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In this study we investigate the compositional changes in dissolved organic matter (DOM) fractions across diverse water sources and treatment processes in three Drinking Water Treatment Plants (DWTPs). High-Performance Size Exclusion Chromatography coupled with Diode Array Detection and Organic Carbon Detection (HPSEC-DAD-OCD) was employed to characterize DOM fractions, offering insights into treatment optimization. We examine bulk water parameters, DOM distributions, and the efficiency of treatment trains in reducing DOM fractions. Results reveal distinct DOM composition profiles in river-sourced versus reservoir-sourced waters, with implications for treatment processes. Coagulation, Granular Activated Carbon (GAC) adsorption, Electrodialysis Reversal (EDR), and Ion Exchange (IEX) were evaluated for their efficacy in removing DOM fractions. The analysis highlights the effectiveness of coagulation in reducing high molecular weight (MW) fractions, while GAC filtration targets lower MW fractions. EDR shows significant removal of anions and aromatics, while IEX demonstrates high removal efficiencies for removing humic substances (HS) fractions. Spectroscopic analysis further elucidates changes HS sub-fractions and their role in disinfection by-products (DBP) formation. To quantitatively assess the relationship between HS sub-fractions and trihalomethane formation potentials (THMFP), Pearson correlation analysis were conducted, unveiling robust associations between HS sub-fractions and THM-FP that can be predicted by surrogate parameters such as A 254 ., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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. CHCl 3, CHBrCl 2, CHBr 2 Cl, and CHBr 3 ). 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 (S 206-240 ) and high (S 350-380 ) 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 - ., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

Microplastics (MPs) are emerging pollutants detected everywhere in the environment, with the potential to harm living organisms. The present study investigated the concentration, morphology, and composition of MPs, between 20 μm and 5 mm, in a drinking water treatment plant (DWTP) located close to Barcelona (Catalonia, NE Spain). The sampling included different units of the DWTP, from influent to effluent as well as sludge line. Sampling strategy, filtration, allows sampling of large volumes of water avoiding sample contamination, and during 8 h in order to increase the representativeness of MPs collected. The pre-treatment of the samples consisted of advanced oxidation with Fenton's reagent and hydrogen peroxide, followed by density separation of the particles with zinc chloride solution. Visual identification was performed with an optical and stereoscopic microscope with final Fourier-transform infrared spectroscopic (FTIR) confirmation. MPs were found in all DWTP samples, with concentrations from 4.23 ± 1.26 MPs/L to 0.075 ± 0.019 MPs/L in the influent and effluent of the plant, respectively. The overall removal efficiency of the plant was 98.3%. The most dominant morphology was fibers followed by fragments and films. Twenty-two different polymer types were identified and synthetic cellulose, polyester, polyamide, polypropylene, polyethylene, polyurethane, and polyacrylonitrile were the most common. Although MPs could be incorporated from the distribution network, MPs intake from drinking water from this DWTP was not an important route compared to fish and seafood ingestion., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

Drinking water treatment plants (DWTPs) face changes in raw water quality, which affect the formation of disinfection by-products. Several empirical modelling approaches have been reported in the literature, but most of them have been developed with lab-scale data, which may not be representative of real water systems. Therefore, the application of these models for real-time operation of DWTPs might be limited. At the present study, multiple linear regression (MLR) and multi-layer perceptrons (MLP) were benchmarked using field-scale data for predicting the THMs formation in a case-study DWTP in Barcelona, Spain. After fitting the studied models, MLR exhibited good fit with the validation data set (R 2  = 0.88 and MAE = 4.0 μg·L -1 ) and described the most plausible input-output relationships with field-scale data. The MLR predictive model was incorporated into an environmental decision support system (EDSS) for assessing the THMs formation at two critical points of the distribution network. A Monte Carlo scheme was applied for quantifying uncertainty of model predictions at these points, considering low and high water quality scenarios and different degrees of treatment by an electrodialysis reversal process. The results show that the use of the proposed EDSS can help in real operation of complex drinking water systems, which face important changes in water quality throughout the year., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

A safe water supply requires distinct treatments and monitoring to guarantee the absence of pathogens and substances potentially hazardous for human health. In this study we assessed the efficiency of the dead-end ultrafiltration (DEUF) method to concentrate faecal indicator organisms (FIO) and pathogens in water samples with different physicochemical characteristics. Water samples were collected at the treatment stages of two drinking water treatment plants to analyse the concentration of a variety of 7 FIO and 4 reference microbes which have some species that are pathogenic to humans: Campylobacter spp., enteroviruses, Cryptosporidium spp. and Giardia spp. The samples were analysed before and after concentration by DEUF, detecting FIO concentrations about 1 log 10 higher in non-concentrated samples from both catchments. Percent recoveries were highly variable with a mean of 43.8 ± 17.5%, depending on the FIO and inherent sample characteristics. However, DEUF enabled FIO concentration in high volumes of water (100-500 l), allowing a reduction in the detection limit compared to the non-concentrated samples due to the high volume processing capabilities of the method. As a consequence, the detection of FIO removal from water in the drinking water treatment process was 1.0-1.5 logarithms greater in DEUF-treated water compared to unfiltered samples. The DEUF method improved the detection of target indicators and allowed for the detection of pathogens in low concentrations in water after the treatment stages, confirming the suitability of DEUF to concentrate high volumes of different types of water. This method could be useful for microbial analysis in water treatment monitoring and risk assessment, allowing the identification of critical points during the water treatment process and potential hazards in water destined for several uses., (Copyright © 2020 Elsevier GmbH. All rights reserved.)

Drinking water treatment plants (DWTPs) face changes in raw water quality, and treatment needs to be adjusted to produce the best water quality at the minimum environmental cost. An environmental decision support system (EDSS) was developed for aiding DWTP operators in choosing the adequate permanganate dosing rate in the pre-oxidation step. To this end, multiple linear regression (MLR) and multi-layer perceptron (MLP) models are compared for choosing the best predictive model. Besides, a case-based reasoning (CBR) model was approached to provide the user with a distribution of solutions given similar operating conditions in the past. The predictive model consisted of an MLP and has been validated against historical data with sufficient good accuracy for the utility needs (R 2 = 0.76 and RSE = 0.13 mg·L -1 ). The integration of the predictive and the CBR models in an EDSS gives the user an augmented decision-making capacity of the process and has great potential for both assisting experienced users and for training new personnel in deciding the operational set-point of the process.