Pub Date : 2026-01-01Epub Date: 2025-12-24DOI: 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 : 2026-01-01Epub Date: 2026-01-06DOI: 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 : 2026-01-01Epub Date: 2026-01-13DOI: 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 : 2026-01-01Epub Date: 2025-12-11DOI: 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 : 2026-01-01Epub Date: 2026-01-18DOI: 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 : 2026-01-01Epub Date: 2025-11-25DOI: 10.1016/j.wroa.2025.100459
Sheldon V. Masters , Timothy A. Bartrand , Kylie M. Boenisch-Oakes , Yun Yu , Marylia Duarte Batista , Audrey Young Keightley , Dienye L. Tolofari , Chad J. Seidel , R. Scott Summers
Disinfection byproducts (DBPs) can transform within building plumbing systems, altering both concentrations and toxicity at the point of use. This study evaluated how pipe material (copper, PEX, PVC), pipe diameter, and water use frequency affect the fate of four regulated trihalomethanes (THM4), nine haloacetic acids (HAA9) and four haloacetonitriles (HAN4) using controlled pipe racks operated for one year under chlorinated and chloraminated conditions. The calculated additive toxicity (CAT) metric for these DBP groups was also evaluated. Random Forest analysis revealed that water use frequency and disinfectant type were the strongest predictors of DBP occurrence and CAT with pipe material and size playing secondary roles. Under low-use conditions, HAN4 concentrations decreased by 60–90%, resulting in a 40–80% reduction in CAT relative to feed water, primarily due to the degradation of nitrogenous DBPs. In contrast, high-use conditions increased CAT by 25–50% across all pipe types. Complementary batch experiments, using copper and PEX pipes, expanded the DBP scope to 52 regulated and unregulated species and showed that, while HANs again declined, overall CAT did not decrease due to elevated levels of unregulated DBPs, particularly haloacetaldehydes which dominated CAT. These findings underscore the limits of relying on regulated DBPs or narrow toxicity metrics and suggest that whole-water assays offer a stronger framework for assessing health risk changes in plumbing systems. The apparent decline in DBP toxicity during stagnation coincided with much higher microbial activity (HPCs) across all pipe materials, emphasizing the challenge of balancing chemical and microbial risks in premise plumbing.
{"title":"Impact of cold-water premise plumbing on the fate and associated additive toxicity of disinfection byproducts","authors":"Sheldon V. Masters , Timothy A. Bartrand , Kylie M. Boenisch-Oakes , Yun Yu , Marylia Duarte Batista , Audrey Young Keightley , Dienye L. Tolofari , Chad J. Seidel , R. Scott Summers","doi":"10.1016/j.wroa.2025.100459","DOIUrl":"10.1016/j.wroa.2025.100459","url":null,"abstract":"<div><div>Disinfection byproducts (DBPs) can transform within building plumbing systems, altering both concentrations and toxicity at the point of use. This study evaluated how pipe material (copper, PEX, PVC), pipe diameter, and water use frequency affect the fate of four regulated trihalomethanes (THM4), nine haloacetic acids (HAA9) and four haloacetonitriles (HAN4) using controlled pipe racks operated for one year under chlorinated and chloraminated conditions. The calculated additive toxicity (CAT) metric for these DBP groups was also evaluated. Random Forest analysis revealed that water use frequency and disinfectant type were the strongest predictors of DBP occurrence and CAT with pipe material and size playing secondary roles. Under low-use conditions, HAN4 concentrations decreased by 60–90%, resulting in a 40–80% reduction in CAT relative to feed water, primarily due to the degradation of nitrogenous DBPs. In contrast, high-use conditions increased CAT by 25–50% across all pipe types. Complementary batch experiments, using copper and PEX pipes, expanded the DBP scope to 52 regulated and unregulated species and showed that, while HANs again declined, overall CAT did not decrease due to elevated levels of unregulated DBPs, particularly haloacetaldehydes which dominated CAT. These findings underscore the limits of relying on regulated DBPs or narrow toxicity metrics and suggest that whole-water assays offer a stronger framework for assessing health risk changes in plumbing systems. The apparent decline in DBP toxicity during stagnation coincided with much higher microbial activity (HPCs) across all pipe materials, emphasizing the challenge of balancing chemical and microbial risks in premise plumbing.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100459"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797621","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}
Droughts are projected to become more frequent and severe, threatening groundwater resources in Mediterranean regions by altering water quality and hydrological dynamics. This study provides for the first time a field-scale assessment of 85 emerging contaminants (ECs), including 41 pharmaceuticals (PhACs), 10 metabolites and transformation products, and 34 endocrine disrupting compounds (EDCs) plus caffeine as stimulant, under drought conditions in the Onyar River Basin, a representative Mediterranean catchment. Results showed that prolonged drought disrupted stream–aquifer interactions, increasing aquifer vulnerability to multiple pollution sources. The most widely detected PhACs were carbamazepine (0.8–4 ng/L), and sulfamethoxazole (0.6–20 ng/L), while dominant EDCs, were tolyltriazole (3.9–239.54 ng/L) and tris(2-chloroethyl) phosphate (TCEP; 3.3–260 ng/L), and caffeine (6.5–154 ng/L). Concentrations were generally highest in wells near the Onyar River, reflecting river–aquifer interactions, whereas agricultural activities using livestock waste as fertilizer contributed mostly to sulfonamide inputs. During a prolonged drought (2020–2024) the stream-aquifer relationship changed its dynamics, modifying groundwater flow and contaminant transport pathways, increasing aquifer vulnerability to diffuse pollution sources.
Indicators, as the Groundwater Ubiquity Score (GUS) and the Persistent-Mobile-Toxic (PMT) classification are valuable tools for predicting contaminant behavior, especially when estimated site-specific sorption parameters are used. Both support groundwater management in Mediterranean catchments, where drought conditions influence the occurrence, spatial distribution and fate of ECs, complicating predictions of contaminant transport and aquifer protection. Therefore, this study underscores that the occurrence of ECs must be interpreted in the context of hydrological conditions prevailing at sampling.
{"title":"Drought-driven changes in emerging contaminant occurrence and distribution in groundwater: A Mediterranean catchment study","authors":"Nonito Ros-Berja , Elisa García-Gómez , Lúcia H.M.L.M. Santos , Anna Menció , Josep Mas-Pla , Meritxell Gros","doi":"10.1016/j.wroa.2026.100509","DOIUrl":"10.1016/j.wroa.2026.100509","url":null,"abstract":"<div><div>Droughts are projected to become more frequent and severe, threatening groundwater resources in Mediterranean regions by altering water quality and hydrological dynamics. This study provides for the first time a field-scale assessment of 85 emerging contaminants (ECs), including 41 pharmaceuticals (PhACs), 10 metabolites and transformation products, and 34 endocrine disrupting compounds (EDCs) plus caffeine as stimulant, under drought conditions in the Onyar River Basin, a representative Mediterranean catchment. Results showed that prolonged drought disrupted stream–aquifer interactions, increasing aquifer vulnerability to multiple pollution sources. The most widely detected PhACs were carbamazepine (0.8–4 ng/L), and sulfamethoxazole (0.6–20 ng/L), while dominant EDCs, were tolyltriazole (3.9–239.54 ng/L) and tris(2-chloroethyl) phosphate (TCEP; 3.3–260 ng/L), and caffeine (6.5–154 ng/L). Concentrations were generally highest in wells near the Onyar River, reflecting river–aquifer interactions, whereas agricultural activities using livestock waste as fertilizer contributed mostly to sulfonamide inputs. During a prolonged drought (2020–2024) the stream-aquifer relationship changed its dynamics, modifying groundwater flow and contaminant transport pathways, increasing aquifer vulnerability to diffuse pollution sources.</div><div>Indicators, as the Groundwater Ubiquity Score (GUS) and the Persistent-Mobile-Toxic (PMT) classification are valuable tools for predicting contaminant behavior, especially when estimated site-specific sorption parameters are used. Both support groundwater management in Mediterranean catchments, where drought conditions influence the occurrence, spatial distribution and fate of ECs, complicating predictions of contaminant transport and aquifer protection. Therefore, this study underscores that the occurrence of ECs must be interpreted in the context of hydrological conditions prevailing at sampling.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100509"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394315","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 : 2026-01-01Epub Date: 2026-02-21DOI: 10.1016/j.wroa.2026.100513
Asli Aslan , Sérgio Henrique Godinho Silva , Bruno Teixeira Ribeiro , Victor Obi , Marcela Vieira da Costa , Luiza Maria Pereira Pierangeli , Talita Amorim Santos , Dyego Maradona Ataide de Freitas , Mariene Helena Duarte , Eduane José de Pádua , Geila Santos Carvalho , Marco Aurélio Carbone Carneiro , Nilton Curi , David Weindorf
Field-deployable screening technologies are emerging as critical tools for water quality testing where laboratory capacity is limited. This pilot study evaluated portable X-ray fluorescence (pXRF) spectroscopy as a complementary approach for microbial water quality assessment. Fifteen samples from surface waters and municipal wastewater were collected near Lavras, Minas Gerais, Brazil and analyzed for pH, turbidity, total coliforms and Escherichia coli, alongside elemental composition by pXRF in unfiltered water, filtered water, and particulates concentrated on filters scanned wet and after drying. Filter-based elemental fingerprints discriminated wastewater-impacted from ambient sites, whereas liquid-phase pXRF did not. Partial least squares–discriminant analysis classified samples by E. coli thresholds (≤100 vs >100 CFU 100 mL⁻¹) with 80 % validation accuracy (Kappa = 0.62). P, Ca, and Fe were the strongest predictors. While not a substitute for microbiological assays, this pilot study shows that filter-based pXRF analysis offers a rapid, field-ready screening tool to prioritize high-risk samples for confirmatory analysis in resource-limited settings.
可现场部署的筛选技术正在成为实验室能力有限的地方水质检测的关键工具。该试点研究评估了便携式x射线荧光（pXRF）光谱作为微生物水质评估的补充方法。从巴西米纳斯吉拉斯州拉夫拉斯附近的地表水和城市污水中收集了15个样本，并通过pXRF分析了未过滤水、过滤水和浓缩在过滤器上的颗粒的pH值、浊度、总大肠菌群和大肠杆菌以及元素组成。基于过滤器的元素指纹图谱能够区分受环境影响的废水，而液相pXRF则不能。偏最小二乘-判别分析通过大肠杆菌阈值（≤100 vs >；100 CFU 100 mL毒血症）对样品进行分类，验证准确率为80% （Kappa = 0.62）。P、Ca和Fe是最强的预测因子。虽然不能替代微生物分析，但该试点研究表明，基于过滤器的pXRF分析提供了一种快速的现场筛选工具，可以在资源有限的情况下优先考虑高风险样品进行验证分析。
{"title":"Portable X-ray fluorescence spectrometry as a rapid and complementary approach for fecal pollution assessment in water","authors":"Asli Aslan , Sérgio Henrique Godinho Silva , Bruno Teixeira Ribeiro , Victor Obi , Marcela Vieira da Costa , Luiza Maria Pereira Pierangeli , Talita Amorim Santos , Dyego Maradona Ataide de Freitas , Mariene Helena Duarte , Eduane José de Pádua , Geila Santos Carvalho , Marco Aurélio Carbone Carneiro , Nilton Curi , David Weindorf","doi":"10.1016/j.wroa.2026.100513","DOIUrl":"10.1016/j.wroa.2026.100513","url":null,"abstract":"<div><div>Field-deployable screening technologies are emerging as critical tools for water quality testing where laboratory capacity is limited. This pilot study evaluated portable X-ray fluorescence (pXRF) spectroscopy as a complementary approach for microbial water quality assessment. Fifteen samples from surface waters and municipal wastewater were collected near Lavras, Minas Gerais, Brazil and analyzed for pH, turbidity, total coliforms and <em>Escherichia coli</em>, alongside elemental composition by pXRF in unfiltered water, filtered water, and particulates concentrated on filters scanned wet and after drying. Filter-based elemental fingerprints discriminated wastewater-impacted from ambient sites, whereas liquid-phase pXRF did not. Partial least squares–discriminant analysis classified samples by <em>E. coli</em> thresholds (≤100 vs >100 CFU 100 mL⁻¹) with 80 % validation accuracy (Kappa = 0.62). P, Ca, and Fe were the strongest predictors. While not a substitute for microbiological assays, this pilot study shows that filter-based pXRF analysis offers a rapid, field-ready screening tool to prioritize high-risk samples for confirmatory analysis in resource-limited settings.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100513"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394312","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 : 2026-01-01Epub Date: 2026-01-21DOI: 10.1016/j.wroa.2026.100499
Shuangbao Han , Fuyang Huang , Jiaqing Liu , Fucheng Li , Rui An , Rongzhen Xu , Yan Zheng , Shengpin Li , Wenpeng Li
Nitrogen fertilizers are widely used in agricultural production, and their residues can migrate to aquifers, threatening groundwater safety. As an important region for agricultural and energy production in China, the Yellow River Basin has seen the long-term application of nitrogen fertilizers. In this study, Based on the nitrate (NO₃⁻-N) data from 3116 groundwater monitoring sites collected over 2018–2022, this study investigated the occurrence and distribution characteristics of NO₃⁻-N in groundwater. Specifically, the driving factors of the migration and occurrence of NO₃⁻-N in groundwater were identified. The results show that the average concentration of NO₃⁻-N is 9.04 mg/L in the monitoring sites, and the rate of concentration exceed 10 mg/L is 24.71%. The average concentration of NO₃⁻-N gradually increases from the upstream to the downstream. The average concentration of NO₃⁻-N in groundwater decreases significantly with increasing depth, decreased from 5.75 mg/L (depth: 0–50 m) to 1.8 mg/L (depth: ≥200 m). The concentration of NO₃⁻-N in phreatic water is notably higher than that in confined water. High-concentrations of NO₃⁻-N (>10 mg/L) are mainly distributed in the areas with developed agriculture and industry. Especially in the areas of phreatic aquifers with suitable temperature, abundant rainfall, intensive industrial and agricultural activities, an oxidizing, Na-Cl and Ca-Mg-Cl type groundwater environment. In some monitoring sites of phreatic aquifers with depths <50 m, NO₃⁻-N pose risks to human health.
{"title":"Distribution characteristics, driving factors and risk assessment of nitrate in groundwater of the Yellow River Basin","authors":"Shuangbao Han , Fuyang Huang , Jiaqing Liu , Fucheng Li , Rui An , Rongzhen Xu , Yan Zheng , Shengpin Li , Wenpeng Li","doi":"10.1016/j.wroa.2026.100499","DOIUrl":"10.1016/j.wroa.2026.100499","url":null,"abstract":"<div><div>Nitrogen fertilizers are widely used in agricultural production, and their residues can migrate to aquifers, threatening groundwater safety. As an important region for agricultural and energy production in China, the Yellow River Basin has seen the long-term application of nitrogen fertilizers. In this study, Based on the nitrate (NO₃⁻-N) data from 3116 groundwater monitoring sites collected over 2018–2022, this study investigated the occurrence and distribution characteristics of NO₃⁻-N in groundwater. Specifically, the driving factors of the migration and occurrence of NO₃⁻-N in groundwater were identified. The results show that the average concentration of NO₃⁻-N is 9.04 mg/L in the monitoring sites, and the rate of concentration exceed 10 mg/L is 24.71%. The average concentration of NO₃⁻-N gradually increases from the upstream to the downstream. The average concentration of NO₃⁻-N in groundwater decreases significantly with increasing depth, decreased from 5.75 mg/L (depth: 0–50 m) to 1.8 mg/L (depth: ≥200 m). The concentration of NO₃⁻-N in phreatic water is notably higher than that in confined water. High-concentrations of NO₃⁻-N (>10 mg/L) are mainly distributed in the areas with developed agriculture and industry. Especially in the areas of phreatic aquifers with suitable temperature, abundant rainfall, intensive industrial and agricultural activities, an oxidizing, Na-Cl and Ca-Mg-Cl type groundwater environment. In some monitoring sites of phreatic aquifers with depths <50 m, NO₃⁻-N pose risks to human health.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100499"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077343","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 : 2026-01-01Epub Date: 2025-12-27DOI: 10.1016/j.wroa.2025.100478
Mehran Janmohammadi , Baiqian Shi , Tanveer M. Adyel , David McCarthy
Understanding the spatial and temporal dynamics of nitrates is crucial to mitigate pollution that causes eutrophication and poor aquatic health. However, in-situ sensors for direct nitrate detection are often limited by high costs, frequent maintenance requirements, and low sensitivity. Soft-sensing has emerged as a promising alternative, where nitrates are predicted using surrogate sensors using models or machine learning. This study addresses a central challenge with soft-sensors: their transferability to sites with limited or no training data. We propose a transferable framework that predicts nitrate concentrations using only a small number of training data points together with simple, low-cost sensors such as electrical conductivity, temperature, and turbidity. The approach selects a pre-trained model (PR-TR) from a large model library using only historical surrogate data, with site similarity determined through Euclidean distance and a relative difference metric. For sites with relative differences below 100%, the PR-TR model achieved an average NSE of 0.51 using only 15 data points. For more dissimilar sites, higher data requirements and careful tuning of the learning rate (0.01) were essential, yet PR-TR still outperformed traditional approaches. Compared with artificial neural networks (ANN) and multiple linear regression (MLR), which required >40 data points to reach similar performance, PR-TR delivered robust and efficient predictions using significantly fewer data samples. The model selection process identified suitable PR-TR models capable of achieving positive NSE values even without nitrate data from the validation site. These findings demonstrate that PR-TR offers a practical, data-efficient method for reliable water quality monitoring.
{"title":"Transferable soft-sensors for predicting nitrate in diverse watersheds","authors":"Mehran Janmohammadi , Baiqian Shi , Tanveer M. Adyel , David McCarthy","doi":"10.1016/j.wroa.2025.100478","DOIUrl":"10.1016/j.wroa.2025.100478","url":null,"abstract":"<div><div>Understanding the spatial and temporal dynamics of nitrates is crucial to mitigate pollution that causes eutrophication and poor aquatic health. However, in-situ sensors for direct nitrate detection are often limited by high costs, frequent maintenance requirements, and low sensitivity. Soft-sensing has emerged as a promising alternative, where nitrates are predicted using surrogate sensors using models or machine learning. This study addresses a central challenge with soft-sensors: their transferability to sites with limited or no training data. We propose a transferable framework that predicts nitrate concentrations using only a small number of training data points together with simple, low-cost sensors such as electrical conductivity, temperature, and turbidity. The approach selects a pre-trained model (PR-TR) from a large model library using only historical surrogate data, with site similarity determined through Euclidean distance and a relative difference metric. For sites with relative differences below 100%, the PR-TR model achieved an average NSE of 0.51 using only 15 data points. For more dissimilar sites, higher data requirements and careful tuning of the learning rate (0.01) were essential, yet PR-TR still outperformed traditional approaches. Compared with artificial neural networks (ANN) and multiple linear regression (MLR), which required >40 data points to reach similar performance, PR-TR delivered robust and efficient predictions using significantly fewer data samples. The model selection process identified suitable PR-TR models capable of achieving positive NSE values even without nitrate data from the validation site. These findings demonstrate that PR-TR offers a practical, data-efficient method for reliable water quality monitoring.</div></div>","PeriodicalId":52198,"journal":{"name":"Water Research X","volume":"30 ","pages":"Article 100478"},"PeriodicalIF":8.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924828","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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