AIMC Topic: Soil Pollutants

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A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution.

PloS one Jan 24, 2025
Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means th...
Petroleum Pollution Environmental Monitoring Support Vector Machine Hydrocarbons Petroleum Soil Pollutants Neural Networks, Computer Machine Learning Soil
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Using machine learning to predict selenium content in crops: Implications for soil health and agricultural land utilization in longevity regions.

The Science of the total environment Jan 21, 2025
Selenium (Se) is an indispensable trace element to human health, yet its biological tolerance threshold is relatively narrow. The potential application of machine learning methods to indirectly predict the Se content in crops across regional areas, t...
Agriculture Environmental Monitoring Crops, Agricultural Soil Pollutants Machine Learning Soil Selenium Neural Networks, Computer
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Artificial neural networks to estimate the sorption and desorption of the herbicide linuron in Brazilian soils.

Environmental pollution (Barking, Essex : 1987) Jan 15, 2025
Generally, herbicides used in Brazil follow manufacturer's recommendations, which often do not consider soil attributes. Statistical models that include the physicochemical properties of the soil involved in herbicide retention processes could enable...
Environmental Monitoring Adsorption Soil Pollutants Soil Brazil Linuron Models, Chemical Herbicides Neural Networks, Computer
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Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect.

Water research Jan 12, 2025
Microplastics (MPs) easily migrate into deeper soil layers, posing potential risks to subterranean habitats and groundwater. However, the mechanisms governing the vertical migration of MPs in soil, particularly aged MPs, remain unclear. In this study...
Machine Learning Soil Porosity Microplastics Water Pollutants, Chemical Soil Pollutants
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Classification of soil contamination by heavy metals (Cr, Ni, Pb, Zn) in wildfire-affected areas using laser-induced breakdown spectroscopy and machine learning.

Environmental science and pollution research international Jan 8, 2025
The assessment of soil contamination by heavy metals is of high importance due to its impact on the environment and human health. Standard high-sensitivity spectroscopic techniques for this task such as atomic absorption spectrometry (AAS) and induct...
Soil Environmental Monitoring Lead Machine Learning Spectrum Analysis Lasers Soil Pollutants Metals, Heavy Fires
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Quantifying the contributions of factors to bioaccessible Cd and Pb in soil using machine learning.

Journal of hazardous materials Jan 2, 2025
The bioaccessibility of cadmium (Cd) and lead (Pb) in the gastrointestinal tract is crucial for health risk assessments of contaminated soils. However, variability in In vitro analytical conditions and soil properties introduces bias and uncertainty ...
Cadmium Soil Lead Hydrogen-Ion Concentration Biological Availability Soil Pollutants Machine Learning
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Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches.

Biodegradation Dec 28, 2024
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Eleme...
Insecticides Pesticides Diazinon Soil Pollutants Biodegradation, Environmental Machine Learning Soil
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Interpretation of machine learning-based prediction models and functional metagenomic approach to identify critical genes in HBCD degradation.

Journal of hazardous materials Dec 25, 2024
Hexabromocyclododecane (HBCD) poses significant environmental risks, and identifying HBCD-degrading microbes and their enzymatic mechanisms is challenging due to the complexity of microbial interactions and metabolic pathways. This study aimed to ide...
Metagenomics Metagenome Soil Microbiology Machine Learning Bacteria Soil Pollutants Hydrocarbons, Brominated Biodegradation, Environmental
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Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning.

Ecotoxicology and environmental safety Dec 16, 2024
Cadmium (Cd) is a heavy metal recognized for its notable biotoxicity. Excessive Cd levels can have detrimental effects on crop growth, development, and yield. Real-time, rapid, and nondestructive monitoring of Cd content in leaves (LCd) is essential ...
Cadmium Plant Leaves Soil Pollutants Machine Learning Environmental Monitoring Oryza
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Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Journal of hazardous materials Dec 14, 2024
Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ens...
Food Contamination Soil Pollutants Edible Grain Oryza Arsenic Soil China Environmental Monitoring Machine Learning Cadmium Ensemble Learning
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