Statistical evaluation of a new air dispersion model against AERMOD using the Prairie Grass data set

Abstract

In this work, the authors present a statistical assessment of two atmospheric dispersion models. One of them, AERMOD (American Meteorological Society/Environmental Protection Agency Regulatory Model), adopted by the U.S. Environmental Protection Agency, is widely used in many countries and here is taken as a baseline to assess the performance of a newly proposed model, MODELAR (Modelo Regulatório de Qualidade do Ar). In terms of parameterizations and modeling options, MODELAR is a somewhat simple model. It is currently being considered for adoption as the regulatory model in Paraná State, Brazil. The well-known Prairie Grass data set, already used in earlier evaluations of the same version of AERMOD analyzed here, was used to perform model assessment. The evaluations employed well-established statistical performance descriptors and techniques. The results indicate that MODELAR is a slightly better predictor, for the Prairie Grass data set, of concentrations under unstable conditions, whereas AERMOD has a better performance under near-neutral and stable conditions. Moreover, cases of severe overestimation and underestimation, as detected by the Factor of Two index, are clearly associated with extreme stability conditions (both unstable and stable), stressing the need for better parameterizations under these conditions. Statistical evaluation of a proposed numerical dispersion model, called MODELAR, and of the well-established dispersion model AERMOD was performed, using the Prairie Grass data set as reference. The results showed that MODELAR performs well under unstable conditions, making it competitive with other dispersion models. However, the results also showed that both models require improvement of the dispersion parameterizations for extreme stability conditions, both unstable and stable. © 2014 Copyright 2013 A&WMA.