AIMC Topic: Soil Pollutants

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Co-exposure to microplastics and soil pollutants significantly exacerbates toxicity to crops: Insights from a global meta and machine-learning analysis.

The Science of the total environment Sep 24, 2024
Environmental contamination of microplastics (MPs) is ubiquitous worldwide, and co-contamination of arable soils with MPs and other pollutants is of increasing concern, and may lead to unexpected consequences on crop production. However, the overall ...
Environmental Pollution Microplastics Crops, Agricultural Environmental Monitoring Soil Pollutants Machine Learning Soil
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Machine learning and structural equation modeling for revealing the influence factors and pathways of different water management regimes acting on brown rice cadmium.

The Science of the total environment Sep 24, 2024
Excessive cadmium (Cd) in brown rice has detrimental effects on rice growth and human health. Water management is a cost-effective, eco-friendly measure to suppress Cd accumulation in rice. However, there is no acknowledged water management regime th...
Machine Learning Soil Soil Pollutants Cadmium Oryza Agricultural Irrigation
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Synergistic biochar and Serratia marcescens tackle toxic metal contamination: A multifaceted machine learning approach.

Journal of environmental management Sep 20, 2024
Metal contamination in soil poses environmental and health risks requiring effective remediation strategies. This study introduces an innovative approach of synergistically employing biochar and bacterial inoculum of Serratia marcescens to address to...
Charcoal Serratia marcescens Metals Soil Pollutants Machine Learning Soil
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Deep learning models for predicting plant uptake of emerging contaminants by including the role of plant macromolecular compositions.

Journal of hazardous materials Sep 19, 2024
Deep learning models can predict uptake of emerging contaminants in plants with improved accuracy because they leverage advanced data-driven approaches to capture non-linear relationships that traditional models struggle to address. Traditional model...
Deep Learning Neural Networks, Computer Plant Roots Plants Soil Pollutants
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Identifying the habitat suitability of Pteris vittata in China and associated key drivers using machine learning models.

The Science of the total environment Sep 18, 2024
Pteris vittata (P. vittata) possesses significant potential in remediating arsenic (As) soil pollution. Understanding the habitat suitability of P. vittata in China and pinpointing the key drivers that influence its distribution can facilitate the id...
Soil Pollutants Arsenic Pteris China Environmental Monitoring Soil Ecosystem Machine Learning
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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 Pollutants Soil Models, Chemical Selenium Environmental Monitoring Sulfur 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...
Genome-Wide Association Study Soil Pollutants Biodegradation, Environmental Machine Learning Phenotype Cadmium
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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...
Soil Pollutants Kinetics Biodegradation, Environmental Fungi Molecular Docking Simulation Neural Networks, Computer Molecular Dynamics Simulation Polychlorinated Biphenyls Bayes Theorem Quantitative Structure-Activity Relationship
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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 Pollutants Triticum Machine Learning Cadmium Soil China Oryza Crops, Agricultural Environmental Monitoring
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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...
Environmental Monitoring Metabolic Reprogramming Machine Learning Soil Herbicides Soil Pollutants Polyethylene Microplastics Glycine max
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