AIMC Topic: Metals, Heavy

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Synergistic microbial consortia in the bioremediation of heavy metal-contaminated wastewater: mechanisms and sustainability perspectives.

Environmental geochemistry and health
Heavy metals (HMs) are mostly toxic to all forms of life and are tenacious environmental pollutants. Rapid industrialization, urban development, and unsustainable agricultural implications lead to their accumulation in soil and water ecosystems, prom...

Application of machine learning for identification of key exposure predictors for heavy metal accumulation in hair of traffic police officers in Tehran.

The Science of the total environment
In order to determine variability and measure the major exposure factors affecting the levels of hazardous metals (such as Fe, Mn, Ni, Pb, As, Cr, and Cu) in the scalp hair of Tehran traffic police personnel, an advanced statistical method is used. T...

Machine learning-based prediction of deep soil metal(loid) contamination in industrial areas: Role of surface environmental factors.

Environmental pollution (Barking, Essex : 1987)
Predicting the distribution of soil contamination is crucial for targeted remediation efforts and risk prevention, especially considering the high costs associated with in-situ contamination surveys. This study proposes a random forest (RF)-based app...

Quantitative inversion of soil heavy metal pollution using a GA-BP neural network model.

Environmental monitoring and assessment
With the rapid development of industrialization in China, significant economic benefits have been accompanied by varying degrees of threat to the soil environment, particularly from heavy metal pollution. The rapid quantitative inversion of heavy met...

Emerging nanosensor technologies for the rapid detection of heavy metal contaminants in agricultural soils.

Analytical methods : advancing methods and applications
The accumulation of heavy metals in agricultural soils presents a growing threat to food safety and human health. Conventional laboratory-based methods for heavy metal detection, while highly sensitive, are impractical for widespread, real-time soil ...

Geographically weighted random forest fusing multi-source environmental covariates for spatial prediction of soil heavy metals.

Environmental pollution (Barking, Essex : 1987)
Efficient spatial prediction models for soil heavy metals are crucial for maintaining soil ecosystem health, promoting high-quality regional agriculture, and national food security. Traditional machine learning (ML) models often overlook spatial auto...

Comparative analysis of multiple machine learning models: identifying impact factors in biochar heavy metal adsorption mechanisms.

Environmental geochemistry and health
The contribution analysis of influencing factors governing biochar-mediated heavy metal adsorption in aqueous systems holds significant implications for cost-effective water remediation. Current studies predominantly rely on single-model approaches t...

A data-intensive framework for evaluating ecological and human health impacts of soil potentially toxic elements (PTEs) in the mining-endemic region of Singida, Tanzania.

Environmental geochemistry and health
Uncontrolled soil contamination by potentially toxic elements (PTEs) poses serious threats to environmental and public health in mining-intensive regions. Against this background, this study assessed the distribution, sources, ecological impact, and ...

Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation.

The Science of the total environment
The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial dis...

Simulation, prediction and optimization of heavy metal adsorption by metal-organic frameworks with machine learning.

Environmental research
The unique structures and complex characteristics of Metal-organic frame (MOFs) obscure understanding the processes and mechanisms of heavy metal (HM) removal. This study established an interpretable machine learning (ML) framework predicting adsorpt...