Laurent Spinelle

TL;DR: In this article, the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques are compared, and 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.

TL;DR: A literature review of sensors for the monitoring of benzene in ambient air and other volatile organic compounds considers commercially available sensors, including PID-based sensors, semiconductor (resistive gas sensors) and portable on-line measuring devices as for example sensor arrays.

TL;DR: In this paper, the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques, are compared and the accuracy of each regression method was evaluated on a five months field experiment at a semi-rural site using different indicators and techniques.

TL;DR: In this article, quantitative data regarding the performance of low-cost sensors (LCS) against reference measurement are presented. And the authors highlight the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

TL;DR: The manuscript provides an overview of the AirSensEUR project, which establishes an affordable open software/hardware multi-sensor platform, which is nonetheless able to monitor air pollution at low concentration levels.