Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model

Abstract

The pollutant dispersion models of this work fall into two classes: physical and statistical. We propose a large-scale physical particle dispersion model and a dynamic version of the well-known Gaussian plume model, based on statistical filters. Both models are based on wind measurements, wind interpolations, and mass corrections of certain wind stations installed in an alpine valley in Carinthia/Austria. Every 10 minutes the wind field is updated, and the dispersion of the pollutant is calculated. Vegetations like forest and grassland are fully considered. The dispersion models are used to predict pollutant concentrations resulting from the emissions of a cement plant. Both models are compared to each other and give almost equivalent results. The great advantage of the statistical model is that it does not scale like the particle model with the number of emitters, but its computational burden is constant, no matter how many emitters are included in the model. To test and validate these models, we developed the R-package PDC using the CUDA framework for GPU implementation.