Natural organic matter (NOM) is of concern in drinking water because it causes adverse aesthetic qualities such as taste, odour, and colour; impedes the performance of treatment processes; and decreases the effectiveness of oxidants and disinfectants while contributing to undesirable disinfection by-products. The effective removal of NOM during drinking water treatment requires a good understanding of its character. Because of its heterogeneity, NOM characterization necessitates the use of multiple analytical techniques. In this study, NOM in water samples from two drinking water treatment trains was characterized using liquid chromatography with organic carbon detection (LC-OCD), and fluorescence excitation-emission matrices (F-EEMs) with parallel factor analysis (PARAFAC). These characterization methods indicate that the raw and treated waters are dominated by humic substances. The results show that whereas the coagulation process for both plants may be optimized for the removal of bulk DOC, it is not likewise optimized for the removal of specific NOM fractions. A five component PARAFAC model was developed for the F-EEMs, three of which are humic-like, while two are protein-like. These PARAFAC components and the LC-OCD fractions represented effective tools for the performance evaluation of the two water treatment plants in terms of the removal of NOM fractions.

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