Pub Date : 2025-09-20DOI: 10.1016/j.wroa.2025.100417
Wenqiang Zhang , Songjie Han , Baoqing Shan , Quan Zhou
Phosphite is supposed to an essential compound for the origin of early life, and exist as the main form of phosphorus (P) in the P cycle of ancient anoxic marine environments. Recent researches have discovered the presence of phosphite in contemporary aquatic ecosystems, considering its utilization by microorganisms via assimilatory and dissimilatory phosphite oxidation, as well as the indirect photooxidization to orthophosphate by ultraviolet light, which indicate the potential contribution of phosphite for P cycle in modern earth. Given its high solubility, phosphite is believed to expedite P transformations in freshwater aquatic environments, particularly in lacustrine systems. Compared to oceans, these systems have shallower waters, which favor the rapid transport of released phosphite from reducing sediments to surface water. This is especially significant if the sediment is a major production site for phosphite, indicating that its role in the P cycle might have been previously understated.
{"title":"Does phosphite accelerate the phosphorus cycle in freshwater ecosystems?","authors":"Wenqiang Zhang , Songjie Han , Baoqing Shan , Quan Zhou","doi":"10.1016/j.wroa.2025.100417","DOIUrl":"10.1016/j.wroa.2025.100417","url":null,"abstract":"<div><div>Phosphite is supposed to an essential compound for the origin of early life, and exist as the main form of phosphorus (P) in the P cycle of ancient anoxic marine environments. Recent researches have discovered the presence of phosphite in contemporary aquatic ecosystems, considering its utilization by microorganisms via assimilatory and dissimilatory phosphite oxidation, as well as the indirect photooxidization to orthophosphate by ultraviolet light, which indicate the potential contribution of phosphite for P cycle in modern earth. Given its high solubility, phosphite is believed to expedite P transformations in freshwater aquatic environments, particularly in lacustrine systems. Compared to oceans, these systems have shallower waters, which favor the rapid transport of released phosphite from reducing sediments to surface water. This is especially significant if the sediment is a major production site for phosphite, indicating that its role in the P cycle might have been previously understated.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100417"},"PeriodicalIF":8.2,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120792","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}
Pub Date : 2025-09-17DOI: 10.1016/j.wroa.2025.100411
Hong Wang , Kaiyang Jiang , Jinhao Yang , Yuxing Hu , Min Rui , Yueyi Wang , Yinyin Ye
To address the challenges of traditional down-flow biological activated carbon (BAC) filters, up-flow filters have been increasingly applied in drinking water treatment plants (DWTPs), yet their microbial characteristics and underlying assembly mechanisms are not fully explored. This study presents the first comprehensive comparison of bacterial and eukaryotic communities, functional traits, ecological interactions and assembly mechanisms in up-flow and down-flow BAC filters across 18 full-scale DWTPs spanning diverse geographic and operational contexts in China. Despite site-specific variability, distinct bacterial and eukaryotic community structures were observed between the two configurations (ANOSIM, R=0.345-0.353, P < 0.05), highlighting the strong influence of filter design on microbiomes. Functional gene profiling revealed significant enrichment of high-abundance pathways related to carbon, sulfur, and nitrogen metabolism in up-flow filters (P<0.05), indicating elevated biogeochemical activity. HAllA and network analyses revealed the pivotal role of eukaryotes in structuring microbial interactions and uncovered distinct cross-domain interaction patterns between filter types. Community assembly analysis showed deterministic processes dominated BAC microbiomes, with significantly stronger homogeneous selection in up-flow systems (P < 0.05). Together, these findings provide new ecological insights into BAC filter microbiomes and support the broader adoption of up-flow designs to enhance treatment performance and microbial stability in full-scale DWTPs.
为了解决传统的下流式生物活性炭（BAC）过滤器的挑战，上流式过滤器在饮用水处理厂（dwtp）中得到越来越多的应用，但其微生物特性和潜在的组装机制尚未得到充分的探讨。本研究首次全面比较了中国18个不同地理和操作环境的全规模dwtp上、下流式BAC过滤器中的细菌和真核生物群落、功能特征、生态相互作用和组装机制。尽管存在位点特异性差异，但在两种配置之间观察到不同的细菌和真核生物群落结构（ANOSIM， R=0.345-0.353, P < 0.05），突出了过滤器设计对微生物组的强烈影响。功能基因谱分析显示，上流式过滤器中碳、硫和氮代谢相关的高丰度途径显著富集（P<0.05），表明生物地球化学活性升高。HAllA和网络分析揭示了真核生物在构建微生物相互作用中的关键作用，并揭示了过滤器类型之间不同的跨域相互作用模式。群落组装分析显示，确定性过程主导BAC微生物组，在上游系统中具有显著更强的均匀选择（P < 0.05）。总之，这些发现为BAC过滤器微生物群提供了新的生态学见解，并支持更广泛地采用向上流设计来提高全尺寸dwtp的处理性能和微生物稳定性。
{"title":"Flow configuration shapes microbiome assembly and function in full-scale drinking water BAC filters","authors":"Hong Wang , Kaiyang Jiang , Jinhao Yang , Yuxing Hu , Min Rui , Yueyi Wang , Yinyin Ye","doi":"10.1016/j.wroa.2025.100411","DOIUrl":"10.1016/j.wroa.2025.100411","url":null,"abstract":"<div><div>To address the challenges of traditional down-flow biological activated carbon (BAC) filters, up-flow filters have been increasingly applied in drinking water treatment plants (DWTPs), yet their microbial characteristics and underlying assembly mechanisms are not fully explored. This study presents the first comprehensive comparison of bacterial and eukaryotic communities, functional traits, ecological interactions and assembly mechanisms in up-flow and down-flow BAC filters across 18 full-scale DWTPs spanning diverse geographic and operational contexts in China. Despite site-specific variability, distinct bacterial and eukaryotic community structures were observed between the two configurations (ANOSIM, R=0.345-0.353, P < 0.05), highlighting the strong influence of filter design on microbiomes. Functional gene profiling revealed significant enrichment of high-abundance pathways related to carbon, sulfur, and nitrogen metabolism in up-flow filters (P<0.05), indicating elevated biogeochemical activity. HAllA and network analyses revealed the pivotal role of eukaryotes in structuring microbial interactions and uncovered distinct cross-domain interaction patterns between filter types. Community assembly analysis showed deterministic processes dominated BAC microbiomes, with significantly stronger homogeneous selection in up-flow systems (P < 0.05). Together, these findings provide new ecological insights into BAC filter microbiomes and support the broader adoption of up-flow designs to enhance treatment performance and microbial stability in full-scale DWTPs.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"29 ","pages":"Article 100411"},"PeriodicalIF":8.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120793","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}
Pub Date : 2025-09-16DOI: 10.1016/j.wroa.2025.100415
Ruozhou Lin , Wenchong Tian , Ruihong Qiu , Lihan Hu , Zhiguo Yuan
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.
{"title":"Low-cost, data-efficient, on-device soft sensors for sewer flow monitoring—learning from adjacent water level sensors","authors":"Ruozhou Lin , Wenchong Tian , Ruihong Qiu , Lihan Hu , 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}
Pub Date : 2025-09-16DOI: 10.1016/j.wroa.2025.100414
Yu Jian Cheong , Lloyd Ling , Ren Jie Chin , Steven Lim , Yu Heng Cheong , Zulkifli Yusop
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 (Ia) to retention (S) relationship (Ia = λS, where λ is initial abstraction ratio) with a power law-based formulation (Ia = SL, 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.
{"title":"Beyond the Curve Number methodology: Power law-based calibration and a nonparametric approach for enhancing urban runoff estimation","authors":"Yu Jian Cheong , Lloyd Ling , Ren Jie Chin , Steven Lim , Yu Heng Cheong , 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<sub>a</sub>) to retention (S) relationship (I<sub>a</sub> = λS, where λ is initial abstraction ratio) with a power law-based formulation (I<sub>a</sub> = S<sup>L</sup>, 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}
Pub Date : 2025-09-14DOI: 10.1016/j.wroa.2025.100413
Rong Zhao , Min Xu , Lili Han , Moyang Li , Ehui Tan , Shiheng Tang , Hui Shen , Wenhao Su , Zhiwen Fu , Shan Sun , Silin Ni , Xindong Ma , Zhenzhen Zheng , Shuh-Ji Kao
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 (<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 (amo A = 0 copies/mL, amo B ≤ 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.
广泛分布的浅湖/池塘接收大量人为活性氮(N)输入，成为全球水生生态系统的脆弱组成部分。然而，在这些系统中控制氮保留的机制仍然没有得到充分的探索。我们结合15N示踪标记技术和分子分析来量化中国南方一个热带浅湖（<；1 m深度）的N转化网络，包括水柱中铵态氮（NH4+）吸收、再矿化、硝态氮（NO3−）吸收和硝化作用与沉积N去除能力。高分辨率日报社监测（在36小时内每2小时）显示，日报社中NH4+的显著波动是由白天浮游植物的吸收（高达8.5 μ M h- 1）和颗粒有机氮（PN）的NH4+再生（高达12.3 μ M h- 1）驱动的。相比之下，NO3−保持稳定，浮游植物的吸收或通过硝化作用产生的NO3−可以忽略不计。NH4+和PN之间的相互转移形成了一个闭合的N环。尽管夜间NH4+可用性充足，但硝化作用几乎不存在，这可以通过低氮化物基因丰度（amo A = 0拷贝/mL， amo B≤8 × 103拷贝/mL）来证明，这表明浮游植物的竞争性排斥。这种抑制硝化作用限制了沉积物的NO3−供应，并可能限制了反硝化作用，特别是由上覆水扩散所贡献的反硝化作用，尽管测量的反硝化速率表明在NO3−升高的条件下具有很强的潜力。本研究阐明了富营养化光照良好的浅层生态系统中氮循环和生态位竞争在驱动氮保持和自净化能力中的关键作用。
{"title":"Phytoplankton-induced nitrification suppression limits sediment nitrogen removal via nitrate diffusion in shallow illuminated eutrophic lake","authors":"Rong Zhao , Min Xu , Lili Han , Moyang Li , Ehui Tan , Shiheng Tang , Hui Shen , Wenhao Su , Zhiwen Fu , Shan Sun , Silin Ni , Xindong Ma , Zhenzhen Zheng , 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 <sup>15</sup>N tracer-labeling techniques and molecular analysis to quantify N transformation networks, including ammonium (NH<sub>4</sub><sup>+</sup>) uptake, remineralization, nitrate (NO<sub>3</sub><sup>−</sup>) uptake and nitrification in water column versus sedimentary N removal capacity in a tropical shallow lake (<1 m depth) in southern China. High-resolution diel monitoring (every 2 h over 36 h) revealed pronounced diel fluctuations in NH<sub>4</sub><sup>+</sup>, driven by daytime phytoplankton uptake (up to 8.5 µM h<sup>−1</sup>) and NH<sub>4</sub><sup>+</sup> regeneration from particulate organic nitrogen (PN) (up to 12.3 µM h<sup>−1</sup>) in diel rhythms. In contrast, NO<sub>3</sub><sup>−</sup> remained stable, with negligible uptake by phytoplankton or production via nitrification. The reciprocal transfer between NH<sub>4</sub><sup>+</sup> and PN formed a closed N cycle loop. Nitrification was nearly absent despite ample NH<sub>4</sub><sup>+</sup> availability at night, as evidenced by low nitrifier gene abundances (<em>amo A</em> = 0 copies/mL, <em>amo B</em> ≤ 8 × 10<sup>3</sup> copies/mL), suggesting competitive exclusion by phytoplankton. This suppression of nitrification restricted NO<sub>3</sub><sup>−</sup> supply to sediments and likely limited denitrification particularly contributed from the overlying water diffusion, though measured denitrification rates indicated strong potential under elevated NO<sub>3</sub><sup>−</sup> 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}
Pub Date : 2025-09-14DOI: 10.1016/j.wroa.2025.100412
Mirvahid Mohammadpour Chehrghani , Jamal Seyyed Monfared Zanjani , Doekle Yntema , David Matthews , Matthijn de Rooij
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 s⁺ ≈ 14.5, their performance differed significantly versus Reynolds numbers. The MSI design sustained drag reduction over a wider range (2500 < Re < 20,000), while the BSI design was effective only within 2500 < Re < 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 (), provided the most consistent predictor for optimal riblet performance in pipe flow. However, the mean optimal value of 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.
由于湍流引起的阻力，饮用水分配系统（DWDS）经历了巨大的能量损失。虽然鲨鱼纹表面在受控条件下可以减少阻力，但其在DWDS中的有效性仍然不确定，特别是在动态流动变化的情况下。这项实验研究探索了仿生波纹设计作为在这种环境中减少阻力的潜在解决方案。评估了两种肋骨配置：一种是根据短鳍鲭鲨（MSI）设计的，具有较小的，紧密间隔的肋骨，另一种是根据黑鳍鲨（BSI）设计的，具有较大的，广泛间隔的肋骨。波纹结构是3d打印的，并在水流循环系统中进行了测试。结果表明，尽管MSI和BSI在s +≈14.5的无量纲间距附近实现了相似的最大减阻约6%，但它们的性能与雷诺数有显著差异。MSI设计在更大的范围内（2500 < Re < 20,000）持续减少阻力，而BSI设计仅在2500 <； Re <； 8500范围内有效。然而，超过这个范围，两种设计都开始经历阻力增加。此外，几何描述符的比较表明，槽横截面积的平方根（lg+）提供了最一致的预测器，用于管道流动中最佳的波纹性能。然而，lg+的平均最优值约为8.45，低于通道流的参考值10.7。这种偏差可能是由于管道几何形状的限制和曲率效应造成的，与平面流动相比，它们改变了涡纹相互作用。这些发现强调了针对管道特定条件定制纹管设计的必要性，并表明结合几何参数和流动参数可以改善DWDS的性能评估。
{"title":"Shark-inspired riblet design and optimization for drag reduction in drinking water distribution pipes across varying flow rates","authors":"Mirvahid Mohammadpour Chehrghani , Jamal Seyyed Monfared Zanjani , Doekle Yntema , David Matthews , 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 < <em>Re</em> < 20,000), while the BSI design was effective only within 2500 < <em>Re</em> < 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}
Pub Date : 2025-09-13DOI: 10.1016/j.wroa.2025.100410
Hao Wang , Satoshi Ishii
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 critically evaluate 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.
{"title":"Bioremediation of agricultural nitrate pollution – challenges and opportunities","authors":"Hao Wang , 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}
Pub Date : 2025-09-13DOI: 10.1016/j.wroa.2025.100409
Acme Afrin Jahan , Do Hwan Jeong , Jae Uk Youn , Tae Kwon Lee , MoonSu Kim
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 <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.
依赖地下水的农村社区面临越来越大的污染风险，但对实际饮用水源的大规模评估仍然很少。本研究开创了一个新的地质统计学框架，利用2349口地下水井作为韩国忠清南道无水地区人口的主要饮用水源，对污染模式进行量化。我们的综合分析显示，NO₃⁻-N是最紧迫的问题，16.1%的水井超过了国家饮用水标准。我们的分析揭示了前所未有的空间污染结构。硝酸盐表现出62公里的空间相干性，大大超过了微量元素的20公里范围。方差划分量化了相邻井在任何位置对硝酸盐变异的贡献为38 - 40%，表明整个区域含水层系统存在大量的水力互连。地方空间关联指标确定了6个农业乡镇为污染热点，平均硝酸盐浓度达到64.7 mg L -毒血症-超过安全饮用水限量的6倍。这些热点地区的耕地覆盖率为68%，而非热点地区的耕地覆盖率为33%。这些发现将对地下水污染的认识从局部现象转变为区域现象，需要流域尺度的管理，而不是逐井修复，以保护农村饮用水供应。
{"title":"Multi-scale variance partitioning reveals hidden regional connectivity in groundwater contamination: Implications for drinking water security","authors":"Acme Afrin Jahan , Do Hwan Jeong , Jae Uk Youn , Tae Kwon Lee , 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 <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}
Pub Date : 2025-09-10DOI: 10.1016/j.wroa.2025.100402
Xiaodong Ji , Lu Liu , Bentao Duan , Ying Li , Haoran Xing , Bin Wang , Dashe Li
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%, 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.
{"title":"Long-term multivariate water quality forecasting for sustainable aquaculture management","authors":"Xiaodong Ji , Lu Liu , Bentao Duan , Ying Li , Haoran Xing , Bin Wang , 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}
Pub Date : 2025-09-08DOI: 10.1016/j.wroa.2025.100408
Rui Bian , Ting Su , Xiaofeng Cao , Jianfeng Peng , Weixiao Qi , Jiuhui Qu
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 (< 6.22), mixed (6.22 − 9.86), and industrial (> 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.
{"title":"Identification of characteristic factors for water quality indicators and development of a wastewater source signature system for receiving rivers","authors":"Rui Bian , Ting Su , Xiaofeng Cao , Jianfeng Peng , Weixiao Qi , 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 (< 6.22), mixed (6.22 − 9.86), and industrial (> 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}
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