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Multi-wavelength models expand the validity of DBP-differential absorbance relationships in drinking water.

Author:
Beauchamp, Nicolas, Dorea, Caetano, Bouchard, Christian, Rodriguez, Manuel
Source:
Water research 2019 v.158 pp. 61-71
ISSN:
0043-1354
Subject:
absorbance, byproducts, chlorination, databases, disinfection, drinking water, haloacetic acids, models, prediction, regression analysis, wavelengths
Abstract:
Differential UV absorbance (ΔA) is an important indicator that could allow operators and utility managers to routinely monitor disinfection by-product (DBP) concentrations, even in real-time applications, without the limitations of regulatory sampling and analyses. While determination coefficients between differential UV absorbance at a single wavelength (often 272 nm) and chlorination DBP concentrations are usually very high (R2 > 0,90), the fitting parameters of these relationships vary from one water source to another, or vary within the same water source depending on the time of year. The objectives of this study are to apply multiple regression models to a rich database of ΔA and DBPs (trihalomethanes (THMs) and haloacetic acids (HAAs)) that was generated from lab experiments using multiple waters with low bromide concentrations, in order to identify wavelengths that improve the applicability of DBP-ΔA relationships, as well as develop a widely applicable multi-wavelength DBP-ΔA relationship. The results show that using ΔA at two wavelengths, in addition to initial absorbance (A) at one wavelength, greatly improves the determination coefficients of the relationships, when compared with the best possible single-wavelength relationships. The significant predictors identified are A270, ΔA270 and ΔA425 for THMs; A255, ΔA255 and ΔA425 for DCAA; A250, ΔA250 and ΔA425 for TCAA; and A250, ΔA250 and ΔA425 for HAA6. This paper also discusses the applicability of the models developed for predicting DBPs in treatment plants and drinking water, as well as their limitations and the required future research to improve their performance.
Agid:
6374010