Uncertainty propagation of meteorological and emission data in modeling pollutant dispersion in the atmosphere

Variability is true heterogeneity existing within a population that cannot be reduced or eliminated by more or better determinations. Uncertainty represents ignorance about poorly characterized phenomena, but it can be reduced by collecting more data. The aim of this paper was to study the impact of the variability and uncertainty of the main variables, i.e., emissions and meteorology, of the PM10 concentration caused by a point source located at Malagueno (Cordoba, Argentina). To perform this analysis, a scheme was developed using the USEPA Industrial Source Complex model algorithms with a Monte Carlo methodology. Using a simulation with one hundred thousand iterations, the concentration distribution was obtained and showed that the uncertainty in wind direction had the greatest impact on the estimates.