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 parameters from observed concentrations. The estimation tool is tested with the Fusion Field Trial 2007 (FFT-07) data set.