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-.

Screening of microplastics in water and sludge lines of a drinking water treatment plant in Catalonia, Spain.

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.

Benchmarking empirical models for THMs formation in drinking water systems: An application for decision support in Barcelona, Spain.

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 (R2 = 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.

Assessment of dead-end ultrafiltration for the detection and quantification of microbial indicators and pathogens in the drinking water treatment processes.

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 log10 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.

Implementation of an environmental decision support system for controlling the pre-oxidation step at a full-scale drinking water treatment plant.

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 (R2 = 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.

Chemical characterization and relative toxicity assessment of disinfection byproduct mixtures in a large drinking water supply network.

In order to reduce the formation of disinfection by-products (DBPs) during potabilization of water it is necessary to explore the potential of the source water and the applied treatment to generate these chemicals. This is actually more challenging in large drinking water networks that use different source waters to satisfy drinking water demand. In this regard, this work investigated the formation of DBPs in water matrices that are commonly supplied to the city of Barcelona and its metropolitan area. The regulated trihalomethanes and haloacetic acids were the most abundant DBP classes in these waters, followed by haloacetamides and haloacetonitriles or trihalogenated acetaldehydes (THALs). On the contrary, the formation potential of iodo-DBPs was minor. Mixing of drinking water treatment plant finished waters with desalinated water decreased the overall DBP formation potential of the water but resulted in the increased formation of brominated DBPs after long chlorine contact time. The formation of most DBPs was enhanced at high water temperatures (except for Br-THALs) and increasing residence times. Potential cytotoxicity and genotoxicity of the DBP mixtures were mainly attributed to the presence of nitrogen-containing DBPs and HAAs.