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A bayesian inference procedure based on inverse dispersion modelling for source term estimation in built-up environments
Abstract In atmospheric physics, reconstructing a pollution source is a challenging and important question. It provides better input parameters to dispersion models, and gives useful information to first-responder teams in case of an accidental toxic release. Various methods already exist, but using them requires an important amount of computational resources, especially when the accuracy of the dispersion model increases which is necessary in complex built-up environments. In this paper, a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source is proposed. More precisely, an Adaptive Multiple Importance Sampling (AMIS) algorithm is considered and enhanced by an efficient use of a Lagrangian Particle Dispersion Model (LPDM) in backward mode. Twin experiments empirically demonstrate the efficiency of the proposed inference strategy in very complex cases.
Highlights Source term estimation is at stakes in case of surreptitious accidental or malicious releases into the air. A Bayesian solution is designed to quantify all the statistical information regarding a pollutant source term. Strategies to reduce the overall time complexity of the proposed adaptive Monte-Carlo algorithm are proposed. Initialization of the adaptive algorithm is efficiently performed using output from a dispersion model run in a backward mode. The overall approach has been successfully verified for twin experiments of various releases in a complex urban environment.
A bayesian inference procedure based on inverse dispersion modelling for source term estimation in built-up environments
Abstract In atmospheric physics, reconstructing a pollution source is a challenging and important question. It provides better input parameters to dispersion models, and gives useful information to first-responder teams in case of an accidental toxic release. Various methods already exist, but using them requires an important amount of computational resources, especially when the accuracy of the dispersion model increases which is necessary in complex built-up environments. In this paper, a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source is proposed. More precisely, an Adaptive Multiple Importance Sampling (AMIS) algorithm is considered and enhanced by an efficient use of a Lagrangian Particle Dispersion Model (LPDM) in backward mode. Twin experiments empirically demonstrate the efficiency of the proposed inference strategy in very complex cases.
Highlights Source term estimation is at stakes in case of surreptitious accidental or malicious releases into the air. A Bayesian solution is designed to quantify all the statistical information regarding a pollutant source term. Strategies to reduce the overall time complexity of the proposed adaptive Monte-Carlo algorithm are proposed. Initialization of the adaptive algorithm is efficiently performed using output from a dispersion model run in a backward mode. The overall approach has been successfully verified for twin experiments of various releases in a complex urban environment.
A bayesian inference procedure based on inverse dispersion modelling for source term estimation in built-up environments
Septier, François (author) / Armand, Patrick (author) / Duchenne, Christophe (author)
Atmospheric Environment ; 242
2020-06-22
Article (Journal)
Electronic Resource
English
Turbulent Schmidt number for source term estimation using Bayesian inference
| British Library Online Contents | 2017
Turbulent Schmidt number for source term estimation using Bayesian inference
| British Library Online Contents | 2017