气候变化领域集成服务门户(CCPortal): Development and evaluation of a robust temperature sensitive algorithm for long term NO2 gas sensor network data correction

CCPortal

DOI	10.1016/j.atmosenv.2020.117509
	Development and evaluation of a robust temperature sensitive algorithm for long term NO2 gas sensor network data correction
	Wei P.; Sun L.; Abhishek A.; Zhang Q.; Huixin Z.; Deng Z.; Wang Y.; Ning Z.
发表日期	2020
ISSN	1352-2310
卷号	230
英文摘要	Low-cost sensors (LCS) for air-quality monitoring have shown huge potential in enhancing spatial and temporal resolutions at a lower cost, greater flexibility in use and with less maintenance than fixed-air quality monitoring stations (AQMS). There are numerous reports showing sensor-based systems perform well under laboratory conditions. However, the accuracy of LCS operated under field conditions has been reported to deviate from their use in these controlled environments. Previous studies have employed various mathematical techniques to improve system performance. These include classic multivariate regression model (MLR) to incorporate ambient factors (e.g. temperature & relative humidity) and machine learning (ML) models for integrating complex non-linear sensor responses for gaseous pollutant concentration predictions. However, routine field calibrations may cause data inconsistency during measurement periods. This study first illustrates the limitations of short-term routine calibration by comparing performances of existing MLR and ML models. Further, based on a long-term evaluation period, a newly principle-based method named Temperature Look-Up (TLU) model was built and compared with the existing MLR and ML models. Measurements were taken for 8 months (August 2017 to March 2018) in SanMenXia (SMX) city, China, using 8 sets of sensor systems (Mini Air Stations, MAS). One MAS was co-located with AQMS to provide data to assess the principle, whereas the other 7 MAS were used for verification. The TLU model showed the best performance for long term validation (4 months) and high model inner coherence (R2 > 0.91). Meanwhile, the TLU model also shows the fast convergence (40 days) to reach stable prediction performance, and spatial representativeness in the sensor network. Recommendations on sensor network deployment and strategy for data maintenance based on our observations are discussed. © 2020 Elsevier Ltd
关键词	Robustness analysis Sensor calibration Sensor network
语种	英语
scopus关键词	Air quality; Fog; Logistic regression; Sensor networks; Air quality monitoring; Air quality monitoring stations; Controlled environment; Laboratory conditions; Multivariate regression models; Prediction performance; Spatial and temporal resolutions; Temperature sensitive; Costs; nitrogen dioxide; air quality; air sampling; algorithm; environmental monitoring; machine learning; nitrogen dioxide; prediction; sensor; temperature effect; air monitoring; air pollutant; air quality; algorithm; Article; calibration; China; city; comparative study; data analysis; electrochemical analysis; gas; humidity; laboratory test; machine learning; prediction; priority journal; spatiotemporal analysis; temperature; validation study; China
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/249192
作者单位	Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong SAR, Hong Kong; Sapiens Environmental Technology Co Ltd., Hong Kong SAR, Hong Kong
推荐引用方式 GB/T 7714	Wei P.,Sun L.,Abhishek A.,et al. Development and evaluation of a robust temperature sensitive algorithm for long term NO2 gas sensor network data correction[J],2020,230.
APA	Wei P..,Sun L..,Abhishek A..,Zhang Q..,Huixin Z..,...&Ning Z..(2020).Development and evaluation of a robust temperature sensitive algorithm for long term NO2 gas sensor network data correction.ATMOSPHERIC ENVIRONMENT,230.
MLA	Wei P.,et al."Development and evaluation of a robust temperature sensitive algorithm for long term NO2 gas sensor network data correction".ATMOSPHERIC ENVIRONMENT 230(2020).