Pub Date : 2026-01-01DOI: 10.1016/j.wroa.2025.100479
A. Belmondo Bianchi, H.H.M. Rijnaarts, S. Shariat Torbaghan
Water distribution networks comprise interconnected components such as pipes, tanks, and pumps, whose hydraulic behavior is inherently nonlinear and nonconvex. Modeling head loss in pipes and pump performance curves is a major challenge for optimization-based planning and operations. These challenges arise, for instance, when solving the Optimal Water Flow (OWF) problem, which aims to determine pump schedules that minimize energy costs while satisfying hydraulic and operational constraints. While various approximation techniques exist, they often lack sufficient accuracy, raising concerns about their reliability in practice. To address this, we propose a hybrid approach that integrates deep learning with mathematical optimization to solve the OWF problem. We design a modified Input Convex Neural Network (ICNN) capable of capturing complex nonlinear relationships, focusing on pipe friction losses and pump hydraulics. To ensure tractable optimization, we introduce a novel regularization that enforces input convexity, enabling neural network inference to be reformulated as a linear program. This convex approximation is embedded into the OWF formulation, enabling end-to-end optimization with standard solvers. Empirical results demonstrate significant improvements in accuracy and scalability over existing OWF approximations, offering a practical tool for cost-effective, energy-efficient water distribution management.
{"title":"Neural network-informed Optimal Water Flow problem: Modeling, algorithm, and benchmarking","authors":"A. Belmondo Bianchi, H.H.M. Rijnaarts, S. Shariat Torbaghan","doi":"10.1016/j.wroa.2025.100479","DOIUrl":"10.1016/j.wroa.2025.100479","url":null,"abstract":"<div><div>Water distribution networks comprise interconnected components such as pipes, tanks, and pumps, whose hydraulic behavior is inherently nonlinear and nonconvex. Modeling head loss in pipes and pump performance curves is a major challenge for optimization-based planning and operations. These challenges arise, for instance, when solving the Optimal Water Flow (OWF) problem, which aims to determine pump schedules that minimize energy costs while satisfying hydraulic and operational constraints. While various approximation techniques exist, they often lack sufficient accuracy, raising concerns about their reliability in practice. To address this, we propose a hybrid approach that integrates deep learning with mathematical optimization to solve the OWF problem. We design a modified Input Convex Neural Network (ICNN) capable of capturing complex nonlinear relationships, focusing on pipe friction losses and pump hydraulics. To ensure tractable optimization, we introduce a novel regularization that enforces input convexity, enabling neural network inference to be reformulated as a linear program. This convex approximation is embedded into the OWF formulation, enabling end-to-end optimization with standard solvers. Empirical results demonstrate significant improvements in accuracy and scalability over existing OWF approximations, offering a practical tool for cost-effective, energy-efficient water distribution management.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100479"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976698","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 : 2026-01-01DOI: 10.1016/j.wroa.2026.100495
Rongchao He, Shasha Chen, Zhenxin Chen, Weisong Ma, Mei Fu, Hongjian Lü, Weizhi Yao
Cascade-dam systems impose repeated hydrological discontinuities that convert a river continuum into alternating reservoir–river segments, enhancing environmental heterogeneity and longitudinal gradients beyond the influence of a single dam. Seasonal shifts in temperature and hydrologic conditions also modulate connectivity along the cascade, yet how these drivers jointly shape bacterial community assembly remains poorly understood. This study investigated 10 cascade-dammed reaches of the Qijiang River, collecting paired sediment and water samples in summer and winter. Using an integrated approach of 16S rRNA gene amplicon sequencing, functional gene quantification, and potential process assays, we analyzed the impacts of spatial fragmentation and seasonality on bacterial community structure, assembly mechanisms, and carbon-nitrogen functions. The results revealed a habitat partitioning phenomenon characterized by two divergent assembly mechanisms. The sediment community, predominantly shaped by stable spatial gradients, followed a deterministic track where assembly was consistently dominated by selection. Conversely, the highly sensitive water community followed a season-responsive track, with its assembly shifting from mixed assembly with elevated stochasticity in summer to deterministic control in winter. This functional partitioning was also evident: sediment functions were more strongly associated with community structure, while the water community exhibited high functional redundancy, maintaining relatively stable functional potential despite marked seasonal compositional shifts. Together, these results provide a mechanistic explanation for why dam cascades matter by demonstrating that repeated, season-modulated fragmentation generates habitat-specific assembly pathways and may buffer functional stability in regulated river landscapes.
{"title":"Habitat partitioning shapes divergent bacterial community assembly and carbon–nitrogen functional responses under cascade-dam fragmentation and seasonality","authors":"Rongchao He, Shasha Chen, Zhenxin Chen, Weisong Ma, Mei Fu, Hongjian Lü, Weizhi Yao","doi":"10.1016/j.wroa.2026.100495","DOIUrl":"10.1016/j.wroa.2026.100495","url":null,"abstract":"<div><div>Cascade-dam systems impose repeated hydrological discontinuities that convert a river continuum into alternating reservoir–river segments, enhancing environmental heterogeneity and longitudinal gradients beyond the influence of a single dam. Seasonal shifts in temperature and hydrologic conditions also modulate connectivity along the cascade, yet how these drivers jointly shape bacterial community assembly remains poorly understood. This study investigated 10 cascade-dammed reaches of the Qijiang River, collecting paired sediment and water samples in summer and winter. Using an integrated approach of 16S rRNA gene amplicon sequencing, functional gene quantification, and potential process assays, we analyzed the impacts of spatial fragmentation and seasonality on bacterial community structure, assembly mechanisms, and carbon-nitrogen functions. The results revealed a habitat partitioning phenomenon characterized by two divergent assembly mechanisms. The sediment community, predominantly shaped by stable spatial gradients, followed a deterministic track where assembly was consistently dominated by selection. Conversely, the highly sensitive water community followed a season-responsive track, with its assembly shifting from mixed assembly with elevated stochasticity in summer to deterministic control in winter. This functional partitioning was also evident: sediment functions were more strongly associated with community structure, while the water community exhibited high functional redundancy, maintaining relatively stable functional potential despite marked seasonal compositional shifts. Together, these results provide a mechanistic explanation for why dam cascades matter by demonstrating that repeated, season-modulated fragmentation generates habitat-specific assembly pathways and may buffer functional stability in regulated river landscapes.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100495"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037312","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 : 2026-01-01DOI: 10.1016/j.wroa.2025.100476
Yueyi Liu , Hang Zheng , Jianshi Zhao
Observable improvements in surface water quality are often interpreted as evidence of effective governance. However, such conclusions may be misleading when hydrological variability and socio-economic activities obscure inconsistencies in policy implementation. This study develops a deep learning prediction-error framework that combines Convolutional Neural Networks and Long Short-Term Memory networks to predict multiple water quality indicators across China’s Pearl River Basin and to retrospectively assess governance performance. By comparing predicted and observed water quality, the approach identifies temporal and spatial patterns where regulatory signals are strong or weak. The analysis reveals that prediction errors serve as sensitive markers of governance inconsistency, particularly in economically underdeveloped regions where seemingly good water quality does not necessarily reflect robust pollution control. Occasional anomalies, such as short-term degradation coinciding with major public holidays, are presented as examples of governance-related temporal irregularities detectable through this method. Overall, the results demonstrate that deep learning models can serve not only as predictive tools but also as diagnostic instruments for uncovering hidden governance issues, offering a more nuanced evaluation of environmental management than water quality observations alone.
{"title":"Unveiling the illusion of successful water quality governance using deep learning","authors":"Yueyi Liu , Hang Zheng , Jianshi Zhao","doi":"10.1016/j.wroa.2025.100476","DOIUrl":"10.1016/j.wroa.2025.100476","url":null,"abstract":"<div><div>Observable improvements in surface water quality are often interpreted as evidence of effective governance. However, such conclusions may be misleading when hydrological variability and socio-economic activities obscure inconsistencies in policy implementation. This study develops a deep learning prediction-error framework that combines Convolutional Neural Networks and Long Short-Term Memory networks to predict multiple water quality indicators across China’s Pearl River Basin and to retrospectively assess governance performance. By comparing predicted and observed water quality, the approach identifies temporal and spatial patterns where regulatory signals are strong or weak. The analysis reveals that prediction errors serve as sensitive markers of governance inconsistency, particularly in economically underdeveloped regions where seemingly good water quality does not necessarily reflect robust pollution control. Occasional anomalies, such as short-term degradation coinciding with major public holidays, are presented as examples of governance-related temporal irregularities detectable through this method. Overall, the results demonstrate that deep learning models can serve not only as predictive tools but also as diagnostic instruments for uncovering hidden governance issues, offering a more nuanced evaluation of environmental management than water quality observations alone.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100476"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976533","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 : 2026-01-01DOI: 10.1016/j.wroa.2026.100493
Lianbao Chi , Jing Chen , Tianhao Zheng , Wentao Wang , Xiuxian Song , Zhiming Yu
Global efforts to mitigate climate change emphasize the critical need to enhance carbon sinks. Harmful algal blooms (HABs) areas represent hot spots for labile organic carbon production, yet their carbon sequestration capacity is diminished by rapid microbial mineralization. Modified clay (MC) technology, used for HABs mitigation, could enhance the sedimentation and influence the transformation dynamics of algal-derived organic carbon. Nevertheless, the impacts of MC on the mineralization of algal-derived organic carbon and the mechanisms involved remain inadequately understood. In this study, employing ¹³C-labeled batch incubation experiments, we demonstrated that algal-derived organic carbon undergoes rapid mineralization, with 46%–59% of the total organic carbon (TOC) being mineralized within 30 days. MC substantially reduced the mineralization rate of algal-derived organic carbon from 0.68–0.80 mg/(L·d) to 0.09 mg/(L·d), thereby boosting organic carbon sequestration potential by approximately 55-70% compared to the control group. Multiple lines of evidence, including microscopic imaging, fluorescence spectroscopy, and microbial analysis, revealed that MC promoted organic carbon downward export, reduced bioavailability through flocculation and encapsulation, and suppressed heterotrophic bacteria. Notably, the MC treatment group exhibited a significant reduction, with the abundance of heterotrophic bacteria decreasing by approximately 60% and the functional genes associated with microbial mineralization dropping by 50%. Overall, this study presents direct evidence and mechanistic insights that demonstrate the feasibility of employing MC to enhance carbon sequestration in mitigating HABs.
{"title":"Enhancing carbon sequestration through flocculation of harmful algal blooms by modified clay technology","authors":"Lianbao Chi , Jing Chen , Tianhao Zheng , Wentao Wang , Xiuxian Song , Zhiming Yu","doi":"10.1016/j.wroa.2026.100493","DOIUrl":"10.1016/j.wroa.2026.100493","url":null,"abstract":"<div><div>Global efforts to mitigate climate change emphasize the critical need to enhance carbon sinks. Harmful algal blooms (HABs) areas represent hot spots for labile organic carbon production, yet their carbon sequestration capacity is diminished by rapid microbial mineralization. Modified clay (MC) technology, used for HABs mitigation, could enhance the sedimentation and influence the transformation dynamics of algal-derived organic carbon. Nevertheless, the impacts of MC on the mineralization of algal-derived organic carbon and the mechanisms involved remain inadequately understood. In this study, employing ¹³C-labeled batch incubation experiments, we demonstrated that algal-derived organic carbon undergoes rapid mineralization, with 46%–59% of the total organic carbon (TOC) being mineralized within 30 days. MC substantially reduced the mineralization rate of algal-derived organic carbon from 0.68–0.80 mg/(L·d) to 0.09 mg/(L·d), thereby boosting organic carbon sequestration potential by approximately 55-70% compared to the control group. Multiple lines of evidence, including microscopic imaging, fluorescence spectroscopy, and microbial analysis, revealed that MC promoted organic carbon downward export, reduced bioavailability through flocculation and encapsulation, and suppressed heterotrophic bacteria. Notably, the MC treatment group exhibited a significant reduction, with the abundance of heterotrophic bacteria decreasing by approximately 60% and the functional genes associated with microbial mineralization dropping by 50%. Overall, this study presents direct evidence and mechanistic insights that demonstrate the feasibility of employing MC to enhance carbon sequestration in mitigating HABs.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100493"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037310","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 : 2026-01-01DOI: 10.1016/j.wroa.2026.100485
Yangmo Zhu , Rodney Nelson Leary III , Tianyuan Xu , Ke He , Lee Blaney , Xiaodi Hao , Dongye Zhao
Per- and polyfluoroalkyl substances (PFAS) are ubiquitous in surface waters. While numerous technologies have been investigated to mitigate human exposure, limited information is available for treatment of PFAS in actual field waters. Based on the “concentrate-and-destroy” strategy, we prepared and evaluated an adsorptive photocatalyst, namely gallium-doped activated carbon-supported titanate nanotubes (Ga/TNTs@AC), for treatment of six PFAS in a model surface water. Being most prevalent in the field water, perfluorooctane sulfonate (PFOS) was selected as a representative compound for feasibility and optimization studies. Batch experiments revealed that at a dosage of 1 g/L, Ga/TNTs@AC adsorbed 98% of 100 µg/L PFOS in the surface water within 10 min. Background cations enhanced PFOS removal by suppressing repulsive forces and enabling the cation-bridging effects. Upon UV irradiation, 35.5% of adsorbed PFOS was effectively degraded and 25.8% defluorinated. The photocatalytic defluorination of PFOS was boosted to 70.0% by addition of 60 µM Fe3+ during the photodegradation, where formation of Fe3+−PFOS and Fe3+−DOM complexes reduced the energy barrier, facilitated activation of PFOS, and diminished inhibitory effects of DOM. Acidic conditions were found favorable for both adsorption and photocatalysis of PFOS. Fixed-bed column tests confirmed the effective adsorption of PFOS and other PFAS in the field water, with complete PFOS breakthrough occurred after 5100 bed volumes. Subsequently, the PFAS-laden Ga/TNTs@AC successfully degraded the pre-concentrated PFAS, which also regenerated the Ga/TNTs@AC media for reuse. Ga/TNTs@AC appeared to be a promising material for enabling the “concentrate-&-destroy” strategy for more efficient removal and degradation of PFAS in field waters.
{"title":"Photocatalytic degradation of PFAS under field water matrix conditions using an adsorptive photocatalyst","authors":"Yangmo Zhu , Rodney Nelson Leary III , Tianyuan Xu , Ke He , Lee Blaney , Xiaodi Hao , Dongye Zhao","doi":"10.1016/j.wroa.2026.100485","DOIUrl":"10.1016/j.wroa.2026.100485","url":null,"abstract":"<div><div>Per- and polyfluoroalkyl substances (PFAS) are ubiquitous in surface waters. While numerous technologies have been investigated to mitigate human exposure, limited information is available for treatment of PFAS in actual field waters. Based on the “concentrate-and-destroy” strategy, we prepared and evaluated an adsorptive photocatalyst, namely gallium-doped activated carbon-supported titanate nanotubes (Ga/TNTs@AC), for treatment of six PFAS in a model surface water. Being most prevalent in the field water, perfluorooctane sulfonate (PFOS) was selected as a representative compound for feasibility and optimization studies. Batch experiments revealed that at a dosage of 1 g/L, Ga/TNTs@AC adsorbed 98% of 100 µg/L PFOS in the surface water within 10 min. Background cations enhanced PFOS removal by suppressing repulsive forces and enabling the cation-bridging effects. Upon UV irradiation, 35.5% of adsorbed PFOS was effectively degraded and 25.8% defluorinated. The photocatalytic defluorination of PFOS was boosted to 70.0% by addition of 60 µM Fe<sup>3+</sup> during the photodegradation, where formation of Fe<sup>3+</sup>−PFOS and Fe<sup>3+</sup>−DOM complexes reduced the energy barrier, facilitated activation of PFOS, and diminished inhibitory effects of DOM. Acidic conditions were found favorable for both adsorption and photocatalysis of PFOS. Fixed-bed column tests confirmed the effective adsorption of PFOS and other PFAS in the field water, with complete PFOS breakthrough occurred after 5100 bed volumes. Subsequently, the PFAS-laden Ga/TNTs@AC successfully degraded the pre-concentrated PFAS, which also regenerated the Ga/TNTs@AC media for reuse. Ga/TNTs@AC appeared to be a promising material for enabling the “concentrate-&-destroy” strategy for more efficient removal and degradation of PFAS in field waters.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100485"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037315","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 : 2026-01-01DOI: 10.1016/j.wroa.2025.100467
Jingchao Yang , Tarek Zayed , Dramani Arimiyaw , Mohamed Nashat , Ridwan Taiwo , Ghasan Alfalah , Xianyang Liu , Abdelazim Ibrahim
Global urban sewer infrastructure faces an unprecedented aging crisis, with cascading failures threatening public health, environmental protection, and urban resilience. The American Society of Civil Engineers estimates a $271 billion investment gap for US sewer systems alone, highlighting the urgent need for sewer aging analysis to optimize resource allocation. Current analysis methodologies face a critical implementation barrier: their complex data type requirements limit practical adoption across diverse municipal contexts. This study is inspired by the recognition that sewer pipelines, like human populations, experience age-related deterioration, and the demographic life table can be applied to analyze the dominant factors in this process. The methodology transforms traditional multi-parameter models into a two-input approach requiring only current age and dominant analytical factor, while maintaining statistical rigor through Wilcoxon signed-rank tests with Bonferroni correction. As one of Asia's leading metropolitan centers, Hong Kong presents an ideal case study for sewer aging analysis. Therefore, comprehensive empirical validation was conducted across 148,389 pipeline segments spanning four major regions, 18 districts, six soil types, and diverse environmental conditions, culminating in a quartile-based risk classification system integrated with GIS visualization for immediate spatial risk assessment. This streamlined approach enables immediate implementation using minimal data requirements and facilitates the transition from reactive repair strategies to predictive management approaches. This ease of implementation supports sustainable urban development and resilient sewer systems globally, providing a viable solution to the global infrastructure crisis.
{"title":"When population science meets urban sewer networks: Decoding remaining life using life table analytics","authors":"Jingchao Yang , Tarek Zayed , Dramani Arimiyaw , Mohamed Nashat , Ridwan Taiwo , Ghasan Alfalah , Xianyang Liu , Abdelazim Ibrahim","doi":"10.1016/j.wroa.2025.100467","DOIUrl":"10.1016/j.wroa.2025.100467","url":null,"abstract":"<div><div>Global urban sewer infrastructure faces an unprecedented aging crisis, with cascading failures threatening public health, environmental protection, and urban resilience. The American Society of Civil Engineers estimates a $271 billion investment gap for US sewer systems alone, highlighting the urgent need for sewer aging analysis to optimize resource allocation. Current analysis methodologies face a critical implementation barrier: their complex data type requirements limit practical adoption across diverse municipal contexts. This study is inspired by the recognition that sewer pipelines, like human populations, experience age-related deterioration, and the demographic life table can be applied to analyze the dominant factors in this process. The methodology transforms traditional multi-parameter models into a two-input approach requiring only current age and dominant analytical factor, while maintaining statistical rigor through Wilcoxon signed-rank tests with Bonferroni correction. As one of Asia's leading metropolitan centers, Hong Kong presents an ideal case study for sewer aging analysis. Therefore, comprehensive empirical validation was conducted across 148,389 pipeline segments spanning four major regions, 18 districts, six soil types, and diverse environmental conditions, culminating in a quartile-based risk classification system integrated with GIS visualization for immediate spatial risk assessment. This streamlined approach enables immediate implementation using minimal data requirements and facilitates the transition from reactive repair strategies to predictive management approaches. This ease of implementation supports sustainable urban development and resilient sewer systems globally, providing a viable solution to the global infrastructure crisis.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100467"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976727","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 : 2026-01-01DOI: 10.1016/j.wroa.2026.100486
Jianqiang Zhou , Xiaojuan Wang , Xichen Song , Jiangtao He , Yawen Zhou , Jie Qin , Yifei Xiao , Suxia Gao , Hua Li , Jianlin Liu , Wei Li , Lianbao Cao , Tingting Zhang , Bigui Wei
To enhance the removal of nitrogen and phosphorus pollutants from urban stormwater runoff in bioretention systems, this study developed an iron-carbon bioretention system with a saturated zone. The system's performance in enhanced pollutant removal was systematically investigated, and the synergistic removal mechanisms between the vadose and saturated zones were elucidated. Experimental results demonstrated that the iron-carbon bioretention system achieved high and stable removal efficiencies for dissolved pollutants, with removal rates of 95.97±2.42% for nitrate-nitrogen, 84.63±3.75% for total nitrogen, 94.88±1.92% for total phosphorus, and 86.99±5.57% for COD. These values represent significant improvements of 69.05%, 44.73%, 49.11%, and 18.63%, respectively, compared to a conventional sand-based bioretention system. Mechanistic analysis of nitrogen and phosphorus removal reveals that the system establishes functional zones in the vertical direction. The aerobic environment in the vadose zone facilitated nitrification and the formation of iron oxides, enabling nitrogen transformation and phosphorus adsorption. Conversely, the anaerobic conditions in the saturated zone drove continuous iron-carbon micro-electrolysis, generating Fe2+ and [H] as inorganic electron donors. This process promoted autotrophic denitrification and precipitated phosphorus as stable iron phosphate. The iron-carbon also enhanced microbial diversity and enriched functional genera involved in autotrophic denitrification (e.g., Hydrogenophaga, Geobacter) and iron cycling (e.g., Shewanella, Geobacteraceae). Furthermore, the presence of iron oxides suppressed CH4 production by competing with methanogens for organic substrates. The higher abundances of Desulfobacterota and Bacteroidota contributed to reduced N2O emissions, thereby mitigating the greenhouse gas footprint of the bioretention system. This study provides a novel strategy for enhancing stormwater purification in bioretention systems.
{"title":"An iron-carbon bioretention system for enhancing nitrogen and phosphorus removal: Synergy of vadose and saturated zones","authors":"Jianqiang Zhou , Xiaojuan Wang , Xichen Song , Jiangtao He , Yawen Zhou , Jie Qin , Yifei Xiao , Suxia Gao , Hua Li , Jianlin Liu , Wei Li , Lianbao Cao , Tingting Zhang , Bigui Wei","doi":"10.1016/j.wroa.2026.100486","DOIUrl":"10.1016/j.wroa.2026.100486","url":null,"abstract":"<div><div>To enhance the removal of nitrogen and phosphorus pollutants from urban stormwater runoff in bioretention systems, this study developed an iron-carbon bioretention system with a saturated zone. The system's performance in enhanced pollutant removal was systematically investigated, and the synergistic removal mechanisms between the vadose and saturated zones were elucidated. Experimental results demonstrated that the iron-carbon bioretention system achieved high and stable removal efficiencies for dissolved pollutants, with removal rates of 95.97±2.42% for nitrate-nitrogen, 84.63±3.75% for total nitrogen, 94.88±1.92% for total phosphorus, and 86.99±5.57% for COD. These values represent significant improvements of 69.05%, 44.73%, 49.11%, and 18.63%, respectively, compared to a conventional sand-based bioretention system. Mechanistic analysis of nitrogen and phosphorus removal reveals that the system establishes functional zones in the vertical direction. The aerobic environment in the vadose zone facilitated nitrification and the formation of iron oxides, enabling nitrogen transformation and phosphorus adsorption. Conversely, the anaerobic conditions in the saturated zone drove continuous iron-carbon micro-electrolysis, generating Fe<sup>2+</sup> and [H] as inorganic electron donors. This process promoted autotrophic denitrification and precipitated phosphorus as stable iron phosphate. The iron-carbon also enhanced microbial diversity and enriched functional genera involved in autotrophic denitrification (e.g., <em>Hydrogenophaga, Geobacter</em>) and iron cycling (e.g., <em>Shewanella, Geobacteraceae</em>). Furthermore, the presence of iron oxides suppressed CH<sub>4</sub> production by competing with methanogens for organic substrates. The higher abundances of <em>Desulfobacterota</em> and <em>Bacteroidota</em> contributed to reduced N<sub>2</sub>O emissions, thereby mitigating the greenhouse gas footprint of the bioretention system. This study provides a novel strategy for enhancing stormwater purification in bioretention systems.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100486"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924827","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 : 2026-01-01DOI: 10.1016/j.wroa.2026.100492
Dave T.F. Kuo, Song-Yan Ho
Inter-sample sorption variability and nonlinearity can ambiguate fate and ecotoxicity assessment of organic pollutants and remediation strategy. Nonlinear sorption models that assume constancy in organic carbon (OC) normalized sorption coefficient (KOC) are often adopted across different sorbents. This study explored sorbent-side influence on sorption through meta-analysis and modeling of sorption data on chlorpyrifos, a recently Stockholm Convention enlisted non-ionizable organophosphate pesticide, and its main metabolite 3,5,6-trichloropyridinol (TCP). LogKOC varies by approximately 2 log units among soils and sediments. In predicting sorption with natural geosorbents (n = 519), linear-OC and Freundlich models are outperformed by models considering nonlinear sorbate-side (concentration) and sorbent-side (OC content) effects ( or ). All nonlinear chlorpyrifos models are C-linear but fOC-nonlinear. The resulting models predict fairly against out-of-domain (untrained) sorbents including biosolids, peat, dissolved organic matter (DOM), biochar, vegetative residues, and wood pieces with root mean squared error (RMSE) ranging from 0.17 to 0.72 (n = 304), hinting the possibility of a universal sorption model across different carbonaceous sorbents. These models capture sorbate competition and sorbent configuration, and may apply or adapt to other chemicals and carbonaceous sorbents to accommodate changing sorbent configurations, compositions, or volume/domain accessibility. Metaphysically, the proposed nonlinear models are grounded in a reference-adjustment framework, where overall effects are quantified as adjustments relative to a base-case scenario, as in thermodynamics and kinetics. Overall, results challenge KOC constancy that underlies most current models while demonstrating the importance of inter-sorbent variations in composition and configuration of organic components for accurate sorption assessment.
{"title":"Sorbent- and sorbate-influenced sorption variability and nonlinearity: a meta-analysis on chlorpyrifos with soils, sediments, and other carbonaceous materials","authors":"Dave T.F. Kuo, Song-Yan Ho","doi":"10.1016/j.wroa.2026.100492","DOIUrl":"10.1016/j.wroa.2026.100492","url":null,"abstract":"<div><div>Inter-sample sorption variability and nonlinearity can ambiguate fate and ecotoxicity assessment of organic pollutants and remediation strategy. Nonlinear sorption models that assume constancy in organic carbon (OC) normalized sorption coefficient (<em>K</em><sub>OC</sub>) are often adopted across different sorbents. This study explored sorbent-side influence on sorption through meta-analysis and modeling of sorption data on chlorpyrifos, a recently Stockholm Convention enlisted non-ionizable organophosphate pesticide, and its main metabolite 3,5,6-trichloropyridinol (TCP). Log<em>K</em><sub>OC</sub> varies by approximately 2 log units among soils and sediments. In predicting sorption with natural geosorbents (<em>n</em> = 519), linear-OC and Freundlich models are outperformed by models considering nonlinear sorbate-side (concentration) and sorbent-side (OC content) effects (<span><math><mrow><mi>S</mi><mo>=</mo><msub><mi>K</mi><mrow><mi>O</mi><mi>C</mi></mrow></msub><msubsup><mi>f</mi><mrow><mi>O</mi><mi>C</mi></mrow><mi>a</mi></msubsup><msup><mrow><mi>C</mi></mrow><mi>b</mi></msup></mrow></math></span> or <span><math><mrow><msub><mi>K</mi><mrow><mi>O</mi><mi>C</mi></mrow></msub><msub><mi>f</mi><mrow><mi>O</mi><mi>C</mi></mrow></msub><msup><mrow><mi>ϕ</mi></mrow><mi>a</mi></msup><msup><mrow><mi>C</mi></mrow><mi>b</mi></msup></mrow></math></span>). All nonlinear chlorpyrifos models are <em>C</em>-linear but <em>f</em><sub>OC</sub>-nonlinear. The resulting models predict fairly against out-of-domain (untrained) sorbents including biosolids, peat, dissolved organic matter (DOM), biochar, vegetative residues, and wood pieces with root mean squared error (RMSE) ranging from 0.17 to 0.72 (<em>n</em> = 304), hinting the possibility of a universal sorption model across different carbonaceous sorbents. These models capture <em>sorbate competition</em> and <em>sorbent configuration</em>, and may apply or adapt to other chemicals and carbonaceous sorbents to accommodate changing sorbent configurations, compositions, or volume/domain accessibility. Metaphysically, the proposed nonlinear models are grounded in a reference-adjustment framework, where overall effects are quantified as adjustments relative to a base-case scenario, as in thermodynamics and kinetics. Overall, results challenge <em>K</em><sub>OC</sub> constancy that underlies most current models while demonstrating the importance of inter-sorbent variations in composition and configuration of organic components for accurate sorption assessment.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100492"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037314","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 : 2026-01-01DOI: 10.1016/j.wroa.2026.100497
Tianlong Zheng , Yuxin Wang , Lin Li , Junxin Liu , Pengyu Li
Activated sludge models (ASMs), the most widely used mathematical models for biological wastewater treatment, offer a simplified matrix-based representation of pollutant biochemical degradation. As understanding of wastewater treatment mechanisms has advanced, the simplifying assumptions of general ASMs have proven unreasonable under certain conditions, prompting their improvement. Existing reviews often focus on the specific application of ASMs, with limited comprehensive analyses of their multi-dimensional extensions and cross-model integrations. This review provides the first systematic overview of the latest developments in ASMs, focusing on model mechanism extension and multi-scale model integration. In terms of mechanism extension, the incorporation of new theories and secondary reaction has enhanced the accuracy of models in simulating membrane bioreactor systems, phosphorus removal, and industrial wastewater treatment. It has also quantified the generation and dissipation pathways of N2O and provided a basis for sludge reduction and sedimentation control. Regarding model integration, this review focuses on the coupling interfaces between ASMs and other models, such as anaerobic reaction models, convection-diffusion theory, hydrodynamic models, and machine learning. These coupled models enable full-scale simulation from micro-level biochemical reactions to macro-level environmental dynamics. Finally, the review emphasizes that future ASMs developments should focus on improving mechanisms and addressing emerging contaminants. It highlights that integrating artificial intelligence can serve as a key tool to balance model accuracy and parameter identifiability. The present review aims to establish a systematic research framework for ASMs, analyze the limitations of existing models, and ultimately provide insights for enhancing the precision and application of ASMs in wastewater treatment.
{"title":"The crucial role of activated sludge models (ASMs) on wastewater treatment processes: Developments, applications, and future perspectives","authors":"Tianlong Zheng , Yuxin Wang , Lin Li , Junxin Liu , Pengyu Li","doi":"10.1016/j.wroa.2026.100497","DOIUrl":"10.1016/j.wroa.2026.100497","url":null,"abstract":"<div><div>Activated sludge models (ASMs), the most widely used mathematical models for biological wastewater treatment, offer a simplified matrix-based representation of pollutant biochemical degradation. As understanding of wastewater treatment mechanisms has advanced, the simplifying assumptions of general ASMs have proven unreasonable under certain conditions, prompting their improvement. Existing reviews often focus on the specific application of ASMs, with limited comprehensive analyses of their multi-dimensional extensions and cross-model integrations. This review provides the first systematic overview of the latest developments in ASMs, focusing on model mechanism extension and multi-scale model integration. In terms of mechanism extension, the incorporation of new theories and secondary reaction has enhanced the accuracy of models in simulating membrane bioreactor systems, phosphorus removal, and industrial wastewater treatment. It has also quantified the generation and dissipation pathways of N<sub>2</sub>O and provided a basis for sludge reduction and sedimentation control. Regarding model integration, this review focuses on the coupling interfaces between ASMs and other models, such as anaerobic reaction models, convection-diffusion theory, hydrodynamic models, and machine learning. These coupled models enable full-scale simulation from micro-level biochemical reactions to macro-level environmental dynamics. Finally, the review emphasizes that future ASMs developments should focus on improving mechanisms and addressing emerging contaminants. It highlights that integrating artificial intelligence can serve as a key tool to balance model accuracy and parameter identifiability. The present review aims to establish a systematic research framework for ASMs, analyze the limitations of existing models, and ultimately provide insights for enhancing the precision and application of ASMs in wastewater treatment.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100497"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037414","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 : 2026-01-01DOI: 10.1016/j.wroa.2025.100475
Ioan Petculescu , Anna Majury , R. Stephen Brown , Kevin McDermott , Paul Hynds
Groundwater accounts for approximately 98% of available freshwater, with >2 billion people relying on it as a primary drinking water source. Notwithstanding its importance, specific groundwater quality parameters - namely microbial concentrations and non-Escherichia coli coliforms (NEC) - remain understudied. The current study sought to address this gap by modelling three distinct Contamination Indices (CI) corresponding to E. coli concentration, NEC concentration, and the NEC:E. coli concentration ratio. CIs were developed for south Ontario (115,693 km2) using ∼1 million samples from ∼290,000 wells collected between 2010 and 2021. To permit modelling, CIs were linked to 50 subregion-specific variables which impact groundwater quality (e.g., well depth, aquifer type, mean daily precipitation volumes); Generalized Additive Models (GAM) were subsequently developed and associated non-linear partial effects were calculated. Findings suggest NEC concentrations may appropriately indicate a source’s long-term potential for generalized contamination, as the NEC model exhibited high deviance explained (91.9%) due to significant associations (p < 0.05) with factors influencing and/or representing groundwater recharge. A daily summer rainfall “tipping point” was identified, with volumes >3 mm being associated with NEC concentration reductions (p < 0.0001), potentially due to subsoil saturation and/or aquifer contamination dilution. Regions with predominantly deep wells in bedrock aquifers were associated (p < 0.0001) with low NEC:E. coli ratios, i.e., localized contamination mechanisms (e.g., contaminant bypass or short-circuiting) likely dominate in these regions. The presumption that deeper aquifers/wells are “safer” may thus be due for reconsideration. The importance of understanding and inferring contamination mechanisms cannot be overstated, as it serves as a foundation for evidence-based source protection and testing recommendations.
{"title":"Using “big data” and non-linear machine learning to infer groundwater contamination mechanisms across a spatially extensive, geologically heterogeneous region","authors":"Ioan Petculescu , Anna Majury , R. Stephen Brown , Kevin McDermott , Paul Hynds","doi":"10.1016/j.wroa.2025.100475","DOIUrl":"10.1016/j.wroa.2025.100475","url":null,"abstract":"<div><div>Groundwater accounts for approximately 98% of available freshwater, with >2 billion people relying on it as a primary drinking water source. Notwithstanding its importance, specific groundwater quality parameters - namely microbial concentrations and non-<em>Escherichia coli</em> coliforms (NEC) - remain understudied. The current study sought to address this gap by modelling three distinct Contamination Indices (CI) corresponding to <em>E. coli</em> concentration, NEC concentration, and the NEC:<em>E. coli</em> concentration ratio. CIs were developed for south Ontario (115,693 km<sup>2</sup>) using ∼1 million samples from ∼290,000 wells collected between 2010 and 2021. To permit modelling, CIs were linked to 50 subregion-specific variables which impact groundwater quality (e.g., well depth, aquifer type, mean daily precipitation volumes); Generalized Additive Models (GAM) were subsequently developed and associated non-linear partial effects were calculated. Findings suggest NEC concentrations may appropriately indicate a source’s long-term potential for generalized contamination, as the NEC model exhibited high deviance explained (91.9%) due to significant associations (<em>p</em> < 0.05) with factors influencing and/or representing groundwater recharge. A daily summer rainfall “tipping point” was identified, with volumes >3 mm being associated with NEC concentration reductions (<em>p</em> < 0.0001), potentially due to subsoil saturation and/or aquifer contamination dilution. Regions with predominantly deep wells in bedrock aquifers were associated (<em>p</em> < 0.0001) with low NEC:<em>E. coli</em> ratios, i.e., localized contamination mechanisms (e.g., contaminant bypass or short-circuiting) likely dominate in these regions. The presumption that deeper aquifers/wells are “safer” may thus be due for reconsideration. The importance of understanding and inferring contamination mechanisms cannot be overstated, as it serves as a foundation for evidence-based source protection and testing recommendations.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100475"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924863","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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