Solving Environmental Retrieval Problems via Mathematical Modeling, Satellite Informatics and Statistical Derivation

Abstract

Combating with environmental pollution problems and reducing human exposure to airborne chemicals are important global missions. Existing monitoring network for pollutant measurements are currently too sparse, thus may not effectively capture and detect spatial changes occurred in places with mixed land use patterns. In this talk, we will discuss several environmental retrieval problems by combining different mathematical approaches, including the use of satellite remote sensing for tropospheric NO2 retrieval within China, combination of statistical regression algorithms and modeling techniques in ground NO2 retrieval, as well as the development of a real-time and urban air quality modeling system for analyzing and forecasting air quality in Hong Kong, down to individual street level. A recently developed app, PRAISE-HK will be introduced at the end of this talk