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Urban air quality nowadays is one of the major environmental issues, due to its impact to&#x0D;various health problems, caused by the daily exposure of the population in dangerous air&#x0D;pollutants. The highest levels of air pollutants are usually observed in street canyons, emitted&#x0D;by urban traffic. To understand the way that pollutants dispersed in a street canyon&#x0D;environment, various modeling techniques are used. Both the accuracy of the predictions and&#x0D;the quickness of the calculations, are significant factors to adopt a modeling technique.&#x0D;In the present study, we used benzene as an indicator of traffic pollution. Two different&#x0D;modeling techniques, an artificial neural network (ANN) and a semi empirical deterministic&#x0D;model (DET) are used, to predict benzene concentrations in a street canyon environment.&#x0D;The ANN was based on a training procedure using measurements collected in a specific&#x0D;street canyon (benzene concentrations, traffic density, vehicle’s type distribution), while the&#x0D;DET model was based on road traffic emission rate, wind speed and direction, and the&#x0D;geometrical characteristics of the road.&#x0D;After the validation of models, their response to preselected “what if” scenarios was&#x0D;attempted.&#x0D;Although both models produced very good results, given the limited amount of data available,&#x0D;the ANN succeeded slightly better than DET in predicting benzene concentrations. On the&#x0D;contrary, ANN presents lower response in predicting the effect of significant changes in traffic&#x0D;flow patterns on benzene concentrations.&#x0D;The results from the simulations indicate that the ANN is a promising technique for benzene&#x0D;modeling in an urban environment, since DET seems to be ideal for environmental&#x0D;management purposes.

Assessment and prediction of benzene concentrations in a street canyon using a variety of models, in the means of environmental management purposes