Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables

Marcos Orellana; Juan Fernando Lima; Priscila Cedillo

doi:10.1007/978-3-030-73103-8_13

Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables

Marcos Orellana
, Juan Fernando Lima
, Priscila Cedillo

Universidad del Azuay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC
Editors	Kohei Arai
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	205-215
Number of pages	11
ISBN (Print)	9783030731021
DOIs	https://doi.org/10.1007/978-3-030-73103-8_13
State	Published - 2021
Event	Future of Information and Communication Conference, FICC 2021 - Virtual, Online Duration: 29 Apr 2021 → 30 Apr 2021

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1364 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Future of Information and Communication Conference, FICC 2021
City	Virtual, Online
Period	29/04/21 → 30/04/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Air pollutant
Big data
Correlation
Data Mining
Knowledge
Rolling correlations

Access to Document

10.1007/978-3-030-73103-8_13

Cite this

Orellana, M., Lima, J. F., & Cedillo, P. (2021). Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables. In K. Arai (Ed.), Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC (pp. 205-215). (Advances in Intelligent Systems and Computing; Vol. 1364 AISC). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73103-8_13

Orellana, Marcos ; Lima, Juan Fernando ; Cedillo, Priscila. / Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables. Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. editor / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2021. pp. 205-215 (Advances in Intelligent Systems and Computing).

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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.",

keywords = "Air pollutant, Big data, Correlation, Data Mining, Knowledge, Rolling correlations",

author = "Marcos Orellana and Lima, \{Juan Fernando\} and Priscila Cedillo",

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Orellana, M, Lima, JF & Cedillo, P 2021, Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables. in K Arai (ed.), Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. Advances in Intelligent Systems and Computing, vol. 1364 AISC, Springer Science and Business Media Deutschland GmbH, pp. 205-215, Future of Information and Communication Conference, FICC 2021, Virtual, Online, 29/04/21. https://doi.org/10.1007/978-3-030-73103-8_13

Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables. / Orellana, Marcos; Lima, Juan Fernando; Cedillo, Priscila.
Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. ed. / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2021. p. 205-215 (Advances in Intelligent Systems and Computing; Vol. 1364 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables

AU - Orellana, Marcos

AU - Lima, Juan Fernando

AU - Cedillo, Priscila

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Air pollutant

KW - Big data

KW - Correlation

KW - Data Mining

KW - Knowledge

KW - Rolling correlations

UR - https://www.scopus.com/pages/publications/85105946376

U2 - 10.1007/978-3-030-73103-8_13

DO - 10.1007/978-3-030-73103-8_13

M3 - Contribución a la conferencia

AN - SCOPUS:85105946376

SN - 9783030731021

T3 - Advances in Intelligent Systems and Computing

SP - 205

EP - 215

BT - Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC

A2 - Arai, Kohei

PB - Springer Science and Business Media Deutschland GmbH

T2 - Future of Information and Communication Conference, FICC 2021

Y2 - 29 April 2021 through 30 April 2021

ER -

Orellana M, Lima JF, Cedillo P. Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables. In Arai K, editor, Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. Springer Science and Business Media Deutschland GmbH. 2021. p. 205-215. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-73103-8_13

Discovering Patterns of Time Association Among Air Pollution and Meteorological Variables

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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