AIMC Topic: Soil Pollutants

Clear Filters Showing 31 to 40 of 117 articles

Natural factor-based spatial prediction and source apportionment of typical heavy metals in Chinese surface soil: Application of machine learning models.

Environmental pollution (Barking, Essex : 1987) Dec 7, 2024
Predicting the natural distribution of heavy metals (HMs) in soil is important to understand the potential risk of pollution. However, suitable technologies are still lacking for wide scale due to the large spatial heterogeneity. In this study, we de...
China Environmental Monitoring East Asian People Machine Learning Soil Soil Pollutants Metals, Heavy
View on PubMed DOI

Machine learning-based identification of critical factors for cadmium accumulation in rice grains.

Environmental geochemistry and health Nov 28, 2024
The aggregation of Cadmium (Cd) in rice grains is a significant threat to human healthy. The complexity of the soil-rice system, with its numerous influencing parameters, highlights the need to identify the crucial factors responsible for Cd aggregat...
Edible Grain Oryza Soil Soil Pollutants Machine Learning Cadmium
View on PubMed DOI

Using machine learning to predict soil lead relative bioavailability.

Journal of hazardous materials Nov 22, 2024
Although the relative bioavailability (RBA) can be applied to assess the effects of Pb on human health, there is no definition and no specific data of Pb-RBA to different soil sources and endpoints in vivo. In this study, we estimated the Pb-RBA from...
Machine Learning Soil Humans Animals Soil Pollutants Biological Availability Environmental Monitoring Lead
View on PubMed DOI

Mining soil heavy metal inversion based on Levy Flight Cauchy Gaussian perturbation sparrow search algorithm support vector regression (LSSA-SVR).

Ecotoxicology and environmental safety Nov 8, 2024
Soil heavy metal pollution in mining areas poses severe challenges to the ecological environment. In recent years, machine learning has been widely used in heavy metal inversion by hyperspectral data. However, deterministic algorithms and probabilist...
Support Vector Machine Soil Pollutants Mining Algorithms Normal Distribution Metals, Heavy Soil Environmental Monitoring
View on PubMed DOI

Global meta-analysis and machine learning reveal the critical role of soil properties in influencing biochar-pesticide interactions.

Environment international Nov 6, 2024
Biochar application in soils is increasingly advocated globally for its dual benefits in enhancing agricultural productivity and sequestering carbon. However, lingering concerns persist regarding its environmental impact, particularly concerning its ...
Machine Learning Soil Soil Pollutants Charcoal Agriculture Pesticides Pesticide Residues
View on PubMed DOI

Reducing cadmium and arsenic accumulation in rice grains: The coupled effect of sulfur's biomass dilution and soil immobilization analyzed using meta-analysis and machine learning.

The Science of the total environment Oct 30, 2024
The biogeochemical cycling of sulfur (S) in paddy soil influences cadmium (Cd) and arsenic (As) migration. However, the impact of S application on Cd and As within the soil-rice system has not been fully explored. This study aimed to examine the effe...
Arsenic Biomass Sulfur Cadmium Machine Learning Oryza Soil Pollutants Soil
View on PubMed DOI

Impacts of micro/nano plastics on the ecotoxicological effects of antibiotics in agricultural soil: A comprehensive study based on meta-analysis and machine learning prediction.

The Science of the total environment Oct 29, 2024
Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of this study was to systematically evaluate the ecological effects of the co-exposure of M/NPs and antib...
Soil Ecotoxicology Agriculture Plastics Soil Pollutants Environmental Monitoring Machine Learning Anti-Bacterial Agents
View on PubMed DOI

Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning.

Ecotoxicology and environmental safety Oct 23, 2024
Micro/nanoplastics (MNPs) and heavy metals (HMs) coexist worldwide. Existing studies have reported different or even contradictory toxic effects of co-exposure to MNPs and HMs on plants, which may be related to various influencing factors. In this st...
Microplastics Cadmium Metals, Heavy Soil Pollutants Plants Machine Learning Nanoparticles
View on PubMed DOI

Enhanced phytoremediation of vanadium using coffee grounds and fast-growing plants: Integrating machine learning for predictive modeling.

Journal of environmental management Oct 9, 2024
Vanadium (V) contamination posed a significant environmental challenge, while phytoremediation offered a sustainable solution. Phytoremediation performance was often limited by the slow growth cycles of traditional plants. A novel approach to enhanci...
Coffee Lolium Biodegradation, Environmental Vanadium Machine Learning Soil Pollutants
View on PubMed DOI

Leveraging machine learning for sustainable cultivation of Zn-enriched crops in Cd-contaminated karst regions.

The Science of the total environment Oct 3, 2024
Karst soils often exhibit elevated zinc (Zn) levels, providing an opportunity to cultivate Zn-enriched crops. (meanwhile) However, these soils also frequently contain high background levels of toxic metals, particularly cadmium (Cd), posing potential...
Soil China Zinc Machine Learning Soil Pollutants Agriculture Cadmium Environmental Monitoring Crops, Agricultural
View on PubMed DOI
Popular Topics
Recent Journals