2024-03-28T13:22:32Zhttps://riubu.ubu.es/oai/requestoai: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
2023-01-17T11:48:36Z
2023-01-17T11:48:36Z
2019-08
1367-0751
http://hdl.handle.net/10259/7255
10.1093/jigpal/jzz049
1368-9894
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.
application/pdf
eng
Oxford University Press
Logic Journal of the IGPL. 2019, V. 28, n. 4, p. 596-614
https://doi.org/10.1093/jigpal/jzz049
Air quality
Time evolution
Self-organizing maps
Trajectories
Data visualization
Informática
Computer science
Self-Organizing Maps to Validate Anti-Pollution Policies
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/openAccess
Logic Journal of the IGPL
28
4
596
614