Water Research X最新文献

{"title":"Long-term multivariate water quality forecasting for sustainable aquaculture management","authors":"Xiaodong Ji ,&nbsp;Lu Liu ,&nbsp;Bentao Duan ,&nbsp;Ying Li ,&nbsp;Haoran Xing ,&nbsp;Bin Wang ,&nbsp;Dashe Li","doi":"10.1016/j.wroa.2025.100402","DOIUrl":"10.1016/j.wroa.2025.100402","url":null,"abstract":"<div><div>Accurate water quality prediction is essential for intelligent aquaculture management, enabling timely intervention, risk mitigation, and sustainable resource use. Key parameters such as dissolved oxygen, chlorophyll-a, and pH are influenced by complex spatiotemporal dynamics, making long-term forecasting particularly challenging in high-density aquaculture systems. Traditional methods struggle to balance local details and global trends, while circadian rhythms, feeding cycles, and seasonal shifts cause dynamic dependencies and distribution drift. To address these issues, we propose a novel deep learning framework with three core components: (1) a multi-scale decomposition module with time–frequency enhancement, which removes cross-scale redundancy, suppresses noise, and integrates local–global features via hierarchical decomposition and feature reorganization; (2) an adaptive sequence perception attention mechanism based on graph learning, which captures dynamic variable dependencies and models spatiotemporal interactions, including environmental coupling and aquaculture disturbances; and (3) a GRU-MoE network with a dynamic expert selection strategy that adjusts to data characteristics, mitigating distribution drift caused by human interventions like feeding and oxygenation. Extensive experiments on four real-world water quality datasets show the proposed method outperforms six deep learning baselines, achieving an average MAE reduction of 53.17%, RMSE reduction of 51.68%, <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> improvement of 0.4945, and KGE improvement of 0.1979. Furthermore, Kolmogorov–Smirnov test results confirm the model’s ability to recover real data distributions and their temporal evolution. This high-precision long-term prediction method enhances aquaculture system resilience, reduces risks from water quality fluctuations, and provides a robust foundation for informed decision-making and sustainable aquaculture management.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100402"},"PeriodicalIF":8.2,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Synergistic ozonation–ion exchange strategy for nutrient recovery from algal filtrate","authors":"Ji Wu ,&nbsp;Qimeng Li ,&nbsp;Xuan Huang ,&nbsp;Liu Yang ,&nbsp;Nan Shen ,&nbsp;Wentao Li ,&nbsp;Zhenkun Ma ,&nbsp;Chen Xie ,&nbsp;Ziwu Fan ,&nbsp;Guoxiang Wang","doi":"10.1016/j.wroa.2025.100407","DOIUrl":"10.1016/j.wroa.2025.100407","url":null,"abstract":"<div><div>Harmful algal blooms pose a growing threat to freshwater ecosystems due to nutrient over-enrichment. While mechanical separation of algal biomass is commonly employed, it often produces highly concentrated algal filtrate rich in algal organic matter (AOM), nitrogen, and phosphorus, leading to secondary pollution risks. In this study, a synergistic treatment approach combining short-duration ozonation and ion-exchange processes was investigated to effectively degrade AOM and recover nutrients from algal filtrate. Anion-exchange resins with quaternary ammonium groups, along with nanoconfined La(OH)₃-loaded resins, were utilized to achieve selective adsorption of nitrate and phosphate, respectively. Advanced spectroscopic techniques were employed to elucidate the structural transformations of AOM during ozonation. A 5-minute ozone treatment rapidly decomposed fluorophores, and the La(OH)₃-loaded resins achieved nearly 100% phosphate removal with excellent reusability with minimal decline (less than 5%) in efficiency over five adsorption–desorption cycles. Furthermore, the in situ transformation of LaPO₄ back to La(OH)₃ under alkaline conditions enabled efficient regeneration of the adsorbent. This study demonstrates a promising integrated strategy for algae-laden water treatment, offering both pollutant control and resource recovery.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100407"},"PeriodicalIF":8.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Unraveling pathogen dynamics in rivers flowing into Taihu Lake: Insights from high-throughput sequencing and environmental correlations","authors":"Wenjing Liu ,&nbsp;Min Deng ,&nbsp;Yuren Wang ,&nbsp;Lu Li ,&nbsp;Yeerken Senbati ,&nbsp;Yunpeng Xue ,&nbsp;Kang Song ,&nbsp;Fengchang Wu","doi":"10.1016/j.wroa.2025.100406","DOIUrl":"10.1016/j.wroa.2025.100406","url":null,"abstract":"<div><div>Rapid urbanization and industrialization have intensified microbial health risks in river–lake systems, yet region-specific pathogen monitoring frameworks remain limited. We characterized pathogen communities in ten rivers flowing into Taihu Lake using a manually curated pathogen database combined with 16S rRNA gene amplicon sequencing (<em>n</em> = 28 sampling sites). A total of 38 potential pathogenic genera and 68 putative species were detected, with <em>Mycobacterium</em> (32.14 %) and <em>Pseudomonas</em> (16.76 %) being the most abundant. Pathogen risk prediction suggested that these taxa may pose potential health threats. Water samples exhibited significantly higher pathogen richness and diversity than sediments (<em>P</em> &lt; 0.0001), and pathogen abundance declined along the river–lake continuum. Community composition was significantly associated with water temperature, pH, conductivity, and nutrient concentrations (all <em>P</em> &lt; 0.05). Null model analyses indicated that stochastic processes predominated in community assembly, while deterministic selection was strongest at estuarine sites. These results suggest that pathogen distributions are jointly shaped by environmental filtering, hydrodynamic transport, and stochastic events such as dispersal limitation and ecological drift. Given the taxonomic resolution limitations of 16S rRNA amplicons, species-level assignments should be interpreted cautiously. Nevertheless, integrating amplicon sequencing with a pathogen-specific database provides a practical framework for early risk warning. This study advances understanding of spatial dynamics and ecological drivers of pathogen communities in river–lake networks and highlights the need for higher-resolution approaches, including metagenomics and virulence gene profiling, to refine microbial risk assessments in rapidly urbanizing watersheds.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100406"},"PeriodicalIF":8.2,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"A data-driven early warning system for Escherichia coli in water based on microbial community analysis using flow cytometry 2D histograms","authors":"Isabel K. Erb ,&nbsp;Niklas Gador ,&nbsp;Moa Jinbäck ,&nbsp;Elisabet Lindberg ,&nbsp;Catherine J. Paul","doi":"10.1016/j.wroa.2025.100404","DOIUrl":"10.1016/j.wroa.2025.100404","url":null,"abstract":"<div><div>Traditional methods for microbial water quality testing take up to two days to produce results, putting humans in contact with this water risk during this period. Flow cytometry, including with online capacity, is a fast and efficient way to profile microbes in water. In this study, <em>Escherichia coli</em> concentrations determined by Colilert18 and flow cytometry profiles from the same water samples were taken from sixteen bathing locations in Southern Sweden. Applying machine learning algorithms confirmed correlations and identified patterns in the microbial community described by the flow cytometry 2D histograms associated with the presence of <em>E. coli</em>. A Random Forest algorithm was best in discriminating between water containing &gt; 100 CFU/100 mL and water containing &lt; 100 CFU/100 mL <em>E. coli</em> when compared to logistic regression and support vector machines, improving prediction accuracy to 80 % from a baseline approach of 55 % when using optimised parameters. The introduction of a two-threshold model, which only considered safe predictions, further improved accuracy to 87 % by utilizing the prediction probability information in random forest. This approach, however, could only predict 65 % of the samples. A feature importance ranking using random forest identified the most important region within the flow cytometric 2D histogram for classification. This study suggests machine learning can leverage microbial community information from flow cytometry, that when combined with established methods quantifying indicators, can rapidly assess microbial water quality as an early warning system that complements traditional approaches.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100404"},"PeriodicalIF":8.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Metagenome-informed QMRA and resistome profiling reveal hidden health risks in Yongding River wastewater","authors":"Zixuan Zhang ,&nbsp;Miaomiao Teng ,&nbsp;Lihui Zhao ,&nbsp;Jiaqi Sun ,&nbsp;Yunxia Li ,&nbsp;Hongbo Ma ,&nbsp;Wen Li ,&nbsp;Wanting Zhou","doi":"10.1016/j.wroa.2025.100403","DOIUrl":"10.1016/j.wroa.2025.100403","url":null,"abstract":"<div><div>Emerging pathogens and antibiotic resistance in wastewater pose significant threats to public health and the environment. This study presents a comprehensive metagenomic and quantitative microbial risk assessment (QMRA)-based evaluation of microbial diversity, pathogen prevalence, and associated health risks in agricultural and urban wastewater from Beijing and Tianjin between 2022 and 2024. Metagenomic analysis revealed that bacteria accounted for 86.02 % of the sequenced reads, with <em>Pseudomonadota, Bacteroidota</em>, and <em>Verrucomicrobiota</em> dominating the microbial communities. A total of 238 pathogen species were identified, including <em>Pseudomonas aeruginosa, Vibrio cholerae</em>, and <em>Salmonella enterica</em>, with seasonal peaks in pathogen abundance, such as <em>V. cholerae</em> reaching 1.3 × 10⁶ copies/L in the dry season. Additionally, 922 ARG subtypes were detected in Beijing wastewater, and 1339 ARG subtypes were identified in Tianjin, with multidrug-resistant genes contributing 38.08 % of the total ARG burden in Beijing and 40.7 % in Tianjin. Risk assessments using QMRA revealed that <em>Campylobacter</em> spp. was the leading contributor to cumulative illness risk in effluent-impacted waters, with an illness risk probability of 0.062 per exposure event in Beijing agricultural wastewater. In addition, <em>V. cholerae</em> posed heightened seasonal risks, especially during the wet season, with a predicted 0.47 % probability of infection per exposure. The study also emphasized that human and bovine fecal markers were the primary sources of contamination, where Tianjin agricultural wastewater exhibited the highest health risk index (HRI) due to the mobility and diversity of ARG hosts. This study aims to integrate QMRA with resistome profiling to establish a comprehensive framework for assessing the health risks associated with pathogens and ARGs in wastewater systems. The findings provide actionable insights for improving wastewater management and mitigating the public health risks posed by emerging pathogens and antibiotic resistance.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100403"},"PeriodicalIF":8.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Nitrogen cycling blocked in constructed wetlands: Mechanisms, developments, and challenges—A review","authors":"Yingming Liu ,&nbsp;Hang Gao ,&nbsp;Zhiyuan Wang ,&nbsp;Peiquan Xue ,&nbsp;Xiaojie Chen ,&nbsp;Baoshan Wang ,&nbsp;Gang Wen","doi":"10.1016/j.wroa.2025.100401","DOIUrl":"10.1016/j.wroa.2025.100401","url":null,"abstract":"<div><div>Constructed wetlands (CWs) are widely used for the denitrification of wastewater because of their high efficiency and low pollutant consumption. However, insufficient internal dissolved oxygen (DO) or a lack of electron donors has resulted in a blocked electron supply and acceptance process for the nitrogen removal (N-removal) process, severely restricting the N-removal efficiency of CWs. In this study, the electron transfer mechanism of the N-removal process in CWs was reviewed, and the effects of plant action and substrate adsorption on the nitrogen cycle were discussed. To address the challenge of restricted nitrogen cycling in CWs, innovative strategies such as intermittent aeration to optimize the distribution of DO, introduction of metal oxide substrates to strengthen the electron transfer efficiency, and coupled bioelectrochemical systems (BES) have been proposed to induce system electron donors and acceptors to maintain the transfer balance. In the future, further research should explore the deep synergy between CWs and BES, development of new types of CWs fillers, and overcome the effects of low temperatures, as well as to implement further intelligent monitoring and management measures to address impeded nitrogen cycling in CWs and enhance the potential of the application of CWs in the field of wastewater denitrification.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100401"},"PeriodicalIF":8.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"An overview of biological cyanide elimination from tailing wastewater as a promising tool for sustainable utilization","authors":"Ainur Berkinbayeva ,&nbsp;Bagdaulet Kenzhaliyev ,&nbsp;Kenzhegali Smailov ,&nbsp;Alan Aimagambetov ,&nbsp;Bekzat Kamenov ,&nbsp;Shynar Saulebekkyzy ,&nbsp;Nazerke Tolegenova ,&nbsp;Primandari Sri Rizki Putri","doi":"10.1016/j.wroa.2025.100400","DOIUrl":"10.1016/j.wroa.2025.100400","url":null,"abstract":"<div><div>Cyanide compounds, both organic and inorganic, are widely present in natural and industrial environments, especially in effluents from mining and metallurgical processes. Their high toxicity, particularly in the form of free cyanides and hydrogen cyanide, poses severe risks to ecosystems and public health by disrupting cellular respiration via inhibition of cytochrome c oxidase. Conventional chemical treatments such as alkaline chlorination are effective but can be costly, energy-intensive, and generate secondary pollutants. In contrast, microbial bioremediation has emerged as a potentially more sustainable and cost-effective alternative, particularly for on-site treatment of cyanide-laden wastewater from massive tailings dams. Microorganisms including cyanotrophs utilize cyanide as a nitrogen or carbon sources, transforming it into less toxic compounds such as ammonia and carbon dioxide through enzymatic systems like cyanide hydratase, nitrilase, and rhodanese. While bioremediation may operate more slowly than chemical methods, its advantages lie in lower energy consumption, reduced material input, simpler maintenance, and minimized toxic by-products. This review synthesizes current understanding of cyanide’s chemical nature, toxicity, and environmental impact, and explores microbial cyanide degradation mechanisms. It further highlights how advances in metagenomics and synthetic biology (“cyanomics”) are enabling the design of more robust biocatalytic systems. Integrating these biological approaches into environmental management frameworks could reduce long-term operational costs and improve sustainability across cyanide-intensive industries.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100400"},"PeriodicalIF":8.2,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"The superior coupled model with innovative strategy for accurate imputation of missing hydrological monitoring data in water research-A case of groundwater level data","authors":"Xian-Qi Zhang ,&nbsp;Zi-Yu Li ,&nbsp;Xian-Liang Liu ,&nbsp;Xiao-Yan Wu ,&nbsp;Xin-Yong Xu ,&nbsp;En-Kuan Li ,&nbsp;Bo-Wen Wang ,&nbsp;Su-Bo Han ,&nbsp;Shao-Bo Liu","doi":"10.1016/j.wroa.2025.100398","DOIUrl":"10.1016/j.wroa.2025.100398","url":null,"abstract":"<div><div>The missing hydrological monitoring data pose challenges to regional water resource analysis and research. This paper proposes a strategy of “Decomposition-Classification-Feature Extraction-Imputation” and establishes a coupled model integrating Extreme-point Symmetric Mode Decomposition (ESMD), Permutation Entropy (PE), Singular Value Decomposition (SVD), and Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-BiLSTM) for the hydrological monitoring data imputation. The practicality of this model in imputing discrete and continuous missing data is verified using daily groundwater level data from monitoring stations around Qinghai Lake, spanning from April 1, 2019, to June 30, 2020. The results show that ESMD can decompose the groundwater level sequences into IMFs and R; the PE-SVD method for complexity-based reclassification and feature extraction can reasonably reduce the number of components while guaranteeing imputing accuracy. Compared with other models, the ESMD-PE-SVD-WOA-BiLSTM coupled model has the best imputing accuracy (MAE=17.43, RMSE=21.08, MAPE=0.32 %, IoA=90 %), providing an effective and reliable tool for precise hydrological monitoring data imputation and regional water resource research.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100398"},"PeriodicalIF":8.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Governance challenges in water data management: Insights from the application of the practitioner governance assessment tool in Palestine","authors":"Amro Wawi,&nbsp;Gül Özerol","doi":"10.1016/j.wroa.2025.100399","DOIUrl":"10.1016/j.wroa.2025.100399","url":null,"abstract":"<div><div>As data becomes increasingly crucial for the water sector, understanding the governance context surrounding its management becomes essential. In Palestine, the Water Sector Regulatory Council was established as part of the broader sector reform, with a mandate to monitor the performance of water service providers through its database system. We apply the practitioner version of the Governance Assessment Tool to assess how the governance context supports or hinders the implementation of the water data management requirements within the water sector reform. Four criteria (completeness, coherence, flexibility, and pressure for change) were used to assess the governance context along four dimensions (actors and networks, problem perceptions and goal ambitions, strategies and instruments, and responsibilities and resources). The results indicate that governance context supports the implementation of requirements through the policy instruments developed for this purpose, the stakeholders’ flexibility and their pressure for change. Resources and responsibilities are the most hindering, primarily due to financial limitations, institutional fragmentation, and the political conflict. A lack of cross-organizational collaboration and limited awareness of the strategic value of data among service providers also hinder water data management. The study reveals that technical solutions alone are insufficient; they should be equipped with sustainable resources and strategic alignment across actors.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100399"},"PeriodicalIF":8.2,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"A high-performance approach with cellular automata framework and GPU parallelization for real-time waterflows and pollutant transport simulations of pluvial and fluvial floods","authors":"Hsiang-Lin Yu ,&nbsp;Tsang-Jung Chang","doi":"10.1016/j.wroa.2025.100397","DOIUrl":"10.1016/j.wroa.2025.100397","url":null,"abstract":"<div><div>The present study provides a new tool for real-time high-accuracy water flows and pollutant transport modeling during pluvial and fluvial floods. Firstly, a Cellular Automata (CA)-based advanced solute transport (ASTCA) solver is developed to simulate solute transport due to turbulent diffusion, flow advection, and material decay mechanisms. Constraints are added to prevent unwanted numerical issues in high Pélect number flows from distorting the results. Through three benchmark cases, the ASTCA solver is more accurate than the state-of-the-art Godunov-type FV-TVD model with second-order accuracy. Next, the ASTCA solver is coupled with the well-performing CA-based shallow water flow (SWFCA) solver as a novel CA-based coupled (SWFASTCA) approach. Through real-world pluvial and fluvial flood cases, the ASTCA solver is more reliable than the FV-TVD model. As to the efficiency, the SWFASTCA approach is further GPU-parallelized by OpenCL 2.1 under Nvidia CUDA to see how fast it can be. From the outcomes, the GPU-parallelized SWFASTCA approach is up to 74.2 times faster than its FV-based alternative and can finish a 24 h simulation with 110 thousand grids within 20 s, which is satisfactory as the simulations are performed on a PC without a professional graphics card. Hence, the SWFASTCA approach has a strong potential to become a real-time simulator for water flows and solute transport.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"28 ","pages":"Article 100397"},"PeriodicalIF":8.2,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}