This study presents the application of self-organizing maps to air-quality data in order to analyze episodes of high pollution in
Madrid (Spain’s capital city). The goal of this work is to explore the dataset and then compare several scenarios with similar
atmospheric conditions (periods of high Nitrogen dioxide concentration): some of them when no actions were taken and
some when traffic restrictions were imposed. The levels of main pollutants, recorded at these stations for eleven days at four
different times from 2015 to 2018, are analyzed in order to determine the effectiveness of the anti-pollution measures. The
visualization of trajectories on the self-organizing map let us clearly see the evolution of pollution levels and consequently
evaluate the effectiveness of the taken measures, after and during the protocol activation time.
