Investigating the impacts of atmospheric diffusion conditions on source parameter identification based on an optimized inverse modelling method

Accurate identification of source parameters (source strength and location) of sudden air pollution accidents (SAPAs) is important for implementation of adequate responses. However, the potential impact of atmospheric diffusion conditions on source parameter identification may be significant. An inversion model that combines the hybrid particle swarm optimization and the Nelder–Mead simplex search method (PSO-NM) with the Gaussian dispersion model was proposed to identify the source parameters and to investigate the influences of different atmospheric conditions on the identifications. A case study based on 68 SO2 leakage tests from the Prairie Grass field experiment was conducted. The source strengths and locations of the 68 tests were estimated by the combined inversion model. The results indicated that the inversion model can effectively get accurate and robust source parameter estimations. The average absolute value of relative deviation of source strength was 13.8% ± 11.4%; the average absolute deviations for parameters x0, y0, z0 and the total distances were 18.9 ± 36.9 m, 2.7 ± 5.2 m, 3.5 ± 9.7 m and 19.6 ± 38.1 m, respectively. A comprehensive evaluation method was also proposed for analyzing the impacts of atmospheric conditions on source parameter estimations. The results showed that the source parameter estimations under atmospheric stability classes E and C have the best accuracy and robustness, followed by stability classes A and D; while the worst occurred under atmospheric stability classes B and F. The analysis results can provide scientific support for the formulation or adjustment of emergency response strategies used in sudden air pollution accidents. The new inversion model proposed is a supplement to the methodology of inversing source parameters.