Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers DOI
Jiaqi Zhang, Clarence Edward Choi

Water Research, Journal Year: 2024, Volume and Issue: 272, P. 122961 - 122961

Published: Dec. 12, 2024

Language: Английский

Settling velocity of microplastics in turbulent open-channel flow DOI

Usama Ijaz,

Abul Basar M. Baki, Weiming Wu

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 946, P. 174179 - 174179

Published: June 24, 2024

Language: Английский

Citations

9

Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect DOI
Yifei Qiu,

Jingyu Niu,

Chuchu Zhang

et al.

Water Research, Journal Year: 2025, Volume and Issue: 274, P. 123129 - 123129

Published: Jan. 12, 2025

Language: Английский

Citations

1

Machine learning-supported determination for site-specific natural background values of soil heavy metals DOI
Jian Wu, Chengmin Huang

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 487, P. 137276 - 137276

Published: Jan. 18, 2025

Language: Английский

Citations

1

Centrifugal microfluidic chip for multi-stage sorting and detection of microplastics at micron scale DOI
Feifei Feng, Wenqi Ye,

Xuecong Zhao

et al.

Analytica Chimica Acta, Journal Year: 2025, Volume and Issue: 1351, P. 343883 - 343883

Published: March 5, 2025

Language: Английский

Citations

1

Microplastic pollution in Taihu lake: Spatial distribution from the lake inlet to the lake centre and vertical stratification in the water column DOI
Long Chen, Shenglü Zhou,

Bo Su

et al.

Environmental Pollution, Journal Year: 2024, Volume and Issue: 363, P. 125102 - 125102

Published: Oct. 10, 2024

Language: Английский

Citations

6

Towards better predicting the settling velocity of film-shaped microplastics based on experiment and simulation data DOI

Chaoqun Ji,

Jinfeng Zhang, Guangwei Liu

et al.

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 203, P. 116493 - 116493

Published: May 16, 2024

Language: Английский

Citations

5

Principles, Challenges, and optimization of Indigenous Microalgae-Bacteria consortium for sustainable swine wastewater treatment DOI
Sheng Yu, Zhipeng Chen, Mengting Li

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: 406, P. 131055 - 131055

Published: June 27, 2024

Language: Английский

Citations

5

Tracing the Footprint of Microplastics: Transport Mechanism, Degradation, and Remediation in Marine Environment DOI
Arkadeep Mukherjee,

Shivani Kumar S,

Randeep Singh

et al.

Water Air & Soil Pollution, Journal Year: 2025, Volume and Issue: 236(2)

Published: Jan. 24, 2025

Language: Английский

Citations

0

Abundance of microplastics in a typical urban wetland in China: Association with occurrence and carbon storage DOI
Haowen Zhang, Mengjie Pu, Ming Zheng

et al.

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 488, P. 137451 - 137451

Published: Jan. 30, 2025

Language: Английский

Citations

0

Machine learning-based prediction for airflow velocity in unpressured water-conveyance tunnels DOI
Shangtuo Qian,

Xiaofeng Meng,

Pengcheng Li

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(2)

Published: Feb. 1, 2025

Spillway and drainage tunnels have an open-channel flow pattern when operating under unpressured condition, above which air is driven resisted by water flow, wall friction, pressure difference. Unpressured present many airflow-related safety environmental issues, including fluctuation, gate vibration, shaft cover blow-off, odor emission; therefore, it valuable to study predict their airflow velocity. Given the difficulty in accurate prediction of velocity complicated influences hydraulic, structural, boundary parameters, this focuses on establishing high-performance models understanding importance independent coupled each parameter using machine learning. It found that Froude number, ratio free-surface width unwetted perimeter, relative ventilation area, tunnel length are four key parameters. By these parameters input combination, learning can well tunnels, achieving significantly higher performance than existing empirical theoretical models. Among models, built Random Forest XGBoost demonstrate best with R2 ≥ 0.911. The interpretability analysis reveals highest number increases generally result enhancement plays a dominant role ≤11.5, continuous increase exhibits marginal effect. area close importances, either promoting To help researchers engineers unfamiliar easily accurately GPlearn algorithm employed establish explicit expressions, validated good 0.900.

Language: Английский

Citations

0