This study will support environmental public health tracking programs by utilizing aerosol optical depth (AOD) data from NASA's Terra and Aqua satellites to create fine particulate matter (PM2.5) datasets that reflect the spatial and temporal variations in ambient PM2.5 concentrations that occur on local scales and are thus representative of the true PM2.5 field. PM2.5 is a criteria air pollutant, and its adverse impacts on human health are well established. Traditionally, studies that analyze the health effects of exposure to PM2.5 use data from ground-based monitors. There are large spatial and temporal gaps in PM2.5 measured with monitors, however, so a new approach is needed to generate representative fields. Remote sensing data, such as AOD, can provide information about particulate concentrations in areas where monitors do not exist. The proposal team has developed an innovative methodology using a hierarchical Bayesian model (HBM) to combine monitor data with estimates of PM2.5 derived from NASA AOD and the CMAQ air quality model. This approach promises to be a significant step toward creating accurate and representative PM2.5 datasets that officials can use to make informed decisions regarding public health. This study will focus on the Baltimore, MD and New York City, NY metropolitan regions for the period 2004-2006. For each region, combined monitor/satellite/air quality model datasets will be generated using the HBM and correlated with hospital and emergency room data for seven common respiratory and cardiovascular diseases using case-crossover analyses. The accuracy of the combined monitor/satellite/air quality model datasets will be determined in relation to monitor values, and their performance over datasets analogous to the current best estimates of PM2.5 fields will be quantified. Environmental public health tracking programs associated with Maryland, the CDC, and USEPA have expressed interest in using the results of the feasibility study to enhance their existing decision-making activities.