AIMC Topic: Soil Pollutants

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Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data.

The Science of the total environment
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils ...

A novel prediction approach driven by graph representation learning for heavy metal concentrations.

The Science of the total environment
The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental f...

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils.

Journal of hazardous materials
To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive...

New strategy to optimize in-situ fenton oxidation for TPH contaminated soil remediation via artificial neural network approach.

Chemosphere
In-situ remediation of total petroleum hydrocarbon (TPH) contaminated soils via Fenton oxidation is a promising approach. However, determining the proper injection amount of HO and Fe source over the Fenton reaction in the complex geological conditio...

Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning.

Journal of hazardous materials
Biochar is effective in mitigating heavy metal pollution, and cadmium (Cd) is the primary pollutant in agricultural fields. However, traditional trial-and-error methods for determining the optimal biochar remediation efficiency are time-consuming and...

Predicting the governing factors for the release of colloidal phosphorus using machine learning.

Chemosphere
Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent th...

Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach.

Chemosphere
The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil sp...

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) prediction model based on limited peat samples using an evolved artificial neural network.

Chemosphere
Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high ...

Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis.

Journal of hazardous materials
The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-c...