Source Reconstruction of Atmospheric Releases by Bayesian Inference and the Backward Atmospheric Dispersion Model: An Application to ETEX-I Data

Source term reconstruction methods attempt to calculate the most likely source parameters of an atmospheric release given measurements, including both location and release amount. However, source term reconstruction is vulnerable to uncertainties. In this paper, a method combining Bayesian inference with the backward atmospheric dispersion model is developed for robust source term reconstruction. The backward model is used to quantify the relationship between the source and measurements and to reduce the search range of the Bayesian inference. A Markov chain Monte Carlo method is used to sample from the multidimensional parameter space of the source term. The source location and release rate are estimated simultaneously, and the posterior probability distribution is produced by applying Bayes’ theorem. The proposed method is applied to a set of real concentration data from the ETEX-I experiment. The results demonstrate that the source location is estimated to be −2.86° ± 1.01°E, 48.25° ± 0.33°N, and the release rate is estimated to be 20.16 ± 3.56 kg/h. The true source location is correctly estimated to be within a one standard deviation interval, and the release rate is correctly determined to be within a three standard deviation interval.