Garcia, V. 1 , Özkaynak, H. 1 , Dimmick, F. 1 , Holland, D. 1 , Hall, E. S. 1 , and Lin, S. 2

1. Development of Alternative PM and Ozone Exposure Prediction Methodologies for Environmental Epidemiology and Public Health Tracking Studies Garcia, V.1, Özkaynak, H.1, Dimmick, F.1, Holland, D.1, Hall, E. S.1, and Lin, S.2 1. U.S EPA; 2. New York State Department of Health

2. Overview • Current Situation: • Spatial and temporal variations of air pollutants - ozone (O3) and fine particulate matter (PM2.5) • Present difficulty when developing concentration/response models for specific public health outcomes • Asthma • Myocardial infarction • EPA in collaboration with state health agencies, state air quality agencies and CDC • Applying a Hierarchical Bayesian space-time Modeling System (HBM) incorporating: • Air pollution concentration measurements from FRM monitors • Air pollution modeling data from the Community Multiscale Air Quality (CMAQ) model • Delivering a single estimated air pollution concentration ‘surface’ (usable for health studies) • HBM statistically combines monitor and model estimates • Gives more weight to monitor values where monitors exist • Uses bias-adjusted model output in locations without monitors • HBM approach is being developed into an operational system • Allowing EPA to deliver air pollution concentration surfaces (for O3 and PM2.5) • to CDC for use in Environmental Public Health Tracking (EPHT) Network – (Dec 2008) • to correlate and evaluate effect of O3 and PM2.5 air pollution levels on health outcomes • EPA collaborating with New York State (NYS) to use: • HBM concentration surfaces • Population exposure predictions from APEX and SHEDS exposure models • to evaluate air pollution health effects (i.e., respiratory-related hospital admissions) • to evaluate use of ambient pollution concentrations as a surrogate for exposure versus modeled exposures in conducting air pollution health risk assessments

3. Motivation • Clean Air Act requires EPA to set National Ambient Air Quality Standards to protect human health. • Implementing these regulations costs billions of dollars annually. • Quantifying improvement in air quality and human health is imperative in evaluating whether these actions are making the difference originally anticipated.

4. Discussion • EPA’s National Exposure Research Laboratory (NERL) • Conducts research to characterize exposures • Exposure research designed to identify and characterize • environmental stressors (pollutants) • their effects on receptors (people) • Objective is to assess and link: • processes affecting propagation of pollutants from their sources • their fate in environment • the intersection/interaction of pollutants with individuals and populations • NERL’s exposure research: • conducted within a source-to-outcome continuum • provides a framework to evaluate the critical linkages • between pollution sources • associated human health impacts

5. NERL Source-To-Outcome Framework

6. NERL Models • NERL develops models: • General types • source apportionment/receptor models • air quality models [community/local/regional/national scale] • Bayesian environmental concentration [‘data fusion’] models • exposure models and databases [CHAD] • dose models • focused on air pollutants within the source-to-outcome framework • to conduct exposure research • to summarize our knowledge of exposure processes • mathematically quantify/predict concentrations of chemical pollutants, biological and physical conditions, exposures, and dose • use of models is central to EPA’s regulatory decision making • Key use of these models is in ‘accountability’ research • to determine the impact of regulatory changes • on pollution levels • on human health impacts

7. XXXXXXXXXXXXXXXX Mechanistic Air Quality Models Air Quality: Exposure Framework Regional Scale, Population Level CMAQ SHEDS ERDEM • Exposure: • Population • Demographic Info • Time-activity & Commuting • Housing factors AQ Model Input • Ambient Concentration • Ozone, PM2.5 species, toxics • Regional Airshed • Spatially gridded (36km,12km, 4km) • Hourly values: months, years • Dose: • Population • Physiological • Activity level • ADME • Effects: • Population • Acute • Chronic • Source/Stressor Characterization • National Emission Inventory (NEI) • “SMOKE” processing • Spatial allocation to county, road, or point source locations • Temporal allocation (hourly to annual) AERMOD • National to Region Scale Concentration: • Fusion of observational data and model output • Ozone, PM2.5 total • Spatially gridded (12km) • Daily values: years Time-Series and Multi-City Epi Studies • Ambient Concentration • Ozone, PM2.5 species, toxics • Regional Airshed • Spatially gridded (36km,12km, 4km) • Hourly values: months, years • Environmental Characterization • Land Use/Landscape • Biogenic emissions (vegetation type, meteorology) • Plume rise from stacks • Meteorology model (WRF, MM5) Improved Air Quality and Health Associations Statistical Models Air Quality: Exposure Framework Urban Scale, Community Level • Transport/Transformation • Dispersion and advection • Atmospheric chemistry • Atmospheric deposition • Spatially gridded (36km, 12km, 4km) • Hourly values: months to years Bayesian & Other • Ambient Concentration • Ozone, PM2.5 species, toxics • Regional Airshed • Spatially gridded (36km,12km, 4km) • Hourly values: months, years • Urban-Scale Concentration • Toxics, other? • Local scale Airshed • Spatially gridded (? km) • Hourly Values: months, years? Intra-urban and Cohort Studies Land Use Regression • Air Quality Monitoring Data • Best estimate where measured • Spatially sparse when large concentration gradients exist • Temporally strong (hourly for PM2.5/ozone) to weak (1 in 3 days) • Monitoring sites change over time • Ambient Concentration • Ozone, PM2.5 species, toxics • Regional Airshed • Spatially gridded (36km,12km, 4km) • Hourly values: months, years SHEDS ERDEM PMF, UNMIX, APTR • Exposure: • Community • Demographic info • Human activity patterns • Housing characteristics • Dose: • Community • Physiological • Activity level • ADME • Effects: • Community & • Individual • Acute • Chronic • Source/Receptor Models • Ambient Concentrations • Emissions profiles • Met Data

8. Project 1: EPA/NYS DOH • EPA conducting research with NYS • to evaluate respiratory and cardiovascular health impact • of NOx State Implementation Plan (SIP) call • characterize the change (during 1997 – 2006): • air pollution levels • hospitalizations • compare number of hospital admissions • Before NOx SIP Call • After NOx SIP Call • Three time periods examined: • 1997 – 2000 (pre-implementation) • 2001 – 2003 (intervention) • 2004 – 2006 (post-implementation) • health outcomes of interest: • respiratory disease • cardiovascular disease

9. ACCOUNTABILITY INDICATORS FOR AIR QUALITY • What is unique about this study with NYS? • First attempt to assess impact of regulation from source to exposure to effects • Done to establish baseline for [now vacated] CAIR rule • Investigates various scenarios (e.g., “no emissions” vs. “controls” vs. “no controls”) • Evaluates range of statistical techniques for combining observed and modeled values • Assesses the importance of exposure information and intercomparison of exposure models • Demonstrates feasibility of operational capability to produce air quality and exposure indicators

10. Health Assessment Objectives • Evaluate potential respiratory and cardiovascular health impact of NOx SIP Call in New York State and to: • Characterize and track the magnitude of changes in air pollution levels and hospital admissions for respiratory and cardiovascular disease during 1997-2006 • Compare hospital admissions before and after the NOx SIP went into effect

11. ACCOUNTABILITY INDICATORS FOR AIR QUALITY - Activities • Generated indicator maps for Eastern U.S. to quantify and track the impact of control actions • Impact of NOx SIP Call on ozone transport • Ambient concentrations • Exposure estimates • Assessing value of indicator maps: • Risk assessment • Health assessment • Delivery Architecture/Tool • GIS-based application to layer and align air quality exposure, risk and health assessment data

12. Study Population and Period • NYS Residents, 1997 to 2005 • Upstate and NYC • By 12km grid, ZIP code, and U.S. Census tract • Health outcomes and association with environmental data being examined for trends in 3 time periods: • Baseline period (1997-2000) • Pre-implementation period (2001-2003) • 2003 may have partial implementation • Post-implementation period (2004-2006)

13. Can the NOx SIP Call signal be detected in health endpoints? • Assess whether exposure estimates and enriched air quality information makes a difference in detecting signal • Evaluate scenarios with regard to how risk and health assessments change • Analyze NYS hospital admissions for respiratory diseases pre and post-NOx SIP • Longitudinal component • Cross-Sectional - geographic regions

14. Total Continuous Emission Monitoring Data – Ozone Season NOx State Implementation Plan (SIP) Call Pre-NOx SIP Call: 1997 – 2000 Intervention Period: 2001 –2003 Post-NOx SIP Call: 2004 – 2005

15. Health Outcomes • Daily and weekly counts and rates of admissions • Summers only (June – August) for each year and across all summers from 1997 to 2005 • Respiratory disease • Asthma (496) • Chronic Bronchitis (491) • COPD (496) • Emphysema (492) • Pneumonia and Influenza (480-487) • Cardiovascular disease • Ischemic heart disease (410-414) • Hypertensive disease (401-405) • Cerebrovascular Disease (430-438)

16. Analysis • Longitudinal component • Simple descriptive analyses of health and exposure data • Time-series models of relationship between respiratory and CVD hospital admissions and O3, PM2.5 levels • Intervention model: compare rate of admissions before and after implementation • Pre-NOx SIP Call (1997-2000) • Intervention (2001-2003) • Post-NOx SIP Call (2004-2006) • Cross-sectional comparison • Hospital admission rates being compared between geographic regions, socio-demographic subgroups, and disease characteristics at single time points • Targeted analyses will focus on identified susceptible groups

17. Confounders & Effect Modifiers • Controlled for in models or by stratification • Day-of-the-week, long term trends • Meteorological variables • Temperature, humidity • Age • Race/ethnicity • Gender • Income

18. All respiratory hospital admissions by disease group, NYS, 1997-2004* *2005 data currently unavailable; † Children 0-4 years only

19. Baseline Period Trends in all respiratory admissions, NYS, 1997-2004 Pre-implementation (-6%) Post- implementation (-17%)

20. Project 2: EPA/CDC EPHT Network • EPA’s collaborative research with CDC: • uses HBM to estimate population exposure to ground-level O3, and PM2.5 • assessing health impacts to individuals and susceptible subpopulations • guide public health actions • facilitate analytical studies linking human health outcomes and ambient concentrations • EPA worked with CDC and state public health agencies (2004 through 2006) • New York • Maine • Wisconsin • Public Health Air Surveillance Evaluation (PHASE) research project • study impact of air pollution on asthma and heart disease • From O3 and PM2.5 (traffic and manufacturing facilities) • Follow-on to the PHASE research project (current) • public health researchers and agencies (New York, California, Pennsylvania, Minnesota, Utah, Massachusetts, Maryland, Oregon, New Jersey, New Hampshire, New Mexico, and Louisiana) • provide public health information on hospitalizations • asthma and heart ailments to CDC’s EPHT network. • EPA provides HBM concentration ‘surfaces’ • data to decision makers on effectiveness of current health-based air pollution standards

21. Current Activities – Project 1 • Research EPA is conducting with NYS utilizes: • air pollution concentration measurement data • air pollution concentration model estimates from CMAQ • ‘fused’ air quality surface to provide a relatively • finely-resolved map of O3 and PM2.5 concentrations for NY State • enriched air pollution maps and estimates of exposure probability from the APEX and SHEDS models • used to evaluate the health impact of the NYS NOx SIP (respiratory and cardiovascular health)

23. Current Activities – Project 2 • EPA provides for CDC’s EPHT Network : • ambient concentration measurements (monitors) • QA’ed monitor data directly provided • ambient concentration model estimates (CMAQ) • not directly provided but is input to HBM • ambient concentration ‘surface’ via HBM • covers continental United States • regions with air quality monitors • regions with no air quality monitors • HBM concentration surface • input to CDC EPHT Network • will be correlated with CDC health data • assess the linkage between concentrations and adverse health effects (conc.- response)

24. PM2.5 Monitor Data and CMAQ (HBM)

25. Scientific Impact • Combiningair pollution measurement data and air pollution modeling data: • provides concentration estimates using strengths of both techniques • can yield daily concentration values for PM2.5 or 8-hour maximum values for O3 levels. • provides concentration values distributed throughout a region of interest as a concentration 'response' surface.

26. Outlook for Research Area • NERL will apply resources to: • improve spatial and temporal estimates of pollutant concentrations for health studies • data 'fusion' techniques • address effect of spatio-temporal variability of concentrations on health effects • part of an emerging research program • underlying science is continually being assessed and improved • provide a scientifically based, credible estimate of air quality (O3 and PM2.5 concentrations) at locations without monitors • improvement over using monitor concentrations alone

27. Conclusion of Presentation • Disclaimer • Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.