AIMC Topic: Soil Pollutants

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Prediction of PFAS bioaccumulation in different plant tissues with machine learning models based on molecular fingerprints.

The Science of the total environment Jul 28, 2024
Due to the wastewater irrigation or biosolid application, per- and polyfluoroalkyl substances (PFASs) have been widely detected in agriculture soil and hence crops or vegetables. Consumption of contaminated crops and vegetables is considered as an im...
Bioaccumulation Machine Learning Soil Pollutants Fluorocarbons Environmental Monitoring Plant Roots
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Quantifying the impact of factors on soil available arsenic using machine learning.

Environmental pollution (Barking, Essex : 1987) Jul 17, 2024
Arsenic (As) can accumulate in edible plant parts and thus pose a serious threat to human health. Identifying the contributions of various factors to soil available As is crucial for evaluating environmental risks. However, research quantitatively as...
Soil Environmental Monitoring Arsenic Soil Pollutants Machine Learning
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Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area.

Environmental geochemistry and health Jul 13, 2024
Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of s...
Cadmium Machine Learning Mining Soil China Soil Pollutants Environmental Monitoring Hydrogen-Ion Concentration
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Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning.

Ecotoxicology and environmental safety Jul 11, 2024
Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In ...
Deep Learning Zinc Soil Pollutants Biodegradation, Environmental Cadmium Support Vector Machine Plant Leaves Sedum Hyperspectral Imaging Metals, Heavy
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Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data.

The Science of the total environment Jul 10, 2024
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils ...
Edible Grain Rhizosphere Soil Pollutants Triticum Zinc Environmental Monitoring Soil Trace Elements Neural Networks, Computer
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A novel prediction approach driven by graph representation learning for heavy metal concentrations.

The Science of the total environment Jul 10, 2024
The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental f...
Metals, Heavy Soil Pollutants Oryza Machine Learning Environmental Monitoring Neural Networks, Computer
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Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils.

Journal of hazardous materials Jul 5, 2024
To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive...
Soil Risk Assessment Environmental Monitoring Soil Pollutants Metals, Heavy Machine Learning China
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New strategy to optimize in-situ fenton oxidation for TPH contaminated soil remediation via artificial neural network approach.

Chemosphere Jul 3, 2024
In-situ remediation of total petroleum hydrocarbon (TPH) contaminated soils via Fenton oxidation is a promising approach. However, determining the proper injection amount of HO and Fe source over the Fenton reaction in the complex geological conditio...
Soil Hydrogen Peroxide Oxidation-Reduction Neural Networks, Computer Iron Hydrocarbons Petroleum Soil Pollutants Environmental Restoration and Remediation
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Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning.

Journal of hazardous materials Jun 27, 2024
Biochar is effective in mitigating heavy metal pollution, and cadmium (Cd) is the primary pollutant in agricultural fields. However, traditional trial-and-error methods for determining the optimal biochar remediation efficiency are time-consuming and...
Agriculture Machine Learning Soil Pollutants Charcoal Soil Environmental Restoration and Remediation Cadmium China
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Predicting the governing factors for the release of colloidal phosphorus using machine learning.

Chemosphere Jun 27, 2024
Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent th...
Iron Environmental Monitoring Colloids Phosphorus Machine Learning Linear Models Soil Pollutants Soil
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