Office of Research and Development National Exposure Research Laboratory CMAS Special Session on Human Health October 13, 2010 Combining Models and Observations of Air Quality for Human Health Studies

1 Office of Research and Development National Exposure Research Laboratory Health data analysis Tiers of Exposure Metrics Personal Behavior/Time Activity Microenvironmental Characteristics Ambient Monitoring Data Land-Use Regression Modeling Air Quality Modeling (CMAQ, AERMOD, hybrid) Exposure Modeling (SHEDS, APEX) Statistical modeling (Data blending) Monitoring Data Emissions Data Meteorological Data Land-Use/Topography Monitoring Data Emissions Data Meteorological Data Land-Use/Topography Input data Epidemiological statistical models : log(E(Y kt )) = α + β exposure metric kt +  k γ k a kt + … other covariates Hypothesis High spatio-temporal resolution in air quality/exposure data will better reveal the relationships between ambient air quality and health outcomes Exposure Metrics Used in Health Studies

2 Office of Research and Development National Exposure Research Laboratory Health data analysis Tiers of Exposure Metrics Personal Behavior/Time Activity Microenvironmental Characteristics Ambient Monitoring Data Land-Use Regression Modeling Air Quality Modeling (CMAQ, AERMOD, hybrid) Exposure Modeling (SHEDS, APEX) Statistical modeling (Data blending) Monitoring Data Emissions Data Meteorological Data Land-Use/Topography Monitoring Data Emissions Data Meteorological Data Land-Use/Topography Input data Epidemiological statistical models : log(E(Y kt )) = α + β exposure metric kt +  k γ k a kt + … other covariates Hypothesis High spatio-temporal resolution in air quality/exposure data will better reveal the relationships between ambient air quality and health outcomes Exposure Metrics Used in Health Studies

3 Office of Research and Development National Exposure Research Laboratory Land Use Regression (LUR): One Approach That Combines Observations with a Model

4 Office of Research and Development National Exposure Research Laboratory Land Use Regression (LUR)

5 Office of Research and Development National Exposure Research Laboratory Source: Jerrett et al., JEAEE (2005). Land Use Regression (LUR)

6 Office of Research and Development National Exposure Research Laboratory Example of Land Use Regression (LUR)

7 Office of Research and Development National Exposure Research Laboratory Health data analysis Tiers of Exposure Metrics Personal Behavior/Time Activity Microenvironmental Characteristics Ambient Monitoring Data Land-Use Regression Modeling Air Quality Modeling (CMAQ, AERMOD, hybrid) Exposure Modeling (SHEDS, APEX) Statistical modeling (Data blending) Monitoring Data Emissions Data Meteorological Data Land-Use/Topography Monitoring Data Emissions Data Meteorological Data Land-Use/Topography Input data Epidemiological statistical models : log(E(Y kt )) = α + β exposure metric kt +  k γ k a kt + … other covariates Hypothesis High spatio-temporal resolution in air quality/exposure data will better reveal the relationships between ambient air quality and health outcomes Exposure Metrics Used in Health Studies

8 Office of Research and Development National Exposure Research Laboratory Source: Kang et al., GMD (2010). Use of a Bias-Adjustment Method of Combining Observations and Model Results

9 Office of Research and Development National Exposure Research Laboratory Use of a Bayesian Technique to Combine Observations and Model Results (See also Fuentes, AQAH, 2009)

10 Office of Research and Development National Exposure Research Laboratory NERL is developing AQ surfaces from AQS data and CMAQ results using HB model 1 –Daily PM2.5 and 8-hr Ozone –36km CONUS and 12km eastern half US –2001 to 2006 done; subsequent years underway CDC’s Tracking program is using HBM to develop AQ indicators –Currently available on the Tracking Network –Made available to states and other CDC programs MATCH program / county health rankings CDC and its partners are also using HBM predictions for health associations and impact assessments 1 McMillan, N., Holland, D. M., Morara, M., and Feng, J. (2010). Environmetrics 21, 48-65; Hierarchical Bayesian Model Approach used in an EPA-CDC Collaboration Courtesy of Ambarish Vaidyanathan (CDC) and Fred Dimmick (EPA)

11 Office of Research and Development National Exposure Research Laboratory Health data analysis Tiers of Exposure Metrics Personal Behavior/Time Activity Microenvironmental Characteristics Ambient Monitoring Data Land-Use Regression Modeling Air Quality Modeling (CMAQ, AERMOD, hybrid) Exposure Modeling (SHEDS, APEX) Statistical modeling (Data blending) Monitoring Data Emissions Data Meteorological Data Land-Use/Topography Monitoring Data Emissions Data Meteorological Data Land-Use/Topography Input data Epidemiological statistical models : log(E(Y kt )) = α + β exposure metric kt +  k γ k a kt + … other covariates Hypothesis High spatio-temporal resolution in air quality/exposure data will better reveal the relationships between ambient air quality and health outcomes Exposure Metrics Used in Health Studies