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一种新型低成本二氧化氮传感器漂移校正方法的开发与评估

Development and Evaluation of A Novel and Cost-Effective Approach for Low-Cost NO₂ Sensor Drift Correction.

作者信息

Sun Li, Westerdahl Dane, Ning Zhi

机构信息

School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.

Guy Carpenter Climate Change Centre, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.

出版信息

Sensors (Basel). 2017 Aug 19;17(8):1916. doi: 10.3390/s17081916.

DOI:10.3390/s17081916
PMID:28825633
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5580082/
Abstract

Emerging low-cost gas sensor technologies have received increasing attention in recent years for air quality measurements due to their small size and convenient deployment. However, in the diverse applications these sensors face many technological challenges, including sensor drift over long-term deployment that cannot be easily addressed using mathematical correction algorithms or machine learning methods. This study aims to develop a novel approach to auto-correct the drift of commonly used electrochemical nitrogen dioxide (NO₂) sensor with comprehensive evaluation of its application. The impact of environmental factors on the NO₂ electrochemical sensor in low-ppb concentration level measurement was evaluated in laboratory and the temperature and relative humidity correction algorithm was evaluated. An automated zeroing protocol was developed and assessed using a chemical absorbent to remove NO₂ as a means to perform zero correction in varying ambient conditions. The sensor system was operated in three different environments in which data were compared to a reference NO₂ analyzer. The results showed that the zero-calibration protocol effectively corrected the observed drift of the sensor output. This technique offers the ability to enhance the performance of low-cost sensor based systems and these findings suggest extension of the approach to improve data quality from sensors measuring other gaseous pollutants in urban air.

摘要

近年来,新兴的低成本气体传感器技术因其体积小、部署方便而在空气质量测量中受到越来越多的关注。然而,在各种应用中,这些传感器面临着许多技术挑战,包括长期部署中的传感器漂移,使用数学校正算法或机器学习方法难以轻易解决。本研究旨在开发一种新颖的方法,对常用的电化学二氧化氮(NO₂)传感器的漂移进行自动校正,并对其应用进行全面评估。在实验室中评估了环境因素对低ppb浓度水平测量中NO₂电化学传感器的影响,并评估了温度和相对湿度校正算法。开发了一种自动归零协议,并使用化学吸收剂去除NO₂作为在不同环境条件下进行零点校正的手段进行评估。传感器系统在三种不同环境中运行,并将数据与参考NO₂分析仪进行比较。结果表明,零点校准协议有效地校正了观察到的传感器输出漂移。该技术能够提高基于低成本传感器的系统的性能,这些发现表明该方法可扩展,以提高城市空气中测量其他气态污染物的传感器的数据质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/925e2c4f9a43/sensors-17-01916-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/0362fa4351d1/sensors-17-01916-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/ce8658380df6/sensors-17-01916-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/0c192d8066d3/sensors-17-01916-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/b08d3b417fa5/sensors-17-01916-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/e2a0095ee7e5/sensors-17-01916-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/3c752d50ce03/sensors-17-01916-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/9c4c1f0b5501/sensors-17-01916-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/b9a1e244e927/sensors-17-01916-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/925e2c4f9a43/sensors-17-01916-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/0362fa4351d1/sensors-17-01916-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/ce8658380df6/sensors-17-01916-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/0c192d8066d3/sensors-17-01916-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/b08d3b417fa5/sensors-17-01916-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/e2a0095ee7e5/sensors-17-01916-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/3c752d50ce03/sensors-17-01916-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/9c4c1f0b5501/sensors-17-01916-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/b9a1e244e927/sensors-17-01916-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b85/5580082/925e2c4f9a43/sensors-17-01916-g009.jpg

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