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Abstract: For the first time, the application of different robust data mining techniques to the assessment of water treatment performance is considered. Principal components analysis (PCA), parallel factor analysis (PARAFAC), and a self-organizing map (SOM) were used in the analysis of multivariate data characterising organic matter (OM) removal at 16 water treatment works. Decomposed fluorescence data from PCA, PARAFAC and SOM were used as input to calibrate fluorescence data with OM concentrations using stepwise regression (SR), partial least squares (PLS), multiple linear regression (MLR), and neural network with back-propagation algorithm (BPNN). The best results were obtained with combined PARAFAC/PLS and SOM/BPNN. Both the numerical accuracy and feasibility of the adopted solutions were compared and recommendations on the use of the above techniques for fluorescence data analysis are presented. [Copyright &y& Elsevier]

The utilization of groundwaters containing high levels of arsenic (As) for drinking water purposes presents major health and economic challenges for water utilities. One low-cost approach is to mix arsenic-rich groundwater (GW) with arsenic-free surface waters (SW) to achieve acceptable As levels. In this study we investigated the effect of different mixing ratios on water quality in an eastern Croatian water distribution system (WDS). To investigate the effects of mixing on drinking water quality, we measured the organic matter (OM) composition, disinfection byproduct (DBP) and metal concentrations in differently mixed ratios of GW and SW within the WDS. Fluorescence analysis revealed that the GW and SW had similar OM composition, with an almost equal ratio of humic- and protein-like OM throughout the WDS despite fluorescence indices revealing slightly different OM sources between the two water types. The tyrosine-like OM component was more variable, increasing during warmer months and towards the end of the WDS, most likely due to enhanced biofilm formation. Arsenic concentrations decreased to below 10 μg/L in the second half of the sampling campaign. Acceptable water quality was achieved after a period of destabilization and solubilization of loose deposits within the WDS resulting in their mobilization caused by water quality changes. Principal component and classification analysis, regression models and Spearman correlation coefficients revealed an association between As, OM and DBP concentrations with these correlations suggestive of their role in As mobilization in the WDS. Changing source waters, with different OM content and characteristics, corresponded to variable As release within the WDS. [Display omitted] • Mixing ground-with surface waters decreased arsenic concentrations. • Water quality changes caused a destabilization of metals from pipe deposits. • Stable arsenic concentrations were achieved after six months of water mixing. • Tyrosine-like OM components were most variable in the WDS. • OM compositional changes altered DBP formation. [ABSTRACT FROM AUTHOR]