Estimating the number of atmospheric releases and other parameters by Bayesian inference

We propose a methodology to estimate unknown atmospheric releases, including the number of emissions, addressing overfitting, and using an economical number of unknowns. It is based on the combination of accurate modeling to solve the dispersion problem with Bayesian inference to identify the parame...

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Published inAir quality, atmosphere and health Vol. 17; no. 5; pp. 1007 - 1019
Main Authors Albani, Roseane A. S., Albani, Vinicius V. L., Gomes, Luiz E. S., Migon, Helio S., Silva Neto, Antonio J.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.05.2024
Springer Nature B.V
Subjects
Atmospheric Protection/Air Quality Control/Air Pollution
Bayesian analysis
Earth and Environmental Science
Environment
Environmental Health
Estimation
Health Promotion and Disease Prevention
Parameter identification
Statistical inference
Source estimation
Finite elements methods
Bayesian inference
Overfitting
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ISSN1873-9318
1873-9326
DOI10.1007/s11869-023-01497-9

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Summary:We propose a methodology to estimate unknown atmospheric releases, including the number of emissions, addressing overfitting, and using an economical number of unknowns. It is based on the combination of accurate modeling to solve the dispersion problem with Bayesian inference to identify the parameters from observed concentrations. The estimation tool is tested with the Fusion Field Trial 2007 (FFT-07) data set.
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ISSN:1873-9318
1873-9326
DOI:10.1007/s11869-023-01497-9