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基于流式细胞术测量的高分辨率浮游植物数据化学计量分析的水质监测。

Water quality monitoring based on chemometric analysis of high-resolution phytoplankton data measured with flow cytometry.

作者信息

Tinnevelt Gerjen H, Lushchikova Olga, Augustijn Dillen, Lochs Mathijs, Geertsma Rinze W, Rijkeboer Machteld, Kools Harrie, Dubelaar George, Veen Arnold, Buydens Lutgarde M C, Jansen Jeroen J

机构信息

Radboud University, Institute for Molecules and Materials, (Analytical Chemistry), P.O. Box 9010, 6500 GL Nijmegen, the Netherlands; TI-COAST, Science Park 904, 1098 XH Amsterdam, the Netherlands.

Radboud University, Institute for Molecules and Materials, (Analytical Chemistry), P.O. Box 9010, 6500 GL Nijmegen, the Netherlands; TI-COAST, Science Park 904, 1098 XH Amsterdam, the Netherlands.

出版信息

Environ Int. 2022 Dec;170:107587. doi: 10.1016/j.envint.2022.107587. Epub 2022 Oct 17.

Abstract

River water is an important source of Dutch drinking water. For this reason, continuous monitoring of river water quality is needed. However, comprehensive chemical analyses with high-resolution gas chromatography [GC]-mass spectrometry [MS]/liquid chromatography [LC]-MS are quite tedious and time consuming; this makes them poorly fit for routine water quality monitoring and, therefore, many pollution events are missed. Phytoplankton are highly sensitive and responsive to toxicity, which makes them highly usable for effect-based water quality monitoring. Flow cytometry can measure the optical properties of phytoplankton every hour, generating a large amount of information-rich data in one year. However, this requires chemometrics, as the resulting fingerprints need to be processed into information about abnormal phytoplankton behaviour. We developed Discriminant Analysis of Multi-Aspect CYtometry (DAMACY) to model the "normal condition" of the phytoplankton community imposed by diurnal, meteorological, and other exogenous influences. DAMACY first describes the cellular variability and distribution of phytoplankton in each measurement using principal component analysis, and then aims to find subtle differences in these phytoplankton distributions that predict normal environmental conditions. Deviations from these normal environmental conditions indicated abnormal phytoplankton behaviour that happened alongside pollution events measured with the GC/MS and LC/MS systems. Thus, our results demonstrate that flow cytometry in combination with chemometrics may be used for an automated hourly assessment of river water quality and as a near real-time early warning for detecting harmful known or unknown contaminants. Finally, both the flow cytometer and the DAMACY algorithm run completely autonomous and only requires maintenance once or twice per year. The warning system results may be uploaded automatically, so that drinking water companies may temporary stop pumping water whenever abnormal phytoplankton behaviour is detected. In the case of prolonged abnormal phytoplankton behaviour, comprehensive analysis may still be used to identify the chemical compound, its origin, and toxicity.

摘要

河水是荷兰饮用水的重要来源。因此,需要对河流水质进行持续监测。然而,采用高分辨率气相色谱[GC]-质谱[MS]/液相色谱[LC]-MS进行全面的化学分析相当繁琐且耗时;这使得它们不太适合用于常规水质监测,因此许多污染事件被遗漏。浮游植物对毒性高度敏感且反应迅速,这使其非常适用于基于效应的水质监测。流式细胞仪可以每小时测量浮游植物的光学特性,一年内就能生成大量信息丰富的数据。然而,这需要化学计量学,因为所得的指纹图谱需要处理成有关浮游植物异常行为的信息。我们开发了多方面流式细胞术判别分析(DAMACY)来模拟由昼夜、气象和其他外源影响所施加的浮游植物群落的“正常状态”。DAMACY首先使用主成分分析描述每次测量中浮游植物的细胞变异性和分布,然后旨在找出这些浮游植物分布中预测正常环境条件的细微差异。与这些正常环境条件的偏差表明浮游植物出现了异常行为,这些行为与用GC/MS和LC/MS系统测量的污染事件同时发生。因此,我们的结果表明,流式细胞术与化学计量学相结合可用于对河流水质进行每小时一次的自动评估,并作为检测已知或未知有害污染物的近实时预警。最后,流式细胞仪和DAMACY算法都完全自主运行,每年只需维护一到两次。预警系统的结果可以自动上传,以便饮用水公司在检测到浮游植物异常行为时能够暂时停止抽水。在浮游植物行为长期异常的情况下,仍可使用综合分析来识别化合物、其来源和毒性。

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