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Soil Pollutants

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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
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...

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

Environmental geochemistry and health
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...

Using machine learning to predict soil lead relative bioavailability.

Journal of hazardous materials
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...

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

Environment international
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 ...

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

Ecotoxicology and environmental safety
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...

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

Ecotoxicology and environmental safety
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...

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

Journal of environmental management
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...

Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Journal of hazardous materials
Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ens...

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)
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...

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
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...