Source term estimation for continuous plume dispersion in Fusion Field Trial-07: Bayesian inference probability adjoint inverse method

Hong-Liang Zhang; Bin Li; Jin Shang; Wei-Wei Wang; Fu-Yun Zhao

doi:10.1016/j.scitotenv.2023.169802

Source term estimation for continuous plume dispersion in Fusion Field Trial-07: Bayesian inference probability adjoint inverse method

Sci Total Environ. 2024 Mar 10:915:169802. doi: 10.1016/j.scitotenv.2023.169802. Epub 2024 Jan 11.

Authors

Hong-Liang Zhang¹, Bin Li¹, Jin Shang¹, Wei-Wei Wang¹, Fu-Yun Zhao²

Affiliations

¹ School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China.
² School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China; School of Civil Engineering, Hunan University of Technology, Zhuzhou, Hunan Province, PR China. Electronic address: fyzhao@whu.edu.cn.

PMID: 38215839
DOI: 10.1016/j.scitotenv.2023.169802

Abstract

In scenarios involving sudden releases of unidentified gases or concealed pollution emergencies, source control emerges as a critical procedure to safeguard residential air quality. Appropriate inverse source tracking methodology depending on diverse measurement data could be utilized to promptly identify pollutant source parameters. In this study, source term estimation (STE) method, i.e., jointly combining probability adjoint method with the Bayesian inference method, has been proposed. General form of the pollutant inverse transport equation was firstly established. Subsequently, the pollution source information, assumed from single continuous point releases during Fusion Field Trials 2007 under an unsteady wind field, was identified using the Bayesian inference probability adjoint inverse method. Metropolis-Hastings Markov Chain Monte Carlo (MH-MCMC) and Differential Evolution Markov Chain Monte Carlo (DE-MCMC) were then compared as sampling methods for Bayesian inference. Results indicated that the DE-MCMC algorithm has superior convergence and could present higher accuracy of pollutant source information than that of MH-MCMC algorithm, particularly for highly nonlinear and multi-modal distribution systems. Furthermore, the integration of Union standard Adjoint Location Probability (UALP) as prior information into the Bayesian inference probability adjoint inverse method effectively narrowed the sampling range, enhancing both the accuracy and robustness of the proposed approach. Finally, the impact of the covariance matrix on the inverse identification accuracy was explored. Overall, this research has provided insights into the future applicability of this Bayesian inference inversion technique for point source identification.

Keywords: Atmospheric dispersion; Bayesian inference; Dual adjoint equations; Probability adjoint method; Source term estimation.