Keywords: Bayesian analysis, conditional autoregressive models, covariance modelling, GPM, Gaussian plume model, Kincaid tracer experiment, non-stationary covariance, WinBUGS, MCMC, Markov chain Monte Carlo, atmospheric dispersion modelling, air pollution, air quality, uncertainty
Uncertainty adjustments to deterministic atmospheric dispersion models
Atmospheric Dispersion Models (ADMs) are routinely used in environmental impact assessment, risk analysis, and source apportionment studies. There are a variety of such computational ADMs, but these models usually only provide deterministic predictions or estimation of uncertainty. By introducing error components in ADMs, we formulate statistical modelling to obtain more precise prediction. These error components are based on the default neighbourhood structures created by the point source and already recognised by ADMs. Application is made to a real dataset. Posterior inference and model choice are assessed via Markov Chain Monte Carlo techniques, deviance information criterion, and mean squared predicted error.