2026-06-30T02:17:40Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72552023-01-18T01:05:26Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Arroyo Puente, Ángel Cambra Baseca, Carlos Herrero Cosío, Álvaro Tricio Gómez, Verónica Corchado, Emilio Air quality Time evolution Self-organizing maps Trajectories Data visualization 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. 2023-01-17 2023-01-17 2019-08 info:eu-repo/semantics/article 1367-0751 http://hdl.handle.net/10259/7255 10.1093/jigpal/jzz049 1368-9894 eng Logic Journal of the IGPL. 2019, V. 28, n. 4, p. 596-614 https://doi.org/10.1093/jigpal/jzz049 info:eu-repo/semantics/openAccess Oxford University Press