AIMC Topic: Soil Pollutants

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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 ...
Machine Learning Soil Hydrogen-Ion Concentration Biological Availability Cadmium Lead Soil Pollutants
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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...
Soil Pollutants Metagenome Metagenomics Biodegradation, Environmental Bacteria Hydrocarbons, Brominated Soil Microbiology Machine Learning
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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 ...
Oryza Cadmium Plant Leaves Soil Pollutants Machine Learning Environmental Monitoring
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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...
Machine Learning Environmental Monitoring Food Contamination Soil Pollutants Soil China Ensemble Learning Oryza Cadmium Arsenic Edible Grain
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Natural factor-based spatial prediction and source apportionment of typical heavy metals in Chinese surface soil: Application of machine learning models.

Environmental pollution (Barking, Essex : 1987) Dec 7, 2024
Predicting the natural distribution of heavy metals (HMs) in soil is important to understand the potential risk of pollution. However, suitable technologies are still lacking for wide scale due to the large spatial heterogeneity. In this study, we de...
Metals, Heavy Machine Learning East Asian People China Soil Pollutants Soil Environmental Monitoring
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Machine learning-based identification of critical factors for cadmium accumulation in rice grains.

Environmental geochemistry and health Nov 28, 2024
The aggregation of Cadmium (Cd) in rice grains is a significant threat to human healthy. The complexity of the soil-rice system, with its numerous influencing parameters, highlights the need to identify the crucial factors responsible for Cd aggregat...
Oryza Machine Learning Cadmium Soil Soil Pollutants Edible Grain
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Using machine learning to predict soil lead relative bioavailability.

Journal of hazardous materials Nov 22, 2024
Although the relative bioavailability (RBA) can be applied to assess the effects of Pb on human health, there is no definition and no specific data of Pb-RBA to different soil sources and endpoints in vivo. In this study, we estimated the Pb-RBA from...
Lead Machine Learning Soil Pollutants Soil Animals Humans Environmental Monitoring Biological Availability
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Mining soil heavy metal inversion based on Levy Flight Cauchy Gaussian perturbation sparrow search algorithm support vector regression (LSSA-SVR).

Ecotoxicology and environmental safety Nov 8, 2024
Soil heavy metal pollution in mining areas poses severe challenges to the ecological environment. In recent years, machine learning has been widely used in heavy metal inversion by hyperspectral data. However, deterministic algorithms and probabilist...
Mining Soil Soil Pollutants Algorithms Support Vector Machine Normal Distribution Metals, Heavy Environmental Monitoring
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Global meta-analysis and machine learning reveal the critical role of soil properties in influencing biochar-pesticide interactions.

Environment international Nov 6, 2024
Biochar application in soils is increasingly advocated globally for its dual benefits in enhancing agricultural productivity and sequestering carbon. However, lingering concerns persist regarding its environmental impact, particularly concerning its ...
Soil Agriculture Soil Pollutants Machine Learning Charcoal Pesticide Residues Pesticides
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