4.1 Article

Contributions of spatial, temporal, and treatment impacts on natural organic matter character using fluorescence-based measures

期刊

WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY
卷 15, 期 3, 页码 589-598

出版社

IWA PUBLISHING
DOI: 10.2166/ws.2015.013

关键词

drinking water treatment; fluorescence spectroscopy; natural organic matter; parallel factors analysis; principal component analysis

资金

  1. Canadian Water Network
  2. Natural Sciences and Engineering Research Council of Canada (NSERC) Chair in Drinking Water Research at the University of Toronto

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The potential application of fluorescence spectroscopy for monitoring of organic matter concentration and character at four water treatment facilities was investigated. Results are presented showing impacts on natural organic matter (NOM) due to intake location on the same water body and from individual unit processes including ozonation, granular-activated carbon filtration, and coagulation/ flocculation. For validation and comparison of fluorescence methods, organic matter was quantified and characterized using liquid chromatography-organic carbon detection (LC-OCD). Principal component analysis (PCA) and parallel factors analysis were used for dimensionality reduction and to represent individual organic components observed through fluorescence excitation-emission matrices. Fluorescence results generally agreed with LC-OCD characterization, indicating that complete treatment reduced organic concentrations and preferential removal of humic-like material was associated with coagulation/ flocculation. PCA results indicated higher concentrations of humiclike material at the Island water treatment plant intake that was not well reduced by inline polyaluminum chloride coagulation and direct filtration. Through fluorescence spectroscopy, ozonation increased Rayleigh scattering, which is correlated to small colloidal/ particulate concentrations. Full-scale results from four water treatment plants presented demonstrate that fluorescence methods can characterize NOM, providing similar identification of trends to LC-OCD, with possible online application and use in real-time water treatment process control.

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