AI Article Synopsis

  • Karst soils have high zinc (Zn) levels, which can benefit the cultivation of zinc-enriched crops, but they often also contain toxic metals like cadmium (Cd) that pose health risks.
  • The study utilized machine learning models, such as Random Forest, Support Vector Machine, and Extreme Gradient Boosting, to analyze bioaccumulation factors (BAFs) of Zn and Cd in crops grown in a Guangxi karst region, using nearly 11,000 soil samples.
  • Results showed that SVM and XGBoost were effective in predicting BAFs based on soil properties like iron and manganese content, pH, and metal interactions, leading to a framework for safely cultivating Zn-enriched crops on contaminated soils.

Article Abstract

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 health risks. Understanding the bioaccumulation of Cd and Zn and the related drivers in a high geochemical background area can provide important insights for the safe development of Zn-enriched crops. Traditional models often struggle to accurately predict metal levels in crop systems grown on soils with high geochemical background. This study employed machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), to explore effective strategies for sustainable cultivation of Zn-enriched crops in karst regions, focusing on bioaccumulation factors (BAF). A total of 10,986 topsoil samples and 181 paired rhizosphere soil-crop samples, including early rice, late rice, and maize, were collected from a karst region in Guangxi. The SVM and XGBoost models demonstrated superior performance, achieving R values of 0.84 and 0.60 for estimating the BAFs of Zn and Cd, respectively. Key determinants of the BAFs were identified, including soil iron and manganese contents, pH level, and the interaction between Zn and Cd. By integrating these soil properties with machine learning, a framework for the safe cultivation of Zn-enriched crops was developed. This research contributes to the development of strategies for mitigating Zn deficiency in crops grown on Cd-contaminated soils.

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Source
http://dx.doi.org/10.1016/j.scitotenv.2024.176650DOI Listing

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