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Advances in Meteorology
Volume 2012 (2012), Article ID 609328, 16 pages
http://dx.doi.org/10.1155/2012/609328
Research Article

Modeling the Short-Term Effect of Traffic and Meteorology on Air Pollution in Turin with Generalized Additive Models

1Department of Biella, Environmental Protection Agency of Piemonte, 13900 Biella, Italy
2Department of Applied Mathematics, University of Colorado-Boulder, Boulder, CO 80309, USA
3Department of Economics, University of Turin, 10123 Turin, Italy

Received 15 February 2012; Revised 27 May 2012; Accepted 9 June 2012

Academic Editor: Tareq Hussein

Copyright © 2012 Pancrazio Bertaccini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Vehicular traffic plays an important role in atmospheric pollution and can be used as one of the key predictors in air-quality forecasting models. The models that can account for the role of traffic are especially valuable in urban areas, where high pollutant concentrations are often observed during particular times of day (rush hour) and year (winter). In this paper, we develop a generalized additive models approach to analyze the behavior of concentrations of nitrogen dioxide (NO2), and particulate matter (PM10), collected at the environmental monitoring stations distributed throughout the city of Turin, Italy, from December 2003 to April 2005. We describe nonlinear relationships between predictors and pollutants, that are adjusted for unobserved time-varying confounders. We examine several functional forms for the traffic variable and find that a simple form can often provide adequate modeling power. Our analysis shows that there is a saturation effect of traffic on NO2, while such saturation is less evident in models linking traffic to PM10 behavior, having adjusted for meteorological covariates. Moreover, we consider the proposed models separately by seasons and highlight similarities and differences in the predictors’ partial effects. Finally, we show how forecasting can help in evaluating traffic regulation policies.