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Related Experiment Video

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Machine learning-driven optimization of arsenic phytoextraction using amendments.

Huading Shi1, Yunxian Yan2, Zhaoyang Han2

  • 1Key Laboratory of Resource Utilization and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China.

Ecotoxicology and Environmental Safety
|July 20, 2025
PubMed
Summary

Machine learning optimizes Pteris vittata phytoextraction of arsenic from contaminated soils. Phosphate fertilizers are the most cost-effective amendment for enhancing arsenic accumulation in P. vittata biomass.

Keywords:
Economic costEnhanced phytoremediationHyperaccumulatorMain factorRandom forestSustainability

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Area of Science:

  • Environmental Science
  • Soil Science
  • Bioremediation

Background:

  • Arsenic contamination poses significant environmental and health risks.
  • Pteris vittata (Chinese brake fern) is a hyperaccumulator plant used for arsenic (As) phytoextraction.
  • Exogenous amendments can enhance P. vittata's As remediation efficiency, but their application is limited by variable effectiveness and economic costs.

Purpose of the Study:

  • To develop a machine learning model to predict and optimize amendment performance for enhancing As phytoextraction by P. vittata.
  • To identify key factors influencing As accumulation in P. vittata.
  • To evaluate the economic feasibility of different amendments for As phytoextraction.

Main Methods:

  • A random forest model was developed using 2299 data points from 121 published datasets.
  • The model considered 18 parameters across P. vittata characteristics, amendment properties, soil properties, and cultivation conditions.
  • Parameter importance was assessed using %IncMSE, and cost-effectiveness was calculated based on CNY per 1 µg of As accumulation.

Main Results:

  • The random forest model achieved a high predictive accuracy (R² = 0.846).
  • Pteris vittata biomass was a more significant factor than As concentration in predicting As accumulation.
  • Amendment type, application timing, cultivation duration, and soil-available As were identified as key factors influencing As uptake.
  • Phosphate fertilizers demonstrated the lowest cost for enhancing As accumulation, while other amendments like calcium acetate, ethylenediamine-N,N'-disuccinic acid, and glutathione were less economically viable.

Conclusions:

  • Machine learning provides a robust tool for predicting and optimizing amendment strategies for arsenic phytoextraction.
  • Biomass and amendment characteristics are critical for maximizing As accumulation in P. vittata.
  • Phosphate fertilizers offer a cost-effective amendment solution for practical arsenic remediation using P. vittata.