Stochastic Event Reconstruction of Atmospheric Contaminant Dispersion using Bayesian Inference
This is an author-produced, peer-reviewed version of this article. The final, definitive version of this document can be found online at Atmospheric Environment , published by Elsevier. Copyright restrictions may apply. doi: 10.1016/j.atmosenv.2008.05.024
Environmental sensors have been deployed in various cities for early detection of contaminant releases into the atmosphere. Event reconstruction and improved dis- persion 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 with Markov chain Monte Carlo (MCMC) sampling. Fast-running Gaussian plume dispersion models are adopted as the forward model in the Bayesian inference approach to achieve rapid-response event reconstructions. The Gaussian plume model is substantially enhanced by in- troducing stochastic parameters in its turbulent diffusion parameterizations and estimating them within the Bayesian inference framework. Additionally, parame- ters of the likelihood function are estimated in a principled way using data and prior probabilities to avoid tuning in the overall method, The event reconstruction method is successfully validated for both real and synthetic dispersion problems, and posterior distributions of the model parameters are used to generate probabilistic plume envelopes with specified confidence levels to aid emergency decisions.
Inanc Senocak, Nicolas W. Hengartner, Margaret B. Short, and W. Brent Daniel. "Stochastic Event Reconstruction of Atmospheric Contaminant Dispersion using Bayesian Inference" Atmospheric Environment 42.33 (2008): 7718-7727.
Available at: http://works.bepress.com/inanc_senocak/1