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Area of Science:

  • Environmental Science
  • Nuclear Science
  • Bayesian Statistics

Background:

  • Accurate source term reconstruction is vital for environmental monitoring and nuclear safety.
  • Noble gas (Xenon-133) measurements from the International Monitoring System provide data for source localization.
  • Previous methods faced challenges in accurately modeling uncertainties.

Purpose of the Study:

  • To apply a Bayesian probabilistic inferential methodology for reconstructing the location and emission rate of a contaminant source.
  • To utilize limited Xenon-133 activity concentration measurements from the International Monitoring System.
  • To compare the performance of different measurement models in source parameter recovery.

Main Methods:

  • Bayesian probabilistic inferential methodology.
  • Markov chain Monte Carlo (MCMC) technique with a multiple-try differential evolution adaptive Metropolis algorithm.
  • Analysis of Xenon-133 measurements from three International Monitoring System stations.
  • Consideration of two distinct measurement models for incorporating model error.

Main Results:

  • Successful reconstruction of source parameters (location and emission rate) from a real-world emission (Chalk River Laboratories).
  • Identified model error specification as the primary challenge in Bayesian source term reconstruction.
  • Demonstrated the impact of different measurement models on the accuracy and precision of source parameter recovery.

Conclusions:

  • Bayesian inference provides a robust framework for contaminant source reconstruction.
  • Accurate characterization of model errors is essential for reliable source term estimation.
  • The choice of measurement model significantly influences the precision and accuracy of reconstructed source parameters.