El papel del análisis por componentes principales en la evaluación de redes de control de la calidad del aire
The role of principal component analysis in the evaluation of air quality monitoring networks
Abstract (en)
One of the most statistical techniques used in environmental sciences is the Principal Component Analysis (PCA). This technique consist in a linear decomposition of a set of correlated variables into a set of uncorrelated variables named principal components. It is one of the simplest and most robust ways of doing dimensionality reduction. The PCA is widely used in the study of environmental phenomena, from the analysis of meteorological fields to the evaluation of air quality monitoring networks (AQMN). Due to the potential use of this method, more information in Spanish is required. For these reasons, we are highly motivated to contribute with this review paper, which contains the state of the art to evaluate AQMN by means of PCA. Additionally, some examples (simulated and real-world data) are presented to exemplify the use of this technique. Keywords: principal component analysis, air quality monitoring networks, redundant sensor detection.
Abstract (es)
Una de las técnicas estadísticas de más amplio uso en estudios ambientales es el análisis por componentes principales (ACP). Esta técnica consiste en la descomposición lineal de un conjunto de variables correlacionadas en términos de funciones de base ortogonal, de tal modo que reducen el número de variables y eliminan la correlación entre ellas. El ACP es utilizado en una amplia gama de aplicaciones en el estudio de fenomenos ambientales, desde el analisis de campos meteorol ́ogicos hasta en la evaluacion de redes de control y vigilancia de la calidad del aire (RCVCA). Hoy por hoy, es posible encontrar una buena cantidad de publicaciones en ingles sobre este último tipo de aplicaciones, pero hay una carencia de informacion en español. Por estas razones, en este artıculo de revisi ́on se presenta de manera concisa toda la informaci ́on pertinente para evaluar RCVCA mediante el ACP, así como algunos ejemplos con datos simulados y reales.
References
Abdi, H. & Williams, L. J. (2010), ‘Principal Component Analysis’, Wiley Inter- disc. Rev.: Comp. Stat. 2(4), 433–459.
Albizuri, A. (2008), in ‘Caracterización de patrones meteorológicos a escala regional y local y su relación con los niveles de calidad del aire registrados en la C.A.P.V. Análisis de episodios’, Memorias de la 3a. Jornada técnica sobre con- terminación atmosférica, Dept. de Medio Ambiente, Planificación Territorial, Agricultura y Pesca, Gobierno Vasco.
Aránguez, E., Ordóñez, J. M., Serrano, J., Aragonés, N., Fernández-Patier, R., Gandarillas, A. & Galán, I. (1999), ‘Contaminantes atmosféricos y su vigilancia’, Revista Española de Salud Pública 73(2), 123–132.
Berkooz, G., Holmes, P. & Lumley, J. L. (1993), ‘The proper orthogonal decomposition in the analysis of turbulent flows’, An. Rev. of Fluid Mech. 25(1), 539– 575.
Cambra, E., Alonso, E., F., C. & Martínez-Rueda, T. (2005), ‘Health impact assessment of air pollution’, ENHIS-1 project WP5 health impact assessment, Local City Report Bilbao.
Dray, S. (2008), ‘On the number of principal components: A test of dimensionality based on measurements of similarity between matrices’, Comp. Statand Data Analysis 52(4), 2228–2237.
Estaciones remotas de la red de vigilancia de la calidad del aire. Departamento de Medio Ambiente y Política Territorial, Gobierno Vasco (2016),
http://www.ingurumena.ejgv.euskadi.eus/informacion/ la-red-de-control-de-calidad-del-aire/r49-3614/es/.
Gangoiti, G., Alonso, L., Navazo, M., Albizuri, A., Pérez-Landa, G., Matabuena, M., Valdenebro, V., Maruri, M., Antonio Garc ́ıa, J. & Mill ́an, M. M. (2002), ‘Regional transport of pollutants over the Bay of Biscay: analysis of an ozone episode under a blocking anticyclone in west-central Europe’, Atm. Env. 36(8), 1349–1361.
Gramsch, E., Cereceda-Balic, F., Oyola, P. & Von Baer, D. (2006), ‘Examination of pollution trends in Santiago de Chile with cluster analysis of PM10 and Ozone data’, Atm. Env. 40(28), 5464–5475.
Hannachi, A., Jolliffe, I. T. & Stephenson, D. B. (2007), ‘Empirical orthogonal functions and related techniques in atmospheric science: A review’, Int. J. of Clim. 27(9), 1119–1152.
Henry, R. C. (1997), ‘History and fundamentals of multivariate air quality receptor models’, Chem. and Intel. Lab. Syst. 37(1), 37–42.
Hotelling, H. (1933), ‘Analysis of a complex of statistical variables into principal components’, J. of Educational Psychology 24(6), 417–441.
Ibarra-Berastegi, G., Elías, A., Barona, A., Sáenz, J., Ezcurra, A. & Díaz de Argandoña, J. (2007), ‘From diagnosis to prognosis for forecasting air pollution using neural networks: Air pollution monitoring in Bilbao’, Env. Mod. and Soft. 23(5), 622–637.
Ibarra-Berastegi, G., Sáenz, J., Ezcurra, A., Ganzedo, U., D ́ıaz de Argandoña, J., Errasti, I., Fernandez-Ferrero, A. & Polanco-Martínez, J. (2009), ‘Asses- sing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps, cluster, and Principal Component Analysis’, Atm. Env. 43(25), 3829–3836.
Jolliffe, I. T. (2002), Principal component analysis, Springer-Verlag, New York.
Kendall, S. M. (1980), Multivariate analysis, Charles Griffin, London.
Lau, J., Hung, W. T. & Cheung, C. S. (2009), ‘Interpretation of air quality in relation to monitoring station’s surroundings’, Atm. Env. 43(4), 769–777.
Lˆe, S., Josse, J. & Husson, F. (2008), ‘FactoMineR: an R package for multivariate analysis’, J. of Stat. Soft. 25(1), 1–18.
Ligges, U. & M ̈achler, M. (2003), ‘Scatterplot3d–an R package for Visualizing Multivariate Data’, J. of Stat. Soft. 8(11), 1–20.
Martínez-Ataz, E. M. & de Mera-Morales, Y. D. (2004), Contaminación atmosférica, Ed. Universidad de Castilla-La Mancha,.
Monahan, A. H., Fyfe, J. C., Ambaum, M. H. P., Stephenson, D. B. & North, G. R. (2009), ‘Empirical orthogonal functions: The medium is the message’, J. Clim. 22(24), 6501–6514.
Mudelsee, M. (2014), Climate Time Series Analysis: Classical Statistical and Bootstrap Methods, Springer.
Nunnari, G., Dorling, S., Schlink, U., Cawley, G., Foxall, R. & Chatterton, T. (2004), ‘Modelling SO2 concentration at a point with statistical approaches’, Env. Mod. and Soft. 19(10), 887–905.
Pearson, K. (1901), ‘On lines and planes of closest fit to systems of points in space’, Phil. Mag. 2(11), 559–572.
Peres-Neto, P. R., Jackson, D. A. & Somers, K. M. (2005), ‘How many principal components? Stopping rules for determining the number of non-trivial axes revisited’, Comp. Stat. and Data Analysis 49(4), 974–997.
Pires, J. C. M., Pereira, M. C., Alvim-Ferraz, M. C. M. & Martins, F. G. (2009), ‘Identification of redundant air quality measurements through the use of principal component analysis’, Atm. Env. 43(25), 3837–3842.
Pires, J. C. M., Sousa, S. I. V., Pereira, M. C., Alvim-Ferraz, M. C. M. & Martins, F. G. (2008), ‘Management of air quality monitoring using principal component and cluster analysis Part I: SO2 and PM10’, Atm. Env. 42(6), 1249–1260.
Polanco-Martínez, J. (2012), Aplicación de técnicas estadísticas en el estudio de fenómenos ambientales y ecosistémicos, PhD thesis, University of Basque Country, España.
*https://addi.ehu.es/handle/10810/11295
Preisendorfer, R. W. (1988), Principal components analysis in Meteorology and Oceanography, Elsevier, Amsterdam.
R Development Core Team (2009), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. *http://www.R-pro ject.org
Seinfeld, J. H. (1978), Contaminación atmosférica. Fundamentos físicos y químicos, Inst. de Estudios de Adm. Local, Madrid.
Shrestha, S. & Kazama, F. (2007), ‘Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan’, Env. Mod. and Soft. 22(4), 464–475.
Singh, K. P., Malik, A., Mohan, D. & Sinha, S. (2004), ‘Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India) a case study’, Water Res. 38(18), 3980–3992.
Sportisse, B. (2010), Fundamentals in air pollution: from processes to modelling, Springer, Heidelberg.
Von Storch, H. & Zwiers, F. W. (1999), Statistical analysis in climate research, Cambridge University Press, Cambridge, U.K.
Wark, K. & Warmer, C. F. (1994), Contaminación del aire: origen y control, Limusa, México.
Wilks, D. S. (1995), Statistical Methods in the Atmospheric Sciences, Academic Press, London.
World-Health-Organization, W. H. O. (2000), Air Quality Guidelines for Europe, number 91, WHO Reg. Pub. European series; No. 91.
Wunderlin, D. A., Diaz, M. P., Ame, M. V., Pesce, S. F., Hued, A. C., Bistoni, M. A. et al. (2001), ‘Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquia river basin (Cordoba, Argentina)’, Water Res. 35(12), 2881–2894.
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