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Analysing the Effect of Recent Anti-pollution Policies in Madrid City Through Soft-Computing

  • Ángel Arroyo
  • Verónica Tricio
  • Álvaro Herrero
  • Emilio Corchado
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 649)

Abstract

This study presents the application of dimensionality reduction and clustering techniques to episodes of high pollution in Madrid City (Spain). The goal of this work is to compare two scenarios with similar atmospheric conditions (periods of high NO2 concentration): one of them when no actions were taken and the other one when traffic restrictions were imposed. The analyzed data have been gathered from two acquisition stations from the local air control network of Madrid City. The main pollutants recorded at these stations along four days during two time intervals are analyzed in order to determine the effectiveness of the anti-pollution measures. Dimensionality-reduction and clustering techniques have been applied to analyse the pollution public datasets.

Keywords

Clustering k-means Air quality Time evolution Dimensionality reduction Principal components analysis Locally Linear Embedding 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ángel Arroyo
    • 1
  • Verónica Tricio
    • 2
  • Álvaro Herrero
    • 1
  • Emilio Corchado
    • 3
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of PhysicsUniversity of BurgosBurgosSpain
  3. 3.Departamento de Informática y AutomáticaUniversity of SalamancaSalamancaSpain

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