5 4 1999 2002MD Baltimore 240053001 39.310833 -76.474444 POP SUB RES NEI 5 2 2004 2005MD Baltimore (City) 245100018 39.314167 -76.613333 POP URB RES NEI 4 2 1997 1998A-21YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastMD Baltimore (City) 245100036 39.265 -76.536667 HIC URB RES NEI 5 1 1997 1997MA Bristol 250051004 41.683279 -71.169171 HIC SUB COM NEI 5 9 1997 2006MA Essex 250090005 42.709444 -71.146389 HIC URB RES NEI 4 5 1997 2001MA Essex 250091004 42.515556 -70.931389 UNK SUB RES 1 1997 1997MA Essex 250091005 42.525 -70.934167 UNK SUB RES 1 1997 1997MA Essex 250095004 42.772222 -71.061111 OTH SUB RES 9 3 1997 2000MA Hampden 250130016 42.108581 -72.590614 POP URB COM NEI 4 9 1997 2006MA Hampden 250131009 42.085556 -72.579722 HIC SUB RES NEI 5 3 1997 1999MA Hampshire 250154002 42.298279 -72.333904 OTH RUR FOR URB 5 9 1998 2006MA Middlesex 250171701 42.474444 -71.111111 UNK SUB RES 2 1997 1999MA Middlesex 250174003 42.383611 -71.213889 POP RUR AGR NEI 4 2 1997 1998MA Suffolk 250250002 42.348873 -71.097163 HIC URB COM NEI 5 8 1997 2006MA Suffolk 250250019 42.316394 -70.967773 OTH RUR RES 5 9 1997 2005MA Suffolk 250250020 42.309417 -71.055573 OTH URB COM 5 8 1997 2005MA Suffolk 250250021 42.377833 -71.027138 HIC URB RES NEI 4 9 1997 2005MA Suffolk 250250040 42.340251 -71.03835 POP URB IND NEI 4 9 1997 2005MA Suffolk 250250042 42.3294 -71.0825 POP URB COM NEI 5 5 2001 2006MA Suffolk 250251003 42.401667 -71.031111 POP SUB RES NEI 4 3 1997 1999MA Worcester 250270020 42.267222 -71.798889 HIC URB COM NEI 3 4 1998 2002MA Worcester 250270023 42.263877 -71.794186 POP URB COM URB 4 3 2004 2006MI Delta 260410902 45.796667 -87.089444 UNK RUR IND 7 1997 2003MI Genesee 260490021 43.047224 -83.670159 POP URB RES NEI 4 8 1997 2006MI Genesee 260492001 43.168336 -83.461541 GEN RUR AGR 1 2004 2004MI Kent 260810020 42.984173 -85.671339 POP URB IND NEI 5 8 1997 2005MI Macomb 260991003 42.51334 -83.005971 POP SUB RES NEI 3 10 1997 2006MI Missaukee 261130001 44.310555 -84.891865 GEN RUR FOR 1 2003 2003MI St. Clair 261470005 42.953336 -82.456229 HIC SUB RES NEI 4 10 1997 2006MI Schoolcraft 261530001 46.288877 -85.950227 GEN RUR FOR 1 2005 2005MI Wayne 261630001 42.22862 -83.2082 POP SUB COM NEI 4 1 1997 1997MI Wayne 261630005 42.267231 -83.132086 HIC SUB IND NEI 4 4 1997 2000MI Wayne 261630015 42.302786 -83.10653 HIC URB COM NEI 4 9 1997 2006MI Wayne 261630016 42.357808 -83.096033 POP URB RES NEI 4 9 1997 2006MI Wayne 261630019 42.43084 -83.000138 POP SUB RES NEI 4 7 1997 2006A-22YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastMI Wayne 261630025 42.423063 -83.426263 POP SUB COM NEI 4 1 1997 1997MI Wayne 261630027 42.292231 -83.106807 HIC URB IND MID 3 3 1997 1999MI Wayne 261630033 42.306674 -83.148754 HIC SUB IND MID 5 2 1997 1998MI Wayne 261630062 42.340833 -83.0625 POP URB RES NEI 5 1 1997 1997MI Wayne 261630092 42.296111 -83.116944 HIC URB RES MID 7 1 1997 1997MN Anoka 270031002 45.13768 -93.20772 POP SUB RES URB 4.57 4 2003 2006MN Carlton 270176316 46.733611 -92.418889 SRC RUR AGR 3 2 2001 2002MN Dakota 270370020 44.76323 -93.03255 UNK RUR IND NEI 3 8 1997 2006MN Dakota 270370423 44.77553 -93.06299 UNK RUR IND NEI 3.66 9 1997 2006MN Dakota 270370439 44.748039 -93.043266 UNK RUR IND 4 1 1999 1999MN Dakota 270370441 44.7468 -93.02611 UNK RUR IND 3 7 2000 2006MN Dakota 270370442 44.73857 -93.00496 UNK RUR AGR NEI 3.5 6 2001 2006MN Hennepin 270530954 44.980995 -93.273719 HIC URB COM NEI 3 7 1997 2006MN Hennepin 270530957 45.021111 -93.281944 HIC URB IND MID 10 6 1997 2002MN Koochiching 270711240 48.605278 -93.402222 UNK URB IND 10 2 1997 1999MN Ramsey 271230864 44.991944 -93.183056 POP SUB RES NEI 6 6 1997 2002MN Sherburne 271410003 45.420278 -93.871667 UNK RUR AGR 1 1997 1997MN Sherburne 271410011 45.394444 -93.8975 UNK RUR IND NEI 2 1997 1998MN Sherburne 271410012 45.394444 -93.885 UNK URB MOB NEI 1 1997 1997MN Sherburne 271410013 45.369444 -93.898056 UNK RUR IND NEI 2 1997 1998MN Washington 271630436 44.84737 -92.9954 UNK SUB IND MID 4.88 9 1997 2006MN Wright 271710007 45.329167 -93.835833 UNK RUR AGR 1 1997 1997MS Harrison 280470007 30.446806 -89.029139 HIC SUB RES NEI 4 8 1997 2004MS Hinds 280490018 32.296806 -90.188306 POP URB COM NEI 4 9 1997 2005MS Jackson 280590006 30.378425 -88.533985 POP URB COM NEI 7 1997 2006MS Lee 280810004 34.263333 -88.759722 UNK SUB COM 4 1 1997 1997MO Buchanan 290210009 39.731389 -94.8775 GEN URB IND NEI 3 3 1997 1999MO Buchanan 290210011 39.731389 -94.868333 GEN URB IND NEI 3 1 2001 2001MO Clay 290470025 39.183889 -94.4975 POP SUB RES NEI 4 5 1997 2001MO Greene 290770026 37.128333 -93.261667 POP SUB RES 3 10 1997 2006MO Greene 290770032 37.205278 -93.283333 UNK URB RES 3 10 1997 2006MO Greene 290770037 37.11 -93.251944 POP RUR RES 4 10 1997 2006MO Greene 290770040 37.108889 -93.252778 SRC SUB RES 4 2003 2006A-23YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastMO Greene 290770041 37.108611 -93.272222 SRC SUB RES 4 2003 2006MO Iron 290930030 37.466389 -90.69 SRC RUR RES NEI 4 7 1997 2003MO Iron 290930031 37.519444 -90.7125 UNK RUR AGR 2 6 1997 2003MO Jackson 290950034 39.104722 -94.570556 UNK URB COM 9 1997 2006MO Jefferson 290990004 38.2633 -90.3785 POP RUR IND 3 3 2004 2006MO Jefferson 290990014 38.267222 -90.379444 OTH RUR RES NEI 4 4 1997 2000MO Jefferson 290990017 38.252778 -90.393333 UNK SUB RES 5 2 1999 2000MO Jefferson 290990018 38.297694 -90.384333 HIC SUB RES NEI 5 1 2002 2002MO Monroe 291370001 39.473056 -91.789167 UNK RUR UNK 10 1997 2006MO Pike 291630002 39.3726 -90.9144 HIC RUR RES NEI 3 1 2006 2006MO Platte 291650023 39.3 -94.7 UNK SUB MOB 3 8 1997 2004MO Saint Charles 291830010 38.579167 -90.841111 UNK RUR AGR 3 1 1997 1997MO Saint Charles 291831002 38.8725 -90.226389 UNK RUR AGR 2 3 1997 1999MO Saint Louis 291890001 38.521667 -90.343611 POP SUB RES NEI 3 1 1997 1997MO Saint Louis 291890004 38.5325 -90.382778 POP SUB RES NEI 3 6 1999 2004MO Saint Louis 291890006 38.613611 -90.495833 UNK RUR RES 4 8 1997 2004MO Saint Louis 291890014 38.7109 -90.4759 HIC RUR RES NEI 3 1 2006 2006MO Saint Louis 291893001 38.641389 -90.345833 UNK SUB COM 4 10 1997 2006MO Saint Louis 291895001 38.766111 -90.285833 UNK SUB COM 2 8 1997 2004MO Saint Louis 291897002 38.727222 -90.379444 POP SUB RES NEI 4 4 1997 2000MO Saint Louis 291897003 38.720917 -90.367028 POP SUB RES NEI 4 2 2002 2003MO St. Louis City 295100007 38.5425 -90.263611 HIC URB RES NEI 4 10 1997 2006MO St. Louis City 295100072 38.624167 -90.198611 POP URB COM NEI 14 4 1997 2000MO St. Louis City 295100080 38.682778 -90.246667 UNK URB RES 4 3 1997 1999MO St. Louis City 295100086 38.672222 -90.238889 POP URB RES NEI 4 7 2000 2006MT Cascade 300132000 47.532222 -111.271111 SRC SUB AGR 3 3 1997 1999MT Cascade 300132001 47.53 -111.283611 SRC SUB IND NEI 3.5 5 2001 2005MT Jefferson 300430903 46.557679 -111.918098 UNK RUR AGR 4 1997 2000MT Jefferson 300430911 46.548056 -111.873333 UNK RUR AGR 4 4 1997 2000MT Jefferson 300430913 46.534722 -111.861389 UNK RUR AGR 4 4 1997 2000MT Lewis and Clark 300490702 46.583333 -111.934444 UNK RUR AGR 3 4 1997 2000MT Lewis and Clark 300490703 46.593889 -111.92 UNK RUR RES 3 4 1997 2000MT Rosebud 300870700 45.886944 -106.628056 UNK SUB RES 4 4 1998 2001A-24YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastMT Rosebud 300870701 45.901944 -106.637778 UNK RUR AGR 5 3 1997 1999MT Rosebud 300870702 45.863889 -106.557778 UNK RUR AGR 5 2 1997 2000MT Rosebud 300870760 45.668056 -106.518889 SRC RUR FOR 4 5 1998 2003MT Rosebud 300870761 45.603056 -106.464167 SRC RUR FOR 5 1997 2003MT Rosebud 300870762 45.648333 -106.556667 OTH RUR FOR 5 1998 2003MT Rosebud 300870763 45.976667 -106.660556 UNK RUR IND 3 1 1997 1997MT Yellowstone 301110016 45.656389 -108.765833 UNK RUR AGR 4 9 1997 2005MT Yellowstone 301110066 45.788318 -108.459536 SRC RUR RES NEI 3.5 10 1997 2006MT Yellowstone 301110079 45.769439 -108.574292 POP SUB COM 4.5 2 2002 2003MT Yellowstone 301110080 45.777149 -108.47436 UNK RUR AGR 4 4 1997 2000MT Yellowstone 301110082 45.783889 -108.515 POP URB COM NEI 3 2 2002 2003MT Yellowstone 301110083 45.795278 -108.455833 SRC SUB AGR 4 3 2000 2002MT Yellowstone 301110084 45.831453 -108.449964 POP SUB RES NEI 4.5 3 2004 2006MT Yellowstone 301111065 45.801944 -108.426111 UNK SUB RES 4 9 1997 2005MT Yellowstone 301112005 45.803889 -108.445556 UNK SUB IND 4 9 1997 2005MT Yellowstone 301112006 45.81 -108.413056 OTH SUB AGR 3 8 1997 2004MT Yellowstone 301112007 45.832778 -108.377778 OTH RUR RES 3 9 1997 2005NE Douglas 310550048 41.323889 -95.942778 HIC URB RES NEI 5 1 1997 1997NE Douglas 310550050 41.332778 -95.956389 HIC URB RES NEI 6 2 2002 2003NE Douglas 310550053 41.297778 -95.9375 POP URB IND NEI 4 4 2002 2006NE Douglas 310550055 41.362433 -95.976112 HIC SUB RES NEI 8 2 2005 2006NV Clark 320030022 36.390775 -114.90681 REG RUR IND NEI 3.5 5 1998 2002NV Clark 320030078 35.46505 -114.919615 REG RUR DES REG 4 2 2001 2002NV Clark 320030539 36.144444 -115.085556 POP SUB MOB URB 3.5 8 1998 2005NV Clark 320030601 35.978889 -114.844167 POP SUB COM NEI 4 1 2002 2002NH Cheshire 330050007 42.930556 -72.277778 UNK URB COM NEI 7 1997 2003NH Coos 330070019 44.488611 -71.180278 POP UNK UNK NEI 4 5 1997 2001NH Coos 330070022 44.458333 -71.154167 UNK RUR IND 1 1997 1997NH Coos 330071007 44.596667 -71.516667 POP URB IND NEI 5 4 1997 2001NH Hillsborough 330110016 42.992778 -71.459444 HIC URB COM NEI 5 1 1997 1997NH Hillsborough 330110019 43.000556 -71.468056 UNK URB COM 5 1 2000 2000NH Hillsborough 330110020 43.000556 -71.468056 UNK URB COM NEI 5 5 2002 2006NH Hillsborough 330111009 42.764444 -71.4675 HIC URB COM NEI 3 3 1997 2001A-25YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastNH Hillsborough 330111010 42.701944 -71.445 UNK SUB IND MIC 5 6 1997 2002NH Merrimack 330130007 43.206944 -71.534167 UNK URB COM NEI 6 1997 2003NH Merrimack 330131003 43.177222 -71.4625 UNK RUR RES NEI 3 7 1997 2003NH Merrimack 330131006 43.132444 -71.45827 OTH SUB RES NEI 3 4 2003 2006NH Merrimack 330131007 43.218491 -71.45827 OTH URB COM URB 9 1 2005 2005NH Rockingham 330150009 43.078056 -70.762778 UNK SUB COM 3 3 1997 2000NH Rockingham 330150014 43.075278 -70.748056 POP URB RES NEI 2 3 2004 2006NH Rockingham 330150015 43.0825 -70.761944 POP SUB COM NEI 4 1 2002 2002NH Sullivan 330190003 43.364444 -72.338333 UNK URB RES NEI 5 1997 2001NJ Atlantic 340010005 39.53024 -74.46069 UNK RUR RES 4 8 1997 2005NJ Bergen 340035001 40.88237 -74.04217 POP URB COM NEI 4 9 1997 2005NJ Burlington 340051001 40.07806 -74.85772 HIC URB COM NEI 4 9 1997 2005NJ Camden 340070003 39.92304 -75.09762 POP SUB RES NEI 5 8 1997 2005NJ Camden 340071001 39.68425 -74.86149 GEN RUR COM URB 4 9 1997 2005NJ Cumberland 340110007 39.42227 -75.0252 UNK RUR IND 4 9 1997 2005NJ Essex 340130011 40.726667 -74.144167 UNK URB IND 4 2 1997 1998NJ Essex 340130016 40.722222 -74.146944 POP URB IND NEI 5 1 2002 2002NJ Gloucester 340150002 39.80034 -75.21212 UNK RUR AGR 4 9 1997 2005NJ Hudson 340170006 40.67025 -74.12608 POP URB COM NEI 5 9 1997 2005NJ Hudson 340171002 40.73169 -74.06657 HIC URB COM NEI 4 8 1997 2005NJ Middlesex 340232003 40.50888 -74.2682 HIC URB COM NEI 5 9 1997 2005NJ Morris 340273001 40.78763 -74.6763 UNK RUR AGR 5 9 1997 2005NJ Union 340390003 40.66245 -74.21474 POP URB COM MID 5 9 1997 2005NJ Union 340390004 40.64144 -74.20836 HIC SUB IND NEI 4 9 1997 2005NM Dona Ana 350130008 31.930556 -106.630556 UNK RUR AGR 2 6 1997 2002NM Dona Ana 350130017 31.795833 -106.5575 SRC SUB COM URB 9 1997 2005NM Eddy 350151004 32.855556 -104.411389 SRC URB COM NEI 9 1997 2005NM Grant 350170001 32.759444 -108.131389 UNK SUB IND 4 5 1997 2001NM Grant 350171003 32.691944 -108.124444 SRC RUR IND NEI 5 1999 2005NM Hidalgo 350230005 31.783333 -108.497222 UNK RUR UNK 3 5 1997 2001NM San Juan 350450008 36.735833 -108.238333 UNK RUR DES 6 1997 2002NM San Juan 350450009 36.742222 -107.976944 SRC RUR IND NEI 3 1997 2005NM San Juan 350450017 36.752778 -108.716667 UNK RUR UNK 3 1 1997 1997A-26YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastNM San Juan 350451005 36.796667 -108.4725 UNK UNK UNK 9 7 1997 2005NY Albany 360010012 42.68069 -73.75689 HIC RUR AGR NEI 5 10 1997 2006NY Bronx 360050073 40.811389 -73.91 UNK URB RES 13 2 1997 1998NY Bronx 360050080 40.83608 -73.92021 HIC URB RES MID 12 3 1997 1999NY Bronx 360050083 40.86586 -73.88075 UNK URB COM 6 2001 2006NY Bronx 360050110 40.81616 -73.90207 OTH URB RES 5 2000 2006NY Chautauqua 360130005 42.29073 -79.58958 POP URB IND 5 4 1997 2000NY Chautauqua 360130006 42.49945 -79.31888 HIC URB IND NEI 4 7 2000 2006NY Chautauqua 360130011 42.29073 -79.58658 POP RUR AGR REG 4 10 1997 2006NY Chemung 360150003 42.11105 -76.80249 UNK URB COM 4 9 1998 2006NY Erie 360290005 42.87684 -78.80988 POP URB RES NEI 4 10 1997 2006NY Erie 360294002 42.99549 -78.90157 HIC SUB IND NEI 4 10 1997 2006NY Erie 360298001 42.818889 -78.840833 HIC URB IND NEI 4 2 1997 1998NY Essex 360310003 44.39309 -73.85892 GEN RUR FOR NEI 4 10 1997 2006NY Franklin 360330004 44.434309 -74.24601 GEN RUR COM 2 2005 2006NY Hamilton 360410005 43.44957 -74.51625 POP RUR COM URB 5 10 1997 2006NY Herkimer 360430005 43.68578 -74.98538 POP RUR FOR REG 4 8 1997 2006NY Kings 360470011 40.73277 -73.94722 HIC URB IND NEI 13 1 1998 1998NY Kings 360470076 40.67185 -73.97824 POP URB RES 11 2 1997 1999NY Madison 360530006 42.73046 -75.78443 POP RUR AGR REG 10 1997 2006NY Monroe 360551004 43.16545 -77.55479 POP SUB RES NEI 4 7 1997 2003NY Monroe 360551007 43.146198 -77.54813 POP URB RES 2 2005 2006NY Monroe 360556001 43.161 -77.60357 HIC URB COM NEI 12 7 1997 2003NY Nassau 360590005 40.74316 -73.58549 UNK SUB COM NEI 5 9 1997 2006NY New York 360610010 40.739444 -73.986111 HIC URB RES NEI 38 2 1997 1999NY New York 360610056 40.75917 -73.96651 HIC URB COM MID 10 8 1997 2006NY Niagara 360632008 43.08216 -79.00099 POP SUB IND NEI 4 8 1999 2006NY Onondaga 360671015 43.05238 -76.0592 POP SUB COM NEI 5 10 1997 2006NY Putnam 360790005 41.44151 -73.70762 UNK RUR FOR 10 1997 2006NY Queens 360810097 40.75527 -73.75861 GEN URB RES 12 3 1999 2001NY Queens 360810124 40.7362 -73.82317 POP SUB RES 5 2002 2006NY Rensselaer 360830004 42.78187 -73.46361 OTH RUR FOR 3 2002 2004NY Rensselaer 360831005 42.72444 -73.43166 GEN RUR FOR 5 3 1998 2000A-27YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastNY Richmond 360850067 40.59733 -74.12619 POP SUB RES NEI 20 3 1997 1999NY Schenectady 360930003 42.79963 -73.94019 POP SUB RES NEI 5 10 1997 2006NY Suffolk 361030002 40.74529 -73.41919 HIC SUB IND NEI 5 2 1997 1998NY Suffolk 361030009 40.8275 -73.05694 UNK SUB RES 7 2000 2006NY Ulster 361111005 42.1438 -74.49414 POP RUR COM URB 5 9 1997 2006NC Alexander 370030003 35.903611 -81.184167 GEN SUB COM URB 2 1999 2003NC Beaufort 370130003 35.3575 -76.779722 SRC RUR IND NEI 3 3 1997 1999NC Beaufort 370130004 35.377241 -76.748997 HIC RUR FOR NEI 3 2 1997 1998NC Beaufort 370130006 35.377778 -76.766944 SRC RUR IND NEI 3 5 2001 2006NC Chatham 370370004 35.757222 -79.159722 GEN RUR AGR MIC 2 1998 2001NC Cumberland 370511003 34.968889 -78.9625 POP SUB COM NEI 2 1999 2006NC Davie 370590002 35.809289 -80.559115 GEN SUB IND 2 1997 2000NC Duplin 370610002 34.954823 -77.960781 GEN URB RES NEI 1 1999 1999NC Edgecombe 370650099 35.988333 -77.582778 GEN RUR AGR REG 4 2 1999 2004NC Forsyth 370670022 36.110556 -80.226667 POP URB RES NEI 3 8 1997 2004NC Johnston 371010002 35.590833 -78.461944 GEN RUR AGR URB 1 1999 1999NC Lincoln 371090004 35.438556 -81.27675 GEN RUR RES NEI 2 1997 2000NC Martin 371170001 35.81069 -76.89782 GEN RUR AGR URB 5 2 1998 2001NC Mecklenburg 371190034 35.248611 -80.766389 POP SUB RES NEI 5 2 1997 1998NC Mecklenburg 371190041 35.2401 -80.785683 POP URB RES NEI 5 6 2000 2006NC New Hanover 371290002 34.364167 -77.838611 POP RUR AGR URB 3 1 2005 2005NC New Hanover 371290006 34.268403 -77.956529 GEN RUR IND URB 3 10 1997 2006NC Northampton 371310002 36.48438 -77.61998 SRC RUR COM URB 2 1997 2000NC Person 371450003 36.306965 -79.09197 GEN RUR AGR URB 4 2 1998 2004NC Pitt 371470099 35.583333 -77.598889 GEN RUR COM REG 2 1997 2000NC Swain 371730002 35.435509 -83.443697 GEN SUB RES NEI 2 1998 2004ND Billings 380070002 46.8943 -103.37853 GEN RUR AGR REG 12.2 5 2000 2006ND Billings 380070111 47.296667 -103.095556 HIC RUR IND NEI 4 1 1997 1997ND Burke 380130002 48.9904 -102.7815 SRC RUR AGR REG 4 6 2000 2005ND Burke 380130004 48.64193 -102.4018 REG RUR AGR REG 4 3 2004 2006ND Burleigh 380150003 46.825425 -100.76821 POP SUB RES URB 4 1 2006 2006ND Cass 380171003 46.910278 -96.795 POP SUB RES URB 4 1 1997 1997ND Cass 380171004 46.933754 -96.85535 POP SUB AGR URB 3 8 1999 2006A-28YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastND Dunn 380250003 47.3132 -102.5273 GEN RUR AGR REG 4 10 1997 2006ND McKenzie 380530002 47.5812 -103.2995 GEN RUR AGR REG 4 6 1997 2006ND McKenzie 380530104 47.575278 -103.968889 SRC RUR AGR URB 3 9 1998 2006ND McKenzie 380530111 47.605556 -104.017222 SRC RUR IND URB 3 7 2000 2006ND McLean 380550113 47.606667 -102.036389 POP RUR AGR URB 3 6 1998 2003ND Mercer 380570001 47.258853 -101.783035 POP SUB RES NEI 5 1 1997 1997ND Mercer 380570004 47.298611 -101.766944 POP RUR AGR URB 4 8 1999 2006ND Mercer 380570102 47.325 -101.765833 SRC RUR IND URB 3 10 1997 2006ND Mercer 380570118 47.371667 -101.780833 SRC RUR IND URB 3 10 1997 2006ND Mercer 380570123 47.385725 -101.862917 SRC RUR IND URB 4 10 1997 2006ND Mercer 380570124 47.400619 -101.92865 SRC RUR IND URB 4 10 1997 2006ND Morton 380590002 46.84175 -100.870059 SRC SUB IND NEI 4 8 1997 2004ND Morton 380590003 46.873075 -100.905039 SRC SUB IND NEI 4 6 1999 2004ND Oliver 380650002 47.185833 -101.428056 SRC RUR AGR URB 3 9 1997 2006ND Steele 380910001 47.599703 -97.899009 GEN RUR AGR REG 3 2 1997 1999ND Williams 381050103 48.408834 -102.90765 SRC RUR IND URB 4 9 1997 2006ND Williams 381050105 48.392644 -102.910233 SRC RUR IND URB 4 9 1997 2005OH Adams 390010001 38.795 -83.535278 POP SUB RES NEI 5 10 1997 2006OH Allen 390030002 40.772222 -84.051944 POP UNK AGR URB 6 10 1997 2006OH Ashtabula 390071001 41.959444 -80.5725 POP SUB RES URB 8 10 1997 2006OH Belmont 390133002 39.968056 -80.7475 POP SUB IND NEI 6 7 2000 2006OH Butler 390170004 39.383333 -84.544167 POP SUB COM NEI 7 10 1997 2006OH Butler 390171004 39.53 -84.3925 POP SUB COM NEI 4 10 1997 2006OH Clark 390230003 39.855556 -83.9975 POP RUR AGR NEI 4 10 1997 2006OH Clermont 390250021 38.961273 -84.09445 HIC URB RES URB 5 8 1997 2004OH Columbiana 390290016 40.634722 -80.546389 POP SUB RES NEI 7 1 1997 1997OH Columbiana 390290022 40.635 -80.546667 POP SUB COM MIC 6 5 2002 2006OH Columbiana 390292001 40.620278 -80.580833 POP URB COM NEI 20 1 1998 1998OH Cuyahoga 390350038 41.476944 -81.681944 HIC URB IND NEI 4 9 1997 2006OH Cuyahoga 390350045 41.471667 -81.657222 POP URB IND NEI 4 10 1997 2006OH Cuyahoga 390350060 41.493955 -81.678542 POP URB COM NEI 4 10 1997 2006OH Cuyahoga 390350065 41.446389 -81.661944 HIC URB RES NEI 5 9 1998 2006OH Cuyahoga 390356001 41.504722 -81.623889 POP SUB COM NEI 6 6 1997 2002A-29YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastOH Franklin 390490004 39.992222 -83.041667 HIC SUB COM NEI 5 3 1997 1999OH Franklin 390490034 40.0025 -82.994444 POP URB COM NEI 4 9 1997 2006OH Gallia 390530002 38.944167 -82.112222 POP SUB RES NEI 10 5 2002 2006OH Hamilton 390610010 39.214931 -84.690723 POP RUR IND NEI 5 9 1998 2006OH Hamilton 390612003 39.228889 -84.448889 HIC SUB IND NEI 3 1 1997 1997OH Jefferson 390810016 40.362778 -80.615556 POP URB COM NEI 10 4 1999 2002OH Jefferson 390810017 40.366104 -80.615002 HIC URB COM NEI 3 3 2004 2006OH Jefferson 390811001 40.321944 -80.606389 HIC URB IND MID 6 6 1998 2003OH Lake 390850003 41.673056 -81.4225 UNK SUB RES NEI 5 10 1997 2006OH Lake 390853002 41.7225 -81.241944 HIC SUB COM MID 16 10 1997 2006OH Lawrence 390870006 38.520278 -82.666667 POP SUB RES NEI 8 9 1998 2006OH Lorain 390930017 41.368056 -82.110556 POP URB COM NEI 6 3 2001 2003OH Lorain 390930026 41.471667 -82.143611 POP SUB IND NEI 5 6 1997 2002OH Lorain 390931003 41.365833 -82.108333 HIC URB COM NEI 9 3 1997 1999OH Lucas 390950008 41.663333 -83.476667 HIC URB IND NEI 8 7 1998 2006OH Lucas 390950024 41.644167 -83.546667 POP URB IND NEI 8 8 1999 2006OH Mahoning 390990009 41.098333 -80.651944 HIC URB COM NEI 6 3 1997 1999OH Mahoning 390990013 41.096111 -80.658611 GEN URB RES NEI 6 7 2000 2006OH Meigs 391051001 39.037778 -82.045556 POP SUB RES URB 4 10 1997 2006OH Montgomery 391130025 39.758333 -84.2 HIC URB COM NEI 3 7 1997 2003OH Morgan 391150003 39.631667 -81.673056 HIC RUR AGR URB 5 9 1997 2005OH Morgan 391150004 39.634221 -81.670038 SRC RUR AGR URB 4 1 2006 2006OH Scioto 391450013 38.754167 -82.9175 HIC SUB IND MID 10 10 1997 2006OH Scioto 391450020 38.609048 -82.822911 HIC RUR FOR NEI 4 2 2005 2006OH Scioto 391450022 38.588034 -82.834973 UPW RUR IND NEI 4 2 2005 2006OH Stark 391510016 40.827778 -81.378611 HIC SUB RES NEI 5 7 1997 2003OH Summit 391530017 41.063333 -81.468611 HIC SUB IND NEI 4 10 1997 2006OH Summit 391530022 41.080278 -81.516389 POP URB COM NEI 3 10 1997 2006OH Tuscarawas 391570003 40.516389 -81.476389 POP URB IND URB 5 6 1997 2002OH Tuscarawas 391570006 40.511416 -81.639149 POP RUR RES NEI 10 3 2004 2006OK Cherokee 400219002 35.85408 -94.985964 REG RUR RES NEI 3 4 2001 2005OK Kay 400710602 36.705328 -97.087656 UNK URB RES 4 8 1997 2005OK Kay 400719003 36.662778 -97.074444 POP RUR RES NEI 3 2 2002 2003A-30YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastOK Kay 400719010 36.956222 -97.03135 GEN RUR AGR NEI 3 2 2004 2005OK Mayes 400979014 36.228408 -95.249943 GEN RUR AGR NEI 3 1 2005 2005OK Muskogee 401010167 35.793134 -95.302235 SRC RUR COM NEI 5 9 1997 2005OK Oklahoma 401090025 35.553056 -97.623611 POP SUB RES URB 4 4 1999 2002OK Oklahoma 401091037 35.614131 -97.475083 POP SUB RES URB 4 2 2004 2005OK Ottawa 401159004 36.922222 -94.838889 UNK RUR RES NEI 3 2001 2004OK Tulsa 401430175 36.149877 -96.011664 UNK SUB IND NEI 4 9 1997 2005OK Tulsa 401430235 36.126945 -95.998941 SRC URB IND MID 4 9 1997 2005OK Tulsa 401430501 36.16127 -96.015784 UNK URB COM 6 2000 2005PA Allegheny 420030002 40.500556 -80.071944 POP SUB RES NEI 6 8 1997 2006PA Allegheny 420030010 40.445577 -80.016155 POP URB COM URB 4 9 1998 2006PA Allegheny 420030021 40.413611 -79.941389 POP SUB RES NEI 6 7 1997 2006PA Allegheny 420030031 40.443333 -79.990556 POP URB COM NEI 13 3 1997 1999PA Allegheny 420030032 40.414444 -79.942222 UNK SUB RES 5 2 1997 1998PA Allegheny 420030064 40.323611 -79.868333 POP SUB RES NEI 8 10 1997 2006PA Allegheny 420030067 40.381944 -80.185556 GEN RUR RES NEI 9 9 1997 2006PA Allegheny 420030116 40.473611 -80.077222 POP SUB RES NEI 5 7 1997 2005PA Allegheny 420031301 40.4025 -79.860278 HIC SUB RES NEI 9 4 1997 2000PA Allegheny 420033003 40.318056 -79.881111 POP SUB IND 5 7 1997 2005PA Allegheny 420033004 40.305 -79.888889 UNK SUB RES 8 4 1997 2000PA Beaver 420070002 40.56252 -80.503948 REG RUR AGR REG 3 10 1997 2006PA Beaver 420070004 40.635575 -80.230605 HIC URB IND NEI 4 2 1997 1998PA Beaver 420070005 40.684722 -80.359722 POP RUR AGR URB 3 10 1997 2006PA Beaver 420070014 40.747796 -80.316442 POP URB RES URB 4 10 1997 2006PA Berks 420110009 40.320278 -75.926667 HIC SUB RES NEI 4 9 1997 2005PA Berks 420110100 40.335278 -75.922778 UNK URB COM 4 2 1997 1998PA Blair 420130801 40.535278 -78.370833 POP SUB IND NEI 6 10 1997 2006PA Bucks 420170012 40.107222 -74.882222 POP SUB RES NEI 2 10 1997 2006PA Cambria 420210011 40.309722 -78.915 HIC URB COM NEI 12 10 1997 2006PA Centre 420270100 40.811389 -77.877028 POP RUR AGR NEI 3 3 2004 2006PA Dauphin 420430401 40.245 -76.844722 HIC RUR COM NEI 4 10 1997 2006PA Delaware 420450002 39.835556 -75.3725 HIC URB IND NEI 4 10 1997 2006PA Delaware 420450109 39.818715 -75.413973 UNK URB IND 3 1997 1999A-31YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastPA Erie 420490003 42.14175 -80.038611 HIC SUB COM NEI 4 10 1997 2006PA Indiana 420630004 40.56333 -78.919972 POP RUR COM NEI 3 2 2005 2006PA Lackawanna 420692006 41.442778 -75.623056 HIC SUB RES NEI 4 10 1997 2006PA Lancaster 420710007 40.046667 -76.283333 HIC SUB IND NEI 4 10 1997 2006PA Lawrence 420730015 40.995848 -80.346442 POP SUB IND NEI 4 10 1997 2006PA Lehigh 420770004 40.611944 -75.4325 POP SUB COM NEI 3 10 1997 2006PA Luzerne 420791101 41.265556 -75.846389 POP SUB RES NEI 4 9 1997 2005PA Lycoming 420810100 41.2508 -76.9238 POP URB RES URB 3.5 5 2002 2006PA Lycoming 420810403 41.246111 -76.989722 POP URB COM NEI 8 4 1997 2000PA Mercer 420850100 41.215014 -80.484779 POP URB COM NEI 3 9 1997 2006PA Montgomery 420910013 40.112222 -75.309167 POP SUB RES NEI 4 10 1997 2006PA Northampton 420950025 40.628056 -75.341111 POP SUB COM NEI 3 9 1998 2006PA Northampton 420950100 40.676667 -75.216667 UNK SUB IND 3 2 1997 1998PA Northampton 420958000 40.692224 -75.237156 POP SUB RES NEI 4 7 2000 2006PA Perry 420990301 40.456944 -77.165556 GEN RUR UNK REG 4 10 1997 2006PA Philadelphia 421010004 40.008889 -75.097778 POP URB RES NEI 7 8 1997 2004PA Philadelphia 421010022 39.916667 -75.188889 HIC URB IND NEI 7 2 1997 1998PA Philadelphia 421010024 40.076389 -75.011944 UNK SUB IND 4 2 1997 1998PA Philadelphia 421010027 40.010556 -75.151944 UNK URB MOB 5 2 1997 1998PA Philadelphia 421010029 39.957222 -75.173056 POP URB COM NEI 11 8 1997 2004PA Philadelphia 421010047 39.944722 -75.166111 POP URB RES NEI 4 2 1997 1998PA Philadelphia 421010048 39.991389 -75.080833 UNK RUR RES 5 2 1997 1998PA Philadelphia 421010055 39.922517 -75.186783 POP URB RES NEI 4 1 2005 2005PA Philadelphia 421010136 39.9275 -75.222778 POP URB RES NEI 4 7 1997 2004PA Schuylkill 421070003 40.820556 -76.212222 POP RUR RES NEI 4 9 1998 2006PA Warren 421230003 41.857222 -79.1375 HIC SUB RES NEI 4 10 1997 2006PA Warren 421230004 41.844722 -79.169722 HIC RUR FOR NEI 4 10 1997 2006PA Washington 421250005 40.146667 -79.902222 POP SUB COM NEI 2 10 1997 2006PA Washington 421250200 40.170556 -80.261389 POP SUB RES NEI 4 10 1997 2006PA Washington 421255001 40.445278 -80.420833 REG RUR AGR REG 4 10 1997 2006PA Westmoreland 421290008 40.304694 -79.505667 POP SUB COM URB 4 9 1998 2006PA York 421330008 39.965278 -76.699444 HIC SUB RES NEI 4 10 1997 2006RI Providence 440070012 41.825556 -71.405278 POP URB COM NEI 20 10 1997 2006A-32YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastRI Providence 440071005 41.878333 -71.378889 HIC URB RES NEI 6 1 1997 1997RI Providence 440071009 41.823611 -71.411667 HIC URB COM NEI 3 10 1997 2006SC Aiken 450030003 33.342226 -81.788731 HIC SUB RES URB 4.02 2 1997 1998SC Barnwell 450110001 33.320344 -81.465537 SRC RUR FOR URB 3.1 10 1997 2006SC Charleston 450190003 32.882289 -79.977538 POP URB COM NEI 4.3 10 1997 2006SC Charleston 450190046 32.941023 -79.657187 SRC RUR FOR REG 4 8 1997 2006SC Georgetown 450430006 33.362014 -79.294251 SRC URB IND NEI 2.13 7 1997 2006SC Greenville 450450008 34.838814 -82.402918 POP URB COM NEI 4 9 1997 2006SC Greenville 450450009 34.899141 -82.31307 WEL SUB RES NEI 4 2 2005 2006SC Lexington 450630008 34.051017 -81.15495 SRC SUB COM NEI 3.35 9 1997 2006SC Oconee 450730001 34.805261 -83.2377 REG RUR FOR REG 4.3 9 1997 2006SC Orangeburg 450750003 33.29959 -80.442218 SRC RUR FOR NEI 3.2 1 2003 2003SC Richland 450790007 34.093959 -80.962304 OTH SUB COM NEI 3 7 1999 2006SC Richland 450790021 33.81468 -80.781135 GEN RUR FOR URB 4.42 4 2002 2005SC Richland 450791003 34.024497 -81.036248 POP URB COM MID 4 10 1997 2006SC Richland 450791006 33.817902 -80.826596 GEN RUR FOR MIC 5 2 1997 1999SD Custer 460330132 43.5578 -103.4839 REG RUR FOR REG 3.35 2 2005 2006SD Jackson 460710001 43.74561 -101.941218 GEN RUR AGR REG 3 2 2005 2006SD Minnehaha 460990007 43.537626 -96.682001 POP URB RES NEI 4 3 2004 2006TN Anderson 470010028 36.027778 -84.151389 UNK SUB RES 3 8 1997 2006TN Blount 470090002 35.775 -83.965833 HIC RUR COM MID 4 8 1997 2006TN Blount 470090006 35.768056 -83.976667 HIC SUB RES MID 4 8 1997 2006TN Blount 470090101 35.63149 -83.943512 GEN RUR FOR REG 10 1 1999 1999TN Bradley 470110102 35.283164 -84.759371 UNK URB RES 8 1997 2006TN Coffee 470310004 35.582222 -86.015556 UNK RUR AGR 4 1 1998 1998TN Davidson 470370011 36.205 -86.744722 POP URB RES NEI 13 10 1997 2006TN Hawkins 470730002 36.366944 -82.977778 UNK RUR AGR 1 6 1998 2004TN Humphreys 470850020 36.051944 -87.965 UNK RUR AGR 4 8 1997 2006TN McMinn 471070101 35.29733 -84.75076 HIC SUB AGR NEI 4 8 1997 2005TN Montgomery 471250006 36.520056 -87.394167 UNK RUR IND NEI 3 10 1997 2006TN Montgomery 471250106 36.504529 -87.396675 HIC RUR RES MID 4 10 1997 2006TN Polk 471390003 35.026111 -84.384722 POP SUB COM NEI 8 9 1997 2005TN Polk 471390007 34.988333 -84.371667 POP URB COM NEI 1 9 1997 2005A-33YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastTN Polk 471390008 34.995833 -84.368333 UNK RUR RES 3 3 1998 2000TN Polk 471390009 34.989722 -84.383889 UNK RUR IND 4 3 1997 2000TN Roane 471450009 35.947222 -84.522222 UNK SUB RES 4 6 1998 2005TN Shelby 471570034 35.0434 -90.0136 HIC SUB RES NEI 3 4 2002 2005TN Shelby 471570043 35.087778 -90.025278 HIC SUB COM NEI 3 2 1997 1998TN Shelby 471570046 35.272778 -89.961389 POP SUB IND URB 10 1997 2006TN Shelby 471571034 35.087222 -90.133611 UNK RUR AGR MID 3 10 1997 2006TN Stewart 471610007 36.389722 -87.633333 OTH RUR AGR 3 7 1997 2005TN Sullivan 471630007 36.534804 -82.517078 HIC SUB RES NEI 3 9 1998 2006TN Sullivan 471630009 36.513971 -82.560968 HIC RUR RES NEI 3 10 1997 2006TN Sumner 471651002 36.341667 -86.398333 OTH RUR AGR 3 7 1997 2004TX Cameron 480610006 25.892509 -97.493824 HIC URB COM NEI 3 1998 2000TX Dallas 481130069 32.819952 -96.860082 POP URB COM NEI 6 10 1997 2006TX Ellis 481390015 32.436944 -97.025 HIC SUB AGR NEI 4 9 1998 2006TX Ellis 481390016 32.482222 -97.026944 GEN SUB AGR NEI 4 7 1998 2006TX Ellis 481390017 32.473611 -97.0425 OTH RUR RES 1 2005 2005TX El Paso 481410037 31.768281 -106.501253 POP URB COM NEI 3 9 1998 2006TX El Paso 481410053 31.758504 -106.501023 HIC URB COM NEI 5 8 1999 2006TX El Paso 481410058 31.893928 -106.425813 POP URB RES NEI 5 5 2001 2005TX Galveston 481670005 29.385236 -94.931526 HIC URB RES NEI 2 2005 2006TX Galveston 481671002 29.398611 -94.933333 HIC SUB RES NEI 5 7 1997 2003TX Gregg 481830001 32.37871 -94.711834 GEN RUR RES NEI 4 6 2000 2005TX Harris 482010046 29.8275 -95.283611 POP SUB RES NEI 4 8 1997 2006TX Harris 482010051 29.623611 -95.473611 SRC SUB RES NEI 4 8 1997 2006TX Harris 482010059 29.705833 -95.281111 HIC SUB RES NEI 6 1 1997 1997TX Harris 482010062 29.625833 -95.2675 POP SUB RES NEI 5 9 1997 2006TX Harris 482010070 29.735129 -95.315583 GEN SUB RES NEI 11 6 2001 2006TX Harris 482011035 29.733713 -95.257591 POP SUB IND NEI 6 9 1997 2006TX Harris 482011050 29.583032 -95.015535 HIC SUB RES MID 11 5 2002 2006TX Jefferson 482450009 30.036446 -94.071073 HIC SUB RES NEI 6.31 10 1997 2006TX Jefferson 482450011 29.89403 -93.987898 SRC URB IND NEI 4 10 1997 2006TX Jefferson 482450020 30.06607 -94.077383 SRC URB IND NEI 5 8 1998 2006TX Kaufman 482570005 32.564969 -96.31766 HIC SUB COM NEI 5 6 2001 2006A-34YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastTX Nueces 483550025 27.76534 -97.434272 POP URB RES NEI 4 9 1997 2005TX Nueces 483550026 27.832409 -97.555381 HIC URB RES NEI 6 8 1998 2005TX Nueces 483550032 27.804482 -97.431553 POP SUB RES 4 8 1998 2005UT Cache 490050004 41.731111 -111.8375 POP URB COM 4 3 2003 2005UT Davis 490110001 40.886389 -111.882222 POP SUB COM 3 6 1997 2002UT Davis 490110004 40.902967 -111.884467 POP SUB RES NEI 4 2 2004 2005UT Salt Lake 490350012 40.8075 -111.921111 UNK SUB IND 4 6 1999 2004UT Salt Lake 490351001 40.708611 -112.094722 HIC SUB RES NEI 6 9 1997 2005UT Salt Lake 490352004 40.736389 -112.210278 HIC RUR IND 7 1997 2003VT Chittenden 500070003 44.478889 -73.211944 HIC URB COM NEI 4 3 1997 1999VT Chittenden 500070014 44.4762 -73.2106 POP URB COM MID 1 2004 2004VT Rutland 500210002 43.608056 -72.982778 POP URB COM NEI 4 7 1997 2005VA Charles 510360002 37.343294 -77.260034 HIC SUB RES NEI 5 10 1997 2006VA Fairfax 510590005 38.893889 -77.465278 POP RUR AGR NEI 4 9 1997 2006VA Fairfax 510590018 38.7425 -77.0775 UNK SUB RES 4 1 1997 1997VA Fairfax 510591004 38.868056 -77.143056 UNK SUB COM 11 4 1997 2000VA Fairfax 510591005 38.837517 -77.163231 POP SUB RES 4 2003 2006VA Fairfax 510595001 38.931944 -77.198889 UNK SUB RES 4 9 1998 2006VA Madison 511130003 38.521944 -78.436111 UNK RUR FOR 3 2000 2003VA Roanoke 511611004 37.285556 -79.884167 HIC SUB RES NEI 4 10 1997 2006VA Rockingham 511650002 38.389444 -78.914167 POP RUR AGR NEI 7 6 1998 2003VA Rockingham 511650003 38.47732 -78.81904 POP SUB COM NEI 6 2 2005 2006VA Alexandria City 515100009 38.810833 -77.044722 POP URB RES NEI 10 10 1997 2006VA Hampton City 516500004 37.003333 -76.399167 HIC SUB RES NEI 4 10 1997 2006VA Norfolk City 517100023 36.850278 -76.257778 POP URB COM NEI 5 8 1997 2004VA Richmond City 517600024 37.562778 -77.465278 HIC URB COM NEI 5 8 1999 2006WA Clallam 530090010 48.113333 -123.399167 UNK SUB RES 4 1 1997 1997WA Clallam 530090012 48.0975 -123.425556 UNK SUB RES NEI 5 5 1999 2004WA King 530330057 47.563333 -122.3406 HIC SUB IND NEI 11 2 1997 1998WA King 530330080 47.568333 -122.308056 POP URB RES URB 5 4 2001 2004WA Pierce 530530021 47.281111 -122.374167 HIC SUB RES NEI 5 1 1997 1997WA Pierce 530530031 47.2656 -122.3858 POP SUB IND NEI 5 1 1997 1997WA Skagit 530570012 48.493611 -122.551944 UNK SUB RES 5 1 1997 1997A-35YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastWA Skagit 530571003 48.486111 -122.549444 UNK RUR IND 3 1 1997 1997WA Snohomish 530610016 47.983333 -122.209722 UNK URB COM 4 1 1997 1997WA Whatcom 530730011 48.750278 -122.482778 UNK URB IND 12 1 1998 1998WV Brooke 540090005 40.341023 -80.596635 POP SUB IND NEI 4 10 1997 2006WV Brooke 540090007 40.389655 -80.586235 POP RUR RES NEI 4 10 1997 2006WV Cabell 540110006 38.424133 -82.4259 POP SUB COM NEI 13.6 10 1997 2006WV Greenbrier 540250001 37.819444 -80.5125 UNK RUR AGR 4 1 1997 1997WV Hancock 540290005 40.529021 -80.576067 POP SUB RES URB 4 10 1997 2006WV Hancock 540290007 40.460138 -80.576567 POP RUR RES URB 10 1997 2006WV Hancock 540290008 40.61572 -80.56 POP SUB RES NEI 5 10 1997 2006WV Hancock 540290009 40.427372 -80.592318 POP SUB RES NEI 10 1997 2006WV Hancock 540290011 40.394583 -80.612017 POP SUB RES NEI 10 1997 2006WV Hancock 540290014 40.43552 -80.600579 POP SUB RES MID 7 1997 2003WV Hancock 540290015 40.618353 -80.540616 POP URB RES URB 4 10 1997 2006WV Hancock 540290016 40.411944 -80.601667 HIC SUB RES 4 7 1997 2003WV Hancock 540291004 40.421539 -80.580717 HIC SUB RES NEI 3 10 1997 2006WV Kanawha 540390004 38.343889 -81.619444 POP SUB COM NEI 8 2 1997 1998WV Kanawha 540390010 38.3456 -81.628317 POP URB COM URB 13 6 2001 2006WV Kanawha 540392002 38.416944 -81.846389 HIC SUB IND NEI 4 1 1997 1997WV Marshall 540511002 39.915961 -80.733858 POP SUB RES URB 4 10 1997 2006WV Monongalia 540610003 39.649367 -79.920867 POP SUB COM URB 4.6 10 1997 2006WV Monongalia 540610004 39.633056 -79.957222 UNK SUB RES 4 1997 2000WV Monongalia 540610005 39.648333 -79.957778 UNK SUB RES URB 10.7 9 1997 2005WV Ohio 540690007 40.12043 -80.699265 HIC SUB RES NEI 8 6 1997 2002WV Wayne 540990002 38.39186 -82.583923 POP RUR IND NEI 4 6 1997 2002WV Wayne 540990003 38.390278 -82.585833 HIC RUR RES NEI 3 8 1997 2005WV Wayne 540990004 38.380278 -82.583889 HIC RUR RES NEI 3 8 1997 2005WV Wayne 540990005 38.372222 -82.588889 HIC RUR RES NEI 3 8 1997 2005WV Wood 541071002 39.323533 -81.552367 POP SUB IND URB 4 10 1997 2006WI Brown 550090005 44.516667 -87.993889 POP URB RES NEI 11 7 1997 2005WI Dane 550250041 43.100833 -89.357222 POP URB RES NEI 5 2 1997 1998WI FOR 550410007 45.56498 -88.80859 GEN RUR FOR REG 6 2 2004 2005WI Marathon 550730005 45.028333 -89.652222 HIC RUR FOR MID 5 3 1997 1999A-36YearsState County Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4Heightn First LastWI Milwaukee 550790007 43.047222 -87.920278 POP URB COM NEI 7 4 1997 2000WI Milwaukee 550790026 43.061111 -87.9125 POP URB COM NEI 9 4 2002 2005WI Milwaukee 550790041 43.075278 -87.884444 HIC URB RES NEI 7 4 1997 2001WI Oneida 550850996 45.645278 -89.4125 UNK URB IND 6 9 1997 2005WI Sauk 551110007 43.435556 -89.680278 GEN RUR FOR REG 6 1 2003 2003WI Vilas 551250001 46.048056 -89.653611 GEN RUR FOR REG 15 1 2003 2003WI Wood 551410016 44.3825 -89.819167 POP URB RES NEI 7 2 1998 1999WY Campbell 560050857 44.277222 -105.375 SRC RUR IND NEI 4 3 2002 2004PR Barceloneta 720170003 18.436111 -66.580556 UNK RUR RES 3 5 1997 2005PR Bayamon 720210004 18.412778 -66.132778 HIC SUB IND NEI 6 1997 2004PR Bayamon 720210006 18.416667 -66.150833 POP SUB IND NEI 3 7 1997 2005PR Catano 720330004 18.430556 -66.142222 UNK SUB RES 4 7 1997 2005PR Catano 720330007 18.444722 -66.116111 POP URB RES NEI 2 1 2002 2002PR Catano 720330008 18.440028 -66.127076 POP URB COM 1 2005 2005PR Catano 720330009 18.449964 -66.149043 POP URB RES 1 2005 2005PR Guayama 720570009 17.966844 -66.188014 SRC RUR COM NEI 4 4 2002 2005PR Salinas 721230001 17.963002 -66.254749 SRC RUR AGR 1 2004 2004VI St Croix 780100006 17.706944 -64.780556 HIC RUR IND NEI 4 5 1998 2004VI St Croix 780100011 17.719167 -64.775 HIC RUR IND NEI 4 5 1997 2004VI St Croix 780100013 17.7225 -64.776667 POP SUB RES NEI 5 1999 2004VI St Croix 780100014 17.734444 -64.783333 POP RUR AGR NEI 4 5 1999 2004VI St Croix 780100015 17.741667 -64.751944 SRC RUR AGR NEI 4 4 2000 2004Notes:1Objectives are POP=Population Exposure; HIC=Highest Concentration; SRC=Source Oriented; GEN=General/Background; REG=RegionalTransport; OTH=Other; UNK=Unknown; UPW=Upwind Background; WEL=Welfare Related Impacts2Settings are R=Rural; U=Urban and Center City; S=Suburban3Land Uses are AGR=Agricultural; COM=Commercial; IND=Industrial; FOR=Forest; RES=Residential; UNK=Unknown; DES=Desert; MOB=Mobile.4Scales are NEI=Neighborhood; MID=Middle; URB=URBAN; REG=Regional; MIC=MicroA-37Table A.1-4. Population density, concentration variability, and total SO2emissions associated with 809 ambientmonitors in the broader SO2monitoring network.Population Residing within:Analysis BinsState CountyMonitor ID5km 10km 15km 20km Population1COV2GSD3Emissions(tpy)4AL Colbert 010330044 2195 7954 25394 62838 low c a 50041AL Jackson 010710020 1902 8137 19317 29686 low c b 45357AL Jefferson 010731003 76802 196682 344386 489181 hi b b 6478AL Lawrence 010790003 3952 28674 73092 91057 low b b 8937AL Mobile 010970028 5966 7758 17087 39111 low c c 66130AL Mobile 010972005 3017 18106 52682 111608 low b a 1187AL Montgomery 011011002 45389 156786 213606 259730 mod a a 3650AR Pulaski 051190007 67784 178348 270266 334649 hi a a 20AR Pulaski 051191002 45800 109372 230200 310362 mod a a 20AR Union051390006 21877 29073 32652 36340 mod b a 2527AZ Gila040070009 7801 14076 17280 17633 low b bAZ Gila040071001 1359 1359 3098 5401 low c c 18438AZ Maricopa 040130019 197458 613618 1036233 1447648 hi a b 186AZ Maricopa 040133002 144581 490123 980730 1612687 hi a a 185AZ Maricopa 040133003 91955 340325 829051 1518806 hi a a 180AZ Pima040191011 111215 354473 561487 639921 hi a a 3119AZ Pinal040212001 4375 7679 9577 10125 low c aCA Alameda 060010010 236320 532827 841443 1342267 hi a a 369CA Contra Costa 060130002 136288 303088 445297 598861 hi b a 15056CA Contra Costa 060130006 119088 231479 471471 968983 hi b a 5032CA Contra Costa 060130010 29809 123220 403137 685185 mod a a 17834CA Contra Costa 060131001 53051 181259 321500 610171 hi b a 19592CA Contra Costa 060131002 4033 39708 117118 173196 low a a 79CA Contra Costa 060131003 146336 256417 420619 856435 hi a a 5032CA Contra Costa 060131004 125350 233220 433669 876585 hi a a 5032CA Contra Costa 060132001 34743 155226 433934 807706 mod a a 17834CA Contra Costa 060133001 64019 152758 303597 478310 hi b a 8105CA Imperial 060250005 27033 31895 56234 84405 mod b c 7CA Los Angeles 060371002 167653 827729 2001363 3286038 hi a a 51CA Los Angeles 060371103 378843 1618324 3027507 4530714 hi a a 551A-38Population Residing within:Analysis BinsState CountyMonitor ID5km 10km 15km 20km Population1COV2GSD3Emissions(tpy)4CA Los Angeles 060374002 240505 913176 1850549 3218392 hi a b 5869CA Los Angeles 060375001 276378 890302 2071144 3561110 hi a a 6282CA Los Angeles 060375005 94836 652173 1628468 2848126 hi a a 2304CA Orange 060591003 200253 744882 1303743 1829713 hi a a 68CA Riverside 060658001 78757 360234 734267 1141466 hi a a 299CA Sacramento 060670002 92433 328190 645533 916197 hi a a 5CA Sacramento 060670006 132584 472019 866437 1180898 hi a a 58CA San Bernardino 060710012 6720 17620 29756 69717 low a a 8CA San Bernardino 060710014 58937 114149 193928 224008 hi a a 251CA San Bernardino 060710306 59772 114149 193046 224008 hi a a 251CA San Bernardino 060711234 0 0 1911 1911 low a a 290CA San Bernardino 060712002 89732 314392 650533 1142460 hi a a 203CA San Bernardino 060714001 40799 114888 174610 219525 mod a a 32CA San Diego 060730001 168237 528890 866015 1177835 hi a a 21CA San Diego 060731007 169117 616102 1097387 1449106 hi a a 34CA San Diego 060732007 9376 15849 218480 452120 low a a 21CA San Francisco 060750005 433367 827164 1227784 1729715 hi a a 399CA San Luis Obispo 060791005 4725 56677 85064 152491 low c b 3755CA San Luis Obispo 060792001 39236 55657 61709 121393 mod a a 3755CA San Luis Obispo 060792004 2135 34056 113260 162669 low b c 3755CA San Luis Obispo 060794002 0 51508 95245 141786 low b b 3755CA Santa Barbara 060830008 655 1678 17486 67965 low a a 118CA Santa Barbara 060831012 0 0 960 3201 low a a 1109CA Santa Barbara 060831013 6617 41576 59590 89777 low a a 1109CA Santa Barbara 060831015 0 0 2391 17826 low a a 18CA Santa Barbara 060831016 0 0 4034 17826 low a a 18CA Santa Barbara 060831019 0 0 4689 17826 low a a 18CA Santa Barbara 060831020 39222 71015 117832 170206 mod a a 118CA Santa Barbara 060831025 655 1678 11216 56132 low a a 118CA Santa Barbara 060831026 655 1678 15659 63963 low a a 118CA Santa Barbara 060831027 655 1678 13618 62298 low b a 118CA Santa Barbara 060832004 38688 49356 58271 59279 mod a a 1109CA Santa Barbara 060832011 55496 105491 170865 181894 hi a a 118A-39Population Residing within:Analysis BinsState CountyMonitor ID5km 10km 15km 20km Population1COV2GSD3Emissions(tpy)4CA Santa Barbara 060834003 0 0 8430 51692 low a a 1109CA Santa Cruz 060870003 0 6016 51831 124792 low a a 722CA Solano 060950001 27872 130319 359105 620107 mod a a 17821CA Solano 060950004 102003 166693 247861 374613 hi a a 17763CA Ventura 061113001 47248 227525 401656 427503 mod a b 19CO Adams 080010007 45071 360261 903964 1344766 mod b b 24028CO Adams 080013001 81896 334611 784343 1205604 hi b b 23817CO Denver 080310002 189782 574752 1158644 1608099 hi b b 26354CO El Paso 080416001 0 24520 54194 111518 low b b 5010CO El Paso 080416004 84979 242841 368203 430076 hi a a 8547CO El Paso 080416011 97849 288563 407401 448545 hi b b 8547CO El Paso 080416018 93065 266008 388801 438812 hi a a 8537CT Fairfield 090010012 164887 291072 393358 528453 hi b b 4671CT Fairfield 090010017 30184 188214 330125 672435 mod b b 757CT Fairfield 090011123 72689 126452 191805 277225 hi a bCT Fairfield 090012124 121109 209567 343909 476656 hi b b 766CT Fairfield 090019003 28181 151905 313449 546288 mod b b 5039CT Hartford 090031005 33414 147625 319902 484462 mod a b 1268CT Hartford 090031018 152497 329646 523045 693079 hi a b 113CT Hartford 090032006 91965 333744 510929 671515 hi a b 83CT New Haven 090090027 140329 290735 389117 529118 hi b b 4761CT New Haven 090091003 156879 293853 414381 552021 hi b b 5085CT New Haven 090091123 154781 292598 417546 557442 hi b b 5085CT New Haven 090092123 104191 189838 276310 447334 hi a b 430CT New London 090110007 58457 97870 141173 182476 hi a a 3898CT Tolland 090130003 23441 47285 78649 115317 mod a aDC District of Columbia 110010041 216129 813665 1461563 2029936 hia a 18325DE New Castle 100031003 68790 223079 369450 603736 hi b b 33133DE New 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223185OH Jefferson 390810017 30069 71838 96408 122094 mod b c 223185OH Jefferson 390811001 21833 53684 91514 119322 mod b c 78071OH Lake390850003 48791 145694 238216 407417 mod b b 72266OH Lake390853002 40430 92415 141902 209471 mod b c 4799OH Lawrence 390870006 26563 71376 94538 131453 mod b b 11400OH Lorain390930017 58361 129195 249878 362235 hi b b 495OH Lorain390930026 54867 114602 202571 298148 hi b b 53OH Lorain390931003 58580 124277 251182 365323 hi b b 495OH Lucas390950008 62606 205665 356815 487567 hi b b 37337OH Lucas390950024 134960 319708 466184 528531 hi b b 37450OH Mahoning 390990009 79207 210961 293714 378289 hi b b 21074OH Mahoning 390990013 78376 214611 294367 375287 hi b b 21074OH Meigs391051001 5440 15029 21812 31834 low b b 190311OH Montgomery 391130025 123978 304826 511565 645130 hi b b 9652OH Morgan 391150003 1122 3168 9162 22426 low c c 115526OH Morgan 391150004 1122 3168 9871 24252 low c b 115526OH Scioto391450013 15699 47369 61292 77940 mod b bOH Scioto391450020 4530 11216 45697 87756 low b c 4351OH Scioto391450022 3469 12081 40548 82103 low b c 4351OH Stark391510016 99075 208779 291216 350367 hi a b 1269OH Summit 391530017 104817 292059 470747 574282 hi b c 11053OH Summit 391530022 140332 329963 454363 570258 hi b b 11053OH Tuscarawas 391570003 26914 40238 61526 85938 mod b b 2579OH Tuscarawas 391570006 2710 15439 38518 72765 low b b 2556OK Cherokee 400219002 993 22584 28182 36130 low c aOK Kay400710602 25029 31461 31461 36740 mod b b 7003OK Kay400719003 6614 29697 32746 35459 low b b 7003OK Kay400719010 1123 3516 16273 20121 low b aOK Mayes400979014 1947 14224 26265 29243 low a b 19079A-55Population Residing within:Analysis BinsState CountyMonitor ID5km 10km 15km 20km Population1COV2GSD3Emissions(tpy)4OK Muskogee 401010167 5633 39252 56271 64455 low b b 30011OK Oklahoma 401090025 78654 254952 384825 552894 hi a a 182OK Oklahoma 401091037 46197 141934 258441 459371 mod a a 182OK Ottawa 401159004 6272 22614 29716 37508 low b a 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76679 176978 low b b 86717VA Fairfax 510590005 34561 183637 408647 687195 mod a b 156VA Fairfax 510590018 87725 293189 730360 1388941 hi a b 18204VA Fairfax 510591004 215952 660586 1410007 2092422 hi a a 18303VA Fairfax 510591005 203219 670880 1238334 1844099 hi a a 18405VA Fairfax 510595001 80603 358173 1098236 2041931 hi a a 17221VA Madison 511130003 1316 4823 13930 28417 low b c 7VA Roanoke 511611004 33161 123148 197615 235072 mod a a 677VA Rockingham 511650002 17897 58020 76316 85276 mod a a 277VA Rockingham 511650003 13821 47577 71219 92912 mod a a 235VA Alexandria City 515100009 137533 622283 1320784 1894197 hi b b 18293VA Hampton City 516500004 73011 182507 356676 601943 hi b b 4274VA Norfolk City 517100023 124263 379455 703082 871632 hi b b 36499VA Richmond City 517600024 109306 309672 524083 656099 hi b b 2675VI St Croix 780100006 0 0 0 0 low b cVI St Croix 780100011 0 0 0 0 low b cVI St Croix 780100013 0 0 0 0 low c cA-60Population Residing within:Analysis BinsState CountyMonitor ID5km 10km 15km 20km Population1COV2GSD3Emissions(tpy)4VI St Croix 780100014 0 0 0 0 low c cVI St Croix 780100015 0 0 0 0 low c cVT Chittenden 500070003 50990 89229 110853 133530 hi b a 6VT Chittenden 500070014 54166 87471 110853 133749 hi a a 6VT Rutland 500210002 21330 30052 35316 46525 mod b cWA Clallam 530090010 17871 26073 30255 37672 mod b b 756WA Clallam 530090012 20830 27014 30036 36843 mod b a 756WA King530330057 131605 394412 730218 1093083 hi a a 1203WA King530330080 116769 423064 811856 1157199 hi b a 1203WA Pierce530530021 68072 250876 548806 839357 hi b b 538WA Pierce530530031 55628 275358 555755 820805 hi b a 538WA Skagit530570012 3580 22573 32120 70660 low b b 8951WA Skagit530571003 1733 21622 32120 75069 low b b 8951WA Snohomish 530610016 46071 152230 303720 432356 mod a a 381WA Whatcom 530730011 60525 83632 111425 126291 hi a a 4391WI Brown 550090005 79060 158940 201226 215144 hi b b 23888WI Dane550250041 79610 189421 306132 353861 hi b b 9049WI Forest 550410007 1330 3913 5514 6669 low b a 5WI Marathon 550730005 5095 42173 61417 93151 low c a 12120WI Milwaukee 550790007 248317 606921 865925 1037293 hi b b 15753WI Milwaukee 550790026 214859 572784 834939 1014161 hi b a 15753WI Milwaukee 550790041 137816 455868 765734 964876 hi b b 15753WI Oneida 550850996 8351 17018 17018 23821 low c c 2304WI Sauk551110007 2743 15039 24240 43368 low b a 63WI Vilas551250001 934 934 8639 10755 low a aWI Wood551410016 19525 33790 43315 50360 mod c b 14245WV Brooke 540090005 25010 64711 92813 118070 mod b b 78071WV Brooke 540090007 30794 70187 95823 120385 mod b b 223185WV Cabell540110006 50835 88879 125923 164495 hi b b 7504WV Greenbrier 540250001 2158 9273 18280 23902 low a aWV Hancock 540290005 6006 23418 77160 125873 low b b 176554WV Hancock 540290007 14924 44311 83167 128126 mod b b 148520WV Hancock 540290008 24095 49351 63727 91485 mod b b 186262A-61Population Residing within:Analysis BinsState CountyMonitor ID5km 10km 15km 20km Population1COV2GSD3Emissions(tpy)4WV Hancock 540290009 20946 61117 90717 115283 mod b b 148404WV Hancock 540290011 31890 76198 95992 115162 mod b b 223185WV Hancock 540290014 22857 58620 89998 120724 mod b b 148520WV Hancock 540290015 20793 45848 65851 102031 mod b b 186262WV Hancock 540290016 19278 70483 96151 114992 mod b b 169771WV Hancock 540291004 24761 63677 91977 115615 mod b b 169771WV Kanawha 540390004 46977 80511 120631 164476 mod b b 6115WV Kanawha 540390010 48231 83340 123101 172217 mod b b 6115WV Kanawha 540392002 21694 61059 111812 164912 mod b b 113491WV Marshall 540511002 13403 32048 55054 95735 mod b c 138904WV Monongalia 540610003 43902 65672 80405 98315 mod b b 91984WV Monongalia 540610004 44079 63708 80385 98966 mod b b 97887WV Monongalia 540610005 46591 61019 77800 99544 mod b b 96396WV Ohio540690007 20818 60048 91967 126981 mod b b 74781WV Wayne540990002 17320 62645 124477 178576 mod a b 10172WV Wayne540990003 17320 59989 123349 177744 mod b b 10172WV Wayne540990004 16553 54251 122072 179815 mod b b 10172WV Wayne540990005 13314 48330 114824 173807 mod b b 10172WV Wood541071002 24917 70324 104458 128127 mod b b 48124WY Campbell 560050857 3288 11413 23902 25752 low b b 10106Notes:1Population bins: low (≤10,000); mid (10,001 to 50,000); hi (>50,000) using population within 5 km of ambient monitor.2COV bins: a (≤100%); b (>100 to≤200); c (>200).3GSD bins: a (≤2.17); b (>2.17 to≤2.94); c (>2.94).4Sum of emissions within 20 km radius of ambient monitor based on 2002 NEI.A-62 A.1.2 Analysis of SO 2 Emission Sources Surrounding Ambient Monitors Distances of the 5-minute and 1-hour ambien t monitoring sites to stationary sources emitting SO 2 were estimated using data from the 2002 National Emissions Inventory 1 (NEI). The NEI database reports emissions of SO 2 in tons per year (tpy) for 98,667 unique emission sources at various points of release. The re lease locations were all taken from the latitude longitude values within the NEI. First, all SO 2 emissions were summed for identical latitude and longitude entries while retaini ng source codes for the emissions (e.g., Standard Industrial Code (SIC), or North American Industr ial Classification System (NAICS )). Therefore, any facility containing similar emission processes were summ ed at the stack loca tion, resulting in 32,521

observations. These data were then screened for sources with emissi ons greater than 5 tpy, yielding 6,104 unique SO 2 emission sources. Locations of these stationary source emissions were compared with ambient monitoring locations using the following formula:



##   rlonlon lat lat lat lat d 

      )) cos()cos()cos()sin( arccos(sin 12 2 1 2 1 where

d = distance (kilometers)

lat 1 = latitude of a monitor (radians)

lat 2 = latitude of sour ce emission (radians)

lon 1 = longitude of monitor (radians)

lon 2 = longitude of source emission (radians)

r

= approximate radius of the earth (or 6,371 km)

Location data for monitors and sources provi ded in the AQS and NEI data bases were given in units of degrees ther efore, these were first convert ed 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 stationa ry source emissions to each

of the monitors in the broader SO 2 monitoring network. There were varying numbers of sources emitting >5 tpy of SO 2 and located within a 20 km radius fo r many of the monitors. Some of the monitors are point-source oriented, that is, site d 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: h ttp://www.epa.gov/ttn/chief/n et/2002inventory.html.A-63 influenced by a specific single sour ces (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 monito rs contained no source emissions >5 tpy (e.g., Iowa monitor IDs 191770005, 191770006)

.A-64Table A.1-5. Distance of ambient SO2monitors (all usedin analysis) to stationary sources emitting > 5 tons of SO2per year, within a 20 kilometerdistance of monitoring site, and SO2emissions associated with those stationarysources.SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100010330044 3 16680 28821 30 30 51 49960 49960 6.0 0.7 5.5 5.5 5.9 6.8 6.8010710020 3 15119 25004 98 98 1276 43983 43983 5.7 2.4 3.1 3.1 6.2 7.8 7.8010731003 43 151 227 5 5 38 786 982 11.4 5.5 1.1 1.2 13.1 16.8 19.8010790003 5 1787 3416 6 6 58 7852 7852 8.4 1.7 5.5 5.5 8.6 9.8 9.8010970028 10 6613 13057 14 14 214 38917 38917 7.5 5.8 1.4 1.4 6.1 19.1 19.1010972005 9 132 154 5 5 72 440 440 7.7 2.1 4.3 4.3 7.2 10.1 10.1011011002 4 913 1183 180 180 403 2663 2663 12.7 7.2 4.5 4.5 13.2 19.9 19.9040070009 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0040071001 2 9219 10723 1637 1637 9219 16801 16801 1.3 0.5 0.9 0.9 1.3 1.6 1.6040130019 8 23 19 10 10 19 69 69 11.0 3.6 5.6 5.6 10.2 16.9 16.9040133002 9 21 19 10 10 14 69 69 10.8 6.6 1.9 1.9 11.2 19.2 19.2040133003 9 20 19 6 6 14 69 69 12.5 4.7 5.5 5.5 12.4 18.5 18.5040191011 1 3119 3119 3119 3119 3119 3119 6.1 0.0 6.1 6.1 6.1 6.1 6.1040212001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0051190007 1 20 20 20 20 20 20 6.3 0.0 6.3 6.3 6.3 6.3 6.3051191002 1 20 20 20 20 20 20 13.7 0.0 13.7 13.7 13.7 13.7 13.7051390006 6 421 689 8 8 22 1689 1689 7.7 4.2 1.9 1.9 8.8 11.7 11.7060010010 7 53 66 5 5 14 187 187 8.9 5.4 1.2 1.2 9.0 16.8 16.8060130002 15 1004 2007 6 6 58 7009 7009 13.5 2.8 9.6 9.6 13.3 17.8 17.8060130006 9 559 789 5 5 38 1829 1829 13.0 6.4 2.5 2.5 15.0 19.3 19.3060130010 15 1189 1977 6 6 419 7009 7009 8.3 5.6 1.6 1.6 6.4 19.7 19.7060131001 13 1507 2036 6 6 793 7009 7009 10.1 5.9 0.2 0.2 9.9 19.8 19.8060131002 3 26 21 6 6 25 48 48 11.7 1.4 10.1 10.1 12.4 12.7 12.7060131003 9 559 789 5 5 38 1829 1829 12.6 4.8 5.4 5.4 12.2 19.0 19.0060131004 9 559 789 5 5 38 1829 1829 12.8 5.7 4.1 4.1 13.5 19.1 19.1060132001 15 1189 1977 6 6 419 7009 7009 8.8 5.4 2.3 2.3 6.7 19.9 19.9060133001 16 507 1104 6 6 48 4337 4337 9.8 6.1 0.7 0.7 11.4 18.6 18.6060250005 1 7 7 7 7 7 7 18.0 0.0 18.0 18.0 18.0 18.0 18.0060371002 3 17 7 10 10 17 24 24 6.8 2.1 4.7 4.7 6.9 8.8 8.8060371103 15 37 36 7 7 29 119 119 13.8 5.1 6.3 6.3 12.5 19.8 19.8060374002 32 183 313 5 5 46 1503 1503 10.4 5.2 4.1 4.1 9.3 19.5 19.5060375001 31 203 342 5 5 61 1503 1503 13.4 5.9 3.7 3.7 16.4 19.6 19.6A-65SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100060375005 12 192 332 6 6 33 1119 1119 9.1 5.9 2.3 2.3 6.0 19.8 19.8060591003 7 10 5 5 5 7 18 18 13.9 5.7 5.3 5.3 15.6 19.7 19.7060658001 4 75 76 17 17 50 181 181 16.8 4.7 9.8 9.8 18.8 19.6 19.6060670002 1 5 5 5 5 5 5 14.8 0.0 14.8 14.8 14.8 14.8 14.8060670006 1 58 58 58 58 58 58 9.7 0.0 9.7 9.7 9.7 9.7 9.7060710012 1 8 8 8 8 8 8 11.9 0.0 11.9 11.9 11.9 11.9 11.9060710014 2 126 132 32 32 126 219 219 8.0 3.0 5.9 5.9 8.0 10.1 10.1060710306 2 126 132 32 32 126 219 219 8.1 3.3 5.7 5.7 8.1 10.4 10.4060711234 3 97 85 6 6 110 175 175 4.9 5.8 1.3 1.3 1.9 11.7 11.7060712002 2 102 112 22 22 102 181 181 13.2 2.9 11.2 11.2 13.2 15.3 15.3060714001 1 32 32 32 32 32 32 6.5 0.0 6.5 6.5 6.5 6.5 6.5060730001 1 21 21 21 21 21 21 4.0 0.0 4.0 4.0 4.0 4.0 4.0060731007 3 11 9 5 5 7 21 21 12.9 1.3 11.8 11.8 12.5 14.4 14.4060732007 1 21 21 21 21 21 21 16.4 0.0 16.4 16.4 16.4 16.4 16.4060750005 6 66 83 5 5 39 224 224 13.3 6.2 1.8 1.8 15.2 18.3 18.3060791005 7 536 1369 6 6 24 3642 3642 1.0 0.2 0.8 0.8 1.0 1.5 1.5060792001 7 536 1369 6 6 24 3642 3642 10.5 0.2 10.2 10.2 10.4 10.9 10.9060792004 7 536 1369 6 6 24 3642 3642 2.5 0.1 2.3 2.3 2.6 2.7 2.7060794002 7 536 1369 6 6 24 3642 3642 18.4 0.1 18.3 18.3 18.5 18.5 18.5060830008 3 39 43 10 10 18 89 89 9.7 6.2 2.8 2.8 11.3 14.9 14.9060831012 2 554 357 302 302 554 807 807 16.5 0.1 16.4 16.4 16.5 16.5 16.5060831013 2 554 357 302 302 554 807 807 14.1 0.1 14.1 14.1 14.1 14.2 14.2060831015 1 18 18 18 18 18 18 15.6 0.0 15.6 15.6 15.6 15.6 15.6060831016 1 18 18 18 18 18 18 15.2 0.0 15.2 15.2 15.2 15.2 15.2060831019 1 18 18 18 18 18 18 13.7 0.0 13.7 13.7 13.7 13.7 13.7060831020 3 39 43 10 10 18 89 89 7.3 8.2 2.0 2.0 3.2 16.7 16.7060831025 3 39 43 10 10 18 89 89 10.9 8.9 0.8 0.8 14.2 17.7 17.7060831026 3 39 43 10 10 18 89 89 9.9 8.0 0.9 0.9 12.6 16.2 16.2060831027 3 39 43 10 10 18 89 89 10.3 7.7 1.7 1.7 12.8 16.4 16.4060832004 2 554 357 302 302 554 807 807 4.2 0.1 4.2 4.2 4.2 4.2 4.2060832011 3 39 43 10 10 18 89 89 10.4 8.4 3.9 3.9 7.5 20.0 20.0060834003 2 554 357 302 302 554 807 807 16.4 0.1 16.4 16.4 16.4 16.5 16.5060870003 1 722 722 722 722 722 722 0.8 0.0 0.8 0.8 0.8 0.8 0.8060950001 13 1371 2071 6 6 790 7009 7009 7.1 3.3 2.1 2.1 7.5 13.8 13.8060950004 12 1480 2124 6 6 791 7009 7009 13.0 4.9 5.5 5.5 13.6 19.6 19.6061113001 2 9 3 7 7 9 11 11 10.4 5.1 6.8 6.8 10.4 14.0 14.0A-66SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100080010007 24 1001 3352 8 8 25 15958 15958 9.8 6.1 2.4 2.4 8.2 19.7 19.7080013001 20 1191 3657 8 8 28 15958 15958 8.3 5.8 1.6 1.6 5.9 19.8 19.8080310002 24 1098 3356 6 6 28 15958 15958 9.2 4.5 3.9 3.9 7.0 19.5 19.5080416001 3 1670 2857 7 7 34 4969 4969 6.2 8.6 0.8 0.8 1.8 16.1 16.1080416004 3 2849 4920 7 7 10 8530 8530 13.0 3.9 10.7 10.7 10.9 17.6 17.6080416011 3 2849 4920 7 7 10 8530 8530 9.9 7.6 2.5 2.5 9.6 17.7 17.7080416018 2 4268 6026 7 7 4268 8530 8530 6.8 0.5 6.5 6.5 6.8 7.2 7.2090010012 11 425 1198 5 5 21 4024 4024 6.1 4.9 2.1 2.1 4.8 19.7 19.7090010017 3 252 423 5 5 11 741 741 9.6 6.4 5.7 5.7 6.2 17.0 17.0090011123 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0090012124 4 192 366 5 5 10 741 741 7.4 6.0 2.3 2.3 6.5 14.4 14.4090019003 10 504 1257 5 5 10 4024 4024 13.2 5.1 4.0 4.0 14.0 19.5 19.5090031005 28 45 106 5 5 12 522 522 14.2 3.6 3.0 3.0 14.2 19.9 19.9090031018 7 16 9 5 5 15 30 30 7.7 6.3 1.9 1.9 3.7 18.4 18.4090032006 6 14 7 5 5 15 25 25 4.5 5.1 0.5 0.5 1.8 11.4 11.4090090027 8 595 1388 5 5 32 4012 4012 6.3 7.3 1.0 1.0 3.1 18.6 18.6090091003 9 565 1302 5 5 43 4012 4012 7.4 8.2 0.7 0.7 2.6 19.7 19.7090091123 9 565 1302 5 5 43 4012 4012 7.3 8.2 0.8 0.8 2.7 19.7 19.7090092123 5 86 96 9 9 28 198 198 9.0 5.7 0.8 0.8 8.9 15.2 15.2090110007 6 650 1088 7 7 110 2755 2755 8.4 3.0 3.3 3.3 8.7 12.7 12.7090130003 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100031003 34 975 1619 5 5 112 6720 6720 9.0 5.2 1.5 1.5 8.1 19.8 19.8100031007 11 3126 6528 15 15 103 19923 19923 10.5 2.2 9.1 9.1 9.8 16.2 16.2100031008 24 1657 4554 5 5 60 19923 19923 10.9 6.9 2.2 2.2 13.9 19.7 19.7100031013 34 975 1619 5 5 112 6720 6720 9.1 4.8 2.8 2.8 8.3 18.9 18.9100032002 36 802 1272 5 5 97 5051 5051 10.2 6.2 1.1 1.1 9.4 19.7 19.7100032004 39 1526 3681 5 5 116 19923 19923 10.9 6.2 1.3 1.3 11.1 19.8 19.8110010041 13 1410 4437 7 7 24 16141 16141 11.7 6.5 0.6 0.6 11.5 19.8 19.8120110010 8 2397 6653 17 17 41 18861 18861 11.0 6.1 5.1 5.1 7.5 19.2 19.2120310032 14 2715 5784 5 5 287 20908 20908 9.0 4.2 1.3 1.3 9.1 18.5 18.5120310080 15 2534 5617 5 5 257 20908 20908 12.0 4.7 1.1 1.1 13.3 19.7 19.7120310081 13 2923 5965 5 5 317 20908 20908 7.9 4.6 1.3 1.3 6.5 15.5 15.5120310097 14 2715 5784 5 5 287 20908 20908 9.2 4.7 3.1 3.1 7.7 19.5 19.5120330004 6 7262 14101 6 6 330 35417 35417 9.3 4.2 4.9 4.9 8.9 14.6 14.6120330022 6 7262 14101 6 6 330 35417 35417 7.6 4.4 2.4 2.4 8.4 12.3 12.3120470015 3 755 1268 18 18 27 2218 2218 3.0 0.5 2.6 2.6 2.7 3.6 3.6A-67SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100120570021 18 4986 11445 6 6 341 47103 47103 11.6 6.5 1.4 1.4 14.3 18.2 18.2120570053 19 4728 11180 6 6 104 47103 47103 10.8 3.5 5.9 5.9 12.1 17.3 17.3120570081 18 6781 13097 6 6 1116 47103 47103 14.4 4.5 8.1 8.1 15.1 19.6 19.6120570095 17 3845 11285 6 6 61 47103 47103 10.1 6.2 2.5 2.5 14.2 19.3 19.3120570109 16 4084 11610 6 6 83 47103 47103 9.9 4.0 6.7 6.7 7.4 19.9 19.9120571035 18 4986 11445 6 6 341 47103 47103 10.6 5.8 1.6 1.6 12.9 16.3 16.3120574004 3 2872 4949 11 11 19 8587 8587 14.2 8.5 4.4 4.4 18.9 19.3 19.3120813002 5 73 93 6 6 9 208 208 7.2 8.3 0.7 0.7 2.2 16.5 16.5120860019 7 34 45 5 5 12 130 130 9.6 5.8 2.6 2.6 6.9 19.1 19.1120890005 4 1262 1594 11 11 765 3509 3509 4.5 5.0 1.1 1.1 2.5 12.0 12.0120890009 4 1262 1594 11 11 765 3509 3509 4.2 4.6 1.1 1.1 2.4 11.0 11.0120952002 5 9 4 5 5 10 14 14 13.8 2.8 10.1 10.1 13.6 17.6 17.6120993004 6 39 38 5 5 32 103 103 12.0 3.1 7.0 7.0 12.0 16.7 16.7121030023 7 3546 7041 6 6 104 18822 18822 7.4 6.4 2.3 2.3 3.7 19.6 19.6121033002 6 4136 7521 23 23 156 18822 18822 10.3 4.2 3.5 3.5 10.0 15.4 15.4121035002 2 15398 21767 7 7 15398 30790 30790 13.6 0.1 13.5 13.5 13.6 13.7 13.7121035003 2 15398 21767 7 7 15398 30790 30790 9.8 4.0 7.0 7.0 9.8 12.6 12.6121050010 9 2386 2929 6 6 1210 8587 8587 10.4 3.1 3.7 3.7 10.8 14.4 14.4121052006 13 1691 2627 6 6 230 8587 8587 11.9 6.2 2.7 2.7 13.7 19.9 19.9121071008 3 9965 12565 12 12 5799 24083 24083 5.8 3.4 2.6 2.6 5.6 9.3 9.3121151002 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0121151005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0121151006 2 71 90 7 7 71 135 135 15.8 0.8 15.2 15.2 15.8 16.4 16.4130090001 2 36975 52282 6 6 36975 73943 73943 11.3 5.4 7.5 7.5 11.3 15.1 15.1130150002 4 40604 80047 21 21 862 160673 160673 10.4 5.2 2.5 2.5 13.0 13.0 13.0130210012 11 245 468 6 6 17 1576 1576 10.1 5.2 1.5 1.5 8.8 19.9 19.9130510019 14 1362 2664 8 8 235 7969 7969 7.1 4.1 0.4 0.4 6.6 12.0 12.0130510021 14 1362 2664 8 8 235 7969 7969 6.8 4.4 1.4 1.4 7.2 14.0 14.0130511002 14 1362 2664 8 8 235 7969 7969 6.2 3.4 1.6 1.6 6.6 10.0 10.0130950006 4 1693 2220 5 5 932 4905 4905 6.3 5.3 2.2 2.2 4.4 14.1 14.1131110091 1 1900 1900 1900 1900 1900 1900 1.6 0.0 1.6 1.6 1.6 1.6 1.6131150003 8 4057 9625 5 5 101 27594 27594 1.4 0.4 1.1 1.1 1.2 2.3 2.3131210048 7 4339 10445 68 68 169 27993 27993 10.3 2.1 8.4 8.4 9.2 14.0 14.0131210055 7 4339 10445 68 68 169 27993 27993 15.1 3.3 8.0 8.0 15.6 18.1 18.1131270006 3 821 948 14 14 586 1865 1865 3.6 2.8 1.8 1.8 2.2 6.8 6.8132150008 4 1740 3214 8 8 197 6559 6559 12.5 2.6 10.1 10.1 12.4 15.1 15.1A-68SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100132450003 15 1335 2379 8 8 545 8275 8275 8.0 1.4 4.7 4.7 8.2 10.0 10.0150030010 7 2231 2339 79 79 1566 6978 6978 5.0 4.6 2.5 2.5 3.3 15.3 15.3150030011 7 2231 2339 79 79 1566 6978 6978 5.7 5.3 2.2 2.2 4.1 17.5 17.5150031001 3 1043 1509 6 6 350 2774 2774 10.1 8.2 0.7 0.7 13.8 15.7 15.7150031006 7 2231 2339 79 79 1566 6978 6978 6.1 4.8 1.8 1.8 5.0 16.5 16.5160050004 2 804 606 376 376 804 1233 1233 1.3 0.1 1.2 1.2 1.3 1.4 1.4160290003 13 967 2904 7 7 33 10544 10544 2.9 0.4 2.7 2.7 2.8 4.3 4.3160290031 13 967 2904 7 7 33 10544 10544 1.4 1.1 0.8 0.8 1.2 4.9 4.9160770011 2 804 606 376 376 804 1233 1233 0.8 0.1 0.7 0.7 0.8 0.8 0.8170010006 4 965 614 392 392 817 1834 1834 4.8 4.5 1.9 1.9 2.9 11.5 11.5170190004 3 121 182 10 10 21 331 331 1.8 0.9 0.8 0.8 2.3 2.4 2.4170310050 47 900 1775 5 5 65 5951 8443 11.0 5.0 2.1 3.4 10.2 19.7 19.8170310059 40 910 1928 5 5 65 7381 8443 7.5 5.2 1.5 1.5 5.8 19.3 19.5170310063 23 1041 1800 5 5 17 6229 6229 11.0 6.8 0.9 0.9 9.3 19.7 19.7170310064 50 1015 1902 5 5 51 6229 8443 14.6 4.1 3.9 6.4 16.4 19.9 19.9170310076 36 930 1976 5 5 26 8443 8443 13.2 4.0 4.9 4.9 13.3 19.7 19.7170311018 26 924 1721 5 5 16 6229 6229 10.7 6.8 0.5 0.5 11.6 19.8 19.8170311601 12 3807 5540 7 7 1090 15934 15934 14.1 6.4 4.0 4.0 18.5 19.3 19.3170312001 43 920 1807 5 5 64 6229 8443 16.5 3.2 3.4 8.4 17.7 19.3 19.9170314002 25 982 1738 5 5 17 6229 6229 9.0 3.1 3.9 3.9 9.5 18.5 18.5170314201 4 165 230 7 7 77 498 498 18.0 3.0 13.4 13.4 19.4 19.7 19.7170318003 36 835 1797 5 5 70 8443 8443 8.8 2.4 2.9 2.9 8.4 14.7 14.7170436001 12 2986 5690 6 6 17 15934 15934 16.5 5.1 1.5 1.5 18.1 19.8 19.8170990007 4 890 1527 6 6 189 3178 3178 7.2 6.1 0.5 0.5 6.7 14.8 14.8171150013 11 1251 2596 22 22 164 8032 8032 3.3 2.3 1.8 1.8 3.2 9.9 9.9171170002 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0171190008 15 4510 11972 6 6 111 45960 45960 10.1 3.7 3.2 3.2 9.5 19.7 19.7171190017 40 877 2339 6 6 117 9663 12063 9.5 6.6 0.5 0.7 11.2 19.0 19.6171191010 28 954 2564 6 6 183 12063 12063 10.5 6.8 0.7 0.7 15.6 18.4 18.4171193007 28 2595 8875 6 6 214 45960 45960 12.2 6.9 2.4 2.4 16.1 18.9 18.9171193009 26 2789 9193 6 6 247 45960 45960 12.4 7.5 2.9 2.9 14.7 19.8 19.8171430024 10 7333 11752 5 5 67 35748 35748 13.2 5.8 1.3 1.3 15.5 18.8 18.8171570001 2 13148 18554 28 28 13148 26268 26268 6.4 0.4 6.0 6.0 6.4 6.7 6.7171610003 10 945 1612 7 7 169 4963 4963 11.4 5.6 2.3 2.3 12.3 17.2 17.2171630010 30 445 1152 6 6 68 6250 6250 9.3 4.1 1.3 1.3 9.6 18.5 18.5171631010 30 445 1152 6 6 68 6250 6250 10.4 4.3 1.1 1.1 11.7 19.4 19.4A-69SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100171631011 2 13148 18554 28 28 13148 26268 26268 4.0 0.6 3.6 3.6 4.0 4.4 4.4171670006 5 2170 3169 9 9 202 7210 7210 7.3 3.6 4.9 4.9 5.6 13.5 13.5171790004 6 12212 13311 22 22 10290 35748 35748 5.4 5.2 0.8 0.8 3.6 13.8 13.8171850001 3 42452 25439 27097 27097 28443 71817 71817 2.9 0.1 2.8 2.8 2.9 3.1 3.1171851001 3 42452 25439 27097 27097 28443 71817 71817 5.9 0.1 5.8 5.8 5.8 6.0 6.0171970013 19 2439 6269 6 6 37 25224 25224 6.6 4.8 1.1 1.1 5.2 18.6 18.6180270002 6 10869 16456 9 9 2241 41536 41536 6.3 0.6 5.8 5.8 6.0 7.3 7.3180290004 7 21579 32930 174 174 1574 85699 85699 4.2 4.1 1.2 1.2 3.4 12.8 12.8180430004 8 6500 10778 12 12 484 23995 23995 13.4 3.1 8.8 8.8 12.3 17.7 17.7180430007 10 6721 10131 12 12 516 23995 23995 9.2 6.4 1.1 1.1 7.3 19.9 19.9180431004 9 7442 10470 12 12 798 23995 23995 10.0 3.5 5.0 5.0 9.8 14.7 14.7180450001 3 18552 32099 10 10 28 55617 55617 9.8 8.7 4.5 4.5 5.1 19.8 19.8180510001 3 42452 25439 27097 27097 28443 71817 71817 2.0 0.1 1.8 1.8 2.0 2.1 2.1180510002 3 42452 25439 27097 27097 28443 71817 71817 2.9 0.0 2.9 2.9 2.9 3.0 3.0180630001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0180630002 1 147 147 147 147 147 147 19.2 0.0 19.2 19.2 19.2 19.2 19.2180630003 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0180730002 4 6874 1422 6085 6085 6204 9002 9002 4.3 1.0 3.5 3.5 4.0 5.8 5.8180730003 4 6874 1422 6085 6085 6204 9002 9002 10.2 1.2 9.5 9.5 9.7 12.1 12.1180770004 2 19099 1297 18182 18182 19099 20016 20016 4.3 0.1 4.3 4.3 4.3 4.4 4.4180890022 50 1014 1502 5 6 188 5951 6318 14.1 4.0 0.8 1.8 14.6 19.8 19.9180892008 39 938 1945 5 5 72 8443 8443 6.4 4.1 1.6 1.6 5.6 17.6 17.6180910005 3 4166 4640 20 20 3301 9178 9178 9.1 9.7 0.4 0.4 7.3 19.6 19.6180910007 2 4599 6476 20 20 4599 9178 9178 6.0 0.8 5.4 5.4 6.0 6.5 6.5180970042 22 2358 6820 5 5 36 30896 30896 11.2 2.9 7.8 7.8 11.0 17.0 17.0180970054 20 2554 7138 5 5 23 30896 30896 3.3 2.3 0.9 0.9 2.4 9.2 9.2180970057 20 2554 7138 5 5 23 30896 30896 4.2 2.0 0.9 0.9 4.3 9.8 9.8180970072 21 2433 6980 5 5 19 30896 30896 6.9 3.5 0.8 0.8 6.6 18.7 18.7180970073 20 2547 7141 5 5 18 30896 30896 13.7 2.3 6.2 6.2 14.5 15.3 15.3181091001 3 6006 9709 242 242 561 17216 17216 4.3 2.4 2.1 2.1 4.0 6.9 6.9181230006 8 7033 17145 7 7 38 49028 49028 7.7 4.3 2.8 2.8 7.0 14.3 14.3181230007 8 7033 17145 7 7 38 49028 49028 6.8 4.2 2.1 2.1 5.7 13.1 13.1181250005 6 10869 16456 9 9 2241 41536 41536 3.0 4.7 0.9 0.9 1.1 12.7 12.7181270011 23 1703 2266 20 20 1062 9178 9178 6.7 6.2 2.2 2.2 3.6 18.7 18.7181270017 22 1363 1612 20 20 1029 6318 6318 5.4 5.7 2.0 2.0 2.6 17.8 17.8181270023 21 1427 1623 23 23 1062 6318 6318 4.1 4.4 1.1 1.1 2.4 14.6 14.6A-70SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100181470002 7 15627 24405 7 7 66 53196 53196 13.0 3.6 8.0 8.0 15.0 16.6 16.6181470010 4 15099 25616 20 20 3589 53196 53196 12.3 6.6 3.3 3.3 14.0 17.9 17.9181530004 3 9270 8089 10 10 12846 14955 14955 12.1 6.4 4.8 4.8 14.7 16.8 16.8181630012 5 1806 2589 5 5 382 6004 6004 13.1 7.7 3.1 3.1 18.0 19.6 19.6181631002 5 1806 2589 5 5 382 6004 6004 8.5 5.3 3.4 3.4 9.5 16.5 16.5181670018 6 10842 25028 12 12 417 61901 61901 6.8 3.6 5.0 5.0 5.5 14.1 14.1181671014 6 10842 25028 12 12 417 61901 61901 6.8 5.9 1.9 1.9 5.5 17.3 17.3181730002 8 13636 16457 50 50 3559 41049 41049 2.9 0.4 2.5 2.5 3.0 3.3 3.3181731001 8 13636 16457 50 50 3559 41049 41049 3.0 0.5 2.5 2.5 2.9 3.7 3.7181770006 2 6446 9089 19 19 6446 12873 12873 2.1 1.4 1.1 1.1 2.1 3.1 3.1181770007 2 6446 9089 19 19 6446 12873 12873 3.2 2.5 1.4 1.4 3.2 5.0 5.0190330018 4 2684 3305 20 20 1934 6850 6850 3.9 3.7 0.4 0.4 3.2 8.8 8.8190450018 2 4694 839 4101 4101 4694 5287 5287 1.4 0.9 0.7 0.7 1.4 2.0 2.0190450019 2 4694 839 4101 4101 4694 5287 5287 1.3 1.0 0.6 0.6 1.3 2.0 2.0190450020 2 4694 839 4101 4101 4694 5287 5287 3.4 0.6 3.0 3.0 3.4 3.8 3.8191110006 1 29 29 29 29 29 29 3.7 0.0 3.7 3.7 3.7 3.7 3.7191111007 2 104 105 29 29 104 179 179 13.3 6.3 8.8 8.8 13.3 17.7 17.7191130028 7 2200 2428 12 12 1954 5480 5480 5.8 2.4 2.8 2.8 6.7 8.8 8.8191130029 7 2200 2428 12 12 1954 5480 5480 3.8 3.1 0.5 0.5 4.0 9.2 9.2191130031 7 2200 2428 12 12 1954 5480 5480 4.3 3.2 0.5 0.5 4.7 9.3 9.3191130032 7 2200 2428 12 12 1954 5480 5480 3.5 2.7 0.6 0.6 3.1 8.8 8.8191130034 7 2200 2428 12 12 1954 5480 5480 3.6 2.5 0.2 0.2 2.9 7.4 7.4191130038 7 2200 2428 12 12 1954 5480 5480 3.9 1.9 0.6 0.6 4.2 6.2 6.2191130039 7 2200 2428 12 12 1954 5480 5480 4.6 3.0 1.1 1.1 4.2 10.3 10.3191390016 5 6227 6934 83 83 3790 15901 15901 8.7 6.9 2.4 2.4 7.4 19.2 19.2191390017 4 7763 6956 463 463 7345 15901 15901 3.8 3.6 0.6 0.6 3.1 8.5 8.5191390020 4 7763 6956 463 463 7345 15901 15901 4.9 4.4 0.9 0.9 4.0 10.4 10.4191630015 7 1345 1810 17 17 336 4963 4963 9.5 5.0 1.1 1.1 11.7 15.1 15.1191630017 7 2120 3515 17 17 303 8983 8983 9.6 4.2 1.1 1.1 11.2 13.6 13.6191770004 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0191770005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0191770006 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0191930018 4 9208 10818 15 15 7845 21127 21127 6.4 4.3 0.7 0.7 7.1 10.7 10.7201070002 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0201250006 4 468 464 11 11 428 1006 1006 5.8 9.3 0.5 0.5 1.6 19.7 19.7201450001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0A-71SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100201730010 3 269 448 6 6 15 785 785 11.4 3.1 9.0 9.0 10.2 14.9 14.9201910002 3 269 448 6 6 15 785 785 16.3 2.4 13.6 13.6 17.3 18.0 18.0201950001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0202090001 14 1388 2341 6 6 34 7625 7625 9.2 5.9 3.5 3.5 7.1 19.8 19.8202090020 14 1388 2341 6 6 34 7625 7625 9.0 6.1 0.6 0.6 7.7 18.9 18.9202090021 13 1494 2402 6 6 40 7625 7625 8.6 5.5 3.4 3.4 6.6 19.1 19.1210190015 9 1323 2058 25 25 401 6285 6285 12.3 5.5 1.6 1.6 14.6 17.7 17.7210190017 10 1193 1983 25 25 343 6285 6285 12.8 5.4 2.9 2.9 13.8 19.5 19.5210191003 8 1271 2194 25 25 343 6285 6285 9.3 5.4 1.3 1.3 9.9 15.4 15.4210370003 11 6817 20950 12 12 268 69953 69953 12.0 3.0 8.1 8.1 10.8 17.8 17.8210371001 11 465 664 12 12 213 1848 1848 8.5 3.4 4.2 4.2 7.5 15.5 15.5210590005 4 15241 25506 26 26 3871 53196 53196 7.4 6.8 2.2 2.2 5.5 16.5 16.5210670012 3 209 316 12 12 42 573 573 3.2 2.2 1.2 1.2 2.7 5.6 5.6210890007 5 961 1147 25 25 401 2589 2589 10.9 6.2 5.1 5.1 7.6 19.8 19.8210910012 9 12162 22226 7 7 38 53196 53196 10.4 5.1 1.2 1.2 10.6 18.9 18.9211010013 4 2256 2755 5 5 1508 6004 6004 10.2 5.5 2.0 2.0 12.7 13.3 13.3211010014 10 10948 15581 5 5 2980 41049 41049 12.9 1.4 11.5 11.5 12.8 16.6 16.6211110032 14 6208 8948 38 38 516 23995 23995 11.4 5.6 2.4 2.4 13.7 18.3 18.3211110051 12 3259 5326 38 38 168 14977 14977 10.6 7.5 1.6 1.6 14.6 18.7 18.7211111041 11 6268 9779 12 12 234 23995 23995 9.1 7.3 1.3 1.3 7.7 19.3 19.3211390004 4 444 869 6 6 11 1747 1747 8.0 6.9 3.1 3.1 5.4 17.9 17.9211450001 7 8769 13010 174 174 7435 37077 37077 7.5 3.4 2.0 2.0 9.4 11.2 11.2211451024 3 587 1005 6 6 7 1747 1747 18.2 2.1 15.8 15.8 19.3 19.5 19.5211451026 3 587 1005 6 6 7 1747 1747 15.3 2.2 12.7 12.7 16.5 16.7 16.7212270008 1 52 52 52 52 52 52 19.1 0.0 19.1 19.1 19.1 19.1 19.1220150008 2 77 21 62 62 77 91 91 8.7 0.1 8.6 8.6 8.7 8.8 8.8220190008 16 3352 5531 6 6 184 18851 18851 7.6 6.1 1.2 1.2 5.8 16.7 16.7220330009 28 1406 3913 6 6 45 18680 18680 5.8 5.6 1.5 1.5 3.2 20.0 20.0220730004 1 2166 2166 2166 2166 2166 2166 10.1 0.0 10.1 10.1 10.1 10.1 10.1220870002 18 419 846 8 8 52 3009 3009 8.8 4.2 0.5 0.5 7.8 19.0 19.0221210001 28 1116 3650 6 6 33 18680 18680 5.4 4.7 2.4 2.4 3.4 18.1 18.1230010011 9 31 41 5 5 23 140 140 6.6 4.3 1.3 1.3 6.5 13.3 13.3230030009 1 90 90 90 90 90 90 1.9 0.0 1.9 1.9 1.9 1.9 1.9230030012 1 90 90 90 90 90 90 1.0 0.0 1.0 1.0 1.0 1.0 1.0230031003 1 90 90 90 90 90 90 1.3 0.0 1.3 1.3 1.3 1.3 1.3230031013 3 16 17 5 5 7 36 36 4.7 4.5 1.4 1.4 2.8 9.9 9.9A-72SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100230031018 4 193 233 7 7 133 499 499 8.5 5.6 0.3 0.3 10.3 13.0 13.0230050014 12 267 628 5 5 16 2091 2091 6.1 4.8 1.2 1.2 5.0 16.8 16.8230050027 12 267 628 5 5 16 2091 2091 6.0 4.7 0.8 0.8 4.8 16.6 16.6230172007 2 249 344 6 6 249 492 492 1.0 0.1 1.0 1.0 1.0 1.1 1.1240010006 2 681 685 197 197 681 1166 1166 8.9 4.0 6.0 6.0 8.9 11.7 11.7240032002 20 3247 9622 5 5 21 39974 39974 11.9 4.5 2.7 2.7 13.3 19.9 19.9240053001 22 4429 11101 5 5 27 39974 39974 11.9 3.3 4.6 4.6 12.1 19.2 19.2245100018 21 3101 9402 5 5 22 39974 39974 9.1 4.6 1.4 1.4 7.6 16.7 16.7245100036 21 4635 11331 5 5 22 39974 39974 6.6 3.3 1.6 1.6 6.8 16.0 16.0250051004 24 1867 8085 6 6 31 39593 39593 7.5 6.7 0.1 0.1 3.8 18.9 18.9250090005 25 65 148 6 6 26 762 762 9.6 6.6 0.3 0.3 9.2 19.9 19.9250091004 23 878 3071 5 5 16 14132 14132 11.3 6.3 0.8 0.8 12.8 20.0 20.0250091005 22 917 3137 5 5 16 14132 14132 11.2 6.3 0.7 0.7 11.9 18.6 18.6250095004 14 88 197 8 8 25 762 762 8.6 4.2 0.7 0.7 10.1 14.7 14.7250130016 34 216 907 5 5 14 5282 5282 7.6 5.2 0.5 0.5 7.4 19.2 19.2250131009 32 65 148 5 5 13 671 671 8.4 4.7 1.7 1.7 7.4 18.9 18.9250154002 12 72 113 6 6 29 363 363 15.8 3.4 9.1 9.1 16.8 19.7 19.7250171701 55 139 678 5 5 15 640 5007 13.3 4.6 0.4 2.9 15.0 19.4 20.0250174003 57 127 663 5 5 13 460 5007 12.2 4.0 0.6 5.6 12.4 19.5 19.7250250002 62 129 639 5 5 14 640 5007 9.6 6.1 0.7 1.1 8.6 19.5 19.7250250019 50 156 710 5 5 14 640 5007 12.0 3.8 0.7 4.2 12.0 18.1 18.4250250020 58 138 660 5 5 15 640 5007 10.0 5.0 1.1 3.0 9.1 19.2 19.2250250021 58 137 660 5 5 14 640 5007 10.6 4.7 1.8 3.4 9.3 19.5 20.0250250040 59 135 654 5 5 14 640 5007 10.2 5.3 1.0 1.4 9.5 19.5 19.8250250042 60 133 649 5 5 14 640 5007 9.4 5.8 0.5 0.7 9.1 19.1 19.3250251003 58 380 1952 5 5 15 5007 14132 11.0 4.6 1.0 2.1 10.4 19.3 19.4250270020 28 25 35 6 6 12 178 178 5.0 5.9 0.1 0.1 2.8 19.5 19.5250270023 28 25 35 6 6 12 178 178 5.1 5.8 0.6 0.6 2.9 19.1 19.1260410902 3 1407 1264 671 671 685 2867 2867 2.5 1.5 0.8 0.8 3.3 3.4 3.4260490021 4 42 24 7 7 48 63 63 10.9 4.5 4.2 4.2 13.1 13.1 13.1260492001 2 64 79 7 7 64 120 120 19.0 0.4 18.8 18.8 19.0 19.3 19.3260810020 9 60 96 9 9 12 280 280 10.5 5.6 4.3 4.3 10.6 19.4 19.4260991003 3 239 287 10 10 148 560 560 14.0 3.4 10.2 10.2 15.2 16.7 16.7261130001 1 58 58 58 58 58 58 10.3 0.0 10.3 10.3 10.3 10.3 10.3261470005 3 524 431 31 31 715 826 826 8.7 5.9 3.8 3.8 6.9 15.2 15.2261530001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0A-73SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100261630001 36 1780 5390 5 5 109 30171 30171 10.9 4.0 5.4 5.4 9.6 20.0 20.0261630005 34 1894 5529 5 5 117 30171 30171 6.1 5.4 1.2 1.2 4.4 19.0 19.0261630015 32 1070 2436 5 5 117 8913 8913 5.5 4.2 1.5 1.5 3.8 17.9 17.9261630016 31 1104 2469 5 5 121 8913 8913 9.0 2.7 3.6 3.6 8.6 17.0 17.0261630019 23 1358 2828 10 10 121 8913 8913 17.3 4.5 3.7 3.7 18.9 19.8 19.8261630025 6 13 14 5 5 9 42 42 14.8 2.4 11.2 11.2 15.2 17.8 17.8261630027 33 1952 5605 5 5 121 30171 30171 5.5 5.2 0.4 0.4 3.9 19.7 19.7261630033 32 1070 2436 5 5 117 8913 8913 5.0 4.5 0.4 0.4 4.2 15.8 15.8261630062 31 1104 2469 5 5 121 8913 8913 9.0 2.9 3.1 3.1 8.5 17.2 17.2261630092 33 1952 5605 5 5 121 30171 30171 5.4 5.1 0.9 0.9 3.0 19.9 19.9270031002 10 1332 4067 5 5 11 12904 12904 14.3 4.4 4.7 4.7 15.5 18.9 18.9270176316 5 72 84 5 5 26 190 190 13.7 6.8 2.2 2.2 16.4 19.7 19.7270370020 15 610 1015 9 9 104 3071 3071 11.9 6.1 0.9 0.9 12.4 19.6 19.6270370423 17 805 1227 9 9 205 3821 3821 11.6 5.5 0.4 0.4 12.4 18.8 18.8270370439 14 639 1047 9 9 79 3071 3071 12.5 5.8 2.6 2.6 13.1 20.0 20.0270370441 12 720 1114 9 9 79 3071 3071 11.6 5.7 1.6 1.6 12.6 19.0 19.0270370442 11 506 873 9 9 54 2869 2869 12.2 5.5 2.3 2.3 13.8 18.8 18.8270530954 24 913 2729 5 5 48 12904 12904 10.9 5.8 0.6 0.6 12.2 19.0 19.0270530957 21 878 2877 5 5 12 12904 12904 10.7 5.3 0.9 0.9 10.9 18.3 18.3270711240 1 67 67 67 67 67 67 0.3 0.0 0.3 0.3 0.3 0.3 0.3271230864 27 769 2540 5 5 46 12904 12904 12.0 4.8 3.9 3.9 12.6 19.7 19.7271410003 1 26742 26742 26742 26742 26742 26742 4.9 0.0 4.9 4.9 4.9 4.9 4.9271410011 1 26742 26742 26742 26742 26742 26742 1.7 0.0 1.7 1.7 1.7 1.7 1.7271410012 1 26742 26742 26742 26742 26742 26742 1.8 0.0 1.8 1.8 1.8 1.8 1.8271410013 1 26742 26742 26742 26742 26742 26742 1.1 0.0 1.1 1.1 1.1 1.1 1.1271630436 21 545 997 7 7 104 3821 3821 11.1 5.6 0.9 0.9 11.4 18.4 18.4271710007 2 13397 18873 52 52 13397 26742 26742 11.8 6.5 7.2 7.2 11.8 16.3 16.3280470007 2 12535 17718 6 6 12535 25064 25064 6.5 7.9 0.9 0.9 6.5 12.1 12.1280490018 5 51 45 15 15 30 128 128 7.3 5.4 3.2 3.2 6.0 16.6 16.6280590006 7 4903 10049 12 12 96 27207 27207 7.0 4.9 3.3 3.3 5.4 17.3 17.3280810004 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0290210009 1 3563 3563 3563 3563 3563 3563 0.7 0.0 0.7 0.7 0.7 0.7 0.7290210011 1 3563 3563 3563 3563 3563 3563 0.9 0.0 0.9 0.9 0.9 0.9 0.9290470025 15 1682 2364 6 6 105 7625 7625 11.9 4.8 2.8 2.8 10.8 18.2 18.2290770026 4 2302 2728 5 5 1772 5657 5657 8.2 4.5 2.3 2.3 9.3 11.8 11.8290770032 4 2302 2728 5 5 1772 5657 5657 7.8 3.9 3.0 3.0 8.5 11.0 11.0A-74SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100290770037 4 2302 2728 5 5 1772 5657 5657 9.2 6.1 0.6 0.6 11.0 14.0 14.0290770040 4 2302 2728 5 5 1772 5657 5657 9.2 6.2 0.5 0.5 11.0 14.1 14.1290770041 4 2302 2728 5 5 1772 5657 5657 8.6 5.5 1.2 1.2 9.7 13.8 13.8290930030 1 43340 43340 43340 43340 43340 43340 1.7 0.0 1.7 1.7 1.7 1.7 1.7290930031 1 43340 43340 43340 43340 43340 43340 4.6 0.0 4.6 4.6 4.6 4.6 4.6290950034 14 1388 2341 6 6 34 7625 7625 8.7 4.9 1.4 1.4 8.1 15.4 15.4290990004 5 11145 10277 243 243 15223 23258 23258 9.7 7.4 0.2 0.2 11.4 17.1 17.1290990014 5 11145 10277 243 243 15223 23258 23258 9.8 7.4 0.7 0.7 11.9 17.5 17.5290990017 5 11145 10277 243 243 15223 23258 23258 10.2 7.1 1.6 1.6 10.6 17.3 17.3290990018 4 8117 8927 243 243 7889 16447 16447 8.3 6.6 1.4 1.4 8.2 15.3 15.3291370001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0291630002 2 6747 934 6087 6087 6747 7408 7408 7.3 6.6 2.7 2.7 7.3 12.0 12.0291650023 4 2757 3602 19 19 1693 7625 7625 17.8 1.3 16.0 16.0 18.1 19.1 19.1291830010 1 47610 47610 47610 47610 47610 47610 1.7 0.0 1.7 1.7 1.7 1.7 1.7291831002 15 4516 11970 6 6 136 45960 45960 12.6 3.4 4.3 4.3 13.5 17.3 17.3291890001 14 1748 4547 8 8 35 16447 16447 14.8 4.4 6.4 6.4 15.9 19.7 19.7291890004 9 2535 5610 8 8 13 16447 16447 14.0 3.1 9.8 9.8 15.2 18.2 18.2291890006 7 27 48 6 6 8 136 136 14.7 4.6 8.4 8.4 15.7 19.9 19.9291890014 8 33 47 6 6 10 136 136 11.9 6.2 3.2 3.2 11.3 19.7 19.7291893001 29 370 1164 6 6 60 6250 6250 15.2 4.2 5.1 5.1 16.0 20.0 20.0291895001 35 1911 7823 6 6 111 45960 45960 15.1 3.1 6.7 6.7 15.9 20.0 20.0291897002 14 50 75 6 6 16 277 277 13.2 5.7 3.9 3.9 14.6 20.0 20.0291897003 18 403 1461 6 6 37 6250 6250 14.1 5.4 3.5 3.5 16.2 19.4 19.4295100007 19 1312 3936 8 8 50 16447 16447 12.5 6.0 0.5 0.5 14.0 19.6 19.6295100072 30 445 1152 6 6 68 6250 6250 8.8 3.8 2.0 2.0 9.7 19.2 19.2295100080 34 397 1088 6 6 61 6250 6250 10.7 4.3 0.4 0.4 10.5 19.7 19.7295100086 32 421 1118 6 6 68 6250 6250 9.8 3.9 1.7 1.7 10.0 18.6 18.6300132000 2 351 481 11 11 351 691 691 4.1 3.6 1.5 1.5 4.1 6.7 6.7300132001 2 351 481 11 11 351 691 691 4.1 4.9 0.7 0.7 4.1 7.5 7.5300430903 1 234 234 234 234 234 234 3.3 0.0 3.3 3.3 3.3 3.3 3.3300430911 1 234 234 234 234 234 234 4.5 0.0 4.5 4.5 4.5 4.5 4.5300430913 1 234 234 234 234 234 234 4.9 0.0 4.9 4.9 4.9 4.9 4.9300490702 1 234 234 234 234 234 234 6.2 0.0 6.2 6.2 6.2 6.2 6.2300490703 1 234 234 234 234 234 234 7.3 0.0 7.3 7.3 7.3 7.3 7.3300870700 1 16735 16735 16735 16735 16735 16735 19.8 0.0 19.8 19.8 19.8 19.8 19.8300870701 1 16735 16735 16735 16735 16735 16735 19.0 0.0 19.0 19.0 19.0 19.0 19.0A-75SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100300870702 1 16735 16735 16735 16735 16735 16735 19.8 0.0 19.8 19.8 19.8 19.8 19.8300870760 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0300870761 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0300870762 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0300870763 1 16735 16735 16735 16735 16735 16735 15.2 0.0 15.2 15.2 15.2 15.2 15.2301110016 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0301110066 4 1370 1322 75 75 1135 3135 3135 3.1 0.5 2.6 2.6 3.1 3.7 3.7301110079 4 1370 1322 75 75 1135 3135 3135 7.8 3.0 5.8 5.8 6.7 12.2 12.2301110080 4 1370 1322 75 75 1135 3135 3135 2.4 1.8 0.9 0.9 1.9 5.0 5.0301110082 4 1370 1322 75 75 1135 3135 3135 3.4 2.7 1.7 1.7 2.3 7.3 7.3301110083 4 1370 1322 75 75 1135 3135 3135 3.4 0.7 2.7 2.7 3.4 4.4 4.4301110084 6 2550 2627 75 75 1976 7415 7415 10.3 6.6 3.1 3.1 7.4 18.6 18.6301111065 4 1370 1322 75 75 1135 3135 3135 4.7 2.7 0.7 0.7 5.7 6.7 6.7301112005 4 1370 1322 75 75 1135 3135 3135 4.1 1.8 1.5 1.5 4.6 5.7 5.7301112006 6 2550 2627 75 75 1976 7415 7415 10.1 7.2 1.1 1.1 7.6 18.8 18.8301112007 6 2550 2627 75 75 1976 7415 7415 11.4 3.9 4.7 4.7 11.2 15.3 15.3310550048 5 6370 9218 6 6 58 20257 20257 12.7 7.5 0.5 0.5 13.6 19.3 19.3310550050 5 6370 9218 6 6 58 20257 20257 13.4 7.5 1.0 1.0 14.7 19.6 19.6310550053 5 6370 9218 6 6 58 20257 20257 11.3 5.7 3.3 3.3 10.6 18.0 18.0310550055 3 3845 6637 6 6 20 11509 11509 13.0 7.3 4.7 4.7 16.1 18.2 18.2320030022 4 45 27 16 16 44 75 75 3.9 0.0 3.8 3.8 3.9 3.9 3.9320030078 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0320030539 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0320030601 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0330050007 1 81 81 81 81 81 81 0.3 0.0 0.3 0.3 0.3 0.3 0.3330070019 1 638 638 638 638 638 638 1.7 0.0 1.7 1.7 1.7 1.7 1.7330070022 1 638 638 638 638 638 638 2.3 0.0 2.3 2.3 2.3 2.3 2.3330071007 2 9 4 6 6 9 12 12 0.6 0.1 0.6 0.6 0.6 0.7 0.7330110016 3 10269 10386 149 149 9754 20902 20902 17.3 1.3 16.5 16.5 16.6 18.8 18.8330110019 3 10269 10386 149 149 9754 20902 20902 17.0 2.3 15.7 15.7 15.7 19.6 19.6330110020 3 10269 10386 149 149 9754 20902 20902 17.0 2.3 15.7 15.7 15.7 19.6 19.6330111009 11 41 42 6 6 20 149 149 12.7 6.0 4.4 4.4 14.7 19.0 19.0330111010 16 48 42 6 6 38 149 149 13.0 3.0 7.2 7.2 12.0 19.0 19.0330130007 4 7708 9906 41 41 4945 20902 20902 7.3 3.9 1.4 1.4 9.0 9.6 9.6330131003 4 7708 9906 41 41 4945 20902 20902 7.7 5.4 4.0 4.0 5.6 15.4 15.4330131006 4 7708 9906 41 41 4945 20902 20902 8.2 8.8 1.3 1.3 5.8 19.8 19.8A-76SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100330131007 4 7708 9906 41 41 4945 20902 20902 9.3 2.1 7.5 7.5 8.6 12.3 12.3330150009 9 1523 2990 6 6 52 8057 8057 9.0 6.9 2.0 2.0 4.4 19.2 19.2330150014 9 1523 2990 6 6 52 8057 8057 9.6 7.0 1.0 1.0 5.5 19.9 19.9330150015 9 1523 2990 6 6 52 8057 8057 8.9 7.1 1.9 1.9 4.1 19.5 19.5330190003 2 110 81 53 53 110 168 168 2.5 1.7 1.3 1.3 2.5 3.7 3.7340010005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0340035001 61 457 2442 6 6 22 2302 18958 14.8 3.7 2.2 5.2 15.7 19.7 19.9340051001 21 719 3104 5 5 35 14266 14266 10.7 6.7 1.5 1.5 12.3 19.9 19.9340070003 60 179 644 5 5 25 2378 4450 9.7 3.4 2.0 2.8 9.6 17.2 19.9340071001 2 8 1 8 8 8 9 9 10.2 0.5 9.9 9.9 10.2 10.5 10.5340110007 4 161 198 28 28 81 456 456 7.5 6.6 1.8 1.8 5.7 16.8 16.8340130011 59 465 2471 5 6 25 1845 18958 13.1 4.9 1.6 2.2 14.2 19.2 19.4340130016 61 453 2431 5 6 25 1845 18958 13.4 5.0 1.8 2.7 14.3 19.8 19.9340150002 50 529 1281 5 6 44 4450 6720 13.2 3.7 2.1 4.6 12.9 19.2 19.7340170006 59 467 2471 5 5 25 1845 18958 13.0 4.6 2.0 3.2 13.5 19.9 19.9340171002 71 421 2267 5 5 18 2302 18958 11.9 5.0 0.8 0.8 11.6 19.7 19.8340232003 21 80 206 6 6 16 958 958 8.6 4.6 1.8 1.8 9.2 15.8 15.8340273001 2 19 8 13 13 19 25 25 17.7 3.1 15.5 15.5 17.7 19.8 19.8340390003 38 610 3074 5 5 19 18958 18958 11.5 5.3 2.3 2.3 12.4 20.0 20.0340390004 38 609 3075 5 5 19 18958 18958 11.2 5.6 0.7 0.7 12.1 19.9 19.9350130008 1 37 37 37 37 37 37 17.9 0.0 17.9 17.9 17.9 17.9 17.9350130017 13 44 92 5 5 11 345 345 14.8 4.0 1.7 1.7 15.7 17.7 17.7350151004 4 1058 973 168 168 983 2099 2099 8.6 8.4 0.9 0.9 8.7 16.1 16.1350170001 1 263 263 263 263 263 263 6.1 0.0 6.1 6.1 6.1 6.1 6.1350171003 1 263 263 263 263 263 263 1.5 0.0 1.5 1.5 1.5 1.5 1.5350230005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0350450008 7 2478 2496 11 11 2554 5919 5919 17.2 3.5 11.9 11.9 19.2 19.3 19.3350450009 2 293 378 25 25 293 560 560 3.3 2.0 2.0 2.0 3.3 4.7 4.7350450017 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0350451005 8 6274 10983 11 11 2630 32847 32847 6.1 3.8 3.2 3.2 3.5 11.9 11.9360010012 9 40 46 7 7 20 153 153 10.8 5.2 3.5 3.5 9.0 18.0 18.0360050073 68 399 2309 5 6 22 2302 18958 10.0 4.9 3.4 3.4 9.1 19.2 19.7360050080 66 406 2344 5 6 18 2302 18958 10.6 5.0 1.8 3.0 9.6 19.5 19.9360050083 56 119 355 6 6 19 1129 2302 11.2 5.6 1.6 1.8 11.3 19.6 19.6360050110 67 402 2326 5 6 21 2302 18958 10.1 4.9 2.7 2.8 9.0 19.2 19.7360130005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0A-77SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100360130006 1 52177 52177 52177 52177 52177 52177 2.0 0.0 2.0 2.0 2.0 2.0 2.0360130011 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0360150003 2 202 270 11 11 202 393 393 10.2 13.6 0.6 0.6 10.2 19.9 19.9360290005 10 4073 12273 8 8 182 38999 38999 10.2 4.7 2.5 2.5 11.1 15.4 15.4360294002 16 2608 9706 8 8 166 38999 38999 10.4 6.2 1.6 1.6 12.3 18.3 18.3360298001 9 4518 12932 8 8 247 38999 38999 13.5 5.6 4.6 4.6 14.7 19.0 19.0360310003 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0360330004 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0360410005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0360430005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0360470011 77 377 2178 5 5 18 2302 18958 10.3 5.5 0.7 1.9 10.8 19.2 19.7360470076 67 428 2333 5 5 17 2302 18958 11.6 4.8 2.3 3.1 11.5 19.4 19.9360530006 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0360551004 4 12595 14519 8 8 11988 26395 26395 11.0 4.2 7.6 7.6 10.0 16.5 16.5360551007 4 12595 14519 8 8 11988 26395 26395 11.3 4.1 6.4 6.4 11.9 15.0 15.0360556001 4 12595 14519 8 8 11988 26395 26395 10.5 6.8 5.2 5.2 8.5 19.8 19.8360590005 12 151 301 6 6 26 1057 1057 11.8 4.8 1.9 1.9 11.8 19.1 19.1360610010 77 375 2178 5 5 17 2302 18958 10.4 5.4 0.3 1.4 11.1 19.4 19.6360610056 76 382 2192 5 5 18 2302 18958 9.9 5.4 0.3 1.4 10.6 19.9 19.9360632008 13 3134 10777 8 8 118 38999 38999 9.3 7.3 0.3 0.3 12.2 19.8 19.8360671015 4 820 1602 8 8 24 3223 3223 5.9 4.3 1.9 1.9 5.2 11.5 11.5360790005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0360810097 60 136 358 5 6 22 1129 2302 14.8 4.0 2.9 5.0 15.5 19.9 20.0360810124 66 122 342 5 6 21 1129 2302 12.5 4.0 2.1 2.3 12.4 19.5 20.0360830004 3 126 106 10 10 153 217 217 18.4 1.8 16.3 16.3 19.3 19.6 19.6360831005 2 94 124 6 6 94 182 182 17.6 1.6 16.5 16.5 17.6 18.8 18.8360850067 48 515 2737 5 6 17 1845 18958 14.0 4.0 5.5 6.2 14.2 19.6 19.9360930003 4 24 26 6 6 14 62 62 9.5 6.6 2.0 2.0 9.7 16.5 16.5361030002 9 156 344 6 6 19 1057 1057 9.3 5.8 1.9 1.9 7.3 18.2 18.2361030009 10 734 2013 11 11 42 6453 6453 11.3 5.7 2.0 2.0 11.9 19.3 19.3361111005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0370030003 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0370130003 1 4730 4730 4730 4730 4730 4730 2.2 0.0 2.2 2.2 2.2 2.2 2.2370130004 1 4730 4730 4730 4730 4730 4730 2.7 0.0 2.7 2.7 2.7 2.7 2.7370130006 1 4730 4730 4730 4730 4730 4730 1.1 0.0 1.1 1.1 1.1 1.1 1.1370370004 4 119 71 12 12 148 165 165 17.2 3.7 11.8 11.8 18.6 19.9 19.9A-78SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100370511003 5 295 264 17 17 173 675 675 15.8 2.5 11.5 11.5 16.5 17.9 17.9370590002 4 1949 3658 13 13 175 7432 7432 15.3 4.3 10.4 10.4 15.6 19.6 19.6370610002 5 83 132 6 6 36 317 317 12.3 4.9 4.1 4.1 13.1 17.0 17.0370650099 1 325 325 325 325 325 325 16.1 0.0 16.1 16.1 16.1 16.1 16.1370670022 9 438 848 5 5 46 2591 2591 6.3 5.7 1.2 1.2 3.9 17.8 17.8371010002 2 15 4 12 12 15 17 17 10.3 7.5 5.0 5.0 10.3 15.6 15.6371090004 1 10 10 10 10 10 10 10.7 0.0 10.7 10.7 10.7 10.7 10.7371170001 2 1713 2329 66 66 1713 3360 3360 6.6 7.8 1.1 1.1 6.6 12.2 12.2371190034 12 86 121 5 5 11 320 320 13.3 4.7 6.3 6.3 12.8 19.8 19.8371190041 12 68 103 5 5 11 320 320 12.7 5.0 6.3 6.3 12.2 19.8 19.8371290002 9 3325 6800 6 6 313 20865 20865 14.5 4.9 2.3 2.3 15.4 19.0 19.0371290006 12 2502 5987 6 6 50 20865 20865 6.9 4.8 0.6 0.6 7.1 14.5 14.5371310002 3 805 759 16 16 871 1529 1529 4.2 1.8 2.1 2.1 5.1 5.3 5.3371450003 3 32251 54874 5 5 1136 95610 95610 18.8 0.5 18.4 18.4 18.7 19.3 19.3371470099 2 14 3 12 12 14 16 16 1.3 0.0 1.3 1.3 1.3 1.3 1.3371730002 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0380070002 1 283 283 283 283 283 283 11.4 0.0 11.4 11.4 11.4 11.4 11.4380070111 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0380130002 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0380130004 1 426 426 426 426 426 426 18.6 0.0 18.6 18.6 18.6 18.6 18.6380150003 1 4592 4592 4592 4592 4592 4592 9.8 0.0 9.8 9.8 9.8 9.8 9.8380171003 3 257 226 15 15 294 462 462 7.7 6.9 3.0 3.0 4.6 15.7 15.7380171004 2 378 119 294 294 378 462 462 9.0 1.1 8.2 8.2 9.0 9.7 9.7380250003 1 5 5 5 5 5 5 13.9 0.0 13.9 13.9 13.9 13.9 13.9380530002 1 210 210 210 210 210 210 17.3 0.0 17.3 17.3 17.3 17.3 17.3380530104 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0380530111 2 411 522 42 42 411 781 781 16.1 0.1 16.1 16.1 16.1 16.2 16.2380550113 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0380570001 2 45808 55924 6264 6264 45808 85352 85352 2.5 2.6 0.7 0.7 2.5 4.3 4.3380570004 2 45808 55924 6264 6264 45808 85352 85352 2.7 2.0 1.3 1.3 2.7 4.1 4.1380570102 2 45808 55924 6264 6264 45808 85352 85352 5.4 2.3 3.8 3.8 5.4 7.0 7.0380570118 2 45808 55924 6264 6264 45808 85352 85352 10.7 2.2 9.1 9.1 10.7 12.2 12.2380570123 2 45808 55924 6264 6264 45808 85352 85352 14.3 1.4 13.3 13.3 14.3 15.3 15.3380570124 2 45808 55924 6264 6264 45808 85352 85352 18.6 1.0 17.9 17.9 18.6 19.3 19.3380590002 1 4592 4592 4592 4592 4592 4592 2.6 0.0 2.6 2.6 2.6 2.6 2.6380590003 1 4592 4592 4592 4592 4592 4592 5.1 0.0 5.1 5.1 5.1 5.1 5.1A-79SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100380650002 1 28565 28565 28565 28565 28565 28565 8.5 0.0 8.5 8.5 8.5 8.5 8.5380910001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0381050103 1 1605 1605 1605 1605 1605 1605 2.8 0.0 2.8 2.8 2.8 2.8 2.8381050105 1 1605 1605 1605 1605 1605 1605 1.8 0.0 1.8 1.8 1.8 1.8 1.8390010001 1 19670 19670 19670 19670 19670 19670 11.4 0.0 11.4 11.4 11.4 11.4 11.4390030002 9 442 535 16 16 45 1469 1469 8.5 0.4 7.9 7.9 8.3 9.3 9.3390071001 5 1731 3761 12 12 34 8458 8458 17.3 0.6 16.6 16.6 17.2 18.2 18.2390133002 5 27781 23029 795 795 35454 56009 56009 14.5 5.1 6.0 6.0 15.8 19.8 19.8390170004 11 907 1265 56 56 233 3998 3998 14.7 6.9 0.9 0.9 18.5 19.3 19.3390171004 9 1546 2186 56 56 309 6275 6275 6.5 6.5 1.7 1.7 3.3 19.8 19.8390230003 4 509 349 105 105 492 946 946 12.2 6.1 5.8 5.8 12.0 19.2 19.2390250021 6 15304 28111 26 26 145 69953 69953 15.0 2.7 12.7 12.7 14.1 18.7 18.7390290016 9 20696 19955 18 18 24766 59928 59928 12.7 3.6 7.2 7.2 13.5 18.1 18.1390290022 9 20696 19955 18 18 24766 59928 59928 12.7 3.6 7.2 7.2 13.6 18.2 18.2390292001 8 22401 20621 18 18 25596 59928 59928 11.4 4.1 4.6 4.6 10.8 19.3 19.3390350038 10 740 916 15 15 382 2453 2453 9.8 4.9 1.9 1.9 11.7 14.3 14.3390350045 10 740 916 15 15 382 2453 2453 10.1 5.5 1.2 1.2 10.4 15.8 15.8390350060 10 740 916 15 15 382 2453 2453 10.4 5.7 1.0 1.0 13.3 15.5 15.5390350065 10 740 916 15 15 382 2453 2453 9.8 4.3 2.0 2.0 9.8 14.5 14.5390356001 13 5759 16867 8 8 382 61629 61629 13.8 7.1 1.7 1.7 16.8 20.0 20.0390490004 6 75 74 5 5 64 192 192 8.7 3.4 2.9 2.9 9.2 12.9 12.9390490034 6 75 74 5 5 64 192 192 9.5 3.0 3.4 3.4 10.4 11.5 11.5390530002 6 31718 26583 9 9 29551 74452 74452 7.0 7.4 1.0 1.0 3.6 16.5 16.5390610010 10 9265 26865 12 12 537 85699 85699 16.1 3.0 8.6 8.6 16.8 19.7 19.7390612003 11 660 817 12 12 268 2164 2164 8.7 5.5 0.4 0.4 8.0 19.4 19.4390810016 17 13129 20063 10 10 361 59928 59928 9.5 7.1 1.7 1.7 5.6 19.0 19.0390810017 17 13129 20063 10 10 361 59928 59928 9.6 6.9 2.0 2.0 5.9 18.6 18.6390811001 13 6005 15392 10 10 234 53414 53414 4.9 5.6 0.3 0.3 2.9 18.0 18.0390850003 6 12044 24426 8 8 2390 61629 61629 9.1 4.2 5.6 5.6 7.4 15.2 15.2390853002 3 1600 2615 18 18 163 4618 4618 5.3 6.0 1.1 1.1 2.6 12.3 12.3390870006 8 1425 2178 25 25 343 6285 6285 13.7 6.0 2.2 2.2 15.5 19.3 19.3390930017 3 165 241 6 6 47 442 442 11.4 2.2 8.9 8.9 12.5 12.8 12.8390930026 2 27 29 6 6 27 47 47 3.3 0.5 3.0 3.0 3.3 3.6 3.6390931003 3 165 241 6 6 47 442 442 11.6 2.1 9.2 9.2 12.5 13.1 13.1390950008 9 4149 4513 204 204 3712 13581 13581 8.1 5.5 2.5 2.5 4.5 14.6 14.6390950024 10 3745 4443 113 113 2406 13581 13581 11.4 6.4 3.9 3.9 9.5 18.6 18.6A-80SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100390990009 10 2107 5350 6 6 353 17244 17244 12.4 7.3 2.0 2.0 15.6 19.6 19.6390990013 10 2107 5350 6 6 353 17244 17244 12.4 7.5 1.7 1.7 15.8 19.6 19.6391051001 6 31718 26583 9 9 29551 74452 74452 13.6 2.2 11.6 11.6 13.0 17.8 17.8391130025 6 1609 2326 105 105 753 6275 6275 13.4 5.4 7.3 7.3 13.4 19.4 19.4391150003 2 57763 38696 30401 30401 57763 85125 85125 4.8 0.2 4.6 4.6 4.8 4.9 4.9391150004 2 57763 38696 30401 30401 57763 85125 85125 5.1 0.3 4.9 4.9 5.1 5.3 5.3391450013 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0391450020 3 1450 1306 25 25 1737 2589 2589 9.6 6.9 4.6 4.6 6.7 17.5 17.5391450022 3 1450 1306 25 25 1737 2589 2589 8.4 7.5 2.8 2.8 5.4 16.9 16.9391510016 7 181 213 10 10 43 510 510 6.6 1.5 4.5 4.5 5.9 8.7 8.7391530017 4 2763 2244 863 863 2091 6009 6009 5.0 2.4 1.4 1.4 6.0 6.6 6.6391530022 4 2763 2244 863 863 2091 6009 6009 3.9 0.7 3.0 3.0 4.1 4.6 4.6391570003 7 368 741 15 15 38 2017 2017 12.0 6.4 0.6 0.6 13.3 18.6 18.6391570006 6 426 795 15 15 38 2017 2017 6.4 6.1 0.4 0.4 5.3 14.2 14.2400219002 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0400710602 2 3502 457 3178 3178 3502 3825 3825 3.4 2.3 1.8 1.8 3.4 5.0 5.0400719003 2 3502 457 3178 3178 3502 3825 3825 1.8 2.0 0.4 0.4 1.8 3.2 3.2400719010 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0400979014 6 3180 5200 173 173 713 13428 13428 4.7 1.3 2.7 2.7 5.5 5.7 5.7401010167 8 3751 4529 23 23 1130 9866 9866 5.9 4.2 3.7 3.7 3.7 15.8 15.8401090025 2 91 110 13 13 91 169 169 8.7 4.5 5.6 5.6 8.7 11.9 11.9401091037 2 91 110 13 13 91 169 169 8.8 7.9 3.2 3.2 8.8 14.4 14.4401159004 1 62 62 62 62 62 62 5.2 0.0 5.2 5.2 5.2 5.2 5.2401430175 10 938 1088 9 9 263 2729 2729 11.8 6.9 1.4 1.4 13.9 18.3 18.3401430235 10 938 1088 9 9 263 2729 2729 10.7 6.9 1.5 1.5 13.4 18.1 18.1401430501 10 938 1088 9 9 263 2729 2729 12.6 6.8 2.7 2.7 14.2 19.2 19.2420030002 19 103 137 7 7 30 468 468 7.4 5.9 0.6 0.6 8.6 18.1 18.1420030010 55 85 101 5 7 49 407 468 14.2 5.6 2.5 2.5 15.5 20.0 20.0420030021 64 819 5274 5 7 47 5395 42018 11.7 3.3 3.2 4.8 13.1 18.0 18.7420030031 62 757 5327 5 7 46 468 42018 13.9 5.1 1.3 1.4 14.4 18.7 19.8420030032 64 819 5274 5 7 47 5395 42018 11.7 3.3 3.1 4.7 13.2 18.1 18.7420030064 54 213 741 5 6 52 1164 5395 6.0 5.2 2.0 2.0 3.1 17.9 18.2420030067 16 73 105 7 7 29 407 407 15.1 3.5 6.1 6.1 15.7 19.7 19.7420030116 19 103 137 7 7 30 468 468 7.4 5.1 2.1 2.1 7.7 17.0 17.0420031301 57 914 5587 5 7 47 5395 42018 9.9 4.6 1.1 1.1 11.0 17.5 17.8420033003 54 213 741 5 6 52 1164 5395 5.6 5.4 1.0 1.0 2.3 17.8 17.8A-81SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100420033004 55 209 735 5 6 49 1164 5395 5.9 6.0 0.6 0.7 3.3 18.8 18.8420070002 10 18726 19819 18 18 15912 59928 59928 13.0 3.2 9.2 9.2 11.4 18.6 18.6420070004 7 5881 11104 9 9 118 30312 30312 14.5 5.1 7.4 7.4 16.0 19.8 19.8420070005 8 5173 10474 9 9 157 30312 30312 9.6 5.6 2.5 2.5 8.8 17.1 17.1420070014 10 4400 9400 8 8 157 30312 30312 12.0 3.1 7.1 7.1 12.0 17.2 17.2420110009 13 1140 3818 14 14 37 13841 13841 9.8 7.1 1.3 1.3 10.3 19.8 19.8420110100 12 1231 3973 14 14 34 13841 13841 8.7 6.3 1.5 1.5 7.5 17.2 17.2420130801 1 441 441 441 441 441 441 1.3 0.0 1.3 1.3 1.3 1.3 1.3420170012 22 687 3033 5 5 27 14266 14266 11.1 6.5 1.2 1.2 12.4 19.6 19.6420210011 4 4195 5171 34 34 3004 10738 10738 8.5 7.4 1.5 1.5 8.9 14.9 14.9420270100 4 1090 1267 53 53 834 2638 2638 10.4 6.2 2.3 2.3 11.4 16.6 16.6420430401 8 107 99 10 10 78 313 313 5.4 4.0 0.8 0.8 3.7 12.1 12.1420450002 57 681 1415 5 5 47 5051 6720 13.6 5.5 1.3 1.9 15.8 19.8 19.8420450109 45 855 1553 5 5 91 5051 6720 12.4 6.4 0.5 1.6 13.3 19.9 20.0420490003 5 824 1068 10 10 228 2398 2398 3.1 1.9 1.2 1.2 2.6 5.4 5.4420630004 3 4796 5156 1497 1497 2154 10738 10738 18.4 1.4 17.0 17.0 18.4 19.8 19.8420692006 5 13 5 6 6 15 18 18 10.9 7.4 2.1 2.1 8.2 19.6 19.6420710007 5 75 109 6 6 23 264 264 3.7 3.7 0.6 0.6 2.7 10.1 10.1420730015 9 3206 8423 6 6 28 25551 25551 12.5 5.6 0.6 0.6 13.2 18.0 18.0420770004 13 703 1041 7 7 120 2888 2888 12.5 5.8 0.3 0.3 12.0 19.3 19.3420791101 4 117 160 9 9 53 351 351 12.3 3.4 7.8 7.8 12.9 15.8 15.8420810100 3 28 28 6 6 18 59 59 11.3 0.7 10.6 10.6 11.2 12.0 12.0420810403 3 28 28 6 6 18 59 59 15.8 1.1 14.9 14.9 15.4 16.9 16.9420850100 2 14 4 11 11 14 17 17 10.8 11.8 2.4 2.4 10.8 19.1 19.1420910013 28 171 704 5 5 15 3753 3753 15.3 4.5 1.4 1.4 16.2 20.0 20.0420950025 18 676 1020 7 7 86 2888 2888 13.1 4.3 4.0 4.0 14.1 19.7 19.7420950100 15 2179 5602 7 7 120 22057 22057 10.4 5.5 2.5 2.5 10.7 19.3 19.3420958000 16 2045 5439 7 7 86 22057 22057 10.1 5.9 0.6 0.6 9.1 18.8 18.8420990301 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0421010004 61 102 316 5 6 20 560 2378 10.5 5.2 1.0 1.3 10.9 19.2 19.7421010022 66 285 1022 5 5 26 4450 6720 8.0 5.6 0.9 1.0 7.0 19.4 20.0421010024 36 46 77 5 5 13 407 407 13.0 3.8 6.3 6.3 12.6 19.9 19.9421010027 63 99 311 5 6 20 560 2378 9.8 4.6 0.8 1.7 11.0 19.7 19.7421010029 67 262 1007 5 5 24 4450 6720 8.3 4.7 1.1 1.8 6.8 18.9 19.6421010047 65 270 1022 5 5 26 4450 6720 7.9 4.5 0.6 0.8 6.4 17.6 17.9421010048 60 104 318 5 6 22 560 2378 10.4 4.9 0.9 1.7 10.7 18.6 19.2A-82SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100421010055 66 286 1022 5 5 26 4450 6720 7.9 5.4 1.3 1.4 6.8 18.8 20.0421010136 68 319 1042 5 5 27 4450 6720 8.8 5.4 1.1 1.4 9.3 18.7 19.8421070003 6 831 687 8 8 674 1743 1743 10.4 7.4 3.3 3.3 8.8 19.2 19.2421230003 2 2445 659 1979 1979 2445 2911 2911 4.0 1.2 3.2 3.2 4.0 4.9 4.9421230004 2 2445 659 1979 1979 2445 2911 2911 3.0 1.6 1.9 1.9 3.0 4.1 4.1421250005 33 257 945 5 5 47 5395 5395 15.7 4.7 1.1 1.1 17.5 18.7 18.7421250200 1 7 7 7 7 7 7 1.1 0.0 1.1 1.1 1.1 1.1 1.1421255001 8 321 439 7 7 82 1017 1017 15.9 4.1 9.3 9.3 17.2 19.7 19.7421290008 3 24 9 16 16 22 34 34 9.8 1.4 8.7 8.7 9.3 11.5 11.5421330008 9 8943 22698 14 14 171 68932 68932 9.3 5.8 0.8 0.8 10.1 17.7 17.7440070012 54 41 90 5 5 13 392 521 8.4 5.8 0.3 0.4 5.9 18.9 19.0440071005 55 41 89 5 5 13 392 521 9.1 5.5 0.9 1.0 8.4 18.5 19.0440071009 55 41 89 5 5 13 392 521 8.6 6.0 0.1 0.4 6.3 19.5 19.9450030003 13 1654 2599 8 8 549 8275 8275 15.3 1.5 11.4 11.4 15.3 17.5 17.5450110001 1 65 65 65 65 65 65 13.2 0.0 13.2 13.2 13.2 13.2 13.2450190003 16 2183 6339 6 6 28 25544 25544 7.2 5.0 1.1 1.1 6.2 16.3 16.3450190046 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0450430006 7 5834 14038 6 6 24 37622 37622 4.6 4.3 0.2 0.2 3.4 13.2 13.2450450008 12 89 136 6 6 20 411 411 11.7 4.5 2.1 2.1 10.7 17.4 17.4450450009 13 83 132 6 6 19 411 411 10.1 5.7 4.0 4.0 5.4 17.3 17.3450630008 11 948 2944 5 5 9 9820 9820 11.5 5.4 0.5 0.5 13.0 19.2 19.2450730001 1 5 5 5 5 5 5 14.9 0.0 14.9 14.9 14.9 14.9 14.9450750003 5 1433 1913 5 5 211 4088 4088 8.5 5.1 3.4 3.4 9.6 15.8 15.8450790007 10 61 103 5 5 18 343 343 14.0 4.1 6.4 6.4 15.9 18.7 18.7450790021 8 5061 12720 7 7 89 36378 36378 14.7 1.2 12.3 12.3 15.3 15.6 15.6450791003 13 995 2730 5 5 52 9820 9820 10.9 5.9 1.4 1.4 10.9 18.5 18.5450791006 10 4289 11350 7 7 89 36378 36378 17.5 3.3 8.2 8.2 18.9 19.1 19.1460330132 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0460710001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0460990007 1 496 496 496 496 496 496 17.5 0.0 17.5 17.5 17.5 17.5 17.5470010028 8 5595 14808 7 7 34 42188 42188 12.2 6.5 0.9 0.9 12.8 18.8 18.8470090002 3 1421 2325 6 6 153 4104 4104 5.7 5.7 0.7 0.7 4.5 11.9 11.9470090006 3 1421 2325 6 6 153 4104 4104 5.4 5.3 1.4 1.4 3.3 11.3 11.3470090101 3 1421 2325 6 6 153 4104 4104 12.1 6.9 4.2 4.2 15.4 16.7 16.7470110102 2 2719 3687 112 112 2719 5326 5326 2.5 1.2 1.6 1.6 2.5 3.4 3.4470310004 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0A-83SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100470370011 9 891 2248 9 9 60 6842 6842 10.4 3.6 5.6 5.6 10.7 17.6 17.6470730002 3 11831 10420 6 6 15822 19666 19666 2.9 2.1 1.7 1.7 1.7 5.2 5.2470850020 6 18599 44191 12 12 281 108788 108788 3.2 1.8 1.6 1.6 2.6 6.3 6.3471070101 3 1834 3024 64 64 112 5326 5326 7.6 10.2 0.5 0.5 3.0 19.3 19.3471250006 6 222 401 8 8 35 1025 1025 6.2 6.9 1.0 1.0 2.5 15.0 15.0471250106 6 222 401 8 8 35 1025 1025 7.1 7.3 1.5 1.5 3.5 16.3 16.3471390003 1 1900 1900 1900 1900 1900 1900 3.1 0.0 3.1 3.1 3.1 3.1 3.1471390007 1 1900 1900 1900 1900 1900 1900 1.6 0.0 1.6 1.6 1.6 1.6 1.6471390008 1 1900 1900 1900 1900 1900 1900 1.4 0.0 1.4 1.4 1.4 1.4 1.4471390009 1 1900 1900 1900 1900 1900 1900 1.0 0.0 1.0 1.0 1.0 1.0 1.0471450009 4 19470 22311 9 9 19188 39495 39495 10.9 6.7 5.3 5.3 9.5 19.1 19.1471570034 18 1204 2391 5 5 32 6540 6540 11.4 2.2 4.8 4.8 11.8 15.3 15.3471570043 18 1204 2391 5 5 32 6540 6540 9.6 1.7 5.3 5.3 10.0 11.4 11.4471570046 2 1973 2640 106 106 1973 3839 3839 6.0 6.7 1.3 1.3 6.0 10.8 10.8471571034 19 1150 2336 5 5 35 6540 6540 3.5 5.6 0.5 0.5 0.7 18.0 18.0471610007 3 5561 5107 21 21 6580 10081 10081 1.8 0.2 1.7 1.7 1.7 1.9 1.9471630007 10 3010 5303 22 22 495 16855 16855 3.7 2.6 1.7 1.7 2.6 10.7 10.7471630009 12 2513 4935 13 13 286 16855 16855 5.7 6.0 2.0 2.0 2.7 18.7 18.7471651002 4 8593 10129 88 88 7029 20226 20226 4.2 1.8 2.9 2.9 3.5 6.9 6.9480610006 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0481130069 9 34 25 9 9 18 69 69 12.1 5.7 2.0 2.0 12.9 20.0 20.0481390015 12 664 993 13 13 57 3003 3003 9.5 5.8 2.3 2.3 9.4 16.6 16.6481390016 12 664 993 13 13 57 3003 3003 9.0 6.3 2.9 2.9 6.1 17.4 17.4481390017 12 664 993 13 13 57 3003 3003 9.6 6.9 1.9 1.9 7.6 18.6 18.6481410037 13 44 92 5 5 11 345 345 9.7 1.8 4.5 4.5 10.0 12.0 12.0481410053 13 44 92 5 5 11 345 345 9.7 1.6 5.1 5.1 9.9 11.9 11.9481410058 16 38 83 5 5 12 345 345 13.9 2.3 9.5 9.5 14.7 16.0 16.0481670005 43 185 611 5 6 22 1937 3599 2.3 1.3 1.2 1.3 2.0 3.3 9.5481671002 43 185 611 5 6 22 1937 3599 3.6 1.1 2.5 2.5 3.3 4.6 9.5481830001 5 13289 12287 6 6 19024 24837 24837 18.9 0.5 18.6 18.6 18.7 19.9 19.9482010046 29 606 1182 6 6 161 5097 5097 12.8 3.1 6.2 6.2 13.1 19.6 19.6482010051 2 13 8 7 7 13 18 18 19.1 0.6 18.7 18.7 19.1 19.5 19.5482010059 38 674 1486 6 6 48 6968 6968 10.3 5.9 1.8 1.8 8.5 19.5 19.5482010062 37 694 1503 6 6 49 6968 6968 14.8 3.8 7.8 7.8 15.7 20.0 20.0482010070 31 790 1622 6 6 161 6968 6968 10.7 5.3 2.2 2.2 8.7 19.5 19.5482011035 39 657 1470 6 6 46 6968 6968 8.6 5.4 1.6 1.6 7.7 17.6 17.6A-84SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100482011050 46 243 1028 6 7 36 829 6968 16.5 3.9 5.0 5.3 17.9 19.1 19.9482450009 16 863 2732 6 6 80 11064 11064 14.8 6.8 0.4 0.4 18.7 19.7 19.7482450011 27 999 2362 6 6 45 11064 11064 9.0 5.3 2.8 2.8 7.0 18.1 18.1482450020 8 170 306 6 6 64 908 908 10.8 8.1 1.8 1.8 11.3 19.9 19.9482570005 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0483550025 17 468 1086 6 6 43 3955 3955 6.7 3.0 4.2 4.2 5.2 16.4 16.4483550026 19 424 1032 6 6 43 3955 3955 10.0 3.3 4.6 4.6 11.0 13.6 13.6483550032 17 468 1086 6 6 43 3955 3955 3.9 4.1 0.4 0.4 1.7 16.0 16.0490050004 1 5 5 5 5 5 5 1.8 0.0 1.8 1.8 1.8 1.8 1.8490110001 6 468 500 8 8 366 1332 1332 8.2 5.8 1.5 1.5 8.1 17.7 17.7490110004 6 468 500 8 8 366 1332 1332 9.7 6.0 2.3 2.3 9.8 19.2 19.2490350012 6 468 500 8 8 366 1332 1332 4.9 3.7 0.6 0.6 4.5 8.9 8.9490351001 7 833 1006 8 8 712 2788 2788 13.0 6.5 2.1 2.1 13.0 19.6 19.6490352004 3 1245 1415 8 8 939 2788 2788 9.8 8.0 2.4 2.4 8.9 18.3 18.3500070003 1 6 6 6 6 6 6 1.6 0.0 1.6 1.6 1.6 1.6 1.6500070014 1 6 6 6 6 6 6 1.9 0.0 1.9 1.9 1.9 1.9 1.9500210002 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0510360002 18 4818 17274 7 7 35 73839 73839 12.1 7.2 2.0 2.0 13.6 19.9 19.9510590005 5 31 46 8 8 11 114 114 17.2 1.6 15.0 15.0 17.3 19.4 19.4510590018 10 1820 5043 8 8 74 16141 16141 13.5 3.9 8.4 8.4 15.7 17.5 17.5510591004 11 1664 4813 7 7 59 16141 16141 10.9 3.5 3.7 3.7 11.2 16.3 16.3510591005 13 1416 4435 7 7 59 16141 16141 13.6 4.3 4.6 4.6 13.8 19.0 19.0510595001 11 1566 4837 6 6 24 16141 16141 14.8 4.4 5.1 5.1 16.0 19.8 19.8511130003 1 7 7 7 7 7 7 10.8 0.0 10.8 10.8 10.8 10.8 10.8511611004 8 85 117 5 5 34 341 341 9.3 5.5 2.9 2.9 9.7 19.1 19.1511650002 7 40 36 8 8 32 108 108 12.3 5.1 5.1 5.1 13.9 17.8 17.8511650003 6 39 40 5 5 25 108 108 11.4 5.4 6.3 6.3 10.3 17.9 17.9515100009 11 1663 4813 7 7 59 16141 16141 9.6 5.1 1.1 1.1 8.6 17.9 17.9516500004 15 285 505 6 6 92 1983 1983 11.1 4.9 4.0 4.0 11.3 17.9 17.9517100023 21 1738 7026 5 5 85 32344 32344 8.3 3.4 3.6 3.6 8.3 18.8 18.8517600024 14 191 363 6 6 16 1148 1148 9.4 5.8 1.2 1.2 10.3 20.0 20.0530090010 1 756 756 756 756 756 756 5.6 0.0 5.6 5.6 5.6 5.6 5.6530090012 1 756 756 756 756 756 756 5.3 0.0 5.3 5.3 5.3 5.3 5.3530330057 5 241 301 63 63 117 771 771 4.0 6.0 0.6 0.6 1.3 14.7 14.7530330080 5 241 301 63 63 117 771 771 5.0 4.2 2.5 2.5 3.1 12.5 12.5530530021 3 179 213 11 11 109 419 419 3.2 1.1 2.1 2.1 3.2 4.3 4.3A-85SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100530530031 3 179 213 11 11 109 419 419 1.8 0.9 1.2 1.2 1.3 2.8 2.8530570012 4 2238 2630 21 21 1793 5345 5345 2.2 0.8 1.3 1.3 2.3 3.1 3.1530571003 4 2238 2630 21 21 1793 5345 5345 1.7 0.6 1.1 1.1 1.7 2.4 2.4530610016 2 191 194 53 53 191 328 328 0.5 0.1 0.4 0.4 0.5 0.6 0.6530730011 9 488 695 8 8 349 2286 2286 16.9 6.2 0.5 0.5 19.3 19.7 19.7540090005 13 6005 15392 10 10 234 53414 53414 5.3 5.3 0.9 0.9 2.7 16.8 16.8540090007 17 13129 20063 10 10 361 59928 59928 10.7 5.3 3.9 3.9 8.3 18.8 18.8540110006 5 1501 2677 124 124 401 6285 6285 13.2 7.1 0.5 0.5 16.2 17.2 17.2540250001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0540290005 8 22069 20983 18 18 25596 59928 59928 9.3 5.3 4.7 4.7 7.5 17.6 17.6540290007 16 9282 17668 10 10 238 59928 59928 13.1 3.8 4.8 4.8 13.1 18.3 18.3540290008 9 20696 19955 18 18 24766 59928 59928 12.1 4.2 6.3 6.3 11.2 19.8 19.8540290009 15 9894 18112 10 10 243 59928 59928 11.0 3.5 1.0 1.0 12.0 17.7 17.7540290011 17 13129 20063 10 10 361 59928 59928 10.7 5.2 3.2 3.2 8.8 18.8 18.8540290014 16 9282 17668 10 10 238 59928 59928 11.8 4.0 1.5 1.5 11.1 19.4 19.4540290015 9 20696 19955 18 18 24766 59928 59928 12.1 3.5 7.1 7.1 12.4 18.2 18.2540290016 16 10611 17732 10 10 302 59928 59928 10.8 4.3 1.1 1.1 10.6 18.3 18.3540291004 16 10611 17732 10 10 302 59928 59928 11.5 3.9 1.8 1.8 11.8 19.8 19.8540390004 4 1529 1146 854 854 1008 3245 3245 10.2 4.3 6.0 6.0 10.0 14.8 14.8540390010 4 1529 1146 854 854 1008 3245 3245 9.7 4.6 5.2 5.2 9.8 14.0 14.0540392002 5 22698 47491 750 750 1009 107633 107633 9.1 5.6 2.3 2.3 6.7 15.5 15.5540511002 5 27781 23029 795 795 35454 56009 56009 10.1 4.7 2.2 2.2 11.4 15.0 15.0540610003 2 45992 63840 850 850 45992 91134 91134 4.6 1.4 3.6 3.6 4.6 5.6 5.6540610004 4 24472 44468 850 850 2952 91134 91134 11.8 8.9 0.8 0.8 13.5 19.4 19.4540610005 3 32132 51128 850 850 4412 91134 91134 9.2 9.7 1.0 1.0 6.7 19.9 19.9540690007 2 37391 22660 21367 21367 37391 5341453414 13.9 1.8 12.7 12.7 13.9 15.2 15.2540990002 8 1271 2194 25 25 343 6285 6285 9.7 5.5 1.7 1.7 10.6 16.0 16.0540990003 8 1271 2194 25 25 343 6285 6285 9.6 5.5 1.5 1.5 10.7 15.8 15.8540990004 8 1271 2194 25 25 343 6285 6285 9.6 6.0 1.0 1.0 11.3 15.8 15.8540990005 8 1271 2194 25 25 343 6285 6285 9.5 6.4 0.9 0.9 11.4 16.2 16.2541071002 11 4375 9095 7 7 1517 31006 31006 8.5 5.4 2.7 2.7 8.8 17.0 17.0550090005 7 3413 5045 9 9 850 13470 13470 4.2 3.4 1.1 1.1 3.1 9.7 9.7550250041 7 1293 2743 7 7 71 7417 7417 7.4 4.7 2.8 2.8 5.2 14.7 14.7550410007 1 5 5 5 5 5 5 8.3 0.0 8.3 8.3 8.3 8.3 8.3550730005 3 4040 6715 24 24 303 11792 11792 10.7 9.2 0.1 0.1 15.8 16.2 16.2550790007 9 1750 4858 5 5 28 14686 14686 6.5 3.4 1.8 1.8 5.9 12.9 12.9A-86SO2emissions (tpy) from sources within 20 km of monitor1Distance of monitor to SO2emission source (km)1Monitor ID nmean std min p2.5 p50 p97.5 p100 mean std min p2.5 p50 p97.5 p100550790026 9 1750 4858 5 5 28 14686 14686 7.6 3.0 3.5 3.5 7.5 12.8 12.8550790041 9 1750 4858 5 5 28 14686 14686 10.1 3.0 5.9 5.9 10.2 14.5 14.5550850996 2 1152 1617 9 9 1152 2295 2295 0.9 0.1 0.9 0.9 0.9 1.0 1.0551110007 2 31 35 7 7 31 56 56 14.7 7.4 9.5 9.5 14.7 19.9 19.9551250001 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0551410016 6 2374 2368 6 6 2032 5782 5782 5.3 2.6 2.3 2.3 4.9 9.8 9.8560050857 4 2527 3868 23 23 896 8291 8291 4.6 6.5 1.1 1.1 1.6 14.4 14.4Notes:1Mean, std , min, p2.5, p50, p97.5, max are the arithmetic average, standard deviation, minimum, 2.5th, 50th, 97.5thpercentiles, and maximum distancesand emissions.2There were no emissions above 5 tpy for sources located within 20km of the monitors sited in Puerto Rico and the Virgin Islands.A-87 A.2 Analysis of Duplicate SO 2 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 SO 2 measurements to discern if there were any differences in the re ported/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 pres ent at a particular monitoring site or if there were duplicate reporting of SO 2 concentrations, only to determine that the selection of a particular value used in the fi nal data sets were not biased. In selecting which of the duplic ate concentrations to use for final REA data sets, staff made the following judgements. First, the am bient 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 meas urements, staff selected the 5-minute maximum

SO 2 concentration from the continuous-5 data se t. And finally, where continuous-5 data were available and used to estimate a 1-hour average SO 2 concentration that co incided 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 selecti on 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 anticipat ed that small fluctuations in concentration between the duplicat e data would have a greater in fluence on the RPD at lower concentrations than at higher con centrations. Therefore, staff se parated 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 

 

 CC CC RPD equation A.2-1

where,

RPD = Relative percent difference (%)A-88 C 1 = First SO 2 concentration value

C

2 = Second SO 2 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 th e maximum difference between any two values,

while an RPD of zero indicates no difference. The sign of the value ca n also indicate the

direction of bias when comparing the first concen tration to the second. In the first comparison (i.e., th e within max-5 duplicates), C 1 was selected as the ambient monitor containing the overall greater sample size/duration. Tabl e A.2-1 summarizes the

distribution of RPDs for wh ere duplicate values of SO 2 concentrations were less than 10 ppb within the max-5 monitoring data set. On averag e, there were relatively small differences in the duplicate values reported at each of the monitori ng 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 SO 2 concentrations are well below that of potential interest in the exposure and risk analysis, this degree of ag reement between the two values at these concentration levels is acceptable.
Table A.2-1. Distributi on of the relative percent difference (RPD) between duplicate 5-minute maximum SO 2 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 ar ithmetic average, standard deviation, minimum, 5 th , median, 95 th , 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 withA-89 the greatest number of samples as the base m onitor, but on average the difference was typically

less than 3%.
Table A.2-2. Distributi on of the relative percent difference (RPD) between duplicate 5-minute maximum SO 2 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 ar ithmetic average, standard deviation, minimum, 5 th , median, 95 th , 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 deviatin g from zero (i.e., 1.1%), the following analysis included only the samples that had a non-zero diffe rence and at any concentration levels. The distribution for the RPD given these monitors an d 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 exis ted, 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 addi tion, selection of the continuous-5 measurement preserves the relationship between the actua l 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. Distributi on of the relative percent difference (RPD) between simultaneous 5-minute SO 2 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 wh ere 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 arit hmetic average, standard deviation, minimum, 5 th

, median, 95 th , and maximum, respectively.

In the last comparison (i.e., the 1-hour concentration duplicates), the 1-hour

concentration from the continous-5 am bient monitors was selected as C 1 in equation A.2-1.

Table A.2-4 summarizes the dist ribution of RPDs for where duplicate measurements of SO 2 concentrations were less than 10 ppb within the max-5 monitoring data set. While nearly 20% had no difference between the duplicate values, on av erage, 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 not ed at or above 100% one another (absolute difference), however all of these were due to wh ere 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 positiv e bias at most of the monitors , however in considering that

these 1-hour SO 2 concentrations are below that of poten tial interest in th e 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. Distributi on of the relative percent difference (RPD) between duplicate 1-hour SO 2 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 200A-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 ar ithmetic average, standard deviation, minimum, 5 th , median, 95 th , and maximum, respectively.

When considering duplicate 1-hour concentrat ions > 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 duplicat e 1-hour values at concentrations above 10 ppb. A very small positive bias may exist with se lection 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 co ncentrations above 10 ppb.
Table A.2-5. Distributi on of the relative percent difference (RPD) between duplicate 1-hour SO 2 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 ar ithmetic average, standard deviation, minimum, 5 th , median, 95 th , 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 SO 2 concentrations. PMRs were seperated into 19 groups 2 based on the observed variability (3 bins) and concen trations ranges (7 bins) in measured 1-hour

ambient monitor concentrations (n=2,367,686). Table A.3-1 summa rizes the PMR distributions used for estimating 5-minute maximum concentrations from 1-hour measurements where

ambient monitors were characteri zed by the 1-hour coefficient of variation (COV). These are the

PMR distributions used in the statis tical modeling of 5-minute maximum SO 2 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 SO 2 concentrations from 1-hour

measurements where ambient monitors were char acterized by the 1-hour co efficient of variation (GSD). Peak-to-mean ratios estimated by catego rizing the ambient monitors by GSD were used only in evaluating an alternative method of estimating 5-minute SO 2 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 distri bution 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 SO 2 REA.A-93Table A.3-1. Distribution of 5-minute maximum peak to 1-hour mean SO2concentration ratios (PMRs) using ambientmonitors categorized by 1-hour coefficient ofvariation (COV) and 1-hour mean concentration.COV≤100% 100% < COV≤200% COV > 200 %Concbin10 1 2 3 4 0 1 2 3 4 5 6 0 1 2 3 4 5 6Pct2- 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 1.001.00 1.00 1.00 1.08 1.13- 1 1.00 1.00 1.00 1.03 1.02 1.001.00 1.00 1.00 1.05 1.08 1.04 1.00 1.00 1.00 1.12 1.14 1.18 1.25- 2 1.00 1.00 1.00 1.03 1.02 1.001.00 1.00 1.03 1.07 1.12 1.14 1.00 1.00 1.06 1.17 1.18 1.21 1.28- 3 1.00 1.00 1.00 1.04 1.04 1.001.00 1.00 1.04 1.08 1.14 1.14 1.00 1.00 1.08 1.21 1.21 1.22 1.29- 4 1.00 1.00 1.00 1.04 1.04 1.001.00 1.05 1.06 1.10 1.16 1.15 1.00 1.00 1.08 1.24 1.24 1.25 1.30- 5 1.00 1.00 1.05 1.05 1.05 1.001.00 1.06 1.06 1.11 1.20 1.16 1.00 1.00 1.09 1.26 1.26 1.28 1.33- 6 1.00 1.00 1.06 1.06 1.06 1.001.00 1.06 1.07 1.12 1.21 1.18 1.00 1.00 1.10 1.29 1.28 1.31 1.34- 7 1.00 1.00 1.06 1.06 1.07 1.001.00 1.07 1.08 1.13 1.21 1.18 1.00 1.00 1.11 1.31 1.30 1.34 1.37- 8 1.00 1.00 1.06 1.07 1.08 1.001.00 1.08 1.09 1.14 1.22 1.22 1.00 1.00 1.14 1.33 1.32 1.37 1.38- 9 1.00 1.00 1.07 1.07 1.09 1.001.00 1.08 1.10 1.15 1.23 1.24 1.00 1.00 1.15 1.36 1.33 1.40 1.43- 10 1.00 1.00 1.07 1.07 1.09 1.001.00 1.08 1.10 1.16 1.23 1.24 1.00 1.00 1.17 1.38 1.35 1.42 1.47- 11 1.00 1.00 1.08 1.07 1.10 1.001.05 1.09 1.11 1.17 1.25 1.24 1.00 1.00 1.18 1.40 1.37 1.44 1.48- 12 1.00 1.00 1.08 1.08 1.10 1.001.08 1.09 1.12 1.18 1.27 1.30 1.00 1.00 1.20 1.42 1.38 1.47 1.50- 13 1.00 1.00 1.08 1.08 1.11 1.001.11 1.10 1.12 1.19 1.27 1.30 1.00 1.06 1.20 1.44 1.40 1.49 1.51- 14 1.00 1.00 1.08 1.09 1.11 1.001.11 1.10 1.13 1.19 1.28 1.32 1.00 1.11 1.22 1.46 1.42 1.51 1.53- 15 1.00 1.00 1.08 1.09 1.11 1.001.11 1.10 1.14 1.20 1.28 1.33 1.00 1.11 1.24 1.48 1.43 1.54 1.54- 16 1.00 1.00 1.08 1.10 1.12 1.001.13 1.10 1.15 1.21 1.29 1.34 1.00 1.13 1.26 1.50 1.45 1.57 1.57- 17 1.00 1.00 1.09 1.10 1.13 1.001.13 1.11 1.15 1.22 1.30 1.36 1.00 1.13 1.27 1.52 1.46 1.59 1.58- 18 1.00 1.04 1.09 1.11 1.14 1.001.13 1.13 1.16 1.23 1.31 1.36 1.00 1.14 1.30 1.53 1.48 1.60 1.59- 19 1.00 1.11 1.09 1.11 1.15 1.001.13 1.13 1.17 1.24 1.32 1.37 1.00 1.14 1.30 1.55 1.50 1.64 1.59- 20 1.00 1.11 1.09 1.11 1.15 1.001.14 1.14 1.17 1.24 1.32 1.38 1.00 1.14 1.33 1.57 1.51 1.65 1.61- 21 1.00 1.11 1.10 1.12 1.16 1.001.14 1.14 1.18 1.25 1.34 1.39 1.00 1.14 1.34 1.59 1.53 1.68 1.61- 22 1.00 1.11 1.10 1.12 1.17 1.001.14 1.15 1.19 1.26 1.34 1.43 1.00 1.17 1.36 1.61 1.54 1.72 1.63- 23 1.00 1.13 1.10 1.12 1.17 1.001.14 1.16 1.20 1.27 1.35 1.45 1.00 1.17 1.38 1.62 1.56 1.75 1.64- 24 1.00 1.13 1.10 1.13 1.18 1.001.15 1.17 1.20 1.28 1.36 1.45 1.00 1.17 1.40 1.64 1.57 1.76 1.64- 25 1.00 1.13 1.10 1.13 1.18 1.001.17 1.17 1.21 1.29 1.37 1.46 1.00 1.17 1.42 1.66 1.59 1.78 1.67- 26 1.00 1.13 1.11 1.13 1.19 1.001.17 1.18 1.22 1.30 1.38 1.46 1.00 1.17 1.44 1.68 1.60 1.80 1.69- 27 1.00 1.13 1.12 1.14 1.19 1.001.17 1.18 1.23 1.30 1.38 1.46 1.00 1.18 1.46 1.70 1.62 1.81 1.71- 28 1.00 1.13 1.13 1.14 1.20 1.001.17 1.19 1.23 1.31 1.38 1.47 1.00 1.20 1.50 1.71 1.64 1.83 1.73- 29 1.00 1.14 1.13 1.15 1.20 1.001.17 1.20 1.24 1.32 1.39 1.47 1.00 1.20 1.50 1.73 1.65 1.87 1.73A-94COV≤100% 100% < COV≤200% COV > 200 %Concbin10 1 2 3 4 0 1 2 3 4 5 6 0 1 2 3 4 5 6- 30 1.00 1.14 1.13 1.15 1.23 1.001.17 1.20 1.25 1.33 1.40 1.48 1.00 1.20 1.53 1.75 1.67 1.90 1.76- 31 1.00 1.14 1.14 1.16 1.23 1.001.18 1.20 1.26 1.34 1.42 1.49 1.00 1.20 1.55 1.76 1.69 1.91 1.77- 32 1.00 1.14 1.14 1.16 1.23 1.001.20 1.20 1.27 1.35 1.42 1.51 1.00 1.20 1.57 1.78 1.70 1.93 1.78- 33 1.00 1.14 1.15 1.16 1.24 1.001.20 1.21 1.28 1.36 1.43 1.51 1.00 1.20 1.60 1.80 1.73 1.96 1.79- 34 1.00 1.14 1.15 1.17 1.24 1.001.20 1.22 1.28 1.36 1.44 1.51 1.00 1.20 1.62 1.81 1.74 1.97 1.79- 35 1.00 1.15 1.16 1.17 1.24 1.001.20 1.23 1.29 1.38 1.44 1.54 1.00 1.20 1.64 1.83 1.77 1.99 1.80- 36 1.00 1.17 1.17 1.18 1.24 1.001.20 1.24 1.30 1.38 1.46 1.54 1.00 1.22 1.67 1.85 1.78 2.02 1.81- 37 1.00 1.17 1.17 1.18 1.25 1.001.20 1.25 1.31 1.39 1.46 1.55 1.00 1.24 1.69 1.87 1.80 2.05 1.82- 38 1.00 1.17 1.17 1.19 1.26 1.041.20 1.26 1.32 1.40 1.48 1.55 1.00 1.25 1.71 1.88 1.82 2.08 1.82- 39 1.00 1.17 1.18 1.19 1.29 1.081.20 1.27 1.33 1.41 1.49 1.56 1.00 1.27 1.74 1.90 1.84 2.10 1.83- 40 1.00 1.17 1.18 1.19 1.29 1.111.22 1.27 1.34 1.42 1.50 1.57 1.00 1.29 1.76 1.92 1.86 2.14 1.84- 41 1.00 1.17 1.18 1.20 1.29 1.131.22 1.29 1.35 1.43 1.51 1.57 1.00 1.29 1.80 1.94 1.88 2.16 1.84- 42 1.00 1.17 1.18 1.21 1.30 1.181.24 1.29 1.36 1.44 1.52 1.58 1.00 1.33 1.82 1.96 1.90 2.18 1.87- 43 1.00 1.17 1.19 1.21 1.30 1.221.25 1.30 1.37 1.45 1.53 1.60 1.00 1.33 1.84 1.98 1.93 2.20 1.89- 44 1.00 1.17 1.20 1.22 1.31 1.251.25 1.30 1.38 1.46 1.55 1.64 1.00 1.33 1.87 2.00 1.95 2.21 1.91- 45 1.00 1.20 1.20 1.22 1.31 1.251.27 1.31 1.39 1.47 1.57 1.64 1.00 1.34 1.90 2.02 1.97 2.23 1.91- 46 1.00 1.20 1.20 1.23 1.32 1.251.29 1.33 1.40 1.48 1.57 1.65 1.00 1.38 1.92 2.04 1.99 2.24 1.93- 47 1.00 1.20 1.20 1.23 1.32 1.251.29 1.33 1.41 1.49 1.58 1.67 1.00 1.40 1.94 2.06 2.01 2.26 1.94- 48 1.00 1.20 1.20 1.24 1.34 1.251.29 1.35 1.42 1.50 1.59 1.68 1.00 1.40 2.00 2.08 2.04 2.28 1.96- 49 1.00 1.20 1.21 1.24 1.35 1.291.33 1.36 1.43 1.51 1.61 1.68 1.00 1.40 2.00 2.10 2.06 2.30 1.96- 50 1.00 1.20 1.21 1.25 1.35 1.331.33 1.36 1.44 1.52 1.62 1.69 1.00 1.40 2.03 2.12 2.09 2.31 1.97- 51 1.00 1.20 1.22 1.25 1.36 1.331.33 1.38 1.46 1.54 1.62 1.72 1.00 1.43 2.07 2.14 2.12 2.34 1.97- 52 1.00 1.20 1.23 1.26 1.37 1.331.33 1.40 1.47 1.55 1.63 1.72 1.00 1.44 2.09 2.17 2.15 2.36 1.98- 53 1.00 1.20 1.24 1.27 1.39 1.331.33 1.40 1.48 1.56 1.64 1.74 1.00 1.50 2.11 2.19 2.18 2.38 2.01- 54 1.00 1.20 1.25 1.27 1.40 1.331.37 1.41 1.50 1.57 1.65 1.74 1.00 1.50 2.15 2.21 2.21 2.41 2.02- 55 1.00 1.20 1.25 1.28 1.41 1.331.38 1.42 1.51 1.58 1.67 1.76 1.00 1.50 2.18 2.24 2.24 2.43 2.04- 56 1.00 1.20 1.25 1.28 1.42 1.421.40 1.44 1.52 1.60 1.68 1.78 1.00 1.56 2.20 2.26 2.27 2.44 2.06- 57 1.00 1.22 1.27 1.29 1.42 1.431.40 1.45 1.54 1.61 1.70 1.81 1.00 1.57 2.24 2.29 2.30 2.47 2.08- 58 1.05 1.22 1.27 1.30 1.45 1.501.40 1.47 1.55 1.62 1.71 1.82 1.04 1.60 2.27 2.31 2.34 2.50 2.09- 59 1.11 1.24 1.27 1.31 1.45 1.501.40 1.49 1.57 1.63 1.73 1.82 1.11 1.60 2.30 2.34 2.36 2.53 2.13- 60 1.20 1.25 1.29 1.31 1.46 1.501.40 1.50 1.58 1.65 1.74 1.83 1.17 1.63 2.34 2.37 2.40 2.57 2.14- 61 1.25 1.25 1.29 1.32 1.46 1.501.43 1.50 1.60 1.66 1.75 1.83 1.25 1.67 2.38 2.39 2.44 2.60 2.15- 62 1.25 1.25 1.30 1.32 1.47 1.501.43 1.53 1.61 1.67 1.75 1.86 1.25 1.67 2.41 2.42 2.48 2.62 2.17A-95COV≤100% 100% < COV≤200% COV > 200 %Concbin10 1 2 3 4 0 1 2 3 4 5 6 0 1 2 3 4 5 6- 63 1.25 1.29 1.30 1.33 1.52 1.501.45 1.55 1.63 1.69 1.77 1.90 1.25 1.74 2.45 2.45 2.52 2.64 2.17- 64 1.25 1.29 1.31 1.34 1.54 1.501.50 1.56 1.65 1.70 1.79 1.93 1.33 1.78 2.50 2.48 2.56 2.67 2.19- 65 1.30 1.29 1.31 1.35 1.55 1.501.50 1.58 1.67 1.71 1.81 1.93 1.33 1.80 2.53 2.51 2.60 2.70 2.21- 66 1.33 1.33 1.33 1.36 1.57 1.501.50 1.60 1.68 1.72 1.82 1.93 1.33 1.83 2.56 2.54 2.66 2.73 2.24- 67 1.33 1.33 1.33 1.37 1.58 1.581.55 1.62 1.70 1.74 1.83 1.96 1.33 1.86 2.60 2.57 2.71 2.77 2.27- 68 1.33 1.33 1.35 1.38 1.58 1.671.57 1.64 1.72 1.76 1.86 1.99 1.42 1.89 2.64 2.61 2.76 2.80 2.28- 69 1.33 1.33 1.36 1.39 1.60 1.671.58 1.67 1.74 1.78 1.88 2.02 1.50 2.00 2.69 2.64 2.80 2.84 2.30- 70 1.33 1.33 1.36 1.40 1.64 1.671.60 1.68 1.76 1.79 1.90 2.02 1.50 2.00 2.73 2.68 2.85 2.88 2.31- 71 1.43 1.33 1.38 1.42 1.64 1.751.60 1.70 1.78 1.81 1.92 2.04 1.50 2.00 2.77 2.72 2.89 2.90 2.33- 72 1.50 1.38 1.39 1.42 1.65 1.851.63 1.73 1.80 1.83 1.93 2.06 1.50 2.10 2.82 2.76 2.95 2.93 2.33- 73 1.50 1.38 1.40 1.44 1.65 2.001.67 1.75 1.82 1.84 1.96 2.07 1.50 2.14 2.87 2.80 3.01 2.97 2.35- 74 1.50 1.40 1.40 1.44 1.65 2.001.67 1.78 1.85 1.86 1.98 2.07 1.50 2.18 2.92 2.84 3.06 2.99 2.37- 75 1.50 1.40 1.42 1.46 1.66 2.001.71 1.80 1.87 1.88 2.00 2.08 1.50 2.22 2.96 2.89 3.11 3.02 2.41- 76 1.50 1.40 1.43 1.47 1.67 2.001.75 1.83 1.90 1.90 2.02 2.09 1.60 2.29 3.00 2.93 3.16 3.06 2.44- 77 1.50 1.40 1.45 1.48 1.68 2.001.78 1.86 1.92 1.93 2.05 2.11 1.67 2.34 3.07 2.97 3.22 3.10 2.49- 78 1.50 1.40 1.46 1.50 1.69 2.001.80 1.90 1.96 1.95 2.06 2.13 1.71 2.40 3.13 3.03 3.30 3.16 2.52- 79 1.50 1.43 1.48 1.52 1.70 2.001.83 1.92 1.98 1.97 2.08 2.16 1.85 2.46 3.18 3.09 3.35 3.19 2.53- 80 1.50 1.44 1.50 1.52 1.71 2.001.86 1.96 2.01 2.00 2.14 2.20 2.00 2.56 3.25 3.14 3.41 3.24 2.55- 81 1.58 1.50 1.50 1.54 1.74 2.001.89 2.00 2.05 2.02 2.15 2.23 2.00 2.60 3.31 3.20 3.47 3.26 2.57- 82 1.67 1.50 1.53 1.57 1.75 2.002.00 2.05 2.08 2.05 2.17 2.25 2.00 2.67 3.38 3.26 3.57 3.32 2.60- 83 1.75 1.50 1.55 1.59 1.77 2.002.00 2.09 2.12 2.08 2.22 2.29 2.00 2.78 3.46 3.33 3.65 3.38 2.64- 84 2.00 1.55 1.58 1.61 1.77 2.002.00 2.14 2.15 2.11 2.25 2.29 2.00 2.83 3.54 3.41 3.72 3.42 2.65- 85 2.00 1.57 1.60 1.64 1.82 2.002.11 2.19 2.20 2.14 2.27 2.31 2.00 3.00 3.62 3.48 3.80 3.49 2.67- 86 2.00 1.60 1.63 1.67 1.85 2.112.17 2.24 2.24 2.17 2.39 2.32 2.00 3.00 3.70 3.57 3.90 3.55 2.70- 87 2.00 1.60 1.65 1.70 1.86 2.332.20 2.30 2.29 2.20 2.47 2.39 2.00 3.17 3.80 3.67 4.00 3.62 2.71- 88 2.00 1.63 1.69 1.72 1.88 2.502.29 2.36 2.35 2.23 2.50 2.39 2.00 3.29 3.90 3.77 4.10 3.69 2.74- 89 2.00 1.67 1.71 1.76 1.91 2.502.35 2.43 2.40 2.27 2.53 2.39 2.00 3.40 4.00 3.90 4.21 3.80 2.82- 90 2.00 1.71 1.75 1.80 1.98 2.942.43 2.50 2.46 2.31 2.58 2.50 2.00 3.56 4.12 4.04 4.35 3.88 2.84- 91 2.00 1.78 1.80 1.85 2.10 3.002.56 2.60 2.54 2.37 2.66 2.51 2.44 3.68 4.25 4.18 4.44 3.94 2.94- 92 2.00 1.80 1.83 1.89 2.25 3.002.65 2.70 2.62 2.43 2.73 2.57 2.67 3.86 4.39 4.35 4.62 4.07 2.98- 93 2.00 1.86 1.90 1.96 2.26 3.002.80 2.81 2.71 2.48 2.77 2.59 3.00 4.00 4.56 4.55 4.82 4.18 3.03- 94 2.00 2.00 1.95 2.02 2.30 3.333.00 2.94 2.81 2.56 2.81 2.65 3.00 4.29 4.76 4.77 5.03 4.28 3.09- 95 2.00 2.00 2.05 2.10 2.50 4.003.14 3.10 2.93 2.66 2.91 2.65 3.75 4.57 5.00 5.03 5.24 4.40 3.13A-96COV≤100% 100% < COV≤200% COV > 200 %Concbin10 1 2 3 4 0 1 2 3 4 5 6 0 1 2 3 4 5 6- 96 2.33 2.14 2.14 2.22 2.53 4.003.38 3.30 3.08 2.82 3.11 2.66 4.75 4.88 5.27 5.37 5.48 4.48 3.33- 97 2.67 2.29 2.27 2.37 2.56 5.003.67 3.56 3.28 3.01 3.25 2.71 6.00 5.29 5.69 5.80 5.94 4.63 3.38- 98 3.00 2.50 2.50 2.63 3.12 6.004.17 3.93 3.56 3.33 3.30 3.16 10.00 5.86 6.30 6.51 6.48 5.06 3.48- 99 4.00 2.89 2.91 3.02 3.61 10.005.00 4.64 4.07 3.77 3.82 3.27 10.00 6.86 7.27 7.50 7.29 5.36 3.70- 100 11.67 10.60 10.08 6.81 6.10 11.7511.67 11.94 11.41 8.51 6.63 3.51 11.75 11.50 11.93 11.45 11.39 6.48 5.39n3352735 74053 42876 6895 147 802624 259701 179452 53053 3807 398 104 475572 55341 35502 20077 4019 989 341Notes:11-hour SO2concentration bins are: 0 = 1-hour mean < 5 ppb; 2 = 5≤1-hour mean < 10 ppb; 2 = 10≤1-hour mean < 25 ppb; 3 = 25≤1-hour mean < 75ppb; 4 = 75≤1-hour mean < 150 ppb ; 5 = 150≤1-hour mean 250 ppb; 6 = 1-hour mean > 250 ppb.2pct –xindicates the percentile of the distribution.3nis the number of 5-minute maximum and 1-hour SO2measurements used to develop distribution.A-97Table A.3-2. Distribution of 5-minute maximum peak to 1-hour mean SO2concentration ratios (PMRs) using ambientmonitors categorized by 1-hour geometric standarddeviation (GSD) and 1-hourmean concentration.GSD≤2.17 2.17 < GSD≤2.94 GSD > 2.94Concbin10 1 2 3 4 0 1 2 3 4 5 6 0 1 2 3 4 5 6Pct2- 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 1.001.00 1.00 1.00 1.07 1.02- 1 1.00 1.00 1.00 1.03 1.07 1.001.00 1.00 1.00 1.05 1.13 1.19 1.00 1.00 1.00 1.05 1.08 1.14 1.14- 2 1.00 1.00 1.00 1.04 1.17 1.001.00 1.00 1.03 1.08 1.21 1.26 1.00 1.00 1.04 1.07 1.10 1.17 1.16- 3 1.00 1.00 1.00 1.04 1.21 1.001.00 1.00 1.04 1.10 1.23 1.28 1.00 1.00 1.06 1.08 1.12 1.20 1.18- 4 1.00 1.00 1.00 1.06 1.22 1.001.00 1.05 1.05 1.12 1.26 1.29 1.00 1.00 1.07 1.09 1.14 1.21 1.24- 5 1.00 1.00 1.00 1.07 1.24 1.001.00 1.06 1.06 1.14 1.28 1.30 1.00 1.00 1.08 1.10 1.15 1.21 1.25- 6 1.00 1.00 1.00 1.08 1.25 1.001.00 1.06 1.07 1.16 1.29 1.33 1.00 1.00 1.08 1.12 1.17 1.22 1.29- 7 1.00 1.00 1.04 1.10 1.27 1.001.00 1.07 1.08 1.17 1.31 1.34 1.00 1.03 1.09 1.13 1.18 1.23 1.30- 8 1.00 1.00 1.06 1.11 1.29 1.001.00 1.07 1.08 1.18 1.32 1.35 1.00 1.05 1.10 1.14 1.19 1.25 1.31- 9 1.00 1.00 1.06 1.12 1.30 1.001.00 1.08 1.09 1.20 1.34 1.37 1.00 1.07 1.10 1.15 1.20 1.27 1.33- 10 1.00 1.00 1.07 1.13 1.30 1.001.00 1.08 1.10 1.21 1.35 1.38 1.00 1.09 1.11 1.16 1.21 1.28 1.36- 11 1.00 1.00 1.07 1.14 1.31 1.001.00 1.08 1.11 1.23 1.38 1.43 1.00 1.10 1.12 1.17 1.22 1.30 1.37- 12 1.00 1.00 1.07 1.14 1.32 1.001.05 1.09 1.12 1.24 1.39 1.45 1.00 1.11 1.13 1.18 1.24 1.32 1.38- 13 1.00 1.00 1.08 1.15 1.32 1.001.10 1.09 1.12 1.25 1.40 1.46 1.00 1.11 1.14 1.19 1.25 1.35 1.45- 14 1.00 1.00 1.08 1.16 1.33 1.001.11 1.10 1.13 1.26 1.42 1.47 1.00 1.13 1.15 1.20 1.26 1.36 1.46- 15 1.00 1.00 1.08 1.17 1.34 1.001.11 1.10 1.14 1.28 1.43 1.48 1.00 1.13 1.16 1.21 1.27 1.38 1.46- 16 1.00 1.00 1.08 1.19 1.35 1.001.11 1.10 1.15 1.29 1.44 1.50 1.00 1.13 1.17 1.22 1.29 1.39 1.47- 17 1.00 1.00 1.09 1.19 1.36 1.001.13 1.10 1.15 1.30 1.46 1.51 1.00 1.14 1.18 1.23 1.30 1.42 1.49- 18 1.00 1.00 1.09 1.20 1.36 1.001.13 1.11 1.16 1.31 1.47 1.52 1.00 1.14 1.18 1.24 1.31 1.43 1.51- 19 1.00 1.00 1.09 1.21 1.37 1.001.13 1.12 1.17 1.32 1.49 1.53 1.00 1.14 1.19 1.25 1.32 1.44 1.54- 20 1.00 1.00 1.09 1.22 1.40 1.001.13 1.13 1.17 1.34 1.50 1.54 1.00 1.15 1.20 1.26 1.33 1.46 1.55- 21 1.00 1.00 1.10 1.23 1.43 1.001.14 1.13 1.18 1.35 1.52 1.54 1.00 1.16 1.20 1.27 1.34 1.49 1.57- 22 1.00 1.07 1.10 1.24 1.44 1.001.14 1.14 1.19 1.36 1.53 1.56 1.00 1.17 1.21 1.29 1.35 1.50 1.58- 23 1.00 1.11 1.10 1.25 1.44 1.001.14 1.15 1.20 1.38 1.55 1.58 1.00 1.17 1.23 1.30 1.36 1.53 1.60- 24 1.00 1.11 1.10 1.26 1.46 1.001.14 1.15 1.21 1.39 1.57 1.58 1.00 1.17 1.24 1.31 1.37 1.56 1.61- 25 1.00 1.11 1.10 1.27 1.47 1.001.14 1.16 1.21 1.40 1.57 1.58 1.00 1.18 1.25 1.32 1.39 1.58 1.64- 26 1.00 1.13 1.11 1.28 1.48 1.001.17 1.17 1.22 1.41 1.59 1.59 1.00 1.19 1.25 1.33 1.40 1.60 1.64- 27 1.00 1.13 1.12 1.29 1.49 1.001.17 1.17 1.23 1.42 1.60 1.59 1.00 1.20 1.27 1.34 1.41 1.62 1.64- 28 1.00 1.13 1.13 1.30 1.50 1.001.17 1.18 1.24 1.43 1.61 1.61 1.00 1.20 1.27 1.35 1.42 1.64 1.68- 29 1.00 1.14 1.14 1.30 1.50 1.001.17 1.18 1.25 1.44 1.64 1.63 1.00 1.20 1.29 1.37 1.43 1.65 1.68A-98GSD≤2.17 2.17 < GSD≤2.94 GSD > 2.94Concbin10 1 2 3 4 0 1 2 3 4 5 6 0 1 2 3 4 5 6- 30 1.00 1.14 1.14 1.31 1.52 1.001.17 1.18 1.26 1.46 1.65 1.64 1.00 1.20 1.30 1.38 1.45 1.67 1.74- 31 1.00 1.14 1.15 1.32 1.53 1.001.17 1.19 1.27 1.47 1.68 1.65 1.00 1.20 1.30 1.39 1.46 1.71 1.76- 32 1.00 1.14 1.16 1.33 1.55 1.001.17 1.20 1.28 1.48 1.70 1.66 1.00 1.22 1.32 1.40 1.47 1.73 1.77- 33 1.00 1.14 1.17 1.34 1.57 1.001.18 1.20 1.29 1.50 1.72 1.68 1.00 1.23 1.33 1.42 1.48 1.75 1.79- 34 1.00 1.15 1.17 1.35 1.58 1.001.20 1.20 1.30 1.51 1.74 1.69 1.00 1.25 1.34 1.43 1.50 1.76 1.81- 35 1.00 1.17 1.18 1.36 1.59 1.001.20 1.21 1.31 1.52 1.75 1.69 1.00 1.25 1.36 1.44 1.51 1.77 1.83- 36 1.00 1.17 1.18 1.37 1.60 1.001.20 1.21 1.32 1.54 1.76 1.70 1.00 1.26 1.36 1.46 1.52 1.80 1.83- 37 1.00 1.17 1.18 1.39 1.60 1.001.20 1.23 1.32 1.55 1.78 1.72 1.00 1.28 1.38 1.47 1.53 1.82 1.84- 38 1.00 1.17 1.20 1.40 1.62 1.001.20 1.23 1.33 1.57 1.80 1.73 1.03 1.29 1.39 1.49 1.54 1.85 1.90- 39 1.00 1.17 1.20 1.40 1.63 1.001.20 1.25 1.35 1.58 1.82 1.73 1.05 1.29 1.40 1.50 1.56 1.90 1.91- 40 1.00 1.17 1.20 1.42 1.63 1.001.20 1.25 1.36 1.60 1.83 1.74 1.11 1.32 1.42 1.52 1.57 1.92 1.91- 41 1.00 1.17 1.20 1.43 1.64 1.001.20 1.26 1.37 1.61 1.85 1.76 1.11 1.33 1.43 1.53 1.58 1.94 1.93- 42 1.00 1.18 1.21 1.44 1.65 1.001.20 1.27 1.38 1.63 1.86 1.77 1.11 1.33 1.45 1.55 1.60 1.96 1.96- 43 1.00 1.20 1.22 1.44 1.66 1.001.22 1.27 1.39 1.64 1.89 1.78 1.15 1.33 1.46 1.56 1.61 1.99 1.96- 44 1.00 1.20 1.23 1.46 1.67 1.001.22 1.29 1.40 1.66 1.90 1.78 1.18 1.35 1.47 1.58 1.62 2.02 1.96- 45 1.00 1.20 1.25 1.47 1.70 1.001.25 1.29 1.42 1.67 1.91 1.79 1.21 1.37 1.50 1.59 1.64 2.03 1.97- 46 1.00 1.20 1.25 1.48 1.70 1.041.25 1.30 1.43 1.69 1.93 1.80 1.25 1.38 1.50 1.61 1.65 2.08 1.98- 47 1.00 1.20 1.27 1.50 1.71 1.111.25 1.30 1.44 1.70 1.95 1.80 1.25 1.40 1.53 1.63 1.67 2.12 1.98- 48 1.00 1.20 1.27 1.51 1.72 1.131.29 1.31 1.45 1.72 1.97 1.81 1.25 1.40 1.54 1.64 1.68 2.16 2.00- 49 1.00 1.20 1.27 1.52 1.74 1.201.29 1.33 1.47 1.74 1.99 1.82 1.25 1.40 1.56 1.66 1.69 2.17 2.01- 50 1.00 1.20 1.29 1.54 1.74 1.251.29 1.33 1.48 1.75 2.00 1.82 1.29 1.41 1.58 1.68 1.71 2.21 2.03- 51 1.00 1.20 1.30 1.56 1.75 1.251.29 1.35 1.49 1.77 2.02 1.82 1.33 1.43 1.59 1.69 1.72 2.22 2.04- 52 1.00 1.20 1.30 1.57 1.76 1.251.33 1.36 1.51 1.79 2.05 1.83 1.33 1.44 1.61 1.71 1.74 2.24 2.06- 53 1.00 1.20 1.31 1.59 1.76 1.251.33 1.37 1.52 1.81 2.06 1.84 1.33 1.47 1.63 1.73 1.77 2.26 2.08- 54 1.00 1.20 1.32 1.61 1.77 1.251.33 1.38 1.54 1.83 2.09 1.84 1.33 1.50 1.65 1.75 1.78 2.28 2.09- 55 1.00 1.22 1.33 1.63 1.78 1.291.33 1.40 1.56 1.84 2.11 1.87 1.38 1.50 1.67 1.77 1.80 2.31 2.11- 56 1.00 1.25 1.35 1.65 1.80 1.331.33 1.40 1.57 1.86 2.14 1.88 1.43 1.50 1.69 1.79 1.82 2.37 2.14- 57 1.00 1.25 1.36 1.67 1.81 1.331.38 1.42 1.59 1.88 2.15 1.89 1.43 1.54 1.71 1.80 1.84 2.38 2.15- 58 1.00 1.27 1.37 1.69 1.81 1.331.38 1.43 1.61 1.90 2.17 1.91 1.48 1.57 1.73 1.82 1.86 2.41 2.16- 59 1.07 1.29 1.38 1.71 1.83 1.331.40 1.45 1.62 1.92 2.19 1.91 1.50 1.58 1.76 1.84 1.88 2.43 2.18- 60 1.14 1.29 1.40 1.72 1.83 1.331.40 1.46 1.64 1.94 2.21 1.93 1.50 1.60 1.79 1.86 1.91 2.47 2.19- 61 1.22 1.32 1.40 1.74 1.84 1.331.40 1.47 1.66 1.96 2.22 1.94 1.50 1.60 1.81 1.88 1.93 2.50 2.20- 62 1.25 1.33 1.42 1.75 1.91 1.431.40 1.50 1.68 1.98 2.25 1.95 1.50 1.63 1.83 1.90 1.96 2.52 2.26A-99GSD≤2.17 2.17 < GSD≤2.94 GSD > 2.94Concbin10 1 2 3 4 0 1 2 3 4 5 6 0 1 2 3 4 5 6- 63 1.25 1.33 1.44 1.78 1.93 1.501.40 1.50 1.70 1.99 2.27 1.96 1.50 1.67 1.86 1.92 1.98 2.56 2.28- 64 1.29 1.33 1.45 1.80 1.93 1.501.43 1.52 1.72 2.02 2.29 2.00 1.54 1.67 1.89 1.95 2.01 2.59 2.29- 65 1.33 1.33 1.47 1.81 1.99 1.501.43 1.54 1.74 2.04 2.31 2.02 1.62 1.70 1.92 1.97 2.04 2.62 2.31- 66 1.33 1.38 1.50 1.84 2.00 1.501.44 1.56 1.76 2.07 2.32 2.04 1.67 1.72 1.94 2.00 2.07 2.63 2.31- 67 1.33 1.40 1.50 1.87 2.05 1.501.50 1.58 1.79 2.10 2.35 2.06 1.67 1.76 1.99 2.02 2.10 2.67 2.33- 68 1.33 1.40 1.53 1.89 2.08 1.501.50 1.60 1.81 2.12 2.37 2.07 1.71 1.80 2.00 2.04 2.14 2.70 2.35- 69 1.36 1.40 1.55 1.91 2.09 1.501.50 1.62 1.83 2.15 2.41 2.08 1.80 1.80 2.03 2.07 2.18 2.72 2.36- 70 1.50 1.40 1.58 1.94 2.11 1.501.56 1.64 1.85 2.17 2.43 2.11 1.86 1.83 2.07 2.10 2.21 2.77 2.37- 71 1.50 1.40 1.60 1.97 2.15 1.501.57 1.67 1.88 2.20 2.44 2.13 2.00 1.86 2.10 2.13 2.24 2.81 2.39- 72 1.50 1.43 1.62 2.00 2.18 1.501.60 1.69 1.91 2.23 2.49 2.14 2.00 1.89 2.14 2.15 2.29 2.85 2.41- 73 1.50 1.43 1.64 2.02 2.19 1.671.60 1.71 1.94 2.26 2.51 2.15 2.00 1.98 2.18 2.18 2.33 2.90 2.44- 74 1.50 1.49 1.67 2.04 2.23 1.671.60 1.73 1.97 2.30 2.55 2.17 2.00 2.00 2.21 2.21 2.37 2.93 2.50- 75 1.50 1.50 1.69 2.07 2.26 1.681.67 1.77 2.00 2.33 2.61 2.17 2.00 2.00 2.25 2.25 2.42 2.98 2.51- 76 1.50 1.50 1.71 2.10 2.27 1.751.67 1.80 2.03 2.36 2.63 2.21 2.00 2.03 2.29 2.28 2.48 3.01 2.53- 77 1.50 1.56 1.73 2.12 2.28 2.001.71 1.82 2.06 2.41 2.69 2.23 2.00 2.13 2.33 2.32 2.54 3.03 2.53- 78 1.50 1.57 1.77 2.15 2.31 2.001.75 1.86 2.10 2.46 2.73 2.24 2.00 2.17 2.38 2.36 2.60 3.09 2.54- 79 1.67 1.60 1.80 2.19 2.40 2.001.80 1.90 2.13 2.49 2.75 2.27 2.00 2.20 2.43 2.40 2.68 3.12 2.56- 80 1.75 1.60 1.83 2.22 2.46 2.001.80 1.93 2.17 2.54 2.79 2.27 2.00 2.24 2.49 2.44 2.77 3.17 2.58- 81 2.00 1.63 1.87 2.27 2.47 2.001.83 2.00 2.22 2.60 2.84 2.30 2.00 2.31 2.54 2.48 2.85 3.21 2.60- 82 2.00 1.67 1.91 2.30 2.47 2.001.86 2.00 2.26 2.67 2.87 2.31 2.29 2.37 2.60 2.53 2.95 3.25 2.64- 83 2.00 1.71 1.94 2.36 2.49 2.001.96 2.08 2.31 2.72 2.89 2.33 2.50 2.40 2.65 2.58 3.05 3.30 2.65- 84 2.00 1.77 2.00 2.43 2.53 2.002.00 2.11 2.36 2.78 2.92 2.37 2.50 2.50 2.71 2.63 3.13 3.35 2.69- 85 2.00 1.80 2.00 2.48 2.68 2.002.00 2.18 2.41 2.85 2.97 2.44 2.75 2.57 2.79 2.69 3.24 3.39 2.71- 86 2.00 1.83 2.09 2.56 2.74 2.002.11 2.23 2.47 2.90 3.00 2.48 3.00 2.63 2.87 2.75 3.35 3.46 2.73- 87 2.00 1.86 2.13 2.63 2.78 2.002.17 2.30 2.54 2.97 3.11 2.56 3.00 2.73 2.94 2.82 3.47 3.54 2.81- 88 2.00 1.97 2.20 2.69 2.81 2.002.20 2.38 2.60 3.06 3.19 2.59 3.33 2.83 3.00 2.89 3.62 3.59 2.84- 89 2.00 2.00 2.27 2.79 2.85 2.002.30 2.45 2.68 3.14 3.25 2.62 3.33 2.94 3.10 2.96 3.73 3.68 2.84- 90 2.00 2.00 2.33 2.88 2.96 2.252.40 2.54 2.76 3.26 3.31 2.66 4.00 3.00 3.20 3.06 3.86 3.78 2.94- 91 2.00 2.14 2.42 2.97 3.06 2.502.50 2.64 2.86 3.36 3.41 2.67 4.67 3.18 3.31 3.16 4.03 3.88 2.97- 92 2.00 2.20 2.54 3.08 3.24 2.502.60 2.75 2.96 3.44 3.55 2.68 5.00 3.33 3.46 3.28 4.22 3.98 3.02- 93 2.00 2.29 2.64 3.24 3.39 3.002.78 2.89 3.08 3.59 3.67 2.70 5.50 3.50 3.61 3.41 4.41 4.10 3.06- 94 2.25 2.40 2.79 3.50 3.55 3.002.92 3.02 3.22 3.74 3.84 2.71 10.00 3.71 3.77 3.57 4.65 4.18 3.12- 95 2.50 2.56 2.93 3.61 3.68 3.003.14 3.21 3.40 3.92 3.92 3.01 10.00 4.00 4.00 3.78 4.94 4.35 3.16A-100GSD≤2.17 2.17 < GSD≤2.94 GSD > 2.94Concbin10 1 2 3 4 0 1 2 3 4 5 6 0 1 2 3 4 5 6- 96 3.00 2.71 3.13 3.91 3.93 3.503.40 3.44 3.62 4.12 4.22 3.11 10.00 4.25 4.23 4.07 5.23 4.44 3.27- 97 3.00 3.00 3.40 4.23 4.13 4.003.75 3.73 3.93 4.35 4.38 3.41 10.00 4.67 4.57 4.48 5.59 4.58 3.33- 98 3.50 3.29 3.85 4.71 4.49 5.004.20 4.17 4.37 4.84 4.52 3.44 10.00 5.22 5.04 5.06 6.11 4.89 3.35- 99 4.00 4.00 4.68 5.58 5.09 6.005.14 5.00 5.20 5.50 5.13 3.79 10.00 6.20 5.91 6.19 6.89 5.45 3.51- 100 11.75 11.57 11.94 10.14 6.10 11.7511.50 11.93 11.41 9.67 6.48 5.39 11.67 11.67 11.93 11.45 11.39 6.63 3.62n3456580 54454 16117 1925 150 876986 271059 186098 49555 3888 613 219 297365 63582 55615 28545 3952 759 224Notes:11-hour SO2concentration bins are: 0 = 1-hour mean < 5 ppb; 2 = 5≤1-hour mean < 10 ppb; 2 = 10≤1-hour mean < 25 ppb; 3 = 25≤1-hour mean < 75ppb; 4 = 75≤1-hour mean < 150 ppb ; 5 = 150≤1-hour mean 250 ppb; 6 = 1-hour mean > 250 ppb.2pct –xindicates the percentile of the distribution.3nis the number of 5-minute maximum and 1-hour SO2measurements used to develop distribution.A-101 A.4 Factors Used in Adjusting Air Quality to Just Meet the Current and Potential Alternative SO 2 Air Quality Standards The adjustment factors used for simulating ju st meeting particular forms and levels of

SO 2 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 th e 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 concentrati ons used in deriving the factors applied to simulate just meeting potential alternative standards.

A.4.1 Adjustment factors for ju st meeting the current standard Both annual and daily adjustment factors we re 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 (s ee 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., C OV) was used as a measure to indicate the variability associated with each of the calculated factors when cons idering 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 count y however, year-to-year

differences in the number and location of am bient monitors may confound this comparison. Lower COVs indicate similarity in that concen tration metric in the county, while higher values indicate less homogeneity in concentrati ons (whether spatially or temporally).

Table A.4-1. Adjustment factors used in simulating air quali ty just meeting the current SO 2 NAAQS in selected counties by year.

State Abbreviation County Year Monitors (n) Adjustment Factor COV Closest Standard 1 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 DA-102 State Abbreviation County Year Monitors (n) Adjustment Factor COV Closest Standard 1 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 DA-103 State Abbreviation County Year Monitors (n) Adjustment Factor COV Closest Standard 1 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 AA-104 State Abbreviation County Year Monitors (n) Adjustment Factor COV Closest Standard 1 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 DA-105 State Abbreviation County Year Monitors (n) Adjustment Factor COV Closest Standard 1 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 DA-106 State Abbreviation County Year Monitors (n) Adjustment Factor COV Closest Standard 1 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 SO 2 concentrations were closest to either the annual (A) or daily (D) NAAQS level.A-107 A.4.2 Adjustment factors for just meet ing the potential alternative standards Five potential alternative standards (i.e., 50, 100, 150, 200, and 250 ppb daily maximum 1-hour) given a 99 th percentile form and one alternat ive standard (200 ppb daily maximum 1- hour) given a 98 th percentile form were selected for evalua tion (for details, see REA chapter 5). Adjustment factors were derived for each of tw o 3-year groups of recent air quality (i.e., 2001- 2003 and 2004-2006). For the sake of brevit y, 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 factor s 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 c oncentrations in New Castle DE to simulate just meeting a 99 th percentile daily maximum 1-hour of 100 ppb is 100/164 = 0.61. That is to say, to m eet this particular st andard, the hourly con centrations need to be adjusted downward by a factor of 0.61. The C OV is also used to represent the temporal variability over the three years of mo nitoring (where such data exist).

Table A.4-2. Concentrations used in developing adjustment factors when simulating air quality just m eeting potential alternative SO 2 NAAQS in selected counties by year.

98 th Percentile 99 th 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 19A-108 98 th Percentile 99 th 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 Cuyahoga 1 2004-2006 3 67 8 3 80 9 OH Cuyahoga 1 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 35A-109 98 th Percentile 99 th Percentile State Abbreviation County Year Group Years (n) Conc (ppb) COV (%) Years (n) Conc (ppb) COV (%) TN Shelby 1 2004-2006 3 72 35 3 85 33 TN Shelby 1 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 98 th 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 DataA-111Table A.5-1. Annual average SO2concentrations and number of measured 5-minute daily maximum SO2concentrations above potential health effect benchmark levels. Data used werefrom 98 monitors that reported boththe 5-minute maximum and 1-hour SO2concentrations for years 1997 through 2007.Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbAR Pulaski 051190007 2002 339 7138 2.76 1.43 2.44 1.65 1 0 0 0AR Pulaski 051190007 2003 365 7799 2.47 1.3 2.18 1.64 0 0 0 0AR Pulaski 051190007 2004 359 7687 2.08 1.61 1.69 1.84 0 0 0 0AR Pulaski 051190007 2005 350 6702 1.91 1.17 1.65 1.69 0 0 0 0AR Pulaski 051190007 2006 365 8356 3.2 1.13 3.03 1.39 0 0 0 0AR Pulaski 051190007 2007 90 2062 2.88 1.12 2.71 1.39 0 0 0 0AR Pulaski 051191002 1997 365 6607 2.33 1.5 1.99 1.74 0 0 0 0AR Pulaski 051191002 1998 329 5997 1.62 1.3 1.35 1.74 0 0 0 0AR Pulaski 051191002 1999 275 3833 2.31 1.51 1.85 2.04 0 0 0 0AR Pulaski 051191002 2000 352 5596 2.38 1.63 1.77 2.44 0 0 0 0AR Pulaski 051191002 2001 364 6529 2.28 1.18 2.02 1.63 0 0 0 0AR Union051390006 1997 365 7624 5.27 11.3 3.28 2.15 30 11 5 0AR Union051390006 1998 313 6766 6.4 7.45 5.14 1.73 17 3 1 0AR Union051390006 1999 275 5101 5.39 6.94 3.66 2.44 12 1 0 0AR Union051390006 2000 357 5792 6.21 10.95 3.76 2.29 44 7 2 0AR Union 051390006 2001 364 7474 3.09 3.86 2.28 2.06 5 1 1 1AR Union 051390006 2002 275 6296 2.92 2.27 2.5 1.65 1 0 0 0AR Union 051390006 2003 364 7239 2.14 5.13 1.59 1.88 2 2 2 1AR Union 051390006 2004 334 4267 2.15 2.74 1.63 1.89 3 2 0 0AR Union 051390006 2005 249 4922 2.36 2.58 1.94 1.76 2 1 0 0AR Union 051390006 2006 365 8364 2.89 2.19 2.61 1.49 1 1 1 0AR Union 051390006 2007 90 2061 2.99 1.3 2.81 1.39 0 0 0 0CO Denver080310002 1997 365 7014 6.77 9.36 3.75 2.86 23 0 0 0CO Denver080310002 1998 360 4311 7.37 9.45 4.29 2.79 18 2 0 0CO Denver 080310002 1999 156 1626 6.77 8.21 4.01 2.76 3 0 0 0CO Denver 080310002 2000 137 2434 6.53 8.62 3.84 2.69 4 0 0 0CO Denver 080310002 2001 360 5575 6.63 8.85 3.84 2.75 8 0 0 0CO Denver 080310002 2002 365 6830 5.36 7.27 3.11 2.67 6 0 0 0CO Denver 080310002 2003 362 6250 3.83 4.62 2.54 2.34 1 0 0 0CO Denver 080310002 2004 337 4412 3.68 4.09 2.48 2.31 0 0 0 0A-112Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbCO Denver 080310002 2005 337 3599 3.92 4.2 2.57 2.42 0 0 0 0CO Denver 080310002 2006 349 6199 3.38 3.62 2.33 2.26 1 0 0 0DE New Castle 100031008 1997 330 7490 10.29 17.99 5.23 2.86 103 33 1 0DE New Castle 100031008 1998 257 4898 8.86 14.99 4.35 3.03 64 16 2 0DC District of Columbia 1100100412000 160 3731 8.64 6.17 7.25 1.77 1 0 0 0DC District of Columbia 110010041 2001 358 7774 7 6.51 4.83 2.45 3 1 1 0DC District of Columbia 1100100412002 365 8365 6.89 5.62 5.29 2.11 1 0 0 0DC District of Columbia 1100100412003 181 4267 8.63 5.92 7.28 1.75 5 1 1 1DC District of Columbia 1100100412004 119 2765 7.88 5.51 6.3 2.06 1 0 0 0DC District of Columbia 1100100412007 268 6394 5.05 3.74 4.24 1.76 1 1 1 0FL Nassau 120890005 2002 357 8415 6.39 15.33 2.65 2.95 69 23 6 2FL Nassau 120890005 2003 365 8662 3.44 8.95 1.6 2.5 26 5 1 0FL Nassau 120890005 2004 275 6507 3.2 7.18 1.68 2.37 11 5 1 1FL Nassau 120890005 2005 175 4120 4.06 10.16 1.65 2.71 26 4 0 0IA Cerro Gordo 190330018 2001 38 513 1.22 3.38 0.44 3.16 0 0 0 0IA Cerro Gordo 190330018 2002 254 3325 1.16 3.83 0.33 4.07 0 0 0 0IA Cerro Gordo 190330018 2003 296 5032 1.88 7.57 0.27 4.83 4 0 0 0IA Cerro Gordo 190330018 2004 366 8141 0.8 2.84 0.23 3.4 0 0 0 0IA Cerro Gordo 190330018 2005 173 3528 0.69 1.49 0.31 3.16 0 0 0 0IA Clinton 190450019 2001 70 1276 2.14 1.69 1.52 2.54 0 0 0 0IA Clinton 190450019 2002 345 6516 3.29 3.37 1.96 3.02 3 0 0 0IA Clinton 190450019 2003 333 5939 2.89 3.2 1.68 3.14 4 1 0 0IA Clinton 190450019 2004 353 7093 2.83 3.06 1.67 3.12 3 0 0 0IA Clinton 190450019 2005 177 3323 3.99 4.31 2.35 3.11 2 0 0 0IA Muscatine 191390016 2001 91 1733 3.27 4.61 1.89 2.93 0 0 0 0IA Muscatine 191390016 2002 365 7391 4.07 5.36 2.78 2.39 4 0 0 0IA Muscatine 191390016 2003 353 6570 3.87 7.01 2.21 2.86 4 0 0 0IA Muscatine 191390016 2004 365 6664 3.92 5.67 2.43 2.7 5 0 0 0IA Muscatine 191390016 2005 181 3629 4.22 7.55 2.34 2.79 9 0 0 0IA Muscatine 191390017 2001 83 1373 2.14 1.86 1.42 2.66 0 0 0 0IA Muscatine 191390017 2002 364 7242 3.12 3.82 2.05 2.62 3 1 0 0IA Muscatine 191390017 2003 365 7586 3.93 4.26 2.69 2.51 4 0 0 0IA Muscatine 191390017 2004 363 7322 3.56 3.92 2.24 2.79 2 0 0 0IA Muscatine 191390017 2005 181 3441 3.16 4.14 2.03 2.59 4 0 0 0A-113Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbIA Muscatine 191390020 2001 92 1909 5.36 9.76 2.04 3.95 1 0 0 0IA Muscatine 191390020 2002 363 7682 5.27 10.27 2.22 3.61 31 1 0 0IA Muscatine 191390020 2003 365 7695 5.31 11.2 2.11 3.71 42 5 0 0IA Muscatine 191390020 2004 366 7757 7.36 15.39 3.02 3.2 60 14 0 0IA Muscatine 191390020 2005 181 3931 5.55 13.61 2.02 3.64 27 12 1 0IA Scott 191630015 2001 85 1345 1.15 2.13 0.45 4 0 0 0 0IA Scott 191630015 2002 364 7505 2.28 3.17 0.87 4.7 0 0 0 0IA Scott 191630015 2003 364 7451 2.09 2.68 1.02 3.79 0 0 0 0IA Scott 191630015 2004 336 6696 2.11 2.65 1.06 3.68 0 0 0 0IA Scott 191630015 2005 177 3436 2.56 3.05 1.17 4.17 0 0 0 0IA Van Buren 191770005 2001 65 597 0.9 0.92 0.64 2.33 0 0 0 0IA Van Buren 191770005 2002 353 6350 1.03 0.92 0.72 2.48 0 0 0 0IA Van Buren 191770005 2003 358 7118 1.1 0.91 0.78 2.48 0 0 0 0IA Van Buren 191770005 2004 305 5011 0.88 1.45 0.5 2.87 0 0 0 0IA Van Buren 191770006 2004 53 877 0.85 0.94 0.55 2.53 0 0 0 0IA Van Buren 191770006 2005 181 3349 0.9 0.79 0.69 2.09 0 0 0 0IA Woodbury 191930018 2001 85 1578 1.32 2.28 0.77 2.45 0 0 0 0IA Woodbury 191930018 2002 280 3875 1.5 2.94 0.7 3.14 0 0 0 0LA West Baton Rouge 221210001 1997 277 4966 7.04 12.51 3.94 2.65 42 13 4 1LA West Baton Rouge 221210001 1998 353 7566 7.52 10.67 5.03 2.29 50 18 2 1LA West Baton Rouge 221210001 1999 354 7272 6.4 9.59 4.01 2.44 55 12 1 1LA West Baton Rouge 221210001 2000 361 7360 7.3 11.13 4.51 2.46 76 26 7 1MO Buchanan 290210009 1997 361 8484 8.3 31.64 2.77 2.8 94 79 57 39MO Buchanan 290210009 1998 364 8161 7.06 24.17 2.8 2.64 92 67 44 19MO Buchanan 290210009 1999 362 7415 2.77 3.07 2.08 2 3 0 0 0MO Buchanan 290210009 2000 264 5297 2.37 3.04 1.81 1.88 7 0 0 0MO Buchanan 290210011 2000 72 1672 5.27 8.53 3.45 2.15 8 0 0 0MO Buchanan 290210011 2001 329 6412 3.7 5.3 2.52 2.15 6 0 0 0MO Buchanan 290210011 2002 331 6457 4.01 7.33 2.52 2.23 21 0 0 0MO Buchanan 290210011 2003 253 5141 4.06 7.04 2.59 2.25 13 0 0 0MO Greene290770026 1997 339 4763 4.32 9.65 2.02 2.69 20 2 0 0MO Greene290770026 1998 350 5810 5.73 11.66 2.35 3.07 39 1 0 0MO Greene290770026 1999 362 7242 4.09 7.53 2.22 2.5 13 1 0 0MO Greene290770026 2000 366 8721 4.97 10.21 2.41 2.67 52 1 0 0A-114Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbMO Greene290770026 2001 365 8304 4.52 9.62 2.17 2.63 36 0 0 0MO Greene290770026 2002 360 7054 4.28 9.08 1.94 2.72 27 0 0 0MO Greene 290770026 2003 362 7935 3.5 6.16 2.02 2.36 5 0 0 0MO Greene 290770026 2004 274 6574 3.21 6.41 1.64 2.45 3 0 0 0MO Greene 290770026 2005 365 8756 2.95 5.94 1.58 2.35 5 0 0 0MO Greene 290770026 2006 365 8753 3.15 6.77 1.58 2.42 8 0 0 0MO Greene 290770026 2007 272 6520 3.2 7.07 1.59 2.43 9 0 0 0MO Greene290770037 1997 356 6559 4.98 14.73 1.89 2.78 52 21 8 5MO Greene290770037 1998 361 8134 4.27 7.37 2.76 2.18 30 2 0 0MO Greene290770037 1999 363 8554 3.13 7.72 1.72 2.23 31 3 0 0MO Greene290770037 2000 341 5318 6.36 17.9 2.13 3.04 46 23 3 0MO Greene290770037 2001 355 6707 4.04 10.65 1.91 2.49 37 9 2 0MO Greene290770037 2002 335 6373 4 9.68 2.15 2.27 40 11 1 0MO Greene290770037 2003 363 8179 3.32 6.96 1.93 2.21 19 1 0 0MO Greene290770037 2004 274 6575 2.71 4.79 1.79 2.05 13 0 0 0MO Greene290770037 2005 365 8760 3.05 6.06 1.93 2.11 20 1 0 0MO Greene290770037 2006 365 8745 3.26 8.44 1.57 2.38 37 4 0 0MO Greene290770037 2007 272 6496 2.42 6.03 1.37 2.08 16 0 0 0MO Iron 290930030 1997 365 8575 8.24 26.43 3.12 2.89 93 78 63 54MO Iron 290930030 1998 365 8475 7.9 25.09 2.73 2.99 85 70 62 52MO Iron 290930030 1999 356 6546 9.33 28.07 3.29 3.09 83 74 63 49MO Iron 290930030 2000 324 4071 14.3 46.11 3.2 3.95 95 77 69 55MO Iron 290930030 2001 356 5388 9.32 32.18 2.37 3.41 88 74 64 56MO Iron 290930030 2002 354 7960 6.95 23.55 2.2 2.98 99 73 58 52MO Iron 290930030 2003 363 6963 7.58 23.2 2.69 2.94 99 81 64 48MO Iron 290930030 2004 90 1846 2.47 2.56 1.76 2.11 0 0 0 0MO Iron 290930031 1997 352 6177 8.09 24.57 2.92 3.17 77 55 37 27MO Iron 290930031 1998 363 7991 7.56 22.94 3.03 2.94 88 57 37 22MO Iron 290930031 1999 341 7918 8.41 25.99 3.93 2.63 92 54 37 23MO Iron 290930031 2000 332 5170 8.27 24.93 2.81 3.21 86 53 35 23MO Iron 290930031 2001 365 8426 6.62 23.42 2.47 2.79 95 60 40 22MO Iron 290930031 2002 365 8665 6.32 18.53 3.19 2.35 88 54 28 19MO Iron 290930031 2003 350 8230 6.6 21.05 2.89 2.64 88 54 39 23MO Iron 290930031 2004 91 2172 3.82 2.74 3.2 1.74 0 0 0 0A-115Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbMO Jefferson 290990004 2004 346 8033 10.32 22.63 4.78 2.96 106 41 26 13MO Jefferson 290990004 2005 351 7144 11.41 24.87 4.62 3.34 118 68 47 28MO Jefferson 290990004 2006 343 6524 13.12 27.2 4.3 4.02 134 78 53 41MO Jefferson 290990004 2007 90 2125 6.31 11.92 3.08 2.88 21 8 4 1MO Jefferson 290990014 1997 359 7174 8.38 19 4.14 2.79 87 54 31 23MO Jefferson 290990014 1998 365 7770 4.57 9.67 2.62 2.48 37 23 13 6MO Jefferson 290990014 1999 363 7591 4.6 9.49 2.48 2.57 32 19 11 5MO Jefferson 290990014 2000 361 6588 3.87 7.06 2.36 2.35 28 7 4 2MO Jefferson 290990014 2001 132 2433 3.15 5.64 1.95 2.25 7 1 0 0MO Jefferson 290990017 1998 289 5721 7.37 18.87 3.47 2.87 59 33 22 16MO Jefferson 290990017 1999 360 7289 8.65 22.19 3.8 3.01 90 57 42 29MO Jefferson 290990017 2000 355 7153 6.06 16.54 2.87 2.77 59 40 26 17MO Jefferson 290990017 2001 74 1044 7.72 16.53 3.69 3.02 13 9 5 3MO Jefferson 290990018 2001 219 3492 5.33 11.74 2.53 2.84 34 18 9 6MO Jefferson 290990018 2002 352 6305 5.51 14.84 2.59 2.75 56 36 24 18MO Jefferson 290990018 2003 272 6009 4.41 10.38 2.4 2.54 27 18 10 9MO Monroe 291370001 1997 364 8280 2.92 2.86 2.38 1.79 0 0 0 0MO Monroe 291370001 1998 364 8411 2.35 2.25 1.86 1.87 0 0 0 0MO Monroe 291370001 1999 365 8714 3.58 2.36 3.13 1.63 0 0 0 0MO Monroe 291370001 2000 366 8617 2.93 2.06 2.54 1.65 0 0 0 0MO Monroe 291370001 2001 309 4346 1.78 1.44 1.47 1.74 0 0 0 0MO Monroe 291370001 2002 321 5358 1.81 1.48 1.48 1.75 0 0 0 0MO Monroe 291370001 2003 336 5948 1.82 1.48 1.51 1.73 0 0 0 0MO Monroe 291370001 2004 316 5123 2.29 2.31 1.77 1.91 0 0 0 0MO Monroe 291370001 2005 348 6518 2.03 1.81 1.63 1.81 0 0 0 0MO Monroe 291370001 2006 338 6169 1.73 1.26 1.47 1.68 0 0 0 0MO Monroe 291370001 2007 51 526 1.86 2 1.48 1.8 0 0 0 0MO Pike 291630002 2005 311 4879 4.37 5.43 2.89 2.33 5 0 0 0MO Pike 291630002 2006 348 6469 3.94 4.67 2.78 2.2 3 0 0 0MO Pike 291630002 2007 68 1019 3.08 3.69 2.09 2.24 0 0 0 0MO Saint Charles 291830010 1997 365 8152 4.35 7.95 2.6 2.45 5 1 1 1MO Saint Charles 291830010 1998 230 4810 4.32 5.69 2.77 2.38 1 0 0 0MO Saint Charles 291831002 1997 365 8514 5.72 6.95 3.65 2.5 23 2 1 0MO Saint Charles 291831002 1998 362 8122 6.31 7.9 4.02 2.5 25 0 0 0A-116Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbMO Saint Charles 291831002 1999 363 7969 5.61 7.24 3.58 2.5 17 2 0 0MO Saint Charles 291831002 2000 331 6421 4.6 5.45 3.01 2.42 5 0 0 0MT Yellowstone 301110066 1997 362 6873 8.06 10.76 4.4 3 45 7 1 1MT Yellowstone 301110066 1998 357 7198 7.14 9.49 4 2.9 42 5 1 0MT Yellowstone 301110066 1999 352 5767 7.75 9.65 4.31 2.99 34 5 0 0MT Yellowstone 301110066 2000 355 6099 7.72 10.26 4.25 2.97 66 7 2 1MT Yellowstone 301110066 2001 365 6872 7.77 10.46 4.13 3.06 56 4 0 0MT Yellowstone 301110066 2002 364 8347 6.81 11.61 3.46 3.04 52 4 2 1MT Yellowstone 301110066 2003 347 5691 7.37 9.92 4.06 2.93 39 2 0 0MT Yellowstone 301110079 1997 180 3166 3.84 4.06 2.65 2.28 1 0 0 0MT Yellowstone 301110079 2001 55 837 4.64 3.71 3.43 2.23 0 0 0 0MT Yellowstone 301110079 2002 353 8034 1.9 1.91 1.48 1.83 0 0 0 0MT Yellowstone 301110079 2003 350 5107 3.02 2.55 2.3 2.06 0 0 0 0MT Yellowstone 301110080 1997 363 5433 7.54 10.11 4.29 2.86 59 11 3 0MT Yellowstone 301110080 1998 358 5371 6.85 9.12 3.98 2.79 38 14 6 0MT Yellowstone 301110080 1999 350 5588 6.36 7.81 3.79 2.75 47 7 4 2MT Yellowstone 301110080 2000 360 5999 6.22 7.65 3.68 2.74 59 10 1 0MT Yellowstone 301110080 2001 150 2015 5.55 6.3 3.54 2.56 12 2 1 1MT Yellowstone 301110082 2001 169 2605 4.19 4.62 2.87 2.32 1 0 0 0MT Yellowstone 301110082 2002 365 8212 2.32 2.77 1.7 1.99 0 0 0 0MT Yellowstone 301110082 2003 361 5173 2.93 3.25 2.11 2.11 1 1 0 0MT Yellowstone 301110083 1999 112 2087 8.07 8.01 5.01 2.81 4 0 0 0MT Yellowstone 301110083 2000 341 3845 4.68 5.36 3 2.49 10 1 1 1MT Yellowstone 301110083 2001 357 5604 4.36 5.59 2.71 2.51 11 1 0 0MT Yellowstone 301110083 2002 360 6847 2.31 3.21 1.65 1.98 1 0 0 0MT Yellowstone 301110083 2003 166 1641 2.29 3.08 1.62 1.99 0 0 0 0MT Yellowstone 301110084 2003 99 759 2.99 4.51 1.99 2.19 0 0 0 0MT Yellowstone 301110084 2004 294 2465 3.48 5.45 2.14 2.37 2 0 0 0MT Yellowstone 301110084 2005 291 2577 2.96 4.98 1.79 2.28 2 0 0 0MT Yellowstone 301110084 2006 273 1983 2.75 4.56 1.71 2.23 1 0 0 0MT Yellowstone 301112008 1997 177 2579 3.96 4.57 2.65 2.35 2 0 0 0NC Forsyth 370670022 1997 362 7822 7.06 6.91 5.13 2.2 10 0 0 0NC Forsyth 370670022 1998 364 7122 6.98 7.54 4.72 2.48 13 1 1 1NC Forsyth 370670022 1999 352 6428 5.85 5.92 4.13 2.29 3 0 0 0A-117Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbNC Forsyth 370670022 2000 266 5203 5.52 5.58 3.77 2.39 1 0 0 0NC Forsyth 370670022 2001 361 7634 5.12 5.64 3.46 2.38 5 0 0 0NC Forsyth 370670022 2002 362 7022 6.12 8.19 3.87 2.51 15 3 0 0NC Forsyth 370670022 2003 363 8075 5.87 6.19 4.17 2.24 11 0 0 0NC Forsyth 370670022 2004 259 4710 5.56 8.21 3.37 2.55 6 1 0 0NC New Hanover 371290006 1999 360 8208 4.1 8.34 1.92 2.73 54 8 4 3NC New Hanover 371290006 2000 335 7980 4.67 8.92 2.13 2.87 76 6 3 0NC New Hanover 371290006 2001 358 8168 5.71 13.73 2.08 3.09 109 54 10 3NC New Hanover 371290006 2002 352 8028 6.44 13.85 2.61 3.12 127 39 7 2ND Billings 380070002 1998 143 1940 1.31 1.04 1.16 1.48 0 0 0 0ND Billings 380070002 1999 276 3216 1.38 1.04 1.21 1.53 0 0 0 0ND Billings 380070002 2000 248 2724 1.42 1.1 1.24 1.56 0 0 0 0ND Billings 380070002 2001 283 2860 1.37 1.12 1.2 1.51 0 0 0 0ND Billings 380070002 2002 275 3113 1.43 1.11 1.26 1.53 0 0 0 0ND Billings 380070002 2003 26 341 1.48 0.87 1.32 1.54 0 0 0 0ND Billings 380070002 2004 164 1256 1.24 0.85 1.13 1.41 0 0 0 0ND Billings 380070002 2005 128 835 1.44 0.92 1.27 1.55 0 0 0 0ND Billings 380070002 2006 106 418 1.53 1.25 1.29 1.64 0 0 0 0ND Billings 380070002 2007 43 221 1.5 1.26 1.29 1.6 0 0 0 0ND Billings 380070003 1997 167 2657 1.72 1.52 1.43 1.7 0 0 0 0ND Burke 380130002 1999 297 3852 2.79 4.61 1.65 2.31 3 0 0 0ND Burke 380130002 2000 347 5268 2.96 5.77 1.77 2.27 7 1 1 0ND Burke 380130002 2001 338 5653 2.72 4.97 1.62 2.25 3 1 0 0ND Burke 380130002 2002 346 5367 2.64 4.72 1.58 2.24 4 0 0 0ND Burke 380130002 2003 353 6328 2.6 4.77 1.62 2.16 7 1 0 0ND Burke 380130002 2004 340 5229 2.77 5.03 1.65 2.26 6 0 0 0ND Burke 380130002 2005 263 3098 2.88 4.99 1.67 2.33 4 0 0 0ND Burke 380130004 2003 63 882 2.89 3.99 1.84 2.26 0 0 0 0ND Burke 380130004 2004 315 3198 2.76 3.59 1.83 2.21 0 0 0 0ND Burke 380130004 2005 244 2238 2.47 3.18 1.72 2.09 0 0 0 0ND Burke 380130004 2006 302 3152 2.27 3.16 1.59 2.02 1 0 0 0ND Burke 380130004 2007 99 1227 3.8 5.18 2.27 2.53 1 0 0 0ND Burleigh 380150003 2005 60 683 3.4 2.97 2.47 2.2 0 0 0 0ND Burleigh 380150003 2006 294 3686 2.33 2.6 1.68 2.04 0 0 0 0A-118Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbND Burleigh 380150003 2007 97 947 3.77 4.32 2.49 2.36 0 0 0 0ND Cass 380171003 1997 206 2254 1.74 2.31 1.32 1.79 0 0 0 0ND Cass 380171003 1998 132 2943 1.88 1.83 1.5 1.8 0 0 0 0ND Cass 380171004 1998 162 2501 1.11 0.43 1.07 1.27 0 0 0 0ND Cass 380171004 1999 246 3325 1.32 0.75 1.2 1.46 0 0 0 0ND Cass 380171004 2000 213 1868 1.37 0.84 1.23 1.5 0 0 0 0ND Cass 380171004 2001 203 1686 1.34 0.93 1.2 1.49 0 0 0 0ND Cass 380171004 2002 274 2476 1.12 0.43 1.08 1.27 0 0 0 0ND Cass 380171004 2003 200 1297 1.25 0.82 1.15 1.41 0 0 0 0ND Cass 380171004 2004 256 3140 1.21 0.6 1.13 1.37 0 0 0 0ND Cass 380171004 2005 146 928 1.24 0.68 1.15 1.41 0 0 0 0ND Cass 380171004 2006 358 7385 0.39 0.42 0.28 2.19 0 0 0 0ND Cass 380171004 2007 116 2256 0.55 0.74 0.33 2.6 0 0 0 0ND Dunn 380250003 1997 224 3313 1.38 1.14 1.2 1.54 0 0 0 0ND Dunn 380250003 1998 242 2688 1.78 2.07 1.39 1.79 0 0 0 0ND Dunn 380250003 1999 323 5099 1.5 1.56 1.26 1.62 0 0 0 0ND Dunn 380250003 2000 353 7455 1.4 1.44 1.2 1.55 0 0 0 0ND Dunn 380250003 2001 276 3575 1.6 1.48 1.34 1.66 0 0 0 0ND Dunn 380250003 2002 334 4484 1.31 1.09 1.16 1.48 0 0 0 0ND Dunn 380250003 2003 355 7289 1.5 1.28 1.29 1.58 0 0 0 0ND Dunn 380250003 2004 347 6019 1.34 1.13 1.17 1.51 0 0 0 0ND Dunn 380250003 2005 183 1314 1.48 1.53 1.23 1.62 0 0 0 0ND Dunn 380250003 2006 262 2213 1.53 1.57 1.26 1.65 0 0 0 0ND Dunn 380250003 2007 79 667 1.65 1.5 1.37 1.69 0 0 0 0ND McKenzie 380530002 1997 238 2552 1.5 1.23 1.28 1.61 0 0 0 0ND McKenzie 380530002 1998 144 1989 1.66 1.57 1.36 1.7 0 0 0 0ND McKenzie 380530002 2001 108 754 1.31 0.84 1.18 1.47 0 0 0 0ND McKenzie 380530002 2002 262 3361 1.23 0.77 1.13 1.4 0 0 0 0ND McKenzie 380530002 2003 305 5345 1.5 1.29 1.28 1.6 0 0 0 0ND McKenzie 380530002 2004 303 4614 1.4 1.19 1.22 1.55 0 0 0 0ND McKenzie 380530002 2005 225 2515 1.29 0.82 1.17 1.46 0 0 0 0ND McKenzie 380530002 2006 276 2896 1.28 0.85 1.16 1.45 0 0 0 0ND McKenzie 380530002 2007 73 511 1.64 1.34 1.38 1.67 0 0 0 0ND McKenzie 380530104 1998 224 1525 2.38 4.92 1.59 2.04 4 0 0 0A-119Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbND McKenzie 380530104 1999 240 1500 2.3 3.7 1.66 1.97 3 3 1 0ND McKenzie 380530104 2000 294 2755 1.96 4.07 1.44 1.85 5 2 1 1ND McKenzie 380530104 2001 283 2281 1.68 1.75 1.38 1.72 1 0 0 0ND McKenzie 380530104 2002 236 1526 1.9 4.04 1.34 1.83 9 2 0 0ND McKenzie 380530104 2003 293 2333 1.98 5.29 1.3 1.84 15 3 1 0ND McKenzie 380530104 2004 271 2231 1.34 1.34 1.19 1.49 1 0 0 0ND McKenzie 380530104 2005 245 1900 1.32 2.32 1.14 1.46 2 0 0 0ND McKenzie 380530104 2006 234 1827 1.32 1.78 1.14 1.46 4 1 0 0ND McKenzie 380530104 2007 71 764 1.44 1.13 1.26 1.56 0 0 0 0ND McKenzie 380530111 1998 258 2063 3.11 7.34 1.8 2.29 7 2 0 0ND McKenzie 380530111 1999 294 2379 2.36 5.4 1.56 2.02 7 2 1 1ND McKenzie 380530111 2000 329 2805 2.68 8.27 1.65 2.1 7 5 4 2ND McKenzie 380530111 2001 336 3183 1.81 2.09 1.4 1.81 0 0 0 0ND McKenzie 380530111 2002 297 2255 1.87 3.52 1.38 1.8 8 3 1 0ND McKenzie 380530111 2003 288 2243 2.03 3.84 1.44 1.87 7 2 1 0ND McKenzie 380530111 2004 308 2857 1.82 5.94 1.27 1.72 3 1 1 0ND McKenzie 380530111 2005 296 2790 1.39 3.28 1.14 1.5 5 2 0 0ND McKenzie 380530111 2006 304 2896 1.35 2.43 1.16 1.48 4 1 0 0ND McKenzie 380530111 2007 78 722 1.61 1.89 1.3 1.69 1 1 0 0ND Mercer 380570001 1997 243 2824 2.93 4.29 1.87 2.26 0 0 0 0ND Mercer 380570001 1998 319 4735 3.33 6.47 2.09 2.28 5 2 0 0ND Mercer 380570001 1999 14 320 5.18 3.12 4.43 1.73 0 0 0 0ND Mercer 380570004 1999 334 5584 2.6 3.94 1.66 2.2 3 1 0 0ND Mercer 380570004 2000 362 7348 2.29 3.8 1.55 2.06 3 1 0 0ND Mercer 380570004 2001 338 4647 2.9 5.34 1.76 2.26 8 0 0 0ND Mercer 380570004 2002 336 3701 2.65 4.59 1.73 2.17 2 1 0 0ND Mercer 380570004 2003 351 5555 2.21 3.11 1.55 2.01 1 0 0 0ND Mercer 380570004 2004 344 4678 2.62 3.57 1.73 2.19 1 0 0 0ND Mercer 380570004 2005 273 3037 2.43 3.25 1.68 2.08 0 0 0 0ND Mercer 380570004 2006 301 2755 2.77 3.37 1.86 2.21 0 0 0 0ND Mercer 380570004 2007 107 1133 2.48 3.44 1.7 2.1 0 0 0 0ND Morton380590002 1997 346 6547 9.31 20.26 2.93 3.67 102 19 1 0ND Morton380590002 1998 290 4696 9.3 22.47 2.78 3.75 75 8 0 0ND Morton380590002 1999 359 6837 7.7 16.99 2.53 3.55 90 4 0 0A-120Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbND Morton380590002 2000 363 7964 6.47 14.58 2.22 3.31 73 3 0 0ND Morton380590002 2001 346 5947 7.48 13.57 2.81 3.5 66 2 0 0ND Morton380590002 2002 355 6258 6.26 12.03 2.49 3.25 59 1 0 0ND Morton380590002 2003 365 8033 6.25 13.66 2.33 3.18 82 3 1 0ND Morton380590002 2004 363 7532 6.74 13.2 2.62 3.29 76 2 0 0ND Morton 380590002 2005 111 1450 4.85 6.08 2.7 2.82 1 0 0 0ND Morton 380590003 1998 95 1924 3.71 7.47 2.01 2.48 8 0 0 0ND Morton380590003 1999 353 6522 5.06 8.84 2.48 2.88 41 2 1 0ND Morton380590003 2000 351 5984 4.71 8.04 2.44 2.74 24 0 0 0ND Morton380590003 2001 357 6345 4.94 8.17 2.54 2.81 27 1 0 0ND Morton380590003 2002 342 5245 4.41 7.53 2.35 2.68 26 1 0 0ND Morton380590003 2003 364 7991 3.55 6.34 1.96 2.49 27 0 0 0ND Morton380590003 2004 344 6338 4.44 7.03 2.5 2.59 24 0 0 0ND Morton 380590003 2005 106 1012 3.84 5.1 2.42 2.39 1 0 0 0ND Oliver 380650002 1997 244 2356 4.28 7.23 2.3 2.63 7 0 0 0ND Oliver380650002 1998 319 4175 3.92 7.23 2.1 2.58 12 1 0 0ND Oliver380650002 1999 349 4856 3.47 6.94 1.93 2.42 15 1 0 0ND Oliver 380650002 2000 351 4765 3.14 5.54 1.89 2.32 8 0 0 0ND Oliver 380650002 2001 214 2404 3.42 5.86 1.96 2.42 1 0 0 0ND Oliver 380650002 2002 350 4482 2.71 4.75 1.69 2.21 4 0 0 0ND Oliver380650002 2003 357 6953 2.37 5.58 1.47 2.05 10 1 0 0ND Oliver 380650002 2004 354 6138 2.76 5.16 1.65 2.24 7 1 1 0ND Oliver 380650002 2005 275 2443 3.86 6.7 2.05 2.62 6 2 0 0ND Oliver 380650002 2006 325 3369 2.85 4.32 1.77 2.28 1 0 0 0ND Oliver 380650002 2007 101 780 4.12 6.99 2.35 2.53 2 0 0 0ND Steele 380910001 1997 216 3134 1.41 0.74 1.28 1.5 0 0 0 0ND Steele 380910001 1998 202 2804 2.22 2.1 1.72 1.91 0 0 0 0ND Steele 380910001 1999 152 1845 1.25 0.79 1.14 1.42 0 0 0 0ND Steele 380910001 2000 83 805 1.11 0.4 1.07 1.26 0 0 0 0ND Williams 381050103 2002 319 2724 3.18 7.56 1.68 2.36 8 3 1 0ND Williams 381050103 2003 339 3323 2.48 3.71 1.64 2.13 3 0 0 0ND Williams 381050103 2004 348 3438 2.52 5.21 1.62 2.12 5 3 1 0ND Williams 381050103 2005 301 2331 3.51 8 1.85 2.45 20 3 1 0ND Williams 381050103 2006 322 2976 1.88 2.32 1.4 1.87 0 0 0 0A-121Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbND Williams 381050103 2007 86 834 3.35 4.62 2.07 2.4 0 0 0 0ND Williams 381050105 2002 302 2843 6.77 10.88 2.93 3.34 35 4 1 0ND Williams 381050105 2003 342 3523 5.67 9.39 2.55 3.12 13 1 0 0ND Williams 381050105 2004 346 4129 5.64 10.64 2.55 3.1 19 2 2 1ND Williams 381050105 2005 349 4492 6.79 13 2.49 3.46 52 12 1 0ND Williams 381050105 2006 262 2938 3.74 6.66 1.91 2.62 14 1 0 0ND Williams 381050105 2007 24 263 3.59 5.63 1.99 2.53 1 0 0 0PA Allegheny 420030002 1997 357 7821 12.57 15.05 7.69 2.68 70 8 2 0PA Allegheny 420030002 1998 3 72 43.18 32.27 31.63 2.43 3 1 0 0PA Allegheny 420030002 1999 325 6986 11.04 11.16 7.36 2.53 31 2 0 0PA Allegheny 420030021 1997 355 7830 18.11 18.87 11.07 2.93 87 19 5 2PA Allegheny 420030021 1998 3 72 10.22 8.23 7.48 2.27 0 0 0 0PA Allegheny 420030021 1999 362 8279 9 7.94 6.64 2.2 3 0 0 0PA Allegheny 420030021 2002 313 7291 7.32 7.33 4.49 2.85 3 0 0 0PA Allegheny 420030031 1997 362 8000 10.98 9.63 8.05 2.24 12 1 0 0PA Allegheny 420030031 1998 3 68 11.38 9.36 8.2 2.3 0 0 0 0PA Allegheny 420030031 1999 360 7443 8.98 7.84 6.43 2.33 1 0 0 0PA Allegheny 420030032 1997 364 7951 15.4 19.34 9.39 2.73 84 15 6 4PA Allegheny 420030032 1998 3 60 35.2 20.65 27.51 2.26 2 0 0 0PA Allegheny 420030032 1999 210 4326 8.18 7.8 5.66 2.41 2 0 0 0PA Allegheny 420030064 1997 361 7526 11.9 13.08 7.16 2.86 17 2 0 0PA Allegheny 420030064 1998 3 71 20.11 7.99 18.41 1.56 0 0 0 0PA Allegheny 420030064 1999 355 7232 12.11 14.34 7.35 2.78 18 3 2 1PA Allegheny 420030064 2002 350 8239 10.9 13.26 5.91 3.15 18 5 1 0PA Allegheny 420030067 1997 364 8231 10.43 11.13 6.69 2.62 12 2 1 1PA Allegheny 420030067 1998 3 72 17.01 12.54 12.63 2.25 0 0 0 0PA Allegheny 420030067 1999 257 5891 10.05 8.81 7.35 2.22 1 0 0 0PA Allegheny 420030116 1997 361 7767 13.26 17.76 8.33 2.6 60 19 12 8PA Allegheny 420030116 1998 3 70 17 11.04 12.59 2.46 0 0 0 0PA Allegheny 420030116 1999 299 5684 12.12 16.01 7.82 2.54 50 26 13 8PA Allegheny 420030116 2002 232 5403 7 7.96 4.56 2.5 3 0 0 0PA Allegheny 420031301 1997 363 7663 9.37 9.8 6.25 2.48 21 4 1 1PA Allegheny 420031301 1998 3 70 12.66 6.88 11.29 1.58 0 0 0 0PA Allegheny 420031301 1999 363 8161 9.64 9.62 6.57 2.44 21 3 1 1A-122Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbPA Allegheny 420033003 1997 356 7422 11.8 13.86 7.01 2.85 27 1 0 0PA Allegheny 420033003 1998 2 45 11.47 6.31 9.35 2.09 0 0 0 0PA Allegheny 420033003 1999 350 6998 13.59 19.91 7.86 2.86 37 2 2 2PA Allegheny 420033003 2002 316 7363 12.66 18.25 6.32 3.29 53 8 5 3PA Allegheny 420033004 1997 362 7461 9.18 9.66 6.17 2.47 12 2 0 0PA Allegheny 420033004 1998 3 66 13.12 6.01 11.71 1.65 0 0 0 0PA Allegheny 420033004 1999 361 7408 8.55 9.09 5.79 2.47 6 3 2 0PA Beaver 420070002 1997 351 7889 11.83 15.38 6.83 2.84 91 11 1 1PA Beaver 420070002 1998 270 6205 12.96 16.48 7.8 2.71 74 6 2 0PA Beaver 420070005 1997 359 7447 16.57 25.11 8.65 3.14 98 39 17 11PA Beaver 420070005 1998 277 6388 16.14 26.85 8.36 3.01 92 39 21 13PA Beaver 420070005 2002 361 8491 14.24 26.51 5.28 4.12 113 49 23 13PA Beaver 420070005 2003 365 8706 10.79 17.07 4.38 3.83 75 16 3 2PA Beaver 420070005 2004 364 8656 11.59 17.68 5.55 3.39 74 22 10 3PA Beaver 420070005 2005 362 8578 12.57 18.18 6.82 3.04 75 26 12 7PA Beaver 420070005 2006 361 8457 9.26 18.5 3.49 3.78 71 30 11 5PA Beaver 420070005 2007 324 7556 9.79 13.98 4.94 3.26 45 12 4 1PA Berks420110009 1997 350 7805 8.66 8.87 5.87 2.44 35 4 0 0PA Berks420110009 1998 365 8641 8.93 7.56 7.11 1.92 33 3 0 0PA Berks 420110009 1999 119 2790 9.22 8.38 6.86 2.17 9 1 0 0PA Cambria 420210011 1997 361 8129 9.76 9.15 6.72 2.47 8 0 0 0PA Cambria 420210011 1998 356 7908 8.78 9.69 5.65 2.62 16 1 0 0PA Cambria 420210011 1999 120 2835 9.74 7.99 7.61 1.99 1 0 0 0PA Erie420490003 1997 363 8169 9.76 11.22 6.68 2.33 60 9 1 0PA Erie420490003 1998 363 8416 10.57 13.5 7.09 2.35 60 12 1 0PA Erie420490003 1999 120 2778 11.48 15.12 7.46 2.48 26 7 3 0PA Philadelphia 421010022 1997 364 8297 8.56 8.74 5.63 2.57 7 1 0 0PA Philadelphia 421010022 1998 363 8065 7.3 7.04 4.82 2.56 2 0 0 0PA Philadelphia 421010022 1999 137 2665 7.79 8.26 4.76 2.79 4 1 0 0PA Philadelphia 421010022 2000 179 3630 7.63 6.88 5.05 2.62 1 0 0 0PA Philadelphia 421010022 2001 98 2094 7.53 7.17 5.16 2.44 0 0 0 0PA Philadelphia 421010048 1997 365 8456 8.88 18.38 4.96 2.74 59 40 27 23PA Philadelphia 421010048 1998 356 7285 6.27 6.03 4.21 2.48 0 0 0 0PA Philadelphia 421010048 1999 178 3939 6.08 6.57 3.95 2.53 1 1 0 0A-123Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbPA Philadelphia 421010136 1997 360 7532 4.99 5.52 3.29 2.43 1 0 0 0PA Philadelphia 421010136 1998 339 6491 5.25 5.52 3.5 2.44 2 0 0 0PA Philadelphia 421010136 1999 337 7144 5.63 6.04 3.71 2.48 2 1 0 0PA Philadelphia 421010136 2000 351 7044 5.76 5.97 3.74 2.54 0 0 0 0PA Philadelphia 421010136 2001 266 5149 6.77 7.43 4.38 2.55 2 0 0 0PA Philadelphia 421010136 2002 359 7271 5.38 5.7 3.57 2.47 3 0 0 0PA Philadelphia 421010136 2003 119 2585 6.74 6.71 4.62 2.42 2 0 0 0PA Warren 421230003 1997 346 7157 10.53 11.59 6.64 2.68 26 3 0 0PA Warren 421230003 1998 89 2126 7.62 7.38 5.41 2.26 0 0 0 0PA Warren 421230004 1997 355 7022 17.14 28.18 7.47 3.66 148 44 14 8PA Warren 421230004 1998 89 1966 13.97 21.76 6.8 3.18 30 6 2 0PA Washington 421250005 1997 364 8374 8.95 8.41 6.45 2.25 7 0 0 0PA Washington 421250005 1998 362 8540 8.88 7.78 6.68 2.14 4 0 0 0PA Washington 421250005 1999 120 2821 8.32 7.68 6.36 2.02 1 0 0 0PA Washington 421250200 1997 364 8369 10.52 11.23 6.99 2.45 17 0 0 0PA Washington 421250200 1998 365 8656 10.46 10.49 7.18 2.37 15 1 0 0PA Washington 421250200 1999 120 2829 10.15 9.81 7 2.4 3 1 0 0PA Washington 421255001 1997 365 8425 12.71 15.24 8.39 2.36 57 5 1 0PA Washington 421255001 1998 277 6559 13.46 13.09 10.28 1.97 42 3 0 0SC Barnwell 450110001 2000 100 789 3.95 2.83 3.39 1.66 0 0 0 0SC Barnwell 450110001 2001 267 2625 2.72 2.61 2.13 1.93 1 0 0 0SC Barnwell 450110001 2002 202 2544 2.11 1.72 1.67 1.88 0 0 0 0SC Charleston 450190003 2000 114 1703 6.24 5.36 4.77 2.02 1 0 0 0SC Charleston 450190003 2001 344 4806 4.16 4.12 2.95 2.22 1 0 0 0SC Charleston 450190003 2002 201 3509 2.85 3.49 1.97 2.16 0 0 0 0SC Charleston 450190046 2000 100 1252 4.61 3.9 3.71 1.84 0 0 0 0SC Charleston 450190046 2001 269 3497 2.64 2.6 1.99 2 0 0 0 0SC Charleston 450190046 2002 189 2927 2.34 2.89 1.68 2.02 0 0 0 0SC Georgetown 450430006 2000 71 604 4.92 4.35 3.97 1.82 0 0 0 0SC Georgetown 450430006 2001 241 2218 4.76 6.11 3.13 2.33 3 0 0 0SC Georgetown 450430006 2002 140 1169 2.5 4.33 1.67 2.08 1 0 0 0SC Greenville 450450008 2000 113 1987 4.84 3.75 3.95 1.82 0 0 0 0SC Greenville 450450008 2001 356 6418 4.24 3.86 3.18 2.1 3 0 0 0SC Greenville 450450008 2002 212 4679 3.06 2.8 2.29 2.09 1 0 0 0A-124Annual Hourly (ppb) Number of 5-minute Daily MaximumState County Monitor ID YearDays(n)Hours(n)Mean std GM GSD > 100 ppb > 200 ppb > 300 ppb > 400 ppbSC Lexington 450630008 2001 263 3941 4.2 7.8 2.37 2.44 26 3 0 0SC Lexington 450630008 2002 211 4242 4.5 8.74 2.33 2.61 22 3 0 0SC Oconee 450730001 2000 89 1218 3.85 2.87 3.26 1.7 0 0 0 0SC Oconee 450730001 2001 288 4304 2.9 2.1 2.35 1.89 0 0 0 0SC Oconee 450730001 2002 188 3063 1.82 1.52 1.43 1.95 0 0 0 0SC Richland 450790007 2000 110 1808 4.48 2.81 3.86 1.69 0 0 0 0SC Richland 450790007 2001 365 6419 3.88 3.47 2.99 2.02 0 0 0 0SC Richland 450790007 2002 210 4335 2.95 2.71 2.23 2.04 0 0 0 0SC Richland 450790021 2000 109 911 4.43 5.47 3.4 1.85 0 0 0 0SC Richland 450790021 2001 283 2700 3.73 4.89 2.64 2.1 0 0 0 0SC Richland 450790021 2002 202 2505 2.94 4.85 1.92 2.16 0 0 0 0SC Richland 450791003 2001 193 3346 3.14 2.8 2.46 1.96 0 0 0 0SC Richland 450791003 2002 212 4323 2.87 2.8 2.16 2.04 0 0 0 0UT Salt Lake 490352004 1997 335 4524 2.31 2.5 1.76 1.94 6 1 0 0UT Salt Lake 490352004 1998 354 5792 1.94 1.66 1.58 1.78 0 0 0 0WV Wayne 540990002 2002 365 8711 7.49 7.14 5.13 2.42 1 0 0 0WV Wayne 540990003 2002 361 7417 8.48 9.1 5.21 2.75 7 2 1 1WV Wayne 540990003 2003 362 8057 8.76 9.73 5.56 2.58 8 0 0 0WV Wayne 540990003 2004 366 8659 9.21 9.46 6.38 2.31 5 1 0 0WV Wayne 540990003 2005 365 8141 9.58 11.8 5.96 2.61 6 0 0 0WV Wayne540990004 2002 362 8560 9.21 9.18 6.37 2.37 22 1 1 1WV Wayne540990004 2003 365 8570 8.53 9.77 5.84 2.35 26 4 3 1WV Wayne 540990004 2004 366 8673 7.22 6.66 5.36 2.12 6 0 0 0WV Wayne 540990004 2005 363 8586 7.67 6.39 5.97 2 7 0 0 0WV Wayne540990005 2002 365 8283 8.44 9.75 5.38 2.58 67 3 0 0WV Wayne540990005 2003 365 7927 8.31 11.03 5.02 2.7 52 20 5 0WV Wayne 540990005 2004 366 8681 7.03 5.92 5.25 2.16 2 0 0 0WV Wayne 540990005 2005 365 8453 6.68 5.52 4.89 2.26 4 1 0 0WV Wood 541071002 2001 92 2152 7.76 12.51 4.04 3.04 9 3 2 1WV Wood541071002 2002 365 8648 9.9 11.29 6.21 2.63 42 7 1 0WV Wood541071002 2003 365 8641 9.48 12.26 5.8 2.61 53 9 2 0WV Wood541071002 2004 366 8581 10.88 13.25 7 2.55 57 13 3 1WV Wood541071002 2005 266 6219 8.34 12.71 4.07 3.23 42 12 1 1B-1 

## Appendix B: Supplement to the SO 2 

## Exposure AssessmentB-2 

## B.1 OVERVIEW This appendix contains supplemental descrip tions of the methods and data used in the

SO 2 exposure assessment, as well as detailed re sults from the exposure analyses performed. First, a broad description of the exposure m odeling 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 measurem ents in each modeling domain. Section B.4 has additional input and out put data for APEX. A series of Attachments also follow, furthe r documenting some of the data sources and modeling approaches used, as well as previous ly conducted uncertainty analyses on selected input parameters in APEX:

Attachment 1. Technical Memorandum on Mete orological Data Preparation for AERMOD for

SO 2 REA for Greene County And St. L ouis Modeling Domains, Year 2002.

Attachment 2. Technical Memorandum on the Analysis of NHIS Asthma Prevalence Data.

Attachment 3. Technical Memorandum on Estimati ng Physiological Parame ters for the Exposure

Model

Attachment 4. Technical Memorandum on L ongitudinal 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 Uncerta inty Analysis of Residential Air Exchange

Rate Distributions

Attachment 8. Technical Memorandum on the Dist ributions of Air Exchange Rate Averages Over Multiple DaysB-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 pollu tants at the local, urban, and consolidated metropolitan levels. APEX, al so known as TRIM.Expo, is the human inhalation exposure module of EPA’s Total Risk Integr ated 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 po llutants. It is devel oped to support evaluations with a scientifically sound, fl exible, and user-friendly methodology. Additional information on the TRIM modeling system, as well as downloads of the APEX Model, us er’s guide, and other supporting documentation, are on EPA’s T echnology Transfer Network (TTN) at http://www.epa.gov/ttn/fera. 

### B.2.1 History

APEX was derived from the National Am bient Air Quality Standards (NAAQS) Exposure Model (NEM) series of models, deve loped to estimate exposure to the criteria pollutants (e.g., carbon monoxide (CO), ozone O 3 ). 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., 19 79). These data were then co mbined 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 pr obabilistic 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 O 3 (i.e., pNEM/O 3 ). In 1991, a probabilistic version of NEM was extended to CO (pNEM/CO) that include d a one-compartment mass-balance model to

estimate CO concentrations in indoor microenviro nments. The application of this model to Denver, Colorado has been documented in Johnson et al. (1992). Additi onal enhancements to pNEM/O 3 in the early- to mid-1990’ s allowed for probabilistic e xposure assessments in nine urban areas for the general population, outdoor children, and outdoor workers (Johnson et al., 1996a; 1996b; 1996c). Between 1999 and 2001, update d versions of pNEM/CO (versions 2.0 and 2.1) were developed that re lied on activity diary data from EPA’s Consolidated Human Activities Database (CHAD) and enhanced algorith ms for simulating gas stove usage, estimating alveolar ventilation rate (a measure of human respirati on), 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 fr om national databases, a series of new output tables providing summary exposure a nd dose statistics, and a thoroughly reorganized method of describing microenvironments and their pa rameters. 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 Technica l 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 expos ure 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 th eir 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 individu als who live in (or work and live in) a geographic area and then continuously monitoring their act ivities and subsequent inhalati on 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 bala nced according to various demographic A microenvironmentis 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 rela tive frequencies, in order to obtain a representative sample (to the extent possibl e) of the actual people in the study area

 The activity patterns of the sampled indivi duals (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 es timated by the model using a set of userinput ambient outdoor concentrations (eithe r 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 alternativ e ambient standards, or by reducing source emissions and obtaining resulting air quali ty modeling outputs that reflect these potential emission reductions, and

 The model accounts for the most signifi cant factors contributing to inhalation exposure – the temporal and spatial distribution of people and pollutant concentrations throughout the study area a nd 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 populat ion 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 simu lated to represent this population. APEX then generates a personal profile for each simulated pe rson that specifies various parameter values required by the model. The model next uses di ary-derived time/activity data matched to each personal profile to generate an exposu re 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 pe rformed. 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, dependingB-6 on the microenvironment, available data, and es timation method selected by the user. Because the modeled individuals represent a random sample of the population of in terest, the distribution of modeled individual exposures can be extra polated to the larger population. Additional discussion regarding the fi ve basic exposure modeli ng steps noted in the SO 2 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 mode l larger areas such as combined statistical areas (CSAs). In the exposure analyses perfor med 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 distribu tion of age, gender, race, and work status within the study population. Each block has a lo cation specified by latitu de 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 cens us tracts, block groups, and blocks in the 50 United States, based on th e 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 expo sure analysis used modeled air quality data, whereas the principal emission so urces included both mobile and st ationary sources as well as fugitive emissions. Air quality data used for input to APEX were generated using AERMOD, a steady-state, Gaussian plume m odel (US EPA, 2004). The following steps were performed using

AERMOD. In APEX, the ambient air quality data are assi gned to geographic areas called districts.

The districts are used to a ssign pollutant concentrations to the blocks/tracts and microenvironments being modeled. The ambient ai r 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 calcula tes 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 dist rict. Each block/tract can beB-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 (loca tions). 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 W eather 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 fi le, contains the locations of meteorological data recordings expressed in latitude and longitude coordinates. This file also c ontains start and end dates for the data recording periods. The temp erature data file contains the data from the locations in the temperature zone location file. This file contains hourly temp erature readings for the period being modeled for the meteorological st ations 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 simulate d person is represented by a personal profile, a summary of personal attributes th at define the individual. APEX generates the simulated person or profile by probabilisti cally selecting values for a set of pr ofile 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 ba sed on literature-deri ved distribution data that change daily duri ng the simulation period.

APEX first selects demographic and physical attributes for ea ch specified individual, and

then follows the individual over time and calcu lates 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 Indicate s employment outside home Air conditioner Indicates presen ce of air conditioning at home Physical Gas Stove Indicates presen ce of gas stove at home

Population Demographics APEX takes population characteristics into acco unt to develop accurate representations of study area demographics. Specifically, populat ion 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 populati on data provided. As long as the data are available, any resolution can be used (e.g., c ounty, census tract, census block). For this application of the model, census block level data were used. Block-le vel population counts come from the 2000 Census of Population and Housing Summ ary File 1 (SF-1). This file contains the 100-percent data, which is the information compil ed from the questions asked of all people and about every housing unit. As part of the population demographics input s, it is important to integrate working patterns into the a ssessment. In the 2000 U.S. Census, es timates 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/ag e 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 conduc ted at the census block leve l, 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 Cens us Transportation Planning Package (CTPP) (US DOT, 2007). The

data used contain counts of indi viduals 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 di fferentiate people that work at home from those that

commute within their home tract.

Commuting Distance Cutoff A preliminary data analysis of the home-wor k 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 relationshi p also had a fairly constant sl ope 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 distan ce. 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 patt erns 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 da ily. This meant that the list of destinations for each home tract was restricted to only those work tracts that are w ithin 120 km of the home tract. When the same cutoff was performed on the 1990 census data, it resu lted in 4.75% of the home-work pairs in the nationwide database being eliminated, repr esenting 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 eliminat ed from the database for various reasons. A fair number of tract-to-tract pair s represented workers who either worked outside of the U.S.B-10 (9,631 tract pairs with 107,595 workers) or work ed 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 tr eatment of persons in the modeled population

who commute to destinations out side the study area. By specify ing “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 modele d. 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 concentra tion is assumed to be related to the average

concentration over all air districts at the sa me point in time, and is calculated as:

LeaverAdd tavg LeaverMult ion Concentrat Ambient    )( 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 concentra tion 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 sa me function as applies inside the study

area. Block-level commuting For census block simulations, APEX require s block-level commuting file. A special software preprocesser was created to generate these files for APEX on the basis of the tract-levelB-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).

land pop tract block FFFlow Flow   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 p opulation residing in the home block.

F land = fraction of work tract’s commercia l/industrial land area in the work

block

Thus, it is assumed that the frequency of co mmuting to a workplace block within a tract is proportional to the amount of commerc ial and industrial land in the block.

Profile Functions

A Profile Functions file contains settings us ed to generate results for variables related to

simulated individuals. While cer tain settings for individuals are generated automatically by APEX based on other input files, including demogr aphic characteristics, others can be specified using this file. For example, the file may cont ain settings for determin ing 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 prev alence 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 de rivation of particular da ta 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 criti cal to understand thei r 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 ac tivity pattern surveys with vari ed structures (Table B.2-2), however the surveys have commonality in cont aining 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 ad justed 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 contai ns 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 microenvir onments; this file is discussed in the section describing microenvironmental calcul ations (Section B.2.2.4.4).

Personal Information file

Personal attribute data are c ontained 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 codeB-13  Time, in minutes, during this diary day for wh ich no data are included in the database

Diary Events file The human activity diary data ar e contained in the events file that is distributed with

APEX. This file contains the activities for the nearly 23,000 people with in tervals ranging from

one minute to one hour. An individu als’ diary varies in length fr om 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 activit y pattern studies used in CHAD.

Construction of Longitudinal Activity Sequences Typical time-activity pattern data available fo r inhalation exposure modeling consist of a sequence of location/activity combinations span ning 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 exampl e, even for pollutant h ealth effects with short averaging times (e.g., SO 2 5-minute average concentration) it may be desirable to know the frequency of exceedances of a concentration ove r a long period of time (e.g., the annual number of exceedances of a 24-hour average SO 2 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 met hod used for combining them will influence the variability of the long-term activ ity patterns across the simulate d 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 expos ed multiple times to short-term high-end

concentrations. A common approach for construc ting 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 ac ross the population, and therefor e, underestimate the high-end exposure concentrations or th e 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 ac tivities are perfectly correlated. This approach tends to result in longterm activity patterns that are very different across the simulated populat ion, and therefore may over-estimate the variability across the population.

Cluster-Markov Algorithm A new algorithm has been developed and incorp orated into APEX to represent the dayto-day correlation of activities for individuals. Th e 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 empiri cally-derived transition probabilities. This approach is intermediate between the assumption of no day-to-day correl ation (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, empl oyment status), temperature range, and day-of-week combination, the associated ti me-activity records are partitioned into 3B-16 groups using cluster analysis. The clustering criterion is a vector of 5 values: the time spent in each of 5 microenvironment categor ies (indoors – residence; indoors – other building; outdoors – near road; outdoor s – 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 patte rn of the previous day and cluster-tocluster transition probabilitie s. 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 tran sition probabilities ar e estimated from the frequency of time-activity records in each cluster in the CHAD data base.

Details regarding the Cluster-Markov algorith m and supporting evaluatio ns are provided in Attachments 4 and 5.

B.2.2.4 Calculating Microenvir onmental Concentrations Probabilistic algorithms estimate the polluta nt concentration associated with each exposure event. The estimated pollutant con centrations account for the effects of ambient (outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate, and proximity to microenvironments can use the tr ansfer 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 th e various microenvironments visited by the simulated person by using the ambient air data for the relevant blocks , the user-specified estimation method, and input parame ters specific to each microe nvironment. 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 encl osed microenvironment as a well-mixed volume in which the air concentration is spatia lly 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 microenvironmentB-17  Outflow of air from the microenvironment

 Removal of a pollutant from the microe nvironment due to deposition, filtration, and chemical degradation

 Pollutant emissions inside the microenvironment.

Table B.2-3 lists the parameters require d by the mass balance method to calculate concentrations in a microenvironment. A proximity factor ( f

proximity ) is used to account for

differences in ambient concentrations between the geographic locati on represented by the ambient air quality data (e.g., a re gional 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) repr esents the emission rate for the emission source and concentration source (CS) is the mean air c oncentration resulting from the

source. R removal is defined as the removal rate of a pollutant from a microenvironment due to deposition, filtration, and chemical re action. The air exchange rate (R air 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 R removal ≥ 0

R

air exchange Air exchange rate 1/hr R air exchange ≥ 0 V Volume of microenvironment m 3 V > 0

The mass balance equation for a pollutant in a microenvironment is described by:

source removal out in C CCC  dt (t)dC ME equation (B-3)

where:

d C ME (t) = Change in concentration in a microenvironment at time t (ppb),

 C in = Rate of change in microenvironmental concentration due to influx of air (ppb/hour),

 C out = Rate of change in microenvironm ental concentration due to outflux of air (ppb/hour),B-18  C removal = Rate of change in microenvironmental concentration due to removal processes (ppb/hour), and

 C source = 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,  C in ,  C out ,  C removal , and

 C source , is assumed to be constant. At each time step 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 pro cesses. Details regarding these equations are provided in the APEX TSD (US EPA, 2009b). The calcula tion continues to the next timestep by using the end concentrati on for the previous timestep as the initial

microenvironmental concentration. A brief description of the i nput parameters estimates used for microenvironments using the mass ba lance approach is provided below.

Factors Model The factors method is simpler than the ma ss balance method. It does not calculate

concentration in a microenvironment from the concentration in the pr evious hour and it has fewer parameters. Table B.2-4 lists the paramete rs required by the factors method to calculate

concentrations in a microenvironm ent 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 times tep concentration in

a microenvironment from the use r-provided hourly air quality data:

npenetratio proximity ambient timestep ME 

### fxfxCC  equation (B-4)

where:

timestep ME C = Timestep concentration in a microenvironment (ppb)

C

ambient = Timestep concentration in ambient environment (ppb) f

proximity = Proximity factor (unitless) f

penetration = Penetration factor (unitless)B-19 The ambient NO 2 concentrations are from the air quali ty data input file. The proximity factor is a unitless parameter that represents the proximity of the microenvironment to a monitoring station. The penetrati on 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 penetra tion factors used in this analysis are discussed below for each microenvironment using this approach.

Microenvironments Modeled In APEX, microenvironments repr esent the exposure locations for simulated individuals. For exposures to be estimated accu rately, 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 diffe rent microenvironments in the Microenvironment Mapping File (see below). There are many more CHAD locations than microenvironment locations (the re are 113 CHAD codes versus 12 microenvironments in this assessment), therefore most of the microenvir onments have multiple CHAD locations mapped to

them.B-20 Table B.2-5. List of microenvir onments 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 Microenvir onments 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-ResidenceB-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-OtherB-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 co ncentrations in each microenvironment based on these ambient air concentrations (and indoor so urces 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 simu lation period is determined using the following equation:



### T tC C N

j j timestep jME

i 

# 

 

###  1 )()( equation (B-5)

where: C i = Hourly exposure concentration at clock hour i of the simulation period (ppb) N

= Number of events (i.e., micr oenvironments visited) in clock hour i of the simulation period.
timestep jME C )( = 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 tim e series of 1-hour, 8-hour and daily average exposures that a simulated indi vidual would experien ce during the simulation period. APEX then statistically summarizes and ta bulates 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 ar e not produced. Exposures are calculated independently for all pollutants in the simulation.

From the timestep exposures, APEX can calcu late the time-series of 1-hour, 8-hour, and daily average exposures that a simulated indi vidual would experien ce during the simulation period. APEX then statistically summarizes a nd 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 pe ople exposed to a specified SO 2 concentration level and the number of times per year that they are so exposed; the latter metric is typically expressed in te rms 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 es timates the number of times per season that individuals are exposed to the exposure indi cator of interest and then accumulates these estimates for the entire popul ation residing in an area. APEX tabulates and displays the two measur es for exposures above levels ranging from any number of benchmark levels, by any incr ement (e.g., 0 to 800 ppb by 50 ppb increments for 5-minute exposures). These exposure results ar e 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, ventilati on, and energy expenditures, and even detailed event-level information, if desired. The names a nd locations, as well as the output table levelsB-24 (e.g., output percentiles, cu t-points), for these out put 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, t he log file indicates the input files and parameter settings used for the simulati on and reports on a number of different factors. If the simulation ends prematurel y, the log file contains error messages describing the critical errors t hat 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 DataB-26B.3-1 AERMOD Input dataTable B.3-1. Emission parameters by stackfor all major facility stacks in Missouri.Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod25049LABADIEAMERENUE-LABADIEPLANTNEI 7514688,3924,270,39410,9702134446.228Tier 15050LABADIEAMERENUE-LABADIEPLANTNEI 7514688,3574,270,43914,7532134446.228Tier 15051LABADIEAMERENUE-LABADIEPLANTNEI 7514688,4614,270,33814,2852134448.828Tier 15054LABADIEAMERENUE-LABADIEPLANTNEI 7514688,4424,270,3227,6022134448.828Tier 15063SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525476,8424,106,9441,1371074222.515Tier 25064SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525476,8534,106,9221,4331074222.515Tier 15066SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525476,9134,106,929757614223.76Tier 15068SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525476,8844,106,932159614223.76Tier 15069SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525476,8904,106,922660614223.75Tier 15070SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525476,9184,106,919567614223.75Tier 15073SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525476,9194,106,930218604223.76Tier 15074SPRINGCITY UTILITIES OFSPRINGFIELDNEI 7525255604223.76Tier 1B-27Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod2FIELDMISSOURI-JAMESRIVER POWER PLANT476,9524,106,9405076SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525477,0504,106,880219604223.76Tier 15077SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURI-JAMESRIVER POWER PLANTNEI 7525476,9924,106,881252604223.76Tier 15084SPRINGFIELDCITY UTILITIES OFSPRINGFIELDMISSOURISOUTHWEST POWERPLANTNEI 12640465,4164,111,8163,3901173973.421Tier 25113WESTALTONAMERENUE-SIOUXPLANTNEI 7516735,0344,310,87624,9321834275.829Tier 15114WESTALTONAMERENUE-SIOUXPLANTNEI 7516735,0274,310,81921,0251834275.829Tier 15115WESTALTONAMERENUE-SIOUXPLANTNEI 7516734,9484,310,8642654361.415Tier 15131HERCULANEUMDOE RUN COMPANY-HERCULANEUMSMELTERNEI 34412729,5894,238,084232950.00Tier 25141HERCULANEUMDOE RUN COMPANY-HERCULANEUMSMELTERNEI 34412729,5434,237,936292870.36Tier 35145HERCULANEUMDOE RUN COMPANY-HERCULANEUMSMELTERNEI 34412729,5374,237,97315,2191683506.118Tier 25147FESTUSAMERENUE-RUSHISLAND PLANTNEI 12618739,9104,223,9342765771.59Tier 15148FESTUSAMERENUE-RUSHISLAND PLANTNEI 12618739,8934,223,82710,5112134058.825Tier 15149FESTUSAMERENUE-RUSHISLAND PLANTNEI 12618739,9314,223,86912,7442134058.825Tier 15244STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,3584,207,06562235193.24Tier 35245STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,3844,207,01589234693.46Tier 3B-28Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod25246STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,6974,206,939103234693.46Tier 35247STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,6664,206,950106234693.46Tier 35248STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,6974,206,981105234693.46Tier 35261STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,5614,206,9881,290353431.711Tier 35262STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,7354,206,9711,394353431.711Tier 35263STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,7274,206,9971,505353441.713Tier 35264STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,5504,206,96467353462.19Tier 35265STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,5244,206,92477353462.19Tier 35267STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,6334,206,9992203671.115Tier 25270STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,6274,206,9891203621.211Tier 35271STE. GENEVIEVEMISSISSIPPI LIMECOMPANY-MISSISSIPPI LIME CONEIMO1860001757,5404,206,9311,199353431.711Tier 35276ST. LOUISAMERENUEMERAMEC PLANTNEI 7515732,5844,253,7995,1951074634.933Tier 15277ST. LOUISAMERENUEMERAMEC PLANTNEI 7515732,6314,253,7906,4631074474.331Tier 15278ST. LOUISAMERENUEMERAMEC PLANTNEI 7515732,6774,253,7842,359764363.427Tier 15279ST. LOUISAMERENUEMERAMEC PLANTNEI 7515732,7144,253,7792,430764363.227Tier 15293ST. LOUISANHEUSER-BUSCHINC-ST LOUISNEI 34732742,7364,275,7862303711.23Tier 2B-29Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod25295ST. LOUISANHEUSER-BUSCHINC-ST LOUISNEI 34732742,7754,275,743176694503.06Tier 25296ST. LOUISANHEUSER-BUSCHINC-ST LOUISNEI 34732742,7504,275,704256694503.06Tier 25297ST. LOUISANHEUSER-BUSCHINC-ST LOUISNEI 34732742,7814,275,753249694503.06Tier 25298ST. LOUISANHEUSER-BUSCHINC-ST LOUISNEI 34732742,8004,275,764158694503.06Tier 25299ST. LOUISANHEUSER-BUSCHINC-ST LOUISNEI 34732742,7594,275,7143,066694613.06Tier 25302ST. LOUISANHEUSER-BUSCHINC-ST LOUISNEI 34732742,7394,275,6772,339694393.06Tier 25304ST. LOUISANHEUSER-BUSCHINC-ST LOUISNEI 34732742,7114,275,7404224861.29Tier 2Notes:1UTM Zone 15 values in all cases.2Three methods were possible to convert annual total emissions data from the NEI into hourly temporal profiles required for AERMOD, based on availability ofdata:Tier 1: CAMD hourly concentrationsto create relative temporal profiles.Tier 2: EMS-HAP seasonal and diurnal temporal profiles for source categorization codes (SCCs).Tier 3: Flat profilesB-30Table B.3-2. Emission parameters by stack for all major cross-border facility stacks in the St. Louis scenario.Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod21EastAltonDYNEGYMIDWESTGENERATIONINCNEI52119748,6544,305,5181536.276.2427.65.28.5Tier 12EastAltonDYNEGYMIDWESTGENERATIONINCNEI52119748,6544,305,5185725.8106.7416.54.634.6Tier 13BaldwinDYNEGYMIDWESTGENERATIONINCNEI52781775,3164,233,2029931.4184.4425.45.939.7Tier 14BaldwinDYNEGYMIDWESTGENERATIONINCNEI52781775,3164,233,2029053184.4428.75.938.3Tier 15BaldwinDYNEGYMIDWESTGENERATIONINCNEI52781775,3164,233,2027283184.4424.85.938.4Tier 19HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159753,0034,302,381131.9578633.5533.21.53.1Tier 210HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159752,7834,302,408907.2419.9502.01.16.5Tier 211HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159752,8864,302,285132.924.1519.32.17.0Tier 2B-31Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod212HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159752,8834,302,377106.6724.4533.21.82.6Tier 213HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159753,0034,302,3817936.0533.21.23.1Tier 214HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159752,8864,302,28566.4316.8677.61.86.2Tier 215HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159752,7834,302,4084.9021935.4570.41.57.8Tier 216HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159753,0034,302,381171.3600630.5533.21.54.1Tier 217HartfordPremcorRefining Group(prev. Clark Oiland RefiningCorp.)NEI52159752,7834,302,4087.4230.7513.21.711.4Tier 218SaugetBIG RIVERZINC CORPNEI53013746,4294,276,3391.3421.3317.60.710.6Tier 219SaugetBIG RIVERZINC CORPNEI53013746,4294,276,3391377.2825.9422.00.941.3Tier 220RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835755,0004,302,59915.38106.7472.04.611.4Tier 2B-32Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod221RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835755,0004,302,5997.27106.7463.74.60.3Tier 222RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835755,1884,302,5501.245.7628.22.37.9Tier 123RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835754,4054,303,1051.2556.4432.62.46.7Tier 224RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835754,9974,302,6911.4561.0672.03.76.7Tier 225RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835754,5054,302,9841.5395.1483.74.30.3Tier 226RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835754,9944,302,7833.3940.2491.52.113.2Tier 227RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835755,0844,303,0031.1545.7699.82.37.0Tier 128RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835754,6584,302,515385.2536.9754.83.45.9Tier 229RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835754,9944,302,7833.2445.7431.53.015.9Tier 230RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835755,0004,302,59916.73106.7483.74.60.3Tier 231RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835754,6584,302,51511677.8210.1293.70.10.1Tier 232RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835755,2314,302,561212.4145.7699.82.48.8Tier 2B-33Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod233RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835755,2314,302,561206.9645.7672.02.48.2Tier 234RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835753,8014,303,085110.638.1792.02.25.4Tier 235RoxanaConocoPhillipsCo. (prev.Phillips 66 Co.)NEI55835755,2314,302,561108.645.7672.02.44.3Tier 236GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848747,7954,286,72361.8830.5616.52.117.9Tier 237GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848750,0414,286,824506.746.3441.52.110.6Tier 238GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848749,7784,286,692228.4724.5372.01.56.2Tier 239GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848749,8974,286,788421.5888368.6460.94.34.5Tier 240GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848749,9704,286,761375.1915.4453.70.99.9Tier 241GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848750,0414,286,824351.9346.3441.52.11.2Tier 242GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848749,8474,286,849264.9544261.0460.93.43.1Tier 2B-34Stack IDCityFacility NameNEI Site IDUTM X(m)1UTM Y(m)1SO2Emissions(tpy)StackHeight(m)ExitTemp.(K)StackDiam.(m)ExitVelocity(m/s)ProfileMethod243GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848750,2804,286,925923.5243.5538.72.09.2Tier 244GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848749,8284,286,663501.1943.5538.72.09.2Tier 246GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848747,8424,286,75585.8630.5616.52.117.9Tier 247GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848750,1804,286,98320.9924.9335.91.58.9Tier 250GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848748,0534,287,055819.2323.72.113.1Tier 251GraniteCityNATIONALSTEEL CORP -GRANITE CITYDIVNEI55848750,2554,286,924959.8243.5538.72.09.2Tier 2Notes:1UTM Zone 15 values in all cases.2Three methods were possible to convert annual total emissions data from the NEI into hourly temporal profiles required for AERMOD, based onavailability of data:Tier 1: CAMD hourly concentrationsto create relative temporal profiles.Tier 2: EMS-HAP seasonal and diurnal temporal profiles for source categorization codes (SCCs).Tier 3: Flat profilesB-35 

### B.3.2 AERMOD Air Quality Evaluation Data

Table B.3-2. Measure d ambient monitor SO 2 concentration distributions and the modeled monitor receptor and receptors with in 4 km of the ambient monitors in Greene County for year 2002. Percentile Concentration (ppb)

Ambient 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 t he given percentile (p2.5 = 2.5 th ; p50 = 50 th ; p97.5 = 97.5 th ) 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. Measure d ambient monitor SO 2 concentration diurnal profile and the modeled monitor receptor and receptors with in 4 km of the ambient monitors in Greene County for year 2002. Annual Average SO 2 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 3.4B-37 Annual Average SO 2 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 1.8B-38 Annual Average SO 2 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 0.6B-39 Table B.3-4. Measure d ambient monitor SO 2 concentration distributions and the modeled monitor receptor and receptors with in 4 km of the ambient monitors in St. Louis for year 2002. Percentile Concentration (ppb)

Ambient 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 t he given percentile (p2.5 = 2.5 th ; p50 = 50 th ; p97.5 = 97.5 th ) 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. Measure d ambient monitor SO 2 concentration diurnal profile and the modeled monitor receptor and receptors with in 4 km of the ambient monitors in St. Louis for year 2002. Annual Average SO 2 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 3.4B-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 3.9B-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 4.0B-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 4.8B-44 

## B.4 SUPPLEMENTAL APEX EXPOSURE MODELING DATA

B.4.1 APEX Input Data Distributions for SO 2 

### deposition In recognizing the relationship between SO 2 deposition rate and various surface types within indoor microenvironments and th at the presence of these surfaces would vary in proportions dependent on the microe nvironment, staff estimated the APEX input

SO 2 deposition rate distributions using a M onte Carlo sampling approach. First, 1,000 different hypothetical indoor microenvironmen ts 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 ra tios 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 ceiling floor

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 D eff is the effective deposition rate. If more than one su rface type is present in the sample indoor microenvironment (e.g. both car pet 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 SO 2 deposition rate within simulated indoor microenvironments are provide d in the following sections.

B.4.1.1 Surface deposition data and surface type mapping Staff obtained SO 2 deposition velocities from a li terature review conducted by

Gr Ø ntoft and Raychaudhuri (2004). These auth ors categorized the data by several

relative humidities and consid ering several different surface types. Staff mapped theB-45 surface classes reported in Gr Ø ntoft and Raychaudhuri (2004) to surface types typically found within indoor microenvironments (Table B.4-1).

Table B.4-1. Classification of SO 2 deposition data for several microenvironmental surfaces.

Deposition in cm/s 1 Surface Category Surface Type Surface Class 1 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 0.048 0.16 0.27 Wall

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 Gr Ø ntoft and Raychaudhuri (2004).

B.4.1.2 Indoor Microenvironment Configurations Because the configuration of rooms within a building w ill affect the wall area to floor area ratio, staff first estimated the area s 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 th e indoor microenvironment could represent all potential rooms within the particular build ing type. Secondly, st aff assumed all rooms were square to calculate the area distributi ons. Additional assumptions specific to the type of indoor microenvironment are provi ded 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 probabil ities 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 independe nt 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 correla tion 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 calcu late 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 collec tion 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, mini mum, 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 occupant s 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 di vided by the number of rooms to obtain the representative office area. For schools, the distribution of the total bu ilding square footage is available from the Commercial Building Energy Consumption Survey (CBECS; US DOE, 2003); however, information on the number of room s 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 f eet) to generate a uniform distribution bounded by these extrem es (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 f ootage distributions (Table B.4-3).

Table B.4-3. Distributions used to calculate representative room size for non-residential microenvironments.

Microenvironment Parameter 1 a Parameter 2 b Parameter 3 c Parameter 4 d Distribution Type Office, Building size (ft 2 ) 16,632 8,035 4,612 69,530 Normal

Office, number of people per m 2

. 4.0 1.5 1.5 8.5 Normal

1 http://www.epa.gov/iaq/base/pdfs/te st_space_characteristics/tc-0.pdf 2 http://www.epa.gov/iaq/base/pdfs/te st_space_characteristics/tc-1.pdfB-48 School (ft 2 ) 600 1,400 N/A N/A Uniform

Restaurant (ft 2 ) 5,340 31 668 42,699 Lognormal

Other Buildings (ft 2 ) 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 requi red to calculate the room volumes and surface areas: the ceiling height s and surface area of furniture within the rooms. Table B.4-4 provides the data values and sources us ed to estimate each of these variables.

Table B.4-4. Ceiling hei ghts and furniture surface area to floor ratios for simulated indoor microenvironments.

Indoor Microenvironment Ceiling Height a Furniture Surface Area to Floor Ratio Residence 8 ft 2 b Office 10 ft 4 c School 10 ft 4 c Restaurant 10 ft 4 c Other Buildings 10 ft 4 c 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 di mensions 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 floor s and no carpet). However, in other cases, a typical building may have multiple surface types (e.g., a residence may have a mixtureB-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 approxi mated 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 SO 2 deposition distributions Following the estimation of the room dimensions and surface types within each simulated indoor microenvironment, an effec tive 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 re lative 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 microe nvironments, residences, offices, and restaurants are explicitely modeled microe nvironments. The daycare microenvironment used the school deposition distribution, wh ile other indoor microenvironments (i.e., shopping or other) used the othe r building deposition distribution.B-50Table B.4-5. Probability of occurrence and fractional quantity for surfacetypes in indoor micronenvironments.Floor Ceiling WallIndoorMicroenvironmentCarpet Hard floor Wallboard Ceiling Tile Wallboard Wallpaper Wood PanelingResidenceP = 1F =N{0.52, 0.23}aP = 1F =1 - fractioncarpetedaP = 1cP = 0cP = 1F= 5/6if wallpaper ispresentcP = 0.225F= 1/6 ifwallpaper ispresentdP = 0cOfficeP = 1F = 5/6 if hardfloor presentcP = 0.34F= 1/6 if hardfloor is presentbP = 0cP = 1cP = 1F is adjusted ifwallpaper and/orwood paneling ispresentcP = 0.11F= 1/6 ifwallpaper ispresentbP = 0.13F = 1/6 if woodpaneling ispresentbSchoolP = 0cP = 1cP = 0cP = 1cP = 1cP = 0cP = 0cRestaurantP = 0.1dP = 0.9dP = 0.55dP = 0.45dP = 1F is adjusted ifwallpaper and/orwood paneling ispresentcP = 0.09F = 1/2 ifwallpaper ispresentdP = 0.25F = 1/10 If woodpaneling ispresentdOther Buildings P = 0.1dP = 0.9dP = 0.19dP = 0.81dP = 1F is adjusted ifwallpaper and/orwood paneling ispresentcP = 0.09F = 1/2 ifwallpaper ispresentdP = 0.045F=1/10 if woodpaneling ispresentdNotes:aUS EPA, 2008b.bBASE study, Table 4 (US EPA, 2008a); the fractionof 1/6 is based on professional judgment.cAssumed by staff.dSource Ranking Database (SRD, US EPA,2004b). The fraction of buildings value inthe database was used tospecify a probability each surface typeoccurs in the microenvironment.SRD names were matched to the APEXenvironments. Most categoriesin the SRD have the same fraction of buildingvalues. To map to the necessary surface types: Carpet – Networx represented carpet; Ceiling tile represented ceiling tile; vinyl coated wallpaperrepresented wallpaper; and Hardwood plywood paneling represented wood paneling. Fractions were assumed by staff. Then, probabilities in theremaining surface types were calculated assuming either onlyone type could be present or multiple types could be present.B-51 Table B.4-6. Final pa rameter estimates of SO 2 deposition distribut ions 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 SO 2 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 0.00B-53 Table B.4-8. APEX estimated SO 2 exposures in Greene County (current standard air quality scenario) while at m oderate 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 0.00B-54 Table B.4-9. APEX estimated SO 2 exposures in Greene County (99 th %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 0.00B-55 Table B.4-10. APEX estimated SO 2 exposures in Greene County (99 th %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 0.00B-56 Table B.4-11. APEX estimated SO 2 exposures in Greene County (99 th %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 0.00B-57 Table B.4-12. APEX estimated SO 2 exposures in Greene County (99 th %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 0.00B-58 Table B.4-13. APEX estimated SO 2 exposures in Greene County (99 th %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 0.00B-59 Table B.4-14. APEX estimated SO 2 exposures in Greene County (98 th %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 0.00B-60 Table B.4-15. APEX estimated SO 2 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 0.00B-61 Table B.4-16. APEX estimated SO 2 exposures in St. Louis (c urrent standard air quality scenario) while at mode rate 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 0.03B-62 Table B.4-17. APEX estimated SO 2 exposures in St. Louis (99 th %ile, 50 ppb air quality scenario) while at mode rate 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 0.00B-63 Table B.4-18. APEX estimated SO 2 exposures in St. Louis (99 th %ile, 100 ppb air quality scenario) while at mode rate 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 0.00B-64 Table B.4-19. APEX estimated SO 2 exposures in St. Louis (99 th %ile 150 ppb air quality scenario) while at mode rate 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 0.00B-65 Table B.4-20. APEX estimated SO 2 exposures in St. Louis (99 th %ile, 200 ppb air quality scenario) while at mode rate 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 0.00B-66 Table B.4-21. APEX estimated SO 2 exposures in St. Louis (99 th %ile, 250 ppb air quality scenario) while at mode rate 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 0.00B-67 Table B.4-21. APEX estimated SO 2 exposures in St. Louis (98 th %ile 200 ppb air quality scenario) while at mode rate 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 0.00B-68 

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. 36:1811-1819. Tsang AM and Klepeis NE. (1996). Descriptive St atistics Tables from a Detailed Analysis of the National Human Activity Pattern Survey (NHAPS) Data. U.S. Environmental Protection Agency. EPA/600/R-96/148. US Army Corps of Engineers. (2002). Unif ied Facilities Criteria (Ufc) Design: Child Development Centers. Report no. UFC 4-740-14, (Table 2.6). Available at: http://www.wbdg.org/ccb/DOD/UFC/ufc_4_740_14.pdf. US Census Bureau. (2007). Employment Stat us: 2000- Supplemental Tables. Available at: http://www.census.gov/population/www/cen2000/phc-t28.html. US DOE (2003). Commercial Building Energy C onsumption Survey. U. S. Department of Energy. Available at: http://www.eia.doe.gov/emeu/cbecs/cbecs 2003/public_use_2003/cb ecs_pudata2003.html. Last accessed December 17, 2008. US DOT. (2007). Part 3-The Journey To Work files. Bureau of Transportation Statistics (BTS). Available at: http://transtats.bts.gov/

. US EPA. (1999). Total Risk In tegrated Methodology. Website: http://www.epa.gov/ttnatw01/urban/trim/trimpg.html

. US EPA. (2002). Consolidated Human Activi ties Database (CHAD) User s Guide. Database and documentation available at: http://www.epa.gov/chadnet1/. US EPA. (2004a). AERMOD: Description of Model Formulation. Office of Air Quality Planning and Standards. EPA-454/R-03-004. Available at: http://www.epa.gov/scram001/7thconf/aermod/aermod_mfd.pdf.B-70 US EPA. (2004b). Source Ranki ng Database. Available at: http://www.epa.gov/opptintr/exposure/pubs/sr ddl.htm. Last accessed September 30, 2008. US EPA. (2008a). Building Assessme nt Survey and Evaluation (BASE) Study. http://www.epa.gov/iaq/base/summarized_dat a.html. Last accessed December 17, 2008. US EPA. (2008b). The Approach Used for Esti mating Changes in Children’s IQ from Lead Dust Generated During Renovati on, Repair, and Painting in Re sidences and Child-Occupied Facilities: Appendix A. Inputs for the Resident ial and Child-occupied Facilities (COFs) RRP Models. Office of Pollution Prevention and Toxics. March 28, 2008. US EPA. (2009a). Total Risk Integrated Met hodology (TRIM) - Air Poll utants Exposure Model Documentation (TRIM.Expo / APEX, Version 4.3) Volume I: User’s Guide. Office of Air Quality Planning and Standards, Research Triangle Park, NC. January 2009. Available at: http://www.epa.gov/ttn/fera/human_apex.html. US EPA. (2009b). Total Risk Integrated Methodology (TRIM) - Air Pollut ants Exposure Model Documentation (TRIM.Expo / APEX, Version 4.3) Volume II: Technical Support Document. Office of Air Quality Planning and Standards, Research Triangle Park, NC. January 2009. Available at: http://www.epa.gov/ttn/fera/human_apex.html

. Wiley JA, Robinson JP, Cheng Y-T, Piazza T, Stor k L, Pladsen K. (1991b). Study of Children's Activity Patterns: Final Report. California Air Resources Board, Sacramento, CA. ARB-R93/489. Wiley JA, Robinson JP, Piazza T, Garrett K, Cirkse na K, Cheng Y-T, Martin G. (1991a). Activity Patterns of California Residents: Fi nal Report. California Air Resources Board, Sacramento, CA. ARB/R93/487. Availabl e from: NTIS, Springf ield, VA., PB94-108719. Williams R, Suggs J, Creason J, Rodes C, La wless P, Kwok R, Zweidinger R, Sheldon L. (2000). The 1998 Baltimore particulate ma tter epidemiology-exposure study: Part 2. Personal exposure associated wi th an elderly population. J Expo Anal Environ Epidemiol

. 10(6):533-543.B-71 

## ATTACHMENT 1. TECHNICAL MEMORANDUM ON METEOROLOGICAL DATA PREPARATION FOR AERMOD FOR SO 2 

## REA FOR GREENE COUNTY AND ST. LOUIS MODELING DOMAINS, YEAR 2002B-72 Meteorological data preparation for AERMOD for SO 2 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 da ta are often used as the source of input meteorological data for AERMOD (U. S. EPA, 2004a). For the SO 2 Risk and Exposure Assessment, two study areas were chosen: Green e 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 betw een 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 an d 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 SO 2 study areas. Latitude an d 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 SO 2 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 6B-73 Figure 1. Location of surfac e stations (red dots) relative to upper air stations (crosses) for Greene County and St. Louis, MO.

A potential concern related to th e use of NWS meteorological data for dispersion modeling is the often high incidence of calms and variable wi nd 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 di spersion 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 us ed to calculate hourly average wind speed and directions, which were used to supplement th e standard archive of winds reported for each station in the Integrated Surface Hourly (ISH) database. Details regarding this procedure are described below.B-74 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. S ection 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.B-75 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. Curren tly, the wind speed and direction used to represent the hour in AERMOD is based on a singl e two-minute average, usually reported about 10 minutes before the hour. The METAR system re ports winds of less than three knots as calm (coded as 0 knots), and winds up to six knots will be reported as va riable when the variation in the 2-minute wind direction is more than 60 degr ees. 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 numbe rs of calm and variable hours may compromise the representativeness of NWS surface data for AE RMOD applications. Th is is especially of concern for applications involving low-level rele ases 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 to ward 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 variab le winds. Recent work in AQMG has focused on utilizing these 1 minute winds to calculate hourly aver age winds to reduce the number of calms and variable winds for a given station and year. Fo r 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 pe rformed 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 wi nd speed were in the co rrect general location. If a minute was listed twice, the second line fo r 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 knot s, 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 windB-76 sample below threshold when processing sample s to obtain hourly averages. At the same time, the x- and y-components of the wind di rection were calculat ed using equations 1 and 2 below, which are the functions insi de 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.

 sin

  x v (1)  cos

  y v (2) where v x and v y are the x- and y-components of the one minute wind direction θ

.

3. For all minutes that passed the QA check in st ep 1, the wind speeds were converted from knots to m/s. 4. Before calculating hourly averages, the num ber 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-ove rlapping 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 coul d be used were 30 2-minute values (30 even or 30 odd). 5. For wind speed averages, all available non-ove rlapping 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 v xi and v yi calculated in equations 1 and 2. The hourly wind direction was calculated base d 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 di rections 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 whic h it is blowing, as shown by the flow correction in 6.2.19. Instead, the one minut e program corrected for the direction from which the wind was blowing. 

# 

  N

i xi X v N V

1 1 (3)



# 

  N

i yi Y v N V

1 1 (4)

CORR V V Arc

Y X       

 tan  (5)

Where Vx and Vy are the hourly averag ed x- and y-components of the wind, θ is the hourly averaged wind direction, N is the num ber of observations used for the hour, andB-77 = 180 for V x > 0 and V y > 0 or V x < 0 and V y > 0 = 0 for V x < 0 and V y < 0 CORR = 360 for V x ≥ 0 and V y < 0

2.2 Upper air data

For AERMET processing, an upper ai r 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 System s 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.B-78 3. AERSURFACE

The AERSURFACE tool (U.S. EPA, 2008b) was used to determine surface characteristics (albedo, Bowen ratio, and surface roughness) fo r 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 towe r to ensure accurate represen tation of the conditions around the site. The approximate locations of the meteorol ogical towers were dete rmined 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 si nce the 2001 land cover data will be more representative of the meteor ological 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 SO 2 REA (U.S. EPA, 2008a). Monthly seasonal assignments did no t follow the defaults as outlined in the AERSURFACE User’s Guide (U.S. EPA, 2008a) a nd the monthly seasonal assignments were defined as shown in Table 3. Since the defau lt seasonal assignments were not used, the surface characteristics were output by month.

Table 3. Seasonal m onthly 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 pr ecipitation distribution are considered wet. Those in the bottom 30% of the distribution are dry. Otherwise, a given year is co nsidered average. For the two surface stations, the 2007 local climatological da tabase was used to l ook at 30 years (1978-2007) annual precipitation. For SGF, 2002 was consider ed dry while STL was considered average. The ranked 30 year distributions ar e shown in Table 4 with time se ries of the annual precipitation in Figure 3.B-79 Table 4. Annual precipitation (inches) for Sp ringfield and St. Louis. Years in green are top 30% of distribution (wette st), years in brown are the bottom 30% of the distribution (driest) and years in white are the middl e 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 39.14B-80 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 roughne ss to be defined by up to 12 sectors. The landuse around SGF and STL were an alyzed using the NLCD data and aerial photographs. The resulting sectors are shown in Figures 4 and 5.

After determining the moisture conditions an d 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.B-81 Figure 4. Surface roughness sectors for SGF with a) 2001 NLCD landuse and b) 2003 aerial photograph.B-82 Figure 5. Surface roughness sectors for STL with a) 2001 NLCD landuse and b) 2002 aerial photograph.B-83 4. AERMET

The meteorological data files fo r 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 IS H data was processed via the SURFACE pathway, and the one minute hourly av erage winds were proc essed 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 ho urly averaged winds were merged together for each station. After Stage 2, Stage 3 was run to create the input file s 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, cl oud 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 in put file. As noted in Section 3, surface characteristics from AERSURFACE are input into Stage 3. The resu lting output from Stage 3 were the .SFC and .PFL files input into AERMOD.

An AERMOD run, using a singl e 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. Miss ing hours can also result from va riable 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 onl y the ISH winds for surface winds, no hourly averaged one minute winds included. Note that including the hourly averaged winds dramatically reduces the numb er of calms and missing hours.

Table 5. Number of calms and missing hours fo r 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 401B-84 5. Analysis

Wind roses for 2002 for the two stations are s hown in Figure 6. For SGF, the wind was predominantly from the south and south-southeas t. 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 agains t 30-year climatology for precipitation and temperature. Precipitation has been discusse d 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 precipita tion was slightly above the 30-year average (Table 4 and Figure 3) with the mean temperature about one degree abov e the 30-year average (Table 6 and Figure 7).B-85 Table 6. 30-year distributi on 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 56.8B-86 Figure 7. 30 year time series of m ean annual temperatures (Fahrenheit) for a) Springfield, and b) St. Louis. An nual averages are in red, 30-year averages in blue, and 2002 denoted by asterisk.B-87 6. References

U.S. EPA, 2000: Meteorologi cal Monitoring Guidance for Regul atory Modeling Applications. EPA-454/R-99-005. U.S. Environmental Protec tion Agency, Research Triangle Park, NC 27711.

U.S. EPA, 2004: User’s Guide for the AE RMOD Meteorological Preprocessor (AERMET). EPA-454/B-03-002. U.S. Environmental Prot ection Agency, Research Triangle Park, NC 27711.

U.S. EPA, 2008a: Risk and Exposure A ssessment to Support the Review of the SO 2 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 Guid e. EPA-454/B-08-001. U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/7thcon f/aermod/aersurface_userguide.pdfB-88 Appendix A. Surface characteristics.

Tables A1 and A2 show the su rface characteristics fo r 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 0.022B-89 Table A2. St. Louis monthly su rface 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 0.041B-90 

## ATTACHMENT 2. TECHNICAL MEMORANDUM ON THE ANALYSIS OF NHIS ASTHMA PREVALENCE DATAB-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 child ren’s asthma prevalence data from the National Health Interview Survey (NHIS) for 2003. Asth ma 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 que stion CASHMEV: “Ever be en 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 desi gn of the NHIS survey. Standard errors and confidence intervals for the prevalence were ca lculated 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 pl otted. 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 signifi cant differences in pr evalence by gender and by region. Therefore we did not combine the prev alence 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 uncertain ties 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 wa s initially used, two problems are that the distributions of the estimat ed 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] interv al. 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 variab les 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 be ta 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 conf idence intervals for beta, the co rresponding confidence intervals for prob (asthma) will always be inside [0, 1]. A nother advantage of the logistic modeling is that it can be used to compare alternative statistica l models, such as models where the prevalence probability depends upon age, region, and ge nder, or on age and region but not gender.

A variety of logistic models for asthma preval ence were fit and compared, where the transformed probability variable beta is a given function of age, gender, and region. SAS’s SURVEYLOGISTIC procedure was us ed 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 lo g-likelihood goodness-of-fit measures. 16 models were fitted. The Strata co lumn 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 independent ly 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 18B-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 describe s how beta depends upon the age:

Linear in age: Beta =  +  × age, where  and  vary with the strata. Quadratic in age: Beta =  +  × age +  × age 2 where   and  vary with the strata. Cubic in age: Beta =  +  × age +  × age 2 +  × age 3 where   ,  , and  vary with the strata. f(age) Beta = arbitrary function of age, w ith 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 pol ynomial of degree 16 in age (sin ce 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 co mplex model (16) has a prevalence that is an arbitrary function of age, gender, and region. Mo del 16 is equivalent to calculating independent prevalence estimates for each of the 144 combina tions 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, wher e r is the difference in the DF. This is a likelihood ratio test. For all pairs of models from Table G-1, a ll the differences are at least 70,000 and the likelihood ratios ar e all extremely statis tically significant at levels well below 5 percent. Therefore the model 16 is clearly pref erred and was used to model the prevalences.

The SURVEYLOGISTIC model predictions are ta bulated 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 scatte rplot smoother, as discussed below.

The SURVEYLOGISTIC procedure produces estimat es 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 predicti ons 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 asB-94 Std Error {Prob (asthma)} = Std Error (b eta) × exp(- beta) / (1 + exp(beta) ) 2

, which follows from the delta method (a fi rst 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 f unctions of age modeled by SURV EYLOGISTIC were one strategy for smoothing the curves, but they did not prov ide a good fit to the data. One reason for this might be due to the attempt to fit a global regressi on 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 regre ssion 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 smoo thing 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 fitte d these local regression curves to the beta values, the logits of the asthma prevalence estima tes, and then converted them back to estimated prevalence rates by applying the inverse logit functi on exp(beta) / (1 + exp(be ta) ). 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 estimat e 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 avoi d 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 ra w 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 para meter of 0.2 cannot be used becau se 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 pr edicted values with vertical lines indicating the confidence intervals in Figu res 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 applicati on 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 estimat ed standard errors by 1 / estimated  , and adjusted the widths of the c onfidence intervals by the same factor.

Additionally, because the true value of  equals 1, the best choi ces of smoothing parameter should give residual standard errors close to one. Using this crite rion 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 diagnos tic the residual errors from th e 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, combini ng all the studentized residuals across genders, regions, and ages. The plots for smoothing para meters seem to be equally straight for each smoothing parameter.

The final regression diagnostic is a plot of the studentized resi duals against the smoothed beta values. Ideally there should be no obvious pattern and an averag e studentized residual close to zero. The plots indeed showed no unusual patterns, and the result s 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 c hoices of 0.5 or 0.6. We therefore chose the final value of 0.5. These predictions, standard errors, and confidence inte rvals are presented in tabular form below as Table G-2.B-96 region=Midwest

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=Northeast

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17B-97 region=South

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17B-98 region=Midwest

gender Female Male predprob

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=Northeast

gender Female Male predprob

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17B-99 region=South

gender Female Male predprob

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male predprob

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17B-100 region=Midwest

gender Female Male prev

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=Northeast

gender Female Male prev

0.0 0.1 0.2 0.3 0.4 0.5 0.6

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17B-101 region=South

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male prev

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17B-102 region=Midwest

gender Female Male prev

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=Northeast

gender Female Male prev

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17B-103 region=South

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17B-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 0.35959B-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 0.25050B-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 0.38783B-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 0.15522B-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 0.18067B-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 0.23028B-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 0.21409B-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 0.18736B-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 0.18508B-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 0.31153B-114 

## ATTACHMENT 3. TECHNICAL MEMORANDUM ON ESTIMATING PHYSIOLOGICAL PARAMETERS FOR THE EXPOSURE MODELB-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 Physiologica l 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 a nd 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 We ights for the Combined NHANES Dataset. ................................................................................................................... 120 3.3 Fitting the Body Mass Data. ............................................................................. 120 4. Derivation of New Distributio ns for Normalized Vo2max.................................... 126 4.1 The Nvo2max Data ........................................................................................... 126 4.2 Determining the NVo2max Distributions......................................................... 130 5. Derivation of New Distributions for He moglobin Content (Hemoglobin Density) 138 6. Blood Volume as a Function of Height and Weight.............................................. 141 References..................................................................................................................... . 142 Appendix A. SAS Code for Estimati ng the Body Mass Distributions ..................... 156 Appendix B. SAS Code for Estimating th e 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 Ph ysiological Parameters ................................................. 170B-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, Deri ved from NHANES 1999-2004 Study Data...... 123 Figure 3. Minimums (1 st Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data. .................................................................................. 124 Figure 4. Maximums (99 th 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 Measuremen ts for Males and Females, Derived from Literature Studies............................................................................................................ 1 28 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 M eans and Smoothed Fits. ... 134 Figure 11. Nvo2max Normal Distribution Fi ts: Raw Fit Standard Deviations and Smoothed Fits. ................................................................................................................ 1 35 Figure 12. Nvo2max Minimums. 1 st Percentile of the Best-f it Normal Distribution. .. 136 Figure 13. Nvo2max Maximums. 99 th Percentile of the Best-f it Normal Distribution. 137 Figure 14. Mean Values of Hemoglobi n Content as Derived from the 1999-2002 NHANES Dataset, with Comparison to Current Physiology.txt Values........................ 139 Figure 15. Values of Hemoglobin Content St andard Deviation as Derived from the 1999- 2002 NHANES Dataset, with Comparison to Current Physiology.txt Values............... 140B-117 

## 1. INTRODUCTION

The purpose of this memo is to presen t an updated version of the physiological parameters input file (Physiology.txt) for the AP EX model. Portions of this file are also used as input for SHEDS-PM and SHEDS-AirToxics.

The physiology file contains age- and gender-b ased information for several physiological parameters used in human exposure modeling. This information in cludes 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 pres ent 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 th e current state of the physiology file is presented, followed by th e derivation of new physiological data for body mass, normalized vo2max, and hemoglobin c ontent. Portions of the SAS code used for analysis are included (Appendices A-C), as is the new Physiology.txt file (Appendix D). The final appe ndix (Appendix E) contains tables of all the derived physiological parameters.



## 2. EVALUATION OF THE CURRENT PHYSIOLOGY FILE DATA

The physiology.txt file was originally generate d 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 include d in a previous memo to the WA-COR under this work assignment. A summary of those findings is repeated here.



### 2.1 Normalized Maxima l Oxygen Uptake (nvo2max).

The nvo2max data were derived from a nu mber of sources. The data for males, especially, were pieced together from a vari ety of studies (a tota l of 6), leading to discontinuities in the distribut ional 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. Th e data for males at age 20 and at 28-69 came from a study from 1960 [1]. Data for male s 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 gr oups 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 Exam ination 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 anal ysis of more recent data is necessary to accurately account for changes in human activity pattern s in adults and especially children.



### 2.3 Resting Metabolic Rate.

Not included for evaluation, per discussion with WA-COR.



### 2.4 Hemoglobin Content a nd Blood Volume Factor.

The original references for the hemoglobin content or blood volume factor values given in the current physiology.txt f ile 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, th e data are unnecessar ily 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 c ontent 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). Th is 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 ch aracterizing 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 we bsite. 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 da tasets 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).B-120 

### 3.2 Calculation of the New Samp ling 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 repres ent 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

  w w combined (1)

20021999 3 2

  w w combined (2)

where w combined is the sampling weight for the combined dataset, w 2003-2004 is the weight for the subjects in the most recent study, and w 1999-2002 is the weight for subjects in combined 4-year (1999-2000 and 2001-2002) NHANES dataset. (Both weights are provided with the appropriate NHANES re lease. The combined 1999-2002 weight, which is not a simply half of that for th e corresponding 2-year periods, was explicitly calculated for researcher use by CDC since th e two 2-year periods use different census data.)

By using the sampling weights, once can c onsider any 2-year NHA NES 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 type s of distributions (including normal, beta, and three-parameter l ognormal distributions). It was determined that overall, the distribution that provided the best comb ination of good behavior over ages and good fit to the data was a two-parameter lognormal distribution.

The data were fit to the lognormal dist ributions 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 ma ss distributions is pr ovided 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 base d on the data for ages 60 and greater.

3.4 Body Mass Results.B-121 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 P hysiology.txt file, which were derived from earlier NHANES studies. The new means also capture the tr end towards decreasing body weight in older persons that was previously negl ected in the Physiology.txt file.

The maximum and minimum values for the distri butions are presented in Figures 3 and 4. The minimums and maximums were calculated as the 1 st and 99 th percentile of the raw body mass data for the cohort. (Note th at these values differ from the 1 st and 99 th 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 parame ters (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 ma y desirable to use age- grouped data or running averages over years in these age ranges. Wh ile the attached prepar ed 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 th e “official” release Physiology. txt file would be trivial.B-122 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.B-123 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.B-124 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 (1 st Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.B-125 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 (99 th Percentile) for Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.B-126 

## 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 distributi onal shapes were determined from a large database of experimental and literature vo2max measur ements for different age/gender cohorts.

A PubMed-based literature search located a number of studies in which vo2max was directly measured. In add ition, a large number of scien tific 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 broa d or contained both males and females were discarded. Also discarded were any st udies in which vo2max was not normalized by body mass, or for which no age data were reported. Data fo r 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 comprise d 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 sour ce was digitized and the data points were pulled from the digital figure using gra phics software. (This was accomplished by calibrating the pixels of the digitized image w ith 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 derive d from 136 studies [32-167]. These data comprised approximately 550 means and SDs fo r 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 fo r 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 va lues are shown in Figure 7.B-127 

### MALES: Nvo2max

0 20 40 60 80 100

0 20 40 60 80 100 Age (years)nvo2max (ml/kg)nnn



### 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 Measurement s for Males and Females, Derived from Literature Studies and Experimental Measurements. N=1558 N=1949B-128 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.B-129 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.B-130 

### 4.2 Determining the NVo2max Distributions

Both the grouped mean and the individual data sets 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 (w eighted by the number of subjects in each study) and retransforming the variances to SD s. The combined group means and SDs are given in Figures 8 and 9.

The combined group means were fairly well-b ehaved 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 in cluded 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 seve ral types of distributi ons (including normal, beta, and lognormal distributions). It was determined that the normal distribution fit the data best. The parameters (means and standa rd deviations) of the best-fit distributions were obtained using the SAS PR OC UNIVARIATE procedure. The SAS code used to fit the data is given in Appendix B.

Both raw and smoothed nvo2max fits were calculated. Calcul ating 5-year running averages did not smooth the data considerab ly. 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 ra w 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 (proba bly 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 cons istent 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 l onger demonstrate nonsensical discontinuities across ages.

The minimum and maximum nvo2max values were assumed to be the 1 st and 99 th 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 ap propriate for use as minimum and maximum. As the nvo2max data cohorts had much sm aller N than the NHANES studies, the raw percentiles were less appropriate.) The maximum and minimum values are shown in Figures 12 and 13.B-132 MALES: Nvo2max, Combined Group Means

0 10 20 30 40 50 60

0 2 0 4 0 6 0 8 0 Age (Years)nv02max (ml/kg

FEMALES: Nvo2max, Combined Group Means

0 10 20 30 40 50

0 10 20 30 40 50 60 70 Age (Years)nv02max (ml/kg

Figure 15. Combined Nvo2max Group Means for Males and FemalesB-133 MALES: Nvo2max, Combined Group SD

0 2 4 6 8 10 12 14 16

0 2 0 4 0 6 0 8 0 Age (Years)nv02max (ml/kg

FEMALES: Nvo2max, Combined Group SD

0 2 4 6 8 10

0 10 20 30 40 50 60 70 Age (Years)nv02max (ml/kg

Figure 16. Combined Nvo2max Group Standard Deviations.B-134 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.B-135 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 Standa rd Deviations and Smoothed Fits.B-136 

### MALES: Nvo2max Minimum

0 10 20 30 40 50 60

0 20 40 60 80 100 Age (Years)nvo2max (ml/kg 

### New Values Current Physiology.txt

FEMALES: Nvo2max Minimum

0 5 10 15 20 25 30 35 40 45

0 20 40 60 80 100 Age (Years)Nvo2max (ml/kg 

### New Values Current Physiology.txt

Figure 19. Nvo2max Minimums. 1 st Percentile of the Best-f it Normal Distribution.B-137 

### MALES: Nvo2max Maximum

0 10 20 30 40 50 60 70 80

0 20 40 60 80 100 Age (Years)nvo2max (ml/kg



### New Values Current Physiology.txt

FEMALES: Nvo2max Maximum

0 10 20 30 40 50 60 70

0 20 40 60 80 100 Age (Years)Nvo2max (ml/kg



### New Values Current Physiology.txt

Figure 20. Nvo2max Maximums. 99 th Percentile of the Best -fit Normal Distribution.B-138 

### 5. Derivation of New Distrib utions 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, previ ously 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 datase t comprised 20,321 indivi duals; appropriate sample weights were used for the combined 4-year (1999-2002) data set as provided with the NHANES 2001-2002 data release. Similarly to the body mass data, the hemoglobin content values were analyzed in SAS. Th e age and hemoglobin datasets were merged and fit to normal distributions using th e SAS PROC UNIVARIATE procedure. The FREQ option of the procedure was used to ap ply 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 we re smooth in this age range. For persons 20 and over, the data were groupe d in 5-year cohorts (20-24, 25-29, etc.) No blood count data were available for subject s 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 whic h 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 deviat ions did not appear to behave as smoothly with age as did th e 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 gi ven 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 Phys iology.txt input file overestimates mean hemoglobin content in child ren 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 smoot h over ages; EPA may elect to continue to use the current values. It should be noted that the original refe rence for the current hemoglobin statistics is unknown.

Note: In the current implementation of APEX , the hemoglobin conten t statistics affect only the CO dose algorithm calculations.B-139 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 C ontent as Derived from the 1999-2002 NHANES Dataset, with Comparison to Current Physiology.txt ValuesB-140 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 ValuesB-141 

## 6. BLOOD VOLUME AS A FUNCTION OF HEIGHT AND WEIGHT

In APEX, blood volume is estimated as a func tion of height and weight by the following equation:

30 3 - + K*Height BVF*Weight V

blood 

where V blood 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 in clude the age/gender dependent BVF and adjusted for the given units.

As previously mentioned, the data upon wh ich the BVF values in the physiology file were based could not be iden tified. 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 fo r use with the Allen equations. It should be noted however, that these equations were m odified 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 specifi cally in children [170,171] or body surface area [172]. In particular, Linderkamp et al. [170] derive d prediction equations for blood volume as a function of a number of physiological parame ters for children in three different age groups. It is recommended that further analys is 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 sp ecific improvements to the current BVF values in the physiology file can be made.B-142 

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### 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 UpdateB-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 confor m to the current APEX read formats. That is, the number of decimal places for each parame ter 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 41.8B-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 43.3B-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 50.5 130.0B-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 12.1B-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 91.4B-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 7.3B-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 5.7B-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 1.0B-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 1.8B-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 1.3B-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 1.6B-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 61.94B-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 37.52B-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 57.76B-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 30.97B-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 41.5 133.1B-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 47.2 123.8B-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 40.4B-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 47.08 141.36B-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 89.83615B-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 1.552685662B-180 

## ATTACHMENT 4. TECHNICAL MEMORANDUM ON LONGITUDINAL DIARY CONSTRUCTION APPROACHB-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 ge nerally include an accurate estimate of both the average exposure concentr ation and the high end of the e xposure 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 repres ents these exposure scenarios well depends on whether the treatment of activity pattern da ta accurately characterizes differences among individuals.

Human time-activity data for population exposure models are generally derived from demographic surveys of individual s’ daily activities, the amount of time spent engaged in those activities, and the ME locations where the activi ties occur. Typical time-activity pattern data available for inhalation exposure modeling co nsist 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 exposur e to air pollutants typically require information on activity patterns over long periods of time, e.g., a full ye ar. 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 con centration over a long pe riod of time (e.g., the annual number of exceedances of an 8-hour aver age 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 fo r combining them will in fluence the variability of the long-term activity patterns across the simulated population. Th is in turn will influence the ability of the model to accurately represent e ither long-term average high-end exposures, or the number of individuals exposed multiple times to short-term high-end concentrations.

A common approach for construc ting 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 toB-182 underestimate the variability ac ross the population, and therefor e, 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 th at an individual’s day to day activities are perfectly correlated. This approach tends to result in long-term activity patterns that are very different acro ss the simulated population, and therefore may over-estimate the variability across the population.

The Cluster-Markov Algorithm Recently, a new algorithm has been developed a nd 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 pa ttern records into groups that are similar, and then select a si ngle 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 da ily record to represent all days).

The steps in the algorithm are as follows.

 For each demographic group (age, gender, em ployment status), temperature range, and day-of-week combination, the associated time-activity records are partitioned into 3 groups using cluster analysis. The cluste ring criterion is a vect or of 5 values: the time spent in each of 5 microenvironment categories (indoors – residence; indoors – other building; outdoors – near road; outdoor s – 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 pr obability of a given time-activity pattern occurring on a given day based on the time- activity pattern of th e previous day and cluster-to-cluster transition probabilities. The cluster-to-cluster transition probabilities are estimated from the availa ble multi-day time-activity records. (If insufficient multi-day time-activity record s are available for a demographic group, season, day-of-week combination, then the cluster-to-cluster tran sition probabilities are estimated from the frequency of time-ac tivity records in each cluster in the CHAD data base.).

Figure 1 illustrates the Cluster-Markov algorithm in flow chart format.B-183Figure 1.Flow chart of Cluster-Markov algorithm used for constructing longitudinal time-activity diaries.B-184 Evaluation of modeled diary prof iles versus observed diary profiles The Cluster-Markov algorithm is also incorporated into the Hazardous Air Pollutant Exposure Model (HAPEM). Rose baum 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 S outhern California Chr onic Ozone Exposure Study (Xue et al. 2005, Geyh et al. 2000). In th is 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 a ggregate microenvironments: indoors-home, indoors-school, indoors-other, outdoors, and in-transit. For purposes of defining diary pools and for clustering and calculating tran sition probabilities the activity pattern records were divided by day type (i.e., wee kday, 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 simu lated. 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 mi croenvironment, 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 vari ance 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 m easure used in dispersion model performance and was also calculated as follows. 

#    N observed observed predicted N NBIAS

1 ) (100

The predicted time-in-microenvironmen t 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 th e predicted averages have bias between –9% and +9%, and the m ean bias across any mi croenvironment 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-fi ve percent of the pr edicted variances had bias between –22% and +24%. The mean nor malized bias across any microenvironment ranged from –10% to +28%. Eighteen predictio ns 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 microe nvironment/age/gender/season. Seventy percent of the predicted va riances had bias between –25% and +30%. The mean normalized bias across any micr oenvironment ranged from –11% to +47%. Twenty-eight predictions had positive bias and 12 had ne gative bias, suggesting some tendency for overprediction of this variance measure.

The overall conclusion was that the propos ed algorithm appeared to be able to replicate the observed data reasonably well. Although some discrepancies were rather large for some of the “variance across pe rsons” and “within-person variance” subsets, about two-thirds of the predictions for each ca se were within 30% of the observed value. A detailed description of the evaluation using HAPEM is presented in Attachment 5.

Comparison of Cluster-Markov a pproach 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 EP A, 2007). One of these was to make parallel simulations using each of the three algor ithms for constructing multi-day time-activity sequences that are incorporated into APEX.

Table 1 presents the resu lts for the number of pe rsons in Atlanta population groups with moderate exertion exposed to 8- hour average concentrations exceeding 0.07ppm. The results show that the predictions made with alternative algorithm ClusterMarkov algorithm are substantiall y 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 valu es 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 ex ceeding 0.07 ppm with moderate or greater exertion. The results show that although th e 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 in ter-individual variability.B-186 Table 1. Sensitivity to longitudinal diary algor ithm: 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 mo derate or greater exertion (after US EPA 2007). One or more exposures Three or more exposures Population Group Simple re-sampling DiversityAutocorrelation ClusterMarkov Simple re-sampling DiversityAutocorrelation ClusterMarkov

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 algor ithm: 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: Assessi ng 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 Expos ure Analysis for Selected Urban Areas. EPA452/R-07-010. Available at: http://www.epa.gov/ttn/naaqs/standard s/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 us ing 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 ALGORITHMB-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 proce ss to better represent the variability among individuals. This section summarizes th e 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 cat egory associated with each given first-day category, where the same individual was obser ved for two consecutive days. (Consecutive day activity pattern records for a single indivi dual 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 ar e 3 cluster categories for each of 3 day types, 9 unique activity pattern s are selected to be averaged toge ther 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 pa tterns used in the averaging process is determined by the product of two factors. The fi rst 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 wei ghting for the selected activity pattern is determined by simulating a sequence of category-types as a one-stage Markov chai n process using the transition probabilities. The category for the fi rst day is selected according to the relative frequencies of each category. The category fo r 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 proba bilities for the categoryB-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 freque ncy of the category-type in th e sequence associated with the selected activity pattern is the second factor in the weighting.

PROPOSED ALGORITHM STEPS The proposed algorithm is summarized in Figur e 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 – aw ay 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 gr oup, 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 indi vidual, are analyzed to determine the category-to-category transition frequencies in the CHAD data base. These transition frequencies are used to calculate cate gory-to-category tran sition probabilities.

For example, if there are 2 categories, A and B, then

P AA = the probability that a type A pa ttern is followed by a type A pattern,

P AB = the probability that a type A patte rn is followed by a type B pattern (P AB = 1 – P AA ),

P BB = the probability that a type B patte rn is followed by a type B pattern, and

P BA = the probability that a type B patte rn is followed by a type A pattern (P BA = 1 – P BB ).

Activity Pattern Selection For each day-type and demographic group in each census tract: Step 5: One activity pattern is randomly sel ected 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 th e first day of the day-type sequence, according to the relative fre quency 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-t o-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 fr equencies (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 ac tivity patterns are averaged together using the relative frequency of day-types as weight s, 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 va riability, 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, a nd 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 appr oach 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 se t 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 simula ted as a function of the age. Step 4: For each simulated day, the user de fines 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 ma tch the target gender and employment status exactly and the age w ithin 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 probabil ity of selecting a given diary day is equal to the age weight divided by the total of th e 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 clus ter 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 temperatur e bins for the clustering and transition probability calculations so that the clus ter definitions would be fairly uniform across temperature bins. Thus we woul d 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 transiti on 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 speci fied for transitions between summer weekdays of each category and summer weekends of each category.

Another issue for dividing the CHAD activ ity records for the purposes of clustering and calculating transition probabi lities is that the diary pools specified for the APEX activity selection algorithm use varying and overlapp ing age ranges. One way to address this problem would be to simply not include consid eration of age in the clustering process, under the assumption that cluster categories are simila r 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 stra tified by age group that were de veloped for HAPEM5. If the assumption is found to be valid, then the cluste r categories could be pre-determined for inputB-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 alternativ e approach could be implemented that would create overlapping age groups for purposes of clustering as follows. APEX age group ranges and age window per centages would be constrained to some maximum values. Then a set of overlapping age ra nges 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 resul ting sets of cluster ca tegories 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 selecti ng the second day’s activity pattern, each age weight would be multiplied by the transition probability P AB 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 P AB divided by the total of the products of age weight and P AB for all diary days in the pool for day 2. Similarly, for the transitions fr om 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 availa ble multi-day data set. For purposes of clustering we characterized the ac tivity 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 diar y pools and for clustering and calculating transition probabilities we divided the ac tivity pattern records by day ty pe (i.e., weekday, weekend), season (i.e., summer or ozone season, non-summ er 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 appear ed to group in three clusters. In this case, we simulated week-long se quences (Wednesday through Tuesday) for each of 100 people in each age/gender gr oup 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 spen t in that microenvironment

In each case we compared the predicted statis tic for the stratum to the statistic for the corresponding stratum in th e actual diary data.5 We also calculated the mean normalized bi as for the statistic, which is a common performance measure used in dispersion mode l performance and which is calculated as follows. 

#    N observed observed predicted 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 pr edicted 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 te st that the median bias across the 40 combinations = 0 % was not significant (p-v alue = 0.40) supporti ng 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/seas on. 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%. Ei ghteen predictions have positive bias and 20 have negative bias. Figure 4 suggests a reas onably 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 person s of the mean time was appr opriately 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) suppor ting 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 per cent 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 ha ve 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 overpre dicting outliers at th e higher end of the distribution. So although the positive bias is significant in a sta tistical sense (i.e., the variance is more likely to be overpredicted than underpre dicted), it is not clea r 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 va riance is somewhat overpredicted. It would be informative to compare this algorithm with the earlier alternative approaches in order to gain perspective on th e degree of improvement, if any, afforded by this approach.

Two earlier approaches were:

1. Select a single activity pattern for ea ch 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 da y in the multi-day sequence from the appropriate set for the corres ponding 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 microenvironm ent: 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-201Figure 1.Flow diagram of proposed algorithm for creating annual average activitypatterns for HAPEM5.Consolidated Human Activity Database - CHAD (CHAD)Cluster AnalysisCluster 1PatternGroupCluster 2PatternGroupCluster 3PatternGroupTransitionAnalysisTransitionProbabilitiesMarkov SelectionIndividual Annual Average ActivityPatternWinter WeekdayPattern GroupSummer WeekdayPattern GroupWeekendPattern GroupCluster 1PatternCluster 2PatternCluster 3PatternWeightsAverage WinterWeekday Pattern0.54Cluster AnalysisCluster AnalysisB-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 aver age 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 aver age 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

ObservedSimulated 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 vari ance across persons for time spent in each of 5 microenvironments for ag e/gender groups and seasons.

0 5 10 15 20 25 30

0 10 20 30

ObservedSimulated 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 th e 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 DATAB-205 

### DRAFT MEMORANDUM

To: John Langstaff From: Jonathan Cohen, Hemant Mallya, Arlene Rosenbaum Date: September 30, 2005 Re: EPA 68D01052, Work Assignment 3-08. Anal ysis of Air Exchange Rate Data

EPA is planning to use the APEX exposure mode l 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 in cluded 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) da ta were obtained from the following seven studies:

Avol: Avol et al, 1998. In this study, ozone c oncentrations 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. Da ta 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 a nd 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 st udy particulate matter concentrations and daily average AERs were measured at 37 residences in centra l North Carolina during 2000 and 2001 (averaging about 23 AER measurem ents per residence). The residences belong to two specific cohorts: a mostly Cau casian, 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 c ontrolled hypertension living in a low-tomoderate SES neighborhood in Raleigh. Da ta included the da ily average outdoor temperature, and the number of air conditione r 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 concentr ations 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. Meas urements were made at residences in Elizabeth, NJ, Houston TX, and Los Angele s 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 toxi cs. 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 cond itioning, room air conditioning, or a swamp (evaporative) cooler.

TEACH: Chillrud at al, 2004, Kinne y 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 expos ures to air toxics among hi gh 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 resi dences 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 (whi ch were converted to daily av erages 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 th e 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 cond itioner. We matched these data by month and zip code to the corresponding monthly average temperat ures obtained from EPA’s SCRAM website as well as from the archives in www.wunderground.com (personal and airport meteorological stations). Resi dences more than 25 miles aw ay 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 sufficien t 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 w ith one two-day test in the winter for each home. We obtained the data di rectly 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 cond itioner, 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 we bsite as well as from the archives in www.wunderground.com (personal and airport me teorological stations). Residences more than 25 miles away from the nearest av ailable meteorological st ation were excluded from the analysis. For our analyses, the c ity/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 re sidential AER data from several studies conducted between 1982 and 1987. These data were originally compiled by the Lawrence Berkeley National Laboratory. We acknowle dge Mr. Murray’s assistance in obtainingB-207 these data for us. The available data consis ted of AER measurements, dates, cities, and degree-days. Information on air conditione r 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 de sign, although some of these studies included information on whether air conditioners or fa ns 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 th e homes were deliberatel y sampled from specific subsets of the population at a given location ra ther than the entire population: The RTP Panel study selected two specific cohorts of older subj ects 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 thes e 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-208Table A-1. Summary of Studies ofResidential Air Exchange RatesAvolRTP PanelRIOPATEACHWilson 1984Wilson 1991MurrayandBurmasterLocationsLancaster, LakeGregory,Riverside, SanDimas. All inSouthern CAResearch TrianglePark, NCCA; NJ; TXLos Angeles, CA;New York City, NYSouthern CA Southern CAAZ, CA, CO,CT, FL, ID,MD, MN, MT,NJYears1994 2000; 2001 1999; 2000; 2001 1999; 20001984, 19851984 1982 – 1987Months/SeasonsFeb; Mar; Apr;May; Jun; Jul;Aug; Sep; Oct;Nov2000 (Jun; Jul;Aug; Sep; Oct;Nov), 2001 (Jan;Feb; Apr; May)1999 (July toDec); 2000 (allmonths); 2001(Jan and Feb)1999 (Feb; Mar; Apr;Jul; Aug); 2000 (Jan;Feb; Mar; Sep; Oct)Mar 1984, Jul 1984, Jan1985 Jan, Mar, Jul VariousNumber ofHomes86 37 284 85 581 288 1,884Total AERMeasurements161 854 524 151 1,362 316 2,844AverageNumber ofMeasurementsper Home1.87 23.08 1.85 1.78 2.34 1.10 1.51MeasurementDurationNot Available 24 hour24 to 96 hoursSample time (hours)reported. Rangesfrom about 1 to 7days.7 days7 daysNot availableMeasurementTechniqueNot AvailablePerflourocarbontracer. PMCH tracerPerflourocarbontracer. Perflourocarbon tracer. Perflourocarbon tracer. Not availableMin AER Value0.01 0.02 0.08 0.12 0.03 0.01 0.01Max AER Value2.70 21.44 87.50 8.87 11.77 2.91 11.77Mean AERValue0.80 0.72 1.41 1.71 1.05 0.57 0.76MinTemperature(C)-0.04 -2.18 -6.82 -1.36 11.00 3.00NotavailableB-209AvolRTP PanelRIOPATEACHWilson 1984Wilson 1991MurrayandBurmasterMaxTemperature(C)36.25 30.81 32.50 32.00 28.00 25.00NotavailableAir ConditionerCategoriesNo A/C; Centralor Room A/C;Swamp Cooleronly; Swamp +[Central or Room]Central or RoomA/C (Y/N)Window A/C(Y/N); EvapCoolers (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) Not availableAir ConditionerMeasurementsA/C use inminutes Not AvailableDurationmeasurements inHrs and Mins Not Available Not Available Not Available Not availableFan CategoriesNot available Fan (Y/N) Fan (Y/N) Not Available Not Available Not Available Not availableFanMeasurementsTime on or off forvarious fan typesduring samplingwas recorded, butnot included indatabase provided.Not AvailableDurationmeasurements inHrs and MinsNot Available Not AvailableNot Available Not availableWindow Open/Closed DataDuration openbetween times6am-12 pm; 12pm- 6 pm; and 6pm -6amWindows (open /closed along withduration open ininch-hours unitsWindows (Open /Closed) along withwindow opendurationmeasurements Not Available Not Available NotAvailable NotavailableCommentsCA sample was arandom sample ofhomes. NJ and TXhomes weredeliberatelychosen to be nearto ambientsources.Restricted to innercity homes with highschool students.Contemporaneoustemperature dataobtained for theseanalyses from SCRAMandwww.wunderground.commeteorological data.Contemporaneoustemperature dataobtained for theseanalyses from SCRAMandwww.wunderground.commeteorological 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 ty pe 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” “C entral and Room A/C” “Central or Room A/C” “No A/C” “Swamp + (Central or Ro om)” “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 a nd Burmaster data were excluded because of the absence of information on air conditioner presence. (However, a subset of these data was used for a supplemen tary analysis described below.) .

Based on our review of the AER data we excl uded seven outlying high AER values – above 10 per hour. The main data analysis used all th e 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 preval ence for the subsequent ozone modeling cities was not available from the American Housing Su rvey. It is plausible that AER distributionsB-211 depend upon the presence or absence of a swamp cooler. It is also plausible that AER distributions also depend upon wh ether the residence sp ecifically has a cent ral 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 th e presence of a swamp cooler are approximately proportionately represented in the surveyed residences.

Most of the studies had more than one AER meas urement 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 si nce the AER will be affected by the use of the available ventilation (air c onditioners, windows, fans), which in turn will depend upon the outside meteorology. Therefore it is not appropriate to averag e 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 sa me house on different da ys 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 st udy, 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 Be rnardino. 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 temper ature 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 defi ned 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, 10 th , 20 th ... 90 th percentiles, Max)

These calculations exclude all seven outliers and resu lts are not used for strata with 10 or fewer values, since those summary statistics are extremely unreliable.

Examination of these summary tables clearly de monstrates that the AER distributions vary greatly across cities and A/C type s and temperatures, so that the selected AER distributions for the modeled cities should also depend upon the c ity, 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 m ean 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 th e A/C type and temperature is illustrated by theB-212 result that for residences with A/C and betw een 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 we re carried out between the different strata, for the AER and its logarith m. The various strata are define d 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 th e Type of A/C and the temperature range; there are twelve such combinations. For each of th ese twelve combinations, we compare the AER distributions across differe nt cities. This analysis determin es whether the AER distribution is appropriately defined by the A/C type and temp erature 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 va lues 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 th e 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 si gnificantly different at the 5 pe rcent level. Those results are summarized in the last two colu mns of the above table “Summary of Comparisons of Means” which gives the number of cases where the m eans 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 st ratify by temperature range , A/C type, or both.B-213 The Kruskal-Wallis Statistic comparisons are non-p arametric 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 consis tent 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 increasi ng function and the test is non-para metric, the Kruskal-Wallis tests give identical results for AER and Log (AER).

The Mood Statistic comparisons are non-parametric te sts that compare the s cale statistics for two or more groups. The scale statistic measures va riation 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 th e 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-parametr ic, 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 comb ination 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 Wei bull distributions to the AER values.

The first analysis used the same stratificati ons as in the above “S ummary Statistics” and “Statistical Comparisons” secti ons. Results are not reported fo r 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), Ande rson-Darling (A-D). Each goodness-of-fit test compares the empirical distributi on of the AER values to the fitted distribution. The K-S and CM tests are different tests examining the overall fi t, 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-va lue and is marked by an x in th e final three columns. The mean and standard deviation (Std_Dev) are the values for the fitted di stribution. 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 geomet ric 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 di stributions, and so, for consistency, we recommend using the fitted log-normal distributions for all the cases.

One limitation of the initial analysis was that dist ributions 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 dist ributions were not available by using the AER distributions across all citie s and dates for a given temp erature range and A/C type.

Another important limitation of the initial analysis was that distributions we re 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 enti re range of temperatures from minus to plus infinity, although obviously the available data to fit these ranges ha ve finite temperatures. Stratifying by A/C type, city, and the new temperature ra nges produces results for four ci ties: Houston (AC and NA); Los Angeles (AC and NA); New York (AC and NA); Res earch Triangle Park (AC). For each of the fitted distributions we created histograms to co mpare the fitted distributions with the empirical distributions.

AER Distributions for Th e First Nine Cities. Based upon the results fo r the above four cities and the corresponding graphs, we propose using thos e fitted distributions for the three cities Houston, Los Angeles, and New York. For anothe r 6 of the cities to be modeled, we propose using the distribution for one of th e four cities thought to have similar charact eristics to the city to be modeled with respect to factors that mi ght 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 hum idity and wind speed patterns. The distributions proposed for these citi es are as follows:

 Atlanta, GA, A/C: Use log-normal distributi ons 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-norma l 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 ci ty using data from the same city, but this approach was chosen as a reasonable alte rnative, given the available AER data.

AER Distributions for Sa cramento 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 repr esent St. Louis. The results for the city Sacramento were obtained by combining all the av ailable 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 Burmaste r AER data for Maryland with AER data from each of those cities. The Murray and Burmaste r 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 distribut ions. Between Research Triangle Park and Maryland, the central values and the mean AE R values are not statistically significantly different, and the differences in the mean log (A ER) values are much less statistically significant than between New York and Maryland. The scal e statistic comparisons are not statistically significantly different between New York and Ma ryland, but were statistically significantly different between Research Triangle Park a nd Maryland. Since matching central and mean values is generally more important than matc hing the scales, we propos e to model Washington DC residences with air conditioning using the Res earch Triangle Park distributions, stratified by temperature:

 Washington DC, A/C: Use l og-normal distributions fo r 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 re sidences without air conditioning are discussed below.

AER Distributions for Washi ngton DC and Atlanta GA Re sidences 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 Triangl e 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 f itted to all the study data for residences without A/C, stratified by temp erature. We propose applying the “No A/C”B-216 distributions for modeling these two cities for re sidences 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 valu es also need to be simulated using APEX in order to select the appropriat e AER distribution. Mean daily temperatures are one of the available APEX inputs for each modeled city, so that the temperature ra nge 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 “N o A/C.” For comparison with Washington DC and Atlanta, we have included the A/C type percen tage 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 Triangl e 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 cen tral 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 re sidential and non-residential AER studies that might provide additional information or data. In door / outdoor ozone and PAN distributions were studied by Jakobi and Fabian (1997). Liu et al (1995) studied resi dential ozone and AER distributions in Toronto, Cana da. Weschler and Shields (2000 ) describes a modeling study ofB-217 ventilation and air exchange rate s. Weschler (2000) includes a us eful overview of residential and non-residential AER studies.

AER Distributions for Other Indoor Environments

To estimate AER distributions for non-resident ial, indoor environments (e.g., offices and schools), we obtained and analyzed two AER data sets: “Turk” (T urk et al, 1989); and “Persily” (Persily and Gorfain 2004; Persily et al. 2005).

The earlier “Turk” data set (T urk et al, 1989) includes 40 AER m easurements from offices (25 values), schools (7 values), libraries (3 valu es), 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 Surv ey and Evaluation (BASE) study, which was conducted to assess indoor air qu ality, including ventilation, in a large number of randomly selected office buildings throughout the U.S. The data base co nsists 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 da ta, the AER values for each office space were averaged, rather using the individual measuremen ts, 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 th e two studies are presented in Table A-4.

Table A-4. AER summary statistics for of fices and other non-residential buildings

Study Variable N Mean Std Dev Min 25 th %ile Median 75 th %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 1.4110B-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 dist ributions were derived from the more recent Persily data only.

Similarly to the analyses of the residential AE R distributions, we fitted exponential, log-normal, normal, and Weibull distributions to the 96 o ffice 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-ValueKolmogorovSmirnov P-ValueCramervon Mises P-ValueAndersonDarling 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 reside ntial AER distributi ons above.) According to all three goodnessof-fit measures the best-fitting distribution is th e log-normal. Reasonable choices for the lower and upper bounds are the observed mi nimum 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 tr ansit micro-environments, an approach using proximity and penetra tion factors is proposed, as follows.

Outdoors Near Road

Penetration factor = 1.

For the Proximity factor, we propose using ratio distributions develope d from the Cincinnati Ozone Study (American Petroleum Institute, 1997, Appendix B; Johnson et al. 1995). The field study was conducted in the greate r Cincinnati metropolitan area in August and September, 1994. Vehicle tests were conducted ac cording to an experimental de sign 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 compac t car. Road types were interstate highways (interstate), principal ur ban arterial roads (urban), and local roads (local). Nominal vehicleB-219 speeds (typically met over one minute intervals w ithin 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 vehi cle, outside the vehicle, and at six fixed site monitors in the Cincinnati area.

The proximity factor can be estimated from the di stributions of the ratios of the outside-vehicle ozone concentrations to the fixe d-site ozone concentrations, repor ted 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 th e ratios of outside-vehicle concen trations to the concentrations at the nearest fixed site mon itor, as shown in Table A-6.

Table A-6. Ratio of outside-vehicle oz one to ozone at nearest fixed site 1

Road Type 1 Number of cases 1 Mean 1 Standard Deviation 1 25 th Percentile 1 50 th Percentile 1 75 th Percentile 1 Estimated 5 th Percentile 2 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 ex cluded 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 oz one exposure will occur on local roads. The summary data from the Cincinna ti Ozone Study are too limited to allow fitting of distributions, but the 25 th and 75 th percentiles appear to be approximately equidistant from the median (50 th percentile). Therefore we propos e 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 5 th percentile, as shown in the final column of th e above table. Therefore in summary we propose:

 Penetration factor for outdoors, near road: 1.B-220  Proximity factor for outdoors, near road: No rmal 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 out doors-near-road microenvironment. We therefore recommend the same distributi ons 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 fo r outdoors, other: 1.

 Proximity factor for outdoors, other: 1.

In-Vehicle

For the proximity factor for in-vehicle, we also recommend using the resu lts of the Cincinnati Ozone Study presented in Table A-6. For this mi croenvironment, 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, a nd the lower bound to be the estimated 5 th percentile.

 Proximity factor for in-vehicle , local roads: Normal distribu tion. Mean = 0.755. Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

 Proximity factor for in-vehicle, urban ro ads: Normal distribution. Mean = 0.754. Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.

 Proximity factor for in-vehicle, interstates: Normal distribu tion. Mean = 0.364. Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.

To complete the specification, th e 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.ht m). 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 arte rial, 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 Urbani zed 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 combin ed with others for reporting purposes are not shown separately. The Illinois portion of R ound 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 Cincinna ti 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 ventil ation type are shown in Table A-8.

Table A-8. Ratio of inside-vehicle ozone to outside-vehicle ozone 1

Ventilation 1 Number of

cases 1 Mean 1 Standard Deviation 1 25 th Percentile 1 50 th Percentile 1 75 th Percentile 1 Estimated 5 th Percentile 2 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 0.000 3

All 812 0.300 0.232 0.117 0.251 0.463 0.000 3B-222 1. From Table 7 of Johnson et al .(1995). Data excluded if outsi de-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 in dicate that the inside-to-outside ozone ratios strongly depend upon the ventilation type, it would be very di fficult to find suitable data to estimate the ventilation type distributions for each modeled city. Furthermor e, 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 di stribution. 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 th e same APEX modeling approach:

 Proximity factor for mass transit, local ro ads: Normal distribution. Mean = 0.755. Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

 Proximity factor for mass transit, urban ro ads: Normal distribution. Mean = 0.754. Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.

 Proximity factor for mass transit, intersta tes: Normal distribution. Mean = 0.364. Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.

 Road type distributions for mass transit: See Table A-6

 Penetration factor for mass transit: Normal distribution. Mean = 0.300. Standard Deviation = 0.232. Lower Bound = 0.000. Upper Bound = 1.

References

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Avol, E. L., W. C. Navidi, and S. D. Colome (1998) Modeling ozone levels in and around southern California homes. Environ. Sci. Technol. 32, 463-468.

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Colome, S.D., A. L. Wilson, Y. Tian (1993). California Residential Indoor Air Quality Study, Volume 1, Methodology and Descriptive Statistics. Report prepared for the Gas Research Institute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Southern California Gas Co.B-223 Colome, S.D., A. L. Wilson, Y. Tian (1994). California Residential Indoor Air Quality Study, Volume 2, Carbon Monoxide and Air Exchange Rate : An Univariate and Mu ltivariate Analysis. Chicago, IL. Report prepared for the Gas Research Inst itute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Southe rn California Gas Co. GRI-93/0224.3

Jakobi, G and Fabian, P. (1997). Indoor/outdoor con centrations of ozone and peroxyacetyl nitrate (PAN) . Int. J. Biometeorol. 40: 162-165.

. Johnson, T., A. Pakrasi, A. Wisbeth, G. Meiner s, W. M. Ollison (1995). Ozone exposures within motor vehicles – results of a fi eld study in Cincinnati, Ohio. Proceedings 88 th annual meeting and exposition of the Air & Waste Management Association, June 18-23, 1995. San Antonio, TX. Preprint paper 95-WA84A.02.

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Meng, Q. Y., B. J. Turpin, L. Korn, C. P. Weisel, M. Morandi, S. Colome, J. J. Zhang, T. Stock, D. Spektor, A. Winer, L. Zhang, J. H. Lee, R. Giovanetti, W. Cui, J. Kwon, S. Alimokhtari, D. Shendell, J. Jones, C. Farrar, S. Maberti (2004). Influence of ambient (outdoor) sources on residential indoor and personal PM 2.5 concentrations: Analyses of RIOPA data. Journal of Exposure Analysis and Environ Epidemiology . Preprint.

Murray, D. M. and D. E. Burmaster (1995). Residential Air Exchange Rates in the United States: Empirical and Estimated Parametric Di stributions by Season and Climatic Region. Risk Analysis , Vol. 15, No. 4, 459-465.

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Persily, A., J. Gorfain, G. Brunner.(2005). Ventil ation design and performance in U.S. office buildings. ASHRAE Journal. April 2005, 30-35.

Sax, S. N., D. H. Bennett, S. N. Chillrud, P. L. Kinney, J. D. Spengler (2004) Differences in source emission rates of volatile organic compounds in inner-city residences of New York City and Los Angeles. Journal of Exposure Analysis and Environ Epidemiology . Preprint.

Turk, B. H., D. T. Grimsrud, J. T. Brown, K. L. Geisling-Sobotka, J. Harrison, R. J. Prill (1989). Commercial building ventilation ra tes and particle concentrations . ASHRAE, No. 3248.B-224 Weschler, C. J. (2000) Ozone in indoor envir onments: concentration a nd chemistry. Indoor Air 10: 269-288.

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Weisel, C. P., J. J. Zhang, B. J. Turpin, M. T. Morandi, S. Colome, T. H. Stock, D. M. Spektor, L. Korn, A. Winer, S. Alimokhtari, J. Kwon, K. Mohan, R. Harrington, R. Giovanetti, W. Cui, M. Afshar, S. Maberti, D. Shendell (2004). Re lationship of Indoor, Outdoor and Personal Air (RIOPA) study; study design, methods and quality assurance / control results. Journal of Exposure Analysis and Environ Epidemiology . Preprint.

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## ATTACHMENT 7. TECHNICAL MEMORANDUM ON THE UNCERTAINTY ANALYSIS OF RESIDENTIAL AIR EXCHANGE RATE DISTRIBUTIONSB-226 

### MEMORANDUM

To: John Langstaff, EPA OAQPS

From: Jonathan Cohen, Arlene Ro senbaum, 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 th e uncertainty of cityspecific distributions of residentia l air exchange rates that were f itted to the available study data. City-specific distributions for use with the AP EX ozone model were developed for 12 modeling cities, as detailed in the memorandum by C ohen, Mallya and Rosenbaum, 20056 (Appendix A of this report). In the first part of the memora ndum, we analyze the between-city uncertainty by examining the variation of the geometric means a nd 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 variati on on the fitted geometric means and standard deviations for each city. The bootst rap distributions assess the uncertainty due to random sampling variation but do not address uncerta inties 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 Rose nbaum, 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 distribu tion to the AER data, so that the logarithm of the AER for a given city, temperature range, and A/C type is as sumed to be normally distributed. If the AER data has geometric mean GM and geometric standa rd 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 repres ent the AER distributions for h ouses without A/C. Sacramento, CA AER distributions were estimat ed using the AER data from the inland California counties of Sacramento, Riverside, and San Bernardino; thes e 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 di stributions were estima ted 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 Residentia l 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-Californi a, 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 Lo s Angeles (“Inland California”)

St. Louis All non-California (“Outside California”) Washington, DC, A/C Resear ch 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 c ities 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 th at also included the necessary temperature and A/C type information. One way to assess the imp act of these assignments on the uncertainty of the AER distributions is to evaluate the variati on 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 da ta, 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 de signed 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 car diac defibrillators living in Chapel Hill; aB-228 group of non-smoking, African Amer icans 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 pref erentially 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 loca tions is to evaluate the varia tion of the fitted log-normal distributions within ea ch 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 di stributions 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 th e outside temperature and the A/ C type (whether or not the residence has air conditioning), th is analysis was stratified by th e outside temperature range and the A/C type. Otherwise, study or city di fferences would have been confounded by the temperature and A/C type differe nces and you would not be able to tell how much of the AER difference was due to the variati on of temperature and A/C type acr oss cities or studies. In order to be able to compare cities and studies we could not use different te mperature ranges for the different modeled cities as we did for the origin al 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 Windo w A/C” versus ‘No A/C,” givi ng 8 temperature and A/C type strata.

Table D-2 shows the geometric means and stan dard deviations by city and study. These geometric mean and standard deviation pairs ar e plotted in Figure D-1 through D-8. Each figure shows the variation across cities and studies for a given temper ature 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 i ndividual studies and the city overall distribution is designated by a blank value for the study name.

Table D-2. Geometric means and standa rd 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 1.34B-229 Table D-2. Geometric means and standa rd 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 1.30B-230 Table D-2. Geometric means and standa rd 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 availa ble 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 und efined if the sample size equals 1. In general, there is a relatively wide variation across different ci ties. This implies that the AER modeling results would be very different if the matching of m odeled 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 pa rticular 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 th e AER value from the assigned distribution for the city, A/C type and temperature range. Note that th is will result in a very different distribution toB-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 statisti cal weight for each city and fits a mixture of log-normal distributions rather than a single distribution.

In general, there is also some va riation 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 da ta points are less precise and the differences would not be statistically significant. For exam ple, 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 Wils on 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 st udy 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 th eir allocation to the 12 modeled c ities 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 thes e 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 20-25B-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 <=10B-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 ex ample figures D-9 through D-19 corresponds to a single GM/GSD “Original Data” pair. The GM a nd GSD values for the “O riginal Data” are at the intersection of the horizontal and vertical lines that are parall el to the x- and y-axes in the figures.B-234 Table D-4. Geometric means and standard devi ations 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 2.0373B-235 Table D-4. Geometric means and standard devi ations 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 GS D values, a bootstrap si mulation was performed, as follows. Suppose that a given AER subset ha s N values. A bootstrap sample is obtained by sampling N times at random with replacement fr om 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 valu es in the bootstrap sample are also selected randomly from the N values, so that for each selec tion, each of the N values is equally likely. The same value can be selected more than on ce. 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 th e part of the uncertainty of th e 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 di stribution 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 possi ble samples of N values from the population. Since each GM, GSD pair uniquely defines a f itted log-normal distribution, the pairs also estimate the uncertainty of the fitted log-normal distribution. Th e 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 pair s, 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 largeB-236 and/or the variability is small, so that there are a large number of valu es that are all close together.

The bootstrap analyses show that the geometri c 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 uncerta inty due to the random sampling. It does not account for the uncertainty due to th e lack of representativeness, whic h in turn is due to the fact that the samples were not always random sample s from the entire population of residences in a city, and were sometimes used to represent di fferent cities. Since only the GM and GSD were used, the bootstrap analyses does not account fo r uncertainties about the true distributional shape, which may not necessarily be log-normal. Furthermore, the bootstr ap 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 distributi ons were intended to repr esent distributions of 24 hour average AER values although the study AE R 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 AP EX model. Instead of using the Original Data distributions, a bootstrap GM, GSD pair could be selected at ra ndom and the AER value could be selected randomly from the log-normal di stribution with the bootstrap GM and GSD.B-237Figure D-1B-238Figure D-2B-239Figure D-3B-240Figure D-4B-241Figure D-5B-242Figure D-6B-243Figure D-7B-244Figure D-8B-245Figure D-9B-246Figure D-10B-247Figure D-11B-248Figure D-12B-249Figure D-13B-250Figure D-14B-251Figure D-15B-252Figure D-16B-253Figure D-17B-254Figure D-18B-255Figure D-19B-256 

## ATTACHMENT 8. TECHNICAL MEMORANDUM ON THE DISTRIBUTIONS OF AIR EXCHANGE RATE AVERAGES OVER MULTIPLE DAYSB-257 

### MEMORANDUM

To: John Langstaff, EPA OAQPS From: Jonathan Cohen, Arlene Ro senbaum, ICF International Date: June 8, 2006 Re: Distributions of air exchange rate averages over multiple days

As detailed in the memorandum by Cohen, Ma llya 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 Triang le Park Panel Study that show extremely strong correlations between consecutive 24-hour air excha nge rates measured at the same house. This provides support for the simplified approach of tr eating all averaging periods as if they were 24- hour averages.

In the current version of the APEX model, ther e are several options for stratification of time periods with respect to AER distributions, and fo r when to re-sample from a distribution for a given stratum. The options selected for this curre nt 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 independ ent. Although we have not identified sufficient data to test the assumption of uniform AERs th roughout a 24-hour period, the analyses described in this memorandum suggest that AERs on consecutiv e days are highly correlated. Therefore, we performed sensitivity simulati ons 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 av erages 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 we re in different seasons and/or calendar years. Daily outside temperatures were also provided. A ll the residences had either window or room air conditioners or both. We used these data to co mpare 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 temperatur e, and the daily temper atures often varied considerably. Two altern ative 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 actua l number of groups was 124. For the second approach, B, we again sort ed the data by the HOUSE_ID number and date, but this time we began a new group of days fo r 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, memora ndum (Appendix A): <= 10, 10-20, 20-25, and > 25 oC. For example, if the first week of meas urements 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 trea t 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 a pproach we treat groups of days as being independent following a transiti on 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 c ontinued this process to obtain three-, four-, five-, six-, and seven-day averag es. There were insufficiently re presentative data for averaging periods longer then seven days. Averages ove r 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 temperatur e 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, st andard 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 geom etric standard deviation equals e raised to the power Std Dev log (AER). The results are shown in Tabl es 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 sa me temperature range but different averaging periods. This is expected if the daily AER valu es 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 a pproximately constant for the same temperature range but different averaging periods, except fo r the data at > 25 oC where the standard deviations and variances tend to decrease as the length of the av eraging 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 diffe rent values of K in the same temperature range (except for the relatively limite d data at > 25 oC), this shows that the daily AER values are strongly correlated. Of course the correlation is not perfect, sin ce otherwise the AER for a given day would be identical to the AE R 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 th e RTP Panel study data, to a reasonable approximation, the distribution of an AER meas urement does not depend upon the length of the averaging period for the measurement, alt hough 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 le ngth of the averaging period.

The above argument suggests that the assumpti on that daily AER values are statistically independent is not justified. St atistical modeling of the correlation structure between consecutive daily AER values is not easy because of the prob lem of accounting for temp erature 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 eff ects are the population average values of AER or log(AER), and ar e assumed to depend upon the temperature range. The random effects have expected values of zero and define the correla tions between pairs ofB-261 measurements from the same Group, where the Gr oups 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 mixe d 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 th at 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. Ther e is a different overall mean value for each of the four temperature ranges.

A(Group, Temp Range) is the random effect of te mperature. For each Group, four error terms are independently drawn from four different norma l 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 calend ar 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(Gr oup 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-re gressive 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 ar e 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 interes ting 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 corre lation) although values exactly equal to +1 or -1 are impossible for a stationary model. Nega tive 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 “r andom” 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 dis tinguish from the other te rms. 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, sh owing 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, ther e are several options for stratification of time periods with respect to AER distributions, and fo r when to re-sample from a distribution for a given stratum. The options selected for this curre nt 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 independ ent. Although we have not identified sufficient data to test the assumption of uniform AERs th roughout 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 intr oduced into the APEX estimates with respect to either the magnitudes or variability of e xposure concentrations by implicitly assuming uncorrelated air exchange rates, we re-ran the 20 02 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 micr oenvironments 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 exce edances 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 PercentileDays/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 PercentileDays/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 Offi cer, Contract No. EP-W-05-022 Nancy Riley, Project Officer, Contract No. EP-D-08-100

June 2009Abt Associates Inc. June 2009 i DISCLAIMER

This report is being furnished to th e U.S. Environmental Protection Agency (EPA) by Abt Associates Inc. in partial fu lfillment 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 thos e 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 Harv ey 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. June 2009 ii 

## Table of Contents

1 INTRODUCTION .................................................................................................. 1-1

2 PRELIMINARY CONSIDERATIONS................................................................ 2-1 2.1 The Broad Empirical Basis for a Relationship Between SO 2 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 endpoi nts 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 ....................................................................................................... 5-1Abt Associates Inc. June 2009 iii 

## List of Tables

Table 3-1. Study-Specific SO 2 Exposure-Response Data for Lung Function Decrements .......................................................................................................... 3-4 Table 3-2. Example: Calculation of Number of Occurrenc es 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 SO 2 Concentrations that Just Me et an Alternative 1-Hour 99 th 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 Esti mated to Experience at Least One Lung Function Response, Defined as an Increase in sRaw ≥ 100%, Associated with Exposure to SO 2 Concentrations that Just Me et an Alternative 1-Hour 99 th Percentile 100 ppb Standard ..............................................................................3-11 Table 4-1. Number of O ccurrences (in Hundreds) of Lung Function Response (Defined in Terms of sRaw) Among As thmatics Engaged in Moderate or Greater Exertion Associated with Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards ........................................ 4-16 Table 4-2. Number of O ccurrences (in Hundreds) of Lung Function Response (Defined in Terms of sRaw) Am ong Asthmatic Children Engaged in Moderate or Greater Exertion Associ ated with Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards.......................... 4-17 Table 4-3. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of FEV 1 ) Among Asthmatics Engaged in Moderate or Greater Exertion Associated with Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards ........................................ 4-18 Table 4-4. Number of O ccurrences (in Hundreds) of Lung Function Response (Defined in Terms of FEV 1 ) Among Asthmatic Children Engaged in Moderate or Greater Exertion Associ ated with Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards.......................... 4-19 Table 4-5. Number of Asthmatics Enga ged in Moderate or Greater Exertion Estimated to Experience At Least On e Lung Function Response (Defined in Terms of sRaw) Associated w ith Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards ........................................ 4-20 Table 4-6. Number of Asthmatic Children E ngaged in Moderate or Greater Exertion Estimated to Experience At Least On e Lung Function Response (Defined in Terms of sRaw) Associated w ith Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards ........................................ 4-21Abt Associates Inc. June 2009 iv Table 4-7. Number of Asthmatics Enga ged in Moderate or Greater Exertion Estimated to Experience At Least On e Lung Function Response (Defined in Terms of FEV 1 ) Associated with Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards ........................................ 4-22 Table 4-8. Number of Asthmatic Children E ngaged in Moderate or Greater Exertion Estimated to Experience At Least On e Lung Function Response (Defined in Terms of FEV 1 ) Associated with Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards ........................................ 4-23 Table 4-9. Percent of Asthmatics Enga ged in Moderate or Greater Exertion Estimated to Experience At Least On e Lung Function Response (Defined in Terms of sRaw) Associated w ith Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 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 On e Lung Function Response (Defined in Terms of sRaw) Associated w ith Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards ........................................ 4-25 Table 4-11. Percent of Asthmatics Enga ged in Moderate or Greater Exertion Estimated to Experience At Least On e Lung Function Response (Defined in Terms of FEV 1 ) Associated with Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 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 On e Lung Function Response (Defined in Terms of FEV 1 ) Associated with Exposure to "As Is" SO 2 Concentrations, SO 2 Concentrations that Just Meet the Current Standards, and SO 2 Concentrations that Just Meet Alternative Standards ........................................ 4-27Abt Associates Inc. June 2009 v 

## List of Figures

Figure 3-1. Components of SO 2 Health Risk Assessment Based on Controlled Human Exposure Studies..................................................................................... 3-2 Figure 3-2. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw > 100% for 5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion...................................................................................................3-7 Figure 3-3. Bayesian-Estimated Median Exposure-Response Functions: Increase in sRaw > 200% for 5-Minute Exposures of Asthmatics Engaged in Moderate or Greater Exertion...................................................................................................3-7 Figure 3-4. Bayesian-Estimated Median Exposure-Response Functions: Decrease in FEV 1 > 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 FEV 1 > 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 SO 2 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 Re sponse (Defined as an Increase in sRaw ≥ 100%) Attributable to SO 2 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 Respons e (Defined as a Decrease in FEV 1 ≥ 15%) Attributable to SO 2 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 Re sponse (Defined as a Decrease in FEV 1 ≥ 15%) Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios...............................................................................................4-31 Figure 4-5. Number of O ccurrences of Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Among Asthmatics Engaged in Moderate or Greater Exertion in St. Louis Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.............................................................. 4-32 Figure 4-6. Number of Occu rrences of Lung Function Re sponse (Defined as an Increase in sRaw ≥ 100%) Among Asthmatic Children Engaged in Moderate or Greater Exertion in St. Louis Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.............................................................. 4-33 Figure 4-7. Number of Occu rrences of Lung Function Response (Defined as a Decrease in FEV 1 ≥ 15%) Among Asthmatics Engaged in Moderate or Greater Exertion in St. Louis Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.............................................................. 4-34 Figure 4-8. Number of Occu rrences of Lung Function Response (Defined as a Decrease in FEV 1 ≥ 15%) Among Asthmatic Children Engaged in ModerateAbt Associates Inc. June 2009 vi or Greater Exertion in St. Louis Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.............................................................. 4-35 Figure 4-9. Percent of Asthmatics Engage d in Moderate or Greater Exertion in Greene Co. Exhibiting Lung Function Res ponse (Defined as an Increase in sRaw ≥ 100%) Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios...............................................................................................4-36 Figure 4-10. Percent of Asthmatic Child ren Engaged in Moderate or Greater Exertion in Greene Co. Exhibiting Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.........................................................................4-37 Figure 4-11. Percent of Asthmatics Engage d in Moderate or Greater Exertion in Greene Co. Exhibiting Lung Function Re sponse (Defined as a Decrease in FEV 1 ≥ 15%) Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios...............................................................................................4-38 Figure 4-12. Percent of Asthmatic Child ren Engaged in Moderate or Greater Exertion in Greene Co. Exhibiting L ung Function Response (Defined as a Decrease in FEV 1 ≥ 15%) Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.........................................................................4-39 Figure 4-13. Number of O ccurrences of Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Among Asthmatics Engaged in Moderate or Greater Exertion in Greene Co. Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.............................................................. 4-40 Figure 4-14. Number of O ccurrences 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 SO 2 Within Given Ranges Under Different Air Quality Scenarios.............................................................. 4-41 Figure 4-15. Number of O ccurrences of Lung Function Response (Defined as a Decrease in FEV 1 ≥ 15%) Among Asthmatics Engaged in Moderate or Greater Exertion in Greene Co. Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.............................................................. 4-42 Figure 4-16. Number of O ccurrences of Lung Function Response (Defined as a Decrease in FEV 1 ≥ 15%) Among Asthmatic Children Engaged in Moderate or Greater Exertion in Greene Co. Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios.............................................................. 4-43 Figure 4-17. Legend for Figures 4-1 - 4-16. .................................................................4-44Abt Associates Inc. June 2009 1-1 

## Sulfur Dioxide Health Risk Assessment

1 INTRODUCTION

The U.S. Environmental Protection Ag ency (EPA) is presently conducting a review of the national ambient air quality standards (NAAQS) for sulfur dioxide (SO 2 ). Sections 108 and 109 of the Clean Air Act (A ct) 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 di verse mobile or stationary sources. The NAAQS are to be based on air qua lity criteria, which are to a ccurately reflect the latest scientific knowledge useful in indicating th e kind and extent of identifiable effects on public health or welfare that may be expect ed from the presence of the pollutant in ambient air. The EPA Administrator is to promulgate and periodica lly review, at fiveyear intervals, “primary” (health-based) and “secondary” (welfare-based) NAAQS for such pollutants. 1 Based on periodic reviews of the ai r quality criteria and standards, the Administrator is to make revisions in the cr iteria and standards, and promulgate any new standards, as may be appropria te. 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 Scient ific Advisory Committee (CASAC).

EPA’s plan and schedule for this SO 2 NAAQS review is presented in the “Integrated Plan for Review of the Primar y National Ambient Air Quality Standards for Sulfur Oxides” (U.S. EPA, 2007a). The plan disc usses 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 oxi des of sulfur (SO x ) in the ambient air. The risk/exposure assessme nts 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 Stan dards (OAQPS) released a first draft of its “Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standard,” henceforth referred to as the 1 st draft REA (U.S. EPA, 2008b). Both of these documents were reviewed by the CASAC SO 2 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 assessmen t 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. June 2009 1-2 bronchoconstriction experienced by asthmatic subjects associated with 5 to 10 minute exposures to SO 2 while engaged in moderate or greater exertion. In September 2008, NCEA released the final version of the ISA, “Integrated Science Assessment for Oxides of Sulfur – Health Criteria, henceforth referred to as the ISA (U.S. EPA, 2008c). A second draft REA (EPA, 2009a) was made availa ble to the CASAC and public in March 2009. The second draft REA was reviewed by the CASAC SO 2 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 a nd conclusions contained in the final ISA.

SO 2 is one of a group of compounds known as sulfur oxides (SO x ), which include multiple chemicals (e.g., SO 2 , SO, SO 3 ). However only SO 2 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, SO 2 is considered as a surrogate for gaseous SO x species in this assessment, with the secondarily formed particulat e species (i.e., sulfate or SO 4 ) addressed as part of the particulate matter (PM) NAAQS review.

In the previous review, concluded in 1996, it was clearly estab lished that subjects with asthma are more sensitive to the respiratory effects of SO 2 exposure than healthy individuals (ISA, section 3.1.3.2) . Asthmatics exposed to SO 2 concentrations as low as 0.2-0.3 ppm for 5-10 minutes during exercise ha ve 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 (FEV 1 ) ( ≥ 15%) after correction for exercise-induced responses in clean air.

The basic structure of the SO 2 health risk assessment de scribed in this document reflects the fact that we have availabl e controlled human exposure study data from several studies involving volunteer asthmatic subjects who were exposed to SO 2 concentrations at specified exposure levels wh ile 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 SO 2 , (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 alternativ e 1-hour standards in select ed locations encompassing a variety of SO 2 emission source types in the Greene County and the St. Louis area within Missouri.

The SO 2 health risk assessment build s upon the methodology, analyses, and lessons learned from the assessments conducted for the last SO 2 NAAQS review in 1996, as well as the methodology and lessons learne d from the health risk assessment work conducted for the recently concluded O 3 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 SO 2 concentrations. 3 There is a 3-hr secondary standard as well. Howeve r, this risk assessment is taking into account only the primary standards. The current primary SO 2 standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and an annual standard set at 0.03 ppm, calculated as the arithmetic m ean of hourly averages.Abt Associates Inc. June 2009 1-3 particular, the assessment of risk based on controlled human exposure studies described in Chapter 3 of that document. The SO 2 risk assessment is based on our current understanding of the SO 2 scientific literature as reflected in the evaluation provided in the final ISA.

The goals of this SO 2 health risk assessment are: (1) to develop health risk estimates of the number and percent of the as thmatic population in the selected study area locations that would experience moderate or greater lung function decr ements in response to daily 5-minute maximum peak exposures while engaged in m oderate or greater exertion for a recent year of air quality and under a scenario in which the SO 2 concentrations are adjusted to simulate just meeting the current 24-hour standard; (2) to develop a better understanding of the influence of various i nputs and assumptions on the risk estimates; and (3) to gain insights into th e risk levels and patterns of risk reductions associated with air quality simulatin g just meeting alternative 1-hour SO 2 standards. The risk assessment is intended as a tool th at, together with other information on lung function and other health e ffects evaluated in the SO 2 ISA, can aid the Administrator in judging whether the current primary standards protect public health with an adequate margin of safety, or whether revision s to the standards are appropriate.

Preliminary considerations and the basi c 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. June 2009 2-1 2 PRELIMINARY CONSIDERATIONS

The health risk assessment described in this document estimated lung function decrements (measured as increase s in sRaw or decreases in FEV 1 ) associated with SO 2 exposures under several scenarios: (1) recent ambient levels of SO 2 , (2) air quality adjusted to simulate just meeting the current 24-hour and annual standards, and (3) air quality adjusted to simulate just meeting se veral alternative 1-hour standards. In this section we address preliminary considerati ons. Section 2.1 briefl y discusses the broad empirical basis for a relationship between SO 2 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 SO 2 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 SO 2 ” (ISA, p. 3- 33). In support of this conclu sion, the ISA notes the following:

The strongest evidence for this causal relationship comes from human clinical studies re porting respiratory symptoms and decreased lung function following peak exposures of 5-10 min duration to SO 2 . These effects have been observed consis tently across st udies 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 SO 2 . Although studies have not reported statistically sign ificant respiratory effects following exposure to 0.2-0.3 ppm SO 2 , some asthmatic subjects (5-30%) have been shown to experience moderate to large decr ements in lung function at these exposure concentrations. A larger body of evidence s upporting this determination of causality comes from numerous ep idemiological studies reporting associations with respiratory sy mptoms, ED visits, and hospital admissions with short-term SO 2 exposures, generally of 24-h avg. Important new multicity studies and se veral other studies have found an association between 24-h avg ambient SO 2 concentrations and respiratory symptoms in children, partic ularly those with asthma.... ... Collectively, the findings from both human clinical and epidemiological studies provide a st rong basis for concluding a causal relationship between respiratory morb idity and short-term exposure to SO 2 .Abt Associates Inc. June 2009 2-2 2.2 Basic Structure of the Risk Assessment

As noted above, this SO 2 health risk assessment is based on controlled human exposure studies involving volunteer subjects who were exposed while engaged in different exercise regimens to specified levels of SO 2 under controlled conditions for 5 or 10 minute periods. The responses measured in these studies were measures of lung function decrements, including increases in sRaw and decreases in FEV 1 . We used probabilistic exposure-response re lationships, based on analysis of individual data, that describe the relationships between a measure of personal exposure to SO 2 and the measure(s) of lung function recorded in these studies. These probabilistic exposureresponse relationships were combined w ith 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. Es timates of personal exposures to varying ambient co ncentrations a ssociated with several air quality scenarios including recent air quality le vels, and air quality levels simulating just meeting the current SO 2 primary standard and several altern ative primary 1-hour standards were derived through exposure modeling. The detail s 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 requ ires as input (modeled) personal exposures to SO 2 .

 Controlled human exposure studies, car ried out in laborat ory settings, are generally not specific to any particular real wo rld location. A controlled human exposure studies-based risk asse ssment can therefore appropriately be carried out for any location for which th ere are adequate air quality data on which to base the modeling of personal exposures.

The methods for the SO 2 risk assessment are discussed in section 3 below. The risk assessment was implemented within a new probabilistic version of TRIM.Risk, the component of EPA’s Total Risk Integrated Methodology (TRIM) model that estimates human health risks. 4

2.3 Air Quality Considerations

The SO 2 health risk assessment estimates l ung function risks associated with (1) “as is” ambient levels of SO 2 , (2) air quality simulating just meeting the cu rrent 24-hour and annual standards, and (3) air quality simu lating just meeting several alternative 1-

4 TRIM.Risk was most recently applied to EPA’s O 3 health risk assessment. A User’s Guide for the Application of TRIM.Risk to the O 3 health risk assessment (Abt Associates, 2007b) is available online at: http://epa.gov/ttn/f era/data/trim/trimrisk_ozone _ra_userguide_8-6-07.pdf

.Abt Associates Inc. June 2009 2-3 hour standards in a recent year (2002) in two select ed locations encompassing a variety of SO 2 emission source types in Greene County, Missouri and St. Louis, Missouri.

In order to estimate health risks associat ed with just meeting the current 24-hour and annual standards and alternative 1-hour SO 2 standards, it is nece ssary to estimate the distribution of short- term (5-minute) SO 2 concentrations that would occur under any given standard. Since comp liance with the current SO 2 standards is based on a single year, air quality data from 2002 were used to determine the change in SO 2 concentrations required to meet the current standards. Estim ated design values were used to determine the adjustment necessary to just meet th e 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 O 3 NAAQS reviews represented risks a ssociated with PM and O 3 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 Nort h America (defined as the United States, Canada, and Mexico). The ISA notes that PRB SO 2 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 SO 2 emissions cause high PRB concentrati ons, PRB contributes less than 1% to present-day SO 2 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 SO 2 health risk assessment.Abt Associates Inc. June 2009 3-1 3 METHODS

The major components of the SO 2 lung function risk assessment are illustrated in Figure 3-1. The air quality and exposure analysis components that are in tegral to the risk assessment are discussed in Chapters 6 and 7, respectively, of the 2 nd draft REA. As described in the ISA and the 2 nd draft REA, there are numerous controlled human exposure studies reporting lung function decrem ents (as measured by increases in SRaw and/or decreases in FEV 1 ) among mild and/or moderate asth matic adults associated with short-term (5 or 10 minute) peak exposures to various levels of SO 2 while engaged in moderate or greater exercise. The SO 2 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 suffici ent evidence to infer a causal relationship between respiratory morbidity and short-term exposure to SO 2 (ISA, section 5.2). This determination was based in large part on controlled human exposure studies demonstrating a relationship between s hort-term (5- or 10-minute) peak SO 2 exposures and adverse effects on the respiratory system in exercising asthmatics. More specifically, the ISA found consistent evidence from nume rous controlled human exposure studies demonstrating increased respiratory symp toms (e.g. cough, chest tightness, wheeze) and decrements in lung function in a substa ntial proportion of exercising asthmatics (generally classified as mild to moderate asthmatics) following short-term peak exposures to SO 2 at concentrations ≥ 0.4 ppm (400 ppb). As in previous reviews, the ISA also concluded that at concentr ations below 1.0 ppm (1,000 ppb) , healthy individuals are relatively insensitive to the respirat ory effects of short-term peak SO 2 exposures (ISA, sections 3.1.3.2). Therefore, the SO 2 lung function risk as sessment focuses on asthmatics. Exposure estimates for asthmatic children and adult asthmatics were combined separately with probabilistic e xposure-response relationships (described below) for lung function response associat ed with daily 5-minute maximum peak exposures while engaged in m oderate or greater exertion. 5

5 Only the highest 5-minute peak exposure (with moderate or great er 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 re fractory period where the individual was relatively insensitive to additional SO 2 challenges.Abt Associates Inc.June 20093-2Figure 3-1. Components of SO2Health Risk Assessment Based on Controlled Human Exposure StudiesAmbientMonitoring forSelected UrbanAreasAir QualityAdjustmentProceduresCurrent andAlternativeProposedStandardsRisk Estimates:•Recent AirQuality•CurrentStandard•AlternativeStandardsHealthRiskModelControlled HumanExposure StudiesProbabilisticExposure -ResponseRelationshipsExposure-ResponseAir QualityRecent(“As Is”)AmbientSO2LevelsExposureModelExposure EstimatesAssociated with:•Recent Air Quality•Current Standard•AlternativeStandardsExposureAmbientMonitoring forSelected UrbanAreasAir QualityAdjustmentProceduresCurrent andAlternativeProposedStandardsRisk Estimates:•Recent AirQuality•CurrentStandard•AlternativeStandardsHealthRiskModelControlled HumanExposure StudiesProbabilisticExposure -ResponseRelationshipsExposure-ResponseAir QualityRecent(“As Is”)AmbientSO2LevelsExposureModelExposure EstimatesAssociated with:•Recent Air Quality•Current Standard•AlternativeStandardsExposureAbt Associates Inc. June 2009 3-3 Two measures of lung function response – sp ecific airway resistance (sRaw) and forced expiratory volume in one second (FEV 1 ) – have been used in the controlled human exposure studies that have focused on the effects of exposure to SO 2 on exercising asthmatics. Negative effects are measured as the percent increase in sRaw or the percent decrease in FEV 1 . As explained below, we estimated exposure-response re lationships for four di fferent definitions of response:

 An increase in sRaw ≥ 100%

 An increase in sRaw ≥ 200%

 A decrease in FEV 1 ≥ 15%

 A decrease in FEV 1 ≥ 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, usin g 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 defi ned in terms of sRaw and 5- or 10-minute exposures to SO 2 at levels of 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, and 1.0 ppm. 6 As noted above, two definitions of response were used: (1) an increase in sRaw ≥ 100% and (2) an increase in sRaw ≥ 200%.

The combined data set from Linn et al. (1987, 1988, 1990) provide data with which to estimate exposure-response relationships be tween responses defined in terms of FEV 1 and 5- or 10-minute exposures to SO 2 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 FEV 1 ≥ 15% and a decrease in FEV 1 ≥ 20%.

Before being used to estimate exposure-re sponse relationships for 5-minute exposures, the data from these controlled human exposure studi es were corrected for th e 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 SO 2 -exposed cases. The resulting st udy-specific resu lts, 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 us ing a mouthpiece rather than a chamber. 7 Corrections were subject-specific. A correction was ma de by subtracting the subject’s percent change (in FEV 1 or sRaw) under the no-SO 2 protocol from his or her percent change (in FEV 1 or sRaw) under the given SO 2 protocol, and rounding the result to the nearest integer. For example, if a subject’s percent change in sRaw under the no-SO 2 protocol was 110.12% and his percent change in sRaw under the 0.6 ppm (600 ppb) SO 2 protocol was 185.92%, then his percent change in sRaw due to SO 2 is 185.92% - 110.12% = 75.8%, which rounds to 76%.Abt Associates Inc. June 20093-4Table 3-1. Study-Specific SO2Exposure-Response Data for Lung Function DecrementsIncrease in sRaw >100% Increase in sRaw >200% Decrease in FEV1>15% Decrease in FEV1>20%Study and SO2LevelNumberExposedNumberRespondingNumberExposedNumberRespondingNumberExposedNumberRespondingNumberExposedNumberResponding0.2 ppm SO2Linnetal.(1987) 40 2 40 0 40 5 40 20.25 ppm SO219 6 19 3Bethel et al. (1985)9 2 9 0Roger et al. (1985) 2812800.3 ppm SO2Linnetal.(1988) 20 2 20 1 20 3 20 0Linnetal.(1990) 21 7 21 2 21 5 21 30.4 ppm SO2Linn et al. (1987) 40940340124090.5 ppm SO2Bethel et al. (1983) 106104Roger et al. (1985) 285281Magnussen et al. (1990)* 45164570.6 ppm SO2Linnetal.(1987) 40 14 40 11 40 21 40 19Linn et al. (1988) 201220720112011Linnetal.(1990) 21 13 21 6 21 9 21 71.0 ppm SO2Roger et al. (1985) 2814287Kehrl et al. (1987) 106102*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 amouthpiece 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 th e data in Table 3-1 to estimate the logistic function,

)1( 1 ),;( )*ln( x e xy  

   (3-1)

and the probit function, 

#  

   )ln( 2/ 2/1 2 )2( 1 ),;( x t dte xy 

  (3-2)

for each of the four lung function responses defined above, where x denotes the SO 2 concentration (in ppb) to which the individual is exposed, ln( x ) is the natural logarithm of x , y denotes the corresponding probability of response (inc rease in sRaw > 100% or > 200% or decrease in FEV 1 > 15% or > 20%), and β and γ are the two parameters whose values are estimated. 8

We assumed that the number of responses, s i , out of N i subjects exposed to a given SO 2 concentration, x i , has a binomial distribution with re sponse probability given by equation (3-1) when we assume the logistic model a nd equation (3-2) when we assume the probit model. The likelihood function is therefore

ii i sN i s i i i i xy xy s N dataL 

        

 

##  )],;(1[),;( );,(   

.

In some of the controlled human exposure st udies, subjects were exposed to a given SO 2 concentration more than once. However, b ecause there were insufficient data to estimate subject-specific response probabilit ies, we assumed a single res ponse 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 pos terior distribution us ing this binomial likelihood function in combinati on with uniform prior distri butions 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 conve rgence of the resulting posterior density. Each iteration corresponds to a set of values for the parameters of the logi stic or probit exposure-response function.

8 For ease of exposition, we use the same two Greek lette rs 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 SO 2 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 (50 th percentile) logistic and probit exposureresponse functions are shown toge ther, along with the data used to estimate these functions, for increases in sRaw > 100% and > 200% and decreases in FEV 1 > 15% and > 20% in Figures 3-2, 3-3, 3-4, an d 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-respons e functions, and in all cases it wasn’t clear that one function fit the data be tter than the other. In fact, for each of the four lung function response definitions there was little differen ce between the estimated logistic and probit models in the range of the da ta used to estimate the functi ons. 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 risk s associated with exposure to SO 2 under the different air quality scenarios considered using both the l ogistic and the probit exposureresponse functions. The 2.5 th percentile, median, and 97.5 th 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 substantia l differences in estimates of risk.Abt Associates Inc. June 2009 3-7 Figure 3-2. Bayesi an-Estimated Median Exposure-Respo nse Functions: Increase in sRaw > 100% for 5- Minute Exposures of Asthmatics Engag ed 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 SO 2 Concentration (ppm)Response Rate The data probit 2-parameter logistic

Figure 3-3. Bayesi an-Estimated Median Exposure-Respo nse Functions: Increase in sRaw > 200% for 5- Minute Exposures of Asthmatics Engag ed 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 SO 2 Concentration (ppm)Response Rate The data probit 2-parameter logisticAbt Associates Inc. June 2009 3-8 Figure 3-4. Bayesian -Estimated Median Exposure-Respo nse Functions: Decrease in FEV 1 > 15% for 5- Minute Exposures of Asthmatics Engag ed 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 SO 2 Concentration (ppm)Response Rate The data probit 2-parameter logistic

Figure 3-5. Bayesian -Estimated Median Exposure-Respo nse Functions: Decrease in FEV 1 > 20% for 5- Minute Exposures of Asthmatics Engag ed 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 SO 2 Concentration (ppm)Response Rate The data probit 2-parameter logisticAbt 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., asthmatic s) in a year associated with SO 2 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 with in 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 e xposure bin by the response probability (given by our logistic or probit exposure-response f unction for the specified de finition of lung function response) associated with the midpoint of that bin and summing the results across the bins.

Because response probabilitie s are calculated for each of several percentiles of a probabilistic exposure-response di stribution, estimated numbers of occurrences are similarly percentile-specific. The k th percentile number of occurrences, O k , associated with SO 2 concentrations under a given air quality scenario is:

)|(

1 jk n

j j k eRxNO 

## 

  (3-3)

where:

e j = (the midpoint of) the jth ca tegory of personal exposure to SO 2 ;

N

j = the number of exposures to e j ppb SO 2 , given ambient SO 2 concentrations under the specified air quality scenario;

jk eR | = the k th percentile response probability at SO 2 concentration e j ; and

n = the number of interv als (categories) of SO 2 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 SO 2 . In at least one of the a lternative 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 a ssigned 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 En gaged in Moderate or Greater Exertion Associated with Exposure to SO 2 Concentrations that Just Me et an Alternative 1-Hour 99 th Percentile 100 ppb Standard* Number of Probability of Expected Number of Lower Upper Midpoint Exposures Response at Midpoint Occurrences of Lung Bound Bound SO 2 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 SO 2 Exposure Bin (ppb)

*Calculations were made using the l ogistic exposure-response function.
The second measure of risk generated for each lung function response definition is the number of individuals in the designated popul ation to experience at least one lung function response in a year associated with SO 2 concentrations under a speci fied air quality scenario. The calculation of this measure of risk is similar to the calculation of th e 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 SO 2 or higher, for x = 0, 50, 100, etc. From this we calculated the number of individu als exposed at least once to SO 2 concentrations within each SO 2 exposure bin defined above. We then multi plied the numbers of individuals in an exposure bin by the response proba bility (given by our logistic or probit exposure-response function for the specified definition of lung f unction response) corresponding to the midpoint of the exposure bin, and summed the results across the bins.

Because response probabilitie s are calculated for each of several percentiles of a probabilistic exposure-response dist ribution, estimated numbers of individuals with at least one lung function response are simila rly percentile-specific. The k th percentile number of individuals, Y k , associated with SO 2 concentrations under a given air quality scenario is:

)|(

1 jk n

j j k eRxNIY 

## 

  (3-4)

Where e j , jk

eR | , and n are as defined above, and NI j is the number of individuals whose highest exposure is to e j ppb SO 2 , given ambient SO 2 concentrations under the specified airAbt 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 Leas t One Lung Function Response, Defined as an Increase in sRaw ≥ 100%, Associated wi th Exposure to SO 2 Concentrations th at Just Meet an Alternative 1-Hour 99 th 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 SO 2 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 SO 2 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 SO 2 concentration to which they are e xposed will not respond to any lower SO 2 concentrations to which they are exposed.

Note also that, in contrast to th e risk estimates calculated for the O 3 health risk assessment, the risk estimates calculated for the SO 2 health risk assessment do not subtract out risk given the personal exposures associated w ith estimated policy rele vant background (PRB) ambient SO 2 concentrations, because PRB SO 2 concentrations are so low (see section 2.3).

3.2.2 Selection of urban areas

Although it would be useful to characterize SO 2 -related lung function risks associated with “as is” SO 2 ambient concentrations and SO 2 concentrations that just meet the current and alternative SO 2 standards nationwide, because the m odeling 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 ther efore the risk assessment, took into consideration the availability of ambient monitoring, the desire to represent a range ofAbt Associates Inc. June 2009 3-12 geographic areas considering SO 2 emission sources, population demographics, general climatology, and results of the ambi ent air quality characterization.

The first area of interest was initially iden tified based on the results of a preliminary screening of the 5-minute ambient SO 2 monitoring data that were available. The state of Missouri was one of only a few states havi ng both 5-minute maximum and continuous 5- minute SO 2 ambient monitoring, as we ll as having over 30 1-hour SO 2 monitors in operation at some time during the period from 1997 to 2007. In addition, the air quality characterization, described in Chapter 6 of the 1 st draft REA (EPA, 2008b), estimated frequent exceedances above the potential health effect benchmark leve ls at several of the 1-hour ambient monitors. In a ranking of estimated SO 2 emissions reported in the Nation al Emissions Inventory (NEI), Missouri ranked 7th for the num ber of stacks with > 1000 tpy SO x emissions out of all U.S. states. These stack emissions were associated wi th a variety of source ty pes such as electrical power generating units, chemical manufacturing, cement processing, and smelters. For all these reasons, the current SO 2 lung function risk assessment fo cuses on Missouri and, within Missouri, on those areas within 20 km of a major point source of SO 2 emissions in Greene County and the St. Louis area.

3.2.3 Addressing variability and uncertainty

Any estimation of risks associated with “as is” SO 2 concentrations or with SO 2 concentrations that just meet the current or alternative SO 2 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 relations hips (model uncertainty – e.g., the shapes of exposure-response and concentratio n-response functions). The goal of the an alyst 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 charact erized, sometimes quantitatively. For example, the statistical uncerta inty surrounding the estimated SO 2 coefficients in the exposure-response functions is reflected in conf idence or credible inte rvals provided for the risk estimates.

As described in section 3.2 above, we us ed a Bayesian Markov Chain Monte Carlo approach to estimate exposure-response functio ns 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 SO 2 concentration, x , as described above (see section 3.2). Because our exposure estimates were generated at the midpoints of 0.05 ppm intervals (i.e., for 0. 025 ppm, 0.075 ppm, etc.), we derived 2.5 th percentile, 50 th percentile (median), and 97.5 th percentile response estimates for SO 2 concentrations at these midpoint values. The 2.5 th percentile and 97.5 th 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 fro m sampling variability, other uncertainties associated with the use of the exposure-respons e relationships for lung function responses are briefly summarized below. Additional uncertaintie s 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 es timates are based on a combined data set from several controlled human e xposure studies, most of which evaluated responses associated with 10-minute exposur es. 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-mi nute 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 e xposure studies represen t the distribution of mild and moderate asthmatics in the U.S. popula tion. As indicated in the ISA (p. 3-9), the subjects studied represent the responses “am ong groups of relatively healthy asthmatics and cannot necessarily be extrapolated to the most sensitive asthmatics in the population who are likely more suscep tible to the respiratory effects of exposure to SO 2 ."

 Extrapolation of exposure-response relationships . It was necessary to estimate responses at SO 2 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 alte rnative 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 informa tion provides insights on how much of the estimated risk is attributed to SO 2 exposures at the lower exposur e levels (i.e., 0 to 50 ppb, 50 to 100 ppb, 100 to 150 ppb, etc.). One can us e this information to get a rough sense of the SO 2 -related risk that would exist under alternative thresh old assumptions.

 Reproducibility of SO 2- induced responses

. The risk assessment assumed that the SO 2 - induced responses for individuals are reprodu cible. 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 aut hors reported a high degree of correlation (r > 0.7 for mild asthmatics and r > 0.8 for mode rate 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 func tion responses were conducted w ith adult subjects, the risk assessment relies on data from adult asthma tic subjects to estimate exposure-response relationships that were applied to all asth matic individuals, incl uding children. The ISAAbt Associates Inc. June 2009 3-14 (section 3.1.3.5) indicates that there is a st rong body of evidence that suggests adolescents may experience many of the same respiratory effects at similar SO 2 levels, but recognizes that these studies administered SO 2 via inhalation through a mouthpiece rather than an exposure chamber. This techniqu e bypasses nasal absorption of SO 2 and can result in an increase in lung SO 2 uptake. Therefore, the uncertainty will be greater in the risk estimates for asthmatic children.

 Exposure history . The risk assessment assumed that the SO 2 -induced response on any given day is independe nt of previous SO 2 exposures.

 Interaction between SO 2 and other pollutants. Because the contro lled human exposure studies used in the risk assessment involved only SO 2 exposures, it was assumed that estimates of SO 2 -induced health responses would not be affected by the presence of other pollutants (e.g., PM 2.5 , O 3 , NO 2 ).

Variability refers to the heterogeneity in a populati on or parameter. Even if there is no uncertainty surrounding inputs to the analysis, ther e may still be variability. For example, there may be variability among exposure-res ponse functions describing the relationship between SO 2 and lung function in different locations . This variability does not imply uncertainty about the exposure-re sponse 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 SO 2 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 SO 2 lung function risk assessment addresses variability-related concerns by using location-specific inputs for th e exposure analysis (e.g., locatio n-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 in cluded in the risk assessment residing in different geogr aphic areas is currently unknown.

Temporal variability is more difficult to a ddress, 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 uns pecified time, we are using the most current inputs available.Abt Associates Inc. June 2009 4-15 4 RESULTS

The results of the SO 2 risk assessment are presented in Tables 4-1 through 4-12. Each table includes results for both of the lo cations included in the risk assessment and for all of the air quality scenarios consider ed, 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 m oderate or greater exer tion associated with SO 2 concentrations under each of the different ai r 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 FEV 1 . 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 func tion response in a year, defined in terms of sRaw, under each of the differen t 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 FEV 1 . 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 attr ibutable to exposure to SO 2 within different concentration ranges are shown in Figures 4- 1 through 4-16. The exposure ranges are in 50 ppb increments – i.e., SO 2 < 50 ppb, 50 ppb ≤ SO 2 < 100 ppb, 100 ppb ≤ SO 2 < 150 ppb, ... , SO 2 ≥ 500 ppb. Figures 4-1a and b show th e 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 SO 2 in each exposure “bin.” Figures 4-2a and b show the corresponding percents for asthmatic children engaged in moderate or gr eater exertion in St. Louis, MO, using the logistic and probit exposure-re sponse 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 f unction response is defined as a decrease in FEV 1 ≥ 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, attr ibutable to exposure to SO 2 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 co rresponding numbers of occurrences of lung function response for asth matics and asthmatic children, respectively, when lung function response is defined as a decrease in FEV 1 ≥ 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. June 20094-16Table 4-1. Number of Occurrences (in Hundreds) of Lung FunctionResponse (Defined in Terms of sRaw) Among Asthmatics Engaged in Moderate orGreater Exertion Associated with Exposure to "As Is" SO2Concentrations, SO2Concentrations that Just Meetthe Current Standards, and SO2Concentrations that Just Meet Alternative Standards*99/5099/10099/15099/20099/25098/200125127125125125126126126(24 - 572) (25 - 577) (24 - 572) (24 - 572) (24 - 573) (24 - 573) (24 - 575) (24 - 574)1618161616161717(0 - 256) (1 - 261) (0 - 256) (0 - 256) (1 - 257) (1 - 257) (1 - 258) (1 - 258)65716726526867628801036997(128 - 2985) (663 - 4740) (125 - 2975) (141 - 3041) (176 - 3184) (234 - 3398) (315 - 3673) (295 - 3604)9093386111170264392360(4 - 1346) (393 - 3107) (3 - 1336) (11 - 1402) (33 - 1543) (72 - 1756) (128 - 2031) (114 - 1963)3839383838383939(4 - 310) (4 - 312) (4 - 310) (4 - 310) (4 - 310) (4 - 310) (4 - 311) (4 - 311)23222222(0 - 123) (0 - 124) (0 - 122) (0 - 123) (0 - 123) (0 - 123) (0 - 123) (0 - 123)201560199211237278332319(21 - 1614) (165 - 2407) (20 - 1609) (24 - 1639) (32 - 1703) (47 - 1799) (68 - 1923) (63 - 1892)13 258 12 18 33 59 95 86(0 - 643) (86 - 1388) (0 - 639) (1 - 666) (5 - 725) (12 - 814) (24 - 930) (21 - 901)***The current primary SO2standards include a 24-hour standard set at 0.14 parts per million (ppm), not to be exceeded more than once per year, and anannual standard set at 0.03ppm, calculated as the arithmetic mean of hourly averages.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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 uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number.Probit2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticAbt Associates Inc. June 20094-17Table 4-2. Number of Occurrences (in Hundreds) of Lung FunctionResponse (Defined in Terms of sRaw) Among Asthmatic Children Engaged inModerate or Greater Exertion Associated with Exposure to "As Is" SO2Concentrations, SO2Concentrations that JustMeet the Current Standards,and SO2Concentrations that JustMeet Alternative Standards*99/5099/10099/15099/20099/25098/2007172717171717171(13 - 324) (14 - 327) (13 - 324) (14 - 324) (14 - 324) (14 - 325) (14 - 325) (14 - 325)9 109 9 9 910 10(0 - 145) (1 - 148) (0 - 145) (0 - 145) (0 - 145) (0 - 146) (0 - 146) (0 - 146)4171179413439497586704674(81 - 1893) (484 - 3209) (80 - 1885) (91 - 1935) (118 - 2043) (162 - 2206) (222 - 2413) (207 - 2361)586925574118189286262(3 - 855) (296 - 2176) (2 - 847) (8 - 896) (25 - 1004) (53 - 1166) (96 - 1373) (85 - 1321)2222222222222222(2 - 175) (2 - 177) (2 - 175) (2 - 175) (2 - 175) (2 - 176) (2 - 176) (2 - 176)12111111(0 - 69) (0 - 71) (0 - 69) (0 - 69) (0 - 69) (0 - 70) (0 - 70) (0 - 70)128397126135155186227217(13 - 1023) (122 - 1618) (13 - 1019) (15 - 1042) (22 - 1091) (33 - 1164) (49 - 1257) (45 - 1234)8 1928 12 24 43 70 63(0 - 408) (65 - 967) (0 - 405) (1 - 425) (4 - 470) (9 - 538) (18 - 625) (16 - 603)***The current primary SO2standards 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.2-Parameter Logistic*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number.Exposure-ResponseModel**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.St. Louis, MOProbit2-Parameter LogisticProbit2-Parameter LogisticProbitSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitGreene County, MOSt. Louis, MOGreene County, MO"As is" SO2Concentrations**Abt Associates Inc. June 20094-18Table 4-3. Number of Occurrences (in Hundreds) ofLung Function Response (Defined in Terms of FEV1) Among Asthmatics Engaged in Moderate orGreater Exertion Associated with Exposure to "As Is" SO2Concentrations, SO2Concentrations that Just Meetthe Current Standards, and SO2Concentrations that Just MeetAlternative Standards*99/5099/10099/15099/20099/25098/2006971696969697070(6 - 675) (7 - 680) (6 - 675) (6 - 675) (6 - 675) (6 - 676) (6 - 677) (6 - 677)68666676(0 - 418) (0 - 424) (0 - 417) (0 - 418) (0 - 418) (0 - 419) (0 - 421) (0 - 420)3661341361391461570718681(33 - 3520) (454 - 5632) (32 - 3507) (41 - 3587) (66 - 3759) (108 - 4016) (169 - 4346) (154 - 4264)368663355109198322291(1 - 2189) (322 - 4471) (0 - 2175) (5 - 2262) (20 - 2448) (49 - 2727) (94 - 3084) (82 - 2995)33333333(0 - 53) (0 - 54) (0 - 53) (0 - 53) (0 - 53) (0 - 53) (0 - 53) (0 - 53)00000000(0 - 5)(0 - 7)(0 - 5)(0 - 5)(0 - 5)(0 - 6)(0 - 6)(0 - 6)153101420356210493(1 - 279) (133 - 1045) (0 - 276) (2 - 299) (7 - 351) (17 - 435) (34 - 550) (30 - 521)12400313336557(0 - 32) (120 - 697) (0 - 30) (1 - 47) (5 - 89) (14 - 158) (29 - 256) (25 - 232)St. Louis, MOProbit**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.St. Louis, MOSO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations**2-Parameter LogisticProbit2-Parameter LogisticProbitGreene County, MOSO2Concentrationsthat Just Meet theCurrentStandards***Greene County, MO***The current primary SO2standards 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.2-Parameter LogisticExposure-ResponseModel*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number.Probit2-Parameter LogisticResponse = Decrease in FEV1 >= 15%Response = Decrease in FEV1 >= 20%Abt Associates Inc. June 20094-19Table 4-4. Number of Occurrences (in Hundreds) of Lung Function Response (Defined in Terms of FEV1) Among Asthmatic Children Engaged inModerate or Greater Exertion Associated with Exposure to "As Is" SO2Concentrations, SO2Concentrations that JustMeet the Current Standards,and SO2Concentrations that JustMeet Alternative Standards*99/5099/10099/15099/20099/25098/2003940393939394040(3 - 382) (4 - 386) (3 - 382) (3 - 382) (3 - 382) (3 - 383) (4 - 384) (4 - 383)34333444(0 - 236) (0 - 240) (0 - 236) (0 - 236) (0 - 237) (0 - 237) (0 - 238) (0 - 238)232965229252304387499471(21 - 2231) (338 - 3816) (20 - 2222) (27 - 2282) (46 - 2412) (77 - 2608) (123 - 2857) (112 - 2795)23648213879146239216(1 - 1389) (242 - 3101) (0 - 1379) (4 - 1444) (15 - 1585) (37 - 1797) (70 - 2066) (62 - 1999)12111222(0 - 30) (0 - 31) (0 - 30) (0 - 30) (0 - 30) (0 - 30) (0 - 30) (0 - 30)00000000(0 - 3)(0 - 4)(0 - 3)(0 - 3)(0 - 3)(0 - 3)(0 - 3)(0 - 3)1023191324457668(0 - 178) (99 - 753) (0 - 175) (1 - 192) (5 - 232) (13 - 295) (26 - 382) (22 - 360)01800210254943(0 - 21) (90 - 521) (0 - 19) (1 - 32) (3 - 63) (10 - 116) (21 - 190) (18 - 171)***The current primary SO2standards 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.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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 uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest whole number.Probit2-Parameter LogisticResponse = Decrease in FEV1 >= 15%Response = Decrease in FEV1 >= 20%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticAbt Associates Inc. June 20094-20Table 4-5. Number of Asthmatics Engaged in Moderate or GreaterExertion Estimated to Experience At Least One Lung Function Response (Definedin Terms of sRaw) Associatedwith Exposure to "As Is" SO2Concentrations, SO2Concentrations that Just Meet the Current Standards, and SO2Concentrations that Just Meet Alternative Standards*99/5099/10099/15099/20099/25098/200902108090100120160140(20 - 390) (80 - 620) (20 - 380) (20 - 390) (20 - 420) (30 - 460) (50 - 520) (40 - 500)10 110 10 10 20 40 70 60(0 - 180) (40 - 410) (0 - 170) (0 - 180) (0 - 210) (10 - 250) (20 - 310) (20 - 280)10101346073019903650552075007050(340 - 3010) (9740 - 18510) (220 - 2490) (860 - 4690) (1900 - 7100) (3230 - 9490) (4770 - 11850) (4410 - 11320)5001305029013402930481068606400(140 - 1990) (9430 - 18100) (70 - 1470) (520 - 3690) (1450 - 6200) (2760 - 8710) (4310 - 11190) (3950 - 10640)3070303030405050(0 - 210) (20 - 310) (0 - 210) (0 - 210) (0 - 220) (10 - 240) (10 - 270) (10 - 260)0 300 010 10 20 10(0 - 80) (10 - 180) (0 - 80) (0 - 90) (0 - 100) (0 - 110) (0 - 140) (0 - 130)33055202306701280201028302640(70 - 1520) (3400 - 8960) (40 - 1290) (210 - 2270) (510 - 3360) (940 - 4470) (1470 - 5590) (1340 - 5330)120518060350870156023802190(20 - 880) (3150 - 8570) (10 - 660) (90 - 1590) (310 - 2680) (690 - 3820) (1200 - 5000) (1070 - 4730)Response = Increase in sRaw >= 100%Greene County, MOSt. Louis, MO"As is" SO2Concentrations**Exposure-ResponseModelSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):2-Parameter LogisticProbit2-Parameter Logistic*Numbers are median (50th percentile) numbers of asthmatics. Numbers in parentheses below the median are 95% credible intervals based on statistical uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.2-Parameter LogisticResponse = Increase in sRaw >= 200%ProbitSt. Louis, MOProbitGreene County, MO***The current primary SO2standards 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.2-Parameter LogisticProbit**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.Abt Associates Inc. June 20094-21Table 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" SO2Concentrations, SO2Concentrations that Just Meet the Current Standards, andSO2Concentrations that JustMeet Alternative Standards*99/5099/10099/15099/20099/25098/20030 110 30 30 40 50 70 60(10 - 130) (40 - 270) (10 - 130) (10 - 140) (10 - 150) (20 - 180) (30 - 210) (20 - 200)10 60 0 10 10 20 40 30(0 - 60) (20 - 200) (0 - 60) (0 - 60) (0 - 80) (10 - 100) (10 - 140) (10 - 130)590802040012202240337045604290(220 - 1570) (6080 - 10370) (130 - 1210) (560 - 2620) (1240 - 4010) (2090 - 5350) (3060 - 6680) (2840 - 6390)34079501908901910308043304060(100 - 1150) (6020 - 10320) (50 - 790) (360 - 2220) (1000 - 3690) (1860 - 5110) (2870 - 6510) (2640 - 6210)1040101010202020(0 - 70) (10 - 130) (0 - 70) (0 - 70) (0 - 80) (0 - 90) (10 - 110) (10 - 100)020000101010(0 - 30) (0 - 90) (0 - 30) (0 - 30) (0 - 40) (0 - 50) (0 - 60) (0 - 60)1903380130410800125017501640(50 - 780) (2190 - 5070) (30 - 610) (140 - 1240) (340 - 1870) (620 - 2500) (970 - 3140) (890 - 3000)80329040240580103015601440(10 - 500) (2110 - 5000) (10 - 350) (60 - 950) (220 - 1590) (480 - 2250) (830 - 2940) (740 - 2790)***The current primary SO2standards 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.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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% credibleintervals based on statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.Probit2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticAbt Associates Inc. June 20094-22Table 4-7. Number of Asthmatics Engaged in Moderate or GreaterExertion Estimated to Experience At Least One Lung Function Response (Definedin Terms of FEV1) Associated with Exposure to "As Is" SO2Concentrations, SO2Concentrations that Just Meet the Current Standards, and SO2Concentrations that Just Meet Alternative Standards*99/5099/10099/15099/20099/25098/2005017050506080110100(10 - 460) (50 - 730) (0 - 450) (10 - 460) (10 - 490) (20 - 540) (30 - 610) (20 - 590)1010001020305050(0 - 290) (30 - 590) (0 - 280) (0 - 290) (0 - 330) (10 - 380) (10 - 460) (10 - 430)7501522051017003460557079107370(180 - 3580) (10280 - 22530) (100 - 2950) (580 - 5590) (1520 - 8500) (2880 - 11400) (4550 - 14280) (4160 - 13640)4101504022012502970513075506990(80 - 2880) (10140 - 22670) (30 - 2200) (370 - 5070) (1230 - 8210) (2580 - 11280) (4280 - 14280) (3880 - 13610)030000102010(0 - 40) (10 - 130) (0 - 40) (0 - 40) (0 - 50) (0 - 60) (0 - 80) (0 - 80)0 200 0 0 010 10(0 - 10) (10 - 80) (0 - 0)(0 - 10) (0 - 10) (0 - 20) (0 - 40) (0 - 40)1009240503501020210035403190(20 - 570) (6110 - 13840) (10 - 380) (110 - 1290) (430 - 2680) (1060 - 4450) (1990 - 6540) (1760 - 6050)40926020240870195034303070(10 - 320) (6200 - 13820) (0 - 170) (80 - 960) (390 - 2340) (1020 - 4170) (1980 - 6340) (1740 - 5830)***The current primary SO2standards 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.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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 uncertainty surrounding the SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest ten.Probit2-Parameter LogisticResponse = Decrease in FEV1>= 15%Response = Decrease in FEV1>= 20%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticAbt Associates Inc. June 20094-23Table 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" SO2Concentrations, SO2Concentrations that Just Meet the Current Standards, andSO2Concentrations that JustMeet Alternative Standards*99/5099/10099/15099/20099/25098/2002090202030405050(0 - 160) (30 - 320) (0 - 150) (0 - 160) (0 - 180) (10 - 210) (10 - 250) (10 - 240)0 600 010 20 30 30(0 - 100) (20 - 280) (0 - 100) (0 - 100) (0 - 120) (0 - 160) (10 - 200) (10 - 180)460931029010802200351049504630(120 - 1870) (6620 - 12680) (60 - 1440) (390 - 3130) (1030 - 4810) (1930 - 6440) (3030 - 8070) (2780 - 7720)28093201508401970335048704530(50 - 1630) (6630 - 12800) (20 - 1160) (260 - 2990) (860 - 4800) (1790 - 6510) (2930 - 8190) (2660 - 7830)0 200 0 0 010 10(0 - 10) (10 - 70) (0 - 10) (0 - 10) (0 - 20) (0 - 30) (0 - 40) (0 - 40)0 100 0 0 0 0 0(0 - 0)(0 - 40)(0 - 0)(0 - 0)(0 - 10) (0 - 10) (0 - 20) (0 - 20)70615030240700143024102170(10 - 350) (4190 - 8700) (10 - 220) (80 - 820) (300 - 1710) (740 - 2830) (1400 - 4160) (1240 - 3850)30621010170610137023802140(10 - 220) (4280 - 8780) (0 - 110) (60 - 650) (280 - 1560) (730 - 2750) (1410 - 4140) (1240 - 3820)***The current primary SO2standards 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.2-Parameter Logistic*Numbers are median (50th percentile) numbers of asthmatic children. Numbers in parentheses below the median are 95% credibleintervals based on statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Numbers are rounded to the nearest hundred.Exposure-ResponseModel**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.St. Louis, MOProbit2-Parameter LogisticProbit2-Parameter LogisticProbitSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):2-Parameter LogisticResponse = Decrease in FEV1>= 15%Response = Decrease in FEV1>= 20%ProbitGreene County, MOSt. Louis, MOGreene County, MO"As is" SO2Concentrations**Abt Associates Inc. June 20094-24Table 4-9. Percent of Asthmatics Engaged in Moderate or Greater Exertion Estimated toExperience At Least One Lung Function Response (Definedin Terms of sRaw) Associatedwith Exposure to "As Is" SO2Concentrations, SO2Concentrations that Just Meet the Current Standards, and SO2Concentrations that Just Meet Alternative Standards*99/5099/10099/15099/20099/25098/2000.4%1%0.4%0.4%0.5%0.6%0.7%0.7%(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% - 2.4%) (0.2% - 2.3%)0.1%0.5%0.1%0.1%0.1%0.2%0.3%0.3%(0% - 0.8%) (0.2% - 1.9%) (0% - 0.8%) (0% - 0.8%) (0% - 1%) (0% - 1.2%) (0.1% - 1.4%) (0.1% - 1.3%)1%13.1%0.7%1.9%3.6%5.4%7.3%6.9%(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% - 11.6%) (4.3% - 11.1%)0.5%12.7%0.3%1.3%2.9%4.7%6.7%6.2%(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% - 10.9%) (3.9% - 10.4%)0.1%0.3%0.1%0.1%0.2%0.2%0.2%0.2%(0% - 1%) (0.1% - 1.5%) (0% - 1%) (0% - 1%) (0%- 1%) (0% - 1.1%) (0% - 1.3%) (0% - 1.2%)0%0.1%0%0%0%0%0.1%0.1%(0% - 0.4%) (0% - 0.8%) (0% - 0.4%) (0% - 0.4%) (0% - 0.5%) (0% - 0.5%) (0% - 0.6%) (0% - 0.6%)0.3%5.4%0.2%0.7%1.3%2%2.8%2.6%(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% - 5.5%) (1.3% - 5.2%)0.1%5.1%0.1%0.3%0.8%1.5%2.3%2.1%(0% - 0.9%) (3.1% - 8.4%) (0% - 0.6%) (0.1% - 1.6%) (0.3% - 2.6%) (0.7%- 3.7%) (1.2% - 4.9%) (1% - 4.6%)***The current primary SO2standards 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.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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 statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth.Probit2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticAbt Associates Inc. June 20094-25Table 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" SO2Concentrations, SO2Concentrations that Just Meet the Current Standards, andSO2Concentrations that JustMeet Alternative Standards*99/5099/10099/15099/20099/25098/2000.4%1.4%0.4%0.4%0.5%0.7%1%0.9%(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% - 2.9%) (0.3% - 2.7%)0.1%0.9%0.1%0.1%0.2%0.3%0.5%0.4%(0% - 0.9%) (0.3% - 2.7%) (0% - 0.8%) (0% - 0.9%) (0% - 1.1%) (0.1% -1.4%) (0.2% - 1.9%) (0.1% - 1.7%)1.4%19.2%0.9%2.9%5.4%8.1%10.9%10.3%(0.5% - 3.8%) (14.6% - 24.9%) (0.3% - 2.9%) (1.3% - 6.3%) (3% - 9.6%) (5% - 12.8%) (7.3% - 16%) (6.8% - 15.3%)0.8%19.1%0.4%2.1%4.6%7.4%10.4%9.7%(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% - 15.6%) (6.3% - 14.9%)0.1%0.5%0.1%0.1%0.2%0.2%0.3%0.3%(0% - 1%) (0.1% - 1.8%) (0% - 1%) (0% - 1%) (0%- 1.1%) (0% - 1.3%) (0.1%- 1.5%) (0.1% - 1.4%)0%0.2%0%0%0%0.1%0.1%0.1%(0% - 0.4%) (0.1% - 1.2%) (0% - 0.4%) (0% - 0.4%) (0% - 0.5%) (0% - 0.6%) (0% - 0.8%) (0% - 0.8%)0.5%8.1%0.3%1%1.9%3%4.2%3.9%(0.1% - 1.9%) (5.3% - 12.2%) (0.1% - 1.5%) (0.3% - 3%) (0.8% - 4.5%) (1.5% - 6%) (2.3% - 7.5%) (2.1% - 7.2%)0.2%7.9%0.1%0.6%1.4%2.5%3.7%3.4%(0% - 1.2%) (5% - 12%) (0% - 0.8%) (0.2% - 2.3%) (0.5% - 3.8%) (1.2% - 5.4%) (2% - 7%) (1.8% - 6.7%)***The current primary SO2standards 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.2-Parameter Logistic*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth.Exposure-ResponseModel**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.St. Louis, MOProbit2-Parameter LogisticProbit2-Parameter LogisticProbitSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):2-Parameter LogisticResponse = Increase in sRaw >= 100%Response = Increase in sRaw >= 200%ProbitGreene County, MOSt. Louis, MOGreene County, MO"As is" SO2Concentrations**Abt Associates Inc. June 20094-26Table 4-11. Percent of Asthmatics Engaged in Moderate or Greater Exertion Estimated toExperience At LeastOne Lung Function Response (Definedin Terms of FEV1) Associated with Exposure to "As Is" SO2Concentrations, SO2Concentrations that Just Meet the Current Standards, and SO2Concentrations that Just Meet Alternative Standards*99/5099/10099/15099/20099/25098/2000.2%0.8%0.2%0.2%0.3%0.4%0.5%0.5%(0% - 2.1%) (0.2% - 3.4%) (0% - 2.1%) (0% - 2.1%) (0% - 2.3%) (0.1% -2.5%) (0.1% - 2.9%) (0.1% - 2.8%)0%0.5%0%0%0.1%0.1%0.3%0.2%(0% - 1.3%) (0.1% - 2.8%) (0% - 1.3%) (0% - 1.4%) (0% - 1.5%) (0%- 1.8%) (0.1% - 2.1%) (0% - 2%)0.7%14.9%0.5%1.7%3.4%5.4%7.7%7.2%(0.2% - 3.5%) (10% - 22%) (0.1% - 2.9%) (0.6% - 5.5%) (1.5% - 8.3%) (2.8% - 11.1%) (4.4% - 13.9%) (4.1% - 13.3%)0.4%14.7%0.2%1.2%2.9%5%7.4%6.8%(0.1% - 2.8%) (9.9% - 22.1%) (0% - 2.1%) (0.4% - 4.9%) (1.2% - 8%) (2.5% - 11%) (4.2% - 13.9%) (3.8% - 13.3%)0%0.1%0%0%0%0%0.1%0.1%(0% - 0.2%) (0% - 0.6%) (0% - 0.2%) (0% - 0.2%) (0% - 0.2%) (0% - 0.3%) (0% - 0.4%) (0% - 0.4%)0%0.1%0%0%0%0%0%0%(0% - 0%) (0% - 0.4%) (0% - 0%) (0% - 0%) (0% - 0.1%) (0% - 0.1%) (0% - 0.2%) (0% - 0.2%)0.1%9%0.1%0.3%1%2.1%3.5%3.1%(0% - 0.6%) (6% - 13.5%) (0% - 0.4%) (0.1% - 1.3%) (0.4% - 2.6%) (1% -4.3%) (1.9% - 6.4%) (1.7% - 5.9%)0%9%0%0.2%0.8%1.9%3.4%3%(0% - 0.3%) (6% - 13.5%) (0% - 0.2%) (0.1% - 0.9%) (0.4% - 2.3%) (1% -4.1%) (1.9% - 6.2%) (1.7% - 5.7%)***The current primary SO2standards 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.2-Parameter Logistic*Percents are median (50th percentile) percents of asthmatic children. Percents in parentheses below the median are 95% credible intervals based on statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth.Exposure-ResponseModel**The "as is" exposure scenario was based on monitoring and modeling using 2002 air quality information.St. Louis, MOProbit2-Parameter LogisticProbit2-Parameter LogisticProbitSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):2-Parameter LogisticResponse = Decrease in FEV1 >= 15%Response = Decrease in FEV1 >= 20%ProbitGreene County, MOSt. Louis, MOGreene County, MO"As is" SO2Concentrations**Abt Associates Inc. June 20094-27Table 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" SO2Concentrations, SO2Concentrations that Just Meet the Current Standards, andSO2Concentrations that Just Meet Alternative Standards*99/5099/10099/15099/20099/25098/2000.2%1.2%0.2%0.2%0.3%0.5%0.7%0.6%(0% - 2.2%) (0.4% - 4.4%) (0% - 2.1%) (0% - 2.2%) (0.1% - 2.4%) (0.1% -2.9%) (0.2% - 3.5%) (0.2% - 3.2%)0%0.8%0%0%0.1%0.2%0.4%0.3%(0% - 1.4%) (0.2% - 3.8%) (0% - 1.3%) (0% - 1.4%) (0% - 1.7%) (0% -2.1%) (0.1% - 2.8%) (0.1% - 2.5%)1.1%22.3%0.7%2.6%5.3%8.4%11.9%11.1%(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% - 19.3%) (6.7% - 18.5%)0.7%22.3%0.4%2%4.7%8%11.7%10.9%(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% - 19.6%) (6.4% - 18.8%)0%0.2%0%0%0%0.1%0.1%0.1%(0% - 0.2%) (0.1% - 0.9%) (0% - 0.2%) (0% - 0.2%) (0% - 0.3%) (0% - 0.4%) (0% - 0.5%) (0% - 0.5%)0%0.1%0%0%0%0%0.1%0%(0% - 0%) (0.1% - 0.6%) (0% - 0%) (0% - 0%) (0%- 0.1%) (0% - 0.2%) (0% - 0.3%) (0% - 0.3%)0.2%14.7%0.1%0.6%1.7%3.4%5.8%5.2%(0% - 0.8%) (10.1% - 20.8%) (0% - 0.5%) (0.2% - 2%) (0.7% - 4.1%) (1.8% - 6.8%) (3.4% - 10%) (3% - 9.2%)0.1%14.9%0%0.4%1.5%3.3%5.7%5.1%(0% - 0.5%) (10.3% - 21%) (0% - 0.3%) (0.1% - 1.6%) (0.7% - 3.7%) (1.7% - 6.6%) (3.4% - 9.9%) (3% - 9.2%)**The current primary SO2standards 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.2-Parameter LogisticSO2Concentrationsthat Just Meet theCurrentStandards***SO2Concentrations that Just Meet Alternative nth Percentile 1-Hr Daily Maximum Standards, with Levels (inppb) of m (Standard Denoted n/m):"As is" SO2Concentrations****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 statistical uncertainty surroundingthe SO2coefficient in the logistic and probit exposure-response functions. Percents are rounded to the nearest tenth.Probit2-Parameter LogisticResponse = Decrease in FEV1 >= 15%Response = Decrease in FEV1 >= 20%ProbitProbit2-Parameter LogisticProbitExposure-ResponseModelGreene County, MOSt. Louis, MOGreene County, MO2-Parameter LogisticAbt Associates Inc. June 2009 4-28 Figure 4-1. Percent of As thmatics Engaged in Moderate or Grea ter Exertion in St. Louis Exhibiting Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Attributable to SO 2 Within Given Ranges Under Different Ai r Quality Scenarios* a) Logistic Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

b) Probit Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 As thmatic Children Engaged in Moderate or Greater Exertion in St. Louis Exhibiting Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios* a) Logistic Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

b) Probit Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 As thmatics Engaged in Moderate or Grea ter Exertion in St. Louis Exhibiting Lung Function Response (Defined as a Decrease in FEV 1 ≥ 15%) Attributable to SO 2 Within Given Ranges Under Different Ai r Quality Scenarios* a) Logistic Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

b) Probit Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 As thmatic Children Engaged in Moderate or Greater Exertion in St. Louis Exhibiting Lung Function Response (Defined as a Decrease in FEV 1 ≥ 15%) Attributable to SO 2 Within Given Ranges Under Diff erent Air Quality Scenarios* a) Logistic Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

b) Probit Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 (Def ined as an Increase in sRaw ≥ 100%) Among Asthmatics Engaged in Moderate or Greater Exertion in St. Louis Attributable to SO 2 Within Given Ranges Under Diff erent Air Quality Scenarios* a) Logistic Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number 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 (Defin ed as an Increase in sRaw ≥ 100%) Among Asthmatic Children Engaged in Mo derate or Greater Exertion in St. Louis Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios* a) Logistic Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number 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 Func tion Response (Defined as a Decrease in FEV 1 ≥ 15%) Among Asthmatics Engaged in Moderate or Greater Exertion in St. Louis Attributable to SO 2 Within Given Ranges Under Diff erent Air Quality Scenarios* a) Logistic Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number 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 (D efined as a Decrease in FEV 1 ≥ 15%) Among Asthmatic Children Engaged in Mo derate or Greater Exertion in St. Louis Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios* a) Logistic Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number 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 Exertio n in Greene Co. Exhibiting Lung Function Response (Defined as an Increase in sRaw ≥ 100%) Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios* a) Logistic Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

b) Probit Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 SO 2 Within Given Ranges Under Different Air Quality Scenarios* a) Logistic Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

b) Probit Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 FEV 1 ≥ 15%) Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios* a) Logistic Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

b) Probit Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 FEV 1 ≥ 15%) Attributable to SO 2 Within Given Ranges Under Diff erent Air Quality Scenarios* a) Logistic Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent Responding (and 95% CI)

b) Probit Exposure-Response Function

0% 5% 10% 15% 20% 25% 30%"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Percent 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 Functi on Response (Defined as an Increase in sRaw ≥ 100%) Among Asthmatics Engaged in Moderate or Greater Exertion in Greene Co. Attributable to SO 2 Within Given Ranges Under Diff erent Air Quality Scenarios* a) Logistic Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number 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 Mo derate or Greater Ex ertion in Greene Co. Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios* a) Logistic Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number 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 F unction Response (Defined as a Decrease in FEV 1 ≥ 15%) Among Asthmatics Engaged in Moderate or Greater Exertion in Greene Co. Attributable to SO 2 Within Given Ranges Under Diff erent Air Quality Scenarios* a) Logistic Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number 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 Functi on Response (Defined as a Decrease in FEV 1 ≥ 15%) Among Asthmatic Children Engaged in Mod erate or Greater Exertion in Greene Co. Attributable to SO 2 Within Given Ranges Under Different Air Quality Scenarios* a) Logistic Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number of Occurences, in Thousands (and 95% CI)

b) Probit Exposure-Response Function

0 100 200 300 400 500 600"As Is" AirQualityCurrent Std.99/5099/10099/15099/20099/25098/200Number 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 QualityCurrent Std.99/5099/10099/15099/20099/25098/200 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 SO 2 standards include a 24-hour standard se t at 0.14 parts per million (ppm), not to be exceeded more than on ce per year, and an annual standard set at 0.03 ppm, calculated as the arithmetic mean of hourly averages. In St. Louis, SO 2 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. Conse quently, 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 qual ity 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 99 th percentile 1-hr standard, set at 50 ppb (denoted “99/50” in the above tables of results), is pr edicted to lower the numbers of responders below the levels estim ated under the “as is” scenario. As theAbt 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 f unction response, defined as an increase in sRaw ≥ 100%, is also seen for the other defi nitions of lung function response. For example, of the estimated roughly 102,400 as thmatics engaged in moderate or greater exertion, 750 are estimated to have at l east one lung function re sponse, defined as a decrease in FEV 1 ≥ 15%, under the “as is” air qualit y 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 quali ty 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 SO 2 concentrations to which asthmatics are expos ed under the current standards scenario – i.e., substantial percentages of asthmatics are exposed to relatively higher concentrations of SO 2 under this scenario. There is thus much room for improvement. Under the most stringent alternative 1-hr standard (99/50), mu ch of that exposure is pushed down to the lowest SO 2 concentration “bins.” Under the curren t standards scenario, for example, only about 22 percent of asthmatics in St. Louis have exposures no greater than 100 ppb; under the most stringent alternat ive 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 curr ent standards scenario. Ther e 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 scenar io, 99.9 percent of person days of exposure are to ≤ 100 ppb SO 2 in Greene County; the correspondi ng figure for St. Louis is 95.2 percent.

The generally lower levels of SO 2 to which asthmatics in Greene County are exposed, relative to asthmatics in St. Louis, and the corresponding greater preponderance of responses associated with the lowest SO 2 concentration “bins” in Greene County, can be readily seen in Figures 4-1 through 4-8. 12

Although the numbers are smaller for asth matic children (because the underlying populations are smaller), the pa tterns seen in St. Louis a nd in Greene County across the different air quality scenarios, and the comp arisons between the two locations, are fairly similar for asthmatic children as for asthma tics for all lung function response definitions.

12 In several cases, responses a ssociated with exposures in SO 2 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” esse ntially 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. Th is 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 ai r quality scenarios the great majority of occurrences of lung function re sponse are in the lowest e xposure bin, the numbers of individuals with at least one lung function response attribut able to exposures in that lowest bin are typically quite small. This is because the calcul ation of numbers of individuals with at least one lung function response uses individuals’ highest exposure only. While individuals may be exposed mostly to low SO 2 concentrations, many are exposed at least occasionally to higher levels. Thus, the pe rcentage of individuals in a designated population with at least one lung function re sponse associated with SO 2 concentrations in the lowest bin is likely to be very small, since most individuals are exposed at least once to higher SO 2 levels. For example, defining lung function response as an increase in sRaw ≥ 100%, under a scenario in which SO 2 concentrations just meet an alternative 1-hour 99 th percentile 100 ppb standard, about 93 percent of occurrences of lung function response among asthmatics in St. Louis are associated with SO 2 exposures in the lowest exposure bin (0 – 50 ppb). However, the lowest SO 2 exposure bin accounts for only about 0.2 percent of asthmatics estimated to experience at least 1 SO 2 -related lung function response. For th is very small percent of the population, the lowest exposure bin represents their highest SO 2 exposures under moderate exertion in a year. Thus Figure 4-5b shows virtually all of th e occurrences among asthmatics in St. Louis associated with the lowest SO 2 exposure bin; however, Fi gure 4-1b shows a relatively small proportion of asthmatics in St. Louis experiencing at least one response to be experiencing those responses because of exposur es in that lowest exposure bin.Abt Associates Inc. June 2009 5-1 5 REFERENCES

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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, Ha ckney JD. 1987. Replicated dose-response study of sulfur dioxide effects in nor mal, 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 sulf ate on mild asthmatics’ respons e to sulfur dioxide exposure and exercise. Arch Environ Health 43:399-406.

Linn WS, Shamoo DA, Peng RC, Clark KW, Av ol 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 Nied ing G. 1990. Relationship between the airway response to inhaled sulfur dioxide, isocapnic hyperventilati on, 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 di oxide during repe ated 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 Re view 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 Environmenta l 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 Environmenta l Assessment. 2008c. Integrated Science Assessment (ISA) for Sulfur Oxides – H ealth Criteria (Fin al 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 Qu ality Planning and Standards. 2008b. Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standard: First Draft. EPA-452/P-08-003. June 2008. Available online at: http://www.epa.gov/ttn/naaqs/st andards/so2/s_so2_cr_rea.html

. U.S. EPA, Office of Air Qual ity Planning and Standards. 2009a. Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standards: Second Draft. EPA-452/P-09- 003. March 2009. Available online at: http://www.epa.gov/ttn/naaqs/st andards/so2/s_so2_cr_rea.html

. U.S. EPA, Office of Air Qual ity Planning and Standards. 2009b. Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standards. EPA-452/R-09-007. Ju ly 2009. Available online at: http://www.epa.gov/ttn/naaqs/st andards/so2/s_so2_cr_rea.html

.Abt Associates Inc. June 2009 A-1 Appendix A: Bayesian-Est imated Logistic and Probit Exposure-Response Functions: Median, 2.5 th Percentile, and 97.5 th Percentile CurvesAbt 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-E stimated Probit Exposure-Response Function: Increase in sRaw > 100% for 5-Minute Exposures of Asthmatics Engage d 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 curveAbt 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-E stimated Probit Exposure-Response Function: Increase in sRaw > 200% for 5-Minute Exposures of Asthmatics Engage d 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 curveAbt Associates Inc. June 2009 A-4 Figure A-3a. Bayesian-Estimat ed Logistic Exposure-Response Function: Decrease in FEV 1 > 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-Res ponse Function: Decrease in FEV 1 > 15% for 5-Minute Exposures of Asthmatics En gaged 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 curveAbt Associates Inc. June 2009 A-5 Figure A-4a. Bayesian-Estimat ed Logistic Exposure-Response Function: Decrease in FEV 1 > 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-Res ponse Function: Decrease in FEV 1 > 20% for 5-Minute Exposures of Asthmatics En gaged 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 curveD-1 

## APPENDIX D: SUPPLEMENT TO THE POLICY ASSESSMENTD-2 Table D-1. 99 th percentile 24-hour average SO 2 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 194D-3 Table D-2. 99 th percentile 24-hour average SO 2 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 53D-4 Table D-3. 2 nd highest 24-hour average SO 2 concentrations (i.e. the current 24- hour standard) for 2005 given just meetin g 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 213D-5 Table D-4. 2 nd highest 24-hour average SO 2 concentrations (i.e. the current 24- hour standard) for 2006 given just meetin g 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 57D-6 Table D-5. Annual average SO 2 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 26.8D-7 Table D-6. Annual average SO 2 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 9.9United StatesOffice of Air Quality Planning and StandardsEPA-452/R-09-007Environmental ProtectionHealth and Environmental ImpactsDivisionJuly 2009AgencyResearch Triangle Park, NC