On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario

• Published in: Sensors and actuators. B, Chemical Vol. 129; no. 2; pp. 750 - 757
• Main Authors: De Vito, S., Massera, E., Piga, M., Martinotto, L., Di Francia, G.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 22-02-2008
• Subjects: Air pollution; Algorithms; Artificial neural networks; Benzene; Benzene monitoring; Calibration; Devices; Electronic nose; Errors; Multi-sensor device; On-line calibration; Pollution monitoring; Sensors; Urban atmospheric pollution; Multi-sensor device; Urban atmospheric pollution; Benzene monitoring; On-line calibration; Electronic nose; Artificial neural networks
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2007.09.060

Low-cost gas multi-sensor devices could be efficiently used for densifying the sparse urban pollution monitoring mesh if equipped with a reliable calibration able to counter specificity and stability issues of solid-state sensors they rely on. In this work, we present a neural calibration for the prediction of benzene concentrations using a gas multi-sensor device (solid-state) designed to monitor urban environment pollution. The feasibility of a sensor fusion algorithm as a calibrating tool for the multi-sensor device is discussed. A Conventional air pollution monitoring station is used to provide reference data. Results are assessed by means of prediction error characterization throughout a 13 months long interval and discussed. Relationship between training length and performances are also investigated. A neural calibration obtained using a small number of measurement days revealed to be capable to limit the absolute prediction error for more than 6th month, after which seasonal influences on prediction capabilities at low-concentrations suggested the need for a further calibration.