Authors: Tae-Young Heo, Jacqueline M. Hughes-Oliver
Addresses: Department of Data Information, Korea Maritime University, Pusan, 606-791, South Korea. ' Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA
Abstract: 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.
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.
International Journal of Environment and Pollution, 2010 Vol.42 No.1/2/3, pp.85 - 106
Published online: 30 Jul 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article