AIMC Topic: Soil Pollutants

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Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Environmental science. Processes & impacts Sep 18, 2024
Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attrib...
Machine Learning Soil Soil Pollutants Models, Chemical Sulfur Selenium Environmental Monitoring Europe
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Machine learning phenotyping and GWAS reveal genetic basis of Cd tolerance and absorption in jute.

Environmental pollution (Barking, Essex : 1987) Sep 10, 2024
Cadmium (Cd) is a dangerous environmental contaminant. Jute (Corchorus sp.) is an important natural fiber crop with strong absorption and excellent adaptability to metal-stressed environments, used in the phytoextraction of heavy metals. Understandin...
Machine Learning Phenotype Genome-Wide Association Study Cadmium Soil Pollutants Biodegradation, Environmental
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Investigating PCB degradation by indigenous fungal strains isolated from the transformer oil-contaminated site: degradation kinetics, Bayesian network, artificial neural networks, QSAR with DFT, molecular docking, and molecular dynamics simulation.

Environmental science and pollution research international Sep 6, 2024
The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network...
Quantitative Structure-Activity Relationship Bayes Theorem Fungi Neural Networks, Computer Kinetics Soil Pollutants Molecular Docking Simulation Molecular Dynamics Simulation Biodegradation, Environmental Polychlorinated Biphenyls
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Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models.

The Science of the total environment Aug 24, 2024
The traditional prediction of the Cd content in grains (Cd) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cd) and pH, neglecting inter-factorial interactions and nonlinear causal links between external e...
Soil China Environmental Monitoring Machine Learning Cadmium Crops, Agricultural Soil Pollutants Triticum Oryza
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Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean.

Journal of hazardous materials Aug 22, 2024
The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant quality and yield, as well as affect human health and food chain cycles. Therefore, developing rapid and effective detection methods is crucial. In this stu...
Herbicides Metabolic Reprogramming Machine Learning Polyethylene Glycine max Soil Soil Pollutants Microplastics Environmental Monitoring
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Application of machine learning approaches to predict ammonium nitrogen transport in different soil types and evaluate the contribution of control factors.

Ecotoxicology and environmental safety Aug 17, 2024
The loss of nitrogen in soil damages the environment. Clarifying the mechanism of ammonium nitrogen (NH-N) transport in soil and increasing the fixation of NH-N after N application are effective methods for improving N use efficiency. However, the ma...
Machine Learning Nitrogen Soil Ammonium Compounds Environmental Monitoring Soil Pollutants
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Contribution assessment and accumulation prediction of heavy metals in wheat grain in a smelting-affected area using machine learning methods.

The Science of the total environment Aug 11, 2024
Due to the diverse controlling factors and their uneven spatial distribution, especially atmospheric deposition from smelters, assessing and predicting the accumulation of heavy metals (HM) in crops across smelting-affected areas becomes challenging....
Environmental Monitoring Soil Pollutants Triticum Edible Grain Metals, Heavy Metallurgy Machine Learning
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Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation.

Journal of hazardous materials Aug 10, 2024
Urban fragmented vegetable fields offer fresh produce but pose a potential risk of heavy metal (HM) exposure. Thus, this study investigated HM sources and health risks in the soil-vegetable systems of Chongqing's central urban area. Results indicated...
Metals, Heavy Environmental Monitoring Food Contamination Soil Pollutants Vegetables Risk Assessment Monte Carlo Method China Agriculture Humans Machine Learning
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Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study.

ACS applied materials & interfaces Aug 1, 2024
The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long. We here investigate the c...
Animals Soil Pollutants Iron Machine Learning Copper Nanostructures Oligochaeta
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Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations.

Journal of hazardous materials Jul 31, 2024
Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the surrounding soil with potentially toxic elements (PTEs). Herein, 117 topsoil samples from gas statio...
Metals, Heavy Risk Assessment Female Humans Environmental Monitoring Soil Pollutants Adult Machine Learning Male Monte Carlo Method Beijing
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