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Progress in heterogeneous advanced oxidation processes (AOPs) is hampered by several issues including mass transfer limitation, limited diffusion of short-lived reactive oxygen species (ROS), aggregation of nanocatalysts, and loss of nanocatalysts to treated water. These issues have been addressed in recent studies by executing the heterogeneous AOPs in confinement, especially in the nanopores of catalytic membranes. Under nanoconfinement (preferably at the length of less than 25 nm), the oxidant-nanocatalyst interaction, ROS-micropollutant interaction and diffusion of ROS have been observed to significantly improve, which results in enhanced ROS yield and mass transfer, improved reaction kinetics and reduced matrix effect as compared to conventional heterogenous AOP configuration. Given the significance of nanoconfinement effect, this study presents a critical review of the current status of membrane-based nanoconfined heterogeneous catalysis system for the first time. A succinct overview of the nanoconfinement concept in the context of membrane-based nanofluidic platforms is provided to elucidate the theoretical and experimental findings related to reaction kinetics, reaction mechanisms and molecule transport in membrane-based nanoconfined AOPs vs. conventional AOPs. In addition, strategies to construct membrane-based nanoconfined catalytic systems are explained along with conflicting arguments/opinions, which provides critical information on the viability of these strategies and future research directions. To show the desirability and applicability of membrane-based nanoconfined catalysis systems, performance governing factors including operating conditions and water matrix effect are particularly focused. Finally, this review presents a systematic account of the opportunities and technological constraints in the development of membrane-based nanoconfined catalytic platform to realize effective micropollutant elimination in water treatment.

Water pollution generated from intensive anthropogenic activities has emerged as a critical issue concerning ecosystem balance and livelihoods worldwide. Although optimizing wastewater treatment efficiency is widely regarded as the foremost step to minimize pollutants released into the environment, this widespread application has encountered two major problems: firstly, the significant variation of influent wastewater constituents; secondly, complex treatment processes within wastewater treatment plants (WWTPs). Based on the data collected hourly using real-time sensors in three different full-scale WWTPs (24 h × 365 days × 3 WWTPs × 10 wastewater parameters), this work introduced the potential application of Machine Learning (ML) to predict wastewater quality. In this work, six different ML algorithms were examined and compared, varying from shallow to deep learning architectures including Seasonal Autoregressive Integrated Moving Average (SARIMAX), Random Forest (RF), Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Long Short-Term Memory (LSTM). These models were developed to detect total phosphorus in the outlet (Outlet-TP), which served as an output variable due to the rising concerns about the eutrophication problem. Irrespective of WWTPs, SARIMAX consistently demonstrated the best performance for regression estimation as evidenced by the lowest values of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the highest coefficient of determination (R

This study evaluated and compared the performance of two vertical flow constructed wetlands (VF) using expanded clay (VF1) and biochar (VF2), of which both are low-cost, eco-friendly, and exhibit potentially high adsorption as compared to conventional filter layers. Both VFs achieved relatively high removal for organic matters (i.e. Biological oxygen demand during 5 days, BOD5) and nitrogen, accounting for 9.5 - 10.5 g.BOD5.m-2.d-1 and 3.5 - 3.6 g.NH4-N.m-2.d-1, respectively. The different filter materials did not exert any significant discrepancy to effluent quality in terms of suspended solids, organic matters and NO3-N (P > 0.05), but they did influence NH4-N effluent as evidenced by the removal rate of that by VF1 and VF2 being of 82.4 ± 5.7 and 84.6 ± 6.4%, respectively (P 2) and the root mean square error (RMSE) of whole fitting data achieved 74.0% and 5.0 mg.L-1, 80.0% and 0.3 mg.L-1, 90.1% and 2.9 mg.L-1, and 48.5% and 0.5 mg.L-1 for BOD5_VF1, NH4-N_VF1, BOD5_VF2, and NH4-N_VF2, respectively.

This study aims to extend and demonstrate the application of fluorescence spectroscopy for monitoring the water quality of three differently operated full-scale drinking water treatment plants located in the Shenzhen city (China). A ratio of fluorescent dissolved organic matter (FDOM), which describes relative changes in humic-like to protein-like fluorescence, was used to explain mechanisms behind the physicochemical processes. The fluorescence components obtained through individual and combined parallel factor analysis (PARAFAC) modeling revealed the presence of humic-like (C1) and protein-like (C2) structures in the DOM. The C1/C2 ratio provided a direct relationship between the seasonal variations and DOM composition. Wet season generated DOM enriched with humic-like fluorescence, while dry season caused a higher release of protein-like fluorescence. The fluorescence ratio presented unique patterns of DOM in treatment trains. The chemical pretreatment and disinfection unit processes showed a higher tendency to remove the humic-like fluorescence. However, the C1/C2 ratio increased during physical treatment processes such as coagulation-precipitation and sand filtration, indicating preferential removal of protein-like fluorescence. The DOM composition in influent directly (R2 = 0.77) influenced the relative intensities of fluorescence components in the treated water. Compared to the dry season, the wet season caused significant changes in DOM composition and produced treated water enriched with humic-like fluorescence. This fluorescence ratio offers an approach to explore the role of different treatment units and determine the factors affecting the composition of DOM in the surface water and drinking water treatment plants.

The increasing accumulation of pharmaceuticals in aquatic ecosystems could impair freshwater quality and threaten human health. Despite the adsorption of pharmaceuticals on biochars is one of the most cost-effective and eco-friendly removal methods, the wide variation of experimental designs and research aims among previous studies pose significant challenge in selecting biochar for optimal removal. In this work, literature data of 1033 sets with 21 variables collected from 267 papers over ten years (2010–2020) covering 19 pharmaceuticals onto 88 biochars were assessed by different machine learning (ML) algorithms i.e., Linear regression model (LM), Feed-forward neural networks (NNET), Deep neutral networks (DNN), Cubist, K-nearest neighbor (KNN), and Random forest (RF), to predict equilibrium adsorption capacity (Qe) and explore adsorption mechanisms. LM showed the best performance on ranking importance of input variables. Except for initial concentration of pharmaceuticals, Qe was strongly governed by biochars' properties including specific surface area (BET), pore volume (PV), and pore structure (PS) rather than pharmaceuticals’ properties and experimental conditions. The most accurate model for estimating Qe was achieved by Cubist, followed by KNN, RF, KNN, NNET and LM. The generalization ability was observed by the tuned Cubist with 26 rules for the prediction of the unseen data. This study not only provides an insightful evidence for data-based adsorption mechanisms of pharmaceuticals on biochars, but also offers a potential method to accurately predict the biochar adsorption performance without conducting any experiments, which will be of high interests in practice in terms of water/wastewater treatment using biochars.

The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.

Over the last several decades, the frequent occurrence of algal bloom in drinking water supplies, driven by increasing anthropogenic input and climate change, has posed serious problems for membrane filtration processes, resulting in reduced membrane permeability and increased energy consumption. It is essential to comprehensively understand the characteristics of algal dissolved organic matter (DOM) and the subsequent effects on the filtration processes for better insight into membrane fouling mitigation. Many studies have revealed that algal DOM has displayed unique characteristics distinguished from other sources of DOM with respect to the chemical composition, the structures, and the molecular weight distributions. Algal DOM is considered to be a major obstacle in understanding membrane fouling due to its complicated interactions among dissimilar algal DOM constituents as well as between algal DOM and membrane material matrices. The present review article summarizes (1) recent characterizing methods for algal DOM, (2) environmental factors affecting the characteristics of algal DOM, (3) the discrepancies between algal DOM and other sources of aquatic DOM, particularly terrestrial sources, and (4) potential fouling effects of algal DOM on membrane filtration processes and their associations with algal DOM characteristics. A broad understanding of algal DOM-driven membrane fouling can lead to breakthroughs in efficient membrane filtration processes to treat algal bloom water sources.

In biological wastewater treatment systems, extracellular polymeric substances (EPS) are continuously excreted as a response to environmental changes and substrate conditions. It could severely affect the treatment efficacy such as membrane fouling, dewaterability and the formation of carcinogenic disinfection by-products (DBPs). The heterogeneous dissolved organic matter (DOM) with varying size and chemical nature constitute a primary proportion of EPS. In the last few decades, fluorescence spectroscopy has received increasing attention for characterizing these organic substances due to the attractive features of this low-cost spectroscopic approach, including easy sample handling, rapid, non-destructive and highly sensitive nature. In this review, we summarize the application of fluorescence spectroscopy for characterizing EPS and provide the potential implications for online monitoring of water quality along with its limitations. We also link the dynamics of fluorescent dissolved organic matter (FDOM) in EPS with operational and environmental changes in wastewater treatment systems as well as their associations with metal binding, membrane fouling, adsorption, toxicity, and dewaterability. The multiple modes of exploration of fluorescence spectra, such as synchronous spectra with or without coupling with two-dimensional correlation spectroscopy (2D-COS), excitation-emission matrix (EEM) deconvoluted fluorescence regional integration (FRI), and parallel factor analysis (PARAFAC) are also discussed. The potential fluorescence indicators to depict the composition and bulk characteristics of EPS are also of interest. Further studies are highly recommended to expand the application of fluorescence spectroscopy paired with appropriate supplementary techniques to fully unravel the underlying mechanisms associated with EPS.

N-nitrosamines have been identified as emerging contaminants with tremendous carcinogenic potential for human beings. This study examined the seasonal changes in the occurrence of N-nitrosamines and N-nitrosodimethylamine formation potential (NDMA-FP) in drinking water resources and potable water from 10 drinking water treatment plants in a southern city of China. The changes in N-nitrosamines are well correlated with dissolved organic matter (DOM), particularly fluorophores, which were measured and compared between traditional fluorescence indices and excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Four of N-nitrosamine species including N-nitrosodimethylamine (NDMA), N-Nitrosodibutylamine (NDBA), N-Nitrosopyrrolidine (NPYR), and N-Nitrosodiphenylamine (NDPhA) are found to be abundant compounds with an average of 29.5% (26.7%), 20.0% (25.2%), 18.9% (16.0%), and 9.0% (9.9%) in the source (and treated) water, respectively. The sum of N-nitrosamines concentration is recorded to be low in the wet season (July-September), whereas the dry season (October-December) provided opposite impacts. EEM-PARAFAC modeling indicated the predominance of humic-like component (C1) in the wet season while in the dry season the water was dominant in protein-like component (C2). All the N-nitrosamines excluding NDPhA and N-Nitrosomorpholine (NMOR) showed a strong association with protein-like component (C2). In contrast, humic-like C1, which was directly influenced by rainfall, was found to be a suitable proxy for NMOR and NDPhA. The results of this study are valuable to understand the correlation between different N-nitrosamines and DOM through adopting fluorescence signatures.

In this study, the complex degradation behavior of natural organic matter (NOM) was explored using photocatalytic oxidation systems with a novel catalyst based on a hybrid composite of zinc-bismuth oxides and g-C3N4 (ZBO-CN). The photooxidation system demonstrated the effective removal of NOM under low-intensity visible light irradiation, presenting removal rates of 53–74% and 65–88% on the basis of dissolved organic carbon (DOC) and the UV absorption coefficient (UV254), respectively, at 1.5 g/L of the catalyst. The NOM removal showed an increasing trend with a higher ZBO-CN dose. Comparative experiments with the hole and OH radical scavengers revealed that the direct oxidation occurring on the catalyst's surface might be the governing photocatalytic mechanism. Fluorescence excitation emission matrix-parallel factor analysis (EEM-PARAFAC) revealed the individual removal behavior of the different constituents in bulk NOM. Different tendencies towards preferential adsorption and subsequent oxidative removal were found among dissimilar fluorescent components within a bulk terrestrial NOM, following the order of terrestrial humic-like (C1) > humic-like (C2) > microbial humic-like (C3) components. The result suggests the dominant operation of π-π and/or hydrophobic interactions between the NOM and the catalyst. The discriminative removal behavior was more pronounced in visible light versus UV-activated systems, probably due to the incapability of visible light to excite e - h+ pairs of ZnO and the triplet state of NOM. The high photoactivity and structural stability of ZBO-CN under visible light implies its potential for an effective, low-cost and energy-saving treatment technology to selectively remove large sized humic-like substances from water.

© 2018 Elsevier Ltd The relative ratios of chemical oxygen demand (COD) to nitrogen (N) in wastewater are known to have profound effects on the characteristics of soluble microbial products (SMP) from activated sludge. In this study, the changes in the SMP characteristics upon different COD/N ratios and the subsequent effects on ultrafiltration (UF) membrane fouling potentials were examined in sequencing batch reactors (SBR) using excitation emission matrix-parallel factor analysis (EEM-PARAFAC) and size exclusion chromatography (SEC). Three unique fluorescent components were identified from the SMP samples in the bioreactors operated at the COD/N ratios of 100/10 (N rich), 100/5 (N medium), and 100/2 (N deficient). The tryptophan-like component (C1) was the most depleted at the N medium condition. Fulvic-like (C2) and humic-like (C3) components were more abundant with N rich wastewater. Greater abundances of large size biopolymer (BP) and low molecular weight neutrals (LMWN) were found under the N deficient and N rich conditions, respectively. SMPs from various COD/N exhibited a greater degree on membrane fouling following the order of 100/2 > 100/10 > 100/5. C1 and C2 had close associations with reversible and irreversible fouling, respectively, while the reversible fouling potential of C3 depended on the COD/N ratios. No significant impact of COD/N ratio was observed on the relative contributions of SMP size fractions to either reversible or irreversible fouling potential. However, the COD/N ratios likely altered the BP foulants' composition with greater contribution of proteinaceous substances to reversible fouling under the N deficient condition than at other N richer conditions. The opposite trend was observed for irreversible fouling. Our results provided further insight into changes in different SMP constitutes and their membrane fouling in response to microbial activities under different COD/N ratios.

This study assessed the relative contributions of different constitutes in dissolved organic matter (DOM) with two different sources (i.e., urban river and effluent) to membrane fouling on three types of ultrafiltration (UF) membranes via excitation emission matrix - parallel factor analysis (EEM-PARAFAC), size exclusion chromatography (SEC), and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). Two polyethersulfone membranes with different pore sizes and one regenerated cellulose membrane were used as representative hydrophobic (HPO) and hydrophilic (HPI) UF membranes, respectively. Although size exclusion effect was found to be the most prevailing rejection mechanism, the behaviors of individual fluorescent components (one tryptophan-like, one microbial-humic-like, and terrestrial humic-like) and different size fractions upon the UF filtration revealed that chemical interactions (e.g., hydrophobic interactions and hydrogen bonding) between DOM and membrane might play important roles in UF membrane fouling, especially for small sized DOM molecules. Based on the molecular level composition determined by FT-ICR-MS, the CHOS formula group showed a greater removal tendency toward the HPO membrane, while the CHONS group was prone to be removed by the HPI membrane. The changes in the overall molecular composition of DOM upon UF filtration were highly dependent on the sources of DOM. The molecules of more acidic nature tended to remain in the permeate of effluent DOM, while the river DOM was shifted into more nitrogen-enriched composition after filtration. Regardless of the DOM sources, the HPO membrane with a smaller pore size led to the most pronounced changes in the molecular composition of DOM.