Stochastic approach to study the atmospheric pollutants in the urban area of Rome

The aim of this article is to present some results obtained by adapting ‘structural” and ‘temporal series’ models to data on atmospheric chemical contaminants detected by stations for continuous monitoring of the Istituto Superiore di Sanità (ISS), located in an urban area of Rome. The reported results allow us to quantify the interrelation among contaminants, periodicity, persistence, behavioral trend over 24 h with particular reference to carbon monoxide (CO) and its correlations with benzene. The results are in agreement with the information available on the traffic flow in this area. The present work, which constitutes part of methodological research on the treatment of data supplied by monitoring stations points out that by using mathematical models it is possible to produce consistent and reliable information, reproducible in space and time, on the basis of actual data on contaminants. The results obtained point towards the advantage of using the predictive model for CO.