Air pollution exposure is often estimated using a single or small number of outdoor monitors and assuming that pollutant levels are homogenous over a given area. However, concentrations may differ within a community or across persons due to numerous factors such as transportation emissions, activity patterns, and occupational exposures. We are interested in developing better methods of estimating air pollution exposure. Our research aimed at improving the use of ambient monitoring data includes studies of spatial heterogeneity of air pollution in the U.S. and Brazil, spatial analysis methods for study of lung function in Korea, and development of statistical methods to address uncertainty introduced by differences in data’s spatial domains. We performed several studies to measure personal exposure, including, to the best of our knowledge, the first use of personal monitoring in Nepal or China. Current projects explore emerging exposure methods, including source factor analysis, traffic modeling, land-use modeling, and satellite imagery. In other work, we investigate the application of air quality modeling in health-based research.