Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model

Journal of Hazardous Materials, Год журнала: 2024, Номер 473, С. 134708 - 134708

Опубликована: Май 23, 2024

Язык: Английский

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

The Science of The Total Environment, Год журнала: 2024, Номер 934, С. 173284 - 173284

Опубликована: Май 18, 2024

Язык: Английский

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Identification of driving factors for heavy metals and polycyclic aromatic hydrocarbons pollution in agricultural soils using interpretable machine learning

The Science of The Total Environment, Год журнала: 2025, Номер 960, С. 178384 - 178384

Опубликована: Янв. 1, 2025

Язык: Английский

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Heavy metal enrichment characteristics and synergistic evaluation in soil-crop-human systems of agricultural land with different soil parent materials

Environmental Geochemistry and Health, Год журнала: 2025, Номер 47(3)

Опубликована: Фев. 8, 2025

Язык: Английский

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Multiple Environmental Variables as Covariates to Improve the Accuracy of Spatial Prediction Models for SOM on Karst Aera

Land Degradation and Development, Год журнала: 2025, Номер unknown

Опубликована: Янв. 12, 2025

ABSTRACT Aims accurately predicting the spatial distribution of soil organic matter (SOM) is essential for environmental management and carbon storage estimation. However, diversity sources variables poses a challenge in studying SOM. Methods order to address this issue, we propose leveraging multiple employing machine learning models, specifically Lightweight gradient boosting (LightGBM) random forest (RF), SOM distribution. 128 samples were collected from Caohai National Nature Reserve, their content was measured. Results study found that average 36.75 g/kg. Compared traditional linear regression models such as ordinary kriging (OK), least squares (OLS), geographically weighted (GWR), based on nonlinear regression, LightGBM RF, demonstrated higher cross‐validated coefficients determination ( R 2 ) 0.62 0.60, respectively, outperforming other models. Additionally, RF exhibited lower mean absolute error (MAE) root square (RMSE), indicating stability generalization capability. The among showed consistency, with observed southern near‐Caohai Lake regions northern farther Lake. Shapley additive explanations (SHAP) model highlighted agricultural land (AL), pH, Elevation (ELV) primary covariates influencing Conclusions provides valuable insights support estimation karst plateau region.

Язык: Английский

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Hotspot mapping and risk prediction of fluoride in natural waters across the Tibetan Plateau

Journal of Hazardous Materials, Год журнала: 2024, Номер 465, С. 133510 - 133510

Опубликована: Янв. 13, 2024

Язык: Английский

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Digital Mapping and Scenario Prediction of Soil Salinity in Coastal Lands Based on Multi-Source Data Combined with Machine Learning Algorithms

Remote Sensing, Год журнала: 2024, Номер 16(14), С. 2681 - 2681

Опубликована: Июль 22, 2024

Salinization is a major soil degradation process threatening ecosystems and posing great challenge to sustainable agriculture food security worldwide. This study aimed evaluate the potential of state-of-the-art machine learning algorithms in salinity (EC1:5) mapping. Further, we predicted distribution patterns under different future scenarios Yellow River Delta. A geodatabase comprising 201 samples 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, Sentinel-2) was used compare predictive performance empirical bayesian kriging regression, random forest, CatBoost models. The model exhibited highest with both training testing datasets, an average MAE 1.86, RMSE 3.11, R2 0.59 datasets. Among explanatory factors, Na most important for predicting EC1:5, followed by normalized difference vegetation index organic carbon. Soil EC1:5 predictions suggested that Delta region faces severe salinization, particularly coastal zones. three increases carbon content (1, 2, 3 g/kg), 2 g/kg scenario resulted best improvement effect saline–alkali soils > ds/m. Our results provide valuable insights policymakers improve land quality plan regional agricultural development.

Язык: Английский

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Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils

Journal of Hazardous Materials, Год журнала: 2024, Номер 481, С. 136536 - 136536

Опубликована: Ноя. 19, 2024

Язык: Английский

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Machine learning for membrane bioreactor research: principles, methods, applications, and a tutorial

Frontiers of Environmental Science & Engineering, Год журнала: 2024, Номер 19(3)

Опубликована: Дек. 20, 2024

Язык: Английский

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Potential risk of heavy metals release in sediments and soils of the Yellow River Basin (Henan section): A perspective on bioavailability and bioaccessibility

Ecotoxicology and Environmental Safety, Год журнала: 2025, Номер unknown, С. 117799 - 117799

Опубликована: Янв. 1, 2025

Язык: Английский

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