Pub Date : 2026-02-01DOI: 10.1007/s13201-025-02746-1
Romina Roozbeh, Narjes Keramati, Mehdi Mousavi Kamazani
{"title":"Synthesis and application of bimetallic ZIF-11 as an adsorbent for tetracycline: understanding the performance-enhancing role of cobalt in the framework","authors":"Romina Roozbeh, Narjes Keramati, Mehdi Mousavi Kamazani","doi":"10.1007/s13201-025-02746-1","DOIUrl":"https://doi.org/10.1007/s13201-025-02746-1","url":null,"abstract":"","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"87 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-02-01DOI: 10.1007/s13201-025-02734-5
Ahmad Makhdoomi, Maryam Sarkhosh, Ali Akbar Dehghan, Somayyeh Ziaei
{"title":"Advanced photocatalytic degradation of reactive blue 248 using BiOI: synthesis, performance evaluation, optimization, kinetic, and machine learning-based prediction","authors":"Ahmad Makhdoomi, Maryam Sarkhosh, Ali Akbar Dehghan, Somayyeh Ziaei","doi":"10.1007/s13201-025-02734-5","DOIUrl":"https://doi.org/10.1007/s13201-025-02734-5","url":null,"abstract":"","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"216 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-31DOI: 10.1007/s13201-025-02733-6
Hadi Beitollahi, Fariba Garkani Nejad, Zahra Dourandish, Reza Zaimbashi, Somayeh Tajik, Samuel Adeloju
{"title":"Electrochemical sensor based on ZIF-67/MWCNTs nanocomposite for 4-aminophenol determination in water samples","authors":"Hadi Beitollahi, Fariba Garkani Nejad, Zahra Dourandish, Reza Zaimbashi, Somayeh Tajik, Samuel Adeloju","doi":"10.1007/s13201-025-02733-6","DOIUrl":"https://doi.org/10.1007/s13201-025-02733-6","url":null,"abstract":"","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"140 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-31DOI: 10.1007/s13201-026-02752-x
Ben kouider Tayeb, Souli Lahcene, Derouiche Yazid, Messaoudi Mohammed, Taoufik Soltani, Huda Alsaeedi, David Cornu, Mikhael Bechelany, Ahmed Barhoum
{"title":"Dye removal by adsorption using Fe₃O₄ and ε-Fe₂O₃-based kaolinite nanocomposites synthesized with an apricot kernels shell extract","authors":"Ben kouider Tayeb, Souli Lahcene, Derouiche Yazid, Messaoudi Mohammed, Taoufik Soltani, Huda Alsaeedi, David Cornu, Mikhael Bechelany, Ahmed Barhoum","doi":"10.1007/s13201-026-02752-x","DOIUrl":"https://doi.org/10.1007/s13201-026-02752-x","url":null,"abstract":"","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-31DOI: 10.1007/s13201-026-02750-z
Dianyan Ning, KaiPeng Zhu, Yongsheng Zhu, Shuxia Yuan, Nan Hanchen
{"title":"Numerical simulation of coal seam floor water inrush based on acoustic emission technology","authors":"Dianyan Ning, KaiPeng Zhu, Yongsheng Zhu, Shuxia Yuan, Nan Hanchen","doi":"10.1007/s13201-026-02750-z","DOIUrl":"https://doi.org/10.1007/s13201-026-02750-z","url":null,"abstract":"","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"91 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-31DOI: 10.1007/s13201-026-02748-7
Shoukat Ali Shah, Songtao Ai, Tahira Khurshid
{"title":"Statistical analysis of long-term climate variability and drought trends: a case study of Punjab province, Pakistan","authors":"Shoukat Ali Shah, Songtao Ai, Tahira Khurshid","doi":"10.1007/s13201-026-02748-7","DOIUrl":"https://doi.org/10.1007/s13201-026-02748-7","url":null,"abstract":"","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"94 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-31DOI: 10.1007/s13201-025-02741-6
Maryam Rezaei, Rozita Monsef, Elmuez A. Dawi, Forat H. Alsultany, Hadil Hussain Hamza, Ahmad Akbari, Hanieh Ansarinejad, Masoud Salavati-Niasari
{"title":"Sol–gel auto-combustion synthesis and characterization of CeO2/PbFe12O19/g-C3N4 nanocomposites with enhanced visible-light photocatalytic activity","authors":"Maryam Rezaei, Rozita Monsef, Elmuez A. Dawi, Forat H. Alsultany, Hadil Hussain Hamza, Ahmad Akbari, Hanieh Ansarinejad, Masoud Salavati-Niasari","doi":"10.1007/s13201-025-02741-6","DOIUrl":"https://doi.org/10.1007/s13201-025-02741-6","url":null,"abstract":"","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"288 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Biogenic nanoparticles produced using plant and microbial sources have emerged as low cost and environmentally benign alternatives for wastewater treatment applications. This review examines the underlying mechanisms of plant and microbe mediated nanoparticle synthesis, highlighting how naturally occurring biomolecules act as reducing, stabilizing, and capping agents to regulate nanoparticle surface characteristics. The discussion outlines key practical advantageous, including lower energy inputs, avoidance of hazardous reducing agents, use of renewable biological resources, and the potential for in situ or decentralized production, while also noting constraints like variability in plant extracts or microbial cultures. Applications in the removal of organic dyes, heavy metals, and pharmaceuticals are discussed with emphasis on performance indicators such as adsorption capacity, degradation efficiency, selectivity, and nanoparticle recovery and reuse. Alongside future opportunities for advancing green nanotechnologies through improved standardization, process control, integration with existing treatment systems, and comprehensive lifecycle under techno-economic evaluations. A comparative assessment indicates that plant-based synthesis is typically rapid, scalable, and suitable for high throughput production due to its procedural simplicity and abundance of phytochemicals. In contrast microbial synthesis generally allows finer control over nanoparticles size, shape and crystallinity. Unlike existing reviews that largely describe individual synthesis approaches or application specific studies, this review offers a critical, integrative comparison of biogenic nanoparticle synthesis routes, highlighting key performance and practical limitations across systems. The analysis indicates that no single biogenic route is universally optimal; rather, application driven selection is required, balancing efficiency, scalability and environmental capability. These insights clarify current progress while identifying priority directions for advancing biogenic nanomaterials towards real-world wastewater treatment applications.
{"title":"Biogenic nanoparticles-the future of eco-friendly wastewater treatment: a review","authors":"Aishwarya Bhaskaralingam, Mu. Naushad, Pooja Dhiman, Amit Kumar, Tongtong Wang, Dinesh Kumar, Gaurav Sharma","doi":"10.1007/s13201-025-02736-3","DOIUrl":"https://doi.org/10.1007/s13201-025-02736-3","url":null,"abstract":"Biogenic nanoparticles produced using plant and microbial sources have emerged as low cost and environmentally benign alternatives for wastewater treatment applications. This review examines the underlying mechanisms of plant and microbe mediated nanoparticle synthesis, highlighting how naturally occurring biomolecules act as reducing, stabilizing, and capping agents to regulate nanoparticle surface characteristics. The discussion outlines key practical advantageous, including lower energy inputs, avoidance of hazardous reducing agents, use of renewable biological resources, and the potential for in situ or decentralized production, while also noting constraints like variability in plant extracts or microbial cultures. Applications in the removal of organic dyes, heavy metals, and pharmaceuticals are discussed with emphasis on performance indicators such as adsorption capacity, degradation efficiency, selectivity, and nanoparticle recovery and reuse. Alongside future opportunities for advancing green nanotechnologies through improved standardization, process control, integration with existing treatment systems, and comprehensive lifecycle under techno-economic evaluations. A comparative assessment indicates that plant-based synthesis is typically rapid, scalable, and suitable for high throughput production due to its procedural simplicity and abundance of phytochemicals. In contrast microbial synthesis generally allows finer control over nanoparticles size, shape and crystallinity. Unlike existing reviews that largely describe individual synthesis approaches or application specific studies, this review offers a critical, integrative comparison of biogenic nanoparticle synthesis routes, highlighting key performance and practical limitations across systems. The analysis indicates that no single biogenic route is universally optimal; rather, application driven selection is required, balancing efficiency, scalability and environmental capability. These insights clarify current progress while identifying priority directions for advancing biogenic nanomaterials towards real-world wastewater treatment applications.","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"80 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-27DOI: 10.1007/s13201-025-02677-x
Hongyan Shao, Ka Yin Chau, Ahmad Zaman, Massoud Moslehpour, Xiaotian Pan
Climate change profoundly impacts hydropower productivity, a cornerstone of renewable energy, necessitating advanced predictive tools for sustainable water-energy management. This study presents novel machine learning (ML) frameworks to forecast climate-induced variations in hydropower output by synergistically integrating climate, hydrological, and operational data with reanalysis datasets. Distinct from existing approaches, our methodology introduces unique contributions, including synthetic climate scenario generation via Generative Adversarial Networks (GANs), neural network-driven feature ranking to prioritize key climate variables, and robust preprocessing techniques such as outlier detection, normalization, and time-series feature engineering. Using a dataset of 650 records with 11 features from a hydropower plant in the Middle East, split into 70% training, 15% validation, and 15% testing subsets, we evaluated the performance of ARIMA, GAN, Autoregressive Deep Neural Network (ARDNN), and Long Short-Term Memory (LSTM) models using RMSE and R² metrics. The LSTM model outperformed the others, achieving an RMSE of 2892.61, a MAPE of 1.3237, and an R² of 0.9985, owing to its superior ability to capture long-term temporal dependencies. These advancements surpass traditional models by offering enhanced predictive accuracy and adaptability, enabling optimized resource management and bolstering the resilience of hydropower systems against climate variability, thus contributing significantly to global sustainable energy strategies.
{"title":"Machine learning frameworks to analyze climate change impact on hydropower productivity","authors":"Hongyan Shao, Ka Yin Chau, Ahmad Zaman, Massoud Moslehpour, Xiaotian Pan","doi":"10.1007/s13201-025-02677-x","DOIUrl":"https://doi.org/10.1007/s13201-025-02677-x","url":null,"abstract":"Climate change profoundly impacts hydropower productivity, a cornerstone of renewable energy, necessitating advanced predictive tools for sustainable water-energy management. This study presents novel machine learning (ML) frameworks to forecast climate-induced variations in hydropower output by synergistically integrating climate, hydrological, and operational data with reanalysis datasets. Distinct from existing approaches, our methodology introduces unique contributions, including synthetic climate scenario generation via Generative Adversarial Networks (GANs), neural network-driven feature ranking to prioritize key climate variables, and robust preprocessing techniques such as outlier detection, normalization, and time-series feature engineering. Using a dataset of 650 records with 11 features from a hydropower plant in the Middle East, split into 70% training, 15% validation, and 15% testing subsets, we evaluated the performance of ARIMA, GAN, Autoregressive Deep Neural Network (ARDNN), and Long Short-Term Memory (LSTM) models using RMSE and R² metrics. The LSTM model outperformed the others, achieving an RMSE of 2892.61, a MAPE of 1.3237, and an R² of 0.9985, owing to its superior ability to capture long-term temporal dependencies. These advancements surpass traditional models by offering enhanced predictive accuracy and adaptability, enabling optimized resource management and bolstering the resilience of hydropower systems against climate variability, thus contributing significantly to global sustainable energy strategies.","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"87 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2026-01-25DOI: 10.1007/s13201-025-02717-6
Yinglan A, Yan Cheng, Puze Wang, Guoqiang Wang, Libo Wang, Baolin Xue, Yuntao Wang, Jin Wu
With its increasingly serious and continuous need, effective spatiotemporal water quality prediction has become key to effective pollution control and decision-making. Current research primarily focuses on utilizing continuous time monitoring data to predict trends in time series within specific sections. However, the lack of spatially continuous and reliable observations limits the ability to achieve full spatial coverage prediction. To address this limitation, this study proposes an integrated framework, named SELC, which utilizes the Soil and Water Assessment Tool (SWAT), Environmental Fluid Dynamics Code (EFDC), Convolutional Neural Network (CNN), and Long Short-term Memory (LSTM), to predict the continuous spatiotemporal water quality of the Xiaoqing River Basin (China) using discrete cross-section monitoring data and mechanism model simulation. The SELC model framework integration is as follows: The CNN training uses on-site monitoring data and high-resolution spatial simulations from the coupled SWAT-EFDC models. LTSM is used to generate future temporal forcing data for SELC at monitoring sections. The verification results showed that CNN successfully replicated the spatially continuous distribution of pollutants, and the prediction results were highly consistent with the trend, peak position, and minimum value EFDC simulation results. In the verification, the average coefficients of determination (R2) of the model were 0.62 (NH₃-N) and 0.65 (chemical oxygen demand, COD), confirming its reliability. This study achieved high-resolution spatiotemporal water quality prediction by using only segmented monitoring input and future scenario prediction, thus overcoming the limitation of sparse spatial data. This framework provides a practical tool for identifying high-risk pollution areas and periods and supports targeted aquatic environmental management.
{"title":"Continuous spatial prediction of river water quality based on a novel hybrid physical-data framework","authors":"Yinglan A, Yan Cheng, Puze Wang, Guoqiang Wang, Libo Wang, Baolin Xue, Yuntao Wang, Jin Wu","doi":"10.1007/s13201-025-02717-6","DOIUrl":"10.1007/s13201-025-02717-6","url":null,"abstract":"<div><p>With its increasingly serious and continuous need, effective spatiotemporal water quality prediction has become key to effective pollution control and decision-making. Current research primarily focuses on utilizing continuous time monitoring data to predict trends in time series within specific sections. However, the lack of spatially continuous and reliable observations limits the ability to achieve full spatial coverage prediction. To address this limitation, this study proposes an integrated framework, named SELC, which utilizes the Soil and Water Assessment Tool (SWAT), Environmental Fluid Dynamics Code (EFDC), Convolutional Neural Network (CNN), and Long Short-term Memory (LSTM), to predict the continuous spatiotemporal water quality of the Xiaoqing River Basin (China) using discrete cross-section monitoring data and mechanism model simulation. The SELC model framework integration is as follows: The CNN training uses on-site monitoring data and high-resolution spatial simulations from the coupled SWAT-EFDC models. LTSM is used to generate future temporal forcing data for SELC at monitoring sections. The verification results showed that CNN successfully replicated the spatially continuous distribution of pollutants, and the prediction results were highly consistent with the trend, peak position, and minimum value EFDC simulation results. In the verification, the average coefficients of determination (R2) of the model were 0.62 (NH₃-N) and 0.65 (chemical oxygen demand, COD), confirming its reliability. This study achieved high-resolution spatiotemporal water quality prediction by using only segmented monitoring input and future scenario prediction, thus overcoming the limitation of sparse spatial data. This framework provides a practical tool for identifying high-risk pollution areas and periods and supports targeted aquatic environmental management.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 2","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-025-02717-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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