Enhanced Air Pollution Epidemiology using a Source-Oriented Chemical Transport ModelEPA Grant Number: R833864
Title: Enhanced Air Pollution Epidemiology using a Source-Oriented Chemical Transport Model
Investigators: Kleeman, Michael J. , Chen, Shuhua , Kaufman, Joel D. , Ostro, Bart , Reynolds, Peggy , Sampson, Paul , Ying, Qi
Institution: University of California - Davis , Northern California Cancer Center , Texas A & M University , University of Washington
EPA Project Officer: Hunt, Sherri
Project Period: December 1, 2008 through November 30, 2012 (Extended to June 30, 2014)
Project Amount: $900,000
RFA: Innovative Approaches to Particulate Matter Health, Composition, and Source Questions (2007) RFA Text | Recipients Lists
Research Category: Health Effects , Particulate Matter , Air
The objective of this study is to combine existing atmospheric science tools and epidemiological tools to improve our understanding of the health effects of airborne particulate matter (PM). The underlying hypothesis is that central monitors provide a poor exposure estimate for primary pollutants that have sharp spatial gradients, and air quality model calculations provide the most realistic method to “interpolate” between air quality measurements at regional scales. The combination of air quality model predictions and existing epidemiological studies will bring source-oriented health effects into sharper focus.
The UCD/CIT model will predict PM concentrations with 24-hr time resolution continuously over the period from 2000-2006 across the entire continental United States with 36 km resolution. UCD/CIT results will be nested downward to a maximum resolution of ~4-5km in 3 different subregions (California, Midwest, and East Coast) that overlap with epidemiological populations. Further nesting using statistical regression models will increase the maximum resolution to 10m in selected regions. UCD/CIT model results will separately resolve the primary contribution to PM10, PM2.5, and PM0.1 from every PM source in the emissions inventory (estimated ~2000 sources). The PM chemical composition for each source will be calculated based on the most recent source profile measurements. Both the UCD/CIT model and CMAQ will be used to predict secondary PM concentrations. All model predictions will be compared to measurements from the U.S. EPA Speciation Trends Network (STN), California Air Resources Board, and MESA-AIR monitors. Final model results will describe PM source / component concentrations by time, and location.
Air quality model results will be incorporated into three longitudinal cohort studies (MESA Air, WHI-OS, CTS) and one time series study (CALFINE). The MESA Air study will examine the association between chronic exposure to PM sources/components and carotid itima-media thickness (CIMT) in 6 urban areas across the United States. CIMT is a strong predictor of cardiovascular disease. The WHI-OS and CTS studies will examine how chronic exposure to PM sources and components is associated with cardiovascular mortality in California (CTS) and across the United States (WHI-OS). Finally, the CALFINE study will examine the association between acute exposure to PM sources/components and mortality in California. The examination of both chronic and acute exposures in multiple populations will provide a robust test of the use of chemical transport models for epidemiological studies.
Air quality model predictions describing source-oriented PM component concentrations in multiple size cuts will provide new inputs to examine the effects of acute and chronic PM exposure on mortality and morbidity. Associations between adverse health effects and PM sources/components/size fractions may be identified years earlier than would otherwise be possible using only central monitor measurements. Hypotheses about PM health effects will be explored over historical time periods for which comprehensive epidemiological information already exists, saving significant resources compared to new epidemiological studies. Improved understanding of the PM sources/components/size fractions responsible for adverse health effects will enable regulators to design efficient control strategies.