Remote sensing techniques provide a means to obtain air quality, climate, and exposure information in the absence of available ground monitoring data. The focus of this theme is on studying the feasibility of applying remote sensing, both ground and satellite observations, to enrich the air quality database in Israel for use in epidemiological studies. Namely, to assess and develop models that can predict ground PM concentrations based on remotely sensed observations. Another direction we pursue is to mine the air quality monitoring network data and allocate measured concentrations to various sources, develop risk metrics, study spatiotemporal pollutant patterns, and provide information to policy makers on the prospects of attaining the standards. Finally, we develop a spectrum of models to estimate exposures, from data driven models to mechanistic (dispersion and fate) models, land-use regression (LUR) models, and a transportation-model based emission-dispersion model in dense urban setting.