Water Research X最新文献

{"title":"Low-cost, data-efficient, on-device soft sensors for sewer flow monitoring—learning from adjacent water level sensors","authors":"Ruozhou Lin ,&nbsp;Wenchong Tian ,&nbsp;Ruihong Qiu ,&nbsp;Lihan Hu ,&nbsp;Zhiguo Yuan","doi":"10.1016/j.wroa.2025.100415","DOIUrl":"10.1016/j.wroa.2025.100415","url":null,"abstract":"<div><div>Flow measurements are critical for sewer monitoring, but direct measurements with flow meters are often expensive due to high sensor costs and frequent sensor maintenance. Soft sensors that derive flow rates from water depth measurements are a more cost-effective approach; however, the training of such sensors still requires extensive direct flow measurements. In this paper, we propose on-device soft flow sensors based on water depth measurements at two adjacent manholes, rather than a single manhole, to reduce the demand for flow data for training. Three model structures, namely the Saint-Venant equations (SVE), a multilayer perceptron (MLP), and a physics-informed neural network (PINN), are used to implement soft sensors for two real-life pipes and one simulated pipe. In all cases, the SVE- and MLP-based soft sensors reliably estimate flow rates with a low computational load that can be implemented on a Raspberry Pi 5 that powers a water level sensor. In contrast, the PINN-based soft sensor failed due to its high computational demand. The SVE-based sensor requires much less flow data for training, while the MLP-based soft sensor delivers more accurate flow estimates but requires more flow data. Both sensors are robust against noise and bias associated with the water depth and flow rate measurements, suitable for real-life applications. The SVE-based sensor is preferrable when scarce flow data are available.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100415"},"PeriodicalIF":8.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095092","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":"Beyond the Curve Number methodology: Power law-based calibration and a nonparametric approach for enhancing urban runoff estimation","authors":"Yu Jian Cheong ,&nbsp;Lloyd Ling ,&nbsp;Ren Jie Chin ,&nbsp;Steven Lim ,&nbsp;Yu Heng Cheong ,&nbsp;Zulkifli Yusop","doi":"10.1016/j.wroa.2025.100414","DOIUrl":"10.1016/j.wroa.2025.100414","url":null,"abstract":"<div><div>This study presents a statistically grounded reformulation of the Natural Resources Conservation Service (NRCS) Curve Number (CN) rainfall-runoff model by replacing the conventional linear initial abstraction (I_a) to retention (S) relationship (I_a = λS, where λ is initial abstraction ratio) with a power law-based formulation (I_a = S^L, where L is gradient of log-log graph) in order to restore mathematical correctness. A nonparametric bias-corrected and accelerated (BCa) bootstrap framework was employed to test the NRCS universal λ = 0.20 assumption, revealing its statistical invalidity (derived optimum λ value at 99 % BCa confidence interval: 0.032 - 0.079) for the urban Malaysian catchment studied. The proposed model achieved higher theoretical coherence and improved runoff estimate accuracy while preserving model parsimony. Importantly, it accommodates full rainfall-runoff datasets and dynamically captures catchment saturation-dependent retention behavior, addressing limitations of the conventional CN practices. The newly developed parsimonious two-parameter (S, L) runoff estimation model ensures practical adaptability by enabling catchment specific calibration without resorting to arbitrary CN selection. This study bridges traditional hydrology with modern statistical rigor, offering a scalable, data-driven alternative to conventional CN practices. The findings support a paradigm shift in runoff modelling by demonstrating the potential of nonparametric methods to refine legacy hydrological models and better capture real world nonlinearity and variability under changing climatic conditions.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100414"},"PeriodicalIF":8.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219338","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":"Phytoplankton-induced nitrification suppression limits sediment nitrogen removal via nitrate diffusion in shallow illuminated eutrophic lake","authors":"Rong Zhao ,&nbsp;Min Xu ,&nbsp;Lili Han ,&nbsp;Moyang Li ,&nbsp;Ehui Tan ,&nbsp;Shiheng Tang ,&nbsp;Hui Shen ,&nbsp;Wenhao Su ,&nbsp;Zhiwen Fu ,&nbsp;Shan Sun ,&nbsp;Silin Ni ,&nbsp;Xindong Ma ,&nbsp;Zhenzhen Zheng ,&nbsp;Shuh-Ji Kao","doi":"10.1016/j.wroa.2025.100413","DOIUrl":"10.1016/j.wroa.2025.100413","url":null,"abstract":"<div><div>Widespread shallow lakes/ponds receive substantial anthropogenic reactive nitrogen (N) inputs to be vulnerable components of global aquatic ecosystems. However, the mechanisms governing N retention in these systems remain inadequately explored. We combined ¹⁵N tracer-labeling techniques and molecular analysis to quantify N transformation networks, including ammonium (NH₄⁺) uptake, remineralization, nitrate (NO₃⁻) uptake and nitrification in water column versus sedimentary N removal capacity in a tropical shallow lake (&lt;1 m depth) in southern China. High-resolution diel monitoring (every 2 h over 36 h) revealed pronounced diel fluctuations in NH₄⁺, driven by daytime phytoplankton uptake (up to 8.5 µM h⁻¹) and NH₄⁺ regeneration from particulate organic nitrogen (PN) (up to 12.3 µM h⁻¹) in diel rhythms. In contrast, NO₃⁻ remained stable, with negligible uptake by phytoplankton or production via nitrification. The reciprocal transfer between NH₄⁺ and PN formed a closed N cycle loop. Nitrification was nearly absent despite ample NH₄⁺ availability at night, as evidenced by low nitrifier gene abundances (<em>amo A</em> = 0 copies/mL, <em>amo B</em> ≤ 8 × 10³ copies/mL), suggesting competitive exclusion by phytoplankton. This suppression of nitrification restricted NO₃⁻ supply to sediments and likely limited denitrification particularly contributed from the overlying water diffusion, though measured denitrification rates indicated strong potential under elevated NO₃⁻ conditions. This study elucidated the pivotal role of diel N cycling and ecological niche competition in driving N retention and self-purification capacity in eutrophic well-lit shallow systems.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100413"},"PeriodicalIF":8.2,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095093","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":"Shark-inspired riblet design and optimization for drag reduction in drinking water distribution pipes across varying flow rates","authors":"Mirvahid Mohammadpour Chehrghani ,&nbsp;Jamal Seyyed Monfared Zanjani ,&nbsp;Doekle Yntema ,&nbsp;David Matthews ,&nbsp;Matthijn de Rooij","doi":"10.1016/j.wroa.2025.100412","DOIUrl":"10.1016/j.wroa.2025.100412","url":null,"abstract":"<div><div>Drinking water distribution systems (DWDS) experience significant energy losses due to turbulence-induced drag. While shark-inspired riblet surfaces have been shown to reduce drag in controlled conditions, their effectiveness in DWDS remains uncertain, particularly under the dynamic flow variations. This experimental study explores biomimetic riblet designs as a potential solution for drag reduction in such environments. Two riblet configurations were evaluated: one designed after the shortfin mako shark (MSI), with smaller, tightly spaced riblets, and another based on the blacktip shark (BSI), with larger, widely spaced riblets. Riblet structures were 3D-printed and tested in a water flow loop system. The results show that although MSI and BSI achieved similar maximum drag reduction of approximately 6 % near a nondimensional spacing of <em>s</em>⁺ ≈ 14.5, their performance differed significantly versus Reynolds numbers. The MSI design sustained drag reduction over a wider range (2500 &lt; <em>Re</em> &lt; 20,000), while the BSI design was effective only within 2500 &lt; <em>Re</em> &lt; 8500. However, beyond these ranges, both designs began to experience drag increase. In addition, a comparison of geometric descriptors revealed that the square root of the groove cross-sectional area (<span><math><msubsup><mi>l</mi><mi>g</mi><mo>+</mo></msubsup></math></span>), provided the most consistent predictor for optimal riblet performance in pipe flow. However, the mean optimal value of <span><math><msubsup><mi>l</mi><mi>g</mi><mo>+</mo></msubsup></math></span> was approximately 8.45, which is lower than the reference value of 10.7 reported for channel flows. This deviation likely results from confinement and curvature effects in pipe geometries, which modify vortex–riblet interactions compared to planar flows. These findings highlight the need to tailor riblet design to pipe-specific conditions and show that combining geometric and flow parameters improves performance evaluation in DWDS.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100412"},"PeriodicalIF":8.2,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095087","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":"Bioremediation of agricultural nitrate pollution – challenges and opportunities","authors":"Hao Wang ,&nbsp;Satoshi Ishii","doi":"10.1016/j.wroa.2025.100410","DOIUrl":"10.1016/j.wroa.2025.100410","url":null,"abstract":"<div><div>Agriculture is the major cause of nitrogen pollution worldwide, leading to eutrophication in the surrounding and downstream rivers, lakes, and oceans. Nitrogen runs out from the field mostly in the form of nitrate where subsurface drainage is installed, which is common in areas with poorly drained soils such as the U.S. Midwest and northern Europe. Nitrate contamination in groundwater wells can also cause human diseases, and therefore, is a serious public health concern. Agricultural drainage displays distinct characteristics from municipal wastewater and animal manure, which include high nitrate, low ammonium, and low organic carbon concentrations as well as low temperature. The remediation technologies also need to be deployable in rural settings, low cost, and have minimum impacts on agricultural production. In this review article, we first summarize the challenges associated with agricultural nitrate pollution. We also briefly summarize microbial nitrogen transforming reactions that are potentially useful for nitrate bioremediation. We then <u>critically evaluate</u> currently available nitrate remediation technologies. Because bioremediation is much less expensive than physical and chemical treatments, we mostly focus on bioremediation technologies, including wetlands, denitrification bioreactors, saturated riparian buffers, controlled drainage, and controlled drainage ditches. Current bioremediation technologies exhibit substantial variability in performance when implemented at field scale. This review discusses recent advances and emerging strategies to enhance nitrate removal under challenging field conditions, including bioaugmentation, biostimulation, and other novel technologies. Looking forward, the effective management of agricultural subsurface drainage will likely depend on the integration of multiple conservation practices to achieve targeted nitrate reduction goals.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100410"},"PeriodicalIF":8.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095081","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":"Multi-scale variance partitioning reveals hidden regional connectivity in groundwater contamination: Implications for drinking water security","authors":"Acme Afrin Jahan ,&nbsp;Do Hwan Jeong ,&nbsp;Jae Uk Youn ,&nbsp;Tae Kwon Lee ,&nbsp;MoonSu Kim","doi":"10.1016/j.wroa.2025.100409","DOIUrl":"10.1016/j.wroa.2025.100409","url":null,"abstract":"<div><div>Rural communities dependent on groundwater face increasing contamination risks, yet large-scale assessments of actual drinking water sources remain rare. This study pioneers a novel geostatistical framework to quantify contamination patterns using 2349 groundwater wells that serve as primary drinking water sources for populations in unsupplied areas of Chungcheongnam-do, South Korea. Our integrated analysis revealed that NO₃⁻-N is the most pressing concern with 16.1 % of wells exceeding the national drinking water standard. Our analysis revealed unprecedented spatial contamination architecture. Nitrate demonstrated spatial coherence extending 62 km, vastly exceeding the &lt;20 km ranges observed for trace elements. Variance partitioning quantified that neighboring wells contribute 38–40 % to nitrate variability at any location, indicating substantial hydraulic interconnection across the regional aquifer system. Local Indicators of Spatial Association identified six agricultural townships as contamination hotspots where mean nitrate concentrations reach 64.7 mg L⁻¹—over six times the safe drinking water limit. These hotspots exhibited 68 % cropland coverage compared to 33 % in non-hotspot areas. These findings transform understanding of groundwater contamination from local to regional phenomena, necessitating watershed-scale management rather than well-by-well remediation to protect rural drinking water supplies.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100409"},"PeriodicalIF":8.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095091","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":"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":"Identification of characteristic factors for water quality indicators and development of a wastewater source signature system for receiving rivers","authors":"Rui Bian ,&nbsp;Ting Su ,&nbsp;Xiaofeng Cao ,&nbsp;Jianfeng Peng ,&nbsp;Weixiao Qi ,&nbsp;Jiuhui Qu","doi":"10.1016/j.wroa.2025.100408","DOIUrl":"10.1016/j.wroa.2025.100408","url":null,"abstract":"<div><div>Wastewater treatment plant (WWTP) discharge has become a focal point in watershed management, and its aggregate impacts on receiving rivers have been preliminarily elucidated. However, the characteristic water quality patterns in receiving rivers under the influence of different WWTP discharges (domestic, mixed, and industrial) remain unclear. To address this gap, water quality indicators were analysed in samples collected upstream and downstream of the outfall during different water periods and characteristic factors were identified. A threshold system for identifying the characteristic water quality patterns was established based on indicator concentration ratios, and the threshold ranges for source-type water quality signature ratios were determined. The characteristic patterns were validated by selecting three characteristic section types (different regions, double outfalls, and long distances). The results showed that the concentrations of most indicators at the downstream of outfalls were 5 % − 70 % higher than those at the upstream, and the water quality index quantified downstream deterioration (0.46 − 0.69). Furthermore, anions and metallic elements were identified as the characteristic factors. Based on these analyses, threshold ranges for source-type water quality signature ratio were determined: domestic (&lt; 6.22), mixed (6.22 − 9.86), and industrial (&gt; 9.86). Validation across the other characteristic sections confirmed that the results were within the threshold ranges. The strength of the indicator interaction by industrial wastewater discharge exceeded that of other wastewaters, thereby elucidating the differential characteristics mechanisms. This study provides a novel methodological framework for watershed water quality characterization, and the established threshold system holds significant practical value for aquatic environment management.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100408"},"PeriodicalIF":8.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095084","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}