A Bayesian Inference Model for the Estimation of Time-Dependent Pollutant Emissions

Roseane Albani, Hélio S. Migon, Antônio J. Silva Neto, Vinicius Albani

Resumo


Source identification methodologies use inverse problems techniques combined with a dispersion model and observational data to estimate relevant source parameters. This work proposes a time-dependent model to estimate source parameters of multiple point releases. The forward problem or dispersion model accounts for the time variation of the wind field using a Fourier series that best fits the wind field time series of the experimental data. The source parameters are estimated by an adaptive Monte Carlo Markov Chain algorithm.


Palavras-chave


Source Estimation; Atmospheric Dispersion; Bayesian Inference; Monte Carlo Markov; Chain Algorithms; Inverse Modeling.

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Referências


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DOI: https://doi.org/10.5540/03.2022.009.01.0228

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