Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme through sensitivity analysis combined with optimization model

Shushuai Mao; Jianlei Lang; Tian Chen; Shuiyuan Cheng

doi:10.1016/j.envpol.2021.117186

Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme through sensitivity analysis combined with optimization model

Environ Pollut. 2021 Sep 1:284:117186. doi: 10.1016/j.envpol.2021.117186. Epub 2021 Apr 19.

Authors

Shushuai Mao¹, Jianlei Lang², Tian Chen¹, Shuiyuan Cheng¹

Affiliations

¹ Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China.
² Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China; Beijing Laboratory for Intelligent Environmental Protection, Beijing University of Technology, Beijing, 100124, China. Electronic address: jllang@bjut.edu.cn.

PMID: 33906039
DOI: 10.1016/j.envpol.2021.117186

Abstract

Estimating accurately airborne pollutant emissions source information (source strength and location) is important for achieving effective air pollution management or adequate emergency responses to accidents. Inversion method is one of the useful tools to identify the source parameters. The atmospheric dispersion scheme has been proven to be the key to determining the source inversion performance by influencing the accuracy of the dispersion models. Modifying the atmospheric dispersion scheme is an important potential method to improve the inversion performance, but this has not been studied previously. To fill this gap, a novel approach for parameter sensitivity analysis combined with an optimization method was proposed to improve the source inversion performance by optimizing empirical scheme. The dispersion coefficients σ_y and σ_z of the typical BRIGGS scheme under different atmospheric dispersion conditions were optimized and used for air pollutant dispersion and source inversion. The results showed that the prediction performance of the air pollutant concentrations was greatly improved with statistical indices |FB| and NMSE decreased by 0.22 and 2.07, respectively; FAC2 and R increased by 0.10, and 0.08, respectively. For source inversion, the results of the significance analysis suggested that the accuracy in the source strength and location parameter (x0) were both significantly improved by ∼271% (relative deviation reduced from 60.0% to 16.2%) and ∼121% (absolute deviation reduced from 27.6 to 12.5 m). The improvement of source strength inversion accuracy was more significant under unstable atmospheric conditions (stability class A, B, and C); the mean absolute relative deviation was reduced by 97.5%. These results can help to obtain more accurate source information and to provide reliable reference for air pollution managements or emergency response to accidents. This study provides a novel and versatile approach to improve estimation performance of pollutant emission sources and enhances our understanding of source inversion.

Keywords: Air pollutant emissions; Atmospheric dispersion coefficients; Dispersion scheme optimization; Sensitivity analysis; Source inversion.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Environmental Monitoring
Environmental Pollutants*

Substances

Air Pollutants
Environmental Pollutants