Modelling The Effects Of Traffic Emissions On The Air Quality
Free (open access)
G. Genon & E. Brizio
European directives 1999/30/CE and 2000/69/CE set severe limits for ambient concentrations of sulphur dioxide, nitrogen dioxide, fine particles, lead, benzene and carbon monoxide; in particular, the concentrations of PM10 and nitrogen dioxide, measured in urban areas (even in towns of 20,000 inhabitants) of Northern Italy, often don’t respect the air quality limits. The forementioned pollution levels are heavily determined by traffic emissions. In order to match the imposed limits, public administrations have to plan strong actions such as the restriction of car circulation; therefore, it is very important to have at their disposal a reliable instrument to model the effects of different traffic restriction policies on the air quality. In the present paper we modelled the effects of the traffic emissions on the air quality for two streets of a 50,000-inhabitant town in Piedmont, NW Italy, by means of different atmospheric dispersion models, the Industrial Source Complex 3 model (U.S. EPA), AERMOD (U.S. EPA) and the Operational Street Pollution Model (N.E.R.I., Denmark). We also had at our disposal measured concentrations for the pollutants CO and NOx in the analysed streets, so we could validate the results. We obtained the best results by applying the OSPM model: the mean deviation from the measured concentrations of CO came out between 50 % and 80 %, according to the analysed period, while ISC3 and AERMOD results were 10–15 % worse. Consequently, the OSPM model has been applied to foresee air quality scenarios corresponding to different traffic restriction policies. Keywords: traffic, emissions, street, OSPM, concentration. 1 Introduction The pollution levels (in particular PM10 and NO2) measured in Northern Italy are very high because of the low wind conditions of the Po Valley that don’t help the dilution of the pollutants. Industrial emissions, civil heating and traffic are held
traffic, emissions, street, OSPM, concentration.