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Spatial and meteorological relevance in NO2 estimations: a case study in the Bay of Algeciras (Spain)

  • Javier González-EnriqueEmail author
  • Ignacio J. Turias
  • Juan Jesús Ruiz-Aguilar
  • José Antonio Moscoso-López
  • Leonardo Franco
Original Paper
  • 140 Downloads

Abstract

This study focuses on how to determine the most relevant variables in order to estimate the hourly NO2 concentrations in a monitoring network located in the Bay of Algeciras (Spain). For each station of the network, artificial neural networks and multiple linear regression have been used to compute hourly estimation models. Meteorological variables and hourly NO2 concentrations from the nearby stations have been used as inputs, and a feature selection procedure has been applied as a previous step. The different models developed have been statistically compared. The inputs used in the best estimation model for each station were the most important to estimate each hourly NO2 concentration level. These estimations can be a very useful resource to provide autonomous capacities as automatic decalibration detection or missing data imputation in monitoring networks. Finally, the similarities between stations, according to the relevance of variables, have been analysed with the aid of a hierarchical clustering algorithm.

Keywords

Artificial neural networks Monitoring networks Air pollution Feature relevance 

Notes

Acknowledgements

This work is part of the coordinated research projects TIN2014-58516-C2-1-R and TIN2014-58516-C2-2-R supported by MICINN (Ministerio de Economía y Competitividad-Spain). Monitoring data have been kindly provided by the Environmental Agency of the Andalusian Government.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science Engineering, Polytechnic School of EngineeringUniversity of CádizAlgecirasSpain
  2. 2.Department of Industrial and Civil Engineering, Polytechnic School of EngineeringUniversity of CádizAlgecirasSpain
  3. 3.Department of Computer Science, ETS Computer ScienceUniversity of MálagaMálagaSpain

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