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.
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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.
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González-Enrique, J., Turias, I.J., Ruiz-Aguilar, J.J. et al. Spatial and meteorological relevance in NO2 estimations: a case study in the Bay of Algeciras (Spain). Stoch Environ Res Risk Assess 33, 801–815 (2019). https://doi.org/10.1007/s00477-018-01644-0
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DOI: https://doi.org/10.1007/s00477-018-01644-0