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 ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors was operated. The sensors were either of metal oxide or electrochemical type or based on miniaturized infra-red cell. For each method, a two-week calibration was carried out at a semi-rural site against reference measurements. Subsequently, the accuracy of the predicted values was evaluated for about five months using a few indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. The study assessed if the sensors were could reach the Data Quality Objective (DQOs) of the European Air Quality Directive for indicative methods (between 25 and 30% of uncertainty for O3 and NO2). In this study it appears that O3 may be calibrated using simple regression techniques while for NO2 a better agreement between sensors and reference measurements was reached using supervised learning techniques. The hourly O3 DQO was met while it was unlikely that NO2 hourly one could be met. This was likely caused by the low NO2 levels correlated with high O3 levels that are typical of semi-rural site where the measurements of this study took place. © 2015 The Authors.

Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide

Villani, M. G.;
2015

Abstract

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 ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors was operated. The sensors were either of metal oxide or electrochemical type or based on miniaturized infra-red cell. For each method, a two-week calibration was carried out at a semi-rural site against reference measurements. Subsequently, the accuracy of the predicted values was evaluated for about five months using a few indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. The study assessed if the sensors were could reach the Data Quality Objective (DQOs) of the European Air Quality Directive for indicative methods (between 25 and 30% of uncertainty for O3 and NO2). In this study it appears that O3 may be calibrated using simple regression techniques while for NO2 a better agreement between sensors and reference measurements was reached using supervised learning techniques. The hourly O3 DQO was met while it was unlikely that NO2 hourly one could be met. This was likely caused by the low NO2 levels correlated with high O3 levels that are typical of semi-rural site where the measurements of this study took place. © 2015 The Authors.
Multivariate linear regression
Measurement uncertainty
Air Quality Directive
Gas sensors
Validation
Neural network
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/3366
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