1. Characterize populations and health outcomes Project 4 Multi-City Morbidity Study Characterize daily pollutant mixtures Source apportionment Source apportionment Examine impacts of air pollution mixtures on acute severe morbidity Examine and explain heterogeneity of associations across cities ED visits Hospital admissions Sociodemographic and housing factors Conduct multi-city epidemiologic analysis How and why do associations between air pollution mixtures and acute morbidity vary across cities? Atmospheric modeling (e.g., ROS) Atmospheric modeling (e.g., ROS) Direct measurements

2. Project 4 Study Team Stefanie Sarnat Andrea Winquist Mitch Klein Lyndsey Darrow Howard Chang Jim Mulholland Ted Russell Paige Tolbert Joe Abrams Mariel Friberg Katie Gass Brook Hixson Jenna Krall John Pearce Cassie O’Lenick Dongni Ye

3. Outline Study status 2013 SAC comments and response Data and analysis progress Selected results presentations SCAPE exposure metrics comparison Effect modification of air pollution-asthma associations Effect modification of air pollution-CVD associations (Andrea Winquist) PM 2.5 sources and cardiovascular ED visits (Jenna Krall) Project 4 plans over coming year

4. Population vulnerability (age, race, comorbidity, SES) Temperature, seasonality Measurement error (ambient pollutant spatial variability) CR Functions Personal exposures (to ambient) True modifiers Apparent modifiers Cities: ATL BHM DFW PGH STL Joint effects of single pollutants Air Pollution Mixtures ED Outcomes Single Pollutants (O 3, NO 2, PM 2.5 ) Mixtures based on chemical properties (ROS) Data-based exposures (SOM, C&RT) Respiratory (All, ASW, COPD) Cardiovascular (All, IHD, CHF) PM Sources (CMB) Project 4 Overview

5. 2013 SAC Comments & Response Suggestion to focus on cardiovascular endpoints, not only asthma Several analyses of CVD endpoints in progress Interest in the alternative methods of specifying exposure Comparison of SCAPE exposure metrics in epidemiologic analyses underway With only 5 cities, limited ability to examine reasons for heterogeneity in air pollution-health associations Focus on consistencies observed across cities, including patterns of within-city effect modification Recommendation to refine data on potential effect modifiers In-depth examination of neighborhood socioeconomic conditions Extended air exchange rates estimation approach to multiple cities Consider how to formally incorporate uncertainty in source apportionment estimates in health analyses Multi-city source apportionment epi analyses in progress Plans to incorporate Bayesian ensemble source apportioned estimates, as done for Project 3

6. Data Collection and Processing ED visit data Final for Atlanta, Birmingham, Dallas, St. Louis Note - no Children’s data for Birmingham Pittsburgh data processed by UPitt; working on data use agreements AQ data Various metrics providing data on 12 pollutants (gases, PM 2.5, major PM 2.5 components) in 4 cities Including CMAQ-OBS fused data, finalized over past year PM 2.5 source apportionment in 4 cities Receptor-based ensemble CMB at selected monitoring sites

7. Analysis Summary 1.Application of spatially-refined modeled estimates of ambient concentrations in epidemiologic analyses (Dionisio et al., 2014) 2.Examination of health effects of PM 2.5 components (ions, carbon, speciated organics, elements) (Winquist et al., in press; Sarnat et al., provisional acceptance) 3.Methods for classifying and analyzing air pollution mixtures using multi- pollutant monitoring data, including self-organizing maps (Pearce et al., 2014), classification and regression trees (Gass et al., 2014), assessing multi- pollutant joint effects (Winquist et al., 2014), estimating health effects of organic chemical groupings, PM 2.5 sources, and retrospectively- predicted reactive oxygen species 4.Assessment of potential modifiers of the effects of ambient air pollution, including age, sex, race, neighborhood socioeconomic conditions, season, temperature, and air exchange rates 5.Statistical modeling approach to quantify projection uncertainties in future ambient ozone levels and their health impact due to climate change (Chang et al., 2014) and assessment of heat-related morbidity

8. Analysis Summary 1.Application of spatially-refined modeled estimates of ambient concentrations in epidemiologic analyses (Dionisio et al., 2014) 2.Examination of health effects of PM 2.5 components (ions, carbon, speciated organics, elements) (Winquist et al., in press; Sarnat et al., provisional acceptance) 3.Methods for classifying and analyzing air pollution mixtures using multi- pollutant monitoring data, including self-organizing maps (Pearce et al., 2014), classification and regression trees (Gass et al., 2014), assessing multi- pollutant joint effects (Winquist et al., 2014), estimating health effects of organic chemical groupings, PM 2.5 sources, and retrospectively- predicted reactive oxygen species 4.Assessment of potential modifiers of the effects of ambient air pollution, including age, sex, race, neighborhood socioeconomic conditions, season, temperature, and air exchange rates 5.Statistical modeling approach to quantify projection uncertainties in future ambient ozone levels and their health impact due to climate change (Chang et al., 2014) and assessment of heat-related morbidity

9. Population vulnerability (age, race, comorbidity, SES) Temperature, seasonality Measurement error (ambient pollutant spatial variability) CR Functions Personal exposures (to ambient) True modifiers Apparent modifiers Cities: ATL BHM DFW PGH STL Joint effects of single pollutants Air Pollution Mixtures ED Outcomes Single Pollutants (O 3, NO 2, PM 2.5 ) Mixtures based on chemical properties (ROS) Data-based exposures (SOM, C&RT) Respiratory (All, ASW, COPD) Cardiovascular (All, IHD, CHF) PM Sources (CMB) SCAPE Exposure Metrics Comparison

10. Cities St. Louis, MO-IL (15 counties + city; 8,623 mi 2 ; 2,812,896 people; 326 people/mi 2 ) airport central site Dallas, TX (12 counties; 8,928 mi 2 ; 6,371,773 people; 714 people/mi 2 ) Ellis Hunt Wise Collin Dallas Parker Denton Tarrant Kaufman Johnson Delta Rockwall Birmingham, AL (7 counties; 5,280 mi 2 ; 1,128,047 people; 214 people/mi 2 ) Atlanta, GA (20 counties; 6,096 mi 2 ; 5,110,183 people; 838 people/mi 2 )

11. Background Lack of spatial representativeness of available monitoring data for large metropolitan areas considered here Monitored ambient concentrations not fully comparable across different metro areas Different numbers and placement of monitors AQ core developed a data fusion method to combine monitored observations and modeled CMAQ outputs Compare use of CMAQ-OBS with other SCAPE exposure assignment approaches in epidemiologic analyses

12. SCAPE Exposure Metrics Four metrics provide data on 12 pollutants in 4 cities Central monitoring site (CMS) Population-weighted average (PWA) of observations from available monitors CMAQ-OBS PWA (CF PWA) CMAQ-OBS at ZIP code tabulation areas, ZCTAs (CF ZIP) Each ZCTA assigned pollutant values from each exposure metric on each study day Analysis restricted to periods with daily data available for each exposure metric and ED data # ZCTAs2002200320042005200620072008 Atlanta191 Birmingham*125 Dallas252 St. Louis256 *Birmingham NO 2 analysis restricted to 2004-2008 due to lack of CMS data in earlier years

13. Modeling Approach Examined associations between each pollutant- exposure metric and ED visits Poisson regression models allowing for overdispersion Same-day (lag 0) and 3-day (lag 0-2) moving average pollutant concentrations Control for ZCTA (maintains temporal analysis) Control for time: Time splines with monthly knots, day of week and holidays, season and interaction between day of week and holidays and season, hospital Control for meteorology: Cubic terms for lag 0 max temp, and interaction of max temp with season; lag 1-2 moving average min temp (when modeling 3-day moving average exposure); lag 0-2 moving average mean dew point

14. Respiratory & Cardiovascular ED Visits* *Identified using primary ICD-9 codes [indicated in brackets]: Respiratory disease (RD): includes visits for pneumonia [480-486], chronic obstructive pulmonary disease [491, 492, 496], asthma/wheeze [493, 786.07], and other RD [460-466, 477] Cardiovascular disease (CVD): includes visits for ischemic heart disease [410-414], cardiac dysrhythmia [427], congestive heart failure [428], and other CVD [433-437, 440, 443-445, 451-453] Atlanta (2002-2008, 2557 days) Birmingham (2002-2008, 2557 days) Dallas (2006-2008, 1096 days) St. Louis (2002-2007, 2004 days) Total # Visits Mean Visits / Day Total # Visits Mean Visits / Day Total # Visits Mean Visits / Day Total # Visits Mean Visits / Day RD1,022,735400.0173,31767.8490,826447.8572,494285.7 CVD263,944103.279,05730.9138,946126.8200,787100.2

15. Pollutant Summary CMS = central monitoring site; PWA = population-weighted average of observations from available monitors; CF PWA = CMAQ-OBS data fusion, PWA; CF ZIP = CMAQ-OBS at ZCTAs

16. Criteria Pollutants & RD ED Visits Rate Ratio (95% CI) per Standard Unit ATLANTA 2002-2008 ST. LOUIS 2002-2007 DALLAS 2006-2008 BIRMINGHAM 2004-2008 3-day (lag 0-2) moving average pollution and respiratory disease (RD) ED visits ATLANTA 2002-2008 ST. LOUIS 2002-2007 DALLAS 2006-2008 BIRMINGHAM 2004-2008 CO NO 2 O3O3 PM 2.5

17. Criteria Pollutants & CVD ED Visits Same-day (lag 0) pollution and cardiovascular disease (CVD) ED visits ATLANTA 2002-2008 ST. LOUIS 2002-2007 DALLAS 2006-2008 BIRMINGHAM 2004-2008 ATLANTA 2002-2008 ST. LOUIS 2002-2007 DALLAS 2006-2008 BIRMINGHAM 2004-2008 CO NO 2 O3O3 PM 2.5 Rate Ratio (95% CI) per Standard Unit

18. PM Components & CVD ED Visits Same-day (lag 0) pollution and cardiovascular disease (CVD) ED visits Rate Ratio (95% CI) per Standard Unit PM 2.5 SO 4 2- NO 3 - OCEC ATLANTA, 2002-2008

19. Summary Differences between CMS and CF metrics larger for CO and NO 2 than O 3 and PM 2.5 Often stronger associations with CF metrics than with CMS  suggestive of reduced exposure measurement error Some unanticipated weaker associations using CF ZIP vs. CF PWA Due to greater error in ZIP estimates? ZIP-level estimates driven by CMAQ, with error estimates greater for primary pollutants when away from monitors Due to exposure measurement error from mobility patterns that take patients away from home ZIP codes? Future work to assess whether error estimates are related to observed pattern of RRs across pollutant metrics

20. Population vulnerability (age, race, comorbidity, SES) Temperature, seasonality Measurement error (ambient pollutant spatial variability) CR Functions Personal exposures (to ambient) True modifiers Apparent modifiers Cities: ATL BHM DFW PGH STL Joint effects of single pollutants Air Pollution Mixtures ED Outcomes Single Pollutants (O 3, NO 2, PM 2.5 ) Mixtures based on chemical properties (ROS) Data-based exposures (SOM, C&RT) Respiratory (All, ASW, COPD) Cardiovascular (All, IHD, CHF) PM Sources (CMB) Effect Modification of Air Pollution- Asthma Associations

21. Objectives Strong associations of ozone and traffic-related pollutants on asthma ED visits in Atlanta and other cities Examine potential within-city modification of air pollution-asthma associations Understanding of susceptible subpopulations Are results any more/less consistent across cities within defined strata? Modifiers considered Age, sex, race (Brooke Hixson) Neighborhood socioeconomic conditions (Cassie O’Lenick)

22. Methods Daily ED visits for children (5-18 yrs) with a primary or secondary diagnosis of asthma or wheeze in 20- county Atlanta, 2002-2008 128,758 visits overall Distributed across 191 ZCTAs Daily ZCTA-level pollutant concentrations from CMAQ-OBS fusion Paired with data on ZCTA-level socioeconomic conditions Counts of asthma ED visits by ZCTA in Atlanta

23. Socioeconomic Variables Acquired data from two sources to account for potential changes to neighborhood socioeconomic conditions over time Census 2000 data: assigned to 2002-2004 ED data 2007-2011 ACS data: assigned to 2005-2008 ED data Data from Geolytics, standardized to 2010 borders Selected variables describing different ZCTA level socioeconomic conditions: Education, income, poverty, wealth, working class, unemployment Composite metrics (e.g., Townsend index of deprivation, Carstairs index, neighborhood deprivation index, others) ZCTAs grouped into strata according to various definitions: Deprivation area (yes/no), e.g. Undereducated area (≤25% of population with high school diploma) Poverty area (≥20% of population living below the federal poverty line) Above/below median, 75th percentile, 90th percentile, or in quartiles of continuous SES variables

24. Modeling Approach Conditional logistic regression Matching on day of week, month, and year of ED visit, and ZCTA of patient residence Additional control for time (spline with 2 knots per year), meteorology, season, and hospital entry/exit 3-day (lag 0-2) moving average pollutant concentrations Effect modification by ZCTA-level socioeconomic factors examined via stratification Here, focus on analyses considering poverty Mean % below poverty in Atlanta ZCTAs = 13.2%, range 45%

25. Modification by Poverty via Poverty Area* O3O3 CONO 2 PM 2.5 EC *ZCTA labeled as an ‘poverty area’ if ≥20% of population living below the federal poverty line Odds Ratio (95% CI) per Standard Unit Census 2000 2007- 2011 ACS # Asthma ED Visits # ZCTAs Not Poverty Area (PA)174156100,194 Poverty Area173528,564 Census 2000 2007-2011 ACS ATLANTA, 2002-2008, 5-18 yr olds

26. Modification by Poverty via Quartiles of % Below Poverty O3O3 CONO 2 PM 2.5 EC Census 2000 2007-2011 ACS Odds Ratio (95% CI) per Standard Unit Census 20002007-2011 ACS# Asthma ED Visits Quartiles# ZCTAsQuartiles#ZCTAs Q1<5.547<8.54728,623 Q2>5.5 ≤ 8.549>8.5 ≤ 13.14929,151 Q3>8.5 ≤ 11.947>13.1 ≤ 17.84829,884 Q4>11.948>17.84741,100 ATLANTA, 2002-2008, 5-18 yr olds

27. Summary Strong air pollution-asthma associations among 5-18 yr olds in Atlanta Associations generally stronger among patients living in commonly-designated ‘deprived’ areas based on poverty and education (not shown) Consistent u-shaped pattern of effect mod for % BP and most other SES metrics: strongest associations among patients living in Q1 (high SES) and Q4 (low SES) ZCTAs Observed for most pollutants, for different asthma outcome definitions, for both CF PWA and CF ZIP Examination of factors that may differ by SES may facilitate results interpretation, e.g., patient characteristics, ambient concentrations, etc.

28. Modification by Patient-Level Sex and Race in Atlanta, 5-18 yr olds Atlanta findings from Hixson et al. analysis Slightly stronger association among males Significantly stronger association among non- white than white patients Same findings in St. Louis Corresponds with Project 3 findings Overall Female Rate Ratio (95% CI) per Standard Unit O3O3 NO 2 Male White Non- White

29. Population vulnerability (age, race, comorbidity, SES) Temperature, seasonality Measurement error (ambient pollutant spatial variability) CR Functions Personal exposures (to ambient) True modifiers Apparent modifiers Cities: ATL BHM DFW PGH STL Joint effects of single pollutants Air Pollution Mixtures ED Outcomes Single Pollutants (O 3, NO 2, PM 2.5 ) Mixtures based on chemical properties (ROS) Data-based exposures (SOM, C&RT) Respiratory (All, ASW, COPD) Cardiovascular (All, IHD, CHF) PM Sources (CMB) Effect Modification of Air Pollution- CVD Associations (Andrea Winquist)

30. Air Pollution and Cardiovascular Disease Daily ambient air pollutant concentrations have been found to be associated with the daily number of hospitalizations and emergency department visits for cardiovascular diseases The strength of association has varied across geographic locations Reasons for heterogeneity in the observed strength of association are not clear; some potential reasons include: Differences in population characteristics that may influence susceptibility to cardiovascular effects of air pollution Differences in seasonality and meteorology Differences in pollutant mixtures Differences in factors influencing personal exposure to ambient air pollution Differences in measurement error Non-linear dose-response curves

31. Meteorologic Characteristics by City, 2006 MeanSDMin25%50%75%Max Max Temp (⁰F) Atlanta 73.114.23862738598 Birmingham 75.714.940657689100 Dallas 80.516.031708394107 St. Louis 68.318.616536985101 Min Temp (⁰F) Atlanta 53.814.31942546777 Birmingham 54.015.41741546879 Dallas 58.215.82044617283 St. Louis 48.917.3535476582 Dewpt Temp (⁰F) Atlanta 49.115.34.637.552.161.772.1 Birmingham 50.315.16.339.452.563.571.9 Dallas 50.215.21238.854.363.373.2 St. Louis 44.916.8-4.432.346.65974.4 Dallas is overall the warmest and St. Louis is overall the coldest St. Louis is the least humid

32. American Community Survey Data, 2010 St. Louis and Birmingham have a higher percentage of the population at older ages than Atlanta and Dallas Dallas has a higher percentage of the population at younger ages Age

33. AtlantaBirminghamDallasSt. Louis Number of ZCTAs with ED visits and census data191125252256 Overall % below poverty level*13.2%14.3%14.0%12.4% Range of ZCTA-specific % below poverty level At study period start2.9-45.92.8-52.41.3-46.01.5-50.1 At study period end1.7-45.50-54.20-48.10-70.7 Number (%) of ZCTAs with ≥20% below poverty level (“poverty area”) At study period start20 (10%)23 (18%)54 (21%)32 (13%) At study period end35 (18%)37 (30%)58 (23%)48 (19%) Overall % less than a high school education (among people aged ≥ 25 years)* 12.6%14.8%17.1%11.3% Range of ZCTA-specific % with < high school education At study period start2.5-39.12.2-49.41.4-57.82.2-47.2 At study period end0.8-34.10.5-54.60-56.80-49.1 Number (%) of ZCTAs with ≥25% with < HS education (“low education area”) At study period start44 (23%)65 (52%)54 (21%)49 (19%) At study period end22 (12%)31 (25%)54 (21%)21 (8%) Note: There was one ZCTA in St. Louis and one ZCTA in Dallas for which census information was not available. Socioeconomic Characteristics by City, 2006 *2008-2010 American Community Survey

34. Model Specification Poisson Regression Models allowing for overdispersion Outcome: All cardiovascular disease (CVD; ICD9 codes 410 – 414, 427, 428, 433 – 437, 440, 443 – 445, 451 – 453) Air quality estimates at lag 0 PM 2.5 and EC CMAQ fused estimates previously described Control for ZCTA (within ZCTA analysis) Time control Time splines with monthly knots Day of week and holidays Season and interaction between day of week and holidays and season Time period of participation for each hospital Meteorology control Cubic terms for maximum temperature (lag 0) Interaction between cubic terms for maximum temperature and season Cubic terms for dew point (moving average of lags 0-2) Estimates scaled to common unit (close to IQR; PM 2.5 10 μg/mᵌ, EC 0.5 μg/mᵌ)

35. Overall Model Results PM 2.5 and EC Largely null results for PM 2.5 For EC, suggestion of stronger associations in Atlanta and St. Louis than in Birmingham and Dallas AtlantaBirminghamDallasSt. Louis PM 2.5 AtlantaBirminghamDallasSt. Louis EC

36. No consistent pattern of effect modification by season Significant associations in both Atlanta and St. Louis observed in both seasons Associations not more similar across cities within season By Season EC (ZCTA-specific estimates) Warm Season: May-Oct Cold Season: Nov-Apr AtlantaBirminghamDallasSt. Louis Both Warm Cold Both Warm Cold EC

37. By Age and Gender EC (ZCTA-specific estimates) Not much variation across age groups, except for Dallas Associations slightly stronger for women than for men (except Atlanta) Associations not more similar across cities within age and gender categories AtlantaBirminghamDallasSt. Louis EC

38. By ZCTA Socioeconom ic Characteristic s EC (ZCTA-specific estimates) AtlantaBirminghamDallasSt. Louis Lower SES groups had stronger associations in Dallas and St. Louis, opposite pattern in Birmingham Associations not more similar across cities within these SES categories EC

39. Preliminary Conclusions There are differences between the cities in age distribution, SES, meteorology and pollutant levels For CVD, there is not a lot of variation across the cities in the strength of associations with PM 2.5 and EC (generally weak associations overall), but statistically significant associations observed for EC in Atlanta and St. Louis There was no consistent evidence of effect modification of the association between CVD and EC These stratifications for this outcome and pollutant did not explain between-city heterogeneity (but there was not a lot of heterogeneity) Future analyses will consider specific types of cardiovascular outcomes and additional pollutants

40. Population vulnerability (age, race, comorbidity, SES) Temperature, seasonality Measurement error (ambient pollutant spatial variability) CR Functions Personal exposures (to ambient) True modifiers Apparent modifiers Cities: ATL BHM DFW PGH STL Joint effects of single pollutants Air Pollution Mixtures ED Outcomes Single Pollutants (O 3, NO 2, PM 2.5 ) Mixtures based on chemical properties (ROS) Data-based exposures (SOM, C&RT) Respiratory (All, ASW, COPD) Cardiovascular (All, IHD, CHF) PM Sources (CMB) PM 2.5 Sources and Cardiovascular Disease ED Visits (Jenna Krall)

41. Aims 1.Provide a framework for conducting multi-city analyses of the associations between PM 2.5 sources and emergency department (ED) visits 2.Estimate city-specific associations between short-term exposure to sources of PM 2.5 and ED visits for cardiovascular disease (CVD) Current analyses show no significant associations between central monitor measurements of total PM 2.5 or major PM 2.5 chemical constituents and ED visits for CVD causes

42. For Atlanta, Birmingham, Dallas, and St. Louis: ED visits for CVD causes including including ischemic heart disease [410-414], cardiac dysrhythmia [427], congestive heart failure [428], and other CVD [433-437, 440, 443-445, 451-453] Concentrations of source apportioned PM 2.5 Estimated from PM 2.5 chemical speciation data measured at a single central monitoring site in each city Daily data for Atlanta (Jefferson St. SEARCH site) 1-in-3 day data for Birmingham, Dallas, St. Louis (CSN sites) Chemical mass balance (CMB) run with ensemble-based source profiles (EBSP) derived using Atlanta data EBSPs generated for warm and cold seasons using CMB with molecular markers, CMB with gas constraints, positive matrix factorization (PMF), and chemical transport model using CMAQ Sources vary across cities (e.g. no PM 2.5 from metals in Atlanta) Total PM 2.5 mass from central site monitors Data

43. City-specific associations between PM 2.5 sources and ED visits for CVD causes were estimated using Poisson time series regression models with overdispersion Same-day (lag 0) exposure to PM 2.5 and source apportioned PM 2.5 Single pollutant models (PM 2.5 source or total PM 2.5 and covariates) Multiple source models (all PM 2.5 sources and covariates) Covariates Time splines with monthly knots Temperature Cubic terms for lag 0 maximum temperature Cubic terms for lag 1-2 moving average minimum temperature Cubic terms for lag 0-2 moving average dew point temperature Indicator variables for season, day of week, holidays, hospitals Interaction terms between season and maximum temperature and season and day of week/holidays Methods

44. AtlantaBirminghamDallasSt. Louis Start date1999-01-032004-01-012006-01-052001-01-01 End date2009-12-312010-12-312009-08-292007-06-26 Observation days3,667821350735 Number of hospitals411236 ED visits for CVD causes N356,07527,56444,49973,107 Daily mean (standard deviation) 97 (28)34 (7)127 (18)99 (14) Results: Time frame and days of data for each city ED visits for CVD causes for each city

45. Concentrations in μg/m 3 IQRAtlantaBirminghamDallasSt. Louis Total PM 2.5 9.1015.58 (7.84)16.98 (9.28)10.69 (4.64)13.61 (7.10) Source apportioned PM 2.5 Mobile1.472.16 (1.73)1.71 (1.70)0.81 (0.79)1.80 (1.16) Gas vehicles0.781.01 (0.93)0.69 (0.75)0.46 (0.37) N/A Diesel vehicles1.171.15 (1.15)1.03 (1.35)0.35 (0.57) N/A Burning0.961.65 (1.26)1.08 (1.14)1.87 (3.22)1.36 (1.15) Coal-fired power plant0.130.13 (0.12)0.22 (0.29)0.01 (0.02) N/A Metals0.43 N/A0.63 (0.57) N/A0.22 (0.24) Dust0.330.46 (0.51)0.60 (0.73)0.62 (1.04)0.47 (0.69) Secondary organic carbon3.691.98 (1.57)2.08 (1.93)1.19 (0.89)1.13 (1.14) Ammonium sulfate1.563.27 (3.32)3.35 (3.43)2.28 (2.55)3.50 (3.60) Ammonium bisulfate0.942.20 (1.80)1.69 (1.54)1.17 (0.98)1.68 (1.87) Ammonium nitrate1.581.10 (1.00)1.08 (1.01)1.04 (1.18)3.05 (3.24) Results: Concentrations of total PM 2.5 and source apportioned PM 2.5 Median interquartile range (IQR, Q3 – Q1) across cities Mean (standard deviation) concentration for each city N/A indicates source was not measured for a particular city

46. Results: Associations of lag 0 pollution and ED visits for CVD causes Single pollutant models (total PM 2.5 or PM 2.5 source and covariates only) Multiple source models (all PM 2.5 sources and covariates simultaneously) * statistically significant at α = 0.05 * * **

47. In general, associations between PM 2.5 sources and ED visits for CVD causes were null across cities Results mostly consistent with observed null associations between CVD ED visits and PM 2.5 from central monitoring sites in Atlanta We observed positive, statistically significant associations of CVD ED visits with gas vehicles and mobile sources in Atlanta We did not observe these associations in other cities, which had shorter time series and temporally sparser pollution data compared with Atlanta Future work: 1.Associations between PM 2.5 sources and ED visit subcategories CVD: ischemic heart disease and congestive heart failure Respiratory: asthma and wheeze 2.Incorporate uncertainty from estimating PM 2.5 sources into health effect regression models 3.Develop multi-city model 4.Long-term goal to incorporate CMAQ-CMB outputs Conclusions and future work

48. Project 4 Plans Over Coming Year Submit drafted manuscripts: Hixson et al., Modification of air pollution-asthma by age Gass et al., 3-city C&RT Finalize analyses, draft and submit manuscripts: Sarnat et al., SCAPE exposure metrics comparison O’Lenick et al., Modification of air pollution-asthma by SES (poster) Winquist et al., Modification of air pollution-CVD associations Krall et al., Multi-city PM 2.5 sources and CVD (poster) Pearce et al., Atlanta SOM epi application Make progress on additional analyses ROS-DTT and ED visits in Atlanta Organic chemical groups and ED visits in Atlanta Assessment of effect modification by air exchange rates Assessment of effect modification by temperature Quintiles analysis

49. Accepted/Published in Past Year Sarnat SE, Winquist A, Schauer JJ, Turner J, Sarnat JA. Fine particulate matter components and emergency department visits for respiratory and cardiovascular diseases in St. Louis. Environmental Health Perspectives, provisional acceptance. Winquist A, Schauer JJ, Turner J, Klein M, Sarnat SE. Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity. Journal of Exposure Science and Environmental Epidemiology, in press. Dionisio KL, Baxter LK, Chang HH. An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models. Environmental Health Perspectives 122:1216-1224, 2014. Winquist A, Kirrane E, Klein M, Strickland M, Darrow LA, Sarnat SE, Gass K, Mulholland JA, Russell AG, Tolbert PE. Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004. Epidemiology 25:666-673, 2014. Pearce JL, Waller LA, Chang H, Klein M, Mulholland J, Sarnat J, Sarnat S, Strickland M, Tolbert P. Using self-organizing maps to classify days by air quality for air pollution epidemiological mixtures research. Environmental Health 13:56, 2014. Chang HH, Hao H, Sarnat SE. A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change. Atmospheric Environment 89:290-297, 2014. Gass K, Klein M, Chang HH, Flanders WD, Strickland MJ. Classification and regression trees for epidemiologic research. Environmental Health 13:17, 2014.

51. Pollutant Spearman Correlations *Average of ZCTA-specific correlations CMS = central monitoring site; PWA = population-weighted average of observations from available monitors; CF PWA = CMAQ-OBS data fusion PWA; CF ZIP = CMAQ-OBS at ZCTAs