Uncertainty quantification and atmospheric source estimation with a discrepancy-based and a state-dependent adaptative MCMC.

Autor: Albani RAS; Polytechnic Institute, Rio de Janeiro State University, 28.625-570, Nova Friburgo, Brazil. Electronic address: roseanealves75@gmail.com., Albani VVL; Dept of Mathematics, Federal University of Santa Catarina, Campus Trindade, 88.040-900 Florianopolis, Brazil., Migon HS; Polytechnic Institute, Rio de Janeiro State University, 28.625-570, Nova Friburgo, Brazil; Institute of Mathematics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil., Silva Neto AJ; Polytechnic Institute, Rio de Janeiro State University, 28.625-570, Nova Friburgo, Brazil.
Jazyk: angličtina
Zdroj: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2021 Dec 01; Vol. 290, pp. 118039. Date of Electronic Publication: 2021 Aug 27.
DOI: 10.1016/j.envpol.2021.118039
Abstrakt: We address the source characterization of atmospheric releases using adaptive strategies in Bayesian inference in combination with the numerical solution of the dispersion problem by a stabilized finite element method and uncertainty quantification in the measurements. The adaptive techniques accelerate the convergence of Monte Carlo Markov Chain (MCMC) algorithms, leading to accurate reconstructions of the source parameters. Such accuracy is illustrated by the comparison with results from previous works. Moreover, the technique used to simulate the corresponding dispersion problem allowed us to introduce relevant meteorological information. The uncertainty quantification also improves the quality of reconstructions. Numerical examples using data from the Copenhagen experimental campaign illustrate the effectiveness of the proposed methodology. We found errors in reconstructions ranging from 0.11% to 8.67% of the size of the search region, which is similar to results found in previous works using deterministic techniques, with comparable computational time.
(Copyright © 2021 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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