AIMC Topic: Metals, Heavy
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Machine learning models for water safety enhancement.
Scientific reports
Jan 30, 2025
Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. These radionuclides can infiltrate the human body through various pathways, potentially lead...
Classification of soil contamination by heavy metals (Cr, Ni, Pb, Zn) in wildfire-affected areas using laser-induced breakdown spectroscopy and machine learning.
Environmental science and pollution research international
Jan 8, 2025
The assessment of soil contamination by heavy metals is of high importance due to its impact on the environment and human health. Standard high-sensitivity spectroscopic techniques for this task such as atomic absorption spectrometry (AAS) and induct...
Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning.
Ecotoxicology and environmental safety
Dec 24, 2024
Machine learning exhibits excellent performance in terms of predictive power. We aimed to construct an interpretable machine learning model utilizing National Health and Nutrition Examination Survey data to investigate the relationship between heavy ...
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...
Simulation, prediction and optimization for synthesis and heavy metals adsorption of schwertmannite by machine learning.
Environmental research
Nov 26, 2024
Due to its sea urchin-like structure, Schwertmannite is commonly applied for heavy metals (HMs) pollutant adsorption. The adsorption influence parameters of Schwertmannite are numerous, the traditional experimental enumeration is powerless. In recent...
Enhanced food authenticity control using machine learning-assisted elemental analysis.
Food research international (Ottawa, Ont.)
Nov 13, 2024
With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namel...
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...
Carcinogenic and non-carcinogenic risks caused by rice contamination with heavy metals and their effect on the prevalence of cardiovascular disease (Using machine learning).
Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
Nov 7, 2024
INTRODUCTION: The safety and health of food products are essential in the food industry, and the risk of contamination from various contaminants must be evaluated. Exposure to HMs from the environment (especially food) causes various adverse effects ...
Machine learning model for age-related macular degeneration based on heavy metals: The National Health and Nutrition Examination Survey 2005 to 2008.
Scientific reports
Nov 6, 2024
Age-related macular degeneration (AMD) is the leading cause of blindness in older people in developed countries. It has been suggested that heavy metal exposure may be associated with the development of AMD, but most studies have focused on the effec...
Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.
Ecotoxicology and environmental safety
Oct 28, 2024
There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on hea...
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