Removal assessment of disinfection by-products (DBPs) from drinking water supplies by solar heterogeneous photocatalysis: A case study of trihalomethanes (THMs)

Optimization of anode positioning in constructed wetlands coupled with microbial fuel cells based on C/O microenvironment for simultaneous removal of disinfection by-products and nitrogen

Constructed wetland coupled with microbial fuel cell (CW-MFC) has been applied for the advanced removal of emerging contaminants and nitrogen due to its sustainability potential. However, the impact of anode positioning on the synergistic removal of disinfection by-products (DBPs) and nitrogen in CW-MFC remains insufficiently understood. In this study, three CW-MFCs with different anode positions were constructed to explore the response differences in the removal of DBPs (represented by haloacetic acids-HAAs) and nitrogen. It was observed that the CW-MFC with the anode positioned centrally exhibited considerable nitrogen removal (TN removal efficiency: 56.3 ± 8.6 %) and HAAs degradation performance (dichloroacetic acid removal efficiency: 97.8 ± 1.3 %). Correlation analysis identified the advantageous carbon-oxygen environment at the anode as the most critical factor. Furthermore, this carbon-oxygen environment (CODcr-anode/DO=27.7) directly provided favorable conditions for electroactive bacteria to inhabit the anode, significantly enriching denitrifiers and HAAs-degrading bacteria at the cathode. Key genes (HAAs and carbon-nitrogen metabolic) were upregulated, clarifying the mechanisms of synergistic removal of HAAs and nitrogen in CW-MFCs with centrally positioned anodes. This study highlights the importance of CW-MFCs with anode positioning in the synergistic removal of DBPs and nitrogen, providing straightforward and feasible strategy for optimizing CW-MFC performance and scaling up.

Assessing the toxic potency of absorbed trihalomethanes in leafy vegetables: the effects of different Chlorine pretreatment

Trihalomethanes (THMs) are a class of compounds formed when organic substances in water interact with halogen disinfectants such as chlorine. The specific THMs include CHBr3, CHClBr2, CHCl2Br, and CHCl3. THMs are toxic disinfection by-products (DBPs) that pose potential risks to human health and can be present in ready-to-eat vegetables. Our study examined key variables such as contact time, chlorine concentration, and vegetable type on the formation and absorption of these contaminants. Laboratory simulations involved 22 samples characterized by differing chlorine concentrations, contact durations, and three vegetable types: celery, lettuce, and leek. The result showed that the maximum concentration of THMs (354.73 µg L- 1) in celery was observed when 300 mg L-1 of chlorine for 15 min was employed. The results demonstrated that contact time significantly affected the formation and absorption of THMs. Celery demonstrates a greater absorption of THMs than others. The evaluation of lifetime cancer risk (LTCR) and hazard index (HI) for THMs across 22 simulated test conditions indicated that CHClBr2 exhibited the highest LTCR at 7.34 × 10^-6. Also, the average influence of LTCR for CHBr2Cl constituted 64%, CHBr3 accounted for 21%, CHBrCl2 represented 10%, and CHCl3 was 5%. The results showed that CHBr3 had the most effect on the hazard index, while CHCl3 showed the lowest impact. These findings assist food industry professionals in reducing THM absorption by regulating chlorine concentration and contact time during vegetable disinfection.

Graphical Abstract

Predicting few disinfection byproducts in the water distribution systems using machine learning models

Concerns regarding disinfection byproducts (DBPs) in drinking water persist, with measurements in water treatment plants (WTPs) being relatively easier than those in water distribution systems (WDSs) due to accessibility challenges, especially during adverse weather conditions. Machine learning (ML) models offer improved predictions of DBPs in WDSs. This study developed multiple ML models to predict Trihalomethanes (THMs), Haloacetic Acids (HAAs), Dichloroacetonitrile (DCAN), and N-nitrosodimethylamine (NDMA) in WDSs using data collected over 13 years (2008-2020) from 113 water supply systems (WSS) in Ontario. Data were collected tri-monthly (four times/year) following Ontario's regulatory requirements. Four common ML models-linear regressor (LR), random forest regressor (RFR), support vector regressor (SVR), and artificial neural networks with multiple folds cross-validation (ANN-MV) and single fold validation (ANN-SV)-were trained and tested using different datasets. R2 values for training datasets of THMs, HAAs, DCAN, and NDMA models ranged from 0.533 to 0.976, 0.560 to 0.980, 0.602 to 0.993, and 0.449 to 0.858, respectively. For testing datasets, R2 ranged from 0.517 to 0.939, 0.437 to 0.945, 0.565 to 0.973, and 0.517 to 0.718, respectively. Among THMs, HAAs, and DCAN, ANN-SV models were identified as the best, followed by the RFR model, whereas for NDMA, SVR was the superior model, followed by the LR model. Some models reliably predicted DBPs, suggesting they could replace costly sampling and experimental analysis for DBPs in the WDSs, thereby enhancing DBPs control in WDSs and reducing human exposure and associated risks.

Adsorption kinetics, isotherms, and selectivity of trihalomethanes and haloacetonitriles by granular activated carbon

The performance capability of granular activated carbon (GAC) adsorption in terms of disinfection by-product (DBPs) removal was investigated with synthetic water containing 1) trihalomethanes (THMs), 2) haloacetronitriles (HANs), and 3) Mix-THMs & HANs. The initial 20 min of adsorption resulted in the maximum adsorption rate, with the total THMs, total HANs, and total Mix-THMs & HANs being 4.972, 2.071, and 6.460 µg/gGAC-min, respectively. GAC dosage affects the adsorption selectivity of THMs and HANs. Under a low GAC dosage, the selectivity of GAC adsorbs more bromo-THMs than chloro-THMs. The adsorption selectivity of THMs on GAC following bromoform > dibromochloromethane > bromodichloromethane > chloroform was investigated. As the GAC concentration increased, the selectivity of THM adsorption by GAC became comparable. Chloro-HAN, in contrast to THMs, has a higher adsorption selectivity than bromo-HAN. Trichloroacetonitrile was removed by GAC more rapidly than the other HAN species when the GAC dose was increased. The toxin of bromoform was primarily eliminated through GAC adsorption, caused by a greater removal rate than that of the other THMs. As an implemented measure, GAC is introduced to reduce THMs and HANs and the toxic contents associated with THMs and HANs.

Analysis of THM formation potential in drinking water networks: Effects of network age, health risks, and seasonal variations in northwest of Iran

Various factors influence the formation of disinfection by-products (DBPs) in drinking water. Therefore, it is crucial to study the formation of DBPs and identify the associated influencing agents in water distribution networks (WDNs) to effectively prevent and control the health risks posed by DBPs. This research aimed to examine THM concentrations in the WDNs of Maragheh, Iran, focusing on seasonal variations. It also compared THM levels between new and old WDNs and assessed the health risks associated with exposure to THMs through various exposure routes. The mean concentrations of Chloroform, BDCM, DBCM, and Bromoform were 44.28 ± 18.25, 12.66 ± 5.19, 3.16 ± 0.89, and 0.302 ± 0.89 μg/L, respectively. Therefore, Chloroform was the predominant compound among the THM species, accounting for over 72 % of the total THMs (TTHMs). The average TTHMs concentration in summer (69.89 μg/L) was significantly higher than in winter (50.97 μg/L) (p < 0.05). Except for Bromoform, concentrations of other THM species in the new WDNs were considerably lower than in the old WDN (p < 0.05). The mean lifetime cancer risk (LTCR) rates for oral and dermal exposure routes to THMs were negligible and within acceptable risk levels. However, the LTCR mean values for inhalation exposure routes to THMs in winter and summer were within low (1 × 10-6 ≤ LTCR <5.1 × 10-5) and high acceptable risk levels (5.1 × 10-5 ≤ LTCR <10-4), respectively. Inhalation exposure presented the highest cancer risk among the various exposure routes. The hazard index values for oral and dermal contact with THMs were less than 1. Finally, sensitivity analysis revealed that the ingestion rate and exposure duration of THMs had the most significant positive effect on chronic daily intake (CDI) values and cancer risk. However, further comprehensive investigations are needed to develop effective solutions for reducing and controlling the precursors of DBP formation, as well as identifying suitable alternative disinfection compounds that minimize by-product formation.

Understanding How Chemical Pollutants Arise and Evolve in the Brewing Supply Chain: A Scoping Review

In this study, a critical review was carried out using the Web of ScienceTM Core Collection database to analyse the scientific literature published to date to identify lines of research and future perspectives on the presence of chemical pollutants in beer brewing. Beer is one of the world's most popular drinks and the most consumed alcoholic beverage. However, a widespread challenge with potential implications for human and animal health is the presence of physical, chemical, and/or microbiological contaminants in beer. Biogenic amines, heavy metals, mycotoxins, nitrosamines, pesticides, acrylamide, phthalates, bisphenols, microplastics, and, to a lesser extent, hydrocarbons (aliphatic chlorinated and polycyclic aromatic), carbonyls, furan-derivatives, polychlorinated biphenyls, and trihalomethanes are the main chemical pollutants found during the beer brewing process. Pollution sources include raw materials, technological process steps, the brewery environment, and packaging materials. Different chemical pollutants have been found during the beer brewing process, from barley to beer. Brewing steps such as steeping, kilning, mashing, boiling, fermentation, and clarification are critical in reducing the levels of many of these pollutants. As a result, their residual levels are usually below the maximum levels allowed by international regulations. Therefore, this work was aimed at assessing how chemical pollutants appear and evolve in the brewing process, according to research developed in the last few decades.

Adsorption, boiling or membrane filtration for disinfection by-product removal: How to make our drinking water safer?

Disinfection by-products (DBPs) generated in drinking water have become a global concern due to their potential harm to human health. Nevertheless, there are few studies about different point-of-use water treatments in household drinking water. The study aims to compare the effectiveness of three point-of-use water treatments: adsorption, boiling, and membrane filtration. The experimental results showed that the initial average concentration of volatile DBPs and non-volatile DBPs for tap water were 63.71 μg/L and 6.33 μg/L. The removal efficiency of DBPs for adsorption which were 75.6 % (the filter volumes from 0 L to 20 L) and 45.4 % (the filter volumes from 20 L to 50 L) during the service life of the filter element (50 L). Boiling had a high removal efficiency for volatile DBPs like trihalomethanes (THMs), haloacetaldehydes (HALs), haloacetonitriles (HANs), and haloketones (HKs) (90.5 %, 100 %, 100 %, and 100 %, respectively). However, boiling had a low removal efficiency which was 15 % in removing non-volatile DBPs like haloacetic acids (HAAs). Membrane filtration had a middle removal efficiency for THMs, HAAs, HALs, HKs, and HANs (45.3 %, 75.2 %, 46.5 %, 47.6 %, and 100 %, respectively). Through analysis of the correlation between dissolved organic matter (DOM) removal efficacy and DBP removal efficiency, it was found that the strongest correlation was observed between UV254 and DBP removal efficiency. Boiling showed a lower estimated cytotoxicity of DBPs compared to adsorption and membrane filtration. Cancer risk assessment of DBPs was below the specified risk range for three point-of-use water treatments. This study provides a reference for choosing point-of-use water treatments in household drinking water.

Trihalomethane prediction model for water supply system based on machine learning and Log-linear regression

Laboratory determination of trihalomethanes (THMs) is a very time-consuming task. Therefore, establishing a THMs model using easily obtainable water quality parameters would be very helpful. This study explored the modeling methods of the random forest regression (RFR) model, support vector regression (SVR) model, and Log-linear regression model to predict the concentration of total-trihalomethanes (T-THMs), bromodichloromethane (BDCM), and dibromochloromethane (DBCM), using nine water quality parameters as input variables. The models were developed and tested using a dataset of 175 samples collected from a water treatment plant. The results showed that the RFR model, with the optimal parameter combination, outperformed the Log-linear regression model in predicting the concentration of T-THMs (N25 = 82-88%, rp = 0.70-0.80), while the SVR model performed slightly better than the RFR model in predicting the concentration of BDCM (N25 = 85-98%, rp = 0.70-0.97). The RFR model exhibited superior performance compared to the other two models in predicting the concentration of T-THMs and DBCM. The study concludes that the RFR model is superior overall to the SVR model and Log-linear regression models and could be used to monitor THMs concentration in water supply systems.

New methods based on a genetic algorithm back propagation (GABP) neural network and general regression neural network (GRNN) for predicting the occurrence of trihalomethanes in tap water

Monitoring trihalomethanes (THMs) levels in water supply systems is of great significance in ensuring drinking water safety. However, THMs detection is a time-consuming task. Developing predictive THMs models using parameters that are easier to obtain is an alternative. To date, there is still no application of optimization algorithms and general regression neural networks in predicting disinfection by-products levels. This study was to explore the feasibility of back propagation neural network (BPNN), genetic algorithm back propagation (GABP) neural network and general regression neural network (GRNN) for predicting THMs occurrence in real water supply systems. The results showed that the BPNN models' prediction ability was limited (test r_p = 0.571-0.857, N₂₅ = 61.5 %-91.5 %). Optimized by the genetic algorithm (GA), GABP models were generated and exhibited better prediction performance (test r_p = 0.573 and 0.696-0.863, N₂₅ = 68.2 %-93.6 %). However, GABP models took a lot of time and their prediction performance was unstable. A GRNN was then used to generate simpler neural network models, and the resulting prediction performance was excellent (total trihalomethanes and bromodichloromethane: test r_p = 0.657-0.824, N₂₅ = 81.8 %-100 %). In general, GRNN was the best at predicting THMs concentrations among the three models. However, it is worth noting that the prediction accuracy of bromodichloromethane (BDCM) was not high, which may be due to the absence of key parameters affecting BDCM formation. Accurate predictions of THMs by GRNN with these nine water parameters made THMs monitoring in real water supply systems possible and practical.

A comprehensive appraisal on status and management of remediation of DBPs by TiO2 based-photocatalysts: Insights of technology, performance and energy efficiency

Disinfection has been acknowledged as an inevitable technique in water treatment. However, an inadvertent consequence of generation of carcinogenic and mutagenic disinfection byproducts (DBPs) is associated with the reaction of disinfectants and natural organic matter (NOM) present in water. More than 700 DBPs have been identified in drinking water. The conventional processes carried out in WTPs do not optimally ensure NOM elimination, which evokes the need for the incorporation of other processes. In this context, several physicochemical and advanced oxidation processes (AOP), such as adsorption, membrane techniques, photocatalysis, etc., have been studied for the removal of NOM from water. Photocatalysis using semiconductors has been one of the most proficient technologies, which utilizes light energy for the degradation of recalcitrant organics. The present study aims to provide a comprehensive appraisal on the performance of titanium dioxide (TiO₂) based photocatalysts in the remediation of DBPs concerning the efficacy and energy requirements of the system. Furthermore, the effect of process parameters, such as pH, catalyst dose, light intensity, etc. on the efficacy of the process was also studied. It was observed that conventional P25-TiO₂ powders were efficient in the degradation of dissolved organic carbon (DOC) (up to 90%). However, low photocatalytic activity under visible light activation is one of its significant downsides. Several modifications on the catalyst surface in many studies exhibited advantages, such as high humic acid (HA) degradation under visible light. Furthermore, doped TiO₂ catalysts have shown high total organic carbon (TOC) degradation. The photocatalytic systems have achieved a better decrease in trihalomethane formation potential (THMFP) when compared to haloacetic acid formation potential (HAAFP). The energy requirements of the photocatalytic systems are determined by electrical energy per order (EE/O), which has been observed to be lesser for doped TiO₂ and engineered TiO₂ catalysts when compared with P25-TiO₂ powders. Carbon, iron, silver, etc., based catalysts can be a promising alternative to TiO₂-based photocatalysts for the degradation of NOM, although further research is required in this direction. The present review provides critical highlights on the uses, opportunities, and challenges of TiO₂-based photocatalytic techniques for the management of DBPs and their precursors pertaining to an emerging area of water treatment.

Extra-modification of zirconium dioxide for potential photocatalytic applications towards environmental remediation: A critical review

Photocatalytic degradation is a valuable direction for eliminating organic pollutants in the environment because of its exceptional catalytic activity and low energy requirements. As one of the prospective photocatalysts, zirconium dioxide (ZrO₂) is a promising candidate for photoactivity due to its favorable redox potential and higher chemical stability. ZrO₂ has a high rate of electron-hole recombination and poor light-harvesting capabilities. Still, modification has demonstrated enhancements, especially extra-modification, and is therefore worthy of investigation. This present review provides a comprehensive overview of the extra-modifications of ZrO₂ for enhanced photocatalytic performance, including coupling with other semiconductors, doping with metal, non-metal, and co-doping with metal and non-metal. The extra-modified ZrO₂ showed superior performance in degrading the organic pollutant, particularly dyes and phenolic compounds. Interestingly, this review also briefly highlighted the probable mechanisms of the extra-modification of ZrO₂ such as p-n heterojunction, type II heterojunction, and Z-scheme heterojunction. The latter heterojunction with excellent electron-hole space separation improved the photoactivity. Extensive research on ZrO₂'s photocatalytic potential is presented, including the removal of heavy metals, the redox of heavy metals and organic pollutants, and the evolution of hydrogen. Modified ZrO₂'s photocatalytic effectiveness depends on its band position, oxygen vacancy concentration, and metal defect sites. The opportunities and future problems of the extra-modified ZrO₂ photocatalyst are also discussed. This review aims to share knowledge regarding extra-modified ZrO₂ photocatalysts and inspire new environmental remediation applications.