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

Understanding microplastic retention in surface flow constructed wetlands: The impact of aquatic macrophytes DOI Creative Commons

Martina Miloloža,

Ula Putar,

Mark Starin

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 116097 - 116097

Published: March 1, 2025

Language: Английский

Citations

0

Advancing microplastics detection and prediction: integrating traditional methods with machine learning for environmental and food safety application DOI
Chi Zhang,

Liwen Xiao,

Jing Jing Wang

et al.

Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104964 - 104964

Published: March 1, 2025

Language: Английский

Citations

0

Factors Influencing the Vertical Distribution and Transport of Plastics in Riverine Environments: Theoretical Background and Implications for Improved Field Study Design DOI Creative Commons
Jenna Brooks,

Julia F. Hopkins

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 126151 - 126151

Published: March 1, 2025

Language: Английский

Citations

0

Revisit the actual roles of catalytic sites in a Fenton-like system DOI

Lan Liang,

Rui Wang,

Yongsheng Xu

et al.

Journal of Colloid and Interface Science, Journal Year: 2025, Volume and Issue: 693, P. 137639 - 137639

Published: April 18, 2025

Language: Английский

Citations

0

Predicting single-cell protein production from food-processing wastewater in sequencing batch reactors using ensemble learning DOI

Rong Rong Huang,

Hui Xu,

Ezequiel Santillan

et al.

Bioresource Technology, Journal Year: 2025, Volume and Issue: 430, P. 132561 - 132561

Published: April 19, 2025

Language: Английский

Citations

0

Nationwide meta-analysis of microplastic distribution and risk assessment in China's aquatic ecosystems, soils, and sediments DOI
Qiannan Duan, Baoxin Zhai, Chen Zhao

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 477, P. 135331 - 135331

Published: July 26, 2024

Language: Английский

Citations

3

Machine learning modeling of thermally assisted biodrying process for municipal sludge DOI
Kaiqiang Zhang,

Ningfung Wang

Waste Management, Journal Year: 2024, Volume and Issue: 188, P. 95 - 106

Published: Aug. 10, 2024

Language: Английский

Citations

3

Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors DOI
Xuewen Wu, Ming Chen, Tengyi Zhu

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175411 - 175411

Published: Aug. 10, 2024

Language: Английский

Citations

2

Assessing the Impact of Straw Burning on PM2.5 Using Explainable Machine Learning: A Case Study in Heilongjiang Province, China DOI Open Access
Zehua Xu,

Baiyin Liu,

Wei Wang

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(17), P. 7315 - 7315

Published: Aug. 26, 2024

Straw burning is recognized as a significant contributor to deteriorating air quality, but its specific impacts, particularly on PM2.5 concentrations, are still not fully understood or quantified. In this study, we conducted detailed examination of the spatial and temporal patterns straw in Heilongjiang Province, China—a key agricultural area—utilizing high-resolution fire-point data from Fengyun-3 satellite. We subsequently employed random forest (RF) models alongside Shapley Additive Explanations (SHAPs) systematically evaluate impact various determinants, including (as indicated by crop data), meteorological conditions, aerosol optical depth (AOD), levels across dimensions. Our findings statistically nonsignificant downward trend number fires Province 2015 2023, with hotspots mainly concentrated western southern parts province. On monthly scale, was primarily observed February April October November—which critical periods calendar—accounting for 97% annual fire counts. The RF achieved excellent performance predicting levels, R2 values 0.997 0.746 predictions. SHAP analysis revealed points be determinant variations during straw-burning periods, explaining 72% variance. However, significance markedly reduced analysis. This study leveraged machine learning interpretable modeling techniques provide comprehensive understanding influence both temporally spatially. offers valuable insights policymakers formulate more targeted effective strategies combat pollution.

Language: Английский

Citations

2

Mangrove mud clam as an effective sentinel species for monitoring changes in coastal microplastic pollution DOI
Yinglin Wu, Zitong Li,

Yanxia Deng

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 472, P. 134617 - 134617

Published: May 14, 2024

Language: Английский

Citations

1