Source-specific soil heavy metal risk assessment in arsenic waste mine site of Yunnan: integrating environmental and biological factors DOI

Weigang Huang,

Yanwei Liu,

Xiaoyang Bi

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 486, P. 136902 - 136902

Published: Dec. 20, 2024

Language: Английский

Source-oriented comprehensive assessment framework for identifying priority heavy metals in agricultural soils DOI
Furong Yu,

Yuekun Ji,

Lin Wu

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 115579 - 115579

Published: Jan. 1, 2025

Language: Английский

Citations

2
Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models DOI

Deyu Duan,

Peng Wang, Xin Rao

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 934, P. 173284 - 173284

Published: May 18, 2024

Language: Английский

Citations

13
Source analysis and distribution prediction of soil heavy metals in a typical area of the Qinghai-Tibet Plateau DOI Creative Commons
Xinjie Zha, Liyuan Deng, Wei Jiang

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 166, P. 112460 - 112460

Published: Aug. 8, 2024

The excessive presence of heavy metals (HMs) in soil poses a significant threat to both ecosystems and human health. Consequently, there is compelling need for quantitative analysis HMs concentration the prediction potential contamination. In this study, 58 surface samples were systematically collected from 11 different townships Luolong County. Using ArcGIS 10.7, fishing net interpolation resampling was performed obtain model data. GeoDetector employed determine key driving factors their interrelationships affecting composition. Subsequently, influential with higher explanatory power Random Forest (RF) generate contamination map. results revealed that arsenic (As), cadmium (Cd) lead (Pb) exceeded risk screening values by 8.62%, 10.34%, respectively. identified such as elevation, annual average precipitation, distance nearest river, geomorphic type natural sources, geological roads, proximity mining sites, per capita income inhabitants, total potassium content organic matter anthropogenic sources significantly influencing spatial distribution soil. interactions among primary increased capacity. By using RF predict main HMs, it found areas high probability As mainly concentrated northern, central southeast regions Regions Cd exceeding value primarily east, northeast few northern County, while likelihood Pb southwestern This study integrates stratified heterogeneity random forest mitigate overfitting HM contamination, common issue traditional machine learning methods. approach essential elucidating environmental drivers pollution, predicting high-risk complex conditions limited data, ensuring safety stability agricultural production well well-being local residents.

Language: Английский

Citations

6
Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models DOI

Xiaosong Lu,

Li Sun,

Ya Zhang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175787 - 175787

Published: Aug. 24, 2024

Language: Английский

Citations

6
Biofilm-mediated Heavy Metal Bioaccumulation and Trophic Transfer in a Mining-contaminated River DOI
Wen Chen, Qi Li, Dan Zhu

et al.

Water Research, Journal Year: 2024, Volume and Issue: 267, P. 122487 - 122487

Published: Sept. 20, 2024

Language: Английский

Citations

4
Chemical Fractionation and Isotopic Analysis of Lead in Sediments from the Taipu River, Integrated Demonstration Zone in the Yangtze River Delta, China DOI
Yao-Jen Tu, Pengcheng Luo,

Meichuan Chien

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: 13(2), P. 115392 - 115392

Published: Jan. 9, 2025

Language: Английский

Citations

0
Study on remediation effect and consolidation behavior of Cu-Zn contaminated clays at vertical profile after bottom vacuum leaching combined with EDTA DOI

Yajun Wu,

Yujie Dong, Xudong Zhang

et al.

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106937 - 106937

Published: Feb. 1, 2025

Language: Английский

Citations

0
Analysis of the relationship between soil particle fractal dimension and physicochemical properties DOI

Yongxing Pan,

Meng Chen,

Yudao Chen

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(8)

Published: April 1, 2025

Language: Английский

Citations

0
Spatial Distribution and Health Risk Assessment of Potentially Toxic Elements Along GT Road from Sialkot to Rawalpindi DOI Creative Commons
Ufra Naseer,

Atif Nisar Ahmad,

Muhammad Adnan Khan

et al.

Environmental Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100632 - 100632

Published: April 1, 2025

Language: Английский

Citations

0
Atmospheric Trace Metal Exposure in a 60-Year-Old Wood: A Sustainable Methodological Approach to Measurement of Dry Deposition DOI
Kaan Işınkaralar, Öznur Işınkaralar,

İsmail Koç

et al.

International Journal of Environmental Research, Journal Year: 2025, Volume and Issue: 19(4)

Published: April 12, 2025

Language: Английский

Citations

0