Journal
SENSORS AND ACTUATORS B-CHEMICAL
Volume 129, Issue 2, Pages 750-757Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2007.09.060
Keywords
electronic nose; multi-sensor device; urban atmospheric pollution; benzene monitoring; artificial neural networks; on-line calibration
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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. (C) 2007 Elsevier B.V. All rights reserved.
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