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Data Mining Paradigm in the Study of Air Quality

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Data Mining Paradigm in the Study of Air Quality

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dc.contributor.author Represa, Natacha Soledad es_ES
dc.contributor.author Fernández-Sarría, Alfonso es_ES
dc.contributor.author Porta, Andrés es_ES
dc.contributor.author Palomar-Vázquez, Jesús es_ES
dc.date.accessioned 2021-02-06T04:33:37Z
dc.date.available 2021-02-06T04:33:37Z
dc.date.issued 2020-03 es_ES
dc.identifier.issn 2198-7491 es_ES
dc.identifier.uri http://hdl.handle.net/10251/160828
dc.description.abstract [EN] Air pollution is a serious global problem that threatens human life and health, as well as the environment. The most important aspect of a successful air quality management strategy is the measurement analysis, air quality forecasting, and reporting system. A complete insight, an accurate prediction, and a rapid response may provide valuable information for society¿s decision-making. The data mining paradigm can assist in the study of air quality by providing a structured work methodology that simplifies data analysis. This study presents a systematic review of the literature from 2014 to 2018 on the use of data mining in the analysis of air pollutant measurements. For this review, a data mining approach to air quality analysis was proposed that was consistent with the 748 articles consulted. The most frequent sources of data have been the measurements of monitoring networks, and other technologies such as remote sensing, low-cost sensors, and social networks which are gaining importance in recent years. Among the topics studied in the literature were the redundancy of the information collected in the monitoring networks, the forecasting of pollutant levels or days of excessive regulation, and the identification of meteorological or land use parameters that have the most substantial impact on air quality. As methods to visualise and present the results, we recovered graphic design, air quality index development, heat mapping, and geographic information systems. We hope that this study will provide anchoring of theoretical-practical development in the field and that it will provide inputs for air quality planning and management. es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Environmental Processes es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Air quality es_ES
dc.subject Environmental management es_ES
dc.subject Air pollution es_ES
dc.subject Data mining es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Data Mining Paradigm in the Study of Air Quality es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s40710-019-00407-5 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANPCyT//PICT-2015-0618/AR/Estudio de potenciales emergencias químicas en escenarios urbanos y suburbanos con modelos simples y complejos/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria es_ES
dc.description.bibliographicCitation Represa, NS.; Fernández-Sarría, A.; Porta, A.; Palomar-Vázquez, J. (2020). Data Mining Paradigm in the Study of Air Quality. Environmental Processes. 7(1):1-21. https://doi.org/10.1007/s40710-019-00407-5 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s40710-019-00407-5 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 21 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 7 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\399193 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Universidad Nacional de La Plata, Argentina es_ES
dc.contributor.funder Agencia Nacional de Promoción Científica y Tecnológica, Argentina es_ES
dc.contributor.funder Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina es_ES
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