AIMC Topic: Metals, Heavy

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Machine learning-powered fluorescent sensor arrays for rapid detection of heavy metals and pesticides in complex environments.

Biosensors & bioelectronics
The co-contamination of multiple pollutants in complex environmental matrices poses a significant threat to ecosystems and public health, necessitating advanced detection methods. In this study, we developed a machine learning-powered chemical sensor...

Urban-rural inequality in soil heavy metal health risks: Insights from Baoding, China.

Ecotoxicology and environmental safety
Soil heavy metal contamination poses serious health risks, but few studies have quantitatively assessed disparities in these risks between urban and rural populations. To address this gap, we introduce a novel framework integrating machine learning a...

A novel method for achieving ecological indicator based on vertical soil bacterial communities coupled with machine learning: A case study of a typical tropical site in China.

Journal of hazardous materials
Global industrialization has resulted in severe contamination of soil with heavy metals (HMs). Nevertheless, it is unclear if it affects the depth-resolved bacterial communities. Herein, we collected soil samples at different depths from a typical HM...

Stricter cadmium and lead standards needed for organic fertilizers in China.

The Science of the total environment
This study aims to evaluate the adequacy of China's national standards for heavy metals in organic fertilizers by predicting their concentrations in grains using machine leaning. A comprehensive dataset was collected from literature, including soil p...

Spatial distribution and risk assessment of heavy metal in coastal waters of China.

Marine environmental research
In order to better understand the status of heavy metal pollution in surface seawater of China's coastal waters, this paper compiled research results on seven heavy metals (i.e. Hg, As, Cu, Cd, Cr, Pb, Zn) in surface seawater of 35 coastal areas of C...

Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh.

Environmental geochemistry and health
Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Developing a reliable predictive model for heavy metal concentrations in this region has proven to be a...

Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.

Frontiers in public health
BACKGROUND: Exposure to heavy metals has been implicated in adverse auditory health outcomes, yet the precise relationships between heavy metal biomarkers and hearing status remain underexplored. This study leverages a machine learning framework to i...

Machine learning predicts selectivity of green synthesized iron nanoparticles toward typical contaminants: critical factors in synthesis conditions, material properties, and reaction process.

Environmental research
Green synthesized iron nanoparticles (FeNPs) have gained popularity in contaminant removal due to their low cost and environmentally friendly properties. However, a gap remains in understanding how synthesis conditions (SC), material properties (MP),...

Predicting the amount of toxic metals and metalloids in silt loading using neural networks.

Environmental monitoring and assessment
Material deposited on road surfaces, called road dust, are known to contain different toxic elements. According to particle size, there are different fractions. Particles with an aerodynamic size less than or equal to 75 µm are called silt loading. A...

The interpretable machine learning model for depression associated with heavy metals via EMR mining method.

Scientific reports
Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient machine learning (ML) model with robust performance to identify depression linked to heavy metal exposur...