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Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/38209
Title: Discovering patterns of time association among air pollution and meteorological variables
Other Titles: 
Authors: Orellana Cordero, Marcos Patricio
Lima Sigua, Juan Fernando
Cedillo Orellana, Irene Priscila
metadata.dc.ucuenca.correspondencia: Orellana Cordero, Marcos Patricio, marore@uazuay.edu.ec
Keywords: Air pollutant
Knowledge
Data mining
Correlation
Big data
Rolling correlations
metadata.dc.ucuenca.areaconocimientofrascatiamplio: 1. Ciencias Naturales y Exactas
metadata.dc.ucuenca.areaconocimientofrascatidetallado: 1.2.1 Ciencias de la Computación
metadata.dc.ucuenca.areaconocimientofrascatiespecifico: 1.2 Informática y Ciencias de la Información
metadata.dc.ucuenca.areaconocimientounescoamplio: 06 - Información y Comunicación (TIC)
metadata.dc.ucuenca.areaconocimientounescodetallado: 0613 - Software y Desarrollo y Análisis de Aplicativos
metadata.dc.ucuenca.areaconocimientounescoespecifico: 061 - Información y Comunicación (TIC)
Issue Date: 2021
metadata.dc.ucuenca.embargoend: 30-Dec-2050
metadata.dc.ucuenca.volumen: Volumen 1364
metadata.dc.source: Advances in Intelligent Systems and Computing
metadata.dc.identifier.doi: 10.1007/978-3-030-73103-8_13
Publisher: Springer
metadata.dc.description.city: 
Vancouver
metadata.dc.type: ARTÍCULO DE CONFERENCIA
Abstract: 
Lately, there is a concern about to air pollution, which leads to environmental specialists discovering relevant causes of this phenomenon. Several factors determine the level of pollution, but it is necessary to find behavior patterns between air pollution and meteorological variables. The relations between these variables in distinct hours a day could give clues to discover essential patterns in their relationships. This study revealed relations among five air pollution variables and nine meteorological variables collected for one month in the city Cuenca-Ecuador. The method used considerer an evaluation of the essential time associations using time rolling windows and correlations. The results were revelated using visualization frames for dimensions such as time, correlation rate, and component relation, highlighting 57 strong correlations from 91 pairs of variables, the best positive correlation is between Ozone and Radiation UVA. The best negative correlation is Ozone and Dew Point, both throughout the day.
URI: https://www.scopus.com/record/display.uri?eid=2-s2.0-85105946376&origin=resultslist&sort=plf-f&src=s&st1=Discovering+Patterns+of+Time+Association+Among+Air+Pollution+and+Meteorological+Variables&sid=2069e1fb1a7a1d019370e228f7d95ff8&sot=b&sdt=b&sl=104&s=TITLE-ABS-KEY%28Discovering+Patterns+of+Time+Association+Among+Air+Pollution+and+Meteorological+Variables%29&relpos=0&citeCnt=0&searchTerm=
metadata.dc.ucuenca.urifuente: https://www.springer.com/series/11156
ISBN: 978-303073102-1
ISSN: 2194-5357
Appears in Collections:Artículos

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