Artificial neural network based line source models for vehicular exhaust emission predictions of an urban roadway
نویسندگان
چکیده
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.
منابع مشابه
Line source emission modelling
Line source emission modelling is an important tool in control and management of vehicular exhaust emissions (VEEs) in urban environment. The US Environmental Protection Agency and many other research institutes have developed a number of line source models (LSMs) to describe temporal and spatial distribution of VEEs on roadways. Most of these models are either deterministic and/or statistical ...
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