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Hollow fiber microfiltration (MF) and ultrafiltration (UF) membrane processes have been extensively used in water purification and biotechnology. However, complicated filtration hydrodynamics wield a negative influence on fouling mitigation and stability of hollow fiber MF/UF membrane processes. Thus, establishing a mathematical model to understand the membrane processes is essential to guide the optimization of module configurations and to alleviate membrane fouling. Here, we present a comprehensive overview of the hollow fiber MF/UF membrane filtration models developed from different theories. The existing models primarily focus on membrane fouling but rarely on the interactions between the membrane fouling and local filtration hydrodynamics. Therefore, more simplified conceptual models and integrated reduced models need to be built to represent the real filtration behaviors of hollow fiber membranes. Future analyses considering practical requirements including complicated local hydrodynamics and nonuniform membrane properties are suggested to meet the accurate prediction of membrane filtration performance in practical application. This review will inspire the development of high-efficiency hollow fiber membrane modules.

Three types of biomass of invasive plants and agrowastes, namely, the wattle bark of Acacia auriculiformis (BA), mimosa (BM), and coffee husks (BC), were converted into biochars through slow pyrolysis and investigated for their ability to remove dyes in water. The properties of the materials were characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) analysis. The BET surface area (total pore volume) of BC was 2.62 m2/g (0.007 cm3/g), far below those of BA and BM with 393.15 cm2/g (0.195 m3/g) and 285.53 cm2/g (0.153 m3/g), respectively. The optimal adsorption doses for the removal of methylene blue (MB) were found to be 2, 5, and 5 g/L for BC, BA, and BM, respectively. The suitable pH ranges for MB removal were 6–12 for BA, 7–12 for BC, and 2–10 for BM. The majority of MB (over 83%) was removed in the initial 30 min, followed by a more quasisteady state condition after the removal rate exceeded 90%. The experimental data were fitted with the kinetic models (PFO, PSO, Bangham, IDP), indicating that physicochemical adsorption, pore diffusion process, and multiple stages are the dominant mechanisms for the MB adsorption onto biochars. Finally, BA and BM showed similar adsorption efficiencies, while BC may not be favorable for use as an adsorbent due to its low surface area and low pore volume.

A submerged photocatalytic membrane reactor (SPMR) was used with suspended and immobilized N–TiO2 under visible irradiation for diclofenac (DCF) removal from wastewater. The effects of initial N–TiO2 concentrations for the SPMR with suspended N–TiO2 were determined for batch processes. Hydrogen peroxide was also coupled with the photocatalytic process. In continuous conditions, a reverse osmosis (RO) membrane was combined with the SPMR for enhancing effluent quality. DCF removal by the SPMR with suspended and immobilized N–TiO2 at a low N–TiO2 dosage (0.5 g/L) was not much different between the two systems, but increased with higher N–TiO2 dosages for the reactor with suspended N–TiO2. Coupling H2O2 with the photocatalytic process under visible irradiation enhanced the DCF removal efficiency. In continuous conditions, DCF concentrations in the photoreactor increased during the reaction time, while those in the effluent (RO permeate) were steady for both systems and both processes. The permeate flux in the reactor with suspended N–TiO2 declined faster than in the reactor with the immobilized N–TiO2. Coupling H2O2 with the photocatalytic process yielded more resistant permeate flux rates. The cake layer formed on the microfiltration membrane of the SPMR with suspended N–TiO2 under visible irradiation was denser than others after completing the process.

This study presents a greener approach to fabricate mixed matrix membranes (MMMs) by introducing malachite nanoparticles (MLC NPs) synthesized from copper sulfate, a toxic waste from many industrial applications, into polyethersulfone (PES) membrane to remove organic matter for water reclamation. The PES/MLC MMMs were characterized by XRD, FTIR, SEM(EDX), Raman Spectroscopy, Photoluminescence Spectrometry, and Contact angle measurements. Results showed that the incorporation of MLC NPs into PES altered the membrane morphology, and made the membrane more hydrophilic. At the optimal amount of 0.1 wt.% MLC NPs, PES/MLC showed the best water flux of 931.1 L/(m2 h) (LMH) with the highest breaking stability of 5.06 ± 0.33 MPa. The membrane also exhibited enhanced adsorption of organic foulants with a corresponding flux recovery rate of 0.55 ± 0.03%. The inevitable agglomeration of MLC NPs witnessed at MLC NP dosage of 1 wt.% compromised either the throughput or the mechanical integrity. The elevated NP loading rate from 0 to 1 wt.% enhanced the removal rate of organic carbon (C) from 20.8 ± 1.7% to 26.3 ± 2.3%, respectively. Optical methods demonstrated the preferential rejection of aromatic constituents by the fabricated membranes suggests their potential applicability of optical methods, particularly the fluorescence spectrometry for better prediction of treated water quality by membrane filtration.

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 investigated the influence of three different organic carbon sources including sodium acetate (SOD), glucose (GLU), and starch (STAR), on soluble microbial products (SMP), which presumably have dissimilar uptake rates and metabolic pathways, in sequencing batch reactors (SBR) and their subsequent effects on membrane fouling of ultrafiltration (UF). SMP were mainly characterized by fluorescence excitation emission matrix coupled with parallel factor analysis (EEM-PARAFAC) and size exclusion chromatography (SEC). SMP produced in SOD-fed SBR showed higher abundances of protein-like fluorescent component and large sized aliphatic biopolymer (BP) than GLU- or STAR-fed counterpart did, while the STAR-based operation resulted in more SMP enriched with humic-like fluorescence. The differences in SMP exerted marked effects on UF membrane fouling as indicated by the highest fouling potential with reversibility shown for the SMP from the SOD-fed reactor. Regardless of the carbon source, BP fraction and protein-like component exhibited the greatest extent of reversible fouling, suggesting that size exclusion plays a critical role. However, notable differences in the reversible fouling propensity of relatively smaller size fractions among the three SBRs signified the possible involvement of chemical interactions as a secondary fouling mechanism and its dependency on different carbon sources. Our results provide a new insight into the roles of carbon sources in the characteristics of SMP in biological treatment systems and their effects on the post-treatment using membrane filtration, which is ultimately beneficial to the optimization of biological treatment design and membrane filtration operation.

Tracking the variation of the algogenic organic matter (AOM) released during the proliferation of green algae and subsequent treatment processes is crucial for constructing and optimizing control strategies. In this study, the potential of the spectroscopic tool was fully explored as a surrogate of AOM upon the cultivation of green algae and subsequent coagulation/flocculation (C/F) treatment processes using ZrCl4 and Al2(SO4)3. Fluorescence excitation emission matrix coupled with parallel factor analysis (EEM-PARAFAC) identified the presence of three independent fluorescent components in AOM, including protein-like (C1), fulvic-like (C2) and humic-like components (C3). Size exclusion chromatography (SEC) revealed that C1 in AOM was composed of large-sized proteins and aromatic amino acids. The individual components exhibited their unique characteristics with respect to the dynamic changes. C1 showed the highest correlation with AOM concentrations (R2 = 0.843) upon the C/F processes. C1 could also be suggested as an optical predictor for the formation of trihalomethanes upon the C/F processes. This study sheds a light for the potential application of the protein-like component (C1) as a practical surrogate to track the evolution of AOM in water treatment or wastewater reclamation systems involving Chlorella vulgaris green algae.

In this study, the stability of the total nitrogen removal efficiency (TNRE) was modeled using an artificial neural network (ANN)-based binary classification model for the anaerobic ammonium oxidation (AMX) process under saline conditions. The TNRE was stabilized to 80.2 ± 11.4% at the final phase under the salinity of 1.0 ± 0.02%. The results of terminal restriction fragment length polymorphism (T-RFLP) analysis showed the predominance of Candidatus Jettenia genus. Real-time quantitative PCR analysis revealed the average abundance of Ca. Jettenia and Kuenenia spp. increased in 3.2 ± 5.4 × 10

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.

© 2018 Elsevier B.V. Membrane fouling by dissolved organic matter (EfOM) in secondary treated effluent is a problematic and inevitable issue during wastewater reclamation using low pressure membrane filtration. This study evaluates the performance of coagulation/flocculation (C/F) using two recently developed coagulants (namely TiCl4 and ZrCl4) in comparison to conventional alum (i.e. Al2(SO4)3) as pretreatment to remove EfOM for subsequent ultrafiltration (UF) membrane fouling mitigation. At the optimal dosage, TiCl4-based C/F pretreatment showed the greatest performance in membrane fouling mitigation, followed by ZrCl4 and then alum. The underlying mechanisms were well explained by classical fouling models and the extended Derjaguin-Landau-Verwey-Overbeek (xDLVO) theory, highlighting a dominant role of standard blocking in the fouling potential of the C/F treated EfOM. The interfacial free energy of cohesion and adhesion showed that C/F pretreatment using TiCl4 and ZrCl4 as coagulant can lower the binding affinity between EfOM molecules and between EfOM molecules and membrane surface, ultimately reduce membrane fouling. The results of size exclusion chromatography (SEC) and fluorescence excitation emission matrix- parallel factor analysis (EEM-PARAFAC) also supported the classical fouling mechanisms, providing additional insights into the potential roles of chemical interactions in the preferential removal of certain organic substances by C/F pretreatment and the chemical composition of subsequent membrane foulants. Protein-like components were highly associated with reversible fouling after the C/F, while the reversibility of humic-like substances was enhanced upon C/F pretreatment. After C/F pretreatment, small sized EfOM molecules became the dominant fraction responsible for UF membrane fouling.

In this study, different fluorescent constituents of dissolved organic matter (DOM) with terrestrial sources and their membrane fouling potentials were tracked in the hybrid ultrafiltration (UF) processes adopting UV photooxidation with TiO2 nanoparticles (NPs) as a form of pretreatment via fluorescence excitation emission matrix coupled with parallel factor analysis (EEM-PARAFAC). The photocatalytic oxidation processes consistently resulted in enhanced removal rates of DOM and membrane fouling mitigation regardless of DOM sources. EEM-PARAFAC decomposed bulk DOM into four different fluorescent DOM (FDOM) components including three humic-like components (C1, C2, and C3) and one protein/polyphenol-like component (C4). The results showed that direct hole oxidation was primarily involved in attacking large sized humic-like C1 on TiO2 surfaces, while indirect oxidation with hydroxyl radicals was responsible for the removal of the small sized humic-like C3 and C4. Under a short irradiation period of 30 min, the intermediate-sized humic-like C2 was released into solutions from adsorbed C1. Among the FDOM components, C4 was the most associated with reversible fouling of the hybrid UF system, while C1 contributed most of the fouling of the UF system without the photocatalysis. Meanwhile, C3 was the most important FDOM component responsible for irreversible fouling, and the contribution was more pronounced at longer irradiation. This study revealed the great applicability of EEM-PARAFAC in probing the extent of membrane fouling in TiO2/UV-UF hybrid systems. Considering the unique characteristics of the individual FDOM components, EEM-PARAFAC can provide valuable information providing further insight into system optimization as well as predictions of the treated water quality.

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.

Subsurface constructed wetland (SCW) appears to be an economical and environmental-friendly practice to treat nitrogen-enriched (waste) water. Nevertheless, the removal mechanisms in SCW are complicated and rather time-consuming to conduct and assessment the efficiency of new experiments. This work mined data from literature and developed the machine learning models to elucidate the effect of influent inputs and predict ammonium removal rate (ARR) in SCW treatment. 755 sets and 11 attributes were applied in four modeled algorithms, including Random forest, Cubist, Support vector machines, and K-nearest neighbors. Six out of ten input features including ammonium (NH4), total nitrogen (TN), hydraulic loading rate (HLR), the filter height (i.e., Height), aeration mode (i.e., Aeration), and types of inlet feeding (i.e., Feeding) have posed pronounced influences on the ARR. The Cubist algorithm appears the most optimal model showing the lowest RMSE i.e., 0.974 and the highest R 2 i.e., 0.957. The contribution of variables followed the order of NH4, HLR, TN, Aeration, Height and Feeding corresponding to 97, 93, 71, 49, 34, and 34%, respectively. The generalization ability to forecast ARR using testing data achieved the R 2 of 0.970 and the RMSE of 1.140 g/m 2 d, indicating that Cubist is a reliable tool for ARR prediction. User interface and web tool of final predictive model are provided to facilitate the application for designing and developing SCW system in real practice.

This study presents possible applications of machine learning (ML) methods for estimating water levels without a throughout understanding of hydrological processes and complex databases of irrigation systems. The Bac-Hung-Hai catchment, the biggest irrigation and drainage area in Vietnam, is selected as a case study due to the large database on this case consisting of 3348 samples drawn over a 21-year monitoring period. The state-of-the-art Gradient tree boosting (GTB)-based model was developed and is compared with eight other common ML methods. The proposed GTB-based model consistently showed the best performance, with the lowest value of mean-squares-error and the greatest values for R 2 and adjusted R 2 in all case studies. Moreover, over 91% of the total samples had an error rate of below 10% between the predicted and the observed values. The results suggested that the GTB model can predict water level with high accuracy, thus helping researchers and policy-makers devise proactive strategies for hydraulic regulation and 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.

Wastewater reuse is considered one of the most promising practices for the achievement of sustainable water management on a global scale. In the context of the safe reuse of water, membrane filtration is a competitive technique due to its superior efficiency in several processes. However, membrane fouling by organics is an inevitable challenge that is encountered during the practical application of membrane processes. The resolution of the membrane fouling challenge requires an in-depth understanding of many complex interactions between organic foulants and the membrane. In the last few decades, the forward osmosis (FO) membrane process, which exploits osmosis as a driving force, has emerged as an effective technology for water production with low energy consumption, thus leveraging the water-energy nexus. However, their successful application is severely hampered by membrane fouling, which is caused by such complex fouling mechanisms as cake enhanced osmotic pressure (CEOP), reverse salt diffusion (RSD), internal, and external concentration polarization as well as by the traditional fouling processes encompassing colloids, microbial (biofouling), inorganic, and organic fouling. Of these fouling types, the fouling potential of organic matter in FO has not been given sufficient attention, in particular, when FO is applied to wastewater treatment. This paper aims to provide a comprehensive overview of FO membrane fouling for wastewater applications with a special focus on the identification of the major factors that lead to the unique properties of organic fouling in this filtration process. Based on the critical assessment of organic fouling formation and the governing mechanisms, proposals were advanced for future research aimed at the mitigation of FO membrane fouling to enhance process efficiency in wastewater applications. Keywords: Forward osmosis, Organic fouling, Wastewater, Influencing factors, Characterization

Several factors play a role in chlorine consumption in drinking water networks such as microbial contamination and dissolved organic matter (DOM). This study investigated the simultaneous impacts of seasonal variations on chlorine consumption and DOM composition for a year-long period in three different full-scale water distribution networks in a southern city of China. Efforts were made to determine the association between different fluorescence moieties in DOM of finished water and chlorine consumption through excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). The chlorine consumption was also found to be directly related to N-Nitrosodimethylamine (NDMA) and NDMA-formation potential (NDMA-FP), presenting consequence of excess residual chlorine. The wet season with high rainfall showed elevated consumption of chlorine on the opposite of dry season. During wet season, humic-like component in finished water was dominant than protein-like features and its abundance decreased in dry season. Results proved that chlorine consumption was season dependent and highly influenced by DOM composition in the finished water. Unlike the bulk quality parameters and protein-like component, fluorescence moieties related to humic substances acted as a surrogate for the chlorine consumption. Based on humic-like component, a single excitation based fluorescence peak, I245/410 or I335/410, was also extracted from EEMs of finished water samples and validated on different water distribution networks. These two single peaks well-explained the trends of residual chlorine and have the potential for on-line monitoring and portable purposes. This field-based study demonstrated application of fluorescence spectroscopy in designing chlorine dose to obtain optimal residual chlorine at consumer’s tap.

Polyethersulfone (PES)/sulfonated polysulfone (SPSf)/TiO2 mixed matrix membranes (MMMs) were fabricated using non-solvent induced phase inversion (NIPS) technique for oil-in-water emulsion separation. The performance of the membrane was tailored by tuning the morphology through the addition of TiO2 nanoparticles (NPs) (0.05–0.15 wt %) and by varying the polymer concentrations (18–28 wt). The optimum concentration of TiO2 was found to be 0.075 wt %), where the water permeability and solute rejection trade-off neutralised. In this membrane the polymer concentration was 22 wt %. In terms of performance, the membrane had pure water permeance of 555.2 LMH bar-1, 90% oil rejection and 89.5% permeance recovery rate (PRR) at an initial concentration of 900 ppm. The surface porosity was 13.1%. Molecular dynamic (MD) simulations and spectroscopic analyses proved that the NPs formed hydrogen bonds with the polymer chains. This resulted in two effects with ripple impacts: a) slowed movement of polymer chains or slow solid-liquid phase separation, leading to the MMMs with the thicker top layer and high surface porosity, and b) stable and even distribution of NPs within the framework as observed with elemental mapping. Accordingly, the MMMs obtained desired asymmetric features corresponded to their overall superior performance. In conclusion, small concentrations of TiO2 NPs can be used to successfully modify morphology and separation performance of membranes used for oil-in-water emulsion filtration.

A sequential anode-cathode double-chamber microbial fuel cell (MFC) is a promising system for simultaneously removing contaminants, recovering nutrients and producing energy from swine wastewater. To improve sulfonamide antibiotics (SMs)’s removal in the continuous operating of MFC, one new pomelo peel-derived biochar was applied in the anode chamber in this study. Results demonstrated that SMs can be absorbed onto the heterogeneous surfaces of biochar through pore-filling and π-π EDA interaction. Adding biochar to a certain concentration (500 mg/L) could enhance the efficiency in removing sulfamethoxazole, sulfadiazine and sulfamethazine to 82.44 - 88.15%, 53.40 - 77.53% and 61.12 - 80.68%, respectively. Moreover, electricity production, COD and nutrients removal were improved by increasing the concentration of biochar. Hence, it is proved that adding biochar in MFC could effectively improve the performance of MFC in treating swine wastewater containing SMs.

The abatement of NOx emission, which is one of the most critical global environmental issues, is traditionally addressed using risky and costly approaches that require high reaction temperatures. To counter this drawback, much attention has been directed at the development of photocatalytic NOx abatement techniques, including photocatalytic oxidation, photocatalytic decomposition, and photo-selective catalytic reduction (photo-SCR), as they allow one to reduce the reaction temperature and increase conversion efficiency. Therefore, photocatalyst design is of crucial importance for the development of next-generation NOx abatement technologies. This review rationally summarizes and discusses recent progress in photocatalytic NOx abatement, highlighting catalyst type, removal pathway, and parameters governing the removal efficiency for each approach as well as considering strategies for the advancement of photocatalysts featuring high catalytic activities at low reaction temperatures for each process. Finally, the remaining challenges obstacles and directions for the future development of photocatalytic NOx abatement are provided.

Fouling mitigation is of critical importance to warrant the widespread application of membrane filtration. Mixed matrix membranes (MMMs) have emerged as the attractive modified edition of traditional membrane due to their enhanced filtration performance, higher rejection and less fouling potential. This study examined the performance of MMMs by incorporating double layered hydroxide nanomaterials (DLH NMs) within the polyethersulfone/sulfonate polysulfone (PES/SPSf) blend matrix. The MMM with a loading rate of 0.5 wt.% DLH NMs showed an improved performance. The MMM exhibited enhanced antifouling ability than the pristine PES/SPSf membrane for terrestrial-derived organic matter. For sodium alginate (SA), the two membranes showed similar fouling potential, and the MMM even displayed worse irreversible fouling ascribed to higher roughness, reduced negative charge alongside the specific gel-box structure of SA-Ca2+. The fouling potential was further investigated by the thermodynamic analysis. The obtained results suggested that the specific free energies (ΔGho) should be used with the free energies as a function of distance (U) to prevent any fouling misinterpretation. The work clearly implied that the organic fouling behavior during water treatment is somewhat complex and DLH NMs could be promising materials for MMMs to address terrestrial organic matter dominated water resources.

This study evaluated the performance of the high performance nanofiltration (NF) membrane by grafting the recently developed hyperbranched polyglycerol (hPG) with a unique three-dimensional (3D) globular structure and superhydrophilicity on thin-film composite (TFC) polyamide layer. The hPG modified TFC (hPG-TFC) NF membrane possessed an outstanding hydrophilic surface and high permeance without compromising salt rejection. Compared with the pristine TFC membrane, the hPG modified membrane exhibited a permeate flux increase of 30% while maintaining Na2SO4 rejection of 97%. Irrespective of foulants, the hPG grafted TFC persistently generated an enhanced antifouling ability. Among the model foulants, the humid acid shows the most severe fouling potential for NF membranes. Notwithstanding, the hPG-TFC exhibits an improved normalized water flux of 65% and flux recovery of 75%, much higher than the pristine TFC membrane (44% and 47%, respectively). In addition, in order to better understand the antifouling behavior of our hPG modified NF membranes, the Q-sense quartz-crystal microbalance with dissipation (QCM-D) was used to determine both the adsorption rate and amount of model foulants on the membrane surface. Therefore, the successful application of 3D hPG polymers on the NF membrane should serve as a source of inspiration for the surface engineering of high performance TFC membrane with high water permeability and enhanced antifouling performance in pressure-driven NF membrane processes.

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.

We introduced a facile method for the fabrication of WS2 hollow spheres and used the spheres as efficient catalysts for hydrogen evolution reaction. First, thioacetamide nanocrystals were crystallized from a saturated solution as the temperature was reduced from 40 to 25 °C. These nanocrystals acted as initial cores for inducing WS2 hollow spheres. Next, thioacetamide-WCl6 core-shells were formed upon adding WCl6 into the prepared precursor. Through hydrolysis, the thioacetamide nanocrystals were decomposed to generate H2S and NH3 gases, which in turn caused the expansion and sulfurization of WCl6 simultaneously, giving rise in WS2 hollow spheres. The diameters of the as-prepared WS2 hollow spheres were in the range of 300 nm to 2 μm. The performance of WS2 hollow spheres as catalysts for hydrogen evolution reactions was evaluated and compared with that of WS2 nanoflowers. Interestingly, WS2 hollow structures exhibited an onset potential of 125 mV and a Tafel slope of 60 mV dec−1, which was lower than that of WS2 nanoflowers (82.3 mV dec−1). Furthermore, the conductivity of WS2 hollow structures was higher than that of WS2 nanoflowers, as confirmed by impedance spectroscopy. These results indicate that the WS2 hollow spheres are promising candidates for energy conversion and storage applications.

Membrane fouling can hinder the widespread application of thin film composite (TFC) reverse osmosis (RO) for water treatment. This study evaluated a novel zwitterion-grafted TFC RO as a mean to address organic fouling for water reclamation. The membrane exhibited the best permeability at the grafting condition of 45 °C in 1 h. This modified membrane consistently possessed improved antifouling ability irrespective of organic foulants. Among individual foulants, surfactant Dodecyl Trimethyl Ammonium Chloride (DTAC) posed the worst fouling potential due to its low molecular weight and positive charge, whereas fouling induced by other substances were relatively analogous and minor. In the mixture of DTAC and proteins, the former played a key role in governing the membrane fouling. While, their interplay affected membrane fouling, the fouling extent varied upon the membrane materials. The extended Derijaguin, Landau, Verwey and Overbeek (xDLVO) theory was unable to fully describe the interactions between surfactant foulants and the membrane materials. The complementary use of quartz crystal microbalance with dissipation (QCM-D), otherwise, concurred the fouling potential and gave the plausible interpretation for fouling mechanisms by providing insightful information of foulant layer on the polyamide-coated sensor. This study provided critical insights of organic foulants’ behavior on TFC RO membrane and offered the promising industrial implication of the novel membrane.

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.

Carbon-based materials are promising candidates as charge transport layers in various optoelectronic devices and have been applied to enhance the performance and stability of such devices. In this paper, we provide an overview of the most contemporary strategies that use carbon-based materials including graphene, graphene oxide, carbon nanotubes, carbon quantum dots, and graphitic carbon nitride as buffer layers in polymer solar cells (PSCs). The crucial parameters that regulate the performance of carbon-based buffer layers are highlighted and discussed in detail. Furthermore, the performances of recently developed carbon-based materials as hole and electron transport layers in PSCs compared with those of commercially available hole/electron transport layers are evaluated. Finally, we elaborate on the remaining challenges and future directions for the development of carbon-based buffer layers to achieve high-efficiency and high-stability PSCs.