Performance of an industrial source complex model: Predicting long‐term concentrations in an urban area

Abstract

AbstractThe short‐term and long‐term versions of Industrial Source Complex Models (ISCST3 and ISCLT3) are evaluated for estimating long‐term concentrations using sulfur dioxide data from emission inventory of Lucas County, Ohio for the year 1990. The ISCST3 and ISCLT3 models are used to predict monthly and quarterly averages of sulfur dioxide concentrations. Uncertainties associated with the model's predictions are estimated using the bootstrap resampling method. Confidence intervals on the fractional mean bias, normalized mean square error, geometric mean bias and geometric variance are calculated for each model and differences between models. Intercomparison of the ISCST3 and ISCLT3 models indicated that these models yielded relatively good performance in their prediction of monthly and quarterly average concentrations, with relative fractional biases of 0.26 to 0.55 and normalized mean square error values that are about 0.12 to 0.44. Both the ISCST3 and ISCLT3 models predicted concentrations that are lower than the observed concentrations. The concentrations predicted by the ISCST3 model were closer to the observed concentrations when compared with the concentrations obtained using the ISCLT3 model. The study suggests that the ISCST3 model is better for estimating long‐term concentrations of sulfur dioxide as compared to the ISCLT3 model.