Authors: Valentí-Quiroga, Meritxell; Daunis-i-Estadella, Pepus; Emiliano, Pere; Valero, Fernando; Martin, Maria J.

NOM fractionation by HPSEC-DAD-OCD for predicting trihalomethane disinfection by-product formation potential in full-scale drinking water treatment plants

Chlorination is a common method for water disinfection; however, it leads to the formation of disinfection by-products (DBPs), which are undesirable toxic pollutants. To prevent their formation, it is crucial to understand the reactivity of natural organic matter (NOM), which is considered a dominant precursor of DBPs. We propose a novel size exclusion chromatography (SEC) approach to evaluate NOM reactivity and the formation potential of total trihalomethanes-formation potentials (tTHMs-FP) and four regulated species (i.e. CHCl3, CHBrCl2, CHBr2Cl, and CHBr3). This method combines enhanced SEC separation with two analytical columns working in tandem and quantification of apparent molecular weight (AMW) NOM fractions using C content (organic carbon detector, OCD), 254-nm spectroscopic (diode-array detector, DAD) measurements, and spectral slopes at low (S206–240) and high (S350–380) wavelengths. Links between THMs-FP and NOM fractions from high performance size exclusion chromatography HPSEC-DAD-OCD were investigated using statistical modelling with multiple linear regressions for samples taken alongside conventional full-scale as well as full- and pilot-scale electrodialysis reversal and bench-scale ion exchange resins. The proposed models revealed promising correlations between the AMW NOM fractions and the THMs-FP. Methodological changes increased fractionated signal correlations relative to bulk regressions, especially in the proposed HPSEC-DAD-OCD method. Furthermore, spectroscopic models based on fractionated signals are presented, providing a promising approach to predict THMs-FP simultaneously considering the effect of the dominant THMs precursors, NOM and Br.

Authors:Valentí-Quiroga, Meritxell; Daunis-i-Estadella, Pepus; Emiliano, Pere; Valero, Fernando; Martin, Maria J.
Reference:Water Research, Open Access, Volume 2271, December 2022, Article number 119314