Microbial biofilms pose persistent challenges in industrial water systems due to their contribution to biofouling, biocorrosion, and reduced operational efficiency. Advances in in vitro biofilm models, ranging from static microtiter plate assays to dynamic systems such as flow cells and microfluidic platforms, have enabled detailed investigation of biofilm structure, physiology, and antimicrobial tolerance towards the management of biofilms in varied systems. In parallel, microscopy, imaging technologies, spectroscopy, and molecular tools provide complementary insights into biofilm composition, heterogeneity, and ecological function. Despite widespread use of these tools in medical and environmental microbiology, their application to industrial water systems remains uneven. This review critically evaluates in vitro biofilm growth models and characterization techniques with specific emphasis on their relevance, strengths, and limitations in the context of industrial water systems. We highlight knowledge gaps and methodological considerations essential for improving biofilm monitoring and control strategies in these engineered environments.
To address the challenges associated with elevated salinity-related background conditions and low temperature in wastewater treatment plants (WWTPs) in Northwest China, this study investigates dissolved oxygen (DO) soft-sensing and interpretable decision support for under-instrumented conditions. Existing approaches often depend on online chemical instrumentation, whose maintenance burden and unstable long-term operation limit their applicability in resource-constrained plants. To address this gap, a DO soft-sensing and interpretable decision-support framework was developed and evaluated in a full-scale plug-flow Anaerobic-Anoxic-Oxic (AAO) WWTP in Northwest China. Using 15,676 hourly observations collected across seasons, an Entropy-Weighted TOPSIS Fusion Model (ETFM) ensemble was constructed through Bayesian hyperparameter optimization and multi-model benchmarking to improve prediction stability under complex operating regimes. Under the random-split benchmark, ETFM achieved an R2 of 0.966 while additional time-ordered validation retained acceptable temporal generalization with an R2 of 0.915. Multi-level interpretability analyses based on SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), 3D Partial Dependence Plots (3DPDP), and 3D Accumulated Local Effects (3DALE) were then used to characterize the nonlinear relationships learned by the model. These analyses suggested model-inferred diminishing marginal returns for upstream DO, beyond which further increases in upstream DO provided limited predicted downstream benefit. Based on these model-explanation results, a conceptual decision-support logic was proposed to support state-informed downstream DO soft sensing, ORP-based process-state screening, and preliminary decision-support evaluation.
The removal of anions from water remains a major challenge in water treatment and water resource protection. Capacitive deionization (CDI) is an energy-saving and eco-friendly ion removal technology and has gained growing attention. Great advances have been made recently in its electrode materials, system structures, and ion storage principles. This review systematically summarizes the recent advances in CDI for anion removal, with particular emphasis on the roles of carbon-based materials, transition metal compounds, and organic electrode materials in removing different anions from water. Unlike previous reviews, this work comparatively analyzes the removal performance of various electrode materials toward different anions and correlates these performances with the corresponding adsorption and charge-storage mechanisms. In addition, the review highlights the important roles of advanced characterization techniques and computational methods in elucidating ion transport and storage behaviors. Furthermore, optimization strategies for CDI system configurations and the challenges associated with practical large-scale applications are discussed. CDI systems boast prominent strengths in selectivity, treatment efficiency, operational stability, and resource recovery. Nevertheless, multiple key obstacles restrict its practical use in complex water environments. These difficulties involve poor long-term electrode stability during dynamic operation, low selective removal capacity amid mixed ions, and the balance between energy efficiency and large-scale application. Addressing these challenges will be essential for the future development and practical application of CDI technologies in water treatment.
Surface modification is an effective strategy for mitigating surface passivation and enhancing the reactivity of zero-valent iron(ZVI). The increased reactivity of modified ZVI is considered to be primarily attributed to enhanced electron transfer capability and generation of highly reactive electron donors on the modified ZVI surface, whereas the role of protons is often overlooked. In this research, a mild and rapid method for preparing Fe3O4-loaded micron ZVI(mZVI) composite material was proposed and the proton-related mechanism of the iron oxide layer was further investigated. Trace sulfide ions(S2-) were introduced to modulate the redox intensity, enabling rapid preparation of Fe3O4-loaded mZVI composite material(r-Fe3O4/mZVI) via aerial oxidation of Fe2+. XRD, XPS, and FTIR analyses confirmed that the iron oxide layer loaded on r-Fe3O4/mZVI was primarily composed of magnetite. 500 mg/L p-nitrophenol (PNP) was completely removed within 25 min by using 5 g/L r-Fe3O4/mZVI at an initial pH of 3. Cyclic voltammetry, alcohol quenching, products identification, and ferrozine inhibition experiments confirmed that surface-adsorbed Fe(II) was the reactive species. Quantitative calculation based on electron and proton mass balance shows that in r-Fe3O4/mZVI reaction, the oxidation of surface-adsorbed Fe(II) on the iron oxide layer provided 94.17% of the protons and up to 31.39% of the electrons required for PNP removal. Mechanistic analysis revealed that the iron oxide layer suppressed electron transfer from mZVI but promoted the oxidation of surface-adsorbed Fe(II), thereby enhancing proton generation. The pre-loading an iron oxide layer on mZVI surface to enhance proton generation represented an effective strategy for promoting pollutant removal from mZVI reduction.
Quorum quenching (QQ) of signaling molecules plays a critical role in reducing biofilm and controlling membrane biofouling for sustained performance. The aiiO gene serves as a key microbial regulatory gene responsible for implementing quorum quenching. Appropriate expression of the aiiO gene can address key issues in wastewater treatment and avoid the excessive microbial load that results from overproduction. In this study, promoter-engineering strategy is used to enhance QQ effect in Pseudomonas putida KT2440 by modulating expression of the aiiO lactonase. Promoters of graded strength (p51, p46, pJ23119, and plac) were cloned upstream of aiiO and aiiO–gfp in the broad-host-range plasmid pBBR1-MCS2, enabling tunable, dose-dependent attenuation of acyl-homoserine lactone (AHL) signaling. In activated sludge systems, the p51-driven strain delivered the most pronounced effect, reducing biofilm formation by ∼50–60% relative to natural quorum quenching Rhodococcus BH4 after 84h, while concurrently lowering EPS secretion (51.42%) and weakening biofilm adhesion (28.5%). Importantly, p51-mediated aiiO expression imposed no detectable metabolic burden on the host KT2440, and QQ performance remained stable over extended cultivation, indicating genetic and phenotypic robustness suitable for long-term operation. Through targeted regulation of aiiO expression, this study achieved tunable quorum quenching activity in activated sludge systems, leading to reduced AHL-mediated biofilm formation, lower EPS secretion, and weaker biofilm adhesion, thereby providing a promising strategy for mitigating biofouling in wastewater treatment applications.
The land-sea interaction zone, as an ecologically sensitive interface linking terrestrial and marine systems, features complex material and energy exchange processes. The identification of contaminant sources in coastal groundwater is challenging due to the dual influence of anthropogenic activities and complex surface water-groundwater interactions. Over the years, various source tracing techniques have been developed from multiple perspectives, such as indicators, analytical methods, and data processing. This paper systematically reviews main contamination source tracing techniques applied in coastal groundwater, basing on chemical, geophysical prospecting, inverse modeling and biological methods. The advantages and limitations of these techniques in terms of tracing principles, applicable scope, and effectiveness are discussed comprehensively. A major challenge is that most existing source tracing techniques require a large number of sampling points, provide only coarse spatial resolution, and lack sufficient accuracy in identifying contamination source types. This study aims to offer technical references for tracing groundwater contamination sources in land-sea interaction zones. Future research should focus on developing more advanced tracing techniques based on information-rich microbial communities and organic matter fingerprints, integrated with machine learning techniques.
Accurate estimation of baseflow and discharge is critical for sustainable water resource planning, particularly in tropical catchments with limited data quality. This study evaluated the sensitivity, uncertainty, and multi-objective performance of the WetSpass-M model in the Akaki catchment and its sub-catchments (Legedadi and Gefersa) in the central highlands of Ethiopia. The model incorporates meteorological, physiographic, and groundwater data. By varying the model parameters and using two potential evapotranspiration (PET) datasets—FAO Penman–Monteith (S1) and GLEAM (S2)—a total of 78 simulations were conducted, followed by one-at-a-Time sensitivity and uncertainty analyses. The results indicate that the runoff timing factor (X) is the most sensitive parameter across all sites, whereas the recharge contributing factor (∅) and soil moisture parameter (LP) have a moderate influence. In Akaki, GLEAM (S2) overestimated the baseflow more than FAO Penman–Monteith (S1). Legedadi predominantly overestimated baseflow, whereas Gefersa showed more variable and inconsistent responses. In both the catchment and sub-catchments, the discharge uncertainty was lower than the baseflow uncertainty. Multi-objective assessment further revealed that in Akaki, S1 generally outperformed S2 in baseflow prediction, whereas subcatchment-scale simulations demonstrated low model performance, particularly in Legedadi, highlighting the need for localized calibration-verification and high-quality observed data. Overall, the study underscores the critical role of PET dataset selection and model parameterization in hydrological modeling and highlights the need for comprehensive sensitivity and uncertainty analyses to improve model reliability and support informed water management decisions in rapidly urbanizing catchments.
Conventional advanced oxidation processes (AOPs) are constrained by several limitations, including reliance on radical oxidation mechanisms, spatiotemporal constraints on operation, and a narrow focus on pollutant removal. Conceptual limitations constrain the development of AOP. In contrast, the synergistic oxidation process (SOP) achieves multi-mechanism and multi-objective synergy through strategic process design. This review clarifies the concepts of SOP, and its differences from traditional AOPs and combined/integrated AOPs. It introduces typical cases of SOPs and their mechanisms, including pre-chlorination and vacuum UV/UV process for semiconductor wastewater reuse, ozone/UV/chlorine synergistic disinfection, magnetic adsorption-oxidation synergistic process, and the combination of UV/H2O2 and biological activated carbon. Through quantitative analysis of the synergistic effects, cost reduction, and risk control of SOP cases, the synergistic mechanisms and advantages of SOPs are highlighted. The design strategy and evaluation framework for SOP are also established. SOP represents a more promising solution for the safe reuse of reclaimed water and for controlling trace contaminants in aquatic environments.
Assessing future climate change impacts is essential for developing adaptive water resource management strategies in river basins. This study presents an integrated modeling framework combining the Soil and Water Assessment Tool (SWAT+) hydrological model with bias-corrected CMIP6 climate projections to assess hydro-climatic changes and drought propagation in the Arno River basin, Tuscany, Italy. We employed five General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathways SSP2-4.5 and SSP5-8.5, Quantile Delta Mapping (QDM) bias correction, and Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) drought indices to examine climate extremes spanning from 2015 to 2100. Results demonstrate: (1) pronounced warming trends with temperature increases of 3 degrees C under SSP2-4.5 and 5 degrees C under SSP5-8.5 by century's end; (2) declining precipitation trends at rates of-0.51 mm year-1 and-1.77 mm year-1 for moderate and high emission pathways respectively; (3) streamflow reductions of-0.07 m3 s-1 year-1 and-0.19 m3 s-1 year-1 under SSP2-4.5 and SSP5-8.5 scenarios; and (4) increasing drought severity with total drought duration reaching 99 months for meteorological droughts and 106 months for hydrological droughts under high-emission pathways, compared to historical baselines of 41 and 32 months respectively. Strong correlations (r = 0.80-0.95) between meteorological and hydrological drought indices demonstrate robust drought propagation from precipitation deficits to streamflow reductions. This study provides crucial understanding of hydroclimatic changes, enabling policymakers and water managers to develop resilient strategies for the Arno River basin.
Carbon source deficiency is the crucial factor constraining conventional biological nitrogen removal in low C/N wastewater. Therefore, this study investigated nutrients and dimethyl phthalate (DMP) removal and mechanisms in an algal pond-constructed wetland system treating wastewater at different low C/N ratio. The findings indicated that the system exhibited optimal comprehensive treatment performance at a C/N ratio of 5. Under this condition, the removal efficiencies of ammonia nitrogen, total nitrogen and DMP reached 98.5%, 56.8% and 78.0%, respectively, while nitrous oxide emissions were significantly reduced. Scenedesmus obliquus exhibited good growth. Meanwhile, extracellular polymeric substances (EPS) secreted by microorganisms were mainly soluble EPS, which served as a slow-release carbon source to supplement the electron donors required for denitrification and enhanced DMP biodegradation. Although an elevated C/N ratio reduced microbial community biodiversity and abundance, it significantly enhanced the relative abundance of functional microbes related to nitrogen transformation. Notably, the levels of denitrification functional genes (norB, nosZ, nirK, nirS) all improved, indicating enhanced genetic and metabolic potential for denitrification. These findings indicate moderately optimizing the influent C/N ratio can effectively and synergistically remove multiple pollutants and reduce greenhouse gas emissions in the integrated system, offering a theoretical foundation for the co-treatment and ecological remediation of multiple pollutants in low C/N wastewater.
This study analyses post-treatment Escherichia coli regrowth in drinking water after UVC LED disinfection and proposes a kinetic model to predict it. The model integrates the effects of UVC dose (0.06-0.15 J cm(-2)), storage temperature (15, 20, 25 and 37 degrees C) and water matrix (wastewater secondary effluent vs isotonic water). Regrowth was monitored for up to 7 days under dark-storage conditions, representative of covered tanks, bottles and enclosed distribution systems where photoreactivation is negligible. The model, fitted to experimental data, achieved a global NRMS Error of 9.38 % for kinetic constants and a NRMSL Error of 19.77 % for bacterial concentrations, indicating good agreement with observations. Results showed an inverse relationship between UVC dose and regrowth, identifying a threshold dose (D0) of 0.129 J cm(-2) above which no detectable increase in bacteria concentration was detected, regardless of temperature or water matrix. Temperature affected regrowth kinetics, with slower proliferation at <= 25 degrees C and rapid recovery at 37 degrees C, close to the optimal growth temperature for E. coli. The water matrix also influenced outcomes, with lower bacterial recovery in wastewater effluents than in isotonic water. Importantly, the model indicates that ensuring compliance with stringent standards after 7 days of storage may require UVC fluences up to four times higher than those typically recommended based solely on immediate inactivation, highlighting the need to account for regrowth in system design. Overall, the model is a useful tool to assess microbial risks and optimize UVC-based disinfection strategies.
Industrial wastewater containing mixed ionic dyes poses a significant treatment challenge. Herein, a conjugated microporous poly(pyrrole methylene) (Py-PPA-CMP) was synthesized using pyrrole and p-phthalaldehyde as monomers, and its adsorption performance was evaluated with the representative anionic dye Acid Red G (ARG) and cationic dye Methylene Blue (MB) as model pollutants. Under neutral conditions, the adsorption performance of Py-PPA-CMP in the dual-component system significantly surpassed that in single-component systems, with the equilibrium adsorption capacity for ARG increasing from 20 mg g(-1) to 109 mg g(-1) and for MB from 60 mg g(-1)to 147 mg g(-1). Remarkably, in the dual-component system, Py-PPA-CMP realized efficient simultaneous removal of ARG and MB across a wide pH range. ARG maintained high removal efficiencies (93-96%) under strongly acidic (pH 2), neutral (pH 7), and strongly alkaline (pH 11) conditions. Concurrently, MB removal was substantial (75%) at pH 2 and nearly reached completion (> 98%) at pH 7 and 11. This pH stability stands in stark contrast to that of single-pollutant systems, where adsorption is severely inhibited by pH-dependent electrostatic repulsion, resulting in a drastic decline in removal efficiency from 85% to 30%. This breakthrough overcomes the selective limitation of traditional adsorbents, highlighting its practical potential for complex wastewater treatment. Furthermore, this study establishes a design paradigm for the development of broad-spectrum dye adsorbents.
Catalysts are crucial for the catalytic ozonation treatment of refractory industrial wastewater. While carbon-based catalysts are considered as promising alternatives to metal-based ones, their practical application is severely hindered by costly heteroatom doping (e.g., N, P) and difficulty in separating and reusing their predominant powder forms. Algal biomass is a promising raw material for carbon-based catalyst, given its inherent nitrogen and phosphorus content, and wide availability from diverse sources such as algal bloom waste or industrial algal residues. Here, we report a novel strategy to synthesize granular carbon-alumina composite catalysts directly from algal biomass. The granular catalysts were fabricated via a single-step process involving mixing algae biomass with alumina, granulation, and thermal treatment in an inert atmosphere, which simultaneously induced the pyrolysis of algae and the sintering of alumina. The resulting granular catalyst exhibited excellent performance in catalytic ozonation, achieving removal rate of 97.3% for oxalic acid in synthetic wastewater, and 63.9% for COD in actual hypersaline industrial wastewater. This study provides a feasible technical pathway for fabricating granular algal-biomass-based catalysts, advancing the research and application of novel green algal-derived materials in catalytic ozonation.
Toxicity assessments using microalgae are commonly conducted to reflect the ecological risks posed by exogenous pollutants. In natural water, pollutant concentrations often remain constant due to continuous import from point or non-point sources. However, in the laboratory, pollutants are mixed with test organisms at the beginning of the toxicity assessment and are gradually biodegraded, which may not accurately reflect the continuous pollution present in natural conditions. Therefore, this study proposed a new method that fresh pollutants (three typical sulfonamides, SAs) were continuously provided to the testing organism (microalgae, Chlorella vulgaris) in the detection system. The difference in algal growth ratio between test group and control group (named Delta r) was applied to reflect the toxicity of antibiotics. It was revealed that the three SAs caused greater inhibition or less promotion effect on 5-7 days' microalgae growth when using the proposed method, compared to the traditional method. This result demonstrated that the traditional test could under-estimate the antibiotic toxicity. Furthermore, a blueprint of the extended method and its corresponding device were proposed based on the idea of plug flow to determine the toxicity of antibiotics and their intermediate products generated during various treatment processes.
Reclaimed water reuse is critical to alleviate urban water resources shortage. However, applying it to artificial landscape might pose ecological issues, and key factors driving water quality deterioration especially in isolated waters with weak self-purification capacity remains unclear. This study unveiled the relationship between water quality deterioration with dissolved organic matter (DOM), algal and bacterial communities in isolated artificial landscape replenished by reclaimed water. Results showed that the trophic level index was increased by 58.6-88.2% with increase in turbidity, chemical oxygen demand, and chlorophyll a after reclaimed water reuse. Carbon, nitrogen, phosphorus as key factors drove the deterioration of water quality. Fluorescence characteristics suggested that increased DOM was derived from endogenous microorganisms. The algae of Chlorelia, Scenedesmus, Ankistrodesmus and bacteria of Porphyrobacter, Fluviimonas, Rhodobacter were dominant contributors. Partial least squares path modeling revealed that bacteria were more sensitive to reclaimed water, while algal proliferation more likely induced water quality deterioration. These findings deepened our understanding on water quality deterioration process, and highlighted the superiority of monitoring algae when reusing reclaimed water in artificial landscapes.
This study assesses the ability of satellite, meteorological, and climate teleconnection indices to forecast drought at 3-, 6-, 9-, and 12-month intervals in the Urmia Lake basin (ULB) using machine learning models. Data sources include TRMM precipitation, MODIS-derived NDVI and daytime land surface temperature (LSTday), gridded potential evapotranspiration (PET), and large-scale indices (MEI, SOI, AMO, NAO) from 2001 to 2019. The methods compared feature Decision Tree (DT), Random Forest (RF), and Extremely Randomized Trees (ERT). Model performance was evaluated through cross-validated R-squared, root mean square error (RMSE), and mean absolute error (MAE), along with variable importance, SHAP (SHapley Additive exPlanations) interpretation, and wavelet coherence analysis. A key finding is that ERT consistently outperformed other algorithms across different accumulation periods, showing superior predictive accuracy (higher R-squared and lower RMSE/MAE) and more consistent cross-validation results; SHAP and wavelet coherence analyses indicate a systematic shift from local drivers (precipitation, NDVI, LSTday) at shorter timescales to teleconnection indices (MEI, AMO, SOI, NAO) at longer ones. Practical implications suggest that the ERT-based framework can support operational drought monitoring and water management in the Basin by integrating satellite data with climate indices to enhance early-warning systems and resource planning. The novelty of this approach lies in integrating multiscale SHAP interpretation and wavelet coherence to elucidate the shift from local to teleconnected drought drivers, offering a transferable framework for basin-scale drought assessment.
Sand filtration systems are cornerstone technologies in water treatment, yet they frequently suffer from clogging due to particle and biofilm accumulation, compromising efficiency and escalating maintenance demands. Ultrasonic treatment has shown significant potential in mitigating the clogging of filtration systems; however, most existing studies have focused predominantly on its applications in ultrafiltration and microfiltration. The potential of ultrasonic treatment in sand filtration remains largely unexplored. This study investigates the effectiveness and underlying mechanisms of in situ ultrasonic treatment (20-40 kHz) for reducing substrate clogging in sand filters. Results indicate that intermittent continuous ultrasound application delays clogging progression, with the 40 kHz treatment at 60 kW/m2 achieving the best performance. Conversely, pulsed treatment at 28 kHz and 60 kW/m2 was most effective in restoring permeability in sand filters, due to an optimal balance of cavitation intensity and stability. Ultrasonic treatment promoted particle disaggregation, reduced aggregate size, altered surface charge, and oxidized organic matter, thereby enhancing filter permeability. The approach was particularly effective against clogging caused by inorganic particles and showed high performance in treating clogging influenced by organic matter. Intermittent continuous ultrasound outperformed pulsed treatments in systems with high organic content, attributed to enhanced release or degradation of organic compounds. However, filters clogged by large particles and high volatile solids content exhibited limited recovery, suggesting that ultrasonic treatment is more effective for clogging dominated by small, inorganic particles. These findings highlight ultrasound as a sustainable and effective strategy for mitigating clogging in sand filtration systems.
Chlorine disinfectants, such as sodium hypochlorite (NaClO) and trichloroisocyanuric acid (TCCA), are widely used in outdoor swimming pools. However, the effects of solar irradiation on the formation of disinfection byproducts (DBPs) in the presence of diverse organic precursors in outdoor pools remain poorly understood. This study systematically evaluated DBP formation in outdoor pool water disinfected with NaClO or TCCA under sunlight versus dark conditions. Solar irradiation markedly enhanced targeted DBP formation in actual pool water, with a greater increase for TCCA (75.9-104.2 %) than for NaClO (49.1-60.1 %). In simulated pool water, sunlight consistently elevated DBP yields across varying concentrations of body fluid analogues, chlorine doses, and light intensities. Notably, while TCCA produced fewer DBPs than NaClO in the dark, it generated comparable or higher concentrations under sunlight. Quenching and deoxygenation experiments indicated that photogenerated reactive species (e.g., OH & sdot;, Cl & sdot;, O(3P), O3) significantly promote DBP formation under light, with a particularly pivotal role in the TCCA system. Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) further revealed that sunlight increased both the number and oxidation state of novel chlorinated DBPs. This increase corresponds to the light-enhanced conversion of precursor formulas, particularly larger, more saturated, and oxygen-rich molecular structures. These findings provide the first molecular-level evidence that sunlight not only elevates the abundance of known DBPs but also drives the formation of more diverse and oxidized species, highlighting the need to explicitly account for solar exposure in the risk assessment and management of outdoor pools.
C/N imbalanced and toxic wastewaters destabilize biological treatment processes by disrupting microbial structure and function. Ethylene glycol (EG), commonly encountered in de-icing-related effluents, represents a relevant model stressor for examining such instability. This study redefines aerobic granular sludge (AGS) recovery pathways under EG-induced C/N imbalance through alginate-aided microbial encapsulation. Under progressively increasing EG concentrations (120-2000 mg/L), AGS maintained COD removal exceeding 90 % and nearly complete simultaneous nitrification-denitrification (SND) at concentrations up to 800 mg/L, but granule destabilization and biomass washout occurred beyond 1200 mg/L. Alginate-aided encapsulation actively confined microbial fragments within a transient protective hydrogel scaffold, transforming spontaneous fragmentation into a guided reaggregation process that restored structural cohesion and metabolic stability. Optical coherence tomography (OCT) imaging confirmed structural restabilization, consistent with restored secretion of extracellular polymeric substances (EPS), whereas microsensor-based oxygen profiling revealed improved diffusivity within encapsulated biomass. Although conventional nitrifiers, Nitrosomonas and Nitrospira, became undetectable post-encapsulation, stress-tolerant genera, including Comamonas and Pseudomonas, sustained nitrogen transformation with >85% total nitrogen removal, while Pedobacter potentially facilitated EPS-mediated reassembly. Together, this work establishes alginate-aided encapsulation as a facile, retrofittable intervention that converts passive AGS recovery into a controllable bioprocess, strengthening structural resilience and treatment stability under organic loading stress.
The menace of antibiotic contamination of water bodies has highlighted the need to develop efficient strategies to effectively remove them from contaminated water for safe discharge. In this study, a high-performance bioTiO2@ZIF-8/PVA-PVDF membrane was developed to effectively remove cefixime, which is a commonly used antibiotic, from contaminated water. Through harnessing the hydrophilicity and photocatalytic capability of the membrane, significant improvements in the membrane's permeability, CFX removal, and self-cleaning propensity were achieved. Analysis of the bio-TiO2@ZIF-8/PVA-PVDF membrane's hydrophilicity indicates that the water contact angle (WCA) was reduced to 61.00 +/- 4.59o, as compared to the 88.56 +/- 2.79o WCA of the pristine PVDF. The band gap energy (Eg) of the bio-TiO2@ZIF-8 composite was narrowed to 2.96 eV from 3.25 eV obtained for the bio-TiO2, which infused the bio-TiO2@ZIF-8 modified membrane with photocatalytic ability in the visible light range. The pure water flux (PWF) of the bio-TiO2@ZIF-8/PVA-PVDF membrane was 246.50 +/- 3.11 L/m2h, and the long-term filtration of the CFX-contaminated water demonstrated that the membrane could maintain a stable CFX flux for 78 h, while being able to remove up to 99.5 % CFX from the wastewater. This notable performance of the bio-TiO2@ZIF-8/PVA-PVDF membrane is attributed to the enhanced hydrophilic and photocatalytic capacity of the membrane, which contributed to increasing the membrane's water affinity and its CFX degradation under visible light. Absorbed CFX on the membrane surface, due to the saturation of the feed solution and CFX blockage of the membrane's photoactive sites, was noted after the long filtration. However, the absorbed CFX on the membrane was self-cleaned within 1 h.
