AIMC Topic: Metals, Heavy
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Machine learning-based analysis of heavy metal contamination in Chinese lake basin sediments: Assessing influencing factors and policy implications.
Ecotoxicology and environmental safety
Aug 1, 2024
Sediments are important heavy metal sinks in lakes, crucial for ensuring water environment safety. Existing studies mainly focused on well-studied lakes, leaving gaps in understanding pollution patterns in specific basins and influencing factors.We c...
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...
Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning.
Environmental pollution (Barking, Essex : 1987)
Jul 29, 2024
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
Jul 11, 2024
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
Jul 10, 2024
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
Jul 5, 2024
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...
Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies.
World journal of microbiology & biotechnology
Jun 24, 2024
This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizi...
Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.
Frontiers in public health
Jun 14, 2024
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
Jun 6, 2024
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
May 29, 2024
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...
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