Cleavage of the C-F bond is a critical challenge in effectively controlling pollution from Per- and Polyfluoroalkyl Substances (PFAS). Herein, the Cu-NSC electrocatalyst was constructed for the efficient electrocatalytic reduction of perfluorooctanoic acid (PFOA). Multi-modal characterization has revealed a multifunctional micro-scale synergistic structure: atomically dispersed Cu sites with N0.5S2.1 coordination serve as active centers, whilst trace sub-nanometer clusters act as ‘fluorine traps’, exhibiting high reductive activity. Consequently, a PFOA degradation of up to 96.32% was achieved after 12 h processing of simulated wastewater in the Cu-NSC electrocatalytic reduction system. Also, a defluorination efficiency of 88.36% and a TOC reduction of 63.29% confirmed the deep mineralization-defluorination of PFOA. LC-MS comparative analyses of single/dual-pool systems and in-situ EPR spectra, collectively reveals that direct electron transfer (DET) is the dominant degradation mechanism. Further DFT calculations demonstrates that the C-F bond cleavage energy barrier at the α-CF₂ site substantially reduced by 303.79 kJ/mol. Eventually, the optimal PFOA degradation efficacy in actual industrial wastewater (initial 9.77 mg/L) was obtained at -1.5 V after 2 h electrolysis, with 91.40% degradation and exceeding 95% current efficiency. These demonstrate the system’s excellent suitability for use in actual wastewater matrices. This work furnishes a novel paradigm for electrocatalyst design, which will facilitate controlling PFAS contamination in effluent from chemical industrial parks.
River ecosystems are hierarchically organized systems in which biological responses to environmental stressors emerge across multiple organizational levels. River health indices (RHIs) operationalize this hierarchy by aggregating biological sub-metrics into assemblage-specific multi-metric indices (MMIs) and an integrated assessment index (IAI). However, most predictive models treat RHI estimation as a flat learning problem, disregarding the hierarchical dependencies and producing predictions that violate aggregation relationships. To address this limitation, we developed the hierarchically constrained deep learning (HCDL) framework that combines hierarchical learning with a reconciliation layer to ensure structurally coherent prediction of multi-level RHIs. HCDL employs transformer modules organized across three hierarchical levels: biological sub-metrics, assemblage-specific MMIs for diatoms, benthic macroinvertebrates, and fish, and the IAI. The raw multi-level HCDL outputs are reconciled using exact aggregation rules to obtain the final reconciled predictions, denoted R-HCDL. The framework was evaluated using nationwide biomonitoring data from 2,833 sites across South Korea, including the four major river basins and Jeju Island. R-HCDL achieved NNSE values of 0.599–0.716 across MMIs and the IAI, with the highest NNSE for the IAI and BAI among the compared models, while enforcing exact cross-level consistency and generally reducing regression toward the mean at the distributional extremes. Hierarchical interpretation based on propagated attention weights summarized model-derived information-use patterns across the predefined assessment hierarchy, which were broadly consistent with established ecological understanding. Exploratory scenario analysis showed structurally consistent model-predicted responses to specified changes in environmental inputs across sub-metrics, MMIs, and the IAI. These results suggest that R-HCDL provides a predictive and structurally consistent framework for modeling hierarchical ecological indices, with potential utility for exploratory screening and hypothesis generation pending external validation.
Sediment organic phosphorus (OP) is a key internal source sustaining lake eutrophication after external P reduction, yet its molecular-level response to trophic gradients and regulatory mechanisms remain unclear. In this study, we characterized OP fractions, bioavailability, and molecular composition in sediments from 11 lakes across trophic states in China using chemical extraction, enzymatic hydrolysis, and Fourier transform ion cyclotron resonance mass spectrometry. The results showed that sediment OP content was positively correlated with trophic states. Bioavailable OP, including labile OP (LOP) and moderately labile OP (MLOP), accounting for 28.8%-76.5% of sediment OP. With increasing trophic level, LOP and MLOP contents increased significantly; LOP hydrolysis shifted from labile monoester P to diester P dominance, whereas MLOP remained labile monoester P dominated. The P‑containing molecules showed higher molecular weight, enhanced aromaticity, lower oxidation state, and greater labile molecules abundance with increasing trophic states. Relative abundances of lipid‑, protein‑, and lignin‑like compounds of P‑containing molecules were positively correlated with trophic level, indicating algal production and external inputs synergistically drove sediment OP accumulation. Partial least squares path modeling showed that OP bioavailability had a negative direct effect on trophic level, but a positive indirect effect via molecular composition, suggesting molecular composition may act as a important mediator converting potentially available OP into long‑term ecological effects. Our findings indicate that enrichment of labile molecules (lipids and proteins) in sediment OP enhances bioavailability, exacerbates eutrophication and promotes algal sedimentation, forming a self‑reinforcing positive feedback. This study reveals the molecular mechanisms by which sediment OP drives persistent eutrophication, providing a basis for internal P risk assessment and targeted eutrophication control.
Aquatic ecosystems serve as critical carbon sinks, with sediments playing a key role in long-term organic carbon sequestration. However, the global patterns and trajectories of sedimentary total organic carbon concentration (TOC) and organic carbon burial rates (OC-BR) during the Anthropocene remain poorly constrained. Here, we present a comprehensive global synthesis of sediment core records from lakes, reservoirs, inland wetlands, and coastal systems, integrating over 18,000 rows data from 585 sites across diverse climatic and geographic settings. Generally, our results show that both TOC and OC-BR increased since the 1950s-1960s, coinciding with the onset of the Great Acceleration. These trends are primarily attributable to anthropogenic-driven changes in temperature, atmospheric CO₂, and nitrogen deposition. Spatially, TOC and OC‑BR exhibit higher values in shallow, anthropogenically influenced, low‑latitude regions, though carbon sequestration efficiency varies considerably across ecosystem types. Projections under future socio-economic scenarios suggest that sedimentary carbon sequestration may approach a ceiling (e.g. sedimentary carbon-store capacity could not increase without limitation), limiting the future capacity of aquatic systems as carbon sinks. These findings highlight the influence of climate change on sedimentary systems during the Anthropocene epoch.
Hospital wastewater (HWW) is a critical reservoir of contaminants of emerging concern (CECs), antibiotic resistant bacteria (ARB) and genes (ARGs), and a major hotspot for accelerating the spread of antimicrobial resistance (AMR). Despite this, occurrence data remain scarce in India, especially for contrast agents and parabens. For Indian scenario, this study acts as the first account of non-targeted analysis (NTA) coupled with liquid-chromatography-high-resolution mass-spectrometry (LC-HRMS) for characterizing the CECs in the effluents of three hospitals in Delhi region. 728 CECs were tentatively identified, of which 38 were structurally confirmed (at level 2) and 23 quantified. Pharmaceuticals dominated the CEC profile, with antibiotics being the predominant subclass (concentrations of 44–3509 ng/L). Iohexol was detected at high concentration of 80 µg/L, further its toxic transformation products (TPs) were identified during HWW treatment. The effluent treatment plant (ETP) was ineffective in removing CECs, including antibiotics, implying AMR spread. Amoxicillin, levofloxacin, and azithromycin (along with antibiotic TPs) exhibited AMR risk (RQAMR>1), with detection of WHO priority and ESKAPE pathogens- Acinetobacter baumannii, Klebsiella sp., Pseudomonas aeruginosa, and Enterobacter sp. in the treated effluent. Out of 51 ARGs identified, 14 of them belonged to the efflux mediated multidrug resistance class. With HWW discharged directly into municipal sewers, there could be overburdening of urban treatment networks with CECs and spread of AMR. This study provides one of the most comprehensive datasets on CECs, their TPs, ARBs and ARGs in Indian HWW and underscores the urgent need of upgradation of existing ETPs and integrated AMR surveillance.
