The dispersion characteristics of vehicular exhaust emissions on urban roadways are highly non-linear and the presence of 'traffic wake' adds complexities to the dispersion. Gaussian deterministic line source models may not then be able to explain variations in related meteorological and traffic characteristic variables. Artificial neural networks comprising of interconnected adaptive processing units have the capability to recognize the non-linearity present in incomplete or noisy data. One-hour average artificial neural network based carbon monoxide models are developed for two air quality control regions in Delhi city - a traffic intersection and an arterial road. Ten meteorological and six traffic characteristic variables are used in the model. The results demonstrate that neural network models are able to explain the effects of 'traffic wake' on the CO dispersion in the near field regions of a roadway. (A) "Reprinted with permission from Elsevier".
Abstract