An integrated low-cost road traffic and air pollution monitoring platform to assess vehicles' air quality impact in urban areas

In this work, a statistical analysis was performed to investigate the influence of local road traffic and meteorological conditions on CO, NO2 and CO2 concentrations. Two different methods were applied: a linear regression model and an artificial neural network. To investigate the role played by emissions from road traffic, the influence of all drivers by period of the year (cold vs. warm months) and day of the week (weekdays vs. weekends) was analysed. As a result, the contribution of local road traffic on pollutant concentrations proved to be lower than meteorological parameters.