Abstract
The objectives of this paper are the application of uncertainty and sensitivity analysis methods in
atmospheric dispersion modeling to study the prediction of the dispersion of pollutants in the
atmosphere. The Gaussian Plume Model is used to study the impact of meteorology on the dispersion
of the emissions from an industrial source complex. The determination of ground-level concentration
and maximum ground-level concentration is useful for the prediction of violations of air quality
regulations. The Industrial Source Complex Short-Term (ISCST-3) air pollution model was adopted to
predict the ground-level concentration of sulfur dioxide (SO2) emitted by a power plant located in an
industrial region site in Spain. Quantitative uncertainty analysis has become a common component of
risk assessments. Uncertainties were defined a priori for each of the following variables: wind speed,
wind direction, and pollutant emission rate. In order to obtain information about the uncertainty of
computer code results, a number of code runs was performed using the nonparametric tolerance limits
method. The Monte Carlo method was used to propagate uncertainty across codes. The Spearman rank
correlation coefficient was used as a sensitivity measure.
