The application of satellite remote sensing in estimating ground level fine particle concentrations

Due to the rapid economic development and population growth, many metropolitan areas in developing countries such as China and India are suffering public health and social welfare damage from increasingly severe air pollution. Among the major pollutants, fine particulate matters (PM2.5) have received widespread attention due to its significant association with adverse health effects. Recent studies also showed that importance of monitoring long-term fine particles concentrations in order to evaluate the health effects of low or moderate exposure as well as repeated exposure to elevated pollution. However, ground monitoring networks are costly to maintain and often not available in regions with limited financial resources.; In this thesis, a new method of monitoring ground level PM2.5 concentrations using satellite remote sensing is developed. This method uses the aerosol optical thickness (AOT) measurements from the Multi-angle Imaging SpectroRadiometer (MISR) onboard NASA's Terra satellite as a major predictor of ground level particle abundance. Three logical steps are followed to developing this method. The quality of MISR AOT is first evaluated using observations from Aerosol Robotic Network (AERONET) over the contiguous United States. Then an empirical regression model is developed in eastern United States to study the feasibility of predicting ground level PM2.5 concentrations using MISR AOT and simple meteorological and geographical parameters. Finally, MISR AOT measurements and aerosol vertical profiles generated by a global 3-D atmospheric chemistry model (GEOS-CHEM) are integrated in order to develop a physical and fully transferable model for estimating ground level PM 2.5 concentrations.; This work makes a valuable contribution by introducing satellite remote sensing technology as a quantitative tool in long-term air pollution monitoring. Due to their excellent performance and near global coverage, the empirical and physical models established in this work can be used to extend existing ground monitoring networks and provide a cost-effective data source to power long-term health studies in both developing and developed countries.