(447e) Use of Surface Measurements and MODIS Aerosol Optical Depth for Improved Model Based PM2.5 Prediction in the United States

Epidemiological studies of the health effects of air pollution and particulate matter have found robust associations between PM_2.5 and mortality rates. However, these studies have limited ability to investigate species- or source-specific PM health effects, and suffer from exposure misclassification resulting from the use of central site monitors. Multiple studies have shown that substantial spatial gradients exist, especially for primary pollutants emitted from low elevation sources such as motor vehicles and industrial area sources. Accordingly, PM_2.5 exposure estimates for United States at high spatial and temporal resolution are highly desirable to conduct further epidemiological studies focused on identifying more and less harmful types of particulate matter. Data assimilation via optimal interpolation (OI) was used to improve the Models-3 Community Multiscale Air Quality Model (CMAQ) modeling system estimates of aerosol pollution. Model predicted concentrations over North America for 2002 with and without assimilation of MODIS satellite-based aerosol optical depth are compared to PM_2.5 measurements for performance evaluation. Effect of various AOD regridding and interpolation schemes on performance statistics is discussed, as is the role of uncertainty in the conversion between PM mass and optical properties. Furthermore, the effect of quality checks and regression with AERONET on reducing bias and noise in the MODIS AOD products is discussed. Our preliminary results show that the best MODIS assimilation parameters may be region and/or season specific. OI improved the PM_2.5 simulations in 78% of regions relative to IMPROVE and in 44% regions relative to STN. The OI method will be extended to include the assimilation of PM_2.5 ground observations, and used to produce optimal PM_2.5 estimates for the time period 2000 to 2004.