Source term estimation in complex urban environments based on Bayesian inference and unsteady adjoint equations simulated via large eddy simulation

Abstract

Establishing an accurate source–receptor relationship is essential for identifying unknown sources of air pollution in complex urban environments. Existing source term estimation (STE) methods determine this relationship via steady simulations of adjoint equations with ensembled-averaged flow fields. However, the effect of turbulent diffusion on the dispersion of pollutants is simplified according to the gradient dispersion hypothesis, which may result in considerable STE errors, especially in the complex urban areas. Therefore, we developed a new STE method by embedding unsteady adjoint equations modeled via large eddy simulation (LES) into a Bayesian inference framework. The tremendous storage requirement of the unsteady simulation was mitigated using a wavelet-based compression method. The performance of the proposed method was then evaluated based on dispersion measurements collected in a wind tunnel experiment for a regular, block-arrayed building group model. The estimation results were compared with those derived from an existing method, in which the steady simulation of adjoint equations was employed based on the mean LES flow field. Our results suggest that the LES-based approach significantly improved the modeling accuracy of the adjoint equations and the resulting STEs. If the 50th percentile of the posterior probability was regarded as the point estimate, the absolute errors in source location and strength were reduced by 89% and 99%, respectively, when compared to the existing method.