Table of Contents
International Scholarly Research Notices
Volume 2014, Article ID 507634, 12 pages
Research Article

Bayesian Inference for Source Reconstruction: A Real-World Application

1Defence Research and Development Canada, Suffield Research Centre, P.O. Box 4000 Stn Main, Medicine Hat, AB, Canada T1A 8K6
2Health Canada, Radiation Protection Bureau, 775 Brookfield Road, A.L. 6302A, Ottawa, ON, Canada K1A 1C1

Received 9 May 2014; Revised 13 June 2014; Accepted 14 June 2014; Published 25 September 2014

Academic Editor: Ka-Veng Yuen

Copyright © 2014 Her Majesty the Queen in Right of Canada. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper applies a Bayesian probabilistic inferential methodology for the reconstruction of the location and emission rate from an actual contaminant source (emission from the Chalk River Laboratories medical isotope production facility) using a small number of activity concentration measurements of a noble gas (Xenon-133) obtained from three stations that form part of the International Monitoring System radionuclide network. The sampling of the resulting posterior distribution of the source parameters is undertaken using a very efficient Markov chain Monte Carlo technique that utilizes a multiple-try differential evolution adaptive Metropolis algorithm with an archive of past states. It is shown that the principal difficulty in the reconstruction lay in the correct specification of the model errors (both scale and structure) for use in the Bayesian inferential methodology. In this context, two different measurement models for incorporation of the model error of the predicted concentrations are considered. The performance of both of these measurement models with respect to their accuracy and precision in the recovery of the source parameters is compared and contrasted.