Prediction of air pollution peaks generated by urban transport networks

This paper illustrates the first results of an ongoing research for developing novel methods to analyse and simulate the relationship between trasport-related air pollutant concentrations and easily accessible explanatory variables. The final scope of the analysis is to integrate the new models in traditional traffic management decision-support systems for a sustainable mobility of road vehicles in urban areas. This first stage concerns the relationship between the mean hourly concentration of nitrogen dioxide and explanatory factors like traffic and weather conditions, with particular reference to the prediction of pollution peaks, defined as exceedances of normative concentration limits. Two modelling frameworks are explored: the Artificial Neural Network approach and the ARIMAX model. Furthermore, the benefit of a synergic use of both models for air quality forecasting is investigated. The analysis of findings points out that the prediction of extreme pollutant concentrations is best performed by the integration of the two models into an ensemble. The neural network is outperformed by the ARIMAX model in foreseeing peaks, but gives a more realistic representation of the relationships between concentration and wind characteristics. So, it can be exploited to direct the ARIMAX model specification. At last, the study shows that the ability at forecasting exceedances of pollution regulative limits can be enhanced by requiring traffic management actions when the predicted concentration exceeds a threshold that is pretty high but lower than the normative one