Study of the principal component analysis in air quality databases

Technological development has facilitated daily habits, business, the manufacture of large quantities of products, among other types of industrial activities; however, these advances have caused environmental deterioration that seriously threatens the development of society. The increase of greenhou...

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Autores:
Silva, Jesús
Londoño, Luz Adriana
Varela Izquierdo, Noel
Pineda, Omar
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7956
Acceso en línea:
https://hdl.handle.net/11323/7956
https://repositorio.cuc.edu.co/
Palabra clave:
Analysis of correlation matrix
Selection of factors
Interpretation of factors
Factorial matrix analysis
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_1263f246c93785d08e18c8ffafa57769
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7956
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Study of the principal component analysis in air quality databases
title Study of the principal component analysis in air quality databases
spellingShingle Study of the principal component analysis in air quality databases
Analysis of correlation matrix
Selection of factors
Interpretation of factors
Factorial matrix analysis
title_short Study of the principal component analysis in air quality databases
title_full Study of the principal component analysis in air quality databases
title_fullStr Study of the principal component analysis in air quality databases
title_full_unstemmed Study of the principal component analysis in air quality databases
title_sort Study of the principal component analysis in air quality databases
dc.creator.fl_str_mv Silva, Jesús
Londoño, Luz Adriana
Varela Izquierdo, Noel
Pineda, Omar
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Londoño, Luz Adriana
Varela Izquierdo, Noel
Pineda, Omar
dc.subject.spa.fl_str_mv Analysis of correlation matrix
Selection of factors
Interpretation of factors
Factorial matrix analysis
topic Analysis of correlation matrix
Selection of factors
Interpretation of factors
Factorial matrix analysis
description Technological development has facilitated daily habits, business, the manufacture of large quantities of products, among other types of industrial activities; however, these advances have caused environmental deterioration that seriously threatens the development of society. The increase of greenhouse gases in the atmosphere affects the health of millions of people and is the main factor that has modified the climate on planet Earth. Faced with this situation, it is necessary to carry out actions that allow to quickly adapt to this change and mitigate its effects. The present study proposes the analysis of main components in the data of the pollutant measurements in the city of Bogota, Colombia with the purpose of obtaining a more compact representation of these data, to later apply grouping techniques and obtain factors that allow the emission of an alert for pre-contingency and contingency.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-09-15
dc.date.accessioned.none.fl_str_mv 2021-03-03T19:36:30Z
dc.date.available.none.fl_str_mv 2021-03-03T19:36:30Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 17578981
1757899X
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7956
dc.identifier.doi.spa.fl_str_mv doi:10.1088/1757-899X/872/1/012195
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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identifier_str_mv 17578981
1757899X
doi:10.1088/1757-899X/872/1/012195
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7956
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Dogruparmak S. C., Keskin G. A., Yaman S. and Alkan A. 2014 Using principal component analysis and fuzzy c-means clustering for the assessment of air quality monitoring Atmospheric Pollution Research 5 656-663
[2] Sanchez L., Vásquez C. and Viloria A. 2018 In International Conference on Data Mining and Big data (Cham: Springer) Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector 759-766 June
[3] Viloria A. and Gaitan-Angulo M. 2016 Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company Indian Journal Of Science And Technology 9
[4] Lin Y. C., Lee S. J., Ouyang C. S. and Wu C. H. 2020 Air quality prediction by neuro-fuzzy modeling approach Applied Soft Computing 86 105898
[5] Ding C. and He X. Proceedings of the 20th International Conference on Machine Learning (2004) K-means clustering via principal component analysis
[6] Zare A., Young N., Suen D., Nabelek T., Galusha A. and Keller J. 2017 In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE) Possibilistic fuzzy local information c-means for sonar image segmentation 1-8 November
[7] Pholsena K. and Pan L. 2018 In 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) (IEEE) Traffic status evaluation based on possibilistic fuzzy c-means clustering algorithm 175-180 June
[8] Stockwell W. R., Saunders E., Goliff W. S. and Fitzgerald R. M. 2020 A perspective on the development of gas-phase chemical mechanisms for Eulerian air quality models Journal of the Air & Waste Management Association 70 44-70
[9] Psiloglou B. E., Kambezidis H. D., Kaskaoutis D. G., Karagiannis D. and Polo J. M. 2020 Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece Renewable energy 146 1372-1391
[10] Johnson T. 2002 A guide to selected algorithms, distributions, and databases used in exposure models developed by the office of air quality planning and standards Research Triangle Park, NC, US Environmental Protection Agency, Office of Research and Development
[11] Singh K. P., Gupta S. and Rai P. 2013 Identifying pollution sources and predicting urban air quality using ensemble learning methods Atmospheric Environment 80 426-437
[12] Elbir T. 2004 A GIS based decision support system for estimation, visualization and analysis of air pollution for large Turkish cities Atmospheric Environment 38 4509-4517
[13] Yatkin S., Gerboles M., Belis C. A., Karagulian F., Lagler F., Barbiere M. and Borowiak A. 2020 Representativeness of an air quality monitoring station for PM2. 5 and source apportionment over a small urban domain Atmospheric Pollution Research 11 225-233
[14] Ganbold G. and Chasia S. 2017 Comparison between Possibilistic c-Means (PCM) and Artificial Neural Network (ANN) Classification Algorithms in Land use/Land cover Classification International Journal of Knowledge Content Development & Technology 7 57
[15] Grace R. K. and Manju S. 2019 A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems Wireless Personal Communications 108 2499-2515
[16] Rodríguez-Camargo L. A., Sierra-Parada R. J. and Blanco-Becerra L. C. 2020 Spatial analysis of PM2 5 concentrations according to WHO air quality guideline values for cardiopulmonary diseases in Bogotá, DC, 2014-2015. Biomedical 40
[17] Casallas A., Celis N., Ferro C., Barrera E. L., Peña C., Corredor J. and Segura M. B. 2020 Validation of PM 10 and PM 2.5 early alert in Bogotá, Colombia, through the modeling software WRF-CHEM Environmental Science and Pollution Research 1-11
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spelling Silva, JesúsLondoño, Luz AdrianaVarela Izquierdo, NoelPineda, Omar2021-03-03T19:36:30Z2021-03-03T19:36:30Z2020-09-15175789811757899Xhttps://hdl.handle.net/11323/7956doi:10.1088/1757-899X/872/1/012195Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Technological development has facilitated daily habits, business, the manufacture of large quantities of products, among other types of industrial activities; however, these advances have caused environmental deterioration that seriously threatens the development of society. The increase of greenhouse gases in the atmosphere affects the health of millions of people and is the main factor that has modified the climate on planet Earth. Faced with this situation, it is necessary to carry out actions that allow to quickly adapt to this change and mitigate its effects. The present study proposes the analysis of main components in the data of the pollutant measurements in the city of Bogota, Colombia with the purpose of obtaining a more compact representation of these data, to later apply grouping techniques and obtain factors that allow the emission of an alert for pre-contingency and contingency.Silva, JesúsLondoño, Luz AdrianaVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaRetractedCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2IOP Conf. Series: Materials Science and Engineeringhttps://iopscience.iop.org/article/10.1088/1757-899X/872/1/012030/pdfAnalysis of correlation matrixSelection of factorsInterpretation of factorsFactorial matrix analysisStudy of the principal component analysis in air quality databasesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Dogruparmak S. C., Keskin G. A., Yaman S. and Alkan A. 2014 Using principal component analysis and fuzzy c-means clustering for the assessment of air quality monitoring Atmospheric Pollution Research 5 656-663[2] Sanchez L., Vásquez C. and Viloria A. 2018 In International Conference on Data Mining and Big data (Cham: Springer) Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector 759-766 June[3] Viloria A. and Gaitan-Angulo M. 2016 Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company Indian Journal Of Science And Technology 9[4] Lin Y. C., Lee S. J., Ouyang C. S. and Wu C. H. 2020 Air quality prediction by neuro-fuzzy modeling approach Applied Soft Computing 86 105898[5] Ding C. and He X. Proceedings of the 20th International Conference on Machine Learning (2004) K-means clustering via principal component analysis[6] Zare A., Young N., Suen D., Nabelek T., Galusha A. and Keller J. 2017 In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE) Possibilistic fuzzy local information c-means for sonar image segmentation 1-8 November[7] Pholsena K. and Pan L. 2018 In 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) (IEEE) Traffic status evaluation based on possibilistic fuzzy c-means clustering algorithm 175-180 June[8] Stockwell W. R., Saunders E., Goliff W. S. and Fitzgerald R. M. 2020 A perspective on the development of gas-phase chemical mechanisms for Eulerian air quality models Journal of the Air & Waste Management Association 70 44-70[9] Psiloglou B. E., Kambezidis H. D., Kaskaoutis D. G., Karagiannis D. and Polo J. M. 2020 Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece Renewable energy 146 1372-1391[10] Johnson T. 2002 A guide to selected algorithms, distributions, and databases used in exposure models developed by the office of air quality planning and standards Research Triangle Park, NC, US Environmental Protection Agency, Office of Research and Development[11] Singh K. P., Gupta S. and Rai P. 2013 Identifying pollution sources and predicting urban air quality using ensemble learning methods Atmospheric Environment 80 426-437[12] Elbir T. 2004 A GIS based decision support system for estimation, visualization and analysis of air pollution for large Turkish cities Atmospheric Environment 38 4509-4517[13] Yatkin S., Gerboles M., Belis C. A., Karagulian F., Lagler F., Barbiere M. and Borowiak A. 2020 Representativeness of an air quality monitoring station for PM2. 5 and source apportionment over a small urban domain Atmospheric Pollution Research 11 225-233[14] Ganbold G. and Chasia S. 2017 Comparison between Possibilistic c-Means (PCM) and Artificial Neural Network (ANN) Classification Algorithms in Land use/Land cover Classification International Journal of Knowledge Content Development & Technology 7 57[15] Grace R. K. and Manju S. 2019 A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems Wireless Personal Communications 108 2499-2515[16] Rodríguez-Camargo L. A., Sierra-Parada R. J. and Blanco-Becerra L. C. 2020 Spatial analysis of PM2 5 concentrations according to WHO air quality guideline values for cardiopulmonary diseases in Bogotá, DC, 2014-2015. Biomedical 40[17] Casallas A., Celis N., Ferro C., Barrera E. L., Peña C., Corredor J. and Segura M. B. 2020 Validation of PM 10 and PM 2.5 early alert in Bogotá, Colombia, through the modeling software WRF-CHEM Environmental Science and Pollution Research 1-11PublicationORIGINALStudy of the principal component analysis in air quality databases.pdfStudy of the principal component analysis in air quality databases.pdfapplication/pdf1399024https://repositorio.cuc.edu.co/bitstreams/dc0565c2-a3c3-477f-94d9-36b0a273e1e2/downloadaf0131a051278d75a3a5f5bd1780dac1MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/a4132e7b-6d56-4dc3-b038-67ac6769dcac/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/323cb07e-6e0e-4108-84dc-c7ede1e19386/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILStudy of the principal component analysis in air quality databases.pdf.jpgStudy of the principal component analysis in air quality 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