On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario
On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario
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DOI：
10.1016/j.snb.2007.09.060
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发表时间：
2008-02-22
影响因子：
8.4
通讯作者：
Di Francia, G.
中科院分区：
文献类型：
--
作者：
De Vito, S.;Massera, E.;Di Francia, G.
低成本的气体多传感器设备可以有效地用于加密稀疏的城市污染监测网络，如果配备能够应对其所依赖的固态传感器的特异性和稳定性问题的可靠校准。在这项工作中，我们提出了一种使用气体多传感器设备(固态)来预测苯浓度的神经校准方法，该设备用于监测城市环境污染。讨论了传感器融合算法作为多传感器设备标定工具的可行性。常规的空气污染监测站用于提供参考数据。在长达13个月的时间间隔内，通过预测误差特征对结果进行评估并进行讨论。研究了训练时长与运动成绩的关系。使用少量测量天数获得的神经校准表明，能够在6个月以上限制绝对预测误差，之后对低浓度下的预测能力的季节性影响表明需要进一步校准。(C)2007 Elsevier B.V.保留所有权利。
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.