A case study for assessing uncertainty in local-scale regulatory air quality modeling applications

In this paper, we expand upon established uncertainty analysis techniques to demonstrate a general method for assessing variability and uncertainty in Gaussian air pollutant dispersion modeling systems. To illustrate this method, we estimated variability and uncertainty in predicted hexavalent chromium concentrations generated by welding operations at a shipbuilding and repair facility in California. Using Monte Carlo statistical techniques, we propagated uncertainty across both ISCST3 and AERMOD, and estimated the contribution of variability and uncertainty from four model components: emissions, spatial and temporal allocation of emissions, model parameters, and meteorology. Our results indicated the 95% confidence interval uncertainty at each receptor spanned an order of magnitude. From a practical perspective uncertainty is most important at receptors with highest predicted concentrations. In this case study, emissions were the primary source of uncertainty. However, Gaussian models are also sensitive to location of emission releases, meteorology, and model parameters. Simplified modeling approaches may lead to errors in pollutant concentration estimates, especially in close proximity to emissions sources where predicted concentrations are highest.