Per- and polyfluoroalkyl substances (PFAS) are persistent contaminants. Predicting their fate in natural or engineered porous media, using accurate models, is essential for effective remediation and contamination management strategies. The mechanisms of transport and retention included in such models, and the associated parameters, are mostly inferred from PFAS concentration vs. time breakthrough curves (BTCs) measured during transport experiments. Still, the interpretation of BTCs may not be unique as they result from a succession of mechanisms taking place inside the porous media. We addressed this issue using F magnetic resonance imaging (MRI) to monitor the transport of perfluorobutanoic acid (PFBA) inside a sand-packed column. The experimental BTC was slightly asymmetric, suggesting that some PFBA may have been adsorbed onto the sand. Hence, a transport model based on the hypothesis that PFBA behaved as a non-sorbing tracer slightly overestimated the concentrations in two regions of the BTC. Surprisingly, the same model matched well the MRI profiles, pointing out that the BTC asymmetry stemmed from an imperfect column exit. Although F MRI requires PFAS concentration above those found in environmental samples, this study showed that the combination of this technique and modeling constitutes a powerful tool to determine the mechanisms involved in PFAS transport in natural or engineered porous media andselect appropriate fate models.

Biofilm-based process such as integrated fixed-film activated sludge (IFAS) appears to be a promising alternative to upgrade and retrofit the conventional activated sludge (CAS) technology for sustainable wastewater treatment. However, limited studies have attempted to evaluate the effects of biofilm carriers on the nitrous oxide (NO) emissions, which contributes ∼50 % of total greenhouse gas emissions at wastewater treatment plants. In this study, comparisons were conducted between a CAS reactor versus a IFAS reactor, both performing partial nitritation (PN). It was experimentally demonstrated that the biofilm carriers significantly increased NO production by 3.04 times in the sidestream PN system under low dissolved oxygen (DO) conditions (e.g., ˂0.5 mg O/L) via stimulating the hydroxylamine oxidation and heterotrophic denitrification pathways, which was primarily attributed to the introduced anoxic microenvironments and altered microbial community distributions. When the biofilm carriers were removed from the IFAS reactor, the NO emission factor decreased from (1.64±0.04)% to (0.63±0.06)%, and the sludge microbial community evolved towards that of the CAS reactor. In addition, enhancing aeration for higher DO levels would narrow the gaps between two reactors and NO production in the IFAS reactor was even 16 % lower at higher DO levels (e.g., 1-1.5 mg O/L). This work reveals that biofilm presence stimulates NO emissions from the sidestream nitritation process under low DO concentrations, thus informing the development of IFAS technology for efficient pollutant removal with a minimized carbon footprint.

Hydroxylamine (NHOH) has significant effects on accelerating Fenton and Fenton-like systems, while little attention was paid to the contribution of N-containing species to the generation of ROS, resulting in the limited utilization of enhancement capacity of NHOH. Herein, the generation of ·OH was initially evaluated in Fe(III)/HO system under different NHOH dosing modes. With the same total dosages of NHOH or Fe(III), continuous dosing of NHOH always achieved a uniform production of ·OH with the accumulated concentration 6 %-61 % higher than that in the single-dosing system, which was different from the typical two-stage ·OH generation profile. The reaction stoichiometry between Fe(III) and NHOH was calculated to be around 1-1.3, while the value of NHOH utilization efficiency for ·OH production (R) kept around 2, indicating the existence of ignored ·OH generation pathway through the direct participation of NHOH. The combination of transformation products analysis, ESR spectra and kinetic modeling demonstrated that NHO· generated from the reaction between Fe(III) and NHOH could activate HO to generate ·OH, which contributed >25 % of the accumulated ·OH in the continuous-dosing system. On the contrary, single-dosing of NHOH facilitated the dimerization of NHO·, decreasing its contribution to ·OH production and attenuating the enhancement capacity of NHOH in the end. By selecting the dosing rate within a proper range related to the total dosages of NHOH and Fe(III) (i.e. dosing rate ≤ 0.13·[NHOH]·k·[Fe(III)]), the rate-limiting step for ·OH production would switch from the reduction of Fe(III) to the dosing of NHOH, which established the dynamic equilibrium of both Fe(III) and NHOH, thereby achieving the controllable generation of ·OH regulated by NHOH dosing rate. This controllable ·OH generation system was successfully used in predicting the exact demand of NHOH at desired treatment efficiency and evaluating the ·OH scavenging capacity of real water, which was conductive to the experimental design and parameter selection for the future studies of reductant-enhanced Fenton-like systems.

Dewatering is essential for improving the handling efficiency of anaerobic digestate. However, the separated liquid fraction, anaerobic digestate liquor (ADL), always contains high concentrations of fine colloidal particles (D < 50 μm, 2000-3000 mg/L), which hinder subsequent biological processes and membrane-based treatments. This study proposed a strategy to enhance colloidal particle removal by briefly exposing ADL to subcritical conditions through rapid heating and quenching, a process that modulates water polarity, weakens solid-liquid interface polar affinity, and minimizes undesirable side reactions. Under optimized conditions (350 °C, 15 min heating, followed by 3000 g centrifugation for 10 min), >90 % of suspended solids were removed from ADL with limited solubilization (soluble COD increase <30 %). Ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) further revealed molecular transformations of dissolved organic matter (DOM) during the above process, including the breakdown of aromatic and unsaturated CHON compounds and the formation of aliphatic, saturated structures, indicating partial degradation of humic-like substances and improved biodegradability. Notably, the stable relative abundances of amine-N and pyridine-N suggested that Maillard-derived compounds preferentially partitioned into the solid phase, underscoring the advantage of the newly proposed approach in controlling refractory byproducts of hydrothermal process. The mechanism of colloidal particle destabilization was investigated through Raman and sum frequency generation (SFG) spectroscopy, focusing on the conformational transformation of interfacial water molecules during rapid exposure to subcritical conditions. A transition from ordered to disordered interfacial water caused by surface restructuring and carbonization was identified. This disordering reduced hydrogen-bond saturation in the hydration layer, facilitated molecular exchange with the bulk water phase, and lowered the interparticle energy barrier imposed by hydration films. Consequently, hydration repulsion was weakened, promoting aggregation of colloidal particles. Overall, these findings advance the mechanistic understanding of colloid stability and demonstrate a physical strategy to substantially reduce the pollution load of ADL by modifying the molecular conformation of interfacial water while minimizing secondary pollution.

The formation of disinfection by-products (DBPs) during water disinfection is a health concern. A limited number of DBPs, such as trihalomethanes (THMs), are regulated and used as indicators of human exposure to the broader group of DBPs. However, it remains poorly understood whether the formation mechanisms and precursors of unregulated DBPs are similar to that of regulated ones. In this study, lab-scale chlorination and chlorine dioxide (ClO) disinfection were conducted on four different source waters (two surface water and two groundwaters). DBP formation was assessed through targeted analysis of THMs via gas chromatography-mass spectrometry, novel sulfonated DBPs via supercritical fluid chromatography-mass spectrometry, and non-targeted analysis using liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FT-ICR-MS). The formation of THMs during chlorination was higher in surface waters (49-111 µg per mg dissolved organic carbon, DOC, at 48 h) than in groundwaters (21-27 µg per mg DOC) and corresponded to their higher initial specific UV-absorbance (SUVA) and higher humic acid fractions as determined by LC-organic carbon detection. ClO disinfection led to significantly lower THM levels (below the limit of detection of 0.20 µg/L) across all samples. Similarly, the formation of sulfonated DBPs was one order of magnitude lower. However, unlike THMs, sulfonated DBPs were formed to a greater extent in both groundwaters (3.0-3.6 µg per mg DOC) than in surface waters (2.0-2.2 µg per mg DOC), suggesting that sulfonated DBPs are preferentially formed from other precursors than THMs. This was further elucidated by LC-FT-ICR-MS analysis showing that the higher levels of sulfur- and nitrogen- containing dissolved organic matter in the studied groundwater samples likely contributed to the increased formation of sulfonated DBPs. Furthermore, LC-FT-ICR-MS analysis outlined that disinfection by ClO, while reducing halogenated DBPs, resulted in even higher levels of non-chlorinated, sulfur- and nitrogen-containing DBPs. In conclusion, strategies focused on reducing regulated THMs may be insufficient to mitigate the formation of sulfonated and other novel heteroatom-containing DBPs during drinking water treatment.

Understanding watershed emerging contaminants (ECs) transport is vital for pollution control but challenging due to complex land-climate interactions and limited models. This study collected 517 seasonal water samples from the Huangshui River (2020-2024) and quantified microplastics (MPs), antibiotics, heavy metals, and water quality indicators. A novel machine learning (ML-SHAP) framework was developed to model ECs transport (train R² = 0.94, test R² = 0.65), integrating multiscale land use (200, 500, 1000, 2000 m riparian buffers), landscape metrics (Patch Density (PD), Largest Patch Index (LPI), Contiguity Index Mean (CONTIG-MN)), and 11 climate variables. Overall, the water quality and heavy metals complied with Class III and Class I standards (GB3838-2002), respectively. However, MPs (1831 items/L) and antibiotics (55.33 ng/L) posed significant threats to regional water security. MPs transport was enhanced in fragmented urban land (PD > 1 in 2000-m buffer) and highly connected cropland (LPI > 50 in 500-m buffer), whereas antibiotic transport intensified in cropland with low landscape connectivity (LPI < 50 in 1000-m buffer). Notably, forest (cover > 45 % in 1000-m buffer) and grassland (CONTIG-MN > 0.5 in 500-m buffer) effectively mitigated ECs transport. Therefore, enhancing riparian forest and grassland connectivity while reducing urban fragmentation within a 2000 m buffer could substantially mitigate the transport of ECs. MPs transport increased under heavy rainfall (>6 mm) and low wind speeds (<1.2 m/s), while antibiotic concentrations rose under strong winds (>2 m/s), low rainfall (<2 mm) and weak solar radiation (<1.7 × 10⁷ J/m²). Climate warming under SSP585 increased MPs by 10.90 items/L and antibiotics by 0.007 ng/L per decade. Low-emission SSP245 with 40 % riparian reforestation reduced pollutants. These findings provide new mechanistic insights into ECs transport and offer a novel model for watershed ECs management.

Pathogenic bacteria can proliferate in disinfectant-depleted tap water when they utilize organic matter as growth substrates. Although the concentration of assimilable organic carbon has been used to estimate substrate quantities in tap water, the full qualitative composition remains largely uncharacterized. In this study, we used a liquid chromatograph-hybrid mass spectrometer for non-targeted analysis of dissolved organic matter to screen for potential growth substrates for Pseudomonas aeruginosa in drinking water. From 4247 components in tap water, 81 substrate candidates were screened. Three dicarboxylic acids-suberic acid, azelaic acid, and sebacic acid-were identified by MS/MS analysis and co-chromatography. Quantum chemical calculations were used to explore the fragmentation pathways of the suberic acid anion in MS/MS, and the results supported the validity of the observed product ion spectra. These dicarboxylic acids, which have not been previously listed in metabolic databases, were identified as substrates capable of acting as sole carbon sources for P. aeruginosa. This study demonstrates a new approach for discovering previously unrecognized organic substrates utilized by specific microorganisms in environmental samples.

Understanding how climate-driven environmental changes shape the community stability of phytoplankton in rivers is crucial for maintaining freshwater ecosystem services and functions. Here, a novel evaluation framework of phytoplankton community stability was developed based on 21 functional traits of eight categories relevant to climate-driven environmental gradients. The proposed framework was applied to evaluate the compositional stability of phytoplankton communities in 16 rivers flowing through a transitional climate zone of China. The results indicated that both species and functional trait compositions significantly differed between communities from the northern (semi-humid) and southern (humid) slopes of the Qinling Mountains across seasons. Algae with large cell volume, defense, and photosynthesis traits were present at higher abundances on the northern slope, while algae with medium cell volume and mobility traits preferentially occurred on the southern slope. Community stability analysis revealed that in contrast to species composition, functional trait composition was less stable on the northern slope than on the southern slope. Due to stronger environmental filtering on the northern slope, closer associations emerged between species and functional traits within phytoplankton communities, reducing functional redundancy and thus compromising trait composition stability. The findings underscore the importance of integrating functional traits with species-based methods in comprehensive evaluation of phytoplankton community stability in rivers. Our new framework provides a powerful tool to unravel the impacts of climate-driven environmental filtering on the stability of riverine phytoplankton communities through a functional trait lens.

As reservoirs become the primary drinking water sources, autochthonous contamination and runoff pollution have become major causes of periodic water quality deterioration at downstream treatment plants. Therefore, implementing in-situ water quality enhancement has become essential. This 3-year study assessed the feasibility of reservoirs using Water-lifting aerators (WLA) as pre-treatment units under autochthonous and runoff pollution, analyzed its effect on downstream oxidant demand and provides practical guidance. This will contribute to achieving more efficient treatment and reducing energy waste and other costs during reservoir management and drinking water treatment processes. Results showed that thermal stratification created anoxic conditions in bottom water of SBY Reservoir, triggering a sequential cascade of dissolved pollutant mobilization and deterioration of water supply quality. Runoff pollution deteriorates the water quality of reservoirs and downstream water treatment plants by introducing substantial loads of particulate and dissolved pollutants. The WLA system, via bottom aeration and full-layer mixing, raises the dissolved oxygen (DO) levels and temperature of bottom water in summer. This approach can reduce the amount of reductive pollutants from autochthonous pollution and, when dealing with runoff pollution, induce high-turbidity runoff to flow to the reservoir bottom, thereby promoting particle settlement. WLA operation has led to a considerable reduction in oxidant dosage at the downstream plant. These findings provide operational frameworks for optimizing reservoir management through artificial mixing and aeration technology, thereby enhancing the role of reservoirs in integrated drinking water treatment systems.

Municipal Separate Storm Sewer Systems (MS4s) are designed to route and discharge stormwater runoff directly into local water bodies. However, contamination from sources such as urban runoff and illicit connections conveyed through MS4s poses serious environmental risks, underscoring the need for source tracing research. This paper presents a comprehensive review of research conducted on tracing contamination in MS4s. The review includes a literature review, an analysis of water quality and hydrological data, and an analysis of tracing techniques, with the aim of identifying research gaps and proposing future directions. The findings indicate a growing research interest in MS4-related contamination over the past decade. Various types of water quality and hydrological data are shown to play distinct but critical roles in tracing efforts. Tracing techniques fall into three categories: (1) source identification, providing quantitative insights into source contributions; (2) movement tracking, integrating conventional approaches with computational models to estimate source locations; and (3) risk analysis, assessing current conditions and predicting future risks. Each category of techniques has its strengths and limitations, and their integration may enhance overall tracing effectiveness. This review also highlights key research gaps, including underutilization of water quality and hydrological data, insufficient integration of economic considerations, limited accuracy of tracing techniques, continued reliance on conventional methods, and the absence of a systems-level perspective. Future development should therefore focus on integrating economic analysis and a system-level perspective with more accurate, data-driven tracing techniques to achieve effective and sustainable MS4s pollution control.

Arsenic (As) contamination of groundwater is primarily driven by microbially mediated redox processes and the dynamic evolution of dissolved organic matter (DOM). The influence of cycled methanogenesis and methane oxidation processes on As species transformation in geogenic As-contaminated groundwater, however, remain mechanistically elusive. In this study, quantitative relationships among DOM molecular characteristics, microbial functional networks, and As speciation were established using sediment microcosm experiments, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), and metagenomic sequencing. The results indicate that rates of methanogenesis and methane oxidation are regulated by thermodynamic properties of DOM. Labile DOM promoted As(III) mobilization at a rate of 1.04 μg kg⁻¹ d⁻¹ through methyl-related metabolism. Remarkably, enhanced methane oxidation further elevated the As(III) generation rate to 3.30 μg kg⁻¹ d⁻¹, underscoring the accelerating effect of methane cycling on As release. In contrast, humified DOM decoupled the geochemical linkage between iron and As. Microbial succession governed the redox transitions, as the proliferation of methanogens substantially increased methane production (up to 7.23 mg kg⁻¹ d⁻¹), while methanotrophs enhanced oxidation rates from 94.99 to 190.76 mg kg⁻¹ d⁻¹. This microbial progression coupled sulfate and As(V) reduction through the up-regulation of key functional genes (dsrAB, arsC). Energy conversion during DOM biodegradation governs As migration stages. These findings highlight the interactive constraints on As speciation dynamics by molecular characteristics of DOM and microbial functional networks during methane biotransformation processes in groundwater systems. This research provides new mechanistic insights into As biogeochemical cycling in geogenic contaminated groundwater.

Safe drinking water supply from karst aquifers faces several challenges due to their high vulnerability to contamination, which can result in abrupt water quality variations in matter of hours. The analysis of faecal bacterial activity requires time-consuming cultures and expensive reagents in the laboratory, which can delay the detection of an imminent contamination event. An innovative methodology is proposed in this research to overcome these limitations and provide real-time insights about water quality in drinking sources. Fieldwork activities included the continuous monitoring of water parameters (spring discharge, electrical conductivity, turbidity and Tryptophan-like fluorescence) and groundwater sampling for Escherichia coli determination in two springs draining a binary karst aquifer in S Spain during three hydrological years (2020/21 to 2022/23). Ten supervised Machine Learning models were then tested to infer five sanitary risk levels (based on E. coli activity) from continuous measurements at the springs. The combination of two water parameters was the most effective predictor at the two drinking water sources, which showed different optimal configuration of proxy parameters depending on their hydrogeological features and contaminant transport mechanisms. Gaussian Processes, Neural Networks, Naïve Bayes and Quadratic Discriminant Analysis provided the highest probability of correctly discriminating between sanitary risk levels. This methodology holds significant potential to be integrated as an early-warning protection tool for real-time sanitary risk assessment and, thus, safeguarding drinking water supplies worldwide against microbial threats.

Freshwater scarcity intensifies in arid/semi-arid inland regions where conventional extraction is hindered by low humidity, infrastructural limitations, and energy deficits. Sorption-based atmospheric water harvesting (SAWH) using hygroscopic salt composites (HSCs) shows promise but suffers from salt leakage at high loadings. Herein, we present here for the first time a novel class of HSCs based on covalent organic framework (COF) gels, synthesized via a one-step gelation strategy. The COF gel matrix, characterized by the rigid and open microporous-macroporous architecture without observable swelling behaviors, enables nano-confinement of lithium chloride without requiring strong ionic interactions. The resulting COF gel-based HSCs achieve stable salt loading up to 55 wt% with no observed leakage, a maximum water uptake of 3.7 kg kg, and a rapid water harvesting rate of 1.2 kg kg h. Moreover, under outdoor conditions with 60% RH, the COF gel-based HSCs achieve six solar-driven adsorption-desorption cycles per day, with a water production rate reaching 3.2 L kg day, demonstrating its high efficiency and potential for sustainable water collection in natural environments. This work establishes a robust strategy for salt confinement within COF networks and offers a scalable pathway toward high-performance, leakage-resistant SAWH materials.

The dissemination of antibiotic resistance genes (ARGs) facilitated by coexisting microplastics (MPs) in the "source-sink" hotspots of waste activated sludge (WAS) raises great concern. Despite real-world MPs undergoing aging, whether and how naturally aged microplastics (AMPs) affect ARG dissemination during sludge treatment remains largely unknown. Herein, we systematically explored the evolved effects and underlying mechanisms of environmentally relevant MPs (0, 3, and 30 mg/kg TS) aging on ARG propagation in anaerobic sludge digestion via multi-omics analyses. Specifically, microplastic exposure increased total ARG abundance by 2.59-15.31 % with enriched mobile genetic elements (MGEs, 0.22-16.71 %). These effects were escalated at higher microplastic dosages and aging degrees. Mechanistically, metagenomic and metaproteomic analyses revealed the drivers for ARG amplification in the sludge digester evolved from the pristine microplastics (PMPs)-induced higher oxidative stress and membrane permeability to AMPs-induced higher multidrug efflux coupled with pilus-mediated conjugation. Subsequently, metagenomic binning identified key multidrug-resistant hosts of Sedimentibacter, Alicycliphilus, and Sulfuricurvum genera. Moreover, high-resolution metabolomics and reactomics network analysis uncovered that AMPs stimulated microbial metabolite turnover, particularly of nitrogenous and sulfurous compounds, and enhanced the complexity and communication frequency of molecular transformation networks centered on lignin and protein nodes, thereby promoting ARG exchange. Finally, Mantel tests reconfirmed that reactive oxygen species level (Mantel's r = 0.93, p = 0.04) and metabolite network connectivity (Mantel's r = 0.82, p = 0.04) are paramount drivers of ARG spread. These findings offer novel insights into the ARG amplification risk driven by MPs aging, guiding targeted strategies to mitigate ARG spread and improve resource recovery in sludge bioengineering systems.

Accurate calibration of hydraulic models of water distribution systems (WDSs) is essential for reliable simulations. After eliminating gross errors, including those in estimated demands, pipe roughness coefficients (PRCs) are the most often used calibration parameters. Although numerous PRC calibration methods exist to ensure model simulations align well with field observations, they typically assume error-free pressure gauge elevations and overlook the compensatory interactions between elevation errors and PRC uncertainties. This often results in biased PRC calibration outcomes. To overcome this limitation, this paper introduces a novel framework that decouples elevation errors by minimizing the standard deviation of pressure residual time series, rather than relying on traditional residual minimization techniques. Additionally, a clustering-based data preprocessing approach is employed to reduce the impact of uncertain nodal demands and measurement noise. Tests on three benchmark networks demonstrate that the proposed method accurately calibrates PRCs, even when accounting for elevation inaccuracies, nodal demand uncertainties and measurement noise simultaneously. This establishes a new paradigm that leverages the statistical characteristics of residual time series to enable error-decoupled model calibration. Crucially, the method also quantifies pressure gauge elevation errors through post-calibration residual analysis, eliminating the need for costly field surveys. This advancement is particularly valuable for regions with missing or erroneous elevation data, significantly improving WDS calibration practices.

Eucalyptus trees are grown as short-cycle, high-efficiency species, but there is concern about their impact on the composition of dissolved organic matter (DOM) and its fate during drinking water disinfection treatment. DOM leached from Eucalyptus leaf (EL-DOM) was characterized by ultrafiltration, UV-visible, three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy-parallel factor analysis (PARAFAC), and high-resolution mass spectrometry (HRMS), and the potential toxicity of by-products associated with water disinfection processes was calculated. Increasing pH and temperature (10-30 °C) enhanced release of dissolved organic carbon (DOC)/dissolved organic nitrogen (DON) from EL litter compared to other tree tissues. PARAFAC identified four fluorescent components, while molecular characterization revealed a predominance of lignin/carboxylic-rich alicyclic molecules (CRAM)-like structures compared to aliphatic/peptide and aromatic constituents. EL-DOM showed higher carbonaceous disinfection byproducts (C-DBPs) and nitrogenous DBPs (N-DBPs) formation potentials and reactivity than the standard Suwannee River natural organic matter for three investigated disinfection scenarios, which were in the order HOCl > NHCl > ClO. Calculated cytotoxicity of DBPs in disinfected water was derived mainly from haloacetic acids (HAAs) in absence of halide ions whereas it was derived mainly from haloacetamides (HAMs) and HAAs in the presence of bromide and iodide. Also, elevated Br and I levels in water caused a significant shift in speciation to mixed brominated/iodinated-DBPs, and resulted in DBPs with increased total toxicity. Non-target screening using HRMS tentatively assigned molecular formulae to > 1000 putative DBPs, including 179, 439, and 287 aliphatic DBP formulae from HOCl, NHCl, and ClO treatments, respectively. Furthermore, relationships were established between specific DBP formation potential (DBPFP) and measured properties from EL-DOM, thus emphasizing the need for development of source-water protection in Eucalyptus plantation catchment areas, and optimization of disinfection strategies to mitigate DBP risks in drinking water supplies.

Water supply systems-spanning from water sources to treatment plants, distribution networks, and ultimately consumers-function as both significant sources and sinks of microplastic pollution. Due to factors such as light exposure, mechanical force of water flow and chemical corrosion by disinfectants, microplastics (MPs) undergo a series of transformations, thereby posing a higher water safety risk. Nevertheless, the distribution of MPs within water supply systems and their potential risks to water safety remain to be fully understood and systematically reviewed. This review provides the first comprehensive synthesis that traces MPs across the entire water supply chain-from source to consumer-to quantitatively link global occurrence, transformation risks, and potential health implications. A comprehensive analysis of MPs abundance in global drinking water sources, tap water, and bottled water from global data (Web of Science Core Collection, up to 2025) revealed a rapidly escalating daily intake of MPs through drinking water, with structural equation modeling identifying the Human Development Index and wastewater treatment rate as key predictors of global microplastic distribution. Beyond occurrence, a cascade of underappreciated risks arising from microplastic persistence and transformation in water supply systems was critically examined-including enhanced disinfection by-product formation, additive leaching, contaminant carrier effects, and human exposure. By integrating evidence on microplastic transformation, aging, leaching, and exposure, this review establishes a conceptual link between microplastic environmental behavior and population-level health implications, offering a mechanistic perspective on risks posed by MPs in water supply systems. The synthesis provides a scientific foundation to guide future research, policy-making, and standard-setting for emerging contaminants in drinking water.

The assessment factor (AF) method remains the most widely used method for deriving predicted no-effect concentrations (PNEC) in aquatic ecological risk assessment (ERA), particularly for emerging contaminants (ECs). However, the inherent conservatism of the AF method may result in the systematic overestimation of ecological risks. This study critically evaluates PNEC derivation for sulfamethoxazole and other representative ECs. The findings reveal that studies identifying these pollutants as posing high ecological risks consistently relied on AF-derived PNEC values, which were typically 2-3 orders of magnitude lower than those obtained using the species sensitivity distribution (SSD) method. Bibliometric analysis further indicates that these conservative PNEC have been extensively adopted and cited, potentially shaping regulatory decisions and environmental management practices. To address this issue, we propose methodological advancements, including standardized toxicity data screening protocols, simplified derivation methods, adaptive assessment factor schemes, and the creation of an open, dynamically updated global PNEC database. These innovations aim to improve the scientific accuracy of ERA, mitigate regulatory overprotection, and optimize resource allocation in environmental management.

The co-occurrence of UV-irradiated nanoplastics and heavy metals (HMs) in aquatic environments raises significant environmental concerns, yet the irradiation-induced surface electric field (E) enhancement on nanoplastics and its implications for colloidal stability remain unclear. By integrating experimental characterization with theoretical modeling, this study systematically compared the aggregation kinetics of pristine and aged PET-NPs (polyethylene terephthalate nanoplastics) mediated by Zn, Cd, Caand Mg using dynamic light scattering (DLS), spectroscopic analyses, interaction forces calculation and density functional theory (DFT) simulations. UV irradiation facilitated surface oxidation of PET-NPs, significantly amplifying E from 0.55 to 3.49×10 V/m within 48 h. The measured E intensities exhibited strong positive correlations with both the critical coagulation concentration (CCC) across all cation systems (r > 0.90, p < 0.05) and the CCC differences (ΔCCC) among different cations (r > 0.90, p < 0.05). These findings identified E as a key regulator enhancing colloidal stability of PET-NPs and governing specific ion effects on their aggregation during irradiation. Further calculations revealed that, beyond Coulombic forces, two critical but previously unrecognized E-dependent interactions occurred between HM cations and surface oxygen atoms (primarily on the COH groups): polarization-enhanced induction force (PEIF) and polarization-induced covalent bonding (PICB). These interactions contributed nearly 40 % to PET-NPs aggregation in Zn/Cd systems after 10-h of irradiation. These results were strongly validated by FTIR and XPS analyses. Our findings provide fundamental mechanistic insights into the sedimentation dynamics of photoaged nanoplastics and establish a theoretical framework for assessing the environmental risks associated with combined nanoplastics-HMs pollution in aquatic ecosystems.

Coastal marine water quality emerges from complex and dynamic feedbacks between natural processes and human activities, yet disentangling their respective influences remains a persistent challenge. This study proposes an interpretable, data-driven framework that integrates clustering, explainable machine learning (XML), and causal inference to investigate long-term coastal water quality dynamics. Using 36 years of monthly in-situ measurements of ten water quality parameters from 76 monitoring stations across ten water control zones (WCZs) in densely urbanized Hong Kong, we clustered into several representative water quality regimes with distinct spatiotemporal characteristics: low pollution, microbial, and eutrophic. XML-based SHAP analysis revealed regime-specific patterns: microbial pollution was best predicted by urban proximity, clean waters by shipping intensity, and nutrient enrichment by agricultural land use, with salinity and SiO consistently ranking as dominant environmental drivers. Building on SHAP results, we applied CausalForestDML to estimate the marginal effects of human drivers while controlling for a comprehensive set of environmental confounders. While SHAP importance generally aligned with causal effects, notable discrepancies underscored the added value of causal modeling. Further, temporal causal analysis revealed attenuated urban and shipping influences on DO, while agricultural impacts on nutrient concentrations have intensified and become more spatially heterogeneous. Although proximity to natural land consistently mitigated Escherichia coli, its buffering capacity on nutrient pollution appears to be weakening under expanding agricultural pressure. The proposed framework demonstrates how combining predictive and causal analytics can integratively reveal mechanistic insights into water quality evolution and regulation, offering transferable tools for sustainability-oriented coastal governance.

Rapid prediction of reservoir vertical thermal structure is crucial for implementation flexible reservoir optimization strategies aimed at ecological protection. Data-driven models can efficiently forecast water temperature dynamics but have limitations: limited measured data reduces the prediction accuracy, weak physical interpretability of stratification, and extrapolation to future scenarios remains unreliable. To overcome these challenges, this study proposes a physically constrained deep learning framework (P-DL). Mechanism-driven process models are used to augment training data and identify key influencing key factors. Vertical temperature profiles are transformed into physically interpretable parameters to describe stratification intensity and improve extrapolation through weak physical constraints. The Xiangjiaba (XJB) reservoir was selected as a case study, to compare the proposed framework was compared with Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) model. All models captured historical temperature variations, whereas P-DL provided more accurate representation of short-term local fluctuations. Among the outputs, parameter D reflects the temporal evolution of stratification, and the difference between A and B indicates overall intensity and peak timing. Under the SSP5-8.5 scenario, the framework outperformed others in predicting surface temperatures during strong stratification (RMSE: 0.83-1.1 °C; R²: 0.88-0.9) and showed superior consistency at both local and overall consistency (KLD: 2.85-5.71; KSS: 0.2-0.4). Overall, the framework improves prediction accuracy, physical interpretability, and extrapolation stability, providing a reference for intelligent thermal management of reservoirs. The hybrid model and weak physical constraints concept involved in the framework can also guide improving data-driven predictions for other environmental factors.