Supporting Multi-year, Spatial-Temporal Analysis of Health Data with Air Pollution through the Chemical Transport Modeling

Jaemeen Baek (1), Charles O. Stanier (1), Sinan Sousan (1), Jacob Oleson (1), Naresh Kumar (1), Gregory R. Carmichael (1)

(1) University of Iowa, Iowa

     Abstract Number: 736
     Last modified: May 14, 2010

Abstract
Epidemiological studies have shown that elevated concentrations of fine particulate matter with aerodynamic diameter less than 2.5 micro-meters (PM2.5) are associated with cardiovascular and respiratory related hospital admissions or mortality rates. PM2.5 measurements are crucial to understanding exposures, but they are limited by time (one measurement for every three to six days) and space (discrete point locations). Chemical transport models, such as the Community Multiscale Air Quality (CMAQ) model, provide another way to obtain exposure estimates, but this approach has seen limited use in epidemiological studies. Our current goal is to evaluate a range of air quality modeling practices within the Weather Research and Forecasting (WRF)/CMAQ/Sparse Matrix Operator Kernel Emissions (SMOKE) framework for supporting health studies. Three areas of current focus within our work will be discussed. First is characterization of model performance (and therefore exposure uncertainties) for total, speciated, and source-resolved PM concentrations in ways that are appropriate with time series and long term epidemiological analysis. Second focus area is the selection of model grid resolution. And third is the selection of regridding schemes to map from the regular model grid to irregular geographic domains to match health data. Multi-year (initially 2002 and 2003) and multi-resolution (36km, 12km and 4km) CMAQ results will be evaluated against standard IMPROVE and CSN monitoring data.