Laref Rachid, Losson Etienne, Sava Alexandre, Siadat Maryam
Laboratoire de Conception, Optimisation et Modélisation des Systèmes, LCOMS EA 7306, Université de Lorraine, 57000 Metz, France.
Sensors (Basel). 2021 May 21;21(11):3581. doi: 10.3390/s21113581.
This paper investigates the long term drift phenomenon affecting electrochemical sensors used in real environmental conditions to monitor the nitrogen dioxide concentration [NO]. Electrochemical sensors are low-cost gas sensors able to detect pollutant gas at part per billion level and may be employed to enhance the air quality monitoring networks. However, they suffer from many forms of drift caused by climatic parameter variations, interfering gases and aging. Therefore, they require frequent, expensive and time-consuming calibrations, which constitute the main obstacle to the exploitation of these kinds of sensors. This paper proposes an empirical, linear and unsupervised drift correction model, allowing to extend the time between two successive full calibrations. First, a calibration model is established based on multiple linear regression. The influence of the air temperature and humidity is considered. Then, a correction model is proposed to solve the drift related to age issue. The slope and the intercept of the correction model compensate the change over time of the sensors' sensitivity and baseline, respectively. The parameters of the correction model are identified using particle swarm optimization (PSO). Data considered in this work are continuously collected onsite close to a highway crossing Metz City (France) during a period of 6 months (July to December 2018) covering almost all the climatic conditions in this region. Experimental results show that the suggested correction model allows maintaining an adequate [NO] estimation accuracy for at least 3 consecutive months without needing any labeled data for the recalibration.
本文研究了在实际环境条件下用于监测二氧化氮浓度[NO]的电化学传感器所受的长期漂移现象。电化学传感器是低成本的气体传感器，能够检测十亿分之一水平的污染气体，可用于加强空气质量监测网络。然而，它们会受到气候参数变化、干扰气体和老化等多种形式漂移的影响。因此，它们需要频繁、昂贵且耗时的校准，这构成了这类传感器应用的主要障碍。本文提出了一种经验性、线性且无监督的漂移校正模型，可延长两次连续完全校准之间的时间间隔。首先，基于多元线性回归建立校准模型，考虑了气温和湿度的影响。然后，提出了一个校正模型来解决与老化相关的漂移问题。校正模型的斜率和截距分别补偿传感器灵敏度和基线随时间的变化。使用粒子群优化（PSO）确定校正模型的参数。本研究中考虑的数据是在法国梅斯市一个公路交叉路口附近连续6个月（2018年7月至12月）现场收集的，涵盖了该地区几乎所有的气候条件。实验结果表明，所建议的校正模型能够在至少连续3个月内保持足够的[NO]估计精度，而无需任何用于重新校准的标记数据。
