AIMC Topic: Soil Pollutants

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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...

Influence of sample size and machine learning algorithms on digital soil nutrient mapping accuracy.

Environmental monitoring and assessment
The objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, viz., multi-layer perceptron (MLP), random forest (RF), extra trees regressor (ETR), CatBoost, and gradient boost (GB), considering ...

Integrating machine learning for enhanced spatial prediction and risk assessment of soil heavy metal(loid)s.

Environmental pollution (Barking, Essex : 1987)
Accurately predicting the concentrations and spatial distribution of soil heavy metal(loid)s is crucial for effective environmental management and human health risk assessment. However, existing studies are often limited by poor model accuracy, featu...

Mapping hotspots and identifying drivers of lead bioaccumulation in Oryza sativa L. in tropical agroecosystems.

Journal of hazardous materials
Tropical rice systems exhibit high annual rates of heavy metal accumulation, requiring accurate identification of accumulation drivers in rice-growing ecosystems to ensure regional food security. Therefore, we collected 229 paired soil and rice sampl...

Quantitative evaluation of hydrocarbon contamination in soil using hyperspectral data-a comparative study of machine learning models.

Environmental monitoring and assessment
This study aims to evaluate the applicability of existing machine learning and deep learning techniques for the rapid prediction of hydrocarbon contamination in soils using hyperspectral data. Soil samples of three types, i.e., clayey, silty, and san...

Predicting arsenic bioaccessibility: A global data-driven machine learning approach and its implication for reducing carbon emissions.

Journal of hazardous materials
Site-specific arsenic (As) bioaccessibility data can improve the accuracy of health risk assessments, but direct measurements are costly and time-consuming. Even when available, measured values such as the mean still yield remediation targets below n...

Predicting the Sorption Capacity of Perfluoroalkyl and Polyfluoroalkyl Substances in Soils: Meta-Analysis and Machine Learning Modeling.

Environmental science & technology
Predicting the soil sorption capacity for perfluoroalkyl and polyfluoroalkyl substances (PFAS) is pivotal for environmental risk assessment. However, traditional experimental methods are inefficient, necessitating computational model development. We ...

Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces.

Ecotoxicology and environmental safety
Global urbanization has significantly contributed to soil contamination by heavy metals (HMs), posing serious ecological risks, particularly within urban green spaces (UGS). This study focused on UGS soils in Lanzhou, a major river-valley city in Chi...

Unlocking urban soil secrets: machine learning and spectrometry in Berlin's heavy metal pollution study considering spatial data.

Environmental monitoring and assessment
Berlin has historically been impacted by heavy metal (HM) emissions, raising concerns about soil pollution. In this study, machine learning (ML) techniques were applied to predict HM concentrations across the Berlin metropolitan area. A dataset of 66...