Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach

This paper applies artificial neural network to predict hourly air pollutant concentrations near an arterial in Guangzhou, China. Factors that influence pollutant concentrations are classified into four categories: traffic-related, background concentration, meteorological and geographical. The hourly averages of these influential factors and concentrations of carbon monoxide, nitrogen dioxide, particular matter and ozone were measured at three selected sites near the arterial using vehicular automatic monitoring equipments. Models based on back-propagation neural network were trained, validated and tested using the collected data. It is demonstrated that the models are able to produce accurate prediction of hourly concentrations of the pollutants respectively more than 10 h in advance. A comparison study shows that the neural network models outperform multiple linear regression models and the California line source dispersion model.