Analysing the effect of recent anti-pollution policies in Madrid city through soft-computing

Abstract

This study presents the application of dimensionality reduction and clustering techniques to episodes of high pollution in Madrid City (Spain). The goal of this work is to compare two scenarios with similar atmospheric conditions (periods of high NO2 concentration): one of them when no actions were taken and the other one when traffic restrictions were imposed. The analyzed data have been gathered from two acquisition stations from the local air control network of Madrid City. The main pollutants recorded at these stations along four days during two time intervals are analyzed in order to determine the effectiveness of the anti-pollution measures. Dimensionality-reduction and clustering techniques have been applied to analyse the pollution public datasets.