Separation membranes with inherent antiwettability and stability are highly desirable for membrane distillation (MD) in practical applications. In this study, hydrophilic–hydrophobic dual-layer membranes composed of a dense poly (vinyl alcohol)/halloysite nanotube (PVA-HNT) layer and a microporous polytetrafluoroethylene (PTFE) layer were fabricated to improve wetting and fouling resistance during the MD process. The incorporation of the HNT manipulated the crystallization and chain mobility of PVA, endowing the PVA-HNT layer with tunable water transport properties by adjusting the level of HNT loading. Benefiting from the hydrophilic top layer on PTFE, the dual-layer membrane with an optimal HNT loading of 5 wt% showed stable water vapor flux (7.6 kg/m2·h) while maintaining salt rejection above 99.95%. This performance was achieved using a 3.5 wt% NaCl feed solution with 0.4 mM sodium dodecyl sulfate at a feed temperature of 50 °C and permeate temperature of 10 °C. In contrast, the pristine PTFE membrane suffered from severe pore wetting, with its salt selectivity dropping from 99.5% to 91.5%. Antifouling performance was further evaluated using real landfill leachate in a 50 h treatment. The dual-layer membrane with a 5 wt% HNT maintained stable separation behavior with a 15.3% decrease in water flux, whereas the flux of the PTFE membrane declined by 70.5% in 30 h of operation. A distinct fouling layer was observed on the PTFE membrane surface after the operation, while no obvious fouling was identified on the dual-layer membrane, confirming its superior antifouling properties.

Membrane fouling is a key issue limiting membrane distillation (MD) technology, especially when organic and inorganic fouling coexist and produce complex interactions. In this study, Bovine Serum Albumin (BSA) was employed as a model organic foulant to represent proteinaceous contaminants. The impact of a protein-inorganic salt system on MD performance was investigated by adding inorganic salts with different valencies, namely calcium chloride (CaCl2) and sodium chloride (NaCl). The results indicate that the addition of cations significantly exacerbates membrane fouling and increases the difficulty of cleaning. Na+ reduces the interaction energy barrier between the membrane and BSA to the lowest level, while facilitating the adsorption of substantial quantities of hydrophobic compounds on the membrane surface, resulting in a thicker fouling layer. In contrast, the presence of Ca2+ promotes the adsorption of small molecules onto the membrane surface, forming a more loosely structured and hydrophilic fouling layer. Compared with Na+-induced fouling, Ca2+-induced fouling exhibits lower overall severity but shows greater resistance to cleaning. This increased resistance is attributable to BSACa2+ bridging complexes, which are more difficult to dislodge during cleaning. In the mixed inorganic salt ion system, the interaction between the two inorganic salt ions produces an intermediate level of contamination compared to the single cation system.

Polyamide (PA) membranes hold considerable promise for the removal of emerging contaminants (ECs) from water and wastewater. However, the key factors that govern EC rejection remain elusive, hindering the rational design of high-performance membrane for ECs. Herein, we present an integrated machine learning (ML) framework that couples molecular structure with physics-based models to elucidate the determinants of EC rejection across both macroscopic and molecular levels. Model-interpretation analyses at the macroscopic level identify size exclusion as the primary factor governing rejection across all ECs. At the molecular level, attention scores elucidate the role of functional groups of ECs in altering solute-membrane interactions, revealing that the same type of functional groups can exert category-dependent effects. For example, polar groups such as -COOH and -OH predominantly regulate electrostatic interactions in per- and polyfluoroalkyl substances (PFASs), whereas they participate mainly in hydrogen bonding and hydrophobic interactions for personal care products (PPCPs) and endocrine disrupting chemicals (EDCs). This approach offers a novel strategy for mechanism identification, paving the way for the design of advanced membranes with enhanced EC removal efficiency.

Tire microplastics (TMs), as a prominent environmental source of microplastic pollution, are aged and accumulated during their migration through drainage systems and sewage treatment facilities, most of which trapped in excess sludge. Consequently, this study systematically examines the concentration-dependent effects of TMs and aged TMs (ATMs) on anaerobic fermentation of excess sludge. Results showed that TMs and ATMs negatively affected acidogenic fermentation and organic matter hydrolysis. Low TMs concentration (0.002 g/g-VSS) and high ATMs concentration (0.2 g/g-VSS) resulted in concentration reductions of volatile fatty acids, soluble chemical oxygen demand, proteins, and polysaccharides ranged from 16.28 % to 40.40 % relative to the control group. In general, the detrimental effect demonstrated a positive correlation with ATM concentration. Conversely, low TMs concentration exerted a ‌significant inhibitory impact compared with high concentration, which contradicted the hormetic effect. In response to TMs/ATMs stress, the microorganisms employed extracellular polymeric substances (EPS) secretion as a defense mechanism, with pronounced‌ variations examined in protein content within tightly bound EPS and polysaccharide content in loosely bound EPS. Comprehensive mechanistic analysis revealed that although the incorporation of TMs/ATMs into fermentation system improved electron transfer efficiency through elevating electron transport system activity and sludge conductivity, TMs demonstrated superior interspecies electron transfer (IET) enhancement compared to ATMs, coupled with material-dependent suppression of hydrolytic and acidogenic bacterial consortia, which potentially altering the acid generation and microbial dynamics. Overall, our study provides critical insights for optimizing TMs control in sludge treatment by leveraging their differential profiles toward IET and bacterial consortia.

Environmental transformations and multi-source mixing of organic contaminants (OCs) in real river systems substantially complicate reliable source identification during downstream transport. This study proposes a molecularly resolved source-tracking method within a non-targeted screening (NTS) framework that integrates multidimensional chemical fingerprinting with along-stream dynamic tracking, enabling stable source identification under complex transport and transformation conditions. By organizing compounds into chemically coherent molecular families, the proposed multidimensional fingerprinting approach amplifies chemical information, expanding 162-929 source-specific compounds into 253-1733 structurally and transformation-resolved attribute entries per source. This expansion allows source information to propagate downstream despite continuous molecular-level structural evolution. Across the river network, the method continuously tracked source-associated chemical signals and generated spatial contribution patterns that converged with those obtained through least-squares similarity analysis, while retaining compound-level and process-level interpretability. Compared with conventional static, compound-based tracking approaches, the number of traceable compounds increased by approximately 4.61-fold (from 23 to 106). Notably, effective source discrimination was maintained even when the chemical compositions of OCs from different sources were highly similar. A chemically interpretable and process-oriented source-tracking strategy is presented, advancing NTS from static detection toward dynamic analysis and providing a feasible and robust pathway for contaminant source identification in complex riverine systems.

Membrane fouling and scaling are significant impediments to the application of membrane distillation (MD) in treating actual hypersaline organic wastewater. Identifying effective cleaning strategies is a key approach to addressing membrane fouling caused by complex substances with diverse chemical properties. This work investigated the characteristics of membrane fouling evolution under acid and alkaline cleaning in actual hypersaline organic wastewater systems for the first time. Both acid and alkaline cleaning effectively restored membrane flux, with acid cleaning achieving up to 99.36 % recovery and alkaline cleaning exceeding 80 %. The efficiency of acid cleaning was consistently superior to that of alkaline cleaning for feed solutions with varying organic-to-mineral ratios. SEM and EDS mapping analyses showed that acid cleaning more effectively thinned the fouling layer and exposed the underlying hydrophobic membrane surface. A non-targeted screening method based on GC x GC-TOFMS identified the evolution of the fouling layer, challenging the conventional understanding that alkaline cleaning is the most effective approach for organic membrane fouling. Acid cleaning effectively removed carboxylic acids by disrupting Ca2+ complexes and reducing membrane affinity under low pH conditions, while alkaline cleaning preferentially degraded esters, amides, and amines through hydrolysis, highlighting their distinct mechanisms for targeting different DOM types. This work provides new insights into membrane fouling control and offers critical guidance for advancing the practical implementation of MD systems.

Understanding the hydrochemical characteristics and formation mechanisms of rivers in the Huxi Catchment is essential for water resource conservation, as these rivers serve as the primary water source for Taihu Lake. A total of 14 surface water samples were collected from the rivers in Huxi catchment, and the concentrations of seven major ions- namely, Na+, K+, Ca2+, Mg2+, Cl-,SO42-, and HCO3- -were determined. Positive Matrix Factorization (PMF), Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR), and the Principal Component Analysis-based Endmember Mixing Model (PCA-EMM) were employed to quantify the contributions of anthropogenic activities. While APCS-MLR can only identify the impacts of human activities, PMF and PCA-EMM can further distinguish between agricultural activities and wastewater discharge. Significant positive correlations were observed between the PMF and PCA-EMM results, but PMF overestimated the contribution of anthropogenic impacts. PCA-EMM showed that the natural background accounted for 63%, while human activities contributed 37% (domestic sewage 23%, agricultural activities 14%). By integrating ion composition data from representative sources, PCA-EMM overcomes the limitations of traditional methods that lack source verification and provides robust methodological support for the source apportionment of water chemistry.

Underwater defect detection is markedly hampered by image degradation from light scattering in turbid water, obscuring critical defect features. Herein, the CTS-SH polyelectrolyte complexes were constructed in a two-stage synergistic flocculation strategy for rapid clarification under turbid conditions. CTS was first added to induce pre-flocculation and form initially small flocs. Then, the addition of SH and CTS constructed complexes for further floc growth. Flocculation performance was evaluated in simulated turbid water, while zeta potential, SEM, FTIR, and XPS were employed to elucidate the structural characteristics and underlying mechanisms. Raw water sampled from the SX Dam was used to verify practical applicability. Under simulated turbid-water conditions, the synergistic mode required only 2.45 ± 0.14 min of settling after stirring to reach the target turbidity for clear defect detection, reducing the required time by 64.80% compared with CTS-only system. The CTS-SH complexes formed a three-dimensional leaf network, which offered numerous attachment sites and enhanced sweep capture, increasing floc projected area by approximately 103 times. In raw water tests, defect contours and clear defects could be identified within 2.5 and 7.5 min, respectively. These findings demonstrate a green and effective strategy for rapid clarification and underwater visual detection in turbid environments.

Innovative wastewater treatment strategies are required to achieve industrial zero liquid discharge and promote resource recovery. After reverse osmosis (RO) treatment of food processing wastewater, the resulting concentrate contains elevated nitrogen and phosphorus as well as high levels of salinity related ions, posing new challenges for wastewater treatment. In this study, we developed a continuous flow photobioreactor using Chlorella vulgaris to treat reverse osmosis concentrate (ROC) from a food processing facility, enabling simultaneous pollutant removal and biomass valorization. By adjusting the update rate via peristaltic pumps, we investigated nutrient removal performance and microalgal growth dynamics under continuous operation. To optimize the update rate, quadratic regression was used to describe the relationships between the update rate and both nutrient removal performance and microalgal growth status. The results showed that an update rate of 0.15-0.20 d(-1) yielded the highest biomass productivity (37.10 mg center dot L-1 center dot d(-1)) and the highest nitrogen removal rate (5.50 mg center dot L-1 center dot d(-1)). In addition, changes in nutrient loading driven by the update rate altered the chemical environment for microalgal growth. Transcriptomic analysis was used to elucidate stress related molecular responses in Chlorella cultivated in ROC under different nutrient loading conditions. Under low nutrient loading (50% water recovery ROC), genes involved in photosynthesis, carbon fixation, nitrogen metabolism, ascorbate biosynthesis, and carotenoid biosynthesis were significantly altered. In contrast, under high nutrient loading (80% water recovery ROC), the metabolic state was more stable. ROC produced at 80% water recovery was a promising low cost and nutrient rich alternative medium for microalgal cultivation, offering potential for sustainable water reuse and biomass production. These findings provide a theoretical and practical basis for integrating microalgal biotechnology into ROC management in food industry wastewater treatment.

Nanofiltration (NF) membranes hold promise for energyefficient liquid separation, but achieving high permeance and precise separation membrane via a facile approach remains a great challenge. Herein, we introduce guanidinoacetic acid (GAA) as an economical additive to modify interfacial polymerization (IP) for highperformance NF membranes. Comprehensive characterizations and simulations revealed that GAA regulates the IP process through two mechanisms: (1) modulation of piperazine (PIP) diffusion kinetics at the aqueous-organic interface and (2) covalent incorporation of GAA that triggers in situ modification within the polyamide (PA) layer. This controlled diffusion and reaction kinetics produced a thinner, more electronegativity PA layer with an optimized microstructure. Benefiting from these optimized physicochemical properties, the GAA-functionalized membranes exhibit a 2.3-fold increase in water permeance compared to conventional NF membranes, while maintaining high Na2SO4 rejection (>98.5 %) and excellent removal efficiency for organic micropollutants. Moreover, the modified membranes achieve superior ion selectivity, particularly for the separation of Ca2+/SO42-, surpassing the state-of-the-art PA-based NF membranes. The incorporation of GAA presents a rational design paradigm for engineering selective layers in NF membranes, thereby advancing the prospects of NF technology for sustainable water purification.

ABSTRACT Switching water sources is essential for effective water resource management and pipeline maintenance, ensuring water supply stability and reducing reliance on a single source. However, improper water source selection can disrupt the chemical balance within pipelines and accelerate corrosion. This study simulated the process of switching water sources and employed electrochemical analysis, such as scanning electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy, to investigate the impact of water source switching on pipeline corrosion. During the initial switching phase, severe pipeline corrosion was observed, along with significant variations in water quality indicators that substantially influenced corrosion. Electrochemical analysis revealed a decrease in pipeline open circuit potential and a negative shift in the polarization curve, indicating an increased tendency for corrosion. Corrosion could be relieved as the pipeline adapted to the new water quality. Microscopic analysis demonstrated that switching water sources affected the structure and stability of the corrosion product film. Investigations into different water source switching ratios indicated that corrosion levels should not be evaluated based on a single parameter; an appropriate blend of water sources is necessary to effectively mitigate corrosion. This study provides valuable insights for water source management, pipeline maintenance, and efficient water supply strategies.

Microbial electrolysis cell (MEC) is an alternative to conventional sludge treatment process with great energy-recovery potential. However, hydrolysis is considered as a rate-limiting step in MEC. In this study, ozone (O3) pretreatment was successfully applied to disintegrate sludge matrix and accelerate microbial electrolysis. At 100-250 mg·g-1 (O3/SS), rapid SCOD increment and SS reduction rates were observed with increased O3 dosage. Afterwards, the mass transfer from gas to liquid was inhibited and oxidation reactions between O3 and organics occurred, which resulted in a declining disintegration rate. At favorable dosage of 250 mg·g-1 (O3/SS), the degree of disintegration was 17 % and SS reduction reached 44.9 %. A lab-scale MEC experiment was performed by feeding ozonated sludge. Results showed that O3 pretreatment yielded 8.3-times increment in biogas production rate. In addition, O3 pretreatment improved the organics removal and bioelectrochemical efficiency during microbial electrolysis, achieving 74.50 % of VSS removal rate and 77.56 % of TCOD removal rate, with gas yield increased by 7.5 times and cathodic hydrogen recovery increased by 7.40 %. The FT-IR spectra indicated negligible difference between influent extracellular biological organic matter (EBOM) and effluent EBOM, which suggested the function of O3 pretreatment was to accelerate microbial electrolysis reactions due to sludge disintegration. Furthermore, the ozonation pretreatment facilitated the enrichment of exoelectrogens and collaborative bacteria in MEC, collectively enhancing MEC performance. This study provides a theoretical reference for enhanced bioelectrochemical treatment of complex heterogeneous mixture with soluble/insoluble organic matters.

With the increasing prevalence of emerging contaminants (ECs) in the environment, gaining a deeper understanding of the chemical information pertaining to the contamination source is a crucial step toward effective prevention and control of these ECs. This study presents a novel strategy for analyzing the chemical information of contamination sources using gas chromatography-high resolution mass spectrometry (GC-HRMS) and demonstrates it on landfill leachate, a common and representative environmental contamination source. Initially, a non-targeted screening approach using HRMS was used to characterize a total of 5344 organic compounds with identification confidence levels 1 and 2 in 14 landfill leachate samples. Leveraging this as a base data set, the similarity analysis was first performed, and the classification fingerprints exhibited a pronounced level of similarity. Second, 169 characteristic marker contaminants with important and significant differences were identified in the 3 groups of landfill leachate with different solid waste compositions (mostly kitchen waste, mostly plastic & daily chemical product waste, and proportion average) by difference analysis. Finally, 101 hazardous chemicals (HCs) were screened in the data set. The results demonstrated that a class of contamination source exhibited certain common characteristics, while different groups of samples had their own distinct contamination signatures. This work offers a unique perspective on the interpretation of chemical information from contamination sources, aiming to provide a valuable reference for environmental pollution management.

The ozone micro-bubbles (OCBs) technology is increasingly gaining traction as a promising alternative method for organic compounds removal in wastewater. Nevertheless, there is a scarcity of literature addressing the molecular-level transformation of organic compounds during OCBs treatment. In this work, the secondary effluent from a wastewater treatment plant was treated with ozone milli-bubbles (OLBs) and OCBs, and the fate of organic compounds at the molecular level was investigated using comprehensive two-dimensional gas chromatography quadrupole time-of-flight mass spectrometry (GC x GC-QTOF-MS). The findings revealed that, compared to OLBs, OCBs increased the total mass transfer coefficient by 1.46 times and the half-life of ozone by 4 times. Consequently, OCBs enhanced the removal rates of CODcr, NH4+-N, UV254, and TOC at the 30-min mark by 8.91%, 8.65%, 10.11%, and 2.15%, respectively. In the raw water, 710 organic compounds were detected, decreasing to 668 and 478 after treatment with OLBs and OCBs, respectively. Furthermore, the organic compounds with higher molecular weight and unsaturation degree were more prone to mineralization in the OCBs process. It was also identified that OCBs exhibited nearly 100% removal of amines, unsaturated hydrocarbons, aldehydes, phenols, and aromatic amides. It is noteworthy that, among the 15 identified emerging contaminants (ECs), the removal efficiency of OCBs (53.3%) was higher than that of OLBs (33.3%), with fewer by-products. More deeply, based on 30 common reactions, the primary reactions occurring in OLBs treatment were dealkylations, whereas the abundant hydroxyl radicals in OCBs treatment facilitated the oxidation reaction (+O). This study contributes to the exploration of the potential of OCBs technology in treating secondary effluent, providing invaluable insights for its rational application in practical scenarios.

Membrane distillation (MD) is a promising technology for the reclamation of hypersaline organic wastewater due to its complete rejection of non-volatile compounds. In this study, a hybrid treatment process combining conventional biological treatment (two-stage activated sludge), coagulation-sedimentation, and MD was evaluated for achieving 80 % wastewater recovery. Experimental results showed that when flocculated effluent was used as the feed, stable MD performance was maintained over four consecutive running cycles without membrane cleaning, producing high-quality permeate with TDS of 8.76 +/- 1.35 mg/L and COD of 1.29 +/- 0.09 mg/L. To gain molecular-level insights into membrane fouling mechanisms, a non-targeted screening method based on twodimensional gas chromatography-time-of-flight mass spectrometry was coupled with traditional water quality analysis and surface characterization. The results revealed that polar compounds, such as carboxylic acids, alcohols, and amides, tended to remain dissolved in the aqueous phase rather than contributing directly to membrane fouling. In contrast, membrane foulants were predominantly composed of low-polarity and structurally stable compounds that adsorbed and enriched on the hydrophobic membrane surface. Comparative analysis identified five key structural classes of dissolved organic matter that were most prone to accumulation and fouling: aliphatic saturated hydrocarbons, aliphatic esters, aromatic esters, oxygen-containing heterocycles, and nitrogen-containing heterocycles. This work provides a comprehensive understanding of pollutant fate and membrane fouling behavior in a two-stage biological-coagulation-MD hybrid system, offering practical implications for optimizing pretreatment strategies and improving the operational stability of MD in hypersaline wastewater reuse.

Electrocatalytic degradation of urea contaminant for wastewater treatment presents great promise to enable green environmental remediation and simultaneously produce clean H-2 energy. However, the energy efficiency is mainly restricted by the sluggish urea oxidation reaction (UOR). In this study, we report a temple-assisted approach for the synthesis of Zn doped based-Co nanoarray catalysts for urea electrolysis. The nanoarray structure and Zn doping enlarge the exposure of the active sites and tune the electronic configuration of based-Co catalysts, favoring the hydrogen evolution reaction (HER) and UOR. Specifically, Zn-CoSe2 cathode presents a relatively low overpotential (200 mV) and a desirable Faradaic efficiency (97.2 %) of HER to support the current density of 100 mAcm(-2) in alkaline solution. When the Zn-CoP anode is utilized for the UOR, a potential of 1.3 V is required to drive a current density of 50 mAcm(-2) in a urea-alkaline solution. In-situ Raman unveiled that the Se sites are identified as the H-2-evolving center while the Co site was responsible for dissociating H2O. Further operando characterization indicated that a shortened oxidation pathway, characterized by low Co3+ generation, accounts for the efficient degradation of urea., which is obviously distinguished from the four-electron-transfer pathway of OER with higher Co4+. This work exhibits significant potential for removing urea coupled with economical and sustainable H-2 production.

Gas chromatography-high resolution mass spectrometry (GC-HRMS), with its superior qualitative capability, has become a powerful tool for non-targeted screening (NTS) of volatile and semi-volatile organic contaminants in complex water matrices. However, conventional workflows rely heavily on reference standards, limiting their ability to capture the chemical diversity present in real samples. This study proposes an alternative three-stage strategy that prioritizes real sample data for method development prior to standard-based validation. Using liquid-liquid extraction (LLE), the extraction efficiency of four common solvents-dichloromethane (DCM), ethyl acetate (EAC), n-hexane (HEX), and methyl tert-butyl ether (MTBE)-was systematically evaluated across five representative water types according to this strategy. Multidimensional data analysis revealed that the DCM-MTBE solvent combination achieved the highest average chemical space coverage and satisfactory extraction efficiency across different matrices. Validation with 152 reference compounds confirmed 100 % detection accuracy and an average recovery of 92.38 %. The study highlights the value of integrating real sample data into NTS workflow development and provides a practical, extensible solution for enhancing the detection of unknown pollutants in aquatic environments. This work also offers a generalizable framework that can guide future pretreatment optimization in non-targeted analysis and support more reliable environmental risk assessments.

To adapt to the trend of increasing miniaturization and high integration of microelectronic equipments, there is a high demand for multifunctional thermally conductive (TC) polymeric films combining excellent flame retardancy and low dielectric constant (ε). To date, there have been few successes that achieve such a performance portfolio in polymer films due to their different and even mutually exclusive governing mechanisms. Herein, we propose a trinity strategy for creating a rationally engineered heterostructure nanoadditive (FG@CuP@ZTC) by in situ self-assembly immobilization of copper-phenyl phosphonate (CuP) and zinc-3, 5-diamino-1,2,4-triazole complex (ZTC) onto the fluorinated graphene (FG) surface. Benefiting from the synergistic effects of FG, CuP, and ZTC and the bionic lay-by-lay (LBL) strategy, the as-fabricated waterborne polyurethane (WPU) nanocomposite film with 30 wt

Sequence decomposition improves the prediction accuracy of deep learning models by preprocessing time series into different frequency components. However, research on the systematic evaluation of hybrid models (decomposition-deep learning) on large datasets and the impact of data sparsity on their scalability remains limited. This study assessed the predictive capabilities of 60 hybrid model combinations for total nitrogen and total phosphorus using data from 350 monitoring stations in China. The Empirical Wavelet Transform-Gated Recurrent Unit (EWT-GRU) model achieved optimal performance, improving prediction accuracy at 88 % of stations with an average 47.7 % reduction in Root Mean Square Error (RMSE). Evaluation under various data sparsity scenarios demonstrated that the EWT-GRU model maintained robust performance in both temporal (lowfrequency) and spatial (cross-site) predictions. This study establishes a reliable framework for large-scale water quality prediction and sets a new paradigm for understanding the spatiotemporal scalability in water quality prediction.