Assessment on fast simulation of wind-driven pollutant dispersion around a street canyon with regime-switching Markov chain

Abstract

High-density cities shorten commutes but also create proximity to air pollution sources. Sensors coupled with simulations can achieve real-time monitoring and risk assessment, and the recently emerging data-driven models provide opportunities that allow minimizing the computation burden of physical models. This study investigates the use of a time inhomogeneous Markov chain (CLF-MC) to fast simulation of wind-driven pollutant dispersion around a street canyon. The CLF-MC model is trained and tested using synthetic datasets obtained from large eddy simulations (LES). The LES model is validated using a benchmark wind tunnel test, which yields correlation coefficients above 0.95. The results show that the CLF-MC can faithfully reproduce the cumulative distribution of air pollution concentrations at the receptor site using the optimal model configuration identified in this study. For the hold-out test dataset, the release condition is a time-varying release strength, and the CLF-MC shows improved prediction results compared to the baseline models, i.e. the weighted average deviation is 15 % and 51 % smaller, respectively. In comparison to LES, the CLF-MC model is 4.8 × 106 times faster. These findings provide scientific references for fast and accurate wind-driven pollutant dispersion simulation using the probabilistic modelling approach.