RT journal article
T1 An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis
A1 Sales Lérida, Diego
A1 Bello Espina, Alfonso José
A1 Sánchez Alzola, Alberto
A1 Martínez Jiménez, Pedro Manuel
A2 Estadística e Investigación Operativa
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 air air quality
K1 metal-oxide sensor
K1 monitoring
K1 multivariable regression models
K1 model calibration
AB Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O-3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring.
PB MDPI
SN 1424-8220
YR 2021
FD 2021-07
LK http://hdl.handle.net/10498/25603
UL http://hdl.handle.net/10498/25603
LA eng
NO The researchers would like to thank the University of Cadiz for the grant obtained through its "Programa de Fomento e Impulso de la actividad de Investigacion y Transferencia". The authors would also like to thank to the Environmental Technology researching group and Acoustic Engineering Laboratory researching group, TEP-181 and TEP-195, respectively, for the access to the devices and data of the EcoBici Project (number G-GI3002/IDIC). Alfonso J. Bello acknowledges the support received from the 2014-2020 ERDF Operational Program and by the Department of Economy, Knowledge, and Business and the University of the Regional Government of Andalusia, Spain, under grant: FEDER-UCA18-107519.
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026