A pre-deployment calibration and a field validation of two low-cost (LC) stations equipped with O3 and NO2 metal oxide sensors were addressed. Pre-deployment calibration was performed after developing and implementing a comprehensive calibration framework including several supervised learning models, such as univariate linear and non-linear algorithms, and multiple linear and non-linear algorithms.

Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors

Cavaliere, Alice
;
Brilli, Lorenzo;Carotenuto, Federico;Gioli, Beniamino;Giordano, Tommaso;Vagnoli, Carolina;Zaldei, Alessandro;Gualtieri, Giovanni
2023

Abstract

A pre-deployment calibration and a field validation of two low-cost (LC) stations equipped with O3 and NO2 metal oxide sensors were addressed. Pre-deployment calibration was performed after developing and implementing a comprehensive calibration framework including several supervised learning models, such as univariate linear and non-linear algorithms, and multiple linear and non-linear algorithms.
2023
Istituto per la BioEconomia - IBE
AirQino, NO2, O3, machine learning, explainable AI, air quality low-cost sensor
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/513548
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