Evaluation of groundwater quality and health risk assessment in Dawen River Basin, North China DOI Creative Commons

Shanming Wei,

Yaxin Zhang, Zizhao Cai

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 264, P. 120292 - 120292

Published: Nov. 7, 2024

Language: Английский

Appraisal of potential toxic elements pollution, sources apportionment, and health risks in groundwater from a coastal area of SE China DOI
Denghui Wei, Shiming Yang, Lin Zou

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124691 - 124691

Published: Feb. 27, 2025

Language: Английский

Citations

1
Hydrochemical insights, water quality, and human health risk assessment of groundwater in a coastal area of southeastern China DOI
Yanhong Zheng, Denghui Wei,

Jie Gan

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(10)

Published: Sept. 20, 2024

Language: Английский

Citations

5
Optimized groundwater quality evaluation using unsupervised machine learning, game theory and Monte-Carlo simulation DOI
Yuting Yan,

Yunhui Zhang,

Shiming Yang

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 371, P. 122902 - 122902

Published: Nov. 11, 2024

Language: Английский

Citations

4
Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis DOI
Abu Reza Md. Towfiqul Islam, Md. Abdullah-Al Mamun, Mehedi Hasan

et al.

Journal of Contaminant Hydrology, Journal Year: 2024, Volume and Issue: 269, P. 104480 - 104480

Published: Dec. 10, 2024

Language: Английский

Citations

4
Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices DOI Creative Commons

Caixia Hu,

Jie Li, Yong Pang

et al.

Land, Journal Year: 2025, Volume and Issue: 14(1), P. 69 - 69

Published: Jan. 2, 2025

Nitrate leaching from soil presents a significant threat to health, as it can result in nutrient loss, acidification, and structural damage. It is crucial quantify the spatial heterogeneity of nitrate its drivers. A total 509 observational data points regarding northern China were collected, capturing temporal variations across crops such winter wheat, maize, greenhouse vegetables. machine learning (ML) model for predicting was then developed, with random forest (RF) outperforming support vector (SVM), extreme gradient boosting (XGBoost), convolutional neural network (CNN) models, achieving an R2 0.75. However, performance improved significantly after integrating four models Bayesian optimization (all had > 0.56), which realized quantitative prediction capabilities loss concentrations. Moreover, XGBoost exhibited highest fitting accuracy smallest error estimating losses, value 0.79 average absolute (MAE) 3.87 kg/ha. Analyses feature importance SHAP values optimal identified organic matter, chemical nitrogen fertilizer input, water input (including rainfall irrigation) main indicators loss. The ML-based modeling method developed overcomes difficulty determination functional relationship between intensity influencing factors, providing data-driven solution nitrate–nitrogen farmlands North strengthening sustainable agricultural practices.

Language: Английский

Citations

0
Assessing groundwater quality and suitability in Qatar: Strategic insights for sustainable water management and environmental protection DOI Creative Commons
Sarra Aloui, Adel Zghibi, Annamaria Mazzoni

et al.

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: unknown, P. 100582 - 100582

Published: Jan. 1, 2025

Language: Английский

Citations

0
Hydrogeochemical analysis of the groundwater composition and risk to human health of an abandoned mine area, southwest China DOI
Jiajun Fan,

Mingtan Zhu,

Dong Sun

et al.

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(3)

Published: Feb. 19, 2025

Language: Английский

Citations

0
Overviewing the Machine Learning Utilization on Groundwater Research Using Bibliometric Analysis DOI Open Access
Kayhan Bayhan, Eyyup Ensar Başakın, Ömer Ekmekcioğlu

et al.

Water, Journal Year: 2025, Volume and Issue: 17(7), P. 936 - 936

Published: March 23, 2025

Groundwater, which constitutes 95% of the world’s freshwater resources, is widely used for drinking and domestic water supply, agricultural irrigation, energy production, bottled commercial use. In recent years, due to pressures from climate change excessive urbanization, a noticeable decline in groundwater levels has been observed, particularly arid semi-arid regions. The corresponding changes have analyzed using diverse range methodologies, including data-driven modeling techniques. Recent evidence shown notable acceleration utilization such advanced techniques, demonstrating significant attention by research community. Therefore, major aim present study conduct bibliometric analysis investigate application evolution machine learning (ML) techniques research. this sense, studies published between 2000 2023 were examined terms scientific productivity, collaboration networks, themes, methods. findings revealed that ML offer high accuracy predictive capacity, especially quality, level estimation, pollution modeling. United States, China, Iran stand out as leading countries emphasizing strategic importance management. However, outcomes demonstrated low international cooperation led deficiencies solving transboundary problems. aimed encourage more widespread effective use management environmental planning processes drew transparent interpretable algorithms, with potential yield rewarding opportunities increasing adoption technologies decision-makers.

Language: Английский

Citations

0
Linking fluorescence spectral to machine learning predicts the emissions fates of greenhouse gas during composting DOI
Bing Bai,

Hongtao Liu,

Aizhen Liang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110430 - 110430

Published: April 26, 2025

Language: Английский

Citations

0
Heavy Metal Distribution and Health Risk Assessment in Groundwater and Surface Water of Karst Lead–Zinc Mine DOI Open Access

Jin-Mei Zhou,

Zhongcheng Jiang,

Xiaoqun Qin

et al.

Water, Journal Year: 2024, Volume and Issue: 16(15), P. 2179 - 2179

Published: Aug. 1, 2024

Heavy metal pollution seriously threatens the drinking water safety and ecological environment in karst lead–zinc mines. Fifteen groundwater surface samples were collected a mine Daxin, Chongzuo. Ten heavy (Mn, Zn, As, Pb, Cr, Cd, Ni, Co, Cu, Fe) concentrations detected. Correlation cluster analysis utilized to explore distribution characteristics sources. The health risks appraised using risk assessment model. had more types than water, of which average exceeded class III quality standard. drainage contributed most (65.10%) concentrations. Mn, Fe primarily originated from mining mine, Cr came fuel combustion wear metals, As was connected with regional geological background. higher total (5.12 × 10−4 a−1) (2.17 a−1). In comparison non-carcinogenic risk, carcinogenic increased by three five orders magnitude. Cd represented pattern. pathway posed two magnitude amount that dermal contact posed. Children suffered greater risks. Water security for children should be strictly controlled. must paid attention terms protection management.

Language: Английский

Citations

2