AIMC Topic: Soil Pollutants
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Spatial Risks of Microplastics in Soils and the Cascading Effects Thereof.
Environmental science & technology
May 21, 2025
Microplastic (MP) pollution has become a significant global concern in soil systems. The spatial risk of MPs in soils, the cascading effects of climate, human activities, and air quality, and the ecosystem gradients from natural habitats, agricultura...
Unraveling soil salinity on potentially toxic element accumulation in coastal Phragmites australis: A novel integration of multivariate and interpretable machine-learning models.
Marine pollution bulletin
May 5, 2025
Revealing the key mechanisms influencing the behavior of potentially toxic elements (PTEs) in soil-plant systems is of great significance for environmental protection and grassland development in coastal areas. This study utilized redundancy analysis...
Uncovering rare earth and precious metal in landfill-mined soil-like-fractions: distribution prediction, ecological risk and resource potential.
Environmental pollution (Barking, Essex : 1987)
May 5, 2025
The introduction of rare earth elements (REEs) and precious metals (PMs) containing wastes in aged landfills leads to a significant pollutant and resource potential. Against this backdrop, the accumulation of REEs and PMs in soil-like-fractions (SLF)...
Stricter cadmium and lead standards needed for organic fertilizers in China.
The Science of the total environment
Apr 30, 2025
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...
Estimating soil cadmium concentration using multi-source UAV imagery and machine learning techniques.
Environmental monitoring and assessment
Apr 24, 2025
Urbanization and industrialization have led to widespread soil heavy metals contamination, posing significant risks to ecosystems and human health. Conventional methods for mapping heavy metal distribution, which rely on soil sampling followed by che...
Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh.
Environmental geochemistry and health
Apr 23, 2025
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...
Modeling PFAS Sorption in Soils Using Machine Learning.
Environmental science & technology
Apr 11, 2025
In this study, we introduce PFASorptionML, a novel machine learning (ML) tool developed to predict solid-liquid distribution coefficients () for per- and polyfluoroalkyl substances (PFAS) in soils. Leveraging a data set of 1,274 entries for PFAS in ...
Applications of machine learning in potentially toxic elemental contamination in soils: A review.
Ecotoxicology and environmental safety
Apr 6, 2025
Soil contamination by potentially toxic elements (PTEs) poses substantial risks to the environment and human health. Traditional investigational methods are often inadequate for large-scale assessments because they are time-consuming, costly, and hav...
Assessing potential toxic metal threats in tea growing soils of India with soil health indices and machine learning technologies.
Environmental monitoring and assessment
Mar 25, 2025
This study explores the impact of potentially toxic metals (PTMs) contamination in Indian tea-growing soils on ecosystems, soil quality, and human health using machine learning and statistical analysis. A total of 148 surface soil samples were collec...
Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland.
Journal of environmental management
Mar 24, 2025
For efficient decision-making and optimal land management trajectories, information on soil properties in relation to safety guidelines should be processed from point inventories to surface predictive maps. For large-scale predictive mapping, very fe...
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