npj Clean Water最新文献

{"title":"Compounded fuzzy entropy-based derivation of uncertain critical factors causing corrosion in buried concrete sewer pipeline","authors":"Tasaduq Ismail Wani, Srinivas Rallapalli, Anupam Singhal, Dilan Robert, Jayantha Kodikara, Biplob Kumar Pramanik, Shashi Bhushan Arya","doi":"10.1038/s41545-025-00467-1","DOIUrl":"https://doi.org/10.1038/s41545-025-00467-1","url":null,"abstract":"<p>Corrosion in buried concrete sewer pipelines remains a critical challenge for infrastructure sustainability, driven by the complex interplay of environmental, material, operational, pipe-related, and physical factors with inherent uncertainty and interdependency, aspects often overlooked previously. This study introduces a novel compounded fuzzy entropy-based approach to systematically prioritize critical corrosion-inducing factors, integrating environmental (H₂S, pH, humidity, temperature, O₂), material (cement content, alkalinity, w/c ratio, porosity, permeability), pipe-related (age, length, diameter, depth, slope), operational (flow velocity, water pressure, hydraulic energy loss, sewage residence time, sewer type), and physical (soil type, corrosivity, moisture, groundwater level, external load) factors. Results identify H₂S (0.2073), pH (0.2055), humidity (0.2031), pipe age (0.2039), length (0.2019), cement content (0.2026), alkalinity (0.2015), water pressure (0.2073), flow velocity (0.2043), soil type (0.2042), and soil corrosivity (0.2025) as the most influential contributors, enabling targeted corrosion mitigation strategies and enhancing infrastructure resilience.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"110 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909730","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":"Analysis of microstructure properties of Ni-Fe MOFs and their influence on selective recognition of single and binary dye systems","authors":"Dan Xu, Xiaolin Cao, Jie Zhou, Zhihao Lin, Jing Wang, Jiatong Han, Ge Chen, Guangyang Liu, Xiaomin Xu, Yanguo Zhang, Donghui Xu","doi":"10.1038/s41545-025-00470-6","DOIUrl":"https://doi.org/10.1038/s41545-025-00470-6","url":null,"abstract":"<p>To address the environmental threats posed by dye pollution, it is crucial to develop adsorbents with high selectivity and adsorption capacity for the removal of chemical dye pollutants. In this study, five types of Ni/Fe-MOFs were synthesized via a one-pot solvothermal method by varying the Ni/Fe molar ratio. Their physicochemical structures, surface morphologies, and compositions were characterized through a series of techniques, and their adsorption performances for different dyes were preliminarily investigated. The results demonstrated that the Ni/Fe ratio significantly affects the microstructure and adsorption performance of the MOFs. As the molar ratio of Ni/Fe increased, the structures of the five materials transformed from nanoflowers_(Fe-MOFs) to nanospheres_{(Ni/Fe(3:1)MOFs)}, and eventually to rhombic blocks_(Ni-MOFs). The specific surface area decreased from 281.3 m²/g_(Fe-MOFs) to 67.11 m²/g_{(Ni/Fe(1:1)MOFs)} and further to 0.5369 m²/g_(Ni-MOFs). XPS analysis confirmed the synergistic coordination between Ni and Fe, and FT-IR spectra revealed characteristic peaks for hydroxyl groups on the MOF surface and carboxylate ligands. XRD analysis indicated that materials with higher Ni content were more prone to ligand decomposition, which is consistent with the weight loss observed in the TG-DSC between 230 and 350 °C. The materials exhibited selective adsorption for different dyes. Under optimal conditions, the maximum adsorption capacities for Eosin Y (EY), Neutral Red (NR), and Acid Fuchsin (AF) were 54.92 mg/g_(Fe-MOFs), 636.31 mg/g_(Ni-MOFs), and 79.30 mg/g_{(Ni/Fe(1:1)MOFs)}, respectively. The adsorption kinetics followed a pseudo-second-order model, and the adsorption isotherms conformed to the Langmuir model, while Fe-MOFs and Ni/Fe(1:1) MOFs also exhibited compatibility with the Freundlich model. The adsorption mechanism primarily involved monolayer adsorption as the dominant process, with localized multilayer adsorption, as well as chemisorption, hydrogen bonding, hydroxyl interactions, and electrostatic interactions. These MOFs demonstrated high dye removal efficiency in actual water samples and fruit and vegetable juices, exhibiting excellent reusability and anti-interference capabilities. Importantly, compared with the single system, Fe-MOFs and Ni/Fe(1:1)MOFs exhibit competitive adsorption, and Ni-MOFs exhibit synergistic adsorption in the binary system. This study confirms that by adjusting the Ni/Fe molar ratio, MOFs with high selectivity and adsorption performance for different dyes can be designed, providing new theoretical insights and technical support for environmental remediation and food safety applications.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"82 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884861","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":"Low toxicity mechanistic insights into Z-scheme WO3/BiFeO3/DSB photocatalysts for efficient ampicillin degradation","authors":"Junxin Yu, Tianyu Gu, Ruiying Wang, Xiaohui Zhu, Zhexuan Li, Weiming Zhu, Li Jiang, Zhiying Dong, Bing Li","doi":"10.1038/s41545-025-00442-w","DOIUrl":"https://doi.org/10.1038/s41545-025-00442-w","url":null,"abstract":"<p>Ampicillin (AMP) poses a significant environmental hazard to aquatic ecosystems, and previous research has been inadequate in addressing the ecological toxicity of its byproducts. A novel photocatalyst, WO₃-BiFeO₃/digestate biochar (DSB), denoted WBD, was synthesized for efficient photocatalytic degradation of AMP. In our study, 150 mg WBD achieved 99.93% AMP degradation in 3 h at a concentration of 100 mg/L. WBD maintained a stable degradation performance under various environmental stressors, including pH, and the presence of Cl⁻, NO₃⁻, and HA. •O₂⁻, h⁺, •OH, and ¹O₂ were identified as primary active oxygen species. WBD effectively targeted the -NH₂, -SH, and β-lactam ring-forming -COOH of AMP, facilitating its transformation into low-toxic or non-toxic degradation products, and establishing a detoxification pathway. Overall, this study introduces a novel environmentally material that demonstrates a high degradation efficiency, long-lasting effectiveness, and low product toxicity, offering a promising approach for the effective management of emerging pollutants.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"37 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884862","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":"Deep representation learning enables cross-basin water quality prediction under data-scarce conditions","authors":"Yue Zheng, Xiaoran Zhang, Yongchao Zhou, Yiping Zhang, Tuqiao Zhang, Raziyeh Farmani","doi":"10.1038/s41545-025-00466-2","DOIUrl":"https://doi.org/10.1038/s41545-025-00466-2","url":null,"abstract":"<p>Artificial intelligence has been extensively used to predict surface water quality to assess the health of aquatic ecosystems proactively. However, water quality prediction in data-scarce conditions is a challenge, especially with heterogeneous data from monitoring sites that lack similarity in water quality, hindering the information transfer. A deep learning model is proposed that utilizes representation learning to capture knowledge from source river basins during the pre-training stage, and incorporates meteorological data to accurately predict water quality. This model is successfully implemented and validated using data from 149 monitoring sites across inland China. The results show that the model has outstanding prediction accuracy across all sites, with a mean Nash-Sutcliffe efficiency of 0.80, and has a significant advantage in multi-indicator prediction. The model maintains its excellent performance even when trained with only half of the data. This can be attributed to the representation learning used in the pre-training stage, which enables extensive and accurate prediction under data-scarce conditions. The developed model holds significant potential for cross-basin water quality prediction, which could substantially advance the development of water environment system management.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"44 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875760","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":"Comprehensive HRMS-screening for persistent, mobile, and toxic compounds in first flush urban stormwater","authors":"Sergio Santana-Viera, Francesc Labad, Marina G. Pintado-Herrera, Nicola Montemurro, Marc Teixidó, Pablo A. Lara-Martín, Sandra Pérez","doi":"10.1038/s41545-025-00463-5","DOIUrl":"https://doi.org/10.1038/s41545-025-00463-5","url":null,"abstract":"<p>Water scarcity drives water-stressed regions to make use of all available resources. Consequently, urban stormwater is gaining recognition as a valuable resource, for instance to replenish aquifers; thus, enhancing water supply. However, it carries contaminants that could limit its potential uses, highlighting those recently categorized as persistent, mobile, and toxic (PMT) compounds. In order to conduct broad screening for the presence of PMT compounds in stormwater first-flush samples and rainwater, two instruments based on Gas and Liquid Chromatography coupled to Quadrupole-Time-of-Flight Mass Spectrometry were used, and both suspect and target screening were performed. After prioritization 42 PMTs were detected, of which 24 PMTs were quantified. The results showed that 66% of the quantified PMTs were present in more than 50% of the samples, with average concentrations ranging from 2 ng L^-1 to 2.78 µg L^-1. Of the target PMTs, 11 were quantified for the first time in runoff samples.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"108 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862912","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":"Antibiotic resistance profile of nitrogen-metabolizing microbes in anoxic‒oxic processes for swine wastewater treatment","authors":"Yiwen Yang, Shuang Cai, Feng Huang, Chunhao Mo, Yongbao Wu, Junting Cao, Sheng Chen, Zhiguo Wen, Xindi Liao","doi":"10.1038/s41545-025-00464-4","DOIUrl":"https://doi.org/10.1038/s41545-025-00464-4","url":null,"abstract":"<p>The anoxic-oxic (A/O) process is the most common biological method for removing nitrogen (N) from wastewater, but the antibiotic resistance profile of N-metabolizing microbes in A/O processes remains largely underexplored. Here we demonstrated a significant positive correlation between various types of N-metabolizing genes and antibiotic resistance genes (ARGs) in swine wastewater A/O processes across China. We assembled 180 high-quality genomes of dominant N-metabolizing microbes (12.6% of the total metagenome-assembled genomes), all harboring transcriptionally active ARGs. And Pseudomonas was identified as the primary N-metabolizing genus and major ARG host. Among 1110 culturable N-metabolizing isolates, 22.34% were Pseudomonas strains showing high N removal capacity and multi-antibiotic resistance. Moreover, plasmid-mediated ARG transfer further heightened resistance risks. Overall, these findings highlight a significant ARG risk among predominant N-metabolizing microbes in A/O treatment processes, underscoring the urgency of balancing N removal performance with resistance control in wastewater treatment processes.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"11 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853462","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":"Piloting batch reverse osmosis with a flexible bladder for water recovery from scaling-prone brine","authors":"Emily W. Tow, Quantum J. Wei, Audrey R. Abraham, Kei L. Chua, Michael J. Plumley, John H. Lienhard","doi":"10.1038/s41545-025-00462-6","DOIUrl":"https://doi.org/10.1038/s41545-025-00462-6","url":null,"abstract":"<p>A pilot-scale batch reverse osmosis (RO) system with a flexible bladder was designed to recover additional water from RO concentrate. The sulfate-rich, ~6400-ppm concentrate was sourced from the Yuma Desalting Plant (Arizona, USA), which desalinates agricultural drainage water. The pilot produced 4.4 m³/day of permeate with 150 ppm total dissolved solids from the facility’s concentrate stream with a recovery ratio of 82.6%. Despite producing supersaturated brine, there was no performance deterioration due to scaling. Using a bladder for retentate pressurization limited average power to 633 W and the specific energy consumption to 3.3 kWh/m³. The pilot’s energy data informed a model of large-scale batch RO, which has the potential to desalinate the same water for less than 1 kWh/m³. Additionally, a model was developed to predict scaling likelihood in batch RO. This investigation demonstrates that batch RO is a viable technology for low-energy brine concentration beyond saturation limits.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"7 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831764","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":"Geographical features and management strategies for microplastic loads in freshwater lakes","authors":"Huike Dong, Ruixuan Zhang, Xiaoping Wang, Jiamin Zeng, Lei Chai, Xuerui Niu, Li Xu, Yunqiao Zhou, Ping Gong, Qianxue Yin","doi":"10.1038/s41545-025-00459-1","DOIUrl":"https://doi.org/10.1038/s41545-025-00459-1","url":null,"abstract":"<p>In recent years, microplastic contamination in freshwater lakes has become a significant environmental concern. Despite this, there remains a lack of comprehensive understanding of the distribution patterns and regional characteristics of microplastic loads in global lacustrine environments under a unified standard. To address this gap, our study utilizes Machine Learning (the random forest algorithm), combined with number-to-mass transformation techniques to generate a global prediction. The results indicate an average microplastic concentration of 0.57 items/m³ in lakes and reservoirs worldwide, with an accumulated microplastic load of 10167 tons within top 20 m of water—equivalent to 508 million plastic bottles. The primary sources of microplastics are linked to agricultural land use and the proportion of urban areas within watersheds. Notably, the highest microplastic loads are observed in North America, Africa, and Asia, though the contributing factors vary, including concentration-dependent and area-dependent influences, as well as differences in shape composition. These findings provide valuable insights that can guide the development of targeted policies to effectively mitigate microplastic pollution in freshwater ecosystems.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"59 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789804","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":"Viral particle prediction in wastewater treatment plants using nonlinear lifelong learning models","authors":"Jianxu Chen, Ibrahima N’Doye, Yevhen Myshkevych, Fahad Aljehani, Mohammad Khalil Monjed, Taous-Meriem Laleg-Kirati, Pei-Ying Hong","doi":"10.1038/s41545-025-00461-7","DOIUrl":"https://doi.org/10.1038/s41545-025-00461-7","url":null,"abstract":"<p>Predicting new unseen data using only wastewater process inputs remains an open challenge. This paper proposes lifelong learning approaches that integrate long short-term memory (LSTM), gated recurrent unit (GRU) and tree-based machine learning models with knowledge-based dictionaries for real-time viral prediction across various wastewater treatment plants (WWTPs) in Saudi Arabia. Limited data prompted the use of a Wasserstein generative adversarial network to generate synthetic data from physicochemical parameters (e.g., pH, chemical oxygen demand, total dissolved solids, total suspended solids, turbidity, conductivity, NO₂-N, NO₃-N, NH₄-N), virometry, and PCR-based methods. The input features and predictors are combined into a coupled dictionary learning framework, enabling knowledge transfer for new WWTP batches. We tested the framework for predicting total virus, adenovirus, and pepper mild mottle virus from WWTP stages, including conventional activated sludge, sand filter, and ultrafiltration effluents. The LSTM and GRU models adapted well to new data, maintaining robust performance. Tests on total viral prediction across four municipal WWTPs in Saudi Arabia showed the lifelong learning model’s value for adaptive viral particle prediction and performance enhancement.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"73 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784829","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":"Integrated emulsion separation and fog collection with functionalized Janus wood membrane for water scarcity solutions","authors":"Kaiwen Chen, Jianyi Zhu, Cheng Hao, Haonan Zhang, Yujing Tan, Xianfu Xiao, Fengze Sun, Xuewen Han, Hui Peng, Tianyi Zhan, Jianxiong Lyu, Ning Yan","doi":"10.1038/s41545-025-00460-8","DOIUrl":"https://doi.org/10.1038/s41545-025-00460-8","url":null,"abstract":"<p>Global climate change has exacerbated water scarcity, while traditional water treatment technologies are often unsustainable due to high energy consumption and negative environmental impacts, posing an urgent need for a sustainable solution. This study developed a novel wood-based flexible Janus membrane coupled with a spine structure for efficient oil-water emulsion separation and fog harvesting. The Janus wood membrane showed high separation efficiency (&gt; 99.6%), high filtration flux (water-in-oil and oil-in-water emulsions exceeded 810 L/m²·h and 747 L/m²·h, respectively), and good reusability. Additionally, the introduction of spine and conical pores significantly enhanced fog collection efficiency (19.23 kg/m²·h), expanding the application potential of Janus membranes. Moreover, this Janus wood membrane offered excellent mechanical properties, dimensional stability, mildew resistance, and environmental benefits. This study underscored the potential of Janus membranes in water management and liquid separation, providing a sustainable solution to water scarcity.</p><figure></figure>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"58 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775696","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}