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Combining Models and Observations of Air Quality for Human Health Studies

Combining Models and Observations of Air Quality for Human Health Studies. Exposure Metrics Used in Health Studies. Hypothesis High spatio-temporal resolution in air quality/exposure data will better reveal the relationships between ambient air quality and health outcomes.

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Combining Models and Observations of Air Quality for Human Health Studies

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  1. Combining Models and Observations of Air Quality for Human Health Studies

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

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

  4. Land Use Regression (LUR): One Approach That Combines Observations with a Model

  5. Land Use Regression (LUR)

  6. Land Use Regression (LUR) Source: Jerrett et al., JEAEE (2005).

  7. Example of Land Use Regression (LUR)

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

  9. Use of a Bias-Adjustment Method of Combining Observations and Model Results Source: Kang et al., GMD (2010).

  10. Use of a Bayesian Technique to Combine Observations and Model Results (See also Fuentes, AQAH, 2009)

  11. NERL is developing AQ surfaces from AQS data and CMAQ results using HB model1 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 http://ephtracking.cdc.gov/ Made available to states and other CDC programs MATCH program / county health rankings http://www.countyhealthrankings.org/ CDC and its partners are also using HBM predictions for health associations and impact assessments Hierarchical Bayesian Model Approach used in an EPA-CDC Collaboration 1 McMillan, N., Holland, D. M., Morara, M., and Feng, J. (2010). Environmetrics 21, 48-65; http://www3.interscience.wiley.com/cgi-bin/fulltext/122546906/PDFSTART. Courtesy of Ambarish Vaidyanathan (CDC) and Fred Dimmick (EPA)

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

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