A Bayesian Inference Approach for the Identification of Multiple Atmospheric Emissions With Uncertainty Quantification

Albani, Roseane; Albani, Vinicius; Neto, Antônio José da Silva; MIGON, HELIO

doi:10.29327/154013.24-62

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2022

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Cited by 1 publication

(3 citation statements)

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“…If we compare the present results with those from Ref. [3], we observe that they are more accurate. This is probably due to the improvements made in the modelling of the forward operator, that considers the dynamic change in the wind direction.…”

Section: Resultssupporting

confidence: 67%

“…Such changes considerably improved estimates if we compare with those from Ref. [3]. However, further improvements are still needed in order to find more accurate results.…”

Section: Discussionmentioning

confidence: 81%

“…These sets are defined based on the wind direction and on the isopleth in Figure 1 in Ref. [3]. It is worth mentioning that the true source locations are inside the resulting searching region.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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

Albani¹,

Migon²,

Neto³

et al. 2022

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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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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Section: Resultssupporting

confidence: 67%

“…Such changes considerably improved estimates if we compare with those from Ref. [3]. However, further improvements are still needed in order to find more accurate results.…”

Section: Discussionmentioning

confidence: 81%