Mechanical Engineering Commons^™

Stochastic Event Reconstruction Of Atmospheric Contaminant Dispersion Using Bayesian Inference, Inanc Senocak, Nicolas W. Hengartner, Margaret B. Short, W. Brent Daniel

Mechanical and Biomedical Engineering Faculty Publications and Presentations

Environmental sensors have been deployed in various cities for early detection of contaminant releases into the atmosphere. Event reconstruction and improved dispersion modeling capabilities are needed to estimate the extent of contamination, which is required to implement effective strategies in emergency management. To this end, a stochastic event reconstruction capability that can process information from an environmental sensor network is developed. A probability model is proposed to take into account both zero and non-zero concentration measurements that can be available from a sensor network because of a sensor’s specified limit of detection. The inference is based on the Bayesian paradigm …