Evaluation of the ADMS, AERMOD and ISC3 Models with the Optex, Duke Forest, Kincaid, Indianapolis and Lovett Field Data Sets
The model evaluation exercise addresses the question whether the new models, ADMS and AERMOD, produce improvements over ISC3 when compared with a range of field observations. ADMS and AERMOD have similar state-of-the-art scientific components, whereas ISC3 contains 1960s technology. The five sets of field observations used in the statistical evaluation represent a cross-section of typical scenarios encountered by modelers. The OPTEX data base concerns non-buoyant tracer releases within an oil refinery complex, and the Duke Forest data base involves non-buoyant tracer releases from area and volume sources in an open field. The Kincaid, Indianapolis, and Lovett data bases all deal with buoyant plumes from tall stacks at power plants. However, the settings are quite different, since the Kincaid plant is surrounded by flat farmland, the Indianapolis plant is located in an urban environment, and the Lovett plant is sited in a valley surrounded by complex terrain with monitors at elevations higher than the stack. Analysis of the model performance measures suggest that ISC3 typically overpredicts, has a scatter of about a factor of three, and has about 33% of its predictions within a factor of two of observations. The ADMS performance is slightly better than the AERMOD performance and both perform better than ISC3. On average, ADMS underpredicts by about 20% and AERMOD underpredicts by about 40%, and both have a scatter of about a factor of two. ADMS and AERMOD have about 53% and 46% of their predictions within a factor of two of observations, respectively. Considering only the highest predicted and observed concentrations, ISC3 overpredicts by a factor of seven, on average, while ADMS and AERMOD underpredict by about 20%, on average.