Urban water scarcity increasingly requires demand-side management (DSM) to complement conventional supply-side engineering, yet evaluation is challenging when behavior is reversible and heterogeneous. This study proposes an integrated framework that combines time-series analysis with agent-based modeling (ABM) to simulate how non-price DSM policies—environmental education and behavioral nudges—jointly promote water-saving behavior. High-resolution dormitory meter data from a leading university were analyzed to extract baseline consumption trends, periodicities, and holiday effects, which were then translated into empirically grounded behavioral rules for agents in the ABM. The calibrated model reproduced the observed campus dynamics with high fidelity (R²=0.96). Key findings include: (1) education-only and nudge-only policies deliver short-term water savings that regress toward a low-level equilibrium due to the ∼15% endogenous reversion tendency. (2) A combined policy activates an ordered cascade effect: high-quality education first seeds early adopters, generating a conservation signal that is subsequently amplified by nudges across the social network, driving a system-wide shift toward water conservation. (3) Across adoption stages, the combined DSM strategy reduced per capita water usage by 1.8% to 10.7%, and increased the share of water-saving students. Threshold analysis reveals that, when the non-water saving ratio is 70%, adding nudges expands the feasible intervention space by 65.3%, while education quality outweighs coverage for crossing behavioral tipping points. The model results were validated through a sensitivity analysis using the Hornberger-Spear-Young algorithm. This study provides a data-driven framework for evaluating DSM policies and offers a roadmap for designing staged, cascade-oriented policies aimed at achieving water savings.
{"title":"Coupling Time-Series Analysis and Agent-Based Modeling to Design Non-Price Demand Side Management Policies for Water Saving","authors":"Yacong Hu, Chen Feng, Bingqian Zhang, Haoge Xu, Hao Xiao, Shiyu Wan, Hanbo Gao, Kun Yan, Jinping Tian, Lyujun Chen","doi":"10.1016/j.watres.2026.125501","DOIUrl":"https://doi.org/10.1016/j.watres.2026.125501","url":null,"abstract":"Urban water scarcity increasingly requires demand-side management (DSM) to complement conventional supply-side engineering, yet evaluation is challenging when behavior is reversible and heterogeneous. This study proposes an integrated framework that combines time-series analysis with agent-based modeling (ABM) to simulate how non-price DSM policies—environmental education and behavioral nudges—jointly promote water-saving behavior. High-resolution dormitory meter data from a leading university were analyzed to extract baseline consumption trends, periodicities, and holiday effects, which were then translated into empirically grounded behavioral rules for agents in the ABM. The calibrated model reproduced the observed campus dynamics with high fidelity (R²=0.96). Key findings include: (1) education-only and nudge-only policies deliver short-term water savings that regress toward a low-level equilibrium due to the ∼15% endogenous reversion tendency. (2) A combined policy activates an ordered cascade effect: high-quality education first seeds early adopters, generating a conservation signal that is subsequently amplified by nudges across the social network, driving a system-wide shift toward water conservation. (3) Across adoption stages, the combined DSM strategy reduced per capita water usage by 1.8% to 10.7%, and increased the share of water-saving students. Threshold analysis reveals that, when the non-water saving ratio is 70%, adding nudges expands the feasible intervention space by 65.3%, while education quality outweighs coverage for crossing behavioral tipping points. The model results were validated through a sensitivity analysis using the Hornberger-Spear-Young algorithm. This study provides a data-driven framework for evaluating DSM policies and offers a roadmap for designing staged, cascade-oriented policies aimed at achieving water savings.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"41 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid and precise toxicity assessment of chemical pollutants and their byproducts formed during water treatment and in aquatic environments remains a significant environmental challenge, as the predictive power of conventional quantitative structure–activity relationship (QSAR) models is limited by their reliance on simplified molecular descriptors. To address this, ToxD4C, a novel multi-modal deep learning framework, was developed to simultaneously classify and regress 31 toxicity endpoints, covering nuclear receptor and enzyme panels, stress response assays, mutagenicity, carcinogenicity, cardiopulmonary toxicity, and various environmental toxicities of concern for water quality management. ToxD4C uniquely integrates three-dimensional molecular geometries, graph attention networks, and SE(3)-equivariant Transformer architectures, effectively capturing complex stereochemical and electronic molecular features. In parallel, a pretrained Uni-Mol model was fine-tuned via transfer learning on Density Functional Theory (DFT)-optimized structures, independently generating normalized toxicity predictions with enhanced reliability and generalization. Both approaches outperformed traditional descriptor-based models across validation tests. Feature‑attribution analysis (SHAP) highlighted key physicochemical drivers of predicted toxicity, and receptor docking offered mechanistic context for selected receptor‑mediated endpoints. Applied to realistic UV/H₂O₂ advanced oxidation scenarios in a real water matrix, this approach efficiently identified high-risk transformation products, and their predicted toxicity was further validated in vitro using JC-1 mitochondrial membrane potential, CCK-8 cell viability, and nuclear receptor/stress-response reporter assays. These tools are integrated within the open-source Tox-Agents platform, enabling rapid and interpretable decision-making for water treatment and environmental risk assessment.
{"title":"Screening toxic transformation products of emerging pollutants in advanced oxidation processes with 3D deep learning and in vitro assays","authors":"Fulin Shao, Weiying Li, Zhiwei Liang, Yu Zhou, Dawei Zhang, Yu Chang","doi":"10.1016/j.watres.2026.125499","DOIUrl":"https://doi.org/10.1016/j.watres.2026.125499","url":null,"abstract":"The rapid and precise toxicity assessment of chemical pollutants and their byproducts formed during water treatment and in aquatic environments remains a significant environmental challenge, as the predictive power of conventional quantitative structure–activity relationship (QSAR) models is limited by their reliance on simplified molecular descriptors. To address this, ToxD4C, a novel multi-modal deep learning framework, was developed to simultaneously classify and regress 31 toxicity endpoints, covering nuclear receptor and enzyme panels, stress response assays, mutagenicity, carcinogenicity, cardiopulmonary toxicity, and various environmental toxicities of concern for water quality management. ToxD4C uniquely integrates three-dimensional molecular geometries, graph attention networks, and SE(3)-equivariant Transformer architectures, effectively capturing complex stereochemical and electronic molecular features. In parallel, a pretrained Uni-Mol model was fine-tuned via transfer learning on Density Functional Theory (DFT)-optimized structures, independently generating normalized toxicity predictions with enhanced reliability and generalization. Both approaches outperformed traditional descriptor-based models across validation tests. Feature‑attribution analysis (SHAP) highlighted key physicochemical drivers of predicted toxicity, and receptor docking offered mechanistic context for selected receptor‑mediated endpoints. Applied to realistic UV/H₂O₂ advanced oxidation scenarios in a real water matrix, this approach efficiently identified high-risk transformation products, and their predicted toxicity was further validated in vitro using JC-1 mitochondrial membrane potential, CCK-8 cell viability, and nuclear receptor/stress-response reporter assays. These tools are integrated within the open-source Tox-Agents platform, enabling rapid and interpretable decision-making for water treatment and environmental risk assessment.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"8 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1016/j.watres.2026.125493
Zhengxing Chen, Xiufeng Tang, Yirui Su, Tao Liu, Uli Klümper, Feng Ju, Min Liu, Ping Han
{"title":"Impact of human activities on groundwater biogeochemical cycles and microbial communities: Insights from metagenomic analysis","authors":"Zhengxing Chen, Xiufeng Tang, Yirui Su, Tao Liu, Uli Klümper, Feng Ju, Min Liu, Ping Han","doi":"10.1016/j.watres.2026.125493","DOIUrl":"https://doi.org/10.1016/j.watres.2026.125493","url":null,"abstract":"","PeriodicalId":443,"journal":{"name":"Water Research","volume":"8 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1016/j.watres.2026.125495
Linxuan Che, Ziruo Wang, Hui Xu, Lu Lv, Weiming Zhang, Bingcai Pan, Qingran Zhang, Ming Hua
Nanoscale zero-valent iron (nZVI) serves as an effective electron donor to enhance anaerobic methanogenesis, yet its high reducibility often induces microbial nanotoxicity, creating a fundamental conflict between reactivity and biocompatibility. Herein, we synthesized an extracellular polymeric substance (EPS)-modified nZVI bio-composite (nZVI@EPS) via one-step liquid-phase reduction, with a focus on the structural characteristics and functional interplay of its core-shell architecture in bio-nano systems. We found that EPS decelerated precursor reduction kinetics, suppressing supersaturation-induced crystallization and favoring the formation of an amorphous iron core with elongated Fe–Fe bonds that enhanced its thermodynamic driving force for electron donation. Simultaneously, the resulting EPS layer served as a biocompatible and pseudocapacitive biointerface, physically shielding microorganisms from direct contact and electrochemically buffering electron surge from the highly reductive iron core through a storage and controlled-release mechanism. Hydrogen evolution experiments confirmed that the amorphous core ensured sufficient electron supply, while the EPS biointerface merely regulated the electron release kinetics without sacrificing ultimate utilization efficiency. In the anaerobic digestion of waste activated sludge, the optimized core-interface synergistically enhanced methane yield and biogas purity by 31.11% and 37.42%, respectively. Such improvements were underpinned by enhanced enzymatic activities, reinforced energy conservation, and a redirected methanogenic metabolic flux toward the hydrogenotrophic pathway. This study leverages insights from iron core-interface functional decoupling to propose a synchronized optimization strategy, establishing a universal design framework for engineering nZVI materials that integrate high reactivity with biocompatibility for efficient waste-to-energy conversion.
{"title":"Reconciling the reactivity-biocompatibility trade-off in nanoscale zero-valent iron with an amorphous core and pseudocapacitive biointerface for enhanced anaerobic methanogenesis","authors":"Linxuan Che, Ziruo Wang, Hui Xu, Lu Lv, Weiming Zhang, Bingcai Pan, Qingran Zhang, Ming Hua","doi":"10.1016/j.watres.2026.125495","DOIUrl":"https://doi.org/10.1016/j.watres.2026.125495","url":null,"abstract":"Nanoscale zero-valent iron (nZVI) serves as an effective electron donor to enhance anaerobic methanogenesis, yet its high reducibility often induces microbial nanotoxicity, creating a fundamental conflict between reactivity and biocompatibility. Herein, we synthesized an extracellular polymeric substance (EPS)-modified nZVI bio-composite (nZVI@EPS) via one-step liquid-phase reduction, with a focus on the structural characteristics and functional interplay of its core-shell architecture in bio-nano systems. We found that EPS decelerated precursor reduction kinetics, suppressing supersaturation-induced crystallization and favoring the formation of an amorphous iron core with elongated Fe–Fe bonds that enhanced its thermodynamic driving force for electron donation. Simultaneously, the resulting EPS layer served as a biocompatible and pseudocapacitive biointerface, physically shielding microorganisms from direct contact and electrochemically buffering electron surge from the highly reductive iron core through a storage and controlled-release mechanism. Hydrogen evolution experiments confirmed that the amorphous core ensured sufficient electron supply, while the EPS biointerface merely regulated the electron release kinetics without sacrificing ultimate utilization efficiency. In the anaerobic digestion of waste activated sludge, the optimized core-interface synergistically enhanced methane yield and biogas purity by 31.11% and 37.42%, respectively. Such improvements were underpinned by enhanced enzymatic activities, reinforced energy conservation, and a redirected methanogenic metabolic flux toward the hydrogenotrophic pathway. This study leverages insights from iron core-interface functional decoupling to propose a synchronized optimization strategy, establishing a universal design framework for engineering nZVI materials that integrate high reactivity with biocompatibility for efficient waste-to-energy conversion.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"42 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146097834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate estimation of chlorophyll-a (Chl-a) is essential for monitoring harmful algal blooms (HABs), particularly in vulnerable coastal regions. However, most machine learning (ML) approaches rely on purely correlative patterns, often lacking causal interpretability and robustness under changing environmental conditions. This study introduces an enhanced causal machine learning framework that integrates causal discovery, treatment effect estimation, and deep learning within a Causally Informed Neural Network (CINN). Using 31 environmental predictors from MODIS, ERA5, and HYCOM over the Persian Gulf, a region where HABs threaten desalination, fisheries, and coastal ecosystems, the model embeds causal graphs derived from the DECI algorithm and average treatment effects from double machine learning. Monotonic causal constraints were incorporated to align predictions with ecological expectations. Results show that CINN and its monotonic extension (MCINN) consistently outperform baselines, including Random Forests, XGBoost, and Support Vector Machines, achieving R² up to 0.926 (a 10-17% improvement over baselines) while reducing RMSE by up to 25%. Mediation and sensitivity analyses confirm the causal validity of key drivers, including sea surface temperature, non-fluorescence line height, and nutrient fluxes. Uncertainty quantification and counterfactual simulations further demonstrate the framework’s potential for operational early-warning systems and policy interventions. By bridging causality and deep learning, this framework delivers an interpretable, data-efficient, and uncertainty-aware solution for predicting algal blooms in data-scarce, climate-sensitive marine environments.
{"title":"Bridging Causality and Deep Learning for Harmful Algal Bloom Prediction","authors":"Pouya Zarbipour, Mohammad Reza Nikoo, Hassan Akbari, Rouzbeh Nazari, Maryam Karimi","doi":"10.1016/j.watres.2026.125492","DOIUrl":"https://doi.org/10.1016/j.watres.2026.125492","url":null,"abstract":"Accurate estimation of chlorophyll-a (Chl-a) is essential for monitoring harmful algal blooms (HABs), particularly in vulnerable coastal regions. However, most machine learning (ML) approaches rely on purely correlative patterns, often lacking causal interpretability and robustness under changing environmental conditions. This study introduces an enhanced causal machine learning framework that integrates causal discovery, treatment effect estimation, and deep learning within a Causally Informed Neural Network (CINN). Using 31 environmental predictors from MODIS, ERA5, and HYCOM over the Persian Gulf, a region where HABs threaten desalination, fisheries, and coastal ecosystems, the model embeds causal graphs derived from the DECI algorithm and average treatment effects from double machine learning. Monotonic causal constraints were incorporated to align predictions with ecological expectations. Results show that CINN and its monotonic extension (MCINN) consistently outperform baselines, including Random Forests, XGBoost, and Support Vector Machines, achieving R² up to 0.926 (a 10-17% improvement over baselines) while reducing RMSE by up to 25%. Mediation and sensitivity analyses confirm the causal validity of key drivers, including sea surface temperature, non-fluorescence line height, and nutrient fluxes. Uncertainty quantification and counterfactual simulations further demonstrate the framework’s potential for operational early-warning systems and policy interventions. By bridging causality and deep learning, this framework delivers an interpretable, data-efficient, and uncertainty-aware solution for predicting algal blooms in data-scarce, climate-sensitive marine environments.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"253 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1016/j.watres.2026.125498
Kun Shi , Wanying Li , Jiafeng Zhang , Rui Huo , Yuting Zhao , Shilei Zhou
Aerobic denitrifying bacteria are increasingly recognized for nitrogen removal in deep drinking-water reservoirs, yet how dissolved oxygen (DO) gradients relate to their ecological strategies, niche breadth, and co-occurrence networks remains unclear. We investigated nirS-type aerobic denitrifiers in 14 drinking-water reservoirs in North China using depth-stratified water-column samples, and classified communities into four functional groups based on K/r strategies and niche generalization/specialization. Ecological threshold detection, community assembly, and network analyses identified a DO breakpoint at 8.31 mg/L, separating an oxygen-limited niche (OLN) and an oxygen-enriched niche (OEN). Dissolved organic matter (DOM) was dominated by protein-like components in both niches, while fluorescence intensity was higher in OLN. Along the OLN→OEN gradient, dominant genera shifted from Sulfuritalea to Rhodanobacter, Pseudomonas, and Achromobacter. Assembly analyses suggested stronger environmental selection and dispersal limitation for K-strategists and specialists in OLN, whereas stochasticity increased in OEN; r-strategists and generalists exhibited greater stochasticity across both niches. Co-occurrence networks indicated that r-strategists and generalists formed more cohesive networks and showed higher cohesion-based structural stability, particularly in OEN. Random-forest models interpreted with SHapley Additive exPlanations and partial least squares path modeling suggested that stability in OLN was most strongly associated with temperature, manganese, and chemical oxygen demand, whereas in OEN it was more strongly associated with inorganic nitrogen and DOM composition, largely via indirect links through β-diversity. Overall, DO regimes are linked to systematic shifts in community assembly and interaction structure of aerobic denitrifiers in drinking-water reservoirs, offering testable hypotheses for assessing combined effects of oxygen, nitrogen loading, and DOM properties.
{"title":"Linking ecological strategies to niche breadth: interpretable machine learning unravels community patterns of nirS-type aerobic denitrifiers along oxygen gradients in drinking water reservoirs","authors":"Kun Shi , Wanying Li , Jiafeng Zhang , Rui Huo , Yuting Zhao , Shilei Zhou","doi":"10.1016/j.watres.2026.125498","DOIUrl":"10.1016/j.watres.2026.125498","url":null,"abstract":"<div><div>Aerobic denitrifying bacteria are increasingly recognized for nitrogen removal in deep drinking-water reservoirs, yet how dissolved oxygen (DO) gradients relate to their ecological strategies, niche breadth, and co-occurrence networks remains unclear. We investigated <em>nirS</em>-type aerobic denitrifiers in 14 drinking-water reservoirs in North China using depth-stratified water-column samples, and classified communities into four functional groups based on K/r strategies and niche generalization/specialization. Ecological threshold detection, community assembly, and network analyses identified a DO breakpoint at 8.31 mg/L, separating an oxygen-limited niche (OLN) and an oxygen-enriched niche (OEN). Dissolved organic matter (DOM) was dominated by protein-like components in both niches, while fluorescence intensity was higher in OLN. Along the OLN→OEN gradient, dominant genera shifted from <em>Sulfuritalea</em> to <em>Rhodanobacter, Pseudomonas</em>, and <em>Achromobacter</em>. Assembly analyses suggested stronger environmental selection and dispersal limitation for K-strategists and specialists in OLN, whereas stochasticity increased in OEN; r-strategists and generalists exhibited greater stochasticity across both niches. Co-occurrence networks indicated that r-strategists and generalists formed more cohesive networks and showed higher cohesion-based structural stability, particularly in OEN. Random-forest models interpreted with SHapley Additive exPlanations and partial least squares path modeling suggested that stability in OLN was most strongly associated with temperature, manganese, and chemical oxygen demand, whereas in OEN it was more strongly associated with inorganic nitrogen and DOM composition, largely via indirect links through β-diversity. Overall, DO regimes are linked to systematic shifts in community assembly and interaction structure of aerobic denitrifiers in drinking-water reservoirs, offering testable hypotheses for assessing combined effects of oxygen, nitrogen loading, and DOM properties.</div></div>","PeriodicalId":443,"journal":{"name":"Water Research","volume":"294 ","pages":"Article 125498"},"PeriodicalIF":12.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1016/j.watres.2026.125497
Rongdi An , Xiamu Zelang , Donglin Wang , Shiting Liu , Nan Lu , Chao Ma , Hongfeng Bian , Lianxi Sheng , Jiunian Guan
Hydrothermal carbonization demonstrates a potential for converting invasive plants into multifunctional carbonaceous material. Invasive plant-based hydrochar derived dissolved organic matter (HDOM) becomes an important source of anthropogenic dissolved organic matter, however, the molecular composition and bioavailability of HDOM and the controlling factors were not sufficiently revealed. Thus, in this study, a variety of invasive plants were selected to fabricate hydrochar at different hydrothermal temperatures to investigate the molecular composition via FT-ICR-MS and bioavailability based on microbial fuel cell system. The results indicated dissolved organic carbon (DOC) yield peaked at 200°C and pH fluctuated within a range of 5.0 ‒ 6.0. Along with the increase in hydrothermal temperature, macromolecular humic-like substances promoted via depolymerization, dehydration, and condensation of lignocellulose, likewise unsaturated-reduced molecules as well as the diversity of CHO group in HDOMs. Van Krevelen diagrams demonstrated highly unsaturated and phenolic compounds as lignin-like/CRAMs were the dominant components. Biomass feedstocks did not greatly alter the molecular distribution pattern of HDOMs. HDOMs were introduced into the microbial fuel cell system as the substitute carbon source of sodium acetate, according to the output voltage, HDOMs demonstrated a superior bioavailability, and the effects of biomass feedstocks and hydrothermal temperature were in line with the percentage of labile compounds (MLBL%). HDOMs may serve as a carbon substrate that upregulated catabolic pathways to enhance the bioavailability, and act as metabolic driver to promote the nitrogen removal efficiency via enhancing denitrification and anammox. Environmental implications of HDOMs based on molecular composition and bioavailability were further discussed. This work provided theoretical foundation for optimizing the hydrothermal carbonization of invasive plants and reducing the ecological risks of invasive plant-based hydrochar.
{"title":"Effects of biomass feedstock and hydrothermal temperature on the molecular composition and bioavailability of invasive plant-based hydrochar-derived dissolved organic matter","authors":"Rongdi An , Xiamu Zelang , Donglin Wang , Shiting Liu , Nan Lu , Chao Ma , Hongfeng Bian , Lianxi Sheng , Jiunian Guan","doi":"10.1016/j.watres.2026.125497","DOIUrl":"10.1016/j.watres.2026.125497","url":null,"abstract":"<div><div>Hydrothermal carbonization demonstrates a potential for converting invasive plants into multifunctional carbonaceous material. Invasive plant-based hydrochar derived dissolved organic matter (HDOM) becomes an important source of anthropogenic dissolved organic matter, however, the molecular composition and bioavailability of HDOM and the controlling factors were not sufficiently revealed. Thus, in this study, a variety of invasive plants were selected to fabricate hydrochar at different hydrothermal temperatures to investigate the molecular composition via FT-ICR-MS and bioavailability based on microbial fuel cell system. The results indicated dissolved organic carbon (DOC) yield peaked at 200°C and pH fluctuated within a range of 5.0 ‒ 6.0. Along with the increase in hydrothermal temperature, macromolecular humic-like substances promoted via depolymerization, dehydration, and condensation of lignocellulose, likewise unsaturated-reduced molecules as well as the diversity of CHO group in HDOMs. Van Krevelen diagrams demonstrated highly unsaturated and phenolic compounds as lignin-like/CRAMs were the dominant components. Biomass feedstocks did not greatly alter the molecular distribution pattern of HDOMs. HDOMs were introduced into the microbial fuel cell system as the substitute carbon source of sodium acetate, according to the output voltage, HDOMs demonstrated a superior bioavailability, and the effects of biomass feedstocks and hydrothermal temperature were in line with the percentage of labile compounds (MLB<sub>L</sub>%). HDOMs may serve as a carbon substrate that upregulated catabolic pathways to enhance the bioavailability, and act as metabolic driver to promote the nitrogen removal efficiency via enhancing denitrification and anammox. Environmental implications of HDOMs based on molecular composition and bioavailability were further discussed. This work provided theoretical foundation for optimizing the hydrothermal carbonization of invasive plants and reducing the ecological risks of invasive plant-based hydrochar.</div></div>","PeriodicalId":443,"journal":{"name":"Water Research","volume":"294 ","pages":"Article 125497"},"PeriodicalIF":12.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.watres.2026.125489
Yang Shen, Zhonglin Chen, Jimin Shen, Pengwei Yan, Jing Kang, Binyuan Wang, Shengxin Zhao, Yu Ji, Qiang Tan, Ruihang Chen
Improving the interfacial mass transfer efficiencies of ozone and pollutants in heterogeneous catalytic ozonation systems is crucial to solving the technical barrier of low reactive oxygen species (ROS) yield, thereby minimizing interference from coexisting components in water to achieve efficient decontamination. Herein, a biochar-coupled manganese oxide catalyst (MnO@BC) was designed, which achieved a dual adsorption and interfacial reaction between ozone and pollutants, significantly enhanced the hydroxyl radical (·OH) yield, leading to a 92.5% removal efficiency for atrazine (ATZ). The hydroxyl groups on the carbon layer achieve effective adsorption of ozone molecules (Eads=-0.72 eV), inducing Mn-O bond formation with Mn sites and the transfer of 0.29 e⁻, leading to the generation of surface atomic oxygen (*O). Subsequently, this *O spontaneously converts into ·OH, as evidenced by the highly negative Gibbs free energy change (ΔG = -13.9 eV). The improved synergetic process significantly increases ·OH yield by 3.8 times compared to ozone alone. Benefiting from the dual synergy process, the constructed O3/MnO@BC system significantly resists the interference of coexisting components in water, exhibiting unique advantages compared to traditional catalytic systems. It also performed well in purifying broad-spectrum micropollutants, synchronously weakening the toxicity, and blooming superior prospects for filtered water purification. The study designs catalysts from the perspective of the microscopic heterogeneous interface, providing novel theoretical insights and solutions to solve the technical barrier of heterogeneous catalytic ozonation.
{"title":"Interface Synergetic Adsorption and Catalysis Achieve Efficient Ozone Decomposition: Surface Atomic Oxygen-triggered Hydroxyl Radicals Increment for Dependable Water Purification","authors":"Yang Shen, Zhonglin Chen, Jimin Shen, Pengwei Yan, Jing Kang, Binyuan Wang, Shengxin Zhao, Yu Ji, Qiang Tan, Ruihang Chen","doi":"10.1016/j.watres.2026.125489","DOIUrl":"https://doi.org/10.1016/j.watres.2026.125489","url":null,"abstract":"Improving the interfacial mass transfer efficiencies of ozone and pollutants in heterogeneous catalytic ozonation systems is crucial to solving the technical barrier of low reactive oxygen species (ROS) yield, thereby minimizing interference from coexisting components in water to achieve efficient decontamination. Herein, a biochar-coupled manganese oxide catalyst (MnO@BC) was designed, which achieved a dual adsorption and interfacial reaction between ozone and pollutants, significantly enhanced the hydroxyl radical (·OH) yield, leading to a 92.5% removal efficiency for atrazine (ATZ). The hydroxyl groups on the carbon layer achieve effective adsorption of ozone molecules (<em>E</em><sub>ads</sub>=-0.72 eV), inducing Mn-O bond formation with Mn sites and the transfer of 0.29 e⁻, leading to the generation of surface atomic oxygen (*O). Subsequently, this *O spontaneously converts into ·OH, as evidenced by the highly negative Gibbs free energy change (ΔG = -13.9 eV). The improved synergetic process significantly increases ·OH yield by 3.8 times compared to ozone alone. Benefiting from the dual synergy process, the constructed O<sub>3</sub>/MnO@BC system significantly resists the interference of coexisting components in water, exhibiting unique advantages compared to traditional catalytic systems. It also performed well in purifying broad-spectrum micropollutants, synchronously weakening the toxicity, and blooming superior prospects for filtered water purification. The study designs catalysts from the perspective of the microscopic heterogeneous interface, providing novel theoretical insights and solutions to solve the technical barrier of heterogeneous catalytic ozonation.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"253 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.watres.2026.125488
Hiroki Ando, Kelly Reynolds
Left-censored data (i.e., microbial non-detection data) in wastewater surveillance hinder accurate understanding of disease incidence and the early detection of epidemic signals in the initial stages. In this study, we propose state-space models incorporating a logistic model to handle left-censored data. Using simulation data, we show that the state-space models can provide accurate estimates of wastewater concentrations from detection rates. The models outperformed the substitution method (i.e., a method that replaces non-detection data with a specific value), lowering the mean of absolute percentage error from 0.39 to 0.053. We also found that the estimation accuracy of the models was improved by increasing the number of tested samples and sampling frequency. In the simulation analysis, higher sampling frequency was more critical than the number of daily analyzed samples, as long as weekly totals remained consistent. Subsequently, we applied the models to real-world data for influenza A virus (IAV) and respiratory syncytial viruses (RSV) in the USA, estimating wastewater concentration and wastewater-based effective reproduction number (Reww). Estimated Reww ranged from 0.80 to 1.36 for IAV and 0.74 to 1.66 for RSV, which was consistent with Re reported in previous studies using clinical data. As a comparison, dynamics of wastewater concentration and Reww were estimated using the substitution approach. We observed that the substitution approach underestimated concentrations in the period during which left-censored data were observed. The substitution approach also overestimated Reww in early epidemic stages and underestimated Reww in the end stage. These findings demonstrate the utility of the state-space models to handle left-censored data and enhance our ability to understand epidemic dynamics through wastewater surveillance. To facilitate its use, we provided a file for inputting wastewater-based data along with scripts. The state-space models can be run easily by adding wastewater-based data in a provided CSV file without advanced programming skills.
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