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...
- 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
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| 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. |
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2020 |
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2020-09-15 |
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2021-03-03T19:36:30Z |
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2021-03-03T19:36:30Z |
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Artículo de revista |
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https://hdl.handle.net/11323/7956 |
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doi:10.1088/1757-899X/872/1/012195 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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17578981 1757899X doi:10.1088/1757-899X/872/1/012195 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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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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Silva, Jesúse17281d02925301aa71681ad0d7b3e03Londoño, Luz Adriana2d586979cd45bce00fcf3bffcde69404Varela Izquierdo, Noel484160b66adc1de7303e235ec7894532Pineda, Omaraf4b322b3d3157067b1e466da357fb982021-03-03T19:36:30Z2021-03-03T19:36:30Z2020-09-15175789811757899Xhttps://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.application/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. 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