Storm events strongly regulate organic matter (OM) mobilization in agricultural watersheds, yet potential differences in the source evolution of dissolved and particulate OM (DOM and POM) across storm phases remain poorly constrained. Here, we conducted hydrograph phase-resolved storm sampling during two rainfall events and applied end-member mixing analysis (EMMA) to quantify phase-specific OM source dynamics. Source contributions were traced using δ13C–δ15N for POM and fluorescence indices for DOM. Bulk water-quality parameters and POM concentrations increased with discharge during the monitored events, closely tracking suspended solids and particulate nutrients, suggesting cumulative mobilization under event-specific hydrological conditions rather than a clear first-flush-like decline. EMMA results indicated highly dynamic POM source evolution, with leaf litter/riparian vegetation initially contributing 60–67% of POM during the early stage but declining to ∼29% near peak flow as soil-derived POM increased sharply to ∼51%, consistent with discharge-associated particulate mobilization and progressive source-area activation during storm progression. In contrast, DOM showed comparatively buffered dynamics and a strong manure/compost-like signature within the FI–BIX-based EMMA framework, contributing ∼37–58% across storm phases, suggesting persistent leaching and flushing from manure-amended areas once source areas became connected to active flow paths. Source-specific OC fluxes were concentrated in short high-flow windows, with sharp POC pulses near peak discharge and more persistent DOC export into recession. These findings indicate decoupled POM and DOM transport during the monitored storms and highlight the value of phase-resolved, OM-specific source apportionment for interpreting storm-driven carbon export and informing targeted watershed management.

Estuaries represent dynamic interfaces where dissolved organic matter (DOM) undergoes significant transformation through intertwined biogeochemical processes. However, disentangling and quantifying the relative influence of key environmental drivers remains a persistent challenge due to their concurrent and often synergistic nature. This study applied an integrated approach combining seasonal field observations along a dam-affected estuary (the Yeongsan River estuary, South Korea), controlled laboratory simulations, and optical-tracer-based end-member mixing analysis (EMMA) to assess the respective roles of salinity, biodegradation, and photodegradation in shaping DOM composition. Surface water samples were collected from five stations spanning a 26.7 km estuarine gradient under both monsoon and non-monsoon regimes. Laboratory experiments simulating salinity-induced flocculation, microbial degradation, and photochemical transformation were used to derive process-specific end-members. Among various optical indices, specific UV absorbance (SUVA) and dissolved organic carbon-normalized fluorescence regional integration in region 5 emerged as the most responsive and discriminating tracers. Quantitative EMMA results revealed shifting dominance of environmental drivers across space and season: photodegradation accounted for 60.4-91.8% of DOM transformation attributable to the three processes at mid-estuarine sites (YSR2-YSR3) during the non-monsoon period, while biodegradation became influential under monsoonal conditions (up to 47.2% at YSR2), and salinity influence increased to 28.2-33.9% at marine-influenced sites (YSR4-YSR5). Collectively, these results extend the application of EMMA beyond conventional source apportionment to quantitatively resolve process-level controls on DOM transformation. This framework provides a robust basis for improving DOM monitoring, modeling, and ecosystem management in estuarine systems subject to hydrological regulation and climate-driven variability.

Dissolved organic matter (DOM) is a major contributor to membrane fouling in water treatment, yet existing optical and size-based indices often fail to reliably distinguish DOM sources or quantify their contributions. This study introduces a hydrophilic interaction liquid chromatography (HILIC)-derived index, the average logarithmic octanol-water partition coefficient (log KOW), as a hydrophobicity-based tracer for source apportionment of membrane foulants. Using mixtures of Suwannee River fulvic acid (SRFA) and algal-derived organic matter (ADOM), we compared the performance of the average log KOW with conventional UV-vis, fluorescence, and molecular-size indices across feed, permeate, and foulant fractions during ultrafiltration. The average log KOW consistently met stringent evaluation criteria, showing strong linearity, high sensitivity, and close agreement with DOC mass balance. Source apportionment revealed clear differences in fouling pathways: ADOM, enriched in hydrophilic high-molecular-weight biopolymers, contributed up to similar to 85 % of reversible fouling, while SRFA, dominated by hydrophobic low-molecular-weight humic substances, accounted for over 70 % of irreversible fouling. Quantitative apportionment further showed that ADOM contributions to reversible foulants were consistently greater than predicted by ideal mixing, indicating a synergistic effect that enhances reversible fouling in mixed systems. Conversely, SRFA contributions to irreversible foulants exceeded end-member predictions, confirming its strong affinity for pore penetration and persistent fouling layer formation. These results highlight the mechanistic role of hydrophobicity in controlling source-specific fouling behaviors. Compared with conventional indices, the average log KOW provided superior reliability for source discrimination and foulant tracking. This hydrophobicity-based metric offers a powerful diagnostic tool for understanding DOM-membrane interactions and for improving source-specific fouling management in water treatment systems.

Microplastics (MPs) are pervasive in aquatic environments and ultimately accumulate in sediments, yet their role in regulating benthic carbon cycling remains poorly constrained, particularly with respect to dissolved organic matter (DOM) exchange across the sediment-water interface. In this study, we conducted 28-day laboratory sediment-column incubations under controlled oxic and hypoxic conditions to quantify MP-associated changes in DOM quantity and composition in porewater and overlying water. Polyethylene (PE; petroleum-based) and polylactic acid (PLA; bio-based) MPs were added at 5% (w/w) as a high-end (hotspot) loading scenario. MP contamination increased porewater dissolved organic carbon (DOC) concentrations by up to 27-fold relative to controls, resulting in 1.6-18-fold higher cumulative benthic DOC fluxes across the sediment-water interface. Optical and size-fraction analyses revealed polymer-specific shifts in exported DOM quality. PLA treatments were characterized by strong enrichment of low-molecular-weight neutral fractions, consistent with MP-associated DOM inputs, whereas PE treatments were associated with enhanced mobilization and redistribution of sediment-derived DOM. Molecular-level analyses further showed that MP-associated formulas accounted for up to one-third of all detected molecular features, despite MPs comprising only ∼5% of sediment mass. This disproportionate molecular representation suggests that MPs may influence DOM composition beyond their direct mass contribution. Oxygen availability further modulated these molecular patterns, with oxic conditions characterized by higher proportions of formulas classified as metabolically active within the operational reactivity-activity framework, whereas hypoxic conditions favored the accumulation of relatively inactive and more persistent molecular fractions. Overall, these results suggest that sediment-associated MPs may alter both the quantity and molecular characteristics of DOM exchange across the sediment-water interface, highlighting their potential role in modifying benthic carbon exchange processes in MP-impacted aquatic systems.

Microplastic-derived dissolved organic matter (MP-DOM) is an emerging and largely overlooked fraction of dissolved carbon in aquatic and engineered systems, generated in situ through polymer aging, weathering, and oxidative processes. Unlike discrete external inputs, MP-DOM is released continuously and is typically enriched in low-molecular-weight, weakly aromatic, and largely neutral compounds associated with persistent dissolved organic carbon (DOC) fractions in biological treatment systems. However, whether these similarities translate into comparable microbial processing remains unclear. This review develops a process-oriented framework for interpreting MP-DOM as a distinct, continuously generated carbon source in water and wastewater treatment systems. Drawing on established paradigms for natural organic matter (NOM) and soluble microbial products (SMPs), we critically evaluate their applicability and limitations for understanding MP-DOM behavior. Based on currently reported compositional characteristics and estimated occurrence levels, we hypothesize that MP-DOM is processed predominantly through non-growth-associated pathways, including co-metabolic transformation, partial oxidation, and biofilm-mediated processing, while contributing only modestly to biomass production and bulk DOC removal. Rather than serving as a major microbial growth substrate, MP-DOM may therefore act as a persistent modulator of microbial processes by altering oxygen demand, extracellular enzymatic activity, SMP production, extracellular polymeric substance (EPS) composition, and redox microenvironments. This conceptualization highlights a potential decoupling between carbon processing and net carbon removal, with important implications for treatment performance, residual DOC persistence, and carbon cycling. Overall, this review establishes a hypothesis-driven mechanistic framework for understanding MP-DOM in biological treatment systems and identifies key knowledge gaps that require future quantitative validation.

The dynamics of suspended particulate matter (SPM) plays a crucial role in determining water quality, sediment transport, and biogeochemical cycles in inland, estuarine, and coastal water resources. Flocculation processes strongly influence the SPM dynamics via aggregation and breakage under various hydrodynamic and biogeochemical conditions. This study introduces a mechanistic and diagnostic framework that combines a two-class population balance equation (TCPBE) model with Bayesian calibration to simulate flocculation-transport behavior in both laboratory- (time-dependent batch) and field-scale (one-dimensional vertical) systems. Laboratory experiments with biopolymer-clay and microalgae-clay mixtures and field observations from an estuarine turbidity maximum zone are used to derive a comprehensive data set for model validation. Bayesian inference enables the estimation of uncertain model parameters while characterizing their statistical properties, thus supporting the mechanistic interpretation of flocculation dynamics. By quantifying how ionic strength and microbial physiology regulate flocculation kinetics and elucidating the turbulence-driven coupling between flocculation kinetics and sediment transport over tidal cycles, the framework demonstrates its suitability as a process-based diagnostic tool capable of effectively capturing SPM dynamics under various conditions. This framework has strong potential to advance the understanding of flocculation dynamics and support a range of applications in inland and estuarine sediment-laden water systems, including river, reservoir, esturine and coastal waters.

Despite extensive studies on the release behavior of microplastics-derived dissolved organic matter (MP-DOM) under continuous leaching, limited attention has been paid to the sequential leaching dynamics that better simulate realistic environmental conditions. Here, four types of microplastics-polylactic acid (PLA), polyethylene (PE), polystyrene (PS), and commercial PS (CPS)-were subjected to four sequential extractions under dark and UV irradiation conditions to investigate the evolving characteristics of MP-DOM across different leaching phases. Two important aspects of MP-DOM reactivity were examined: trihalomethane formation potential (THMFP) and microbial growth potential . Bio-based PLA exhibited the highest dissolved organic carbon (DOC) release and the fastest leaching rate, while petroleum-based MPs (PE, PS, CPS) showed progressively increasing DOC release under prolonged UV exposure. Phenol/protein-like substances dominated the MP-DOM across all MPs, but their evolution varied by polymer type and leaching condition. Under UV irradiation, PSDOM exhibited a gradual decline in protein-like components and an increase in aromaticity and humification, whereas PLA-, PE-, and CPS-DOM showed opposite trends. THM precursor leaching was more pronounced than DOC leaching under UV irradiation, with simple aromatic phenol/protein-like substances serving as the major precursors. Principal component analysis further revealed that PLA-DOM was characterized by higher lability and lower THMFP, while petroleum-based MP-DOMs exhibited higher THMFP and reduced microbial activity over time. Overall, this study highlights the dynamic compositional changes of MP-DOM during sequential leaching and their implications for disinfection by-product formation and microbial ecosystem function. These findings provide new insights into the environmental behavior of MP-DOM, emphasizing the need for timeresolved assessments of MP-water interactions.

Mud volcanoes (MVs) deliver deep-sourced fluids and hydrocarbons to the seafloor, yet their coupling to cryosphere dynamics remains uncertain. We investigate spatiotemporal variability of pore fluid and gas geochemistry at two Beaufort Sea MVs to assess how cryospheric processes affect MV fluid-gas systems. Central MV sites exhibit persistent freshening (<69% Cl− depletion), whereas peripheries remain seawater-like. Distinctive fluid fingerprints, such as depleted δD, δ18O, and δ7Li values (down to −41.4‰, −4.2‰, and 14.9‰, respectively), elevated 87Sr/86Sr values and B concentrations (up to 0.70924 and 3.06 mM, respectively), and mid-depth minima below 5 pMC in 14CDIC, indicate mixing of post-LGM meteoric groundwater (including submarine permafrost meltwater) with clay dehydration fluids, overprinted by low-temperature water–rock interaction and marine silicate weathering. This freshening also influences gas compositions and isotopic signatures, as reflected in the methane to heavier hydrocarbons ratio (C1/C2+) and the carbon and hydrogen isotopic compositions of hydrocarbons, thereby contributing to decadal-scale shifts between microbial and thermogenic methane that are further modulated by eruption intensity and vent migration. We infer cryosphere-derived freshwater drives pore fluid freshening and perturbs the methane source, shifting the balance in microbial-thermogenic gases at the shallow sediment and thereby modulating net methane delivery from Arctic MVs under present and future warming.

Groundwater nitrate contamination necessitates reliable and efficient methods for source tracking and process understanding. Although dual nitrate isotopes (δ15N-NO3- and δ18O-NO3-) are powerful tracers, their application is limited by their high cost and analytical complexity. Optical spectroscopy of dissolved organic matter (DOM) offers a rapid and cost-effective alternative. Here, we evaluated and advanced the use of DOM fluorescence as an integrative proxy for predicting groundwater nitrate dynamics. Although significant, individual optical indices showed weak correlations with nitrate indicators (R2 = 0.14-0.25, p < 0.05). Integrated DOM indices improved the significant correlations, yet the performance remained moderate (R2 = 0.27-0.62, p < 0.01). We introduced a novel specific fluorescence intensity ratio (FIR), which demonstrated strong and significant linear relationships with all nitrate indicators (p < 0.01). Optimal FIR offered substantially improved prediction (R2 = 0.60-0.76, p < 0.01), while region-based FIR yielded comparable robustness (R2 = 0.56-0.68, p < 0.01) and greater stability. Anthropogenic groundwater recharge markedly strengthened FIR-nitrate relationships, highlighting its role as a key hydrological driver. Critically, the FIR approach generalized well across five groundwater and three surface water systems in China (groundwater: R2 = 0.45-0.99, p < 0.01; surface water: R2 = 0.40-0.86, p < 0.01). These findings established FIR as a viable, cost-effective, and rapid proxy for nitrate and isotopic screening prior to large-scale water quality assessment.

Radiocarbon analysis is an indispensable tool in various fields, including environmental studies, archaeology, geochemistry, and marine science. Two primary oxidation methods for solids, elemental analyzer combustion (EAC) and closed tube combustion (CTC), are routinely used to prepare samples for 14C analysis. While both have been validated with international reference materials, a comprehensive comparison of these two methods using diverse, real-world environmental samples is currently lacking. We conducted such a comparison, analyzing the fraction modern (F14C) values across a range of environmental matrices. For the majority of the samples, the results from both methods were in good agreement. However, we observed a significant F14C discrepancy of up to 30% between the two methods for low-carbon concentration samples (<1% by weight). While intrinsic heterogeneity of organic matter in environmental samples complicated interpretation, this finding suggests that the prolonged combustion time of the CTC method can be important for ensuring complete oxidation for samples containing heat resistant organic matter, given the growing popularity of automated EAC in radiocarbon laboratories.

Accurately apportioning organic pollution sources in mixed land-use watersheds remains challenging due to the limited tracer capacity of conventional fluorescence-based approaches. Traditional methods condense multidimensional excitation-emission matrix (EEM) spectra into a few summarized indices (e.g., BIX, HIX, FI), creating a mismatch between available fluorescence tracers and the number of pollution sources, thereby limiting source discrimination when multiple sources overlap. This study presents an analytical framework for source apportionment integrating three elements. First, full-spectrum EEM images are used as source-specific end-members to preserve spectral information for source-pattern characterization. Second, a statistical assessment of end-member independence and separability performed using SSIM and ANOSIM. Third, a CNN-unmixing architecture was developed to learn source-discriminating patterns from full-spectrum EEM images and estimate source-associated contributions in mixed-source EEMs based on candidate end-member representations. An EEM dataset was constructed from pollution source samples collected in mixed land-use watersheds. Statistical assessment of end-member similarity and separability showed high within-group similarity (Pearson's r = 0.882-0.988; SSIM = 0.838-0.970) and significant between-group separation among the eight end-members in EEM spectral space (ANOSIM R = 0.876; PERMANOVA R2 = 0.899). The framework was evaluated using ground-truth mixtures (2-5 components) and field stream samples. In controlled mixtures, CNN-unmixing showed R2 > 0.7 and MAE <0.07 and generally reproduced dominant-source contributions and rankings across mixture complexities. The field application in the study watershed showed reasonable spatial consistency between the predicted dominant-source contributions and observed pollution-load distribution patterns, supporting the preliminary field-scale applicability of the framework for source-priority assessment in mixed land-use watersheds.

Hydraulic connectivity dynamically regulates water and solute exchange in lake–groundwater systems, but its role in shaping hydrochemical evolution under strong evaporative forcing remains poorly constrained. Here, we integrate hydrochemical analysis and dynamic isotope mass-balance modeling to quantify wet-season lake–groundwater interactions and clarify their hydrochemical consequences in a semi-arid tectonic lake. Lake water preferentially recharges aquifers with high hydraulic connectivity (HHC: 21.65% ± 10.15%) relative to low connectivity aquifers (LHC: 9.47% ± 4.06%, p < 0.01). In contrast, groundwater contributions from HHC back to the lake (16.09% ± 0.03%) are significantly lower than those from LHC (21.29% ± 0.04%, p < 0.01). At the whole-lake scale, the dominant interaction pattern is lacustrine groundwater discharge, as indicated by a positive flux of 7.22 × 106 m3 d−1. Notably, although water stable isotopes indicate stronger evaporative enrichment in HHC groundwater, HHC groundwater exhibits significantly higher dissolved oxygen and lower mineralization than LHC (p < 0.01), indicating limited solute accumulation. This connectivity-regulated evaporation paradox suggests that frequent hydrological cycling under high hydraulic connectivity can limit salinization despite strong evaporative forcing. More importantly, lake–groundwater interactions influence hydrochemical evolution indirectly by regulating hydrochemical conditions, which subsequently constrain major ion accumulation. These demonstrate that hydraulic connectivity can decouple evaporative enrichment from salinization and strongly regulate divergent hydrochemical evolution pathways in lake–groundwater systems.

Dissolved organic matter (DOM) plays a central role in Arctic carbon cycling. However, its molecular composition in sediment porewaters, which links carbon burial and benthic-pelagic exchange, remains poorly constrained. Under ongoing Arctic amplification, increasing terrestrial inputs and subsea permafrost thaw are expected to modify early diagenetic carbon processing across continental shelves. Here, we investigated sediment porewater DOM along an East Siberian Sea-Chukchi shelf-slope transect using fluorescence spectroscopy and ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry to resolve molecular composition, sources, and transformation pathways. Porewater DOM was dominated by lignin- and carboxyl-rich alicyclic molecule (CRAM)-like compounds (33.6-66.5%), indicating persistent terrestrial influence coupled with extensive microbial reworking. Nearshore sediments exhibited elevated dissolved organic carbon (DOC) concentrations, enhanced nutrient regeneration, and stronger humic-like fluorescence signatures, together with higher proportions of carbohydrate-, protein-, and aliphatic-like formulas, consistent with fluvial and permafrost-derived inputs and active early diagenesis. In contrast, slope porewaters were enriched in lipid-like, unsaturated hydrocarbon-like, and high-m/z compounds (∼712), reflecting advanced in situ microbial transformation. Chukchi shelf sediments displayed enhanced condensed aromatic signatures but limited accumulation of structurally stabilized refractory DOM. Nitrogen-containing formulas comprised a substantial fraction of porewater DOM (44.8 ± 8.3%, up to 57.1% on the slope), highlighting pronounced heteroatom enrichment during microbial reprocessing. Refractory components, including CRAM (5.0-45.2%) and island-of-stability (IOS) compounds (0-8.3%), exhibited marked spatial variability, with the outer shelf acting as a preferential zone for refractory DOM accumulation. These results demonstrate that carbon stabilization along the Arctic margin is governed by the interplay among terrestrial supply, permafrost influence, microbial transformation, and hydrodynamic regime. Continued Arctic warming may therefore shift the balance between rapid carbon turnover and long-term stabilization in shelf sediments.

Water quality prediction in freshwater systems and engineered water infrastructure is increasingly supported by machine learning (ML), yet limited model interpretability constrains its scientific and operational utility. Explainable artificial intelligence (XAI) has emerged as a critical framework for addressing this limitation by providing insights into model behavior, dominant predictors, nonlinear responses, and context-dependent interactions. However, existing reviews have largely treated XAI as a complementary component, offering descriptive summaries without critically examining methodological assumptions, interpretive boundaries, or cross-system applicability. In contrast, this review provides a comprehensive and critical synthesis that positions explainability as a central analytical framework and integrates applications across both freshwater systems and engineered water infrastructure. More than 70 studies published since 2018 were analyzed to identify recurring explanatory patterns across key water quality parameters, including nutrients, algal indicators, oxygen- and organic matter–related variables, composite indices, and operational parameters. Across these domains, XAI reveals that model-learned water quality patterns are often structured by nonlinear, threshold-dependent, and context-specific interactions among temperature, nutrient availability, hydrological conditions, light availability, and operational variables. The synthesis highlights the complementary roles of global and local explanations in capturing system heterogeneity and supporting both process understanding and operational decision-making. Key limitations include the non-causal nature of feature attribution and sensitivity to data structure, predictor collinearity, model configuration, and explanation stability. Future work should prioritize integration with process-based and causal inference frameworks, uncertainty-aware explainability, standardized evaluation protocols, and applications across upstream–downstream linkages. Overall, this review reframes XAI as an integrative framework linking prediction, interpretation, and decision support across the water cycle.

High-throughput assessment of microplastic (MP) contamination in complex waters remains challenging because particle-resolved methods are low-throughput and inherently limited by size-dependent detection, particularly for nanoplastics. Here we report a UV-triggered chemiluminescence (CL) bulk-screening assay in which microplastic-derived dissolved organic matter (MP-DOM) generated during controlled UV weathering is transduced into a quantitative luminol-H2O2 flow-injection CL (FIA-CL) readout. Each measurement requires only a 30 min pretreatment, 1 mL of sample, and a few-minute analysis. Polyethylene (PE) and polystyrene (PS) produced reproducible, time-resolved CL transients, and peak intensity responded linearly to MP loading over 0.1-0.5 g L-1 (R2 > 0.92) with polymer-dependent sensitivity. The assay performed optimally under near-neutral pH and 30 min irradiation conditions, while iron oxide (Fe2O3) was identified as a major interferent that markedly suppressed emission. Optical characterization (UV absorbance and EEM-PARAFAC) showed that CL intensity correlated with dissolved organic carbon and humic-like components (R2 = 0.43-0.60), suggesting that both the amount and composition of UV-generated MP-DOM contribute to signal generation. Application to urban streamwater and wastewater effluent demonstrated the assay's utility in realistic matrices. Overall, the UV-assisted FIA-CL approach provides a rapid, mechanistically supported complementary bulk-screening surrogate for comparative assessment of MP contamination across aquatic samples.

The Arctic Ocean is undergoing rapid transformation driven by climate change, including declining sea-ice cover and increasing freshwater inputs, with profound consequences for carbon and nutrient cycling. The East Siberian Sea (ESS), one of the largest Arctic shelf systems, plays a central role in linking terrestrial inputs, primary production, and deep-ocean carbon export. In this study, we combined fluorescence spectroscopy with stable isotope and bulk chemical analyses to examine dissolved organic matter (DOM) dynamics along a shelf-slope transect of the ESS. Our results show that slope-associated sedimentary processes exert a strong control on DOM redistribution and transformation. Enhanced algal production across the Russian Arctic increasingly contributes to organic matter transport toward slope regions. Elevated chlorophyll-a concentrations in waters, together with a pronounced enrichment of protein-like fluorescent DOM (C3), point to strong algal inputs to the continental slope. At mid-slope stations (200-900 m), co-enrichment of dissolved and particulate organic carbon with heavier δ15N signatures suggests intensified microbial remineralization and nitrogen cycling, likely stimulated by sediment resuspension under Atlantic Water influence. These findings highlight the ESS slope as a dynamic benthic-pelagic interface where sedimentary processes reshape DOM composition and act as an important pathway for the supply of bioavailable carbon and nitrogen to the Arctic Ocean interior. Overall, this study highlights the continental slope as an active regulator of Arctic DOM cycling under ongoing Atlantification and sea-ice decline.

Hydrogen sulfide (H2S), a smelly, corrosive and highly toxic byproduct generated from anaerobic digestion processes, is commonly treated using iron (Fe)-based materials, consequently leading to a secondary solid waste. This study offers a novel interfacial catalyst-design approach that valorizes Fe-rich solid waste into a functional cathodic materials in peracetic acid (PAA)-assisted Electro-Fenton (EF). The calcinated waste-derirved FeS@C particles were integrated with reduced graphene oxide (rGO) onto carbon cloth (CC) to construct FeS@C/rGO/CC cathode with active solid–liquid interfaces for oxidant activation and pollutant degradation. The as-prepared cathode achieved 99% degradation of methylene blue (MB) as an indicator within 1 h, with rate constant (kobs) of 0.11 ± 0.02 min−1. The system maintained good performance in the presence of various anions, natural organic matter, and real water matrix, while effectively removing various dyes and antibiotics. Surface and electrochemical analyses revealed that the Fe–S-containing surface, conductive rGO network, and carbon-cloth support collectively enhanced interfacial electron transfer, Fe(II)/Fe(III) redox cycling for effective PAA activation, leading to the dominant involvement of •OH, R-O•, and 1O2 in the degradation pathways. This work provides new physicochemical insight into waste-derived catalytic interfaces for aqueous oxidation and demonstrates a sustainable route for transforming Fe-rich industrial waste into value-added materials for environmental applications.

Biodegradable dissolved organic carbon (BDOC) is the most reactive fraction of dissolved organic matter (DOM) and a major driver of carbon dioxide (CO2) emissions from inland waters, yet its large-scale assessment is constrained by incubation-based measurements. This study conducted a global meta-analysis integrating harmonized BDOC observations, optical DOM indices, and machine-learning (ML) modeling to enable scalable BDOC prediction across freshwater systems. A total of 1063 paired observations were compiled from 17 studies across eight countries, with cross-study comparability improved through correction of variable incubation periods using first-order degradation kinetics, and standardization of UV-visible absorbance wavelengths. The harmonized dataset revealed pronounced hydrological and climatic controls on BDOC dynamics. Biodegradation rate constants were consistently higher in rivers than in lakes, with maximum values observed in temperate rivers during the wet season. Seasonal analyses showed a decoupling between bulk dissolved organic carbon (DOC) and BDOC, with dry conditions favoring DOC accumulation and aromaticity, while wet conditions enhanced inputs of bio-labile DOM. Across climatic gradients, BDOC exhibited greater sensitivity to environmental variability than bulk DOC. Using DOC concentration, absorbance at 254 nm, and humification index (HIX) as predictors, nonlinear ML models outperformed linear regression. Extreme gradient boosting (XGBoost) achieved the highest predictive performance (R2 > 0.6), with stronger accuracy for riverine than lacustrine systems. Overall, this study demonstrates that harmonized optical datasets combined with ML modeling provide a robust and scalable alternative to incubation-based BDOC measurements, advancing quantitative assessment of freshwater carbon biodegradability and inland-water carbon cycling.