Water Research最新文献

Photochemical processing dictates the molecular evolution of dissolved organic matter (DOM) in sunlit waters and its subsequent reactivity during chlorination. However, how source-specific molecular signatures regulate the photochemical transformations of DOM and its associated disinfection by-product (DBP) formation potentials remains poorly understood. Here, DOM samples derived from algae (ADOM), soil (SDOM), litter (LDOM), and wildfire ash (BDOM) were exposed to 28 days of simulated solar irradiation to elucidate source-dependent molecular transformations and their impacts on specific DBP formation. DOM compositional changes were characterized using optical spectroscopy and Fourier transform-ion cyclotron resonance mass spectrometry, and specific DBP formation potentials were assessed via chlorination assays. Distinct molecular signatures differentiated DOM sources, with ADOM enriched in protein- and lipid-like compounds, whereas SDOM, LDOM, and BDOM contained greater proportions of aromatic, lignin-, and tannin-like molecules. Irradiation drove partial convergence of DOM toward a more oxidized state, lower molecular weight, and more aromatic structures via decarboxylation, dealkylation, and deamination. These transformations reduced specific carbonaceous DBP formation (up to 93 %) across most DOM sources, whereas LDOM showed a ∼9 % increase in specific trihalomethane and chloral hydrate formation, likely due to the persistence and transformation of lignin-like precursors. Notably, specific haloacetonitrile formation potentials substantially increased by 99-125 % across all DOM sources, associated with the generation of reactive nitrogenous precursors through photo-oxidation and depolymerization of macromolecules. These findings demonstrate that solar-driven DOM transformation governs distinct carbonaceous and nitrogenous DBP responses, highlighting the need for source-specific molecular insights to predict and manage DBP risks in watersheds.

Unvegetated tidal flats cover extensive areas of global coastlines, but their role in atmospheric CO₂ exchange remains poorly understood. Here we investigate how hydrological regimes regulate carbon uptake in these ecosystems through a marsh organ experiment in the subtropical Zhangjiang Estuary, China that integrated sediment-atmosphere CO₂ and CH₄ flux measurements, porewater geochemistry, and metagenomic sequencing. We identified a site-specific hydrological transition in which sediment-atmosphere CO₂ exchange shifted from a weak source to sustained net uptake under more frequent inundation, with the transition occurring around an annual inundation frequency of approximately 10-30% in this experimental system. This transition coincided with declining porewater NO₃^-/Cl^- and SO₄^2-/Cl^- ratios and increasing pH, dissolved CO₂ concentration, and carbonate system derived estimated alkalinity, consistent with enhanced anaerobic redox processes and alkalinity-associated CO₂ dissolution and retention. Although CH₄ emissions increased under frequent inundation, incorporating CH₄ into global warming potential did not substantially offset the CO₂ sink transition. Metagenomic analyses further showed an enrichment of rTCA-related carbon-fixation taxa under frequent inundation, indicating greater microbial autotrophic carbon-fixation potential. This functional potential was associated with porewater geochemical changes and increased microbial biomass carbon, supporting the possibility of hydrologically modulated geochemical-microbial coupling during the CO₂ source-sink transition. Together, our results unveil a previously unrecognized mechanism in which a hydrologically-modulated geochemical-microbial coupling drives CO₂ uptake in frequently inundated sediments. These findings extend current blue carbon frameworks by highlighting the previously overlooked role of unvegetated tidal flats in coastal carbon cycling and climate regulation.

The efficient dewatering and the deep removal of obstinate emerging contaminants (e.g., perfluorooctanoic acid (PFOA) and antibiotic resistance genes (ARGs) remain critical bottlenecks in current sludge management. Persulfate advanced oxidation processes (PS-AOPs) offer great potential in sludge conditioning and pollutant mineralization, however their efficacy strongly hinges on water-organics binding capacity, solid-liquid interface reactivity, radical generation routes, and iron cycling rate. In this study, a pair of high specific-surface helix-leaf electrodes (a total of 5481.8 mm² ) was designed and integrated into Fe²⁺-activated persulfate oxidation processes to drive Fe²⁺ re-generation and highly active radicals formation. Under the optimal operational conditions of 30 V and 2/1.6 mmol Fe²⁺-S₂O₈^2-/g-VS, the sludge achieved an 87 % decrease in capillary suction time (CST) and low moisture content of 52.2 % after pressure filtration. More importantly, such hybrid option produced a 91.03 % degradation of PFOA from total EPS and an exponential elimination (above 90 %) of ARGs. Mechanistically, the electro-Fe²⁺-activated persulfate oxidation encouraged the continuous production of highly active radicals (·OH, SO₄·⁻, ¹O₂, and ·O₂⁻) via ensuring efficient Fe³⁺/Fe²⁺ cycling driven by a robust electro-reduced circumstance. These radicals catalyzed the degradation of extracellular polymeric substances (EPS) and microbial cell rupture, notably reconfiguring protein secondary structures-characterized by the reduction of stable α-helix and β-sheet proportions to 17.79 % and 14.44 %, respectively. This profound structural transformation disrupted the hydrophilic gel matrix, thereby significantly enhancing sludge dewaterability. Concurrently, PFOA was destructed via direct free radical attacks on its molecular structure, while ARGs were eliminated primarily through the unwinding and subsequent cleavage of DNA strands. The research results will provide theoretical support and technical paradigms for the "reduction and emerging pollutant control" of sludge, and promote the green and low-carbon transformation in the field of waste treatment.

Recovering valuable organic compounds from high-salinity organic wastewater is essential for sustainable chemical manufacturing. However, conventional polymeric membranes suffer from insufficient organic/inorganic selectivity, poor solvent resistance, and severe membrane fouling during practical applications. Herein, we developed a hydroxylated carbon nanotube (CNT-OH)-modulated graphene oxide (GO) membrane to address these challenges. By precisely regulating the inner diameter and hydroxyl content of CNT-OH, rapid transport channels for water molecules and hydrated ions were constructed, which simultaneously suppressed interlayer swelling and enhanced the rejection of organic compounds. The optimized GO/single-walled hydroxylated carbon nanotubes (SWCNT-OH) membrane achieved water permeability of 28.7 l m^-2 h^-1 bar^-1 and separation factor above 15 for negatively charged dyes, antibiotics, and per- and polyfluoroalkyl substances (PFASs) relative to inorganic salts (NaCl), outperforming commercial nanofiltration (NF) membranes by one order of magnitude. During one-month continuous treatment of real erythromycin (ERY) fermentation broth, the membrane showed only a 7.1% flux decline and maintained 99.9% ERY rejection. Compared with conventional ultrafiltration-nanofiltration (UF-NF) processes, the system reduced operating costs by 66.4% and CO₂ emissions by 76.8%, demonstrating pronounced economic and environmental advantages. Overall, the system demonstrates outstanding application potential, strong scalability and engineering feasibility.

Sulfur-mediated chemical regulation has emerged as an attractive strategy for enhancing sludge anaerobic fermentation (AF). However, the non-linear influence of diverse sulfur species on AF performance, and their cross-scale interactions with microbiomes and functional genes, remain poorly understood. Therefore, this study developed a multi-scale machine learning (ML) framework that integrates MacroML for AF performance prediction and MicroML for mechanistic microbiome-gene interpretation, using thiosulfate-mediated AF as a representative sulfur-regulated system. At the macroscopic level, support vector regression (SVR) exhibited superior predictive robustness for short-chain fatty acids (SCFAs) (R²-test = 0.94) and biogas (R²-test = 0.89) using multiple sulfur species and operational conditions as inputs. MacroML interpretation analysis identified H₂S (>3000 ppm), thiosulfate (∼650 mg S/L), sulfite (3.0-3.5 mg S/L), sulfide (120-140 mg S/L), and sulfate (20-30 mg S/L) as determinants for increasing SCFA production, whereas biogas production required low thiosulfate (<200 mg S/L), sulfate (< 10 mg S/L), sulfide (10-20 mg S/L), sulfite (< 2.0 mg S/L), and H₂S (< 2500 ppm). At the microscopic scale, a graph embedding-enhanced random forest (GE-RF) model (R²-test = 0.72-0.89) revealed that sulfur metabolism was associated with a metabolic shift toward acidogenesis, as indicated by the enrichment of core taxa (e.g., Proteiniphilum, Aminobacterium), higher expression of acidogenic genes (e.g., buk, ptb), and increased abundance of electron transfer chain components (e.g., aprA/B, COQ4). Concurrently, sulfur exposure (e.g., thiosulfate, sulfide, H₂S) was associated with inhibition of terminal methanogenic enzymes (i.e., mcr and mtr) and a potential weakening of Methanothrix-centered functional modules, which may collectively contribute to reduced biogas production. This work provides a robust, data-driven roadmap for deciphering sulfur-mediated biochemical pathways and facilitates precision operational control in sludge AF systems.

Given the global prevalence of per- and poly-fluoroalkyl substances (PFASs), notable for their environmental persistence, bioaccumulation potential, and toxicity, in consumer products, water sources, and living organisms, the development of robust analytical techniques for their accurate quantification in complex matrices is imperative. Here, a novel quaternary ammonium-fluorinated dual-site magnetic microporous organic network (QM-MFMON) is developed for efficiently capturing PFASs. The cooperative integration of electrostatic interaction and fluorous affinity within QM-MFMON facilitates high-selectivity and ultrafast PFASs capture. QM-MFMON is integrated into a workflow comprising magnetic solid-phase extraction (MSPE) followed by UPLC-MS/MS to rapidly detect trace PFASs in biological fluids, food, and environmental water with high sensitivity and reliability. This platform demonstrates a wide range of detection (0.5-5000 ng L^-1), low limit of detection (0.01-0.03 ng L^-1), high enrichment efficiency (91.5-97.4), good recoveries (80.2-112.4%), and stable reusability. QM-MFMON is expected to serve as a high-performance adsorbent for MSPE to facilitate the rapid, selective, and ultrasensitive analysis of PFASs in complex matrices.

Cryptosporidium is a waterborne parasite which poses significant risks to drinking water safety. Despite its importance, considerable uncertainty remains regarding the effect of catchment characteristics and environmental conditions on the scale and timing of Cryptosporidium concentrations in rivers. This paper performs a spatio-temporal analysis of the environmental factors influencing Cryptosporidium by applying eXplainable AI (XAI) tools to a large-scale, long-term dataset from across the British mainland. The study brings together extensive land use, topographical, meteorological, hydrological and human/livestock density data to analyse the seasonality and magnitude of Cryptosporidium in rivers and advance understanding of how these factors influence Cryptosporidium concentrations and dynamics across heterogeneous river systems. A machine learning (ML) model - XGBoost - was trained with environmental inputs at different spatial scales. Interpretation of feature effects using SHapley Additive exPlanations (SHAP) found that humidity and rainfall had a strong positive effect on modelled Cryptosporidium concentrations. Human population density was shown to be more highly predictive of Cryptosporidium concentrations than either grassland area or livestock density, suggesting that human sources of Cryptosporidium may be more important than animal sources in many rivers. Densities of combined sewer overflows (CSOs) and sewage treatment works (STWs) were most predictive within 5 km and 25 km upstream, respectively, indicating the possible significance of local point source effects. The study revealed strong seasonality in Cryptosporidium at many sites, with the timing of peak concentrations varying significantly with catchment characteristics, such as human population, STW density, urban area and prevailing meteorological conditions. The work demonstrates how an XAI-based modelling framework can combine static, pseudo-static and dynamic environmental datasets across multiple catchments to identify modelled drivers of pathogen occurrence and seasonality in river systems, yielding more generalisable insights than those provided by previous single-catchment or short-term studies.