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Temporal changes in field calibration relationships for Aeroqual S500 O3 and NO2 sensor-based monitors
Sensors and Actuators B: Chemical ( IF 8.0 ) Pub Date : 2018-07-18 , DOI: 10.1016/j.snb.2018.07.087
Nicola Masey , Jonathan Gillespie , Eliani Ezani , Chun Lin , Hao Wu , Neil S. Ferguson , Scott Hamilton , Mathew R. Heal , Iain J. Beverland

Sensor-based monitors are increasingly used to measure air pollutant concentrations, but require calibration under field conditions. We made intermittent comparisons (6 times over 6-month period) between ozone and nitrogen dioxide concentrations measured by Aeroqual gas-sensitive semiconductor (O3) and electrochemical (NO2) sensors (two of each) and reference analysers in the UK Automatic Urban and Rural Network. Each deployment period was split into equal (n = 48 h) training and test datasets, to derive and test calibration equations respectively. We observed significant bivariate linear relationships between Aeroqual O3 and Reference O3 concentrations, and significant multiple linear relationships between Aeroqual NO2 and both Reference NO2 and Aeroqual O3 concentrations. Changes in monitor responses over time (including apparent baseline drift in O3 sensor output, and discrepancies between the 2 Aeroqual NO2 sensors) resulted in relatively inaccurate concentrations estimates (cf. reference concentrations) from calibration equations derived in the first training period and applied to subsequent test deployments (e.g. NO2 RMSE = 47.2 μg m-3 (n = 286) for a dataset of all test periods combined, for one of the two monitor pairs). Substantial improvements in accuracy of estimated concentrations were achieved by combination of repeated intermittent training data into a single calibration dataset (NO2 RMSE = 8.5 μg m-3 for same test dataset described above). This latter approach to field calibration is recommended.



中文翻译:

基于Aeroqual S500 O 3和NO 2传感器的监测仪的现场校准关系的时间变化

基于传感器的监视器越来越多地用于测量空气污染物浓度,但需要在现场条件下进行校准。我们通过Aeroqual气敏半导体(O 3)和电化学(NO 2)传感器(每个2个)以及英国自动城市中的参考分析仪对臭氧和二氧化氮浓度进行了间歇性比较(在6个月内进行了6次)和农村网络。每个部署阶段分为相等的(n  = 48 h)训练和测试数据集,分别导出和测试校准方程式。我们观察到Aeroqual O 3参考O 3之间存在显着的二元线性关系浓度,以及Aeroqual NO 2参比NO 2Aeroqual O 3浓度之间的显着多重线性关系。监测器响应随时间的变化(包括O 3传感器输出中明显的基线漂移,以及2个Aeroqual NO 2传感器之间的差异)导致在第一次训练期间得出并应用的校准方程相对不准确的浓度估计值(参见参考浓度) 所有数据集的后续测试部署(例如,NO 2 RMSE = 47.2μgm -3n = 286)两个监视器对之一的组合测试时间段)。通过将重复的间歇性训练数据组合到单个校准数据集中,可以显着提高估计浓度的准确性(对于上述相同的测试数据集,NO 2 RMSE = 8.5μgm -3)。建议使用后一种现场校准方法。

更新日期:2018-07-18
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