AIMC Topic: Metals, Heavy

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Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning.

Environmental pollution (Barking, Essex : 1987)
Microplastics (MPs) have the potential to adsorb heavy metals (HMs), resulting in a combined pollution threat in aquatic and terrestrial environments. However, due to the complexity of MP/HM properties and experimental conditions, research on the ads...

Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning.

Ecotoxicology and environmental safety
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 ...

A novel prediction approach driven by graph representation learning for heavy metal concentrations.

The Science of the total environment
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...

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils.

Journal of hazardous materials
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...

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.

Frontiers in public health
BACKGROUND AND OBJECTIVE: Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy me...

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.

Scientific reports
Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinop...

A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik.

Environmental monitoring and assessment
Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosys...

Phytobial remediation advances and application of omics and artificial intelligence: a review.

Environmental science and pollution research international
Industrialization and urbanization increased the use of chemicals in agriculture, vehicular emissions, etc., and spoiled all environmental sectors. It causes various problems among living beings at multiple levels and concentrations. Phytoremediation...

Machine learning-based exploration of biochar for environmental management and remediation.

Journal of environmental management
Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the...