An Adjoint Probability Inverse Modelling Method for Air Pollutant Source Determination with Applications to a Complex Urban Environment

Urban air pollution is one of the major environmental issues. Accurate and rapid identification of air pollutants sources is crucial to adopt efficient strategies to protect public health and to mitigate the harmful effects. Traditional “trial-error” process is time consuming and is incapacity in distinguishing multiple potential sources, which is common in urban pollution. Inverse prediction methods such as probability based adjoint modelling method have shown viability for locating indoor contaminant sources. This paper is a step forward of the adjoint probability method to track constant release of outdoor pollutant source. This study is a step forward in implementation of adjoint probability method in mesoscale weather research and forecasting (WRF) model coupled with multilayer urban canopy (BEP_BEM) model to investigate the possibility of fast and accurate identification of unknown atmospheric releases with limited information collected from limited sensors at an urban scale. Two numerical field experiments are conducted to illustrate and verify the predictions: one in an open space and the other in a real urban case in a high rise and high compact city of Hong Kong. The developed algorithm promptly and accurately identifies the source locations in both cases.