Identification of Pollution Sources in Urban Wind Environments Using the Regularized Residual Method

Abstract

The scale of cities is increasing with continuous urban development. Effective methods, such as the source term estimation (STE) method, must be established for identifying the sources of air pollution in cities to prevent economic losses and casualties caused by pollutant leakage. Herein, methods for optimizing sensor configuration and identifying pollution sources are discussed, and an STE method based on the regularized minimum residual method is proposed. Urban wind environments were simulated using a computational fluid dynamics (CFD) model, and the results were compared with experimental data pertaining to the wind tunnel of an architectural ensemble to verify the model’s accuracy. The sensor layout was optimized using the simulated annealing (SA) algorithm and adjoint entropy, and the relationship between sensor responses and potential pollution sources was established using the CFD model. Pollutant concentrations measured using sensors were combined with the regularization method to extrapolate the pollution source strength, and the regularized minimum residual method was used to obtain the locations of the real pollution sources. The results show that compared with the Bayesian methods, the proposed method can more accurately identify pollution sources (100%), with a smaller source strength error of 2.01% for constant sources and one of 2.62% for attenuation sources.