Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Part B: NO, CO and CO2

• Published in: Sensors and actuators. B, Chemical Vol. 238; pp. 706 - 715
• Main Authors: Spinelle, Laurent, Gerboles, Michel, Villani, Maria Gabriella, Aleixandre, Manuel, Bonavitacola, Fausto
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-01-2017
• Subjects: Air Quality Directive; Artificial neural network; Calibration; Carbon dioxide; Carbon monoxide; Clusters; Low-cost gas sensors; Measurement uncertainty; Multivariate linear regression; Nitrogen oxides; Ozone; Regression; Sensors; Validation; Validation; Artificial neural network; Multivariate linear regression; Measurement uncertainty; Low-cost gas sensors; Air Quality Directive
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2016.07.036

•We tested a high number of commercial sensors on the same site, applying the same data treatment and evaluation.•We tested sensors’ response modelling with and without interfering compounds or temperature and humidity corrections.•Data Quality Objectives of the Air quality Directive should be met for CO using a cluster of sensors.•Long term real field measurements.
In this work the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques, are compared. A cluster of either metal oxide or electrochemical sensors for nitrogen monoxide and carbon monoxide together with miniaturized infra-red carbon dioxide sensors was operated. Calibration was carried out during the two first weeks of evaluation against reference measurements. The accuracy of each regression method was evaluated on a five months field experiment at a semi-rural site using different indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. In addition to the analyses for ozone and nitrogen oxide already published in Part A [1], this work assessed if carbon monoxide sensors can reach the Data Quality Objective (DQOs) of 25% of uncertainty set in the European Air Quality Directive for indicative methods. As for ozone and nitrogen oxide, it was found for NO, CO and CO2 that the best agreement between sensors and reference measurements was observed for supervised learning techniques compared to linear and multilinear regression.