Evaluation of the AERMOD dispersion model as a function of atmospheric stability for an urban area

Abstract

The AERMOD dispersion model was used to compute ambient air concentrations of SO2 for 1-, 3-, and 24-h averaging periods using the emission inventory data for Lucas County, Ohio for the year 1990. The estimated concentrations were classified based on the stability parameter, Monin–Obukhov length (L), for the two monitoring stations located in the area. The data were divided into two atmospheric stability classes (stable and convective cases) as used in the AERMOD model. These categories were further grouped into five subcategories based on the value of L to learn about fine details of model performance. The model evaluation was done using several statistical parameters used in air quality studies. AERMOD did not yield a satisfactory performance in predicting 1- and 3-h average concentrations for the multisource region but showed a slightly better performance in predicting the 24-h concentrations using urban option for the land use parameters. The model had a tendency to underpredict in both the stable and convective cases. In the 24-h averaging period factor of two (Fa2) values suggested a better performance than fractional bias (FB). The model seemed to perform better for the Main Street station than the Collins Park station. Limited analysis using different land use parameters reported in the paper indicates that model performance may improve for certain cases. Other errors include the unavoidable scatter arising from differences between wind direction and actual transport for an averaging period, formulation of the model, and the emission inventory. The results of the study should be used cautiously because of the limited scope of the evaluation. Future work should focus on the role of land use parameters in predicting concentrations at the monitors and finding ways to quantify errors attributed to other factors. However, it is clear that more guidance is needed to apply the AERMOD model for multisource regions. Alternative schemes to divide the data should also be considered for analyzing model performance. © 2006 American Institute of Chemical Engineers Environ Prog, 2006