Atmospheric Pollution Analysis by Unsupervised Learning

  • Angel Arroyo
  • Emilio Corchado
  • Veronica Tricio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


This paper presents a multidisciplinary study on the application of statistical and neural models for analysing data on immissions of atmospheric pollution in urban areas. Data was collected from the network of pollution measurement stations in the Spanish Autonomous Region of Castile-Leon. Four pollution parameters and a pollution measurement station in the city of Burgos were used to carry out the study in 2007, during a period of just over six months. Pollution data are compared, their values are interrelated and relationships are established not only with the pollution variables, but also with different weeks of the year. The aim of this study is to classify the levels of atmospheric pollution in relation to the days of the week, trying to differentiate between working days and non-working days.


artificial neural networks meteorology pollution 


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  1. 1.
    van der Maaten, L.J.P.: An Introduction to Dimensionality Reduction Using Matlab. Report MICC 07-07Google Scholar
  2. 2.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24, 417–441 (1933)CrossRefzbMATHGoogle Scholar
  3. 3.
    Pearson, K.: On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine 2, 559–572 (1901)CrossRefzbMATHGoogle Scholar
  4. 4.
    Everitt, B.: An R and S-PLUS companion to multivariate analysis. Springer, Heidelberg (2005)CrossRefzbMATHGoogle Scholar
  5. 5.
    Tenenbaum, B., de Silva, V., Langford, J.C.: A Globlal Geometric framework for Nonlinear Dimensionality Reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  6. 6.
    Floyd, R.W.: Algorithm 97: Shortest path. Communications of the ACM 5(6), 345 (1962)CrossRefGoogle Scholar
  7. 7.
    Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  8. 8.
    Chan, H.–P., Yeung, D.–Y., Xiong, Y.: Super – resolution throuugh neighbor embedding. In: IEEE Computer Society Conference on computer vision and pattern recognition, vol. 1, pp. 275–282 (2004)Google Scholar
  9. 9.
    Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Mining and Knowledge Discovery 8(3) (2004)Google Scholar
  10. 10.
    Tricio, V., Viloria, R., Minguito, A.: Evolución del ozono en Burgos y provincia a partir de los datos de la red de medida de contaminación atmosférica. Los retos del desar-rollo sostenible en España. In: Informe CONAMA 2006, 31 pages (2006),
  11. 11.
    Arroyo, A., Corchado, E., Tricio, V.: Computational Methods for Immision Analysis of Urban Atmospheric Pollution. In: 9th International Conference Computational and Mathematical Methods in science and engineering, Gijón 2009 (in Press, 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Angel Arroyo
    • 1
  • Emilio Corchado
    • 1
  • Veronica Tricio
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of PhysicsUniversity of BurgosBurgosSpain

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