Pulse exposure to microplastics depolarizes the goldfish gill: interactive effects of DOC and differential degradation DOI Creative Commons
Lauren Zink, Carolyn Morris, Chris M. Wood

и другие.

Environmental Pollution, Год журнала: 2024, Номер unknown, С. 125434 - 125434

Опубликована: Ноя. 1, 2024

Язык: Английский

CFD-aided analysis of inlet velocity and pipeline bending angle effects on flow and energy in urban stormwater manholes DOI
Weipeng He, Yumei Chen, Peng Zhang

и другие.

Journal of Water Process Engineering, Год журнала: 2025, Номер 74, С. 107876 - 107876

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Hyporheic exchange processes of pore-scale microplastics DOI Creative Commons
Ben Stride, Soroush Abolfathi, Gary D. Bending

и другие.

The Science of The Total Environment, Год журнала: 2025, Номер 982, С. 179573 - 179573

Опубликована: Май 14, 2025

Язык: Английский

Процитировано

0
Terminal velocity and drag coefficient of a smooth steel sphere moving in the water-filled vertical and inclined glass pipe (Newton regime) DOI
Volodymyr Brazhenko, Ievgen Mochalin

Powder Technology, Год журнала: 2024, Номер 446, С. 120120 - 120120

Опубликована: Авг. 2, 2024

Язык: Английский

Процитировано

2
Tracing nitrogen and phosphorus pollution in urban runoff: Insights from isotopic tracers and SWMM modeling DOI

Jiaxun Guo,

Ye Pan, Ruidong Chen

и другие.

Journal of Cleaner Production, Год журнала: 2024, Номер 472, С. 143513 - 143513

Опубликована: Авг. 28, 2024

Язык: Английский

Процитировано

2
Removal of contaminants of emerging concern from drinking water using bio-based activated carbon DOI Creative Commons

Paki Israel Dikobe,

Memory Tekere, Vhahangwele Masindi

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 68, С. 106313 - 106313

Опубликована: Окт. 16, 2024

Язык: Английский

Процитировано

2
Assessment of Cu(II) impact on aerobic sludge biomass and its post-exposure self-recovery potential DOI
Rajneesh Kumar, Rajhans Negi, Balwant Singh

и другие.

Journal of Water Process Engineering, Год журнала: 2023, Номер 56, С. 104501 - 104501

Опубликована: Ноя. 4, 2023

Язык: Английский

Процитировано

6
A novel hydrodynamic-water quality coupling model for high-efficiency and high-resolution simulations of urban NSPs DOI

Guangxue Luan,

Tian Wang, Jingming Hou

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 64, С. 105680 - 105680

Опубликована: Июнь 24, 2024

Язык: Английский

Процитировано

1
Mechanisms of influence of confluence containing spur-dike on microplastic transport and fate DOI

Liwei Cao,

Xia Shen, Huanjie Cai

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 641, С. 131720 - 131720

Опубликована: Июль 31, 2024

Язык: Английский

Процитировано

1
Phosphorus prediction in the middle reaches of the Yangtze river based on GRA-CEEMDAN-CNLSTM-DBO DOI Creative Commons

Huaipeng Yao,

Yuling Huang,

Pingyu Lv

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 21, 2024

Accurate and rapid prediction of water quality is crucial for the protection aquatic ecosystems. This study aims to enhance total phosphorus (TP) concentrations in middle reaches Yangtze River by integrating advanced modeling techniques. Using operational discharge data from Three Gorges Reservoir (TGR), along with parameters downstream sections, we used Grey Relational Analysis (GRA) rank factors contributing TP concentrations. The analysis identified turbidity, permanganate index (CODMn), nitrogen (TN), temperature, chlorophyll a, upstream level variation, Dam (TGD) as top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, predict under dynamic control during flood periods TGR. proposed GRA-CEEMDAN-CN1D-LSTM-DBO compared conventional models, including BP, LSTM, GRU. results indicated that significantly outperformed others, achieving correlation coefficient (R) 0.784 root mean square error (RMSE) 0.004, 0.58 0.007 LSTM model, 0.576 BP 0.623 0.006 GRU model. model's accuracy applicability further validated two sections: YC (Yunchi) Yichang City LK (Liukou) Jingzhou City, where it performed satisfactorily predicting (R = 0.776, RMSE 0.007) 0.718, 0.007). Additionally, deep learning revealed distance away dam increased, gradually decreased, indicating reduced impact TGR operations on In conclusion, demonstrates superior performance concentration River, offering valuable insights seasons smart advancement management River.

Язык: Английский

Процитировано

1
Development of soft computing-based models for forecasting water quality index of Lorestan Province, Iran DOI Creative Commons
Balraj Singh, Alireza Sepahvand, Parveen Sihag

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 29, 2024

The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical chemical parameters into single metric to measure the status of Quality. This study explores application five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), combinations bagging. These models aim predict Khorramabad, Biranshahr, Alashtar sub-watersheds in Lorestan province, Iran. dataset consists 124 observations, input variables being sulfate (SO

Язык: Английский

Процитировано

1