Comparative Study of Gaussian and Non-Gaussian Models in Evaluation of Pollutant Dispersion

Abstract

Different degrees of accuracy for Gaussian and non-Gaussian models were analyzed for the evaluation of dispersion processes with homogeneous or spatial dependent dispersion coefficients that were described by different sigma schemes. The aim of this study was to present and investigate a comparison between Gaussian and non-Gaussian models for simulation of pollutant dispersion in the PBL, considering the effect of meteorological parameters. Downwind concentrations of I-131 were measured through five experiments at different meteorological conditions. Observed data were compared with that predicted using Gaussian and non-Gaussian calculations. Models performances were evaluated using different sigma schemes estimation. Results show that non-Gaussian calculations perform much better than Gaussian as Gaussian models have shown to be unreliable at closer range, i.e. at few hundred meters away from the source. At high wind speed, all approaches in case of non-Gaussian calculations perform much better than Gaussian. Power law function methods show reasonable estimates within factors of 1.2 to 2.4 in case of Gaussian and 0.25 to 0.86 in non-Gaussian application. In moderate wind speed, Brigg’s formula (in non-Gaussian) provides reasonable estimates of downwind concentration and has been shown to be accurate to within factors of 0.24 to 1.76 when compared observed data. Although Gaussian models works reasonably not good during weak and variable wind conditions, split sigma shows equitable estimates within factors of 0.5 to 1.08 in low wind speed with Gaussian application. In general, uncertainty increases as going downwind far from the source and decreases with increasing atmospheric stability.