Application Of A Bayesian Inference Method To Reconstruct Short-Range Atmospheric Dispersion Events

In the event of an accidental or intentional release of chemical or biological (CB) agents into the atmosphere, first responders and decision makers need to rapidly locate and characterize the source of dispersion events using limited information from sensor networks. In this study the stochastic event reconstruction tool (SERT) is applied to a subset of the Fusing Sensor Information from Observing Networks (FUSION) Field Trial 2007 (FFT 07) database. The inference in SERT is based on Bayesian inference with Markov chain Monte Carlo (MCMC) sampling. SERT adopts a probability model that takes into account both positive and zero‐reading sensors. In addition to the location and strength of the dispersion event, empirical parameters in the forward model are also estimated to establish a data‐driven plume model. Results demonstrate the effectiveness of the Bayesian inference approach to characterize the source of a short range atmospheric release with uncertainty quantification.