The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain. (November 2016)

• Record Type: Journal Article
• Authors: Laña, Ibai; Del Ser, Javier; Padró, Ales; Vélez, Manuel; Casanova-Mateo, Carlos
• Is Part Of: Atmospheric environment. Volume 145(2016)
• Page Start: 424
• Page End: 438
• Publication Date: 2016-11
• Subjects: Urban air pollution -- Traffic flow -- Meteorological conditions -- Supervised learning -- Random forests; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2016.09.052
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Urban air pollution is a matter of growing concern for both public administrations and citizens. Road traffic is one of the main sources of air pollutants, though topography characteristics and meteorological conditions can make pollution levels increase or diminish dramatically. In this context an upsurge of research has been conducted towards functionally linking variables of such domains to measured pollution data, with studies dealing with up to one-hour resolution meteorological data. However, the majority of such reported contributions do not deal with traffic data or, at most, simulate traffic conditions jointly with the consideration of different topographical features. The aim of this study is to further explore this relationship by using high-resolution real traffic data. This paper describes a methodology based on the construction of regression models to predict levels of different pollutants (i.e. CO, NO, NO2, O3 and PM10 ) based on traffic data and meteorological conditions, from which an estimation of the predictive relevance ( importance ) of each utilized feature can be estimated by virtue of their particular training procedure. The study was made with one hour resolution meteorological, traffic and pollution historic data in roadside and background locations of the city of Madrid (Spain) captured over 2015. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowed by the effects of stable meteorological conditions of this city. Graphical abstract: Highlights: We present a data-based method to inspect the interplay among traffic, meteorological conditions and pollution in Madrid. We examine the coupling between traffic, meteorological features and different pollutants over districts of this city. Background pollution is found to be scarcely influenced by local traffic emissions.