The Accuracy of Gaussian Plume Models and Sensitivity Study in Air Pollution Prediction

The Gaussian Plume Model (GPM) is widely used to predict the concentration of pollutants in the atmosphere. There are significant simplifications with the GPM. The assumptions used to derive the GPM should be understood in order to predict the uncertainties associated with modeling results. In this work, a sensitivity study was performed by assuming reasonable degrees of error in some of the key variables used in the GPM and determining the end results. It was shown that minor changes in some of the key parameters of GPM can result in a propagated over-prediction factor up to 30. Also, in using the GPM, a range of values for the vertical dispersion coefficient should be considered in determining the maximum possible concentration at ground level.