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


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




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


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.


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Biografia do Autor

Roseane Albani

IPRJ/UERJ, Nova Friburgo, RJ

Hélio S. Migon

IPRJ/UERJ, Nova Friburgo, RJ

Antônio J. Silva Neto

IPRJ/UERJ, Nova Friburgo, RJ

Vinicius Albani

LAMMCA/Dept. Matemática/UFSC, Florianópolis, SC


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