RT journal article
T1 Spatial and meteorological relevance in NO2 estimations: a case study in the Bay of Algeciras (Spain)
A1 González Enrique, Francisco Javier
A1 Turias Domínguez, Ignacio José
A1 Ruiz Águilar, Juan Jesús
A1 Moscoso López, José Antonio
A1 Franco, Leonardo
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 Artificial neural networks
K1 Monitoring networks
K1 Air pollution
K1 Feature relevance
AB 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.
PB Springer Nature
SN 1436-3240
YR 2019
FD 2019
LK http://hdl.handle.net/10498/35211
UL http://hdl.handle.net/10498/35211
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026