npj Clean Water最新文献

{"title":"Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation","authors":"Huarong Yu, Yue Wang, Tan Li, Qibo Gan, Dan Qu, Fangshu Qu","doi":"10.1038/s41545-025-00513-y","DOIUrl":"https://doi.org/10.1038/s41545-025-00513-y","url":null,"abstract":"<p>Traditional calibration of Activated Sludge Models (ASM) is often manual, expert-dependent, and inefficient. This study introduces a hyperparameter optimisation framework using Optuna to automate the calibration of the ASM2d model. Built on Python, the model integrates the Tree-structured Parzen Estimator (TPE) for single-objective and NSGA-II for multi-objective optimisation. A 50-day dataset from a full-scale wastewater treatment plant in Shenzhen, China, validates the approach. Compared to traditional methods, TPE reduced average relative errors for TN and COD from 4.587 and 24.846% to 0.798 and 15.291%, respectively, while decreasing iterations by 15–20%. NSGA-II lowered TN and COD errors to 4.72 and 15.17%, further improving to 0.095% and 8.43% with full-parameter tuning. Calibration efficiency increased by 65–75%. By effectively exploring parameter interdependencies, TPE and NSGA-II enhance calibration robustness and generalisation. This automated optimisation method significantly improves the accuracy and efficiency of ASM calibration, advancing intelligent wastewater process modelling.</p><figure></figure>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"25 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pollution-based integrated real-time control for urban drainage systems: a multi-agent deep reinforcement learning approach","authors":"Zhenyu Huang, Yiming Wang, Xin Dong, Wei Li, Yangbo Tang, Dazhen Zhang","doi":"10.1038/s41545-025-00512-z","DOIUrl":"https://doi.org/10.1038/s41545-025-00512-z","url":null,"abstract":"<p>This study presents a multi-agent reinforcement learning (MARL) framework for integrated real-time control (RTC) of urban drainage systems (UDSs), coordinating sewers, wastewater treatment plants (WWTPs), and receiving waters. Trained within a hydraulic–water quality simulation environment using QMIX, the framework enables facility-level decision-making and adaptive system coordination. Applied to Lu’an City, China, MARL achieved a 25.4% reduction in flooding and overflow volumes and an 18.0% decrease in river pollutants relative to benchmark strategies, while maintaining real-time control feasibility (6.35 s per 5-min interval). Under rainfall forecast and sensor noise uncertainty, MARL improved performance stability by 44.7–52.4%. Despite operational trade-offs, the framework supports integrated system optimization and consistent water quality improvements in urban settings.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"29 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating non-target analysis and machine learning: a framework for contaminant source identification","authors":"Peng Liu, Ding Pan, Xin-Yi Jiao, Ji-Ning Liu, Peng-Hui Du, Peng-Cheng Li, Meng-Zhu Xue, Yan-Chao Jin, Cai-Shan Wang, Xue-Rong Wang, Ying-Zhi Ding, Guang-Ning Zhu, Jing-Hao Yang, Wen-Ze Wu, Lu-Feng Liang, Xin-Hui Liu, Li-Ping Li","doi":"10.1038/s41545-025-00504-z","DOIUrl":"https://doi.org/10.1038/s41545-025-00504-z","url":null,"abstract":"<p>Machine learning-based non-target analysis (ML-based NTA) faces the critical challenge of linking complex chemical signals to contamination sources. This review proposes a systematic framework of ML-assisted NTA for contaminant source identification, emphasizing the strategies and considerations of key steps in data processing, pattern recognition, and model validation. The framework provides practical guidance for translating raw NTA data to actionable environmental insights that support informed decision-making.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"14 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Architecting ternary heteronanocomposites Bi2S3/BiOCl@In2S3 and Bi2S3/BiOBr@In2S3 for photocatalytic detoxification of organoarsenic compound","authors":"Namasivayam Dhenadhayalan","doi":"10.1038/s41545-025-00469-z","DOIUrl":"https://doi.org/10.1038/s41545-025-00469-z","url":null,"abstract":"<p>Ternary heteronanocomposites Bi₂S₃/BiOCl@In₂S₃ and Bi₂S₃/BiOBr@In₂S₃ were designed as potent and sustainable photocatalysts and demonstrated their visible light-driven detoxification of the organoarsenic pollutant (carbarsone). A facile wet chemical-based synthesis method was applied to fabricate ternary nanocomposites, and their structural properties were analyzed using several analytical techniques (FETEM, XRD, XPS, etc.). In both nanocomposites, the monodispersed nanorod-structured Bi₂S₃ and nanosheet-structured In₂S₃, BiOCl, and BiOBr materials were combined to form multi-structured nanocomposites. Both nanocomposites exhibited good photocurrent responses and lower band gap energies that led to their use as photocatalysts for the degradation of carbarsone. As expected, Bi₂S₃/BiOCl@In₂S₃ and Bi₂S₃/BiOBr@In₂S₃ displayed excellent catalytic performance, achieving carbarsone degradation within 34 and 20 min, with corresponding higher rate constants of 0.1022 and 0.1572 min⁻¹, respectively. This enhanced photocatalytic activity arose due to synergistic double Z-scheme heterojunctions originating based on the band energies within nanocomposites, which can increase the inhibition of the photogenerated electrons and holes pair’s recombination and relatively maintain the strong catalytic redox properties. Bi₂S₃ acts as an interfacial mediator for effective charge separation, whereas In₂S₃ and BiOCl/BiOBr feasibly generate hydroxyl radical reactive species. Mass spectral analysis was employed to prove the oxidative pathway mechanism wherein hydroxyl radicals effectively degrade carbarsone. Furthermore, these nanocomposites displayed strong structural stability along with sustaining catalytic performance, and insignificant loss during the recycling processes. The integration of visible-light sensitivity with strong oxidative capabilities establishes Bi₂S₃, In₂S₃, BiOCl, and BiOBr as excellent candidates for photocatalytic detoxification of pollutants.</p><figure></figure>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"1 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnesiothermal reduction and doping strategies in engineered TiO2 for pharmaceutical degradation and CO2 conversion","authors":"Junguang Gao, Hashem O. Alsaab, Masoud Habibi Zare, Saeed Shirazian","doi":"10.1038/s41545-025-00508-9","DOIUrl":"https://doi.org/10.1038/s41545-025-00508-9","url":null,"abstract":"<p>Pharmaceutical contaminants in water pose a significant ecological risk and require advanced treatment solutions. In this study, the strategy of oxygen-deficient TiO_2-x and doping is evaluated for its photocatalytic efficiency under visible light for pharmaceutical pollutant degradation, seawater purification and green fuel production. The oxygen vacancies reduced the band gap and improved visible light absorption and charge separation, allowing TiO_2-x to achieve almost complete degradation of aspirin within 6 h. Mechanistic studies (EPR, LC-MS) revealed •O₂⁻ and h⁺ as the dominant reactive species. The TiO_2-x (1:1) catalyst showed excellent stability and reusability. Modified catalysts (TiO₂-Cu, TiO₂-GO) were also evaluated, with TiO₂-Cu and TiO_2-x (1:1) showing superior removal of organic pollutants (&gt;90%), natural organic matter (NOM) and divalent ions (Mg²⁺, Ca²⁺) in seawater. While efficient degradation reduced biotoxicity (95% EC50 reduction in the Microtox assay), incomplete mineralization in some systems resulted in toxic intermediates, highlighting the need for combined chemical and toxicity assessments. In addition, TiO_2-x (1:1) and TiO₂-GO showed increased activity in CO₂ reduction. This work highlights oxygen vacancy engineering as a promising strategy for visible-light-driven environmental photocatalysis.</p><figure></figure>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"11 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144819344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning physics and temporal dependencies: real-time modeling of water distribution systems via Kolmogorov–Arnold attention networks","authors":"Zekun Zou, Zhihong Long, Gang Xu, Raziyeh Farmani, Tingchao Yu, Shipeng Chu","doi":"10.1038/s41545-025-00505-y","DOIUrl":"https://doi.org/10.1038/s41545-025-00505-y","url":null,"abstract":"<p>Real-time modeling is vital for the intelligent management of urban water distribution systems (WDSs), enabling proactive decision-making, rapid anomaly detection, and efficient operational control. In comparison with traditional mechanistic simulators, data-driven models offer faster computation and reduced calibration demands, making them more suitable for real-time applications. However, existing models often accumulate long-term prediction errors and fail to capture the strong temporal dependencies in measured time series. To address these challenges, this study proposes the Kolmogorov–Arnold Attention Network for the real-time modeling of WDSs (KANSA), which combines Kolmogorov–Arnold Networks with attention mechanisms to extract temporal dependency features through bidirectional spatiotemporal processing. Additionally, a multi-equation soft-constraint formulation embeds mass and energy conservation laws into the loss function, mitigating cumulative errors and enhancing physical consistency. Evaluations on a benchmark network and a real-world system demonstrate that KANSA achieves high-accuracy real-time estimation and pattern fidelity while maintaining engineering-grade hydraulic balance.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"289 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144797361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems","authors":"Tianwei Mu, Feiyu Duan, Baokuan Ning, Bo Zhou, Junyu Liu, Manhong Huang","doi":"10.1038/s41545-025-00499-7","DOIUrl":"https://doi.org/10.1038/s41545-025-00499-7","url":null,"abstract":"<p>Data-driven models often neglect the underlying physical principles, limiting generalization capabilities in water distribution systems (WDSs). This study presents a novel spatio-temporal graph physics-informed neural network (ST-GPINN) for water quality prediction in WDSs, integrating hydraulic simulations, physics-informed neural networks (PINNs), and graph neural networks (GNNs) to capture dynamics and graph-based network connectivity while approximating partial differential equations (PDEs). ST-GPINN discretizes WDSs using virtual nodes to enhance spatial granularity, employs an Encoder-Processor-Decoder architecture for predictions. Validated on Network A (a small-scale network with 9 junctions and 11 pipes) and Network B (a real large-scale WDS with 920 junctions and 1032 pipes), ST-GPINN outperforms others, achieving a MAE of 0.0073 mg/L, RMSE of 0.0121 mg/L, and <i>R</i>² of 88.91% in Network A, and a MAE of 0.008 mg/L, RMSE of 0.0098 mg/L, and <i>R</i>² of 98.91% in Network B. Its scalability and accuracy highlight ST-GPINN’s potential for water quality predictions.</p><figure></figure>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"14 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144792787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced nitrogen removal via SNAD-IFAS process treating THP-AD liquor: microbial community and co-metabolism mechanism","authors":"Xiaochen Xu, Xiao Feng, Weichun Gao, Fenglin Yang, Bing Liu, Chao Wang","doi":"10.1038/s41545-025-00502-1","DOIUrl":"https://doi.org/10.1038/s41545-025-00502-1","url":null,"abstract":"<p>The anaerobic digester liquor after thermal hydrolysis pretreatment (THP-AD liquor) is a highly hazardous wastewater containing high concentration of ammonium. Herein, a novel two-stage tandem-type simultaneous partial nitrification, anammox, and denitrification-integrated fixed biofilm activated sludge (SNAD-IFAS) hybrid process was successfully constructed for treating THP-AD liquor. The average removal efficiencies of ammonium, total nitrogen, and chemical oxygen demand in the stable phase were 94.0%, 89.9%, and 66.8%, respectively. The specific anammox activity of the two SNAD-IFAS reactors was 68.72 and 42.15 mg N/(g VSS·h), respectively. <i>Candidatus</i> Kuenenia and <i>Candidatus</i> Brocadia (13.49–20.94%) were the main genus of anammox bacteria. Although the relative abundance of <i>Nitrosomonas</i> (0.21–1.67%) was very lower than other bacteria, <i>Nitrosomonas</i> was a central genus in the co-occurrence network. Moreover, the genes involved in pyridine, dioxin, protein, and carbohydrate degradation were remarkably enriched in SNAD-IFAS, indicating the co-metabolism mechanism of refractory organic degradation. This study provides a low energy consumption, high efficiency, and low-carbon technology for treating THP-AD liquor.</p><figure></figure>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"31 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144792788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CaO2 pretreatment enhances fecal pollution indicator removal during mesophilic anaerobic digestion of sewage sludge","authors":"Yuhang Wang, Wenhao Fang, Hua Li, Ru Wang, Xinxin Wang, Xiaomeng Wang, Lixiang Zhou, Guanyu Zheng","doi":"10.1038/s41545-025-00506-x","DOIUrl":"https://doi.org/10.1038/s41545-025-00506-x","url":null,"abstract":"<p>Municipal sludge retains fecal pathogens that pose challenges in its treatment and utilization. Mesophilic anaerobic digestion (MAD) fails to achieve adequate pathogen reduction while demonstrating inconsistent methane. To address these, the feasibility of reducing fecal pollution indicators, including <i>Escherichia coli</i> (EC), human-specific <i>Bacteroides</i> HF183 (HF183), human adenovirus (HAdV), JC and BK polyomaviruses (JCPyV and BKPyV), and crAssphage by combining MAD with CaO₂ pre-treatment was assessed. Both ·OH and ·O₂⁻ were produced during CaO₂ pre-treatment. ·OH mainly accounted for the removal of these indicators, while ·O₂⁻ reduced their infectivity. In MAD process, CaO₂ pre-treatment accelerated the decay of most indicators with the elevated ammonia nitrogen in digested sludge. Overall, CaO₂ pre-treatment and the MAD process predominantly contributed to the reduction of HF183, JCPyV and HAdV and the reduction of crAssphage, EC, and BKPyV, respectively, and combining MAD with CaO₂ pre-treatment is an effective approach for removing fecal indicators from sludge.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"290 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144786888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Symmetric anion exchange membranes enhance arsenic removal and overcome conductivity limitations in FCDI systems","authors":"Siyao Zhang, Purui Wang, Wenchao Xue, Wanni Zhang, Fangyuan Liu, Chaoge Yang, Hanlong Liu, Yang Zhang, Zimeng Zhang, Chunpeng Zhang","doi":"10.1038/s41545-025-00503-0","DOIUrl":"https://doi.org/10.1038/s41545-025-00503-0","url":null,"abstract":"<p>Conventional flow-electrode capacitive deionization (FCDI) often exhibits performance constraints stemming from elevated ion migration resistance associated with diminished conductivity within the desalination chamber, particularly under complex aqueous matrices. This investigation introduces a symmetric anion-exchange membrane (AEM) configuration engineered to circumvent these conductivity limitations and enhance arsenic removal efficacy. Relative to conventional designs, the symmetric-AEM configuration demonstrated an approximate 19.4% enhancement in arsenic removal efficiency. For influent streams with initial arsenic concentrations of 1000 μg·L⁻¹, effluent concentrations were diminished below the analytical detection limit (0.02 μg·L⁻¹) employing a two-stage sequential process. This configuration sustains or potentially enhances desalination-chamber conductivity by optimizing ion migration pathways and facilitating anion compensation via highly mobile chloride ions. The contributions of chloride ions as supporting electrolytes and the transformations of arsenic valence states were interrogated, providing mechanistic insights into the observed performance improvements. Our findings signify a practical advancement in FCDI, presenting a potentially robust and efficacious strategy for arsenic remediation in contaminated groundwater. Thus, the symmetric-AEM configuration represents a significant advancement toward the broader implementation and practical application of FCDI systems for potable water production.</p><figure></figure>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"28 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}