A city-scale inverse modelling method for air pollutant source determination

Abstract

Urban air pollution is a major environmental hazard. Accurate and rapid identification of pollutant sources is a crucial component of efficient strategies to protect public health. The mostly used back-trajectory analysis and source-receptor likelihood function have limitations in real scenarios. Inverse prediction models such as the adjoint probability method have shown viability in indoor environment. A novel application of this method is conducted to determine the air pollutant source at city-scale. The adjoint probability method is physically deduced, parameterized, and first implemented in the Weather Research and Forecasting (WRF) model coupled with building effect parameterization (BEP) and building energy model (BEM). The predictive performance of the method is investigated using an ideal case and a real urban case study in the city of Hong Kong. The results show that the proposed method can precisely identify both the source location and strength. The main distinction of the method is the fast and accurate determination of the potential source with limited known information collected from three mobile sensors or three fixed sensors. The results of this study can provide insights on sensor distribution and sensor selection in the urban environment. The method of this study can be extended to other source-term problems.