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Uncertainty quantification and atmospheric source estimation with a discrepancy-based and a state-dependent

Roseane A S Albani1, Vinicius V L Albani2, Hélio S Migon3

  • 1Polytechnic Institute, Rio de Janeiro State University, 28.625-570, Nova Friburgo, Brazil.

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Summary

This study enhances atmospheric release source characterization using adaptive Bayesian inference and a stabilized finite element method. The approach improves accuracy and incorporates meteorological data for reliable source parameter reconstructions.

Keywords:
Atmospheric dispersionMCMC algorithmsMetropolis in Gibbs SamplerSource estimationUncertainty quantification

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Computational Science

Background:

  • Accurate source characterization of atmospheric releases is crucial for environmental monitoring and emergency response.
  • Traditional methods often face challenges with computational efficiency and incorporating real-world data complexities.

Purpose of the Study:

  • To develop and validate an adaptive Bayesian inference methodology for atmospheric release source characterization.
  • To integrate numerical dispersion modeling with uncertainty quantification for improved reconstruction accuracy.
  • To assess the methodology's performance using experimental data.

Main Methods:

  • Adaptive strategies were employed to accelerate Monte Carlo Markov Chain (MCMC) algorithms.
  • A stabilized finite element method was used for the numerical solution of the atmospheric dispersion problem.
  • Uncertainty quantification was applied to measurement data to enhance reconstruction quality.

Main Results:

  • The adaptive techniques significantly accelerated MCMC convergence, leading to accurate source parameter reconstructions.
  • The numerical dispersion simulation successfully incorporated relevant meteorological information.
  • Reconstruction errors ranged from 0.11% to 8.67% of the search region, comparable to deterministic techniques.
  • The methodology demonstrated effectiveness using data from the Copenhagen experimental campaign.

Conclusions:

  • The proposed adaptive Bayesian inference methodology provides an effective and accurate approach for atmospheric release source characterization.
  • Integration of advanced numerical methods and uncertainty quantification enhances the reliability of source parameter estimation.
  • The findings offer a valuable tool for environmental monitoring and risk assessment applications.