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城市水再利用方案中的有机物荧光:建立统一的 PARAFAC 模型。

Organic matter fluorescence in municipal water recycling schemes: toward a unified PARAFAC model.

机构信息

School of Civil and Environmental Engineering, The University of New South Wales, Sydney NSW 2052, Australia.

出版信息

Environ Sci Technol. 2011 Apr 1;45(7):2909-16. doi: 10.1021/es103015e. Epub 2011 Mar 1.

Abstract

Organic matter (OM) is a ubiquitous constituent of natural waters quantifiable at very low levels using fluorescence spectroscopy. This technique has recognized potential in a range of applications where the ability to monitor water quality in real time is desirable, such as in water treatment systems. This study used PARAFAC to characterize a large (n=1479) and diverse excitation emission matrix (EEM) data set from six recycled water treatment plants in Australia, for which sources of variability included geography, season, treatment processes, pH and fluorometer settings. Five components were identified independently in four or more plants, none of which were generated during the treatment process nor were typically entirely removed. PARAFAC scores could be obtained from EEMs by simple regression. The results have important implications for online monitoring of OM fluorescence in treatment plants, affecting choices regarding experimental design, instrumentation and the optimal wavelengths for tracking fluorescent organic matter through the treatment process. While the multimodel comparisons provide a compelling demonstration of PARAFAC's ability to distill chemical information from EEMs, deficiencies identified through this process have broad implications for interpreting and reusing (D)OM-PARAFAC models.

摘要

有机物(OM)是自然水体中普遍存在的成分,可以通过荧光光谱法在非常低的水平上进行定量。这项技术在一系列应用中具有潜在的应用价值,例如在水处理系统中,这些应用需要实时监测水质的能力。本研究使用平行因子分析(PARAFAC)对来自澳大利亚六个再生水处理厂的大量(n=1479)和多样化的激发发射矩阵(EEM)数据集进行了特征描述,其中包括地理、季节、处理过程、pH 值和荧光计设置等因素。在四个或更多的工厂中独立识别出了五个组分,这些组分都不是在处理过程中产生的,也不能完全去除。可以通过简单的回归从 EEM 中获得 PARAFAC 得分。这些结果对处理厂中 OM 荧光的在线监测具有重要意义,影响到实验设计、仪器选择以及跟踪荧光有机物通过处理过程的最佳波长的选择。虽然多模型比较为 PARAFAC 从 EEM 中提取化学信息的能力提供了令人信服的证明,但通过该过程发现的缺陷对解释和重复使用(D)OM-PARAFAC 模型具有广泛的影响。

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