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Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity

机译:在线测量的自动实时不确定性估计:水浊度的案例研究

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摘要

Continuous sensor measurements are becoming an important tool in environmental monitoring. However, the reliability of field measurements is still too often unknown, evaluated only through comparisons with laboratory methods or based on sometimes unrealistic information from the measuring device manufacturers. A water turbidity measurement system with automatic reference sample measurement and measurement uncertainty estimation was constructed and operated in laboratory conditions to test an approach that utilizes validation and quality control data for automatic measurement uncertainty estimation. Using validation and quality control data for measurement uncertainty estimation is a common practice in laboratories and, if applied to field measurements, could be a way to enhance the usability of field sensor measurements. The measurement system investigated performed replicate measurements of turbidity in river water and measured synthetic turbidity reference solutions at given intervals during the testing period. Measurement uncertainties were calculated for the results using AutoMUkit software and uncertainties were attached to appropriate results. The measurement results correlated well (R2 = 0.99) with laboratory results and the calculated measurement uncertainties were 0.8–2.1 formazin nephelometric units (FNU) (k = 2) for 1.2–5 FNU range and 11–27% (k = 2) for 5–40 FNU range. The measurement uncertainty estimation settings (such as measurement range selected and a number of replicates) provided by the user have a significant effect on the calculated measurement uncertainties. More research is needed especially on finding suitable measurement uncertainty estimation intervals for different field conditions. The approach presented is also applicable for other online measurements besides turbidity within limits set by available measurement devices and stable reference solutions. Potentially interesting areas of application could be the measurement of conductivity, pH, chemical oxygen demand (COD)/total organic carbon (TOC), or metals.
机译:连续的传感器测量正成为环境监测中的重要工具。但是,现场测量的可靠性仍然常常是未知的,只能通过与实验室方法进行比较或基于有时来自测量设备制造商的不切实际的信息进行评估。在实验室条件下,构建并运行了具有自动参考样品测量和测量不确定性估算功能的水浊度测量系统,以测试一种利用验证和质量控制数据进行自动测量不确定性估算的方法。在实验室中,使用验证和质量控制数据进行测量不确定度估算是一种常见做法,如果应用于现场测量,则可能是增强现场传感器测量可用性的一种方式。研究的测量系统在测试期间以给定的间隔对河水中的浊度进行了重复测量,并测量了合成浊度参考溶液。使用AutoMUkit软件计算结果的测量不确定度,并将不确定度附加到适当的结果上。测量结果与实验室结果之间的相关性很好(R 2 = 0.99),并且在1.2-5 FNU范围和11-11之间,计算的测量不确定度为0.8-2.1甲醛比浊法(FNU)(k = 2)。 5-40 FNU范围为27%(k(= 2)。用户提供的测量不确定度估计设置(例如选择的测量范围和重复次数)对计算的测量不确定度有重大影响。需要进行更多的研究,尤其是针对不同的现场条件找到合适的测量不确定度估计间隔。除了可用的测量设备和稳定的参考解决方案设定的限制内的浊度外,本文介绍的方法还适用于其他在线测量。潜在有趣的应用领域可能是电导率,pH,化学需氧量(COD)/总有机碳(TOC)或金属的测量。

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