Uncertainty and sensitivity analysis in code environmental model for risk assessments from industrial source complex

The objectives of paper are the application of uncertainty and sensitivity analysis methods in atmospheric dispersion modeling to case study for predicting the dispersion of pollutants in the atmospheric environment. Gaussian Plume Model is used to study the impact of meteorology on the dispersion of emissions from industrial source complex. Quantitative uncertainty analysis has become a common component of risk assessments. Uncertainties were defined a priori in each of the following variables: wind speed, wind direction and pollutant emission rate. In order to get information about the uncertainty of computer code results, a number of code runs have to be performed using tolerance limits method. Monte Carlo method is used for propagating uncertainty across of code. Spearman rank correlation coefficient is used as sensitivity measure.