Evaluation of the Industrial Source Complex short-term model in a large-scale multiple source region for different stability classes

Abstract

The original Industrial Source Complex Model (ISCST, 90346) and its latest version, the ISCST2 (92090), are evaluated for the 1- and 24-hour averaging periods, using six statistical parameters. The confidence limits on two parameters were obtained using the bootstrap, jacknife, seductive, and robust resampling techniques. Evaluation is conducted in a multiple point source environment and in all the different stability categories, using a year of meteorological data, emission inventory of the Lucas County, in Toledo, Ohio, and monitoring data from two nearby stations for the year 1987. This was representative of a real time situation in which the ISCST is generally applied for regulatory work. The sensitivity analysis shows that the ISC is a poor performing model in the 1-h and 24-h averaging period, neutral and stable categories, and modifications to it are necessary in order to improve its performance. Its performance changes depending on the chosen paired values. A better relative performance is observed in the unstable category relative to the neutral and stable categories. Improved model performance may be achieved by applying modifications to the physics on which the model is based.

Application of the United States environmental protection agency's AERMOD model to an industrial area

“…They are also used to support laws and/or regulations designed to protect air quality. The models have been the subject of extensive evaluation to determine their performance under a variety of meteorological conditions [1][2][3][4].…”

Application of computational fluid dynamics to dispersion of particulate matter emitted during the injection of biosolids on a farm field

“…To evaluate further the comparison, statistical parameters based on the work of Hanna et al [18], Gudivaka and Kumar [19], and Riswadkar and Kumar [20] were used. Four statistical parameters; normalized mean square error (NMSE), fractional bias (FB), correlation coefficient (R), and geometric mean (GM) were calculated.…”

A field study using open-path FTIR spectroscopy to measure and map air emissions from volume sources

“…These results agree with some previous evaluations of ISCST2 and AERMOD, which found underprediction by the models; these studies were conducted with the use of 1-h averages of meteorological and concentration data and point sample data. 30,36 In this study, both models underpredicted the measured concentrations of SF 6 when compared to the OP-FTIR spectrometer and point sample measurements. This underprediction indicates that corrections may be necessary when using these models and predicting contaminant concentrations over the distances examined in this study (near field, less than 150 m).…”